This study investigates the impact of energy efficiency and energy intensity on firm profitability using econometric and machine learning methods. Analyzing a panel dataset of 713 publicly listed firms from 2016 to 2022, we apply system-GMM and MMQR models to assess the linear effects of key variables on profitability. We complement the analysis with SHAP and PDP visualizations from a panel structured LightGBM quantile model, capturing nonlinearities and marginal effects that are not addressed by econometric methods. Our results reveal a positive relationship between energy efficiency and profitability, especially among high-performing firms, indicating that scale benefits arise from adopting sustainable energy practices. Energy intensity shows nonlinear effects, with reversed U-shapes at lower profitability levels and U-shapes at higher levels. These findings emphasize firm-level heterogeneity in the environmental–profitability link, offering valuable insights for aligning energy strategies with financial performance.
Çoban et al. (Mon,) studied this question.