Energy and CO 2 emissions efficiency of major economies: A network DEA approach

Abstract This study extends the analysis of energy and CO2 emissions efficiency of economies from the black box to a network structure analysis realizing the fact that each economy is a network of two divisions responsible for production and distribution of economic outputs. Hence, its economic and distributive efficiencies must be taken into account while analyzing the energy and CO2 emissions efficiency. In order to analyze energy and CO2 emissions efficiency of economies in terms of economic and distributive efficiencies simultaneously, we have innovatively applied network DEA model free link case under free disposability assumption for all undesirable outputs in both divisions of economies. The results are tantalizing as we have found that in aggregate 85% of energy consumption and 89% of CO2 emissions were just because of economic and distributive inefficiencies. Although none of the economies was overall efficient but a few were efficient in one of the two divisions. China was the largest user of excess energy because of economic inefficiency and the USA was the largest user of excess energy because of distributive inefficiency. We have suggested that inefficient economies not only need rightly directed taxation laws, incentives, and penalties but also need reforms in economic structures.

[1]  Constantin Zopounidis,et al.  A Two-stage Approach for Energy Efficiency Analysis in European Union Countries , 2015 .

[2]  Kaoru Tone,et al.  Network DEA: A slacks-based measure approach , 2009, Eur. J. Oper. Res..

[3]  Qiang Cui,et al.  Has airline efficiency affected by the inclusion of aviation into European Union Emission Trading Scheme? Evidences from 22 airlines during 2008–2012 , 2016 .

[4]  K. Tone,et al.  Dynamic DEA: A slacks-based measure approach , 2010 .

[5]  Jun Bi,et al.  Chinese regional industrial energy efficiency evaluation based on a DEA model of fixing non-energy inputs , 2010 .

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

[7]  José Ramón San Cristóbal,et al.  A multi criteria data envelopment analysis model to evaluate the efficiency of the Renewable Energy technologies , 2011 .

[8]  Yanghon Chung,et al.  The static and dynamic environmental efficiency of renewable energy: A Malmquist index analysis of OECD countries , 2015 .

[9]  Aihua Wu,et al.  Energy efficiency evaluation for regions in China: an application of DEA and Malmquist indices , 2014 .

[10]  Laure Latruffe,et al.  Modelling pollution-generating technologies in performance benchmarking: Recent developments, limits and future prospects in the nonparametric framework , 2016, Eur. J. Oper. Res..

[11]  Tser-yieth Chen,et al.  A comparative study of energy utilization efficiency between Taiwan and China , 2010 .

[12]  Chuanguo Zhang,et al.  Panel estimation for income inequality and CO2 emissions: A regional analysis in China , 2014 .

[13]  A. R. Gómez-Calvet,et al.  Energy efficiency in the European Union: What can be learned from the joint application of directional distance functions and slacks-based measures? , 2014 .

[14]  Ching-Cheng Lu,et al.  Measuring CO2 emission efficiency in OECD countries: Application of the Hybrid Efficiency model , 2013 .

[15]  Wei Zhang,et al.  China's regional energy and environmental efficiency: A DEA window analysis based dynamic evaluation , 2013, Math. Comput. Model..

[16]  Malin Song,et al.  A two-stage DEA approach for environmental efficiency measurement , 2014, Environmental Monitoring and Assessment.

[17]  Joe Zhu,et al.  Data envelopment analysis : a handbook on the modeling of internal structures and networks , 2014 .

[18]  Bai-Chen Xie,et al.  Dynamic environmental efficiency evaluation of electric power industries: Evidence from OECD (Organization for Economic Cooperation and Development) and BRIC (Brazil, Russia, India and China) countries , 2014 .

[19]  Boqiang Lin,et al.  Regional differences of CO2 emissions performance in China’s agricultural sector: A Malmquist index approach , 2015 .

[20]  Yue-Jun Zhang,et al.  The decomposition of energy-related carbon emission and its decoupling with economic growth in China , 2015 .

[21]  H. Wang,et al.  A generalized MCDA–DEA (multi-criterion decision analysis–data envelopment analysis) approach to construct slacks-based composite indicator , 2015 .

[22]  Yue-Jun Zhang,et al.  The CO2 emission efficiency, reduction potential and spatial clustering in China's industry: evidence from the regional level , 2016 .

[23]  Ke Wang,et al.  Energy efficiency of China's industry sector: An adjusted network DEA (data envelopment analysis)-based decomposition analysis , 2015 .

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

[25]  Zhaohua Wang,et al.  Energy and CO2 emissions efficiency of major economies: A non-parametric analysis , 2016 .

[26]  Giorgia Oggioni,et al.  Eco-efficiency of the world cement industry: A data envelopment analysis , 2011 .

[27]  D. Štreimikienė,et al.  A comprehensive review of data envelopment analysis (DEA) approach in energy efficiency , 2017 .

[28]  José Luis Zofío,et al.  Environmental efficiency and regulatory standards: the case of CO2 emissions from OECD industries , 2001 .

[29]  Jin-Li Hu,et al.  Ecological total-factor energy efficiency of regions in China , 2012 .

[30]  Yong Zha,et al.  Measuring regional efficiency of energy and carbon dioxide emissions in China: A chance constrained DEA approach , 2016, Comput. Oper. Res..

[31]  Giorgia Oggioni,et al.  Efficiency analysis of world cement industry in presence of undesirable output: Application of data envelopment analysis and directional distance function , 2012 .

[32]  Jin-Li Hu,et al.  Industry-level Total-factor Energy Efficiency in Developed Countries , 2011 .

[33]  Andrew K. Jorgenson,et al.  Income Inequality and Carbon Emissions in the United States: A State-level Analysis, 1997–2012 , 2017 .

[34]  Yongpei Hao,et al.  Research of Regional Energy Efficiency Based on Undesirable Outputs and Its Influential Factors: A Case of Western China , 2012 .

[35]  Zhaohua Wang,et al.  An empirical analysis of China's energy efficiency from both static and dynamic perspectives , 2014 .

[36]  Satoshi Honma,et al.  Total-factor energy productivity growth of regions in Japan , 2009 .

[37]  Claudiu Cicea,et al.  Environmental efficiency of investments in renewable energy: Comparative analysis at macroeconomic level , 2014 .

[38]  M. Song,et al.  Bootstrap-DEA analysis of BRICS' energy efficiency based on small sample data , 2013 .

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

[40]  Christina Bampatsou,et al.  The Use of the DEA Method for Simultaneous Analysis of The Interrelationships Among Economic Growth, Environmental Pollution And Energy Consumption , 2009 .

[41]  D. Large,et al.  THE ECONOMICS OF ENERGY EXTRAVAGANCE , 1975 .

[42]  William W. Cooper,et al.  Handbook on data envelopment analysis , 2011 .

[43]  Fan Ying,et al.  Does generation form influence environmental efficiency performance? An analysis of China’s power system , 2012 .

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

[45]  Jin-Li Hu,et al.  Renewable energy and macroeconomic efficiency of OECD and non-OECD economies , 2007 .

[46]  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 .

[47]  Jin-Li Hu,et al.  Total-factor energy efficiency of regions in Japan , 2008 .

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

[49]  Ke Chen,et al.  The integrated efficiency of economic development and CO2 emissions among Asia Pacific Economic Cooperation members , 2016 .

[50]  Yiwen Bian,et al.  Energy efficiency analysis of the economic system in China during 1986–2012: A parallel slacks-based measure approach , 2016 .

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

[52]  Osman Zaim,et al.  Environmental efficiency in carbon dioxide emissions in the OECD: A non-parametric approach , 2000 .

[53]  Peter Nijkamp,et al.  An evaluation of energy-environment-economic efficiency for EU, APEC and ASEAN countries: Design of a Target-Oriented DFM model with fixed factors in Data Envelopment Analysis , 2015 .

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

[55]  R. Färe,et al.  PRODUCTIVITY AND INTERMEDIATE PRODUCTS: A FRONTIER APPROACH , 1995 .

[56]  B. W. Ang,et al.  Linear programming models for measuring economy-wide energy efficiency performance , 2008 .

[57]  Yue-Jun Zhang,et al.  The impact of economic growth, industrial structure and urbanization on carbon emission intensity in China , 2014, Natural Hazards.

[58]  Kaoru Tone,et al.  Dynamic DEA with network structure: A slacks-based measure approach , 2013 .

[59]  Jin-Li Hu,et al.  Clean energy use and total-factor efficiencies: An international comparison , 2015 .

[60]  Boqiang Lin,et al.  Impact of energy conservation policies on the green productivity in China's manufacturing sector: Evidence from a three-stage DEA model , 2016 .

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

[62]  Satoshi Honma,et al.  Panel Data Parametric Frontier Technique for Measuring Total-factor Energy Efficiency: Application to Japanese Regions , 2014 .

[63]  Boqiang Lin,et al.  Modeling the dynamics of carbon emission performance in China: A parametric Malmquist index approach , 2015 .

[64]  Ke Wang,et al.  A comparative analysis of China’s regional energy and emission performance: Which is the better way to deal with undesirable outputs? , 2012 .

[65]  C. Hirschhausen,et al.  Technical efficiency and CO2 reduction potentials — An analysis of the German electricity and heat generating sector☆ , 2016 .

[66]  Hirofumi Fukuyama,et al.  Production , Manufacturing and Logistics Identifying the efficiency status in network DEA , 2012 .

[67]  Flávia de Castro Camioto,et al.  Energy efficiency analysis of G7 and BRICS considering total-factor structure , 2016 .

[68]  Mara Madaleno,et al.  The economic and environmental efficiency assessment in EU cross-country: Evidence from DEA and quantile regression approach , 2017 .

[69]  W. Martin,et al.  Stakeholder objectives for public lands: Rankings of forest management alternatives , 2000 .