Applied Science, Faculty of
Engineering, School of (Okanagan)
DSpace
UBCO
Hewa Godella Waththage, Tharindu Prabatha
Doctor of Philosophy - PhD
University of British Columbia
Curbing greenhouse gas (GHG) emissions has become a global need with the raising concerns about climate change. Buildings account for 32-40% of the global energy use and thereby are responsible for a significant portion of GHG emissions. Therefore, the federal and provincial governments in Canada are looking to reduce the emissions associated with buildings. Policy initiatives such as British Columbia Energy Step Code have been launched to reduce the operational emissions of the new constructions. However, embodied emissions associated with energy upgrades are being overlooked in these initiatives. Moreover, initiatives focussing on existing buildings are minimal. Therefore, this research aimed to develop a life cycle thinking-based energy retrofits decision-making approach under uncertain conditions for existing buildings. According to the recent literature, low investor confidence, predicting the performance uncertainties, performance gaps in the design and post-retrofit periods, capital investment barriers, and ensuring the quality of retrofit implementation are critical challenges faced by building energy retrofit projects. Performance gaps during the post-retrofit period are mainly caused by uncertainties associated with the building operations, which inevitably results in low investor confidence. A fuzzy-based performance prediction and retrofit selection approach was used to identify the best retrofit strategies for a given building while accounting for the performance uncertainties. Poor workmanship can also result in lower energy savings in the post-retrofit period. Therefore, the potential of employing energy performance contracts (EPC) to ensure the quality of implementation and deliver guaranteed cost and energy savings were investigated. An optimization algorithm was developed to identify the optimal financial parameters to meet the expectations of all stakeholders involved in an EPC. It was observed that the retrofit solution that produces the highest cost-savings does not necessarily produce the highest emissions savings in provinces with a greener electricity grid. An optimization algorithm was developed to assist in financial incentive strategy planning to promote eco-friendly retrofit solutions. The findings from this thesis will benefit multiple stakeholders including, building owners and managers, utility providers, and governmental bodies. In conclusion, this thesis lays the foundation for developing a comprehensive retrofit strategy for Canadian residences.
A LIFE CYCLE THINKING-BASED ENERGY RETROFITS PLANNING APPROACH FOR EXISTING RESIDENTIAL BUILDINGS by Tharindu Prabatha Hewa Godella Waththage B.Sc. Eng. (Hons), University of Moratuwa, 2014 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) January 2022 © Tharindu Prabatha Hewa Godella Waththage, 2022 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis/dissertation entitled: A LIFE CYCLE THINKING-BASED ENERGY RETROFITS PLANNING APPROACH FOR EXISTING RESIDENTIAL BUILDINGS. submitted by Tharindu Prabatha Hewa Godella Waththage in partial fulfillment of the requirements of the degree of Doctor of Philosophy in Mechanical Engineering. Dr. Kasun Hewage, School of Engineering Supervisor Dr. Rehan Sadiq, School of Engineering Co-supervisor Dr. Shahria Alam, School of Engineering Supervisory Committee Member Dr. Zheng Liu, School of Engineering Supervisory Committee Member Dr. Jian Liu, School of Engineering University Examiner Dr. Fuzhan Nasiri, Concordia University External Examiner iii Abstract Curbing greenhouse gas (GHG) emissions has become a global need with the raising concerns about climate change. Buildings account for 32-40% of the global energy use and thereby are responsible for a significant portion of GHG emissions. Therefore, the federal and provincial governments in Canada are looking to reduce the emissions associated with buildings. Policy initiatives such as British Columbia Energy Step Code have been launched to reduce the operational emissions of the new constructions. However, embodied emissions associated with energy upgrades are being overlooked in these initiatives. Moreover, initiatives focussing on existing buildings are minimal. Therefore, this research aimed to develop a life cycle thinking-based energy retrofits decision-making approach under uncertain conditions for existing buildings. According to the recent literature, low investor confidence, predicting the performance uncertainties, performance gaps in the design and post-retrofit periods, capital investment barriers, and ensuring the quality of retrofit implementation are critical challenges faced by building energy retrofit projects. Performance gaps during the post-retrofit period are mainly caused by uncertainties associated with the building operations, which inevitably results in low investor confidence. A fuzzy-based performance prediction and retrofit selection approach was used to identify the best retrofit strategies for a given building while accounting for the performance uncertainties. Poor workmanship can also result in lower energy savings in the post-retrofit period. Therefore, the potential of employing energy performance contracts (EPC) to ensure the quality of implementation and deliver guaranteed cost and energy savings were investigated. An optimization algorithm was developed to identify the optimal financial parameters to meet the expectations of all stakeholders involved in an EPC. It was observed that the retrofit solution that produces the highest cost-savings does not necessarily produce the highest emissions savings in provinces with a greener electricity grid. An optimization algorithm was developed to assist in financial incentive strategy planning to promote eco-friendly retrofit solutions. The findings from this thesis will benefit multiple stakeholders including, building owners and managers, utility providers, and governmental bodies. In conclusion, this thesis lays the foundation for developing a comprehensive retrofit strategy for Canadian residences. iv Lay Summary This study aimed to develop a comprehensive energy retrofits investment planning approach to improve the environmental and economic performance of Canadian residences. Initially, a cross-country performance assessment was conducted to understand the costs and benefits of building energy retrofits in regional contexts. It was observed that the retrofit performance significantly varies with local conditions including, energy supply, energy prices, and weather. Some retrofits with high environmental benefits had challenges in community penetration due to poor economic performance. Moreover, principal-agent problems (i.e., uneven distribution of costs and benefits between landlords and tenants) and lack of investor confidence were identified as barriers to building energy retrofits' community penetration. An incentive planning framework and an energy performance contracting planning model were developed to address the aforementioned challenges. The developed frameworks and research findings were presented to the local municipalities and utility providers to assist in planning their building energy efficiency and conservation initiatives. v Preface I, Tharindu Prabatha Hewa Godella Waththage, developed the research concepts presented in this thesis and conducted the relevant analyses. The initial research ideas were formulated with the work carried out collaborating with Pacific Institute for Climate Solutions (PICS). With the experience from the PICS collaboration, I conducted a literature review on energy retrofit planning for residential buildings. Thereafter, I conducted an economic and environmental performance assessment for building energy retrofits under varying weather and economic conditions across Canadian provinces in collaboration with Dr. Hirushie Karunathilake. I conducted all the energy simulations and life cycle economic and environmental performance assessment work in the article and wrote the manuscript. The outcomes of the study indicated the need for a comprehensive framework to identify retrofit investment strategies accounting for potential uncertainties. I developed a retrofit scenario ranking procedure to address this need, accounting for the uncertainties in energy prices, emission factors, and occupant behaviors. The retrofit strategies with the best environmental performance did not necessarily have the best cost-saving potential in BC. Findings from the said assessment highlighted the need for financial incentives to motivate the building owners to employ building energy retrofits with better environmental performance. An optimization algorithm was developed to identify the best incentive levels to promote energy retrofits accounting for the stakeholder expectations and environmental goals. This work has been submitted for publication in a peer-reviewed journal. Principal-agent issues, lack of investor confidence, and high capital costs were identified as common challenges associated with residential energy retrofitting projects. Energy performance contracting (EPC) is a popular solution adopted in commercial and large-scale building renovation projects to address the above challenges. However, the applicability of EPCs for small-scale residences is a less investigated area in Canada and the world in general. Therefore, the applicability of EPC models for small residential buildings was investigated. A non-linear EPC planning and finance optimization model was developed considering the uncertainties. The findings were submitted for publication in a peer-reviewed journal. The findings from the thesis were presented to the local municipalities, utility providers, and building managers. Best practices for energy retrofits planning were identified based on the feedback from the said stakeholders and the lessons learned from the research work. Three journal articles and two conference papers were prepared from the findings vi of the research. This research was carried out under the supervision of Dr. Kasun Hewage and Dr. Rehan Sadiq. Dr. Zheng Liu and Dr. Shahria Alam supported the success of the research with continuous support as committee members. Journal Articles: Published ➢ Prabatha., T., Karunathilake, H., Sadiq, R., & Hewage, K. (2020) To retrofit or not? Making energy retrofit decisions through life cycle thinking for Canadian residences, Energy and Buildings, https://doi.org/10.1016/j.enbuild.2020.110393 Submission ready ➢ Prabatha, T., Hewage, K., & Sadiq, R. (2021). Retrofitting strategy development and incentives planning for residential buildings: An uncertainty-based life cycle thinking approach. Will be submitted to a peer reviewed journal. ➢ Prabatha, T., Hewage, K., & Sadiq, R. (2021). Overcoming the financial barriers faced by retrofitting projects in small residences: A non-linear optimization approach for energy performance contract formulation under uncertain conditions. Will be submitted to a peer reviewed journal. Conference proceedings: ➢ Prabatha., T., Hirushie, K., Ruparathna, R., Sadiq, R., & Hewage, K. (2019) A building energy and health conditions monitoring strategy for Canadian building sector. In 7th International Construction Conference (7th ICC) 2019. Montreal ➢ Prabatha, T., Ruparathna, R., Hewage, K., & Sadiq, R. (2018). Operational performance management of multi-unit residential buildings. In 1st International Conference on New Horizons in Green Civil Engineering (NHICE-01) 2018. Victoria. vii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ............................................................................................................................... iv Preface .............................................................................................................................................v Table of Contents ........................................................................................................................ vii List of Tables ............................................................................................................................... xii List of Figures ............................................................................................................................. xiv List of Abbreviations ................................................................................................................. xvi Acknowledgments .................................................................................................................... xviii Dedication .....................................................................................................................................xx Chapter 1: Introduction ................................................................................................................1 Background and pressures .............................................................................................. 1 Research motivation........................................................................................................ 3 Research objectives ......................................................................................................... 4 1.3.1 Research outcomes and deliverables .......................................................................... 5 Meta language ................................................................................................................. 6 Thesis organization ......................................................................................................... 7 Chapter 2: Research methodology .............................................................................................11 Literature review and data collection (Phase: 1) .......................................................... 11 Evaluation of regional applicability of energy retrofits for residential buildings (Phase: 2) ................................................................................................................................... 12 Financial incentives planning and retrofit strategy selection under uncertain conditions (Phase: 3) ..................................................................................................................... 13 Model development to assist financial management of energy retrofitting projects (Phase: 4) ..................................................................................................................... 14 Chapter 3: Literature review ......................................................................................................15 viii Retrofits classification .................................................................................................. 16 Retrofit performance evaluation ................................................................................... 18 3.2.1 Performance indicators selection .............................................................................. 18 3.2.2 Energy performance evaluation ................................................................................ 19 3.2.3 Overcoming the design-performance gap ................................................................. 21 Energy performance contracting for energy retrofit projects ....................................... 24 3.3.1 Challenges for energy performance contracting ....................................................... 24 3.3.1.1 Financing energy retrofit projects ..................................................................... 24 3.3.1.2 Principal-agent issues........................................................................................ 26 3.3.2 Business models ........................................................................................................ 27 3.3.3 Project evaluation...................................................................................................... 29 Research gap ................................................................................................................. 30 Chapter 4: Evaluating the regional applicability of residential building energy retrofits....34 Background ................................................................................................................... 34 Methodology ................................................................................................................. 38 4.2.1 Quantifying life cycle emissions ............................................................................... 40 4.2.1.1 Embodied emissions ......................................................................................... 40 4.2.1.2 Operational emissions ....................................................................................... 42 4.2.1.3 Direct disposal emissions .................................................................................. 43 4.2.2 Life cycle cost assessment ........................................................................................ 43 4.2.3 Performance evaluation and options prioritization ................................................... 46 Retrofit Evaluation for Canada ..................................................................................... 47 4.3.1 Regional climate conditions ...................................................................................... 47 4.3.2 Base building definition ............................................................................................ 48 4.3.3 Retrofit options identification ................................................................................... 51 4.3.4 Scenario Formation and performance assessment .................................................... 54 ix Results and discussion .................................................................................................. 55 4.4.1 Prioritizing energy upgrades with varying priorities ................................................ 57 4.4.1.1 Eco-efficiency Performance.............................................................................. 58 4.4.1.2 Economic performance ..................................................................................... 60 4.4.2 Provincial opportunities and challenges ................................................................... 62 Summary ....................................................................................................................... 67 Chapter 5: Financial incentives planning for building energy retrofits under uncertainty .69 Background ................................................................................................................... 69 Methodology ................................................................................................................. 71 5.2.1 Sensitivity of operational and supply conditions on retrofit performance ................ 72 5.2.1.1 Change in energy demand with operational conditions and user behavior ...... 72 5.2.1.2 Sensitivity of operational cost and emission performance to energy price and emission factors ................................................................................................................ 73 5.2.2 Retrofit performance evaluation ............................................................................... 74 5.2.2.1 Energy performance .......................................................................................... 75 5.2.2.2 Environmental performance.............................................................................. 76 5.2.2.3 Economic performance ..................................................................................... 78 5.2.3 Performance-based retrofits planning ....................................................................... 80 5.2.4 Optimization of incentives ........................................................................................ 82 Case study ..................................................................................................................... 84 5.3.1 Base building definition ............................................................................................ 84 5.3.2 Retrofit options identification ................................................................................... 85 Results and discussion .................................................................................................. 85 5.4.1 Sensitivity of operational and supply conditions on retrofit performance ................ 85 5.4.1.1 Number of occupants and their time spent at home .......................................... 85 5.4.1.2 Major electric appliances .................................................................................. 86 x 5.4.1.3 Hot-water temperature and demand .................................................................. 87 5.4.1.4 Space cooling and heating setpoints ................................................................. 88 5.4.1.5 Operational cost and emission sensitivity to source conditions........................ 90 5.4.2 Retrofit performance outcomes ................................................................................. 93 5.4.3 Incentive performance .............................................................................................. 95 5.4.3.1 Optimal incentive allocation ............................................................................. 95 5.4.3.2 Incentive effectiveness ...................................................................................... 96 Summary ....................................................................................................................... 99 Chapter 6: Energy performance contract formulation for residential buildings ................102 Background ................................................................................................................. 102 Methodology ............................................................................................................... 103 6.2.1 Energy performance simulation .............................................................................. 104 6.2.2 Key performance indicator identification ............................................................... 105 6.2.3 Energy performance contract formulation .............................................................. 107 6.2.3.1 Optimization problem ..................................................................................... 108 6.2.3.2 Monte Carlo Simulation .................................................................................. 110 Case study ................................................................................................................... 110 6.3.1 Base building definition and retrofit strategy selection .......................................... 110 6.3.2 Uncertain conditions ............................................................................................... 112 6.3.3 Economic parameters .............................................................................................. 112 6.3.4 Decision priorities ................................................................................................... 112 6.3.5 Optimization constraints ......................................................................................... 112 Results and discussion ................................................................................................ 113 6.4.1 Cost Optimal Retrofit Strategy ............................................................................... 113 6.4.2 Energy Performance Contract ................................................................................. 115 6.4.2.1 Owner’s profit maximization scenario............................................................ 115 xi 6.4.2.2 ESCO’s profit maximization scenario ............................................................ 118 Summary ..................................................................................................................... 120 Chapter 7: Conclusions and recommendations ......................................................................122 Summary and conclusions .......................................................................................... 122 Originality and contributions ...................................................................................... 129 Limitations of the Study.............................................................................................. 130 Future research ............................................................................................................ 132 References ...................................................................................................................................134 Appendices ..................................................................................................................................150 Appendix 1 .............................................................................................................................. 150 Appendix 2 .............................................................................................................................. 153 Appendix 3 .............................................................................................................................. 154 Appendix 4 .............................................................................................................................. 156 Appendix 5 .............................................................................................................................. 159 Appendix 6 .............................................................................................................................. 161 Appendix 7 .............................................................................................................................. 171 Appendix 8 .............................................................................................................................. 173 Appendix 9 .............................................................................................................................. 176 Appendix 10 ............................................................................................................................ 177 xii List of Tables Table 1 Characteristics of the cities considered in the study ........................................................ 48 Table 2 Base building characteristics ........................................................................................... 50 Table 3 Baseload and occupancy characteristics .......................................................................... 51 Table 4 Retrofitting options summary .......................................................................................... 54 Table 5 Retrofit scenarios ............................................................................................................. 54 Table 6 Canadian eco-efficiency rank of retrofits ........................................................................ 59 Table 7 Life cycle cost-savings of retrofits (values are given in thousands) ................................ 61 Table 8 Operational uncertainties ................................................................................................. 75 Table 9 Fuzzy conditions used to represent emission factors ....................................................... 78 Table 10 Base building characteristics ......................................................................................... 84 Table 11 Baseload and occupancy characteristics ........................................................................ 84 Table 12 Space heating set points ................................................................................................. 88 Table 13 Emission factors and price for energy sources .............................................................. 90 Table 14 Annual energy use of the base buildings ....................................................................... 91 Table 15 Base building characteristics ....................................................................................... 111 Table 16 Baseload and occupancy characteristics ...................................................................... 111 Table 17 Retrofitting options summary ...................................................................................... 111 Table 18 Decision priorities ........................................................................................................ 112 Table 19 Capital cost of cost optimal solution............................................................................ 114 Table 20 Energy performance under uncertain conditions ......................................................... 114 Table A. 1 Embodied emissions of heat pump ........................................................................... 150 Table A. 2 Material list of NG furnace ....................................................................................... 151 Table A. 3 Electricity grid emission factors and prices, NG price, and HDD of each city ........ 153 Table A. 4 Capital cost of retrofits.............................................................................................. 154 xiii Table A. 5 Embodied emissions of retrofits ............................................................................... 156 Table A. 6 Cost, energy, and emissions saving of different energy retrofits when applied on an electrically heated house ............................................................................................................. 159 Table A. 7 Cost, energy, and emissions saving of different energy retrofits when applied to a house with natural gas heating .............................................................................................................. 160 Table A. 8 Eco-efficiency performance scores of retrofits ......................................................... 171 Table A. 9 Capital costs and embodied emission data of the retrofit options considered in the study..................................................................................................................................................... 173 Table A. 10 Percentage inclusion of retrofits in the best hundred retrofit scenarios under varying decision priorities ........................................................................................................................ 176 Table A. 11 Proposed incentive levels under varying decision priorities and the ranks of the retrofits selected for incentivizing .............................................................................................. 177 xiv List of Figures Figure 1 Integration of objectives and thesis organization ........................................................... 10 Figure 2 Expected benefits and stakeholders involved in energy upgrade initiatives .................. 36 Figure 3 Overview of methodology .............................................................................................. 38 Figure 4 Retrofit performance variation among Canadian cities in BB-1 .................................... 56 Figure 5 Retrofit performance variation among Canadian cities in BB-2 .................................... 57 Figure 6 Building operation scenarios under incentives ............................................................... 72 Figure 7 Percentage energy demand change vs. percentage change of no. of occupants and time at home .............................................................................................................................................. 86 Figure 8 Percentage energy demand change vs. percentage change of daily energy demand for major electrical appliances ............................................................................................................ 87 Figure 9 Percentage energy demand change vs. hot water supply temperature change ............... 88 Figure 10 Percentage energy demand change vs. daytime and nighttime heating setpoint change....................................................................................................................................................... 89 Figure 11 Percentage energy demand change vs. setback duration change ................................. 90 Figure 12 Operational cost vs. energy price variation .................................................................. 91 Figure 13 Operational emissions vs. energy emission factor variation ........................................ 92 Figure 14 Percentage inclusion of upgrades vs. relative importance of LCES ............................ 94 Figure 15 Incentive amount vs. relative importance of LCES to incentivize best 100 retrofit scenarios ........................................................................................................................................ 96 Figure 16 Incentive effectiveness vs. relative importance of LCES ............................................. 97 Figure 17 Capital cost and incentives vs. relative importance of LCES ...................................... 98 Figure 18 Overview of the proposed EPC planning methodology ............................................. 104 Figure 19 Owner’s capital cost contribution and guaranteed savings variation against the contract period .......................................................................................................................................... 116 Figure 20 Owner's profit and ESCO's profit against the contract period.................................... 117 xv Figure 21 Owner’s capital cost contribution and guaranteed savings against the contract period..................................................................................................................................................... 118 Figure 22 Owner's profit and ESCO's profit against the contract period.................................... 119 Figure 23 Energy retrofits implementation pathway .................................................................. 128 Figure A. 1 Performance of space heating retrofits .................................................................... 166 Figure A. 2 Performance of water heating retrofits .................................................................... 167 Figure A. 3 Performance of envelope upgrades .......................................................................... 168 Figure A. 4 Performance of LEDs .............................................................................................. 169 Figure A. 5 Performance of water-efficient faucets .................................................................... 169 Figure A. 6 Performance of solar PV integration ....................................................................... 170 Figure A. 7 Incentive level variations under the number of retrofit scenarios considered ......... 179 xvi List of Abbreviations Abbreviations AB Alberta ON Ontario ACH Air changes per hour OSH One-stop-shop ACS Annual cost savings PBP Payback period AES Annual energy savings PV Photovoltaics AFUE Annual fuel utilization efficiency QC Quebec BB Base building SFD Single family detached BC British Columbia SK Saskatchewan BCESC BC Energy STEP Code USD United States Dollars CC Capital cost WWR Window to wall ratio EEP Eco-efficiency parameter EF Energy factor EPC Energy performance contracts EPD Environmental product declaration ESA Energy services agreement ESCO Energy service companies GHG Greenhouse gas HP Heat pump HTAP Housing Technology Assessment Platform HWU Hot water unit IC Initial cost IRR Internal rate of return LCA Life cycle assessment LCC Life cycle cost LCE Life cycle emissions LCIA Life cycle impact assessment MAC Marginal abatement cost MB Manitoba MESA Managed energy services agreement MURB Multi-unit residential buildings NECB National Energy Code of Canada for Buildings NG Natural gas NL Newfoundland and Labrador NPV Net percent value NRCan Natural Resources Canada NZE Net-zero energy O&M Operations and maintenance xvii Units CAD Canadian Dollars GJ Gigajoule HDD Heating degree days kW Kilowatt kWh Kilowatt-hours MWh Megawatt hours xviii Acknowledgments It was a great blessing to work in the multi-disciplinary work environment in the Life Cycle Management Laboratory at the University of British Columbia’s Okanagan (UBCO) Campus. I am grateful to Dr. Kasun Hewage for the opportunity given to me to pursue my post-graduate studies in such an environment under his supervision. His continued guidance and support throughout my research work were instrumental in successfully completing my research. His leadership characteristics and project management knowledge were inspirational in developing my character and career development. Dr. Rehan Sadiq’s support for my research work and career development as a supervisor was invaluable. He always reviewed my research and provided valuable feedbacks despite being a busy individual with numerous responsibilities. His magnanimous approach for professional and personal matters helped me become a better researcher and a person. The multi-criteria decision-making course taught by Dr. Zheng Liu laid an excellent base for my thesis as it is mainly focused on decision-making. Dr. Shahria Alam provided his expert inputs and access to experimental data, which were helpful in formulating my research questions. Therefore, I wish to thank Dr. Zheng Liu and Dr. Shahria Alam for their advice and continuous support as committee members and teachers. Dr. Claire Yu Yan helped me develop the skills needed by an academic to become a better teacher. Dr. Dimitry Sediako was always helpful and trusted my abilities in teaching and related academic work. The support from these professors and the experience I gained by associating them made me confident and shaped my academic career. I want to thank Ms. Shannon Hohl for her continued administrative support and kind words throughout my time at UBCO. I would like to thank Mr. Colin Richardson and Mr. Glen McIntyre from the Energy Team of UBCO for providing expert inputs, training, and data access, which were instrumental in conducting my research work. Moreover, I would like to thank Mr. Robert Stupka, the FortisBC experts, Mr. Chris Ray, and the City of Kelowna for providing expert opinions. Their inputs helped me see the practical challenges and opportunities related to building renovations from industrial and government perspectives. xix I want to thank the Canadian Queen Elizabeth II Diamond Jubilee Scholarship, Natural Sciences and Engineering Research Council (NSERC) of Canada, Silverlake Forest Education Society, Pacific Institute for Climate Solutions (PICS), Mitacs Canada, FortisBC, Green Construction Research and Training Center in UBC for funding support provided for my research work. I thank the University of Moratuwa, Sri Lanka, for supporting me in pursuing my graduate studies by providing me with study leave from my position at the faculty. I thank the academic staff of the Department of Mechanical Engineering, the University of Moratuwa, for kindly handling my workload while I am on leave. I am forever grateful to Professor Kapila Perera for being my mentor and role model since my undergraduate studies. I also wish to thank Dr. Himan Punchihewa and Dr. W.K. Wimalsiri for supporting, inspiring, and guiding me. Moreover, I would like to thank Mr. Sanath Ranjitha for being my personal and professional mentor since my school time. I was always motivated and supported by my colleagues at the Life Cycle Management Laboratory. I especially want to thank Dr. Hirushie Karunathilake for supporting me as a fellow Ph.D. student and then a postdoctoral fellow, most importantly as a sincere friend since my undergraduate studies. I consider myself lucky to have such a friend and a work partner, whom I can trust to help me in victories and failures. I would like to extend my gratitude to Dr. Amin Shotorbani and Dr. Rajeeve Ruparathna for their guidance and support as post-doctoral fellows. I thank my parents, sister, and brother-in-law for supporting me throughout my life and patiently waiting for me to come back to them. I am grateful to my mother, who always motivated me to become my best self and not stop until I achieve my goals. Most importantly, I would like to thank my wife for believing in me. Her unwavering love, emotional support, and understanding helped me overcome the personal and professional challenges I faced during the Ph.D. I believe it is a blessing to have a family who believes in you and supports you thick and thin. xx Dedication Dedicated to my beloved wife, mother, sister, and friends, who believed in me, supported me, and motivated me to do and be my best…. 1 Chapter 1: Introduction This chapter provides a general outline and the background of the research work presented in the thesis. Background and pressures Canada aims to reduce its greenhouse gas emissions to 70% of the 2005 emission levels by 2030 [1][2]. Recently, energy consumption and associated environmental impacts caused by the built environment, particularly the building sector, have faced much scrutiny. Globally, the building sector accounts for 40% of the energy use, which results in significant GHG emissions and other negative environmental impacts. The combined energy use of residential and commercial building sectors approximately account for 33% of the Canadian national energy use and 70% of the electricity generated in the United States [3][4]. The building sector emits approximately one-third of Canada’s GHG emissions, and residential buildings are responsible for 12% of the GHG emissions [5]. Therefore, improving the energy efficiency of residential buildings is essential to meet the Canadian emission reduction targets. In addition to the environmental concerns, energy use has economic impacts for the building owners and occupants in the form of energy bills. Managing these economic impacts of energy use is especially important as 8% of Canadian households are affected by energy poverty [6]. Energy use in buildings results from facilitating occupant needs such as heating, lighting, and operating other appliances. An energy-efficient building provides the same level of service to occupants while consuming a lower amount of energy compared to an average building operating under the same conditions. Net-zero buildings take this concept a step further, where building energy demand is reduced by ultra-efficient measures, and the remaining demand is met through locally available renewable energy sources [7]. Along with the reduced climate and environmental impacts, energy-efficient buildings provide economic benefits to buildings and homeowners through reduced energy costs, leading to reduced energy poverty, socio-economic development, and value creation in a region [8]. The Province of British Columbia (BC) has committed an incremental approach in increasing energy efficiency requirements for its buildings, ultimately aiming to make the new buildings net-zero ready by 2032 [9]. Therefore, adapting the buildings to code compliance is critical, especially in light of the province’s ambitious emissions reduction 2 targets [10]. The provincial government of British Columbia introduced the BC Energy Step Code (BCESC) to promote energy efficiency in the new constructions. BCESC has been successfully introduced for Part-9 buildings (small residential buildings) in many municipalities. Currently, BCESC is being introduced to the Part-3 buildings (i.e., multi-unit residential and institutional buildings). Energy efficiency improvements are greatly promoted for the new constructions via BCESC. However, a structured approach for improving the energy efficiency of the existing buildings is not implemented except for some incentives provided by local municipalities and utility providers to promote energy-efficient building retrofits [11]. It is possible to obtain 50-90% energy savings through effective energy interventions planning [12]. Building energy use in the operational phase can be reduced by the use of efficient appliances, energy conservation measures, and implementing energy efficiency improvements (retrofits) [13][14]. However, it is essential to note that this is a multi-stakeholder problem. Therefore, the cooperation of all the parties involved is imperative to implement retrofit measures successfully. Municipalities and government bodies would like to promote energy retrofits with high GHG emissions reduction potential to achieve national climate change mitigation goals. However, the building owners generally would like to implement energy retrofits that can produce economic benefits. Due to split-incentives issues, rental building owners may hesitate to implement even the energy retrofits with potential cost-savings. Difficulties in spending the required capital for energy-retrofit projects are another barrier to successful community penetration of energy retrofits. Even if the retrofits are implemented after overcoming all the aforementioned barriers, poor quality of implementation may lead to subpar outcomes [15][11]. Therefore, the contractors should be incentivized to adhere to best practices in retrofitting projects. The points highlighted above indicate that the success of an energy retrofitting project can only be assured by adhering to a corporative strategy between building owners, contractors, government bodies, and all related stakeholders. In order to bring this equilibrium, innovative financial structures, performance contracting methods, and incentive schemes are needed. A comprehensive approach for building energy retrofit planning for residential buildings addressing the gaps mentioned above was not found in the literature. Therefore, this study focussed on investigating potential challenges and opportunities to reduce life cycle emissions of the existing buildings while simultaneously 3 reducing the associated costs via policy instruments and financial models promoting energy retrofits. The economic and environmental performance of different retrofits and retrofit combinations need to be evaluated to develop retrofitting strategies for existing buildings. Traditionally, building owners tend to emphasize the capital cost rather than the net benefits of a given retrofit by considering the potential operational cost-savings compared to the capital cost. On the other hand, even the most advanced building energy efficiency standards such as BCESC only account for the operational energy use. However, embodied emissions can be significantly higher compared to operational emissions for some retrofits [11]. Therefore, this study employs life cycle costing and life cycle emissions assessment procedures to provide a complete picture of the costs and benefits associated with building energy retrofits [16]. Moreover, the performance of building energy retrofits can significantly vary with uncertainties created by weather, occupant behavior, building operational conditions, and macro-economic environment [3]. Therefore, the planning frameworks proposed in this research employed fuzzy logic and Monte Carlo simulation-based options ranking and optimization approaches to handle the uncertainty. Accounting for uncertainties helps to provide a better representation of the reality of the outcomes. A major challenge in finding solutions for problems involving multiple stakeholders is addressing the contradicting priorities proposed by each group. Therefore, multi-criteria decision-making principles and multi-objective optimization principles were employed to find the best financial structures and incentive schemes for energy retrofitting projects while satisfying the contradicting stakeholder expectations [6]. Research motivation The increasing concerns about anthropogenic GHG emissions leading to climate change have resulted in the ongoing environmental impact mitigation initiatives related to the built environment [17][16]. Canada has set various emissions reduction targets at municipal, provincial, and federal levels to support climate change mitigation. Improving the energy performance of the existing residential buildings can significantly contribute towards climate change mitigation as this sector accounts for a significant fraction of the current building stock. Therefore, addressing the barriers and challenges that impede the uptake of existing building retrofitting in the residential sector is critical. 4 In addition to the obvious environmental benefits of reduced energy demand, it can also deliver economic benefits to the occupants due to reduced energy costs, leading to an overall reduction in energy poverty [11]. Moreover, from a regional perspective, decelerating the demand growth in urban centers with the rising population and housing density can benefit utility providers due to the reduced need for having new energy infrastructure in place [11]. Improving the energy performance will enhance the affordability of energy services to the residents while also enhancing occupant wellbeing in Northern climates with high building energy intensities due to climatic conditions such as Canada. While energy retrofitting is highly discussed, building and community level stakeholders such as building owners, occupants, and municipal governments face various challenges in its practical implementation. They are impeded by the lack of expertise in selecting the best retrofitting solutions, regional variability and data uncertainties affecting the decision, lack of access to retrofit planning tools, as well as lack of funding and viable financial models [18][19][20]. The motivation for this research arises from the timely need to bridge the above limitations and facilitate communities in adopting retrofits for existing buildings in an optimal manner. By integrating life cycle thinking and the effect of uncertainties into retrofit planning frameworks, predictions closer to the actual post-retrofit performance of buildings can be obtained. The retrofitting strategies need to go beyond mere technical feasibility to consider the interests of various stakeholder groups and triple bottom line sustainability to be economically viable, environmentally responsible, and socially acceptable. Providing user-friendly decision support frameworks for optimal retrofit selection and workable financial models for partnering in retrofit projects will enhance the potential and the acceptability of retrofitting projects. It will also be beneficial for governments and relevant regulatory and policymaking bodies to have an evidence-based approach to facilitate the development of retrofit policies and incentive programs for the residential sector. Research objectives The main goal of this research is to develop an integrated strategy for planning energy retrofits for existing small residential buildings in Canada. The specific sub-objectives of the research are outlined below. 5 1) Identify challenges and opportunities associated with existing retrofits planning and investment strategies for residential buildings. 2) Evaluate the economic and environmental feasibility of potential building energy retrofits under different regional conditions. 3) Develop a retrofits strategy selection framework under uncertain conditions by considering life cycle environmental and economic performance. 4) Develop an incentive planning approach to promote environmentally friendly and economically viable energy retrofits. 5) Develop an energy performance contracting strategy to support retrofit project implementation. This research proposes a mathematical simulation-based energy retrofits planning process for existing residential buildings. Depending on the application, the algorithms developed under each objective can be used individually or in combination with other objectives. For example, in a community level retrofit policy development exercise, objectives 1 to 4 can be followed in the order presented. On the other hand, when planning a retrofit project for an individual building, objectives 3 and 5 can be employed to identify the best retrofit strategy and develop the implementation plan. The proposed approach was demonstrated using residential buildings located in Canadian communities as case studies. The findings were communicated to the local governments, utility providers, and other stakeholders. Even though the demonstrations were done for Canadian communities, the proposed approach can be applied to any community or province worldwide for retrofit policy development by simply modifying the inputs to match the local conditions. 1.3.1 Research outcomes and deliverables The main deliverable of this research is a life cycle thinking-based comprehensive decision-making approach for energy retrofit planning for small residential buildings under uncertain conditions. Specific deliverables from each sub-objective are discussed below. Literature-based findings from the 1st sub-objective were used to identify the challenges and opportunities associated with the energy retrofits planning process. Retrofit strategy selection, financial incentives planning, ensuring the quality of the project delivery, investment planning, and handling uncertainty were identified as critical challenges for the energy retrofitting projects. 6 As a first step in addressing the identified knowledge gaps, the regional applicability of individual retrofits for different Canadian cities across the country was evaluated under the scope of the 2nd sub-objective, and the findings were published in a Journal Article [11]. This regional evaluation indicated that the most environmentally friendly retrofits do not necessarily provide the best economic performance. Moreover, it was noted that the performance benefits associated with individual retrofits also vary with the other retrofit options implemented with that retrofit. Moreover, a sensitivity analysis revealed that the uncertain operational conditions and economic and environmental parameters significantly affect the effectiveness of the retrofit strategies. Therefore, a life cycle thinking-based multi-objective fuzzy ranking methodology was developed under the 3rd sub-objective to identify the best retrofit strategies accounting for varying stakeholder priorities. Canadian municipalities are currently working on delivering financial incentives to promote eco-friendly retrofits by providing financial incentives. Therefore, under the 4th sub-objective, a multi-objective optimization algorithm was developed to identify the optimal financial incentive levels to promote eco-friendly retrofit strategies. Findings from the third and fourth sub-objectives will be submitted for publication in a peer-reviewed journal [14]. Poor project delivery quality and management issues, lack of capital investments, and performance gaps in the design and operational stages were identified as critical challenges faced by the energy retrofit projects. According to the literature, energy performance contracting (EPC) was a viable solution to address the aforementioned challenges. However, a comprehensive EPC planning methodology for retrofitting small residential buildings was not found in the currently published body of knowledge. Therefore, an EPC planning approach was developed under the 5th sub-objective, and the findings will be submitted for publication in a peer-reviewed journal. Meta language The following specific terms were used in the study to explain the context and key concepts. Small residential buildings: The term “small residential buildings” is used throughout this thesis to refer to single family detached residential houses, which belong to Part-9 of the British Columbia Building Code. Energy retrofits: Building upgrades that impact the energy performance of the building were considered energy retrofits. 7 Energy performance evaluation: Energy use in buildings results from meeting occupant needs such as heating, lighting, and operating other appliances. In order to evaluate the operational costs and emissions of a given building, the energy requirement has to be quantified. This process is known as energy performance evaluation. Sensor-based energy monitoring or building energy simulation tools can be used for energy performance evaluation. The process of replicating or predicting the energy performance of real buildings in a computer-based environment using principles of mathematics, physics, and engineering is called building energy simulation. In this thesis, a simulation-based approach was employed as detailed energy monitoring is not common in existing small residential buildings. Energy performance contract: An agreement made between an energy services company and a building owner to operate a building at a pre-determined energy demand or to provide a pre-determined energy-saving level or an agreement to split the energy-cost-savings based on a pre-agreed percentage is referred to as an energy performance contract. Energy services company: A company that provides services related to energy projects, including design and development, implementation, financing options, and performance guarantees and insurances is called an energy services company. Life cycle thinking: Life cycle thinking is used to quantify and integrate the environmental, economic, and social impacts considering the entire life cycle of a product, system, or process. The embodied environmental impacts of retrofits and the operational impacts of retrofitting were quantified using this approach for a more holistic view. In retrofit assessment, the system boundary was defined from raw material extraction to the end of useful life. The life cycle cost assessment covered the implementation costs, including product cost and installation and the operational energy use reduction in existing residential buildings. Small residential buildings: Buildings classified as Part-9 buildings in the Canadian Building Energy Code were considered as small residential buildings in this study. Thesis organization The thesis is presented with eight chapters comprised of the information related to the literature review, methodology, findings, and conclusions. 8 Chapter 1: This chapter provides information about the background, research gaps, motivations, and deliverables of the proposed energy retrofits planning approach. Chapter 2: This chapter summarizes the research methodology followed in achieving the research objectives. Moreover, this chapter provides a brief overview of the research phases and associated goals. Chapter 3: The third chapter provides a comprehensive literature review on the current state, opportunities, and challenges associated with retrofit planning for small residential buildings. Moreover, the literature review contains the literature related to energy simulation, uncertainty handling, and optimization methods used for energy retrofits-related topics such as financial incentives planning and energy performance contracting. Chapter 4 to 7 contains the detailed information related to the methodology and outcomes of the sub-objectives completed to achieve the overall objective. The contents of each chapter are summarized below. Chapter 4: This chapter presents the methodology and results related to the regional applicability evaluation of energy retrofits. In this evolution, life cycle emissions and cost assessments were conducted considering various cities representing different climate and energy supply conditions across Canada. An eco-efficiency parameter was proposed to classify energy retrofits for policy development purposes. Chapter 5: This chapter discusses the details of the life cycle thinking-based retrofit strategy selection and incentives planning process under uncertain conditions while accounting for varying stakeholder priorities. Energy simulations were employed to evaluate the pre- and post-retrofit energy performance of the case-study building. A fuzzy-based multi-criteria decision-making method was employed to select the best retrofitting strategies. Secondly, a multi-objective optimization algorithm was proposed to assist policymakers in developing financial incentive strategies. The proposed optimization algorithm determines the financial incentive level that needs to be provided to individual energy retrofits to promote the most environmentally friendly retrofit strategies (retrofit combinations) considering the budget limitations and stakeholder priorities. A case-study building located in British Columbia (Canada) was employed to demonstrate the proposed process. 9 Chapter 6: This chapter proposes a profit-sharing strategy for the building owners and energy services companies when initiating energy performance contracts. A Monte-Carlo simulation-based non-linear optimization algorithm was proposed to identify the optimum profit split between the two parties under uncertain conditions, depending on the decision priorities. Moreover, the potential opportunities and challenges for introducing energy performance contracts to small residential buildings were explored. The proposed EPC planning approach was demonstrated using a case study building. Chapter 7: This chapter contains the conclusions, recommendations, originality, and potential areas needing future research. Figure 1 presents the overall flow of the thesis indicating the interlinks between the sub-objectives and the chapter contents. 10 Figure 1 Integration of objectives and thesis organization 11 Chapter 2: Research methodology This thesis aims to support the realization of provincial emission reduction targets and energy affordability by improving the environmental and economic performance of existing residential buildings. Energy savings achieved by implementing energy retrofits in residential buildings translates to cost and emissions savings benefiting the building owners and the environment at large. However, there are limitations in the current building energy retrofit planning approaches, as discussed in section 3.4. Therefore, this study discusses a systematic retrofit selection and investment planning approach to improve the energy performance of the existing residential buildings by overcoming the identified research gaps. The proposed building energy retrofit planning approach was developed stage-by-stage through multiple research phases. Buildings located in different Canadian cities were considered in the case studies to demonstrate the proposed retrofit planning approach. This chapter provides a summary of the overall research methodology followed in the research. Detailed methods and results associated with each sub-objective are discussed in subsequent chapters. Literature review and data collection (Phase: 1) The existing knowledge base was explored to identify the trends and advancements in building energy performance enhancement options and energy retrofit planning. The research articles, government publications, and other publications related to residential building energy retrofits were referred. Publications during the last decade were prioritized to maintain the timeliness of the information. The knowledge gaps related to the retrofit planning process were identified through the literature review. Pros and cons of the techniques used to address the multi-stakeholder expectations and associated uncertainties of the energy retrofits planning problem were investigated. An especial focus was given to retrofit selection under uncertainty, methodologies related to life cycle costing and life cycle assessment, retrofit-related financial incentives planning, and energy performance contracting methods. Moreover, cost and emissions data related to building energy retrofits were gathered from literature, state-of-the-art databases, and online vendors. Detailed findings from the literature review are presented in Chapter 3:. 12 Evaluation of regional applicability of energy retrofits for residential buildings (Phase: 2) In order to identify the effectiveness of a building energy retrofit, it is necessary to compare the post-retrofit performance against the original (base) performance of the building of interest. As the first step, two base buildings and their operational conditions were defined. These buildings were used as representative buildings to reflect Canadian single-family detached houses (SFD). Two energy supply scenarios were defined to reflect the current reality in Canada, i.e., fully electric-powered houses vs. natural gas (NG) heated houses. Commonly used retrofits and the envelope characteristics of the representative SFDs were defined based on statistics and literature. The considered retrofit categories were envelope (wall, window, and roof) modifications, airtightness improvements, space heating system upgrades, hot water system upgrades, minor retrofits (water-efficient faucets and LEDs), and roof-top solar PV systems. The energy performance of different interventions was evaluated through energy simulations. HOT2000 is a commonly used energy simulation software in BC Energy STEP Code evaluations and the official energy rating system of Canada (EnerGuide) for small residential buildings [18][21]. Therefore, HOT2000 was used to simulate the performance of retrofits under different climatic conditions. The energy savings associated with retrofits were used to calculate the operational cost and emissions savings. These values were combined with the capital cost and embodied emissions values to obtain the life cycle cost and emissions attributed to different retrofit strategies. Life cycle emissions (LCE) and LCC data associated with the building energy retrofits were gathered from state-of-the-art life cycle databases, costing databases, online information sources, and literature. Energy savings, energy source emission factors, and embodied emissions were used to calculate the LCE of each retrofit. Similarly, the capital and operational costs incurred during retrofits were used to calculate differential LCC. In the scenario analysis, LCC and LCE were combined using a new eco-efficiency indicator. This indicator was employed to categorize the retrofits based on emission reduction potential and associated costs. After the categorization, the eco-efficiency performance of retrofits was used to identify the retrofits that produce the best environmental performance at a minimal expenditure for each province under varying conditions. A case study employing various regions of Canada 13 was used to demonstrate the above methodology and its applications. Details of the methodology and results of this phase is presented in Chapter 4:. Financial incentives planning and retrofit strategy selection under uncertain conditions (Phase: 3) Embodied emissions, contradicting stakeholder expectations, and the associated uncertainties are often overlooked in retrofitting projects. Therefore, a model was proposed under this research phase to identify the best retrofit packages for residential buildings considering local conditions and associated uncertainties. Firstly, a sensitivity analysis was conducted to identify the key parameters that impact the overall energy performance of a building. The prominent parameters were considered in the energy simulation process to identify the performance variation of a given building under uncertain conditions. Energy performance, life cycle cost, and life cycle emissions associated with different energy retrofitting strategies were represented in the form of fuzzy numbers. Maximizing set and minimizing set method was used to rank the retrofit strategies according to different user priority scenarios. The findings were used in the following research phase to plan the finances associated with the residential energy retrofitting projects. The community adoption of energy retrofits heavily depends on the economic viability and associated economic benefits. However, the retrofit strategies that produce the highest economic benefits do not necessarily produce the highest emissions savings. Therefore, an incentive planning approach for building retrofits was proposed to achieve emission reduction targets by compensating for the compromised economic performance. An optimization algorithm was developed to determine the financial incentive amounts needed to promote top-ranked options under varying decision priorities found in the previous step. The use of the proposed retrofits strategy ranking method and incentive planning algorithm was demonstrated using a case study. The chosen retrofit options and the representative buildings defined in Phase:2 were employed in the case study. The LCC and LCE saving potential of each retrofit were evaluated in a preliminary screening. The goal was to find strategies to reduce LCE associated with the buildings within the given financial resources and constraints for incentivizing. Therefore, the retrofits with no emissions saving potential were not considered. The developed optimization tools and the case study are expected to support the policymakers in planning 14 financial incentives to achieve provincial and federal climate goals. Details of the methodology and results of this phase is presented in Chapter 5:. Model development to assist financial management of energy retrofitting projects (Phase: 4) The success of a building energy retrofit project depends on multiple factors, including funding availability, retrofit selection, and quality of retrofit implementation. Energy performance contracts have been identified as a potential solution for the common issues related to building energy retrofitting, including funding availability, design-performance gap, principal-agent issues, and quality of retrofit implementation [22][15][11]. Nevertheless, identifying the optimal financial structure for a given EPC is a challenging task due to contradicting stakeholder expectations, data uncertainties, and variabilities in operational conditions. Therefore, an energy performance contracting approach was proposed to overcome the said challenges. A Monte-Carlo simulation-based non-linear optimization algorithm was developed to assist in planning energy performance contracts to maximize the benefits to the stakeholders under uncertain conditions. A comprehensive literature review on residential EPCs indicated that the exiting planning models overlook the impact of the equity to loan ratio on the project's cost of capital. Moreover, the need for developing a generalizable EPC planning approach for residential buildings under uncertain conditions was identified [11]. Therefore, an EPC planning model was proposed to overcome the explained knowledge gap. The proposed optimization process for EPC-based retrofitting project planning involves five main steps: retrofit options identification, energy performance simulation, key performance indicator (KPI) identification, and EPC modeling. Retrofit options scenario prioritization has been comprehensively discussed in Phase-3. Therefore, this phase mainly focussed on proposing a Monte-Carlo simulation-based non-linear multi-objective optimization approach to identify the optimal cost-savings guarantee amount and capital contributions split between different financial sources, including owner equity and loans for a given contract period. The proposed approach was demonstrated using a case study building located in Kelowna, BC, Canada. Details of the methodology and results of this phase is presented in Chapter 6:. 15 Chapter 3: Literature review Global warming and climate change mitigation efforts have been initiated worldwide with increasing awareness about potential environmental risks involved. The Intergovernmental Panel on Climate Change (IPCC) emphasized the need for maintaining the global temperature increment below 1.5oC compared to pre-industrial times to avoid future adversities [23]. Buildings are responsible for 12% and 40% of the global greenhouse gas (GHG) emissions and energy use, respectively [24][25]. Therefore, IPCC identified the building sector as a key area that needs attention in order to achieve the proposed emissions reduction targets [23]. Aligning with the global efforts, new constructions in Canada aim to reduce the environmental footprint of the building stock through the advent of innovative standards such as the BC Energy STEP Code (BCESC), thereby support the climate action goals [26]. BCESC has a structured process, clearly defining the performance steps that buildings need to achieve in the construction phase. This approach seems to have promising energy use reduction potential. However, no such energy efficiency standards are developed for existing residential buildings in BC and Canada. Moreover, decision support tools that can evaluate and compare retrofit options considering life cycle economic benefits and environmental impacts are lacking. The global rates of new constructions are recorded to be less than 1% and 10% in average cities and booming areas, respectively [27]. There are 14.1million existing residential buildings in Canada, accounting for 17% of the total GHG emissions of the country [27][28]. There lies tremendous energy and emissions saving potential because these buildings have not been constructed according to stringent energy efficiency guidelines in the construction phase except for the most recent ones. Moreover, the condition of existing buildings in Canada is deteriorating [29], which leads to poor energy performance, higher operational costs, and other negative impacts on the occupants and the environment [30]. Reduced energy bills resulting from energy retrofitting initiatives produce immediate benefits for the building owners and renters. In addition to the energy cost savings, renovations increase property values for building owners, which in turn provides increased tax revenue for the local governments. Moreover, existing building renovations can increase employment opportunities in the construction industry, improve the quality of the built infrastructure, enhance indoor living conditions for the occupants, and support the climate 16 action agendas of federal and provincial governments [31]. Therefore, it is essential to devise energy retrofitting strategies to enhance the energy and environmental performance of existing buildings. Some incentive and rebate schemes have already been implemented in Canada by governmental organizations and non-governmental organizations such as utility providers to promote energy retrofit projects[22]. However, the challenges such as principal-agent problems in rental buildings, inability to gather the required capital investments, awareness issues, and lack of investor confidence are still not adequately resolved [22][15][11]. Innovative financing mechanisms, financial and operational risk mitigation approaches, marketing campaigns, and awareness programs can support addressing the discussed issues in promoting energy efficiency for older buildings. This chapter reviews the literature focussing on the current trends, challenges, and opportunities associated with energy retrofitting projects. Retrofits classification Building energy retrofits can produce emission savings, thus contribute to provincial, national, and international emission reduction targets [16]. However, financial pressures such as high life cycle costs (LCC) and capital costs (CC) associated with some retrofits can reduce the stakeholder acceptance and market penetration of retrofits [32]. Moreover, the lack of a financial justification or a business case for retrofits hinders their widespread acceptance [33]. Therefore, building owners have to be not only guided and educated but also need to be provided with an economic rationale to adopt these interventions. Financial incentives such as low-interest loans, rebates, and subsidies can motivate the stakeholders to support green initiatives [11][34]. In order to identify the opportunities and challenges associated with different retrofitting strategies, retrofits can be categorized into three main groups considering the eco-efficiency performance [11]. • Priority retrofits (PR): Retrofits that can deliver both life cycle emissions (LCE) and LCC savings • Secondary retrofits (SR): Retrofits that deliver LCE savings but no LCC savings • Tertiary retrofits (TS): Retrofits that do not deliver LCE savings Retrofit interventions that require energy source switching from a low emission source to a high emission source fall under tertiary retrofits. The retrofits with higher embodied emissions than the 17 operational emissions savings potential also fall under this group. Nevertheless, it is important to note that some of the retrofits in the TS group can produce LCC savings. Priority retrofits can reduce emissions while simultaneously delivering cost savings to the building owners or occupants [11]. If the payback periods are sensible, the owners can be easily convinced to implement these priority retrofits while simultaneously achieving the government emission reduction initiatives. High capital costs can be a barrier for this type of retrofits, preventing building owners from adopting them [35]. In such situations, a loan scheme can be introduced to assist the building owners in implementing such retrofits [36]. Loans can be repaid as a percentage of the cost-savings similar to an energy performance contract [37], or they could be repaid similar to a standard loan with equal installments. If the loan installment is paid together with the energy bill, it is called an ‘on bill payment’. However, it has been identified that stakeholders are generally reluctant to adopt loan schemes due to the high administrative workload and pre-and post-performance uncertainties [38]. However, the involvement of energy performance contracts and energy services companies (ESCO) have been identified as potential solutions for the said administrative issues. Literature pertaining to EPC planning is discussed in detail under section 3.3. Promoting the options in the secondary retrofits is challenging compared to priority retrofits since there is no direct financial benefit for the building owners due to the inability to produce energy cost savings. In most cases, emissions reduction potential alone is not likely to motivate a building owner to adopt retrofits. Therefore, secondary retrofits can be promoted by financial incentives such as tax rebates or subsidies [39]. Another approach is for the government to implement penalties such as carbon taxes to financially penalize carbon emissions, thus developing a business case for emissions reduction initiatives [40]. However, regionally implemented penalties such as provincial carbon taxes can pose challenges to local industries when competing in the global market due to increased production and operational energy costs [41]. Moreover, introducing penalties can create social predicaments that could result in a backlash against the government-led clean energy and climate change mitigation initiatives [42][43]. Therefore, bringing forward the penalties as the first step can hinder the success of energy retrofit promotion campaigns. When looking at the limitations associated with the penalties, incentivizing the performance 18 improvements seems to be a more practical approach at the initial stages of climate change programs. Retrofit performance evaluation Performance evaluation is a key requirement of the retrofits decision-making process. The “good” or “poor” performance of retrofits depends on the context and the decision priorities. This section discusses the key features of the performance prediction and potential opportunities and challenges to minimize the inaccuracies in the predictions. 3.2.1 Performance indicators selection Complexity and the outcomes of the performance evaluation can significantly vary depending on the chosen performance criteria and evaluation method. Depending on the priorities, single or multiple performance evaluation criteria can be involved in an Existing energy performance evaluation (EPE). Existing EPE methods employ different performance criteria, including energy usage (i.e., thermal energy demand intensity - TEDI, total energy use intensity - TEUI), environmental impacts (i.e., CO2, eq), economic impacts (LCC, operational cost), and social impacts [11][18][44][45]. For example, Energy Star Portfolio Manager (ESPM) and EnerGuide use only one parameter for the EPE. On the other hand, LEED and BREEAM use credit-based scoring systems. In EPE processes involving contradicting benchmarking criteria, performance evaluation is done based on multi-criteria decision-making (MCDM) techniques [45][46]. For example, BREEAM uses a weighted sum to determine the overall sustainability score. In energy planning studies, different MCDM techniques, including the analytical hierarchy process (AHP) [47][48], PROMETHEE [49][50], and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), have been widely used [51]. It is important to note that the MCDM approaches can cater to the contradicting expectations of the stakeholders while one-dimensional decision-making attempts fail to do so [52]. The end goal can be significantly different from one EPE exercise to another. Therefore, identifying the goal of EPE is essential for increasing the utility of the information produced by the evaluation. One or more life cycle stages of the energy system from raw material extraction, manufacturing, transportation, operation, and disposal at end-of-life are considered in calculating the performance scores depending on the goal of the evaluation [14][26]. Data requirements and 19 acquisition frequency and accuracy depend on the life cycle stages considered. Suppose the goal is to find measures to improve the overall economic performance of an energy system. In that case, the costs incurred at different life cycle stages including, capital, operational, and disposal costs, have to be taken into account [53][54][55]. If only the operational phase is considered, the conclusions derived about the economic and environmental performance of energy system components may not be accurate. For example, operational performance data related to building-integrated solar photovoltaic (BIPV) panels and envelope upgrades can be misleading because the highest portion of environmental impacts and costs of these components are accounted for in the early stages, including the material recovery, manufacturing, and disposal phases [18][56]. Therefore, the performance indicators have to be carefully chosen to provide a complete picture of the retrofits. Moreover, comparing the goal of the evaluation against the performance indicators and the outcomes of the EPE approach being used at the beginning can help ensure the availability of all the pertinent information required for the decision-making process followed by the EPE. Some of the most common EPE tools, including Energy Star and EnerGuide, can only produce an energy rating or a score. While the scores produced by EPE tools are helpful to compare or rank a building’s energy performance to its’ previous performances or peers, the use of those scores in retrofit and maintenance strategy identification can be limited [18][57]. Different EPE tools have their strengths and weaknesses. ESPM is a more straightforward data-driven approach that provides an energy rating based on actual data. However, it does not provide reasons for poor energy performance. Therefore, this framework is suitable to trigger the need for interventions, but its use in retrofits and maintenance strategy identification is limited. EnerGuide can be used to compare retrofitting options as it is a simulation-based approach, but lack of consideration given to actual energy use may incur problems in identifying the effect of behavioral trends and occupancy patterns. Errors caused due to negligence on actual energy data apply to all simulation-based approaches. However, calibrating energy models with actual data is a potential approach that can be used to minimize the errors [58]. 3.2.2 Energy performance evaluation The energy savings potential of residential retrofit projects is evaluated based on two approaches, including actual monitoring and deemed savings [18][59]. Deemed savings are subjectively decided by the energy experts involved in the project with previous experience or energy 20 simulation-based predictions [11]. On the other hand, monitoring-based approaches determine the savings based on monitored data. EPE following either of the discussed approaches can become challenging due to the time-consuming nature of energy simulations, high technical literacy requirements, limited data availability and higher cost of data collection, and data uncertainties [55]. The deemed savings approach can be practical when energy experts are directly involved in a renovation project that employs established and well-known retrofits. Expert judgments may not be very effective in capturing the operational uncertainties specific to a given building and may not be able to accurately predict the energy and cost-saving potentials of new energy upgrades in the market. Even simulation-based approaches may pose inaccuracies due to unrealistic underlying assumptions and inaccuracies in input data [60]. ASHRAE energy simulation guidelines for existing buildings can be followed to minimize the above-discussed errors in options comparisons [9][10]. However, predefined generic occupancy and operational patterns from the standards and guidelines can be less effective in predicting the actual energy performance due to case-specific uncertainties. On the plus side, deemed savings approach requires no additional instrumentation, thus saves the instrumentation cost. Minimizing additional costs must be a key consideration in residential retrofit projects as the potential profit margins for ESCOs can be already low in this sector. Building energy use and weather monitoring systems can help overcome the limitations of the predictions made by the deemed savings approach [60][60] in the pre-retrofit stage. This approach uses the energy bills or monitored energy use data for evaluations and requires at least one year's worth of energy data records during the pre-and post-retrofit periods to evaluate the performance [18][63]. It is important to note that collecting this data and accounting for missing utility bills can be quite challenging [63]. Moreover, the energy demand can significantly vary with other factors such as weather and occupant behavior, which are independent of the energy efficiency of the building. Data-driven benchmarking approaches may produce inaccuracies due to the lack of similarity between the building types and differences in interactive parameters such as building envelope, heating ventilation and air-conditioning (HVAC) system type, occupant behavior, and environmental conditions directly related to the energy usage [64][65]. Advanced mathematical techniques such as linear mixed effect model, multi-linear regression, and support vector machines 21 have been used in research studies to reduce the said errors [46]. On a positive note, energy monitoring can allow cost-saving disputes or design-performance gaps in the post-retrofit period [66]. Depending on the responsiveness of the building energy system, real-time monitoring opens up the potential for smart operations [67]. If the instrumentation cost can be justified with additional benefits such as smart operations in addition to performance evaluation, building instrumentation projects make more sense from a financial viewpoint. Evaluating the pre-and post-retrofit energy performance of a given building with minimum instrumentation can avoid redundant expenses. Calibrated energy simulations can be a possible solution that can minimize instrumentation costs. In this approach, the building energy simulations are calibrated considering hourly, monthly, or yearly performance records from the building of interest [58]. Automated energy model calibration methods can potentially minimize the simulation time, thereby the costs involved. In literature, automated energy model calibration techniques [58] and novel approaches such as occupant-based energy use prediction models [68] have been proposed to improve the accuracy of performance predictions to support energy upgrade decisions and performance assessments. In summary, it is fair to say that the accuracy of both data-driven and simulation-based EPE approaches heavily depends on the information inputs. Therefore, the lack of locally applicable energy-use data of existing buildings is a significant challenge for EPE. Affordable energy monitoring and data acquisition mechanisms are needed for improving the effectiveness of building energy-related decision-making. Moreover, global scale databases containing energy-use information for different buildings must be developed to produce up-to-date benchmarks for existing buildings and energy system design guidelines for new constructions. The discussion above confirms the criticality of having accurate data and selecting an appropriate EPE approach matching for a given problem context to effectively plan energy interventions while accounting for the uncertainties created by occupant behavior and weather. 3.2.3 Overcoming the design-performance gap In some energy efficiency projects, anticipated performance improvements in the design stage are not realized in actual operations. This phenomenon is commonly referred to as the design-performance gap [66]. One main reason for this can be the lack of awareness of the occupants on how to achieve the best possible energy savings in the operational stage by following energy-22 conscious behavior and using newly implemented technical solutions in the building [69]. The other main reason is the performance evaluation inaccuracies in the design stage due to prediction errors and data unavailability. Lack of post-retrofit performance data and cost data is identified as a challenge for developing comprehensive retrofit plans and accurately predicting the performance of retrofits [70]. Moreover, Reliable cost data is essential for successfully evaluating retrofit projects [59]. Locally applicable databases that contain information on energy savings, life cycle cost, and building characteristics can help overcome the design-performance gap. The Building Performance Database in the United States is a great example of such an energy performance database [71]. Epistemic and aleatory uncertainties can produce inaccuracies in any performance prediction exercise, heightening the design-performance gap [72]. Behavioral practices, operational parameter changes, and weather are known to introduce uncertainties to the energy performance of buildings [68]. Lack of awareness about energy conservation practices and technology use can result in poor operational performance of buildings. The “soft landing approach” was originally proposed for energy efficiency projects in larger buildings to overcome the performance gap by raising awareness among the building occupants on how to use the upgraded building to achieve the best outcomes [66]. This approach can be adapted for residential energy retrofit projects to raise the awareness of occupants and promote better energy conservation practices. Awareness programs can significantly reduce operational uncertainties resulted from behavioral aspects. However, the operational uncertainties resulting from environmental parameters such as outside temperature and humidity cannot be controlled. Therefore, they have to be incorporated into the decision process. Previous studies on energy planning have used techniques such as fuzzy logic, Mote Carlo simulations, Robust Optimization, Taguchi Orthogonal Array, and Grey Numbers to handle these uncertain conditions [29][76][90][72]. Financial incentives planning for energy retrofits Incentivizing energy retrofits can be done following either a prescriptive or a performance-based approach, and each approach has its positives and negatives [43]. In the prescriptive approach, the incentive is issued when a prescribed retrofit is implemented in a building [74]. In practice, the incentive provider checks a pre-defined document such as the bill of sale and provides the incentive based on that. Prescriptive incentives are easier to implement compared to performance-based 23 incentives [75]. From the receivers’ perspective, this boosts confidence in retrofit investments, as they are confident that they will receive the anticipated incentive once the retrofit is installed. From the incentive issuers’ perspective, this is easy to implement as less administrative work is involved. However, it is important to note that the actual energy and emissions savings during the post-retrofit period are highly dependant on locational parameters, including weather and energy prices [11]. Moreover, it can significantly change due to behavioral practices such as heating and cooling temperature settings and occupancy schedule[22]. Retrofits identified at the provincial or national level may not be suitable for a given municipality or a region due to the variations in climate and other conditions. Therefore, if the prescriptive approach is followed, the effectiveness of retrofits needs to be evaluated based on local operational conditions. A critical challenge associated with the prescriptive approach is the lack of motivation. The users are not encouraged to adopt energy-conscious behavior during the post-retrofit period, as the incentives have already been issued upfront. This challenge can be addressed with awareness programs to a certain extent. In the performance-based approach, incentives are given for achieving a predefined goal or benchmark regarding energy intensities or emissions. The building owner is given the flexibility to reach the goal by applying any retrofit combination rather than being limited to pre-defined options[22]. This flexibility for innovative decision-making is the strength of the performance-based approach. In contrast to the prescriptive approach, the owners are compelled to adopt energy-conscious behavior to reach better performance levels, with the promise of higher incentives with enhanced performance [76]. However, assessing the pre-and post-retrofit performance of an existing building and predicting the future outcomes of design decisions can be pretty challenging when issuing performance-based incentives [77]. Local governments and private sector organizations such as utility companies have only a limited budget to incentivize building energy efficiency. Therefore, identifying the best split of this limited funding among the retrofit incentivizing options is essential to harvest the maximum emission reduction benefits. Moreover, the building level stakeholders need to be given the awareness and tools to determine what retrofit options are most suitable for meeting the performance targets to reap higher incentive benefits. A Compendex database search was done using “residential building, energy retrofits, incentive” as keywords to explore the previous literature related to the topic. This search yielded seventy-24 three research articles. The search result did not include research articles locally applicable to Canada that identify the best retrofit strategies and optimal incentive levels to promote the identified retrofit strategies. In addition to the literature review, building management agencies, utility providers, and local governments were consulted to establish the need for an energy retrofits and incentives planning mechanism. All of the above stakeholder groups agreed that standardized retrofit planning methods and decision support tools are essential to enhance the energy performance and achieve the climate action goals of the existing building sector. Energy performance contracting for energy retrofit projects Energy performance contracting has been identified as an effective way to overcome the design-performance gap and many other issues related to the energy retrofits implementation process. This section reviews the literature related to residential energy performance contracting. A keyword search simultaneously including “residential building” and “energy performance contract” recorded 42 research items in the “Compendex Engineering Village” database. Out of the 42 articles, 37 research articles published after 2000 were considered for the study to maintain the timeliness of the discussion. The articles included ten journal articles, fourteen conference articles, ten conference proceedings, two standards, and a book chapter. A manual screening was done to remove the duplicates and to assess the relevance of the search results to energy performance contracting in residential buildings. The screening selected ten journal articles, four conference articles, and a book chapter. The filtered articles were reviewed in detail to understand the current research landscape around the topic. 3.3.1 Challenges for energy performance contracting Even though EPCs have the potential to promote energy efficiency initiatives in the residential building sector, there are some challenges. This section discusses the challenges associated with EPCs and potential solutions for them in reference to literature. 3.3.1.1 Financing energy retrofit projects Energy retrofit projects include multiple stakeholders, including the building owners, tenants, contractors, ESCOs, and third-party incentive providers such as government bodies and utility providers. Depending on the context, all or some of the stakeholders listed above may be involved in the EPC process. Depending on the project delivery model employed, all these stakeholders 25 may have a role in financing an energy retrofit project. This section discusses the financial challenges specific to different stakeholders and energy retrofitting projects in general. Providing a significant portion of the capital and lack of assets to tie the contracts for extended periods are some of the critical challenges faced by ESCOs when attempting to enter the residential energy efficiency market [70]. Legal bonds to pay off the remaining capital investment using the increased property resale value have been proposed to address the potential challenges created by changing ownership. High transaction costs are another barrier to forming EPCs for residential buildings [78]. These transaction costs can include marketing, documentation, legal fees, and other similar costs. Lack of building owner awareness is also a barrier in reducing the said transaction costs [66]. In addition to awareness issues, budget constraints are a key challenge faced by the building owners. These challenges have been addressed in Europe by means such as government subsidies and low-interest loans. The Russian government has followed a preferential loan scheme to overcome the investment challenges for retrofitting residential building projects [67]. Relatively higher leverage that ESCOs can gain over financial bodies such as banks in securing low-interest loans to overcome initial budget barriers faced by the building owners is discussed in the literature [78]. However, this approach binds ESCOs to long-term financial commitments with third-party financial institutes, thus limit the financial freedom and the financial ability to commit to new projects. Selling the energy performance contracts as assets at a profit to improve the ESCO’s equity was proposed as a potential solution for this issue [79]. In addition to discussed solutions, the retrofit implementation costs can be reduced by increasing the number of retrofit projects allowing the suppliers to the ESCOs to enter into mass-scale production opportunities [79]. However, widening the market depends on multiple external factors, including community awareness and policy support. Innovative methods in communications and marketing have the potential to sufficiently minimize the high transaction costs [80][81]. Using cooperative advertisement strategies among the stakeholder groups, including the government organizations, utility providers, and ESCOs, is a potential approach proposed to reduce transaction costs [59]. The retrofit promotions and incentive programs operated by the government and non-government organizations can communicate the potential benefits of EPCs to the community. From another perspective, ESCO operations and 26 promotion work can simultaneously act as community awareness boosters about the benefits of the retrofits and available incentive programs executed by other organizations [67]. Diversifying the ESCO business model can also increase the profitability of the projects. For example, suppose a renovation company becomes an ESCO at the same time. It allows the company to benefit from the profits obtained during the project delivery and the cost-savings during the operational phase. Moreover, it allows the company to attract more renovation projects as it supports the project financing and operations and maintenance work [68]. Positively using the leverage an ESCO has over its’ staff, a UK-based company promised a shared bonus scheme for the trades workers, which helped improve the quality and efficiency of the work while simultaneously reducing the project costs [66]. It is evident that high-quality project delivery and proper coordination between the contractors can be easily ensured by ESCOs using innovative management strategies. Therefore, additional non-energy benefits such as improved property values and rental price increases, and thermal comfort improvements can be successfully achieved by including an ESCO in a renovation process. 3.3.1.2 Principal-agent issues Even though a viable financing plan can be developed for a given retrofit project, uneven distribution of costs and benefits among the stakeholders can challenge the project's progress. This issue is commonly identified as the principal-agent problem or split-incentive issue. This issue can be more prevalent in rental properties, and in this context, it is generally referred to as the landlord-tenant dilemma [82]. The landlord-tenant dilemma can pose challenges for energy retrofits implementation. When the tenants are responsible for the utility bills, building owners are not motivated to improve the energy efficiency of rental buildings as direct financial benefits for the owners are not clear. Landlords receive indirect benefits from the retrofitting projects in the form of increased property resale value and increased rental income when re-renting. However, these outcomes seem to be less obvious, thus not sufficient to motivate the landlords to invest in retrofits. On the other hand, tenants are not willing to invest in energy upgrades projects in a rented property. Therefore, promoting energy retrofits in rental properties is a challenging task. The green lease approach is identified as a potential agreement between the building owners and tenants to split the incentives of the retrofitting process. In this approach, the building owner invests the initial capital, which is recouped by a pre-agreed rental increase. The tenants will be 27 compensated for the increased rental by the subsequent operational cost-savings due to the increased energy efficiency [82]. This approach seems to be a practical and fair model that can solve the landlord-tenant dilemma. However, the lack of expertise in both parties can create issues in determining a fair amount of rent increase for the post-retrofit period. Determining the fraction of renovation investment that can be directly attributed to energy efficiency improvements and handling the instances where the anticipated energy savings are not achieved could be challenging for non-expert stakeholders (landlords and tenants) involved in this approach. Potentially these issues can be resolved by involving ESCOs and viable EPC models. If an ESCO handles the capital cost in an EPC, the building owner does not have to worry about the capital investment. At the same time, if the ESCO caters to the capital investment of a retrofitting project in a rental building, they can potentially keep a higher profit margin while creating some energy cost savings for the tenants. This setup teams up the ESCO and the tenants/occupants for the common goal of energy and cost-savings, which can help increase the acceptance of energy-conscious behavior among the tenants and the building occupants. In summary, an ESCO can work as an intermediate partner to communicate between stakeholders and provide a savings guarantee for the tenants while finding third-party funding sources for the building owners. 3.3.2 Business models Donal Brown has reviewed the opportunities and challenges of different business models in promoting residential retrofits in the United Kingdom. Brown identifies five main business models commonly used in Europe, including the “Atomized” market model, market intermediation model, one-stop-shop, energy services agreement (ESA), managed energy services agreement (MESA). In the " Atomized " market model, the building owner directly deals with different suppliers, energy auditors, and financial bodies. This approach does not facilitate a pathway for educated planning for a comprehensive retrofit strategy to achieve the expected energy and emissions savings. Moreover, it is mentioned that recruiting individual contractors who are not going to assume responsibility for the resultant energy and cost-savings tends to deliver subpar outcomes and quality of workmanship [78]. In the market intermediation model discussed by Donal Brown, singular retrofits are prescribed following a basic energy audit. Trustworthy intermediary partners such as local governments, government institutions, or established private sector players promote the retrofitting work and 28 intervene in the project management work by means of supplying expertise and incentives [78]. This approach has many similarities to the current state in BC, Canada. Local municipalities, governmental bodies such as CleanBC, and utility providers such as FortisBC and BC Hydro are collaboratively working to increasing the community acceptance of retrofits via awareness programs and incentive schemes. These initiatives have increased the motivation in the local communities to implement energy efficiency retrofits. However, as mentioned by Donal Brown, this approach does not promote comprehensive retrofit planning, thus end up adding individual retrofits to the buildings. Moreover, the continuity of the retrofitting initiatives started by these programs depends heavily on the incentives and backing provided by these agencies, which are heavily dependent on government policy [100] and the national economy. Promoting intermediary agencies such as ESCOs can support in overcoming this challenge as they are motivated to promote energy efficiency and high-quality project outcomes as that is an essential part of their business model. One-stop-shop, ESA, and MESA models discussed by Donal Brown can be mapped into different EPC approaches followed by the ESCOs in other countries. The one-stop-shop approach includes a project management company that completes the required retrofitting requirement by dealing with one contractor or a reputable and closely connected group of sub-contractors [27][78]. This approach may create higher pricing pressure for small-scale subcontractors than directly working with the building owner. Moreover, small-scale subcontractors may not always be the first choices of the ESCOs, which may create potential threats to small-scale local businesses [27]. However, on a positive note, the marketing done by the ESCOs for energy upgrades can create new retrofit project opportunities that can benefit many companies at the same time. One main advantage of this approach is the improved communication between the contractors [78]. This approach addresses the potential issues in retrofit planning, such as oversizing issues, inefficiencies in retrofit investments caused by neglected interactions between energy system components, and difficulties in meeting the close tolerances that determine the profitability and the quality of the outcome [11][66]. The ESCO and the owner are closely tied into a financial commitment in an EPC. Both parties benefit from the energy and cost-savings resulting from an EPC-based renovation project because this approach involves financial models such as first-out and split energy savings [79]. Due to the profit-sharing mechanisms involved, this approach ensures the 29 best possible quality of the project outcomes compared to a regular renovation approach. In ESA, the ESCO guarantees a performance level such as a minimum heating temperature, a monthly volume of hot water supply, and total energy supply for a pre-defined period [27][78]. If unable to meet the promised performance levels during the guarantee period, ESCO is liable for pre-defined service penalties [78]. This method can enhance investor confidence in retrofitting projects. MESA approach provides a complete energy management solution by assuming the responsibility of paying the energy bills and maintenance work on top of the ESA. This approach allows the service provider company to attempt aggressive strategies such as net-zero retrofitting with in-house renewable energy generation given long contractual periods such as 30 years are involved [78][67]. A model with similar characteristics to MESA was discussed in a Swedish study under the name “Chauffage” [67]. Capital investments for the latter three business models could be fully covered by the building owner (owner equity or loans from third-party finance bodies) [67] or the ESCO (company equity or loans from third-party finance bodies) [27][78]. On the other hand, it can be a split investment between the owner and the ESCO [79]. Kupchik et al. and Carlo et al. discussed the financial implications of retrofit projects in reference to the outlay from the building owner and the equity from the ESCO. Moreover, impacts of the operational cost-savings split between the owner and the ESCO on the investment decision were previously investigated [67][79]. Assigning a higher share from the cost-savings split to the ESCO can significantly reduce the payback period, therefore, the contract length. However, if the financial benefit for the client is not significant, the project may not take place [70]. Therefore, determining the split of cost savings is a delicate matter. The current project evaluation models proposed in the literature is discussed in the following section. 3.3.3 Project evaluation Economic feasibility is a key determinant of the successful implementation of energy retrofit projects, thus the EPCs. Few approaches have already been proposed for the financial modeling of EPCs. Toppel et al. proposed a method to assess the risk mitigation potential for energy efficiency insurances and EPCs. The proposed model considered stochastic uncertainties in natural, financial, and technological parameters [20]. Kullapa et al. proposed a Monte Carlo simulation approach for developing EPCs for residential energy retrofits. This study highlights that identifying the 30 economically beneficial retrofits is critical for the success of an EPC. Moreover, the lack of information about the distributions of the uncertain parameters was identified as a challenge for this approach [70]. Carlo et al. proposed a comprehensive financial performance assessment tool to evaluate the effectiveness of investments on an energy performance intervention from the building owners' and Escos' views. This study evaluates first-out and shared saving models for EPCs. Moreover, it investigates the impact of different incentive schemes on EPC performance [79]. However, no emphasis was given to potential uncertainties associated with energy prices and behavioral variations. Carlo et al. focus on finding the range of outlay from the building owner that produces mutual benefits to both parties. However, they do not focus on finding an optimum outlay from the building owners’ perspective. Kuristaps et al. compare the performance of three financial models for building renovations in reference to three actual multi-unit residential buildings in Latvia. However, no financial planning model was proposed in this study. The case study results show that EPCs stand out against the other two realities, including loan financed retrofitting and doing no renovation [83]. These practical examples are essential for improving the confidence of the building owners and investors in EPCs. Research gap This research originated based on the need for streamlined retrofit planning strategies to overcome the issues faced by building developers, building owners and residents, and local governments in identifying the best retrofit solutions and financial models for implementing them. In the present, the focus is mainly on new constructions when developing energy performance standards [11]. This is a major limitation of building-sector emissions mitigation initiatives. It is easier to predict and standardize the performance of new constructions as the building is flexible at the design stage. In contrast, existing buildings have often been constructed at different points in time following different codes and standards, with many differences such as type, applications, age, and standards to which those are built [84]. Age-based deterioration makes it more challenging to assess and predict the energy performance of older buildings. Thus, setting up performance evaluation benchmarks and planning upgrades for existing buildings is challenging [85][86]. However, older buildings account for a higher fraction of the current building stock in many places. The Canadian Infrastructure Report Card predicts that the condition of the country’s building stock is deteriorating [29]. Developing mechanisms that assist performance evaluation, 31 retrofit design, and investment planning for the existing building sector is a challenging but utterly necessary endeavor. The singular focus on operational emissions in the building retrofitting discussion obstructs the holistic vision in emissions mitigation. Embodied emissions are often neglected in the retrofit planning and policy-making process, and life cycle thinking is missing in the cost-benefit analysis [87]. Embodied emissions have been considered in some studies focussed on envelop upgrades [88][89]. However, when it comes to energy upgrades, there are many categories, including, heating system, hot water system, lighting system, and renewable energy systems in addition to the envelop upgrades. Embodied impacts associated with the aforementioned energy system components have been greatly overlooked in the literature. Notably, the life cycle impact data related to these energy system retrofits are lacking even in the state-of-the-art life cycle databases [11]. These limitations can introduce errors into the decision-making process that can hinder the effectiveness of building sector sustainability initiatives in the long run. While operational energy demand reductions and corresponding operational emissions reductions can be achieved, it can lead to an overall increase in life cycle emissions and other environmental damages if the embodied impacts of the retrofits are not carefully considered. Moreover, retrofit performance and macro-environment uncertainties are rarely considered in retrofit strategy planning [11][90]. A major problem in defining performance standards and retrofit planning for existing buildings is the regional variability in building energy use and performance. A “one size fits all” approach cannot be successful in retrofit planning as geographic and climatic conditions, energy supply and prices, and occupant behavior vary significantly with location. In many cases, the available data and information on weather and climate conditions, energy supply and prices, energy use patterns, and occupant behavior are variable, imprecise, vague, or incomplete. Therefore, deterministic approaches used in retrofit planning do not provide a complete picture. It is necessary to incorporate life cycle thinking and uncertainty handling into the retrofit planning process to provide better information and a more holistic perspective to investors and policymakers. Upgrade investments in older buildings is hindered and made complex due to the presence of multiple stakeholder groups with conflicting interests, high capital costs, and a lack of financial justification for the investments [91][92][32][33]. While energy performance contracting and other 32 mechanisms support energy efficiency initiatives in the building sector, those are not commonly used in renovation projects related to small residential buildings. Therefore, the most effective models for administering them are yet to be identified, especially under variable and uncertain conditions. This problem is especially prevalent in the residential building sector, where the control of building development, ownership, and occupancy tends to be under different stakeholders during different stages of the building life cycle. This gives rise to principle agent problems, where the party who spends on the energy performance upgrades and the party who benefits from it are not necessarily the same [16]. Due to the lack of methods that can analyze this investment decision and provide solutions that satisfy the interests of the various stakeholder groups, existing building retrofits are slow in the uptake [11][18][19]. Financial incentives (such as subsidies, tax breaks, and rebates) and other fiscal instruments (such as loan schemes and grants) can support the penetration of energy retrofits in the existing building sector [93]. These can also assist in mitigating the above-mentioned principal-agent problems. Financial incentives are currently used in some Canadian jurisdictions to motivate the adoption of retrofits. However, optimal ways to allocate the limited funding available for incentive programs need to be further investigated to support the government climate action targets. In addition, identifying the best incentive mechanisms under given conditions is an issue that has not been sufficiently addressed. The primary question that needs to be addressed in developing community-level retrofitting policies and incentive programs is how to optimize incentives allocation while still meeting the conflicting stakeholder needs [94][95]. For this, accurate performance prediction methods, life cycle thinking-based performance assessment techniques, and uncertainty-integrated financial planning approaches are necessary. Summary The overall research gaps discussed above, which stemmed from the literature review were used to formulate the research questions and the research gaps addressed in the thesis. Specific research gaps related to building energy retrofits planning, identified from the reviewed literature under sections 3.1 and 3.2, are summarized below. ➢ The life cycle thinking aspect was missing in the current energy retrofit planning discussion, and embodied emissions are often neglected in favor of a singular focus on operational emissions [87]. 33 ➢ The uncertainties related to building retrofits and the external environment have been largely neglected in the past studies in retrofit strategy planning [11][90]. ➢ Comprehensive models that can simultaneously account for contradicting stakeholder priorities, and uncertainties have not been developed for making holistic investment decisions on building energy retrofit incentives [11][18][19]. The work carried out to address the aforementioned research gaps is presented in Chapter 4: and Chapter 5:. Findings from the reviewed literature in section 3.3 indicated that a comprehensive investment evaluation model accounting for investment uncertainties and opportunity costs of investments for the multiple stakeholders involved was not developed. Moreover, the previous studies did not model the possibility of securing third-party loans and associated variations in the cost of capital. Therefore, a need for developing a model to identify the best EPC financial parameters for a given contract period was identified. The literature review indicated the following research gaps in EPC planning. ➢ Considering the impact of financial parameters on the cost of capital of the project. ➢ Accounting for multi-stakeholder perspectives in the contract formulation process. ➢ Employing an energy simulation-based approach for EPC planning to allow location-independent usage. ➢ Identifying the optimal profit splits and capital contributions for different contract periods under uncertain conditions. The work carried out to address the aforementioned research gaps is presented in Chapter 6:. This study attempts to develop a streamlined and holistic strategy for retrofitting existing small residential buildings by addressing the identified research gaps. 34 Chapter 4: Evaluating the regional applicability of residential building energy retrofits The contents presented in this chapter have been published in the Elsevier journal Energy and Buildings, under the title “To retrofit or not? Making residential energy performance upgrade decisions through life cycle thinking” [11]. Background In the residential building sector of the Northern latitudes, the energy use is dominated by space and water heating needs. Over 80% of the Canadian residential energy consumption is used up for space conditioning and hot water [96]. Other end-uses are space cooling, lighting, and appliances [18]. The current building energy codes and construction standards aim to develop residences with better energy performance through changes to the building envelope and the energy system. The Province of British Columbia, Canada, has lately introduced BC Energy Step Code, the most innovative beyond-code energy efficiency standard for newly constructed buildings in North America [26]. While new construction can greatly benefit from improved designs and materials in reducing energy demand as per these standards, the existing building stock still faces many problems related to energy efficiency. Poor energy performance in existing buildings could be due to age-related deterioration or less-stringent Code requirements followed in the building constructions in the past [29]. Retrofitting is widely used to improve the energy performance of existing buildings by upgrading the building asset condition. These retrofits may focus on improving energy efficiency, reducing active energy demand, or integrating localized renewable energy sources to a building [97]. Thus, retrofits are expected to deliver economic and environmental benefits and better social outcomes by reducing energy use and maintenance requirements, mitigating emissions and other environmental impacts, and lowering the energy costs for the building users. They can also improve the interior environment of a building for the occupants [98]. While retrofitting existing buildings is a popular choice, the related decision-making is not simple when trying to attain optimal outcomes. A multitude of retrofit options including, building envelope components, space heating and hot water systems, lighting, appliances and equipment, and renewable energy systems, are available to choose from [30][99]. Retrofit-based energy 35 upgrade projects for buildings are of three types, behavioral interventions, minor retrofits, and deep retrofits. In the first approach, energy savings are achieved by improvements in building operations and maintenance (O&M) procedures. This type of upgrade can deliver up to 15% energy savings [100]. Behavioral research is needed to investigate the associated benefits and ensure community penetration of this kind of energy performance upgrades. Minor retrofits are the most cost effective and low risk type of retrofit, and it can deliver around 15-45% energy savings according to literature [101][100]. Deep retrofits involve large-scale changes such as building envelope and mechanical systems retrofits. The energy savings in deep retrofit projects can be above 45% [100]. Residential buildings are the third highest energy end-use sector [102]. Single family residences account for 70% of the residential building energy use in Canada approximately [103]. Moreover, single-family detached (SFD) houses account for 53.6% of the private dwellings of Canada [104]. Therefore, energy efficiency upgrades applied to these buildings can produce significant environmental and economic benefits. In order to select the best options for retrofitting these houses, the current state of buildings and the technological interventions available for improving them need to be studied. The energy upgrade choices for residential buildings can vary, depending on the regional and seasonal variations in weather and climate, macro-environmental conditions, and the involvement of multiple stakeholder groups [105]. In addition, the economic and emissions benefits produced by the upgrades greatly depend on the heating energy supply of the building (i.e., electricity or natural gas) and the regional electricity generation mix (i.e., renewable energy sources or fossil fuel sources). Some regions already have a relatively “clean” grid with a low emissions factor due to the high fraction of renewables in the grid mix [93]. Some energy upgrades may not make environmental and economic sense from a life cycle perspective in such locations due to higher embodied emissions than operational emissions savings [30]. Financial incentives and life cycle assessments are necessary to evaluate the actual costs and benefits of retrofitting projects, performance contracts, and energy policy initiatives [106][107]. Building owners, managers, and other decision-makers involved in the energy sector need to ensure that the best energy upgrade pathways are selected with the limited available funding to ensure the best outcomes considering economics, environment, and stakeholder expectations. 36 Figure 2 provides an overview of the stakeholders and intended benefits of energy upgrades at the building level. While the building owners and occupants appear to be the directly involved group at first glance, the nexus revolves around various other aspects impacting more stakeholder groups, including utilities, government, and policy developers. Reductions in energy use, emissions, and operational costs (i.e., energy bills) hold different importance to each group. The interests of the stakeholder groups in each of these benefit categories vary depending on their group goals. Energy policy development needs to capture all of these diverse interests and factors to be successful. Figure 2 Expected benefits and stakeholders involved in energy upgrade initiatives While it is clear that it is the overall footprint of an upgrade that matters when assessing whether sustainability goals are truly met through building energy upgrades, embodied emissions have been termed as the “blind spot of the buildings industry” [87]. Studies, reports, and policies largely neglect this aspect when discussing the pros and cons of building energy upgrades. This is an area where the application of life cycle thinking can bring in definite benefits. Some studies have focused on life cycle assessment of building energy retrofits for various building types in the past [108]. Out of these, some studies focus exclusively on quantifying the life cycle environmental impacts of various retrofitting options [109][110][30]. Others have considered the life cycle assessment and life cycle costing aspects simultaneously, and yet these are more limited in number [111][112]. The studies that have considered adopting life cycle thinking into the retrofit decision making process have mainly considered the cost aspects without adequately addressing the need 37 for the streamlined strategy that combines life cycle environmental impacts alongside the economic considerations [3][105][113]. There are no recent studies that combine the life cycle environmental and economic consideration in the retrofit decision making process with a regional policy perspective for Canada in the published literature. This gap makes it difficult for the policy makers and the building industry to make holistic decisions and plans in their climate action initiatives and strategizing, leading to the above observation about embodied emissions acting as a “blind spot”. The next issue is the often-adopted “one-size-fits-all” view in retrofit planning and policymaking. In reality, retrofit planning is specific to the building types and condition, application, geography, climate, macro-environmental conditions, and available funding [114]. Another major limitation in the current approaches towards is that the conflicting stakeholder requirements and decision priorities have not been considered together in determining regional policies. Previous work does not provide much insight to the effects of emissions reduction initiatives on economic outcomes through a multi-stakeholder lens and a detailed regional policy discussion is missing in the published literature [18][19]. A comprehensive literature review was conducted on retrofit and energy upgrade planning for the residential sector. Findings indicated that holistic methods and models that assist life cycle thinking-based dynamic decision-making and planning of residential energy performance enhancements lack in the current body of knowledge. A database search conducted in the Compendex Engineering Village returned only ~30 records that fit the criteria of “building energy retrofits, life cycle, decision-making”. Reviewing the relevant literature revealed that current planning models commonly neglect simultaneous consideration of multi-stakeholder expectations as well as the effects of life cycle environmental and economic impacts, geography and climate, macro-economic variables, and regional energy supply scenarios on the energy upgrade decisions. Without addressing these limitations, it is difficult to ensure that the best decisions are being made when selecting upgrades at the building level and developing policies, incentives, and standards to support building energy efficiency. Thus, the main goal of this chapter was to quantify the life cycle cost and emissions impacts associated with residential energy retrofits considering regional conditions and multi-stakeholder perspectives. Moreover, this chapter demonstrates the use of holistic life cycle thinking in 38 evaluating energy retrofit projects in Canada. The energy performance of various upgrade options applicable for single-family detached housing was measured through a simulation-based approach. An eco-efficiency-based method was developed to categorize feasible residential energy upgrades considering life cycle costs and life cycle emissions reduction potential in different regions. In order to demonstrate the use of the proposed evaluation method, a case study was conducted encompassing seven Canadian provinces. The most suitable energy upgrade options for a given region under varying conditions (i.e., climate, building energy systems, energy prices, and provincial energy mix) were identified based on the case study results. The patterns in the distribution of benefits across stakeholder groups in various locations for a specific energy upgrade investment were also discussed to understand the policy implications. The findings will be useful not only for building owners and occupants but also for policy developers and other decision-makers interested in demand-side management in buildings. Methodology Evaluating all possible retrofitting options before implementing the interventions ensures the selection of the most beneficial retrofits for a given building [101]. Therefore, common upgrades used in Canadian residential buildings were considered in the study. The detailed methodology of the research is discussed below. Figure 3 shows the overall flow of work and information inputs of the study. Figure 3 Overview of methodology 39 In order to identify the effectiveness of a building energy retrofit, it is necessary to compare the post-retrofit performance against the original (base) performance. Therefore, as the first step, two base buildings and their operational conditions were defined. Here, the most common characteristics of the envelopes of Canadian single-family detached houses were defined based on average statistics. For this envelope, two energy supply system scenarios were defined to reflect the current reality in Canada, i.e., fully electric-powered houses vs. natural gas (NG) heated houses. Then, the commonly used energy retrofits were identified based on existing standards and literature. The considered retrofit categories were envelope (wall, window, and roof) modifications, airtightness improvements, space heating system upgrades, hot water system upgrades, minor retrofits (water-efficient faucets and LEDs), and solar PV installation. The energy performance of different interventions was evaluated through energy simulations. HOT2000 is a commonly used energy simulation software in BC Energy STEP Code evaluations and the official energy rating system of Canada (EnerGuide) for small residential buildings [18][21]. Therefore, HOT2000 was used to simulate the performance of retrofits under varying climatic conditions. The energy savings associated with retrofits were used to calculate the operational cost and emissions savings. These values were combined with the capital cost and embodied emissions values to obtain the life cycle cost and emissions attributed to different retrofit strategies. Life cycle impact (LCI) and life cycle cost (LCC) data associated with the building energy retrofits were gathered from state-of-the-art life cycle databases, costing databases, online information sources, and literature. Energy savings, grid emission factors, and embodied emissions were used to calculate the LCE of each retrofit. Similarly, the capital and operational costs incurred during retrofits were used to calculate differential LCC. LCC and LCE were combined to define a new eco-efficiency indicator to categorize the retrofits based on emission reduction potential and associated costs. After the categorization, the eco-efficiency performance was used to prioritize the retrofits that produce the best environmental performance at a minimal expenditure for each province under varying conditions. A case study approach covering various regions of Canada was used to demonstrate the above methodology and its applications. 40 4.2.1 Quantifying life cycle emissions ISO 14040 standard was followed in data collection for life cycle inventory development [115]. Goal and the scope of the life cycle assessment (LCA) for retrofits were defined based on literature. The data collection process for the LCA was defined to match the goal and scope. The goal of the study was to conduct a comparative life cycle emissions assessment for commonly used retrofits in Canadian residential buildings. The system boundary in the assessment was defined as cradle-to-grave for retrofits for a holistic assessment that combines embodied emissions with the operational. Thus, the impacts caused by the raw material production and manufacture related to retrofits were not neglected in the decision-making. The assessment of the energy supply had a gate-to-gate system boundary to reflect the energy use phase that is relevant for the study. The focus of the study was limited to emissions as this is the main interest in Canada at present due to the highly ambitious emissions reduction targets [16]. This analysis considers multiple life cycle phases of retrofits, including the construction, manufacture, installation, operations, and disposal. Life cycle inventory data related to different building components that create impacts on energy performance were quantified as per ISO 14040. The life cycle impact assessment (LCIA) process is comprehensively explained in the following sections. 4.2.1.1 Embodied emissions A mid-point impact assessment method was selected to enhance accuracy in the life cycle impact results [116]. The life cycle impact data of the building retrofits from cradle-to-gate was retrieved from the state-of-the-art life cycle assessment software, databases, and EPDs (Environmental Product Declarations). Operational emissions were mainly attributed to the emissions produced by operational energy use. Only direct disposal emissions were accounted for in the study to maintain a standardized disposal emissions calculation process for all Canadian provinces. SimaPro software package and Ecoinvent database were mainly involved in the life cycle assessment. Emission data from literature and EPDs were used in cases where life cycle impact data was unavailable in the Ecoinvent database. The lack of embodied emissions data related to building energy retrofits in Canada was identified as a major challenge to conduct LCA during the study. 41 In previous studies, global warming potential was involved as the representative impact category in climate-related decision-making [90][56]. Therefore, it was used in this study as the environmental performance indicator. It was quantified in the form of an equivalent amount of CO2 in kilograms (kgCO2e) to represent emissions associated with building materials and components [117][118]. Building envelope and fenestration: Building envelope is commonly defined as the separator between the indoor and the outdoor environment or the separator between the conditioned and the unconditioned space of a building [119]. It comprises multiple surfaces, including roof, ceiling, walls (interior, exterior), fenestration, and flooring [118]. When determining the embodied emissions, it is essential to know the amount of material used in a given envelope component. Surface areas and cross-sectional areas of the selected envelope retrofits were used to calculate the material volumes in a given envelope retrofit [118]. Material cross-sectional areas, surface areas, mass densities, and embodied emissions per unit mass/area of envelope retrofits were used to obtain the embodied emissions as demonstrated in Equation 1. 𝑬𝑬𝑪 =∑𝒘𝒊 ∗ 𝒆𝒆𝒊𝒏𝒊 Equation 1 Where, 𝐸𝐸𝐶 –Embodied energy of a given component of the building envelop 𝑤𝑖 –Mass or area of ith material or component used in the given component 𝑒𝑒𝑖 –Embodied emissions associated with a unit mass or unit area of ith material or component Energy system components: Heating system, air conditioning system (optional), hot water system, building envelope, and the baseloads are the main factors that determine the energy performance of a residential building. Equipment capacity, material composition, and the life cycle impact data were used to determine the embodied emissions of each retrofit of interest. Equation 2 can be employed to calculate the embodied emissions of the components of which the system level LCE data are not available. 𝑬𝒊 = ∑𝑴𝒋𝒊 ∗ 𝒇𝒋𝒊 Equation 2 42 Where, 𝐸𝑖 – Embodied emissions of the jth constituent material in component “i” (kg CO2e) 𝑀𝑗𝑖 – Mass of the jth constituent material in component “i” (kg) 𝑓𝑗𝑖 – Embodied emissions of jth constituent material in component “i” (kg CO2e/kg of material) The 𝑀𝑗𝑖 values have to be calculated considering the system-specific information such as area, geometry, and material types. 4.2.1.2 Operational emissions Operational emissions due to energy use are responsible for the highest portion of the environmental footprint during a building life cycle [18]. Therefore, operational emission savings achieved by introducing retrofit scenarios compared to the base building were calculated using𝑶𝑬𝒊=(𝑬𝑬𝑩 − 𝑬𝑬𝑹,𝒊) ∗ 𝑬𝑭𝑬 + (𝑵𝑮𝑬𝑩 −𝑵𝑮𝑬𝑹,𝒊)∗𝑬𝑭𝑵𝑮 Equation 3. 𝑶𝑬𝒊 = (𝑬𝑬𝑩 − 𝑬𝑬𝑹,𝒊) ∗ 𝑬𝑭𝑬 + (𝑵𝑮𝑬𝑩 −𝑵𝑮𝑬𝑹,𝒊) ∗ 𝑬𝑭𝑵𝑮 Equation 3 Where, 𝑂𝐸𝑖 – Operational emissions saving of the ith system configuration (kg CO2e) 𝐸𝐸𝐵 – Electrical energy consumption of the base building (kWh) 𝐸𝐸𝑅,𝑖 – Electrical energy consumption of the ith system configuration (kWh) 𝐸𝐹𝐸 – Emission factor of the local electrical grid (kgCO2e/kWh) 𝑁𝐺𝐸𝐵 – NG energy consumption of the base building (GJ) 𝑁𝐺𝐸𝑅,𝑖 – NG energy consumption of the ith system configuration (GJ) 𝑁𝐺𝐹𝐸 – Emission factor of NG (kgCO2e/GJ) In addition to the emissions due to energy use, direct emissions from refrigerant leakages were calculated using the following equation [120]. 𝑳𝑬𝑯𝑷 = 𝒏 ∗ 𝑳 ∗ 𝒎 ∗ 𝑮𝑾𝑷 43 Equation 4 Where, 𝐿𝐸𝐻𝑃 – Leakage emissions of the heat pump (kg CO2e) 𝑛 – Equipment lifetime (year) 𝐿 – Annual leakage rate (%) 𝑚 – Refrigerant charge (kg) 𝐺𝑊𝑃 – Global warming potential (kgCO2e/kg refrigerant) 4.2.1.3 Direct disposal emissions Calculating the exact disposal emission are challenging due to data availability issues and varied waste management practices across the provinces. The disposal phase includes direct emissions due to gas releases, waste processing emissions, and transportation emissions. The following equation can calculate direct disposal emissions related to refrigerants released from heat pumps [120]. 𝑫𝑬𝑯𝑷,𝑹 = 𝑳𝒅𝒆𝒎𝒐𝒍𝒊𝒕𝒊𝒐𝒏 ∗ 𝒎 ∗ 𝑮𝑾𝑷 Equation 5 Where, 𝐷𝐸𝐻𝑃,𝑅 – Refrigerant related emissions at the disposal (kgCO2e) 𝐿𝑑𝑒𝑚𝑜𝑙𝑖𝑡𝑖𝑜𝑛 – Refrigerant losses during demolition (%) The transportation and landfill emissions at the disposal of building retrofits are not significant compared to embodied and operational emissions in building energy retrofits. Thus these emissions have not been considered in many previous studies [30]. Therefore, only direct emissions were accounted for from the disposal stage, considering the higher-level discussion planned in the study. However, if higher accuracy of the results is imperative, it is recommended to calculate the transportation and landfill emissions associated with each retrofit. 4.2.2 Life cycle cost assessment A key requirement for the success of any project is the availability of required funds. However, Canada Green Building Council has identified the lack of investor confidence as a critical barrier 44 faced by building energy efficiency projects [15]. Life cycle costing is a valuable tool to provide decision-makers with a comprehensive understanding of an investment. Therefore, the LCC of different retrofitting strategies was used to identify the associated economic benefits. Life cycle cost can be calculated as a combination of capital cost, operational cost, and disposal cost [121]. Capital cost is a combination of the cost of equipment and installation. In this study, the RSMeans Building Construction Costs database and literature were referred to identify the capital costs of the identified retrofits. Initial costs (IC) associated with envelope and energy system upgrades can be calculated by Equation 6 and Equation 7, respectively. 𝑰𝑪𝒆𝒏𝒗𝒆𝒍𝒐𝒑,𝒊 =∑𝑰𝑪𝒋,𝒊𝟑𝒋=𝟏× 𝑨𝒋 Equation 6 Where, 𝐼𝐶𝑒𝑛𝑣𝑒𝑙𝑜𝑝,𝑖 – Initial cost of the envelope of the ith strategy 𝐴(𝑊𝑎𝑙𝑙/𝑅𝑜𝑜𝑓/𝑊𝑖𝑛𝑑𝑜𝑤) – Area of the envelope component 𝐼𝐶𝑗,𝑖– Per unit area initial cost to implement the modification proposed under ith system retrofit strategy for jth envelope component (wall/ roof/ windows) 𝑰𝑪𝑬𝒏𝒆𝒓𝒈𝒚𝑺𝒚𝒔𝒕𝒆𝒎,𝒊 =∑𝐈𝐂𝐄𝒌,𝒊𝒏𝒌=𝟏 Equation 7 Where, 𝐼𝐶𝐸𝑛𝑒𝑟𝑔𝑦𝑆𝑦𝑠𝑡𝑒𝑚,𝑖 – Initial cost of ith system retrofit strategy 𝐼𝐶𝐸𝑘,𝑖 – Initial cost of kth energy system component proposed under ith system retrofit strategy 𝑛 – number of energy system components considered for retrofitting The total initial cost of a given retrofitting strategy accounts for costs for implementing building envelope and energy system modifications. 45 𝑰𝑪𝑻𝒐𝒕𝒂𝒍,𝒊 = 𝑰𝑪𝒆𝒏𝒗𝒆𝒍𝒐𝒑,𝒊 + 𝑰𝑪𝑬𝒏𝒆𝒓𝒈𝒚𝑺𝒚𝒔𝒕𝒆𝒎,𝒊 Equation 8 Where, 𝐼𝐶𝑇𝑜𝑡𝑎𝑙,𝑖 – Total initial cost of the ith retrofit strategy The operational cost of a retrofit has three main components, including operational energy cost, maintenance costs, and replacement costs. Reliable maintenance cost data figures associated with different retrofits were not found in the literature. On the other hand, the maintenance costs of residential energy system components are significantly lower compared to replacement costs and the cost of energy use. Therefore, only energy cost savings were considered under the operational costs. The energy cost-savings and replacement costs were calculated in comparison to the base (existing condition) building using building energy simulations. Energy simulation results were fed into 𝑨𝑶𝑺𝒊 = 𝑬𝑺𝑬,𝒊 × 𝑪𝑬,𝒌𝑾𝒉 + 𝑬𝑺𝑵𝑮,𝒊 × 𝑪𝑵𝑮,𝒌𝑾𝒉 Equation 9 to determine the operational cost-savings of a given retrofit strategy. 𝑨𝑶𝑺𝒊 = 𝑬𝑺𝑬,𝒊 × 𝑪𝑬,𝒌𝑾𝒉 + 𝑬𝑺𝑵𝑮,𝒊 × 𝑪𝑵𝑮,𝒌𝑾𝒉 Equation 9 Where, 𝐴𝑂𝑆𝑖 – Annual operational cost-saving achieved by the ith retrofitting strategy 𝐸𝑆𝐸,𝑖 – Electrical energy saving achieved by the ith retrofitting strategy 𝐸𝑆𝑁𝐺,𝑖 – NG saving achieved by the ith retrofitting strategy 𝐶𝐸,𝑘𝑊ℎ – Average cost per kWh of electricity 𝐶𝑁𝐺,𝑘𝑊ℎ – Average cost per kWh of electricity It was assumed that the disposal cost is a one-time cost that occurred at the end-of-life of a given component. Assuming the construction waste is dumped into a landfill, the cost incurred at the landfill and the cost of transport were used to calculate the disposal cost. 𝑫𝑪𝒋 = 𝑾𝒋𝒗 × (𝑪𝑳𝑭 +𝑫𝑫 ∗ 𝑪𝑻) Equation 10 46 Where, 𝐷𝐶𝑗 – Disposal cost of the jth component 𝑊𝑗𝑣 – Amount of waste (kg) produced by the jth component 𝐷𝐷 – The double distance between the landfill and the building (km) 𝐶𝐿𝐹 – Landfill cost per kg of waste 𝐶𝑇 – The cost of transportation per km LCC was calculated considering the initial, operational, replacement, and disposal costs. 𝑳𝑪𝑪𝒊 = (𝑰𝑪𝒊 − 𝑨𝑶𝑺𝒊 ((𝟏 + 𝒓)𝒕 − 𝟏𝒓(𝟏 + 𝒓)𝒕) +∑∑𝑹𝑪𝒋 +𝑫𝑪𝒋(𝟏 + 𝒓)𝒕𝒄,𝒋,𝒌𝒎𝒌=𝟏𝒏𝒋=𝟏) Equation 11 Where, 𝑳𝑪𝑪𝒊 – LCC of ith retrofit strategy 𝒓 – Discount rate 𝒕 – Project period 𝒕𝒄,𝒋,𝒌 – Time at which the jth component is replaced for the kth time 𝒏 – Number of retrofits considered 𝒎 – Number of replacements during the project period 4.2.3 Performance evaluation and options prioritization Eco-efficiency parameters (EEP) are defined in the literature as a sustainability parameter relating “the environmental performance of a product system to its product system value” [122]. In previous studies, eco-efficiency parameters have been used to evaluate energy system planning and hybrid energy system designs to study the combined effect of environmental impacts and the system performance [35][36][125]. It is reasonable to assume that the energy retrofit decisions are mainly driven by the concurrent expectations of economic benefits and climate change mitigation. Therefore, an eco-efficiency parameter was used to evaluate and cluster the retrofits based on combined environmental and economic performance. Annualized emissions and LCE have been commonly used as the environmental parameter of EEP in literature [35][36][125]. Therefore, in 47 this study LCE (kilograms of CO2 eq.) of a given retrofit intervention was selected as the environmental parameter. The LCC and IC were selected to represent the economic aspect of retrofits similar to energy planning studies in literature [125][73][126]. The said parameters were combined to calculate the eco-efficiency of retrofit strategies as shown in Equation 12. 𝑬𝑬𝑷 = { |𝑳𝑪𝑬|𝑰𝑪; 𝒊𝒇 𝑳𝑪𝑬 < 𝟎 𝑨𝑵𝑫 𝑳𝑪𝑪 ≤ 𝟎−|𝑳𝑪𝑬|𝑳𝑪𝑪 ; 𝒊𝒇 𝑳𝑪𝑬 < 𝟎 𝑨𝑵𝑫 𝑳𝑪𝑪 > 𝟎𝟎; 𝒊𝒇 𝑳𝑪𝑬 ≥ 𝟎 } Equation 12 If EEP for a given retrofit is greater than zero, that retrofit can reduce both LCE and LCC. These options were named “primary retrofits”. Retrofits falling into this group are indicated using positive numbers in the results section of this chapter (i.e., Table 6) and ranked within the group based on eco-efficiency performance values. The best performance is denoted by 1, and the performance progressively reduces with the increasing rank values. The options with negative EEP are classified as “secondary retrofits”. These retrofits have the potential to reduce LCE but at a financial cost. The retrofits that fall under this group are indicated in negative numbers in the results section (i.e., Table 6) and ranked according to their eco-efficiency performance (i.e., -1 is the best rank in this category, and performance decreases as the absolute value of the negative number increases). If EEP is zero, that means the retrofit has no emission reduction potential and is indicated by zeros in the results section. This set of options are clustered as “tertiary retrofits”. Retrofit Evaluation for Canada The developed method is demonstrated for different provinces and climate conditions across Canada, with 13 cities spanning seven provinces. The cities were selected to reflect the variations in the energy supply conditions, prices, and emissions factors in Canadian cities. 4.3.1 Regional climate conditions National Energy Code of Canada for Buildings (NECB) and BC Energy Step Code (BCESC) define performance targets for buildings by categorizing the cities under six different climate zones 48 based on the number of heating degree days (HDDs) [127]. However, the ENERGY STAR rating system categorizes the geographic locations into three bins, including Zone-1 (less than 3500 HDD), Zone-2 (less than 6000 HDD and greater than or equal to 3500 HDD), and Zone-3 (greater than or equal to 6000 HDD) [128]. ENERGY STAR classification is at a lower granularity compared to NECB and BCESC. Therefore, climate classification proposed by ENERGY STAR was adopted for this study to keep the number of regions (cities) representing a province at a manageable number while making sure the research goals are achieved. A general description of the considered cities in the evaluation is indicated in Table 1. Annual average HDD, design cooling dry bulb temperature, design cooling wet bulb temperature, design heating dry bulb temperature, and f-value of Solar Index are listed in Table 1 for each city. Table 1 Characteristics of the cities considered in the study Prov. City Annual HDD Design Cooling DB Temperature (oC) Design Cooling WB Temperature (oC) Design Heating DB Temperature (oC) f-Value Solar Index Grid Emissions (gCO2 /kWh) AB Fort McMurray 62,500 280 188 -380 9 790 Calgary 50,000 280 172 -300 11 BC Prince George 47,200 280 180 -320 9 12.9 Vancouver 28,250 280 195 -70 10 MB Thompson 76,000 270 192 -400 9 3.4 Winnipeg 56,700 300 229 -330 11 NL Saint John's 48,000 240 197 -150 9 32 ON Kapuskasing 62,500 290 206 -340 10 40 Toronto 35,200 310 230 -200 11 QC Montreal 42,000 300 229 -230 11 1.2 SK Prince Albert 61,000 280 210 -370 11 660 Regina 56,000 310 212 -340 11 4.3.2 Base building definition BC Energy Step Code Metrics research considers six main archetypes, including MURBs (10units), Row house (6units), quadplex, large single-family detached (SFD) houses, medium SFD, and small SFD to represent Part-9 buildings of the BCESC [127]. The research findings 49 indicate that large SFDs show higher energy savings potential with energy efficiency enhancement attempts than small SFDs [127]. This chapter aims to investigate the effectiveness of retrofitting strategies under varying weather conditions and local factors such as energy prices and grid emission-factor. Therefore, a medium SFD was selected to investigate the potential costs and benefits of retrofitting the SFD houses while keeping the number of energy simulations at a manageable number. However, the common archetypes in a given community can be modeled for better accuracy in local policy planning initiatives. NRCan defines a 2551ft2 two-story house with a basement as a medium SFD [127]. However, the average living area of Canadian residential buildings is 1792ft2 [129]. Therefore, a medium SFD design that has a living area closer to the Canadian average was employed for the evaluation. A 1862ft2 two-storied building was selected as the base building for the study. Window to wall ratio of a typical Canadian SFD varies from 17% to 22% [130]. Therefore, the WWR of the selected housing design was set to be within the range (18.6%). The windows were assumed to be standard single pane windows, while the insulation layers used in the wall and the ceiling to be R-10. Houses are typically categorized under three infiltration levels, namely airtight (<5ACH@50), moderate (5-10ACH@50), and leaky (>10ACH@50) [131]. The base building was considered to be an old, moderately leaky house with a 7.5ACH@50 infiltration value. The ventilation requirement specified by the BC Building Code for small residential buildings with 2-3 rooms and a floor area ranging between 140–280m2 is 28L/s [132]. This value was adopted for the simulation as the considered house meets the said characteristics. Approximately 80% of the residential energy use is attributed to space heating (63%) and water heating (19%) [133]. Over 75% of Canadian residential space heating energy demand is covered by electricity (25-39%) and NG (50%) [134][133]. The remainder is covered by heating oil, wood, and other sources [134]. A building powered by electricity (Base Building-1 or BB-1) and a building with NG heating (space and hot water heating) were considered in the study to represent the common Canadian residential energy supply situations. The most common space heating system used in Canada was NG furnaces (57%), while electric baseboards (27%) were the second most used option by 2011 [134]. However, it is important to note that heating equipment and fuel types greatly vary over Canadian provinces. Electric baseboards and an NG furnace with a 78% AFUE rating (to resemble the performance of an old furnace) [135] were considered for space 50 heating in BB-1 and BB-2, respectively, considering their common usage. The hot water energy demand of over 94% of Canadian residences is catered by two energy sources, electricity (47%) and NG (47%) [136]. Conventional storage tank water heaters are the most common type of hot water system [137]. Therefore, a conventional tank water heater powered by electricity and NG was considered in BB-1 and BB-2, respectively [137]. Generally, the energy factor (EF) of storage tank water heaters varies between 0.53 to 0.70. Therefore, a water heater with an energy factor closer to the lower bound of the considered EF range (0.55) was chosen. The water heater was considered to be powered by electricity and NG in BB-1 and BB-2, respectively. Table 2 contains the summary envelope, space conditioning, and water heating system characteristics of the base buildings. Table 2 Base building characteristics Heating Hot Water Window Wall Ceiling Infiltration Ventilation BB-1 NG Furnace (78% AFUE) Conventional Tank (NG) (EF=0.5543) Single Pane R10 R10 7.5 ACH @50Pa 28 L/s BB-2 Electric Baseboard Conventional Tank (Electric) (EF=0.5543) The electrical appliances, lighting loads, occupancy, and hot water consumption were defined based on Version 15.1 of the EnerGuide rating system [138] and the statistics from NRCan. Major domestic electrical appliances include clothes-dryers (916 kWh/year), clothes-washers (197 kWh/year), dishwashers (260 kWh/year), and refrigerators (565 kWh/year). These appliance loads add up to 1938 kWh/year [138]. Therefore, the daily electrical appliance load was calculated to be 5.31kWh/day following the standard values from EnerGuide, assuming 365days per year. According to NRCan, the major electrical appliance load has varied from 14.15 kWh/day to 7.12 kWh/day from 1990 to 2016 [139]. It was decided that the average of the NRCan statistics (10.68 kWh/day) can better represent the reality of the old buildings compared to the values from EnerGuide. As the focus is on performance enhancements of old buildings, the NRCan average was used as the appliance load in the energy simulations. 51 According to NRCan, the ratio of the emissions between major appliances and minor appliances (other electric loads), including TVs, laptops, and hairdryers, varied from 5.24% to 1.31%, with an average ratio of 2.74% from 1990 to 2016 [140]. Assuming the ratio of the emissions is approximately equal to the energy use ratio, the average energy use of minor appliances was calculated to be 0.29 kWh/day. Incandescent bulbs were the common lighting option of Canadian households by 2011 [134]. Therefore, it was assumed that none of the lighting fixtures were CFLs or LEDs for the base buildings. Thus the energy use of lighting was assumed to be 2.6 kWh/day following the reference values provided by EnerGuide for houses that have installed LED or CFL not more than 25% of the total installed lighting capacity [138]. Average exterior energy use is mainly due to lighting. However, a standard value for the above is not prescribed in the EnerGuide standard. The exterior energy use of a medium SFD was assumed to be negligible compared to interior loads. A report from 2002 declares that the hot water use of a Canadian household is 247 liters/day [136]. As this study focuses on old buildings, this statistic was adopted in the simulations. The Vancouver energy modeling guideline recommends using two occupants for the first bedroom and adding one for each additional bedroom for energy simulations [141]. Therefore, three occupants with 50%-time inside the building were considered in this study. Discussed information is summarized in Table 3. Table 3 Baseload and occupancy characteristics Electrical Appliances Minor (other) electrical appliance Lighting Average exterior energy use Hot water load Occupancy 10.68 kWh/day 0.29 kWh/day 2.6 kWh/day Negligible 247 liters/day 3 x (50%) 4.3.3 Retrofit options identification In general, even though non-heating energy end-uses such as lighting and appliances only account for less than 20% of the total energy use, upgrading them is less intrusive and inexpensive than the envelope, HVAC, and hot water system upgrades [134]. According to EnerGuide, converting the lighting system to LEDs can reduce the energy use for lighting to 0.6 kWh/day [138]. Fifty hot water liters per day can be saved by introducing low-flow shower heads, low-flow bathroom faucets, clothes washers, and dishwashers [138]. As the lifetime of a LED varies between 20,000 to 50,000 hours, the average lifetime was assumed to be 35,000 hours in the assessment. Moreover, 52 assuming 6-7 hours of daily use, the time to replacement was determined to be approximately 15 years. These two options were investigated to study the impact of baseload modifications on the overall energy performance of buildings. Airtightness improvements can be identified as a low-cost option that can significantly reduce building energy use. It is reasonable to achieve an airtightness improvement in the range of 20-30% for an existing building, and the higher bound of the said range can be achieved in leaky buildings [142]. Therefore, a 30% airtightness improvement of the building was considered making the new natural air infiltration rate of the house 5.0 ACH @50Pa. NECB recommends minimum insulation levels for walls ranging from R-18 to R-31 for new constructions based on climate zones. Similarly, the recommended roof insulation range is R-25 to R-40. The maximum R-value of the ranges discussed above was selected for each envelope component for all the Canadian cities being studied to make all the investments and performance of wall and roof insulation enhancements comparable. However, it is recommended to investigate the sensitivity of different R-values to energy and economic savings in future research. When practically conducting the wall insulation upgrade, it is not economical to rip out the dry wall unless a major wall renovation is due. Therefore, the usage of injection foam (i.e., fiberglass, polystyrene injection foam) enables the injection of the insulation material through the holes made in the dry wall, which can be easily sealed and painted after the upgrade without completely dismantling the wall. The injection foam is supposed to have better performance in blocking air infiltrations through the wall than the blown fiberglass. However, performance data, including embodied emissions and capital costs, are not readily available for injection foam. Therefore, the blown-in fiberglass was involved in this study. The blown-in fiberglass is known to have R-values from 2.2-2.9 per inch [143]. Therefore, achieving R-31 would require a 10.7” minimum wall thickness, while R-18 calls for a 6.2” wall cavity under ideal conditions. In an ideal situation, the wall cavity will be uniformly filled with the blown-in fiberglass insulation allowing no thermal bridges to form. Thus, ensuring the maximum insulation of blown fiberglass is achieved. Wall cavity thickness is a potential constraint to consider when planning retrofits for a specific house in practice. However, in this study, it was assumed that there was enough room inside the wall to achieve the intended R-value. Electric baseboards have an efficiency of around 100% [144] and old NG furnaces have efficiencies in the range of 56-70% [145]. However, heat pumps (HP) can produce a higher heating 53 output than the input energy due to coefficient of performance (COP) values exceeding 1. It is important to note that HPs are not allowed to be installed alone in the HOT2000 package and buildings in colder climates. Therefore, the option of adding a heat pump to work parallel with the baseboards was considered. HPs make a robust case from an energy and emissions saving standpoint. However, when it comes to emission reduction potential, the choice of transitioning from NG to electricity will depend on the emission factors of a given region. For an example, in Vancouver the emission factor of grid electricity (0.011 kgCO2e/kWh) is very low compared to NG (0.177-0.185 kgCO2e/kWh) [141] while the emission factor for grid electricity in Alberta is 0.77 kgCO2e [146]. In the discussed scenario, unarguably, it is a straightforward decision for Vancouver to promote HPs with a vision of operational emissions reduction, while it is not the same for a city in Alberta. Therefore, a high efficiency (95%) condensing NG furnace with a 95% AFUE rating was considered as a heating system upgrade option to investigate the discussed matter. The efficiency of the NG furnace was selected following the ENERGY STAR requirements [135]. It is important to note that hot air ducts need to be installed in the house when transferring from electrical baseboards to a central HP or a furnace. However, when transferring between the HP and furnace options, there is a possibility of potentially using the existing ductwork. The average cost of ducting in the US is approximately 2,000USD for 60ft Aluminum ductwork with five vents [147]. This cost value was converted to the Canadian average by using the RSMeans cost factors. Instantaneous water heaters are known for higher EFs. Therefore, an instantaneous electronic ignition condensing hot water system (EF=0.83) operated with NG was considered as a retrofitting option. However, most tankless water heaters powered with electricity cannot meet the domestic hot water demand alone [137]. Therefore, using an ENERGY STAR-rated electric heat pump hot water system (EF=1.901) was considered as a retrofit option. It is important to note that new piping is needed for venting when transforming from an electric hot water system to an NG-based system. In addition to demand-side management options discussed above, building-integrated solar PV systems are getting popular among Canadians. Therefore, a roof-top solar PV array was considered as another retrofit option. Despite relatively higher prices than the Polycrystalline panels, Monocrystalline solar panels were selected due to higher efficiency [148]. According to literature, approximately 40% of the roof area can be utilized for laying solar PV panels [149]. Therefore, 54 the solar array was spread in a 69.2m2 area following the roof slope (28.443o) at an azimuth of 0 degrees. A summary of the retrofit options considered in the study is presented in Table 4. Table 4 Retrofitting options summary Heating Hot Water Window Wall Ceiling Infiltration Solar PV Lighting Plumbing Retrofits Electric Baseboard and HP (12kW, COP~2.5) HP HW system (EF=1.901%) Double Pane R31 @ 7" blown fiberglass R40 @11” batt 5.0 ACH @50Pa 69.2 m2, 28.443o Slope, 0o Azimuth LED Efficient Faucets NG Gas Furnace with 95% efficiency Instantaneous, condensing HW System (EF=83%) Lifetime 25 50 years 25 years 15 years 4.3.4 Scenario Formation and performance assessment The retrofit options considered under each building component are listed in Table 5. It is important to note that all these retrofit options were applied to both BB-1 and BB-2 and evaluated under varying climatic and energy supply conditions across Canada. Table 5 Retrofit scenarios Component Scenario Envelope tightness Air Tightness Improvement (ATI) Windows Double pane windows Wall R31 Blown-in Fiberglass Roof R40 Fiberglass Batt Space heating and hot water system (HWS) Electric Heat Pump (COP2.5) and HP HW system (EF=1.901%) High-efficiency gas furnace (95% AFUE) and Instantaneous, condensing HW System (EF=83%) Lighting system LED Water outlets Water-Saving Faucets Renewable integration Solar PV integration Area: 69.2 m2, Slope: 28.443o, Azimuth: 0o 55 The energy and cost-saving potentials of different retrofitting strategies at different locations around Canada were evaluated using the HOT2000 energy simulator. The simulation results were combined with the financial parameters to obtain the environmental and financial performance of different retrofits. Appendix 2 lists the grid emissions factors, unit energy costs for electricity and NG, and the heating degree days (HDD) for each province and city. Appendix 3 summarizes the capital costs and the quantities of the retrofits. Appendix 4 lists the embodied emissions of the retrofits. Detailed numerical results from the simulations and calculations are presented in 0. The operational energy cost for a particular scenario was estimated employing the energy simulator using the unit energy prices of the province as an input. The applicable discount rate was taken as 3% in the case study, considering a 5% inflation and 2% interest rate [90]. The residential building life was assumed to be 50 years [90]. Results and discussion There was a significant difference in the electrical grid emission factors among some provinces while some had comparable values. Therefore, BC, MB, QC, NL, and ON were clustered into one group, and SK and AB were put into a separate cluster considering the respective grid emission factors . This classification was helpful to identify the common trends followed by the provinces with similar grid energy supply conditions. These two groups were named “Green group” and “Brown group” in the coming sections. As seen in 0, the emissions factors of the Green group range from 1.2 to 40 kgCO2,eq per MWh, indicating relatively “cleaner” electricity grids with low emission factors compared to NG (177-185 kgCO2e/MWh). All these provinces have electricity grids dominated by hydropower, while the ON grid is dominated by nuclear energy. SK and AB have much higher emissions factors ranging from 650 to 800 kgCO2/MWh due to the dominance of fossil fuels in electricity generation. All the provinces discussed above are equipped with NG pipe networks except NL. Different retrofit types deliver emissions and cost-savings at different scales, as observed in Figure 4, Figure 5, and Appendix 5. Therefore, retrofits were discussed in clusters, namely space heating, water heating, envelope, minor retrofits, and solar PV upgrades. A snapshot of the life cycle emissions and differential life cycle cost performance of retrofits quantified through the analysis is presented in Figure 4. Most of the retrofits applied on BB-1 (heated with electricity before 56 upgrade) produce a negative differential life cycle cost (LCC), while the opposite outcome occurs for BB-2 (heated with NG before upgrade). Green group provinces show an increase in life cycle emissions (LCE) when moving from electricity to NG. In contrast, the environmental impacts of this transition are opposite for the Brown group provinces. Nevertheless, moving away from electricity reduces LCC in any province considered. Figure 4 Retrofit performance variation among Canadian cities in BB-1 57 Figure 5 Retrofit performance variation among Canadian cities in BB-2 The performances of retrofit clusters are discussed in the coming sections. The provinces are listed in the order of ascending emission factors in all the performance figures presented. It is important to note that the electricity prices are always higher than the NG prices when a unit of energy is costed on a comparable basis. All the performance graphs show the variation of retrofit performance (LCE, LCC, avoided emissions per dollar investment) among the provinces. The ranges indicated under each province show the performance variation when moving from one climate zone to another in each province. The detailed information about the suitability of different retrofit types by province is provided in Appendix 6. 4.4.1 Prioritizing energy upgrades with varying priorities Different stakeholders define the desired building energy performance according to their priorities. In general, government agencies such as federal and provincial governments are more concerned about climate change mitigation, while the homeowners are more interested in seeing cost-savings. This section discusses the suitability of each retrofit for the two representative buildings considered in the study under the varying regional conditions and stakeholder priorities. 58 4.4.1.1 Eco-efficiency Performance Government agencies and utility providers support the homeowners by bringing in financial justifications to the energy retrofits through incentive and rebate programs with a vision of reducing GHG emissions. This section evaluates the retrofits based on the eco-efficiency performance with a bias towards the priorities of federal and provincial level climate policymakers. Table 6 contains the eco-efficiency ranks determined from the eco-efficiency scores presented in Appendix 7. The Priority and Secondary retrofits are indicated in positive and negative numbers, respectively (important: the positive and negative signs were only used to differentiate the two retrofit groups and the signs do not contain a numerical meaning). Natural gas is not used in residential buildings in NL. Therefore, BB-2 case is not applicable to NL and greyed out in the table. Details of the EEP calculation and interpretation methodology is explained in section 4.2.3. 59 Table 6 Canadian eco-efficiency rank of retrofits QC MB BC NL ON SK AB Retrofit Montreal Thompson Winnipeg Prince George Vancouver St. John's Kapuskasing Toronto Prince Albert Regina Calgary Fort McMurray BB1 Air Tightness 89 75 77 65 72 58 61 68 3 2 12 4 Ceiling 0 91 88 83 79 69 65 72 49 50 52 44 All Windows 0 0 0 87 81 76 71 74 57 57 64 57 Wall 0 0 0 86 80 74 72 73 58 58 65 58 Water Eff. Faucet 0 0 0 74 71 62 63 69 7 4 13 10 Heating System (HP) 0 0 0 82 78 63 67 70 54 52 46 52 Hot Water Unit (HP) 0 0 0 0 0 73 69 71 48 48 44 43 Efficient Furnace (NG) 0 0 0 0 0 0 0 0 1 1 3 1 Instant. HWU (NG) 0 0 0 0 0 0 0 0 6 3 7 5 LED 0 0 0 0 0 -83 -86 -84 -49 -58 -51 -50 Solar PV 0 0 0 0 0 -84 -81 -81 -3 -1 -38 -48 BB2 Air Tightness -7 23 28 38 52 30 50 29 29 47 39 Ceiling -57 -9 -21 55 -47 -3 -47 -54 -62 -46 -13 All Windows -78 -47 -52 -46 -70 -60 -74 -66 -70 -74 -62 Wall -79 -51 -55 -48 -71 -61 -75 -67 -71 -75 -64 Water Eff. Faucet -58 -42 -40 -5 -5 -41 -44 -63 -68 -55 -52 Heating System (HP) -82 -66 -53 -68 -69 -80 -80 -76 0 0 0 Hot Water Unit (HP) -73 -55 -49 -59 -62 -76 -77 0 0 0 0 Efficient Furnace (NG) -8 13 20 22 42 17 38 9 7 37 14 Instant. HWU (NG) -55 -30 -22 -16 -19 -35 -41 -61 -65 -62 -49 LED 0 0 0 0 0 -89 -84 -49 -58 -51 -50 Solar PV 0 0 0 -82 0 -82 -82 -4 -2 -44 -53 The provinces presented in Table 6 are listed in ascending order of the grid emission factor. Here, the priority retrofits are mostly concentrated in the top part of the table. Notably, 82% percent of the retrofits proposed for BB-1 in AB and SK are priority retrofits. Moreover, all the priority retrofits proposed for BB-1 in AB and SK fall in the first quartile of the retrofit population 60 considered. Therefore, SFDs equipped with electric space heating and HWUs in these two provinces should be prioritized in federal attempts to improve the environmental performance of SFDs in Canada. Out of the retrofits proposed for BB-1 houses in NL and ON, 63.5% are priority retrofits, while the same for BC is 54.5%. Most of the retrofits proposed for BB-1 in QC and MB do not have emission reduction potential. Air tightness is a common priority retrofit for these two provinces. The low emission reduction potential of retrofits applied on BB-1 in QC and MB can be attributed to their low grid emission factors. NL is not considered in the BB-2 retrofits discussion as the province does not use NG for residential applications. Except for Montreal, air tightness improvements and efficient NG furnaces are priority retrofits for all BB-2 houses across the provinces. It is important to note that the said two retrofits fall among the best 50% of the priority retrofits. Except for solar PV and LED, all other retrofits fall under secondary retrofits for BB-2 in all the Green provinces. Retrofits implemented in BB-2 except for electrical upgrades on space heating system and HWU fall under secondary retrofits in Brown Group. In summary, air tightness can be identified as the most robust retrofit capable of producing positive eco-efficiency performance in most of the locations considered in the study ranging up to 140 kgCO2e/CAD, irrespective of the base-building type considered. However, NG Furnace upgrades can avoid over 300 kgCO2e over the life cycle per dollar invested when implemented in suitable locations. 4.4.1.2 Economic performance Generally, for residential building occupants, the main driver towards retrofits is cost-savings. Therefore, this section looks into the financial performance of retrofits considering the LCC savings and simple payback period. Table 7 presents the LCC performance of retrofits with less than ten years of simple payback period. Retrofit options having higher payback periods than ten years and the options that do not produce any cost savings are greyed out in the table. 61 Table 7 Life cycle cost-savings of retrofits (values are given in thousands) Province / City QC MB BC NL ON SK AB Base Retrofit Montreal Thompson Winnipeg Prince George Vancouver St. John's Kapuskasing Toronto Prince Albert Regina Calgary Fort McMurray BB1 Air Tightness 10k 12k 9k 16k 7k 18k 24k 13k 25k 24k 14k 18k All Windows 19k 24k 24k 19k Ceiling 7k 15k 7k 11k 17k 10k 18k 15k 10k 14k Wall 8k 8k 7k Efficient Furnace (NG) 100k 96k 69k 152k 68k N/A 210k 117k 229k 202k 122k 171k Heating System (HP) 10k 30k 39k 34k 18k 24k 15k 19k 16k 12k Hot Water Unit (HP) 4k 9k 9k 7k 9k 9k 10k 10k 7k 7k Instant. HWU (NG) 11k 6k 7k 13k 12k 12k 11k 14k 14k 15k 16k Water Efficient Faucet 1k 2k 2k 2k 2k 3k 2k 2k 4k 4k 3k 3k BB2 Air Tightness 2k 3k 3k 2k Efficient Furnace (NG) 3k 2k 5k 2k 4k 2k 1k 2k 3k According to Table 7, heating system and HWU upgrades and water-efficient faucets (WEF) satisfy the abovementioned conditions. Retrofits applied on BB-1 have a higher cost-saving potential compared to BB-2. Introducing efficient NG furnaces to electrically heated houses produce notably higher cost savings. The capital cost of this retrofit is approximately 3,000CAD, and the payback period varies between 0.3-1.1 years across provinces under given conditions. Therefore, converting from electric baseboard heating to NG Furnace is a financially lucrative option in any province. However, this financial benefit comes at an environmental cost in Green provinces. Therefore, governments should develop incentives and policy schemes to regulate this transformation without totally burdening the building occupants. Airtightness improvements in electrically heated houses stand out among other retrofits. In some provinces, window replacements show comparable economic performance with airtightness improvements. However, the capital cost of air tightness improvements is around 10% of the window replacement capital in the considered scenario. Moreover, the embodied emissions associated with window upgrades are relatively higher. Therefore, air tightness improvement is a clear winner among the two. Contradicting outcomes from the economic and environmental analyses show the need for 62 comprehensive energy retrofit evaluation mechanisms and policy tools to reduce the environmental footprint of Canadian buildings. 4.4.2 Provincial opportunities and challenges All the cost bearers of a given retrofit strategy throughout its life cycle should be considered when developing incentive schemes for retrofitting projects. The expectations and stakeholder groups depicted in Figure 2 come into play in this complex decision-making scenario. Federal, provincial, and municipal governments are primarily interested in the interventions that can induce maximum emissions reductions to support the ongoing climate action targets. Thus, the secondary retrofits identified in the study may also be of interest to the climate action policymakers in addition to the priority retrofits. However, priority retrofits are the easiest to penetrate communities as there is the potential for LCC savings while producing emission reductions. Government can promote this kind of retrofits through public awareness programs to inform how the residents of a community can help climate change action while saving money. If the high initial cost becomes a challenge, the governments can support the implementation through loan schemes. Even though Secondary retrofits fail to produce cost savings, they are still worth being considered under climate action programs due to emission reduction potential. However, the cost barrier has to be addressed with incentives or rebate programs to ensure the acceptance of the Secondary retrofits in the community. Moreover, the question “who bears the additional costs?” needs to be carefully addressed. Promoting electricity use to heat homes instead of NG in BC is an initiative that reduces emissions while increasing operational energy costs. However, increasing operational energy costs pose an additional economic burden to the occupants creating potential affordability issues. As seen above, NG heating is the uncontested winner in terms of energy affordability. Installing an efficient NG furnace can deliver significantly higher cost savings than any other retrofit applied to electric-heated houses. Considering the higher cost-saving potential of NG heating systems, more research on novel technologies such as carbon-capturing at the building level is timely to bring its environmental performance up to the mark. If the high initial cost becomes a challenge for new technologies, then the governments can support the implementation through loan schemes. The key findings of the study on the overall verdict regarding the regional and conditional suitability of different retrofit options can be summarized as follows. 63 Air Tightness: This intervention brings positive economic and environmental outcomes across all provinces for residences with electric heating. However, the eco-efficiency performance increases when the emissions factors are higher (i.e., energy supply is “dirtier”). Even for residences with natural gas heating, airtightness improvements can deliver positive outcomes in terms of eco-efficiency. Heating System (Heat pump): Heating systems upgrades with heat pumps bring environmental and economic benefits simultaneously in most places in residences with electric heating. However, there are minimal or zero emissions benefits from this upgrade in locations with very low emissions factors in energy supply. Moreover, only emissions benefits can be obtained by replacing the conventional natural gas heating systems with heat pumps, as the operating energy costs increase when the cheaper natural gas supply is replaced with higher priced electricity required to operate the heat pumps. In fact, in locations with very high emissions factors in the grid electricity (such as Alberta), natural gas heating in fact leads to less emissions compared to electric heating, thus making heat pump retrofitting pointless. Wall: This intervention shows trends mostly similar to those of efficient heating systems with heat pumps in the case of electric heated houses, as it reduces the heating load on the whole. In locations with higher natural gas prices (i.e., BC and ON), it can also deliver economic benefits for natural gas heated houses in terms of energy cost savings. Double pane windows and ceilings: These bring only emissions benefits in natural gas heated houses due to the very high capital investment requirement (as the energy cost saving is so low with the cheap natural gas supply so as to make it relatively uneconomical). However, in electric heated houses, these can bring both emissions and cost savings, since electricity price is higher than that of NG across Canada. Hot Water Unit (HP): These do not deliver emissions savings for electric heated houses in locations with lower grid emission factors, with the embodied emissions being higher than the emissions of the avoided energy use. However, they can deliver emissions benefits for natural gas heated houses in the same locations. It produces emission savings for houses with both electrical and natural gas hot-water systems in places with high emissions electricity grids. This happens due to the same reasons as when using heat pumps for space heating. 64 Instant HWU (NG) and condensing NG furnace: These can simultaneously deliver economic and emissions benefits for electric heated houses in locations with very high grid emissions factors. In other locations with lower grid emissions factors, these interventions do not lead to emissions savings in electric heated houses. In natural gas heated houses located in colder climates with higher heating loads, increasing the efficiency of HWU and furnace can deliver cost savings in addition to emissions savings. Solar PV: This does not deliver much emissions benefits in regions with a clean grid electricity supply, as the grid is already “low-emission and renewable” due to high levels of hydro power in the grid mix. However, solar PV should be particularly promoted in provinces with very high grid emissions factors, such as AB and SK. Water efficient faucets and LED: While efficient faucets can deliver economic benefits in the electric heated houses located in provinces with high emissions factors, LEDs have no such economic benefits. However, both interventions can deliver emissions benefits under both natural gas and electric heating scenarios in such provinces with high emissions energy grids. While energy cost savings can assist in reducing energy poverty and increasing the affordability of energy services for residents, the ownership structures can pose challenges to the community penetration of retrofits. For example, the home ownership rate is slightly below 70% in Canada [150]. This means approximately 30% of the homes are rentals. Thus, “who pays for the energy upgrades and additional operational costs?” becomes a critical question to be answered when promoting the transition towards expensive cleaner fuels and the adoption of secondary retrofits, as the homeowners will not realize any economic benefit. On the other hand, the demand reduction achieved via energy efficiency upgrades is of more interest to the utility providers. Therefore, they contribute to energy efficiency initiatives with the goal of reducing excess residential demand growth, which in turn requires additional investments by utilities on power generation infrastructure. In order to serve the interests of all these parties in an equitable manner, viable financial models for cost-benefit sharing and incentivizing are necessary. In addition, the federal and provincial governments should take a careful look at what investments bring the best returns considering the available funds, climate, and macro-environmental conditions in a region. Planning incentives and residential energy efficiency programs at provincial levels can benefit from the findings detailed above. For example, suppose the economic barrier on transitioning from 65 NG to electric heat pumps in BC is addressed with financial incentives. In that case, it becomes a financially feasible option for the residential users while supporting the federal and provincial climate action programs. The emission reduction potential of upgrades per dollar invested in the Brown Group is almost ten times higher than the Green Group when it comes to the hot-water units. Therefore, federal incentives can be directed towards the Brown Group to achieve higher emission reductions to reach immediate climate goals. However, in the long run, the Brown Group should consider means to reduce their electricity grid emissions. In order to realize higher environmental benefits, the efforts and resources of provincial governments should focus on upgrading the NG heated houses over the electrically heated houses in the “Green” provinces. In contrast, the “Brown” provinces should focus on retrofitting the electric space heaters and HWUs of the houses with NG heating systems from both environmental and economic perspectives. It is important to note that the heating system retrofits can stay long periods spanning more than 20 years. Therefore, the governments of Brown provinces should weigh in the economic and environmental benefits of transitioning towards cleaner electricity grids over the retrofitting options. Considering the trends observed in the “Green” provinces, it is fair to say that the envelope upgrades on electrically heated houses can mainly produce economic benefits. In contrast, the primary motivation behind the envelope upgrades on houses heated with NG is environmental performance enhancement. Therefore, climate action programs in these provinces can incentivize building envelope upgrades for houses heated with NG. For the electrically heated houses in the “Brown” provinces, the envelope upgrades and improved airtightness can deliver significantly higher cost and emissions savings. Therefore, federal priority should be given to envelop upgrades for electrically heated houses in these provinces as it benefits the end-users and the climate mitigation plan. Moreover, the Brown provinces should promote air-tightness improvements considering its high emission reduction potential per dollar invested. It is important to note that both heating system and envelope upgrades can reduce space heating energy consumption. Therefore, in all provinces, careful attention needs to be given before prioritizing envelope upgrades or heating system upgrades for applications such as financial incentive schemes. In this matter, eco-efficiency parameters defined in this study can be taken as an indicator for incentives planning. Minor retrofits do not deliver notable benefits in most cases. 66 However, they remain a low-hanging fruit across all provinces that can generally produce cost and emissions savings due to low capital investment requirements. Roof-top solar PV does not deliver significant economic benefits anywhere in the selected locations under the conditions considered in the study. However, as solar PV can be positive in terms of emissions reduction in certain locations (i.e., SK and AB), this is one area worth investigating in incentives planning. Currently, BC has taken many building energy efficiency improvement initiatives, including the BC Energy STEP Code focusing on the new constructions and rebate programs focussing on existing building renovations. There are focused rebates to motivate the residential users to upgrade to electric heat pumps from older space heating systems powered by electricity or NG. Moreover, utility providers have introduced rebate programs to motivate upgrading the existing NG space heating system with ENERGY STAR-rated efficient NG space heaters. In addition to space heating system upgrades, other means such as envelop upgrades can be used to reduce residential energy demand. In addition to the above initiatives, two other areas of critical interest are inter-province power-sharing agreements and the carbon economy. Some of the environmental impacts associated with buildings located in Brown provinces can be mitigated by purchasing energy from neighboring provinces with “cleaner” electricity grids. This could help the Brown group to reduce building-related emissions while the energy efficiency culture and energy retrofits penetrate the society. On the other hand, this provides more significant meaning to the energy efficiency initiatives in greener provinces like BC. Carbon credit trading can be used as a mechanism to pay for incentive programs. The advantage of carbon credit trading is that it can be used to distribute the benefits across provinces and among different stakeholder groups involved in different stages of a building, including construction, retrofitting, and occupancy. Energy performance contracts and multi-stage incentive programs that target stakeholder groups at different levels can help solve principal-agent problems. For example, the building construction industry can be offered a set of incentives for building efficient homes, while the occupants/building owners can be offered incentives on reducing operational environmental footprint. However, there needs to be a mechanism to fund the incentive programs. This is where the above-mentioned carbon economy can be taken into action, especially with the carbon prices mandated by the Canadian government [151]. In addition, some municipalities are 67 already using their climate action revenues to fund energy efficiency incentive programs for the building sector. The clear inference from the above findings is that retrofit-related decision-making cannot be done ad hoc, either for installing retrofits at a single house or for planning incentives or climate action programs at municipal, provincial or federal levels. The outcomes and effectiveness of these decisions are affected by a multitude of factors, and the best solutions and strategies can change depending on finances, location, climate conditions, energy systems, and various other factors. In allocating the limited government budgets for energy efficiency enhancement, the critical areas that can deliver the highest outcomes considering emissions and operational costs per dollar invested should be prioritized (i.e., NG furnaces for AB and SK). The discussed points infer the pressing need for a national retrofitting code to assist decision-makers and other stakeholders in making better decisions in retrofit planning. Summary It is important to note that all retrofits that can deliver positive environmental outcomes may not have the ability to deliver economic benefits simultaneously. The anticipated economic and environmental benefits depend on the regional energy prices, emission factors, and environmental conditions. Stakeholders’ priorities and expectations from retrofitting projects notably vary from each other. Moreover, the costs and benefits of retrofits are distributed throughout its life cycle, creating impacts on different stakeholders. Therefore, all these factors and cost-bearing stakeholders need to be considered when planning retrofits for different locations and incentivizing such programs through a life cycle thinking approach. Retrofitting is most effective when done at a point where the building components have reached their end of life. In such a situation, the impact of embodied emissions has to be evaluated compared to the base options available because the building components must be replaced with new components at their end-of-life. Therefore, the differential embodied emissions and capital costs calculated compared to the base-retrofits need to be employed in contrast to the absolute values used in this analysis. The energy performance improvements for electrically heated houses made the most sense from an environmental perspective in locations with high grid emissions factors. In contrast, in the provinces with greener electricity grids, upgrading houses heated with NG produce better 68 environmental performance improvements. For the greener provinces, shifting from NG heating to electricity is environmentally beneficial, although it is economically unattractive. In all the provinces in general, NG Furnace upgrades seem to be an economically promising option. Especially in AB and SK, both emission and cost-savings per dollar invested on NG Furnaces are very high compared to all other retrofit scenarios. This chapter highlights the need for a comprehensive retrofit code for Canada that integrates life cycle thinking for holistic decision-making. It also highlights the need for robust incentive and policy planning tools and project deployment strategies such as energy performance contracts that can equitably distribute the benefits of retrofitting projects across the stakeholders engaging with a building throughout its life cycle. Many factors considered, including energy use, energy prices, climate conditions, and energy supply, are subject to uncertainties and variations. In order to overcome this, stochastic and probabilistic mathematical techniques for modeling uncertainties such as Monte Carlo simulations, fuzzy logic, and robust optimization can be used. This chapter investigated retrofits as separate solutions. Further work is necessary to investigate the effects of implementing multiple retrofits simultaneously under uncertain conditions. In the following chapters of this thesis, said gaps are discussed in detail. 69 Chapter 5: Financial incentives planning for building energy retrofits under uncertainty Findings and methods presented in this chapter will be submitted to a peer reviewed journal, under the title “Retrofitting strategy development and incentives planning for residential buildings: An uncertainty-based life cycle thinking approach”. Background The existing building stock in Canada is aging and deteriorating. Older buildings were constructed to less stringent standards and energy efficiency requirements compared to new buildings, which results in poor energy, economic, and environmental performance [29][30]. In the long run, building renovations can produce win-win outcomes for all stakeholders, as highlighted in Section 0 [31]. Results from Chapter 4: confirmed that building energy retrofits have the potential to produce emission savings, thus contributing to provincial, national, and international emission reduction targets [16]. However, financial pressures such as high life cycle costs (LCC) and capital costs (CC) can reduce the stakeholder acceptance and market penetration of retrofits [32]. Moreover, the lack of a financial justification or a business case for retrofits also hinders their widespread acceptance [33]. Therefore, building owners have to be not only guided and educated but also be provided with an economic rationale to adopt these interventions. Financial incentives such as loans, rebates, and subsidies are all proven to be effective in motivating the stakeholders to engage with green initiatives [11][34]. Depending on the energy supply and environmental conditions, certain retrofits have the potential to reduce emissions while simultaneously delivering cost savings to the building owners or occupants [11]. Nevertheless, higher capital costs can still prevent the market penetration of the said retrofits [35]. Loan schemes offered by the governments or third-party organizations can support in overcoming this financial barrier [36][37][38]. The community penetration is even harder for retrofit options, which cannot benefit the building owners financially. Nevertheless, these retrofits may have a greater emission reduction potential. For example, in BC, replacing a natural gas furnace with an electric heat pump produces significant emissions savings but no financial benefits [11]. As the climate change mitigation policymakers are interested in promoting retrofits with higher emissions saving potential, they employ policy 70 tools such as financial incentives or carbon taxes to make a business case for the aforementioned eco-friendly retrofits [40]. Penalties have the potential to create social predicaments due to financial burdens applied to the local businesses and people in general [41][42][43]. Therefore, incentives are a more suitable approach to initiate the energy retrofits discussion in the existing residential building sector [39][152]. Therefore, this chapter focuses on financial incentives planning. Nevertheless, it is suitable to implement penalties for high emitters in later stages as suitable in order to maintain the money circulation to ensure the longevity of incentive programs. Incentivizing energy retrofits can be done following either a prescriptive or a performance-based approach [43]. In the prescriptive approach, the incentive is issued when a prescribed retrofit is implemented in a building [74]. In the performance-based approach, incentives are given for achieving a certain goal with regards to energy intensities or emissions based on a predefined performance benchmark. The strengths and weaknesses of the two approaches are discussed in Section 0. In either approach, developing comprehensive financial models is needed to determine the incentive levels needed to promote environmentally friendly retrofits while staying within the budgetary constraints of the funding agencies. In order to identify the current state of the knowledge relating to the topic, a Compendex database search was done using “residential, building, energy, retrofit, and incentive” as keywords. Out of the 73 research items found in the search, any research article that is locally applicable to Canada that focus on identifying the best retrofit strategies was not found. Moreover, no article focused on determining optimal incentive levels to promote the best retrofit strategies, accounting for environmental and economic decision priorities. These gaps were tabled for discussion with local municipalities (City of Kelowna, City of West Kelowna, City of Penticton, City of Peachland, etc.), local utility providers (FortisBC), and facility managers (Facilities Management Division, University of British Columbia). The stakeholder discussions confirmed the need for a standardized retrofit planning method and decision support tools focussing on the existing building sector. This study aims to propose an incentive planning approach for building retrofits for optimal environmental and economic outcomes under uncertainty, which enables the maximum emission reduction to be achieved within the available incentive budget for energy retrofits. Moreover, this chapter discusses the sensitivity of the proposed retrofit strategies to variations in energy supply, 71 building operational conditions, and macroeconomic parameters. Furthermore, how the incentive schemes can change under different decision priorities was investigated. The study proposes a method to identify the best retrofit packages for a building under given conditions and allocate the limited funding available for retrofitting to achieve the best outcomes considering both emissions and economics. The findings will be of use to the local governments and other decision-makers responsible for regional retrofit strategy planning and incentivizing and the residential building industry and housing owners in making the best decisions for their property. Methodology The study investigated the opportunities to promote the building energy-retrofits (BER) that can produce the best environmental and economic performance via incentives. As the first step of BER performance evaluation, two representative buildings were selected as the base buildings to represent the community selected for the study. Potential retrofit options were identified based on published literature and other relevant resources such as manufacturer data. The LCC and LCE saving potential of the individual retrofits were evaluated in a preliminary screening. The group-3 retrofits (which deliver no emissions savings) discussed in section 4.2.3 were not analyzed in this study, as the goal of this chapter was to find strategies to reduce LCE associated with the buildings within the given financial resources and constraints. The energy-saving potential of each energy retrofit was evaluated with energy simulation results from the HOT2000 software package. All the retrofits with an emission reduction potential were combined to envision all possible retrofitting scenarios under the given conditions. LCC and LCE savings of each retrofit strategy was calculated considering occupant behavior, energy generation, and economic uncertainties. The eco-efficiency parameter proposed in section 4.2.3 was used to simultaneously consider both the economic and environmental performances of retrofits. Then, these retrofit scenarios were ranked with respect to their eco-efficiency performance using the fuzzy maximizing and minimizing sets method [153][154]. Finally, the incentive budget optimization was done considering the best-performing retrofit scenarios under varying decision priorities. Figure 6 contains a summary of the above-described retrofit and incentive evaluation process followed in the study. 72 Figure 6 Building operation scenarios under incentives The detailed methodology followed in evaluating the prescriptive incentives planning strategies is discussed in the coming sections. Base building definition: Natural gas (NG) and electricity are the most commonly used fuel types in Canada for space and water heating. Therefore, the two base buildings proposed in Chapter-Chapter 4: were employed in this study to represent the different heating energy sources in SFDs [11]. BB-1 represents a house in which the space and water heating demand are supplied via electricity, and BB-2 represents a house in which the space and water heating demands are catered from NG. Other than the difference mentioned above in energy supply source, all other characteristics of BB-1 and BB-2 are similar. The retrofit performance and sensitivity analysis were done with reference to these two buildings. The detailed descriptions of the house characteristics are provided in section 5.3 (Case study). 5.2.1 Sensitivity of operational and supply conditions on retrofit performance Building energy simulation output is sensitive to input parameters and assumptions. Therefore, a sensitivity analysis was conducted to understand the behavior of the energy model with respect to variations in specific input parameters, including operational conditions and energy supply-related factors. 5.2.1.1 Change in energy demand with operational conditions and user behavior Percentage energy demand change (PEDC) was investigated with respect to a percentage or an absolute change in various parameters, including the number of occupants, time at home, hot water demand and temperature, and space cooling and heating setpoints. The sensitivity analysis was conducted under the weather conditions in Summerland, BC, which has a moderate climate. It is 73 important to note that the PEDC caused by the variations of different input parameters can be more or less pronounced in harsher or milder climates compared to Summerland. The sensitivity analysis must be conducted again when applying the proposed approach in a different location as the weather, cost factors, and energy supply conditions are location specific. The PEDC variation results in response to the discussed parameters are presented from sections 5.4.1.1 to 5.4.1.4. 5.2.1.2 Sensitivity of operational cost and emission performance to energy price and emission factors While electricity can be cleaner than natural gas depending on the grid supply mix, natural gas is generally cheaper in Canada. Energy cost and emission factors can be decisive parameters for source switching decisions related to building heating and hot water systems. Therefore, it is essential to understand the sensitivity of the overall operational performance of the buildings and upgrades towards these two parameters. The energy use of the two base buildings was simulated using the HOT2000 software package. The simulation results were used to evaluate the effect of energy price and cost variations on the annual operational performance of the two buildings. In doing so, the operational emissions were calculated using Equation 13. 𝑶𝑬 = ∑𝑫𝒆𝒎𝒂𝒏𝒅𝒋 ∗ 𝑬𝑭𝒋 Equation 13 Where, 𝑂𝐸 – Operational emissions (kgCO2,eq) 𝐷𝑒𝑚𝑎𝑛𝑑𝑗 – Demand for jth energy source (kWh) 𝐸𝐹𝑗 – Emissions factor of jth energy source (kgCO2,eq /kWh) Similarly, the operational cost was calculated using Equation 14. 𝑶𝑪 = ∑𝑫𝒆𝒎𝒂𝒏𝒅𝒋 ∗ 𝑼𝑬𝑷𝒋 Equation 14 Where, 𝑂𝐶 – Operational cost ($) 𝑈𝐸𝑃𝑗– Unit energy price of jth energy source ($ /kWh) 74 In order to understand the potential impact on the building performance, operational cost and emissions were plotted against the unit energy price and emission factor variations of the energy sources. Two energy price and emission scenarios were considered for this. Scenario-1 assumed that the electricity price and emission factor stay constant while the NG price and emission factor vary. Scenario-2 assumed that the price and emission factor of NG stay constant while the price and emission factor of the electricity supply vary. Detailed results of this analysis are presented in section 5.4.1.5. 5.2.2 Retrofit performance evaluation The goal of the retrofit performance evaluation was to identify the best retrofit strategies that can maximize the environmental and economic benefits. In literature, LCC and LCE are identified as effective economic and environmental performance indicators, respectively [11]. Therefore, LCC and LCE were employed as performance indicators. Performance-related uncertainties created by occupant behavior and building use patterns have made performance predictions a challenging task [84][155][69]. Moreover, retrofit performance can greatly vary with the uncertainties associated with the emissions factor and economic parameters of the energy source. Fuzzy sets were used in previous studies to handle the uncertainties associated with similar energy planning exercises [18][55]. Therefore, fuzzy sets were used to account for potential uncertainties in the developed retrofit performance evaluation model. Previous studies have evaluated the performance of the retrofits considering individual retrofits [11]. However, the performance of individual retrofits can change significantly due to performance interactions when combined with other retrofits. For example, the emission reduction potential of a heat pump is higher when installed in a house with poor envelope insulation. On the other hand, the same heat pump would produce lesser emission savings at a house with a better envelope. Therefore, all possible combinations of the retrofits that can produce emissions savings (priority and secondary retrofits) were considered when evaluating the retrofit performance. The performance values of individual retrofits under these variable conditions and retrofit combinations were taken forward into the incentive optimization algorithm. The detailed retrofit evaluation process is discussed below. 75 5.2.2.1 Energy performance HOT2000 is a popular software platform used to simulate the energy performance of small residential buildings. It is currently used for energy simulations in BC Energy STEP Code and EnerGuide [6][29]. HOT2000 (Version 11.3) was used to develop the energy models required for the assessment considering the faster learning curve, high community penetration, and familiarity of the software package among the Canadian energy experts. Natural Resources Canada (NRCan) developed the Housing Technology Assessment Platform (HTAP) to support the energy analysts in handling large numbers of energy simulations [156]. Using the strengths of the HTAP and HOT2000 together, all possible combinations of the retrofit options were simulated. The simulation results were used to calculate the operational cost and emissions savings potential of retrofits in the subsequent steps. Fuzzy numbers were used to represent the energy performance of the base buildings and the retrofit upgrades. The key parameters to which the energy use was sensitive were used to characterize the said fuzzy number. Energy simulations were conducted considering three operational conditions to define the lower and upper bounds and the likely values of the said fuzzy numbers. The likely value was defined considering the nominal operational conditions from the study by Prabatha et al., as indicated in Table 8 [11]. Lower and upper bounds were defined by varying the nominal operating conditions considered in the Average User as detailed in Table 8. Table 8 Operational uncertainties Parameter Lower Bound of the fuzzy number Average User of the fuzzy number Upper Bound of the fuzzy number Number of adults Avg+1 3 adults [11] Avg-1 Percentage time inside the house 60% 50% [11] 40% Appliance, lighting, and other loads 90% of avg. Conditions from Table 3 110% of avg. Domestic hot water consumption and temperature 197𝑙, 53oC 247𝑙, 55oC [11] 297𝑙, 57oC Daytime heating temperature 20 oC 21 oC [11] 22 oC Nighttime heating temperature 17 oC 18 oC [11] 19 oC Setback duration 9h 8h [11] 7h 76 Operational energy savings under each fuzzy value (i.e., lower bound, likely value, upper bound) were calculated using Equation 15. 𝑶𝑬𝑺𝒊,𝒋 = 𝑩𝒂𝒔𝒆𝑫𝒆𝒎𝒂𝒏𝒅𝒋 −𝑫𝒆𝒎𝒂𝒏𝒅𝒊,𝒋 Equation 15 Where, 𝑂𝐸𝑆𝑖,𝑘,𝑗 – Operational energy savings of jth energy source by ith retrofit scenario 𝐵𝑎𝑠𝑒𝐷𝑒𝑚𝑎𝑛𝑑𝑘,𝑗 – Demand for jth energy source 𝐷𝑒𝑚𝑎𝑛𝑑𝑖,𝑘,𝑗 – Demand for jth energy source by the ith retrofit combination The fuzzy form of the operational energy savings is given below. 𝑶𝑬𝑺𝒊,𝒋 = [𝑶𝑬𝑺𝒊,𝒋,𝑳𝒐𝒘𝒆𝒓, 𝑶𝑬𝑺𝒊,𝒋,𝑳𝒊𝒌𝒆𝒍𝒚, 𝑶𝑬𝑺𝒊,𝒋,𝑼𝒑𝒑𝒆𝒓] Equation 16 This fuzzy number was combined with the emission factors and energy prices to evaluate the operational performance of the retrofit scenarios. 5.2.2.2 Environmental performance Canadian policy discussions are currently focusing on how to reduce building-related emissions. Moreover, global warming potential is commonly used to indicate environmental impact in climate change mitigation activities [90][56]. Life cycle emissions have been used as an indicator of global warming potential and environmental performance in previous studies [11][117][118]. Therefore, life cycle emissions (kg CO2e) were used as the environmental performance indicator of retrofits. Three main components, including embodied, operational, and disposal emissions, were considered in calculating the life cycle emissions of a given retrofit [11]. Embodied emission data from Chapter 4: was adopted for this study [11]. Equation 17 was used to calculate the life cycle emission savings of each retrofit scenario. 𝑳𝑪𝑬𝑺𝒊 = −∑ 𝑬𝑬𝒊𝒋𝒏𝒋=𝟏 + ∑ 𝑶𝑬𝑺𝒊𝒌 ∗ 𝑬𝑭𝒋𝟐𝒌=𝟏 − ∑ 𝑫𝑬𝒊𝒋𝒏𝒋 Equation 17 77 Where, 𝐿𝐶𝐸𝑆𝑖 –LCE savings of the building after introducing ith retrofit scenario; (in the fuzzy form) 𝑛 – number of retrofits implemented in the ith retrofit scenario 𝐸𝐸𝑖𝑗 – Added embodied emissions by the jth retrofit in the ith retrofit scenario 𝑂𝐸𝑆𝑖𝑘 – Operational energy savings of the ith retrofit scenario related to kth fuel (NG, E); (in the fuzzy form) 𝐸𝐹𝑘 – Emission factor of the kth fuel (NG, E); (in the fuzzy form) 𝐷𝐸𝑖𝑗 – Disposal emissions of the jth retrofit in the ith retrofit scenario The emission factors employed in the study accounted for the life cycle emissions associated with the energy sources, including the emissions created during the production phase. As seen in Equation 17, operational emissions are directly correlated to the emission factors of the two energy sources considered. Therefore, potential variations of the NG and electricity emission factors were considered in the retrofit evaluation process. BC natural gas providers plan to make the domestic NG supply greener by injecting renewable NG (RNG) into the gas network. RNG has a very low emission factor (0.2932 kgCO2e/GJ) as opposed to the much higher emission factor of conventional NG (49.87 kgCO2e/GJ). A major NG provider in BC predicts that RNG could cater to 25-46% percent of the NG demand by 2036 with the technology developments in the biogas sector. Two scenarios were considered to assume a gradual growth of RNG inclusion in the gas network to reach 25% and 46% by 2036 [41]. This study assumed that the same predicted NG inclusion growth rate is maintained from 2036 to 2040 as well. The average emission factor for the project period was represented using a fuzzy number accounting for alternative generation realities aligning with the predictions discussed above. Fuzzy conditions considered in the study for emission factors are shown in Table 9. The lower bound of the emission factors was defined considering that the electricity emissions factor remains the same while the NG emissions factor reduces due to RNG injection to the supply. Under the current conditions, approximately 92% of the installed capacity of the BC electricity grid is supplied renewable energy sources [157]. As of 2020, the installed generation capacity of the province is 18,286MW [158]. According to the literature, the installed capacity is expected to increase at an 78 approximate annual growth of 1% until 2040 [159]. In the upper bound (from Table 9), a 500% increment of the electricity emission factor was used to represent a future where the approximate demand growth (20% from current demand) in the next two decades is supplied by NG-based electricity generation stations. Likely value presents a reality where NG-based generators supply 50% of the excess demand. Table 9 Fuzzy conditions used to represent emission factors Electricity emission factor NG emission factor Lower Bound Stays at the present value Reduces by 28.5% from the present value Likely Value Increases by 250% from the present value Reduces by 15.5% from the present value Upper Bound Increases by 500% from the present value Stays at the present value These fuzzy numbers were used to represent the emission factors of electricity and NG. 5.2.2.3 Economic performance Financial viability is an essential factor for the success of any building upgrade. However, lack of investor confidence has been identified as a common barrier for attracting finances for building energy efficiency projects [15]. Therefore, comprehensive economic performance evaluations are essential to ensure financial viability and boost investor confidence. Life cycle cost (LCC) was used to evaluate the economic performance of different retrofit combinations accounting for capital, operational, and disposal costs [121]. The cost of equipment and labor was accounted under the capital cost. RSMeans Building Construction Cost Database was used wherever the data can be found in it. Online vendor prices and literature were used for any retrofits, which were not included in the database. Equations used in Chapter 4: for LCC calculations of retrofits were adopted for this study [11]. Equation 6, Equation 7, and Equation 8 were used to calculate the initial capital cost of each retrofit strategy. Equation 10 was used to calculate the total disposal costs incurred during the life cycle of a given retrofit strategy. Equation 18 was used to calculate the operational cost savings achieved by a given energy retrofit strategy in fuzzy form. 𝑶𝑪𝑺𝒊 = 𝑬𝑺𝑬,𝒊 × 𝑪𝑬,𝒌𝑾𝒉 + 𝑬𝑺𝑵𝑮,𝒊 × 𝑪𝑵𝑮,𝒌𝑾𝒉 Equation 18 79 Where, 𝑂𝐶𝑆𝑖 – Fuzzy value of annual operational cost-saving achieved by the ith retrofitting strategy 𝐸𝑆𝐸,𝑖 – Fuzzy value of electrical energy saving achieved by the ith retrofitting strategy 𝐸𝑆𝑁𝐺,𝑖 – Fuzzy value of NG saving achieved by the ith retrofitting strategy 𝐶𝐸,𝑘𝑊ℎ – Fuzzy value of average cost per kWh of electricity 𝐶𝑁𝐺,𝑘𝑊ℎ – Fuzzy value of average cost per kWh of electricity Energy prices of NG and electricity can affect the effectiveness of retrofit performance. Therefore, similar to the emission factor, fuzzy numbers were proposed to represent energy prices. Electricity price is linked with the grid energy mix and many other factors, including government policies [160][161]. The current average grid electricity price for BC is 11.62 cents/kWh [11]. The current price was taken as the reference energy price for the study. Electricity price is expected to increase by 8.1% [161] in the coming five years, and it could be approximately represented by an annualized increment of 1.16% each year compared to the previous year. Earlier it was predicted to grow 13.7% from 2013 to 2024 [161], and this prediction can be represented as an annualized value of 1.15%. If an average price hike of 1.155% was assumed for the project period (2020-2040), then the average electricity price is 12.96 cents/kWh. The aforementioned growth predictions are closely falling on each other. Therefore, the uncertainty is minimal according to the predictions. A variation of ±1% was introduced to the electricity price to account for uncertainties due to price fluctuation. NG cost in the geographic area considered is 7.89 CAD/GJ [162]. Approximately 5.5 CAD/GJ is charged for delivery, storage, and transport, while the cost of gas is 2.28 CAD/GJ. Over the past decade, gas prices have varied between 2-5 CAD/GJ approximately [163]. Following the great financial recession in the 2008-2009 period, gas prices have hiked up to 10 CAD/GJ. The fuzzy number of the NG price was developed assuming such price extremities will not be present in the coming two decades, and the NG cost will vary between 2-5 CAD/GJ range with an average of 3.5 CAD/GJ. Charges other than the gas cost were assumed to be constant in the project time. With 80 these assumptions, the average gas price is 9 CAD/GJ. The upper and lower bounds are 10.5 and 7.5CAD/GJ, respectively. Similar to LCES, the LCCS of each scenario can be calculated by Equation 19. 𝑳𝑪𝑪𝑺𝒊 = −𝑰𝑪𝑻𝒐𝒕𝒂𝒍,𝒊 + ((𝟏+𝒓)𝒕−𝟏𝒓(𝟏+𝒓)𝒕) . 𝑶𝑪𝑺𝒊 Equation 19 Where, 𝐿𝐶𝐶𝑆𝑖 – LCC savings of the building after introducing ith retrofit scenario; (in the fuzzy form) 𝒓 – Discount rate 𝑡 – Project period 5.2.3 Performance-based retrofits planning LCCS and LCES were considered as the key performance indicators of retrofit performance. For each retrofit scenario, LCCS and LCES were calculated in the form of fuzzy numbers to account for the uncertainty. The relative importance of the LCCS and LCES can change depending on the priorities of the stakeholders. Municipal decision-makers are the stakeholder group mainly interested in the LCES, while the building owners are more interested in seeing the LCCS. However, if the building owners do not embrace the idea of retrofitting the building, then the emission reduction goals of the municipalities will not be met. Therefore, it is important to establish the relative importance of the KPIs based on a stakeholder survey in practical implementation. In this study, the relative importance of LCCS (W1) and LCES (W2) was varied from 0-100%, ensuring that the sum of the two weights always remains at 100%. The overall performance score of each retrofit combination was calculated by combining the LCCS and LCES of the scenario with the defined weighting scheme, as shown in Equation 20. 𝑭𝒊 = 𝑾𝟏 ∗ 𝑳𝑪𝑪𝑺𝒊 +𝑾𝟐 ∗ 𝑳𝑪𝑬𝑺𝒊 Equation 20 Where, 𝐹𝑖 – Overall performance score of the ith retrofit scenario 𝐹𝑖 was calculated in the form of a fuzzy number for all the retrofit scenarios from 1 to n. 81 𝑭𝒊 = {𝒙|𝒇𝑭𝒊(𝒙) > 𝟎}; 𝒊 = 𝟏, 𝟐,… , 𝒏 Equation 21 Maximizing set and minimizing set method has been used for fuzzy number ranking in several previous studies [90][153][154]. This technique identifies the option closest to the best performance level and furthest from the worst performance level as the best option. Based on this criterion, it ranks the retrofit scenarios. This method is close to the fuzzy TOPSIS method, which is known to be a reasonable approximation of the human thinking pattern in decision-making [56]. The normalizing method employed makes it possible to compare a large number of alternative solutions against each other. It also adapts the TOPSIS concept in a generalizable manner to a fuzzy environment, reducing the loss of fuzzy information [164]. Maximizing set (A) and minimizing set (B) are defined as shown in Equation 22 and Equation 23, respectively. The membership of x to A and B are represented by 𝑓𝐴(𝑥) and 𝑓𝐵(𝑥) respectively [154]. 𝑨 = {(𝒙, 𝒇𝑨(𝒙))|𝒙 ∈ 𝑹} Equation 22 Where, 𝑓𝐴(𝑥) = {𝑥 − 𝑥𝑚𝑖𝑛𝑥𝑚𝑎𝑥 − 𝑥𝑚𝑖𝑛 , 𝑥𝑚𝑖𝑛 ≤ 𝑥 ≤ 𝑥𝑚𝑎𝑥 0 , 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 𝑩 = {(𝒙, 𝒇𝑩(𝒙))|𝒙 ∈ 𝑹} Equation 23 Where, 𝑓𝐵(𝑥) = {𝑥 − 𝑥𝑚𝑎𝑥𝑥𝑚𝑖𝑛 − 𝑥𝑚𝑎𝑥 , 𝑥𝑚𝑖𝑛 ≤ 𝑥 ≤ 𝑥𝑚𝑎𝑥 0 , 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 Parameters used in fA(x) and fB(x) calculations are defined below. 𝑥𝑚𝑖𝑛 = inf(𝑆) ; 𝑥𝑚𝑎𝑥 = sup(𝑆) ; 𝑆 =⋃𝐹𝑖𝑛𝑖=1 82 The right utility, left utility, and the total utility values for each retrofit scenario were calculated as seen in Equation 24, Equation 25, and Equation 26, respectively. 𝑼𝑨(𝑭𝒊) = 𝒔𝒖𝒑 (𝒇𝑭𝒊(𝒙)⋂𝒇𝑨(𝒙)) , 𝒊 = 𝟏, 𝟐,… , 𝒏 Equation 24 𝑼𝑩(𝑭𝒊) = 𝒔𝒖𝒑(𝒇𝑭𝒊(𝒙)⋂𝒇𝑩(𝒙)) , 𝒊 = 𝟏, 𝟐,… , 𝒏 Equation 25 𝑼𝑻(𝑭𝒊) =𝑼𝑨(𝑭𝒊)+𝟏−𝑼𝑩(𝑭𝒊)𝟐 Equation 26 The total utility value was used in retrofit strategy ranking. The retrofit combination that produces the highest UT(FI) was considered to be the best performing retrofit scenario. This information is useful to building owners in selecting the most suitable retrofits for their context and to policymakers in identifying which retrofits need to be promoted and incentivized. 5.2.4 Optimization of incentives Incentivizing building energy retrofits is done with the aim of reducing the emissions associated with building energy use. When looking at the retrofitting opportunities in general, there is an exhaustive number of potential scenarios created by alternative retrofit options combinations [11]. Moreover, building energy retrofits perform differently depending on the combination of retrofits applied at a given retrofit project. The retrofit selection also depends on many factors, including the current condition of the building of interest, the components that need replacement, and the budget available with the building owner. Therefore, the incentive providers cannot prescribe a single retrofitting strategy for a given house. Incentive planners have to come up with incentive schemes that can perform well under all the top-ranked retrofit scenarios applicable for a given building cluster in order to address the aforementioned problem. The following linear optimization problem was defined considering all the aspects discussed above. 83 The specific objective of the proposed incentive optimization was to identify the best incentive levels for individual retrofits to promote the top-ranked retrofit combination scenarios that achieve the maximum performance score (total utility: UT(Fi)). The total utility of all retrofit scenarios was compiled in a row matrix U (1xn), where “n” is the number of scenarios. The availability of retrofits in each retrofit scenario considered was represented in an availability matrix (A). A is a (m x n) matrix where “m” is the total number of retrofits considered for the study. The elements of the availability matrix were defined as shown in Equation 27. 𝑨𝒊𝒋 = {𝟏; 𝑖𝑓 𝑟𝑒𝑡𝑟𝑜𝑓𝑖𝑡 "𝑗" 𝑖𝑠 𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑙𝑒 𝑖𝑛 𝑠𝑐𝑒𝑛𝑎𝑟𝑖𝑜 "𝑖" 𝟎; 𝑖𝑓 𝑟𝑒𝑡𝑟𝑜𝑓𝑖𝑡 "𝑗" 𝑖𝑠 𝑛𝑜𝑡 𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑙𝑒 𝑖𝑛 𝑠𝑐𝑒𝑛𝑎𝑟𝑖𝑜 "𝑖" Equation 27 A performance index matrix P (m x n) was defined by the elementwise multiplication of U and A. The elements of the performance index matrix were defined as shown in Equation 28. 𝑷𝒊𝒋 = 𝑨𝒊𝒋. 𝑼𝒊 Equation 28 The incentive level is the optimization variable. Incentive level was chosen to be (m x 1) matrix indicated by “IN”. The objective function was defined as shown in Equation 29. 𝒁 = 𝒔𝒖𝒎{𝑷𝑻. 𝑰𝑵} Equation 29 Optimization problem-1: 𝒁∗ = 𝐦𝐚𝐱{𝒁} S.T Constraints: i. The capital cost of each retrofit should not exceed the incentive provided for that retrofit 𝐼𝑁𝑖 ≤ 𝐶𝐶𝑖 ii. Total incentives should not exceed the maximum budget allocation for incentivizing retrofits of the given building archetype 𝑠𝑢𝑚{𝐼𝑁} ≤ 𝐼𝑛𝑐𝑒𝑛𝑡𝑖𝑣𝑒𝐵𝑢𝑑𝑔𝑒𝑡 84 Case study The case study focussed on the City of Kelowna, which is located in BC, Canada. It is categorized under climate zone-5 as the number of HDD falls between 3,000-3,999. At this point in time, the City of Kelowna only offers $150 top-ups under the EnerGuide Pre-Upgrade Evaluations Top-Up program. However, the utility providers in the region offer some attractive rebates for the building owners. As the goal of this study was to identify the optimum incentive allocation for retrofits, the total value of incentives given for any retrofit scenario applied to an individual household was assumed to reach a maximum of 10,000 CAD. 5.3.1 Base building definition The two base buildings mentioned under the retrofit performance evaluation section were used to demonstrate the proposed incentive planning approach. However, it is important to note that the proposed method can be expanded to an incentive planning exercise involving more than two base buildings for community-level planning exercises. Table 10 and Table 11 summarizes the details of the base buildings. Table 10 Base building characteristics Heating Hot Water Window Wall Ceiling Infiltration Ventilation BB-1 NG Furnace (78% AFUE) Conventional Tank (NG) (EF=0.5543) Single Pane R10 R10 7.5 ACH @50Pa 28 L/s BB-2 Electric Baseboard Conventional Tank (Electric) (EF=0.5543) Table 11 Baseload and occupancy characteristics Electrical Appliances Minor (other) electrical appliance Lighting Average exterior energy use Hot water load Occupancy 10.68kwh/day 0.29kWh/day 2.6kWh/day Negligible 247 liters/day 3 x (50%) 85 5.3.2 Retrofit options identification The already incentivized retrofits in different municipalities outside Kelowna were considered as the retrofit options for this study. The capital cost and embodied emission data of the retrofits considered in the study are summarized in Appendix 8. Space heating and hot water systems that required source switching from electricity to NG did not show any emission saving potential under any of the future scenarios considered. Therefore, those options were omitted from the BB1 retrofits options list. However, NG space and hot-water space heating upgrades were considered for the house BB-2, which was heated with NG. Results and discussion The proposed research methodology was applied to the case study conditions discussed above. The detailed results and outcomes of the study are discussed in the following sections. 5.4.1 Sensitivity of operational and supply conditions on retrofit performance The sensitivity analysis results considering the demand, user behavior, operational conditions, energy prices, and emissions factor are described below. 5.4.1.1 Number of occupants and their time spent at home The number of occupants was varied from 1 to 5 in the simulation, creating a 66% variation from the nominal value (three occupants) used in the base cases. The percentage of time spent at home was considered to be 50% in the base buildings [11]. In order to evaluate the sensitivity of this parameter, Percentage energy demand change (PEDC) was evaluated for the occupants spending 10%, 25%, 75%, 90% of their time at home. PEDC with the percentage change of time at home compared to the nominal value (occupied during 50% of the day) used in the base building was calculated. PEDC variation with the number of occupants and their time at home is presented in Figure 7. 86 Figure 7 Percentage energy demand change vs. percentage change of no. of occupants and time at home The graph shows that even with a significant variation in the number of occupants and the time spent at home, the total energy use changes by less than 1%. In reality, the energy demand of a house is responsive to these two parameters due to changes in energy demand for lighting, heating, cooling, hot water, and appliances. However, in the HOT2000 modeling platform, all those details act as separate inputs. Thus, the HOT2000 model output regarding energy demand does not vary significantly with the above parameters. Therefore, it should be noted that those parameters need to be manually adjusted when altering the number of occupants in this modeling process. However, as independent parameters, the number of occupants and their time at home will not significantly impact the building energy demand prediction from the simulated model. Notably, when the number of occupants and time inside the house increases, the energy demand reduces due to the energy savings from the space heating system with the increasing passive heat gains from the occupants. The visible reduction of energy use is understandable as the heating energy demand significantly dominates over all other building energy end-uses in most parts of BC and Canada (including Summerland). 5.4.1.2 Major electric appliances From 1990 to 2016, the daily energy demand of major electrical appliances decreased from 14.15 to 7.12 kWh [139]. In the base building definition, the average value (10.68 kWh/day) of this range was selected to be a suitable representation of the existing building cluster [11]. The range from 7.12 to 14.15 kWh/day was considered in assessing the sensitivity of the total energy demand to -0.8%-0.6%-0.4%-0.2%0.0%0.2%0.4%0.6%0.8%-80% -60% -40% -20% 0% 20% 40% 60% 80%PEDC (%)Change in the number of occupants (%)-0.8%-0.6%-0.4%-0.2%0.0%0.2%0.4%0.6%0.8%-80% -30% 20% 70%PEDC (%)Change in time spent at home (%)BB1BB287 major electric appliances. PEDC variation with the percentage appliance load variation is presented in Figure 8. Figure 8 Percentage energy demand change vs. percentage change of daily energy demand for major electrical appliances The energy demand variations of the major appliances do not change the PEDC by over 1%. Similarly, the changes in lighting energy demand also did not introduce any significant change to the PEDC. 5.4.1.3 Hot-water temperature and demand Hot water consumption and the supply temperature of the base buildings were set at 247l/day and 55oC, respectively. A temperature difference of ±2oC and ±15% variation of the hot water consumption was considered to analyze the sensitivity of PEDC to hot-water (HW) temperature and consumption. The PEDC response to the HW supply temperature difference and percentage change of HW demand is presented in Figure 9. -1.0%-0.8%-0.6%-0.4%-0.2%0.0%0.2%0.4%0.6%0.8%1.0%-40% -30% -20% -10% 0% 10% 20% 30% 40%PEDC (%)ApplianceLoad Change (%)BB1BB288 Figure 9 Percentage energy demand change vs. hot water supply temperature change Out of the two parameters discussed, a minor change of the HW temperature (±2oC) shows the potential to reduce the PEDC by 0.25%, which is not a significant saving. However, it is important to note that this saving resulted from a simple temperature adjustment. Water-efficient faucets (WEF) have the potential to reduce the daily HW consumption by approximately 50L/day, which accounts for about 25% of the daily HW consumption in the base buildings considered herein. This HW saving can translate to an energy saving of up to 2%. In addition to technical solutions such as WEFs, and behavioral changes brought in through awareness programs and incentives can also make significant changes to HW consumption. 5.4.1.4 Space cooling and heating setpoints The base houses considered in this study do not have any cooling systems. Therefore, no sensitivity analysis was conducted for the cooling system operational conditions. The base temperature setpoints for the heating system are listed in the table below. Table 12 Space heating set points Parameter Value Daytime Heating Setpoint (oC) 21 Nighttime Heating Setpoint (oC) 18 Setback Duration (h) 8 -0.25%-0.15%-0.05%0.05%0.15%0.25%-2 -1 0 1 2PEDC (%)HW Tempeature Change (oC)-1.9%-1.4%-0.9%-0.4%0.1%0.6%1.1%1.6%-25% -15% -5% 5% 15% 25%PEDC (%)HW Demand %ChangeBB1BB289 Heating setpoints In order to evaluate the sensitivity of the total energy demand to the temperature set points, up to 2oC changes were applied to both heating set points. The PEDC variation with the heating set points is presented in Figure 10. Figure 10 Percentage energy demand change vs. daytime and nighttime heating setpoint change Both nighttime and daytime heating temperature set points have a high potential to change the PEDC. This observation shows the importance of maintaining the daytime temperature of a house at the minimum possible level while applying the temperature set back at nighttime. Moreover, this highlights the importance of energy-conscious behaviors from the building occupants, which are not equipped with programmable thermostats. On another note, the introduction of programmable thermostats can be a great retrofit in this type of situation. Setback duration Set back duration of the base building was set to 8 hours. The setback duration was changed by ±2hours to evaluate the PEDC variation with the setback time variation. The response of the PEDC to the Setback duration is presented in Figure 11. -12%-10%-8%-6%-4%-2%0%2%4%6%8%10%12%-2 -1 0 1 2PEDC (%)Day time HS point change (oC)-6%-4%-2%0%2%4%6%-2 -1 0 1 2PEDC (%)Night time HS point change (oC)BB1BB290 Figure 11 Percentage energy demand change vs. setback duration change As seen in Figure 11, dropping the setback duration by one hour per day can cause a 1% energy demand increase. It is important to note that the average daily setback duration can change greatly over the year under manual operation. With energy-conscious behavior, it has the potential to reap more savings by keeping the setback temperature applied whenever the house is not in use and during the nighttime. However, the negligence of occupants can cause significant increases in the energy demand by compromising the average daily setback time. Similar to the temperature setpoint controlling, programmable thermostats can avoid potential energy losses due to low average daily setback durations resulting from occupant behavior. Even with programmable thermostats, energy-conscious behaviors can further improve energy savings. 5.4.1.5 Operational cost and emission sensitivity to source conditions The energy costs and operational emissions at present were used as the baseline for the comparison. A summary of the current energy source details of BC is presented in Table 13 [11]. Table 13 Emission factors and price for energy sources Source Emission factor Energy price Electricity 0.0129 kgCO2/kWh 11.62 cents/kWh Natural Gas 49.58 kg/GJ 0.1784 kg/kWh 7.36 $/GJ 2.65 cents/kWh -2%0%2%-2 -1 0 1 2PEDC (%)Setback duration change (h)BB1BB291 The annual energy use of the base buildings is summarized in Table 14 [11]. Table 14 Annual energy use of the base buildings Building Electricity Natural Gas BB-1 56,767 kWh - BB-2 5,804 kWh 6,015 m3, 236 GJ, 65,556 kWh In Figure 12, a 50% price variation was introduced to energy prices following the scenarios discussed in section 5.2.1.2. The results are presented below. Scenario-1 Scenario-2 Figure 12 Operational cost vs. energy price variation According to Figure 12, it is evident that even after a 50% increment in NG price or 50% decrement of electricity price, the electrically heated house has a higher operational cost. However, it is important to note that BB-1 is only equipped with electric baseboards for heating. Suppose an electric heat pump was used for heating BB-1. In that case, there is a possibility of BB-1 operating at a lower cost compared to the NG heated house (BB-2) when the electricity price drops to the 150,000 250,000 350,000 450,000 550,000 650,000-50% -30% -10% 10% 30% 50%Operational CostNG Price VariationBB1BB2 150,000 250,000 350,000 450,000 550,000 650,000 750,000 850,000 950,000-50% -30% -10% 10% 30% 50%Operational CostElectricity Price VariationBB1BB292 lowest levels presented above. However, it is safe to say that under the given configurations of BB-1 and BB-2, NG is a clear winner from an operational cost perspective. In Figure 13, NG and electricity emission factors were varied following scenarios 1 and 2 from section 5.2.1.2, respectively. In scenario-1, the NG emission factor was varied in a range of ±50%. BC electricity demand is growing due to a number of factors, including population and industrial growth. On top of that, some governmental and private initiatives motivate the conversion of fossil fuel-based operations (such as building space heating and transportation) to electricity. With the said conversions, alternative energy sources such as NG and solar will be integrated into the grid if hydroelectricity becomes insufficient to cater to the provincial energy demand. However, according to a demand forecast by a major electricity supplier in the province, the expected electricity demand growth from 2020 to 2040 is less than 20% [159]. A 500% increment of electricity emission factor will be observed in a hypothetical future where the total approximate demand growth (20% from current demand) in the next two decades is supplied by NG-based electricity generators. As renewable energy sources already dominate the BC electricity grid, the potential for emission factor reduction is minimal. However, a 50% emission factor reduction was considered with the aim of studying the potential operational savings in a scenario like that. In scenario-2, the electricity emission factor was varied in a range of −50% to 500%. Scenario-1 Scenario-2 Figure 13 Operational emissions vs. energy emission factor variation 0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000-50% -30% -10% 10% 30% 50%Operational Emissoins (kgCO2.eq)NG Emission Factor VariationBB1BB2 - 2,000 4,000 6,000 8,000 10,000 12,000-50% 150% 350%Operational Emissoins (kgCO2.eq)Electricity Emission Factor VariationBB1BB293 As seen in Figure 13, under all the conditions considered in both scenarios, electrically heated houses (BB-1) produce lower operational emissions. Operational emissions performance of the electrically heated house is better even after reducing the emission factor of NG by 50% and increasing the emission factor of electricity to 500% of the current value. It is important to note that an electrically heated house's performance can be improved by introducing heat pumps instead of the baseboards used in BB-1 as the COP values can exceed 1. 5.4.2 Retrofit performance outcomes W1 and W2 were used as weights to represent the priorities assigned for LCES and LCCS, respectively. The sum of these two parameters was maintained at 100% on all occasions. This was done to represent the split between the importance assigned for each criterion as a percentage, enabling a more straightforward interpretation of priorities. In order to understand the effect of the weighting assigned for LCES on the percentage occurrence of building component upgrades in the best 100 ranks, the priority assigned to LCCS (W2) was varied from 0% to 100%. Detailed results are presented in Appendix 9 and Appendix 10. Figure 14 indicates the variation of percentage inclusion of upgrades vs. relative importance of LCES. Here, the top-ranked 100 retrofit combinations were explored in-depth to quantify the number of times a particular retrofit type appeared in the solutions. For example, Figure 14 makes it clear that the HWU appeared in 80-100% of the solutions under all varying priority levels assigned to LCES. 94 Figure 14 Percentage inclusion of upgrades vs. relative importance of LCES When W2 is below 50%, all the upgrades in the best ranked 100 options were proposed to be applied on BB1. Similarly, when W2 is above 50%, all the upgrade scenarios ranked in the top 100 options were proposed to be applied on BB2. When LCES and LCCS were given similar importance majority of the upgrade scenarios in the first 100 ranks were proposed to be applied on BB1, while 12% of the upgrades were proposed to be applied on BB2, this is the only decision scenario (W2 = 50%) where upgrades for both BB1 and BB2 were included in the best 100 options at the same time. From Figure 14, it is evident that space heating upgrades are commonly selected under any weighting scenario. It is closely followed by the HWU upgrades. However, HWU upgrade percentage inclusion reduces slightly below 85% when the relative importance of LCES is below 50% yet being the second most used upgrade. Wall upgrades and airtightness are the third and fourth best performing retrofits irrespective of weighting schemes. Ceiling upgrades are included 50%-70% times in the best 100 retrofit scenarios irrespective of the decision priority. However, when the relative importance of LCES exceeds 70%, 80%, and 90%, the use of window upgrades becomes a common inclusion compared to ceiling, airtightness, and wall upgrades, respectively. Window upgrades show a steep increase of inclusion with the increasing importance of LCES and 304050607080901000% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%% Inclusion in to 100 retrofit scenariosImportance of LCES%Inclusion of Upgrades vs. relative importance of LCESAirTightnessWallWindowCeilingSpaceHHWU95 the decreasing importance of LCCS. Windows are mainly at a disadvantage when compared to other retrofits due to the high capital cost. It is important to note that at 100% importance for LCES, windows are included in all of the best 100 retrofit scenarios. A detailed summary of the upgrades included in the best 100 ranked options under varying decision priorities is presented in 0. 5.4.3 Incentive performance The incentive performance was evaluated under varying decision priorities. Only the top-ranked retrofit scenarios were selected for the incentive optimization as the goal of the incentivizing is to promote the best-performing retrofit scenarios. The incentive amounts applied to each retrofit vary with the number of scenarios considered. The incentive amounts applicable for individual retrofits were determined considering the best 50, 100, and 500 retrofit scenarios out of the 2736 retrofit scenarios considered in the study. The incentive results under a varying number of retrofit scenarios for prioritization are presented in 0. The number of scenarios considered for the incentives planning can be determined based on benchmark LCCS and LCES levels for the retrofit scenarios being promoted. 5.4.3.1 Optimal incentive allocation In this discussion, the optimal incentive amounts to promote the top 100 retrofit scenarios were analyzed for demonstrative purposes. In the top 100 retrofit scenarios, any incentive scheme proposed when the decision priority is assigned over 50% is for BB2. Similarly, any incentive scheme proposed for less than 50% decision priority to LCES is proposed for BB1, as discussed in section 5.4.2. Incentive amounts determined from the proposed optimization algorithm under each decision priority are presented in Figure 15. The legend contains the capital costs of each retrofit option. 96 Figure 15 Incentive amount vs. relative importance of LCES to incentivize best 100 retrofit scenarios According to Figure 15, the Electric-HP HWU upgrade and the airtightness upgrades are suitable under any decision priority, and the incentives are proposed to match the capital cost of the retrofit. Upgrading the wall to R-31 is the other retrofit that is proposed to be incentivized under any decision priority. Multi-split ASHP is also a retrofit that is incentivized under any decision priority except when the only decision priority is LCES. When the focus is more biased towards LCCS than LCES, the multi-split ASHP is incentivized to match its capital cost. These are the decision scenarios where the wall upgrade is not incentivized to match its capital cost. If a higher priority is given to LCES, then the proposed incentives cover the entire capital cost of the wall upgrades. When 100% focus is on LCES, then Two-pane windows are incentivized instead of multi-split ASHP. Window upgrades cannot compete with the other retrofits due to their high initial cost when the financial performance is a priority. 5.4.3.2 Incentive effectiveness If the evaluation contained buildings with varying sizes (square footage), then interpreting the costs, emissions, and energy savings using a relative indicator such as per square foot energy demand, emissions, and cost is more suitable. However, the buildings considered in this study have identical envelops. Therefore, comparing the absolute values of LCCS and LCES achieved by different retrofit strategies provide a comprehensive idea about the potential economic and 2399 2399 2399 2399 2399 2399 2399 2399 2399 2399 23991103 1103 1103 1103 1103 1103 1103 1103 1103 1103 11032798 2798 2798 27984059 4059 4059 4059 4059 4059 405924393700 3700 3700 37002439 2439 2439 2439 2439 24390 % 1 0 % 2 0 % 3 0 % 4 0 % 5 0 % 6 0 % 7 0 % 8 0 % 9 0 % 1 0 0 %INCENTIVE AMOUNT (CAD)WEIGHTING FOR LCESElectricHP HWU (2,399) ACH 5 (1,103) Wall_R31 (4,059) 2 Pane Window (24,448) MultiSplitASHP (3,700)97 environmental benefits. However, these figures do not provide a clear indication about the effectiveness of incentive strategies. Therefore, incentive effectiveness was evaluated relative to the amount of incentives assigned to each retrofit strategy. The average LCES and LCCS reduction per dollar incentive were employed as the performance indicators for incentive effectiveness evaluation. The incentive effectiveness variation with the changing decision priorities (representing the variation in the relative importance assigned to the decision criteria) was calculated for the best 100 retrofit scenarios under each decision priority. The effectiveness results are summarized in Figure 16. The solid lines in both Figure 16 (a) and (b) indicate the average performance that can be expected. The dotted lines indicate the possible range within which the performance can vary due to uncertainties such as emission factors, energy prices, and occupant behaviors. (a) LCES per dollar investment vs. decision priorities (b) LCCS per dollar incentive vs. decision priorities Figure 16 Incentive effectiveness vs. relative importance of LCES As seen in Figure 16, the decision priorities significantly impact per dollar LCES and LCCS values. It is observed that LCES shows a rapid increment, and LCCS experiences a dip when the priority for LCES exceeds 50%. This observation can be mainly attributed to the characteristics of the base building to which the retrofits were applied. When the LCES priority is above 50%, all the top retrofit scenarios are related to BB2. When LCES priority is less than 50%, all the top retrofits are proposed for BB1 house. This observation highlights that more LCES can be achieved by retrofitting houses with NG heating. In contrast, more LCCS savings can be achieved from the retrofits applied on houses heated with electricity. 0510152025303540450% 50% 100%LCES Per $ IncentiveWeighting for LCESUpper BoundLCES per$IncentiveLower Bound-800-600-400-2000200400600800100012000% 20% 40% 60% 80% 100%LCCS Per $ IncentiveWeighting for LCESLower BoundLCCS per$IncentiveUpper Bound98 The retrofits applied on BB1 can produce 1 to 10 kgCO2,e LCES per dollar incentive. Even though this is not matching the LCES levels of BB2, this shows promising potential for community penetration due to the attractive LCCS delivered to building owners. Energy-conscious behavior can approximately double the emission savings achieved by retrofitting an electrically heated house compared to consumeristic user patterns. Therefore, if the city can promote energy-conscious behaviors, it can achieve the upper threshold of emissions and cost savings. Retrofitting BB2 houses makes more sense from a municipal point of view as those can create emission savings as high as seven times the savings of a similar building heated with electricity. As seen in Figure 16, implementing retrofits on BB2 with the view of emission savings can create life cycle financial losses in the range of 0-25,000$ even with the incentives proposed. Therefore, promoting energy retrofits to BB2 owners is still a challenging task even with the proposed incentives. When determining the financial viability of a retrofit project, it is essential to look into the capital cost in addition to the LCC figures because this is a critical barrier that can refrain the building owners from implementing energy retrofits. Figure 17 shows the capital cost and incentive variation with the increasing importance assigned to LCES. Figure 17 Capital cost and incentives vs. relative importance of LCES 05,00010,00015,00020,00025,00030,00035,00040,00045,00050,0000% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%Capital cost\IncentivesRelative importance of LCESLowerBound (CC) Average (CC) UpperBound (CC)LowerBound (Incentive) Average Incentive Level UpperBound (Incentive)99 Under any decision priority, the upper bound and the average incentive levels stay reasonably constant. The minimum incentive level increases slightly with the increasing priority assigned to LCES. However, the small increments observed in incentive amounts are negligible compared to the rapid increase of average capital cost with the increasing priority assigned to LCES. Here, the capital cost increase is proportional to the importance assigned to LCES because more retrofits and costly interventions are necessary to achieve higher emissions reduction levels. Below 50% importance assigned to LCES, the average incentive level approximately covers more than 25% of the average capital cost. With the high LCCS values and a high percentage of capital cost covered with the incentives, the retrofits for BB1 buildings show a higher community penetration potential. The average capital cost of best retrofit scenarios for BB2 varies from 25,000 to 40,000. Compared to these numbers, the proposed incentive levels will not make a convincing business case for building owners, especially when looking at the poor LCCS associated with the proposed upgrades. In short, there is a limit to the emissions savings that can be achieved via retrofitting while still maintaining financial viability and a business case for the building owners. Summary This chapter proposed a novel strategy to find the optimum incentive levels for individual energy retrofits to promote the best-performing retrofit scenarios (combination of retrofit options). The study was conducted considering two identical base buildings except for the energy source used for the space and water heating. It was observed that the retrofits applied on the house with electric space and water heating (BB1) showed a greater cost reduction potential with a relatively small amount of life cycle emissions savings compared to retrofitting the house with NG space and water heating (BB2). On the other hand, any retrofit applied on BB2 led to a very high life cycle emissions saving than BB1. However, the high LCES produced by the BB2 came at a net positive LCC even with the proposed incentives. Therefore, promoting emission reduction in BB2 is challenging under prescribed incentive approaches due to low cost savings and high capital costs in the best emission-saving scenarios. Moreover, merely by adopting energy-conscious behavior patterns over consumeristic practices, BB2 owners can create 112,182 kgCO2,e LCES and LCCS closer to 20,000$ without implementing any major physical retrofit over the twenty-year project period considered in the study. Therefore, adopting behavioral changes and smart controllers to BB-2 can be very effective in emissions saving. 100 When comparing the performance-based and prescriptive incentives, the latter seems to be a more straightforward process in the sense of implementation, as discussed in the background section of this chapter. This could be the potential reason behind the broader adoption of prescriptive incentives among local municipalities and utility providers in BC and many countries in Europe. Performance-based incentivizing could be potentially introduced as a second stage of building energy retrofit promotion campaigns after establishing the community awareness, infrastructure, and adequate human resources to assess building energy retrofit performance. In order to cultivate emission savings in the houses heated with natural gas in BC, the closest step is promoting energy-conscious behavior and the adoption of smart controllers. Reducing the emission factor of the NG supply by mixing RNG is another solution that is already being discussed. The life cycle emissions saving that is achieved by increasing the RNG fraction in the NG supply and simply adopting energy-conscious behavior in natural gas heated houses is almost two times the maximum emission savings achieved by any retrofit scenario related to electrically heated houses. This observation brings in a powerful justification for incentives planning for emissions reduction in the natural gas heated housing sector, especially via performance-based schemes. Prescriptive incentive programs for retrofitting the houses heated with NG will not be financially effective due to net positive life cycle costs, especially when source switching is involved. However, if the incentives were focused on adopting energy-conscious behavior, then the building owners will automatically experience cost and emissions savings when trying to achieve the benchmark performance levels while avoiding the need to apply costly renovations on BB2. Therefore, performance-based incentives can greatly support the local governments in BC to achieve the climate change targets compared to the prescriptive incentives. However, executing performance-based incentive programs involves more administrative and technical work, including building performance monitoring, evaluation, and benchmarking. Pushing the community towards switching from NG to electricity can help reduce the emission savings in the residential building sector. However, it is important to note that this source switching approach will increase the utility bills for building owners. A blinkered vision on source switching to reduce emissions could add other negative impacts with regards to housing affordability and energy poverty issues already faced by residents in British Columbia and other urban centers of the world. Therefore, promoting performance-based incentives alongside some prescriptive 101 incentives (subsidies and rebates) for building emission savings can be a more practical and effective approach. This approach will allow the building owners to select the best retrofit options flexibly to reach the emission reduction goals and reap the benefits. Chapter-6 investigates innovative project deployment strategies such as energy performance contracts and performance guaranties to share the financial and performance risks associated with energy retrofits. In addition to risk management, these project deployment strategies will help address the existing gaps in performance monitoring, prediction, and evaluation. 102 Chapter 6: Energy performance contract formulation for residential buildings The contents presented in this chapter is in the submission ready state and planned to be submitted under the title “Overcoming the financial barriers of retrofitting projects in smaller residences: A non-linear optimization approach for energy performance contract formulation under uncertainty”. Background Following the global efforts, North American climate leaders such as British Columbia (BC) have come up with innovative energy efficiency standards such as BC Energy Step Code (BCESC) for new constructions [26]. However, no such energy efficiency standards are developed for existing residential buildings in BC, Canada. As emphasized in the literature review and the previous chapters, retrofitting existing buildings is necessary to reduce the environmental impacts and operational costs associated with the building sector. Despite the incentive schemes initiated by the governments and non-governmental organizations, challenges including the principal-agent problems in rental buildings, inability to gather the required capital investments, awareness issues, and lack of investor confidence are still not adequately resolved [15][11]. Energy service companies (ESCO) provide financial risk mitigation models such as energy efficiency insurances and energy performance contracts (EPC). These financial models can be used to address the operational and financial risks faced by the owners employing means such as guaranteed savings [20]. Generally, ESCOs focus on larger buildings such as multi-unit residential buildings, commercial buildings, and building clusters owned by institutions [165]. Large-scale projects involve high investments and returns, which motivate both building owners and the ESCOs to be involved. Some literature suggests that EPCs are less successful in small-scale residential projects unless tied with other service agreements such as repair and maintenance [166]. However, introducing EPCs to small residential buildings can support streamline the retrofit implementation process, as discussed in section 3.3. Moreover, implementing EPCs in small residential buildings can help overcome the challenges faced by ESCOs and small players in financing large-scale projects [165]. Therefore, developing a workable EPC model for the residential sector can provide an opportunity for small-scale entrepreneurs to enter the energy 103 services market [165] and mitigate the challenges faced by building owners in acquiring capital investments for energy efficiency projects while managing financial and operational risks [20]. On the other hand, successful EPCs can produce energy and emissions savings. For example, some EPC ventures have reportedly improved the energy efficiency of the buildings by 22-45% [67]. On the other hand, the involvement of an ESCO in a renovation project can boost investor confidence and resolve principal-agent problems leading to wider community penetration of building energy retrofits. Moreover, it is likely that the quality of the retrofitting process is maintained at the best possible level by the ESCO as it profits from the energy savings produced during the contract period. Therefore, this is a topic worthy of investigation from both financial and environmental viewpoints. According to a Danish study, supporting tools and finances related to EPCs are stuck in a deadlock as one cannot proceed without the other aspect being improved [82]. EPC-related information needs to be provided to the investors through research studies as the finances are less likely to flow into an area where investor confidence is low. Therefore, financial models and support tools need to be developed as the first step towards successful community penetration of EPCs and energy retrofits focused on residential buildings. This chapter presents an EPC planning approach for residential buildings to address the research gaps presented in section 6.3.4. Methodology This chapter aimed to propose an approach for optimally planning EPCs under uncertain conditions. The proposed optimization process for EPC-based retrofitting project planning involves five main steps, including retrofit options identification, energy performance simulation, key performance indicator identification, and EPC modeling. Retrofit options scenario prioritization has been comprehensively discussed in Chapter 4: and Chapter 5:. Therefore, this chapter focused on the other main steps of retrofit project delivery. Overall decision-making process of the EPC planning approach proposed in this chapter is summarized in Figure 18. 104 Figure 18 Overview of the proposed EPC planning methodology The detailed methodology followed in the study is presented in the sections below. 6.2.1 Energy performance simulation For simulating the energy performance of small residential buildings in Canada, HOT2000 is the recommended and the most used energy simulation software package [6][29]. Therefore, HOT2000 (Version 11.3) was employed to simulate the energy performance of the selected case study building. The Housing Technology Assessment Platform (HTAP) developed by Natural Resources Canada (NRCan) was employed to feed alternative retrofit strategies (combinations of different retrofit options) into HOT2000 [156]. Energy performance results from the simulations were combined with the emissions and cost factors in the later stages of the study to evaluate the economic and environmental performance of different retrofit strategies. Building energy performance is sensitive to building operational conditions such as heating setpoint, cooling setpoint, etc. [11]. Therefore, the energy performance uncertainties associated with the case study building were evaluated considering potential operational variabilities. Details of the operational condition variations considered in this study are presented under the Case Study section. 105 6.2.2 Key performance indicator identification Selecting the performance indicators is critical for options comparison or optimization in any decision-making problem. In this study, the performance indicators were selected based on the literature. Life cycle cost (LCC) and net percent value (NPV), internal rate of return (IRR), payback period (PBP), and marginal abatement cost1 (MAC) were used in literature to evaluate the financial performance of retrofit and EPC projects [27][79][167]. Life cycle cost is the net percent value of all the cash flows involved during the life cycle of a given project. LCC assists decision-makers in understanding the anticipated financial performance of a project by the end of its life cycle. IRR is the discount rate at which the NPV of the project becomes zero. IRR is commonly compared against the weighted average cost of capital (WACC) to determine the profitability of a project. A higher IRR than the WACC usually indicates a good investment opportunity. The difference between IRR and WACC translates into the profit or the value addition created by a given investment to the investors [168]. Similarly, the WACC of the capital put forward for a retrofit project can be used to understand whether that investment is financially feasible. In this study, the net present life cycle cost-savings for the owner and the ESCO were considered as the performance indicators. The weighted average cost of capital The owner can solely finance the project, or a loan can be secured to cater to a portion of the capital cost requirements in which the owner bears the balance of the initial capital from available funds to him/her. This decision depends on the owner's financial constraints and/or the cost of equity. It was assumed that the ESCO leverages loans from third-party financial bodies to cover the initial investment balance when the owner cannot or does not want to fully fund the project. For given conditions, the WACC of a project can be calculated using Equation 30 [168]. 𝒓 =𝑰𝑪𝒐 ∗ 𝒓𝒆 + (𝑰𝑪 − 𝑰𝑪𝒐) ∗ (𝟏 − 𝒓𝒕) ∗ 𝒓𝒅(𝑻𝑪)𝑰𝑪 = (1 − 𝑟𝑡). 𝑟𝑑(𝑇𝐶) +𝐼𝐶𝑜𝐼𝐶(𝑟𝑒 − (1 − 𝑟𝑡) ∗ 𝑟𝑑(𝑇𝐶)) 1 The cost of avoided unit of pollution 106 When 𝜷𝟏 = (1 − 𝑟𝑡). 𝑟𝑑(𝑇𝐶) and 𝜷𝟐 = (𝑟𝑒 − (1 − 𝑟𝑡) ∗ 𝑟𝑑(𝑇𝐶)) 𝒓 = 𝜷𝟏 + 𝜷𝟐𝑰𝑪𝒐𝑰𝑪 Equation 30 Where, 𝑟 − Weighted average cost of capital 𝐼𝐶 − Total initial capital requirement of the retrofit project 𝐼𝐶𝑜 − Capital investment from owner 𝑟𝑒 − Cost of equity 𝑇𝐶 − Contract period 𝑟𝑑(𝑇𝐶)−Debt Rate for a given contract period 𝑟𝑡 −Tax Rate The WACC calculated above was used to discount the future cash flows of the proposed retrofit interventions in a given retrofitting project. Third-party financing When the ESCO involves a third-party financing body, the loan installment can be calculated using Equation 31, assuming that the loan continues throughout the contract period. 𝑳𝑰 = (𝑰𝑪 − 𝑰𝑪𝒐) ∗𝒓𝒅(𝑻𝑪)𝟏 − (𝟏 + 𝒓𝒅(𝑻𝑪))−𝑻𝑪 Equation 31 Where, 𝐿𝐼 − Loan installment Life cycle profits realized by the ESCO For a given contract period (Tc), the net present value (NPV) of the costs and savings observed by the ESCO throughout the contract life cycle is represented in Equation 32. 𝒇𝟏(𝑥) = (𝑨𝑪𝑺 − 𝒌 − 𝑳𝑰) ∗ {(𝟏 + 𝒓)𝑻𝑪 − 𝟏𝒓. (𝟏 + 𝒓)𝑻𝑪} Equation 32 107 Where, 𝑥 = (𝐼𝐶0, 𝑘) 𝑘 − Guaranteed energy savings to the owner 𝐴𝐶𝑆 − Annual cost-savings 𝑇𝐶 − Contract period The life cycle profits realized by the owner The total net present cost-savings realized by the building owner during the proposed retrofit project are represented in Equation 33. 𝒇𝟐(𝒙) = −𝑰𝑪𝒐 + 𝒌 ∗∑{𝟏(𝟏 + 𝒓)𝒕}𝑻𝑪𝒕=𝟏 + 𝑨𝑪𝑺 ∗ ∑ {𝟏(𝟏 + 𝒓)𝒕}𝑻𝑷𝒕=𝑻𝑪+𝟏 Equation 33 Where, 𝑇𝑃 −Project period The project period is the total time the owner plans to own the house after the renovation. After the contract period, the owner receives the entire cost-savings produced by the retrofitting project as the ESCO moves out of the contract. The project period indicates the time that the current owner expects to own the retrofitted house. Even though the implemented retrofits generate savings until the end of the lifetime of the retrofits, neither the ESCO nor the owner is going to receive any direct benefits after the project period, as the home ownership changes. Therefore, the cost-savings incurred after the project period were not considered in the EPC evaluation process. 6.2.3 Energy performance contract formulation After determining the costs of a given retrofit project, ESCO and building owners compete for profits. The ESCO help in increasing the confidence of the building owner to commit to a retrofit project by sharing the risk through an energy performance contract. The ESCO has to build a compelling argument to secure the project while achieving the highest possible profit margins. Generally, any residential building owner has budget constraints on the maximum capital investment that can be allocated for a building renovation project. In such situations, the ESCO 108 can cover the capital cost deficit by securing a loan by a third-party financial body, as discussed above. Two main financial models, including the energy/cost-savings guarantee and profit-sharing, are prevalent in most of the EPC models discussed in the literature. However, according to the literature, the building owners are generally more interested in avoiding losses and knowing the expected profits upfront [20]. Therefore, this study employs the energy/cost-savings guarantee model to formulate the EPC. Nevertheless, the proposed optimization model can be easily modified to plan EPCs formulated according to the profit-share model by changing the expressions for profits realized by the owner (𝑓2(𝑥)) and ESCO (𝑓2(𝑥)). The proposed model will help enhance the confidence of Canadian residential building owners in investing in energy retrofitting projects by allowing informed decision-making. 6.2.3.1 Optimization problem The optimization variable 𝑘 represents the optimal value of the annual energy cost-savings guarantee. 𝐼𝐶𝑜 represents the optimal capital investment amount from the building owner under given conditions. The optimal values for the above parameters can vary depending on the decision priorities, the capital contribution from the owner, contract period, and operational uncertainties. Therefore, an optimization algorithm was developed to find the optimal values for 𝑘 and 𝐼𝐶𝑜, accounting for stakeholder decision priorities for a given contract period. The Monte Carlo approach was employed to account for uncertainties. The following vector can present the optimization variables: 𝑥 = (𝐼𝐶0, 𝑘) The optimization was iteratively conducted for each contract period (TC). For each 𝑇𝑐 = 1,2, … , 𝑇𝑃, the objective function 𝐺(𝑥) can be written as below: 𝑮(𝒙) = 𝑾𝟏 ∗ 𝒇𝟏(𝒙) +𝑾𝟐 ∗ 𝒇𝟐(𝒙) Equation 34 Where, 𝑊1 – Weight of maximizing profits for the ESCO 𝑓1(𝑥) –Profits realized by the ESCO 𝑊2 – Weight of maximizing profits for the owner 𝑓2(𝑥) –Profits realized by the owner 109 The optimization has to be conducted binding to the constraints discussed below. The building owner should at least be able to provide sufficient initial capital to close the gap between the total capital requirement and the maximum capital contribution provided by the ESCO (𝐼𝐶𝐸𝑆𝐶𝑂,𝑀𝑎𝑥) via the discussed third-party loan. Otherwise, the project cannot move forward. Therefore, the following inequality should be met for the project to move forward. 𝑰𝑪 − 𝑰𝑪𝑬𝑺𝑪𝑶,𝑴𝒂𝒙 ≤ 𝑰𝑪𝒐 ≤ 𝑰𝑪𝑶𝑾𝑵𝑬𝑹,𝑴𝒂𝒙 Equation 35 Overall project profitability can be determined by considering the internal rate of return (IRR) of the cashflows against the WACC. Following inequality must be satisfied for the investments to be profitable for both players in the game. 𝑰𝑹𝑹 > 𝑾𝑨𝑪𝑪 Equation 36 As the contract period increases, the ESCO has to keep on monitoring the project, and they increase the risk of facing cost-saving reductions due to component deterioration. Therefore, for each year the ESCO is in contract, there should be a compelling profit increment. Thus, the minimum profit expectation of the ESCO (𝜂1) was defined as a function of the contract period. 𝜼𝟏 = 𝜽𝟏 + 𝜽𝟐 ∗ 𝑻𝑪 Equation 37 Where, 𝜃1 − Starting profit level in order to enter the performance contract 𝜃2 − Additional profit share per each year the performance contract continues 𝜃1 and 𝜃2 has to be defined considering the overhead costs and minimum profit expectations of the company. Total profits realized by the project have to be higher than or equal to the minimum profit expectation of the ESCO. 𝜼𝟏 < 𝒇𝟏(𝒙) Equation 38 Total profits realized by the owner has to be higher than or equal to their minimum profit expectation (𝜂2) to motivate the building owner to enter into the EPC. 110 𝜼𝟐 < 𝒇𝟐(𝒙) Equation 39 The discussed optimization problem is summarized below. Optimization problem-2: 𝒙∗ = 𝐦𝐚𝐱{𝑮(𝒙)} S.T Constraints: i. IC − ICESCO,Max ≤ ICo ≤ ICOWNER,Max ii. IRR > WACC iii. η1 < f1(x) iv. η2 < f2(x) 6.2.3.2 Monte Carlo Simulation A Monte Carlo simulation was conducted to investigate the uncertainties associated with the project profits. Annual energy savings and energy prices can vary over the project lifetime due to operational condition variations, weather, and energy supply conditions. Therefore, these parameters were modeled as uncertain variables. Normal probability distribution was used to model the said variables. The probability distributions were defined based on the operational energy saving values obtained from the energy simulations and the energy price variations predicted based on potential future energy supply conditions. Each variable was represented using 10,000 points in the Monte Carlo simulation. The particulars of the uncertain variables are discussed in Section 6.3.2. Case study The proposed research methodology was demonstrated using a case study. The details of the case study are presented below. 6.3.1 Base building definition and retrofit strategy selection An electrically heated building (BB-1) from Chapter 4: was used in this case study [11]. Table 15 and Table 16 summarizes the building details. 111 Table 15 Base building characteristics Heating Hot Water Window Wall Ceiling Infiltration Ventilation NG Furnace (78% AFUE) Conventional Tank (NG) (EF=0.5543) Single Pane R10 R10 7.5 ACH @50Pa 28 L/s Table 16 Baseload and occupancy characteristics Electrical Appliances Minor (other) electrical appliance Lighting Average exterior energy use Hot water load Occupancy 10.68kwh/day 0.29kWh/day 2.6kWh/day Negligible 247 liters/day 3 x (50%) According to Chapter 4:, upgrading the BB-1 can produce cost savings, and sometimes life cycle emission savings can be negative. The total energy requirement of BB-1 before retrofitting was 56,767 kWh (all energy provided by electricity). The energy performance of the base-building after applying different retrofit strategies was simulated using the HOT2000 software. Space heating systems, hot water units, and building envelope-related retrofits are commonly used to improve the energy performance of existing buildings. Therefore, best-performing retrofit options from the Chapter 4: were considered for this case study and are presented in Table 17 [11]. Table 17 Retrofitting options summary Heating Hot Water Window Wall Ceiling Infiltration Multi-Split ASHP HSPF-9.9 BTU/watt-hr COP-2.9 28kBTU Heat Pump system (EF=1.901%) Double Pane Low-E Hard Coat Air Fill R31 R40 5.0 ACH @50Pa Tier-1 ASHP Tier-2 ASHP The retrofit options considered above were combined to produce 128 retrofit strategies for the reference building. The LCCS produced by all retrofits was evaluated considering a 10year project period. The most cost-effective retrofit strategy was selected based on LCC savings. The proposed EPC planning model was applied to the most cost-effective retrofit strategy. 112 6.3.2 Uncertain conditions The uncertainties associated with the energy savings of each retrofit strategy were quantified by simulating the varying operational conditions. Operational variabilities presented in Chapter 5: (shown in Table 8) were adopted to represent the associated uncertainties with this case study. The average electricity price was taken as 11.62 cents/kWh with a 10% price variability [11]. 6.3.3 Economic parameters This section contains the economic parameters used in the case study. The tax and risk-free rates for home renovation projects were taken as 5% and 2.04%, respectively. The cost of equity for green and renewable energy projects was found to be 5.6% from the literature [169]. The debt rate variation with time was derived based on a data set on Canadian building renovation loan rates from an online resource [170]. The following trendline equation was developed to reflect the variation of the loan rate with the contract period. 𝑟𝑑 = (2.3746 ∗ 𝑙𝑛(𝑇𝐶) + 0.1866) ∗ 10−2 Equation 40 6.3.4 Decision priorities The decision priorities have to be found based on the owner's and the ESCO’s expectations for maximizing priorities. For demonstration purposes of the proposed algorithm, the decision priorities were adopted from the literature [20]. In order to understand the impact of the decision priorities on the final results, the algorithm was executed for decision scenarios listed in Table 18. Table 18 Decision priorities Scenarios W1 (ESCO) W2 (Owner) ESCO Profit Maximisation Scenario 2.25 1 Owner Profit Maximisation Scenario 1 2.25 6.3.5 Optimization constraints Minimum profit expectations of the ESCO depend on the company agenda. For demonstration purposes, the minimum profit requirement of the ESCO for entering the EPC (𝜃1) and the minimum net present profit for staying an additional year in the contract (𝜃2) were taken as 1000CAD and 500CAD/year, respectively. In this analysis, the annual overhead costs for the 113 ESCO were not included as data relating to that are not available. However, when data is available, the overhead costs can be included as an annual cash flow that has to be borne by the ESCO. The minimum net present profit expectation of the owner from the project was set as 1000CAD assuming that the building owner does not bear any additional cost than the planned capital cost contribution. During the contract period, the owner has assurance from the ESCO regarding the energy savings. However, extended contract periods mean that the owner has to share the profits with the ESCO for a longer period. Details of the optimization (optimization problem-2) are presented in Section 6.2.3.1. Results and discussion This section presents and analyzes the results generated from the case study. 6.4.1 Cost Optimal Retrofit Strategy The cost-optimal retrofit strategy was selected by ranking the considered retrofit strategies using a fuzzy number ranking mechanism (min-max method)[154]. In this ranking process, the LCCS was considered as the performance indicator. The chosen retrofit options and the capital costs associated with each retrofit are presented in Table 19. In this scenario analysis, the potential of source switching from electricity to NG was not considered as it is not promoted in the British Columbian climate change mitigation discussion. However, if the potential of source switching was considered, substantially higher cost-savings can be obtained due to the low energy cost of NG compared to electricity in BC. 114 Table 19 Capital cost of cost optimal solution System Option Capital Cost ($) ACH ACH_5 1,103 Wall R31 4,059 Window Do not upgrade - Ceiling Do not upgrade - Space Heating Multi-Split ASHP HSPF 9.9 BTU/watt-hr COP2.9 28kBTU 3,700 Hot water system Electric Heat Pump operated hot water system 2,399 Total cost 11,261 The energy and cost savings achieved by applying the cost-optimal retrofit strategy is presented in Table 20. Savings were calculated assuming the operational parameters do not significantly differ from the usual operational conditions when the retrofits are implemented. The variability for the operational conditions for both pre- and post-retrofit periods was modeled considering the conditions presented in Table 8. A price variability of ±10% was considered for electricity unit price when calculating the annual cost-savings. The performance outcomes under uncertain conditions are presented in Table 20. Table 20 Energy performance under uncertain conditions Lower Bound Likely Value Upper Bound Base Building (kWh/year) 51,086 56,767 62,596 Retrofitted Building (kWh/year) 22,383 24,932 27,627 Energy Savings (kWh/year) 28,703 31,835 34,969 Annual cost-savings (CAD/year) 3,410 3,820 4,238 115 6.4.2 Energy Performance Contract The Monte-Carlo simulation was conducted considering a normal distribution for the annual energy savings (mean = 3820, range = [3410, 4238]) and the unit energy price (mean = 11.62 cents/kWh, range = [10.458, 12.782]). Both parameters were considered to be normally distributed in the given range and were modeled by 10,000 data points. The optimization results obtained from the proposed model are presented in this section. In the optimization, it was assumed that the owner was going to own the house for ten years for demonstration purposes. Suppose the owner decides to keep the house for more than ten years. In that case, the selected retrofit strategy will keep generating cost savings until the end-of-life cycle of the selected retrofits, which can be expected to be around 15 to 25 years. Therefore, if the owner keeps the house for a longer period, they can realize higher profits than the values discussed in the sections below. 6.4.2.1 Owner’s profit maximization scenario The owner’s profit maximization scenario was defined to give higher priority to the owner’s profit by assigning a higher weight to the owner’s expectations over the ESCO (2.25:1), as discussed in section 6.3.4. Depending on the factors, including the capital constraints and stakeholder’s risk preferences and profit expectations, they can agree on a contract period. The optimal range of guaranteed energy savings level and the optimal capital cost born by the owner for each contract period is presented in Figure 19. The stakeholders can use this information to agree on a contract period. 116 Figure 19 Owner’s capital cost contribution and guaranteed savings variation against the contract period The dashed lines indicate the possible range of values that a given parameter can take due to uncertainties, while the solid lines indicate the likely scenario. Figure 19 helps both stakeholders to understand the worst case, best case, and likely conditions to be expected from this contract. When the contract period is shorter, the ESCO has to secure a higher portion of the profits in order to achieve the expected minimum profit level by the end of the contract. Therefore, the optimal guaranteed savings level stays at the minimum guaranteed cost-savings value defined in the algorithm (100CAD) up until the contract period exceeds four years. When the ESCO stays in the contract longer, ESCO realizes more profits than ending the contract in a shorter period. In that case, the owner capital cost contribution has to reduce, and the guaranteed savings amount has to increase in order to maximize the owner’s profit. Figure 20 presents the impact of the contract period and the optimization variables on the profits realized by the stakeholders. 0200400600800100012001400160018000100020003000400050006000700080009000100001 2 3 4 5 6 7 8 9 10Guaranteed Savings ($)Capital Cost contribution from owner ($)TC (Years)ICGuranteed Savings117 Figure 20 Owner's profit and ESCO's profit against the contract period Until the TC=3, the total project profit keeps increasing, according to Figure 20. After three years, the total project profit starts declining due to increasing WACC with decreasing owner capital contribution. This phenomenon can be understood by paying attention to the constants 𝛽1 and 𝛽2 presented in Equation 30. Until Tc=3, 𝛽1 is higher than 𝛽2. Therefore, decreasing capital contribution from the owner help improve the total project profit by minimizing the WACC. However, when Tc exceeds 3, the debt rate keeps on increasing, causing the 𝛽2 to be higher than 𝛽1, thereby increases the WACC causing a lower overall project profit. However, the owner has to contribute a lower capital cost portion as their profit tends to drop with ESCO’s demand of 500CAD more from the net present profits for each year they stay in the contract. Under the given decision priorities, it is beneficial for the ESCO to stay longer in the contract and maximize the profits. The owner’s profit maximizes if a contract period of 2 years is selected. However, it is important to note that the building owner has no performance guarantee when the ESCO moves out of the performance contract after that point. Therefore, the owner has to assume the risk of not receiving the promised savings if they want to maximize the profits. Even though longer contracts enhance ESCO’s profits, the ESCO has to assume the risk of having to compensate 05000100001500020000250000100020003000400050006000700080009000100001 2 3 4 5 6 7 8 9 10Owner's profit ($)ESCO's profit / Total profit ($)TC (Years)ESCO ProfitOwner ProfitTotal Profit118 for not being able to meet the promised energy savings by staying longer in the performance contract. Moreover, longer contract periods demand the ESCO increase the debt portion of the initial investment, therefore, the company’s liability. From the owner’s point of view, longer contract periods result in very low to zero capital contributions. This is beneficial for owners who are facing issues in securing the required capital cost for the project. Moreover, longer contract periods can help resolve principal-agent issues in rental properties when the ESCO is bearing the total capital cost. In such a situation, the ESCO can make its profit maximization a top priority. In conclusion, it is safe to mention that the contract period selection is a discussion that both parties must have at the contract formulation stage, considering their risk appetite and other conditions discussed above. 6.4.2.2 ESCO’s profit maximization scenario This scenario was defined to prioritize ESCO’s profit by assigning a higher weight to the ESCO’s expectations over the owner (2.25:1), as discussed in section 6.3.4. The optimal range of guaranteed energy savings levels and the optimal capital cost born by the owner for each contract period under given decision priorities are presented in Figure 21. Figure 21 Owner’s capital cost contribution and guaranteed savings against the contract period 0204060801001201400200040006000800010000120001 2 3 4 5 6 7 8 9 10Guaranteed Savings ($)Capital cost contribution from owner ($)TC (Years)Capital CostGuranteed Savings119 In this decision scenario, a higher priority was assigned to the profits realized by the ESCO compared to the owner. Therefore, the goal should be to get the maximum capital contribution from the owner while providing the minimum guaranteed savings during the contract period. At lower contract periods, maximizing the profits for the ESCO is difficult due to the shorter time frame. Therefore, when the contract period is shorter (≤5 years), the building owner bears the total capital cost, and only the minimum guaranteed cost-saving amount will be provided. As the contract period gets closer to the project life, then the capital cost contribution from the owner decreases to enable realizing minimum profit expectations of the owner. When the contract period is ten years, the guaranteed savings slightly increase to meet the owner's minimum profit expectations. Figure 22 Owner's profit and ESCO's profit against the contract period As shown in Figure 22, the profits realized by the owner decrease with the increasing contract length. If the contract period extends beyond five years, the owner can only realize the minimum profit expectation agreed before (set as a constraint in the contract formulation). On the other hand, the profits realized by the ESCO rapidly increase when the contract period changes from 1 to 5 02000400060008000100001200014000160001800020000010002000300040005000600070008000900010000110001200013000140001500016000170001800019000200001 2 3 4 5 6 7 8 9 10Owner's profit ($)ESCO's profit \Total Profit ($)TC (Years)ESCO's ProfitOwner's ProfitTotal Profit120 years. After, there is a gradual increase in the profit until Tc=7. The slowdown of the profit increase and profit drop observed when Tc exceeds seven years results from the increasing WACC due to increasing loan interest rates with the increasing contract period and the decreasing capital contribution from the owner. Similar to the previous decision scenario, profits realized by the owner are higher when the contract period is lower. At the same time, the ESCO can realize higher profits when the contract periods are longer. Moreover, the owner may also like extended contract periods in rental situations as the capital cost requirement decreases. However, the same risks discussed in the previous decision scenario have to be borne by both parties when attempting to maximize profits. Summary This chapter proposed a novel energy simulation-based EPC formulation technique for residential building renovation projects. The uncertainties in energy prices and building operational conditions were accounted for by employing the Monte-Carlo simulation approach. The results showed that the uncertainties could change the optimal contract parameters and the expected profits significantly. The proposed approach allows the decision-makers to be aware of the outcomes of the performance contracts, thereby selecting the contract parameters to match their risk appetite and other priorities and constraints. The proposed contract optimization approach can be applied for EPC formulation for any building irrespective of the archetype or the location. The decision priorities (weights) can change the contract parameters leading to significant changes in the profits. However, it was observed that irrespective of the decision priorities (weights) assigned to the objective function, the profits realized by the ESCO maximize with the increasing contract periods while the profits realized by the owner have a general decreasing trend with the contract period. It is important to note that the increasing contract period reduces the risks faced by the owner of being affected by the performance gap. However, extended contract periods increase the risk for the ESCO of having to compensate for the building owner if the upgrades fail to produce the anticipated savings. Moreover, the extended contract period results in lower capital contribution from the building owner, binding the ESCO to secure higher loan amounts. Lower capital contributions can help overcome the uneven benefit distributions experienced by the owners in the rental housing market. Therefore, EPCs can help to promote energy retrofits to the rental building sector as well. 121 The success of an EPC project totally depends on the economic performance of the implemented retrofits. It is safe to say that the EPCs can be employed to effectively deliver building energy retrofitting projects by overcoming common challenges such as capital investment barriers, lack of investor confidence, principal-agent problems, and pre- and post-retrofit performance gap. However, it is important to note that the most cost-effective energy retrofit strategies may not necessarily provide life cycle emission savings. Therefore, policy tools and incentives have to be developed to simultaneously realize emission savings parallel to cost savings from energy retrofitting projects. 122 Chapter 7: Conclusions and recommendations The increasing awareness about climate change has initiated numerous efforts to mitigate anthropogenic GHG emissions. Energy efficiency improvements and energy conservation have become global priorities with the aim of reducing associated GHG emissions. Building energy efficiency and energy demand management strategies have been a key focus of the climate change mitigation initiatives. However, energy efficiency discussion is unlikely to gain momentum if financial implications associated with the problem are not carefully handled. Moreover, it is essential to evaluate the actual outcomes of the climate change mitigation initiatives from a life cycle lens, going beyond the traditional approach of focussing only on the operational phase. This research proposes a decision support framework to address three critical gaps in the energy efficiency enhancement of existing buildings, including retrofits selection, retrofits promotion, and project delivery under uncertain conditions from a life cycle thinking lens. The proposed strategy is expected to support municipalities, government bodies, and climate change initiatives by providing means to develop incentive strategies to improve the acceptance of environmentally friendly retrofits by the building owners. Moreover, it supports the building owners and contractors by providing means to identify optimal retrofit strategies under uncertain conditions, including energy price, occupant behavior, and other operational conditions. Moreover, it assists in the financial planning of retrofit projects via energy performance contracts. The proposed approach can provide solutions to multiple issues, including capital cost constraints, design performance gap, and split-incentive issues. In summary, the findings of this research can be used as the initial step for developing a comprehensive retrofits promotion, planning, and implementation strategy for communities. Notably, the proposed approach can support the climate change initiatives while supporting the local companies and building owners in realizing financial benefits. Summary and conclusions Key takeaways and conclusions derived from each chapter are presented below. Chapter 3 presents the findings from literature related to the thesis discussion. This literature review critically discusses the key areas related to the retrofits planning process, including costs and benefits, performance evaluation approaches, financial planning methods, and contracting strategies. 123 The findings from the literature review indicated that life cycle assessment is not commonly employed in environmental performance evaluation exercises of building energy retrofit projects. Especially the embodied impacts of retrofits have been overlooked, which can be a significant portion of the life cycle impacts. Findings from the life cycle assessments conducted in the thesis confirmed the importance of employing life cycle assessments in retrofit project evaluation. Another key research gap identified during the literature review was the need for comprehensive financial modeling tools to promote energy retrofits and facilitate retrofit project delivery. It was identified that financial incentives planning needs attention in retrofits promotion exercises. Moreover, findings indicated that energy performance contracting could help mitigate multiple issues faced during the retrofit project delivery, including capital cost barriers, low investor confidence, split-incentive issues, and poor quality of project delivery. Further investigations indicated the need for a comprehensive energy performance contract design approach that can account for multi-stakeholder nature and the uncertainties associated with the problem. The research gaps addressed in the thesis are derivatives of the findings from this chapter. Moreover, the literature review was instrumental in choosing the methods to achieve the goals of the proposed retrofits planning approach. Chapter 4 focussed on understanding the regional applicability of different building energy retrofits. Economic and environmental performance of commonly used building energy retrofits were evaluated under different local conditions (i.e., grid emission factor, energy prices, weather) employing a life cycle thinking approach. The energy performance of various retrofit options applicable for single-family detached houses was simulated using HOT2000 energy simulation software for the varying climatic conditions and energy supply scenarios across Canada. The retrofits were evaluated in terms of the life cycle cost and emissions reduction. An eco-efficiency parameter was defined to classify building energy retrofits depending on the economic and environmental performance. The defined eco-efficiency parameter helps the stakeholders involved in the retrofits discussion to identify a set of suitable energy retrofit options that can be used to achieve their goals. It was identified that operational emission savings do not necessarily mean that a given retrofit essentially produces emission savings when accounted for the entire life cycle, including production and disposal phases. This observation confirmed the need for employing life cycle 124 assessments to understand the true environmental performance of building energy retrofits. According to the findings, not all retrofits that reduce emissions make economic sense and vice versa. The provincial energy mix and the heating energy source used in a house (i.e., electricity or natural gas) play a significant role in determining the effectiveness of a retrofit. It is important to note that the meaning of “effectiveness” changes for different stakeholders depending on their expectations (i.e., economic benefits, environmental benefits). In provinces with low emission electricity grids compared to natural gas, switching from natural gas to electric heating can produce significant emissions savings. In such provinces, upgrades applied to the house heated with natural gas showed higher emissions saving potential compared to the electrically heated house. On the other hand, in provinces with high emission electricity supplies compared to natural gas, moving from electricity to natural gas heating can produce emissions savings. In such provinces, upgrades applied to the house heated with electricity showed higher emissions saving potential compared to the house heated with natural gas. Irrespective of the emission factors of energy supply, natural gas heating systems proved to produce higher economic benefits compared to electric heating systems. The findings from this chapter will be helpful for building owners and occupants, and policy developers and other decision-makers to obtain a high-level understanding about the regional applicability of retrofits. Chapter 5 proposes a fuzzy-based retrofit selection approach under uncertain conditions and a linear optimization algorithm to determine the optimal incentive levels to promote energy retrofits. This chapter integrates commonly overlooked factors such as embodied emissions, contradicting stakeholder expectations, and the associated uncertainties into the retrofits planning process. Retrofits may deliver both or either emissions savings or cost savings depending on the chosen retrofit strategy, the condition of the building in which the retrofits were applied, and the local conditions, as discussed earlier. Moreover, the uncertainties associated with the operational phase, such as weather and occupant behavior, can change the project outcomes, including the cost and emissions saving potential. Unpredictable outcomes have the potential to reduce the confidence of investors, thus refrain them from entering retrofitting projects. Therefore, a sensitivity analysis was conducted to identify the uncertainties introduced by different operational parameters to the expected outcomes. A fuzzy-based multi-criteria retrofits ranking model was developed to identify 125 the best retrofit packages for residential buildings considering local conditions, associated uncertainties, and contradicting decision priorities. Promoting emissions reduction via retrofitting can face challenges depending on the local energy prices and emission factors, the retrofit implementation cost (i.e., capital cost, labor), and user priorities. Financial incentives can be used to enhance the economic viability of energy retrofits with high emission saving benefits and low economic viability. Therefore, an incentive planning approach was proposed to promote retrofits with higher emission saving potential. An optimization algorithm was developed to determine the amounts of financial incentives needed to promote retrofits that can produce environmental and cost benefits under different decision priorities. The use of proposed retrofits selection and incentive planning methods was demonstrated using a case study building located in Kelowna, BC. It was observed that the retrofits applied on the house with electric space and water heating have a significant cost-saving potential. On the other hand, retrofits applied on the house heated with natural gas showed greater emissions reduction potential. Notably, the emissions savings achieved in a house heated with natural gas by simultaneously adopting energy-conscious behavior or smart controllers, and renewable natural gas inclusion in the supply grid can be approximately two times the emissions savings achieved by any retrofit strategy applied on an electrically headed house. This indicates the importance of employing smart controllers and improving community awareness regarding energy-conscious behaviors. However, the retrofits applied on houses heated with natural gas tend to incur net positive life cycle costs even after applying financial incentives. Prescriptive incentives incentivize an action (i.e., implementing an energy retrofit), while performance-based incentives incentivize an outcome (i.e., reduction in energy use). Even though the latter seems to be more accurate in appraising the outcomes, the prescriptive approach is straightforward and easy to implement. Due to this reason, prescriptive incentives are relatively common in retrofits promotion strategies. Performance-based incentivizing could be potentially introduced as a second stage of building energy retrofit promotion campaigns after establishing the community awareness, infrastructure, and adequate human resources to assess pre- and post-energy retrofit performance of buildings. The results from the case study showed that electric heat pumps, wall upgrades, and air tightness improvements should be incentivized under all decision priorities (the relative importance assigned to life cycle cost savings compared to life cycle 126 emissions savings). When the importance of emissions savings is higher, the incentives provided to the wall upgrades have to increase while the incentives applied to the multi-split air source heat pumps decrease. Window upgrades were chosen for incentivizing when the decision priority was only to reduce emissions. Window upgrades were not chosen for incentivizing when both life cycle emissions and cost savings were simultaneously considered as decision priorities due to the relatively higher capital cost compared to the other retrofits. Research outcomes from this chapter provide means to identify effective retrofit strategies for the building owners. Moreover, a systematic approach for incentives planning for the policy makers under contradicting decision priorities was proposed to achieve climate change mitigation goals. Chapter 6 proposes an energy performance contracts (EPC) planning approach. EPCs have been identified as an effective solution for challenges faced by energy retrofitting projects, including lack of investor confidence and awareness, insufficient funding availability, poor quality of project delivery, and split incentive issues pose challenges for energy efficiency projects in the residential building sector. However, identifying the optimal financial structure for a given EPC is challenging due to uncertainties and contradicting stakeholder expectations. The lack of literature focusing on implementing EPCs for the small residential building sector is not helping this situation either. Exiting residential EPC planning models overlook the impact of the cost-of-capital in the project evaluation and formulation process. Overlooking the cost-of-capital in project evaluation can lead to inaccurate conclusions about the financial performance of retrofit projects involving third-party financing support such as loans. Therefore, a non-linear multi-objective optimization approach was developed to identify the optimal cost-savings guarantee amount and capital contributions split between different financial sources, including owner equity and loans for a given contract period. The proposed approach was demonstrated using a case study. The findings confirmed that short contract periods are profitable for the building owners while extended contract periods increase the profits for the energy services company (ESCO). However, the building owners benefit from the performance guarantee for the entire contract period. Therefore, longer contract periods are suitable for risk-averse building owners. Moreover, with the longer contract periods, the capital cost borne by the owner reduces. Therefore, longer contract periods can be employed to overcome the capital barriers and the split-incentive issues faced by rental building owners and occupants. 127 Despite the higher profits, extended contract periods increase the risk for the ESCO of having to compensate for the building owner if the upgrades fail to produce the anticipated savings. Moreover, extended contract periods result in lower capital contribution from the building owner, binding the ESCO to secure higher loan amounts. Therefore, the contract period negotiation has to be done by the ESCO and owners considering their risk appetites, profit expectations, and willingness to enter into long-term financial commitments. The structured EPC formulation process presented in this chapter can increase the popularity of EPCs in the residential retrofit market by allowing the stakeholders to make informed decisions about their retrofitting projects. Chapter 7 provides concluding remarks for the thesis based on the research findings. It summarizes the pathway for promoting and implementing energy retrofits in the residential building sector. This chapter discusses the roles of stakeholders and the connectivity of the planning solutions proposed in the thesis based on the research findings from the previous chapters. Figure 23 presents the connections and flow of the planning solutions proposed in the thesis to promote and effectively implement energy retrofits in small residential buildings. This research covers key areas in energy retrofit planning including, retrofits selection under uncertain conditions, retrofit promotion through incentivizing, and implementation strategies employing energy performance contracts. However, implementation of the proposed retrofits planning approach needs regulatory support and community awareness programs. 128 Figure 23 Energy retrofits implementation pathway Implementation of the proposed retrofitting strategy needs support from the government, industry, and the community. The local governments can enhance community awareness, provide means to improve industry readiness through training programs and workshops, and develop policies and incentive strategies to promote energy retrofits. The industry partners can make themselves open to eco-friendly retrofitting strategies while maintaining profitability. Local governments, utility providers, researchers, sustainable material producers, energy consultancy companies and architects, and contractors all have to be onboard to enable the implementation of the retrofits with economic and environmental benefits. Utility providers can work on providing low emission energy sources at competitive prices. Researchers and academics can work on important areas including, retrofit-related design improvement approaches, sustainable material development, locally applicable life cycle impact and cost database development, and planning support tools development. Financial institutes can contribute by providing loan facilities for energy retrofit projects to overcome capital barriers. energy services companies (ESCOs) can facilitate the streamlined implementation of retrofit projects while providing performance guarantees to the building owners. Entrepreneurs have to step into address the lack of ESCOs that provide energy performance contracting services for small residences. Government agencies and local start-up promotors can support the initiation of ESCOs. Providing training opportunities for the existing 129 project management companies to provide energy services can be another viable approach. This approach can potentially fast-track the introduction of EPCs to the small residential building market, as these project management companies are already established entities in the market. In summary, it is fair to conclude that energy efficiency improvement projects in the existing buildings can only succeed through a combined effort of all the stakeholders discussed above. Originality and contributions This thesis presents four unique contributions to support the energy retrofits planning process at the policy and individual building levels. An eco-efficiency classifier to identify the regional applicability of retrofits: Even though some studies have discussed the pros and cons of different retrofit options, a comprehensive study has not been done to evaluate the regional applicability of different retrofit options for Canadian cities. Moreover, no published study has proposed a systematic evaluation approach and an eco-efficiency parameter to classify retrofits depending on the life cycle cost and emissions saving potential. The eco-efficiency parameter proposed in this thesis helps the stakeholders identify the best retrofit options to reduce building-related emissions or costs or reduce a bit of both. Moreover, the life cycle thinking-based approach adopted in evaluating the regional applicability of the retrofits can be extended to develop lists of retrofit options for individual cities to achieve the intended cost and environmental benefits under the local conditions such as weather, energy mix, and building archetypes. The proposed classification approach will support the policymakers in identifying the retrofit options that can be incentivized to achieve regional emission reduction targets. Moreover, it will support the building owners and contractors in identifying the retrofit options to consider in renovation projects. A life cycle thinking-based energy retrofits strategy selection approach under uncertain conditions: The published studies on residential retrofits planning generally overlook the retrofits' life cycle impacts, uncertainties, and contradicting stakeholder expectations in the retrofits planning process. The interactions of retrofits when simultaneously implemented are also generally overlooked. The proposed retrofits strategy selection approach addressed all the aspects discussed above, allowing the stakeholders to select the best retrofits strategies suitable for their priorities under uncertain environmental, energy supply, and operational conditions. 130 An incentives optimization approach to promote selected retrofits strategies: Currently, many financial incentive programs are already in operation in Canada and other countries. However, a literature review indicated that no systematic approach had been proposed in the existing body of knowledge on how to promote selected retrofits strategies (retrofit combinations) by incentivizing individual retrofits. The proposed optimization approach allows determining the optimum financial incentive levels for individual retrofits depending on the relative decision priorities assigned to economic and environmental performance. This algorithm can be combined with the proposed retrofits strategy selection approach to identify the optimal retrofit incentive levels for a given region. An energy performance contract planning approach for residential buildings: A comprehensive literature review indicated a lack of studies focussing on energy performance contract planning for residential buildings. Moreover, the energy performance contracts related literature overlooks the operational and data uncertainties and cost-of-capital variations in the retrofitting projects caused by the employed funding mechanisms (i.e., owner’s equity, third party loans). Some studies focused on local data sets, therefore, cannot be adopted for regions in which comprehensive data sets are not available. This thesis proposed an energy simulation-based energy performance contracts planning approach employing the Monte-Carlo simulations to take the aforementioned uncertainties into consideration. The proposed planning model is expected to support the community penetration of energy retrofits and performance contracts by empowering the stakeholders with the means to understand the anticipated outcomes under uncertain conditions. Limitations of the Study During the completion of this thesis, some challenges and limitations were encountered. The impact of these challenges was mitigated via suitable assumptions and adjustments. Limitations in life cycle impact data: This research indicated the importance of employing life cycle thinking in the retrofits planning. However, it was noted that even the state-of-the-art databases employed in this study lacked locally applicable life cycle impact data relating to some building energy retrofits. Especially, the embodied impacts of energy system components such as heat pumps, furnaces, and hot water tanks were not commonly found in the life cycle impact databases. In order to overcome these data limitations, the environmental product declarations 131 (EPDs), online resources, and data from other regions were employed with reasonable approximations. Moreover, uncertainty handling techniques were employed to account for the potential errors introduced by the uncertain parameters to the final decisions. Nevertheless, it is recommended for future studies to develop locally applicable and up-to-date life cycle cost and impact databases in order to reduce the data uncertainties. Limitations in details of building archetype data related to existing buildings: In comprehensive city-level and regional-level retrofits planning exercises, multiple residential building archetypes need to be considered. However, the literature did not provide comprehensive building archetype data and drawings related to Canadian residential buildings. The analyses in this thesis were conducted considering a representative building defined according to Canadian residential building statistics to overcome this challenge. Natural gas and electricity were selected as alternative energy supply scenarios as those are the most commonly used energy sources. Nevertheless, if data is available, the proposed retrofits planning approaches can handle any building archetype as an input. Performance prediction challenges posed by data uncertainties and other variabilities: Data uncertainties and other variabilities (i.e., operational conditions, weather, and energy supply conditions and prices) pose challenges in predicting the energy, economic, and environmental performance of pre- and post-retrofit periods of buildings. Key operational parameters affecting the energy performance of a building were identified through a sensitivity analysis. Uncertainties introduced by these operational parameters and data limitations were modeled using fuzzy-logic and Monte-Carlo approaches. This enabled the integration of the uncertainties into the proposed retrofits planning process. Validating the building energy models in policy studies: The developed building energy models were formulated to represent Canadian averages, in order to assist in decision making at community level. Validating these models with experimental data is time and cost prohibitive, as it requires comprehensive pre- and post-retrofit performance monitoring of multiple buildings located in different climate zones over a long time span. To address the said issue, this study employed the recommended energy simulation platform (HOT2000) for Canadian residences following the best practices from the Vancouver energy simulation guideline and EnerGuide for 132 energy model development. Validating the energy models in policy studies can be further investigated in future studies. Challenges in identifying stakeholder priorities: The optimal solutions for each step of the proposed retrofits planning approach depend on stakeholder expectations. This aspect was addressed by considering multiple decision priority scenarios based on the literature. Community surveys can improve the accuracy of the decision priority scenarios. The decision priorities were kept as input variables to allow future users of the proposed approach to use survey data or applicable data for their context. Future research Future studies can help improve the limitations and challenges associated with the retrofits planning process identified during the study. Following research areas can greatly support and improve the accuracy of the results of the proposed retrofits planning approach. Develop locally applicable life cycle databases: This study demonstrated the importance of adopting life cycle thinking in retrofits-related decision-making. However, even the state-of-the-art life cycle impact databases employed in the study lacked locally applicable life cycle impact data for building energy retrofits, especially for HVAC and hot water systems. Future research focussing on developing accurate embodied impact data can help improve the accuracy of the life cycle emission assessment. Moreover, this will make the adoption of the life cycle assessment in green initiatives a common practice, allowing the stakeholders to understand the actual environmental performance of the energy efficiency projects without limiting to operational performance. Develop building archetype databases: Finding local building archetype data was a challenge faced during the current study. Developing locally relevant archetype databases and building energy models will facilitate the policymakers to identify the most applicable energy efficiency policies for existing building clusters. Moreover, this will support the building owners and contractors in identifying optimal retrofitting strategies for their buildings without having to spend time and money on developing energy models from scratch. Investigate opportunities for community-level centralized heating systems: The current study did not account for the option of employing centrally operated heating systems to cater to residential 133 communities. However, local energy sharing mechanisms can help reduce the total energy requirements of a community as the heating and cooling requirements of different buildings can offset each other. Further research on this aspect is needed to be done to support the community-level energy efficiency improvement policy development discussion. Investigate behavioral interventions for energy efficiency: This research focussed on investigating the performance and proposing policy solutions for energy retrofit interventions. The current study did not explicitly investigate behavioural and smart technology interventions. Investigating these areas can support in identifying and promoting occupant behaviour-related energy efficiency upgrades. Investigate the impact of building upgrades on occupant health: Building upgrades can change the indoor environment and thereby impact occupant health. Therefore, further studies are necessary to identify potential occupant health benefits associated with energy upgrades. Investigate energy efficiency retrofit planning challenges for other building types: The study focused on the single-family detached housing sector. This study can be expanded to other building types in the future. References [1] Environment Canada, “Government of Canada announces 2030 emissions target,” 2015. [Online]. 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Table A. 1 Embodied emissions of heat pump Parameter Value Unit Labeled heat output 12 kW Refrigerant charge: 2.70 kg (407c) GWP R-407c: 1530 kg CO2e/kgRefrigerant Equipment lifetime: 15 years Annual leakage rate: 2% - Refrigerant losses during demolition: 15% - Emissions due to leakage 1239.30 kg CO2e Direct emissions at demolition 619.65 kg CO2e Embodied emissions 170.10 kg CO2e Total Non-Operational (energy) Emissions 2029.05 kg CO2e Heat pump hot water unit (EF=1.901%) (65Gal) Embodied emissions of a 12 kW Heat Pump = 1014.525 kgCO2e; (assuming 50% of the 23.5 kg HP) Embodied emissions of steel = 2.7 kgCO2e/kg (from SimaPro) Embodied emissions of a (65gal) hot water tank made of steel (175 lb or 79.38 kg) = 2.7*79.38 kg =214.326 kgCO2e 151 Total embodied emissions of the HP HW system =1014.525+214.326 =1228.851 kgCO2e Natural gas furnace An NG furnace weight varies from 120 to 190 kgs. Therefore, the average value of the range 155 kg was taken as the weight of the furnace. Natural gas furnaces are mainly built out of stainless steel (heat exchangers), aluminum (frame, blowers, and burner), brass (valves), copper (wiring), and fiberglass (insulation) [171]. The exact mass fractions were not found in the literature. Therefore, expert opinions were used as the reference for the calculation. Steel, Cu, and Plastic all have the same embodied emissions (2.7 kgCO2e/kg), according to SimaPro. Table A. 2 Material list of NG furnace Constituent kgCO2e per kg Mass percentage Copper, Steel, Plastic (heat exchangers) 2.7 80% Aluminum (frame, blowers, and burner) 11.5 10% Brass (valves) 4.5 5% Fiberglass (insulation) 8.1 5% Therefore, the embodied emissions of the NG Furnace = 150 x (80% x 2.7 + 10% x 11.5 + 5% x 4.5 + 5% x 8.1) = 591 kgCO2e Instantaneous NG hot water unit (6.4GPM) The weight of a water heater of this type is approximately 20kg. The device is equipped with copper tubing, an aluminum enclosure, and some minor circuitry. Therefore, considering the densities and the volume of the materials used in the device, it was assumed to be 50% Copper and 50% Aluminum. Embodied emissions of Copper = 2.7 kgCO2e/kg (from SimaPro) Embodied emissions of Aluminum = 11.5 kgCO2e/kg (from SimaPro) Embodied emissions of the Instantaneous NG hot water unit 152 = 20 x 50% x (2.7 + 11.5) =135 kgCO2e Aluminum ducting used when introducing a heat pump or a furnace for BB-1 The perimeter of a domestic duct ranges between 20” to 80”. The gauge of the ducts varies from 0.013” to 0.018”. For the study purposes, the average of the ranges 50” (1.27m) and 0.0155” (0.0003937m) were taken as the duct perimeter and the gauge, respectively. These values were combined with the average duct length of 30ft (12.192m) to find the total aluminum volume, as indicated below. Aluminum volume = 1.27 x 0.0003937 x 12.192 = 0.006 m3 Density of Aluminum = 2,710 kg/m3 Aluminum mass = 0.006 x 2,710 = 16.52 kg Embodied emissions of Aluminum = 11.5 kgCO2e/kg (from SimaPro) Embodied emissions of the duct = 16.52 x 11.5 = 190 kgCO2e 350W Electric Baseboard Heaters The average weight of a 350W baseboard heater is 2.36kg. The device is made of copper wiring, Aluminum fins, and Steel housing. Assuming 75% of the weight of the baseboard is accounted under Steel and Copper and 25% of the weight under Aluminum. Steel, Cu, and Plastic all have the same embodied emissions (2.7kgCO2e/kg), according to SimaPro. Embodied emissions of Aluminum = 11.5 kgCO2e/kg (from SimaPro) Embodied emissions of the electric baseboard = 2.36 x (75% x 2.7 + 25% x 11.5) = 11.56 kgCO2e 153 Appendix 2 Electricity grid emission factors and prices, NG price, and HDD of each city Table A. 3 Electricity grid emission factors and prices, NG price, and HDD of each city Province City NG Price ($/GJ) Average Electricity Cost ($/kWh) Grid Emission Factor (kgCO2/ kWh) Annual HDD BC Vancouver 7.36 11.62 0.0129 2825 Prince George 4720 MB Winnipeg 3.06 9.37 0.0034 5670 Thompson 7600 QB Montreal 1.14 7.3 0.0012 4200 ON Toronto 4.73 13.89 0.04 3520 Kapuskasing 6250 NL St. John's N/A 12.8 0.032 4800 AB Calgary 3.99 15.74 0.79 5000 Fort McMurray 6250 SK Regina 2.42 16.51 0.66 5600 Prince Albert 6100 154 Appendix 3 Capital cost The capital cost values for the proposed retrofits are mainly found from the RSMeans 2019 database [172]. The capital cost variation among the provinces for a given system component was noted to be minimal among the cities considered in the study. Therefore, an average of the cost factors given in the RSMeans database was used to derive a Canadian average for the capital costs. Wherever assigning exact cost figures from the RSMeans database was not possible, online resources were employed. Determining costs for air-tightness improvements and wall insulation enhancements is difficult without taking quotations for the given case. Therefore, for analytical purposes, a US average cost figure to improve the air-tightness of a 2,500ft2 house was interpolated to match the area of the house being evaluated (1862 ft2) [173]. Upgrade costs considered in the study are summarized in Table A. 1 in Canadian Dollars. Table A. 4 Capital cost of retrofits Retrofit Sizing Capital Cost (CAD) Reference Air Tightness Improvement (ATI) 173m2 (living area) 1,103 [173] All windows: Double pane windows 39.15m2 11,788 RSMeans 2019 North windows: Double pane windows 17.57m2 4,566 South windows: Double pane windows 9.76m2 2,769 East windows: Double pane windows 4.13m2 1,751 West windows: Double pane windows 7.69m2 2,701 R40 Fiberglass Batt 103.5m2 (plan area of the house) 3,944 R31 Blown-in Fiberglass 211m2 4,059 Electric Heat Pump (COP2.5) 23.5kW 4,597 HP HW system (EF=1.901%) (65Gal) 12kW 2,399 [174] 155 Retrofit Sizing Capital Cost (CAD) Reference Gas furnace (95% AFUE) 23.5kW 1,140 RSMeans 2019 Instantaneous, condensing HW System (EF=83%) 6.4GPM 12kW 888 LED (100W equivalent) 15Wx15 225 [175] Water Saving Faucets 4 faucets 400 Assumed based on market prices Solar PV system (Monocrystalline) 69.2m2 (10kW approximate) 30,700 [176] Baseboards to work with HP in BB-2 23.5kW (Approximately 7 baseboards x 350W) 798 RSMeans 2019 Aluminum ducting used when introducing a HP or a furnace to BB-1 60ft Al ducting with 5vents 1920 [147] It is important to note that the cost per unit area values for windows vary in four directions, not because of the direction but because of the different sizes of windows used on different surfaces of the considered building. 156 Appendix 4 Embodied emissions Embodied emissions (non-operational emissions including both production and disposal) data were mainly found from the SimaPro database, and wherever lacking EPDs and literature were used with reasonable assumptions. The embodied emission values were assumed to be similar for all the locations considered due to data limitations. However, embodied emission data has to be made available at city levels to assist in informed energy retrofits decision-making to achieve Canadian climate goals. Table A. 5 summarizes the embodied emissions values used in this study. Table A. 5 Embodied emissions of retrofits Retrofit Sizing Unit Embodied Emissions Total Embodied Emissions (kgCO2e) Reference Air Tightness Improvement (ATI) 173m2 - Negligible - All windows: Double pane windows 39.15m2 40.05 kgCO2e/m2 2614.4 SimaPro (GLOBAL, Cut-off) North windows: Double pane windows 17.57m2 1173.3 South windows: Double pane windows 9.76m2 651.8 East windows: Double pane windows 4.13m2 275.8 West windows: Double pane windows 7.69m2 513.5 Ceiling: R40 Fiberglass Batt 103.5m2 (plan area of the house) 5.382 kgCO2e/m2 557.1 EPD by EcoTouch (Canada) [177] 157 Retrofit Sizing Unit Embodied Emissions Total Embodied Emissions (kgCO2e) Reference Wall: R31 Blown in Fiberglass 211m2 3.702 kgCO2e/m2 781.1 Electric Heat Pump (COP2.5) 23.5kW - 2029.1 The calculation procedure is explained in section 4.2.2. Detailed calculations are included in 0 HP HW system (EF=1.901%) (65Gal) 12kW - 1228.9 Detailed calculations are included in Appendix 1 Gas furnace (95% AFUE) 23.5kW - 591 Instantaneous, condensing HW System (EF=83%) 6.4GPM 12kW - 135 LED (100W equivalent) 15Wx15 2.4 kgCO2e/bulb 36 Embodied emission data of 8W LED Bulb [178] Water Saving Faucets 4 faucets 24.93 kgCO2e/faucet 99.7 EPD by NextGen [179] Rooftop solar PV system (Monocrystalline) and mounting system 69.2m2 (10kW approximate) 84 kgCO2e/m2 5812.8 SimaPro (Global, Cut-off) Electric baseboards to work with HP in BB-2 23.5kW (Approximately 7 baseboards x 350W) 11.56 kgCO2e/board 81 Detailed calculations are included in 0 158 Retrofit Sizing Unit Embodied Emissions Total Embodied Emissions (kgCO2e) Reference Aluminum ducting used when introducing a HP or a furnace for BB-1 60ft Al ducting with 5vents - 190 Detailed calculations are included in 0 Appendix 5 Retrofit performance when applied on an electrically heated house Table A. 6 Cost, energy, and emissions saving of different energy retrofits when applied on an electrically heated house Prov. City Air Tightness All Windows Ceiling Efficient Furnace (NG) Heating System (HP) Hot Water Unit (HP) Instant. HWU (NG) LED Solar PV with Net Metering Wall Water Efficient Faucet AB Calgary ACS ($) 573 855 532 4910 1013 407 634 13 1338 294 150 LCE -199468 -295346 -184762 -1499769 -351292 -139317 -197582 -4281 -454280 -101601 -49076 AES (kWh) 5028 7528 4694 -2639 8917 3583 472 111 11778 2583 1250 Fort McMurray ACS ($) 740 1111 681 6833 824 420 656 13 1171 371 150 LCE -257838 -384244 -236547 -2088830 -285541 -143933 -204516 -4281 -396102 -128482 -49076 AES (kWh) 6528 9778 6000 -3667 7250 3694 472 111 10306 3278 1250 BC Prince George ACS ($) 672 1185 724 6086 1546 474 546 11 1252 397 125 LCE -2930 -2571 -2599 467293 -5172 206 48400 36 5456 -960 -507 AES (kWh) 4528 8028 4889 -2778 10444 3500 389 111 9556 2694 1250 Vancouver ACS ($) 305 671 413 2812 1891 1381 507 11 1938 229 125 LCE -1331 -310 -1245 230622 -3963 98 43218 36 5811 -219 -507 AES (kWh) 2083 4556 2806 -1361 8583 3667 861 111 9028 1556 1250 MB Thompson ACS ($) 491 668 408 3892 367 203 296 10 602 222 113 LCE -1477 605 -669 814623 457 1848 54978 89 9816 113 87 AES (kWh) 8667 11806 7194 -4639 6500 3583 250 111 10639 3917 1250 Winnipeg ACS ($) 381 496 306 2860 420 219 306 10 642 167 113 LCE -1147 1120 -362 599867 298 1799 51287 89 9697 278 87 AES (kWh) 6750 8806 5417 -3389 7444 3889 778 111 11361 2972 1250 NL Saint John's ACS ($) 758 942 576 0 1701 396 0 12 1048 318 138 LCE -10109 -9944 -7122 0 -21114 -2816 0 -70 -2344 -3453 -1701 AES (kWh) 6306 7861 4806 0 14167 3306 0 111 8722 2639 1250 ON Kapuskasing ACS ($) 975 1391 825 8321 1080 491 504 11 1456 466 125 LCE -14034 -17404 -11317 532460 -13977 -4751 40667 -114 -10070 -5924 -2201 AES (kWh) 7028 10028 5944 -3750 7778 3611 444 111 10861 3361 1250 Toronto ACS ($) 549 870 530 4726 1299 489 464 11 1322 293 125 LCE -7892 -9907 -7064 311201 -17128 -4983 34787 -114 -8696 -3439 -2201 AES (kWh) 3944 6250 3806 -2194 9333 3722 944 111 10167 2111 1250 QC Montreal ACS ($) 418 632 392 4069 777 303 493 7 852 214 75 LCE -269 2208 305 431334 1064 2229 48873 101 10972 643 224 AES (kWh) 4472 6778 4222 -2389 8306 3833 917 111 10889 2306 1250 SK Prince Albert ACS ($) 998 1400 834 9095 946 526 592 17 1670 472 188 LCE -231357 -322164 -192750 -1660572 -217913 -119536 -160827 -3559 -375726 -108675 -40951 AES (kWh) 7000 9833 5861 -3722 6639 3694 528 111 11722 3306 1250 Regina ACS ($) 975 1212 753 8034 1113 543 602 17 1747 412 188 160 Prov. City Air Tightness All Windows Ceiling Efficient Furnace (NG) Heating System (HP) Hot Water Unit (HP) Instant. HWU (NG) LED Solar PV with Net Metering Wall Water Efficient Faucet LCE -226100 -278528 -174107 -1471974 -256638 -123455 -163945 -3559 -393617 -94674 -40951 AES (kWh) 6861 8528 5278 -3306 7833 3806 722 111 12278 2889 1250 Table A. 7 Cost, energy, and emissions saving of different energy retrofits when applied to a house with natural gas heating Prov. City Air Tightness All Windows Ceiling Efficient Furnace (NG) Heating System (HP) Hot Water Unit (HP) Instant. HWU (NG) LED Solar PV with Net Metering Wall Water Efficient Faucet AB Calgary ACS ($) 99 148 92 133 -3853 -204 23 13 1298 51 18 LCE -58420 -84924 -53733 -81183 1099250 43901 -14498 -4281 -432467 -29236 -10856 AES (kWh) 6194 9306 5778 9222 21056 4806 1722 111 11966 3194 1250 Fort McMurray ACS ($) 128 192 117 184 -5928 -210 26 13 1129 64 18 LCE -75423 -110716 -68880 -113005 1726580 44307 -16355 -4281 -377202 -37094 -10856 AES (kWh) 8028 12056 7389 12778 24000 5139 1917 111 10463 4028 1250 BC Prince George ACS ($) 155 275 167 259 -4254 -90 40 11 939 92 33 LCE -49185 -84751 -52470 -85893 -562473 -62150 -13959 36 -1354 -28495 -10856 AES (kWh) 5611 9944 6028 9778 23278 4694 1583 111 9764 3333 1250 Vancouver ACS ($) 70 156 95 127 -1416 -76 41 11 893 53 33 LCE -22380 -46851 -29768 -41647 -277335 -57353 -14236 36 1161 -16060 -10856 AES (kWh) 2528 5611 3444 4778 14944 4389 1611 111 9123 1917 1250 MB Thompson ACS ($) 132 179 109 187 -3246 -71 23 10 680 60 14 LCE -94146 -125398 -77693 -142988 -964266 -71029 -17899 89 1452 -42024 -10856 AES (kWh) 10722 14556 8889 16167 30028 5333 2028 111 10845 4861 1250 Winnipeg ACS ($) 102 133 82 138 -2219 -62 25 10 767 45 14 LCE -72896 -92335 -57906 -104932 -709136 -68903 -19416 89 2839 -31188 -10856 AES (kWh) 8306 10806 6667 11889 25111 5306 2194 111 11501 3639 1250 ON Kapuskasing ACS ($) 168 240 142 236 -6978 -193 33 11 1148 80 21 LCE -76079 -106016 -63851 -116157 -670375 -62487 -17076 -114 -16846 -35610 -10856 AES (kWh) 8611 12306 7278 13139 27194 5083 1917 111 11007 4111 1250 Toronto ACS ($) 95 150 91 137 -3288 -167 32 11 1086 51 21 LCE -42816 -65377 -40813 -67189 -399640 -56440 -16677 -114 -13317 -22143 -10856 AES (kWh) 4861 7722 4694 7667 21250 4694 1889 111 10282 2611 1250 QC Montreal ACS ($) 29 43 27 36 -2985 -125 8 7 626 15 5 LCE -48350 -70675 -44884 -74135 -506667 -64018 -17375 101 4648 -24079 -10856 AES (kWh) 5500 8333 5167 8444 21222 4861 1972 111 11029 2833 1250 SK Prince Albert ACS ($) 95 134 79 116 -8120 -329 16 17 1608 45 11 LCE -80154 -110398 -66460 -114688 -111649 -15066 -17254 -3559 -359438 -37204 -10856 AES (kWh) 8611 12139 7194 12972 25806 5167 2000 111 11919 4083 1250 Regina ACS ($) 93 115 72 103 -6923 -321 17 17 1699 39 11 LCE -78410 -94876 -60018 -101743 1071412 21976 -18588 -3559 -378564 -32465 -10856 AES (kWh) 8417 10472 6500 11528 24972 5250 2139 111 12450 3556 1250 Appendix 6 Retrofit performance under different regional conditions A6-1: Space Heating System Retrofits Space heating system performance under varying climatic conditions is depicted in Figure A. 1. Green group: Despite the climate variations and minor variations in energy costs and emission factors, some general trends can be observed in space heating upgrades in the Green group. None of the space heating upgrades applied on BB-1 produce significant LCE reduction in any Green province. The environmental performance of the efficient NG-Furnace is worse than the existing electric baseboard system. Nevertheless, both upgrades produce negative LCCs showing a better economic performance compared to the existing system. Therefore, any space heating system upgrade for BB-1 in these provinces can be justified only in an economic sense. In contrast, for BB-2 (heated with an old NG Furnace), any space heating upgrade can improve the environmental performance resulting in negative LCEs. The introduction of an EHP (electric heat pump) is the best way to improve environmental performance. However, the net positive LCC of HPs, when shifting away from NG heating, makes it an economically unpopular option. Irrespective of the climate conditions or the current state of the building, HP and NG-Furnace produce the best environmental and economic performance, respectively. Better environmental performance of HP mainly results from the COP values above 100% and the low electricity grid emission factors of the Green group. In contrast, the economic performance of NG-Furnaces is backed by low investment and operational costs. Brown Group: Heating system upgrades on the Brown group produce comparable economic savings with the Green group. However, the environmental performance has changed by a greater degree compared to the Green group. For the Brown group, any space heating upgrade on BB-1 is able to create emission reduction while reducing the LCC. However, an efficient NG Furnace upgrade can produce significantly higher emissions and cost savings compared to HP when applied on BB-1. Even though the benefits from the space heating upgrades applied on BB-2 are relatively low compared to BB-1, upgrading BB-2 with efficient NG furnaces helps in reducing both LCC and LCE. However, the introduction of electric HPs to BB-2 cannot be justified in either a financial or an environmental sense. 162 A6-2: Hot Water System Upgrades Performance of the hot water system upgrades at the provincial level is presented in Figure A. 2. Green group: No environmental performance enhancement can be obtained by introducing an efficient NG furnace to BB-1 in the Green group due to the clean electricity grids. Even the introduction of HP-operated HWU to BB-1 shows the potential to slightly increase the emissions for QC, MB, and BC. With the cleaner energy grids in these three provinces, emissions offset due to the energy savings are lesser than the embodied emissions of the proposed hot-water system upgrades. HP-operated HWU can reduce the LCE of the BB-1 in NL and ON, as their grid emissions factors are relatively higher than the other three provinces. HWU upgrades applied to BB-1 under the discussed conditions can only be justified through a financial perspective in all these provinces. On the other hand, any HWU upgrade done on BB-2 produces emission reductions but shows a positive LCC. Therefore, for climate change mitigation from a governmental perspective, HWU upgrades for BB-2 should be prioritized over BB-1. However, the upgrades for BB-2 come at a cost. Therefore, government interventions such as incentive schemes are needed to promote this transformation. Brown Group: It is important to note that the LCE scales of Brown and Green Groups are significantly different. In this group, both HWU upgrades reduce LCE and LCC irrespective of the location when applied on BB-1. This is a direct result of relatively high emissions and the price of grid electricity compared to NG. Therefore, introducing either NG or HP HWU upgrades to BB-1 can be strongly supported with environmental and economic justifications. The preferred upgrade for BB-1 is NG HWU. In this region, LCC and LCE of HP HWU retrofit are positive when applied on BB-2 (important: SK has an exception in LCE). Therefore, applying a HP HWU upgrade for a house that is currently using a NG HWU cannot be encouraged. However, emissions of BB-2 can be reduced by a high-efficiency NG HWU upgrade but at an increased LCC. A6-3: Envelope upgrades Figure A. 3 depicts the overall outcomes of envelope upgrades, including walls, windows, ceiling, and airtightness upgrades. Green group: QC, MB, and BC have the cleanest electricity supplies in this group. Thus, the emission reduction due to envelope upgrades done on BB-1 is not significant enough to offset the associated embodied emissions. Therefore, the LCE values are near zero (+/-) for BB-1envelop 163 upgrades. NL and ON show negative LCEs with envelope upgrades on BB-1 due to relatively high grid emission factors. It is important to note that envelope upgrades on BB-1 can produce small to very high LCC savings. On the other hand, any envelope upgrade done on BB-2 does not show comparable cost-savings with BB-1. However, all envelope upgrades on BB-2 can produce significant LCE reductions. Airtightness improvements on BB-2 standards are above the other options as it can produce cost-savings and produces significant emission reductions per dollar invested in almost all provinces except QC. Brown Group: Envelope upgrades on both houses (BB-1 and BB-2) produce significant emission savings for all the locations considered. Any envelope upgrade on BB-1 produces LCC savings. However, only airtightness improvements can produce LCC savings for BB-2 out of all the envelope upgrades considered. It is important to note that LCC and LCE savings produced by BB-1 envelope upgrades are always higher than BB-2 upgrades due to high costs and emissions of the electricity supply. A6-4: Minor retrofits Figure A. 4 and Figure A. 5 summarize the outcomes of the minor retrofits, including LED and efficient faucets. The interactions of the heating system and the lighting system of the buildings and natural lighting level variations over the provinces were not considered. With this assumption, the performance improvements achieved by BB-1 and BB-2 with the introduction of LED are the same. Therefore, the results are not separately presented for BB-1 and 2. As seen in Figure A. 4 Performance of LCC of LED is positive for any province in the discussion under the given conditions. The positive LCE values for LEDs in QC, MB, and BC indicate that there will be no emission saving associated with this intervention. At first sight, this gives the idea that LEDs are not suitable for these provinces. However, in new construction or end-of-life replacements, embodied emissions of the alternative options that are being replaced should also be taken into account when deciding on the environmental performance of LED. For NL and ON, the introduction of LEDs produces negative LCEs implying an environmentally friendly transformation for the buildings under investigation. The emission savings potential of LEDs in SK and AB is much higher, and the LCC values for these provinces are comparable with the provinces in Green Group. In any province, water-efficient faucets (WEF) can create negative LCCs on BB-1 while it creates positive LCCs for BB-2. This is due to relatively high electricity prices in all the provinces compared 164 to NG. The introduction of WEF for BB-2 always produces similar emission savings for all the provinces in the study. However, it is important to note that the emission saving potential of WEF is very high for BB-1 houses in Brown Group. In contrast, WEF produces positive LCEs for QC and MB. Moreover, the emission savings achieved by WEF on BB-1 houses in the other provinces of Green Group are also relatively low compared to Brown Group. WEF work best for houses with electric HWUs in Brown Group. A6-5: Solar PV integration For all the provinces and cities in the study, Solar PV integration produces significantly high positive LCCs for the considered base houses during the project period (50 years) considering a 25-year lifetime for solar panels, as seen in Figure A. 6Error! Reference source not found.. Green Group: LCE values for QC and MB are positive for both BB-1 and BB-2. Therefore, under these conditions, solar PV upgrade is not desirable for QC and MB. In BC, Solar PV only produces a small LCE saving in Prince George when applied on BB-2. However, the emission reduction potential of this particular scenario is still minimal. Therefore, it is not advisable to introduce solar PV from an environmental point of view in BC. PV upgrade on BB-1 in NL shows potential for reducing emissions. However, this emission reduction is relatively low per dollar invested compared to other retrofits. Introduction of Solar PV reduces LCE in ON for both BB-1 and BB-2. However, Solar PV cannot be recommended for ON, considering the high LCC and low emission reduction potential per dollar invested. Green Group: Introduction of Solar PV in the Brown Group help reduce LCE significantly for both BB-1 and BB-2 cases irrespective of the location. In SK, the LCC values are positive but not too high when considering the corresponding emission reduction potential. However, in AB, the LCC is significantly higher. Therefore, it is arguable to incentivize this upgrade in AB, especially when comparing with the emission reduction potential per dollar investment of other retrofits in AB. Importantly, when the embodied emission component is neglected, it looks like a zero-emission energy supply and could potentially mislead decision-makers. Therefore, it is important to employ life cycle performance evaluation techniques in assessing Solar PV integration to buildings. 165 166 Figure A. 1 Performance of space heating retrofits QC HDD (4200, 8550) Price~ E: 7.3c/kWh NG: 1.14$/GJ Emissions~ E: 1.2g/kWh MB HDD (5670,7600) Price~ E: 9.37c/kWh NG: 3.06$/GJ Emissions~ E: 3.4g/kWh BC HDD (2825, 4720) Price~ E: 11.62c/kWh NG: 7.36$/GJ Emissions~ E: 12.9g/kWh NL HDD (4800) Price~ E: 12.8c/kWh Emissions~ E: 32g/kWh ON HDD (3520, 6250) Price~ E: 13.89c/kWh NG: 4.73$/GJ Emissions~ E: 1.2g/kWh SK HDD (5600, 6100) Price~ E: 16.51c/kWh NG: 4.73$/GJ Emissions~ E: 660g/kWh AB HDD (5000, 6250) Price~ E: 15.74c/kWh NG: 4.73$/GJ Emissions~ E: 790g/kWh 167 Figure A. 2 Performance of water heating retrofits 168 Figure A. 3 Performance of envelope upgrades 169 Figure A. 4 Performance of LEDs Figure A. 5 Performance of water-efficient faucets Figure A. 6 Performance of solar PV integration Appendix 7 Eco-efficiency performance scores of retrofits Table A. 8 Eco-efficiency performance scores of retrofits Province / City QC MB BC NL ON AB SK Base Retrofit Montreal Thompson Winnipeg Prince George Vancouver St. John's Kapuskasing Toronto Calgary Fort McMurray Prince Albert Regina BB1 Air Tightness 0.2 1.3 1.0 2.7 1.2 9.2 12.7 7.2 180.8 233.8 209.8 205.0 All Windows 0.0 0.0 0.0 0.2 0.0 0.8 1.5 0.8 25.1 32.6 27.3 23.6 Ceiling 0.0 0.2 0.1 0.7 0.3 1.8 2.9 1.8 46.8 60.0 48.9 44.1 Efficient Furnace (NG) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 490.1 682.6 542.7 481.0 Heating System (HP) 0.0 0.0 0.0 0.8 0.6 3.2 2.1 2.6 53.9 43.8 33.4 39.4 Hot Water Unit (HP) 0.0 0.0 0.0 0.0 0.0 1.2 2.0 2.1 58.1 60.0 49.8 51.5 Instant. HWU (NG) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 222.5 230.3 181.1 184.6 LED 0.0 0.0 0.0 0.0 0.0 -0.3 -0.5 -0.5 -24.0 -24.0 -38.4 -38.4 Solar PV 0.0 0.0 0.0 0.0 0.0 -0.1 -1.3 -0.8 -41.5 -26.0 -157.5 -981.6 Wall 0.0 0.0 0.0 0.2 0.1 0.9 1.5 0.8 25.0 31.7 26.8 23.3 Water Efficient Faucet 0.0 0.0 0.0 1.3 1.3 4.3 5.5 5.5 122.7 122.7 102.4 102.4 BB2 Air Tightness -131.4 85.4 66.1 44.6 20.3 69.0 38.8 53.0 68.4 72.7 71.1 All Windows -6.6 -17.5 -11.0 -18.0 -6.0 -18.9 -8.3 -10.6 -16.2 -13.2 -10.8 Ceiling -13.8 -68.7 -31.4 13.3 -20.0 -227.1 -25.6 -34.0 -74.7 -34.9 -28.6 Efficient Furnace (NG) -99.3 125.4 92.0 75.3 36.5 101.9 58.9 71.2 99.1 100.6 89.2 Heating System (HP) -6.0 -10.5 -10.9 -4.8 -6.2 -3.6 -4.3 0.0 0.0 -0.5 0.0 Hot Water Unit (HP) -9.5 -13.2 -13.4 -10.6 -10.4 -7.3 -7.2 0.0 0.0 0.0 0.0 Instant. HWU (NG) -15.5 -24.6 -28.8 -49.2 -55.1 -36.6 -34.4 -20.0 -25.3 -19.1 -21.5 LED 0.0 0.0 0.0 0.0 0.0 -0.5 -0.5 -24.0 -24.0 -38.4 -38.4 Solar PV 0.0 0.0 0.0 -0.1 0.0 -1.1 -0.8 -36.1 -23.1 -90.4 -228.9 172 Province / City QC MB BC NL ON AB SK Base Retrofit Montreal Thompson Winnipeg Prince George Vancouver St. John's Kapuskasing Toronto Calgary Fort McMurray Prince Albert Regina Wall -6.5 -16.7 -10.7 -16.9 -6.0 -17.9 -8.0 -10.6 -15.4 -12.8 -10.7 Water Efficient Faucet -13.7 -18.9 -18.9 -144.5 -144.5 -28.6 -28.6 -23.3 -23.3 -16.8 -16.8 173 Appendix 8 Cost and emissions data of retrofits Table A. 9 Capital costs and embodied emission data of the retrofit options considered in the study System Option Unit Embodied Emission Embodied Emission (kgCO2e) Embodied Emission Remarks Capital Cost Costing Remarks ACH ACH_5 - 0 Assumed Negligible 1,103 [11] Wall R22 3.235 kgCO2,e/m2 682.6 EPD 3,588 RSMeans R31 3.702 kgCO2,e/m2 781.1 EPD 4,059 Window Double pane, Low-E High gain, Air Fill 40.05 kgCO2,e/m2 2607.3 [11] 24,448 [11] Triple pane, Low-E High gain, Argon Fill, U1.36 66.78 kgCO2,e/m2 4347.5 SimaPro (Not enough data to differentiate between the two three-pane windows) 27,504 [180] Triple pane, Low-E low gain, Argon Fill, U1.14 66.78 kgCO2,e/m2 4347.5 SimaPro (Not enough data to differentiate between the two three-pane windows) 27,504 Ceiling R50 6.374 kgCO2,e/m2 659.7 Interpolated from the 3 values used in this study 4,461 Interpolated from the R values in the table R40 5.382 kgCO2,e/m2 557.1 [11] 3,944 [11] 174 System Option Unit Embodied Emission Embodied Emission (kgCO2e) Embodied Emission Remarks Capital Cost Costing Remarks Space Heating Tier1 Central Ducted ASHP - 2219.1 [11] 4,620 From vendors and [11] Tier2 Central Ducted ASHP - 2219.1 This was assumed to be the same as Tier1. Not enough data to differentiate within the same type of systems. 5,020 From vendors and [11] Multi Split ASHP HSPF 9.9 BTU/watt-hr COP 2.9 Capacity 28kBTU - 2219.1 Not sufficient data to calculate this one. Therefore, assumed to be similar to Central Ducted HPs 3,700 Vendors Tier1 gas-furnace - 781 [11] 1,360 Vendors Tier2 gas-furnace - 781 This was assumed to be the same as Tier1. Not enough data to differentiate within the same type of systems. 2,990 From this sheet DHWS Conventional Electric Tank replacing the existing one - 800 Replacement with a conventional DHWS (This sheet) 509 [11] Electric HP - 1228.9 [11] 2,399 [11] Gas Instantaneous - 135 [11] 2,889 Vendors 175 System Option Unit Embodied Emission Embodied Emission (kgCO2e) Embodied Emission Remarks Capital Cost Costing Remarks Gas storage tank EF 0.67 Capacity 65gal - 800 Assumed to be the same as a conventional heat pump 2,149 Vendors Condensing Gas storage tank EF 0.8 Capacity 65gal - 800 929 Vendors 176 Appendix 9 Retrofit selection information Table A. 10 Percentage inclusion of retrofits in the best hundred retrofit scenarios under varying decision priorities Weights Base ACH5 Wall Window Ceiling Space Heater HWU W1 W2 BB1 BB2 R 22 R 31 Two Pane Low-E Hard Coat Air Fill Three Pane R50 R40 Central Ducted ASHP Multi Split ASHP HSPF9.9 COP2.9 28kBTU Gas Furnace Electric HP NG High Gain, u1.36 Low Gain, u1.14 Tier1 Tier2 Tier 1 Tier2 Instantaneous Storage tank EF0.67 65gal Condensing Gas storage tank EF0.8 65gal 0% 100% 0 100 66 42 45 42 41 17 34 35 28 36 36 0 0 100 0 0 0 10% 90% 70 37 51 39 39 15 33 34 21 39 40 100 20% 80% 71 35 53 32 35 8 29 33 17 36 47 100 30% 70% 70 37 49 26 33 5 28 32 17 36 47 100 40% 60% 74 33 54 18 24 2 24 32 13 36 51 84 10 0 6 50% 50% 88 12 69 32 52 16 24 1 25 29 16 34 50 97 1 0 1 60% 40% 100 0 66 34 47 16 25 3 27 29 20 34 46 95 0 0 0 70% 30% 66 34 46 15 23 2 26 29 21 32 47 92 80% 20% 64 34 46 14 20 1 25 28 20 32 48 87 90% 10% 64 34 46 13 19 1 25 28 20 32 48 85 100% 0% 63 33 45 11 18 1 25 28 20 32 48 83 177 Appendix 10 Incentive levels for individual retrofits Table A. 11 Proposed incentive levels under varying decision priorities and the ranks of the retrofits selected for incentivizing Considering the best 50 Scenarios Considering the best 100 Scenarios Considering the best 500 Scenarios CC W1 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 1103 ACH 5 1103 1103 1103 3588 Wall R22 0 0 0 2439 4059 Wall R31 4059 2798 4059 2798 4059 2798 4059 24448 2Pane LowE Hard Coat AirFill 2439 0 2439 0 0 3700 Multi Split ASHP HSPF9.9,COP2.9 0 2439 0 2439 3700 0 2439 3700 0 178 Considering the best 50 Scenarios Considering the best 100 Scenarios Considering the best 500 Scenarios CC W1 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 2399 Electric HP HWU 2399 2399 2399 179 Figure A. 7 Incentive level variations under the number of retrofit scenarios considered (a) Considering Best 50 Retrofit Scenarios (b) Considering Best 100 Retrofit Scenarios (c) Considering Best 500 Retrofit Scenarios 0200040006000800010000100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%Incentive Amount (CAD)Weighting for LCCSElectricHP HWU Multi Split ASHP 2 Pane Window Wall_R31 ACH 50200040006000800010000100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%Incentive Amount (CAD)Weighting for LCCSElectricHP HWU (2,399) MultiSplitASHP (3,700) 2 Pane Window (24,448)0200040006000800010000100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%Incentive Amount (CAD)Weighting for LCCSElectricHP HWU GasInstantaneous Multi Split ASHP 2 Pane Window Wall_R22 Wall_R31 ACH 5
Thesis/Dissertation
eng
Mechanical Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Hewage, Kasun
Sadiq, Rehan
A life cycle thinking-based energy retrofits planning approach for existing residential buildings
Text