MétaCan
Menu
Back to cohort
Record W3190249869 · doi:10.1142/s2010007821500081

OPTIMAL CLIMATE POLICY IN 3D: MITIGATION, CARBON REMOVAL, AND SOLAR GEOENGINEERING

2021· article· en· W3190249869 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueClimate Change Economics · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Geoengineering
Canadian institutionsUniversity of Waterloo
FundersNational Science Foundation
KeywordsDiscountingGeoengineeringBaseline (sea)Environmental scienceSoftware deploymentClimate changeClimate policyClimate change mitigationGreenhouse gasEconometricsComputer scienceEnvironmental economicsNatural resource economicsEconomicsEcology

Abstract

fetched live from OpenAlex

We introduce solar geoengineering (SG) and carbon dioxide removal (CDR) into an integrated assessment model to analyze the trade-offs between mitigation, SG, and CDR. We propose a novel empirical parameterization of SG that disentangles its efficacy, calibrated with climate model results, from its direct impacts. We use a simple parameterization of CDR that decouples it from the scale of baseline emissions. We find that (a) SG optimally delays mitigation and lowers the use of CDR, which is distinct from moral hazard; (b) SG is deployed prior to CDR while CDR drives the phasing out of SG in the far future; (c) SG deployment in the short term is relatively independent of discounting and of the long-term trade-off between SG and CDR over time; (d) small amounts of SG sharply reduce the cost of meeting a [Formula: see text]C target and the costs of climate change, even with a conservative calibration for the efficacy of SG.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.475
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.221
Teacher spread0.202 · 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