Impact of Various Feedstock Attributes on the Social Acceptance on Bioethanol Promotion in South Korea

doi:10.15266/KEREA.2021.30.1.049

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2021 Vol.30, Issue 1 Preview Page Next Page

Research Paper

Impact of Various Feedstock Attributes on the Social Acceptance on Bioethanol Promotion in South Korea 바이오에탄올 보급에 대한 사회적 수용성 분석: 바이오에탄올 원료 속성을 중심으로: 이 드미트리*‧배정환**
Dmitriy D. Li* and Jeong Hwan Bae**; *전남대학교 경제학부 시간강사, 제1저자(e-mail: lddima82@yahoo.com)
**전남대학교 경제학부 교수, 교신저자(e-mail: jhbae@jnu.ac.kr)

*Lecturer, Department of Economics, Chonnam National University, First author(e-mail: lddima82@ yahoo.com)
**Professor, Department of Economics, Chonnam National University, Corresponding author(e-mail: jhbae@jnu.ac.kr)

31 March 2021. pp. 49-77

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Abstract

This study uses a choice experiment approach to examine whether different types of feedstocks as well as other attributes such as the cost of bioethanol, bioethanol blending ratio, and government support policies affect consumers’ biofuel preferences. We apply a standard conditional logit model, a mixed logit model (MLM), and individual coefficient estimation model (ICM) to estimate the parameters of the investigated attributes. The results show that people prefer domestic and non-food feedstock, along with tax exemption as a support policy. All the attributes show unobservable preference heterogeneity in the MLM and ICM. In particular, willingness to pay for attributes are higher in the genetically modified (GM) feedstock-unknown group than in the known one. We show the importance of using domestic and non-food feedstocks and managing GM feedstocks carefully to avoid consumer resistance when producing bioethanol in South Korea.

Keywords

Bioethanol

Biofuel

Feedstock

Choice experiment

Preference heterogeneity

본 연구는 선택실험법을 이용하여 바이오에탄올 원료유형, 바이오에탄올 혼합율, 바이오에탄올 비용, 정부지원 정책과 같은 속성들이 바이오에탄올 보급정책에 대한 사회적 수용성에 영향을 미치는지를 분석하였다. 바이오에탄올 속성 계수를 추정하기 위해 조건부로짓모형, 혼합로짓모형, 개별계수추정모형을 적용하였다. 추정 결과에 따르면, 소비자들은 국산원료와 비식량원료를 사용한 바이오에탄올을 선호하고 지원정책 가운데는 면세정책을 선호하는 것으로 나타났다. 혼합로짓모형과 개별계수추정모형에 의하면 모든 속성들이 관찰불가능한 이질성을 갖고 있는 것으로 나타났다. 또한 속성별 지불용의액을 추정한 결과, 유전자조작기반 바이오에탄올임을 사전에 인지한 응답자일수록 그렇지 못한 응답자보다 바이오에탄올에 대한 지불용의액이 더 낮게 나타났다. 추정결과를 종합하면, 우리나라에서 바이오에탄올을 보급하기 위해서는 국산원료 및 비식량원료에 기반한 바이오에탄올을 중점적으로 보급해야 하고, 특히 유전자 조작 기반 바이오에탄올에 대한 수용성이 낮게 나타나므로 보급시 이를 충분히 고려해야 할 것이다.

키워드

바이오에탄올

원료

선택실험법

선호 이질성

References

Bae, J. H., “Supply Portfolio of Bioethanol in the Republic of South Korea,” The South Korean Economic Review, Vol. 30, No. 1, 2014, pp. 133~161.
Bae, J. H., and M. Rishi, “Increasing consumer participation rates for green pricing programs: A choice experiment for South South Korea,” Energy Economics, Vol. 74, 2018, pp. 490~502. 10.1016/j.eneco.2018.06.027
Bujosa, A., A. Riera, and R. L. Hicks, “Combining Discrete and Continuous Representations of Preference Heterogeneity: A Latent Class Approach,” Environmental and Resource Economics, Vol. 47, No. 4, 2010, pp. 477~493. 10.1007/s10640-010-9389-y
Fimereli, E., and S. Mourato, Consumer preferences for bioethanol blends, 2009 EAERE Summer School, 5th-11th July 2009, Venice, pp. 1~17, 2009.
Hensher, D. A., J. M. Rose, and W. H. Green, Applied Choice Analysis. Second ed, Cambridge University Press, Cambridge, pp. 1~1698, 2015.
Hole, A. R., “Fitting mixed logit models by using maximum simulated likelihood,” Stata J, Vol. 7, 2007, pp. 388~401.10.1177/1536867X0700700306
International Service for the Acquisition of Agri-biotech Applications(ISAAA), “Global status of commercialized biotech/GM Crops:2017,” ISAAA Briefs, Vol. 53, 2017, pp. 1~100.
Jensen, K. L., C. D. Clark, B. C. English, R. J. Menard, D. K. Skahan, and A. C. Marra, “Willingness to pay for E85 from corn, switchgrass, and wood residues,” Energy Economics, Vol. 32, No. 6, 2010, pp. 1253~1262. 10.1016/j.eneco.2010.06.002
Lanzini, P., F. Testa, and F. Iraldo, “Factors affecting drivers’ willingness to pay for biofuels: the case of Italy,” Journal of Cleaner Production, Vol. 112, 2016, pp. 2684~2692. 10.1016/j.jclepro.2015.10.080
Li, T., and J. J. Mccluskey, “Consumer preferences for second-generation bioethanol,” Energy Economics, Vol. 61, 2017, pp. 1~7. 10.1016/j.eneco.2016.10.023
Lim, S. Y., H. J. Kim, and S. H. Yoo, “Public's willingness to pay a premium for bioethanol in South Korea: A contingent valuation study,” Energy Policy, Vol. 101, 2017, pp. 20~27. 10.1016/j.enpol.2016.11.010
Loureiro, M. L., X. Labandeira, and M. Hanemann, “Transport and low-carbon fuel: A study of public preferences in Spain,” Energy Economics, Vol. 40, 2013, pp. 1~26. 10.1016/j.eneco.2013.09.010
Mamadzhanov, A., J. J. McCluskeym, and T. Li, “Willingness to pay for a second-generation bioethanol: A case study of South Korea,” Energy Policy, Vol. 127, 2019, pp. 464~474. 10.1016/j.enpol.2018.12.001
McFadden, D., Conditional logit analysis of qualitative choice behavior, Berkeley: CA. University of California, pp. 1~38, 1973.
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Mueller, S., J. Lie, B. Keesom, and S. Unnasch, The impact of higher ethanol blend levels on vehicle emissions in 5 global cities, Workshop at the University of Illinois at Chicago, pp. 1~104, 2018.
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Information

Publisher :Environmental and Resource Economics Review
Publisher(Ko) :자원 · 환경경제연구
Journal Title :자원·환경경제연구
Journal Title(Ko) :Environmental and Resource Economics Review
Volume : 30
No :1
Pages :49-77
DOI :https://doi.org/10.15266/KEREA.2021.30.1.049

[1] Bae, J. H., “Supply Portfolio of Bioethanol in the Republic of South Korea,” The South Korean Economic Review, Vol. 30, No. 1, 2014, pp. 133~161.

[2] Bae, J. H., and M. Rishi, “Increasing consumer participation rates for green pricing programs: A choice experiment for South South Korea,” Energy Economics, Vol. 74, 2018, pp. 490~502. 10.1016/j.eneco.2018.06.027

[3] Bujosa, A., A. Riera, and R. L. Hicks, “Combining Discrete and Continuous Representations of Preference Heterogeneity: A Latent Class Approach,” Environmental and Resource Economics, Vol. 47, No. 4, 2010, pp. 477~493. 10.1007/s10640-010-9389-y

[4] Fimereli, E., and S. Mourato, Consumer preferences for bioethanol blends, 2009 EAERE Summer School, 5th-11th July 2009, Venice, pp. 1~17, 2009.

[5] Hensher, D. A., J. M. Rose, and W. H. Green, Applied Choice Analysis. Second ed, Cambridge University Press, Cambridge, pp. 1~1698, 2015.

[6] Hole, A. R., “Fitting mixed logit models by using maximum simulated likelihood,” Stata J, Vol. 7, 2007, pp. 388~401.10.1177/1536867X0700700306

[7] International Service for the Acquisition of Agri-biotech Applications(ISAAA), “Global status of commercialized biotech/GM Crops:2017,” ISAAA Briefs, Vol. 53, 2017, pp. 1~100.

[8] Jensen, K. L., C. D. Clark, B. C. English, R. J. Menard, D. K. Skahan, and A. C. Marra, “Willingness to pay for E85 from corn, switchgrass, and wood residues,” Energy Economics, Vol. 32, No. 6, 2010, pp. 1253~1262. 10.1016/j.eneco.2010.06.002

[9] Lanzini, P., F. Testa, and F. Iraldo, “Factors affecting drivers’ willingness to pay for biofuels: the case of Italy,” Journal of Cleaner Production, Vol. 112, 2016, pp. 2684~2692. 10.1016/j.jclepro.2015.10.080

[10] Li, T., and J. J. Mccluskey, “Consumer preferences for second-generation bioethanol,” Energy Economics, Vol. 61, 2017, pp. 1~7. 10.1016/j.eneco.2016.10.023

[11] Lim, S. Y., H. J. Kim, and S. H. Yoo, “Public's willingness to pay a premium for bioethanol in South Korea: A contingent valuation study,” Energy Policy, Vol. 101, 2017, pp. 20~27. 10.1016/j.enpol.2016.11.010

[12] Loureiro, M. L., X. Labandeira, and M. Hanemann, “Transport and low-carbon fuel: A study of public preferences in Spain,” Energy Economics, Vol. 40, 2013, pp. 1~26. 10.1016/j.eneco.2013.09.010

[13] Mamadzhanov, A., J. J. McCluskeym, and T. Li, “Willingness to pay for a second-generation bioethanol: A case study of South Korea,” Energy Policy, Vol. 127, 2019, pp. 464~474. 10.1016/j.enpol.2018.12.001

[14] McFadden, D., Conditional logit analysis of qualitative choice behavior, Berkeley: CA. University of California, pp. 1~38, 1973.

[15] Ministry of Trade, Industry and Energy, Implementation Plan for Renewable Energy 3020 Agenda, 2017.

[16] Miren, I. S., S. Javier, and W. Carmina, “Consumer reaction to information on the labels of genetically modified food,” Rev Saúde Pública 2014, Vol. 48, No. 1, 2014, pp. 154~169.

[17] Mueller, S., J. Lie, B. Keesom, and S. Unnasch, The impact of higher ethanol blend levels on vehicle emissions in 5 global cities, Workshop at the University of Illinois at Chicago, pp. 1~104, 2018.

[18] Petrolia, D. R., S. Bhattacharjee, D. Hudson, and C. W. Herndon, “Do Americans want ethanol? A comparative contingent-valuation study of willingness to pay for E-10 and E-85,” Energy Economics, Vol. 32, No. 1, 2010, pp. 121~128. 10.1016/j.eneco.2009.08.004

[19] Revelt, D., and K. Train, Customer-specific Taste Parameters and Mixed Logit: Households' Choice of Electricity Supplier, Econometrics 0012001, University Library of Munich, Germany, 2001.

[20] Shin, J., and W. -S. Hwang, “Consumer preference and willingness to pay for a renewable fuel standard (RFS) policy: Focusing on ex-ante market analysis and segmentation,” Energy Policy, Vol. 106, 2017, pp. 32~40.10.1016/j.enpol.2017.03.042

[21] Solomon, B. D., and N. H. Johnson, “Valuing climate protection through willingness to pay for biomass ethanol,” Ecological Economics, Vol. 68, No. 7, 2009, pp. 2137~2144.10.1016/j.ecolecon.2009.02.010

[22] Stein, R. A., J. E. Anderson, and T. J. Wallington, “An Overview of the Effects of Ethanol-Gasoline Blends on SI Engine Performance, Fuel Efficiency, and Emissions,” SAE International Journal of Engines, Vol. 6, No. 1, 2013, pp. 470~487.10.4271/2013-01-1635

[23] Train, K. E., Discrete choice methods with simulation, Cambridge, Cambridge University Press, pp. 1~388. 2009.

[24] Wang, M., J. Han, J. Dunn, H. Cai, and A. Elgowainy, “Well-to-Wheels Energy Use and Greenhouse Gas Emissions of Ethanol From Corn, Sugarcane, and Cellulosic Biomass for US Use,” Environmental Research Letters, Vol. 7, No. 4, 2012, pp. 1~13. 10.1088/1748-9326/7/4/045905

[25] Yoo, J., and R. C. Ready, “Preference heterogeneity for renewable energy technology,” Energy Economics, Vol. 42, 2014, pp. 101~114. 10.1016/j.eneco.2013.12.007

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