IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i23p5899-d1528335.html
   My bibliography  Save this article

Research on Spatial Heterogeneity, Impact Mechanism, and Carbon Peak Prediction of Carbon Emissions in the Yangtze River Delta Urban Agglomeration

Author

Listed:
  • Pin Chen

    (School of Economics and Management, Changzhou Institute of Technology, Changzhou 213032, China)

  • Xiyue Wang

    (School of Economics and Finance, Hohai University, Changzhou 213200, China)

  • Zexia Yang

    (School of Economics, Fuyang Normal University, Fuyang 236041, China)

  • Changfeng Shi

    (School of Economics and Finance, Hohai University, Changzhou 213200, China)

Abstract

Urban agglomerations with a high economic activity and population density are key areas for carbon emissions and pioneers in achieving carbon peaking and the Sustainable Development Goals (SDGs). This study combines machine learning with an extended STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model to uncover the mechanisms driving carbon peaking disparities within these regions. It forecasts carbon emissions under different scenarios and develops indices to assess peaking pressure, reduction potential, and driving forces. The findings show significant carbon emission disparities among cities in the Yangtze River Delta, with a fluctuating downward trend over time. Technological advancement, population size, affluence, and urbanization positively impact emissions, while the effects of industrial structure and foreign investment are weakening. Industrially optimized cities lead in peaking, while others—such as late-peaking and economically radiating cities—achieve peaking only under the ER scenario. Cities facing population loss and demonstration cities fail to peak by 2030 in any scenario. The study recommends differentiated carbon peaking pathways for cities, emphasizing tailored targets, pathway models, and improved supervision. This research offers theoretical and practical insights for global urban agglomerations aiming to achieve early carbon peaking.

Suggested Citation

  • 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 1-21, November.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:23:p:5899-:d:1528335
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/23/5899/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/23/5899/
    Download Restriction: no
    ---><---

    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. Li, Pei-Hao & Pye, Steve & Keppo, Ilkka, 2020. "Using clustering algorithms to characterise uncertain long-term decarbonisation pathways," Applied Energy, Elsevier, vol. 268(C).
    3. Kerong Jian & Ruyun Shi & Yixue Zhang & Zhigao Liao, 2023. "Research on Carbon Emission Characteristics and Differentiated Carbon Reduction Pathways in the Yangtze River Delta Region Based on the STIRPAT Model," Sustainability, MDPI, vol. 15(21), pages 1-18, November.
    4. Xu, Guangyue & Schwarz, Peter & Yang, Hualiu, 2019. "Determining China's CO2 emissions peak with a dynamic nonlinear artificial neural network approach and scenario analysis," Energy Policy, Elsevier, vol. 128(C), pages 752-762.
    5. Fang, Guochang & Gao, Zhengye & Tian, Lixin & Fu, Min, 2022. "What drives urban carbon emission efficiency? – Spatial analysis based on nighttime light data," Applied Energy, Elsevier, vol. 312(C).
    6. Shi, Changfeng & Zhi, Jiaqi & Yao, Xiao & Zhang, Hong & Yu, Yue & Zeng, Qingshun & Li, Luji & Zhang, Yuxi, 2023. "How can China achieve the 2030 carbon peak goal—a crossover analysis based on low-carbon economics and deep learning," Energy, Elsevier, vol. 269(C).
    7. Ma, Xuejiao & Jiang, Ping & Jiang, Qichuan, 2020. "Research and application of association rule algorithm and an optimized grey model in carbon emissions forecasting," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    8. Cai, Bofeng & Liu, Helin & Zhang, Xiaoling & Pan, Haozhi & Zhao, Mengxue & Zheng, Tianming & Nie, Jingxin & Du, Mengbing & Dhakal, Shobhakar, 2022. "High-resolution accounting of urban emissions in China," Applied Energy, Elsevier, vol. 325(C).
    9. Wang, Yanan & Yin, Shiwen & Fang, Xiaoli & Chen, Wei, 2022. "Interaction of economic agglomeration, energy conservation and emission reduction: Evidence from three major urban agglomerations in China," Energy, Elsevier, vol. 241(C).
    10. Anu Ramaswami & Daqian Jiang & Kangkang Tong & Jerry Zhao, 2018. "Impact of the Economic Structure of Cities on Urban Scaling Factors: Implications for Urban Material and Energy Flows in China," Journal of Industrial Ecology, Yale University, vol. 22(2), pages 392-405, April.
    11. Liang, Xiaoying & Min Fan, & Xiao, Yuting & Yao, Jing, 2022. "Temporal-spatial characteristics of energy-based carbon dioxide emissions and driving factors during 2004–2019, China," Energy, Elsevier, vol. 261(PA).
    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. Zeng, Qingshun & Shi, Changfeng & Zhu, Wenjun & Zhi, Jiaqi & Na, Xiaohong, 2023. "Sequential data-driven carbon peaking path simulation research of the Yangtze River Delta urban agglomeration based on semantic mining and heuristic algorithm optimization," Energy, Elsevier, vol. 285(C).
    2. Shi, Changfeng & Zhi, Jiaqi & Yao, Xiao & Zhang, Hong & Yu, Yue & Zeng, Qingshun & Li, Luji & Zhang, Yuxi, 2023. "How can China achieve the 2030 carbon peak goal—a crossover analysis based on low-carbon economics and deep learning," Energy, Elsevier, vol. 269(C).
    3. Ye, Li & Yang, Deling & Dang, Yaoguo & Wang, Junjie, 2022. "An enhanced multivariable dynamic time-delay discrete grey forecasting model for predicting China's carbon emissions," Energy, Elsevier, vol. 249(C).
    4. Qiangyi Li & Lan Yang & Shuang Huang & Yangqing Liu & Chenyang Guo, 2023. "The Effects of Urban Sprawl on Electricity Consumption: Empirical Evidence from 283 Prefecture-Level Cities in China," Land, MDPI, vol. 12(8), pages 1-27, August.
    5. Sang, Meiyue & Shen, Liyin, 2024. "An international perspective on carbon peaking status between a sample of 154 countries," Applied Energy, Elsevier, vol. 369(C).
    6. Yali Wang & Yangyang Liu & Zijun Wang & Yan Zhang & Bo Fang & Shengnan Jiang & Yijia Yang & Zhongming Wen & Wei Zhang & Zhixin Zhang & Ziqi Lin & Peidong Han & Wenjie Yang, 2023. "Assessing the Spatio-Temporal Dynamics of Land Use Carbon Emissions and Multiple Driving Factors in the Guanzhong Area of Shaanxi Province," Sustainability, MDPI, vol. 15(9), pages 1-23, May.
    7. Hongqiang Wang & Wenyi Xu & Yingjie Zhang, 2023. "Research on Provincial Carbon Emission Reduction Path Based on LMDI-SD-Tapio Decoupling Model: The Case of Guizhou, China," Sustainability, MDPI, vol. 15(17), pages 1-20, September.
    8. Zhi Wang & Fengwan Zhang & Shaoquan Liu & Dingde Xu, 2023. "Land Use Structure Optimization and Ecological Benefit Evaluation in Chengdu-Chongqing Urban Agglomeration Based on Carbon Neutrality," Land, MDPI, vol. 12(5), pages 1-22, May.
    9. Yue, Wencong & Li, Yangqing & Su, Meirong & Chen, Qionghong & Rong, Qiangqiang, 2023. "Carbon emissions accounting and prediction in urban agglomerations from multiple perspectives of production, consumption and income," Applied Energy, Elsevier, vol. 348(C).
    10. Sun, Lu & Fujii, Minoru & Li, Zhaoling & Dong, Huijuan & Geng, Yong & Liu, Zhe & Fujita, Tsuyoshi & Yu, Xiaoman & Zhang, Yuepeng, 2020. "Energy-saving and carbon emission reduction effect of urban-industrial symbiosis implementation with feasibility analysis in the city," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    11. Wang, Zheng-Xin & Jv, Yue-Qi, 2021. "A non-linear systematic grey model for forecasting the industrial economy-energy-environment system," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    12. Meng Guo & Shukai Cai, 2022. "Impact of Green Innovation Efficiency on Carbon Peak: Carbon Neutralization under Environmental Governance Constraints," IJERPH, MDPI, vol. 19(16), pages 1-18, August.
    13. Du, Xiaoyun & Meng, Conghui & Guo, Zhenhua & Yan, Hang, 2023. "An improved approach for measuring the efficiency of low carbon city practice in China," Energy, Elsevier, vol. 268(C).
    14. Chen, Lu & Li, Xin & Liu, Wei & Kang, Xinyu & Zhao, Yifei & Wang, Minxi, 2024. "System dynamics-multiple the objective optimization model for the coordinated development of urban economy-energy-carbon system," Applied Energy, Elsevier, vol. 371(C).
    15. Xiaoxu, Xing & Qiangmin, Xi & Weihao, Shi, 2024. "Impact of urban compactness on carbon emission in Chinese cities: From moderating effects of industrial diversity and job-housing imbalances," Land Use Policy, Elsevier, vol. 143(C).
    16. Lili Sun & Huijuan Cui & Quansheng Ge, 2021. "Driving Factors and Future Prediction of Carbon Emissions in the ‘Belt and Road Initiative’ Countries," Energies, MDPI, vol. 14(17), pages 1-21, September.
    17. Feng, Qianqian & Sun, Xiaolei & Hao, Jun & Li, Jianping, 2021. "Predictability dynamics of multifactor-influenced installed capacity: A perspective of country clustering," Energy, Elsevier, vol. 214(C).
    18. Meixia Wang, 2024. "Predicting China’s Energy Consumption and CO 2 Emissions by Employing a Novel Grey Model," Energies, MDPI, vol. 17(21), pages 1-25, October.
    19. Mengyao Liu & Yan Hou & Hongli Jiang, 2023. "The Energy-Saving Effect of E-Commerce Development—A Quasi-Natural Experiment in China," Energies, MDPI, vol. 16(12), pages 1-22, June.
    20. Luo, Haizhi & Zhang, Yiwen & Gao, Xinyu & Liu, Zhengguang & Song, Xia & Meng, Xiangzhao & Yang, Xiaohu, 2024. "Unveiling land use-carbon Nexus: Spatial matrix-enhanced neural network for predicting commercial and residential carbon emissions," Energy, Elsevier, vol. 305(C).

    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:17:y:2024:i:23:p:5899-:d:1528335. 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.