This study proposes a causal analytical framework for examining the dynamics of tourism-induced carbon footprints at the community level in Tha Sala Subdistrict, Chiang Mai Province. Nitrogen dioxide (NO₂) concentration is employed as a proxy indicator. Multidimensional satellite data (Sentinel, MODIS, VIIRS) processed on Google Earth Engine are integrated with synthetically generated tourist data based on the Poisson distribution through a Hybrid AI architecture combining Random Forest Regression (RFR) and Diverse Counterfactual Explanations (DiCE). The results indicate that the RFR model achieves high predictive accuracy (R² = 0.973). The primary drivers of emissions are infrastructure-related factors reflected by land surface temperature (LST; 58.22%) and energy consumption proxied by nighttime lights (NTL; 20.80%), whereas the direct influence of tourist volume is relatively marginal (1.50%). Furthermore, what-if scenario simulations using DiCE reveal that increasing green space (NDVI) and reducing surface temperature can decrease NO₂ levels by up to 11.94% without restricting tourist numbers. These empirical findings provide a decision-support mechanism, demonstrating that sustainable tourism policies should prioritize spatial supply-side management and green infrastructure development rather than relying solely on demand-side control of tourist volumes.