COP27 Making a Case for a Net Zero-Carbon Emissions Future by Implementing Technological Solutions and Mindset Transformation
Bibliographic record
Abstract
Carbon emissions pose a massive risk to our planet's health. According to the Paris Climate Agreement, nations pledged to limit global warming to 1.5°C to mitigate climate change's impacts. This target will not be achieved without immediate and deep emissions reductions across all sectors. Unfortunately, the Russian-Ukrainian conflict and the new natural gas discoveries in some countries have also slowed down the pace of decarbonization. Aside from that, a faint light at the end of the tunnel could be seen from the new Intergovernmental Panel on Climate Change (IPCC) report, which pointed to increasing actions on climate change. Fortunately, achieving future zero carbon emission is still possible via the implementation of holistic frameworks that promote existing and emerging green technologies and helps the community to transform. This policy paper proposes a framework that integrates technology use and mobilizes the transformation of communities' mindsets to cope and adapt to climate change. The proposed framework will then be implemented in the country hosting the 27th Conference of the Parties of the UNFCCC (COP 27), Egypt, and it is recommended to be used by scholars and policymakers for future assessment of the country's climate change performance. Finally, the paper provides a set of recommendations to governments, policymakers, and communities to accelerate the movements toward a net zero-carbon future.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".