Prospects for achieving carbon neutrality by economically developed countries
Bibliographic record
Abstract
The prospects for achieving carbon neutrality by economically developed countries (USA, EU, Norway, Canada, Japan and Australia) are studied. An analysis of the structure of energy and land use in these countries is carried out. Scenario estimates of the dynamics of carbon indicators of the economies of the world’s leading countries have been developed. It is shown that the current rates of decarbonisation and development of the carbon capture and storage industry do not guarantee the achievement of climate neutrality by 2050, even in the world’s leading economies. A central challenge in achieving climate neutrality is the rapid and large-scale deployment of CCS in all its possible manifestations. All of the countries studied, except Japan, have their own capacity to store carbon for more than a hundred years. To achieve climate neutrality, the leading OECD countries will need to ensure the annual capture of at least 6 billion tons of CO2 by 2050, which is almost 25 times higher than their current capacities (operating, under construction and under design) Despite the fact that climate change occupies almost a leading place on the global agenda, the actual results of efforts in this area are far from declared. It is no longer realistic to keep warming within 1.5°C, and at the current rate of decarbonization, even by world leaders, the defense of the second critical frontier in 2°C will soon be threatened.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".