Environmental Assessment of European Union Countries

This study utilizes the dynamic data envelopment analysis (DEA) model by considering time to measure the energy environmental efficiency of 28 countries in the European Union (EU) during the period 2006–2013. There are three kinds of variables: input, output, and carry-over. The inputs are labor, capital, and energy consumption (EC). The undesirable outputs are greenhouse gas emissions (GHE) and sulfur oxide (SOx) emissions, and the desirable output variable is gross domestic product (GDP). The carry-over variable is gross capital formation (GCF). The empirical results show that first the dynamic DEA model can measure environment efficiency and provide optimum improvement for inefficient countries, as more than half of the EU countries should improve their environmental efficiency. Second, the average overall scores of the EU countries point out that the better period of performance is from 2009 to 2012. Third, the output variables of GHE, SOx, and GDP exhibit a significant impact on environmental efficiency. Finally, the average value of others is significantly better than high renewable energy utilization (HRE) with the Wilcoxon test. Thus, the EU’s strategy for environmental energy improvement should be to pay attention to the benefits of renewable energy (RE) utilization, reducing greenhouse gas emissions (GHE), and enhancing the development of RE utilization to help achieve the goal of lower GHE.

[1]  Philipp von Geymueller,et al.  Static versus dynamic DEA in electricity regulation: the case of US transmission system operators , 2009, Central Eur. J. Oper. Res..

[2]  Cheng-bo Zhao,et al.  Does Regulation on CO2 Emissions Influence Productivity Growth?–The Empirical Test Based on DEA Analysis , 2012 .

[3]  Kazuyuki Sekitani,et al.  Returns to scale in dynamic DEA , 2005, Eur. J. Oper. Res..

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

[5]  Gerald A Klopp,et al.  THE ANALYSIS OF THE EFFICIENCY OF PRODUCTIVE SYSTEMS WITH MULTIPLE INPUTS AND OUTPUTS. , 1985 .

[6]  Dequn Zhou,et al.  Inefficiency and Congestion Assessment of Mix Energy Consumption in 16 APEC Countries by using DEA Window Analysis , 2014 .

[7]  M. Goto,et al.  Measurement of Dynamic Efficiency in Production: An Application of Data Envelopment Analysis to Japanese Electric Utilities , 2003 .

[8]  P. Rhodes Administration. , 1983 .

[9]  Reza Farzipoor Saen,et al.  A new data envelopment analysis (DEA) model to select eco-efficient technologies in the presence of undesirable outputs , 2013, Clean Technologies and Environmental Policy.

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

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

[12]  Andrés Camilo,et al.  Evaluación de la eficiencia relativa de los sistemas de producción porcícolas del departamento de Cundinamarca, utilizando análisis envolvente de datos (DEA) , 2020 .

[13]  Chao Feng,et al.  A performance evaluation of the energy, environmental, and economic efficiency and productivity in China: An application of global data envelopment analysis , 2015 .

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

[15]  Toshiyuki Sueyoshi,et al.  Environmental assessment on coal-fired power plants in U.S. north-east region by DEA non-radial measurement , 2015 .

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

[17]  Zhang Li-bo,et al.  The Evaluation and Selection of Renewable Energy Technologies in China , 2014 .

[18]  Z. Hajduová,et al.  Determinants of Environmental Efficiency of the EU Countries Using Two-Step DEA Approach , 2018, Sustainability.

[19]  杨力 Evaluation of regional environmental efficiencies in China based on super-efficiency-DEA , 2015 .

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

[21]  M. Madaleno,et al.  Advanced scoring method of eco-efficiency in European cities , 2017, Environmental Science and Pollution Research.

[22]  Ioannis E. Tsolas,et al.  Assessing power stations performance using a DEA-bootstrap approach , 2010 .

[23]  Gumersindo Feijoo,et al.  Eco-efficiency analysis of Spanish WWTPs using the LCA + DEA method. , 2015, Water research.

[24]  Spyros Niavis,et al.  A DEA approach for estimating the agricultural energy and environmental efficiency of EU countries , 2014 .

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

[26]  H. Kuo,et al.  Analysis of Farming Environmental Efficiency Using a DEA Model with Undesirable Outputs , 2014 .

[27]  Zhen Liu,et al.  Regional Environmental Performance Evaluation: A Case of Western Regions in China , 2012 .

[28]  Mika Goto,et al.  Dynamic data envelopment analysis: modeling intertemporal behavior of a firm in the presence of productive inefficiencies , 1999 .

[29]  Chaido Dritsaki,et al.  Causal Relationship between Energy Consumption, Economic Growth and CO2 Emissions: A Dynamic Panel Data Approach , 2014 .

[30]  George Emm. Halkos,et al.  Regional sustainability efficiency index in Europe: an additive two-stage DEA approach , 2015, Oper. Res..

[31]  William W. Cooper,et al.  Using Malmquist Indexes to measure changes in the productivity and efficiency of US accounting firms before and after the Sarbanes–Oxley Act , 2009 .

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

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

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

[35]  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 .

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

[37]  Can Tansel Tuğcu,et al.  Energy Efficiency in Electricity Production: A Data Envelopment Analysis (DEA) Approach for the G-20 Countries , 2015 .

[38]  Sudhir Kumar Singh,et al.  Measuring productivity change in Indian coal‐fired electricity generation: 2003‐2010 , 2013 .

[39]  Boqiang Lin,et al.  The improvement gap in energy intensity: Analysis of China's thirty provincial regions using the improved DEA (data envelopment analysis) model , 2015 .

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

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