Negative emissions and the long history of carbon removal
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
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".