Skip to main content
Log in

The spatial and temporal evolution of provincial eco-efficiency in China based on SBM modified three-stage data envelopment analysis

  • Research Article
  • Published:
Environmental Science and Pollution Research Aims and scope Submit manuscript

Abstract

Eco-efficiency plays a significant role in expressing how efficient the economic activity consumes nature’s goods and services. To accurately measure eco-efficiency, the method slack-based measure modified three-stage data envelopment analysis (DEA) is adopted to evaluate environmental conditions in China’s 30 provinces from year 2004 to 2016. This study treats carbon emissions and three industrials wastes as undesirable outputs and excludes the influences from external environment and random errors when make adjustments. Based on the results, this study makes the following conclusions: Firstly, industrial structure, trade openness, and population have negative effects on eco-efficiency while technology investment, urbanization process, foreign direct investment, and fiscal decentralization have positive effects on eco-efficiency. Secondly, the eco-efficiency for most provinces after adjusted is lower than the pre-adjusted, which indicates the overestimation in eco-efficiency when using traditional approaches. Thirdly, the eco-efficiency in China showed a clear geographical step distribution, with the highest eco-efficiency in the east area, followed by the central, northwest, and southwest regions.

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

Similar content being viewed by others

Notes

  1. http://reports.weforum.org/global-energy-architecture-performance-index-2017/.

  2. http://www.stats.gov.cn/tjsj/ndsj/.

  3. http://olap.epsnet.com.cn/index.html.

References

  • Angulo-Meza L, González-Araya M, Iriarte A et al (2019) A multiobjective DEA model to assess the eco-efficiency of agricultural practices within the CF + DEA method. Comput Electron Agric 161:151–161

  • Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2:429e444. https://doi.org/10.1016/0377-2217(78)90138-8

    Article  Google Scholar 

  • Coelli T (1998) A multi-stage methodology for the solution of orientated DEA models. Oper Res Lett 23(3-5):143–149

  • Cui Y, Huang G, Yin Z (2015) Estimating regional coal resource efficiency in China using three-stage DEA and bootstrap DEA models. Int J Min Sci Technol 25:861–864

    Article  Google Scholar 

  • Ebrahimnejad A, Tavana M, Lotfi FH, Shahverdi R, Yousefpour M (2014) A three-stage data envelopment analysis model with application to banking industry. Measurement 49:308–319

    Article  Google Scholar 

  • Egilmez G, Gumus S, Kucukvar M, Tatari O (2016) A fuzzy data envelopment analysis framework for dealing with uncertainty impacts of input–output life cycle assessment models on eco-efficiency assessment. J Clean Prod 129:622–636

    Article  Google Scholar 

  • Färe R, Grosskopf S, Lovell C A K, et al (1989) Multilateral productivity comparisons when some outputs are undesirable: a nonparametric approach. Rev Econ Stat 71:90–98

  • Farrell M J (1957) The measurement of productive efficiency. J Royal Stat Soc Series A (General) 120:253–281

  • Fried HO, Lovell CAK, Schmidt SS et al (2002) J Prod Anal 17:157. https://doi.org/10.1023/A:1013548723393

    Article  Google Scholar 

  • Jia S, Wang C, Li Y, Zhang F, Liu W (2017) The urbanization efficiency in Chengdu City: an estimation based on a three-stage DEA model. Phys Chem Earth Parts A/B/C 101:59–69

    Article  Google Scholar 

  • Li H, He H, Shan J, Cai J (2019) Innovation efficiency of semiconductor industry in China: a new framework based on generalized three-stage DEA analysis. Socio-Econ Plan Sci 66:136–148

    Article  Google Scholar 

  • Li H, Zhang J, Wang C, Wang Y, Coffey V (2018) An evaluation of the impact of environmental regulation on the efficiency of technology innovation using the combined DEA model: a case study of Xi’an, China. Sustain Cities Soc 42:355–369

    Article  Google Scholar 

  • Lin B, Ge J (2019) Carbon sinks and output of China’s forestry sector: an ecological economic development perspective. Sci Total Environ 655:1169–1180

    Article  CAS  Google Scholar 

  • Liu J, Zhang J, Fu Z (2017a) Tourism eco-efficiency of Chinese coastal cities – analysis based on the DEA-Tobit model. Ocean Coast Manag 148:164–170

    Article  Google Scholar 

  • Liu X, Chu J, Yin P, Sun J (2017b) DEA cross-efficiency evaluation considering undesirable output and ranking priority: a case study of eco-efficiency analysis of coal-fired power plants. J Clean Prod 142:877–885

    Article  Google Scholar 

  • Lorenzo-Toja Y, Vázquez-Rowe I, Chenel S, Marín-Navarro D, Moreira MT, Feijoo G (2015) Eco-efficiency analysis of Spanish WWTPs using the LCA + DEA method. Water Res 68:651–666

    Article  CAS  Google Scholar 

  • Lu X, Xu C (2019) The difference and convergence of total factor productivity of inter-provincial water resources in China based on three- stage DEA-Malmquist index model. Sustain Comput Inform Syst 22:75–83

    Google Scholar 

  • Ma X, Li Y, Zhang X, Wang C, Li Y, Dong B, Gu Y (2018a) Research on the ecological efficiency of the Yangtze River Delta region in China from the perspective of sustainable development of the economy-energy-environment (3E) system. Environ Sci Pollut Res 25:29. https://doi.org/10.1007/s11356-018-2852-y

    Article  Google Scholar 

  • Ma X, Wang C, Yu Y, Li Y, Dong B, Zhang X, Niu X, Yang Q, Chen R, Li Y, Gu Y (2018b) Ecological efficiency in China and its influencing factors—a super-efficient SBM Metafrontier-Malmquist-Tobit model study. Environ Sci Pollut Res 25(21):20880–20898. https://doi.org/10.1007/s11356-018-1949-7

    Article  Google Scholar 

  • Mavi RK, Saen RF, Goh M (2019) Joint analysis of eco-efficiency and eco-innovation with common weights in two-stage network DEA: a big data approach. Technol Forecasting Social Change 144:553–562

  • Mickwitz P, Melanen M, Rosenström U, Seppälä J (2006) Regional eco-efficiency indicators – a participatory approach. J Clean Prod 14:1603–1611

    Article  Google Scholar 

  • Moutinho V, Fuinhas JA, Marques AC, Santiago R (2018a) Assessing eco-efficiency through the DEA analysis and decoupling index in the Latin America countries. J Clean Prod 205:512–524

    Article  Google Scholar 

  • Moutinho V, Mara M, Margarita R, José V (2018b) Advanced scoring method of eco-efficiency in European cities. Environ Sci Pollut Res 25.2:1637–1654. https://doi.org/10.1007/s11356-017-0540-y

    Article  CAS  Google Scholar 

  • Schaltegger S, Sturm A. (1990) Ökologische rationalität: ansatzpunkte zur ausgestaltung von ökologieorientierten managementinstrumenten. Die Unternehmung 44:273–290

  • Schmidheiny S, Timberlake L (1992) Changing course: A global business perspective on development and the environment. MIT press

  • Shyu J, Chiang T (2012) Measuring the true managerial efficiency of bank branches in Taiwan: a three-stage DEA analysis. Expert Syst Appl 39:11494–11502

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Tone K (2003) Dealing with undesirable outputs in DEA: a slacks-based measure (SBM) approach. GRIPS Research Report Series, 2003

  • Torregrossa D, Marvuglia A, Leopold U (2018) A novel methodology based on LCA + DEA to detect eco-efficiency shifts in wastewater treatment plants. Ecol Indic 94:7–15

    Article  Google Scholar 

  • Wang K, Yu S, Zhang W (2013) China’s regional energy and environmental efficiency: a DEA window analysis based dynamic evaluation. Math Comput Model 58:1117–1127

    Article  CAS  Google Scholar 

  • Wang S, Fang C, Guan X, Pang B, Ma H (2014) Urbanisation, energy consumption, and carbon dioxide emissions in China: a panel data analysis of China’s provinces. Appl Energy 136:738–749

    Article  Google Scholar 

  • Wang X, Ding H, Liu L (2019) Eco-efficiency measurement of industrial sectors in China: a hybrid super-efficiency DEA analysis. J Clean Prod 229:53–64

    Article  Google Scholar 

  • Wu Y, Chen Z, Xia P (2018) An extended DEA-based measurement for eco-efficiency from the viewpoint of limited preparation. J Clean Prod 195:721–733

    Article  Google Scholar 

  • Yin K, Wang R, An Q, Yao L, Liang J (2014) Using eco-efficiency as an indicator for sustainable urban development: a case study of Chinese provincial capital cities. Ecol Indic 36:665–671

    Article  Google Scholar 

  • Yu Y, Huang J, Zhang N (2019) Modeling the eco-efficiency of Chinese prefecture-level cities with regional heterogeneities: a comparative perspective. Ecol Model 402:1–17

    Article  Google Scholar 

  • Zhang J, Li H, Xia B, Skitmore M (2018) Impact of environment regulation on the efficiency of regional construction industry: a 3-stage Data Envelopment Analysis (DEA). J Clean Prod 200:770–780

    Article  Google Scholar 

  • Zhang J, Liu Y, Chang Y, Zhang L (2017) Industrial eco-efficiency in China: a provincial quantification using three-stage data envelopment analysis. J Clean Prod 143:238–249

    Article  Google Scholar 

  • Zhang J, Wu G, Zhang J (2004) Estimation of China's inter-provincial physical capital stock: 1952-2000. Econ Res 10:35-44. (In Chinese)

  • Zhao H, Guo S, Zhao H (2019) Provincial energy efficiency of China quantified by three-stage data envelopment analysis. Energy 166:96–107

    Article  Google Scholar 

  • Zhao Y, Wang S, Zhou C (2016) Understanding the relation between urbanization and the eco-environment in China’s Yangtze River Delta using an improved EKC model and coupling analysis. Sci Total Environ 571:862–875

    Article  CAS  Google Scholar 

  • Zhou X, Xu Z, Chai J, Yao L, Wang S, Lev B (2019) Efficiency evaluation for banking systems under uncertainty: a multi-period three-stage DEA model. Omega 85:68–82

    Article  Google Scholar 

  • Zhu Q, Wu J, Li X, Xiong B (2017) China’s regional natural resource allocation and utilization: a DEA-based approach in a big data environment. J Clean Prod 142:809–818

    Article  Google Scholar 

Download references

Funding

This study is funded by the Shenzhen Municipal Development and Reform Commission, Shenzhen Environmental Science and New Energy Technology Engineering Laboratory, Grant No. SDRC [2016]172.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ying Kong.

Additional information

Responsible editor: Philippe Garrigues

Publisher’s note

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

Appendix

Appendix

Table 5 Eco-efficiency in first step
Table 6 Eco-efficiency in third step

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, Y., Kong, Y. & Zhang, T. The spatial and temporal evolution of provincial eco-efficiency in China based on SBM modified three-stage data envelopment analysis. Environ Sci Pollut Res 27, 8557–8569 (2020). https://doi.org/10.1007/s11356-019-07515-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11356-019-07515-7

Keywords

Navigation