The Effect of Financial Materiality on ESG Performance Assessment
Bibliographic record
Abstract
The effect of considering the financial materiality of ESG (environmental, social and governance) issues on firms’ ESG performance scores and rankings is investigated using Morgan Stanley Capital International (MSCI) ESG Ratings and the financial Materiality Map® developed by the Sustainability Accounting Standard Board (SASB). Results show that when financial materiality is applied, firms’ ESG performance scores change significantly. Further corroboration is provided by significant changes in firms’ ESG rankings when ESG performance assessment is based on SASB-adjusted ESG performance scores. Environmental pillar issues, and particularly natural resource use, are predominantly responsible for the changes. Overall, the results suggest that financial materiality affects the informative value of ESG scores and rankings, allowing the identification of investment opportunities in firms with high scores on business-critical ESG issues. We argue that consideration of financial materiality can better inform investment decisions based on ESG performance. This study adds to the understanding and assessment of ESG performance and its information content.
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How this classification was reachedexpand
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".