Artificial Intelligence, Tourism Development, and Ecological Footprint in Advanced Economies: Evidence from MMQR and PQQKRLS Approaches

Achieving environmental sustainability, particularly the targets outlined in Sustainable Development Goal 13 (Climate Action), is a critical global imperative. This investigation analyzes the heterogeneous effects of artificial intelligence (AI), tourism intensity, tourism expenditure, the Gross Domestic Product (GDP) share contributed by tourism, natural resource rents, and environmental policy stringency on the ecological footprints of advanced countries from 2000 to 2022. Using a robust analytical framework featuring advanced econometric methods, specifically the Method of Moments Quantile Regression (MMQR) and an innovative machine learning approach—Panel Quantile-on-Quantile Kernel-Based Regularized Least Squares (PQQKRLS)—the research elucidates complex, nonlinear interdependencies. Key empirical results show that AI adoption significantly mitigates ecological footprints across all quantile distributions. Conversely, heightened tourism intensity and increased tourism expenditure are associated with greater environmental degradation. The analysis further indicates a U-shaped tourism–ecological footprint relationship, suggesting that tourism’s economic contribution may initially reduce ecological pressure but may increase it again beyond a certain expansion threshold. These conclusions underscore the necessity for advanced nations to adopt synergistic policy frameworks that strategically leverage AI technologies, promote sustainable tourism practices, and reinforce rigorous environmental governance to advance climate action and ecological sustainability.