Economic Activities and Carbon Dioxide Emissions in Thailand: A Quantile Regression Analysis

Abstract

This study examines the heterogeneous determinants of carbon dioxide (CO₂) emissions in Thailand using quantile regression to capture variation across the emission distribution. Monthly data from 2015 to 2024 were analyzed, incorporating economic, energy, and demographic indicators such as crude oil production, power consumption, the Leading Economic Index (LEI), Cost, Insurance, and Freight (CIF), tourism, unemployment, population, and the Private Consumption Index (PCI). The analysis reveals significant cross-quantile asymmetry, indicating that emission drivers differ based on intensity.  At the lower quantiles, tourism and unemployment are statistically significant, suggesting that low-emission periods are more sensitive to service-sector activity and labor market conditions. At the median and higher quantiles, energy-related variables dominate; power consumption is consistently significant across all quantiles, while crude oil production becomes increasingly important at upper quantiles. Economic indicators such as LEI, CIF, and PCI show weak or inconsistent effects, while population and CIF are not significant at any quantile. These findings highlight the need to enhance energy efficiency, promote renewable energy adoption, and encourage sustainable tourism to support Thailand’s low-carbon transition.