IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v15y2018i10p2291-d176688.html
   My bibliography  Save this article

Can Industrial Structural Adjustment Improve the Total-Factor Carbon Emission Performance in China?

Author

Listed:
  • Zhonghua Cheng

    (China Institute of Manufacturing Development, Nanjing University of Information Science & Technology, Nanjing 210044, China
    School of Management Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
    Reading Academy, Nanjing University of Information Science & Technology, Nanjing 210044, China)

  • Xiai Shi

    (China Institute of Manufacturing Development, Nanjing University of Information Science & Technology, Nanjing 210044, China
    School of Economics and Management, Southeast University, Nanjing 210096, China)

Abstract

How to improve the industrial total-factor carbon emission performance (TCPI), or total-factor carbon productivity, through industrial structural adjustment, is crucial to China’s energy conservation and emission reduction and sustainable growth. In this paper, we use a dynamic spatial panel model to empirically analyze the effect of industrial structural adjustment on TCPI of 30 provinces in China from 2000 to 2015. The results show that most of the provinces with high TCPI are located in the eastern coastal areas, while the provinces with relatively low TCPI are to be found in the central and western regions. The spatial auto-correlation tests show that there are significant global spatial auto-correlation and local spatial agglomeration characteristics in TCPI. The regression results of the dynamic spatial panel models show that at the national level, the structure of industrialization, the industrial structure of heavy industrialization, the coal-based energy consumption structure and the endowment structure have significant negative effects on the improvement of TCPI. The expansion of industrial enterprise scale, on the other hand, is conducive to an improvement in TCPI while the effects of foreign direct investment (FDI) structure and ownership structure on TCPI are not significant. At the regional level, there are certain differences in the effects of different types of industrial structural adjustment on TCPI.

Suggested Citation

  • Zhonghua Cheng & Xiai Shi, 2018. "Can Industrial Structural Adjustment Improve the Total-Factor Carbon Emission Performance in China?," IJERPH, MDPI, vol. 15(10), pages 1-20, October.
  • Handle: RePEc:gam:jijerp:v:15:y:2018:i:10:p:2291-:d:176688
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/15/10/2291/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/15/10/2291/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kukenova, Madina & Monteiro, Jose-Antonio, 2008. "Spatial Dynamic Panel Model and System GMM: A Monte Carlo Investigation," MPRA Paper 11569, University Library of Munich, Germany, revised Nov 2008.
    2. 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.
    3. Long, Ruyin & Shao, Tianxiang & Chen, Hong, 2016. "Spatial econometric analysis of China’s province-level industrial carbon productivity and its influencing factors," Applied Energy, Elsevier, vol. 166(C), pages 210-219.
    4. Tan, Zhongfu & Li, Li & Wang, Jianjun & Wang, Jianhui, 2011. "Examining the driving forces for improving China’s CO2 emission intensity using the decomposing method," Applied Energy, Elsevier, vol. 88(12), pages 4496-4504.
    5. Yang, Yuan & Cai, Wenjia & Wang, Can, 2014. "Industrial CO2 intensity, indigenous innovation and R&D spillovers in China’s provinces," Applied Energy, Elsevier, vol. 131(C), pages 117-127.
    6. Yao, Xin & Guo, Chengwen & Shao, Shuai & Jiang, Zhujun, 2016. "Total-factor CO2 emission performance of China’s provincial industrial sector: A meta-frontier non-radial Malmquist index approach," Applied Energy, Elsevier, vol. 184(C), pages 1142-1153.
    7. Nabavieh, Alireza & Gholamiangonabadi, Davoud & Ahangaran, Ali Asghar, 2015. "Dynamic changes in CO2 emission performance of different types of Iranian fossil-fuel power plants," Energy Economics, Elsevier, vol. 52(PA), pages 142-150.
    8. Brock, William A. & Taylor, M. Scott, 2005. "Economic Growth and the Environment: A Review of Theory and Empirics," Handbook of Economic Growth, in: Philippe Aghion & Steven Durlauf (ed.), Handbook of Economic Growth, edition 1, volume 1, chapter 28, pages 1749-1821, Elsevier.
    9. Oh, Dong-hyun, 2010. "A metafrontier approach for measuring an environmentally sensitive productivity growth index," Energy Economics, Elsevier, vol. 32(1), pages 146-157, January.
    10. Feng Dong & Jingyun Li & Yue-Jun Zhang & Ying Wang, 2018. "Drivers Analysis of CO 2 Emissions from the Perspective of Carbon Density: The Case of Shandong Province, China," IJERPH, MDPI, vol. 15(8), pages 1-24, August.
    11. Zhang, Ning & Wei, Xiao, 2015. "Dynamic total factor carbon emissions performance changes in the Chinese transportation industry," Applied Energy, Elsevier, vol. 146(C), pages 409-420.
    12. Jacobs, J.P.A.M. & Ligthart, J.E. & Vrijburg, H., 2009. "Dynamic Panel Data Models Featuring Endogenous Interaction and Spatially Correlated Errors," Other publications TiSEM d473cc67-03f6-4389-9a9f-3, Tilburg University, School of Economics and Management.
    13. Liu, Nan & Ma, Zujun & Kang, Jidong, 2015. "Changes in carbon intensity in China's industrial sector: Decomposition and attribution analysis," Energy Policy, Elsevier, vol. 87(C), pages 28-38.
    14. González, Domingo & Martínez, Manuel, 2012. "Changes in CO2 emission intensities in the Mexican industry," Energy Policy, Elsevier, vol. 51(C), pages 149-163.
    15. Alwyn Young, 2003. "Gold into Base Metals: Productivity Growth in the People's Republic of China during the Reform Period," Journal of Political Economy, University of Chicago Press, vol. 111(6), pages 1220-1261, December.
    16. Fan, Ying & Liu, Lan-Cui & Wu, Gang & Tsai, Hsien-Tang & Wei, Yi-Ming, 2007. "Changes in carbon intensity in China: Empirical findings from 1980-2003," Ecological Economics, Elsevier, vol. 62(3-4), pages 683-691, May.
    17. Wang, Zhaohua & Zhang, Bin & Liu, Tongfan, 2016. "Empirical analysis on the factors influencing national and regional carbon intensity in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 34-42.
    18. Wang, H. & Ang, B.W. & Su, Bin, 2017. "Multiplicative structural decomposition analysis of energy and emission intensities: Some methodological issues," Energy, Elsevier, vol. 123(C), pages 47-63.
    19. Jiangfeng Hu & Zhao Wang & Yuehan Lian & Qinghua Huang, 2018. "Environmental Regulation, Foreign Direct Investment and Green Technological Progress—Evidence from Chinese Manufacturing Industries," IJERPH, MDPI, vol. 15(2), pages 1-14, January.
    20. 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.
    21. Li, Ke & Lin, Boqiang, 2017. "Economic growth model, structural transformation, and green productivity in China," Applied Energy, Elsevier, vol. 187(C), pages 489-500.
    22. Cheng, Zhonghua & Li, Lianshui & Liu, Jun, 2018. "Industrial structure, technical progress and carbon intensity in China's provinces," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2935-2946.
    23. Zhang, Wei & Li, Ke & Zhou, Dequn & Zhang, Wenrui & Gao, Hui, 2016. "Decomposition of intensity of energy-related CO2 emission in Chinese provinces using the LMDI method," Energy Policy, Elsevier, vol. 92(C), pages 369-381.
    24. Zhang, Youguo, 2009. "Structural decomposition analysis of sources of decarbonizing economic development in China; 1992-2006," Ecological Economics, Elsevier, vol. 68(8-9), pages 2399-2405, June.
    25. Zhang, Ning & Choi, Yongrok, 2013. "Total-factor carbon emission performance of fossil fuel power plants in China: A metafrontier non-radial Malmquist index analysis," Energy Economics, Elsevier, vol. 40(C), pages 549-559.
    26. Dong-hyun Oh & Jeong-dong Lee, 2010. "A metafrontier approach for measuring Malmquist productivity index," Empirical Economics, Springer, vol. 38(1), pages 47-64, February.
    27. Rashid Gill, Abid & Viswanathan, Kuperan K. & Hassan, Sallahuddin, 2018. "The Environmental Kuznets Curve (EKC) and the environmental problem of the day," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 1636-1642.
    28. Wang, Qunwei & Su, Bin & Zhou, Peng & Chiu, Ching-Ren, 2016. "Measuring total-factor CO2 emission performance and technology gaps using a non-radial directional distance function: A modified approach," Energy Economics, Elsevier, vol. 56(C), pages 475-482.
    29. Zhi-Fu Mi & Yi-Ming Wei & Bing Wang & Jing Meng & Zhu Liu & Yuli Shan & Jingru Liu & Dabo Guan, 2017. "Socioeconomic impact assessment of China's CO2 emissions peak prior to 2030," CEEP-BIT Working Papers 103, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ruiqing Yuan & Xiangyang Xu & Yanli Wang & Jiayi Lu & Ying Long, 2024. "Evaluating Carbon-Emission Efficiency in China’s Construction Industry: An SBM-Model Analysis of Interprovincial Building Heating," Sustainability, MDPI, vol. 16(6), pages 1-16, March.
    2. Jianshi Wang & Shangkun Yu & Mengcheng Li & Yu Cheng & Chengxin Wang, 2022. "Study of the Impact of Industrial Restructuring on the Spatial and Temporal Evolution of Carbon Emission Intensity in Chinese Provinces—Analysis of Mediating Effects Based on Technological Innovation," IJERPH, MDPI, vol. 19(20), pages 1-18, October.
    3. Zhou, Tao & Huang, Xuhui & Zhang, Ning, 2023. "The effect of innovation pilot on carbon total factor productivity: Quasi-experimental evidence from China," Energy Economics, Elsevier, vol. 125(C).
    4. Chao Bi & Jingjing Zeng, 2019. "Nonlinear and Spatial Effects of Tourism on Carbon Emissions in China: A Spatial Econometric Approach," IJERPH, MDPI, vol. 16(18), pages 1-17, September.

    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. Cheng, Zhonghua & Li, Lianshui & Liu, Jun & Zhang, Huiming, 2018. "Total-factor carbon emission efficiency of China's provincial industrial sector and its dynamic evolution," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 330-339.
    2. Zha, Donglan & Yang, Guanglei & Wang, Qunwei, 2019. "Investigating the driving factors of regional CO2 emissions in China using the IDA-PDA-MMI method," Energy Economics, Elsevier, vol. 84(C).
    3. Zhonghua Cheng & Wenwen Li, 2018. "Independent R and D, Technology Introduction, and Green Growth in China’s Manufacturing," Sustainability, MDPI, vol. 10(2), pages 1-14, January.
    4. Cheng, Zhonghua & Li, Lianshui & Liu, Jun, 2018. "Industrial structure, technical progress and carbon intensity in China's provinces," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2935-2946.
    5. Wei, Yigang & Li, Yan & Wu, Meiyu & Li, Yingbo, 2019. "The decomposition of total-factor CO2 emission efficiency of 97 contracting countries in Paris Agreement," Energy Economics, Elsevier, vol. 78(C), pages 365-378.
    6. Nabavieh, Alireza & Gholamiangonabadi, Davoud & Ahangaran, Ali Asghar, 2015. "Dynamic changes in CO2 emission performance of different types of Iranian fossil-fuel power plants," Energy Economics, Elsevier, vol. 52(PA), pages 142-150.
    7. Wang, Qunwei & Chiu, Yung-Ho & Chiu, Ching-Ren, 2017. "Non-radial metafrontier approach to identify carbon emission performance and intensity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 664-672.
    8. Tan, Xiujie & Choi, Yongrok & Wang, Banban & Huang, Xiaoqi, 2020. "Does China's carbon regulatory policy improve total factor carbon efficiency? A fixed-effect panel stochastic frontier analysis," Technological Forecasting and Social Change, Elsevier, vol. 160(C).
    9. 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.
    10. Xinlin Zhang & Yuan Zhao & Qi Sun & Changjian Wang, 2017. "Decomposition and Attribution Analysis of Industrial Carbon Intensity Changes in Xinjiang, China," Sustainability, MDPI, vol. 9(3), pages 1-16, March.
    11. Wang, Zhenguo & Su, Bin & Xie, Rui & Long, Haiyu, 2020. "China’s aggregate embodied CO2 emission intensity from 2007 to 2012: A multi-region multiplicative structural decomposition analysis," Energy Economics, Elsevier, vol. 85(C).
    12. Lin, Boqiang & Xu, Mengmeng, 2018. "Regional differences on CO2 emission efficiency in metallurgical industry of China," Energy Policy, Elsevier, vol. 120(C), pages 302-311.
    13. Cheng, Zhonghua & Liu, Jun & Li, Lianshui & Gu, Xinbei, 2020. "Research on meta-frontier total-factor energy efficiency and its spatial convergence in Chinese provinces," Energy Economics, Elsevier, vol. 86(C).
    14. Liu, Nan & Ma, Zujun & Kang, Jidong & Su, Bin, 2019. "A multi-region multi-sector decomposition and attribution analysis of aggregate carbon intensity in China from 2000 to 2015," Energy Policy, Elsevier, vol. 129(C), pages 410-421.
    15. Feng, Chao & Zhang, Hua & Huang, Jian-Bai, 2017. "The approach to realizing the potential of emissions reduction in China: An implication from data envelopment analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 859-872.
    16. Lin, Boqiang & Sai, Rockson, 2022. "Sustainable transitioning in Africa: A historical evaluation of energy productivity changes and determinants," Energy, Elsevier, vol. 250(C).
    17. Zaijun Li & Xiang Zheng & Dongqi Sun, 2021. "The Influencing Effects of Industrial Eco-Efficiency on Carbon Emissions in the Yangtze River Delta," Energies, MDPI, vol. 14(23), pages 1-19, December.
    18. You, Jianmin & Zhang, Wei, 2022. "How heterogeneous technological progress promotes industrial structure upgrading and industrial carbon efficiency? Evidence from China's industries," Energy, Elsevier, vol. 247(C).
    19. Shixiong Cheng & Jiahui Xie & De Xiao & Yun Zhang, 2019. "Measuring the Environmental Efficiency and Technology Gap of PM 2.5 in China’s Ten City Groups: An Empirical Analysis Using the EBM Meta-Frontier Model," IJERPH, MDPI, vol. 16(4), pages 1-22, February.
    20. Xu, Chong, 2023. "Towards balanced low-carbon development: Driver and complex network of urban-rural energy-carbon performance gap in China," Applied Energy, Elsevier, vol. 333(C).

    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:jijerp:v:15:y:2018:i:10:p:2291-:d:176688. 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.