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Record W1978964746 · doi:10.1080/23251042.2015.1008384

Climate governance in the post-industrial city: the urban side of ecological modernisation

2015· article· en· W1978964746 on OpenAlexaff
Emiliano Scanu

Bibliographic record

VenueEnvironmental Sociology · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainability and Climate Change Governance
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsDecentralizationCorporate governanceModernization theoryContext (archaeology)Ecological modernizationEuropean unionClimate changeEconomic geographyEconomic systemPolitical scienceEconomyBusinessGeographyEconomicsEconomic growthEconomic policyEcologyPolitics

Abstract

fetched live from OpenAlex

Ecological modernisation (EM) is probably the most widely adopted approach to climate policy, as evidenced by the Kyoto Protocol Flexible Mechanisms, or by the European Union Emissions Trading System. However, far from being deployed only at national and global scales, EM seems to be deployed also locally, as the growing global phenomenon of cities’ involvement in climate governance suggests. That is what this study wants to verify by focusing on the city of Genoa, Italy, whose climate policy is assessed through a set of four criteria already used by EM scholars: win-win stance, policy integration, decentralisation and smart regulation, technological and institutional innovation. Results show that Genoa’s approach to climate change can be firmly qualified as EM and that this outcome is due as much to elements specific to the municipality as to external influences. However, EM observed in Genoa seems qualitatively different from EM which can be observed within nation-states or some industrial sectors, mainly because it displays a specifically urban character. Thus, the article concludes with the hypothesis that an ‘urban’ EM is emerging, namely an EM that reflects the peculiarities of the governance of post-industrial cities within a global context.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.048
GPT teacher head0.247
Teacher spread0.199 · 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

Citations11
Published2015
Admission routes1
Has abstractyes

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