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Record W1772273256 · doi:10.1002/csr.1321

Market Responses to Firms' Voluntary Climate Change Information Disclosure and Carbon Communication

2013· article· en· W1772273256 on OpenAlexaff
Su‐Yol Lee, Y Park, Robert D. Klassen

Bibliographic record

VenueCorporate Social Responsibility and Environmental Management · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsWestern University
Fundersnot available
KeywordsVoluntary disclosureBusinessCapital marketEnterprise valueShareholderValue (mathematics)TurnoverEvent studyShareholder valueSample (material)AccountingMonetary economicsCorporate governanceFinanceEconomics

Abstract

fetched live from OpenAlex

Abstract Despite the importance of the Carbon Disclosure Project (CDP), the question of how firms' voluntary carbon disclosure influences capital markets and shareholder value remains unanswered. Using the event study methodology with a sample of firms from the CDP Korea 2008 and 2009, this paper investigates market responses to firms' voluntary carbon information disclosure. The results suggest that the market is likely to respond negatively to firms' carbon disclosure, implying that investors tend to perceive carbon disclosure as bad news and thus are concerned about potential costs facing firms for addressing global warming. In addition, the study examines the moderating effect of frequent carbon communication on the relationship between carbon disclosure and shareholder value. The results suggest that a firm can mitigate negative market shocks from its carbon disclosure by releasing its carbon news periodically through the media in advance of its carbon disclosure. Copyright © 2013 John Wiley & Sons, Ltd and ERP 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 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)0.004
metaresearch head score (Gemma)0.035
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 score0.004
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.035
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.025
GPT teacher head0.223
Teacher spread0.197 · 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

Citations235
Published2013
Admission routes1
Has abstractyes

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