Quantification of ESG Regulations: A Cross-Country Benchmarking Analysis
Bibliographic record
Abstract
Environmental, social and governance (ESG) criteria mean investment in economic choices which, without interference with the environment, are intended to promote long-term economic and social well-being. Due to high environmental and social awareness, customers expect companies to devote time and efforts to such sustainable practices. This attitude has led to an overall rise in ESG disclosures and reporting instruments globally with a focus on influence of ESG disclosures on financial performance of companies. Many European countries have already introduced mandatory disclosure of non-financial information. This transition from voluntary to mandatory motivated other countries to adopt mandatory ESG disclosure practices for sustainable development. The practice of reporting non-financial disclosures has been rising due to several reasons, such as increasing visibility, informing customers, avoiding the risk associated with firm performance and achieving sustainability. Countries in the early stages of ESG disclosure need to understand the benchmark practices used by countries with a well-developed ESG system. For preparing the ESG disclosure index and benchmarking based on disclosure score, this study considers a set of developed and developing countries with their ESG disclosures. On the basis of ESG disclosures, the countries have been classified into four different categories. We found Norway, Sweden, Denmark, Finland, United Kingdom, Belgium and France, to have high ESG scores and have been classified as Countries with Well-Developed ESG Framework. Germany, Italy, USA, Australia, Switzerland, Canada, Japan, Brazil and South Africa have medium to high ESG scores and fall under the category Rapidly improving ESG framework. While Singapore, India, China, Philippines, Malaysia and Argentina are categorized as countries with ESG framework at developing stage, Russia, Indonesia, Thailand, Nigeria and Vietnam are classified as Countries with early-stage framework due to low ESG scores.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".