Bibliographic record
Abstract
Abstract Decarbonizing the energy system is a major challenge facing the richest countries, whereas provision of energy services is a major challenge facing the poorest countries. What would be the climate consequences if only richer countries focus on decarbonization, and only poorer countries focus on provision of energy services? To address this question, we create future scenarios in which carbon dioxide (CO2) emissions increase according to a historical trend and then start to decline only when countries reach specified income levels. In our central case, we assume that when countries start to decarbonize, they reduce emissions at 2% yr−1. With this assumption and if all countries begin to decarbonize in 2020, the world would be expected to warm by 2.0 °C relative to pre-industrial times. If countries begin to decarbonize only when their per capita gross domestic product (GDP) exceeds $10 000, there would be less than 0.3 °C of additional warming. Yet over half the world’s population currently lives in countries below such an income threshold, and continued direct CO2 emissions by people who live in these countries, while they remain underdeveloped, would increase global average temperature rise by 14% relative to the case, in which all people begin to decarbonize in 2020. The primary concern of developments driven by fossil fuels in lower income countries might relate to issues such as the technological lock-in to high-emission technologies.
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.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| 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".