What Really Explains ESG Performance? Disentangling the Asymmetrical Drivers of the Triple Bottom Line
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
Why is there such great heterogeneity in environmental, social, and governance (ESG) performance between firms? Drawing inspiration from the locus of performance literature, we use variance partitioning methods to analyze the extent to which CEO, firm, industry, year, and state effects explain variation in ESG performance over recent decades. Our findings show that internal effects (i.e., CEO and firm) are the strongest determinants. Yet, disaggregation of the multidimensional ESG construct shifts the salience of the factors significantly, revealing the importance of the external environment (i.e., industry and year) in explaining ESG concerns. Our research extends the locus of performance literature to our understanding of the triple bottom line and contributes to understanding the complex determinants of firm-level ESG performance across an array of positive and negative ESG indicators.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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