Renewable realities: Charting a greener course for the world's high‐emitting nations through information technology insights
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
Abstract Carbon dioxide (CO₂) is the most abundant gas among all greenhouse gas emissions, severely impacting global warming. This study examines the impact of Information and Communication Technology (ICT), population dynamics, Per Capita Gross Domestic Product (PGDP), and Renewable Energy Consumption (REC) on CO₂ on a global scale, representing 38 countries selected using the Pareto principle. Results from the panel regression model indicate a significantly positive relationship between ICT, PGDP, and population on CO₂ emissions. In contrast, REC exhibits a negative relationship. The Multiple Linear Regression model shows that an increase in PGDP leads to higher CO₂ emissions, except in Uzbekistan. ICT increases emissions in the United States, Argentina, Australia, Canada, and Egypt. Population growth raises emissions, except in the United States, France, Germany, and Russia. REC reduces CO₂ emissions in most countries. Policymakers in individual countries can gain a precise understanding of how these variables impact CO₂ emissions, enabling them to mitigate the risks associated with global warming.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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