IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i20p7777-d948617.html
   My bibliography  Save this article

Classifying Regional and Industrial Characteristics of GHG Emissions in South Korea

Author

Listed:
  • Hyungsu Kang

    (Korea Institute of Civil Engineering and Building Technology, 283, Goyang-daero, Ilsanseo-gu, Goyang-si 10223, Korea)

  • Hyunmin Daniel Zoh

    (WWF-Korea, Jongno-gu, Gongpyeong-dong, Jong-ro, 47, Seoul 03160, Korea)

Abstract

South Korea officially committed to reducing 40% of its total carbon emissions by 2030, but the country has a carbon-dependent economic structure based on the manufacturing industry. Additionally, the industrial structure of each region in South Korea is heterogeneous. In this regard, policymakers should analyze the carbon emission condition at a regional level because abatement aspects are heterogeneous by urban spatial production. However, although various studies have developed a methodology to evaluate the GHG emission condition, these studies failed to consider the fundamental aspect of regional heterogeneity. In this regard, this study suggests a quantitative method to assess the potential of the carbon neutrality of regions and industries by using both shift-share analysis and the Log Mean Divisia Index method. Shift share analysis is used to quantify the relation between the industry and regional characteristics, and the Log Mean Divisia Index method can decompose each effect for economic growth and technological progress. By combining these two methods, this study suggests four classifications to evaluate regional and industrial characteristics of GHG emissions and analyze each region’s emission status in terms of the mining and manufacturing industry in South Korea.

Suggested Citation

  • Hyungsu Kang & Hyunmin Daniel Zoh, 2022. "Classifying Regional and Industrial Characteristics of GHG Emissions in South Korea," Energies, MDPI, vol. 15(20), pages 1-16, October.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:20:p:7777-:d:948617
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/20/7777/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/20/7777/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ang, B.W., 2015. "LMDI decomposition approach: A guide for implementation," Energy Policy, Elsevier, vol. 86(C), pages 233-238.
    2. Jung, Seok & An, Kyoung-Jin & Dodbiba, Gjergj & Fujita, Toyohisa, 2012. "Regional energy-related carbon emission characteristics and potential mitigation in eco-industrial parks in South Korea: Logarithmic mean Divisia index analysis based on the Kaya identity," Energy, Elsevier, vol. 46(1), pages 231-241.
    3. Tae, Sungho & Shin, Sungwoo, 2009. "Current work and future trends for sustainable buildings in South Korea," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 1910-1921, October.
    4. Baran Doda, 2018. "Tales From The Tails: Sector-Level Carbon Intensity Distribution," Climate Change Economics (CCE), World Scientific Publishing Co. Pte. Ltd., vol. 9(04), pages 1-27, November.
    5. Touseef Hussain & Jaffar Abbas & Zou Wei & Mohammad Nurunnabi, 2019. "The Effect of Sustainable Urban Planning and Slum Disamenity on The Value of Neighboring Residential Property: Application of The Hedonic Pricing Model in Rent Price Appraisal," Sustainability, MDPI, vol. 11(4), pages 1-20, February.
    6. Scarlat, Nicolae & Prussi, Matteo & Padella, Monica, 2022. "Quantification of the carbon intensity of electricity produced and used in Europe," Applied Energy, Elsevier, vol. 305(C).
    7. Reitler, W. & Rudolph, M. & Schaefer, H., 1987. "Analysis of the factors influencing energy consumption in industry : A revised method," Energy Economics, Elsevier, vol. 9(3), pages 145-148, July.
    8. Battese, G E & Coelli, T J, 1995. "A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data," Empirical Economics, Springer, vol. 20(2), pages 325-332.
    9. Sato, Kazuo, 1976. "The Ideal Log-Change Index Number," The Review of Economics and Statistics, MIT Press, vol. 58(2), pages 223-228, May.
    10. Wang, Miao & Feng, Chao, 2018. "Using an extended logarithmic mean Divisia index approach to assess the roles of economic factors on industrial CO2 emissions of China," Energy Economics, Elsevier, vol. 76(C), pages 101-114.
    11. Wang, Qiang & Song, Xiaoxin, 2021. "How UK farewell to coal – Insight from multi-regional input-output and logarithmic mean divisia index analysis," Energy, Elsevier, vol. 229(C).
    12. Salvia, Monica & Reckien, Diana & Pietrapertosa, Filomena & Eckersley, Peter & Spyridaki, Niki-Artemis & Krook-Riekkola, Anna & Olazabal, Marta & De Gregorio Hurtado, Sonia & Simoes, Sofia G. & Genele, 2021. "Will climate mitigation ambitions lead to carbon neutrality? An analysis of the local-level plans of 327 cities in the EU," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    13. Andersson, Fredrik N.G. & Karpestam, Peter, 2013. "CO2 emissions and economic activity: Short- and long-run economic determinants of scale, energy intensity and carbon intensity," Energy Policy, Elsevier, vol. 61(C), pages 1285-1294.
    14. Li, Jun, 2011. "Decoupling urban transport from GHG emissions in Indian cities--A critical review and perspectives," Energy Policy, Elsevier, vol. 39(6), pages 3503-3514, June.
    15. Cai, Bofeng & Cui, Can & Zhang, Da & Cao, Libin & Wu, Pengcheng & Pang, Lingyun & Zhang, Jihong & Dai, Chunyan, 2019. "China city-level greenhouse gas emissions inventory in 2015 and uncertainty analysis," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    16. Roberts, J. Timmons & Grimes, Peter E., 1997. "Carbon intensity and economic development 1962-1991: A brief exploration of the environmental Kuznets curve," World Development, Elsevier, vol. 25(2), pages 191-198, February.
    17. McKitrick, Ross, 1999. "A Derivation of the Marginal Abatement Cost Curve," Journal of Environmental Economics and Management, Elsevier, vol. 37(3), pages 306-314, May.
    18. Daniel Witte, 2020. "Business for Climate: A Qualitative Comparative Analysis of Policy Support from Transnational Companies," Global Environmental Politics, MIT Press, vol. 20(4), pages 167-191, Autumn.
    19. Suyi Kim, 2017. "LMDI Decomposition Analysis of Energy Consumption in the Korean Manufacturing Sector," Sustainability, MDPI, vol. 9(2), pages 1-17, February.
    20. Lau, Lee Chung & Lee, Keat Teong & Mohamed, Abdul Rahman, 2012. "Global warming mitigation and renewable energy policy development from the Kyoto Protocol to the Copenhagen Accord—A comment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 5280-5284.
    21. Jaime Vallés-Giménez & Anabel Zárate-Marco, 2020. "A Dynamic Spatial Panel of Subnational GHG Emissions: Environmental Effectiveness of Emissions Taxes in Spanish Regions," Sustainability, MDPI, vol. 12(7), pages 1-22, April.
    22. Seoyoung Yu & Donghyun Kim, 2021. "Changes in Regional Economic Resilience after the 2008 Global Economic Crisis: The Case of Korea," Sustainability, MDPI, vol. 13(20), pages 1-14, October.
    23. Kumbhakar,Subal C. & Lovell,C. A. Knox, 2003. "Stochastic Frontier Analysis," Cambridge Books, Cambridge University Press, number 9780521666633, November.
    24. Celil Aydin & Ömer Esen, 2018. "Reducing CO2 emissions in the EU member states: Do environmental taxes work?," Journal of Environmental Planning and Management, Taylor & Francis Journals, vol. 61(13), pages 2396-2420, November.
    25. Sheinbaum, Claudia & Ozawa, Leticia & Castillo, Daniel, 2010. "Using logarithmic mean Divisia index to analyze changes in energy use and carbon dioxide emissions in Mexico's iron and steel industry," Energy Economics, Elsevier, vol. 32(6), pages 1337-1344, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Trotta, Gianluca, 2020. "Assessing energy efficiency improvements and related energy security and climate benefits in Finland: An ex post multi-sectoral decomposition analysis," Energy Economics, Elsevier, vol. 86(C).
    2. Giovanni Calice & Levent Kutlu & Ming Zeng, 2021. "Understanding US firm efficiency and its asset pricing implications," Empirical Economics, Springer, vol. 60(2), pages 803-827, February.
    3. Kangile, Rajabu Joseph, 2015. "Efficiency In Production By Smallholder Rice Farmers Under Cooperative Irrigation Schemes In Pwani And Morogoro Regions, Tanzania," Research Theses 265681, Collaborative Masters Program in Agricultural and Applied Economics.
    4. Juan Luo & Chong Xu & Boyu Yang & Xiaoyu Chen & Yinyin Wu, 2022. "Quantitative Analysis of China’s Carbon Emissions Trading Policies: Perspectives of Policy Content Validity and Carbon Emissions Reduction Effect," Energies, MDPI, vol. 15(14), pages 1-20, July.
    5. Cuéllar Martín, Jaime & Martín-Román, Ángel L. & Moral, Alfonso, 2017. "A composed error model decomposition and spatial analysis of local unemployment," MPRA Paper 79783, University Library of Munich, Germany.
    6. Kristiana Dolge & Dagnija Blumberga, 2023. "Transitioning to Clean Energy: A Comprehensive Analysis of Renewable Electricity Generation in the EU-27," Energies, MDPI, vol. 16(18), pages 1-27, September.
    7. Ebru Solakoglu & M. Solakoglu & Nazmi Demir, 2013. "The Role of Progress Factors Explaining Inefficiencies in Transition Countries," Transition Studies Review, Springer;Central Eastern European University Network (CEEUN), vol. 19(3), pages 261-274, February.
    8. Chen, Jiandong & Cheng, Shulei & Song, Malin, 2018. "Changes in energy-related carbon dioxide emissions of the agricultural sector in China from 2005 to 2013," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 748-761.
    9. P. Fernández-González & M. Landajo & M.J. Presno, 2013. "Factors Influencing Changes In Aggregate Energy Consumption. An European Cross-Country Analysis," Regional and Sectoral Economic Studies, Euro-American Association of Economic Development, vol. 13(2), pages 18-30.
    10. Singbo, Alphonse G. & Emvalomatis, Grigorios & Alfons, Oude Lansink, 2013. "Assessing the impact of crop specialization on farms’ performance in vegetables farming in Benin: a non-neutral stochastic frontier approach," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 149172, Agricultural and Applied Economics Association.
    11. Alajmi, Reema Gh, 2021. "Factors that impact greenhouse gas emissions in Saudi Arabia: Decomposition analysis using LMDI," Energy Policy, Elsevier, vol. 156(C).
    12. Phu Nguyen-Van & Nguyen To-The, 2016. "Technical efficiency and agricultural policy: evidence from the teaproduction in Vietnam," Review of Agricultural, Food and Environmental Studies, INRA Department of Economics, vol. 97(3), pages 173-184.
    13. Uwe Jensen & Hermann Gartner & Susanne Rässler, 2010. "Estimating German overqualification with stochastic earnings frontiers," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 94(1), pages 33-51, March.
    14. Azam, Muhammad & Younes, Ben Zaied & Hunjra, Ahmed Imran & Hussain, Nazim, 2022. "Integrated Spatial-Temporal decomposition analysis for life cycle assessment of carbon emission intensity change in various regions of China," Resources Policy, Elsevier, vol. 79(C).
    15. Lee, Chi-Chuan & Huang, Tai-Hsin, 2017. "Cost efficiency and technological gap in Western European banks: A stochastic metafrontier analysis," International Review of Economics & Finance, Elsevier, vol. 48(C), pages 161-178.
    16. Xiaolei Huang & Jinpei Ou & Yingjian Huang & Shun Gao, 2023. "Exploring the Effects of Socioeconomic Factors and Urban Forms on CO 2 Emissions in Shrinking and Growing Cities," Sustainability, MDPI, vol. 16(1), pages 1-20, December.
    17. Fernández González, P. & Landajo, M. & Presno, M.J., 2014. "Tracking European Union CO2 emissions through LMDI (logarithmic-mean Divisia index) decomposition. The activity revaluation approach," Energy, Elsevier, vol. 73(C), pages 741-750.
    18. Jian Liu & Qingshan Yang & Yu Zhang & Wen Sun & Yiming Xu, 2019. "Analysis of CO 2 Emissions in China’s Manufacturing Industry Based on Extended Logarithmic Mean Division Index Decomposition," Sustainability, MDPI, vol. 11(1), pages 1-28, January.
    19. Liu, Yingying & Chen, Sha & Jiang, Kejun & Kaghembega, Wendkuuni Steve-Harold, 2022. "The gaps and pathways to carbon neutrality for different type cities in China," Energy, Elsevier, vol. 244(PA).
    20. Chien-Ming Chen & Magali A. Delmas & Marvin B. Lieberman, 2015. "Production frontier methodologies and efficiency as a performance measure in strategic management research," Strategic Management Journal, Wiley Blackwell, vol. 36(1), pages 19-36, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:20:p:7777-:d:948617. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.