MétaCan
Menu
Back to cohort
Record W4405721139 · doi:10.1086/733652

The Economics and Governance of Solar Geoengineering

2024· article· en· W4405721139 on OpenAlex
Juan Moreno‐Cruz, David M. McEvoy, Matthew McGinty, Todd L. Cherry

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

VenueReview of Environmental Economics and Policy · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Geoengineering
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsGeoengineeringCorporate governanceEconomicsEnvironmental scienceEnvironmental economicsNatural resource economicsClimate changeFinance

Abstract

fetched live from OpenAlex

Limited progress on reducing global greenhouse gas (GHG) emissions has sparked increasing interest in whether the global community should consider the use of solar geoengineering (SGE)—technologies designed to reflect sunlight away from Earth—as a short-term approach to reduce climate change damages. Through theory, surveys, simulations, and experiments, economists have studied the strategic implications of SGE, how these technologies interact with incentives to mitigate GHG emissions, and the challenges of governing them. This article provides a comprehensive review of the literature, starting with how SGE is incorporated into economic models. One issue is whether SGE will crowd out efforts to mitigate GHG or will enhance mitigation efforts. We identify conditions under which each of those results is likely. We review the economics of governing SGE, particularly the issue of a single actor unilaterally deploying SGE to manipulate global temperatures. Our review synthesizes the main findings from the literature with the goal of better informing global climate policies.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.959
Threshold uncertainty score0.333

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 head0.006
GPT teacher head0.204
Teacher spread0.197 · 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