Policy Portfolio Options for Encouraging Deployment of Negative Emissions Technologies in the Electricity System
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Negative emissions technologies, like direct air capture (DAC) of CO 2 and carbon capture and storage (CCS), could play a role in the transition to net-zero emissions in the electricity system. These technologies both capture CO 2: CCS captures CO 2 from a point source, such as a fossil-fueled power pant, while DAC captures CO 2 from the ambient air. DAC could be used to offset residual emission from CCS-enabled power plants with imperfect (<100%) CO 2 capture rates. Studies have shown that they can reduce the cost of achieving net-zero, but this prior work has focused on outcomes achieved in the final year of the transition to net-zero, such as cost and technological portfolio. Less attention is paid to important transition dynamics, including cumulative emissions and the effect of deployment on electricity prices. Here, we study the impact of negative emissions technologies on transition dynamics within the electricity system, focusing on how they affect cumulative emissions, electricity prices, and abatement costs. We model several policy portfolios, or scenarios, with differing net-zero commitments, deployment subsidies, and carbon pricing. Across scenarios, we find that these technologies play a minimal role in achieving net-zero. Instead, the policy portfolio combining graduated carbon pricing and a renewables subsidy yields the smoothest transition dynamics for electricity prices, and the lowest cumulative emissions and abatement costs.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".