Energy efficiency development in German and Colombian non-energy-intensive sectors

This paper measures energy efficiency development in non-energy-intensive sectors (NEISs) in Germany and Colombia from a production-based theoretical framework using Data Envelopment Analysis (DEA). Using data from the German and Colombian Annual Surveys of Industries from 1998 to 2005, the analysis compares energy efficiency performances in German and Colombian NEISs at two levels of aggregation and then applies several alternative models. The results show considerable variation in energy efficiency performance in the NEISs of both countries. Comparing the results across models, it was found that in the German and Colombian NEISs, the measures of energy efficiency are similar, indicating that an appropriate combination of technical efficiency and cost minimisation are necessary to improve energy efficiency. However, energy efficiency based on cost minimisation is greater in both countries, demonstrating that energy prices in this sector are not the key variable for improving energy efficiency. This is due to the low share of energy costs, making it preferable to change other inputs rather than energy. A second-stage regression analysis reveals that in the German and Colombian NEISs, labour productivity and investments are fundamental to changes in energy efficiency. Finally, the energy efficiency measures of the DEA models show significant correlations with the traditional energy efficiency measure, indicating that energy efficiency as measured through DEA could be complementary to measures of energy intensity when analysing other key elements of energy efficiency performance in the industrial sector.

[1]  Kankana Mukherjee,et al.  Energy use efficiency in the Indian manufacturing sector: An interstate analysis , 2008 .

[2]  Patrik Söderholm.  Environmental Regulations and Interfuel Substitution in the Power Sector: A Generalized Leontief Model , 2000 .

[3]  Renuka Mahadevan,et al.  A DEA Approach to Understanding the Productivity Growth of Malaysia's Manufacturing Industries , 2002 .

[4]  Gilbert E. Metcalf,et al.  An Empirical Analysis of Energy Intensity and Its Determinants at the State Level , 2008 .

[5]  S. Sorrell,et al.  UKERC Review of evidence for the rebound effect: Technical report 3: Elasticity of substitution studies , 2007 .

[6]  Richard S. Eckaus,et al.  Explaining Long-Run Changes in the Energy Intensity of the U.S. Economy , 2004 .

[7]  Fridtjof Unander.  Decomposition of manufacturing energy-use in IEA countries: How do recent developments compare with historical long-term trends? , 2007 .

[8]  篠原 正明,et al.  William W.Cooper,Lawrence M.Seiford,Kaoru Tone 著, DATA ENVELOPMENT ANALYSIS : A Comprehensive Text with Models, Applications, References and DEA-Solver Software, Kluwer Academic Publishers, 2000年, 318頁 , 2002 .

[9]  Jesús T. Pastor,et al.  Units invariant and translation invariant DEA models , 1995, Oper. Res. Lett..

[10]  Axel Pierru,et al.  Modelling and allocation of CO2 emissions in a multiproduct industry: The case of oil refining , 2007 .

[11]  Louise Trygg,et al.  Reduction of electricity use in Swedish industry and its impact on national power supply and European CO2 emissions , 2008 .

[12]  Rodrigo Taborda,et al.  Perfomance and Efficiency in Colombia's Power Distribution Sistem: Effects of the 1994 Reform , 2006 .

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

[14]  Patrik Söderholm,et al.  Modeling technical change in energy system analysis: analyzing the introduction of learning-by-doing in bottom-up energy models , 2006 .

[15]  William W. Cooper,et al.  DEA and stochastic frontier analyses of the 1978 Chinese economic reforms , 1995 .

[17]  B. W. Ang,et al.  A survey of index decomposition analysis in energy and environmental studies , 2000 .

[18]  Ali Azadeh,et al.  An integrated DEA PCA numerical taxonomy approach for energy efficiency assessment and consumption optimization in energy intensive manufacturing sectors , 2007 .

[19]  A. Charnes,et al.  Data Envelopment Analysis Theory, Methodology and Applications , 1995 .

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

[21]  Günter Fandel,et al.  On the performance of universities in North Rhine-Westphalia, Germany: Government's redistribution of funds judged using DEA efficiency measures , 2007, Eur. J. Oper. Res..

[22]  Kankana Mukherjee,et al.  Energy use efficiency in U.S. manufacturing: A nonparametric analysis , 2008 .

[23]  Subhash C. Ray,et al.  Data Envelopment Analysis: Theory and Techniques for Economics and Operations Research , 2004 .

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

[25]  Timothy Coelli,et al.  An Introduction to Efficiency and Productivity Analysis , 1997 .

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

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

[28]  M. Farrell.  The Measurement of Productive Efficiency , 1957 .

[29]  Ramakrishnan Ramanathan,et al.  A multi-factor efficiency perspective to the relationships among world GDP, energy consumption and carbon dioxide emissions , 2006 .

[30]  F. Lescaroux,et al.  Decomposition of US manufacturing energy intensity and elasticities of components with respect to energy prices , 2008 .

[31]  A. Nwaokoro.  Translog Model Of Employment Substitution And Economies Of Scale In The U.S. Steel Industry, 2003 , 2011 .

[32]  Borge Hess,et al.  Efficiency analysis of East and West German electricity distribution companies – Do the “Ossis” really beat the “Wessis”? , 2007 .

[33]  T. Rutherford,et al.  Combining bottom-up and top-down , 2008 .

[34]  Ramazan Sarı,et al.  The relationship between energy and production: Evidence from Turkish manufacturing industry , 2007 .

[35]  Peter Lund,et al.  Impacts of EU carbon emission trade directive on energy-intensive industries -- Indicative micro-economic analyses , 2007 .

[36]  G. Boyd,et al.  Estimating the linkage between energy efficiency and productivity , 2000 .

[37]  Rosa Duarte,et al.  Comparison of energy intensities in European Union countries. Results of a structural decomposition analysis , 2004 .

[38]  Kingsley E. Haynes,et al.  Regional Efficiency in the Manufacturing Sector: Integrated Shift-Share and Data Envelopment Analysis , 1999 .

[39]  Aie,et al.  Tracking Industrial Energy Efficiency and CO2 Emissions , 2007 .

[40]  Subhash C. Ray.  Data Envelopment Analysis: Contents , 2004 .

[41]  Kihoon Lee,et al.  Analysis of CO2 emissions in APEC countries: A time-series and a cross-sectional decomposition using the log mean Divisia method , 2006 .

[42]  G. Boyd,et al.  Modeling of industrial energy consumption: An introduction and context , 2007 .

[43]  S. Sorrell,et al.  The rebound effect: Microeconomic definitions, limitations and extensions , 2008 .

[44]  Kemal Sarica,et al.  Efficiency assessment of Turkish power plants using data envelopment analysis , 2007 .

[45]  An Application of Post-DEA Bootstrap Regression Analysis to the Spillover of the Technology of Foreign-Invested Enterprises in China , 2000 .

[46]  Astrid Kander,et al.  The energy-capital relation—Sweden 1870–2000 , 2007 .

[47]  Henri L. F. de Groot,et al.  International Comparisons of Sectoral Energy- and Labour-Productivity Performance: Stylised Facts and Decomposition of Trends , 2004 .

[48]  Pierre-André Haldi,et al.  Dynamic formulation of a top-down and bottom-up merging energy policy model , 2003 .