IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i24p16903-d1005751.html
   My bibliography  Save this article

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

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/24/16903/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/24/16903/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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).
    8. 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.
    9. 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.
    10. 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).
    11. 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.
    12. 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.
    13. 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).
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. 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.
    22. Kumbhakar,Subal C. & Lovell,C. A. Knox, 2003. "Stochastic Frontier Analysis," Cambridge Books, Cambridge University Press, number 9780521666633, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lee, Chi-Chuan & Huang, Tai-Hsin, 2017. "Cost efficiency and technological gap in Western European banks: A stochastic metafrontier analysis," International Review of Economics & Finance, Elsevier, vol. 48(C), pages 161-178.
    2. Wanke, Peter F., 2013. "Physical infrastructure and shipment consolidation efficiency drivers in Brazilian ports: A two-stage network-DEA approach," Transport Policy, Elsevier, vol. 29(C), pages 145-153.
    3. Zhang, Dongyang, 2021. "Marketization, environmental regulation, and eco-friendly productivity: A Malmquist–Luenberger index for pollution emissions of large Chinese firms," Journal of Asian Economics, Elsevier, vol. 76(C).
    4. Iparraguirre, José Luis & Ma, Ruosi, 2015. "Efficiency in the provision of social care for older people. A three-stage Data Envelopment Analysis using self-reported quality of life," Socio-Economic Planning Sciences, Elsevier, vol. 49(C), pages 33-46.
    5. Li, Ke & Lin, Boqiang, 2016. "Impact of energy conservation policies on the green productivity in China’s manufacturing sector: Evidence from a three-stage DEA model," Applied Energy, Elsevier, vol. 168(C), pages 351-363.
    6. Mai, Nhat Chi, 2015. "Efficiency of the banking system in Vietnam under financial liberalization," OSF Preprints qsf6d, Center for Open Science.
    7. Franz R. Hahn, 2007. "Determinants of Bank Efficiency in Europe. Assessing Bank Performance Across Markets," WIFO Studies, WIFO, number 31499.
    8. Shanwei Li & Yongchang Wu & Qi Yu & Xueyuan Chen, 2023. "National Agricultural Science and Technology Parks in China: Distribution Characteristics, Innovation Efficiency, and Influencing Factors," Agriculture, MDPI, vol. 13(7), pages 1-26, July.
    9. Hongwei Liu & Ronglu Yang & Zhixiang Zhou & Dacheng Huang, 2020. "Regional Green Eco-Efficiency in China: Considering Energy Saving, Pollution Treatment, and External Environmental Heterogeneity," Sustainability, MDPI, vol. 12(17), pages 1-19, August.
    10. Ruijing Zheng & Yu Cheng & Haimeng Liu & Wei Chen & Xiaodong Chen & Yaping Wang, 2022. "The Spatiotemporal Distribution and Drivers of Urban Carbon Emission Efficiency: The Role of Technological Innovation," IJERPH, MDPI, vol. 19(15), pages 1-22, July.
    11. Junlong Li & Chuangneng Cai & Feng Zhang, 2020. "Assessment of Ecological Efficiency and Environmental Sustainability of the Minjiang-Source in China," Sustainability, MDPI, vol. 12(11), pages 1-15, June.
    12. Jiandong Chen & Ping Wang & Jixian Zhou & Malin Song & Xinyue Zhang, 2022. "Influencing factors and efficiency of funds in humanitarian supply chains: the case of Chinese rural minimum living security funds," Annals of Operations Research, Springer, vol. 319(1), pages 413-438, December.
    13. Jens Kjærsgaard & Niels Vestergaard & Kristiaan Kerstens, 2009. "Ecological Benchmarking to Explore Alternative Fishing Schemes to Protect Endangered Species by Substitution: The Danish Demersal Fishery in the North Sea," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 43(4), pages 573-590, August.
    14. Angela Stefania Bergantino & Enrico Musso, 2011. "A Multi-step Approach to Model the Relative Efficiency of European Ports: The Role of Regulation and Other Non-discretionary Factors," Chapters, in: Kevin Cullinane (ed.), International Handbook of Maritime Economics, chapter 18, Edward Elgar Publishing.
    15. Qiang Yue & Xicui Chai & Yujie Zhang & Qi Wang & Heming Wang & Feng Zhao & Wei Ji & Yuqi Lu, 2023. "Analysis of iron and steel production paths on the energy demand and carbon emission in China’s iron and steel industry," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(5), pages 4065-4085, May.
    16. Nolwenn Roudaut & Anne Vanhems, 2012. "Explaining firms efficiency in the Ivorian manufacturing sector: a robust nonparametric approach," Journal of Productivity Analysis, Springer, vol. 37(2), pages 155-169, April.
    17. Yuhanis Ladewi & Meiryani Meiryani & Ahmad Syamil & Agustini Agustini & Agustinus Winoto, 2024. "The Relation between Climate Change and Carbon Emission Trading: A Bibliometric Analysis," International Journal of Energy Economics and Policy, Econjournals, vol. 14(1), pages 686-697, January.
    18. Wang, Quan-Jing & Wang, Hai-Jie & Chang, Chun-Ping, 2022. "Environmental performance, green finance and green innovation: What's the long-run relationships among variables?," Energy Economics, Elsevier, vol. 110(C).
    19. Pei Zhao & Junhua Guo & Yang Wang, 2023. "How Does the Digital Economy Affect Green Development?—Evidence from 284 Cities in China," Sustainability, MDPI, vol. 15(15), pages 1-24, July.
    20. Samuele Centorrino & María Pérez‐Urdiales & Boris Bravo‐Ureta & Alan Wall, 2024. "Binary endogenous treatment in stochastic frontier models with an application to soil conservation in El Salvador," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(3), pages 365-382, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:24:p:16903-:d:1005751. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.