A "cap and invest" strategy for managing the intergenerational burden of financing energy transitions
Bibliographic record
Abstract
The investment in sustainable energy required to meet the climate change commitments made by 190 countries signatory to the 2015 Paris Accord is in the order of $100 trillion over the next 2 decades. Reducing carbon emissions requires a financing strategy for managing risk that is an intergenerational burden. This paper proposes a "cap and invest" strategy for building up the necessary infrastructure to reduce greenhouse gas (GHG) emissions consistent with national commitments. "Cap and invest" is in sharp contrast to "cap and trade." An economy-wide general environmental tax (GET) on consumption is the basis for financing the energy transition. The GET creates a large "pool of capital" to de-risk investment in emerging low-carbon solutions in support of an energy infrastructure resilient to the threat of climate change. Innovation in governance is an integral part of the policy to leverage the capital markets through public-private partnership in green financing.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".