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Spatial Evolution and Scenario Simulation of Carbon Metabolism in Coal-Resource-Based Cities Towards Carbon Neutrality: A Case Study of Jincheng, China

Author

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  • Li Zhu

    (School of Architecture, Tianjin University, Tianjin 300072, China
    APEC Sustainable Energy Center, Asia-Pacific Economic Cooperation (APEC)/National Energy Administration (NEA) of China, Tianjin 300072, China)

  • Mengying Cao

    (School of Architecture, Tianjin University, Tianjin 300072, China)

  • Wenyuan Wang

    (Faculty of Innovation and Design, City University of Macau, Macau 999078, China
    College of Architectural Engineering, Henan Polytechnic Institute, Nanyang 473000, China)

  • Tianyue Zhang

    (School of Future Technology, Tianjin University, Tianjin 300072, China)

Abstract

As important energy suppliers in China, coal-resource-based cities are pivotal to achieving the nation’s 2060 carbon-neutrality goal. This study focused on Jincheng City, utilizing the LOW EMISSIONS ANALYSIS PLATFORM (LEAP) model to predict carbon emissions from energy consumption under various scenarios from 2020 to 2060. Then, combined with the Markov-PLUS model to map carbon emissions to land-use types, it evaluated spatial changes in carbon metabolism and analyzed carbon-transfer patterns across different land-use types. The results showed the following: (1) Across all scenarios, Jincheng’s carbon emissions exhibited an initial increase followed by a decline, with the industrial sector accounting for over 70% of total emissions. While the baseline scenario deviated from China’s carbon peaking target, the high-limit scenario achieved an early carbon peak by 2027. (2) High-negative-carbon-metabolism areas were concentrated in central urban zones and industrial parks. Notably, arable land shifted from a carbon-sink area to a carbon source area by 2060 in both the low- and high-limit scenarios. (3) In the baseline scenario, industrial and transportation land uses were the primary barriers to carbon metabolism balance. In the low-carbon scenario, the focus shifted from industrial and transportation emissions to urban construction land emissions. In the high-limit scenario, changes in urban–rural land-use relationships significantly influenced carbon metabolism balance. This study emphasizes the importance of industrial green transformation and land-use planning control to achieve carbon neutrality, and it further explores the significant impact of territorial spatial planning on the low-carbon transition of coal-resource-based cities.

Suggested Citation

  • Li Zhu & Mengying Cao & Wenyuan Wang & Tianyue Zhang, 2025. "Spatial Evolution and Scenario Simulation of Carbon Metabolism in Coal-Resource-Based Cities Towards Carbon Neutrality: A Case Study of Jincheng, China," Energies, MDPI, vol. 18(6), pages 1-20, March.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:6:p:1532-:d:1616142
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    References listed on IDEAS

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    1. Dandan Liu & Dewei Yang & Anmin Huang, 2021. "LEAP-Based Greenhouse Gases Emissions Peak and Low Carbon Pathways in China’s Tourist Industry," IJERPH, MDPI, vol. 18(3), pages 1-15, January.
    2. Yeo, In-Ae & Yoon, Seong-Hwan & Yee, Jurng-Jae, 2013. "Development of an urban energy demand forecasting system to support environmentally friendly urban planning," Applied Energy, Elsevier, vol. 110(C), pages 304-317.
    3. Deng, Yue & Jiang, Wanyi & Wang, Zeyu, 2023. "Economic resilience assessment and policy interaction of coal resource oriented cities for the low carbon economy based on AI," Resources Policy, Elsevier, vol. 82(C).
    4. Dong, Jia & Li, Cunbin, 2022. "Scenario prediction and decoupling analysis of carbon emission in Jiangsu Province, China," Technological Forecasting and Social Change, Elsevier, vol. 185(C).
    5. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    6. Emodi, Nnaemeka Vincent & Emodi, Chinenye Comfort & Murthy, Girish Panchakshara & Emodi, Adaeze Saratu Augusta, 2017. "Energy policy for low carbon development in Nigeria: A LEAP model application," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 247-261.
    7. Badeeb, Ramez Abubakr & Szulczyk, Kenneth R. & Zahra, Samia & Mukherjee, Tanusree Chakravarty, 2023. "Innovation dynamics in the natural resource curse hypothesis: A new perspective from BRICS countries," Resources Policy, Elsevier, vol. 81(C).
    8. Shu, Yunxia & Deng, Nanxin & Wu, Yuming & Bao, Shuming & Bie, Ao, 2023. "Urban governance and sustainable development: The effect of smart city on carbon emission in China," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    9. Hailiang Huang & Changfeng Shi, 2023. "Analysis of the Path Optimization of the Sustainable Development of Coal-Energy Cities Based on TOPSIS Evaluation Model," Energies, MDPI, vol. 16(2), pages 1-17, January.
    10. Nieves, J.A. & Aristizábal, A.J. & Dyner, I. & Báez, O. & Ospina, D.H., 2019. "Energy demand and greenhouse gas emissions analysis in Colombia: A LEAP model application," Energy, Elsevier, vol. 169(C), pages 380-397.
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