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Record W4388622472 · doi:10.1073/pnas.2207727120

Governing the net-zero transition: Strategy, policy, and politics

2023· article· en· W4388622472 on OpenAlexaff
James Meadowcroft, Daniel Rosenbloom

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainability and Climate Change Governance
Canadian institutionsUniversity of TorontoCarleton University
Fundersnot available
KeywordsGreenhouse gasConsumption (sociology)PoliticsIntermediaryProduction (economics)Transition (genetics)EconomicsDistributive propertyResource (disambiguation)Natural resource economicsEnvironmental economicsBusinessEconomic systemPolitical scienceSociologyMicroeconomicsComputer scienceEcology

Abstract

fetched live from OpenAlex

This paper applies insights from the literature on transitions in major consumption-production systems to clarify the nature of the challenge of moving to a net-zero greenhouse gas (GHG) emission society. It highlights critical features of transitions including their multiactor/multicausal logic, phased development, and distributive impacts. Because current systems are so dependent on fossil energy resources, and on GHG-emitting industrial processes and agricultural practices, multiple transitions across a range of distinct consumption-production systems will be required for net zero. The transformation of each system faces different barriers and enabling conditions and is influenced by varied nonclimate-related disruptions. Important policy implications follow, including the need to focus on sector and regional transitions, link climate policy to other societal goals, and adopt policy mixes appropriate to the transition phase. The article discusses recent policy and politics-related findings from the transitions literatures including those dealing with policy mixes, transition intermediaries, and green industrial policy.

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)0.001
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 score0.481
Threshold uncertainty score0.617

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
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 head0.046
GPT teacher head0.302
Teacher spread0.256 · 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

Citations80
Published2023
Admission routes1
Has abstractyes

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