The relative importance of <scp>ESG</scp> pillars: A two‐step machine learning and analytical framework
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it