DEVELOPING COUNTRIES AND THE UNFCCC PROCESS: SOME SIMULATIONS FROM AN ARMINGTON EXTENDED CLIMATE MODEL
Bibliographic record
Abstract
We report simulation results for alternative multilateral emissions cuts and accompanying policies which could come under renewed reconsideration for the process to follow the Durban UNFCCC negotiations. The model is an Armington type trade model extended to capture climate change. We calibrate the model to alternative BAU damage scenarios following the Stern report and the literature that has followed. We consider different depths, forms, and timeframes for emission reductions by China, India, Russia, Brazil, US, EU, Japan, and a residual row both jointly and block wise. We assume regionally uniform percentage of both climate change and damages by region, which are relaxed later in sensitivity analysis. The welfare impacts of both emission reductions and accompanying measures are computed in Hicksian money metric equivalent form over three alternative potential commitment periods: 2012–2020, 2012–2030, and 2012–2050. Our multiyear multicounty global modeling framework captures the benefit of emission mitigation through preferences incorporating temperature change. Countries are linked not only through shared welfare impacts of global temperature change but also through trade among country subscripted goods. These trade impacts influence net country benefits from alternative emission reduction agreements. We also evaluate the potential impacts of potential accompanying mechanisms including funds/transfers, border adjustments, and tariffs.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".