The study examines the relationship between ESG variables and financial risk, measured through logarithmic volatility. It introduces the Hierarchical Variable Selection (HVS) algorithm, designed for ESG datasets, which is reported to outperform aggregated ESG scores and traditional selection models by providing higher explanatory power with fewer variables. Findings suggest that ESG risk factors vary across sectors and between large- and small‑cap firms, influenced by differences in regulation, expectations, and strategy. The authors highlight the robustness and adaptability of HVS, noting its effectiveness in identifying risk‑relevant ESG variables across industries and its potential for broader applications in hierarchical datasets.