The ESG Disclosure and the Financial Performance of Norwegian Listed Firms
Bibliographic record
Abstract
The world is constantly changing, and with an evolving global environmental crisis, there is a growing trend of Corporate Social Responsibility, and Environmental, Social, and Governance (ESG) disclosure initiatives. The final report on the new E.U. taxonomy for sustainable activities was released in 2020, making ESG disclosure more relevant. This paper investigates the effects of ESG initiatives on the financial performance of Norwegian listed companies from 2010 to 2019. ESG is measured through the Thomson Reuters Eikon ESG disclosure score and financial performance through ROA and Tobin’s Q. To the best of our knowledge, this is the first time this relationship has been investigated in Norway. Using panel data regression analysis and two proxies for the dependent variable (financial performance), the results of this study are mixed. In particular, findings suggest a strong significant relationship between ESG initiatives and financial performance. More specifically, the regression model, with ROA as the dependent variable, suggests that ESG initiatives have a clear negative impact. On the other hand, the variable Tobin’s Q increases when ESG increases. This could be explained by the different horizons of the measures and other factors affecting the business environment.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".