IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i11p4510-d1163463.html
   My bibliography  Save this article

Decomposition Analysis and Trend Prediction of Energy-Consumption CO 2 Emissions in China’s Yangtze River Delta Region

Author

Listed:
  • Yue Yuan

    (Graduate School of Fisheries and Environmental Sciences, Nagasaki University, 1-14 Bunkyo-machi, Nagasaki 852-8521, Japan)

  • Sunhee Suk

    (Graduate School of Fisheries and Environmental Sciences, Nagasaki University, 1-14 Bunkyo-machi, Nagasaki 852-8521, Japan)

Abstract

This study calculated CO 2 emissions related to the consumption of primary energy by five sectors in the Yangtze River Delta region over 2000 to 2019. The Logarithmic Mean Divisia Index (LMDI) decomposition method was used to establish the factor decomposition model of CO 2 emissions change. The LMDI model was modified to assess the impact of five influencing factors, namely energy structure, energy intensity, industrial structure, economic output, and population size, on CO 2 emissions in the Yangtze River Delta region over the study period. The empirical results show that economic output has the largest positive effect on the growth in CO 2 emissions. Population size is the second most important factor promoting the growth in CO 2 emissions. Energy intensity is the most inhibitory factor to restrain CO 2 emissions, with a significant negative effect. Energy structure and industrial structure contribute insignificantly to CO 2 emissions. Using data on CO 2 emissions in the Yangtze River Delta region from 2000 to 2019, the GM (1, 1) model was applied for future forecasts of primary energy consumption and CO 2 emissions. Specific policy suggestions to mitigate CO 2 emissions in Yangtze River Delta region are provided.

Suggested Citation

  • Yue Yuan & Sunhee Suk, 2023. "Decomposition Analysis and Trend Prediction of Energy-Consumption CO 2 Emissions in China’s Yangtze River Delta Region," Energies, MDPI, vol. 16(11), pages 1-18, June.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:11:p:4510-:d:1163463
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/11/4510/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/11/4510/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Chong, ChinHao & Ma, Linwei & Li, Zheng & Ni, Weidou & Song, Shizhong, 2015. "Logarithmic mean Divisia index (LMDI) decomposition of coal consumption in China based on the energy allocation diagram of coal flows," Energy, Elsevier, vol. 85(C), pages 366-378.
    3. Wang, Miao & Feng, Chao, 2017. "Decomposition of energy-related CO2 emissions in China: An empirical analysis based on provincial panel data of three sectors," Applied Energy, Elsevier, vol. 190(C), pages 772-787.
    4. B. W. Ang & Ki-Hong Choi, 1997. "Decomposition of Aggregate Energy and Gas Emission Intensities for Industry: A Refined Divisia Index Method," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 59-73.
    5. Ang, B. W., 2005. "The LMDI approach to decomposition analysis: a practical guide," Energy Policy, Elsevier, vol. 33(7), pages 867-871, May.
    6. Wang, Can & Chen, Jining & Zou, Ji, 2005. "Decomposition of energy-related CO2 emission in China: 1957–2000," Energy, Elsevier, vol. 30(1), pages 73-83.
    7. Zhang, Ming & Li, Huanan & Zhou, Min & Mu, Hailin, 2011. "Decomposition analysis of energy consumption in Chinese transportation sector," Applied Energy, Elsevier, vol. 88(6), pages 2279-2285, June.
    8. Zhang, Wei & Li, Ke & Zhou, Dequn & Zhang, Wenrui & Gao, Hui, 2016. "Decomposition of intensity of energy-related CO2 emission in Chinese provinces using the LMDI method," Energy Policy, Elsevier, vol. 92(C), pages 369-381.
    9. 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.
    10. Wang, W.W. & Zhang, M. & Zhou, M., 2011. "Using LMDI method to analyze transport sector CO2 emissions in China," Energy, Elsevier, vol. 36(10), pages 5909-5915.
    11. Pao, Hsiao-Tien & Fu, Hsin-Chia & Tseng, Cheng-Lung, 2012. "Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model," Energy, Elsevier, vol. 40(1), pages 400-409.
    12. Xu, Jin-Hua & Fleiter, Tobias & Eichhammer, Wolfgang & Fan, Ying, 2012. "Energy consumption and CO2 emissions in China's cement industry: A perspective from LMDI decomposition analysis," Energy Policy, Elsevier, vol. 50(C), pages 821-832.
    13. Liu, Lan-Cui & Fan, Ying & Wu, Gang & Wei, Yi-Ming, 2007. "Using LMDI method to analyze the change of China's industrial CO2 emissions from final fuel use: An empirical analysis," Energy Policy, Elsevier, vol. 35(11), pages 5892-5900, November.
    14. Zhao, Min & Tan, Lirong & Zhang, Weiguo & Ji, Minhe & Liu, Yuan & Yu, Lizhong, 2010. "Decomposing the influencing factors of industrial carbon emissions in Shanghai using the LMDI method," Energy, Elsevier, vol. 35(6), pages 2505-2510.
    15. Wu, Lifeng & Liu, Sifeng & Liu, Dinglin & Fang, Zhigeng & Xu, Haiyan, 2015. "Modelling and forecasting CO2 emissions in the BRICS (Brazil, Russia, India, China, and South Africa) countries using a novel multi-variable grey model," Energy, Elsevier, vol. 79(C), pages 489-495.
    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. Wang, Miao & Feng, Chao, 2018. "Decomposing the change in energy consumption in China's nonferrous metal industry: An empirical analysis based on the LMDI method," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2652-2663.
    2. Wang, Miao & Feng, Chao, 2017. "Decomposition of energy-related CO2 emissions in China: An empirical analysis based on provincial panel data of three sectors," Applied Energy, Elsevier, vol. 190(C), pages 772-787.
    3. Xianrui Liao & Wei Yang & Yichen Wang & Junnian Song, 2019. "Uncovering Variations, Determinants, and Disparities of Multisector-Level Final Energy Use of Industries Across Cities," Sustainability, MDPI, vol. 11(6), pages 1-16, March.
    4. Yang Yu & Qiuyue Kong, 2017. "Analysis on the influencing factors of carbon emissions from energy consumption in China based on LMDI method," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 88(3), pages 1691-1707, September.
    5. Suyi Kim, 2017. "LMDI Decomposition Analysis of Energy Consumption in the Korean Manufacturing Sector," Sustainability, MDPI, vol. 9(2), pages 1-17, February.
    6. Jiang, Jingjing & Ye, Bin & Xie, Dejun & Li, Ji & Miao, Lixin & Yang, Peng, 2017. "Sector decomposition of China’s national economic carbon emissions and its policy implication for national ETS development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 855-867.
    7. Vaninsky, Alexander, 2014. "Factorial decomposition of CO2 emissions: A generalized Divisia index approach," Energy Economics, Elsevier, vol. 45(C), pages 389-400.
    8. 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.
    9. Linwei Ma & Chinhao Chong & Xi Zhang & Pei Liu & Weiqi Li & Zheng Li & Weidou Ni, 2018. "LMDI Decomposition of Energy-Related CO 2 Emissions Based on Energy and CO 2 Allocation Sankey Diagrams: The Method and an Application to China," Sustainability, MDPI, vol. 10(2), pages 1-37, January.
    10. Wang, Miao & Feng, Chao, 2017. "Analysis of energy-related CO2 emissions in China’s mining industry: Evidence and policy implications," Resources Policy, Elsevier, vol. 53(C), pages 77-87.
    11. Wang, Zhiping & Feng, Chao & Chen, Jinyu & Huang, Jianbai, 2017. "The driving forces of material use in China: An index decomposition analysis," Resources Policy, Elsevier, vol. 52(C), pages 336-348.
    12. Lei Liu & Ke Wang & Shanshan Wang & Ruiqin Zhang & Xiaoyan Tang, 2019. "Exploring the Driving Forces and Reduction Potential of Industrial Energy-Related CO 2 Emissions during 2001–2030: A Case Study for Henan Province, China," Sustainability, MDPI, vol. 11(4), pages 1-25, February.
    13. Jie-fang Dong & Qiang Wang & Chun Deng & Xing-min Wang & Xiao-lei Zhang, 2016. "How to Move China toward a Green-Energy Economy: From a Sector Perspective," Sustainability, MDPI, vol. 8(4), pages 1-18, April.
    14. Wang, Qunwei & Wang, Yizhong & Zhou, P. & Wei, Hongye, 2017. "Whole process decomposition of energy-related SO2 in Jiangsu Province, China," Applied Energy, Elsevier, vol. 194(C), pages 679-687.
    15. Shiraki, Hiroto & Matsumoto, Ken'ichi & Shigetomi, Yosuke & Ehara, Tomoki & Ochi, Yuki & Ogawa, Yuki, 2020. "Factors affecting CO2 emissions from private automobiles in Japan: The impact of vehicle occupancy," Applied Energy, Elsevier, vol. 259(C).
    16. Jialing Zou & Weidong Liu & Zhipeng Tang, 2017. "Analysis of Factors Contributing to Changes in Energy Consumption in Tangshan City between 2007 and 2012," Sustainability, MDPI, vol. 9(3), pages 1-14, March.
    17. Lin, Boqiang & Ouyang, Xiaoling, 2014. "Analysis of energy-related CO2 (carbon dioxide) emissions and reduction potential in the Chinese non-metallic mineral products industry," Energy, Elsevier, vol. 68(C), pages 688-697.
    18. Jialing Zou & Zhipeng Tang & Shuang Wu, 2019. "Divergent Leading Factors in Energy-Related CO 2 Emissions Change among Subregions of the Beijing–Tianjin–Hebei Area from 2006 to 2016: An Extended LMDI Analysis," Sustainability, MDPI, vol. 11(18), pages 1-17, September.
    19. Lei Liu & Shanshan Wang & Ke Wang & Ruiqin Zhang & Xiaoyan Tang, 2016. "LMDI decomposition analysis of industry carbon emissions in Henan Province, China: comparison between different 5-year plans," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 80(2), pages 997-1014, January.
    20. Li, Hao & Zhao, Yuhuan & Qiao, Xiaoyong & Liu, Ya & Cao, Ye & Li, Yue & Wang, Song & Zhang, Zhonghua & Zhang, Yongfeng & Weng, Jianfeng, 2017. "Identifying the driving forces of national and regional CO2 emissions in China: Based on temporal and spatial decomposition analysis models," Energy Economics, Elsevier, vol. 68(C), pages 522-538.

    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:16:y:2023:i:11:p:4510-:d:1163463. 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.