Energy Innovation, Artificial Intelligence, and Fossil Fuels: Implications for Ecological Footprints in <scp>G7</scp> Economies

ABSTRACT The group of seven (G7) economies remains central to global ecological pressure despite rapid technological progress. However, the extent to which energy innovation, artificial intelligence, and fossil fuel dependence shape this pressure remains poorly understood, particularly when their effects are uneven across countries and emission intensities. To investigate the heterogeneous policy effects on the ecological footprint (ECF) among the G7 countries, this study employed the methods of moments quantile regression (MMQR) and parametric and nonparametric robustness tests to understand how the drivers influence ECF outcomes on the basis of different levels of ECF among the countries. The research relied on the G7 countries' dataset from 2000 to 2021, which was collected from trusted sources, and explored some valuable insights. The MMQR results revealed that both renewable energy innovation and its intensity effectively lower ecological pressure in high‐footprint countries but have a limited impact on low‐footprint economies. Artificial intelligence innovation generates a nonlinear pattern, with modest ECF reductions in low quantiles but reversed to substantial increases in high‐footprint nations, which points to energy‐intensive data infrastructure. The GDP per capita consistently intensifies minor ECF across all quantiles, which supports the environmental Kuznets curve pattern. These heterogeneous patterns challenge the assumption of uniform environmental responses to innovation and growth and reveal structural trade‐offs between technological modernization and ecological performance. We show that effective climate strategies in advanced economies require differentiated interventions that accelerate clean innovation while actively managing the environmental rebound of digital and fossil fuel systems rather than relying on uniform market‐based instruments or green growth dynamics.