Do asymmetric green technology innovation and institutional quality shocks matter for CO2 emissions in OECD countries? New evidence from an ARDL–PMG approach
Bibliographic record
Abstract
Harmful climatic effects caused by increasing levels of carbon emissions are nowadays considered a serious problem for countries all over the world. Some nations are not yet making best use of their resources to promote long-term growth, while others are making great efforts to maintain a clean environment. Governments and policymakers worldwide however are considering climate challenges and global warming as critical risks. This research enriches previous literature on reducing CO2 emissions by exploring effects on carbon dioxide emissions from asymmetric green technology innovation and institutional quality within OECD nations. The short- and long-term impact of upward and downward fluctuations of GTI and IQ on CO2 emissions are assessed across a panel of 35 OECD nations for the period 1995-2020. The findings show: (i) that the EKC hypothesis is supported for long term effect but not short term in the countries studied; (ii) the existence of asymmetric long-term effects for GTI and dimensions of IQ; and (iii) that controlling corruption seems to have the most important effect on environmental degradation compared to other IQ measures. The study contributes to current understandings by revealing the nuanced and complex relations linking technological and institutional factors and environmental outcomes in developed economies. Based on the results, OECD countries must stimulate and support green technological innovation by defining appropriate governance reforms to foster sustainable development and meet sustainable development goals.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".