Distribution of climate damages in convergence-consistent growth projections
Bibliographic record
Abstract
Climate-econometric estimates assuming that climate changes affect economic growth result in larger projected damages than estimates restricting the effect to economic income levels. We show that the latter is consistent with neoclassical macroeconomic theory by explicitly accounting for income growth convergence in our empirical investigation. We show that accounting for convergence does not statistically change the point estimates capturing climate’s macroeconomic effect, but it has significant implications for assessing the long-term economic consequences of climate change. The magnitude and spread of long-term losses from climate change are reduced. Aggregated damages are found to be convex in the extent of climate change and are projected to continuously increase over time with on-going climate change, in contrast to growth-effects-only estimates where the gains experienced by the winners of climate change eventually surpass the losses incurred by the losers. For example, projections of climate change damages based on climate-econometric estimates by Burke et al., 2015 find that global warming could reduce average global incomes by 20% and drastically increase intercountry income inequality, reflected by a 118% increase in the Gini coefficient in 2100 under RCP8.5. We reestimate and project climate damages under the same scenario accounting for convergence and find global climate damages around 8.5% of global incomes and an increase in intercountry income inequality by 8% in 2100.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".