Climate change exposure and internal carbon pricing adoption
Bibliographic record
Abstract
Abstract Governments and corporations around the world are increasingly pressured to manage climate‐related business risks and reduce their carbon footprint. Consequently, a growing number of corporations have started implementing internal carbon pricing (ICP) programs, assigning a monetary value to their carbon emissions as a mitigation and adaptation mechanism. This paper explores the motives underlying voluntary ICP adoption and examines whether a firm's exposure to climate‐related risks is a relevant driver of ICP adoption. Using a worldwide sample of firms reporting to the Carbon Disclosure Project between 2016 and 2018, we find that firm‐level climate change exposure is significantly and positively related to the likelihood of ICP adoption. More specifically, the probability of adoption is largely linked to regulatory shocks and opportunity exposure. Moreover, we find that board independence acts as a moderator in the climate change exposure–ICP adoption relation. The findings of this study shed light on the factors contributing to the acceleration in ICP implementation in the context of a coordinated effort between public and private sectors to reduce global emissions.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".