An integrated framework for agricultural non-point source pollution control technology evaluation: Application of an improved multiple attribute decision making method

Abstract

An Intuitionistic Fuzzy - Combined Weight - Mahalanobis distance - Multiple Attribute Decision Making (IFCM-MADM) method is proposed to solve comprehensive evaluation problem of the pollution control technologies for non-point sources in planting industry. The method is able to achieve cooperative improvement of the traditional TOPSIS on quantitative transformation of qualitative data in the evaluation indices by integrated intuitionistic fuzzy theory, subjective–objective combination weight calculation for the evaluation indicators by coupling the AHP and entropy weighting method, and accurately calculation on the distance between the evaluation object and the positive/negative solution by introducing the Mahalanobis distance to avoid the impact of correlation between indicators. Then, 34 typical technologies for agricultural non-point source pollution control selected from Major Science and Technology Program for Water Pollution Control and Treatment (MSTPWPCT) in China are employed for case application and validation, and the evaluation result obtained by IFCM- MADM has been deeply analyzed by comparing with the recommendation technologies provided by MSTPWPCT and the other two evaluation methods. The result shows that, the IFCM- MADM method can provide a scientific evaluation and selection of the technologies with high comprehensive benefits (including economic benefits, environmental benefits, and technical performance), which is highly consistent with the recommendation result from MSTPWPCT, and is capable to weaken the disadvantage of the objective weighting method and subjective weighting method, to a certain extent. The proposal of IFCM-MADM is the first attempt to enhance the traditional TOPSIS by extending its abilities on quantitative data processing, index weight calculation and accurate calculation of relative closeness, concurrently, and the method is also appropriate to other practical environmental evaluation problems with high complexity and multiple attributes.