On measuring climate risks using attention search and testing the clean energy-climate hypothesis
Bibliographic record
Abstract
We measure physical and regulatory climate risks as innovations in several attention to climate change indexes that we construct using search volume data in Google Trends. The intention is to use the constructed risk indexes to test the empirical validity of the clean energy-climate hypothesis, which posits that clean energy prices fall (rise) following a drop (rise) in attention to climate risks. We test the empirical validity of this hypothesis at the macro (market level) using regime switching models and at the micro (firm level) using time-series regressions across clean energy sub-sectors. The macro analysis reveals that our hypothesis is only valid for climate pledges and physical climate risks. When attention to climate pledges drops or when physical climate risks are low, investors become reluctant to hold clean energy assets and vice-versa. Climate policy and climate solution risks have no impact on the nonlinear dynamic behaviour of clean energy prices. These results mean that investors and the public believe that climate policies lack credibility and climate solutions are not effective. The firm-level analysis confirms the findings of the macro analysis and reveals the heterogeneity of climate risks across clean energy sub-sectors.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".