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Record W4224816978 · doi:10.31235/osf.io/tj2d3

Impacts of Meeting Minimum Access on Critical Earth Systems amidst the Great Inequality

2022· preprint· en· W4224816978 on OpenAlexaff
Crelis Rammelt, Joyeeta Gupta, Diana Liverman, Joeri Scholtens, D. C. Ciobanu, Jesse F. Abrams, Xuemei Bai, Lauren Gifford, Christopher Gordon, Margot Hurlbert, Cristina Yumie Aoki Inoue, Lisa P. Jacobson, Steven J. Lade, Timothy M. Lenton, David I. Armstrong McKay, Nebojša Nakićenović, Chukwumerije Okereke, Ilona M. Otto, Laura Pereira, Klaudia Prodani, Johan Rockström, Ben Stewart‐Koster, Peter H. Verburg, Caroline Zimm

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsUniversity of Regina
FundersPorticus FoundationGlobal Environment FacilityGordon and Betty Moore Foundation
KeywordsInequalityRedistribution (election)OperationalizationEnvironmental degradationNatural resource economicsNatural resourceSustainable developmentSustainabilityEarth system scienceEnvironmental planningGeographyEnvironmental resource managementDevelopment economicsEnvironmental sciencePolitical scienceEconomicsEcology

Abstract

fetched live from OpenAlex

The UN 2030 Agenda includes 17 Sustainable Development Goals towards improving access to resources and services, reducing environmental degradation and bringing down inequality. However, there is debate on the magnitude of the environmental burden that would arise from meeting the needs of the poorest, especially compared to much larger burdens from the rich. We first show that the ‘Great Acceleration’ of human impacts is characterized by a ‘Great Inequality’ in utilising and damaging the environment. We then operationalize ‘just access’ to minimum energy, water, food and infrastructure. Third, in an unequal world, we show that hypothetically meeting ‘just access’ would add 2-26% to current impacts on the Earth’s natural systems of climate, water, land and nutrients. These additional impacts, hypothetically caused by about a third of humanity, equal those currently caused by the wealthiest 1-4%. Nevertheless, achieving ‘just access’ calls for redistribution within stable Earth System Boundaries.

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)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.293
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.004
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.064
GPT teacher head0.381
Teacher spread0.318 · 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.

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

Citations12
Published2022
Admission routes1
Has abstractyes

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