Unlocking the dynamic linkages between sustainable equity investment and economic policy uncertainty: An empirical analysis for <scp>G‐20</scp> countries
Bibliographic record
Abstract
Abstract The study examines the dynamic relationships between sustainable equity investment and economic policy uncertainty of G‐20 countries using monthly environmental, social, and governance ( ESG) equity and economic policy uncertainty ( EPU) indices. Using the autoregressive distributed lag model and nonlinear autoregressive distributed lag model models, the study finds a negative long‐run relationship between sustainable equity investments and economic policy uncertainties in Australia, Canada, the USA , Brazil, Mexico, Germany, Italy, and Japan. Investors in these G20 countries may perceive that companies with higher ESG performance are more likely to face regulatory scrutiny, legal action, or reputational damage if they are associated with high levels of economic policy uncertainties. As a result, ESG market indices may underperform when EPU is high and vice versa. It supports prospect theory and suggests that individuals are more sensitive to potential losses than gains. On the contrary, the relationship is positive in the case of the USA , Brazil, China, and to some extent India. This might be because firms with high ESG performance could manage risks better and seize opportunities associated with EPU , which helps ESG market indices to outperform when EPU is high. It is supported by the legitimacy theory that says to maintain the legitimacy and credibility of the company, the investment must be invested in ESG initiatives, which can lead to improved long‐term financial performance and market value.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".