The value of reducing the Green Premium: cost-saving innovation, emissions abatement, and climate goals
Bibliographic record
Abstract
Abstract Carbon-emitting technologies often cost less than carbon-emission-free alternatives; this difference in cost is known as the Green Premium. Innovations that decrease the Green Premium contribute to achieving climate goals, but a conceptual framework to quantify that contribution has been lacking. Here, we devise a framework to translate reductions in the Green Premium into equivalent reductions in carbon emissions. We introduce a new integrated assessment model designed for teaching and communication, the Climate Optimized INvestment model, to facilitate transparent investigation of cost-saving innovation. We look at consequences of introducing a new technology with potential for learning and improvement for scenarios with three levels of stringency of carbon constraint: an Unlimited budget scenario in which carbon emissions abatement is determined only by balancing marginal costs; a Large budget scenario with a maximum budget for future cumulative emissions equivalent to 50 times the initial-year emissions; and a Small budget scenario with a maximum budget for future cumulative emissions equivalent to 15 times the initial-year emissions. At all of these stringency levels, we find the least-cost solutions involve investing in a learning subsidy to bring the cost of the new technology down the learning curve. Reducing the Green Premium can lead to enhanced carbon abatement, lower abatement costs even after reaching net-zero emissions, less climate damage, and increased net-present-value of consumption. We find both the value of Green Premium reductions and the value of carbon dioxide removal are greater under more stringent mitigation targets. Our study suggests a crucial role for both public and private sectors in promoting and developing innovations that can contribute to achieving zero emissions goals.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".