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Evaluation Research of Green Innovation Efficiency in China’s Heavy Polluting Industries

Author

Listed:
  • Zhong Fang

    (School of Economics, Fujian Normal University, Fuzhou, Fujian 350007, China)

  • Hua Bai

    (School of Economics, Fujian Normal University, Fuzhou, Fujian 350007, China)

  • Yuriy Bilan

    (Centre for Applied Economic Research, Faculty of Management and Economics, Tomas Bata University in Zlín, nám. Masaryka 5555, 76-001 Zlín, Czech Republic)

Abstract

Recently, green innovation efficiency, which considers innovation and environmental factors, is gradually becoming important for the sustainable development of Chinese heavy polluting industries because of the increasing strictness in China’s environmental regulations. Previous studies ignore the impact of external environmental factors on the efficiency of green industry innovation and fail to explain the complex relationship between environmental and technical efficiency fully. Therefore, a non-radial directional distance function-data envelopment analysis (DDF-DEA) three-stage green innovation efficiency evaluation model was constructed to measure the green innovation efficiency of China’s heavy polluting industries objectively and explore the impact mechanism of external factors. Then, the aforementioned model was used to conduct an empirical test on China’s heavy polluting industries. Results indicate that the green innovation efficiency of heavy polluting industries is generally low in China, and the entire industry is in the transitional stage of “effective innovation but not green.” The uncertainty of the effect of the environmental regulation policy, the over-reliance on external technologies, and the scale diseconomies of industries, which are the key factors in improving the green innovation efficiency of China’s heavy polluting industries, have a significant negative impact on green innovation efficiency. The conclusions of this study can provide a useful reference for China and other emerging markets to formulate reasonable environmental regulations and green transition of heavy polluting industries.

Suggested Citation

  • Zhong Fang & Hua Bai & Yuriy Bilan, 2019. "Evaluation Research of Green Innovation Efficiency in China’s Heavy Polluting Industries," Sustainability, MDPI, vol. 12(1), pages 1-21, December.
    • Handle: RePEc:gam:jsusta:v:12:y:2019:i:1:p:146-:d:301238
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    as
    1. Fare, Rolf & Grosskopf, Shawna, 1996. "Productivity and intermediate products: A frontier approach," Economics Letters, Elsevier, vol. 50(1), pages 65-70, January.
    2. Paul Lanoie & Michel Patry & Richard Lajeunesse, 2008. "Environmental regulation and productivity: testing the porter hypothesis," Journal of Productivity Analysis, Springer, vol. 30(2), pages 121-128, October.
    3. Brown, Marilyn A., 2001. "Market failures and barriers as a basis for clean energy policies," Energy Policy, Elsevier, vol. 29(14), pages 1197-1207, November.
    4. Vlontzos, George & Niavis, Spyros & Manos, Basil, 2014. "A DEA approach for estimating the agricultural energy and environmental efficiency of EU countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 91-96.
    5. Chang, Young-Tae & Zhang, Ning & Danao, Denise & Zhang, Nan, 2013. "Environmental efficiency analysis of transportation system in China: A non-radial DEA approach," Energy Policy, Elsevier, vol. 58(C), pages 277-283.
    6. Giulio Cainelli & Massimiliano Mazzanti & Sandro Montresor, 2012. "Environmental Innovations, Local Networks and Internationalization," Industry and Innovation, Taylor & Francis Journals, vol. 19(8), pages 697-734, November.
    7. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    8. Sueyoshi, Toshiyuki & Goto, Mika, 2011. "DEA approach for unified efficiency measurement: Assessment of Japanese fossil fuel power generation," Energy Economics, Elsevier, vol. 33(2), pages 292-303, March.
    9. Bian, Yiwen & Yang, Feng, 2010. "Resource and environment efficiency analysis of provinces in China: A DEA approach based on Shannon's entropy," Energy Policy, Elsevier, vol. 38(4), pages 1909-1917, April.
    10. Zhou, Yan & Xing, Xinpeng & Fang, Kuangnan & Liang, Dapeng & Xu, Chunlin, 2013. "Environmental efficiency analysis of power industry in China based on an entropy SBM model," Energy Policy, Elsevier, vol. 57(C), pages 68-75.
    11. William Cooper & Kyung Park & Jesus Pastor, 1999. "RAM: A Range Adjusted Measure of Inefficiency for Use with Additive Models, and Relations to Other Models and Measures in DEA," Journal of Productivity Analysis, Springer, vol. 11(1), pages 5-42, February.
    12. Fare, Rolf, et al, 1989. "Multilateral Productivity Comparisons When Some Outputs Are Undesirable: A Nonparametric Approach," The Review of Economics and Statistics, MIT Press, vol. 71(1), pages 90-98, February.
    13. Yongzhong Jiang & Xueli Chen & Vivian Valdmanis & Tomas Baležentis, 2019. "Evaluating Economic and Environmental Performance of the Chinese Industry Sector," Sustainability, MDPI, vol. 11(23), pages 1-17, November.
    14. Klaus Rennings & Christian Rammer, 2009. "Increasing Energy and Resource Efficiency through Innovation: An Explorative Analysis Using Innovation Survey Data," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 59(5), pages 442-459, December.
    15. 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.
    16. Guo, Chuanyin & Abbasi Shureshjani, Roohollah & Foroughi, Ali Asghar & Zhu, Joe, 2017. "Decomposition weights and overall efficiency in two-stage additive network DEA," European Journal of Operational Research, Elsevier, vol. 257(3), pages 896-906.
    17. Popp, David, 2004. "ENTICE: endogenous technological change in the DICE model of global warming," Journal of Environmental Economics and Management, Elsevier, vol. 48(1), pages 742-768, July.
    18. Zhao, Haoran & Guo, Sen & Zhao, Huiru, 2019. "Provincial energy efficiency of China quantified by three-stage data envelopment analysis," Energy, Elsevier, vol. 166(C), pages 96-107.
    19. Unknown, 2016. "Energy for Sustainable Development," Conference Proceedings 253270, Guru Arjan Dev Institute of Development Studies (IDSAsr).
    20. 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.
    21. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
    22. Du, Limin & Hanley, Aoife & Zhang, Ning, 2016. "Environmental technical efficiency, technology gap and shadow price of coal-fuelled power plants in China: A parametric meta-frontier analysis," Resource and Energy Economics, Elsevier, vol. 43(C), pages 14-32.
    23. Zhuang Miao & Tomas Baležentis & Zhihua Tian & Shuai Shao & Yong Geng & Rui Wu, 2019. "Environmental Performance and Regulation Effect of China’s Atmospheric Pollutant Emissions: Evidence from “Three Regions and Ten Urban Agglomerations”," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 74(1), pages 211-242, September.
    24. Levinson, Arik, 1996. "Environmental regulations and manufacturers' location choices: Evidence from the Census of Manufactures," Journal of Public Economics, Elsevier, vol. 62(1-2), pages 5-29, October.
    25. Hamamoto, Mitsutsugu, 2006. "Environmental regulation and the productivity of Japanese manufacturing industries," Resource and Energy Economics, Elsevier, vol. 28(4), pages 299-312, November.
    26. Zhou, P. & Ang, B.W. & Wang, H., 2012. "Energy and CO2 emission performance in electricity generation: A non-radial directional distance function approach," European Journal of Operational Research, Elsevier, vol. 221(3), pages 625-635.
    27. 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.
    28. Song, Malin & Wang, Shuhong & Sun, Jing, 2018. "Environmental regulations, staff quality, green technology, R&D efficiency, and profit in manufacturing," Technological Forecasting and Social Change, Elsevier, vol. 133(C), pages 1-14.
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    1. Ying Sun & Jianzhong Xu, 2021. "Evaluation Model and Empirical Research on the Green Innovation Capability of Manufacturing Enterprises from the Perspective of Ecological Niche," Sustainability, MDPI, vol. 13(21), pages 1-21, October.
    2. Caiming Wang & Jian Li, 2020. "The Evaluation and Promotion Path of Green Innovation Performance in Chinese Pollution-Intensive Industry," Sustainability, MDPI, vol. 12(10), pages 1-22, May.
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    4. Shan Feng & Yawen Kong & Shuguang Liu & Hongwei Zhou, 2022. "Study on the Spatio-Temporal Evolution and Influential Factors of Green Innovation Efficiency in Urban Agglomerations of China," Sustainability, MDPI, vol. 15(1), pages 1-19, December.

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