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Decomposition and Scenario Analysis of Factors Influencing Carbon Emissions: A Case Study of Jiangsu Province, China

Author

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  • An Cheng

    (Wu Jinglian School of Economics, Changzhou University, Changzhou 213159, China
    Jiangsu Energy Strategy Research Base, Changzhou University, Changzhou 213159, China)

  • Xinru Han

    (Institute of Agricultural Economics and Development, Chinese Academy of Agricultural Sciences, Beijing 100081, China)

  • Guogang Jiang

    (Wu Jinglian School of Economics, Changzhou University, Changzhou 213159, China
    Jiangsu Energy Strategy Research Base, Changzhou University, Changzhou 213159, China)

Abstract

It is crucial for China to take the characteristics and development stage of every province in the region into account in order to achieve the “dual carbon” development goal. Using data collected from 2000 to 2019, this study identifies the factors that influence carbon emissions using the logarithmic mean Divisia index (LMDI) method and establishes a revised stochastic impacts by regression on population, affluence, and technology (STIRPAT) model to investigate the effects of four key factors on carbon emissions in Jiangsu province: population size, economic output, energy intensity, and energy structure. The following conclusions were drawn: (1) energy intensity contributes to a slowed rate of carbon emission production in Jiangsu, whereas population size, economic output, and energy structure contribute to a pulling effect; (2) under different scenarios, Jiangsu’s carbon dioxide emissions peak at different times and reach different values; and (3) two low-carbon scenarios are more in line with the current development situation and future policy orientation of Jiangsu Province and are therefore better choices. Our policy recommendations are as follows: (1) the development of economic and social activities should be coordinated and greenhouse gas emissions should be reduced; (2) the province’s energy structure should be transformed and upgraded by taking advantage of the “dual carbon” development model; and (3) regionally-differentiated carbon emission reduction policies should be developed.

Suggested Citation

  • An Cheng & Xinru Han & Guogang Jiang, 2023. "Decomposition and Scenario Analysis of Factors Influencing Carbon Emissions: A Case Study of Jiangsu Province, China," Sustainability, MDPI, vol. 15(8), pages 1-16, April.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:8:p:6718-:d:1124595
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    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. Elzen, Michel den & Fekete, Hanna & Höhne, Niklas & Admiraal, Annemiek & Forsell, Nicklas & Hof, Andries F. & Olivier, Jos G.J. & Roelfsema, Mark & van Soest, Heleen, 2016. "Greenhouse gas emissions from current and enhanced policies of China until 2030: Can emissions peak before 2030?," Energy Policy, Elsevier, vol. 89(C), pages 224-236.
    4. Yu, Shiwei & Zheng, Shuhong & Li, Xia, 2018. "The achievement of the carbon emissions peak in China: The role of energy consumption structure optimization," Energy Economics, Elsevier, vol. 74(C), pages 693-707.
    5. Hu, Ying & Yu, Yang & Mardani, Abbas, 2021. "Selection of carbon emissions control industries in China: An approach based on complex networks control perspective," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    6. Chong, ChinHao & Liu, Pei & Ma, Linwei & Li, Zheng & Ni, Weidou & Li, Xu & Song, Shizhong, 2017. "LMDI decomposition of energy consumption in Guangdong Province, China, based on an energy allocation diagram," Energy, Elsevier, vol. 133(C), pages 525-544.
    7. Ding, Suiting & Zhang, Ming & Song, Yan, 2019. "Exploring China's carbon emissions peak for different carbon tax scenarios," Energy Policy, Elsevier, vol. 129(C), pages 1245-1252.
    8. Chai, Jian & Liang, Ting & Lai, Kin Keung & Zhang, Zhe George & Wang, Shouyang, 2018. "The future natural gas consumption in China: Based on the LMDI-STIRPAT-PLSR framework and scenario analysis," Energy Policy, Elsevier, vol. 119(C), pages 215-225.
    9. Camille Parmesan & Gary Yohe, 2003. "A globally coherent fingerprint of climate change impacts across natural systems," Nature, Nature, vol. 421(6918), pages 37-42, January.
    10. Xu, Haitao & Pan, Xiongfeng & Guo, Shucen & Lu, Yuduo, 2021. "Forecasting Chinese CO2 emission using a non-linear multi-agent intertemporal optimization model and scenario analysis," Energy, Elsevier, vol. 228(C).
    11. Wang, Yuan & Zhang, Chen & Lu, Aitong & Li, Li & He, Yanmin & ToJo, Junji & Zhu, Xiaodong, 2017. "A disaggregated analysis of the environmental Kuznets curve for industrial CO2 emissions in China," Applied Energy, Elsevier, vol. 190(C), pages 172-180.
    12. Behera, Smruti Ranjan & Dash, Devi Prasad, 2017. "The effect of urbanization, energy consumption, and foreign direct investment on the carbon dioxide emission in the SSEA (South and Southeast Asian) region," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 96-106.
    13. Kaltenegger, Oliver, 2020. "What drives total real unit energy costs globally? A novel LMDI decomposition approach," Applied Energy, Elsevier, vol. 261(C).
    14. Lin, Boqiang & Teng, Yuqiang, 2022. "Structural path and decomposition analysis of sectoral carbon emission changes in China," Energy, Elsevier, vol. 261(PB).
    15. 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).
    16. York, Richard & Rosa, Eugene A. & Dietz, Thomas, 2003. "STIRPAT, IPAT and ImPACT: analytic tools for unpacking the driving forces of environmental impacts," Ecological Economics, Elsevier, vol. 46(3), pages 351-365, October.
    17. Wang, Ping & Wu, Wanshui & Zhu, Bangzhu & Wei, Yiming, 2013. "Examining the impact factors of energy-related CO2 emissions using the STIRPAT model in Guangdong Province, China," Applied Energy, Elsevier, vol. 106(C), pages 65-71.
    18. Wu, Rong & Wang, Jieyu & Wang, Shaojian & Feng, Kuishuang, 2021. "The drivers of declining CO2 emissions trends in developed nations using an extended STIRPAT model: A historical and prospective analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    19. Mei, H. & Li, Y.P. & Suo, C. & Ma, Y. & Lv, J., 2020. "Analyzing the impact of climate change on energy-economy-carbon nexus system in China," Applied Energy, Elsevier, vol. 262(C).
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