Skip to main content

Advertisement

Log in

Green innovation efficiency across China’s 30 provinces: estimate, comparison, and convergence

  • Original Article
  • Published:
Mitigation and Adaptation Strategies for Global Change Aims and scope Submit manuscript

Abstract

Considering government and market failure of environmental regulation to combat increasing GHG (greenhouse gas) emissions, green innovation can mitigate pollution through production processes and clean production. This paper aims to investigate endogenous green innovation efficiency and its convergence across China’s 30 provinces from 2004 to 2014. Due to factor endowment heterogeneity, it is important to explore the convergence of green innovation efficiency among China’s different regions, which can compare green innovation efficiency spatially and propose scientific policy implications for regions with relatively weaker green innovation efficiency. Green innovation efficiency is evaluated through epsilon-based measure (EBM) global Malmquist-Luenberger (ML) in order to overcome the demerits of radial model and slacks-based measure (SBM). Panel unit root test is implemented to explore the convergence of green innovation efficiency across different provinces of China, which addresses the invalid inference of conventional β convergence. The empirical analysis revealed that green innovation efficiency in the east is the highest among four regions of China. Unit root test of panel data revealed that the northeast had the highest convergence among China’s four regions. It is important to enhance green innovation capacity, and expand knowledge spillover of green innovation technology in order to mitigate GHG emissions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. https://apps.ipcc.ch/report/authors/report.authors.php?q=37&p=.

References

  • Aldieri L, Carlucci F, Cirà A, Ioppolo G, Vinci CP (2019) Is green innovation an opportunity or a threat to employment? An empirical analysis of three main industrialized areas: the USA, Japan and Europe. J Clean Prod 214:758–766

    Google Scholar 

  • Aloise PG, Macke J (2017) Eco-innovations in developing countries: the case of Manaus free trade zone (Brazil). J Clean Prod 168:30–38

    Google Scholar 

  • Annala CN, Chen S (2011) Convergence of state and local fiscal policies: an application of panel unit root test. J Econ Econ Educ Res 12(1):27

    Google Scholar 

  • Arranz N, Arroyabe MF, Molina-García A, Fernandez DAJC (2019) Incentives and inhibiting factors of eco-innovation in the Spanish firms. J Clean Prod 220:167–176

    Google Scholar 

  • Barassi MR, Cole MA, Elliott RJR (2008) Stochastic divergence or convergence of per capita carbon dioxide emissions: re-examining the evidence. Environ Resour Econ 40(1):121–137

    Google Scholar 

  • Barro R, Sala-i-Martín X (1992) Convergence. J Polit Econ 100:223–251

  • Beyaert A, Camacho M (2008) TAR panel unit root tests and real convergence. Rev Dev Econ 12(3):668–681

    Google Scholar 

  • Bhattacharya M, Inekwe JN, Sadorsky P, Saha A (2018) Convergence of energy productivity across Indian states and territories. Energy Econ 74:427–440

    Google Scholar 

  • Cai YF, Chang TY, Inglesi-Lotz R (2018) Asymmetric persistence in convergence for carbon dioxide emissions based on quantile unit root test with Fourier function. Energy 161:470–481

    Google Scholar 

  • Casu B, Ferrari A, Girardone C, Wilsonet JOS (2016) Integration, productivity and technological spillovers: evidence for eurozone banking industries. Eur J Oper Res 255(3):971–983

    Google Scholar 

  • Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2(6):429–444

    Google Scholar 

  • Chen JN (2015) http://news.sohu.com/20150307/n409460582.shtml.

  • Chen XH, Li CQ, Ji HL, Bai SZ, Zhang GR (2013) Spatial conditional β convergence analysis of society-wide energy efficiency based on technological diffusion. Chin Pop Resour Environ 23(8):7–13

    Google Scholar 

  • Cheng Y, Yin Q (2016) Study on the regional difference of green innovation efficiency in China—an empirical analysis based on the panel data. Proceedings of the 6th International Asia Conference on Industrial Engineering and Management Innovation. Atlantis Press:69–78

  • Chung YH, Färe R, Grosskopf S (1997) Productivity and undesirable outputs: a directional distance function approach. J Environ Manag 51:229–240

    Google Scholar 

  • Churchill SA, Inekwe J, Ivanovski K (2018) Conditional convergence in per capita carbon emissions since 1900. Appl Energy 228:916–927

    Google Scholar 

  • Claudia K (2005) Induced technological change in a multi-regional, multi-sectoral, integrated assessment model (WIAGEM) impact assessment of climate policy strategies. Ecol Econ 54:293–305

    Google Scholar 

  • Cooper WW, Seiford LM, Tone K (2007) Data envelopment analysis: a comprehensive text with models, applications. Springer, References and DEA-Solver Software

    Google Scholar 

  • Duan HB, Zhu L, Fan Y (2015) Modelling the evolutionary paths of multiple carbon-free energy technologies with policy incentives. Environ Model Assess 20:55–69

    Google Scholar 

  • Duan HB, Zhang GP, Wang SY, Fan Y (2019) Integrated benefit-cost analysis of China’s optimal adaptation and targeted mitigation. Ecol Econ 160:76–86

    Google Scholar 

  • Evans P (1998) Using panel data to evaluate growth theories. Int Econ Rev 39:295–306

    Google Scholar 

  • Evans P, Karras G (1996) Convergence revisited. J Monet Econ 37:249–265

    Google Scholar 

  • Färe R, Grosskopf S, Pausurka CAJ (2007) Environmental production functions and environmental directional distance functions. Energy 32:1055–1066

    Google Scholar 

  • Farrell MJ (1957) The measurement of productive efficiency. J R Stat Soc 120(3):253–281

    Google Scholar 

  • Feng Y, Wang X, Du W, Wu H, Wang J (2019) Effects of environmental regulation and FDI on urban innovation in China: a spatial Durbin econometric analysis. J Clean Prod 235:210–224

    Google Scholar 

  • Ghisetti C, Quatraro F (2017) Green technologies and environmental productivity: a cross-sectoral analysis of direct and indirect effects in Italian regions. Ecol Econ 132:1–13

    Google Scholar 

  • Goulder LH, Schneider SH (1999) Induced technological change and the attractiveness of CO2 abatement policies. Resour Energy Econ 21(3–4):211–253

    Google Scholar 

  • Grazia C, Nicoletta C, Cédric G, Muge O (2014) Technological pervasiveness and variety of innovators in Green ICT: a patent-based analysis. Res Policy 43:1827–1839

    Google Scholar 

  • Grossman GM, Helpman E (1993) Innovation and growth in the global economy. The MIT Press.

  • Hall BH, Helmers C (2013) Innovation and diffusion of clean/green technology: can patent commons help? J Environ Econ Manag 66(1):33–51

    Google Scholar 

  • Han L, Han BT, Shi XP, Su B, Lv X, Lei X (2018) Energy efficiency convergence across countries in the context of China’s Belt and Road initiative. Appl Energy 213:112–122

    Google Scholar 

  • Herrerias MJ (2013) The environmental convergence hypothesis: carbon dioxide emissions according to the source of energy. Energy Policy 61(10):1140–1150

    Google Scholar 

  • Hu X (2016) Research on China’s provincial environmental total factor productivity calculation, convergence and influencing factors. Jiangxi University of Finance and Economics

  • Karakaya,E., Hidalgo,A., Nuur,C..Diffusion of eco-innovations: a review, Renewable and Sustainable Energy Reviews,2014,33: 392-399.

  • Kim T, Maskus KE, Oh KY (2009) The effects of patents on productivity growth in Korean manufacturing. Pac Econ Rev 13(4):137–154

    Google Scholar 

  • Lambertini L, Poyago-Theotoky J, Tampieri A (2017) Cournot competition and ‘green’ innovation: an inverted-u relationship. Energy Econ 68:116–123

    Google Scholar 

  • Levin A, Lin CF, Chu CSJ(2002) Unit Root Tests in Panel Data: Asymptotic and Finite-Sample Properties. Journal of econometrics, 108, 1-24.

  • Li XS, Zhu JP (2013) Innovation efficiency and convergence research on China’s provincial industrial enterprises. J Appl Stat Manag 32(6):1090–1099

    Google Scholar 

  • Lin PC, Huang HC (2012) Convergence in income inequality? Evidence from panel unit root tests with structural breaks. Empir Econ 43:153–174

    Google Scholar 

  • Liu MG (2017) Research on spatial distribution and convergence of green innovation efficiency in regional innovation system. J Ind Technol Econ 282(4):10–18

    Google Scholar 

  • Liu ZM, Ma SC, Ma WT (2017) The research on innovation efficiency of pharmaceutical manufacturing and its convergence based on dynamic network SBM model. J Ind Technol Econ 284(6):63–69

    Google Scholar 

  • Long XL, Chen YQ, Du JG, Oh KY, Han IS (2017a) Environmental innovation and its impact on economic and environmental performance: evidence from Korean-owned firms in China. Energy Policy 107:131–137

    Google Scholar 

  • Long XL, Chen YQ, Du JG, Oh KY, Han IS, Yan JH (2017b) The effect of environmental innovation behavior on economic and environmental performance of 182 Chinese firms. J Clean Prod 166:1274–1282

    Google Scholar 

  • Long XL, Sun M, Cheng FX, Zhang JJ (2017c) Convergence analysis of eco-efficiency of China’s cement manufacturers through unit root test of panel data. Energy 134:709–717

    Google Scholar 

  • Long XL, Chen B, Byounggu P (2018a) Effect of 2008’s Beijing Olympic Games on environmental efficiency of 268 China’s cities. J Clean Prod 172:1423–1432

    Google Scholar 

  • Long XL, Wu C, Zhang JJ, Zhang J (2018b) Environmental efficiency for 192 thermal power plants in the Yangtze River Delta considering heterogeneity: a metafrontier directional slacks-based measure approach. Renew Sust Energ Rev 82:3962–3971

    Google Scholar 

  • Luo YS, Long XL, Wu C,Zhang JJ(2017)Decoupling CO2 emissions from economic growth in agricultural sector across 30 Chinese provinces from 1997 to 2014. Journal of Cleaner Production, 159: 220-228.

  • Mensah CN, Long XL, Dauda L, Boamah KB, Salman M, Appiah-Twum F, Tachie A K(2019)Technological innovation and green growth in the Organization for Economic Cooperation and Development economies, Journal of Cleaner Production,

  • Ma HL, Ding YQ, Wang L (2017) Measurement and convergence analysis of green water utilization efficiency. J Nat Res 32(3):406–417. https://doi.org/10.1016/j.jclepro.2019.118204

  • Pan XF, Liu FC (2010) Research on industrial enterprise’s innovation efficiency in China based on regional comparison. Manag Rev 22(2):59–64

    Google Scholar 

  • Pang RZ, Li P (2011) Transformation performance of China’s industrial growth pattern. J Quant Tech Econ 9:34–46

    Google Scholar 

  • Pastor JT, Lovell CAK (2005) A global Malmquist productivity index. Econ Lett 88(2):266–271

    Google Scholar 

  • Popp D (2002) Induced innovation and energy prices. Am Econ Rev 92(1):160–180

    Google Scholar 

  • Ren Y, Niu CK, Niu T, Yao XL (2014) The theoretical model and empirical analysis of green innovation efficiency. Management World 7:176–177

    Google Scholar 

  • Reyer G (2007) Measuring the value of induced technological change. Energy Policy 35:5287–5297

    Google Scholar 

  • Robalino-López A, García-Ramos JE, Golpe AA, Mena-Nieto A (2016) CO2 emissions convergence among 10 South American countries. A study of Kaya components(1980-2010). Carbon Management 7(1-2):1–12

    Google Scholar 

  • Romer P (1986) Increasing returns and long-run growth. J Polit Econ 99:1002–1037

    Google Scholar 

  • Shephard RW (1970) Theory of cost and production functions. Princeton University Press

  • Song ML, Tao J, Wang SH (2015) FDI, technology spillovers and green innovation in China: analysis based on Data Envelopment Analysis. Ann Oper Res 228(1):47–64

    Google Scholar 

  • Stahlke T (2019) The impact of the Clean Development Mechanism on developing countries’ commitment to mitigate climate change and its implications for the future. Mitig Adapt Strateg Glob Chang. https://doi.org/10.1007/s11027-019-09863-8

  • Sun C, Ma T, Xu M (2018) Exploring the prospects of cooperation in the manufacturing industries between India and China: a perspective of embodied energy in India-China trade. Energy Policy 113:643–650

    Google Scholar 

  • Tian G, Shi J, Sun L, Long X, Guo B (2017) Dynamic changes in the energy-carbon performance of Chinese transportation sector: a meta-frontier non-radial directional distance function approach. Nat Hazards 1:1–23

    Google Scholar 

  • Tone K (2001) A Slacks-based measure of efficiency in data envelopment analysis. J Oper Res 130(3):498–509

    Google Scholar 

  • Tone K, Tsutsui M (2010) An epsilon-based measure of efficiency in DEA – a third pole of technical efficiency. Eur J Oper Res 207(3):1554–1563

    Google Scholar 

  • Tu ZG (2008) The coordination of industrial growth with environment and resource. Econ Res 2:93–105

    Google Scholar 

  • Wang W, Fan D (2012) Influential factors and convergence of total factor energy efficiency in China based on the Malmqulist-Luenberger index. Resources Science 34(10):1816–1824

    Google Scholar 

  • Wang Y, Wang J (2019) Does industrial agglomeration facilitate environmental performance: new evidence from urban China? J Environ Manag 248:109244

    Google Scholar 

  • Wang ZP, Tao CQ, Shen PY (2014) Regional green technical efficiency with its influencing factors analysis based on ecological footprint. Chin Pop Resour Environ 1:35–40

    Google Scholar 

  • Wang QW, Hang Y, Sun LC, Zengyao Zhao ZY (2016a) Two-stage innovation efficiency of new energy enterprises in China: a non-radial DEA approach. Technol Forecast Soc Chang 112:254–261

    Google Scholar 

  • Wang QW, Su B, Zhou P, Chiu CR (2016b) Measuring total-factor CO2 emission performance and technology gaps using a non-radial directional distance function: a modified approach. Energy Econ 56:475–482

    Google Scholar 

  • Wang QW, Hang Y, Hu JL, Chiu CR (2018) An alternative metafrontier framework for measuring the heterogeneity of technology. Nav Res Logist 65(5):427–445

    Google Scholar 

  • Wang, K., Zhang, J., Geng, Y., Xiao, L., Xu, Z., Rao, Y., Zhou, X.. Differential spatial-temporal responses of carbon dioxide emissions to economic development: empirical evidence based on spatial analysis .Mitigation and Adaptation Strategies for Global Change,2019, https://doi.org/10.1007/s11027-019-09876-3.

  • Westerlund J, Basher SA (2008) Testing for convergence in carbon dioxide emissions using a century of panel data. Environ Resour Econ 40(1):109–120

    Google Scholar 

  • Wu, J.L. http://news.sciencenet.cn/htmlnews/2012/8/267652.shtm. 2012

  • Xia D, Chen B, Zheng Z (2015) Relationships among circumstance pressure, green technology selection and firm performance. J Clean Prod 106:487–496

    Google Scholar 

  • Xu JX, Lin LM, Huang SW, Zheng YF (2015) Analysis of the regional technical innovation efficiency and its convergence. J Fuj Agric Fores Univ 18(2):31–35

    Google Scholar 

  • Yang L, Hu XZ (2010) Analysis on regional difference and convergence of the efficiency of China’s green economy based on DEA. Economist 2:46–54

    Google Scholar 

  • Yang F, Yang M (2015) Analysis on China’s eco-innovations: regulation context, intertemporal change and regional differences. Eur J Oper Res 247(3):1003–1012

    Google Scholar 

  • Yavuz NC, Yilanci V (2013) Convergence in per capita carbon dioxide emissions among G7countries: a TAR panel unit root approach. Environ Resour Econ 54(2):283–291

    Google Scholar 

  • Zhang N, Wang B, Liu Z (2016) Carbon emissions dynamics, efficiency gains, and technological innovation in China’s industrial sectors. Energy 99:10–19

    Google Scholar 

  • Zhang Y, Shen L, Shuai C, Bian J, Zhu M, Tan Y, Ye G (2019) How is the environmental efficiency in the process of dramatic economic development in the Chinese cities? Ecol Indic 98:349–362

    Google Scholar 

Download references

Acknowledgements

This work received financial support from the National Natural Science Foundation of China (Nos. 71911540483, 71603105, 71673230, 71803068, and 71303199), Natural Science Foundation of Jiangsu, China (No. SBK2016042936), and China Postdoctoral Science Foundation (No.2017M610051, 2018T110054).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xingle Long.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Long, X., Sun, C., Wu, C. et al. Green innovation efficiency across China’s 30 provinces: estimate, comparison, and convergence. Mitig Adapt Strateg Glob Change 25, 1243–1260 (2020). https://doi.org/10.1007/s11027-019-09903-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11027-019-09903-3

Keywords

Navigation