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Annals of Operations Research

, Volume 255, Issue 1–2, pp 277–300 | Cite as

A target-based method for energy saving and carbon emissions reduction in China based on environmental data envelopment analysis

  • Linlin Zhao
  • Yong ZhaEmail author
  • Kangning Wei
  • Liang Liang
S.I.: Energy and Climate Policy Modeling

Abstract

It is an important issue for China’s energy and climate policy to achieve the targeted goals of energy saving and carbon emissions reduction. In this study, we develop a data envelopment analysis-based framework for energy saving and carbon emissions reduction in China. We first separate the related \(\hbox {CO}_{2}\) emissions into various classifications and propose a framework for customized adjustment of energy saving strategy. Customized targets for the inefficient decision-making unites (DMUs) are introduced to improve the efficiencies based on energy conservation technology and energy structural adjustment. Step-by-step mechanisms of energy saving and carbon emissions reduction are provided for inefficient DMUs. To overcome the difficulties of energy consumption in China, the proposed approach provides a flexible way by making proper energy saving and carbon emissions reduction strategies. Detailed analysis of the regional energy saving and carbon emissions reduction targets in China is illustrated to better verify the proposed approach.

Keywords

Energy saving Carbon emissions reduction Data envelopment analysis Customized adjustment 

Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant Nos. 71371008, 71001093), Major International (Regional) Joint Research Projects (Grant No. 71110107024) and the National Natural Science Foundation of China for Innovative Research Groups (Grant No. 71121061).

References

  1. Ang, B. W., Mu, A. R., & Zhou, P. (2010). Accounting frameworks for tracking energy efficiency trends. Energy Economics, 32(5), 1209–1219.CrossRefGoogle Scholar
  2. Argyris, C., & Schön, D. A. (1997). Organizational learning: A theory of action perspective. Reis, (77/78), 345–348.Google Scholar
  3. Bi, G., Luo, Y., Ding, J. J., & Liang, L. (2012). Environmental performance analysis of Chinese industry from a slacks-based perspective. Annals of Operations Research 1–16.Google Scholar
  4. Bian, Y. W., He, P., & Xu, H. (2013). Estimation of potential energy saving and carbon dioxide emission reduction in China based on an extended non-radial DEA approach. Energy Policy, 63, 962–971.CrossRefGoogle Scholar
  5. Brissimis, S. N., & Zervopoulos, P. D. (2012). Developing a step-by-step effectiveness assessment model for customer-oriented service organizations. European Journal of Operational Research, 223(1), 226–233.CrossRefGoogle Scholar
  6. Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444.CrossRefGoogle Scholar
  7. Christoff, P. (2010). Cold climate in Copenhagen: China and the United States at COP15. Environmental Politics, 19(4), 637–656.CrossRefGoogle Scholar
  8. Estrada, S. A., Song, H. S., Kim, Y., Namn, S. H., & Kang, S. C. (2009). A method of stepwise benchmarking for inefficient DMUs based on the proximity-based target selection. Expert Systems with Applications, 36(9), 11595–11604.CrossRefGoogle Scholar
  9. EU, D. (2010). A digital agenda for Europe. Communication, COM.Google Scholar
  10. Färe, R., & Grosskopf, S. (2004). Modeling undesirable factors in efficiency evaluation: Comment. European Journal of Operational Research, 157(1), 242–245.CrossRefGoogle Scholar
  11. Färe, R., & Primont, D. (1994). Multi-output production and duality: Theory and applications. Berlin: Springer.Google Scholar
  12. Fang, L. (2015). Centralized resource allocation based on efficiency analysis for step-by-step improvement paths. Omega, 51, 24–28.CrossRefGoogle Scholar
  13. Golembiewski, R. T., Billingsley, K., & Yeager, S. (1976). Measuring change and persistence in human affairs: Types of change generated by OD designs. The Journal of Applied Behavioral Science, 12(2), 133–157.CrossRefGoogle Scholar
  14. Guo, X. D., Zhu, L., Fan, Y., & Xie, B. C. (2011). Evaluation of potential reductions in carbon emissions in Chinese provinces based on environmental DEA. Energy Policy, 39(5), 2352–2360.CrossRefGoogle Scholar
  15. Hou, J., Zhang, P., Tian, Y., Yuan, X., & Yang, Y. (2011). Developing low-carbon economy: Actions, challenges and solutions for energy savings in China. Renewable Energy, 36(11), 3037–3042.CrossRefGoogle Scholar
  16. Hu, J. L., & Kao, C. H. (2007). Efficient energy-saving targets for APEC economies. Energy Policy, 35(1), 373–382.CrossRefGoogle Scholar
  17. Hu, J. L., & Wang, S. C. (2006). Total-factor energy efficiency of regions in China. Energy Policy, 34(17), 3206–3217.CrossRefGoogle Scholar
  18. Jiang, B., Sun, Z., & Liu, M. (2010). China’s energy development strategy under the low-carbon economy. Energy, 35(11), 4257–4264.CrossRefGoogle Scholar
  19. Lee, Y. C., Hu, J. L., & Kao, C. H. (2011). Efficient saving targets of electricity and energy for regions in China. International Journal of Electrical Power & Energy Systems, 33(6), 1211–1219.CrossRefGoogle Scholar
  20. Leung, G. C. (2011). China’s energy security: Perception and reality. Energy Policy, 39(3), 1330–1337.CrossRefGoogle Scholar
  21. Li, H., Mu, H., Zhang, M., & Gui, S. (2012). Analysis of regional difference on impact factors of China’s energy-related \(\text{ CO }_{2}\) emissions. Energy, 39(1), 319–326.CrossRefGoogle Scholar
  22. Lim, S., Bae, H., & Lee, L. H. (2011). A study on the selection of benchmarking paths in DEA. Expert Systems with Applications, 38(6), 7665–7673.CrossRefGoogle Scholar
  23. Liu, L. C., Wang, J. N., Wu, G., & Wei, Y. M. (2010a). China’s regional carbon emissions change over 1997–2007. International Journal of Energy and Environment, 1(1), 161–176.Google Scholar
  24. Liu, W. B., Meng, W., Li, X. X., & Zhang, D. Q. (2010b). DEA models with undesirable inputs and outputs. Annals of Operations Research, 173(1), 177–194.Google Scholar
  25. Lo, K. (2014). A critical review of China’s rapidly developing renewable energy and energy efficiency policies. Renewable and Sustainable Energy Reviews, 29, 508–516.CrossRefGoogle Scholar
  26. Lovell, C. K., & Pastor, J. T. (1995). Units invariant and translation invariant DEA models. Operations Research Letters, 18(3), 147–151.CrossRefGoogle Scholar
  27. Moore, R. (1999). Making common sense common practice: Models for manufacturing excellence. Houston, Texas: Gulf Publishing Company.Google Scholar
  28. Seiford, L. M., & Zhu, J. (2003). Context-dependent data envelopment analysis-measuring attractiveness and progress. Omega, 31(5), 397–408.CrossRefGoogle Scholar
  29. Shan, H. J. (2008). Re-estimating the capital stock of China: 1952–2006. Journal of Quantitative and Technical Economics, 10, 17–31. (in Chinese).Google Scholar
  30. 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(10), 6172–6179.CrossRefGoogle Scholar
  31. Statistics, I. E. A. (2011). \(\text{ CO }_{2}\) emissions from fuel combustion-highlights. IEA, Paris. http://www.iea.org/CO2highlights/CO2highlights.pdf.
  32. Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130(3), 498–509.CrossRefGoogle Scholar
  33. Wang, B. (2007). An imbalanced development of coal and electricity industries in China. Energy Policy, 35(10), 4959–4968.CrossRefGoogle Scholar
  34. Wang, K., Yu, S., & 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.CrossRefGoogle Scholar
  35. Wang, Q., Zhou, P., & Zhou, D. (2012). Efficiency measurement with carbon dioxide emissions: The case of China. Applied Energy, 90(1), 161–166.CrossRefGoogle Scholar
  36. Yu, M. M., Chern, C. C., & Hsiao, B. (2013). Human resource rightsizing using centralized data envelopment analysis: Evidence from Taiwan’s Airports. Omega, 41(1), 119–130.CrossRefGoogle Scholar
  37. Zhou, P., Ang, B. W., & Han, J. Y. (2010). Total factor carbon emission performance: A Malmquist index analysis. Energy Economics, 32(1), 194–201.CrossRefGoogle Scholar
  38. Zhou, P., Ang, B. W., & Poh, K. L. (2008a). Measuring environmental performance under different environmental DEA technologies. Energy Economics, 30(1), 1–14.CrossRefGoogle Scholar
  39. Zhou, P., & Ang, B. W. (2008). Linear programming models for measuring economy-wide energy efficiency performance. Energy Policy, 36(8), 2911–2916.CrossRefGoogle Scholar
  40. Zhou, P., Ang, B. W., & Poh, K. L. (2008b). A survey of data envelopment analysis in energy and environmental studies. European Journal of Operational Research, 189(1), 1–18.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Linlin Zhao
    • 1
  • Yong Zha
    • 1
    Email author
  • Kangning Wei
    • 2
  • Liang Liang
    • 1
  1. 1.School of ManagementUniversity of Science of Technology of ChinaHefeiPeople’s Republic of China
  2. 2.School of ManagementShandong UniversityJinanPeople’s Republic of China

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