Energy efficiency measures and convergence in China, taking into account the effects of environmental and random factors

• Autorzy: Shen N. , Zhou J. , Zou W.
• Języki publikacji: EN

Abstrakty

EN

This paper measures energy efficiency in China using the three-stage data envelopment analysis (DEA) model and then tests the convergence of China’s energy efficiency. The study finds that environmental factors and random factors both have significant impacts on energy efficiency. After eliminating the influences of environmental and random error factors, the results present that the pure technical efficiency improves and the scale efficiency decreases, but pure technical efficiency is far lower than scale efficiency in terms of energy utilization, which indicates that low pure technical efficiency is the main factor constraining China’s energy efficiency. China’s energy efficiency presents obvious regional differences, and the energy efficiency in eastern regions is higher than that in midwestern regions. Based on the matching relationship between energy efficiency and input level, China can be regionally divided into four energy utilization modes: high efficiency and high input mode, high efficiency and low input mode, low efficiency and high input mode, and low efficiency and low input mode. Nationally, the difference in regional energy efficiency should maintain a relatively high level in the short term; divergence occurs in terms of pure technical efficiency and overall technical efficiency, while scale efficiency manifests a significant absolute convergence feature. Differential energy strategy should be carried out according to the features of different districts. Eastern regions should decrease the dependence on external energy, and develop advanced techniques with lower energy consumption. The improvement of energy efficiency in Midwest regions should depend on changing a traditionally highly energy-intensive industrial structure, undertaking clear industrial transfer from the east, excavating latent energy savings with the high-energy industry sector, and accelerating the transformation to an intensive pattern. Strengthening the energy corporation of China not only enhances energy efficiency in eastern regions but also improves energy efficiency in midwestern regions by spillover effect. Accordingly, it could improve energy efficiency balance and robustness.

• Wydawca: -
• Czasopismo: Polish Journal of Environmental Studies
• Rocznik: 2015
• Tom: 24
• Numer: 1
• Opis fizyczny: p.257-267,ref.

Twórcy

autor

Shen N.

• School of Business, Soochow University, Suzhou 215021, Republic of China

autor

Zhou J.

• School of Business, Soochow University, Suzhou 215021, Republic of China

autor

Zou W.

• School of Economics, Fujian Normal University, Fuzhou 350108, Republic of China

Bibliografia

• 1. ANG B.W. Monitoring changes in economy-wide energy efficiency: from energy-GDP ratio to composite efficiency index. Energy Policy. 34, (2), 547, 2006.
• 2. GAO Z.Y., WANG Y. Classification of China provinces according to energy productivity and analysis for the differences. The Journal of Quantitative & Technical Economics. 9, 46, 2006.
• 3. HU J L., WANG S.C. Total-factor energy efficiency of regions in China. Energy Policy. 34, (17), 3206, 2006.
• 4. HU J.L., KAO C.H. Efficient energy-saving targets for APEC economies. Energy Policy. 35, (1), 373, 2007.
• 5. MUKHERJEE K. Energy use efficiency in the Indian manufacturing sector : An interstate analysis. Energy Policy. 36, (2), 662, 2008.
• 6. HONMA S., HU J.L. Total-factor Energy Efficiency of Regions in Japan. Energy Policy. 36, (2), 821, 2008.
• 7. CHEN T.Y., LAI P.Y. A comparative study of energy utilization efficiency between Taiwan and China. Energy Policy. 38, (5), 2386, 2010.
• 8. CHUNG Y.H., FARE R., GROSSKOPF S. Productivity and undesirable outputs: A directional distance function approach. Journal of Environmental Management. 51, 229, 1997.
• 9. MACPHERSON A.J., PRINCIPE P.P., SMITH E. R. A directional distance function approach to regional environmental-economic assessments. Ecological Economics., 69, (10), 1918, 2010.
• 10. POVEDA A.C. Economic development and growth in colombia: An Empirical analysis with super-efficiency DEA and panel data. Socio-Economic Planning Sciences. 45, (4), 154, 2011.
• 11. KIM K., KIM Y. International comparison of industrial CO₂ emission trends and the energy efficiency paradox utilizing production based decomposition. Energy Economics. 34, (5), 1724, 2012.
• 12. STERN D.I. Modeling international trends in energy efficiency. Energy Economics. 34, (6), 2200, 2012.
• 13. BERTHOLT A., PAN J.N. Evolving the latent variable model as An environmental DEA technology. Omega. 41, (2), 315, 2013.
• 14. LU C. C., CHIU Y.-H., SHYU M.-K., LEE J.-H. Measuring CO₂ emission efficiency in OECD courtiers: Application of the Hybrid Efficiency model. Economic Modeling, 32, 130, 2013.
• 15. NOAILLY J. Improving the energy efficiency of buildings: The impact of environmental policy on technological innovation. Energy Economics. 34, (3), 795, 2012.
• 16. BALEZENTIS A., BALEZENTIS T., STREIMIKIENE D. The Energy Intensity in Lithuania during 1995-2009: A LMDI Approach. Energy Policy. 48, (1), 7322, 2011.
• 17. HERRERIAS M.J. World energy intensity convergence revisited: A weighted distribution dynamics approach. Energy Policy. 49, (10), 383, 2012.
• 18. MENG M., PAYNE J., LEE J. Convergence in per capita energy use among OECD countries. Energy Economics. 36, (3), 536, 2013.
• 19. STERN D.I. Modeling international trends in energy efficiency. Energy Economics. 34, (6), 2200, 2012.
• 20. FRIED L. Accounting for environmental effects and statistical noise in data envelopment analysis. Journal of Productivity Analysis. 33, (17), 157, 2002.
• 21. SHANG J.K., HUANG W.T., LO C.F,WANG F.C. Ecommerce and hotel performance: three-stage DEA analysis. The Service Industries Journal. 28, 529, 2008.
• 22. SHYU J., CHIANG T. Measuring the true managerial efficiency of bank branches in Taiwan: A three-stage DEA analysis. Expert Systems with Applications. 39, 147, 2012.
• 23. CHARNESS A., COOPER W.W.,RHODES E. Measuring the efficiency of decision units. European Journal of Operational Research. 94, (2), 95, 1978.
• 24. SHI F., SHEN K.R. The Total Factors Energy Efficiency under the Condition of Market Segmentations. World Economy. 9, 49, 2008.

Identyfikatory

DOI

10.15244/pjoes/28347