MétaCan
Menu
Back to cohort
Record W2726231039

Is the Financial Market a Mechanism for Environmental Overcompliance

2012· dissertation· en· W2726231039 on OpenAlexaboutno aff
Julie Mallory

Bibliographic record

VenueTSpace (University of Toronto) · 2012
Typedissertation
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsnot available
Fundersnot available
KeywordsMechanism (biology)BusinessPhysics
DOInot available

Abstract

fetched live from OpenAlex

Climate change legislation is financially and politically costly. Financial markets have the capacity to encourage companies to do more than what is required by law (i.e. overcomply), and this could lead to socially optimal outcomes without the costs.\n\n\tFirst, I examine how the responses of Canadian companies to a voluntary survey regarding carbon emission levels affect those companies' valuations. I employ a signaling framework where companies choose between two signals - disclosure and nondisclosure - and where investors are uncertain about the likelihood of legislation in addition to company type. I test the prediction of the model that disclosure increases company value only when investors believe legislation is likely. I find that withholding emissions information resulted in average daily abnormal returns of 3 basis points, and that disclosure resulted in average daily abnormal returns of -11 basis points in the days surrounding the submission of survey responses. The level of emissions disclosed is found to be irrelevant. \n\t\n\tSecond, I examine the credibility of green legislative threat. The economic climate impacts the government's ability to credibly threaten new environmental law, and so I model a company's pollution decision as a function of the economic climate. In times of recession, companies may choose to pollute heavily since they believe that the likelihood of legislation is low. As a first step in evaluating the model empirically, I use differences-in-differences regressions to estimate the effect of legislative threat during recession on company value. Although the value of carbon-intensive companies decreased initially in reaction to legislative threat, the relative value of these companies increased as the depth of the recession becomes more apparent. I find that on average the legislative threat of an emission trading scheme reduced Tobin's Q by 18% in the initial stages of the recession, but as the recession deepened the legislative threat effect was eliminated. \n\t\n\tMy results suggest that financial markets combined with a credible threat of legislation could provide encouragement to companies to overcomply with current regulations, possibly to the extent that is socially optimal. More research on factors affecting company carbon emissions levels and intensity is required.

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)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.019
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0060.008
Open science0.0010.001
Research integrity0.0050.002
Insufficient payload (model declined to judge)0.0270.002

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 head0.239
Teacher spread0.218 · 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 designTheoretical or conceptual
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

Citations0
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueTSpace (University of Toronto)Same topicCorporate Social Responsibility ReportingFrench-language works237,207