Regional environmental efficiency in China: Analysis based on a regional slack-based measure with environmental undesirable outputs

Abstract As the largest developing country, China has been suffering from resource shortage and serious environmental pollution. Evaluation and improvement of the regional environmental efficiency is crucial to pursuing the balance between economic development and environmental protection. This paper used a regional environmental efficiency SBM (slack-based measure) (REES) model which treats environmental pollution as undesirable outputs to evaluate the environmental efficiency of 30 provincial administrative regions (PARs) in China from 2005 to 2011 and investigated the factors affecting environmental efficiency using the Tobit regression model. The results indicate that there is distinct difference in environmental efficiency amongst each PAR. The GDP per capita, industrial structure, innovation capability, environmental awareness of local government and population density have significant positive impacts, while energy intensity exerts a significantly negative effect on environmental efficiency. In order to make more effective policies for improving China’s regional environmental efficiency, the hierarchical cluster analysis was applied to divide the 30 PARs into 3 sub-regions. A number of policy recommendations were provided for improving environmental efficiency according to the characteristics of each sub-region, which are helpful for the Chinese government to achieve the targets of environmental protection along with the economic development in the coming years.

[1]  George Emm. Halkos,et al.  A conditional directional distance function approach for measuring regional environmental efficiency: Evidence from UK regions , 2013, Eur. J. Oper. Res..

[2]  Jie Wu,et al.  Environmental efficiency evaluation based on data envelopment analysis: A review , 2012 .

[3]  P. Zhou,et al.  Efficiency and abatement costs of energy-related CO2 emissions in China: A slacks-based efficiency measure , 2012 .

[4]  B. W. Ang,et al.  Total factor carbon emission performance: A Malmquist index analysis , 2010 .

[5]  C.A.K. Lovell,et al.  Multilateral Productivity Comparisons When Some Outputs are Undesirable: A Nonparametric Approach , 1989 .

[6]  Kaoru Tone,et al.  Dealing with Undesirable Outputs in DEA: A Slacks-based Measure (SBM) Approach , 2003 .

[7]  Nicky Rogge,et al.  Undesirable specialization in the construction of composite policy indicators: The Environmental Performance Index , 2012 .

[8]  Dequn Zhou,et al.  Scenario-based energy efficiency and productivity in China: A non-radial directional distance function analysis , 2013 .

[9]  Chu Wei,et al.  Economic development and carbon dioxide emissions in China: Provincial panel data analysis , 2012 .

[10]  Mohamed M. Mostafa,et al.  Modeling the efficiency of top Arab banks: A DEA-neural network approach , 2009, Expert Syst. Appl..

[11]  Bo Zhang,et al.  Physical sustainability assessment for the China society: Exergy-based systems account for resources use and environmental emissions , 2010 .

[12]  Soumyananda Dinda.  Environmental Kuznets Curve Hypothesis: A Survey , 2004 .

[13]  C. A. Knox Lovell,et al.  Environmental efficiency with multiple environmentally detrimental variables; estimated with SFA and DEA , 2000, Eur. J. Oper. Res..

[14]  Peng Zhou,et al.  A survey of data envelopment analysis in energy and environmental studies , 2008, Eur. J. Oper. Res..

[15]  Jun Zhang.  Estimation of China's provincial capital stock (1952–2004) with applications , 2008 .

[16]  Aydın Çelen,et al.  Efficiency and productivity (TFP) of the Turkish electricity distribution companies: An application of two-stage (DEA&Tobit) analysis , 2013 .

[17]  Peng Zhou,et al.  A non-radial DEA approach to measuring environmental performance , 2007, Eur. J. Oper. Res..

[18]  E. Zervas,et al.  The Environmental Kuznets Curve (EKC) theory—Part A: Concept, causes and the CO2 emissions case , 2013 .

[19]  Hui Wang,et al.  Energy and CO2 emission performance in electricity generation: A non-radial directional distance function approach , 2012, Eur. J. Oper. Res..

[20]  Lawrence M. Seiford,et al.  Modeling undesirable factors in efficiency evaluation , 2002, Eur. J. Oper. Res..

[21]  Ning Zhang,et al.  Environmental energy efficiency of China's regional economies: A non-oriented slacks-based measure analysis , 2013 .

[22]  Lawrence M. Seiford,et al.  Recent developments in dea : the mathematical programming approach to frontier analysis , 1990 .

[23]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .

[24]  Holger Scheel,et al.  Undesirable outputs in efficiency valuations , 2001, Eur. J. Oper. Res..

[25]  B. W. Ang,et al.  Slacks-based efficiency measures for modeling environmental performance , 2006 .

[26]  Alex S. Mayer,et al.  Classification of watersheds into integrated social and biophysical indicators with clustering analysis , 2014 .

[27]  Dinesh Mohan,et al.  Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of Gomti River (India)--a case study. , 2004, Water research.

[28]  Zhimin Huang,et al.  The performance evaluation of regional R&D investments in China: An application of DEA based on the first official China economic census data , 2011 .

[29]  Bin Liu,et al.  Energy efficiency and energy saving potential in China: An analysis based on slacks-based measure model , 2012, Comput. Ind. Eng..

[30]  Ilhan Ozturk,et al.  Testing Environmental Kuznets Curve hypothesis in Asian countries , 2015 .

[31]  Rolf Färe,et al.  Modeling undesirable factors in efficiency evaluation: Comment , 2004, Eur. J. Oper. Res..

[32]  G. Grossman,et al.  Economic Growth and the Environment , 1994 .

[33]  Jin-Li Hu,et al.  Total-factor energy efficiency of regions in China , 2006 .

[34]  Kaoru Tone,et al.  A slacks-based measure of super-efficiency in data envelopment analysis , 2001, Eur. J. Oper. Res..

[35]  Ning Zhang,et al.  Environmental efficiency analysis of transportation system in China:A non-radial DEA approach , 2013 .

[36]  Yolanda Fernandez Lommen.  Toward Sustainable Growth in the People's Republic of China: The 12th Five-Year Plan , 2011 .

[37]  Michael D. Hammig,et al.  Institutions and the Environmental Kuznets Curve for Deforestation: A Crosscountry Analysis for Latin America, Africa and Asia , 2001 .

[38]  P. Zhou,et al.  Total-factor energy efficiency with congestion , 2017, Ann. Oper. Res..

[39]  Surender Kumar,et al.  Environmental regulation, productive efficiency and cost of pollution abatement: a case study of the sugar industry in India. , 2006, Journal of environmental management.

[40]  Malin Song,et al.  Calculation of China's environmental efficiency and relevant hierarchical cluster analysis from the perspective of regional differences , 2013, Math. Comput. Model..

[41]  G. Zambrano.  Modified maximum likelihood estimationof tobit models with fixed effects:theory and an application to earningsequations , 2005 .

[42]  S. Kuznets.  Economic Growth and Income Inequality , 2019, The Gap between Rich and Poor.

[43]  P. Zhou,et al.  Does there exist energy congestion? Empirical evidence from Chinese industrial sectors , 2016 .

[44]  Malin Song,et al.  Evaluation of environmental efficiency in China using data envelopment analysis , 2015 .

[45]  Gumersindo Feijoo,et al.  The link between operational efficiency and environmental impacts. A joint application of Life Cycle Assessment and Data Envelopment Analysis. , 2009, The Science of the total environment.

[46]  A. Hailu,et al.  Non‐Parametric Productivity Analysis with Undesirable Outputs: An Application to the Canadian Pulp and Paper Industry , 2001 .

[47]  Bing Xue,et al.  Empirical study on the environmental pressure versus economic growth in China during 1991–2012 , 2015 .

[48]  Xiaohong Zhang,et al.  The interactions between China's economic growth, energy production and consumption and the related air emissions during 2000–2011 , 2014 .

[49]  Yuqi Wang,et al.  Transverse and longitudinal analysis of the environmental efficiency of Chinese industries , 2013, Math. Comput. Model..

[50]  Malin Song,et al.  Statistical analysis and combination forecasting of environmental efficiency and its influential factors since China entered the WTO: 2002–2010–2012 , 2013 .