Renewable energy and CO2 emissions: New evidence with the panel threshold model
Bibliographic record
Abstract
The increased concerns over climate change led to a large body of literature that examined the impact of energy and economic growth on carbon dioxide (CO2) emissions per capita. The majority of the existing studies employed various linear panel estimation techniques ignoring the potential nonlinear effects of energy and income on CO2 emissions per capita. To fill this gap, this study uses panel data consisting of 97 countries between 1995 and 2015 and examines the nonlinear impact of renewable, non-renewable energy consumption, economic growth on CO2 emissions per capita by using a dynamic panel threshold model that is robust to cross-section dependence. Our findings indicate the effect of growth in renewable energy consumption per capita on the growth of CO2 emissions per capita is negative and significant if countries surpass a certain threshold of renewable energy consumption. This finding mainly holds for developed countries and countries with stronger institutions and is robust to the use of an alternative proxy for renewable energy consumption. Our findings highlight the fact that increased renewable energy consumption would only reduce CO2 emissions per capita if and only if countries surpass a certain threshold of renewable energy consumption.
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.
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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".