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Measurement and Driving Factors of Carbon Emissions from Coal Consumption in China Based on the Kaya-LMDI Model

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  • Di Peng

    (School of Management, China University of Mining and Technology, Beijing 100083, China)

  • Haibin Liu

    (School of Management, China University of Mining and Technology, Beijing 100083, China)

Abstract

As the top emitter of carbon dioxide worldwide, China faces a considerable challenge in reducing carbon emissions to combat global warming. Carbon emissions from coal consumption is the primary source of carbon dioxide emissions in China. The decomposition of the driving factors and the quantification of regions and industries needs further research. Thus, this paper decomposed five driving factors affecting carbon emissions from coal consumption in China, namely, carbon emission intensity, energy structure, energy intensity, economic output, and population scale, by constructing a Kaya-Logarithmic Mean Divisia Index (Kaya-LMDI) decomposition model with data on coal consumption in China from 1997 to 2019. It was revealed that the economic output and energy intensity effects are major drivers and inhibitors of carbon emissions from coal consumption in China, respectively. The contribution and impact of these driving factors on carbon emissions from coal consumption were analyzed for different regions and industrial sectors. The results showed that carbon emissions from coal consumption increased by 3211.92 million tons from 1997 to 2019. From a regional perspective, Hebei Province has the most significant impact on carbon emissions from coal consumption due to the effect of economic output. Additionally, the industrial sector had the most pronounced influence on carbon emissions from coal consumption due to the economic output effect. Finally, a series of measures to reduce carbon emissions including controlling the total coal consumption, improving the utilization rate of clean energy, and optimizing the energy structure is proposed based on China’s actual development.

Suggested Citation

  • Di Peng & Haibin Liu, 2022. "Measurement and Driving Factors of Carbon Emissions from Coal Consumption in China Based on the Kaya-LMDI Model," Energies, MDPI, vol. 16(1), pages 1-19, December.
    • Handle: RePEc:gam:jeners:v:16:y:2022:i:1:p:439-:d:1020485
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    1. 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.
    2. Zhang, F. Q. & Ang, B. W., 2001. "Methodological issues in cross-country/region decomposition of energy and environment indicators," Energy Economics, Elsevier, vol. 23(2), pages 179-190, March.
    3. Shining Zhang & Fang Yang & Changyi Liu & Xing Chen & Xin Tan & Yuanbing Zhou & Fei Guo & Weiyi Jiang, 2020. "Study on Global Industrialization and Industry Emission to Achieve the 2 °C Goal Based on MESSAGE Model and LMDI Approach," Energies, MDPI, vol. 13(4), pages 1-21, February.
    4. Katarzyna Aleksandra Wójtowicz & Jolanta Małgorzata Szołno-Koguc & Jan Braun, 2021. "The Role of Public Spending in CO 2 Emissions Reduction in Polish Regions: An LMDI Decomposition Approach," Energies, MDPI, vol. 15(1), pages 1-21, December.
    5. Ang, B. W., 2004. "Decomposition analysis for policymaking in energy:: which is the preferred method?," Energy Policy, Elsevier, vol. 32(9), pages 1131-1139, June.
    6. Chen, Jiandong & Wang, Ping & Cui, Lianbiao & Huang, Shuo & Song, Malin, 2018. "Decomposition and decoupling analysis of CO2 emissions in OECD," Applied Energy, Elsevier, vol. 231(C), pages 937-950.
    7. Kristiāna Dolge & Dagnija Blumberga, 2021. "Key Factors Influencing the Achievement of Climate Neutrality Targets in the Manufacturing Industry: LMDI Decomposition Analysis," Energies, MDPI, vol. 14(23), pages 1-23, November.
    8. Xu, Shi-Chun & He, Zheng-Xia & Long, Ru-Yin, 2014. "Factors that influence carbon emissions due to energy consumption in China: Decomposition analysis using LMDI," Applied Energy, Elsevier, vol. 127(C), pages 182-193.
    9. Tian Zhao & Zhixin Liu, 2022. "Drivers of CO 2 Emissions: A Debt Perspective," IJERPH, MDPI, vol. 19(3), pages 1-18, February.
    10. Jianghua Liu & Mengxu Li & Yitao Ding, 2021. "Econometric analysis of the impact of the urban population size on carbon dioxide (CO2) emissions in China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(12), pages 18186-18203, December.
    11. Wang, Miao & Feng, Chao, 2018. "Investigating the drivers of energy-related CO2 emissions in China’s industrial sector: From regional and provincial perspectives," Structural Change and Economic Dynamics, Elsevier, vol. 46(C), pages 136-147.
    12. Wang, H. & Ang, B.W. & Su, Bin, 2017. "Assessing drivers of economy-wide energy use and emissions: IDA versus SDA," Energy Policy, Elsevier, vol. 107(C), pages 585-599.
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