Monte Carlo Simulation–Aided Analytic Hierarchy Process Approach: Case Study of Assessing Preferred Non-Point-Source Pollution Control Best Management Practices

Abstract

This paper presents an innovative Monte Carlo simulation—aided analytical hierarchy process (MC-AHP) approach by integrating the beta-PERT distribution, pairwise comparison, and Monte Carlo simulation. A case study related to non-point-source pollution management in southwest China is used to demonstrate the applicability of the proposed approach. Five alternative best management practices (BMPs) (i.e., constructed wetlands technology, conservation tillage, fertilizer and pesticide management, irrigation management, and livestock grazing management) are evaluated by experts using a number of criteria such as reliability, capital costs, and environmental benefits. Statistical analysis indicates that conservation tillage has the highest score (0.25–0.36) in most replications, and its overlap with the second-best alternative is statistically negligible. Constructed wetlands technology, along with fertilizer and pesticide management, are mostly found as the second- and third-best alternatives, with scores ranging from 0.18 to 0.28 and 0.17 to 0.25, respectively. Compared with the traditional analytic hierarchy process (AHP), the proposed MC-AHP approach addresses the uncertainty resulting from insufficient information and opinion difference in group decision-making problems.