Bibliographic record
Abstract
Regulations play a crucial role in promoting the development of sustainable finance and investments. The European Union (EU) is leading the way in regulating and influencing the sustainable finance market on a global scale. The EU Sustainable Finance Disclosures Regulation (SFDR) and the EU Green Taxonomy are examples of regulations that directly impact sustainable finance markets. Additionally, the International Sustainability Standards Board (ISSB) and the mandatory application of Task Force on Climate-related Financial Disclosures (TCFD) reporting around the world are expanding the scope of the field. This chapter provides an overview of regulations in various countries, including Australia, Canada, China, Colombia, India, Indonesia, Malaysia, Russia, Singapore, South Africa, the United Arab Emirates, the United Kingdom, the United States, and Turkey, as well as the EU. Moreover, this chapter sheds light on the issue of “greenwashing” in institutions and sustainable finance instruments. To prevent the proliferation of greenwashing and lack of clarity undermines confidence in the market, taxonomy regulation plays a vital role. It is, therefore, crucial for countries to take prompt and decisive steps toward developing their national taxonomy and standards. This chapter aims to delve into this process in greater detail.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| 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".