IDEAS home Printed from https://ideas.repec.org/a/sae/engenv/v31y2020i6p943-960.html
   My bibliography  Save this article

Comparing regional differences in global energy performance

Author

Listed:
  • Liang-Han Ma
  • Jin-Chi Hsieh
  • Yung-Ho Chiu

Abstract

This study comprehensively considers any input and output that has a certain physical dimension, utilizes the super slacks-based measure directional distance function data envelopment analysis (DDF-DEA) model to measure global energy performance in the period 2010–2016, and compares regional differences in Americas, Europe and Asia. We employ contained directional, non-directional, and undesirable inputs and outputs, which include population number, fossil fuels energy consumption, gross capital formation, gross domestic product, renewable energy consumption, and carbon dioxide emission. From the full energy efficiency and ranking of the DDF-DEA approach herein, the empirical results show that Trinidad and Tobago exhibits the best efficiency (2.8194) and Uzbekistan has the worst efficiency (0.5734). The best regional energy performance is Americas, and the worst is Asia for 2010–2016, showing that regional energy policies have a significant impact. The Environmental Performance Index is an important sustainable environment index, and most Environmental Performance Index levels are quite consistent with the trend of energy efficiency and ranking with DDF-DEA in this study. The energy efficiencies of the higher Environmental Performance Index group and higher renewable energy consumption group are significantly larger than the lower Environmental Performance Index group and better than the lower renewable energy consumption group, respectively. Therefore, we suggest that all countries should adjust their future energy using a strategy based on annual Environmental Performance Index. Their goals can be to reduce fossil fuels energy consumption, increase renewable energy use, and reduce undesirable output of carbon dioxide. Doing so will help them to develop their economies while taking into account a sustainable environment, thus achieving sustainable economic development.

Suggested Citation

  • Liang-Han Ma & Jin-Chi Hsieh & Yung-Ho Chiu, 2020. "Comparing regional differences in global energy performance," Energy & Environment, , vol. 31(6), pages 943-960, September.
    • Handle: RePEc:sae:engenv:v:31:y:2020:i:6:p:943-960
    DOI: 10.1177/0958305X19882404
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0958305X19882404
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0958305X19882404?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
    ---><---

    References listed on IDEAS

    as
    1. Dariush Khezrimotlagh & Yao Chen, 2018. "Data Envelopment Analysis," International Series in Operations Research & Management Science, in: Decision Making and Performance Evaluation Using Data Envelopment Analysis, chapter 0, pages 217-234, Springer.
    2. Sueyoshi, Toshiyuki & Goto, Mika, 2015. "Environmental assessment on coal-fired power plants in U.S. north-east region by DEA non-radial measurement," Energy Economics, Elsevier, vol. 50(C), pages 125-139.
    3. Tone, Kaoru & Sahoo, Biresh K., 2004. "Degree of scale economies and congestion: A unified DEA approach," European Journal of Operational Research, Elsevier, vol. 158(3), pages 755-772, November.
    4. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    5. Sueyoshi, Toshiyuki & Goto, Mika, 2011. "DEA approach for unified efficiency measurement: Assessment of Japanese fossil fuel power generation," Energy Economics, Elsevier, vol. 33(2), pages 292-303, March.
    6. Nuri Ozgur DOGAN & Can Tansel TUGCU, 2015. "Energy Efficiency in Electricity Production: A Data Envelopment Analysis (DEA) Approach for the G-20 Countries," International Journal of Energy Economics and Policy, Econjournals, vol. 5(1), pages 246-252.
    7. Guo, Xiaoying & Lu, Ching-Cheng & Lee, Jen-Hui & Chiu, Yung-Ho, 2017. "Applying the dynamic DEA model to evaluate the energy efficiency of OECD countries and China," Energy, Elsevier, vol. 134(C), pages 392-399.
    8. Wang, Zhaohua & Feng, Chao, 2015. "A performance evaluation of the energy, environmental, and economic efficiency and productivity in China: An application of global data envelopment analysis," Applied Energy, Elsevier, vol. 147(C), pages 617-626.
    9. Samuel De Alencar Bezerra & Francisco Jackson dos Santos & Plácido Rogerio Pinheiro & Fábio Rocha Barbosa, 2017. "Dynamic Evaluation of the Energy Efficiency of Environments in Brazilian University Classrooms Using DEA," Sustainability, MDPI, vol. 9(12), pages 1-14, December.
    10. Woo, Chungwon & Chung, Yanghon & Chun, Dongphil & Seo, Hangyeol & Hong, Sungjun, 2015. "The static and dynamic environmental efficiency of renewable energy: A Malmquist index analysis of OECD countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 367-376.
    11. Goto, Mika & Otsuka, Akihiro & Sueyoshi, Toshiyuki, 2014. "DEA (Data Envelopment Analysis) assessment of operational and environmental efficiencies on Japanese regional industries," Energy, Elsevier, vol. 66(C), pages 535-549.
    12. Aparicio, Juan & Pastor, Jesus T. & Vidal, Fernando, 2016. "The directional distance function and the translation invariance property," Omega, Elsevier, vol. 58(C), pages 1-3.
    13. R. G. Chambers & Y. Chung & R. Färe, 1998. "Profit, Directional Distance Functions, and Nerlovian Efficiency," Journal of Optimization Theory and Applications, Springer, vol. 98(2), pages 351-364, August.
    14. Li, Ke & Lin, Boqiang, 2015. "The improvement gap in energy intensity: Analysis of China's thirty provincial regions using the improved DEA (data envelopment analysis) model," Energy, Elsevier, vol. 84(C), pages 589-599.
    15. Tone, Kaoru, 2002. "A slacks-based measure of super-efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 143(1), pages 32-41, November.
    16. Chaido Dritsaki & Melina Dritsaki, 2014. "Causal Relationship between Energy Consumption, Economic Growth and CO2 Emissions: A Dynamic Panel Data Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 4(2), pages 125-136.
    17. 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.
    18. Chen, Nengcheng & Xu, Lei & Chen, Zeqiang, 2017. "Environmental efficiency analysis of the Yangtze River Economic Zone using super efficiency data envelopment analysis (SEDEA) and tobit models," Energy, Elsevier, vol. 134(C), pages 659-671.
    19. Rolf Färe & Shawna Grosskopf, 2000. "Theory and Application of Directional Distance Functions," Journal of Productivity Analysis, Springer, vol. 13(2), pages 93-103, March.
    20. Mardani, Abbas & Zavadskas, Edmundas Kazimieras & Streimikiene, Dalia & Jusoh, Ahmad & Khoshnoudi, Masoumeh, 2017. "A comprehensive review of data envelopment analysis (DEA) approach in energy efficiency," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1298-1322.
    21. Chambers, Robert G. & Chung, Yangho & Fare, Rolf, 1996. "Benefit and Distance Functions," Journal of Economic Theory, Elsevier, vol. 70(2), pages 407-419, August.
    22. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
    Full references (including those not matched with items on IDEAS)

    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. Jin-chi Hsieh & Ching-cheng Lu & Ying Li & Yung-ho Chiu & Ya-sue Xu, 2019. "Environmental Assessment of European Union Countries," Energies, MDPI, vol. 12(2), pages 1-18, January.
    2. Abbas Mardani & Dalia Streimikiene & Tomas Balezentis & Muhamad Zameri Mat Saman & Khalil Md Nor & Seyed Meysam Khoshnava, 2018. "Data Envelopment Analysis in Energy and Environmental Economics: An Overview of the State-of-the-Art and Recent Development Trends," Energies, MDPI, vol. 11(8), pages 1-21, August.
    3. Rafael Benítez & Vicente Coll-Serrano & Vicente J. Bolós, 2021. "deaR-Shiny: An Interactive Web App for Data Envelopment Analysis," Sustainability, MDPI, vol. 13(12), pages 1-19, June.
    4. Laura Calzada-Infante & Ana María López-Narbona & Alberto Núñez-Elvira & Javier Orozco-Messana, 2020. "Assessing the Efficiency of Sustainable Cities Using an Empirical Approach," Sustainability, MDPI, vol. 12(7), pages 1-13, March.
    5. Li, Hai-ling & Zhu, Xue-hong & Chen, Jin-yu & Jiang, Fei-tao, 2019. "Environmental regulations, environmental governance efficiency and the green transformation of China's iron and steel enterprises," Ecological Economics, Elsevier, vol. 165(C), pages 1-1.
    6. Tavana, Madjid & Izadikhah, Mohammad & Toloo, Mehdi & Roostaee, Razieh, 2021. "A new non-radial directional distance model for data envelopment analysis problems with negative and flexible measures," Omega, Elsevier, vol. 102(C).
    7. Fang-Rong Ren & Ze Tian & Yu-Ting Shen & Yung-Ho Chiu & Tai-Yu Lin, 2019. "Energy, CO 2 , and AQI Efficiency and Improvement of the Yangtze River Economic Belt," Energies, MDPI, vol. 12(4), pages 1-17, February.
    8. Arabmaldar, Aliasghar & Sahoo, Biresh K. & Ghiyasi, Mojtaba, 2023. "A generalized robust data envelopment analysis model based on directional distance function," European Journal of Operational Research, Elsevier, vol. 311(2), pages 617-632.
    9. Juan Aparicio & Magdalena Kapelko, 2019. "Enhancing the Measurement of Composite Indicators of Corporate Social Performance," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 144(2), pages 807-826, July.
    10. Fangqing Wei & Junfei Chu & Jiayun Song & Feng Yang, 2019. "A cross-bargaining game approach for direction selection in the directional distance function," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 41(3), pages 787-807, September.
    11. Chen, Jiabin & Wen, Shaobo & Liu, Yuchen, 2022. "Research on the efficiency of the mining industry in China from the perspective of time and space," Resources Policy, Elsevier, vol. 75(C).
    12. 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.
    13. Si-Si Dong & Liang-Qun Qi & Jia-Quan Li, 2022. "Evaluation of the Implementation Effect of China’s Industrial Sector Supply-Side Reform: From the Perspective of Energy and Environmental Efficiency," Energies, MDPI, vol. 15(9), pages 1-17, April.
    14. Khalid Mehmood & Yaser Iftikhar & Shouming Chen & Shaheera Amin & Alia Manzoor & Jinlong Pan, 2020. "Analysis of Inter-Temporal Change in the Energy and CO 2 Emissions Efficiency of Economies: A Two Divisional Network DEA Approach," Energies, MDPI, vol. 13(13), pages 1-17, June.
    15. Aparicio, Juan & Monge, Juan F. & Ramón, Nuria, 2021. "A new measure of technical efficiency in data envelopment analysis based on the maximization of hypervolumes: Benchmarking, properties and computational aspects," European Journal of Operational Research, Elsevier, vol. 293(1), pages 263-275.
    16. Yuan, Qianqian & Fang Chin Cheng, Charles & Wang, Jiayu & Zhu, Tian-Tian & Wang, Ke, 2020. "Inclusive and sustainable industrial development in China: An efficiency-based analysis for current status and improving potentials," Applied Energy, Elsevier, vol. 268(C).
    17. Jin XU & Panagiotis ZERVOPOULOS & Zhenhua QIAN & Gang CHENG, 2012. "A Universal Solution For Units - Invariance In Data Envelopment Analysis," Theoretical and Practical Research in the Economic Fields, ASERS Publishing, vol. 3(2), pages 121-128.
    18. Huang, Beijia & Zhang, Long & Ma, Linmao & Bai, Wuliyasu & Ren, Jingzheng, 2021. "Multi-criteria decision analysis of China’s energy security from 2008 to 2017 based on Fuzzy BWM-DEA-AR model and Malmquist Productivity Index," Energy, Elsevier, vol. 228(C).
    19. Hongli Liu & Xiaoyu Yan & Jinhua Cheng & Jun Zhang & Yan Bu, 2021. "Driving Factors for the Spatiotemporal Heterogeneity in Technical Efficiency of China’s New Energy Industry," Energies, MDPI, vol. 14(14), pages 1-21, July.
    20. Pastor, Jesus T. & Lovell, C.A. Knox & Aparicio, Juan, 2020. "Defining a new graph inefficiency measure for the proportional directional distance function and introducing a new Malmquist productivity index," European Journal of Operational Research, Elsevier, vol. 281(1), pages 222-230.

    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:sae:engenv:v:31:y:2020:i:6:p:943-960. 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: SAGE Publications (email available below). General contact details of provider: .

    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.