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Wang, Haiyan
- Trend and Factor Analysis of Beijing Areas’ Economic Performance under Restrictions of Resource and Environment
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1 School of Information Science and Technology, Beijing Forestry University, Beijing 100083, CN
1 School of Information Science and Technology, Beijing Forestry University, Beijing 100083, CN
Source
Nature Environment and Pollution Technology, Vol 15, No 4 (2016), Pagination: 1125-1132Abstract
Combining the feather of SBM directional distance function and Luenberger Index, and using the new method of productivity index's construction and decomposition, the paper studied the trend and factor analysis of Beijing area's economic performance under restrictions of resource and environment over the period of 2009-2013. Results showed that: (1) Energy consumption and pollution emissions mainly contributed to the inefficiency of Beijing's economy growth. And the regional environmental efficiency presented obvious differences from the city centre to the edge of the city, the central area with developed economy and the edge area with good environment. (2) During the 2009-2013, the trends of Beijing's Luenberger Total Factor Productivity (LTFP) was opposite to those of environmental efficiency, with the middle of the city highest. The development of suburban areas was relatively quick. (3) From a dynamic perspective, Beijing's economic performance was mainly influenced by changes in technical borders. Finally, the paper put forward relevant suggestions to enhance economic growth of Beijing and every area.Keywords
Environmental Efficiency, Total Factor Produtivity, Luenberger Total Factor, Productivity, Data Envelope Analyze, Model.References
- Arabia, B., Munisamyb, S., Emrouznejadc, A. and Shadman, F. 2014. Power industry restructuring and eco-efficiency changes: A new slacks-based model in Malmquist-Luenberger Index measurement. Energy. Policy, 68: 132-145.
- Bai, Y.P., Zhang, X.Z., Hao, Y.P. and Song, X.W. 2013. Research on regional environmental performance and its influential factors based on SBM-Malmquist-Tobit Model. Areal Research and Development, 32(2): 90-95.
- Caves, D.W., Christensen, L.R. and Diewert, W.E. 1982. The Economic theory of index numbers and the measurement of input, output and productivity. Econometrica, 50(6): 1393-1414.
- Chambers, R.G., Fare, R. and Grosskopf, S. 1996. Productivity growth in APEC countries. Pacific Economic Review, 1: 181-190.
- Chung, Y. H., Fare, R. and Grosskopf, S. 1997. Productivity and Undesirable Outputs: A directional distance function approach.
- Journal of Environmental Management, 51(3): 229-240.
- Färe, R., Grosskopf, S. and Pasurka Jr., C.A. 2007. Environmental production functions and environmental directinal distance functions. Energy, 32(7): 1055-1066.
- Jing, W.M. and Zhang, L. 2014. Environmental regulation, economic opening and China’s industrial green technology progress. Economic Research Journal, 9: 34-47.
- Li, S.W. and Li, D.S. 2008. China’s industrial total factor productivity fluctuations: 1986-2005. The Journal of Quantitative & Technical Economics, 5: 43-54.
- Liu, R.X. and An, T.L. 2012. Trend and factor analysis of Chinese economic growth performance under restrictions of resource and environment - A research based on a new method of productivity index’s. Economic Research Journal, 11: 34-46.
- Meng, F.Y., Fan, L.W., Zhou, P. and Zhou, D.Q. 2013. Measuring environmental performance in China’s industrial sectors with nonradial DEA. Mathematical and Computer Modelling, 58(5): 10471056.
- Reinhard, S., Lovell, C.A.K. and Thijssen, G.J. 2000. Environmental efficiency with multiple environmentally detrimental variables; estimated with SFA and DEA. European Journal of Operational Research, 121(2): 287-303.
- Shephard, R.W. 1970. Theory of Cost and Production Functions. New York: Princeton University Press.
- Tu, Z.G. and Shen, R.J. 2013. Does environment technology efficiency measured by traditional method underestimate environment governance efficiency? From the evidence of China’s industrial provincial panel data using environmental directional distance function based on the network DEA model. Economic Review, 5: 89-99.
- Wang, B., Wu, Y.R. and Yan, P.F. 2010. Environmental efficiency and environmental total factor productivity growth in China’s regional economies. Economic Research Journal, 5: 95-109.
- Wang, K., Yu, S.W. and Zhang, W. 2013. China’s regional energy and environmental efficiency: a DEA window analysis based dynamic evaluation. Mathematical and Computer Modelling, 58(5): 1117-1127.
- Xie, B.C. and Du, G. 2010. Utility Malmquist Index and listed thermal power Corporations empirical analysis. Chinese Journal of Management Science, 1: 46-51.
- Yan, P.F. and Wang B. 2004. Technical efficiency, technical progress and productivity growth: An empirical analysis based on DEA. Economic Research Journal, 12: 55-65.
- Yang, J., Shao, H.H. and Hu, J. 2010. Empirical study on evaluation and determinants of environmental efficiency of China. China Population Resources and Environment, 20(2): 49-55.
- The Evaluation and Spatial Correlation Analysis of Chinese Industrial Environmental Efficiency
Abstract Views :128 |
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Authors
Affiliations
1 School of Information Science and Technology, Beijing Forestry University, Beijing, 100083, CN
1 School of Information Science and Technology, Beijing Forestry University, Beijing, 100083, CN
Source
Nature Environment and Pollution Technology, Vol 15, No 3 (2016), Pagination: 1041-1048Abstract
Since the reform and opening up, with the progress of science and technology, China's economy developed rapidly, but the attendant environmental problems are becoming increasingly serious, environmental efficiency evaluation has been paid more and more attention in China. This paper used the SBM model to measure the environmental efficiency of the mainland of China's 30 provinces, autonomous regions and municipalities from 2000 to 2010 and analysed the overall situation of the industrial environment efficiency during the 10th and the 11th five-year plan in China. The results show that the overall industrial environmental efficiency of China is low, but it shows a rising trend, and there is a big gap between provinces and regions. At the same time, this paper used the Moran's I index to analyse the spatial correlation of the environmental efficiency. The results show that the industrial environmental efficiency agglomerate in the whole country and it has an obvious spatial autocorrelation. High-high environmental efficiency agglomeration area most distribute in the eastern zone, and low-low environmental efficiency agglomeration area is mainly distributed in the western zone, the location distribution showed significant differences.Keywords
Industrial Environmental Efficiency, SBM Model, Spatial Correlation Analysis.References
- Charnes, A., Cooper, W. W. and Rhodes, E. 1978. Measuring the efficiency of decision making units. European Journal of Operational Research, 2(78): 429-444.
- Guo, Ping-bo and Zhao, Hua 2009. Study on the temporal and spatial variation of the per capita income of farmers in Shandong Province based on GeoDa-GIS. Statistics and Decision Making, (4): 88-91.
- Li, Jing 2008. Environmental efficiency evaluation based on SBM model. Journal of HeFei University of Technology: Natural Science Edition, 31(5): 771-775.
- Liang, Yan-ping, Zhong, Er-shun and Zhu, Jian-jun 2003. Spatial correlation analysis of urban population distribution. Engineering Investigation, (4): 48-50.
- Lu, Feng 2004. Spatial statistical analysis of regional economic disparity in China. East China Normal University.
- Peng, Jun 2015. Analysis of regional environmental efficiency and spatial effects in China. Anhui Finance and Economics University.
- Tone, K. 2004. Dealing with undesirable outputs in DEA: a slacksbased measure (SBM) approach. The Operations Research Society of Japan, 44-45.
- Tu, Zheng-ge and Liu, Lei-ke 2011. China’s industry efficiency evaluation considering energy and environment factors-analysis of provincial data based on SBM model. Economic Review, (2): 55-65.