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Record W4323320233 · doi:10.1108/jec-10-2022-0147

The use of ESG scores in academic literature: a systematic literature review

2023· article· en· W4323320233 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Enterprising Communities People and Places in the Global Economy · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsUniversité du Québec à Trois-RivièresUniversité du Québec à Montréal
Fundersnot available
KeywordsCorporate social responsibilitySystematic reviewCorporate governanceSustainability reportingAccountingPsychologySustainabilityMetric (unit)BusinessPublic relationsPolitical scienceMEDLINEMarketingFinance

Abstract

fetched live from OpenAlex

Purpose Environmental, social and governance (ESG) scores are becoming increasingly relevant in academic literature and the corporate world. This is partly because the themes covered by ESG scores are intended to resolve multiple major social and environmental issues. However, there is little consensus among academics about the definition of ESG scores and their measures. Many scholars have used ESG scores to represent various issues. The purpose of this study is to gather all definitions that were used by scholar when using ESG scores in their research. Design/methodology/approach This systematic literature review aims to identify how ESG scores are presented in the academic literature. A total of 4,145 articles were identified, of which 342 articles from influential peer-reviewed journals were retained. Findings In the articles, five different thematic definitions emerged in terms of how scholars have used ESG scores in their research: sustainability, corporate social responsibility, disclosure, finance and the analysis of ESG scores. Although some definitions are consistent with the methodologies of the agencies that produce ESG scores, others raise further questions. Caution is required when using ESG scores as a metric. They represent financial adjusted risk-return for some and are used to express business sustainability for others. Research limitations/implications Only top-ranked journals were analyzed. In addition, only the key terms “ESG Score” and “ESG Scores” were used to gather all research papers. Practical implications Researchers could improve the accuracy of their results by developing specific methodologies that are closely related to the issues intended to be measured. The underlying variables composing the ESG scores could be used instead of the final score for more accurate environmental or social issues measurements. Originality/value This research shows that scholars use ESG scores to represent multiple issues that are not always captured by ESG scores’ official methodologies. ESG scores can express the overall performance of environmental and social issues, but they cannot be used to track specific underlying issues.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.778
Threshold uncertainty score0.778

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.044
GPT teacher head0.295
Teacher spread0.250 · 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