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Record W4414871654 · doi:10.2308/tar-2023-0276

The Impact of Mandatory Sustainability Reporting on Institutional Investment: The Role of Reporting Location

2025· article· en· W4414871654 on OpenAlexaff
Mark L. DeFond, Mingyi Hung, Emily Jing Wang

Bibliographic record

VenueThe Accounting Review · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSustainability reportingSustainabilityMandateComparabilityDirectiveSustainability organizationsInvestment (military)Corporate governance

Abstract

fetched live from OpenAlex

ABSTRACT We investigate whether foreign institutional investors respond to the sustainability disclosures mandated by the EU’s Non-Financial Reporting Directive and whether disclosure location affects their response. We find that foreign institutions increase ownership in companies affected by the mandate and that the increase is greater in countries that locate the sustainability disclosures within their annual reports, referred to as combined reporting. This is consistent with combined reporting reducing investors’ disclosure processing costs by providing timelier disclosure and better integration of sustainability and financial information. We further find that the increase in ownership is greater in countries that experience a larger increase in the number of firms issuing combined reports, consistent with combined reporting increasing comparability of the sustainability disclosures. Our findings suggest that the location of sustainability reporting plays an important role in cross-border investment decisions, which provides policy implications for the implementation of global sustainability disclosure regulation.

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)0.066
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 score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.066
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.026
GPT teacher head0.331
Teacher spread0.305 · 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

Citations5
Published2025
Admission routes1
Has abstractyes

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