IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v10y2018i4p1052-d139244.html
   My bibliography  Save this article

Green Total Factor Productivity Growth and Its Determinants in China’s Industrial Economy

Author

Listed:
  • Chaofan Chen

    (School of Economics and Resource Management, Beijing Normal University, Beijing 100875, China
    School of Statistics, Beijing Normal University, Beijing 100875, China)

  • Qingxin Lan

    (School of International Trade and Economics, University of International Business and Economics, Beijing 100029, China)

  • Ming Gao

    (School of Economics and Resource Management, Beijing Normal University, Beijing 100875, China)

  • Yawen Sun

    (School of Economics and Resource Management, Beijing Normal University, Beijing 100875, China)

Abstract

This paper employs directional distance function (DDF) and the global Malmquist–Luenberger (GML) productivity index to measure the green total factor productivity (GTFP) growth of China’s 36 industrial sectors from 2000 to 2014. Based on this, this paper ascertains the determinants of GTFP from the perspectives of institution, technology, and structure, and the determinant factors that affect GTFP are empirically tested by a dynamic panel data (DPD) model. The research shows that, considering energy consumption and environmental undesirable outputs, the industrial GTFP goes backwards by 0.02% per year on average, and the contributions of GTFP to output growth are far from the target value of 50% in all industrial sectors, which indicates that the growth of industrial economy sacrifices resources and environment to a certain degree. In terms of the determinant factors of GTFP, environmental regulation does improve the GTFP, while environmental regulation is difficult to promote GTFP by the route of technological innovation. Compared with technology importation, the driving effect of independent research and development on GTFP is obvious, especially promoting the GTFP of moderately and lightly polluting industries, while the driving effect in heavily polluting industries is poor. Endowment structure and property right structure play a positive role in improving GTFP, but the impacts of capital structure and energy structure on GTFP are poor.

Suggested Citation

  • Chaofan Chen & Qingxin Lan & Ming Gao & Yawen Sun, 2018. "Green Total Factor Productivity Growth and Its Determinants in China’s Industrial Economy," Sustainability, MDPI, vol. 10(4), pages 1-25, April.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:4:p:1052-:d:139244
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/10/4/1052/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/10/4/1052/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chaofan Chen & Jing Han & Peilei Fan, 2016. "Measuring the Level of Industrial Green Development and Exploring Its Influencing Factors: Empirical Evidence from China’s 30 Provinces," Sustainability, MDPI, vol. 8(2), pages 1-20, February.
    2. Ananda, Jayanath & Hampf, Benjamin, 2015. "Measuring environmentally sensitive productivity growth: An application to the urban water sector," Ecological Economics, Elsevier, vol. 116(C), pages 211-219.
    3. Dong-hyun Oh, 2010. "A global Malmquist-Luenberger productivity index," Journal of Productivity Analysis, Springer, vol. 34(3), pages 183-197, December.
    4. Dariush Khezrimotlagh & Yao Chen, 2018. "Data Envelopment Analysis," International Series in Operations Research & Management Science, in: Decision Making and Performance Evaluation Using Data Envelopment Analysis, chapter 0, pages 217-234, Springer.
    5. Chung, Yeimin & Heshmati, Almas, 2013. "Measurement of Environmentally Sensitive Productivity Growth in Korean Industries," IZA Discussion Papers 7235, Institute of Labor Economics (IZA).
    6. Adam B. Jaffe & Karen Palmer, 1997. "Environmental Regulation And Innovation: A Panel Data Study," The Review of Economics and Statistics, MIT Press, vol. 79(4), pages 610-619, November.
    7. Arabi, Behrouz & Munisamy, Susila & Emrouznejad, Ali & Shadman, Foroogh, 2014. "Power industry restructuring and eco-efficiency changes: A new slacks-based model in Malmquist–Luenberger Index measurement," Energy Policy, Elsevier, vol. 68(C), pages 132-145.
    8. Li, Ke & Lin, Boqiang, 2015. "Measuring green productivity growth of Chinese industrial sectors during 1998–2011," China Economic Review, Elsevier, vol. 36(C), pages 279-295.
    9. Chen, Shiyi & Jefferson, Gary H. & Zhang, Jun, 2011. "Structural change, productivity growth and industrial transformation in China," China Economic Review, Elsevier, vol. 22(1), pages 133-150, March.
    10. 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.
    11. Johnes, Jill, 2006. "Data envelopment analysis and its application to the measurement of efficiency in higher education," Economics of Education Review, Elsevier, vol. 25(3), pages 273-288, June.
    12. Aparicio, Juan & Pastor, Jesus T. & Zofio, Jose L., 2013. "On the inconsistency of the Malmquist–Luenberger index," European Journal of Operational Research, Elsevier, vol. 229(3), pages 738-742.
    13. Arellano, Manuel & Bover, Olympia, 1995. "Another look at the instrumental variable estimation of error-components models," Journal of Econometrics, Elsevier, vol. 68(1), pages 29-51, July.
    14. Fan, Meiting & Shao, Shuai & Yang, Lili, 2015. "Combining global Malmquist–Luenberger index and generalized method of moments to investigate industrial total factor CO2 emission performance: A case of Shanghai (China)," Energy Policy, Elsevier, vol. 79(C), pages 189-201.
    15. Gale Boyd & George Tolley & Joseph Pang, 2002. "Plant Level Productivity, Efficiency, and Environmental Performance of the Container Glass Industry," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 23(1), pages 29-43, September.
    16. 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.
    17. Pastor, Jesus T. & Lovell, C.A. Knox, 2005. "A global Malmquist productivity index," Economics Letters, Elsevier, vol. 88(2), pages 266-271, August.
    18. Tao, Feng & Zhang, Huiqin & Hu, Jun & Xia, X.H., 2017. "Dynamics of green productivity growth for major Chinese urban agglomerations," Applied Energy, Elsevier, vol. 196(C), pages 170-179.
    19. 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.
    20. Hampf, Benjamin & Ananda, Jayanath, 2015. "Measuring environmentally sensitive productivity growth: An application to the urban water sector," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 77008, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    21. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    22. Mundaca, Luis & Markandya, Anil, 2016. "Assessing regional progress towards a ‘Green Energy Economy’," Applied Energy, Elsevier, vol. 179(C), pages 1372-1394.
    23. Watanabe, Michio & Tanaka, Katsuya, 2007. "Efficiency analysis of Chinese industry: A directional distance function approach," Energy Policy, Elsevier, vol. 35(12), pages 6323-6331, December.
    24. Aghayi, Nazila & Maleki, Bentolhoda, 2016. "Efficiency measurement of DMUs with undesirable outputs under uncertainty based on the directional distance function: Application on bank industry," Energy, Elsevier, vol. 112(C), pages 376-387.
    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. Emrouznejad, Ali & Yang, Guo-liang, 2016. "CO2 emissions reduction of Chinese light manufacturing industries: A novel RAM-based global Malmquist–Luenberger productivity index," Energy Policy, Elsevier, vol. 96(C), pages 397-410.
    27. Färe, Rolf & Grosskopf, Shawna & Pasurka, Carl A., 2007. "Environmental production functions and environmental directional distance functions," Energy, Elsevier, vol. 32(7), pages 1055-1066.
    28. Wang, Ke & Wei, Yi-Ming & Zhang, Xian, 2013. "Energy and emissions efficiency patterns of Chinese regions: A multi-directional efficiency analysis," Applied Energy, Elsevier, vol. 104(C), pages 105-116.
    29. Alwyn Young, 1995. "The Tyranny of Numbers: Confronting the Statistical Realities of the East Asian Growth Experience," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 110(3), pages 641-680.
    30. Färe, Rolf & Grosskopf, Shawna, 2010. "Directional distance functions and slacks-based measures of efficiency," European Journal of Operational Research, Elsevier, vol. 200(1), pages 320-322, January.
    31. Zhu, Xuehong & Chen, Ying & Feng, Chao, 2018. "Green total factor productivity of China's mining and quarrying industry: A global data envelopment analysis," Resources Policy, Elsevier, vol. 57(C), pages 1-9.
    32. Cropper, Maureen L & Oates, Wallace E, 1992. "Environmental Economics: A Survey," Journal of Economic Literature, American Economic Association, vol. 30(2), pages 675-740, June.
    33. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
    34. Chambers, Robert G. & Chung, Yangho & Fare, Rolf, 1996. "Benefit and Distance Functions," Journal of Economic Theory, Elsevier, vol. 70(2), pages 407-419, August.
    35. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
    36. 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.
    37. Windmeijer, Frank, 2005. "A finite sample correction for the variance of linear efficient two-step GMM estimators," Journal of Econometrics, Elsevier, vol. 126(1), pages 25-51, May.
    38. Fare, Rolf & Grosskopf, Shawna & Norris, Mary, 1997. "Productivity Growth, Technical Progress, and Efficiency Change in Industrialized Countries: Reply," American Economic Review, American Economic Association, vol. 87(5), pages 1040-1043, December.
    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. Tao, Feng & Zhang, Huiqin & Hu, Jun & Xia, X.H., 2017. "Dynamics of green productivity growth for major Chinese urban agglomerations," Applied Energy, Elsevier, vol. 196(C), pages 170-179.
    2. Emrouznejad, Ali & Yang, Guo-liang, 2016. "CO2 emissions reduction of Chinese light manufacturing industries: A novel RAM-based global Malmquist–Luenberger productivity index," Energy Policy, Elsevier, vol. 96(C), pages 397-410.
    3. Fan, Meiting & Shao, Shuai & Yang, Lili, 2015. "Combining global Malmquist–Luenberger index and generalized method of moments to investigate industrial total factor CO2 emission performance: A case of Shanghai (China)," Energy Policy, Elsevier, vol. 79(C), pages 189-201.
    4. Deshan Li & Rongwei Wu, 2018. "A Dynamic Analysis of Green Productivity Growth for Cities in Xinjiang," Sustainability, MDPI, vol. 10(2), pages 1-13, February.
    5. Li, Hai-ling & Zhu, Xue-hong & Chen, Jin-yu & Jiang, Fei-tao, 2019. "Environmental regulations, environmental governance efficiency and the green transformation of China's iron and steel enterprises," Ecological Economics, Elsevier, vol. 165(C), pages 1-1.
    6. Qin, Quande & Li, Xin & Li, Li & Zhen, Wei & Wei, Yi-Ming, 2017. "Air emissions perspective on energy efficiency: An empirical analysis of China’s coastal areas," Applied Energy, Elsevier, vol. 185(P1), pages 604-614.
    7. Gao, Kang & Yuan, Yijun, 2022. "Spatiotemporal pattern assessment of China’s industrial green productivity and its spatial drivers: Evidence from city-level data over 2000–2017," Applied Energy, Elsevier, vol. 307(C).
    8. Ke Wang & Yujiao Xian & Chia-Yen Lee & Yi-Ming Wei & Zhimin Huang, 2019. "On selecting directions for directional distance functions in a non-parametric framework: a review," Annals of Operations Research, Springer, vol. 278(1), pages 43-76, July.
    9. Ke Li & Malin Song, 2016. "Green Development Performance in China: A Metafrontier Non-Radial Approach," Sustainability, MDPI, vol. 8(3), pages 1-21, March.
    10. Gang Tian & Jian Shi & Licheng Sun & Xingle Long & Benhai Guo, 2017. "Dynamic changes in the energy–carbon performance of Chinese transportation sector: a meta-frontier non-radial directional distance function approach," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 89(2), pages 585-607, November.
    11. Wang, Zhaohua & Feng, Chao, 2015. "Sources of production inefficiency and productivity growth in China: A global data envelopment analysis," Energy Economics, Elsevier, vol. 49(C), pages 380-389.
    12. Zhang, Yijun & Song, Yi, 2020. "Unified efficiency of coal mining enterprises in China: An analysis based on meta-frontier non-radial directional distance functions," Resources Policy, Elsevier, vol. 65(C).
    13. Ke Wang & Yujiao Xian & Yi-Ming Wei & Zhimin Huang, 2016. "Sources of carbon productivity change: A decomposition and disaggregation analysis based on global Luenberger productivity indicator and endogenous directional distance function," CEEP-BIT Working Papers 91, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
    14. 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.
    15. Huijun Li & Jianhua Zhang & Edward Osei & Mark Yu, 2018. "Sustainable Development of China’s Industrial Economy: An Empirical Study of the Period 2001–2011," Sustainability, MDPI, vol. 10(3), pages 1-18, March.
    16. Yang, Jun & Zhang, Tengfei & Sheng, Pengfei & Shackman, Joshua D., 2016. "Carbon dioxide emissions and interregional economic convergence in China," Economic Modelling, Elsevier, vol. 52(PB), pages 672-680.
    17. Weixiang Zhao & Yankun Xu, 2022. "Public Expenditure and Green Total Factor Productivity: Evidence from Chinese Prefecture-Level Cities," IJERPH, MDPI, vol. 19(9), pages 1-27, May.
    18. Emrouznejad, Ali & Yang, Guo-liang, 2016. "A framework for measuring global Malmquist–Luenberger productivity index with CO2 emissions on Chinese manufacturing industries," Energy, Elsevier, vol. 115(P1), pages 840-856.
    19. Juan Du & Yongrui Duan & Jinghua Xu, 2019. "The infeasible problem of Malmquist–Luenberger index and its application on China’s environmental total factor productivity," Annals of Operations Research, Springer, vol. 278(1), pages 235-253, July.
    20. Xi Qin & Xiaoling Wang & Yusen Xu & Yawen Wei, 2019. "Exploring Driving Forces of Green Growth: Empirical Analysis on China’s Iron and Steel Industry," Sustainability, MDPI, vol. 11(4), pages 1-11, February.

    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:10:y:2018:i:4:p:1052-:d:139244. 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.