The relative importance of <scp>ESG</scp> pillars: A two‐step machine learning and analytical framework
Bibliographic record
Abstract
Abstract This study aims to contribute to the ongoing and inconclusive debates regarding the relative significance of environmental, social, and governance (ESG) sustainability key performance indicators and their correlation with overall sustainability performance. We present a unique two‐step analytical framework to leverage Thomson Reuters' ESG Score (ASSET4) database to determine the most impactful ESG pillars and their subcomponents at both the firm and industry levels. This framework integrates the Method based on the Removal Effects of Criteria (MEREC) with K‐means cluster analysis. Through the MEREC‐K‐means framework, we determine the two most noteworthy ESG pillars within various industries, subsequently clustering them to form peer groups for comprehensive comparative analysis. We find that while the social and economic pillars are the two fundamental pillars of ESG performance in all industries in general, this prioritization sometimes differs from industry to industry. This research makes theoretical and practical contributions by introducing a novel dimensionality reduction technique in ESG pillars, offering valuable insights for strategic resource allocation in corporate social responsibility (CSR) and sustainability initiatives. The implications of our findings extend broadly to investors, policymakers, and practitioners, empowering them to make more informed decisions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".