Climate impact auctions: an underused tool for green subsidies in the Global South
Bibliographic record
Abstract
The urgency of reducing emissions globally requires the participation of Low- and Middle-Income Countries, which represent over 70% of global emissions. Funding from High-Income Countries’ support for a ‘just transition’ in developing countries through institutions such as the Green Climate Fund has almost exclusively been given as investment-cost subsidies. In contrast, the same industrialized countries extensively use performance-based mechanisms to drive emissions reductions domestically. We explore the advantages and disadvantages of using results-based subsidies allocated through reverse auctions as a tool to support mitigation in Low- and Middle-Income Countries. Results-based subsidies would drive strong responses by giving greater rewards for better performance. By reducing administrative discretion, results-based subsidies would decrease costs and facilitate participation by small- and medium-sized firms. Results-based subsidies would, however, increase capital costs and would reallocate risks from donors to project proponents. Overall, they could be attractive in specific circumstances, specifically for projects that can be competitive, have measurable results, and currently face socially suboptimal investment.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 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".