Context-dependent DEASort: A multiple criteria sorting method for ecological risk assessment problems

With the increasing urban economic development and awareness of environment protection, ecological risk assessment (ERA), as a management mode combined with ecological research and risk assessment, has become a highly relevant topic in environment sustainable development. In this paper, we propose a novel sorting model based on Data Envelopment Analysis (DEA) and Best Worst method (BWM) to solve multi-criteria sorting problems and apply it to ERA. The proposed Context-Dependent DEASort method based on the idea of Context-Dependent DEA to position decision-making units (DMUs) into diverse categories in uncertain circumstance. It also takes experts' preference into full account as well, which makes the sorting solution more flexible and reasonable. Besides, a common set of weight-based model is introduced to deal with the situation when evaluating attractiveness and progress of each DMU. The common weight set model can ensure to provide an overall evaluation context compared to the original Context-Dependent DEA model, which only distinct the DMU from a single virtual DMU. Furthermore, a case study concerning ecological risk assessment of Yangtze River Economic Zone in China is provided to illustrate the applicability of the developed environment. Finally, comparative analysis and sensitivity analysis are carried out to demonstrate the practicality and effectiveness of the proposed method. (c) 2021 Published by Elsevier Inc.

Automated summary

This study proposes a novel sorting model based on DEA and BWM methods to address multi-criteria sorting problems in ecological risk assessment. The Context-Dependent DEASort method positions decision-making units into diverse categories under uncertain circumstances, taking into account experts' preferences for a more flexible and reasonable sorting solution. Additionally, a common weight set model is introduced to evaluate the attractiveness and progress of each DMU efficiently.