What Can We Learn About Carbon-reducing Innovations From the Joint Dynamics of CO <sub>2</sub> Emissions and GDP?
Bibliographic record
Abstract
Technological innovations targeting energy efficiency, carbon storage, and clean energy are often promoted as solutions to reduce emissions without sacrificing economic growth. However, theoretical studies offer conflicting views on the feasibility of this approach, and empirical assessments at the aggregate level are sparse. This paper contributes new evidence on the macroeconomic implications of carbon-reducing technological innovations. We analyze the joint dynamics of U.S. per capita emissions and GDP and identify a novel shock that lowers emissions without reducing economic output. This statistical shock is uncorrelated with past macroeconomic variables, energy prices, estimates of the leading macroeconomic shocks, and measures of environmental policy stringency. Our novel shock exhibits characteristics of an energy demand-reducing shock specific to the U.S. energy market, with pronounced effects concentrated in the residential and commercial sectors. This shock appears to be linked to improvements in energy efficiency, possibly stemming from changes in energy requirements for homes and buildings. These improvements may result from energy policies, occur exogenously, or reflect shifts in preferences for energy conservation. JEL Classification: E32, Q43, Q50, Q55
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".