Strategic Implications of Counter-Geoengineering: Clash or Cooperation?
Bibliographic record
Abstract
Solar geoengineering has received increasing attention as an option to temporarily stabilize global temperatures. A key concern is that heterogeneous preferences over the optimal amount of cooling combined with low deployment costs may allow the country with the strongest incentive for cooling, the so-called free-driver, to impose a substantial externality on the rest of the world. We analyze whether the threat of counter-geoengineering technologies capable of negating the climatic effects of solar geoengineering can overcome the free-driver problem and tilt the game in favour of international cooperation. Our game-theoretical model of countries with asymmetric preferences allows for a rigorous analysis of the strategic interaction surrounding solar geoengineering and counter-geoengineering. We find that counter-geoengineering prevents the free-driver outcome, but not always with benign effects. The presence of counter-geoengineering leads to either a climate clash where countries engage in a non-cooperative escalation of opposing climate interventions (negative welfare effect), a moratorium treaty where countries commit to abstain from either type of climate intervention (indeterminate welfare effect), or cooperative deployment of solar geoengineering (positive welfare effect). We show that the outcome depends crucially on the degree of asymmetry in temperature preferences between countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".