Carbon Emissions and Business Cycles
Bibliographic record
Abstract
U.S. carbon dioxide emissions are highly procyclical-they increase during expansions and fall during recessions.Given this empirical fact, we estimate the response of emissions to four prominent technology shocks from the business-cycle literature using structural vector autoregressive methodologies and data for 1973-2012.By studying the response of emissions to these shocks, we provide a novel approach to assess the shocks' relevance as sources of aggregate output fluctuations.We find that emissions rise on impact only after an anticipated investmentspecific technology shock; the response is statistically significant after the first quarter.The same shock explains most-roughly a third-of the total variation in emissions at a horizon of 5 years.Notably, emissions decrease on impact after an unanticipated neutral technology shock in a statistically significant way.This negative empirical response has the opposite sign from its theoretical counterpart in recent environmental DSGE (E-DSGE) models.Since the positive response of emissions drives the E-DSGE models' recommendation for an optimal procyclical policy, our findings suggest that such a policy recommendation should be treated cautiously.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".