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Record W3216530770 · doi:10.1177/09722629211054173

Quantification of ESG Regulations: A Cross-Country Benchmarking Analysis

2021· article· en· W3216530770 on OpenAlexaboutno aff
Monica Singhania, Neha Saini

Bibliographic record

VenueVision The Journal of Business Perspective · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsnot available
Fundersnot available
KeywordsBenchmarkingBusinessAccountingCorporate social responsibilityCorporate governanceSustainability reportingSustainabilityDeveloping countryChinaFinanceEconomic growthMarketingEconomicsPublic relationsPolitical science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.013
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.012
GPT teacher head0.281
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations109
Published2021
Admission routes1
Has abstractyes

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