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Quantitative evaluation and ranking of PM2.5 and hotspot mitigation action plans in Thailand using dynamic panel data analysis |
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| รหัสดีโอไอ | |
| Title | Quantitative evaluation and ranking of PM2.5 and hotspot mitigation action plans in Thailand using dynamic panel data analysis |
| Creator | Waritthorn Na Nagara |
| Contributor | Aussadavut Dumrongsiri, Advisor |
| Publisher | Thammasat University |
| Publication Year | 2568 |
| Keyword | PM2.5 mitigation, Policy evaluation, Dynamic panel data, Difference GMM |
| Abstract | This research addresses the critical challenge of evaluating government policies for PM2.5 and wildfire mitigation in Thailand. While numerous action plans have been implemented, their effectiveness remains unquantified due to the complex characteristics of pollution data and the endogeneity problem, where policies are often reactive to high pollution levels. This study proposes an implementation of the Difference Generalized Method of Moments to identify the policy effectiveness of specific action plans on PM2.5 concentration and Hotspot counts across 73 provinces during the 2025 haze season. Unlike traditional regression models, this method controls for unobserved province-specific effects and persistence in pollution levels. The results indicate a clear hierarchy of effectiveness: punitive measures, specifically Strict Law Enforcement, show the strongest statistically significant impact on reducing pollution (p-value < 0.001), followed by operational waste management. Conversely, voluntary awareness campaigns yielded the smallest reduction in PM2.5 and no significant effect on fire hotspots. These findings provide a scientifically ranked hierarchy of action plans to support evidence-based policymaking and resource allocation. In short, the analysis shows that strict enforcement most effectively lowers PM2.5 concentration, operational waste management is uniquely effective at reducing the number of fires, and voluntary measures contribute least, pointing to a targeted, enforcement-led policy mix. |
