Exnovations in Energy and Mobility in Europe: Impacts on and Engagement of Vulnerable Groups
Notice bibliographique
Résumé
Abstract Exnovation refers to processes of destabilization, decline, and phase-out of carbon-intensive industries, technologies, business models, and practices, as well as those that create other systemic sustainability challenges. Implementing successful low-carbon transitions across Europe that are socially fair, just, and effective is challenging. While transition policies are usually promoting innovation and diffusion of technological advancements, less focus is dedicated to systemic decarbonization and phasing-out of non-sustainable technologies, materials and practices. Phase-out policies and their implementation supporting a just-low-carbon transition are fundamental to achieve current climate change goals. Nevertheless, the engagement of and the impacts on citizens exposed to most severe effects of transition policies remain underexplored, but are crucial to avoid reinforcement of existing injustices, such as inequitable distribution of costs, or non-inclusive decision-making processes. Therefore, in this paper we explore 27 past and present initiatives related to transition policies in the mobility and energy sector across EU, Canada and Australia to understand their undesired (negative) impacts, affected vulnerable groups and their participation in the decision-making process. This study stems from TANDEM, a Horizon Europe project, that is utilising an innovative transdisciplinary approach to assess and mitigate negative impacts on citizens at risk of vulnerability due to implementation of low-carbon transition policies. Our analysis shows that although it depends on the type, location, and scale of the transition initiatives, vulnerability factors are often related to level of education, level of income, age, gender, house ownership, ethnicity, job sector, geographic location, migration background, and disability. Unfortunately, there is a lack of understanding, awareness or recognition among policy and decision makers when it comes to vulnerability factors and undesired impacts associated with transition policies and initiatives. One-third of the analysed initiatives did not even identify any vulnerable groups and only less than half of the initiatives undertook some effort to engage citizens or account for concerns of vulnerable groups, while only two (out of 27 initiatives analysed) allowed vulnerable groups to take part in the decision-making process. This lack of analysis of inequalities and vulnerabilities prior to implementing low-carbon transition initiatives and policies can not only lead to lower acceptability of these initiatives, but also can cause serious negative consequences, such as deepening inequalities and increasing energy and mobility poverty of certain societal groups. Effective addressal of issues related to fairness and justice are imperative for successful implementation of transition policies.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».