The Value Relevance of a Firm's Carbon Risk Profile
Bibliographic record
Abstract
The aim of this paper is to provide insights into the capital market's role in incentivizing firms to engage meaningfully in the transition to a net zero carbon emissions economy. We investigate whether capital markets negatively value a broader concept of carbon risk exposure in addition to its historic carbon footprint and offset assessed penalties by considering carbon mitigation activities undertaken by the firm. We develop a conceptual framework of a firm's ‘carbon risk profile’ from the literature comprising: (a) carbon risk exposure (current emissions and broader risk notions of fossil fuel dependency and carbon visibility); and (b) carbon mitigation activities (realized emissions reductions and anticipatory proactive activities). We confirm and operationalize this framework using interviews with managers and environmental, social, and governance analysts. Based on a sample of 310 firm‐year observations for ASX200 firms from 2014–2020 in high‐carbon sectors, our results suggest material valuation penalties for the broader carbon risk exposure concept. Further, we find that capital markets attach value to a firm's intangible capability to proactively mitigate its carbon risk exposure. Building on these results, to further mobilize capital markets in the push towards net zero emissions, policymakers and regulators may wish to undertake initiatives to increase carbon‐related disclosures on both risks and mitigation activities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".