Applied Science, Faculty of
Engineering, School of (Okanagan)
DSpace
UBCO
Akbariansaravi, Niloofar
Doctor of Philosophy - PhD
University of British Columbia
Biocomposites have become a significant sustainable alternative in green engineering. The production of biocomposites relies on strategic decisions regarding biomass collection and size reduction during pre-processing within the supply chain. This dissertation aims to evaluate different biomass collection and pre-processing equipment configurations in a case study of hemp-based biocomposites supply chain in Saskatchewan, Canada, to identify the most sustainable operational options. A novel sustainable decision-making framework is proposed in three phases, integrating economic, environmental, social (job opportunity), and technical considerations. The first phase employs a Techno-Economic Analysis tool to quantify the economic criteria of pre-processing scenarios, including Mean Net Present Value derived from Monte Carlo simulations, Net Present Value under Risk, and selling price ranges. The selling price is identified as the most influential factor, contributing 72%-78% to Net Present Value fluctuations. In the second phase, environmental impacts of the scenarios are assessed using an attributional Life Cycle Assessment tool. The cultivating and harvesting stage, linked to the use of biomass, fertilizers, and diesel fuels, is identified as a critical contributor to the environmental impact in all the important impact categories. Additionally, the social dimension is evaluated by estimating potential job creation associated with this biocomposite supply chain. Technical factors are captured by gathering industrial expert insights on product quality, system reliability, and Technology Readiness Levels (this dataset in particular included an Unreliability Factor). The outputs from the first two phases are used as the inputs for the third phase, for which an Analytic Network Process model is employed with interdependencies among criteria and alternatives. To assess the robustness of the alternatives’ rankings from the Analytic Network Process model, a sensitivity analysis using Non-Linear Programming is developed. Ultimately, the proposed framework effectively selects best hemp pre-processing equipment (namely half-screen hammer mill and round baler) by introducing sustainable metrics. Incorporating “interdependencies” among criteria and alternatives enhances the solution's robustness for decision-makers, as validated by the sensitivity model compared to the conventional Analytical Hierarchy Process. This Analytic Network Process-based supply chain framework could be adapted to various biocomposite contexts and production regions by modifying input data, broadening its impact.
A SUSTAINABILITY ASSESSMENT MODEL FOR SELECTING PRE-PROCESSING EQUIPMENT IN HEMP-BASED BIOCOMPOSITE SUPPLY CHAINS UNDER TECHNO-ECONOMIC, ENVIRONMENTAL, AND SOCIAL MEASURES by Niloofar Akbariansaravi B.Sc., Ferdowsi University of Mashhad, 2016 M.Sc., University of Tehran, 2018 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 2025 © Niloofar Akbariansaravi, 2025 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a dissertation entitled: A SUSTAINABILITY ASSESSMENT MODEL FOR SELECTING PRE-PROCESSING EQUIPMENT IN HEMP-BASED BIOCOMPOSITE SUPPLY CHAINS UNDER TECHNO-ECONOMIC, ENVIRONMENTAL, AND SOCIAL MEASURES submitted by Niloofar Akbariansaravi in partial fulfilment of the requirements of the degree of Doctor of Philosophy Dr. Abbas Milani, School of Engineering Supervisor Dr. Taraneh Sowlati, University of British Columbia Co-Supervisor Dr. Kasun Hewage, School of Engineering Supervisory Committee Member Dr. Feng Jiang, University of British Columbia Supervisory Committee Member Dr. Amir Ardestani-Jaafari, Faculty of Management University Examiner Dr. Manjusri Misra, University of Guelph External Examiner iii Abstract Biocomposites have become a significant sustainable alternative in green engineering. The production of biocomposites relies on strategic decisions regarding biomass collection and size reduction during pre-processing within the supply chain. This dissertation aims to evaluate different biomass collection and pre-processing equipment configurations in a case study of hemp-based biocomposites supply chain in Saskatchewan, Canada, to identify the most sustainable operational options. A novel sustainable decision-making framework is proposed in three phases, integrating economic, environmental, social (job opportunity), and technical considerations. The first phase employs a Techno-Economic Analysis tool to quantify the economic criteria of pre-processing scenarios, including Mean Net Present Value derived from Monte Carlo simulations, Net Present Value under Risk, and selling price ranges. The selling price is identified as the most influential factor, contributing 72%-78% to Net Present Value fluctuations. In the second phase, environmental impacts of the scenarios are assessed using an attributional Life Cycle Assessment tool. The cultivating and harvesting stage, linked to the use of biomass, fertilizers, and diesel fuels, is identified as a critical contributor to the environmental impact in all the important impact categories. Additionally, the social dimension is evaluated by estimating potential job creation associated with this biocomposite supply chain. Technical factors are captured by gathering industrial expert insights on product quality, system reliability, and Technology Readiness Levels (this dataset in particular included an Unreliability Factor). The outputs from the first two phases are used as the inputs for the third phase, for which an Analytic Network Process model is employed with interdependencies among criteria and alternatives. To assess the robustness of the alternatives’ rankings from the Analytic Network Process model, a sensitivity analysis using Non-Linear Programming is developed. Ultimately, the proposed iv framework effectively selects best hemp pre-processing equipment (namely half-screen hammer mill and round baler) by introducing sustainable metrics. Incorporating “interdependencies” among criteria and alternatives enhances the solution's robustness for decision-makers, as validated by the sensitivity model compared to the conventional Analytical Hierarchy Process. This Analytic Network Process-based supply chain framework could be adapted to various biocomposite contexts and production regions by modifying input data, broadening its impact. v Lay Summary Biomass collection and size reduction activities during pre-processing of biocomposites production significantly influence the supply chain sustainability across economic, environmental, social, and technical dimensions. This dissertation presents a decision-making framework, integrated with an optimization model and applied to a Canadian case study to evaluate different supply chain configurations for hemp-based biocomposite pellet production. The work introduces comprehensive sustainable metrics such as profitability under market uncertainty conditions, ecosystem and human health-related environmental impacts, job creation, and technical factors including final product quality. The proposed decision-making framework would help in selecting the best supply chain configuration by addressing interdependencies among supply chain decision criteria. Results show that incorporating a broader range of techno-economic, social and environmental factors into the decision-making model enhances the biocomposite supply chain’s reliability. In particular, it is emphasized that selecting appropriate biomass pre-processing equipment can enable the decision-makers to effectively allocate economic and human resources while meeting sustainability goals. vi Preface All the work presented in this thesis was carried out by the author, Niloofar Akbarian-Saravi, during the PhD program. The work was conducted under the co-supervision of Prof. Abbas S. Milani and Prof. Taraneh Sowlati at the Biocomposites Research Cluster of the University of British Columbia, Canada. A number of publications and conference presentations have arisen from this work, a summary of which with respect to the Chapter-based content of the thesis is provided below. I was responsible for conducting all the analyses within this research as well as data collection. Prof. Abbas S. Milani and Prof. Taraneh Sowlati were the supervisory authors who advised in defining the problem, reviewed and approved methodologies and models, provided support in the communications and meetings with our industrial partner, and guided manuscript preparation and revisions. Also, generative AI was not used for any aspects of this research. 1) A version of Chapter 3 is accepted to be published in “Biocomposites and the Circular Economy” Elsevier Book. Mr. Darrel Fry was our industrial partner from ABC3D Inc, who provided the technical data, identified the case study, and verified the results. • Niloofar Akbarian-Saravi, Taraneh Sowlati, Darrel Fry, Abbas S. Milani, “Techno-economic analysis and multi-criteria assessment of a hemp-based biocomposite production supply chain”, Biocomposites and the Circular Economy Book, Elsevier, 2025 (in press). 2) A version of Chapter 4 has been submitted to a peer-reviewed journal for publication. Also, part of Chapter 4 (related to the environmental mitigation strategies) has been accepted to be published in “Biocomposites and Circular Economy” Elsevier Book. Hammad Ahmad, Prof. Kasun Hewage, and Prof. Rehan Sadiq contributed to the Simapro software access and implementations as well as editing the manuscript. vii • Niloofar Akbarian-Saravi, Taraneh Sowlati, Abbas S. Milani, “Cradle-to-gate life cycle assessment of hemp utilization for biocomposite pellet production: A case study with data quality assurance process”, Cleaner Engineering and Technology, 2024 (under review). • Niloofar Akbarian-Saravi, Taraneh Sowlati, Hammad Ahmad, Kasun Hewage, Rehan Sadiq, Abbas S. Milani, “Life cycle assessment of hemp-based biocomposites production: agricultural emission mitigation strategies based on harvesting energy source and fertilizer type selection”, Biocomposites and the Circular Economy Book, Elsevier, 2025 (in press). 3) A version of Chapter 5 is being submitted to a peer-reviewed journal for publication. • Niloofar Akbarian-Saravi, Taraneh Sowlati, Abbas S. Milani, “A Robust Analytical Network Process for Biocomposites Supply Chain Design incorporating Techno-economic, environmental, and social measures, Sustainable Materials and Technologies, 2024 (under submission). Conference Proceedings and presentations • Niloofar Akbarian-Saravi, Bryn Crawford, Taraneh Sowlati, Iman Jalilvand, Abbas S. Milani, “Modelling and Sustainability Assessment of a Biocomposite Supply Chain Network using Bayesian Belief Networks”, IISE Annual Conference & Expo, 31 May- 3 June, Atlanta, Georgia, 2025 (Upcoming). • Niloofar Akbarian-Saravi, Taraneh Sowlati, Abbas S. Milani, “Analytic Network Process for Multicriteria Decision-Making in Sustainable Composites Supply Chain Management”, IISE Annual Conference & Expo, 18-21 May, Montreal, Canada, 2024 (received the best student paper award; 1st place). viii • Niloofar Akbarian-Saravi, Taraneh Sowlati, Abbas S. Milani, “Economic and environmental performance of a hemp-based biocomposite supply chain”, Operations Management & UN Sustainable Development Goals Cluster, Production and Operations Management Society (POMS) International Conference, 18–20 July, Paris, France, 2023 • Niloofar Akbarian-Saravi, Taraneh Sowlati, Abbas S. Milani, “A techno-economic environmental investigation of hemp-based biocomposite supply chain: case study in western Canada”, Energy, Natural Resources, and the Environment Cluster, CORS/INFORMS International Conference, 5-7 June, Vancouver, Canada, 2022. • Niloofar Akbarian-Saravi, Taraneh Sowlati, Abbas S. Milani, “Developing a sustainable biocomposite supply chain assessment model under uncertainty: a case study in western Canada”, CORS - Forestry SIG Cluster, CORS/INFORMS International Conference, 5-7 June, Vancouver, Canada, 2022. Additional Project Collaborations Also, I collaborated on a number of collaborative projects during my doctoral studies, which resulted in the following journal publications. My contribution in these works included the material synthesis, sample preparation and experimental works including mechanical tests, performing the statistical and data analysis, writing and editing parts of the manuscripts. • Niloofar Akbarian-Saravi, Ibrahim Alper Basar, Olivia Helena Margoto, Nadia Abdollahi G., Bryn Crawford, Benjamin Magel, Mehrdad Gharibnavaz, Cigdem Eskicioglu, Abbas S. Milani, “Characterization of Mechanical, Biodegradation, and Morphological Properties of NBR/Biopolymer Blend, integrated with a Risk Assessment, ACS Omega, 2024, doi/10.1021/acsomega.3c08301. ix • Niloofar Akbarian-Saravi, Kinga Vojnits, Mohammad Amin Batouei, Bryn Crawford, Hyuk Joon Jung, Sepideh Pakpour, Parisa Mehrkhodavandi, Mehrdad Gharibnavaz, Abbas S. Milani, “Integrated Framework for Analysis of biopolymer-Blended Nitrile Butadiene Rubber Copolymer: Biodegradability, Mechanical Properties, and Biosafety”, Composites Communication, 2024 (under submission). x Table of Contents Abstract .........................................................................................................................................iii Lay Summary ................................................................................................................................. v Preface ........................................................................................................................................... vi Table of Contents ........................................................................................................................... x List of Tables ............................................................................................................................... xiv List of Figures ............................................................................................................................ xvii List of Symbols ............................................................................................................................. xx List of Abbreviations ................................................................................................................. xxii Acknowledgement ...................................................................................................................... xxv Dedication ................................................................................................................................. xxvii Chapter 1: Introduction ................................................................................................................ 1 1.1 Background .......................................................................................................................... 1 1.2 Research objectives .............................................................................................................. 5 1.3 Outline of the thesis .............................................................................................................. 7 Chapter 2: Literature Review ..................................................................................................... 11 2.1 Synopsis .............................................................................................................................. 11 2.2 Techno-economic analysis in biocomposites supply chain ................................................ 11 2.3 Environmental assessment of the biocomposites supply chain .......................................... 13 2.4 Multi-criteria decision-making models in supply chain modeling ..................................... 19 2.5 Summary: research gap ...................................................................................................... 21 Chapter 3: Case Study Description and Techno-Economic Analysis Model ......................... 23 3.1 Synopsis .............................................................................................................................. 23 xi 3.2 Selected case study of hemp-based biocomposite production ........................................... 23 3.3 Techno-economic problem description and data collection ............................................... 29 3.3.1 Assessment framework ................................................................................................... 35 3.3.1.1 Deterministic model ............................................................................................... 36 3.3.1.2 Uncertainty analysis ............................................................................................... 40 3.3.1.2.1 Monte-Carlo simulation .................................................................................... 41 3.3.1.2.2 Economic risk assessment method: conditional risk analysis .......................... 42 3.4 Results and discussion ........................................................................................................ 44 3.4.1 A sub-scenario considering operators wellness .............................................................. 48 3.4.2 Sensitivity analysis ......................................................................................................... 49 3.4.3 Demand scenarios analysis ............................................................................................. 50 3.4.4 Economic assessment under risk .................................................................................... 52 3.5 Summary ............................................................................................................................ 54 Chapter 4: Life Cycle Assessment Model .................................................................................. 56 4.1 Synopsis .............................................................................................................................. 56 4.2 Problem definition and analysis strategy ............................................................................ 57 4.3 Materials and methods ........................................................................................................ 57 4.3.1 Goal and scope definition ............................................................................................... 58 4.3.2 Life cycle inventory and its assumption ......................................................................... 62 4.3.2.1 Alternatives based on biomass pre-processing equipment ..................................... 66 4.3.3 Life cycle impact assessment ......................................................................................... 68 4.3.4 Uncertainty analysis ....................................................................................................... 69 4.4 Results and discussion ........................................................................................................ 70 xii 4.4.1 Sensitivity analysis ......................................................................................................... 78 4.4.2 Effect of data quality uncertainty on impact categories ................................................. 80 4.4.3 Key environmental hotspots in agricultural machinery and conversion ........................ 83 4.4.4 Emission mitigation strategies ........................................................................................ 85 4.5 Summary ............................................................................................................................ 92 Chapter 5: Multi-Criteria Decision-Making Method ............................................................... 95 5.1 Synopsis .............................................................................................................................. 95 5.2 Problem definition and criteria clusters .............................................................................. 95 5.2.1 Analytical Network Process (ANP) ............................................................................. 100 5.2.2 ANP concept for sustainable SC configuration selection............................................. 105 5.2.2.1 Supply Chain Configuration Scenario 1 ............................................................... 109 5.2.2.2 Supply chain configuration scenario 2 ................................................................. 110 5.2.3 Sensitivity analysis (the proposed optimization model) ............................................... 112 5.3 Results and discussions .................................................................................................... 115 5.3.1 Optimization-based sensitivity analysis ....................................................................... 128 5.4 Summary .......................................................................................................................... 132 Chapter 6: Conclusion............................................................................................................... 134 6.1 Summary and conclusion ................................................................................................. 134 6.2 Strengths: contributions to knowledge ............................................................................. 136 6.2.1 Practical trade-offs/insights perceived ......................................................................... 137 6.3 Limitations ........................................................................................................................ 138 6.4 Future Work ..................................................................................................................... 139 Bibliography ............................................................................................................................... 142 xiii Appendices ................................................................................................................................. 168 Appendix A : Detailed data from sensitivity analysis of TEA model. ......................................... 168 Appendix B : Monte-carlo simulation results in TEA.................................................................. 170 Appendix C : Detailed data from sensitivity analysis of LCA model. ......................................... 172 xiv List of Tables Table 2-1. A summary of previous studies on the Life Cycle Assessment (LCA) of biocomposites. ............................................................................................................................................................ 17 Table 3-1. Parameter values for each baler and hammer mill type. ................................................... 28 Table 3-2. Input parameters considered for the four Supply Chain (SC) alternative focusing on biomass pre-processing equipment. .................................................................................................... 31 Table 3-3. Capital investment costs (source: personal communication with industry expert [June, 2021]).................................................................................................................................................. 32 Table 3-4. Data for calculating Operation and Maintenance (O&M) costs within each supply chain stage. ................................................................................................................................................... 33 Table 3-5. Other assumptions made in techno-economic analysis. .................................................... 34 Table 3-6. The considered criteria in the investment decision-making. ............................................. 43 Table 3-7. Costs components for each alternative (capital costs and annual operating costs). .......... 44 Table 3-8. Results of economic assessment. ...................................................................................... 44 Table 3-9. The parameters and the Net Present Value (NPV) outcome for all alternatives (considering one shift). ............................................................................................................................................ 49 Table 3-10. Selling price of biocomposite pellet for each supply chain configuration case according to different demand scenarios. ............................................................................................................ 52 Table 3-11. The ranges assumed for uncertain selling prices to run the Monte-Carlo simulation. .... 54 Table 4-1. Inventory inputs to produce 1 tonne of hemp-based biopolymer. .................................... 63 Table 4-2. Inventory inputs to produce 1 tonne PET resin (sourced from a study conducted by Papong et al. [137]). ........................................................................................................................................ 64 xv Table 4-3. Life cycle inventory of SC alternatives (all values are calculated based on 80 tonnes of biocomposites per day). ...................................................................................................................... 67 Table 4-4. Pedigree matrix employed for agricultural stage data quality uncertainties. .................... 70 Table 4-5. Pedigree matrix employed for conversion stage data quality uncertainties. ..................... 70 Table 4-6. Environmental burdens per impact category for all four alternatives evaluated (data reported per functional unit: 1 tonne of biocomposites pellet). .......................................................... 71 Table 4-7. The Life Cycle Impact Assessment (LCIA) of PET production (the functional unit was based on 1 tonne of PET). .................................................................................................................. 78 Table 4-8. A summary of fertilization requirements conditions related to the different scenarios. ... 86 Table 4-9. A summary of all considered scenarios represented the share of each source in the total energy and fertilizer source (inputs for two-way ANOVA). .............................................................. 86 Table 4-10. Environmental results for each scenario (different fertilizer sets and energy types). ..... 91 Table 4-11. ANOVA results for the carcinogenic impact category. .................................................. 91 Table 4-12. ANOVA results for the non-carcinogenic impact category. ........................................... 91 Table 4-13. ANOVA results for the ecotoxicity impact category. ..................................................... 92 Table 4-14. ANOVA results for the eutrophication impact category. ................................................ 92 Table 5-1. Decision matrix for criteria values for all alternatives (the effect (direction) of each sustainable SC parameter on the performance of SC is indicated with “+” or “-” sign). ................... 99 Table 5-2. Saaty’s fundamental scale [177]. .................................................................................... 102 Table 5-3. Normalized decision matrix (benefit type). .................................................................... 118 Table 5-4. Normalized pair-wise comparison matrix for enablers under mean NPV. ..................... 118 Table 5-5. (Weighted) supermatrix for the network of Figure 5.3 with feedback. .......................... 121 Table 5-6. Limit matrix obtained for the network of Figure 5.3 with feedback. ............................. 122 xvi Table 5-7. Relative and global weights in scenario 2 (without inter/outer dependency). ................ 125 Table 5-8. Sensitivity analysis of criteria weight changes required to make an alternative the top-ranked option. ................................................................................................................................... 130 xvii List of Figures Figure 1-1. Hemp structure: a) hemp stem; b) hemp stem cross section [12] . .................................... 2 Figure 1-2. Thesis structure. ............................................................................................................... 10 Figure 3-1. Flowchart of the hemp-based biocomposite SC in this case study. ................................. 25 Figure 3-2. The supply chain activities and different alternatives considered in the current study. .. 30 Figure 3-3. Schematic of the techno-economic assessment framework considered in the case study. ............................................................................................................................................................ 36 Figure 3-4. Illustration of the Conditional Value-at-Risk (CVaR) [118]. .......................................... 43 Figure 3-5. Net Present Value and annual Return on Investment for the four alternatives presented in this study. ............................................................................................................................................ 46 Figure 3-6. Annual net cash flows of alternatives in the 20-year project lifetime. ............................ 47 Figure 3-7. Total costs breakdown a) for all alternatives (Operation and Maintenance costs); b) overall contributions (example of alternative C). ........................................................................................... 48 Figure 3-8. Results of sensitivity analysis on Net Present Value (NPV) of alternative B: full-screen hammer mill and square baler. ........................................................................................................... 50 Figure 3-9. Break-even price benchmark. .......................................................................................... 52 Figure 3-10. Probability of Net Present Value (NPV) of Alternative B: full-screen hammer mill & square baler. ........................................................................................................................................ 54 Figure 4-1. The hemp based biocomposite Life Cycle Assessment (LCA) steps considered in this study.................................................................................................................................................... 58 xviii Figure 4-2. The system boundary and material flow of producing 1 tonne of hemp-based biocomposite pellet in the case study. ....................................................................................................................... 59 Figure 4-3. The system boundary and material flow of producing 1 tonne of PET pellet in the case study [68], [131]. ................................................................................................................................ 61 Figure 4-4. Percentage of impact categories contribution for alternative (B) for 1 tonne of hemp-based biocomposites. .................................................................................................................................... 72 Figure 4-5. Life Cycle Impact Assessment (LCIA) results for the best (alternative B) and worst (alternative C) Supply Chain (SC) practice implementations. The percentage value above the arrow are the changes in the absolute impacts, a) carcinogenic, b) ecotoxicity, c) eutrophication, d) non-carcinogenic. ....................................................................................................................................... 75 Figure 4-6. Life Cycle Impact Assessment (LCIA) results for the best and worst SC practice implementation focusing on Global Warming Potential (GWP) impact category. ............................ 76 Figure 4-7. The contribution of impact categories in PET pellet production. .................................... 77 Figure 4-8. Sensitivity of the important impact categories for alternative B, to different input parameters including biomass yield, diesel and fertilizer use during cultivation, and ethanol use during the hemp-based biocomposites production (alternative B); a) carcinogenic impact (human health-related), b) eutrophication (ecosystem-related). ................................................................................. 80 Figure 4-9. Characterized Life Cycle Impact Assessment (LCIA) results and uncertainty analysis for the harvesting process (environmental impact hotspot) of hemp-based biocomposites (error bars represent the 95% confidence interval of total emissions based on Monte-Carlo simulation). ......... 81 Figure 4-10. The conversion process environmental impact in each impact category under uncertainty for alternative B, a) with ethanol consideration; b) without ethanol consideration. .......................... 82 xix Figure 4-11. Hotspots in the agricultural and manufacturing activity of biocomposite supply chain. ............................................................................................................................................................ 85 Figure 4-12. Environmental Profile of different alternative scenarios relative to scenario 1 (reference scenario). ............................................................................................................................................. 88 Figure 4-13. Comparison of Global Warming Potential (GWP) impact (in the agricultural stage) for all scenarios considering CO2 uptake of the plants. ........................................................................... 89 Figure 5-1. Sustainable decision-making framework for the present SC pre-processing equipment configuration selection. .................................................................................................................... 100 Figure 5-2. Samples of a (a) hierarchy, and (b) a network in Multi Criteria Decision Making (MCDM) modeling [178]. ................................................................................................................................ 102 Figure 5-3. Analytic Network Process (ANP) model (scenario 1). .................................................. 109 Figure 5-4. Analytic Hierarchy Process (AHP) model (scenario 2). ................................................ 111 Figure 5-5. Criteria weights using pairwise comparisons; a) Analytic Network Process (ANP) (interdependencies), b) Analytic Hierarchy Process (AHP) model (without interdependencies). ... 123 Figure 5-6. The alternatives ranking of the Supply Chain (SC) configurations in scenario 1. ........ 124 Figure 5-7. Sensitivity analysis of cluster importance (economic, environmental, social, and technical) on alternatives ranking within scenario 1. ........................................................................................ 127 Figure 5-8. Sensitivity analysis of the Unreliability Factor (UF) on alternative scores, focusing on qualitative factors such as product quality and system reliability. ................................................... 128 Figure 5-9. Sensitivity analysis of criteria weight changes in (a) ANP model and (b) AHP model (under alternative C). ........................................................................................................................ 131 xx List of Symbols 𝐶𝑜𝑠𝑡 𝑖 Cost of equipment 𝑖 𝐶𝑎𝑝 𝑖 Capacity of equipment 𝑖 𝑔 Scaling factor 𝐶𝑃𝐼 𝑅𝑒𝑓 Consumer price index for the reference year 𝜏° Efficiency of each piece of equipment TC𝑜𝑢𝑡𝑝𝑢𝑡 Output capacity of each piece of equipment TC𝑖𝑛𝑝𝑢𝑡 Input capacity of each piece of equipment 𝑁𝐶𝐹𝑡 Net cash flow after tax at period 𝑡 𝑛 Total number of periods (years) 𝑟 Interest rate 𝑁𝑖𝑡𝑒𝑟 Number of iterations µ Mean value of the NPV model output under market uncertainty 𝜎 Standard deviation of the NPV model’s output under market uncertainty 𝑍(𝛼) Z-score associated with a given confidence level (𝛼) 𝜀 Maximum allowable marginal error 𝐶𝑉𝑎𝑅𝛼 Conditional expectation of NPV with a confidence level of 𝛼 𝛽1,𝛽2 Pricing ratios of biofiller and the shredded hemp 𝛿1, 𝛿2 Mass ratios of biofiller and the shredded hemp xxi 𝛼𝑖𝑗 The relative importance of criterion or alternative 𝑖 over criterion or alternative 𝑗 𝑠𝑖𝑗 Normalized values of decision matrix for criterion or alternative 𝑖, 𝑗 𝑈𝑖𝑗 Weighted metrics for the alternatives 𝑖 and criteria 𝑗 based on the normalized pairwise metrics in ANP model (elements from supermatrix in ANP) 𝑤𝑖𝑗, 𝑤𝑗 Weight of criteria 𝑗 independent from alternative or criteria 𝑗 under alternative 𝑖 𝑑𝑖𝑗+ , 𝑑𝑖𝑗− , 𝑑𝑗+, 𝑑𝑗− Positive and negative deviations from the importance of criteria 𝑗 under alternative 𝑖; or deviations of criteria 𝑗 𝑘 Power of convergence of supermatrix 𝐴𝑊𝑖𝑗 Normalized weights of alternative 𝑖 under criteria 𝑗 in AHP model 𝑙𝑖𝑚𝑖𝑡𝑚𝑎𝑡𝑟𝑖𝑥𝑖𝑗 Element from limit matrix after the convergence of supermatrix 𝑊 𝜑𝑖𝑗 Unreliability factor regarding the criteria 𝑗 under alternative 𝑖 xxii List of Abbreviations SDMF Sustainable Decision-Making Framework SC Supply Chain TEA Techno-Economic Analysis LCA Life Cycle Assessment MC Monte Carlo MCDM Multi-Criteria Decision making ANP Analytic Network Process IRR Internal Rate of Return NPV Net Present Value VaR Value at Risk CVaR Conditional Value at Risk PLA Polylactic Acid PP Polypropylene TBL Triple Bottom Line PET Polyethylene Terephthalate AW Annual Worth RoI Return on Investment CPI Consumer Price Index FCC Fixed and Capital Costs SV Salvage Value NIBT Net Income Before Tax xxiii NCF Net Cash Flow MS Excel Microsoft Excel O&M Operation and Maintenance FU Functional Unit LA Lactic Acid MEG Mono Ethylene Glycol PTA Purified Terephthalic Acid PX Paraxylene LCI Life Cycle Inventory GHG Greenhouse Gas Emission LCIA Life Cycle Impact Assessment ODP Ozone Depletion Potential GWP Global Warming Potential AP Acidification Potential EuP Eutrophication Potential EP Ecotoxicity Potential CP Carcinogenic Potential NCP Non-Carcinogenic Potential FFDP Fossil Fuel Depletion Potential SP Smog Potential REF Respiratory Effects Potential MTTR Mean Time to Repair xxiv ANP Analytic Network Process AHP Analytic Hierarchy Process NLP Non-Linear Programming LP Linear Programming CEO Chief Executive Officer TRL Technology Readiness Level e-vector Eigenvector SS Sum of Squares Df Degree of Freedom MS Mean Squares xxv Acknowledgement I would like to express my sincere gratitude to my supervisors, Prof. Abbas S. Milani and Prof. Taraneh Sowlati, for their unwavering support, trust, and encouragement throughout my time at UBC. I feel incredibly fortunate to have experienced the ideal balance of freedom and guidance during my graduate studies. Their constant mentorship, through continuous meetings and thoughtful advice, taught me the importance of patience and self-confidence in shaping my Ph.D. research. I would like to express my gratitude to the "Industrial Engineering Group" peers at UBC Vancouver, under the leadership of Prof. Taraneh Sowlati, for the invaluable opportunity to connect with them during my visits. Especially, thanks to Dr. Bertrand Laratte for his insightful guidance in my work. I would like to extend my appreciation to my thesis committee members, Prof. Kasun Hewage and Prof. Feng Jiang, for their valuable feedback and suggestions, which significantly enhanced the quality of my research. I am also deeply thankful to Mr. Darrel Fry from Advanced Biocarbon 3D (ABC3D) Company, who consistently supported my research by providing data, a case study, and helping to verify the results. My special thanks to the MITACS Accelerate Program (grant no. IT29287) for supporting my PhD research. I also thank Dr. Bryn Crawford from CRN for providing invaluable insights and expert knowledge into biocomposites case study modeling. I would also like to also acknowledge Dr. Peyman Yousefi for his years of mentorship, particularly regarding teaching, leadership, and his ongoing advice throughout my Ph.D. journey. I would like to thank my beloved off-campus friends and colleagues from CRN, especially Elnaz Ghahremani Rad, Yasaman Lotfizadeh, Mahshid Mahbod, Olivia Margoto, Haoyang Liu, Dr. Tina Olfatbakhsh, and Dr. Farzan Gholamreza, for their friendship, care, and joyful gatherings made this experience even more special. To my closest friends, Atash Tahaei and Mitra Tavakkoli, who became xxvi like family during my immigration journey through long-distance conversations and video calls, made those challenging days more tolerable. I thank you. Lastly and most importantly, I would like to express my deepest gratitude to my parents, Maryam and Mohammadreza, and my dear brother Ali. Their love, belief in me, and endless support have been the cornerstone of my strength and inspiration, making this academic dream a reality. I am equally most thankful to my beloved husband and best friend, Iman, whose constant presence, encouragement, patience, and love have been the cornerstone of my success throughout this journey. xxvii Dedication ...To my kind husband Iman And my lovely parents Mohammadreza & Maryam... 1 Chapter 1: Introduction 1.1 Background Synthetic polymers, commonly known as plastics, are typically originated from petrochemical sources and require energy, frequently sourced from fossil fuels, for their manufacturing [1]. Large-scale plastic manufacturing took place during the 1940s and as the plastic industry plans to expand production on a large scale, the World Economic Forum predicts a 3.8% annual growth in plastic production and usage by 2030, which will fall to 3.5% annually from 2030 to 2050 [2], [3]. Many plastic wastes are abandoned in the environment due to their lack of recycling or their inability to yield financial returns from recycling. Due to rising plastic production, the total greenhouse gas emissions throughout the plastic life cycle may surpass 56 gigatons by 2050 [3]. This accounts for 10%-13% of the permissible CO2 emissions to adhere to a specified global warming limit [3]. In Canada, a national goal is defined to reduce greenhouse gas emissions to 40%–50% of that for the 2005 level to keep the average global temperatures rising below 2 °𝐶 by 2100 [4]. Therefore, the negative impacts of accumulated non-biodegradable wastes from petroleum-based polymers have led to the development of renewable materials [5]. In addition, the cost of petro-based commodities polymers is high ranging from 2.74- 7.67 $/kg. As a result, in order to reduce the cost of final products and their environmental impacts, the production of biocomposites incorporating bio-based natural fibers as either the matrix or reinforcement components, has generated significant attention from researchers [6], [7]. Natural fibers, categorized as plant, animal, or mineral-based, are widely used as reinforcements in polymer and bio-based composites. Their popularity stems from their low cost, light weight, biodegradability, easy availability, and straightforward processing [8], [9]. Among plant-based fibers, non-wooden fibers such as hemp (cannabis), flax, and kenaf are regarded as the most ecologically 2 friendly fibers and have already achieved commercial success in the design of multiple biocomposite products [10], [11]. Industrial hemp, which was legalized in 1998 and had a growth of over 70% in Canada between 2011 and 2016, possesses great potential in diverse applications from textiles to food and medicine industries. As shown in Figure 1-1, the hemp stem comprises two primary components: fibers, which are long and strong, and hurd, which is the inner woody core. These components contribute to its versatility in various industrial applications. There are numerous benefits to utilizing hemp in lieu of petroleum-based products and other natural fibers such as an eco-friendly production process, high tensile strength, lower water consumption, and an economically competitive production process to name a few [12], [13]. Moreover, the high carbohydrate content of industrial hemp makes it a promising source for bioproduct production [14]. Despite the great advantages of utilizing hemp, their large-scale and commercial production for biocomposites has been less investigated [15]. (a) (b) Figure 1-1. Hemp structure: a) hemp stem; b) hemp stem cross section [12] . To overcome this challenge, however, having a proper Supply Chain (SC) management becomes pivotal [16]. The production SC is complex and includes a series of activities required from raw material supply to the production of biocomposites. A well-structured SC ensures the effective flows 3 of raw materials and products as well as efficient manufacturing processes, all of which are essential components in achieving a competitive advantage in the market [17]. The SC economic and environmental performance depends on particular aspects such as processing and manufacturing techniques, and equipment selection, which varies according to the unique condition the material is being produced [18]. Within biocomposites production, biomass may need to undergo multiple pre-processing steps to make it compatible with conversion technology and feedstock requirements. A fundamental step in biobased composite production is the mechanical pre-processing of biomass, encompassing tasks such as particle-size reduction and mechanical separation. Size reduction is a process that adjusts the particle size distribution of biomass to meet the requirements of its intended final application [19]. Fine size reduction equipment, such as hammer mills with classifiers like screens, is essential for producing the finer particles needed in biobased materials and engineered products. The screen plate design in these grinders significantly influences particle fineness, capacity, and energy use, impacting the efficiency of the SC and subsequent processing steps [20]. Therefore, analyzing every interlinked activity within the SC system is essential for comprehending its collective impact on the economic and environmental performance. This underscores the necessity for a comprehensive study to assess the economic and environmental repercussions of a product or service, encompassing stages from extraction to the production gate [21]. Economic and environmental assessments in the literature overlook aspects related to pre-processing within product manufacturing, hindering the management process and important aspects of the carbon footprints [18]. Analyzing decisions on the type of equipment to use for biomass harvesting, drying, baling, and shredding plays a crucial role in determining the properties and overall environmental performance of biocomposites. Neglecting the difference in equipment options and in 4 the assessments may lead to higher costs or emissions being omitted from carbon footprint evaluations [18]. Techno-Economic Analysis (TEA) is a suitable tool to assess economic performance of SC, especially for the production process without any industrial reference. Thus, the investors will be provided with a guideline for the commercialization of their product [22], [23]. In addition, the most significant area/parameters in terms of revenue generation in the model should also be identified to be able to compete with other more established products/technologies [24], [25]. Furthermore, for monitoring the environmental impact of new product designs, previous studies used Life Cycle Assessment (LCA) model. LCA aims to assess the environmental suitability and evaluate the potential for mitigating environmental contributions [26], [27], [28]. This method can also help to decide which SC configurations are favourable for improving the environmental impacts of the material [18]. Furthermore, initial assumptions and the quality of available data can affect the LCA results [29], [30]. In this regard, data quality indicators are necessary to reflect data reliability [29]. These indicators encompass the age of data, geographical area, completeness, representativeness, and reliability assessed via Monte Carlo (MC) simulation. Additionally, initial assumptions in LCA are another source of uncertainty that can be effectively examined through sensitivity analysis [29]. Aside from economic and environmental impacts, notably, activities within the SC have significant impacts on social aspects as well, collectively known as the Triple Bottom Line (TBL) [31]. Social impacts can include factors like job quantities, while environmental impacts involve considerations such as carbon emissions and pollution [31]. Economic impacts assess factors like the production profit. A sustainable SC should ideally integrate economic, social, and environmental goals to enhance long-term performance [31]. To tackle such problems, Multi-Criteria Decision Making (MCDM) approaches have been widely adopted in logistics performance evaluation, helping 5 with resolving conflicting criteria and reaching key decisions among the feasible options/alternatives [32]. Current studies on sustainability often overlook a full range of criteria, creating a gap in addressing the holistic requirements for sustainable biocomposites SC configurations [33].Therefore, the Sustainable Decision-Making Framework (SDMF) is required to fill this gap by integrating approaches across all sustainability dimensions in biocomposite production. 1.2 Research objectives Based on a detailed literature review and the identified gaps in the field (see Section 2.5), the overarching objective of this thesis has been to develop a comprehensive Multi-Criteria Decision-Making framework based on the Analytic Network Process, which can support the sustainable pre-processing decision-makings within hemp-based biocomposites supply chains, incorporating techno-economic, environmental, and social (job opportunity) measures. The chosen case study for the thesis is based on a bioproduct manufacturing company in Canada, aiming to produce hemp-based biocomposites pellets. The above goal is achieved through the following sub-objectives/tasks: 1) Modelling a financial stochastic Techno-Economic Analysis (TEA) model for comparisons among four different biomass collection and size-reduction (i.e., pre-processing) methods, including: (1) a full-screen hammer mill and round baler (full-round); (2) a full-screen hammer mill and square baler (full-square), (3) a half-screen hammer mill and round baler (half-round), and (4) a half-screen hammer mill and square baler (half-square). • Overview: this financial analysis, based on Techno-Economic Assessment (TEA), evaluates key metrics including payback period, internal rate of return (IRR), return on investment (ROI), and stochastic measures such as the mean and risk value of Net Present Value (NPV). The analysis is conducted using a spreadsheet-based model with a 10% discount rate and a 20-year project timeline. Inputs include equipment efficiency, material 6 consumption, total capital investment, and Operation and Maintenance (O&M) costs, along with other technical and economic factors. An automated Monte Carlo simulation is integrated into the spreadsheet to account for the combined effects of stochastic inputs, such as selling price and demand variation, on financial outcomes. This simulation generates a range of potential NPV values, providing insight into financial risks and opportunities. 2) Employing a Life Cycle Assessment (LCA) model to assess the relative difference in environmental performance associated with the above four different biomass collection and size-reduction alternatives. • Overview: The analysis is done to identify the areas for improvement by comparing the best and worst scenarios. The difference between the biocomposite's environmental impacts contribution with those of the petroleum-based products (Polyethylene Terephthalate (PET) as a control material) is also assessed. A sensitivity analysis is conducted to investigate the impact of variations in agricultural-stage-related inputs on the above performance and suggest potential areas for improvement. The impact of data quality uncertainty through Monte Carlo simulation within the SimaPro tool is evaluated. Using the best-case scenario for biomass collection and size-reduction methods, a scenario analysis is conducted to explore environmental impact reductions in agricultural practices. The analysis specifically examines the use of organic versus chemical fertilizers to further enhance the environmental performance of the final product. 3) Applying and testing robustness of a Multi-Criteria Decision-Making (sustainability assessment) model based on the Analytic Network Process model to support the sustainable development of 7 the hemp-based biocomposite SC in the case study, considering the “interrelationships” among criteria. • Overview: The decision model integrates economic, environmental, social (only job opportunity), and technical parameters. In practice, it is difficult (or very time consuming) to evaluate the robustness of a decision model’s rankings by investigating all possible criteria weight combinations (i.e., subjective weighting uncertainties) that would potentially change the decision outcome. While Multi-Criteria Decision-Making (MCDM) has been widely applied in various studies [34], this work introduces the application of a Non-Linear Programming (NLP) model combined with Analytic Network Process (ANP) for the first time. This approach addresses a key challenge: evaluating the robustness of decision model rankings under subjective weighting uncertainties, which is typically time-consuming and computationally intensive. The NLP-based sensitivity analysis automates this process by optimizing an objective function to determine the minimum sum of changes in criteria weights required to make an alternative the top-ranked option. This Novel method enhances the applicability of sensitivity analysis in complex decision-making scenarios. Of note, the analysis values obtained in the first (TEA analysis) and second (LCA analysis) are the inputs for the MCDM problem in the third phase, among other social and technical criteria including job creation, product quality, etc. that are added directly in sub-objective 3. Details of the selected case study will be provided in Chapter 3. 1.3 Outline of the thesis This dissertation contains six chapters, including the present chapter, as presented in Figure 1-2. 8 In Chapter 2, a comprehensive review of the relevant literature will be provided which includes classification and analysis of research works conducted for the economic, environmental, and sustainable assessment of SC for biocomposite production. Chapter 3 will expand on the problem statement, case study, and the techno-economic model developed for the economic feasibility evaluation of hemp-based biocomposites SC. The model results are reported, and their sensitivity is analyzed against variations in important parameters such as selling price, capital costs, and O&M costs. A MC simulation is also performed to evaluate the Net Present Value of the project under uncertainty of the selling price. In Chapter 4, in order to quantify the environmental impacts of the SC in the biocomposite production for different biomass pre-processing equipment, a LCA model will be developed using SimaPro software combined with the MC simulation to evaluate the impact of the data uncertainty on the impact categories. The results of this model are analyzed, and then among different SC scenarios with the biomass pre-processing equipment, the best and worst options are compared to provide a recommendation to improve the environmental impacts. Alternatives are compared with the traditional petroleum-based composites from the environmental perspective. Similar to the TEA model, the sensitivity analysis is performed to evaluate the impact of the important parameters on the most important impact categories. Further analysis on evaluating the different scenarios for the fertilizer type and energy sources are conducted. In Chapter 5, after quantifying the economic and environmental aspects of the hemp-based biocomposites performed in Chapter 3 and Chapter 4, the ANP model will be used given the tradeoff between the indicators, involving social and technical factors as well. Finally, to evaluate the robustness of the solution obtained by the ANP model, a new proposed NLP mathematical model is 9 applied, and the results are provided. Finally, in Chapter 6, conclusions, strengths, and limitations of this work as well as the potential future research areas will be covered. 10 Figure 1-2. Thesis structure. 11 Chapter 2: Literature Review 2.1 Synopsis The current literature on TEA, LCA, and MCDM models plays a crucial role in understanding the sustainability of biocomposite SCs [35]. This chapter explains the review of previous studies on these areas. TEA studies focus on bioproduct conversion processes, highlighting the impact of factors like biomass pre-processing on SC economic metrics. LCA research studies further examine environmental impacts, with a significant body of work on natural fiber composites across different regions, though Canadian-specific assessments remain sparse. Also, MCDM approaches are becoming essential for managing complex trade-offs, as they allow for the simultaneous evaluation of multiple sustainability dimensions including economic, environmental, social, etc. Therefore, the studies are categorized into three groups: 1) TEA in biocomposites SC, 2) Environmental impact of biocomposite SC, and 3) MCDM studies in SC modeling. 2.2 Techno-economic analysis in biocomposites supply chain There is a considerable number of studies that performed TEA to assess the viability and profitability of biomass-to-value added product processes such as biofuel, bioenergy, forestry-based product production, etc. [35]. The profitability of producing any materials/bioproducts may be higher for some processes/strategies than for others. This is measured by a few economic estimators, such as the minimum selling price, the cost of capital, the Internal Rate of Return (IRR), the Net Present Value (NPV) or the payback period [22]. Also, several research projects have been mostly focused on the economic evaluation of the different SC by focusing more on conversion processes involved in the biomass SC to produce added-value products [35], [36], [37]. However, it is important to emphasize that reducing the cost of delivered feedstock is a key to enhance the profitability [24]. 12 As the majority of biomass must undergo pre-processing operations, such as size reduction or collection, the efficient biomass pre-processing can improve the economic performance [38]. One of the most relevant studies was performed by Sokhansanj and Turhollow [39], who compared two square and round baling systems for corn stover collection. Costs, including those for operations up to and including storage, were calculated as $19.7/dry tonne for round baling and $21.40/dry tonne for square baling. The cost variation between the two systems stemmed from the need for an extra raking operation and the higher capital investment in equipment for rectangular baling and transportation. Similar studies carried out TEA in the context of bioproducts and biomass SC, however, the assessment of biomass pre-processing strategies considering all upstream SC activities using hemp biomass was not done [40]. Aside from technical and economic performance estimation, uncertainty within SC could be assessed to identify the elements that strongly impact the profitability and viability of SC (e.g., biomass availability, logistics, etc.) [16]. Most researchers have attempted to deal with uncertainties in the biomass SC via sensitivity analysis and studied the impact of biomass and product pricing, electricity and fuel price, and installment and equipment cost on the outcome of the biomass SC such as the NPV and capital investment costs [41], [42], [43], [44]. As an example, Khatiwada et al. [45] demonstrated that the feasibility of bioenergy produced from biomass depends on electricity price fluctuations. Aside from the sensitivity analysis approach, another well-established method is the MC simulation which can assess the uncertainties associated with biomass SC by incorporating probability distributions [16]. This involves repeatedly sampling probability distributions that reflect the variability of factors such as biomass availability, demand variation, and material price fluctuations. During each MC iteration, all model calculations and outputs are stored [46], [47]. In 13 this way, the solutions for risk-averse investors are reported by involving the distribution of the economic metric in assessing different production technology configurations [46], [48]. The previous works on assessing the uncertainties could be categorized into those that only considered one economic factor while assessing the risk [49], and those that incorporated multiple metrics while assessing the appropriate option in their assessment [50]. As highlighted by Wu et al. [51], while tools like Oracle Crystal Ball and @RISK facilitate MC sampling, the application of MC methods in stochastic TEA remains relatively limited in research. For example, Li and Hu [49] performed MC simulation model to convert the uncertainty of input variables into a probability and assess the economic risk of the gasification investment. Other approaches have been presented by Ferjan et al. [40] and and Khadivi and Sowlati [50] in the context of biocomposites recycling and bioenergy production, respectively. The latter work included NPV at Value-at-Risk (VaR) in the assessment of biomass gasification options. NPV at VaR provides a singular value to represent risk at a designated confidence level. In contrast, Conditional Value at Risk (CVaR) calculates the expected NPV when NPV surpasses the NPV at VaR threshold. Therefore, CVaR is more comprehensive in assessing the risk by considering the expected NPV beyond the VaR threshold, providing insights into potential worst-case scenarios [52]. Conducting a risk analysis for the TEA could provide investors with a comprehensive view of the financial outcomes related to the evaluated SC [53]. 2.3 Environmental assessment of the biocomposites supply chain Replacing traditional composites with bio-based alternatives requires a thorough understanding of material flow in the SC and its life cycle impacts. It has also been argued that renewable-based composites using natural fibers are not necessarily environmentally friendly materials [54]. Therefore, in recent years, there has been an increasing amount of environmental assessment studies on natural fiber-reinforced biodegradable polymer as almost fully biobased products [55]. In the 14 category of papers related to comparative analysis of LCA of petroleum polymer reinforced with natural fibers, much of the research up to now has been focused on natural fiber such as corn, potato, kenaf, and hemp fiber [56], [57], [58]. Each paper addressed the comparison between the different material types and used the impact categories focusing more on ReCipe. Among the reviewed literature on comparative analyses in the field, the analysis performed by Gomez-Campos et al. [55] involved cradle-to-gate analysis of the environmental impact of producing a biobased reinforcement material using flax fiber. The comprehensive analysis included the valorization of co-products, outsourced manufacturing processes in China, and a sensitivity analysis on the electricity mix. Another example is the study conducted by David et al. [59], who investigated the environmental benefits of incorporating vine shoots, an agricultural residue, as filler in composite trays made of poly (3-hydroxybutyrate-co-3-hydroxyvalerate), polylactic acid (PLA), or polypropylene (PP). The SC entities in their study involved drying, transportation, milling, cutting, compounding, and final product production step. The study also recommended the maximum filler content for enhanced environmental and economic gains. Within the LCA studies of natural fiber-based composites, activities such as harvesting and baling influence not only the mechanical properties of final biocomposites used in engineered applications, but also significantly impact the overall environmental footprint [60]. Neglecting these activities could result in an incomplete assessment, with key emissions in the SC configuration potentially excluded from the carbon footprint evaluation [18], leading to decisions that might inadvertently impose higher emissions on the SC. In this regard, while the environmental performance of natural fiber reinforced composites/biocomposites has been assessed and compared with the petroelum-based biocomposites in previoussni LCA studies [61], a little attention has been paid to the SC configuration decisions, 15 specially related to the agricultural field operation such as biomass pre-processing equipment selection. Moreover, it has been shown that the cultivation stage plays an important role in hemp natural-fiber based biocomposites [61]. Scrucca et al. [61] employed LCA to assess the energy and environmental implications of hemp cultivation in France. Andrianandraina et al. [62] identified trends and recommended technological measures and fertilizer scenarios for hemp farmers to reduce environmental impacts in the agricultural phase. The existing body of the literature concerning LCA of hemp-based biocomposites has predominantly focused on European regions due to its abundance availability [57]. However, it is imperative to acknowledge that geographical-specific parameters, notably biomass yield and fertilizer usage, can influence the sustainability assessment substantially [63]. There has been insufficient attention given to the LCA of fully hemp-based biocomposites accounting for all SC activities within Canada. Addressing this gap by focusing on practices specific to the Canadian context is essential for a comprehensive environmental assessment. Agricultural farming results in significant emissions due to the application of fertilizers. The considerable chemical and energy inputs needed to break recalcitrant lignocellulosic bonds result in emissions, including ammonia, dinitrogen monoxide, nitrogen, nitrate, and phosphorous [58]. These releases have diverse impacts, contributing to acidification, eutrophication, biodiversity decline, and the contamination of soil, water, and air [64]. Previous research conducted on hemp-based SC has also found the fertilizer (especially nitrogen) and pesticides as hotspots [1]. In light of this, a growing body of literature has examined LCA and developed strategic policies for mitigating agricultural activities' environmental impacts. The environmental impacts of only field production practices of hemp fiber regarding nitrate leaching have been explored by Van Der Werf et al. [65] in which eutrophication is identified as the main impact category on the overall environmental impacts. 16 Introducing an agricultural practice production through the substitution of mineral fertilizer with organic material (slurry), climate change and energy use are reduced, but the eutrophication and acidification are increased due to NH3 leaching. Focusing on reducing eutrophication impacts, research conducted by Scrucca et al. [61] used a cradle-to-gate approach for assessing the energy-environmental aspects associated with the hemp hurds in the building application including the cultivation and processing of hemp phases. Existing literature on agricultural mitigation policies often focuses on field-level actions, overlooking the conversion stages needed for final value-added products [66]. Moreover, most studies in the field of LCA in biocomposites and biopolymers have only focused on the data uncertainty using sensitivity analysis on the input parameters [67], [61], while few studies have incorporated data quality uncertainty into the analysis. For example, a recent study by Ita-Nagy et al. [67] involved data quality assessment using MC simulation in evaluating the LCA of bagasse fiber-reinforced biocomposites. Table 2-1 summarzies the literature on the LCA of biocomposites, showcasing the diversity of biomass types, products, and methodologies employed across different case studies. Each study location (shown in Table 2-1) highlights the importance of geographic focus based on the availability of the biomass type among other strategic factors. The comparative literature review highlights the key aspects considered in each study, as outlined in the "Focus" column. This comparison emphasizes the critical factors to be considered for the LCA scope, such as the evaluation of natural fiber composites against petroleum-based materials or the influence of fiber content, filler types, and polymer matrix. The table underscores the diversity of approaches and parameters used in LCA studies within these categories. 17 Table 2-1. A summary of previous studies on the Life Cycle Assessment (LCA) of biocomposites. Study LCA method Biomass type Product/application System boundary Case study Uncertainty Focus Khoshnava et al. [56] ReCiPe Kenaf bast fibers Biocomposites Cradle-to-Gate Canada N/A Natural fiber reinforced polymer/composites v.s. petroleum-based composites/polymer Rivas-Aybar et al. [30] GWP 100a Hemp hurds Hemp-based board Cradle-to-Gate Australia Energy source Chen et al. [68] TRACI Corn, Switchgrass, and Wheat Bottle Cradle-to-Gate N/A Impact allocation Chen et al. [58] Matrix-based model Potato Pots/rigid packaging Cradle-to-Gate Italy N/A Korol et al. [69] ReCipe Cotton, Juite, Kenaf Plastic pellet Cradle-to-Gate Poland N/A La Rosa et al. [57] ReCipe Hemp Biocomposite Cradle-to-Gate Spain N/A Ita-Nagy et et al. [67] ReCiPe Sugarcane Plastic pellet Cradle-to-Gate Brazil Fiber content, Electricity source Gomez-Campos et al. [55] ILCD 2001 Flax fiber Biobased reinforcement Textile fabric Cradle-to-Gate France Electricity source 18 Study LCA method Biomass type Product/application System boundary Case study Uncertainty Focus Guo et al. [70] ECER Corn starch Polylactic Acid (PLA) bottle Cradle-to-Gate China Electricity source, PLA particle, water amount, etc. Different polymer matrix/binder, filler and reinforcement types Scrucca et al. [61] IPCC Hemp Biocomposites Cradle-to-Gate France Fertilizer type David et al. [59] ReCiPe midpoint Vine shoots Trays (packaging material) Cradle-to-Grave France N/A This Study TRACI Hemp Biocomposites Cradle-to-Gate Canada Data quality, Biomass yield, ethanol use, diesel use, fertilizer amount Difference between biomass pre-processing and collection equipment for fully hemp-based biocomposite 19 2.4 Multi-criteria decision-making models in supply chain modeling The current literature on sustainable bioproducts SCs management, emphasizes heavily on environmental and economic aspects, while less focusing on social and technical dimensions, as well as human/ecosystem-related environmental factors [71]. Particularly, in the field of biocomposites, the previous studies have primarily focused on material selection decisions given the sustainable assessment emphasizing costs, ecology aspect, and product mechanical properties factors in SC [72]. To tackle such problems, Multi-Criteria Decision Making (MCDM) approaches have been widely adopted, helping with resolving conflicting criteria and reaching key decisions among the feasible options/alternatives [73]. For instance, in the study by Chanpuypetch et al. [74], an integrated AHP-TOPSIS framework is used to assess bamboo-based products, highlighting its application in SC management for sustainable materials. By evaluating criteria such as production cost, market demand, and environmental impact, the model provides a systematic approach for product selection that can easily be applied to biocomposites. Focusing on product quality, in a recent study, Saha et al. [75] employed the VIKOR method to evaluate the properties of biocomposites for use in automobile interiors. By considering tensile strength, thermal stability, and moisture resistance, the research highlighted how bamboo-based biocomposites meet both performance requirements and sustainability goals [76]. Given the sustainability dimensions, however, the inclusion of several criteria, their importance and interdependence can further complicate the selection in biocomposites application, requiring more sophisticated decision methods to find a practical solution [77]. Among different MCDM methods, some studies also have applied the Analytic Network Process (ANP) method as an effective and well-established tool based on pairwise comparisons to address the interdependencies between 20 decision factors [77]. ANP integrates both quantitative and qualitative criteria, yielding more realistic outcomes and providing managerial insights when systematically selecting suppliers, for example in [19], [78]. The use of ANP in SC decision-making underscores its importance in navigating complex trade-offs, making it a valuable tool in biocomposite production [78]. To the best of my knowledge, no previous MCDM studies considered sustainable SC assessment within the biocomposites context. Also, previous efforts in this field have primarily concentrated on sustainability assessments of materials selection or supplier selection within SC relying on the mechanical and economic properties, neglecting an evaluation of the SC operational decisions based on sustainable dimensions. In MCDM models, especially involving both qualitative and quantitative factors, results are often influenced by input data affected by personal opinions, cognitive biases, and measurement errors [79]. Hence, in such cases, the sensitivity analysis is crucial for understanding how changes in criteria weights would impact decision rankings [79]. Several researchers have developed sensitivity analysis models for MCDM methods. For example, Masuda [80] and Huang [81] proposed models for AHP, while Chen and Kocaoglu [82] introduced an algorithm for hierarchical decision models. More recently, studies by Badi et al. [76] and Bouraima et al. [83] analyzed the effects of adjusting the most important criteria weights on the rankings of alternatives, offering insight into the stability of MCDM models. In some studies, weight fluctuations are examined, calculating the range within which rankings remained consistent [29], while some researchers introduced more advanced sensitivity techniques including standard deviation to compare variations of alternative scores across MCDM methods [84], [85]. Despite these advances in analyzing the sensitivity analysis results, most above methods are based on adjusting criteria weights manually to evaluate ethe robustness of the solution rankings, and few studies employed optimization models to calculate the minimum changes necessary to shift the 21 ranking of alternatives. As instance, Wolters and Mareschal [86] and Sowlati and Asadi [87] addressed this by developing Linear Programming (LP) models for PROMETHEE and AHP methods, respectively, to calculate the minimum required changes in criteria weights to alter the top-ranked alternative. However, ANP remains underexplored in this area of research due to its complexity in modeling dependencies among elements. This highlights the need for a more systematic sensitivity analysis approach within ANP, providing decision-makers with a robust method to address uncertainties and interdependencies in ANP-based decision models, particularly for emerging SC designs including those of biocomposites. 2.5 Summary: research gap This dissertation addresses a significant gap in the field of sustainable biocomposite SCs by integrating comprehensive economic, environmental, and decision-making analyses. Namely, based on the above literature review, while there has been extensive research on the techno-economic viability and environmental impacts of biocomposites, gaps have persisted in holistically assessing the SCs due to the complexities involved in biocomposite production (particularly at the biomass pre-processing stage). • Most existing studies have focused on isolated aspects such as economic performance or environmental impacts without considering the real-life interdependencies among social, technical, and environmental factors. • While existing studies have often centered on economic and environmental performance assessments, there is a lack of holistic stochastics analyses tailored for risk-averse investors, particularly those that incorporate uncertainty in both input parameters and data quality assessments. 22 • Furthermore, the reliance on traditional/subjective sensitivity analyses of the undergone decision models would limit the robustness of these evaluations, particularly in the face of uncertain market conditions and variable biomass characteristics. In this work, the techno-economic model (to be presented in Chapter 3) and the LCA model (to be presented in Chapter 4) advance our understanding of these decision criteria interdependencies, while also incorporating MC simulations to address uncertainties in economic and environmental input model parameters. Subsequently, given the trade-offs between the output of these model, the need for Multi-Criteria Decision-Making (MCDM) approaches remains crucial, as will be shown in Chapter 5, among other factors such as social and technical, which have been also largely overlooked in prior research. This study’s novel approach of using ANP and a new NLP model to automatically capture the criteria trade-offs towards decision model rankings, represents a step forward in providing more nuanced insights into sustainable SC designs within biocomposites context. 23 Chapter 3: Case Study Description and Techno-Economic Analysis Model 3.1 Synopsis In this chapter, the first sub-objective of the study is achieved by employing a TEA model to quantify various deterministic and stochastic economic criteria. To evaluate the impact of the input parameters and find out the most contributing parameters on the economic performance, a sensitivity analysis is also performed. After defining the most important parameter, the MC Simulation is employed to calculate the economic factors under that parameter variability and calculate the NPV at CVaR. The outcome of this analysis includes: 1) Mean NPV derived from MC Simulation, 2) CVaR-NPV, and 3) selling price range. Through this model, the biocomposite product selling price under all SC configuration alternatives is estimated and compared with its substitute product price in the market. Additionally, the uncertainty analysis reveals how fluctuations in key economic factors, such as the selling price and operational costs, impact overall profitability. A key finding of the analysis is that changes in selling price, identified as the most influential parameter, result in significant variations in NPV. By modeling these fluctuations through MC simulations, the study accounts for possible market uncertainties, offering a range of probable outcomes rather than relying on static assumptions. 3.2 Selected case study of hemp-based biocomposite production The global adoption of biocomposite pellets is increasing, especially in automotive industry, due to their multiple benefits including the preservation of natural fibre resources, and the utilization of environmentally friendly materials in their manufacturing [69]. In Canada, approximately 34,262 hectares (84,660 acres) of hemp were farmed in 2015. Hemp stands out as a promising material for biocomposites due to its superior tensile properties and modulus compared to many other natural fibers, as reported by multiple studies. These characteristics, coupled with its bio-based nature, make 24 hemp an ideal choice for use as either a matrix or reinforcement component in sustainable composite materials [88]. In Saskatchewan, seedlings occurred on about 15,335 hectares (37,892 acres) with over 400 total licenses issued to grow hemp in 2015 [89]. In this research, a start-up company is interested in producing sustainable plastics from hemp including 3D printing products. The company's bioplastics are non-toxic, biodegradable, and carbon-negative with engineered-grade quality (i.e., strong, not flammable, high heat and moisture resistance), which helps mitigate climate change and environmental issues. The company specializes in producing bioplastic pellets and aerogels. Additionally, the cellulose synthesis process is explored to produce a structured and gel-like, porous material that serves as the backbone of aerogels. This research focuses only on the production of biocomposite pellets using two primary components derived from hemp: hemicellulose and lignin. A facility with an input capacity of 300 tonnes/day of hemp for extracting lignin and hemicellulose is considered, based on insights from an interview with company executives. The process flow diagram for the ligno-chemical biorefinery commercial plant located in Saskatchewan, Canada, is shown in Figure 3-1. The SC starts from hemp supply sources located in Saskatchewan, and ends with the production of biocomposite pellets. 25 Figure 3-1. Flowchart of the hemp-based biocomposite SC in this case study. As shown in Figure 3-1, the SC entities include biomass and biorefinery SC activities. In the biomass SC, the activity starts with the harvesting of the entire hemp plant and then baling the whole hemp using baler machines. For the purposes of this study, it is assumed that hemp availability is consistent across all seasons, ensuring a continuous supply for the production process. No resource shortages are considered in the model, as it is assumed that sufficient quantities of hemp are readily accessible in the region throughout the year. Next, the baled hemp is transported to an intermediate 26 facility, where is size-reduced using a hammer mill (25% of hemp used as the composites reinforcement) and shredder (75% of hemp to be processed for the biopolymer conversion) before undergoing the biopolymer conversion process (the ratios are obtained according to the data gathered by the industry expert) Preprocessing at the field or facility could also reduce the costs and provide higher-value feedstock. The majority of previous studies focused on using hammer mills due to lower maintenance than any other size reduction machine [90]. Self-unloading trucks are used to transport and unload hemp bales. The travel distance is assumed to be around 50 km. In the biorefinery facility, hemp ingredients including lignin and hemicellulose are extracted, and then the biopolymer is produced with further processing. Biopolymers from hemp can be produced by extracting hemicellulose from stalks and fibers of hemp [91]. To enhance the mechanical properties of biocomposites, natural fibers are recognized as one of the most effective green fillers for polymer matrices [92]. Consequently, ground hemp fibers are utilized as reinforcement in the composite materials [93]. Ultimately, the blend of ground hemp (biofiller), lignin, and the biopolymer undergo compounding and extrusion processes, which yield biocomposite pellets. Within the biomass SC, the baling activity can be done using square or round balers [94]. Round balers require smaller tractors than that of large-square balers, which is an advantage for smaller farms [95]. The pickup area for the square baler is wider than that of the round baler, which results in difference in their efficiency [39], [96], [39]. Also, the impact of wrap/eject time for round balers reduces the throughput capacity to 50%. Arising from the variant efficiency in baler types, the biomass amount feeding to balers is different. Also, while within an hour the energy consumption of the square baler is higher (almost 1.25 times compared to the round baler), given the higher throughput in an hour, the energy consumption of the square baler becomes lower per tonne. The estimation has been derived from study performed by Lemos et al. [97]. Additionally, it is noted that 27 the round baler yields bale with lower density during the packing and rolling phases, whereas square balers compact biomass into "flakes" before compressing them into bales [94]. The lesser density of biomass from round baling facilitates, more effective drying in the agricultural field, consequently augmenting biomass quality. This enhancement in biomass quality is known to positively impact the overall quality of the final biocomposites manufactured [98]. From the perspective of the size reduction activity, two types of hammer mills including full-screen and half-screen can be used for crushing purposes to produce biofillers as biocomposite reinforcement [99], [100]. Hammer mills are energy-intensive machines, and factors such as screen size and particle size affect their energy requirement [101]. The finer the output particle size, the higher the energy needed to achieve a micrometric particle size [59]. Full-screen hammer mills require less power and energy along with higher throughput and efficiency compared to all hammer mill styles [101]. The energy consumption of half-screen hammer mill can be at least five times higher than that of the full-screen hammer mill [100], [102]. When the screen size increases from 1 mm to 1.5 mm, the production efficiency increases by at least 50% [103]. Also, decreasing the screen size mesh reduces the production capacity arising from the increased residence time during the grinding [104]. Moreover, the maintenance cost for equipment that produces finer particles is nearly double that for equipment handling larger particles [96]. On the other hand, finely ground hemp used as a reinforcement in biocomposite presents many benefits for sample preparation because it increases homogeneity and surface area, and decreases particle size, which improves extraction efficiency [105]. Therefore, evaluating the economic performance of different hammer mill and baler equipment combinations is crucial in decision-making. Finally, within biorefinery facility, lignin is extracted through the organosolv technique mixing with an organic solvent including ethanol in the bio-reactor, known as the company’s proprietary 28 solvent, then the mixed product is cooked in the bio-reactor under pressure and the extracted black liquor solution is cooled down, followed by filtering and drying processes for lignin production. The hemp components are then washed with water to extract a hemicellulose solution, which undergoes fermentation with the aid of bacteria to produce a biopolymer. This biopolymer is subsequently polymerized and utilized in bioplastic compounding. For producing a biopolymer/lignin biocomposites pellet, the lignin and biopolymers along with bio-fillers (hemp grounds) are compounded using a compounder (twin-screw extruder). It should be noted that the capacities of the full-screen hammer mill and square baler were established based on the daily demand/production and industry expert recommendations. Subsequently, the capacities and energy requirements for the other options were adjusted relative to these benchmarks, using data from previous studies. The key parameters of different balers and hammer mill types are listed in Table 3-1. Table 3-1. Parameter values for each baler and hammer mill type. Options Capacity Efficiency Energy consumption (diesel/electricity) Full-screen hammer mill 3.5 (tonne/hour)1 90% [103] 160 (KWh/tonne) [100] Half-screen hammer mill 1.5 (tonne/hour)1 70% [103] 900 (KWh/tonne) [100] Round baler 7.2 (tonne/hour)1 50% [96] 20 (liter/tonne)1 Square baler 37.5 (tonne/hour)1 65% [96] 3 (liter/tonne)1 1 Data is retrieved from industry experts or adjusted based on the daily productions. Currently, the company does not use any sustainable assessment framework for upscaling the natural-fiber based products. The company aims to scale-up the product and build a commercial plant for 20 years. 29 3.3 Techno-economic problem description and data collection In this section, the economic feasibility of biocomposites pellet production is assessed considering four alternatives defined based on biomass collection and processing equipment as shown in Figure 3-2. For biomass processing, the choice of equipment and its availability can impact the SC performance and total costs depending on the energy consumption, productivity maintenance, etc. [106]. Therefore, to collect the biomass from the field, different baler machines, i.e., square and round balers are used.. Moreover, the grinding operation’s efficiency depends on some variables including but not limited to screen size. Hence, the impact of hammer mill screen size, half-screen and full-screen, on the energy and power usage are taken into account, affecting the total SC costs [107]. The purchase cost of full-screen and half-screen hammer mills are estimated based on the full throughput of the hemp feedstock to the hammer mill. The combination of these assumptions resulted in four SC equipment pieces (Alternatives) (as shown in Figure 3-2) considering different capacities, which includes: 1) full-screen hammer mill and round baler (Alternative A); 2) full-screen hammer mill and square baler (Alternative B), 3) half-screen hammer mill and round baler (Alternative C), 4) half-screen hammer mill and square baler (Alternative D). 30 Figure 3-2. The supply chain activities and different alternatives considered in the current study. For the different balers and hammer mill types, the values shown in Table 3-2 are the key parameters affecting the economic viability of the project. The input parameters used in the Techno-Economic Analysis are selected based on industry-relevant data and were adjusted to reflect the specific scale of hemp biomass pre-processing (75 tonnes of biofiller (hemp grounds) per day). For the hammer mill costs, the observed differences compared to literature are due to the use of scaling factors and magnitude techniques. These methods are applied to account for the specific operational scale of processing of hemp per day. Scaling factors, derived from industry data, are used to proportionately estimate costs for different capacity equipment. For balers, supplementary data from equipment manufacturers are also used. All technical and cost-related input parameters for the concerned SC are detailed Table 3-3, Table 3-4, and Table 3-5. 31 Table 3-2. Input parameters considered for the four Supply Chain (SC) alternative focusing on biomass pre-processing equipment. Parameters Baler Types Hammer Mill Types References Square Baler Round baler Full-Screen Half-screen Purchase cost $19000 $7000 $6700 $4500 [108] Maintenance cost 10% of purchase price 2% of purchase price 10% of purchase price 20% of purchase price [108] # of operators 2 12 1 2 Calculated # of equipment pieces 1 3 1 1 Calculated Shifts 1 2 1 2 Personal communication with industry expert [June, 2021] Speed 4.2 km/hour 3.5 km/hour 1200 rpm 1200 rpm [108] 32 Table 3-3. Capital investment costs (source: personal communication with industry expert [June, 2021]). Parameter Value (CAD$) Land for farming 110,000,000 Land for building 110,000 Heating and cooling equipment installation 36,000,000 Reactors’ purchase cost 80,000 Tank purchase cost 3,500,000 Filter press purchase cost 4,000,000 Compounder extruder twin 500,000 Trucks 120,000 Loaders 220,000 Contractor 32,000,000 Design engineering 1,000,000 Unforeseen costs 5% of capital costs Total capital costs 211,398,256 Funding (incentives provided by Federal and Provincial governments) 350,000 Total capital costs after incentives 211,048,256 33 Table 3-4. Data for calculating Operation and Maintenance (O&M) costs within each supply chain stage. Parameter value References Feedstock cost (CAD$/ODT) 200 Personal communication with industry expert [June, 2021] Electricity cost including taxes and customer fee price (CAD$/KWh) 5 [25] Diesel fuel price (CAD$/liter) 1.56 Personal communication with industry expert [June, 2021] Staff salary (operation) (CAD$/hour) (24/7) 30 Personal communication with industry expert [June, 2021] Staff Salary (professional engineer) (CAD$/hour) 60 Personal communication with industry expert [June, 2021] Annual maintenance rate for equipment and building 2% of total investment [109] 34 Table 3-5. Other assumptions made in techno-economic analysis. Parameter Value References Service life (year) 20 Personal communication with industry expert [June, 2021] Land area for farming (hectare) 20,000 Personal communication with industry expert [June, 2021] Land area for building (hectare) 20 Personal communication with industry expert [June, 2021] Reactor processing capacity (tonne/year) 180,000 Personal communication with industry expert [June, 2021] Discount rate 10% [110] Equipment depreciation rate1 30% [111] Building depreciation rate1 5% [111] Corporate tax rate 38% [110], [112] Operating hours (per day) 24 Personal communication with industry expert [June, 2021] Operating hours (yearly) 8736 Personal communication with industry expert [June, 2021] Batch reactor 1 capacity for solvent extraction (dry tonne/cycle)2 2 Personal communication with industry expert [June, 2021] 35 Parameter Value References Batch reactor capacity for solvent extraction (ODT/cycle)2 2 Personal communication with industry expert [June, 2021] Batch reactor capacity for hot water extraction (ODT/day) 18 Personal communication with industry expert [June, 2021] Ethanol consumption (tonne/day) 45 Personal communication with industry expert [June, 2021] Hemp yield (tonne/acre) 2.5 Personal communication with industry expert [June, 2021] 1Declining balancing method. 2 Cycle time is approximately 8 hours per batch. 3.3.1 Assessment framework Most of the economic evaluations in the literature estimated the annuity of all the relevant costs including capital, installation, O&M costs [95]. Also, the NPV, incorporating the sum of initial investment and the present value of all future cash flows, is a method for evaluating the economics of a project based on four alternatives (A), (B), (C), (D) at a specific discount rate. For the purpose of this study, as shown in Figure 3-3, an interlinked spreadsheet-based model is prepared in Excel for all alternatives and the model is designed using a parallel data structure by which rapid comparison of parameters and results is allowed. First, the selling price is calculated to ensure a specific profit margin is achieved (in this study, a 50% profit margin is assumed based on the company’s instruction). Following this, to reflect on market demand uncertainty, the selling price is determined for different demand scenarios, such as 40%, 60%, 80%, and 100%, where 100% indicates that the entire demand is met by the production level. The range of selling price under different demand 36 scenarios is another comparable value to assess the alternatives. While deterministic models such as NPV results in the evaluation of the economics of the project, they do not indicate the feasibility when parameters change. As a second step, stochastic analysis using MC simulation is done to calculate NPV resulted from the selling price variability. A detailed description of data-collection, deterministic, and stochastic models is provided in the following sub-sections. Figure 3-3. Schematic of the techno-economic assessment framework considered in the case study. 3.3.1.1 Deterministic model To analyze the economic performance of biocomposites SC, a 20-year service life and a 10% discount rate are assumed. As depicted in Figure 3-3 , different deterministic engineering economics methods are used for comparing different options. To compare the economics of the four SC design alternatives, NPV, Annual Worth (AW), IRR, Return on Investment (RoI), and payback period of each alternative are calculated. For performing a detailed techno-economic study, data and information on technical aspects and costs are required. The costs include capital costs such as equipment purchase, installation, and engineering costs as well as operational costs including biomass purchase, energy, maintenance, and labor costs. All technical and economic data including bioplastic demand, total hemp area availability, equipment efficiency, capital costs, and per unit costs (e.g., for 37 feedstock, energy, labour, and maintenance costs) are obtained from the literature and industry experts (as can be seen in Table 3-3, Table 3-4, and Table 3-5). Since this is a new development project, the demand is assumed to be equal to the annual production capacity. The cost parameters are categorized as follows: Capital expenses: The major pieces of equipment in this study include a baler, a hammer mill, a shredder, reactors, etc. The purchase price of each piece of equipment is determined according to the referenced purchase price from the literature and from quotes provided by manufacturers (e.g., Alibaba website). The capital cost of each equipment piece is scaled to the required capacity for each option, using the order of magnitude technique as shown in Equation (3-1) [109]. 𝐶𝑜𝑠𝑡 1 = 𝐶𝑜𝑠𝑡 2(𝐶𝑎𝑝1𝐶𝑎𝑝2)𝑔 (3-1) Where 𝐶𝑎𝑝1 and 𝐶𝑎𝑝2 are the capacity of equipment 1 and 2, respectively; 𝐶𝑜𝑠𝑡 1 𝑎𝑛𝑑 𝐶𝑜𝑠𝑡 2 are the cost of equipment 1 and equipment 2, respectively; and g is the specific scaling factor for a particular type of equipment ranging from 0.4 to 0.8 (0.6 is used in this study). The adjustment of costs are done using Consumer Price Index (CPI) as shown in Equation (3-2) [95]. To reflect its impact and account for inflation, prior cost data for years other than 2022 (the reference year) are standardized to 2022. 𝐶𝑜𝑠𝑡 2022 = (𝐶𝑜𝑠𝑡 𝑅𝑒𝑓) (𝐶𝑃𝐼2022𝐶𝑃𝐼𝑅𝑒𝑓) (3-2) For estimating the installation costs of equipment pieces, as per communication with the experts, an escalation factor of 2.6 is applied to the purchase cost, whereas the purchase costs of equipment are taken from the literature and communication with experts [109]. The installation costs include costs associated with civil, structural, mechanical and electrical components. 38 Operating costs: The operating costs include the costs related to raw materials, energy for the conversion/size reduction process depending on the type of fuel used (electricity, diesel fuel, etc.), maintenance, personnel (e.g., preprocessing, transportation, storage, construction, etc.), and insurance. For operators, labour costs are assumed to be $30/hour, and a professional mechanical engineer costs C$60/hour as per personal communication with the project manager of the company. In total, a maximum of 2 shifts are considered, and for each shift a professional engineer, a project manager, and other personnel for shipping, administration, and maintenance are required. The number of required operators for each baler and hammer mill depends on their capacity and required number of equipment, which is shown in Table 3-2. The maintenance cost of equipment and building are assumed to be 2% of the capital cost. A higher maintenance cost rate is considered for the hammer mill due to its high repair and maintenance costs [108]. The biomass costs include the purchase cost of hemp as well as the land area cultivation and management costs. Energy and electricity consumption costs depend on the biomass input for each piece of equipment as well as equipment efficiency. Electricity consumption costs of hammer mill operation are estimated based on the desired particle size of the biomass, which can be changed by determining the screen size [25]. Also, the output capacity of each piece of equipment (TC𝑜𝑢𝑡𝑝𝑢𝑡) is calculated using the efficiency of that equipment piece (𝜏°) and the input capacity (TC𝑖𝑛𝑝𝑢𝑡) as detailed in Equation (3-3). 𝜏° =TC𝑜𝑢𝑡𝑝𝑢𝑡TC𝑖𝑛𝑝𝑢𝑡 (3-3) According to Equation (3-4), the amount of required biomass to be harvested is adjusted based on the equipment’s (baler/hammer mill) efficiency [50], because higher efficiency equipment enhances 39 the system’s throughput. It is also assumed that area width and yield are constant, so it is not included in the analysis. 𝑅𝑒𝑞𝑢𝑖𝑟𝑒𝑑 𝑏𝑖𝑜𝑚𝑎𝑠𝑠 = 𝐵𝑖𝑜𝑚𝑎𝑠𝑠 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦 ∗ 𝑆𝑝𝑒𝑒𝑑 𝑟𝑎𝑡𝑖𝑜 (3-4) As a part of economic analysis, the following steps are followed: Step 1: Estimating the selling price Gross profit margin defines the capability of the company to return revenue into profit, and is impacted by the annuity of Fixed and Capital Costs (FCC) including equipment purchase and installation costs, etc. [113]. The formula for selling price based on profit margin is shown in Equation (3-5) [113]: 𝑆𝑒𝑙𝑙𝑖𝑛𝑔 𝑝𝑟𝑖𝑐𝑒 = 𝑈𝑛𝑖𝑡 𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 𝑐𝑜𝑠𝑡1 − 𝑝𝑟𝑜𝑓𝑖𝑡 𝑚𝑎𝑟𝑔𝑖𝑛 (%) (3-5) Step 2: Calculating economic metrics The Net Income Before Tax (NIBT) is calculated as Equation (3-6) [114]: 𝑁𝐼𝐵𝑇 = 𝑃𝑟𝑜𝑑𝑢𝑐𝑡 𝑅𝑒𝑣𝑒𝑛𝑢𝑒𝑠 − 𝐵𝑖𝑜𝑚𝑎𝑠𝑠 𝐶𝑜𝑠𝑡𝑠 − 𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔 𝐶𝑜𝑠𝑡 = 𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑆𝑒𝑙𝑙𝑖𝑛𝑔𝑃𝑟𝑖𝑐𝑒 × 𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 𝑣𝑜𝑙𝑢𝑚𝑒 − 𝐵𝑖𝑜𝑚𝑎𝑠𝑠𝐼𝑛𝑝𝑢𝑡 ×𝐵𝑖𝑜𝑚𝑎𝑠𝑠𝑃𝑟𝑖𝑐𝑒 − 𝑀𝑎𝑖𝑛𝑡𝑎𝑛𝑎𝑐𝑒𝐶𝑜𝑠𝑡 − 𝑆𝑎𝑙𝑎𝑟𝑦 − 𝐸𝑛𝑒𝑔𝑦 & 𝐹𝑢𝑒𝑙 𝐶𝑜𝑠𝑡𝑠 (3-6) The depreciation and Salvage Value (SV) are estimated using the Half-year rule and declining balance deprecation method required by Canada Revenue Agency [115]. The taxable income calculation is also required to estimate the Net Cash Flow (NCF) after taxes as shown in Equation (3-7): 40 𝑇𝑎𝑥𝑎𝑏𝑙𝑒 𝑖𝑛𝑐𝑜𝑚𝑒 = 𝑁𝐼𝐵𝑇 − 𝑑𝑒𝑝𝑟𝑒𝑐𝑖𝑎𝑡𝑖𝑜𝑛 𝑟𝑎𝑡𝑒 × 𝑏𝑜𝑜𝑘 𝑣𝑎𝑙𝑢𝑒 𝑜𝑓 𝑎𝑠𝑠𝑒𝑡 (3-7) 𝑆𝑉 = 𝑅𝑒𝑚𝑎𝑖𝑛𝑖𝑛𝑔 𝑈𝑛𝑑𝑒𝑝𝑟𝑒𝑐𝑖𝑎𝑡𝑒 𝑐𝑎𝑝𝑖𝑡𝑎𝑙 𝑐𝑜𝑠𝑡 𝑜𝑓 𝑓𝑖𝑥𝑒𝑑 𝑎𝑠𝑠𝑒𝑡 (𝑒𝑞𝑢𝑖𝑝𝑚𝑒𝑛𝑡 𝑎𝑛𝑑 𝑙𝑎𝑛𝑑) (3-8) The salvage value and the land resale price in the last year (year 20) are added to the income after tax in the cash flow. The annual NCF is calculated as follows: 𝑁𝐶𝐹 = 𝑁𝐼𝐵𝑇 − 𝑇𝑎𝑥𝑎𝑏𝑙𝑒 𝑖𝑛𝑐𝑜𝑚𝑒 × 𝑇𝑎𝑥𝑅𝑎𝑡𝑒 (3-9) Finally, the NPV, AW, RoI, and payback period are estimated: NPV = ∑𝑁𝐶𝐹𝑡(1+𝑟)𝑛𝑛𝑡=1 − 𝐹𝐶𝐶 (3-10) Where 𝑟 is the interest rate (discount rate), 𝑁𝐶𝐹𝑡 is the net cash flow after tax in period t, and n is the service time of the project (20-year). To do so, the AW of the project is calculated using Equation (3-11) [116]. 𝐴𝑊 = 𝑁𝑃𝑉 ×𝑟(1 + 𝑟)𝑛(1 + 𝑟)𝑛 − 1 (3-11) Finally, the payback period as another metric of the investment, which identifies when cash flows cover the capital cost, and the RoI as a method for quick investment screening are calculated (RoI is estimated using Equation (3-12) as derived from Mitkidis et al. [95]). 𝑅𝑜𝐼 = 𝑁𝐶𝐹 𝐹𝐶𝐶 (3-12) 3.3.1.2 Uncertainty analysis To improve the reliability of TEA model, it is important to quantify the degree of uncertainty associated with variations in input parameters, which may be caused by the nature of the parameter 41 or a lack of large-scale operating data [26]. In the present study, sensitivity analysis is carried out by changing the input parameters (±20% from the original value) to assess the influence of five factors on NPV. These factors include product selling price, hemp purchase cost, electricity price, fixed capital investment cost, and O&M costs. Moreover, the mean value and risk associated with the outputs are calculated using the MC Simulation model. The detailed analysis is explained in the subsequent sub-sections. 3.3.1.2.1 Monte-Carlo simulation To address the uncertainty associated with model assumptions, MC simulation is performed. A Microsoft (MS) Excel add-on and Python code are used to generate 60,000 possible cases by varying the selling price parameter as the most influential factor identified by sensitivity analysis. According to the available data, probability distributions are fitted and used. The outcome of the MC simulation provides: 1) the probability distribution of the model outputs; and 2) an evaluation of the possible risks caused by variations in input parameters. Due to the absence of more detailed information, uniform distribution with minimum and maximum range is assumed. The minimum value given in the range indicates the minimum selling price at which the NPV of the project equals zero and the project is marginally acceptable. The maximum value represents is the future product price at which the 50% profit margin is achieved, considering product demand to be equal to the 100% of production. MS Excel is used to generate samples from the specified ranges and distributions. Next, NPV for each sample is calculated. As a first step, the number of required iterations is estimated using Equation (3-13) [50]. 𝑁𝑖𝑡𝑒𝑟 = (𝑍(𝛼) × 𝜎𝜀 × 𝜇)2 (3-13) 42 Where N is the number of iterations, 𝜎 and 𝜇 are the standard deviation and mean value of model’s output, respectively. 𝜀 is the maximum allowable marginal error and Z(α) is the critical value or Z-score that corresponds to the desired level of confidence in the two-tailed normal distribution. The MC simulation is conducted for 1000 iterations, in which iteration that the uncertain selling price were samples based on the defined range distribution. Other assumptions include a confidence level of 95% (Z=1.96) and a marginal error of 5%. Then, for the given confidence level and marginal error, 60,000 iterations (N=60,000) for all alternatives are required. 3.3.1.2.2 Economic risk assessment method: conditional risk analysis After calculating the NPV by solving Equation (3-10) for 60,000 iterations, the economic risk caused by the selling price uncertainty is assessed. VaR as a financial metric, estimates the minimum NPV (worst scenario) that can be achieved at a given confidence level ((1 − α) × 100). CVaR is an extension of VaR and gives more detailed information by estimating the expected VaR [117]. CVaR helps to calculate the expected NPV that occurs if VaR threshold is crossed (expected value of all worst-case scenarios). Within this study focusing on NPV as an output, the larger the CVaR ensures the higher risk regarding having an infeasible project (negative NPV) as shown in Figure 3-4. 43 Figure 3-4. Illustration of the Conditional Value-at-Risk (CVaR) [118]. (3-14) (3-15) Equation (3-14) indicates the use of mean value (𝜇) and standard deviation (𝜎) to quantify the risk associate with the model outputs (here NPV), while in Equation (3-15), 𝐶𝑉𝑎𝑅 (𝑎) of NPV is the conditional expectation of NPV subjected to 𝑁𝑃𝑉 ≤ 𝑉𝑎𝑅 (𝑎) with a confidence level of (1 − 𝛼). Therefore, the finalized economic factors under uncertainty (derived from MC simulation and under different demand scenarios) are shown in Table 3-6. Table 3-6. The considered criteria in the investment decision-making. Criteria Short Description C1: Mean NPV Expected estimate of profitability under selling price variations. C2: CVaR- NPV Expected profit losses in the worst-case scenarios. C3: Product Price Range Expresses the selling price range estimated with 50% profit margin and under different demand scenarios. 𝑉𝑎𝑅 (𝑎) = 𝜇 + 𝑍(𝑎)𝜎 𝐶𝑉𝑎𝑅 (𝑎) = 1𝑎 ∫𝑉𝑎𝑅(𝑥)𝑑𝑥𝑎0 44 3.4 Results and discussion Given the assumptions and data presented in Table 3-3, Table 3-4, and Table 3-5, the deterministic economic analysis is performed. The detailed cost components for each alternative are summarized in Table 3-7 and the results of each alternative are also provided in Table 3-8. The project’s cash flow considered the capital cost in the base year (i.e., 2022), O&M costs, and feedstock costs. Table 3-7. Costs components for each alternative (capital costs and annual operating costs). Options Fixed costs Operating costs Selling price (50% profit margin) Energy and fuel costs Labor costs Maintenance costs Biomass costs Total A $ 211M $190 $115 $173 $855 $1,335 $3688 B $ 211M $70 $26 $173 $566 $837 $3069 C $ 211M $372 $173 $173 $888 $1,559 $3964 D $ 211M $247 $566 $173 $587 $1,044 $3326 Table 3-8. Results of economic assessment. Economic Metrics Alternatives 𝐀: Full-Round 𝐁: Full-Square 𝐂: Half-Round D: Half-Square Net Present Value $123M $108M $130M $114M Annual Return on Investment (RoI) 145.1% 141.1% 149.7% 145.7% Payback Period 15 years 14 years 15 years 14 years Internal Rate of Return (IRR) 15.98% 15.02% 16.41% 15.42% Annual Worth (AW) $14.5M $12.7M $15.3M $13.45M Selling Price (Profit margin 50%) $3,687 $3,069 $3,964 $3,326 45 According to Table 3-8, the NPV increases when the capacity of equipment decreases to meet the 50% profit margin. Reducing the capacity leads to an increase in the number of equipment required, resulting in higher overall costs. Consequently, this has a direct influence on selling prices, as they need to maintain a 50% profit margin. Alternative (C) ($130 million) has the highest NPV with 20% higher NPV compared to that of the alternative (B) ($108 million). On the other hand, Alternative (C) poses a 30% higher selling price compared to that in Alternative (B) to meet the 50% profit margin constraint. As described earlier, this is because in Alternative (C) with a half-screen hammer mill and round baler equipment, a higher number of equipment pieces is required which results in higher total operational and investment costs. Focusing on the baler options, the fixed capital cost of a square baler (including purchase and installation costs) is three times more expensive than that of the round baler, however, the higher investment cost compensates for the smaller number of required equipment pieces. While the operating costs can vary due to parameters such as equipment efficiency, energy consumption, and total number of labors required for each option, the total fixed costs for all alternatives are relatively in close proximity to one another as per the calculation. As it can be seen in Figure 3-5, it is surprising that the option with the highest total costs (half-screen hammer mill and round baler) reaches a higher NPV (more profitable). The reason behind the high NPV is attributed to the high estimated selling price required to reach the targeted profit margin (50%). Given the highest total costs incurred by the half-screen hammer mill and round baler, to cover 50% of total costs, the higher selling price is estimated compared with full-screen hammer mill and square baler. Comparing the screen size of the hammer mill and baler types individually, the baler’s impact is 15% higher than that of the screen size types. The baler processes a larger amount of biomass compared to the hammer mill, which only handles 25% of harvested biomass. This difference impacts energy consumption and operating costs differently for each system. 46 Figure 3-5. Net Present Value and annual Return on Investment for the four alternatives presented in this study. Table 3-8 also presents the AW, IRR, and payback period for each alternative. Obviously, all alternatives have a positive AW aligning with NPV and annual RoI results to obtain 50% profit margin over 20 years at a 10% discount rate. The annual cash flow of all alternatives is shown in Figure 3-6. All alternatives have a positive net cash flow per year and the project for all alternatives is profitable. As shown in Figure 3-6, the 15% variation in the annual cash flow between the best and worst alternatives is primarily influenced by differences in biomass resource consumption. This discrepancy arises from varying energy consumption and salary costs in operational activities, leading to differences in NPV between the options with the highest and lowest values. Alternatives (B) and (D) achieved 14 years payback period meaning that the cumulative cash flow becomes positive within 14 years, while other alternatives with round baler had the longest payback period turning to profitability after 15 years. The slight shortest payback period arises from the lower operational cost especially due to using square baler. Therefore, from the payback period perspective, alternative (B) and (D) are the best options while from the NPV, AW, and RoI point of views, alternative (C) is the most economic one followed by alternatives (A), (D), and (B). Therefore, more reliable economic metrics needs to be included for the investment assessment. 47 Figure 3-6. Annual net cash flows of alternatives in the 20-year project lifetime. Over the 20-year service life of a commercial plant, the raw material cost accounts for 41% of the total production cost in the biocomposites SC. Also, the operating cost tends to outweigh the initial capital investment costs as can be seen in Figure 3-7. The cost of biomass accounts for 41% of the total operating costs of plant as shown in previous analysis of biomass plants as well [119]. The energy and fuel costs contributed largely to the operational expenses in all alternatives except for alternative (B). This trend can be explained by the higher maintenance costs compared to energy and fuel costs of full-screen hammer mills and square balers. The lower O&M cost of option B (full-square) can be attributed to employing an equipment with higher capacity and efficiency that does not need an additional equipment to be installed, which in turn saves some energy and power consumption. As the efficiency increases, the value of required energy along with a mass of inlet biomass decrease, leading to a reduction in the number of required equipment. However, capital investment costs, which include costs of building, equipment procurement, and installation, vary very 48 slightly across scenarios due to only changes in equipment purchase and installation costs for biomass processing. (a) (b) Figure 3-7. Total costs breakdown a) for all alternatives (Operation and Maintenance costs); b) overall contributions (example of alternative C). 3.4.1 A sub-scenario considering operators wellness The developed model took into account a maximum of 2 shifts per operator. However, the model is flexible and can be adapted to changes in parameters. For instance, to present the capability of the model to be adjusted to meet the specific needs in that sector, a scenario where the maximum shift duration is limited to one shift (e.g., 7:00 am- 3:00 pm) is considered in order to improve job performance and incorporate wellness management. As shown in Table 3-9, our analysis reveals a slight decrease in NPV due to increasing the number of equipment pieces and operators for baling and biomass size reduction operations to meet the demand. 49 Table 3-9. The parameters and the Net Present Value (NPV) outcome for all alternatives (considering one shift). Scenario Parameters/Alternatives A: Full-Round B: Full-Square (no change) C: Half-Round D: Half-Square #of operators 13 3 14 4 # of equipment pieces 7 2 8 3 NPV $120 M $108 M $128 M $113 M 3.4.2 Sensitivity analysis To deal with the uncertainty affecting the feasibility and outcomes of the project, a sensitivity analysis is carried out to identify the sensitivity of NPV of all alternatives with respect to the changes in each individual parameter. Figure 3-8 illustrates the sensitivity of NPV of alternative (B) with respect to ±20% changes in parameters including electricity price, fixed capital investment cost, hemp purchase price, O&M cost, and selling price. The specific 20% variation is assumed similar to a study by Khadivi and Sowlati [50] for the feasibility project in the industries. The results indicate that ±20% variation in the selling price led to about ±72 % change in the project’s NPV due to the direct impact of the selling price on the annual revenue obtained in each alternative. The sensitivity analysis of all alternatives is the same but for brevity, the results for only one alternative (full-screen hammer mill and square baler) is shown, and the rest are included in Appendix A. 50 Figure 3-8. Results of sensitivity analysis on Net Present Value (NPV) of alternative B: full-screen hammer mill and square baler. 3.4.3 Demand scenarios analysis The results of sensitivity analysis reveal that the selling price is a key input parameter of the model affecting the variation in NPV. The results also show that demand-based pricing considering fluctuations in customer demand is another method to further evaluate the minimum selling price to meet and keep the profit margin at 50%. As it can be seen in Table 3-10, with decreasing demand scenarios from 60% to 40%, the selling price increases from $5,115 to $7,673 (by 50%), which is significant and much higher than the increase when the demand scenario decreases from 100% to 80%, by which the selling price increases from $3,069 to $3,836 (by 20%). This huge difference arises from the impact of the time value of the money on NPV (as per Equation (3-10)). Overall, the selling price varies from 3,000 to 8,500 in a polynomial trend in different SC configurations. The slope of the curve becomes smoother closer to 51 100% market penetration stating that the gap between the selling price in each alternative decreases with further increase of market penetration. Although in terms of NPV, alternative (C) (half-round option) is superior, alternative (B) (full-square option) shows a better performance in terms of selling price variation. In this regard, less selling price fluctuation under market uncertainties suggests another criterion for the project investment evaluation. To benchmark the selling price, the minimum selling price for all alternatives are compared with those reported in relevant previous studies. It is worth mentioning that most existing literature include biobased plastics using natural sources biofillers [120]. As an example, Haylock and Rosentrater [121] analyzed organic fillers including hemp, flax, etc. for PLA-based composites. They performed TEA and used all operating costs including material, processing, equipment purchases and installation, maintenance, and labor costs. In the current study, the price of PLA depending on the parts weight of filler is used as a benchmark in hemp-based biocomposites pricing. As a reference, 0.01 kg, 0.1 kg, and 1 kg hemp weights are considered in PLA materials with different pricing according to the study performed by Haylock and Rosentrater [121]. According to the previous study, the price of hemp-based PLA with partial hemp filler varies from 2,750–10,000 $/tonne depending on the biofiller’s (hemp) weight. As it can be seen in Figure 3-9, although the fixed capital costs are not considered [39], the break-even price for most of the scenarios is aligned with hemp-based PLA price and even the price is well-below the reference price. Interestingly, Figure 3-9 shows that by increasing the biofiller’s weight, the price decreases in agreement with existing research findings regarding reduced selling prices. Overall, discrepancies in our findings compared to previous studies originate from different assumptions in feedstocks, and other economic and technological parameters. 52 Table 3-10. Selling price of biocomposite pellet for each supply chain configuration case according to different demand scenarios. Alternatives 40% 60% 80% 100% Selling price range A: Full-Round 9,219 6,146 4,609 3,688 5,531 B: Full-Square 7,673 5,115 3,836 3,069 4,604 C: Half-Round 9,912 6,608 4,956 3,964 5,948 D: Half-Square 8,316 5,544 4,158 3,326 4,990 Figure 3-9. Break-even price benchmark. 3.4.4 Economic assessment under risk The impact of selling price uncertainty resulting from the MC simulation is shown in Table 3-11. The maximum value of the selling price arises from the selling price assuming 100% demand scenario at which 50% of the revenue covers the total costs. On the other hand, the minimum selling price is 53 dependent on uncertain parameters such as product demand. Therefore, this parameter enables the comparison between the current study and the output of the economic analysis of other investigations. The MC simulation results show that uncertainty in selling price decreases NPV and increases the risk of a lower NPV than the mean value. To evaluate the investment risk, the NPV-at-Risk of each option is calculated using the mean and standard deviation of the simulated NPV results, with a chosen confidence level determining the expected value of the NPV. As it can be seen from the investment at risk of each alternative in Figure 3-10, Figure B1, Figure B2, Figure B3 in Appendix B , having uncertain input parameters can lead to the possibility of unfavorable NPV results, making the project financially unviable. According to Figure 3-10, alternative B (Full screen hammer mill & square baler) can lead to a CVaR-NPV of - $9.7 million. Contrarily, the CVaR of alternatives (C) (Half-screen hammer mill & round baler), A (Full-screen hammer mill & round baler), and (D) (Half-screen hammer mill & square baler) is even worse than that of alternative (B), equal to - $11.26 million, - $11.18 million, and - $10.02 million, respectively. It further reveals that despite greater mean NPV for alternatives (C), (A), and (D), they are exposed to higher risks of generating negative NPV. Alternative (B) with a full-screen hammer mill and square baler is exposed to a lower risk of having negative NPV, but at the expense of having a lower mean NPV. 54 Table 3-11. The ranges assumed for uncertain selling prices to run the Monte-Carlo simulation. Selling Price Options Minimum Maximum Alternative (A) $ 2,725 $ 3,688 Alternative (B) $ 2,226 $ 3,069 Alternative (C) $ 2,948 $ 3,964 Alternative (D) $ 2,434 $ 3,326 Figure 3-10. Probability of Net Present Value (NPV) of Alternative B: full-screen hammer mill & square baler. 3.5 Summary This chapter highlighted the importance of biomass collection and pre-processing equipment in commercializing the hemp-based biocomposites production which has not been performed in previous works to the best of my knowledge. The case study of a company in Saskatchewan, Canada (per section 3.2) was employed to determine the best option for investment under risk over a 20-year planning horizon. Also, the main three economic factors under uncertainty were introduced and 55 considered including CVaR-NPV, Mean NPV derived from MC simulation, and selling price fluctuations. The results of this chapter suggested that the capacity and efficiency of equipment for biomass baling or size reduction before converting them to the biobased materials were important to increase the profitability of project in the long-term. Full screen hammer mill and square baler combination had the highest CVaR-NPV and lowest selling price range among the alternatives. But half-screen hammer mill and round baler combination had the highest average NPV. Considering multiple criteria, no alternatives outweighed the other one in all criteria. Based on the stochastic analysis, the full-screen hammer mill and square baler combination is the preferred choice for most risk-averse decision-makers within the SC configuration. Moreover, to indicate the most significant component in determining the NPV, the sensitivity analysis was conducted and the selling price was determined as an impactful parameter with 72% changes on NPV resulting from 20% change in product selling price The results also supported the idea that balers have 15% higher impact on the SC economic performance compared to that of the hammer mill choice as supported by NPV metric. From the customers’ perspective, the selling price variation as another factor was impacted exponentially by the different demand scenarios. Based on the assessment of NPV under risk and variations in selling price, the full-screen hammer mill and square baler ranked as the top-performing alternative. 56 Chapter 4: Life Cycle Assessment Model 4.1 Synopsis In this chapter, the second sub-objective of the study is achieved by evaluating the environmental impacts of producing a fully hemp-derived biopolymer/lignin biocomposite pellet (based on the case study section 3.2) considering different biomass collection and pre-processing equipment alternatives. The overarching motivation remains the same as the earlier chapter: to arrive at efficient and eco-friendly operational decisions towards this pre-processing equipment selection. The system boundary covers the initial stages (upstream activities) through to the point where the finished biocomposite products are manufactured (factory gate), similar to the TEA model used in Chapter 3 (section 3.3). The lifecycle assessment boundary concludes at the point where the finished product exits the manufacturing facility, excluding subsequent stages such as transportation to customers, product usage, or end-of-life disposal. An attributional LCA is performed using SimaPro 8 software, focusing on four SC design strategies involving different baler (round/square) and size reducer machine (full/half screen hammer mill) options. The relative difference of the best and worst case is also evaluated to determine key areas in enhancing the environmental impact of the SC. Furthermore, as an essential step towards providing mitigation policy measures, the main sources of emissions, identified as “hotspots” are identified. Ultimately, to correctly support decision-makers in environmental impact mitigation actions, the evaluation is integrated with a sensitivity analysis that considered the impact of data quality and parameter variations. This approach facilitated a discussion on the significance of identifying viable solutions to enhance environmental aspects along with quantifying the environmental impacts of all alternatives that are needed for the sub-objective 3 of the research. 57 4.2 Problem definition and analysis strategy Considering the key parameters of different balers and hammer mill types including efficiency, capacity, and energy consumption as listed in Chapter 3 (Table 3-1), it is evident that the full-screen hammer mill combined with a square baler is the most efficient option, compared to the half-screen hammer mill and round baler. However, as mentioned earlier in section 3.2, a combination of a half-screen hammer mill and a round baler may yield a higher-quality output. Therefore, in this chapter, it is of primary interest to analyze and compare their relative differences in the environmental impact performance within the same environmental impact category (e.g., global warming). 4.3 Materials and methods LCA is commonly used to evaluate the environmental impact of a given product and could reveal the hotspots in each method to reduce the impacts [122]. The main steps used in this work are demonstrated in Figure 4-1, which includes: a) the SC activities, b) the LCA steps, and c) the uncertainty analysis. The SC's main activities include cultivation, harvesting, baling, transportation, and size reduction, where interactions among them occur. According to ISO 14040-44 [123], [124], the LCA technique includes four steps, as depicted in Figure 4-1 part (b): 1) the goal and scope definition for determining objectives and system boundaries; 2) Life Cycle Inventory (LCI) analysis for assembling the input materials and energy and the subsequent outputs; 3) Life Cycle Impact Assessment (LCIA) for quantifying the environmental, ecological, and human health impacts; and 4) interpreting and summarizing the results. Additionally, uncertainty analysis is conducted through sensitivity analysis within the SimaPro tool to evaluate the impacts of changes in critical input parameters. Moreover, the reliability and quality of the data are assessed to understand their influence on the LCA results. 58 Figure 4-1. The hemp based biocomposite Life Cycle Assessment (LCA) steps considered in this study. 4.3.1 Goal and scope definition The ultimate goal of the analysis is to quantify the major environmental impacts associated with fully hemp-derived biocomposites pellets production from cradle to gate. The FU in this work is the production of 1 tonne of biocomposite. The output-based FU is considered here because different pathways require different amounts of hemp supply due to different biomass pre-processing equipment efficiency. As depicted in Figure 4-2, the system boundary of this analysis includes activities related to hemp collection, pre-processing, and conversion to biocomposite pellets drawn from the study performed by Zampori et al. [125]. 59 Figure 4-2. The system boundary and material flow of producing 1 tonne of hemp-based biocomposite pellet in the case study. According to Figure 4-2, the process initiates with the cultivation of hemp, necessitating fertilizer resources, water, and seeds. During its growth, hemp also sequesters CO2 from the atmosphere. The cultivation process further involves transportation via trucks powered by diesel fuels. In this case study, assumptions are made about the conversion process based on insights from industry experts. It is assumed that the extraction of dextrose (sugar) and the subsequent polymerization process is adapted from the PLA production process, forming integral parts of the hemp-based biopolymer conversion stage. PLA is a biopolymer derived from renewable sources and has usage in various applications. For the conversion phase, the extraction of lignin involves the use of an organic solvent that consists of a mixture of 70% water and 30% ethanol per tonne of biomass being processed, as reported by the industry material experts in the case study. Also, hemicellulose, a heterogeneous polymer composed of pentoses, hexoses, and sugar acids, serves as a key carbohydrate source in hemp [126]. Biopolymer production is divided into four steps including i) 60 conversion of starch (carbohydrate) in the hemp into dextrose using enzymatic hydrolysis; ii) microbial fermentation of dextrose into Lactic Acid (LA); iii) Lactide production using treatment of LA; and iv) polymerization of lactide into biopolymer pellets [127], [128]. Following the microbial fermentation of LA, the process proceeds with either LA polycondensation or ring-opening polymerization [127]. The final step involves compounding and extruding the biopolymer, ground hemp (biofiller), and lignin to produce biocomposite pellets for applications like 3D printing and packaging. The mentioned processes need other chemicals including sulfuric dioxide, and calcium hydroxide (limestone) accounting for 96 wt.% of the inputs of chemicals [129], [130]. Purification of LA and the subsequent polymerization and manufacturing steps need to be included for producing biopolymer. All the life cycle stages beyond the manufacturing stage are excluded from the analysis and the conversion stage remains consistent due to the same demand among all alternatives. The impacts from maintenance and installation activities are negligible and not included in the system boundary [56]. To further compare the effect of the transition from petroleum-based polymers to biobased polymers, the environmental impact categories of Polyethylene Terephthalate (PET) polymer are also compared with the hemp-based biopolymer in this study. The comparison between hemp-based biocomposites and PET in this study is based on the objective of evaluating the environmental impacts of different polymer materials within similar application context. The goal of the LCA in this chapter is not only to assess the impacts of these materials but also to highlight the environmental benefits and trade-offs when shifting from petroleum-based polymers to biobased alternatives. The LCA methodology applied here evaluates both materials on a common functional unit, production of 1 tonne of biocomposites, allowing for a fair comparison of their environmental burdens across various impact categories. Focusing on environmental impacts from production through manufacturing 61 (cradle-to-gate) provides valuable insights into the relative sustainability of these polymers for comparable applications, offering a comprehensive perspective on the contributions of biobased and fossil-based materials to global sustainability objectives. The detailed process flow diagram of PET is shown in Figure 4-3. For the PET as a reference material, the two chemicals Purified Terephthalic Acid (PTA) and MonoEthylene Glycol (MEG) are synthesized separately. Using traditional production processes, PTA and MEG are fossil-based refinery products. By separating paraxylene (PX) from a xylene mixture and then oxidization, PTA is produced [131]. Also, hydration and oxidation are processes required for MEG production. Then, PTA and MEG chemicals are polymerized and pelletized to produce PET pellets [132], [68]. It is worth noting that the practical weight-based substitutability of PET with PLA in real-world applications is nearly one-to-one, as interviewed by industry partner. The ethylene is converted to MEG by reacting the intermediate product of ethylene oxide with water. Figure 4-3. The system boundary and material flow of producing 1 tonne of PET pellet in the case study [68], [131]. 62 4.3.2 Life cycle inventory and its assumption The LCI of this study is conducted to create an inventory of input and output flows for a product system. Inputs are the resources, materials and energy utilized in the system, while the outputs are emissions to the air, water and soil, and the exploitation of natural and primary-energy resources. The assumptions made in this case study are as follows: (1) the effect of insulation and maintenance is neglected, (2) there is 20% mass loss during baling, grinding, and pelletizing operations [133] and as a result, 80% of the harvested amount of biomass goes to the conversion plant, (3) there is no material loss in the conversion process, (4) the amount of transported and solid processed biomass is calculated as dry matter [133]. The data collection and calculations are carried out to quantify the inputs and outputs of each unit process based on material flows. The information for material flows and conversion rates are retrieved from lab-scale plant data. Some data are collected from scientific literature with similar production processes, engineering calculations about process technology, approximation of similar operations, and data from raw material suppliers. Another data source is the SimaPro database (EcoInvent library), which provides generic data on transportation, chemicals, electricity production, water, fuels etc. The production capacity of biocomposite pellets is considered equal to 80 tonnes/day similar to the TEA model in chapter 3. A more detailed description of relevant information concerning the various stages of the LCA is described in the following subsections. Also, the unit process input data for the LCA analysis of biocomposites and PET are provided in Table 4-1 and Table 4-2. 63 Table 4-1. Inventory inputs to produce 1 tonne of hemp-based biopolymer. Flow Unit Value Reference Agricultural and farming Hemp yield tonne/hectare 2.2 [89] Diesel consumption (tractor use for fertilization) hour/hectare 1.8 [125] Diesel consumption (tractor use for fertilization) kg/hour 8.78 [125] Diesel use for cutting and harvesting kg/hour 48.24 [125] Diesel use for cutting and harvesting Hours/hectare 4 [125] Seedling rate kg/hectare 34 [89] Fertilizer (𝐾2𝑂, applied as Potassium Oxide) kg/hectare 67 [89], [134] Fertilizer (N, Nitrogen fertilization) kg/hectare 100 [89], [134] Fertilizer (P2O5, phosphate rate) kg/hectare 50 [125], [89], [134] Water for germination m3/hectare 120 [125] CO2 Uptake tonne/hectare - 0.67 [125] Size reduction Electricity use for hammer mill GJ/tonne 0.58 [25] Electricity use for shredder GJ/tonne 0.047 [25] Biopolymer conversion (1 tonne) Enzymes gram/liter biomass 3 [135] N2 (Nitrogen) tonne 0.009 [130] Limestone tonne 0.03 [130] Water Liter 16000 [130] Extruder usage kg/hour 20 Industry expert Electricity usage in extruder kWh/kg 0.27 Industry expert Transportation and loading 64 Flow Unit Value Reference Fuel use in loaders Liter /tonne 10.9 [136] Three-axile semi-trailer Liter/km 0.68 [136] Table 4-2. Inventory inputs to produce 1 tonne PET resin (sourced from a study conducted by Papong et al. [137]). Flow Unit Value MEG (1 kg) Ethylene oxide tonne 720 Water kg 6.18 Electricity kWh 0.39 Ethylene oxide (1 kg) Liquid oxygen kg 0.46 Electricity kWh 0.33 Ethylene kg 0.83 PTA production (1 kg) paraxylene kg 0.66 Water kg 0.43 Electricity kWh 0.42 PET Resin (1 kg) PTA kg 0.87 MEG kg 0.35 Electricity kWh 0.38 For harvesting and collection activities, most data are retrieved from the Saskatchewan agriculture report along with those from previous studies on hemp cultivation [89], [125]. Saskatchewan's hemp farmers adjust land acreage based on market demand, with grain yield varying according to growing 65 conditions. The EcoInvent library provides data for diesel fuel burned in harvesting machinery [138]. The harvesting activities are modelled using the diesel GLO in EcoInvent dataset. The fuel usage for baling depends on the type of baler and its capacity and efficiency. Information regarding fertilizers is also taken from the EcoInvent database. It is worth mentioning that 1 kg of diesel has 45 MJ of energy based on the EcoInvent database. And 1-liter diesel has energy equivalent to 35 MJ. In addition, natural fibres like hemp have high carbon storage for GWP, considering that CO2 uptake is one of the crucial environmental benefits of dealing with biomass-based materials. Plants can store CO2 during photosynthesis and the growth of the material. The EcoInvent data are utilized for the grinding process, assuming electricity (medium voltage) is used for grinding. The electricity mix in Saskatchewan is considered to quantify the impact of electricity usage in equipment pieces such as size reducers, extruders, etc. Saskatchewan's electricity sector produces the second-highest amount of greenhouse gas (GHG) emissions in Canada, following Alberta. This is primarily due to its heavy reliance on fossil fuel-based electricity generation. Electricity in Saskatchewan accounts for 20% of emissions as 80% of the province’s generating capacity comes from fossil fuels sources (40% natural gas, 40% diesel, coal and other fuel sources), and 20% from renewable sources, primarily hydroelectricity (16% hydro, 4% wind) [139], [140]. The EcoInvent database library is revised and utilized to deal with the electricity mix and calculate the overall impact per kWh. During the manufacturing phase, data similar to a full-scale plant design for producing LA and PLA are taken into account [129]. Design data on extruders to compound the biopolymer and ground hemp are used (as per Table 4-1) to estimate the relationship between environmental emissions. 66 4.3.2.1 Alternatives based on biomass pre-processing equipment As outlined earlier in previous chapters, four alternatives related to the pre-processing equipment for biomass, including balers and hammer mills, are possible. The detailed life-cycle inventory data for each alternative is summarized in Table 4-3. To meet the demand, in total one full-screen hammer mill and square baler or two half-screen hammer mills and one round balers are required. The relevant variation in input parameters among different balers is obtained from the literature [141]. The utilization of two distinct pathways for size reduction, involving grinding and shredding, gives rise to challenges in resource allocation ratio [142]. The energy-intensive process of grinding hemp into nanometer particles affects biofiller pricing compared to shredded hemp as interviewed by the industry expert. In this regard, the accumulated ratio is determined using a mass and pricing ratios as shown in Equation (4-1) [125], [1]: 𝜆1 =𝛿1𝛽1𝛿1𝛽1 + 𝛿2𝛽2 (4-1) Where 𝛿1 and 𝛿2 are mass ratios for biofiller and shredded hemp, respectively, and 𝛽1 and 𝛽2 are the pricing ratio of the two size reduction streams. The price ratio of biofiller (𝛽1) to shredded hemp (𝛽2) is assumed 1.5:1 based on discussions with the industry expert. Using Equation (4-1), the ratio for shredded hemp is decreased to 0.66 instead of 0.75 as the price of bioffiller is expected to be higher than shredded hemp, generating more revenue after its production. In the SC configuration, the transportation distance of baled hemp to the intermediate facility and then to the conversion facility is assumed to be 50 km. Baled hemp is transported using a truck lorry with a capacity of 16-32 metric tonnes (EcoInvent database). 67 Table 4-3. Life cycle inventory of SC alternatives (all values are calculated based on 80 tonnes of biocomposites per day). Description (unit/day) A: B: C: D: Full-Round Full-Square Half-Round Half-Square Reference Flows Biocomposite pellets to the external markets (tonne/day) 80 80 80 80 Farm Activities Land cultivation (ha) 181.09 118.63 196.36 129.09 Phosphate, as P2O5 (fertilizer) (tonne) 9.05 5.93 9.81 6.45 Nitrogen fertilizer as N (tonne) 18.1 11.8 19.6 12.9 Potassium oxide, as K2O (fertilizer) (tonne) 12.1 7.9 13.15 8.64 Seeds (tonne) 6.15 4.02 6.67 4.38 Water for germination process (tonne) 5,976 3,924 6,480 4,266 Harvested hemp to be dried (tonne) 398.4 261.6 432 284.4 Diesel consumption for tractor (sowing and fertilization) (MJ) 35,416 23,255 38,403 25,282 Diesel consumption for tractor (harvesting) (MJ) 432,423 283,940 468,892 308,687 Diesel fuel used in baling machine (agricultural machinery) (MJ) 278,880 27,468 302,400 29,862 Intermediate Facility Diesel uses in loader (for unloading and open-pile management) (MJ) 126,658 83,167 137,340 90,416 Baled hemp to intermediate facility (tonne) 332 218 360 237 Amount of biomass transported (tkm) (from farm to intermediate facility) 16,600 10,900 18,000 11,850 Electricity use in hammer mill (KWh) 17,706 12,160 108,000 71,100 Electricity use in shredder (KWh) 10.40 6.83 11.28 7.42 68 4.3.3 Life cycle impact assessment The LCIA is carried out to quantify different indicators. This study applies the mid-point impact categories and characterization factors using the TRACI version 2.1 method performed in the SimaPro 8 software. The TRACI method considers different environmental impact categories namely ozone depletion, global warming, smog, acidification, eutrophication, carcinogenic, non-carcinogenic, respiratory effects, ecotoxicity, fossil fuel depletion [143]. This approach is chosen because the inventory data and impact categories and indicators rely on US-Canadian case studies [144]. There are four steps to perform LCIA, of which two are mandatory and the other two steps are optional. The mandatory steps are: (i) impact categories selection and classification, and (ii) characterization, and the optional steps comprise of: (iii) normalization and (iv) weighting. The first Description (unit/day) A: B: C: D: Full-Round Full-Square Half-Round Half-Square Conversion Facility Amount of biomass transported (tkm) (from intermediate facility to conversion) 1,000 1,000 1,000 1,000 Diesel uses for transportation of shredded hemp using three-axile trailer (MJ) 1,190 1,190 1,190 1,190 Water for biopolymer and lignin production (tonne) 665 665 665 665 Electricity use in twin-screw extruder (KWh) 21,600 21,600 21,600 21,600 Limestone (CaCO3) (tonne) 2.4 2.4 2.4 2.4 Enzymes (e.g., Alpha-amylase) (kg) 0.6 0.6 0.6 0.6 Nitrogen gas (N2) (tonne) 0.72 0.72 0.72 0.72 Ethanol used for precipitation and lignin extraction (tonne) 190 190 190 190 Positive flow CO2 uptake (hemp growth) (tonne CO2 eq) -34.77 -22.91 -36.18 -23.81 69 two steps entail selecting the environmental impact categories (classification) and allocating the elementary flows from the inventory of each process unit to each impact category (characterization) [144]. Classifications include Global Warming Potential (GWP), Acidification Potential (AP), Ozone Depletion Potential (ODP), Eutrophication Potential (EuP), Ecotoxicity Potential (EP), Carcinogenic Potential (CP), Non-carcinogenic Potential (NCP), Fossil Fuel Depletion Potential (FFDP), Smog Potential (SP), and Respiratory Effects Potential (REP) [143]. 4.3.4 Uncertainty analysis The inventory data may have uncertainties influenced by factors mentioned earlier, including reliability and quality of data and their availability. These uncertainties have the potential to impact the results of LCA. Therefore, an uncertainty analysis is performed using MC simulation within SimaPro 8 software. The uncertainty analysis is performed using a 95% confidence interval with 3000 iterations (sample size) calculating the mean, median, and standard deviation. To characterize the probability distribution arising from each uncertain data, a pedigree matrix is used incorporating a subjective assessment of data quality assessment [145]. In the pedigree matrix, the causes for deviations are grouped into reliability, completeness, temporal, geographical, and technological representativeness, detailed in Table 4-4 and Table 4-5 . The computer takes a random variable for each value, especially for farm and conversion-related input parameters, within the uncertainty range assuming the lognormal distribution. Approximately 75% of process units obtained from the EcoInvent library utilized a lognormal distribution, calculated with the pedigree matrix [67]. 70 Table 4-4. Pedigree matrix employed for agricultural stage data quality uncertainties. Indicator score 1 2 3 4 5 Comment Reliability × The data are taken by the literature (not totally reliable). Completeness × The data are representative of Saskatchewan's location in terms of agricultural-resources to be used. Temporal correlation × Most of data regarding agricultural resources are gathered from the report generated in 2015 (not very old). Geographical correlation × Data are from the areas with similar production condition. Further technological correlation × Data are from the materials and processes under study (e.g., identical technologies) but from different enterprises. The definition of indicator scores “1-5” can be found in [146] (the higher score on an indicator suggests lower reliability of the associated data). Table 4-5. Pedigree matrix employed for conversion stage data quality uncertainties. Indicator score 1 2 3 4 5 Comments Reliability × The data are taken from the industry expert (qualified estimates). Completeness × The data are not representative of the Saskatchewan location (all sites for the market considered; here Polymer conversion). Temporal correlation × The data are gathered based on interview (most of them are up to date considering experiments done in the lab-scale plant). Geographical correlation × Data are gathered from areas with similar production process (e.g., similar technologies for PLA production are assumed). Further technological correlation × Data are from related process on laboratory scales or from different technologies. The definition of indicator scores “1-5” can be found in [146] (the higher score on an indicator suggests lower reliability of the associated data). 4.4 Results and discussion The results obtained from LCA for 1 tonne of hemp-based biocomposites (per Figure 4-2) according to each alternative are detailed in Table 4-6 indicates that alternative (B), which utilizes a full-screen hammer mill and a square baler, is the most environmentally efficient SC design, exhibiting the lowest environmental impacts across all categories. In contrast, alternative (C) has the highest environmental impacts due to lower equipment efficiency and higher energy consumption, as detailed in section 3.2. Despite these higher environmental impacts, alternative (C) offers the advantage of a higher quality final product due to the smaller biofiller size achieved with a half-screen hammer mill and the less densified bales produced by the round baler. 71 Table 4-6. Environmental burdens per impact category for all four alternatives evaluated (data reported per functional unit: 1 tonne of biocomposites pellet). Impact Category Unit A: Full-Round B: Full-Square C: Half-Round D: Half-Square Global Warming Potential (GWP) Kg CO2 eq 9,377.77 6,521.229 10,985 7,514 Acidification Potential (AP) kg SO2eq 73.09 51.4 82.99 57.57 Ozone Depletion Potential (ODP) kg CFC-11 eq 0.001 0.0007 0.0013 0.0008 Eutrophication Potential (EuP) kg N eq 34.98 26.05 37.51 27.61 Ecotoxicity Potential (EP) CTUe 78,763.91 53,656.53 87,393.42 58,914.38 Carcinogenic Potential (CP) CTUh 0.0007 0.0004 0.0008 0.0005 Non-carcinogenic Potential (NCP) CTUh 0.01 0.009 0.017 0.0107 Fossil Fuel Depletion Potential (FFDP) MJ surplus 12,258 8,271.25 15,175 10,085 Smog Potential (SP) kg O3 eq 1,071.83 693.44 1,201 771 Respiratory Effects Potential (REP) kg PM2.5 eq 13.79 9.49 15.13 10.297 Figure 4-4 presents the breakdown of environmental impacts for the best option, alternative (B). The carcinogenic impact category is the most contributing indicator (53%) followed by non-carcinogenic (27%), ecotoxicity (12%), and eutrophication (5%). The trend is similar for all alternatives and for brevity, only one option is presented here. As shown in Figure 4-4, although utilizing a source of renewable raw materials exhibits superior performance relative to petrochemical polymers in the context of fossil energy utilization and greenhouse gas emissions, these materials demonstrate comparatively inferior performance concerning carcinogenic, ecotoxicity, and eutrophication due to the use of fertilizer, water, land and other material sources, as supported by previous works [69]. The findings of the current study are consistent with those of Groot and Boren 72 [129], who compared biopolymers (PLA) with fossil-based polymers. They showed greater impacts of biopolymers on the ecosystem including acidification, photochemical ozone generation, eutrophication, and farmland utilization compared to fossil-based polymers, due to N-leakage and NH3 volatilization from the ammonia-based fertilizers. Figure 4-4. Percentage of impact categories contribution for alternative (B) for 1 tonne of hemp-based biocomposites. To evaluate the trade-offs associated with the most and least environmentally efficient SC alternatives (i.e., (B) and (C), respectively), the percentage change in the LCIA results along with the contribution analysis of the source of impact for the representative case are analyzed in Figure 4-5. The significant processes found in contribution analysis are further analyzed to determine ways to improve the performance of hemp-based biocomposite production. Focusing on the most important impact categories considering a 5% cut-off, the indicators including carcinogenicity, non-carcinogenicity, ecotoxicity, and eutrophication are examined, as illustrated in Figure 4-5. As shown in Figure 4-5 (a), the changes in strategy from the worst (half-round) to the best (full-square) SC design are estimated to reduce carcinogenic impacts by 41%. More than half of this reduction is due to reduced biomass requirements and fewer harvester operations. Harvesting 39% less biomass, attributed to the higher efficiency and capacity of the equipment, is the largest resource 73 saver. The remaining savings come from reduced fuel use (less 9%) during the baling process for balers. Similarly, ecotoxicity impact is estimated to be reduced by 38% as shown in Figure 4-5 (b) followed by eutrophication, and non-carcinogenic by the reduction percentage of 30%, and 44%, respectively, as illustrated in Figure 4-5 (c) and Figure 4-5 (d). For all these impact categories, these differences are due to the superior performance of the square baler and full-screen hammer mill, which are assumed to have 30% higher efficiency, 420%-600% greater capacity, and 85% lower energy consumption. In most impact categories, harvesting is the most contributing process (55%-70%) as shown in Figure 4-5 (a-d) followed by the conversion process (15%-35%), baling, transportation, and size reduction activities. There is, however, a slightly different trend in the eutrophication impact category. Although harvesting and conversion activities collectively contribute 30% to 60% across all impact categories, respectively, there is a notable increase in the share of the conversion process specifically for eutrophication. The significant eutrophication impact from biocomposite conversion is mainly due to ethanol use and related nutrient run-off (N-leakage) into emissions and land use [147]. Overall, there is no significant contribution of transportation and size reduction processes to the overall environmental impacts in each impact category, which is derived from the lower electricity and energy consumption. Also, transportation share is not quite significant due to the 50 km transportation distance between facilities. 74 (a) carcinogenic (b) ecotoxicity (c) eutrophication 75 (d) non-carcinogenic Figure 4-5. Life Cycle Impact Assessment (LCIA) results for the best (alternative B) and worst (alternative C) Supply Chain (SC) practice implementations. The percentage value above the arrow are the changes in the absolute impacts, a) carcinogenic, b) ecotoxicity, c) eutrophication, d) non-carcinogenic. The TRACI method does not include carbon capture by plants, so carbon dioxide from the air was added as a negative emission to account for the carbon sequestered in hemp biomass. Although the contribution to the GWP is minimal, carbon emissions in the best and worst scenarios were also analyzed to highlight the impact of using more efficient equipment in reducing CO2 emissions. A significant benefit of utilizing a sustainable source of raw material is that renewable resources absorb carbon dioxide from the atmosphere, thereby decreasing CO2 emissions [147]. As shown in Figure 4-6, the higher carbon uptake (environmental credit) from growing more hemp in the worst-case scenario (a 70% increase from 9.7E+2 to 1.6E+3 kg CO2 eq.) does not necessarily reduce the net GWP emissions. This is due to higher GWP emissions associated with agricultural machinery operations, including baling and harvesting. Consequently, the full-square option has 40% lower GWP emissions compared to that of the half-round worst-case scenario. The GWP emissions from harvesting and biocomposite pellet conversion in the full-square option (Alternative B) are 3.829E+3 kg CO2 eq. and 2.231E+3 kg CO2 eq., respectively, showing a 50% decrease in harvesting impacts 76 compared to the half-round option (Alternative C). While hemp captures CO2 from the atmosphere through photosynthesis, reducing greenhouse gas contributions during cultivation and harvesting by 20%, this benefit is offset by the emissions from machinery use, as shown in Figure 4-6. Figure 4-6. Life Cycle Impact Assessment (LCIA) results for the best and worst SC practice implementation focusing on Global Warming Potential (GWP) impact category. Finally, given the similar scope (as outlined in section 4.3), the LCA of a petroleum-based product, made of virgin PET, is also analyzed here as shown in Figure 4-7. The results of the PET indicate that carcinogenic impact is the highest, with a value of 6.06E-05 CTUh (as shown in Table 4-7) followed by ecotoxicity, fossil fuel depletion, and non-carcinogenic. Comparing various biopolymers with petroleum-based products is challenging, primarily due to the variable cultivation and raw material inputs. These disparities consequently result in distinct environmental impacts across various categories [132]. Contrary to expectations, the results shown in Table 4-7 indicate that in all impact categories, biopolymer-based products have had overall worse environmental performance than their 100% fossil-based counterpart. However, this negative distinction may be justifiable by delving into more details of profiles of impact by other assumptions such as considering the emissions reductions resulting from the substitution of fossil-based composites with biocomposite 77 options. This result aligns with the finding of Chen et al [68], which highlighted the specific benefits associated with utilizing biocomposites as a sustainable alternative. As an example, the GWP of PET in this study, assuming the same demand as biopolymers, is estimated to be 2,213 kg CO2/tonne polymer. Previous studies such as Gracida-Alvarez et al. [148] and Groot and Boren [129] reported the GWP of PET as approximately equivalent to 3,000 kg CO2/tonne polymer. Specifically, the greatest contribution to PET production is caused by PTA and ethylene production, as has been previously supported by Tamburini et al. [132]) (Table 4-7). Comparing hemp-based biocomposites and PET, the relative contribution increases in GWP and fossil fuel depletion are associated with the usage of fossil-based resources. Reduction in non-carcinogenic, ecotoxicity, and eutrophication contribution compared to all impact categories is observed due to the removal of agricultural activities and requirements (e.g., fertilizer use) and diesel use for field operations. Figure 4-7. The contribution of impact categories in PET pellet production. 78 Table 4-7. The Life Cycle Impact Assessment (LCIA) of PET production (the functional unit was based on 1 tonne of PET). Impact categories MonoEthylene Glycol (MEG) Purified Terephthalic Acid (PTA) Total Unit Water Ethylene Oxide Electricity Ethylene Ozone depletion kg CFC-11 1.36E-06 8.48E-06 4.37E-09 1.12E-05 2.10E-05 GWP kg CO2 3.65 430.173 101.60 1,677.62 2,213.05 Smog kg O3 0.22 18.044 3.48 67.01 88.76 Acidification kg SO2 0.019 1.57 0.49 4.86 6.95 Eutrophication kg N 0.014 0.56 0.010 0.36 0.95 Carcinogenic CTUh 5.34E-07 1.03E-05 1.03E-06 4.88E-05 6.06E-05 Non carcinogenic CTUh 1.21E-06 2.94E-05 1.06E-05 2.70E-05 6.82E-05 Respiratory effects kg PM2.5 0.007 0.28 0.023 0.39 0.71 Ecotoxicity CTUe 30.09 1,100.05 211.69 2,145.92 3,487.76 Fossil fuel depletion MJ surplus 2.92 1,921.59 221.23 9,269.80 11,415.56 4.4.1 Sensitivity analysis The one-at-a-time sensitivity analysis results for the ±10%, ±20%, and ±30% changes in ethanol use in conversion process, and diesel, biomass yield, and fertilizer use in the agricultural stages are shown in Figure 4-8. Focusing on the best scenario (alternative B), increasing the amount of biomass yield from 10%-30% reduces the amount of impacts in all five impact categories by approximately 7%-20%. The results confirm that achieving higher biomass yield helps to increase sustainability 79 indicators of biocomposite production, as it affects the land use changes. Reducing diesel usage by 10%-30% primarily affects human health impact factors (by 7%-22%) more significantly than it does GWP and ecosystem impacts (by 4%-12%). Also, fertilizer usage in the agricultural stage accounted for a range of 3%-10% of influence in the ecosystem and GWP impacts, while having an impact of approximately 1%-4% on the human health factors. The results align with previous research, indicating that emissions from fertilizer significantly contribute to ecotoxicity and eutrophication [149]. This is attributed to the accumulation of heavy metals in the soil caused by the use of mineral fertilizers [149]. Moreover, ethanol presents a higher impact on eutrophication and acidification, mostly impacted by the production of fertilizers used in sugarcane/corn cultivation [147]. The rest of the results for eutrophication, non-carcinogenic, and global warming are shown in Figure C1 in Appendix C. (a) carcinogenic (human health-related) 80 (b) eutrophication (ecosystem-related) Figure 4-8. Sensitivity of the important impact categories for alternative B, to different input parameters including biomass yield, diesel and fertilizer use during cultivation, and ethanol use during the hemp-based biocomposites production (alternative B); a) carcinogenic impact (human health-related), b) eutrophication (ecosystem-related). 4.4.2 Effect of data quality uncertainty on impact categories The MC simulation demonstrated that, with a 95% confidence interval, on average alternative B (best scenario) has 40% less environmental impacts with 20% standard deviation compared to alternative C (worst case) with standard deviation of 35%. Conducting statistical analysis such as t-test and p-value investigation, Figure 4-9 shows that there is a statistical difference between full-square and half-round options under uncertainty of input data corresponding to the agricultural stage (a p-value less than 0.05 indicates that there is less than 5% probability that the observed differences occurred by random chance). The considerable standard deviation within the carcinogenic impact category, compared to ecotoxicity and non-carcinogenic categories, highlights the need for higher precision in harvesting-level input parameters to reduce uncertainty in human health assessments. Since the contribution of 81 eutrophication and its associated uncertainty are minor in comparison, they are not depicted in the simulation analysis. The comparison of alternatives under data quality uncertainties supports the previous LCIA findings. Figure 4-9. Characterized Life Cycle Impact Assessment (LCIA) results and uncertainty analysis for the harvesting process (environmental impact hotspot) of hemp-based biocomposites (error bars represent the 95% confidence interval of total emissions based on Monte-Carlo simulation). Focusing on conversion-related data uncertainty, the results show that the carcinogenic impact has the highest uncertainty and contribution, followed by non-carcinogenic, ecotoxicity, and eutrophication impacts, as shown in Figure 4-10 (a). These findings are consistent with previous results from the model without uncertainty, and they are attributed to the use of ethanol in the conversion process. When ethanol is removed, the environmental impact of the conversion phase is significantly reduced (e.g., eutrophication impact category is reduced by 700% as shown in Figure 4-10 (b)). The key finding is that data uncertainty in the conversion stage emissions for the default case is much higher compared to the case without ethanol use. Although uncertainty bars are comparable across all impact categories, the carcinogenic impact category has notably higher uncertainty, underscoring the importance of data quality in minimizing variations in this category. 82 (a) with ethanol (b) without ethanol Figure 4-10. The conversion process environmental impact in each impact category under uncertainty for alternative B, a) with ethanol consideration; b) without ethanol consideration. In the harvesting stage, identified as the most influential process in this work, improving hemp cultivation practices is recommended to mitigate environmental impacts. Literature suggests that substituting mineral fertilizers with organic options, such as sludge, could reduce emissions by 0.4 to 1.5 tonnes CO2 eq./ha/year [134]. Employing nitrogen-reduced fertilizers can also mitigate the significant influence of nitrogen fertilizers, which are key for plant growth [150]. Some studies argue that fertilizers, pesticides, and land use enhancements needed for biobased materials might have greater environmental impacts, especially in eutrophication, carcinogens, and ozone depletion, compared to fossil fuels [70]. Therefore, optimizing fertilizer application and using sustainable agricultural techniques are crucial, necessitating a comprehensive LCA to fully understand hemp cultivation's environmental implications. Additionally, cleaner energy sources would help achieve the sustainable development goals. Our results align with the studies suggesting agricultural activities significantly impact natural fiber-based biomaterial development [55]. However, our findings contradict those from La Rosa et 83 al. [57], who observed negligible eutrophication and ecotoxicity impacts in hemp agricultural practices due to the avoidance of fertilizers and pesticides. This discrepancy is likely due to varying site-specific inputs, such as biomass yield and agricultural inputs [151], [152]. As an example, Zampori et al. [125], who conducted the LCA of hemp cultivation in Europe, reported a biomass yield of 13-16 tonnes/hectare, which was 1.5 times more than biomass productivity considered for the other study in an Australian case study, i.e., 10 tonnes/hectare and approximately twice the biomass yields estimated for the France study (i.e., 8 and 9 tonnes/hectare). In the current study, 2.2 tonne/hectare was assumed as the biomass productivity given the climate conditions in Saskatchewan. These variations highlight the importance of geographical context in LCA outcomes. Prioritizing best management practices is essential to harness the potential of hemp-based biocomposites for sustainability. Figure 4-10 indicates that uncertainties in conversion stage input parameters significantly affect LCA results. Collecting more accurate data, especially for carcinogenic-related indicators, can improve LCA outcomes, underscoring the need for actual biomass extraction data. Comparing biocomposites to conventional PET polymers revealed that hemp-reinforced biocomposites did not outperform PET. Previous studies suggested that including the use phase in the system boundary could reduce the environmental impact of natural fiber-reinforced composites due to lower density and significant energy recovery during recycling [153]. 4.4.3 Key environmental hotspots in agricultural machinery and conversion For a more comprehensive insight into the causes and components influencing environmental impacts, a detailed examination of each major contributing process is conducted. The result in Figure 4-11 implies that with the exemption of the conversion process, the high contribution of agricultural practices among all activities is mainly a result of diesel use followed by using nitrogen fertilizer. The 84 use of diesel energy sources in agricultural machinery mainly contributed to human and ecological toxicities including non-carcinogenic, carcinogenic, and smog indicators. From a nitrogen fertilizer perspective, a similar trend for human-related toxicity has been found with the addition of the respiratory effects indicator. Surprisingly, in the conversion process, the contribution of ethanol use is much higher than other resources and energy sources, especially to the ecosystem-related emissions (e.g., eutrophication) compared to the production of fertilizer and diesel. In ethanol production, the cultivation of sugarcane inherently involves land occupation, a factor that substantially contributes to water and soil emissions due to the consumption of pesticides and fertilizers in sugarcane cultivation [147]. This preliminary study provides a summary of impact categories, and the corresponding sources associated with those impacts in the context of LCA results. Although those results differ from some published studies performed by Andrianandraina et al. [62] reporting post-farm activities as the main hotspot of the system, they are consistent with Sinka et al. [154] and Heidari et al. [1], who report nitrogen fertilizer and leaching are the main hotspots of hemp-based material used in construction and building applications. 85 Figure 4-11. Hotspots in the agricultural and manufacturing activity of biocomposite supply chain. The results illustrate that when evaluating the environmental impacts of biocomposites, human and eco-system emissions ranking above CO2 emissions (GWP) should be taken into consideration to avoid the environmental problem-shifting [70]. 4.4.4 Emission mitigation strategies From LCA results, it can be indicated that using the strategies to reduce the diesel and nitrogen fertilizer impacts in agricultural practices can efficiently minimize the environmental impacts of hemp-based biocomposites production. While ethanol use in the pretreatment process of the conversion process impacts the environment, various solvents like ethanol, acetone, and sulfuric acids interact differently with matrix materials, affecting fiber bonding and influencing thermal and mechanical properties [155]. Compost is an ideal substitute for supplying soil nitrogen as indicated by Scrucca et al. [61]. Four fertilizer set scenarios focusing on the replacement of chemical fertilizers with compost by 50%, 75%, and 100% ratios of nitrogen replacement are summarized in Table 4-8. 86 Also, two energy source options including diesel and natural gas are considered, given the constant desire for replacement of liquid petroleum origin fuels with d in agricultural machinery like tractor engines [156] . In this regard, the eight scenarios are defined as described in Table 4-9. Table 4-8. A summary of fertilization requirements conditions related to the different scenarios. Fertilizer kg/hectare Current Fertilizer set 1 Fertilizer set 2 Fertilizer set 3 Phosphate, as P2O5 50 33.55 16.6 5.56 Nitrogen fertilizer as N 100 55.8 25 - Potassium oxide, as K2O (fertilizer) 67 57.7 45.8 38 Compost - 862 1724 2486 Table 4-9. A summary of all considered scenarios represented the share of each source in the total energy and fertilizer source (inputs for two-way ANOVA). Scenarios Energy Source Fertilizer set Description 1 Diesel Current The current agricultural practices as a baseline 2 Natural gas Current Diesel is replaced with natural gas for operations 3 Diesel Set 1 50% of nitrogen replacement with compost 4 Natural gas Set 1 50% of nitrogen replacement with compost 5 Diesel Set 2 75% of nitrogen replacement with compost 6 Natural gas Set 2 75% of nitrogen replacement with compost 7 Diesel Set 3 100% of nitrogen replacement with compost 8 Natural gas Set 3 100% of nitrogen replacement with compost It is shown that incorporating organic fertilizer and other replacements for supplying nitrogen allowed for reducing environmental impacts. Apart from the fertilizer application and its impact on 87 environmental impacts, the use of diesel fuel for agricultural machinery to cultivate and harvest biomass significantly contributes to high levels of NOx, Sulfur dioxide (SO2), and other fields [157]. It is crucial to reduce energy consumption during the agricultural process recognized as the most energy-intensive operation in the biocomposites SC. Hence, mitigation scenarios are taken into account for the agricultural stage of the SC, particularly focusing on activities identified as hotspots. As shown in Table 4-8, the complete replacement of the chemical fertilizer (e.g., nitrogen) with organic fertilizer involves replacing the active ingredient with 100%. Given that the total nitrogen requirements are unchanged, it is assumed that there would be no changes in overall biomass production. The replacement of nitrogen with organic fertilizer has previously been recognized by Scrucca et al. [61] and Van Der Werf et al. [65] as a promising strategy for reducing emissions and recovering eutrophication impacts. According to Scrucca et al. [61], the calculations considered 11 g N/kg compost, 6 g P2O5/kg compost, and 8 g K2O/kg compost. This estimation enabled the determination of the remaining quantities of conventional fertilizers required to meet the recommended active ingredient rates per hectare. For the compost, the eco-invent data is employed as “compost (GLO), for market” in the SimaPro software. As can be seen in Figure 4-12, scenario 8, in which diesel fuel is replaced with natural gas as the energy source for machinery operations and compost is used as the source of nutrition for soil, offers the highest decrease (by 50%-80%) in the environmental profile, especially in human and ecological-related impacts (carcinogenic, non-carcinogenic, and ecotoxicity indicators). Supplying all energy sources with natural gas is investigated in scenarios 2, 4, 6, and 8 providing a substantial reduction in all impact categories (by approximately 40%). The results show that given the same energy source, a reduction in nitrogen fertilizer source and an increase in organic source mainly affects all impact categories along with the trivial impact on non-carcinogenic and respiratory effects indicators. 88 Figure 4-12. Environmental Profile of different alternative scenarios relative to scenario 1 (reference scenario). Results for global warming impacts are presented in Figure 4-13. For the favourable scenario, it is shown that the organic fertilizer assists in reducing CO2 emissions relative to the default scenario (scenario 1). While the global warming impacts was minimal in this study, it is noteworthy that 70%-100% substituting nitrogen fertilizer with compost results in a favourable environmental impact, as evidenced by absorbing CO2 and negative values of -173 and -650 kg CO2 equivalent (Figure 4-13). Incorporating CO2 uptake from hemp growth, these negative CO2 emissions are achieved. 89 Figure 4-13. Comparison of Global Warming Potential (GWP) impact (in the agricultural stage) for all scenarios considering CO2 uptake of the plants. An ANOVA (Analysis of Variance) is conducted to evaluate the impact of different fertilizer sets and energy sources on four major environmental impact categories: carcinogenic (CTUh), non-carcinogenic (CTUh), ecotoxicity (CTUe), and eutrophication (kg N eq). The aim was to determine whether there are statistically significant differences in these environmental impacts based on the variations in fertilizer sets and energy sources. The analysis involved calculating the Sum of Squares (SS) to measure total variation, Degrees of Freedom (df) to indicate the number of independent values and Mean Squares (MS) as the ratio of SS to df. The F-value (F0) represents the ratio of the variance explained by the factor to the unexplained variance, with a higher F-value suggesting a more significant effect. The F-critical value, obtained from the F-distribution table, is used to compare against F0 to determine significance [158]. Table 4-10 shows the results for four major impact categories which are carcinogenic, non-carcinogenic, eutrophication, and ecotoxicity based on different fertilizer and energy source 90 variations. Such results are used in the ANOVA model as input parameters and the calculated statistical metrics results are given in Table 4-10, Table 4-11, Table 4-12, Table 4-13, and Table 4-14. According to the ANOVA model, a p-value lower than 0.05 rejects the H0 hypothesis meaning that the outputs under factor levels are statistically different. For instance, according to Table 4-11, the choice of energy source for agricultural machinery significantly influences the carcinogenic indicator, as concluded by ANOVA results with the p-value below the 5% threshold. Conversely, the substitution of nitrogen fertilizer with compost showed no statistically significant impact with 95% of the confidence level within carcinogenic impact. The same analysis is reported for non-carcinogenic, ecotoxicity, and eutrophication indicators. However, the statistical results show the significant impact of both fertilizer and energy sources (diesel/natural gas) on the environmental impacts as all p-values are lower than 5%. To analyze the pairwise comparison between four types of fertilizer sets, a Tukey test using Q-distribution is used [159]. Obtaining the Q critical value for non-carcinogenic, ecotoxicity, and eutrophication impact categories in which the fertilizer’s impact is significant, the difference in impact between various fertilizer scenarios (including baseline and replacing nitrogen with compost at different ratios) is found to be statistically significant as well. 91 Table 4-10. Environmental results for each scenario (different fertilizer sets and energy types). Scenarios Energy Source Fertilizer set CP (CTUh) NCP (CTUh) EXP (CTUe) EP (kg N eq) 1 Diesel Current 0.0004 0.0097 53,656.53 26.05 2 Natural gas Current 0.0002 0.0027 32,973.05 19.39 3 Diesel Set 1 0.0004 0.0094 48,215.03 23.76 4 Natural gas Set 1 0.00018 0.0024 27,531.55 17.10 5 Diesel Set 2 0.0004 0.0093 44,079.43 21.98 6 Natural gas Set 2 0.00016 0.0022 23,395.951 15.32 7 Diesel Set 3 0.00042 0.0091 40,885.69 20.62 8 Natural gas Set 3 0.00014 0.0021 20,177.17 13.95 Table 4-11. ANOVA results for the carcinogenic impact category. Source of Variation Sum of Squares Degree of freedom Mean Square F0 P-value F critical Energy sources 1.10E-07 1 1.1E-07 189.3 0.0008 10.12 Fertilizer type 5.5E-10 3 1.8E-10 0.31 0.81 9.27 Error 1.75E-09 3 5.8E-10 Total 1.12E-07 7 Table 4-12. ANOVA results for the non-carcinogenic impact category. Source of Variation Sum of Squares Degree of freedom Mean Square F0 P-value F critical Energy sources 9.83E-05 1 9.83E-05 256,908,323 5.35E-13 10.12 Fertilizer type 3.83E-07 3 1.27E-07 333,872.38 8.79E-09 9.27 Error 1.14E-12 3 3.82E-13 Total 9.87E-05 7 92 Table 4-13. ANOVA results for the ecotoxicity impact category. Source of Variation Sum of Squares Degree of freedom Mean Square F0 P-value F critical Energy sources 856,130,761 1 856,130,761 10,928,500.4 6.10E-11 10.12 Fertilizer type 183,015,544 3 61,005,181.5 778,730.51 2.47E-09 9.27 Error 235.01 3 78.33 Total 1,039,146,540 7 Table 4-14. ANOVA results for the eutrophication impact category. Source of Variation Sum of Squares Degree of freedom Mean Square F0 P-value F critical Energy sources 88.72 1 88.72 4,092,269.57 2.66E-10 10.12 Fertilizer type 33.11 3 11.03 509,148.22 4.67E-09 9.27 Error 6.50E-05 3 2.16E-05 Total 121.84 7 Regarding equipment types for hemp biomass collection and pre-processing, our study identified a trade-off between environmental impacts and biocomposite quality. The half-round option, while less efficient environmentally, produces higher quality biocomposites due to finer biofiller particle size and less densified bales. This assessment provides a tangible way to evaluate trade-offs and identify the environmental improvement strategies, and inform decisions on environmental and health policies. 4.5 Summary In this chapter, we quantified the environmental sustainability of a novel hemp-based biocomposite using the whole hemp plant grown in Saskatchewan, Canada, by capturing region- 93 specific inputs for hemp growth. The main finding of this LCA study is that selecting appropriate agricultural practices and operational equipment for hemp collection and pre-processing can balance the quality of the final product with environmental effectiveness. To assess the carbon footprint of this material, the research utilized LCA, which thoroughly examined operational processes like hemp collection and size reduction equipment. The LCA evaluation considered all aspects of production, including biomass supply, collection, pre-processing, transportation, and conversion facilities. Similar to the previous chapter, four logistic practices were evaluated, focusing on the best and worst-case scenarios with the best case as the reference point: 1) full-screen hammer mill and round baler, 2) full-screen hammer mill and square baler, 3) half-screen hammer mill and round baler, and 4) half-screen hammer mill and square baler. To evaluate the environmental impact assessment system, the ISO 14044 as the principal framework was utilized. The half-round option exhibits 30%-44% higher environmental impacts due to 30% higher harvested biomass and 9% higher diesel usage per ton of produced biocomposite, but results in higher product quality compared to the full-square option. The most significant impact categories are identified as human health and ecosystem indicators, including carcinogenics, non-carcinogenics, ecotoxicity, and eutrophication. The harvesting stage, linked to the use of biomass, fertilizers, and diesel fuels, is identified as a critical contributor to the environmental impact in all the important impact categories. The sensitivity analysis revealed that a 10%-30% increase in biomass yield could reduce impacts across all categories by approximately 7% to 20%. Analyzing potential improvements indicated substantial environmental savings from resource management while maintaining product quality. The primary contributing process in the studied SC was the harvesting and cultivation activity, attributed to the biomass yield, use of diesel fuel, and fertilizers consumption. Additionally, reducing 94 the majority of impacts associated with agricultural activities could be achieved through better fertilizer management, using nitrogen-reduced fertilizer sources, and enhancing crop yields as per the previous works. Moreover, the data quality uncertainties significantly affected key impact categories, especially in the conversion phase involving ethanol as the solvent for extracting biomass ingredients (e.g., carcinogenic effects). These findings provided essential references for policymakers in biocomposite SC management. Also, integrating LCA under uncertainty, along with mitigation actions targeting hotspots and supported by statistical analysis (ANOVA), could yield more transparent and reliable LCA results. This approach enhances clarity in identifying critical environmental impacts and provides a robust basis for making informed sustainability decisions. 95 Chapter 5: Multi-Criteria Decision-Making Method 5.1 Synopsis This chapter focuses on presenting the proposed Sustainable Decision-Making Framework (SDMF) that integrates technoeconomic, social, and sustainability measures within an ANP-based MCDM model. In particular, it shows how the selection of SC configuration can be viewed as a network process as opposed to a conventional hierarchical decision-making process. The ANP, coupled with an optimization-based sensitivity analysis, assess the sustainable development of the hemp-based biocomposite SC Configuration described in Section 3.2, and ranks the best pre-processing equipment configurations, based on the combination of hammer mill and baler options. The economic and environmental impacts/values are directly used based on Chapters 3 and 4 results, respectively. A social dimension is added in this chapter under MCDM by examining potential job creation of different alternatives. Expert insights on product quality, system reliability, and technology readiness level are also collected through qualitative methods to account for technical factors (this latter dataset in particular included an Unreliability Factor (UF)). The developed sensitivity model identifies the smallest criteria weight changes necessary to shift the ANP rankings of alternatives. 5.2 Problem definition and criteria clusters As also outlined in Chapter 3, a SC is defined as a network connecting every component from customers to suppliers, encompassing manufacturing, to ensure the efficient flow of materials, finances, and information [85]. Organizations must assess not only their internal operations but also those of their entire SC, taking into account the following pillars/decision clusters of sustainability: environmental, economic, and social factors, along with technical factors [160]. A narrow, short-term approach without including all sustainability factors in a SC design may be 96 inadequate [161]. Instead, the SC performance should be evaluated by balancing these sustainability dimensions, as outlined in the Triple Bottom Line (TBL) framework, ensuring long-term operational effectiveness while addressing ecological and societal responsibilities [162]. Economic and environmental criteria values were derived from Chapter 3 and Chapter 4 (and also according to Akbarian-Saravi et al. [163] and Akbarian-Saravi et al. [164]) using the TEA and the LCA approaches, while social and technical indicators/factors have relied on inputs from industry experts, and will be added herein into the MCDM framework. While the MCDM framework was developed in previous study by Akbarian-Saravi et al. [165], it was previously limited to only product-specific technical factors, and a more comprehensive set of criteria is required to include the additional dimensions. Economic factors: As concluded in Chapter 3, the finalized economic metrics using stochastic TEA include the mean NPV, NPV under risk, including the worst scenarios which are quantified using the CVaR-NPV metric, along with the selling price range given the unstable market and demand. Environmental factors: from Chapter 4, the most contributing environmental impact categories (5% cut-off) were selected (from Figure 4-4) as the most influential environmental criteria, relating to human and ecosystem impacts. These criteria consist of carcinogenic, non-carcinogenic, ecotoxicity, and eutrophication impact categories. Additionally, the current base scenario for energy sources, as outlined in Table 4-9 (Scenario 1), is used for MCDM analysis. Social factor: the perceived number of created jobs under each decision scenario is considered as a social factor in this study (as a sample representative of social factors). In this study, social factors are assessed by estimating the number of jobs created under different operational configurations. Specifically, these jobs are calculated based on equipment usage, including the 97 capacity and hours of operation per machine (such as hammer mills or round balers) and the number of shifts assumed per day for each piece of equipment. The total number of hours needed for equipment operation is determined based on the amount of material required to meet the production demand. Once the equipment hours are estimated, the number of workers needed for each configuration is calculated. This approach links the operational requirements directly to job creation, which is considered a key social factor in this study. Therefore, the number of jobs created is considered as a proxy for social impacts. Technical factors: Another key characteristic of a sustainable SC is the technical aspect, for which product quality, system reliability, and Technology Readiness Level (TRL) are included in the present problem. From the current literature, clearly there is an essential variability in the quality of the processed biocomposites as well as system reliability and TRL under different pre-processing equipment [166]. All the qualitative data are collected using industry experts’ opinion as shown in Table 5-1. These experts provide insights based on their experience and knowledge of the equipment and processes involved in biocomposite material production. The effect (direction) of each sustainable SC parameter on the performance of SC is indicated with “+” or “-” sign. For instance, to ensure the sustainability of biocomposites upscale production, it would require a higher Mean NPV (𝐶2+) and a lower Carcinogenic impact (𝐶4−). Product quality: Product quality in biocomposites is significantly influenced by the properties of biofillers used as reinforcements [167]. The quality and mechanical characteristics of these biofillers are affected by various factors across multiple stages including filler production (griding), and the hemp storage processes. The quality of biofillers, such as hemp grinds, plays a crucial role in determining the modulus (stiffness) of the final biocomposite product. A higher-quality biofiller, with smaller particle size and well-preserved fiber integrity, can enhance the 98 modulus of the composite by providing better reinforcement to the polymer matrix. Conversely, poor-quality biofillers, with irregularities or degradation due to improper handling, may result in weaker bonding with the matrix, reducing the product's stiffness and overall mechanical performance [168]. System reliability: Reliability is often quantified as the percentage of time the system operates without failure within that period. One key reliability metric, Mean Time to Repair (MTTR), measures the average time required to restore the system to full functionality after a failure [169]. For instance, comparing the maintenance time of square and round balers, the criteria have been assessed. It indicates the level at which the system maintains the level of quality over time. Square baling needs more maintenance and repair compared to round baling leading to higher maintenance cost of square baler compared to the round baler. Due to less complicated handling system for round balers to tie the bales, they require less frequent maintenance [97]. Besides, storage of square bales is more difficult than round bales because they must be under a structure or out of the weather to decrease mass loss due to weathering [170]. Failure of the equipment on the other hand can be a major contribution to the maintenance costs [171]. Therefore, the equipment with the most maintenance costs has less reliability. From the hammer mill’s perspective, the components of a hammermill that experience the most wear are its screens, which may appear costly to replace at first glance. Typically, depending on factors like the material being processed and screen hole size, a set of high-quality, hard-faced hammer mill screens will wear out after 2-4 replacements before the hammers need attention. Smaller screen hole sizes may necessitate even more frequent replacements [108], which makes the half-screen hammer mill system less reliable compared to full-screen hammer mill according to MTTR factor. Technology Readiness Level: Another qualitative technical factor considered is the TRL, which 99 is a framework that has been used in many variations across industries to provide a measurement of technology maturity from idea generation (basic principles) to commercialization [172]. TRL can also be adapted to support understanding of capabilities and resources required to develop technologies at different stages of development. The TRL stages are based on the Government of Canada assessment [173]. As shown in Table 5-1, when evaluating the qualitative criteria for the combination of the hammer mill and baler equipment, the baler’s importance is deemed more significant than the hammer mill due to the proportion of raw materials processed by each. Specifically, only 25% of the raw materials are processed using the hammer mill. The MCDM methods can systematically account for the abovementioned influential factors and their relative significance in prioritizing alternatives [78]. Using the ANP model in this work, the outer-dependence and inter-dependence between criteria are allowed (as will be further discussed in Section 5.2.1). The ANP approach is often applied specifically to allow for such interdependencies among decision factors in different applications [174]. The overview of proposed MCDM framework is shown in Figure 5-1. Table 5-1. Decision matrix for criteria values for all alternatives (the effect (direction) of each sustainable SC parameter on the performance of SC is indicated with “+” or “-” sign). A: Full-Round B: Full-Square C: Half-Round D: Half-Square Type of Criteria Economic Selling Price (SP) Range ($) (C1) 5,531 4,604 5,948 4,990 - (cost-like) Mean Net Present Value (NPV) (Million $) (C2) 61.65 53.96 65.28 57.25 + (benefit) Conditional Value at Risk (CVaR) - NPV (Million $) (C3) -11.18 -9.73 -11.26 -10.02 + Environmental Carcinogenic (CTUh) (C4) 0.00076 0.00049 0.00084 0.00054 - 100 A: Full-Round B: Full-Square C: Half-Round D: Half-Square Type of Criteria Non-carcinogenic (CTUh) (C5) 0.015 0.0097 0.0175 0.010 - Eutrophication (kg N eq) (C6) 34.98 26.05 37.51 27.61 - Ecotoxicity (CTUe) (C7) 78,763 53,656 87,393 58,914 - Social Job quantities (C8) 13 3 14 4 + Technical (fuzzy data) System reliability (C9) High Low Medium Very Low + Product (mechanical) quality expected (C10) Medium Very Low High Low + Technology Readiness Level (C11) 4 6 4 7 + Figure 5-1. Sustainable decision-making framework for the present SC pre-processing equipment configuration selection. 5.2.1 Analytical Network Process (ANP) ANP was first introduced by Saaty [175] as a more comprehensive alternative to the Analytic Hierarchy Process (AHP). While AHP uses a strict hierarchical structure to analyze decision-making problems, ANP allows for the modeling of a decision problem using a network-based 101 structure. In this way, not only can the importance of the criteria influence the importance of the alternatives, but the alternatives themselves can affect the importance of the criteria (see Figure 5-2). Additionally, feedback loops can exist within the set of alternatives and criteria, making the analysis more dynamic and realistic. For instance, the selling price range significantly impacts both the mean NPV and CVaR-NPV, as variability in prices directly affects the expected value and risk-adjusted outcomes. As shown in Figure 5-2, ANP does not require a hierarchy, but rather, a network of elements. In this network, elements are represented as nodes within multiple clusters, where each node can both dominate and be dominated through pairwise comparisons, depending on its hierarchical level within the network [175]. In this work, we have adopted ANP-based valuation framework for the selection of best SC alternative. ANP is used due to the following reasons [160]: 1) analyzing the multi-criteria decision problem for sustainable SC performance, 2) incorporating interdependencies among some factors and enablers (instead on independency like AHP), 3) subjectiveness of some of the criteria, enablers, and dimensions, making it challenging to derive a composite score using a straightforward weighting method. The basic assumption of this study is that economic, environmental, social, and technical can be seen as subsystems of an integrated performance management system. This work is the extension of the previous work performed by Akbarian-saravi et al. [165], including additional qualitative factors under technical criteria. The classification of the comparative answers was based on the fundamental scale of Saaty [177] (as shown in Table 5-2), utilized for both ANP and AHP. On this scale, a value of 1 indicates that both elements are of equal importance, while a value of 9 signifies that the row element is significantly more important than the column element. If the column element is more important 102 than the row element, a reciprocal value is used, with 1 representing equal importance and 1/9 indicating that the column element holds much greater importance. Also, in the implementation of the ANP method, a 5-point Likert scale (very low: 1, low: 3, medium: 5, high: 7, very high: 9) is used for assessing the qualitative criterion, which in this context is the product quality (C9), system reliability (C10), and TRL (C11). Figure 5-2. Samples of a (a) hierarchy, and (b) a network in Multi Criteria Decision Making (MCDM) modeling [178]. Table 5-2. Saaty’s fundamental scale [177]. Value Definition Explanation 1 Equal importance/contribution/relation Two elements contribute equally 3 Weak importance/contribution/relation Judgment slightly favors one activity over another 5 Strong importance/contribution/relation Judgment strongly favors one activity over another 7 Very strong importance/contribution/relation An activity is strongly favored, and its dominance demonstrated in practice 9 Extremely strong importance/contribution/relation The evidence favoring one activity over another is of the highest possible order of affirmation Implementation of ANP includes two phases as follows [179]. 103 Phase 1: • Identify Influential Factors: The first step involves identifying the key factors that affect the system or decision in question including economic, environmental, etc. • Define Relationships: The next step is determining the interactions and relationships among the factors and alternatives. This can be done using correlation analysis or expert judgment. In ANP, the factors (criteria) and alternatives may influence one another, leading to a complex web of dependencies. Phase 2: • Step 1 (Pair-wise comparison matrices between component/attribute levels and interdependencies) Pairwise comparisons are performed among the criteria alternatives and their relationships. The decision-maker is asked to perform a series of pairwise comparisons based on an upper-level control criterion, evaluating the relative importance of each option in relation to this control criterion. In cases where interdependencies exist, elements within the same level are considered as controlling components for one another. Additionally, levels themselves can also exhibit interdependencies. These comparisons quantify the relative importance of each factor or alternative. The e-vector (eigenvector) represents the priority of each dimension. With a single question: “with regard to the importance to the company, compare the dimensions of performance”. The comparison matrix (as shown in Equation (5-1)) is created where each element 𝑎𝑖𝑗 represents the relative importance of criterion or alternative 𝑖 over 𝑗. If 𝑎𝑖𝑗 ≥ 1, then 𝑖 is more important than 𝑗; otherwise, the reciprocal value is used. 𝐶𝑛 refers to the nodes of the network. 104 𝐴 =[ 𝐶1 𝐶2 𝐶3 . 𝐶𝑛𝐶1 1 𝑎12 𝑎13 . 𝑎1𝑛⋮ ⋮ ⋮ ⋮ ⋮ ⋮𝐶4 𝑎41 𝑎42 𝑎43 ⋮ 𝑎4𝑛⋮ ⋮ ⋮ ⋮ ⋮ ⋮𝐶𝑛 𝑎𝑛1 𝑎𝑛2 𝑎𝑛3 . 𝑎4𝑛] (5-1) • Step 2 (Normalization) Once the pairwise comparison matrix is established, it is normalized and the normalized value 𝑠𝑖𝑗 is calculated using the Equation (5-2). 𝑠𝑖𝑗 = 𝑎𝑖𝑗 × [∑ 𝑎𝑖𝑗𝑛𝑖 ]−1 ∀𝑖, 𝑗 (5-2) • Step 3 (Weighted matrix) The next step involves determining the weighted metrics for the alternatives based on the normalized pairwise metrics. This is done by multiplying the normalized pairwise metrics 𝑠𝑖𝑗 by the corresponding weights 𝑤𝑖𝑗 for each criterion under each alternative as shown in Equation (5-3): 𝑈𝑖𝑗 = 𝑤𝑖𝑗 × 𝑠𝑖𝑗 ∀𝑖, 𝑗 (5-3) • Step 4 (Supermatrix creation) In this step, the supermatrix, which is the key component of ANP is constructed, capturing the relationships between all the components of the network, including criteria and alternatives. Assume that each cluster 𝑘 includes 𝑛 elements. These elements can be used to build a supermatrix 𝑊 (as shown in Equation (5-4)), and pairwisely be compared using Saaty’s scale. 105 𝑾 =[ 𝑈11⋮𝑈𝑘1….…𝑈1𝑘⋮𝑈𝑘𝑘….…𝑈1𝑛⋮𝑈𝑘𝑛⋮𝑈𝑛1.…⋮𝑈𝑛𝑘.…⋮𝑈𝑛𝑛] (5-4) • Step 5 (limit matrix and selection of best alternative) The supermatrix is raised to powers through multiplication until it converges, meaning that all the values in each row become stable. The final result provides the relative importance or ranking of each alternative in relation to the decision criteria. 𝐿𝑖𝑚𝑖𝑡 𝑀𝑎𝑡𝑟𝑖𝑥𝑖𝑗 = [𝑊]𝑘 ∀𝑖, 𝑗 (5-5) Where 𝑘 is the power of convergence of the Supermatrix with updated elements of 𝑈𝑖𝑗. An alternative with a higher global score (or weight) is consistently preferred. Likewise, a criterion with a higher score is regarded as more significant in the decision-making process. In some cases, the values in a row of the limit matrix may become identical, indicating equal weight among elements. Ultimately, the best alternative is determined by their global weights. 5.2.2 ANP concept for sustainable SC configuration selection In this section, another motivation behind the ANP decision aid tool is described for multiple criteria sustainable SC configuration selection, which is related to the criteria weights assignments by allowing a greater degree of freedom [180] Practically, the ANP network could be applicable compared to the hierarchy framework in cases where the expert during qualitative data collection in evaluating a given alternative under specific criteria may have more/less reliable data/confidence compared to the other alternatives under the same criterion (note that in the standard MCDM methods, the weight 106 of a given criterion is fixed, regardless of alternatives and it cannot vary from one alternative to another; this is where ANP present a great advantage over many MCDM methods such as AHP [171]). To further exemplify, assume we are comparing two alternatives for a SC strategy (𝐴1 and 𝐴2) under two criteria (𝐶1 and 𝐶2) with the goal of choosing the better strategy. Inspired by the work of Milani et al. [180], the expert prefers to use the additional information (e.g., past data availability/experience/reliability/etc.) they may have and define a higher weight for 𝐶1 while scoring 𝐴1 (as more reliable alternatively, i.e., with more certain data), while considering a lower weight for 𝐶1 while scoring 𝐴2 (less reliable alternative). In this decision-making framework, the level of confidence of the decision-maker is influenced by how the combination of equipment contributes to the overall end-stream performance under specific criteria, such as product quality. For example, the combination of the half-screen hammer mill and round baler (Alternative C) demonstrates consistently a high product quality for both equipment, leading to a high overall score and greater confidence by the designer in its selection. In contrast, the full-screen hammer mill and round baler (Alternative A) presents a mix of extremes, with high product quality for the round baler but low product quality for the full-screen hammer mill, making it difficult to determine whether the overall performance is medium or low. This variability introduces uncertainty and lowers confidence in evaluating this alternative. Similarly, the half-screen hammer mill and square baler (Alternative D) exhibits a trade-off, where the high product quality of the half-screen hammer mill is offset by the lower quality of the square baler, resulting in a low overall score. However, due to the superior performance of the half-screen hammer mill and round baler, this alternative might inspire slightly more confidence compared to Alternative A or D. By adjusting the weights based on reliability, the model allows decision-maker to prioritize specific criteria and balance trade-offs, ensuring decisions align with their confidence in the robustness and 107 performance of the selected SC configuration. In this case, for an ANP model, the adjusted aggregated score for each alternative 𝑖 is as follows [181]: 𝑈𝑖𝑗 = 𝑤𝑖𝑗 × 𝑠𝑖𝑗 (5-6) Adjusted 𝑈𝑖𝑗 = 𝑤𝑖𝑗 × 𝜑𝑖𝑗 × 𝑠𝑖𝑗 (5-7) Where 𝑈𝑖𝑗 is the weighted metrics for the alternatives 𝑖 and criteria 𝑗 based on the normalized pairwise metrics in ANP model (elements from supermatrix in ANP), 𝑠𝑖𝑗 is the average performance value of 𝐴𝑖 under criteria 𝐶𝑗; 𝜑𝑖𝑗 defined as Unreliability Factor (UF) is the adjusting weight representing the extent to the unreliability of 𝑤𝑖𝑗 for 𝐴𝑖; 0 ≤ 𝜑𝑖𝑗 ≤ 1. Therefore, in this case study, ANP is used to not only respond to the interdependency of criteria but also to the unreliability in assessing some alternatives under specific criteria. Note that the 𝑤𝑖𝑗 depends on the alternative. In this case, lower weights (𝑤𝑖𝑗) are assigned to alternatives with lower confidence. For example, Alternative A (full-screen hammer mill and round baler) and Alternative D (half-screen hammer mill and square baler) receive reduced weights (reflected by multiplying with UF) because their product quality varies, one equipment performs well while the other does not. Conversely, Alternative C (half-screen hammer mill and round baler), which demonstrates consistently high product quality, is assigned higher weights, reflecting greater confidence in its performance. This decision-making flexibility is a key advantage of the ANP approach, which is often unattainable using earlier MADM SC selection methods. Additionally, ANP can accommodate a large number of criteria and alternatives simultaneously, making it suitable for complex networks and large-scale problems [181]. Incorporating the UF into the decision-making process introduces a practical and adaptive approach to account for uncertainties in data quality or external dependencies. By using UF to adjust the criteria weights for specific alternatives, the model can better reflect decision-making 108 conditions where not all criteria are equally reliable or equally relevant for every alternative. This method allows for a more flexible evaluation of alternatives, adapting to different degrees of reliability or uncertainty associated with the criteria. The rationale behind using UF to modify the criteria weights is that it allows decision-makers to systematically explore how increasing or decreasing uncertainty impacts the performance of alternatives. By scaling the importance of specific criteria according to their unreliability (i.e., using smaller UF values to represent greater uncertainty), the model becomes more realistic in handling external factors that might not always be predictable or stable. After adjusting the criteria weights with the UF, these modified weights are integrated into the supermatrix to calculate the final results. The supermatrix in ANP captures the outerdependencies among criteria and alternatives, and by incorporating UF-adjusted weights, the model dynamically reflects how changes in uncertainty affect the relative importance of each criterion for each alternative. An interdependencies among the network elements under economic criteria is assumed in this case study. The focus of the study is on specific relationships within one cluster (e.g., economic impact), and limited data or expert judgment are available for other clusters. These interdependencies modify the priority weights assigned to each criterion in the network. Unlike the traditional AHP (where criteria are assumed independent), ANP allows these relationships to modify the relative importance of each criterion dynamically. The evaluation issue was addressed using two different scenarios through the excel spreadsheet and Python programming-based model. Scenario 1 allows interdependencies between variables and alternatives to be represented. Scenario 2, on the other hands, assumes that the explanatory 109 variables are independent, structuring the valuation problem as a hierarchical model and solving it using AHP. 5.2.2.1 Supply Chain Configuration Scenario 1 In this scenario, it is assumed that the expert and decision-maker have unequal preference over criteria and that their relative importance can change from one alternative to another depending on the specific criteria raised by the unreliability factor in assessing that criterion (refer to Equation (5-7) and Figure 5-3). An integrated framework is developed to evaluate the sustainable performance using the same network as Figure 5-3. Figure 5-3. Analytic Network Process (ANP) model (scenario 1). 110 Focusing on one cluster, the interdependencies among the mean NPV, CVaR-NPV, and selling price range are determined using expert opinion and the measured TEA outcomes in Table 5-1. A higher Mean NPV indicates greater profitability, which generally reduces NPV under risk (CVaR measures the risk of extreme losses) and allows for more flexible pricing and larger selling price range. Also, the selling price range impacts both mean NPV and CVaR-NPV. A wider selling price range or higher variations increases financial risk, reducing NPV under risk measured by CVaR. 5.2.2.2 Supply chain configuration scenario 2 In this scenario, as shown in Figure 5-4, it is assumed that the decision-maker has no preference for one criterion over another one and no additional information on each alternative is present. Hence, the question of e.g., comparing 𝐶1 to 𝐶2 with respect to 𝐴1 yields a similar value for all alternatives. 111 Figure 5-4. Analytic Hierarchy Process (AHP) model (scenario 2). 112 5.2.3 Sensitivity analysis (the proposed optimization model) After ranking alternatives using ANP method and determining the rank of alternatives, the proposed mathematical model can be used to assess the extent to which the weight of each criterion has to change so as to make an alternative the top-ranked one. The proposed approach within ANP method is inspired by the similar analysis for AHP which was originally pioneered by Sowlati and Asadi [87] for decision-making problems in management. The results provide valuable insights into the robustness of the rankings. The objective function and required constraint for performing sensitivity analysis within ANP model are shown in Equations (5-8) -(5-14). Objective function: 𝑀𝑖𝑛 (𝑍1) = ∑∑(𝑑𝑖𝑗+𝑁𝑖=1+ 𝑑𝑖𝑗−𝑀𝑗=1) (5-8) Constraints: 𝐿𝑖𝑚𝑖𝑡 𝑀𝑎𝑡𝑟𝑖𝑥𝑎𝑗 > 𝐿𝑖𝑚𝑖𝑡 𝑀𝑎𝑡𝑟𝑖𝑥𝑧𝑗 ∀𝑧 z ≠ a, ∀𝑗 (5-9) ∑(𝑤𝑖𝑗 + 𝑑𝑖𝑗+ + 𝑑𝑖𝑗−) = 1𝑀𝑗=1 ∀𝑖 (5-10) 𝑤𝑖𝑗 + 𝑑𝑖𝑗+ + 𝑑𝑖𝑗− ≤ 1 ∀𝑖, 𝑗 (5-11) 𝑤𝑖𝑗 + 𝑑𝑖𝑗+ + 𝑑𝑖𝑗− ≥ 0 ∀𝑖, 𝑗 (5-12) ∑𝑑𝑖𝑗+ + 𝑑𝑖𝑗− = 0𝑀𝑗=1 ∀𝑖 (5-13) 𝑑𝑖𝑗+ , 𝑑𝑖𝑗− ≥ 0 ∀ 𝑖, 𝑗 (5-14) 113 Where variables 𝑑𝑖𝑗+ and 𝑑𝑖𝑗− are positive and negative deviations from the importance of criteria 𝑗 under alternative 𝑖. 𝑤𝑖𝑗 is weights of criteria 𝑗 under alternative 𝑖. Objective function (5-8) minimizes the sum of deviations of the new weights from the existing weights of criteria. Constraint (5-9) guarantees that the new global weight of alternative 𝑎 under criterion 𝑗 in the limit matrix (𝐿𝑖𝑚𝑖𝑡 𝑀𝑎𝑡𝑟𝑖𝑥𝑖𝑎) as calculated in Equation (5-5) that must be greater than the composite weight of any other alternative 𝑧 (𝐿𝑖𝑚𝑖𝑡 𝑀𝑎𝑡𝑟𝑖𝑥𝑧𝑗) under the same criterion 𝑗. Constraint (5-10) ensures that the new weight of all criteria is equal to 1. Constraint (5-11) and (5-12) ensure that each new criteria weight under the specific alternative must be within a range of 0 and 1. Constraint (5-13) specifies that under each alternative, any increase in the weight of one criterion must be offset by a corresponding decrease in the weight of another criterion to maintain the overall balance. Lastly, the non-negativity constraint for criteria weights changes is also stated in constraint (5-14). The specified NLP formulation includes 2𝑀 × 𝑁 variables and approximately 6 × 𝑀 × 𝑁 +𝑀 × (𝑁 − 1) constraints, where 𝑁 represents the number of alternatives being evaluated (e.g., different SC configurations), and 𝑀 denotes the number of criteria or attributes used for the evaluation (e.g., economic, environmental, social, and technical factors). To solve the mathematical model, Python programming is employed, and the corresponding open-source Python script code used for the robustness analysis is freely accessible in the author’s GitHub repository and can be applied to replicate the procedure with any generic dataset, ensuring transparency and reproducibility [182]. Due to the non-linear nature of the model, the "IPOPT" solver was used to handle its non-linearity. The percentage variation in the weight of each criterion (∆CW) required to make an alternative a top-ranked one can be calculated using Equation (5-15). 114 ∆W =𝑑𝑖𝑗+ + 𝑑𝑖𝑗−𝑤𝑖𝑗× 100 ∀𝑖, 𝑗 (5-15) To compare the robustness of ANP with AHP, the Linear Programming (LP) mathematical optimization model shown in Equations (5-16) -(5-23), inspired by Sowlati and Asadi [206], is utilized and developed using Python programming [182]. In this model, due to the linear nature of the model, the "GLPK (GNU Linear Programming Kit)" solver is used, which is an open-source solver designed for linear programming problems. 𝑀𝑖𝑛 (𝑍2) = ∑(𝑑𝑗+ + 𝑑𝑗−)𝑀𝑗=1 (5-16) ∑𝐴𝑊𝑎𝑗(𝑤𝑗𝑀𝑗=1+ 𝑑𝑗+ − 𝑑𝑗−) > ∑𝐴𝑊𝑍𝑗(𝑊𝑗𝑀𝑗=1+ 𝑑𝑗+ − 𝑑𝑗−) ∀𝑧 z ≠ a (5-17) ∑(𝑤𝑗𝑀𝑗=1+ 𝑑𝑗+ − 𝑑𝑗−) = 1 (5-18) 𝑤𝑗 + 𝑑𝑗+ − 𝑑𝑗− ≤ 1 ∀𝑗 (5-19) 𝑤𝑗 + 𝑑𝑗+ − 𝑑𝑗− ≥ 0 ∀𝑗 (5-20) ∑(𝑀𝑗=1𝑑𝑗+ − 𝑑𝑗−) = 0 (5-21) 𝑑𝑗+ ≥ 0 ∀𝑗 (5-22) 115 𝑑𝑗− ≥ 0 ∀𝑗 (5-23) Where 𝐴𝑊𝑎𝑗 is the weight of alternative 𝑎 under criteria 𝑗. Similar to the ANP-based sensitivity analysis model, the objective is to minimize changes in criteria weights (as per constraint (5-16)), while ensuring that the preferred alternative remains top-ranked (constraint (5-17)), the total weight distribution remains balanced (constraint (5-18)), and individual weights are non-negative and do not exceed 1, maintaining logical consistency and preventing any disproportionate influence (constraints (5-19)–(5-23)). Also, the percentage variation in the weight of each criterion (∆CW) required to make an alternative a top-ranked one can be calculated using Equation (5-24). ∆W =𝑑𝑗+ + 𝑑𝑗−𝑤𝑗× 100 ∀𝑗 (5-24) 5.3 Results and discussions As discussed in section (5.2.1), ANP is an extension of the AHP model that incorporates feedback loops and interactions between decision elements. Following the screening of alternatives and the selection of relevant criteria as discussed previously in Table 5-1, a normalized decision matrix was then created from Table 5-1 by converting all cost-related criteria into benefit-related criteria. Following, each row is normalized by dividing by the sum of the values. The resulting normalized values are presented in Table 5-3. Subsequently, pairwise comparisons between the alternatives for each criterion were calculated directly from the normalized decision matrix by determining the ratio of values for each pair of alternatives based on the corresponding criterion. 116 As the first step, in the network, pairwise comparisons among all attribute enablers along with the interdependencies among criteria are conducted. The importance of each criterion for the alternatives is first assessed by experts using Saaty's 5-point scale. Experts evaluate the relative significance of each criterion. Following this, a pairwise comparison matrix is constructed, and the eigenvector (e-vector) method is applied to determine the weight of each criterion for each alternative. For the pairwise comparisons including interdependencies among economic sub-criteria, Table 5-4 illustrates one such case. The weighted priority (e-Vector) is calculated through the pairwise comparisons. By adding a feedback relationship between alternatives and criteria, a more complex decision situation should be solved. In the case of assuming scenario 1, the decision-maker has the preference of one criterion over another one (e.g., comparing 𝐶1 to 𝐶2 with respect to 𝐴2 is calculated). Under the same conditions applied to all pairwise comparisons in the feedback analysis, a weighted supermatrix is generated as shown in Table 5-5, where the sum of each column is equal to 1. In this weighted matrix, the normalized weights for each criterion are located in the top-right section, where. The unreliability factor for the product quality and system reliability under specified alternatives is assumed to be 0.5 (as per Equation (5-7)). The normalized data from Table 5-1 appears in the bottom-left section. The zero values in Table 5-5 indicate no interdependence was defined within either the criteria cluster or the alternatives cluster. Next, the limit matrix is presented in Table 5-6, where the final scores and rankings of the candidate materials are outlined. The supermatrix reaches convergence at the power of 5. Each row of the limit matrix represents the global score for the alternatives and criteria. The normalized criteria and alternative scores are then calculated as shown in Figure 5-5 (a) and Figure 5-6. 117 It is important to note that the convergence power (k) is not fixed for all problems and depends on the specific case study or dataset being analyzed. The value of 𝑘 = 5 in this study was based on the stopping criteria defined for the supermatrix to achieve steady-state convergence. These criteria are typically determined by the decision-maker or analyst and may vary depending on the complexity and structure of the interdependencies among clusters. This underscores the importance of carefully defining the stopping criteria to ensure the robustness of the analysis. The use of non-linear optimization in this study was required (as per section 5.2.3) due to the necessity of raising the supermatrix to the power of 5 to achieve convergence. This process inherently introduces non-linearity into alternative ranking calculations, as the interdependencies propagate through iterative matrix powers. In contrast, if the supermatrix achieves steady-state convergence without requiring iterative powers, the sensitivity analysis could be performed using a simpler linear optimization model. This would significantly reduce computational complexity and streamline the process. Thus, the choice of optimization model, linear or non-linear, depends on the specific convergence requirements of the limit matrix, highlighting the importance of customizing the approach to the characteristics of the analysis. 118 Table 5-3. Normalized decision matrix (benefit type). Criteria Alternatives Full-Round Full-Square Half-Round Half-Square Index A B C D Selling price range C1 0.2359 0.2834 0.2193 0.2614 Mean Net Present Value (NPV) C2 0.2589 0.2266 0.2741 0.2401 Conditional Value at Risk- NPV C3 0.2348 0.2699 0.2332 0.2621 Carcinogenic C4 0.2056 0.3189 0.1860 0.2894 Non-Carcinogenic C5 0.2039 0.3154 0.1748 0.3059 Eutrophication C6 0.2202 0.2956 0.2053 0.2789 Ecotoxicity C7 0.2125 0.3119 0.1915 0.2841 Job quantity C8 0.3824 0.0882 0.4118 0.1176 System reliability C9 0.4375 0.0625 0.3125 0.1875 Product quality C10 0.3125 0.0625 0.4375 0.1875 Technology readiness level C11 0.1905 0.2857 0.1905 0.3333 Table 5-4. Normalized pair-wise comparison matrix for enablers under mean NPV. mean Net Present Value (NPV) Selling price range CVaR-NPV e-vector Selling price range 0.888 0.875 0.881 Conditional Value at Risk (CVaR)-NPV 0.111 0.125 0.118 119 As illustrated in Figure 5-5 (a), the ANP analysis identified the top four most important criteria for sustainable supply chain evaluation as follows: Selling Price Range in the economic cluster, Carcinogenic Impact (a human health risk) within the environmental category, Product Quality from the technical aspect of sustainability assessment, and Job Quantity in the social cluster. When comparing these rankings to the local weights produced using AHP model (as shown in Figure 5-5 (b), the only notable difference is Mean NPV, which ranked as the top criterion in the economic cluster when interdependencies were disregarded. This emphasizes the crucial role of interdependencies in ANP for accurately capturing the relationships between criteria, which are often overlooked in simpler models like AHP. Additionally, in the global weights produced by ANP (as per Figure 5-5), Mean NPV and CVaR-NPV rank higher than that of AHP, likely due to the strong interdependencies between economic metrics and other sustainability dimensions, which ANP is positioned to capture. Furthermore, as shown in Figure 5-6, the half-round alternative (C) has achieved the highest rank, primarily driven by its superior Mean NPV ($65.28M) and product quality (ranked high), which hold the greatest weights in the local supermatrix. Additionally, this alternative offers the most substantial Job quantities (14 jobs), further underscoring its social dimension. These factors underscore the economic and technical strengths of this alternative, contributing significantly to its overall rankings The full-round alternative is positioned closely in second place, achieving a score of 0.267. Although its Mean NPV ($61.65M) is slightly lower than half-round, it still ranks highly. This alternative also performs well in product quality (medium) and has strong system reliability (high). Despite having more moderate environmental impacts and Job Quantities (13 jobs), its moderate performance across multiple categories leads to a competitive ranking just behind the half-round. 120 With a score of 0.221, the full-square alternative ranks lowest, primarily due to its poor performance in key areas such as product quality and job quantities. Although it has the lowest environmental impacts, including Eutrophication (26.05 kg N eq) and Carcinogenic Impact (0.00049 CTUh), its weaker economic performance, especially Mean NPV ($53.96M) and other technical disadvantages lead to this alternative’s lower ranking. The difference between the top two alternatives and the fourth-ranked full-square is more significant, reflecting a reduction in performance, particularly in technical and social dimensions. The closeness in scores between the half-round and full-round alternatives indicates that small changes in criteria weights or performance can significantly affect the rankings. This sensitivity underscores the value of using an optimization model to evaluate the minimal adjustments needed to alter the rankings. Furthermore, as shown in Table 5-7, Although both the AHP and ANP methods result in the same ranking of alternatives, their final scores differ, which has important implications for decision-making. This difference in scores suggests that the robustness of the two models may vary. ANP, by accounting for interdependencies between criteria, can produce more nuanced results that may better reflect the complex relationships between different factors. AHP, on the other hand, assumes independence between criteria, which could simplify the model but might also lead to less reliable outcomes in more intricate decision scenarios. Accordingly, by applying the optimization model the robustness of these rankings are tested. By comparing how sensitive the AHP and ANP models are to such changes, the stability and reliability of rankings are assessed. 121 Table 5-5. (Weighted) supermatrix for the network of Figure 5.3 with feedback. Definition Ind. 𝐶1 𝐶2 𝐶3 𝐶4 𝐶5 𝐶6 𝐶7 𝐶8 𝐶9 𝐶10 𝐶11 A B C D Selling price range 𝐶1 0 0.441 0.441 0 0 0 0 0 0 0 0 0.099 0.095 0.095 0.099 Mean NPV 𝐶2 0.169 0 0.119 0 0 0 0 0 0 0 0 0.178 0.171 0.171 0.178 CVaR-NPV 𝐶3 0.703 0.099 0 0 0 0 0 0 0 0 0 0.059 0.057 0.057 0.059 Carcinogenic 𝐶4 0 0 0 0 0 0 0 0 0 0 0 0.139 0.133 0.133 0.139 Non-Carcinogenic 𝐶5 0 0 0 0 0 0 0 0 0 0 0 0.059 0.057 0.057 0.059 Eutrophication 𝐶6 0 0 0 0 0 0 0 0 0 0 0 0.020 0.019 0.019 0.020 Ecotoxicity 𝐶7 0 0 0 0 0 0 0 0 0 0 0 0.099 0.095 0.095 0.099 Job quantity 𝐶8 0 0 0 0 0 0 0 0 0 0 0 0.099 0.095 0.095 0.099 System reliability 𝐶9 0 0 0 0 0 0 0 0 0 0 0 0.099 0.048 0.048 0.099 Product quality 𝐶10 0 0 0 0 0 0 0 0 0 0 0 0.089 0.171 0.171 0.089 TRL 𝐶11 0 0 0 0 0 0 0 0 0 0 0 0.059 0.057 0.057 0.059 Full-round A 0.118 0.129 0.117 0.206 0.204 0.220 0.212 0.382 0.438 0.313 0.190 0 0 0 0 Full-square B 0.142 0.113 0.135 0.319 0.315 0.296 0.312 0.088 0.063 0.063 0.286 0 0 0 0 Half-round C 0.110 0.137 0.117 0.186 0.175 0.205 0.192 0.412 0.313 0.438 0.190 0 0 0 0 Half-square D 0.131 0.120 0.131 0.289 0.306 0.279 0.284 0.118 0.188 0.188 0.333 0 0 0 0 NPV: Net Present Value, CVaR: Conditional Value at Risk, TRL: Technology Readiness Level. 122 Table 5-6. Limit matrix obtained for the network of Figure 5.3 with feedback. Definition Ind. 𝐶1 𝐶2 𝐶3 𝐶4 𝐶5 𝐶6 𝐶7 𝐶8 𝐶9 𝐶10 𝐶11 A B C D Selling price range 𝐶1 0.116 0.116 0.116 0.117 0.117 0.117 0.117 0.117 0.117 0.117 0.117 0.115 0.115 0.115 0.115 Mean NPV 𝐶2 0.074 0.074 0.074 0.074 0.074 0.074 0.074 0.074 0.074 0.074 0.074 0.094 0.094 0.094 0.094 CVaR-NPV 𝐶3 0.080 0.080 0.080 0.080 0.080 0.080 0.080 0.080 0.080 0.080 0.080 0.086 0.086 0.086 0.086 Carcinogenic 𝐶4 0.050 0.050 0.050 0.050 0.050 0.050 0.050 0.050 0.050 0.050 0.050 0.065 0.065 0.065 0.065 Non-Carcinogenic 𝐶5 0.022 0.022 0.022 0.022 0.022 0.022 0.022 0.022 0.022 0.022 0.022 0.028 0.028 0.028 0.028 Eutrophication 𝐶6 0.007 0.007 0.007 0.007 0.007 0.007 0.007 0.007 0.007 0.007 0.007 0.009 0.009 0.009 0.009 Ecotoxicity 𝐶7 0.036 0.036 0.036 0.036 0.036 0.036 0.036 0.036 0.036 0.036 0.036 0.047 0.047 0.047 0.047 Job quantity 𝐶8 0.036 0.036 0.036 0.036 0.036 0.036 0.036 0.036 0.036 0.036 0.036 0.047 0.047 0.047 0.047 System reliability 𝐶9 0.027 0.027 0.027 0.027 0.027 0.027 0.027 0.027 0.027 0.027 0.027 0.035 0.036 0.036 0.035 Product quality 𝐶10 0.048 0.048 0.048 0.048 0.048 0.048 0.048 0.048 0.048 0.048 0.048 0.062 0.062 0.062 0.062 TRL 𝐶11 0.024 0.024 0.024 0.022 0.022 0.022 0.022 0.022 0.022 0.022 0.022 0.028 0.028 0.028 0.028 Full-round A 0.128 0.128 0.128 0.128 0.128 0.128 0.128 0.128 0.128 0.128 0.128 0.102 0.102 0.102 0.102 Full-square B 0.099 0.099 0.099 0.099 0.099 0.099 0.099 0.099 0.099 0.099 0.099 0.086 0.086 0.086 0.086 Half-round C 0.130 0.130 0.130 0.130 0.130 0.130 0.130 0.130 0.130 0.130 0.130 0.124 0.125 0.124 0.124 Half-square D 0.117 0.117 0.117 0.117 0.117 0.117 0.117 0.117 0.117 0.117 0.117 0.094 0.094 0.094 0.094 NPV: Net Present Value, CVaR: Conditional Value at Risk, TRL: Technology Readiness Level. 123 (a) (b) Figure 5-5. Criteria weights using pairwise comparisons; a) Analytic Network Process (ANP) (interdependencies), b) Analytic Hierarchy Process (AHP) model (without interdependencies). 124 Figure 5-6. The alternatives ranking of the Supply Chain (SC) configurations in scenario 1. 125 Table 5-7. Relative and global weights in scenario 2 (without inter/outer dependency). 𝐶1 𝐶2 𝐶3 𝐶4 𝐶5 𝐶6 𝐶7 𝐶8 𝐶9 𝐶10 𝐶11 Global weight Rank Criteria 0.0909 0.1636 0.0545 0.1273 0.0545 0.0182 0.0909 0.0909 0.0909 0.1636 0.0545 Alts. Alt. A 0.2359 0.1847 0.2348 0.2056 0.2039 0.2202 0.2125 0.3824 0.4375 0.3125 0.1905 0.2733 2 Alt. B 0.2834 0.1888 0.2699 0.3189 0.3154 0.2956 0.3119 0.0882 0.0625 0.0625 0.2857 0.2086 4 Alt. C 0.2193 0.2689 0.2332 0.1860 0.1748 0.2053 0.1915 0.4118 0.4375 0.4375 0.1905 0.2797 1 Alt. D 0.2614 0.3576 0.2621 0.2894 0.3059 0.2789 0.2841 0.1176 0.1875 0.1875 0.3333 0.2384 3 126 Presenting decision-making problems through performance clusters, as shown in Figure 5-7, is particularly useful for large-scale SC selection processes. This approach facilitates assessing alternatives based on specific clusters, such as economic, environmental, social, and technical properties. By adjusting the weight of a particular cluster to one while setting the others to zero, the decision-maker can repeat the same model. For instance, to evaluate alternatives merely on economic performance, the weight for the economic cluster is set to one and others to zero, where Alternative (B) performs best. Under environmental considerations, Alternative (A) is selected due to its superior environmental performance. In another scenario, equal weights may be assigned to all clusters. As presented in Figure 5-7, the rankings demonstrate the model’s adaptability across various scenarios. While the results shift depending on the importance of different criteria clusters, Alternative (C) consistently ranks as a top alternative, particularly when social and technical factors are given priority. However, further analysis reveals that the economic criteria have the most significant impact on the rankings due to their direct influence on cost and profitability, as key decision-making factors in this study. Environmental criteria also exhibit a substantial effect, especially when resource usage or human-related environmental emissions are prioritized, as seen in the selection of Alternative (B). While social and technical factors contribute less to drastic rank changes compared to economic or environmental considerations, they play a pivotal role when decision-making focuses on labor impact, product quality, or system reliability. These observations highlight the importance of tailoring decision models to the priorities of specific stakeholders, ensuring that the most relevant criteria drive the selection process. 127 Figure 5-7. Sensitivity analysis of cluster importance (economic, environmental, social, and technical) on alternatives ranking within scenario 1. As shown in Figure 5-8, the sensitivity of ranking results to UF in ANP has been examined. It is particularly noteworthy that in most cases, the rankings remain consistent across different UF values, demonstrating the robustness of the model. However, as indicated in the magnified section of the graph, at a UF value of 0.1, where uncertainty is highest, the ranking between Alternatives (A) and (C) changes. In this case, Alternative (A) overtakes Alternative (C), highlighting how greater uncertainty can lead to a shift in rankings. This shift shows that when uncertainty is significant, the model becomes sensitive to the slightest variations in the criteria's influence, resulting in ranking changes that are closer to real-world unpredictability. For the final analysis, a UF of 0.5 was selected to represent a balanced level of uncertainty, ensuring that the results are reliable while still accounting for realistic uncertainty. 128 Figure 5-8. Sensitivity analysis of the Unreliability Factor (UF) on alternative scores, focusing on qualitative factors such as product quality and system reliability. 5.3.1 Optimization-based sensitivity analysis To test the robustness of the model and solutions, a non-linear programming model is developed for the sensitivity analysis in the ANP model. The new criteria weights and the corresponding percentage changes have been calculated and are detailed based on the model solution in Table 5-8 and Figure 5-9. As shown in Figure 5-9 (a), the sensitivity analysis reveals that in the ANP model, significant adjustments are required for Alternative (A) (originally ranked second) to become the top-ranked option. Specifically, the weight of “system reliability” would need to increase by 100%, while the weight of “product quality” would need to decrease by 30%. These recalibrated weights and their impacts on alternative scores are presented in detail in Table 5-8. This implies that if “system reliability” is prioritized over “technology readiness level”, Alternative (A) (full-round) could surpass the other alternatives, given its strength in that criterion. 129 In contrast, in the AHP model, as indicated in Figure 5-9 (b), smaller adjustments are necessary for Alternative (A) to rank first. A 37% increase in the weight of “system reliability”, along with decreases of 11% and 31% in “product quality” and “technology readiness level”, respectively, would suffice to change the rankings. AHP assumes no interdependencies between criteria, which simplifies the problem and allows for smaller changes to criteria weights to affect the rankings. The analysis highlights that the ANP model demands more substantial changes in criteria weights to alter the rankings, indicating a higher level of stability and resilience to variations in input parameters compared to AHP. This robustness makes ANP a preferred choice in decision-making processes where stability is critical. Additionally, the developed non-linear programming model for ANP approach, solved using the IPOPT solver in Python, offers the capability to estimate the minimum changes required to alter alternative rankings. This sensitivity analysis reinforces that while AHP and ANP may yield similar rankings under stable conditions, ANP demonstrates greater resilience when facing changes in criteria or weightings. This makes it a more robust choice for complex decision-making processes like sustainability assessments, where criteria interdependencies and external factors can play a crucial role. The optimization model further enhances the decision-making process by quantifying the minimum changes needed to shift rankings, confirming the reliability and adaptability of ANP in dynamic scenarios. While practitioners might choose simpler model like AHP, which are easier to understand and provide greater transparency, incorporating sensitivity analysis can enhance the evaluation of solution robustness. By accounting for the effects of complexity, sensitivity analysis provides deeper insights into how changes in inputs affect outcomes, ensuring that the solutions remain resilient under varying conditions. 130 Table 5-8. Sensitivity analysis of criteria weight changes required to make an alternative the top-ranked option. Criteria ANP Model AHP model Original New ∆W (%) Original New ∆W(%) Selling Price Range 0.095 0.095 0 0.090 0.090 0 Mean NPV 0.171 0.171 0 0.163 0.163 0 CVaR-NPV 0.057 0.057 0 0.054 0.054 0 Carcinogenic 0.133 0.133 0 0.127 0.127 0 Non-carcinogenic 0.057 0.057 0 0.054 0.054 0 Eutrophication 0.019 0.019 0 0.018 0.018 0 Ecotoxicity 0.095 0.095 0 0.090 0.090 0 Job quantities 0.095 0.095 0 0.090 0.090 0 System reliability 0.048 0.096 +100 0.090 0.124 +37.4 Product quality 0.171 0.123 -30 0.163 0.144 -11.61 Technology Readiness Level 0.057 0.057 0 0.054 0.037 -31.16 Alternatives scores A (Full-round) 0.273 0.267 0.273 0.278 B (Full-square) 0.208 0.222 0.208 0.204 C (Half-round) 0.279 0.266 0.279 0.278 D (Half-square) 0.238 0.243 0.238 0.235 131 (a) (b) Figure 5-9. Sensitivity analysis of criteria weight changes in (a) ANP model and (b) AHP model (under alternative C). 132 5.4 Summary The focus of this chapter was on developing a comprehensive ANP-based MCDM framework for evaluating the sustainable SC selection for a hemp-based biocomposite production. The framework accounted for economic, environmental, social, and technical dimensions while explicitly addressing the interdependencies among criteria, particularly within the economic cluster an aspect often overlooked in traditional MCDM methods such as AHP. The analysis of four biomass pre-processing alternatives identified the half-round option as the top choice, primarily due to its strong economic and technical performance, notably in Mean NPV and product quality. Its positive contributions to job creation further strengthened its ranking in the social dimension. The full-round option closely followed, performing well across multiple dimensions, including system reliability and product quality. Conversely, the full-square alternative ranked lowest due to its weaker performance in technical and social criteria, despite some environmental benefits. A key strength of the ANP model herein lied in its ability to capture interdependencies between criteria. For example, among decision criteria, Mean NPV and CVaR-NPV ranked higher in ANP than in AHP due to their interconnections with other economic factors. Cluster weight changes were explored through sensitivity analysis, showing that although rankings shifted when prioritizing specific clusters (economic, environmental, social, or technical), the half-round alternative consistently ranked high, especially when social and technical factors were prioritized. This adaptability highlights ANP's versatility in accommodating shifts in decision priorities while maintaining result stability. In addressing uncertainties in criteria weights, the UF was crucial. While rankings remained stable across most UF values, a higher uncertainty (e.g., UF = 0.1) caused significant shifts, 133 particularly between the top two alternatives ranking. Under increased uncertainty, the full-round alternative overtook the half-round, illustrating how uncertainty can impact outcomes. This underscores the importance of incorporating uncertainty in decision models, especially in qualitative factors assessment. The final analysis used a balanced UF of 0.5 as a quantitative measure of uncertainty in qualitative factors, providing both reliability and realism. To evaluate the robustness of ANP against AHP due to criterial weights changes, a NLP model was developed as part of ANP sensitivity analysis, alongside a LP model for AHP. The results demonstrated that ANP required significant changes in criteria weights to alter/reverse the rankings, indicating its stability in decision-making with variable inputs. Conversely, AHP, while simpler, required smaller weight adjustments to affect rankings, suggesting that it may be less effective in handling complex interdependencies. This robustness makes ANP particularly valuable for industries facing dynamic sustainability challenges, where decision stability is essential despite fluctuating criteria weights or external conditions. In conclusion, the proposed ANP-based framework, combined with optimization-based sensitivity analysis, provided a robust tool for decision-makers aiming to ensure the sustainable development of biocomposite SCs. By incorporating interdependencies and conducting sensitivity analysis, this framework could offer a more reliable and adaptable decision-making process, ensuring that selected alternatives align with both economic viability and sustainability goals. 134 Chapter 6: Conclusion 6.1 Summary and conclusion The main goal of this dissertation was to develop a robust decision-making framework that integrates sustainability considerations for the production of a hemp-based biocomposite, particularly at its biomass pre-processing phase. The case study in Saskatchewan, Canada, provided insights into the performance of various biomass collection and pre-processing configurations under economic, environmental, social, and technical criteria. This research employed a combination of TEA, LCA, and MCDM methods to address key research gaps and support sustainable decision-making in the biocomposite supply chain. In Chapter 2, the literature review identified gaps in existing studies across three main areas: economic, environmental, and MCDM models. While the literature has extensively covered techno-economic and environmental assessments, the integration of social and technical factors in the evaluation of biomass supply chain configurations has been limited. This gap highlighted the need for a multi-faceted approach combining TEA, LCA, and MCDM tools. TEA was chosen to address uncertainties in economic parameters, while LCA was employed to provide a comprehensive view of environmental impacts. MCDM was utilized to handle the complex interdependencies among criteria, allowing for a holistic assessment that incorporates various sustainability dimensions. In Chapter 3, the economic feasibility of four biomass processing configurations were explored: full-screen hammer mill with square baler, full-screen hammer mill with round baler, half-screen hammer mill with square baler, and half-screen hammer mill with round baler. TEA involved deterministic and stochastic analyses to understand economic outcomes. Through 135 sensitivity analysis, selling price emerged as the most critical parameter, significantly impacting NPV. MC simulations further revealed that changes in selling price led to substantial variations in profitability, thus underscoring the importance of price stability in achieving economic sustainability. The study concluded that configurations with full-screen hammer mills, despite higher initial costs, demonstrated better profitability due to enhanced processing efficiency. This financial framework addressed the gap in prior studies that often overlooked uncertainty in economic assessments. In Chapter 4, the LCA was conducted to evaluate the environmental impacts associated with each biomass pre-processing configuration, including key stages like cultivation, processing, transportation, and conversion. Using SimaPro software combined with MC simulations, the research assessed the impact of variations in input parameters, such as fertilizer use and diesel fuel consumption. The results identified the environmental hotspots, revealing the 41% environmental reduction of full-square option (best case scenario) compared to that of half-round SC configuration option (worst case), providing a comparative assessment framework followed by the agricultural mitigation strategies. The study also highlighted that using nitrogen-reduced fertilizers and optimizing agricultural practices could significantly reduce GWP and eutrophication effects. These findings addressed a gap in the literature by offering a full-spectrum environmental assessment specifically tailored to hemp-based biocomposites in Canada, emphasizing the role of input selection in minimizing environmental impacts. To integrate the economic, environmental, social, and technical criteria, the ANP model was applied in Chapter 5. This method allowed for the incorporation of interdependencies among criteria, a gap identified in previous studies that often assumed independent criteria. The ANP 136 model was paired with a NLP approach to conduct sensitivity analysis, automatically determining the minimum weight changes needed to alter the ranking of alternatives. The results showed that configurations with half-screen hammer mills and round baler ranked higher when social and technical criteria were prioritized, while configurations with square balers were more favorable under economic and environmental considerations. By addressing interdependencies and providing a flexible model to accommodate varying criteria, this approach enhanced decision-making accuracy, underscoring the importance of adaptive decision frameworks in complex supply chains. 6.2 Strengths: contributions to knowledge 1) This thesis provided a novel decision-making framework for biocomposites SC design that integrates sustainable metrics across multiple dimensions (techno-economic, environmental and social) with inner and outer dependencies among select criteria, while providing actionable managerial insights towards selection of biomass pre-processing equipment. Each tool used in developing this framework served a unique purpose: TEA handled economic factors and their uncertainties, LCA provides insights into environmental impacts and their uncertainties, and MCDM incorporated the complexities of interdependencies among criteria. This work also suggested that the interrelationships between economic factors needs to be explored more along with other factors for the biocomposites SC. Exploring the effects of risks and uncertainties in economic and environmental assessment models provides valuable guidance to practitioners working to commercialize hemp-based and other natural fiber biocomposites. A notable strength of this framework is its adaptability to various geographic and industrial contexts. For 137 instance, the model could be re-applied to regions such as the United States, Europe, or other locations by updating key input parameters to reflect local conditions, such as biomass availability, regional economic factors, environmental policies, and energy mixes. This adaptability is particularly useful for practitioners aiming to commercialize natural fiber biocomposites in diverse markets, as it allows for customizing the framework to specific SC dynamics and sustainability objectives. 2) Another key innovation in this framework was the model optimization developed and coded as an open-source Python script to expedite the sensitivity analysis within the MCDM phase. This approach addresses a common current challenge in decision-making models: the computational burden associated with manually evaluating criteria weight effects. By automating this process, the framework could significantly reduce the time required to assess how minor adjustments in criteria weights affect rankings, thus offering a more efficient way to conduct sensitivity analysis for users. 6.2.1 Practical trade-offs/insights perceived Further to the above modeling contributions that this work presented, the case study itself uncovered managerial insights as being a key strength for actual implementation of such SC designs to resolve underlying trade-offs. For instance, although the half-screen hammer mill with a round baler configuration overall ranked highest overall due to its higher economic and technical performance, it was identified as having shortcomings in environmental impact. This insight would allow decision-makers to take a targeted approach to improve the selected configuration’s sustainability based on perceived trad-offs. Using the LCA tool, revisiting specific stages such as the agricultural phase where substantial environmental impacts arise stakeholders can explore alternative practices to further mitigate these trade-offs for each given application. For example, 138 the case study suggested that substituting diesel fuel with renewable energy sources or natural gas could significantly lower the environmental footprint during cultivation and transportation. This strategy, when applied, could reduce emissions and enhance the overall sustainability of the chosen configuration. Such trade-offs and associated managerial insights would be valuable in practice for two reasons: they allow for the fine-tuning of specific processes within the SC, and they offer a pathway for continuous improvement as the SC is deployed. 6.3 Limitations While the present modeling and case study provided a base foundation for sustainable decision-making in biocomposites production, several limitations should be acknowledged. First, the use of a static LCA restricts the ability to capture long-term changes in environmental impacts. A dynamic LCA model would allow for a more realistic evaluation by incorporating temporal variations, such as changes in resource availability, technological improvements, and shifting regulatory landscapes. This dynamic approach could significantly enhance the model's ability to reflect the long-term sustainability performance of biocomposite SCs. Additionally, while the framework accounts for economic uncertainties through MC simulations, it employs deterministic approximations for qualitative criteria, such as social and technical factors. Expanding uncertainty analysis to include these aspects would offer a more comprehensive understanding of sustainability under varying market and social conditions. Moreover, experimental testing is required to confirm the findings of this modeling study from economic and environmental perspectives, as some inputs and process conditions were sourced from previous research. 139 Moreover, while the ANP model considered some interdependencies, they were primarily focused on economic aspects. To achieve a truly holistic view, it would be beneficial to evaluate interdependencies across all sustainability clusters, economic, environmental, social, and technical. This would allow for a deeper exploration of trade-offs, such as the interplay between environmental impacts and social benefits like job creation, beyond just economic considerations. Additionally, while the framework incorporated uncertainty in economic and environmental impacts through data quality assurance and input parameters variability, expanding this analysis could provide greater insight into the variability and robustness of analysis outputs. Addressing these limitations could enhance the framework’s utility for long-term sustainability planning, enabling decision-makers to make more informed and nuanced choices that consider a broader range of interrelated sustainability factors across the biocomposite SC. 6.4 Future Work To advance the present modeling framework's capabilities, future research could focus on integrating a dynamic LCA to capture temporal changes in environmental impacts. Unlike the current static LCA, a dynamic model would allow for the analysis of evolving factors such as shifts in resource availability due to biomass seasonality, advancements in technology, and updates in environmental regulations. This approach would provide a more accurate long-term view of environmental sustainability and enable decision-makers to assess how these variables influence biocomposite production over time. Additionally, expanding the uncertainty analysis to encompass social and technical criteria would provide a more comprehensive evaluation. This would allow for a deeper understanding of how fluctuating social dynamics and technological innovations affect sustainability outcomes across the supply chain. 140 Another important consideration is the recyclability of biocomposites compared to conventional petroleum-based polymers like polyethylene terephthalate (PET). While biocomposites can be assigned a Resin Identification Code (RIC) [183] based on their polymer matrix, this classification reflects only the plastic resin type and not the recyclability or sustainability of the composite material as a whole. The natural fiber component in biocomposites introduces complexities that are not captured by the existing RIC system. For example, while the polymer matrix may fall under a recyclable category like RIC 5 (Polypropylene), the presence of natural fibers can alter recycling processes and end-of-life outcomes, potentially limiting their recyclability using conventional systems. This emphasizes the need for future research to develop more comprehensive recycling and sustainability indices that account for the unique properties of biocomposites, including their natural fiber content and hybrid composition. Further research could also involve simulating the biocomposite production processes in greater detail, enabling a refined examination of operational parameters and their impacts. Considering the sensitivity of the LCA outputs on the input parameters like the agricultural inputs, future research should prioritize improving the availability and reliability of LCI data. Developing detailed impact scenarios, particularly focusing on different types of solvents and agricultural sources, is essential for a more comprehensive understanding within biocomposites conversion facility. Therefore, by incorporating process simulations modeling, it would be possible to assess specific stages like cultivation, pre-processing, and transportation with higher precision, identifying potential areas for efficiency gains and optimization. Also, the utilization of process simulation and mass and energy balance modelling could provide more precise insights into investment decisions for projects. 141 Exploring interdependencies across all sustainability dimensions economic, environmental, social, and technical would further enhance the model. This approach could reveal additional trade-offs and synergies, fostering a more integrated view of sustainability that considers how these dimensions influence one another beyond economic factors. These advancements would strengthen the framework, providing decision-makers with a versatile tool for sustainable supply chain management that is adaptable to a range of scenarios and evolving conditions. In exploring interdependencies across all sustainability dimensions economic, environmental, social, and technical, making the model more complicated, future research could employ methods such a probabilistic predictive model based on a knowledge-driven Bayesian Belief Network (BBN). These approaches would enable the modeling of uncertainties within interdependent criteria, allowing for a more nuanced understanding of how various sustainability factors influence each other under uncertain conditions. The benefit of this modelling framework is that it can examine the effect of inter- and intra-dependencies among key decision factors and process outcomes (performance indicators), through a user-friendly, visual and interactive approach. 142 Bibliography [1] M. D. Heidari, M. 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Results of sensitivity analysis on NPV of alternative A: full-screen hammer mill and round baler. Figure A2. Results of sensitivity analysis on NPV of alternative C: half-screen hammer mill and round baler. 169 Figure A3. Results of sensitivity analysis on NPV of alternative D: half-screen hammer mill and square baler. 170 Appendix B : Monte-carlo simulation results in TEA. Figure B1. Probability of NPV of Alternative A: full-screen hammer mill & round baler. Figure B2. Probability of NPV of Alternative C: half-screen hammer mill & round baler. 171 Figure B3. Probability of NPV of Alternative D: half-screen hammer mill & square baler. 172 Appendix C : Detailed data from sensitivity analysis of LCA model. (a) Non-carcinogenic (b) Ecotoxicity (c) Global warming potential Figure C1. 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Thesis/Dissertation
eng
Mechanical Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Milani, Abbas S.
Sowlati, Taraneh
A sustainability assessment model for selecting pre-processing equipment in hemp-based biocomposite supply chains under techno-economic, environmental, and social measures
Text