Analysis of the Economic Ripple Effect of the United States on the World due to Future Climate Change
Bibliographic record
Abstract
Abstract Solomon Hsiang's study, which was recently published in Science, has caused extensive discussion by indicating that future climate change may exert influences on the agricultural yield, labor supply, and energy demand, among others of the United States. Based on the above study, we use an optimized input‐output model to evaluate the economic ripple effect (ERE) of the United States on the world due to climate change under Representative Concentration Pathway 4.5 with different increases in the annual mean temperature (AMT; 1, 1.5, and 2°C) between 2020 and 2100. The results show that if a loss of gross domestic product of 0.88% occurs with a 1°C AMT increase in the United States, the ERE of approximately 0.12% will be generated onto the global gross domestic product; with a 2°C increase, the ERE will triple. The time variation trend of the ERE conforms to the future variations in AMT. The degree of this effect on other regions of the world is closely related to the trade links between the United States and economic aggregates. Among these regions, Canada shows the greatest impact, followed by China. The ERE that China suffers may increase by 4.5 times as the AMT in the United States increases from 1 to 2°C. In cold regions, the benefit from global warming will decrease due to the ERE from other regions, which will inhibit their economic development. This paper aims to provide new perspectives and data‐based support for regions around the world to cope with climate change and to develop policies by studying the ERE behind international trade.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".