Balancing green power: Hydropower and biomass energy's impact on environment in OECD countries

The climate crisis, driven by greenhouse gas (GHG) emissions and environmental degradation, demands a transition to renewable energy for sustainable development. This paper analyzes the asymmetric effects of hydroelectric and biomass energy consumption on the ecological footprint (EFP) for 24 OECD countries from 1970 to 2022. By using a combination of advanced econometric approaches, including Method of Moments Quantile Regression (MMQR), Generalized Linear Models (GLM), and Robust Least Squares (RLS), with machine learning techniques such as Multivariate Adaptive Regression Splines (MARS) and Neural Networks (NN), this study will be able to identify complex nonlinearities that are not captured by traditional models. The results reveal that hydroelectric energy significantly reduces the EFP, particularly in high-pollution contexts, while biomass energy consumption worsens environmental degradation. These findings emphasize the urgent need for targeted policies to maximize the benefits of renewable energy sources and mitigate their risks. The study contributes to the literature by offering a comprehensive framework to analyze the environmental impacts of renewable energy, emphasizing the importance of methodological diversity and advanced modeling techniques as ways to achieve sustainability goals.