How Can European Regulation on ESG Impact Business Globally?
Bibliographic record
Abstract
The European Union (EU) has impacted regulation worldwide in areas ranging from data protection to trade or antitrust. In select fields, it has defined stringent standards and has had an impact on global business because of the size of its market and the price of participating in it. The purpose of this paper is to analyze the main provisions of the EU regulation on Environmental, Social, and Governance (ESG) and determine whether and how it will have an impact on business globally, including regulations around disclosure for companies, taxonomy for the asset management sector, supply chain due diligence requirements, new mechanisms such as carbon markets, or non-tariffs restrictions on international trade. For this, our analysis includes an in-depth review of the literature on EU regulation of the past 20 years, complemented with interviews with experts in the field, in order to understand the main tools used by European policymakers in ESG regulations to understand their effect. The analysis adds to the body of research pertaining to the impact of regulation on business and the growing body of research on sustainable finance. We find that the new ESG regulation impacts countries outside of the EU, influencing regulation worldwide, and raising the question of possible regulatory arbitrage.
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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 | 0.019 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".