Negative emissions and the long history of carbon removal
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Recent IPCC assessments highlight a key role for large‐scale carbon removal in meeting the objectives of the Paris Agreement. This focus on removal, also referred to as negative emissions, is suggestive of novel opportunities, risks, and challenges in addressing climate change, but tends to build on the narrow techno‐economic framings that characterize integrated assessment modeling. While the discussion on negative emissions bears important parallels to a wider and older literature on carbon sequestration and carbon sinks, this earlier scholarship—particularly from the critical social sciences—is seldom engaged with by the negative emissions research community. In this article, we survey this “long history” of carbon removal and seek to draw out lessons for ongoing research and the emerging public debate on negative emissions. We argue that research and policy on negative emissions should proceed not just from projections of the future, but also from an acknowledgement of past controversies, successes and failures. In particular, our review calls attention to the irreducibly political character of carbon removal imaginaries and accounting practices and urges acknowledgement of past experiences with the implementation of (small‐scale) carbon sequestration projects. Our review in this way highlights the importance of seeing continuity in the carbon removal discussion and calls for more engagement with existing social science scholarship on the subject. Acknowledging continuity and embracing an interdisciplinary research agenda on carbon removal are important aspects in making climate change mitigation research more responsible, and a precondition to avoid repeating past mistakes and failures. This article is categorized under: The Carbon Economy and Climate Mitigation > Benefits of Mitigation
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it