Unveiling Sustainability Pathways: Machine Learning‐Based Insights Into Trade in Low‐Carbon Technology, Economic and Financial Dynamics, and the Ecological Footprint

ABSTRACT Low‐carbon technology (LCT) (i.e., solar panels, wind turbines, biomass systems, and carbon capture technologies) trade is an emerging yet understudied driver of environmental sustainability. LCT trade lowers costs, enhances global accessibility, and accelerates renewable energy adoption. This study examines the impacts of LCT export (LCTEX) and LCT import (LCTIM), along with economic and financial dynamics, on the ecological footprint (EF) of the 15 largest emitters from 2000 to 2022. Advanced machine learning models, namely random forest (RF) and artificial neural network (ANN), are employed along with econometric techniques, including the method of moments quantile regression (MM‐QR) and feasible generalized least squares (FGLS). Econometric results unfold that LCTEX is negatively associated with EF across its entire distribution, with an increasing impact from the 10th to 90th quantiles. Likewise, LCTIM is also negatively associated with EF, with increasing intensity, though its significant impact is observed at middle to high percentiles. Higher development and forests are linked with lower EF, whereas financial globalization (FGL) yields heterogeneous effects. Machine learning analysis reinforces these results, with RF (R 2 = 0.984) decisively outperforming ANN (R 2 = 0.855). RF feature importance shows that GDP and GDP 2 are the dominant predictors of EF, while LCTEX and LCTIM jointly explain 3.6% of variation, exceeding the role of FGL. The ANN, despite weaker predictive accuracy, identified the same key factors, though in a different order of importance. This convergence strengthens confidence in the robustness of the findings. Lastly, policies are directed to enhance LCT trade's role in advancing sustainability.