Carbon Emissions and Stock Returns: The Case of Russia
Bibliographic record
Abstract
Russia is taking the first steps in the formation of an emissions trading system. In this article, we studied the impact of carbon risk on Russian stock returns. We link carbon risk to CO2 emissions and air protection costs. We suggest that carbon firms are exposed to carbon risk and hence require a premium in stock returns. We use an approach based on the asset pricing methodology for carbon, carbon-free, and “carbon-minus-carbon-free” portfolios. Based on the Newey–West estimate, we perform a linear regression analysis for the period from January 2014 to December 2021. We find a positive and statistically significant carbon premium. This means that carbon firms show higher expected returns. Carbon risk does not have a statistically significant impact on the carbon premium. The carbon firms’ stock returns are not sensitive to CO2 emissions and air protection costs. Our analysis shows that a quarter of the carbon premium is explained by the market premium and is not sensitive to size, value, and momentum premiums. Our results inform policymakers and investors about the implications of environmental regulation. Policymakers should take into account the results obtained in the development of national climate and, in general, environmental policies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".