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Enregistrement W3194536865 · doi:10.1111/ropr.12445

Decarbonization and climate change

2021· article· en· W3194536865 sur OpenAlexaboutno aff
Nils C. Bandelow, Johanna Hornung, Ilana Schröder, Colette S. Vogeler

Notice bibliographique

RevueReview of Policy Research · 2021
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueSocial Acceptance of Renewable Energy
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésClimate changeWork (physics)Political sciencePublic relationsEngineeringEcology

Résumé

récupéré en direct d'OpenAlex

The summer months of 2021 brought extreme weather events like floods and wildland fires event to previously less affected areas. As a consequence, the topic of climate change is as up-to-date as ever, and with it the question of how to contain and mitigate CO2 emissions. At the intersection of science, technology, and environmental policy, but also public health, the Review of Policy Research (RPR) presents a platform for this discussion. All articles of our current issue deal with decarbonization and/or climate policy. All of these articles have still been managed by the previous editorial team of the RPR. We take this opportunity to again thank them for their great work and the supportive transition towards us as the new editorial team. We also take this opportunity to announce that we have recruited new support in our team. Since August 2021, Ilana Schröder is the editorial director of the journal and thereby responsible for all administrative questions and concerns related to the RPR journal. She is experienced in this task because she also performs this role for the European Policy Analysis (EPA) journal, and her outstanding work there leaves no doubt that she will continue this excellent performance with the RPR (Bandelow et al., 2021). The current issue compiles five articles that address the topic of decarbonization and climate change from different theoretical and methodological angles: Starting on the individual level, Boudet et al. (2021) explore public preferences towards decarbonization policies that have so far received less media attention, such as the promotion of residential electrification and the funding of microgrids. Conducting a binary logistic regression, they find that most people only support the requirement of solar panels on new development, while support for decarbonization policies generally increases with concerns about climate change and openness for smart home technologies, and when respondents are higher educated men. Jagers et al. (2021) also investigate public acceptance of a specific policy measure, focusing on carbon taxes. With three identical surveys carried out in Canada, German, and the United States, the authors find that citizens’ acceptance of costs associated with carbon taxes can be increased by decreasing income taxes and returning revenue to the public. Overall, citizens are more ready to accept carbon taxes if they feel that the burden is fairly allocated. The article by Guo et al. (2021) zooms into a low-carbon pilot city project in China and asks whether this indeed meets its goal of developing innovative low-carbon policy instruments, or simply implements policies decided upon at the national level. Taking a closer look at the subnational authorities and local governments, the authors conclude that rather the latter is the case, and that the initial objective of experimenting with low-carbon development initiatives, which is traced back to a lack of enthusiasm and political will at the local level. Connecting to the implementation of policies related to the combat against climate change, Cann (2021) analyzes why it was possible to pass substantial climate-energy policies in the state of Illinois despite the contested partisan debate on these policies. Drawing on the concept of strategic framing and results from a mixed-method approach, the analysis presents evidence for the claim that outlining specific aspects of the policy's design, such as economic benefits, and putting less emphasis on climate change and climate sciences enabled a cross-partisan consensus on these policies. The paper fits into a couple of recent publications that also stress the importance of framing and narratives in climate and environmental policies (Derwort et al., 2021; Gjerstad & Fløttum, 2021; Lawlor & Crow, 2018; Schlaufer et al., 2021; Shanahan et al., 2018; Tosun & Schaub, 2021; Vogeler et al., 2021). As opposed to the framing of policies, the article by Purdon et al. (2021) focuses on the interaction and sequencing of low-carbon regulations in the transportation sector. Comparing California and Quebec, the authors state that emission trading has had divergent effects in the two cases, that Quebec has replicated the low-carbon transportation policies from California that promote electric vehicles, and that the stringency of the policy mix generally increased but that those regulations remain highly flexible. Closing this editorial introduction, and besides encouraging you to read the journal's contributions, we always want to invite you to contribute to the journal as well—as an author who submits an individual paper, as a reviewer who contributes to ensuring the highest quality of the content, as a guest editor who considers approaching the RPR for high-quality Special Issue publication projects. We have just updated our general call for papers and special issues and are happy about your interest in the journal (https://onlinelibrary.wiley.com/page/journal/15411338/homepage/news.html). For any questions, comments or ideas regarding publications in RPR, feel free to always contact us via email: rpr@ipsonet.org. We'd love to connect with you! Don't forget to follow us on Twitter (@RPR_Journal) to never miss news and updates related to the journal.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,004
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,960
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,004
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,002
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,176
Tête enseignante GPT0,516
Écart entre enseignants0,340 · 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 tête enseignante, pas un consensus.

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

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

En bref

Citations2
Publié2021
Routes d'admission1
Résumé présentoui

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Même revueReview of Policy ResearchMême sujetSocial Acceptance of Renewable EnergyTravaux en français237 207