Assessing the asymmetric effects of clean and dirty energy budgets on load capacity factor: Evidence from top investing countries
Bibliographic record
Abstract
This study examines the relationship between national research, development, and demonstration (RD&D) budgets - both in total and split into clean and fossil categories - and environmental quality, as measured by the Load Capacity Factor (LCF). The analysis covers eight advanced economies from January 1990 to December 2023 and applies a kernel-based quantile method designed to capture non-linear and heterogeneous effects. The results indicate that the link between energy budgets and environmental outcomes is not uniform across countries or quantiles. Moreover, aggregate and clean energy budgets show consistent positive impacts in Germany, the United States, and Sweden, particularly at higher levels of technological maturity and environmental performance, which supports the presence of threshold effects. On the other hand, France and Norway exhibit weak or negative associations, which are likely explained by energy system saturation or misaligned RD&D strategies. Meanwhile, dirty energy budgets produce limited benefits, with some short-term improvements at low environmental performance levels in Canada and Australia. Therefore, clean energy budgets are more likely to generate reliable gains, especially in countries with strong innovation capacity and supporting infrastructure. However, mixed results are found in Japan, France, and Sweden. Based on these findings, RD&D policies should be context-specific and aligned with the maturity of energy systems, the level of innovation, and prevailing environmental conditions. Instead of uniformly increasing RD&D budgets, policymakers in leading investor countries should focus on targeted allocations to clean energy, supported by enabling infrastructure and appropriate regulatory frameworks, in order to maximize environmental gains.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".