IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v71y2014i2p1145-1157.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11069-013-0658-2
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11069-013-0658-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sueyoshi, Toshiyuki & Yuan, Yan & Goto, Mika, 2017. "A literature study for DEA applied to energy and environment," Energy Economics, Elsevier, vol. 62(C), pages 104-124.
    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. 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.
    4. 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.
    5. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cova-Alonso, David José & Díaz-Hernández, Juan José & Martínez-Budría, Eduardo, 2021. "A strong efficiency measure for CCR/BCC models," European Journal of Operational Research, Elsevier, vol. 291(1), pages 284-295.
    2. Erbetta, Fabrizio & Menozzi, Anna & Corbetta, Guido & Fraquelli, Giovanni, 2013. "Assessing family firm performance using frontier analysis techniques: Evidence from Italian manufacturing industries," Journal of Family Business Strategy, Elsevier, vol. 4(2), pages 106-117.
    3. Franz R. Hahn, 2007. "Determinants of Bank Efficiency in Europe. Assessing Bank Performance Across Markets," WIFO Studies, WIFO, number 31499.
    4. Büschken, Joachim, 2009. "When does data envelopment analysis outperform a naïve efficiency measurement model?," European Journal of Operational Research, Elsevier, vol. 192(2), pages 647-657, January.
    5. Matthias Klumpp & Dominic Loske, 2021. "Sustainability and Resilience Revisited: Impact of Information Technology Disruptions on Empirical Retail Logistics Efficiency," Sustainability, MDPI, vol. 13(10), pages 1-20, May.
    6. Soteriou, Andreas C. & Zenios, Stavros A., 1999. "Using data envelopment analysis for costing bank products," European Journal of Operational Research, Elsevier, vol. 114(2), pages 234-248, April.
    7. Margareta Gardijan & Zrinka Lukač, 2018. "Measuring the relative efficiency of the food and drink industry in the chosen EU countries using the data envelopment analysis with missing data," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 26(3), pages 695-713, September.
    8. Fethi, Meryem Duygun & Pasiouras, Fotios, 2010. "Assessing bank efficiency and performance with operational research and artificial intelligence techniques: A survey," European Journal of Operational Research, Elsevier, vol. 204(2), pages 189-198, July.
    9. Ruiz, Jose L. & Sirvent, Inmaculada, 2001. "Techniques for the assessment of influence in DEA," European Journal of Operational Research, Elsevier, vol. 132(2), pages 390-399, July.
    10. Peyrache, Antonio & Rose, Christiern & Sicilia, Gabriela, 2020. "Variable selection in Data Envelopment Analysis," European Journal of Operational Research, Elsevier, vol. 282(2), pages 644-659.
    11. Imanirad, Raha & Cook, Wade D. & Aviles-Sacoto, Sonia Valeria & Zhu, Joe, 2015. "Partial input to output impacts in DEA: The case of DMU-specific impacts," European Journal of Operational Research, Elsevier, vol. 244(3), pages 837-844.
    12. Glover, Fred & Sueyoshi, Toshiyuki, 2009. "Contributions of Professor William W. Cooper in Operations Research and Management Science," European Journal of Operational Research, Elsevier, vol. 197(1), pages 1-16, August.
    13. Vittadini, Giorgio & Sturaro, Caterina & Folloni, Giuseppe, 2022. "Non-Cognitive Skills and Cognitive Skills to measure school efficiency," Socio-Economic Planning Sciences, Elsevier, vol. 81(C).
    14. Eugenia Nissi & Massimiliano Giacalone & Carlo Cusatelli, 2019. "The Efficiency of the Italian Judicial System: A Two Stage Data Envelopment Analysis Approach," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 146(1), pages 395-407, November.
    15. M. Agovino & A. Rapposelli, 2016. "Disability and Work: A Two-Stage Empirical Analysis of Italian Evidence at Provincial Level in Providing Employment for Disabled Workers," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 125(2), pages 635-648, January.
    16. Tavana, Madjid & Ebrahimnejad, Ali & Santos-Arteaga, Francisco J. & Mansourzadeh, Seyed Mehdi & Matin, Reza Kazemi, 2018. "A hybrid DEA-MOLP model for public school assessment and closure decision in the City of Philadelphia," Socio-Economic Planning Sciences, Elsevier, vol. 61(C), pages 70-89.
    17. Mercedes Beltrán-Esteve & José Gómez-Limón & Andrés Picazo-Tadeo & Ernest Reig-Martínez, 2014. "A metafrontier directional distance function approach to assessing eco-efficiency," Journal of Productivity Analysis, Springer, vol. 41(1), pages 69-83, February.
    18. Adrian Nerja, 2023. "An efficiency analysis of Spanish airports," Papers 2311.16156, arXiv.org.
    19. Petridis, Konstantinos & Malesios, Chrisovalantis & Arabatzis, Garyfallos & Thanassoulis, Emmanuel, 2013. "Efficiency analysis of forestry journals: Suggestions for improving journals’ quality," Journal of Informetrics, Elsevier, vol. 7(2), pages 505-521.
    20. K. Tone & M. Tsutsui, 2015. "How to Deal with Non-Convex Frontiers in Data Envelopment Analysis," Journal of Optimization Theory and Applications, Springer, vol. 166(3), pages 1002-1028, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:nathaz:v:71:y:2014:i:2:p:1145-1157. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.