Pemodelan Spasial Temporal Emisi CO2 di ASEAN dengan Pendekatan Regresi Terboboti Geografis dan Temporal - Nabil Naufal; Anik Djuraidah; Yenni Angraini; Alfa Nugraha Pradana (Limits: Journal of Mathematics and Its Applications, 2026)
Carbon dioxide (CO₂) emissions in the ASEAN region exhibit significant spatial and temporal heterogeneity, causing the relationship between emissions and their driving factors to vary across regions and time. This study aims to analyze the dynamics of the factors influencing CO₂ emissions in ASEAN countries using the Geographically and Temporally Weighted Regression (GTWR) method. The analysis utilized panel data from the 2018–2023 period, encompassing emissions data (EDGAR), environmental data (MODIS, ERA5), as well as socio-economic and energy data (World Bank, EMBER). The main results indicate a shift in dominant factors due to the COVID-19 pandemic. Before 2020, economic growth and electricity consumption were the primary drivers, whereas dince 2020, environmental factors (NDVI) and fossil fuel composition showed a more significant influence in several countries. The resulting GTWR model demonstrated very high performance with an R² value of 0.99, confirming the method's capability to capture local variations. This finding implies that emission mitigation policies in ASEAN must be adaptive to the evolving spatial contexts and temporal dynamics.