Willingness to pay for green electricity in vietnam

VIETNAM NATIONAL UNIVERSITY, HANOI VIETNAM JAPAN UNIVERSITY DO THAO NGAN WILLINGNESS TO PAY FOR GREEN ELECTRICITY IN VIETNAM MASTER'S THESIS …………………………… VIETNAM NATIONAL UNIVERSITY, HANOI VIETNAM JAPAN UNIVERSITY DO THAO NGAN WILLINGNESS TO PAY FOR GREEN ELECTRICITY IN VIETNAM MAJOR: PUBLIC POLICY CODE: 8340402.01 RESEARCH SUPERVISOR: PhD VU HOANG LINH Hanoi, 2021 ACKNOWLEDGEMENT I want to express my deepest thanks of gratitude to my respectable supervisor, Dr Vu Hoang Linh, for his friendly and sympathetic assistance and dedicated involvement throughout the process of this thesis With profound knowledge and experience, he helped me improve and finish my research I especially owe many thanks to my lectures in the MPP program: Dr.Vu Hoang Linh; Dr Nguyen Thuy Anh; Assoc Prof Phung Duc Tuan; Dr Dang Quang Vinh, Prof Okamoto Naohisa, Pro Kawashima Hiroichi for their invaluable guidance and continuous encouragement Their generous assistance and meaningful suggestions helped me study, research, and prepare for my master's thesis It was my honor to work with my supervisor and my program lectures I have highly appreciated their wisdom, patience, and continuous support during my thesis preparation Without the helpful discussion and the enormous support of my lectures, it would have been difficult for me to complete my research Further, I would like to thank Ms Pham Lan Huong (Program Assistant) for the best support during my study and thesis preparation I also express my deep appreciation to the respondents who agreed to be answered my survey Without their support and essential and valuable information, I could hardly have completed my research Last but not least, I express warm and sincere thanks to my beloved family that supports and encouraged me to finish my thesis TABLE OF CONTENTS LIST OF TABLE ii LIST OF FIGURE iii LIST OF ABBREVIATIONS iv CHAPTER I: INTRODUCTION 1.1 Research Background .1 1.2 Problem Statement 1.3 Purpose of the study 1.4 Research Questions 1.5 Research Method 1.6 Limitation of the study 1.7 Structure and overview of the thesis CHAPTER II: LITERATURE REVIEW AND THE THEORETICAL FRAMEWORK 2.1 Method to mesure the WTP 2.2 Mean value and factors affecting WTP for green electricity 2.3 Researchs about WTP in Vietnam 10 CHAPTER III: THE SITUATION OF RENEWABLE RESOURCES IN OTHER COUNTRIES AND VIETNAM 12 3.1 Overview 12 3.2 European energy policies 14 3.3 Asian energy policies 16 3.4 Vietnam energy policies 17 CHAPTER IV: METHODOLOGY 20 4.1 Measurement method 20 4.2 Survey Design 20 4.3 Estimation technique 24 CHAPTER V: FINDING AND DISCUSION 26 CHAPTER VI: CONCLUSION AND POLICY IMPLICATION 32 REFERENCES 35 APPENDIX 39 i LIST OF TABLE Table 2.1: Classification Table of WTP methods Table 3.1: Production of renewable energy in the period 2007 – 2017 18 Table 4.1: Summary table of review of WTP for green electricity literature 21 Table 5.1: Distribution of responses by bid amount 26 Table 5.2: Variables Descriptive Statistics 26 Table 5.3: Regression result 29 Table 5.4: Logarit Model Result 30 ii LIST OF FIGURE Figure 3.1: World electricity generation mix by fuel, 1971-2018 12 Figure 3.2: CO2 emissions by energy source, World 1990-2018 13 Figure 3.3:Structure of primary energy supply in Vietnam 18 Figure 4.1: The double bounded – Dichotomous Choices Model 23 Figure 5.1: Money Saving for WTP 28 iii LIST OF ABBREVIATIONS CVM DB DBDC Contingent Valuation Method Double Bounded Single Bounded - Double Bounded DC Dichotomous Choice DCs Dichotomous Choices EC Euroupean Commission GE Green Electricity GHG Greenhouse Gas IEA International Energy Agency MLR Multiple Linear Regression RE Renewable Energy RS Renewable Resources SB Single Bounded WTP Willingness to pay iv CHAPTER I: INTRODUCTION 1.1 Research Background In recent years, the world has faced climate change, which seriously affects human health The concept of sustainable development was first appeared and mentioned in the report of the UN World Commission on Environment and Development in 1987 (WCED, 1987), and then, it has become the essential goal for national development orientation In particular, energy resources are one of them Energy supply is necessary for human life and economies for lighting, transport, internet, etc., and its purchasing accounts for to 10% of GNP in developed countries (Twidell & Weir, 2015) Fossil fuel is one of the main sources of energy used, especially in developing countries However, it is the factor that causes rising greenhouse gas emissions and becomes a challenge in sustainable development Besides, the energy demand has increased dramatically due to economic growth and population growth, while natural resources are limited Therefore, there is necessary for a drastic transformation, distribution, and use of energy towards reducing emissions into the environment and sustainable resources The opposite of fossil fuels is renewable energy, which can restore themselves quickly and unlimited in particular solar, thermal, photovoltaic, bioenergy, hydro, tidal, wind, wave, and geothermal (Boyle, 2004) Green electricity, generated from renewable energy sources, can be viewed as an environmental public good and provided by the private sector According to Menges et al (2005), green electricity options help to reduce the demand for fossil fuels, reduce pollution and greenhouse gas emissions, and become a public benefit However, because the cost of investment in infrastructure to produce electricity using renewable energy is very high, its price is usually higher than fossil fuels Therefore, some policy mechanisms have been designed in different countries to support green electricity One of them is voluntary purchases, which require well-educated consumers willing to foster green energy with their assets This amount is called willingness to pay for green goods to improve the environment and ensure their stability in the future WTP is becoming more and more popular and widely studied in all countries Determining the customer's willingness to pay helps companies and governments develop an appropriate product pricing mechanism to stimulate customer consumption In Vietnam, the Ministry of Industry and Trade forecasts that demand for electricity in economic development from 2021 - 2025 will still grow at a high rate of 8.5% per year (Thao, 2020) However, energy is mainly dependent on imported fuels of coal, gas, and liquefied gas Besides, because the impacts of climate change lead to drought, the hydropower reservoir lacks water for production Therefore, the development of renewable energy sources is an inevitable and necessary trend in the development of Vietnam The Resolution No.55-NQ/TW of The Politburo about National energy development strategy has set out, "Prioritize the exploitation, thorough and efficient use of renewable energy, new energy, and clean energy." The period of 2013 - 2019 marks the rapid development of renewable energy sources in Vietnam According to the EVN report (2019), the average annual total power capacity increases by 10.6% Specifically, the renewable energy source increases at the rate of 31.4%/year, wind power at the rate of 42%/year, biomass 54.5%/year, and especially solar power increased 53 times, from 86 MW to over 4,600 MW Although the growth rate of renewable energy is relatively high, its ability to supply electricity to the national grid only accounts for 7.16%, and the largest source of energy is still coal-fired with 38.12% On the other hand, the Government has a privatization strategy for the power sector, which also opens great opportunities and challenges for using renewable energy After many years of monopolization by state-owned enterprises, the private sector is officially allowed to participate in all areas of the electricity industry Specifically, more and more renewable energy projects have been approved and built, which is the first step in privatizing the electricity sector in Vietnam If WTP and influencing factors are focused on measurement and research, it will help the government or businesses have long-term strategies to promote green electricity Moreover, according to the Resolution No 55, the roadmap for the implementation of a competitive electricity market; mechanism of electricity sale contracts between producers and consumers; The bidding and auction mechanism for energy supply is appropriate, which should be accelerated, especially in renewable energy investment projects, and the electricity purchase price must be transparent 1.2 Problem Statement This thesis assumes that if the power sector in Vietnam is privatized, there will be competition between suppliers of green electricity and suppliers of fossil electricity or nuclear electricity It can be seen clearly that the green electricity supplier will be disadvantaged in terms of price Because there are some costs and benefits with no monetary value, it is not easy to assess them accurately Then these additional production costs are often charged to the end-user of electricity, that is, households (Kowalska-Pyzalska, 2019) On the other hand, customers tend to choose the lower prices for the same product so that the need for fossil electricity will be increased until it is exhausted There is increasing concern that the emission of fossil fuels will make the climate change situation more severe About two-thirds of greenhouse gas emissions are generated from burning fossil fuels for heating, electricity, transport, and industry (European Environment Agency, 2021) It is believed that by expanding environmental protection campaigns and disseminating information about sustainable development, more and more people are willing to pay a certain amount for green products According to Nielsen (2017), up to 80% of Vietnamese consumers are willing to pay more to buy products with environmentally friendly materials However, different consumers will lead to different levels of payment Therefore, it is necessary to investigate the willingness to pay (WTP) level for green power among different households to develop preferential policies and set reasonable prices 1.3 Purpose of the study This paper aims to examine the WTP per month for Green Electricity in Vietnam, and the focus objectives will be household Some different elements are mentioned to analyze which effect on WTP and customers' behavior on choosing GE Then some recommendations are given to increase GE user rates and raise WTP value 1.4 Research Questions The research focuses on answering the following questions: Question 1: How much is the Vietnamese people’s willingness to pay for green electricity? CHAPTER V: FINDING AND DISCUSION The survey-based on the responses of 241 samples provided an overview of the WTP for green electricity in Vietnam According to Table 5.1, it can be seen that the WTP level of green electricity is in the range of less than 100,000 VND, with a rate of 46% of respondents There is 39% of respondents choose to pay about from 100,000 VND to 300,000 VND more for green electricity The percentage of people offering for levels greater than 300,000 is deficient, only 8% Table 5.1: Distribution of responses by bid amount Bid (VND) Sample size Rate WTP < 100,000 113 47% 100,000  WTP  300,000 95 39% WTP> 300,000 19 8% (1 USD = 23,000 VND) In addition, some descriptive statistics for the variables are described in Table 5.2 As can be seen, the number of people agreeing to pay for green electricity is relatively high About 85% of respondents agree to support green electricity with an additional WTP of 136,832 VND/month, equivalent to USD 5.95 per month This result is slightly higher than the number in Beijing, China (Guo et al., 2014) and even higher than those in South Korea (Seung-Hoon Yoo and So-Yoon Kwak, 2009) The rate of people knowing about Vietnam's environmental protection law is not high, about 63.9% It shows that about 37.1% of the respondents not know the law on environmental protection Besides, the proportion of people with a college education or undergraduate is quite large, accounting for 47.7%, and Postgraduate is 46.9% Table 5.2: Variables Descriptive Statistics Variable KNOWLEDGE ELAW Description Knowledge about renewable energy (1 - 5) Dummy variable, = yes, Mean Std Dev Min Max 2.975104 0.6003146 4.6 0.6390041 0.4812889 26 = no Belief on the authority for BELIEF environmental governance 3.6639 0.4885179 3.679668 0.7963206 1.2 4.9 0.8506224 0.3572021 136832 154396.8 900000 (1 - 5) Awareness of the AWARE environmental issues (1 - 5) GREEN Dummy variable, = yes, = no WTP Willingness to pay (VND) BILL Electricity bill (VND) 1083071 694165.9 AGE Age of respondents 34.56017 9.386022 18 70 0.6680498 0.4718933 3.626556 0.7538513 0.1742739 0.3801343 0.0082988 0.0909075 0.0373444 0.1899989 0.4771784 0.5005184 0.4688797 0.5000692 GENDER Dummy variable, = Female, = Male Total number of household HSIZE members (people) LOCATION edulevel1 edulevel2 edulevel3 edulevel4 Dummy variable, = rural, = urban Dummy variable, Primary school/Elementary school Dummy variable, High school College/ Undergraduate Postgraduate 180000 4000000 edulevel5 Others 0.0082988 0.0909075 incomegr1 Less than million VND 0.0290456 0.168284 incomegr2 7-10 million VND 0.0705394 0.2565868 incomegr3 11-20 million VND 0.3029046 0.4604706 incomegr4 21-30 million VND 0.2116183 0.4093057 incomegr5 Above 31 million VND 0.3858921 0.4878184 Furthermore, respondents will have to answer what kind of spending they will cut to pay for green electricity since their income is the same This question is 27 designed to reduce bias between respondents' answers and reality (Noboru Nomura, Makoto Akai, 2004) There are ten basic household expenses mentioned in the answer, and respondents will choose a maximum of types of expenses they will cut The results are shown in Fig.5.1 with a rate greater than 100% due to more than one choice in the answer Less than 10% of people choose to cut costs for education, medical care, housing, transportation & communication, and other living expenses It can be seen that these are necessary expenses of households in Vietnam that they will hardly change On the other hand, a basic expense such as food was chosen to cut by 22% of respondents There are about 40% of options for cutting furniture & household utensils and reading & recreation Clothes and footwear are cut by 60% of respondents One of the reasons is that due to the epidemic's impact, the demand for clothes is not too necessary and popular Not surprisingly, 65% of people choose to reduce power consumption to save costs It is the easiest and most effective method Already about 90% of the choices are mixed with other choices Other living expenses 7% Decrease electricity use 65% Reading and recreation 41% Education 1% Transportation and communication 8% Medical care 1% Clothes and footwear 60% Furniture and household untensils 37% Housing 2% Food 22% 0% 10% 20% 30% 40% 50% 60% 70% Figure 5.1: Money Saving for WTP Corresponding coefficient estimates based on MLR and Logit models, determined using Stata 14.0, are presented in the table It can be seen that the difference in the level of consent to pay for green electricity can be explained by an 28 understanding of renewable energy (KNOWLEDGE), trust in government (BELIEF), the average monthly electricity bill of household (BILL), Respondent's age (AGE), Education level In contrast, not as expected, environmental pollution perception and household income did not affect the difference in WTP for green electricity In addition, the variables of gender, household size (HSIZE), and location also did not affect the level of household payment Based on table 5.3, knowledge of renewable energy, monthly electricity bill, age, and education positively affect WTP Respondents who are aware of renewable energy and its benefits will tend to pay more for green electricity Concerns about the effects of environmental pollution make more people interested in practical solutions The development of technology and the internet has helped the knowledge of renewable energy to spread to more people For a sustainable future, people accept to pay for supporting it In addition, those who tend to use more electricity agree to pay higher The high electricity bills show that their electricity demand is significant Although there has been propaganda about saving electricity, their power consumption remains high, indicating that their electricity demand is challenging to reduce Green electricity is a perfect choice for these objects because they can use it comfortably without worrying about its impact on the environment The elderly often have concerns and worries about the health of themselves and their families, so they choose to pay more to use green electricity The coefficient for education is positive, which means that the higher the education level of the respondents, the higher their WTP Receiving a higher education allows respondents to have more access to knowledge about the environment and sustainable development They understand the benefits of developing renewable energy, which leads to support for clean energy development Besides, their income is stable, and they accept to exchange money for sustainable development for the future Table 5.3: Regression result WTP KNOWLEDGE Coef Std Err T P>t 37402.86 16779.27 2.23 0.027** [95% Conf Interval] 4337.441 70468.28 ELAW -7125.99 20112.9 -0.35 0.723 -46760.69 32508.71 BELIEF -33006.89 19679.84 -1.68 0.095* -71788.21 5774.417 AWARE -7334.117 12471.94 -0.59 0.557 -31911.46 17243.23 29 BILL 0.080666 0.0144167 AGE 2107.831 1048.693 GENDER -24787.29 19791.24 edulevel2 110731 edulevel3 5.6 0.00*** 41.2652 4174.396 -1.25 0.212 -63788.13 14213.55 103170.3 1.07 0.284 -92577.44 314039.4 155416.5 92713.63 1.68 0.095* -27286.03 338119 edulevel4 168847.3 92392.62 1.83 0.069* -13222.63 350917.2 HSIZE 21853.27 16245.02 1.35 0.18 -10159.35 53865.89 incomegr2 -21472.42 72889.24 -0.29 0.769 -165108.8 122163.9 incomegr3 -15982.55 67003.31 -0.24 0.812 -148020 116054.9 incomegr4 -19561.42 67777.65 -0.29 0.773 -153124.8 114002 incomegr5 -6801.892 66782.4 -0.1 0.919 -138404 124800.2 7360.194 26831.74 0.27 0.784 -45514.73 60235.12 -190505.8 133474.2 -1.43 0.155 -453531.6 72519.9 LOCATION _cons 2.01 0.046** 0.0522563 0.1090757 Most previous studies have shown that green energy awareness, education, age, and monthly bills positively impact WTP (Xiea and Zhao, 2018; Yoo and Kwak, 2009) However, the fact that trust in the government has a negative impact on WTP is almost nonexistent The results of Xie and Zhao (2018) showed that belief in the government's ability to control pollution positively affected the respondents' behavior of choosing green electricity and WTP Specifically, the more they trusted the government, the more they would use green electricity, and WTP would increase It suggests that it is necessary to study other consumer behavior towards government actions on the environment to improve propaganda and policy implementation effectiveness.Then the government can increase the rate of green electricity usage and the level of WTP to meet the transition to a green economy, suitable for the trend of sustainable development Table 5.4: Logarit Model Result [95% Conf Interval] GREEN Coef Std Err z P>z KNOWLEDGE 0.6585289 0.3680748 1.79 0.074* -0.0628844 1.379942 ELAW 0.4786972 0.4525327 1.06 0.29 -0.4082506 1.365645 BELIEF 0.0228161 0.4615827 0.05 0.961 -0.8818693 0.9275015 AWARE 0.0000572 0.2700327 -0.5291971 0.5293115 30 [95% Conf Interval] GREEN Coef Std Err z P>z BILL -1.66E-07 3.09E-07 -0.54 0.59 -7.72E-07 4.39E-07 AGE 0.0192827 0.0269778 0.71 0.475 -0.0335928 0.0721583 GENDER 0.3826248 0.4457346 0.86 0.391 -0.490999 1.256249 edulevel2 59.6371 7793.797 0.01 0.994 -15215.92 15335.2 edulevel3 48.40145 7287.996 0.01 0.995 -14235.81 14332.61 edulevel4 33.69725 6966.911 0.996 -13621.2 13688.59 HSIZE 14.76166 1235.183 0.01 0.99 -2406.153 2435.676 incomegr2 -91.92191 9590.959 -0.01 0.992 -18889.86 18706.01 incomegr3 -93.15112 9590.959 -0.01 0.992 -18891.09 18704.78 incomegr4 -92.69361 9590.959 -0.01 0.992 -18890.63 18705.24 incomegr5 -92.87091 9590.959 -0.01 0.992 -18890.81 18705.06 LOCATION -0.7653513 0.5890536 -1.3 0.194 -1.919875 0.3891726 _cons -0.9249655 4699.914 -9212.587 9210.737 The logit function shows that only knowledge about renewable energy slightly influences the behavior of choosing to use renewable energy (Table 5.4) Meanwhile, other factors, especially awareness of environmental issues or household income, not affect choosing green electricity This is an unexpected result as there is only onefactor influencing green electricity choice behavior As mentioned above, the energy transition to a green economy will contribute to combating climate change by reducing greenhouse gas emissions to safe levels The efficient use of renewable energy will improve the air quality and limit the dependence on energy imports to ensure energy security Therefore, those knowledgeable about renewable energy will prioritize using and supporting the development of green electricity In addition, According to Litvine and Wüstenhagen (2011), besides the barrier of using green electricity is the price, raising awareness about the benefits of using green electricity is also an essential factor 31 CHAPTER VI: CONCLUSION AND POLICY IMPLICATION After 36 years of implementing Doi Moi (1986), Vietnam has achieved many impressive achievements Vietnam has become one of the fastest-growing economies; the material and mental life of the people is constantly being improved; health care quality is enhanced; national defense and security are consolidated and stabilized; foreign relations, deeper and more effective international integration, etc However, the government then focused on economic development without a vision of sustainable development, which had severe consequences More specifically, environmental pollution in Vietnam is getting worse and seriously affecting people's lives For example, in 2016, more than 60,000 people died from heart disease, stroke, lung cancer, chronic obstructive pulmonary disease, and pneumonia in Vietnam, all of which are related to air pollution (WHO, 2018) It can be said that environmental pollution is an urgent issue that needs attention for sustainable development in the future Although the energy sector is not the only cause of greenhouse gas emissions, it is still one primary source Therefore, it also needs to be paid more attention to the transition to clean energy, helping improve environmental quality This study adopts a survey to investigate the willingness of households to pay for green electricity Linear and logit regression models were used to determine the factors that might influence their attitudes towards using green electricity and WTP Some conclusions have been reached as follows First, the proportion of people who support the development is relatively higher than expected The survey results show that about 85% of respondents agree to pay extra costs to support green electricity, and the mean WTP is 136,832 VND/month, equivalent to USD 5.95 per month Although Vietnam is a developing country, it can be seen that the level of WTP for green electricity is no less than that of developed countries such as Korea with 2.2 USD/month (Seung-Hoon Yoo and So-Yoon Kwak, 2009) and China with 4.8 USD/ month (Bai-Chen Xiea, Wei Zhao, 2018) However, many people still not know about renewable energy, which leads to not choosing to use green electricity or low WTP 32 Second, people who agree to pay for green electricity are significantly affected by knowledge about renewable energy and electricity bills Respondents who know renewable energy will recognize its importance for sustainable development Reducing greenhouse gas emissions is a must to ensure their health and living environment So they accept to pay extra to use green electricity to improve the environment On the one hand, people with high electricity bills will pay more to use electricity produced from renewable energy The inability to reduce electricity consumption while aware of the environmental impact makes green electricity an optimal choice for respondents In addition, older people tend to pay more for green electricity and higher education leads to a higher amount of WTP Older people tend to pay more attention to the factors that affect the health of themselves and their families They worry about the severe consequences of climate change, and green electricity is a practical action to reduce those effects Moreover, the elderly have savings makes it easier for them to pay more to improve the environment Those with access to higher education will be more knowledgeable about sustainable development and recognize the harmful effects of climate change on ecosystems and human health They will accept additional payments to help ameliorate those consequences Third, only knowledge about renewable energy affects the green electricity choice behavior of respondents The results show that people who know about green electricity will choose to use and pay more to support the development of green electricity It is evident that this is an important influence on behavior and WTP levels in other developed countries such as Korea (Yoo and Kwak, 2009) and China (Xiea and Zhao, 2018) Thus enhancing understanding of renewable energy will help promote the development of green electricity Based on the above conclusions, some recommendations are given to improve the percentage of households using green electricity and raise the WTP level (1) Focus on promoting propaganda activities to raise public awareness to understand the benefits and necessity of using green electricity in sustainable development Specifically, organizing seminars and programs to exchange and disseminate information on renewable energy Bringing the basic knowledge of green electricity to schools activities helps raise awareness from the younger generations In 33 addition, the quality of the staff who participate in the propaganda also needs to be concerned avoiding incorrect or missing important information In order to that, propaganda organizations need to be well-trained and have opportunities to approach developed countries to learn and update the world's development trends These approaches also help raise awareness and make it easier to adopt policies that support clean energy (2) In addition to proposing green electricity development goals, the electricity price mechanism should be made clear and understandable for everyone to access easily Almost citizens cannot clearly understand the calculation of electricity prices in additional tariffs and policies to support green electricity development On the other hand, choosing the suitable electricity price calculation method will help raise the WTP level in the future Public acceptance plays a vital role in devising appropriate policies Different groups may have different attitudes and opinions about developing clean energy This diversity leads to comprehensiveness and relevance in policymaking and implementation However, this study has limitations and needs to be remedied and developed in other future studies The study was carried out in May 2021; household electricity consumption is not too high It is also not a peak hot month, so respondents will not feel the consequences of global warming Therefore, it may affect a household's total WTP to some extent In addition, the study only used a small sample size (241 samples), so a comprehensive study with larger sample size is needed to represent all subjects across the country entirely 34 REFERENCES Ajzen and Driver (1992) Contingent value measurement: On the nature and meaning of the willingness to pay Journal of Consumer Psychology, 1, 297-316 André Hansla, Amelie Gamble, Asgeir Juliusson, Tommy Gärling (2008) Psychological determinants of attitude towards and willingness to pay for green electricity Energy Policy Volume 36, Issue 2, 768-774 Aoshima, Momoko, Yorita, Yasuo Tsunoda, Miyuki (2017) Economic and energy outlook of Japan through FY2018 Bai-Chen Xiea, Wei Zhao (2018) Willingness to pay for green electricity in Tianjin, China: Based on the contingent valuation method Energy Policy, 114, 98-107 Boyle, G (2004) Renewable Energy Christoph Breidert, Michael Hahsler, Thomas Reutterer (2006) A Review of Methods for Measuring Willingness-to-Pay Innovative Marketing Cooper, J., Hanemann, W M (1995) Referendum contingent valuation: How many bounds are enough? USDA Economic Research Search Service, Food and Consumer Economics Division Corporation, K E M (2011) South Korean new and renewable Energies (NRE) EC (2009) European Commission, 2020 Climate & Energy Package - Climate Changes EC (2015) European Commission, European development policy EC (2017) uropean Commission, Energy union and climate Ekstrand & Draper (2000) CONTINGENT VALUATION METHOD: AN INTRODUCTION In e M D Soden and B Steel, Handbook of Global Environmental Policy and Administration (p Chapter 7) NY European Environment Agency (2021, 05 11) Retrieved from Energy and climate change: https://www.eea.europa.eu/signals/signals-2017/articles/energy-andclimate-change EVN (2019) EVN Annual Report Faure-Schuyer, A., Welsch, M., & Pye, S (2017) A market-based European energy policy In Europe’s energy transition - insights for policy making 41-48 Foundation, A.-E (2017) Asia-europe energy policy forum Goldthau, A (2016) The handbook of global energy policy Guo, Xiurui & Liu, Haifeng & Mao, Xianqiang & Jin, Jianjun & Chen, Dongsheng & Cheng, Shuiyuan (2014) Willingness to pay for renewable electricity: A contingent valuation study in Beijing, China Energy Policy Volume 68, 340347 Hanemann, W M (1984) Welfare Evaluations in Contingent Valuation Experiments with Discrete Responses American Journal of Agricultural Economics, 332– 341 35 Hanemann, W M., J Loomis, and B.J Kanninen (1991) Statistical Efficiency of Double-bounded Dichotomous Choice Contingent Valuation American Journal of Agricultural Economics, 73, pp.1255- 63 Hoang Van Minh, Hung Nguyen Viet, Nguyen Hoang Thanh, Jui-Chen Yang (2012) Assessing willingness to pay for improved sanitation in rural Vietnam Environmental Health and Preventive Medicine, volume 18, pages 275–284 IEA (2011) International Energy Agency, New and Renewable Energies (NRE) IEA (2014) International Energy Agency, National Energy Policy (Government Regulation No 79/2014) IEA (2019) World Energy Outlook J.T Houghton, Y Ding, D.J Griggs, M Noguer, P.J van der Linden, X Dai, K Maskell, C.A Johnson (2001) Climate Change 2001: The Scientific Basis NY: THE PRESS SYNDICATE OF THE UNIVERSITY OF CAMBRIDGE Klaus G Grunert, Hans Jorn Juhl, Lars Esbjerg, Birger Boutrup Jensen, Tino BechLarsen, Karen Brunso, Charlotte Oland Madsen, (2009) Comparing methods for measuring consumer willingness to pay for a basic and an improved ready made soup product Food Quality and Preference, 607-619 Klaus M Miller, Reto Hofstetter, Harley Krohmer, Z John Zhang R, and Z JOHN ZHANG (2011) How should consumers' willingness to pay be measured? An empirical comparison of state-of-the-art approaches Journal of Marketing Research, 48(1), 172-184 Kotchen, M J (2006) Green Markets and Private Provision of Public Goods, University of Chicago Press , Volume 114, Number Kowalska-Pyzalska, A (2019, 03 02) Do Consumers Want to Pay for Green Electricity? A Case Study from Poland Sustainability , p 02 Kumar Singh, B (2013) South Asia energy security: Challenges and opportunities Energy Policy, 458-468 Leon A.G Oerlemans, Kai-Ying Chan, Jako Volschenk (2016) Willingness to pay for green electricity: A review of the contingent valuation literature and its sources of error Renewable and Sustainable Energy Reviews, 66 (2016) 875– 885 M Hasanuzzaman, NA Rahim, M Hosenuzzaman, Rahman Saidur, IM Mahbubul, MM Rashid (2012) Energy savings in the combustion based process heating in industrial sector In Renewable and Sustainable Energy Reviews (pp 16(7), 4527e4536.) Michael Hahsler and Thomas Reutterer (2015) A Review of Methods for Measuring Willingness-to-Pay Innovative Marketing Mitchell and Carson (1989) Using Surveys to Value Public Goods: the Contingent Valuation Method (Resources for the Future) Washington, D.C Ngai Nguyen Van, Phuoc Kim Nguyen, Hung Phan Dinh (2012) WTP for clean water supply in Cao Lanh City, Dong Thap Province 36 Nguyen, B V (2020) Orientation for national energy development to 2030, vision to 2045 Hanoi: National Economics University Nielsen.(2017, 04) Retrieved from “Green” and “clean” consumption trends: https://www.nielsen.com/wpcontent/uploads/sites/3/2019/04/Nielsen_Sustainability20in20Vietnam_Apr201 7_VN.pdf Noboru Nomura, Makoto Akai (2004) Willingness to pay for green electricity in Japan as estimated through contingent valuation method Applied Energy Volume 78, Issue 4, 453-463 Nur Iqtiyani Ilhama, M Hasanuzzaman, M.A.A Mamun (2020) World energy policies Energy for Sustainable Development Demand, Supply, Conversion and Management, 179-198 Pinuccia Calia & Elisabetta Strazzera (2000) Bias and efficiency of single versus double bound models for contingent valuation studies: a Monte Carlo analysis Economics, 1329-1336 Robert Cameron Mitchell and Richard T Carson (1989) Valuing Public Goods Using the Contingent Valuation Method In Using Surveys to Value Public Goods: The Contingent Valuation Method (p 22) NY: Resources for the Future Roland Menges, C S (2005) Altruism, Warm Glow and the Willingness-to-Donate for Green Electricity: An Artefactual Field Experiment Seung-Hoon Yoo and So-Yoon Kwak (2009) Willingness to pay for green electricity in Korea: A contingent valuation study Energy Policy, 37; 5408–5416 Thai and Hang (2019, 09 09) Vietnamese Energy Retrieved from http://nangluongvietnam.vn Thao, P (2020, 06 18) Portal of the Ministry of Industry and Trade Retrieved from Vietnam Energy Forum 2020 "Clean energy development - Trends and challenges": https://www.moit.gov.vn/tin-chi-tiet/-/chi-tiet/dien-đan-nangluong-viet-nam-2020-phat-trien-nang-luong-sach-xu-the-va-thach-thuc 1972316.html The World Bank (2021, 04 07) The World Bank Retrieved from The World Bank in Vietnam: https://www.worldbank.org/en/country/vietnam/overview Tien Dat Pham, Naoko Kaida, Kunihiko Yoshino, Xuan Huu Nguyen, Hao Thi Nguyen, Dieu Tien Bui (2018) Willingness to Pay for Mangrove Restoration in the Context of Climate Change in the Cat Ba Biosphere Reserve, Vietnam Ocean & Coastal Management, 163(1) 269-277 Twidell & Weir (2015) Renewable Energy Resources UN (2015) United Nations, Paris agreement UN (2017) The Sustainable Development Goals Report New York V, C.-W S (1947) Capital returns from soil-conser- vation practices Journal Farm Economics, 29: 1181–96 Voelckner, F (2006) An empirical comparison of methods for measuring consumers’ willingness to pay Marketing Letters, No 17(2) – pp 137-149 37 WCED (1987) Our common future WHO (2018, May 2) World Health Organization Retrieved from More than 60,000deaths in viet nam each year linked to air pollution: https://www.who.int/vietnam/vi/news/detail/02-05-2018-more-than-60-000deaths-in-viet-nam-each-year-linked-to-air-pollution Wind & Green (2004) Marketing Research and Modeling: Progress and Prospects 38 APPENDIX Data aggregation No Valuables Questionnaires Do you know the difference between renewable and non-renewable energy? Renewable energy is unlimited KNOWLEDGE Fossil fuel are the best form of energy sources Better incentives should be given for consumption of renewable energy The change to renewable energy is an urgent need ELAW Are you aware of Vietnam’s laws to reduce environmental pollution? Do you think Government’s laws that restrict pollution be as they are? BELIEF Proportion 0.2 0.2 0.2 0.2 0.2 0.5 Do you think your government is spending too much time, money and resources to reduce 0.5 environmental pollution? your agreement with the following statements on environmental pollution: AWARE It poses a hazard to the whole world 0.1 Is on the rise 0.1 It is already impacting life as we know it 0.1 It will impact future generations on the planet 0.1 It is causing extinction of flora and fauna 0.1 There’s not much that can be done to stop it 0.1 All of us have to contribute towards reducing it 0.1 It is causing global warming 0.1 The earth can automatically recover from environmental pollution 0.1 Do you think that remedial action on environmental pollution should be 0.1 the most important aspect to take care of? 39 BILL AGE 10 11 How much you pay for average monthly electricity bill? How old are you? GENDER What is your gender? edulevel1 Primary school/ Middle school edulevel2 High school edulevel3 College/Graduate edulevel4 After Graduated edulevel5 Other HSIZE Including yourself, how many people currently live in your household? incomegr1 Less than million VND incomegr2 7-10 million VND incomegr3 11-20 million VND incomegr4 21-30 million VND incomegr5 Above 31 million VND incomegr6 Prefer not to say Where you live? LOCATION 40 ... willingness to pay for green electricity? Question 2: Which factors influence willingnes to pay for green electricity in Vietnam? Question 3: How to change household behavior to support green electricity? ... survey to investigate the willingness of households to pay for green electricity Linear and logit regression models were used to determine the factors that might influence their attitudes towards... Glow and the Willingness- to- Donate for Green Electricity: An Artefactual Field Experiment Seung-Hoon Yoo and So-Yoon Kwak (2009) Willingness to pay for green electricity in Korea: A contingent valuation