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Sustainability evaluation of seasonal snow storage for building cooling systems : a life cycle approach Chinraj, Venkatesh Kumar 2015

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SUSTAINABILITY EVALUATION OF SEASONAL SNOW STORAGE FOR BUILDING COOLING SYSTEMS: A LIFE CYCLE APPROACH  by  Venkatesh Kumar Chinraj  B.E., Coimbatore Institute of Technology, Anna University, 2008  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF APPLIED SCIENCE  in  THE COLLEGE OF GRADUATE STUDIES  (Civil Engineering)   THE UNIVERSITY OF BRITISH COLUMBIA  (Okanagan)   October 2015   © Venkatesh Kumar Chinraj, 2015 ii  Abstract In Canada, the residential building sector consumes 17% of the total energy and contributes 15% of the total GHG emissions. Predominantly, the energy demand for cooling in the residential sector is increasing due to large occupancy floor area and high usage of air-conditioning. Minimizing energy use and GHG emissions is one of the highest priority goals set for national energy management strategies in developed countries including Canada. In this research, a sustainability assessment framework is developed to evaluate the techno-economic and environmental performance of different building cooling systems, namely conventional snow storage system, watertight snow storage system, high-density snow storage system, and the conventional chiller cooling system. The framework is implemented in a low-rise residential building in Kelowna (BC, Canada) to appraise its practicality.  The Life cycle assessment (LCA) approach is used to assess the environmental impacts of different building cooling systems. LCA results revealed that the systems have varying energy requirements and associated environmental impacts during the different life cycle phases (extraction and construction, utilization, and end of life). The annual cooling energy demands for different cooling systems are also estimated. The LCA is carried out using SimaPro 8.1 software and the TRACI 2.1 method. Multi-criteria decision analysis is employed using the ‘Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE-II)’ to evaluate the sustainability of different cooling systems over their life cycle. The results showed that the snow storage systems tend to reduce the greenhouse gas emissions and associated environmental impacts more than the conventional cooling system.   A probabilistic feasibility evaluation tool is developed to evaluate the techno-economic performance of different cooling systems. The incremental economic performance of alternatives is estimated in terms of the total cooling cost per kWh at the facility. Monte-Carlo simulation was performed to consider the uncertainty factors involved in the techno-economic parameters of cooling systems. Results of this analysis verified that the snow storage systems are more energy efficient and low-cost options for building cooling systems. The developed frameworks will support decision-makers in evaluating the sustainability of building cooling systems. Moreover, socio-economic benefits, i.e. improving affordability, equity, and enhancing energy sustainability, could be achieved.  iii  Preface  This research has been conducted and prepared in the form of thesis and scientific journal papers by the author under the supervision of Dr. Kasun Hewage and Dr. Rehan Sadiq. The third author of the articles of this research work, Dr. Husnain Haider (Post-Doctoral Fellow), has reviewed all the manuscripts and provided critical feedback for the improvement of the manuscripts and thesis.  • Some of the content in the Chapters 2, 3 and 4 is under preparation for submission to the journal Building and Environment titled “Framework for Life Cycle Assessment of Building Cooling Systems: A Comparative study of Snow Storage and Conventional Chiller Systems”.   • Some of the content in the Chapters 2, 3 and 5 is under preparation for submission to the journal Energy and Buildings titled “Techno-Economic Sustainability Evaluation Framework for Building Cooling Systems: A Comparative study of Snow Storage and Conventional Chiller Systems”.   iv  Table of Contents  Abstract .................................................................................................................................... ii	Preface ..................................................................................................................................... iii	List of Tables ........................................................................................................................ viii	List of Figures ......................................................................................................................... ix	List of Abbreviations and Acronyms ................................................................................... xi	Acknowledgements ............................................................................................................... xii	Chapter 1 Introduction .......................................................................................................... 1	1.1	 Background ............................................................................................................... 2	1.2	 Research Objectives .................................................................................................. 6	1.3	 Research Outline ....................................................................................................... 7	Chapter 2 Literature Review ............................................................................................... 10	2.1	 Building Cooling System Alternatives ................................................................... 10	2.1.1	 Conventional Snow Storage System (CSS) ........................................................ 11	2.1.2	 Watertight Snow Storage System (WSS) ........................................................... 12	2.1.3	 High-Density Snow Storage System (HSS) ........................................................ 13	2.1.4	 Conventional Chiller ........................................................................................... 14	2.2	 Environmental Performance of Cooling Systems ................................................... 15	2.2.1	 Life Cycle Analysis ............................................................................................. 16	2.2.2	 Multi-Criteria Decision Analysis Techniques .................................................... 17	v  2.3	 Techno-Economic Sustainability evaluation of Cooling Systems .......................... 23	2.3.1	 Cooling Demand Analysis .................................................................................. 23	2.3.2	 Technical Evaluation .......................................................................................... 24	2.3.3	 Economic Evaluation .......................................................................................... 25	2.3.4	 Uncertainty Analysis ........................................................................................... 28	Chapter 3 Methodology ........................................................................................................ 30	3.1	 Building Cooling Systems Evaluation Framework ................................................. 30	3.2	 Life Cycle Assessment ............................................................................................ 33	3.2.1	 Goal and Scope Definition .................................................................................. 34	3.2.2	 Inventory Analysis .............................................................................................. 34	3.2.3	 Life Cycle Impact Assessment ............................................................................ 34	3.2.4	 Interpretation ....................................................................................................... 38	3.3	 Environmental Performance Evaluation Criteria .................................................... 38	3.3.1	 Global Climate Change ....................................................................................... 38	3.3.2	 Ozone Depletion ................................................................................................. 39	3.3.3	 Acidification ....................................................................................................... 39	3.3.4	 Eutrophication ..................................................................................................... 39	3.3.5	 Human Health Particulate ................................................................................... 40	3.3.6	 Carcinogens, Non-Carcinogens, and Ecotoxicity ............................................... 40	3.3.7	 Resource Depletion ............................................................................................. 40	3.3.8	 Smog Formation ................................................................................................. 41	3.4	 Multi-Criteria Analysis Methods ............................................................................ 41	3.5	 Techno-Economic Sustainability Evaluation Framework ...................................... 44	vi  3.6	 Demand Analysis .................................................................................................... 45	3.7	 Technical Evaluation .............................................................................................. 47	3.8	 Economic Evaluation .............................................................................................. 48	3.9	 Uncertainty Analysis ............................................................................................... 51	Chapter 4 Life Cycle Impact Assessment of the Building Cooling Systems .................... 52	4.1	 Life Cycle Inventory ............................................................................................... 52	4.2	 Life Cycle Impact Assessment ................................................................................ 55	4.3	 Sustainability Evaluation of Cooling Systems Using the PROMETHEE II ........... 58	4.4	 Summary of the Environmental performance of the Building Cooling System Alternatives ......................................................................................................................... 59	Chapter 5 Techno-economic analysis of cooling systems for residential building .......... 61	5.1	 Demand Analysis .................................................................................................... 61	5.2	 Technical Evaluation .............................................................................................. 66	5.3	 Economic Evaluation .............................................................................................. 71	5.3.1	 Capital Investment Cost ...................................................................................... 72	5.3.2	 Operational and Maintenance Cost ..................................................................... 75	5.3.3	 Cost of Energy Options ....................................................................................... 76	5.4	 Summary of the Techno-Economic Evaluation of the Building Cooling System Alternatives ......................................................................................................................... 77	Chapter 6 Conclusions and Recommendations .................................................................. 80	6.1	 Research Contributions ........................................................................................... 81	6.2	 Limitations .............................................................................................................. 83	6.3	 Recommendations ................................................................................................... 83	vii  References .............................................................................................................................. 85	Appendices ............................................................................................................................. 99	Appendix A: Application of PROMETHEE II for ranking of building cooling systems alternatives .......................................................................................................................... 99	Appendix B: Feasibility Evaluation tool ........................................................................... 103	    viii  List of Tables  Table 2.1 Evaluation of energy storage technologies for cooling applications ................... 20	Table 4.1 Summary of material quantities for snow storage system ................................... 54	Table 5.1 Thermal properties of the studied building recommended by UBC REAP (2006) ................................................................................................................................... 62	Table 5.2 Monthly mean temperatures and total monthly precipitation (data from Environment Canada 2015) ................................................................................. 64	Table 5.3 Results of technical evaluation of snow storage system ...................................... 71	Table 5.4 Capital investment needed for cooling system alternatives ................................. 72	Table 5.5 Operation and Maintenance cost needed for cooling system alternatives ........... 75	Table 5.6 Levelized cost of cooling system alternatives ..................................................... 77	 ix  List of Figures   Figure 1.1  Canada’s GHG emissions trend 1990-2013 adopted from (Environment Canada 2015a) .......................................................................................................................... 1 Figure 1.2  Outline of research methodology ................................................................................. 8 Figure 2.1  Forms of seasonal snow storage; from the left to right; indoors, on the ground, in the ground, and underground (Nordell and Skogsberg 2007) (Reprinted with the permission of Springer) .............................................................................................. 10 Figure 2.2  Schematic diagram of watertight snow storage system (Skogsberg and Nordell 2001) (Reprinted with the permission of Elsevier limited) .................................................. 12 Figure 2.3  Schematic diagram of High-Density snow storage system (Hamada et al. 2010) (Reprinted with the permission of Elsevier limited) .................................................. 13 Figure 2.4  Schematic diagram of chiller unit (Stanford 2003) .................................................... 14 Figure 3.1  Proposed framework for sustainability evaluation of environmental performance of building cooling systems ............................................................................................ 31 Figure 3.2  Life Cycle Assessment methodology (Reprinted with the permission of ISO 14040) ........................................................................................................................ 33 Figure 3.3  Life Cycle Impact Assessment framework (Reprinted with the permission of ISO 14040) ........................................................................................................................ 35 Figure 3.4  Proposed framework for sustainability evaluation of techno-economic performance of building cooling systems ....................................................................................... 46 Figure 4.1  Life cycle phases and chosen system boundary of the snow storage system (Hossain et al. 2011) (a) and Life cycle phases of the chiller cooling system (b) .................... 53 x  Figure 4.2  Material contributions in chillers (obtained from the TRANE corporation) .............. 55 Figure 4.3  SimaPro results of Environmental Impact categories for different cooling systems during the different lifecycle phases (a-j) .................................................................. 56 Figure 4.4  Summary of the MCDA results sharing aggregated indices of impact categories for the different lifecycle phase and net flow (i.e. balancing effects between outgoing and incoming flows) for final ranking of building cooling system alternatives. ....... 59 Figure 5.1  Actual image of the studied green building (a) and DesignBuilder model of the studied green building (b) (Feng 2013) ...................................................................... 63 Figure 5.2  Weather report of Kelowna (DesignBuilder results vs. Environment Canada 2015) 65 Figure 5.3  Energy consumption of the studied building for heating, cooling and lighting (DesignBuilder results vs. the actual data from UBCO facilities management) ....... 65 Figure 5.4  Schematic layout of snow storage system in the feasibility evaluation tool  (Skogsberg 2005) ....................................................................................................... 67 Figure 5.5  Snow loss and remaining quantity of snow by month (a) HSS system, (b) WSS system and (c) CSS system ........................................................................................ 69 Figure 5.6  Summary of Coefficient of Performance (COPtotal) ................................................. 78 Figure 5.7  Summary of Total cooling cost (Levelized cost) ....................................................... 79	 xi  List of Abbreviations and Acronyms  COP   Coefficient of Performance  CSS    Conventional Snow Storage  EER   Energy Efficiency Ratio  GHG   Green House Gas  GDP   Gross Domestic Product  HSS    High-density Snow Storage  HVAC   Heating, Ventilation, and Air-Conditioning LEED    Leadership in Energy & Environmental Design LCA   Life Cycle Analysis  LCIA    Life Cycle Impact Assessment   MCDA  Multi-Criteria Decision Aiding  NPV    Net Present Value  O&M    Operation and Maintenance  PROMETHEE  Preference Ranking Organization METHod for Enrichment Evaluation  TRACI Tool for the Reduction and Assessment of Chemical and other environmental Impacts USEPA    U.S. Environmental Protection Agency  WSS    Watertight Snow Storage  WSM    Weighted Sum Method   xii  Acknowledgements  First of all, I would like to express my sincerest gratitude to my research supervisors, Dr. Kasun Hewage and Dr. Rehan Sadiq. Your enthusiasm and thoroughness in research motivated me to strive for the best and this will forever remain an extremely enriching experience. I thank you for providing me guidance, continuous support, and knowledge. I value your patience and cherish the inspirational discussions and advice during the entire program. A special thanks for accepting my transfer application from M.Eng to MASc program and the financial support for my academic career. In addition to my supervisors, I would like to thank Dr. Husnain Haider for giving me immense support, constructive criticism, and helpful comments for the thesis and paper writing. Besides my supervisors, I would like to thank the rest of my thesis committee, Dr. Ahmed Idris, Dr. Loic Markley for their insightful comments and suggestions, all of whom made valuable contributions to this thesis. I am also thankful to Dr. Jannik Eikenaar for his valuable time and attention.  My sincerest appreciation goes to the School of Engineering, the College of Graduate Studies and the Center for Scholarly Communication for providing the support and facilities I needed for my research study. I am grateful to Shannon Hohl, Angela Perry and Teija Wakeman for providing me with assistance whenever I needed it. I am deeply indebted to Dr. Gordon Lovegrove, Dr. Abbas Milani and Professor Laura Patterson for teaching the academic courses, Social Cost Benefit Analysis, Design of Experiments and Multi-Criteria Decision Analysis, and Technical Communication for Engineering Research, which gave me valuable professional and research knowledge. I would also like to thank Amanda Brobbel, who warmly guided me in thesis writing.  I thank my colleagues in the Project Lifecycle Management Laboratory, especially Rajeev Ruparathna, Gyan Kumar Shrestha, Adil Umer, Aziz Alghamdi, Mohammad Al Hashmi, and Fawaz Al Nazzar, for their joyful and supportive company. Also, I thank my friends Vignesh, Raja, Guhan, Gopinath, Jackie, Oleg and so many more for sharing space and ideas. My greatest gratitude goes to the Okanagan Sikh Temple and the Sikh community. They definitely made my stay in Kelowna one of the best times of my life.  xiii  I would like to express my deepest gratitude to my parents. I am thankful for their love, support and constant encouragement. Words cannot express how grateful I am to my sister (Pushpa Latha); I always believe that she is with me and happy to see me completing my Masters. I thank my little sister (Dharshini) for her prayers. I would also like to express my special thanks to the special person of my life, Akshaya, from the deepest side of my heart for always being there, day or night, whenever I needed her. The thesis would not have been possible without her love and support.   Last but not least, I thank God for strength and health.   xiv               To My Family 1  Chapter 1 Introduction  Currently, the world primarily depends on fossil fuels to meet its energy requirements resulting in depletion of non-renewable resources, solid waste generation, high energy consumption, and production of greenhouse gas (GHG) emissions (Holdren 1987; Di Stefano 2000; Reza et al. 2011; Bianchini and Hewage 2012). Energy sector is segmented into buildings, transportation, industrial and others. The buildings are one of the most influential economic segments in the energy sector, accounting for 10% of the global gross domestic product (GDP) (Ortiz et al. 2009) and 40% of the global energy consumption (Dong 2014). Canada is the seventh largest energy consumer in the world (Energy Information Administration 2015). In Canada, the residential building sector alone consumes 17% of the total energy and contributes 15% of the total GHG emissions (Natural Resources Canada 2013). The energy demand has been increasing rapidly due to population growth, urbanization, industrialization, improved lifestyle, and new building technologies across the world (Skogsberg 2005). In particular, the cooling energy demand is increasing due to the large occupancy floor area and high usage of air-conditions in residential buildings. However, through innovative and efficient cooling systems, significant energy reduction can be achieved.   Although by committing to the Copenhagen accord in 2009, Canada should reduce its GHG emissions to 611 Mt by 2020, the total GHG emissions generated in Canada has increased by 18.4% of the 1990 levels (Figure 1.1). More than 80% of Canada’s GHG emissions inventory is due to fossil fuels production and consumption  (Environment Canada 2015a).  Figure 1.1  Canada’s GHG emissions trend 1990-2013 adopted from (Environment Canada 2015a) 2   At present, minimizing the energy use and associated environmental impacts (i.e., GHG emissions) of buildings is one of the highest priority goals set for national energy management strategies in developed countries (Blengini and Di Carlo 2010). In this regard, the Canadian government has also adopted several initiatives at the national and regional levels (Environment Canada 2014; Ministry of Environment BC 2010), such as, the government of Canada controlled the GHG emissions through stringent regulations for electricity and transportation sectors, two of the largest sources of GHG emissions in Canada. As part of Economic Action Plan 2013, the government of Canada support the development and demonstration of renewable and clean energy sources in the energy system (Government of Canada 2015). British Columbia (BC) government has also implemented various ambitious policies to minimize the GHG emissions through the Climate Action Plan and has set GHG emissions reduction targets of 33% by 2020 and 80% by 2050 from 2007 levels. According to the 2008 Utilities Commission Amendment Act, the BC government support the low-carbon energy generation and clean technology projects to attain the target (Ministry of Environment BC 2015). Consequently, public and private institutions are making efforts to address climate change by achieving emission reduction targets (Environment Canada 2015b).   1.1 Background Currently, more than 70% of the energy demand has been fulfilled by the conventional fossil fuel energy sources. In order to minimize the GHG emissions and the increasing fossil fuel prices, various researches implement the clean and renewable energy sources into the energy system. Energy storage technologies are one of the valuable components in most of the energy systems, due to their high energy efficiency, cost savings, and they could be an important tool in achieving a low-carbon future. These technologies consume less primary energy and allow for the decoupling of energy supply and demand. Since the energy crisis from the 1970s, the long-term (seasonal) cold energy storage technologies have gained considerable attention of the decision-makers (Yan et al. 2016). Both natural and artificially produced snow can be stored and used as a heat sink in the warmer seasons. In recent years, numerous seasonal cold storage techniques for cooling applications have been identified. During the late 20th century, about 100 projects in Japan and about 50-100 projects in China used the seasonal ice storage technologies (Skogsberg 2005).  3  In Canada, Vigneault (2000) conducted many studies on winter coldness storage in the agriculture sector. All of these studies focused on minimizing energy consumption and investment cost in the production of low temperatures for the cooling and refrigerated storage of fruits and vegetables.  However, the winter snow can be effectively stored using suitable processes to fulfill the cooling needs of buildings. To promote the implementation of energy storage technology, the International Energy Agency (1995) identified, collected and analyzed the performance data of 50 seasonal cold storage projects in the Canada, Germany, Netherlands, and Sweden. In addition to these 50 projects, Morofsky (1997) collected and evaluated the performance data of additional 52 seasonal cold storage projects to determine the efficacy of the cold storage system for building and process cooling applications. Results of these studies have demonstrated that the improving cost-effectiveness of the most seasonal cold storage projects with the payback period of five years or less.   The relevant literature was reviewed to assess the present state of snow generation and storage technologies to identify the specific snow storage concept for this study. Seasonal snow storage is one of the sustainable alternatives with low primary energy consumption to meet the demand for cooling during the summer months (Nordell 2007). Blahnik (1980) stated that ice is one of the most promising techniques for storing cold energy (free cooling) and is abundantly available as well. Snow storage is an ancient, simple, and low-cost technique and it is feasible in countries like Canada which have high energy demands for both cooling and heating during a year. It requires minimal infrastructure and produces fewer GHG emissions (UNEP 2015). In this technique, snow can be produced naturally using winter ambient air, freezing lakes, and ponds, covered with tarpaulin or sawdust, stored, and used as a cooling resource for summer (Bahadori 1984). Blahnik and Brown (1983) reported that the use of free cooling in a building was firstly recorded in 1833; buckets of ice were hung to cool the Charleston hospital building in South Carolina, USA. During the latter part of 19th century, prominent hotels, restaurants, and commercial buildings were cooled by placing ice in ventilation systems. At the beginning of 20th century, this technique was overridden by the mechanical chiller systems (Blahnik and Brown 1983), because of the high land use and relatively high capital cost of storage systems. Snow storage systems can be used for the long-term (seasonal), which stores enough snow produced during the winter season, as well as for short-term (diurnal), which stores only enough snow to provide for a short (~1-14 days) operational 4  period during peak cooling demand (Minturn et al. 1979). Currently, cold energy storage systems are primarily used for utility load management and for cost saving where off-peak rates are available (Zhao et al. 2010).   There are various techniques available for seasonal cold storage systems, including:  • Annual cycle energy system (ACES), ice is frozen in a tank during the winter by a heat pump, which provides space heat. The ice is melted during the summer to provide cooling (Abbatiello et al. 1981; Holman and Brantley 1978; Krause 1975; Minturn et al. 1979). • Project Icebox, ice is formed in a louvered box through which natural winter chill air flows and freezes water, which is continuously flooded or sprinkled within the bed. The box is insulated after charging, and provides cooling for summer use on an annual basis (Klassen 1981).  • Natural Ice maker, shallow ponds used for forming ice. The ice maker relied mainly on the thermal radiation losses of the pond to the night sky. Ice periodically stored in an earth pit for summer use (Bahadori and Kosari 1978).  • Artificial Snow/ice maker, ice is produced outdoors by spraying water into winter air using a commercial snowmaking machine. The ice is collected in a pond and mounded. The pond and ice mound are covered with an insulation system for use in summer (Brown 1997; Chen and Kevorkian 1971). • Heat pipe tank, a tank of water is buried underground or near the building to be cooled. A series of one-way heat pipes extends from the bottom of the tank to above the soil surface. During the winter months, when the ambient temperature is below freezing, the water in the tank is chiller passively by the heat pipes, which discharge the heat into the atmosphere. Ice is formed around the lower end of the heat pipes (Gorski and Schertz 1982; Gorski et al. 1979; Schertzer 1981). • Project snow bowl, city snow is to be removed to a nearby quarry where the snow is stored for summer use to cool a building (Morofsky and Merrifield 1981; Morofsky 1982). • Ice tank with fan, ice is formed in successive layers as water is incrementally placed in a tank and frozen by a fan-driven winter ambient air jet stream  (Vigneault et al. 1988).  • Ice pond, an ice pond is filled incrementally with water each day and cold ambient winter air freezes the water. At the end of winter the pond is covered with insulation. Melted ice 5  water is circulated between the building for cooling and back to store. Excess melted water is drained off (Bahadori 1984).  • Frozen saturated earth, this system freezes saturated earth in a lined pit using a tube heat exchanger in the pit. In winter a fan coil cools an antifreeze solution, which circulates through the earth heat exchanger to freeze the saturated earth. In summer the chill solution is pumped from the storage unit to cool a building via in indoor coil, then back to storage  (Francis 1982).  There are basically four sources of coolness that can be utilized, including ambient air, sky radiation, ground, and water. • The use of ambient air for cooling, the absorption of solar radiation by the earth’s atmosphere and the ground during the day and thermal radiation by the atmosphere and ground to the space at night cause the diurnal temperature fluctuations. The magnitude of this fluctuation depends on the atmosphere and geographical conditions. At low elevations and in hazy, humid climates the daily temperature range is small, whereas at higher elevations and in dry, clear regions this temperature fluctuation is large. In many parts of the Canada the ambient air temperature may drop below the comfort range during the night. This cool air can be effectively used by circulating it directly through a building using an attic fan or through other media and storing the air coolness in them (Barnaby et al. 1980; Block and Hodges 1979). • The use of sky as a cooling source, when a black body radiator is placed in outer space, which behaves as a black body at a temperature of a few degrees absolute, and shielded against solar radiation, it emits thermal radiation according to the Stefan-Boltzmann law. If the radiator is placed on earth the radiation exchange with the sky will be affected by the atmosphere and the surface surrounding the radiator. It is possible to employ a selective surface with very small solar absorptivity and very large thermal emissivity (especially between 8 and 13µm) and a transparent cover (to reduce convective heat gain from the ambient air) with high transmissivity between these wavelengths to produce a radiative cooling effect during the day (Catalanotti et al. 1975; Sakkal 1979).  • The use of ground as a cooling source, ground provides an excellent sink for heat rejection, or source for cooling. Daily and annual temperature fluctuations in the ground 6  depend on the distance from the ground surface, the moisture content and type of the soil (Reuss et al. 1997). For a soil with a thermal conductivity of k=1W/mC, a heat capacity of ρ=2X106 J/m3C, and assuming a sinusoidal temperature fluctuation at the ground surface, can be shown that the fluctuation’s amplitude decreases to 10% of its surface value within 0.3m, for daily fluctuations. For annual fluctuations the same amplitude reduction is accomplished within a depth of approximately 6m (ECKERT 1976). At depths between about 6-100m the ground temperature may be safely assumed to be constant. This temperature is nearly equal to annul mean ambient air temperature. In many parts of the Canada the temperature of undisturbed ground at a depth of 50cm is appreciably lower than the comfort range and has the potential to provide summer cooling (Givoni 1981). • The use of water as cooling source, water provides a great potential for passive cooling. Winter cooled water may be stored in underground tanks or cisterns (Bahadori 1978) or in aquifers (Nordell et al. 2015); similarly ice may be produced in winter and stored in underground storages for summer (Bahadori and Kosari 1978; Bahadori 1978). Alternatively water may be evaporated adiabatically in summer where the cooled air or water may be utilized.   A comprehensive literature revealed that there is no published comprehensive framework available to: i) evaluates different snow storage systems, ii) provides cradle to grave Life Cycle Assessment (LCA) for different snow storage systems, iii) performs complete Life Cycle Impact Assessment (LCIA) for different impact categories, iv) estimates the uncertainty factors involved in the techno-economic parameters.  1.2 Research Objectives The goal of this research is to develop a methodology for the sustainability evaluation of seasonal snow storage for building cooling systems. Following are the specific objectives of this study: establish an integrated life cycle assessment framework to evaluate the environmental performance of the different cooling system alternatives for their energy requirements and potential environmental impacts and to develop a novel techno-economic sustainability evaluation framework, with uncertainty considerations for the different building cooling system alternatives.   7  For justifying the practicality of the proposed objectives, a low-rise residential apartment building located in Kelowna, BC (Canada) was chosen as a case study. The framework can be used to identify the cooling system with the lowest environmental impacts; also, be useful to evaluate the performance of various snow storage systems and to select the more energy efficiency cooling system with least total cooling cost.   1.3 Research Outline This thesis is arranged into six chapters. Chapter 1 presents the introduction to the research. Chapter 2 presents the literature review of the building cooling system alternatives and their performance evaluation techniques. Chapter 3 presents the methodology used for this research, an overview of life cycle assessment (LCA) using multi-criteria decision analysis technique (MCDA), and discusses the techno-economic feasibility evaluation of building cooling systems. Chapter 4 presents a detailed discussion on multi-criteria based LCA of cooling systems. Chapter 5 presents a detailed discussion on the development and application of techno-economic sustainability evaluation framework for snow storage systems. Finally, Chapter 6 summarizes key conclusions of this research, identifies original contributions, and suggests future research.   Figure 1.2 outlines the schematic presentation of the research methodology for the techno-economic and environmental evaluation of the building cooling systems.   8   Figure 1.2  Outline of research methodology  																												 		Chapter 1 Introduction • Problem Formulation • Background • Objectives Chapter 3 Methodology • Establishment of framework for selection of building cooling systems using Life Cycle Analysis (LCA) and Multi-Criteria Decision Aiding (MCDA) approach • Establishment of framework for sustainability evaluation of techno-economic performance of building cooling systems 	Chapter 4 Life Cycle Assessment of Building cooling systems  Life Cycle Impact Assessment • Develop environmental impact categories • Perform comparative LCI and LCIA  Ranking Alternatives • Aggregate impact categories • Rank the alternatives using PROMETHEE-II method  Chapter 5 Techno-economic sustainability evaluation of snow storage for building cooling systems  Techno-Economic Performance • Estimation and validation of annual cooling demand  • Establish framework for evaluating techno-economic performance Analyzing Results • Develop the probabilistic tool based on the framework  with uncertainty considerations • Identify the feasible alternative Chapter 2 Literature review • Identification of building cooling systems alternatives • Review of MCDA and Uncertainty analysis techniques  • Identification of building cooling systems alternatives • Establishment of framework for selection of building cooling systems using Life Cycle Analysis (LCA) and Multi-Criteria Decision Aiding (MCDA) approach • Demand analysis, Technical evaluation, and economic evaluation 	Chapter 6 Conclusions • Original contributions • Future Research areas 9  Section 2 describes the different building cooling system alternatives. Subsequently, it contains a review of the major decision-making methods and criteria used for evaluating the environmental performance of various energy source options in energy generation systems. Last part of section 2 reviews the technical and economic performance of cooling systems and the probabilistic uncertainty modeling. A new multi-criteria based life cycle assessment framework was established in Section 3.1 to investigate the life cycle assessment of cooling systems. The following sections briefly explain the research methodology used to apply the MCDA and uncertainty analysis for evaluating the environmental and techno-economic performance of cooling systems.   Section 4.1 collects the energy and emissions details from the facility during the service life of the cooling systems. Primary materials used for the storage construction and landfill or recycling of the materials after the service life are included. In section 4.2, the LCI results are characterized based on impact categories in the US EPA’s TRACI method. To rank the alternatives, the PROMETHEE- II method has been applied in section 4.3.   Chapter 5 presents the estimation of annual cooling energy demand for a building. A probabilistic techno-economic sustainability evaluation framework and Excel customizable tool is developed to evaluate the performance of different cooling systems. The incremental economic performance of alternatives is estimated in terms of the total cooling cost per kWh at the facility. Monte-Carlo simulations were performed to consider the uncertainty factors involved in the techno-economic parameters of cooling systems.       10  Chapter 2 Literature Review  The contents of this chapter are prepared to publish in two scientific journals. A part of this chapter is prepared to publish in the journal, Building and Environment titled “Framework for Life Cycle Assessment of Building Cooling Systems: A Comparative study of Snow Storage and Conventional Chiller Systems”; and another part in the journal Energy and Buildings titled as “Techno-Economic Sustainability Evaluation Framework for Building Cooling Systems: A Comparative study of Snow Storage and Conventional Chiller Systems”.  With regard to the objectives defined in Chapter 1, this chapter describes the various building cooling system alternatives and includes the literature review of the environmental and techno-economic performance of cooling systems, MCDA, and Uncertainty analysis.  2.1  Building Cooling System Alternatives Natural cooling systems can be classified based on the sources for producing cool energy, characteristics of storage materials, and types of circulation system and heat transfer. Also, the duration of cold storage is an important factor in the selection process of natural cooling system. For the long term storage, the main source for producing snow is the winter ambient air. Water evaporation and sky radiation could also be used to produce snow, particularly in mild winters (Bahadori 1984). Moreover, for the seasonal storage, snow can be stored in different forms, i.e. indoors, on the ground, in the ground, and underground as shown in Figure 2.1 (Nordell and Skogsberg 2007).  Figure 2.1  Forms of seasonal snow storage; from the left to right; indoors, on the ground, in the ground, and underground (Nordell and Skogsberg 2007) (Reprinted with the permission of Springer) 11  This study focuses on an open pond (in the ground), where cold energy is stored in the form of snow, the melted snow is extracted and pumped to a heat exchanger, and distributed to buildings. Based on the components and materials used, the snow storage system is classified into three types: i) conventional snow storage system (CSS), where snow is stored on a coarse grain filled underground pit; ii) watertight snow storage system (WSS), where snow is stored in a pit covered with asphalt or plastic liner on the ground for waterproofing purpose. The liners avoid the penetration of contaminants present in the snow into the drinking water aquifers, easing the water treatment process; and iii) high-density snow storage system (HSS), where snow is stored in a waterproof pit after mechanically compacting and increasing the density.  In this study, these three snow storage systems are compared to the conventional chiller, a proven technology that has been used widely since the 1900s.   2.1.1 Conventional Snow Storage System (CSS) The first CSS system was developed in Ottawa, Canada in 1980 with the objective to increase the efficiency of energy consumption and to minimize operation and maintenance costs. Snow was dumped into a coarse sand filled pond near the public office building and stored for building cooling in the summer (Morofsky 2007). In the snow storage technique, the melted water from the snow pond is transmitted to the building through a pipe circulation system equipped with a heat exchanger. Melted water is pumped through the heat exchanger and is then led back to the snow pond. The higher temperature of recirculated water helps to melt snow to generate chilled water (Morofsky & Merrifield, 1981; Morofsky, 1982).  This system has three distinct components: development of the snow pond, an energy transfer system, and thermal insulation. The proposed pond has a storage capacity of 88689 m3, potential refrigeration capacity is 1,236,000-ton-hours, and the estimated cost of preparing this is $74,000. The energy transfer system consists of a heat exchanger of 450-ton capacity in which 850 gallons per minute of melted water can be circulated, at an estimated system cost of $137,000.  The application of CSS concept was successfully performed, but the present status of this system is not available. However, the results of this experiment emphasized that the CSS system is suitable for Continental climate regions. With the proposed system, the estimated payback period to deliver 7MW of mean cooling energy with 90000 m3 of snow is about ten years (Morofsky and Merrifield 1981). 12  2.1.2 Watertight Snow Storage System (WSS) The first large-scale WSS system was developed and operated in June 2000, at the regional hospital in Sundsvall, Sweden (Nordell 2007). The conceptual diagram for the WSS system outlined in Figure 2.2, which basically comprises a thermally insulated snow pit, circulation pumps, filters, and a heat exchanger (Skogsberg and Nordell 2001).  Figure 2.2  Schematic diagram of watertight snow storage system (Skogsberg and Nordell 2001) (Reprinted with the permission of Elsevier limited) In 2010, the supplied cooling was 3000 MWh, with a maximum cooling power of 3000kW during the cooling season from May to September. In recent years, almost 100% of the hospital’s cooling (90% for comfort cooling, 5% for kitchen, and 5% miscellaneous) has been covered by this snow storage (Skogsberg 2005). According to Nordell and Skogsberg (2007), during the first six years of operations, the snow cooling cost was considerably lower than that of conventional cooling machines due to increased storage volumes, more efficient operation, and increased energy prices. The estimated payback period of this system to deliver 6000MWh of cooling energy with 120,000 m3 is around three years. The overall cost of the WSS system was found to be considerably lower than that of conventional cooling (Nordell 2015; Skogsberg 2005).   13  2.1.3 High-Density Snow Storage System (HSS) Due to the density of snow (i.e., 450-550 kg/m3) which is less than the density of ice (i.e., 900-920 kg/m3), the snow storage systems require large space for storage which increases construction cost as well as decreases the efficiency of energy use (i.e., high snow mass loss). In order to overcome these challenges, Hamada et al. (2010) developed the HSS system in which the collected snow is mechanically compacted to increase its density to 750-800 kg/m3. This kind of storage system is most suitable in high-density urban areas. The conceptual diagram of the HSS system is presented in Figure 2.3. The HSS system is similar to the WSS system with an additional storage tank to use melted cold water during unexpected peak cooling demands.   Figure 2.3  Schematic diagram of High-Density snow storage system (Hamada et al. 2010) (Reprinted with the permission of Elsevier limited) The developed HSS system was implemented in an office building with a floor area of 989m2 in Oshu, Japan, which required around 450 tons of stored snow for cooling. At the time of data collection, the system had been operated for 8 hours a day for 17 days. The supplied energy was 2.49 MWh that is equivalent to 25.7 tons of snow. Each ton of snow corresponded to 0.094 MWh of cold. However, an average of 756 kg/m3 density was achieved which is much higher than other snow storage systems. Also, this system was operated with the energy efficiency ratio (EER) of 6.0 (Hamada et al. 2010). EER is the ratio of the cooling output under steady-state operation to the electrical power input.  14  2.1.4 Conventional Chiller Cooling machines were first used in the early 1900's.  The chiller principle has been applied from household refrigerators and freezers to space cooling for mega structures. The working principle of the chiller is based on four main components and a refrigerant.   Figure 2.4  Schematic diagram of chiller unit (Stanford 2003) Figure 2.4 shows that low-pressure liquid refrigerant in the evaporator absorbs heat from the building and boils or evaporates at a low pressure to form a gas. This superheated gas transfers the heat it has gained to ambient air or water and condenses into a liquid. The pressure difference between the evaporator and the condenser, and the refrigerant thermal properties determines the desirable range for operations (Stanford 2003).  The major problem of the chiller cooling system includes refrigerant losses and the formation of toxic chlorofluorocarbons (CFC). Recently, the CFC has been replaced with hydro-chlorofluorocarbons (HCFC); although HCFCs are not harmful to the ozone layer, they are still considered to be greenhouse gasses (Calm 2002; Mutel et al. 2011).  15  2.2 Environmental Performance of Cooling Systems Typically, the seasonal snow storage systems use snow from streets and roads, which may contain several contaminants. The water quality of urban snow is poor due to de-icing chemicals, regular atmospheric fallout, airborne residue, friction material, animal excrement, and debris from road surfaces. A major cause of pollution in the snow is vehicular movement. Abrasion of tires, oil leakage, motor and brake wear as well as from the exhaust are the main reasons for foreign particles in the snow. These particles contain organic materials, including zinc, lead, chromium, copper and nickel. Abrasion of studded winter tires contributes iron, nickel, molybdenum, tungsten, chromium, cobalt, cadmium, titanium, and copper (Viklander 1997).  During the operation of snow storage system, the excess melted water is pumped out to sedimentation ponds or district sewer system. Treating the contaminated snow is often a challenge due to complex geophysical interfaces between the atmosphere, landscape, soils and hydrosphere (Podolskiy et al. 2015). Various studies have been performed for handling of urban snow with snow quality. Reinosdotter and Viklander (2006) proposed an environmentally sustainable snow handling strategy. Also, to minimize the costs for analyzing pollutants in snow, this study established guidelines for standard metal concentrations in snow. Tatarniuk et al. (2010) investigated the characteristics of the snow and melted water in the snow deposit facility in Edmonton, Canada. The study found that the road salts and sediment contents were high in the snow pile. Several other factors affecting the characteristics of snow in the storage facility include, retention time on streets, location and nature of the snow source, climate, sampling technique, and snow site operations. Lundberg et al. (2014) compared the snowmelt pollutant releases from urban snow deposits and snow storage cooling systems in northern Sweden. This study found that the snow storage system has largest pollution-control advantage over a surface deposit. During the operational phase, snow storage systems used piping and filters (particularly, oil and water separation filter) for filtering the melted snow water and this can be used also for filtering the surface snow melt runoff before rejection.   16  2.2.1 Life Cycle Analysis According to ISO 14040 (2006) LCA technique aids to evaluate the environmental aspects and potential impacts associated with a product, process, or system throughout its life cycle. In general, the life cycle of a product or a process has consecutive and interrelated phases, from the acquisition of raw materials (mostly natural resources), manufacturing, transportation, use, operation and maintenance, recycling and final disposal (Khasreen et al. 2009). LCA is internationally acknowledged holistic, scientific, and standardized environmental assessment methodology, and it is used in several sectors, including the building industry, with a broad range of applications (Blengini and Di Carlo 2010). LCA follows a structured approach, and it performed based on ISO 14040 – 43 standards (ISO 14040 2006; ISO 14041 1998; ISO 14042 2000; ISO 14043 2000).  In order to assess the sustainability of snow storage systems, their environmental performance needs to be evaluated over their total lifecycle (from cradle to grave). Various studies have evaluated the environmental performance of building energy systems, and 35% of them focused primarily on energy consumption and global climate change (Hamada et al. 2010; Lotteau et al. 2015; Miller et al. 2011; Skogsberg 2005). Building energy systems are mostly influenced by the Global Warming Potential (GWP), Acidification Potential (AP), and Photochemical Oxidant Creation Potential (POCP) impact categories. Furthermore, the non-renewable share of the final energy use is represented as the Non-Renewable Energy (NRE) consumption impact category (Citherlet and Defaux 2007).   In the past, a number of studies have been conducted to evaluate environmental impacts of building projects using the life cycle analysis (LCA) technique, particularly for building materials, heating, ventilation, and air conditioning systems, sewage systems and complete building lifecycles (Abd Rashid and Yusoff 2015; Ortiz et al. 2009). However, the LCA studies on snow storage systems were limited to either a particular lifecycle phase or compared a single snow storage type to the existing cooling systems. Hamada et al. (2010) and Skogsberg (2005) performed Life Cycle Analysis (LCA) for the extraction, construction, and utilization phases; however, the comparison was made between one type of snow storage system and the conventional chiller technique only. There are different types of snow storage systems which have varying energy requirements and associated environmental impacts during the different life cycle phases. Evaluating different types 17  of cooling systems with varying energy requirements for several environmental impact categories during different lifecycle phases is certainly a multi-criteria decision-making problem.   2.2.2 Multi-Criteria Decision Analysis Techniques The LCA results for overall life cycle phases are complex and usually involve a wide range of impact categories and several alternatives. Multi-criteria approach can handle a broad range of assessment criteria into account for different alternatives under consideration. Moreover, this technique confirms the consistency between the structure of the problem and the ranks the alternatives based on their performance to facilitate the decision makers (Medineckiene et al. 2015). Hwang and Yoon (1981) are one of the pioneers who reviewed MCDA methods and applications. MCDA methods have been used primarily to rank the predefined alternatives that are characterised by multiple, usually conflicting, attributes. Currently, this technique has evolved into various dimensions and is being applied in various sectors.  The first attempt of multi-criteria technique to rank the alternatives was found in the study of Churchman (1957). The Weighted Sum Method (WSM) was used to select the enterprise investment schemes. The WSM calculates a single value for each alternative, which is the sum of the alternative’s performance by the relative importance of each criterion. A variation of the WSM is the weighted product Method (WPM). The main difference is that instead of addition in this model, the attributes are multiplied, and the weights become exponents to each of the associated attribute value (Bridgman 1922; Miller and Starr 1969). Saaty (1977) proposed the Analytic Hierarchy Process (AHP) MCDA method based on the hierarchical degeneration of decision-making problem. AHP is developed to degenerate complex decision-making problems into simpler and manageable elements which create different hierarchical layers or levels. AHP degenerates complex decision-making problems into a hierarchy involving of a primary objective, a set of alternatives for attaining the objective, and a set of criteria that relate the alternatives to the objective. Roy (1968) proposed the Elimination and Choice Expressing Reality (ELECTRE) which formulates concrodance and discordance matrices based on how each alternative outranks or outranked by others to strengthen the outranking relationships, then renders a set of preferred alternatives in the form of a kernel. The alternatives in the kernel should satisfy the following 18  conditions: i) alternative is not outranked by any other alternative and ii) all the alternative outside of kernel is outranked by at least one alternaitve in kernel. The original ELECTRE method may not reach a full ranking order, thus various versions of the ELECTRE method has been developed. Most famous versions of the ELECTRE method include: ELECTRE I, ELECTRE II, ELECTRE III, ELECTRE IV, and ELECTRE TRI (Figueira et al. 2005).   Using outranking technique Brans and Vincke (1985) proposed the Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE) method, which defines preference functions based on the differences between attributes among different schemes. The PROMETHEE method has many advantages over intricate ELECTRE methods, including: i) calculations are straight forward, simple and transparent to decision makers; ii) require a few parameters to evaluate the ranking as well as the model parameters have a real economic meanings and make sense for decision makers; iii) technical problems, such as concordance discrepancies and discrimination thresholds will not influence the results; iv) less calculation efforts are required to achieve the results compared to ELECTRE methods; and v) the alternatives can be ranked based on baseline or standard value, whereas ELECTRE facilitates ranking only within the alternatives (Georgopoulou et al. 1997). This method offers two possibilities to rank the alternatives, PROMETHEE I and PROMETHEE II; the former provides a partial ranking and later provides total ranking of the alternatives.   Numerous studies have adopted MCDA methods to solve energy issues (Keefer et al. 2004), e.g., energy and environmental modelling (Huang et al. 1995), renewable energy planning (Ghafghazi et al. 2010), etc.  Pohekar and Ramachandran (2004) and Taha and Daim (2013) reviewed the multi-criteria applications and found that AHP, PROMETHEE, and ELECTRE are the most commonly used methods for renewable energy analysis. Kowalski et al. (2009) and Topcu and Ulengin (2004) used PROMETHEE method to develop a renewable energy planning and policy framework in agreement with the geographical and political conditions. Haralambopoulos and Polatidis (2003) and Oberschmidt et al. (2010) developed a group decision-making framework using PROMETHEE method for renewable energy performance evaluation.    19  Anand et al. (2015) reviewed various types of solar cooling technologies to minimize the fossil fuels consumption and associated harmful emissions. The study concluded that the evacuated tube collectors are most suitable for solar cooling where desiccant cooling improves the indoor air quality. Also, the study found that the thermal energy storage and ejector based solar cooling system increases the performance efficiency as well as energy savings. Pintaldi et al. (2015) reviewed several types of thermal energy storage technologies and control approaches for solar cooling. This review was mainly focused on the high-temperature high-efficiency cooling applications. The study revealed that the new phase change materials are suitable for decreasing the size of thermal storage in high-efficiency high-temperature solar cooling applications. Wimmler et al. (2015) reviewed the application of multi-criteria approach in renewable energy technologies. The study developed a new concept for island energy planning by implementing renewable energy technologies in combination with storage devices for sustainable development.   Extensive research in the literature has been undertaken to assess the performance of energy storage technologies for cooling applications. Therefore, the types of energy systems, the number of cooling systems, and environmental impacts, technical and economic considerations were reviewed. An evaluation of life cycle approaches and MCDA applications has also been conducted. Table 2.1 summarizes the evaluation of energy storage technologies for cooling applications.  20  Table 2.1 Evaluation of energy storage technologies for cooling applications Author Types of Energy Systems No. of Cooling Systems Environmental Impacts Technical Considerations Economic Considerations Life Cycle Approach MCDA Application Remarks (Yan et al. 2016) Seasonal cold storage system based on heat pipe 1 No Weather data and heat pipe parameters (condensor area, evaporator size)  Simple payback period N N A high-efficient and environmental friendly seasonal cold storage system using heat pipe for sustainable building cooling was studied. The effectiveness and sustainability of this system are evaluated using a Quasi-steady-two dimensional mathematical model and validated with field measurement data in Beijing, China. (Blackman et al. 2015) Solar-assisted heating and cooling systems 3 No Cooling power Levelized cost N N To reduce the primary energy consumption and to improve the cost-effectiveness, the solar-assisted heating and cooling systems with sorption module integrated solar collectors were evaluated. Three system models were applied to retrofit a residential building in Madrid, Spain.  The study found that the photovoltaics are more appropriate where high cooling demands.  (Maxim 2014) Sustainability assessment of energy generation technologies 13 Land use and external costs Ability to respond to demand, efficiency, and capacity factor Levelized cost Y Y This study provided a comprehensive sustainability assessment of 13 energy generation technologies using MCDA technique and life cycle approach. The study found that the large hydroelectric projects are the most sustainable technology, followed by small hydro, onshore wind and solar.  (Raza et al. 2014) Renewable energy storage systems 3 Solid waste generation during the manufacturing process Fast load response capability, reliability, system life, efficiency, capacity or efficiency variation, risk factor, modularity production, energy density ratio Net Present Value Y Y A feasibility study has been conducted using sustainability index approach to select the energy storage system of an intermittent renewable energy source. Weighted Sum Method was used to aggregate the comprehensive quantitative and qualitative parametric system. (Khoukhi 2013) Desiccant cooling system 1 N Dry Bulb Temperature and Humidity Ratio  N N N Studied the feasibility of Desiccant based cooling and dehumidifying system and found that it is suitable for the hot-humid climate. Furthermore, Validated the simulation results using field data from Tohoku, Japan. (Yang et al. 2013) Soil cool storage system 1 No Coefficient of performance (COP) Capital and operational cost N N Seasonal soil cool storage system was proposed to reduce the energy consumption and to utilize the renewable resources. A Mathematical model, numerical simulation and experimental validation had performed to describe the charging and discharging process, to provide the theory foundation and technology support of the proposed system. 21  Author Types of Energy Systems No. of Cooling Systems Environmental Impacts Technical Considerations Economic Considerations Life Cycle Approach MCDA Application Remarks (Singh et al. 2011) Heat pipe based cold energy storage system 2 No Thermal load and Coefficient of performance (COP) Simple payback period and Levelized cost N N Two types of heat-pipe based cold energy storage systems (ice storage system and cold water storage system) were proposed. The proposed systems were applied in datacenters located in areas with average yearly temperature is less than 25 ºC. The study found that the ice storage systems were more efficient for datacenters in cold locations.  (Hamada et al. 2010) Mobile High-density Snow Storage (HSS) 1 CO2 emissions Primary energy consumption  Simple payback period N N Introduction and promotion of mobile HSS system for efficient energy use and cost saving (Mokhtar et al. 2010) Solar cooling technologies 25 No Coefficient of performance (COP) and Overall Efficiency of solar field, cooling equipment, and storage Levelized cost Y N This study performed the systematic, comprehensive techno-economic assessment of 25 solar cooling techniques using location-specific climate data. The study concluded that the expense of the solar collection techniques and the performance of the refrigeration techniques are determining the most efficient cooling system.  (Wang et al. 2010) Solar-assisted ground-coupled heat pump system with solar seasonal thermal storage 1 No Coefficient of performance (COP) No N N The experimental study of a solar-assisted ground-coupled heat pump system with solar seasonal thermal storage in a residential building in Harbin, China was conducted. The study found that the soil can be used as the heat sink to cool the building. (Hamada et al. 2007) Hybrid system for snow storage and air-conditioning  1 CO2 emissions Energy Efficiency Ratio (EER) Levelized cost N N The studied hybrid system was comprised of vertical earth heat-exchangers for snow melting and seasonal snow and ice cryogenic storage for space cooling. Also, underground thermal utilization system used to prevent freezing of a road in severe winter in Japan. A Numerical Analyzes were used to measure the efficiency of the hybrid system in terms of energy conservation, environmental protection, and cost, and the results were validated with field measurement data from Sapporo, Japan in 2001.  (Skogsberg 2005) Watertight snow storage 1 Climate change, acidification, nitrification, and ground ozone Coefficient of performance (COP)  Levelized cost, payback period Y N Application of WSS system in a hospital building and presented the results of first six years of operation. (Kirkpatrick et al. 1985) Watertight snow storage system with artificial snow 1 N Coefficient of performance (COP) Levelized cost, payback period Y N Presented the operational experience from two experimental ice storage systems. Low-cost and high-cost scenarios were established to calculate the cost projections.  22  Author Types of Energy Systems No. of Cooling Systems Environmental Impacts Technical Considerations Economic Considerations Life Cycle Approach MCDA Application Remarks (Bahadori 1984) Natural Cooling Storage Systems 5 N Coolness recovery factor and utilization factor  Levelized cost Y N Seasonal storage of coolness in rocks, ground, aquifer, ice, and frozen soil were compared with conventional chiller cooling system  (Masoero 1984) Watertight snow storage 1 N Ice melting parameters and Coefficient of performance (COP) Levelized cost, payback period Y N Evaluated the physics and engineering of ice production, preservation, and utilization prior to the establishment of first commercial watertight snow storage system in Princeton area. (Francis 1982) Long-term frozen saturated earth 1 N Coefficient of performance (COP) and Average freezing degree days N N N Feasibility study has been conducted for five cities in the USA and found that ice storage in milder climates was difficult (MacCracken 1982) Ice storage with plastic tubes 1 N Coefficient of performance (COP)   N N N Studied the daily and seasonal ground freezing with buried plastic tubes carrying cooled glycol based on U.S. Patents Nos. 3751935, 3893507, 3636725, and 3910059. (Morofsky and Merrifield 1981) Conventional snow storage (CSS) system  1 N Storage capacity, efficiency of energy transfer systems and insulation  Payback period  N N Implemented snow storage concept for cooling a building for the first time in Canada.  (JITCO 1977) Natural cold seawater district cooling system 1 N Primary energy consumption  Levelized cost, Net Present Value Y N Reviewed the concept of natural cold water district cooling as well as evaluated the feasibility in terms of substantial energy savings, long-term cost benefits, and environmental benefits.  23 Most of the past studies summarized in Table 2.1 used one type of the snow storage technology and none of the studies conducted the comprehensive evaluation of environmental and techno-economic performance under uncertainty using the MCDA for building cooling systems. Maxim (2014) and Raza et al. (2014) performed sustainability assessment of renewable energy technologies by applying both the life cycle approach and  MCDA; however, these studies did not conduct the LCA to evaluate the environmental performance. The majority of researches used the coefficient of performance (COP) and Levelized cost to evaluate the technical and economic performance of cooling systems, respectively.   2.3 Techno-Economic Sustainability evaluation of Cooling Systems 2.3.1 Cooling Demand Analysis The residential buildings at universities are required to fulfill the secondary energy uses, such as heating, cooling, lighting, and small-scale electronic appliances including IT equipment, kettles, etc. Usually, individual or centralized air-conditioners are used for cooling in summer or as a heat pump in winter are considered as a constant load to the building energy system.  In order to evaluate the annual energy requirement of the cooling system in a building, an energy demand analyses are conducted. Building energy simulation is a well-known and suitable procedure for evaluating the dynamic interfaces between the outside atmospheres, building envelope, HVAC system, and the related energy utilization. It has performed an essential role in the enhancement of building energy efficiency codes and various design tools.  For accurate forecasting of energy demand, sophisticated energy simulation tools obtain detailed information from a building, including hour-by-hour heating and cooling demand, and thermal comfort throughout the year. In recent years, many studies have used various simulation techniques to evaluate the energy demand particularly for residential buildings. Most of these studies hypothetically examined the energy performance of buildings by creating models consisting of various factors, such as building envelope, location, climate zone, and thermal properties.  At present, many building energy simulation programs are available to calculate the energy performance of a building under various circumstances. Maile et al. (2007) compared the usage  24 of thermal simulation engines (EnergyPlus and DOE-2) and user interfaces (RIUSKA, eQUEST and DesignBuilder) for different life-cycle stages of a building, and evaluated the strength and weakness of each tool as well as their data exchanging capabilities. Attia (2011) compared the ten energy simulation software based on the five key parameters, including usability, optimization, accuracy, interoperability and design process integration of the tools and listed out the limitations of software and major requirements to attain the net zero energy building objective implications. Tronchin and Fabbri (2008) analyzed and compared three different types of energy performance calculations of a building to estimate their gap with the actual energy consumption. The study evaluated the energy performance of a single family house in Italy using real energy consumption, operating rating simulation (BestClass) and software simulation (DesignBuilder) for both the static and dynamic conditions. Chowdhury et al. (2007) used DesignBuilder Software to forecast the energy consumption, especially, the operation of HVAC system and lighting system of institutional building in Queensland, Australia. They identified the factors that influence the building energy performance and thermal comforts of the occupants during summer and winter. Dragicevic et al. (2013) used the DesignBuilder software to estimate the thermal performance of the building materials. In particular, this study compared the performance of conventional and energy efficient windows in the school buildings. The results of this study used to develop generalized guidelines to determine the efficiency of energy saving measures and to assess the low-energy buildings. However, very few of the studies validated the obtained simulation results with the actual energy consumption.   2.3.2 Technical Evaluation Canadian climate favors the snow or ice making process at low cost because of its excellent source of coldness in winter. Most of the SSS use rotary blowers to produce snow with a density of around 450-550 kg/m3 (Hamada et al. 2007) which is much less than the density of ice (i.e., 900-920 kg/m3). Due to low density, year-round storage of snow demands large land requirements, and high construction costs (due to extensive requirements of insulation materials). Besides compaction of snow in the storage unit exerts pressure on the insulated exterior walls and may reduce the performance of the insulation material (Hamada et al. 2010). These requirements can be significantly reduced, if snow is mechanically compacted to achieve the density of ice.   25 Vigneault and Gameda (1994) developed and tested a new technique to enhance the snow compaction process. Their study conducted a series of experiments to determine the effect of additions of 0, 10, 15 and 20% water (on a weight basis) on snow compaction, and found that the addition of 10%more water increased the snow density to 920 kg/m3 (density of ice) as well as significantly reduced the energy and pressure required to compact the snow. Gaméda et al. (1996) further investigated the compressive characteristics of snow by adding 0, 3, 6, 9, and 12% of water at an initial temperature of -5, -8,-11, and -18 ºC. Their study also illustrated that the addition of 12 and 9% water during the initial snow temperatures of -11, and -18 ºC resulted in reduced stress and greater density. The snow density increased up to 800-920 kg/m3 by adding water on snow compaction, which is equivalent to the density of ice.  2.3.3 Economic Evaluation The major concern for the widespread application of seasonal snow storage systems is their economic performance compared to the conventional chiller cooling system. For reliable evaluation, consistent methods are required for the collection and analysis of economic costs and benefits using appropriate indicators, including annual capital investment cost, annual operation and maintenance cost, total cooling cost, net present value, and levelized cost.     Minturn et al. (1979) and Abbatiello et al. (1981) studied the techno-economic feasibility of the Annual Cycle Energy System (ACES) technique, for space heating and cooling of the residential buildings. For this study, the efficiency and economic performance of the ACES technique were compared with three conventional electric HVAC systems in 115 U.S. cities based on the actual results as well as analytic calculations. The study results revealed that the performance of ACES was better than other systems; however, its estimated capital cost was found to be remarkably higher than conventional systems. Though, the capital costs are large and payback periods are relatively long, the annual benefits beyond the payback period greater than the other alternatives. Also, this study argued about indirect benefits to the country, e.g., the significant reduction in the use of fossil fuels and the commensurate decrease in an unfavorable balance of trade, lower utility bills, etc.     26 Gordon et al. (1986) studied the economics of passive solar cooling energy in commercial buildings in terms of the construction and operational costs. The study found that the operational cost of passive solar commercial buildings was significantly lesser than the conventional cooling system, and the initial construction cost is as same as other conventional methods. Morofsky et al. (1985) evaluated the techno-economic feasibility of seasonal ice storage development through preliminary design, experiment, and pilot study. The main objective of this study was to establish an automated, energy efficient ice storage with minimum operation and maintenance cost. The study found that the goal of minimum operation and maintenance cost could be achieved by natural ice making facilities. Also, economic assessment of various small-scale seasonal ice storage systems have been evaluated, and the results were very positive in terms of combined capital cost and energy cost. However, the study discussed that a large-scale application of ice storage system might be potentially competitive with conventional cooling systems.   Taylor (1985) reviewed the techno-economic feasibility of several seasonal ice storage systems in the USA and estimated that these systems were substantially profitable when implemented in large buildings having floor area more than 10,000 to 20,000 m2. Since, a large fraction of the capital cost highly depends on the ice storage area; the volume of ice per unit area tends to increase with size and attains economies of scale.  When the total capital costs are fully covered, annual storage costs range from about $25/ton of stored ice, for 10,000 tons capacity down to about $10/ton for 20,000 tons capacity of ice storage system. The cost of the thermal insulations was accounted for more than 2/3 of the total cost of the system. The cost of land also plays an important role in ice storage system, and this study estimated that the land cost less than $20/m2 was allowable for this technique; however, this value is certainly not applicable to present high land prices.  Marquette (1985) evaluated the comprehensive installation cost and operational cost of ice storage systems and compared with a wide array of mechanically operated conventional cooling system designs for 20,000 ft2 and 400,000 ft2 buildings. The study found that the installation cost of ice storage systems was considerably less, even when compared to significantly inexpensive systems including air-cooled rooftop and self-contained water source heat pumps. Also, this study emphasized that the application of ice storage systems be practically feasible in building industry.    27 Kirkpatrick et al. (1985) evaluated the environmental performance of ice storage systems by comparing the range of high-cost to low-cost scenarios. At the time of their study the ice storage concept was quite new and not fully developed; therefore, the estimating cost of conventional cooling could be found to be highly variable, i.e., by a factor of more than 5. For this study design, and construction methods and materials of the ice storage system were considered according to the chosen scenarios. An ice storage system with the volume of 26000m3 (14,300 tons of ice) was insulated with inflated aluminized plastic sheets. Three to six snow guns were used to produce artificial snow, and the expected service life was 20 years. The estimated payback period ranged from 0.8 to 14 years and 5 to 86 years for low-cost and high-cost scenarios respectively.   Vigneault (2000) performed the economic study of ice storage system in the agriculture sector where ice blocks were insulated with sawdust and stored to refrigerate the vegetables. This system was found to be economical because of negligible installation cost, and the total operational cost is mere $700 per season. The conventional system accounted for $22,000 for installation and $400 per season for operational cost.   Morofsky and Merrifield (1981) studied the economic performance of conventional snow storage system and recommended that large-scale application along with heat exchanger system has many advantages. The study resulted in fulfilling 7 MW mean cooling load, the capital cost of snow storage was $240,000 with an estimated 9.5 years payback period. Skogsberg (2005) evaluated the economic performance of watertight snow storage in Sweden. The cost for 3850 MWh of cooling load was compared with various alternatives (i.e. thermal insulation, construction method and materials) and the estimated that the snow storage system required around 50% less total cost than the conventional district cooling system.   The review of the economic studies for seasonal cold storage systems illustrates that with technological advances, these systems are becoming more economically feasible. Furthermore, the subsidies and financial supports offered by the government also play an important role, particularly for the installation cost. The economic feasibility of existing cold storage technologies is influenced by fossil fuel and electricity price variation; in this regard, stringent policy measures, such as a carbon tax, might increase the existing trends towards renewable options.  28 2.3.4 Uncertainty Analysis The most significant uncertainties in the economic analysis of cooling systems are due to: i) inclusion of some costs and benefits and overlooking others, ii) using present estimations for the future, and iii) uncertainty in the cost of materials (Soltani 2015). Uncertainty is often, but not always associated with variables that will be revealed in future. Typically, the probabilistic assessment is performed for the critical parameters that influence the economic analysis. In this assessment, an uncertainty of the variables is described in terms of the Probability Density Function (PDF) or its equivalent, and correlations between dependent variables must be determined. PDF represents a mathematical description of the probability or likelihood of an uncertain variable taking on a given value. The probability of the underlying variable being in a range around that value is given by the area under the density function between those two values (Walter Short et al. 1995): !"#$%$&'&()	[% ≤ -	 ≤ $] = 0 1 2134 	 (2.1)	 where f(x) is the density function. This density function can be determined analytically in simple cases and by Monte Carlo Simulation (MCS). MCS is problem-solving technique used to assess the impact of risk and uncertainty in quantitative analysis and decision-making. In most of the energy systems, MCS is used due to their complex relationships and involvement of large number of variables (Karki et al. 2010).  In the operation of MCS, a random number (generally pseudo-random number) on the interval of 0-1 is generated. The random number is transformed into an appropriate statistical distribution; the resulting number is referred to as a random variate. Then the desired output parameters are calculated by substituting the random variates into the appropriate variables in the model. The outcome of the analysis is recorded, and the process is repeated for m number of iterations. The notable point is the random numbers must be different for each iteration. After m repetitions, an empirical evaluation of the distribution of the system output is obtained (Li and Zio 2011).  Monte Carlo methods are now extensively used by the industries, researchers, and private and government organizations to study the behavior of complex systems of all sorts. Monte Carlo simulations (MCS) technique analyzes the uncertainty associated with data and generates a range of possible outcomes and the probabilities that can be used for quantitative analysis and decision  29 making. This method was first to use in developing the atom bomb in the 2nd World War and have been used to model many physical and conceptual systems ever since (Palisade Corporation 2015). In order to conduct MCS, the cost of construction materials has been normally distributed. Sebastian Stinner (2014) conducted uncertainty analysis of the building performance using Quasi-Monte-Carlo approach to maintain the computational results in minimum range. The study found that at least 64 simulations were necessary to have a potential reproducibility of the dynamics of the energy demand. Jamil et al. (2012) reviewed the techno-economic feasibility analysis of energy generation technique using a solar photovoltaic system and emphasized to perform an uncertainty analysis to obtain a realistic estimate in energy studies.    30 Chapter 3 Methodology  The contents of this chapter are prepared to publish in two scientific journals. A part of this chapter is prepared to publish in the journal, Building and Environment titled “Framework for Life Cycle Assessment of Building Cooling Systems: A Comparative study of Snow Storage and Conventional Chiller Systems”; and another part in the journal Energy and Buildings titled as “Techno-Economic Sustainability Evaluation Framework for Building Cooling Systems: A Comparative study of Snow Storage and Conventional Chiller Systems”.  In the first section of this chapter presented new multi-criteria based life cycle assessment framework to evaluate the environmental impacts of building cooling systems. The subsequent sections briefly explained the methodology used to apply the MCDA and uncertainty analysis to assess the environmental and techno-economic performance of cooling systems.   3.1 Building Cooling Systems Evaluation Framework This study investigates the environmental performance of cooling systems throughout their life cycle. A conceptual framework for evaluating the potential environmental impacts associated with building cooling systems is proposed in Figure 3.1. The framework initiates with the identification of various types of building cooling systems, followed by the identification of various activities responsible for the greatest environment impacts during the cooling systems’ life cycle. The consequences of choosing different LCIA method of an LCA are identified. Finally, the cooling system that contributes the least environmental impact throughout its life cycle will be selected using MCDA technique.       31  Figure 3.1  Proposed framework for sustainability evaluation of environmental performance of building cooling systems 	Literature	Review	and	Identification	of	building	cooling	systems			  Estimation	of	impacts	for	three	lifecycle	phases	of	cooling	systems	  Watertight	snow	storage	(WSS)	High-density	snow	storage	(HSS)	Life	Cycle	Assessment	using	SimaPro	v8.1	      Goal	and	scope	definition	Life	Cycle	Impact	Assessment	(LCIA)	using	TRACI	2.1	Life	Cycle	Inventory	(LCI)	using	Ecoinvent	v3.0	Environmental	Impact	Categories		      Aggregating	environmental	impact	categories	–	Weighted	Sum	Method		Ranking	feasible	alternatives	–	PROMETHEE	application	  CSS	 WSS	 HSS	Conventional	snow	storage	(CSS)	Chillers	Extraction	and	Construction	Utilization	 End	of	Life	• Respiratory	effects	• Ozone	depletion	• Ecotoxicity	• Smog	• Resource	depletion		DesignBuilder	software	for	estimating	operational	energy	demand		Chillers	• Global	Warming	• Acidification	• Eutrophication	• Carcinogens	• Non-carcinogens		 32 Many researchers have investigated the different types of building cooling systems, particularly natural cooling systems.  In recent years, the application of seasonal cold storage has been gradually increased due to high energy efficiency and environmental benefits. The major natural source for producing ice is the ambient air in winter. The ambient air temperature drops well below the freezing point almost everywhere in Canada, which makes it possible to of establish seasonal snow storage system to meet the building cooling energy demand. Also, this method can be adapted to more sites to other seasonal storage methods. Thus, three types of seasonal snow storage systems have been selected, and their environmental performance has been compared with the conventional chiller cooling system. In order to understand the complete performance of a system, the LCA study has to be conducted for its overall lifecycle. This study estimated the impacts for three important lifecycle phases of cooling systems namely, Extraction and Construction (E&C), Utilization (use) and End of life (EOL).  To compare the environmental performance of building cooling systems, the cooling energy and the peak energy demands of a building have to be determined. It can be identified either by the actual data from facilities management or by simulating a building model. In this study, DesignBuilder has been used for energy simulation; it is a powerful energy modeling software that can model the complex systems, including HVAC, daylighting, and airflow. It provides the secondary energy consumption of a building in terms of end use, i.e. heating, cooling, and lighting, and others.  The obtained secondary energy consumption can be input into the SimaPro software to evaluate the primary energy consumption according to the energy mix and the geographical location. Furthermore, the lifecycle inventory details of the cooling systems can be gathered using the Ecoinvent database. Establishing the environmental impact categories is one of the most significant activities in the comparative LCA study. For this study, the impact categories are characterized according to the TRACI 2.1 LCIA method. Sustainability of the cooling systems has been evaluated based on the ten impact categories. Typically, the selection of a sustainable cooling system involves various selection criteria, such as ten impact categories, three lifecycle phases and four alternatives, which clearly demonstrate the necessity of using the MCDA technique. Since this study incorporated two different sets of selection criteria, the first set of criteria should be aggregated according to the second set of criteria. For example, the attributes of the impact categories can be aggregated according to the lifecycle phases using the Weighted Sum Method (WSM) and finally, the alternatives can be ranked from the best to worst using PROMETHEE II method.    33 3.2 Life Cycle Assessment LCA is used to compare two competing products or systems over their life cycle phases; to compare the life cycle phases within the system; to compare a system and its alternatives; to compare a system with a baseline or reference system (Reiter et al. 2010). Moreover, LCA study provides a comprehensive view of the stated environmental impact categories; hence it can aid researchers, industries, governments and environmental groups on identifying key environmental aspects at any life cycle phase of a system. Also, it aids the selection of performance indicators to promote the marketing and communication strategies of a product, such as the development of eco-labelling scheme and decision-making for environment-related strategies and materials selection, such as for product design or redesign (Reiter et al. 2010).   According to ISO 14040 (2006), LCA method consists of four distinct analytical phases (see Figure 3.2):  Step 1: Defining the goal and scope of the LCA Step 2: A life-cycle inventory (LCI) of the materials and their associated environmental impacts Step 3: A life-cycle impact assessment of the system using the LCI data Step 4: Interpretation of the results  Figure 3.2  Life Cycle Assessment methodology (Reprinted with the permission of ISO 14040)  34  3.2.1 Goal and Scope Definition  Goal and scope definition, in a simple description, shall clearly state the goal includes the purpose of carrying out the LCA study, the intended audience, and consistent with the intended application. The scope is related to the methodology used to perform the LCA study, leading to the definition of important key aspects, such as the functional unit, system boundaries, and quality criteria for inventory data (Baumann and Tillman 2004; Cabeza et al. 2014; ISO 14040 2006).  3.2.2 Inventory Analysis  The second step of the LCA, the life cycle inventory analysis (LCI), identifies and quantifies the quantitative and qualitative data for all inflows and outflows, such as raw materials, energy, ancillary products, land use and emissions (Kumar et al. 2015a). In an ideal case, this inventory flow data comprises only elementary flows, such as consumption of energy and resources, emissions or waste energy. Gathering data for the LCA study is a time-consuming process hence it is very common to have the aid of computer software for this process. Moreover, Adoption of existing LCI databases, e.g. Ecoinvent can significantly improve efficiency. The aim of the inventory analysis is to sum the overall amount of resource used and emissions of the studied system in relation to the functional unit of the LCA study (Baumann and Tillman 2004; Dong 2014; ISO 14040 2006).   In agreement with the ISO 14040-14043, SimaPro (Version 8.1) software is used for evaluating the environmental performance of different cooling systems in a systematic and transparent way across all the life cycle phases. This software was developed by the Dutch company Pré (Pré Consultants 2013), which is equipped with an updated inventory database (i.e. Ecoinvent 3.0). It identifies the hotspots from the extraction of raw materials to the end of life. Furthermore, it is comprised of updated LCIA methods, such as TRACI 2.1, ReCiPe, and Impact 2002+.   3.2.3 Life Cycle Impact Assessment The next phase, the life cycle impact assessment (LCIA) aims to describe or characterize the inventory analysis with specific environmental impacts. During the inventory analysis, a huge  35 amount of data regarding resource utilization or emissions is gathered; however, they do not directly report to any environmental impacts. Hence, LCIA evaluates the environmental performance of a system in terms of specific impact categories including global warming, and acidification (Baumann and Tillman 2004; Yang 2005). According to ISO 14042 (ISO, 2000a) the impact assessment should include three compulsory steps, impact category definition, classification, and characterization (see Figure 3.3).  Figure 3.3  Life Cycle Impact Assessment framework (Reprinted with the permission of ISO 14040)  Impact Category Definition: This is the first step of an LCIA, in which impact categories are selected according to the defined goal and scope of the LCA study. Impact categories refer to those recognized areas of environmental protection, as such global warming, ozone depletion, and others.   Classification: Classification step required knowledge of the impacts of specific pollutants. This step involves assigning the inventoried LCI results to the impact categories according to their contribution to environmental impact. If an LCI result must be assigned to multiple impact categories, it can be partitioned if its effects are dependent on each other category; For example, both SO2 and NOx contribute to acidification potential; therefore they should be assigned to the acidification impact category (Pinto 2013).   36 Characterization: After the definition and classification of impact categories, the relative contributions of each element assigned to the same impact category will be quantified according to the corresponding potential impacts on the environment. For example, acidification potential can be measured by the release of ions H+ per kilo of substance relatively to the SO2 (Baumann and Tillman 2004). The equation (3.1) expresses this relation:   Impact	indicator	=	Amount	of	substance	x	Equivalence	Factor	 (3.1)	The quantitative step which transfer inventory results into quantified environmental impact by using equivalency factors as a reference while modeling cause-effect chains (Menoufi et al. 2013; Salieri et al. 2013).  The above steps are mandatory in an LCIA method; sometimes the impact assessment steps given below can also be implemented. Other optional steps to be conducted under the impact assessment phase are:   Normalization: According to (ISO 14040 2006) normalization is an optional step yet it fuels the additional values of placing the characterization results in a broader context. The impact category results can be normalized to a different magnitude of impacts (e.g. kg CO2 eq) by dividing it by a baseline or reference number (e.g. kg CO2 eq in the region or country). Normalization relates the micro world of an LCA study to the macro world in which the system is embedded. In other words, the normalization step can increase the environmental significance and more meaningful of the impact assessment results when the comparison is made between the total impact of the total use of the product and the total impact in the region (Baumann and Tillman 2004). The equation (3.2) expresses this relation: R#"S%'&TU2	&V2&W%(#" = XYZ4[\	X]^_[4\`a			bcdcac][c	e4fgc	 	 (3.2)	 Grouping: Involves sorting the characterization results into fewer categories of impacts. Examples can be global; regional or local impacts, or even grouping the results into high; medium or low priority impacts. Grouping can facilitate the understanding of an impact assessment for the common audience (Baumann and Tillman 2004).    37 Weighting: In this step relative importance is attributed to the different impact categories resulted from characterization or normalization, making possible therefore a direct comparison between them. The weighting process is the multiplication of weighting factors and impact category result. The weighting factors are dependent on the integration of social, political, and ethical factors (Menoufi et al. 2013). The equation (3.3) expressed this relation:  Weighted	indicator	=	Indicator	x	Weighting	factor		 (3.3)	 Due to its subjectivity ISO 14042 standard (ISO 14042 2000) recommends that weighting methods and operations used in the LCA shall be documented to provide transparency (Baumann and Tillman 2004). However, according to (ISO 14047 2003)“One of the required steps in conducting the LCA is choosing relevant impact categories that are consistent in reaching the goal and scope of the study”. Moreover, there is an ongoing debate regarding the normalization and weighting in LCA process because of the possibility of misinterpretation and misuses (Amir Hamzah Sharaai 2011).  TRACI (Bare et al. 2012) and ReCiPe (ReCiPe 2015) evaluates the environmental performance of a system in terms of two approaches (i.e. midpoint and endpoint approach) (Goedkoop et al. 2013). The midpoint approach evaluates the performance in terms of environmental impact categories (i.e. global warming, acidification, etc.) and the endpoint approach refers to the damage to the area of protection (i.e. damage to human health, ecosystems, and resources) (Baumann and Tillman 2004; ILCD Handbook 2010; Yang 2005). The Tool for the Reduction and Assessment of Chemical and Other Environmental Impacts (TRACI 2.1) was developed in 2008 by the U.S. Environmental Protection Agency (USEPA) for conducting LCA with much wider applications, like pollution prevention and sustainability metrics, and is the only LCIA method which is specific to North American context (Amir Hamzah Sharaai 2011). TRACI 2.1 assesses the environmental performance by ten impact categories, including ozone depletion, global climate, acidification, eutrophication, smog formation, fossil fuel depletion, human health particulate, human health cancer, human health non-cancer, and ecotoxicity (Flynn et al. 2011). In TRACI 2.1, emissions details are integrated with the US EPA’s human exposure factors and risk assessment guidelines.    38 3.2.4 Interpretation Finally, the interpretation in LCA is an iterative process that is present during all other phases of the study. If two or more alternatives are compared, and one alternative contributes to higher environmental impacts of each material and of each resource, an interpretation purely based on the LCI can be conclusive (Kumar et al. 2015b; Rebitzer et al. 2004). It is here where the findings of the inventory analysis and impact assessment are combined to formulate recommendations and to attain conclusions of the study (ISO 14043 2000).   3.3 Environmental Performance Evaluation Criteria All energy sources cause negative impacts to our environment, and the world pays more attention only to the environmental impacts resulting from the fossil fuels. Though the degree of impacts caused by renewable technologies is substantially lower than fossil fuels, it is still important to assess the environmental impacts associated with renewable energy generation methods. Hence, the recent studies focused on environmental impacts of renewable energy (Spellman 2014). Abbasi and Abbasi (2000) listed out the possible adverse environmental impacts of renewable energy sources and relative magnitude of minimum, medium, and major scale. Remarkably, renewable energy sources like solar, geothermal and biomass may adversely impact the environment in terms of air pollution, eutrophication, etc. The seasonal storage system also involves huge land requirements and more infrastructure facilities to produce energy. Unlike the conventional energy sources, renewable energy technologies have the environmental impacts of different nature and to a degree, as such the construction phase rather than their operational phase.  3.3.1 Global Climate Change In 1999, the Intergovernmental Panel on Climate Change (IPCC) developed the Global climate change impact category consisting of CO2 (carbon dioxide), CH4 (methane), N2O (nitrous oxide), O3 (ozone) as well as Volatile Organic Compounds (VOC). Emission of these gasses, primarily from the burning of fossil fuels, contributes to the greenhouse effect causing: the rise in sea levels, altered hydrological cycle, and the increase in the spread of infections.  Global climate change is expressed in kg of CO2 equivalents (eq) per kg emission (Bare et al. 2012). In this study, the category global climate change is considered due to snow collection process and pumping generate CO2 emissions while consuming fossil fuels.  39 3.3.2 Ozone Depletion The Ozone depletion category has been presented by the World Meteorological Organization (WMO) for various halogenated compounds (Pyle et al. 1991). This impact category indicates the potential damage of the stratospheric ozone layer at the global scale, due to the emissions of Chlorofluorocarbons (CFCs), Bromofluorocarbons (BFCs), and chlorinated hydrocarbons (HCs). Due to the depletion of the stratospheric ozone layer, more Ultraviolet B (UV-B) radiation reaches the earth’s surface and causes adverse health effects on human beings and animals, terrestrial and aquatic ecosystems, and minerals (Pré Consultants 2013). Ozone depletion is expressed in kg of CFC-11 eq per kg emission (Bare et al. 2012). The chiller cooling system uses refrigerants, which emit CFCs during operation, which is necessary for considering this impact category in this study.  3.3.3 Acidification The factors of acidification are categorized on the basis of their impacts on the base saturation (i.e., an indicator of soil fertility). The primary emissions that contribute to acidification are SO2 (sulfur dioxide), NOx (nitrogen oxides), HCl, NH3, and HF.  These pollutants along with oxidants have the potential to increase the concentration of hydrogen ion (H+) to form acids and reach earth as acid rain.  The primary impact is a reduction of the diversity of species; however, its significance may vary from one ecosystem to another. Acidification is expressed in kg of sulfur dioxide (SO2) eq per kg emission (Hossaini et al. 2014). In this study, different components of cooling systems likely use fossil fuels in the manufacturing process and produce SO2 and NOx emission in the air, which is necessary for considering the acidification impact category for evaluating the environmental impact.  3.3.4 Eutrophication The eutrophication can be defined as an increase in the biomass production due to the excess  of macronutrients (i.e., phosphorus or nitrogen, NOx, NH3, and N2O) in soil and/or water (Guinée et al. 2004a).  These excess nutrients create an undesirable change in species composition and form an undesirable biomass in the aquatic and terrestrial ecosystems.  This excess biomass causes low dissolved oxygen and other aesthetic water quality problems (Guinée et al. 2004b; ILCD Handbook 2010). The eutrophication is also known as nitrification, and it is expressed in kg of Nitrogen (N) eq and/or kg of Phosphorus (P) eq per kg emission (Dahlstrøm 2011). In this study,  40 the category eutrophication is considered because of the manufacturing process of the cooling systems components are the main reason for eutrophication.  3.3.5 Human Health Particulate The US EPA reported that “particulates” have significant deteriorating impacts on the human respiratory system, including symptoms of asthma, bronchitis, and acute pulmonary disease. The wood chips and sawdust generate these particulates, which are used as thermal insulation for the seasonal storage system.  The secondary emissions in this impact category are SO2 and NOx that create sulfate and nitrate aerosols. The fine particulates of sizes up to 10µm diameter and 2.5 µm diameter (PM10 and PM2.5) have considerable impacts on human health. The human health impacts are calculated in terms of the sum of the particulate matters that are released into the environment from different activities at the snow storage facilitates. Human health particulate expressed in PM2.5 has been considered as the reference substance (Bare et al. 2012; ILCD Handbook 2010a).   3.3.6 Carcinogens, Non-Carcinogens, and Ecotoxicity The impact categories of carcinogens, non-carcinogens, and ecotoxicity cause an adverse impact on human health effects. While each of these impact categories has been studied separately in the LCIA, they have been discussed together because they have similar characterization factors derived from USEtox model. The USEtox research team was developed by the United Nations Environment Program (UNEP) and the Society for Environmental, Toxicology, and Chemistry (SETAC) life cycle initiative (Huijbregts 2010). The USEtox developed the environmental model for characterizing the human and ecotoxicological impacts due to pollutants. This model evaluates the impacts, including fate, exposure, and effect parameters according to the original USEPA Risk Assessment Guidelines. As per the initiative, reducing radiation and toxic materials in all the environmental compartments (i.e., air, water, and soil) is one of the key objectives to attain sustainability. Human health cancer, non-cancer, and ecotoxicity are characterized as  Comparative Toxicity Unit (CTU)cancer, CTUnon-cancer, and CTUeco, respectively (Bare et al. 2012).  3.3.7 Resource Depletion Resource depletion is the most significant issue for the development of sustainability matrices; unfortunately, because the resource depletion category lacks a monitoring and controlling system,  41 it is difficult to quantify (Bare et al. 2012). This impact category is focused on protection of the human welfare, human health, and ecosystems. The characterization factor of resource depletion refers to the land use, water use and the extraction of minerals and fossil fuels resources, including of hydrocarbons, e.g. volatile materials like CH4 (methane), liquid petrol, and non-volatile materials like anthracite coal. Resource depletion is mainly concerned with the unforeseen GDP growth due to the extraction of fossil fuel resources, based on the upper heating value in terms of crude oil equivalents.   3.3.8 Smog Formation  The causes of smog formation are characterized by the Photochemical Ozone Creation Potential (POCP) (Derwent and Jenkin 1991) and Maximum Incremental Reactivity (Carter 1994). Ground level smog or ozone is formed by various chemical reactions that occur between NOx and volatile organic compounds (VOC) in sunlight. Emissions during the manufacturing process of cooling system components and electric utilities are some of the major sources of NOx and VOC. The US EPA reported that breathing ozone causes various respiratory problems to humans; particularly prolonged exposure to ozone might lead to permanent lung damage.  It can also have harmful effects on sensitive crops and ecosystems (USEPA 2015). Smog formation is expressed in kg of ozone (O3) eq per kg emission (Bare et al. 2012).  3.4 Multi-Criteria Analysis Methods The predominantly used Weighted Sum Method (WSM) has been chosen for this study to aggregate different impact categories under each life cycle phase. Due to the different measurement units, each incommensurable attribute (impact category) has been normalized using the following linear scale method: -_k = l mnol pqrs mso 											(3.4)	where xij  is j-th criterion’s value for i-th alternative, mink{xkj}  is the smallest i-th criterion’s value for all the alternatives compared, Ẍij  denotes the converted values. Thus, the smallest criterion value xij = mink{xkj}  acquires the largest value equal to unity.     42 In order to use the LCA results for decision-making and comparative analysis, weighting and aggregating the environmental impact categories of system alternatives is essential. Thus, several LCA studies and commercial LCA software employ different weighting schemes to make comparison between different impact categories. However, there is no widely agreed method to determine the relative importance of different impacts (Reza et al. 2011). Assigning different weight to a particular impact category, often can completely alter the design options (Calkins 2009). In this study, the weightings of impact categories are assessed from the ranks, in which the attributes are assigned in a simple rank order, listing the most important attribute first and the least important at the last. The most important attribute is assigned with 1, and the least important attribute with n (the total number of attributes). The cardinal weights have been derived from the rank sum weights method as: uk = (]vaowl)(]vaswl)xsyz 				(3.5)	where rj is the rank of the j-th attribute, wj is the weight of the j-th attribute  (Stillwell et al. 1981). Certainly, the sum of all weights must be equal to ‘1’ (Rao 2007). Furthermore, the total value for each phase is calculated by multiplying the comparable rating for each impact category with the corresponding weights and then summing the product values using the following equation (Mateo 2012): Vq = uk-_kr}~l 							i = 1, . . , m	 (3.6)	where Vi is the score for i-th alternative,  , wj is the weight of criteria and Ẍij is the normalized score of i-th alternative with respect to j-th criterion  (Fishburn 1967; Yoon and Hwang 1995).  In this research, Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE) developed by Brans and Vincke (1985), has been used to evaluate different cooling system alternatives. This outranking MCDA method has been employed in energy management (LOKEN 2007; Pohekar and Ramachandran 2004; Wang et al. 2009), renewable energy projects (Diakoulaki and Karangelis 2007; Haralambopoulos and Polatidis 2003; Madlener et al. 2007), energy exploitation projects (Goumas and Lygerou 2000), and geothermal energy projects (Goumas et al. 1999). This method is comprised of two important phases (i) the construction of an outranking relation, (ii) the exploitation of this relation in order to aid the decision maker (Brans and Vincke 1985).    43 In PROMETHEE II, a pair-wise comparison of the alternatives is performed using preference functions to measure the difference between alternatives under given criteria. Considering two alternatives Ai and Ak, the preference functions can be defined as: Å&!ÅÇ			&00			0ℎ Å& > 	 0ℎ ÅÇ 	Å&ÖÅÇ			&00			0ℎ Å& = 	 0ℎ ÅÇ 	 (3.7)	AiPAk means that alternative Ai is preferred over alternative Ak, if alternative Ai is performing better than alternative Ak with regard to criterion h, and AiIAk means that alternatives Ai and Ak are indifferent with regard to criterion h.  This method provides a numerical value between 0 and 1 to the preference relationship in Equation (3.7) by introducing the preference function P(Ai, Ak) such that: !	(Å&, ÅÇ) = 0																																	&0		0ℎ Å& ≤ 	 0ℎ ÅÇ 	à 0ℎ Å& , 0ℎ ÅÇ 			&0		0ℎ Å& > 	 0ℎ ÅÇ 		 (3.8)	where 0<p[fh(Ai), fh(Ak)]≤1.  For practical applications, it is then reasonable to assume that: à[0ä Å_ , 0ä Åã ] = à[0ä Å_ − 0ä Åã ]	 (3.9)	Let Dh(Ai, Ak) be the difference between alternative Ai and  Ak with regard to criterion h is shown as: éℎ Å&, ÅÇ	 = 0ℎ Å& − 0ℎ ÅÇ 	 (3.10)	In order to facilitate the selection of a specific preference function, six basic types have been developed (Brans and Vincke 1985). The “usual preference” function, which considers absolute preference for any difference observed between two alternatives with regard to a certain criterion, is used, as shown in Equation (3.11)  à[0ä Å_ , 0ä(Åã)] = 0					&0				éä Å_, Åã 	≤ 01					&0				éä Å_, Åã 	> 0	 (3.11)	  44 Then, the multi-criteria preference index, π (Ai, Ak) and an integrated preference functions of alternative Ai over Ak, for all the criteria is arranged as: è Å_, Åã = êëíë ìn,ìsxoyz îëxëyz 											(3.12)	where wh is the weight assigned to the criteria h; In order to develop the outranking relation among the selected alternatives, PROMETHEE II method established three outranking measures for each alternative, namely outgoing flow (Ø+(Ai)), incoming flow (Ø-(Ai)), and net flow (Ø (Ai)) which can be calculated using the following equations, respectively: ∅w Aq = π Aq, 1ó∈ô 											(3.13)	Ø+(Ai) is the outranking index of Ai; the larger is the value of Ø+(Ai), the more Ai dominates the other alternatives in the set M.  ∅v Å_ = è 1, Å_m∈ö 										(3.14)	Ø-(Ai) is the outranked index of Ai; the smaller is the value of Ø-(Ai), the less Ai has been dominated by the other alternatives in the set M.  ∅ Aq = ∅w Aq − ∅v Aq 													(3.15)	where Ø(Ai) is the net ranking of Ai. From equation (3.15), the alternative having highest net flow Ø(Ai) will be considered as the most environmentally sustainable cooling system (Brans and Vincke 1985; Brans et al. 1986).  3.5 Techno-Economic Sustainability Evaluation Framework A conceptual framework for evaluating the techno-economic performance of different building cooling systems throughout their life cycle is proposed in Figure 3.4. The framework starts with the identification of various types of building cooling systems followed by the estimation of monthly mean cooling energy demand using DesignBuilder software. Next, the framework uses the historical climate data from the nearest weather station to estimate the monthly mean  45 temperature. Then, the framework identifies the various components that defined the technical and economic performance during the cooling system’s life cycle. Monte-Carlo simulation is performed with uncertainty considerations. Finally, the cooling systems are ranked based on the coefficient of performance and levelized cost.  3.6 Demand Analysis In this study, the DesignBuilder version 3.1.0.068 was used to perform the energy simulation. DesignBuilder is comprised of high-productivity energy simulation tools, including energy plus to assist sustainable building design. It also compares different building design alternatives, optimizes the materials and design at any stage of the building, and imports files from related software products. High quality technical and graphical outputs explicitly illustrate the key performance indicators, including energy consumption, carbon emissions, thermal comfort, daylight availability, and the cost. Moreover, simulation results illustrate the secondary energy consumption, such as heating, cooling, lighting, and other factors of the building. It is the first and most powerful user interface for EnergyPlus HVAC modeling, it estimates heating and cooling energy demand using the ASHRAE 90.1 approved ‘Heat Balance’ method  (DesignBuilder 2015).  The following step-by-step procedure was adopted for energy simulations using DesignBuilder software: Step 1: Obtain detailed information about a building, including site layout, architectural and structural drawings, and technical specifications from the facilities management. Input building geometry layout data to the DesignBuilder software.  Step 2: Assign the building's architectural elements (e.g., external walls, roofs, internal partitions, doors and windows, etc.) and then elaborate on their thermal characteristics. Step 3: Specify the occupancy characteristics, building utility supplies and thermal equipment in the energy model Step 4: Perform the annual and monthly energy simulations for the studied residential building.   46  Figure 3.4  Proposed framework for sustainability evaluation of techno-economic performance of building cooling systems  	Literature	Review	and	Identification	of	building	cooling	systems			  Estimation	of	the	monthly	mean	cooling	energy	demand	using	DesignBuilder	software	  Watertight	snow	storage	(WSS)	High-density	snow	storage	(HSS)	Techno-economic	sustainability	evaluation			         Final	ranking	of	building	cooling	system	alternatives	  Perform	Monte-Carlo	simulation	for	uncertainty	analysis	CSS	 WSS	 HSS	Conventional	snow	storage	(CSS)	Technical	evaluation	• Snow	Density	• Natural	snow	melt	• Heat	of	fusion	• Thermal	Insulation,	etc.	Chillers	Chillers	Obtain	the	monthly	mean	temperature	from	nearest	weather	station Defining	life	cycle	phases	for	evaluation								Economic	evaluation	• Service	life		• Economic	indicators	• Uncertainty	factors	• Capital	cost,	etc.		Storage	unit	Co-efficient	of	Performance	Auxiliary	unit	 Operational	Levelized	Cost	 47 3.7 Technical Evaluation Snow storage systems are chosen to minimize the energy use and associate GHG emissions. Generally, snow and ice have a great potential for the storage of coldness due to its 0ºC melting temperature, high heat capacity and heat of fusion. The amount of heat (E) required raising the temperature of ice from t1 to 0 º C, melting it, and raising the water temperature to t2 is as given õ = 0 − (l ú_ + ûd + (üúî[dž Ç°]	 (3.16)	where Ci is the heat capacity of ice at -5ºC (2.1 kJ/kg) (Dorsey 1940); Hf  is the heat of fusion of water (333.6 kJ/kg); and Cw is the heat capacity of water at 5 ºC (4.2 kJ/kg.K) (Hobbs 1974; Skogsberg and Nordell 2001).  Estimation of the snow storage system’s performance and characteristics. The total annual cooling energy demand (QL) can be calculated with help of the following equation (Bahadori 1984): ¢£ = §_•_(ûd + úî∆(î)ßa	 	 (3.17)	where ρi and Vi are the density and volume of the snow respectively; ∆tw, is the temperature increase of melted snow before its disposal circulation to the building (generally, 10ºC); and Fr is the coolness recovery factor. Fr represents the heat gains by the ice or losses of cold energy while in the storing and operational stages. Typically, natural melting is segmented in surface melt, rain melt, and ground melt. All these heat transfers depend on the location and dimensions of the selected storage facility. Generally, recovery factor of 0.7 is considered in the equation (3.17) (Bahadori 1984).  The Volume of storage facility depends on several factors, including: i) the maximum height of snow and ice that can be accumulated in winter; ii) the costs for construction of the storage facility including materials (i.e. thermal insulation, vapour barrier in the bottom, water proof liner, and protection liner); and iii) the ground water table (Bahadori 1984).  Estimation of heat gain through natural melting. Most of the natural melting in the storage facility occurred due to the surface melting caused by heat transfer from the air and solar radiation to the snow. The energy balance (W/m2) of the thermal insulation can be stated as (Skogsberg 2005):  48    !®î + !fî + ![`]e = !®\` + ![`]^  (3.18) where Psw is the net solar (short wave) radiation; Plw is the net long wave radiation; Pconv is the net convective net heat transfer to the insulation; Psto is the energy increase of thermal insulation; and Pcond is the thermal conduction from the insulation of the snow.    Covering the storage facility with thermal insulation can reduce Snow melting by rain. The factors and influence the snow melt rate include: the intensity and duration of rain, positive monthly temperature and upper storage area (Skogsberg 2005).  Ground melt represents the heat transfer to the storage facility due to heat conduction from the surrounding ground and leakage of melted snow water. However, introducing the water proof liner (i.e. plastic liner and asphalt) can significantly eliminate the ground melt due to leakage. The ground melt due to heat conduction can be calculated using the thermal conductivity of the ground, snow density and annual mean temperature (Skogsberg 2005).     3.8 Economic Evaluation The economic feasibility of seasonal snow storage systems highly depends on the type of application, location of the storage, weather condition, cost of land, proximity of the building to the storage system. In the covered snow storage system, the land above the storage unit can be used for other purposes (i.e. landscaping, parking, etc.); hence, it is not necessary to include the cost of land in the economic assessment (Bahadori et al. 1982). The main benefits of the seasonal SSS are energy savings and energy efficiency. In order to evaluate their economic feasibility, the cost of electricity, inflation rate, depreciation rates, etc. should be identified for at least the next 20 years (Bahadori 1984). Typically, the economic feasibility of the energy storage systems are evaluated based on financial indices i.e. net present value, internal rate of return, and Initial investment payback period (Yan et al. 2014). In addition, Wang et al. (2009) listed out the economic indicators to evaluate the economic feasibility of the system. The economic indicators are as follows:  49 Investment or Capital Cost are fixed, one-time expenses incurred to establish the building cooling systems including the purchase of land and mechanical equipment, construction activities, technological installations, grid connections, establishment of infrastructure, logistics, engineering services, and other incidental construction work. It is the most widely used economic criteria to evaluate the energy systems.  Operation and Maintenance Cost are recurring during the service life of the energy systems, which comprises of employees’ wages, energy and fuel expenses, and lifetime maintenance expenses including repair and refurbishment. Operational and maintenance cost is segmented into fixed and variable costs and it is one of the most used criteria to evaluate energy systems.  Net Present Value (NPV) also known as net present worth is defined as the total present value of all cash flows during the service life of the energy system as:  R!• = ©™´(lw_)´\¨~≠ 			 (3.19)	where t is the time of the cash flow, CFt is the net cash flow for period t, i is the capital investment cost, and N the number of periods making up the economic life of the investment. Cash inflows and cash outflows are represented as positive values and negative values of CFt respectively. For the specified period t, all the cash flows (inflows and outflows) are processed and summed together. Generally, a positive NPV represents the profit of the project and negative NPV represents the net loss of the project. NPV using the time value of money to evaluate long-term projects and it is the most widely used methods for capital budgeting and assessing the economic feasibility of energy systems. The influence of inflation and deflation can be considered in this method.  Payback Period is defined as the period of time required for the return on investment to repay the sum of the original investment. Due to the simplicity and ease to use this method is widely used in decision making process.  Generally, shorter paybacks are more preferable than longer paybacks for energy systems.    50 Service Life represents the expected lifetime or the acceptable period of service use for an energy system. Generally, the mechanical cooling systems has 20 years of service life whereas the renewable energy generation sources like seasonal snow storage system has 60 years of service life.   Discount, Depreciation, and Tax Rates are established for the sake of economic assessment. The discount rate represents the rate at which the investment should be amortized. The value of building units and equipment depreciate over time. Taxes are charged on purchases, property, energy sales, and revenues at various rates, which should be explicitly considered.   The Levelized Cost is defined as the ratio of the total life cycle cost (including capital and operational and maintenance costs) for establishing the cooling energy production system to the expected life-time energy output. The total annual cost is the NPV of the unit-cost of electricity over the lifetime of a cooling energy production system. It is a first-order economic evaluation of the studied system’s feasibility, which integrates all costs (capital, operation and maintenance, and fuel cost) over its lifetime (Walter Short et al. 1995).  ÆUØU'&TU2	ú#∞( = ±`\4f	ì]]g4f	©`®\	ì]]g4f	≤g\Zg\ 	#"	 ≥gY	`d	[`®\®	`eca	f_dc	\_Yc≥gY	`d	\äc	c]ca¥µ	Za`^g[c^	`eca	f_dc	\_Yc						 (3.20)	ÆUØU'&TU2	W#∞( = ∂´∑	∏´∑	π´(z∑∫)´x´yz ª´(z∑∫)´x´yz 					(3.21)  where Ct  is the capital investment cost in the period t, Ot is the operational and maintenance cost in the period t, Ft is the fuel cost in the period t, Et is the energy production in the period t, r is the discount (depreciation) rate, and n is the expected lifetime of the system.   Levelized cost is a measure to evaluate the feasibility of the cooling energy production systems and comparing these to the conventional cooling systems in Canada, a standard measure of costs needs to be assigned to the technologies. Generally, the standard measure is set as price per unit of electricity, or $/kWh.     51 3.9 Uncertainty Analysis The economic feasibility of cooling systems has been evaluated based on various economic criteria, it is important to estimate these parameters with robust data. Generally, results from literature review and simulation relying on erroneous parameter values may lead to inaccuracies and small perturbations to a sensitive parameter can significantly influence the results (Burhenne et al. 2010).   In this study, the Monte Carlo Simulations were performed to calculate the annual capital investment cost, annual operation and maintenance cost and levelized cost of the cooling system alternatives. The analysis was conducted for 5,000 simulations using the Excel based @risk software, to ensure that all the input data for economic performance were randomly selected.     52 Chapter 4 Life Cycle Impact Assessment of the Building Cooling Systems  A part of this chapter is prepared to publish in the journal, Building and Environment titled “Framework for Life Cycle Assessment of Building Cooling Systems: A Comparative study of Snow Storage and Conventional Chiller Systems”.  In this chapter, the life cycle impact assessment of different building cooling system alternatives is presented. Section 4.1 collects the energy and emissions details from the facility during the service life of the cooling systems. Major materials used for the facility construction and landfill or recycling of the materials after the service life are included. In section 4.2, the LCI results are characterized based on impact categories in the US EPA’s TRACI method. In order to rank the alternatives, the PROMETHEE- II method has been applied in section 4.3.   4.1 Life Cycle Inventory  The Comparative LCA has been performed to identify the potential environmental impacts of four cooling systems. The functional unit used in this study was 1 MWh of electric energy production delivered to the building cooling system to dissipate heat. Figure 4.1 (a) & (b) presents the study boundaries in the activities involved in the different life cycle phases of a snow storage system and a chiller cooling system, respectively.  In this study, the time boundary is defined as the expected life span of 20 years for the chiller cooling system and 40 years for the snow storage system. As the evaluation of different cooling systems is carried out in BC, Canada, most of the data used for analysis has been collected from the same province. However, as needed, the data for some processes has also been obtained from other Canadian provinces, the USA, or elsewhere.     53  (a)  (b) Figure 4.1  Life cycle phases and chosen system boundary of the snow storage system (Hossain et al. 2011) (a) and Life cycle phases of the chiller cooling system (b) 	Raw material Extraction Material manufacture Construction Use End of life Limestone mining Iron extraction Non-ferrous materials extraction Aggregation extraction Transportation Cement production Aggregation production Concrete production Steel production Non-ferrous materials production Hot-rolling of steel Transportation Concrete placing Excavation and surface leveling Insulation placing  Asphalt placing  Finishing  Transportation Electricity Hydraulic loader Repair Retrofit Replacement Transportation Demolition Sorting and disposal Recycling System Boundary  54 All the building details, including floor plans, elevations, and material quantities, were obtained from the facilities management, University of British Columbia-Okanagan (UBCO) and the building has been modeled in the DesignBuilder Software to estimate the annual cooling energy demand, which resulted in 230 MWh/yr. Moreover, the Ecoinvent 3.0 database was used to obtain the data for lifecycle process units and their associated materials and energy flows and data on emissions and discharges into the water, soil, and air. Table 4.1 presents the summary of components and estimated material quantities, for the snow storage systems and Figure 4.2 presents the details of the contributions of materials in the chiller components.  Table 4.1 Summary of material quantities for snow storage system Flows		 Total	material	and	energy	flows		 CSS	system	 WSS	system	 HSS	system		Sizing	(m3)	Estimated	quantity	(ton)	Sizing	(m3)	Estimated	quantity	(ton)	Sizing	(m3)	Estimated	quantity	(ton)	Excavation	 	 5150	 6180	 3250	 3900	 2250	 2700	Crushed	stone	 	 1030	 1236	 	 	 	 	Coarse	gravel	 	 515	 618	 975	 1170	 450	 540	Urethane	foam	 	 	 	 	 	 225	 270	Asphalt	 	 	 	 163	 195	 	 	Light	weight	Concrete	 	 1.3	 1.5	 1.3	 1.5	 382	 458	Polyethylene	cover	 	 103	 124	 	 	 	 	Wood	barks	 	 	 	 650	 780	 	 	Polystyrene	foam	 	 	 	 163	 195	 	 	 The data for the chiller system was obtained from the chiller unit manufacturer, TRANE Corporation, USA. Typically, the cooling system is comprised of three major infrastructure components, such as snow pond/chiller unit, circulation system to the building, and circulation system in the building. In this study, only the two former components are considered. The latter component is not considered in this study because usually the building LCA study included this data. In the End of Life (EOL) cycle phase, it is assumed that all the metals will be recycled, and all non-metals will be incinerated or recycled.   55  Figure 4.2  Material contributions in chillers (obtained from the TRANE corporation)  4.2 Life Cycle Impact Assessment Figure 4.3 (a-j) shows the SimaPro results of environmental impacts for meeting the 230 MWh/yr of energy demand for all the identified cooling systems. It also presents the LCIA results for the entire life cycle of the different systems, including Extraction and Construction (E&C), Utilization (use), End of Life (EOL), and total emissions.  Figure 4.3 (a-j) illustrates that overall, the HSS system contributes the least environmental impacts in all the ten categories, and the chiller system has the greatest environmental impacts in eight out of ten categories. However, the contribution of the cooling systems varies during different life cycle phases for most of the impact categories. The environmental impacts of the chiller cooling system are the highest during the operation phase due to fossil fuel combustion, particularly for electricity production. The EOL phase modeled for the cooling systems are considered as ideal, mainly due to reuse and/ or recycling of the metal components; therefore, negative results are presented for every impact category evaluated. It is important to state that the negative values shown in Figure 4.3 for EOL phase represent the mitigation of impacts, having a positive effect on the overall result.  Steel,	731,	50%Cast	iron,	248,	17%Copper,	386,	27%Aluminium,	14,	1%Polycarbonate,	0.6,	Refrigerant	R134a,	74,	5% 56                          a) Ozone Depletion             b) Global warming                                           c) Acidification                                                        d) Eutrophication                                   e) Carcinogens                                                          f) Non Carcinogens                            g) Respiratory effects                           h) Ecotoxicity       i) Fossil fuel depletion      j) Smog formation Figure 4.3  SimaPro results of Environmental Impact categories for different cooling systems during the different lifecycle phases (a-j) -2.000.002.004.006.00E&C Use EOL TotalKg	CFC-11	eqCSS HSS WSS Chillers-20000200040006000800010000E&C Use EOL TotalKg	SO2	eqCSS HSS WSS Chillers-50000500010000E&C Use EOL TotalKg	N	eqCSS HSS WSS Chillers-0.04-0.020.000.020.040.060.080.100.12E&C Use EOL TotalCTUhCSS HSS WSS Chillers-0.500.000.501.001.502.00E&C Use EOL TotalCTUhCSS HSS WSS Chillers-2000200400600800E&C Use EOL TotalKg	PM2.5	eqCSS HSS WSS Chillers050000100000150000E&C Use EOL TotalCTUe	(Thousands)CSS HSS WSS Chillers-10000000100000020000003000000E&C Use EOL TotalMJ	surplusCSS HSS WSS Chillers-50000050000100000E&C Use EOL TotalKg	O3	eqCSS HSS WSS Chillers-5000000500000100000015000002000000E&C Use EOL TotalKg	CO2	eqCSS HSS WSS Chillers 57  The results of the ozone depletion category for the cooling systems in different life cycle phases are shown in Figure 4.3a. This figure shows that the contribution of different systems varies in each life cycle phase, for instance, the CSS has the highest environmental impacts in the EOL phase; similarly the chiller system has the largest environmental impacts during the E&C and utilization phases. Overall, the chiller system causes the greatest impacts, i.e., 30, 36, and 65 times greater than the CSS, WSS, and HSS system respectively. Figure 4.3b illustrates the characterization factor of global climate change, in which the chiller system generates around 97% of its total CO2 eq GHGs during the utilization phase. Also, the chiller system impacts are found to be 3.0 times higher than the HSS.   The results of the acidification category shown in Figure 4.3c demonstrate that out of the cumulative 8.53 tons of SO2 equivalents generated during the entire lifecycle, the utilization phase is responsible for 95% of the total emissions. Figure 4.3d demonstrates that the chiller system claims the highest impact with 6.7 tons of nitrogen equivalents. Electricity, as the dominating component here, seems to be responsible for this trend. Notably, the chiller system mitigates 15% of nitrogen eq emissions in the EOL phase; however, it contributes the highest eutrophication emissions.   Figure 4.3e illustrates that the chiller system produces the same cumulative carcinogenic effects (i.e., 0.11) as the CSS and the WSS systems. Although the chiller system is found to be the least contributor during the EOL phase, it produces the highest carcinogenic impacts during the utilization phase. The human health effects of carcinogens are characterized based on exposure, i.e. short-term (acute) and long-term (chronic). The EC and EOL phases produce acute health effects, whereas the utilization phase can certainly cause chronic health effects. Hence, more importance has been given to the utilization phase for MCDA in the following sections. Figure 4.3f shows that the trend of the non-carcinogenic emissions is different from other impact categories; here, the CSS system has 2 and 3 times higher contributions than the chiller and HSS systems.  Unlike other impact categories, the chiller systems mitigate around 35% of emissions during the EOL phase, mainly because of reuse and/ or recycling of the metal components. The LCIA results for the category of respiratory effects are shown in Figure 4.3g. The chiller system  58 cumulatively generates 659 kg of PM2.5 eq particulate matter over its life cycle, which is around 2.5 times greater than the lowest emitter amongst snow storage systems.   Figure 4.3h graphically demonstrates the ecological toxicity effects of the different cooling systems. Similar to the most of the other impact categories, the CSS system has the highest toxicity generation due to the incineration of the polyethylene snow cover. Thus, its toxicity is 1.10, 1.35, and 2.85 times greater than the WSS, Chillers, and CSS systems. Figure 4.3i demonstrates that of the cumulative 2257 GJ surplus eq generated by the total life cycle of the Chiller system, and the utilization phase is responsible for 99% of the impacts, which is 1.3, 2.22, and 3.55 times higher than the CSS, WSS, and HSS systems respectively.  The impacts to the category of smog formation are shown in Figure 4.3j. The chiller system cumulatively generates 92 tons of O3 eq during the total lifecycle phases, which is 1.88, 2.23, and 3.86 times higher than the CSS, WSS, and HSS systems respectively.   4.3 Sustainability Evaluation of Cooling Systems Using the PROMETHEE II The result of LCA is shown in Figure 4.3 (a-j) reveals that there is no consistent trend observed for a specific cooling system during different life cycle phases. Therefore, MCDA has been performed using the PROMETHEE II method. The step-by-step procedure of the MCDA is attached in Appendix A. Table A.6 and Figure 4.4 illustrates that the HSS system has the highest aggregated indices of impact categories in all the lifecycle phases. Moreover, the HSS system has the largest outgoing flow, smallest incoming flow, and maximum net flow; thus it is the most sustainable cooling system and it outranks the other cooling systems. Certainly, the conventional cooling system is the least sustainable alternative.    59  Figure 4.4  Summary of the MCDA results sharing aggregated indices of impact categories for the different lifecycle phase and net flow (i.e. balancing effects between outgoing and incoming flows) for final ranking of building cooling system alternatives.  4.4 Summary of the Environmental performance of the Building Cooling System Alternatives In order to minimize the energy consumption and GHG emissions due to the operation of cooling systems in a building, the concept of a seasonal snow storage system was presented, and the pragmatic application of the concept in a residential building in Kelowna (BC, Canada) was studied.  For the purpose of evaluating environmental performance of the cooling systems a novel framework using multi criteria technique was developed. The life cycle environmental impacts of three snow storage systems (conventional snow storage, watertight snow storage, and high-density snow storage) were compared to the conventional chiller cooling system. Ten impact categories were considered in the cycle analysis mainly human health impacts (carcinogenic, non-carcinogens, and human health particulates), ozone layer depletion, aquatic and terrestrial ecosystem impacts (ecotoxicity, acidification, smog formation, and eutrophication), global warming, and resource consumption. In order to evaluate the complete environmental performance of the cooling systems, the life cycle analysis was performed for different life cycle phases (extraction and construction, utilization, and end-of-life). Typically in life cycle analysis, it is -1.00 -0.80 -0.60 -0.40 -0.20 0.00 0.20 0.40 0.60 0.80 1.00ChillersCSSWSSHSSE&C Use EOL Net	Flux 60 difficult to be concluded that a single cooling system outperforms the others with regards to the impact categories. Thus, in order to support the decision making process, multi-criteria approach was employed for selecting the cooling system alternatives. In this study, Weighted Sum Method (WSM) was used to aggregate the impact categories with respect to the life cycle phases, next the PROMETHEE- II method was used to rank the alternatives based on the incoming, outgoing and net flows of the cooling systems. The results of PROMETHEE- II method concluded that the effect of HSS systems over the service life contributes low environmental impacts rather than the other cooling system alternatives. The final ranking of alternatives can be defined as HSS > WSS > CSS > Chillers.      61 Chapter 5 Techno-economic analysis of cooling systems for residential building  A part of this chapter is prepared to publish in the journal, Energy and Buildings titled as “Techno-Economic Sustainability Evaluation Framework for Building Cooling Systems: A Comparative study of Snow Storage and Conventional Chiller Systems”.  This chapter evaluates the techno-economic performance of the different building cooling systems. Section 5.1 presents the estimation of annual cooling energy demand for a building. A simple probabilistic feasibility evaluation tool is developed to evaluate the performance of different cooling systems. The incremental economic performance of alternatives is estimated in terms of the total cooling cost per kWh at the facility. Monte-Carlo simulations were performed to consider the uncertainty factors involved in the techno-economic parameters of cooling systems.   5.1 Demand Analysis The building under study is a five-storey high residence green building, located in Kelowna, British Columbia, Canada, constructed in 2011. The total size of this building is 68,000 square feet accommodating 212 residents. This building is comprised of the solar heating panels at rooftop terrace, heat-recovery ventilators, occupancy and window sensors, and geothermal heating/cooling system. This building is operated by combining various energy sources, such as the natural gas from Fortis BC, grid electricity from BC Hydro, solar heating panels, and geothermal system for preheating the water for space heating as well as domestic hot water. Also, heat-recovery ventilators are used to minimize the building peak energy demand. It was built to attain LEED Gold standard and granted UBC Residential Environmental Assessment Program (REAP) Gold accreditation (Feng 2013). This building comprises all the thermal properties, as recommended in the REAP, illustrated in Table 5.1.    62 Table 5.1 Thermal properties of the studied building recommended by UBC REAP (2006)  Thermal	Property	 Standard	value	Roof	Insulation	 R	≥	40	External	Wall	Insulation	 R	≥	18	Energy	Star	Windows	 U	≤	0.31	Floor	Insulation	 R	≥	20	Domestic	Hot	Water	 EF	=	0.94	Boiler	Management	 EF	≥	0.96	Heat	Recovery	System	 ≥50%	efficiency	 Other than the significant information in Table 5.1, the estimations of the occupancy rate and the set point temperature for heating and cooling were inferred from the everyday operations data of the green building under study. Different parameters of this building, for example, lighting systems, auxiliary loads for computers and room supplies, occupancy metabolism were described as standard values through the university bedroom template from the DesignBuilder Software (Feng and Hewage 2014). Actual image of the building and the model developed in DesignBuilder are presented in Figure 5.1 (a & b).    63    (a)	  (b) Figure 5.1  Actual image of the studied green building (a) and DesignBuilder model of the studied green building (b) (Feng 2013)     64 The primary source of seasonal snow storage is ambient air in winter. To design the storage systems, it is necessary to determine the total period or the number of hours during the year that the ambient air temperature is within a specified range when snow can be produced. Air temperature, precipitation, relative humidity, solar radiation, and wind velocity were obtained in the Kelowna (BC, Canada) weather station. The mean climate data of 1981-2010 were obtained from the Environment Canada (2015) and presented in Table 5.2, for the Kelowna Airport that is located about 5km from the snow storage system.  Table 5.2 Monthly mean temperatures and total monthly precipitation (data from Environment Canada 2015) 	 	 	 Monthly temperature	 Temperature Range	Month	Precipitation (mm)	Rainfall (mm)	Daily Minimum (°C)	Daily Maximum (°C)	Daily Average (°C)	> 0 °C	 <= 2 °C	 <= 0 °C	 < -2 °C	 < -10 °C	Jan	 44.0	 19.7	 -2.8	 1.8	 -0.5	 10	 28	 21	 12	 3.3	Feb	 22.8	 15	 -2.1	 4.1	 1.0	 8	 25	 20	 11	 1.5	Mar	 26.0	 22.5	 0.3	 9.3	 4.8	 16	 22	 15	 6	 0.1	Apr	 30.9	 30.6	 3.8	 15.0	 9.4	 26	 9	 4	 0	 0	May	 42.6	 42.6	 8.0	 19.8	 13.9	 31	 1	 0	 0	 0	Jun	 43.6	 43.6	 11.7	 23.8	 17.8	 30	 0	 0	 0	 0	Jul	 35.0	 35	 14.1	 27.3	 20.7	 31	 0	 0	 0	 0	Aug	 32.3	 32.3	 13.9	 26.8	 20.4	 31	 0	 0	 0	 0	Sep	 31.5	 31.5	 9.7	 20.9	 15.3	 30	 1	 0	 0	 0	Oct	 31.9	 31.7	 5.0	 13.1	 9.1	 29	 6	 3	 1	 0.1	Nov	 44.1	 35.8	 0.7	 6.0	 3.4	 18	 19	 12	 5	 1.0	Dec	 40.5	 16	 -2.2	 2.1	 0.0	 9	 28	 23	 12	 1.9	 Similarly, the weather data of Kelowna was derived from the DesignBuilder climate file using the database of the U.S. Department of Energy (DOE 2011).  A strong correlation of air temperature and precipitation between the simulated results and the values obtained from the airport climate station can be seen in Figure 5.2  65  Figure 5.2  Weather report of Kelowna (DesignBuilder results vs. Environment Canada 2015)  In order to assure the validity of the simulation results of the DesignBuilder software, the simulated annual energy consumption for heating, cooling and lighting system of the studied green building was compared with the actual energy consumption of the building. Figure 5.3 illustrates the comparative results of energy consumption. Figure 5.3  Energy consumption of the studied building for heating, cooling and lighting (DesignBuilder results vs. the actual data from UBCO facilities management)  According to Figure 5.3, it can be presumed that the simulation results largely match with the actual yearly energy consumption of the building. A perceptible distinction between the simulation outcomes and the actual yearly energy consumption is found in the start of spring and fall months of the year, i.e., April, September, and October. Energy variations in the solar panel system and the geothermal system of the building might be the key reasons for this difference. The amount of heat transmitted from the solar panel and the geothermal system to the building solely depends -5.00.05.010.015.020.025.0Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecTemperature (°c)MonthSimulation	results Weather	station020406080100120Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecMWhActual	monthly	energy	consumption Energy	Consumption	from	simulation	results 66 upon the daily weather patterns, and the studied building does not have the sophisticated equipment to quantify the variations. In addition, the occupancy behaviour such as final exams and the additional activities in the beginning of school makes a significant difference in actual consumption in these months. The increasing lighting energy consumptions and the plug and process loads has a significant deviation from the default schedule and occupancy rate in the building.   Moreover, the input weather information for the simulation was based only on the EnergyPlus database of the US Department of Energy's Canadian Weather for Energy Calculations (CWEC) and this signifies average weather pattern in a region. As indicated by the Environment Canada daily weather data reports, the actual daily temperatures in January, November, and December of 2012 were slightly higher than the temperature in the EnergyPlus database (Feng and Hewage 2014).   5.2 Technical Evaluation The best system for a certain building cooling system application depends on several factors, including the energy demand, climate, type of soil, land availability, groundwater level, aesthetic concerns, thermal insulation, local conditions, performance factors, and economical aspects.   A simple feasibility evaluation tool has been developed for the feasibility and simulation studies of snow storage concepts. Snapshots of the tool are presented in Appendix B. The aim of the tool is to facilitate the different stakeholders in the process of planning and building such snow storage systems described in this study. This tool primarily focused on the detailed technical design and economic evaluations of snow storage systems. An upside down frustum of a cone shape shown in Figure 5.4 developed by Kirkpatrick et al. (1985) and Skogsberg (2005) has been used in this study. The assumptions made for technical feasibility evaluation are, i) snow is formed above the bank segment in a sphere shape, and ii) one ton of snow corresponded to 0.1 MWh of cold energy.  67  Figure 5.4  Schematic layout of snow storage system in the feasibility evaluation tool (Skogsberg 2005)  This tool comprises a set of climatic variables and a set of design variables to guide the selection of shape and size. The climatic variables include the annual mean air temperature, hourly values of solar radiation, air velocity, precipitation, and distance to undisturbed ground temperature. The design variables are the cooling energy demand, initial snow volume, upper storage area, depth side slope of the storage, thermal insulation material and thickness, and thermal conductivity of the ground.   In order to predict the snow storage capacity, hourly values of forced melt, rain melt, ground melt, and surface melt are also estimated. Typically, the snow melt starts from the top due to the outside air temperature. Force melt is directly proportional to the cooling energy demand of a building. Rain melt is calculated using positive monthly temperature and upper storage area, because of the rainfall in the storage area is added to the melted water. Rain melt can be estimated by simulations and uniformly distributing the total rain precipitation over the whole summer. Typically, rain temperature is same as the air temperature; and 500mm rain with a mean temperature of 15 ºC contributes 8.7 kWh/m2 of heat gain or melts 0.09 tons of ice. In the ground melt calculations, the heat flow transfer occurs through the storage area in contact with the snow. Snow melting due to undisturbed ground temperature is negligible and does not play a crucial role in the calculations (Skogsberg 2005).     68 The surface melt rate has been calculated using the Equation (3.18). Figure 5.5 (a-c) illustrate the mass of snow loss due to forced and natural melting as well as the remaining quantity of snow by month. With the same amount of snow, if space cooling is to begin on May 1, approximately 97%, 95%, and 87% of the snow could be preserved for the HSS, WSS, and CSS system respectively. The main reason for this difference is the characteristics of snow density, storage facility, and natural melting. In the CSS system, the snow is deposited directly on the ground and there is huge mass loss due to thermal conductivity of the ground and the permeability of snow water in to the ground.  Thus it needed large volume of pit to store sufficient snow to fulfill the energy demand. However, in the HSS system the snow mass loss is lesser than the WSS and CSS systems, due to high density of snow, smaller volume of pit, watertight and insulated storage facility.  An important factor to be considered in the design of storage system is the natural melting, including rain melt, ground melt, and surface melt. Surface melt accounts for the major part with 83% of the total natural melting; thus, it is beneficial to build more compact storages. In order to prevent the intrusion of contaminated snow water to the aquifers, a watertight lining is preferable. The best ground surface for snow storage system is a coarse filled area or solid rock. If the ground consists of fine grained material, it requires insulation to avoid frost heave under the storage area.      69  (a)  (b)    (c)  Figure 5.5  Snow loss and remaining quantity of snow by month (a) HSS system, (b) WSS system and (c) CSS system 0%20%40%60%80%100%120%0100200300400500600Mar Apr May Jun Jul Aug Sep OctPreserved snow rate (%)Mass of snow loss (m3)Mass	of	snow	loss Preserved	snow	rate0%20%40%60%80%100%120%0100200300400500600700800900Mar Apr May Jun Jul Aug Sep OctPreserved snow rate (%)Mass of snow loss (m3)Mass	of	snow	loss Preserved	snow	rate0%10%20%30%40%50%60%70%80%90%100%020040060080010001200140016001800Mar Apr May Jun Jul Aug Sep OctPreserved snow rate (%)Mass of snow loss (m3)Mass	of	snow	loss Preserved	snow	rate 70 In conventional snow storage system, the snow is stored on the unlined coarse-grained material. This system involves excavation, thermal insulation, mechanical and electrical installations. According to Morofsky and Merrifield (1981), the average snow density of this storage system is 450 kg/m3. The watertight snow storage comprises of excavation, waterproof lining material, thermal insulation, and mechanical and electrical installations. The average density of this storage system is 650 kg/m3 (Nordell and Skogsberg 2007; Skogsberg 2005). In high-density snow storage system, the collected snow is mechanically compacted using heavy machineries and the density is increased to 920 kg/m3 (Gaméda et al. 1996; Hamada et al. 2010; Vigneault and Gameda 1994). These design parameters are provided in the evaluation tool, and the obtained results are presented in Table 5.3.   The density of snow is directly linked to the storage area; this criterion is very crucial in situations when there is limited storage area, and the cost of land is high. The results demonstrated that the volume of the storage area remarkably changes with the snow density. Depending on the storage technology, space constraints, and/or floor area may indeed be a challenge, particularly in urban areas and sustainable communities.   The performance of the snow storage system operations can be characterized by a seasonal coefficient of performance (COP) defined as the ratio of delivered cooling energy over the total auxiliary input for snow handling and water circulation including pumping. In other words, the amount of work consumed by the cooling system to remove the heat from the building:  úº! = Ωcf_ecac^	[``f_]¥	c]ca¥µìgm_f_4aµ	c]ca¥µ	_]Zg\  (5.1) However, the equation (5.1) only describes the performance of the snow storage system operation. To invoke the total performance of a system, a new coefficient COPtotal has been established which represents the ratio between delivered cooling energy and total energy required to generate the cold, including the total driving energy and annual material depreciation (i.e. the total energy required for manufacturing and constructing the system divided by estimated technical lifetime). COPtotal is always lower than COPoperation but is a more rational coefficient when comparing different cooling systems (Skogsberg 2005).  71  Table 5.3 Results of technical evaluation of snow storage system Snow	storage	parameters	High-Density	Snow	storage	watertight	Snow	storage	Conventional	Snow	storage	Snow	density	(kg/m3)	 920	 650	 450	Initial	snow	volume	(m3)	 2081	 3363	 6614	Snow	volume	above	bank	(m3)	 496	 723	 1195	Volume	beneath	ground	level,	to	excavate	(m3)	 942	 1463	 2664	Side	slope	(degree)	 27	 27	 27	Top	diameter	(m)	 30	 35	 45	Bottom	diameter	(m)	 14	 14	 20	Pit	depth	(m)	 4.0	 5.3	 6.2	Bank	height	(m)	 1.1	 1.5	 2.1	COPoperation	 22.2	 19.4	 14.6	COPtotal	 19.3	 15.9	 10.9	  To make a rational comparison, the snow storage systems are compared with highly efficient chillers such as Trane water-cooled screw chillers. The TRANE series R® chillers, start and operate satisfactorily over a range of loading conditions with uncontrolled entering condenser water temperature. While meeting the same cooling energy demand the chillers have the maximum coefficient of Performance of 5.6 (TRANE 2015), which is far lesser than snow storage systems. The main reason for this variation is different amounts of snow cooling energy and electricity used. The performance of snow storage systems is certainly higher than the conventional cooling systems due to lesser energy consumption. However, it is important to mention that the operation of a pump has been classified under the building air-conditioning, and its energy requirements are not considered in the snow storage system (Bahadori et al. 1982).    5.3 Economic Evaluation The life-cycle costing takes into account of costs over the lifecycle of the system, rather than the capital investment cost only. Thus, it includes the operational and maintenance cost, thermal insulation costs, and salvage values. To identify the alternative with best economic performance,  72 this study utilized a uniform comparison methodology. The economic comparison of different cooling systems for a building has been evaluated based on the present value in the base year (i.e., 2015) of the produced energy cost. The energy production cost ($/kWh) includes both the capital investment and operation and maintenance (O&M) costs.  5.3.1 Capital Investment Cost Table 5.4 illustrates capital investments needed for snow storage systems as well as the chiller cooling system.   Table 5.4 Capital investment needed for cooling system alternatives Item	Description	High-Density	Snow	storage	watertight	Snow	storage	Conventional	Snow	storage	Chillers	Excavation	 7674	 11259	 21714	 	Lining	 47800	 11373	 0	 	Chiller	unit	 	 	 	 14620	Cooling	Tower	 	 	 	 7466	Mechanical	installations	 108575	 108575	 108575	 108575	Electrical	installations	 26942	 26942	 26942	 26942	Fences		 10725	 10725	 10725	 	Planning	 50000	 50000	 50000	 50000	Total	Capital	cost	 251716	 218875	 217956	 207603	 Common activities for the construction of snow storage systems includes excavation, filling gravel, perhaps a watertight lining, and a pipe circulation system for carrying melted water to the building. However, the volume of the storage and quantities of construction materials may vary depending on snow density.   Typically, economic evaluation of the snow storage systems stems from geographic, temporal (i.e., time of construction), and other factors. For this study, the cost details are obtained from local contractors, literature review, and manufacturers. The discount rate, labor, an efficiency of snow storage system, energy consumption, and the cost of materials are the variables that change based on the projects and location (Bianchini and Hewage 2012; Murillo 2012). To overcome this  73 challenge, a generic methodology that takes into consideration of these uncertainties, within an acceptable confidence level is required to evaluate the economic performance of cooling systems. Thus, price risks within the market are scrutinized through statistical tests. Optimal values for key performance indicators and cost variables are derived with the help of probabilistic uncertainty modeling.  Monte-Carlo simulations with 5000 runs have been performed (using Palisade’s @Risk software) to include the stochastic variation in the construction activities and materials costs.   The excavation cost of a storage pit in Kelowna range from $2.2 to $16 per cubic meter, according to the ‘RSMeans’ cost data book and quotes from local contractors. In the CSS system collected snow is placed on the gravel bed, whereas in the WSS and HSS methods a plastic or asphalt liner is introduced between the snow and ground. The cost of High-Density Polyethylene (HDPE) protective geo-membrane on each side costs between $20 and $30 per square meter. However, for large-scale projects this price ranges from $8 per square meter. Moreover, a vapor barrier has been used in WSS and HSS systems to prevent diffusion of moisture through ground and side walls of the storage facility to avoid interstitial condensation. Usually, urethane or polystyrene foam used as a vapor barrier, which costs between $2.7 and $7 per square meter.  Additional construction costs including mechanical and electrical installations are common for all the cooling systems, furthermore for the sake of simplicity the distance between cooling systems and the building is considered same. The investment costs of electrical and mechanical installations have been obtained from TRANE USA website and were converted to the Canadian dollar.   For the building under study, the chiller options will fall into two categories: air-cooled or water-cooled. Air-cooled chillers have a refrigerant to air condenser that directly rejects heat to the atmosphere. While, water-cooled chillers have a refrigerant to the water condenser and water loop to a remote heat rejection source (typically a cooling tower), but it could also be a geothermal grid, a lake, or any other heat sink. Although, air cooled chiller is a cheaper and simpler option since there is less piping involved, they are also generally less efficient. They also take up more space on the roof and generate more noise than a typical cooling tower. Usually, a water-cooled chiller has higher capital costs but it has higher efficiency than the other type of chillers, so the lifecycle  74 costs are very low, especially in large-scale chillers. Water-cooling also offers more installation flexibility; typically the chiller will be installed in a mechanical room in the building and a cooling tower will be installed on the roof. There are many types of water-cooled chiller that can be selected depending on the application and the pinnacle of efficiency, reliability, and longevity. The cost of 70-250 ton water-cooled chiller varies from $119 to $161 per kW of the cooling load. Besides, the cooling tower costs from $48 to $95 per kW of the load.  The cost of pumps can be calculated according to their horsepower or in terms of peak cooling load, which  ranges from $266 to $400 per horsepower and from $350 to $535 per kW (Bryant 2011). The cost of plates and frame heat exchangers varies between $11 and $23 per kW of cooling load capacity. The total estimated installation cost for piping (buried) ranges between $225 and $328 per running meter, this includes trenching, insulation, fittings, backfill, and moderate amounts of surfacing repairs. The estimated cost for electrical installations are in the range of $175 to $475 per kW and control systems are in the range of $11 to $23 per kW of cooling energy demand.  The cost for oil water separator tanks was obtained from Xerxes, USA web site. The cost range varies from $26800 to $45000 for a double wall tank and $1000 to $2000 for the transportation. To maintain the safety standards, the snow storage systems have to be properly fenced. The cost for fencing varies between $8,500 and $15,000 which including corner posts, gate posts, entry and exit gate and chain link fence. These cost details were obtained from local contractors. The cost for pump house ranging between $1175 and $2000 was obtained from local contractors that include the cost of transportation and installation at the project site. The cost of snow compactor varies from $1750 to $4500 including transportation up to 100 km.   Nordell and Skogsberg (2007) reported the experience of six years operations of the WSS system in the Sundsvall Hospital, Sweden. He reported that the wood chips have to be supplemented every year and replaced after two to four years due to decay and contamination. However, wood barks showed better performance in terms of economic and availability. The cost of pine wood barks is available in a range between $40 and $65.    75 Morofsky and Merrifield (1981) used polyethylene sheets as a thermal insulation in CSS system in Canada, Probably, due to low density, low-density snow in CSS may has large pores in the snow pile and the wood chips may fell into it and consequently affect the performance of the system. Thus, plastic sheets have been used to cover the snow pile in CSS. Skogsberg (2005) used wood chips as a thermal insulation in WSS system in Sweden. However, no specific insulations sheets have been identified for snow storage systems because of their unique characteristics (i.e. small, expensive, fragile, and difficult handling process). Perhaps superstructures are economically feasible when the snow is collected from natural snow share (Skogsberg and Lundberg 2005). Hamada et al. (2010) proposed the mobile superstructure to insulate the storage system and to reduce the capital costs. In this approach, without placing an additional load on the insulation material, the snow density can be increased by compaction of the snow, using construction machinery on the pit. Once the adequate snow is collected and compacted, the mobile superstructure is moved on the pit (usually in summer). Whereas, during the snow collection process in winter, the mobile superstructure located by the snow storage facility can be used as an indoor multi-purpose space. Since the superstructure has been used for multi-purpose, the capital cost is not necessary to be included in the snow storage system.   5.3.2 Operational and Maintenance Cost Table 5.5 illustrates the operation and maintenance cost needed for snow storage systems as well as the chiller cooling system.  Table 5.5 Operation and Maintenance cost needed for cooling system alternatives Item	Description	High-Density	Snow	storage	watertight	Snow	storage	Conventional	Snow	storage	Chillers	Handling	charges	 581	 809	 1622	 	Pump	house	electricity	 293	 293	 293	 	Annuity	Thermal	Insulation	 2469	 3078	 5554	 	Electricity	for	chillers	 	 	 	 13710	Total	operational	cost	 3343	 4180	 7469	 13710	   76 The operational and maintenance cost pivots primarily on storage construction, snow collection, snow and insulation handling. Therefore, constructing a deeper storage with a smaller upper area will reduce the natural melt as well as reduce snow and insulation handling. Reducing the benefits of natural melt lowers the snow requirement and increases the efficiency. Snow and insulation handling is reduced due to a less upper area, and thereby less snow has to be moved from where it is dumped. Moreover, this can be reduced further by using artificial snow share with movable water sprays or snow guns (Skogsberg 2005).  Depending on the local regulations, urban snow might be a cost, income or for free. For example, when natural snow is collected only for cooling then it is a cost, whereas if the storage facility is used as a city’s snow deposit, the owner might be able to charge the depositors or at least get the snow for free. In this study, it has been assumed that the snow is deposited on the storage facility for free of charge. In case of artificial snow generation, the cost of snow depends on the weather, type of snow guns, and the cost of electricity and water (Skogsberg 2005).  To estimate the economic performance of snow storage systems, an Excel-based probabilistic feasibility evaluation tool has been created using the parameters, including monthly mean cooling demand, pond top diameter, bank height, snow height above bank, side slope, type and thickness of thermal insulation, plastic liner at sides and in bottom, monthly mean temperatures, annual precipitation, proportion artificial snow, number of snow guns, interest rate, and a number of other economic parameters listed by Skogsberg (2005).   The aim of the development of this tool is to evaluate the total cooling cost including investment depreciation. Moreover, the estimated cooling cost can be compared by simulating with various design options. Typically, it is appropriate to compare the economic results of more detailed design choices.    5.3.3 Cost of Energy Options In this study, the cost of produced energy has been evaluated for the cooling systems over its service life. For calculating the total energy cost, the service life of snow storage systems is assumed to be 40 years and 20 years for the chillers. Since the parameters in estimating economic  77 performance are involved with uncertainty factors, the probabilistic analyzes are performed. Based on the Monte Carlo simulation, the 80th percentile of the annual capital investment and operational & maintenance costs have been obtained. The 80th percentile represents the percentage of preference for cost in the project. Then the Levelized cost has been obtained using the equation (3.20) and (3.21) and the results are presented in Table 5.6.   Table 5.6 Levelized cost of cooling system alternatives Item	Description	High-Density	Snow	storage	watertight	Snow	storage	Conventional	Snow	storage	Chillers	Annual	Capital	cost	($)	 30796	 30460	 29937	 35875	Annual	operational	cost	($)	 4297	 5380	 9699	 14418	Total	($)	 35093	 35840	 39636	 50293	Cooling	cost	($/kWh)	 0.38	 0.39	 0.42	 0.51	 As shown in Table 5.6, the Levelized cost of energy produced from the HSS system is 0.38$/kWh. The capital investment cost accounts for 88% of the total cost of energy production. It should be noted that the operating cost includes the cost of thermal insulation, snow and insulation handling, and the cost of operating staff. Also, the results illustrated that the HSS system accounted for the cheapest energy-producing alternative. The total cooling cost for the chiller system is 0.51 $/kWh. The operational and maintenance cost of the chiller system is much higher than the seasonal snow storage systems. Moreover, the initial investment cost is increased due to the short service life of chiller system, which has to be replaced after 20 years. The CSS system has considerably lower capital cost than the WSS while the higher operational cost requirement for the CSS system offsets the effect of capital cost.   5.4 Summary of the Techno-Economic Evaluation of the Building Cooling System Alternatives In this study, Excel-based feasibility evaluation tool has been developed based on the proposed framework for the techno-economic sustainability evaluation of the seasonal snow storage systems. Currently, different types and techniques of seasonal snow storage systems are available. However, in order to make the rational comparison this study evaluated the three snow storage  78 systems, (Conventional snow storage, watertight snow storage and high-density snow storage) which successfully implemented in the large-scale project. Also, the technique that has sufficient data based on the operational experience to perform the sustainability evaluation in terms of environmental and techno-economic performance.   The shape and dimensions of the snow storage systems are estimated based on the snow density, natural melting (ground melt, rain melt, and surface melt), annual mean temperature, annual cooling energy demand, and other factors. The technical performance of the building cooling system alternatives has been evaluated based on the coefficient of performance and the results are shown in Figure 5.6.         Figure 5.6  Summary of Coefficient of Performance (COPtotal)  Among the building cooling systems alternatives, the COP of snow storage systems is much greater than the chiller cooling systems. The HSS system is the most efficient alternative to all snow storage systems, mainly due to its higher COP. To make the fair comparison, the most efficient conventional chiller system type was compared with the snow storage systems, yet the performance is much lower than the other alternatives.   Typically, in the economic evaluation, uncertainties about the future can be mitigated using various methods, such as Monte Carlo simulation, sensitivity analysis, regression analysis, scenario forecasting, probability analysis, and decision trees (Soltani 2015). The economic performance of 0.05.010.015.020.025.0High	Density	Snow	storagewatertight	Snow	storageConventional	Snow	storageChillersCoefficient	of	Performance	(COP) 79 the cooling system alternatives was compared against each other using the present value of the capital investment cost and operational and maintenance cost. The economic performance of the building cooling system alternatives has been evaluated based on the Levelized cost, and the results are shown in Figure 5.7.    Figure 5.7  Summary of Total cooling cost (Levelized cost)  Among the building cooling systems alternatives, the total cooling cost of snow storage systems is cheaper than the chiller cooling systems, which require significantly higher initial investments of all options. The HSS system is the less expensive alternative of all snow storage systems, mainly due to its lower operational cost. The CSS system requires higher total cost due to the lower overall efficiency among the snow storage systems.     0.000.100.200.300.400.500.60High	Density	Snow	storagewatertight	Snow	storageConventional	Snow	storageChillersLevelized	cost	($/kWh) 80 Chapter 6 Conclusions and Recommendations Seasonal snow storage is an ancient technique and has been proven as a sustainable technology to minimize energy consumption and Greenhouse gas emissions. In order to improve the body of knowledge in this domain, this study performed a comparative Life Cycle Assessment (LCA) between the most common cooling production systems: conventional snow storage, watertight snow storage, high-density snow storage, and the chiller unit system. This study evaluated the environmental impacts and the consumption of renewable and non-renewable primary energy using the cradle-to-grave approach, which is based on the literature. Understanding the environmental behavior of the cooling systems will support better decision-making. Also, the study is scientifically sound because the Life Cycle Impact Assessment results were achieved through a consistent (US EPA’s TRACI) methodology that takes into consideration of the most recent North American standards. This study is also innovative and provides up-to-date LCA results for the building cooling systems, particularly because they include a comparison of the environmental performance and life-cycle stages for the first time.   The cradle to grave LCA results for each life-cycle stage and environmental impact category of each cooling system have been computed, along with the identification of the processes that contribute most to each of the impact categories. The study revealed that the chiller cooling system has the biggest environmental impacts on eight out of ten impact categories, primarily due to the significant consumption of electricity in the utilization phase. Conventional snow storage causes relatively higher environmental impacts, partly because it occupies much space and uses high environmental impact materials. Specifically, the use of polyethylene causes various negative consequences at the end of life phase. Watertight snow storage performs better in the operational phase, yet it contributes higher environmental impacts than the chiller cooling systems in the extraction and construction and the end of life phase because of it uses asphalt.    Several LCA studies and commercial LCA software employ different weighting schemes (i.e., point scoring, or %) to find out the life cycle impacts for construction projects. There is no widely agreed method to determine the relative importance of different impacts. Secondly, these weighting schemes were established to estimate the life cycle impacts of the cooling systems and not for evaluation of different alternatives. In this study, different building cooling systems are  81 evaluated using a hierarchical based MCDA framework. The impact categories are ranked based on their importance and the weights are finally determined using rank sum weights method. Subsequently, the impacts are aggregated for different life cycle phases using PROMOTHEE-II. Therefore, due to the unique characteristics of the proposed framework, weights at different hierarchical levels are estimated using the given approach in this research. . Vagueness in expert opinion for assigning the weights should be considered in the future studies.  Certainly the LCA results illustrate that the performance of the different systems varies during different lifecycle phases. Consequently, the MCDA technique is employed using the PROMETHEE –II method to rank the alternatives from the best to the weakest one. The final ranking of alternatives can be defined as HSS > WSS > CSS > Chillers.  Coefficient of Performance of the snow storage systems are significantly higher than the operation of the conventional cooling system, and a high energy-saving effect was thus confirmed. Typically, implementation of new systems over the existing systems requires comparative economic study. The study found that the storage systems could be established at a capital investment cost similar to that of a conventional chiller cooling system. Moreover, the operational and maintenance cost of the snow storage systems are significantly less than the chiller cooling systems, and this indicates that the snow storage systems could be a good substitute for the conventional chiller system as a source of cooling.  6.1 Research Contributions  Main contribution of this research is to guide the decision makers for selecting the high-energy efficient and environmentally safe cooling systems for a building. Developed a novel framework for the sustainability evaluation of seasonal snow storage for building cooling systems using multi-criteria approach and uncertainty analysis. This research has developed Excel based probabilistic tool for estimating the snow storage characteristics, technical performance and economic performance of the cooling systems. This customizable tool can be used anywhere in Canada and other continental climate regions for evaluating the techno-economic performance by providing necessary input data.     82 The unique feature of the present study is that it includes an in-depth investigation of the impacts of multi-criteria and uncertainty analysis on the performance of the seasonal snow storage systems as the primary energy source alternative to a building cooling system. Other studies in the field, however, only focus on the evaluation of one snow storage system and neglect or briefly address other important issues present in the evaluation of such energy systems.   In the initial stage of a project, decision-makers, planners, developers, and engineers, often face the challenge of the selection of building materials, energy systems, etc. particularly, to employ the new technology in a project. The sustainability evaluation techniques are used to highlights the benefits of adopting such alternatives or strategies in terms of technical, economic, and environmental aspects. However, evaluating the performance of a system in the initial stages of a project is tough, mainly due to the influence of significant uncertainties and the integration of huge decision parameters and indicators. Thus, multi-criteria approach and probabilistic approach is required to handle the uncertainties and to aid the decision-making process with more robust results.   Moreover, in the energy management, many of the MCDA studies have focused mainly at the regional and national level, while very few research studies are available on local energy systems with renewable energy sources. In this regard, this study employed the MCDA technique to assess the snow storage system concept in a building for a local energy system. Unlike the multi-attribute theory methods such as SAW and AHP, the outranking methodology (PROMETHEE II) used for ranking the cooling system alternatives with respect to their impact categories. This feature is the advantage of the PROMETHEE II method for comparison of the fossil fuels based chiller system and the renewable energy source based snow storage systems where an environmental criterion is emphasized.  The seasonal storage technique can also be implemented in district energy systems and large institutions, (i.e. hospitals, universities, and industries) to reduce the peak demand in the grid as well as to reduce the peak demand rates.     83 6.2 Limitations The following are the main limitations of the research presented in this thesis: The area above the snow storage system cannot be utilized for any application, as it could with the previously mentioned designs. Moreover, the land requirement for establishment of snow storage system is significantly higher than the chiller system, which makes this system unsuitable in urban areas and densely populated areas. The likelihood that these systems will be feasible when the snow is deposited in the facility and the land costs are cheap or where these costs are not accounted in the snow storage system.  The seasonal snow storage system primarily depends on snowfall and ambient air; thus, climate factors will influence the economic feasibility. One of the significant factor to be considered in the choice of system is the increasing global mean temperature, and this may adversely impact the annual variation in snowfall and temperature, and hence the operation of this system.  The low domestic electricity price may reduce the opportunities for the implementation of this system. Also, the electricity generation in BC, Canada highly depends on Hydro energy, which has lower environmental impacts than the conventional energy generation techniques. Domestic renewable energy technology will not directly contribute to the economy, especially in a provincial or federal level.   6.3 Recommendations While this study compares the environmental and techno-economic performance of building cooling systems, the other factors, such as social performance, should also be considered in future studies to enhance the efficacy of the decision-making process. Various scenarios could be developed by considering carbon tax levels, different materials, and future technologies.   This study considered only the uncertainties of the input data. However, the assigned weights and the preference function variables are also underlying uncertainties; it is recommended that future studies perform a detailed sensitivity analysis of these variables. Furthermore, both the LCA and MCDA contain the underlying uncertainties associated with data limitations and the difference in  84 expert opinion, respectively; it is recommended that the future studies address these uncertainties. Also, this study was not considered the correlation between the different criteria in the MCDA. For example, it is most likely that the cost of a technique and the impacts of facility may affect the acceptance of a certain technique. The future studies can account such correlations using the Monte Carlo Simulation technique.  In this study, the proposed multi-criteria based LCA framework and the probabilistic techno-economic sustainability evaluation framework for assessing the performance have been applied only to the building cooling systems. The framework and the feasibility evaluation tool can be applied to a broader-scale development such as district cooling systems. Moreover, this study focused only on the cooling energy production systems. With the studied approach, it is recommended that the sustainability evaluation also be performed in terms of environmental and techno-economic performance for heat generation systems and Combined Heat and Power (CHP) systems. In recent years, CHP systems have received increasing interest, particularly in the district energy production systems. Seasonal cold storage is likely to be less expensive when coupled with the other seasonal storage alternatives, such as solar heat, and the disposal of waste heat from power plants, production industries, and others. It is recommended to evaluate the performance of combined storage of heat and coolness.   The energy production systems and the associated environmental impacts are depending on the geographical region, although LCA is traditionally a site-independent tool, there is currently a trend towards making LCA more site-dependent in order to get more detailed and accurate environmental performance on a regional scale. 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(2010). “Ice Thermal Storage Systems for LWR Supplemental Cooling and Peak Power Shifting.” Proceedings of 10th International Congress on Advances in Nuclear Power Plants, American Nuclear Society, San Diego, CA, 2249–2257.  99 Appendices Appendix A: Application of PROMETHEE II for ranking of building cooling systems alternatives  Step 1: Formation of decision matrix - Based on the LCA results, the decision matrix developed for three lifecycle phases of the cooling systems is presented in Table A.1.  Table A.1 Decision Matrix including all the impact categories of cooling systems for three lifecycle phases 	Cooling	System	 Ozone	depletion	Global	warming	 Smog	 Acidification	 Eutrophication	 Carcinogenic	Non-	carcinogenic	Respiratory	effect	 Ecotoxicity	Fossil	fuel	depletion	Extraction	and	Construction	CSS	 0.01	 250322	 14760	 1058	 239	 0.01	 0.04	 150	 1404934	 1052523	HSS	 0.01	 171662	 10349	 684	 352	 0.01	 0.05	 105	 1280821	 260114	WSS	 0.05	 209436	 17001	 1249	 669	 0.01	 0.06	 171	 1847397	 485012	Chillers	 4.64	 91472	 3755	 1430	 2741	 0.04	 0.75	 141	 14815384	 40335	Utilization	CSS	 0.1	 525225	 27320	 1945	 1103	 0.03	 0.15	 376	 19019822	 1045613	HSS	 0.1	 287296	 15092	 1179	 812	 0.02	 0.10	 213	 16831160	 488583	WSS	 0.1	 364165	 19043	 1426	 906	 0.03	 0.12	 266	 17538266	 668546	Chillers	 1.2	 1705396	 89805	 7654	 4991	 0.09	 0.35	 572	 27779912	 2231592	End	of	life	CSS	 0.0	 855928	 6924	 84	 1057	 0.06	 1.25	 -24	 115008099	 -360075	HSS	 0.0	 130512	 -1581	 -203	 104	 0.01	 0.29	 -43	 29430199	 -113564	WSS	 0.0	 953750	 5379	 -21	 859	 0.06	 1.13	 -35	 103927266	 -137829	Chillers	 -1.9	 -34298	 -1349	 -558	 -1036	 -0.01	 -0.28	 -55	 57904399	 -14624	 100 Step 2: Checking the need for normalization- the impact categories in the decision matrix possess different measurement units and need to be normalized. In the normalization process, the recent benchmark established by the provincial and federal government of Canada to minimize 30% of the GHG emissions by 2040 has been used. Consequently, the least value obtained for a given impact category (for a given alternative) is further reduced by 30%, and then the values obtained for all the alternatives have been normalized correspond to this value. The results are presented in Table A.2. 	Table A.2 Normalized decision matrix for all the impact categories of cooling systems 	 C1	 C2	 C3	 C4	 C5	 C6	 C7	 C8	 C9	 C10	Extraction	and	Construction	 	 	 	 	 	 	 	 	CSS	 0.65	 0.26	 0.18	 0.45	 0.70	 0.50	 0.70	 0.49	 0.64	 0.03	HSS	 0.70	 0.37	 0.25	 0.70	 0.48	 0.68	 0.59	 0.70	 0.70	 0.11	WSS	 0.16	 0.31	 0.15	 0.38	 0.25	 0.70	 0.47	 0.43	 0.49	 0.06	Chillers	 0.00	 0.70	 0.70	 0.33	 0.06	 0.18	 0.04	 0.52	 0.06	 0.70	Utilization	 	 	 	 	 	 	 	 	 	CSS	 0.32	 0.38	 0.39	 0.42	 0.52	 0.54	 0.44	 0.40	 0.62	 0.33	HSS	 0.70	 0.70	 0.70	 0.70	 0.70	 0.70	 0.70	 0.70	 0.70	 0.70	WSS	 0.51	 0.55	 0.55	 0.58	 0.63	 0.64	 0.59	 0.56	 0.67	 0.51	Chillers	 0.03	 0.12	 0.12	 0.11	 0.11	 0.19	 0.19	 0.26	 0.42	 0.15	End	of	life	 	 	 	 	 	 	 	 	 	CSS	 0.01	 0.00	 0.00	 0.00	 0.00	 0.00	 0.00	 0.00	 0.18	 0.00	HSS	 0.00	 0.00	 0.00	 0.00	 0.00	 0.00	 0.00	 0.00	 0.70	 0.00	WSS	 0.00	 0.00	 0.00	 0.00	 0.00	 0.00	 0.00	 0.00	 0.20	 0.00	Chillers	 0.00	 0.01	 0.00	 0.00	 0.00	 0.00	 0.00	 0.00	 0.36	 0.00	 Step 3: Evaluation of weights for each environmental impact category - Weights of the each impact categories were calculated using the rank sum weights method, as described in the methodology section. The impact categories were listed out and ranked by the experts working in LCA according to their importance from 1 to n. where, n is the number of impact categories. Using the rank sum weights method as described in equation (3.5), the normalized weights obtained are presented in Table A.3.  	 	 101 Table A.3 Weight estimation using the rank sum method Impact	categories	 Importance	(most	to	least)	Normalised	weights	Carcinogenic	 1	 0.18	Respiratory	effects	 2	 0.16	Ecotoxicity	 3	 0.15	Fossil	fuel	depletion	 4	 0.13	Global	warming	 5	 0.11	Non-carcinogenic	 6	 0.09	Ozone	depletion	 7	 0.07	Smog	 8	 0.05	Acidification	 9	 0.04	Eutrophication	 10	 0.02			 Sum	of	weights	 1.00	 Step 4: Aggregate the environmental impact categories based on lifecycle phases for each alternative - Weighted Sum Method (WSM) is used to aggregate the environmental impact categories. Normalized attributes are multiplied with weights and summed for each alternative according to equation (3.6), and the results are presented in Table A.4.  Table A.4 Aggregated indices of impact categories using the WSM 		 E&C	 Use	 EOL	CSS	 0.42	 0.42	 0.03	HSS	 0.51	 0.66	 0.10	WSS	 0.37	 0.55	 0.03	Chillers	 0.33	 0.20	 0.05	 Step 5: Construct the outranking relation- Table A.4 has been used to rank the alternatives using the PROMETHEE-II method. In order to rank the cooling systems, the weightings 0.3, 0.65, 0.05 were selected by the expert opinion for the extraction and construction, operation, and end of life phases, respectively. The operations phase was given higher importance as it has the longest time period and thus has the highest chronic effects. In order to derive the preference index, a pairwise comparison was performed for all the alternatives against each criterion. Using equation (3.12) the multi-objective outranking index has been calculated and presented in Tale A.5.    102 	Table A.5 The multi-criteria outranking index X	 P(A1,X)	 P(A2,X)	 P(A3,X)	 P(A4,X)	A1	 0.00	 1.00	 0.70	 0.05	A2	 0.00	 0.00	 0.00	 0.00	A3	 0.30	 1.00	 0.00	 0.05	A4	 0.95	 1.00	 0.95	 0.00	 Step 6: Exploitation of the outranking relation- Using the equation (3.15) the outgoing, incoming, and net flows have been estimated and are presented in Table A.6.  Table A.6 outranking relation using the PROMETHEE method Flow	 CSS	 HSS	 WSS	 Chillers	φ+	 0.42	 1.00	 0.55	 0.03	φ-	 0.58	 0.00	 0.45	 0.97	Net	Flow	 -0.17	 1.00	 0.10	 -0.93	Rank	 3	 1	 2	 4	   103 Appendix B: Feasibility Evaluation tool  The snapshots of the developed Excel based feasibility evaluation tool are presented here.  Step 1: To start with the basic data of climate conditions where the feasibility will be analyzed and load conditions of the building cooling system are entered on the “Input data_temp” sheet (see Table B.1).  Table B.1 Project information, temperature and energy demand profile   104 Step 2: After the basic data has been entered, detailed technical and economic information about the snow storage system construction needs to be provided. In which, some of the parameters are enabled with ‘tool tip’ option for explaining the content and providing some guidelines for entering data. At last, choose the dimensions and shape of the snow storage system with the guidance of enabled green and red cells (see Table B.2 and Table B.3).  Table B.2 Input data for Technical Analysis       105 Table B.3 Input data for Economic Analysis      106 Step 3: Finally, optimise the storage construction to achieve the lowest cooling cost. The results of the storage system in terms of design and economic aspects are summarized on a “Results-technical and economical" sheet (see Table B.4 and Table B.5).  Table B.4 Results of Technical Analysis   107 Table B.5 Results of Economic Analysis	  

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