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APPLICATION OF THE BILAN CARBONE MODEL TO MITIGATE GREENHOUSE GAS EMISSIONS IN AIT CAMPUS

by

Chutiya Lamkitcha

A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in

Environmental Engineering and Management

Examination Committee: Prof. Chettiyappan Visvanathan (Chairperson) Prof. Nguyen Thi Kim Oanh Dr. B. Mohanty

Nationality: Thai Previous Degree: Bachelor of Science in Environment

Chulalongkorn University Bangkok, Thailand Scholarship Donor: Royal Thai Government - AIT Fellowship

Asian Institute of Technology School of Environment, Resources and Development

Thailand May 2011

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Acknowledgements

The author wishes to express her deep gratitude to Prof. C. Visvanathan, her advisor for his invaluable comments and suggestions, encouragement, support and especially devotion. This study could not be accomplished without his guidance. The author would also like to express her sincere appreciation to Dr. Brahmanand Mohanty and Professor. Nguyen Thi Kim Oanh for their precious comments, guidance and support. The author also wishes to thank Mr. Pravakar Pradhan, Research Associate at AIT ADEME Project, Energy Field of Study, School of Environment, Resources and Development (SERD), for his assistance in data collection process and his guidance on the Bilan Carbone tool application. The author would also like to take this opportunity to sincerely thank Sodexo especially Mr. Nitikron for his cooperation on sharing information. Furthermore my kind appreciation also goes to ERCO office, purchasing office, AIT extension office and all other shops and restaurants at AIT for sharing their valuable data information. The author wishes to express her thorough gratitude to the Royal Thai Government fellowship for full financial support for her master degree. She also wishes to appreciate all of the secretaries and staff at Environmental Engineering and Management field of study for their full support and helpful guidance throughout her study at Asian Institute of Technology. Lastly the author wishes to express her deepest gratitude to her family for their constant support, encouragements and inspiration during my engagement on this research work and throughout my whole life. The author would also like to thank all of her friends and well-wishers for their company, inspiration and advises when the problems faced.

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Abstract

Nowadays, Global Warming and Climate Change become the biggest environment issues all over the world, in which greenhouse gas (GHG) emissions are considered to be the main sources. Since the ratification of Kyoto protocol, many countries have developed GHG inventories in order to calculate GHG emissions. The Bilan Carbone model developed by ADEME, is an outstanding tool because it covers all of 6 main GHG and also takes into account of water vapor and emission from aircrafts. By using easy available data, the Bilan Carbone tool can give an overall of GHG emissions in a territory. Academic institute however, is considered to have potential in terms of significantly reduce GHG emissions in its premise because people are capable with knowledge. Estimating GHG emissions in academic institutes has been done and commitment to GHG reduction has been concerned through reduction policy. Asian Institute of Technology (AIT) is also emitting some significant amount of GHG in its premise. The estimation on GHG emissions in AIT campus in the year of 2009 has been done using the Bilan Carbone tool. The sources of GHG emissions covered in the study are energy, excluding energy, materials and products purchased (inputs), transportation of goods (freights), transportation of people (travel), solid waste and wastewater (direct waste), and property. The study has shown that in 2009, AIT emitted 6,245 tons Carbon equivalent of GHG emissions. Transportation of people is considered to be the biggest emitter, which accounts for 41% of overall GHG emissions in AIT. The average GHG emissions per capita of AIT is 2.08 tCeq. Scenarios for GHG reduction have been proposed according best practices for energy, transportation, solid waste generation and wastewater aspects. The Bilan Carbone was once again used to estimate GHG emissions from each proposed scenario. As a result, energy conservation scenario for energy aspect has the highest potential in term of reduce GHG emissions, which can reduce GHG emission up to 602 tCeq. And by combining all scenarios together, AIT should be able to reduce about 1,200 tCeq per year. This study has produced a separate spreadsheet for calculating GHG from the whole water supply system. Although reduction scenarios in water and wastewater aspect give very low GHG reduction, it is still important to take into account because it involves people’s participation. In order to motivate AIT to move towards low carbon campus, it is necessary to have proper policy guidelines and measurement tools. Regularly receiving information about their own carbon footprint and ways of reducing it, can raise awareness among AIT individuals in terms of reduce GHG emissions.

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Table of Contents

Chapter Title Page Title Page i Acknowledgements ii Abstract iii Table of Content iv List of Tables vi List of Figures viii List of Abbreviations ix 1 Introduction 1 1.1 Background 1 1.2 Objectives of the study 2 1.3 Scopes of the study 2 2 Literature Review 4 2.1 GHG Emissions and Climate Change 4 2.2 Low Carbon Society 6 2.3 Best Practices 7 2.3.1 Water and Wastewater Sector 7 2.3.2 Solid Waste 8 2.3.3 Transportation 10 2.3.4 Building (Lighting) 13 2.3.5 Green Campus at Harvard 14 2.4 Concerns for Selecting Scenarios 15 2.4.1 Changes in Lifestyle and Behavior Patterns 15 2.4.2 National Policies and Instruments 16 2.5 Bilan Carbone Model 17 2.5.1 Strengths 18 2.5.2 Overall Principles of the Bilan Carbone 19 2.6 Carbon Footprint 19 3 Methodology 20 3.1 General 20 3.2 Overall Framework of the Study 20 3.3 Detailed Methodology 21 3.3.1 Defining the Boundary of the study 21 3.3.2 Description of Methodology 22 4 Results and Discussions 30 4.1 Introduction 30 4.2 Phase 1: Data Collection 30 4.2.1 Energy 30 4.2.2 Excluding Energy 31 4.2.3 Material and Products Purchased (Inputs) 32 4.2.4 Transportation of Goods (Freights) 34 4.2.5 Transportation of People (Travel) 35 4.2.6 Solid Waste and Wastewater (Direct Waste) 37

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4.2.7 Property 39 4.3 Phase 2: Data Analysis 39 4.3.1 Energy 39 4.3.2 Excluding Energy 40 4.3.3 Material and Products Purchased (Inputs) 41 4.3.4 Transportation of Goods (Freights) 42 4.3.5 Transportation of People (Travel) 43 4.3.6 Solid Waste and Wastewater (Direct Waste) 44 4.3.7 Property 45 4.3.8 Overall GHG Emissions 45 4.3.9 Producing Spreadsheet for Water and Wastewater 46 4.3.10 GHG Emissions from Water and Wastewater 48 4.4 Phase 3: Comparison of Scenarios 49 4.4.1 Energy 51 4.4.2 Transportation of People (People) 51 4.4.3 Solid Waste Generation 53 4.4.4 Water and Wastewater 54 4.5 Phase 4: Analysis of Possibility for Implementation of

Scenarios 56

4.5.1 Energy: Electricity Consumption 56 4.5.2 Transportation of People (People) 57 4.5.3 Solid Waste Generation 57 4.5.4 Water and Wastewater 58 4.5.5 The Possible Policies to Help AIT Achieve Low

Carbon Campus 60

4.6 Limitations of the Study Regarding Emission Factors 60 4.6.1 Energy 60 4.6.2 Excluding Energy 61 4.6.3 Material and Products Purchased (Inputs) 61 4.6.4 Transportation of Goods (Freights) 62 4.6.5 Transportation of People (Travel) 63 4.6.6 Solid Waste and Wastewater (Direct Waste) 63 4.6.7 Property 64 4.6.8 Water and Wastewater 65 5 Conclusion and Recommendations 66 5.1 Conclusion 66 5.2 Recommendations 66 References 68 Appendices 71

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List of Tables Table Title Page 3.1 List of GHG Emission Sources in AIT Campus 213.2 Detail on Activities in Which Consumed Particular Type of

Fuels 23

3.3 Detail on Activities for Each Type of Road Freight 243.4 Data Information for Each Type of Asset 263.5 The Amortization Periods of the Assets 274.1 Information for Fuel Consumptions 314.2 Assumption of Materials Purchased in AIT 324.3 Building Materials and Chemical Use 334.4 Number of Meals According Types of AIT’s Individuals 334.5 Amount of Fuel Consumption by AIT’s Vehicles in 2009 344.6 Data Information of Fuel Consumption for Outgoing Road

Freight 34

4.7 Fuel Consumption for AIT Incoming Road Freight 354.8 Travelling Distance for Each Type of Transportation 354.9 Distance Travelled (km) by AIT’s Vehicles 364.10 Distance Travelled (km) by AIT’s Vehicles 364.11 Solid Waste Generation in AIT 374.12 Amount of Recyclable Waste in AIT 384.13 Summery for Surface Area of All Buildings in AIT

Campus 39

4.14 GHG Emissions from Energy Aspect 404.15 GHG Emissions from Excluding Energy 414.16 GHG Emissions from Input Materials Aspect 424.17 GHG Emissions from Freight Aspect 434.18 GHG Emissions from Travel Aspect 434.19 GHG Emissions from Direct Waste Aspect 444.20 GHG Emissions from Property Aspect 454.21 Data input for Estimating GHG Emission from Water and

Wastewater Sector 48

4.22 GHG Emission per Capita in Some Other University 494.23 GHG Emission per Capita for Asian Countries in 2008 494.24 Data Information on Scenario Electricity Consumption 514.25 Results of Calculation on Scenario Electricity

Consumption 51

4.26 Data Information on Scenario 1 of Travel 524.27 Results of calculation on Scenario 1 of Travel 524.28 Data Information on Scenario 2of Travel 524.29 Results of Calculation on Scenario 2 of Travel 534.30 Data Information on Scenario 1 of Solid Waste Generation 534.31 Results of Calculation on Scenario 1 of Solid Waste

Generation 53

4.32 Results of Calculation on Scenario 2 of Solid Waste Generation 544.33 Results of Calculation on Scenario 3 of Solid Waste Generation 544.34 Data Information on Scenario 1 of Water and Wastewater 554.35 Data Information on Scenario 2 of Water and Wastewater 55

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4.36 Data Information on Scenario 3 of Water and Wastewater 554.37 Amount of GHG Reduced in Each Scenario 564.38 Summary of Advantages and Disadvantages for Each

Proposed Scenario 59

4.39 Summary of Emission Factor Adaptation 65

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List of Figures Figure Title Page 2.1 An idealized model of the natural greenhouse effect 52.2 The 3-step Process of Tungsong’s Organic Fertilizer Production 92.3 Songteaw: Chiang Mai’s Public Transit 122.4 Bio-Diesel Refueling Station 123.1 Overall framework of the study 213.2 Detail on data collection for Direct Waste in AIT 274.1 Monthly Electricity Consumption in AIT for the Year of 2009 334.2 Trend of water consumption in AIT, 2009 414.3 Comparison of GHG Emission from Sectors in Energy Aspect 434.4 Comparison of GHG Emission from Sectors in Excluding

Energy 44

4.5 Comparison of GHG Emission from Sectors in Input Materials Aspect

45

4.6 Comparison of GHG Emission from Sectors in Freight Aspect 464.7 Comparison of GHG Emissions from Each Sector in Travel

Aspect 47

4.8 Comparison of GHG Emissions from Each Sector in Direct Waste Aspect

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4.9 Overall GHG Emissions in AIT 494.10 Total Water Supply in AIT 494.11 Water Production Process for Provincial Waterworks Authority 504.12 Overall Possible Sources of GHG Emission from Wastewater

Treatment Plant 51

4.13 Share of GHG emission by sources in AIT campus 53

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List of Abbreviation ADEME Agence de l’Environment et de la Maitrise de l’Energie (French

Environmrnt and Energy Management Agency) AIT Asian Institute of Technology ASE Alliance to Save Energy BCA Benefit Cost Analysis BRT Bus Rapid Transit CDM Clean Development Mechanism CFC Chlorofluorocarbons CH4 Methane C/N Carbon/Nitrogen CO2 Carbon Dioxide EU European Union GHG Greenhouse Gas GWP Global Warming Potential HFCs Hydrofluorocarbons HKSAR Hong Kong Special Administrative Region IEA International Energy Agency IPCC Intergovernmental Panel on Climate Change ISO International Organization for Standardization LCS Low Carbon Society LED Light Emitting Diodes NMHC Non Methane Hydrocarbons N2O Nitrous Oxide PFCs Perfluorocarbons RDF Refuse-Derived Fuel SF6 Sulphur Hexafluoride TWM Total Water Management UNDP United Nations Development Program USD United States Dollar VEEPL Vietnam Energy Efficiency Public Lighting Project VOC Volatile Organic Compounds

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Chapter 1

Introduction

1.1 Background Asian region is home of many developing countries with lower literacy rates than in developed countries. Moreover Asian region is the most populous region in the world with its economic has been growing very rapidly in the past two decades which results in steady increase of energy and resources demands by Asian populations. Apparently many Asian cities have grown without concerns of proper design in land use and transportation systems that should be relevant to sustainable development. According to World Energy Outlook by IEA (2009), the world demand of oil will rise from 85 million barrels per day in 2008 to 105 million barrels per day in 2030 and all the growth comes from developing countries where the transport sector accounts for 97% of the increase in oil use. A continuous rapid rise in energy-related CO2 emissions through to 2030, resulted from increased global demand for fossil energy. Having already increased from 20.9 gigatons (Gt) in 1990 to 28.8 Gt in 2007, CO2 emissions are projected to reach 34.5 Gt in 2020 and 40.2 Gt in 2030. The basic trend of energy and resources consumption in Asia nowadays, is more like linear metabolism that there is always wasted energy being released from the activities which is not considered as environmental friendly. Since the ratification of the Kyoto Protocol in 2005, all 6 greenhouse gases (GHGs) namely CO2, SF6, CH4, N2O, HFCs and PFCs, are taken into account in terms of reduction by all member countries around the world. With the awareness of that these GHGs have high potential in causing adverse effects on the environment, e.g. increase of the earth’s average temperature, changes in wind patterns and droughts. Countries especially, developed countries have adopted their environmental policies to achieve the goal of reductions on GHG emissions. To be successful in reducing GHG emissions, country or local authorities need tools and methodologies to measure GHG emissions in their own territory so that many kinds of GHG inventory tools and methodologies have been widely developed in the past several years. The Bilan Carbone (Carbon Balance) model which has been developed by ADEME (French Environment and Energy Management Agency) since 2004 serves as a tool to calculate GHG emissions using easily-available data to properly assess the direct and indirect emissions produced by the different activities of all stakeholders in a territory (Mohanty, 2010). With outstanding strengths, the Bilan Carbone covers all 6 greenhouse gases in Kyoto protocol and not like the other GHG inventories, it takes into account all the possible emission sources including sources from air craft and maritime vehicles. Moreover, by using the Bilan Carbone tool, GHG emissions from different types of reduction scenarios can be estimated which will be useful in identifying suitable and effective policy as well as available implementation methods to reduce the adverse impacts of GHG emission on climate change. The Bilan Carbone tool has been widely used in France to estimate GHG emission from all activities in companies, territories and even educational institutions. Number of suitable policies and GHG reduction actions have been identified and implemented in each participated territory. Since huge amount of GHG emission has been reduced from all significant activities in France, it is seen that this will be strongly benefit in Asian region in

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terms of GHG reduction as well. The Asian Institute of Technology (AIT) with support from the French Environment and Energy Management Agency (ADEME) has initiated a two year “Action towards Resource-efficient and Low Carbon Cities in Asia” program with the objective to assist a number of small and medium cities of Asia in their efforts towards low carbon to achieve resource efficiencies and environmental sustainability (Action Towards Resource-Efficient and Low Carbon Cities in Asia, 2010). However, it is helpful to start learning the practice with AIT campus. AIT is currently emitting huge amount of GHG emissions through its daily activities. From this study, it has been found that Transportation sector emitted around 6,000 tons Carbon equivalent per year which is indicated to be the biggest source of GHG emissions on campus. When the second biggest source is from Energy sector and large amount of money has been paid for utility supply. According to report on utility bills, AIT consumed the electricity for more than 12 Gwh in 2008 and increased to about 12.5 Gwh in 2010 that cost around 42 million baht. Moreover waste generation is also another significant source of GHG emission in AIT, everyday solid waste is generated about 2-3 tons and contributes about 350 tons Carbon equivalent per year. This study shows that by implementing the Bilan Carbone tool, the appropriate scenarios and available implementation options for reduction of GHG emissions have been identified to help AIT become a low carbon campus. 1.2 Objectives of the study The objectives of the study are as follows:

1. To measure the current situation of GHGs emission in AIT campus using Bilan Carbone tool.

2. To compare GHG emissions in different kind of reduction scenarios in AIT campus.

3. To identify suitable policy as well as available implementation methods to reduce the adverse impacts of climate change.

1.3 Scope of the study The scopes of the study are as follows:

1. Studied areas: Campus of Asian Institute of Technology (AIT), Pathumthani, Thailand

2. GHG emissions analysis was mainly measured from the following four sectors; namely:

(a) Transportation sector: In transportation sector includes travel of AIT’s faculty, staff and students by the transportation mode of road and planes.

(b) Energy sector: Includes energy consumption by air-conditioning and lighting systems in buildings in AIT campus.

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(c) Solid Waste sector: Consider consumption and generation of food waste, paper, plastics, and metals.

(d) Water and Wastewater: This sector focuses on reduction of consumption and having potential to produce its own water to be used on campus by either water harvesting or wastewater treatment.

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Chapter 2

Literature Review 2.1 GHG emission and Climate Change Climate Change 2007 report by IPCC describes the relationship between GHG emissions and climate change that the sun provides power to the earth’s climate, giving out energy at very short wavelengths, predominately in the visible or near-visible (e.g., ultraviolet) part of the spectrum. Around one-third of the solar energy that reaches the top of Earth’s atmosphere is reflected directly back to atmospheric space. The remaining two-thirds is absorbed by the earth’s surface and, to a smaller extent, by the atmosphere. In order to balance the absorbed incoming energy, the earth must radiates the same amount of energy back to space. Since the Earth is quite colder than the sun, it radiates at longer wavelengths, primarily in the infrared part of the spectrum (see Figure 1). Most of this thermal radiation emitted by the land and ocean is absorbed by the atmosphere, including clouds, and reradiated back to earth. This is called the greenhouse effect. The glass walls in a greenhouse decrease airflow and rise the temperature of the air inside. Analogously, but through a different physical process, the Earth’s greenhouse effect warms the surface of the planet. Without the natural greenhouse effect, the temperature at Earth’s surface would be lower than the water’s freezing point. Thus, Earth’s natural greenhouse effect makes life as we know it possible. However, human activities, especially the burning of fossil fuels and clearing of forests, have largely extended the natural greenhouse effect, causing global warming. The two most abundant gases in the earth’s atmosphere, nitrogen (consisting 78% of the dry atmosphere) and oxygen (21%), include almost no greenhouse effect. Instead, the greenhouse effect is caused by molecules that are more complex. Water vapor is the most important greenhouse gas, and carbon dioxide (CO2) comes second. Methane, nitrous oxide, ozone and several other gases present in the atmosphere in small amounts also are potential to the greenhouse effect. In the humid equatorial regions, where there is high level of water vapor in the air that the greenhouse effect is very huge, adding a small amount of CO2 or water vapor has only a little effect on downward infrared radiation. However, in the dry, cold polar regions, the effect of a small rise in CO2 or water vapor is much greater. The same is true for the cold, dry upper atmosphere where a small increase in water vapor has a larger impact on the greenhouse effect than the same change in water vapor would have near the surface. Several compositions of the climate system, namely the oceans and living organisms, affect atmospheric concentrations of greenhouse gases. A good example of this is plants taking CO2 out of the atmosphere and changing it (and water) into carbohydrates through photosynthesis. In the industrial period, human activities have added greenhouse gases to the atmosphere, primarily through the combustion of fossil fuels and clearing of forests. Adding more of a greenhouse gas, for example CO2 to the atmosphere extends the greenhouse effect, therefore warming Earth’s climate. The amount of warming depends on various feedback mechanisms. For example, as the atmosphere warms because of rising levels of greenhouse gases, its concentration of water vapor increase as well, further intensifying the greenhouse effect. This case causes more warming, which effects an additional increase in water vapor, in a self-reinforcing cycle. This water vapor reaction may be strong enough to approximately double the increase in the greenhouse effect due to the added CO2 alone. Another important feedback

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mechanisms include clouds. Clouds are capable at absorbing infrared radiation and thus exert a huge greenhouse effect, thus warming the Earth. Clouds are also effective at giving away incoming solar radiation, therefore cooling the Earth. An adjustment in almost any aspect of clouds, for example their type, location, water content, cloud altitude, particle size and shape, or lifetimes, affects the degree to which clouds are able to warm up or cool down the Earth. Some changes enhance warming while others weaken it. Many researches are in progress to better understanding how clouds change due to climate warming, and how these changes affect climate through different feedback mechanisms.

Figure 2.1 An idealized model of the natural greenhouse effect.

(IPCC, 2007)

The Earth system that relates a changing climate to atmospheric composition, chemistry, the carbon cycle and natural ecosystems. The science of the time provided a clear case for anthropogenic interference with the climate system. In terms of greenhouse agents (1) emissions resulted from anthropogenic are substantially increasing the atmospheric concentrations of the greenhouse gases: CO2, CH4, CFCs, N2O; (2) some gases are substantially more effective (at greenhouse warming); (3) feedbacks between the carbon cycle, ecosystems and atmospheric greenhouse gases in a warmer world will be likely to affect CO2 abundances; and (4) GWPs provide a metric for comparison of the climatic impact of different greenhouse gases, one that integrates both the radiative influence and biogeochemical cycles. The climatic importance of tropospheric ozone, sulphate aerosols and atmospheric chemical feedbacks were given by scientists at the time and remarked in the assessment. For example, early global chemical modeling results argued that global tropospheric ozone, a greenhouse gas, was controlled by emissions of the greatly reactive gases nitrogen oxides (NOx), carbon monoxide (CO) and non-methane hydrocarbons (NMHC, also known as volatile organic compounds, VOC). Developing countries especially in Asian region have been emitting a huge amount of greenhouse gases caused by anthropogenic. Because most of developing countries are poor so that not many high energy efficient devices are operating in terms of reducing the GHG emissions. Not like many cities in developed countries, that have high potential in reducing

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GHG emission from their territory because they are capable of investment and participation of their people. In developing countries, like Thailand, to be able to be successful in reducing GHG emission, the concept of Low Carbon City needs to be implemented. 2.2 Low Carbon Society At a workshop on “Developing Visions for a Low-Carbon Society (LCS) through Sustainable Development” that was held in Japan, where participants were trying to come up with the definition of Low Carbon Society. The definition is a flexible framework which would allow fruitful discussions, leading to practical actions that (National Institute for Environmental Studies, 2006) a low-carbon society should:

• take actions that are compatible with the principles of sustainable development, ensuring that the development needs of all groups within society are met.

• make an equitable contribution towards the global effort to stabilize the atmospheric concentration of CO2 and other greenhouse gases at a level that will avoid dangerous climate change, through deep cuts in global emissions.

• demonstrate a high level of energy efficiency and use low-carbon energy sources for and production technologies.

• adopt patterns of consumption and behavior that are consistent with low levels of greenhouse gas emissions.

Although the definition is trying to cover all national circumstances, the implications are rather not the same for countries at different stages of development. For developed countries, to be able to achieve a low-carbon society should involve making cuts in CO2 emissions by the middle of the 21st century. It would involve the development and deployment of low-carbon technologies and changes to lifestyles and institutions. For developing countries, the successfulness of a low-carbon society must go as participation with the achievement of development goals. This would be with a view to achieving an important state of development, with CO2 intensity compatible with that succeeded by low-carbon societies in developed countries (Skea and Nishioka, 2008). To summarize, for an area to be able to achieve in moving towards low carbon society, two major components are strongly required namely: Technology development and changing in behaviors of stakeholders. It is essential for developing countries to gain helps from other developed countries in terms of increasing investment in this area and reducing prices of imported technology. AIT camps has quite high potential in helping reduce GHG emissions because it is a higher education institution that commits to combating against Climate Change. All AIT individuals are concerned and willing to participate. Studying the best practices for reducing GHG emissions played an important role in the study for see examples as well as pros and cons of each reduction plans. Although practices for reducing GHG emissions from other cities need to be examined, AIT should have its own policies suitable for its current activities.

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2.3 Best Practices To be able to identify the suitable policies for reducing GHG emission in study areas, best practices in cities around the world have been studied. Following are best practices that have been chosen to be used for scenarios in Chapter 3. In many European cities, their best practices require such a high technology and investment since they are capable of that. Therefore, the criteria in selecting best practices to be used in the study areas should not be practices that require such a huge investments and majority of people in study areas can participate. 2.3.1 Water and Wastewater Sector 2.3.1.1 Emfuleni, South Africa

A water efficiency system known as advanced pressure management reduces pressure in the city's water network, thus reducing the amount of water leaking through small undetected holes, (C40 cities, 2005a) The Alliance to Save Energy facilitated a performance contract for the implementation of a water efficiency system known as advanced pressure management on the bulk water supply pipeline into the Sebokeng/Evaton residential area. This area was characterized by excessive water wastage due to leakage. Because of the consumption of large amounts of electricity to pump water to where it is needed, the water saved as a result of implementing this project translates into savings in electricity, which also implies savings in the use of fossil fuels and especially low-grade coal to generate the electricity. This in turn translates into substantial reductions in CO2 emissions.

The Alliance to Save Energy (ASE) was made aware of this potential project and cooperated with Metsi-a-Lekoa to provide the conceptualization, planning and implementation of an appropriate intervention measure. ASE concluded that the best technical solution was advanced pressure management. By reducing pressure in the network, the amount of water that leaks through small undetected holes is also reduced.

Advanced pressure management consists of time and flow related pressure control valves housed in a single large above-and-below ground chamber together with other appurtenant plant and equipment.

In this instance, the equipment is comprised of five horizontal pressure control valves in parallel to the two co-located and parallel bulk water supply pipelines supplying the greater Sebokeng/Evaton area with water. 2.3.1.2 Hong Kong, China

Retrofitting plumbing appurtenance with water saving devices in government buildings and schools. (C40 cities, 2007b) The Hong Kong Special Administrative Region (HKSAR) Government is implementing Total Water Management (TWM) for the sustainable use of water resources. The TWM program aims to manage the demand and supply in an integrated, multi-sectoral and

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sustainable manner. One of the key demand management initiatives of the TWM program is the promotion of the use of water saving devices. The Government will take a lead to install water-saving devices in its buildings as far as practicable. In this connection, the Water Supplies Department of the HKSAR Government is implementing minor works projects for retrofitting plumbing appurtenance with water saving devices in Government buildings and schools. The water saving devices use less water than conventional plumbing fixtures. The retrofitting of water saving devices in the Government buildings and schools will reduce the water consumption in these premises for water conservation. It is estimated that about 2 million m3 of fresh water and 0.8 million m3 of seawater used for flushing could be saved annually upon the completion of the projects. It will also reduce the sewage discharge and the energy consumption for treatment and delivery of the water. 2.3.1.3 Australia

Rainwater tanks in multi-unit buildings: A case study for three Australian cities (Eroksuz and Rahman, 2010) Rainwater tanks have become popular in large Australian cities due to water shortage and greater public awareness towards sustainable urban development. Rainwater harvesting in multi-unit building in Australia is less common. The study investigates the water saving potential of rainwater tanks fitted in multi-unit residential buildings, in three cities in Australia: Sydney, Newcastle and Wollongong. It is found that for multi-unit buildings, a larger tank size is more appropriate to maximize water savings. It is also found that rainwater tank of appropriate size in a multi-unit building can provide significant mains water savings even in dry years. A prediction equation is developed which can be used to estimate average annual water savings from having a rainwater tank in a multi-unit building in these three Australian cities. 2.3.2 Solid Waste 2.3.2.1 AIT campus Application of 3R principals to solid waste management on the Asian Institute of Technology (AIT) campus. (Dev, 2007) The solid waste audit conducted in AIT showed that 700 tons/year of solid waste was generated and the per capita of solid waste was about 0.5 kg/day. The percentage composition of organic and inorganic solid waste was 60 % and 40% respectively. The audit study also showed that 93% of the solid waste in AIT is disposed in the landfill, 4% of solid waste is recycled and 3% of solid waste is gardening waste that is composted inside the AIT campus. The chemical analysis of the solid waste at AIT showed that the carbon and nitrogen content of the waste were very high, but due to the low C/N ratio, the solid waste cannot be composted and used in the production of manure. The moisture content of the wastes was also as high as 68% that shows a high rate of organic degradation of solid waste at AIT. Thus, the calorific value of the solid waste at AIT indicated that the solid waste was suitable for use as RDF. The study of the formal and

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informal sectors in the Tha Kong Municipality showed that the amount of solid waste recycled was 44% and the amount of solid waste disposed in the landfill was 56%. The BCA showed that there was no benefit in the recycling activities in AIT. The physical analysis of the solid waste showed that 25.1% of the total solid generated was plastic, which showed that there is a lack of awareness in solid waste management at AIT. Hence, awareness programs in solid waste management are necessary at AIT. 2.3.2.2. Dhaka, Bangladesh Organic Waste is Composted and Sold as Bio-rich Fertilizer for Reducing Emissions, Generating Jobs and Cleaning up the City. (C40 cities, 1990) Waste Concern is a non-profit organization established in 1995 that has had major successes in reducing emissions in several cities around Bangladesh, Sri Lanka and Vietnam by composting solid waste instead of burning or flaring, and selling it to fertilizer companies. So far, Waste Concern’s model of managing waste has reduced more than 18,000 tons of CO2 emissions each year in Bangladesh and generated 414 new jobs for the urban poor. It is helping to reduce the 52% of generated solid waste that remains uncollected in Dhaka. The Waste Concern organization works with municipal governments to use solid waste as a resource by composting waste in 5 community-based composting plants (One 10-12 tons/day capacity plant, Two 3 tons/day capacity plant and Two 1 ton/day capacity plant) rather than burning or flaring it and then selling it to fertilizer companies. To scale-up its model, Waste Concern as a Social Business Enterprise partnered with a for profit private Dutch company using CO2 emissions trading of CDM (Clean Development Mechanism). Moreover composting all organic waste in Dhaka would create new jobs for 16,000 people from lower socio-economic background, especially women. 2.3.2.3 Nakhon Si Thammarat, Thailand Tungsong’s Organic Fertilizer & Liquid Detergent Production (Local Governments for Sustainability, 2004) Organic wastes from the fresh markets are collected from the stall owners, group, mixed with molasses and then composted for 10-20 days. The end products are 3,000 liters of liquid detergent and 1000 kilograms of ground fertilizer per month. The detergent, used to clean the market floor, is worth US$25 cents per liter, whereas the ground fertilizer is worth US$10 cents per kilograms. The overall process of Tungsong’s Organic Fertilizer Production is shown in Figure 2.4 Step 1: Collection of Step 2: Shredding Step 3: Composting Biodegradable waste

Figure 2.2 The 3-step Process of Tungsong’s Organic Fertilizer Production

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The project started in 2001. Annual operational expenses is pegged at US$2,250 for the salary of the workers and for the purchase of molasses used to hasten waste decomposition. Carbon offsets from the organic fertilizer production is pegged at 29 tons of equivalent Carbon dioxide. The project brought not only financial rewards but other benefits as well. First, it has reduced a substantial volume of waste that would have been directly dumped to the landfill. Second, it has improved waste management in the markets. Third, it has reduced expenses for market building maintenance because the detergent is derived at minimal cost. Forth, the education of households which lead to waste minimization protects the soil from pollution. 2.3.2.4 Copenhagen, Denmark Copenhagen's waste plan 2008: Copenhagen puts only 3% of waste into landfill (C40 cities, 1990) Copenhagen has taken an innovative and adaptive approach to waste management. Its motto is less waste, more separation. The system works because it's flexible taking into consideration the differing needs and habits of every citizen and business around the clock. For example, people can return paint waste to the paint shop or medicine waste to the pharmacy. Waste drop off points are local and have flexible hours of operation. The system has reduced CO2 emissions by 40,000 tons CO2 and generated 1,000,000 mWh of additional energy enough to power 70,000 homes annually by turning waste to energy. Copenhagen's "Waste Plan 2008" is a detailed plan to reduce waste and improve management over the period of 2005-2008. The Plan is revised every four years and covers a twelve-year period to ensure it delivers long-term solutions. It is very flexible and is constantly adapting to input from businesses and the community. The City has five objectives for waste management: 1. There must be less waste and less hazardous substances in the waste. 2. Waste resources must be better utilized, reducing the amount of waste to be

incinerated and put in landfill 3. As much of the environment must be protected as possible for the money 4. The waste system must be adapted to the city 5. The waste system must be logical and well known 2.3.3 Transportation 2.3.3.1 Copenhagen, Denmark Copenhagen: The world's best city for cyclists (City of Copenhagen, 2006) Every day, 55% of all Copenhageners cycle to and from work, jointly pedaling more than 1.17 million kilometers a day. Bike lanes, cycle parking and special traffic lights for cyclists are part and parcel of the Copenhagen cityscape. The infrastructure has been meticulously planned to show consideration for more than 150,000 citizens for whom the bicycle is their chosen form of everyday transport. The city has some 340 km of cycle lanes and the vast majority of major roads has cycle lanes in both directions, either as separate tracks or delineated by markings at road level.

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On selected stretches through the city, between main residential areas and the centre so-called 'green waves' have been implemented. Series of traffic lights are timed to allow cyclists to ride the entire stretch without stopping at a red light if they maintain speed of 20 km/h. The 'wave' functions on the way into the city in the mornings and on the way out at the end of the working day. At traffic lights, cars stop 5 m behind the cyclists' stop line and the cyclists have their own miniature set of traffic lights that give them priority over motor vehicles. Safety measures such as 'cycle pockets' at traffic lights are currently undergoing trials. The 'pockets' make space for cyclists to stop in front of the cars at red lights. This makes the cyclists more visible, especially to lorries, and accidents occurring when vehicles turn right are avoided. Bicycle parking problems have been solved by the installation of bike stands throughout the city; on streets, in public parking lots and private underground car parks or sheds at most housing complexes. Shopkeepers are also making life easier for cyclists by placing bike stands in front of their shops. Despite the change of the seasons it is possible to cycle all year round in Copenhagen. When it snows, the council clears the snow off the cycle lanes before starting on the roadways and 70% of cyclists keep on cycling. If the weather does become too much of a challenge it is also possible to take your bike with you on the train or underground.

It is Copenhagen City Council’s vision to be hailed the world's best city for cyclists in 2015 and they are striving continuously to improve conditions for cyclists. Growth in the use of bicycles in Copenhagen has increased the need for more, wider, safer cycle lanes. New cycle lanes can accommodate 15-20% more bicycles and reduce the number of cars in the cityscape by 10%. Proposals have also been presented for the imposition of a congestion charge on motor vehicles to reduce the number of cars in the city centre, increasing the use of cycles and public transport. The citizens of Copenhagen are neither cycling fanatics nor environment activists - they simply use a bicycle as a means of transport because two wheels get them quickly and safely from A to B.

2.3.3.2 Bogotá, Colombia

BRT system reduced traveling time 32%, reduced gas emissions 40% and reduced accidents 90%. (C40 cities, 2007a) The Bogotá Transmilenio system has attained a very high productivity level averaging 1,600 passengers per day per bus, reducing traveling time by 32%, eliminating 2,109 public-service vehicles, reducing gas emissions by 40%, and making zones around the trunk roads safer thus decreasing accident rates by 90% throughout the system. Transmilenio is a rapid bus transit system throughout the city of Bogotá, which consists of 850 buses and has a demand of 1,400,000 passengers per day.

Mayor Penalosa created a team, separate and external to his own administration, to oversee the implementation of a new transport system. The Municipality created the company Transmilenio S.A. to plan, organize, and construct the transportation infrastructure, as well as to supervise the bus service.

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System Operations:

• Transmilenio is responsible for all areas of infrastructure, such as segregated lanes, stations, terminals and their upkeep, along with all areas of finance.

• Transmilenio oversees all Finances, as Transmilenio pays each operator according to each specific contract.

• Buses (including drivers) are contracted through private firms/operators. • There are over 15 operators in areas of buses, fare collecting, maintenance,

communications, etc. Bus Network Infrastructure:

• The System operates 18 hours every day. • Dedicated lanes, large capacity buses and elevated bus stations that allow pre-board

ticketing and fast boarding. • Smaller units offering feeder services to main stations are integrated into the

system. • A centralized coordinated fleet control providing monitoring and communications

to schedule services and real-time response to contingencies.

2.3.3.3 Chaing Mai, Thailand Fuel-Substitution for Diesel-Fed Songteaws (Local Governments for Sustainability, 2004)

According to Chaing Mai’s Greenhouse Gas Emissions Inventory in 2002, the city’s transportation sector accounted for 106,978 tons of CO2 emissions. Majority of emissions, pegged at 27,169 tons CO2 a year, come from songteaws. Once completed, the shifts to alternative fuel by substituting diesel with 20% bio-fuel blend derived from used vegetable oil of 2,710 songteaws will reduce 724 of CO2 a year. The recently launched demonstration project on bio-fuel use for 1,000 songteaws is made possible with the assistance of the Ministry of Energy and the National Pollution Control Department. The key objectives are to demonstrate the use of bio-fuel mixed with regular diesel, to construct small-scale bio-fuel plants at the community level, and to promote the use of bio-fuel to mitigate harmful environmental and health impacts caused by pollutant emissions. The benefits from the projects can be as follow: First, air pollutions like Nitrogen oxides would be reduced by 10%. And bio-diesel emits less particulate matter. Second, hazardous health impacts are reduced due to abatement in air pollution.

Figure 2.3 Songteaw: Chiang Mai’s Figure 2.4 Bio-Diesel Refueling Public Transit Station

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2.3.4 Building (Lighting)

2.3.4.1 Vietnam One of the biggest lighting retrofit programs benefiting an entire country, Citywide of Vietnam (C40 cities, 2005b) In 2000, the United Nations Development Program approved the Vietnam Energy Efficiency Public Lighting Project (VEEPL) – an ambitious and comprehensive $15 m USD plan to install and promote the use of energy efficient lighting in streets, schools, and hospitals across the country. The investment has succeeded in reducing CO2 emissions by 8,300 tons CO2 annually in the first three regions – this will grow to 100,000 tons as the program rolls out nationally. Importantly VEEPL is setting up a sustainable long-term lighting industry that is generating local manufacturing, services, with jobs and expertise. Public lighting in Vietnam has been characterized by low efficiency light sources housed within poorly designed luminaries and installed in inappropriate locations. For example, 85% of street lighting in Vietnam is provided by obsolete technology – either mercury or incandescent lamps) and installed without benefit of proper planning or engineering analysis. In addition, Vietnam’s existing market forces have not resulted in a high penetration of energy-efficient public lighting. VEEPL is working with the Government of Vietnam and local governments to develop public lighting policies that support the market forces to bring cities and towns to more efficient public lighting. 2.3.4.2 Rayong, Thailand Energy Switch and Lamp Retrofits for Traffic Signals (Local Governments for Sustainability, 2004) In March 2004, the municipal government of Muangklang completed the retrofitting of three signals using photovoltaic sources and replacing incandescent lamps with light emitting diodes (LED). The technology was easily accessible and cost the municipal government 20,400 Baht (US$821) for the project. Since then, studies have been conducted to expand this initiative city-wide. If all the traffic signals are covered, the city could potentially generate an annual savings of up to US$ 821 and reduce emissions by as much as 7 tons. Energy savings would accrue to the Province Electric Authority which pays 10% of the city’s energy bills, or accrue to the city’s energy allowance to finance the other energy needs of the city. Muangklang is the sixth and most recent local government in Thailand that joined the Cities for Climate Protection Campaign. In 2000, the corporate sector emitted 815 tons of Carbon dioxide and is projected to release 1,436 tons of Carbon dioxide in 2010. Energy use of streetlights and traffic signals account for 33% of these emissions. Muangklang aims to avoid Carbon dioxide emissions by 20% from its 2010 projected emissions (equivalent to 123 tons of e CO2).

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2.3.4.3 AIT Campus Implementation of Electricity Saving Strategies in Academic Building to Mitigate Greenhouse Gas Emission (Kosonkarn, 2008) Asian Institute of Technology (AIT) is an academic institute, which is working as a leader to promote energy efficiency and conservation to address and mitigate global warming. AIT consumes about 13(on an average from 2003 to 2007)GWh/year of electricity, which leads to bout 9,300 tons/year of CO2 emission at power generation. Energy building, one of academic buildings in AIT consumes about 120 MWh of electricity per year, and generates about 86.4 tons of CO2. Among the direct electricity consumption, lighting is the largest consumer, about 38 and 27 percent during weekday and weekend, respectively. Therefore, technical measures were focused on lighting system. The technical measures to reduces electricity consumption are the replacement of reflector and reduce lights, the replacement of high efficient linear fluorescent lamps and electronic ballasts in offices and classroom, the replacement of linear fluorescent lamps by compact fluorescent lamps at corridor and toilets, the reduction of occupancy sensor & LED at toilet, the introduction of individual pull switches and the rearrangement of lighting switches. The overall saving is about 23.5 MWh/year (22% of overall electricity consumption) or 78,800 Baht/year. This reduction can mitigate CO2 emission about 16.9 tons/year. 2.3.4.4. AIT Campus Introduction of pull switches Kosonkarn(2008) has found that some lamps in Energy building at Asian Institute of Technology(AIT) are rarely needed but are always turned on. Moreover, in some cases when only few occupants are in the room many lamps are turned on. These lead to extravagant use of electricity consumption. Individual pull switches can be installed to reduce unnecessary electricity consumption. One more additional benefit from pull switches installation is that the life span of lights and ballasts can be extended. Because the life span of light and ballast depends on the used time. To summarize, best practices are good example for GHG reduction plans however guidelines for selecting appropriate plans need to be concerned. 2.3.5 Green Campus at Harvard The FAS Campus Sustainability Report (2009) by Harvard University indicated that the greenhouse gas reduction program launched in 2008 provided a framework for many of the school’s initiatives in Harvard University. This strong energy reduction focus is best reflected in the significant 16% reduction in GHG emissions from 2006-2009. 85% of buildings now have a green cleaning program and many have seen impressive reductions in their water usage due to the installation of flow fixtures and irrigation system upgrades. Other initiatives worth noting include organic landscaping in the yard, sustainable dining in houses and dorms, increased usage of alternative transportation, and the largest number of LEED certified and registered buildings of any school at Harvard.

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2.3.5 .1 Success in Reduction of GHG Emission at Harvard Waste Recycling Harvard’s recycling rate increased from 32% to 49% in 4 years which made the amount of solid waste generated reduced from 7,209 tons in 2006 to 6,221 tons in 2009. Energy About 3 million square feet of buildings have been audited to identify GHG reduction opportunities. The estimated GHG potential of identified measured is about 6,000 t CO2 e. The overall energy intensity at Harvard had reduced by 8% in 4 years. Food Waste For food waste in dinning halls, students have reduced their food waste by 27%. This was resulted from 2.61 oz. of food waste per person in 2006 to 1.88 in 2009. University has about 25% organic compostable material in total waste, which was found to be an opportunity to expand the accessibility of compost bins throughout campus. They have installed one bin in every building to collect food waste. Organic waste is taken to Brick Ends Farm in Hamilton, MA, where it is turned into a valuable soil amendment. Water Consumption At Harvard, a dual flush pilot in residential buildings has been installed. With its low cost, a quick pay back and students gave great feedback, water efficient fixtures are now part of every renovation project and buildings. For the bathroom upgrades in buildings over campus, water consumption can be reduced by about 30%. The overall water intensity has been reduced from 15% at Harvard. 2.4 Concerns for choosing scenario In order to achieve the significant reduction of GHG emission, not only roles of scientists and engineers in creating technology is important, there are also many factors that should be concerned as follows: 2.4.1 Changes in lifestyle and behavior patterns Changes in lifestyle and behavior patterns can contribute to climate change mitigation across all sectors. Management practices can also have a positive role (IPCC, 2007).

• Lifestyle changes can reduce GHG emissions. Changes in lifestyles and

consumption patterns that emphasize resource conservation can contribute to developing a low-carbon economy that is both equitable and sustainable.

• Education and training programs can help overcome barriers to the market acceptance of energy efficiency, particularly in combination with other measures.

• Changes in occupant behavior, cultural patterns and consumer choice and use of technologies can result in considerable reduction in CO2 emissions related to energy use in buildings.

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• Transport Demand Management, which includes urban planning that can reduce the demand for travel and provision of information and educational techniques that can reduce car usage and lead to an efficient driving style can support GHG mitigation.

• In industry, management tools that include staff training, reward systems, regular feedback, documentation of existing practices can help overcome industrial organization barriers, reduce energy use, and GHG emissions.

To start with climate change mitigation, changing in patterns of behavior of individual is very significant. No other technology or innovation will be useful if people do not change their way of generating GHG emissions. 2.4.2 National policies and instruments A wide variety of national policies and instruments are available to governments to create the incentives for mitigation action. Their applicability depends on national circumstances and an understanding of their interactions, but experience from implementation in various countries and sectors shows there are advantages and disadvantages for any given instrument. • Four main criteria are used to evaluate policies and instruments: environmental

effectiveness, cost effectiveness, distributional effects, including equity, and institutional feasibility.

• All instruments can be designed well or poorly and be stringent or lax. In addition, monitoring to improve implementation is an important issue for all instruments. General findings about the performance of policies are: 1) Integrating climate policies in broader development policies makes implementation

and overcoming barriers easier. 2) Regulations and standards generally provide some certainty about emission levels.

They may be preferable to other barriers prevent producers and consumers from responding to price signals. However, they may not induce innovations and more advanced technologies.

3) Taxes and charges can set a price for carbon, but cannot guarantee a particular level of emissions. Literature identifies taxes as an efficient way of internalizing costs of GHG emissions.

4) Tradable permits will establish a carbon price. The volume of allowed emissions determines their environmental effectiveness, while the allocation of permits has distributional consequences. Fluctuation in the price of carbon makes it difficult to estimate to total cost of complying with emission permits.

5) Financial incentives (subsidies and tax credits) are frequently used by governments to stimulate the development and diffusion of new technologies. While economic costs are generally higher than for the instruments listed above, they are often critical to overcome barriers.

6) Voluntary agreements between industry and governments are politically attractive, raise awareness among stakeholders and have played a role in the evaluation of many national policies. The majority of agreements have` not achieved significant emissions reductions beyond business as usual. However, some recent agreement, in a few countries, have accelerated the application of best available technology and led to measurable emission reductions.

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7) Information instruments (e.g. awareness campaigns) may positively affect environmental quality by promoting informed choices and possibly contributing to behavioral change, however their impact on emissions has not been measured yet.

• Some corporations, local and regional authorities, NGOs and civil groups are adopting a wide variety of voluntary actions, stimulate innovative policies and encourage the deployment of new technologies. On their own, they generally have limited impact on the national or regional level emissions.

2.5 Bilan Carbone Model The model will be used to calculate greenhouse gases emissions in study areas and also will be used to estimate greenhouse gases emissions from each scenario for reduction of greenhouse gases emissions. The “Bilan Carbone Collectivités - Territoires” builds on a GHG inventory tool which the French environmental agency “Agence de l’environnement et de la maîtrise de l’énergie” (ADEME) developed for companies. The first version of the tool was tested in 2005 in 15 municipal and territorial authorities. The results of this testing phase contributed to the improvement of the tool in view of its dissemination by ADEME. The current tool – version 6.0 - has been available since June 2009. The tool can in principle be applied to any entity provided that the required data are available. The tool can be used for measuring emissions of the local authority (module “patrimoine & services”) and/or for measuring the emissions of all GHG emitting activities of the territory (module “territoire”). The “Bilan Carbone” is mainly geared towards the needs of French cities. However, some of the French cities are located in overseas territories and thus in different climatic zones than the cities of mainland France. Specific emission factors have therefore been calculated for these territories (DOM-TOM). Furthermore, ADEME has launched a corperation with the United Nations Development Program (UNDP) in view of compiling carbon inventories in emerging and developing countries (Bader and Bleischwitz, 2009). The “Bilan Carbone® Collectivités - Territoires” measures all the six Kyoto GHG. In addition, it also takes account of other directly emitted GHG such as chlorofluorocarbons (CFC) or the water vapor emitted by planes in the stratosphere. The tool can take inventory of principally any gas that has an impact on the global climate provided that sufficient scientific knowledge of its global warming potential exists. A limitation of the inventory to the Kyoto gases only is possible. The tool takes account of manifold possible emissions sources. In fact, many inventories do not take account of international aircraft or maritime emissions because they cannot be assigned to a state or territory. As the “Bilan Carbone” is based on the principle that any emission that can be assigned to a specific activity must be taken into account, it also takes inventory of indirect emissions like those of international aircraft and maritime transport that are related to the activities of a local territory. The tool does not take inventory of GHG emissions from biomass since its developers assumed that it will mainly be used in the industrialized world where deforestation is balanced by afforestation. Furthermore, the tool (module “territoire”) does not take account of materials that enter the territory due to the lack of data that correctly reflect this flux for a given territory. The Bilan Carbone calculates with “carbon equivalents”; final results can, however, also be displayed in “CO2 equivalents”. The values for the global warming potential of gases

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were initially derived from the third assessment report of 2001. Since the publication of the fourth assessment report in 2007 the values have been updated. The tool is pre-loaded with emissions factors for French cities and territorial authorities. However, for the French overseas territories (DOM-TOM) specific emissions factors partly apply. Given that these territories are located in different climatic zones than mainland France (higher average temperature), the energy consumption of their buildings sector differs. Thus specific emissions factors had to be calculated. The tool can also be used for reporting emissions according to existing standards. Extractions can be made for the EU Emission Trading Directive as well as for the ISO 14064 guidelines for corporate emissions reporting. The results for the latter can be broken down into three scopes. The three ISO scopes are almost identical with those recommended by the GHG Protocol. Once a GHG inventory has been created, the local authority can use the tool to investigate what combinations of emission reductions in different sectors yield a total reduction of X. The use of the tool is principally for free. Yet, cities are required to attend a two day training (cost of training: €1.250) after which they get access to the tool in its standard version. The version for cities and territories requires also that future users attend a second specialized training (€500) after which they receive the tool free of charge. In addition, ADEME trains experts who may support cities and territories when they use the tool. Thus, there is a wide network of experts available in France which may be used by cities when they need external help for carrying out the inventory. The costs of such a service can amount to up to €30.000 for the compilation of an inventory representing the GHG emitting activities of a whole territory. The idea behind the compulsory trainings is to explain the tool and the underlying philosophy to city officials. The “Bilan Carbone” does not only aim at creating an inventory but at activating cities and territories and to make them understand what measures are needed to reduce emissions. The trainings are therefore a cornerstone of the “Bilan Carbone”. In addition, ADEME tries to be as transparent as possible and discloses all the information related to the calculations of emission factors and the methodology in general. Following the rationale of reaching out to the public and disseminating the use of the Bilan Carbone ADEME subsidies also the trainings of experts who may provide support for cities. 2.5.1 Strengths The “Bilan Carbone” can be used for calculating almost all GHG emissions of a territory and allows for compiling detailed inventories. Detailed reports describe how the emission factors were calculated and give a thorough introduction to the methodology and use of the tool. One of the main strengths of the “Bilan Carbone” is therefore its transparency. Another great strengths of the “Bilan Carbone” are the manifold services that ADEME provides: • Users of the tool must attend training seminars where experts give an introduction into

the methodology, the use and limits of the tool.

• City officials can cooperate with external experts who have been trained by ADEME. The costs for these external experts, however, may be too high for some cities. Therefore ADEME provides funding which covers up to 50% of the costs of the external expertise whereby the total maximum amount of funding is of € 15.000.

• To ensure that services provided by an external expert are of good quality and

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comprehensive, ADEME has published a performance requirements template. It details the different steps of GHG accounting and the services that the external expert should provide.

• A rich documentation on the tool and its underlying methodology is provided.

2.5.2 Overall Principal of Bilan Carbone According to ADEME (2009), the Carbone Balance methodology enables users to evaluate the greenhouse gas emissions resulting from all the necessary physical processes required for the existence of a human activity or organization. By necessary physical process that the entity would not exist in its resent day form, or in its present day outline, if the physical process in question was not possible. One of the method’s fundamental points consists of placing on an equal footing: • Greenhouse gas emissions which occur directly within the entity (which, in a certain

manner, are its legal or immediate territorial responsibility). • Emissions which occur outside the entity, but which are the offset for the processes

necessary for the existence of the activity or the organization in its present day form. Therefore the emissions that are included in a GHG emissions assessment are not only those that the entity either is or feels responsible for, but are above all those that it is dependent on. One of the consequences of using the method to take account of direct and indirect emissions is indifference to the location if the greenhouse gas emissions analyzed. This choice, which is dictated by the benefits from an overall assessment of the emissions that an activity is dependent on, is also consistent with physical considerations. 2.6 Carbon footprint

Wiedmann and Minx (2007) proposed that the carbon footprint is a measure of the exclusive total amount of carbon dioxide emissions that is directly and indirectly caused by an activity or is accumulated over the life stages of a product. This includes activities of individuals, populations, governments, companies, organizations, processes, industry sectors etc. Products include goods and services. In any case, all direct (on-site, internal) and indirect emissions (off-site, external, embodied, upstream and downstream) need to be taken into account. Reffold et al., (2008) has given the definition of Carbon Footprint that a carbon footprint is a measure of the impact that human activities have on the environment in terms of the amount of greenhouse gases (GHG) emitted over the full life cycle of a process or product measured in units of carbon dioxide (CO2). Non-carbon GHG (e.g. methane) are converted to CO2-equivalent (CO2e). In conclusion, Carbon footprint is the amount of greenhouse gasses generated to the atmosphere due to human activities. This takes into account of the amount of CO2 emitted either directly or indirectly as a result of its everyday operations from its premises, company-owned vehicles, business travel and waste to landfill etc. In order to have effective policies for GHG reductions in particular areas, it is important to study about the Carbon footprint in its premises.

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Chapter 3

Methodology

3.1 General This chapter describes in depth of the overall methodology in achieving objectives of the study proposed in chapter 1. The concept of this study was to estimate the current situation of GHG emissions on AIT campus using the Bilan Carbone® tool. Comparison of GHG reduction scenarios was observed and suitable reduction policies to be implemented on AIT campus were identified. 3.2 Overall framework of the study The overall framework of the study is described in the flow chart below, see figure 3.1.

Figure 3.1 Overall framework of the study

Phase I Data Collection for

• Primary Data • Secondary

Data

Phase II Calculation of GHG emission using

Bilan Carbone®

Phase III Best Practices

Scenarios

Primary Data

Secondary Data

Scenarios

GHG Emission Phase IV

• Advantages • Disadvantages

Suitable Scenarios Scenarios

GHG Emission Estimation

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The methodology of this study was divided into 4 phases. Phase 1: Data Collection, in this phase, secondary data was obtained from all the possible sources according to input data required in the Bilan Carbone® spreadsheet. In order to fulfill with the spreadsheet requirements, not only secondary data but also primary data was collected when necessary. In phase 2 for Data Analysis, the Bilan Carbone® was the main tool to calculate for GHG emissions on AIT campus using collected data from Phase 1. Best Practices for GHG reductions have been studied and chosen to be used as GHG reduction scenarios in Phase 3. Moreover, GHG emissions and percentage of reduction were also compared in this phase. Finally, implementation of analysis on advantages and disadvatages was used in phase 4 in order to sort out the suitable reduction policies. 3.3 Detailed Methodology This study was carried out to measure greenhouse gases emissions from the area of AIT campus. With strength of the Bilan Carbone® tool, all possible human activities lead to GHG emissions were taken into account so that the overall GHG emission was very close to reality. However, the assumption and estimation needed to be done when the required input data did not exist. The baseline year for calculating GHG emission on AIT campus was 2009. 3.3.1 Defining the boundary of the study In order to have an effective data collection process, a clear determination of emission sources was necessary. Based on literature review, the emission sources of greenhouse gases can be classified into two categories as follow; Direct Emission Source: is the source of emission from activities which are owned and controlled by AIT. For example, emissions from fuel consumption by vehicles and machines owned by AIT, refrigerant leakage and LPG combustion for cooking activities on campus. Indirect Emission Source: is the source of emission from activities that are a consequence of another entity not directly link with AIT. For example, travel by planes, materials used for offices, electricity supply and waste generation. List of GHG emission sources from AIT’s activities were classified as in table 3.1. Table 3.1 List of GHG emission sources in AIT campus

Direct Emission Source Indirect Emission Source

Fuel consumption by AIT’s vehicles and machines

Travel by planes

Refrigerant leakage Materials used Combustion from cooking Food consumption AIT’s property Waste generations Use of fertilizer

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3.3.2 Description of methodology In this study, the emission sources of GHG have been classified into seven categories according to the Bilan Carbone® spreadsheet namely: 1. Energy, 2. Excluding Energy, 3. Materials and Products Purchased (Inputs), 4.Transportation of Goods (Freight), 5. Transportation of People (Travel), 6. Solid Waste and Wastewater (Direct Waste) and 7. Property. For the calculation of GHG emissions in this study, the Bilan Carbone® spreadsheet was used as the main tool. The basic calculation method is by multiplying activity data with the emission factors. The Exel spreadsheet automatically dose the calculation and reports the results in numerical data and bar charts. Moreover, there are many types of input data to the spreadsheet for users to choose relevant to their available information. For example, fuel consumption, the input data can be liters/year or tons/year. However, emission factors for specific country which is Thailand in this case, have been used. In case of unavailable emission factors for some activities, the provided emission factors in the spreadsheet were used. The Bilan Carbone® spreadsheet does not pay much attention on water and wastewater sector by including this part in the Direct Waste tab and focuses only on GHG emission from wastewater. But, in fact, water typically requires treatment prior to use and after it becomes wastewater before released to the environment. It is pumped and pressurized to reach residential areas. All of these activities require energy and therefore result in greenhouse gas emissions. So that this study has produced a separate spreadsheet for specific use to calculate GHG emission from water and wastewater by considering its whole circle from production to wastewater generation. As mentioned above, the overall methodology was divided into four phases as follows: 3.3.2.1 Phase 1: Data Collection Most of the data information in this study was collected from Sodexo, previous research studies, AIT purchasing office, ERCO office and all other possible sources of information. The data information was collected through interviews, literature reviews, as well as surveys and questionnaire when primary data was needed. It is important to note that units for data information that have been gathered might not be suitable for input to the spreadsheet so that prior conversion of units need to be done. Otherwise, mistakenly input of units can cause a huge difference in GHG emission results. The estimation methods for each category of GHG emission sources are described as follow: 1. Energy In this category of GHG emission sources, two main activities were taken into account: Electricity consumption and Direct Consumption of Fuels in AIT campus. Calculation and Assumption method: Electricity Consumption • Electricity Consumption: AIT is supplied by Provincial Electricity Authority (PEA),

Thailand. • Data information for Electricity Consumption in 2009 was given by Sodexo. • Electricity Consumption covered all activities on campus that included energy use in

academic buildings, administration buildings, residential areas, recreation and leisure areas as well as streetlights.

• Emission factor for specific country (Thailand) was used accordingly.

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Calculation and Assumption method: Direct Consumption of Fuels • This took into account for all types of fuels that have been purchased for the year of

2009 within the entity. The table 3.2 below shows the detail on activities in which consumed particular type of fuels.

Table 3.2 Detail on activities in which consumed particular type of fuels

Type of fuels Activities Units Required

Diesel oil Operation of electricity generator and Sodexo’s machines

Liters/year

Petro Operation of Sodexo’s machines Liters/year LPG (Liquefied Petroleum

Gas) Cooking in restaurants and residential areas

Tons/year

Fuel oil For boiler in AITCC Liters/year Cooking Coal Cooking in snack bar restaurant Tons/year

Note: For operations of Sodexo’s machines, basically, it is from gardening activities. For example, grass cutter, leaves blowers and trucks for collecting yard waste. 2. Excluding Energy In this category of GHG emission sources, emissions from activities or agricultural processes were taken into account except for those resulting from energy use. Calculation and assumption method • AIT had activities that required the use of fertilizer and refill of refrigerant leakage. • Fertilizer was used in gardening maintenance and research works. Data information was

collected from Agricultural Systems and Engineering (ASE) and Aquaculture and Aquatic Resources Management (AARM) fields of study.

• This study takes CFCs into account, since these are also greenhouse gases. Even though they are not in the Kyoto protocol, their emissions are considered in the summaries of the result. CFCs are basically used in cooling process of air-conditioning systems and refrigerators.

• The data information required from air-conditioning systems and refrigerators were capacity loads, type of refrigerants and cold water network.

3. Materials and Products Purchased (Inputs) In this category of GHG emission sources, covers all the materials used by activities in AIT campus for one year period. Calculation and assumption method • Consumption of Metals, Plastics and Glass was estimated from repair and maintenance

activities. Data information was collected from AIT purchasing office and Sodexo. • Consumption of paper, since AIT is an educational institution, huge amount of paper is

consumed everyday. In this study, the assumption of paper consumption was done by assuming that paper waste in 2009 would be 30% of total paper consumption.

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• Consumption of Thailand’s typical meals, data information was estimated by number of faculty, staff, students, visitors and family members regarding to whether they stay on or off campus. If one person stays on campus, number of meals would be two(2). When number of meal would be one(1) for person stays off campus.

4. Transportation of Goods (Freights) In this category of GHG emission sources, covers transportation of road transport within AIT campus (Internal Road Freights), road transport of materials going out from AIT to outside (Outgoing Road Freights) and road transport of materials and goods coming to AIT from outside (Incoming Road Freights). Calculation and assumption method • Data information for this part was basically from surveys and interviews. • For Internal Road Freights, data was given by Sodexo since all internal vehicles are

owned by AIT. • For Outgoing and Incoming Road Freights, data was gathered from restaurants, post

office, grocery shops and AIT book store. • Required data information included type of fuel, amount of fuel consumption (liters or

tons) or kilometers travelled. • Detail on activities for each type of road freight is described in table below: Table 3.3 Detail on activities for each type of road freight

Type of Road Transport Activities

Internal Road Freight - AIT’s trucks for yard waste collection - AIT’s vehicles for construction

Incoming Road Freight - Delivery of goods, such as food, cooking gas, drinking water, ice and vegetables to shops and restaurants within AIT campus

Outgoing Road Freight - Transportation of solid waste from AIT to landfill. - Transportation of outgoing mails.

5. Transportation of people (Travel) In this category of GHG emission sources, three main types of transportations of people were taken into account namely: 1. Transportation from home to work (AIT), 2. Business travel in context of work and 3. Travel of AIT’s visitors. Calculation and assumption method • For transportation from home to work (AIT), this considers all types of travelling by

AIT individuals (faculty, staff and students) for coming to work and study in AIT and going back home from AIT. Required data information were type of vehicles, type of fuel consumption, amount of fuel consumption and distance travelled as well as capacity of vehicles which were obtained by surveys, interviews and previous research studies.

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• Business travel in context of work, in this segment takes into consideration for transportation of AIT people (faculty, staff and students) in the framework of their professional activities for example conferences, field trips and etc. The spreadsheet takes into account of all types of transportation when AIT only uses cars, vans and air planes in this category. Data information was collected from Sodexo who owns road vehicles and AIT’s offices for registered travel by AIT individuals.

• Travel of AIT’s visitors, this segment takes into consideration for only registered visitors that could get the information from ERCO office. Visitors to AIT for pleasure purpose were not considered for example visiting of students’ family and friends.

6. Solid Waste and Wastewater (Direct Waste) In this category of GHG emission sources, there are three types of waste that was taken into consideration namely: non-hazardous waste, hazardous waste and sewage (wastewater). Calculation and assumption method • For non-hazardous waste, there are two ways in which it can lead to greenhouse gas

emissions: 1. The fermentation of organic waste placed either in a landfill or in a biological

treatment system. 2. The combustion of plastics produces CO2 emissions since plastic is made from petro

or gas. • Data information for non-hazardous waste in AIT was collected from Sodexo who is

responsible for collecting solid waste from all garbage bins everyday in AIT campus everyday. Data required for this segment such as; generated amount in one year of metals, glass, plastics, paper and food waste as well as yard waste.

• Moreover, type of solid waste management also needed to be taken into consideration whether it is landfill or incineration or else. Recyclable waste for each type of material is considered to emit some amount of greenhouse gas emissions so that amount of recycled waste was also collected.

• For hazardous waste, it has been used and generated from laboratories in some fields of study in AIT in which the assumption was made based on hazardous waste generation from EEM (Environmental Engineering and Management) laboratory.

• Data information required for wastewater segment was total BOD in one year in which the components were amount of waster consumption in one year (data gathered from Sodexo) and BOD value (data gathered from AIT Eco-Campus).

The flow chart below shows detail on data collection for Direct Waste.

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Figure 3.2: Details on data collection for Direct Waste in AIT

7. Property In this category of GHG emission sources, covers investments in long-live products and property (assets) in the entity which are subject to depreciation in accounting. The manufacture of those products and property can lead to greenhouse gas emission. Products and property that are taken into account in this category were Buildings, Road and Parking Areas, Vehicles, Machines, Materials used, Furniture and IT equipments. It is important to note that the Bilan Carbone spreadsheet does not account for the depreciation of products and buildings that are older than their amortization periods because it emits negligible GHG. Calculation and assumption method • The data information that was collected for each type of asset is shown in the table 3.4. Table 3.4 Data information for each type of asset

Type of assets Data Required Units 1.Buildings Surface Area m3 2.Roads and Parking Areas Length and Width m 3.Vehicles Weight tons 4.Machines Weight tons 5.Material used Weight tons 6.Furniture Weight tons 7.IT equipments Weight tons

Direct Waste in AIT Campus

Solid Waste

Wastewater Non-Hazardous Waste Hazardous Waste

Type and amount of

waste

Amount Recycled

Type of Waste

Management

Sodexo

Tha Klong Municipality

Amount of Hazardous

Waste EEM lab

Amount of Water

Consumption

BOD value

Sodexo

AIT Eco-Campus

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• In the Bilan Carbone® spreadsheet, if the assets have already crossed their amortization year, the data information of the assets does not need to be input into the spreadsheet. So that it is necessary to identify the year of construction for buildings and the year of purchase for products. The amortization periods of the assets are shown in table 3.5.

Table 3.5 The amortization periods of the assets

Assets Amortization period (years) Buildings 30

Vehicles and Machines 10 Furniture 10

IT 5 3.3.3.2 Phase 2: Data Analysis In analysis of GHG emission, the Bilan Carbone® model was the main instrument to calculate GHG emission from study areas. At this phase, objective 1 of the study has been achieved. Methodology for Bilan Carbone® tool The organization of the Bilan Carbone’s methodology’s spreadsheets into three modules (company, local authority, territory) that means they can be used for • Any industrial activity, private or public • Any territory activity, whatever its legal form (public authority, profit-making

company, association, foundation, etc) • Any local authority, whatever its nature (commune, department, conurbation, region,

intercommunality), for its own activities taking place in its area. • Any regional organization (Regional Nature Park, Country, etc), similarly for its assets

or the activities in its area. Since study area is considered as academic institution thus the spreadsheet for local authority has been used.

Documents and spreadsheets associated with the method The set of available documents actually includes: 1. The present methodological document which provides a detailed explanation of the

method and what it takes into account, 2. The “Emission factors guide or Carbon Base guide” which, as its name suggests,

contains the calculations or the origins of all the emission factors used in the Bilan Carbone® spreadsheets, and also in other calculators made available by ADEME,

3. For the local authority module: a. a master spreadsheet identical to the company module spreadsheet a local authority

can be considered to be a service activity which allows you to calculate emissions and manage reduction objectives (Excel file: “Bilan_carbone_V6.xls”),

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b. a utility spreadsheet allowing you to combine the emissions for several of the authority’s services (Excel file “Multisites_V6.xls”), which also allows you to compare the emissions for several projects,

c. an economic and forecasting utility spreadsheet allowing you to import the emissions output from the master spreadsheet or the multi-site utility to carry out economic simulations. The two simulations proposed consist of assessing the impact of a rise in hydrocarbon prices, or the impact of wide-scale taxation on emissions (Excel file: “Eco_collectivité_V6.xls”). This utility is adapted to local authority accounting rules.

4. A utility spreadsheet which – in order to fill in the “freight per route” for any of the Bilan Carbone master spreadsheets – allows you to reconstruct the tons. km for each type of road vehicle when the user only has highly consolidated data (Excel file: “fret_route_tkm_V6.xls”),

5. A utility spreadsheet which allows you to assess halocarbon leaks from refrigeration facilities when the user only has summary data, and which therefore allows you to fill in the “halocarbon emissions” part of the Bilan Carbone master spreadsheets (Excel file: “Clim_froid_V6.xls”).

After using the Bilan Carbone® model, the current situation of GHG emission from study areas was obtained in terms of carbon equivalent. Scenarios for reducing GHG emission were proposed in next Phase. 3.3.3.3 Producing spreadsheet for water and wastewater

Process for producing spreadsheet for water and wastewater are described as follows: • Studied the whole system of treatment, distribution and collection of supply water. • Studied GHG emission estimation for each type of water and wastewater treatment

plant. • Data required for producing spreadsheet for water and wastewater

- Capacity of pumps (m3/hr) - Distance of water distribution - GHG estimation for treatment plants - Water consumption per day

• Produced spreadsheet relevant to data information • Estimated GHG emissions for water and wastewater in AIT.

3.3.3.4 Phase 3: Comparison of Scenarios In this Phase 3 of the study, best practices for reduction of GHG emission in many cities around the world have been studied. In choosing appropriate scenarios to be analyzed in this step, the possibilities of implementing the scenarios in the real study areas are concerned. Possibilities for example, budget investment and participation of people. Procedure for phase 3: • After getting the overall GHG emission for AIT in phase 2, prioritization for the

magnitude for each type of emission source was done.

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• In this study, four major sources of GHG emissions in AIT were identified namely: 1. Energy, 2. Transportation, 3. Solid Waste Generation and 4. Wastewater.

• For each aspect, three scenarios for reduction of GHG emission were proposed. • For each proposed reduction scenario, the assumption for amount of GHG reduction

was estimated. • Greenhouse gas emission from each proposed scenario was measured using the Bilan

Carbone spreadsheet. At this phase, objective 2 was achieved. Aspect 1: Energy

o Scenario 1: Energy Conservation

Aspect 2: Transportation o Scenario 1: Use AIT buses o Scenario 2: Video conference to reduce air travel

Aspect 3: Solid Waste Generation

o Scenario 1: Reduce, Reuse and Recycle o Scenario 2: Fertilizer o Scenario 3: Produce of Bio Gas

Aspect 4: Wastewater

o Scenario 1: Reduce water consumption o Scenario 2: Pump retrofit o Scenario 3: Rain Water Harvesting

3.3.3.5 Phase 4: Analysis of Possibility for Implementation of Scenarios In order to achieve objective 3 of the study that is “To identify suitable policy as well as available implementation methods to reduce the adverse impacts of climate change using”, not only estimation for GHG emission was taken into consideration. Analysis of advantages and disadvantages for each proposed scenario has been applied in order to sort out the most effective and applicable policy for the context of AIT. From the data analysis process using Bilan Carbone® model to estimate the current trend of GHG emission in AIT campus, after comparing the emissions estimations for several scenarios in phase 3. Study of good practices in GHG reduction scenarios from other provinces in the country as well as other countries has helped compare the potential of implementing the policies in study areas. Moreover, advantages and disadvantages analysis was considered in the study as an analytical tool in identifying efficient policies. Comparison between advantages and disadvantages to investment cost of a particular scenario has been studied. Because the most potential scenario does not have to always be the one that can reduce the most GHG emission if that requires a very high investment, it will not be attractive to authorities or stakeholders to adapt their administration and people to change their behaviours.

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Chapter 4

Results and Discussions

4.1 Introduction This chapter explains about details in results and discussions of the study from data collection in Phase1 to analysis of different scenarios in Phase 4. Data collection has been basically done through literature review and previous research study as well as surveys. In addition, surveys and interviews were conducted in order to get primary data. Because of time limitation, assumption of data information needed to be done with the aspects that contribute very small GHG emission that is not very significant to the overall emissions. Data information for input to the Bilan Carbone spreadsheet, results for overall GHG emissions as well as GHG emissions for reduction scenarios were discussed. AIT campus is an academic institution, established in Bangkok since 1959. Although AIT is not only a school, it is also a community which composes of academic area, administration area, residential area and recreation area. Greenery spaces account for huge area in AIT campus as well. Map of AIT campus and identification of emission sources is presented in Appendix A.1. With more than 3,000 individuals living and working at AIT, bicycles and walking are mainly used for daily transportation of people within campus. 4.2 Phase 1: Data Collection 4.2.1 Energy In this aspect, two of GHG emissions sources were taken into account namely: electricity consumption and fuel consumption. 1. Electricity Data information for Electricity consumption was obtained from Sodexo, a company that is responsible for administration of utility and facilities on campus. Electricity in AIT is supplied by PEA (Provincial Electricity Authority). With the total electricity consumption in 2009 was about 12,735,035 kWh, Figure 4.1 shows trend for monthly electricity consumption in AIT for the year of 2009 (Appendix A.2).

Figure 4.1 Monthly Electricity Consumption in AIT for the Year of 2009

02468101214

Jan

Feb

Mar

Apr

May Jun Jul

Aug Sep

Oct

Nov Dec

Electricity Co

nsum

ption (x 

105 kW

h)

Month

31

Electricity consumption had constantly increased in the first 3 months and reached the peak for the highest consumption of the year in March, which was in the period of mid term exams. During semester break in June to early August, not many AIT students stayed on campus which resulted in lowest electricity consumption. 2. Fuel Consumption Data information for fuel consumption was obtained by interviews and surveys. The estimation of the amount consumption was estimated and assumed by key persons who are responsible for activities related to fuel consumptions. The information for fuel consumption in 2009 is presented in the table 4.1. Table 4.1 Information for Fuel Consumptions

Fossil Fuel Data Units Domestic Fuel Oil for Boiler 8,000 liters Diesel oil for electric generator

4,000 liters

Diesel oil for Machines use 711 liters Petrol for Machines use 7,223 liters LPG 22.85 tons Cooking coal 4,000 kg

All the above data was provided by Sodexo, except for the consumption of cooking coal. The only activity that requires cooking coal is the cooking in SU snack bar restaurant and about 4,000 kg was consumed in one year. Liquefied Petroleum Gas (LPG) was mainly used for about 22.85 tons in staff’s residential areas and other restaurants on campus. Hot water was supplied for hotel rooms in AIT Conference Center by using about 8,000 liters of domestic fuel oil for boiler. About 4,000 liters of diesel oil was used to operate electricity generators in case of the blackouts from regular electricity supply. Carrying out gardening activities also requires consumption of fuel in order to operate grass cutters or leaves blowers. 4.2.2 Excluding Energy This takes into account of GHG emissions from other types of sources except for those resulting from energy usage. In context of AIT, the activities considered to be in this category are leakage of refrigerants and usage of fertilizer. 1. Refrigerant leakage According to Sodexo, refrigerators are located in some residential areas and restaurants with total installation load of about 101 kW. R134a is used as refrigerant with no cold water network. AIT also owns individual air conditioning system in some part of residential areas with load of installation about 694 kW and refrigerant type R22 no cold water network is used to refill the leakage. In academic and administration areas as well as AIT Conference Center, the chiller plant has been installed for air conditioning system for more than 30 years. The capacity load for chiller plant is about 2,880 kW with cold water network. Refrigerant type R134a is used to refill the leakage.

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2. Fertilizer Usage In context of AIT, fertilizer was used in research and studies in two fields of study namely; Agricultural Systems and Engineering (ASE) and Aquaculture & Aquatic Resources Management (AARM). Data information for fertilizer usage was estimated and assumed by field of studies that total 300 kg of Nitrous Oxide has been used in 2009. 4.2.3 Materials and Products Purchased (Inputs) In this category of GHG emission source, purchasing of incoming materials and products was taken into consideration. In context of AIT, materials and products are mainly consumed due to activities in offices and classrooms as well as repair and maintenance activities of AIT’s fixed assets. Materials taken into account in this category consists of metal, glass, plastics, paper, food consumptions with the unit of tons per year. 1. Materials Purchasing office at the administration building kept the records for the items of materials purchased as well as the payment under the consumption by AIT activities. In order to find the exact weight for each type of material, it should take quite sometime. Moreover, Sodexo also keeps the same types of record for purchasing of materials. Because of time limitation, this study has assumed the percentage of waste generation according to its related activities, see table 4.2. Table 4.2 Assumption of Materials Purchased in AIT

Type of Materials

Amount generated (tons/year)

% Assumption for Generated Amount

Amount Purchased (tons/year)

Paper 128.7 70 183.90 Metal 13.2 50 26.45 Plastic 102.8 80 128.45 Glass 166.7 50 333.51

Regarding academic activities at AIT, large amount of paper has been consumed for academic activities as well as office works. After graduation, graduated students usually move out from dormitories and generate huge amount of paper, that is when the peak of generation of paper. Thus this study assumed that paper was generated as solid waste for 70% of the total consumption. For metal and glass, these two materials are normally consumed for the activities of construction and repair of buildings and facilities. The materials are fixed to the assets and long lasting so that 50% of total consumption becomes solid waste. About 80% of total consumption of plastic is assumed to be generated as solid waste. Because the main component of plastic generation in AIT are plastic bottles which is basically for one time use packaging. The assumption of purchasing materials has been made in order to fulfill the data requirement in the Bilan Carbone spreadsheet so that the overall GHG emissions and main sources of emissions can be identified. However the real record of data information in this aspect can be in the further study in order to get the exact GHG emissions and carbon footprint.

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2. Construction According to Sodexo, there was no major construction in AIT during 2009 but only a construction for one parking lot and minor repair of footpath and building in residential areas. The data information for building materials and chemical use is presented in table 4.3. Table 4.3 Building Materials and Chemical Use

Materials Data

Masonry wall 500 m2

Cement concrete for footpath 5 tons

Cement 3 tons

In 2009, Cement has been used in construction and repair activities for buildings and facilities in AIT for about 3 tons when concrete was used to fixing the footpath for around 5 tons per year. Additionally, 500 m2 of Masonry walls in concrete blocks were used in construction of parking lot. 3. Food GHG emission from food consumption is also taken into consideration in this study. The data information required in this aspect is the number of meals that have been served to all AIT’s individuals throughout one year. The estimations for number of meals were done according to types and number of AIT’s individuals. Table 4.4 describes number of meals according to types of AIT’s individuals.   Table 4.4 Number of Meals According Types of AIT’s Individuals.

Types of AIT’s Individuals

Numbers Number of Members

in Resident

Number of Meal on Campus (per day)

Number of Days on Campus (Days)

Number of Meals on Campus

(per year) Staff, faculty and students with LPG tanks

260 3 2 x 365 569,400

Students living in dormitory without LPG tanks

1,700 1 2 x 365 1,241,000

Faculty and staff living off campus

300 1 1 x 256 76,800

Total 1,887,200 Number of AIT staff, faculty and students who lived in the residence that have LPG tanks indicates that these people cook at their homes and live with their family members. Appendix A.3 presents number of rooms that use LPG gas. In one residential unit, it is assumed that there are around 3 persons in average e.g. family members in faculty and staff residents or student fellows in student villages.

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Moreover, there are a big portion of students who live in dormitories that are not allowed to cook. These students normally eat at the restaurants and cafeteria on campus about twice a day in average. However in some new student dormitories, hot plates are provided for easy cooking. Since hot plates require electricity for its operations, its GHG emission is already covered in Energy aspect. Although the majority of AIT’s individual live on campus, there is still some significant number of AIT’s faculty and staff living off campus. They are considered to have only one meal per day on AIT campus and work for 256 days a week. 4.2.4 Transportation of Goods (Freights) Activities that are considered into this aspect are Internal Road Freights, Outgoing Road Freights and Incoming Road Freights. Data information was obtained by interviews and surveys with key persons. Since AIT is a community, entities within campus have their own activities related to transportation. Various entities, such as grocery shops, restaurants, mailing services, laundry services have company vehicles from outside come to deliver goods and services at AIT. The data were collected by interviews with vehicle drivers and shop owners to get relevant information. 1. Internal Road Freights GHG emissions from road transportation within AIT’s premise are mainly caused by vehicles that are owned by AIT. According to Sodexo, majority of AIT’s vehicles are used for gardening activities. Table 4.5 shows data of amount of fuel consumption by AIT’s vehicles in 2009. Details information of fuel consumption for each type of vehicles can be referred in Appendix A.4. Table 4.5 Amount of Fuel Consumption by AIT’s Vehicles in 2009

Type of Fuel Consumption Amount (liter/year)

Diesel 12,178Gasoline 11,239

2. Outgoing Road Freight AIT dose not conduct any kind of major transportation of goods from its premise. However transportation of out going mails by postal service and out going solid waste by Tha Klong municipality’s truck are taken into consideration in this aspect. AIT does not have many things to send out from its premises. Only some mails, recycled wastes and solid wastes were sent outside in 2009. Table 4.6 presents data information of fuel consumption for outgoing road freight. Data information was obtained by the interview with a postal officer and a driver for Tha Klong municipality’s truck to get the estimation of fuel consumption related to their activities. Table 4.6 Data Information of Fuel Consumption for Outgoing Road Freight

Activity Type of Fuel Consumption

Amount of Fuel Consumption (liters/year)

Postal Service Diesel 540 Solid Waste Transfer Diesel 72

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3. Incoming Road Freights Incoming Road Freights in AIT campus are considered to be delivery of food products from outside companies to shops in AIT campus. Regularly vehicles come to deliver various goods such as drinking water supply, ice, food products, LPG tanks for example. Some data information related to fuel consumption, distance traveled was collected. From this data, the actual fuel consumption for AIT incoming road freight has been calculated and shown in the table 4.7. Table 4.7 Fuel Consumption for AIT Incoming Road Freight

Type of Fuel Consumption Amount Diesel oil 19,473 liters/year NGV 2,384 kg/year

Data in table shows total amount of fuel consumption in one year. However details information for transportation of each type of goods as well as frequency is described in Appendix A.5. 4.2.5 Transportation of People (Travel) GHG emissions from transportation of people in AIT can be categorized as follows: 1. Transportation of AIT’s faculty and staff from home to work For AIT’s faculty and staff who live off campus, the main modes of transportation are AIT staff busses and staff’s personal cars. In 2009, there were 8 AIT staff busses in service for dropping off staff, faculty and students at assigned destinations outside AIT after work. Busses were run by two types of fuel namely; diesel and NGV. From the study, the information provided by Human Resources Office for fuel consumption shows that AIT purchased 27,231 liters of diesel and 74,880 kg of NGV fuel for the bus services. Total number of AIT faculty and staff was 878 which 176 (20%) stayed on campus. According to Human Resources office, 30% of AIT faculty and staff who lived off campus used AIT bus’s services. Which means that 211 of AIT faculty and staff were already taken into consideration for emissions from AIT bus’s services. That results in 491 of AIT faculty and staff who lived off campus used other types of mode of transportation. Poohngamnil, (2009) reported that 70% (344) of AIT faculty and staff who lived off campus drove their own cars to come to work in AIT. Thus this study assumed that 147 of AIT staff and faculty commuted by vans since it is the most preferable mode of transportation among Thai people. Because most of AIT faculty and staff are Thai and live in Bangkok, 50 km away from AIT was assumed to be travelling distance. There are 256 working days in one year. Travelling distance for each type of transportation is shown in table 4.8 below. Table 4.8 Travelling distance for each type of transportation

Mode of Transportation

Number Travelling Distance (km/day)

Travelling Distance (km/year)

Own car 344 100 8,806,400 Van (minibus) 147 100 3,763,200

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2. Transportation of AIT’s faculty and staff in context of work by car Information for business travelling was gathered from the records in the AIT Finance department. In the data, AIT purchased 29,209 liters of petrol to operate vehicles in 2009. Other travel information is shown in the next page: Sodexo has also shared information in terms of distance travelled (km) by AIT’s vehicles in context of business trips such as field trips and road show for example. Data information for distance travelled (km) by AIT’s vehicles is presented in table 4.9.

Table 4.9 Distance Travelled (km) by AIT’s Vehicles

Travel mode Total distance travelled (km)

Car Diesel 9,614 Petrol 33,648 Van 3,423,957

3. Transportation of AIT’s faculty and staff in context of work by airplanes Since AIT is an international academic institution, AIT’s faculty, staff and sometimes students need to fly to other countries for conferences and their professional activities. This takes into account of GHG emissions from airplanes and data information required for this aspect is total distance travelled (km) in one year, see table 4.10. Table 4.10 Distance Travelled (km) by AIT’s Vehicles

Type of travel Type of AIT individual Distance Travelled (km)

International Business Travel Faculty and Staff 10,052,616 Domestic Business Travel Faculty and Staff 352,142 International Travel Students 256,972

The most travelled distance in the context of work in AIT’s International business travels is from AIT’s faculty and staff because of longer distance and higher frequency of traveling. However detail information in travelling for each type travel can be found in Appendix A.6.

4. Transportation of AIT’s visitors AIT regularly conducts conferences, meetings as well as workshops on campus which results in significant number of transportations of visitor coming to AIT. Data information for this aspect was obtained by survey and interview with ERCO office for information of registered visitors. Detail information for travel by visitors to AIT is presented in Appendix A.7 Total distance travelled by AIT’s visitors by airplanes in 2009 was 1,603,807 km. Moreover, visitors usually take AIT’s vans as their mode of transportation from AIT campus to the airport. Distance from AIT campus to the airport is around 140 km for round trip. From Appendix A.7 total number of visitors is 247 so that number of trips from AIT

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to the airport is 494. Total distance travelled by visitors from AIT to the airport is 69,160 km. 4.2.6 Solid Waste and Wastewater (Direct Waste) Sources of GHG emissions from Solid Waste and Wastewater are taken into consideration in this category.

1. Solid Waste According to the Bilan Carbone spreadsheet, this category takes into account for three types of waste; non-hazardous waste, hazardous waste and waste water. Different types of solid waste are generated from different activities on campus. Majority of solid waste generated from residential area is basically organic waste when office waste is mainly composed of inorganic waste. Everyday, Sodexo’s employees come to pick up waste from all garbage bins around AIT campus using tricycles. Solid waste is then gathered in a storage house located in the back of AIT campus. About twice a week, the Tha Klong municipality’s garbage truck is responsible for picking up solid waste in the storage house and delivery to the landfill. According to Thammarachatee (2011), in 2010, AIT generated about 2.7 tons of solid waste per day. Waste generation audit is presented in Appendix A.8. For overall solid waste generation, data information is detailed in table 4.11. Table 4.11 Solid Waste Generation in AIT

Type of Waste Amount generated (tons/year)

Percentage of Generation (%)

Food Waste 505.06 50.31 Paper 128.73 12.82 Metal 13.22 1.32 Plastic 102.77 10.23 Glass 166.76 16.61 Hazardous Waste 40.36 4.02 Others 47.09 4.69 Total 1003.99 100

In 2010, solid waste was generated for about 1,000 tons. Food waste from residence and restaurants accounts for 50% of total generation when metal was the fewest generated. Paper and plastics were generated for quite the same amounts which accounts for 12.8 and 10.2% respectively. In which, the main composition of plastic waste are plastic bottles for drinking water. Moreover, the generation of chemicals from laboratory waste is considered to be around 40.36 tons per year. For current situation in AIT campus, very few waste separation of solid waste prior to its generation, is taking place. Once a month, private recycle company comes to buy recyclable wastes under the trash for cash project in the supervision of AIT Auxiliary Services. However not many AIT students are participating in the project. Although there is another separation of recyclable waste taking place at the storage house by Sodexo’s employees, a few amounts of recyclables are able to be separated out. Since this is a manual separation by people, only clean enough waste is separated out. The majority of solid waste in AIT is mixed components which results in huge amount of recyclable waste is sent to the landfill. Details on amount of recyclable waste is shown in table 4.11 Paper

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waste had been the most recycled among other types of recyclable waste. Glass comes second for 3.5 tons per year in terms of weight recycled. Each type of recyclable waste has different percentage of recycle due to it amount of generations. It is noticeable that percentage of recycle for recyclable waste is very small, this is because of lack of awareness in terms of waste separation for AIT individuals. Another factor that has an impact on participation of people is prices of recyclables. Dev (2007) explained that prices of recyclables are varied depending on the market mechanisms, as the theory of demand and supply and the competition of the market. The price of the main trading materials (shown in table 4.12), however varies according to the quality of the materials. The materials that are of the highest selling price are the scrap metals due to the variation of the quality of scraps. The material that can be sold at a very high price is aluminum, which is of the highest quality. Table 4.12 amount of recyclable waste in AIT

Type of Waste Total recyclable waste (tons/year)

Percentage of Recycle (%)

Paper 6.6 5.12 Metal 1.2 9.1 Plastic 2.9 2.8 Glass 3.5 2.1

In addition, AIT campus covers huge area of greenery spaces which results in large amount of yard waste generations. AIT generates around 574.7 tons of yard wastes per year. 2. Wastewater Data information of water consumption in AIT campus is presented in Appendix A.9. Water consumption in 2009 reached the peak at the beginning of the year in January and constantly reduced in the first three months as shown in figure 4.2.

Figure 4.2Trend of water consumption in AIT, 2009 Total Water Consumption in AIT for the year of 2009 was 588,215 m3. And 80 % of consumed water became wastewater or around 470,572 m3. BOD of the final treatment wastewater is 35 mg/L.

01234567

Jan

Feb

Mar

Apr

May Jun Jul

Aug Sep

Oct

Nov Dec

Wat

er C

onsu

mpt

ion

(x10

,000

m

3)

Month

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4.2.7 Property In the Bilan Carbone spreadsheet takes into consideration of GHG emission from property such as buildings, roads, parking areas, vehicles, machines, furniture and IT equipments. Because the manufacture of those products can lead to greenhouse gas emissions. Data information was obtained by conducting surveys and interviews with key persons from Sodexo. 1. Buildings Data information required for buildings is surface area. Table 4.13 shows summery for surface area of all buildings in AIT campus. Break down for detail information of surface areas in each type of building is referred in Appendix A.9. AIT is an academic institution that the main activities are study and research. Which results in the biggest surface area is from office and academic buildings which accounts for about 50,000 square meters.

Table 4.13 Summery for Surface Area of All Buildings in AIT Campus No. Type of Buildings Surface Area (m2) 1 Office and Academic Buildings 50,200 2 AIT Community School 1,600 3 Auxiliary Service 2,200 4 AIT Conference Center 10,790 5 Student Accommodation Buildings 33,850 6 Campus Residential Service Unit 22,900 Total Surface Area 121,540

 There are many other items that are taken into account in the Bilan Carbone spreadsheet e.g. weight of machines and vehicles owned by premise, number of IT equipments and surface areas of road and parking lots. Although property is considered to emit GHG emissions, this study does not take into consideration of property because it is necessary to the operation of AIT’s activities. It can be almost impossible to implement the reduction plans. 4.3 Phase 2: Data Analysis For data analysis in phase 2 of the study, the Bilan Carbone model was used as a tool in order to calculate greenhouse gas emissions in AIT campus. The overall GHG emissions of AIT campus was identified and Objective 1 of the study was achieved. In order to use the Bilan Carbone spreadsheet effectively, the proper training is required. The emission factors used in the spreadsheet are more suitable in the context of France, this study however used the same ones in the spreadsheet. Although most of the emission factors can be used in the context of Thailand e.g. fuel combustion, there are still some specific emission factors need to be adapted in further study. GHG emissions in terms of Carbon equivalent for each studied aspect are discussed as follows: 4.3.1 Energy Electricity and fuel consumption are taken into account in energy aspect. Electricity consumption is considered to emit greenhouse gas because its production and generation require burning of fuel. Majority of power plants in Thailand however rely on natural gas.

40

The emission factor for electricity in Thailand is provided in the spreadsheet. Appendix B.1 presents data input to the Bilan Carbone spreadsheet. According to table 4.14, it can be found that overall GHG emission from energy aspect at AIT emitted to 1,958 tons Carbon equivalent in 2009. The largest portion is from electricity consumption when the consumption of electricity of 12 GWh contributed to 1,917 tons Carbon equivalent. The rest of 41 tons Carbon equivalent of emission is from fuel consumption. Table 4.14 GHG Emissions from Energy Aspect

Energy used in AIT kg equ. C t C equ. Fuels, direct accounting 40,825 41 Electricity purchased 1,916,785 1,917 TOTAL 1,957,610 1,958

There is a big difference of GHG emission from electricity consumption and fuel consumption at AIT. In order to significantly reduce GHG emission from this sector, it is better to focus on reduction of electricity consumption. Figure 4.3 presents the comparison of GHG emission between sectors in energy aspect.

Figure 4.3 Comparison of GHG Emission from Sectors in Energy Aspect

4.3.2 Excluding Energy Greenhouse gas emissions from sources that do not produce energy are taken into account in this aspect. The main activities considered are fertilizer usage and refrigerant leakage. Refrigerants are potent greenhouse gases, as a result that they can represent a significant fraction of total GHG emissions for a given area, even if they are emitted in very small quantities. Appendix B.2 presents data input to the Bilan Carbone spreadsheet. The overall GHG emission from excluding energy aspect is 78 tons Carbon equivalent, see table 4.15. Refrigerant type R22 that has been used in individual air conditioning system in AIT campus emitted to about 10 tons Carbon equivalent. Furthermore, the chiller plant has been installed for the air conditioning system in academic areas and refrigerators all over campus use R134a as refrigerant. In which contributed to 43 tons of Carbon equivalent.

41

1,917

0

500

1,000

1,500

2,000

2,500

Fuels, direct accounting Electricity purchased

Energy 1, emissions in t Carbon equ.

41

Table 4.15 GHG Emissions from Excluding Energy Excl energy in AIT kg equ. C t C equ.

Nitrous oxide 24,382 24 Kyoto halocarbons (R134a) 43,290 43 Gas excl. Kyoto (R22) 10,366 10 TOTAL 78,038 78

Usage of fertilizer in fields of study related to agricultural activities emitted to GHG emission about 24 tons Carbon equivalent. Even though this aspect emitted to a very small amount of GHG emission at AIT and is not significant to the overall emissions. This spreadsheet can be well applied to be used in agricultural areas that some significant amount of fertilizer is used.

Figure 4.4 Comparison of GHG Emission from Sectors in Excluding Energy Aspect Figure 4.4 shows that Kyoto Halocarbon which is R134a in this case contributed the largest amount of GHG emissions. Nitrous Oxide from usage of fertilizer comes second in terms of GHG emission. It is important to note that the fertilizer usage in this study took into account only in field of study. Fertilizer usage from gardening activities in AIT campus should be taken into consideration in further study. 4.3.3 Materials and Products Purchased (Inputs) The greenhouses gases emitted from production of input materials in this aspect come essentially from the fossil fuel used in its industrial manufacturing processes e.g. coal used to make steel for example. As presented in table 4.16, overall GHG emissions from Input materials aspect at AIT emitted to about 1,000 tons of Carbon equivalent in 2009. Table 4.16 describes GHG emissions from each source of emission in this aspect. GHG emissions from agricultural products emitted 736 tons Carbon equivalent which is considered the highest among other sources in the aspect. Number of meals consumed at AIT in 2009 however is used to calculate for GHG emission by multiplying with emission factor. Appendix B.3 shows detail on data input to the Bilan Carbone spreadsheet.

24

43

10

0

10

20

30

40

50

Nitrous oxide Kyoto halocarbons Gas excl. Kyoto

Non-energy emissions 1, tonnes carbon equ.

42

Table 4.16 GHG Emissions from Input Materials Aspect Inputs kg equ. C t C equ.

Metals 26,450 26 Plastics 83,493 83 Glass 138,164 138 Paper & cardboard 66,204 66 Building materials 2,398 2 Agricultural products 736,008 736 TOTAL 1,052,716 1,053

Manufacturing of Plastic and paper emitted to quite the same amount which are 83 and 66 tons Carbon equivalent respectively. Although the GHG emissions from plastic and paper are not very high, it has a potential to significantly reduce GHG emission because they are materials used in daily activities at AIT. Moreover metals and glass also emitted some significant amount of GHG emission by 26 and 138 tons Carbon equivalent respectively. Although metal and glass contributed to some amount of GHG emissions in AIT campus, it is not effective to implement the action plan on these sectors since these two materials are necessary used in fixed assets. Figure 4.5 shows comparison of GHG emission between sectors in input materials aspect.

Figure 4.5 Comparison of GHG Emission from Sectors in Input Materials Aspect

4.3.4 Transportation of Goods (Freights) Fossil fuel combustion by vehicles in different mode of transportations emits significant amount of GHG emissions. In AIT campus, GHG emissions from transportation of goods are not a major source compare to other GHG emission sources at AIT. It contributed to only 37 tons Carbon equivalent, see table 4.17. AIT dose not operate transportation of goods (freight) in its premise except for transportation of gardening vehicles. The main activity in this aspect is delivery of food and products from companies outside. For the comparison of GHG emission between sectors in input freight aspect, see Figure 4.6. Data input to the Bilan Carbon spreadsheet is shown in Appendix B.4.

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47

4.3.9.2 Water Production Process for Provincial Waterworks Authority  

Figure 4.11 Water Production Process for Provincial Waterworks Authority (Pathum Thani Water Supply Authority Branch, 2011)

Raw water is pumped to water treatment plant passing through the channel pipes to the clarification section. Alum is added in controlled quantities in order to improve water quality and activate sedimentation efficiency during the clarification process. Water is then sent to sedimentation tank and filtration tank. Electricity is mainly consumed for water pumping and operating the treatment processes. Tall water tower is used as storage of water before distribution to residential areas in order to increase the water pressure. 4.3.9.3 Consideration for producing Spreadsheet for Water and Wastewater GHG emissions from water and wastewater treatment plants vary in different areas according to types and characteristics of water and wastewater. Basically, water and wastewater treatment plants are designed according to water and wastewater characteristics. Capacity of treatment plants as well as landscape of the areas can also results in electricity consumption of water pumping. This Exel spreadsheet mainly focuses on GHG emissions from electricity consumption in the whole system and GHG emission from production of water and wastewater. However, there should be GHG emissions from other sources in the system to be taken into consideration e.g. fuel production and chemical production. See figure 4.12 for overall possible sources of GHG emission from wastewater treatment plant. Example of the spreadsheet and manual can be found in Appendix B.8.

Raw Water

Sedimentation T k

Filtration

Water St

Raw Water Pumping station

Clarification T k

Alum

High Pressure Pumping station

Water Tower

48

Figure 4.12 Overall Possible Sources of GHG Emission from Wastewater Treatment Plant (Shahabadi et al., 2010)

4.3.10 GHG emission from water and wastewater In 2009, AIT consumed about 588,000 m3 of water which was sent from Prapa Pathumthani’s water treatment plant. About 80% of consumed water became wastewater that treated by AIT’s wastewater treatment plant. Electricity consumption from each activity related to the whole water supply and treatment was estimated. Table 4.21 presents data input for estimating GHG emissions from water supply system. Table 4.21 Data input for estimating GHG emission from water and wastewater sector

Data Information Number Units Water Consumption 588,000 m3/year Generated Wastewater 470,400 m3/year Electricity Consumption for pumping raw water

99,960 kWh/year

Electricity for water treatment process

470,400 kWh/year

Electricity Consumption for pumping water at AIT

99,960 kWh/year

Total BOD 16,470 kg/year After input data information into the separated spreadsheet for water and wastewater, it is shown that GHG emission from total water and wastewater system at AIT contributed 113.8 tons Carbon equivalent in 2009. Appendix B.9 presents Input data for water and wastewater spreadsheet. In order to reduce GHG emissions from water and wastewater aspect, it can be from reduction of water consumption and electricity consumption.

Energy Production

Fuel Production

Solid for disposal

Wastewater Treatment Plant

Wastewater Production

Chemical Production

Treated Water

49

4.4 Phase 3: Comparison of scenarios The overall GHG emissions at AIT have already been identified in phase 2. In 2009, AIT contributed to GHG emission of 6,245 tons Carbon equivalent. With about 3,000 populations at AIT, the average GHG emission is estimated to be 2.08 tons Carbon equivalent per capita. It is importance to note that in real situation, some people emitted more and some emitted less depending on people’s daily life style. GHG emission per capita of other universities can be found in Table 4.22. University of Colorado at Boulder accounts 0.3 tCe, which is the lowest GHG emission per capita when Yale University accounts for the highest emission. It is important to note that the amount of GHG emissions in any different academic institutions can not actually be compared due to the differences in emission scopes for each specific area and differences in climate and people’s activities. Table 4.22 GHG emission per capita in some other university

University Emission per Capita(t CO2 e)

Emission per Capita (t C e)

University of Colorado at Boulder

1.2 0.3

Tufts University 2.2 0.6 College of Charleston 3.4 0.9 Tulane University 4.1 1.1 University of New Hampshire

4.8 1.3

California State University 6 1.6 University if Texas 5.8 1.6 Vermont University 6.2 1.7 Connecticut College 9 2.4 Carleton College 9.2 2.5 Florida 9.4 2.5 ETH 9.3 2.5 Harvard University 10 2.7 Middlebury College 11.7 3.2 Yale University 12.6 3.4

(Modified from Poohngamnil, 2010) Table 4.23 GHG Emission per capita for Asian countries in 2008

Country GHG emission (t CO2) GHG emission (t C equ) Bangladesh 0.29 0.08 Brunei Darussalam 18.87 5.14 Cambodia 0.31 0.08 Chinese Taipei 11.53 3.14 India 1.25 0.34 Indonesia 1.69 0.46 Malaysia 6.7 1.82 Myanmar 0.24 0.06 Nepal 0.12 0.03 Pakistan 0.81 0.22 Philippines 0.80 0.22

50

Singapore 9.16 2.5 Sri Lanka 0.61 0.16 Thailand 3.41 0.93 Vietnam 1.19 0.32

(Modified from International Energy Agency, 2010)

Data information in table 4.23 presents GHG emission per capita for countries in Asian region in the year of 2008. Apparently, countries with less technology development such as Cambodia, Myanmar and Nepal emitted quite small amount of GHG emissions. When Taipei and Singapore are two developed countries that emitted some significant amount of GHG emissions for 3.14 and 2.5 t C equ per capita respectively. The GHG emissions increase in countries that are well developed due to the requirement on huge amount of fuel to be consumed in a wide variety of activities in their countries. Thailand however emitted about 0.93 t C equ per capita in 2008 when AIT has estimated to emitted about 2.08 t C equ per capita in 2009. Even though the GHG emissions in different locations can not be perfectly compared because of the differences in scopes of estimation in the area, it is useful to have emissions from other countries with similar geographic to compare with. Figure 4.13 illustrates the share of GHG emission by sources in AIT campus. As mentioned earlier in phase 2 that the largest source of GHG emissions at AIT is from transportations of people. This sector emitted to 2,546 tons Carbon equivalent which accounts for 41%. Energy sector is responsible for 31% of overall GHG emission in 2009, which is considered the second largest source of emission. In real situation, reduction of GHG emission from all sources can be difficult and time consuming, so that prioritization of magnitudes needs to be done. Implementing the reduction plans on the main sources of emission can help reduce the overall GHG emission effectively. So that this study took into consideration on travel and energy aspects in terms of proposing the GHG reduction plans.

Figure 4.13 Share of GHG emission by sources in AIT campus Inputs and property aspects also emitted some significant amount of GHG emissions, which account for 17 and 8% of GHG emission respectively. However these two aspects were not taken into consideration in terms of proposing the GHG reduction plans in this

Energy31%

Excl Energy1%

Inputs17%

Fregith1%

Travel41%

Direct Waste1%

Property8%

51

study because of its necessity for AIT’s activities. Instead this study focused on the aspect of direct waste, in which solid waste generation and wastewater are consisted. As in literature reviews on section 2.4, considerable reduction in GHG emission in a premise can be overcome by changing people’s behaviors and installation of new technology related to energy efficiency. Studying existing best practices in terms of greenhouse gas reduction in a wide variety of areas can help identify ones that are suitable for the context of AIT. The proposed reduction plans were chosen accordingly. 4.4.1 Energy The main components in this aspect are fuel and electricity consumption. The study concentrated on reduction plans for electricity consumption because it is the main GHG emission source in this aspect. In phase 2, it is shown that overall GHG emission from energy aspect contributed to 1,958 tCeq when electricity consumption accounts for 1,917 tCeq. Scenario: Energy Conservation In this scenario, AIT can promote energy saving programs and raise awareness of AIT students to use electricity only when necessary. A wide variety of activities can be done in order to reduce electricity consumption. The main source of high electricity consumption at AIT are air-conditioning and lighting systems. Furthermore, installation of light bulbs and air-conditioners with more energy efficiency is another activity that has been widely done in developed countries so that AIT is capable to do so as well. It is important to note that retrofitting of electrical equipments can be costly, therefore analysis of cost-benefit can be interesting for decision makers. By having AIT individuals concern more of energy conservation and light retrofitting, AIT should be able to reduce its electricity consumption for about 30%, see table 4.24. Appendix C.1 presents input data in the Bilan Carbone. Table 4.24 Data Information on Scenario for Electricity Consumption

Current Electricity Consumption (kWh)

% Reduction Scenario Electricity Consumption (kWh)

12,735,035 30 8,914,524 After implementing reduction scenario in AIT, GHG emissions should be reduced to 1,383 t C equ. Table 4.25 shows results of calculation on Scenario for Electricity Consumption. Table 4.25 Results of Calculation on Scenario Electricity Consumption

Energy 1 kg equ. C t C equ. Fuels, direct accounting 40,825 41 Electricity purchased 1,341,749 1,342 TOTAL 1,382,575 1,383

4.4.2 Transportation of People (Travel) Overall GHG emissions from travel aspect is 2,546 tCeq. The biggest contributor is travelling from home to work because the majority of AIT individuals use their own cars.

52

Another source of GHG emissions that is significant and has potential in terms of reducing GHG emissions is travelling by planes in the context of work. Scenario 1: Promote AIT bus services Currently, AIT’s faculty and staff who live off campus prefer using their own cars to commute from their homes to AIT. If AIT improves the level of services for AIT buses and encourages AIT individuals to use more of bus services than personal cars. Suppose, 30% of people using own cars change their life style and use AIT bus service instead, see table 4.26 for travelling distance in scenario. Appendix C.2 presents input data in the Bilan Carbone spreadsheet. Table 4.26 Data Information on Scenario 1 of Travel

Mode of Transportation

Travelling Distance

(km/year)

% Reduction

Scenario Travelling Distance

(km/year) Own car 8,806,400 30 6,164,480

Table 4.27 shows that after implementing scenario 1, GHG emissions from travel aspect can be reduced from 2,546 t C equ to 2,392 t C equ. Table 4.27 Results of Calculation on Scenario 1 of Travel

Travel kg equ. C t C equ. Home-work 962,875 963 Employees, car 23,009 23 Employees, other road 467,755 468 Employees, plane 807,712 808 Visitors, all modes 130,994 131 TOTAL 2,392,345 2,392

Scenario 2: Video conferences to reduce air travel Among sources of GHG emissions in this aspect, traveling by airplanes in the context of work by AIT individuals contributed the biggest amount. Conducting more video conferences on campus can help reduce number of trips travelled by 50%, see table 4.28. Table 4.28 Data Information on Scenario 2 of Travel

Type of travel Type of AIT individual

Distance Travelled (km/year)

% Reduction

Scenario Distance Travelled (km/year)

International Business Travel

Faculty and Staff 10,052,616 50 5,023,308

Domestic Business Travel

Faculty and Staff 352,142 50 176,071

International Travel

Students 256,972 50 128,486

53

Table 4.29 shows that after implementing scenario 2, the GHG emissions should be reduced to be around 2,142 t C equ by conducting video conferences at AIT. Table 4.29 Results of Calculation on Scenario 2 of Travel

Travel kg equ. C t C equ. Home-work 1,116,667 1,117 Employees, car 23,009 23 Employees, other road 467,755 468 Employees, plane 403,629 404 Visitors, all modes 130,994 131 TOTAL 2,142,054 2,142

4.4.3 Solid Waste Generation From the calculation in the Bilan Carbone spreadsheet, the direct waste aspect contributed to 92 tons Carbon equivalent of GHG emission. Solid waste generation in AIT contributed to 63 tons Carbon equivalent. This study has proposed reduction scenarios as follows: Scenario 1: Promote Reduce, Reuse and Recycle concept Raising awareness of AIT individuals in terms of reduce and reuse of materials in their daily activities can help reduce generation of solid waste. Excess amount of paper and plastic bottles has been generated, assumed that AIT has potential to reduce generation of those two materials for 50%. For metal and glass that are used in fix assets, it should be able to reduce about 20%, see table 4.30 for generated amount of solid waste in the scenario. Table 4.30 Data Information on Scenario 1 of Solid Waste Generation

Type of Waste Current Generated Amount (tons/year)

% Reduction

Scenario Generated Amount (tons/year)

Food Waste 505.06 0 505.06 Paper 128.73 50 64.4 Metal 13.22 20 10.6 Plastic 102.77 50 51.4 Glass 166.76 20 133.4 Hazardous Waste 40.36 0 40.36 Others 47.09 0 47.09 Total 1003.99 100 852.31

Table 4.31 shows that after implementing scenario 1 by promoting 3Rs concept, AIT will be able to reduce GHG emission from 92 t C equ to 89 t C equ.

Table 4.31 Results of Calculation on Scenario 1 of Solid Waste Generation Direct waste kg equ. C t C equ.

TPS 59,884 60Waste recycled or recovered 71 0Hazardous waste 1,376 1Sewage 28,074 28TOTAL 89,405 89

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Scenario 2: Organic waste for fertilizer Suppose AIT has its own fertilization process to manage with organic waste on campus. In this scenario, assume all the food waste that is generated goes to fertilization process. On the other hand, there is no food waste goes to landfill. The overall GHG emission from solid waste generation in scenario 2 is reduced to 50 t C equ from 92 t C equ, see table 4.32 for GHG emissions from solid waste in Scenario 2. Table 4.32 Results of Calculation on Scenario 2 of Solid Waste Generation

Direct waste kg equ. C t C equ. TPS 5,550 6 Waste recycled or recovered 15,223 15 Hazardous waste 1,376 1 Sewage 28,074 28 TOTAL 50,222 50

Scenario 3: Organic waste for Bio Gas Having bio gas system in AIT campus, not only help reduce amount of organic waste generation but also give alternative in fuel supply that can be used in activities such as cooking or heating boiler. In another word, AIT can reduce fuel consumption and organic waste at the same time. In this scenario, assume all the food waste that is generated goes to Bio Gas system. On the other hand, there is no food waste goes to landfill. The overall GHG emission from solid waste generation in scenario 3 can be reduced to 40 t C equ from 92 t C equ, see table 4.33 for GHG emission from solid waste in Scenario 3. Table 4.33 Results of Calculation on Scenario 3 of Solid Waste Generation

Direct waste kg equ. C t C equ. TPS 5,550 6 Waste recycled or recovered 4,617 5 Hazardous waste 1,376 1 Sewage 28,074 28 TOTAL 39,616 40

4.4.4 Water and Wastewater Water and wastewater sector in AIT emitted about 113.8 t C equ in 2009. GHG emissions from this sector can be decreased by reducing water consumption and electricity consumption related to water supply activities. Scenario 1: Reduce Water Consumption By raising awareness of AIT faculty, staff and students in terms of reduction of water consumption, AIT should be able to have 30% reduction of water consumption. From calculation, AIT will reduce GHG emissions to 107.6 t C equ through water reduction programs. See table 4.34 for Data Information on Scenario 1 of Water and Wastewater.

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Table 4.34 Data Information on Scenario 1 of Water and Wastewater Data Information Current Data

(per year) %

Reduction Scenario Data

(per year) Water Consumption

588,000 m3 30 411,600 m3

Total BOD 16,470 kg 30 11,529 kg Scenario 2: Pump retrofitting Installing new water pumps with energy saving devices can require high investment. However it can help reduce electricity consumption and save money. Since scenarios in this study are proposed to implement in AIT campus, only water pumps in campus are taken into consideration. New water pumps with energy efficient should be able to reduce electricity consumption for 20%. See Table 4.35 for Data Information on Scenario 2 of Water and Wastewater. Table 4.35 Data Information on Scenario 2 of Water and Wastewater

Data Information Current Data (per year)

% Reduction

Scenario Data (per year)

Generated Wastewater 470,400 0 470,400 Electricity Consumption for pumping raw water

99,960 0 99,960

Electricity for water treatment process

470,400 0 470,400

Electricity Consumption for pumping water at AIT

99,960 20 79,968

From calculation in the spreadsheet, AIT will reduce GHG emissions to 111.1 t C equ. Scenario 3: Rainwater Harvesting Having rainwater tanks installed in AIT campus can help harvest rainwater to be used in some activities e.g. gardening or washing. Another option for AIT to have rainwater harvesting is to build its own ponds or canal on campus. AIT has many empty spaces in the very back of campus, instead abandon those spaces, AIT can make use of that. It is also adequate to harvest huge amount of rainwater during rainy season. This is expected to reduce water consumption from authority by 50%. See Table 4.36 Data Information on Scenario 3 of Water and Wastewater. Table 4.36 Data Information on Scenario 3 of Water and Wastewater

Data Information Current Data (per year)

% Reduction

Scenario Data (per year)

Water Consumption 588,000 50 294,000 From calculation in the spreadsheet, AIT will reduce GHG emissions to 111.7 t C equ by installing rainwater tank.

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Table 4.37 Amount of GHG Reduced in Each Scenario Aspect Scenario Amount of GHG reduced

(tCeq) Energy Energy Conservation 602 Transportation of People

AIT bus service 154 VDO conferences 404

Solid Waste 3Rs 3 Fertilizer 42 Biogas 52

Water and Wastewater

Reduce Consumption 6.2 Pump Retrofit 2.7 Rainwater Harvesting 2.1

Table 4.37 performs that the scenario of energy conservation has the highest potential in term of reduce GHG emissions. Although reduction scenarios in water and wastewater aspect give very low GHG reduction, it is still important to take into account because it involves people’s participation which can lead to significant reduction. 4.5 Phase 4: Analysis of Possibility for Implementation of Scenarios In order to motivate AIT to move towards low carbon campus, it is important to have proper policy guidelines and measurement tools. According to Poohngamnil, 2010, there is no explicit policy at AIT to reduce GHG emissions on campus except for the awareness programs. Some knowledge related to low carbon emissions can be gained from some coursework about issues on climate change. Moreover meetings and conferences to raise awareness on climate change are conducted regularly at AIT. This study has measured the current situation of GHG emissions at AIT using 2009 as base line year. Also this study has proposed scenarios for reduction of GHG emissions suitable for each aspect. GHG emissions from each proposed scenario has been estimated in order to acknowledge its potential in reducing overall GHG emissions in AIT campus. This segment describes in detail about analysis of advantages and disadvantages for each proposed scenario that has been applied in the study in order to identify the most effective and applicable policy for the context of AIT. 4.5.1 Energy: Electricity Conservation Electricity Conservation can be done by promoting energy saving programs and light retrofitting. Advantages: 1. By having all AIT individuals participate in energy saving programs, some noticeable amount of GHG emissions can be reduced. Raising awareness for electricity conservation that means people are always concerned to use electricity only when necessary. 2. Light retrofitting has been proved to effectively reduce electricity consumption. Disadvantages:

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1. Light retrofitting requires high investment however the cost-benefit as well as payback period need to be analyzed in further study. 4.5.2 Transportation of People (Travel) Scenario 1: Promote AIT bus services Advantages: 1. Less fuel consumption in personal cars which results in less GHG emission. Disadvantage: 1. It might not be convenient for AIT faculty, staff if the bus’s level of service is low e.g. number of bus is not enough or buses do not come on time. 2. It requires some investment to improve the level of services for buses. Scenario2: Video conference Advantages: 1. Huge amount of GHG emission can be reduced due to its high potential in contributing to GHG emissions. 2. It can reduce payments on flight tickets. Disadvantage: 1. It can cause an inconvenience during conferences. 4.5.3 Solid Waste Generation Scenario 1: 3Rs Advantage: 1. GHG emission due to materials can be reduced. 2. Money gained from selling recyclable waste. Disadvantage: 1. It requires disciplinary of people in order to regularly do the waste separation. Scenario 2: Fertilizer Advantage: 1. It can reduce GHG emission from food and yard waste. 2. Organic fertilizer can be used for activities on campus. 3. Having its own fertilizer, some money can be saved.

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Disadvantage: It requires large area and proper knowledge. Scenario 3: Biogas 1. It can reduce GHG emission from food and yard waste. 2. Biogas production can be used for activities on campus. Disadvantage High investment 4.5.4 Water and Wastewater Scenario 1: Reduce water consumption Advantage: 1. Reduce GHG emission from water consumption 2. Reduce payment on water consumption. Disadvantage 1. It requires participation for people. Scenario 2: Pump retrofitting Advantage: 1. Reduce water consumption and GHG emission from water. 2. Reduce electricity consumption related to water supply. Disadvantage High investment Scenario 3: Rainwater Harvesting Advantage: 1. Reduce amount of water supply from authority. 2. Reduce electricity on water pumping. Disadvantage High investment In order to select the proper the appropriate GHG reduction scenarios to be implemented at AIT, monetary tools need to be considered in further study.

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This study has also summarized the possible policies to help AIT achieve low carbon campus. 1. AIT should have its own GHG reduction target using base line emissions of a certain year e.g. AIT should be able to reduce GHG emissions by 20% of GHG emissions in 2009 within the year of 2020. 2. Prioritize major source of emissions and study its characteristics so that reduction plans can be proposed accordingly. 3. AIT should introduce retrofitting programs on energy efficiency for existing electrical equipments e.g. light bulbs and water pumps. 4. Subsidizing the energy efficiency equipments can help achieve retrofitting programs. Raise awareness of students by conducting activities related to reduction of GHG emissions. 5. Regularly announce or publish AIT’s GHG emissions per capita so that AIT individuals can know how much GHG they are emitting and how they can reduce. Improve knowledge on greenhouse gas and climate change through activities and assignments in classes. For the summary of advantages and disadvantages for each proposed scenario, can be found in table 4.38. Table 4.38 Summary of Advantages and Disadvantages for Each Proposed Scenario

Aspects Scenarios Advantages Disadvantages Energy Electricity

Conservation Save money Save Energy

Require Investment

Transportation AIT buses Less fuel consumption Not always convenient

Video Conference

-Less fuel consumption -Save money

Not always convenient

Solid Waste 3Rs - Reduce waste generation - Money gained from recyclables

- Require disciplinary

Fertilizer - Reduce organic waste - Fertilizer can be used at AIT.

High Investment

Biogas - Reduce organic waste - Biogas can be used at AIT.

High Investment

Water and Wastewater

Reduce consumption

Reduce payment Participation of people

Pump Retrofitting

Reduce electricity consumption

High Investment

Rainwater Harvesting

Reduce amount of water supply

High Investment

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4.5.5 The possible policies to help AIT achieve low carbon campus In order to achieve in reducing GHG emissions in a territory, implementation of effective technology and changing in people’s life style must be taken into consideration. Followings are possible policies to help AIT achieve low carbon campus: 1. Set GHG reduction target using base line emissions of a certain year 2. Prioritize major source of emissions and study its characteristics so that reduction plans can be proposed accordingly. 3. AIT should introduce retrofitting programs on energy efficiency for existing electrical equipments e.g. light bulbs and water pumps. 4. Subsidizing the energy efficiency equipments can help achieve retrofitting programs. 5. Raise awareness of students by conducting activities related to reduction of GHG emissions. 6. Regularly announce or publish AIT’s GHG emissions per capita so that AIT individuals can know how much GHG they are emitting and how they can reduce. 7. Improve knowledge on greenhouse gas and climate change through activities and assignments in classes. 4.6 Limitations of the study regarding emission factors The Bilan Carbone spreadsheet used in this study was developed to be used mainly for the context of France that results in all of the emission factors provided in the spreadsheet are relevant to France’s situations. The Bilan Carbone’s emission factors guide was carefully studied in order to categorize the eligibility of emission factors. In this study however, some emission factors in the spreadsheet can be used due to similarity of conditions. The emission factors for fuel combustion are estimated according to type of fuel. So that emission factors for fuel consumptions in the spreadsheet are used accordingly that includes in energy, freight and travel aspects. Moreover, emission factor for electricity consumption in Thailand is provided in the spreadsheet. Below describes about emission factors for each item. 4.6.1 Energy 1. Electricity:

Electricity is generated by different type of power plant in different countries in the world e.g. coaled fired plant, nuclear power plant or hydropower dam. But it is always generated by a primary source, for instance petroleum, natural gas, nuclear fuel or solar energy. In Thailand however, the electricity production is mainly based on natural gas. The Bilan Carbone spreadsheet provides emission factors relevant to electricity consumption in each country. 2. Fuel Consumption Data input to the Bilan Carbone spreadsheet for fuel consumption requires clarification for types of fuel. The emission factors given in the spreadsheet are designed to calculate GHG emissions from data that are readily available for the reporting data. Each type of fuel contributes to GHG emissions in different level of emissions however the fuel combustion occurs in the same process even in different location. By choosing type of fuel relevant to

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specific use, the emission factors in the Bilan Carbone spreadsheet are changed accordingly. 4.6.2 Excluding Energy Most of these “non-energy” emissions involve gasses other than CO2. To convert emissions of these gasses into carbon equivalent units, the factors suggested by the IPCC are used. 1. Refrigerant Leakage Most of the recent refrigerant fluids are halogenated hydrocarbon compounds obtained by substituting halogens (Fluorine, Chlorine, Bromine, Iodine) for all or part of the hydrogen in a hydrocarbon molecule. HFCs, CFCs and HCFCs are potent GHG gases and as a result they can represent a significant fraction of total GHG emissions for a given area even if they are emitted in very small amount. Since the level of emissions from refrigerant leakage depends on types of refrigerant so that having the same type of refrigerant fluids in different parts of the world can give the same level of GHG emissions. By choosing type of refrigerant relevant to its specific use, the emission factors in the Bilan Carbone spreadsheet are changed accordingly. 2. Fertilizer Usage The fraction of nitrogen in fertilizer that is converted to gas depends on climate conditions, soil type and type of fertilizer. And since Thailand is located in different type of climate conditions than France, the emission factor for fertilizer usage in Thailand needs to be used accordingly. 4.6.3 Materials and Products Purchased (Inputs) In the Bilan Carbone spreadsheet, the emission factors for these materials have been studied by life-cycle analysis and direct calculation when data is available. These emission factors are intended to be updated as changes in industrial progress or new knowledge is obtained. 1. Materials Metal: Making steel metal causes GHG emissions primarily due to CO2 emissions from coal used to smelt iron ore as well as emissions due to combustion of coke-oven gas. Apparently the GHG emissions of metal are mainly from its manufacturing process. The Bilan Carbone spreadsheet is focused on activities in France. In order to calculate GHG emissions from Metal in Asian region especially in Thailand, it is necessary to study both similarity and differences in its manufacturing process. Aluminum: Production of aluminum can be source of GHG emissions due to energy used for instance, generating if heat during the process. Another cause of GHG emissions from aluminum production is the release of perfluorocarbons (CF4) during electrolysis of alumina.

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As aluminum production requires large amounts of electricity and the emission factors for electricity may vary by a factor of 10 from one country to another. Actual emissions for production of one ton of aluminum can widely vary depending on circumstance. In order to calculate actual GHG emissions from aluminum production in other part of the world such as Thailand, it is important to study through its manufacturing process and concerns for the differences in emission factors for electricity. Glass: The emission factors for glass manufacture is compiled by CEREN, a study for ADEME. The summary of emission factors for each type of glass is provided in the spreadsheet. Since the CEREN study used the average mix of primary energy sources other than electricity in France, it is quite applicable for emission factors to be used elsewhere. Plastic: In the Bilan Carbone spreadsheet, emission factors for plastic production are obtained from the study by the Association of Plastics Manufacturers in Europe (APME). APME has studied life cycle analyses giving atmospheric releases of CO2, methane, N2O and halocarbons linked to production of a certain number of basic chemicals and plastics however emissions from halocarbons are generally negligible and are not included. In case of types of plastic is difficult to identify, the average values must be used to report average emissions for production of one ton of plastic material. The memo for Policymakers from French Inter-ministerial Commission on the Greenhouse Effect (MIES) provides an emission factor for average type of plastic in the Bilan Carbone spreadsheet to be used in context of France and elsewhere when type of plastic is not determined. Paper: Emission factor for paper production is taken from the study by the US Environmental Protection Agency. The emission factors from papermaking and paper pulp from recycled materials are quite the same amount. In some cases of recycled materials, the emission factor can be higher due to the preparation process (de-inking) that consumes as much energy as making paper pulp from wood. However this emission factor can also be used in the rest part of the world. 2. Food Consumption The emission factor used in the study is one that provided in the spreadsheet. For typical meal in France, emission factor should be slightly different from Thailand’s typical meals because of its ingredients. Because of unavailability of emission factors for some items in the context of Thailand, provided emission factors in the spreadsheet were used in order to get overall GHG emissions in macro level. To be very exact in estimating of GHG emissions, study on emission factors for Thailand needs to be focused in further study. 4.6.4 Transportation of Goods (Freights) In this sector of transportation of goods or freights, the input data into the Bilan Carbone spreadsheet requires amount of fuel consumption during transport of goods whether internal, outgoing or incoming the study areas. On the basis of fuel consumption in good

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transport, the emission factor can be derived using the emission factors for fuel consumptions (refer to 4.6.1). 4.6.5 Transportation of People (Travel) 1. Emission factors for people commuting by car, when distance traveled is know In the Bilan Carbone spreadsheet, the emission factors are produced relevant to type of itinerary that takes into account of the place of residences. Taking distances and emission factors for each type of itinerary, approximate emissions for commuting travel can be calculated. In France, residence areas can be categorized as non-urban, mixed, urban and urban in rush hour. In order to calculate GHG emissions from commuting by car in areas other than France, study of emission factors relevant to type of residence needs to be taken into consideration. 2. Emission factor for air travel Aircrafts emit GHG emissions which is accounted for using the fuel’s emission factors and they also emit other types of greenhouse gases e.g. water vapor, condensed water in various forms, nitrous oxide and methane that are potential to produce ozone. In the Bilan Carbone spreadsheet, emission factors are used according to classes in the airplane. As long as types of classes are chosen accordingly, emission factors of this item can be used elsewhere beside France. 4.6.6. Solid Waste and Wastewater (Direct Waste) GHG emissions from solid waste takes into account of transportation of solid waste to the treatment plants (land filling, incineration or recycle). The Bilan Carbone spreadsheet assumed that the treatment plants located about 80 km away from study area and the garbage truck carries about 20 tons of garbage. This can be varied from different circumstances. Metal and Glass: Metal and Glass are considered to be inert waste that cannot be decomposed and burnt. Inert materials do not cause GHG emissions whether they are disposed of in landfill or by incineration. So that the only emissions linked to inert materials to landfill are the emissions for waste transportation to landfill. Plastic: Plastic sent to landfill is not subject to chemical reaction since it does not ferment. The only emissions for end of life disposal are linked to transportation. In order to calculate exact GHG emission from plastic waste, it is better to focus on emission from waste transportation which can be varied in different circumstance in different areas. Paper: Paper can be decomposed when it is sent to landfill that engenders methane and CO2 emissions and a proportion of the carbon with non-fossil as paper is made from wood, is sequestered in soil. The Bilan Carbone spreadsheet uses emission factors for paper waste from the US EPA study. The emission factor takes into account for methane emissions and carbon sequestration in landfills. And this emission factor for paper waste can also be used

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in the rest part of the world. However it is important to note that temperature is one of the factors that involve in methane produced during the decomposition process. Study on effect of the difference in average temperature of different part of the world should be concerned in further study. Food Waste: Decomposition of food waste sent to landfill engenders methane emissions and nitrous oxide. The emission factor for food waste is also taken from the study by the USEPA. Similar to paper waste, the emission factor takes into account of methane produced and sequestered carbon. The use of emission factor for food waste can be application in elsewhere as well. Hazardous Waste: The emission factor for hazardous waste used in the Bilan Carbone spreadsheet was taken from a study conducted by FNADE. The study uses life-cycle analysis (LCA) methodology and the data was based on average values representing the situation in France. On the other hand, the emission factor used in the Spreadsheet cannot really represent to situation in Thailand or Asian region. Study on life cycle analysis of hazardous waste in Asia needs to be taken into consideration in further study. 4.6.7 Property Buildings and machinery are made of basic materials and then transported and assembled that contribute to GHG emissions. Emission factors can be refined by successive iterations when accurate estimations have been carried out by builders and manufacturers. Basically, the emission factors for buildings are calculated from energy consumption for construction of different types of buildings. In order to accurately estimate emission factor for buildings in other places besides France, it is important to look into the emission from energy use which is varied in different places. 4.6.8 Water and Wastewater This study has produced a separated spreadsheet for calculating GHG from overall process of water production and wastewater treatment. Emission factors taken into account in this sector are from electricity consumption, supply water and wastewater. Electricity consumption: Activities involved with electricity consumption are raw water pumping, operating of treatment plants and wastewater pumping. Emission factor for electricity consumption needs to be changed according to electricity production in each country. Supply water: Supply water is considered to be very clean with very tiny amount of contaminants but still supply water can emit some amount of GHG emissions. Qualities of raw water and types of treatment process are main factors of supply water’s qualities which results in level of GHG emissions. These two factors are different in different geography so that study on emission factor for supply water in other types of study areas needs to be focused.

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Wastewater: Emission factor for wastewater concerns methane emission and nitrous oxide released from the decomposition of nitrogen compounds. Emission factor for wastewater takes into account of types of treatment and climate conditions which should be changed in different areas. To summarize, emission factors provided in the spreadsheet are compatible to the context of France however some of them are capable be used in other areas other than France. Table 4.38 presents summary of data parameter in the study with the necessity for adaptation on emission factors. For fertilizer usage and food consumption are two main items that need to be studied for emission factors since food ingredients are varied in different countries in the world and climate results in production of nitrous oxide from fertilizer. Emission factors for travelling by cars were assumed by characteristics of towns in France. Moreover, emissions from waste delivered to landfill are estimated from transportation of waste to the landfill sites that should be adapted in different circumstances. Table 4.39 Summary of Emission Factor Adaptation

Data Parameter No need for Emission Factors Adaptation

Require for Emission Factors Adaptation

Electricity x Fuel Consumption x Refrigerant Leakage x Fertilizer Usage x Metal x Aluminum x Glass x Plastics x Paper x Food Consumption x Freight x Travel by cars x Travel by airplanes x Metal and Glass to landfill x Paper to landfill x Food waste to landfill x Hazardous waste to landfill x Property x Supply water x Wastewater x

Although the Bilan Carbone model is a very effective tool used to estimate GHG emission in a territory, there is another limitation that should be mentioned about. In the context of AIT, for example, many greenery spaces are comprised all over the campus e.g. trees and grass fields. Plants are considered to be source of carbon sink that remove carbon dioxide from the atmosphere. In another word, some significant amount of carbon dioxide emitted from activities in AIT could have been removed by those greenery spaces on campus. Consequently, in further study and development on the Bilan Carbon tool, issue in carbon sinks should be taken into consideration.

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Chapter 5

Conclusion and Recommendations

5.1 Conclusion This study has estimated GHG emissions in AIT campus in the year of 2009. The sources of GHG covered in the study are energy, excluding energy, materials and products purchased (inputs), transportation of goods (freights), transportation of people (travel), solid waste and wastewater (direct waste), as well as property. Data collection was conducted through questionnaires, surveys for primary data as well as review on previous study for secondary data. The Bilan Carbone model was used in order to estimate the overall image of GHG emissions in AIT campus that resulted in the data collection process needed to be done in macro level. With all of the required data inputs into the spreadsheet, it is quite difficult and time consuming to get the exact number for each data. Correct assumption and estimation of data information have been done as well as guesswork, when some required data did not exist. Results of the study have shown that in 2009, AIT emitted about 6,245 tons Carbon equivalent of GHG emissions. Transportation of people is considered to be the biggest contributor, which accounts for 41% of overall GHG emissions in AIT. The average GHG emissions per capita of AIT is 2.08 tCeq. In real situation, reduction of GHG emission from all sources can be difficult and time consuming, so that prioritization of magnitudes needs to be done. Implementing the reduction plans on the main sources of emission can help reduce the overall GHG emission effectively. So that, this study has proposed reduction scenarios for aspects of transportation of people, energy, solid waste generation and wastewater. Moreover separate spreadsheet for calculating GHG emission from the whole system of water supply had been produced. GHG emissions from water supply system can be from electricity consumption in water pumping, water supply, operations of water and wastewater treatment plants as well as wastewater. The scenario of energy conservation has the highest potential in terms of reduce GHG emissions. By combining all of those reduction scenarios, AIT should be able to reduce about 1,268 tCe per year. Although reduction scenarios in water and wastewater aspect give very low GHG reduction, it is still important to take into account because it involves people’s participation. In order to motivate AIT to move towards low carbon campus, it is important to have proper policy guidelines and measurement tools that involve new technology on energy efficiency and changing in life style of people in study areas. Regularly receiving information about their own carbon footprint and ways of reducing it is capable in order to raise awareness among AIT individuals in terms of reduce GHG emissions from their daily activities. 5.2 Recommendations for Further Study 1. Some sources of GHG emissions that are not taken into account in the study and need to be further studied, namely:

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• The amount of materials and products that have been purchased to be used in AIT activities.

• Exact measurement should be done on fuel consumption for transportation of goods. • Exact measurement should be done on fuel consumption for traveling by cars by AIT

individuals. • There should be record of electricity consumption related to water supply at AIT. 2. AIT should start collecting its own records of data information related to all GHG emission sources in order to plan the reduction policy accordingly. 3. Student Union at AIT should be the main driving force to raise participation among AIT students. 4. Study on emission factors for the context of Asia especially, Thailand needs to be done according to 4.6.

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of Climate Policy, 8, S5-S16. Wiedmann, T. and Minx, J. (2007). A Definition of Carbon Footprint. ISAUK Research &

Consulting, Durham, United Kingdom. Retrieved March 2011 from Center for Sustainability Accounting. http://www.censa.org.uk/docs/ISA-UK_Report_07-01_carbon_footprint.pdf.

 

71

Appendix A

Information on Data Collection

72

Appendix A.1 Map of AIT campus and identification of emission sources

Residential Areas Academic Areas

Leisure Areas Administration Area

Emissions - Electricity Consumption - Inputs - Direct Waste -Property - Travel (Staff)

Emissions - Electricity Consumption - Property - Travel (Students) - Inputs - Direct Waste

Emissions - Electricity Consumption - Property - Inputs - Freight - Direct Waste

Emissions - Electricity Consumption - Inputs - Direct Waste -Property - Travel (Faculty and Staff)

73

Appendix A.2 Numerical Data for Electricity Consumption in 2009

Month Year 2009 Electricity

Unit (kWh) Cost (baht) Jan 833,580 2,869,640 Feb 1,011,085 3,445,181 Mar 1,204,280 4,008,535 Apr 1,144,030 3,836,077 May 1,135,540 3,797,004 Jun 995,730 3,308,347 Jul 1,016,165 3,386,986

Aug 1,120,760 3,755,118 Sep 1,143,200 3,857,403 Oct 1,143,165 3,849,017 Nov 1,055,650 3,635,190 Dec 931,850 3,148,253

Total 12,735,035 42,896,757 (Source: Sodexo, 2010)

74

Appendix A.3 Number of Rooms that Use LPG Gas

Type of Residents Number of resident units with LPG gas tank

Student Dormitory none Student Village 140 Faculty Resident 30 Staff Dormitory 90

Total 260

Appendix A.4 Fuel Consumption for Each Type of Vehicle Owned by AIT Diesel Consumption

Type of vehicle Code No. Amount of fuel (L/year) Tractor E 2-5 2960 Tractor E 2-1 1880 Tractor E2-2 645 Tractor E2-4 791 Tractor E 2-7 1910 Tractor Kubota 1738 ROAD Grader E1-1 210 Steamroller E 2-6 131 Truck (4 wheeled) E 2-10 695 Hydraulic Platform Truck for Gardening

E 2-9 205

Fogging Machine GD 00031 180 Hydraulic Platform Truck for Electrician

FM 80

Toyota pick up truck FM 408 Dumper Truck 90 Water pump 155 Other 100 Total 12,178

Gasoline Consumption

Type of vehicle Code No. Amount of fuel (L/year) Water Pump attached with tractor

AIT-01 565

Collecting Truck for yard waste

GD00037 906

Leave Blower GD 00033-36 2093 Collecting Truck for yard waste (Jacobson)

AIT-02 331

Honda Lawn Mover (shoulder type)

GD00001-16 2574

Pruning Machine GD00019-22 400 Fogging Machine GD00031 45 Fogging Machine (wired) GD00030 108 Lawn Mower (Craftsman PYT 9000)

GD 00027 715

Lawn Mower (OREC RM88)

GD00028 923

75

Gardening Saw (36”) AIT-03 80 Gardening Saw (12”) GD00023-24 115 Pick up Truck (Toyota) AIT-GD 2189 Forklift FM 25 Soil Compactor 60 Other 42 Electric Generator (Honda 13hp)

47

Lawn Mower (Cricket field)

21

Total 11,239

Appendix A.5 Detail information for Incoming Road Freights in 2009

Items Shop Vehicles Type of fuel

Amount of fuel

consumption (L/two ways)

Average travel

(Frequency)

Amount of fuel

consumption (L/ year)

Note

Water Grocery Pick up truck

Diesel 6 Once a week 312 x 52

Ice Grocery Container truck

Diesel 3 2 times/week 312 x 52

Bread (Farm House)

Grocery Container Truck

Diesel 8 Once a week 416 x 52

Vegetables and fruits

Grocery Pick up truck

Diesel 5 Everyday 1,490 x 298

Coca Cola Grocery Container Truck

Diesel 20 2 times/week 2,080 x 52

Ice Cream (Wall’s)

Grocery Container Truck

Diesel 20 2 times/ week 2,080 x 52

LPG gas Cafeteria Pick up truck

Diesel 4 Once a week 208 x 52

Food Products

Cafeteria Container truck

Diesel 4 Everyday 1,192 x 298

Ice Snack Bar Pick up truck

Diesel 2 Everyday 596 x 298

Food Products

Snack bar Pick up truck

Diesel 2.5 Everyday 745 x 298

Bread UFM Container truck

Diesel 25 Everyday 7,450 x 298

Food products

Grocery@ UFM

Pick up truck

Diesel 6 Everyday 1,788 x 298

Stationary Book Store Container truck

Diesel 28 Once a month 336 x12

Laundry Sodexo Container truck

Diesel 9 Once a week 468 x 52

Food products

Noodle shop

Pick up truck

LPG 8 kg Everyday 2,384 kg x 298

76

Appendix A.6 Detail Information Transportation of AIT’s faculty and staff in context of work travel by airplanes

International Business Trip by AIT faculty and staff (2008) Country Distance from

Thailand (km) Number of trips Total Distance

(km) Cambodia 567 236 133,812 Myanmar 856 144 123,264 Laos 471 436 205,356 Vietnam 808 1,228 992,224 Bangladesh 1,410 128 180,480 Singapore 1,572 80 125,760 Malaysia 1,950 168 327,600 China 2,412 316 762,192 Nepal 2,191 52 113,932 Indonesia 2,334 224 522,816 Japan 4,315 180 776,700 India 2,383 196 467,068 Pakistan 3,485 60 209,100 Bhutan 1,692 8 13,536 Taiwan 2,412 112 270,144 South Korea 1,654 96 158,784 Philippines 2,265 164 371,460 Sri Lanka 2,374 188 446,312 Afghanistan 3,885 32 124,320 United Arab Emirates

4,986 12 59,832

Iran 5,101 16 81,616 Kyrgyzstan 3,782 4 15,128 Greece 7,980 12 95,760 France 9,395 40 375,800 UK 9,541 72 686,952 Italy 8,709 28 243,852 Australia 5,791 32 185,312 Belgium 9,102 4 36,408 Finland 7,684 4 30,736 Netherland 9,001 8 72,008 Switzerland 8,936 28 250,208 New Zealand 9,862 12 118,344 Sweden 8,273 4 33,092 Norway 8,623 12 103,476 USA 13,392 76 1,017,792 Mexico 14,984 12 179,808 Columbia 17,704 8 141,632 Total 10,052,616

77

Domestic Business Travel of AIT faculty and staff (2008) (short hual) Provinces Distance from AIT Number of Trips Total Distance

(km) Ayuthaya 76 12 912 Bangkok 46 4 184 Chaing Mai 696 110 76,560 Chaing Rai 785 2 1,570 Chainat 194 12 2,328 Chantaburi 245 14 3,430 Cholburi 81 88 7,128 Chachengsao 82 2 164 Hadyai 950 8 7,600 Huahin 153 16 2,448 Karnchanaburi 128 10 1,280 KhonKaen 449 46 20,654 Nakornratchasima 259 20 5,180 Krabi 841 4 3,364 Lamphoon 670 2 1,340 Lopburi 153 4 612 Mukdahan 642 10 6,420 Nakornnayok 107 4 428 Nakornsithumrat 780 18 14,040 Nonthaburi 45 14 630 Pathumthani 30 2 60 Pattalung 840 2 1,680 Petchabun 345 30 10,350 Phisanulok 377 26 9,802 Phuket 862 116 99,992 Pichit 344 4 1,376 Prachinburi 136 12 1,632 Ratchaburi 100 12 1,200 Rayong 568 32 18,176 Roiet 512 10 5,120 Samuthsakorn 72 14 1,008 Saraburi 97 8 776 Singburi 142 2 284 Suphanburi 100 2 200 Suratthani 664 2 1,328 Tak 426 10 4,260 Trad 365 2 730 Ubon 592 24 14,208 Udonthani 564 42 23,688 Total 352,142

78

International travel by AIT students in 2009

Country No. of trips

Distance from airport (km)

Two ways (km)

France 11 9554 19248 USA 7 14111 28362 Japan 11 4490 9120 Nepal 11 2371 4882 Germany 5 8847 17834 Vietnam 26 813 1766 Malaysia 3 1063 2266 Korea 4 3655 7450 Afghanistan 5 4016 8172 Sweden 1 8254 16648 Lao PDR 7 710 1560 Scotland 1 9632 19404 Norway 3 8810 17760 Indonesia 6 2168 4476 Cambodia 7 476 1092 Pakistan 4 3711 7562 Tanzania 1 7602 15344 Cook Islands 1 11517 23174 Myanmar 9 1045 2230 Philippines 3 2276 4692 Uganda 1 7646 15432 Bangladesh 3 1559 3258 China 1 2489 5118 Nigeria 2 9991 20122 Total 133 126806 256972

79

Appendix A.7 Detail Information for Registered Visitors to AIT

No.

Visitors from (Company Name)

No. of Visitors

City

Distance from AIT

two ways (*2) km

Total Distance (*Number of visitors) km

1 Balochistan University of Engineering&Technology 3 Khuzdar,

Pakistan 3943 7886 23658

2 Andalas University (UNAND) 7 (Padang) 1709 3418 23926

3 Department of Science and Technology 3 (Manila) 2261 4522 13566

4

Rehabilitation and Reconstruction, Natinal Agency for Disaster Prevention

1

(Jarkata Pusat) 2389 4778 4778

5 International University 4 ( Ho Chi Minh City) 800 1600 6400

6

University of Transportation and Communication

2 Hanoi 1059 2118 4236

7 Visitors from China 1 Beijing (ASSUMED) 3371 6742 6724

8

Multi Functional Cooperative Association, Ministry of Science, Research and Technology

1

Tehran 5560 11120 11120

9 Vishvakarma Institute of Technology 2 Pune,

Maharashtra 3000 6000 12000

10 University of Boras 2 STOCKHOLM

SWEDEN 8368 16736 33472

11 Industrial Management Institute 2 Tehran 5560 11120 22240

12 Military Institute of Science and Technology 3

Dhaka 1637 3274 9822

13 Chubu of University 1 Aichi, Japan 4412 8824 8824

14 Nippon Koei Co Ltd. 2 Tokyo, Japan 4667 9334 18668

15 Universiti Putra Malaysia 2 Selangor, malaysia 1215 2430 4860

16 Univesal College 5 Kathmandu 2312 4624 23120

17

South Asian Institute of Technology and Management (SAITM)

1 Malabe, Sri Lanka 2475 4950 4950

18

Siam City Bank Public Company Limited 2

1101 Newpetchburi Rd.,Rajthevi,

Bangkok, Thailand 38.4 76.8 153.6

19

International Trainees from Chulalongkom University 10 Chulalongkorn

University 36.8 73.6 736

20 Hokkaido University 2 Hokkaido, Japan 5142 10284 20568

21 Graduate School for Advance Studies 6 Kanagawa,

Japan 4652 9304 55824

22

Japan Advanced Institute of Science and Technology 2

Ishikawa, Japan 4456 8912 17824

23 University of Tokyo 4 Tokyo, Japan 4667 9334 37336

24 Flinders Universiity 2 Adelaide, australia 6800 13600 27200

25 Ministry of Education 4 Vientiane, Laos 595 1190 4760

26 GTZ/engineering capacity building program 1

Addis Ababa 6843 13686 13686

80

27

Agency for Agricultural Research and Development, Ministry of Agriculture

2

Jakarta 2398 4796 9592

28 SJ College of Engineering Mysore 4 Mysore, India 2695 5390 21560

29 National Taiwan University 6 Taipei, Taiwan 2598 5196 31176

30 Sultan Qabos University 3 Muscat, Oman 4639 9278 27834

31 Univesity of Fukui 2 Fukui 4398 8796 17592

32 Academy for Planning and Development 4 Dhaka 1637 3274 13096

33 Jawarharlal Nehru Technological University 3 Andhra

Pradesh, India 2324 4648 13944

34 State Bureau of Surveying and Mapping of China 2 Beijing 3371 6742 13484

35 Osmania University 3 Andhra Pradesh, India 2324 4648 13944

36

Center of Integrated Rural Development for Asia and Pacific

1 Dhaka 1637 3274 3274

37

Ministry of Agriculture &Tashkent Irrigation Institute

4 Tashkent, Uzbekistan 4402 8804 35216

38 Indian Ambassador to Thailand 1 Indian Embassy 38.2 76.4 76.4

39

Embassy of the Republic of Poland to the Kingdom of Thailand

1 36.3 72.6 72.6

47 AIFOMD 1 0 0

40 AIT Center in Indonesia 1 Bandung, Indonesia 2493 4986 4986

41 Ambassador of Timor-Leste to Thailand 1 38.4 76.8 76.8

42 Ministry of Education Etophia 2 Addis Ababa 6843 13686 27372

43 Faculty of Applied Statistics, NIDA 2 53 106 212

44 International ESD Center 3 Tokyo, Japan 4667 9334 28002

45

National Pintung Unversity of Science and Technology 1

Pingtung 2386 4772 4772

46

College of Social Science an Humanities, Vietnam National University

3 Hanoi 1059 2118 6354

47 Ministry of Local Development 1 Lalitpur 2310 4620 4620

48 Discussion on Organizing "ICSS-Asia" at AIT 1 Tokyo, Japan 4667 9334 9334

49 Visitor from Taiwan 1 Taipei, Taiwan 2598 5196 5196

50 Visitor from World Bank 1 Pathumwan, Bangkok 46 92 92

51 Ministry of Foreign Affairs 1 36 72 72

52 The Royal Norwegian Embassy Bangkok 2 Sukumvit 38 76 152

53 Eastern Samar State University 10 Eastern Samar

Philippines 2776 5552 55520

54 University of Gadjah Mada 1 Yogyakarta, Indonesia 2695 5390 5390

55 National Remote Sensing Center of Vietnam 10 Hanoi 1059 2118 21180

56

Ford Foundation International Fellowships Program

4 37 74 296

57

Institute of International Relations, Yunnan University

2 Yunnan, China 1359 2718 5436

81

58

Institute of Aboriginal tertiary Education, Charles Darwin University

1

Darwin, Northern Territory, Australia 4491 8982 8982

59 Jimber University 4 Jember, East Java, Indonesia 2900 5800 23200

60 Korean Delegation 7 Seoul (Assumed) 3790 7580 53060

61 University of Dalat 13

Dalat, Prowincja Lâm Đồng, Vietnam 936 1872 24336

62 MCC Group 2 Yangon, Myanmar 681 1362 2724

63 Indian Delegation 4 New Delhi, India 3021 6042 24168

64 Kyoto University 10 Kyoto, Japan 4310 8620 86200

65 Fraunhofer IFF Germany 2 Magdeburg, Germany 8830 17660 35320

66

Australian Ambassador of Thailand 1

Australian Embassy, Bangkok 40 80 80

67 Environment Development Center DPR 4 Pyongyang 3815 7630 30520

68 Burapha University 4 96 192 768

69 President of APIC 1

Sydney, New South Wales,

Australia 7592 15184 15184

70 University of Nice-Sophia Antipolis/Polytech 2

Nice, France 9316 18632 37264

71 Japanese Scholarship Granting Ceremony 4 Tokyo, Japan 4667 9334 37336

71 New JAXA 1 Tokyo, Japan 4667 9334 9334

73 La Reunion 4 La Réunion, France 9852 19704 78816

74 University of Nottingham Malaysia Campus 2 Selangor Darul

Ehsan Malaysia 1261 2522 5044

75 Provost of RICE University 2 Houston, TX,

United States 14963 29926 59852

76 Ochanomizu University 2 Tokyo, Japan 4667 9334 18668

77 CIRAD 3 Paris, France 9546 19092 57276

78 MINATEC 2 ( Ho Chi Minh City) 800 1600 3200

79 Bauhas University Weimar 1 Weimar, Germany 8869 17738 17738

80 Taiwan University Accreditation Board 1 Taipei, Taiwan 2598 5196 5196

81 Tokyo Institute of Technology 2 Tokyo, Japan 4667 9334 18668

82 University Technology Malaysia 2 Johor Malaysia 1419 2838 5676

83 Ministry of Science and Technology of Pakistan 1 Islamabad,

Pakistan 3651 7302 7302

84 Saitama University 1 Saitama, Japan 4670 9340 9340

85 Ministry of National Education DIKTI 2 Jarkata 2398 4796 9592

86 UPPSALA University 2 Uppsala, Sweden 8390 16780 33560

87 National Institute for environmental studies 4

Ibaraki, Japan 4757 9514 38056

88 The Bridge Fund 2

Washington D.C., DC,

United States 14243 28486 56972

Total 247 1,603,807

82

Appendix A.8 Waste generation audit 2010 Type of Waste %

Generated Amount

Generated (kg/day)

Recyclable waste from

Cash for Trash

(kg/day)

Recyclable waste from

Sodexo (kg/day)

Total recyclable

waste (kg/day)

% recyclable

waste

Amount generated (tons/year)

Food Waste 50.3 1563.7 505.06 Rubber/Leather 0.2 6.8 2.5 Textile 1.3 34.9 12.7 Paper 12.8 352.7 11.37 6.70 18.07 5.12 128.7 Metal 1.3 36.2 1.66 1.60 3.26 9.0 13.2 Plastic 10.2 281.5 2.24 5.78 8.02 2.85 102.7 Glass 16.6 456.9 1.66 8.06 9.72 2.12 166.7 Hazardous Waste

4.0 110.6 40.4

Foam 0.8 18.4 6.7 Others 2.5 68.9 25.1 Total 100 2930.6 1003.76 Appendix A.9 Data information of water consumption in AIT campus 2009

Month Year 2009

Waterm3 Cost(baht)

Jan 57,641 1,075,898 Feb 49,942 932,139 Mar 42,502 847,181 Apr 43,223 806,740 May 50,496 942,525 Jun 49,017 914,893 Jul 49,691 927,437

Aug 50,789 947,932 Sep 47,525 886,980 Oct 52,422 978,396 Nov 47,877 893,560 Dec 47,090 878,873 Total 588,215 11,032,560

(Source: Sodexo, 2010)

83

Appendix A.10 Detail Information of Surface Areas in Each Type of Building in AIT

1. Office and Academic

No. Building Bldg. Floor Estimated Area (Sq.m)

1 AFE 1 2 1,350 2 Academic North 1 2 4,005 3 Academic South 1 2 8,052 4 Computer Science 1 2 960 5 ET-1 1 2+1+1 1,770 6 ET-1 1 2 1,229 7 TC 1 2 1,570 8 Library 1 2 6,781 9 RCC 1 2 3,418 10 REC (AFE) New 1 1 1,080 11 EC (AFE) Old 1 1 2,800 12 Administration (Old Wing) 1 2 2,755 13 Administration (Extension) 2 640 14 Chiller Extension 128 15 Student Union 1 1 960 16 Gatehouse + Pump House 1 1+1 100 17 Korea House 1 1 340 18 Nursery, New 1 1 130 19 Physical Plant & Chiller 1 2+1 3,048 20 WRE, New Office 1 2 883 21 Chalermprakiat 1 2 1,450 22 Squash Court 1 1 200 23 Biotechnology 1 2 804 24 PPT 1 1,583 25 Acrors 1 2 800 26 Som & Extension 1 2 2,353 27 DEC 1 1 120 28 DEC InterLab 1 1 880

TOTAL AREA 50,189

84

2. AIT Community School

No. Building Bldg. Floor Estimated Area (Sq.m)

1 AIT Community School 1 2 1,600 TOTAL AREA 1,600

3. Auxiliary Service

No. Building Bldg. Floor Estimated Area (Sq.m)

1 Outreach 1 3 1,920 2 AITCC Arcade 1 1 100 3 Vietnamese Restaurant 1 1 66 4 Academic Kiosk 1 1 10 5 West-end Kiosk 1 1 10 6 AITCS Kiosk 1 1 15 7 RAST Office 1 1 36 8 Bicycle Shop 1 1 36

TOTAL AREA 2,193

4. AIT Conference Center

No. Building Bldg. Floor Estimated Area (Sq.m)

1 AITCC 1 4 8,518 2 AITCC Annex 1 4 2,260

TOTAL AREA 10,778

5. Student Accommodation Building

No. Building Bldg. Floor Estimated Area (Sq.m)

1 Student Dormitory A-H 8 3 5,052 2 Student Dormitory J-K 2 3 1,135 3 Student Dormitory L-N 3 3 4,884 4 Student Dormitory Q-S 3 3 4,884 5 Student Dormitory X-Y 2 3 1,720 6 Student Village I 20 2 2,295 7 Student Village II 20 2 2,295 8 Student Village III 29 2 3,290 9 Student Dormitory P 1 3 1,222

85

10 Student Dormitory T, U, V, W 4 4 6,849 11 CUC Guest House 1 2 200

TOTAL AREA 33,826

6. Campus Residential Service Unit

No. Building Bldg. Floor Estimated Area (Sq.m)

1 Faculty House, Duplex + Maid 4 2 628 2 Bangalo House 6 1 676 3 Faculty House, N.Z. + Maid 8 2 1,199

4 Faculty House, Townhoue + Maid 10 2 1,356

5 Staff Housing 1 4 2 1,600 6 Staff Housing 2 1 3 747 7 Staff Housing 3 4 2 1,258 8 Staff Housing 4 1 3 795 9 Staff Housing 5 1 3 1,475 10 Staff Housing 6 2 3 3,500 11 Staff Housing 7 1 3 2,148 12 Staff Housing 8 1 3 2,582 13 Staff Housing 9-12 4 3 4,880

TOTAL AREA 22,844

86

Appendix B

Data Information on Data Analysis

87

Appendix B.1 Data Input for Energy

Electricity Consumption 1 - Electricity purchased, average by country Recap Recap Emissions Emissions

Country of the electricity consumption kg equ C kg equ CO2 Cons. kg equ. C kg equ. (kW.h) per kWh carbon

Thailand 1,774,801 6,507,603 12,735,035 0.139 1,774,801 Total 1,774,801 6,507,603 1,774,801

Fuel Consumption

1 - Fossil fuels, fixed sources Recap Recap Emissions Emissions

Fuel kg equ C kg equ CO2 Cons. Cons. kg equ. C per litre (tonnes) combustion (litres) upstream

Domestic fuel oil 6,420 23,540 856 8,000 0.08Diesel oil 3,781 13,862 856 4,711 0.08Petrol for land engines 5,582 20,468 833 7,223 0.11Liquefied petroleum gas (LPG) 21,765 79,806 23 731 0.08Coking coal (PCS>23,865 kJ/kg) 3,277 12,016 4 1,085 Coal (PCS>23,865 kJ/kg) 0 0 1,085 Total 40,825 149,692

88

Appendix B.2 Data Input for Excluding Energy Kyoto Halocarbons

1 - Kyoto halocarbons Recap Recap Emissions Emissions kg equ C kg equ CO2 emissions in kg equ C kg equ.

tonnes per

year per tonne carbon R134a 1,170 4,290 0.003 390,000 1,170HFC – 134a 42,120 154,440 0.108 390,000 42,120 Total 43,290 158,730 43,290

Gas Excl. Kyoto

1 - Gas excl. Kyoto Recap Recap Emissions Emissions kg equ C kg equ CO2 emissions in kg equ C kg equ.

tonnes per

year per tonne carbon R22 - HCFC excl. Kyoto 10,366 38,010 0.021 493,636 10,366Total 10,366 38,010 10,366

Nitrous Oxide

2 - Other N2O emissions Recap Recap Emissions Emissions

kg equ C kg equ CO2 Tonnes of

N20 equ. C kg equ. per year of N2O carbon

Nitrous oxide 24,382 89,400 0 81.3 24,382Total 24,382 89,400 24,382

89

Appendix B.3 Data Input for Materials and Products Purchased (Inputs)

Metals Metals Recap Recap

Emissions Emissions Tonnes % from kg equ. C supplement total kg equ C kg equ CO2 used recycled mat. per tonne 2nd transf. kg equ. C

Other current metals - average 26,450 96,983 26 1,000 26,450 Total 26,450 96,983 26,450

Plastics

Plastics Recap Recap Emissions Emissions Tonnes % from kg equ. C supplement total kg equ C kg equ CO2 used recycled mat. per tonne 2nd transf. kg equ. C

Plastic - average 83,493 306,139 128.45 650 83,493 Total 83,493 306,139 83,493

Glass

Glass Recap Recap Emissions Emissions Tonnes % from kg equ. C supplement total kg equ C kg equ CO2 used recycled mat. per tonne 2nd transf. kg equ. C

Flat glass 138,164 506,602 333.51 414 138,164 Total 138,164 506,602 138,164

90

Paper Paper, cardboard Recap Recap

Emissions Emissions Tonnes % from kg equ. C supplement kg equ. kg equ C kg equ CO2 used recycled mat. per tonne 2nd transf. carbon

Paper 66,204 242,748 183.9 360 66,204

Total 66,204 242,748 66,204

Construction Building materials - INIES Recap Recap

Emissions Emissions Quantities kg equ. C % supp for kg equ. kg equ C kg equ CO2 Unit used used per qty implement. carbon

Masonry wall in concrete blocks 1,509 5,534 m2 500 3.02 1,509 Total 1,509 5,534 1,509

Building materials - bulk Recap Recap

Emissions Emissions Tonnes kg equ. C % supp for kg equ. kg equ C kg equ CO2 used per tonne implementation carbon

Cement concrete (road) 183 672 5 37 183Cement 705 2,586 3 235 705Total 889 3,258 889

Agricultural Products

Evaluation based on meals Recap Recap Emissions Emissions number kg equ. C kg equ. kg equ C kg equ CO2 of meals per meal carbon

Typical meal 736,008 2,698,696 1,887,200 0.39 736,008Typical meal 0 0 0.39 0Total 736,008 2,698,696 736,008

91

Appendix B.4 Data Input for Transportation of Goods (Freights)

Internal Road Freight 1 - Internal road freight, transport owned Recap Recap

Fuel Emissions Emissions kg equ. C kg equ C kg equ CO2 (litres)

Diesel oil 9,773 35,834 12,178Petrol for land engines 8,686 31,848 11,239Total 18,459 67,682

Outgoing Road Freight

1 - Outgoing road freight, vehicles owned Recap Recap Emissions Emissions

Fuel kg equ C kg equ CO2 kg equ. C (litres)

Diesel oil 491 1,801 612Total 491 1,801

Incoming Road Freight

1 - Incoming road freight, lorries owned, direct accounting of fuel Recap Recap

Fuel Emissions Emissions kg equ C kg equ CO2 (litres)

Diesel oil 15,627 57,300 19,473Liquefied natural gas (NGV) 2,167 7,947 Total 17,794 65,246

92

Appendix B.5 Data Input for Transportation of People (Travel)

Transportation of AIT’s faculty and staff from home to work 1 - Home-work: fuel supplied or reimbursed (means of transport owned) Recap Recap

Fuel Emissions Emissions Cons. Cons. kg equ C kg equ CO2 (tonnes) (litres)

Diesel oil 21,853 80,128 27,231Liquefied natural gas (NGV) 68,072 249,598 75 Total 89,925 329,725

3 - car, home - work: calculation based on number of vehicle.km and location of driver's home Recap Recap

Emissions Emissions Vehicle owned kilometres kg equ C kg equ CO2 controlled, etc? travelled

rural periphery 512,642 1,879,689 8,806,400Total 512,642 1,879,689

5 - Bus & coach, home-work: calculation based on vehicle.km Recap Recap Emissions Emissions vehicles.km kg equ C kg equ CO2 Minibus 514,100 1,885,033 3,763,200Total 514,100 1,885,033

93

Transportation of AIT’s faculty and staff in context of work by car Recap Recap

Fuel Emissions Emissions Cons. kg equ C kg equ CO2 (litres)

Petrol for land engines 22,574 82,770 29,209Total 22,574 82,770

2 - car travel in the context of work: calculation based on fiscal ratings for petrol passenger cars Recap Recap

Taxable horse power Emissions Emissions Vehicle owned vehicle.km (HP) kg equ C kg equ CO2 controlled, etc?

6 336 1,233 33,648Total 336 1,233

2b - car travel in the context of work: calculation based on fiscal ratings for diesel passenger cars Recap Recap

Taxable horse power Emissions Emissions Vehicle owned vehicle.km (HP) kg equ C kg equ CO2 controlled, etc?

6 99 364 9,614Total 99 364

1 - Bus & coach travel by employees: calculation based on vehicle.km Recap Recap Emissions Emissions Vehicle owned vehicles.km kg equ C kg equ CO2 controlled, etc? Minibus 467,755 1,715,102 3,423,957Total 467,755 1,715,102

94

Transportation of AIT’s faculty and staff in context of work by airplanes 2 - Plane travel by employees, passengers.km Recap Recap

Emissions Emissions plane

owned, cumulative

kg equ C kg equ CO2 controlled,

etc? distances

(km) Long-haul, class not known 761,846 2,793,437 10,052,616Short-haul, class not known 26,391 96,767 352,142Long-haul, class not known 19,475 71,408 256,972Total 807,712 2,961,612

Transportation of AIT’s visitors

3 - visitors by bus and coach, vehicles.km Recap Recap Emissions Emissions vehicles.km kg equ C kg equ CO2 Minibus 9,448 34,643 69,160Total 9,448 34,643

5 - visitors by plane, persons.km Recap Recap

Emissions Emissions plane

owned, cumulative

kg equ C kg equ CO2 controlled,

etc? distances

(km) Long-haul, class not known 121,546 445,668 1,603,807Total 121,546 445,668

95

Appendix B.6 Data Input for Solid Waste and Wastewater (Direct Waste)

Solid Waste Materials from TPS utilisation Recap Recap

Emissions Emissions Tonnes kg equ C kg equ CO2 discarded

food waste 57,072 209,263 505.06Paper 4,506 16,521 128.735Metals 119 436 13.224Plastic 925 3,391 102.766Glass 1,501 5,503 166.757Average household waste 1,978 7,251 47.087 Total 62,621 229,612

Amount of recyclable waste in AIT

Metals recycled or reused Recap Recap Emissions Emissions Tonnes kg equ C kg equ CO2 recycled

Other current metals - average 6 22 1.2Total 6 22

Plastics recycled or reused Recap Recap

Emissions Emissions Tonnes kg equ C kg equ CO2 recycled

Plastic - average 15 53 2.9Total 15 53

96

Glass recycled or reused Recap Recap Emissions Emissions Tonnes kg equ C kg equ CO2 recycled

Flat glass 18 64 3.5Total 18 64

Paper/cardboard recycled or reused Recap Recap

Emissions Emissions Tonnes kg equ C kg equ CO2 recycled

Paper 33 121 6.6Total 33 121

Dangerous waste Recap Recap

Emissions Emissions Tonnes discard. kg equ. C kg equ.

kg equ C kg equ CO2 or emitted per tonne carbon Stabilisation and storage 1,376 5,045 40.362 34 1,376Total 1,376 5,045 1,376

sewage - method by BOD weight Recap Recap

kg of CH4 per kg of BOD released in nature Emissions Emissions 0.25 kg equ C kg equ CO2 Kg of BOD sewage 28,074 102,938 16,470Total 28,074 102,938

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Appendix B.7 Data Input for Property

1 - buildings, method by surface areas Recap Recap Emissions Emissions surface areas Amort period kg equ.

kg equ C kg equ CO2 (m2) (years) carbon Dwellings (concrete) 482,109 1,767,732 121,540 30 482,109 offices (concrete) 0 0 0 industrial blgs metal 0 0 0 health (concrete) 0 0 0 Total 482,109 1,767,732 482,109

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Appendix B.8 Example of the spreadsheet and manual for water and wastewater

Electricity Consumption for

Total GHG

pumping raw water to water kg e C

Electricity Consumption for kg e C

Amount of consumed kg e C

Electricity consumption for kg e C

Amount of generated

BOD value

Total BOD kg e C

treatment plant (kWh)

per kWh

water treatment process (kWh) per kWh

supply water (m3) per m3

pumping water in AIT (kWh)

per kWh

wastewater (m3) (mg/l) kg

0.139 0.139 0.0072 0.139 0 Remark1: Pump Capacity (m3/h) Operating Hours (hrs) Electricity Consumption (kWh) Electrcicity Consumption/m3 #DIV/0! Amount of water (m3) Total Electricity Consumption (kWh) #DIV/0!

Remark2: 0.0264*12/44

0.0072 Remark3: Total BOD (kg) BOD (mg) * wastewater (L)/1000000

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Description of Exel Spreadsheet Remark 1: To be relevant to the Bilan Carbon spreadsheet, all input data should be amount in one year. Remark2: Fill in data information in white areas. Column B: Input data for electricity consumption for pumping raw water to water treatment plant Data information for this column can be found from company who provides supply water in the area. One water treatment plant can be responsible for many households. To calculate electricity consumption for water pumping to the area consideration, it is better to consider from kWh/m3 (the calculation is provided on remark 1). Column C: Emission factor for electricity in Thailand Data information is taken from the Bilan Carbon spreadsheet. Column D: Electricity consumption for water treatment plant Data information can be gathered from water provider. Column E: Emission factor for electricity in Thailand Data information is taken from the Bilan Carbon spreadsheet. Column F: Amount of consumed supply water Data information can be gathered by meter reading. Column G: Emission factor for supply water in Thailand Data information is taken from report on Carbon Footprint by Thailand Greenhouse Gas Management Organization (Public Organization). Emission factor for water supply in Thailand is 0.0264 kg CO2 e. Calculation in converting to kg C e is shown in Remark 2.

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Column H: Electricity consumption for pumping water for generation in AIT Data information can be gathered by meter reading. Column I: Emission factor for electricity in Thailand Data information is taken from the Bilan Carbon spreadsheet. Column J: Amount of generated wastewater Data information is taken from measurement or capacity of wastewater storage. Column K: BOD value Data information is from analysis. Column L: Total BOD Data information can be calculated from Remark 3. Column M: Total GHG emissions Summary of GHG emission

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Appendix B.9 Input data for water and wastewater spreadsheet

Electricity Consumption for Total GHG pumping raw water to water kg e C

Electricity Consumption for kg e C

Amount of consumed kg e C

Electricity consumption for kg e C

Amount of generated

BOD value

Total BOD kg e C

treatment plant (kWh) per kWh

water treament process (kWh)

per kWh

supply water (m3) per m3

pumping water in AIT (kWh) per kWh

wastewater (m3) (mg/l) kg

99,960 0.139 470,400 0.139 588,000 0.0072 99,960 0.139 470,400 35 16,470 113878.08

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Appendix C

Data Information for Comparison of Scenarios

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Appendix C.1 Data Inputs for Energy

1 - Electricity purchased, average by country Recap Recap Emissions Emissions

Country of the electricity consumption kg equ C kg equ CO2 Cons. kg equ. C (kW.h) per kWh

Thailand 1,242,360 4,555,322 8,914,524 0.139France 0 0 0.023France 0 0 0.023France 0 0 0.023 Total 1,242,360 4,555,322

Appendix C.2 Data Inputs for Travel

Travel scenario 1

3 - car, home - work: calculation based on number of vehicle.km and location of driver's home Recap Recap

Emissions Emissions Vehicle owned kilometres kg equ C kg equ CO2 controlled, etc? travelled

rural periphery 358,850 1,315,782 6,164,480All of France 0 0 urban suburbs 0 0 town centre 0 0 Total 358,850 1,315,782

Travel scenario 2

2 - Plane travel by employees, passengers.km Recap Recap

Emissions Emissions plane

owned, cumulative

kg equ C kg equ CO2 controlled,

etc? distances

(km) Long-haul, class not known 380,696 1,395,885 5,023,308Short-haul, class not known 13,196 48,384 176,071Long-haul, class not known 9,737 35,704 128,486Short-haul in 2nd Class 0 0

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Appendix C.3 Data Inputs for Solid Waste Generation

Solid Waste Scenario 1

Materials from TPS utilisation Recap Recap

Emissions Emissions Tonnes Collection kg equ C kg equ CO2 discarded

food waste 57,072 209,263 505.06 O Paper 2,254 8,265 64.4 O Metals 95 350 10.6 O Plastic 463 1,696 51.4 O Glass 1,201 4,402 133.4 O Average household waste 1,978 7,251 47.087 O Total 59,884 219,574

Solid Waste Scenario 2

Materials from TPS utilisation Recap Recap Emissions Emissions Tonnes Collection kg equ C kg equ CO2 discarded

food waste 0 0 O Paper 4,506 16,521 128.735 O Metals 119 436 13.224 O Plastic 925 3,391 102.766 O Glass 1,501 5,503 166.757 O Average household waste 1,978 7,251 47.087 O Total 5,550 20,349

Biological treatment of fermentable waste Recap Recap Emissions Emissions Tonnes type of kg equ C kg equ CO2 treated Recovery

All waste 15,152 55,557 505 Composting All waste 0 0 Methanisation Total 15,152 55,557

Solid Waste Scenario 3

Biological treatment of fermentable waste Recap Recap

Emissions Emissions Tonnes type of kg equ C kg equ CO2 treated Recovery

All waste 0 0 Composting All waste 4,546 16,667 505 Methanisation Total 4,546 16,667