Climate-Related Regulations and Financial Markets: A Meta-Analytic Literature Review
Bibliographic record
Abstract
Countries are confronting climate change using climate-related regulations that require firms and investors to disclose their green strategies and activities. Using the Meta-Analysis Structural Equation Modeling (MASEM) technique, this study evaluates the relationship between climate-related regulations and financial markets. The meta-regression analysis is conducted based on the outcomes of 52 empirical studies screened from 143 relevant articles. The results show the predictive power of the climate-related disclosure (CRD) laws and environmental regulations (ERs) on financial performance across all studies. ERs create mixed impacts on the equity market and support the debt market. Firm value is affected by ERs either negatively or positively. Methodologies and risk-related factors (market, industry, and firm risks) are important in explaining the relationships between ER/CRD and financial performance. The more developed the market, the less the impact of ERs and CRD on the equity market. Considering industry risk is recommended because different industries are exposed to changes in policies differently. The ER/CRD–firm value relationship is affected by all market, industry, and firm risks. The downside effect of mandatory CRD on the equity market suggests that policy makers, firms, and investors should be cautious in passing a new CRD regulation for transformation towards a sustainable economy.
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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 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.014 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".