Rethinking climate engineering categorization in the context of climate change mitigation and adaptation
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
The portfolio of approaches to respond to the challenges posed by anthropogenic climate change has broadened beyond mitigation and adaptation with the recent discussion of potential climate engineering options. How to define and categorize climate engineering options has been a recurring issue in both public and specialist discussions. We assert here that current definitions of mitigation, adaptation, and climate engineering are ambiguous, overlap with each other and thus contribute to confusing the discourse on how to tackle anthropogenic climate change. We propose a new and more inclusive categorization into five different classes: anthropogenic emissions reductions ( AER ), territorial or domestic removal of atmospheric CO 2 and other greenhouse gases (D‐ GGR ), trans‐territorial removal of atmospheric CO 2 and other greenhouse gases (T‐ GGR ), regional to planetary targeted climate modification ( TCM ), and climate change adaptation measures (including local targeted climate and environmental modification, abbreviated CCAM ). Thus, we suggest that techniques for domestic greenhouse gas removal might better be thought of as forming a separate category alongside more traditional mitigation techniques that consist of emissions reductions. Local targeted climate modification can be seen as an adaptation measure as long as there are no detectable remote environmental effects. In both cases, the scale and intensity of action are essential attributes from the technological, climatic, and political viewpoints. While some of the boundaries in this revised classification depend on policy and judgement, it offers a foundation for debating on how to define and categorize climate engineering options and differentiate them from both mitigation and adaptation measures to climate change. WIREs Clim Change 2014, 5:23–35. doi: 10.1002/wcc.261 This article is categorized under: Climate, History, Society, Culture > Ideas and Knowledge Social Status of Climate Change Knowledge > Knowledge and Practice
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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