IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0305769.html

Decomposition and decoupling analysis between economic growth and carbon emissions at the regional level: Evidence from six central provinces, China

Author

Listed:
  • Wensheng Wang
  • Xuanyi Zhu
  • Xiaoxuan Kao
  • Hui Wei

Abstract

As the six central provinces account for 23% of total national carbon emissions (CE), research into the decoupling status of their economic growth (EG) and carbon emissions is critical to achieving the Dual Carbon Goals and the Rise of Central China Plan. This research initially examines the decoupling status between CE and EG using the Tapio decoupling model, based on energy consumption (EC) dataset from six central provinces in China between 2000 and 2019. The decoupling index (DI) is then divided into five decoupling drivers using the LMDI method. Finally, an enhanced STIRPAT model is used to examine the decoupling status of CE and EG in the six central provinces from 2020 to 2040. The research findings are: (1) The six central provinces exhibited a stable decoupling status between 2000 and 2019. The DI of the six central provinces ranged from -1.2 to 3.4. (2) The decoupling performance is influenced mainly by the inhibitory effect of economic development (GI) and the promoting effect of energy intensity (EI). The GI consistently maintains an impact value of around 0.9. EI performance varies widely across provinces. (3) From 2020 to 2040, Anhui, Hubei, Henan, and Hunan show significantly strong decoupling indices distributed between -2.21 and -0.07 in all three scenarios. It is important to note that Shanxi and Jiangxi provinces will experience a Reverse Decoupling phenomenon. These findings are helpful in developing regionally coordinated development policies and strategies for reducing CE.

Suggested Citation

  • Wensheng Wang & Xuanyi Zhu & Xiaoxuan Kao & Hui Wei, 2024. "Decomposition and decoupling analysis between economic growth and carbon emissions at the regional level: Evidence from six central provinces, China," PLOS ONE, Public Library of Science, vol. 19(9), pages 1-23, September.
    • Handle: RePEc:plo:pone00:0305769
    DOI: 10.1371/journal.pone.0305769
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0305769
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0305769&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0305769?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Fang, Kai & Tang, Yiqi & Zhang, Qifeng & Song, Junnian & Wen, Qi & Sun, Huaping & Ji, Chenyang & Xu, Anqi, 2019. "Will China peak its energy-related carbon emissions by 2030? Lessons from 30 Chinese provinces," Applied Energy, Elsevier, vol. 255(C).
    2. Ang, B.W., 2015. "LMDI decomposition approach: A guide for implementation," Energy Policy, Elsevier, vol. 86(C), pages 233-238.
    3. Jia, Junsong & Deng, Hongbing & Duan, Jing & Zhao, Jingzhu, 2009. "Analysis of the major drivers of the ecological footprint using the STIRPAT model and the PLS method--A case study in Henan Province, China," Ecological Economics, Elsevier, vol. 68(11), pages 2818-2824, September.
    4. Xu, X.Y. & Ang, B.W., 2014. "Analysing residential energy consumption using index decomposition analysis," Applied Energy, Elsevier, vol. 113(C), pages 342-351.
    5. Wang, Juan & Li, Ziming & Wu, Tong & Wu, Siyu & Yin, Tingwei, 2022. "The decoupling analysis of CO2 emissions from power generation in Chinese provincial power sector," Energy, Elsevier, vol. 255(C).
    6. Wang, Zhaojing & Jiang, Qingzhe & Dong, Kangyin & Mubarik, Muhammad Shujaat & Dong, Xiucheng, 2020. "Decomposition of the US CO2 emissions and its mitigation potential: An aggregate and sectoral analysis," Energy Policy, Elsevier, vol. 147(C).
    7. Zhang, Yue-Jun & Liu, Zhao & Zhou, Si-Ming & Qin, Chang-Xiong & Zhang, Huan, 2018. "The impact of China's Central Rise Policy on carbon emissions at the stage of operation in road sector," Economic Modelling, Elsevier, vol. 71(C), pages 159-173.
    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. You, Jianmin & Zhang, Wei, 2022. "How heterogeneous technological progress promotes industrial structure upgrading and industrial carbon efficiency? Evidence from China's industries," Energy, Elsevier, vol. 247(C).
    10. Zhou, P. & Wang, M., 2016. "Carbon dioxide emissions allocation: A review," Ecological Economics, Elsevier, vol. 125(C), pages 47-59.
    11. Ang, B.W. & Zhang, F.Q., 2000. "A survey of index decomposition analysis in energy and environmental studies," Energy, Elsevier, vol. 25(12), pages 1149-1176.
    12. Xu, X.Y. & Ang, B.W., 2013. "Index decomposition analysis applied to CO2 emission studies," Ecological Economics, Elsevier, vol. 93(C), pages 313-329.
    13. Pan, Xiongfeng & Guo, Shucen & Xu, Haitao & Tian, Mengyuan & Pan, Xianyou & Chu, Junhui, 2022. "China's carbon intensity factor decomposition and carbon emission decoupling analysis," Energy, Elsevier, vol. 239(PC).
    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. Mousavi, Babak & Lopez, Neil Stephen A. & Biona, Jose Bienvenido Manuel & Chiu, Anthony S.F. & Blesl, Markus, 2017. "Driving forces of Iran's CO2 emissions from energy consumption: An LMDI decomposition approach," Applied Energy, Elsevier, vol. 206(C), pages 804-814.
    2. Jaruwan Chontanawat & Paitoon Wiboonchutikula & Atinat Buddhivanich, 2020. "Decomposition Analysis of the Carbon Emissions of the Manufacturing and Industrial Sector in Thailand," Energies, MDPI, vol. 13(4), pages 1-23, February.
    3. Kaltenegger, Oliver, 2020. "What drives total real unit energy costs globally? A novel LMDI decomposition approach," Applied Energy, Elsevier, vol. 261(C).
    4. Rui Jiang & Rongrong Li & Qiuhong Wu, 2019. "Investigation for the Decomposition of Carbon Emissions in the USA with C-D Function and LMDI Methods," Sustainability, MDPI, vol. 11(2), pages 1-15, January.
    5. Rivera-Niquepa, Juan David & De Oliveira-De Jesus, Paulo M. & Yusta, Jose M., 2025. "Trend-based multi-period decomposition and decoupling methodology for energy-related carbon dioxide emissions: A case study of Portugal," Utilities Policy, Elsevier, vol. 93(C).
    6. Ang, B.W. & Goh, Tian, 2019. "Index decomposition analysis for comparing emission scenarios: Applications and challenges," Energy Economics, Elsevier, vol. 83(C), pages 74-87.
    7. Sun, Xiaoqi & Liu, Xiaojia, 2020. "Decomposition analysis of debt’s impact on China’s energy consumption," Energy Policy, Elsevier, vol. 146(C).
    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. Guang, Fengtao & Wen, Le & Sharp, Basil, 2022. "Energy efficiency improvements and industry transition: An analysis of China's electricity consumption," Energy, Elsevier, vol. 244(PA).
    10. Shigetomi, Yosuke & Matsumoto, Ken'ichi & Ogawa, Yuki & Shiraki, Hiroto & Yamamoto, Yuki & Ochi, Yuki & Ehara, Tomoki, 2018. "Driving forces underlying sub-national carbon dioxide emissions within the household sector and implications for the Paris Agreement targets in Japan," Applied Energy, Elsevier, vol. 228(C), pages 2321-2332.
    11. Banie Naser Outchiri, 2020. "Contributing to better energy and environmental analyses: how accurate are decomposition analysis results?," Cahiers de recherche 20-11, Departement d'économique de l'École de gestion à l'Université de Sherbrooke.
    12. Huang, Yun-Hsun, 2020. "Examining impact factors of residential electricity consumption in Taiwan using index decomposition analysis based on end-use level data," Energy, Elsevier, vol. 213(C).
    13. Junghwan Lee & Jinsoo Kim, 2021. "A Decomposition Analysis of the Korean Manufacturing Sector: Monetary vs. Physical Outputs," Sustainability, MDPI, vol. 13(11), pages 1-13, May.
    14. Edyta Sidorczuk-Pietraszko, 2020. "Spatial Differences in Carbon Intensity in Polish Households," Energies, MDPI, vol. 13(12), pages 1-21, June.
    15. Rongrong Li & Rui Jiang, 2019. "Is carbon emission decline caused by economic decline? Empirical evidence from Russia," Energy & Environment, , vol. 30(4), pages 672-684, June.
    16. Eskander, Shaikh M.S.U. & Nitschke, Jakob, 2021. "Energy use and CO2 emissions in the UK universities: an extended Kaya identity analysis," LSE Research Online Documents on Economics 110764, London School of Economics and Political Science, LSE Library.
    17. De Oliveira-De Jesus, Paulo M., 2019. "Effect of generation capacity factors on carbon emission intensity of electricity of Latin America & the Caribbean, a temporal IDA-LMDI analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 101(C), pages 516-526.
    18. Su, Bin & Ang, B.W., 2017. "Multiplicative structural decomposition analysis of aggregate embodied energy and emission intensities," Energy Economics, Elsevier, vol. 65(C), pages 137-147.
    19. Jain, Princy & Goswami, Binoy, 2021. "Energy efficiency in South Asia: Trends and determinants," Energy, Elsevier, vol. 221(C).
    20. 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.

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0305769. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.