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Green technology innovation efficiency of energy-intensive industries in China from the perspective of shared resources: Dynamic change and improvement path

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  • Zhu, Lin
  • Luo, Jian
  • Dong, Qingli
  • Zhao, Yang
  • Wang, Yunyue
  • Wang, Yong

Abstract

Energy-intensive industries are high-energy-consumption and high-pollution industries, and their green technology innovation efficiency deserves in-depth investigation. This paper explores the efficiency of green technology innovation and its combinatorial improvement path in energy-intensive industries from 2005-2015 with a two-stage data envelopment analysis model based on shared and additional input resources and fuzzy-set qualitative comparative analysis. The results indicate that (1) the overall efficiency of green technology innovation in energy-intensive industries as a whole showed a fluctuating upward trend from 2005 to 2015, benefiting from the improvement of technology R&D efficiency and achievement conversion efficiency; (2) there is high industry heterogeneity in the green technology innovation capacity of energy-intensive industries, and the difference in the average efficiency in green technology innovation between the industries with the strongest innovation strength and that with the weakest is as high as 0.615; (3) small-scale enterprises’ strategies should be based on foreign scientific research support or environmental regulations, supplemented by a small amount of industry-university-research cooperation. Large-scale enterprises’ strategies should be based on foreign scientific research support and industry-university-research cooperation, supplemented by appropriate environmental regulations and government investment. This study provides a reference for the formulation of green technology innovation development strategies for energy-intensive industries.

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  • Zhu, Lin & Luo, Jian & Dong, Qingli & Zhao, Yang & Wang, Yunyue & Wang, Yong, 2021. "Green technology innovation efficiency of energy-intensive industries in China from the perspective of shared resources: Dynamic change and improvement path," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
  • Handle: RePEc:eee:tefoso:v:170:y:2021:i:c:s004016252100322x
    DOI: 10.1016/j.techfore.2021.120890
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    References listed on IDEAS

    as
    1. Wu, Jie & Zhu, Qingyuan & Ji, Xiang & Chu, Junfei & Liang, Liang, 2016. "Two-stage network processes with shared resources and resources recovered from undesirable outputs," European Journal of Operational Research, Elsevier, vol. 251(1), pages 182-197.
    2. Yung-ho Chiu & Chin-wei Huang & Yu-Chuan Chen, 2012. "The R&D value-chain efficiency measurement for high-tech industries in China," Asia Pacific Journal of Management, Springer, vol. 29(4), pages 989-1006, December.
    3. Guan, Jiancheng & Chen, Kaihua, 2012. "Modeling the relative efficiency of national innovation systems," Research Policy, Elsevier, vol. 41(1), pages 102-115.
    4. Yu, Ming-Miin & Lin, Erwin T.J., 2008. "Efficiency and effectiveness in railway performance using a multi-activity network DEA model," Omega, Elsevier, vol. 36(6), pages 1005-1017, December.
    5. Jiandong Chen & Chong Xu & Qianjiao Xie & Malin Song, 2020. "Net primary productivity‐based factors of China's carbon intensity: A regional perspective," Growth and Change, Wiley Blackwell, vol. 51(4), pages 1727-1748, December.
    6. Costa-Campi, M.T. & Duch-Brown, N. & García-Quevedo, J., 2014. "R&D drivers and obstacles to innovation in the energy industry," Energy Economics, Elsevier, vol. 46(C), pages 20-30.
    7. Daniel Palacios-Marqués & Salvador Roig-Dobón & Irene Comeig, 2017. "Background factors to innovation performance: results of an empirical study using fsQCA methodology," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(5), pages 1939-1953, September.
    8. Zhou, Xiaoxiao & Cai, Ziming & Tan, Kim Hua & Zhang, Linling & Du, Juntao & Song, Malin, 2021. "Technological innovation and structural change for economic development in China as an emerging market," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    9. 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.
    10. Song, Malin & Zhu, Shuai & Wang, Jianlin & Zhao, Jiajia, 2020. "Share green growth: Regional evaluation of green output performance in China," International Journal of Production Economics, Elsevier, vol. 219(C), pages 152-163.
    11. Ghosh, Ranjan & Kathuria, Vinish, 2016. "The effect of regulatory governance on efficiency of thermal power generation in India: A stochastic frontier analysis," Energy Policy, Elsevier, vol. 89(C), pages 11-24.
    12. Li, Lan-bing & Liu, Bing-lian & Liu, Wei-lin & Chiu, Yung-Ho, 2017. "Efficiency evaluation of the regional high-tech industry in China: A new framework based on meta-frontier dynamic DEA analysis," Socio-Economic Planning Sciences, Elsevier, vol. 60(C), pages 24-33.
    13. Jian-Wen Fang & Yung-ho Chiu, 2017. "Research on Innovation Efficiency and Technology Gap in China Economic Development," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(02), pages 1-22, April.
    14. Seifert, Stefan & Cullmann, Astrid & von Hirschhausen, Christian, 2016. "Technical efficiency and CO2 reduction potentials — An analysis of the German electricity and heat generating sector," Energy Economics, Elsevier, vol. 56(C), pages 9-19.
    15. Griliches, Zvi, 1980. "R & D and the Productivity Slowdown," American Economic Review, American Economic Association, vol. 70(2), pages 343-348, May.
    16. Zhu, Lin & Wang, Yong & Shang, Peipei & Qi, Lin & Yang, Guangchun & Wang, Ying, 2019. "Improvement path, the improvement potential and the dynamic evolution of regional energy efficiency in China: Based on an improved nonradial multidirectional efficiency analysis," Energy Policy, Elsevier, vol. 133(C).
    17. Gao, Yanyan & Zang, Leizhen & Roth, Antoine & Wang, Puqu, 2017. "Does democracy cause innovation? An empirical test of the popper hypothesis," Research Policy, Elsevier, vol. 46(7), pages 1272-1283.
    18. Yang, Zhenbing & Shao, Shuai & Li, Chengyu & Yang, Lili, 2020. "Alleviating the misallocation of R&D inputs in China's manufacturing sector: From the perspectives of factor-biased technological innovation and substitution elasticity," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    19. Albino, Vito & Ardito, Lorenzo & Dangelico, Rosa Maria & Messeni Petruzzelli, Antonio, 2014. "Understanding the development trends of low-carbon energy technologies: A patent analysis," Applied Energy, Elsevier, vol. 135(C), pages 836-854.
    20. Chen, Xiafei & Liu, Zhiying & Zhu, Qingyuan, 2018. "Performance evaluation of China's high-tech innovation process: Analysis based on the innovation value chain," Technovation, Elsevier, vol. 74, pages 42-53.
    21. Skarmeas, Dionysis & Leonidou, Constantinos N. & Saridakis, Charalampos, 2014. "Examining the role of CSR skepticism using fuzzy-set qualitative comparative analysis," Journal of Business Research, Elsevier, vol. 67(9), pages 1796-1805.
    22. Li, Yanfei & Ji, Qiang & Zhang, Dayong, 2020. "Technological catching up and innovation policies in China: What is behind this largely successful story?," Technological Forecasting and Social Change, Elsevier, vol. 153(C).
    23. Poorkavoos, Meysam & Duan, Yanqing & Edwards, John S. & Ramanathan, Ramakrishnan, 2016. "Identifying the configurational paths to innovation in SMEs: A fuzzy-set qualitative comparative analysis," Journal of Business Research, Elsevier, vol. 69(12), pages 5843-5854.
    24. Guan, Jian Cheng & Yam, Richard C.M. & Mok, Chiu Kam & Ma, Ning, 2006. "A study of the relationship between competitiveness and technological innovation capability based on DEA models," European Journal of Operational Research, Elsevier, vol. 170(3), pages 971-986, May.
    25. Zha, Donglan & Kavuri, Anil Savio & Si, Songjian, 2017. "Energy biased technology change: Focused on Chinese energy-intensive industries," Applied Energy, Elsevier, vol. 190(C), pages 1081-1089.
    26. Wu, Yinyin & Wang, Ping & Liu, Xin & Chen, Jiandong & Song, Malin, 2020. "Analysis of regional carbon allocation and carbon trading based on net primary productivity in China," China Economic Review, Elsevier, vol. 60(C).
    27. Hashimoto, Akihiro & Haneda, Shoko, 2008. "Measuring the change in R&D efficiency of the Japanese pharmaceutical industry," Research Policy, Elsevier, vol. 37(10), pages 1829-1836, December.
    28. Wang, Qunwei & Hang, Ye & Sun, Licheng & Zhao, Zengyao, 2016. "Two-stage innovation efficiency of new energy enterprises in China: A non-radial DEA approach," Technological Forecasting and Social Change, Elsevier, vol. 112(C), pages 254-261.
    29. Chen, Yao & Du, Juan & David Sherman, H. & Zhu, Joe, 2010. "DEA model with shared resources and efficiency decomposition," European Journal of Operational Research, Elsevier, vol. 207(1), pages 339-349, November.
    30. Song, Malin & Xie, Qianjiao & Tan, Kim Hua & Wang, Jianlin, 2020. "A fair distribution and transfer mechanism of forest tourism benefits in China," China Economic Review, Elsevier, vol. 63(C).
    31. Mozas-Moral, Adoración & Bernal-Jurado, Enrique & Medina-Viruel, Miguel Jesús & Fernández-Uclés, Domingo, 2016. "Factors for success in online social networks: An fsQCA approach," Journal of Business Research, Elsevier, vol. 69(11), pages 5261-5264.
    32. Chia‐Hung Sun & Kaliappa P. Kalirajan, 2005. "Gauging The Sources Of Growth Of High‐Tech And Low‐Tech Industries: The Case Of Korean Manufacturing," Australian Economic Papers, Wiley Blackwell, vol. 44(2), pages 170-185, June.
    33. Wang, H. & Ang, B.W. & Wang, Q.W. & Zhou, P., 2017. "Measuring energy performance with sectoral heterogeneity: A non-parametric frontier approach," Energy Economics, Elsevier, vol. 62(C), pages 70-78.
    34. Bi, Kexin & Huang, Ping & Wang, Xiangxiang, 2016. "Innovation performance and influencing factors of low-carbon technological innovation under the global value chain: A case of Chinese manufacturing industry," Technological Forecasting and Social Change, Elsevier, vol. 111(C), pages 275-284.
    35. Wurlod, Jules-Daniel & Noailly, Joëlle, 2018. "The impact of green innovation on energy intensity: An empirical analysis for 14 industrial sectors in OECD countries," Energy Economics, Elsevier, vol. 71(C), pages 47-61.
    36. Wang, Miao & Feng, Chao, 2020. "The impacts of technological gap and scale economy on the low-carbon development of China's industries: An extended decomposition analysis," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    37. Haibo Zhou & Hanhui Hu, 2017. "Sustainability Evaluation of Railways in China Using a Two-Stage Network DEA Model with Undesirable Outputs and Shared Resources," Sustainability, MDPI, vol. 9(1), pages 1-23, January.
    38. Li, Xibao, 2009. "China's regional innovation capacity in transition: An empirical approach," Research Policy, Elsevier, vol. 38(2), pages 338-357, March.
    39. Chen, Jiandong & Gao, Ming & Mangla, Sachin Kumar & Song, Malin & Wen, Jie, 2020. "Effects of technological changes on China's carbon emissions," Technological Forecasting and Social Change, Elsevier, vol. 153(C).
    40. Chun, Hyunbae & Kim, Jung-Wook & Lee, Jason, 2015. "How does information technology improve aggregate productivity? A new channel of productivity dispersion and reallocation," Research Policy, Elsevier, vol. 44(5), pages 999-1016.
    41. Jie Wu & Beibei Xiong & Qingxian An & Jiasen Sun & Huaqing Wu, 2017. "Total-factor energy efficiency evaluation of Chinese industry by using two-stage DEA model with shared inputs," Annals of Operations Research, Springer, vol. 255(1), pages 257-276, August.
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