CO 2 Emission of Hotel Sector in Sri Lanka: A Case study & Scenario Analysis.

Transcription

1 CO 2 Emission of Hotel Sector in Sri Lanka: A Case study & Scenario Analysis. Amila Gamage Master of Science Thesis KTH School of Industrial Engineering and Management Energy Technology EGI MSC EKV1066 Division of Heat and Power Technology SE STOCKHOLM

2 Master of Science ThesisEGI 2014:105MSC EKV1066 CO2 Emission of Hotel Sector in Sri Lanka: A Case study & Scenario Analysis Amila Gamage Approved Examiner Prof. Torsten Fransson Supervisors Ms. Sumudu Jathunarachchi Dr. Jeevan Jayasuriya Commissioner Contact person Abstract This research was addressed the CO2 emission and energy consumption pattern of Sri Lanka s Hotel sector in both present and 2020 scenarios. It was proven from the literature survey that there was no study carried out to assess the current and future CO2 emission in Sri Lankan Hotel sector which is the government main focused area to develop within next 10 years of time. Also the writer is working to tourism sector in last few years and understood the importance of Sustainability of tourism business and identified the Green concept, Low carbon emission and less environmental impact are the key attributes to concern for the business sustainability. The research was identified the CO2 emission of 2020 is 404,234 tonnes against the 121,458 tonnes of 2010 which is a huge impact to the environment because of expected growth of tourism sector. Also it was identified the CO2 emission per room in 2020 is kg against the same kg in This was slight change compared to the emission per room in 2010 but it will not considerably reduce the impact to the expected environmental pollution. The main energy consumption is from 5-star hotel category, which was contributed to 47% of total energy consumption even though this sector has contributed only 24% of total room capacity of the country in the present scenario of 2010, which was forecasted and identified the contribution to the total CO2 emission is 49.2% in This analyzing and modeling was done by using LEAP (Long Range Energy Alternative Planning System). ii

3 Acknowledgements First I would like to express my great gratitude to KTH Royal institute of Technology in Sweden for offering me this valuable opportunity to learn under their Distance Learning Master Program in Sustainable Energy Engineering and granting a free scholarship. I gained wonderful knowledge through this program even though it is a distance learning process. So I must thank all the lecturers, instructors and staff who has been engaged in various parts of this course, specially the online practical sessions are great experience and memorable and the support of the instructors are remarkable. Also I would like to request from KTH to offer this course continuously for distance based students, because the knowledge and application of sustainable energy industry are more important in future than today. I would like to remind and express my gratitude to Ms. Shara Ousman of ICBT who gave her dedicated service in the academic part of this course which taking all troubles on her shoulders and providing all facilities of ICBT to success this program. Furthermore, I will not be successful without her great support and the continuous guidance as a student. As well as I would like to appreciate Dr. Primal Fernando who guided us during the initial stage of this Program. Specially, I would like to grant my special thanks to Ms. Sumudu Jathunarachchi and Dr. Jeevan Jayasuriya for in accord to be the supervisors and giving a great guidance and valuable instructions to success this research. Their expert knowledge and professional experience are turn in as valuable input to succeed this study. Also I would like to thanks, Mr. Ruchira Abeyweera for his great dedication towards the success of our thesis projects. Especially his guidance, encouragement and co-ordination of evaluation are highly affected to the success of this study. And special thanks to all DSEE coordinating staff of Open University who facilitated us to success our thesis work in the final stage of the Program. Also I would like to grant my sincere thanks to Stockholm Environment Institute that provide free of charge LEAP usage facility throughout this research and was extensively helped me to success this study. Besides, I would like to thank to my family members of father, mother, brother, sister in law and their little daughter Thishakya who always encourage me and behind me in my life for all the success. Finally, this will be granted my affable gratitude to many friends and colleagues those who have not been mentioned here in namely who helped to succeed my education to an accomplishment. iii

4 Table of Contents Abstract... ii Acknowledgements... iii List of Tables... v List of Figures... vi 1 Introduction Background Tourism Industry in Sri Lanka Long Range Energy Alternative System (LEAP) Problem formulation and objectives Problem formulation Objectives Literature Review Method of Attack Results and Observations CO 2 Emission Factors Background information of Sample Hotels Hotels Energy Consumption Scenario Analysis Analysis of Sample Hotels Analysis of Actual Scenario of Sri Lankan Hotels Analysis of CO 2 emission of Sri Lankan Hotels Analyze the impact of available mitigation technologies Discussion Conclusion and Recommendation...48 iv

5 List of Tables Table 1.1: Tourists arrivals in Table 4.1: Current room capacity of the Sri Lanka Table 4.2: Sample selection for the Analysis Table 4.3: Forecasted sample for Table 4.4: Data Collection Chart Table 5.1: CO2 emission factors developed UK energy department Table 5.2: CO2 emission factors Sustainable Energy Authority, Sri Lanka Table 5.3: CO2 emission factors for CEB power Generation, Sri Lanka Table 5.4: CO2 emission factors for Hotels Energy Consumption, Sri Lanka Table 5.5: Sample Hotels information Table 5.6: Total Energy consumption of Sample Hotels Table 5.7: Total Energy consumption of Sample Hotels Table 5.8: Total Energy consumption of Sample Hotels Table 6.1.1: Occupancy forecast Sample Table 6.1.2: Summary of forecast for energy consumption pattern in 2010 & 2020 Sample Table 6.1.3: Summary of forecast for total energy consumption pattern from 2010 to 2020 Sample Table 6.2.1: Occupancy forecast Table 6.2.2: Summary of forecast for energy consumption pattern in 2010 & Table 6.2.3: Summary of forecast for total energy consumption pattern from 2010 to Table 6.3.1: Summary of forecast for CO2 emission pattern in 2010 & Table 6.3.2: Summary of forecast for total CO2 emission pattern from 2010 to v

6 List of Figures Figure 5.2.1: Location of sample hotels Figure 6.1.1: Available Room forecast Sample Figure 6.1.2: Occupancy forecast Sample Figure 6.1.3: Total energy consumption from different source - Sample Figure 6.1.4: Total electricity consumption - Sample Figure 6.1.5: Total energy consumption per room behaviour - Sample Figure 6.1.6: Total electricity consumption per room behavior - Sample Figure 6.1.7: Total energy consumption for categories Sample Figure 6.1.8: Total electricity consumption for categories - Sample Figure 6.1.9: Total energy in different sources/room in category wise - Sample Figure : Total electricity in different sources/room - Sample Figure 6.2.1: Available Room forecast Figure 6.2.2: Occupancy forecast Figure 6.2.3: Total energy consumption from source Figure 6.2.4: Total electricity consumption Figure 6.2.5: Total energy consumption per room behavior Figure 6.2.6: Total electricity consumption per room behavior Figure 6.2.7: Total energy consumption for categories Figure 6.2.8: Total electricity consumption for categories Figure 6.2.9: Total energy in different sources/room in category wise Figure : Total electricity in different sources/room Figure : Total energy consumption per occupied room Figure 6.3.1: Total emission from different sources Figure 6.3.2: Total emission in different categories Figure 6.3.3: Total CO2emission forecast from different sources per room Figure 6.3.4: Total CO2emission per room forecast from Electricity Figure 6.3.5: Total emission in different categories Figure 6.3.6: Total emission from Electricity in different categories Figure 6.3.7: Forecast of total CO2 emission against occupancy of Hotel Industry Figure 6.3.8: Trend of CO2 emission per room in the Hotel Industry vi

7 NOMENCLATURE ABBREVIATION CO2 GDP SLTDA FE LEAP SLSEA NCPC SLEMA CDM NGO INGO SLTB HFO CFL LED VSD VFD PV DENOMINATION Carbon Dioxide Gross Domestic Production Sri Lanka Tourism Development Authority Foreign Exchange Long range Energy Alternative Planning Sri Lanka Sustainable Energy Authority National Cleaner Production Centre Sri Lanka Energy Managers Association Clean Development Mechanism Non-Government Organization International Non-Government Organization Sri Lanka Tourism Board Heavy Furnace Oil Compacted Florescent Light Light Emitting Diode Variable Speed Drive Variable Frequency Drive Photo Voltaic vii

8 1 Introduction 1.1 Background The Sri Lankan economy has shown tremendous growth in past three years. The GDP growth in 2011 was recorded at 8.3%, the highest annual rate of growth since the country achieved independence in The inflation rate in the country is also under control at 6.7% and the unemployment rate had decreased to 4.2% in 2011 compared to the rate of 5.8% in 2009[1].The robust growth of tourism industry is a key contributor for the economic growth coupled with investment in infrastructure, businesses and property investments. The Sri Lanka Tourism Development Authority is the key state body of identifying targets and preparing strategic plans for Sri Lanka s tourism sector. According to the newly launched Tourism Development Strategy, there is a Master Plan for the tourism industry for the period of 10 years starting from 2010, which states this industry contributes a large share of Sri Lanka s economic development in next 10 years while gearing all efforts towards achieving sustainability. However there should be a proper initiative to sustain this trend of tourism in Sri Lanka and it is vital to maintain better carbon foot print. Because the future of tourism industry mainly guides on the low carbon economy which is mainly depended by energy consumption of the industry. Specially, now there are international pressures arising on this industry to move forward with green concept because the recent climate changes happened all over the world and most countries starting to work out with their greenhouse gas emissions. Also there may be a number of international responsibilities and pressures will apply in future almost all countries and industries in the world to mitigate their carbon emissions and to approach many international conventions. 1.2 Tourism Industry in Sri Lanka The Sri Lankan Tourism industry has shown a tremendous growth from 2010 and it has recorded the highest tourism arrival in the history by achieving 1 million arrivals in Tourist arrivals are in line with the government s set target of achieving 1.5 million tourists in 2015 and 3 million tourists in The contribution of tourism to the national economy is remarkable and it is one of key contributors of the country s Foreign Exchange (FE) earnings of last three years. It has 1

9 recorded 91,926 million of FE in 2011 and as compared to 65,018 million of FE in 2010 which is a recorded rate of 4.3% increase [1]. In addition to earnings of foreign revenues, tourism industry has created large potential of job opportunities for the society to help reduce the unemployment rate of the country. The employment opportunities generated by the tourism industry has increased by 5% in the year 2011 as compared to job opportunities opened during the year It is anticipated that the job opportunities to be increased drastically in the tourism industry in near future since the developments and the new investments in the industry. Table 1.1: Tourists arrivals in [2] Month No. of Tourists arrivals January 85,874 February 83,549 March 91,102 April 69,591 May 57,506 June 65,245 July 90,338 August 79,456 September 71,111 October 80,379 November 109,202 December 122,252 TOTAL 1,005, Long Range Energy Alternative System (LEAP) LEAP, the Long range Energy Alternatives Planning System, is a widely used software tool for energy policy analysis and climate change mitigation assessments. Software is developed by the Stockholm Environment Institute [3]. LEAP is being used by thousands of organizations in worldwide and in more than 190 countries. LEAP is very popular among the government bodies, academics, non-governmental organizations, consulting companies and energy utilities. The example of such applications are, the scenario analysis of energy consumption for a village of Lao People s Republic in 2010 by Mustonen[4] and the research of prospects of low-carbon economic development in China done by the students of School of Management, Tianjin University of China[5]. LEAP can be used for various types of analysis, such as resources planning, greenhouse gas (GHG) mitigation assessments, etc., especially in developing world. In addition to the energy 2

10 analysis, LEAP can be used for the non-energy analysis as well. Few examples of such applications are, the Energy modeling for policy analysis in 2012 [6], Assessing the long term scenario alternatives of the electricity sector and the implications of the policy implementation in Panama [7] and Analyze the greenhouse gas mitigation possibility using available biomass in Vietnam [8]. The biggest advantage of LEAP is that, itcan be customized to model different systems and flexible for introducing various inputs as required by the user. LEAP can analyze alternative scenarios together to get a proper understanding of the result in a flexible manner. Also LEAP is capable of analyzing energy systems considering the aspects of the current requirement, future scenario, cost, alternatives, cost of alternatives, benefits of alternatives and the environmental impact of present and future. Therefore LEAP is popular among the users especially energy experts who design energy policies and make decisions as LEAP can be presented complex systems in more transparent way. 3

11 2 Problem formulation and objectives 2.1 Problem formulation Since the tourism industry in Sri Lanka is a considerable entity and it is continued expand significantly, it is important to analyze its current CO2 emission potential and future CO2 emission trends of Sri Lankan hotel sector under the various scenarios of tourism development. This study is particularly important since there has not been any attempt made so far to estimate CO2 emission potential of this important sector. This study is intended to estimate; What is the current potential of CO2 emissions in the Sri Lankan hotel sector and forecast the emission potential by 2020 in relation with the expansion of the tourism industry in Sri Lanka. What will be the impact on CO2 emission pattern when adopting various CO2 emissions mitigation options, use of energy saving techniques and the use of renewable energy sources for the energy needs of hotel industry. Since the hotel sector in Sri Lanka is significantly larger and it is formidable task to collect information from all the hotels in the country to estimate and analyze CO2 emission potential, the study was conducted only by selecting a sample of hotels to represent the entire sector qualitatively as well as quantitatively. Future CO2 emission potential has been estimated along with the government s tourism industry expansion targets up to the year 2020 while assuming that the hotel sector expansion will be progressed with the proportion as the various types of hotels present at present time. The analyzing and forecasting are done by using LEAP software. LEAP is a tool which can be customized to model any system with giving unique inputs to its different scale of models as it is not a specific model to simulate emission or energy patterns. LEAP can be used to analyze and view of any system how it will evolve over time with its long range scenario analysis. There is a facility to assess the present requirement and impact, how future demand and the impact and the ability of applying hypothetical alternative scenarios to observe the change of impact. Therefore it is helpful to identify the proper 4

12 alternative scenarios, best practicing methods and how should align the future plans to reduce the environmental impact. This research also used the same facilities to model the present and future CO2 emission patterns and it was further modeled by using hypothetical scenario for the replacement of burning diesel and furnace oil in the boiler operation with the use of green energies to assess how it will impact to the CO2 emission and environment pollution at The CO2 emission factors are given as the customized input to the system which is in line with Sri Lanka s current emission factors to increase the accuracy of the analysis instead of using the programmed emission factors of LEAP. 2.2 Objectives The main objective of this study is to analyze the current CO2 emission in the Hotel industry in Sri Lanka and forecast the future trend in This forecasting will be in line with the expansion targets of tourist hotels room capacity and the expected tourist arrivals targets of tourism master plan. Also the CO2 emission will be analyzed based on the various type of energy sources used in the hotel industry and same will be predicted for And also To evaluate the present carbon emission per occupied room of the Hotel sector and forecast the same in 2020 under current mix of energy sources which are being used in the hotel sector by assuming the sources and the proportion will be similar to To study the impact of various mitigation options and renewable sources available in the industry. 5

13 3 Literature Review Many studies have attempted to understand the carbon emission of different sectors and the carbon mitigation technologies. Also there are few projects and studies have been carried out to reduce the carbon emission in different type of industries. In addition to that there are many studies relevant to the analysis of carbon emission and mitigation potentials in Sri Lanka and Internationally. In Sri Lanka few acting government bodies and non-government bodies have carried out many studies and under taken projects to mitigate the carbon foot print of hotel sector like Sri Lanka Sustainable Energy Authority (SLSEA), National Cleaner Production Centre (NCPC), Sri Lanka Energy Managers Association (SLEMA) and Switch Asia Greening Sri Lankan Hotels project. Earth check is one of internationally leading institute of governing carbon emission of Sri Lankan hotels. The SLSEA plays a main role of Sri Lanka s energy management and monitoring in addition to the institute s main scope of developing sustainable energy sources up to 20% of total energy demand by SLSEA has focused on all industries which leads more consumption of energy including the leading hotels. NCPC is a Non-Government Organization (NGO) which is monitoring and working to reduce both energy consumption and the waste generation of industries. SLEMA is one of the leading professional bodies which are working to implement proper energy management practices among the industries through training and development of various levels of employees in the industry. Switch Asia is an International Non-Government Organization (INGO) which completely focused on hotels. They have implemented many energy saving initiatives to reduce the carbon foot print of Sri Lankan hotels. Also the training and development programs, awarding ceremonies and free audits are a wide help to encourage most hotels all over the island to move new initiatives of energy saving and mitigation of carbon emission. But most of projects and researches are addressed to the carbon emission or the equivalent carbon dioxide emission to monitor specific hotel s footprints and the capability of development of mitigation options and Clean Development Mechanism (CDM) projects. There are no studies carried out to identify CO2 emission in Sri Lankan hotel sector as a macro picture and future forecast in relation to the industry growth. Therefore this study is addressed to highly important area because it will give a clear picture about the carbon footprint of Sri Lankan hotel sector in macro scenario and also it will help to identify the future emission pattern which is extremely valuable for the sustainability of this industry. 6

14 There is a good example in Sri Lanka to show how it is important for any industry to reduce their carbon foot print. In 1980 s Sri Lankan government introduced a concept which was called One Factory for One village and the main focus is to popular apparel industry and motivate investors to invest in apparel industry because of huge market opportunity at that time. But today the situation is changed and there are few leading factories only able to manage to run their businesses. The main reason behind this change is the market demand was changed from 1980 s to The market demand was changed for less carbon foot print productions which lead less environmental impact. The same scenario can be applied in future to tourism industry of Sri Lanka which is presently having high market advantage after the end of country s terrorist activities. There are many new tourism projects all over the country in new tourism development zones and investing by both local and foreign entrepreneurs who are motivated with government encouragement of many exceptions. Now the tourism industry is changing for demand more eco-friendly products and less environmental impact of travelling. So the same challenge ahead of tourism sector if there is no proper plan to reduce their environmental impact from their businesses. Therefore this study is highly important to understand where the industry stands at present and what will be the future scenario in Also this study will be focused to study the impact of currently available renewable energy sources on the total emission and consumption. There are also few international studies have been carried out in several countries but they are not related to the hotel sector. Few similar researches of CO2 emission analysis are carried out in China and analysis through LEAP software. China is the country which is having world largest population and second leading carbon emission country in the world only next to United States of America. A study Comparison of CO2 emission scenarios and mitigation opportunities in China s five sectors in 2020 [9] was addressed key carbon emission sectors of Electricity, Iron and Steel, Cement, Pulp & Paper and Transport and then predicted through LEAP. The similar analysis was done through in the research of Research on the prospects of lowcarbon economic development in China based on LEAP model [5] Another research was carried out in China to identify the potential of CO2 emission reduction in the electricity sector which is modeled by LEAP name as Scenario analysis on CO2 emission reduction potential in China s electricity sector [10]. Also there is two similar researches were carried out in China to analysis the CO2 emission pattern of their non-ferrous metal industry and urban areas. The diversified models and policies are associated with urban 7

15 development of China which includes the sustainable city, eco-city and low-carbon city and urban recycling economy. The world cities account for nearly 75% of the world s energy consumption and contribute more than 80% to global greenhouse gas emission. Therefore most of the energy conservation and emission reductions initiatives attract to the cities where same thing follow in China. So they want to predict the effects of different development alternatives on future energy consumption and carbon emission of their cities and this research was conducted by taking Beijing as a case study [11]. The research of Analysis of potential energy conservation and CO2 emissions reduction in China s non-ferrous metals industry from a technology perspective [12] is one of similar study carried out in non-ferrous industry and addressed the future scenario and the both energy conservation and CO2 mitigation potentials. As this research has addressed to the CO2 emission scenario of Sri Lanka in 2020 there is another similar study carried out by State Grid Energy Research institute of China to analyze the Energy demand scenario in 2030 and same was modeled through LEAP[13]. There was a study carried out in Thailand to analyze CO2 mitigation potential of their transport sector by using energy efficiency and bio-energy. The road transport is the major mode of transport in Thailand which account for about 78% of the total energy consumption of transport sector of the country. It is interesting research which was investigated the effect of two realistic and idealistic scenarios in three actions of electric vehicles, fuel switching and model shift to reduce the demand of energy as well as to reduce the CO2 emission by modeling through LEAP [14]. It can be found the similar analysis relation to the transport sector to predict energy demand, CO2 emission and mitigation potential in Iran and Pakistan which are General procedure for long-term energyenvironmental planning for transportation sector of developing countries with limited data based on LEAP (long-range energy alternative planning) and Energy PLAN [15] and Monitoring urban transport air pollution and energy demand in Rawalpindi and Islamabad using LEAP model [16]. There was another study carried out in Taiwan The long-term forecast of Taiwan s energy supply and demand: LEAP model application [17] which is extremely important to know the future scenario as country s lack of natural resources and depend on the energy imports. In Lebanon, similar study was done to identify the implication of alternative scenarios on their electricity sector and future behavior on different climate conditions [18]. There are two similar type of LEAP modeling was carried out in Korea to assess the environmental and economic feasibility of chemical absorption process [19] and landfill gas electricity generation [20] with regard to the greenhouse gas emission and the environmental impact. Even though there are many type of scenario analysis through LEAP to 8

16 predict energy demand and CO2 emissions, it cannot be found any analysis on the CO2 emission scenario of hotel sector or tourism industry. Since no study has been carried out specifically to address the carbon emission pattern of hotel sector this study will be the latest area of analysis. The most important thing is this study was started at the right time because country vastly focusing for tourism development. 9

17 4 Method of Attack There are many types of hotels all over the island and there are many differences between hotels. Therefore it is extremely difficult and important to identify the proper categories and a reasonable sample. The main and standard classification for hotels is star classification other than that there are classifications like Eco, Ayurvedic, City, Luxury, Leisure and Resort according to their operating background. Also there is a classification according to the climate zones like dry zone, wet zone, beach and hills. The sample was selected according to the star classification of Sri Lanka Tourist Board (SLTB) and the each category consists with the hotels with different climate zones and different operational background. Therefore the study is based on the star classification and also the selected sample represented same percentage of rooms to the actual capacity which leads more accurate results from this research. Table 4.1: Current room capacity of the Sri Lanka [2, 21] Category No. of Hotels No. of Rooms % of Rooms to the Total 5-star 15 3,476 24% 4-star 14 1,363 9% 3-star % 2-star 31 1,842 13% 1-star % Unclassified 149 6,150 42% TOTAL , % The current capacity of total hotel rooms are 14,714 and the expected growth in 2020 is 40,000. The study has based on the assumption of the same percentage composition in 2020 to avoid complexity of analysis but it depends on the future investments. Currently there is no proper source to get the exact composition in Therefore LEAP model also assumed the same proportions of room capacity from each star category at The prime objective of this research is to calculate CO2 emission of the hotel sector based on their energy consumption in the last three years. The main carbon emission sources are from the consumption of key energies because the other carbon dioxide generation possibility is very low and negligible compared to the emission of main energy sources. 10

18 Table 4.2: Sample selection for the Analysis [2, 21] Category No. of Hotels No. of Rooms % of Rooms to the Total 5-star % 4-star % 3-star % 2-star % 1-star % Unclassified 1,240 42% TOTAL 21 2, % The sample was segregated to two categories which is star class hotels and the unclassified hotels. The unclassified hotels contribute 42% of room availability which should be with 1,240 rooms of the sample. Therefore a sub-sample of 09 hotels consists with 171 rooms is selected and model through LEAP for the 1,240 capacity to avoid the complexity. Then the main sample developed for star categories including the result of sub-sample as mentioned in the table 4.2. The main sample is analyzed through LEAP for the modeled and forecasted the future scenario in 2020 and then same developed for the actual state of the country. The occupied room is taken as the base unit for this analysis. According to the Master plan for Sri Lankan tourism sector it can be easily identified the expected tourism arrival and the expected tourist room capacity in the country by Also last year the tourist arrivals to the country has reached the maximum of Sri Lankan history and recorded as over one million and it exceeds the expected growth rate of tourist s arrivals [2] and the current room capacity of the country is 14,714 [22]. The hotel room capacity of the forecasted sample in 2020 is given in table 4.3 as per the expected development of hotel sector in parallel to the government plan. The expected room capacity in 2020 is 40,000 in the country which should be contributed to the 8,057 rooms in the sample. The hotels key consuming energies are electricity, diesel, furnace oil and LPG. Also there are few hotels which consume biomass in small scale. The emissions are generated from the consumption stage of all energies therefore the individual hotel energy consumption data are collected according to the format given in Table

19 Table 4.3: Forecasted sample for 2020 Category No. of Hotels No. of Rooms % of Rooms to the Total Forecasted Sample % of Rooms to the Total 5-star % % 4-star % 725 9% 3-star % 490 6% 2-star % % 1-star % 477 6% Unclassified 149 1,240 42% % TOTAL 170 2, % % Expansion of Sample on ,057 *40000x2964/14714* Then the CO2 emission patterns were analyzed according to the collected data of unclassified hotels and star class hotels based on the developed sample and then modeled the future patterns through LEAP to get results in First the unclassified sample was analyzed and developed for the forecasted model and then star categories were separately developed. Finally the total results were analyzed against the present actual scenario. Also the impacts of different types of energy saving measures were analyzed through LEAP for a sample hotel to understand the behaviour of consumption and emission patterns. Table 4.4: Data Collection Chart January February March April May June July August September October November December Year Electricity Diesel Furnace LPG Biomass Biogas other 12

20 5 Results and Observations 5.1 CO2 Emission Factors The CO2 emission factors found from several international and national sources. Both these institutions and Non-government bodies are followed Intergovernmental Panel on Climate Change (IPCC) 2006 Guidelines[23] for national greenhouse gas inventories which were produced at the invitation of the United Nations Framework Convention on Climate Change (UNFCC) [24]. The energy department of UK has developed international emission factors referring to the IPCC Guidelines and the GHG protocol for company reporting purpose which is summarized in table 5.1. Table 5.1: CO2 emission factors developed UK energy department [25] Fuel Type Specific Gravity (kg/m 3 ) Emission Factor kg of CO2/litre kg of CO2/kg Diesel (100% mineral) Diesel (Biofuel blend) LPG Also emission factors mentioned in the Sri Lanka Sustainable Energy Authority (SLSEA) website are summarized in table 5.2. Table 5.2: CO2 emission factors Sustainable Energy Authority, Sri Lanka [26] Fuel Type (kg-co2/tj) Fuel Net Calorific Values (kcal/kg) Specific Gravity (kg/litre) Fuel Net Calorific Values (kj/litre) (kg- CO2/litre) Fuel Oil (LFO/ HSFO 180 CST ) , Coal N/A - - LSFO 180 CST , Residual Fuel (LHF/HSFO 380 CST ) , Diesel (LAD) , Naphtha , The emission factors for Ceylon Electricity Board (CEB) power generation are summarized from 2006 to 2011 in table

21 Table 5.3: CO2 emission factors for CEB power Generation, Sri Lanka [27] Year Emissions from Power Plants (t- CO 2) 3,255, ,018, ,820, ,038, ,333, ,995, Net Electricity Generation 4, , , , , , (GWh) Simple operating margin CO 2 emission factor (t- CO 2/MWh) Annual Three-year weighted average The emission factors used by Switch Asia Greening Sri Lanka project relevant to the hotel energy consumption are mentioned in table 5.4. Table 5.4: CO2 emission factors for Hotels Energy Consumption, Sri Lanka [13] Type of Energy Units Emission Factor Electricity (70% thermal & 30% hydro) kg of CO 2/kWh 0.63 LPG kg of CO 2/kg 2.9 Diesel kg of CO 2/litre 2.7 Furnace oil kg of CO 2/litre 3 Petrol kg of CO 2/litre

22 5.2 Background information of Sample Hotels The star category sample consists 21 hotels from 1-star to 5-star with the capacity of 1,724 rooms. All hotels consume Electricity as their main source of energy and LP Gas for the cooking purpose. Most of hotels use diesel or furnace oil for their boiler operation for the hot water generation and Laundry operation. In addition to those three energy sources few hotels have taken initiatives to implement renewable energy sources as the green energy as well as cost saving option. The solar hot water systems are the most common and widely used renewable sources among the Sri Lankan hotel sector but there are very few hotels have implemented Bio mass systems for their boiler operation. The most of hotels above 2-star category has proper energy management and monitoring process in place except few hotels. Also most hotels engage with government institute or nongovernment body for proper management and enhance of energy consumption that was the additional thing observed within the data survey. Most unclassified and below 2-star level hotels do not have proper energy management and monitoring process in place but it was observed there is a trend of those hotels to move with the energy enhancement programs with proper guidance of non-government bodies specially like Switch Asia Greening Sri Lanka project. The table 5.5 shows the more information about selected hotels from all categories for this study which was collected through the literature survey. 15

23 Table 5.5: Sample Hotels information Category 5- star 4 - star 3 - star 2- star 1- star Unclassified Hotel No. of Rooms Location Hotel Seeduwa, Sri Lanka Hotel Kandy, Sri Lanka Hotel Ahungalla, Sri Lanka Hotel Kandy, Sri Lanka Hotel MountLavinia,Colombo, Sri Lanka Hotel NuwaraEliya, Sri Lanka Hotel Dambulla, Sri Lanka Hotel kalutara, Sri Lanka Hotel Kandy, Sri Lanka Hotel Colombo, Sri Lanka Hotel Kandy, Sri Lanka Hotel Kandy, Sri Lanka Hotel Koggala, Sri Lanka Hotel kalutara, Sri Lanka Hotel Colombo, Sri Lanka Hotel Dambulla, Sri Lanka Hotel Polonnaruwa, Sri Lanka Hotel Marawila, Sri Lanka Hotel Colombo, Sri Lanka Hotel Negambo, Sri Lanka Hotel Kandy, Sri Lanka Hotel U.1 25 kalutara, Sri Lanka Hotel U.2 24 NuwaraEliya, Sri Lanka Hotel U.3 18 Galle, Sri Lanka Hotel U.4 22 Polonnaruwa, Sri Lanka Hotel U.5 15 Kandy, Sri Lanka Hotel U.6 15 Mount Lavinia, Colombo, Sri Lanka Hotel U.7 17 Tricomalee, Sri Lanka Hotel U.8 18 Kandy, Sri Lanka Hotel U.9 17 Gampaha, Sri lanka 16

24 Figure : Location of sample hotels 5.3 Hotels Energy Consumption Scenario The energy consumption data was collected through data survey of the sample hotels for the last three calendar years of 2010, 2011 & The five star hotels contribute 24% of room capacity in the selected sample which contribute around 45% of total energy consumption of 17

25 the sample hotels. It clearly shows the luxury hotels consumption per room is very high compared to the other categories which are having considerable facilities. When compared the next level of 4, 3 & 2 star hotels in sample contribute to the room capacity by 9%, 6% & 13% but the energy consumption is around 14% which does not have much difference. Also it can be noted the 1 & 3 star hotels have room capacity around 6% but the energy consumption of 1-star hotels are 2/3 of the consumption of 3-star hotels. The most important scenario is the unclassified hotels which contributes 42% of room capacity but only consumed around 4% of total consumption. Table 5.6: Total Energy consumption of Sample Hotels Category TOTAL Consump: (MJ)*10 3 % Consump: (MJ)*10 3 % Consump: (MJ)*10 3 % Consump: (MJ)* star 15, , , , star 4, , , , star 4, , , , star 5, , , , star 2, , , , Unclassified 1, , , , TOTAL % This primary data survey clearly indicates the more consumption from the 5-star category and there is no considerable difference of energy consumption between 4, 3 & 2 star categories even though the room capacities are different. Also the 1-star and unclassified categories are the most less energy consumption categories. But the emission patterns can be varied and depended on the usage of different energy sources and the percentage of the contribution of a particular source. Therefore it is the one of focused fact of this study and it was noted the future mix of energy resources cannot forecast accurately with the available data and it was assumed that the same mix of energy resources will remain in 2020 and predicted the CO2 emission. So it can be clearly mentioned the future investments should be considered the emission potential of different energy sources and linked to the reduction of CO2 emission. Also it is difficult to compare the unclassified category as an alternative to the 5, 4 or 3 star categories because those hotels having very basic facilities which not attract high clientele of the market. The energy consumption data of sample hotels for the last three calendar years are listed in table 5.7 and 5.8 according to the source of energy. 18

26 Table 5.7: Total Energy consumption of Sample Hotels Category Hotel Electricity (kwh) Diesel (l) HFO LPG (kg) Occupied Rooms Electricity (kwh) Diesel (l) HFO LPG (kg) Occupied Rooms 1- star 2- star 3 - star 4 - star 5- star Hotel 5.1 2,488, ,752 36,636 31,572 2,514, ,876 39,309 32,175 Hotel 5.2 1,852,312 81,977 30,681 29,347 1,724,616 81,459 22,538 26,046 Hotel 5.3 3,424,081 19, ,250 23,661 39,730 3,272,059 14, ,257 22,702 32,909 Hotel 5.4 2,393, ,776 39,486 37,765 2,228, ,108 28,963 33,498 Hotel 5.5 5,330,198 35, ,600 59,112 62,359 6,047,589 23, ,300 55,122 73,740 TOTAL 15,488, , , , ,773 15,786, , , , ,368 Hotel ,557 47,647 10,411 15, ,775 27,099 11,302 15,261 Hotel 4.2 1,990,418 86,332 32,877 31,556 1,793,268 84,563 23,208 27,238 Hotel 4.3 3,520, ,849 38,846 32,065 3,054, ,846 39,159 30,877 TOTAL 5,511,396 86, ,849 71,723 63,621 4,847,647 84, ,846 62,367 58,115 Hotel ,909 63,400 8,285 11, ,205 53,782 7,794 8,985 Hotel 3.2 4,961, ,176 54,758 45,203 4,304, ,535 55,199 43,529 TOTAL 5,278, ,176 63,400 63,043 56,694 4,607, ,535 53,782 62,993 52,514 Hotel ,387 18,338 16, ,234 20,727 19,173 Hotel , ,635 23,380 22, , ,046 26,269 23,931 Hotel 2.3 2,216,705 12,290 79,353 14,940 25,734 2,117,751 9,222 74,150 14,556 21,199 Hotel 2.4 1,968, ,029 28,981 24,976 1,988, ,935 31,095 25,454 Hotel ,889 34,574 10,512 8, ,747 33,491 10,601 8,358 TOTAL 5,136, ,893 79,353 54,433 59,391 4,931, ,648 74,150 56,252 55,011 Hotel ,844 4,166 5, ,599 3,839 4,603 Hotel ,058 69,823 8,991 13, ,473 60,575 8,785 10,126 Hotel 1.3 1,019,016 11,343 9, ,093 11,435 9,018 Hotel ,734 7,182 6, ,105 7,724 6,280 Hotel ,658 9,997 8, ,558 10,727 8,780 Hotel ,061 7,367 7, ,850 8,297 7,521 TOTAL 3,012,371 69, ,046 50,325 2,867,678 60, ,807 46,328 19

27 2010 Category Hotel Electricity (kwh) Diesel (l) HFO LPG (kg) Occupied Rooms 5- star Hotel 5.1 2,499, ,591 39,309 30,433 Hotel 5.2 1,262,122 71,428 20,489 23,110 Hotel 5.3 3,141,736 14, ,975 23,047 35,419 Hotel 5.4 1,630,887 92,144 26,318 56,685 Hotel 5.5 6,136,066 31, ,000 65,939 67,001 TOTAL 14,670, , , , , star Hotel ,864 28,153 14,888 15,228 Hotel 4.2 1,321,805 74,124 21,422 24,318 Hotel 4.3 2,645,300 11,595 37,815 29,053 TOTAL 3,967,105 74,124 11,595 59,237 53, star Hotel ,058 49,238 8,558 8,070 Hotel 3.2 3,727, ,271 53,305 40,959 TOTAL 3,977, ,271 49,238 61,863 49, star Hotel ,264 17,069 18,666 Hotel , ,539 21,732 23,522 Hotel 2.3 2,044,800 9,249 72,666 14,778 22,835 Hotel 2.4 1,977, ,246 32,821 24,076 Hotel ,154 30,182 10,236 7,865 TOTAL 4,737, ,677 72,666 57,835 54, star Hotel ,073 4,318 4,075 Hotel ,618 55,457 9,644 9,096 Hotel ,749 11,041 8,486 Hotel ,750 8,345 5,930 Hotel ,781 11,566 8,306 Hotel ,663 6,835 7,388 TOTAL 2,608,634 55, ,749 43,281 Source: Data Survey 20

28 Table 5.8: Total Energy consumption of Sample Hotels Category Unclassified Hotel Electricity (kwh) LPG (kg) Occupied Rooms Electricity (kwh) LPG (kg) Occupied Rooms Electricity (kwh) LPG (kg) Occupied Rooms Hotel U.1 195, , , , , ,627 Hotel U.2 267,182 5,330 4, ,066 5,153 3, ,705 4,555 3,980 Hotel U.3 193,976 2,549 2, ,105 2,645 2, ,087 2,723 2,614 Hotel U.4 103,337 3,470 3,997 94,168 2,999 3,198 85,305 2,532 3,648 Hotel U.5 41,692 1,766 2,406 51,196 1,985 2,137 45,013 1,545 2,178 Hotel U.6 200,676 3,832 2, ,546 3,650 2, ,964 3,202 2,488 Hotel U.7 72,310 2,697 3,090 63,642 2,263 2,474 81,225 1,861 2,822 Hotel U.8 196,408 6,566 3, ,880 6,485 3, ,971 9,155 3,959 Hotel U.9 210,526 2,865 2, ,976 2,926 2, ,017 3,071 2,471 TOTAL 1,481,565 29,840 29,835 1,445,523 28,922 25,956 1,552,190 29,516 27,787 Source: Data Survey 21

29 6 Analysis The whole analysis, forecast and simulation are done through the LEAP. The data collected by the survey which were processed according to the selected sample and uploaded to the LEAP to analyze the sample based on the energy consumption data and then the sample was taken as the model to develop the actual scenario of the industry. The actual scenario first simulates to identify the energy consumption behavior in the industry from current scenario to forecasted 2020 scenario. Then the forecasted scenario was developed to identify the emission pattern of the industry for both present scenario and future scenario in Analysis of Sample Hotels The sample consists with 21 star class hotels and 09 unclassified hotels as given in the table 4.2 and the star class hotels contribute to 1,724 rooms while the 09 unclassified hotels with 171 rooms developed for 1,240 rooms which all together the sample consists with 2,964 rooms. The forecasted sample for 2020 is shown in the table 4.3. The available rooms of the sample hotels for the year 2010 = 2964*365 = 1,081,860 The available room capacity of the sample forecasted through LEAP as in figure Figure 6.1.1: Available Room forecast Sample 22

30 No of Occ. rooms In relation to the occupancy forecast of Sri Lankan government they are expecting 1.5 million tourist arrivals on 2015 and 3.0 million arrivals on This gives a clear guidance to the tourism growth rate of the country for the future. The tourist arrival of 2010 is 1.0 million therefore the expected growth rate at 2015 is 1.5 and 3.0 at So it can be made a reasonable assumption of hotel occupancy growth rate also similarly parallel to this rate. The occupancy forecasted through LEAP is as below figure Figure 6.1.2: Occupancy forecast Sample 7,000, ,000, ,000, ,000, ,000, Occupied Rooms 2,000, ,000, Year 23

31 Table 6.1.1: Occupancy forecast Sample Year Occupied Rooms 440, , , , , , , , ,058, ,190, ,322, * * * * * Unclassified

32 Thousands of MJ Thousands of MJ Then the energy consumption behaviour of the sample hotels is analyzed from 2010 to 2020 as shown in below graphs. Figure 6.1.3: Total energy consumption from different source - Sample 100, , , , , , , , , , Year Diesel HFO Biomass LPG The graph shows the consumption pattern of Diesel, HFO, Biomass and LPG for the sample and the electricity consumption pattern is shown in the graph separately. The total energy consumption always increased as the volume of the rooms increased year on year. Figure 6.1.4: Total electricity consumption - Sample 400, , , , , Electricity 150, , , Year 25

33 MJ MJ The behaviour of total energy consumption per room shown in the graphs and which are not shown a significant variation for the Diesel, Biomass and LPG even though there are considerable difference for HFO and Electricity consumption. Electricity consumption per room is reduced with increase of volume which shares the base load. Figure 6.1.5: Total energy consumption per room behaviour - Sample Year Diesel HFO Biomass LPG Figure 6.1.6: Total electricity consumption per room behavior - Sample Year Electricity 26

34 Thousands of MJ Thousands of MJ The total energy consumption analyzed for different categories as shown in below two graphs. Figure 6.1.7: Total energy consumption for categories Sample LPG Biomass HFO Diesel * 4* 3* 2* 1* u 5* 4* 3* 2* 1* u 5* 4* 3* 2* 1* u 5* 4* 3* 2* 1* u Year Figure 6.1.8: Total electricity consumption for categories - Sample Electricity * 4* 3* 2* 1* u 5* 4* 3* 2* 1* u 5* 4* 3* 2* 1* u 5* 4* 3* 2* 1* u Year 27

35 MJ Then the total energy consumption behaviours are analyzed to identify the per occupied room behavior of consumptions. Figure 6.1.9: Total energy in different sources/room in category wise - Sample * * * 5* Diesel * 1 * 1 * 3 * HFO LPG * U 3 * 2 * * 4 * Year 28

36 MJ Figure : Total electricity in different sources/room - Sample Year 5* 4* 3* 2* 1* U The graph shows the electricity consumption per room for the different categories of the hotels. It can be observed the consumption per room for the 5-star category was reduced with the time while other sources were not varied in considerably. The energy consumption patterns are identified and analyzed through LEAP to understand the future behaviour of the same. These results are summarized in the below mentioned tables. 29

37 Table 6.1.2: Summary of forecast for energy consumption pattern in 2010 & 2020 Sample star 4-star 3-star 2-star 1-star Unclass: 5-star 4-star 3-star 2-star 1-star Unclass: Total Consumption (1000*MJ) 92, , , , , , Occupancy (No. of rooms) Avg. Con:/Occ. Room (MJ/room) Electricity Diesel HFO Biomass LPG Table 6.1.3: Summary of forecast for total energy consumption pattern from 2010 to 2020 Sample Electricity (GJ) 113, , , , , , , , , , , Diesel (GJ) 29, , , , , , , , , , , HFO (GJ) 23, , , , , , , , , , , Biomass (GJ) 6, , , , , , , , , , , LPG (GJ) 20, , , , , , , , , , , Total (GJ) 193, , , , , , , , , , ,

38 6.2 Analysis of Actual Scenario of Sri Lankan Hotels After analyzing the sample, the same sample was developed for the actual scenario of the industry to identify and simulate the actual energy consumption behaviors, The actual scenario consists with 100 star class hotels and 149 unclassified hotels table 4.1 and the star class hotels contribute to 8,564 rooms while the 149 unclassified hotels contribute to 6,150 rooms which all together the sample consists with 14,714 rooms. The forecasted room capacity of the country in 2020 is 40,000 (Source: SLTB). The available rooms of the all hotels for the year 2010 = 14,714*365 = 5,370,610 The available room capacity of the actual scenario forecasted through LEAP is as in figure Figure 6.2.1: Available Room forecast 31

39 No of Occ. rooms In relation to the occupancy growth rate the occupancy for future years were forecasted through LEAP as below figure Figure 6.2.2: Occupancy forecast 7,000, ,000, ,000, ,000, ,000, Occupied Rooms 2,000, ,000, Year 32

40 Table 6.2.1: Occupancy forecast Year Occupied Rooms 2,199, ,178, ,297, ,631, ,965, ,299, ,959, ,619, ,279, ,939, ,599, * * * * * Unclassified

41 Thousands of MJ Thousands of MJ Then the energy consumption behavior of the actual scenario is analyzed from 2010 to 2020 as shown in below graphs. Figure 6.2.3: Total energy consumption from source 500, , , , , , , , , , Year Diesel HFO Biomass LPG The graph shows the developed actual patterns of consumption in different sources of energies which can be observed the considerable increase except the biomass. It can be clearly said the usage of the renewable energy sources are not increased compared to the development of country s energy demand. Figure 6.2.4: Total electricity consumption 2,000, ,800, ,600, ,400, ,200, ,000, , , , , Year Electricity 34

42 MJ MJ The graph shows the pattern of the total energy consumption per room for diesel, HFO, Biomass and LPG. The pattern of electricity consumption was shown in the graph Figure 6.2.5: Total energy consumption per room behavior Year Diesel HFO Biomass LPG Figure 6.2.6: Total electricity consumption per room behavior Year Electricity 35

43 Thousands of MJ Thousands of MJ Also the total energy consumption is analyzed for different categories as shown in below two graphs. Figure 6.2.7: Total energy consumption for categories LPG Biomass HFO Diesel * 4* 3* 2* 1* u 5* 4* 3* 2* 1* u 5* 4* 3* 2* 1* u 5* 4* 3* 2* 1* u Year Figure 6.2.8: Total electricity consumption for categories Electricity * 4* 3* 2* 1* u 5* 4* 3* 2* 1* u 5* 4* 3* 2* 1* u 5* 4* 3* 2* 1* u Year 36

44 MJ Then the total energy consumption behaviors are analyzed to identify the per occupied room behaviour of consumptions. Figure 6.2.9: Total energy in different sources/room in category wise * * * 5 * * 2 * 4 * 1 * 1 * 2 * 3 * Diesel HFO LPG * 4 * Year 37

45 MJ MJ Figure : Total electricity in different sources/room * 4* 3* 2* 1* U Year The energy consumption patterns are identified and analyzed through LEAP to understand the future behaviour of the same. These results are summarized in the below mentioned tables. Figure : Total energy consumption per occupied room Total energy/room Year 38

46 Table 6.2.2: Summary of forecasted energy consumption pattern in 2010 & 2020 Total Consumption (1000*MJ) star 4-star 3-star 2-star 1-star Unclass: 5-star 4-star 3-star 2-star 1-star Unclass: Occupancy (No. of rooms) Avg. Con:/Occ. Room (MJ/room) Electricity Diesel HFO Biomass LPG Table 6.2.3: Summary of forecast for total energy consumption pattern from 2010 to Electricity (GJ) 565, , , , , , ,137, ,304, ,471, ,639, ,806, Diesel (GJ) 146, , , , , , , , , , , HFO (GJ) 135, , , , , , , , , , , Biomass (GJ) 32, , , , , , , , , , , LPG (GJ) 102, , , , , , , , , , , Total (GJ) 982, ,030, ,070, ,260, ,450, ,639, ,920, ,202, ,483, ,764, ,045,

47 6.3 Analysis of CO2 emission of Sri Lankan Hotels After analyzing the energy consumption pattern of the actual scenario of the industry then the same model was used to identify and simulate the actual emission pattern of the Carbon Dioxide. The CO2 emission was analyzed and forecasted through LEAP and obtained the below results. Figure 6.3.1: Total emission from different sources Figure 6.3.2: Total emission in different categories 40

48 kg kg The actual scenario of emission was forecasted to identify the behaviour of per room emissions and the results are shown in below graphs. Figure 6.3.3: Total CO2emission forecast from different sources per room Diesel HFO Biomass LPG Year The graph shows the forecasted CO2 emission from the consumption of electricity which is shown slight decline with time even though the other energies are not shown considerable deviation. Figure 6.3.4: Total CO2emission per room forecast from Electricity Electricity Year 41

49 Thousands of MJ Thousands of MJ Figure 6.3.5: Total emission in different categories Total Energy in Category wise 5* 4* 3* 2* 1* u 5* 4* 3* 2* 1* u 5* 4* 3* 2* 1* u 5* 4* 3* 2* 1* u Year LPG Biomass HFO Diesel Figure 6.3.6: Total emission from Electricity in different categories Total Energy in Category wise - Electricity Electricity * 4* 3* 2* 1* u 5* 4* 3* 2* 1* u 5* 4* 3* 2* 1* u 5* 4* 3* 2* 1* u Year 42