Policy transition risk, carbon premiums, and asset prices
Bibliographic record
Abstract
We analyze the effects of policy transition risk on asset pricing and the green transition using a global two-sector, macro-finance model of climate and the economy. Policy transition risk results from probabilistic changes between three policy states: no, modest, and ambitious carbon pricing. We show that policy transition risk leads to carbon premiums (i.e. higher expected returns on brown than on green assets), especially if the economy is still quite carbon-intensive and close to the temperature cap, and thus accelerate the green transition. Increased transition risk leads to more precautionary saving and falls in the risk-free rate. We offer extensions to deal with physical risks (temperature-related risk of climate disasters and climate tipping), technology transition risk, and more realistic policy tipping with endogenous transition probabilities. • Policy transition risk leads to carbon premiums and a faster green transition. • Risks of climate-related disasters and climate tipping push up the carbon price. • Climate policy shocks have a significant impact on asset prices and returns. • Price impacts are more pronounced if carbon-intensive capital is more prevalent.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".