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Record W4407958389 · doi:10.59075/kwyny187

Empowering Local Climate Actions: Insights on Environmental Devolution from Global North and Global South

2025· article· en· W4407958389 on OpenAlexaboutno aff
Muhammad Asghar, M. M. Lutfe Elahi, Fouzia Ghani

Bibliographic record

Venue˜The œcritical review of social sciences studies · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainable Development and Environmental Policy
Canadian institutionsnot available
Fundersnot available
KeywordsDevolution (biology)Environmental planningClimate changeGeographyGlobal SouthEnvironmental resource managementPolitical scienceEnvironmental protectionRegional scienceEnvironmental scienceEconomic geographyOceanographyGeology

Abstract

fetched live from OpenAlex

The global climate crisis has necessitated an adaptive governance framework to prioritize sustainable development. Environmental devolution is one of the adaptation-cum-mitigation strategies that transfer decision-making power over climate governance from central governments to local institutions and offers a potential roadmap to navigate climate challenges effectively. This study aims to explore the dynamics of environmental devolution by analyzing the dominant case studies from the Global North (Canada, UK, and Australia) and the Global South (India, Brazil, and South Africa), comprehending their innovative approaches towards sustainable governance. The case study approach helps to do an in-depth analysis of various devolution models across the globe and finds that the patterns of environmental devolution in the Global North often derive from constitutional and institutional frameworks, whereas in the Global South, community-based initiatives and indigenous knowledge systems (re-)shape this. The study concludes by comparing diverse cases and urges for an effective environmental devolution model to address the challenges of climate justice and contribute to sustainable governance. The findings suggest policymakers and practitioners to foster an adaptive governance framework to address the complexities of the climate crisis.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.275
Threshold uncertainty score0.999

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 studies0.0010.004
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.028
GPT teacher head0.340
Teacher spread0.311 · 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 designObservational
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

Citations0
Published2025
Admission routes1
Has abstractyes

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