Energy hardship programmes: a systematic cross-country policy analysis of initiatives addressing equity and low-carbon energy services
Notice bibliographique
Résumé
Abstract Background Energy hardship can be broadly understood as a situation in which individuals or households are unable to afford basic energy services necessary for sustaining their wellbeing. Despite conceptual challenges and overlaps with similar narratives (e.g., fuel poverty), the literature on energy hardship continues to expand. It represents a critical intersection with sustainable energy systems that reveals both challenges and opportunities in the transition towards clean energy solutions. However, few energy hardship programmes have been examined from a policy perspective. Our study aims to address this knowledge gap by providing a systematic analysis of a sample of 67 energy hardship programmes implemented across Australia, Canada, the United Kingdom, the United States, and more than 20 European countries. Guided by specific research questions and supported by directed content analysis, we focus on five areas: dominant policy rationales, main policy goals, supportive policy instruments, stakeholders, and key performance indicators (KPIs). Results Despite an important degree of heterogeneity among the reviewed programmes, findings revealed commonalities across significant design and implementation areas. Policy rationales often rely on two significant pillars: narratives related to energy poverty (and related concepts), and market barriers and failures related to energy efficiency or decentralised renewable energy systems. Policy goals encompass three distinct areas: finance, knowledge, and technology/infrastructure. Policy instruments supporting energy hardship programmes are predominantly economic in nature. However, the review of programmes reveals a significant gap in robust estimates of cost-effectiveness or economic efficiency. Results also show that the design and implementation of programmes often involve a diversity of stakeholders. The review reveals that there is an abundance of KPIs that can (potentially) support the monitoring and assessment of programmes. Conclusions Overall, our study reveals significant policy lessons regarding the links, dynamics, and complexities associated with the design and implementation of energy hardship programmes. It underscores the importance of evidence-based evaluations to enhance the ability of policymakers and managers to effectively alleviate the suffering of those facing energy hardship. Results can be of particular interest to countries where policy discussions about energy hardship are emerging, and where there is a need for knowledge to inform decision-making on future programmes that support just and inclusive clean energy transitions.
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Comment cette classification a été obtenuedéplier
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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».