CO2 Emission of Hotel Sector in Sri Lanka: A Case study & Scenario Analysis

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Master of Science Thesis

KTH School of Industrial Engineering and Management Energy Technology EGI- 2014-105MSC EKV1066

Division of Heat and Power Technology SE-100 44 STOCKHOLM

CO 2 Emission of Hotel Sector in Sri Lanka:

A Case study & Scenario Analysis.

Amila Gamage

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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 61.26 kg against the same 61.85 kg in 2010. 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 2020. This analyzing and modeling was done by using LEAP (Long Range Energy Alternative Planning System).

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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.

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List of Tables

Table 1.1: Tourists arrivals in 2012. ... 2

Table 4.1: Current room capacity of the Sri Lanka ... 10

Table 4.2: Sample selection for the Analysis ... 11

Table 4.3: Forecasted sample for 2020 ... 12

Table 4.4: Data Collection Chart ... 12

Table 5.1: CO2 emission factors developed UK energy department ... 13

Table 5.2: CO2 emission factors Sustainable Energy Authority, Sri Lanka ... 13

Table 5.3: CO2 emission factors for CEB power Generation, Sri Lanka ... 14

Table 5.4: CO2 emission factors for Hotels Energy Consumption, Sri Lanka ... 14

Table 5.5: Sample Hotels information ... 16

Table 5.6: Total Energy consumption of Sample Hotels ... 18

Table 6.1.1: Occupancy forecast – Sample ... 24

Table 6.1.2: Summary of forecast for energy consumption pattern in 2010 & 2020 – Sample ... 30

Table 6.1.3: Summary of forecast for total energy consumption pattern from 2010 to 2020 – Sample ... 30

Table 6.2.1: Occupancy forecast ... 33

Table 6.2.2: Summary of forecast for energy consumption pattern in 2010 & 2020 ... 39

Table 6.2.3: Summary of forecast for total energy consumption pattern from 2010 to 2020 ... 39

Table 6.3.1: Summary of forecast for CO2 emission pattern in 2010 & 2020 ... 44

Table 6.3.2: Summary of forecast for total CO2 emission pattern from 2010 to 2020... 44

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List of Figures

Figure 5.2.1: Location of sample hotels ... 17

Figure 6.1.1: Available Room forecast – Sample ... 22

Figure 6.1.2: Occupancy forecast – Sample ... 23

Figure 6.1.3: Total energy consumption from different source - Sample ... 25

Figure 6.1.4: Total electricity consumption - Sample ... 25

Figure 6.1.5: Total energy consumption per room behaviour - Sample... 26

Figure 6.1.6: Total electricity consumption per room behavior - Sample ... 26

Figure 6.1.7: Total energy consumption for categories – Sample... 27

Figure 6.1.8: Total electricity consumption for categories - Sample ... 27

Figure 6.1.9: Total energy in different sources/room in category wise - Sample ... 28

Figure 6.1.10: Total electricity in different sources/room - Sample ... 29

Figure 6.2.1: Available Room forecast ... 31

Figure 6.2.2: Occupancy forecast ... 32

Figure 6.2.3: Total energy consumption from source ... 34

Figure 6.2.4: Total electricity consumption ... 34

Figure 6.2.5: Total energy consumption per room behavior ... 35

Figure 6.2.6: Total electricity consumption per room behavior ... 35

Figure 6.2.7: Total energy consumption for categories... 36

Figure 6.2.8: Total electricity consumption for categories ... 36

Figure 6.2.9: Total energy in different sources/room in category wise ... 37

Figure 6.2.10: Total electricity in different sources/room ... 38

Figure 6.2.11: Total energy consumption per occupied room ... 38

Figure 6.3.1: Total emission from different sources ... 40

Figure 6.3.2: Total emission in different categories ... 40

Figure 6.3.3: Total CO2emission forecast from different sources per room ... 41

Figure 6.3.4: Total CO2emission per room forecast from Electricity ... 41

Figure 6.3.5: Total emission in different categories ... 42

Figure 6.3.6: Total emission from Electricity in different categories ... 42

Figure 6.3.7: Forecast of total CO2 emission against occupancy of Hotel Industry ... 43

Figure 6.3.8: Trend of CO2 emission per room in the Hotel Industry ... 43

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NOMENCLATURE

ABBREVIATION DENOMINATION

CO2 Carbon Dioxide

GDP Gross Domestic Production

SLTDA Sri Lanka Tourism Development Authority FE Foreign Exchange

LEAP Long range Energy Alternative Planning SLSEA Sri Lanka Sustainable Energy Authority NCPC National Cleaner Production Centre SLEMA Sri Lanka Energy Managers Association CDM Clean Development Mechanism

NGO Non-Government Organization

INGO International Non-Government Organization SLTB Sri Lanka Tourism Board

HFO Heavy Furnace Oil

CFL Compacted Florescent Light LED Light Emitting Diode VSD Variable Speed Drive VFD Variable Frequency Drive PV Photo Voltaic

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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 1948. 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 2012.

Tourist arrivals are in line with the government’s set target of achieving 1.5 million tourists in 2015 and 3 million tourists in 2020.

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

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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 2010. 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 2012. [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 1,005,605

1.3 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

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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.

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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

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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 2020. 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 2020. 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 2020. 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 2010.

 To study the impact of various mitigation options and renewable sources available in the industry.

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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 2020. 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.

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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 2000. 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 2020. 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 low- carbon 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

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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 energy- environmental 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

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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.

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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 13 928 6%

2-star 31 1,842 13%

1-star 27 955 6%

Unclassified 149 6,150 42%

TOTAL 249 14,714 100%

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 2020. Therefore LEAP model also assumed the same proportions of room capacity from each star category at 2020.

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.

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11 Table 4.2: Sample selection for the Analysis [2, 21]

the Total

5-star 5 704 24%

4-star 3 265 9%

3-star 2 188 6%

2-star 5 388 13%

1-star 6 179 6%

Unclassified 1,240 42%

TOTAL 21 2,964 100%

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 2020. 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 4.4.

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12 Table 4.3: Forecasted sample for 2020

the Total

Forecasted Sample

% of Rooms to the Total

5-star 5 704 24% 1934 24%

4-star 3 265 9% 725 9%

3-star 2 188 6% 490 6%

2-star 5 388 13% 1047 13%

1-star 6 179 6% 477 6%

Unclassified 149 1,240 42% 3384 42%

TOTAL 170 2,964 100% 8057 100%

Expansion of Sample on 2020

8,057 *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 2020. 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

Year

Electricity Diesel Furnace LPG Biomass Biogas other

January

February

March

April

May

June

July

August

September

October

November

December

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5 Results and Observations

5.1 CO

2

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/m³) Emission Factor kg of CO2/litre kg of CO2/kg

Diesel (100% mineral) 839.6 2.6569 -

Diesel (Biofuel blend) 839.6 2.5636 -

LPG 522.4 1.5301 2.93

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 ) 75500 10200 0.96 40,989.25 3.09

Coal 92800 6300 N/A - -

LSFO 180 CST 75500 10200 0.96 40,989.25 3.09

Residual Fuel (LHF/HSFO 380

CST ) 75500 10300 0.96 41,391.10 3.13

Diesel (LAD) 72600 10556 0.86 37,985.15 2.76

Naphtha 69300 11491 0.68 32,542.29 2.26

The emission factors for Ceylon Electricity Board (CEB) power generation are summarized from 2006 to 2011 in table 5.3.

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Table 5.3: CO2 emission factors for CEB power Generation, Sri Lanka [27]

Year 2006 2007 2008 2009 2010 2011

Emissions from Power Plants (t- CO2)

3,255,268.23 4,018,189.30 3,820,584.49 4,038,436.27 3,333,783.67 3,995,667.59

Net Electricity Generation (GWh)

4,595.15 5,692.26 5,586.14 5,789.85 4,798.06 5,543.08

Simple operating

margin CO2

emission factor (t- CO2/MWh)

Annual 0.7084 0.7059 0.6839 0.6975 0.6948 0.7208

Three-year weighted average

0.6994 0.6958 0.6921 0.7044

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 CO2/kWh 0.63

LPG kg of CO2/kg 2.9

Diesel kg of CO2/litre 2.7

Furnace oil kg of CO2/litre 3

Petrol kg of CO2/litre 2.3

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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 non- government 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.

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16 Table 5.5: Sample Hotels information

Category Hotel No. of

Rooms Location

5- star

Hotel 5.1 110 Seeduwa, Sri Lanka

Hotel 5.2 104 Kandy, Sri Lanka

Hotel 5.3 152 Ahungalla, Sri Lanka

Hotel 5.5 226 MountLavinia,Colombo, Sri Lanka

4 - star Hotel 4.1 54 NuwaraEliya, Sri Lanka

Hotel 4.2 106 Dambulla, Sri Lanka

Hotel 4.3 105 kalutara, Sri Lanka

3 - star Hotel 3.1 43 Kandy, Sri Lanka

Hotel 3.2 145 Colombo, Sri Lanka

2- star

Hotel 2.3 152 Koggala, Sri Lanka

Hotel 2.4 100 kalutara, Sri Lanka

1- star

Hotel 1.1 17 Dambulla, Sri Lanka

Hotel 1.2 42 Polonnaruwa, Sri Lanka

Hotel 1.3 38 Marawila, Sri Lanka

Hotel 1.5 30 Negambo, Sri Lanka

Unclassified

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

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17 Figure 5.22.1: 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 & 2012. The five star hotels contribute 24% of room capacity in the selected sample which contribute around 45% of total energy consumption of

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18

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 2010 2011 2012 TOTAL

Consump:

(MJ)*10³ % Consump:

(MJ)*10³ % 5-star 15,666.13 46.4 16,813.73 45.9 16,531.85 43.3 49,011.71 45.1 4-star 4,529.85 13.4 5,274.73 14.4 6,020.77 15.8 15,825.35 14.6 3-star 4,246.03 12.6 4,898.56 13.4 5,585.08 14.6 14,729.67 13.6 2-star 5,014.29 14.9 5,206.97 14.2 5,437.68 14.2 15,658.94 14.4 1-star 2,715.84 8.1 2,979.06 8.1 3,131.24 8.2 8,826.14 8.1 Unclassified 1,560.29 4.6 1,474.45 4.0 1,511.41 4.0 4,546.14 4.2 TOTAL 33732.428 36647.495 38218.025 108597.948

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.

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19 Table 5.7: Total Energy consumption of Sample Hotels

Category

Hotel

2012 2011

Electricity

(kWh) Diesel (l) HFO LPG (kg) Occupied Rooms

Electricity

5- star

Hotel 5.1 2,488,744 151,752 36,636 31,572 2,514,083 128,876 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 19,224 122,250 23,661 39,730 3,272,059 14,509 114,257 22,702 32,909

Hotel 5.4 2,393,597 105,776 39,486 37,765 2,228,573 105,108 28,963 33,498

Hotel 5.5 5,330,198 35,757 336,600 59,112 62,359 6,047,589 23,668 390,300 55,122 73,740 TOTAL 15,488,932 394,486 458,850 189,576 200,773 15,786,920 353,620 504,557 168,634 198,368

4 - star

Hotel 4.1 384,557 47,647 10,411 15,555 380,775 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 3,520,978 127,849 38,846 32,065 3,054,379 123,846 39,159 30,877

TOTAL 5,511,396 86,332 127,849 71,723 63,621 4,847,647 84,563 123,846 62,367 58,115

3 - star Hotel 3.1 316,909 63,400 8,285 11,491 303,205 53,782 7,794 8,985

Hotel 3.2 4,961,552 180,176 54,758 45,203 4,304,051 174,535 55,199 43,529

TOTAL 5,278,461 180,176 63,400 63,043 56,694 4,607,256 174,535 53,782 62,993 52,514

2- star

Hotel 2.1 430,387 18,338 16,929 486,234 20,727 19,173

Hotel 2.2 904,970 136,635 23,380 22,717 879,519 138,046 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 1,968,375 120,029 28,981 24,976 1,988,418 101,935 31,095 25,454

Hotel 2.5 951,889 34,574 10,512 8,681 825,747 33,491 10,601 8,358

TOTAL 5,136,969 166,893 79,353 54,433 59,391 4,931,916 144,648 74,150 56,252 55,011

1- star

Hotel 1.1 156,844 4,166 5,695 151,599 3,839 4,603

Hotel 1.2 355,058 69,823 8,991 13,117 341,473 60,575 8,785 10,126

Hotel 1.3 1,019,016 11,343 9,366 891,093 11,435 9,018

Hotel 1.4 498,734 7,182 6,264 503,105 7,724 6,280

Hotel 1.5 673,658 9,997 8,755 679,558 10,727 8,780

Hotel 1.6 309,061 7,367 7,128 300,850 8,297 7,521

TOTAL 3,012,371 69,823 0 49,046 50,325 2,867,678 60,575 0 50,807 46,328

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20 Category Hotel

2010 Electricity

5- star

Hotel 5.1 2,499,843 135,591 39,309 30,433

Hotel 5.2 1,262,122 71,428 20,489 23,110

Hotel 5.3 3,141,736 14,550 111,975 23,047 35,419

Hotel 5.4 1,630,887 92,144 26,318 56,685

Hotel 5.5 6,136,066 31,698 363,000 65,939 67,001

TOTAL 14,670,654 345,411 474,975 175,102 212,648

4 - star

Hotel 4.1 338,864 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 53,371

3 - star Hotel 3.1 250,058 49,238 8,558 8,070

Hotel 3.2 3,727,604 157,271 53,305 40,959

TOTAL 3,977,662 157,271 49,238 61,863 49,029

2- star

Hotel 2.1 451,264 17,069 18,666

Hotel 2.2 708,440 126,539 21,732 23,522

Hotel 2.3 2,044,800 9,249 72,666 14,778 22,835

Hotel 2.4 1,977,156 107,246 32,821 24,076

Hotel 2.5 715,154 30,182 10,236 7,865

TOTAL 4,737,110 146,677 72,666 57,835 54,776

1- star

Hotel 1.1 123,073 4,318 4,075

Hotel 1.2 281,618 55,457 9,644 9,096

Hotel 1.3 771,749 11,041 8,486

Hotel 1.4 504,750 8,345 5,930

Hotel 1.5 681,781 11,566 8,306

Hotel 1.6 245,663 6,835 7,388

TOTAL 2,608,634 55,457 0 51,749 43,281

Source: Data Survey

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21 Table 5.8: Total Energy consumption of Sample Hotels

Category Hotel

2012 2011 2010

Electricity

(kWh) LPG (kg) Occupied Rooms

Electricity

Unclassified

Hotel U.1 195,458 765 4,006 188,944 816 3,560 173,903 872 3,627

Hotel U.2 267,182 5,330 4,362 248,066 5,153 3,488 389,705 4,555 3,980

Hotel U.3 193,976 2,549 2,887 197,105 2,645 2,565 187,087 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 2,726 190,546 3,650 2,182 211,964 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 3,633 198,880 6,485 3,928 173,971 9,155 3,959

Hotel U.9 210,526 2,865 2,728 212,976 2,926 2,424 204,017 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

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22

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 2020.

6.1 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 6.1.1.

Figure 6.1.1: Available Room forecast – Sample

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23

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 2020. 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 2020. 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 6.1.2.

Figure 6.1.2: Occupancy forecast – Sample

0.00 1,000,000.00 2,000,000.00 3,000,000.00 4,000,000.00 5,000,000.00 6,000,000.00 7,000,000.00

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

No of Occ. rooms

Year

Occupied Rooms

CO2 Emission of Hotel Sector in Sri Lanka: A Case study & Scenario Analysis

CO 2 Emission of Hotel Sector in Sri Lanka:

A Case study & Scenario Analysis.

Amila Gamage

Abstract

Acknowledgements

Table of Contents

List of Tables

List of Figures

NOMENCLATURE

ABBREVIATION DENOMINATION

1 Introduction

1.1 Background

1.2 Tourism Industry in Sri Lanka

1.3 Long Range Energy Alternative System (LEAP)

2 Problem formulation and objectives

2.1 Problem formulation

2.2 Objectives

3 Literature Review

4 Method of Attack

5 Results and Observations

5.1 CO

Emission Factors

5.2 Background information of Sample Hotels

5.3 Hotels Energy Consumption Scenario

6 Analysis

6.1 Analysis of Sample Hotels