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Trend Prediction and Decomposed Driving Factors of Carbon Emissions in Jiangsu Province during 2015–2020

Author

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  • Decai Tang

    (Institute of Climate Change and Public Policy, Nanjing University of Information Science & Technology, Nanjing 210044, China)

  • Tingyu Ma

    (School of Economics and Management, Nanjing University of Information Science & Technology, Nanjing 210044, China)

  • Zhijiang Li

    (School of Economics and Management, Nanjing University of Information Science & Technology, Nanjing 210044, China)

  • Jiexin Tang

    (School of Economics and Management, Nanjing University of Information Science & Technology, Nanjing 210044, China)

  • Brandon J. Bethel

    (School of Economics and Management, Nanjing University of Information Science & Technology, Nanjing 210044, China)

Abstract

According to the economic and energy consumption statistics in Jiangsu Province, we combined the GM (1, 1) grey model and polynomial regression to forecast carbon emissions. Historical and projected emissions were decomposed using the Logarithmic Mean Divisia Index (LMDI) approach to assess the relative contribution of different factors to emission variability. The results showed that carbon emissions will continue to increase in Jiangsu province during 2015–2020 period and cumulative carbon emissions will increase by 39.5487 million tons within the forecast period. The growth of gross domestic product (GDP) per capita plays the greatest positive role in driving carbon emission growth. Furthermore, the improvement of energy usage efficiency is the primary factor responsible for reducing carbon emissions. Factors of population, industry structure adjustment and the optimization of fuel mix also help to reduce carbon emissions. Based on the LMDI analysis, we provide some advice for policy-makers in Jiangsu and other provinces in China.

Suggested Citation

  • Decai Tang & Tingyu Ma & Zhijiang Li & Jiexin Tang & Brandon J. Bethel, 2016. "Trend Prediction and Decomposed Driving Factors of Carbon Emissions in Jiangsu Province during 2015–2020," Sustainability, MDPI, vol. 8(10), pages 1-15, October.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:10:p:1018-:d:80396
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    Cited by:

    1. Sheng-Wen Tseng, 2019. "Analysis of Energy-Related Carbon Emissions in Inner Mongolia, China," Sustainability, MDPI, vol. 11(24), pages 1-20, December.
    2. Thi-Nham Le & Chia-Nan Wang, 2017. "The Integrated Approach for Sustainable Performance Evaluation in Value Chain of Vietnam Textile and Apparel Industry," Sustainability, MDPI, vol. 9(3), pages 1-21, March.
    3. Zhengsong Lin & Xinyue Ye & Qian Wei & Fan Xin & Zhang Lu & Sonali Kudva & Qiwen Dai, 2017. "Ecosystem Services Value Assessment and Uneven Development of the Qingjiang River Basin in China," Sustainability, MDPI, vol. 9(12), pages 1-17, December.
    4. Ang, B.W. & Goh, Tian, 2019. "Index decomposition analysis for comparing emission scenarios: Applications and challenges," Energy Economics, Elsevier, vol. 83(C), pages 74-87.
    5. Ming Meng & Manyu Li, 2020. "Decomposition Analysis and Trend Prediction of CO 2 Emissions in China’s Transportation Industry," Sustainability, MDPI, vol. 12(7), pages 1-20, March.
    6. Ying Wang & Peipei Shang & Lichun He & Yingchun Zhang & Dandan Liu, 2018. "Can China Achieve the 2020 and 2030 Carbon Intensity Targets through Energy Structure Adjustment?," Energies, MDPI, vol. 11(10), pages 1-32, October.
    7. Haojia Kong & Lifan Shi & Dan Da & Zhijiang Li & Decai Tang & Wei Xing, 2022. "Simulation of China’s Carbon Emission based on Influencing Factors," Energies, MDPI, vol. 15(9), pages 1-15, April.
    8. Li, Guohao & Chen, Xue & You, Xue-yi, 2023. "System dynamics prediction and development path optimization of regional carbon emissions: A case study of Tianjin," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).

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