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Record W4220876785 · doi:10.1162/glep_a_00658

Transnational Governance and the Urban Politics of Nature-Based Solutions for Climate Change

2022· article· en· W4220876785 on OpenAlexfundno aff
Laura Tozer, Harriet Bulkeley, Linjun Xie

Bibliographic record

VenueGlobal Environmental Politics · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainability and Climate Change Governance
Canadian institutionsnot available
FundersUniversity of Toronto ScarboroughHorizon 2020 Framework ProgrammeUniversiteit UtrechtDurham UniversityEuropean Commission
KeywordsVisionCorporate governanceUrbanismClimate governanceClimate changePoliticsSustainabilityEnvironmental governanceClimate resiliencePolitical scienceResilience (materials science)Political economySociologyBusinessArchitectureGeographyLawEcology

Abstract

fetched live from OpenAlex

Abstract Multiple visions for how urbanism can respond to the climate crisis and foster sustainability have emerged on the international agenda, including the ecocity, low-carbon city, smart city, and resilient city. These competing visions have been joined by one deploying “nature-based solutions.” We examine how nature-based solutions are emerging as a linchpin holding together the nature and climate agendas and what this means for where and by whom nature-based solutions are forming part of transnational urban governance. We argue that this field is animated by four frames connecting urban nature and climate: nature for resilience, nature for mitigation, the integrated benefits of nature, and nature first. Diverse actors, from conservation organizations to design firms to transnational municipal networks, draw on these frames and adopt new governance arrangements such that what it means to govern climate in the city is shifting. How this emerging nature–climate governance complex is structured will generate new momentum for governing urban nature over the coming decade.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.841
Threshold uncertainty score0.599

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 studies0.0010.001
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 head0.012
GPT teacher head0.224
Teacher spread0.213 · 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

Citations19
Published2022
Admission routes1
Has abstractyes

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