IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i6p2689-d1614985.html
   My bibliography  Save this article

Decomposition of Carbon Emission Drivers and Carbon Peak Forecast for Three Major Urban Agglomerations in the Yangtze River Economic Belt

Author

Listed:
  • Ziqian Zhou

    (Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
    Fudan Tyndall Centre, Fudan University, Shanghai 200433, China)

  • Ping Jiang

    (Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
    Fudan Tyndall Centre, Fudan University, Shanghai 200433, China)

  • Shun Chen

    (Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
    Fudan Tyndall Centre, Fudan University, Shanghai 200433, China)

Abstract

Spanning China’s eastern, central, and western regions, the Yangtze River Economic Belt (YREB) is a pivotal area for economic growth and carbon emissions, with its three major urban agglomerations serving as key hubs along the upper, middle, and lower reaches of the Yangtze River. Understanding the driving factors of carbon emissions and simulating carbon peak scenarios in these regions are critical for informing low-carbon development strategies across China’s diverse geographical zones. This study employs Grey Relational Analysis to identify key drivers and applies the Logarithmic Mean Divisia Index (LMDI) decomposition method to quantify the contributions of various factors to carbon emissions from 2005 to 2021. Furthermore, the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model is utilized to project future emission trends under multiple scenarios. The results indicate that (1) the growth rate of carbon emissions in the three urban agglomerations has generally decelerated during the study period; (2) the influence of driving factors varies significantly across regions, with economic development, urbanization, and population size positively correlating with carbon emissions, while energy structure and energy intensity exhibit mitigating effects; and (3) tailored emission reduction strategies for each urban agglomeration—namely, the Yangtze River Delta Urban Agglomeration (YRD), the Middle Reaches of the Yangtze River Urban Agglomeration (TCC), and the Chengdu-Chongqing Urban Agglomeration (CCA)—can enable all three to achieve carbon peaking by 2030. These findings provide a robust foundation for region-specific policy-making to support China’s carbon neutrality goals.

Suggested Citation

  • Ziqian Zhou & Ping Jiang & Shun Chen, 2025. "Decomposition of Carbon Emission Drivers and Carbon Peak Forecast for Three Major Urban Agglomerations in the Yangtze River Economic Belt," Sustainability, MDPI, vol. 17(6), pages 1-28, March.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:6:p:2689-:d:1614985
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/6/2689/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/6/2689/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhao, Fei & Wang, Yuliang & Guo, Jianlong & Wu, Lifeng, 2024. "Chinese provincial energy consumption intensity prediction by the CGM(1,1)," Energy, Elsevier, vol. 292(C).
    2. Yu, Ying & Dai, Yuqi & Xu, Linyu & Zheng, Hanzhong & Wu, Wenhao & Chen, Lei, 2023. "A multi-level characteristic analysis of urban agglomeration energy-related carbon emission: A case study of the Pearl River Delta," Energy, Elsevier, vol. 263(PB).
    3. Pin Chen & Xiyue Wang & Zexia Yang & Changfeng Shi, 2024. "Research on Spatial Heterogeneity, Impact Mechanism, and Carbon Peak Prediction of Carbon Emissions in the Yangtze River Delta Urban Agglomeration," Energies, MDPI, vol. 17(23), pages 1-20, November.
    4. Han, Mengyao & Xiong, Jiao & Wang, Siyuan & Yang, Yu, 2020. "Chinese photovoltaic poverty alleviation: Geographic distribution, economic benefits and emission mitigation," Energy Policy, Elsevier, vol. 144(C).
    5. Liang, Xiaoying & Fan, Min & Huang, Xiaofang & Cai, Can & Zhou, Lele & Wang, Yuanzhe, 2024. "Spatial distributed characteristics of carbon dioxide emissions based on fossil energy consumption and their driving factors at provincial scale in China," Energy, Elsevier, vol. 309(C).
    6. Ang, B.W. & Liu, Na, 2007. "Handling zero values in the logarithmic mean Divisia index decomposition approach," Energy Policy, Elsevier, vol. 35(1), pages 238-246, January.
    7. Shaobo Yang & Shanhua Duan & Linlin Fan & Chongwei Zheng & Xingfei Li & Hongyu Li & Jianjun Xu & Qiang Wang & Ming Feng, 2019. "10-Year Wind and Wave Energy Assessment in the North Indian Ocean," Energies, MDPI, vol. 12(20), pages 1-16, October.
    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. Li, Aijun & Hu, Mingming & Wang, Mingjian & Cao, Yinxue, 2016. "Energy consumption and CO2 emissions in Eastern and Central China: A temporal and a cross-regional decomposition analysis," Technological Forecasting and Social Change, Elsevier, vol. 103(C), pages 284-297.
    2. 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.
    3. Chen, Yufeng & Miao, Jiafeng, 2023. "What Determines China’s Agricultural Non-Point Source Pollution? An Improved LMDI Decomposition Analysis," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 48(2), May.
    4. Wang, Wenwen & Li, Man & Zhang, Ming, 2017. "Study on the changes of the decoupling indicator between energy-related CO2 emission and GDP in China," Energy, Elsevier, vol. 128(C), pages 11-18.
    5. Zhang, Zumeng & Ding, Liping & Wang, Chaofan & Dai, Qiyao & Shi, Yin & Zhao, Yujia & Zhu, Yuxuan, 2022. "Do operation and maintenance contracts help photovoltaic poverty alleviation power stations perform better?," Energy, Elsevier, vol. 259(C).
    6. Xuankai Deng & Yanhua Yu & Yanfang Liu, 2015. "Effect of Construction Land Expansion on Energy-Related Carbon Emissions: Empirical Analysis of China and Its Provinces from 2001 to 2011," Energies, MDPI, vol. 8(6), pages 1-22, June.
    7. 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.
    8. Ma, Chunbo, 2014. "A multi-fuel, multi-sector and multi-region approach to index decomposition: An application to China's energy consumption 1995–2010," Energy Economics, Elsevier, vol. 42(C), pages 9-16.
    9. Kaltenegger, Oliver & Löschel, Andreas & Pothen, Frank, 2017. "The effect of globalisation on energy footprints: Disentangling the links of global value chains," Energy Economics, Elsevier, vol. 68(S1), pages 148-168.
    10. Lizhan Cao & Hui Wang, 2022. "The Slowdown in China’s Energy Consumption Growth in the “New Normal” Stage: From Both National and Regional Perspectives," Sustainability, MDPI, vol. 14(7), pages 1-21, April.
    11. Andreoni, V. & Galmarini, S., 2012. "European CO2 emission trends: A decomposition analysis for water and aviation transport sectors," Energy, Elsevier, vol. 45(1), pages 595-602.
    12. Shrestha, Ram M. & Anandarajah, Gabrial & Liyanage, Migara H., 2009. "Factors affecting CO2 emission from the power sector of selected countries in Asia and the Pacific," Energy Policy, Elsevier, vol. 37(6), pages 2375-2384, June.
    13. 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.
    14. Filippo Antoniolli, Andrigo & Naspolini, Helena Flávia & de Abreu, João Frederico & Rüther, Ricardo, 2022. "The role and benefits of residential rooftop photovoltaic prosumers in Brazil," Renewable Energy, Elsevier, vol. 187(C), pages 204-222.
    15. Liu, Xiao & Zhou, Dequn & Zhou, Peng & Wang, Qunwei, 2017. "What drives CO2 emissions from China’s civil aviation? An exploration using a new generalized PDA method," Transportation Research Part A: Policy and Practice, Elsevier, vol. 99(C), pages 30-45.
    16. Wei, Xiahai & Zeng, Chenyu & Wang, Jiannan & Chen, Yu, 2024. "The road to green development for national-level poverty-stricken counties: Does poverty alleviation help reduce carbon emissions?," Energy Economics, Elsevier, vol. 138(C).
    17. Yueyue Rong & Junsong Jia & Min Ju & Chundi Chen & Yangming Zhou & Yexi Zhong, 2021. "Multi-Perspective Analysis of Household Carbon Dioxide Emissions from Direct Energy Consumption by the Methods of Logarithmic Mean Divisia Index and ? Convergence in Central China," Sustainability, MDPI, vol. 13(16), pages 1-28, August.
    18. Dequn Zhou & Xiao Liu & Peng Zhou & Qunwei Wang, 2017. "Decomposition Analysis of Aggregate Energy Consumption in China: An Exploration Using a New Generalized PDA Method," Sustainability, MDPI, vol. 9(5), pages 1-13, April.
    19. Liu, Chang & Liu, Linlin & Zhang, Dayong & Fu, Jiasha, 2021. "How does the capital market respond to policy shocks? Evidence from listed solar photovoltaic companies in China," Energy Policy, Elsevier, vol. 151(C).
    20. Cansino, José M. & Román-Collado, Rocío & Merchán, José, 2019. "Do Spanish energy efficiency actions trigger JEVON’S paradox?," Energy, Elsevier, vol. 181(C), pages 760-770.

    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:jsusta:v:17:y:2025:i:6:p:2689-:d:1614985. 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.