Strategic Prioritization of Environmental, Social, and Governance Goals: A Data‐Driven Efficiency Framework

ABSTRACT Organizations are increasingly required to integrate environmental, social, and governance (ESG) objectives alongside operational performance, yet empirical guidance on how firms should prioritize among ESG activities under resource constraints remains limited. Existing research often treats ESG as a unified construct, despite differences in the mechanisms and time horizons through which environmental, social, and governance practices operate. Using longitudinal data from 2952 publicly listed firms, we combine Data Envelopment Analysis with interpretable machine learning to examine how individual ESG components are associated with firm level efficiency across different time horizons. The results indicate that social and governance dimensions are more strongly associated with efficiency in the short term, while environmental dimensions become more salient over longer horizons. The study introduces a temporal perspective on ESG efficiency and highlights the importance of timing and sequencing in ESG analysis, while recognizing that the findings reflect empirical associations rather than causal effects.