Near-term benefits from investment in climate adaptation complement long-term economic returns from emissions reduction
Bibliographic record
Abstract
Previous studies have suggested that a combined strategy using both emissions abatement and climate adaptation can improve economic outcomes. Here, using a parsimonious economic-climate assessment model, we have shown that, relative to investment in abatement, adaptation has a much shorter timescale for economic return. Adaptation deployed in conjunction with abatement allows earlier benefits compared to investment in abatement alone. Our results provide evidence of greater net benefit with complementary investments in abatement reducing long-term climate damage and investments in adaptation reducing near-term damage. The timescale of return on investment in abatement is strongly influenced by economic discount rates, whereas the timescale of return on investment in adaptation is strongly influenced by the capital depreciation timescale. Higher levels of abatement investment associated with stringent emissions reduction constraints can reduce returns on adaptation investment. Even so, our results indicate greater near-term and long-term net benefits when investing in both abatement and adaptation. The near-term economic benefits of adaptation to climate change and the longterm return-on-investment from emissions abatement are complementary and most effective in combination, according to an analysis based on an Integrated Assessment Model.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".