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Research on the Spatial-Temporal Distribution Characteristics and Influencing Factors of Carbon Emission Efficiency in China’s Metal Smelting Industry—Based on the Three-Stage DEA Method

Author

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  • Linan Gao

    (School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China
    Beijing Laboratory of National Economic Security Early-Warning Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Xiaofei Liu

    (Postdoctoral Programme of China Centre for Industrial Security Research, Beijing Jiaotong University, Beijing 100044, China)

  • Xinyi Mei

    (Beijing Laboratory of National Economic Security Early-Warning Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Guangwei Rui

    (Beijing Laboratory of National Economic Security Early-Warning Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Jingcheng Li

    (School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China)

Abstract

The threat of global climate change has encouraged the international community to pay close attention to the levels of greenhouse gases, such as carbon dioxide, in the atmosphere. China has the world’s largest metal smelting industry, which is a major energy-consuming and carbon-emitting industry. Thus, this industry’s low-carbon transition is of great significance. Carbon emission efficiency (CEE) is a key indicator for the metal smelting industry to prioritize sustainable development. This paper applies a three-stage data envelopment analysis model with undesirable outputs to estimate CEE for 30 provinces from 2005 to 2020 in China, and analyzes the influencing factors using a spatial Durbin model. The results show that the CEE level generally improved in all Chinese provinces during the sample period, but the average CEE in the eastern region was 1.05 compared to 1.07 in the western and central regions, with the latter two regions progressing faster in terms of low carbon production capacity. The national average Malmquist–Luenberger (ML) index demonstrates a significant increase in technical efficiency across regions in 2010 and 2017, peaking in 2017. The study also suggests that current green credit and environmental regulations are not effective in promoting CEE improvements in the metal smelting industry, and that existing policies should be modified. Moreover, the spatial regression results indicate that the cross-regional transfer of low-carbon production technologies in China is largely complete. This study provides a more objective evaluation of the CEE levels of metal smelting across China, providing the government with a new perspective to guide the green transformation of energy-intensive industries.

Suggested Citation

  • Linan Gao & Xiaofei Liu & Xinyi Mei & Guangwei Rui & Jingcheng Li, 2022. "Research on the Spatial-Temporal Distribution Characteristics and Influencing Factors of Carbon Emission Efficiency in China’s Metal Smelting Industry—Based on the Three-Stage DEA Method," Sustainability, MDPI, vol. 14(24), pages 1-19, December.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:24:p:16903-:d:1005751
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    as
    1. Wang, Zhao-Hua & Zeng, Hua-Lin & Wei, Yi-Ming & Zhang, Yi-Xiang, 2012. "Regional total factor energy efficiency: An empirical analysis of industrial sector in China," Applied Energy, Elsevier, vol. 97(C), pages 115-123.
    2. Wu, Jie & Li, Mingjun & Zhu, Qingyuan & Zhou, Zhixiang & Liang, Liang, 2019. "Energy and environmental efficiency measurement of China's industrial sectors: A DEA model with non-homogeneous inputs and outputs," Energy Economics, Elsevier, vol. 78(C), pages 468-480.
    3. Xu, Mengmeng & Lin, Boqiang & Wang, Siquan, 2021. "Towards energy conservation by improving energy efficiency? Evidence from China’s metallurgical industry," Energy, Elsevier, vol. 216(C).
    4. Zhao, Pengjun & Zeng, Liangen & Li, Peilin & Lu, Haiyan & Hu, Haoyu & Li, Chengming & Zheng, Mengyuan & Li, Haitao & Yu, Zhao & Yuan, Dandan & Xie, Jinxin & Huang, Qi & Qi, Yuting, 2022. "China's transportation sector carbon dioxide emissions efficiency and its influencing factors based on the EBM DEA model with undesirable outputs and spatial Durbin model," Energy, Elsevier, vol. 238(PC).
    5. Jakob Mayer & Gabriel Bachner & Karl W. Steininger, 2017. "Macroeconomic implications of switching to process-emission-free iron and steel production in Europe," Graz Economics Papers 2017-14, University of Graz, Department of Economics.
    6. Ma, Ding & Fei, Rilong & Yu, Yongsheng, 2019. "How government regulation impacts on energy and CO2 emissions performance in China's mining industry," Resources Policy, Elsevier, vol. 62(C), pages 651-663.
    7. Yang Zhou & Jintao Fu & Ying Kong & Rui Wu, 2018. "How Foreign Direct Investment Influences Carbon Emissions, Based on the Empirical Analysis of Chinese Urban Data," Sustainability, MDPI, vol. 10(7), pages 1-19, June.
    8. Abhinav Bhaskar & Mohsen Assadi & Homam Nikpey Somehsaraei, 2020. "Decarbonization of the Iron and Steel Industry with Direct Reduction of Iron Ore with Green Hydrogen," Energies, MDPI, vol. 13(3), pages 1-23, February.
    9. Wang, Keying & Wu, Meng & Sun, Yongping & Shi, Xunpeng & Sun, Ao & Zhang, Ping, 2019. "Resource abundance, industrial structure, and regional carbon emissions efficiency in China," Resources Policy, Elsevier, vol. 60(C), pages 203-214.
    10. Tian, Xu & Dai, Hancheng & Geng, Yong & Huang, Zhen & Masui, Toshihiko & Fujita, Tsuyoshi, 2017. "The effects of carbon reduction on sectoral competitiveness in China: A case of Shanghai," Applied Energy, Elsevier, vol. 197(C), pages 270-278.
    11. Ke, Jing & Price, Lynn & Ohshita, Stephanie & Fridley, David & Khanna, Nina Zheng & Zhou, Nan & Levine, Mark, 2012. "China's industrial energy consumption trends and impacts of the Top-1000 Enterprises Energy-Saving Program and the Ten Key Energy-Saving Projects," Energy Policy, Elsevier, vol. 50(C), pages 562-569.
    12. Wang, Yan & Shen, Neng, 2016. "Environmental regulation and environmental productivity: The case of China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 758-766.
    13. H. Fried & C. Lovell & S. Schmidt & S. Yaisawarng, 2002. "Accounting for Environmental Effects and Statistical Noise in Data Envelopment Analysis," Journal of Productivity Analysis, Springer, vol. 17(1), pages 157-174, January.
    14. Wen, Huwei & Lee, Chien-Chiang & Zhou, Fengxiu, 2021. "Green credit policy, credit allocation efficiency and upgrade of energy-intensive enterprises," Energy Economics, Elsevier, vol. 94(C).
    15. Lin, Boqiang & Wang, Xiaolei, 2014. "Exploring energy efficiency in China׳s iron and steel industry: A stochastic frontier approach," Energy Policy, Elsevier, vol. 72(C), pages 87-96.
    16. Griffin, Paul W. & Hammond, Geoffrey P., 2019. "Industrial energy use and carbon emissions reduction in the iron and steel sector: A UK perspective," Applied Energy, Elsevier, vol. 249(C), pages 109-125.
    17. Simar, Leopold & Wilson, Paul W., 2007. "Estimation and inference in two-stage, semi-parametric models of production processes," Journal of Econometrics, Elsevier, vol. 136(1), pages 31-64, January.
    18. Solarin, Sakiru Adebola & Al-Mulali, Usama & Musah, Ibrahim & Ozturk, Ilhan, 2017. "Investigating the pollution haven hypothesis in Ghana: An empirical investigation," Energy, Elsevier, vol. 124(C), pages 706-719.
    19. Runde Gu & Chunfa Li & Dongdong Li & Yangyang Yang & Shan Gu, 2022. "The Impact of Rationalization and Upgrading of Industrial Structure on Carbon Emissions in the Beijing-Tianjin-Hebei Urban Agglomeration," IJERPH, MDPI, vol. 19(13), pages 1-16, June.
    20. Truett, Lila J. & Truett, Dale B., 1997. "The Korean metals industry and economic development," Journal of Asian Economics, Elsevier, vol. 8(2), pages 333-347.
    21. Dyer, Caroline H. & Hammond, Geoffrey P. & Jones, Craig I. & McKenna, Russell C., 2008. "Enabling technologies for industrial energy demand management," Energy Policy, Elsevier, vol. 36(12), pages 4434-4443, December.
    22. Kumbhakar,Subal C. & Lovell,C. A. Knox, 2003. "Stochastic Frontier Analysis," Cambridge Books, Cambridge University Press, number 9780521666633.
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