IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v13y2016i11p1116-d82519.html
   My bibliography  Save this article

Measurement of Low Carbon Economy Efficiency with a Three-Stage Data Envelopment Analysis: A Comparison of the Largest Twenty CO 2 Emitting Countries

Author

Listed:
  • Xiang Liu

    (Economics and Management School, Wuhan University, Wuhan 430072, China)

  • Jia Liu

    (School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China)

Abstract

This paper employs a three-stage approach to estimate low carbon economy efficiency in the largest twenty CO 2 emitting countries from 2000 to 2012. The approach includes the following three stages: (1) use of a data envelopment analysis (DEA) model with undesirable output to estimate the low carbon economy efficiency and calculate the input and output slacks; (2) use of a stochastic frontier approach to eliminate the impacts of external environment variables on these slacks; (3) re-estimation of the efficiency with adjusted inputs and outputs to reflect the capacity of the government to develop a low carbon economy. The results indicate that the low carbon economy efficiency performances in these countries had worsened during the studied period. The performances in the third stage are larger than that in the first stage. Moreover, in general, low carbon economy efficiency in Annex I countries of the United Nations Framework Convention on Climate Change (UNFCCC) is better than that in Non-Annex I countries. However, the gap of the average efficiency score between Annex I and Non-Annex I countries in the first stage is smaller than that in the third stage. It implies that the external environment variables show greater influence on Non-Annex I countries than that on Annex I countries. These external environment variables should be taken into account in the transnational negotiation of the responsibility of promoting CO 2 reductions. Most importantly, the developed countries (mostly in Annex I) should help the developing countries (mostly in Non-Annex I) to reduce carbon emission by opening or expanding the trade, such as encouraging the import and export of the energy-saving and sharing emission reduction technology.

Suggested Citation

  • Xiang Liu & Jia Liu, 2016. "Measurement of Low Carbon Economy Efficiency with a Three-Stage Data Envelopment Analysis: A Comparison of the Largest Twenty CO 2 Emitting Countries," IJERPH, MDPI, vol. 13(11), pages 1-14, November.
  • Handle: RePEc:gam:jijerp:v:13:y:2016:i:11:p:1116-:d:82519
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/13/11/1116/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/13/11/1116/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Ke & Lu, Bin & Wei, Yi-Ming, 2013. "China’s regional energy and environmental efficiency: A Range-Adjusted Measure based analysis," Applied Energy, Elsevier, vol. 112(C), pages 1403-1415.
    2. Yang, Hongliang & Pollitt, Michael, 2009. "Incorporating both undesirable outputs and uncontrollable variables into DEA: The performance of Chinese coal-fired power plants," European Journal of Operational Research, Elsevier, vol. 197(3), pages 1095-1105, September.
    3. Choi, Yongrok & Zhang, Ning & Zhou, P., 2012. "Efficiency and abatement costs of energy-related CO2 emissions in China: A slacks-based efficiency measure," Applied Energy, Elsevier, vol. 98(C), pages 198-208.
    4. Bi, Gong-Bing & Song, Wen & Zhou, P. & Liang, Liang, 2014. "Does environmental regulation affect energy efficiency in China's thermal power generation? Empirical evidence from a slacks-based DEA model," Energy Policy, Elsevier, vol. 66(C), pages 537-546.
    5. Zhou, P. & Ang, B.W. & Poh, K.L., 2006. "Slacks-based efficiency measures for modeling environmental performance," Ecological Economics, Elsevier, vol. 60(1), pages 111-118, November.
    6. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
    7. ZhongXiang Zhang, 2012. "Who should bear the cost of China’s carbon emissions embodied in goods for exports?," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 24(2), pages 103-117, June.
    8. W. Liu & W. Meng & X. Li & D. Zhang, 2010. "DEA models with undesirable inputs and outputs," Annals of Operations Research, Springer, vol. 173(1), pages 177-194, January.
    9. Chiu, Yung-ho & Huang, Chin-wei & Ma, Chun-Mei, 2011. "Assessment of China transit and economic efficiencies in a modified value-chains DEA model," European Journal of Operational Research, Elsevier, vol. 209(2), pages 95-103, March.
    10. Li, Ke & Lin, Boqiang, 2016. "Impact of energy conservation policies on the green productivity in China’s manufacturing sector: Evidence from a three-stage DEA model," Applied Energy, Elsevier, vol. 168(C), pages 351-363.
    11. Avkiran, Necmi K. & Rowlands, Terry, 2008. "How to better identify the true managerial performance: State of the art using DEA," Omega, Elsevier, vol. 36(2), pages 317-324, April.
    12. Stern,Nicholas, 2007. "The Economics of Climate Change," Cambridge Books, Cambridge University Press, number 9780521700801, October.
    13. Wu, Libo & Kaneko, Shinji & Matsuoka, Shunji, 2005. "Driving forces behind the stagnancy of China's energy-related CO2 emissions from 1996 to 1999: the relative importance of structural change, intensity change and scale change," Energy Policy, Elsevier, vol. 33(3), pages 319-335, February.
    14. Lee, Taehwee & Yeo, Gi-Tae & Thai, Vinh V., 2014. "Environmental efficiency analysis of port cities: Slacks-based measure data envelopment analysis approach," Transport Policy, Elsevier, vol. 33(C), pages 82-88.
    15. Harold Fried & Shelton Schmidt & Suthathip Yaisawarng, 1999. "Incorporating the Operating Environment Into a Nonparametric Measure of Technical Efficiency," Journal of Productivity Analysis, Springer, vol. 12(3), pages 249-267, November.
    16. Ferrier, Gary D. & Lovell, C. A. Knox, 1990. "Measuring cost efficiency in banking : Econometric and linear programming evidence," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 229-245.
    17. H. Fried & C. Lovell & S. Schmidt & S. Yaisawarng, 2002. "Accounting for Environmental Effects and Statistical Noise in Data Envelopment Analysis," Journal of Productivity Analysis, Springer, vol. 17(1), pages 157-174, January.
    18. Liu, Lan-Cui & Fan, Ying & Wu, Gang & Wei, Yi-Ming, 2007. "Using LMDI method to analyze the change of China's industrial CO2 emissions from final fuel use: An empirical analysis," Energy Policy, Elsevier, vol. 35(11), pages 5892-5900, November.
    19. Zhou, P. & Ang, B.W. & Poh, K.L., 2008. "A survey of data envelopment analysis in energy and environmental studies," European Journal of Operational Research, Elsevier, vol. 189(1), pages 1-18, August.
    20. Hu, Jin-Li & Kao, Chih-Hung, 2007. "Efficient energy-saving targets for APEC economies," Energy Policy, Elsevier, vol. 35(1), pages 373-382, January.
    21. Ramanathan, Ramakrishnan, 2006. "Evaluating the comparative performance of countries of the Middle East and North Africa: A DEA application," Socio-Economic Planning Sciences, Elsevier, vol. 40(2), pages 156-167, June.
    22. Seiford, Lawrence M. & Zhu, Joe, 2002. "Modeling undesirable factors in efficiency evaluation," European Journal of Operational Research, Elsevier, vol. 142(1), pages 16-20, October.
    23. Dhakal, Shobhakar, 2009. "Urban energy use and carbon emissions from cities in China and policy implications," Energy Policy, Elsevier, vol. 37(11), pages 4208-4219, November.
    24. Daniel Tyteca, 1997. "Linear Programming Models for the Measurement of Environmental Performance of Firms—Concepts and Empirical Results," Journal of Productivity Analysis, Springer, vol. 8(2), pages 183-197, May.
    25. 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.
    26. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
    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. Z-John Liu & Minh-Hieu Le & Wen-Min Lu, 2022. "An Innovation Perspective to Explore the Ecology and Social Welfare Efficiencies of Countries," IJERPH, MDPI, vol. 19(9), pages 1-18, April.
    2. Xuetong Wang & Wenyong Lai & Xiangnan Song & Chen Lu, 2018. "Implementation Efficiency of Corporate Social Responsibility in the Construction Industry: A China Study," IJERPH, MDPI, vol. 15(9), pages 1-21, September.
    3. Hui Lin & Zhou-Jing Wang, 2017. "Linguistic Multi-Attribute Group Decision Making with Risk Preferences and Its Use in Low-Carbon Tourism Destination Selection," IJERPH, MDPI, vol. 14(9), pages 1-14, September.
    4. Qi Yang & Zhonggen Sun & Hubiao Zhang, 2022. "Assessment of Urban Green Development Efficiency Based on Three-Stage DEA: A Case Study from China’s Yangtze River Delta," Sustainability, MDPI, vol. 14(19), pages 1-20, September.
    5. Yiwei Wang & Shuwang Yang & Canmian Liu & Shiying Li, 2018. "How Would Economic Development Influence Carbon Productivity? A Case from Hubei in China," IJERPH, MDPI, vol. 15(8), pages 1-13, August.
    6. Maria Gouveia & Carla Henriques & Ana Amaro, 2022. "Is the Cohesion Policy Efficient in Supporting the Transition to a Low-Carbon Economy? Some Insights with Value-Based Data Envelopment Analysis," Sustainability, MDPI, vol. 14(18), pages 1-24, September.
    7. Ping Lu & Xuan Yang & Zhou-Jing Wang, 2018. "Fuzzy Group Consensus Decision Making and Its Use in Selecting Energy-Saving and Low-Carbon Technology Schemes in Star Hotels," IJERPH, MDPI, vol. 15(9), pages 1-18, September.
    8. Xiao Gong & Jianing Mi & Chunyan Wei & Ruitao Yang, 2019. "Measuring Environmental and Economic Performance of Air Pollution Control for Province-Level Areas in China," IJERPH, MDPI, vol. 16(8), pages 1-19, April.
    9. Shimei Weng & Jianbao Chen, 2023. "How Does Industrial Upgrading Affect Carbon Productivity in China’s Service Industry?," Sustainability, MDPI, vol. 15(13), pages 1-20, July.
    10. Qing Yang & Lingmei Fu & Xingxing Liu & Mengying Cheng, 2018. "Evaluating the Efficiency of Municipal Solid Waste Management in China," IJERPH, MDPI, vol. 15(11), pages 1-23, November.
    11. Aijun Liu & Qiuyun Zhu & Xiaohui Ji & Hui Lu & Sang-Bing Tsai, 2018. "Novel Method for Perceiving Key Requirements of Customer Collaboration Low-Carbon Product Design," IJERPH, MDPI, vol. 15(7), pages 1-32, July.

    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. 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. Nelson Amowine & Zhiqiang Ma & Mingxing Li & Zhixiang Zhou & Benjamin Azembila Asunka & James Amowine, 2019. "Energy Efficiency Improvement Assessment in Africa: An Integrated Dynamic DEA Approach," Energies, MDPI, vol. 12(20), pages 1-17, October.
    3. Mushtaq Taleb & Ruzelan Khalid & Ali Emrouznejad & Razamin Ramli, 2023. "Environmental efficiency under weak disposability: an improved super efficiency data envelopment analysis model with application for assessment of port operations considering NetZero," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(7), pages 6627-6656, July.
    4. Gómez-Calvet, Roberto & Conesa, David & Gómez-Calvet, Ana Rosa & Tortosa-Ausina, Emili, 2014. "Energy efficiency in the European Union: What can be learned from the joint application of directional distance functions and slacks-based measures?," Applied Energy, Elsevier, vol. 132(C), pages 137-154.
    5. Vaninsky, Alexander, 2010. "Prospective national and regional environmental performance: Boundary estimations using a combined data envelopment – stochastic frontier analysis approach," Energy, Elsevier, vol. 35(9), pages 3657-3665.
    6. Zhou, Haibo & Yang, Yi & Chen, Yao & Zhu, Joe, 2018. "Data envelopment analysis application in sustainability: The origins, development and future directions," European Journal of Operational Research, Elsevier, vol. 264(1), pages 1-16.
    7. Mai, Nhat Chi, 2015. "Efficiency of the banking system in Vietnam under financial liberalization," OSF Preprints qsf6d, Center for Open Science.
    8. Meng, Fanyi & Su, Bin & Thomson, Elspeth & Zhou, Dequn & Zhou, P., 2016. "Measuring China’s regional energy and carbon emission efficiency with DEA models: A survey," Applied Energy, Elsevier, vol. 183(C), pages 1-21.
    9. Jie Wu & Xiang Lu & Dong Guo & Liang Liang, 2017. "Slacks-Based Efficiency Measurements with Undesirable Outputs in Data Envelopment Analysis," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(04), pages 1005-1021, July.
    10. Wang, Zhaohua & Li, Yi & Wang, Ke & Huang, Zhimin, 2017. "Environment-adjusted operational performance evaluation of solar photovoltaic power plants: A three stage efficiency analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 1153-1162.
    11. Noor Ramli & Susila Munisamy & Behrouz Arabi, 2013. "Scale directional distance function and its application to the measurement of eco-efficiency in the manufacturing sector," Annals of Operations Research, Springer, vol. 211(1), pages 381-398, December.
    12. Xuetong Wang & Wenyong Lai & Xiangnan Song & Chen Lu, 2018. "Implementation Efficiency of Corporate Social Responsibility in the Construction Industry: A China Study," IJERPH, MDPI, vol. 15(9), pages 1-21, September.
    13. Liu, John S. & Lu, Louis Y.Y. & Lu, Wen-Min, 2016. "Research fronts in data envelopment analysis," Omega, Elsevier, vol. 58(C), pages 33-45.
    14. Yiwen Bian & Kangjuan Lv & Anyu Yu, 2017. "China’s regional energy and carbon dioxide emissions efficiency evaluation with the presence of recovery energy: an interval slacks-based measure approach," Annals of Operations Research, Springer, vol. 255(1), pages 301-321, August.
    15. Lampe, Hannes W. & Hilgers, Dennis, 2015. "Trajectories of efficiency measurement: A bibliometric analysis of DEA and SFA," European Journal of Operational Research, Elsevier, vol. 240(1), pages 1-21.
    16. 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.
    17. Avkiran, Necmi K. & Rowlands, Terry, 2008. "How to better identify the true managerial performance: State of the art using DEA," Omega, Elsevier, vol. 36(2), pages 317-324, April.
    18. Yang Lin & Longzhong Yan & Ying-Ming Wang, 2019. "Performance Evaluation and Investment Analysis for Container Port Sustainable Development in China: An Inverse DEA Approach," Sustainability, MDPI, vol. 11(17), pages 1-13, August.
    19. Taleb, Mushtaq & Khalid, Ruzelan & Ramli, Razamin & Ghasemi, Mohammad Reza & Ignatius, Joshua, 2022. "An integrated bi-objective data envelopment analysis model for measuring returns to scale," European Journal of Operational Research, Elsevier, vol. 296(3), pages 967-979.
    20. Carla Henriques & Clara Viseu, 2022. "Are ERDFs Devoted to Boosting ICTs in SMEs Inefficient? A Three-Stage SBM Approach," Sustainability, MDPI, vol. 14(17), pages 1-20, August.

    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:gam:jijerp:v:13:y:2016:i:11:p:1116-:d:82519. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.