Carbon emissions efficiency in China: Key facts from regional and industrial sector

Abstract Improving CO2 emissions efficiency has become a crucial task faced by China to develop a low-carbon economy and choose a CO2 emissions mitigation path. Existing researches have focused on estimating national CO2 emissions efficiency, but have ignored the industrial heterogeneity in the region or explanation for inefficiency. To fill the gaps, this paper applies a metafrontier-Malmquist index analysis method combined with a three-level common frontier model to comprehensively estimate and decompose the carbon emissions efficiency and dynamic changes of China's industries and regions during 2010–2015. First, we use data envelopment analysis with directional distance function by considering three-level frontiers to calculate CO2 emissions efficiency. Results show that China's overall CO2 emissions efficiency is at the middle level with an average value of 0.511. The efficiency of CO2 emissions in the eastern region is the highest, followed by the central and western regions, and the CO2 emissions efficiency in the tertiary industries is greater than that in the primary and secondary industries. Second, we decompose CO2 emissions efficiency into technological, industrial and management efficiency, and found that management efficiency is the main reason for low CO2 emissions efficiency in developed regions and industries. Third, the Malmquist-Luenberger index is utilized to decompose CO2 emissions efficiency into 9 items, and we obtained that continuous technological progress is the leading cause driving different degrees of improvement in CO2 emissions efficiency.

[1]  H. Mu,et al.  Regional Total-Factor Coal Consumption Efficiency in China: A Meta-Frontier SBM-Undesirable Approach , 2017 .

[2]  Ruyin Long,et al.  Regional carbon emission performance in China according to a stochastic frontier model , 2013 .

[3]  Shijin Wang,et al.  Influencing factors and regional discrepancies of the efficiency of carbon dioxide emissions in Jiangsu, China , 2018, Ecological Indicators.

[4]  Wei Zhang,et al.  A Malmquist CO2 emission performance index based on a metafrontier approach , 2013, Math. Comput. Model..

[5]  Guohao Zhao,et al.  An analysis of Chinese provincial carbon dioxide emission efficiencies based on energy consumption structure , 2016 .

[6]  Boqiang Lin,et al.  Metafroniter energy efficiency with CO2 emissions and its convergence analysis for China , 2015 .

[7]  Chao Feng,et al.  Analysis of green total-factor productivity in China's regional metal industry: A meta-frontier approach , 2018, Resources Policy.

[8]  G. Boyd,et al.  Plant Level Productivity, Efficiency, and Environmental Performance of the Container Glass Industry , 2002 .

[9]  W. Cai,et al.  How to Measure Carbon Emission Reduction in China’s Public Building Sector: Retrospective Decomposition Analysis Based on STIRPAT Model in 2000–2015 , 2017 .

[10]  R. R. Russell,et al.  Measures of technical efficiency , 1985 .

[11]  Surender Kumar,et al.  Environmentally Sensitive Productivity Growth: A Global Analysis Using Malmquist-Luenberger Index , 2006 .

[12]  A. Charnes,et al.  Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis , 1984 .

[13]  Jesús T. Pastor,et al.  An enhanced DEA Russell graph efficiency measure , 1999, Eur. J. Oper. Res..

[14]  Ali Emrouznejad,et al.  CO2 emissions reduction of Chinese light manufacturing industries: A novel RAM-based global Malmquist–Luenberger productivity index , 2016 .

[15]  R. Färe,et al.  Productivity Growth, Technical Progress, and Efficiency Change in Industrialized Countries , 1994 .

[16]  Osman Zaim,et al.  Productivity growth in OECD countries: A comparison with Malmquist indices , 2005 .

[17]  Toshiyuki Sueyoshi,et al.  DEA (Data Envelopment Analysis) assessment of operational and environmental efficiencies on Japanese regional industries , 2014 .

[18]  Xiaodong Zhu,et al.  Analysis of the Environmental Efficiency of the Chinese Transportation Sector Using an Undesirable Output Slacks-Based Measure Data Envelopment Analysis Model , 2015 .

[19]  Rolf Färe,et al.  Measuring the technical efficiency of production , 1978 .

[20]  Jeong-Dong Lee,et al.  A metafrontier approach for measuring Malmquist productivity index , 2010 .

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

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

[23]  Yi-Ming Wei,et al.  Chinese CO2 emission flows have reversed since the global financial crisis , 2017, Nature Communications.

[24]  Shuai Shao,et al.  How to achieve the 2030 CO2 emission-reduction targets for China's industrial sector: Retrospective decomposition and prospective trajectories , 2017 .

[25]  Ku-Hsieh Chen,et al.  A cross-country comparison of productivity growth using the generalised metafrontier Malmquist productivity index: with application to banking industries in Taiwan and China , 2011 .

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

[27]  Yalin Lei,et al.  Carbon emission efficiency and spatial clustering analyses in China’s thermal power industry: Evidence from the provincial level , 2017 .

[28]  Ching-Ren Chiu,et al.  Decomposition of the environmental inefficiency of the meta-frontier with undesirable output , 2012 .

[29]  Ran Yan,et al.  An extended STIRPAT model-based methodology for evaluating the driving forces affecting carbon emissions in existing public building sector: evidence from China in 2000–2015 , 2017, Natural Hazards.

[30]  Zhujun Jiang,et al.  Total-factor CO2 emission performance of China’s provincial industrial sector: A meta-frontier non-radial Malmquist index approach , 2016 .

[31]  Bin Zhang,et al.  Energy and CO 2 emissions efficiency of major economies: A network DEA approach , 2018 .

[32]  Chiang Kao,et al.  Network data envelopment analysis: A review , 2014, Eur. J. Oper. Res..

[33]  Daniel Tyteca,et al.  On the Measurement of the Environmental Performance of Firms— A Literature Review and a Productive Efficiency Perspective , 1996 .

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

[35]  Ricardo Martinez-Botas,et al.  Reducing China’s road transport sector CO2 emissions to 2050: Technologies, costs and decomposition analysis , 2015 .

[36]  X. Yao,et al.  Regional energy efficiency, carbon emission performance and technology gaps in China: A meta-frontier non-radial directional distance function analysis , 2015 .

[37]  Ming Zhang,et al.  Decomposition analysis of energy consumption in Chinese transportation sector , 2011 .

[38]  David E Calkin,et al.  Production and efficiency of large wildland fire suppression effort: A stochastic frontier analysis. , 2016, Journal of environmental management.

[39]  G. Battese,et al.  Metafrontier frameworks for the study of firm-level efficiencies and technology ratios , 2008 .

[40]  Lei Zhu,et al.  Evaluation of potential reductions in carbon emissions in Chinese provinces based on environmental DEA , 2011 .

[41]  Ching-Cheng Lu,et al.  Applying the dynamic DEA model to evaluate the energy efficiency of OECD countries and China , 2017 .

[42]  R. Goel,et al.  Global CO2 efficiency: Country-wise estimates using a stochastic cost frontier , 2012 .

[43]  William L. Weber,et al.  Productivity Growth and Pollution in State Manufacturing , 2001, Review of Economics and Statistics.

[44]  C. Kroeze.  N2O from animal waste. Methodology according to IPCC Guidelines for National Greenhouse Gas Inventories. , 1997 .

[45]  Yi-Ming Wei,et al.  China’s regional energy and environmental efficiency: A Range-Adjusted Measure based analysis , 2013 .

[46]  Kerui Du,et al.  Sources of the potential CO2 emission reduction in China: A nonparametric metafrontier approach , 2014 .

[47]  Finn R. Førsund,et al.  Good Modelling of Bad Outputs: Pollution and Multiple-Output Production , 2009 .

[48]  Ye Li,et al.  An empirical study on the influencing factors of transportation carbon efficiency: Evidences from fifteen countries , 2015 .

[49]  Timo Kuosmanen.  Weak Disposability in Nonparametric Production Analysis with Undesirable Outputs , 2005 .

[50]  Shuai Shao,et al.  Decoupling CO2 emissions and industrial growth in China over 1993–2013: The role of investment , 2016 .

[51]  Minda Ma,et al.  What drives the carbon mitigation in Chinese commercial building sector? Evidence from decomposing an extended Kaya identity. , 2018, The Science of the total environment.

[52]  Rolf Färe,et al.  Productivity and Undesirable Outputs: A Directional Distance Function Approach , 1995 .

[53]  Huiming Zhang,et al.  Total-factor carbon emission efficiency of China's provincial industrial sector and its dynamic evolution , 2018, Renewable and Sustainable Energy Reviews.

[54]  Ran Yan,et al.  A methodology to assess China's building energy savings at the national level: An IPAT-LMDI model approach , 2017 .

[55]  Kaoru Tone,et al.  A slacks-based measure of efficiency in data envelopment analysis , 1997, Eur. J. Oper. Res..

[56]  W. Cooper,et al.  RAM: A Range Adjusted Measure of Inefficiency for Use with Additive Models, and Relations to Other Models and Measures in DEA , 1999 .

[57]  Dequn Zhou,et al.  Measuring China’s regional energy and carbon emission efficiency with DEA models: A survey , 2016 .

[58]  W. Feng,et al.  The total-factor energy productivity growth of China’s construction industry: evidence from the regional level , 2018, Natural Hazards.

[59]  Lutz Bornmann,et al.  Are there any frontiers of research performance? Efficiency measurement of funded research projects with the Bayesian stochastic frontier analysis for count data , 2017, J. Informetrics.

[60]  Zhijie Zhu,et al.  Research and application of a novel hybrid air quality early-warning system: A case study in China. , 2018, The Science of the total environment.