Carbon removal for a just transition
Bibliographic record
Abstract
There is growing acknowledgement of the need to remove and durably store carbon dioxide. Even with dramatic emissions reductions, achieving net zero will require the creation of new infrastructures, institutions, and processes for carbon removal on the scale of major existing industries. Removal technologies are in development but their material configurations in functioning socio-technical systems are as yet undetermined. As private and public investments flow into research, development, and deployment, the foundations of an emerging carbon removal industry are being laid down via policy decisions and presumptions that will shape the field for decades or more. Here we argue that although deployment of carbon removal is necessary to underpin a just transition, its emerging configurations and governance run counter to just transition principles. With reference to findings from an expert convening, we highlight a set of critical problems and inequities within the emerging political-economic model of the nascent sector. While scholars have previously examined the role of carbon removal in climate policy, and the technical and economic conditions for its effective delivery, we focus here on the prospect of radical interventions to reorient its practical delivery to support a just transition. We suggest interventions to guarantee that carbon removal is done for just purposes (e.g. not to allow high emitters to continue emitting), and ensure that carbon removal can be done sustainably and responsibly at the scales imagined. We call for mandatory substantive participation in decision-making, particularly amongst marginalized groups. We look beyond commodification, markets, and private ownership as models for deploying carbon removal and argue that fossil interests and historical emitters must be held financially responsible for carbon removal without being placed at its helm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".