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Record W1872703860 · doi:10.1002/wcc.261

Rethinking climate engineering categorization in the context of climate change mitigation and adaptation

2013· article· en· W1872703860 on OpenAlexaff
Oliviér Boucher, Piers Forster, Nicolas Gruber, Minh Ha‐Duong, M. G. Lawrence, Timothy M. Lenton, Achim Maas, Naomi E. Vaughan

Bibliographic record

VenueWiley Interdisciplinary Reviews Climate Change · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Geoengineering
Canadian institutionsConcordia University
FundersNatural Environment Research CouncilEngineering and Physical Sciences Research CouncilEidgenössische Technische Hochschule ZürichBrandenburger Staatsministerium für Wissenschaft, Forschung und KulturBundesministerium für Bildung und ForschungAgence Nationale de la Recherche
KeywordsClimate changeGreenhouse gasAdaptation (eye)Climate change mitigationEnvironmental resource managementCategorizationContext (archaeology)Environmental planningEnvironmental scienceGeographyComputer scienceEcologyPsychology

Abstract

fetched live from OpenAlex

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 CO2 and other greenhouse gases (D‐GGR), trans‐territorial removal of atmospheric CO2 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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.043
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.043
Threshold uncertainty score0.227

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0430.036
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0080.007
Science and technology studies0.0090.062
Scholarly communication0.0200.034
Open science0.0070.011
Research integrity0.0090.021
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.061
GPT teacher head0.276
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreReview

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".

Quick stats

Citations89
Published2013
Admission routes1
Has abstractyes

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