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Enregistrement W3094078513 · doi:10.1162/glep_r_00583

Repowering Cities: Governing Climate Change Mitigation in New York City, Los Angeles, and Toronto

2020· article· en· W3094078513 sur OpenAlexaboutno aff
Corina McKendry

Notice bibliographique

RevueGlobal Environmental Politics · 2020
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueSustainability and Climate Change Governance
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésContext (archaeology)Climate governanceClimate changeCorporate governancePoliticsScholarshipArgument (complex analysis)Greenhouse gasCity regionClimate change mitigationPolitical scienceSociologyEnvironmental planningPublic administrationEconomic growthEconomicsEconomyManagementEnvironmental scienceGeography

Résumé

récupéré en direct d'OpenAlex

Cities have been widely promoted as an important political scale for addressing climate change. As Sara Hughes’ Repowering Cities rightly points out, however, much of the research on city climate efforts focuses on the adoption of greenhouse gas reduction goals. Hughes is interested in an even more pressing question: once goals are adopted, how do cities move forward with the complicated process of governing emissions? To answer this question, she offers an excellent synthesis of years of scholarship on cities and climate change, then builds on it with her own study of New York, Los Angeles, and Toronto. In doing so, Repowering Cities offers a useful illumination of the political challenges of achieving city climate goals.Hughes’ main argument is that cities share a set of governance strategies that help them overcome the complexity and uncertainty that define greenhouse gas reduction efforts. These strategies are institution building, coalition building, and capacity building. She argues that they can be seen across city climate initiatives, even as each city’s unique built environment and political context shape the details of its climate policies and the strategies’ implementation.The importance of her first governance strategy, institutional building, is well established. It may include the creation of a new department to oversee the implementation of a city’s policies but also often means that city climate leaders find ways to integrate climate goals throughout the existing sectors and departments of the city. This embedding of climate goals and policies into formal and informal institutions and decision-making is crucial, Hughes asserts, to ensure that implementation continues through changes in political leadership and staff turnover. In other words, though strong leaders are often important in establishing ambitious greenhouse gas reduction goals, it is how well these leaders are able to institutionalize their climate goals that in the long run facilitates emission reductions.The second governance strategy Hughes investigates is coalition building. She argues that coalition building across political bodies, the business community, and nonprofits is necessary for reducing strategic uncertainty in climate policies. As greenhouse gas emissions come from myriad sectors across urban areas, “the capacity and ability of city governments to govern is activated and strengthened by the partnerships and relationships they are able to generate” (p. 69). Coalition building fosters collaboration, communication, and support for the programs across constituents and interests. A failure to build coalitions, her cases make clear, can lead to opposition to climate policies from sectors vital to achieving emission reductions.Finally, capacity building reduces substantive uncertainty about how mitigation actions relate to outcomes. Capacity building can include gathering and understanding emissions data, creating new financial tools and regulations to reduce emissions, and learning from other cities through transmunicipal networks. As governing climate change is a new arena for cities, they often find themselves with ambitious goals they are not sure how to achieve. Capacity building is necessary for operating in this new field. The discussion of capacity building returns the book to one of Hughes’ main arguments: adopting greenhouse gas reduction goals is theeasy part; figuring out how to achieve these goals is the challenge that cities now face.Overall, Repowering Cities illustrates that, for proponents of city climate efforts, a shift needs to be made from sharing best practices in the realm of policy to sharing best practices in the realm of governance. Cities’ emissions come from different sources depending on their built environment, local climatic conditions, and existing infrastructure. Yet much of what needs to be done to reduce emissions, such as changing energy sources and reducing emissions from transportation and buildings, is well known. What needs deeper investigation, Hughes successfully argues, is how to establish city governance practices that facilitate achieving these changes over the long run. For scholars already steeped in the literature on cities and climate governance, nothing in Hughes’ book is particularly groundbreaking. Yet it does provide a useful framework for examining strategies of city climate governance and the challenges even ambitious and well-resourced cities face in achieving their goals.Hughes concludes with a decidedly hedged answer as to whether or not city climate actions are reducing carbon emissions. Indeed, though she holds her case cities up as climate leaders, her analysis of climate governance in New York, Los Angeles, and Toronto shows, perhaps unintentionally, how often climate governance falters. It is clear that some changes have been made, most notably in city energy supplies and municipal operations. Yet Hughes asserts that emissions reductions are only one way of counting progress. Of perhaps even greater importance is the ability of these cities to facilitate and promote the new modes of governance that will be necessary for the “repowering” of city climate politics that is necessary for them to be important players in moving toward a low-carbon world.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,123
Score d'incertitude au seuil0,895

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,002
Études des sciences et des technologies0,0140,008
Communication savante0,0070,003
Science ouverte0,0010,004
Intégrité de la recherche0,0010,003
Charge utile insuffisante (le modèle a refusé de juger)0,0060,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,025
Tête enseignante GPT0,234
Écart entre enseignants0,209 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations27
Publié2020
Routes d'admission1
Résumé présentoui

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