OPTIMISING POLICY SELECTION FOR PM2.5 REDUCTION IN CENTRAL THAILAND’S AGRICULTURAL AREAS: A MULTI-CRITERIA DECISIONMAKING AND SIMULATION APPROACH TOWARDS HEALTH AND SUSTAINABILITY

Abstract

This study focuses on optimising policy directions using the Analytic Hierarchy Process (AHP) and Sensitivity Analysis (SA) algorithm techniques to control burning in agricultural areas, thereby reducing the amount of PM2.5 dust pollution in the atmosphere. Data were collected through pairwise comparisons from a group of 15 individuals, comprising government agency representatives (seven individuals), and agricultural workers (eight individuals) in Nakhon Sawan Province, Thailand. Data analysis was conducted using PriEsT software. The study considered five criteria to determine the success of policies: Environmental, economic, technological, political, and social. The policy alternatives considered were sugar cane, rice, cassava, and rice. By calculating the weighted relevance of each criterion and comparing them, policies were developed to prevent burning in agricultural areas. Based on the study’s findings, economic factors (37.1%) were identified as the most significant, followed by technological (18.2%), social (17.9%), political (13.6%), and environmental (13.2%) factors. To simulate decisionmaking under uncertainty, the SA algorithm was employed through PriEsT. The results indicate that to effectively reduce burning in agricultural areas and subsequently decrease PM2.5 pollution, the government and related agencies should formulate policies that promote economic measures for handling post-harvest rice scraps.