A comparative study of dynamic changes in CO2 emission performance of fossil fuel power plants in China and Korea

This paper aims to conduct a comparative study of the changes in CO2 emission performance of state-owned fossil fuel power plants between China and Korea. For this purpose, we combine the concept of the metafrontier Malmquist productivity index and the non-radial directional distance function to develop a new index called the non-radial metafrontier Malmquist CO2 emission performance index (NMMCPI). This new methodology allows for the incorporation of technological heterogeneities and slack variables into the previously introduced Malmquist CO2 emission performance index (MCPI). The NMMCPI can be derived by solving several non-radial data envelopment analysis (DEA) models. The NMMCPI can be decomposed into an efficiency change (EC) index, a best-practice gap change (BPC) index, and a technology gap change (TGC) index. By fixing the non-energy inputs, we measure the pure CO2 emission performance change. Based on the proposed indices, the comparative study between Chinese and Korean fossil fuel power industries is conducted for the 2005–2010 period. Empirical results indicate significant differences in terms of various decomposed CO2 emission performance changes between China and Korea. Korean power plants demonstrate improvements in innovation, while Chinese power plants demonstrate a higher ability for technological leadership. Some related policy implications are also proposed based on the empirical results.

[1]  Philippe Vanden Eeckaut,et al.  Non-parametric efficiency, progress and regress measures for panel data: Methodological aspects☆ , 1995 .

[2]  Jin-Li Hu,et al.  Total-factor energy productivity growth, technical progress, and efficiency change: An empirical study of China , 2010 .

[3]  Rolf Färe,et al.  New directions : efficiency and productivity , 2004 .

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

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

[6]  Sue J. Lin,et al.  Evaluation of thermal power plant operational performance in Taiwan by data envelopment analysis , 2010 .

[7]  Toshiyuki Sueyoshi,et al.  DEA approach for unified efficiency measurement: Assessment of Japanese fossil fuel power generation , 2011 .

[8]  Rolf Färe,et al.  Environmental production functions and environmental directional distance functions , 2007 .

[9]  B. W. Ang,et al.  Measuring environmental performance under different environmental DEA technologies , 2008 .

[10]  R. Färe,et al.  Benefit and Distance Functions , 1996 .

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

[12]  Corrado Di Maria,et al.  Efficiency, Productivity and Environmental Policy: A Case Study of Power Generation in the EU , 2010 .

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

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

[15]  Ning Zhang,et al.  Technical efficiency, shadow price of carbon dioxide emissions, and substitutability for energy in the Chinese manufacturing industries , 2012 .

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

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

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

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

[20]  Dong-hyun Oh A metafrontier approach for measuring an environmentally sensitive productivity growth index , 2010 .

[21]  R. Wells,et al.  Measuring environmental performance , 1994, Proceedings of 1994 IEEE International Symposium on Electronics and The Environment.

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

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

[24]  Li Yang,et al.  Energy saving in China: Analysis on the energy efficiency via bootstrap-DEA approach , 2013 .

[25]  Michael G. Pollitt,et al.  The necessity of distinguishing weak and strong disposability among undesirable outputs in DEA: Environmental performance of Chinese coal-fired power plants , 2010 .

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

[27]  Carlos Pestana Barros,et al.  Technical efficiency of thermoelectric power plants , 2008 .

[28]  William L. Weber,et al.  A directional slacks-based measure of technical inefficiency , 2009 .

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

[30]  Ning Zhang,et al.  Energy efficiency, CO2 emission performance and technology gaps in fossil fuel electricity generation in Korea: A meta-frontier non-radial directional distance functionanalysis , 2013 .

[31]  Adnan Sözen,et al.  Assessment of operational and environmental performance of the thermal power plants in Turkey by using data envelopment analysis , 2010 .

[32]  Toshiyuki Sueyoshi,et al.  Efficiency-based rank assessment for electric power industry: A combined use of Data Envelopment Analysis (DEA) and DEA-Discriminant Analysis (DA) , 2012 .

[33]  Toshiyuki Sueyoshi,et al.  Performance analysis of US coal-fired power plants by measuring three DEA efficiencies , 2010 .

[34]  M. Porter,et al.  Toward a New Conception of the Environment-Competitiveness Relationship , 1995 .