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Record W4281619308 · doi:10.3390/jrfm15060237

The ESG Disclosure and the Financial Performance of Norwegian Listed Firms

2022· article· en· W4281619308 on OpenAlexvenueno aff
George A. Giannopoulos, Renate Victoria Kihle Fagernes, Mahmoud Elmarzouky, Kazi Abul Bashar Muhammad Afzal Hossain

Bibliographic record

VenueJournal of risk and financial management · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsnot available
Fundersnot available
KeywordsNorwegianAccountingBusinessCorporate social responsibilityCorporate governancePanel dataSustainability reportingRegression analysisVariable (mathematics)VariablesFinanceEconomicsEconometricsPolitical sciencePublic relationsStatisticsMathematics

Abstract

fetched live from OpenAlex

The world is constantly changing, and with an evolving global environmental crisis, there is a growing trend of Corporate Social Responsibility, and Environmental, Social, and Governance (ESG) disclosure initiatives. The final report on the new E.U. taxonomy for sustainable activities was released in 2020, making ESG disclosure more relevant. This paper investigates the effects of ESG initiatives on the financial performance of Norwegian listed companies from 2010 to 2019. ESG is measured through the Thomson Reuters Eikon ESG disclosure score and financial performance through ROA and Tobin’s Q. To the best of our knowledge, this is the first time this relationship has been investigated in Norway. Using panel data regression analysis and two proxies for the dependent variable (financial performance), the results of this study are mixed. In particular, findings suggest a strong significant relationship between ESG initiatives and financial performance. More specifically, the regression model, with ROA as the dependent variable, suggests that ESG initiatives have a clear negative impact. On the other hand, the variable Tobin’s Q increases when ESG increases. This could be explained by the different horizons of the measures and other factors affecting the business environment.

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 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)0.003
metaresearch head score (Gemma)0.001
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 score0.764
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.007
GPT teacher head0.198
Teacher spread0.191 · 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 teacher head, 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

Citations212
Published2022
Admission routes1
Has abstractyes

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