Abstract
The level of development of industrial enterprises is related to a country’s or region’s overall competitiveness. It is critical to assess the green innovation efficiency of regional industrial enterprises scientifically and effectively in order to improve a country’s overall green innovation capability. The green innovation system is divided into three sub-stages in this paper: technology development, economic transformation, and environmental protection. Based on the theory of innovation value chain, a three-stage super-efficiency DEA model of the cooperative game including shared inputs and undesirable outputs is constructed to calculate the overall efficiency, three sub-stages efficiency, and dynamic evolution of green innovation of industrial enterprises in China’s provincial administrative regions and eight economic zones from 2015 to 2019 (divided by the time of ultimate output). The results indicate that (1) in terms of overall efficiency, the efficiency of green innovation is not high, and there are clear regional differences, as evidenced by the following states: the middle reaches of the Yangtze River economic zone > the eastern coastal economic zone > the southern coastal economic zone > the northern coastal economic zone > the northeastern economic zone > the northwestern economic zone > the middle reaches of the Yellow River economic zone, and the overall efficiency of the southwestern economic zone fluctuates around the average level of China; (2) from the standpoint of various stages, economic transformation stage efficiency > overall efficiency > technology development stage efficiency > environmental protection stage efficiency. The improvement of overall efficiency is largely dependent on the high efficiency of the economic transformation stage, but low efficiency in the environmental protection stage results in overall low efficiency; (3) from the perspective of the dynamic evolution trend, the overall efficiency and three sub-stages have been improved to varying degrees. However, due to the low efficiency of the environmental protection stage, there is still a long way to go to achieve the goal of innovation-driven development; (4) based on the classification analysis, it was determined that the green innovation efficiency of industrial enterprises in only a few regions belongs to the “three high innovation type,” which must take targeted measures to improve the inefficient innovation process links.
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Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
References
Adel HM, Saber S (2018) Efficiency evaluation in two-stage data envelopment analysis under a fuzzy environment: a common-weights approach. Appl Soft Comput 72:156–165. https://doi.org/10.1016/j.asoc.2018.07.057
Aigner D, Lovell C, Schmidt P (1977) Formulation and estimation of stochastic frontier production function models. J Econom 6(1):21–37. https://doi.org/10.1016/0304-4076(77)90052-5
Andersen P, Petersen NC (1993) A procedure for ranking efficient units in data envelopment analysis. Manage Sci 39(10):1261–1264. https://doi.org/10.1287/mnsc.39.10.1261
Anis O (2020) Technological innovation and sustainable development: does the stage of development matter? Environ Impact Assess Rev 83:106398. https://doi.org/10.1016/j.eiar.2020.106398
Bian JS, Zhang GQ, Zhou GG (2020) Manufacturer vs. consumer subsidy with green technology investment and environmental concern. Eur J Oper Res 287(3):832–843. https://doi.org/10.1016/j.ejor.2020.05.014
Bronzini R, Piselli P (2016) The impact of R&D subsidies on firm innovation. Res Policy 45(2):442–457. https://doi.org/10.1016/j.respol.2015.10.008
Bulletin on Statistics of National Economic and Social Development, 2020. National Bureau of Statistics of China.
Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2(6):429–444. https://doi.org/10.1016/0377-2217(78)90138-8
Chen X, Liu X, Gong Z, Xie J (2021) Three-stage super-efficiency DEA models based on the cooperative game and its application on the R&D green innovation of the Chinese high-tech industry. Comput Ind Eng 156:107234. https://doi.org/10.1016/j.cie.2021.107234
Chen X, Liu Z, Zhu Q (2018) Performance evaluation of China’s high-tech innovation process: analysis based on the innovation value chain. Technovation 94–95:102094. https://doi.org/10.1016/j.technovation.2018.02.009
Chen Y, Du JA, Sherman HD, Zhu J (2010) DEA model with shared resources and efficiency decomposition. Eur J Oper Res 207(1):339–349. https://doi.org/10.1016/j.ejor.2010.03.031
Chen Y, Yao Z, Zhong K (2022) Do environmental regulations of carbon emissions and air pollution foster green technology innovation: evidence from China’s prefecture-level cities. J Clean Prod 350:131537. https://doi.org/10.1016/j.jclepro.2022.131537
Chen H, Yao M, Chong D (2019) Research on institutional innovation of China’s green insurance investment. J Ind Integr Manag 1:67–70. https://doi.org/10.1142/S2424862219500039
Cook WD, Hababou M (2001) Sales performance measurement in bank branches. Omega 29(4):299–307. https://doi.org/10.1016/S0305-0483(01)00025-1
Deng Z, Lv L, Huang W, Shi Y (2019) A high efficiency and low carbon oriented machining process route optimization model and its application. Int J Precis Eng Manuf-Green Technol 1:23–41. https://doi.org/10.1007/s40684-019-00029-0
Doyle J, Green R (1994) Efficiency and cross-efficiency in DEA: derivations, meanings and uses. J Oper Res Soc 45(5):567–578. https://doi.org/10.1057/jors.1994.84
Dyson RC, Shale EA (2010) Data envelopment analysis, operational research and uncertainty. Oper Res 61(1):25–34. https://doi.org/10.1057/jors.2009.145
Guan JC, Chen KH (2010) Measuring the innovation production process: a cross-region empirical study of China’s high-tech innovations. Technovation 30(5–6):348–358. https://doi.org/10.1016/j.technovation.2010.02.001
Han CJ, Thomas SR, Yang M, Ieromonachou P, Zhang HR (2017) Evaluating R&D investment efficiency in China’s high-tech industry. J High Technol Managem Res 28(1):93–109. https://doi.org/10.1016/j.hitech.2017.04.007
Hatami-Marbini A, Toloo M (2017) An extended multiple criteria data envelopment analysis model. Expert Syst Appl 73:201–219. https://doi.org/10.1016/j.eswa.2016.12.030
Huang H, Wang F, Song M, Balezentis T, Streimikiene D (2021) Green innovations for sustainable development of China: analysis based on the nested spatial panel models. Technol Soc 65(4):101593. https://doi.org/10.1016/j.techsoc.2021.101593
Jin W, Zhang HQ, Liu SS, Zhang HB (2019) Technological innovation, environmental regulation, and green total factor efficiency of industrial water resources. J Clean Prod 211:61–69. https://doi.org/10.1016/j.jclepro.2018.11.172
Ju K, Zhou DJ, Wu JM (2020) Can environmental regulation a “win-win” strategy?——strong porter hypothesis research on China’s industry. J Beijing Inst Technol (Social Sciences Edition) 22(1):21–28. https://doi.org/10.15918/j.jbitss1009-3370.2020.1825
Kao C, Hwang SN (2008) Efficiency decomposition in two-stage data envelopment analysis: an application to non-life insurance companies in Taiwan. Eur J Oper Res 185(1):418–429. https://doi.org/10.1016/j.ejor.2006.11.041
Koronakos G, Sotiros D, Despotis DK, Kritikos MN, Parker BR (2022) Fair efficiency decomposition in network DEA: a compromise programming approach. Socio-Econ Plann Sci 101100. https://doi.org/10.1016/j.seps.2021.101100
Le TH, Wang Y, Liu L, Yang JN, Yung YL, Li GH, Seinfeld JH (2020) Unexpected air pollution with marked emission reductions during the COVID-19 outbreak in China. Science 369:702–706. https://doi.org/10.1126/science.abb7431
Li LB, Liu BL, Liu WL, Chiu YH (2017) Efficiency evaluation of the regional high-tech industry in China: a new framework based on meta-frontier dynamic DEA analysis. Socio Econ Plan Sci 60:24–33. https://doi.org/10.1016/j.seps.2017.02.001
Li HL, Zhu XH, Chen JY, Jiang FT (2019) Environmental regulations, environmental governance efficiency and the green transformation of China’s iron and steel enterprises. Ecol Econ 165:106397. https://doi.org/10.1016/j.ecolecon.2019.106397
Li PZ, Jian LR (2021) Research on innovation process efficiency of regional high-tech industry. Stud Sci Sci. https://doi.org/10.16192/j.cnki.1003-2053.20210720.002
Liang L, Yang F (2006) DEA models for supply chain efficiency evaluation. Ann Oper Res 145(1):35–49. https://doi.org/10.1007/s10479-006-0026-7
Lin TY, Chiu SH, Yang HL (2021) Performance evaluation for regional innovation systems development in China based on the two-stage SBM-DNDEA model. Socioecon Plann Sci 80:101148. https://doi.org/10.1016/j.seps.2021.101148
Luo Q, Miao C, Sun L, Meng X, Duan M (2019) Efficiency evaluation of green technology innovation of China’s strategic emerging industries: an empirical analysis based on Malmquist-data envelopment analysis index. J Clean Prod 238:117782. https://doi.org/10.1016/j.jclepro.2019.117782
Ma J (2015) A two-stage DEA model considering shared inputs and free intermediate measures. Expert Syst Appl 42(9):4339–4347. https://doi.org/10.1016/j.eswa.2015.01.040
Magbouli M, Amirteimoori A, Kordrostami S (2014) Two-stage network structures with undesired outputs: a DEA based approach. Measurement 48:109–118. https://doi.org/10.1016/j.measurement.2013.10.032
Meeusen W, Van Den Broeck J (1977) Efficiency estimation from Cobb-Douglas production functions with composed error. Int Econ Rev 18(2):435–444
Miao C, Duan M, Zuo Y, Wu X (2021) Spatial heterogeneity and evolution trend of regional green innovation efficiency–an empirical study based on panel data of industrial enterprises in China’s provinces. Energy Policy 156:112370. https://doi.org/10.1016/j.enpol.2021.112370
Miao C, Fang D, Sun L, Luo Q, Qian Y (2018) Driving effect of technology innovation on energy utilization efficiency in strategic emerging industries. J Clean Prod 170:1177–1184. https://doi.org/10.1016/j.jclepro.2017.09.225
Michali M, Emrouznejad A, Dehnokhalaji A, Clegg B (2021) Noise-pollution efficiency analysis of European railways: a network DEA model. Transport Res Transport Environ 98:102980. https://doi.org/10.1016/j.trd.2021.102980
Mozaffari MR, Kamyab P, Jablonsky J, Gerami J (2014) Cost and revenue efficiency in DEA-R models. Comput Ind Eng 78:188–194. https://doi.org/10.1016/j.cie.2014.10.001
Mozaffari MR, Mohammadi S, Wanke PF, Correa HL (2021) Towards greener petrochemical production: two-stage network data envelopment analysis in a fully fuzzy environment in the presence of undesirable outputs. Expert Syst Appl 164:113903. https://doi.org/10.1016/j.eswa.2020.113903
Ouyang W, Yang JB (2020) The network energy and environment efficiency analysis of 27 OECD countries: a multiplicative network DEA model. Energy 197:117161. https://doi.org/10.1016/j.energy.2020.117161
Pan SY, Gao M, Kim H, Shah KJ, Pei SL, Chiang PC (2018) Advances and challenges in sustainable tourism toward a green economy. Sci Total Environ 635:452–469. https://doi.org/10.1016/j.scitotenv.2018.04.134
Pan WT, Zhuang ME, Zhou YY, Yang JJ (2021) Research on sustainable development and efficiency of China’s E-Agriculture based on a data envelopment analysis-Malmquist model. Technol Forecast Soc Change 162:120298. https://doi.org/10.1016/j.techfore.2020.120298
Qu JJ, Wang BH, Liu XH (2022) A modified super-efficiency network data envelopment analysis: assessing regional sustainability performance in China. Socio-Econ Plann Sci 101262 Available online. https://doi.org/10.1016/j.seps.2022.101262
Shao X, Liu S, Ran R, Liu Y (2022) Environmental regulation, market demand, and green innovation: spatial perspective evidence from China. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-022-20313-y
Shi XA, Li LS (2019) Green total factor productivity and its decomposition of Chinese manufacturing based on the MML index: 2003–2015. J Clean Prod 222:998–1008. https://doi.org/10.1016/j.jclepro.2019.03.080
Shi Z, Wu F, Huang H, Sun X, Zhang L (2019) Comparing economics, environmental pollution and health efficiency in China. Int J Environ Res Public Health 16(23):4827. https://doi.org/10.3390/ijerph16234827
Simon EB, Krogstie J, Kärrholm M (2020) Compact city planning and development: emerging practices and strategies for achieving the goals of sustainable development. Dev Built Environ 4:100021. https://doi.org/10.1016/j.dibe.2020.100021
Spanos YE, Vonortas NS, Voudouris I (2015) Antecedents of innovation impacts in publicly funded collaborative R&D projects. Technovation 36–37:53–64. https://doi.org/10.1016/j.technovation.2014.07.010
Su S, Zhang F (2020) Modeling the role of environmental regulations in regional green economy efficiency of China: empirical evidence from super efficiency DEA-Tobit model. J Environ Manag 261(C):110227. https://doi.org/10.1016/j.jenvman.2020.110227
Sun J, Li G, Wang Z (2019) Technology heterogeneity and efficiency of China’s circular economic systems: a game meta-frontier DEA approach. Resour Conserv Recycl 146:337–347. https://doi.org/10.1016/j.resconrec.2019.03.046
Sun YH, Ding WW, Yang GC (2022) Green innovation efficiency of China’s tourism industry from the perspective of shared inputs: dynamic evolution and combination improvement paths. Ecol Ind 138:108824. https://doi.org/10.1016/j.ecolind.2022.108824
Walsh JP, Lee YN, Jung T (2016) Win, lose or draw? The fate of patented inventions. Res Policy 45(7):1362–1773. https://doi.org/10.1016/j.respol.2016.03.020
Wang CH, Lu YH, Huang CW, Lee JY (2013) R&D, productivity, and market value: an empirical study from high-technology firms. Omega 41(1):143–155. https://doi.org/10.1016/j.omega.2011.12.011
Wang HR, Cui HR, Zhao QZ (2020a) Effect of green technology innovation on green total factor productivity in China: evidence from spatial durbin model analysis. J Clean Prod 288(1):125624. https://doi.org/10.1016/j.jclepro.2020.125624
Wang Y, Pan JF, Pei RM, Yi BW, Yang GL (2020b) Assessing the technological innovation efficiency of China’s high-tech industries with a two-stage network DEA approach. Socio-Econ Plann Sci 71(C). https://doi.org/10.1016/j.seps.2020.100810
Wang Z, Feng C, Zhang B (2014) An empirical analysis of China’s energy efficiency from both static and dynamic perspectives. Energy 74:322–330. https://doi.org/10.1016/j.energy.2014.06.082
Wang Z, Wang XB (2022) Research on the impact of green finance on energy efficiency in different regions of China based on the DEA-Tobit model. Resour Policy 77:102695. https://doi.org/10.1016/j.resourpol.2022.102695
WB (2020) World development indicators. https://data.worldbank.org.cn/
Wu H (2014) Carbon emission reduction in China’s eight comprehensive economic zones: type division and policy suggestions. http://www.tanpaifang.com/jienenjianpai/2015/0312/43089.html
Wu M (2021) Measurement and spatial statistical analysis of green science and technology innovation efficiency among Chinese provinces. Environ Ecol Stat 28:423–444. https://doi.org/10.1016/10.1007/s10651-021-00491-7
Xiao SA, Ali E, Wenqi Y (2021) Overall efficiency of operational process with undesirable outputs containing both series and parallel processes: a SBM network DEA model. Expert Syst Appl 178:115062. https://doi.org/10.1016/j.eswa.2021.115062
Zhang J, Wang S, Yang P, Fan F, Wang X (2020) Analysis of scale factors on China’s sustainable development efficiency based on three-stage DEA and a double threshold test. Sustainability 12:2225. https://doi.org/10.1016/10.3390/su12062225
Zhao L, Zhu Q, Zhang L (2021) Regulation adaptive strategy and bank efficiency: a network slacks-based measure with shared resources. Eur J Oper Res 295:348–362. https://doi.org/10.1016/j.ejor.2021.02.050
Zhao X, Ma XW, Shang YP, Yang ZH, Shahzad U (2022) Green economic growth and its inherent driving factors in Chinese cities: based on the etafrontier-global-SBM super-efficiency DEA model. Gondwana Res 106:315–328. https://doi.org/10.1016/j.gr.2022.01.013
Zheng WL, Wang JW, Zhang SQ, Syed ARK, Jiang AD, Yang XQ, Xin Z (2021) Evaluation of linkage efficiency between manufacturing industry and logistics industry considering the output of unexpected pollutants. J Air Waste Manag Assoc 71(3):304–314. https://doi.org/10.1080/10962247.2020.1811799
Zhou ZB, Sun WT, Xiao HL, Jin QY, Liu WB (2021) Stochastic leader–follower DEA models for two-stage systems. J Manag Sci Eng 6(4):413–434. https://doi.org/10.1016/j.jmse.2021.02.004
Zhu X, Li H, Chen J, Jiang F (2019) Pollution control efficiency of China’s iron and steel industry: evidence from different manufacturing processes. J Clean Prod 240:118184. https://doi.org/10.1016/j.jclepro.2019.118184
Acknowledgements
We are thankful for the suggestions and efforts from the reviewers and editors, and we also acknowledge the financial support from the General Program of the National Social Science Fund of China (grant no. 18BGL026).
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The General Program of the National Social Science Fund of China “A Study on the Impact of Institutional Gap on the Social Responsibility of Chinese Overseas Investment Enterprise” (grant no. 18BGL026).
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Yueting Zhang: conceptualization, data curation, formal analysis, validation, visualization, writing-original draft, and writing-review and editing. Huaichao Chen: funding acquisition, project administration, resources, supervision, and writing-review and editing. Zhimin He: writing-original draft and writing-review and editing.
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Appendix A
Appendix A
China has been divided into eight economic zones according to a standard of “Strategy and Policy for Coordinated Regional Development” issued by the Development Research Center of the State Council. This standard is put forward on the basis of reference to regional resource endowment, economic status, geographical environment, cultural development and other factors. The specific division scope is shown in Table
7. Due to the inability to obtain necessary data, Tibet is excluded from this study.
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Zhang, Y., Chen, H. & He, Z. Regional green innovation efficiency and dynamic evolution of Chinese industrial enterprises: a three-stage super-efficiency DEA method based on cooperative game. Environ Sci Pollut Res 29, 89387–89410 (2022). https://doi.org/10.1007/s11356-022-21682-0
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DOI: https://doi.org/10.1007/s11356-022-21682-0