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Record W4361275321 · doi:10.3390/jrfm16040215

Can Corporate Sustainability Drive Economic Value Added? Evidence from Larger European Firms

2023· article· en· W4361275321 on OpenAlexvenueno aff
Tiago Gonçalves, Diogo Louro, Víctor Barros

Bibliographic record

VenueJournal of risk and financial management · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsnot available
FundersFundação para a Ciência e a Tecnologia
KeywordsSustainabilityCorporate sustainabilityEconomic Value AddedProfit marginOperating marginReturn on assetsEndogeneityMargin (machine learning)RevenueEconomicsComparabilityTriple bottom lineBusinessProfit (economics)Industrial organizationEconometricsAccountingMicroeconomicsFinanceProfitability index

Abstract

fetched live from OpenAlex

This study analyses the association between firms’ sustainability and economic performance in Europe, considering the channels of margin and turnover. The sample is composed of firms listed in the STOXX Europe 600 Index from 2012 to 2020. The sustainability performance is captured by the combined and individual ESG scores from Refinitiv, and dynamically tested with proxies of economic performance, including economic value added, return on firms’ assets and its components, margin and turnover. The methodological approach comprises different panel data specifications and tackles the potentially unobserved, time-invariant heterogeneity, endogeneity concerns, and reverse causality biases. Our findings point to a strong positive association between firms’ sustainability and economic performance in Europe, although the individual ESG forces are not at play with the same intensity. The environmental pillar is the one that is systematically associated with better economic performance across all estimations. The influence of sustainability performance on economic performance is also channeled by both profit margin and turnover. We find that a 1% improvement in the ESG score yields an increase in the economic value added of 0.08%, EVA over revenues. In general, our findings point to a shift from the conventional business model perspective to the incorporation of a core sustainability proposition and agenda that brings advantages and drives economic performance.

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)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.323
Threshold uncertainty score0.794

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.021
GPT teacher head0.238
Teacher spread0.217 · 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

Citations23
Published2023
Admission routes1
Has abstractyes

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