Enhancing public acceptance of renewable heat obligation policies in South Korea: Consumer preferences and policy implications

    Enhancing public acceptance of renewable heat obligation policies in South Korea: Consumer preferences and policy implications Sesil Lim, Sung-Yoon Huh, Jungwoo Shin, Jongsu Lee, Yong-Gil Lee PII: DOI: Reference:

S0140-9883(15)00035-3 doi: 10.1016/j.eneco.2015.01.018 ENEECO 2988

To appear in:

Energy Economics

Received date: Revised date: Accepted date:

10 February 2014 7 January 2015 8 January 2015

Please cite this article as: Lim, Sesil, Huh, Sung-Yoon, Shin, Jungwoo, Lee, Jongsu, Lee, Yong-Gil, Enhancing public acceptance of renewable heat obligation policies in South Korea: Consumer preferences and policy implications, Energy Economics (2015), doi: 10.1016/j.eneco.2015.01.018

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ACCEPTED MANUSCRIPT Enhancing public acceptance of renewable heat obligation policies in South Korea: Consumer preferences and policy implications Sesil Lima, Sung-Yoon Huha, Jungwoo Shinb, Jongsu Leea, Yong-Gil Leec,*

Gwanak-ro, Gwanak-gu, Seoul 151-742, South Korea b

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Technology Management, Economics, and Policy Program, Seoul National University, 599

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Department of Civil, Architectural and Environmental Engineering, The University of Texas

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at Austin, 1 University Station C1761, Austin TX 78712, United States

Department of Energy Resources Engineering, Inha University, 100 Inharo, Nam-gu,

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Incheon 402-751, South Korea

* Corresponding author.

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E-mail: [email protected]; Tel.: +82-32-860-7555; Fax: +82-32-872-7550.

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Abstract Renewable heat obligation (RHO) policies have continued to attract global attention, and the

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South Korean government plans to start enforcing these policies in 2016. To ensure the

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effectiveness of RHO policies, various preferences of stakeholders, including end users, should be considered in policy development because their benefits and costs can vary

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according to their level of obligation. A choice experiment was conducted to analyze the preferences of end users for two types of RHO policies: one aimed at heat suppliers and the

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other at building owners. The results indicate the necessity to focus on costs when designing RHO policies because this element was the most important factor influencing the public's acceptance of these policies and suggest that, for RHO policies aimed at heat suppliers, the government should convince end users of the stability of the heat supply, a factor considered to be important by consumers. Finally, the minimum level of the subsidy required to gain public approval of both types of RHO was examined. The annual government subsidies for

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the RHO for heating suppliers is expected to be KRW 330 billion, which can be covered by collecting taxes from those consumers not subject to these obligations. The results for RHOs

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for building owners indicate KRW 900 billion to 1.8 trillion as the amount of subsidy

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spending required of the government.

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Keywords: Renewable Heat Obligation, Choice Experiment, Mixed Logit Model

JEL Classification: D12, Q28

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1. Introduction To stabilize the global climate and reduce carbon emissions, promoting the use of renewable

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energy sources has become energy authorities' major focus in recent years. Supporting

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policies and schemes are necessary for expanding the renewable energy market because the initial investment and the cost of renewable energy are higher than those for conventional

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fossil fuels. In the last several decades, many governments have focused on policy instruments for electricity generation. In addition, reducing carbon emissions in the heating

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sector is important because heating and cooling account for approximately 40–50% of global energy demand (Bürger et al., 2008). With the increasing popularity of renewable heating technologies such as biomass, solar, and geothermal heating, several governments have promoted the use of renewable energy in the heating sector through a wide range of policy instruments. Notable examples include the renewable heat obligation (RHO) policy directed at building owners in Germany and the renewable heat incentive (RHI) policy directed at heat

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producers in the United Kingdom (UK).1 The RHO policy enforces the use of renewable energy sources for building heating systems, and the RHI policy provides a monetary

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incentive to suppliers of renewable heat sources. The South Korean government plans to

development.

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introduce RHO policies in 2016, and a detailed policy structure is currently under

Because renewable heating technologies have received close attention only recently,

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few studies have examined the design of policies promoting renewable heating. Steinbach et al. (2013) compared two harmonization scenarios for an RHO scheme developed for the European Union (EU) member states based on the Renewable Directive of the EU and discussed various policy implications and economic benefits of the optimal harmonization design. However, Steinbach et al. focused on efficiently adjusting policies of each member state and thus provided no effective design guidelines considering stakeholders' acceptance or the total cost of the RHO policy. In addition, few studies have examined stakeholders' acceptance of renewable energy by focusing on the supply side through an analysis of preferences of potential investors or experts in financing and investing in renewable energy (Lüdeke-Freund & Loock, 2011; Masini & Menichetti, 2012). With respect to the demand side, most studies of end users' acceptance of renewable energy have focused on preferences of electricity consumers (Islam, 2014; Islam & Meade, 2013; Longo et al., 2008; Shin et al., 1

See Appendix A for detailed information.

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ACCEPTED MANUSCRIPT 2014; Wiser, 2007). In comparison to electricity or transport sectors, promoting the use of renewable energy in the heating sector is much more difficult, given the wide variety of stakeholders

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such as heat end users who obtain a supply of heat from heat suppliers, building owners with

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decentralized and small-scale heating units, and district heat suppliers, among others (Steinbach et al., 2013). Preferences of various stakeholders can also influence the

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implementation of policies in the heating sector. In particular, end users impacted by RHO policies in the heating sector may face increased heating expenses related to the increased

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production cost because of heat suppliers' investment in renewable heating facilities. If the government does not consider the need and preferences of heat end users in the early design stages, then the effectiveness of RHO policies may be compromised by end users’ opposition. Therefore, it is important to enhance the acceptance and support of the public for RHOs, which can be promoted by designing appropriate policies considering preferences of heat end users.

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This study provides a quantitative analysis of consumer preferences by estimating their marginal willingness to pay (MWTP) and the relative importance of various attributes of

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RHO policies to be introduced in South Korea in 2016. The results are expected to be useful for designing effective RHO policies based on preferences of end users and offer some

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guidelines for promoting the public's acceptance in a cost-effective manner. The rest of this paper is organized as follows: Section 2 explains the present status of the RHO policy in

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South Korea and discusses related issues. Section 3 describes the analytical methods, including the design of choice experiments and model descriptions. Section 4 reports the main results and their implications for the appropriate design of RHO policies by emphasizing cost-effectiveness. In addition, further avenues of research such as limitations and suggestions of this study are discussed in this section. Lastly, Section 5 concludes with describing the contribution of this study.

1.1. The background of the heating sector As stated earlier, the demand for heating accounts for a substantial portion of the world's total energy demand (IEA, 2007). Globally, the share of heat in total final energy consumption was 47% in 2009, far exceeding the final energy demand for transportation (27%), electricity (17%), and non‐energy use (9%). In the case of OECD countries, the share of heat in total final energy consumption was 37%, exceeding all other sectors such as transportation (32%), 4

ACCEPTED MANUSCRIPT electricity (21%), and non‐energy use (10%) (Beerepoot & Marmion, 2012). Therefore, heating accounts for the largest portion of final energy consumption. Heating not only accounts for a large share of final energy consumption but also has a high level of

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dependence on fossil fuel heat sources. Among various available heat sources such as gas, oil,

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coal, waste heat, district heating, and renewable energy, natural gas is the most widely used source. The level of worldwide dependence on fossil fuel as a heat source was 66.7% (gas:

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27.1%; coal: 20.3%; oil: 19.3%) as of 2009, and that of OECD dependence was 84.7% (gas: 50.9%; coal: 25.1%; oil: 8.7%) (Beerepoot & Marmion, 2012). In sum, it has been recently

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pointed out that the heating sector should be decarbonized as soon as possible because both its share in final energy consumption and its dependence on fossil fuel as a heat source are high.

For South Korea, which is the subject of this empirical analysis, heat accounts for 30.3% of total energy consumption in 2007: industries (16.8%), households (8.3%), and

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commercial use (5.2%) (KEMCO & Deloitte, 2012). The overall goal of the Korean government is to supply 11% of total primary energy through renewable energy by 2030. With respect to renewable heat, its goal is to supply 41% of domestic heat energy with

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renewable energy by 2030, which was only 8.7% in 2008 (KEMCO & Deloitte, 2012). In sum, although the share of the heat sector in total energy consumption is considerable in

almost absent.

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South Korea, relevant policies on renewable heat sources to meet national supply targets are

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Renewable heat can be generated in a number of ways because there are various technologies for renewable heat sources. Among these, existing technologies for heating include modern biomass, solar thermal, and geothermal energy (Beerepoot & Marmion, 2012).2 Biomass exists in solid, liquid, or gaseous forms derived from various feedstock sources and includes solid biomass, biogas, and biomass materials from waste (IEA, 2007). In the case of solar energy, solar thermal collectors produce heat derived from solar radiation by heating fluid circulated within the collector (Beerepoot & Marmion, 2012). Geothermal applications use heat stored in rock and trapped in vapor or liquid, and shallow and deep geothermal energy sources are distinguished according to the depth of heat used (European Union, 2011). These three renewable resources currently supply not only hot water and space 2

According to IEA 2006 World Energy Outlook, these three renewable heat technologies have considerable potential (IEA, 2006). The use of heat from renewable energy sources in industry and building sectors is expected to increase 20% by 2030 relative to 2003. For example, heat from solar thermal collectors is projected to increase from approximately 280 PJ to 3000 PJ from 2003 to 2030, and geothermal heat is projected to increase from 185 PJ in 2004 to over 1000 PJ by 2030 (IEA, 2007).

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ACCEPTED MANUSCRIPT heating for tens of millions of domestic and commercial buildings worldwide but also heat for industrial processing and agricultural applications (REN21, 2013). In the case of biomass heat, the combustion of various forms of biomass fuels can provide heat at different scales for

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use by industries, agricultural processes, drying, district heating schemes, water heating, and

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space heating in individual buildings. Solar space heating and cooling are also gaining some ground, and their advanced applications include water and space heating for buildings of all

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sizes, large-scale plants for district heating, and air conditioning and cooling. Finally, geothermal energy can be used for district heating, industrial purposes, aquaculture pond

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heating, agricultural drying, snow melting, and other uses (REN21, 2013). In sum, all of these three sources can be used for district heating and cooling. From this perspective, heat suppliers are defined in this study as providers of district heating that produce and supply heat through large-scale heating generation facilities.

2. Current issues in RHO policies in South Korea

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The South Korean government has only recently started to consider RHO policies. In August 2013, the government announced the “Activation Plan for Renewable Energies,” including its

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plan for introducing an RHO policy for owners of private buildings (other than residential buildings) with a total floor area of >10,000 m2 in 2016. According to the plan, such owners

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are required to use renewable energy for more than 10% of total heat consumption. Although the government plans to extend this policy to buildings with a total floor area of >3,000 m2 in

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stages, beginning in 2030, details are under development. Extending the policy to residential buildings and implementing an additional RHO policy aimed at heat suppliers have also been proposed.

Although RHO policies have immediate impacts with minimal financial burdens and ensure the consistent use of renewable energy for heating, considerable resistance from stakeholders is expected, which may threaten the implementation of RHO policies. Therefore, such policies require mechanisms to enhance stakeholder acceptance. In addition, it is important to determine who would be impacted first by their obligations for renewable heating before discussing other details of RHO policies because the overall design and effects of implementation can vary according to the stakeholder. Accordingly, this study compares the details and effects of enforcing two different RHO policies: one intended for heat suppliers and the other for building owners (including owners of residential buildings). In RHO policies intended for building owners, owners of existing buildings typically 6

ACCEPTED MANUSCRIPT receive incentives from the government to use renewable heating, whereas those of remodeled or new buildings are generally obligated to use renewable energy. If owners of existing buildings also assume this obligation, then their installation costs would be higher

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than those of new-building owners because existing buildings likely require some degree of

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reconstruction to install facilities for renewable heating. This immense initial expense is expected to cause considerable opposition from owners of existing buildings to the

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enforcement of RHO policies, and these high initial costs may not be offset by relatively low fuel and maintenance costs of renewable heating. Additional social costs may also be

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incurred to implement and monitor policy compliance for owners of existing buildings. For these reasons, most governments impose RHOs on owners of remodeled and new buildings. The South Korean government is also considering enforcing these obligations but only on owners of new buildings.

On the other hand, there are some drawbacks to RHO policies intended for newbuilding owners that should be considered. First, construction firms are actual buyers of

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renewable heating systems and may choose those systems that have low heating efficiency or are not effective in reducing greenhouse gas emissions because they are concerned more

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about reducing construction costs than about the efficient option of systems. Therefore, a quality standard for renewable heating systems should be established, and compliance should

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be monitored to ensure intended effects of RHO policies. Further, only a portion of the initial investment can be recovered from lower fuel and maintenance costs of renewable heating

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because of the declining performance of heating facilities and fluctuating fuel prices with payback periods longer than a year. In contrast to RHO policies intended for building owners, those policies focusing on heat suppliers have low operating and monitoring costs because the number of entities subject to these obligations is relatively small. On the other hand, effects of RHO policy implementation are limited in comparison to those of policies aimed at building owners because of the smaller number of heat suppliers. The added investment for renewable facilities may be passed on to heat users by increasing the cost of heating. In addition, some imbalance between supply and demand for renewable energy can lead to instability in the heat supply, inconveniencing some heat consumers. For example, for heat suppliers using biomass for heating (e.g., wood pellets), a sudden increase in demand for biomass when this source is in short supply can lead to an unstable heat supply. For solar thermal heating facilities, the consistent production of heat is not possible under all weather conditions. 7

ACCEPTED MANUSCRIPT Further, the performance of heat pump boilers can be sensitive to the weather. In particular, the coefficient of performance of heat pump boilers working on outside air and heat pumps using surface water are affected by seasonal temperature fluctuations (Beerepoot & Marmion,

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2012).

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Given the aforementioned issues, this study evaluates the preferences and MWTP of end users for these two types of RHO policies to provide guidelines for enhancing the

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public’s acceptance of these policies.

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3. Methodology

To provide guidelines for end users' acceptance of new RHO policies in South Korea, this study uses data on stated preferences obtained through choice experiments because the country has yet to implement any RHO policies. Choice experiments allow consumers to choose the most preferred alternative from a list of hypothetical conditions, and resulting choice data are composed of discrete values appropriate for use in a discrete choice model. In

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this study, a discrete choice model was used to analyze consumers’ marginal utility and MWTP for key attributes of RHO policies and calculate acceptance levels of end users for

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specific levels of attributes. In addition, data on socioeconomic characteristics of respondents

scale.

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and their levels of awareness for RHO policies were evaluated using a five-point Likert-type

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3.1 The design of the discrete choice experiment A discrete choice experiment allows the researcher to obtain data on a hypothetical choice by asking respondents to repeatedly choose one alternative from different sets of product or service profiles constructed from core attributes defined at certain levels (Haaijer & Wedel, 2003). In this study, two discrete choice experiments were conducted for two RHO policies aimed at heat suppliers and building owners.

Step 1: Identify attributes A discrete choice experiment requires several stages. The first stage identifies attributes relevant to the choice experiment and assigns levels for each attribute through a qualitative analysis (Louviere et al., 2000). In this study, to determine appropriate attributes, extensive literature reviews of RHO policies and their effects on consumers were conducted. The appropriate scope or range of levels for each attribute was determined to capture trade-offs 8

ACCEPTED MANUSCRIPT between attributes and ensure feasibility (Green & Srinivasan, 1978; Haaijer & Wedel, 2003). Ratcliffe and Longworth (2002) pointed out that respondents tend to put higher value on attributes with more levels. Therefore, the same number of levels was added to all attributes

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whenever possible. Table 1 shows the defined attributes that may influence the public’s

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acceptance of RHO policies. These attributes are related to costs, effects of implementation,

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and dependent factors for individuals affected directly by RHO policies.

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[Insert Table 1 about here.]

Issues related to the cost of implementation, namely the party bearing this cost and the method for paying it, vary according to who is obligated under the RHO policy. For the policy aimed at heat suppliers, the heating expense for heat consumers can increase because heat suppliers are needed to install renewable heating facilities and their production costs rise as a result. Therefore, attributes related to costs were defined as the increase in the heating

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expense for end users after enforcing RHO policies intended for heat suppliers. According to a study by the Korea City Gas Association, approximately 75% of all Korean households

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used city gas for heating in 2011, and the average heating expense per winter month in Seoul was estimated at approximately KRW 100,000.3 Based on this value, the levels of this

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attribute were set to 5%, 10%, and 15%, which assume increases in the per-household monthly heating expense by 5%, 10% and 15% from KRW 100,000, respectively, with the

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implementation of RHO policies. For RHO policies intended for building owners, they have to bear all costs of installing renewable heating facilities. Therefore, attributes related to these costs were defined as the additional installation cost of renewable heating facilities over an area of 83 m2, the average area of a home for a family of four. According to previous research, the installation cost of solar thermal heating facilities ranged from approximately KRW 7 million for 49.6 m2 to KRW 8.3 million for 148.8 m2 as of April 2012, and that of geothermal heating facilities was KRW 4.4 million per 33.1 m2. Therefore, the additional installation cost was set to KRW 6, 7, and 8 million. In addition, reductions in CO2 emissions per year from current levels that could occur as heat energy is generated from renewable sources instead of fossil fuels were examined. 3

The average consumption of gas per household in Seoul was 112 m3/month during the winter of 2011, and therefore the heating expense was KRW 103,390 = 1.1 × (840 + 12 × 826.84 + 100 × 832.29).

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ACCEPTED MANUSCRIPT Because this attribute is related to the effect of RHO policy implementation, which is independent of the subject of RHO obligations, this attribute was defined in the same way in both experiments. Levels of this attribute were selected by assuming that greenhouse gas

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emissions from heating would account for approximately 1/7 to 1/5 of all emissions

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generated from electricity production based on a database maintained by Korea Energy Management Corporation. According to Kydes (2007), if the renewable portfolio standard

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mandates that power utilities generate 20% of their power from renewable sources, which is double the current mandatory rate in South Korea, then CO2 emissions decrease by

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approximately 4% per year. Palmer and Burtraw (2005) predicted that greenhouse gas emissions decrease by approximately 5.8% per year for a 10% mandatory rate under the renewable portfolio standard. Therefore, levels of the attribute for reductions in CO2 emissions were set to 0.5%, 1%, and 1.5% per year based on the assumption that CO2 emissions decrease by 1/7 to 1/5 of all emissions under the current mandatory rate in South Korea.

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The attribute related to new employment per year was defined as the number of new jobs expected to be created through the revitalization of the renewable market based on the

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implementation of the RHO policy. As in the case of the attribute for reductions of CO2 emissions, the level of this attribute was defined in the same way in both experiments because

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its value is independent of the subject of RHO obligations. According the Center for Renewable Energies of Korea Energy Management Corporation, 6,000 new jobs are expected

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to be created per year in the renewable energy market if the number of houses heated by geothermal heating facilities increases to 50,000, which is approximately 10% of the annual housing supply. Based on the data, levels of new employment were set to 5,000, 10,000, and 15,000 per year. Finally, separate attributes for RHO policies directed at heat suppliers and building owners were considered. For those policies directed at heat suppliers, an attribute related to the stability of heat energy supply was analyzed. This attribute suggests that heat supply may become unstable because heat is produced from renewable energy sources instead of fossil fuel and electricity. As discussed in Section 2, because the supply of renewable heat energy can be sensitive to weather conditions, heat demand and production can vary over time (Beerepoot & Marmion, 2012). Therefore, the stability of heat supply may have considerable influence on preferences of end users. For RHO policies directed at building owners, various attributes related to payback 10

ACCEPTED MANUSCRIPT periods and government subsidies for the initial investment were examined. The attribute for payback periods reflected the period during which building owners recouped all installation costs because of their continued operation of renewable heating facilities. Although the

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installation of renewable heating facilities typically requires a large initial investment, the

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annual heating expense may be lower because of lower fuel and maintenance costs. For example, heating from geothermal and solar thermal sources does not have a fuel cost, and

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using wood pellets to generate heat can save 62% in comparison to heating using fossil fuel (Byun, 2012). Therefore, the levels of the attribute related to the payback period were set to 3,

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5, and 7 years.

The attribute related to government subsidies for the initial investment reflected the subsidy rate for the installation cost of renewable heating facilities. The South Korean government currently pays 50% of the total installation cost of solar thermal and geothermal heating facilities per household through the Home Subsidy Program. In Germany, the government matches 12.6% of the initial cost of installing solar thermal heating facilities.

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Therefore, the levels for the subsidy of the installation cost were set to 0% (no subsidy), 25%, and 50%.

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Other policy attributes can be included because the implementation of an RHO policy should be evaluated based on a range of criteria, including the balanced development

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of individual renewable sources, economic feasibility, sufficient financial resources, and the likelihood of realization. Typical attributes reflecting such aspects include improvements in

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domestic energy security, variable heating efficiency, and the type of renewable energy used for heating. The exploitation of various renewable energy sources enables a heavy energyimporting country such as South Korea to improve its national energy security by reducing primary energy imports. Heating efficiency can vary based on the substitution of conventional fossil fuels for renewable energy sources. In addition, consumer preferences can vary for renewable heating sources such as solar, geothermal, and bioenergy energy, although they are all supplied as heat. On the other hand, including all such attributes can lead to too many attributes per alternative, which can confuse respondents, obscure their preferences, and give rise to some bias in their choices. Therefore, the number of attributes in a choice experiment was limited to 4 or 5, and alternatives were composed of those attributes considered to be most important ones by policymakers. Other potential attributes not included in the survey were assumed to have the same level in all alternatives, and each respondent was informed of this assumption 11

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Step 2: Construct choice sets

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The second stage combines attributes and their levels to construct choice sets. Based on

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combinations of these attributes at levels described earlier, the numbers of possible alternatives for RHO policies directed at heat suppliers and building owners were 54 and 243,

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respectively. However, evaluating all possible alternatives is not feasible in practice. To address this problem, fractional factorial designs were considered in each choice experiment.

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Only main effects were considered in choice experiments by assuming all interactions between attributes to be negligible (Addelman, 1962). Louviere (1988) mentioned that main effects explain more than 80% of the variance if two-way interactions account for up to 6%. Nowadays main-effect plan is most common fractional factorial design (Hensher, 1994). Nevertheless, there is some concern that estimated results can be biased if attributes are severely correlated and thus that there are significant interactions. To minimize this bias,

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attributes were designed to vary independently of one another in choice experiments to satisfy orthogonality, which requires attributes and attribute levels to be orthogonal to one

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another within and between alternatives. Therefore, with the orthogonal plan of SPSS 20, 18 separate orthogonal alternatives were selected based on the orthogonal main-effect plan of the

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fractional factorial design.

Then these 18 alternatives were divided into six choice sets consisting of three

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randomly arranged alternatives and “no-choice” options. The no-choice option was a type of basic alternative that stood for the case in which the respondent wanted to maintain the status quo because he or she did not perceive the necessity of RHO policies. Previous studies based on discrete choice experiments have included the no-choice option in the design of choice experiments to increase the efficiency of the choice experiment design (Anderson & Wiley, 1992), make the choice situation more realistic (Haaijer et al., 2001), and directly measure the demand for specific goods in the context of the whole market (Brazell et al., 2006). In this study, choice sets were rearranged to satisfy four criteria for an efficient choice design in Huber and Zwerina (1996).4 Therefore, some specific choice sets were rearranged to avoid 4

Huber and Zwerina (1996) suggested four criteria for efficient choice designs assuring the minimization of errors in estimated parameters: the level balance, orthogonality, the minimal overlap, and the utility balance. The level balance means that levels of an attribute occur with the same frequency. The second criterion, orthogonality, allows the researcher to estimate the effect of one attribute independently (Louviere et al., 2000). The minimal level overlap means that any repeat of attribute levels in each choice set has to be avoided. Finally, the utility balance requires that alternatives within each choice set have similar choice probabilities by limiting

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ACCEPTED MANUSCRIPT the existence of apparently superior or inferior alternatives in the choice set that could bias the respondent's choice. Finally, a pilot test of the design was conducted based on 219 samples, and the

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questionnaire was modified to ensure face validity, which indicates the extent to which the

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choice experiment assesses true utility by including significant factors (Bateman et al., 2002). In the questionnaire, the respondent was allowed to choose the most preferred alternative in

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each choice set. Appendix B shows samples of actual choice sets.

3.2. A discrete choice model: A mixed logit model

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In the discrete choice model, the indirect utility of alternative j (j = 1, …, J) for consumer n, Unj, consists of a deterministic component Vnj and a random component εnj according to the random-utility model. The joint probability density of the random vector, εn’=<εn1, …, εnJ>, is denoted as f(εn). Assuming that consumers attempt to maximize their utility, the probability

Pnj  Pr U nj  U ni , i  j 

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of consumer n choosing alternative j is expressed as follows (McFadden, 1974; Train, 2003):

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 Pr Vnj   nj  Vni   ni , i  j 

,

(1)



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  I  ni   nj  Vnj  Vni , i  j  f  n  d  n where I(·) is an indicator function whose value is 1 if the expression in parentheses is true

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and 0 otherwise.

In this study, a mixed logit model was used to reflect heterogeneous preferences of consumers. Assuming that the deterministic component of indirect utility is affected by attributes of alternatives (Unj = Vnj(xnj) + εnj =  n xnj + εnj) and a vector of coefficients (βn), the evaluation of attributes of alternatives by consumer n varies according to consumers in the population with probability density f(β). If the random component of indirect utility is independent and identically distributed with a type Ι extreme-value distribution, then the choice probability of the mixed logit model can be expressed as (Train, 2003)

 n xnj e Pnj      e n xni   i

  f  d .     

(2)

the occurrence of dominating alternatives in each choice set.

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In the mixed logit model, the researcher can specify the distribution for each coefficient according to its effect on consumer preferences (Train, 2003). Although the

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distribution of coefficients is generally assumed to be normal, a bounded distribution should

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be used if all consumers display similar preferences (Train & Sonnier, 2005). For example, it is appropriate to use a log-normal distribution for a price coefficient with a negative sign for

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all consumers. Preferences of consumers can be understood most accurately if an appropriate distribution satisfying a realistic behavior is assumed.

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Unlike estimates from general econometric models, estimates for coefficients of a mixed logit model do not have comparable meanings across attributes because these estimates express only the marginal contribution to the marginal utility of each attribute with arbitrary units. Therefore, a consumer’s MWTP was calculated based on coefficient estimates. MWTP, or the variation in the compensated surplus of consumers from changes in attributes, indicates the amount of money that consumers are willing to pay to maintain their current

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level of utility when the level of an attribute changes by a unit. If the deterministic component of indirect utility, Vnj, consists of an attribute related to price, xj,price, and other

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attributes xjk , MWTP for each attribute can be calculated as follows:

U nj x j , price



k .  price

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(3)

U nj x jk

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MWTPx jk  

The related importance (RIk) of each attribute in a consumer’s ultimate choice from a series of alternatives can be determined by calculating the “part-worth” of each attribute through the following equation:

RI k 

part  worthK 100 .  part  worthk

(4)

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The part-worth of attribute k can be calculated by multiplying the coefficient of the attribute βk by the difference between the maximum and minimum levels of that attribute.

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4. Results and discussion 4.1. Data

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For the empirical analysis, a professional survey company (Gallup Korea) conducted a

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consumer survey examining RHO preferences from August 30 to September 19, 2012. Adults (n = 500) located in Seoul and other metropolitan cities5 in South Korea participated in face-

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to-face interviews. To maintain a participant component ratio representative of the actual population, a sample was drawn using the purposive quota sampling method based on

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respondent age and gender. Table 2 shows the key characteristics of the respondents.

[Insert Table 2 about here.]

4.2. RHOs for heat suppliers: Consumer preferences

To analyze the public's preferences for RHO policies aimed at heat suppliers, a mixed logit

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model with Bayesian inference6 was used to estimate the utility function7:

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U nj  n1 X j ,cost  n 2 X j ,co2  n3 X j ,employ  n 4 D j , stable  n5 D j ,nochoice   nj ,

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(5)

where Xj,cost, Xj,CO2, and Xj,employ are levels of increased heating expenses, annual reductions in

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CO2 emissions, and annual employment creation, respectively, resulting from RHO implementation. The dummy variable Dj,stable represents whether a stable supply of renewable heating is possible such that a value of 1 denotes a stable measure with the current use of fossil fuel and 0 is a relatively unstable level of supply. The dummy variable Dj,no-choice is an alternative-specific dummy variable indicating the no-choice option (Haaijer et al., 2001; Louviere & Woodworth, 1983).8 The coefficient of this dummy variable is interpreted as the 5

Major metropolitan cities (Busan, Daegu, Inchoen, Gwangju, and Daejeon) and towns in Gyeonggi Province were included. 6 Bayesian inference has the advantage of resolving initial values as well as global maximization problems in comparison to the traditional maximum likelihood estimation (MLE) method (Allenby & Rossi, 1999; Edward & Allenby, 2003; Huber & Train, 2001) 7 Section 3.1 designs the discrete choice experiment based on the orthogonal main-effect plan of the fractional factorial design. Because the main-effect plan assumes that consumers process information in a strictly additive manner (Hensher, 1994), it allows the researcher to define the utility function in a strictly linear and additive manner. Therefore, the utility function in this study is defined as a linear function of attributes. 8 Haaijer et al. (2001) cited three ways to model the “no-choice” option in the discrete choice experiment. The most straightforward way is to code attribute values of the no-choice option as a series of zeros by assuming the

15

ACCEPTED MANUSCRIPT utility of choosing the no-choice option by the respondent (Haaijer et al., 2001; Scarpa et al., 2005). Therefore, the choice probability of the no-choice option can be interpreted as an indicator of the overall preference for a product or service under study (Haaijer et al., 2001;

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Louviere & Woodworth, 1983).

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In general, parameters in a mixed logit model are assumed to have a normal distribution. On the other hand, if a parameter reflects a one-directional preference, then it

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can be assumed to have a log-normal distribution. In this study, a log-normal distribution was assumed for parameters with variables for heating expenses (-) and reductions in CO2

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emissions (+), and all other parameters were assumed to have a normal distribution. Table 3 shows the estimation results for the mixed logit model, including the mean (b) and variance (W) of estimates. The median MWTP and relative importance (RI) of each attribute were calculated based on estimate distributions. Means and variances of all coefficients were significant at the 95% confidence level.

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[Insert Table 3 about here.]

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According to the estimation results, consumer utility increased with a decrease in heating expenses and CO2 emissions and with an increase in annual employment creation. In

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addition, a more stable heat energy supply was preferred to an unstable one. The coefficient of the dummy variable for rejecting RHO policies was high and negative, implying that

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consumer utility was sharply lower when RHO policies were rejected for heat suppliers. Median MWTP was calculated for each attribute based on estimation results (Table 3). Consumers were willing to pay KRW 42.5 for a 1 Mt reduction in CO2 emissions (or KRW 0.4250/10000 t). MWTP was KRW 0.0565 for each new job created through RHO policies. In addition, consumers were willing to pay an average of KRW 5,904 for a stable heat supply. Finally, the average Korean consumer was willing to pay KRW 55,167 per year for implementing RHO policies because the respondents’ MWTP for the rejection of RHO policies was KRW -55,167, which translates to the monthly MWTP of KRW 4,597.25 per utility of the no-choice option as zero, but this method may produce biased results. Second, the researcher may add a specific alternative constant for the no-choice option to increase the model fit by setting the utility level of the no-choice alternative. Another way is to specify a nested logit model by treating the no-choice option and other alternatives as being in different nests. Haaijer et al. (2001) suggested that the model including a constant for the no-choice option is the most appropriate because it can reduce the potential bias in estimates for linear attributes if attribute values of the no-choice option are coded as a series of zeros and produces better results than the nested logit model. Therefore, this study defines the utility function with the no-choice constant, Dj,nochoice.

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ACCEPTED MANUSCRIPT household for RHO implementation. Based on the estimation results, the relative importance (RI) of each attribute representing consumer preferences for RHO policies aimed at heat suppliers was calculated

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(Table 3). The RI for an increase in heating expenses was the highest (19.57%) of all

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attributes, followed by a stable heat supply (10.49%). The analysis result showing the respondents' main emphasis on the stability of heat supply implies that relevant technological

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improvements and appropriate policies should be provided when renewable heat is supplied by heat suppliers. At its current technological level, renewable energy has relatively low

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stability in comparison to conventional fossil fuel and nuclear energy because it has an intermittency problem in which energy supply fluctuates according to natural conditions. From the perspective of consumer preferences, therefore, it is necessary to establish an appropriate policy that can induce heat suppliers to introduce complementary technologies such as energy storage devices to compensate for this intermittency problem. In addition, in the case of solar thermal energy, which has a relatively serious intermittency problem

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according to weather conditions, the government should force heat suppliers to be prepared with auxiliary heat source facilities. Further, given the intermittency problem of renewable

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energy, advanced forecasting techniques for heat energy supply that can reflect geographic and climatic properties of each region should be developed and adopted to improve the

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stability of heat supply.

Changes in the adoption rate of RHO policies with attribute levels were calculated to

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forecast market feasibility. In the standard scenario, the following conditions were assumed: a 10% increase in monthly heating expenses, a 1% annual reduction in CO2 emissions, the annual creation of 10,000 new jobs, and a stable supply of heat energy. For this standard scenario, 86.22% of all respondents reported their willingness to adopt RHO policies. This high adoption rate is consistent with the result showing that 92.00% of all respondents were willing to adopt RHO policies (i.e., the percentage of respondents not rejecting RHO policies in the choice experiment). The high RHO adoption ratio can be explained in part by a large number of Koreans perceiving the necessity of using renewable energy sources and having a positive impression of RHO policies and is supported by answers to preliminary questions. A number of other scenarios were analyzed to examine changes in the adoption rate based on variations in attribute levels. When heating expenses, which had the highest RI among all attributes, increased from 0% to 30% over the standard scenario, the adoption rate decreased from 99.9% to 60.3% (Figure 1). When heating expenses rose to 49%, the adoption 17

ACCEPTED MANUSCRIPT rate decreased below 50% (49.94%), with more than half of all respondents preferring not to

4.3. RHOs for building owners: Owners’ perspectives

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[Inset Fig. 1 about here.]

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implement RHO policies.

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To investigate the acceptance of RHO policies aimed at building owners, a mixed logit model with Bayesian inference was used to estimate the following utility function9:

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U nj  n1 X j ,cost  n 2 X j ,co2  n3 X j ,employ  n 4 X j , payback  n5 X j , subsidy  n 6 D j ,nochoice   nj

(6)

,

where Xj,cost, Xj,CO2, and Xj,employ are initial installation costs of renewable heating facilities,

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annual reductions in CO2 emissions, and the annual creation of new jobs, respectively. The parameter Xj,payback reflects the payback period for new facilities, and Xj,subsidy reflects the size

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of the government subsidy for the initial investment. The dummy variable Dj,no-choice again represents an alternative-specific dummy variable indicating the no-choice option. In this

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study, a log-normal distribution was assumed for additional installation costs, reductions in CO2 emissions, and the payback period, and a normal distribution was assumed for all other parameters.

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Table 4 shows the estimation results for survey data with mean b and variance W. Both the means and variances of all coefficients were significant at either 99% or 90% confidence level.

[Insert Table 4 about here.]

For RHO policies directed at building owners, the result suggests that median 9

Although it was possible to define the utility function as a linear function of attributes because the choice experiment was designed based on the orthogonal main-effect plan, some attributes such as the payback period, government subsidies, and additional installation costs were correlated. However, severe correlations between attributes may induce erroneous estimation results. The payback period varied considerably according to characteristics of heat energy sources and operating conditions such as the location or weather, and the level of government subsidies depended on the government’s will. In addition, additional installation costs varied according to heating technology specifications and were designed based on the initial investment cost without any government subsidy. Therefore, correlations between these three variables were assumed to be negligible, and attributes were designed to vary independently from one another in the choice experiment.

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ACCEPTED MANUSCRIPT MWTP has little effect on policymakers. More specifically, each building owner's preferences and MWTP varied substantially according to characteristics of buildings they owned, including their type, purpose, area (size), and price. In this regard, Table 4 shows

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only the direction and effect of each attribute on total consumer utility. Consumer utility

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increased with a decrease in installation costs and payback periods. In addition, the utility is increased by increasing the number of new jobs and the amount of government subsidies, but

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in a perspective on CO2 emissions, it is increased by decreasing the amount of CO2 emissions. As in the previous section, consumer utility decreased sharply for consumers rejecting RHO

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policies for building owners.

4.4. A comparison of RHO policies for heat suppliers and building owners For RHO policies for heat suppliers, the median Korean consumer’s monthly WTP was approximately KRW 4,600. To examine the feasibility of RHO implementation in South Korea, an additional increase in heating expenses beyond the consumer’s WTP was assumed

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to be subsidized by the national treasury. In this case, the respondents were willing to pay a maximum of KRW 4,600 even when the total expense of the RHO policy exceeded this

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amount. Given that the average heating expense per Korean household was approximately KRW 100,000 per month and based on the assumption of a 10% increase in heating expenses

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with RHO implementation, the government subsidy per household was KRW 5,400 per month (10,000 – 4,600 = KRW 5,400). According to the Korean Statistical Information

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Service (KOSIS), a total of 2,655,080 households in 2010 used heat energy from a central or district heating system,10 corresponding to KRW 170 billion in total government subsidies per year.11 The annual government subsidy was KRW 330 billion if the increase in heating expenses was assumed to be 15% (i.e., the maximum level of the attribute in the discrete choice experiment).12 In addition, many households in South Korea do not acquire heat energy from suppliers. If RHO policies are enforced for heat suppliers, then 14,686,886 households in Korea are expected to realize various benefits of RHO implementation, even though they do not pay for it. In this case, the government may levy a tax of KRW 4,600 per month, which is the median WTP for the RHO policy, on these households to secure necessary financial

10 11 12

There were 820,059 and 1,835,021 households with central and district heating systems, respectively. KRW 5,400/month × 2,655,080 households × 12 months/year = KRW 1.72×1011/year. KRW 10,400/month × 2,655,080 households × 12 months/year = KRW 3.31×1011/year.

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ACCEPTED MANUSCRIPT resources to support heat suppliers. In this scenario, expected financial resources amount to KRW 810 billion,13 which covers the previously calculated expected subsidy for suppliers. Therefore, when imposing RHO regulations on heat suppliers, the government should offer

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subsidies to minimize participant opposition and collect taxes from consumers who are not

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subject to these obligations to raise revenue to support these subsidies.

On the other hand, the government may subsidize a portion of installation costs for

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each new facility to encourage the public's acceptance. As indicated earlier, because each building has its own unique characteristics, it is not appropriate to estimate the size of

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subsidies by calculating the owner’s average WTP. Instead, the required subsidy should be estimated by assuming a situation in which the government offers grants to the primary target specified in the Activation Plan for Renewable Energies in the initial stage. To estimate the total installation cost of renewable heating and cooling systems, the following conditions were assumed: (i) the government subsidizes a portion of installation costs for owners of new buildings with a total floor area >10,000 m2 in the initial step (2016–2019), (ii) the annual

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construction area is the minimum area (10,000 m2), and (iii) the average installation cost of renewable heating and cooling systems is approximately KRW 120,000/m2.14 According to

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the Korean Ministry of Trade, Industry, and Energy, the average number of newly constructed buildings with a total floor area >10,000 m2 is approximately 700/year. Given

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these assumptions, the total installation cost for building owners for renewable heating and cooling facilities was estimated to be approximately KRW 84 billion/year. 15 If the

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government subsidized 25% and 50% (i.e., the maximum level in the discrete choice experiment) of this additional installation cost, then the total subsidy reached KRW 21 and 42 billion, respectively.

Furthermore, with the assumption that the enforcement of RHO policies would be extended to the residential sector in the future, the government can offer grants to owners of detached or multifamily homes. These individuals are less likely to accept RHO policies because their WTP for renewable heat energy is low. To approximate the total installation cost for owners of detached or multifamily homes for renewable heating and cooling systems, the following conditions were assumed: (i) the annual new construction of both types of 13

KRW 4,600/month × 14,686,886 households × 12 months/year = KRW 8.11×1011/year. Although the average installation cost of solar heating and hot-water systems was about KRW 55,000– 142,000 and that of geothermal systems was about KRW 133,000, the fixed value of KRW 120,000 was used as the renewable installation cost because the average cost per unit area decreases with an increase in the total building area. 15 KRW 120,000/m2 × 10,000 m2 × 700 = KRW 8.4×1011. 14

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ACCEPTED MANUSCRIPT homes is maintained at levels similar to those in 2012, (ii) the construction area of these two types of homes is the maximum area, and (iii) all newly constructed homes use only renewable resources for heating. According to KOSIS, there were 71,255 construction

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permits granted in 2012 in South Korea: 51,232 for detached houses and 20,023 for

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multifamily housing. According to South Korean construction regulations, areas of detached and multifamily homes cannot exceed 331 m2 and 660 m2, respectively. The average

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installation cost of renewable heating and cooling systems is approximately KRW 120,000 /m2. Given these assumptions, the total installation cost of renewable heating and cooling facilities in the residential sector of South Korea was estimated to be approximately KRW 3.6

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trillion.16 If the government subsidized 25% and 50% (i.e., the maximum level in conjoint alternatives) of this additional installation cost, then total subsidies were KRW 900 billion and 1.8 trillion, respectively.

Although this study examines expected amounts of the government's financial resources, directly comparing the feasibility of the two types of RHO policies by using these

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estimates remains a challenge. For RHO policies for building owners, the total cost of implementing RHO policies can far exceed these calculations because the government may

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fully subsidize installation costs of public buildings such as government offices and schools. The government may minimize its financial burden by exempting, not subsidizing,

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obligations of individuals with low acceptance levels. Even if all obligations are met, it may be a long time before RHO implementation results in visible outcomes such as greenhouse

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gas reductions or the creation of new jobs because substantial delays are likely before a large percentage of buildings install renewable heating facilities because of their typically long life span. For RHO policies directed at heat suppliers, an increase in additional costs for end consumers is likely to lead to a rapid decrease in the public's acceptance of RHO policies, producing strong opposition to these policies. On the other hand, the public's acceptance may be enhanced by subsidizing costs above end users' WTP. In this study, WTP was calculated for all consumers, including those with no direct interest in RHO policies because they do not acquire their heat from heat suppliers. The government can collect considerable revenue from these individuals by levying new taxes that are lower than this WTP, contributing to the longterm price competitiveness of renewable heat energy. This study has some limitations. First, in discrete choice experiments, variations in consumer acceptance were analyzed at a specific point in time instead of over a longer period. 16

KRW 120,000 /m2 × (331 m2 × 51,232 + 660 m2 × 20,023) = KRW 3.6×1012.

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ACCEPTED MANUSCRIPT In this regard, a time series analysis considering other factors such as price changes and advances in renewable energy technologies should improve the public's acceptance of RHO policies. Second, in the composition of hypothetical RHO alternatives, only four to six

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attributes (among many possible attributes) were included to avoid confusion. However,

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other important attributes of RHO policies, such as heating efficiency, should also be examined. Third, although consumers’ heterogeneous preferences were noted using a mixed

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logit model, the structure of this heterogeneity was not described. In addition, the analysis focused on preferences of end users of heat energy without distinguishing between

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respondents who staunchly supported RHO policies and those who did not. However, these two groups may show clear differences in their preferences for RHO policies. Although this is beyond the scope of this study, which focuses on deducing policy implications based on average preferences of end users of heat energy, segmenting end users by using other models (e.g., a hierarchical Bayesian model or a latent class model) or comparing results between supporters and non-supporters should provide richer information because this study's

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respondents expressed heterogeneous preferences across all attributes. In this regard, future research should take such an approach. Finally, an analysis of characteristics of supporters in

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comparison to non-supporters should provide useful insights for designing efficient RHO policies, and therefore future research should take into account effects of sociodemographic

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and regional factors on the acceptance of RHO policies.

5. Conclusions

This study estimates the MWTP and acceptance level of respondents for various attributes of RHO policies planned for their implementation in South Korea. The average response for RHO policy awareness was 1.89 points on a five-point Likert-type scale, implying that most respondents were unaware of RHO policies. On the other hand, the acceptance rate for the standard scenario for RHO policies directed at heat suppliers was 86.22% after the respondents were educated on advantages and disadvantages of these policies. These results indicate that the respondents had positive attitudes toward these policies. This suggests that the South Korean government should consider extensive public relations and educational programs focusing on RHO policies to enable consumers to better understand these policies and make educated decisions. Such programs are expected to increase the public's acceptance and improve the effectiveness of RHO policies. 22

ACCEPTED MANUSCRIPT The most important attribute for the public's acceptance was the cost (i.e., an increase in heating expenses and initial installation costs of facilities), which suggests that cost considerations, including the use of government subsidies, are likely to play a critical role in

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designing RHO policies. In addition to costs, the respondents also placed great emphasis on

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the stability of heat supply. Therefore, establishing a stable supply system before RHO implementation may be a crucial factor influencing policy success. In general, policymakers

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are likely to have a clear understanding of attributes emphasized by consumers and use those attributes to enhance the public's acceptance. Therefore, policymakers should carefully

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consider costs and heat supply stability when designing RHO policies. Heat production represents a large share of total energy demand, accounting for 47% of it worldwide as of 2009 (Beerepoot & Marmion, 2012). Although there has been a sharp increase in policies focusing on renewable heat energy since 2005, few countries have implemented policies with a strong regulatory component such as RHO obligations (Beerepoot & Marmion, 2012). However, RHOs are starting to become more prevalent. In

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this context, this study's results have several important policy implications for countries other than Korea, particularly those planning to introduce RHO policies. First, it is important to

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promote a better understanding of positive aspects of RHO policies, such as reductions in CO2 emissions and employment creation, to enhance the public’s acceptance of these policies.

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The public's current interest in RHO policies focuses on the potential increase in the cost of heating. Therefore, the public's focus should be redirected to these positive aspects to

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effectively increase its acceptance. Establishing the cost of heating and the stability of heat supply should take priority over other issues in designing RHO policies because the public's acceptance is strongly affected by these two factors. This study not only presents a detailed methodology for an ex ante analysis of the public's preferences for a renewable energy policy but also empirically verifies the methodology using real data. In this regard, the study is expected to serve as a foundation for analyzing the public's preferences for RHO policies that are consistent with data from individual countries.

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Acknowledgments This study was supported by the Energy Information Technology and Policy Program of the

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Korea Institute of Energy Technology Evaluation and Planning (KETEP) funded by the

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Korea Ministry of Knowledge Economy (Grant No. 20128040000020) and the Management of Technology (MOT) of the Korea Institute for Advancement Technology (KIAT) grant

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funded by the Korea Ministry of Trade, Industry, and Energy (MOTIE). Y. G. Lee was supported by the National Research Foundation (2012R1A1A1012649 & 2012-S1A3A-

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2033860) and Inha University.

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ACCEPTED MANUSCRIPT Table 1 Designed attributes and levels for choice experiments

Attributes

Levels

Attributes

Levels

Increase in the

5% increase

Additional

KRW 6 million

T

RHOs for building owners

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Cost

RHOs for heat suppliers

heating expense (KRW 5,000)

average

(KRW 10,000)

monthly

15% increase

cost

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10% increase

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(based on an

installation

KRW 7 million

(for 83 m2) KRW 8 million

heating expense (KRW 15,000) of KRWa 100,000) Reduction in

0.5% decrease

Reduction in

0.5% decrease

implementation

CO2 emissions

(3 Mt CO2 eq

CO2

(3 Mt CO2 eq

emissions

decrease per year)

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Effect of

decrease per year) 1.0 % decrease

1.0 % decrease

(6 Mt CO2 eq

(6 Mt CO2 eq

decrease per year)

decrease per year) 1.5% decrease

(9 Mt CO2 eq

(9 Mt CO2 eq

decrease per year)

decrease per year)

1.5% decrease

Annual new

5,000 new jobs per

Annual new

5,000 new jobs

employment

year

employment

per year

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CE

PT

(common)

(0.02% decrease in

(0.02% decrease

the unemployment

in the

rate)

unemployment rate)

10,000 new jobs per

10,000 new jobs

year

per year (0.04%

(0.04% decrease in

decrease in the

the unemployment

unemployment

rate)

rate)

29

ACCEPTED MANUSCRIPT 15,000 new jobs

year

per year (0.06%

(0.06% decrease in

decrease in the

the unemployment

unemployment

Factors

Stability of heat As stable as the

dependent on

energy supply

obligations

rate)

Payback

3 years

period

5 years

SC

present day

those subject to

7 years

Less stable than the

Government

0%

present day

subsidy for

(no subsidy)

the initial

25%

investment

50%

MA NU

a

RI P

rate)

T

15,000 new jobs per

According to the Bank of Korea (https://www.bok.or.kr). USD 1 was equivalent to KRW

AC

CE

PT

ED

1,126 in 2012.

30

ACCEPTED MANUSCRIPT Table 2 Characteristics of survey respondents Number of Group

(%) Gender

Male

249 (49.8%)

Average

Female

251 (50.2%)

household

MA NU

income Age

<30

120 (24.0%)

(years)

40–49

128 (25.6%)

50–59

Busan

34 (6.8%)

2–3 million

77 (15.4%)

KRW 3–4 million

151 (30.2%)

KRW 4–5 million

112 (22.4%)

KRW 5–6 million

64 (12.8%)

32 (6.4%)

PT

KRW 7–8 million

14 (2.8%)

KRW

80 (16.0%)

8–9 million

7 (1.4%)

KRW 50 (10.0%)

Incheon

50 (10.0%)

Daegu

(%)

KRW

6–7 million

210 (42.0%)

CE

Seoul

respondents

KRW

114 (22.8%)

AC

Region

138 (27.6%)

ED

30–39

<2 million

SC

monthly

T

respondents

RI P

Group

Number of

9–10 million

2 (0.4%)

KRW >10 million

7 (1.4%)

KRW Gwangju

35 (7.0%)

Daejeon Gyeonggi

Housing

Own

329 (65.8%)

35 (7.0%)

Rental

168 (33.6%)

40 (8.0%)

Other

3 (0.6%)

Province

31

ACCEPTED MANUSCRIPT Table 3 Estimation results for consumer preferences for the implementation of the RHO policy for

Attribute

distribution

Mean estimate,

deviation

marginal

Relative

b

of

willingness

importance

(95% CI)

estimates,

to pay

(RI)b

SC

Assumed

(MWTP)a

2.4008***

-

W

Increase in the

(1000

-0.8061***

MA NU

heating expense

Log-normal

(L: -5.2015,

Reduction in

0.1470*** Log-normal

U: 0.5504)

Annual new

0.3912**

Normal

CE

employment (10000

AC

jobs/year) Stability of heat energy supply

Rejection of

Normal

0.4250 KRW/10000 2.39% tons

PT

eq/year)

RHO

3.1712**

(L: 0.0002,

ED

(Mt CO2

19.57%

U: -0.0121)

KRW/month)

CO2 emissions

Median

RI P

Standard

T

heat suppliers

1.3399***

(L: -2.1479, U: 3.0057)

0.0565 KRW/person

4.95%

2.3288*** 1.8113***

(L: -1.2111,

5,904 KRW

10.49%

U: 5.7994) -13.9437*** Normal

4.1143***

(L: -22.1208, U: 5.8888)

-55,167 KRW

62.59%

*** Significant at the 1% level. ** Significant at the 5% level. a

MWTP was calculated based on 2,000 values drawn from the distribution of the estimated

coefficient, and the median of the 2,000 MWTP observations is presented.

32

ACCEPTED MANUSCRIPT b

The RI of each attribute was calculated based on 2,000 values drawn from the distribution

AC

CE

PT

ED

MA NU

SC

RI P

T

of the estimated coefficient, and the mean of the 2,000 RI observations is presented.

33

ACCEPTED MANUSCRIPT Table 4 Estimation results for consumer preferences for the implementation of the RHO policy for building owners

Assumed

Attribute

RI P

T

Standard

Mean estimate, b deviation of estimates,

distribution (95% CI)

Relative importance (RI)

-1.0907***

installation cost

Log-normal

(million KRW)

(L: -6.9858,

MA NU

Additional

SC

W

2.8204***

7.59%

0.0548***

1.13%

1.1286***

6.17%

0.0205***

0.32%

0.5287***

13.86%

6.6901*

70.93%

U: -0.0152) 0.0331***

Reduction in CO2 emissions

Log-normal

(Mt CO2 eq/year)

(L: 0.0011, U: 0.1814) 0.3486***

employment (10000

ED

Annual new

Normal

U: 2.6434)

Log-normal

CE

Payback period (year)

PT

jobs/year)

(L: -1.7992,

AC

Government subsidy for the initial investment

Normal

-0.0096*** (L: -0.0514, U: -0.0002) 0.2143*** (L:-0.8319, U: 1.2796)

(10%)

-14.0100*** Rejection of RHOs

Normal

(L: -26.8326, U: -0.8154)

*** Significant at the 1% level. * Significant at the 10% level.

34

ED

MA NU

SC

RI P

T

ACCEPTED MANUSCRIPT

PT

Fig. 1. Changes in RHO policy acceptance as a function of the heating expense (basis:

AC

CE

2011 average heating expense for Korean households; KRW 100,000).

35

ACCEPTED MANUSCRIPT Highlights: 

The South Korean government will introduce renewable heat obligation (RHO)

T

policies in 2016.

We considered RHO policies for heat suppliers and for building owners.



We analyzed the preferences of end users for both types of RHO policies.



Cost was the critical attribute impacting public acceptance of these policies.



We recommend a subsidy system based on the willingness to pay of end users.

AC

CE

PT

ED

MA NU

SC

RI P



36