Synergy between energy technologies and CO2 emitting goods trade in leading energy-intensive economies: proactive or counterproductive governance?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The trade of CO 2 emissions encompasses the balance of exports and imports related to goods that contribute to carbon dioxide emissions. However, energy technologies, whether clean or carbon-intensive, significantly influence this trade. We investigate the impact of decomposed energy technologies, both clean and fossil fuel-based, on the export of CO 2 emitting goods from the world’s top energy-intensive countries during the period from 1990 to 2022. Given the heterogeneity and endogeneity inherent in the data, we employ quantiles via moments and high-dimensional fixed effects linear regression techniques, respectively. Our findings reveal a monotonic positive impact of clean energy technologies in reducing the export of goods associated with CO 2 emissions. In contrast, fossil fuel technologies tend to increase the export of such goods. Interestingly, the interaction between sustainable and fossil fuel technologies contributes to a reduction in these exports, highlighting the significant role of sustainable technological development. However, when considering the interaction between both types of technologies (clean and fossil fuel) and governance practices, we observe an increase in the exports of goods associated with CO 2 emissions. This underscores the substantial influence of governance practices on increasing the export of these goods. Our results suggest that advancing sustainable energy technologies and proactive governance can lessen the export of CO 2 -emitting goods, promoting environmental safety.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it