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Energy Efficiency

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01-06-2014 | Original Article

Energy efficiency evaluation for regions in China: an application of DEA and Malmquist indices

Authors: Ai-Hua Wu, Yan-Ying Cao, Bo Liu

Published in: Energy Efficiency | Issue 3/2014

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Abstract

With the development of economy, the impression of energy and environment is increasing, and improving energy efficiency plays a vital role in the sustainable economic development in China. An application of data envelopment analysis (DEA) and Malmquist indices has been used in this paper to investigate energy utilization efficiency of 30 provinces, autonomous regions, and municipalities (apart from Hong Kong, Macau, Taiwan, and Tibet) in China. The results show that industries in the eastern area have the best average energy efficiency for the period 2006–2009, followed by the central area. The industrial energy overall technical efficiency, industrial energy pure technical efficiency, industrial energy scale efficiency, etc. in 30 different regions of China are examined. By the comparative analysis of energy efficiency, the results show that the energy efficiency of Beijing, Shanghai, Jiangsu, Zhejiang, Guangdong, and Hainan provinces is effective, and the lowest six regions of energy efficiency are Ningxia, Inner Mongolia, Shaanxi, Liaoning, Chongqing, and Jilin, whose energy efficiency is in the level of 0.5 or so. The results mean that the eastern regions are better than the central and western regions according to energy efficiency.

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Ang, B. W. (2006). Monitoring changes in economy-wide energy efficiency: from energy-GDP ratio to composite efficiency index. Energy Policy, 34, 574–582.CrossRefMathSciNet

Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Models for estimation of technical and scale in efficiencies in data envelopment analysis. Management Science, 30, 1078–1092.CrossRefMATH

Barros, C. P., Managi, S., & Yoshida, Y. (2010). Technical efficiency, regulation, and heterogeneity in Japanese airports. Pacific Economic Review, 15(5), 685–696.CrossRef

Caves, D. W., Christensen, L. R., & Diewert, W. E. (1982). The economic theory of index numbers and the measurement of input, output and productivity. Econometrica, 50, 1393–1414.CrossRefMATH

Chang, T. P., & Hu, J. L. (2010). Total-factor energy productivity growth, technical progress, and efficiency change: an empirical study of China. Applied Energy, 87, 3262–3270.CrossRef

Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(2), 429–444.CrossRefMATHMathSciNet

Cook, W. D., & Seiford, L. M. (2009). Data envelopment analysis (DEA)—thirty years on. European Journal of Operational Research, 192(1), 1–17.CrossRefMATHMathSciNet

Emrouznejad, A., Parker, B. R., & Tavares, G. (2008). Evaluation of research in efficiency and productivity: a survey and analysis of the first 30 years of scholarly literature in DEA. Socio-Economic Planning Sciences, 42, 151–157.CrossRef

Fang, H., Wu, J. J., & Zeng, C. (2009). Comparative study on efficiency performance of listed coal mining companies in China and the US. Energy Policy, 37, 5140–5148.CrossRef

Fare, R., Grosskopf, S., & Norris, M. (1994). Productivity growth, technical progress and efficiency change in industrialized countries. American Economic Review, 84, 66–83.

Fare, R., Grosskopf, S., & Lovell, C. A. K. (1995). The measurement of efficiency of production. Boston: Kluwer.

Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society, Series A, 120(III), 253–281.CrossRef

Filippini, M., & Hunt, L. C. (2011). Energy demand and energy efficiency in the OECD countries: a stochastic demand frontier approach. The energy journal, 32(2), 59–80.CrossRef

Fukuyama, H. (2001). Returns to scale and scale elasticity in Farrell, Russell and additive models. Journal of Productivity Analysis, 16, 225–239.CrossRef

Golany, B., & Roll, Y. (1989). An application procedure for DEA. Omega, 17, 237–250.CrossRef

Greene, W. (2005). Reconsidering heterogeneity in panel data estimators of the stochastic frontier model. Journal of Econometrics, 126, 269–303.CrossRefMathSciNet

Honma, S., & Hu, J. L. (2008). Total-factor energy efficiency of regions in Japan. Energy Policy, 36, 821–833.CrossRef

Honma, S., & Hu, J. L. (2009). Total-factor energy productivity growth of regions in Japan. Energy Policy, 37(10), 821–833.

Hu, J. L., & Kao, C. H. (2007). Efficient energy—saving targets for APEC economies. Energy Policy, 35, 373–382.CrossRef

Hu, J. L., & Wang, S. C. (2006). Total-factor energy efficiency of regions in China. Energy Policy, 34, 3206–3217.CrossRef

Kopsakangas-Savolainen, M., & Svento, R. (2011). Observed and unobserved heterogeneity in stochastic frontier models: an application to the electricity distribution industry. Energy Economics, 33, 304–310.CrossRef

Kuosmanen, T. (2012). Stochastic semi-nonparametric frontier estimation of electricity distribution networks: application of the StoNED method in the Finnish regulatory model. Energy Economics, 34, 2189–2199.CrossRef

Kuosmanen, T., & Kortelainen, M. (2012). Stochastic non-smooth envelopment of data: semi-parametric frontier estimation subject to shape constraints. Journal of Productivity Analysis, 38, 11–28.CrossRef

Lee, W. S., & Lee, K. P. (2009). Benchmarking the performance of building energy management using data envelopment analysis. Applied Thermal Engineering, 29, 3269–3273.CrossRef

Lo, K., & Wang, M. Y. (2013). Energy conservation in China’s Twelfth Five-Year Plan period: continuation or paradigm shift? Renewable and Sustainable Energy Reviews, 18, 499–507.CrossRef

Mester, L. (1997). Measuring heterogeneity at US banks: accounting for heterogeneity is important. European Journal of Operational Research, 98(2), 230–242.CrossRefMATH

Odeck, J. (2000). Assessing the relative efficiency and productivity growth of vehicle inspection services: an application of DEA and Malmquist indices. European Journal of Operational Research, 126, 501–514.CrossRefMATH

Patterson, M. G. (1996). What is energy efficiency? Concepts, indicators and methodological issues. Energy Policy, 24, 377–390.CrossRef

Rao, X., Wu, J., Zhang, Z. Y., & Liu, B. (2012). Energy efficiency and energy saving potential in China: an analysis based on slacks-based measure model. Computers & Industrial Engineering, 63, 578–584.CrossRef

Saen, R. F., Memariani, A., & Lotfi, F. H. (2005). Determining relative efficiency of slightly non-homogeneous decision making units by data envelopment analysis: a case study in IROST. Applied Mathematics and Computation, 165, 313–328.CrossRefMATHMathSciNet

Samoilenko, S., & Osei-Bryson, K. M. (2008). Increasing the discriminatory power of DEA in the presence of the sample heterogeneity with cluster analysis and decision tree. Expert Systems with Applications, 34, 1568–1581.CrossRef

Sarrico, C. S., & Dyson, R. G. (2000). Using DEA for planning in UK university—an institutional perspective. Journal of Operational Research Society, 51(7), 789–800.MATH

Shi, G. M., Bi, J., & Wang, J. N. (2010). Chinese regional industrial energy efficiency evaluation based on a DEA model of fixing non-energy inputs. Energy Policy, 38, 6172–6179.CrossRef

Wang, K., Yu, S. W., & Zhang, W. (2012). China’s regional energy and environmental efficiency: a DEA window analysis based dynamic evaluation. Mathematical and Computer Modelling. doi:10.1016/j.mcm.2011.11.067.

Wei, Y. M., Liao, H., & Fan, Y. (2007). An empirical analysis of energy efficiency in China’s iron and steel sector. Energy, 32, 2262–2270.CrossRef

Wu, Y. (2004). Openness, productivity and growth in the APEC economies. Empirical Economics, 29, 593–604.CrossRef

Zhang, X. P., Cheng, X. M., Yuan, J. H., & Gao, X. J. (2011). Total-factor energy efficiency in developing countries. Energy Policy, 39, 644–650.CrossRef

Zhou, P., Ang, B. W., & Poh, K. L. (2008). A survey of data envelopment analysis in energy and environmental studies. European Journal of Operational Research, 189, 1–18.CrossRefMATHMathSciNet

Title: Energy efficiency evaluation for regions in China: an application of DEA and Malmquist indices
Authors: Ai-Hua Wu
Yan-Ying Cao
Bo Liu
Publication date: 01-06-2014
Publisher: Springer Netherlands
Published in: Energy Efficiency / Issue 3/2014
Print ISSN: 1570-646X
Electronic ISSN: 1570-6478
DOI: https://doi.org/10.1007/s12053-013-9232-8

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