IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i20p13318-d944230.html
   My bibliography  Save this article

Data-Driven Robust Data Envelopment Analysis for Evaluating the Carbon Emissions Efficiency of Provinces in China

Author

Listed:
  • Shaojian Qu

    (School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Yuting Xu

    (School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Ying Ji

    (School of Management, Shanghai University, Shanghai 200444, China)

  • Can Feng

    (School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Jinpeng Wei

    (School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Shan Jiang

    (School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China)

Abstract

To combat global warming, China proposed the “dual carbon” policy in 2020. In this context, it becomes crucial to improve carbon emissions efficiency. Currently, some scholars have utilized data envelopment analysis (DEA) to study carbon emissions efficiency. However, uncertainty about climate and government economic policy is ignored. This paper establishes a robust DEA model to reduce uncertainty and improve robustness. First, robust optimization theory is combined with DEA to establish the robust DEA model. Second, considering three uncertainty sets (box set, ellipsoid set, and polyhedron set), a robust DEA model for different situations is considered. Finally, to address the problem of over-conservatism in robust optimization, this paper applies the data-driven robust DEA model to further analyze the carbon emissions efficiency of China. The results of the data-driven robust DEA model suggest that the government should focus on coordinated regional development, promote the transformation and upgrading of the energy structure, innovate in green technology, and advocate for people to live a green and low-carbon lifestyle.

Suggested Citation

  • Shaojian Qu & Yuting Xu & Ying Ji & Can Feng & Jinpeng Wei & Shan Jiang, 2022. "Data-Driven Robust Data Envelopment Analysis for Evaluating the Carbon Emissions Efficiency of Provinces in China," Sustainability, MDPI, vol. 14(20), pages 1-26, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:20:p:13318-:d:944230
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/20/13318/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/20/13318/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yamaji, Kenji & Matsuhashi, Ryuji & Nagata, Yutaka & Kaya, Yoichi, 1993. "A study on economic measures for CO2 reduction in Japan," Energy Policy, Elsevier, vol. 21(2), pages 123-132, February.
    2. 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.
    3. 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.
    4. Shen, Feifei & Zhao, Liang & Du, Wenli & Zhong, Weimin & Qian, Feng, 2020. "Large-scale industrial energy systems optimization under uncertainty: A data-driven robust optimization approach," Applied Energy, Elsevier, vol. 259(C).
    5. Chassein, André & Dokka, Trivikram & Goerigk, Marc, 2019. "Algorithms and uncertainty sets for data-driven robust shortest path problems," European Journal of Operational Research, Elsevier, vol. 274(2), pages 671-686.
    6. Jianwei Ren & Bin Gao & Jiewei Zhang & Chunhua Chen, 2020. "Measuring the Energy and Carbon Emission Efficiency of Regional Transportation Systems in China: Chance-Constrained DEA Models," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-12, April.
    7. Cullinane, Kevin & Wang, Teng-Fei & Song, Dong-Wook & Ji, Ping, 2006. "The technical efficiency of container ports: Comparing data envelopment analysis and stochastic frontier analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(4), pages 354-374, May.
    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. Dimitris Bertsimas & Melvyn Sim, 2004. "The Price of Robustness," Operations Research, INFORMS, vol. 52(1), pages 35-53, February.
    10. Yang, Jun & Su, Changqi, 2021. "Robust optimization of microgrid based on renewable distributed power generation and load demand uncertainty," Energy, Elsevier, vol. 223(C).
    11. Sadjadi, S.J. & Omrani, H., 2008. "Data envelopment analysis with uncertain data: An application for Iranian electricity distribution companies," Energy Policy, Elsevier, vol. 36(11), pages 4247-4254, November.
    12. 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.
    13. Shaojian Qu & Yefan Han & Zhong Wu & Hassan Raza, 2021. "Consensus Modeling with Asymmetric Cost Based on Data-Driven Robust Optimization," Group Decision and Negotiation, Springer, vol. 30(6), pages 1395-1432, December.
    14. Shaojian Qu & Can Feng & Shan Jiang & Jinpeng Wei & Yuting Xu, 2022. "Data-Driven Robust DEA Models for Measuring Operational Efficiency of Endowment Insurance System of Different Provinces in China," Sustainability, MDPI, vol. 14(16), pages 1-21, August.
    15. 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. Bai, Bing, 2023. "Fiscal stimulus and natural resource efficiency: A comprehensive approach to a green economic recovery," Resources Policy, Elsevier, vol. 86(PB).
    2. Shutian Cui & Renlong Wang, 2024. "A Novel {\delta}-SBM-OPA Approach for Policy-Driven Analysis of Carbon Emission Efficiency under Uncertainty in the Chinese Industrial Sector," Papers 2408.11600, arXiv.org, revised Dec 2024.
    3. Shilei Wang & Ying Ji & M. I. M. Wahab & Dan Xu & Changbao Zhou, 2022. "A New Decision Framework of Online Multi-Attribute Reverse Auctions for Green Supplier Selection under Mixed Uncertainty," Sustainability, MDPI, vol. 14(24), pages 1-23, December.
    4. Cui, Shutian & Wang, Renlong & Li, Xiaoyan & Bai, Xiuguang, 2025. "Policy-driven analysis of carbon emission efficiency under uncertainty and its application in Chinese industry: Hybrid delta-slacks-based model and ordinal priority approach," Energy, Elsevier, vol. 324(C).
    5. Kuang, Hewu & Liang, Yiyan & Zhao, Wenjia & Cai, Jiahong, 2023. "Impact of natural resources and technology on economic development and sustainable environment – Analysis of resources-energy-growth-environment linkages in BRICS," Resources Policy, Elsevier, vol. 85(PB).
    6. Shaojian Qu & Ying Ji, 2023. "Sustainable Supply Chain Management and Optimization," Sustainability, MDPI, vol. 15(4), pages 1-2, February.
    7. Shengqing Chang & Jingjing Ding & Chenpeng Feng & Ruifeng Wang, 2024. "A Hybrid Parallel Processing Strategy for Large-Scale DEA Computation," Computational Economics, Springer;Society for Computational Economics, vol. 63(6), pages 2325-2349, June.

    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. Shaher Z. Zahran & Jobair Bin Alam & Abdulrahem H. Al-Zahrani & Yiannis Smirlis & Stratos Papadimitriou & Vangelis Tsioumas, 2020. "Analysis of port efficiency using imprecise and incomplete data," Operational Research, Springer, vol. 20(1), pages 219-246, March.
    2. Evelin Krmac & Mozhgan Mansouri Kaleibar, 2023. "A comprehensive review of data envelopment analysis (DEA) methodology in port efficiency evaluation," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 25(4), pages 817-881, December.
    3. Daraio, Cinzia & Kerstens, Kristiaan & Nepomuceno, Thyago & Sickles, Robin C., 2019. "Empirical Surveys of Frontier Applications: A Meta-Review," Working Papers 19-005, Rice University, Department of Economics.
    4. 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.
    5. Shaojian Qu & Can Feng & Shan Jiang & Jinpeng Wei & Yuting Xu, 2022. "Data-Driven Robust DEA Models for Measuring Operational Efficiency of Endowment Insurance System of Different Provinces in China," Sustainability, MDPI, vol. 14(16), pages 1-21, August.
    6. K. M. Matawie & A. Assaf, 2010. "Bayesian and DEA efficiency modelling: an application to hospital foodservice operations," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(6), pages 945-953.
    7. Claudio Quintano & Paolo Mazzocchi & Antonella Rocca, 2020. "A competitive analysis of EU ports by fixing spatial and economic dimensions," Journal of Shipping and Trade, Springer, vol. 5(1), pages 1-19, December.
    8. Thiago Victorino & Carlos Rosano Peña, 2023. "The Development of Efficiency Analysis in Transportation Systems: A Bibliometric and Systematic Review," Sustainability, MDPI, vol. 15(13), pages 1-32, June.
    9. Van Meensel, Jef & Lauwers, Ludwig & Van Huylenbroeck, Guido & Van Passel, Steven, 2010. "Comparing frontier methods for economic-environmental trade-off analysis," European Journal of Operational Research, Elsevier, vol. 207(2), pages 1027-1040, December.
    10. António Afonso & Ana Patricia Montes & José M. Domínguez, 2024. "Measuring Tax Burden Efficiency in OECD Countries: An International Comparison," CESifo Working Paper Series 11333, CESifo.
    11. Idiano D’Adamo & Cinzia Daraio & Simone Di Leo & Léopold Simar, 2024. "A Flexible and Sustainable Analysis of Waste Efficiency at the European Level," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 25(4), pages 881-894, December.
    12. Caitlin O’Loughlin & Léopold Simar & Paul W. Wilson, 2023. "Methodologies for assessing government efficiency," Chapters, in: António Afonso & João Tovar Jalles & Ana Venâncio (ed.), Handbook on Public Sector Efficiency, chapter 4, pages 72-101, Edward Elgar Publishing.
    13. Nykamp, Stefan & Andor, Mark & Hurink, Johann L., 2012. "‘Standard’ incentive regulation hinders the integration of renewable energy generation," Energy Policy, Elsevier, vol. 47(C), pages 222-237.
    14. Isabel Narbón-Perpiñá & Maria Teresa Balaguer-Coll & Marko Petrović & Emili Tortosa-Ausina, 2020. "Which estimator to measure local governments’ cost efficiency? The case of Spanish municipalities," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 11(1), pages 51-82, March.
    15. 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).
    16. Thanh Ngo & Kan Wai Hong Tsui, 2022. "Estimating the confidence intervals for DEA efficiency scores of Asia-Pacific airlines," Operational Research, Springer, vol. 22(4), pages 3411-3434, September.
    17. David Mayston, 2015. "Analysing the effectiveness of public service producers with endogenous resourcing," Journal of Productivity Analysis, Springer, vol. 44(1), pages 115-126, August.
    18. Phill Wheat & Alexander D. Stead & William H. Greene, 2019. "Robust stochastic frontier analysis: a Student’s t-half normal model with application to highway maintenance costs in England," Journal of Productivity Analysis, Springer, vol. 51(1), pages 21-38, February.
    19. Sickles, Robin C. & Song, Wonho & Zelenyuk, Valentin, 2018. "Econometric Analysis of Productivity: Theory and Implementation in R," Working Papers 18-008, Rice University, Department of Economics.
    20. Giokas, Dimitris I., 2001. "Greek hospitals: how well their resources are used," Omega, Elsevier, vol. 29(1), pages 73-83, February.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:jsusta:v:14:y:2022:i:20:p:13318-:d:944230. 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.