How Much Does Increasing Non-fossil Fuels in Electricity Generation Reduce Carbon Dioxide Emissions?
Bibliographic record
Abstract
Many international organizations have called for an increased usage of renewable energy as a means to reduce CO2 emissions and address climate change. This paper uses a large panel data set of 93 countries and recently developed panel estimation techniques to answer the question by how much does increasing non-fossil fuels in electricity generation reduce the subsequent carbon dioxide emissions. For the full sample, we find long-run displacement elasticities for non-fossil fuel consumption per capita of approximately −0.38; however, for the share of non-fossil fuels used in electricity generation, those long-run displacement elasticities are −0.82. Thus, a one percent increase of the share of non-fossil fuel electricity generation reduces CO2 emissions per capita from electricity generation by about 0.82%. Long-run share displacement elasticities for non-OECD countries are substantially higher than those for OECD countries (approximately −0.98 to −0.54). These results have a number of policy implications. Our results are important in establishing that a very rapid increase in the share of non-fossil fuels used in electricity generation is needed in order to have a meaningful impact on per capita CO2 emissions from electricity generation.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".