Bibliographic record
Abstract
How global emissions reduction targets can be achieved equitably is a key issue in climate change discussions. This paper presents an analytical framework to encompass contributions to the literature on equity in climate change, and highlights the consequences -- in terms of future emissions allocations -- of different approaches to equity. Progressive cuts relative to historic levels -- for example, 80 percent by industrial countries and 20 percent by developing countries -- in effect accord primacy to adjustment costs and favor large current emitters such as the United States, Canada, Australia, oil exporters, and China. In contrast, principles of equal per capita emissions, historic responsibility, and ability to pay favor some large and poor developing countries such as India, Indonesia, and the Philippines, but hurt industrial countries as well as many other developing countries. The principle of preserving future development opportunities has the appeal that it does not constrain developing countries in the future by a problem that they did not largely cause in the past, but it shifts the burden of meeting climate change goals entirely to industrial countries. Given the strong conflicts of interest in defining equity in emission allocations, it may be desirable to shift the emphasis of international cooperation toward generating a low-carbon technology revolution. Equity considerations would then play a role not in allocating a shrinking emissions pie but in informing the relative contributions of countries to generating such a pie-enlarging revolution.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".