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

Nan Wu, Mengjiao Zhang, Yan Huang () and Jiawei Wang
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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

Mathematics, 2024, vol. 12, issue 17, 1-25

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.

Keywords: data envelopment analysis; benchmarking; interval data; resource allocation optimization; meta-frontier (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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