ESG Data - ARMDAT

ESG (Environmental, Social, and Governance) time series data refers to longitudinal, period-over-period measurements of firm- or portfolio-level sustainability metrics—such as carbon emissions, labor practices, board diversity, and disclosure quality—tracked consistently across years or quarters rather than captured as a single static score. Time series data allows researchers and practitioners to observe how ESG performance evolves, enabling trend analysis, momentum detection, and forecasting of future ESG trajectories using historical patterns.

Separating the E, S, and G components, rather than relying on an aggregated composite score, allows decision-makers to recognize that the three pillars behave very differently and often diverge or even move in opposite directions. Research reports that governance risk (G) tends to be the most inconsistent across rating agencies and least correlated with the other two pillars; environmental factors (E) are often the strongest driver of overall ESG scores, and social risk (S) has been found to negatively affect stock returns and financial stability most strongly.

It is not uncommon to find that environmental risk demonstrates inconsistent or index-dependent effects. In contrast, governance risk can even move opposite to E and S. Because a blended ESG score can mask these offsetting or divergent pillar-level effects, disaggregating E, S, and G is essential for accurate risk attribution, more precise portfolio construction, and theoretically sound modeling of which specific sustainability dimension drives a given financial or operational outcome.