Affiliations: [a]
Fujian Agriculture and Forestry University, Fuzhou, Fujian, China
| [b]
College of Economics and Management, Fujian Agriculture and Forestry University, Fuzhou, Fujian, China
| [c]
The Collective Forestry Reform and Development Research Center of New Types of Think Tanks with Universities in Fujian, Fujian Agriculture and Forestry University, Fuzhou, Fujian, China
Correspondence:
[*]
Corresponding author. Yan Huang, Email: [email protected].
Abstract: Cross-efficiency evaluation is an extension of data envelopment analysis (DEA), which can effectively distinguish between decision-making units (DMUs) through self- and peer-evaluation. The cross-efficiency of each DMU in a set of DMUs is measured in terms of intervals when the input–output data are represented by the number of intervals. Based on the interval cross-efficiency matrix, the interval entropy is defined in terms of the likelihood. Then, considering the influence of peer evaluation, the interval conditional cross-efficiency entropy is proposed and an aggregation model of the interval conditional cross-efficiency entropy is presented to create a ranking index for DMUs. Finally, a simple example is provided to illustrate the effectiveness of the proposed method, which is applied to the evaluation of forest carbon sink efficiency in China. The results indicate that the final cross-efficiencies of all 30 provinces range from 0 to 0.6. Among these provinces, those with a relatively high efficiency include Guangdong, Guizhou, Hainan, Shandong, and Qinghai.
Keywords: Data envelopment analysis, interval data, cross-efficiency, entropy, likelihood
