COP27 Making a Case for a Net Zero-Carbon Emissions Future by Implementing Technological Solutions and Mindset Transformation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it