Level versus Equivalent Intensity Carbon Mitigation Commitments
Bibliographic record
Abstract
Large population / rapidly growing economies such as China and India have argued that in the upcoming UNFCCC negotiations in Copenhagen, any emission reduction targets they take on should be based on their intensity of emissions (emissions/$GDP) on a target date not the level of emissions. They argue that this will allow room for their continued high growth, and level commitments in the presence of sharply differential growth between OECD and non-OECD economies represent asymmetric and unacceptable arrangements. Much of the policy literature agrees with this position, also arguing that while there is equivalence between commitments if growth rates are certain, where growth rates are uncertain equivalence breaks down. However, no explicit models or experimental design are used to support this claim. Here we use a modeling framework in which countries face a business as usual (BAU) growth profile under no mitigation, and can mitigate (reduce consumption) and lower temperature change but with a utility loss. International trade enters through trade in country differentiated goods, and the impact of mitigation on country welfare depends critically on the assumed severity of climate related damage. We then consider cases where country growth rates are uncertain, and compare the impacts of levels versus intensity commitments, with the latter made equivalent in the sense that expected emissions are the same. There are different senses of this equivalence; global equivalence with differing country impacts, or strict country by country equivalence. Under intensity commitments there is more variation in both consumption and emissions than is the case with level commitments, and we show cases where level commitments are preferred to intensity commitments by all countries. Whether this is the case also depends upon how growth rate uncertainty is specified. We are also able to consider packages of mixed level and intensity commitments by country which might be the outcome of UNFCCC negotiations. Outcomes can thus be opposite to prevailing opinion, but it depends on how the equivalent targets are specified.
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How this classification was reachedexpand
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".