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Record W4205381129 · doi:10.33423/jabe.v23i3.4341

False Hopes, Missed Opportunities: How Economic Models Affect the IPCC Proposals in Special Report 15 “Global Warming of 1.5 °C” (2018). An Analysis From the Scientific Advisory Board of BUND

2021· article· en· W4205381129 on OpenAlexvenueno aff

Bibliographic record

VenueJournal of Applied Business and Economics · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsDamagesOvershoot (microwave communication)Consumption (sociology)SustainabilityClimate changeNatural resource economicsGlobal warmingEnvironmental economicsExpert elicitationEnvironmental resource managementEnvironmental scienceBusinessEconomicsEngineeringMeteorologyGeographyPolitical scienceEcology

Abstract

fetched live from OpenAlex

The 2018 IPCC Special Report SR15 developed four scenarios how temperature increases could either be limited to 1.5°, or, in the case of overshoot, could be brought back to that level by 2100. However, the Carbon Dioxide Removal options discussed to achieve “negative emissions” will affect not only the climate system, but also biodiversity and the ecosystem services it provides. Unfortunately, the Integrated Assessment Models, and in particular the economic models incorporated in them were capable of integrating only a selective fraction of these effects, and ignore potential tipping points triggering irreversible processes, policies beyond economic instruments and consumption changes to happen over the next 80 years. Our analysis is based on an interdisciplinary expert elicitation, analysing the options suggested by the IPCC one by one. We find that most of them are associated with biodiversity loss, hazardous chemicals dispersion, enhanced energy consumption and/or other severe other damages. We suggest which measures can be applied sustainably, which should be dropped, and which additional ones have been omitted by the report.

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 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)0.002
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.185
Threshold uncertainty score0.925

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.123
GPT teacher head0.254
Teacher spread0.131 · 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 teacher head, not a consensus.

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

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

Citations4
Published2021
Admission routes1
Has abstractyes

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