Thanakhom Srisaringkarn

Aerosol pollution is a chronic air pollution problem that occurs every year in Thailand. It is mostly caused by human activities and climate conditions, and has severe negative impacts on both human health and the economy. This study investigates the relationship between aerosol pollution and various economic and environmental factors using the Environmental Kuznets Curve (EKC) framework. Additionally, it forecasts aerosol pollution through 2050 by applying four machine learning approaches and comparing their accuracy and predictive performance. The results indicate that the Random Forest (RF) model provided the most accurate estimation across the train, validation, and test periods. The study found an inverted U-shape relationship between aerosol pollution and economic growth, consistent with an EKC pattern. This suggests that as Thailand’s economy expands, aerosol pollution is projected to decline at an average annual rate of − 0.0569% to − 0.0636% from 2000 to 2050, except for the Southern region (R4), which shows an increasing rate. Furthermore, environmental features, specifically regional characteristics (R), Normalized Difference Vegetation Index (NDVI), rainfall, and wind speed, strongly influence aerosol pollution levels. Among economic features, population density was found to positively influence aerosol pollution. The study’s findings indicate that population density and GPP per capita remain primary drivers of aerosol pollution in high-activity regions. Consequently, although not directly analyzed in our models, we propose introducing green finance instruments as a strategic mechanism to decouple economic activity from environmental degradation.

This study investigates the relationship between economic growth and energy consumption using the Energy Kuznets Curve (EKC) framework. Spatial econometric models, including the Spatial Panel Lag Model and the Spatial Dynamic Panel Lag IV Model, are employed to capture both spatial and dynamic effects. The results indicate that energy consumption in Thailand is spatially clustered, with energy use tending to spill over into neighboring provinces and concentrating in specific regions. Key factors that positively influence energy consumption include gross provincial product (GPP) per capita, population density, and road density. Regions characterized by favorable climates, sufficient infrastructure, and high levels of economic activity exhibit higher per capita energy consumption. The EKC analysis reveals a U-shape relationship between GPP per capita and energy consumption in the BKK&VIC, CE, EA, WE, and NE regions. As many regions continue to experience rising energy consumption, the findings underscore the importance of Thailand adopting more efficient energy usage strategies in tandem with its economic development.

This study examines the spatial relationship of PM_2.5 concentrations across provinces in Thailand and explores the relationship between socio-economic factors and PM_2.5 levels from 2012 to 2022. The study results indicate that PM_2.5 pollution in Thailand is spatially clustered, meaning that PM_2.5 spills over into nearby provinces and is not confined to a single area. The factors that positively affect PM_2.5 concentrations include population density and energy consumption per capita, while industrial density has a negative effect on PM_2.5 levels. Additionally, an Environmental Kuznets Curve (EKC) analysis found that the Gross Provincial Product (GPP) per capita has a U-shaped relationship with the PM_2.5 concentration. In the initial stage of economic growth, as the GPP per capita increases, PM_2.5 concentrations gradually decrease. However, once income reaches USD 56,715 and the economy becomes significantly large, further increases in GPP per capita lead to rising PM_2.5 concentrations. In other words, during the early phase of economic development, PM_2.5 pollution does not intensify significantly. However, once Thailand’s economy reaches a certain scale, continued economic expansion exacerbates PM_2.5 pollution, leading to greater economic and social consequences. The study highlights the importance of integrated collaboration among various organizations in mitigating the widespread impacts of PM_2.5 pollution.