Disruptive Solutions for Carbon Neutrality and Sustainable Development: Evidence From <scp>CS</scp>‐<scp>ARDL</scp> Approach
Bibliographic record
Abstract
ABSTRACT Net zero emission and attainment of SDG‐13 was the core agenda in COP 26, 27, and 28. In this regard, the present study measures the nexus between environmental policy stringency, renewable energy, green technology innovation, globalization, and carbon emissions. The CS‐ARDL model is utilized to study the said variables in G7 (Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States) nations. Results reveal that carbon emissions, renewable energy, environmental policy stringency, and globalization are positively associated with green technology innovation and strongly impact the development and implementation of green innovations in the long run, particularly by CO2 emission. Whereas in the short run, the aforementioned associations and impacts are weak and less impactful besides carbon emissions with green innovation. Overall findings show that green technology innovation is the powerful and most crucial tool for the reduction of carbon emissions and climate change impacts. Policy implications are suggested to attain the net zero emission and SDG‐13 in G7 nations through green innovation and its associated products, processes, and practices.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".