Applied Science, Faculty of
Engineering, School of (Okanagan)
DSpace
UBCO
Kotagoda Hetti, Ravihari Kaumadi
Doctor of Philosophy - PhD
University of British Columbia
The increasing energy demand results in numerous environmental concerns. Despite the popularity gained by renewable energy, certain economic activities still require fossil fuels, with natural gas (NG) playing a critical role in Canada's energy security. However, NG supply chain operations emit significant greenhouse gas volumes. As low-carbon fuels, hydrogen and renewable natural gas (RNG) are considered strategies to decarbonize the NG supply chain. However, hardly any studies have been carried out to suggest integrated pathways for RNG and hydrogen inclusion in conventional gas grids. Therefore, the overall aim of this research is to develop a life cycle thinking-based decision support framework to decarbonize the natural gas supply chain through RNG and hydrogen integration. Multi-criteria decision-making methods were used to select the most suitable RNG and hydrogen production paths. Multi-objective optimization was employed to determine the optimum fuel generation mix and gaseous fuel portfolio that caters to key economic sectors. A dynamic modelling approach was used to evaluate investment decisions from a long-range perspective. Policy implications and recommendations were further proposed for successfully decarbonized gas grid planning and management. The study results indicate that integrating RNG and hydrogen into conventional gas grids can reduce emissions of up to 250 tons CO2eq/year and generate substantial carbon tax savings of up to $16,250/year. Furthermore, the long-range simulation indicated the potential to displace FNG 100% by 2045. Moreover, RNG and hydrogen selling prices can be reduced to 0.009 $/ MJ and 4.8 $/kg Hâ by 2050 with policy support. The deliverables of this research will aid in decision-making for pre-project planning and sustenance of a diversified gas supply chain with RNG and hydrogen. The developed decision support framework and the models will assist utility providers and investors in identifying the optimum fuel integration path and fuel portfolio allocation for key economic sectors. The deliverables are geared towards ensuring necessary technical support to ensure community energy security and achieve climate action goals. The study findings will contribute to the development of a diversified and decarbonized gas supply chain and an energy-independent community.
SUSTAINABLE GASEOUS FUEL FUTURES OF CANADA: A PLANNING FRAMEWORK FOR FUEL SUPPLY CHAIN CONFIGURATIONS by Ravihari Kaumadi Kotagoda Hetti B.Sc. Eng. (Hons), University of Peradeniya, 2018 M.A.Sc., The University of British Columbia, Canada, 2020 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE COLLEGE OF GRADUATE STUDIES (Mechanical Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) November, 2024 © Ravihari Kaumadi Kotagoda Hetti, 2024 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, the dissertation entitled: SUSTAINABLE GASEOUS FUEL FUTURES OF CANADA: A PLANNING FRAMEWORK FOR FUEL SUPPLY CHAIN CONFIGURATIONS submitted by Ravihari Kaumadi Kotagoda Hetti in partial fulfillment of the requirements for the degree of Doctor of Philosophy Examining Committee: Dr. Kasun Hewage, School of Engineering Supervisor Dr. Rehan Sadiq, School of Engineering Co-Supervisor Dr. Zheng Liu, School of Engineering Supervisory Committee Member Dr. Abbas Milani, School of Engineering Supervisory Committee Member Dr. Ian Foulds, School of Engineering University Examiner Dr. Ahmed Hammad, University of Alberta External Examiner iii Abstract The increasing energy demand results in numerous environmental concerns. Despite the popularity gained by renewable energy, certain economic activities still require fossil fuels, with natural gas (NG) playing a critical role in Canada's energy security. However, NG supply chain operations emit significant greenhouse gas volumes. As low-carbon fuels, hydrogen and renewable natural gas (RNG) are considered strategies to decarbonize the NG supply chain. However, hardly any studies have been carried out to suggest integrated pathways for RNG and hydrogen inclusion in conventional gas grids. Therefore, the overall aim of this research is to develop a life cycle thinking-based decision support framework to decarbonize the natural gas supply chain through RNG and hydrogen integration. Multi-criteria decision-making methods were used to select the most suitable RNG and hydrogen production paths. Multi-objective optimization was employed to determine the optimum fuel generation mix and gaseous fuel portfolio that caters to key economic sectors. A dynamic modelling approach was used to evaluate investment decisions from a long-range perspective. Policy implications and recommendations were further proposed for successfully decarbonized gas grid planning and management. The study results indicate that integrating RNG and hydrogen into conventional gas grids can reduce emissions of up to 250 tons CO2eq/year and generate substantial carbon tax savings of up to $16,250/year. Furthermore, the long-range simulation indicated the potential to displace FNG 100% by 2045. Moreover, RNG and hydrogen selling prices can be reduced to 0.009 $/ MJ and 4.8 $/kg H2 by 2050 with policy support. The deliverables of this research will aid in decision-making for pre-project planning and sustenance of a diversified gas supply chain with RNG and hydrogen. The developed decision support framework and the models will assist utility providers and investors in identifying the optimum fuel integration path and fuel portfolio allocation for key economic sectors. The deliverables are geared towards ensuring necessary technical support to ensure community energy security and achieve climate action goals. The study findings will contribute to the development of a diversified and decarbonized gas supply chain and an energy-independent community. iv Lay Summary The study involves decarbonizing conventional gaseous fuel supply chains with cleaner gaseous fuels. The main goal is to develop a decision-support framework and models based on life cycle thinking for integrating renewable natural gas and hydrogen fuels into conventional gas grids. With concerns surrounding global warming and achieving emission goals, the oil and gas sector faces numerous pressures to cut down its supply chain emissions. Renewable natural gas and hydrogen are emerging as diversified, greener business directions for the oil and gas (O&G) industry. This study integrates life cycle thinking with decision-making for a diversified gas supply chain to develop robust solutions considering the environment, economy, and stakeholder priorities. The results were used to compile decision support and planning tools for gas supply chain decarbonization and management. These tools will assist utility providers, municipalities, investors, and policy and regulatory bodies in identifying optimum renewable natural gas and hydrogen integration paths and in evaluating the feasibility of Renewable natural gas and hydrogen during the pre-project planning stage of gas grid decarbonization. v Preface I, Ravihari Kotagoda Hetti, developed the research concepts presented in the thesis and conducted all relevant analyses. In total, seven journal articles and two conference articles are prepared from this research. Four journal articles have already been published, two are currently under review, and one journal article is in the submission-ready stage. Some of the content from Chapter 4 is presented at conferences. The research was conducted under the supervision of Dr. Kasun Hewage (University of British Columbia) and Dr. Rehan Sadiq (University of British Columbia). Dr. Abbas Milani (University of British Columbia) and Dr. Zheng Liu (University of British Columbia) were on the supervisory committee, providing critical feedback and suggestions for improvement. I authored one journal article with Dr. Haroon Mian, Dr. Sana Saleem, Dr. Osamah Siddiqui, and Dr. Mohammed Kamali on the life cycle analysis of green hydrogen production paths. The life cycle analysis results presented in the mentioned journal article have been used for the analysis presented in Chapter 4. In all papers listed below, I was the first author and contributed to conceptualization, investigation, methodology, and original draft preparation. The other authors contributed to reviewing and editing. Dr. Kasun Hewage did the supervision and review, and project administration and Dr. Rehan Sadiq did the supervision and reviewing of the below papers. The references of the journals and conference articles completed, under review, and published in this thesis are as follows. Journal Articles (Published and Under review): 1. Kotagodahetti, R., Hewage, K., Sadiq, R. (2024). Conventional gas network decarbonization with low-carbon gaseous fuels: A system dynamics model.Technology Forecasting and Social Change (Elsevier) â Under Review (TFS-S-24-07065) 2. Kotagodahetti, R., Hewage, K., Bhakhtavar, E., Sadiq, R. (2024). Gaseous fuel supply chain configuration selection: A life cycle thinking-based decision support framework. Expert Systems with Applications (Elsevier) â Under Review (ESWA-D-23-15618) 3. Kotagodahetti, R., Hewage, K., Bhakhtavar, E., Sadiq, R. (2024). Life cycle-based multi-objective model for optimal gaseous fuel generation and portfolio allocation in gas grids: A strategic decarbonization. Energy Conversion and Management (Elsevier), 319 (November), 118926, https://doi.org/10.1016/j.enconman.2024.118926. vi 4. Kotagodahetti, R., Hewage, K., Perera, P., Sadiq, R. (2023).Technology and policy options for decarbonizing the natural gas industry: A critical review. Journal of Gas Science and Engineering (Elsevier), 114 (June), 204981, https://doi.org/10.1016/j.jgsce.2023.204981. 5. Kotagodahetti, R., Hewage, K., Razi, F., Sadiq, R. (2023). Comparative life cycle environmental and cost assessments of renewable natural gas production pathways. Journal of Energy Conversion and Management (Elsevier), 278 (February), 116715, htps://doi.org/10.1016/j.enconman.2023.116715. 6. Kotagodahetti, R., Hewage, K., Karunathilake, H., Prabatha, T., Krishnan, H., Kasumu, A.S., Bryant, T., & Sadiq, R., & (2022). Liquefied natural gas exports from Canada to China: An analysis of Internationally Transferred Mitigation Outcomes. Journal of Cleaner Production (Elsevier), 347(May), 131291, https://doi.org/10.1016/j.jclepro.2022.131291. Journal Articles (Under preparation): 7. Kotagodahetti, R., Mian, H., Saleem, S., Siddqui, O, Kamali, M., Hewage, K., Sadiq, R. (2023). Renewable energy-based decentralized hydrogen production potential in British Columbia, Canada: An integrated environmental and techno-economic assessment framework. Energy (Elsevier) â Under Preparation Conference Articles: 1. Kotagodahett, R., Hewage, K., Sadiq, R. (2024). Decarbonized Natural Gas Supply Chain with Low-Carbon Gaseous Fuels: A Life Cycle Environmental and Economic Assessment. In: Gupta, R., et al. Proceedings of the Canadian Society of Civil Engineering Annual Conference 2022. CSCE 2022. Lecture Notes in Civil Engineering, vol 367. Springer, Cham. https://doi.org/10.1007/978-3-031-35471-7_70. 2. Kotagodahetti, R., Hewage, K., Karunathilake, H., Sadiq, R. (2023). Renewable Natural Gas as a Greener Energy Source: A Life Cycle CostâBenefit Analysis. In: Walbridge, S., et al. Proceedings of the Canadian Society of Civil Engineering Annual Conference 2021. CSCE 2021. Lecture Notes in Civil Engineering, vol 239. Springer, Singapore. https://doi.org/10.1007/978-981-19-0503-2_54. *The Grammarly tool was used to review the overall thesis on grammar and language. vii Table of Contents Abstract .......................................................................................................................................... iii Lay Summary ................................................................................................................................. iv Preface..............................................................................................................................................v Table of Contents .......................................................................................................................... vii List of Tables ...................................................................................................................................x List of Figures ............................................................................................................................... xii List of Abbreviations .....................................................................................................................xv Acknowledgments....................................................................................................................... xvii Dedication .................................................................................................................................. xviii Chapter 1: Introduction ............................................................................................................... 1 Background ................................................................................................................. 1 Research Gap .............................................................................................................. 4 Research Objectives .................................................................................................... 7 Thesis Organization .................................................................................................... 7 Research Outcomes and Deliverables ....................................................................... 11 Overview of the Methodology .................................................................................. 12 Chapter 2: Literature Review .................................................................................................... 17 Introduction ............................................................................................................... 17 Overview of the Natural Gas Sector ......................................................................... 18 Decarbonization Strategies ....................................................................................... 27 Decarbonization Strategy Implementation ............................................................... 52 Summary ................................................................................................................... 62 Chapter 3: Gaseous Fuel Supply Chain Configuration Selection: Life Cycle Thinking-based Decision Analysis ..................................................................................................................... 64 viii Background ............................................................................................................... 64 Methodology ............................................................................................................. 66 Results and Discussion ............................................................................................. 92 Summary ................................................................................................................. 110 Chapter 4: Optimal Gaseous Fuel Generation and Portfolio Allocation ................................ 111 Background ............................................................................................................. 111 Methods and Procedure........................................................................................... 113 Case Study .............................................................................................................. 119 Results and Discussion ........................................................................................... 123 Summary ................................................................................................................. 131 Chapter 5: Fossil Gas Decarbonization with Low-carbon Fuels: Dynamic Condition Analysis................................................................................................................................................. 133 Background ............................................................................................................. 133 Methods and Procedure........................................................................................... 134 Results and Discussion ........................................................................................... 147 Summary ................................................................................................................. 156 Chapter 6: Policy Implications and Recommendations .......................................................... 158 Background ............................................................................................................. 158 Best Management Practices for NG Industry Diversification ................................ 158 Recommendations for Stakeholders ....................................................................... 161 Recommendations to Facilitate International Emission Trading with ITMO ........ 169 Summary ................................................................................................................. 173 Chapter 7: Conclusions ........................................................................................................... 174 Summary and Conclusions ..................................................................................... 174 Originality and Contributions ................................................................................. 176 Limitations of the Study.......................................................................................... 177 ix Future Research Directions ..................................................................................... 178 References ....................................................................................................................................180 Appendices ...................................................................................................................................214 Appendix A: Technology Readiness Level Definitions ..................................................... 214 Appendix B: Life Cycle Inventory Data for RNG and Hydrogen Production Facilities .... 214 Appendix C: Life Cycle Impact Normalization and Aggregation Factors ......................... 220 Appendix D: Resource Availability and Plant Capacity Calculation ................................. 223 Appendix E: Life Cycle Cost Estimations of Hydrogen and RNG Production Facilities .. 227 Appendix F: Regional Life Cycle Environmental and Economic Performance of RNG Production Routes ............................................................................................................... 231 Appendix G: Additional Data for MOO ............................................................................. 237 Appendix H: Data Gathered for System Dynamics Model ................................................ 237 x List of Tables Table 2-1: Emission sources by supply chain stage ...................................................................... 21 Table 2-2: Factors affecting supply chain emissions .................................................................... 25 Table 2-3: NG production facility electrification projects ............................................................ 29 Table 2-4: Technologies to reduce/ avoid gas venting and flaring ............................................... 33 Table 2-5: Summary of additional actions for supply chain improvements ................................. 37 Table 2-6: Technological advancements ...................................................................................... 39 Table 3-1: Technology readiness levels of selected technologies ................................................ 70 Table 3-2: Details of Analyzed scenarios ..................................................................................... 71 Table 3-3: Performance criteria indicators used in the study ....................................................... 73 Table 3-4: Cost data used for the economic assessment ............................................................... 79 Table 3-5: Assumptions made for the Operational and maintenance cost evaluation .................. 81 Table 3-6: Cost parameters for the Hydrogen production facility ................................................ 82 Table 3-7: Causal relationships defined ........................................................................................ 87 Table 3-8: Weighting scenarios .................................................................................................... 90 Table 3-9: FCM weights ............................................................................................................. 103 Table 3-10: Criteria weights from BWM.................................................................................... 104 Table 3-11: Criteria indicator weights under defined scenarios ................................................. 105 Table 3-12: RNG/ Hydrogen production technology prioritization ranks .................................. 106 Table 3-13: MCDM results validation with TOPSIS ................................................................. 109 Table 4-1: Biogas production location selection criteria ............................................................ 121 Table 4-2: Theoretical resource potential for renewable-based hydrogen.................................. 121 Table 4-3: Scenarios assessed ..................................................................................................... 122 Table 5-1. Model validation with CEF-based and current simulation-based demand predictions for BC, Canada ................................................................................................................................. 145 Table 5-2. Simulated scenarios ................................................................................................... 146 xi Table 6-1: Action items .............................................................................................................. 163 xii List of Figures Figure 1-1: An overview of the organization of chapters ............................................................. 10 Figure 1-2: Research phases and overall methodology ................................................................ 13 Figure 2-1: Marketable natural gas production prediction with current emission reduction policies....................................................................................................................................................... 19 Figure 2-2: NG Supply Chain ....................................................................................................... 19 Figure 2-3: Emission distribution in the NG supply chain ........................................................... 24 Figure 2-4: Emission categories of NG supply chain ................................................................... 27 Figure 2-5: Design changes to NG supply chain .......................................................................... 36 Figure 2-6: CCSU projects related to NG industry ....................................................................... 45 Figure 2-7: Predicted CO2 capture potential from NG sector ....................................................... 46 Figure 2-8: Additional cost/ benefit components of integrating RNG ......................................... 48 Figure 2-9: Hydrogen markets ...................................................................................................... 49 Figure 2-10: Summary of risks associated with decarbonization investments ............................. 53 Figure 2-11: Road map for decision support in gaseous fuel supply chain investments .............. 58 Figure 3-1. Methodological framework ........................................................................................ 68 Figure 3-2: Life cycle assessment system boundary of RNG production routes .......................... 75 Figure 3-3: Life cycle assessment system boundary of hydrogen production routes ................... 76 Figure 3-4. Life cycle impacts of RNG generation scenarios ....................................................... 93 Figure 3-5: Life cycle impacts of Hydrogen generation scenarios ............................................... 93 Figure 3-6: Aggregated damage categories of RNG production routes ....................................... 94 Figure 3-7: Aggregated damage categories of hydrogen production routes ................................. 95 Figure 3-8: Carbon intensity of RNG production routes .............................................................. 96 Figure 3-9: Carbon intensity of hydrogen production routes........................................................ 96 Figure 3-10: Overall life cycle environmental impact of (a) RNG production routes, (b) Hydrogen production routes .......................................................................................................................... 98 xiii Figure 3-11: LCC of hydrogen and RNG production routes ........................................................ 98 Figure 3-12: LCOE of RNG production routes ............................................................................ 99 Figure 3-13: LCOE of hydrogen production routes .................................................................... 100 Figure 3-14: Profitability Index .................................................................................................. 101 Figure 3-15: Performance indices of RNG and Hydrogen production routes ............................ 108 Figure 4-1. Overall methodological framework ......................................................................... 114 Figure 4-2: Life cycle emissions and levelized cost of energy of (a) hydrogen production paths and (b) RNG production paths ........................................................................................................... 124 Figure 4-3: Gaseous fuel production potential with each resource a) hydrogen, b) RNG.......... 125 Figure 4-4. Optimum fuel portfolio allocation ........................................................................... 127 Figure 4-5: Actual versus optimum fuel portfolio allocation ..................................................... 127 Figure 4-6: Environmental and economic performance of optimal solutions ............................ 128 Figure 4-7: Sector-wise emissions and overall carbon tax savings ............................................ 130 Figure 5-1.The causal relationships of cleaner gaseous fuel integration model for NG displacement..................................................................................................................................................... 137 Figure 5-2. System dynamics model ........................................................................................... 139 Figure 5-3. Total potential RNG supply availability .................................................................. 147 Figure 5-4. Total potential Hydrogen supply availability ........................................................... 148 Figure 5-5. NG displacement potential with RNG and hydrogen .............................................. 149 Figure 5-6. Impact of technology learning on the potential to displace fossil NG ..................... 150 Figure 5-7. Variation of the selling price of (a) RNG and (b) hydrogen with technology learning..................................................................................................................................................... 151 Figure 5-8. Variation of ROI with technology learning (with government subsidies and tax reliefs)..................................................................................................................................................... 152 Figure 5-9. ROI of (a) RNG and (b) hydrogen with T2 tax rate, and ROI of (c) RNG and (d) hydrogen with T1 tax scenario .................................................................................................... 154 xiv Figure 5-10. (a) total avoided emissions, (b) carbon tax rate, and corresponding (c) carbon tax savings......................................................................................................................................... 155 Figure 6-1: Transfer between governments mechanism ............................................................. 171 xv List of Abbreviations Acronyms AD Anaerobic digestion AE Alkaline electrolysis BC British Columbia BWM Best-worst method CAPEX Capital expenditure CCS Carbon capture and Storage CEF Canadaâs Energy Future CFS Clean Fuel Standard CFR Clean Fuel Regulation CHS Chemical scrubbing CIWS Causality-importance weighting system FCM Fuzzy cognitive maps FNG Fossil natural gas GHG Greenhouse gas HPWS High pressure water scrubbing ITMO Internationally transferred mitigation outcomes LCA Life cycle assessment LCCA Life cycle cost assessment LCI Life cycle impacts LCIA Life cycle impact assessment LCOE Levelized cost of energy LFG Landfill gas LNG Liquefied natural gas LVS Livestock MCDM Multi-criteria decisionmaking MOO Multi-objective optimization MOORA Multi-objective optimization by ratio analysis PEME Proton exchange membrane electrolysis PI Profitability index PSA Pressure swing adsorption PV Photovoltaics RES Renewable energy sources RET Renewable energy technologies RNG Renewable natural gas ROI Return on Investment SMR Steam methane reforming SOEC Solid oxide electrolysis cell TBL Triple bottom line TOPSIS Technique for Order of Preference by Similarity to the Ideal Solution TRL Technology readiness level WWS Wastewater sludge xvi Abbreviations and units CAD Canadian Dollars CAPEX Capital expenditure GJ Gigajoule kW Kilowatt kWh Kilowatt-hours TWh Terawatt-hours O&G Oil and gas MWh Megawatt hours OPEX Operational expenditure USD United States Dollars USD/bbl United States Dollars per barrel xvii Acknowledgments I would like to express my sincere gratitude to my supervisors, Dr. Kasun Hewage and Dr. Rehan Sadiq, for their immense support throughout my graduate studies. Furthermore, I would like to thank them for their faith in me and for offering me an opportunity to continue my graduate studies at the University of British Columbia. I am truly grateful for the inspiration and motivation they gave me to complete this work successfully. I would like to thank my supervisory committee members, Dr. Abbas Milani and Dr. Zheng Liu, for the valuable knowledge, advice, and feedback they gave me, which greatly enhanced the quality of this thesis. I am privileged to be a member of an excellent research group, Life Cycle Management Laboratory and would like to thank all my fellow research mates for providing me with great research ideas while making my journey enjoyable and excitable. I would like to express my special thanks to Dr. Haroon Mian for the tremendous support, guidance, and encouragement given to me in completing the thesis. My warm gratitude goes to Dr. Ezzedin Bakhtavar and Dr. Hirushie Karunathilake for the support they provided me for my research work. Moreover, I would like to acknowledge the financial support from FortisBC, Mitacs Canada, and the administrative staff of the University of British Columbiaâs Okanagan Campus for their wonderful academic and work atmosphere over the past years. Reaching this point in my academic journey wouldnât have been possible without Sri Lankaâs free education system, which supported me from Grade 1 all the way through my Bachelorâs degree. I am deeply grateful for the opportunities it made available to me. My special word of gratitude to my spouse, Tharaka Wanniarachchi, for the incredible friendship, support, motivation, and encouragement throughout my graduate studies. Lastly, I would like to thank my beloved parents and my grandmother for the everlasting love and support they have given me, which has enabled me to be the person I am today. xviii Dedication Dedicated to my beloved husband and best friend, Tharaka, who has always been my strength and support since day one, to my parents and my grandmother, who have always taught me to strive for excellence, to my brother, sister, and my friends for their love and companionshipâ¦ 1 Chapter 1: Introduction Background Continuous economic growth has increased energy demand, resulting in numerous environmental concerns. Therefore, nations worldwide have pledged to work towards zero-carbon economies. Canada - a leader in âcleanerâ energy, has set national and provincial emission targets to reduce carbon emissions 40% below 2007 levels by 2030, with the plan to achieve net-zero by 2050 [1]. In addition, the Pan-Canadian Framework on Clean Growth and Climate Change has set key actions to achieve these emission goals. Some of them are carbon pricing, accelerated coal phase-out by 2030, a nationwide strategy for electric vehicles, Clean Fuel Standard (CFS), and methane emissions reduction from the O&G sector by 40-45% by 2025 [2]. These actions apply to six economic sectors: electricity, buildings, transport, heavy industry, agriculture, and O&G. Amongst them, the Canadian O&G sector emits a significant volume of greenhouse gases (GHGs), which is responsible for 191 million tonnes of CO2 eq in 2019 alone [3], [4]. Therefore, the O&G sector demands a special focus on national decarbonizing efforts. The rising energy demand is imperative with the current economic and population growth rate and certain economic activities still require fossil fuels to meet their energy demands. For example, the O&G sector plays a crucial role in ensuring energy security. Canada's Energy Future (CEF) report projects increased natural gas (NG) use by over 30% by 2040 if only the current emission reduction measures are continued [3]. NG emits significant volumes of GHGs from its operations spanning from upstream to downstream. The upstream operations of the industry account for approximately 9% of anthropogenic GHG emissions, while its output (NG combustion) contributes to another 33% of global emissions [5]. Therefore, the O&G industry faces financial, social, and political pressures to reduce its supply chain emissions. Disclosure of climate-related risks, capital allocation towards sustainable industry practices, opposition to new O&G investments, and fracking bans are some of the pressures faced by the industry [6]. Furthermore, policies such as carbon pricing accelerate the actions for decarbonization. Hence, a significant shift in NG business operations is required to achieve industrial emission targets and remain competitive in the energy market. 2 The NG industry has identified some avenues for transitioning to sustainable supply chain operations. One is to focus on increasing operational energy efficiency. It includes strategies to integrate renewable energy to reduce NG consumption and fugitive emissions in field operations [7], [8]. These actions are mainly applicable to upstream and transmission activities. Deploying new technologies, such as carbon capturing, is another option that could be implemented throughout the supply chain. These measures are highly encouraged through sustainable investment funds such as Clean Fuel Fund (CFF) [8], [9]. As another avenue to reduce emissions, the gaseous fuel industries explore paths to diversify their portfolio with cleaner alternatives such as hydrogen and renewable natural gas (RNG). For example, one of Enbridge Gas's low-carbon energy projects involves blending a small amount of hydrogen gas into their existing NG network [10]. FortisBC Inc. offers the option to use RNG instead of conventional NG [11]. According to the FortisBC Clean Growth Pathway to 2050, the NG industry's decarbonization efforts include decarbonizing pipeline gas with RNG and hydrogen, which are further integrated with carbon capture and storage technologies [12]. Another investment pathway would be to export liquefied natural gas (LNG) to regions that heavily depend on emission-intensive fossil fuels such as coal. The abundance of domestic NG resources allows for selling higher quantities of LNG to economically attractive markets in Asia and Europe while generating international emission credits [13]. Various technologies have been introduced to substitute fossil fuels with cleaner gaseous alternatives to meet climate goals. Key renewable gases that have reached considerable maturity include RNG and hydrogen. These are viewed as transformative opportunities for the Canadian natural gas industry, given their lower to zero emissions and the ability to seamlessly integrate into the existing natural gas infrastructure with minimal transformations and interruptions. However, the challenge lies in upgrading the existing business model with RNG and hydrogen to fully cater to the current and future energy demands while ensuring energy security, access, sustainability, and industry growth [1]. Though these fuels are less emission-intensive, they may still carry certain emission levels depending on factors such as feedstock, fuel production technology, and regional conditions [2][3]. Moreover, the technologies associated with RNG and hydrogen production entail high costs. Thus, it is imperative to weigh the environmental and economic impacts of incorporating RNG and hydrogen into the gaseous fuel portfolio to minimize emissions at the 3 lowest cost possible. Consequently, exploring optimum gaseous fuel portfolio strategies is vital for utility providers operating in energy markets. The feasibility and sustenance of the gas network decarbonization with a diversified fuel portfolio depends on various factors that affect the industry's competitive landscape. They fall under the socio-economic, political, technical, and environmental pillars. More specifically, these factors include but are not limited to grid reliability, resource availability, fuel supply and demand, gas prices, environmental impacts, trade regulations and tariffs, technology readiness level (TRL), and the asset mix (offshore vs. onshore, gas vs. oil, upstream vs. downstream) [5], [17], [18]. In addition, policies and regulations involving carbon market mechanisms are anticipated to play a significant role in making investment decisions [19] [17]. The abovementioned factors should be considered simultaneously to make effective, clean energy-related investment decisions. However, it is a complex task and requires comprehensive assessments to determine the feasibility and sustainability of an investment decision. Until now, the developed tools and frameworks have primarily focused on individual evaluations of various fuel production and use within different stages of the supply chain, such as resource extraction, processing, and transmission [20], [21], [22], [23], [24]. However, it is not practical to rely on a single fuel for a diversified gas grid due to varying reasons including resource availability, infrastructure needs, and fuel demand. Instead, a combination of different fuel pathways would be more effective. A handful of studies have assessed upstream gas supply chains through multi-objective optimization (MOO), focusing on designing new supply chain infrastructure integrated with cleaner fuel alternatives [25], [26], [27], [28]. While these studies provide useful information for gaseous fuel supply chain infrastructure planning, there is a notable lack of integrated systematic tools and frameworks that assist in diversifying the overall gas supply chains with RNG and hydrogen. Moreover, most supply chain environmental and cost assessments have concentrated only on the operational stage. However, conducting evaluations from a life cycle perspective is imperative to determine true âcostsâ and âbenefits.â. In addition, the energy sector is subjected to dynamic conditions. Therefore, there is a need to explore the long-range feasibility of diversified gas supply considering future uncertainties like demand and price variations. The motivation for the research originates from the evident need for a consolidated approach in planning decarbonized gas networks with a diversified fuel portfolio. Decarbonizing the natural 4 gas supply chain by substituting it with cleaner alternatives like RNG and hydrogen can significantly reduce carbon emissions, thereby mitigating climate change and improving air quality. Governments and regulatory bodies worldwide are implementing stricter environmental regulations and emissions targets to combat climate change. For example, many regions across the United States have instilled and proposed gas bans [29]. In Canada, Montreal imposes gas bans on new buildings starting October 2024. As a part of BC Zero Carbon Step Code, many cities in British Columbia (Saanich, Victoria, and Nanaimo) adopted bans and restrictions on new natural gas connections [30]. Such polices and bans will affect national energy security and drive the use of other energy sources. Countries can enhance energy security by reducing dependence on one fuel type and diversifying the sources of gaseous fuels used in the supply chain, such as incorporating RNG and hydrogen. This diversification also reduces vulnerability to supply disruptions and risks associated with volatile global energy markets. Moreover, investing in clean energy technologies stimulates economic growth, particularly in regions with abundant energy resources and manufacturing capabilities. Furthermore, as Canada is transitioning towards a low-carbon and net-zero economy, decarbonizing the natural gas supply chain plays a crucial role in achieving long-term sustainability goals. In a nutshell, research on natural gas supply chain decarbonization with cleaner gaseous fuels is motivated by the need to address climate change, meet regulatory requirements, enhance energy security, foster technological innovation, capitalize on market opportunities, and facilitate Canadaâs energy transition toward a more sustainable future. Research Gap This research was originated from the pressures and challenges identified above to make a shift towards a decarbonized gas network with a diversified and cleaner gaseous fuel portfolio. Based on a thorough review of the existing literature and tools for supply chain and energy planning, the following gaps were identified in the current body of knowledge. Limited use of âlife cycleâ thinking in gas network decarbonization with cleaner gaseous fuels: Multiple studies in the literature have assessed the environmental performance of RNG and hydrogen networks, a few have gone beyond the operational stage emissions to evaluate the life 5 cycle emissions and other environmental impacts using a life cycle thinking perspective [31]. However, many studies evaluatef the environmental performance, considering only the global warming potential. Yet, environmental impacts are not limited to global warming as they could further affect waterbodies, ecosystems, land, and human health. Therefore, existing emissions and impact assessments are inadequate to provide a complete picture [32]. Furthermore, most economic decisions are made on an ad-hoc basis, considering only the current conditions and operational impacts. In recent years, several studies have assessed the economies of RNG and hydrogen production pathways, but none have applied life cycle costs (LCC) to assess the financial implications of alternative gaseous fuel projects [33]. A complete system implication could only be estimated through whole-system modelling considering the overall life cycle. Therefore, detailed environmental and socio-economic assessments are required to determine the optimal decarbonization strategies for the NG supply chain. Limited gas network planning tools with integrated life cycle triple-bottom-line performance assessment: Many studies have assessed the cost of investments and associated environmental impacts of RNG and hydrogen processes. Yet, published studies need comprehensive methodologies that simultaneously consider all criteria governing gas industry investments' feasibility [34], [35], [36]. In practical scenarios, decisions are largely affected by stakeholder preferences and causality relationships among decision criteria. Yet, there is limited consideration of stakeholder priorities and causality relationships between decision criteria when making investment decisions [37]. Additionally, considerable uncertainties and variability affect gas networks and supply chain planning. For example, resource availability, supply reliability, energy demand, generation capacity, and efficiency are all subject to uncertainties, and it is essential to include such uncertainties in the decision-making process [38]. Although a handful of studies have incorporated uncertainties in data and decision environments, none have explored a complete and systematic methodology for selecting an RNG or hydrogen production technology that simultaneously addresses practical challenges, such as ambiguity in data, conflicting stakeholder preferences, and causal-effect relationships between performance indicators. Lack of optimization tools and framework for fuel generation and portfolio allocation planning: Many studies have employed multi-objective optimization (MOO) to determine the optimum state of operation within the energy sector, mainly considering the economic and 6 environmental dimensions [39]. Few studies have further offered optimum resource allocation for electricity production [40]. Yet, none of the existing literature has delved into identifying the optimum resource combination for producing hydrogen and RNG. Moreover, there is limited literature on life cycle thinking-based considerations when allocating resources for RNG and hydrogen production integrated into conventional gas grids. Further, there is a significant gap in the literature that addresses the multiple economic sectors that can benefit from the utilization of cleaner gaseous fuel instead of NG [39]. Thus, there is a need for a holistic and systematic methodology for gas supply chain-related decision-making and management. Lack of dynamic assessments exploring the effect of policies on gas network decarbonization investments: Gaseous fuel Supply chain planning is subjected to multiple parameters that are often time-dependent and are influenced by policies and regulatory measures [41]. Therefore, dynamic variations related to each technological option in the supply chain should be considered when planning the infrastructure [42]. However, there are no dynamic analyses to evaluate the long-term feasibility of integrating RNG and hydrogen into the conventional gas network. Only a limited number of studies have examined policy efforts for successful market penetration of these cleaner gaseous fuels from a long-range planning perspective [43] , but none have explored the long-term sustainability of integrating both RNG and hydrogen into the gas grid for decarbonization. The aforementioned concerns related to NG supply chain decarbonization have created a need for a long-range evaluation that assesses and plans the application of decarbonization strategies to the NG supply chain in Canada. Based on above discussed research gaps, this thesis aims to explore the following research questions: 1. How can the best energy sources and technologies be selected for RNG and hydrogen fuel generation? 2. How can life cycle thinking be integrated with the gaseous fuel supply chain for decarbonization planning? 3. How can uncertainties and macro-environmental variations be incorporated into gas network decarbonization planning? 4. How can the optimal RNG and hydrogen fuel generation and gas portfolio diversification strategies be defined in the gaseous fuel supply chain decarbonization process? 7 5. How should policies and regulatory frameworks be formed considering long-range planning of gaseous fuel supply chain decarbonization with RNG and hydrogen? Research Objectives This research aimed to develop a life cycle thinking-based decision support framework to identify optimal gaseous fuel pathways for the British Columbian energy sector. The proposed framework considers the phases of production, distribution, and market conditions along with triple-bottom-line (TBL) sustainability principles. It also incorporates life cycle environmental and economic factors for long-term gaseous fuel investment planning. The specific objectives of this proposed research are as follows: 1. Identify potential low-carbon pathways for gaseous fuel supply chains by considering production capacities, technical and market conditions, and TBL sustainability. 2. Assess the life cycle environmental and economic impacts of âcleanerâ gaseous fuel production technologies and prioritize potential supply chain configurations. 3. Develop an optimization model blended with the life cycle thinking concept to determine the optimal low-carbon gaseous fuel supply chain configurations. 4. Assess the long-term feasibility of cleaner gaseous fuel supply chain configurations under uncertain conditions and provide recommendations for transitioning the gaseous fuel industry towards a low-carbon economy. Thesis Organization This thesis consists of eight chapters, focusing on the literature review, methods used, findings, and conclusions. Chapter 1: This chapter provides an overall introduction to the background, research gaps, motivation, objectives and deliverables, and the overall planning framework for gas network decarbonization with fuel diversification. Chapter 2: This is the overall methodology followed in the thesis. The chapter outlines insight into the key research phases and the methods used to achieve the objectives of each phase. Chapter 3: The third chapter provides a comprehensive literature review on the current state and needs of conventional natural gas supply chain decarbonization, state-of-the-art gas network 8 decarbonization technologies and strategies, and the methods used in developing the supply chain planning framework. Chapters 4 to 6 provide the details of achieving study objectives and the overall goal. The three chapters outline the detailed methodologies and findings, including technology prioritization, optimum resource and fuel portfolio allocation, and the long-range planning model. Chapter 4: This chapter presents the model developed to prioritize and select the most suitable RNG and hydrogen production route based on the technical viability and life cycle triple bottom performance. A fuzzy-based approach is taken to account for the data uncertainties and performance variations in the different fuel production technologies. Chapter 5: The details on the gaseous fuel supply chain optimization model are presented in this chapter. The model was developed in the RNG and hydrogen fuel production stage and the economic sector-wise gaseous fuel supply stage. The optimization model identifies the optimal generation mix and optimum fuel portfolio allocation. Chapter 6: This chapter assesses the long-range feasibility of RNG and hydrogen integration projects and suitable policy measures. The economies and environmental impacts of integrating RNG and hydrogen into the existing gas network were evaluated using multi-period analysis with a system dynamics model. Chapter 7: Based on the findings from Chapters 5 and 6, recommendations are provided for conventional gas supply chain decarbonization with RNG and hydrogen. Furthermore, policy 9 recommendations are provided for creating carbon market mechanisms by exporting displaced NG to replace the emission-intensive fuel use in regions that lack abundant NG resources. Chapter 8: This chapter is focused on the study's conclusions, originality, and avenues for future research. Figure 1-1 illustrates the connection between the research objectives and the chapters, as well as the content in each chapter. The activities carried out to fulfill each objective and the main outcome of the objective are displayed in the figure under the relevant chapters. 10 â¢ Fossil natural gas supply chain challengesâ¢ Fossil natural gas supply chain decarbonization strategies- Improved operations- New business avenues with cleaner fuelsâ¢ Adaptation challengesâ¢ Decision making for emission planning- Life cycle thinking- MethodsChapter 1IntroductionChapter 2MethodologyChapter 3 OBJECTIVE 1Chapter 4 OBJECTIVE 2Technology identificationTechnical criteria selectionFiltered technologiesResource identificationAvailable resourcesScreen out RNG and H2 production technologiesResource availability+Life cycle environmental impact assessment of RNG & H2 production technologiesLife cycle cost assessment of RNG and H2 production technologiesLife cycle impact databaseLife cycle cost databaseMCDM for emission reduction strategy selectionPrioritized RNG and H2 production pathsChapter 6 OBJECTIVE 4â¢ Government subsidiesâ¢ Tax benefitsâ¢ Carbon taxSystem Dynamics Model Optimal RNG and H2 production pathsFuel supply availabilityReturn on InvestmentAvoided emissionsChapter 8ConclusionsChapter 5 OBJECTIVE 3Fuel production routesFuel portfolio configurationsLCA & LCC performance scoresâ¢ Formulating model constraintsâ¢ Modelling objective functionsâ¢ Optimum resource allocation for RNG and H2 productionâ¢ Optimum gaseous fuel portfolioRecommendations for long-range sustainability of diversified gas gridsChapter 7 Figure 1-1: An overview of the organization of chapters 11 Research Outcomes and Deliverables This research's deliverables will aid in decision-making for initial gaseous fuel supply planning and diversification with RNG and hydrogen. The outcome of research will ensuring long-term energy security for communities with a reliable supply of low-carbon fuel, reducing adverse socio-economic and environmental impacts of energy usage. The developed decision tools and models will assist during the conventional gas network decarbonization planning with RNG and hydrogen, focusing on the pre-project planning stage. The research outcomes also align with the climate action goals of the Federal and Provincial Governments across Canada. Policymakers and developers will receive guidance from the decision support framework without imposing additional requirements for extensive technical expertise. â¢ Multicriteria decision support tool to assist in prioritizing RNG and hydrogen production paths: The proposed approach will assist in allocating limited resources effectively and sustainably to produce RNG and hydrogen. The decision support framework will help choose the most appropriate RNG and hydrogen production technologies tailored to local needs and integrate these technologies into conventional gas grids to optimize TBL performance. Leveraging locally available resources for fuel generation will contribute to economic development and enhance the quality of life for communities in those areas. â¢ Multi-objective optimization model to determine the optimum resource allocation for fuel production and fuel portfolio allocation: The proposed life cycle thinking-based multi-objective optimization model determines optimal resource allocation for the production of RNG and hydrogen, integrating them into the conventional gas network. Moreover, it allocates fossil natural gas, RNG, and hydrogen optimally across building, industry, and transport sectors, considering the life cycle performance of fuel integration paths. This model will offer a comprehensive view of supply chain costs and detailed insights into emission benefits when integrating RNG and hydrogen into existing business models. â¢ System dynamics model to evaluate the long-range feasibility and policy support: The long-range decision support tool helps users estimate the future feasibility of gas network decarbonization with RNG and hydrogen with policy reforms. The findings can also be potentially further extended and adapted for different types of gaseous fuel supply chain decarbonization strategy integration. The developed model can help utility providers and 12 policymakers identify the most effective investment strategy and support from a long-range perspective when planning gas network decarbonization. Overview of the Methodology The study examines RNG and hydrogen generation technologies within a selected community, considering technical, socio-environmental, and economic factors. A decision-support framework is proposed to prioritize RNG and hydrogen fuel production paths, which can be integrated into the conventional fossil natural gas supply chains. Furthermore, the developed multi-objective optimization model aims to determine the optimum gaseous fuel supply chain configuration in terms of fuel generation mix and fuel portfolio. A multi-period evaluation was further conducted to examine the factors affecting the long-range feasibility of integrating RNG and hydrogen into conventional gas supply chains. Hence, the current research captures and integrates all scattered knowledge to define and select the optimum gaseous fuel supply chain configuration, ensuring minimum-zero environmental damage while ensuring economic benefits. An overview of the overall methodology employed to attain the research objectives, is illustrated in Figure 1-2. The objectives outlined in section 1.3 were accomplished through various research phases. The application of the proposed framework was demonstrated using the data and information of Kelowna, British Columbia, Canada (Case study). Detailed explanations of the methodologies employed for each sub-objective are provided in respective chapters. Section 1.6.1 summarises the overall roadmap to achieve the main research goal and the methods and procedures adopted at each phase. 1.6.1 Research phases 13 â¢ Investigate the total natural gas industry supply chain; and opportunities and constraints for decarbonizing the supply chainâ¢ Review NG supply chain decarbonization avenuesâ¢ Identify suitable mathematical techniques to compare and prioritise strategies and business pathwaysPhase 1Literature Reviewâ¢ Define assessment criteria for applicable strategies and performance goals of the supply chainâ¢ Define performance indicators under the TBL pillarsâ¢ Gather technical, environmental, and economic performance data for other adoptable gas fuelsâ¢ Identify data requirements for LCA & LCCâ¢ LCI database development Data collection & database developmentâ¢ Applicable decarbonization strategies and other gas fuel business avenuesâ¢ Technical performance, cost & life cycle impact dataâ¢ Energy use, emission, & market dataâ¢ Determine alternative gaseous fuel sources for local availabilityâ¢ Define upstream-midstream supply chain configurationsâ¢ Develop key performance indicatorsPhase 2Fuel production path definitionâ¢ Assess resource availabilityâ¢ Conduct life cycle costing on all scenariosâ¢ Conduct life cycle assessment on all scenariosTechno-economic and life cycle assessmentPrioritized RNG and hydrogen production pathwaysâ¢ Gather model inputs - Resource availability data - LCA and LCC scoresâ¢ Optimization model formulation - Define decision variables - Define objective function - Define model constraintsPhase 3 and 4Multi-objective optimizationâ¢ Identify decision variables and indicators for feasibility of selected supply chain configuration and risk assessmentâ¢ Identify the potential future variations of macro-environmental conditionsâ¢ Develop a system dynamics model to explore the effect of time-sensitive variablesâ¢ Explore and recommend policy instruments to sustain decarbonized gaseous fuel supply chainMulti-period performance assessmentâ¢ System dynamics modelâ¢ RecommendationsObjective 1Objective 2Objective 3 & 4â¢ Define Decision prioritiesâ¢ Assign weights to performance criteriaâ¢ Select and prioritize supply chain configurations for producing RNG and hydrogenMulti-criteria decision modelâ¢ LCA & LCC scoresâ¢ Resource availability dataâ¢ Optimum fuel generation mixâ¢ Optimum fuel portfolio Figure 1-2: Research phases and overall methodology 14 Phase 1 - Content analysis and data collection During this phase, an in-depth review of different decarbonization strategies for the natural gas industry was conducted. Challenges and recommendations for strategy implementation were identified in terms of market, technical, financial, policy, and supply chain management aspects. The literature review was used to identify knowledge gaps in the existing body of research. The review revealed RNG and hydrogen as transformative opportunities for the natural gas industry. Yet, limited work has been conducted on integrating cleaner fuels to decarbonize gas grids. Accordingly, data was collected for the technical and life cycle performance assessments of the identified alternate gaseous fuel pathways to address the research questions. Specific data gathered included â¢ Life cycle inventory for RNG and hydrogen production: o RNG and hydrogen production facility material and energy needed for construction and operation â¢ Life cycle cost data o RNG and hydrogen production facility construction and operation stage cost data for material and energy requirements â¢ Resource availability data for RNG and hydrogen production in a given location to determine the plant capacities and to approximate the corresponding costs Furthermore, additional data for multi-objective optimization and long-range planning model were gathered. The data included. RNG and hydrogen fuel selling prices in different economic sectors, downstream emission data for RNG and hydrogen, technology learning rates of RNG and hydrogen production technologies, RNG and hydrogen demand data, future fuel price forecasts, demand forecasts, technology-wise operational and capital cost variation data, and investment aids, and future policy data etc. The data were collected from literature, published databases such as Ecoinvent, and Canadian Government reports and databases. The mentioned sources provided sufficient coverage to address the research objectives. Further, certain approximations were made in determining location-specific data, such as resource availability to produce RNG and hydrogen in the selected location (Kelowna, BC), and RNG and hydrogen demand data in the selected location. For the uncertainty analysis, specific data ranges, including minimum and maximum values of costs of fuel production and efficiencies of fuel production technologies, were gathered 15 from the literature. However, some approximations were involved in instances where data was not available. Moreover, the comprehensive literature survey selected suitable mathematical methods for technology prioritization, optimization, and long-range planning and assessment of supply chain configuration. A detailed literature review is provided in Chapter 2. Phase 2- Supply chain configuration prioritization Two alternate fuels: RNG and hydrogen integration to fossil natural gas network, were considered as the decarbonization options. RNG and hydrogen production technologies were initially screened based on resource availability and technological maturity. Life cycle assessment (LCA) and life cycle cost assessment (LCCA) tools were employed for the environmental and economic performance assessments of RNG and hydrogen production paths. Different hydrogen and RNG production routes were defined based on resource and fuel production and gas upgrade technologies. LCA and LCC results obtained from the performance assessment were aggregated under different decision perspectives. The current study accounted for both causalities and stakeholder priorities placed on the criteria indicators by employing a hybrid best worst method and fuzzy cognitive maps for allocate criteria weights. Multi objective optimization by ratio analysis (MOORA) method was used in ranking fuel routes based on their techno-economic and environmental performances. The detailed technology prioritization framework is presented in Chapter 3, section 3.2. Phase 3 - Life cycle thinking-based gaseous fuel supply chain configuration optimization model In phase 3, a multi-objective optimization model was presented to determine utility providers' optimum gaseous fuel generation mix and portfolio. The model also provides the proportion of each type of fuel (i.e., natural gas, hydrogen, and RNG) in their portfolio that would cater to key economic sectors (building heating, transport, and industry) based on life cycle environmental and economic performances. A scenario-based assessment was conducted to address the uncertainty in data. Suitable objective functions and constraints were formulated to optimize the gas generation mix and the fuel portfolio. The study employed a Genetic Algorithm (GA) for multi-objective optimization as it provides the optimum solutions with significantly reduced computational time 16 while presenting the mathematical reality more accurately. The proposed model was evaluated and validated via a case study. The detailed model and findings are provided in Chapter 4, section 4.2. Phase 4 - System Dynamics Modelling The developed decision support tool was further extended with a system dynamics model to represent changes in the supply chain with time. The model was used to explain the effect of pre-determined parameters, such as technology evolution, policies, and supply and demand conditions, on an RNG and hydrogen-integrated NG supply chain over an extended period of time. The detailed methodology is provided in Chapter 5, section 5.2. Cognitive mapping was applied to establish the relationship between variables that affect the feasibility of RNG and hydrogen integration into gas networks. Such variables include resource availability, fuel demand and supply, demographics, technology learning, costs, and emissions. These relationships were used to determine the system dynamics model components. Accordingly, the system dynamics model was developed using VENSIM DSS Software to explore the impact of these varying conditions along with applicable policies supporting cleaner fuel integration to the existing fossil natural gas networks. The model was simulated for a 27-year life span of the gaseous fuel supply chain network. Finally, recommendations were provided regarding the sustainable integration of alternate fuels to existing gas networks from a long-range perspective. A detailed discussion on long-term feasibility and recommendations are provided in Chapter 5 and Chapter 6, respectively. 17 Chapter 2: Literature Review The version of this chapter has been published in the Elsevier journal âGas Science and Engineeringâ as an article titled âTechnology and policy options for decarbonizing the natural gas industry: A critical review.â This article was an April-June 2023 Editor's Choice Article for the Journal of Gas Science and Engineering. Introduction The natural gas (NG) industry faces significant pressures to reduce its greenhouse gas (GHG) emissions across its supply chain due to environmental, social, and political concerns. Despite being considered a low-carbon fossil fuel, NG still emits substantial GHGs throughout its operations. These pressures include disclosure of climate-related risks, opposition to new investments, and policies like carbon pricing, necessitating a shift towards sustainable practices to remain competitive [9]. The industry has begun implementing measures such as improving energy efficiency and deploying technologies like carbon capture, but significant changes are needed to meet emission targets and stay viable [12], [13]. Canada's NG industry, a major contributor to national emissions, is crucial in achieving emission reduction goals. While some progress has been made, including methane reduction targets and environmental agreements, more action is needed. New investments are being made to diversify portfolios with cleaner fuels like hydrogen and renewable natural gas (RNG), as well as exporting liquefied natural gas (LNG) to markets reliant on coal [10], [11]. However, implementing these technologies requires substantial infrastructure changes, presenting both challenges and opportunities for the industry's future. A few studies have explored emission reduction strategies and technologies for the NG supply chain in terms of their techno-economic feasibility [20], [21], [22]. Moreover, several studies have conducted environmental assessments for the NG industry operations and have provided strategies for efficient and effective emission tracking [23], [24], [25]. In addition, certain studies have assessed the optimal operational conductions at various stages of the NG supply chain to operate with minimum environmental damage while assuring maximum techno-economic performance [26]. The feasibility and sustenance of the decarbonization pathways assessed in the referred 18 literature depend on various macro and micro-environmental factors. Therefore, decision-making for gas supply chain decarbonization is complex. Only a few studies have explored pathways to select the best emission abatement strategy or combination of strategies [30], [31]. Moreover, there are micro-environmental factors that occur within the industry that involve stakeholders, competitors, employees, and energy demand. Some of them are employee safety, supply reliability, and demand for gas fuels [27]. However, the reviewed studies revealed a lack of integrated knowledge on available decarbonization strategies and implementation guides for the overall NG supply chain. Hence, this chapter aims to identify available decarbonization paths for the NG industry and identify key knowledge gaps within the focus area. The extensive review of the literature findings herein provides information regarding NG supply chain stages that require the most focus in achieving emission goals and provide areas that need immediate attention. Moreover, potential emission abatement strategies are discussed at length regarding their cost-effectiveness and emission reduction potential. Furthermore, the need for policy-level support is explored to ensure the sustenance of discussed strategies. Overview of the Natural Gas Sector NG is the cleaner hydrocarbon yet emits GHGs, including methane, through combustion and other upstream processes [33]. Hence, the industry is challenged to cut its overall emissions while finding means to meet the increasing energy demand. The NG industry is massive in scale and critical to the world economy. North America has the largest NG industry and is the largest market for exports. The United States and Canada make approximately 27% of the global production [34]. Hence, the NG industry has a sizable impact on Canadian economic health through Gross Domestic Product, employment, and taxes and royalty payments to the provincial and federal governments [34], [35]. Moreover, available NG resources in Canada are found to sustain the current production level for up to 300 years [34]. As shown in Figure 2-1, the marketable NG production in Canada is predicted to rise even with the current emission reduction policies [16]. Therefore, the demand for NG is expected to be more resilient over the years to come. Hence, significant shifts are required within the industry to cut down its supply chain emissions. However, before moving towards emission 19 reduction within the supply chain, it is essential to understand supply chain operations and their emission intensiveness. Figure 2-1: Marketable natural gas production prediction with current emission reduction policies As illustrated in Figure 2-2, the NG supply chain can be discussed under three stages: upstream, midstream, and downstream industry. Figure 2-2: NG Supply Chain As given in the figure, the upstream processes involve gas exploration, drilling, and production of raw NG. Upstream companies may also manage the pipelines. Midstream activities are NG processing, storing, and gathering pipelines. Companies in this stage manage processing plants, storage facilities, and pipelines [34]. The downstream industry comprises long-haul transmission pipeline networks and local distribution companies (LDCs) [34]. NG transmission involves a pipeline network that transports NG from the processing facility to the local distribution centers. 020040060080020052007200920112013201520172019202120232025202720292031203320352037203920412043204520472049Natural gas produciton (Mm3/day)Nova Scotia New Brunswick OntarioAlberta Saskatchewan YukonNorthwest Territories British Columbia Canada Total 20 The downstream of the industry is the end-use stage. NG is used extensively in residential and commercial heating and cooling, industrial processes, transportation, and agriculture [34]. In addition, the Canadian NG industry has a bright future with LNG exports [36]. However, NG supply chain operations are responsible for a considerable amount of GHG emissions. 2.2.1 NG supply chain emissions GHG emissions occur throughout the NG supply chain, specifically methane emissions in upstream and downstream CO2 emissions. The emissions within the supply chain can be either intentional (design releases such as venting from well completion) or unintentional (fugitive emissions and system upsets) [37]. There are a number of emission sources within the NG supply chain. For example, the production of NG involves several energy-intensive equipment. Moreover, significant construction activities are involved in the construction stage of the production site. Emission sources during production are intermittent emissions from wells, gas vented from pneumatic devices and fugitive emissions. Upstream emissions may vary depending on the type of gas formation. Thus, emissions may differ largely between different gas deposit sites [38], [39]. Emissions from transmission mainly occur from fugitive methane leaks and fuel combustion for pumping [38]. Table 2-1 provides emission sources at each supply chain activity. According to the table, literature often categorizes the emission sources as fugitives (unintentional releases), flared (undestroyed methane due to incomplete combustion at upstream), vented (intentionally release methane throughout the supply chain), line heating (methane from midstream from incomplete stationary combustion), and burner tip (methane released at end-user burners due to incomplete combustion) [20]. Based on field measurement studies, the largest emission sources within the NG supply chain are fugitive emissions from pneumatic devices, compressors, hydrators, and venting and flaring [20]. 21 Table 2-1: Emission sources by supply chain stage Stage Supply chain activity Emission source Description Reference Upstream Pre-production Site preparation Emissions from construction materials, equipment fuel usage, and electricity for pad development [40], [41], [42] Exploration and drilling Hydraulic fracturing Well completion Extraction Fugitives Embodied emissions from construction materials, chemicals, fuels, and electricity for well drilling Pneumatic venting Liquid tank venting Flared gas Liquids unloading Workovers Abandoned well leakage Gathering Compressor station venting and leaks Venting, flaring, and fugitive emissions from process upsets and equipment depressurization. Embodied emission of pipeline material, electricity for the compressor station emissions from NG combustion for compressor stations, Compressor fuel Midstream Processing Compressor venting and leaks [40], [41], [43] Pneumatic venting 22 Stage Supply chain activity Emission source Description Reference Vented gas fugitive emissions Fuel â compression, H2S scrubbing, CO2 scrubbing, dehydration, hydrocarbon liquids separation Embodied emissions from chemical solvents, Emissions from energy consumption for pre-treatment and deep-cut Vented CO2 Flared gas Downstream Transmission and storage Compressor fuel Venting, flaring, and fugitive emissions [40], [41], [43][37] Compressor venting and leaks electricity for the compressor station, emissions from NG combustion for compressor stations, leaks from compressor seals and packing Pipework leaks Embodied emission of pipeline material, leaks from pneumatic devices, blowdown pipeline segments, equipment leaks such as unit isolation valves and scrubber dump valves Storage site venting and leaks fugitive emissions Distribution Pipework leaks fugitive emissions Metering and regulating station leaks and vents fugitive emissions 23 Stage Supply chain activity Emission source Description Reference Customer meter leaks and vents fugitive emissions End-use Emissions from combustion and emissions from incomplete combustion Burner tip and combustion emissions The projected increase in NG production makes supply chain emissions assessments more important. Supply chain activities are critical contributors to GHG emissions. Therefore, before exploring suitable decarbonization options, it is important to understand the; i. Distribution of GHG emissions from the NG supply chain and the contribution from each stage ii. Different factors that affect and impact emission intensities of each stage Therefore, a greater understanding of NG emission profiles is vital in making informed engineering and investments in the supply chain and making policy decisions to abate/regulate emissions. Several studies have assessed the emission profile of the NG supply chain [40], [44], [45]. However, given the different stages and complexities, credible estimations of the industry's specific energy and emission profiles at each stage are arduous and rigorous to produce. Hence, most of the referred studies have focused on determining the emission profile of a selected supply chain stage [44], [45], [46], and a few have explored the overall supply chain to determine the emission intensities at each stage [40], [47], [48]. Balcombe et al. (2018) determined the distribution of methane and carbon dioxide (CO2) emissions within the NG supply chain and explored factors that govern the distribution. The scope of the study included the whole supply chain from pre-production, production from shale, conventional, and tight reservoirs, to processing, transmission, storage and distribution ending at the customer meter [40]. As per the study by Balcombe et al. (2018), the lowest methane emissions (<0.1%) come from the production, gathering, and processing phases, while the highest come from the 24 transmission and storage phases. In terms of CO2 emissions, the most significant contributor is the NG processing stage, typically representing approximately 57% of overall CO2 emissions [40]. Pinti et al. (2016) extensively studied the anthropogenic and natural methane emissions using the naturally occurring methane in groundwater in shale gas areas [45]. Figure 2-3 provides a breakdown of emissions from NG upstream, midstream, and transmission stages. (The figure was derived using the average values from referred literature) [40], [47], [48]. According to the figure, the highest upstream CO2 emissions come from the NG processing stage. CO2 emissions from fuel usage, methane leaks from transmission, and vents and leaks from compressor stations are major potential emission hotspots in the NG supply chain [40]. In addition, emissions associated with liquid unloading and workovers also contribute to a large proportion of overall emissions [25], [40]. As per the findings, substantial emissions occur through fugitive emissions and losses throughout the supply chain during transmission. According to Charabi (2021), NG venting emissions account for most of the emissions, followed by transmission and storage [49]. Figure 2-3: Emission distribution in the NG supply chain Therefore, it is essential to strengthen the NG supply chain with more sustainable operations. The following section discusses the factors that govern the emission intensiveness of the above stages 2.2.2 Factors that govern the emission intensiveness Key factors that affect NG supply chain emissions are reservoir type, supply chain route, equipment, operational practices, and regional regulations [40]. Depending on the reservoir type, the primary production and secondary and tertiary recovery become highly energy and emission-intensive. However, the O&G operators have lower control over the reservoir type. The well type Well siteTransport from well to processingNG processingTransport from well to processing to utility provider 25 impacts the emission intensity at the well completion stage [40]. Moreover, engineering processes such as equipment age, technology use, and facility type affect the upstream emission intensity. For example, the emission sources during the NG production process are hub platforms that require more labour and equipment, auxiliary systems, emissions due to logistics etc. A small single-steel-jacket platform is less emission-intensive than a floating production, storage and offloading (FPSO) facility with subsea export infrastructure connected to several wells [50]. In addition, older equipment is less efficient and very costly to replace. For example, the piston rod packing systems are one of the largest emission sources in compressor stations, where all packing systems leak under normal conditions. The volume of leaks depends on cylinder pressure, fitting and alignment of packing parts, and the extent of wear on the rings and rod shafts [51]. Hence, aging infrastructure is responsible for higher fugitive emissions due to degraded parts. Process design choices can be options to answer challenges of equipment maturity [50]. Accordingly, the supply chain emissions change depending on categorical factors such as reservoir type, the material of construction, or equipment type. Table 2-2 (adopted from Balcombe et al., 2018) provides a summary of factors that affect the supply chain emission profile [40], [52]. Table 2-2: Factors affecting supply chain emissions Parameter Categories Description Reference Reservoir type High â low API gravity Reservoirs with high American Petroleum Institute (API) gravity and lower complexity have lower emission intensities than the ones with low API gravity and high complexities [50], [53] Well type Conventional, tight, shale Depending on the well type, well depth, and lateral lengths, the drilling technology changes [23][54] Process and engineering Hub platforms, single-steel-jacket platform, storage and offloading (FPSO), frequency of liquid unloading, workovers, the scope of methane leakage Depending on the facility type, the need for equipment and workforce changes. [23] 26 Parameter Categories Description Reference estimates, presence of co-products during NG production Equipment Reduced emissions completions (REC), Non-RECs Vent or flare Vent; Flare Vent due to separation, vaporization, and other routine work; essential flaring and flaring when there is no market and due to transport constraints [54] Type of fuel Renewable or non-renewable The type of fuel used to power operations determines the associated emissions [54] Material Cast iron, bare steel, protected steel, Plastic. Therefore, the key emission-intensive processes of the NG supply chain are production, processing, flaring and venting, and methane losses [54]. These emissions are divided into three categories, as depicted in Figure 2-4. Scope 1 emissions occur from industry operations and are within the control of the industry. Scope 2 and 3 are indirect emissions from purchased energy and end-use, respectively. The latter two emission categories are outside the control of the NG suppliers [55]. Accordingly, three main structural factors govern the emission intensity. They are reservoir type, processes and engineering, and flaring and venting [50]. From the above, the only path to eliminate emissions that depend on reservoir and well type is eliminating emissions from the source. That is replacing NG with cleaner gaseous fuels, which calls for new directions for the industry. The emissions due to flaring and venting can be managed through process optimizations. Proper process management practices can tackle emissions that depend on the process and engineering operations. In the next section, potential emission mitigation strategies are discussed for specific 27 emission sources identified within the NG supply chain stages, including production, processing, transmission, and distribution. Figure 2-4: Emission categories of NG supply chain Decarbonization Strategies Several plausible technological pathways exist to achieve deep decarbonization in NG supply chains. Based on the production strategies, there are three ways to reduce supply chain GHG emissions. First, industrial management where both supply and demand perspectives should be optimized to minimize material and energy losses. Second is making technical improvements at all stages of the supply chain. Third, expanding the portfolio of new business avenues such as hydrogen and Renewable Natural Gas (RNG) blending and shifting the business to producing such alternative fuels and Liquefied Natural Gas (LNG) exports. 2.3.1 Industrial management Management and optimization can be done under four categories: reducing energy demand, replacing energy resources, reducing venting and flaring, and improving the supply chain. Energy demand reduction includes cooperating with low-emission production methods and waste management. In energy resource substitution, energy resources can be substituted with highly efficient and environmentally friendly options. Under an improved supply chain comes activities such as pre-emptive maintenance and a faster response for emission detection [37], [40]. Reduce energy demand 28 The energy demand for supply chain operations can be reduced with simpler, modular, and reusable facilities with lower equipment and manning levels. It will, in turn, reduce emissions from energy use and logistics. Additionally, it can lower the energy costs within the supply chain, around 15% of the total production costs [50]. Furthermore, the energy demand can be reduced by improving process efficiencies and replacing old equipment with newer ones. However, this may result in high initial costs. Therefore, it is essential to assess its financial feasibility using methods such as internal rate of return, net present value, and payback period. Energy resource substitution Most assets in upstream processes are powered by hydrocarbon, including the produced NG. Therefore, the electrification of energy needs allows using renewable power in upstream operations. Electrification replaces additional gas use in the upstream processes. The benefits of electrification are; reduced emissions, maintenance, lower fuel requirement, higher uptime, and reduced consumption of NG [56]. The electrification can be done by either connecting to the grid or via decentralized renewable energy production. If electric engines are not powerful enough, hybrid (duel fuel) equipment can still reduce emissions compared to engines run by fossil fuel [56]. There are a few factors that affect electrification using local grids. Some grids can be powered by renewable energy (RE) sources, while others highly depend on fossil fuels. Emission benefits can only be achieved if the local grid is RE-based. Moreover, accessibility to transmission lines and other infrastructure needs should be assessed prior to the transition since infrastructure development may involve high economic and environmental costs. Furthermore, complete electrification with onsite RE generation faces the existing challenges of renewable power that include intermittency issues, challenges with retrofitting on-stream projects since it is practically difficult to completely electrify installations, space and weight restrictions, and high costs, specially in fields that have reached its maturity, and lack of regulations for upstream electrification [54]. In addition, a waste heat recovery system is also an option to reduce fossil fuel use within upstream and midstream operations. There are a few ongoing projects to electrify the upstream NG sector. One is the CleanBC Facilities Electrification Fund, which supports projects that reduce GHG emissions with the proposed extension of transmission infrastructure into the North Montney region [57]. The facilities are 29 provided with discounts ranging from 7-20% to promote electrification [57]. Existing and planned facilities with grid and renewable-based electrification are given in Table 2-3. Table 2-3: NG production facility electrification projects Facility Year Country Emission reduction RET type Reference Abu Sahaf Development 2003 Saudi Arabia NA Offshore grid-based [50] Johan Sverdrup 2021 NA [50] Hywind Tampen 2022 (due to start) Norway 200,000 Tonnes CO2 and 1000 ton NOx emissions supply 35% of energy demand in upstream Offshore wind [58], [59] Troll West NA Norway 500,000 tonnes CO2 Offshore grid-based [58] Canadian Natural 2022 Canada 270,000 tCO2-e/year reduction Solar [60] Canadian Natural Planning phase Canada 35,000 tCO2-e/year reduction per platform Wind [76] Reduce venting and flaring Venting and flaring in O&G operations occur due to a lack of facilities to collect, treat, transport, and utilize associated gases, offshore sites, gas impurities, and safety and operational reasons [61]. Therefore, venting and flaring can be reduced if three concerns are addressed. First, frequent maintenance and restarts of equipment. Hence, lower-performing assets need to be resolved to avoid repeat failures. Second, ensure operating parameters have not diverged significantly from the design points. The third is asset integrity, that cause increased fugitive emissions from points such as degraded flange joints, valves, and seals [50]. These can be achieved via upgrades to processes. Some of the best practices to reduce venting and flaring are; process optimization to determine the best operating points, predictive maintenance to reduce planned interventions and 30 extended time, and monetizing wasted gas. A summary of approaches to reduce gas flaring and venting is provided in Table 2-4. Gas reinjection: Gas injection is a popular strategy in the O&G industries. Enhanced oil recovery (EOR) by gas reinjection is an established technology. However, enhanced gas recovery (EGR) is a new topic with no field trial [62], [63]. The excess gas can be injected into the reservoir to push NG to the production wells. Major challenges with this method are the high cost of commodity CO2 and the risk of downgrading the NG resource. However, reinjection of mixed CO2 and methane reduces emissions while increasing the gas recovery efficiency. Another toward EGR is reduced costs in gas extraction and production. The method is widely available in North America, Europe, Asia Pacific, and the Middle East [64]. Acid gas injection (AGI): Acid gas flaring and venting were the most economical ways to handle acid gases until the early 1990s. However, with stringent policies and efforts to reduce gas flaring, sulphur recovery and acid gas injection became preferred alternatives [26]. However, sulphur recovery requires higher operating and maintenance costs than an AGI [58]. AGI allows a net reduction of direct GHG emissions from NG processing due to geological storage of CO2 and acid gases, with reduced gas flaring, venting, and reduced fossil fuel use for acid gas treatment [81]. Moreover, the AGI has been an established process since the early 1990s with multiple projects worldwide, including Alberta (Altra Gas) and British Columbia (BC), Canada. By 2002, Wester Canada had reported 42 approved operations for AGI [51], [81]. Power generation: Generating power from excess gas instead of flaring and venting is an existing option to meet onsite energy demand. Moreover, the generated power can be exported to the electricity grid [77]. The exported power is able to generate additional revenue for the industry. However, the quality of gas may impact the equipment's performance and durability. Moreover, the power generated could change depending on the NG production profiles. Hence, the power could be intermittent. To avoid the generated power can be stored in batteries. Moreover, some industries use gas quality detection mechanisms [82]. Flare/ vent gas utilization: Gas-to-liquid (GtL) is a method where methane is converted to transport liquids such as gasoline or diesel [77]. Mini-GtL technologies have been explored to convert smaller volumes of gases to transport fuel. This generates opportunities to utilize flare and vent gases [83], [84]. The technology is still in its development stage. Hence, its application in the 31 field is not viable due to high costs and technical risks. Several gas majors explore the application of GtL technologies. Fleisch (2014) [84] has reviewed companies that apply GtL and Gas-to-chemical (GtC) in the fields to avoid and reduce flare gas. According to Fleisch CompactGTL, Velocys, Oberon Fuels, and GasTechno were reported as the companies closest to applying GtL technologies at the commercial level [84]. Another approach would be converting GtC, such as methanol and ammonia. This is already in practice in the Persian Gulf oil-producing companies [77], [83]. Some effective gas-to-methanol conversion methods are Lurgiâs Mega Methanol, Mega Syn technologies, MTP, and Topsoeâs DME process. Moreover, methane can also be converted to ammonia by using the Haber process to produce fertilizer [85]. 33 Table 2-4: Technologies to reduce/ avoid gas venting and flaring Strategy Description Application Benefits Cons Impact Technology maturity Reference Gas re-injection Method where low-pressure formation gas is re-pressurized via a gas compressor Offshore O&G Increased efficiency Not economical for reservoirs with substantial gas reserves Proven to avoid 49 million tons of CO2 / year in Kazakhstan Commercial [77], [79], [83] Eliminate the need for gas transport facilities Heavy investment Acid gas injection Re-injecting acid gas (H2S+CO2) back to the reservoir to avoid acid gas pollution Applied during sulphur recovery and handling during NG processing when CO2 content is larger than 60% Acid gas injection is lower in cost than a sulphur recovery unit High risks in handling hazardous gases at high pressure Estimated reduction: ~ 60,000 â 110,000 â tonnes CO2-eq/year [81], [86], [87], [88] Avoid safety hazards that could occur from flaring Suitable only for selected reservoirs Avoid acid gas pollution 34 Strategy Description Application Benefits Cons Impact Technology maturity Reference Allow tertiary recovery Gas-to-power Use excess methane to power onsite energy demands Onsite energy generation, connect to the local grid Lower emissions The power could be intermittent depending on the production changes 0.45 million cubic meters per day of flared gas can generate ~ 40 MW of power Commercial [77], [82], [85] Improve economies with smaller size Gas quality may have an impact on the equipment performance and durability Additional revenue generation of generated power is exported to local grids 35 Strategy Description Application Benefits Cons Impact Technology maturity Reference Gas-to-Liquid and Gas-to-chemical Convert gaseous methane to liquid fuel and chemicals Transportation fuel production Reduce flare gas High technical risk Convert 8.5 MMscmd gas to 30,000 bpd diesel or gasoline GtL: Development stage GtC: commercial [77], [84] Ammonia and methanol production using methane Generate revenue through new business avenues High costs 36 Improved upstream operations An improved supply chain can help reduce GHG emissions throughout the value chain. As mentioned earlier, major GHG sources within the supply chain are fugitives, compressors, pneumatic devices, and venting. Emissions from the mentioned sources can be avoided by design changes, pre-emptive maintenance, and substituting or replacing components within the equipment [47]. Design changes: Design changes involve emission mitigation through changing processing systemsâ design and substituting or replacing equipment components [47]. Multiple methods are applied at each supply chain stage to mitigate emissions from its sources. One of the challenging sources is abating methane leaks. Any emission is easy to avoid if its source is detected [47]. Hence, leak detection and repair (LDAR) is a primary method to prevent fugitive emissions [47]. The current most widely used LDAR method is optical gas imaging. In addition to LDAR, there are several other GHG mitigation approaches, as given in Figure 2-5, which are applied at the field level. These mitigation options can reduce emissions from 60% to 100% [47]. Figure 2-5: Design changes to NG supply chain 37 Inspection and maintenance: Lack of proper inspection and maintenance are identified as key reasons behind methane leaks that occur from variations in pressure, temperature, and mechanical stresses on pipeline components such as valves and seals [89]. Moreover, the maintenance process is strictly linked with pipeline management. NG sector has a wide gas distribution network. Since it is scattered across the land, it poses threats to the environment, the safety of civilians, and property [90]. Therefore, adequate maintenance is essential to preserve the pipeline and equipment integrity. Over time, the maintenance of upstream to midstream processes has evolved from corrective, preventive, to predictive maintenance. Corrective maintenance is where an action is taken when a failure has occurred [90], and preventive maintenance is periodical maintenance to prevent or reduce equipment failures [91]. Predictive maintenance is an evolved maintenance strategy that improves the supply chain through process efficiencies and automation for condition-based equipment monitoring for efficient and optimum maintenance without frequent machinery checks [92]. These sensor technologies help predictive maintenance with real-time analytics to foresee and prevent component failures that help save costs. This is reportedly capable of providing cost savings of 10%-40% [92]. More modern strategies include risk-based, opportunistic, reliability-centred, and condition-based maintenance [90], [91]. Irrespective of the maintenance method, inspection is a key step in a maintenance schedule. The inspection involves LDAR. Periodic LDAR are conducted in NG facilities to mitigate fugitive emissions by surveying well pads and mid-stream compressor stations. This can reduce 60-80% of fugitive emissions from sites [47]. In addition to the above, Table 2-5 summarizes further actions to improve the NG supply chain emissions performance [72]. Table 2-5: Summary of additional actions for supply chain improvements Action Supply chain stage Description Benefits Reference Tank-less onsite water/ product management Upstream transport Replace storage tanks and tanker-trucks with pipelines âª Reduce emissions from tanker truck transport âª Reduce noise from transport [72] 38 Action Supply chain stage Description Benefits Reference Cap older wells Upstream extraction Cap older and low-productivity wells with new technology âª Reduce air emissions âª Free-up remediated land [72] Upgrade equipment Upstream, mid-stream operations Replace old operating equipment with upgrades âª Lower emissions [72], [93] Onsite transport reduction Upstream Install water pipelines within sites by replacing trucking âª Reduce onsite vehicular emissions [72] Community air monitoring Upstream, midstream, and downstream Air quality monitoring to establish baseline conditions and serve as an early warning to increase awareness of emissions âª Emissions reduction âª Increased data transparency for local communities [72] 2.3.2 Technological advancements The decarbonization technology at each supply chain stage varies depending on the process and equipment involved. The key emission sources identified in the literature are compressors, dehydrators, equipment leaks, pipelines, pneumatics/ controls, tanks, valves, and wells [94]. There is a portfolio of economically viable technologies to decarbonize extraction and production processes. Hence, technological improvements can be made to the mentioned sources to reduce GHG emissions from each point sources Key emission-intensive sources (equipment) involved in each stage and technological improvements to mitigate emissions from them are given in Table 2-6. 39 Table 2-6: Technological advancements Emission source Applicable stage Technological solution Expected Impact Pros Cost Reference Compressors Upstream production (onshore production platforms) Retrofit wet seal degassing recovery system Avoid methane leaks from compressor venting, 95% emission reduction Economic alternative CAPEX: $105,000 [95] Upstream: processing (Gas processing plants) Low compressor downtime Transport and distribution: Compressor stations Potential revenue generation from recovered gas Retrofit wet seals with dry seals Reduced maintenance: Decrease in the unit capacity loss by removing the drag in seals. 1% decrease in drag cuts fuel consumption by several percent. CAPEX: $675,000 [47], [58], [96] Mechanically simpler and high reliability: Service life prolongation, reduced maintenance (once in 1 to 3 years), lower compressor down 40 Emission source Applicable stage Technological solution Expected Impact Pros Cost Reference time, improved system reliability Reduce the rate of gas leaks: Methane emission reductions and elimination of flash gas emissions from seal oil. Reduced power consumption: As the dry seal system requires no auxiliary circulation pumps and systems Replace gas starters with air or nitrogen Reduce emission from venting during start-up Applicable to all compressors with NG turbine motor starters - [97] Avoid emissions from volume tank leaks Install automated Air/ Fuel ration controls Reduce the emissions from unburned hydrocarbons used in combustion engines Reduced engine fuel consumption - [98] 41 Emission source Applicable stage Technological solution Expected Impact Pros Cost Reference Lower maintenance cost Reduced CO2 emissions and other air pollutants Improved engine reliability and safety Production, processing and distribution Install electric motor starters Avoid emissions by replacing the conventional starter expansion turbine Gas savings and reduced emissions - [99], [100] Short payback period (~3 years) Dehydrators Production, processing, and transmission Replace glycol dehydration units with methanol injection Prevents hydrate formation that avoids methane emissions and loss of valuable product Reduced maintenance and downtime - [101] Sale of previously vented gas volumes Reduced methane emissions 42 Emission source Applicable stage Technological solution Expected Impact Pros Cost Reference Production, processing, and transmission Replace glycol dehydrators with desiccant dehydrators Reduce vented volumes of methane, VOCs, and HAPs Reduced capital costs - [102] Reduced O&M costs Minimal methane, VOC, and HAP emissions. Production, processing, transmission Place Portable dehydrators during maintenance Avoid methane vented from low-pressure wells during glycol dehydrator maintenance Improved revenue from recovering rather than venting gas during glycol dehydrator maintenance - [103] Prevent production interruptions Production, processing, transmission, and distribution Zero-emission dehydrators ( that include flash tanks, electric pumps, and electric control Avoid methane emissions from the venting of still column vapours and leaks from gas-driven glycol circulation pump and pneumatic controls. Reduced maintenance cost - [104], [105] 43 Emission source Applicable stage Technological solution Expected Impact Pros Cost Reference valves, desiccant dehydrators) Emissions are methane, VOC, and HAPs Reduced fuel cost for the reboiler Rapid return on investment through gas savings Increased operational efficiency Reduced compliance cost for HAPs and BTEX Production, processing, and transmission Convert gas-driven pneumatic pumps with alternative pumps Reduce methane emissions from the venting of NG-powered devices Operational and maintenance cost savings [106] Precise control of injection volumes and reduced chemical use Less downtime of wells and pumps 44 Emission source Applicable stage Technological solution Expected Impact Pros Cost Reference Dehydrators, pneumatics, and wells Production, processing, transmission, and distribution Convert pneumatics to mechanical controls Avoid methane bleeding from NG-powered pneumatic controllers Improved equipment reliability at remote sites [107] Lower O&M costs Replace high-bleed devices with low-bleed devices CAPEX: $1241 OPEX: $ 2,090 45 2.3.2.1 Carbon capture, storage, and utilization (CCSU) Carbon capturing technology is the sole means of capturing emissions from fossil fuel combustion. Therefore, CCSU is prominent in the gaseous fuel industryâs strategies and investments [108]. There are multiple drivers for CCSU investments in the industry. Some of them are compliance reasons, to achieve self-imposed targets, and to benefit from CO2 markets. Moreover, CCSU is the only option available to abate the scope 3 emissions from fossil fuel combustion. Another driver towards CCSU would be its potential to reduce additional revenue to the industry via CO2 utilization and for clean power and clean hydrogen production [109]. CO2 utilization is already in practice with enhanced recovery in the O&G sector since it improves recovery rates in closed-loop CO2 systems and improves production and emission performances [67]. The International Energy Agency (IEA) estimates that 700 Mt of CO2 can be captured from O&G operations, while 250 Mt of CO2 can be captured at a cost of less than $50/ tonne of CO2 [109]. According to a report by Global Carbon Capture and Storage (CCS) Institute, lower-cost opportunities for CCSU are available in NG processing. Therefore, in its early stages, CCS has been primarily used in NG processing. Currently, 10-19 CCS facilities are associated with NG plants worldwide. Figure 2-6 presents some of the CCSU projects applied in NG processing facilities over time [110]. Figure 2-6: CCSU projects related to NG industry 46 Another application of CCSU in EOR. This is an established technology that is already in practice at commercial scale. One project is the SaskPower Boundary Dam project in Saskatchewan, Canada [110]. Moreover, CCSU can be applied to capture CO2 emissions during onsite heat and power generation [109]. According to an assessment by the IEA, a cumulative of 72,948 and 26,942 Mt of CO2 can be captured from the overall NG supply chain and NG end-use in power generation, respectively from 2030 to 2070 under a sustainable development scenario [111]. The breakdown of potential emission reduction for the two instances are given in Figure 2-7 [111]. Figure 2-7: Predicted CO2 capture potential from NG sector The above shows that CCSU offers a number of synergies to the NG industry in achieving its decarbonization goals. 2.3.3 New business avenues It is essential for the NG industry to diversify its product portfolio with pressures on downstream to limit fossil fuel use. The NG industry could diversify its business model into a number of cleaner gaseous fuels. Renewable Natural Gas (RNG) and hydrogen are cleaner gaseous fuels that complement petroleum-derived NG as a strategy to reduce environmental impacts. The coming section discusses available new business avenues for cleaner gaseous fuel supply chains. 050010001500200025002030 2035 2040 2045 2050 2055 2060 2065 2070CO2 capture potential (Mt)Overall Supply Chain Power Generation 47 2.3.3.1 Renewable natural gas RNG, also known as upgraded bio-methane, is a gaseous fuel derived from organic materials and waste streams. Suitable feedstock for RNG production are agricultural and agri-food sources, manure and food processing waste, wood waste, municipal solid waste (MSW), and bio-solids from wastewater [47], [112]. Anaerobic digestion (AD) and gasification are available technologies to produce RNG [112]. Although gasification is still in its development stage in producing methane from biomass, it yields a higher methane content and is more versatile [112]. RNG is used to supply heat and power at a community and local scale. With further refining, this is proven to be suitable to power vehicles by converting it to compressed natural gas (CNG) [112], [113], [114]. Although complete displacement of NG by RNG is not practically feasible under current conditions, even partial substitution with RNG allows accelerated emission goal achievement in the NG sector, with minimal disturbance to the existing infrastructure. In particular, Canadaâs RNG potential is reported to be able to reduce up to 10.5 Mt of CO2-eq /year if 5% of NG is displaced [112], [113]. In 2020, there are 900 NG-powered vehicles in BC, Canada alone [115]. Thus, vehicular GHG emissions can reportedly be reduced by 90% when RNG is used for CNG-powered vehicles. In addition, this creates a revenue stream for other economic sectors, such as agriculture, forestry, and MSW management, helping municipalities achieve a circular economy [112]. The current RNG potential in Canada is slightly over half the current annual gas consumption of 2500 bcf / year [112]. The largest RNG potential exists in BC, followed by Ontario and Quebec, due to the availability of feedstock [112]. There is a large opportunity to integrate RNG into NG streams due to existing NG pipeline infrastructure, existing RNG providers, and ongoing RNG projects [116]. Once the fuel is processed to meet the existing NG pipeline specification, NG suppliers can integrate RNG into their business model by using it interchangeably or mixing it with NG [112]. RNG blending can be done without significant modification to existing infrastructure at upstream and end-user equipment. One important factor to consider is the proximity to the existing pipeline infrastructure to avoid additional costs for laying new transmission pipelines. Farm-based RNG production has the potential to provide 36% of the feedstock requirement of RNG production in Canada. However, the lower energy density of agricultural feedstock, fragmented availability, and regulatory barriers limit the RNG production potential [112]. In 48 addition, the municipal sector is another key producer and consumer of RNG, with a potential 13% feedstock supply in Canada [112]. However, extensive purification is required when using MSW for RNG production due to the high concentrations of nitrogen and oxygen in landfill gas. In addition, biomass is an intermittent resource since it is produced seasonally. Moreover, there are challenges to securing long-term biomass supply. Hence, biomass storage is essential for a reliable fuel supply [114], [116]. Furthermore, these barriers add up costs to RNG facilities' capital [112]. Some additional costs include initial capital and operational expenditure, RNG conditioning costs to match pipe specifications, connection costs, and contingency costs. However, these greener initiatives are heavily funded by the government to reduce its costs. Moreover, tax credits and offsets may lower RNG costs. Detailed cost components are provided in Figure 2-8 [113]. A large-scale RNG AD supply costs are approximately $ 8/ GJ, while small-scale farm-based AD projects are as high as $15-20/ GJ [112]. These costs depend on gas plant capacity and facility location [112]. Furthermore, the lack of awareness and higher initial costs limit RNG deployment in Canada. Figure 2-8: Additional cost/ benefit components of integrating RNG 49 2.3.3.2 Hydrogen Hydrogen is a cleaner fuel that can be produced from methane or renewable sources such as wind, solar, biomass etc. [47], [117], [118]. This gaseous fuel is used in power-to-power, power-to-gas, power-to-mobility, power-to-fuels, and power-to-industry applications [47]. In addition, thereâs booming research on using hydrogen in freight transport [119]. Blending hydrogen with NG (hythane) is a power-to-gas application currently in the pilot stage [120]. The accepted level of hydrogen blend often depends on the design specification and operations of all elements in the NG value chain, including pipeline, storage, and end-user systems [47], [120]. Hydrogen has a higher energy content than NG and does not emit GHGs during combustion. Therefore, hydrogen fuel deployment has become a crucial component of community energy planning. For example, according to the report; British Columbia Hydrogen Study [119], hydrogen deployment in BC is essential to meet 2030 and 2050 provincial decarbonization goals [119]. Figure 2-9, adopted from [121], depicts different economic sectors in Canada and their contribution to the national GHG emission inventory while showing the sectors that use hydrogen as their energy source [121]. Figure 2-9: Hydrogen markets 50 According to Figure 2-9, it is clear that GHG emissions can be reduced to a greater extent by blending hydrogen. For example, using hydrogen by displacing fossil fuels in the transport sector improves air quality by reducing other air pollutants such as sulphur dioxide, nitrogen oxides, and particulate emissions [121]. According to the British Columbia hydrogen study, the use of hydrogen shows the potential of abating 1.9 Mt CO2-e/year and 15.6 Mt CO2e/year by 2030 and 2050, respectively, in economic sectors, including O&G, transport, and industries [119]. Moreover, integrating hydrogen into the NG industry will help leverage NG reserves ad infrastructure to meet emission reduction goals [119]. One already existing pilot-scale business model is blending NG with hydrogen. When blending hydrogen with NG, the following factors need to be considered. The blending %, energy density factor, density ratio, and safety factor [122]. Several researchers have studied the optimum hydrogen blending percentage in relation to the energy demand [120], [123], [124], [125]. Originally, 5% hydrogen blending was considered feasible. At present, up to 12% blending has been achieved [47], [123]. Moreover, it is often advised to inject hydrogen close to the principal connections of the NG network. Hence, the proximity to the pipeline infrastructure is a key factor that needs to be considered. Investment planning in the industry should consider pipeline integrity management, and future pipeline projects should account for hydrogen blending into the network. Moreover, NG storage facilities should assess the impact of adding hydrogen to the system [120] The decision on the hydrogen production method is also crucial in determining the business feasibility. The use of renewable-based hydrogen will reduce upstream NG emissions. However, this means high infrastructure costs for decentralized renewable energy plants. Moreover, RE is intermittent. Thus, complete reliance on renewable-based hydrogen will be challenging. One option would be to integrate carbon capturing to Stream methane reforming (SMR)--based hydrogen production from NG [120], [126]. According to studies, SMR with 90% carbon capture can reduce GHG emissions by 3% [120]. Another avenue for the NG industry would be to expand its product portfolio by introducing hydrogen directly in fuel cells for new niche markets such as electric vehicles, stationary power systems, and industrial off-road vehicles[119]. Furthermore, hydrogen produced can be sold as a feedstock in industrial applications such as producing renewable synthetic liquid fuels [119]. Another pathway for the industry would be exporting hydrogen to desired markets in regions within and outside Canada. Some of those likely major 51 hydrogen demand centers are East Asia and Europe because of insufficient local hydrogen production [127]. Some key challenges the industry may face are the impact on end-use systems, system durability and management, safety, and leakage [123]. Therefore, extensive assessments are required to determine the optimum blending levels, associated risks to property and life, system integrity, and optimal system operation and maintenance [123]. Some other challenges are associated costs, regulatory gaps, and lack of social acceptance [128]. Therefore, policy support is essential for accelerated hydrogen deployment. 2.3.3.3 Liquefied natural gas (LNG) LNG is a commercially available business avenue for NG suppliers. The fuel is produced through refrigeration of NG to -1620C after gas pre-treatment [77]. LNG is stored in LNG tankers. Liquefied gas is re-gasified for use after arriving at the gas market [34]. The global market is expanding with mega-liquefaction projects, particularly in Qatar, The United States, Australia, and Russia [129]. Moreover, there is an accelerated demand growth for NG, particularly in Asia, due to its current dependence on coal power [130]. Therefore, coal-to-gas switching is considered an effective initiative to reduce emissions in the Asian regions [20]. Therefore, the Asian region has become a favourable LNG market for NG utilities [60]. This will generate additional revue for the industry while contributing to global air quality improvement. Canadian LNG is less carbon-intensive than coal, and Canadian LNG facilities are specifically known to possess lower emissions intensities compared to LNG produced elsewhere in the world [131]. Hence, NG exports to countries like China creates avenues for the Canadian O&G sector to enter into international energy markets [132]. The global NG liquefaction capacity is predicted to increase by 100%, with the proposed and under construction LNG projects worldwide [133]. Currently, there are 18 proposed LNG export facilities in Canada, with the majority placed in BC (13) [134]. There are key areas that the industry should consider when expanding the product portfolio to LNG. They are; capital efficiency, supply chain optimization, downstream market development, decarbonization, and digital and advanced analytics [130]. This requires extensive feasibility 52 assessments. Moreover, the export of LNG will create a platform for emission trading and internationally transferred mitigation outcome (ITMO) generation [34], [135], [136]. Decarbonization Strategy Implementation As discussed in the previous sections, several decarbonization strategies are applicable for the NG majors. Unfortunately, these effective decarbonization strategy benefits are often overshadowed by their barriers to implementation. This section discusses the challenges to the practical implementation of those strategies and remedial actions. Furthermore, the identified risks and remedial actions have highlighted the need for a systematic guideline for effectively integrating decarbonization technologies. 2.4.1 Risks associated with decarbonizing strategies There are several challenges to the integration of the above decarbonizing strategies, including high costs, lack of expertise, lack of awareness, equipment integrity, and inadequate policies supporting investments in decarbonization [72], [111], [137]. These impose financial, market, management, and technology risks on investment decisions. A summary of barriers to successful decarbonization strategy implementation is provided in Figure 2-10. As given in the diagram, one of the key challenges is the lack of financing within the industry. Some of the discussed decarbonizing strategies, such as integrating advanced technologies and investments for new business avenues, require extensive capital financing. Limited availability of funds within the industry could be a barrier to the successful implementation of those strategies [111]. Moreover, the lack of economies of scale for novel business avenues and by-product generation may pose a financial risk to strategy implementation's initial and operational costs. Furthermore, demand uncertainty is another risk with moving to new markets. Furthermore, a lack of consumer awareness may also be a barrier to gaining profits from new business ventures [127]. In addition to the market and economies, an investment's success largely depends on planning and management. Some of the risks associated with this arena are difficulty in sharing best and differences in priorities of decision-makers [72]. Furthermore, lack of proper management leads to many other issues, such as community resistance and mismanagement of local resources such as water, land, and other cultural resources [72]. 53 The discussed strategies require expertise within the industry since they are highly technical. With the lack of expertise, the sustenance of the strategies may suffer from such technology risks. For example, any technical difficulty may impact the gas quality [85]. Furthermore, depending on the gas well location, some decarbonization efforts could become impractical or uneconomic [85]. For instance, onsite RE generation may not be possible or could be uneconomic, depending on the resource availability. In addition, electrification may not be possible in remote areas. Hence, onsite generators or turbines may be required, which increases the capital expenditure (CAPEX) and operation and maintenance costs of the strategy due to long-distance infrastructure and facility requirements. Moreover, excess gas generated in a remote location may demand infrastructure if the investors plan on exporting the excess power generated to the power grids [85]. Figure 2-10: Summary of risks associated with decarbonization investments In addition to that, the sustenance and drive to continue decarbonization is often motivated by policies and regulations. Limited mandates and incentives, unclear regulations, gaps in codes and standards, and lack of consistent policies de-motivate investing in technologies to improve the supply chain [138], [139]. Moreover, there is a lack of clear guidelines for implementing carbon policies and carbon market mechanisms in industries [140], [141]. Carbon policies play an important role in promoting greener investments in the natural gas industry. Hence, there is a need 54 for standardized guidelines to encourage investments in decarbonization projects and to facilitate carbon market mechanisms and associated policies for the NG industry. 2.4.2 Decision-making within NG supply chain As provided in Section 3.4.1, there are multiple risks and uncertainties associated with investments within a gaseous fuel supply chain. For example, the predicted demand growth of gaseous fuel use may vary depending on climate policies, user patterns, and other unexpected events such as a global pandemic [142]. Prior to the COVID pandemic, O&G majors have made limited investments (~1% of the revenue) in sustainable technologies with the main purpose of selling as a âgreen companyâ [143]. However, with rapid development in RE resources, the industry is putting efforts into decarbonizing its operations to remain competitive within the market [143], [144], [145]. Hence, technology and market evolutions impact investment decisions throughout the fuel supply chain. NG industry operations have opted to utilize potential synergies between NG and RE in powering its supply chain activities, such as NG well operations [146]. Furthermore, the NG industry is looking into market diversifications by integrating hydrogen and RNG into the supply chain [12], [143]. Therefore, multiple parameters affect decisions related to the NG supply chain. Moreover, investment decisions are affected by multiple stakeholder perceptions and priorities involving multiple criteria within sustainability's social, economic, and environmental pillars [147]. Multi-criteria decision-making (MCDM) is an applicable method to identify synergies and trade-offs in decision-making related to gaseous fuel supply chain decarbonization [148]. Multi-criteria analyses aggregate techno-economic, environmental, and social aspects and prioritize and rank suitable supply chain investment options. These alternative investments are assessed using carefully selected criteria based on data availability, and criteria weights are given to suit the decision scenarios [148], [149]. MCDM has been employed in multiple studies in the gaseous fuel supply chain management arena. Analytical Hierarchy Process (AHP) has been employed by Billing and Thraen (2017) to evaluate the TBL performance of different production processes of RNG [150]. Debbarma et al. (2017) have explored MCDM approaches to choose the most suitable fuel combinations among bio-diesel, hydrogen, and diesel alternatives. Analytic Hierarchy Process (AHP) has been used for finding the weightage of each criterion, and ranks of the alternatives have been calculated using VIKOR and PROMETHEE II methods [151]. 55 Holistic performance assessment tools and frameworks serve as systematic guides to effectively incorporate these new technologies into the current energy infrastructure. Wanniarachchi et al. (2023) [152] introduced a holistic model to assess the techno-economic and environmental performance of hydrogen production technologies applicable to the transport sector. The study ranks these technologies based on the triple bottom line performance but is limited to integrating hydrogen into the transport sector. Wijesekara et al. (2022) [153] assess waste-to-hydrogen technologies' environmental and economic performance while proposing a methodology to prioritize and rank these production technologies. However, this study focuses solely on the performance of waste-to-hydrogen technologies, and the supply chain is confined to the production stage. Salkuyeh et al. (2017) [154] present techno-economic analyses and life cycle assessments of four hydrogen production technologies using natural gas as a feedstock. While numerous studies exist on techno-economic and environmental assessments of hydrogen production, only a limited number focus on RNG or biomethane production paths. Kotagodahetti et al. (2023)[15] proposed a novel framework that integrates life cycle thinking and multi-criteria decision-making to select and prioritize RNG technologies. Moreover, the authors demonstrate how these decisions would vary based on stakeholder priorities. However, nearly all the referred literature concentrates on the production stage or the entire supply chain, adhering to a single economic sector and a specific fuel type. Triple bottom-line performance assessments represent the first steps of the decision-making process when integrating novel gaseous fuels into the gas grids. The subsequent step involves jointly considering RNG and hydrogen to determine their combined feasibility. Quintino et al. (2021) [155] stand out as one of the few studies that have assessed the integration of both biomethane and hydrogen into the existing gas grid. The authors conducted a technical analysis encompassing energy, material and operational aspects associated with introducing hydrogen and biomethane to natural gas infrastructure. According to the study findings, existing gas infrastructures generally accommodate hydrogen and biomethane with only minor technical modifications[155]. Zhao et al. (2020) [156] investigated the influence of RNG and renewable hydrogen on the operating performance of residential room furnaces. The mentioned few studies have assessed the integrated technical performance of blending hydrogen and RNG into existing gas infrastructure. 56 2.4.2.1 Multi-objective optimization In addition to selection and prioritization with MCDM approaches, Multi-Objective Optimization (MOO) is employed to determine the optimum state of operation. Several studies have conducted MOO on the fuel supply chain considering multiple criteria [26], [157], [158]. Azadeh et al. (2015) present a multi-objective fuzzy linear programming model to optimize the NG supply chain while in-cooperating uncertainty. The authors have included financial parameters such as NG production costs, transportation costs, and a socio-environmental parameter (social costs of GHG emissions) in the optimization model [157]. Dara et al. (2020) have introduced an economic model that optimizes the profit margin of the NG supply chain considering techno-economic parameters [158]. Moreover, multiple studies have conducted a techno-economic and environmental assessment of the NG supply chain, while a few have included a life cycle perspective in the evaluations [21], [64], [159]. However, there are limited instances of optimizing the supply chain process in the industrial sector, taking into account the entire spectrum from resources to the market. For instance, Bakhtavar et al. (2019) [160] developed a stochastic-based goal programming model to optimize the cement supply chain, spanning from mineral resources to potential demands. In contrast, considerable research has been conducted on the optimization of the hydrogen supply chain, predominantly focusing on the transport sector. Han et al. (2013) [161] utilized Mixed Integer Linear Programming (MILP) to ascertain the optimum number of hydrogen production plants, their locations, and technologies, considering the overall supply chain from hydrogen production to the end-user in the transport sector. Shamsi et al. (2021) [162], developed a MILP-based MOO model solved by GAMS (General Algebraic Modeling Software) to examine the optimal sizes and locations of hydrogen production plants and refuelling stations in Ontario, Canada. The non-dominated sorting genetic algorithm-II (NSGA-II) is another widely used in MOO. It is an evolutionary algorithm that can easily incorporate user-defined preferences and constraints, making them suitable for problems where the decision-makers preferences play a crucial role in the optimization process [163]. While these studies cover the supply chain from fuel production to final distribution, none of the referred literature explores the optimum resource allocation for generating these gaseous fuels. 57 The production of green hydrogen and RNG encounters limitations due to resource availability. Therefore, exploring alternative resources for RNG and hydrogen production is beneficial in ensuring a reliable supply. Zeng et al. (2015) [40] present a multi-objective model aimed at determining the proportion of various energy generation sources within a utility portfolio to minimize both risks and costs per kWh of electricity generated. The authors employed failure mode and effect analysis to calculate the risk, utilizing the levelized cost of electricity as the cost indicator. The model employs the particle swarm optimization (PSO) algorithm in the context of multiple-objective decision-making. Bakhtavar et al. (2020) [164] formulated a MOO model using weighted goal programming and grey pairwise comparison to determine the optimal community energy mix, minimizing life cycle environmental and cost impacts and maximizing renewable contributions and operational energy savings. The authors employed different weighting scenarios to reflect the decision priorities and interests of stakeholders. Although these studies offer optimum resource allocation for electricity production, none of the existing literature has delved into identifying the optimum resource combination for producing hydrogen and RNG. In addition, there is a significant gap in the literature that addresses the multiple economic sectors that can benefit from the utilization of cleaner gaseous fuel instead of NG. The literature review highlights the need for systematic frameworks and tools to support gaseous fuel supply chain planning. The current review suggests a general decision support framework for investments in the gaseous fuel supply chain. The framework is presented in Figure 2-11. The framework provides an overview of a systematic approach to determine the optimal or most suitable gaseous fuel supply chain configuration while providing key performance criteria to focus on. The performance assessment can be conducted using standard tools such as life cycle assessment, life cycle cost assessment, life cycle sustainability assessment, and Greenscope etc. [13], [165]. Sustainability assessments in gaseous fuel supply chains face uncertainties such as fuel price fluctuations, future developments in technology maturity, geopolitical conditions, energy demand, inflation, evolving taxes, tariffs, regulations, etc. are common amongst supply chain sustainability assessments [166]. Therefore, many similar studies have employed methods such as Monte-Carlo, Taguchi-orthogonal array, and fuzzy logic to represent data uncertainties [20], [50], [167]. 58 Figure 2-11: Road map for decision support in gaseous fuel supply chain investments 2.4.3 Life cycle thinking in supply chain planning Environmental and economic viability plays an important role in the pre-project planning stages of gas supply decarbonization. Often, studies are limited to techno-economic and environmental feasibility concerning the operational stage. However, relying solely on operational impacts is inadequate, as it fails to provide a complete picture [15], [168], [169]. Thus, it is imperative to assess overall life cycle environmental and cost impacts. Numerous researchers have appraised the techno-economic and environmental performance of cleaner fuel production, encompassing their overall life cycle [153], [170], [171], [172], [173], [174]. These performance assessments offer an overview of the supply chain of hydrogen and RNG fuels, from production to downstream end-use. Multiple studies in the literature have assessed the environmental performance of RNG networks, while a few have gone beyond the operational stage emissions to evaluate the life cycle emissions and other environmental impacts using a life cycle thinking perspective. Rai et al. [175] explored the global warming potential (GWP) of selected RNG production pathways, namely anaerobic 59 digestion (AD), thermal gasification, and power-to-gas using various feedstock. However, the study only considered the GWP impacts when reaching out at an investment decision. Lee et al. [31] evaluated the life cycle GHG emission savings of RNG over NG for different types of waste feedstock. Moreover, each RNG pathwayâs potential economic value has been explored. The life cycle assessment (LCA) tool has been used in most studies to evaluate GHG emissions. However, other critical environmental impacts, such as human health and the impact on the ecosystem, are often ignored in multiple studies. A few published literature have considered both techno-economic and environmental assessments to determine the overall performance of RNG pathways over NG networks [176], [177]. In this regard, Speirs et al. [178] examined the technical potential and associated environmental and economic impacts of greener gaseous fuels (biomethane and hydrogen) to replace NG. Uddin et al. [177] conducted an economic and environmental impact analysis of RNG production with AD. They explored the impact on the selling price and GHG emissions with economic and process parameters based on process simulations conducted using Aspen PlusTM. However, their methodology lacked the generalizability to determine the cost of RNG pathways. Singlitico et al. [19] review highlighted the need for LCA-integrated techno-economic and thermodynamic assessment modes, GIS-based infrastructure design, and uncertainty quantification to make informed investment decisions considering the technical, economic, and environmental performances of green gas production systems. However, unlike life cycle thinking-based environmental assessments, none of the referred studies have assessed the economic performance of energy projects based on life cycle costs (LCC). A holistic life cycle perspective is essential to determine an investment decisionâs actual costs and benefits. Salkuyeh et al., 2018 [179] conducted a techno-economic and LCA of two hydrogen production methods via biomass gasification. The authors suggest that biomass-based hydrogen production results in negative life cycle emissions since biomass is a carbon-neutral feedstock [179]. However, the study has only evaluated and compared the techno-economic and life cycle environmental performance of biomass-derived hydrogen processes. A harmonization protocol for the life cycle global warming impacts of renewable hydrogen technologies has been proposed by Valente et al. [180], to reduce the disparity in hydrogen LCA studies. However, the study has not considered other environmental impacts such as human health, resource impact, ecosystem 60 damage, etc. Chen et al. [181] conducted an LCA on a solar-based supercritical water gasification process to produce hydrogen. According to this study, 58% of the environmental impacts come from the system operation, and the highest environmental emissions have resulted from the construction of the solar concentrator in the construction stage. This highlights the importance of studying the overall life cycle rather than limiting it to the operational stage. Koroneous et al. [182] conducted a similar study, where a comparative LCA was used on hydrogen production from natural gas SMR and renewable water electrolysis. Moreover, multiple other studies have employed the LCA tool to explore the overall environmental impacts of hydrogen production technologies [183], [184]. Amongst the techno-economic and environmental assessments of hydrogen production routes, a few of the referred studies have extended the life cycle perspective to the economic pillar. Sadeghi et al. [185] investigated the cost and life cycle emissions of four hydrogen production paths, i.e., natural gas SMR, coal gasification, solar photovoltaic (PV), and solar thermal electrolysis, to evaluate and compare GHG abatement costs. As per the study findings, the cost is a key challenge for the market penetration of solar-based hydrogen production [185]. Kazi et al. [186], proposed a strategic framework to design a green hydrogen supply chain network using mixed integer linear programming based on the techno-economic performance of hydrogen routes. While several studies explore the life cycle impacts of hydrogen and dRNG production routes, this information needs to be integrated into a systematic methodology for the decision-making stage. Most energy planners overlook the incorporation of holistic life cycle thinking into the decision-making process. Yet, achieving the objective of minimizing environmental and cost impacts proves challenging without considering the energy system from its cradle-to-grave [15], [187], [188]. Furthermore, decisions are often made considering only the current conditions and operational impacts. However, complete system implications can be estimated with a long-range planning perspective. 2.4.4 Long-range supply chain planning According to the literature survey conducted, RNG and hydrogen socio-economic viability is subjected to a wide range of dynamic parameters, including market conditions, supply and demand variations, infrastructure development, and federal and regional policies and regulations. Therefore, decision-making that considers only the current conditions may not be accurate as it 61 may lead to oversupply, excessive/ insufficient funding, and infrastructure under/overutilization [41]. Hence, effective and efficient decarbonized gaseous fuel industry planning should consider the dynamic parameters simultaneously. Moreover, the causal effect between the parameters should also be taken into consideration. It is important to note that there could be non-linearity between the parameters. Failure to account for that in decision-making could further lead to biased conclusions [189]. In contrast to the traditional long-range analysis techniques such as regression analysis, gray prediction methods, and autoregressive integrated moving average models [41], system dynamics modelling allows the simulation of complex systems while building feedback mechanisms that evolve with time [190]. When modeling complex systems, their problems and causes are identified with a causal loop diagram, which is eventually converted to a system dynamics stock and flow diagram [191]. Furthermore, system dynamics modeling is beneficial in instances where there is a lack of data or if data accuracy is questionable [189], [192]. Model variables and their behavior can be determined from past studies, expert consultations, and similar past system behaviors. System dynamics modeling has been applied by a number of studies for long-range planning of energy systems. Rebs et al. [29] reviewed studies that have employed system dynamics modeling to support strategic supply chain management and decision-making. As per the review, system dynamics modelling has been effective in integrating environmental and social sustainability, government policies, subsidies, incentives, and customer expectations into multi-period supply chain management. Barisa et al [43], evaluated future policy design for biomethane use in the Latvian transport sector employing system dynamics modeling. The model covers the key supply chain stages, including resource availability and corresponding biogas supply and end-use. The developed model aims to determine the efficiency of policy instruments placed on accelerating the transition from biogas for electricity to upgraded biogas in transportation. The key findings of the study indicate the need to limit subsidies for electricity producers while increasing the incentives for biomethane producers. Moreover, the study indicates that tax policies are required to increase the price of competing fossil fuels. Li et al. [189] applied system dynamics modeling to evaluate the effectiveness of subsidy policies for the development of the green hydrogen industry in China. According to the study results, the key policies that promote green hydrogen are subsidies for capital expenditure, hydrogen production electricity charges, and income tax. Furthermore, the 62 study highlights the importance of adjusting the subsidy policy intensiveness according to the change in scale, technological progress, and costs. Gao and Zhang [193] conducted a similar study that evaluated the effect of public funding on the commercialization of on-site hydrogen production using system dynamics modeling. The key findings derived from the study are that moderate funding helps to establish the scale of on-site hydrogen production in its early stages of market development and further enables buffer time to upgrade technology. Additionally, the results highlight the excess funding that could lead to stagnated hydrogen supply and demand. This study further validates the importance of long-range planning while simultaneously considering the causal effect relationships between dynamic parameters. Accordingly, the comprehensive literature review revealed that system dynamics modeling is capable of conducting long-range techno-economic and environmental feasibility assessments and policy simulations to effectively reflect the dynamic evolution of the decarbonized gaseous fuel industry with RNG and hydrogen. However, only a limited number of studies have assessed the dynamic supply and demand relationships of hydrogen and RNG fuels individually. Moreover, most studies focused only on predicting the economic viability of green gas alternatives. Additionally, the preferred system dynamics models have not considered the impact of resource availability on fuel production. Summary NG industry decarbonization involves progressively reducing GHG emissions to a consistent level that helps achieve emission goals. This highlights the need to explore solutions to further reduce the emission generation from the NG sector, considering the overall supply chain. Amongst multiple decarbonization options, creating new business avenues with RNG and hydrogen is considered a transformative opportunity for the gaseous fuel industry. While the inclusion of cleaner gaseous fuel may lead to significant reductions in emissions, many other factors affect the effective integration. RNG and hydrogen fuel integrated supply chains should be configured with careful planning. The common practice is making investment decisions primarily based on rates of return. However, business decisions need to be made by in-cooperating the cost of emission into the calculation. Moreover, several dynamic factors, such as changing regulations and rapidly evolving societal 63 expectations for clean energy, make it difficult to determine the most critical assets across the gaseous fuel value chain. In addition, policy and regulatory interventions are essential to incentivize companies to invest in and sustain decarbonization strategies. The information gathered revealed some key research gaps. Most techno-economic analyses have considered the costs and benefits at the operational stage. However, economic studies on cradle-to-grave life cycle costs of emission abatement are limited. While there are techno-economic and environmental performance assessments of RNG and hydrogen fuels, the knowledge is scattered. The practical inclusion of these fuels in conventional gas networks requires integrated assessments considering all three pillars of sustainability. Yet, the literature lacks holistic frameworks that support supply chain assessment and investment decision-making related to alternative fuel integration. Furthermore, several studies in the existing literature have employed various MOO approaches to study the subject of energy sector supply chain optimization, utilizing diverse focuses and techniques. However, to the best of the authorâs knowledge, the number of studies that have focused on optimizing the gas grid composition is limited, if not non-existent, in the literature. Besides, multi-period feasibility assessments of integrated gaseous fuel supply chain management are limited or, non-existent. Therefore, the practical implementation of decarbonizing strategies remains a challenge. 64 Chapter 3: Gaseous Fuel Supply Chain Configuration Selection: Life Cycle Thinking-based Decision Analysis Parts of this chapter genreated the following journal articles: Kotagodahetti, R., Hewage, K., Razi, F., & Sadiq, R. (2023). Comparative life cycle environmental and cost assessments of renewable natural gas production pathways, which is published in the Energy Conversion and Management (Elsevier) Journal. Another journal article is under review: Kotagodahetti, R., Hewage, K., Bakhtavar, E., & Sadiq, R. (2023). Gaseous fuel supply chain configuration selection: A life cycle thinking-based decision support framework. Expert Systems with Applications (Elsevier). Two conference articles titled: Renewable natural gas as a greener energy source: A life cycle cost-benefit analysis, and Decarbonized natural gas supply chain with low-carbon gaseous fuels: A life cycle environmental and economic assessment are published in CSCE 2021 and 2022 Conferences. Background The energy industry is a major economic sector, but it is increasingly recognized that business-as-usual is no longer applicable to achieve the 2050 net-zero emissions target [194], [195]. Therefore, key players in the energy markets are seeking ways to cut emissions within the industry itself. One emerging approach is gas industry portfolio diversification with RNG and hydrogen. Among the available cleaner gaseous fuels that can be integrated into the existing gas networks, RNG takes a leading role due to various reasons, including the ability to use it interchangeably with existing infrastructure and the ease of directly replacing fossil natural gas with minimum modifications to the gas networks. RNG is also capable of avoiding upstream emissions from fossil natural gas production. However, it still emits a similar quantity of carbon emissions at the user phase due to similar composition of methane. Hydrogen, on the other hand, eliminates emissions at the user phase. However, the overall emissions from the hydrogen supply chain depend largely on the production technology. Currently, the most common approach to hydrogen production is natural gas steam methane reforming (SMR) [196]. The drawback of this method is the emission-intensive natural gas production at upstream phases. Therefore, carbon capturing is integrated into hydrogen production to capture and store carbon emissions from the hydrogen production process [197]. Green hydrogen, on the other hand, is produced entirely from RE sources using the water 65 electrolysis process. This method is considered carbon-neutral, and green hydrogen is thus considered a more promising fuel to alleviate environmental concerns surrounding natural gas systems [198]. While it is evident that the operational phase of RNG and hydrogen carries lower emissions than fossil natural gas, the overall life cycle environmental impacts may not be the same once embodied emissions involved with the processes are considered [199]. Therefore, investment decisions should be made with pre-feasibility assessments spanning the overall life cycle considering environmental, techno-economic, and social pillars [200]. Life cycle assessment (LCA) and life cycle cost assessment (LCCA) tools are useful in determining the environmental and economic costs of RNG and hydrogen routes from its raw material extraction to the end-of-life phase. This would further enable decision-makers to weigh the positives and negatives of investments with complete and comprehensive assessments considering the overall value chain. Additionally, handling uncertainty is a crucial element for realistically planning energy investments. Previous studies have used various techniques to enhance the accuracy of the results, including Monte Carlo analysis, fuzzy logic, and grey numbers, among others [201], [202]. While Monte Carlo simulations are commonly used for incorporating uncertainty into decision-making, the method has limited capacity when dealing with epistemic uncertainties [203]. Fuzzy logic is another useful tool to address vagueness and ambiguity in data, and it allows for the systematic incorporation of variations in importance weights placed on the criteria within the decision environment. As highlighted by similar studies, community energy-related decision making is a multi-criteria process, and there are often causality relationships among the decision [201]. Fuzzy cognitive map (FCM) is a useful approach that considers such causality relationships between criteria while accounting for uncertainties [204]. The conventional approach of investment decision-making often takes an ad-hoc form. The main problem with the ad-hoc approach is that there may be hidden impacts that could cause serious consequences [205]. The comprehensive literature review revealed that numerous studies have conducted assessments on the feasibility of RNG and hydrogen production technologies and supply chains to supplement the natural gas industry for emission reduction. However, none of the reviewed studies have explored a complete and systematic methodology for selecting an RNG or hydrogen production technology that addresses practical challenges. 66 Therefore, this chapter aims to provide a preliminary decision support framework for selecting different RNG and hydrogen production paths for the upstream natural gas industry. A novel hybrid approach is presented by importing weights into multi-objective optimization by ratio analysis (MOORA) as an MCDM method. Criteria weights are derived considering both the importance placed on criteria based on decision makers' perspectives and the causality relationships among them. Hence, this chapter introduces a novel criterion weighting approach that combines fuzzy cognitive maps (FCM) and the best-worst method (BWM). The main advantage of the causality-importance weighting system (CIWS) is its ability to simultaneously consider both internal-external-based causality relationships and the importance weights of the criteria, which could result in more realistic decision-making. The results are further compared using the TOPSIS method. Overall findings will be useful in developing effective and robust decision-making frameworks for prioritizing and selecting the best supply chain configurations for the gas industry with RNG and hydrogen. Methodology This chapter focuses on developing a life cycle thinking-based decision support framework to compare and prioritize suitable âcleanerâ gaseous fuel supply chain configurations for diversifying the natural gas industry portfolio for industrial emission reduction. The proposed framework addresses the following aspects: 1. Screening technically viable RNG and hydrogen production technologies. 2. Defining potential alternative upstream gaseous fuel supply chain configurations with RNG and hydrogen to reduce overall natural gas industry emissions. 3. Prioritizing RNG and hydrogen production routes based on the life cycle triple bottom line (TBL) performance under varying decision environments. The framework first selects potential RNG and hydrogen production technologies based on their technology readiness level (TRL) to ensure minimum market risk. Next, RNG and hydrogen production scenarios were developed and assessed based on their life cycle environmental and economic performance using LCA and LCCA tools. The evaluated scenarios were then ranked and prioritized based on environmental desirability, economic viability, and social acceptability. Gaseous fuel supply chain scenarios were ranked and prioritized using FMOORA method with 67 CIWS derived from FCM and fuzzy BWM. FMOORA was applied due to its ability to solve complex problems with less computational effort. Furthermore, the method is known to have higher stability and robustness when compared to other methods such as AHP, VIKOR, and TOPSIS [206]. Cognitive mapping logically links the causalities between criteria. BWM on the other hand, is capable of handling a large number of criteria and their interrelationships. Moreover, it enables to weight the criteria by their importance. However, the combined use of FCM and BWM is scarce and only a limited number of studies offer any evidence of practical application of these two methods. Hence, other than the simplicity of applying FCM and BWM, the current study combined the two methods due to their ability in facilitating decision making in different context. Four CIWS-based weighting scenarios were considered in the current study to compare the impact of the weights placed upon the criteria indicators subjected to causalities in between and the stakeholder importance placed upon them. The developed framework was demonstrated through a case study for a site in Okanagan, British Columbia (BC), Canada. However, the framework is applicable for any location by employing the parameters considered in the study. Moreover, fuzzy logic has been employed to incorporate the impact of locational variations and data uncertainties. Figure 3-1 provides details of the overall methodological steps. All the employed mathematical techniques are explained in detail below. 68 Figure 3-1. Methodological framework 69 3.2.1 Technology screening and scenario development Multiple existing and emerging RNG and hydrogen production technologies were identified via a thorough literature survey. The literature survey revealed three main RNG production technologies, namely, anaerobic digestion (AD), thermal gasification (TG), and power-to-gas (P2G) [207], [208], [209]. The current study focuses on AD-based RNG production methods since they are already in operation in multiple municipalities across BC. In addition, the availability of organic feedstock in the selected region was also taken into account. The AD is integrated with biogas upgrade units to meet the pipeline specifications and blend RNG with natural gas. Multiple biogas upgrade technologies are listed in Table 3-1. Similarly, existing hydrogen production technologies were reviewed. Hydrogen is classified as grey, blue, and green based on the production technology. Fossil fuel-based hydrogen production technologies such as natural gas steam methane reforming (SMR), and coal gasification are known as grey hydrogen. Grey hydrogen technologies integrated with carbon capture and storage are named as blue hydrogen. Accordingly, the identified RNG and hydrogen production technologies were screened based on their technical maturities, which indicated their closeness to commercial readiness. Technologies with Technology Readiness Levels (TRL) above 7 were carried forward for the next stages. The definitions for TRL are provided in Table A-1. 70 Table 3-1: Technology readiness levels of selected technologies Technology TRL Reference RNG- biogas upgrade Chemical scrubbing 9 [210] High pressure water scrubbing 9 Membrane separation 8 Cryogenic separation 6 Pressure swing adsorption 8 Hydrogen production technologies Wind electrolysis 9 [210] Solar PV electrolysis 9 Grid electrolysis 9 Natural gas steam methane reforming 9 Natural gas steam methane reforming with carbon capture and storage 7-8 Methane pyrolysis 3-5 Biomass gasification 5-6 Coal gasification 9 Coal gasification with carbon capture and storage 6-7 Upon selecting RNG and hydrogen production methods based on the TRLs, gaseous fuel production configurations were generated. For RNG, 12 scenarios were defined considering the feedstock type and biogas upgrade technologies. For hydrogen 13 scenarios were generated 71 depending on the feedstock for grey and blue hydrogen. Green hydrogen production scenarios were defined based on the energy source and water electrolysis method. The details of defined scenarios are provided in Table 3-2. Table 3-2: Details of Analyzed scenarios RNG scenarios Code Scenario Feedstock Biogas Upgrade Technology R1 RNG_LG+MB Landfill waste Membrane separation R2 RNG_LG+PSA Landfill waste Pressure swing adsorption R3 RNG_LG+CHS Landfill waste Chemical scrubbing R4 RNG_LG+HPWS Landfill waste High pressure water scrubbing R5 RNG_WW+MB Waste water sludge Membrane separation R6 RNG_WW+PSA Waste water sludge Pressure swing adsorption R7 RNG_WW+CHS Waste water sludge Chemical scrubbing R8 RNG_WW+HPWS Waste water sludge High pressure water scrubbing R9 RNG_LVS+MB Livestock manure Membrane separation R10 RNG_LVS+PSA Livestock manure Pressure swing adsorption R11 RNG_LVS+CHS Livestock manure Chemical scrubbing R12 RNG_LVS+HPWS Livestock manure High pressure water scrubbing Hydrogen scenarios Code Scenario Energy source/ feedstock Technology H1 H_WIND+AE Wind water electrolysis Alkaline electrolyzer H2 H_WIND+PEME Proton exchange membrane electrolyzer H3 H_WIND+SOEC Solid oxide electrolysis cell H4 H_SOLAR+AE Solar PV water electrolysis Alkaline electrolyzer H5 H_SOLAR+PEME Proton exchange membrane electrolyzer H6 H_SOLAR+SOEC Solid oxide electrolysis cell 72 H7 H_Grid+AE BC grid water electrolysis Alkaline electrolyzer H8 H_Grid+PEME Proton exchange membrane electrolyzer H9 H_Grid+SOEC Solid oxide electrolysis cell H10 H_SMR Natural gas Steam methane reforming (SMR) H11 H_SMR+CCS SMR with carbon capturing H12 H_CG Coal Coal gasification H13 H_CG+CCS Coal gasification with carbon capturing Next, the socio-environmental and economic performance of generated scenarios were conducted using LCA and LCCA tools. 3.2.2 TBL Performance evaluation The TBL performance was evaluated from a life cycle thinking perspective. The economic and socio-environmental performance of defined gaseous fuel configurations were assessed using LCCA and LCA assessment tools, respectively. The performance assessment criteria were selected from published literature to represent the three sustainability pillars: the environment, economy, and society. Table 3-3 provides all the criteria indicators used in the performance assessment. Environmental and human health performances were assessed using LCA. The life cycle impacts obtained from LCA were aggregated into four damage categories, namely, climate change, impact on the ecosystem, resource depletion, and human health. The economic performance was evaluated via LCCA, and mathematical calculations were performed using preliminary data from the literature. Three performance indicators were employed to establish the financial viability and economic feasibility. First, the LCC values for each RNG and hydrogen path were obtained from the LCC model. The LCCA aggregates all cost elements spent over the lifetime of a process [34], [211]. The LCOE was calculated to represent the unit price of RNG and hydrogen to reach breakeven [212] [149]. It is often used in comparing energy projects with varying fuel mixes, investments, and net energy outputs [213], [214], [215], [216]. Unlike conventional studies that use capital expenditure and operational and maintenance costs as separate performance indicators, the current currently study employed LCOE and LCC to represent all cost elements with 73 aggregated performance indicators. The profitability index (PI) was used to determine the viability of investments [217]. Table 3-3: Performance criteria indicators used in the study Criteria Performance indicator Unit Reference Environment Climate change GHG emissions kg CO2 eq [14]â[16][218] Ozone depletion kg CFC11 eq [219] Impact to eco-system Acidification kg SO2 eq [211], [220] Eutrophication kg P eq [218][142], [211] Eco-toxicity kg 1,4-DCB [211], [220] Resource depletion Water depletion m3 [212], [218] Metal depletion kg Cu eq [212], [218] Fossil depletion kg oil eq [212], [218] Society Human health Human toxicity kg 1,4-DCB [212] Ozone formation (human health) kg NOx eq [211], [220], [221], [222] Particulate matter kg PM2.5 eq [219] Ionizing radiation kBq Co-60 eq [212] Economy Financial viability Life cycle cost (LCC) $/kWh [218] Economic feasibility Levelized cost of Energy (LCOE) $/ MMBTU [211], [223] Profitability Index (PI) [217] 74 3.2.2.1 Life cycle assessment LCA assesses the environmental performance of a product, process, or service covering the entire life cycle. The LCA modelling was carried out using the software SimaPro. As per the ISO 14040 standard, LCA consists of four main stages [214], [218]. They are: 1. Goal and scope definition 2. Inventory analysis 3. Impact assessment 4. Result interpretation A project life of 20 years was assumed for both RNG and hydrogen production paths since it is the average plant life as per previous assessment studies and reports [224] [181]. The environmental assessment aims to assess the aggregated environmental impacts of the defined RNG and hydrogen production routes. A functional unit of 1MJ gaseous fuel leaving the plant gate to the utility providerâs facility was considered. LCA of RNG production The LCA was conducted under the phases, namely, facility construction, RNG facility and biogas upgrade unit operation, RNG compression and pipeline injection, and waste disposal. Moreover, the construction and operational impacts of connection pipelines were included in the assessment. The end-of-life was assumed to be incinerated, recycled, or landfilled based on published literature [225]. Figure 3-2 illustrates the system boundary employed. The boundary begins with feeding feedstock to the AD and ends with RNG compression for pipeline injection. It was assumed that any supplemental NG is not required to achieve the target gas purity for pipeline injection [226]. In the second stage, the life cycle inventory (LCI) was compiled. The inventory analysis involves compiling all the inputs and output data for the entire life cycle of the processes [218]. LCI data was obtained from the Ecoinvent Version 3.5 database and published literature [176], [225], [227], [228], [229]. Appendix B provides LCI data gathered from literature and published reports. The impact assessment was conducted using the ReCiPe Midpoint method to ensure higher accuracy of results [167], [218]. 75 Landfill separated wasteWaste water sludgeAnimal manureAnaerobic digestionBiogas upgradeâ¢ Membrane separationâ¢ Pressure swing adsorptionâ¢ Chemical scrubbingâ¢ High pressure water scrubbingRNG Compression and injectionBiogas RNGHeat and electricityEmissionsHeat and electricityHeat and electricityDigestateProcess wasteEmissionsEmissions and leaksLandfillWaste waterFarm animalsPipeline RNG for distributionCONSTRUCTION OPERATION END-OF-LIFE Figure 3-2: Life cycle assessment system boundary of RNG production routes LCA of hydrogen production Similar to RNG production, a cradle-to-gate system boundary was applied for the LCA of hydrogen production paths. Other GHG-generating activities, such as site exploration and land-use change were not considered as they were assumed negligible [205]. Figure 3-3 illustrates the system boundary of the hydrogen production paths considered. In the LCI analysis stage, all inputs and outputs within the defined boundary of the processes were identified, collected, and quantified [26],[29]. The LCI data were gathered from different databases and published literature. The study considered a hypothetical hydrogen production facility with 20,000 H2 tonnes/ year capacity. All background processes, such as construction material production and transport, were modelled with the Ecoinvent Version 3.5 LCI database [183]. 76 WindRun-off river hydroGeothermalAlkaline electrolyzerHydrogen compression and storageCompressed hydrogen at plant gateOPERATION END-OF-LIFESolarBiomassGridDecentralized energy systemProton exchange membrane eletrolyzerSolid oxide fuel electrolyzer cellElectrolyzer technologyCONSTRUCTIONFossil fuel-based technologyNatural gas SMRCoal GasifcationFossil natural gasCoal Figure 3-3: Life cycle assessment system boundary of hydrogen production routes The data for decentralized renewable energy systems were obtained from literature and the Ecoinvent database [179], [181], [183], [185], [224], [232]The study assumed that electricity is 100% provided by the decentralized energy system, except for the grid-powered electrolysis scenarios. Since infrastructure related to grid power is already constructed, the impacts from infrastructure construction were assumed to be sunk environmental impacts. The Renewable Energy (RE) system capacities were calculated according to the assumed hydrogen facility energy requirements. Upon life cycle impact assessments, the results were normalized and aggregated based on criteria weights. Four main indicators, namely climate change, damage to the eco-system, resource depletion, and human health risk, were used for the aggregation, as used in similar studies [218]. 77 Moreover, the impacts were further aggregated to determine a final environmental performance score for fuel production routes. The normalization factors and aggregation weights are given in Appendix C. The data for construction materials for the AD plant and hydrogen production plant in published literature corresponds to plant sizes of 500 m3 AD unit and 20,000-ton H2/ year, respectively. Therefore, the environmental impacts are scaled to the capacity of the currently considered RNG and hydrogen production sites using Equation 3-1 [225], [233]; ð¬ð = ð¬ð (ðªððªð)ð.ð Equation 3-1 Where, E2 = Environmental impacts of current plant E1 = Environmental impacts of the reference plant C2= Capacity of the current plant C2= Capacity of the reference plant Appendix D provides the capacity calculations based on the resource availability in the case- study location (Kelowna, BC). 3.2.2.2 Life cycle cost assessment The life cycle cost assessment (LCCA) tool was used to assess the economic impacts of gaseous fuel production routes during the life cycle. LCC is the aggregation of all costs over the life cycle of a system [234]. Hence, it includes initial capital, operational and maintenance expenses, and the decommissioning and dismantling expenditure. The primary cost figures were obtained from published literature. The economic performance of the defined gaseous fuel scenarios was evaluated using LCC, LCOE, and PI. The LCCs was obtained by adding the present values of each cost element. Equation 3-2 was used to calculate the LCC [235]; ð¿ð¶ð¶ =ð¼ð¶+â ð¹ðð¶ðð=1 +â ððð¶ðð=1 +ð¶ðð·ð¸ Equation 3-2 Where, 78 LCC = life cycle cost ($/kWh); IC = Investment cost ($); FOC = Fixed operation and maintenance cost ($/year); VOC = variable operation and maintenance cost ($/year); COD = Cost of disposal ($); n = Service life; E = Life cycle energy generated (kWh). The present value of non-recurring cash flow was calculated using Equation 3-3 [236]; PV = FV(1+r)t; Equation 3-3 Where, FV = Future cash flow ($); r = Discount rate. A discount rate of 8% was assumed [235]. The present value of recurring cash-flows was calculated using Equation 3-4 [236]; ððð´ = ð´[(1+ð)ð¡â1]ð(1+ð)ð¡; Equation 3-4 Where, A = annually recurring cash-flow ($/year) In the current study, LCOE was calculated to represent both capital and operational expenditure using Equation 3-5 [237]; ð³ðªð¶ð¬ = âð°ð+ ð´ð(ð+ð)ððð=ðâð¬ð (ð +ð)ððð=ðâ Equation 3-5 It = Investment expenditure for year t ($); Mt = O&M expenditure for year t ($/year); Et = Energy generated in year t; The profitability index (PI) was calculated using Equation 3-6 79 ðð¼ =ðð ðð ððð â ðððððð¤ð ðð ðð ððð â ðð¢ð¡ðððð¤ð Equation 3-6 Cost parameter estimations: RNG production routes RNG investments involve several cost elements including gas production costs, network and storage costs, and cost implications on the end user [178]. Capital expenditure: The capital cost element includes the AD plant, biogas upgrade process, RNG compression, and injection to the pipeline. In addition, the quoted costs for the AD plant include costs for the feedstock preprocessing unit, digester unit, gas collection and storage facility, and digestate handling and processing unit. Base cost data were obtained from published literature [177]. The costs for RNG compressor, pipeline, and injection point construction were also obtained from the literature [177] and are provided in Table 3-4. It was assumed that the produced RNG would be taken to a central location of the NG utility provider before being distributed to the RNG end-users. Table 3-4: Cost data used for the economic assessment Cost parameter Cost element Base Capacity/ year Unit Cost Reference Capital investment AD unit 2000 SCFM (2009) $ million 16-18 [217] Compressor Gas separator Dryers Nitrogen injection unit (if N content is high) 2000 SCFM (2009) $ Million 3 [217] Pipeline costs $ / mile 428,333 [177] 80 Cost parameter Cost element Base Capacity/ year Unit Cost Reference Gas monitoring and management facility $ million 1.3 [177] The capital expenditure of plant elements was calculated based on the plant capacity estimates, assuming the gas flow is directly proportional to the extra equipment needed. A scaling factor of 0.6 was used [176], [177], [217]. ð¶ðððð¡ðð ðð¥ðððððð¡ð¢ðð = ðµðð ð ððð ð¡ Ã ( ð ððð¢ðððð ð¶ðððððð¡ð¦ðµðð ð ð¶ðððððð¡ð¦)0.6 Equation 3-7 The capital cost of the biogas upgrade unit was calculated using equation 3-8 [238]; ð¶ðððð¡ðð ððð ð¡ ðð ðððððð ð¢ðððððð ð¢ððð¡ = $ 130,000 Ã (ð ððº ðððð¤)0.56 Equation 3-8 Operational and maintenance costs: The Operational cost includes two components: variable and fixed operating costs. The variable operating cost includes expenses for feedstock, chemicals/consumables, electricity and heating charges, and transport costs [177]. Zero costs were allocated for feedstock since it was assumed to be taken from the landfill/farm/WWTP itself. Moreover, feedstock transport was not considered assuming feedstock is collected on-plant location. The variable operational and maintenance (O&M) costs without the electricity charge for the AD were calculated using equation 3-9 [217]. Fixed O&M costs were assumed to be 15-20% of the capital cost [217]. ðððððððð ð&ð ð¤ðð¡âðð¢ð¡ ð¡âð ððððð¡ððððð¡ð¦ ðâðððð ððð ð´ð· = ðµððððð ð¦ðððð (ðð¶ð¹ð) Ã ð Equation 3-9 Where, m = Operational and maintenance rate (USD 0.22/1000 SCF) [217] The electricity charge was calculated using equation 3-10 [217]. ð¸ð¸ð¡ðð ð¡âð ð´ð· = ðµðº (ðð¶ð¹ð) Ã ð¾ Ã ð·ð¾ðð» Equation 3-10 Where, 81 EEt= Electricity expense of the AD K= kWh electricity consumed per year per cubic ft of gas flow DKWH= price of electricity ($/kWh) The investment and the O&M costs of the biogas upgrade unit and pipeline components were calculated using the assumptions provided in Table 3-5. Table 3-5: Assumptions made for the Operational and maintenance cost evaluation Cost Parameter Unit Value Reference Operational hours Hours/year 8610 Facility construction period year 1 [176] Discount rate % 10 [239] Plant life years 20 [176], [239] Water charge $/m3 1.5 [240] Electricity charge $/kWh 0.128 [241] Cost of insurance % Capital expenditure/month 2 [176] Cost of maintenance %investment cost/year 3 [176] Other operational costs %investment cost/year 0.005 [176] The revenue from the RNG sale was obtained by determining the RNG sale price. The retail price of gas obtained from FortisBC rates [242]. The gap or the premium price that has to be paid by the customer was calculated by reducing the difference between the RNG and natural gas rates. Cost parameter estimations: Hydrogen production routes Similar to the RNG production route cost estimations, the capital costs and operational and investment costs of hydrogen production plants were estimated. The main design parameters directly impacting plant costs are hydrogen production volumes and the electricity mix. The hydrogen production volume governs the capacity of the hydrogen plant and electric energy 82 requirement. The electric mix determines the size of the RE plant [232]. The current study assumed that the decentralized RE system would supply 100% electricity needed. The capital costs include general facilities, engineering permits and start-ups, contingencies and working capital, and land and miscellaneous costs. Moreover, the capital costs are estimated for fuel handling and storage costs [243]. The operational costs include raw materials and water, labour, and electricity. The maintenance cost was considered a faction (6%) of capital expenditure. The key cost data gathered for the hydrogen production facility is presented in Table 3-6. Table 3-6: Cost parameters for the Hydrogen production facility Life cycle phase Cost parameter Unit Description References Construction Investment cost of the electrolyzer CAD/kW AE: 1077 â 2019 PEME: 1885-2827 SOEC: > 2692 [244] Operational Average electricity price CAD/ kWh 0.126 [245] Price of water CAD/L 0.0016 [246] Annual maintenance cost % CAPEX AE: 3-5% PEME: 2-3% SOEC: 2% [244], [247] Replacement year Year AE: 8-15 PEME: 9 SOEC: 5 [16] Labour cost $/year 36,575 [16] Similar to RNG production path cost estimations, the capital expenditure of plant elements were calculated based on the plant capacity estimates, assuming the gas flow is directly proportional to 83 the extra equipment needed. A scaling factor of 0.6 was used employing equation 3-7 [176], [177], [217]. The capital cost of the electrolyzer (ð¶ðð) was calculated using Equation 3-11 [248], [249] ð¶ðð = ððð Ã ð¼ðð Equation 3-11 Where; Pel= rated power of the electrolyzer (kw) Iel = specific investment cost of the electrolyzer ($/kW) The annual operational and maintenance (O&M) cost consists of the cost of electricity, water, and variable O&M costs (ð¶ð&ð) is calculated according to Equation 3-12 [248]. ð¶ð&ð = ( ð Ã ððð Ã ð¢ðð Ã ðð) + (ð¾ Ã ðð»2 Ã ðð¤) + (ð¶ðð Ã ð) Equation 3-12 Where; Ï= Total number of hours in the year (hours) uel= utilization rate of the electrolyzer ce= electricity price ($/kWh) γ= water required to produce 1kg of hydrogen (L/kg H2) cw = price of water ($/L) Ï= Maintenance cost as a fraction of capital cost The annual hydrogen production was calculated using Equation 3-13 [248]; ðð»2 =ðÃðððÃð¢ððð¸ðð Equation 3-13 ð¸ðð is the electrolyzer power consumption (kWh/kg H2 produced). Electrolyzer replacement cost at year "t" was calculated using Equation 3-14 [248]; ð¶ð ð¸ð = ð¶ð ð¹ Ãð¶ððð¡ððð ð¸ð(1+ð)ð¡ Equation 3-14 84 The capital recovery factor (CRF) converts the initial capital into a series of equivalent yearly payments over the system lifetime N, for a given interest rate. CRF was calculated using Equation 3-15 [248]; ð¶ð ð¹ = ðÃ(1+ð)ð(1+ð)ðâ1 Equation 3-15 Appendix E 0provides further data approximations and calculations conducted to obtain the cost figures. 3.2.3 Multi criteria decision making Parameters that affect the feasibility of hydrogen and RNG investments belong to multiple criteria. They can be considered simultaneously when prioritizing and selecting the most suitable investment strategy with MCDM techniques. However, the performance values of the defined alternatives may vary due to data ambiguities and other associated uncertainties. The current research employs fuzzy MCDM to reduce the effect of data uncertainties. Moreover, the methodology presented in this chapter proposes a novel weighting method that combines fuzzy cognitive maps (FCM) and the best-worst method (BWM) to reflect the combined effect of causalities between criteria and stakeholder priorities. The following section provides the detailed methodology applied. Decision-making under uncertainty Gaseous fuel supply chain planning and energy system-related problems are often subjected to uncertainty due to regional conditions, fuel price fluctuations, energy demand variations, etc. Therefore, performance data obtained from the life cycle TBL analysis carry a considerable level of uncertainty [205]. In addition, problems of this nature involve multiple stakeholders whose opinions are often based on linguistic evaluations [250], [251], [252] . Hence, the importance of weights placed upon the performance criteria imposes further uncertainty on the decision-making problem. Fuzzy logic is commonly used in gaseous fuel supply chain problems [250], [251], [252]. Therefore, in the current study, the LCA and LCCA results were assumed to be fuzzy based on the approximations made. 85 A membership function âµâ was used to define an uncertain parameter âð¥â, where âð¥â values within the membership function range from 0 to 1 based on Equation 3-16 [253] . µð´(ð¥) â [0,1] Equation 3-16 The minimum and maximum boundary values of the fuzzy numbers were established, assuming a 10% tolerance for data variability, and the set tolerance limit can be adjusted based on the decision context. A higher tolerance reflects the increased uncertainty associated with data approximations. The LCI data and cost data were converted to fuzzy triangular numbers. Fuzzy arithmetic operations were performed to determine the criteria indicator scores. Criteria weights Supply chain investment planning and management rely heavily on stakeholder preferences placed upon decision criteria. The importance of criteria varies based on the causal relationships [254]. For example, increased environmental pollution could have an increased impact on human health. Moreover, high emissions result in a higher carbon tax, which is a direct cost to the industry. However, traditional weighting techniques are not capable of capturing the causal relationships between decision criteria. Fuzzy cognitive maps (FCM) is a soft computing tool utilized in analyzing complex systems and determining causal weights of criteria indicators. Additionally, to determine the importance weights of criteria, the best-worst method (BWM) was employed (a) as it is a structured approach to conduct pairwise comparisons, (b) rather than AHP, only two vectors are involved instead of a full pairwise comparison matrix, (c) simplicity and revising flexibility to improve the consistency level of comparisons [255], [256]. In the current study a novel criterion weighting system is proposed by considering the impacts of the internal and external causal relationships and the criteria importance arising from stakeholder priorities to derive a more realistic weighting scheme using FCM and BWM. 3.2.3.1 Criteria weights with fuzzy cognitive maps Criteria indicators have complex relationships with each other and with decision goals in a causal situation. The cognitive map method is an approach that analyses complex causal relationships between decision criteria [204], [257]. Internal and external causal relationships among decision criteria and the goal of the investment strategy selection problem affect the reliability of the decisions made [201]. Therefore, the current study employed FCM method to determine criteria 86 weights for the parameters given in Table 3-3 while accounting for the causal relationships between them. The following assumptions were made when developing the FCM. â¢ The relationships between the criteria and goals are linear â¢ Criteria not connected by a causal-effect relationship are independent in terms of their influence on one another â¢ Impact of one criterion on another on a scale of -1 to +1, and there are no qualitative effects that may affect the relationships between the criteria In an FCM, nodes or concepts are denoted by Ci, and the relationships among them are illustrated by weighted arcs [201]. The weight (wij) represents the degree of causality and the type of relationship between the concepts/parameters. Positive relationships are shown by wij > 0, negative by wij < 0, and when wij=0, there is no relationship among the concepts. Equation 3-17 presents the mathematical illustration of FCMs by Ai for each concept Ci with the transformation function âfâ [201]. ð´ðð+1 = ð(ð´ðð + â ð´ðð . ð¤ðð)ðð=1,ðâ ð Equation 3-17 Where, Aik+1 = Concept value of Ci in the k+1 iteration Aik = Concept value of Ci in the kth iteration Ajk = Concept value of Cj in the kth iteration wji = Weight of the interconnection from concept Cj to concept Ci Learning algorithms are applied to cognitive maps to estimate criteria weights, as they increase the accuracy of estimated weights and the reliability of decision-making and limit the dependence on expert inputs. Inputs based on expert knowledge and experience were applied in designing the cognitive map and tracing the initial causal relationship between concepts. A hybrid learning algorithm combining nonlinear Hebbian algorithm and differential evaluation as a meta-heuristic algorithm was used to consider the effect of the main criterion on the goal of increasing the reliability of results [201] . The obtained weights were imported into the fuzzy multi-criteria decision-making environment. 87 For the problem of selecting the most feasible RNG and hydrogen production investment, the main goal was divided into two sub-goals (G1-G2), and the causal relationships between sub-criteria and sub-goals were defined based on expert consultations. Table 3-7 provides the causal relationships defined for sub-criteria and goals. Table 3-7: Causal relationships defined Overall impact GWP Impact to eco-system Human Health Impacts Resource depletion LCOE LCC PI EN EC C1 C2 C3 C4 C5 C6 C7 C8 G1 G2 Overall impact C1 0 0 0 0 0 0.5 0.4 -0.5 0.9 0.4 GWP C2 0.9 0 0.6 0.8 0.6 0.7 0.5 -0.5 0.9 0.5 Impact to eco-system C3 0.6 0.5 0 0.6 0.5 0.2 0.2 -0.1 0.6 0.2 Human health C4 0.6 0.8 0.2 0 0.2 0.7 0.7 -0.6 0.8 0.7 Resource depletion C5 0.5 0.6 0.6 0.5 0 0.4 0.4 -0.4 0.7 0.4 LCOE C6 0 0 0 0 0 0 0 -0.9 0 0.9 LCC C7 0 0 0 0 0 0.8 0 -0.9 0 0.8 PI C8 0 0 0 0 0 0 0 0 0 -0.9 EN G1 0 0 0 0 0 0 0 0 0 0.6 EC G2 0 0 0 0 0 0 0 0 0 0 Investments on RNG and/or hydrogen production should prioritize minimal environmental impact and cost-effectiveness. To achieve these goals, the selection of the most feasible production route was divided into two sub-goals: minimizing environmental impacts (G1) and minimizing life cycle costs (G2). 88 Cognitive maps were developed to address the causal-effect relationships between criteria (C1-C9) and sub-goals (G1-G2), using values between -1 and 1. Pairwise comparisons were conducted on them. Table 3-7 summarizes these causal relationships. In FCM, the variables that cause change are cause variables, and those under the effect are effect variables. Table 3-7 is an adjacency matrix providing the influence level of decision criteria and goals. The value 0 indicates the absence of a relationship between criteria. A positive value indicates a positive relationship where an increase in the cause variable causes an increase in the effect variable. A negative number represents a negative relationship where the effect variableâs change occurs in the opposite direction [258]. For example, the overall environmental impact does not have any impact on the global warming potential. However, global warming potential has a very high positive influence (0.9) on the overall environmental impacts, making global warming a cause variable and overall environmental impact an effect variable. The LCOE has a negative relationship (-0.9) with the PI, where an increase in LCOE, decreases PI largely. Accordingly, the rest of the relationships were defined. The cognitive maps were then modelled in MATLAB to determine the importance weights of each criterion on the sub-goals, minimizing dependency on expert opinions [259]. 3.2.3.2 Criteria weights with best worst method Most studies have utilized subjective judgement or used methods such as AHP to allocate weights based on criteria importance [150]. However, BWM minimizes the subjective bias when determining the importance of criteria. Therefore, the structured process provides more consistent and reliable weight estimates compared to traditional methods. Further, it reduces the cognitive burden on decision-makers compared to the AHP method due to fewer pairwise comparisons and judgment involved [256], [259]. Therefore, BWM was employed to allocate criteria weights based on stakeholder preferences. A neutral stakeholder decision environment was taken for the current analysis. However, other decision environments can be accommodated using the same method. The BWM is a commonly used technique in similar studies to allocate importance to criteria subjected to stakeholder priorities [256], [259]. The steps taken in weight calculation are as follows: Step 1: Identify the best and worst criteria indicators among the criteria indicators ðð (i= 1â¦..n), where n is the number of criteria indicators. 89 Step 2: Define preference ranking for the best criteria indicator over the other indicators using a scale of 1-9. The preference ranking scale used in the current study is provided in supplementary materials. The best indicator over the other indicator vector is defined as: ðð = (ðð,1, ðð,2, â¦ â¦ . ðð,ð), where ðð,ð provides the rating of the best criteria indicator, be over the other indicators i, where ðð,ð= 1. Step 3: Similarly, using the same scale of 1-9 define preference ranking for all criteria over the worst criteria, in a vector form as; ðð¤ = (ð1,ð¤, ð2,ð¤, â¦ â¦ . ðð,ð¤)T, where ððð¤provides the rating of the worst criteria indicator, w over the other indicators i, where ðð¤,ð¤= 1. Step 4: In the next stage, the optimized weights of all criteria indicators are calculated as (ð1â, ð2â, â¦ â¦ . . , ððâ). The weights are calculated such that the maximum absolute differences of all âiâ are minimized of the set {|ðð- ðð,ððð |, |ðð- ððð¤ðð¤|}. Accordingly, Minmax {|ðð- ðð,ððð |, |ðð- ððð¤ðð¤|} s.t. â ðð = 1ð , ðð â¥ 0 for all âiâ Equation 3-18 the above model is converted to a linear model as min βL s.t. |ðð- ðð,ððð | â¤ βL, for all i |ðð- ððð¤ðð¤| â¤ βL, for all i â ðð = 1ð ðð â¥ 0 for all âiâ Equation 3-19 Solving Equations 3-18 and 3-19 provides the optimized weights (ð1â, ð2â, â¦ â¦ . . , ððâ) with the optimum value βL. Values close to 0 are desired for βL[260] . 3.2.4 Weighting scenarios The FCM approach considers the causality of the criteria indicators, while BWM takes the stakeholder priorities and importance of the criteria into account. However, none of the referred literature has considered the above considerations together. Therefore, the current study integrates both aspects using the weighting scenarios given in Table 3-8. 90 Table 3-8: Weighting scenarios Weighting scenario Causality relationships Internal relationships and importance Importance only 0 1 Causality only 1 0 CIWS: Equal causality & importance 0.5 0.5 CIWS: Pro-importance 0.25 0.75 CIWS: Pro-causality 0.75 0.25 The technologies were prioritized under five weighting scenarios. The first only used the weights obtained from FCM, considering only the causality relationships. The second weighting only involved the criteria of importance placed by the stakeholder. In the third, FCM and BWM were equally considered when assigning weights. The fourth and fifth weighting scenarios placed higher weight on criteria importance and causality relationships, respectively. 3.2.4.1 Scenario prioritization with FMOORA FMOORA is often employed in decision-making problems that are subjected to a wide variety of parameters. The current study employed the FMOORA method to enhance the reliability of results under uncertain conditions. Initially, the decision matrix containing fuzzy triangular parameter scores was derived based on the performance assessment of defined gaseous fuel scenarios. X represents the fuzzy decision matrix. Step 1: Define the fuzzy decision matrix ð = [ð¥11ð , ð¥11ð , ð¥11ð¢ ð¥12ð , ð¥12ð , ð¥12ð¢ â¦ â¦ â¦ . . ð¥1ðð , ð¥1ðð , ð¥1ðð¢ â¦â¦â¦.. â¦â¦â¦.. â¦â¦â¦. ð¥ð1ð , ð¥ð1ð , ð¥ð1ð¢ ð¥ð2ð , ð¥ð2ð , ð¥ð2ð¢ â¦ â¦ â¦ . . ð¥ððð , ð¥ððð , ð¥ððð¢ ] Triangular fuzzy number is denoted by ð¥ððð , ð¥ððð, ð¥ððð¢ where, ð¥ððð , ð¥ððð, ð¥ððð¢ are lower, middle, and upper values of the fuzzy number, respectively. The number of alternatives is given by m, and n provides the number of criteria. Step 2: Obtain the normalized decision matrix 91 ðððâ presents the normalized decision matrix with ð¥ðððâ, ð¥ðððâ, ð¥ððð¢â denoting the normalized lower, middle, and upper fuzzy indicator scores. Equations 3-20 to 3-22 were employed in calculating the normalized scores [204] . ð¥ðððâ = ð¥ðððââ (ð¥ððð )2+(ð¥ððð)2ðð=1 +(ð¥ððð¢ )2 Equation 3-20 ð¥ðððâ = ð¥ðððââ (ð¥ððð )2+(ð¥ððð)2ðð=1 +(ð¥ððð¢ )2 Equation 3-21 ð¥ððð¢â = ð¥ððð¢ââ (ð¥ððð )2+(ð¥ððð)2ðð=1 +(ð¥ððð¢ )2 Equation 3-22 Step 3: Calculate weighted normalized decision matrix ððð presents the weighted normalized matrix where ð£ððð , ð£ððð, ð£ððð¢ are derived by multiplying the fuzzy weights of each criterion (wj) with the normalized performance score. Equations 3-23 to 3-25 were employed in calculating the fuzzy weighted normalized decision matrix. ð£ððð = ð¤ð Ã ð¥ðððâ Equation 3-23 ð£ððð = ð¤ð Ã ð¥ðððâ Equation 3-24 ð£ððð¢ = ð¤ð Ã ð¥ððð¢â Equation 3-25 Step 4: Obtain normalized performance scores Equation 3-26 [204] was used in calculating the normalized performance scores scores ð¦ð of each alternative by beneficial criteria â ð£ðððð=1 for 1,â¦.g, deducting the cost criteria â ð£ðððð=ð+1 for g+1,â¦n. ð¦ð = â ð£ðððð=1 - â ð£ðððð=ð+1 Equation 3-26 Step 5: Derive de-fuzzified values Under this step, the triangular fuzzy scores are converted into crisp numbers using Equation 3-27 [204] . ð¦ð is the overall crisp performance score of jth alternative. ð¶ðºð(ð¦ð) =(ð¦ðð¢âð¦ðð)+(ð¦ðð+ð¦ðð)3+ ð¦ðð Equation 3-27 92 Step 6: Rank alternatives The alternatives were ranked according to the crisp performance scores in descending order. Results and Discussion The proposed methodology is applicable to any community in Canada in terms of identifying the most suitable RNG and hydrogen production routes in terms of socio-environmental desirability and economic viability. The following sections provide the life cycle environmental and economic performances of RNG and hydrogen production routes considered in the current research. Detailed decision analysis results is presented in the following sections. 3.3.1 Environmental performance The life cycle impact analysis yielded eighteen mid-point environmental impacts of the 12 RNG and 13 hydrogen production routes. As depicted in Figure 3-4, all RNG production routes have the highest impacts on human health, ranging from 35 - 50% of the overall environmental impacts. This is followed by the impact on the freshwater ecosystems that range from 8-30% of the overall impact. This can be due to certain components (NOx) released into the air and nitrate discharged to groundwater during RNG production [261]. RNG production routes pose minuscule impacts on fine particulate formation and mineral resource scarcity. Moreover, the impact on global warming is negligibly lower due to the avoided impacts associated mainly with landfills and agricultural waste. In addition, the impact on ozone formation and terrestrial eco-toxicity do not indicate a critical issue. Similarly, Figure 3-5 illustrates the mid-point impacts of considered hydrogen production routes. Similar to the impacts associated with RNG production, electrolysis-based hydrogen production routes pose a higher threat to human health in terms of carcinogenic toxicity. This could be due to the heavy metal used in decentralized energy units and electrolyzer units' construction and disposal. However, the values are lower (~8%) for hydrogen production with natural gas SMR and coal gasification coupled with CCS. 93 Figure 3-4. Life cycle impacts of RNG generation scenarios Figure 3-5: Life cycle impacts of Hydrogen generation scenarios0%10%20%30%40%50%60%70%80%90%100%Water consumptionFossil resource scarcityMineral resource scarcityLand useHuman non-carcinogenic toxicityHuman carcinogenic toxicityMarine ecotoxicityFreshwater ecotoxicityTerrestrial ecotoxicityMarine eutrophicationFreshwater eutrophicationTerrestrial acidificationOzone formation, Terrestrial ecosystemsFine particulate matter formationOzone formation, Human healthIonizing radiationStratospheric ozone depletionGlobal warming potential0%10%20%30%40%50%60%70%80%90%100% 94 The impact on the freshwater ecosystem is considerably lower in hydrogen production, with values lying around 1-5% in all hydrogen production paths. However, the impacts on the terrestrial ecosystem have gone beyond 45% for natural gas SMR and coal gasification processes. In contrast to the RNG routes, the global warming potential of the fossil fuel-based hydrogen production routes takes up ~1% of the overall environmental impacts. In addition, the 18 damage categories were aggregated under three key impacts: impact on the ecosystem, human health impacts, and resource depletion. The aggregated damage categories are provided in Figure 3-6. Figure 3-6: Aggregated damage categories of RNG production routes Conforming to Figure 3-4, RNG production routes pose the highest impact on human health, which is followed by the impact on the ecosystem. Amongst the RNG production routes, livestock manure-based chemical scrubbing depicts the highest impact on human health. This can be due to health and safety risks arising from plant operations, such as exposure to pathogens from waste feedstock, high-pressure gas or liquid leaks [262]. The impact on resource depletion is the lowest in livestock-based RNG production. However, resource depletion is higher in WWTP-based RNG production, while it is highest with RNG production routes that include wastewater sludge-based anaerobic digestion with chemical scrubbing methane upgrade. This could be due to the energy intensiveness and chemical use with wastewater sludge anaerobic digestion and biogas upgrade with chemical scrubbing. Moreover, the feedstock volume may have a role to play in the associated impacts. 0.0E+005.0E-061.0E-051.5E-052.0E-052.5E-053.0E-05Normalized impactsResource depletionHuman Health ImpactsImpact to eco-system 95 Similarly, Figure 3-7 present the aggregated damage categories of hydrogen production paths. Figure 3-7: Aggregated damage categories of hydrogen production routes It is observed from this figure that the use of water electrolysis impacts human health the most (>50%) in all scenarios, regardless of the energy source utilized. The electrolyzer units' and energy systems' construction and end-of-life disposal could cause this higher impact on the human health. The particulate matter formation, fugitive emissions, and human health impacts associated with fresh water contamination could have caused the higher impacts associated with hydrogen produced from fossil natural gas and coal. Furthermore, fossil-based hydrogen poses the highest impact on the eco-system. Carbon intensity is a key environmental performance assessment parameter employed by utility providers and policy and regulatory entities. Figure 3-8 indicates the carbon intensity of considered RNG production paths. As seen from the figure, the LFG-based RNG with membrane biogas upgrade has the highest carbon intensity of 2.26 gCO2eq/MJ; in contrast, the livestock manure-based RNG with a PSA biogas upgrade unit has the lowest intensity of 0.39 gCO2eq/MJ. The higher emissions associated with LFG-based RNG could be due to the higher feedstock involved, which demands higher infrastructure, energy, and raw material requirements. However, results could change depending on the locational/ regional variations. Accordingly, 0provides the variation in environmental and techno-economic performance of RNG production paths in three: urban, medium, and large urban municipalities in British Columbia, Canada. 0.00E+002.00E-044.00E-046.00E-048.00E-041.00E-03Normalized impactsResource depletionHuman Health ImpactsImpact to eco-system 96 Figure 3-8: Carbon intensity of RNG production routes Carbon intensity (life cycle CO2-eq emissions) hydrogen production paths are shown in Figure 3-9. The highest emission intensities are associated with fossil fuel-based hydrogen production routes that have not employed carbon capture and storage units. Figure 3-9: Carbon intensity of hydrogen production routes The highest emissions are resulted from the coal gasification technology with ~160 gCO2eq/MJ and it is followed by natural gas SMR process with emission 50% less than coal gasification (~81.5 gCO2eq/MJ). It is noteworthy that SMR coupled with CCS (11.275 gCO2eq/MJ) is less emission-0.00 0.50 1.00 1.50 2.00 2.50RNG_LFG+MBRNG_LFG+PSARNG_LFG+CHSRNG_LFG+HPWSRNG_WW+MBRNG_WW+PSARNG_WW+CHSRNG_WW+HPWSRNG_LVS+MBRNG_LVS+PSARNG_LVS+CHSRNG_LVS+HPWSCarbon intensity (g CO2eq/MJ)0 20 40 60 80 100 120 140 160 180H_WIND+AEH_WIND+PEMEH_WIND+SOECH_SOLAR+AEH_SOLAR+PEMEH_SOLAR+SOECH_Grid+AEH_Grid+PEMEH_Grid+SOECH_SMRH_SMR+CCSH_CGH_CG+CCSCarbon intensity (g CO2eq/ MJ H2) 97 intensive than solar-powered grid electrolysis (~13 gCO2eq/MJ). It is also the most emission intensive green hydrogen (RE-based hydrogen) production route. The lowest emissions come from wind-power based hydrogen routes. It is followed by grid-powered electrolysis routes. This could be due to the high RE mix in the BC grid. However, results may change in a location with an emission-intensive grid mix. Furthermore, the current study did not take inter-regional grid power sharing, which could affect the actual emission factor of grid electricity. The socio-environmental impacts obtained from the LCA were aggregated to a single overall environmental impact value. As shown in Figure 3-10 (a), R11 (RNG from livestock manure upgraded with chemical scrubbing) indicated the highest impact, while the lowest impact was from R5 (RNG from WW sludge upgraded from membrane separation). This is due to higher embodied emissions associated with farming when compared with a wastewater treatment plant. Additionally, human health impacts associated with livestock manure-based RNG are higher than wastewater sludge-based RNG. Moreover, membrane technology is relatively less energy-intensive than chemical scrubbing. This shows the effect of the technology type on the environmental performance [237]. However, all RNG production routes yielded significantly lower environmental impacts than all hydrogen production routes. When hydrogen production is considered separately, the lowest overall environmental impact was from H10 (natural gas SMR), while the higher impacts were from H13 (coal gasification with CCS), which is a result of higher emissions and health impacts from coal use and upstream processes that are more energy, emission, and resource intensive than natural gas. Hydrogen production from renewable sources (H1-H6) remained approximately the same. Accordingly, when only the environmental performance is considered, the best RNG route is R5, and the best hydrogen route is H10. 98 Figure 3-10: Overall life cycle environmental impact of (a) RNG production routes, (b) Hydrogen production routes 3.3.2 Economic performance The economic performance was assessed by estimating the capital and operational costs of considered RNG and hydrogen production routes over its life cycle. Figure 3-11 provides the LCC of considered hydrogen and RNG production paths. In contrast to the environmental performance, certain RNG production routes illustrate significantly higher costs compared to hydrogen. The LCC of RNG from livestock range between (0.10 â 0.11 $/ kWh), which is the highest among all. Figure 3-11: LCC of hydrogen and RNG production routes Lower economies-of-scale resulting from less farming activities in Kelowna is a key reason behind the higher costs. RNG production routes from landfill gas (R1-R4) are the most economical, with LCC ranging from 0.027-0.028 $/ kWh. Kelowna is an urban community with a higher population, 00.020.040.060.080.10.12H1H2H3H4H5H6H7H8H9H10H11H12H13R1R2R3R4R5R6R7R8R9R10R11R12LCC ($/kWh) 99 resulting in higher landfill gas (LFG) potential, leading to higher yields of RNG and increasing economies of scale. Moreover, there is existing infrastructure for biogas production using LFG. However, even when considering RNG and hydrogen production together, the lowest LCC (0.021 $/kWh) is associated with hydrogen production from natural gas SMR (H10) due to the higher yield of hydrogen from natural gas at a lower price. The LCC of green hydrogen (H1-H6) is twice the cost of LCC. Therefore, H10 is the most economical hydrogen route, and R1-R4 is the least costly RNG production option. R9 has the worst economic performance, while H13 (hydrogen from water electrolysis powered by BC grid) is the most expensive hydrogen production route. The cost figures obtained from the LCC model were used to derive the LCOE and PI. The LCOE reflects the minimum selling price of fuel to break even the investment [263]. The LCOE includes the capital investment and all direct and indirect operational and maintenance costs. The LCOE of the considered RNG production scenarios is given in Figure 3-12. Figure 3-12: LCOE of RNG production routes When comparing the LCOEs, as expected, the LCOE is lower for sites with higher RNG production. When the RNG production scenarios are compared, the highest LCOE is from RNG produced from livestock manure with biogas upgrade using membrane technology (R9). Complying with the literature [263], landfill sites are shown to be the least expensive pathway to produce cost-effective RNG. LCOE of LFG-based RNG (R1-R4) is approximately 4.3 $/ MJ in - 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00R1R2R3R4R5R6R7R8R9R10R11R12LCOE RNG ($/MJ) 100 Kelowna, which is an urban population center. These values are comparable to recent studies [263]. Similarly, Figure 3-13 illustrates the LCOE of hydrogen production routes. Unlike for RNG, the hydrogen production routes do not show a similar variance in LCOE, depending on the feedstock used in producing the fuel. The lowest LCOE of 0.012 $/MJ resulted from natural gas SMR process. It is followed by the same process coupled with CCS. Hydrogen production of coal gasification coupled with CCS has the highest LCOE of 0.051 $/MJ. Two other electrolysis-based hydrogen production routes scored the close LCOEs. Figure 3-13: LCOE of hydrogen production routes They are Grid-powered PEM electrolysis and decentralized wind power-based PEM electrolysis. This could be due to the higher cost of the PEM electrolysis technology. Furthermore, it is noticeable that hydrogen production routes that utilized PEME-based electrolysis have higher LCOE values. This could be due to the high capital and operational expenditure associated with the PEME electrolysis plant [46]. Despite being more expensive than the AE and PEME, the SOEC-based hydrogen paths (H3, H6, H9) have relatively lower LCOE than the other electrolysis paths. This can be explained by the lower specific energy consumption (41.17 kWh/ kg H2) and higher efficiency (~81%) [244][247]. This is followed by the AE process. The major advantage behind AE process is its higher stack life and technology maturity over SOEC and PEME technologies. From an overall perspective, grid-powered electrolysis indicated higher costs than 0.000 0.010 0.020 0.030 0.040 0.050H1H2H3H4H5H6H7H8H9H10H11H12H13LCOE H2($/MJ) 101 electrolysis plants powered by decentralized renewable energy plants. The electricity charge is the main cause for the above. However, this result may change from region to region with the variation in the electricity rates. In addition to the above financial indicators, the Profitability Index (PI) of each fuel production path was evaluated to determine its investment attractiveness [169], [264]. Accordingly, Figure 3-14 depicts the PI of each RNG and hydrogen production path. An average selling price was assumed for the two fuel types to calculate the annual revenue. Figure 3-14: Profitability Index As depicted in the diagram, hydrogen illustrates a higher profitability. The higher selling price of hydrogen (0.5 $/kWh) over the same value of RNG (0.056 $/kWh) could be one major attribute of the high PI. However, the value may change if economic sector-wise fuel demand and fuel price values are included in the evaluation. The highest PI resulted from hydrogen production with natural gas SMR coupled with CCS. This can be attributed to the potential additional revenue generation from selling the captured carbon. The PI of wind energy-powered electrolysis-based hydrogen production paths is comparatively lower due to the higher infrastructure costs and lower wind energy yield in the selected case study location. Projects with PI values greater than 1 are deemed good investments, with higher values representing more attractive investments. Accordingly, only the LFG-based RNG production paths 0.002.004.006.008.0010.0012.00R1R2R3R4R5R6R7R8R9R10R11R12H1H2H3H4H5H6H7H8H9H10H11H12H13Profitiability Index 102 (R1-R4) are demonstrated as more attractive RNG production options for Kelowna. WWS-based RNG is slightly below 1 (R5-R8). The PI of LFG and WWS-based RNG have a potential for improved PI in more populated municipalities [15]. On the other hand, LVM-based RNG (R9-R12) yields the lowest PI ~0.25 in the selected location that has lower agricultural activities compared to other regions within BC. The dip in PI is due to lower economies of scale, and the results may change in locations that produce higher biogas volumes. If limited capital is available, fuel production routes with the highest PI are more financially preferable. This could lead to a direct rejection of most RNG fuel production projects. The above results indicate the variation in the best and worst gas production routes when socio-environmental and economic performances are considered. The feasibility of a sustainable gas network should not be governed only by its techno-economic performance. The environmental performance of investments plays an equally important role in governing feasibility. Hence, the study applied MOORA to obtain technology prioritization ranks considering the LCA and LCCA results simultaneously. Moreover, causal relationships can exist between performance indicators and socio-environmental and economic goals, affecting the accuracy and reliability of the MCDM approachâs results. Therefore, the study used an integrated FCM and BWM-based weighting approach to determine important weights while accounting for uncertainty and causalities. Additionally, fuzzy logic was applied to incorporate uncertainties related to technology, infrastructure availability, fuel prices, economies of scale, and location [237], improving the reliability of results and the generalizability of the proposed framework. 3.3.3 Multi-criteria decision making This section presents the MCDM results obtained by employing FCM and BWM-based weighting coupled with FMOORA to prioritize gaseous fuel production investment prioritization. 3.3.3.1 FCM weights FCM was used to determine the criteria weights while accounting for uncertainties. A neutral decision-maker perspective was taken when allocating criteria weights. However, the framework can accommodate other priorities, such as pro-environmental and pro-economic decision environments. Table 3-9 presents the weights derived for each criterion from a neutral decision-making perspective. LCOE obtained the highest weight (0.152), followed by global warming 103 potential (0.145). LCOE provides the sale price to reach the breakeven of a project investment, justifying a higher importance for LCC to ensure zero financial losses. There are multiple other socio-environmental factors that are detrimental to the sustainability. Overall environmental impact aggregates 18 impact categories under global warming potential, resource depletion, human health and impact on the eco-system that have co-relationships with GHG emissions. Moreover, GHG emissions act as a key culprit behind numerous environmental impacts ranging from ecosystem impacts to human health problems. Therefore, providing a higher weight global warming potential (GWP) is acceptable. Profitability index (PI) obtained the least importance, which could have been the opposite if the proposed framework was applied from a pro-economic standpoint. Hence, these weights may vary with stakeholder priorities. Table 3-9: FCM weights Criteria indicator Symbol FCM weight Normalized Weights Overall impact C1 0.880 0.141 GWP C2 0.905 0.145 Impact to eco-system C3 0.845 0.135 Human health impacts C4 0.885 0.141 Resource depletion C5 0.820 0.131 LCOE $/MMBTU C6 0.950 0.152 LCC ($/kWh) C7 0.882 0.141 PI C8 0.071 0.011 3.3.3.2 FBWM weights To compare and contrast the impact on prioritization ranks with different weighting methods, fuzzy BWM was employed using best and worst criteria indicators selected through expert consultation from a neutral perspective. The preference of all best criteria over all other criteria and preference of all criteria over the worst were determined on a scale of 1-9 given below. 104 Preference ranking scale 1-9 adopted from [259]. [260] 1: Equal importance 2: Somewhat between equal and moderate 3: Moderately more important than 4: Somewhat between moderate and strong 5: Strongly more important than 6: Somewhat between strong and very strong 7: Very strongly important than 8: Somewhat between very strong and absolute 9: Absolutely more important than Table 3-10 provides the weights of the criteria indicators and the consistency value obtained from BWM. The highest weight is allotted to LCOE at 0.267, while the profitability index obtained the least importance at 0.038. This is justifiable since the profitability index was initially selected as the worst parameter to govern the feasibility, whereas LCOE was selected as the best parameter. A consistency value close to 0 is desired [260], and in the current study, a consistency value of 0.008 was obtained, making the results reliable. Table 3-10: Criteria weights from BWM Criteria indicator Symbol FBWM weight Consistency value Overall impact C1 0.173 0.008 GWP C2 0.173 Impact to eco-system C3 0.031 Human Health Impacts C4 0.058 Resource depletion C5 0.173 LCOE ($/MJ) C6 0.267 105 Criteria indicator Symbol FBWM weight Consistency value LCC ($/kWh) C7 0.086 Profitability index C8 0.038 When considering only causality (FCM only) or importance weights among criteria (FBWM only), the importance weights placed on each criterion show differences. However, decisions are subjected to both causality and stakeholder preferences. Hence, it is beneficial to merge the two concepts together, and weights were allocated for the defined weighting scenarios, as given in Table 3-11. Table 3-11: Criteria indicator weights under defined scenarios Criteria indicator Causality only Importance only CIWS: Equal causality & importance CIWS: Pro-importance CIWS: Pro-causality Overall impact 0.145 0.173 0.159 0.166 0.152 GWP 0.141 0.173 0.157 0.165 0.149 Impact to eco-system 0.135 0.031 0.083 0.057 0.109 Human health impacts 0.131 0.058 0.095 0.076 0.113 Resource depletion 0.142 0.173 0.157 0.165 0.150 LCOE ($/MJ) 0.152 0.267 0.210 0.239 0.181 LCC ($/kWh) 0.141 0.086 0.114 0.100 0.128 Profitability index 0.011 0.038 0.025 0.032 0.018 As summarized in Table 3-11, regardless of the decision scenario, the highest weight is placed upon LCOE, and the lowest is placed on the profitability index. The above weights from different decision scenarios were employed in RNG and hydrogen production technology prioritization. 3.3.3.3 Technology prioritization with FMOORA Table 3-12 provides the scenario ranking with FMOORA, and the best and the worst ranks remained the same regardless of the weight scenarios, although prioritization ranks of certain gaseous fuel production route differ with the weight scenarios. 106 Table 3-12: RNG/ Hydrogen production technology prioritization ranks RNG/H Production scenarios Causality only Importance only CIWS: Equal causality & importance CIWS: Pro-importance CIWS: Pro-causality R1 3 3 3 3 3 R2 1 1 1 1 1 R3 6 4 4 4 4 R4 2 2 2 2 2 R5 10 7 7 7 9 R6 8 5 5 5 7 R7 13 11 13 13 13 R8 9 6 6 6 8 R9 7 13 11 12 10 R10 5 10 9 10 6 R11 11 12 12 11 11 R12 4 9 8 9 5 H1 19 20 20 20 19 H2 21 22 22 22 21 H3 16 18 17 18 17 H4 15 16 15 15 15 H5 17 17 16 17 16 H6 14 14 14 14 14 H7 20 21 21 21 20 H8 22 23 23 23 22 H9 18 19 18 19 18 H10 12 8 10 8 12 H11 24 15 19 16 24 H12 23 24 24 24 23 H13 25 25 25 25 25 The best production route under all decision scenarios is R2, where RNG is made from LFG upgraded with pressure swing adsorption. This is due to the higher economies of scale from LFG 107 availability coupled with the higher efficiency and lower methane loss associated with pressure swing adsorption [237]. The best hydrogen production route is H10, which achieved the highest rank amongst hydrogen scenarios (Overall rank: 12). H10 is hydrogen produced with natural gas SMR. This is justifiable with its LCOE and overall environmental impact score, which have higher weights among the other criteria from a neutral perspective. The lowest rank was obtained by H13, where hydrogen is produced from coal gasification coupled with CCS due to high investment costs that lower the financial performance, outweighing the emission benefits from CCS. The results are comparable with the weight allotted for criteria indicators. LCOE obtained the highest importance with both FCM and BWM. Hence, the scenario that performed best under the said criteria has achieved better ranks than the other with FCM-weighted fuzzy MOORA. When both causality and stakeholder priorities of criteria indicators are considered for the weighting, the ranks went through slight changes, except for R2 (Rank 1) and H13 (Rank 25). The obtained ranks were used to derive an investment preference index for each gaseous fuel production scenario, illustrated in Figure 3-15. As shown in Figure 3-15, R2 has a higher preference amongst other RNG production routes due to the high economies of scale and efficient bio-gas upgrade technology. Amongst hydrogen production routes, H10 achieved the highest preference index, which is attributed to its higher LCOE achievable with the natural gas feedstock availability at low cost and the efficiency of SMR. Interestingly, when the overall life cycle impacts due to material extraction, facility construction, and end-of-life are considered, green hydrogen technologies that powered by zero-emission energy systems obtained lower rankings in all weighting scenarios. Although green hydrogen technologies H1-H6 are less emission-intensive, they still carry hazards to human health when overall life cycle is considered. Moreover, since electrolysis is still at a lower technology maturity than SMR, initial investment costs for electrolysis and RE systems lower the LCOE of green hydrogen. The variation in the preference indices of RNG technologies could mainly be caused by the differences in economies-of-scale due to the biogas feedstock and the biogas upgrade technology. 108 Figure 3-15: Performance indices of RNG and Hydrogen production routes According to Figure 3-15, it is clear that stakeholder perception and the weighting method affect the investment preference, in addition to the socio-environmental and economic performances. As seen from the diagram, the performance indices of fuel production routes show a slight variance under the considered criteria weighting scenarios indicating that the inter-relationships between criteria as well as stakeholder priorities affect investment decisions. The results obtained using the proposed framework for community energy decision problems was compared with TOPSIS method as shown in Table 3-13. The results indicate that both methods (TOPSIS and MOORA) provide comparable findings. Further, it was concluded that MOORA is an effective and simpler method for comparing and prioritizing gaseous fuel production and supply chain options. 0.000.100.200.300.400.500.600.700.800.901.00R1R2R3R4R5R6R7R8R9R10R11R12H1H2H3H4H5H6H7H8H9H10H11H12H13Causalities onlyImportance onlyCIWS: Equal causality &importanceCIWS: Pro-importanceCIWS: Pro-causality 109 Table 3-13: MCDM results validation with TOPSIS Causalities-only Importance-only CIWS: Equal causality importance CIWS: Pro-importance CIWS: Pro-causality MCDM method MOORA TOPSIS MOORA TOPSIS MOORA TOPSIS MOORA TOPSIS MOORA TOPSIS R1 3 3 3 3 3 3 3 3 3 3 R2 1 1 1 1 1 1 1 1 1 1 R3 6 6 4 4 4 4 4 4 4 4 R4 2 2 2 2 2 2 2 2 2 2 R5 10 10 7 7 7 7 7 7 9 9 R6 8 8 5 5 5 5 5 5 7 7 R7 13 13 11 11 13 13 13 13 13 13 R8 9 9 6 6 6 6 6 6 8 8 R9 7 7 13 13 11 11 12 12 10 10 R10 5 5 10 10 9 9 10 10 6 6 R11 11 11 12 12 12 12 11 11 11 11 R12 4 4 9 9 8 8 9 9 5 5 H1 19 19 20 20 20 20 20 20 19 19 H2 21 21 22 22 22 22 22 22 21 21 H3 16 16 18 18 17 17 18 18 17 17 H4 15 15 16 16 15 15 15 15 15 15 H5 17 17 17 17 16 16 17 17 16 16 H6 14 14 14 14 14 14 14 14 14 14 H7 20 20 21 21 21 21 21 21 20 20 H8 22 22 23 23 23 23 23 23 22 22 H9 18 18 19 19 18 18 19 19 18 18 H10 12 12 8 8 10 10 8 8 12 12 H11 24 24 15 15 19 19 16 16 24 24 H12 23 23 24 24 24 24 24 24 23 23 H13 25 25 25 25 25 25 25 25 25 25 The decision support framework provides added value to the decision-makers in the energy and utilities sectors and government entities specifically the managers, urban planners, and policymakers. Thus, the current research makes various contributions to the project planning and management theory and practice. The procedures followed in the study facilitated the development of an informed, coherent, transparent, and easily applied evaluation framework for gas fuel supply chain decarbonization with cleaner fuel options. 110 Summary The proposed framework encompasses three key stages: technology identification and screening, TBL performance evaluation, and technology ranking and prioritization. First, various RNG and hydrogen production technologies and gas fuel network decarbonization planning objectives were determined based on state-of-the-art literature. The TBL performance analysis was conducted employing LCA and LCCA tools. The decision analysis processed employed FMOORA with combined FCM and BWMs to rank the technologies considering multiple criteria that are weighted considering the: a) the cause-effect dynamics of criteria and b) the most important decision criteria subjected to stakeholder priorities. According to the decision analysis, the LFG-based RNG production upgraded by pressure swing adsorption is the most suitable overall gaseous fuel option. Hydrogen production from natural gas SMR achieved the highest preference index under hydrogen production routes. The results highlighted the impact of economies of scale on the feasibility of gaseous fuel investments. The proposed framework offers multiple benefits for gaseous fuel industry stakeholders, including utility providers, investors, and government entities. The generalizable systematic framework presented in the study provides a guide to determine the pre-feasibility of gaseous fuel industry-related investments. Government entities can determine the fields that require the most funding, and the preference rankings can be used as an initial guide for the investors to select an investment strategy that assures the most benefits. The environmental and economic assessment results can further be adopted by regulatory bodies to set practical emission targets and plausible taxation and carbon credit mechanisms. While fuel production route selection stage of gaseous fuel supply chain planning is an important stage of pre-project planning, complete supply chain decarbonization should consider fuel supply and demand conditions. The inclusion of fuel supply and demand factors may result in requiring multiple fuel production routes and sources to cater to the rising energy demands in different economic sectors. Hence, determining the optimum a) mix of resources to produce fuels and (b) fuel portfolio to cater to the economic sectors served by conventional fossil natural gas is beneficial in the next step of gaseous fuel supply chain planning. The fuel production paths prioritized through the present decision-making phase will be inputs to the next stage in determining the mentioned optimal fuel production mix and portfolio. 111 Chapter 4: Optimal Gaseous Fuel Generation and Portfolio Allocation A part of this chapter is published as a Journal article titled âLife Cycle-Based Multi-Objective Model for Optimal Gaseous Fuel Generation and Portfolio Allocation in Gas Grids: A Strategic Decarbonizationâ, in Elsevier Energy Conversion and Management Journal. Background The technologies associated with RNG and hydrogen production entail high costs. Thus, it becomes imperative to weigh the environmental impacts and their effects on the financial health of utilities when incorporating RNG and hydrogen into their portfolio. Consequently, exploring optimum gaseous fuel portfolio strategies is vital for utility providers operating in energy markets. The previous chapter focused on determining the environmental and economic performance of potential RNG and hydrogen production routes that can be integrated into the conventional natural gas supply chain. As the chapter results revealed, key barriers to RNG and hydrogen market penetration lie with resource availability that impacts the economies of scale and cost of production. Generally, the three main challenges include initial investment, resource availability, and fuel supply and demand. Firstly, investment stands out as a key limitation for converting conventional energy systems [155], [265]. Adapting the existing natural gas infrastructure to accommodate hydrogen and RNG necessitates a substantial capital investment. For example, modifications to the gas network would involve adjustments to pipeline materials, compressors, and storage facilities to handle differences in pressure requirements and gas properties [266]. Furthermore, the associated economies depend on the cost-effectiveness of hydrogen and RNG. Utility providers may need to navigate price fluctuations, production costs, and competitive pricing structures to offer attractive rates to customers [155], [265], [267]. Secondly, resource availability emerges as a critical constraint impacting the scalability of low-carbon gaseous fuel integration attempts. A consistent and sufficient supply of feedstock is essential to ensure the reliability of the fuel supply. Furthermore, resource availability affects the economies of scale in hydrogen and RNG production [14], [15]. Therefore, diversifying resources is an essential factor to consider, as access to a range of feedstock mitigates supply risks and enhances resource resilience [15], [268], [269], [270]. Another critical factor is the existing and future customer demand for hydrogen and RNG. Any 112 mismatch between supply and demand could result in excess capacity or shortages. It is essential to consider the specific demand patterns of the economic sectors served by these gaseous fuels. Overbuilding or underestimating the demand could lead to significant cost implications. Besides upstream processes, several parameters affect the downstream end-use of the fuels. Different economic sectors engage in distinct activities related to their sectors. For example, providing gaseous fuel for transportation necessitates an extra liquefaction step compared to fulfilling heating demands in buildings and industries. Consequently, this results in variations in operational costs, which are reflected in pricing. Such factors contribute to variations in associated environmental impacts stemming from energy consumption and differences in combustion systems. In addition, economic and environmental priorities may differ across economic sectors and regulatory environments. Hence, decision-makers should consider multiple factors when composing an integrated gas grid with hydrogen and RNG while catering to sectoral gas demands. Determining the optimum path to integrate RNG and hydrogen into the existing gas network with minimum environmental impacts is not always feasible without compromising costs. Nevertheless, utility providers must strike a balance in the gas industry's economies when making investment decisions to integrate low-carbon fuels into their portfolios. This indicates the need to address multiple dimensions when addressing supply chain optimization problems. Multi-objective optimization (MOO) is an effective approach to determining the optimum state of operations. Chapter 4 aims to present a MOO model that addresses both environmental and economic dimensions. Furthermore, the uncertainties in the stakeholders' perspectives and objective prioritization are addressed by adjusting the weighting factors assigned to each objective [169]. Accordingly, the chapter addresses two research questions. â¢ What is the optimum source diversification for producing RNG and hydrogen? â¢ What is the optimum NG, RNG, and hydrogen portfolio to cater to the building, industry, and transport sectors? The proposed optimization model and the scenario analyses provide utility providers, investors, and energy policymakers with effective decision-support tools to achieve community emission targets while assuring the sustenance and effectiveness of the gas industry's 2050 zero-emission transition. 113 Methods and Procedure Phase 3 of the study proposes a MOO model to determine the optimum mix of hydrogen and RNG generation and the allocation of gaseous fuel portfolio for three economic sectors: building heating, transport, and industry. The building heating sector includes catering to the residential household energy demand for space and water heating. The transport sector includes passenger and freight on-road transportation and air, rail, marine, and non-industrial off-road travels that consume compressed natural gas. The industrial sector includes heavy industry, O&G, light manufacturing, forestry, fisheries, agriculture, construction, mining, waste, and other industries that utilize fossil natural gas for energy and other industrial purposes [271]. Three objectives were considered when selecting the gaseous fuel generation mix and downstream portfolio allocation. These objectives included minimizing life cycle environmental impacts, minimizing life cycle cost impacts, and maximizing revenue for the utility provider. Life cycle carbon emissions were selected as the performance indicator representing the environmental impact since it is a primary indicator used by the Canadian Government [272]. LCOE and annual revenue generation were used as economic performance indicators. LCOE was considered as it provides the minimum selling price to reach breakeven, representing the cost side of the equation and ensuring that the project is economically competitive and cost-effective. Revenue was used to provide insights into the income side, indicating the potential for income generation. Figure 4-1 outlines the overall methodology employed in this study. The framework is applicable in any community in Canada, facilitating a balance between the environmental and economic impacts associated with the introduction of hydrogen and RNG to the existing gas market. The non-dominated sorting genetic algorithm-II (NSGA-II) was employed to perform Pareto-optimal planning since evolutionary algorithms can easily incorporate user-defined preferences and constraints, making them suitable for problems where the decision-makers preferences play a crucial role in the optimization process [39][163]. Figure 4-1 demonstrates the overall methodlogy applied in developing the MOO model. The key decisions determined by the mathematical model include: â Hydrogen and RNG generation mix 114 â Portfolio allocation of Hydrogen, RNG, and NG for building heating, transport, and industrial sectors â Environmental performance, specifically in terms of Carbon dioxide (CO2) emissions â Economic benefits and costs to the utility provider â Potential revenue generation Supply chain performance goalsLiterature reviewMinimize CostMinimize EmissionsMaximize RevenueOptimization Model InputsLife cycle environmental and cost assessmentsResource availability assessmentEnergy demand and other relevant data collectionModel FormulationDefining decision variablesDefining objective functionsDefining model constraintsRelative importance of criteriaPro-economicPro-environmentalNeutralResults Interpretation & Recommendations Figure 4-1. Overall methodological framework 4.2.1 Problem description Two major supply chain stages of the gaseous fuel industry were considered in determining the optimal pathways for integrating RNG and hydrogen into existing gas networks. They are fuel production and end use. Fuel production paths prioritized in Phase 2 of the research were carried forward as the most suitable approaches to produce RNG and hydrogen. Their environmental and economic performance values were taken from the LCA and LCCA executed in the previous study Phase to quantify the objective functions. 115 The model incorporates constraints: resource availability, total demand for NG, hydrogen, and RNG, economic sector-wise demand for hydrogen and RNG, and capital investment availability. The values for these constraints were derived from pertinent published databases and government websites relevant to the case study location [271], [273]. The following assumptions were made: 1) The gaseous fuel supply chain operates under steady-state conditions 2) The fuel demand is time-invariant The overall optimization model aims to address the following problems: 1) Optimum economic-sector-wise demand for hydrogen and RNG 2) Production capacity and cost of gaseous fuel production Notions used in the mathematical model Indices ð Type of gaseous fuel ð Type of energy source ð¢ Economic sector ð Pareto optimal solution ð Objective function Parameters ð¶?Ì ?ð Levelized cost of energy required to produce fuel type ð with energy source ð $/ MJ ?Ì ?ðð Life cycle emissions of producing fuel type ð with energy source ð kgCO2-eq/MJ ððð¢ Selling price of fuel type ð to the economic sector ð¢ $/MJ 116 ð ðð Maximum annual resource availability of energy source ð to produce gaseous fuel ð MJ/year ð·ð Total demand for gaseous fuel ð MJ/year ?Ì ?ðð¢ Annual demand for fuel ð in economic sector ð¢ MJ/year ð¼ Total investment availability $ ð¶ðð Capital cost of producing fuel ð from energy source ð $/MJ ð¶ððð Reference capital cost of the gaseous production plant ð from energy source ð ðð Score of pareto optimal solution ð ðð Importance weight of objective function ð Continuous variables ð¥ðð Energy content produced from fuel ð based on energy source ð MJ/year ð¥ðð¢ Energy from fuel ð catered to the economic sector ð¢ MJ/year The detailed methodology is given in the following sections. 4.2.2 Hydrogen and RNG production paths Numerous technologies exist for the production of hydrogen and RNG. Hydrogen can be classified into fossil fuel-based and renewable-based categories. Amongst the evaluated hydrogen production routes, natural gas steam methane reforming (SMR) has already attained technical maturity, reaching the âcommercialâ stage [274]. Integrating carbon capture and storage (CCS) can abate carbon emissions from the SMR process, resulting in the production of blue hydrogen, yet its costs are higher than grey hydrogen produced from SMR. The previous prioritization brought the NG-SMR process to a higher priority rank over the blue hydrogen. However, the emission factor for hydrogen production using SMR is 96.8 gCO2e/MJ. The recommended carbon intensity threshold for these routes is set at 36.4 gCO2e/MJ by the Federal Hydrogen Strategy for 117 Canada and the European Commission [275]. Therefore, in the current study, SMR coupled with 90% carbon capture was considered. According to the BC Hydrogen Study, 98% of hydrogen in BC was planned to be produced from electrolysis, with the remaining 2% derived from natural gas-based hydrogen production [269]. Hence, hydrogen production through renewable-based electrolysis was selected as one of the hydrogen production routes. Amongst the available water electrolysis technologies, both alkaline and proton exchange membrane electrolyzers (PEME) have reached commercial stage maturity and ranked high [274]. However, alkaline electrolysis was included in the study due to its well-established use in industrial applications and higher efficiency [276]. RNG is produced from biowaste, initially producing biogas, which then undergoes an upgrade stage to reach the standards of the natural gas pipeline [15]. In the current study, three feedstock types were considered for RNG production: landfill gas (LFG), wastewater sludge (WWS), and livestock manure (LVM). Amongst the technologies that generate biogas, AD was chosen in the current assessment since it has reached its commercial state and is widely used in several municipalities. The Pressure Swing Adsorption (PSA) biogas upgrade technology was selected based on its techno-economic and environmental performance, which placed it in a higher rank in the previous phase of the study [15]. 4.2.3 Multi-objective optimization The current study employed MOO since it enables users to make continuous decisions rather than discrete ones [40]. Various approaches exist for solving MOO problems, with Pareto-optimal solutions being achievable through evolutionary algorithms. This method was selected because it does not require gradient information, rendering its effectiveness across diverse problem types and making it capable of delivering effective results regardless of the nature of the problem. Furthermore, it boasts high computational speed, good convergences, and low computational complexity [277], [278]. The acquired Pareto set was prioritized using the weighted sum method through Equation 4-1, assigning prioritization weights to each objective ð {ðð(ð¥)} [278], [279]. However, the subjective nature of weight assignment, influenced by biases in judgements concerning objectives and decision-maker priorities, must be acknowledged. Consequently, Pareto optimum points were extracted under three decision environments: neutral, pro-economic, and pro-environmental. The genetic algorithm was written in MATLAB to solve the MOO problem. 118 ðð = â ðððð(ð¥)ðð=1 Equation 4-1 The following sub-sections discuss the optimization model in detail. Model formulation First, the MOO model provides decision-makers with the optimal proportions of hydrogen and RNG (indexed on ð =1, 2) that can be produced from various technological paths. This includes the proportions (in MJ/ year) of hydrogen derived from renewable-based electrolysis, grid electricity-based electrolysis, and SMR with CCS, as well as the proportions of RNG from AD sourced from LFG, WWS, and LVM (indexed as ð= 1,2,â¦,6). Next, the model determines the potential total proportions of RNG and hydrogen that can be incorporated into the gas grid of a selected municipality to displace or reduce NG in three economic sectors (indexed as ð¢ for set A= {1: building heating, 2: transport, 3: industry}). The three economic sectors were selected based on their contribution to Canadian GHG emissions and dependence on natural gas [162]. The decision on the amount of hydrogen and RNG produced from the energy source is denoted by ð¥ðð ððð ð = 1 ððð ð = 1,2,3 and ð¥ðð ððð ð = 2 ððð ð = 4,5,6 respectively. The optimum portfolio decision is denoted by ð¥ðð¢. The model also integrated an objective of maximizing revenue from the sale of RNG and hydrogen to the three economic sectors (transport, building heating, and industry). This was added to determine the utility provider's optimum portfolio allocation and demonstrate the potential revenue generation from RNG and hydrogen. Hence, the objective functions were derived as: Objective function 1: Minimizing the LCOE min ðððð ð¡ = â ð¶1Ì ð3ð=1 ð¥1ð + â ð¶2Ì ð6ð=4 ð¥2ð Equation 4-2 Objective function 2: Minimizing the LCE min ððððð ð ðððð = â ?Ì ?1ðð¥1ð3ð=1 + â ?Ì ?2ðð¥2ð6ð=4 Equation 4-3 Objective function 3: Maximize revenue max ðððð£ððð¢ð = â ððð¢ð¥ðð¢3ð¢=1 â ð Equation 4-4 Model Constraints: 119 When producing RNG and hydrogen, it is important to consider the diversification of sources to ensure a continuous fuel supply. However, gas production volumes are subject to resource availability. Hence, the resource availability constraint is formulated as follows: ð¥1ð â¤ ð 1ð , âð = 1, 2, 3 Equation 4-5 ð¥2ð â¤ ð 2ð , âð = 4, 5, 6 Equation 4-6 The model aims to displace NG in the existing market and meet the existing demand for RNG and hydrogen. Therefore, the total amount of RNG and hydrogen produced should adequately cater to the total demand for all gaseous fuels (NG, RNG, and hydrogen). â ð¥ðð â¥ ð·ð ,6j=1 âð Equation 4-7 Where, ð·ð represents the total demand (MJ/year) for gaseous fuel in a selected municipality for NG, RNG and hydrogen. To ensure a reliable fuel supply portfolio for each considered economic sector, the supply of gaseous fuel to the market (RNG and hydrogen) should not be less than the sector-wise fuel demand. â ð¥ðð¢ â¥ ?Ì ?ðð¢ , âð 3u=1 Equation 4-8 Finally, the total investment availability to incur CAPEX on infrastructure and other costs associated with integrating RNG and hydrogen into the existing gas infrastructure. âð¶1ðð¶1ðð ð¥1ð0.6 + âð¶2ðð¶2ðð ð¥2ð0.6 â¤ ð¼6ð=43ð=1 Equation 4-9 The optimization results were analyzed to develop strategies for introducing RNG and hydrogen into the existing gas grid. Case Study The proposed model is a generalizable model that can be applied in any municipality that is planning to decarbonize the conventional gas grid with cleaner gaseous fuels. Based on the BC hydrogen study, it is planned that 5% of hydrogen in BC will be produced in the Okanagan region [269]. Furthermore, RNG is already being produced and used in several communities within the Okanagan [280]. Hence, Kelowna, as a major urban area in the Okanagan, was selected to demonstrate the applicability of the developed optimization model. 120 4.3.1 Energy demand The Energy Future Outlook dataset was employed to find the annual energy demand values of natural gas, hydrogen, and biofuels [271]. It was assumed that biofuels represent 100% RNG. This data was then used to estimate the energy demand values for Kelowna. According to the International Energy Agency, macroeconomic indicators such as gross domestic product and population can be used to estimate the total energy consumption across geographic regions. Therefore, to estimate the energy demands, the population of British Columbia (BC), Canada versus Kelowna was used. Equation 4-10 was applied to estimate energy demand based on the population. ð·ð,ððð ð ð ð¡ð¢ðð¦ ððððð¡ððð = ð·ð,ðµð¶ (ðððð¢ððð¡ððð ðð ððð ð ð ð¡ð¢ðð¦ ððððð¡ððððððð¢ððð¡ððð ðð ðµð¶) Equation 4-10 4.3.2 Resource availability The resource availability was determined to calculate the maximum potential for hydrogen and RNG production. Initially, the feasibility of renewable energy sources was explored for the selected location in Kelowna, BC. RNG production The theoretical initial feasibility based on the resource availability of RNG production in the selected location was decided based on the information provided in Table 4-1. For livestock manure-based RNG production, the average farm animal count in each selected region was obtained from the literature [281], [282], [283]. 121 Table 4-1: Biogas production location selection criteria Biogas generation location Selection criteria Reference Landfill Contain at least 1Million tons of MSW [217], [226] Depth of the landfill â¥ 50ft Annual rain â¥ 25 inches Currently open or have been closed for less than 3 years Landfill gas flowrate >1000 ft3/ minute Waste Water Treatment Plant (WWTP) Biogas flow rate >100 Nm3/ h [217], [226] Preferred if the site has an AD. The resource availability calculation and data for maximum annual RNG generation is provided in Appendix D. Hydrogen production The theoretical feasibility of renewable energy for hydrogen production is summarized in Table 4-2. Table 4-2: Theoretical resource potential for renewable-based hydrogen Resource Potential Source Wind Feasible for electricity generation from predicted wind speeds 3ms-1 and above at ~65m above ground level [284] Solar Average solar radiation received by a south-facing Photo voltaic panel inclined at a latitude equivalent angle: 3-3.5 kWh/m2 /day [284] 122 The resource availability calculations for wind energy yield indicated insufficient resources for wind energy-based electrolysis in Kelowna for hydrogen generation. The detailed resource availability calculation and data for maximum annual hydrogen generation is provided in Appendix D. 4.3.3 Scenario analysis The scenarios provided in Table 4-3 were assessed for the selected municipality under three decision environments. In addition to assessing the impact on the decisions with varying stakeholder priorities, the defined scenarios also intend to compare the results of MOO with two objectives and three objectives to provide decision-makers with various perspectives related to the cost of production and potential revenue from gaseous fuel sales. This comparison aimed at understanding whether revenue maximization significantly alters the trade-offs between emissions, cost, and revenue. Moreover, the scenario analysis intends to understand how variations in objective functions and priorities impact the optimal solutions. The relative importance of emissions, cost, and revenue were adjusted to observe the changes on Pareto fronts. This analysis helps in understanding the sensitivity of solutions to different decision criteria and guides stakeholders in setting appropriate objectives or preferences. Theoretical models of this nature are subjected to numerous data uncertainties and variations. Hence, assessing the robustness of solutions across different scenarios is crucial. By comparing the solutions obtained under the two and three objective functions, decision-makers can evaluate the consistency of optimal portfolios concerning emissions, cost, and revenue. Robust solutions should exhibit stability and maintain their performance despite variations in objective configurations. Identifying such robust solutions enhances confidence in the optimization outcomes. Table 4-3: Scenarios assessed Scenarios Objective functions With Investment constraint Without Investment constraint Without constrained NG With constrained NG S1 LCE & LCOE â â S2 â â S3 â â 123 Scenarios Objective functions With Investment constraint Without Investment constraint Without constrained NG With constrained NG S4 â â S5 LCE, LCOE, & Revenue â â S6 â â S7 â â S8 â â Results and Discussion The applicability of the proposed framework was demonstrated using a selected municipality (Kelowna) in BC, Canada. The following section presents the detailed results and discussion. 4.4.1 LCA and LCC results Figure 4-2 (a) and (b) illustrate the LCE and LCOE derived from LCA and LCCA for hydrogen and RNG production routes, respectively, to refer back to the environmental and economic performances of the RNG and hydrogen production routes selected from the previous phase of the research. Amongst the three hydrogen production routes, grid-electrolysis exhibits the lowest emissions at 0.009 kg CO2eq/ kg H2. This stands in stark contrast to the operational emission assessments of green hydrogen produced from renewable sources such as solar energy, which is approximately 50% higher than grid-electrolysis. The lower grid emission factor in BC, Canada, is a primary contributor to the low emission profile of grid-electricity-based hydrogen production. However, results may flip in regions with higher emission factors. Furthermore, the current study considered the overall life cycle, including the construction, operation, and end-of-life cycle stages of the decentralized renewable energy generation facility, potentially contributing to higher emissions in the solar-electrolysis process. The emissions from SMR technology, coupled with carbon capture, closely match those of grid-electrolysis. In terms of LCOE, hydrogen production from water electrolysis powered by grid energy yields the highest LCOE, followed by solar electrolysis. This can be attributed to electricity costs and additional infrastructure for powering the electrolysis plant. Additional costs associated with both 124 the electrolysis plant and decentralized solar energy plant contribute to higher costs of solar-based electrolysis. However, the LCOE of grid-based electrolysis may vary from region to region due to the disparity in electricity charges. The LCOE of hydrogen production with SMR is approximately 50% less than grid-based electrolysis. The result is justifiable due to the higher costs associated with electrolysis owing to its comparatively lower technology maturity [188], [285], [286]. LCOE ($MJ)LCOE ($/KWh)0.0000.0050.0100.0150.0200.0250.0300.0350.0400.0450.0500.00000.00050.00100.00150.00200.00250.0030Solar_electrolysis Grid_electrolysis NG SMR+CCSLCE (kCO2e/kgH2)(a)LCOE ($/MJ) LCE (kg CO2e/kgH2)0.0000.0500.1000.1500.2000.25000.00050.0010.00150.0020.0025LFG LVM WWSLCE (kgCO2e/MJ)(b)LCOE ($/kWh) LCE (kg CO2e/MJ RNG) Figure 4-2: Life cycle emissions and levelized cost of energy of (a) hydrogen production paths and (b) RNG production paths1 Figure 4-2 (b) depicts the LCE and LCOE of chosen RNG production paths. RNG sourced from LFG has the highest LCE at 0.0024 kgCO2e/MJ RNG, closely followed by WWS-based RNG at 0.0016 kgCO2e/MJ RNG. This aligns with the past literature and can be attributed to larger infrastructure requirements for handling feedstock [15]. The least emissions among the three come from LVM-based RNG. Yet, RNG from LVM has the highest LCOE, with costs nearly four times 1 NG SMR + CCS: Natural gas steam methane reforming with carbon capture and storage, LFG: landfill gas, LVM: livestock manure, WWS; wastewater sludge 125 higher than LFG-based RNG. Accordingly, the lowest costs come from LFG-based RNG, possibly due to higher economies of scale with LFG. Results may vary in locations with higher farming activities [15]. The above results imply the need to account for overall LCE to accurately assess the performances of the technologies and processes. The LCA and LCC results in this section were employed in the MOO model to determine the optimal mix of the technologies based on their economic and environmental performances subjected to resource availability and demand constraints. The model allows decision-makers to choose the optimal resource allocation for fuel production and portfolio allocation path that strikes a balance between LCE and LCC based on decision priorities. The next section provides detailed results on resource and portfolio allocation from the MOO. 4.4.2 Resource allocation Figure 4-3 presents the separate resource allocation for hydrogen and RNG production at minimum cost and LCE. Subject to resource availability, it was found that at optimum conditions (lowest cost and lowest emissions), an average of 18,163 GJâ000 of hydrogen should be produced in the selected region. In contrast, the optimum RNG is comparatively lower, averaging 21 GJâ000/ year. The potential production of hydrogen and RNG from each resource varies based on the environmental and economic objectives and the set constraints. Yet, certain trends are discernible. In Figure 4-3 (a), all eight scenarios recommend producing hydrogen with SMR coupled with CCS, constituting over 90% of the overall potential hydrogen production. The dip in potential - 5,000 10,000 15,000 20,000S1 S2 S3 S4 S5 S6 S7 S82 Objectives 3 ObjectivesPotential Energy (GJ'000/year)(a)H2_Solar Electrolysis H2_Grid ElectrolysisH2_SMR+CCS - 10 20 30 40 50 60S1 S2 S3 S4 S5 S6 S7 S82 Objectives 3 ObjectivesPotential Energy (GJ'000/year)(b)RNG_LFG RNG_LVM RNG_WWSFigure 4-3: Gaseous fuel production potential with each resource a) hydrogen, b) RNG 126 hydrogen in Scenarios 2, 4, 6, and 8 corresponds to the constraints limiting the available NG to produce hydrogen in SMR. An average of 4% of the total hydrogen can be produced with grid electrolysis, while solar electrolysis contributes less than 1%. The value may vary in regions with higher solar energy potential. Figure 4-3 (b) shows that, in all scenarios except for S3 and S7, over 60% of RNG can be produced from LFG. Lower LFG-based RNG could be attributed to higher LCOE and the effect of investment constraints in S3 and S7. Furthermore, higher RNG production with LFG is due to higher resource availability of LFG in the selected location. The lowest potential comes from LVM due to lower resource availability. Only 0.03 GJâ000/ year of RNG is recommended in S7, influenced by higher hydrogen production of 21 455 GJâ000/ year. Hence, the model recommends producing RNG to meet its existing demand. 4.4.3 Portfolio allocation The proposed model also provides optimal portfolio allocation for the assessed gaseous fuels in building heating, transport, and industrial sectors, mainly affected by the sector-wise demand for each fuel. As depicted in Figure 4-4, the highest fuel amount was allocated to the industrial sector, averaging 7,217 GJâ000/year, with over 95% of the energy provided via RNG. A considerable proportion of produced fuel can be allocated to the transport sector, considering emissions, costs, and revenue at optimum levels. Similar to the industrial sector, RNG will contribute to the transport sector at over 70%, while hydrogen will constitute only around 25-30%. This trend differs in S5 and S7, where hydrogen contributes over 50% to the transport sector fuel portfolio, possibly due to the revenue objective in multi-objective optimization and higher NG availability for hydrogen production. Notably, despite the high potential for hydrogen production, the portfolio allocation for hydrogen is less than RNG due to lower hydrogen demand in all three sectors. 127 Figure 4-4. Optimum fuel portfolio allocation Figure 4-5 compares the existing actual portfolio predicted for each sector for the year 2025, versus the average overall optimum portfolio yield from the MOO model. - 2,000.00 4,000.00 6,000.00 8,000.00 10,000.00 12,000.00Actual Optimum Actual Optimum Actual OptimumFNG Suply Hydrogen Supply RNG SupplyFuel Supply (GJ'000/year)Transport Building Heating Industry0%16%84%a) Optimum FNG Supply40%32%28%b) Optimum Hydrogen Supply10%7%83%c) Optimum RNG Supply Figure 4-5: Actual versus optimum fuel portfolio allocation - 2,000 4,000 6,000 8,000 10,000 12,000S1 S2 S3 S4 S5 S6 S7 S82 Objectives 3 ObjectivesOptimum fuel portfolio allocation (GJ'000/year) Industry RNGIndustry H2Building heating RNGBuilding heating H2Transport RNGTransport H2 128 Under optimum conditions, the model recommends a complete displacement of NG usage in the transport sector. Instead, compressed natural gas-based vehicles can be fuelled by RNG. The results suggest a 17% increase in RNG use in the transport sector. Furthermore, the model indicates an optimal hydrogen supply of 396 GJâ000/year to the transport sector. Similarly, given the infrastructure is developed for hydrogen blending by 2025, a 325 GJâ000/year supply is optimal for building heating purposes. Furthermore, there is also room to expand RNG use in building heating by 37.5% at optimum emissions and economic performance. In addition, the use of RNG and hydrogen in the industrial sectors can be increased with only an approximate 11% reduction in the NG supply to industries. Accordingly, NG use in the transport sector can be eliminated while maintaining portfolio proportions at 84% and 16% for the industry and building heating sectors. The total hydrogen produced can be allotted for the transport, building heating, and industrial sectors at 40%, 32%, and 28%, while it is 10%, 7%, and 83% with RNG. 4.4.4 Environmental and economic performance Figure 4-6 presents the environmental and economic performance of Pareto optimal solutions obtained during the optimization scenarios at the considered decision environments neutral, pro-economic, and pro-environmental. Figure 4-6: Environmental and economic performance of optimal solutions N EC EN N EC EN N EC EN N EC EN N EC EN N EC EN N EC EN N EC ENS1 S2 S3 S4 S5 S6 S7 S82 Objectives 3 Objectives - 50,000 100,000 150,000 200,000 250,000 300,000 350,000 400,000 450,000 (500,000) (400,000) (300,000) (200,000) (100,000) - 100,000 200,000 300,000 400,000 500,000 600,000Revenue and cost ($'000/year)LCC Revenue LCELCE (tonCO2eq /year) 129 Scenarios S1 to S4 provide solutions without considering revenue as an objective function, while S5-S8 went through three-objective optimization with revenue as an objective function. It is evident from the figure that Pareto optimal solutions obtained during Scenarios S1-S4 incur higher costs than the potential revenue generation. In contrast, S5-S8 enable generating a net profit by having higher revenue than the life cycle costs incurred. This highlights the importance of considering multiple relevant objectives rather than focusing on one objective when making investment decisions. Scenario S8 exhibits the lowest LCE, possibly due to higher NG displacement and higher proportions of hydrogen recommended for the transport and building heating sectors (The detailed results are provided in supplementary materials). According to the figure, no significant difference can be seen with varying decision environments. However, the weight towards the decision environment is governed by only three criteria. For example, in a pro-environmental decision environment, 75% weight was placed on LCE performance, while placing 12.5% weight each on LCOE and revenue. In a pro-economic decision environment, a 25% weight was placed on LCE, with 37.5% weight each on LCOE and revenue. Therefore, the minimal variance in optimal solutions obtained during the three decision environments is attributed to the limited number of criteria considered in the current evaluation. 4.4.5 Avoided emissions and carbon tax credits Figure 4-7 illustrates the potential emissions savings achievable within the transport, building, and industrial sectors by integrating hydrogen and RNG at optimal levels. Carbon tax savings were calculated for a tax rate of $80/ton of CO2eq [287]. As shown in the figure, Scenario S5, provides the highest emission savings and carbon tax savings in the three economic sectors, possibly due to no constraints on investments and NG to produce hydrogen with SMR. However, this condition is not ideal in realistic cases where resources are not available in abundance. Yet, the next highest savings were achieved in the portfolio derived from S8, where constraints are placed on both investment and available NG to produce hydrogen. This can be attributed to higher displacement of NG in S5 and S8. Furthermore, referring back to Figure 4-4, S5 and S8 have displaced NG use in the transport sector 100% with hydrogen. The study assumed that additional hydrogen produced will contribute to displacing prominent gasoline vehicles. This resulted in a major emission reduction in S5 and S8 due to the significantly low emission factor of hydrogen. Moreover, the potential revenue 130 generation with hydrogen is higher due to its higher selling price, as given in Appendix G. Apart from hydrogen, NG use in the transport sector has also been considerably replaced with RNG when optimally allocated in Scenarios S5 and S8. The potential for higher revenue generation could be one reason behind achieving higher emission savings, even from an economic perspective. Figure 4-7: Sector-wise emissions and overall carbon tax savings The emission savings can be converted into emission credits or carbon tax savings to generate monetary incentives for fuel producers, aligning with the Clean Fuel Standard. Fuel producers of low-carbon fuels, including RNG and hydrogen, can generate emission credits based on the annual amount of low-carbon fuel supply to the Canadian market [288]. Utilizing these carbon credits can help overcome the costs of these fuels and enhance their penetration into the energy market more efficiently. Based on the aforementioned assessment, integrating RNG and hydrogen into the existing gas grid yields significant emission benefits. Moreover, there is potential to generate revenue from the sale of hydrogen and RNG in evaluated economic sectors. However, as the results depict, the major barrier to market penetration is the high production cost of RNG and hydrogen. The overall findings of the study demonstrated the applicability of MOO for gaseous fuel supply chain planning considering both upstream and end-user stage processes considering pillars under the sustainability umbrella simultaneously. As mentioned, decision maker perspectives and priorities play an important role in arriving at solutions. Investigating how decisions would fare under different conditions can help the investors in choosing the most suitable strategy. The - 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18,000 - 50 100 150 200 250 300N EN EC N EN EC N EN EC N EN EC N EN EC N EN EC N EN EC N EN ECS1 S2 S3 S4 S5 S6 S7 S82 Objectives 3 ObjectivesEmission saving (ton CO2eq/year)Transport BH Industry Total carbon tax savingsCarbon tax savings ($/year) 131 obtained results further reflect the need for refining it accuracy depending on the available data and system characteristics under various conditions. The proposed model output solutions considering the current conditions. Yet, energy system decisions involve dynamic factors that are subjected high variance and uncertainty. However, substantial efforts have been to put to overcome the uncertainty in data by employing a scenarios-based approach to MOO. This can be further eliminated by employing system dynamics modeling to in cooperate time-sensitive parameters and in developing a long-range plan for gas grid decarbonization with a diversified fuel portfolio. Summary The third phase of the research proposes a MOO model to investigate the trade-offs between the environmental and economic impacts of RNG and hydrogen integration fuel paths to the existing gas grid in a given community from a life cycle perspective. The proposed model was demonstrated using a case study municipality in BC, Canada. The model initially determines the optimum resource allocation for the production of two fuels, indicating cost and emission optimum fuel generation sources. Next, the MOO model determines the optimum portfolio allocation of NG, RNG, and hydrogen in three economic sectors: transport, building heating, and industry. Key findings include: â¢ The optimum hydrogen production path mainly comprises the NG SMR process coupled with CCS, while the optimum path for RNG production is LFG coupled with PSA biogas upgrade technology. â¢ Based on optimum portfolio allocations, 84% of total NG should be supplied to industry, 16% to the building heading, and the current compressed NG-fuelled vehicle fleet needs to be eliminated. â¢ The optimum hydrogen supply should be apportioned with 40% for transport, 32% for buildings, and 28% for industrial purposes. Similarly, the optimum supply of RNG will be 83% to the industry, 10% to transport, and 7% to, building heating. â¢ Based on the optimum portfolio allocations, the highest emission savings are achieved with Scenarios 5 and 8, due to higher revenue generation coupled with emission benefits from displacing NG in respective economic sectors. The study results further indicated that higher emission savings can be obtained when sufficient capital investments are available. 132 Although gas grid decarbonization with RNG and hydrogen provides emission benefits, limited public acceptance due to a lack of awareness and high infrastructure costs poses challenges. Incentives and comprehensive policy packages promoting low-carbon fuels can address these challenges. Transparent and systematic carbon-tax and credit mechanisms tailored for the gaseous fuel industries can eliminate ambiguities and promote their integration. Yet the policies and recommendations should be created with a long-range perspective due to the dynamic nature of the factors affecting the sustainability of the assessed decarbonization strategies. Furthermore, the suitability of the supply chain configurations provided can vary overtime due to demand variations, public acceptance, global and local political factors, fuel prices, and technology improvements etc. Therefore, supply chain planning tools and frameworks should be expanded to include multi-period considerations. 133 Chapter 5: Fossil Gas Decarbonization with Low-carbon Fuels: Dynamic Condition Analysis A part of this chapter is submitted as a Journal article titled âFossil gas decarbonization with low-carbon fuels: A system dynamic modeling approach,â which is under review in the Elsevier Technology Forecasting and Social Change Journal (TFS-S-24-07065). Background Despite the promise of RNG and hydrogen, their integration into conventional natural gas networks presents several challenges. These challenges include technological limitations, infrastructure requirements, high costs, socio-environmental concerns, and policy and regulatory barriers. For example, although RNG and hydrogen use generate emission benefits over conventional natural gas and other fuels, they come at a high cost, affecting capital and operation expenditure. These high costs are reflected in the price of RNG and hydrogen. Hence, the acceptance of paying a premium charge over the low-cost fossil natural gas (NG) option is low. Moreover, limited public awareness further fuels the lower market penetration of these novel fuels. Regardless, RNG and hydrogen market penetration, even at this level, is mostly a result of complex policies and regulations targeted at net-zero transition. Government funds are known to be driving factors for promoting and developing alternative low-carbon fuels. Many countries globally have introduced legislative and financial incentives to develop low-carbon fuel infrastructure. In California, the Community Energy Commission allocates $20 million a year to fund hydrogen refuelling stations until private sector funding is available without the support of the Government [289]. Some of the key policies that support Canadian low-carbon fuel investment are the Clean Fuel Fund (CFF) and Clean Fuel Regulation (CFR) [290]. Yet, there is a lack of clear and long-term policies and regulations to promote RNG and hydrogen. Moreover, there is an absence of sustained investments to promote investors [14][275], [291]. Therefore, to speed up this transition, there is a need for scientific proof to assist policymakers in creating policies leading to de-risking investments, supporting innovation, and enabling policies and regulations to encourage clean fuel use. Furthermore, complete and comprehensive regulations could raise community awareness to improve its acceptance. 134 The policy and regulator frameworks that are aimed at promoting the market diffusion of RNG and hydrogen should be formulated with long-range thinking. Some of the dynamic factors affecting the viability of decarbonized gas grids with cleaner fuels are inflation, price volatility, political aspects, technological innovations, population and fuel demand, etc. However, there are no dynamic analyses to evaluate the long-term feasibility of integrating RNG and hydrogen into the conventional gas network. Furthermore, only a limited number of studies have examined policy efforts for successful market penetration of these cleaner gaseous fuels from a long-range planning perspective. Amongst the few studies, none of the referred studies have explored the long-term sustainability of integrating both RNG and hydrogen into the gas grid for decarbonization. Therefore, Chapter 6 aims to employ system dynamics modelling to determine the long-range feasibility of RNG and hydrogen integration to existing gas supply chains and the impact of policy initiatives on the long-term feasibility. The main contributions of the chapter are: â¢ The model considers three forms of government policies under the fiscal instruments. They are a) providing government subsidies and funds for investments, b) carbon credits and tax reliefs to promote green gaseous fuel use and emission reduction. â¢ Presents dynamic evolution of return on investments and fuel prices subjected to varying policies and technology progress within the gaseous fuel industry. â¢ Indicates long-range environmental impacts/ benefits associated with RNG and hydrogen integration. Gas supply chain decarbonization with RNG and hydrogen is still in its early stages. Therefore, the developed model, research conclusions, and policy recommendations provided in this chapter are momentous to many countries that are in similar stages of decarbonized gas network development. Additionally, the study findings are useful for investors in determining the long-range viability of integrating RNG and hydrogen into the business models. Methods and Procedure The system dynamic modeling was conducted in 6 stages [41], [191]: 1. Define the problem, aim and the time period 2. Identify causal relationships and create the basic graph a. Define the model boundary 135 b. Define variables c. Determine balancing and reinforcing loops 3. Collect data 4. Convert the causal-effect graph to stock-flow model The problem and aim: The system dynamics modeling approach aims to explore the future techno-economic viability and environmental costs and benefits of integrating RNG and hydrogen into conventional gas networks under different policy scenarios. Moreover, the model attempts to predict the future market potential of hydrogen and RNG in place of NG. The impact of policies and regulations on the market penetration of cleaner gaseous fuels is further explored. Accordingly, the future development of RNG and hydrogen, the corresponding emission reduction, and government expenditure and policy scenarios are comprehensively simulated. A causal relationship model was developed based on the identified parameter relationships. It was then converted to a stock-flow model to assess the feasibility and development of the green gas industry under different governmental fiscal policies and regulatory measures. Vensim DSS simulation environment was employed for the modeling process. While the model was tested and validated for a selected municipality (Kelowna) in the Okanagan Region in BC, Canada, the model components and equations are generalizable and applicable to any region planning to decarbonize its gas network with RNG and hydrogen. 5.2.1 Causal loop diagram A comprehensive literature review identified key parameters affecting the long-term feasibility of RNG and hydrogen. As indicated in the literature review, the feasibility of integrating alternative gaseous fuel into conventional gas networks depends on technical feasibility, economic viability, and socio-environmental acceptability [14]. Funding availability, technology progress, and policies and regulations have a significant role in bolstering confidence in these alternative cleaner fuels. Figure 5-1 shows the main causal relationships between the mentioned parameters within the greener gaseous fuel integration gas grid. Fuel supply When RNG and hydrogen production are considered, the stability of alternative fuel supply is determined by the availability of resources to produce the two fuels. The higher the supply 136 stability, the higher will be the supply reliability. Additionally, the fuel production potential increases with technology efficiency, which increases with the learning rate. This phenomenon strengthens the fuel supply reliability. The increased supply reliability increases the technical feasibility of RNG and hydrogen production paths. Techno-economic and environmental feasibility Technical feasibility has a positive polarity with the initial technology acceptance that leads to increased funds. Funds are further supported by government subsidies. With time, increased learning rates and funds reduce the initial capital expenditure for the investor. Therefore, the costs of fuels are gradually reduced with technological progress. The return on investment (ROI) increases with the reduced investment and operational costs. Furthermore, a higher fuel supply indicates a positive polarity to ROI. The corresponding environmental benefits of RNG and hydrogen can be calculated by the avoided emissions by replacing NG use. Accordingly, carbon tax savings can also be calculated. The avoided emissions and human health impacts associated with alternative fuel supply chains contribute to the environmental performance of the decarbonized gaseous fuel supply chain. 137 Figure 5-1.The causal relationships of cleaner gaseous fuel integration model for NG displacement 138 5.2.2 System dynamics model In compliance with the causal relationship diagram provided in Figure 5-1, the stock-flow model was developed in the VENSIM DSS simulation environment, considering a 27-year span from 2023-2050. The gathered data required for model development are provided in Appendix H. The developed model is illustrated in Figure 5-2. The RNG and hydrogen production paths were determined based on available resources and respective production technologies, with a preference for technologies already at the commercial scale for RNG and hydrogen production. Accordingly, RNG production is coupled with anaerobic digestion and pressure swing adsorption for biomethane upgrade. The feedstock considered are landfill gas (LFG), livestock manure (LVS), and wastewater sludge (WWS). Two hydrogen production methods were further considered. They are NG steam methane with carbon capture and storage (SMR+CCS) and proton exchange membrane electrolysis (PEME). The model assesses the availability of renewable electricity and grid electricity for PEME-based hydrogen. The model mainly comprises three modules, namely, fuel demand and supply, industrial economy, and socio-environmental impacts. 139 Figure 5-2. System dynamics model 140 5.2.2.1 Fuel demand and supply module The demand module estimates the fuel demands in the selected location. The Energy Future Outlook 2023 dataset was employed to obtain the initial annual energy demand values and demand growth of natural gas, hydrogen, and RNG [271]. The dataset provides provincial values. Hence, the model estimates municipal data using the equation below. In instances where the municipal data is not available, the data were approximated based on the population, as given in the equation below. ð· ððð ð ð ð¡ð¢ðð¦ ððððð¡ððð,ð = ð· ðððð£ðððð,ð (ðððð¢ððð¡ððð ðð ððð ð ð ð¡ð¢ðð¦ ððððð¡ððððððð¢ððð¡ððð ðð ð¡âð ðððð£ðððð) Equation 5-1 Where, ð· ððð ð ð ð¡ð¢ðð¦ ððððð¡ððð,ð = Demand for fuel ð in the case study location ð· ðððð£ðððð,ð= Demand for fuel ð in the province Feedstock supply is a limiting factor for RNG and hydrogen production. Therefore, under the resource supply, assessing the availability of feedstock for RNG and hydrogen production was included to predict the future fuel supply potential. The following were considered as feedstock for RNG: livestock manure from the agriculture sector, landfill gas, and wastewater sludge. For hydrogen, it was NG, grid electricity, and solar energy. Livestock manure-based RNG: The availability of manure feedstock is dependent on the population of animals and the manure management system. The current paper considers three main types of farm animals. They are large ruminants, small ruminants, and poultry. The population growth of each type of animal was estimated from [292], [293]. The following formula was used to calculate the biogas potential from livestock manure: ð ððºðð£ð = â ððððððð Ã VS Ã ðð¹ Ã ð¶ð¹ Equation 5-2 Where, ð ððºðð£ð = RNG potential from livestock manure (MJ/year) ð = ðhe type of animal (large and small ruminant, and poultry, etc.) ðð = Farm animal population ðð = Farm animal manure production per animal per year (tons/year. animal) 141 ðð = Dry matter in manure content (%) VS = biogas output per volatile solids in manure dry matter (m3/ ton VS) UF = biogas upgrade to RNG factor CF = Conversion factor Landfill gas-based RNG Municipal solid waste (MSW) is a sustainable energy source [294]. Several projects across Canada use MSW and landfill gas (LFG) to derive biomethane. The current model assumed an MSW growth factor of 0.32 to predict the LFG generation [295]. The amount of LFG produced was determined using the first-degree waste decomposition equation given below: ð ððºð¿ð¹ðº = â ðð Ã ðð¿ð¹ðº Ã ð¶ð¹ Equation 5-3 Where, ð ððºð¿ð¹ðº = RNG potential from LFG (m3/year) ðð = amount of MSW from jth Landfill (t / year) ðð¿ð¹ðº = methane generation rate constant (m3/ton) ð¶ð¹ = Conversion factor WWS-based RNG Similarly, the model included RNG generation potential from wastewater sludge. The wastewater growth rate due to population increase was taken as 0.027 [296]. ð ððºððð = â ðð Ã Qððð Ã Cð¹ Equation 5-4 Where, ð ððºððð = RNG potential from WWS (m3/year) ðð = Wastewater influent flow (t / year) ðððð = methane generation rate constant for wastewater (m3/ton) ð¶ð¹ = Biogas to RNG Conversion factor 142 Hydrogen production The current model assumes three pathways to produce hydrogen. They are SMR with CCS, solar electrolysis and grid-based electrolysis with PEME. The data for the estimation of the dynamic availability of NG and electricity for hydrogen production was obtained from Canada Energy Future [297]. The formula used to calculate the hydrogen production potential is based on specific energy consumption and technology efficiencies. ð»ð = â ð ð Ã ðð Ã ð¸ð¶ð Equation 5-5 Where, ð»ð = Total potential hydrogen supply (kg H2/ year) ð ð = Resource availability to produce hydrogen from technology k (k = 1..3; 1 = NG-based SMR + CCS, 2 = grid electrolysis, 3 = renewable energy-based green hydrogen) (MWh/year) ðð = Efficiency of technology k ð¸ð¶ð = Specific energy consumption of technology k (MWh/ kg H2) The technical feasibility of displacing NG, in addition to catering to actual RNG and hydrogen demands, was established based on the supply and demand relationship. 5.2.2.2 Gaseous fuel industrial economy module Technology efficiencies improve, and costs are reduced with technology learning over time. Hence, the impact of technology learning rates (LR) was incorporated into the developed model. According to the literature, the LR of RNG production is around 4-5% [298]. In contrast, the learning rate of green hydrogen is 15% - 20% [189], [193]. For SMR, the learning rate is around 11±6% [299]. Accordingly, the impact of the learning rate on specific energy consumption and technology efficiency was calculated using the equation below [189]. ð¸ð¶ð = ð¸ð¶ð,0 â â«(ð¸ð¶ð Ã ð¿ð ð)ðð¡ Equation 5-6 ðð = ðð,0 + â« ðð Ã ð¿ð ð)ðð¡ Equation 5-7 Where, ð¸ð¶ð = specific energy consumption of ðð¡â fuel production path (MWh/ kg H2) 143 ð¸ð¶ð,0 = initial specific energy consumption of ðð¡â fuel production path (MWh/ kg H2) ð¿ð ð = learning rate of fuel production ðð¡â fuel production path ðð = efficiency of ðð¡â fuel production path ðð,0 = initial efficiency of the ðð¡â fuel production path Furthermore, the equation below can be used to find the impact of the LR on the unit equipment cost. ð¶ð¶ð¢ððð¡ ððð ð¡,ð = ð¶ð¶ð¢ððð¡,0,ðð¥ðâð¿ð Equation 5-8 Where, ð¶ð¶ð¢ððð¡ ððð ð¡,ð = unit capital cost of the fuel production path ð ð¶ð¶ð¢ððð¡,0,ð = initial unit capital cost of the fuel production path ð ð¥ð = produced fuel capacity The total capital expenditure is indicated by CAPEX, and government subsidies are denoted by GS. ð¶ð´ðð¸ðð¡ðð¡ðð = (1 â ðºð) Ã ð¥ð Ã ð¶ð¶ð¢ððð¡ ððð ð¡,ð Equation 5-9 The profit from fuel sales was calculated using the equation below. ðð = ðð â ððððð¥ð â ð¹ðððð¥ð Equation 5-10 Where, ðð = profit before tax per unit of fuel ð sold ðð = Fuel price of fuel type ð ððððð¥ð = variable operational expenditure of a unit of fuel type m ð¹ð¹ðððð¥ð = fixed operational expenditure of a unit of fuel type m The initial price can be calculated with: ðð = ððð¸ðð¡ðð¡ðð,ð(1 + ðððððð¡ððð ðððððð¡ ðððððð) Equation 5-11 The total operational expenditure (OPEX) is the sum of variable and fixed operational costs. 144 The formula for return on investment of RNG and hydrogen integration modes is indicated in equation 5-12, ð ðð¼ð = ðð(1âðððððð ð¡ðð¥)Ãð ðððð ð£ððð¢ððð ð¶ð¶ð¡ðð¡ðð,ð Equation 5-12 It was assumed that the annual sale of RNG and hydrogen is equal to the respective demand. 5.2.2.3 Gaseous fuel socio-environmental module The socio-environmental module investigates the dynamics of socio-environmental costs and benefits of integrating RNG and hydrogen into the current gas fuel supply chains. Under this module, the emissions generated from RNG and hydrogen production and emissions avoided by displacing NG are evaluated. Furthermore, additional emission savings by integrating CCS into the SMR process are also accounted for when determining the emission credits and carbon tax savings as per Canadaâs Clean Fuel Regulation that allows for carbon capturing utilization and storage-related credit generation [300]. The initial emission values and human health impact values associated with fuel production paths were determined with life cycle analysis and are fed as inputs to the model [15], [301]. 5.2.3 Data and model validation The environmental impact factors of each fuel production path were determined through life cycle analysis. Other initial data for the model were obtained from the literature, including government and industry reports and published research studies. However, it is challenging to validate the model with previous similar models due to the short commercial history of hydrogen and RNG integrated markets. Yet, a set of control parameters was included to avoid deviation from a reasonable range under extreme conditions such as technology progress and upgrades and excessive investment support. Previous studies have employed behavior-reproduction tests to validate system dynamics models) [189]. Therefore, a similar approach was taken to validate the current model. Hence, demand outputs from the model were validated with the demand forecasts provided in Canda Energy Future 2023 data [297]. Fuel demand values in 2022, under the current measures scenario of Canada Energy Future(CEF) 2023 data appendices, were used as the origin of the simulation. The data appendix provides the energy demand for biofuels, which was assumed as RNG in the current study, as RNG qualifies as an advanced biofuel [302]. Accordingly, the initial NG, hydrogen, and RNG demand value inputs to the model are 389.25 PJ/year, 0.03 PJ/year, 145 and 245.27 PJ/year, respectively. The simulation results for the demand values versus CEF data appendix values are provided in Table 5-1. As depicted in the table, the simulation results are close to the CEF data predictions from the Canad Energy Regulator. This implies that the developed model meets the effectiveness requirements for the behavior-production test. Table 5-1. Model validation with CEF-based and current simulation-based demand predictions for BC, Canada Demand (PJ/year) 2024 2025 2030 2040 2050 CEF Model CEF Model CEF Model CEF Model CEF Model NG 379.11 388.82 397.06 388.16 418.17 404.94 421.88 440.40 457.04 486.48 Hydrogen 0.03 0.03 0.03 0.03 0.04 0.036 0.18 0.22 0.26 0.34 RNG 254.40 250.18 262.54 255.18 328.1 281.74 343.18 343.44 357.80 418.65 5.2.4 Assessment scenarios The model was simulated under six policies and two tax support scenarios. The policy scenarios are different government subsidy policies for RNG and hydrogen infrastructure construction, including production equipment and fuel storage, before distributing to the specific end users. Under the subsidy policy, it was considered that the government directly subsidizes the proportion of the investment amount to overcome the challenge of attracting investors. Early-stage government subsidies aim to facilitate technology growth. The scenarios were formulated based on the subsidy support period and intensity. In the scenarios, the maximum subsidy level is 40% of the initial investment. The maximum level is reduced by 5%-10% after the first 3-5 years. Then, the subsidy proportion dropped below 10% after the 10th year before retreating eventually. Two extreme ends of the subsidy scenario maintain a constant subsidy level of 40% throughout the lifetime (S2), while the other involves zero government subsidies (S1). Similar subsidy support scenarios have been formulated by Li et al. (2022) [189]. The rest of the policy scenarios were defined as follows: 146 â¢ S3: 40% investment support for 2023-2026, 30% for 2027-2028, 25% for 2029-2030, 20% for 2031 and 2032, 15% for 2033 and 2034, and 5% for 2035-2037, and 0% from 2038 onwards. â¢ S4: 35% for the first 5 years (2023-2027), 20% for the next 5 years (2028-2032), and 10% from 2033-2037, and 0% from 15th year (2038) onwards. â¢ S5: 30% for 2023-2026, 25% for 2027-2029, 15% for 2030, 10% for 2031-2032, and 0% from 2033 onwards. â¢ S6: 30% for the first 6 years until 2028, 20% from 2029-2030, 10% for 2031 and 2032, and 0% from the 10th year onwards. Tax support policies are considered essential in the penetration of novel fuels and their technology [189]. According to CRA, the corporate Federal tax rate is 15%, and the provincial tax consists of lower and upper rates, of which for BC, it is 2% and 12%, respectively [303]. Therefore, two income tax modes were assumed, as provided below. â¢ T1: 0% income tax on the earnings from 2023-2028, 17% income tax from 2029-2032, 24% income tax from 2033 onwards â¢ T2: no tax benefits. Therefore, 24% income tax on earnings from 2023 onwards. Each of the above policy measures was tested individually and in combination with other instruments to evaluate its effect on RNG and hydrogen production costs and final market price. The developed system dynamics model is used for the analysis of different scenarios for RNG and hydrogen sector development in BC, Canada. Table 5-2 indicates the simulated scenarios. Table 5-2. Simulated scenarios Scenarios Policy Tax A1 S1 T1 A2 S1 T2 A3 S2 T1 A4 S2 T2 A5 S3 T1 A6 S3 T2 A7 S4 T1 A8 S4 T2 147 Scenarios Policy Tax A9 S5 T1 A10 S5 T2 A11 S6 T1 A12 S6 T2 Results and Discussion The model consists of 3 main modules to investigate the benefits of integrating RNG and hydrogen into the conventional gas grid from the fuel supplier's perspective. The first module explores the resource availability and fuel supply capability to cater to the long-term demand for RNG and hydrogen while displacing NG. One main factor that influence the fuel supply and demand matching is resource availability. The resource availability of RNG depends on farming activities and solid waste and wastewater volumes within the municipality. As shown in Figure 5-3, potential RNG supply availability grows exponentially. This is due to the growth of LFG-based and WWS-based RNG. However, the growth of solid waste and wastewater volume could vary over time with the change in population. Figure 5-3. Total potential RNG supply availability Therefore, the impact on RNG generation was further tested by changing the growth rates of solid waste and wastewater by ±10%. It is clear from the figure that the growth slows down when the MSW and WWS growth is reduced by 10%. When it is increased by 10%, the RNG capacity grows faster. The overall growth is slightly affected by the declining proportion of LVS-based RNG. The 148 decline in LVS-based RNG could be due to the rapid urbanization of the area. Yet, the LFG and WWS-based RNG offset a considerable proportion of LVS-based RNG. Figure 5-4 demonstrates a similar hydrogen fuel availability pattern with a slightly faster growth rate than RNG. In the current model, proton exchange membrane electrolysis (PEME) was considered the green, grid-based hydrogen production method. The technology learning of PEME has reduced the specific energy consumption and technology efficiency over time, causing a fast growth of green hydrogen production. A similar trend can be observed with the grid-based electrolysis process. However, the hydrogen production potential is lower than that of green hydrogen due to the declining grid capacity to support hydrogen generation. This suggests the potential to increase the hydrogen supply by increasing the renewable capacity. The NG availability for hydrogen production was derived based on the NG demand for industrial purposes. Figure 5-4. Total potential Hydrogen supply availability The increasing availability of RNG and hydrogen creates room to displace the NG use. As demonstrated in Figure 5-5, the current supply capacity of RNG is only capable of displacing NG demand by approximately 16% and with hydrogen, it is around 1%. However, with the increased supply capacity associated mainly with technology progress and plant expansions, the potential for displacing NG with both RNG and hydrogen increases upto 31% in 10 years. The potential increase up to 55.2% by 2040, and it reaches 100% by 2045. The future 2050 NG demand for the municipality was predicted to be 14 PJ and RNG, and hydrogen demands were 12 PJ and 0.01 PJ, respectively by 2050. Hence, RNG and hydrogen are available enough to cater to their demands 149 while displacing NG. It is clear from Figure 5-5 that after 2046, the availability of both RNG and hydrogen creates an oversupply. This opens up the avenue to distribute additional RNG and hydrogen to regions with lower resource potential. Furthermore, the high availability of resources further qualifies Kelowna as a central location for producing RNG and hydrogen within the region. Figure 5-5. NG displacement potential with RNG and hydrogen One of the factors contributing to the increase in RNG and hydrogen supply potential is technology upgrades and learning. Accordingly, the current model further explored the impact of technology learning on corresponding NG replacement potential and economies. 5.3.1 Impact of technology learning The current model considered the following lower and upper technology learning bounds for the two fuels. The lower bound of RNG technology learning is at 4%, and the upper bound is at 5% [298]. For hydrogen, the lower and upper technology learning bounds of SMR are 6% and 11%, respectively [299]. The green hydrogen technology learning is 15%-20 [193][304]. Figure 5-6 shows the impact of technology learning on the potential to displace NG. As shown in the figure, the NG displacement potential with cleaner fuels at a higher learning rate (LR) leads to a rapid 0.0%50.0%100.0%150.0%200.0%250.0%2023202420252026202720282029203020312032203320342035203620372038203920402041204220432044204520462047204820492050% FNG displacement potentialYearFNG displacement ability with RNGFNG displacement with H2 onlyFNG displacement with combined RNG and H2 150 development from 2038, compared to 2041 with the lower LRs. The key aspect to note is that the potential to completely displace NG is achieved by 2041 with higher LR as opposed to lower LR, which achieves 100% displacement by 2045. Therefore, higher LR can quickly make the municipality a central hub for cleaner fuel production and supply. This could further lead to increased economic success. Figure 5-6. Impact of technology learning on the potential to displace fossil NG With lower LRs, the fuel adoption rates are lower, delaying the point where NG can be fully replaced. Furthermore, it further stretches the time to materialize the investments on the fuels. Lower LRs delay NG displacement, potentially missing out on early economic opportunities. Thus, policies that support R&D, innovation, and infrastructure development are crucial for maximizing learning rates and ensuring the long-term success of RNG and hydrogen adoption. Furthermore, technology learning affects technology efficiency and costs, which are eventually reflected in the selling price and ROI. Therefore, in addition to fossil fuel displacement, the effect of technology learning on RNG and hydrogen fuel production paths was further explored to observe its impact on the fuel selling price, as indicated in Figure 5-7. The RNG production technology is at the commercial scale maturity level. Therefore, the upgrades to anaerobic digestion and biogas upgrade with pressure swing adsorption have limited effect on the cost reduction. Hence, the selling price did not demonstrate a significant variation with the technology learning. The initial RNG price obtained from the model was $0.014/ MJ, which lies closely with 0%50%100%150%200%250%300%350%400%450%2023202420252026202720282029203020312032203320342035203620372038203920402041204220432044204520462047204820492050% FNG displacementLR: lower boundLR: upper bound 151 the current RNG price of $ 0.019/ MJ [305]. According to the model, it reached the lowest ($0.0088/MJ) in 2041 before eventually levelling to a price of $ 0.009/ MJ. Figure 5-7. Variation of the selling price of (a) RNG and (b) hydrogen with technology learning According to Figure 5-7 (b), the hydrogen price declined with the technology learning. The price hits the lowest value of $4.5/ kg H2 earlier with a higher technology learning (20%) in 2034. In contrast, with a technology learning of 15%, the price reached the lowest of $4.75/ kg H2 in 2038. The obtained hydrogen selling price values lie closely with the study by Gao et al. (2022) [193]. Therefore, with high-technology learning, the market penetration of hydrogen can be achieved at a faster phase. ROI is an effective performance indicator to determine the viability of energy investments [15]. Figure 5-8 illustrates the impact of technology learning on the return to the investor from RNG (a) and hydrogen (b) investments. The benefits of technology learning are reflected in the ROI of RNG and hydrogen investments due to the direct effect on fuel prices and costs. Both fuel projects indicate a gradual decline of ROI until 2032, which could be due to reasons including high upfront costs and lower revenue that do no scale-up in the early commercialization phase. Moreover, lower demand for RNG and hydrogen results in limited economies of scale benefits and slower revenue growth that reduces the ROI in the initial stages. The improvement of ROI post-2032 can be attributed to the scale benefits with demand increments leading to higher revenues and effects of technology learning that reduce technology costs. Furthermore, early-stage policies promoting RNG and hydrogen further drive the fuel demands. 152 Figure 5-8. Variation of ROI with technology learning (with government subsidies and tax reliefs) When the dynamics of ROI of the two fuels are compared (as shown in Figure 5-8 (a)), the RNG ROI growth is slower, which could be due to the already achieved technical maturity and lower technology learning rates than that of hydrogen. As visible from Figure 5-8 (b), hydrogen ROI is higher with higher technology learning, and it increases rapidly after 2032 with 20% technology learning. However, it take until 2041 for the ROI to grow with a technology learning of 15%. Furthermore, with high-technology learning, the ROI has increased significantly up to nearly 0.375 by 2050. What is noteworthy is that the benchmark ROIs of 10% can be achieved quickly with higher technology learning rates. Technology learning can be accelerated by providing government funding for technology improvements and research and development [193]. In the current study, the promotional effect of government funding was further reflected through the ROI. 5.3.2 Impact of Government Funding and Tax Policies The developed system dynamics model examined the effect of government funding and tax reliefs on the economies of integrating hydrogen and RNG into the conventional gas grid. Accordingly, Figure 5-9 demonstrates the corresponding ROIs of both RNG and hydrogen. Additionally, the scenarios shown in Figure 5-9 explore the effect of varying income tax rate policies once coupled with the subsidies. The T1 policy is a 0% income tax charge in the first 5 years that increases to 17% from the 6th year until it eventually increases to the actual 24% rate. T2 policy maintains the regular income tax rate of 24% from the beginning. As observed in the figure, with the 24% income tax rate, the initial ROI of RNG is between 0.05 â 0.08, which is below the general 153 benchmark of 0.1 ROI [189]. In contrast, the ROI of hydrogen is better than the ROI within the range of 0.1 â 0.18. With the A2 scenario, without any subsidies, the ROI of both fuels is lower due to high investment costs and lower returns in the initial stages. The growth of ROI with time is slower with scenario A2, as it further lacks any income tax reliefs. Even without tax support, the ROIs slowly improve with stable returns after the 2033 â 2038 period. It is clear from the figure that subsidies and tax support help to slowly gain momentum and improve the economies of RNG and hydrogen. In Figure 5-9 c) and d), the effect of subsidies on the investments and tax reliefs are reflected in the ROI due to the reduced capital expenditure, while the tax benefits increase the profits. Therefore, the initial ROIs lie above 0.06 and 0.19 in RNG and hydrogen, respectively. This is due to the increased cash flow. Moreover, increased returns due to subsidies and tax benefits incentivize companies to invest in cleaner emerging fuel projects due to improved financial attractiveness. After 2033, the ROI improved at a slower rate with the reduced subsidies and tax benefits as ROI is improved through the economies of scale and technology progress. However, for rapid development, sustained subsidies are essential during the mid-term 10-15 years. Moreover, the results indicate the necessity of a combined policy for the current development situation, which matches the scale of development with technological progress. 154 Figure 5-9. ROI of (a) RNG and (b) hydrogen with T2 tax rate, and ROI of (c) RNG and (d) hydrogen with T1 tax scenario 155 5.3.3 Environmental benefits In addition to investment support for carbon capture projects, Canadaâs CFR highlights that CCS creates opportunities to generate credits with low-carbon intensity fuel production [306]. Accordingly, the model includes the potential carbon credit generation with the SMR+CCS process. Furthermore, the environmental benefits of integrating RNG and hydrogen into the existing gas grid were measured in terms of avoided emissions by replacing fossil natural gas with the oversupply of RNG and hydrogen. Figure 5-10 illustrates the avoided emissions and the carbon tax savings along with the British Columbia carbon tax rates from 2023-2050. As shown in the figure, the avoided emissions increase steadily with time. Accordingly, the carbon tax savings for utility providers also increase with time. It is clear that a significant cost saving can be achieved by integrating cleaner fuels into the conventional gas grid. Figure 5-10. (a) total avoided emissions, (b) carbon tax rate, and corresponding (c) carbon tax savings Furthermore, as depicted in Figure 5-10, the avoided emissions and corresponding tax savings gradually increase until 2045, due to the combined use of RNG and hydrogen with NG, leading to 156 incremental emission reductions over time. With the increased potential of RNG and hydrogen due to expanded production capacities and infrastructure driven by demand, their adoption increases, resulting in more significant avoided emissions. The potential to entirely phase out NG use with RNG and hydrogen is feasible after 2045. Thus, post-2045, the shift from incremental substitution to complete replacement accelerates emission savings across key sectors like building energy, industry, and transportation. As NG use is eliminated from these sectors, the rate of avoided emissions increases rapidly. Further, with more emissions avoided, greater tax savings from carbon pricing mechanisms are realized. Decarbonizing the gas networks with a diversified portfolio carrying cleaner alternatives such as RNG and hydrogen is an emerging approach. As noted from the results, high investment costs, public resistance, resource availability, and limited support are key challenges born by the gas industries. Furthermore, the lack of comprehensive policy and project planning guidelines further inhibits the rapid integration of these cleaner gaseous fuels. Summary System dynamics modelling assists decision makers in having a clear vision and equipping them with better information to make alternative fuel integration choices. Chapter 6 presents a system dynamics model for simulating cleaner gaseous fuel supply availability, costs and economies, and socio-environmental performance, along with government policies for the Canadian gaseous fuel industry to support a 2050 net-zero transition. The model evaluated the impact of the combination of subsidy policies and tax reliefs while considering the technology progress. The findings of this chapter underscore several critical points. First, the development of the gaseous fuel industry varies rapidly with time. Therefore, a single fixed subsidy policy is not appropriate. A major proportion of government funding should be provided within the first 10-15 years to bring down the price and increase the ROI to attract more private investors. By aligning subsidies with the benchmark ROI, policymakers can effectively catalyze the transition towards cleaner energy alternatives while ensuring fiscal responsibility. The model simulations project a promising trend of decreasing costs for green hydrogen over time, attributed to technological learning and economies of scale effects. This anticipated reduction in costs could facilitate the market diffusion of hydrogen. RNG, shows an increasing capability to replace NG with supply 157 capacity expansions. The current RNG resource availability can replace only 4-20% of NG demand. However, with time, the simulations indicate that this could go up to 90% by 2040. The value could further increase in areas with higher feedstock availabilities. Such locations can serve as regional central fuel distribution hubs. In conclusion, the study represents a significant step towards understanding and addressing the complexities of transitioning towards cleaner gaseous fuels. The developed model is useful for policymakers, utility providers, and broader research communities in gaining useful insights about long-range planning and integration of RNG, hydrogen, and other emerging gaseous fuels to decarbonize conventional gas networks. 158 Chapter 6: Policy Implications and Recommendations Part of this chapter is published in the journal article: Kotagodahetti, R., Hewage, K., Karunathilake, H., Prabatha, T., Krishnan, H., Adebola, S. K., Bryant, T., & Sadiq, R. (2020). âLiquefied natural gas exports from Canada to China: An analysis of internationally transferred mitigation outcomes (ITMO)â, Journal of Cleaner Production (Elsevier). Background The transition to decarbonized gas networks is critical for global strategies to combat climate change and achieve net-zero emissions targets. Evaluations from phases 3 and 4 of this research indicate that incorporating RNG and hydrogen into the existing gas grid leads to significant emission reductions. Additionally, there is potential for generating revenue from the sale of hydrogen and RNG in the assessed economic sectors. However, the results highlight that the primary barrier to market penetration is the high production cost of RNG and hydrogen. This suggests a need for guidance and support that extends beyond technical expertise to achieve gas supply chain decarbonization. This chapter aims to offer policy implications and recommendations for overcoming the barriers that hinder the growth of RNG and hydrogen. It provides industry guidelines and best practices based on the MOO. Policy implications are discussed using system dynamics model simulations. The study findings indicate the potential to replace natural gas with RNG and hydrogen gradually. Additionally, surplus fossil natural gas could support LNG exports from Canada, potentially facilitating international emission trading mechanisms by reducing the use of emission-intensive fossil fuels in other parts of the world with BC-LNG. Best Management Practices for NG Industry Diversification MOO results from Phase 3 of the study indicate that integrating RNG and hydrogen into the existing gas grid yields significant emission benefits. It was clear from the assessment that prioritizing low-carbon fuel production technologies and integration paths is essential, considering the emission-reduction potential varies among paths. Given the limited resources and budget, going forward with a prioritized investment plan for fuel integration can help decarbonize the current gas grid with optimum GHG emission reductions in a cost-efficient manner. The study 159 results indicated that emission reductions can be achieved even with constrained resources and investments. Accordingly, in Phase 4 of the study, the long-term effectiveness of policy and regulatory support on RNG and hydrogen investments was explored. As seen from the system dynamics model, considerable subsidies are required for the initial investments, especially for those technologies that are in the early stages of technical maturity. Given below are some recommendations to overcome the hurdles that impede the growth of RNG and hydrogen based on findings from MOO and system dynamics model. Develop a standardized portfolio management plan: Establishing a standardized portfolio management plan (PMP) for integrating RNG and hydrogen into the existing gas industry ensures consistency in managing diverse projects and facilitates a uniform approach to incorporating new technologies and practices across the entire portfolio. Utility providers and other stakeholders can use these PMP to choose the optimum low-carbon fuel integration paths to achieve emission targets and economic expectations while fulfilling the investment constraints. Such a plan can help identify and avoid financial and operational risks associated with RNG and hydrogen integration, as it would consider all constraints associated with the gaseous fuel portfolio. Provide planned financial incentives and subsidies: Financial barriers are key hurdles for RNG and hydrogen market penetration. Major emission savings can be achieved if government bodies can allocate a budget to incentivize low-carbon fuel integration via subsidies and tax reliefs. Study findings underline that subsidies should be allocated appropriately to avoid over and under-utilization of investments. In the system dynamics assessment, the subsidy policy that maintains a 40% subsidy level results in high expenditure on the government due to continued investment support on expanding fuel infrastructure. However, the major drawback of this policy would be the blind expansion of installed capacity without matching the actual demand. This could result in the underutilization of developed infrastructure. Providing relatively larger subsidies in the early development period of the first 10 years would improve the economies of the cleaner fuel industry. This could attract investors in the early stages, boost the development of technologies, and stimulate demand growth. Further, it would ensure returns for the investors during the early periods of high costs. Eventually, 160 this could lead to an increase in private investment and a reduction in government spending on infrastructure development. Investigate business models to improve economies: Expanding a municipality's capacity to produce excess fuel not only addresses local energy needs but also positions it as a central hub for distributing surplus fuel to neighbouring regions. By increasing RNG and hydrogen production, municipalities can mitigate the environmental impacts associated with traditional natural gas extraction. Furthermore, another strategic approach would be producing marketable by-products with captured carbon from the SMR+CCS process. This not only maintains the economic viability of fossil fuels but also enhances ROI. Additionally, the NG industry can explore opportunities to boost liquefied natural gas exports while simultaneously earning international emission credits, thereby contributing to global emission reduction efforts while bolstering economic growth. Implement comprehensive policy instruments to promote low-carbon gaseous fuels: The system dynamics model forecasted fuel demands based on the demand predictions of CEF data under current policy measures [307]. The study results indicated that the demand for RNG and hydrogen would still be lower than that for fossil natural gas by 2050. The main reasons behind this are the lack of public awareness and motivation. This indicates the need for policies that promote cleaner fuel use. â¢ Carbon tax: Carbon tax and credits are policies to encourage cleaner fuel use. Carbon credits go on par with low-carbon gaseous fuels in generating financial benefits from emission savings. For example, since 2020, Tesla has generated approximately a billion and a half dollars a year with the carbon credits generated from the sale of electric vehicles [308]. Likewise, a standardized and transparent methodological approach to generating carbon credits and tax savings could promote the public adoption of RNG and hydrogen. Furthermore, RNG certificates are an energy attribute certificate process introduced in the United States, where it provides an entity to claim the clean energy that was brought and used [309]. This allows businesses and the public to purchase clean energy to meet emission mandates and goals. A similar policy for green hydrogen naming green certificates has been introduced by [189]. However, such policies should be transparent and communicated effectively to the general public. 161 â¢ Clean gas mandates: Similar to zero-emission vehicle targets, mandates to include renewable energy or green gas content in other energy-intensive applications, such as building heating, could boost the demand for cleaner fuels. However, building infrastructure for producing, distributing, and storing clean gas is essential for widespread adoption. Governments can invest in building pipelines, storage facilities, and refuelling stations to support the use of clean gas. â¢ Knowledge dissemination: Educating the public about the benefits of clean gas and the importance of reducing emissions can help create demand for cleaner fuels. Government-sponsored campaigns can raise awareness and encourage individuals and businesses to switch to clean gas. Government entities, industries, utility providers, and academia specialists can conduct information sessions and other knowledge dissemination activities to educate the general public regarding the benefits of low-carbon fuels. Research and policies are co-driven. Research drives policies by providing data, insights, and evidence-based recommendations that inform policy decisions. Conversely, policy drives more research by creating a framework and funding opportunities that encourage further investigation into clean gas technologies and low-carbon fuels. This cyclical relationship ensures that policies are grounded in the latest scientific findings while ongoing research continues to refine and improve policy effectiveness. Fostering this interplay could effectively promote the adoption of cleaner fuels for accelerated emission reductions. Successful implementation of the above recommendations and best practices requires coordinated efforts of multiple stakeholders. Recommendations for Stakeholders Table 6-1 summarizes recommendations and responsible stakeholders for effectively implementing the actions. As discussed, the industry can benefit by exploring government programs and policies that could spur decarbonized gaseous fuel markets. [310]. In addition to the above-discussed recommendations, any safety concerns of stakeholders, including the industry personnel and end-users, should be clarified when allocating responsibilities. This could be done by ensuring the 162 transparency of successful demonstration projects. Furthermore, necessary rules and standards for ownership and share of liabilities between utilities and producers should be established. Some other specific standards are gas quality and metering standards, rules for pipeline connections, protocols for sharing data and conditions for accepting gaseous fuels at different points in the gas pipeline systems [311]. Furthermore, research and development in the NG industry decarbonization and new business avenues for scaled-up processes are required to drive down the value chain costs. 163 Table 6-1: Action items Action items Gas producers Gas utilities Technology suppliers Government & municipalities R&D community End-users Policy Introduce policy tools to incent market â Determine green attributes â â â â â Carbon pricing â Low carbon fuel regulations â Tax credits for cleaner gaseous fuel initiatives and clients â â â â Regulate methane emissions from new and existing gas sources â â Set performance targets â â â 164 Action items Gas producers Gas utilities Technology suppliers Government & municipalities R&D community End-users Integrate regional variations to policy mechanisms â Explore opportunities for emission trading schemes â De-risk investments Improve capital efficiency â â â Optimize development strategies for economic value â â â Advocate the GHG benefits on top of the economics and link them to the strategic initiatives â â Advocate for project demonstration grants â â â Strategic partnerships Collaborate and invest in technology solutions â â â 165 Action items Gas producers Gas utilities Technology suppliers Government & municipalities R&D community End-users Collaborations for sharing previous decarbonization experiences â â â â â International collaboration â â â â â Industry-academia collaboration â â â â Technology management Lay virtual pipelines â â â Industry digitalization â â â R&D in scaled up new production processes â â â Build in-house expertise â â â 166 Action items Gas producers Gas utilities Technology suppliers Government & municipalities R&D community End-users Prepare contingency plans in the plantâs designs and operations â â â Codes and Standards Establish necessary rules and standards â â â Implement federal clean fuel standard â â â Establish a common standards and leading practices â â Cleaner gaseous fuel supply chain guidelines and roadmaps â â â Standard repair and maintenance schedule â â Adopt industry-wide quality standards â â 167 Action items Gas producers Gas utilities Technology suppliers Government & municipalities R&D community End-users Clear gas quality and metering standards, and protocols for data sharing â â Community blueprints Raise awareness among key stakeholders â â â â Share knowledge of cost-effective methane reduction technologies and practices â â â â â Raise awareness on safety measures â â â â Manage premiums attached to cleaner fuels â â Public education campaigns â â â â â Showcase successful decarbonization projects â â â â â â 168 Action items Gas producers Gas utilities Technology suppliers Government & municipalities R&D community End-users Ensure transparency and support data collection â â â â 169 Many of the above-discussed recommendations are universally applicable to both the Canadian and global natural gas industries. However, some differences may apply, subject to regional circumstances. For example, certain regions in the world are still highly dependent on emission-intensive coal-based fuels. Such countries are in the phase of coal-to-gas switching. Those countries can create synergies by generating carbon credits and internationally transferred mitigation outcomes by utilizing decarbonized natural gas imported from countries such as the United States (U.S.) and Canada [205]. Recommendations to Facilitate International Emission Trading with ITMO The system dynamics simulation results show that Canada has the potential to displace fossil NG use, creating a surplus gas supply. This surplus gas increases the gas export volumes from Canada. Canadian LNG has lower life cycle carbon emissions compared to coal, and it is noted that Canadian LNG facilities also possess lower emission intensities compared to LNG produced elsewhere in the world [131]. Hence, NG exports to countries like China provide a potential pathway for the Canadian O&G sector to enter into international emission trading, thus reducing net global emissions [132]. Moreover, it can be an opportunity to enhance the sustainability of the Canadian natural gas industry. The 2015 Paris Climate Accord brought 195 countries to a consensus on mitigating global warming [312]. The Countries were required to put forward individual Nationally Determined Contributions (NDC) in the form of emission reduction targets at the national level and internationally transferred mitigation outcomes (ITMO) [313], [314]. The ITMO is a revised emission trading mechanism that is built upon Joint Implementation (JI; Article 6) and Clean Development Mechanism (CDM; Article 12). It is defined as a voluntary agreement between two countries allowing one country to gain credits for reducing emissions in the other country [141], [315]. ITMOs provide countries with an alternative way to reach NDC targets through participation in the global carbon market. As a part of this, Canada is exploring the potential of exporting LNG to Asian markets as an ITMO initiative to replace the existing emissions from carbon-intensive coal-based energy generation [301][136]. 170 6.4.1 Mechanisms that facilitate ITMOs Article 6.2 of the Paris Agreement has not defined a specific mechanism for generating ITMOs. However, the literature suggests few mechanisms for transferring ITMOs regionally and internationally. They are; 1) the linkage of emission systems, 2) the transfer between governments, and 3) crediting mechanisms. The linkage of emission systems An Emission Trading System (ETS) is a market-based instrument that works on the principle of âcap-and-tradeâ. When governments impose a limit (cap) on emissions for one or more sectors, the industries in those sectors require a permit for every ton of emissions they release. The trade part of cap and trade is where these industries either receive or buy permits [316]. These ETS can be linked where the regional and national emission mitigation efforts are linked to cap-and-trade systems from different cities or regions [317]. The ETS sets an emission cap for a defined set of participants. The participants monitor and report actual emissions and submit compliance instruments equal to their actual emissions at the end of the compliance period. These compliance instruments are either allowances issued by the ETS administrator or offset credits generated outside the cap. These compliance instruments can be traded [318]. Thus, ITMOs can be used as compliance instruments (permits) in this regard. There are three forms of creating these links [316], [318]: 1. One-way (Direct) link: Industries in ETS-A buy ITMOs (permits) generated in ETS-B 2. Two-way link: ITMOs can flow in both directions 3. In-direct through offset crediting: Both ETSs are linked to the same offset crediting system Transfers between governments ITMOs are transferred between parties to achieve NDCs in exchange for funds. The buyer party exchanges funds and the seller transfers ITMOs. This supports achieving NDC goals of the buyer. Here ITMOs transferred would be determined by the support provided instead of the results achieved. This mechanism intends to align the support with climate finance contributions under Article 6 [319]. The emission sources and mitigation actions will be the responsibility of the host country. Figure 6-1 depicts how the mechanism works. 171 Figure 6-1: Transfer between governments mechanism Crediting mechanisms This requires a country to implement climate policy or instrument by providing financial assistance. As a reward, the country that receives assistance pays the other a portion of the emission reductions produced due to the policy/ instrument implemented. Thereby, the country that assists with finances receives emissions credits. The emission credits would be conditional on the success of the implemented policy [320]. This mechanism aims to generate emission credits using policies or investments that lead to emission reduction in a given sector. These credits can either be traded or used to offset emission commitments. Moreover, the credits can be banked by the host country for future commitments [321]. Crediting mechanisms can be designed in multiple ways. Potential mechanisms are; 1) Policy-based crediting: credits generated by adopting and implanting GHG-friendly policies in particular sectors 2) Indexed crediting: GHG emission below a certain intensity level generates emission credits 3) Fixed sectoral emission limits: emission credits are generated if a sector/ company emits at a lower limit than an agreed level. The two distinct approaches to setting crediting mechanisms are transnational and national. In transnational crediting mechanisms, the mechanism is set up with worldwide sectors. In the national crediting mechanism, the national governments impose baselines for their sectors to provide incentives to exploit emission abatement opportunities and attract greener investments 172 with the emission credits generated [321]. The above mechanisms can be implemented independently or can be combined. While many parties and observers understand that ITMOs offer a path to carbon markets, no specific references in the Paris Agreement identify carbon markets as the only channel for ITMOs. There is no further guidance on how to engage in cooperative approaches and comply with the goals of Article 6.2. Therefore, the major question revolving around ITMOs is: â¢ How can ITMOs be designed and implemented between countries/ regions to offset domestic emissions in one region based on emission reduction in another? The following are recommended to overcome the confusions around the above for the successful implementation of ITMOs [322]. Government actions: 1) Participate in negotiating guidelines for implementing cooperative approaches. Governments should actively engage in negotiating guidelines for implementing cooperative approaches. Their responsibility lies in customizing rules that aim to maintain environmental integrity and transparency while preventing double counting. 2) Acknowledge the role of different parties. There are concerns about negotiating bilateral agreements that can benefit all parties involved. In that regard, it is necessary to work with other countries to develop pilots or initiatives to know mutual interests, build feasible alternatives, and acknowledge the territorial approach for accounting for emissions. Industry actions: 1) Engage with stakeholders. Stakeholder engagement is essential to show the benefits of implementing ITMOs and the potential overall gains of a successful implementation. Moreover, it is important to acknowledge different groups, such as indigenous communities, and the general public to understand their interests and concerns. This will be critical for supporting ITMO initiatives via LNG exports. 2) Acknowledge local benefits. At a local, provincial, and national level, the benefits of ITMO pilots or initiatives need to be clear for citizens to obtain their support for successful implementation. 173 3) Incentivize the participation of the industry, civil society, and academia. Different organizations are already working on ITMO pilots and cooperating with them. Incentivizing their participation in creating initiatives is necessary to explore successful conceptualization models. Summary While RNG and hydrogen are considered transformative opportunities for the Canadian gas sector, several challenges exist in deploying cleaner fuels into the current gas grid. These barriers prevent the large-scale integration of these fuels into the energy sector. The findings of the previous research phases indicated that significant policy support is needed to accelerate their integration into local gas grids. This chapter provides policy implications and recommendations to support gas supply chain decarbonization with cleaner gaseous fuels. Key recommendations and necessary future work to overcome these barriers include; â¢ Developing a standardized portfolio management plan for diversified gas supply chains: Future studies are needed to develop and test advanced portfolio management models further incorporating dynamic factors, such as demand variability, seasonal fluctuations, and policy transitions. â¢ Analysing infrastructure compatibility and upgrade needs: Further work is needed to evaluate the technical requirements and financial feasibility of retrofitting existing gas infrastructure to handle high concentrations of hydrogen or RNG. This includes studies on material compatibility, safety, and storage capacity modifications for the current pipeline network. â¢ Standardizing emission tracking and validation systems: Further work is required to establish transparent, independent, and standardized systems to validate emission reductions achieved by integrating RNG and hydrogen. This is particularly relevant for generating ITMOs from LNG exports. Blockchain technology and digital verification tools could enhance transparency and credibility in emission tracking. â¢ Raising awareness among the general public: Future studies should explore the impact of public awareness initiatives on RNG and hydrogen acceptance and usage. Social research could identify the most effective messaging strategies and channels for communicating the benefits of cleaner fuels to the public. 174 Chapter 7: Conclusions The growing concerns about the environmental impacts and economic sustainability of fossil fuel usage are pushing the energy sector to adopt cleaner alternatives. Specifically, the NG industry is under pressure to reduce carbon emissions by diversifying its portfolio with cleaner options like RNG and hydrogen. This study addresses a significant gap in planning and decarbonizing gas supply chains by creating a decision-support Package, which includes MCDM framework, MOO model, and a system dynamics model. The proposed framework and models will help identify the best paths for integrating RNG and hydrogen while evaluating the feasibility of such diversified supply chains. The findings of this work will support the gas industry in achieving its energy and climate targets. Additionally, while the focus is on integrating RNG and hydrogen into existing gas networks and supporting clean energy initiatives, the study's implications extend to enhancing energy security for communities. Where possible, certain study findings were further validated by industry experts from FortisBC, BC Energy Regulator, and subject matter experts from the Ministry of Energy, Mines and Low Carbon Innovation. The specifically validated results include LCA and LCCA results of hydrogen and RNG production routes, natural gas supply chain emissions assessment, and the potential to generate emission credits with BC LNG exports. Summary and Conclusions The thesis comprises seven chapters. This section provides a summary of each chapter while outlining the conclusions derived from the findings. Chapter 1 provides the introduction and overall methodology. Chapter 2 presents a comprehensive literature review on conventional gas supply chain decarbonization strategies. The key knowledge gap identified in the literature review was the lack of holistic tools and frameworks for supply chain decarbonization planning and management considering long-range viability. The findings of this chapter were further used to define research concepts and methodologies. Chapter 3, demonstrates the RNG and hydrogen production path selection framework that was developed considering technical feasibility, environmental desirability, and economic viability. The life cycle environmental impacts were assessed using the LCA tool, and an LCC model was developed to determine the fuel production paths' economic viability. The outcomes of the 175 environmental and economic assessments were used as inputs to a multi-criteria decision-making process to identify the most suitable RNG and hydrogen production path. The results demonstrated that the proposed life cycle thinking-based decision-support tool is applicable for prioritizing and classifying sustainable gaseous fuel networks. The developed systematic approach is geared towards assisting utility providers and energy infrastructure investors in selecting the most suitable RNG and hydrogen production path. Chapter 4 presents a MOO model for gas supply chain decarbonization at two levels. MOO modelsâs objective functions include minimization of life cycle emissions and LCOE while maximizing revenue from fuel sales. The optimization model identified Pareto-optimal fuel production paths and portfolios. The model revealed that integrating RNG and hydrogen into the current gas grid can significantly reduce emissions and generate substantial carbon tax savings. This model is useful for gaseous fuel industry stakeholders, offering a comprehensive view of supply chain costs and detailed insights into emission benefits when integrating RNG and hydrogen into existing business models. Chapter 5 provides a dynamic model to assess the long-term viability of integrating RNG and hydrogen into community emission projects subjected to varying conditions. The changes in fuel supply availability, fuel price, return on investment, and emission reduction were simulated under different combinatory government subsidy scenarios, including investment support, tax reliefs, subsidy intensity, and duration. The model simulation results indicated that subsidy policies and tax relief promote the development of RNG and hydrogen market diffusion at varying levels. Flexible policy support was found to be the most suitable for the rapid development of the decarbonized fuel industry. The developed model can help utility providers and policymakers identify the most effective investment strategy and support from a long-range perspective when planning gas network decarbonization. Chapter 6 outlines recommendations for the successful planning and implementation of gas network decarbonization with RNG and hydrogen. The recommendations were provided based on the study findings. Key recommendations outlined are the need for a portfolio management plan for a diversified gas industry, planned government subsidies for RNG and hydrogen market diffusion, alternative fuel promotion with carbon tax and clean gas mandates, and public awareness campaigns. 176 Originality and Contributions The overall study presents a comprehensive solution package for gas supply chain planning for 2050 net-zero transformation. The following unique contributions are made to the field of research to facilitate the decision-making process. An integrated approach to select RNG and hydrogen production paths: At present, evaluations of RNG and hydrogen are conducted as discrete studies. The conventional approach fails to look at the gas supply chain decarbonization with RNG and hydrogen from a holistic perspective. The proposed framework includes a life cycle-based comparison to assess the economic and environmental performance of different fuel options for decarbonizing fossil NG. The framework considers multiple criteria to prioritize and select the best routes for RNG and hydrogen generation. The frameworkâs further novelty lies in the weighting mechanism that simultaneously considers criteria importance/ stakeholder priorities and causal relationships between the criteria. A MOO model to determine optimum resource allocation and gaseous fuel portfolio: There is a lack of a systematic approach to determine the optimum resource allocation for RNG and hydrogen generation, considering resource availability and fuel demand. The current study can help utility providers select the optimum resource configurations of RNG and hydrogen generation at minimum cost and with minimum/zero damage to the environment. Moreover, it is important to determine the optimum fuel supply portfolio in a diversified gas network. Yet there is a lack of comprehensive research on selecting the best fuel portfolio for the gaseous fuel industry considering life cycle TBL performances. The developed comprehensive model is capable of providing the optimum gaseous fuel portfolio to cater to key economic sectors (building, industry, and transport). The MOO model can assist in building a sustainable gaseous fuel supply chain and business operations while aligning with climate goals. Long-range planning model in gaseous fuel decarbonization under uncertain conditions: A system dynamics model was developed to determine the potential to decarbonize the gas grid with low-carbon gaseous fuels. The model explores the effect of government policies and other uncertain macro-environmental conditions on investments in the gaseous fuel supply chain under varying conditions. Moreover, the model is capable of predicting the long-range environmental 177 and economic performance of RNG and hydrogen integration paths. The evaluation of government policies and regulations on the long-term development of RNG and hydrogen fuels within the gaseous fuel industries provides useful insights for the sustenance of a decarbonized gas grid. Limitations of the Study The present study encountered the following limitations and challenges, and adjustments were made where possible to reduce the impacts. Limitations in life cycle impact data: The LCI database development faced certain challenges due to a lack of location-specific data. Hence, reasonable assumptions and approximations were made where necessary. The life cycle impact assessment was conducted based on the available published databases. Specific Canadian data could provide a more accurate representation of life cycle impacts. Yet approximations were made with data developed for other regions when Canada-specific data were not available. North American data were preferred due to the geographical proximity and similar macro-environmental conditions. Limitations and uncertainty in data: Identifying cost factors involved information collection from various published sources since no comprehensive compilation of current data on RNG and hydrogen technologies was available from a single reputable source. Furthermore, mathematical approximations were conducted to derive cost figures. Fuzzy logic and scenario-based assessments were employed to address data uncertainty. Accordingly, fuzzy triangular numbers were used due to its ability to accommodate performance scores that are in a range of possible values. However, assuming a triangular distribution tends to generalize the actual reality of variations and uncertainties into a simpler form. Focus only on municipal fuel generation and supply: The current model focuses only on municipal fuel generation and supply. Therefore, findings are not applicable to centralized fuel production options for a cluster of municipalities in the upstream supply chain stage. Furthermore, the model does not accommodate fuel supply to and sharing among other regional municipalities. While the developed tools can be adapted to decentralized fuel generation supply chain configurations for a selected municipality, further work needs to be conducted to accommodate regional fuel hub planning. Furthermore, discrete optimizations were conducted to determine the 178 fuel generation mix and portfolio mix, as the study was focusing on one municipality. The model can be extended to include the fuel distribution stage as well for regional supply chain planning. Focus only on current technologies: The developed decision support package only considers technologies that have reached commercial maturity. However, radical evolution can occur, especially in hydrogen production technologies. The technology selection stage presented in this study can be updated to include emerging and unproven technologies in addition to mature technologies. These emerging technologies can be evaluated with the MOO and system dynamics model for further model validations and dynamic supply chain planning. Future Research Directions Based on the study conducted, the following recommendations are provided for future research. Overall supply chain optimization: While the current study has focused on upstream fuel generation and downstream fuel use in economic sectors, further work can be done focusing on the integrated optimization of production, distribution, and storage processes to minimize carbon emissions while maximizing efficiency and reliability. The current MOO considered factors such as feedstock availability, energy inputs, production costs, and environmental impacts, along with the revenue to the utility provider with gas sales. Future work can focus on developing an extended model that can help determine the most efficient transportation routes, modes of transport (e.g., pipelines, trucks, or ships), and scheduling of deliveries. Factors such as distance, transportation costs, emissions associated with different transportation modes, and demand variability can be considered in these models to minimize both logistical costs and carbon footprint. Moreover, an integrated model can be updated to determine the optimal storage capacity, location, and inventory management strategies. Such an extended model is applicable to regional fuel hub planning. Real-time risk-based optimization: An extension to the proposed MOO model in the current study could be real-time risk-based optimization. This could integrate dynamic risk assessment with real-time monitoring and adaptive decision-making to enhance the resilience and efficiency of operations. Such a model is useful in the timely identification of emerging risks and the implementation of appropriate mitigation strategies, such as rerouting distributions, reallocating resources, or activating contingency plans. Multi-objective optimization algorithms can consider 179 trade-offs between cost, emissions, reliability, and risk, allowing decision-makers to identify robust solutions that perform well under different risk scenarios. Extended policy and regulators analysis: Future research can focus on analyzing the policy and regulatory frameworks needed to support the integration of RNG and hydrogen into supply chains. This research could involve evaluating existing policies, identifying gaps and inconsistencies, and proposing recommendations for policy reforms to incentivize decarbonization efforts. Furthermore, international emission trading mechanisms such as Internationally Transferred Mitigation Outcomes (ITMOs) by exporting surplus fossil natural gas LNG to replace emission-intensive coal use in international energy markets can be explored. While environmental impact assessments have been conducted on LNG exports, the socio-economic viability of BC-LNG exports can be evaluated in terms of their cost-effectiveness, contribution to BC's economy, and impact on energy security. The feasibility assessment can include a market analysis that examines the current and projected market dynamics of LNG trade in terms of pricing, demand, and competition in energy markets. Emission target compliance tool: The proposed system dynamics model explored the potential avoided emission by replacing fossil natural gas with RNG and hydrogen. This could be extended to derive a a dynamic emission target compliance tool to assess and track emission target compliance across economic sectors. Such a tool will be useful for policymakers, government bodies, and economic sector stakeholders in tracking and monitoring emission sector-wise emissions performance and initiating proactive measures. 180 References [1] CleanBC, â2020 Climate Change Accountability Report,â 2020. [2] Generation Energy Council, âCanadaâs Energy Transition,â 2018. [3] RBC, âThe $ 2 Trillion Transitionâ¯: Canada â s road to Net Zero The $ 2 Trillion Transitionâ¯: Canada â s road to Net Zero,â pp. 1â26, 2021. [4] âGreenhouse gas emissions.â Accessed: Jul. 29, 2019. [Online]. Available: https://www.canada.ca/en/environment-climate-change/services/environmental-indicators/greenhouse-gas-emissions.html [5] C. Beck, S. Rashidbeigi, O. Roelofsen, and E. Speelman, âThe future is now: How oil and gas companies can decarbonize,â no. January, 2020. [6] International Energy Agency (IEA), âThe Oil and Gas Industry in Energy Transitions,â 2020. [7] N. A. Sepulveda, âDecarbonization of Power Systems: Analyzing Different Technological Pathways by,â MASSACHUSETTS INSTITUTE OF TECHNOLOGY, 2016. [8] Center for Climate and Energy Solutions, âDecarboizing U.S. Oil and Gas.â Accessed: Sep. 30, 2020. [Online]. Available: https://www.c2es.org/document/decarbonizing-u-s-oil-and-gas/ [9] R. Chopra, S. Porter, and A. Swart, âThe 2030 decarbonization challenge. The path to the future of energy,â Deloitte Touche Tohmatsu Limited, 2020. [10] âLow-Carbon Energy Project | Enbridge Gas.â [Online]. Available: https://www.enbridgegas.com/about-enbridge-gas/projects/low-carbon-energy [11] â5 reasons weâre investing in our gas system to support BCâs lower-carbon future.â Accessed: Nov. 21, 2021. [Online]. Available: https://www.fortisbc.com/news-events/stories-and-news-from-fortisbc/5-reasons-we-re-investing-in-our-gas-system-to-support-bc-s-lower-carbon-future [12] FortisBC, âClean Growth Pathway to 2050,â 2019. 181 [13] L. S. Abrahams, C. Samaras, W. M. Griffin, and H. S. Matthews, âLife cycle greenhouse gas emissions from U.S. liquefied natural gas exports: Implications for end uses,â Environ Sci Technol, vol. 49, no. 5, pp. 3237â3245, 2015, doi: 10.1021/es505617p. [14] R. Kotagodahetti, K. Hewage, P. Perera, and R. Sadiq, âTechnology and policy options for decarbonizing the natural gas industry: A critical review,â Gas Science and Engineering, vol. 114, p. 204981, Jun. 2023, doi: 10.1016/j.jgsce.2023.204981. [15] R. Kotagodahetti, K. Hewage, F. Razi, and R. Sadiq, âComparative life cycle environmental and cost assessments of renewable natural gas production pathways,â Energy Convers Manag, vol. 278, p. 116715, Feb. 2023, doi: 10.1016/j.enconman.2023.116715. [16] W. C. Nadaleti, E. G. de Souza, and V. A. Lourenço, âGreen hydrogen-based pathways and alternatives: Towards the renewable energy transition in South Americaâs regionsâPart B,â Int J Hydrogen Energy, vol. 47, no. 1, pp. 1â15, Jan. 2022, doi: 10.1016/j.ijhydene.2021.05.113. [17] T. Elsen, âEnergy Transitionâ¯: Impact on the factors shaping International Oil Companies strategy,â Delft University of Technology, 2020. [18] A. Heesterman, âThe pace and practicality of decarbonization,â Clean Technol Environ Policy, vol. 19, no. 2, pp. 295â310, 2017, doi: 10.1007/s10098-016-1277-x. [19] Carbon Market Watch, âCarbon markets 101 The Ultimate Guide to Global Offsetting Mechanisms,â pp. 1â12, 2019. [20] Y. Nie et al., âGreenhouse-gas emissions of Canadian liquefied natural gas for use in China: Comparison and synthesis of three independent life cycle assessments,â J Clean Prod, vol. 258, p. 120701, 2020, doi: 10.1016/j.jclepro.2020.120701. [21] K. Sapkota, A. O. Oni, and A. Kumar, âTechno-economic and life cycle assessments of the natural gas supply chain from production sites in Canada to north and southwest Europe,â J Nat Gas Sci Eng, vol. 52, no. January, pp. 401â409, 2018, doi: 10.1016/j.jngse.2018.01.048. [22] J. Rogers, âLNG Production in British Columbia: Greenhouse Gas Emissions Assessment and Benchmarking for the BC Climate Action Secretariat May 2013,â 2013. 182 [23] R. E. Liu, âLife Cycle Greenhouse Gas Emissions of Western Canadian Natural Gas and a Proposed Method for Upstream Life Cycle Emissions Tracking,â 2019. [24] American Petroleum Institute, âLiquefed Natural Gas (LNG) Operations |Consisten methdology for estimating greenhouse gas emissions,â 2015. [25] J. Zarei, M. R. Amin-Naseri, A. H. Fakehi Khorasani, and A. H. Kashan, âA sustainable multi-objective framework for designing and planning the supply chain of natural gas components,â J Clean Prod, vol. 259, p. 120649, 2020, doi: 10.1016/j.jclepro.2020.120649. [26] E. Santibanez-Borda, A. Korre, Z. Nie, and S. Durucan, âA multi-objective optimisation model to reduce greenhouse gas emissions and costs in offshore natural gas upstream chains,â J Clean Prod, vol. 297, p. 126625, 2021, doi: 10.1016/j.jclepro.2021.126625. [27] T. Andrade, âThe impact of regulation, privatization and competition on gas infrastructure investments,â Energy, vol. 69, pp. 82â85, 2014, doi: 10.1016/j.energy.2014.03.038. [28] C. Beck, D. Bellone, S. Hall, J. Kar, and D. Olufon, âThe big choices for oil and gas in navigating the energy transition,â no. February, p. 11, 2021. [29] A. Stevens and P. Lambermont, âAn Overview of Natural Gas Bans in the U.S.,â 2021. [Online]. Available: https://www.instituteforenergyresearch.org/wp-content/uploads/2021/08/Natural-Gas-Ban-Report_Updated.pdf [30] J. Bleasby, âClimate and Construction: Fossil fuel ban resistance in North America leaves developers in a quandary.â Accessed: May 13, 2024. [Online]. Available: https://canada.constructconnect.com/dcn/news/government/2023/12/climate-and-construction-fossil-fuel-ban-resistance-in-north-america-leaves-developers-in-a-quandary#:~:text=In British Columbia%2C the municipalities,B.C. Zero Carbon Step Code [31] U. Lee, A. Bhatt, T. R. Hawkins, L. Tao, P. T. Benavides, and M. Wang, âLife cycle analysis of renewable natural gas and lactic acid production from waste feedstocks,â J Clean Prod, vol. 311, no. October 2020, p. 127653, 2021, doi: 10.1016/j.jclepro.2021.127653. [32] L. Al-Ghussain, M. Alrbai, S. Al-Dahidi, and Z. Lu, âIntegrated assessment of green hydrogen production in California: Life cycle Greenhouse gas Emissions, Techno-Economic Feasibility, and resource variability,â Energy Convers Manag, vol. 311, no. February, p. 118514, 2024, doi: 10.1016/j.enconman.2024.118514. 183 [33] A. D. Singh, A. Upadhyay, S. Shrivastava, and V. Vivekanand, âLife-cycle assessment of sewage sludge-based large-scale biogas plant,â Bioresour Technol, vol. 309, Aug. 2020, doi: 10.1016/j.biortech.2020.123373. [34] R. Kotagodahetti et al., âLiquefied natural gas exports from Canada to Chinaâ¯: An analysis of internationally transferred mitigation outcomes ( ITMO ),â J Clean Prod, vol. 347, no. March, p. 131291, 2022, doi: 10.1016/j.jclepro.2022.131291. [35] R. Kakodkar et al., âA review of analytical and optimization methodologies for transitions in multi-scale energy systems,â Renewable and Sustainable Energy Reviews, vol. 160, no. December 2021, 2022, doi: 10.1016/j.rser.2022.112277. [36] H. Karunathilake, K. Hewage, T. Prabatha, R. Ruparathna, and R. Sadiq, âProject deployment strategies for community renewable energy: A dynamic multi-period planning approach,â Renew Energy, vol. 152, pp. 237â258, 2020, doi: 10.1016/j.renene.2020.01.045. [37] Y. W. Du and W. Zhou, âNew improved DEMATEL method based on both subjective experience and objective data,â Eng Appl Artif Intell, vol. 83, no. January, pp. 57â71, 2019, doi: 10.1016/j.engappai.2019.05.001. [38] A. Manzardo, J. Ren, A. Mazzi, and A. Scipioni, âA grey-based group decision-making methodology for the selection of hydrogen technologies in life cycle sustainability perspective,â Int J Hydrogen Energy, vol. 37, no. 23, pp. 17663â17670, Dec. 2012, doi: 10.1016/j.ijhydene.2012.08.137. [39] R. Kotagodahetti, K. Hewage, E. Bakhtavar, and R. Sadiq, âLife cycle-based multi-objective model for optimal gaseous fuel generation and portfolio allocation in gas grids: A strategic decarbonization,â Energy Convers Manag, vol. 319, no. July, p. 118926, 2024, doi: 10.1016/j.enconman.2024.118926. [40] Z. Zeng, E. Nasri, A. Chini, R. Ries, and J. Xu, âA multiple objective decision making model for energy generation portfolio under fuzzy uncertainty: Case study of large scale investor-owned utilities in Florida,â Renew Energy, vol. 75, pp. 224â242, Mar. 2015, doi: 10.1016/j.renene.2014.09.030. 184 [41] R. Kotagodahetti, K. Hewage, H. Karunathilake, and R. Sadiq, âLong-term feasibility of carbon capturing in community energy systems: A system dynamics-based evaluation,â J Clean Prod, vol. 377, Dec. 2022, doi: 10.1016/j.jclepro.2022.134460. [42] V. Ghisolfi, L. A. Tavasszy, G. H. de A. Correia, G. de L. D. Chaves, and G. M. Ribeiro, âFreight Transport Decarbonization: A Systematic Literature Review of System Dynamics Models,â Sustainability (Switzerland), vol. 14, no. 6, 2022, doi: 10.3390/su14063625. [43] A. Barisa, V. Kirsanovs, and A. Safronova, âFuture transport policy designs for biomethane promotion: A system Dynamics model,â J Environ Manage, vol. 269, no. September 2019, p. 110842, 2020, doi: 10.1016/j.jenvman.2020.110842. [44] S. Fadeyi, H. A. Arafat, and M. R. M. Abu-Zahra, âLife cycle assessment of natural gas combined cycle integrated with CO2 post combustion capture using chemical solvent,â International Journal of Greenhouse Gas Control, vol. 19, pp. 441â452, 2013, doi: 10.1016/j.ijggc.2013.10.008. [45] McKinsey Climate Change Initiative, âCarbon capture & storage: assessing the economics,â 2008. [46] P. Styring, D. Jansen, H. de Coninck, H. Reith, and K. Armstrong, Carbon Capture and Utilisation in the green economy. 2011. [47] E. Umeozor;, H. Rahmanifard;, E. Nduagu;, and D. Millington;, âEconomic and Environmental Impacts of Methane Emissions Reduction in the Natural Gas Supply Chain,â Calgary, 2019. [48] M. R. Rahimpour and S. M. Jokar, âFeasibility of flare gas reformation to practical energy in Farashband gas refinery: No gas flaring,â J Hazard Mater, vol. 209â210, no. x, pp. 204â217, 2012, doi: 10.1016/j.jhazmat.2012.01.017. [49] C.-H. Chiu, C. Knaus, and C. Lewis, âReduce Greenhouse Gas Emissions Across the LNG Chain,â GasTech 2002, pp. 2â8, 2002. [50] A. S. Kasumu, V. Li, J. W. Coleman, J. Liendo, and S. M. Jordaan, âCountry-Level Life Cycle Assessment of Greenhouse Gas Emissions from Liquefied Natural Gas Trade for Electricity Generation,â Environ Sci Technol, vol. 52, no. 4, pp. 1735â1746, 2018, doi: 10.1021/acs.est.7b05298. 185 [51] S. Bachu Gunter, W.D., âAcid gas injection: A case study of existing operation: Phase I: Final Report; Characzeristics of acid gas injection operations in Western Canada,â no. April, p. 71, 2003. [52] X. Zou et al., âSustainable offshore oil and gas fields development: Techno-economic feasibility analysis of windâhydrogenânatural gas nexus,â Energy Reports, vol. 7, pp. 4470â4482, 2021, doi: 10.1016/j.egyr.2021.07.035. [53] Y. Tao, H. Li, Z. Wen, H. Chen, W. Xu, and S. Evans, âA hybrid scenario analysis for the selection of future greenhouse gas emissions reduction technologies in Chinaâs oil and gas industry,â J Clean Prod, vol. 223, pp. 14â24, 2019, doi: 10.1016/j.jclepro.2019.03.144. [54] A. Vypovska and L. Johnson, âStudy No. 161: Risk Analysis of British Columbia Natural Gas Projects: Environmental and Indigenous Peoples Issues,â no. 161, p. 136, 2016. [55] Government of Canada, âNatural Gas Facts.â Accessed: Aug. 10, 2021. [Online]. Available: https://www.nrcan.gc.ca/science-and-data/data-and-analysis/energy-data-and-analysis/energy-facts/natural-gas-facts/20067 [56] L. Kaplan and Canadian Energy Centre, âThe oil and gas sectorâs contribution to Canadaâs economy,â CEC Fact Sheet, no. 48, pp. 1â7, 2021. [57] Port Long Beach; and T. P. L. Angeles;, âFramework for Developing Feasibility,â 2017. [58] A. Ishkov et al., âUnderstanding methane emissions sources and viable mitigation measures in the natural gas transmission systems: Russian and U.S. experience,â International Gas Research Conference Proceedings, vol. 2, pp. 1038â1057, 2011. [59] P. Balcombe, N. P. Brandon, and A. D. Hawkes, âCharacterising the distribution of methane and carbon dioxide emissions from the natural gas supply chain,â J Clean Prod, vol. 172, no. 2018, pp. 2019â2032, 2018, doi: 10.1016/j.jclepro.2017.11.223. [60] Y. Nie et al., âGreenhouse-gas emissions of Canadian liquefied natural gas for use in China: Comparison and synthesis of three independent life cycle assessments,â J Clean Prod, vol. 258, p. 120701, 2020, doi: 10.1016/j.jclepro.2020.120701. 186 [61] Z. Kong, X. Dong, and G. Liu, âCoal-based synthetic natural gas vs. imported natural gas in China: A net energy perspective,â J Clean Prod, vol. 131, pp. 690â701, 2016, doi: 10.1016/j.jclepro.2016.04.111. [62] FORTISBC, âThe Determination of Change in GHG Emissions Resulting from LNG Export from FortisBC , Tilbury Plant,â 2019. [63] D. T. Allen, âMethane emissions from natural gas production and use: Reconciling bottom-up and top-down measurements,â Curr Opin Chem Eng, vol. 5, pp. 78â83, 2014, doi: 10.1016/j.coche.2014.05.004. [64] D. L. Pinti, Y. Gelinas, A. M. Moritz, M. Larocque, and Y. Sano, âAnthropogenic and natural methane emissions from a shale gas exploration area of Quebec, Canada,â Science of the Total Environment, vol. 566â567, pp. 1329â1338, 2016, doi: 10.1016/j.scitotenv.2016.05.193. [65] A. Azadeh, Z. Raoofi, and M. Zarrin, âA multi-objective fuzzy linear programming model for optimization of natural gas supply chain through a greenhouse gas reduction approach,â J Nat Gas Sci Eng, vol. 26, pp. 702â710, 2015, doi: 10.1016/j.jngse.2015.05.039. [66] Y. Charabi, âDigging deeper into cutting methane emissions from the oil and gas industry in the era of volatile prices,â Mitig Adapt Strateg Glob Chang, vol. 26, no. 2, 2021, doi: 10.1007/s11027-021-09948-3. [67] P. Gergett, S. Hall, and J. Kar, âToward a net-zero future Decarbonizing upstream oil and gas operations,â McKinsey & Company, McKinsey & Company, 2019. [68] EPA, âReducing Methane Emissions From Compressor Rod Packing Systems,â 2006. [69] M. S. Masnadi and A. R. Brandt, âClimate impacts of oil extraction increase significantly with oilfield age,â Nat Clim Chang, vol. 7, no. 8, pp. 551â556, 2017, doi: 10.1038/NCLIMATE3347. [70] O. Engineer, âHow to Decarbonize the Upstream Industry,â Offshore Engineer, 2021. [71] OIES, âCarbon Capture and Storageâ¯: The perspective of oil and gas producing countries,â no. September, pp. 1â9, 2021. 187 [72] J. Engel-Cox et al., âSummary of Findings Innovations in Upstream Oil and Gas Operations in Close Proximity to Communities,â 2019. [73] Oil and Gas Division, âElectrification of the Upstream Natural Gas Sector,â 2021. [74] AkerSolutions, âSolving global energy challenges for future generations .,â 2021. [75] âHywind Tampen - floating wind power project - equinor.com.â Accessed: Apr. 05, 2022. [Online]. Available: https://www.equinor.com/en/what-we-do/hywind-tampen.html [76] Canadian Natural, âCREATING VALUE THROUGH Forward-looking Statements,â 2021. [77] M. Soltanieh, A. Zohrabian, M. J. Gholipour, and E. Kalnay, âA review of global gas flaring and venting and impact on the environment: Case study of Iran,â International Journal of Greenhouse Gas Control, vol. 49, pp. 488â509, 2016, doi: 10.1016/j.ijggc.2016.02.010. [78] R. Moghadasi, A. Rostami, and A. Hemmati-Sarapardeh, Enhanced Oil Recovery Using CO2. Elsevier Inc., 2018. doi: 10.1016/B978-0-12-813027-8.00003-5. [79] J. Zhou, S. Tian, K. Yang, Z. Dong, and J. Cai, Enhanced gas recovery technologies aimed at exploiting captured carbon dioxide. Elsevier Inc., 2022. doi: 10.1016/b978-0-12-824495-1.00010-3. [80] Fortune Buiness Insights, âEnhanced Gas Recovery Market Size, Share & Industry Analysis, By Product Type(Nitrogen Based EGR, Carbon Dioxide Based EGR, Others), By Application(Oil and Gas Industry, Construction Industry, Residential, Others) and regional forecast 2022-2029,â 2021. doi: FBI103922. [81] G. Gas, E. Reduction, and O. Project, âAltaGas Turin Acid Gas Injection Project,â 2012. [82] A. Aggreko, âOvercoming the top 3 hurdles to eliminate flaring,â 2019. [83] C. A. Odumugbo, âNatural gas utilisation in Nigeria: Challenges and opportunities,â J Nat Gas Sci Eng, vol. 2, no. 6, pp. 310â316, 2010, doi: 10.1016/j.jngse.2010.08.004. [84] T. Fleisch, âAssociated Gas Monetization via miniGTL Conversion of flared gas into liquid fuels & chemicals,â 2014. [85] B. Buzcu-Guven, R. Harriss, and D. Hertzmark, âGas flaring and venting: extent, impacts, and remedies,â Energy market consequences, pp. 1â72, 2010. 188 [86] S. Mokhatab, W. A. Poe, and J. Y. Mak, âBasic Concepts of Natural Gas Processing,â Handbook of Natural Gas Transmission and Processing, no. 2004, pp. 177â189, 2019, doi: 10.1016/b978-0-12-815817-3.00004-6. [87] S. Mokhatab, W. A. Poe, and J. Y. Mak, Sulfur Recovery and Handling. 2019. doi: 10.1016/b978-0-12-815817-3.00008-3. [88] Q. Li et al., âEffects of Acid Gas Reinjection on Enhanced Natural Gas Recovery and Carbon Dioxide Geological Storage: Investigation of the Right Bank of the Amu Darya River,â in Carbon Dioxide Capture and Acid Gas Injection, no. July, New York: Wiley, 2016, ch. Effects of, pp. 221â243. doi: 10.1002/9781118938706.ch12. [89] National Gas Machinery Laboratory, Clearstone Engineering, and Environmetnal Innovative Solutions, âCost Effective Directed Inspection and Maintenance Control Opportunities at Five Gas Processing Plants and Upstream Gathering Compressor Stations and Well Sites,â 2006. [90] L. Leoni, F. De Carlo, N. Paltrinieri, F. Sgarbossa, and A. BahooToroody, âOn risk-based maintenance: A comprehensive review of three approaches to track the impact of consequence modelling for predicting maintenance actions,â J Loss Prev Process Ind, vol. 72, p. 104555, 2021, doi: 10.1016/j.jlp.2021.104555. [91] M. F. Mostafa and S. A. Fahmy, âMaintenance Strategy Selection Using AHP: A Case Study in the Oil and Gas Industry,â 2020 IEEE 7th International Conference on Industrial Engineering and Applications, ICIEA 2020, pp. 1049â1053, 2020, doi: 10.1109/ICIEA49774.2020.9101952. [92] Offshore Engineer, âThe Future of Offshore Energy & Technology,â Offshore Engineer, vol. May/June, no. April, 2019. [93] U.S. Environmental Protection Agency, âRisk Assessment Guidance for Superfund Volume I: Human Health Evaluation Manual (Part F, Supplemental Guidance for Inhalation Risk Assessment),â Washington, D.C., 2009. doi: EPA-540-R-070-002. [94] US EPA, âRecommended Technologies to Reduce Methane Emissions | US EPA.â Accessed: Aug. 31, 2021. [Online]. Available: https://www.epa.gov/natural-gas-star-program/recommended-technologies-reduce-methane-emissions 189 [95] EPA, âWet Seal Degassing Recovery System for Centrifugal Compressors,â 2014. [96] US EPA, âReplacing Wet Seals with Dry Seals in Centrifugal Compressors - Lessons learned from Natural Gas STARS Partners,â 2006. [97] U.S. EPA, âReplace Gas Starters with Air or Nitrogen Replace Gas Starters with Air or Nitrogen,â no. 101, p. 2, 2011. [98] US EPA, âInstall Automated Air / Fuel Ratio Controls,â 2011. [99] US EPA, âInstall Electric Motor Starters,â 2011. [100] US EPA, âInstall Electric Compressors,â 2011. [101] US EPA, âReplace Glycol Dehydration Units with Methanol Injection Replace Glycol Dehydration Units with Methanol Injection,â 2011. [102] US EPA, âReplacing Glycol Dehydrators With Desiccant Dehydrators,â 2006. [103] US EPA, âPortable Desiccant Dehydrators Portable Desiccant Dehydrators,â 2011. [104] US EPA, âZero Emissions Dehydrators Zero Emissions Dehydrators,â 2011. [105] EPA, âLessons Learned Replacing Gas-Assisted Glycol Pumps with Electric Pumps Replace Gas-Assisted Glycol Pumps With Electric Pumps,â 2006. [106] US EPA, âConvert Natural Gas-Driven Chemical Pumps,â 2011. [107] US EPA, âConvert Pneumatics to Mechanical Controls,â 2011. [108] R. K. Hetti, H. Karunathilake, G. Chhipi-Shrestha, R. Sadiq, and K. Hewage, âProspects of integrating carbon capturing into community scale energy systems,â 2020, Elsevier Ltd. doi: 10.1016/j.rser.2020.110193. [109] GCCSI, âCCS: Applications and Opportunities for the Oil and Gas Industry,â no. May, 2020. [110] Centre for Climate and Energy Solutions, âCarbon Capture | Center for Climate and Energy Solutions.â Accessed: Apr. 22, 2022. [Online]. Available: https://www.c2es.org/content/carbon-capture/ [111] International Energy Agency (IEA), âEnergy Technology Perspectives 2020 - Special Report on Carbon Capture Utilisation and Storage,â Energy Technology Perspectives 2020 190 - Special Report on Carbon Capture Utilisation and Storage, 2020, doi: 10.1787/208b66f4-en. [112] Canadian Gas Association, âRenewable Natural Gas Technology Roadmap for Canada,â 2014. [113] R. Laszlo; and T. Shorts, âRNG Handbook for Canadian Municipalities,â Toronto, 2021. [114] R. N. Schock; et al., âEnergy Supply Systems,â in Energy Supply Systems, 2013, pp. 1131â1171. [115] âB.C.âs natural gas-powered vehicle fleet is growing - Context Magazine by CAPP.â Accessed: Jul. 05, 2022. [Online]. Available: https://context.capp.ca/energy-matters/2021/itn-natural-gas-vehicles/ [116] Canadian Biogas Association, âClosing the Loop: Primer for Municipalities, Food Processors and Fleets on Fueling Vehicles Using Renewable Natural Gas,â no. June, p. 32, 2021. [117] M. Wang, G. Wang, Z. Sun, Y. Zhang, and D. Xu, âReview of renewable energy-based hydrogen production processes for sustainable energy innovation,â Global Energy Interconnection, vol. 2, no. 5, pp. 436â443, 2019, doi: 10.1016/j.gloei.2019.11.019. [118] S. C. Wijayasekera, K. Hewage, O. Siddiqui, P. Hettiaratchi, and R. Sadiq, âWaste-to-hydrogen technologies: A critical review of techno-economic and socio-environmental sustainability,â Int J Hydrogen Energy, vol. 47, no. 9, pp. 5842â5870, 2022, doi: 10.1016/j.ijhydene.2021.11.226. [119] Zen and the Art of Clean Energy Solutions, âBritish Columbia Hydrogen Study,â 2019. [120] G. Di Lullo, A. O. Oni, and A. Kumar, âBlending blue hydrogen with natural gas for direct consumption: Examining the effect of hydrogen concentration on transportation and well-to-combustion greenhouse gas emissions,â Int J Hydrogen Energy, vol. 46, no. 36, pp. 19202â19216, 2021, doi: 10.1016/j.ijhydene.2021.03.062. [121] KEARNEY Energy Transition Institute, âHydrogen applications and business models Going blue and greenâ¯? Hydrogen â H2 FactBook summary,â 2020. 191 [122] M. Pellegrini, A. Guzzini, and C. Saccani, âA preliminary assessment of the potential of low percentage green hydrogen blending in the Italian Natural Gas Network,â Energies (Basel), vol. 13, no. 21, 2020, doi: 10.3390/en13215570. [123] M. P. M.W. Melaina, O. Antonia, âBlending hydrogen into natural gas pipelines networks: A review of key issues,â 2013. [124] G. Sakthivel, R. Sivakumar, N. Saravanan, and B. W. Ikua, âA decision support system to evaluate the optimum fuel blend in an IC engine to enhance the energy efficiency and energy management,â Energy, vol. 140, pp. 566â583, 2017, doi: 10.1016/j.energy.2017.08.051. [125] R. Harder, H. Holmquist, S. Molander, M. Svanström, and G. M. Peters, âReview of Environmental Assessment Case Studies Blending Elements of Risk Assessment and Life Cycle Assessment,â Environ Sci Technol, vol. 49, no. 22, pp. 13083â13093, 2015, doi: 10.1021/acs.est.5b03302. [126] C. B. Tarun, E. Croiset, P. L. Douglas, M. Gupta, and M. H. M. Chowdhury, âTechno-economic study of CO2 capture from natural gas based hydrogen plants,â International Journal of Greenhouse Gas Control, vol. 1, no. 1, pp. 55â61, 2007, doi: 10.1016/S1750-5836(07)00036-9. [127] NRCan, âHydrogen Strategy for Canada,â 2020. [128] K. Espegren, S. Damman, P. Pisciella, I. Graabak, and A. Tomasgard, âThe role of hydrogen in the transition from a petroleum economy to a low-carbon society,â Int J Hydrogen Energy, vol. 46, no. 45, pp. 23125â23138, 2021, doi: 10.1016/j.ijhydene.2021.04.143. [129] Province of British Columbia, âLiquefied Natural Gas A Strategy for B.C.âs Newest Industry,â Strategy, p. 12, 2012. [130] G. Bresciani, S. Heiligtag, P. Lambert, and M. Rogers, âThe future of liquefied natural gas: Opportunities for growth | McKinsey,â Mckinsey, no. September, p. 10, 2020. [131] T. McMillan, âCanadian LNG could help fight climate change,â Context: Energy Examined. [132] T. McMillan, âCanadian LNG could help fight climate change,â Context: Energy Examined. 192 [133] L. S. Abrahams, C. Samaras, W. M. Griffin, and H. S. Matthews, âLife cycle greenhouse gas emissions from U.S. liquefied natural gas exports: Implications for end uses,â Environ Sci Technol, vol. 49, no. 5, pp. 3237â3245, 2015, doi: 10.1021/es505617p. [134] âCanadian LNG Projects.â Accessed: Apr. 25, 2022. [Online]. Available: https://www.nrcan.gc.ca/our-natural-resources/energy-sources-distribution/clean-fossil-fuels/natural-gas/canadian-lng-projects/5683 [135] Z. Kong, X. Dong, and G. Liu, âCoal-based synthetic natural gas vs. imported natural gas in China: A net energy perspective,â J Clean Prod, vol. 131, pp. 690â701, 2016, doi: 10.1016/j.jclepro.2016.04.111. [136] International Institute for Sustainable Development, âCurrent Status of Article 6 of the Paris Agreementâ¯: Internationally Transferred Mitigation Outcomes (ITMOs),â 2020. [137] M. J. Pickl, âThe renewable energy strategies of oil majors â From oil to energy?,â Energy Strategy Reviews, vol. 26, no. May, p. 100370, 2019, doi: 10.1016/j.esr.2019.100370. [138] C. Krelling and M. G. Badami, âOperational and financial performance of Delhiâs natural gas-fueled public bus transit fleet: A critical evaluation,â Transp Policy (Oxf), vol. 47, pp. 178â188, 2016, doi: 10.1016/j.tranpol.2016.02.001. [139] T. Sugiyama and J. Sinton, âOrchestra of treaties: A future climate regime scenario with multiple treaties among like-minded countries,â Int Environ Agreem, vol. 5, no. 1, pp. 65â88, 2005, doi: 10.1007/s10784-004-3702-6. [140] T. P. S. Bhatia, âEconomic Analysis of Worldâs Carbon Markets,â 2012. [141] B. Riehl, I. Turcotte, and K. T. Wu, âInternationally Transferred Mitigation Outcomes,â 2019. [142] S. Wanniarachchi, K. Hewage, C. Wirasinghe, G. Chhipi-Shrestha, H. Karunathilake, and R. Sadiq, âTransforming road freight transportation from fossils to hydrogen: Opportunities and challenges,â Int J Sustain Transp, vol. 0, no. 0, pp. 1â21, 2022, doi: 10.1080/15568318.2022.2068389. [143] J. D. Hunt, A. Nascimento, N. Nascimento, L. W. Vieira, and O. J. Romero, âPossible pathways for oil and gas companies in a sustainable future: From the perspective of a 193 hydrogen economy,â Renewable and Sustainable Energy Reviews, vol. 160, no. November 2021, p. 112291, 2022, doi: 10.1016/j.rser.2022.112291. [144] Canadian Energy Regulator, âCER â Canadaâs Energy Future 2021 - Key Findings,â 2021. [145] J. I. Marvik, E. V. Ãyslebø, and M. Korpås, âElectrification of offshore petroleum installations with offshore wind integration,â Renew Energy, vol. 50, pp. 558â564, 2013, doi: 10.1016/j.renene.2012.07.010. [146] K. Krah et al., âOpportunities for Clean Energy in Natural Gas Well Operations,â IEEE International Systems Conference (SysCon), pp. 1â7, 2020, doi: 10.1109/SysCon47679.2020.9275853. [147] A. Mattiussi, M. Rosano, and P. Simeoni, âA decision support system for sustainable energy supply combining multi-objective and multi-attribute analysis: An Australian case study,â Decis Support Syst, vol. 57, no. 1, pp. 150â159, 2014, doi: 10.1016/j.dss.2013.08.013. [148] D. Streimikiene, T. Balezentis, I. Krisciukaitien, and A. Balezentis, âPrioritizing sustainable electricity production technologies: MCDM approach,â Renewable and Sustainable Energy Reviews, vol. 16, no. 5, pp. 3302â3311, 2012, doi: 10.1016/j.rser.2012.02.067. [149] P. Naicker and G. Alex, âA framework for sustainable utility scale renewable energy selection in South Africa,â J Clean Prod, vol. 224, pp. 637â650, 2019, doi: 10.1016/j.jclepro.2019.03.257. [150] E. Billig and D. Thraen, âRenewable methane â A technology evaluation by multi-criteria decision making from a European perspective,â Energy, vol. 139, pp. 468â484, 2017, doi: 10.1016/j.energy.2017.07.164. [151] B. Debbarma, P. Chakraborti, P. K. Bose, M. Deb, and R. Banerjee, âExploration of PROMETHEE II and VIKOR methodology in a MCDM approach for ascertaining the optimal performance-emission trade-off vantage in a hydrogen-biohol dual fuel endeavour,â Fuel, vol. 210, no. August, pp. 922â935, 2017, doi: 10.1016/j.fuel.2017.08.016. [152] S. Wanniarachchi, K. Hewage, C. Wirasinghe, H. Karunathilake, and R. Sadiq, âHydrogen fuel supply chains for vehicular emissions mitigation: A feasibility assessment for North American freight transport sector,â Int J Sustain Transp, vol. 17, no. 8, pp. 855â869, 2023, doi: 10.1080/15568318.2022.2116739. 194 [153] S. C. Wijayasekera et al., âSustainability of waste-to-hydrogen conversion pathways: A life cycle thinking-based assessment,â Energy Convers Manag, vol. 270, Oct. 2022, doi: 10.1016/j.enconman.2022.116218. [154] Y. Khojasteh Salkuyeh, B. A. Saville, and H. L. MacLean, âTechno-economic analysis and life cycle assessment of hydrogen production from natural gas using current and emerging technologies,â Int J Hydrogen Energy, vol. 42, no. 30, pp. 18894â18909, Jul. 2017, doi: 10.1016/j.ijhydene.2017.05.219. [155] F. M. Quintino, N. Nascimento, and E. C. Fernandes, âAspects of Hydrogen and Biomethane Introduction in Natural Gas Infrastructure and Equipment,â Hydrogen, vol. 2, no. 3, pp. 301â318, Aug. 2021, doi: 10.3390/hydrogen2030016. [156] Y. Zhao, V. McDonell, and S. Samuelsen, âAssessment of the combustion performance of a room furnace operating on pipeline natural gas mixed with simulated biogas or hydrogen,â Int J Hydrogen Energy, vol. 45, no. 19, pp. 11368â11379, Apr. 2020, doi: 10.1016/j.ijhydene.2020.02.071. [157] A. Azadeh, Z. Raoofi, and M. Zarrin, âA multi-objective fuzzy linear programming model for optimization of natural gas supply chain through a greenhouse gas reduction approach,â J Nat Gas Sci Eng, vol. 26, pp. 702â710, 2015, doi: 10.1016/j.jngse.2015.05.039. [158] S. Dara, H. Abdulqader, Y. Al Wahedi, and A. S. Berrouk, âCountrywide optimization of natural gas supply chain: From wells to consumers,â Energy, vol. 196, p. 117125, 2020, doi: 10.1016/j.energy.2020.117125. [159] A. Prieur-Vernat, P. Pacitto, D. Hec, and V. Bichler, âLCA of the European gas chain: Challenges and results,â International Gas Research Conference Proceedings, vol. 1, no. 1, pp. 463â489, 2011. [160] E. Bakhtavar, R. Lotfian, R. Sadiq, and K. Hewage, âPlanning of cement plants for environmentally friendly production: a fuzzy-weighted stochastic multi-objective model,â Clean Technol Environ Policy, vol. 21, no. 10, pp. 2005â2014, Dec. 2019, doi: 10.1007/s10098-019-01757-2. [161] J. H. Han, J. H. Ryu, and I. B. Lee, âMulti-objective optimization design of hydrogen infrastructures simultaneously considering economic cost, safety and CO2 emission,â 195 Chemical Engineering Research and Design, vol. 91, no. 8, pp. 1427â1439, Aug. 2013, doi: 10.1016/j.cherd.2013.04.026. [162] H. Shamsi, M. K. Tran, S. Akbarpour, A. Maroufmashat, and M. Fowler, âMacro-Level optimization of hydrogen infrastructure and supply chain for zero-emission vehicles on a canadian corridor,â J Clean Prod, vol. 289, Mar. 2021, doi: 10.1016/j.jclepro.2020.125163. [163] J. S. Arora, âMulti-objective Optimum Design Concepts and Methods,â in Introduction to Optimum Design, Elsevier, 2017, pp. 771â794. doi: 10.1016/b978-0-12-800806-5.00018-4. [164] E. Bakhtavar, T. Prabatha, H. Karunathilake, R. Sadiq, and K. Hewage, âAssessment of renewable energy-based strategies for net-zero energy communities: A planning model using multi-objective goal programming,â J Clean Prod, vol. 272, Nov. 2020, doi: 10.1016/j.jclepro.2020.122886. [165] G. J. Ruiz-mercado, R. L. Smith, and M. A. Gonzalez, âAppendix Câ¯: GREENSCOPE Technical User â s Guide Web-based Version Beta,â pp. 513â569, 2014. [166] M. H. Sehatpour and A. Kazemi, âSustainable fuel portfolio optimization: Integrated fuzzy multi-objective programming and multi-criteria decision making,â J Clean Prod, vol. 176, no. x, pp. 304â319, 2018, doi: 10.1016/j.jclepro.2017.12.092. [167] H. P. Karunathilake, âA Life Cycle Thinking Approach for Planning Renewable Energy Systems: Net-Zero Transformation Strategies for Communities,â The University of British Columbia, 2019. [168] S. A. Roman-White et al., âLNG Supply Chains: A Supplier-Specific Life-Cycle Assessment for Improved Emission Accounting,â ACS Sustain Chem Eng, vol. 9, no. 32, pp. 10857â10867, Aug. 2021, doi: 10.1021/acssuschemeng.1c03307. [169] H. Karunathilake, K. Hewage, W. Mérida, and R. Sadiq, âRenewable energy selection for net-zero energy communities: Life cycle based decision making under uncertainty,â Renew Energy, vol. 130, pp. 558â573, Jan. 2019, doi: 10.1016/j.renene.2018.06.086. [170] G. Gonzales, âLIFE CYCLE ASSESSMENT OF HYDROGEN PRODUCTION PATHWAYS IN CANADA,â 2022. 196 [171] A. Verma and A. Kumar, âLife cycle assessment of hydrogen production from underground coal gasification,â Appl Energy, vol. 147, pp. 556â568, Jun. 2015, doi: 10.1016/j.apenergy.2015.03.009. [172] E. Carnevale and L. Lombardi, âComparison of different possibilities for biogas use by Life Cycle Assessment,â in Energy Procedia, Elsevier Ltd, Dec. 2015, pp. 215â226. doi: 10.1016/j.egypro.2015.12.088. [173] W. Wu, C. T. Pai, K. Viswanathan, and J. S. Chang, âComparative life cycle assessment and economic analysis of methanol/hydrogen production processes for fuel cell vehicles,â J Clean Prod, vol. 300, Jun. 2021, doi: 10.1016/j.jclepro.2021.126959. [174] A. Verma, âLife Cycle Assessment and Greenhouse Gas Abatement Costs of Hydrogen Production from Underground Coal Gasification.â [175] S. Rai, D. Hage, J. Littlefield, G. Yanai, and T. J. Skone, âComparative Life Cycle Evaluation of the Global Warming Potential (GWP) Impacts of Renewable Natural Gas Production Pathways,â Environ Sci Technol, 2022, doi: 10.1021/acs.est.2c00093. [176] A. Skorek-Osikowska, M. Martín-Gamboa, and J. Dufour, âThermodynamic, economic and environmental assessment of renewable natural gas production systems,â Energy Conversion and Management: X, vol. 7, no. March, p. 100046, 2020, doi: 10.1016/j.ecmx.2020.100046. [177] M. Mosleh Uddin, Z. Wen, and M. Mba Wright, âTechno-economic and environmental impact assessment of using corn stover biochar for manure derived renewable natural gas production,â Appl Energy, vol. 321, no. January, p. 119376, 2022, doi: 10.1016/j.apenergy.2022.119376. [178] J. Speirs, P. Balcombe, E. Johnson, J. Martin, N. Brandon, and A. Hawkes, âA greener gas grid: What are the options,â Energy Policy, vol. 118, no. December 2017, pp. 291â297, 2018, doi: 10.1016/j.enpol.2018.03.069. [179] Y. K. Salkuyeh, B. A. Saville, and H. L. MacLean, âTechno-economic analysis and life cycle assessment of hydrogen production from different biomass gasification processes,â Int J Hydrogen Energy, vol. 43, no. 20, pp. 9514â9528, 2018, doi: 10.1016/j.ijhydene.2018.04.024. 197 [180] A. Valente, D. Iribarren, and J. Dufour, âHarmonised life-cycle global warming impact of renewable hydrogen,â J Clean Prod, vol. 149, pp. 762â772, 2017, doi: 10.1016/j.jclepro.2017.02.163. [181] J. Chen et al., âSystem development and environmental performance analysis of a solar-driven supercritical water gasification pilot plant for hydrogen production using life cycle assessment approach,â Energy Convers Manag, vol. 184, no. January, pp. 60â73, 2019, doi: 10.1016/j.enconman.2019.01.041. [182] C. Koroneos, A. Dompros, G. Roumbas, and N. Moussiopoulos, âLife cycle assessment of hydrogen fuel production processes,â Int J Hydrogen Energy, vol. 29, no. 14, pp. 1443â1450, 2004, doi: 10.1016/j.ijhydene.2004.01.016. [183] G. Palmer, A. Roberts, A. Hoadley, R. Dargaville, and D. Honnery, âLife-cycle greenhouse gas emissions and net energy assessment of large-scale hydrogen production via electrolysis and solar PV,â Energy Environ Sci, vol. 14, no. 10, pp. 5113â5131, 2021, doi: 10.1039/d1ee01288f. [184] W. K. Biswas, B. C. Thompson, and M. N. Islam, âEnvironmental life cycle feasibility assessment of hydrogen as an automotive fuel in Western Australia,â Int J Hydrogen Energy, vol. 38, no. 1, pp. 246â254, 2013, doi: 10.1016/j.ijhydene.2012.10.044. [185] S. Sadeghi, S. Ghandehariun, and M. A. Rosen, âComparative economic and life cycle assessment of solar-based hydrogen production for oil and gas industries,â Energy, vol. 208, p. 118347, 2020, doi: 10.1016/j.energy.2020.118347. [186] M. K. Kazi, F. Eljack, M. M. El-Halwagi, and M. Haouari, âGreen hydrogen for industrial sector decarbonization: Costs and impacts on hydrogen economy in qatar,â Comput Chem Eng, vol. 145, no. 2021, p. 107144, 2021, doi: 10.1016/j.compchemeng.2020.107144. [187] S. Sun and M. Ertz, âLife cycle assessment and risk assessment of liquefied natural gas vehicles promotion,â Renewable and Sustainable Energy Reviews, vol. 153, Jan. 2022, doi: 10.1016/j.rser.2021.111769. [188] A. Al-Qahtani, B. Parkinson, K. Hellgardt, N. Shah, and G. Guillen-Gosalbez, âUncovering the true cost of hydrogen production routes using life cycle monetisation,â Appl Energy, vol. 281, Jan. 2021, doi: 10.1016/j.apenergy.2020.115958. 198 [189] C. Li, L. Zhang, Z. Ou, and J. Ma, âUsing system dynamics to evaluate the impact of subsidy policies on green hydrogen industry in China,â Energy Policy, vol. 165, no. April, p. 112981, 2022, doi: 10.1016/j.enpol.2022.112981. [190] G. Chhipi-Shrestha, K. Hewage, and R. Sadiq, âWaterâEnergyâCarbon Nexus Modeling for Urban Water Systems: System Dynamics Approach,â J Water Resour Plan Manag, vol. 143, no. 6, Jun. 2017, doi: 10.1061/(asce)wr.1943-5452.0000765. [191] H. Karunathilake, K. Hewage, T. Prabatha, R. Ruparathna, and R. Sadiq, âProject deployment strategies for community renewable energy: A dynamic multi-period planning approach,â Renew Energy, vol. 152, pp. 237â258, Jun. 2020, doi: 10.1016/j.renene.2020.01.045. [192] Y. Yin and J. S. L. Lam, âBottlenecks of LNG supply chain in energy transition: A case study of China using system dynamics simulation,â Energy, vol. 250, Jul. 2022, doi: 10.1016/j.energy.2022.123803. [193] J. Gao and T. Zhang, âEffects of public funding on the commercial diffusion of on-site hydrogen production technology: A system dynamics perspective,â Technol Forecast Soc Change, vol. 175, no. December 2020, p. 121380, 2022, doi: 10.1016/j.techfore.2021.121380. [194] CleanBC, â2020 Climate Change Accountability Report,â 2020. [195] CleanBC, âRoadmap to 2030,â 2021. [196] S. Z. Baykara, âHydrogen: A brief overview on its sources, production and environmental impact,â Int J Hydrogen Energy, vol. 43, no. 23, pp. 10605â10614, 2018, doi: 10.1016/j.ijhydene.2018.02.022. [197] M. Yu, K. Wang, and H. Vredenburg, âInsights into low-carbon hydrogen production methods: Green, blue and aqua hydrogen,â Int J Hydrogen Energy, vol. 46, no. 41, pp. 21261â21273, 2021, doi: 10.1016/j.ijhydene.2021.04.016. [198] I. Dincer, âGreen methods for hydrogen production,â Int J Hydrogen Energy, vol. 37, no. 2, pp. 1954â1971, 2012, doi: 10.1016/j.ijhydene.2011.03.173. 199 [199] A. D. Singh, A. Upadhyay, S. Shrivastava, and V. Vivekanand, âBioresource Technology Life-cycle assessment of sewage sludge-based large-scale biogas plant,â Bioresour Technol, vol. 309, no. April, p. 123373, 2020, doi: 10.1016/j.biortech.2020.123373. [200] T. Prabatha, H. Karunathilake, A. Mohammadpour Shotorbani, R. Sadiq, and K. Hewage, âCommunity-level decentralized energy system planning under uncertainty: A comparison of mathematical models for strategy development,â Appl Energy, vol. 283, no. December 2020, p. 116304, 2021, doi: 10.1016/j.apenergy.2020.116304. [201] E. Bakhtavar, R. Aghayarloo, S. Yousefi, K. Hewage, and R. Sadiq, âRenewable energy based mine reclamation strategy: A hybrid fuzzy-based network analysis,â J Clean Prod, vol. 230, pp. 253â263, 2019, doi: 10.1016/j.jclepro.2019.05.073. [202] P. Nunes, F. Oliveira, S. Hamacher, and A. Almansoori, âDesign of a hydrogen supply chain with uncertainty,â Int J Hydrogen Energy, vol. 40, no. 46, pp. 16408â16418, 2015, doi: 10.1016/j.ijhydene.2015.10.015. [203] E. Bakhtavar, T. Prabatha, H. Karunathilake, R. Sadiq, and K. Hewage, âAssessment of renewable energy-based strategies for net-zero energy communities: A planning model using multi-objective goal programming,â J Clean Prod, vol. 272, p. 122886, 2020, doi: 10.1016/j.jclepro.2020.122886. [204] E. Bakhtavar, S. Yousefi, and A. Jafarpour, âEvaluation of shaft locations in underground mines: Fuzzy multiobjective optimization by ratio analysis with fuzzy cognitive map weights,â J South Afr Inst Min Metall, vol. 119, no. 10, pp. 855â864, 2019, doi: 10.17159/2411-9717/68/2019. [205] R. Kotagodahetti et al., âLiquefied natural gas exports from Canada to Chinaâ¯: An analysis of internationally transferred mitigation outcomes ( ITMO ),â J Clean Prod, vol. 347, no. March, p. 131291, 2022, doi: 10.1016/j.jclepro.2022.131291. [206] S. Chakraborty, H. N. Datta, K. Kalita, and S. Chakraborty, A narrative review of multi-objective optimization on the basis of ratio analysis (MOORA) method in decision making, vol. 60, no. 4. Springer India, 2023. doi: 10.1007/s12597-023-00676-7. 200 [207] T. F. Astrup, D. Tonini, R. Turconi, and A. Boldrin, âLife cycle assessment of thermal Waste-to-Energy technologies: Review and recommendations,â Waste Management, vol. 37, pp. 104â115, 2015, doi: 10.1016/j.wasman.2014.06.011. [208] S. Rai, D. Hage, J. Littlefield, G. Yanai, and T. J. Skone, âComparative Life Cycle Evaluation of the Global Warming Potential (GWP) Impacts of Renewable Natural Gas Production Pathways,â Environ Sci Technol, vol. 56, no. 12, pp. 8581â8589, 2022, doi: 10.1021/acs.est.2c00093. [209] A. Whiting and A. Azapagic, âLife cycle environmental impacts of generating electricity and heat from biogas produced by anaerobic digestion,â Energy, vol. 70, pp. 181â193, 2014, doi: 10.1016/j.energy.2014.03.103. [210] IEA, âETP Clean Energy Technology Guide,â IEA, Paris. Accessed: Apr. 20, 2023. [Online]. Available: https://www.iea.org/data-and-statistics/data-tools/etp-clean-energy-technology-guide [211] H. Karunathilake, K. Hewage, W. Mérida, and R. Sadiq, âRenewable energy selection for net-zero energy communities: Life cycle based decision making under uncertainty,â Renew Energy, vol. 130, pp. 558â573, Jan. 2019, doi: 10.1016/j.renene.2018.06.086. [212] S. C. Wijayasekera et al., âSustainability of waste-to-hydrogen conversion pathways: A life cycle thinking-based assessment,â Energy Convers Manag, vol. 270, no. July, p. 116218, 2022, doi: 10.1016/j.enconman.2022.116218. [213] M. Van Den Broek, N. Berghout, and E. S. Rubin, âThe potential of renewables versus natural gas with CO2 capture and storage for power generation under CO2 constraints,â 2015, Elsevier. doi: 10.1016/j.rser.2015.04.089. [214] H. Karunathilake, P. Perera, R. Ruparathna, K. Hewage, and R. Sadiq, âRenewable energy integration into community energy systems: A case study of new urban residential development,â J Clean Prod, vol. 173, pp. 292â307, 2018, doi: 10.1016/j.jclepro.2016.10.067. [215] H. Karunathilake, K. Hewage, J. Brinkerhoff, and R. Sadiq, âOptimal renewable energy supply choices for net-zero ready buildings: A life cycle thinking approach under 201 uncertainty,â Energy Build, vol. 291, pp. 70â89, Jul. 2019, doi: 10.1016/j.enbuild.2019.07.030. [216] E. S. Rubin, J. E. Davison, and H. J. Herzog, âThe cost of CO 2 capture and storage,â International Journal of Greenhouse Gas Control, vol. 40, pp. 378â400, 2015, doi: 10.1016/j.ijggc.2015.05.018. [217] A. Dyer et al., âThe feasibility of renewable natural gas in new jersey,â Sustainability (Switzerland), vol. 13, no. 4, pp. 1â31, 2021, doi: 10.3390/su13041618. [218] R. Kotagodahetti, K. Hewage, H. Karunathilake, and R. Sadiq, âEvaluating carbon capturing strategies for emissions reduction in community energy systems: A life cycle thinking approach,â Energy, 2021, doi: 10.1016/j.energy.2021.121012. [219] C. Wimmler, G. Hejazi, E. de O. Fernandes, C. Moreira, and S. Connors, âMulti-Criteria Decision Support Methods for Renewable Energy Systems on Islands,â Journal of Clean Energy Technologies, vol. 3, no. 3, pp. 185â195, 2015, doi: 10.7763/jocet.2015.v3.193. [220] V. Campos-guzmán, M. S. García-cáscales, N. Espinosa, and A. Urbina, âLife Cycle Analysis with Multi-Criteria Decision Makingâ¯: A review of approaches for the sustainability evaluation of renewable energy technologies,â Renewable and Sustainable Energy Reviews, vol. 104, no. May 2018, pp. 343â366, 2019, doi: 10.1016/j.rser.2019.01.031. [221] D. Streimikiene, T. Balezentis, I. Krisciukaitien, and A. Balezentis, âPrioritizing sustainable electricity production technologies: MCDM approach,â Renewable and Sustainable Energy Reviews, vol. 16, no. 5, pp. 3302â3311, 2012, doi: 10.1016/j.rser.2012.02.067. [222] M. Yazdani, P. Chatterjee, E. K. Zavadskas, and D. Streimikiene, âA novel integrated decision-making approach for the evaluation and selection of renewable energy technologies,â Clean Technol Environ Policy, vol. 20, no. 2, pp. 403â420, 2018, doi: 10.1007/s10098-018-1488-4. [223] P. Naicker and G. Alex, âA framework for sustainable utility scale renewable energy selection in South Africa,â J Clean Prod, vol. 224, pp. 637â650, 2019, doi: 10.1016/j.jclepro.2019.03.257. 202 [224] E. Cetinkaya, I. Dincer, and G. F. Naterer, âLife cycle assessment of various hydrogen production methods,â Int J Hydrogen Energy, vol. 37, no. 3, pp. 2071â2080, 2012, doi: 10.1016/j.ijhydene.2011.10.064. [225] A. Whiting and A. Azapagic, âLife cycle environmental impacts of generating electricity and heat from biogas produced by anaerobic digestion,â Energy, vol. 70, pp. 181â193, 2014, doi: 10.1016/j.energy.2014.03.103. [226] US EPA, âAn overview of renewable natural gas from biogas,â 2020. [227] A. D. Singh, A. Upadhyay, S. Shrivastava, and V. Vivekanand, âBioresource Technology Life-cycle assessment of sewage sludge-based large-scale biogas plant,â Bioresour Technol, vol. 309, no. April, p. 123373, 2020, doi: 10.1016/j.biortech.2020.123373. [228] C. Florio et al., âA life cycle assessment of biomethane production from waste feedstock through different upgrading technologies,â Energies (Basel), vol. 12, no. 4, pp. 1â12, 2019, doi: 10.3390/en12040718. [229] K. Krich, D. Augenstein, and J. Benemann, âBiomethane from Dairy Waste A Sourcebook for the Production and Use of Renewable Natural Gas in California Prepared for Western United Dairymen,â 2005. [230] World Energy Council, âComparison of Energy Systems Using Life Cycle Assessment,â London, 2004. [231] S. Wanniarachchi, âLife cycle thinking based performance evaluation of shape memory alloy reinforced bridges under seismic conditions,â The University of British Columbia, 2019. [232] M. Minutillo, A. Perna, A. Forcina, S. Di Micco, and E. Jannelli, âAnalyzing the levelized cost of hydrogen in refueling stations with on-site hydrogen production via water electrolysis in the Italian scenario,â Int J Hydrogen Energy, vol. 46, no. 26, pp. 13667â13677, 2021, doi: 10.1016/j.ijhydene.2020.11.110. [233] âecoinvent Database.â Accessed: Nov. 09, 2022. [Online]. Available: https://ecoinvent.org/the-ecoinvent-database/ 203 [234] B. S. Dhillon, âLife Cycle Costing Fundamentals,â in Life cycle costing for Engineers, 1st ed., Taylor and Francis Group, 2009, ch. 3, pp. 27â42. [235] B. S. Dhillon, Life Cycle Costing for Engineers. Taylor and Francis Group, 2010. [236] R. Kotagodahetti, K. Hewage, H. Karunathilake, and R. Sadiq, âEvaluating carbon capturing strategies for emissions reduction in community energy systems: A life cycle thinking approach,â Energy, 2021, doi: 10.1016/j.energy.2021.121012. [237] R. Kotagodahetti, K. Hewage, F. Razi, and R. Sadiq, âComparative life cycle environmental and cost assessments of renewable natural gas production pathways,â Energy Convers Manag, vol. 278, p. 116715, Feb. 2023, doi: 10.1016/j.enconman.2023.116715. [238] N. Parker, R. Williams, R. Dominguez-Faus, and D. Scheitrum, âRenewable natural gas in California: An assessment of the technical and economic potential,â Energy Policy, vol. 111, no. September, pp. 235â245, 2017, doi: 10.1016/j.enpol.2017.09.034. [239] S. Michailos, M. Walker, A. Moody, D. Poggio, and M. Pourkashanian, âBiomethane production using an integrated anaerobic digestion, gasification and CO2 biomethanation process in a real waste water treatment plant: A techno-economic assessment,â Energy Convers Manag, vol. 209, no. December 2019, p. 112663, 2020, doi: 10.1016/j.enconman.2020.112663. [240] WaterBC, âResources for Water and Wastewater Systems in Western Canada.â Accessed: Nov. 08, 2022. [Online]. Available: https://waterbc.ca/community/programs/long-term-financial-planning/water-pricing-plan/ [241] âCanada electricity prices.â Accessed: Nov. 08, 2022. [Online]. Available: https://www.globalpetrolprices.com/Canada/electricity_prices/ [242] FortisBC, âRenewable Natural Gas Rates.â Accessed: Mar. 03, 2021. [Online]. Available: https://www.fortisbc.com/services/sustainable-energy-options/renewable-natural-gas/renewable-natural-gas-rates [243] N. V. S. N. Murthy Konda, N. Shah, and N. P. Brandon, âOptimal transition towards a large-scale hydrogen infrastructure for the transport sector: The case for the Netherlands,â Int J Hydrogen Energy, vol. 36, no. 8, pp. 4619â4635, 2011, doi: 10.1016/j.ijhydene.2011.01.104. 204 [244] A. Buttler and H. Spliethoff, âCurrent status of water electrolysis for energy storage, grid balancing and sector coupling via power-to-gas and power-to-liquids: A review,â Renewable and Sustainable Energy Reviews, vol. 82, no. September 2017, pp. 2440â2454, 2018, doi: 10.1016/j.rser.2017.09.003. [245] âElectricity Prices in Canada 2021.â Accessed: Jul. 03, 2022. [Online]. Available: https://www.energyhub.org/electricity-prices/ [246] âWater Pricing Plan - WaterBC.ca.â Accessed: Jul. 03, 2022. [Online]. Available: https://waterbc.ca/community/programs/long-term-financial-planning/water-pricing-plan/ [247] D. Jang, J. Kim, D. Kim, W. B. Han, and S. Kang, âTechno-economic analysis and Monte Carlo simulation of green hydrogen production technology through various water electrolysis technologies,â Energy Convers Manag, vol. 258, no. November 2021, p. 115499, 2022, doi: 10.1016/j.enconman.2022.115499. [248] P. Benalcazar and A. Komorowska, âProspects of green hydrogen in Poland: A techno-economic analysis using a Monte Carlo approach,â Int J Hydrogen Energy, vol. 47, no. 9, pp. 5779â5796, 2022, doi: 10.1016/j.ijhydene.2021.12.001. [249] T. Holm, T. Borsboom-Hanson, O. E. Herrera, and W. Mérida, âHydrogen costs from water electrolysis at high temperature and pressure,â Energy Convers Manag, vol. 237, 2021, doi: 10.1016/j.enconman.2021.114106. [250] H. AkbaÅ and B. Bilgen, âAn integrated fuzzy QFD and TOPSIS methodology for choosing the ideal gas fuel at WWTPs,â Energy, vol. 125, pp. 484â497, 2017, doi: 10.1016/j.energy.2017.02.153. [251] S. K. Samal, Logistics and supply chain management, vol. 23, no. 6. 2019. doi: 10.37200/IJPR/V23I6/PR190779. [252] M. H. Sehatpour and A. Kazemi, âSustainable fuel portfolio optimization: Integrated fuzzy multi-objective programming and multi-criteria decision making,â J Clean Prod, vol. 176, no. x, pp. 304â319, 2018, doi: 10.1016/j.jclepro.2017.12.092. [253] H. Karunathilake, K. Hewage, W. Mérida, and R. Sadiq, âRenewable energy selection for net-zero energy communities: Life cycle based decision making under uncertainty,â Renew Energy, vol. 130, pp. 558â573, Jan. 2019, doi: 10.1016/j.renene.2018.06.086. 205 [254] E. Bakhtavar, S. Saberi, G. Hu, R. Sadiq, and K. Hewage, âFuzzy cognitive-based goal programming for waste rock management with in-pit dumping priority: Towards sustainable mining,â Resources Policy, vol. 86, no. PB, p. 104095, 2023, doi: 10.1016/j.resourpol.2023.104095. [255] N. Salimi and J. Rezaei, âMeasuring efficiency of university-industry Ph.D. projects using best worst method,â Scientometrics, vol. 109, no. 3, pp. 1911â1938, Dec. 2016, doi: 10.1007/s11192-016-2121-0. [256] A. Mohtashami, âA novel modified fuzzy best-worst multi-criteria decision-making method,â Expert Syst Appl, vol. 181, Nov. 2021, doi: 10.1016/j.eswa.2021.115196. [257] J. L. Salmeron, R. Vidal, and A. Mena, âRanking fuzzy cognitive map based scenarios with TOPSIS,â Expert Syst Appl, vol. 39, no. 3, pp. 2443â2450, Feb. 2012, doi: 10.1016/j.eswa.2011.08.094. [258] F. Tabatabayi, B. Akhgar, S. M. R. Nasserzadeh, and S. Yates, âSimulating online consumer satisfaction using fuzzy cognitive mapping,â Proceedings of the 9th International Conference on Information Technology, ITNG 2012, no. July 2017, pp. 540â547, 2012, doi: 10.1109/ITNG.2012.118. [259] N. Salimi and J. Rezaei, âMeasuring efficiency of university-industry Ph.D. projects using best worst method,â Scientometrics, vol. 109, no. 3, pp. 1911â1938, 2016, doi: 10.1007/s11192-016-2121-0. [260] H. Gupta, âEvaluating service quality of airline industry using hybrid best worst method and VIKOR,â J Air Transp Manag, vol. 68, pp. 35â47, 2018, doi: 10.1016/j.jairtraman.2017.06.001. [261] P. Gittelson et al., âThe False Promises of Biogasâ¯: Why Biogas Is an Environmental Justice Issue,â Environmental Justice, vol. 00, no. 00, pp. 1â10, 2021, doi: 10.1089/env.2021.0025. [262] âRisks and safety measures for anaerobic digestion: How can you make your plant safer? | BiogasWorld.â Accessed: Oct. 13, 2022. [Online]. Available: https://www.biogasworld.com/news/safety-precautions-anaerobic-digestion-systems/ [263] International Energy Agency, ââOutlook for biogas and biomethane. Prospects for organic growth.,ââ IEA Publications, pp. 1â93, 2020. 206 [264] R. Kotagodahetti, K. Hewage, H. Karunathilake, and R. Sadiq, âEvaluating carbon capturing strategies for emissions reduction in community energy systems: A life cycle thinking approach,â Energy, vol. 232, Oct. 2021, doi: 10.1016/j.energy.2021.121012. [265] M. Gustafsson and N. Svensson, âCleaner heavy transports â Environmental and economic analysis of liquefied natural gas and biomethane,â J Clean Prod, vol. 278, Jan. 2021, doi: 10.1016/j.jclepro.2020.123535. [266] F. Sorgulu and I. Dincer, âDevelopment and assessment of renewable hydrogen production and natural gas blending systems for use in different locations,â Energy Sci Eng, vol. 10, no. 5, pp. 1739â1751, May 2022, doi: 10.1002/ese3.1114. [267] O. Norouzi and A. Dutta, âThe Current Status and Future Potential of Biogas Production from Canadaâs Organic Fraction Municipal Solid Waste,â Jan. 01, 2022, MDPI. doi: 10.3390/en15020475. [268] J. Stephen, J. M.â¯; Blair, L. Brennan, and S. Wood-Bohm, âRenewable Natural Gas (Biomethane) Feedstock Potential in Canada,â 2020. [Online]. Available: www.torchlightbioresources.com [269] M. and L. C. I. Ministry of Energy, âB.C Hydrogen Sector,â 2022. [270] M. Yu, K. Wang, and H. Vredenburg, âInsights into low-carbon hydrogen production methods: Green, blue and aqua hydrogen,â Int J Hydrogen Energy, vol. 46, no. 41, pp. 21261â21273, Jun. 2021, doi: 10.1016/j.ijhydene.2021.04.016. [271] C. Energy Regulator, âCanadaâs Energy Future 2021,â 2021. [272] Environment and Climate Change Canada, âLatest environmental indicators.â Accessed: Jan. 09, 2024. [Online]. Available: https://www.canada.ca/en/environment-climate-change/services/environmental-indicators/latest.html [273] Natural Resource Canada, âEnergy Fact Book 2020-2023,â 2022. [274] R. Pinsky, P. Sabharwall, J. Hartvigsen, and J. OâBrien, âComparative review of hydrogen production technologies for nuclear hybrid energy systems,â May 01, 2020, Elsevier Ltd. doi: 10.1016/j.pnucene.2020.103317. 207 [275] CleanBC;, âB.C. Hydrogen Strategy A sustainable pathway for B.C.âs energy transition,â 2019. [276] S. Shiva Kumar and H. Lim, âAn overview of water electrolysis technologies for green hydrogen production,â Nov. 01, 2022, Elsevier Ltd. doi: 10.1016/j.egyr.2022.10.127. [277] R. Cheraghi and M. Hossein Jahangir, âMulti-objective optimization of a hybrid renewable energy system supplying a residential building using NSGA-II and MOPSO algorithms,â Energy Convers Manag, vol. 294, Oct. 2023, doi: 10.1016/j.enconman.2023.117515. [278] J. S. Arora, âMulti-objective Optimum Design Concepts and Methods,â in Introduction to Optimum Design, Elsevier, 2017, pp. 771â794. doi: 10.1016/b978-0-12-800806-5.00018-4. [279] B. Sen, T. Ercan, O. Tatari, and Q. P. Zheng, âRobust Pareto optimal approach to sustainable heavy-duty truck fleet composition,â Resour Conserv Recycl, vol. 146, pp. 502â513, Jul. 2019, doi: 10.1016/j.resconrec.2019.03.042. [280] FortisBC, âMeet our Renewable Natural Gas suppliers.â [281] BC Ministry of Agriculture, âAgriculture in Brief- Fraser Valley Regional District,â 2016. [282] BC Ministry of Agriculture, âAgriculture in Brief - Metro VancouverDistrict, Thompson-nicola Regional,â 2008. [283] BC Ministry of Agriculture, âAgriculture in Brief - Regional District of Okanagan, Similkameen,â 2016. [284] by B. Hirushie Pramuditha Karunathilake, âA LIFE CYCLE THINKING APPROACH FOR PLANNING RENEWABLE ENERGY SYSTEMS: NET-ZERO TRANSFORMATION STRATEGIES FOR COMMUNITIES,â 2014. [285] P. Nikolaidis and A. Poullikkas, âA comparative overview of hydrogen production processes,â Jan. 01, 2017, Elsevier Ltd. doi: 10.1016/j.rser.2016.09.044. [286] S. M. Saba, M. Müller, M. Robinius, and D. Stolten, âThe investment costs of electrolysis â A comparison of cost studies from the past 30 years,â Jan. 18, 2018, Elsevier Ltd. doi: 10.1016/j.ijhydene.2017.11.115. [287] Government of British Columbia, âClean economy.â Accessed: Jan. 16, 2024. [Online]. Available: https://www2.gov.bc.ca/gov/content/environment/climate-change/clean- 208 economy#:~:text=The%20carbon%20tax%20is%20an,tonne%20on%20April%201%2C%202023. [288] Environment and Climate Change Canada, âCLEAN FUEL STANDARD: Regulatory Design Paper,â Gatineau, 2018. [289] U.S Department of Energy, âHydrogen Laws and Incentives in California.â Accessed: Mar. 19, 2024. [Online]. Available: https://afdc.energy.gov/fuels/laws/HY?state=ca [290] Government of Canada, âWhat are the Clean Fuel Regulations?,â Environment and Climate Change Canada (ECCC). Accessed: Mar. 19, 2024. [Online]. Available: https://www.canada.ca/en/environment-climate-change/services/managing-pollution/energy-production/fuel-regulations/clean-fuel-regulations/about.html [291] Canada. Natural Resources Canada, âHydrogen strategy for Canadaâ¯: seizing the opportunities for hydrogenâ¯: a call to action.,â 2020. [292] City of Kelowna, âRDBN Agriculture Plan,â Kelowna, 2017. [293] Ministry of Agriculture, âAgriculture in Brief - British Columbia,â 2016. [Online]. Available: http://www.statcan.gc.ca/tables-tableaux/sum-som/l01/cst01/phys01-eng.htm. [294] P. Seruga, M. Krzywonos, A. Seruga, Å. Nied´zwiecki, H. Pawlak-Kruczek, and A. Urbanowska, âAnaerobic Digestion Performance: Separate Collected vs. Mechanical Segregated Organic Fractions of Municipal Solid Waste as Feedstock,â Energies (Basel), vol. 13, no. 15, Aug. 2020, doi: 10.3390/en13153768. [295] City of Kelowna, â2022 Glenmore Landfill Annual Report,â Kelowna, 2023. [Online]. Available: https://www.kelowna.ca/sites/files/1/docs/related/glenmore_landfill_annual_report_2020.pdf [296] M. Gosselin, â2022 City of Kelowna Wastewater Treatment Annual,â Kelowna, 2023. [Online]. Available: www.kelowna.ca [297] Canada Energy Regulator, âCanadaâs Energy Future Data Appendices.â [Online]. Available: https://apps.rec-cer.gc.ca/ftrppndc/dflt.aspx 209 [298] M. Lambert and G. Oluleye, âA mountain to climb? Tracking progress in scaling up renewable gas production in Europe,â 2019. doi: https://doi.org/10.26889/9781784671471. [299] K. Schoots, F. Ferioli, G. J. Kramer, and B. C. C. van der Zwaan, âLearning curves for hydrogen production technology: An assessment of observed cost reductions,â Int J Hydrogen Energy, vol. 33, no. 11, pp. 2630â2645, 2008, doi: 10.1016/j.ijhydene.2008.03.011. [300] Government of Canada, âWhat are the Clean Fuel Regulations?â Accessed: May 07, 2024. [Online]. Available: https://www.canada.ca/en/environment-climate-change/services/managing-pollution/energy-production/fuel-regulations/clean-fuel-regulations/about.html [301] R. Kotagodahetti et al., âLiquefied natural gas exports from Canada to China: An analysis of internationally transferred mitigation outcomes (ITMO),â J Clean Prod, vol. 347, May 2022, doi: 10.1016/j.jclepro.2022.131291. [302] US Department of Energy, âRenewable Natural Gas Production.â Accessed: May 09, 2024. [Online]. Available: https://afdc.energy.gov/fuels/natural-gas-renewable [303] Government of Canada, âCorporation tax rates.â Accessed: May 09, 2024. [Online]. Available: https://www.canada.ca/en/revenue-agency/services/tax/businesses/topics/corporations/corporation-tax-rates.html [304] IRENA, Making the breakthrough: Green hydrogen policies and technology costs. 2021. [Online]. Available: https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2020/Nov/IRENA_Green_Hydrogen_breakthrough_2021.pdf?la=en&hash=40FA5B8AD7AB1666EECBDE30EF458C45EE5A0AA6 [305] FortisBC, âRenewable Natural Gas now part of the Low Carbon Fuel Standard.â Accessed: Mar. 19, 2024. [Online]. Available: https://www.fortisbc.com/news-events/media-centre-details/2019/04/23/renewable-natural-gas-now-part-of-the-low-carbon-fuel-standard [306] Government of Canada, âCanadaâs Carbon Management Strategy.â Accessed: May 09, 2024. [Online]. Available: https://natural-resources.canada.ca/climate-change/canadas-green-future/capturing-the-opportunity-carbon-management-strategy-for-canada/canadas-carbon-management-strategy/25337 210 [307] Canada Energy Regulator, âCanadaâs Energy Future 2023: Energy Supply and Demand Projections to 2050,â 2023. [308] Jennifer L, âAn Introduction to Hydrogen Energy,â Carbon Credits.com. Accessed: Jan. 21, 2024. [Online]. Available: https://carboncredits.com/introduction-to-hydrogen-energy/ [309] KPMG, âDecarbonizing with renewable natural gas,â 2023. [Online]. Available: https://kpmg.com/kpmg-us/content/dam/kpmg/pdf/2023/decarbonizing-renewable-natural-gas.pdf [310] Canadian Gas Association, âRenewable Natural Gas Technology Roadmap for Canada,â 2014. [311] R. Laszlo; and T. Shorts, âRNG Handbook for Canadian Municipalities,â Toronto, 2021. [312] B. Koo, âPreparing hydropower projects for the post-Paris regime: An econometric analysis of the main drivers for registration in the Clean Development Mechanism,â Renewable and Sustainable Energy Reviews, vol. 73, pp. 868â877, 2017, doi: 10.1016/j.rser.2017.01.095. [313] S. Bracking, âFinancialisation, Climate Finance, and the Calculative Challenges of Managing Environmental Change,â Antipode, vol. 51, no. 3, pp. 709â729, 2019, doi: 10.1111/anti.12510. [314] B. Koo, âPreparing hydropower projects for the post-Paris regime: An econometric analysis of the main drivers for registration in the Clean Development Mechanism,â Renewable and Sustainable Energy Reviews, vol. 73, pp. 868â877, 2017, doi: 10.1016/j.rser.2017.01.095. [315] International Institute for Sustainable Development, âCurrent Status of Article 6 of the Paris Agreementâ¯: Internationally Transferred Mitigation Outcomes (ITMOs),â 2020. [316] L. Emissions and T. Systems, âon the Way To a Global Carbon Marketâ¯: Linking Emissions Trading Systems,â 2020. [317] C. Pan et al., âA linkage framework for the china national emission trading system (Cets): Insight from key global carbon markets,â Sustainability (Switzerland), vol. 13, no. 13, pp. 1â15, 2021, doi: 10.3390/su13137459. [318] PMR, âLessons Learned from Linking Emissions Trading Systems: General Principles and Applications,â no. February, pp. 1â16, 2014. 211 [319] N. Kreibich and W. Obergassel, âNew Paths to Policy Creditingâ¯?â [320] M. A. Mehling, âHarvard Project on Climate Agreements Governing Cooperative Approaches under the Paris Agreement,â no. November, 2018. [321] M. Bosi and J. Ellis, âExploring Options for Sectoral Crediting Mechanisms,â Organisation for Economic Cooperation and Development, pp. 1â44, 2005. [322] C. C. C and Y. Kinoshita, âChallenges for the implementation of Cooperative Approaches under the Paris Agreementâ¯: exploring the transfer of ITMOs in the LNG industry,â 2020. [323] âTechnology readiness levels - Innovation Canada.â Accessed: Nov. 04, 2019. [Online]. Available: https://www.ic.gc.ca/eic/site/080.nsf/eng/00002.html [324] IEAGHG, âAssessment of emerging CO2 capture technologies and their potential to reduce costs,â 2014. [325] S. Rai, D. Hage, J. Littlefield, G. Yanai, and T. J. Skone, âComparative Life Cycle Evaluation of the Global Warming Potential (GWP) Impacts of Renewable Natural Gas Production Pathways,â Environ Sci Technol, 2022, doi: 10.1021/acs.est.2c00093. [326] J. Han, M. Mintz;, and M. Wang;, âWaste-to-Wheel Analysis of Anaerobic-Digestion-Based Renewable Natural Gas Pathways with the GREET Model Energy Systems Division,â 2022. [327] C. Florio et al., âA Life Cycle Assessment of Biomethane Production from Waste Feedstock Through Different Upgrading Technologies,â 2019, doi: 10.3390/en12040718. [328] E. Cetinkaya, I. Dincer, and G. F. Naterer, âLife cycle assessment of various hydrogen production methods,â Int J Hydrogen Energy, vol. 37, no. 3, pp. 2071â2080, Feb. 2012, doi: 10.1016/j.ijhydene.2011.10.064. [329] J. Burkhardt, A. Patyk, P. Tanguy, and C. Retzke, âHydrogen mobility from wind energy â A life cycle assessment focusing on the fuel supply,â Appl Energy, vol. 181, pp. 54â64, Nov. 2016, doi: 10.1016/j.apenergy.2016.07.104. [330] W. and S. National Institute for Public Health and the Environment Ministry of Health, âNormalization scores ReCiPe 2016.â Accessed: Nov. 07, 2022. [Online]. Available: https://www.rivm.nl/en/documenten/normalization-scores-recipe-2016 212 [331] K. Shanmugam, V. Gadhamshetty, M. Tysklind, D. Bhattacharyya, and V. K. K. Upadhyayula, âA sustainable performance assessment framework for circular management of municipal wastewater treatment plants,â J Clean Prod, vol. 339, no. January, p. 130657, 2022, doi: 10.1016/j.jclepro.2022.130657. [332] S. Y. A. Siddiki et al., âTheoretical calculation of biogas production and greenhouse gas emission reduction potential of livestock, poultry and slaughterhouse waste in Bangladesh,â J Environ Chem Eng, vol. 9, no. 3, p. 105204, 2021, doi: 10.1016/j.jece.2021.105204. [333] NREL, âSolar Research: Data and Tools,â Jan. 11, 2010. [334] D. Jang, K. Kim, K. H. Kim, and S. Kang, âTechno-economic analysis and Monte Carlo simulation for green hydrogen production using offshore wind power plant,â Energy Convers Manag, vol. 263, Jul. 2022, doi: 10.1016/j.enconman.2022.115695. [335] NREL, âHydrogen & Fuel Cells.â [336] M. Katebah, M. Al-Rawashdeh, and P. Linke, âAnalysis of hydrogen production costs in Steam-Methane Reforming considering integration with electrolysis and CO2 capture,â Clean Eng Technol, vol. 10, Oct. 2022, doi: 10.1016/j.clet.2022.100552. [337] HTEC, âHTEC FAQs.â Accessed: Jan. 21, 2024. [Online]. Available: https://www.htec.ca/faqs/#:~:text=How%20much%20does%20hydrogen%20cost,a%20price%20per%20kilometre%20basis. [338] Mission Innovation, âHydrogen cost and sales prices.â Accessed: Jan. 21, 2024. [Online]. Available: https://h2v.eu/analysis/statistics/financing/hydrogen-cost-and-sales-prices [339] M. De Rosa, âEconomic assessment of producing and selling biomethane into a regional market,â Energy and Environment, vol. 31, no. 1, pp. 60â76, Feb. 2020, doi: 10.1177/0958305X18762581. [340] Statistics Canada, âNumber of cattle, by class and farm type: Table 32-10-0130-01 Number of cattle, by class and farm type (x 1,000).â Accessed: Jun. 25, 2024. [Online]. Available: https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=3210013001 [341] Livestock and Poultry Environmental Learning Community, âEstimating Routine Beef and Dairy Mortality Masses Based on Systems Operation.â Accessed: Feb. 23, 2024. [Online]. 213 Available: https://lpelc.org/estimating-routine-beef-and-dairy-mortality-masses-based-on-systems-operations/ [342] Poultry Care, âCauses of early chick mortality and 14 ways to reduce mortality.â Accessed: Feb. 23, 2024. [Online]. Available: https://www.poultry.care/blog/causes-of-early-chick-mortality-14-ways-to-reduce-the-mortality#:~:text=1-5%25 of mortality is,this should be taken seriously. [343] J. M. Hoffman and T. G. Valencak, âA short life on the farm: aging and longevity in agricultural, large-bodied mammals,â Geroscience, vol. 42, no. 3, pp. 909â922, 2020, doi: 10.1007/s11357-020-00190-4. [344] S. Y. A. Siddiki et al., âTheoretical calculation of biogas production and greenhouse gas emission reduction potential of livestock, poultry and slaughterhouse waste in Bangladesh,â J Environ Chem Eng, vol. 9, no. 3, p. 105204, 2021, doi: 10.1016/j.jece.2021.105204. [345] A. Whiting and A. Azapagic, âLife cycle environmental impacts of generating electricity and heat from biogas produced by anaerobic digestion,â Energy, vol. 70, pp. 181â193, Jun. 2014, doi: 10.1016/j.energy.2014.03.103. [346] R. N. G. Coalition, âEconomic Analysis of the US Renewable Natural Gas Industry,â no. December, 2022. [347] M. Katebah, M. Al-Rawashdeh, and P. Linke, âAnalysis of hydrogen production costs in Steam-Methane Reforming considering integration with electrolysis and CO2 capture,â Clean Eng Technol, vol. 10, Oct. 2022, doi: 10.1016/j.clet.2022.100552. [348] Z. Abdin, K. Khalilpour, and K. Catchpole, âProjecting the levelized cost of large scale hydrogen storage for stationary applications,â Energy Convers Manag, vol. 270, no. September, p. 116241, 2022, doi: 10.1016/j.enconman.2022.116241. 214 Appendices Appendix A: Technology Readiness Level Definitions The descriptions for each technology readiness levels are given in the Table A-1 [323], [324]. Table A-1: Description of technology readiness levels Readiness level TRL Number Description Research 1 Initial concept development 2 Generating the application 3 Proving the concept at component-level Development 4 Laboratory scale technology validation 5 Pilot scale sub-system validation Demonstration 6 Pilot testing of the complete system 7 Functional prototype demonstration 8 Full-scale technology demonstration at a commercial-scale Deployment 9 First of a kind commercial deployment Appendix B: Life Cycle Inventory Data for RNG and Hydrogen Production Facilities Life cycle inventory (LCI) of RNG production facility construction and operation Table B-1: 500m3 capacity RNG production plant (Anaerobic digester, biogas upgrade unit, and auxiliary equipment) construction [233] Materials/fuels Concrete 120 m3 Copper 250 kg Glued laminated timber, for outdoor use 80 m3 Polyethylene, high density, granulate 170 kg Polystyrene, high impact 570 kg Polyvinylidenchloride, granulate 330 kg Reinforcing steel 10800 kg Steel, chromium steel 1300 kg Synthetic rubber 1200 kg 215 Waste to treatment Waste plastic 113.3793 kg Waste plastic 1256.621 kg Waste polystyrene 38.35038 kg Waste polystyrene 531.6496 kg Waste polyvinylchloride 22.21417 kg Waste polyvinylchloride 307.7858 kg Waste reinforced concrete 19565.54 kg Waste reinforced concrete 279234.5 kg Waste wood, untreated 6472.591 kg Table B-2: Life cycle inventory for the operational stage for 1m3 of biogas from anaerobic digester Parameters Unit Biogas upgradeb Reference Membrane PSA Chemical scrubbing HPWS Input Electricity kWh 0.22 0.34 0.114 0.34 [228], [325] Heat MJ - - 1.83 - [228], [326] Lubricating oil kg 0.0001 0.00015 0.0001 0.0001 [233] Water kg - - 0.55 4 [228] Potassium hydroxide kg - 0.000004 - - MEA kg - - 0.05 - [228] Outputs 216 Waste heat MJ 1.28 1.28 1.28 [233] RNG leak % 0.15-2 0.15-2 0.15-2 0.15-2 [326] CO2 kg 0.99 0.98 0.58 - [233], [327] H2S kg 0.000009 0.000006 0.000002 - [233] SOX kg 0.0000066 0.0000066 0.00037 - [233] Methane kg 0.0044 0.0086 0.645 0.630 [233] Ammonia kg 0.00037 - 0.007 0.00037 [233], [327] Acetaldehyde kg 0.000002 - 0.184 0.00002 [233], [327] Nitrogen kg 0.05 0.048 - - [233] Operational parameters Efficiency % 90-94 90-94 90-94 90-94 [226], [326] Compression pressure bar 5 Life cycle inventory (LCI) of Hydrogen production facility construction Construction data for the gasifier and renewable energy systems that power the electrolyzers were obtained from the Ecoinvent database. Carbon capture unit life cycle inventory data were obtained from previous work [264]. The life cycle inventory of different components of the hydrogen production facility were obtained from literature as listed below NG SMR unit [328] Electrolyzer unit [329] Piping & dispenser [329] Compressor [329] 217 Hydrogen storage unit [329] Table B-3: LCI of hydrogen facility construction Plant/ Equipment Material Amount Unit NG SMR Concrete 12.612 g/kg H2 Steel 4.029 Aluminium 0.033 Iron 0.049 Electrolyzer Capacity 60 Nm3/ h Low alloyed steel 11,410.4 kg High alloyed steel 3,553.0 kg Cast iron 463.1 kg Aluminium 1,004.7 kg Nickel 175.3 kg Polymer 432.9 kg Resin 307.1 kg Zeolith 106.8 kg Electronics 71.0 kg Piping and dispenser Low alloyed steel 158.70 kg High alloyed steel 521.25 kg Electronics 6.00 kg Compressor Nominal capacity 50.3 kWe Low-alloyed steel 11,927.1 kg High alloyed steel 2,085.4 kg Cast iron 739.8 kg Copper 279.5 kg Aluminium 77.3 kg 218 Plant/ Equipment Material Amount Unit Polymer 64.7 kg Electronics 26 kg Hydrogen storage Capacity 360 kg Low alloyed steel 12,278.3 kg Monomere & polymer 11,284.5 kg Table B-4: Hydrogen facility Operational stage LCI Inputs under life cycle stages Unit NG SMR Coal Gasification Electrolyzer Reference Alkaline PEM SOEC Inputs Energy kJ/ kgH2 4150 52,710 - - - [9] Diesel MJ/kgH2 - 16 - - - Electricity kWh/ kg H2 - - 42.28 â 65.64 48.85 â 72 41.17 [11] Coal (in ground) kg/ kgH2 0.005 7.25 - - - [9][12] Natural gas g/ kgH2 694.84 - - - - [9] Oil (in ground) g/ kgH2 1.81 - - - - [9] Water kg/ kgH2 0.3 2.9 10 10 10 [9][13] KOH g/ kgH2 - - 1-2 NA NA [9][13] Outputs CO2 g/ kgH2 41.85 11,299.18a LCA modeling usign Simapro Software [9,12][12] CO g/ kgH2 0.1 - [9] NOX g/ kgH2 0.2 - Nitrous oxides g/ kgH2 0 - 219 Inputs under life cycle stages Unit NG SMR Coal Gasification Electrolyzer Reference Alkaline PEM SOEC Non-methane hydrocarbons g/ kgH2 0.27 - Particulates g/ kgH2 1.26 - SOX g/ kgH2 1.2 2.89 [9,12] Benzene g/ kgH2 0.27 - [9] Operational Parameters Capacity Tons/day 135 660 55 55 55 [14,15] Capacity factor % 90 - - - - [14] Efficiency % 73 51.4-65.8 ##### ##### 61.4 â 87.6% [16] [15] Plant life years 20 - 20-25 20 20 [14][13] Stack life hours - - 80,000 40,000 20,000 [13,17] avalue provides the total CO2-eq / kg H2 from coal gasification [328] 220 Appendix C: Life Cycle Impact Normalization and Aggregation Factors Table C-1: ReCiPe midpoint normalization scores (obtained from [15], [330]) Midpoint impact unit Hierarchic Human health Global Warming - Human health kg CO2 eq. per person in 2010 7.99E+03 Stratospheric ozone depletion - Human health kg CFC11 eq. per person in 2010 6.00E-02 Ionzing Radiation - Human health kBq Co-60 emitted to air eq. per person in 2010 4.80E+02 Fine particulate matter formation - Human health kg PM2.5 eq. per person in 2010 2.56E+01 Photochemical ozone formation - Human health kg NOx eq. per person in 2010 2.06E+01 Toxicity - Human health (cancer) kg 1,4-DCB emitted to urban air eq. per person in 2010 1.03E+01 Toxicity - Human health (non-cancer) kg 1,4-DCB emitted to urban air eq. per person in 2010 3.13E+04 Water consumption - human health m3 consumed per person in 2010 2.67E+02 Terrestrial ecosystems Global Warming - Terrestrial ecosystems kg CO2 eq. per person in 2010 7.99E+03 Photochemical ozone formation - Terrestrial ecosystems kg NOx eq. per person in 2010 1.77E+01 Acidification - Terrestrial ecosystems kg SO2 eq. per person in 2010 4.10E+01 Toxicity - Terrestrial ecosystems kg 1,4-DBC emitted to industrial soil eq. per person in 2010 1.52E+04 Water consumption - terrestrial ecosystems m3 consumed per person in 2010 2.67E+02 Land use - occupation m2âannual crop eq per person in 2010 6.17E+03 Freshwater ecosystems Global Warming - Freshwater ecosystems kg CO2 eq. per person in 2010 7.99E+03 221 Midpoint impact unit Hierarchic Human health Eutrophication - Freshwater ecosystems kg P to freshwater eq. per person in 2010 6.50E-01 Toxicity - Freshwater ecosystems kg 1,4-DBC emitted to freshwater eq. per person in 2010 2.52E+01 Water consumption -aquatic ecosystems m3 consumed per person in 2010 2.67E+02 Marine ecosystems Toxicity - Marine ecosystems kg 1,4-DBC emitted to sea water eq. per person in 2010 4.34E+01 Eutrophication - marine ecosystems kg N to marine water equivalents per person in 2010 4.62E+00 Resources Mineral resource scarcity kg Cu-eq per person in 2010 1.20E+05 Fossil resource scarcity 222 Table C-2: Midpoint impact category aggregation weights (obtained from [212], [218][15]) Midpoint impact Impact Category Weight Climate change Global warming Stratospheric ozone depletion 0.5 0.5 Damage to ecosystem Terrestrial acidification Freshwater eutrophication Marine eutrophication Terrestrial ecotoxicity Freshwater ecotoxicity Marine ecotoxicity Ozone formation, terrestrial ecosystems Land use 0.125 0.125 0.125 0.125 0.125 0.125 0.125 0.125 Human health impacts Particulate matter formation Human carcinogenic toxicity Human non-carcinogenic toxicity Ionizing radiation Ozone formation, human health 0.2 0.2 0.2 0.2 0.2 Resource depletion Mineral resource depletion Water depletion Fossil resource depletion 0.333 0.333 0.333 223 Appendix D: Resource Availability and Plant Capacity Calculation RNG resource availability and plant capaacity The below are adopted from the paper published: [15] RNG production potential from landfill was calculated from equation D-1 [217] ð ððº (ðððð¶ð¹ð) = ð¿ð¹ðº(ðð¶ð¹ð)Ãðð¿ð¹ðºÃðð¸ÃððµðððððµððÃ1000000 Eq. D-1 where; RNG production in Million standard cubic feet per year (MMSCFY) was calculated using LFG = landfill gas input (SCFY) ðð¿ð¹ðº = 0.5 SCF of methane in 1 SCF of LFG ðð¸= 90% efficiency factor [217] ðððµðð= heating conversions rates 1012 BTU per SCF methane (mBTU) and 1050 BTU per SCF natural gas to convert from methane in the LFG to natural gas (ngBTU) and 1,000,000 in the denominator to convert from SCF to MMSCF [217]. Assumptions made: o Amount of LFG produced per year remains the same RNG production from waste water treatment plant (WWTP) was calculated using equation D-2 [217] ð ððº (ðððð¶ð¹ð) = ðððð ðµðº(ðð¶ð¹ð)ÃÃðð¸ÃððµðððððµððÃ1000000 Eq. D-2 Biogas from WWTPs is approximately 63â67% methane [331] ððððð = 0.63 SCF of methane per 1 SCF of WWTP BG RNG production from livestock manure was calculated using equation D-3 [332] ððð¡ððð¡ððð ðððððð (ð3ð¦ððð) = ððð¡ðð ðððð¢ðð (ðð/ð¦ððð) Ã ððð¡ðð ð ððððð ðð ðððð¢ðð Ãðð£ððððððððð¡ð¦ ððððððððððð¡ Ã ðððððð ð¦ðððð (ð3ððððð¡ððð ðððð) Eq. D-3 The data required to calculate the biogas yield is given below. 224 Table D-1: Data for livestock manure-based biogas yield calculation Livestock Manure kg/day Total Solids Biogas yield (m3/kg) Availability co-efficient Reference Large ruminant 10-20 25% 0.6 50% [332] small 2 25% 0.4 13% [332] Poultry (chicken) 0.08-0.1 29% 0.8 99% [332] According to the plant capacities calculated the costing of RNG production plants were conducted based on the data provided in [15]. Hydrogen resource availability and plant capaacity The theoretical feasibility of renewable energy for hydrogen production is summarized below. Table D-2: Theoretical resource potential for renewable-based hydrogen Resource Potential Source Wind Feasible for electricity generation from predicted wind speeds 3ms-1 and above at ~65m above ground level [284] Solar Average solar radiation received by a south-facing Photo voltaic panel inclined at a latitude equivalent angle: 3-3.5 kWh/m2 /day [284] Solar energy The potential for solar energy generation for hydrogen electrolysis was calculated using Equations -4 and D-5. ð´ðð = ð´ðð,ð¢ððð¡ Ã ð¶ðð Eq. D-4 Where, 225 APV = ðð ððð ð¡ððððð ðððð (ð2); APV,unit = ð´ððð ððð ððð ð¡ðððððð 1 ðð ðð ðð (ð2) {6-8 acres per MW [333] } CPV = Installed solar power capacity (kW) {assumed a 1MW plant} ð¸ðð = ð» Ã ð Ã ðð Ã ð¶ðð Eq. D-5 Where, ð¸ðð = ðððð¡âðð¦ ðððððð¦ ððððððð¡ððð ðð ð¡âð ðð ð ð¦ð ð¡ðð (ððâ) ð» = ðððð¡âðð¦ ðððð ððððð¦ ððð ðððð¡ððð ðð ð¡âð ððððð ðð ð¡âð ðð ððððð¦ (ððââð)2 ð = ðð¢ðððð ðð ððð¦ð ðð ð¡âð ðððð¡â PR = Performance ratio (0.75)[284] The resource availability calculations for wind energy yield indicated insufficient resources for wind energy-based electrolysis in Kelowna for hydrogen generation. Grid Energy Availability The grid electricity available for hydrogen production was estimated by considering Kelowna's electricity capacity and usage, utilizing data for the entire province and estimating population differences. 2 [Data/information/map] obtained from the âGlobal Solar Atlas 2.0, a free, web-based application is developed and operated by the company Solargis s.r.o. on behalf of the World Bank Group, utilizing Solargis data, with funding provided by the Energy Sector Management Assistance Program (ESMAP). For additional information: https://globalsolaratlas.info 226 The total solar energy generation potential in a selected location in Canada is given below. Table D-3: Solar insolation and total energy generation potential Month Solar Insolation (kWh/m2/day) Number of days Energy generation (kWh) JAN 2.01 31 1,040,159.37 FEB 3.14 28 1,467,674.72 MAR 3.93 31 2,033,744.43 APR 5.02 30 2,514,010.6 MAY 5.79 31 2,996,279.97 JUN 6.02 30 3,014,809.53 JUL 6.9 31 3,570,696.34 AUG 6.16 31 3,187,752.09 SEP 4.91 31 2,540,886.81 OCT 3.3 31 1,707,724.33 NOV 1.9 30 951,517.958 DEC 1.67 31 864,212.012 Total Annual energy 25,889,468.2 227 Appendix E: Life Cycle Cost Estimations of Hydrogen and RNG Production Facilities Hydrogen production facility cost data Table E-1: Electrolyzer cost data [334] Unit AE PEME SOEC CAPEX baseline H2 production rate kg H2/ year 86488 81819 80760 current rate kg H2/ year 20,000,000 20,000,000 20,000,000 baseline costs Stack cost base $ 340,000 420,000 520,000 Power supply $ 198,225 198,225 198,225 Water circulation $ 87,082 63,541 87,082 H2 processing $ 83,880 83,880 83,880 Cooling $ 28,678 21,517 28,678 Other BOPs $ 6,000 6,000 6,000 Stack life hours 80,000 40,000 20,000 operational hours /year 8,000 8,000 8,000 Replacement years 10 5 2.5 Replacement times 1 3 7 228 Unit AE PEME SOEC Stack replacement cost Current 8,910,937.59 11,380,328.21 14,200,497.34 Operational cost Labor cost $/year 36,575 36,575 36,575 deionized water $/kg water 0.01 0.01 0.01 KOH $/kg 2.92 2.92 2.92 Service and maintenance % of CAPEX 2 2 2 Other operating costs % of CAPEX 1% 1% 1% 229 Table E-2: Hydrogen production unit (SMR and coal gasification) CAPEX [335] Major pieces/systems of equipment Baseline Uninstalled Costs Scaling Factor Exponent Installation Cost Factor NG SMR Baseline: 483,024 kgH2/day Accessory Electric Plant 2,180,000 0.350 1.93 Cooling Water System 5,305,000 0.660 1.358 Feedwater & Miscellaneous BOP Systems 32,610,000 0.457 1.439 Hydrogen Production 16,135,000 0.600 1.080 Instrumentation & Control 8,001,000 0.160 1.138 Reformer & Accessories 39,658,000 0.611 1.499 Syngas Cleanup 3,646,000 0.800 2.3515 NG SMR+CCS Baseline: 483,000 kgH2/day Accessory Electric Plant 3,134,240 0.33 2.09 Cooling Water System 18,595,532 0.69 1.35 Feedwater & Miscellaneous BOP Systems 37,826,538 0.54 1.52 Hydrogen Production 172,415,789 0.70 1.01 Instrumentation & Control 15,465,126 0.63 1.08 Reformer & Accessories 9,596,333 0.60 1.15 230 Major pieces/systems of equipment Baseline Uninstalled Costs Scaling Factor Exponent Installation Cost Factor Syngas Cleanup 40,168,113 0.16 1.51 Coal gasification Baseline: 660,000 kgH2/day Accessory Electric Plant 37,585,959 0.62 1.26 Cooling Water System 22,742,889 0.66 1.22 Feedwater & Miscellaneous BOP Systems 23,872,940 0.68 1.40 Hydrogen Production 64,635,313 0.62 1.57 Instrumentation & Control 834,673,037 0.64 1.42 Reformer & Accessories 19.325,896 0.60 1.08 Syngas Cleanup 14,692.588 0.13 1.25 Table E-3: Operational costs of hydrogen production Operational costs Unit Reference [336] NG cost ($/GJ) $/GJ 5.9 Process water cost $/m3 0.9 Maintenance cost % of CAPEX 7 Operating supplies % maintenance 15 Plant overheads % maintenance and labor 6 Taxes % CAPEX 2 Insurance % CAPEX 1 231 Appendix F: Regional Life Cycle Environmental and Economic Performance of RNG Production Routes The developed methodological framework provided in Chapter 3: was applied to three main cities in BC to establish its applicability and determine the variations in environmental and cost performances of RNG production paths, subjected to the differences in regional population and economic activities. Therefore, by considering a minimum population density of 20 people/ km2, the following three cities were selected for this study; â¢ Fraser Valley: Chilliwack â Medium community â¢ Okanagan Valley â Kelowna - Urban population centre â¢ Great Vancouver âVancouver - Large urban population centre Based on resource and infrastructure availability, potential RNG production volumes were initially calculated for each city for selected RNG source locations (landfill, waste water treatment plant, and livestock farm). As provided in the table below, the highest RNG potential is shown in the large urban population centre (Vancouver). However, the medium population centre shows the second highest potential (Chilliwack). This can be due to the larger landfill capacity and extensive farming activities in Chilliwack (Medium) than the selected urban centre (Kelowna). Table F-1: RNG production potential RNG production route RNG flow (GJ/hour) Urban Medium Large Urban LFG_MB 9.07 18.84 71.37 LFG_PSA 9.46 19.64 74.41 LFG_CHS 9.55 19.84 75.17 LFG_HPWS 9.36 19.44 73.65 WW_MB 2.78 3.04 21.49 WW_PSA 2.90 3.17 22.40 WW_CHS 2.93 3.20 22.63 232 RNG production route RNG flow (GJ/hour) Urban Medium Large Urban WW_HPWS 2.87 3.14 22.18 LVS_MB 0.44 1.19 0.74 LVS_PSA 0.46 1.24 0.78 LVS_CHS 0.47 1.26 0.78 LVS_HPWS 0.46 1.23 0.77 Total RNG potential 50.74 95.25 386.36 Based on the above RNG production potentials, the LCA and LCC evaluations were conducted. Figure F-1 depicts the impact scores obtained by each route for each considered location (medium population - Chilliwack, urban - Kelowna, and large urban- Vancouver). 233 0%10%20%30%40%50%60%70%80%90%100%b) Scenarios_Medium (Chilliwack)0%10%20%30%40%50%60%70%80%90%100%a) Scenarios_Urban (Kelowna)0%10%20%30%40%50%60%70%80%90%100%c) Scenarios_Large Urban (Vancouver)Water consumptionFossil resource scarcityMineral resource scarcityLand useHuman non-carcinogenic toxicityHuman carcinogenic toxicityMarine ecotoxicityFreshwater ecotoxicityTerrestrial ecotoxicityMarine eutrophicationFreshwater eutrophicationFigure F-1: Life cycle impacts of RNG generation scenarios a) Urban, b) Medium, c) Large Urban 234 The aggregated damage categories are provided in Figure F-2. The rest of the figure provide overall life cycle impact and costs. 0.0E+005.0E-061.0E-051.5E-052.0E-052.5E-053.0E-05LFG_MBLFG_PSALFG_CHSLFG_HPWSWW_MBWW_PSAWW_CHSWW_HPWSLVS_MBLVS_PSALVS_CHSLVS_HPWSNormalized impactsa) Urban (Kelowna)0.0E+005.0E-061.0E-051.5E-052.0E-052.5E-053.0E-05LFG_MBLFG_PSALFG_CHSLFG_HPWSWW_MBWW_PSAWW_CHSWW_HPWSLVS_MBLVS_PSALVS_CHSLVS_HPWSNormalized impactsb) Medium (Chilliwack) 0.0E+005.0E-061.0E-051.5E-052.0E-052.5E-053.0E-053.5E-054.0E-05LFG_MBLFG_PSALFG_CHSLFG_HPWSWW_MBWW_PSAWW_CHSWW_HPWSLVS_MBLVS_PSALVS_CHSLVS_HPWSNormalized impactsc) Large urban (Vancouver)Resource depletionHuman Health ImpactsImpact to eco-systemFigure F-2: Aggregated damage categories of RNG production routes a) Urban, b) Medium, c) Large Urban 235 Figure F-3: Global warming potential of RNG production routes Figure F-4: LCI scores of RNG production routes 0.00E+001.00E-032.00E-033.00E-034.00E-035.00E-036.00E-037.00E-038.00E-03GWP (kg CO2/ MJ RNG)KelownaChilliwackVancouver0.0E+001.0E-062.0E-063.0E-064.0E-065.0E-066.0E-067.0E-068.0E-069.0E-061.0E-05Normalized LCI scoreKelowna Chilliwack Vancouver 236 Figure F-5: Life cycle cost of RNG production scenarios Figure F-6: Levelized Cost of Energy of RNG production scenarios 00.020.040.060.080.10.12LCC($/kWh)Urban (Kelowna)Medium (Chilliwack)Large Urban (Vancouver)010203040506070LCOE ($/MMBTU)Urban (Kelowna)Medium (Chilliwack)Large Urban (Vancouver) 237 Appendix G: Additional Data for MOO Table G-1: Fuel Selling Price Fuel Sector $/MJ Reference H2 Transport 0.106 [337] Industry 0.060 [338] Building heating 0.100 [338] RNG Transport 0.016 [339] Industry 0.022 [339] Building heating 0.022 [280] Appendix H: Data Gathered for System Dynamics Model While alternative fuels result in emission benefits, it is important to explore the associated human health impacts. Figure_H-1 indicats the dynamic of human health impacts from alternative fuel integration. In the early years, the impact on human health has shown an increasing trend. By 2045, the impacts peak before starting to decrease. The chart demonstrates the need to manage the human health impacts from the early years as it may raise resistance from communities. Figure H-1: Normalized human health impacts 00.20.40.60.811.21.42023202420252026202720282029203020312032203320342035203620372038203920402041204220432044204520462047204820492050NORMALIZED HUMAN HEALTH IMPACTS 238 Table H-1: Initial data inputs for livestock-based RNG generation Large ruminant Small ruminant Poultry Reference Initial population 1044 360 650 [292] Annual population growth rate (%) 0.9 0.7 0 [340] Animal mortality rate (%) 1a 7b 2.5c [341], [342], [343] Biogas output from dry matter (Nm3/ton volatile solids) 200-680 250-500 350-600 [43] Manure production (tons/year) 10-15a 1-2.4a 0.003b [43]a [344]b Manire dry matter content (%) 25-30 20-25 89 [344] Volatile solids (%) 30-85 21-91 70-80 [43] Table H-2: Initial data inputs for LFG and WWS-based RNG generation Parameter Unit Value Reference Municipal solid waste annual growth rate % 32 [295] Wastewater annual growth rate % 2.7 [296] Wastewater inflow Biogas to RNG upgrade factor % 60 [345] RNG conversion factor % 37 [345] Methane yield CH4 m3/ tonne waste 112 [15] Methane generation rate constant / year 0.031 [15] Table H-2: Initial data inputs for gaseous fuel economic subsystem Parameter Unit Value Reference RNG facility WWS-based unit CAPEX $/MJ 0.097 [346] 239 Parameter Unit Value Reference LVS -based unit $/MJ 0.089 [346] LFG-based unit $/MJ 0.039 [346] WWS-based unit OPEX $/MJ 0.012 [346] LVS -based unit $/MJ 0.014 [346] LFG-based unit $/MJ 0.013 [346] Learning rate % 4-5 [298] Hydrogen production SMR + CCS CAPEX $/ kg H2 0.28 [347] Protone exchange membrane electrolysis CAPEX $/ kg H2 0.71 [193] SMR + CCS OPEX $/ kg H2 0.34 [347] Protone exchange membrane electrolysis $/ kg H2 0.45 [193] SMR Learning rate % 11±6 [299] CCS Learning rate % 3-5 [299] Protone exchange membrane electrolysis Learning rate % 15 [189] Hydrogen storage Salt cavern type (5000 tonnes capacity) CAPEX $/ tonnes 0. 078 [348] FOPEX $/ tonnes 0.052 [348] VOPEX $/ tonnes 0.992 [348] Learning rate % 15 [348]
Thesis/Dissertation
eng
Mechanical Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Hewage, Kasun
Sadiq, Rehan
Sustainable gaseous fuel futures of Canada : a planning framework for fuel supply chain configurations
Text