Laying the foundations for negative emissions technologies: insights from a workshop
Bibliographic record
Abstract
Abstract Pre-empting the worst consequences of climate change requires both mitigation of emissions from the global energy system and carbon dioxide removal through negative emissions technologies. Despite their nascence, negative emissions technologies are being incorporated into nationally determined contributions to achieve ambitious targets. It is therefore urgent to build a scaffolding that enables their expansion. Here, we report results from a workshop that brought together 34 prominent stakeholders, including scientists, engineers, energy system analysts, economists, experts in public policy, and policy makers. Participants discussed the likely cost and performance of these technologies; elucidated the opportunities and risks facing deployment; and envisioned how nations might build the necessary scaffolding for expansion. The majority narrative is that negative emissions technologies will have a bridging role in decarbonizing existing assets. Different models of deployment were proposed. Reaching the scale of deployment necessary to meet emissions targets is lengthy and expensive. Financial and regulatory risks are seen as greater barriers to deployment at scale than technological risk. Greater certainty regarding carbon pricing, production tax credits, and support for geological characterization and trunkline construction could reduce the former. Critical to expansion is a large-scale increase in low-carbon power production; the implementation of regulatory frameworks that remove uncertainty surrounding investment decisions; and prudent societal engagement.
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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 | 0.026 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".