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Spatio-Temporal Evolution and Drivers of Carbon Emission Efficiency in China’s Iron and Steel Industry

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  • Rongbang Xu

    (School of Economics and Management, China University of Geosciences, Beijing 100083, China)

  • Fujie Yang

    (School of Economics and Management, China University of Geosciences, Beijing 100083, China)

  • Sanmang Wu

    (School of Economics and Management, China University of Geosciences, Beijing 100083, China)

  • Qinwen Xue

    (School of Economics and Management, China University of Geosciences, Beijing 100083, China)

Abstract

Improving the carbon emission efficiency (CEE) of the iron and steel industry (ISI) is crucial for China to achieve the goal of carbon peak and carbon neutrality. This study employed the undesirable SBM and Dagum Gini coefficient to measure the ISI’s CEE and analyzed the spatial heterogeneity among three regions of China. This study also used the Tobit model to clarify the influencing factors. The conclusions show that (1) the CEE in eastern provinces is the highest, the central ones rank second, while the western ones rank the worst; the promoting effect of Technical Change is greater than that of Efficiency Change. (2) ISI’s CEE shows a positive spatial correlation and an apparent spatial heterogeneity. The CEE gap between the regions contributes most to the CEE difference among provinces. The regional CEE gap within the western region is the largest, with a maximum difference of 0.520 in the Dagum Gini coefficient. Furthermore, the total CEE gap shows a narrowing trend from 2009 to 2020, with the Dagum Gini coefficient decreasing from 0.414 in 2009 to 0.357 in 2020. (3) Industrial structure, enterprise scale, foreign direct investment, and technology level positively correlate with ISI’s CEE; the marginal impacts are 0.6711, 0.1203, 0.0572, and 3.5191, respectively. While energy intensity, environmental regulation, and product structure negatively correlate with it, the marginal impacts are 0.0178, 1.4673, and 0.2452, respectively.

Suggested Citation

  • Rongbang Xu & Fujie Yang & Sanmang Wu & Qinwen Xue, 2024. "Spatio-Temporal Evolution and Drivers of Carbon Emission Efficiency in China’s Iron and Steel Industry," Sustainability, MDPI, vol. 16(12), pages 1-22, June.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:12:p:4902-:d:1410825
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    References listed on IDEAS

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    1. Tone, Kaoru & Sahoo, Biresh K., 2003. "Scale, indivisibilities and production function in data envelopment analysis," International Journal of Production Economics, Elsevier, vol. 84(2), pages 165-192, May.
    2. Ekkehart Schlicht, 2016. "Directed Technical Change and Capital Deepening: A Reconsideration of Kaldor's Technical Progress Function," Metroeconomica, Wiley Blackwell, vol. 67(1), pages 119-151, February.
    3. Dong-hyun Oh, 2010. "A global Malmquist-Luenberger productivity index," Journal of Productivity Analysis, Springer, vol. 34(3), pages 183-197, December.
    4. Gene M. Grossman & Alan B. Krueger, 1995. "Economic Growth and the Environment," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 110(2), pages 353-377.
    5. Wang, Guofeng & Deng, Xiangzheng & Wang, Jingyu & Zhang, Fan & Liang, Shiqi, 2019. "Carbon emission efficiency in China: A spatial panel data analysis," China Economic Review, Elsevier, vol. 56(C), pages 1-1.
    6. Bin PENG, 2017. "Can China’s Pollution-Fighting Efforts Be a Model for Other Developing Countries?," East Asian Policy (EAP), World Scientific Publishing Co. Pte. Ltd., vol. 9(03), pages 96-103, July.
    7. Hasanbeigi, Ali & Morrow, William & Sathaye, Jayant & Masanet, Eric & Xu, Tengfang, 2013. "A bottom-up model to estimate the energy efficiency improvement and CO2 emission reduction potentials in the Chinese iron and steel industry," Energy, Elsevier, vol. 50(C), pages 315-325.
    8. Arens, Marlene & Worrell, Ernst & Schleich, Joachim, 2012. "Energy intensity development of the German iron and steel industry between 1991 and 2007," Energy, Elsevier, vol. 45(1), pages 786-797.
    9. Huayong Niu & Zhishuo Zhang & Yao Xiao & Manting Luo & Yumeng Chen, 2022. "A Study of Carbon Emission Efficiency in Chinese Provinces Based on a Three-Stage SBM-Undesirable Model and an LSTM Model," IJERPH, MDPI, vol. 19(9), pages 1-19, April.
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