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Staged Resource Allocation Optimization under Heterogeneous Grouping Based on Interval Data: The Case of China’s Forest Carbon Sequestration

Author

Listed:
  • Nan Wu

    (College of Economics and Management, Fujian Agriculture and Forestry University, Fuzhou 350002, China)

  • Mengjiao Zhang

    (College of Digital Economy, Fujian Agriculture and Forestry University, Quanzhou 362406, China)

  • Yan Huang

    (College of Economics and Management, Fujian Agriculture and Forestry University, Fuzhou 350002, China)

  • Jiawei Wang

    (College of Rural Revitalization Academy, Fujian Agriculture and Forestry University, Fuzhou 350002, China)

Abstract

In interval data envelopment analysis (DEA), the production possibility set is variable, which causes traditional resource allocation optimization methods to yield results with limited reachability. This study aims to improve existing resource allocation optimization models so that they can produce meaningful results when handling interval data. Addressing this topic can enhance the applicability of existing models and improve decision-making accuracy. We grouped decision-making units (DMUs) based on heterogeneity to form production possibility sets. We then considered the characteristics of the worst and best production possibility sets in the interval DEA to establish multiple benchmark fronts. A staged optimization procedure is proposed; the procedure provides a continuous optimization solution, offering a basis for decision-makers to formulate strategies. To illustrate this, we provide a numerical example analysis and a case study on forest carbon sequestration. Finally, by applying our method to China’s forest carbon sink data, we show that it better meets the practical needs in China. The practical implication of this procedure is that it provides a basis for decision makers to formulate strategies based on interval data. The theoretical implication is that it extends the application of DEA models to interval data.

Suggested Citation

  • Nan Wu & Mengjiao Zhang & Yan Huang & Jiawei Wang, 2024. "Staged Resource Allocation Optimization under Heterogeneous Grouping Based on Interval Data: The Case of China’s Forest Carbon Sequestration," Mathematics, MDPI, vol. 12(17), pages 1-25, August.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:17:p:2650-:d:1464455
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    References listed on IDEAS

    as
    1. Walheer, Barnabé, 2023. "Meta-frontier and technology switchers: A nonparametric approach," European Journal of Operational Research, Elsevier, vol. 305(1), pages 463-474.
    2. Wang, Miao & Wu, Yi & Zhang, Xinmin & Lei, Lei, 2024. "How does industrial agglomeration affect internal structures of green economy in China? An analysis based on a three-hierarchy meta-frontier DEA and systematic GMM approach," Technological Forecasting and Social Change, Elsevier, vol. 206(C).
    3. Barnabé Walheer, 2019. "Disentangling Heterogeneity Gaps and Pure Performance Differences in Composite Indexes Over Time: The Case of the Europe 2020 Strategy," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 143(1), pages 25-45, May.
    4. Cook, Wade D. & Ruiz, José L. & Sirvent, Inmaculada & Zhu, Joe, 2017. "Within-group common benchmarking using DEA," European Journal of Operational Research, Elsevier, vol. 256(3), pages 901-910.
    5. Das, Gouranga G. & Drine, Imed, 2020. "Distance from the technology frontier: How could Africa catch-up via socio-institutional factors and human capital?," Technological Forecasting and Social Change, Elsevier, vol. 150(C).
    6. 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.
    7. Sebastián Lozano & Narges Soltani, 2020. "A modified discrete Raiffa approach for efficiency assessment and target setting," Annals of Operations Research, Springer, vol. 292(1), pages 71-95, September.
    8. Ruiz, José L. & Segura, José V. & Sirvent, Inmaculada, 2015. "Benchmarking and target setting with expert preferences: An application to the evaluation of educational performance of Spanish universities," European Journal of Operational Research, Elsevier, vol. 242(2), pages 594-605.
    9. Sebastián Lozano & Gabriel Villa, 2010. "Gradual technical and scale efficiency improvement in DEA," Annals of Operations Research, Springer, vol. 173(1), pages 123-136, January.
    10. Jie Wu & Jun-Fei Chu & Liang Liang, 2016. "Target setting and allocation of carbon emissions abatement based on DEA and closest target: an application to 20 APEC economies," 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. 84(1), pages 279-296, November.
    11. William W. Cooper & Kyung Sam Park & Gang Yu, 1999. "IDEA and AR-IDEA: Models for Dealing with Imprecise Data in DEA," Management Science, INFORMS, vol. 45(4), pages 597-607, April.
    12. Despotis, Dimitris K. & Smirlis, Yiannis G., 2002. "Data envelopment analysis with imprecise data," European Journal of Operational Research, Elsevier, vol. 140(1), pages 24-36, July.
    13. Lim, Sungmook & Zhu, Joe, 2019. "Primal-dual correspondence and frontier projections in two-stage network DEA models," Omega, Elsevier, vol. 83(C), pages 236-248.
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