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Research on Carbon Peak Prediction of Various Prefecture-Level Cities in Jiangsu Province Based on Factors Influencing Carbon Emissions

Author

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  • Yu Wang

    (School of Architectural Engineering, Nanjing Institute of Technology, Nanjing 211167, China)

  • Ling Dong

    (School of Architecture, Nanjing Tech University, Nanjing 211816, China)

Abstract

Jiangsu Province is a region with a high concentration of economy and population in China, as well as a spatial unit with relatively concentrated carbon emissions. It is also the pioneer in achieving carbon peak. Analyzing the factors influencing carbon emissions and predicting the peak year of carbon emissions will help Jiangsu Province clarify the direction of carbon reduction and take the lead in achieving carbon peak. This article selects relevant data from Jiangsu Province from 2005 to 2020, uses the STIRPAT model to analyze the influencing factors of carbon emissions in Jiangsu Province, predicts the carbon emissions and peak times of 13 prefecture-level cities in four different scenarios, and constructs a carbon peak prediction model to calculate the carbon peak pressure, carbon emission reduction potential, and carbon peak driving force of each prefecture-level city. Research has found that the population size, wealth level, technological level, urbanization level, and industrial structure have significant impacts on carbon emissions in Jiangsu Province. The prediction results for carbon peak in 13 prefecture-level cities indicate that Nantong, Huai’an, Yancheng, Suzhou, Nanjing, and Wuxi can achieve carbon peak before 2030 in all four scenarios. Changzhou, Xuzhou, Yangzhou, Taizhou, Suqian, Lianyungang, and Zhenjiang are all able to achieve carbon peak between 2025 and 2029 under the low-growth, slow-consumption scenario (P2G2E1) and low-growth, fast-consumption scenario (P2G2E2), but they cannot achieve carbon peak before 2030 under the high-growth, slow-consumption scenario (P1G1E1) and high-growth, fast-consumption scenario (P1G1E2). Finally, based on the carbon peak prediction model, the prefecture-level cities are classified, and differentiated carbon peak implementation paths for different types of prefecture-level cities are proposed.

Suggested Citation

  • Yu Wang & Ling Dong, 2024. "Research on Carbon Peak Prediction of Various Prefecture-Level Cities in Jiangsu Province Based on Factors Influencing Carbon Emissions," Sustainability, MDPI, vol. 16(16), pages 1-24, August.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:16:p:7105-:d:1459168
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    References listed on IDEAS

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    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. Xu, Xianshuo & Zhao, Tao & Liu, Nan & Kang, Jidong, 2014. "Changes of energy-related GHG emissions in China: An empirical analysis from sectoral perspective," Applied Energy, Elsevier, vol. 132(C), pages 298-307.
    3. Chen, Jiandong & Xu, Chong & Cui, Lianbiao & Huang, Shuo & Song, Malin, 2019. "Driving factors of CO2 emissions and inequality characteristics in China: A combined decomposition approach," Energy Economics, Elsevier, vol. 78(C), pages 589-597.
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    Cited by:

    1. Siting Hong & Ting Fu & Ming Dai, 2025. "Machine Learning-Based Carbon Emission Predictions and Customized Reduction Strategies for 30 Chinese Provinces," Sustainability, MDPI, vol. 17(5), pages 1-29, February.
    2. Xuelian Zhu & Jianan Che & Xiaogeng Niu & Nannan Cao & Guofeng Zhang, 2025. "Optimization of Carbon Emission Reduction Path in the Beijing–Tianjin–Hebei Region Based on System Dynamics," Sustainability, MDPI, vol. 17(4), pages 1-24, February.

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