DIRECTIONS OF REGULATORY COORDINATION OF RESPONSIBLE (ESG) INVESTMENT IN THE WORLD: FRAGMENTATION OR UNIFICATION?
Bibliographic record
Abstract
To date, processes of regulatory coordination of responsible or ESG (Environmental, Social, and Corporate Governance) investing are characterized by a combination of fragmentation and unification efforts. On the one hand, there is a noticeable degree of fragmentation of the regulatory landscape for responsible investing. At the level of different regions and countries, their own regulatory frameworks, standards and guidelines for disclosure of ESG information and sustainability reporting are being actively developed and improved. In particular, the European Union (EU), Great Britain, the USA and Canada, some countries of the Asia-Pacific region (Australia, China, Hong Kong, New Zealand, Singapore, etc.) have made special progress. For example, the EU has taken a significant step with the introduction of the so-called triad of regulatory instruments: the Sustainability Taxonomy, the Corporate Sustainability Reporting Directive (CSRD) and the Sustainable Finance Disclosure Regulation (SFDR), aimed at standardizing ESG reporting and classifying investments in sustainable development in member countries. This fragmentation creates difficulties for investors and companies operating in different jurisdictions as they must navigate and comply with different requirements. On the other hand, efforts to unify and harmonize ESG disclosure and regulatory rules are increasing. Recognizing the global nature of ESG challenges, international organizations are working to establish common principles and standards. Initiatives such as the Global Reporting Initiative (GRI), the International Sustainability Standards Board (ISSB), the Task Force on Climate-related Financial Disclosures (TCFD) have gained prominence worldwide, promoting transparency and consistency ESG reporting. While the trend toward unification is promising, achieving full regulatory coordination of responsible investing remains challenging. Different political priorities, cultural and economic differences between countries prevent the creation of a universally recognized regulatory framework. However, the growing recognition of the importance of ESG and the collective efforts of stakeholders around the world indicate a gradual convergence towards more coherent ESG regulations.
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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 | 0.067 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.007 | 0.033 |
| Scholarly communication | 0.034 | 0.055 |
| Open science | 0.006 | 0.022 |
| Research integrity | 0.014 | 0.022 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".