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

  • S.I.: Energy and Climate Policy Modeling
  • Published:
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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.

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References

  • Ang, B. W., Mu, A. R., & Zhou, P. (2010). Accounting frameworks for tracking energy efficiency trends. Energy Economics, 32(5), 1209–1219.

    Article  Google Scholar 

  • Argyris, C., & Schön, D. A. (1997). Organizational learning: A theory of action perspective. Reis, (77/78), 345–348.

  • 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.

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444.

    Article  Google Scholar 

  • Christoff, P. (2010). Cold climate in Copenhagen: China and the United States at COP15. Environmental Politics, 19(4), 637–656.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • EU, D. (2010). A digital agenda for Europe. Communication, COM.

  • Färe, R., & Grosskopf, S. (2004). Modeling undesirable factors in efficiency evaluation: Comment. European Journal of Operational Research, 157(1), 242–245.

    Article  Google Scholar 

  • Färe, R., & Primont, D. (1994). Multi-output production and duality: Theory and applications. Berlin: Springer.

    Google Scholar 

  • Fang, L. (2015). Centralized resource allocation based on efficiency analysis for step-by-step improvement paths. Omega, 51, 24–28.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Hu, J. L., & Kao, C. H. (2007). Efficient energy-saving targets for APEC economies. Energy Policy, 35(1), 373–382.

    Article  Google Scholar 

  • Hu, J. L., & Wang, S. C. (2006). Total-factor energy efficiency of regions in China. Energy Policy, 34(17), 3206–3217.

    Article  Google Scholar 

  • Jiang, B., Sun, Z., & Liu, M. (2010). China’s energy development strategy under the low-carbon economy. Energy, 35(11), 4257–4264.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Leung, G. C. (2011). China’s energy security: Perception and reality. Energy Policy, 39(3), 1330–1337.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

  • 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.

  • 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.

    Article  Google Scholar 

  • Lovell, C. K., & Pastor, J. T. (1995). Units invariant and translation invariant DEA models. Operations Research Letters, 18(3), 147–151.

    Article  Google Scholar 

  • Moore, R. (1999). Making common sense common practice: Models for manufacturing excellence. Houston, Texas: Gulf Publishing Company.

  • Seiford, L. M., & Zhu, J. (2003). Context-dependent data envelopment analysis-measuring attractiveness and progress. Omega, 31(5), 397–408.

    Article  Google Scholar 

  • Shan, H. J. (2008). Re-estimating the capital stock of China: 1952–2006. Journal of Quantitative and Technical Economics, 10, 17–31. (in Chinese).

  • 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.

    Article  Google Scholar 

  • Statistics, I. E. A. (2011). \(\text{ CO }_{2}\) emissions from fuel combustion-highlights. IEA, Paris. http://www.iea.org/CO2highlights/CO2highlights.pdf.

  • Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130(3), 498–509.

    Article  Google Scholar 

  • Wang, B. (2007). An imbalanced development of coal and electricity industries in China. Energy Policy, 35(10), 4959–4968.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Wang, Q., Zhou, P., & Zhou, D. (2012). Efficiency measurement with carbon dioxide emissions: The case of China. Applied Energy, 90(1), 161–166.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Zhou, P., Ang, B. W., & Han, J. Y. (2010). Total factor carbon emission performance: A Malmquist index analysis. Energy Economics, 32(1), 194–201.

    Article  Google Scholar 

  • Zhou, P., Ang, B. W., & Poh, K. L. (2008a). Measuring environmental performance under different environmental DEA technologies. Energy Economics, 30(1), 1–14.

    Article  Google Scholar 

  • Zhou, P., & Ang, B. W. (2008). Linear programming models for measuring economy-wide energy efficiency performance. Energy Policy, 36(8), 2911–2916.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

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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).

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Correspondence to Yong Zha.

Appendix

Appendix

See Table 11.

Table 11 The effects of units variance on the slacks of carbon emissions in model (5)

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Zhao, L., Zha, Y., Wei, K. et al. A target-based method for energy saving and carbon emissions reduction in China based on environmental data envelopment analysis. Ann Oper Res 255, 277–300 (2017). https://doi.org/10.1007/s10479-016-2163-y

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  • DOI: https://doi.org/10.1007/s10479-016-2163-y

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