The Economics and Governance of Solar Geoengineering
Bibliographic record
Abstract
Limited progress on reducing global greenhouse gas (GHG) emissions has sparked increasing interest in whether the global community should consider the use of solar geoengineering (SGE)—technologies designed to reflect sunlight away from Earth—as a short-term approach to reduce climate change damages. Through theory, surveys, simulations, and experiments, economists have studied the strategic implications of SGE, how these technologies interact with incentives to mitigate GHG emissions, and the challenges of governing them. This article provides a comprehensive review of the literature, starting with how SGE is incorporated into economic models. One issue is whether SGE will crowd out efforts to mitigate GHG or will enhance mitigation efforts. We identify conditions under which each of those results is likely. We review the economics of governing SGE, particularly the issue of a single actor unilaterally deploying SGE to manipulate global temperatures. Our review synthesizes the main findings from the literature with the goal of better informing global climate policies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".