Applied Science, Faculty of
Engineering, School of (Okanagan)
DSpace
UBCO
Rana, Anber
Doctor of Philosophy - PhD
University of British Columbia
Green buildings (GBs) are considered to be a more cost-effective and viable solution than transport and other industry sectors to attain carbon mitigation targets. To promote the large-scale adaptation of GBs, one of the key “on-ground efforts” from the government include the development of appropriate financial incentives (FIs). Keeping in view the diverse scope and scale of FIs, an evaluation of FIs is critically important to increase the rate of construction of GBs and to meet desired mitigation targets. The evaluation of FIs involves consideration of the complex nature of building, stakeholders’ perspectives and spatio-temporal design and economic parameters that are rarely considered in conventional studies. Hence, there is a need for pragmatic decision-making that will help in making FIs planning that is beneficial to all stakeholders. In order to address, this need, the main objective of this research is to develop a decision support framework that can evaluate the effectiveness and efficiency of FIs available for GBs in Canada. This objective was achieved in five distinct steps. First, the research reviewed the existing status of FIs in Canada and the techniques used to evaluate FIs, which helped to identify knowledge gaps and specific research requirements in this area. The second step determined the impact of occupancy profiles of residential buildings on estimating viable energy-saving measures. The third step investigated the effectiveness of available FIs for a range of design and economic parameters. The fourth step evaluated the efficiency of available FIs for single buildings and neighborhoods from private and government perspectives. The last step investigated the environmental impacts of operation strategies of a public building and trade-offs between human health and their implication on eligible FIs. The framework presents realistic impacts of the variability in occupancy, operation, building archetypes, stakeholders perspectives, location and economic parameters on the effectiveness and efficiency of incentive policies. The results obtained have the potential to be used by investors and policymakers to increase GBs and achieve sustainable neighborhoods by providing an evaluation method. The policy recommendations can help design and allocate FIs to areas that will yield the largest benefits for all stakeholders.
FINANCIAL INCENTIVES FOR GREEN BUILDINGS IN CANADA: A SPATIO-TEMPORAL ANALYSIS by Anber Rana M.Sc. Eng., University of Engineering and Technology, Lahore, 2012 B.Sc. Eng., University of Engineering and Technology, Lahore, 2008 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE COLLEGE OF GRADUATE STUDIES (Civil Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) August 2021 © Anber Rana, 2021ii The following individuals certify that they have read and recommend to the College of Graduate Studies for acceptance, the dissertation entitled: Financial Incentives for Green Buildings in Canada: A Spatio-temporal Analysis submitted by Anber Rana in partial fulfillment of the requirements for the degree of Doctor of Philosophy Examining Committee: Dr. Rehan Sadiq, School of Engineering Supervisor Dr. Kasun N. Hewage, School of Engineering Co-supervisor Dr. Shahria M. Alam, School of Engineering Co-supervisor Dr. Sumi Siddiqua, School of Engineering Supervisory Committee Member Dr. Sepideh Pakpour, School of Engineering Supervisory Committee Member Dr. Ahmad Rteil, School of Engineering Supervisory Committee Member Dr. Nathan Pelletier, Faculty of Management University Examiner Dr. Thomas Froese, Department of Civil Engineering, University of Victoria External Examiner iii Abstract Green buildings (GBs) are considered to be a more cost-effective and viable solution than transport and other industry sectors to attain carbon mitigation targets. To promote the large-scale adaptation of GBs, one of the key “on-ground efforts” from the government include the development of appropriate financial incentives (FIs). Keeping in view the diverse scope and scale of FIs, an evaluation of FIs is critically important to increase the rate of construction of GBs and to meet desired mitigation targets. The evaluation of FIs involves consideration of the complex nature of building, stakeholders’ perspectives and spatio-temporal design and economic parameters that are rarely considered in conventional studies. Hence, there is a need for pragmatic decision-making that will help in making FIs planning that is beneficial to all stakeholders. In order to address, this need, the main objective of this research is to develop a decision support framework that can evaluate the effectiveness and efficiency of FIs available for GBs in Canada. This objective was achieved in five distinct steps. First, the research reviewed the existing status of FIs in Canada and the techniques used to evaluate FIs, which helped to identify knowledge gaps and specific research requirements in this area. The second step determined the impact of occupancy profiles of residential buildings on estimating viable energy-saving measures. The third step investigated the effectiveness of available FIs for a range of design and economic parameters. The fourth step evaluated the efficiency of available FIs for single buildings and neighborhoods from private and government perspectives. The last step investigated the environmental impacts of operation strategies of a public building and trade-offs between human health and their implication on eligible FIs. The framework presents realistic impacts of the variability in occupancy, operation, building archetypes, stakeholders perspectives, location and economic parameters on effectiveness and efficiency of incentive policies. The results obtained have the potential to be used by investors and policymakers to increase GBs and achieve sustainable neighborhoods by providing an evaluation method. The policy recommendations can help design and allocate FIs to areas that will yield the largest benefits for all stakeholders. iv Lay Summary Green buildings (GBs) offer a viable solution to address the problem of carbon mitigation; however, in practice, the rate of construction of GBs is restricted due to various reasons, such as high initial costs and longer construction times. Therefore, governments are offering financial incentives (FIs) such as rebates, taxes, loans to promote large-scale construction of GBs. These FIs need to be carefully evaluated to gain maximum carbon reduction. The main goal of this research is to provide a decision support framework that evaluates the performance of existing FIs in Canada. The framework generates realistic solutions by considering building design and economic parameters. Results obtained from this research have the potential to improve current FIs planning to attain high environmental and cost benefits. This research will be useful for building owners, managers, developers, local utility and municipality and policy makers to make informed decisions for obtaining GBs and sustainable neighborhoods. v Preface I, Anber Rana, ideated, matured, developed and wrote this thesis under the supervision of Drs. Rehan Sadiq, Kasun Hewage and Shahria Alam. Five journal articles, two conference proceedings and one poster presentation, which are currently published, accepted, or under review, have been prepared directly or indirectly from the research presented in this thesis. The first journal paper focused on the review of financial incentives for green buildings in Canada and methods used for evaluating financial incentives. The second article explored the impacts of occupancy profile on residential building energy end-use, while the third article focused on the impacts of operational management strategies in public swimming pool facilities. The fourth article estimated the effectiveness of FIs for residential archetypes and studied their sensitivity with respect to location, design and economic factors. The fifth paper evaluated the efficiency of the existing FIs’ policy from government and end-user’s perspectives. In addition to these, two conference articles and poster presentation related to this research were also published. Journal Articles (Published) 1. Rana, A., Sadiq, R., Alam, M.S., Karunathilake, H., Hewage, K., 2021. Evaluation of financial incentives for green buildings in Canadian landscape. Renewable and Sustainable Energy Reviews (Elsevier). https://doi.org/10.1016/j.rser.2020.110199 2. Rana, A., Perera, P., Ruparathna, R., Karunathilake, H., Hewage, K., Alam, M.S., Sadiq, R., 2020b. Occupant-based energy upgrades selection for Canadian residential buildings based on field energy data and calibrated simulations. Journal of Cleaner Production (Elsevier). https://doi.org/10.1016/j.jclepro.2020.122430 3. Rana, A., Dyck, R., Hu, G., Hewage, K., Rodriguez, M.J., Alam, M.S., Sadiq, R., 2020a. A process-based LCA for selection of low-impact DBPs control strategy for indoor swimming pool operation. Journal of Cleaner Production (Elsevier) 270. https://doi.org/10.1016/j.jclepro.2020.122372 vi Journal Articles (Under Preparation) 4. Rana, A., Hewage, K., Alam, M.S., Karunathilake, H., Sadiq, R., Financial Incentives for new residential buildings in Canada: Investigating effectiveness using sensitivity analysis. (Expected to be submitted to Applied Energy (Elsevier) in August 2021) 5. Rana, A., Hewage, K., Alam, M.S., Sadiq, R., Rebates Efficiency at Building and Neighborhood level. (Expected to be submitted to Journal of Cleaner Production (Elsevier) in September 2021) Conference Proceedings 6. Rana, A., Alam, M.S., Hewage, K., Sadiq, R., “Evaluation of financial incentives on renewable energy technologies used in residential buildings in Canada”, 5th Annual Engineering Graduate Symposium (AEGS-2019), Kelowna, British Columbia, Canada, 02nd February 2019. https://csce.ca/elf/apps/CONFERENCEVIEWER/conferences/2017/pdfs/CONSPEC/FinalPaper_59.pdf 7. Rana, A., Alam, M. S., Charles, K., Perera, P., Hewage, K., Sadiq, R., Thermal transmittance values for double and triple glazed windows: experimental results from lab and in-situ measurements, 1st International Conference on New Horizons in Green Civil Engineering (NHICE-01), Victoria, British Columbia, Canada, 25-27th April, 2018. https://onlineacademiccommunity.uvic.ca/nhice/wp-content/uploads/sites/2382/2019/02/Proceedings_22Feb2019.pdf Poster Presentations: 8. Rana, A., Hewage, K., Alam, M.S., Sadiq, R., Financial Incentives for Green Buildings-A Scientometric Analysis", Poster presentation at 1st UBCO Inaugural Cluster Trainee Poster Session, Kelowna, British Columbia, Canada, 25th February 2020. vii Table of Contents Abstract .................................................................................................................................... iii Lay Summary ............................................................................................................................... iv Preface ..................................................................................................................................... v List of Tables ............................................................................................................................... xii List of Abbreviations ................................................................................................................. xvi Acknowledgments ....................................................................................................................... xx Dedication ................................................................................................................................. xxii Chapter 1 Introduction ............................................................................................................... 1 1.1 The Challenge ..................................................................................................................... 1 1.2 Research Gap ...................................................................................................................... 3 1.3 Research Motivation ........................................................................................................... 5 1.4 Research Objectives ............................................................................................................ 6 1.5 Meta Language.................................................................................................................... 7 1.6 Thesis Structure and Organization ...................................................................................... 7 Chapter 2 Research Methodology ........................................................................................... 10 Chapter 3 Literature Review ................................................................................................... 13 3.1 Background ....................................................................................................................... 13 3.2 Canada’s Building Energy Efficiency Landscape ............................................................ 16 3.3 Financial Incentives for Green Buildings ......................................................................... 17 3.3.1 Subsidies .............................................................................................................. 19 3.3.2 Rebates ................................................................................................................. 21 3.3.3 Dis-incentives ....................................................................................................... 22 3.3.4 Targeted incentives .............................................................................................. 22 viii 3.4 Financial Incentives in Canada Regulatory Regimes ....................................................... 26 3.4.1 Federal ................................................................................................................. 26 3.4.2 Provincial ............................................................................................................. 27 3.4.3 Municipal ............................................................................................................. 27 3.5 Effectiveness of Financial Incentives ............................................................................... 28 3.5.1 Time of evaluation................................................................................................ 28 3.5.2 Stakeholders’ perspectives ................................................................................... 29 3.5.3 Intended target of FIs effectiveness ..................................................................... 32 3.5.4 Barriers to the success of financial incentives ..................................................... 34 3.6 Summary ........................................................................................................................... 38 Chapter 4 Impacts of Occupant Behaviour on Incentive Policies ........................................ 39 4.1 Background ....................................................................................................................... 39 4.2 Methodology ..................................................................................................................... 40 4.2.1 Data Collection and Processing .......................................................................... 40 4.2.2 Data Analysis ....................................................................................................... 42 4.2.3 Ranking Upgrade Scenarios ................................................................................ 45 4.3 Methodology Demonstration ............................................................................................ 47 4.3.1 Study Area ............................................................................................................ 47 4.3.2 Energy Monitoring ............................................................................................... 49 4.3.3 Energy Modelling................................................................................................. 50 4.3.4 Life Cycle Costing and Carbon Footprint ........................................................... 53 4.4 Results ............................................................................................................................... 55 4.4.1 Occupancy Profile 1 ............................................................................................ 55 4.4.2 Occupancy Profile 2 ............................................................................................ 61 ix 4.5 Discussion ......................................................................................................................... 65 4.5.1 Investor Preference .............................................................................................. 66 4.5.2 Occupancy Profile ............................................................................................... 66 4.5.3 Financial Incentives ............................................................................................. 67 4.5.4 Other Factors Impacting EPUs Ranking ............................................................. 68 4.5.5 Validation of TOPSIS method .............................................................................. 68 4.6 Summary ........................................................................................................................... 69 Chapter 5 Effectiveness of Financial Incentive Policy for Meeting Building Energy Code Requirements............................................................................................................................... 70 5.1 Background ....................................................................................................................... 70 5.2 Methodology ..................................................................................................................... 73 5.2.1 Stage I: Data Collection ...................................................................................... 73 5.2.2 Stage II: Identification of Feasible Energy Performance Upgrades ................... 76 5.2.3 Stage III(A): Effectiveness Index ......................................................................... 79 5.2.4 Stage III (B): Sensitivity Analysis ........................................................................ 80 5.3 Methodology Demonstration ............................................................................................ 80 5.3.1 Archetypes and Location...................................................................................... 82 5.3.2 Energy Performance Upgrades ........................................................................... 84 5.3.3 Cost Data ............................................................................................................. 85 5.4 Results and Discussion ..................................................................................................... 86 5.4.1 Energy Performance Upgrades ........................................................................... 86 5.4.2 Financial Incentives Effectiveness Index ............................................................. 89 5.4.3 Sensitivity Analysis............................................................................................... 90 5.4.4 Key Findings ........................................................................................................ 96 x 5.4.5 Limitations and future direction .......................................................................... 96 5.5 Summary ........................................................................................................................... 97 Chapter 6 Rebates’ Efficiency at Building and Neighborhood Levels ................................. 99 6.1 Background ....................................................................................................................... 99 6.2 Methodology ................................................................................................................... 100 6.2.1 Phase 1: Data Collection and Processing ......................................................... 101 6.2.2 Phase 2: Data Analysis ...................................................................................... 103 6.3 Methodology Demonstration .......................................................................................... 105 6.4 Results and Discussion ................................................................................................... 109 6.4.1 Building Level .................................................................................................... 109 6.4.2 Neighborhood Level ........................................................................................... 113 6.4.3 Limitations and future direction ........................................................................ 114 6.5 Summary ......................................................................................................................... 114 Chapter 7 Operation strategy selection of a Public Building: Decision making with life cycle thinking approach ........................................................................................................... 116 7.1 Background ..................................................................................................................... 116 7.1.1 DBPs Control Strategies .................................................................................... 117 7.1.2 Process-based LCA ............................................................................................ 118 7.2 Methodology ................................................................................................................... 119 7.2.1 Goal and Scope .................................................................................................. 121 7.2.2 Data Collection .................................................................................................. 122 7.2.3 Life Cycle Impact Assessment (LCIA)................................................................ 128 7.2.4 Interpretation ..................................................................................................... 128 7.2.5 Determine Eligible Incentives ............................................................................ 131 xi 7.3 Methodology Demonstration .......................................................................................... 131 7.3.1 Life Cycle Inventory ........................................................................................... 133 7.3.2 TOPSIS Results .................................................................................................. 138 7.3.3 Eligible Incentives .............................................................................................. 141 7.4 Summary ......................................................................................................................... 141 Chapter 8 Conclusions and Recommendations .................................................................... 143 8.1 Summary ......................................................................................................................... 143 8.2 Conclusions ..................................................................................................................... 145 8.3 Originality and Contributions ......................................................................................... 147 8.4 Limitations ...................................................................................................................... 148 8.5 Future Research .............................................................................................................. 149 References ............................................................................................................................... 151 Appendices ................................................................................................................................. 211 Appendix A Financial Incentives............................................................................................ 211 Appendix B Energy Tools ...................................................................................................... 223 Appendix C Calibration Benchmarks ..................................................................................... 225 Appendix D Energy Rates ...................................................................................................... 226 Appendix E Annual Energy Demand Reduction with EPUs .................................................. 227 Appendix F BC-Energy Step Code Metrics............................................................................ 229 Appendix G Environmental Impacts under DBPs Control Strategies .................................... 230 Appendix H Local Annual Weather Conditions ..................................................................... 231 xii List of Tables Table 1-1 Meta-Language of key terms .......................................................................................... 7 Table 3-1 Major journals used in the review ................................................................................ 16 Table 3-2 Categories of market-based instruments ...................................................................... 19 Table 3-3 Financial Incentives for green buildings ...................................................................... 20 Table 3-4 Summary of Targeted Financial Incentives in Canada ................................................. 23 Table 3-5 Summary of provincial level Financial Incentives in Canada ...................................... 28 Table 3-6 Ex-ante and ex-post methods for Financial Incentives effectiveness ........................... 30 Table 3-7 Effectiveness Models for FI ......................................................................................... 33 Table 3-8 Cost-effectiveness for different perspectives ............................................................... 33 Table 3-9 Barriers to the success of different FIs ......................................................................... 35 Table 3- 10 Interaction Matrix between financial and non-financial policy instruments ............. 37 Table 4-1 Criteria weights for different occupant preferences ..................................................... 46 Table 4-2 Household characteristics ............................................................................................. 48 Table 4-3 Information used to calibrate energy models ............................................................... 52 Table 4-4 Energy upgrades and associated costs .......................................................................... 54 Table 4-5 Occupancy Profile 1: Ranking for energy upgrade for decision-maker preferences ... 60 Table 4-6 Occupancy Profile 2: Ranking for energy upgrade for decision-maker preferences ... 60 Table 5-1 Common metrics used of energy performance levels .................................................. 75 Table 5-2 Cost metrics .................................................................................................................. 78 Table 5-3 Effectiveness metrics .................................................................................................... 80 Table 5-4 Details of the sensitivity analysis ................................................................................. 81 Table 5-5 Architectural plans of the houses ................................................................................. 83 Table 5-6 Detail of base case buildings ........................................................................................ 84 xiii Table 5-7 Details of energy performance upgrades ...................................................................... 84 Table 5-8 Details of Electricity and Natural Gas Utility Rates (Source: FortisBC [426]) ........... 85 Table 5-9 Applicable rebates ........................................................................................................ 88 Table 6-1 Variables for DMUs ................................................................................................... 103 Table 6-2 Description of DMUs at neighborhood level ............................................................. 107 Table 6-3 Building Level: Government Perspective .................................................................. 108 Table 6-4 Building Level: Investor Perspective ......................................................................... 108 Table 6-5 Neighborhood Level: Governments’ Perspective ...................................................... 108 Table 6-6 Neighborhood Level: Investor Perspective ................................................................ 109 Table 6-7 Building Level: Rebates efficiency Government Perspective .................................... 111 Table 6-8 Building Level: Investment efficiency Investor Perspective ...................................... 112 Table 7-1 Background information for energy calculations ....................................................... 125 Table 7-2 Models used for water calculations ............................................................................ 127 Table 7-3 Characteristics of pools in UBC aquatic centre ......................................................... 132 Table 7-4 DBPs control strategies used in UBC aquatic centre ................................................. 132 Table 7-5 ReCiPe Midpoint environmental impacts of DBPs control strategies applied on UBC aquatic centre .............................................................................................................................. 137 xiv List of Figures Figure 1-1 Thesis Organization ...................................................................................................... 9 Figure 2-1 Integration of Objectives, Information Flow and Thesis Organization ...................... 10 Figure 3-1 Research methodology ................................................................................................ 15 Figure 3-2 Energy improvements and investors for residential and commercial buildings ......... 17 Figure 3-3 Typology of market-based instruments for green buildings in Canada ...................... 18 Figure 3-4 Environmental cost-effectiveness from a societal perspective ................................... 34 Figure 4-1 Proposed methodology on the evaluation of upgrades for SFD ................................. 41 Figure 4-2 Floor plans of case study homes ................................................................................. 47 Figure 4-3 System architecture for the Living Lab ....................................................................... 51 Figure 4-4 Trend of measured energy use in STH (top) and ATH (bottom) graph ...................... 52 Figure 4-5 Occupancy Profile 1: Reduction in annual energy demand ........................................ 56 Figure 4-6 Occupancy Profile 1: Bubble chart with details of IC, PBT and GHG emission reduction associated with energy upgrades ................................................................................... 58 Figure 4-7 Occupancy Profile 1: Marginal abatement cost curve ................................................ 59 Figure 4-8 Occupancy Profile 2: Reduction in annual energy demand ....................................... 62 Figure 4-9 Occupancy Profile 2 (b) Bubble chart with details of IC, PBT and GHG emission reduction associated with energy upgrades ................................................................................... 64 Figure 4-10 Occupancy Profile 2: Marginal abatement cost curve .............................................. 65 Figure 5-1 Research methodology ................................................................................................ 74 Figure 5-2Criteria considered for defining residential building archetypes ................................. 75 Figure 5-3 Effectiveness Index Matrix ........................................................................................ 79 Figure 5-4 Rebates at different performance levels ...................................................................... 82 xv Figure 5-5 Code compliance levels achieved by residential archetypes under EPUs combinations....................................................................................................................................................... 87 Figure 5-6 Effectiveness Index for tested residential archetypes ................................................ 90 Figure 5-7 Sensitivity Analysis: Impact of design factors on cost-effectiveness ......................... 91 Figure 5-8 Sensitivity Analysis: Impact of design factors on environmental effectiveness ......... 92 Figure 5-9 Sensitivity Analysis: Impact of fuel price escalation on cost-effectiveness ............... 94 Figure 5-10 Sensitivity Analysis: Impact of carbon tax rate increment on cost-effectiveness ..... 95 Figure 6-1 Research Methodology.............................................................................................. 101 Figure 6-2 Distribution of residential archetypes ....................................................................... 106 Figure 6-3 GHG Emission reduction at neighborhood level for Thompson Okanagan (CZ-5) . 110 Figure 6-4 Economic Efficiency from investors and government perspective at the building level..................................................................................................................................................... 112 Figure 6-5 Economic Efficiency from investors and government perspective at the neighborhood levels ........................................................................................................................................... 113 Figure 7-1 Proposed P-LCA methodology for selecting low impact DBPs control strategy ..... 120 Figure 7-2 System boundaries for P-LCA of DBPs control strategy.......................................... 122 Figure 7-3 Input and output parameters for DBPs control strategy ............................................ 124 Figure 7-4 Energy use of ISP systems under DBPs control strategies ...................................... 133 Figure 7-5 Water use of ISP systems under DBPs control strategies ........................................ 134 Figure 7-6 Chemical use of ISP systems under DBPs control strategies .................................. 136 Figure 7-7 Ternary diagrams of ranking results of DPBs control strategies .............................. 140 Figure 8-1 Methodology for evaluating the effectiveness of FIs ................................................ 146 Figure 8-2 Strategic Map ............................................................................................................ 147 xvi List of Abbreviations AB Alberta AHU Air handling units APP Appliances upgrade ATH Advance tech home BC British Columbia BC-ESC British Columbia Energy Step Code BEC Building energy codes BEopt Building Energy Optimization Tool BES Building energy simulation CAC Command and Control CCR Charnes, Cooper & Rhodes CIMS Canadian Integrated Modelling System CMHC Canada Mortgage and Housing Corporation CZ Climate Zones DBPs Disinfection by-products DEA Data Envelope Analysis DHW Domestic hot water upgrade dLL30 Change in life cycle cost over 30-year period DMUs Decision making units DSM Demand-Side Management EERE Energy Efficiency and Renewable Energy EPCs Energy Performance Upgrades xvii FI Financial Incentives FN Foundation wall upgrade GB Green Building GHG Greenhouse gas emission GHGI Greenhouse gas emission intensity GM Grey Model HAA Haloacetic acids HDD Heating Degree-Days HELP Home Energy Low-Income HVAC Heating Ventilation and Air Conditioning IC Initial capital costs ICF Insulating concrete foam IEA International Energy Agency ILM Intrusive load monitoring IO-LCA Input-output life cycle assessment ISP Indoor swimming pool LCA Life Cycle Assessment LCC Life Cycle Cost LCI Life cycle inventory LEAP Long-range Energy Alternatives Planning LED Light emitting diode LEED Leadership in Energy and Environmental Design LEV Local exhaust vent xviii MARKAL Market Allocation MB Manitoba MBE Mean Bias Error MBI Market-based Instruments MCDM Multi-criteria decision making MEUI Mechanical Energy Use Intensity MURB Multi-unit Residential Building NEMS National Energy Modeling System NPO Non-profit organizations NRCan Natural Resources Canada NS Nova Scotia NZEB Net-Zero Energy Building OB Occupant behavior ON Ontario PBP Payback Period PEI Prince Edward Island P-LCA Process-based life cycle assessment PV Solar photovoltaic system QC Quebec RECS Residential Energy Consumption Survey RF Roof insulation upgrade RMSE Root Mean Square Error RWH Rainwater harvesting system xix SFD Single Family Detached Homes STH Standard home TABULA Typology Approach for Building Stock Energy Assessment database TEDI Thermal Energy Demand Intensity THM Trihalomethanes TOPSIS Technique for Order of Preference by Similarity to Ideal Solution UBC University of British Columbia US United States UV Ultravoilet treatment WL Wall insulation upgrade WN Window upgrade WWR Window-to-Wall Ratios xx Acknowledgments First and foremost, I would like to acknowledge the continuous support of my supervisor, Dr. Rehan Sadiq. Dr. Sadiq provided me with all the resources, guidance and freedom necessary to ensure success and happiness during my graduate studies. I would like to thank him for his patience and kindness that helped me develop confidence in my work, ability to conduct independent research and to grow professionally. I will be forever grateful to Dr. Sadiq for giving me the opportunity to work in such a diverse research group and for being a constant source of motivation. I would also like to sincerely thank my co-supervisors, Dr. Kasun Hewage and Dr. M. Shahria Alam, for sharing their expertise and boundless knowledge as well as providing continuous guidance throughout the research process. Dr. Hewage and Dr. Alam were fundamental in helping me improve the quality of my work. This thesis would not have succeeded and reached the current form without their professional guidance and continuous encouragement. I thankfully acknowledge Dr. Sumi Siddiqua, Dr. Ahmad Rteil and Dr. Sepideh Pakpour for serving as my supervisory committee members. I would also extend my thanks to the number of industry project partners with whom I was fortunate to work and develop a practical understanding of current problems in the construction of energy-efficient buildings. I would especially thank Karin-Eger Blenk (Wilden), Russ Foster (Wilden), Martin Blenk (Wilden), Scott Tyerman (Authentech Homes), Carol Suhan (FortisBC) and Russell Street (UBC, Vancouver). In addition, financial support from NSERC, Mitacs, Wilden and FortisBC was beneficial in my studies. In the same vein, I would like to thank UBCO staff, especially Shannon Hohl, Stephanie Oslund, Angela Perry, Ms. Brittaney, Ms. Tanya Chartrand, Amanda Brobbel and Barbara Sobol, for their immense support throughout my time at UBC. The support I received from the project life cycle management laboratory and the research team has been enormous. I would especially like to thank Dr. Guangji Hu, Dr. Hirushie Karunathilake, Dr. Rajeev Ruparathna, Dr. Piyaruwan Perara and Roberta Dyck for their valuable inputs and expertise that helped me in improving the presentation of my research. I would also like to acknowledge Dr. Syed Asad Hussain, Dr. KH MD Nahiduzzaman and Dr. Gyan Chhipi-Shrestha who volunteered their advice in reviewing my thesis. xxi I would also like to acknowledge role of my previous teachers and mentors who encouraged the educational endeavors that helped me in achieving this milestone. In this respect, some noteworthy names are Dr. Mohammad Akram Tahir, Prof. Zia-ud-din Mian, Prof. Faiz Ahmad Chishti, Prof. Ahmad Saeed Sheikh, Dr. Tauseef Ahmad, Madam Sabri and Dr. Husnain Haider. In addition, my friends Sadia Ishaq, Humera Ahmad and Humera Mughal were a constant source of support and helped me to get through the hardest of times. I have developed fond memories of my times at UBC thanks to the company of Sadia and Saba Saleem. My long talks with Sadia were especially helpful in developing interest in research not only in my own topic but the practical implications of inter-disciplinary research. Finally, I would like to extend my sincerest gratitude to my loving parents for their love and prayers throughout my life. I would especially like to thank my mother who in spite of her extremely poor health allowed me to study abroad and complete this long journey. I will also commend my siblings, Dr. Mahwash Rana, Dr. Irfan Ahmad Rana and Rafia Rana, for their constant support and encouragement that helped me in closing this chapter of my life. xxii Dedication Dedicated To my Family, Friends and Mentors 1 Chapter 1 Introduction 1.1 The Challenge Green buildings (GBs) are considered one of the most desirable solutions to the climate change challenges; because of their benefits to environment, society and economy [1]. There are multiple definitions of GBs in literature, but broadly GBs can be defined as the use of construction practices or processes that yield resource efficient high-performance buildings with low environmental impacts [2,3]. In this study, GBs refer to sustainable buildings with high energy efficiency, low emissions and high life cycle performance. In addition to low resource consumption, GBs are associated with better indoor air quality and employees' work productivity [4,5]. Given these advantages, the construction of GBs is encouraged by local and national governments around the world [6]. Despite the numerous advantages related to GBs, a number of challenges hinder their implementation. Major impediments are higher initial costs, longer construction times, inadequate information and relevant expertise, reluctance to adopt newer technologies, hidden costs and benefits, market failure and behavioral constraints [7–12]. The majority of the GBs advantages occur during the operation stage [13]; hence, developers for new buildings are hesitant in making GBs with high initial investments [14,15]. Despite the importance of GBs in addressing climate change, there remains a paucity of empirical evidence on the benefits of GBs construction [10]. Furthermore, uneven cost and benefits distribution among building stakeholders is a hurdle to GBs adoption [16]. These challenges need to be addressed by establishing their technical and economic viability so that local and national carbon mitigation targets are achieved. Challenges related to GBs’ adoption can be removed through properly designed and deployed government policy instruments. Among these instruments, financial incentives (FIs) have proved to be pivotal in promoting GBs [17–20]. FIs in the form of subsidies, rebates and disincentives are monetary support provided to achieve an energy efficient building stock [17,18,21]. FIs help remove barriers related to high up-front investment, mobilization of renovation practices, encouragement of private investments, penetration of innovative low-carbon technologies and implementing other policy instruments. FIs are particularly important in reducing risk perception, thus filling existing knowledge gaps, promoting GBs construction practices and diffusing emerging energy technologies [22]. Likewise, the role of FIs in the widespread adaptation of 2 renewable energy technologies is well recognized [23,24]. Incentives can also change the developers' behavior through financial penalizing or rewarding and establishing business cases that ensure GBs viability [25]. The importance of FIs in achieving more sustainable buildings is well recognized in the existing literature. However, proper implementation of FIs is hindered by some challenges. First, FIs require the use of government resources to be directed towards energy efficiency improvement of building stock [26]. Since the government has limited financial resources, FIs are in competition with other community projects and need to be strategically designed and distributed among building archetypes to ensure maximum benefits [27]. Second, there is a need to consider the perspective of all stakeholders to achieve optimal solutions. For instance, the government perspective requires the adoption of energy performance upgrades (EPUs) that yield maximum greenhouse gas (GHG) reductions. At the same time, the investors are interested in meeting regulatory requirements at least costs [28]. These conflicting requirements need to be considered to yield satisfactory results for FIs implementation. Third, the evaluation of energy conservation policies, including FIs policies, are predominantly based on qualitative analysis and more extensive and detailed quantitative studies are needed to achieve better decision making [29–31]. “Effectiveness” is one of the most important criteria that quantitatively measures the outcomes and benefits of applied FIs. To date, limited studies have evaluated the effectiveness of FIs for GBs [16]. Moreover, there is a diversity and disparity in FIs evaluation process that varies with stakeholders perspective (Government, society, utility or end-users) [21]. “Efficiency” is another criterion that measures the quality of outputs obtained with invested resources [32]. Efficiency values are required for proper allocation of resources; however, most efficiency studies are based on surveys performed after a FI program has been deployed. Therefore, in order to promote GBs, there is a need for more systematic planning and deployment of FIs. Despite the presence of widely available energy efficient technologies and improved sustainable practices, the energy use of the building sector continues to rise and the rate of construction of GBs still remains low. The increase is attributed to the rising population, changes in living patterns as more individuals are living alone and longer, and larger floor areas with rising heating and cooling load to meet thermal comfort requirements. In this regard, a scientific evidence-based strategy is needed for effective decision making that will help make FIs planning that yields 3 maximum benefits for the stakeholders. Such a strategy would integrate and measure the economic and environmental impacts of FIs and offer credible results for stakeholders. 1.2 Research Gap An extensive literature review of financial incentives for green buildings revealed a number of research gaps that need to be addressed: Lifecycle thinking in GBs decision making: Desirable benefits of GBs can only be achieved if suitable sustainability decisions are performed. Economic, energy, environmental and social impacts need to be considered for these decisions [33]. Lifecycle thinking (LCT) approaches help to attain sustainability by providing viable solutions that ensure minimum wastage of resources [34]. Application of these approaches during early design stages can yield the highest benefits by assessing construction, operation, maintenance and end-of-life cycle costs, energy and emissions impacts [35]. Establishing the connection between LCT and decision making is a recognized challenge [36]. A disconnect between LCT and decision making makes it difficult to compare findings of different GBs studies. Focus on initial investment is a major challenge in adopting GBs and the use of lifecycle costs can yield benefits [10,14,37]. Furthermore, the literature reveals that the majority of the studies on energy efficiency are focused on cost-optimality with limited consideration of environmental and social impacts [38,39]. Hence, there is a need for a comprehensive evidence-based framework that considers LCT for decision making in GBs. Impact of GBs operation on occupants’ health and incentives: Changing building operation can decrease energy use and hence qualify the buildings for higher FIs [40]. In this regard, performance-based incentives for commercial and large buildings are particularly important. Interaction between human health and energy use of building is complex. A building operating at low-energy may adversely impact occupants’ health due to an increase in indoor pollutants. Likewise, the use of LCT approaches for selecting the operation strategy should consider occupants’ well-being for decision making [41]. However, existing literature has often selected processes or products ignoring the direct impacts on human health [42,43]. Similarly, studies considering the relationship between FIs for GBs and human health are rare and most concentrated on the impact of low carbon technology, such as replacement of stoves with wood pellets [44–46]. There is a need for a framework that can inform about the impact of change in building operation 4 on incentives and human health. Hence, an integrated framework is required that can capture the impacts of decreased energy use on human health and the environment. Impact of all building characteristics on FIs evaluation: Buildings have distinctive architectural characteristics, climate location and operational features that need to be rationally investigated for the selection of EPUs[47]. Hence, the selection of EPUs that yield desired energy and GHG reduction targets for a particular archetype is a complex process. Likewise, energy savings potential varies with archetype and will be higher in some projects compared to others [48]. Many studies on FIs effectiveness have considered either a single building archetype or the impact of a particular EPU [49–54]. Regional geography and climate variations do not allow for a universal GBs solution [54]. Existing frameworks have inadequately considered the influence of regional parameters in sustainable buildings design [55]. Similarly, in order to improve FIs, local conditions cannot be ignored [16]. Hence, FIs need to be directed towards archetypes and EPUs according to all building characteristics to yield scalable benefits. If FIs are not properly distributed, the desired targets are not achieved and the FIs policy becomes meaningless [54]. Impact of occupant behavior on the effectiveness of FIs: A growing body of literature recognizes occupant behavior (OB) as the major reason which creates a gap between actual and simulated energy use of a building [56,57]. OB is a major area of interest within the field of building energy performance. OB includes occupancy period, occupant movement and their interactions with the buildings’ systems and appliances [58]. Literature indicates that ignoring the impacts of actual OB can result in an over prediction of savings from the energy upgrades. The difference in energy use can be as high as 80% [59]. The energy differences can exist even for homes with similar vintage sizes and number of occupants [60]. Hence, this inaccurate measurement of energy savings will impact the effectiveness of the FIs. Furthermore, the majority of the utility-based FIs are based on the energy savings reported from simulation results. With the change in building operation, the number of applicable incentives will change [40]. Post-occupancy evaluation studies can provide empirical evidence on the actual performance of GBs [16]. Therefore, more robust energy models based on monitored or realistic occupant behavior will help accurately evaluate the effectiveness of FIs. To date, there are few studies that have used monitored data to generate energy model for low rise residential buildings [61,62]; and consequently, a limited understanding of the impacts of OBs on the effectiveness of available FIs. 5 Neighborhood energy planning frameworks for allocating FIs: FIs are scarce economic resources that need to be properly allocated to obtain maximum social benefits [63]. Solutions for energy efficiency have largely focused on single buildings, while solutions for building stock are largely overlooked [27]. Despite the significant impacts of building archetypes, neighborhood size, climate and energy sources [64–66]; the relationship between the impact of neighborhood design and energy efficiency is not well understood. Knowledge of neighborhood level energy demand will help to properly allocate FIs and hence obtain a higher level of benefits. Hence, there is a need for a comprehensive framework that can be used to allocate FIs. Considering the above limitations and gaps in the existing literature, the following research questions arose: 1) How can lifecycle thinking be incorporated into FIs planning for GBs? 2) How can buildings be operated to yield energy, environmental and human health benefits under existing FIs? 3) How can building characteristics, occupancy and preferences affect FIs planning? 4) How can uncertainties and regional variations be integrated with FIs evaluation framework? 5) How can limited public resources be directed for effective allocation of FIs at neighborhood scale? 1.3 Research Motivation The building sector can be a major player in sustaining and improving the global environmental quality compared to the transport and other industry sectors; hence, a paradigm shift is required from conventional buildings towards GBs. This research is strongly motivated by the urgent need to evaluate FIs policy instruments to increase GBs stock. Under the short time window needed to perform all possible actions for a safer future, limited resources and finances need to be strategically allocated. This is especially important in the Canadian context, where the building sector is a major consumer of energy and is responsible for up to 12% of the country's GHG emissions [67]. In Canada, the building sector's energy use continues to rise, with the residential sector alone showing an 8% increase from 1990 to 2015 [68]. Researchers argue the potential of 6 the building sector of Canada to address climate change is underestimated and needs to be prioritized, especially for deep energy retrofits in the next decade. Hence, new building energy codes and complimentary FIs are being deployed, e.g. BC Energy Step Code and the tiered rebates for different levels [69]. In order to reach GHG reduction targets, GBs stakeholders must be provided with a proper planning framework and checklists to support FIs planning and decision making. This thesis contributes to the on-going debate on identifying pathways to achieve GBs through FIs while focusing on topics of life cycle costs, energy monitoring and modelling, environmental impacts, indoor emissions and human health. A robust decision support framework is proposed that evaluates the effectiveness and efficiency of FIs considering OB, operation, building archetypes and regional variations at individual building and neighborhood levels is developed and tested through case studies. The case studies include new residential and a public building with field and simulated data located in British Columbia, Canada. The framework will help in assessing the effectiveness of existing FIs and point out the inconsistencies that need to be considered for their improvement. The research will be beneficial to policy makers, end-users, developers and building managers. The research makes a valuable contribution to the existing literature by use of monitored data, calibrated energy models and a detailed sensitivity analysis that combines the application of existing life cycle thinking approaches with FIs to attain realistic solutions and provides direction for FIs planning strategy improvements. 1.4 Research Objectives The main objective of this research is to develop a decision support framework for a life cycle thinking-based evaluation of financial incentives for green buildings in Canada. The specific sub-objectives of this study are as follows: 1) Investigate and rank energy conservation measures of new residential buildings based on occupancy, environmental and economic parameters 2) Evaluate and compare the cost and environmental effectiveness of financial incentives for residential buildings under uncertainty 3) Determine the efficiency of financial incentives for residential buildings and neighborhoods 7 4) Prioritize and rank public building operation using life cycle thinking approaches based on human health and environmental impacts 1.5 Meta Language This thesis has used ubiquitous scientific terms that are widely understood and accepted. Some technical terms, however, may carry a broader meaning and have been defined in Table 1-1 to ensure a consistent understanding of the current work throughout the thesis. Table 1- 1 Meta-Language of key terms Keywords Description Effectiveness The degree to which objectives are achieved and the extent to which targeted problems are solved. E.g. energy target for BC building energy code. Efficiency To reach an intended target by using the fewest possible resources. E.g. “X” % cut in energy use of the house by the provision of “Y” $ of rebates with least negative environmental impacts compared to alternatives. Financial incentives (FIs) Financial Incentives are external monetary incentives for energy efficiency in green buildings unless stated otherwise. Examples: rebates, loans etc. Life cycle thinking (LCT) Life cycle thinking is used to incorporate economic, environmental and/or social impacts of a product, process, or a system throughout its life cycle, from raw material extraction to the eventual end-of-life. These approaches were used to evaluate impacts of energy upgrades and building operation strategies on economics and/or environment. Decision support system (DSS) Represents a system of model, tool and/or framework to aid decision making. It may comprise models, modules, sub-models, calculation tools and frameworks and is executable. Framework Refers to holistic methods used to achieve the overall goal of the research (E.g. decision support framework for evaluating financial incentives policy). Methodology Terms methodology, methods and techniques are used interchangeably to define the applied procedure. 1.6 Thesis Structure and Organization This thesis consists of eight chapters that focus on the evaluation of financial incentives for green buildings. Figure 1-1 shows the interconnections between the research objectives and thesis chapters. A description of these chapters is given below: Chapter 1 provides a broad view of the research area and identifies research gaps. It also provides 8 the motivation behind research and the objectives and deliverables, research concepts and the overall financial incentives evaluation framework proposed in the study. Chapter 2 provides an insight into the key research phases and the methods followed in achieving the goal of each phase. Each phase of this methodology is further detailed in the content chapters 4 to 7. Chapter 3 provides a comprehensive literature review on the state-of-the-art financial incentives, effectiveness evaluation in context green buildings and current limitations in the existing literature. The gaps identified in this chapter were used to develop the research objectives and methodologies. The literature-based database developed in this section was used in the content chapters 4 to 7. Chapter 4 presents the operational energy and life cycle cost analysis of energy upgrades provided under different occupancy profiles. This chapter makes use of field energy data and uses calibrated energy models to assess the life cycle economic and GHG reduction potential of residential buildings under different occupancy profiles. Chapter 5 provides the core methodology of the location-based impact effectiveness index, which aligns with the expectations of the 2nd objective. A new index is introduced that can help in assessing the cost and environmental effectiveness of the FIs. The effectiveness index is determined with respect to individual buildings. Sensitivity analysis is also incorporated considering findings from Chapter 3. Chapter 6 evaluates the efficiency at building and neighborhoods scale under existing incentives structures. The data envelopment analysis method was used to evaluate efficiency from both investors and government perspectives. Chapter 7 provides the effects of change in public building operation on human health, ecosystems and resources. Field data related to indoor emissions and life cycle assessment method are used to rank operation strategy and determine ranking implication on existing incentive eligibility criteria. The chapter also fulfills the last objective of the study. Chapter 8 provides the main findings, recommendations, originality and future research potential. 9 Note-TOPSIS: Technique for Order of Preference by Similarity to Ideal Solution; MCDM: Multiple-criteria decision analysis; FIs: Financial incentives; GHG: greenhouse gas. Figure 1-1 Thesis Organization 10 Chapter 2 Research Methodology The focus of the research is to develop a planning framework for financial incentives to enhance the construction of green buildings (GBs). The objectives provided in section 1.4 were achieved through 5 research phases. The details of the proposed framework and the demonstration steps are explained in the body of this thesis. Figure 2-1 depicts the connection between the research phases. Note-TOPSIS: Technique for Order of Preference by Similarity to Ideal Solution; FIs: Financial incentives; GHG: greenhouse gas; LCC: Lifecycle costing; LCA: Life cycle assessment; DBPs: Disinfection byproducts Figure 2-1 Integration of Objectives, Information Flow and Thesis Organization 11 Phase 1-Literature review and data collection This phase involved a comprehensive review of literature related to FIs available for GBs in Canada, FIs evaluation criteria and methodology and challenges pertaining to the success of FIs. The systematic review was performed using the document analysis technique. High impact journal articles were given priority for review. In addition, literature was collected from peer-reviewed articles, technical reports, conference publications, books and graduate theses. Data on FIs to make Canadian residential and commercial buildings more energy efficient was collected from municipal, utilities, provincial and Natural Resources Canada (NRCan) databases. Data related to energy performance upgrades, building archetypes, utility costs and building energy codes were collected from local developers and construction practitioners. ‘RS Means’ data was extensively used for cost data in addition to values obtained from construction experts. The detailed findings of the literature review are provided in Chapter 3. Phase 2-Ranking and prioritization of energy performance upgrades based on occupancy profile This phase proposed a methodology for ranking and prioritization of energy performance upgrades for newly constructed residential buildings based on occupancy profiles. Here two identical residential buildings with different occupancies were tested utilizing calibrated energy models, life cycle cost models and operation related GHG emissions. On-site data was collected from the intrusive-load monitoring technique and used to calibrate energy models on HOT2000 using Federal Energy Management Program (FEMP) and ASHRAE 14 benchmarks. In order to represent occupants’ preference that can range from pro-environmental to pro-economic, different weights were assigned to the environmental parameter (i.e., GHG emissions reduction) and economic parameters resulting in five preference scenarios. TOPSIS method was utilized to rank and prioritize energy performance upgrades for different occupancy profiles and preferences. This methodology is detailed in Chapter 4, section 4.2. Phase 3-Location-based effectiveness evaluation methodology A multi-stage methodology was proposed in Phase 4 that utilizes a rule-based screening method to identify the most appropriate energy performance upgrades; evaluate the cost and environmental effectiveness of applicable FIs for residential archetypes; and check the sensitivity of FIs impacts 12 against key design and economic parameters. The methodology was demonstrated through a case study of rebates offered with a new building energy code applied to seven residential archetypes. Environment, technical and cost data related to energy performance upgrade collected in Phase 1 were used to develop energy models on Building Energy Optimization Tool (BEopt) while the lifecycle cost analysis was conducted on MS-excel. Local sensitivity analysis was conducted to determine the impact of uncertain design and economic parameters on the effectiveness of FIs. Detailed methodology is present in Chapter 5, section 5.2 Phase 4-Rebates efficiency at building and neighborhood level Energy, cost and GHG emission results evaluated in Phase 4 for different building archetypes were used to determine the economic efficiency of rebates at building and neighborhood levels. The bottom-up energy modelling technique was utilized to evaluate energy, cost and GHG emissions at the neighborhood level. To determine the economic efficiency, the data envelope analysis (DEA) method was used. Hypothetical residential building neighborhoods in three locations were constructed based on NRCan Statistics reports. The efficiency of FIs was determined using the DEA-Solver tool to identify the building stock with the highest benefits and identify the direction where maximum FIs should be allocated. A more detailed methodology for this phase is provided in Chapter 6, section 6.2. Phase 5-Prioritization of building operation strategy for environmental and health benefits Incentives for commercial and public buildings are assigned based on savings in operational energy intensity. For a holistic assessment of incentives, it is essential that the impacts of building operational energy are evaluated for overall environmental and health impacts. In this phase, a methodology was proposed for prioritizing building operation strategy that provides the highest health, environmental and resource benefits. The methodology was developed for a public aquatic center building in Vancouver, BC. Indoor emissions collected from field study, operational use of water, energy and chemical resources were integrated to determine operation strategy with the highest benefits. Detailed energy modelling and LCA were conducted to prioritize the most suitable building operation strategy using the TOPSIS method. Ultimately the eligibility criteria of incentives were compared with the impacts of operational strategy. The detailed methodology for prioritization of public building operation strategy is provided in Chapter 7, section 7.2. 13 Chapter 3 Literature Review Parts of this chapter has been published in the Elsevier journal Renewable and Sustainable Energy Reviews and Renewable Energy, as articles titled “Evaluation of financial incentives for green buildings in Canadian landscape” and as conference proceedings in CSCE General Conference 2017 as “Financial incentives for green residential buildings in Canada: A review from a regulatory lens” [21,70]. 3.1 Background Climate change like COVID-19 pandemic is a global emergency; however, its negative outcomes are slower in materializing and have much graver impacts in the long run [71]. Greenhouse gases (GHG) from anthropogenic activities are a major driver for climate change. According to the International Energy Agency (IEA), the building sector is a major contributor to the global GHG and accounts for more than one-third of annual emissions [72]. Making buildings more energy efficient can help to address climate change. Financial incentives (FIs) are important and widely implemented policy instruments that can help to reduce buildings' energy and GHG emissions [18,19]. FIs1for making green buildings (GBs) can be in the form of grants, loans, rebates, and tax credits [18,25,73,74]. FIs assist in increasing the energy efficiency of buildings by removing financial barriers, penetration of innovative low-carbon technologies and/or helping the implementation of other policy instruments [75–78]. They are important in setting a stage for the implementation of more stringent bylaws and policies promoting higher building performance standards (e.g. net-zero energy buildings). Due to COVID-19, the impacts of existing FIs for buildings will become even more important as energy retrofitting and integration of clean carbon technologies in buildings are seen as an ideal response to simultaneously pursue climate change mitigation and revival of an economy damaged due to the pandemic [79]. Therefore, successfully utilizing FIs in promoting GB is crucial to ensure minimum wastage of national resources with maximized outcomes. 1 The term “Financial Incentives” or “FIs” throughout the paper will refer to Financial Incentives for energy efficiency in green buildings unless stated otherwise 14 Knowledge on a country’s state-of-the-art FIs is needed to lay a solid foundation for further improvements in the effectiveness of its energy policy. Reviews on FIs are available for a number of countries. However, these reviews either focus on a single FI in a number of countries like the work by Shazmin et al. [17] on property tax incentives for a number of countries including Canada, or they focus on policy instruments in countries other than Canada; example, Sebi et al. [80] work on incentives in Germany, France, and the US or Bertoldi et al. [81] work on financial instruments for residential buildings in Europe. Some recent reviews focus on policy instruments in a single country, such as G. Liu et al. [82] work on policy instruments (including FIs) in China. Alternatively, they look at FIs present for a particular energy upgrade, such as Curtin et al. [83] study on FIs present for low-carbon technologies (solar systems, heat pumps etc.). The existing literature predominantly focuses on policy instruments in the US, Germany, or China, and not specifically on the state of FIs in Canada. The limited studies that do consider Canada either focus on a particular building type, such as a study by Hoicka et al. [84] (residential building retrofits); focus on a particular program, such as work of Nadel and McMahon [85] on lighting energy, or are too old to extract meaningful results, such as Stern et al. [86] (performed in 1986). Overall, the published body of knowledge either focuses on specific FIs (e.g., tax incentive); specific buildings (e.g., residential); specific energy measure (e.g. heat pumps) or a specific stakeholder (e.g., the private-home owners); and a holistic examination of available FIs for buildings in Canada is still missing in the literature. Therefore, with a particular focus on Canada, this chapter attempts to take a closer look at the state-of-the-art green building financial incentives to lay a ground for designing future incentives while improving the existing incentive strategies. This chapter provides a comprehensive review of the state-of-practice of energy related FIs available for residential and commercial buildings in Canada and effectiveness evaluation of FIs in GBs. In order to achieve these objectives, a systematic review using the document analysis technique has been employed [87]. The methodology for this chapter involved the use of specific keywords, specialized databases, and document types that ranged from journal articles to books. A keyword search was conducted in two main bibliographic databases: Compendex Engineering Village and Web of Science. The literature published after 2010 was given a priority, so as to provide a more up-to-date status of the GB related FIs literature. Document analysis technique method used for this research involved 15 the review of all forms of technical writings; for example, peer-reviewed articles, technical reports, conference publications, books and theses, that can help in evaluation. The number of peer-reviewed journals, conference proceedings, books, and other literature sources relevant to the field of GBs considered are shown in Figure 3-1. In addition to these, various municipal, utilities, provincial and Natural Resources Canada (NRCan) databases were explored for determining the FIs available for residential and commercial buildings in Canada. This review provides an assessment of FIs in Canada implemented until 2018 unless new incentives come to the practice. Note- FIs: Financial incentives; NRCan: Natural Resources Canada Figure 3-1 Research methodology The literature is collected from articles of journals with high impact factors. The impact factor 16 represents how frequently the scientific community is citing the journal [88] and this parameter is considered when comparing and ranking journals [89]. Journal articles with impact factors greater than 2.5 were prioritized in this chapter. Table 3-1 shows the journals that were cited more than twice in this review study along with their impact factors. In the research presented, the terms FIs, financial instruments, and the economic instrument are used interchangeably and represent the same meaning. Table 3-1 Major journals used in the review Journal 5-year impact factor Renewable and Sustainable Energy Reviews 12.348 Journal of Cleaner Production 7.491 Energy 6.046 Energy Policy 5.693 Energy Economics 5.790 Energy and Buildings 5.055 Technological Forecasting and Social Change 5.179 Building Research & Information 3.744 The Energy Journal 2.739 The relevant literature was screened by reviewing the titles, summaries of reports, abstracts of articles, keywords, conclusions and key findings. In order to synthesized the information a database of reviewed information was constructed in excel spreadsheets. This enabled comparison of findings and analyzing how financial incentives are being addressed in literature. Through this database it was possible to identify research trends and missing knowledge gaps. 3.2 Canada’s Building Energy Efficiency Landscape The Paris agreement and the Pan-Canadian Framework have encouraged the federal and provincial governments in Canada to set carbon mitigation targets for the year 2030, which will ensure that the global temperature remains below the limit of 1.50C of the pre-industrialized levels. This demanding goal can be achieved through the application of effective energy efficiency measures in all sectors. Canada’s building sector is a major consumer of energy and is responsible for up to 12% of its GHG emissions [90]. The majority of the energy consumed in the residential sector is used for space heating (64%) and water heating (19%), followed by electrical appliances, lighting and space cooling that use the remaining portion of energy [91]. The commercial sector accounts for 11% of the total energy consumption, with the majority accounted for space heating (56%), followed by auxiliary equipment (14%)[91]. Furthermore, more than 80% of the existing buildings 17 were constructed before 1996 and did not conform to the more stringent energy efficiency codes for new buildings [90,92,93]. Low energy GBs can be achieved by three types of energy interventions: energy conservation, energy efficiency and energy saving measures [94]. New buildings can be designed to be green by developing better building thermal envelopes, using energy efficient equipment and appliances and using renewable energy technologies [95]. Existing buildings can be improved through retrofitting, which includes improvements in the thermal envelope, use of energy efficient equipment and appliances, use of renewable energy technologies and changes in human behavior [96,97]. In addition, ownership structures of residential and commercial buildings vary significantly, leading to different stakeholders' involvement responsible for energy efficiency improvement (Figure 3-2). Note- HVAC: Heating, ventilation, and air conditioning Figure 3-2 Energy improvements and investors for residential and commercial buildings 3.3 Financial Incentives for Green Buildings A multitude of different policy tools are available that can help in the market penetration of GBs and achieving energy reduction targets. Policy tools can be divided into two groups: (1) Regularity or Command and Control (CAC); and (2) Market Based Instruments (MBI) [98–100]. CAC provides legislation defining the legal limits; for example, energy efficiency regulations [100]. 18 Note- ASHRAE: American Society of Heating, Refrigerating and Air-Conditioning Engineers; NECB: National Energy Code of Canada for Buildings; LEEDs: Leadership in Energy and Environmental Design; PST: Provincial Sales Tax; PEI: Prince Edward Island. Figure 3-3 Typology of market-based instruments for green buildings in Canada 19 MBIs are considered more powerful than CAC as they offer some form of incentive to the user [98]. MBIs can be sub-divided into three categories: market friction reduction, rights-based and price-based [101]. Table 3-2 describes these instruments, while Figure 3-3 provides examples of MBI available for the promotion of GBs in Canada. Table 3-2 Categories of market-based instruments (Source: [100,102–105]) Category Description Instrument examples Market friction-based Market friction-based instruments help in achieving policy targets by improving the conditions of existing private markets. These market-based instruments are non-financial -Eco-labelling -LEED (Leadership in Energy and Environmental Design) Standards -Energy Star ratings of appliances Rights-based Rights-based or quantity-based instruments specify the amount of emission permitted under a specified condition -Cap-and-trade scheme for provinces (emissions, quotas, or permits) -Carbon offset scheme -Lead trading Price-based Price-based instruments provide changes in prices in existing market conditions -Subsidies on high efficiency appliances -Carbon tax on fossil fuel used FIs are the types of price-based MBIs that are provided extensively by government and utility providers. A brief description of different types of FIs is provided in Table 3-3. It should be noted that among the policy instruments described in this section, no single policy instrument can be considered as a silver bullet for achieving a country’s carbon mitigation goals and the effectiveness of an instrument changes with respect to context. 3.3.1 Subsidies Price-based or financial instruments can be in the form of subsidies, rebates and dis-incentives. The subsidies can be provided in the form of loans, tax-allowances or grants [106]. Loan incentives: Loans are a common type of FI offered by governmental organizations and commercial banks [83,107]. Loans for GBs typically charge a lower interest rate over a longer duration compared to other commercial loan types [107]. The amount of loans offered vary from time to time and depend on macroeconomic conditions [108]. A list of GB loan incentives present in Canada is provided in Appendix A, Table A1. In Canada, loan incentives for GBs are offered 20 by a number of institutions: Canada Mortgage and Housing Corporation (CMHC) [109], banks [110], utility companies and municipalities [111]. The majority of the loans are offered for a five-year period. Loans are available for the construction of new homes [112], retrofitting of commercial buildings [113] and upgrade of individual systems; such as Residential Earth Power Loan - for Cold Climate Air Source Heat Pumps, Manitoba (MB) [114]. Compared to other provinces, MB offers the largest number of loans for GBs. Table 3-3 Financial Incentives for green buildings Types Description Ref. Financial Incentives FIs are the monetary support provided by the government or utility providers. Financial Incentives offered in the forms of subsidies, rebates, or disincentives require certain energy efficiency-related conditions to be fulfilled by the investors. [17,18] Subsidies Subsidies are offered on energy upgrades/retrofits that enable investors to perform energy upgrades at a lower rate than the market price. The subsidies can be in the form of grants, loans or taxes. [106] Loans Loan incentives are used to enable the installation of an energy retrofit or energy efficient equipment at a low-interest rate. Low interest enables the viability of a larger number of retrofits compared to the higher interest rate. [115,116] Grants Grants are monetary incentives that do not require to be paid back and are popular due to their simplicity. Grants account for a large sum of money and are usually offered by the government at the federal level. [83,117] Tax incentives A tax incentive can be defined as monetary credit, deduction or exemption on the tax required to be paid if the energy target/energy upgrade was not performed for the building. [118] Rebates Rebate is the full or partial amount returned on the applied energy upgrade measure. The rebates are usually offered by utility providers on the purchase of energy efficient equipment. [119] Disincentives Dis-incentives are financial instruments that work as negative reinforcement towards energy efficiency improvements. A carbon tax is one of the most common dis-incentive and has been found to be successful in mitigating carbon emission. [120] Tax incentives: Tax incentives are one of the most popular GB incentive offered by governments [121,122]. Tax incentives can be used either in the form of positive incentives that encourage GBs construction such as tax rebates, reductions, exemptions (or tax breaks), preferential tax rate, tax deferral and tax reimbursement [123], or as disincentives to discourage unsustainable practices [110]. Compared to other energy efficiency-related policy instruments such as increments in interest rates or energy prices, taxes provide swifter changes in energy use [123]. The bases of these incentives can also be used to include accelerated property tax assessments, rate of property 21 tax assessment and certification levels for GB [17]. Tax exemption incentives or tax breaks are provided to developers for a limited time period and scope. Compared to other countries, Canada’s incentives are based on the exemption model rather than rebates and/or reductions [17]. In Canada, there are limited number of tax incentives for GBs (see Table A2). British Columbia (BC) has 100% property tax exemption for eligible devices as well as energy upgrades that result in Gold or Platinum level Leadership in Energy and Environmental Design (LEED) certified buildings [124,125]. In Quebec (QC), RénoVert Tax Credit is provided for the energy renovation of residential buildings [126]. Among various FIs, tax incentives have been most effective from an environmental and economic point of view [127]. Hence, there is a need to increase the number of tax incentives available for GBs in Canada. Grant incentives: Grants are another way to offset green residential building costs [128]. They are used as an incentive for the adoption of GBs at an individual and community level [83]. Grants are applied to a certain percentage of per capita costs or investment costs of a component. Since a specific economic benefit is associated with GB financial grants, they have proven to be quite successful. Due to a large amount of capital needed for grant incentives, they are mostly suitable for regulatory incentives at the provincial or national level [129]. In contrast to the financial tax incentives offered for green construction, numerous grants are available at both provincial and municipality levels in Canada. A list of GB grants present in Canada is provided in Table A3. It is observed that similar to loan incentives; grants are offered for new construction, energy renovation and specific systems upgrading. The majority of the grants are provided through utility companies in different provinces. The largest number of grants are available in QC through utility provider “Énergir” [130]. Some special grants, such as Home Energy Low-Income (HELP) [131] are specifically designed for building users with low income [130]. 3.3.2 Rebates Rebates are financial gains received by building owners or developers [18], usually when the energy improvement is implemented in the building. Globally, the deployment of rebates has been successful in achieving high energy savings [132]. Rebates offered by utility programs exceed loan and grant incentives for buildings. Energy saving potential through upgrades is high for commercial buildings, but this is accompanied by higher initial investment requirements. To counter these costs, rebate amounts up to CA$ 500,000 are provided by the government of Nova 22 Scotia (NS), while most other provinces are offering much less (up to CA$ 50,000). In the residential sector, rebates offered by municipalities are common for Alberta (AB), while utilities offered rebates are more common in BC, QC, Ontario (ON) and Newfoundland and Labrador. Provincial rebates are offered for residents of NS, Prince Edward Island (PEI) and Yukon. Overall, AB and BC provinces are highly committed to developing green residential and commercial buildings through rebates with the aim of creating a sustainable future for their residents. 3.3.3 Dis-incentives Dis-incentives are financial instruments that work as negative reinforcement towards energy efficiency improvements [120]. The tax follows the rationale that the polluter pays for the negative impacts of his energy use in the form of a penalty if the emission rate exceeds a specific limit [133,134]. Carbon taxes have been employed in few other countries, including the Netherlands, Sweden, Norway and others [135]. These are determined to be cost-effective instruments that utilize minimum resources [136]. BC has a carbon tax on the use of natural fossil fuels in order to discourage the use of these resources. Carbon tax in BC is applied based on the taxable fuel consumption of a household that relates to cooking, heating and transportation. The prices are applicable to gasoline, diesel, propane, coal and natural gas [137]. Carbon-tax introduced in BC has been deemed successful in reducing GHG emissions by 19% with no economic losses [138]. Similarly, a recent study determined the impact of BC’s carbon tax in reducing natural gas use by 7% in the residential sector [139]. AB and QC have similar carbon pricing systems. Recently, a nationwide tax system has been introduced in Canada, which is applicable to provinces where the carbon pricing and/or carbon tax systems are still absent under the “Greenhouse Gas Pollution Pricing Act”[140]. The federal government's proposed carbon tax is currently offered at CA$45/ton and will be increased at the rate of CA$10/ton per year until the year 2022 [141]. 3.3.4 Targeted incentives Targeted incentives are needed to address the energy improvement needs of specific stakeholders and/or buildings that are vulnerable to high costs risks [142,143]. Targeted incentives provided on sub-sets populations can yield a higher degree of desired outcomes [84]. Table 3-4 shows the distribution of some important targeted incentives in different provinces of Canada. 23 Table 3-4 Summary of Targeted Financial Incentives in Canada Province Residential Commercial Low-income Heritage buildings Non-profit Aboriginals Landlord and tenants Low-income Heritage buildings Non-profit Aboriginals Landlord and tenants Alberta ✔ ✔ ✔ ✔ British Columbia ✔ ✔ ✔ ✔ ✔ ✔ Manitoba ✔ ✔ ✔ ✔ ✔ New Brunswick ✔ ✔ Newfoundland and Labrador ✔ Nova Scotia ✔ Nunavut Ontario ✔ ✔ ✔ ✔ Prince Edward Island ✔ Quebec ✔ ✔ ✔ ✔ Saskatchewan Yukon Low-income population: Lack of affordable housing is a recognized problem in major cities of Canada. The low-income population is especially vulnerable and needs financial aid to improve their houses' energy performance and reduce the energy bills. Studies have shown the willingness to participate in an energy efficiency program is especially minimal for low-income families dwelling in older residences [144]. These incentives are being offered by both utilities and provincial governments [131,145–152]. Some of the incentive programs cover the total costs of specific energy improvements [131,151,152], while others offer increased incentives such as those offered by Énergir and Gazifère utilities in QC [149,150]. In QC, the rebates for low-income residents are double the amount of those offered for a normal housing energy upgrade. Manitoba Hydro offers free small scale energy upgrades for such buildings [153]. This implies low-income incentives are present in most provinces, but the amount and type of incentive being offered vary. Recent studies on COVID-19 indicate an expected increase in energy poverty as the impacts of lockdown and related economic losses become more evident [154,155]. Hence, more specialized incentives for the low-income population will be required to face the current pandemic' economic impacts. 24 Buildings built during a specific time period: The majority of Canadian buildings were constructed prior to the implementation of new energy efficient building codes. Furthermore, old buildings that form a part of the cultural heritage have limited energy upgrades options and require special incentives for energy improvement [143,156]. Hence it is essential to reduce the energy use in these old buildings to meet carbon targets. The Home Weatherization program in Ontario (ON) [157] (for buildings constructed before 1975) and the Power Smart Home Insulation Program in MB [153] (for buildings built before 1999) are good examples of FIs targeting buildings built under older building codes. Though a number of rebates and subsidies are available for retrofitting existing buildings, only ON and MB provinces seem to target buildings belonging to a specific time period. Such incentives are required to ensure that buildings retrofits that will yield maximum benefits are prioritized. Non-profit organizations: Non-profit organizations (NPOs) can help increase energy efficiency by providing awareness and help to marginalized communities. The buildings directly under NPOs usually have high operational costs and FIs can reduce the above costs. Energy efficiency requirements dep end upon the particular services being performed by the NPO [158]. The type of buildings that can benefit from these FIs are sports and recreation, community centers, health centers, religious, social housing, co-op housing, housing for aboriginals etc. These incentives are usually bundled under the same program with incentives for aboriginal housing [151], low-income housing [149], or residential and commercial buildings [148]. Aboriginal population: The future sustainability of Canada's aboriginal communities faces economic, social and environmental challenges [159]. For this reason, specialized incentives have been provided by different provinces to meet sustainability goals. Over time, ON has updated its incentives programmes the aboriginal communities, starting from tax exemptions and moving towards differentiated Feed-in Tariffs and specialized contract and tendering schemes with local ownership criteria [160]. In addition, free upgrades are also offered to aboriginal communities in ON under the First Nations Conservation Program [161]. Manitoba Hydro offers “Pay As You Save (PAYS) Financing” to the aboriginal population to improve energy efficiency through the installation of geothermal systems [162]. Similarly, BC is offering incentives for residential and commercial buildings belonging to aboriginal communities [163]. Aboriginal people are one of the most marginalized communities in Canada with limited energy resources. Only BC, ON and 25 MB are currently offering incentives for aboriginals. Similar to the scenario with low-income populations, more incentives are required to be deployed for aboriginals at provincial and municipal levels as the current COVID-19 pandemic unevenly impacts Canada's vulnerable populations. Landlords and tenants: Extensive research has shown that the investment cost for energy upgrades and limited return on investment are major barriers to energy efficiency improvement of the rental housing sector [164–166]. In rental buildings, benefits and costs associated with energy efficient upgrades are not equally shared by the owners and the tenants. In most cases, the benefits of lower energy bills and comfort are reaped by the tenants while the owners have to make capital investments for the energy efficiency improvements [167–170]. This principal agent problem results in a low willingness to invest in energy upgrades even when FIs are available. This is illustrated by Phillips’ [171] study that showed that the uptake of grants for insulation was much higher for owner-occupied buildings compared to rented buildings. Therefore, special incentives are needed to ensure both landlords and tenants obtain the benefits. In order to improve the energy efficiency of the rental housing sector, utility providers in two provinces, MB and QC, offer incentives specifically designed for landlords and tenants. Hence, only a limited FIs are available specifically for landlord and tenants in Canada. In addition, studies indicate rental occupancy is increasing in major cities such as Toronto (ON) and Vancouver (BC) where more than 40% of citys’ GHG emissions are related to high-rise apartments [172]. Buildings built to specific building standard/code: Provision of green certification to a building adds additional burdens to the costs of GBs [173]. Hence, some FIs in Canada are specifically designed for the rating system and certification of GBs. CMHC and the NS government provides incentives for houses built to Energy Star or R-2000 Standards [109,174]. Similarly, incentives for EnerGuide homes are offered by utility providers in BC and governments of NS and PEI [131,152,174]. The government of PEI also offers incentives for new construction with energy performance either 20% better than Energy Star Homes or 50% better than R-2000 [131]. Some municipalities also offer incentives for energy evaluation before and after energy upgrade of an existing home, such as Edmonton and City of Medicine Hat municipalities in AB evaluation and improved performance [175,176]. In addition, building energy codes are being revised and upgraded in Canada at national and provincial levels, such as the BC energy step code for new 26 buildings [177]. At present, FIs offered in PEI and NS are most adaptable by builders and developers as they are offered over a wider range of energy standards and ratings. Similar efforts are required from other provinces for a faster transition to a more energy efficient building stock. 3.4 Financial Incentives in Canada Regulatory Regimes Canada's climate and energy policies are interrelated and form a crucial part of FIs deployed for the buildings [178]. Politically, the governance of Canada is distributed into three main levels: (1) federal and (2) provincial and (3) municipal territories. The federal government is mainly responsible for developing national energy policies in line with international agreements and deploying the necessary resources such as provincial-level grants to assist the accomplishment of climate targets. Building standards, as well as assessment criteria, also form a part of the federal government responsibilities [179]. In addition, under the Canadian Environmental Protection Act of 1999, the federal government can place limits on carbon emissions such as through the application of carbon tax on provinces. The provincial governments are responsible for the resources and can follow the standards set by the federal government. There is a wide variety of economic and environmental conditions among different provinces that change the energy demand. Similarly, socio-economic conditions vary and change at urban and rural scales with development [180]; hence, the types of energy upgrades and FIs will vary according to a buildings’ location and time of FIs application. Energy decisions performed at provincial levels are used as the basis of FIs offered at the municipality level. Though energy and emission-related decisions are performed at all three government levels, the decisions do not always follow from federal to municipal level [179]. 3.4.1 Federal At the Federal level, most FIs for green residential buildings are in the form of loans offered by financial institutions [130]. The number of incentives offered depends upon the life stage (i.e. design, construction, operation, demolition) of a building and GB certification type. For example, CMHC offers refunds on financial loans up to 15% for an Energy Star rated house and up to 25% for an R-2000 certified building [109]. Different banking organizations in Canada are also offering incentives for homeowners who want to construct or upgrade a house to GB standards. Banks are providing incentives, especially for the installation of solar panels and high energy efficiency 27 equipment [110]. The Canadian Green Building Council also offers registration and certification fee waivers for buildings complying with LEED® Canada under the Homes Affordable Housing Program [181]. The Genworth Financial Canada incentive program for green homes provides a premium refund for energy efficient homes [130]. Overall, several FIs are offered at the Federal level. However, it should be noted that federal incentives cannot be equally availed at all locations in Canada because of differences in demographics, weather, types of constructions, local resources, technologies and components along with certification methods in different regions. 3.4.2 Provincial Provincial incentives commonly offered through the provincial governments or the utilities are useful in the promotion of GBs. Table 3-5 provides an overview of FIs available in different provinces of Canada. It is seen that a large portion of FIs are offered by utility companies, mostly in the form of rebates on individual energy upgrades. Compared to other provinces, AB, NS and PEI governments offer the largest FIs. [131,148,174]. AB, however, does not have any FIs from utility providers. In some provinces, organizations play an important part in the energy performance improvement of the building. For example, the Northwest Territories are dependent on incentives provided by Arctic Energy Alliance [182] and do not have FIs from the provincial government, local municipalities and utilities. At the provincial level, BC, MB and SK have a wider variety of FIs. 3.4.3 Municipal FIs utilized at the municipal level particularly target energy efficiency of housing stocks [183]. Municipal FI models are based on local conditions and are most effective in the generation of GB neighborhoods. Since municipal GB incentives have significant variations regarding end goals, some municipalities are becoming much more efficient with regard to their energy and water use. For instance, the District of Saanich in BC has one of the most elaborate incentive schemes for green residential buildings and offers rebates for houses designed to any four of the energy standards: EnerGuide 80, R-2000, Built Green or Power Smart for New Homes [184]. This increased scope and flexibility have enabled the city to improve its residential buildings. In the same vein, Markham, Calgary and Vancouver are good examples of Canadian cities that have made significant progress in increasing their green building stocks [184]. For commercial 28 buildings in Toronto, two programs, a loan and an Eco-Roof incentive program, specifically target the construction of green and/or cool roofs [113,185]. Programs such as Eco-roof incentive encourage the reduction in energy use and management of storm water of buildings [186]. Therefore, more flexible incentives are needed at the municipality level. Table 3-5 Summary of provincial level Financial Incentives in Canada Province Residential Commercial Tax Loans Grants Rebates Tax Loans Grants Rebates Alberta ✔ ✔ ✔ British Columbia ✔ ✔ ✔ ✔ Manitoba ✔ ✔ ✔ ✔ New Brunswick ✔ Newfoundland and Labrador ✔ ✔ ✔ Nova Scotia ✔ ✔ ✔ Nunavut ✔ ✔ ✔ Ontario ✔ ✔ ✔ ✔ ✔ Prince Edward Island ✔ ✔ Quebec ✔ ✔ ✔ Saskatchewan ✔ ✔ ✔ ✔ ✔ Yukon ✔ ✔ ✔ 3.5 Effectiveness of Financial Incentives The evaluation of FIs in the Canadian context can be examined by a comprehensive review of research performed in Canada and other countries. Research review revealed four major classifications criteria of FIs’ effectiveness evaluation that are explained in the following sub-sections. The summary of publications on FI used in GB are provided in Appendix A, Table A4. 3.5.1 Time of evaluation The time of evaluation of FIs divides the analysis methods into the either ex-post or ex-ante analysis. The ex-post analysis evaluates the effect of energy efficiency interventions over time and provides empirical results. The ex-ante analysis forecasts the impacts of incentives before they are implemented and give expected results [187,188]. Ex-ante analysis for FIs related to energy efficiency is usually based on engineering economics. Some researchers have shown that ex-ante analysis results tend to be over-optimistic compared to more realistic results from the ex-post analysis [127,189,190]. In contrast, Lang and Siler [191] 29 did not find overoptimistic results from the ex-ante analysis. Over time the researchers' focus has shifted from ex-ante towards ex-post analysis, possibly due to the accountability of institutional behavior and accurate ex-post analysis results [192]. However, the ex-ante analysis can help reduce risks related to the resources used to implement FIs, provided suitable assumptions are made for analysis. Details of some of the most common methods are provided in Table 3-6. Ex-post analysis can be conducted through five major methods [78]: a top-down approach, bottom-up approach, an amalgamation of top-down and bottom-up approaches, econometric modelling and policy theory. Among these methods, econometric modelling has been most extensively applied and used to study the diffusion of energy conservation technologies, free-riding effect and comparing FIs [193–198]. However, econometric modelling methods are expensive to implement and need specific data. Other ex-post analysis methods include the use of Panel Data Models [53], Data Envelopment Analysis [199], Life Cycle Assessment (LCA) [200] and Grey Model (GM) [201]. Ex-ante analysis can be conducted through two major methods: forecasting and backcasting. Forecasting methods are based on bottom-up energy-economy models and are more common in effectiveness evaluation studies. Energy-economy models can be categorized into four types: simulation, optimization, accounting and hybrid models [202]. The majority of these models are being used to determine the impact of policy instruments on building energy cost, use and emissions [202–206]. Among these models Canadian Integrated Modelling System (CIMS) is specially designed for Canadian building stocks and can determine the interaction between energy supply and macroeconomic performance. The backcasting method, as opposed to the forecasting method, is capable of determining the path needed to achieve the desired target [207,208]. In short, both ex-post and ex-ante methods have their strengths and weaknesses and lessons learned from these methods can help in obtaining FIs with desired effectiveness. 3.5.2 Stakeholders’ perspectives The optimal energy performance of a building will vary from different stakeholder’s perspectives [209]; hence, the evaluation of FIs from different perspectives will provide different results. Stakeholders for an energy efficiency program include program administrator (e.g. utility), government, society or the end-users. It is quite possible that an incentive has high effectiveness 30 Table 3-6 Ex-ante and ex-post methods for Financial Incentives effectiveness Evaluation Approach Method Advantage Drawbacks Ref. Ex-post Top-down approach Aggregation of different indicators in achieving the target is made by assuming that the amount of energy efficiency is constant over period of evaluation. The amount of energy used with the application of instruments is compared with the energy efficiency baseline and gives the energy saved Less time consuming Not possible to assess the impact of individual instruments [78,210] Bottom-up Approach Effects of individual instruments are assessed to determine the impact on energy target achievement Easy to identify the performance of the individual instrument in kWh, GJ etc. Difficult evaluation in case of instruments assigned in packages [78,210] Combination of Top-down and Bottom-up Approach Two methods (Top-down and Bottom-up Approach) are combined to assess the impact on energy targets Weaknesses of top-down and bottom approach methods are covered. [78,211] Econometric Modelling Based on a statistical analysis of factors that can potentially affect the instrument evaluation [78,156,196,197,212] Policy Theory Complementary method to top-down, bottom-up and econometric modelling with particular emphasis on bottom-up approaches Policy Theory is also called Logic Model Analysis, Intervention Theory, Theory-Based Approach, Realistic Evaluation Theory or Program Theory A comprehensive evaluation of the whole policy implementation process. Easy to identify factors impacting success or failure of incentive [78,213] 31 Evaluation Approach Method Advantage Drawbacks Ref. Ex-ante Forecasting (Energy Economy Models) Energy-economy models are developed based on robust economic and engineering principles and determine impacts of FIs on the energy savings or reduction in carbon emissions Four main methods are used for generating these models: (1) (Market) Simulation (2) Optimization (3) Accounting (4) Hybrid Models Several models are developed and extensively used depending upon the evaluation goal. Examples of some of these methods are: Canadian Integrated Modelling System (CIMS) National Energy Modeling System (NEMS) Long-range Energy Alternatives Planning (LEAP) MARKet Allocation (MARKAL) Capable of identifying the best combination of energy efficiency improvements needed under cost and time constraints CIMS hybrid energy-economy model capable of determining the interaction between energy supply and macroeconomic performance NEMS model provides long term projections of energy technologies based on operating and investment costs LEAP is capable of analyzing both economic and environmental impacts MARKAL is a bottom-up energy-based model that can determine the evolution of end-use energy systems It can be restricted to a specific region and sector [202–206] Backcasting Analysis A future can be achieved by exploring scenarios that give the desired target The process involves methodological steps that have sequenced order that varies with a specific backcasting approach A conventional backcasting method is applicable to long term analysis only [207,208] 32 from an end-user’s point of view but has low effectiveness from the society’s or program administrator’s perspective. Researchers have evaluated effectiveness from the end-users, society and government perspectives [77,78,194,195,198,214–218]; technical, program administrator and multiple stakeholders perspectives [75,219–225]; and micro-economic (end-users) and macro-economic (societal) perspectives [226,227]. 3.5.3 Intended target of FIs effectiveness The effectiveness of a FI can be defined as the degree to which it contributes to the achievement of the intended target of FI [228]. The effectiveness value can help decision-makers in justifying the continuation of investment in GB incentives by the stakeholders [18]. Depending on the intended goal of the FIs, studies on the effectiveness of FIs of GBs can be broadly categorized into two groups: environmental effectiveness and cost-effectiveness. Table 3-7 shows some common models used to evaluate FI effectiveness. Environmental effectiveness can be defined in the same manner Jurušs and Brizga [229] defined tax incentives effectiveness, i.e. the degree of achievement in terms of pollution mitigation targets, technical innovation or substitution of existing products with a more environmentally friendly product. Environmental effectiveness is an important parameter that can also help assess the rebound effect due to incentives [230]. Cost-effectiveness is a common parameter used in engineering economic evaluations of FIs. Harmelink et al.[78] defined cost-effectiveness as the result of the application of a policy instrument to the finances needed to achieve the desired target. Cost-effectiveness results impact energy policy, program design and budget allocation [219]. Table 3-8 provides an overview of different parameters that need to be considered for evaluating cost-effectiveness from different perspectives. Cost-effectiveness from end-user perspectives is concerned with the direct benefits and investments related to energy saving measures [78]. Some studies on end-users cost-effectiveness have also included the change in building sales value or rent ability in the evaluation [231–234]. Some studies found that subsidies and taxes are highly cost-effective from an end-users' perspective while costs on emissions are not; however, from a societal perspective, costs on emission (carbon taxes) have high cost-effectiveness while tax credits and subsidies have low effectiveness [127,226]. 33 Table 3-7 Effectiveness Models for FI Effectiveness type Model Ref. Environmental Specific environmental target in comparison with that achieved by an alternative FI [197,235–237] Emission reduction Reduction in CO2 emission (tCO2) [75,197,217,228,238] Energy EEff = 100X(PEex−ante − PEex−post)PEex−ante=∆PEPEex−ante (%) where: EEff=Energy effectiveness PE= Primary Energy Demand [239] Eco-logical cost ECE =CfVE where: ECE= Eco-logical cost effectiveness Cf=financial costs of energy upgrade (Cost/m2) VE=reduction in environmental impact due to energy upgrade (Pt/m2) Pt= LCA eco-indicator point [234] Cost CEff =program effects in physical terms costs (e. g. CAD$) where: CEff=Cost-Effectiveness [240] %-age reduction in PBP where: PBP= Payback Period [51,54,241,242] Benefit-to-cost Ratio (B/C) [127,219,222] Table 3-8 Cost-effectiveness for different perspectives Parameter End users perspective Society perspective Government perspective Ref. Costs Additional costs to be paid by people responsible for energy saving measure implementation compared to a reference case -Costs to be incurred by society compared to the reference case. -Societal cost-effectiveness Amount spend by the government in the form of both financial and non-Financial Incentives provided to end-users [78,219] Time Actual costs at time of payments Longer time frame with discounted costs evaluation Longer time frame with discounted costs evaluation [78] Indicators -Simple PBP -Investment profit -Marginal costs -Cost of saved energy -Benefit-to-cost Ratio (B/C) -Change in LCC -Total costs per unit of energy saved -Program costs per unit of energy saved -FI per unit of energy saved -Fiscal environment -Non-energy impacts (e.g. health and welfare) -Total costs per unit of energy saved Program costs per unit of energy saved -FI per unit of energy saved [219] 34 Limited studies have measured both the environmental effectiveness and cost-effectiveness of FIs. Most studies either focused on a single energy upgrade (example: natural gas furnace, electric appliances); a single incentive (example: rebate, tax); or from the end-users perspective [50,230]. The most comprehensive study on multiple targets was performed by Ürge-Vorsatz et al. [127]. The review study compared 20 policy instruments that had been evaluated in 60 different ex-post evaluation studies. In order to enable ease in comparison, three qualitative scales (High, medium and low) were applied for cost- and environmental effectiveness [127] (Figure 3-4). Appraisal results on societal level showed that carbon tax had low environmental and cost-effectiveness; capital subsidies, grants and soft-loans had high environmental-effectiveness but low cost-effectiveness; whereas, tax-exemptions had both high environmental and cost-effectiveness among the FIs [127]. Similar comparison plots are needed for end-users and government perspectives. Figure 3-4 Environmental cost-effectiveness from a societal perspective (Source: [127]) 3.5.4 Barriers to the success of financial incentives Despite the proven advantages of FIs [75,212] a number of barriers are present that reduce 35 effectiveness. Table 3-9 describes some major barriers affecting effectiveness. These are split-incentives, weak incentives, time of implementation, negative impacts of FIs interaction, behavioral impacts and others. They are briefly explained as follows. Table 3-9 Barriers to the success of different FIs Barrier/Anti-incentive Description Ref. Split incentives Particularly important in rental housings where interests of two parties conflict and neither the landlord (due to low ROI) nor the tenant (high initial costs) wants to invest in an energy efficiency upgrade [168,243–245] Negative impacts of FIs interaction Mitigating impacts are negative interaction between two policy instruments that result in reduced savings [246–249] Weak incentives Lack of attractive amount of incentive and connection between the government budget and the energy target needed to be achieved by FIs [76,78,244,250] Time Application of FIs at wrong time [251] Behavioral impacts -Low priority towards energy efficiency (Low resource consumption culture present in most developed countries makes it difficult for FIs to be successful) -Free riders (Free-riders are consumers who would have performed energy upgrade regardless of the introduction of FIs.) - Non-takers (Non-takers are consumers who do not perform energy upgrade even with the introduction of FIs) -Rebound effect (Rebound effect results in less energy savings compared to expected due to introduction of FIs and is a source of energy efficiency gap) [78,195,198,212,218,225,230,250,252] Other Barriers -Tax exemptions -High initial investment -Long payback periods -Transaction costs -Limited incentives for large buildings -Lack of information -Lack of technical expertise [76,78,230,244,250,252–254] Split incentives: Split incentives are a common barrier that results in the ineffectiveness of FIs in the rented residential and commercial buildings [167,168,245]. This barrier occurs when the benefits and costs are not equally distributed among stakeholders resulting in an unwillingness to invest in expensive technological options [94,168,245,255]. Split incentives can lead to underinvestment in the energy efficiency programs offered by utilities and government [168,245] or a performance gap between the energy model and the actual building energy use in non-domestic buildings [243]. Charlier [168] suggested the introduction of Energy Performance Contracts (EPCs) as a solution to this problem. As urbanization in major cities continues to 36 increase and trends of high-rise apartment buildings continue in Canada [172], special efforts are needed for removing split-incentives from buildings. Weak incentives: Weak incentives are another common problem that results in low cost and/or environmental effectiveness. Weak incentives discourage investors from making energy efficient choices even if the investment is cost-effective over time for the end-users or society [76,156]. The unclear benefits associated with weak FIs and consumers preference for immediate savings lead to low confidence in making energy efficiency choices [76,256,257]. Weak FIs can also occur due to a low incentive amount than needed to achieve the desired target [132,156,258]. Time of implementation: Provision of FI at a suitable time is important to achieve full sustainability targets. For instance, incentives such as investment tax credits are attractive for building users; however, when applied during the buildings’ operation period, the replacement of building equipment (old appliances, furnaces or/and HVAC systems) may occur prior to the end of useful life. This will offset the maximum possible sustainability targets. On the other hand, the application of utility rebate incentives during a buildings’ operation time will encourage the use of equipment to their full useful life and meet sustainable goals of equipment energy upgrade [251]. Similarly, the local socio-economic conditions will affect the type of FIs that needs to be deployed. For example, currently (under COVID-19 situation), retrofit of buildings is seen as a solution to economic revival and sustainability [79], more FIs related to building retrofits will be needed. Negative impacts of FIs interaction: A number of FIs and policy instruments are present for energy savings in buildings and it is important to understand how they will interact with each other and impact effectiveness. Interaction effects are influenced by the policy instrument steering mechanism, scope and timing of implementation [246,259]. This interaction can result in either positive, neutral or negative impact on effectiveness [246,259]. For example, Boonekamp [247] showed negative interaction between energy taxes, subsidies and regulations in the Netherlands that resulted in 13-30% less effectiveness compared to the sum of effectiveness for individual instruments. Despite the importance of interaction between FIs and other instruments, there are scant studies addressing the problem [246–249]. Table 3-10 shows interaction among instruments for a study performed by Rosenow et al. [249]. More studies are needed on interaction, especially in the Canadian context. 37 Behavioral impacts: Behavioral impacts are often complex and constrained by the type of adopter (free riders, switchers and non-takers) and social, economic, physical parameters [260]. Free riders and rebound effect are most common behavioral impacts [195,198,218,225,230,237,261,262]. Table 3- 10 Interaction Matrix between financial and non-financial policy instruments Financial Incentives Other Policy Instruments Tax rebates Low-interest Loans Grants On-Bill Finance Carbon Tax Building Standard Eco-Labelling Regulations Tax rebates ✖ ✖ * ✔ ✔ ✔ o Low-interest Loans ✖ ✖ ✔ ✔ ✔ o Grants ✖ ✔ ✔ ✔ o On-Bill Finance ✔ ✔ ✔ o Carbon Tax ✔ ✔ ✔ Building Standard ✔ o Eco-Labelling o Regulations Note- (✔): positive interaction; (o): nuetral interaction; - (✖): negative interaction; (*): information not present Source: (adapted from Rosenow et al. [249]) Researchers have also shown that the free-ridership problem is more for energy measures that are replaced more frequently; hence, boiler incentive will have a higher number of free riders compared to FIs for insulation [252]. The energy use behavior and preferences of stakeholders also impact the effectiveness of FIs [144]. For example, a low priority may be given to energy efficiency when energy is low priced, resulting in a lower effectiveness of FIs [230,250]. Similarly, some adopters may prefer tax credit over interest-free loans [144]. In addition to these, occupant behavior is important in assessing the realistic energy savings due to energy upgrades [56,57]. For instance, Rana et al. [263] showed that energy, emission and cost savings vary for the same energy upgrades with different occupancy profiles. Hence, occupant behavior may result in either an increase or a decrease in FIs’ effectiveness. Other Barriers: In addition to above, researchers have also identified other barriers impacting effectiveness. These include: provision of tax exemptions [244], high initial investments [230], long payback periods [250], transaction costs (indirect costs) [264], limited incentives or large buildings [220], lack of information [76,78,230,253] and technical expertise [253,254]. 38 3.6 Summary A detailed literature review was performed to identify the types and quantities of FIs present for promoting GBs in Canada. This work revealed major regional variations in the FIs available for residential and commercial buildings. Loans supplied by financial institutions and rebates by utilities are the most common FIs in all provinces of Canada. Three provinces (ON, QC and BC) are leading the way in the availability and promotion of FIs for GBs construction in Canada. These provinces have FIs that ensure the penetration of energy efficient technologies and are hence paving the path towards deployment of more stringent building energy standards. Despite the presence of a number of FIs, more targeted FIs are required for low-income housing, aboriginal communities, non-profit organizations, rental housing stakeholders and heritage buildings. In addition, published literature on FIs effectiveness also revealed that a number of factors impact the evaluation results. These factors can be grouped into four criteria: stakeholder (end-users, government, society, etc.) perspective considered, time of evaluation (ex-ante and ex-post), type of incentive (subsidy, rebate, dis-incentive) and the intended target. There also exists a non-uniformity in the definition of effectiveness, as well as the methods and models used for evaluation. These differences make it difficult to compare evaluation studies and make conclusive decisions. A deeper understanding of the interaction between different policy instruments, investor behavior and preferences of targeted stakeholders is essential to remove barriers and ensure the success of FI. There is also a need for more comparative studies on FIs effectiveness from different perspectives (end-users, society and government) using ex-ante and ex-post evaluation approaches. 39 Chapter 4 Impacts of Occupant Behaviour on Incentive Policies Parts of this chapter have been published in the Elsevier Journal of Cleaner Production, as an article titled “Occupant-based energy upgrades selection for Canadian residential buildings based on field energy data and calibrated simulations” [263]. 4.1 Background A growing body of literature recognizes occupant behaviour (OB) as the major reason for a gap between actual and simulated energy use of a building [56,57]. OB is a major area of interest within the field of building energy performance. OB includes occupancy, occupant movement and interactions with the building’s systems and appliances [58]. Ignoring the impact of OBs can over predict energy savings or increase the energy use up to 80 % even in identical homes [265] [59] [266–270]. Furthermore, researchers agree that for energy efficient residential buildings, such as net-zero energy houses, OB's influence in BES will become even more significant [60,271]. Since OB results in a significant impact on energy use, it is vital to understand occupancy implications on the selection of suitable EPUs. Detailed monitoring studies further affirm the significance of OB for obtaining realistic BES results. In contrast to commercial buildings, limited research works are focusing on the effects of OB in residential buildings [62]. There remains a paucity of energy models based on monitored data [61,62]; and, consequently, a limited understanding of the impacts of OBs on EPU selections. In Canada, energy monitoring studies on residential buildings have mostly focused on old buildings constructed prior to implementation of the current more stringent building energy codes, such as NECB-2015, BC Energy Step Code 2017. Saldanha and Beausoleil-Morrison [60] focused on residential buildings in Ottawa, Ontario, constructed between the 1930s and 1990s. A recent energy-monitoring study on Gemini home, Toronto, Ontario, is performed on a retrofitted home originally constructed in 1879 [272]. Other studies have generated energy models based on data collected through interviews and surveys [273,274]. Results from survey studies are able to present impacts of occupant behaviour but are unable to provide a good prediction of occupant behaviour on energy savings due to EPUs [275,276]. To date, there are few studies that have investigated the association between the impacts of occupants on energy savings using calibrated energy models. 40 Hence, there is a need to determine the influence of occupant behaviour on energy use along with the consideration of occupant choice and limitations to make informed decisions. This chapter's main objective is to investigate the possibility of catering to the energy demand of a new residential building according to expected occupant behavior (specifically occupancy profile) and compare energy performance upgrades that can reduce this energy demand. To meet this objective, a decision support methodology is developed that ranks the best energy performance upgrades for a specific occupancy profile. The methodology is demonstrated for two single-family residential buildings as a case study in Canada. 4.2 Methodology In this chapter, a methodology is developed to identify the most appropriate energy performance upgrades for new residential buildings with expected occupancy profiles. The methodology shown in Figure 4-1 has two main phases: (1) Data Collection and (2) Data analysis. 4.2.1 Data Collection and Processing The two key sources of data include: (1) literature-based data used to identify potential energy upgrades; and (2) data captured from the field monitoring system to create calibrated energy models. The main sources used for literature were peer-reviewed articles and technical reports related to residential building energy and cost modelling. A thorough literature review was performed for current building energy standards for residential buildings, energy and cost modelling methods, energy monitoring techniques, occupancy profiles and energy performance upgrades possible for residential buildings. Energy performance upgrades can be categorized into three categories: (1) passive, (2) active and (3) renewable energy upgrades. MCDM methods available for ranking and prioritization of energy upgrades were also explored. The collected information was used for developing preliminary energy simulation models, calibrating and validating models, analyzing the effects of energy performance upgrade combinations, assessing associated life cycle costs, PBT and GHG emission reductions and finally prioritizing the most appropriate upgrades for a specific occupancy profile. The intrusive load monitoring (ILM) method is proposed for collecting energy use data related to the occupancy profile. ILM method monitors energy use through individual devices with sensing monitors distributed within the building [277]. Contrary to the non-intrusive method, the data 41 Note-GHG: greenhouse gases; IC: Capital Investment; PBT: Payback period; TOPSIS: Technique for Order of Preference by Similarity to Ideal Solution; HVAC: Heating, Ventilation, and Air Conditioning; DHW: Domestic hot water; HRV: Heat recovery ventilator Figure 4-1 Proposed methodology on the evaluation of upgrades for SFD 42 obtained from ILM are more accurate and helps generate better energy models. The design and installation of energy data monitoring are beyond the scope of this research work and should be designed with the consultation of building developers, owners and data monitoring experts. 4.2.2 Data Analysis Energy modelling: A preliminary energy model was developed to determine the end-use energy demand of the residential building under study. HOT2000V15.1, a simulation tool developed by CANMET Energy Technology Center was used to generate the model. HOT2000 is tailored for residential buildings in Canada [278] and hence ideal for assessing the operational energy use of low-rise residential buildings in Canada (More details on HOT2000 can be seen in Appendix B). The data needed to generate a standard energy model includes location, house geometry, building envelope characteristics, energy systems and equipment present in the house, number of occupants and occupancy hours. Calibration: Calibration is defined as a measure of the BES model's accuracy and ensures that the energy performance gap between simulated and the actual energy use of the building lies within acceptable limits [279]. The calibration is an inverse approximation method where the values of inputs are changed until output values are approximately similar to actual energy use [280,281]. Three most commonly used guidelines that define benchmarks for calibration are the American Society of Heating, Ventilating and Air Conditioning Engineers (ASHRAE) Guidelines 14 [282]; Federal Energy Management Program (FEMP) [283]; and International Performance Measurement and Verification Protocol (IPMVP) [284]. ASHRAE 14 requires two calibration benchmarks to be achieved for acceptable BES. If results exceed these limits, the calibrated model becomes invalid and needs to be recalibrated until the error is within permissible limits. These include Mean Bias Error (MBE) (%) and the Coefficient of Variation of Root Mean Square Error (CV(RMSE)) (%). MBE is a non-dimensional bias measure (i.e., the sum of errors) between measured and simulated data for each hour. It captures the mean difference between measured and simulated data points and is considered a good indicator of the overall bias in the model. RMSE index assesses data by capturing offsetting errors between measured and simulated data and does not suffer from the cancellation effect in MBE. The positive bias compensates the negative bias and can result in incorrect validation. Acceptable 43 tolerance limits for the two benchmarks vary according to the type of calibration hourly or monthly and are provided in Appendix C, Table C1. The following steps are involved in performing the calibration process: 1) Constructing an initial BES model on HOT2000 based on the engineering drawings, local weather data, occupancy, equipment specifications and indoor temperatures. 2) Adjusting constructed BES model with data obtained from monitoring and making suitable assumptions for unknown parameters. 3) Determining MBE and CV(RMSE) values on monthly energy based on results of BES model and actual energy use with Equation 4-1(a) and (b). 𝑀𝐵𝐸 (%) =∑ (𝑚𝑘−𝑠𝑘)𝑁𝑝𝑘=1∑ (𝑚𝑘)𝑁𝑝𝑘=1 Equation 4-1(a) C𝑉(𝑅𝑀𝑆𝐸) (%) =√∑ (𝑚𝑘−𝑠𝑘)2/𝑁𝑝𝑁𝑝𝑘=1𝑚 Equation 4-1(b) Where, mk and sk are the respective measured and simulated data points for each model instance ‘k’; Np is the number of data points at interval ‘p’ (i.e., Nmonthly=12) and m is the average of the measured data points. 4) Re-adjusting the energy model parameters until the results of the benchmark metrics are within acceptable limits of MBE and CV(RMSE) [282]. Calibrated energy models were tested for variation in energy consumption due to changes in energy upgrades. The energy consumptions under upgrades combinations were used to find the change in the house's carbon footprint and operational costs. Life cycle costing and carbon footprint: For this study, the Net Present Value (NPV) method was used to determine the lifecycle cost analysis (LCC) for assessing building upgrades. LCC is an economic evaluation technique that determines the costs of the entire life span of a product or process [285]. Typically, LCC for buildings includes construction, maintenance, repair, replacement and disposal costs [286,287]. Previous studies on residential buildings in Canada have considered a 30-year timeline for building operation [288,289]. Therefore, the 30-year operational period was considered appropriate for the proposed methodology. 44 In this study the relative life cycle cost model by US Federal Energy Management Program [290] was used. Here the 𝑑𝐿𝐶𝐶𝑡 is the cost difference between the life cycle cost (LCCi) for a house with energy upgrades and the life cycle cost of a reference case (LCCr) house. The reference case is a SFD made according to local code compliance or the status of residential building prior to any upgrades. This cost model does not require costs related to all building components but only the cost differences of the energy components that are being changed [291,292]. Cost differences associated with the additional investment, energy bills, maintenance and replacement cost, as well as end-of-life cycle costs, are considered for 𝑑𝐿𝐶𝐶𝑡 calculation as indicated by Equation 4-2(b). 𝑑𝐿𝐶𝐶𝑡 = 𝐿𝐶𝐶𝑖 − 𝐿𝐶𝐶𝑟 (CA$) Equation 4-2(a) 𝑑𝐿𝐶𝐶𝑡 = 𝑑𝐶𝑐 + 𝑑𝐶𝑈 + 𝑑𝐶𝐸𝑂𝐿 (CA$) Equation 4-2(b) where, dLCCt = difference in total life cycle costs of SFD with upgrades and base case, LCCt = total life cycle costs of house with energy upgrades, 𝐿𝐶𝐶𝑟 = total life cycle costs of reference house, t = Study period in years; dCC = difference in costs of SFD with upgrades and base case; dCU = difference in costs in the use stage (operational energy use, maintenance and replacement costs) of SFD with upgrades and base case; and dCEOL = difference in costs of the end-of-life of SFD with upgrades and base case. The end-of-life cycle costs were considered negligible for determining LCC. Carbon footprint is defined by the total GHG emissions and is usually expressed in units of carbon dioxide equivalent (CO2-eq) [293]. Energy-based GHG reduction is dependent on the grid emission factors and the quantity of energy demand reduced through the application of EPUs. Grid emission factors are dependent upon the source of energy production and will change with the location of the building [294]. Energy results from the calibrated energy models were used to calculate the long-term GHG impacts of the occupancy profile under the application of EPUs. Equation 4-3 is used to calculate the GHG emissions associated with energy fuel consumption, where the emission factor changes with the type of fuel. 𝐺𝐻𝐺𝑓 = 𝐸𝐹𝑓 × 𝐹𝑓 Equation 4-3 45 Where, 𝐺𝐻𝐺𝑓 is greenhouse gas emission rate of fuel f consumed (kg CO2-e), 𝐸𝐹𝑓 is the emission factor for energy source (natural gas or electricity) while 𝐹𝑓 is the annual energy consumed for operating the house equipment and systems. 4.2.3 Ranking Upgrade Scenarios The selection of suitable EPUs is governed by a number of conflicting parameters that include: energy performance, environmental impacts, economic requirements, regulatory and social aspects [295]. The decision-maker has to arrive at a compromise among these parameters when selecting the EPUs. Multi-criteria decision-making (MCDM) methods help solve this problem and prioritize and rank the most desirable alternatives [296]. MCDM can treat qualitative and quantitative criteria and come up with a ranking of alternatives [297]. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is one of the most well-established multi-criteria methods that is used to test competing alternatives [298,299]. By using the TOPSIS method, the solution that is the nearest to the ideal among all the sets of alternatives is selected. The method has been successfully used in previous studies for selecting the best energy upgrade [300–303]. Therefore, the TOPSIS method was considered appropriate to meet the goals of this study. The following steps are involved in performing the TOPSIS technique for this particular methodology. 1) Construction of the decision matrix and weighting matrices. The decision matrix considered 3 criteria: reduction in GHG emissions (kg CO2-e), initial investment (CA$) and payback period (Years). The weighting matrix is generated by defining five weighting schemes (Table 4-1) representative of investors’ preference for pro-environmental or pro-economic investments. In the pro-environmental option, more weight is given to the reduction of GHG emissions, while for a pro-economic option lowest LCC is given maximum weight normalization of the decision matrix through the conversion of different criteria into non-dimensional values using Equation 4-4. 46 zij =zij√∑ z2ijji=1⁄; for i= 1, n to j=1, m Equation 4-4 Where, “zij" is an entry in the decision matrix and m represents the total number of rows and n represents the total number of columns. Table 4-1 Criteria weights for different occupant preferences Selection Criteria Description Pro-Environmental Higher weightage for environment Equal importance Higher weightage for economy Pro-Economic GHG emission reduction (kg CO2-e ) 1 2/3 1/3 1/6 0 Capital Investment (IC) 0 1/6 1/3 1/6 0 Payback Period (PBT) 0 1/6 1/3 2/3 1 Generation of the weighted matrix through multiplication of weights of each criterion with entries in normalized matrix. 2) Determination of the best and worst alternatives for each criterion. The best solutions (Vb+)for the weighted normalized matrix are selected out of all alternatives: Vb+ = [MaxGHG, MinIC, MinPBT, ] The worst solutions (Vb−)for the weighted normalized matrix are selected out of all alternatives: Vb− = [MinGHG, MaxIC, MaxPBT, ] 3) Calculation of distance from ideal solution using Euclidean distance. The distance of the alternative from the positive ideal was found by using Equation 4-5(a)and for the negative ideal using Equation 4-5(b). di+ = √∑ (vij − Vb+mj=1 ) Equation 4-5(a) di− = √∑ (vij − Vb−mj=1 ) Equation 4-5(b) The relative closeness to the ideal solution was found by Equation 4-5(c) 47 CLi∗ =di−di− + di+⁄ Equation 4-5(c) 4) Ranking the alternatives based on relative closeness to the ideal solution. 4.3 Methodology Demonstration 4.3.1 Study Area A case study approach is taken to apply and demonstrate the methodology using field data obtained from two living laboratory houses located in Kelowna city, Okanagan Valley (Canada). Although other living laboratories are present in Canada, including the Canadian Centre for Housing Technology (CCHT) houses in Ottawa [304] and the Archetype houses near Toronto [305], these living labs are the first of their kind in Canada conforming to the Okanagan region construction practices. The two houses are located in one of the fastest growing areas in Canada, the Okanagan Valley (Figure 4-2). Figure 4-2 Floor plans of case study homes 48 The case study homes were constructed in 2016 and occupied in 2017. These mid-size residences are identical in their architectural design, location and orientation and are exposed to the same external features. However, the two homes vary with respect to the building envelope, heating and cooling systems and occupancies. The household characteristics for the homes are provided in Table 4-2. The construction materials and thermal characteristics obtained from the industrial partners were used to develop the energy model. The geometry and other relevant information of this house were extracted from the relevant drawings and bills of quantities. The thermal characteristics of specific envelope components and equipment such as HVAC, lighting and appliances were collected from the manufacturers’ specifications. Table 4-2 Household characteristics Parameter Standard Home (STH) Advanced Tech Home (ATH) Occupancy Profile 2 working adults (aged 35-49) 2 teenagers (aged 15-19) retired couple (aged 50-64) Total area of the building (m2) Gross floor area (m2) Area of walls (m2) Area of doors (m2) Area of windows (m2) 291.25 249.91 364.57 6.60 57.91 291.25 249.91 364.57 6.60 57.91 Space cooling and heating (HVAC) Payne (PG92SCS) 17.58 kWh AFUE 92.1% Natural Gas & Payne (PA14NC) 9.96 kWh 14 SEER Split A/C 5 series (500A11) – Geothermal c/w ECM variable speed blower (Heating 4 COP, Cooling 5.6 COP) Domestic hot water (DHW) Standard DHW system, 227 litres, EF 1.901, Electricity-based GeoSpring™ hybrid electric water heater, 303 litres, EF 3.14, Electricity-based Wall-Insulation (WL) USI 0.28 Batt (Eff. USI 0.32) 9.525 mm EPS Styrofoam & USI-0.28 Batt (Eff. USI 0.27) Celling- Insulation (RF) USI 0.26 Batt, USI 0.14 blown (Eff. USI 0.14) USI 0.28 Batt, USI 0.11 blown (Eff. USI 0.09) Foundation (FN) USI 0.26 Batt (Eff. USI 0.32) ICF blocks (Eff. USI 0.25) Windows (WN) Vinyl double glazed windows c/w 180 low-E Vinyl triple glazed windows c/w 366 low-E Appliances (APP) Standard Appliances: Dishwasher, washer & dryer, fridge, oven and hood fan Energy-Star Rated Appliances: Hood fan with LED lighting and ultra-quiet blower, dishwasher, heat pump dryer and washer, 5 door fridge, double ovens Lighting (LED) Incandescent bulbs LED bulbs Solar (PV) None 10 panels (16.3 m2) azimuth 15' and slope 25' 49 The case study homes cover an area of 291.25 m2 and consist of two storeys, the main floor and a partially underground basement. One house called Standard Home (STH) is built by following the BC building code 2012 [306] represents typical construction practices such as standard building characteristics, building systems and appliances. It represents the base case scenario for this study. The other home, called Advanced Tech Home (ATH), is representative of an energy efficient home and is constructed with the most energy efficient materials and systems locally available in Kelowna (Okanagan Valley). In addition to having higher fabric insulation (higher insulation and foundation made of insulated concrete forms- ICF blocks), better performance windows (Vinyl triple glazed windows c/w 366 Low-E), an HVAC system consisting of a geothermal heat source pump and energy star rated appliances the ATH is supplied with renewable energy from a solar (PV) system installed on the roof. The two houses represent occupancy profiles most common in Canada: (a) a family with dependent children where the parents work full time; and (b) a retired elderly couple who spend most of their time indoors. The two profiles are representative of 52.3% of Canadian private household structures with couples with children (Occupancy Profile 1 for this study) representing 26.5% and couples without children (Occupancy Profile 2 for this study) representing 25.8% [307]. Canada does not have a mandatory retirement age though the average age of retirement for most Canadians is 63.5 years [308]. Since it was known that the residents pertaining to Occupancy Profile 2 are retired, the profile is called retired couple for this study. The STH model has previously been used in the study by Perera et al. [303] for assessing household and transport incentives for clean energy. This study extends the original model by calibration and considering additional EPUs. The detailed monitoring ensures a more realistic representation of occupants’ behavior and savings from energy upgrades. In order to make a comparison between the influence of two occupancy profiles on energy and GHG reduction potential of various EPSs, STH was upgraded with energy upgrades present for ATH while ATH was downgraded with the standard materials, equipment and appliances present in STH. 4.3.2 Energy Monitoring An ILM method was employed for determining the energy use by various building components. The living laboratory was installed with temperature, humidity sensors as well as with electricity 50 and natural gas consumption monitors. The system architecture of the data monitoring and acquisition system is shown in Figure 4-3. The real-time data was captured for one-year and a comprehensive database was developed for evidence-based research. Figure 4-4 shows the energy use pattern for the two homes’ HVAC systems, minor appliances (including small kitchen appliances such as toaster oven, microwave oven, electric kettle), lighting, domestic hot water (DHW) system, laundry washer and dryer. It is observed that the energy used in appliances by the ATH is more despite being more efficient appliances. The presence of a ground source heat pump also indicates a higher use of energy during summer months as compared to a conventional HVAC system. The current research assesses monthly energy use of different systems and average temperature and humidity levels for the two homes, while daily, weekly and seasonal variations in energy use are not part of this research. 4.3.3 Energy Modelling The BC Building Code-2012 and local municipality by-laws were used while designing the base-case models of the two buildings. The energy models for the two homes were generated based on the detailed drawings provided by the developer, local metrological data (Appendix H) and suitable assumptions regarding the operation of different appliances and systems. The energy models were constructed using HOT2000 version 11.7b23[309]. HOT2000 uses long term weather data in bin method and steady-state models for assessing monthly energy loads [310]. HOT2000 is used extensively for the design and certification of low-rise residential buildings in Canada [309]. To determine the error in the base case models, the monthly energy results of the simulation and monitored energy were compared using MBE and CV(RMSE) metrics [282,311]. Studies have shown that minor errors in simulated and measured energy use are due to the simplification of simulation tools, while more significant errors are due to incorrect model inputs [312]. The information used to calibrate the energy models is provided in Table 4-3. The base models for two occupancy profiles were constructed based on the architectural drawings provided by the developers. In this regard, HOT2000 guidelines for constructing an energy model for Canadian homes were followed. Architectural drawings and material specifications were used to develop these preliminary models. 51 Note- HVAC: Heating, Ventilation, and Air Conditioning; UBCO: university of British Columbia Figure 4-3 System architecture for the Living Lab 52 . Note- HVAC: Heating, Ventilation, and Air Conditioning; DHW: Domestic hot water; HRV: Heat recovery ventilation; STH: Standard Home; ATH: Advanced tech home Figure 4-4 Trend of measured energy use in STH (top) and ATH (bottom) graph Table 4-3 Information used to calibrate energy models No. Source Description 1 Logged measured data Lighting and general equipment electrical load data 2 Spot measured data Measured electrical load data 3 Infiltration rates data Air change rate from blower door test tests 4 As-built documentation Architectural and mechanical as-built drawings Wall materials and constructions taken from as-built drawings 5 Standards and guidelines BC Building Code 2012 HOT2000 input/output reference guidelines 6 Stakeholder consultation Validation of drawings and assumptions 7 Energy bills Monthly energy bills collected over one year time These models were upgraded with general information such as the number of occupants and air change rate of the home provided by energy consultants blower door tests. The missing information, such as the performance of appliances and electrical equipment, was based on the measured lighting and electrical data and initial base models generated large errors between the actual and simulated 53 energy uses. Manual calibration was performed to decrease these discrepancies in the energy results. Most researchers use manual calibration of energy models and involve iterative adjustments until the model is validated for the specified standard [313]. For calibration first, the energy use calculated by HOT2000 was compared with the monthly energy bills obtained from the home owners. One parameter was changed at a time starting with matching performance results of equipment with monitored energy. Some of the appliances were operating for a longer duration than standard values used in energy tools. Likewise, the actual efficiency of the equipment was determined by decreasing the values compared to the manufacturers specifications. In addition, the R-values of the walls, roofs, floors and foundations were varied as the value on drawings can be changed by construction errors. Multiple trials were done by upgrading the model with field measurements until acceptable errors were obtained. The final base case models had MBE and CV (RMSE) 0.99 and 3.46 for STH and -0.06 and 0.88 for ATH calibrated models on HOT2000, respectively. It was possible to obtain low errors due to the detailed energy, temperatures and humidity data collected through the intrusive load monitoring technique. The calibrated models were then run for different EPU combinations to determine the variation in annual energy use. A total of 514 simulation runs were performed on the nine EPU options. In order to increase the simulation time interaction impact between EPUs combinations was assumed to be negligible. The operational energy consumptions obtained from the aforementioned models upgraded with EPUs were used in the next phases to quantify the GHG emissions, operational costs and life cycle costs. It should be noted that the comparison between the validated models for the two profiles was not possible based on the calibrated models due to the difference in materials, equipment and appliances of the two homes. In order to compare the impact of energy upgrades for the two homes with respect to two occupancy profiles, the EPUs were added in STH while energy systems, equipment and appliances in ATH were downgraded with those present in ATH to find the annual operational energy, GHG emissions and LCC. Hence, it was possible to obtain the impact of occupancy profiles for the same energy systems, equipment and appliances. 4.3.4 Life Cycle Costing and Carbon Footprint The initial construction cost of the SFD base case was estimated using RSMeans residential cost database [314], market prices of appliances, ASHRAE report on the economic database for green 54 residential buildings [315] and data obtained from the Okanagan developers [316]. The costs obtained from RSMeans and the consultation process were adjusted to give a reasonable estimate of the Kelowna construction costs. The costs of construction were limited to additional investments for building energy upgrades. Costs relating to home insurance, landscaping, furniture, mortgage payment, government incentives for green upgrades were not considered. Table 4-3 shows the costs associated with different energy upgrades. The maintenance period for different building systems is used as defined by Kirk et al. [317] which varies with different upgrades. Moreover, costs due to the replacement of existing systems, pollution damage and hazard prevention at the rehabilitation process were not considered for this study. Table 4-4 Energy upgrades and associated costs No. Energy upgrades Abbreviation Type of energy upgrades Expected service life (Years) Annual Maintenance as a percentage of capital cost (%) Initial cost difference CA$(US$) 1 Appliancesa APP Active 16.5 5 3,000 (2,130) 2 Domestic hot water DHW Active 15 5 1,200 (852) 3 Foundation-ICF FN Passive 30+ _c 10,000 (7,100) 4 Space cooling and heatingb HVAC Active & Renewable 15 5 9,000 (6,390) 5 Lighting LED Active 15 3 43 (31) 6 Solar PV Renewable 25 1.5 8,600 (6,106) 7 Roof- Insulation RF Passive 30+ _ c 300 (213) 8 Wall-Insulation WL Passive 30+ _ c 1,000 (710) 9 Windows WN Passive 40 2.8 12,127 (8,610) a Appliances include kitchen (dishwasher, exhaust hood, stove and oven) and laundry appliances (dryer and clothes washer) b HVAC systems upgrade was considered to be ground source heat pump that serves part of both heating and cooling systems of the house and involves the use of electricity for running pumps and renewable energy c Maintenance and replacement of Wall-insulation, Ceiling insulation and ICF foundation is beyond the study period of 30-year The discount rate of 3% and an inflation rate of 5% were assumed based on the literature [318,319]. The energy costs for the house were calculated using the electricity and natural gas tariff defined by the local utility providers. Accordingly, the cost of electricity was assumed as 9.845CA¢/kWh (7.1US¢/kWh) for the consumption up to 1600 kWh and 15.198 CA¢/kWh (10.90 US¢/kWh) for the consumption above 1600 kWh. The cost of natural gas was assumed as 1.141 CA$/GJ (0.82 US$/GJ) [320]. Additional charges related to electricity and natural gas supply such as customer charge, basic daily charge, delivery charge, storage and fuel transportation charges were also 55 considered in the calculation of operational energy in the building life cycle. Total GHG emissions of a residential building depending on the supply energy mix and the energy consumption of the residential activities. In Canada, the emission factors vary with the primary energy sources [321]. Unlike other provinces in Canada, BC has a low-emission electricity supply since the major portion of the grid electricity is hydro-based [322]. Electricity emission factor is 2.80 kgCO2-eq./GJ, whereas the emission factor of natural gas is 65.75 kgCO2-eq./GJ [323]. 4.4 Results The above-described methodology was used to assess the variation in annual operational energy, GHG emissions and life cycle costs associated with the application of EPUs in a single-family detached house under two different occupancy profiles. Since the operational life cycle period of 30-year was considered, only EPUs that had PBT of 30-year or less were considered for comparison and ranking. The EPUs were then ranked with respect to five scenarios representing the priority of the investor. The results of the application of the methodology for the two residential occupancies are presented in this section. 4.4.1 Occupancy Profile 1 Occupancy Profile 1 comprised four family members, including two working adults and 2 school-going children. EPUs that fulfilled the PBT limit of 30-year or less with respect to the energy savings, GHG emissions reduction and PBT are discussed in this section. Annual operational energy: Figure 4-5 depicts the energy savings possible with respect to the selected upgrades for Occupancy Profile 1. These EPUs have been distributed into four groups with respect to percentage energy savings to the initial energy use (119.33 GJ/year) of the Occupancy Profile 1. The majority of the EPUs with a PBT of 30-year provide energy savings in the range 0-20%. As expected, the upgrades yield more savings in combinations and only four upgrades, LED, RF, DHW and PV, give reasonable savings. From the chart, it can be seen that by far, the greatest energy savings are up to 71% for the combination “RF WL LED HVAC DHW PV”. Among the passive energy upgrades, wall (WL) and roof (RF) insulations yielded savings in the majority of the combinations, while among the active systems, domestic hot water (DHW) was determined to have high-energy benefits. HVAC energy upgrade yielded more than 50% in 56 Figure 4-5 Occupancy Profile 1: Reduction in annual energy demand 57 operational energy savings; however, the system had a high initial cost that increased its PBT (beyond 30-year) and made this upgrade financially unfeasible. Payback period and carbon footprint: Figure 4-6 shows the relationship between the extra initial expenditure and PBT and reduction in GHG for the 47 shortlisted energy upgrades. Three main clusters of energy upgrades associated with initial expenditure and PBT can be clearly identified from this figure. It was observed that for investments below CA$ 5,000, the PBT are all less than 15 years. This kind of investment may not only be suitable for most owner-occupants since the average tenure calculated for homeowners in North America is 8-years [324]. The cluster in the middle shows options of energy upgrades when the extra expenditure ranges between CA$ 8,600 to 1,200. It was observed that some of the energy upgrades that have the potential for decreasing GHG emissions beyond kgCO2-eq have a high PBT of up to 30-years. The energy upgrades were also seen to offer more GHG reduction in combinations as compared to individual upgrade. For instance, the energy upgrade combination “RF WL LED HVAC DHW” provides about 200% more GHG reduction than PV systems for the Occupancy Profile 1. For the investment between CA$ 17,000 to 22,000, the majority of the energy combinations provide high reductions in GHG emissions. Since the budget required for these upgrades is high, financial help in the form of financial incentives from the government or utility providers would be needed to make these energy upgrades desirable for the users. Marginal abatement cost curve: Marginal abatements curves are another effective tool to visualize the economic and carbon reduction potential of EPUs. One strength of these curves is the ability to also represent the negative costs associated with adopting some EPUs [325]. Each box on the abatement curve represents the consequences of adopting an EPUs combination. The data generated from EPUs evaluation for Occupancy Profile 1, relative life cycle costs and GHG reduction potential, is used to create a marginal abatement curve. The curve shown in Figure 4-7 is generated for individual energy upgrades. A similar curve can be drawn for all 514 simulations performed. Likewise, abatement curves can be drawn for the 47 short listed EPUs used in Figure -5. The drawn curve shows that the HVAC system has the highest potential to reduce GHG emissions. In contrast, the incorporation of higher wall insulation will be a cheaper option for the residents. It is also observed that the costs of reducing GHG emissions are high for PV system. These high costs of PV are expected to decrease with time. 58 Note- HVAC: Heating, Ventilation, and Air Conditioning; DHW: Domestic hot water; HRV: Heat recovery ventilation; PV: Photovoltaic panels; LED: Light emitting diode; RF: Roof insulation; WL: Wall insulation; FN: Insulated concrete form foundation Figure 4-6 Occupancy Profile 1: Bubble chart with details of IC, PBT and GHG emission reduction associated with energy upgrades 59 Note- HVAC: Heating, Ventilation, and Air Conditioning; DHW: Domestic hot water; HRV: Heat recovery ventilation; PV: Photovoltaic panels; LED: Light emitting diode; RF: Roof insulation; WL: Wall insulation; FN: Insulated concrete form foundation Figure 4-7 Occupancy Profile 1: Marginal abatement cost curve TOPSIS Results: In order to rank the energy upgrade for each profile decision matrix composed of initial cost investment, the payback period and GHG emissions were constructed. Table 4-5 show the results of the first ten highest-ranked energy upgrades. It is seen that among the given scenarios, the choices for pro-economic scenario shows the highest variation in GHG emission reduction, IC and PBT. It is seen that as the decision-maker preference changes from pro-environment towards pro-economic, lesser energy upgrades are used. Roof insulation (RF), wall insulation (WL), lighting upgrade (LED) and upgrade of the domestic hot water system (DHW) are the only upgrades that were found in the list of top ten energy upgrades for the five user preferences. It is interesting to note that for a family of four, the DHW forms part of EPUs combinations under all scenarios. Water heating in Canadian homes accounts for 20% of the energy consumed and also accounts for a large portion of the residential carbon footprint [326]. A family of four is likely to spend more hot water in showering and bathing and hence are able to get more benefits by using a more efficient water heating system. An upgraded HVAC system forms part of the majority of the EPU combinations. The upgraded HVAC has a ground source heat pump and is known to bring 60 Table 4-5 Occupancy Profile 1: Ranking for energy upgrade for decision-maker preferences Table 4-6 Occupancy Profile 2: Ranking for energy upgrade for decision-maker preferences Note- HVAC: Heating, Ventilation, and Air Conditioning; DHW: Domestic hot water; HRV: Heat recovery ventilation; PV: Photovoltaic panels; LED: Light emitting diode; RF: Roof insulation; WL: Wall insulation; FN: Insulated concrete form foundation 61 a number of technical, environmental and socio-economic benefits [327]; however, this system's initial investments are high and show a long payback period unless some external financial incentives are present. Hence, it is not surprising that this upgrade did not form part of the top ten pro-economic choices. Contrary to the HVAC upgrade, the LED is a very cost-effective EPU and forms part of a suitable energy upgrade choice. Solar photovoltaic (PV) system installation is another renewable option that was tested for case studies. Though the costs of PV system have decreased substantially over the past decade, PV was not part of scenario-5 choice. However, the electricity costs are rising; for instance, over the past decade, electricity rates in Ontario, Canada rose by 7.7% [328]. This implies that the use of PV upgrades may soon become a pro-economic in addition to a pro-environment choice. In addition, three energy upgrades that did not form part of top-ranked combinations in any of the five preference scenarios were windows (WN), ICF foundation wall (FN) and appliances (APP). All these options required large expenditure and the associated GHG reduction were not significant to form part of the top pro-environment related choices. Hence, for a typical family of four, the energy upgrades that need to be emphasized are DHW, HVAC, LED, PV, WL and RF. 4.4.2 Occupancy Profile 2 Occupancy Profile 2 comprised of a retired couple who spend most of their time at home. Annual operational energy: Figure 4-8 depicts the energy savings possible with respect to the selected upgrades for Occupancy Profile 2. Similar to energy savings groups made for occupancy, the selected EPUs were grouped into four categories based on their energy saving potential with respect to a base case annual energy 105.62 GJ/yr. It can be seen that the filtered EPUs for the retired couple give significantly more savings for similar EPUs than those for Occupancy Profile 1 EPUs. The majority of EPU give energy savings in the range of 21-39%. The highest savings of 71% are observed for the combination “WL LED HVAC DHW PV”. Similar to Occupancy Profile 1, WL and RF insulations yielded the highest savings among the passive systems and DHW gave savings among the active systems. 62 Figure 4-8 Occupancy Profile 2: Reduction in annual energy demand 63 Payback period and carbon footprint: Occupancy Profile 2 is depicted in energy use under different energy upgrades for the stay at home retired couple. The initial costs, PBT and GHG emissions for feasible energy upgrade options for Occupancy Profile 2 are shown in Figure 4-9. Similar to Occupancy Profile 1, energy upgrades can be seen as clusters between expenditure and PBT. However, a clear contrast between the two profiles is a very low GHG reduction (less than 100 kgCO2-eq) associated with the majority of the upgrades. Hence, it is indicated that the energy use by Occupancy Profile 2 is significantly different from Occupancy Profile 1 and offers few options to reach the same level of GHG reduction targets. The highest reduction potential for Occupancy Profile 2 for the case study was found for the combination “RF WL LED HVAC DHW PV”. It is observed that for this profile, a higher investment of up to CA$ 11,000 is not significantly decreasing GHG emissions. Therefore, for Occupancy Profile 2 in order to achieve high reduction targets needed to achieve sustainable buildings will require external help from the government and other organizations. This is also significant since the majority of the studies have shown senior residents often own bigger houses and usually have a low-income source [329]. Marginal abatement cost curve: The data generated for Occupancy Profile 2 is used to create marginal abatement curve for individual energy upgrades (Figure 4-10). It is observed that the HVAC system has the highest potential to reduce GHG emissions, while the incorporation of more efficient windows is the most expensive option. Comparing the two abatement curves demonstrates that even the individual energy upgrades yield different performance according to the occupancy profile. It is also interesting to note that for Occupancy Profile 1 the LED and APP showed negative abatement curves while they are positive for Occupancy Profile 2. In addition, the change to ICF foundation does not provide notable emission reductions. These abatement curves need to be expanded to include EPUs combinations' performance and make more informed decisions. TOPSIS results: As Table 4-6 shows ranking for the top ten upgrade choices for the house with the retired couple defined by Occupancy Profile 2. Similar to the trends shown for Occupancy Profile 1, 64 Note- HVAC: Heating, Ventilation, and Air Conditioning; DHW: Domestic hot water; HRV: Heat recovery ventilation; PV: Photovoltaic panels; LED: Light emitting diode; RF: Roof insulation; WL: Wall insulation; FN: Insulated concrete form foundation Figure 4-9 Occupancy Profile 2 (b) Bubble chart with details of IC, PBT and GHG emission reduction associated with energy upgrades 65 Note- HVAC: Heating, Ventilation, and Air Conditioning; DHW: Domestic hot water; HRV: Heat recovery ventilation; PV: Photovoltaic panels; LED: Light emitting diode; RF: Roof insulation; WL: Wall insulation; FN: Insulated concrete form foundation Figure 4-10 Occupancy Profile 2: Marginal abatement cost curve more energy upgrades are needed for a greater reduction in GHG emissions. It is seen that single EPUs form part of the highest ranked EPUs for Occupancy Profile 2. These finding is in line with the results shown by abatement curve in Figure 4-10. For instance, WN upgrades does not form part for any top ten EPUs combinations. The main reason for this is the higher cost required to achieve per unit of GHG reduction. These findings imply that a larger expenditure is needed for achieving meaningful GHG reduction. Some studies have also shown that the older occupants belong to the low-income group. This implies that increased energy use accompanied by higher initial investment and longer PBT needs to be addressed by utilities and the government. Some utilities in Canada are already offering incentives for increasing the energy efficiency of homes occupied by senior citizens. However, more extensive and tailored incentives for this age group are still needed. 4.5 Discussion The above results demonstrate the suitability of different EPUs with respect to occupancy profile and investor’s priority. It is observed that low-cost energy upgrades have a higher potential for energy savings for Occupancy Profile 2. However, combinations of high-cost EPUs provide greater energy saving and GHG emission reduction potential for Occupancy Profile 1 as compared to Occupancy Profile 2. This finding validates simulated results found by Motuziene and Vilutiene 66 [271], who observed higher savings for houses with four occupants compared to working and retired couple profiles. This higher energy savings for Occupancy Profile 1 can be attributed to a higher number of occupants. De Meester et al. [330] work on different occupancy profiles also showed that as a house becomes more efficient, occupants' internal heat gains play an important role in decreasing heating energy demand. Higher savings are observed for Occupancy Profile 1 since space heating is still the predominant end-use energy (64%) in residential buildings of Canada [331]. 4.5.1 Investor Preference This study did not find a significant difference between the energy upgrade for the pro-economic preference for the two profiles. However, changes in both ranking order and the type and number of energy upgrades vary with the pro-environment preference. This finding has important implications for developing tailored energy audit and energy saving incentives programs. Currently, energy audit and energy saving incentives programs by government and utility providers are designed on the basis that decisions related to EPU selections are based on the most cost-effective measure [332]. Since a visible difference is present between the ranking of energy upgrades from pro-environment to pro-economic, there is a need to consider factors influencing the investors' decisions. Future research should collect the investor’s preferences through a questionnaire survey. This will help both the home dwellers assess the most feasible energy upgrades according to their criteria and the policymakers for designing effective energy policies. 4.5.2 Occupancy Profile The results from this case study are important, as they are representative of energy use based on actual occupancy profiles. The differences in the EPUs ranked for the two profiles show there is a need to incorporate the type of occupancy and the preference of owner-investors in selecting EPU. Occupancy Profile 2 represented retired couple (aged between 50 and 64) that accounted for about 21% of Canada’s national population [333]. It is predicted that this age group will keep increasing, usually accompanied by smaller household sizes (single or couples). Furthermore, the occupancy patterns of the retired couple are similar for demographic groups such as people working from home, jobless, or homebound due to disability [334]. Hence, it is imperative that future energy policies and incentives for energy efficient buildings are designed accordingly. The study results 67 can be applied to other countries having similar occupancy behaviors as Canada, such as the US and UK [335]. The methodology can be applied in other countries of the world through the update of local weather conditions, local terrain, types of home dwellers and socio-economic conditions. The findings of this study are restricted to two occupancy profiles that represent only 52.3% of the Canadian private households. According to Statistics Canada [307], private households are divided into 7 occupancy profiles:(1) Couples with children (Occupancy Profile 1 for this study) (26.5%), (2) Couples without children (Occupancy Profile 2 for this study) (25.8%), (3) Lone parents (8.9%) (4) Multi-generational households (2.9%), (5) One-person households (28.2%), (6) Households with two or more persons (4.1%) and (7) Other households. Therefore, caution is recommended when applying the results of this study to houses with different occupancy profiles. The type of occupancy profile will affect the amount of energy savings associated with EPUs as well as the priority of stakeholders related to investment in energy upgrades. For example, Poortinga et al. [336] showed that single and senior occupants have a low willingness to invest in energy savings. Similarly, high–income residents consume more energy than low-income earners but are also willing to pay more for energy investments [336,337]. Likewise, the occupancy patterns can change over time and will affect the selection of EPUs. For example, the COVID-19 pandemic has increased occupancy period at home [338]. As a result of this pandemic, homes have been transformed into mixed-use spaces where home-schooling, office work, recreational activities and social interaction have become norms. Similarly, the percentage of remote or home workers had been steadily increasing in Canada and other countries of the world, even in pre-pandemic conditions [339,340]. EPUs selection should consider these emerging dynamics. 4.5.3 Financial Incentives Financial incentives in the form rebates, subsidies, grants are offered to promote the residential building energy upgrades and form a major part of energy policies [18,25]. These incentives reduce financial risks for investors and help in meeting carbon mitigation goals. A number of financial incentives are also present in British Columbia and other regions of Canada through government and private organizations [341]. The largest financial incentives are present in the form of rebates offered by local utility providers. Currently, these rebate incentives are available for various 68 household appliances, water heater, heating equipment and insulation [342]. These incentives were not considered in the life cycle cost analysis, as incentives are subject to change over time as new energy policies and building energy codes are implemented. The inclusion of these and other government incentives (for a specific time period) will reduce the LCC and change the ranking of the EPUs options for the two occupancy profiles. 4.5.4 Other Factors Impacting EPUs Ranking In addition to the occupancy profile, other factors can also influence the selection of optimal energy upgrades. Financial incentives were not considered, as the study aimed to analyse the impact of OB alone. When FIs are considered, the LCC will be reduced and rankings of EPUs will change. Similarly, the carbon footprint of the energy mix in British Columbia is very low compared to the other regions of Canada [343]. Therefore, the GHG emission reduction potential for energy upgrades will be higher for regions with energy generation based on fossil fuels. Another factor affecting the ranking of energy upgrades will be the typology of the house. The energy consumption and the expenditure involved in the installation of the energy upgrades will vary with the type (detached, semi-detached and terraced) of the residence [329]. In the current study, single-family detached homes of medium size were considered for methodology demonstration. Bastos et al. [344] study indicate the energy demand and carbon emissions per covered area from large houses is less compared to smaller residences. Compared to large single-family detached homes, multi-unit residential buildings have higher operational energy requirement per unit area. Furthermore, the operation and maintenance cost of apartments are high [345]. A commonly observed split-incentive problem may also impact the selection of EPUs for multi-unit residential buildings due to uneven cost and benefits distribution between the building owners and renters [346]. Considering the impact of these factors it is suggested the influence of these factors is not ignored for the selection and ranking of EPUs. 4.5.5 Validation of TOPSIS method TOPSIS method was used to rank energy performance upgrades for the two occupancy profiles. TOPSIS method was selected due to its and ability to rank alternatives based on the shortest distance from the ideal. This method has been successfully used on energy performance upgrade selection in literature [300–303]. However, rank reversal is a common problem associated with 69 TOPSIS [347] and the performance of any MCDM method varies with application [348]. Therefore, it is essential to verify ranking results obtained for the tested EPUs and occupancy. This research did not validate the results obtained from the TOPSIS method. Future researchers can validate these results by comparing these results with ranking obtained from at least three other MCDM techniques [349,350]. Compared to TOPSIS, a different MCDM method (AHP, PROMETHEE, ELECTRE) may provide a different ranking. Comparing ranking results from three MCDM methods will verify the findings obtained from TOPSIS. 4.6 Summary This chapter provided a two-step methodology that is able to assess the effect of occupancy profile on the selection of the most suitable EPUs in new residential buildings. In particular, this chapter (1) developed calibrated models using one-year monitored data for predicting energy use associated with individual occupancy profile; (2) applied the calibrated model to holistically evaluate energy upgrade options and lastly (3) enabled prioritization of different upgrades through the application of TOPSIS with a variable weighting scale to enable flexibility in defining user preferences. The proposed methodology was demonstrated through comparison in EPUs required for the two most common occupancy profiles in Canada: a family of two working adults and dependent teenagers and a stay at home retired couple. The results showed that a single-family detached house family of four is capable of reducing GHG emissions at a higher rate as compared to the application of the same upgrades in the house of a retired couple. The preferences based weighting showed that when pro-economic is the sole criterion, the choices of applicable EPUs are limited and similar for different occupancy profiles, but when decision-makers move towards a pro-environmental behavior their choice will change both the ranking and EPUs options. The new understandings from this study can be used for community-level residential building planning. This unique methodology can assist developers, planners, potential owners and practitioners in developing single-family detached houses for retired communities, typical urban communities in Canada and other developed countries. 70 Chapter 5 Effectiveness of Financial Incentive Policy for Meeting Building Energy Code Requirements Parts of this chapter will be submitted in the Elsevier journal Energy, as an article titled “Financial Incentives for new residential buildings in Canada: Investigating effectiveness using sensitivity analysis” [263]. 5.1 Background Greenhouse gas (GHG) emissions related to energy are a significant cause of the climate emergency. The building sector accounts for up to 39% of the energy-related GHG emissions. The residential building sector, in particular, is responsible for up to 25% of the total energy consumption and 17% of GHG emission [351,352]. The construction of energy efficient residential buildings can play a crucial role in addressing increasing energy demands, depleting resources and causing negative environmental impacts [353,354]. Moreover, energy efficient residential buildings are associated with economic savings, improved thermal comfort and better indoor air quality leading to improved health and well-being of occupants [108,355–358]. Building energy codes (BEC), standards and regulations are common policy instruments used to increase the ratio of the energy efficient residential building stock and obtain the above-mentioned benefits. In order to combat environmental issues, there has been a pragmatic shift towards the implementation of more stringent sets of BEC and standards as part of regional, national and international energy policies over the recent years. [359]. BEC as regularity policy instruments mandate energy performance targets to be obtained by a building and are usually enforced by regional and provincial governments, such as the British Columbia Energy Step Code (BC-ESC) and California Title 24 [306,360,361]. Building standards, on the other hand, provide guidelines for energy efficient buildings and are not mandatory. These are typically provided by a national organization, such as the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) standards [361]. In North America, net-zero energy building (NZEB) codes are the most popular due to the potential benefits at individual building and community levels [362–364]. Some BEC aim NZEB such as BC-ESC, California Title 24 and NYStretch Energy Code-2020 [306,360,365]. Despite the benefits associated with advanced BEC, their implementation is often delayed due to obstacles that either delay or hinder 71 widespread implementation. Among these, the most common and frequently cited obstacle is the large cost premium needed to construct such buildings. For instance, Yip and Richman [366] performed economic analysis for a new residential home in Ontario, Canada. It was found that a premium of up to 51,564 US$ is required to reduce heating energy 80% below the existing BEC. Similarly, Barkokebas et al. [288] performed a life cycle cost analysis over a 30-year period for a new residential home in Alberta, Canada. It was revealed that the operational energy savings did not cover the initial investment and required an additional amount of at least 66,640 US$. Thus, high economic risks are associated with the implementation of stringent BEC. Financial Incentives (FIs) are policy instruments most often provided by the government and utilities to make the implementation of BEC economically viable [216]. FIs are able to change the consumer’s behavior and investment convenience either by rewarding (e.g. grants, subsidy, tax-credits) or through penalizing (e.g. carbon tax)[21]. Similarly, the role of FIs is well recognized in widespread applications of renewable energy sources and technologies [23,24]. The synergy between BEC and FIs is well established in the literature and these two policy instruments have been proven to positively reinforce each other in achieving desired targets [249]. Despite the importance of FIs in achieving energy efficiency targets, there is limited research in this area. The majority of studies have dealt with BEC compliance and FIs impacts separately. Recent studies on FIs evaluation are mostly restricted to single energy upgrades, such as solar photovoltaic system and heat pumps [52,54,367]. Furthermore, most studies have predominantly focused either on commercial buildings or on one residential archetype (i.e. single-family detached home) [54,368–370]. The impact of FIs is subject to uncertainties related to building design, occupancy and the local economic conditions, although a limited number of studies have incorporated these factors for assessing FIs [21,40]. In order to provide a comprehensive decision direction for improving FIs designed for emerging BEC, the environmental and economic impacts should be quantifiable and easily understandable. Hence, there is an urgent need for a methodology that can provide a comprehensive evaluation of FIs and accurately provide the economic and environmental benefits associated with BEC adoption. “Effectiveness” is one of the most fundamental criteria that can quantitatively measure the outcomes and benefits of FIs [18,21]. The effectiveness of FIs has been defined in a number of domains and perspectives, ranging from the effectiveness of FIs from government perspective (e.g. 72 degree of energy efficient technologies penetration) to end users’ perspectives (e.g. energy savings possible with the use of FIs) [52,75,78,369–373]. Similarly, FIs effectiveness will vary according to the intended goal of the study and is most commonly expressed in terms of cost and/or environmental effectiveness [21]. Environmental performance indices have been successfully used in literature to aggregate economic and environmental parameters/indicators and evaluate the policy [199,374–376]. Such indices are able to represent complex information in an easily comprehensible form [377]. Hence, aggregation of economic and environmental indicators as a single index can be used to comprehensively assess the impacts of FIs related to BEC. This chapter endeavors to fulfill the above-mentioned gaps through the introduction of a FIs evaluation methodology from end-users’ perspectives. Here, end users are defined as the developers and building owners who will make decisions regarding the investment in energy upgrades of the residential buildings. Specifically, this methodology will help understand the relationship between FIs and related energy, cost and GHG reduction among residential archetypes. A large number of FIs are provided in the form of rebates. Therefore, this research considers a case study of rebates offered for meeting new BEC targets. From this part onwards, the terms financial incentives, FIs and rebates will be used interchangeably. The proposed methodology will measure effectiveness from the end-users’ perspective. The specific objectives of this research are: (1) Develop a methodology for effectiveness evaluation of FIs related to BEC. (2) Assess the FIs effectiveness sensitivity under key design and economic parameters of residential archetypes. The original contributions of the study are as follows: (1) A multi-stage methodology that utilizes a rule-based screening method to identify the most appropriate energy efficiency measures and applicable FIs for meeting BEC requirements. (2) Introduces a “FIs effectiveness index” that can be a useful tool for decision-makers to visualize cost and environmental implications of FIs. (3) Uses rebates offered on an advanced BEC called stretch code that is being adopted in different regions of North America. 73 (4) Demonstrates the proposed methodology by application on FIs related to BC-ESC considering seven residential archetypes (that represent 90% of archetypes in British Columbia’s residential sector) and three climatic locations. 5.2 Methodology The specific aim of this research is to assess the effectiveness of FIs offered for residential buildings to meet performance-based targets. To meet the research objectives, a multi-stage research methodology is introduced (Figure 5-1). The stages include (1) data collection, (2) determination of applicable rebate and (3) evaluation of FIs effectiveness index and its sensitivity analysis. The methodology is illustrated through a case study of FIs in the form of rebates available for a new BEC in British Columbia, Canada, called BC-ESC [177]. The BC-ESC was chosen because it is considered to be the blueprint for future upgrades in the national building energy code of Canada [378]. 5.2.1 Stage I: Data Collection The first stage involved the data collection related to building archetypes, BEC, FIs, occupancy profiles, energy upgrades, cost data and energy carbon emission factors. Design and economic factors that may impact effectiveness were also identified. The primary sources utilized for data collection included peer-reviewed journal articles, local and international databases, local building energy regulations and policies and technical reports. Building Archetype: The terms “building typology” or “archetype” refers to the categorization of buildings according to their function [288]. Archetypes aim to distribute building stock into homogenous groups in order to reduce computation time and determine the optimal solution. Criteria used for classification of archetypes involved (1) building geometry; (2) energy systems; (3) structural type; and (4) location (Figure 5-2). Energy performance level: BEC with fixed targets (such as stretch codes) gaining popularity since they provide a higher degree of certainty and a time to revise BEC to attain energy policy targets. These codes also help bring innovation and provide time for a smooth transition for the industry [379]. Many regions in North America have started to adopt or plan to adopt BEC with fixed targets; therefore, the energy performance levels set by these codes were considered for this research. Table 5-1 provides metrics used in the methodology for FIs evaluation. 74 Note- TEDI: Thermal Energy Demand Intensity; MEUI: Mechanical Energy Use intensity; SFD: Single family detached home Figure 5-1 Research methodology 75 Note- HVAC: Heating, Ventilation, and Air Conditioning Figure 5-2 Criteria considered for defining residential building archetypes (Source: [288,380,381]) Table 5-1 Common metrics used of energy performance levels Metrics Metric (Abbreviation) Units Formula Ref. Energy Thermal Energy Demand Intensity (TEDI) kWh/m2/yr TEDI measures the amount of energy required to have comfortable indoor temperatures 𝑇𝐸𝐷𝐼=𝑆𝑝𝑎𝑐𝑒 𝑎𝑛𝑑 𝑉𝑒𝑛𝑡𝑖𝑙𝑎𝑡𝑖𝑜𝑛 𝐻𝑒𝑎𝑡𝑖𝑛𝑔 𝑂𝑢𝑡𝑝𝑢𝑡𝑀𝑜𝑑𝑒𝑙𝑙𝑒𝑑 𝐹𝑙𝑜𝑜𝑟 𝐴𝑟𝑒𝑎 [382] Mechanical Energy Use intensity (MEUI) kWh/m2/yr MEUI measures the total energy required for heating, cooling, ventilation and water heating systems. Lighting, plug loads and consumer appliances are not part of typically MEUI indicator 𝑀𝐸𝑈𝐼=𝑇𝑜𝑡𝑎𝑙 𝐴𝑛𝑛𝑢𝑎𝑙 𝐸𝑛𝑒𝑟𝑔𝑦 𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 − 𝐵𝑎𝑠𝑒𝑙𝑜𝑎𝑑𝑠𝑀𝑜𝑑𝑒𝑙𝑙𝑒𝑑 𝐹𝑙𝑜𝑜𝑟 𝐴𝑟𝑒𝑎 [382] Environment Greenhouse Gas Intensity (GHGI) kg.CO2/m2 Where, GHGI is greenhouse gas intensity and dependent on the energy used on-site, using emission factors of different energy sources used 𝐺𝐻𝐺𝐼=𝑆𝑖𝑡𝑒 𝐸𝑛𝑒𝑟𝑔𝑦 𝑈𝑠𝑒 𝑥 𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛 𝐹𝑎𝑐𝑡𝑜𝑟𝑀𝑜𝑑𝑒𝑙𝑙𝑒𝑑 𝐹𝑙𝑜𝑜𝑟 𝐴𝑟𝑒𝑎 [383] 76 Energy performance upgrades: Energy performance upgrades (EPUs) are alternative materials, equipment or appliances that improve building energy performance to achieve BEC. EPUs for a building are typically divided into three categories: passive, active and renewable EPUs [263,384,385]. Locally available technologies were used in the methodology to predict the impact of rebates. Financial Incentives and other cost data: Incentives are provided for energy investment and are generally in the form of rebates for achieving BEC requirements. The amount offered is independent of the energy saving actually achieved, provided the minimum requisites are fulfilled, so that the economic efficiency of the intervention and, most importantly, the action on the entire building stock is left to the owners’ decision [28]. Investment-related cost data were collected through quotations from local suppliers, online market rates of materials and/or from established and verified databases such as RSMeans [386]. Local utility rates were used for evaluating operational energy costs, while the replacement and maintenance rates were obtained from expert opinion or values used in literature. Similarly, discount and utility escalation rates were adopted based on local historical data. Uncertainty factors: A number of uncertainties related to engineering design and economic parameters can affect the ultimate decisions. Building heating and cooling loads are especially impacted by the local climatic conditions [387]. Similarly, design factors such as window-to-wall ratios (WWR), building aspect–ratio, orientation, distance from neighboring buildings or trees and number of floors in a building are considered as uncertain parameters in existing literature [388–390]. These design parameters will ultimately impact the energy costs of a house. Uncertain economic parameters most commonly considered are discount rates, fuel escalation rates, mortgage and interest rates and digression cost of technology [47,391,392]. These factors were obtained from the literature and technical reports. 5.2.2 Stage II: Identification of Feasible Energy Performance Upgrades A stepwise rule-based screening methodology was used to determine the feasible energy upgrades required to achieve BEC. The method consisted of energy model development, calculation of energy performance metrics, determination of applicable FIs and evaluation of cost and GHG emission. The screening was performed from the end users’ perspective. These steps are explained 77 as follows. Energy modelling: Energy performance can be evaluated either through a forward or inverse approach [393]. The forward approach, also called the classical approach, is most common and utilizes information on building characteristics to construct energy models and assess the energy performance level achieved. Most common energy analysis software, such as EnergyPlus, DOE-2 and TRNSYS, utilize the forward approach. In contrast, the inverse approach, also known as the data-driven approach, is used when actual energy performance, weather, temperature and/or humidity data are available. The approach is used to construct a new or calibrate an existing model [394,395]. The proposed methodology utilized the forward approach to construct energy models for new residential buildings. Building Energy Optimization Tool (BEopt) version 2.8 was used to determine the energy performance of shortlisted archetypes. BEopt is a graphical user interphase (GUI) focusing on residential buildings in North America and uses EnergyPlus as a simulation engine [396,397]. BEopt provides good modeling capability of house plans though its 3D modelling capacity is limited [398]. The occupancy profiles are based on the number of bedrooms assigned to the model. The energy models constructed in BEopt were used to determine annual energy consumption by keeping intervals of one minute. A parametric analysis study was performed to evaluate the impact of individual and combined EPUs. Heating and cooling loads determined from BEopt were used to estimate the energy performance metrics. The metrics values were compared with the BEC energy performance metrics. EPUs with values equal to or above desired targets were shortlisted while the remaining were discarded from further analysis. The short listed EPUs were used evaluating GHG emission reduction compared with base case values. Selection of rebates: The quantity of rebates applicable varies with region and local bylaws. The maximum TEDI and MEUI levels achieved by EPUs was used to determine corresponding rebates. These values can be obtained from utility providers, local municipality, provincial and federal databases [21]. These rebates can be in the form of lump-sum amounts or can be grouped with additional incentives available for special EPUs (such as ground source heat pump or installment of solar photovoltaic systems) and additional rebates from the local municipality. The rebates 78 associated with BEC level were used in cost analysis. It should be noted that for attached homes, the rebates were calculated with respect to each unit; therefore, the rebate for a single unit was multiplied with a number of units in an attached home to determine total rebates. Cost Modelling: Cost modeling was conducted to further shortlist and refine the applicable EPUs. Cost modelling results are often determined as internal rate of return, benefit to cost ratio, life cycle cost assessment (LCC), profitability index and simple or discounted payback period [96,399]. Among these economic indicators, LCC determines a product or process's costs for an entire life span [285]. Usually, LCC for buildings includes costs from the construction to final demolition and disposal stages [286,287]. Recent studies on energy evaluation of new residential buildings in Canada have used relative LCC and a timeline of 30-year [263,288,289]. Relative LCC is based on the model provided by the US Federal Energy Management Program [290]. Compared to conventional LCC analysis, this model does not require extensive and time-intensive data collection. Relative LCC only requires costs associated with energy-related components before and after EPUs [291,292]. For current work, these costs included investments and operation- related costs (i.e. operational energy bills, maintenance and replacement of energy components), while the end of life cycle costs considered negligible. Hence, this research also adopted relative LCC as an economic metric, evaluated through the Net Present Value method [290] and a 30 year operation period. Table 5-2 shows the cost metrics and their description used in this study. The last two metrics in this table: future energy costs and carbon tax rates, were utilized for conducting sensitivity analysis. Table 5-2 Cost metrics No. Metric Units Model Ref. 1. Difference in total Life Cycle Costs (dLCCt) CA ($) 𝑑𝐿𝐶𝐶𝑡 = 𝑑𝐶𝑐 + 𝑑𝐶𝑈 + 𝑑𝐶𝐸𝑂𝐿 [263,384] 2. Present value of future energy costs (P) under different fuel price escalation and discount rate CA ($) 𝑃 = 𝐴0 ∗ (1 + 𝑒𝑑 − 𝑒) [1 − (1 + 𝑒1 + 𝑑)𝑁] [400] 3. Carbon tax on natural gas (CA$/tonnes CO2e) 𝐶𝑎𝑟𝑏𝑜𝑛 𝑡𝑎𝑥 = 𝑡𝑎𝑥 𝑟𝑎𝑡𝑒 𝑥 𝐸𝐹𝑓 [323] Selection of EPU: The EPUs were selected based on GHG reduction potential and associated LCC. GHG emission reduction potential was assessed from the GHGI metric defined in Table 5-1. Most 79 studies on code compliance follow the least cost approach as seen in recent studies in Alberta and Ontario [288,366]. However, EPUs with the least cost are not always environmentally efficient. In fact, EPUs selected by this method serve as low-hanging fruits that can lead to a lockdown effect that would restrict further energy efficiency improvements. Therefore, to obtain maximum benefit, both for the end-users and society, EPUs with the highest GHG reduction selected. The costs were determined for the EPUs selected from the second screening. 5.2.3 Stage III(A): Effectiveness Index Cost (Ceff) and environmental (Eneff) effectiveness of FIs were evaluated from the end users’ perspective. End users for this study refer to the developers or building owners who are major decision-makers in BEC implementation. An effectiveness index matrix was constructed that visually represented the impacts of FIs (Figure 5-3). Ceff metrics were adopted from Boza-Kiss [372] work on societal cost-effectiveness and helped identify the amount spent per energy savings. In this matrix, a negative value of Ceff shows that applied incentives are highly-cost effective and able to provide monetary savings for the end-users and vice versa. Eneff was defined as the GHG reduction potential and was expressed as the percentage change from the base case. Positive Eneff indicates a reduction in GHG emissions, while its negative value showed that GHG emissions had increased. Figure 5-3 Effectiveness Index Matrix 80 Table 5-3 Effectiveness metrics Effectiveness Description Unit Ref. Cost 𝐶𝑜𝑠𝑡 𝑜𝑓 𝑖𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡 − 𝐸𝑛𝑒𝑟𝑔𝑦 𝑐𝑜𝑠𝑡 𝑠𝑎𝑣𝑖𝑛𝑔𝑠𝐸𝑛𝑒𝑟𝑔𝑦 𝑠𝑎𝑣𝑖𝑛𝑔𝑠 CA$/ kWh [372] Environment 𝐺𝐻𝐺 𝑒𝑚𝑖𝑠𝑠𝑜𝑛𝑠 𝑜𝑓 𝑏𝑎𝑠𝑒 𝑐𝑎𝑠𝑒−𝐺𝐻𝐺 𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛 𝑤𝑖𝑡ℎ 𝑐𝑜𝑑𝑒 𝑐𝑜𝑚𝑝𝑙𝑎𝑖𝑛𝑐𝑒𝐺𝐻𝐺 𝑒𝑚𝑖𝑠𝑠𝑜𝑛𝑠 𝑜𝑓 𝑏𝑎𝑠𝑒 𝑐𝑎𝑠𝑒 % [75,197,217,228,238] 5.2.4 Stage III (B): Sensitivity Analysis Sensitivity analysis can help improve the efficiency of the design process and extract useful information for improvement and application for a larger audience [401,402]. To assess the impact of different uncertain parameters on Ceff and Eneff of available rebates, a local sensitivity analysis was used. Local sensitivity analysis uses a one-parameter-at-a-time (OAT) approach [387]. Here, one parameter is changed at a time keeping other factors constant [401,403]. The parameter values selected for sensitivity analysis are based on average values under normal conditions and tested against extreme possibilities. For instance, cost parameters such as interest, discount rates and fuel escalation prices should be based on most recent reports and databases [384]. In this methodology, uncertain parameters related to design and economic factors and based on previous studies were considered (Table 5-4). Design factors affected both Ceff and Eneff, since they impact energy usage and associated costs, while economic factors only affected the Ceff. 5.3 Methodology Demonstration Canada's residential building sector has high energy use intensity values due to high space heating further augmented in the past few decades with rising space cooling demands [404]. To meet mitigation targets, the Pan-Canadian Framework on Clean Growth and Climate Change mandated provincial governments to develop advanced and innovative BEC codes for buildings. Step-codes (also called stretch codes) help ensure a smooth transition to net-zero energy pathways [405]. In the same vein, BC’s provincial government introduced the BC energy step code (BC-ESC) in April 2017 [69]. Currently, BC-ESC is a voluntary standard for new residential and commercial buildings that aims to move the construction to a net-zero status beyond the base case BEC of 2012. It establishes measurable TEDI and MEUI targets according to the building archetype and 81 Table 5-4 Details of the sensitivity analysis Factors Parameter(s) Investigated values Justification Ref. Design Window-to-wall ratios (WWR) 4 ratios 10%; 12%; 15%; 18% WWR are the area of openings relative to the building envelope. Windows are responsible for large portion of heat gains and losses; therefore, they directly impact heat balance and resultant energy usage. [406,407] Orientation 8 Orientations N; NE; E; SE; S: SW; W; NW Orientation is important design parameter and forms part of a number of sensitivity studies. It is important with respect to solar radiation; lighting and ventilation of building. 8 orientations representing a 45 degree interval rotation of each archetype [406] Neighboring buildings 4 neighbor distances None; Left/right 10 ft; Left/right 15 ft; Left/right 20 ft The neighboring buildings can act as shading to adjacent buildings. The distance to adjacent building varies with density of the neighborhood. The impacts may not be significant for individual buildings but would become significant at building stock scale. [406] Economic Fuel Price (%) 3 price escalation: Low; Medium and High Electricity (2%; 6%;10%) Natural gas (2%; 5%; 8%) The three fuel escalation rates low; medium and high are considered from Canadian context. The escalation rates are based on Energy Escalation Rate Calculator and data by National Energy Board of Canada. [384,405,408] Carbon Tax Rate (CA$/tonne CO2e) 4 carbon tax -Current Carbon tax ratea (40) -Carbon tax rate 2021(50) -Double Carbon tax rate 2021 (100) -Equal to Sweden tax rate (183) A carbon tax is a form of disincentives that push the market towards energy efficiency and conservation. Commodities with high GHG emissions are assigned higher tax value and additional cash flow under abated carbon policy is achieved. Carbon taxes have been applied to fossil fuel commodities in British Columbia since 2008. These taxes are increasing every year and are projected to be 50 CA$ in 2021. Sweden has the highest carbon tax in the world and its consider an extreme case that can be adopted to curtain carbon emissions and consequent impact on cost-effectiveness of existing rebates. [216,402,409,410] (Note: due to the COVID-19 situation, the increase in carbon tax for 2020 had been postponed (Source: [411]). N: North; E; East; S: South; W:West 82 location. These energy metrics are defined in Table 5-1. This research is limited to the application of this code on new residential buildings, since their rate of construction, is high in BC; for instance, construction of new residential buildings exceeded 81,000 in the year 2018 alone [412]. Hence, making new residential buildings complaint to BC-ESC will curtail the energy demands and the associated GHG emissions. Among the various incentives present in BC, rebates from local utility providers are the most extensive in number [21]. In addition to the rebates offered for BC-ESC specifically [413] (Figure 5-4), other incentives and rebates are available that can be coupled with these rebates. The additional financial sources include rebates for special EPUs (such as air-source heat pump and energy star appliances), individual municipalities (to accelerate the rate of adoption of BC-ESC) and national energy efficiency schemes. Since the objective of this work was to assess the effectiveness of BEC related rebates, these additional sources were beyond this study’s scope. Figure 5-4 Rebates at different performance levels (Source:[177,413]) 5.3.1 Archetypes and Location This study considered seven residential buildings based on residential building stock in British Columbia [382,414]. These buildings comprised low-rise residential buildings up to 4-storey high and represented approximately 90% of the residential building stock. Table 5-5 shows the architectural plans of the houses. The province of BC is divided into 6 climate zones (CZ). Each climate zone pertains to the average heating degree-days (HDD) below 180C and accounts for the impact of local weather conditions such as temperature, humidity, wind speeds, rainfall and 83 Table 5-5 Architectural plans of the houses No. Archetype Planimetric view Archetype Planimetric view 1. Small single-family detached home: Single storey on a heated crawlspace, 69.7 m2(750ft2) 5. Duplex: Semi-detached houses, at least one side is common with another dwelling, preventing heat loss through that side 81.48 m2 (877ft2)/ unit, 2 storeys 2. Medium single-family detached home: 2 storeys, 107.8 m2 (1,160ft2) 6. Triplex: Three attached units, 81.48 m2 (877ft2)/ unit, 2 storeys 3. Large single-family detached home: 2 storeys with basement 556.7 m2 (5,992ft2) 7. Quadplex: Four attached units, 81.48 m2 (877ft2)/ unit, 2 storeys 4. Six-Unit: Six-attached units, 81.48 m2 (877ft2)/ unit, 2 storeys 84 snowfall on the energy performance of buildings [415]. The lowest CZ-4 has an HDD less than 3,000 while the highest CZ-8 has an HDD greater than 7,000 [416].Three cities, i.e. Vancouver (CZ-4 <3,000 HDD); Kamloops (CZ-5 3,000-3,999 HDD) and Prince George (CZ-6 4,000-4,999 HDD), were selected, since they are hubs of major construction activities in BC [412]. 5.3.2 Energy Performance Upgrades New residential buildings built to BC building code 2012 [306] are considered as the base case (Table 5-6). Traditional buildings can be made more energy efficient by increasing their energy efficiency and/or use of renewable energy generation systems [417]. Table 5-7 provides EPUs considered for meeting BC-ESC targets. Table 5-6 Detail of base case buildings Energy performance upgrade Variable Details Costa US ($) Ref. Wall Insulation (WL) R30 -Blanket Insulation kraft faced fiberglass, 9" thick, R30, 11" wide 1.43 /ft2 [386] Roof Insulation (RF) R40 -Blown Insulation Ceiling with 16" thick 2.57 /ft2 [386] Windows (WN) Double -DG+ 1 HM 88 Low E 20 hard Wood Krypton 9 mm 43 /ft2 [289] Space Heating (SH) 92.5 AFUE -Payne (PG92SCS) 60000 btu AFUE 92.1% Natural Gas & 961.84 1 unit [289] Heat recovery ventilator (HRV) No HRV Domestic Water Heater (DHW) Gas tank -Rheem (630083) Natural gas hot water EF: 0.59 547 1 unit [289] Table 5-7 Details of energy performance upgrades Variable Details Costa US ($) Ref. WL R40 R60 -Blanket Insulation kraft faced fiberglass, 12" thick, R38, 11" wide -Blanket Insulation kraft faced fiberglass, 18" thick, R38, 11" wide 1.8 4.03 /ft2 [386] RF R70 R100 -Blown Insulation Ceiling with 20" thick -Blown insulation Ceiling with 40" thick 5.14 6.56 /ft2 [386] WN Triple -TG+ 2 coating Low E 20 hard Vinyl Krypton 9 mm 47.25 /ft2 [289] SH 98 AFUE -Payne (PA14NC) 34000btu 14SEER Split A/C 1,570 1 unit [289] HRV 70% HRV -Lifebreath METRO Series 849 [418] DHW Electric tankless Gas tankless Electric DWHP -Rheem 36kW Electric tankless -Instantaneous, tankless, gas water heaters, Natural gas/propane, 6.4 GPM -GeoSpring™ hybrid electric water heater, 303 L, EF 3.14, Electricity-based 964 1,250 1,322 1 unit [289] [289] [263] 85 Air-tightness was kept constant for the base case and upgraded residential archetypes. This constant value was considered mainly due to: (a) lack of validated cost data related to different levels of air-tightness and (b) to remove uncertainty related to the quality of construction that affects air-tightness value. Previous researchers have also avoided air-tightness as a variable to avoid a potential source of inaccuracy in the building model [384,419]. Airtightness value of 0.6 Air Changes per Hour at 50 Pascal was kept so that higher steps of BC-ESC are achievable. 5.3.3 Cost Data Cost data for the EPUs was collected from existing literature, RSMeans (2019) database and local suppliers [386]. The cost of EPUs varies for the three cities. Hence, in order to represent cost on a uniform scale, the cost of EPUs were shown in US national dollar values (conversion rate year: 2020). Utility energy rates were uniform for the three cities as they were supplied from the same utility. Electricity and natural gas are the dominant energy use sources in BC. The rate structure of these two sources is provided in Table 5-8. Among different taxes and charges, the carbon tax on natural gas is especially high and has been increasing per year since 2008 based on BC carbon policy [137]. In fact, the rate of the carbon tax in BC surpassed the natural gas energy rate in 2013 [420]. In addition to these, a 5% discount rate was adopted based on previous studies on residential buildings in Canada [263]. Table 5-8 Details of Electricity and Natural Gas Utility Rates (Source: FortisBC [421]) Energy source Charge Parameter Value Electricity Delivery charges Basic daily charge 30 days at 0.4085 CA$/day (0.31 US$/day) Commodity charges Electricity used Block 1: ≤1,600 kWh 10.799 CA¢/ kWh (8.26 US¢/kWh) Electricity used Block 2: >1,600 kWh 14.320 CA CA¢/ kWh (10.95 US¢/kWh) Taxes Goods and Services Tax 5% of a amounts Natural Gas Delivery charges Basic daily charge 30 days at 0.4261 CA$/day (0.29 US$/day) Delivery 4.596 CA$/GJ (3.51 US$/GJ) Commodity charges Storage and transport 1.019 CA$/GJ (0.78 US$/GJ) Cost of gas 2.279 CA$/GJ (1.74 US$/GJ) Other charges and taxes Municipal operating fee 3.09% of a amounts Carbon tax 1.9864 CA$/GJ (1.52 US$/GJ) Clean energy levy 0.40% of a amounts Goods and Services Tax 5% of b amounts 86 5.4 Results and Discussion The present research provides a comprehensive methodology to evaluate incentives' performance for achieving compliance with an advanced residential building energy code. The methodology is demonstrated through a case study of incentives (rebates for the specific case study) applicable to seven residential archetypes. It should be noted that the results determined here are very specific to the archetypes, location and building energy code considered; since the results will vary with local environmental and economic conditions. Here, an attempt is made to examine the variation of the effectiveness of these incentives and generate trends that can help policymakers generate tailored incentives. 5.4.1 Energy Performance Upgrades Energy performance upgrades are known to decrease the energy and GHG emission of buildings. In this case study, the seven archetypes are first modeled in BEopt based on BC Building Code 2012 requirements and represent base cases for the study. These basic energy models were upgraded with EPUs provided in Table 5-7 to run an extensive parametric analysis. The parametric analysis aimed to shortlist the EPUs combination that could help achieve the highest code compliance level possible. In this regard, a total of 432 energy simulations are run for each house at each location. This analysis considered the impacts of individual EPUs as well as their combinations in reducing energy performance. The TEDI and MEUI values for each archetype are then calculated using energy metrics. A total of 432 energy simulations are run for each detached house, while 288 different simulations are run for each attached house and separately for each location. The TEDI and MEUI values obtained for each archetype are shown in Figure 5-5. The same EPUs combination performed better for Vancouver's milder climate compared to Prince George's much colder climate. As shown in Figure 5-5 (c), the resulting TEDI and MEUI points overlapped for large homes, especially for Vancouver and Kamloops. For the majority of the archetypes, two clear clusters are observed for EPUs combinations provided. The TEDI and MEUI values for various EPUs were compared with code requirements (Appendix F, Table F1). The color-coded shaded area represents the maximum BC-ESC level satisfied by EPUs combinations for the tested cities. All the points falling in the shaded region represent the possible EPUs used for code compliance of each archetype. It is observed that the same combinations of EPUs that perform well for a particular archetype located in a lower CZ also perform well for a more extreme 87 Note-Each graph represent single archetype tested at three locations defined by different colors. The points represent TEDI and MEUI performance of EPUs combinations. The shaded area represent the maximum code level achieved by house under different climate zones. SFD: Single family detached Figure 5-5 Code compliance levels achieved by residential archetypes under EPUs combinations 88 climate. However, the resulting energy savings vary and will result in different economic and environmental impacts. Interestingly, the same EPUs combination also resulted in the archetypes complying to different levels despite more stringent requirements for warmer climates than colder ones. Hence, in most cases, the best performing EPUs resulted in Prince George's homes falling at much lower levels than Vancouver and Kamloops. The only outlier are EPUs for large SFD, which can achieve Step 4 compliance in all three tested locations. Hence all archetypes in Vancouver are eligible for higher rebates and large SFD will be eligible for the highest rebates compared to other archetypes at other locations. In contrast to Vancouver, small and medium SFD in Prince George do not comply with any eligible rebate level. The maximum steps achieved along with rebates for 7 archetypes at three locations are provided in Table 5-9. It is seen that all archetypes in Vancouver can achieve Step 4 while the compliance level decreases in colder climates. Most homes in Kamloops can achieve Step 3, while medium SFD performs worst and only achieves Step 2. Likewise, homes in Prince George also comply to a lower level, with most homes achieving Step 1 or Step 2. This disparity in steps achieved is due to higher savings achievable in warmer climates and differences in the code specified TEDI and MEUI intensity targets for different archetypes and locations. Table 5-9 Applicable rebates Location Archetype Small SFD Medium SFD Large SFD Duplex Triplex Quadplex Six-Unit Vancouver (CZ 4) Compliance level Step 4 Step 4 Step 4 Step 4 Step 4 Step 4 Step 4 Rebate (CA$) 6,000 6,000 6,000 12,000 18,000 24,000 36,000 Kamloops (CZ 5) Compliance level Step 3 Step 2 Step 4 Step 3 Step 3 Step 3 Step 3 Rebate (CA$) 4,000 3,000 6,000 8,000 12,000 16,000 24,000 Prince George (CZ 6) Compliance level Step 1 Step 1 Step 4 Step 2 Step 2 Step 3 Step 2 Rebate (CA$) 0 0 6,000 6,000 9,000 16,000 18,000 Note- SFD: Single family detached; CZ: Climate zone 89 5.4.2 Financial Incentives Effectiveness Index Figure 5-6 provides the effectiveness index matrix generated for the seven archetypes. The results of code compliance levels were used to determine energy savings and GHG reductions achieved and the best performing EPUs were selected to determine the applicable rebate amount. The operational energy savings were used to determine savings in operational bills and added in the life cycle costing model to determine the extra expenditure required for EPU. It is observed that archetypes located in Vancouver provide higher environmental effectiveness that ranges from about 50% to 90%. However, for most cases, the cost-effectiveness for the applied incentives was lower for homes located in the warmer climate of Vancouver despite showing higher energy savings. This result is due to higher construction and investment costs in Vancouver compared to the other two cities. Likewise, under the applied incentives, the archetypes n Kamloops had higher environmental effectiveness but required more expenditure to achieve energy savings than Prince George. The cost-effectiveness for Kamloops showed the lowest value for a six-unit attached home and required 4 CA$ to save a kWh energy. The cost-effectiveness of Prince George ranged from about 1 CA$ to 3.5 CA$ to save a kWh of energy. It is also observed that compared to SFD, the cost and environmental effectiveness showed similar trends in attached homes for each location. The attached homes also show higher GHG reduction potential than SFD and required more expenditure resulting in their lower cost-effectiveness. These results have important implications for the energy policies and highlight the need to consider local building archetypes and trends in construction. For instance, if the main aim of building energy policy is to reduce the energy expenditure of the building stock, then colder climates would achieve more significant energy savings at lower costs compared with similar archetypes in milder climates. However, if the policy aims to reduce GHG emissions, emphasis should be on attached homes in all climates. The local neighborhood design will also be essential to guide incentive design and deployment. Currently, the residential sector in Canada is dominated by single-family detached homes [422]. The distribution of these archetypes varies from one location to another. For the three cities number of SFD homes have a higher percentage in Kamloops and Prince George, while for Vancouver, they only occupy up to 20% of the archetypes. This distribution may require more incentives to be directed at SFD homes located in Kamloops and Prince George. In comparison, more incentives 90 may be assigned to Vancouver attached homes to yield higher benefits. Therefore, some archetypes may even be eliminated from the incentive program to yield maximum cost and environmental benefits depending upon the local building stock profile. Note- SFD: Single family detached Figure 5-6 Effectiveness Index for tested residential archetypes 5.4.3 Sensitivity Analysis This section represents the results of sensitivity analysis to the design and economic factors. These factors are explicitly considered for the Canadian context and the results should be applied with necessary precautions and modifications. Design factors: Figure 5-7 and Figure 5-8 show the variation in cost and environmental effectiveness, respectively, for the three design factors: window-to-wall ratios (WWR), building orientation and distance from neighboring buildings. In these figures, the horizontal axis of the 91 Note- SFD: Single family detached: WWR: Window to wall ratio; CZ: climatic zone Figure 5-7 Sensitivity Analysis: Impact of design factors on cost-effectiveness 92 Note- SFD: Single family detached: WWR: Window to wall ratio; CZ: climatic zone Figure 5-8 Sensitivity Analysis: Impact of design factors on environmental effectiveness 93 represents the seven archetypes, while the vertical axis represents the percentage variation in cost and environmental effectiveness for the latter figure. Two general observations can be made from these figures about the sensitivity of effectiveness. Firstly, the variation is more significant in cost-effectiveness (ranges -56--69%) for different archetypes than environmental effectiveness (ranges -4--34%). Secondly, the variation is more pronounced for archetypes located in colder climates of Kamloops (range -32--12%) and Prince George (range -56--69%) compared to Vancouver (range -4--3%). Though all the design parameters impact archetypes' performance, the variation is most pronounced in medium and large SFD. This variation is also a reflection of the heat losses possible due to large wall areas. These changes are mainly reflected for homes in Kamloops and Prince George with colder climates and thus more heat losses. When consequent cost and environmental effectiveness are also changed. This finding is important because existing neighborhoods in Kamloops and Prince George have more than 60% of the residences as single-family detached homes [422]. These trends of SFD homes construction are predicted to continue, as seen from BC statistics of year 2020, where more than 9,600 new SFD were registered despite the low economic progress during the COVID-19 pandemic. Compared to multi-unit residential homes, whose registrations decrease by 37.1 %, the SFD home saw an increase of 11.5% in the year 2020 [423]. Overall, the effects of design factors on effectiveness are essential, especially when considering the effectiveness of rebates at a building stock level. The current work explored sensitivity analysis to individual design factors. In most cases, these factors will interact with each other to reinforce or decrease the effectiveness of rebates in achieving desired targets. Furthermore, these impacts may seem insignificant in some cases for single archetypes, but their impacts will be compounded when considered at community levels. Therefore, the impacts of design factors cannot be ignored from a policy perspective. Economic factors: Two leading economic factors, energy escalation price and a carbon tax on fossil fuels, were considered in the study. Previous researchers have shown that energy savings (and hence savings on energy bills) are mainly affected by the local energy prices [368]. depicts the impact of energy price scenarios on cost-effectiveness. As expected, the increase in Figure 5-9 energy price reduces the effectiveness of rebates. However, the degree of impact varies significantly with archetype and location. More variation is observed for attached homes, with the impact is more pronounced for homes located in Vancouver than Kamloops and Prince George. These results are due to higher investment requirements for homes in Vancouver compared to the colder and smaller cities. Likewise, it is seen that as the number of 94 attached units increases, the cost-effectiveness decreases. For instance, for duplex homes in Vancouver, about 3 CA$ is required to save a kWh of energy and as the energy price rises to the highest scenario (10% rise in electricity and 8% rise in gas prices), an additional 5 CA$ need to spend to achieve the same amount of savings. In the same vein, the six-unit attached house sees a reduction in cost-effectiveness and an additional 8 CA$ are needed to reduce a kWh of energy. In contrast, attached homes do not show a significant decrease in cost-effectiveness and a maximum 2CA$ is required to achieve the same amount of savings in these homes. This difference may not be significant compared to attached homes but can be magnified depending upon local building stock. As stated in the previous section, SFD homes dominate building stocks in Kamloops and Prince George. Furthermore, it was estimated that EPUs for medium SFD in Prince George were not eligible for any rebate (Table 5-8). Therefore, the impacts of fuel price escalation need to be accounted for when allocating incentives, and the archetype dominating local building stock should become part of future rebate policy. Note- SFD: Single family detached; CZ: climatic zone Figure 5-9 Sensitivity Analysis: Impact of fuel price escalation on cost-effectiveness The carbon tax increases fossil fuels prices, thus encouraging the adoption of low carbon technologies and energy sources [424]. Often, the carbon tax is considered a complementary instrument that can influence the impact of available rebates. BC is one of the few regions of the world where a carbon tax has been actively applied to address climate change. The application of carbon tax in BC is considered a successful policy that reduced provincial carbon emissions [425,426]. Xiang and Lawley [139] noted that carbon tax in BC reduced natural gas use by 7% in 95 the residential sector. Therefore, due to the significance of carbon tax in BC, the impacts of its variation were analyzed. Figure 5-10 shows carbon tax scenarios for the tested archetypes. It is seen that in contrast to fuel price escalation (Figure 5-9), which had high impact on cost-effectiveness related to attached homes, the carbon tax has a more significant impact on SFD homes. In addition, the carbon tax is seen to impact homes in colder cities of Kamloops and Prince George compared to Vancouver. Interestingly, the cost-effectiveness decreases more significantly for small and medium SFD compared to larger homes. In Vancouver, the increase in cost requirements for homes ranges from about 1CA$ for attached homes to few cents in attached homes. The attached homes in Kamloops and Prince George require approximately 2CA$ more dollars while attached homes require about 75CA¢. It is also noteworthy that the decrease in the cost-effectiveness of attached homes at each location is almost uniform. Overall, a carbon tax does not have a significant impact as compared to fuel (electricity and natural gas) price escalation. However, these results indicate that at a very high carbon tax rate, implementation and implementation of more stringent BEC targets may become difficult for SFDs. In short, if more extreme carbon taxes are implemented, there may be a need to revise the number of rebates for SFD homes, especially for low-income occupants. Note- SFD: Single family detached; CZ: climatic zone Figure 5-10 Sensitivity Analysis: Impact of carbon tax rate increment on cost-effectiveness 96 5.4.4 Key Findings Based on the above results and findings, following implications need to be considered that need to be considered for designing and allocating rebates to yield maximum cost savings and environmental benefits. Small SFD homes do not significantly contribute to energy and cost requirements, especially for homes in Vancouver, where a more significant portion of archetypes is composed of attached and multi-unit residential apartments. Depending upon the local building stock, the rebates for small SFD can be directed towards larger SFD and attached homes to yield more significant savings. Medium SFD homes form a large percentage of the residential building stock and require a considerable investment to improve energy efficiency. Especially under COVID-19, which has a disproportionally affected population, the end-users of medium SFD can be unwilling to make significant investments. Under changing economic conditions, there may be a need to direct additional rebates to achieve efficient building stock. Large SFD construction is standard, especially in newer suburban residential communities. The analysis indicates that the rebates offered on large buildings can offer higher GHG reduction. Furthermore, compared to other archetypes, large SFD shows higher variation under changing design parameters, especially for the colder climate of Prince George. Hence, additional rebates may be needed for large SFD located in Prince George. Attached homes can provide higher environmental effectiveness under available rebates but require more expenditure to save per unit of energy. There is a need to direct additional financial resources towards these homes because of the more considerable investment required for installing EPUs, increased construction rates and uncertain economic conditions. contribute to energy and cost requirements, and the rebates can be directed towards larger homes to yield greater savings. 5.4.5 Limitations and future direction Despite an extensive study on many residential archetypes, some cautions need to be considered for adopting our results for other archetypes, locations and economic conditions. A limited number of locally available EPUs were utilized for rebates effectiveness evaluation. Future studies should 97 carefully consider the local context and construction practices during financial incentives assessment. This research was based on virtual buildings simulated on BEopt. Although simulations are powerful techniques, each energy modeling tool has limitations and cannot accurately represent complex built environments. The uncertainty present in these models needs to be accounted for to predict energy performance and ensure BEC compliance accurately. In the same vein, another critical factor that needs to be explored in future studies is the order of interaction among various uncertain parameters (design and economic). This chapter only explored the impact on cost and environmental effectiveness due to changing one parameter at a time (called One-At-a-Time (OAT) or local sensitivity analysis). However, this type of analysis rarely provides the full impact on the output variables [427].Multivariate sensitivity analysis can help determine the magnitude of interaction among input parameters [428]. Therefore, future work on incentives effectiveness should evaluate the interaction among various uncertain parameters. Another critical limitation was the consideration of standard occupancy profiles, same investment capability and preference of end-users. The next phase of this study will consider the impact of different end-users’ occupancy variation and cost constraints on the effectiveness of financial incentives. The overall impact of financial incentives policy should be evaluated at the building stock level using bottom-up energy modeling techniques. The framework was introduced for new buildings. However, it can be modified and applied to retrofit existing buildings by carefully evaluating and classifying existing archetypes. Compared to new houses, existing homes are more complex and will have a more extensive set of constraints that will vary with a local context. Similarly, the seven archetypes represent generic models; a more detailed study should be conducted that utilizes actual buildings and uses their calibrated energy models to study rebates effectiveness. Detailed survey studies will be required to identify the archetypes of the existing building stock and design effective financial incentives. Based on the above results and findings, a set of recommendations are being provided for each archetype that can help in rebates design and allocation to yield maximum cost savings and environmental benefits. 5.5 Summary The chapter presented a novel methodology to understand the impacts of financial incentives available for new residential buildings. The methodology has the potential to determine the 98 effectiveness of FIs for residential archetypes from end-user’s perspective and hence will be useful in the implementation of advanced building energy codes. The main novelty of the methodology is the introduction of a FIs effectiveness matrix. To the best of authors’ knowledge, an effectiveness matrix related to FIs of green buildings is still missing from the literature. The methodology consists of three main stages: screening mechanism to determine suitable energy performance upgrade and applicable FIs; evaluation method of effectiveness index; and checking the sensitivity of FIs effectiveness against key design and economic parameters. Parametric analysis of energy upgrades in BEopt and excel based algorithms are used to determine the impact of FIs on cost, energy and carbon mitigation metrics. In this regard, a case study is performed on rebates related to building energy code in British Columbia (Canada) and applied on seven low-rise residential buildings. These archetypes represented approximately 90% of the total residential building stock. Hence a comprehensive evaluation is performed. Results indicate a significant difference in cost and environmental effectiveness with a change in archetype and location. The results further demonstrate the importance of sensitivity factors in effectiveness evaluation. Hence, in order to introduce or improve existing FIs the local context and uncertain parameters related to a particular neighborhood cannot be ignored. 99 Chapter 6 Rebates’ Efficiency at Building and Neighborhood Levels A version of this chapter is under preparation for a peer-reviewed journal paper, as an article titled “Efficiency of utility rebates: A case study of British Columbia” that will be submitted to the Elsevier journal Journal of Cleaner Production in June 2021. 6.1 Background In Chapter 5, comprehensive energy and cost models were generated to determine rebates' “effectiveness index” for different residential archetypes. The proposed methodology was built from the end-user’s perspective and restricted to individual buildings. The rebates are part of government limited budgetary resources [429] and need to be allocated strategically to ensure maximum efficiency. The problem of climate change adds to the current management and strategic challenges for the government and the utilities. Hence, it is imperative to utilize the resources efficiently and allocate them to attain maximum benefit. Adopting economically feasible rebate structures can help utilities meet the growing energy needs and meet the desired mitigation goals. To allocate resources, the performance of buildings under these rebates must be assessed for the amount of energy saved, the carbon mitigation potential and consequent preference of the stakeholders. Rebates are the most common FIs offered by the utilities in Canada [21,132]. Hence, it is essential to determine how the current rebate structures are performing and which building archetype can perform the best under the existing allocation system. In recent years, policymaking for climate change has shifted from the impact of individual buildings towards local and regional levels [430]. Therefore, the offered energy efficiency rebates need to be efficient at both building and regional levels. Like effectiveness, efficiency is a core criterion for evaluating FIs policy. Efficiency can provide quantitative information for decision makers and help in strategic allocation. Efficiency can be defined as the ratio of outputs obtained to the invested resources. Energy-related decisions can be defined as the ratio of outputs, such as energy performance, emissions; to inputs such as investment and material consumption [32]. Therefore, the “efficiency” of rebates can help evaluate the quality of outputs with minimum use of resources [431]. Among efficiency evaluation methods, data envelopment analysis (DEA) has been especially popular for its non-parametric nature, capability to use multiple inputs and outputs, eliminating the dependence on expert judgments and ease of application [432–434]. DEA has been applied for 100 evaluating the efficiency of subsidies in agriculture, tourist industry, business, power plants and transportation [434–437]. Likewise, DEA has also been used to select optimal energy performance upgrades and to benchmarking green cities and regions [438–441]. The method has been used successfully in identifying the most efficient measure and in allocating resources. Hence, DEA method can be adopted to evaluate efficiency of rebates offered for energy efficient buildings. A government subsidy program for residential buildings' overall economic efficiency depends on of the government and private investors' perspective [28]. The subsidy providers are interested in allocating minimum resources, i.e., subsidy amount that will yield the highest energy and GHG emission reduction. At the same time, the investors’ want to spend the least-cost amount that can enable the government-mandated targets [28]. Hence, evaluating both government and investors' perspectives is needed to assess a subsidy program's success. Among subsidies, rebates provided by utility companies and governments are the most popular policy instruments used globally [132]. Studies on efficiency evaluation of the subsidies have largely been based on surveys that have been often performed at national levels. Hence, there is a shortage of the impact of the efficiency of rebates at the building and neighborhood level. This chapter proposes a methodology for evaluating the efficiency of rebates for energy-efficient residential buildings at building and neighborhood levels. The specific objectives of this research are: (1) Assessment of technical efficiency of rebates at building and neighborhood levels; (2) Assessment of end-user’s investment efficiency at building and neighborhood levels; (3) Comparison between technical efficiency of rebates from government and end-user’s perspectives. The assessment provides unique insights to the policymakers regarding the challenges and opportunities in the existing subsidy allocation procedure and provides effective policy design guidelines. 6.2 Methodology A three-phase methodology is introduced to determine efficiency from investors and government perspectives. The phases are (1) data collection, (2) data processing and (3) data analysis. The methodology is illustrated for financial rebates on the Building Energy Step Code (BC-ESC), 101 Canada. The methodology is illustrated for financial rebates on Building Energy Step Code (BC-ESC), Canada [177]. Subsequently, the proposed research methodology is explained in Figure 6-1. 6.2.1 Phase 1: Data Collection and Processing The energy, cost and GHG emission calculated at building and neighborhood levels are used to develop DEA indicators and DEA models. Phase 1 consisted of three main components (a) data collection; (b) energy performance upgrades (EPUs) selection and (c) energy performance and cost evaluation at neighborhood levels. Among these components, the first two components, i.e. (a) data collection and (b) EPUs selection, are similar to Section 5.2.1 and Section 5.2.2, respectively in Chapter 5. Hence, only details on the second and third phase of this methodology are provided in this section. Figure 6-1 Research Methodology 102 Assessment of building stock performance at regional scales can help derive suitable incentive policies. Despite the importance of regional level building stocks for effective policy design, few studies have evaluated resource consumption and performance of residential building stocks [397]. Residential buildings for a specific region or country can be classified into archetypes based on their form, built and use. Building stock models at neighborhood levels can be constructed based on previous literature, surveys and national and regional building energy codes [442]. Archetypes of existing buildings can be based on regional and national surveys. Examples of such databases are the US Census Bureau’s American Housing Survey (AHS) [443], the DOE Residential Energy Consumption Survey (RECS) in the United States [444] and the Statistics Canada 2016 survey in Canada [422]. Some databases contain information on a number of countries, such as the Typology Approach for Building Stock Energy Assessment database (TABULA) that represents residential archetypes for 13 European countries [445]. Energy stock models can be prepared through top-down or bottom-up modelling approaches. Top-down modelling is mostly based on econometric techniques but are unable to capture the impact of new technologies and policies. On the other hand, Bottom-up modeling utilizes statistical or engineering techniques that estimate end-use energy aggregation to find building stock performance [446]. Since these models are based on building physics, census surveys and other statistical datasets, they can be successfully used to predict the energy of past, present and future residential building stocks [447–449]. Hence, bottom-up modelling can better represent the impact of new technology or policy compared to the top-down modelling approach [450]. Bottom-up modelling commonly uses weighting approaches for determining building stock performance at regional or national levels [275,450]. Hence, this research uses bottom-up modelling using the weighting approach. The building stock models at neighborhood levels are generated on a method similar to Krarti (2018) [446]. The specific method followed to achieve neighborhood-level results are: (1) Classify building stock based on geometry, systems, structural types and location factors. (2) Construct building energy models based on archetypes determined in the previous step. The number of energy models constructed is proportional to computational time; hence, an appropriate number of models representing the largest portion of building stock is selected. (3) Evaluate end-use energy for each archetype using the BeOPT tool. 103 (4) Determine neighborhood-level energy use by aggregation of archetypes end-use energy through a weighting technique based on archetype distribution. 6.2.2 Phase 2: Data Analysis Phase 2 Phase 2 consisted of three main components (a) evaluation of indicators, (b) assessment of decision-making units (DMUs) and (c) application of the DEA model. (a) Evaluation of Indicators: Three key indicators were selected, each for government and investors’ perspective based on existing literature [28,132,263,451,452]. The government perspective considered “rebates” as inputs and “energy saved” and “GHG emissions reduction” as outputs. Investors’ perspective included “additional life cycle costs (dLCC)” as input and “energy saved” and “GHG emissions reduction” as outputs. These are illustrated in Table 6-1 and described in the following sections. Table 6-1 Variables for DMUs Perspective Variable Indicator Description Government Input Rebates (CAD$) Rebates are an important investment from the government and utility providers. Output Energy saved (kWh/year)a Energy saved is considered to be a cost-effective and easily applicable method to mitigate climate change[453]. In addition to saving the planet, reduction in energy use or increased energy efficiency of residential buildings has a number of co-benefits that include reduced energy bills for consumers, better air quality, health and well-being of occupants. Due to the vast importance of the topic, the energy efficiency has recently become part of efficiency evaluation studies. GHG reduction (tCO2-eq./year)b Energy savings cannot completely represent the benefit gained to the environment. Hence GHG emissions reduction are an essential factor. Investor Input Difference in life cycle cost (CAD$) Previous studies show that the implementation of BEC targets includes additional investment from the investors (end-users or developers) perspectives. This is the most important investment and decision parameter from the investors’ perspective. Output Energy saved (kWh/year) See (a) GHG reduction (tCO2-eq./year) See (b ) (b) Assessment of Decision making units (DMUs): Environment efficiency was determined at (a) building and (b) neighborhood level. At the building level, the DMUs are different residential 104 archetypes, while at the neighborhood levels distribution of these archetypes per unit area is DMUs. At the neighborhood level, the archetypes depend on local weather, usage and economy of the area. For example, multi-unit residential buildings dominate metropolitan areas, while suburban areas with new construction are predominantly single-family detached homes. Hence, the efficiency of rebates will vary both at building and neighborhood levels. (c) Application of DEA model: DEA technique was developed by Charnes, Cooper & Rhodes [432] based on Farrells work [454]. DEA utilizes the basic efficiency concept and uses it to convert multiple inputs and multiple outputs into a single efficiency reports [455]. The DEA method has the advantage of requiring no specific functional form. As such, qualitative and quantitative factors can be evaluated. Efficiency is assessed based on the frontier created by compared DMUs. Existing MCDM methods can perform subsidy allocation; however, weighing all of them is a big challenge due to numerous environmental and economic parameters. Hence, traditional allocation procedures can fail if a wrong set of weights is adopted. The utilization of DEA is proven to effectively address this problem and allocate subsidies in other areas [455]. Models developed by Charnes, Cooper & Rhodes (CCR) models evaluate the efficiency of DMUs based on self–evaluation under existing rebates conditions [432]. Among various DEA models, CCR-output based model has been frequently used for assessing the efficiency of subsidies [435,456]. Input-based models are more frequently used in literature due to their easier interpretation; however, output-based models are more advantageous for subsidies as they will enable maximum benefit from available resources [38]. Hence, this research used a standard CCR-output model for efficiency evaluation. The CCR model aims to maximize the outputs with a fixed sum of inputs and is determined by Equation 6-1 subject to the constraints defined below. 𝑚𝑎𝑥𝜃𝑝 =∑ 𝑢𝑟𝑠𝑟=1 𝑦𝑟𝑝∑ 𝑣𝑖𝑚𝑖=1 𝑥𝑖𝑝 Equation 6-1 s.t. ∑ ursr=1 yrp∑ vimi=1 xip≤ 1 j=1,2,3…n, ur ≥ 0 r=1,2,3…, s, 105 vi ≥ 0 i=1,2,3…, m, where 𝑢𝑟 and , 𝑣𝑖 are the inputs and outputs weighting vectors for a DMU j, respectively. The DEA method distributes DMUs into two main groups: efficient and in-efficient. The DMUs that depict the value of efficiency as “1” are efficient and those with lower value fall will be inefficient. A performance-based perspective is considered [435] for this research; hence, buildings and neighborhoods with higher efficiency from a government perspective will be assigned more significant rebates compared to those with low efficiency. Similarly, from investors' perspectives, archetypes and neighborhoods that are efficient would be the preferred investment choice for investors. The residential building archetypes are fixed and cannot be changed while the amount of rebates and investors costs can be reallocated. Therefore, output based model was considered to evaluate efficiency. The individual DMUs performance is reflected on overall group performance. In this regard, best performers assert positive impacts while negative impacts are influenced by weak DMUs [437]. The research method proposed by Ang et al. (2018) [437] or group efficiency evaluation has been used. The inputs and outputs for individual DMUs were first aggregated to determine the group inputs and outputs. Then the CCR-output model was applied to assess group efficiency. Therefore, this research investigated the efficiency at two levels and compares how the efficiency varies between the two perspectives. This will help both policymakers design rebates that will yield maximum benefit and investors direct their investments at locations that will yield more energy savings and GHG emission reductions. 6.3 Methodology Demonstration The case study considers the rebates available for building energy step code in British Columbia (Canada), BC Energy Step Code. Currently, the residential sector in Canada is dominated by single-family detached homes [422]. Seven archetypes representative of common residential archetypes in Canada are selected as DMUs at the building level. At the neighborhood level, three DMUs are used that represent the distribution of these archetypes in three climate zones: Mainland/Southwest (CZ4), Thompson Okanagan (CZ 5) and Northeast (CZ 6) (Figure 6-2). 106 Note- SFD: Single family detached; CZ: climatic zone Figure 6-2 Distribution of residential archetypes The details of DMUs at neighborhood levels are described in Table 6-2, respectively. Here the planimetric views shows distribution of the seven archetypes in at each location. Based on results of energy, cost and GHG for residential archetypes in Chapter 5 the final indicators values for the case study at the building level from government and investor perspective are shown in Table 6-3 and Table 6-4. The rebates value for each archetype varies with the location along with the associated energy savings and GHG reduction potential. The bottom-up energy modelling method provided in section 6.2.1 is used for obtaining final indicators at the neighborhood level. Here the amount of rebates, energy and GHG reduction for each archetype are multiplied by the number of houses present in neighborhood following local statistics and assumptions considered in Table 6-2. From these it is observed that the differences present in indicators values are not linearly increased, rather the specific neighborhood pattern will rule how much the costs and savings of individual home contribute towards the whole neighborhood performance. Table 6-5 and Table 6-6 show the 3 DMUs details at the neighborhood level. As an illustration of buildings impact at neighborhood level a 3D model is constructed for Thompson Okanagan (CZ-5). A color scale is also allotted to the amount of GHG reduction by each archetype. Figure 6-3 hence enables a visualization of the neighborhood performance and can be used for comparing different neighborhoods. The overall GHG reduction potential of Thompson Okanagan is seen to be 281,547 kgCO2-eq./GJ. IT should be noted that this value is obtained from application of a limited set of energy upgrades. As more renewable energy systems and more energy efficient systems are 107 Table 6-2 Description of DMUs at neighborhood level No. DMU Description Planimetric view 1. Mainland/Southwest (CZ 4) A new neighborhood consists of 100 residential buildings. The neighborhood is dominated by multi-unit residential apartments and utilized Vancouver city statistics for residential archetype distribution present in Statistics Canada 2016 [422]. There 5 Small SFD; 11 medium SFD; 5 large SFD; 28 Duplex; 2 triplex; 46 quadplex and 3 six-unit homes. 2. Thompson-Okanagan (CZ 5) A new neighborhood consists of 100 residential buildings. The neighborhood is dominated by single family detached homes and utilized Kamloops city statistics for residential archetype distribution present in Statistics Canada 2016 [422]. There 17 Small SFD; 34 medium SFD; 17 large SFD; 8 Duplex; 4 triplex; 17 quadplex and 4 six-unit homes. 7. Northeast (CZ 6) A new neighborhood consists of 100 residential buildings. The neighborhood is dominated by single family detached homes and utilized Prince George city statistics for residential archetype distribution present in Statistics Canada 2016 [422]. There 16 Small SFD; 33 medium SFD; 16 large SFD; 13 Duplex; 3 triplex; 16 quadplex and 3 six-unit homes. Key Note- SFD: Single family detached; CZ: climatic zone; DMU: Decision making unit 108 Table 6-3 Building Level: Government Perspective Location DMU Input Output Rebates (CA$) Energy saved (kWh) GHG reduction (tCO2-eq.) Mainland/Southeast (CZ 4) DMU1 6,000 4,058.43 923.26 DMU2 6,000 5,265.48 1,444.47 DMU3 6,000 11,484.18 2,303.69 DMU4 12,000 7,443.16 2,630.93 DMU5 18,000 10,687.53 3,862.56 DMU6 24,000 14,211.05 5,143.55 DMU7 36,000 20,398.15 7,551.89 Thompson Okanagan (CZ 5) DMU1 4,000 4,110.83 942.69 DMU2 3,000 5,995.64 1,584.67 DMU3 6,000 12,538.54 2,487.40 DMU4 8,000 3,128.24 2,891.63 DMU5 12,000 12,885.40 4,291.47 DMU6 16,000 16,996.14 5,690.36 DMU7 24,000 25,260.88 8,492.96 Northeast (CZ 6) DMU1 0 5,354.14 1,187.93 DMU2 0 7,643.07 1,937.25 DMU3 6,000 15,748.88 3,104.44 DMU4 6,000 9,096.34 3,541.48 DMU5 9,000 16,483.21 5,246.44 DMU6 16,000 21,612.41 6,934.37 DMU7 18,000 32,328.13 10,381.73 Table 6-4 Building Level: Investor Perspective Location DMU Input Output Difference in life cycle costs (CA$) Energy saved (kWh) GHG reduction (tCO2-eq.) Mainland/ Southeast (CZ 4) DMU1 11,915.41 4,058.43 923.26 DMU2 25,684.00 5,265.48 1,444.47 DMU3 36,419.94 11,484.18 2,303.69 DMU4 41,019.66 7,443.16 2,630.93 DMU5 64,346.14 10,687.53 3,862.56 DMU6 100,951.70 14,211.05 5,143.55 DMU7 144,975.19 20,398.15 7,551.89 Thompson Okanagan (CZ 5) DMU1 11,463.85 4,110.83 942.69 DMU2 24,014.28 5,995.64 1,584.67 DMU3 33,384.58 12,538.54 2,487.40 DMU4 38,527.47 3,128.24 2,891.63 DMU5 67,985.84 12,885.40 4,291.47 DMU6 97,336.90 16,996.14 5,690.36 DMU7 139,280.98 25,260.88 8,492.96 Northeast (CZ 6) DMU1 11,002.30 5,354.14 1,187.93 DMU2 23,733.67 7,643.07 1,937.25 DMU3 31,615.77 15,748.88 3,104.44 DMU4 38,815.31 9,096.34 3,541.48 DMU5 63,504.19 16,483.21 5,246.44 DMU6 99,757.38 21,612.41 6,934.37 DMU7 142,712.34 32,328.13 10,381.73 Note- CZ: climatic zone; DMU: Decision making unit 109 Table 6-5 Neighborhood Level: Governments’ Perspective DMU Input Output Rebates (CA$) Energy saved (kWh) GHG reduction (tCO2-eq.) DMU1 1,710,000 1,081,572.60 372,758.44 DMU2 748,000 946,276.27 281,547.41 DMU3 512,500 1,201,808.63 336,585.24 Note- DMU: Decision making unit Table 6-6 Neighborhood Level: Investor Perspective DMU Input Output Difference in life cycle costs (CA$) Energy saved (kWh) GHG reduction (tCO2-eq.) DMU1 6,879,389.00 1,081,572.60 372,758.44 DMU2 4,347,703.78 946,276.27 281,547.41 DMU3 4,183,254.40 1,201,808.63 336,585.24 Note- DMU: Decision making unit adopted this value of overall GHG emissions reductions would be much higher. Likewise, when a different neighborhood layout is considered this value will change. 6.4 Results and Discussion This section presents the results obtained from two points of view at two levels: the individual residential archetype at the building level and efficiency at the neighborhood level. 6.4.1 Building Level The results of efficiency at the building level from the investors’ point of view can help establish a performance benchmark and help in comparing investment potential in different residential archetypes. The results from the government perspective inform about the best-performing archetypes where the subsidy funds allocation will yield maximum benefits. The building level efficiency from the government perspective at the building level is shown in Table 6-7. It is seen that the efficiency rankings change with location. For the milder climate of Mainland/Southwest, the rebates are efficient for large SFD. In-efficient archetypes in Mainland/Southwest show values from 0.627 to 0.4008. The results indicate that small SFD is the most in-efficient from a government perspective, while attached homes investigated show similar values. Thus government subsidies allocated to single-family detached homes will give the highest benefits. 110 Note- SFD: Single family detached home; CZ: climatic zone; GHG: greenhouse gases Figure 6-3 GHG Emission reduction at neighborhood level for Thompson Okanagan (CZ-5) 111 Archetypes in the Thompson Okanagan show a similar ranking to archetypes in the Mainland/Southwest; however, the in-efficient units in this region behave better to rebates compared to the milder climate and their efficiency values range from 0.6843 to 0.5023. In addition, the Thompson Okanagan contains two efficient archetypes: Medium SFD and Large SFD. In contrast to the Mainland/Southwest and the Thompson Okanagan, the archetypes in the Northeast show an overall poor performance. The most efficient archetype is Medium SFD, while the range of inefficient DMUs ranges from 0.7005 to 0.0018. Overall, attached homes show the lowest efficiency under current rebates. Table 6-7 Building Level: Rebates efficiency Government Perspective DMU Mainland/Southwest (CZ4) Thompson Okanagan (CZ 5) Northeast (CZ 6) Efficiency Rank Efficiency Rank Efficiency Rank DMU1 0.4008 7 0.5023 6 0.7005 2 DMU2 0.627 2 1 1 1 1 DMU3 1 1 1 1 0.0021 6 DMU4 0.571 3 0.6843 2 0.0018 7 DMU5 0.5589 4 0.677 3 0.0027 5 DMU6 0.5582 5 0.6733 4 0.0036 4 DMU7 0.5464 6 0.6699 5 0.0053 3 The building level efficiency from investors’ perspective at the building level is shown in Table 6-8. Similar to government rebates efficiency, the rankings for investment vary with location. In contrast to government rebates, the efficiencies from investors’ perspective are better and do not show high variation. In this respect, Small SFD is the most efficient unit for investment in all three locations. For the Mainland/Southwest, efficiency values range from 0.9258 to 0.6576. In the Thompson Okanagan and the Northeast, in addition to Small SFD, the investment in Large SFD is also efficient. The inefficient units range from 0.9127 to 0.7109 and 0.845 to 0.6438 for the Thompson Okanagan and the Northeast, respectively. Investors’ perspective also indicates that investment in single-detached homes is a more efficient option compared to attached homes. Overall results indicate that investment in almost all homes of the Thompson Okanagan will be beneficial for investors. Figure 6-4 shows a comparison of efficiencies between the two perspectives for the tested regions and the seven archetypes used as DMUs. The quadrant chart enables the comparison of the DMUs in a straightforward manner. It is seen that all DMUs fall in the higher investors’ efficiency 112 quadrant; however, for the more extreme colder climate of the Northeast, most homes are inefficient from a government perspective. Table 6-8 Building Level: Investment efficiency Investor Perspective DMU Mainland/Southwest (CZ4) Thompson Okanagan (CZ 5) Northeast (CZ 6) Efficiency Rank Efficiency Rank Efficiency Rank DMU1 1 1 1 1 1 1 DMU2 0.7258 5 0.8025 3 0.756 4 DMU3 0.9258 2 1 1 1 1 DMU4 0.8278 3 0.9127 2 0.845 2 DMU5 0.7747 4 0.7676 4 0.7652 3 DMU6 0.6576 7 0.7109 6 0.6438 6 DMU7 0.6723 6 0.7415 5 0.6738 5 Note- CZ: climatic zone; DMU: Decision making unit Note- SFD: single family detached home Figure 6-4 Economic Efficiency from investors and government perspective at the building level 113 6.4.2 Neighborhood Level The results for efficiencies at the neighborhood level from both investors and government perspectives are shown in Figure 6-5. It is seen that both from government and investors perspectives, investment in neighborhoods located in the Northeast is most efficient. This is in contrast to the results obtained for the building level. This change is observed because of how the archetypes are actually distributed among the three regions. Figure 6-5 Economic Efficiency from investors and government perspective at the neighborhood level: (a) Mainland/Southwest (CZ 4), (b) Thompson-Okanagan (CZ 5) and (c) Northeast (CZ 6) The Mainland/Southwest is dominated by attached homes that had lower efficiencies compared to detached homes, while more efficient detached homes dominate the Northeast. In addition, the cost factors play an important role, the costs are higher for the same energy performance upgrades in the Mainland/Southwest and Thompson Okanagan as compared to those in the Northeast climate. It is also observed that government and investor efficiencies are better for the Thompson Okanagan (0.80 and 0.57) compared to the Mainland/Southwest (0.67 and 0.33). Similar to results at the building level, the investors’ perspective has better efficiencies compared to the government perspective. These results indicate that under current rebates the government can obtain maximum benefits by directing maximum rebates towards Northeast neighborhoods. Investors' perspective can help benchmark neighborhoods' performance and direct future investors to obtain more sustainable neighborhoods. 114 6.4.3 Limitations and future direction The same set of limitations related to the residential archetypes, limited set EPUs, locations and economic conditions that govern chapter 5, section 5.4.5 should also be considered for determining DMUs at individual building and neighborhood levels. As preliminary research on rebates efficiency, the DEA method using CCR-output based model proves a good approach. However, CCR-output based models have low discriminating ability and give more weight to favorable inputs and outputs than unavoidable ones, resulting in overestimating results [456]. Future research on rebates efficiency should consider using more advanced DEA models, such as cross-efficiency, to help address these shortcomings. Another direction for future research is conducting a sensitivity analysis that can highlight the main factors making a residential archetype or neighborhood more efficient than others. A robustness test can also be performed by removing one input or output and running DEA models and comparing the results [457]. In addition, a similar layout of neighborhood was considered for three locations. In reality, the neighborhoods layout varies even in the same city. Therefore, more comprehensive studies with various possible neighborhood layouts need to be explored and compared for rebates performance. This will help improve existing and new neighborhoods. 6.5 Summary Chapter 6 investigated the efficiencies of existing rebates and investments from government and investors perspectives at the building and neighborhood level through the application of the CCR-output based model. The feasibility of the methodology is demonstrated through an application on rebates offered for a recently upgraded building energy code in British Columbia, Canada. The results provide insights into the impacts of current rebates offered for meeting targets of the recently upgraded building energy code in British Columbia, Canada. It is observed under the current rebates at the building level, the rebates directed towards Large SFD and Medium SFD will have the highest efficiency. At the neighborhood level, the most economically sound investment by government and investors are for archetypes located in the Northeast. It should be noted that the research was limited to only three locations, climate zones and seven archetypes. It is possible that other archetypes and neighborhoods are more efficient than the tested samples. CCR-output based model used for this methodology can provide relative efficiencies and is 115 sufficient for the current investigation. More advanced models are present for the reallocation of subsidies that can be used for future research. Overall, the results obtained from this research can help the regional government is allocating subsidies to archetypes while the efficiency of investments can help end-users make informed decisions regarding their future expenditure. At neighborhood levels the efficiency values can help in the distribution of national and provincial subsidies. Likewise, the investment efficiency at neighborhood level can help in benchmarking and comparing regions and indicate what changes need to be incorporated for improvement in performance. 116 Chapter 7 Operation strategy selection of a Public Building: Decision making with life cycle thinking approach Parts of this chapter have been published in Elsevier’s Journal of Cleaner Production, as an article titled “A process-based LCA for selection of low-impact DBPs control strategy for indoor swimming pool operation”[458]. 7.1 Background Financial Incentives (FIs) are assigned according to the level of energy savings targets achieved. However, buildings' energy use changes with the building operation schedule and can result in higher energy savings and hence eligible incentives [40]. FIs for achieving energy efficient buildings can either yield a positive impact on human health, such as introducing incentives on wood pellets instead of conventional coal stoves or using renewable energy sources. In other cases, reducing building energy use can overlook the indoor environmental problems leading to a negative impact on building occupants [43]. Likewise, if importance is given to reducing the impacts on human health, the eligible incentives will be reduced. This chapter explores the implication of building operating conditions on FIs eligibility criteria. The chapter uses a public aquatic centre building as a case study to demonstrate the problem, methodology and solution. Swimming is an important recreational activity suitable for all age groups with varying physical abilities [459]. The World Health Organization recommends swimming for health benefits, mental relaxation and social interaction [460]. Indoor swimming is often preferred over outdoor swimming by the population living in cold regions as it offers a comfortable swimming environment throughout the year. Hence, the number of indoor swimming pools (ISPs) exceeds outdoor swimming pools in many countries. A similar trend is observed in Canada, where ISPs account for 75% of the total 3,615 public pools [461]. Maintaining good water quality in swimming pools is of great importance to protecting the health of swimmers and non-swimmers [462]. Swimming pool water can be a host to various harmful microorganisms, such as bacteria, viruses and algae, which could pose risks to bathers' health. Disinfectants are added to swimming pool water to control the population of microorganisms 117 [463]. Chlorine is the most common disinfectant applied to swimming pools due to its low cost and high effectiveness in deactivating microorganisms [464]. However, chlorine can react with anthropogenic organic matter (e.g., human sweat, saliva, personal care products) in swimming pool water to form disinfection by-products (DBPs), such as trihalomethanes (THMs) and haloacetic acids (HAAs) [465,466]. Excessive exposure to DBPs can cause harm to the health of bathers and employees who spent considerable time in ISPs [467]. DBPs may affect human health through dermal contact, accidental oral intake and inhalation pathways [468–470]. Extensive research has identified links between DBPs exposure and various human health effects ranging from respiratory irritation to different cancers [471–474]. Hence, it is essential to develop and implement DBPs control strategies in ISPs to protect bathers and ISP staff's health. DBPs control strategies can be developed by changing swimmer’s behavior and/or the operating conditions of engineering systems that remove or reduce DBPs and their precursors in the ISP environment [459]. On the other hand, implementation of a DBPs control strategy will alter the energy and resource consumption of the existing pool operation. A large quantity of energy, water, chemical and other resources are required in ISP operation, resulting in significant environmental impacts. ISPs also have a long operating or service life; for example, a typical ISP is expected to have a service lifespan of 40–75 years [475]. Hence, considerable environmental impacts can be generated with the application of DBPs control strategies throughout the long service life of an ISP. To the best of the authors’ knowledge, limited studies have investigated the environmental impacts of DBPs control strategies in ISP operation. ISPs are important energy consumers and contributors to global warming. Thus, it is important to assess the lifecycle impact of DBPs control strategies and select suitable strategies that can achieve promising DBPs control performance with minimum resource consumption. 7.1.1 DBPs Control Strategies Human health risks due to DBPs exposure can be mitigated through the adoption of appropriate control strategies. Development of a specific DBPs control strategy is based on a number of ISP design and management factors including, pool types (e.g., leisure pools, lap pools, hot tubs) [476,477], source water quality (e.g., organic and inorganic precursors, pH, temperature) [465,478–481], disinfection processes (methods) (e.g., UV treatment, chlorination, ozonation) 118 [482–484], bathers (e.g., number, behavior and personal hygiene condition) [477,485,486] and air ventilation conditions [487,488]. DBPs control strategies are developed based on a modification of existing engineering systems as well as users’ behaviors [459,489]. These strategies can be divided into three main categories: precursors removal, modification of water treatment processes and removal of DBPs from the pool environment [490,491]. The selection of an effective DBPs control strategy depends upon the specific pool conditions, bathers’ behaviors, engineering systems, resource availability and preference and facility managers’ expertise. The formation of DBPs in pools occurs at a high rate; hence some researchers recommend that DBPs control strategies should focus on the removal of anthropogenic organic precursors [462]. The reduction of precursors can be achieved by changing bathers’ behaviors such as pre-swim showering and wearing proper bathing caps and swimsuits [492]. DBPs precursors can also be reduced by keeping the pH of water at a relatively low level and reducing water age (retention time) by increasing turnover rates [493]. Improving the water treatment process is another important strategy that involves modification and/or combination of existing disinfection methods; such as a combination of enhanced coagulation with filtration [493,494]. Disinfection methods include chlorination, electrochemically generated/mixed oxidants, UV- treatment, use of hydrogen peroxide, ozonation and their combinations [493,495]. Once formed, DBPs can be present in the pool water and air of an ISP. DBPs removal from water involves membrane filtration, advanced oxidation, thermal and chemical reduction [495]; while their removal from the air can be achieved by changing the operation of heating and ventilation systems and controlling surface air flows through exhaust fans [459,496]. The three categories may be implemented individually or in combinations for effective DBPs control. Different strategies are associated with varied energy, water and chemical use during the long pool service life and therefore, investigating their lifecycle environmental impacts is necessary. 7.1.2 Process-based LCA Life cycle assessment (LCA) is a standard procedure to evaluate the environmental performance of various processes and man-made products, from raw materials acquisition through operation and maintenance to the ultimate waste disposal [497]. LCA can be performed using three methods: (1) process-based (P-LCA), (2) input-output-based (IO-LCA) and (3) hybrid LCA. P-LCA 119 calculates environmental impacts due to inputs and potential outputs of processes in each life-cycle phase [498]. It can assess the impact of a series of processes on consequential outcomes and is considered to provide highly accurate results. In comparison, IO-LCA is a top-down approach that uses sector-level data based on the economy to generate inventories. Though IO-LCA considers all processes, data from its inventories are not detailed enough to conduct a comprehensive assessment to reach decisions at the product/process level [499]. Hybrid-LCA involves the combination of P-LCA and IO-LCA methods and has the advantages of both methods. However, Yang et al. [500] found that hybrid-LCA does not always give more accurate results than P-LCA. P-LCA is known to provide more accurate results than IO-LCA [499]. P-LCA is a part of ISO standard guidelines [501]. The system boundary of P-LCA is based on the goal and scope of a study; hence, the data inventory is based on the processes within system boundaries [502]. Cut-off and truncation errors are a common limitation of any P-LCA [503,504]. Therefore, cut-off criteria should be selected carefully to minimize errors and obtain realistic environmental impacts. Although several studies have explored the energy use [505–509], water use [505,510] and chemical emissions [470,511] in ISP operations, the lifecycle environmental impact of ISP operation is still not well understood. Forrest & Williams [512] developed a hybrid-LCA model to determine the carbon footprint of private residential pools in the United States. However, the study was limited to private outdoor residential pools associated with significantly different energy and resource consumption compared to ISPs. Furthermore, the study only investigated carbon emissions but did not explore other environmental impacts such as ozone depletion or human toxicity. In short, a significant knowledge gap exists in the LCA of public ISPs. 7.2 Methodology A P-LCA methodology is developed to help select DBPs control strategies with promising DBPs control performance and low environmental impacts. The processes and products of each DBPs control strategy are considered, including energy, water and chemical use as well as emissions of contaminants. The proposed methodology consists of four main phases, as shown in Figure 7-1. 120 Note- ISP: indoor swimming pool; TOPSIS: Technique for Order of Preference by Similarity to Ideal Solution; DBPs: Disinfection byproducts; LCA: Life cycle assessment; P-LCA: process based LCA Figure 7-1 Proposed P-LCA methodology for selecting low impact DBPs control strategy 121 7.2.1 Goal and Scope According to ISO 14044 [513], the temporal and spatial boundaries of the system being assessed should be defined in accordance with the goal of the study. The goal in this phase of the study was to evaluate DBPs control strategies applied to engineering systems in ISP and determine the strategy with the lowest environmental impacts by using P-LCA. DBPs emitted in pools are a known human health risk, however the determination of the direct human health impacts is outside the scope of this methodology. Cradle-to-gate boundary is considered for determining the environmental impacts of different processes of DBPs control strategies. The functional unit was defined as operating a DBPs control strategy in a standard ISP facility to ensure low DBPs emissions in air and water over 50 years. The standard ISP has four pools, including a 50-m pool, a 20-m pool, a leisure pool and a hot tub and the total pool surface area is around 3100 m2. This time period is selected as most commercial buildings have a service life of 50 years [514,515]. Statistics Canada [461] has reported that pools with a length ≥ 50 m are expected to have a service life of 48 years, while pools with a length ≤ 25 m have an expected service life of 38 years. Therefore, a temporal boundary of 50 years is considered appropriate for determining the lifecycle environmental impacts of public ISPs. Permissible levels of DBPs emissions, defined by air and water quality guidelines, are another important criterion in the selection of DBPs control strategies. For example, a few countries have implemented DBPs emission levels (between 20-100μg/L) for swimming pool waters [516–518]. However, there is no available DBP concentration guideline for swimming pool water in Canada. Thus, the DBP concentration guidelines were not considered in defining the functional unit in this study. The system boundaries for the P-LCA are provided in Figure 7-2. Since this study's goal was to assess the environmental impacts associated with DBPs control strategies, only processes involved in ISP operation within the pool environment are included in the system boundaries. DBPs control strategies are applied during the operation stage of ISP. Hence, other life cycle phases such as extraction of materials, transportation, construction and end of life stages are excluded from system boundaries. Similarly, DBPs control strategies do not alter the infrastructure of the ISP, so the environmental impacts associated with the pool infrastructure and systems, such as routine maintenance and replacement of swimming pool materials and 122 systems, building envelope not part of the P-LCA study. Hence, the terms ISP operation and DBPs control strategies can be used interchangeably in this study as they represent the same meaning. Note- DBPs: Disinfection byproducts; UV: ultraviolet treatment; PHE: plate heat exchanger Figure 7-2 System boundaries for P-LCA of DBPs control strategy 7.2.2 Data Collection Energy, water and chemical use data in ISP operations can be obtained from a variety of sources, including calculations based on the engineering drawings, pool operation records, expert judgments, existing literature and databases. Ecoinvent 3 is the world's largest and most transparent database on unit processes and is extensively used in different LCAs [519]; hence, Ecoinvent 3 database is appropriate for generating life cycle inventory (LCI) for ISP operation. Three main flows: energy, water and chemicals, are considered in the LCI. Scenario analysis is the most commonly used method for the comparison of environmental impacts under varying operating conditions [520]. Therefore, the scenario analysis was used for investigating the environmental 123 impacts as a result of implementing different ISP operation management strategies that include a combination of energy-reducing strategies and DBPs control strategies. The operating condition of an ISP without implementing any DBPs control strategy represented the baseline condition. DBPs control strategies applicable to a specific ISP need to be explored and their impacts will be determined using scenario-based assessments. The LCI is generated based on the collected operational data and the measured DBPs concentrations in the ISP environment. As shown in Figure 7-3, the ISP operational data, as inputs to the methodology, include disinfectants and water softeners added to water, energy used by heating, ventilation and air conditioning (HVAC) systems, water consumption and generation of DBPs and wastewater. Transmissions of energy and water from the nearest available source present in the Ecoinvent 3 database are used. Energy use under a specific DBPs control strategy depends on the number of bathers present in pools, type of pool, water heating system (e.g. water heat pumps, heat heaters, etc.), water treatment systems (e.g. UV treatment, filtration, ozonation etc.), on and off schedules of pools’ water features (e.g., water sprayers, water wheel), chemical pumps in operation and the setting of water and air circulation systems. The proposed methodology focuses on the operation of swimming pools alone. Thus the energy uses in other ISP amenities, such as exercise rooms and washrooms, is not evaluated. Table 7-1 shows models and tools used for determining energy use in ISP operations. The total energy used for swimming pool operation can be expressed as a combination of energy consumption of operating different systems/components in an ISP, such as the energy used for water heating, pump operation, rainwater harvesting system (if applicable), HVAC system, lighting system and water treatment. The energy used to heat water in swimming pools is evaluated through the Enerpool tool which is specifically designed for swimming pools in Canada [521]. A variety of pumps are used in swimming pools that include chemical, chlorination, pH adjustment, circulation and by-pass pumps. The flow rate and pressure head are used to determine the amount of energy used by each pump. Energy used by rainwater harvesting systems (RWH) ranges from 0.19-5.51 kWh/m3, 124 Figure 7-3 Input and output parameters for DBPs control strategy 125 Table 7-1 Background information for energy calculations Energy use Model Used/Source Description Ref. Pool heating Enerpool Tool 3.0 Enerpool Tool is a program developed by NRCan Canada. This program is capable of calculating heating energy required for swimming pools of different types located in Canada and US cities. [521] Pool pumps 𝐸 = (𝐹𝑟 ∗ 𝐻3196) Where: E=Energy used by pump (kW) Fr=Flow rate (m3/day) H=Total head (m) Energy consumed by the pumps used for refilling pools depends upon the pump design and the flow rate of water. [522] RWH 0.19-5.51 kWh/m3 Energy consumed by rain water harvesting system depends upon the water storing capacity. [523] HVAC Energy Calculator Energy Calculator is an online energy tool that can calculate the energy used by air handling units by the provision of operating design parameters of HVAC units. [524] Local exhaust vent 𝐸 = (𝐹𝑟 ∗ 𝑃 ∗ 𝑡) Where: E = Energy use by local exhaust vent (kWh) 𝐹𝑟=Flow rate P= Local exhaust power (W) 𝑡= Operating time (Hours) Local exhaust vents help in the removal of DBPs on the pool surface and are needed because the majority of the DBPs emitted in the air from the pools are present near the surface of the water [525] Lighting 𝐸 = (P ∗ ∆𝑡) Where: E = Energy use per luminaire (kWh) P = Luminaire power (W) ∆𝑡 = Full load hours per luminaire (hrs) Energy consumed by the lighting present in the indoor swimming pool facility. [526] UV-treatment 𝐸 = (0.0145 ∗ 𝑄) Where: 𝐸 = Energy use (kWh) 𝑄 = Water flow rate (m3/day) Ultraviolet (UV)- treatment is used for removing chlorine-resistant pathogens in swimming pool water. The energy consumed through UV treatment depends upon the intensity of UV system to destroy pathogens. Compared to other water treatment systems UV-treatment is more energy-intensive. [527,528] Chlorination 𝐸 = 0.0021 ∗ 𝑄 Where: E = Energy use (kWh) Q = Water flow rate (m3/day) Chlorination is the most common disinfection method with fast and long-lasting effects. Energy used in chlorination depends upon the water flow rate in a swimming pool. [529] Filtration 𝐸 = (0.1717 ∗ 𝑄) Where: E = Energy use (KWh) Q = Water flow rate (m3/day) Energy used in filtration depends upon the flow rate of swimming pool water filtered. [527] 126 depending on the system's size and type [523]. Energy used by HVAC systems mainly depends on the number of air handling units (AHU), type of exhaust system in use and their operational conditions. Lighting energy is determined by the operation schedules as well as the types and number of lighting fixtures in an ISP. Energy used by UV-treatment, chlorination and filtration depends on the water circulation rate within pools and hence varies with the size and type of pools. The water data for LCI can be calculated using a water balance model similar to the model by Gallion et al. [530]. The water balance model is provided: total i jW W W   Equation 7-1 where Wtotal is the total water use, Wi represents the replenished water and Wj represents the water loss due to backwash, evaporation and drainage. The specific models used for evaluating water replenishment and loss are provided in Table 7-2. The amount of water refilled depends on the number of swimmers, size of the pool and maintenance frequency. Water provided by RWH depends on the system capacity and local weather and topography conditions. It should be noted that RWH is not a commonly used feature in ISPs and the value will become zero if RWH is not used for a pool water refill. Evaporation is not only a significant contributor to the total water loss but also to energy use because extra energy is required to heat the pool water refilled [531]. The evaporation model developed by Shah [532] is the most commonly used for determining the evaporation of swimming pool water. This model describes the correlations of water, air and energy use in swimming pools based on the experimental data. Shah’s model [532] calculates water evaporation in both occupied and unoccupied pools and is used to generate water use data in the LCI. The amount of water drained also varies with the maintenance frequency and volume of pools. Water used for cleaning filters and pools through backwashing depends on the flow rate of backwashing pumps as well as the duration and frequency of backwashing [512]. Chemical use data varies from one ISP facility to another and should be collected through consultation with facility managers and operators. The concentrations of DBPs generated in pool 127 Table 7-2 Models used for water calculations Water use Model/Source Used Ref. Pool refills: Pools are refilled either to maintain water level due to spilling and evaporation or after the pool has been completely drained for maintenance and cleaning purpose. 𝑃𝑟𝑒𝑓𝑖𝑙𝑙 = (𝑉𝑝𝑜𝑜𝑙 ∗ 𝐹𝑟𝑒𝑓𝑖𝑙𝑙) Where: 𝑃𝑟𝑒𝑓𝑖𝑙𝑙= Volume of water refilled in pool (m3) 𝑉𝑝𝑜𝑜𝑙= Volume of pool (m3) 𝐹𝑟𝑒𝑓𝑖𝑙𝑙= Refill frequency [512] Rainwater harvesting system: Depends on the rain water system present in the facility 𝑃𝑅𝑊𝐻=Capacity of rainwater harvesting system (m3) [533] Evaporation: Evaporation models consider the principle of natural convection occurring due to temperature and density differences between air on the pool surface and room air, number of swimmers and air circulation due to ventilation systems. Where: b = 0.00005 in SI units and 0.0346 in I-P units C = 35 in SI units and 290 in I-P units E=Evaporation from occupied pool 𝐸𝑜=Evaporation from unoccupied pool 𝜌𝑟 = Density ratio of air at room conditions 𝜌𝑤 = Density ratio of air at the water surface N=Number of occupants per unit pool area 𝑊𝑟= Humidity ratio of air at room conditions 𝑊𝑤= Humidity ratio of air at the water surface Evaporation from unoccupied pools is larger of Eq.1. and Eq. 2. 𝐸𝑜 = 𝐶 ∗ 𝜌𝑤(𝜌𝑤 − 𝜌𝑟)13⁄ (𝑊𝑤 − 𝑊𝑟) (Eq. 1) 𝐸𝑜 = 𝑏 ∗ (𝜌𝑤 − 𝜌𝑟) (Eq. 2) Evaporation from occupied pools if N ≥0.05 𝐸𝐸𝑜⁄ = 1.9 − 21(𝜌𝑟 − 𝜌𝑤) + 5.3𝑁 (Eq. 3) Evaporation from occupied pools if N<0.05 𝐸 ranges between 0.05 and 1 [534] Water drainage: Water drainage is required for maintenance and cleaning of the swimming pools. The frequency of drainage depends upon the size and usage of pools Example: 50-m pool is usually drained annually while leisure pools /children pools can be drained monthly. 𝑃𝑑𝑟𝑎𝑖𝑛 = (𝑉𝑝𝑜𝑜𝑙 ∗ 𝐹) Where: 𝑃𝑑𝑟𝑎𝑖𝑛= Volume of water drained from pool (m3) 𝑉𝑝𝑜𝑜𝑙 = Volume of pool (m3) F = Frequency of drainage [535] Backwashing: Over time, the filters of the pools are clogged and regular backwashing is performed to maintain water quality of the swimming pool. 𝑃𝑏𝑎𝑐𝑘 = (𝑄𝑝𝑢𝑚𝑝 ∗ 𝑇𝑏𝑤 ∗ 𝐹𝑏𝑤) Where: 𝑃𝑏𝑎𝑐𝑘= Volume of water used in backwashing (m3) 𝑄𝑝𝑢𝑚𝑝 =Pump flow rate (m3/day) 𝑇𝑏𝑤=Duration of backwash (hrs) 𝐹𝑏𝑤=Frequency of backwashing [512] 128 water and released to ambient air are affected by the chemical use, disinfection methods and the specific uses of the ISP facility. While chemical input data can be obtained from the annual ISP operation records, the chemical emission data can be determined by analysis of air and water samples collected from specific ISPs. It should be noted that the accuracy of P-LCA results is affected by the number of DBPs investigated. Trihalomethanes (THMs) and chloramine groups are the most prevalent DBPs in swimming pool water [469,480]. The concentrations of THMs are regulated in water quality guidelines for swimming pools in some countries [495]. THMs consist of chloroform, bromodichloromethane, dibromochloromethane and bromoform [464,536]. Both THMs and chloramine groups are considered chemical emissions in the proposed methodology. 7.2.3 Life Cycle Impact Assessment (LCIA) The potential environmental impacts are quantified based on the inventory data [513]. The LCA methods can be categorized as problem-oriented (midpoints) and damage-oriented (endpoints) depending on the objectives. The midpoint impact assessment determines the potential impacts on the environment [537], while the endpoint assessment evaluates the impacts to the public in a specific area [538]. Since the objective of this study is to assist ISP facility managers in selecting a low-impact operation strategy midpoint impact assessment is used in the methodology. ReCipe is the most commonly used method for midpoint assessment. Midpoint impacts in ReCipe can be determined from three perspectives: individualist (I), hierarchist (H) and egalitarian (E). ReCipe (I) determines midpoint impacts over a short duration and is representative of a positive mindset; ReCipe (H) is considered to be the default scenario where midpoint impacts are quantified for a 100-year period; and ReCipe (E) represents midpoint impacts over a long duration (example 1000 years for climate change) and follows a precautionary thinking including impacts that have not been fully established [539,540]. ReCipe (H) is used in the methodology based on the consideration of the temporal boundaries of the LCA. For this study, ReCiPe 2016 Midpoint (H) V1.12 / World Recipe H in SimaPRo tool was used. 7.2.4 Interpretation The final step of this methodology is results interpretation, which involves analysis of assessment outcomes to evaluate robustness and sensitivity of the inventory [541]. In this step, conclusions are also drawn with reference to the LCA's goals and objectives [542]. Interpretation consists of 129 three main parts: (i) identification of significant issues in the results obtained from the LCI and impact assessment phases; (ii) validation of results by testing the study for completeness, consistency and sensitivity and (iii) drawing the conclusion, limitations and recommendations [543]. Interpretation of P-LCA results is performed in two stages: (i) using normalization to compare DBPs control strategies based on environmental impacts and (ii) using the TOPSIS method to compare DBPs control strategies using environmental impacts and DBPs emissions. Stage 1-Interpretation: The results from an impact assessment are commonly normalized for interpretation. Normalization of impacts can be done either through internal or external normalization methods. Internal normalization enables case-specific normalization using the reference impact determined within the study [544]. On the other hand, external normalization is performed based on a selected reference system and is more appropriate when the relative importance of different indicators in a single system is determined. External normalization can dominate the weighting scheme and is not appropriate for comparative LCA study [545]. Since the objective of this study was to compare the impacts of DBPs control strategies, internal normalization was deemed suitable for impact interpretation. The internal normalization was performed using Equation 7-2. 𝑁𝑖𝑗 = 1 −max𝑖𝑥𝑖𝑗−𝑥𝑖𝑗max𝑖𝑥𝑖𝑗−min𝑖𝑥𝑖𝑗 Equation 7-2 where N is the normalized value of impact (e.g., climate change, ozone depletion), x is the value of impact category 𝑖 under DBPs control strategy j. The max xij and min xij are the maximum (assigned value one) and minimum (assigned value zero) values in each impact category i. Stage 2-Interpretation: Stage 2-Interpretation is performed by aggregation of midpoint impacts using the MCDM method that increases the usability, robustness and implementation of LCA [546]. Midpoint impact categories have been used extensively for LCA studies and are important for green building-related programs such as LEED, environmental product declarations etc. [546]. Numerous researchers have used midpoint impacts and combined them with other criteria such as human health risks, economic indicators etc., to rank the studied systems [544,547–550]. In the same vein, in order to represent possible preference variation of different stakeholders, 18 midpoint categories and the DBPs generated are aggregated under 3 main criteria: (1) Human health; (2) 130 Ecosystems; and (3) Resources. TOPSIS method is used to select the best DBPs control strategy with minimum impacts on the environment. TOPSIS is a widely used multi-criteria decision-making technique, in which the ideal solution is determined by identifying the most ideal and least ideal alternatives. The integration of LCA results into TOPSIS yields more robust solutions than those obtained from ReCiPe single scores and linear weighted sum methods [551,552]. The following six steps are used in TOPSIS to rank DBPs control strategies: Step I: Normalizing the quantified impacts to human health, resources and eco-systems under different DBPs control strategies. 𝑥𝑖𝑗 =𝑥𝑖𝑗√∑ 𝑥2𝑖𝑗𝑚𝑖=1⁄ Equation 7-3 DBPs concentrations in air and water are used as indicators of human health concern. The human health-related midpoint impacts and DBPs emissions are aggregated to the human health (HH) impact category, while other midpoint impacts are aggregated to the corresponding ecosystem (ES) and resources (RE) impact categories. The distribution of midpoint impacts into endpoints categories follows the same path as used for ReCiPe2016. All individual impacts are assumed to contribute equally into the main category. Step II: Creating a weighted normalized matrix based on the weights determined for the three categories (HH, ES and RE) by stakeholders. Step III: Evaluation of positive and negative ideal solutions. The best solutions (Vj+) for the weighted normalized matrix are selected out of all alternatives: 𝑉𝑗+ = [𝑀𝑖𝑛𝐻𝐻 , 𝑀𝑖𝑛𝐸𝑆, 𝑀𝑖𝑛𝑅𝐸] The worst solutions (Vj−)for the weighted normalized matrix are also selected out of all alternatives 𝑉𝑗− = [𝑀𝑎𝑥𝐻𝐻 , 𝑀𝑎𝑥𝐸𝑆, 𝑀𝑎𝑥𝑅𝐸] Step IV: Calculation of Euclidean distances between an alternative and positive (𝑑𝑖+)and negative (𝑑𝑖−) ideal solutions, respectively. Each DBPs control strategy is considered an alternative. 𝑑𝑖+ = √∑ (𝑣𝑖𝑗 − 𝑉𝑗+𝑚𝑗=1 ) Equation 7-4(a) 131 𝑑𝑖− = √∑ (𝑣𝑖𝑗 − 𝑉𝑗−𝑚𝑗=1 ) Equation 7-4(b) Step V: The relative closeness to the ideal solution was found by Eq. (5-c) 𝐶𝐿𝑖∗ =𝑑𝑖−(𝑑𝑖− + 𝑑𝑖+)⁄ Equation 7-4(c) Step VI: Ranking DBPs control strategies based on the relative closeness calculated. The alternative with the smallest relative closeness is preferred. The weights assigned to various criteria are subject to the stakeholders’ preferences. In the absence of survey data on stakeholder preferences, the weights were changed from 0 to 1 to simulate stakeholders' preferences. Ternary plots are generated to visualize the change of ranking of DBPs control strategy changes with the variation in weights of the criteria. 7.2.5 Determine Eligible Incentives Incentives for non-residential buildings such as aquatic centre can be determined as a percentage of energy saving per unit floor area. Higher the savings, more incentives will be applicable for the operation scenario. Based on the ranking results obtained from two interpretation stages, the energy savings are compared with each scenario. Based on the comparison, the impact of building operation environmental impacts on existing FIs eligibility criteria can be assessed. 7.3 Methodology Demonstration The aquatic centre at the University of British Columbia (UBC), Vancouver, Canada, was selected as a case application of the P-LCA methodology. The aquatic centre is a new facility that has been granted a green building Leadership in Energy and Environmental Design (LEED) gold certification [553]. This facility consists of four types of swimming pools: a 50-m lap pool, a 25-m lap pool, a leisure pool with a variety of water recreational features (e.g., sprays, jets) and a hot tub (Table 7-3). The aquatic centre is equipped with an RWH system as a supplementary water source capable of supplying 2,700 m3 of water annually [533]. The ISP hall with four pools (50-m, 25-m, leisure and hot tub pool) covers a total area of 3162.6 m2 and are assumed to be in operation for 50 years’ period. The UBC aquatic centre employs a combined HVAC system to remove DBPs from the air 132 The combined HVAC system consists of two AHUs and an LEV in the deck surrounding the pools. The pool water is also treated by combined chlorination and UV-treatment process. Though a number of DBPs control strategies are possible through a change in treatment and engineering systems this study focuses on strategies based on the change in the ventilation system. These strategies were adopted because they do not require additional resources and expenditure to implement, do not disturb pool operation and users. Table 7-3 Characteristics of pools in UBC aquatic centre (Source:[554]) in the ISP Description 50-m pool 25-m pool Leisure pool Hot tub Design capacity (bathers) 200 353 374 34 Surface area (m2) 1,251 534 347 31 Volume (m3) 3,135.5 1,325.5 387.7 34.6 Average depth (m) 2.51 2.49 1.12 1.12 No. Of regenerative media filters 2 2 2 1 Flow rate (l/s) 177 95 120 40 Turnover duration (hour) 5 4 1 0.25 No. of pool refill per year 3 3 2 12 Note-UV: ultraviolet treatment; NaOCl: sodium hypochlorite In total, seven DBPs control strategies (S1-S7) were investigated based on the operating conditions of AHUs and LEV (Table 7-4). These DBPs control strategies were developed and applied to the UBC aquatic centre in the study by Saleem et al. [555]. The DBPs concentrations reported in their study were used as part of LCI. The baseline case S1 represented the ISP operation without using any DBPs control strategy. The rest DBPs control strategies (S2-S7) were developed by changing the AHU fan speed and the amount of fresh air intake. Table 7-4 DBPs control strategies used in UBC aquatic centre Strategy AHU 1 AHU 2 Local exhaust fan Fan Speed Fresh Air Fan Speed Fresh Air S1 N N (72%) N N (72%) N S2 N 86% N N (72%) C S3 N 100% N N (72%) C S4 -20% N (72%) -20% N (72%) C S5 -10% N (72%) -10% N (72%) C S6 N N (72%) N C C S7 N 100% N C C Note-N: normal operating condition; C: closed (not operating) 133 7.3.1 Life Cycle Inventory The detailed energy, chemical and water use related to seven DBPs control strategies are explained in the following sections. Energy use for operating the major ISP systems in the aquatic centre was estimated based on engineering drawings and schedules provided by the pool managers. The energy use included consumption by water treatment systems (UV-treatment and filtration), water and chemical pumps, RWH system, water heating system, water features in the leisure pool, lighting and HVAC system. Figure 7-4 presents the breakdown of individual components' energy demands under seven DBPs control strategies. It was observed that the HVAC system's operation accounts for > 25% of the total energy consumption. The results are consistent with the results from previous studies, which also shown that the HVAC system consumes a major portion of the total energy used in aquatic centres [509,556]. For swimming pools, high energy consumption is due to the high water circulation rates required for maintaining the required water quality and operating water features. Among the four types of pools, the 50-m pool is the largest energy consumer (25-32%) due to the highest water use. A large amount of energy is required to circulate, disinfect and heat the water. The leisure pool consumes the third-highest amount of energy (15-21%) under different strategies. The high energy demand of this pool is due to the night pumps that ensure the pool water is continuously circulated to accommodate high bather loads. In addition, water features used in this pool also increased energy consumption, which accounts for 15% of this pool's total energy use. In comparison, the lighting system, LEV, additional pool pumps (bulk feed and super chlorinating) and the RWH system are associated with much lower energy consumption (≤ 1%) under all strategies. Note-RWH: Rainwater harvesting; HVAC: Heating, ventilation and air conditioning system. Figure 7-4 Energy use of ISP systems under DBPs control strategies 134 The water used to fill and operate the four pools was sourced from Metro Vancouver’s drinking water system [557] and the RWH system [558]. The inventory generated for the ISP water use is provided in Figure 7-5. Since the strategies implemented mainly involved air circulation changes, the water use profile is almost constant. The variation of water loss under the seven strategies is correlated to the change of air circulation rate because the air circulation rate positively affects the rate of evaporation. Also, the 50-m pool has the largest surface, resulting in the highest water loss by evaporation. The highest water loss is due to drainage that varies within individual pools. RWH was able to compensate approximately 22% of the water loss to a certain degree, which helps achieve lower water usage. The water losses were determined the highest (1.34E+08 USG) for S6 and the lowest (1.27E+08 USG) for S2. The total water use within a 50-year period for all strategies was approximately 6.5 × 107 US gallons. Note-RWH: Rainwater harvesting Figure 7-5 Water use of ISP systems under DBPs control strategies 135 Figure 7-6 shows the chemical usage associated with seven DBPs control strategies. The aquatic centre employs combined chlorination and UV-filtration disinfection method [555]. The total concentration of THMs was used as a parameter indicating the chemical emissions to air and water due to limitation in data availability. The dosage data of disinfectants and other chemicals used for controlling water hardness and turbidity were provided by the facility manager [559], while the concentrations of THMs under different DBPs control strategies were obtained from field sampling and analysis [555]. Sodium hypochlorite, sodium bisulphate and hydrochloric acid were the most extensively used chemicals in the aquatic centre. A small quantity of calcium chloride was added to the pool water to maintain the acceptable level of calcium hardness. Calcium hardness needs to maintain between 180-220 ppm in public swimming pools in British Columbia (Canada) [560]). The amount of chemical inputs was assumed to be constant for all DBPs control strategies during the service period. Saleem et al. [555] analyzed water samples for four THMs, total organic compounds (TOC), free chlorine, combined chlorine (an indicator of chloramines) and total chlorine; while air samples were analyzed for the four THMs and trichloramine. The measured emissions show distinct variations among DBPs control strategies. DBPs in water were found in a range of 26.3 to 95.5 μg/L and chloroform and trichloramine in air were ranged from 27.7 to 106.2 μg/m3 and 0.052 to 0.224 mg/m3, respectively, under the seven strategies [555]. Table 7-5 shows the life cycle impact assessment (LCIA) midpoint scores for the seven strategies or 18 midpoint environmental categories evaluated through ReCiPe Midpoint (H) V1.12. S3 and S7 showed higher environmental impacts in most categories except terrestrial ecotoxicity. Indoor DBPs released by both S3 and S7 were low compared to S4, S5 and S6; however, the two strategies were responsible for the higher energy consumption due to the need for heating a higher amount of fresh air. Hence, under these two strategies, better indoor air quality was reached at the expense of higher consumptions of resources. The higher environmental impacts point out the undesirability of S3 and S7. Terrestrial ecotoxicity impacts were determined to be highest for S1 and S6. Interestingly, for both strategies, chemical emissions were also high compared to other strategies. 136 Note-BDCM: bromodichloromethane; DBCM: dibromochloromethane; TOC: total organic compounds Figure 7-6 Chemical use of ISP systems under DBPs control strategies 137 Table 7-5 ReCiPe Midpoint environmental impacts of DBPs control strategies applied on UBC aquatic centre Environmental impact categories Unit S1 S2 S3 S4 S5 S6 S7 Climate change kg CO2-eq 4.1E+07 4.4E+07 4.9E+07 3.9E+07 4.0E+07 3.9E+07 4.7E+07 Ozone depletion kg CFC-11 eq 6.4E+00 6.8E+00 7.5E+00 6.0E+00 6.2E+00 6.1E+00 7.3E+00 Human toxicity kg 1,4-DB eq 2.5E+07 2.7E+07 3.0E+07 2.3E+07 2.4E+07 2.4E+07 2.9E+07 Agricultural land occupation m2 6.4E+07 6.9E+07 7.7E+07 6.0E+07 6.2E+07 6.2E+07 7.5E+07 Fossil depletion kg oil eq 1.2E+07 1.3E+07 1.4E+07 1.1E+07 1.2E+07 1.2E+07 1.4E+07 Freshwater ecotoxicity kg 1,4-DB eq 9.5E+05 1.0E+06 1.1E+06 9.0E+05 9.3E+05 9.2E+05 1.1E+06 Freshwater eutrophication kg P eq 3.4E+04 3.6E+04 4.0E+04 3.2E+04 3.3E+04 3.2E+04 3.9E+04 Ionising radiation kBq U235 eq 5.9E+06 6.4E+06 7.1E+06 5.6E+06 5.8E+06 5.7E+06 6.8E+06 Marine ecotoxicity kg 1,4-DB eq 8.6E+05 9.2E+05 1.0E+06 8.1E+05 8.3E+05 8.3E+05 9.9E+05 Marine eutrophication kg N eq 1.2E+04 1.3E+04 1.5E+04 1.2E+04 1.2E+04 1.2E+04 1.4E+04 Metal depletion kg Fe eq 1.4E+06 1.5E+06 1.6E+06 1.4E+06 1.4E+06 1.4E+06 1.6E+06 Natural land transformation m2 7.0E+03 7.5E+03 8.3E+03 6.6E+03 6.8E+03 6.7E+03 8.0E+03 Particulate matter formation kg PM10 eq 1.4E+05 1.5E+05 1.7E+05 1.3E+05 1.4E+05 1.4E+05 1.7E+05 Photochemical oxidant formation kg NMVOC 1.3E+05 1.4E+05 1.5E+05 1.2E+05 1.2E+05 1.2E+05 1.5E+05 Terrestrial acidification kg SO2 eq 1.7E+05 1.8E+05 2.0E+05 1.6E+05 1.7E+05 1.6E+05 2.0E+05 Terrestrial ecotoxicity kg 1,4-DB eq 8.4E+03 6.1E+03 7.2E+03 7.8E+03 7.9E+03 1.0E+04 7.7E+03 Urban land occupation m2a 5.0E+05 5.3E+05 5.9E+05 4.7E+05 4.8E+05 4.8E+05 5.7E+05 Water depletion m3 7.3E+06 7.8E+06 8.7E+06 6.8E+06 7.1E+06 7.0E+06 8.4E+06 Note-CFC-11: Chlorofluorocarbon; 1,4-DB: 1,4 dichlorobenzene; NMVOC: Non Methane Volatile Organic Carbon compound 138 This indicates a proportional relationship between higher DBPs releases and terrestrial ecotoxicity. Although the energy associated with these two strategies is relatively low compared to S3 and S7, the higher amounts of DBPs released could increase the exposure for ISP users. S4 shows the lowest impacts in all impact categories except human toxicity, indicating its potential of achieving environmentally friendly ISP operation. Under S4, the fan speed of AHUs was reduced, which significantly lowered the energy use compared to other strategies. It is also seen that S4 is associated with high impacts in terrestrial ecotoxicity. It is evident that S4 is associated with the overall lowest impacts, while S3 is associated with the highest impacts. The normalized results again indicate the feasibility of S4 for low impact ISP operation. Compared to the baseline case (S1), S2, S3 and S7 have very high environmental impacts, suggesting that providing a higher amount of fresh air (for example, compared to S1 fresh air quantity of 72%, S2, S3 and S7 are getting 86%, 100% and 100% air respectively) could reduce the levels of DBPs, but it could have a significant adverse impact on the environment. In the case study, building electricity was used as an energy source. The generation of electricity and other conventional energy are often associated with high environmental impacts [561]. A hotspot analysis was also performed to determine the most important processes and materials influencing the environmental impacts of ISP operation. The percentages of environmental impacts contributed by water, energy and chemical uses were compared for seven control strategies. The results show that the contribution of energy use was the highest (i.e., 87-94%) for all impact categories. The contributions of chemical and water use only account for ≤ 13% of the total impact under all strategies. In addition to the midpoint, ReCiPe 2016 Endpoint (H) V1.12 / World Recipe H method was used to determine the impacts of DBPs control strategies on human health, resources and ecosystem. These results are provided in Appendix G, Figure G1. The results show similar trends as a midpoint, i.e. S3 and S7 have the highest impacts while S4 has low impacts. 7.3.2 TOPSIS Results The comparisons between environmental impacts and DBPs released can help ISP managers select the most suitable strategy with minimum environmental impacts. High DBPs levels emitted can cause direct human health impacts on ISP users. However, estimation of these direct human health 139 impacts is beyond the scope of the proposed methodology. The TOPSIS was performed by considering three criteria under 21 sets of weights (Appendix G, Table G1). The best and worst cases for the ranking are shown in Figure 7-7. The symbol at the convergence of the three criteria lines shows the most suitable DBPs control strategy for the specific weights in Figure 7-7(a), while the symbol at the convergence of the three criteria in Figure 7-7(b) represent the least suitable strategy under the specific weight lines. The chemical use data included the amounts of chemicals used for all four pools annually; therefore, the LCA was not performed separately for the individual pools. It is observable from Figure 7-7(a) that S4 is the most suitable control strategy for the ISP understudy, which was ranked as the top alternative under 15 weighting scenarios. When the weight of human health increases, S2 and S5 become more suitable alternatives. This is due to the fact that the fresh air circulation rate is high in S2, which leads to a low concentration of DBPs in the ISP, while for S5, the reduced energy use leads to lower emissions related to energy production, resulting in a lower use of resources but a higher human health impact. The provision of fresh air is an important approach to improve indoor air quality [562]. Despite the low concentrations of DBPs, S3 is associated with very high environmental impacts, indicating that the improvement of air quality needs to be balanced with the overall environmental impact. As shown in Figure 7-7(b), S3 is the least suitable strategy under different weights; however, it is replaced by S6 when human health is given the maximum weight of 1 and ecosystems and resources are given a weight of zero, respectively. S6 is not favourable for human health protection considering the poor indoor air quality. This is because the LEV was switched off and the fresh air supply by the AHUs was reduced. Saleem et al. [555] also indicated that as the amount of fresh air entering the ISP decreases, the DBP levels in the air are likely to rise. The ranking of various DBPs control strategies suggests that the most promising alternative is S4 and the least favourable is S3. Thus, the results of Stage 1 and 2 interpretation and endpoint impacts show that ranking of DBPs control strategies for environmental impacts show similar result that S4 is most suitable while S3 is least preferable. This study only focused on the interpretation of LCA results while direct human health impacts associated with indoor DBPs emissions were not determined. It is possible that DBPs control strategy with the lowest environmental impacts have high human health impacts. 140 Figure 7-7 Ternary diagrams of ranking results of DPBs control strategies 141 In order to select DBPs control strategy that establishes a reasonable balance between LCA related environmental impacts and the direct human health risks, the current methodology can be integrated with human health risks evaluated for DBPs exposure to the users in a similar manner as proposed by Csiszar et al.[563]. 7.3.3 Eligible Incentives The incentives assigned to non-residential buildings are assigned according to the percentage of energy reduction. The inventory for the seven tested strategies showed that compared to base case S1, the energy savings increased by 7.5%, 18.2% and 15.9% for S2, S3 and S7 respectively, while they deceased 6.4% for S4 and 3.2% for S5. This implies that while the positive savings are eligible for incentives, extra operational bills will be needed for S4 and S5. However, S4 came to be the most suitable scenario for maximum environmental and health benefits. This implies that the most energy saving options are not necessarily the most optimal. Despite the importance of human health and environmental impacts that can result due to load shifting, FIs are predominantly emphasizing energy savings. Hence, there is need to revise existing FIs eligibility criteria and extend it to include degree of reduction in environmental impacts and human health risks. 7.4 Summary The main objective of this chapter was to compare different DBPs control strategies or building operation strategies applied to the engineering systems and assess the environmental impacts using process-based LCA. The DBPs control strategies were developed by adjusting ISP operation conditions and thus the environmental impacts of DBPs control strategies are the same as those of different ISP operation methods. To meet this objective, a methodology is developed to facilitate ISP managers in selecting low impact DBPs control strategies for sustainable ISP management. The application of the methodology is illustrated using an indoor aquatic centre located in British Columbia (Canada). In particular, this study (i) generates an inventory of energy, water and chemicals used during the operation stage of an ISP; (ii) uses the inventory data to estimate the environmental impacts using the ReCipe method; (iii) enables prioritization of different DBPs control strategies using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) [299]; and lastly (iv) revisit the eligibility criteria for FIs application. This study makes 142 an original contribution by developing a methodology for estimating the environmental impacts of DBPs control strategies in ISPs. The outcomes of this study are expected to increase understanding of the link between DBPs control strategies and their potential environmental impacts, which will help select and implement suitable strategies for sustainable ISP management. The findings from this can also help improve existing ISP operation guidelines. A case study of an ISP in Vancouver, Canada, was used to demonstrate the application of methodology. Seven strategies based on changes in ventilation rate and quantity of fresh air exchange were applied and compared. The results have shown that energy use in swimming pools was the major contributor to the environmental impacts (i.e., 87–94%), followed by chemical use and associated emissions. Since rainwater was used in the case study, the impacts from water use were relatively low. In most swimming pools, these impacts are expected to be higher than those estimated for the specific case study. These higher impacts are expected as the case study pool operates on hydroelectric energy while fossil fuels are still the most dominant source of electricity production around the world [564]. The results of P-LCA also showed that increase in ventilation rate and air quantity results in lower DBPs emissions, but the associated environmental impacts increased for the same strategies. Therefore, it is important for strategies to achieve a balance between the indoor DBPs levels and the environmental impacts generated by implementing DBPs control activities. The overall findings indicated that the proposed P-LCA methodology was suitable for the selection of a DBPs control strategy to operate a healthy and environmentally friendly ISP. Furthermore, the existing FIs criteria for GBs need to include the impacts on the environment and human health. To conclude, there is a need to assess DBPs control strategies that ensure the healthy and sustainable operation of swimming pool facilities. Integration and assessment of DBPs control strategies with energy efficient technologies could be a path for future studies. The proposed P-LCA methodology can help in designing and implementing DBPs control strategies to achieve sustainable ISP operation and protection of human health from DBPs exposure. 143 Chapter 8 Conclusions and Recommendations This research addresses critical gaps in the literature related to the evaluation of financial incentives’ effectiveness and efficiency. A decision support framework was proposed and developed that can help attain low-energy green buildings and sustainable neighborhoods. The developed framework will help advance existing evaluation methods and direct financial resources to options that yield high environmental, health and cost-related benefits. The framework helps in decision making related to the selection of the most feasible operation strategy for public swimming pool facilities, selection of energy performance upgrades for new residential buildings based on occupancy profile of the residents, evaluation of cost and environmental effectiveness for residential archetypes and evaluation rebates efficiency of building and neighborhood levels. The framework will help end-users, developers, building managers and policy makers take informed decisions. Furthermore, though the framework is explicitly developed considering case studies in Canada, it can easily be replicated in other countries with similar social and economic conditions. 8.1 Summary A summary of the specific study sections and the main conclusions are given below. Chapter 3: An in-depth literature review was conducted on the types of financial incentives available for green buildings in Canada. Along with this, state-of-art practices employed for evaluation of the effectiveness of financial incentives were investigated. The findings of this chapter identify research gaps in financial incentives evaluation studies and different evaluation indicators and methodologies Chapter 4: A systematic two-phase methodology was proposed to assess the impacts of occupancy on residential energy performance upgrade selection. Feasible EPUs were selected based on economic and environmental performance criteria and ranked through the TOPSIS method. The methodology was tested on two identical homes with different occupancy profiles. Calibrated energy models and detailed life cycle cost analysis were performed to observe the impact of occupancy. In addition, the methodology offered flexibility in describing the preference of occupants that could range from pro-economic to pro-environment. The proposed methodology in 144 this chapter would assist developers, planners, potential owners and practitioners in developing single-family detached houses for retired as well as typical urban communities in Canada and other developed countries. Chapter 5: This chapter provided a multi-stage methodology for evaluating the effectiveness of incentives available for new residential archetypes. The proposed methodology also introduced a location-based effectiveness index matrix that enables assessment of the environment and economic impacts of rebates from end-users’ perspectives. In addition, a parametric sensitivity analysis approach is proposed that will enable the identification of the most important factors for obtaining effective rebates. The proposed methodology can help in understanding the relationship between FIs and their impact in achieving environmental and economic targets for different building archetypes. The case study conducted on seven residential archetypes common in Canada and showed the feasibility of the proposed methodology in making informed decisions. The results derived from the case study were used for providing recommendations for policy makers. Chapter 6: The chapter proposed a methodology to evaluate the efficiency of FIs for residential archetypes at individual building and neighborhood level. The bottom-up energy modelling technique and data envelopment assessment method were used to obtain the feasibility of investment for different archetypes and climate zones. The methodology could determine efficiency from investors and government perspectives. This research can help municipality governments allocate rebates to archetypes, while the efficiency of investments can help end-users make informed decisions regarding their future expenditure. At neighborhood levels, the efficiency could help in the distribution of national and provincial subsidies. Likewise, the investment efficiency at neighborhood level can help to benchmark and comparing regions and indicate what changes need to be incorporated for improvement in performance. Chapter 7: A methodology for evaluating the most feasible operation strategies for public indoor swimming pools based on health and environmental perspective. The methodology was developed on detailed energy modelling, field study data and process-based life cycle assessment to obtain environmental and health impacts. In addition, the relation between eligible incentives and building operation was observed. The research in this chapter fulfilled the significant knowledge gap in the literature related to environmental impact evaluation of public indoor swimming pool facilities. A case study of an actual facility in Vancouver, Canada, was considered. The results 145 indicated the feasibility of methodology to assess the environment and human health impacts along with implication related to eligible public incentives. The proposed methodology can help in designing and implementing strategies that can help achieve sustainable ISP operation and protection of human health from swimming pool disinfectant by-products exposure. 8.2 Conclusions The aim of the study was to develop a planning framework for financial incentives to enhance the construction of green buildings (GBs). The framework was demonstrated by case studies of actual buildings and a hypothetical community. It was clear from the findings that the local, regional parameters (location, building archetype, community design, costs, energy prices), occupancy (number of occupants), building operation and stakeholders’ perspectives play an important role in the effectiveness and efficiency of available financial incentives. The findings can be useful for policy makers, end-users, developers and building managers to achieve more energy savings and carbon emissions reductions at a lower life cycle cost. Based on research findings, a seven-step methodology is proposed to obtain the desired effectiveness of FIs in residential and commercial buildings (Figure 8-1). These steps include: (1) Consideration of major stakeholders’ perspectives in order to minimize the associated risks; (2) Collection of data related to the buildings that include existing incentives, occupancy profiles, socio-demographic conditions and stakeholders; (3) Regulatory level to assess the degree of influence of deployed FIs as well as the available resources; (4) Identification of key barriers that may influence effectiveness is essential to assure minimum wastage of resources; (5) Applicable analysis method (ex-post or ex-ante) depending upon the time of analysis, for example, the ex-ante method is the only option for analysis for new FIs (for instance, a new technology); (6) Comparison of the analysis results with the desired target; and (7) revision if targets are not achieved. In addition, a strategic map (Figure 8-2) is generated that can be used to develop more energy efficient building stocks and more sustainable cities. The map shows that depending upon the level of application, the main targets, key policy instruments and influence of associated stakeholders will change. The design and deployment of future FIs need to be gradual and planned to ensure that municipality, regional, provincial and national targets are achieved. 146 *These are all the most commonly used ex-post and ex-ante methods. Only one method will be selected according to perspective and target under consideration Figure 8-1 Methodology for evaluating the effectiveness of FIs 147 Figure 8-2 Strategic Map 8.3 Originality and Contributions The study critically evaluated green building financial incentives in Canada. The work will help design and modify FIs for the wider adaptation of GBs to meet climate goals in Canada. In summary, three major contributions of this research are as follows. Standardization of FIs appraisal: There is no standard method of FIs effectiveness and efficiency evaluation and different methods have been employed to achieve their results. Hence, this 148 framework strived to provide a standard decision support framework for evaluating green building FIs based on effectiveness and efficiency that will enable engineers, planners and policy makers to make informed decisions. Multi-stakeholders’ perspectives in investment planning: In order to ensure a sustainable future, all stakeholders of buildings should strive towards the same goal. The two most important stakeholders are the investors (end-users, occupants or developers) and the government providing rebates. Most studies have considered only one perspective evaluation. Hence, the research results provide useful information to help in decision making by investors and the government. Incorporation of lifecycle thinking and spatial variations: Limited studies have evaluated the effectiveness and efficiency of the deployed financial incentives. Moreover, the FIs associated with the achievement of specific building energy code targets are absent from the literature. This research proposed effectiveness and efficiency evaluation frameworks that can account for the change in residential building archetype and climate zone for meeting advanced building energy code targets. 8.4 Limitations Data limitations: Data collection related to buildings costs and energy usage is a challenge. This research utilized data from a number of sources, including the RSMeans database, local utility providers, ASHRAE, field measurements and existing literature. Hence, there is non-uniformity in data and assumptions are often made to generate the models with limited data available. Since the purpose of this research was to provide a framework that is capable of evaluating the effectiveness and efficiency of FIs, data limitation did not have a significant impact on decision making and reasonable results were obtained. Focus on one form of financial incentive: The framework proposed utilized only one form of incentive, i.e., rebates offered by the government. Although rebates are the most common policy instruments and highest among existing FIs, other incentives such as grants and taxes can also help in increasing the rate of construction of greener buildings. Therefore, future work can incorporate 149 impact of these instruments combined to determine overall impacts of energy policies at local and national levels. Limited to developed countries: The framework was constructed under the Canadian context and case studies of Canadian buildings were employed. Hence the obtained results can be applied to other countries with a similar economic and social condition like the United States and the United Kingdom. However, the framework cannot be applied to developing countries unless suitable modifications are made according to governing parameters of developing countries. Limited to new buildings: The framework was introduced for new buildings; however, it can be modified and applied to retrofit existing buildings through careful evaluation and classification of existing archetypes. Compared to new houses, existing homes are more complex and will have a larger set of constraints that will vary with the local context. Detailed survey studies will be required to identify the archetypes of the existing building stock and designing effective and efficient FIs. Limited number of EPUs: Despite an extensive study on a number of archetypes of residential buildings, there are some limitations that need to considered for applying the framework results. A limited number of locally available EPUs were utilized for rebates effectiveness evaluation. Future studies should carefully consider the local context and construction practices during FIs assessment. Limited Statistical Analysis: A limited statistical analysis was conducted to investigate the effect of different design and economic parameters on ultimate effectiveness and efficiency of tested incentives. A detailed multivariate sensitivity analysis is required to determine the impact of uncertain parameters on performance of FIs and to propose improvements in current incentive structures. 8.5 Future Research The following areas were identified as a possible extension of this research. Incorporate economic uncertainty: Future research should consider economic uncertainties related to interest rates, energy prices, labor and digression costs [47,391,392]. Costs of low-emission and renewable systems are expected to decrease as more efficient equipment and 150 technologies are developed and GBs become more widely adopted. Hence, the investors will have wider choices of EPUs for making energy efficient buildings. Incorporate the impact of climate change: Climate change is another important factor that will affect the selection of EPUs and the effectiveness and efficiency of FIs in the near future. A recent report by Canada’s Changing Climate Report indicates Canada's temperature is rising at rates twice compared to the other regions of the world [565]. Hence, the cooling load demands of buildings will increase over time. Therefore, future studies should consider the impact of climate change on the EPUs selection for residential buildings. Incorporate the impact of the COVID-19 pandemic and changing occupancy profiles: Another area of research that needs to be addressed urgently is the impact of the COVID-19 pandemic on occupants’ behavior and the relevant EPUs for increased occupancy periods. Energy use in residential buildings has increased as people spend the majority of their time at home as an intervention measure [338]. As a result of this pandemic, homes have been transformed into mixed-use spaces where home-schooling, office work, recreational activities and social interaction have become norms. Similarly, the percentage of remote or home workers had been steadily increasing in Canada and other countries of the world, even in pre-pandemic conditions. COVID-19 has increased the trend of working from homes, thus bringing permanent changes in the energy use of the building sector [339,340]. 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Climate OneBuilding Repository of free climate data for building performance simulation 2020. http://climate.onebuilding.org/default.html (accessed April 11, 2020). 210 [594] Environment Canada. Canadian Climate Normals 1981-2010 Station Data 2020. https://climate.weather.gc.ca/climate_normals/station_select_1981_2010_e.html?searchType=stnName&txtStationName=kelowna&searchMethod=contains (accessed April 11, 2020). [595] Government of Canada. Canadian Climate Normals 2020. https://climate.weather.gc.ca/climate_normals/index_e.html (accessed November 17, 2020). 211 Appendices Appendix A Financial Incentives Table A1 Loan incentives Sector Program Name Provider Source Province Amount (CA$)/Type/Interest Ref. Commercial/ Institutional Religious Buildings Initiative Manitoba Hydro - Power Smart for Business Utility MB $5,000 5 years 6.75% [566] Energy Retrofit Loans City of Toronto Municipality ON 20 years [113] Residential CMHC Green Home Canada Mortgage and Housing Corporation (CMHC) Federal government All 25 % Premium Refund [109] Energy-Efficient Housing Program Genworth Financial Canada Private Insurance Company All 15 % Premium Refund [567] Central or ductless heat pump loans FortisBC - Electricity - For Homes Utility BC $6,500 10 years 1.9% [568] RBC Energy Saver™ Loan RBC Royal Bank Bank BC >$5,000 10 years 1.0% [110] Power Smart Residential Loan Manitoba Hydro Utility MB $7,500 5 years 4.8% [114] Residential Earth Power Loan - for Cold Climate Air Source Heat Pumps Utility 10,000 5 years 4.9% [569] Residential Earth Power Loan - for Solar Water Heaters Utility 7,500 5 years 4.9% takeCHARGE Energy Efficiency Loan Program (EELP) Government of Newfoundland and Labrador and takeCHARGE Utility NL $10,000 5 years 4.95% [570] Home Energy Loan Program (HELP) City of Toronto Municipality ON $75,000 15 years 3.5% [111] ENERGY STAR® Loan Program SaskEnergy Utility SK $15,000 5 years 6.5% [112] Home Repair Program Yukon Housing Corporation Provincial YT $50,000 15 years 1.0% [571] 212 Table A2 Tax incentives Sector Program Name Provider Source Province Amount (CA$)/Type/Interest Ref. Residential Home Energy Loan Program (HELP) City of Toronto Municipality ON $75,000 15 years 3.5% [111] RénoVert Tax Credit Revenu Québec Provincial QC Up to $10,000 [126] 213 Table A3 Grant incentives Sector Program Name Provider Source Province Amount (CA$)/Type/Interest Ref. Commercial/Institutional Grant for an Infrared Heating System Énergir Utility QC Up to $500 per appliance [149] Grant to Encourage Energy Innovation Up to $250,000 demonstration project Grant for a Solar Air Preheating System Up to $200,000 Grant for a Unit Heater Up to $1,700 per appliance Implementation of Energy Efficiency Measures Grant Up to $100,000 Condensing Boiler Grant Up to $25,000 Recommissioning Grants Up to $100,000 Feasibility Studies Grant Up to $50,000 Energy-Efficient Renovations Grant Up to $100,000 New Efficient Construction Grant Up to $275,000 Solar and Wind-powered Pump Grant SaskPower Utility SK Not Specified [572] Residential Empower Me FortisBC - Natural Gas - For Homes Utility BC Home Energy Low-Income Program (HELP) Efficiency PEI Utility PE Varies [131] Tankless Water Heater Grant Énergir Utility QC $400 [149] Smart Thermostat Grant Up to $100 Hot Water Boiler Grants $1,200 Combo System Grant $500 Residential+ Commercial/Institutional High-Efficiency Water Heater Grant Énergir Utility QC Not Specified [149] 214 Table A4 Summary of publications on FI used in GB No. Focus Area Location Incentive Perspective Effectiveness/Amount Key Findings Limitations Ref. 1. Residential & Commercial Athens, Georgia, USA Cost reduction Society End-users Cost effectiveness NPV 10%-14% The impact of cost reduction on green roofs through possible incentives yields benefits for private investors as well as society Actual incentives were not analysed, but cost –reduction were tested through a sensitivity analysis on the basis of which future incentives can be planned [43] 2. Ground source heat pump 1 (GSHP) Residential USA Tax credit Government Providing 30% federal tax on GSHP increases the adoption to 30% of houses and reduces PBP 4.8 years Spatial and economic constraints were defined to determine the suitability of GSHP and were seen to be very low ( only 10% for case study) The study was performed for putting GSHP in an existing house. As cost vary for a new house, further study needs to be performed [51] 3. Solar Water Heaters1 27 cities in China Subsidy Government (National) Environmental effectiveness 0.305-0.744 CNY/kW h Panel data model was used that calculate effectiveness that included savings of energy bills, IRR and payback periods of Solar water heaters. It was seen that the current subsidies were in-effective Detailed subsidies for different kinds of consumers such as urban residents, public institutions and industries with boilers are suggested or future research [53] 4. CHP System 1 (hospital, office, hotel building, secondary school building) 11 different states USA Capital cost Rebate Tax credits Tax loan Utility credits Not specified Grant > 400/kW or with Production incentives > 0.1/kWh are effective for CHP systems Effectiveness determined through percentage payback period reduction For the majority of cases FIs tested are effective but useless in some cases and require additional action to apply CHP systems in buildings. [54] 5. Households ENERGY STAR appliances The U.S.A. Rebates Utility Environmental effectiveness Cost rebates on energy star clothes-washer were found to be cost-effective Impacts of 91% of rebates available were evaluated [75] 215 $28/ per megawatt-hour $140 tonne of CO2 saved Dollar increase in rebates increases the adoption rates by 0.4% only for clothes washer 6. Refrigerators1 Colombia Subsidy Rebates Government Penetration Levels Use of diffusion and discrete choice models in an ex-ante analysis revealed that 75% penetrations of AC by 2032 Willingness to pay for energy-efficient refrigerator was not considered so the effects of penetration due to financial incentive may be over-exaggerated. [77] 7. Low-carbon Technologies (LCT) Global FIT Grants Tax incentives Soft loans End-users Qualitative (based on previous studies) Soft loans are less effective as a stand-alone instruments to motivate the end-users to adopt LCTs as compared to FiT, subsidies and grants [83] 8. Building Stock 30 different countries Multiple Society (Qualitative + Quantitative) Literature review of studies related to green building policy instruments Standards for appliances, tax exemptions, labelling, building codes, demand-side management programs and energy efficiency requirements were revealed as most effective. Literature used for the qualitative evaluation is limited due to the absence of individual instrument evaluation as well as cost-efficiency values [127] 9. Residential EU Generic Subsidy Tax credit Loans Society Econometric modeling Subsidies for renovations that allow minor energy gain are ineffective and inefficient To reduce the overall energy consumption of housing by locking building energy efficiency to a particular level. Old houses have a cultural value and special subsidies are needed to be designed for their renovation [156] 10. Residential Brisbane, Australia Rebate Government Max. cost to government Aus $635 millions The model generated in this study considered financial and non-financial benefits along with interaction among different interventions options [193] 216 11. Refrigerator1 United States Rebate Society Econometric model, choice experiment used to find willingness-to-pay Mail in rebates on energy-star refrigerators increases uncertainty related to refrigerator quality and reduces willingness-to-pay for it. [194] 12. Door/Window Heating system (Residential) Italy Tax credits Society Econometric model used to determine free-riding With presence of subsidies, window renovations can increase to more than 30% , while for heating system tax credits are not effective and free-riding is a problem Low renovations rate made it difficult to measure tax credits impacts precisely More survey are needed for designing policies for window and heating system upgrades since the current study uses a small sample [195] 13. Room Air Conditioner 1 China Subsidy Government Econometric Analysis Most economic subsidy levels for China should be 60% as compared to existing (5-15%) [196] 14. Multi-Sector Technological Improvements China Subsidy Society Econometric Analysis Carbon emission under different proposed scenarios can be reduced from 15.5-19.1% Rebound effect can be reduced for energy conservation programs by the addition of clean energy subsidies and removal of fossil energy subsidies [197] 15. Residential France Tax credit Government Free-riding rate ranged from 40-85% Incentives are most effective after 2 or 3 years of the start of incentive program [198] 16. Refrigerator Washing Machine Boilers Light bulbs France Italy Denmark Poland Subsidy Tax credit Society Refrigerators -185$/tCO2 Washing Machine 190.67$/tCO2 Boilers -23.87 $/tCO2 Light Bulbs CFLs -761.132 $/tCO2 Cost-effectiveness of energy measures varies with country and energy efficiency options evaluated Taxes are more cost-effective than subsidies Effect of change in energy price and removal of less efficient devices from the market on the fuel poor segment of the population is not investigated. Spillover effects such as rebound [212] 217 Tax credit on boilers most effective for Italy and Denmark Subsidy on CFLs cost-effective in France and Poland effect, increased sale of appliances is also not evaluated 17. Commercial 4 cities in China Subsidy Government Comparative analysis Lighting system retrofits were seen to be most prevalent retrofit in commercial buildings. Subsidy incentives offered for educational buildings are more than the offices [214] 18. Energy-efficient appliances (residential) China 22 provinces Subsidy Government Energy subsidy was found to be ineffective Different factors affecting population behaviour for selecting energy efficient appliances were determined The effect of subsidy policy were vanished after 3 years of the end of policy. [215] 19. Residential Switzerland Subsidy Tax allowances Carbon tax End-users Cost effectiveness IRR ranged from energy retrofit varies from 2.1% -12.1% All tested incentive has a positive influence on NPV and IRR, but only combination of instruments pushed the NPV into profitability Energy price is one of the most important parameter in investment analysis Certain cost parameters such as smaller heating system, replacement of system and Willingness-to-pay for an energy upgrade were not considered, which will yield higher cost savings [216] 20. LCD Television1 Seoul, South Korea Rebate Tax deduction Government (National) Environmental effectiveness 21,200 tCO2 50 GWh power saved Combination of rebate and tax allowance on an appliance can result in a significant reduction in electric power demand and CO2 emissions A comparison is made on financing models of Japan, the USA and South Korea and yields current program in Korea fall short on maintenance requirements. Impacts of other products and incentives on the effectiveness were not determined. Adverse effects possible due to FIs are not considered [217] 218 21. Natural Gas Furnace1 Canada Grants Tax credit Government Environmental effectiveness $70-110/tCO2 Free-riding is represented by approximately 70 % of expenditures under the Canadian subsidy and tax credit programs. Limited data used on a single equipment (furnace) [218] 22. Lighting and cooling equipment Commercial USA Multiple Utility Qualitative Incentives suitable for different sizes of commercial buildings are explored Quantitative study needs to be performed to calculate the effect of different incentive types [220] 23. Residential & Non-Residential Netherlands Subsidy Tax credit Energy tax Policymakers Qualitative Energy tax is much more cost-effective than building codes Tested FIs were insufficient to reach the energy savings needed. Energy price was an important criterion for effectiveness The study was unable to quantitatively assess the effectiveness of individual incentive nor a policy mix due to lack of data availability Other impacts resulting from FIs implementation such as housing costs, employment and others were not investigated Newer agreements within different bodies were not considered [221] 24. Gas Furnaces1 USA Rebates Government Maximum Total energy savings =0.442 quads Maximum NPV=5.9 billions A framework is proposed for evaluating energy, economic and environmental impacts of policy instruments [222] 25. Residential sector France Incentive Tax credits Interest free Loans Grants End-users Society Utility provider End-user B/C ratio range 0.93-3.81 Society B/C ratio range 0.30-1.39 Utility provider B/C ratio range 0.12-0.88 Cost –effectiveness is measured from 3 different perspectives It is not necessary that an energy efficiency program is beneficial from all three perspectives Sample size and a lack of data of a control group added a degree of uncertainty to the results Spillover impact and impacts of free-riders are not accounted either [223] 219 26. Residential Sweden Subsidy Tax credit Society End-users Qualitative + Quantitative (System analysis approach plus results from previous studies) The variation in heating systems from different suppliers had a larger impact as compared to subsidy and taxes. Change in house value with different types of heating systems and energy prices is not investigated [224] 27. Residential heating systems 8 EU Countries France Germany Italy Spain Sweden Romania Poland UK Rebates Policy makers Choice experiment used to determine free-riding For 40% rebates free-riders exceeded 50% of the population in most countries Impact of weak and strong free-riders on cost-effectiveness was calculated Study is based on the stated behaviour of upgrade Variation in heating technologies across different countries is not considered Rebound effects and administration costs, as well as hidden costs, are not accounted [225] 28. Residential Estonia Grants (micro-economic level) 25% grant support motivated owners to invest €20/m2 on energy renovations Minor energy renovations are not useful and incentives should be designed to make existing apartment buildings to better energy level or building energy code. [227] 29. Multi-Sector Latvia Environmental Tax Government Economic efficiency Environmental effectiveness (based on previous studies) Low environmental effectiveness is associated with environmental taxes despite high economic efficiency [229] 30. Dishwasher1 Spain Subsidy Tax /energy tax (disincentive) Subsidy + tax End-users Environmental effectiveness and Economic efficiency (using demand and supply models) Welfare loss €24,000-38,000 for average energy Subsidy (rebates) increased adoption rates from 4.8-7.7% for labelled dishwashers with rebound effects Tax decreases the adoption of in-efficient appliances by 1.4-2% with no rebound effects The rebound effect evaluated does not consider the possible over consumption energy and water with penetration of other energy efficient equipment in the market [230] 220 consumed over 10 years Energy bills €192,425-260,902 Tax + Subsidy increases adoption of labelled 0.5-1.2% Welfare impacts as well as interaction with other subsidy, tax allowances etc. are not taken into consideration 31. Solar Panels and solar hot water (Residential) Brisbane, Australia Rebates Society ≈30t/yr The study estimated rebate for a solar hot water heater is more cost-effective as compared to solar panels, which are preferred by high-income households Properly planned timeframe is needed for maximum penetration. If rebates are terminated 30% into the timeframe 90% of the GHG emissions savings will be lost Non-financial benefits as well as impacts of the time frame when rebate is available were not assessed [236] 32. Home Appliances Rizhao City, China Subsidy Society Electricity consumption logarithmic difference Urban areas=4.34 Rural areas=2.86 With the application of subsidy, a significant rebound effect was seen in household electricity consumption Consumer behaviour needs to be taken into consideration for future investigations [237] 33. Solar driven heat engine (Residential) Canada Subsidy FiT Carbon tax End-users Environmental effectiveness ≈0.3 kgCO2kWh-1 Combined use of FiT incentive and carbon tax could result in a payback period of 11.3 years for solar-driven heat engines [238] 34. Solar Water Heaters1 Residential & Commercial 10 locations, Taiwan Subsidy Not specified For residential buildings highest PBP for most locations is 15 years For commercial buildings highest PBP is 5.4 years FIs for SWH are not effective for residential buildings in Taiwan Solar water heaters are more economically feasible for the commercial sector than residential As return on investment increase, people are more willing to invest in solar thermal energy [241] 35. PV Systems Residential 51 cities, Rebates Government NPV 35% of the targeted cities achieved break even point [242] 221 United States Tax deductions Tax credit FIT profitability index (PI) PBP 36. Residential Netherlands Subsidy Carbon tax Program provider Qualitative + Quantitative The study determined interaction effect on the combined use of different policy measured and showed combinations of policy measures yielded 13–30% less effect than the sum of the effects of the separate measures. [247] 37. Residential heating systems Italy Rebates Tax credit Society Environmental effectiveness €279/tCO2 Tax credits are not cost-effective Savings on energy bills are most more cost-effective as compared to the rebates on an initial investment Impact of free-riders not evaluated [262] 38. Residential and Industrial Geneva, Switzerland VAT on electricity tariff Tax deductions Multi-stakeholders Cost –effectiveness B/C Ratios averages Participant 1.83 Ratepayer 0.24 Energy consumers 0.81 Program administrator 1.15 Utility 0.27 Geographic Jurisdiction 0.76 Societal on GDP 1.22 Cost-effectiveness was calculated with respect to different stakeholders Energy efficiency programs have a positive social and economic impact but can increase energy price and costs for utilities [371] 39. Multi-Sector British Columbia, Canada Carbon tax Government Qualitative (based on previous studies) Provincial level emissions decreased from 5-15%, ensuring cost-effectiveness of the carbon tax. The support for tax by the public increased with time. Impact of tax exemptions, reductions or tax credits for low-income houses is not considered, which will reduce the actual cost-[573] 222 effectiveness of the carbon tax 40. Residential Vermont, USA Subsidy Carbon tax Society Marginal cost analysis Carbon taxes ad carbon limits can reduce environmental impacts for a building at a minimum cost, while subsidies and building codes are more cost-effective from society perspective [574] 41. Air Conditioner unit 1 Residential & Commercial Abu Dhabi, UAE FiT Subsidy Government 2.5Mt/CO2 per sector by 2030 A new upfront tariff and subsidy were proposed to reduce energy usage and CO2 and the results were evaluated over short and long term basis [575] 42. PV Systems Residential United States (2002-2012) Rebates Tax incentives Solar renewable energy credits Government 500W/ US$ per 1000 residential customers Cash rebates on solar renewable are much more effective than tax incentives The variation in incentives with the change in rates of installation or [576] 43. Office Building Pretoria, South Africa Tax Incentive End-users Genetic algorithm optimization model to determine the impact of tax incentive Tax incentive had little impact on the payback period of building energy efficiency retrofit Only one type of incentive was considered [577] 44. Heat pumps1 Netherlands Tax incentives Policymakers Demand response module Ad valorem tax are 3.5 times more effective than per-unit tax [578] 223 Appendix B Energy Tools B.1 HOT2000 HOT2000 is used extensively for the design and certification of low-rise residential buildings in Canada [309]. HOT2000 is a grey-box program that uses a series of coupled time-dependent steady-state models to determine the energy demands of the house [579].This tool uses a modified bin based method and long term monthly weather files for determining the energy performance of the residential buildings [580,581]. In the bin method, loads of the house are determined by utilizing steady-state models that are based on the assumption that the temperature remains the same within each interval and the loads can be expressed as linear functions of outdoor temperatures [582,583]. HOT2000 divides the house into three energy zones (i.e. attic, main floors and basements) and uses Alberta Air Infiltration Model (AIM2) and Mitalas method to determine the monthly and annual energy loads [584]. AIM2 model evaluates the infiltration and interaction of wind and stack effects with mechanical ventilation. The infiltration is found by the pressure difference in the windward side of the house and indoors. Hence, all the possible openings that can cause infiltration are accounted for in the model [585]. Mitalas method calculates heat losses from the foundation to the soil. It is also capable of accounting for the seasonal variation in soil temperature that affects heat losses as well as the impacts of insulation configurations [581,586].The tool also considers the impact of internal heat gains and solar transfers among the three zones for calculating heating and cooling loads. HOT2000 uses part load factors and on and off cycles to determine the size of the heating system [584]. The tool tests the energy performance using monthly steps and, owing to its steady-state nature, cannot be readily used for dynamic simulations such as a change in the solar system and wind energies. Despite its limitation as giving loads in monthly time steps, the tool is a good predictor of annual net energy performance and is periodically upgraded and tested with other tools for validation [585,586]. 224 B.2 BEopt Building Energy Optimization Tool (BEopt) is a graphical user interphase (GUI) focusing on residential buildings in North America and uses EnergyPlus as a simulation engine [396,397]. BEopt provides good modeling capability of house plans though its 3D modelling capacity is limited [398].The occupancy profiles are based on the number of bedrooms assigned to the model. The energy models constructed in BEopt can determine annual energy consumption and supports different levels of data-input that makes the adaptability of the tool high [587]. The tool uses Alberta Air Infiltration Model (AIM2) and enhanced ASHRAE infiltration models for air leakage calculations [588].AIM2 model evaluates the infiltration and interaction of wind and stack effects with mechanical ventilation. The infiltration is found by the pressure difference in the windward side of the house and indoors. Hence, all the possible openings that can cause infiltration are accounted in the model [585]. In addition, the Domain Model is used as the foundation for evaluating ground heat exchange and the building. Compared to the Mitalas method (that is based on two-dimensional heat exchanges), the Domain model is limited to one-dimensional heat exchange [588].The tool is capable of performing cost-based optimization and calculating energy generation on-site using renewable energy sources [589].The tool makes use of a sequential search-optimization technique that can help determine optimal and near-optimal solutions in a short time [590]. In addition, parametric analysis can also be performed to assess the impact of individual energy systems. The cost is calculated based on utility rates, incentives (tax credits and rebates) on energy systems and capital costs of energy measures. The cost data present in the tool is based on and National Residential Efficiency Measures Database of the United States [591]. All these values can be manually updated based on local databases to determine optimal energy measures. 225 Appendix C Calibration Benchmarks Table C1 Calibration Benchmarks [282,311] Calibration Metric Acceptable Value (%) Monthly MBEa CV (RMSE)b ≤+5 +15 Hourly MBEa CV (RMSE)b ≤+10 30 ` a MBE: Mean Bias Error bCV (RMSE): Coefficient of Variation of Root Mean Square Error 226 Appendix D Energy Rates Table D1 Detail of Natural Gas Rates [320] Parameter Value Delivery charges Basic daily charge 30 days at 0.4085 CA$/day (0.29 US$/day) Delivery 4.296 CA$/GJ (3.09 US$/GJ) Commodity charges Storage and transport 0.758 CA$/GJ (0.54 US$/GJ) Cost of gas 1.141 CA$/GJ (0.82 US$/GJ) Other charges and taxes Municipal operating fee 3.09% of a amounts Carbon tax 1.7381 CA$/GJ (1.25 US$/GJ) Clean energy levy 0.40% of a amounts GST 5% of b amounts a Delivery charges +Commodity charges b Municipal operating fee + Carbon tax Table D2 Detail of Electricity Rates [320] Parameter Value Basic daily charge 30 days at 0.4085 CA$/day (0.29 US$/day) Electricity used Block 1: 1,600 kWh 9.845 CA¢/ kWh (7.1US¢/kWh) Electricity used Block 2: > 1,600 kWh 15.198 CA CA¢/ kWh (10.90 US¢/kWh) GST 5% of a amounts a Basic Customer charge + Electricity used charges 227 Appendix E Annual Energy Demand Reduction with EPUs Table E1 Annual energy demand reduction with EPUs Occupancy Profile 1 Energy savings (%) Occupancy Profile 2 Energy savings (%) Initial Energy Demand 119.33GJ/yr 105.62 GJ/yr <10% LED 1 RF 2 WL LED 3 RF LED 3 DHW 4 RF WL LED 5 LED DHW 5 WL DHW 6 RF DHW 6 WL LED DHW 7 RF LED DHW 7 PV 8 RF WL DHW 8 LED PV 8 RF WL LED DHW 9 WL PV 9 RF PV 10 WL LED PV 10 RF LED PV 10 10-20% RF WL PV 11 RF 16 DHW PV 12 WL 17 RF WL LED PV 12 RF WL 17 LED DHW PV 12 LED 18 WL DHW PV 14 RF LED 19 RF DHW PV 14 WL LED 19 WL LED DHW PV 14 RF WL LED 20 RF LED DHW PV 14 DHW 20 FN LED DHW PV 15 RF WL DHW PV 16 RF WL LED DHW PV 16 RF FN LED DHW PV 17 WN LED DHW PV 18 RF WN LED DHW PV 20 20-40% RF DHW 21 WL DHW 21 RF WL LED APP PV 21 RF WL DHW 21 LED DHW 22 RF LED DHW 23 WL LED DHW 23 RF WL LED DHW 24 PV 25 RF PV 26 228 WL PV 26 RF WL PV 26 LED PV 27 RF LED PV 28 WL LED PV 28 RF WL LED PV 29 DHW PV 29 RF DHW PV 30 WL DHW PV 30 RF WL DHW PV 31 FN LED DHW PV 31 LED DHW PV 31 WN DHW PV 31 RF FN LED DHW PV 32 WL FN LED DHW PV 32 RF LED DHW PV 32 WL LED DHW PV 32 WL WN DHW PV 32 RF WL FN LED DHW PV 32 RF WL LED DHW PV 33 RF WL WN DHW PV 33 WN LED DHW PV 34 RF WN LED DHW PV 34 WL WN LED DHW PV 35 RF WL WN LED DHW PV 35 >40% LED HVAC DHW 59 LED HVAC DHW PV 68 RF LED HVAC DHW 61 RF LED HVAC DHW PV 69 LED HVAC PV 63 RF WL LED HVAC DHW PV 70 RF WL LED HVAC DHW 63 WL LED HVAC DHW PV 71 RF LED HVAC PV 65 HVAC DHW PV 66 RF WL LED HVAC PV 67 LED HVAC DHW PV 67 WL HVAC DHW PV 68 RF HVAC DHW PV 68 RF LED HVAC DHW PV 69 WL LED HVAC DHW PV 69 RF WL HVAC DHW PV 70 RF WL LED HVAC DHW PV 71 229 Appendix F BC-Energy Step Code Metrics Table F1 BC-Energy Step Code Metricsa (Source: BC-ESC [592]) City Code Step ACHb TEDIc kWh/ (m2.year) MEUId kWh/ (m2.year) Small SFD (Area: 69.7m2) Medium SFD (Area: 214m2) Large SFD (Area: 556m2) Attached Units (Area: 81.9m2/Unit) Vancouver (CZ 4) Step 1 3.0 _ _ _ _ _ Step 2 2.5 43 148 108 65 108 Step 3 1.5 38 128 93 55 93 Step 4 1.0 28 108 78 45 78 Step 5 >1.0 19 83 58 30 58 Kamloops (CZ 5) Step 1 3.0 _ _ _ _ _ Step 2 2.5 52 158 118 75 118 Step 3 1.5 45 143 108 70 108 Step 4 1.0 35 118 88 55 88 Step 5 >1.0 22 88 63 35 63 Prince George (CZ 6) Step 1 3.0 _ _ _ _ _ Step 2 2.5 74 173 133 90 133 Step 3 1.5 64 153 118 80 118 Step 4 1.0 51 123 93 60 93 Step 5 >1.0 32 98 73 45 73 aOnly matrices applicable to the case study homes are provided in this table. For complete list of metrics please refer to BC-ESC [592]) bACH: Air changes per hour @ 50 Pa (ACH50) cTEDI: Thermal Energy Demand Intensity dMEUI: Mechanical Energy Use intensity Note: ACH target changes with the step only; TEDI targets are influenced by location of building and step; while MEUI targets vary with archetype, location and code step 230 Appendix G Environmental Impacts under DBPs Control Strategies Figure G1 Comparison of endpoint environmental impacts under DBPs control strategies Table G1 Weights for different criteria and best and worst ranking for considered DBPs control strategies Case No. Human Health (HH) Ecosystems (ES) Resources (RE) Best Case Worst Case Case 1 1.0 0.0 0.0 S2 S6 Case 2 1.0 0.2 0.0 S2 S3 Case 3 1.0 0.4 0.0 S5 S3 Case 4 1.0 0.6 0.0 S4 S3 Case 5 1.0 0.8 0.0 S4 S3 Case 6 1.0 1.0 0.0 S4 S3 Case 7 0.8 0.2 0.2 S2 S3 Case 8 0.8 0.4 0.2 S5 S3 Case 9 0.8 0.6 0.2 S4 S3 Case 10 0.8 0.8 0.2 S4 S3 Case 11 0.8 1.0 0.2 S4 S3 Case 12 0.6 0.4 0.4 S4 S3 Case 13 0.6 0.6 0.4 S4 S3 Case 14 0.6 0.8 0.4 S4 S3 Case 15 0.6 1.0 0.4 S4 S3 Case 16 0.4 0.6 0.6 S4 S3 Case 17 0.4 0.8 0.6 S4 S3 Case 18 0.4 1.0 0.6 S4 S3 Case 19 0.2 0.8 0.8 S4 S3 Case 20 0.2 1.0 0.8 S4 S3 Case 21 0.0 0.0 1.0 S4 S3 231 Appendix H Local Annual Weather Conditions Figure H1 Local annual weather conditions in Kelowna (Source: [593,594]) 232 Figure H2 Local annual weather conditions in (a) Vancouver (CZ 4); (b) Kamloops (CZ 5); and Prince George (CZ 6) (Source: [593,595])
Thesis/Dissertation
eng
Civil Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Sadiq, Rehan
Hewage, Kasun
Alam, M. Shahria
Financial incentives for green buildings in Canada : a spatio-temporal analysis
Text