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Analysis of regional differences and decomposition of carbon emissions in China based on generalized divisia index method

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  • Liu, Yisheng
  • Yang, Meng
  • Cheng, Feiyu
  • Tian, Jinzhao
  • Du, Zhuoqun
  • Song, Pengbo

Abstract

The achievement of China's carbon dioxide (CO2) emission reduction target is of great significance in the face of global climate change. Accurate identification of key factors that affect CO2 emissions can provide theoretical support to policymakers when designing related policies. Compared to the traditional method, the generalized Divisia index method (GDIM) can capture the influence of multiple scale factors on carbon emissions, providing new tools for studying the decomposition of carbon emissions. The article proposed a GDIM-based decomposition method to analyze the drivers that influence CO2 emissions in China from 2000 to 2017. The results indicate that investment activity is the primary element in promoting China's carbon emissions, followed by energy use and economic activities. On the contrary, investment carbon intensity is the vital inhibitory factor, followed by GDP carbon intensity. Specifically, the positive driving force of investment and energy use is gradually weakening, while the contribution of economic activities is continuously strengthening. The effectiveness of carbon emission reduction in the Northeast, East, and Southwest is actively promoting China's carbon emission reduction, while the effectiveness of CO2 emission reduction in the Northwest is not performing well. The findings provide support and reference for carbon emission control in China.

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  • Liu, Yisheng & Yang, Meng & Cheng, Feiyu & Tian, Jinzhao & Du, Zhuoqun & Song, Pengbo, 2022. "Analysis of regional differences and decomposition of carbon emissions in China based on generalized divisia index method," Energy, Elsevier, vol. 256(C).
    • Handle: RePEc:eee:energy:v:256:y:2022:i:c:s0360544222015699
    DOI: 10.1016/j.energy.2022.124666
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    Cited by:

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    3. Shangjiu Wang & Shaohua Zhang & Liang Cheng, 2023. "Drivers and Decoupling Effects of PM 2.5 Emissions in China: An Application of the Generalized Divisia Index," IJERPH, MDPI, vol. 20(2), pages 1-19, January.
    4. Xianpu Xu & Yuchen Song, 2023. "Is There a Conflict between Automation and Environment? Implications of Artificial Intelligence for Carbon Emissions in China," Sustainability, MDPI, vol. 15(16), pages 1-22, August.
    5. Liyuan Fu & Qing Wang, 2022. "Spatial and Temporal Distribution and the Driving Factors of Carbon Emissions from Urban Production Energy Consumption," IJERPH, MDPI, vol. 19(19), pages 1-29, September.
    6. Mengmeng Liu & Hao Wu & Haopeng Wang, 2023. "Will Trade Protection Trigger a Surge in Investment-Related CO 2 Emissions? Evidence from Multi-Regional Input–Output Model," Sustainability, MDPI, vol. 15(13), pages 1-21, June.
    7. Wang, Yaxian & Zhao, Zhenli & Wang, Wenju & Streimikiene, Dalia & Balezentis, Tomas, 2023. "Interplay of multiple factors behind decarbonisation of thermal electricity generation: A novel decomposition model," Technological Forecasting and Social Change, Elsevier, vol. 189(C).

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