Welfare Impacts of Climate Change on Agriculture: Evidence from Over 1, 000 Yield Studies
Bibliographic record
Abstract
There is now a large body of scientific evidence based on experiments, process-based crop models, and econometric studies, documenting the expected impact of climate change on crop productivity. However, the implications of these changes for more salient economic outcomes such as production, prices, consumption, and welfare are poorly understood. In particular, recent scientific findings are not reflected in the calibration of damage functions in Integrated Assessment Models (IAMs), used to calculate the social cost of carbon (SCC), which are instead based on a small number of studies from the early-to-mid 1990s. In this paper we perform the first end-to-end analysis directly linking the scientific literature on biophysical climate impacts to the SCC. We do this by connecting a comprehensive meta-analysis of crop yield response to climate change, a computable general equilibrium model (GTAP), and the FUND IAM. We find negative effects of warming on most crops in most places and very limited potential for adaptation to offset declines. These yield impacts cause prices to increase between 24% (maize) and 1% (wheat). The welfare effects of these changes are mediated by terms-of-trade effects that tend to moderate negative impacts in net exporters (Brazil, Canada, United States) and exacerbate them in net importers (Middle East, Japan). Overall, damages from warming are negative in most regions and increase approximately linearly with temperature. Incorporating these new damage functions into FUND more than triples the SCC from $7 per ton to $23 per ton. This is due to impacts in the agricultural sector changing from benefits of $7 per ton to costs of $9 per ton. This has direct policy implications given FUND is one of three models used by the US government to calculate the SCC applied to cost-benefit analysis of climate-relevant regulations.
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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 | 0.034 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.012 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".