Carbon Neutrality Challenge: Analyse the Role of Energy Productivity, Renewable Energy, and Collaboration in Climate Mitigation Technology in OECD Economies
Bibliographic record
Abstract
Carbon neutrality has been widely acknowledged as a challenge to environmental mitigation and global climate change policy. The current study examines the association between collaboration in climate change mitigation technologies (CMTs), energy productivity (EP), natural resources rent (NRR), renewable energy consumption (REC), and environmentally related tax (ET) on CO2 emissions for a panel dataset of 30 OECD economies from 1990 to 2020. This paper employs panel data econometric techniques such as AMG, CCEMG, and CS-ARDL. The empirical outcomes show that CMTs, EP, REC, and ET have a negative effect on CO2 emissions, indicating that their increase will bring about the reduction of carbon emissions, whereas NRR has a positive impact on CO2 emissions, suggesting that its increase will raise CO2 emissions. Most interestingly, REC and EP play a leading role in all selected variables by decarbonizing and effectively converting conventional energy into clean, green energy in the process of energy production and utilization. Finally, the OECD countries are anticipated to transition their energy from conventional resources to renewable sources, which will be validated by the increase in energy productivity and the adoption of clean and green technology in the short term.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".