Analyzing global renewable energy generation trends amid volatile economic and social environments
Bibliographic record
Abstract
As renewable energy (RE) rapidly integrates into society to meet the growing demand for affordable and clean energy (SDG 7, United Nations’ Sustainable Development Goal 7), it is crucial to analyze the merits and flaws of renewable energy generation by assessing its impact on the global economy and social wellbeing. This paper performs an investigative study on the correlation and causality between renewable energy, economy, and environmental indicators. The data was gathered from diverse sources, including The Center for Climate and Energy Solutions, National Aeronautics and Space Administration Goddard Institute for Space Studies (NASA GISS), and Our World in Data, for the temperature, carbon dioxide (CO2) emissions, and gross domestic product (GDP) data. Significant Granger p-values were obtained for RE generation’s ability to forecast CO2 emissions and temperature, while discovering a strong positive correlation between CO2 and RE generation. The findings revealed that RE has limited effects on the global economy but has considerable implications on social and ecological well-being.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".