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Efficiency assessment of the energy consumption and economic indicators in Beijing under the influence of short-term climatic factors: based on data envelopment analysis methodology

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  • Zaiwu Gong
  • Yue Zhao
  • Xinming Ge

Abstract

In this paper, the data envelopment analysis (DEA) method is introduced to analyze the input–output efficiency of energy consumption and economic indicators in Beijing city under the influence of short-term climatic factors. Total energy consumption, fixed asset investment, average temperature, precipitation, sunshine hours, average wind velocity and the average pressure being employed as the input variables, gross domestic product (GDP) and per capita disposable income being employed as the output variables, effective technology and the validity of the scale of DEA of 31 decision-making units (DMUs) under the influence of the short-term climatic factors are analyzed, and the inefficient DMUs are improved. Empirical analysis shows that both energy consumption and economic growth are sensitive to short-term climate condition, and the reasonable employing of extreme climatic conditions is a question worthy of consideration. This study provides effective basis for the scientific and reasonable arrangement of Beijing city’s short-term climatic resources and energy–economic development. Copyright The Author(s) 2014

Suggested Citation

  • Zaiwu Gong & Yue Zhao & Xinming Ge, 2014. "Efficiency assessment of the energy consumption and economic indicators in Beijing under the influence of short-term climatic factors: based on data envelopment analysis methodology," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 71(2), pages 1145-1157, March.
    • Handle: RePEc:spr:nathaz:v:71:y:2014:i:2:p:1145-1157
    DOI: 10.1007/s11069-013-0658-2
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    References listed on IDEAS

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    1. William W. Cooper & Lawrence M. Seiford & Joe Zhu, 2011. "Data Envelopment Analysis: History, Models, and Interpretations," International Series in Operations Research & Management Science, in: William W. Cooper & Lawrence M. Seiford & Joe Zhu (ed.), Handbook on Data Envelopment Analysis, chapter 0, pages 1-39, Springer.
    2. Ju-Liang Jin & Yi-Ming Wei & Le-Le Zou & Li Liu & Juan Fu, 2012. "Risk evaluation of China’s natural disaster systems: an approach based on triangular fuzzy numbers and stochastic simulation," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 62(1), pages 129-139, May.
    3. A. Charnes & W. W. Cooper & E. Rhodes, 1981. "Evaluating Program and Managerial Efficiency: An Application of Data Envelopment Analysis to Program Follow Through," Management Science, INFORMS, vol. 27(6), pages 668-697, June.
    4. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
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    Cited by:

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    2. Shaojian Qu & Yongyi Zhou, 2017. "A Study of The Effect of Demand Uncertainty for Low-Carbon Products Using a Newsvendor Model," IJERPH, MDPI, vol. 14(11), pages 1-24, October.
    3. Chen, Ya & Pan, Yongbin & Wang, Mengyuan & Ding, Tao & Zhou, Zhixiang & Wang, Ke, 2023. "How do industrial sectors contribute to carbon peaking and carbon neutrality goals? A heterogeneous energy efficiency analysis for Beijing," Structural Change and Economic Dynamics, Elsevier, vol. 66(C), pages 67-80.
    4. Ma-Lin Song & Yuan-Xiang Zhou & Rong-Rong Zhang & Ron Fisher, 2017. "Environmental efficiency evaluation with left–right fuzzy numbers," Operational Research, Springer, vol. 17(3), pages 697-714, October.
    5. Jia-Yin Yin & Yun-Fei Cao & Bao-Jun Tang, 2019. "Fairness of China’s provincial energy environment efficiency evaluation: empirical analysis using a three-stage data envelopment analysis model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 95(1), pages 343-362, January.

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