Incentives for investment in clean technologies
Bibliographic record
Abstract
The fulfilment of the commitment to decarbonization at a global level implies the need to invest in clean technologies on a global scale that requires large volumes of financing in a complex context where there are significant technological uncertainties, and the distribution of resources is not homogeneous around the world. Against this backdrop, incentives for investment in clean technologies are an instrument that, when properly designed, can boost private investment to achieve environmental goals. This report addresses the funding needs for investment in clean technologies, the current financing gap to move towards environmental sustainability, and conceptualizing the term investment incentive. It proposes classifying the different incentives into six broad categories: economic, financial, fiscal, market, regulatory, and of knowledge and collaboration, and then describes how they are being implemented in the United States, the European Union, China, Canada, India and the United Kingdom. Finally, conclusions and reflections are presented on the elements and dimensions to consider when designing, implementing, monitoring and evaluating the impacts of incentives for investment in clean technologies.
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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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".