Profit-seeking solar geoengineering exemplifies broader risks of market-based climate governance
Bibliographic record
Abstract
Despite uncertainties about its feasibility and desirability, start-up companies seeking to profit from solar geoengineering have begun to emerge. One company is releasing balloons filled with sulfur dioxide to sell “cooling credits”, claiming that the cooling achieved when 1 g of SO 2 is released is equivalent to offsetting one ton of carbon dioxide for one year. Another aspires to deliver returns to investors from the development of a proprietary aerosol for dispersal in the stratosphere. Such for-profit solar geoengineering enterprises should not be understood merely as rogue opportunists. These proposals are not only scientifically questionable, and premature in the absence of effective governance, but they are a predictable consequence of neoliberal, market-driven climate governance. The structures and incentives of market-based climate policy - circumscribed by neoliberalism's emphasis on technological innovation, venture capital, and the marketization of environmental goods - have generated repeated efforts to profit from various forms of geoengineering. With a climate governance regime wherein private, for-profit actors significantly influence and weaken climate policy, de facto governance of solar geoengineering has emerged, dominated by actors linked to Silicon Valley funders and ideologies. Without more explicit efforts to curb the power of private sector actors, including commercial geoengineering bans and non-use provisions, pursuit of techno-market “solutions” could lead to both inadequate mitigation and increasingly risky reliance on geoengineering.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| 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".