Skip to main content

Advertisement

SpringerLink
Log in

The average environmental efficiency technique and its application to Chinese provincial panel data

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

Abstract

In this study, we propose average environmental efficiency, a more comprehensive, fair, comparable, and robust environmental efficiency measurement considering all projection directions to the efficient frontier, and then it is used to evaluate the environmental efficiency of Chinese provinces from 2006 to 2017. Furthermore, we investigate the most influential factors of regional environmental efficiency via a feasible generalized least squares regression approach. The empirical results show that only nine Chinese provinces have average environmental efficiency greater than the national average, implying that two-thirds of the provinces still have much room for improvement. Additionally, environmental efficiency disparities exist between provinces and between four larger geographical areas. The east area achieved the best environmental efficiency over the studied period, better than the whole country, followed in order by the west area, central area, and northeast area. Moreover, we find that the energy consumption structure, government intervention, and economic openness significantly and negatively influence regional environmental efficiency. Finally, we provide policy implications in terms of energy consumption structure optimization, government supervision, and foreign investment introduction while considering the local conditions in different provinces.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data availability

The datasets analyzed during the current study are available from the corresponding author on reasonable request.

References

  • Afzalinejad, M. (2021). Evaluating radial efficiency considering environmental factors: a generalization of classical DEA. Measurement, 179, 109497.

  • Banker RD, Charnes A, Cooper WW (1984) Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manag Sci 30(9):1078–1092

    Article  Google Scholar 

  • Beltrán-Esteve M, Picazo-Tadeo AJ (2015) Assessing environmental performance trends in the transport industry: eco-innovation or catching-up? Energy Econ 51:570–580

    Article  Google Scholar 

  • Bhattarai M, Hammig M (2001) Institutions and the environmental Kuznets curve for deforestation: a crosscountry analysis for Latin America Africa and Asia. World Develop 29(6):995–1010

    Article  Google Scholar 

  • Cecchini L, Venanzi S, Pierri A, Chiorri M (2018) Environmental efficiency analysis and estimation of CO2 abatement costs in dairy cattle farms in Umbria (Italy): a SBM-DEA model with undesirable output. J Clean Prod 197:895–907

    Article  Google Scholar 

  • Chambers RG, Chung Y, Färe R (1996) Benefit and distance functions. J Econ Theory 70(2):407–419

    Article  Google Scholar 

  • Chambers RG, Chung Y, Färe R (1998) Profit, directional distance functions, and Nerlovian efficiency. J Optim Theory Appl 98(2):351–364

    Article  Google Scholar 

  • Chang YT, Zhang N, Danao D, Zhang N (2013) Environmental efficiency analysis of transportation system in China: a non-radial DEA approach. Energy Policy 58:277–283

    Article  Google Scholar 

  • Chen N, Xu L, Chen Z (2017) Environmental efficiency analysis of the Yangtze River Economic Zone using super efficiency data envelopment analysis (SEDEA) and tobit models. Energy 134:659–671

    Article  Google Scholar 

  • Chen Y, Xu JT (2019) An assessment of energy efficiency based on environmental constraints and its influencing factors in China. Environ Sci Pollut Res 26(17):16887–16900

    Article  Google Scholar 

  • Chen YJ, Li P, Lu Y (2018) Career concerns and multitasking local bureaucrats: evidence of a target-based performance evaluation system in China. J Develop Econ 133:84–101

    Article  Google Scholar 

  • Chen, Z., Kourtzidis, S., Tzeremes, P., & Tzeremes, N. (2020). A robust network DEA model for sustainability assessment: an application to Chinese Provinces. Oper Res 1–28.

  • Cheng G (2014) Data envelopment analysis: Methods and MaxDEA software, 1st edn. Intellectual Property Press, Beijing ((Chapter 4))

    Google Scholar 

  • Cheng G, Zervopoulos PD (2014) Estimating the technical efficiency of health care systems: a cross-country comparison using the directional distance function. Eur J Oper Res 238(3):899–910

    Article  Google Scholar 

  • Chung YH, Färe R, Grosskopf S (1997) Productivity and undesirable outputs: a directional distance function approach. J Environ Manag 51(3):229–240

    Article  Google Scholar 

  • Copeland BR, Taylor MS (1994) North-South trade and the environment. Q J Econ 109(3):755–787

    Article  Google Scholar 

  • Copeland BR, Taylor MS (2004) Trade, growth, and the environment. J Econ Lit 42(1):7–71

    Article  Google Scholar 

  • Crompton P, Wu Y (2005) Energy consumption in China: past trends and future directions. Energy Econ 27(1):195–208

    Article  Google Scholar 

  • Cui Q, Li Y (2020) A cross efficiency distinguishing method to explore the cooperation degree in dynamic airline environmental efficiency. Transp Policy 99:31–43

    Article  CAS  Google Scholar 

  • Dickey DA, Fuller WA (1979) Distribution of the estimators for autoregressive time series with a unit root. J Am Stat Assoc 74(366a):427–431

    Article  Google Scholar 

  • Du K, Lu H, Yu K (2014) Sources of the potential CO2 emission reduction in China: a nonparametric metafrontier approach. Appl Energy 115:491–501

    Article  Google Scholar 

  • Fang, Z., Chang, Y., & Shigeyuki, H. (2017). Energy and human capital: a driver or drag for economic growth. Singapore Econ Rev

  • Färe R, Grosskopf S (2004) Modeling undesirable factors in efficiency evaluation: comment. Eur J Oper Res 157(1):242–245

    Article  Google Scholar 

  • Färe, R., & Grosskopf, S. (2006). New directions: efficiency and productivity (Vol. 3). Springer Sci Bus Media

  • Färe R, Grosskopf S, Weber WL (2006) Shadow prices and pollution costs in US agriculture. Ecol Econ 56(1):89–103

    Article  Google Scholar 

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

  • Färe R, Grosskopf S, Pasurka CA Jr (2007) Environmental production functions and environmental directional distance functions. Energy 32(7):1055–1066

    Article  Google Scholar 

  • Goldsmith, R. W. (1951). A perpetual inventory of national wealth. In Studies in Income and Wealth, Volume 14 (pp. 5–73). NBER.

  • Halkos GE, Polemis ML (2018) The impact of economic growth on environmental efficiency of the electricity sector: a hybrid window DEA methodology for the USA. J Environ Manag 211:334–346

    Article  Google Scholar 

  • Halkos GE, Tzeremes NG (2013) A conditional directional distance function approach for measuring regional environmental efficiency: evidence from UK regions. Eur J Oper Res 227(1):182–189

    Article  Google Scholar 

  • Hatzigeorgiou E, Polatidis H, Haralambopoulos D (2008) CO2 emissions in Greece for 1990–2002: a decomposition analysis and comparison of results using the Arithmetic Mean Divisia Index and Logarithmic Mean Divisia Index techniques. Energy 33(3):492–499

    Article  CAS  Google Scholar 

  • Kaika D, Zervas E (2013) The environmental Kuznets curve (EKC) theory—part A: concept, causes and the CO2 emissions case. Energy Policy 62:1392–1402

    Article  Google Scholar 

  • Koçak E, Kınacı H, Shehzad K (2021) Environmental efficiency of disaggregated energy R&D expenditures in OECD: a bootstrap DEA approach. Environ Sci Pollut Res 28(15):19381–19390

    Article  Google Scholar 

  • Kong Y, Zhao T, Yuan R, Chen C (2019) Allocation of carbon emission quotas in Chinese provinces based on equality and efficiency principles. J Clean Prod 211:222–232

    Article  Google Scholar 

  • Kounetas K (2015) Heterogeneous technologies, strategic groups and environmental efficiency technology gaps for European countries. Energy Policy 83:277–287

    Article  Google Scholar 

  • Kumar S, Khanna M (2009) Measurement of environmental efficiency and productivity: a cross-country analysis. Environ Develop Econ 14(4):473–495

    Article  Google Scholar 

  • Lahdelma R, Salminen P (2006) Stochastic multicriteria acceptability analysis using the data envelopment model. Eur J Oper Res 170(1):241–252

    Article  Google Scholar 

  • Lee H, Choi Y (2018) Greenhouse gas performance of Korean local governments based on non-radial DDF. Technol Forecast Soc Change 135:13–21

    Article  Google Scholar 

  • Levin A, Lin CF, Chu CSJ (2002) Unit root tests in panel data: asymptotic and finite-sample properties. J Econ 108(1):1–24

    Article  Google Scholar 

  • Li K, Fang L, He L (2018) How urbanization affects China’s energy efficiency: a spatial econometric analysis. J Clean Prod 200:1130–1141

    Article  Google Scholar 

  • Li K, Lin B (2016) Impact of energy technology patents in China: evidence from a panel cointegration and error correction model. Energy Policy 89:214–223

    Article  Google Scholar 

  • Li M, Wang Q (2014) International environmental efficiency differences and their determinants. Energy 78:411–420

    Article  Google Scholar 

  • Li, Y., Li, J., Gong, Y., Wei, F., & Huang, Q. (2020a). CO2 emission performance evaluation of Chinese port enterprises: a modified meta-frontier non-radial directional distance function approach. Trans Res Part D: Trans Environ 89, 102605.

  • Li Y, Lin TY, Chiu YH, Cen H, Lin YN (2021) Efficiency assessment of coal energy and non-coal energy under bound dynamic DDF DEA. Environ Sci Pollut Res 28(16):20093–20110

    Article  CAS  Google Scholar 

  • Li, Y., Zhang, Q., Wang, L., & Liang, L. (2020b). Regional environmental efficiency in China: an empirical analysis based on entropy weight method and non-parametric models. J Clean Prod 276, 124147.

  • Lin S, Sun J, Marinova D, Zhao D (2018) Evaluation of the green technology innovation efficiency of China’s manufacturing industries: DEA window analysis with ideal window width. Technol Anal Strat Manag 30(10):1166–1181

    Article  Google Scholar 

  • Liu, H., Zhang, Z., Zhang, T., & Wang, L. (2020). Revisiting China’s provincial energy efficiency and its influencing factors. Energy, 208, 118361.

  • Liu, X., Ji, X., Zhang, D., Yang, J., & Wang, Y. (2019). How public environmental concern affects the sustainable development of Chinese cities: an empirical study using extended DEA models. J Environ Manag 251, 109619.

  • Liu Z, Guan D, Crawford-Brown D, Zhang Q, He K, Liu J (2013) A low-carbon road map for China. Nature 500(7461):143–145

    Article  CAS  Google Scholar 

  • Lozano S, Soltani N (2020) Efficiency assessment using a multidirectional DDF approach. Int Trans Oper Res 27(4):2064–2080

    Article  Google Scholar 

  • Ma B (2015) Does urbanization affect energy intensities across provinces in China? Long-run elasticities estimation using dynamic panels with heterogeneous slopes. Energy Econ 49:390–401

    Article  Google Scholar 

  • Ma X, Zhao X, Zhang L, Zhou Y, Chen H (2021) Spatial-temporal characteristics and influencing factors of atmospheric environmental efficiency in China. Environ Sci Pollut Res 28(10):12428–12440

    Article  CAS  Google Scholar 

  • Mahmoudi, R., Emrouznejad, A., Shetab-Boushehri, S. N., & Hejazi, S. R. (2020). The origins, development and future directions of data envelopment analysis approach in transportation systems. Soc Econ Plan Sci 69, 100672.

  • Mandal SK, Madheswaran S (2010) Environmental efficiency of the Indian cement industry: an interstate analysis. Energy Policy 38(2):1108–1118

    Article  CAS  Google Scholar 

  • Mardani A, Zavadskas EK, Streimikiene D, Jusoh A, Khoshnoudi M (2017) A comprehensive review of data envelopment analysis (DEA) approach in energy efficiency. Renew Sustain Energy Rev 70:1298–1322

    Article  Google Scholar 

  • Mavi NK, Mavi RK (2019) Energy and environmental efficiency of OECD countries in the context of the circular economy: common weight analysis for Malmquist productivity index. J Environ Manag 247:651–661

    Article  Google Scholar 

  • Meng F, Su B, Thomson E, Zhou D, Zhou P (2016) Measuring China’s regional energy and carbon emission efficiency with DEA models: a survey. Appl Energy 183:1–21

    Article  Google Scholar 

  • Montalbano P, Nenci S (2019) Energy efficiency, productivity and exporting: firm-level evidence in Latin America. Energy Econ 79:97–110

    Article  Google Scholar 

  • Moutinho V, Madaleno M, Robaina M (2017) The economic and environmental efficiency assessment in EU cross-country: evidence from DEA and quantile regression approach. Ecol Indic 78:85–97

    Article  Google Scholar 

  • National Bureau of Statistics of People’s Republic of China. (2021a). China Statistical Yearbook 2021. http://www.stats.gov.cn/tjsj/ndsj/2021/indexch.htm.

  • National Bureau of Statistics of People’s Republic of China. (2021b). Statistical Bulletin of People’s Republic of China on the 2020 National Economic and Social Development, accessed February 28, 2021. http://www.stats.gov.cn/tjsj/zxfb/202102/t20210227_1814154.html.

  • National Bureau of Statistics of People’s Republic of China. Statistical system and classification standards, accessed June 19, 2020. http://www.stats.gov.cn/tjzs/cjwtjd/201308/t20130829_74318.html.

  • Oh DH (2010) A global Malmquist-Luenberger productivity index. J Prod Anal 34(3):183–197

    Article  Google Scholar 

  • Ouyang, W., & Yang, J. B. (2020). The network energy and environment efficiency analysis of 27 OECD countries: a multiplicative network DEA model. Energy, 117161.

  • Pablo-Romero MDP, Sánchez-Braza A (2015) Productive energy use and economic growth: energy, physical and human capital relationships. Energy Econ 49:420–429

    Article  Google Scholar 

  • Ramli NA, Munisamy S, Arabi B (2013) Scale directional distance function and its application to the measurement of eco-efficiency in the manufacturing sector. Ann Oper Res 211(1):381–398

    Article  Google Scholar 

  • Ray SC (2008) The directional distance function and measurement of super-efficiency: an application to airlines data. J Oper Res Soc 59(6):788–797

    Article  Google Scholar 

  • Salim R, Yao Y, Chen GS (2017) Does human capital matter for energy consumption in China? Energy Econ 67:49–59

    Article  Google Scholar 

  • Salo A, Punkka A (2011) Ranking intervals and dominance relations for ratio-based efficiency analysis. Manag Sci 57(1):200–214

    Article  Google Scholar 

  • Sarkodie SA, Strezov V (2019) Effect of foreign direct investments, economic development and energy consumption on greenhouse gas emissions in developing countries. Sci Total Environ 646:862–871

    Article  CAS  Google Scholar 

  • Shahbaz M, Nasreen S, Abbas F, Anis O (2015) Does foreign direct investment impede environmental quality in high-, middle-, and low-income countries? Energy Econ 51:275–287

    Article  Google Scholar 

  • Sharma S, Majumdar K (2021) Efficiency of rice production and CO2 emissions: a study of selected Asian countries using DDF and SBM-DEA. J Environ Plan Manag 64(12):2133–2153

    Article  Google Scholar 

  • Singh A, Gundimeda H (2021) Impact of bad outputs and environmental regulation on efficiency of Indian leather firms: a directional distance function approach. J Environ Plan Manag 64(8):1331–1351

    Article  Google Scholar 

  • Song M, Peng J, Wang J, Zhao J (2018) Environmental efficiency and economic growth of China: a Ray slack-based model analysis. Eur J Oper Res 269(1):51–63

    Article  Google Scholar 

  • Stergiou, E., & Kounetas, K. E. (2021). Eco-efficiency convergence and technology spillovers of European industries. J Environ Manag 283, 111972.

  • Sueyoshi, T., & Goto, M. (2012). Weak and strong disposability vs. natural and managerial disposability in DEA environmental assessment: comparison between Japanese electric power industry and manufacturing industries. Energy Econ 34(3), 686–699.

  • Sueyoshi T, Yuan Y, Goto M (2017) A literature study for DEA applied to energy and environment. Energy Econ 62:104–124

    Article  Google Scholar 

  • Sun J, Yuan Y, Yang R, Ji X, Wu J (2017) Performance evaluation of Chinese port enterprises under significant environmental concerns: an extended DEA-based analysis. Transp Policy 60:75–86

    Article  Google Scholar 

  • Tovar B, Wall A (2019) Environmental efficiency for a cross-section of Spanish port authorities. Transp Res Part d: Transport Environ 75:170–178

    Article  Google Scholar 

  • Vlontzos G, Niavis S, Manos B (2014) A DEA approach for estimating the agricultural energy and environmental efficiency of EU countries. Renew Sustain Energy Rev 40:91–96

    Article  Google Scholar 

  • Wang J, Wang S, Li S, Cai Q, Gao S (2019a) Evaluating the energy-environment efficiency and its determinants in Guangdong using a slack-based measure with environmental undesirable outputs and panel data model. Sci Total Environ 663:878–888

    Article  CAS  Google Scholar 

  • Wang K, Xian Y, Lee CY, Wei YM, Huang Z (2019b) On selecting directions for directional distance functions in a non-parametric framework: a review. Ann Oper Res 278(1–2):43–76

    Article  Google Scholar 

  • Wang K, Wei YM, Huang Z (2018) Environmental efficiency and abatement efficiency measurements of China’s thermal power industry: a data envelopment analysis based materials balance approach. Eur J Oper Res 269(1):35–50

    Article  Google Scholar 

  • Wang, K. L., Zhao, B., Ding, L. L., & Miao, Z. (2021). Government intervention, market development, and pollution emission efficiency: evidence from China. Sci Total Environ 757, 143738.

  • Wang S, Chu C, Chen G, Peng Z, Li F (2016) Efficiency and reduction cost of carbon emissions in China: a non-radial directional distance function method. J Clean Prod 113:624–634

    Article  Google Scholar 

  • Wang, Z., Sun, Y., Yuan, Z., & Wang, B. (2019c). Does energy efficiency have a spatial spill-over effect in China? Evidence from provincial-level data. J Clean Prod 241, 118258.

  • Wei F, Chu J, Song J, Yang F (2019) A cross-bargaining game approach for direction selection in the directional distance function. Or Spectr 41(3):787–807

    Article  Google Scholar 

  • Wei, F., Zhang, X., Chu, J., Yang, F., & Yuan, Z. (2021a). Energy and environmental efficiency of China’s transportation sectors considering CO2 emission uncertainty. Trans Res Part D: Trans Environ 97, 102955.

  • F Wei X Zhang J Song F Yang 2021 Efficiency evaluation of healthcare services in China based on stochastic multicriteria acceptability analysis and directional distance function Int Trans Oper Res https://doi.org/10.1111/itor.13085

  • Xian Y, Wang K, Wei YM, Huang Z (2019) Would China’s power industry benefit from nationwide carbon emission permit trading? An optimization model-based ex post analysis on abatement cost savings. Appl Energy 235:978–986

    Article  Google Scholar 

  • Yang H, Pollitt M (2010) The necessity of distinguishing weak and strong disposability among undesirable outputs in DEA: environmental performance of Chinese coal-fired power plants. Energy Policy 38(8):4440–4444

    Article  Google Scholar 

  • Yang F, Wei F, Li Y, Huang Y, Chen Y (2018) Expected efficiency based on directional distance function in data envelopment analysis. Comp Ind Eng 125:33–45

    Article  Google Scholar 

  • Yu, J., Zhou, K., & Yang, S. (2019). Regional heterogeneity of China’s energy efficiency in “new normal”: a meta-frontier Super-SBM analysis. Energy Policy, 134, 110941.

  • Zafar, M. W., Qin, Q., & Zaidi, S. A. H. (2020). Foreign direct investment and education as determinants of environmental quality: the importance of post Paris Agreement (COP21). J Environ Manag 270, 110827.

  • Zha Y, Zhao L, Bian Y (2016) Measuring regional efficiency of energy and carbon dioxide emissions in China: a chance constrained DEA approach. Comp Oper Res 66:351–361

    Article  Google Scholar 

  • Zhang N, Choi Y (2014) A note on the evolution of directional distance function and its development in energy and environmental studies 1997–2013. Renew Sustain Energy Rev 33:50–59

    Article  CAS  Google Scholar 

  • 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 

  • Zhou H, Yang Y, Chen Y, Zhu J (2018) Data envelopment analysis application in sustainability: the origins, development and future directions. Eur J Oper Res 264(1):1–16

    Article  Google Scholar 

  • Zhu, L., Wang, Y., Shang, P., Qi, L., Yang, G., & Wang, Y. (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, 133, 110883.

  • Zhu Q, Aparicio J, Li F, Wu J, Kou G (2022) Determining closest targets on the extended facet production possibility set in data envelopment analysis: modeling and computational aspects. Eur J Oper Res 296(3):927–939

    Article  Google Scholar 

  • Zhu, Q., Li, X., Li, F., Wu, J., & Zhou, D. (2020). Energy and environmental efficiency of China’s transportation sectors under the constraints of energy consumption and environmental pollutions. Energy Econs, 89, 104817.

  • Zografakis N, Menegaki AN, Tsagarakis KP (2008) Effective education for energy efficiency. Energy Policy 36(8):3226–3232

    Article  Google Scholar 

Download references

Funding

The research is supported by the University Humanities and Social Sciences Research Project of Anhui Province (No. SK2020A0430), the National Natural Science Foundation of China (Nos. 72101246, 71631006, 71991464, and 71921001), the China Postdoctoral Science Foundation (No. 2019M662210), the Xin Wenke Program of the University of Science and Technology of China (No. XWK2019029), and the Fundamental Research Funds for the Central Universities (WK2040000024).

Author information

Authors and Affiliations

  1. School of Management, University of Science and Technology of China, Hefei, Anhui, 230026, People’s Republic of China

    Jing Tang & Feng Yang

  2. School of Economics and Management, Hefei University, Hefei, Anhui, 230601, People’s Republic of China

    Jing Tang

  3. School of Economics, Hefei University of Technology, Hefei, Anhui, 230009, People’s Republic of China

    Fangqing Wei

Authors
  1. Jing Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Feng Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fangqing Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Jing Tang: conceptualization, methodology, formal analysis, and writing—original draft. Feng Yang: supervision, writing—review and editing, and funding acquisition. Fangqing Wei: conceptualization, methodology, formal analysis, writing—review and editing, and funding acquisition.

Corresponding author

Correspondence to Fangqing Wei.

Ethics declarations

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Competing interest

The authors declare no competing interests.

Additional information

Responsible Editor: Eyup Dogan

Publisher's Note

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

Appendix

Appendix

Table 10 Statistical descriptions of environmental efficiencies for 30 provinces from 2006 to 2017
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tang, J., Yang, F. & Wei, F. The average environmental efficiency technique and its application to Chinese provincial panel data. Environ Sci Pollut Res 29, 39665–39683 (2022). https://doi.org/10.1007/s11356-022-18751-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11356-022-18751-9

Keywords

Search

Navigation