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Influential Factors and Spatiotemporal Characteristics of Carbon Intensity on Industrial Sectors in China

Author

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  • Ying Han

    (School of Business Administration, Northeastern University, Shenyang 110169, China)

  • Baoling Jin

    (School of Business Administration, Northeastern University, Shenyang 110169, China)

  • Xiaoyuan Qi

    (School of Business Administration, Northeastern University, Shenyang 110169, China)

  • Huasen Zhou

    (School of Business Administration, Northeastern University, Shenyang 110169, China)

Abstract

Based on the extended STIRPAT model and panel data from 2005 to 2015 in 20 industrial sectors, this study investigates the influential factors of carbon intensity, including employee, industry added value, fixed-assets investment, coal consumption, and resource tax. Meanwhile, by expanding the spatial weight matrix and using the Spatial Durbin Model, we reveal the spatiotemporal characteristics of carbon intensity. The results indicate that Manufacturing of Oil Processing and Coking Processing (S7), Manufacturing of Non-metal Products (S10), Smelting and Rolling Process of Metal (S11), and Electricity, Gas, Water, Sewage Treatment, Waste and Remediation (S17) contribute most to carbon intensity in China. The carbon intensity of 20 industrial sectors presents a spatial agglomeration characteristic. Meanwhile, industry added value inhibits the carbon intensity; however, employee, coal consumption, and resource tax promote carbon intensity. Finally, coal consumption appears to have spillover effects, and the employee has an insignificant impact on the carbon intensity of industrial sectors.

Suggested Citation

  • Ying Han & Baoling Jin & Xiaoyuan Qi & Huasen Zhou, 2021. "Influential Factors and Spatiotemporal Characteristics of Carbon Intensity on Industrial Sectors in China," IJERPH, MDPI, vol. 18(6), pages 1-18, March.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:6:p:2914-:d:515801
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    Cited by:

    1. Xin Xu & Yuming Shen & Hanchu Liu, 2022. "What Cause Large Spatiotemporal Differences in Carbon Intensity of Energy-Intensive Industries in China? Evidence from Provincial Data during 2000–2019," IJERPH, MDPI, vol. 19(16), pages 1-18, August.
    2. He Zhang & Jingyi Peng & Rui Wang & Yuanyuan Guo & Jing He & Dahlia Yu & Jianxun Zhang, 2023. "Efficiency and Potential Evaluation to Promote Differentiated Low-Carbon Management in Chinese Counties," IJERPH, MDPI, vol. 20(4), pages 1-19, February.
    3. Quito, Byron & del Río-Rama, María de la Cruz & Álvarez- García, José & Bekun, Festus Victor, 2023. "Spatiotemporal influencing factors of energy efficiency in 43 european countries: A spatial econometric analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).

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