Capturing Carbon, Weighing Choices: Essays in Climate Policy, Willingness to Pay, and Heterogeneous Preferences
Notice bibliographique
Résumé
Carbon capture and storage (CCS) has gained attention for its potential to reduce carbon emissions from energy-intensive and industrial processes. However, public acceptance of CCS remains uncertain, shaped by perceptions of its effectiveness, safety, and cost, as well as its role in the broader landscape of climate solutions. This dissertation investigates the complexity of CCS deployment through the lens of public preferences, focusing on five countries: Canada, Germany, Netherlands, Norway, and the UK. Chapter 1 examines where CCS stands in the broader landscape of climate policy options from the public’s perspective. Reaching net-zero emissions will require a diverse portfolio of solutions as no single policy can achieve this goal in isolation. While CCS often faces resistance when considered on its own, a comparative lens provides important insights: if the public is willing to accept CCS compared to other mitigation policies, this suggests potential support for future CCS operations under the right conditions. Using a best-worst scaling experiment, this study compares the public acceptance of CCS deployment with six alternative climate change mitigation policies (i.e., Increase the share of renewable energy, Nurture forest landscapes, End fossil fuel subsidies, Put a price on CO2 emissions, Enforce reductions in personal vehicle transport to encourage public transportation, and Force households to adopt energy efficiency measures for home heating and electricity consumption). While renewable energy and forest landscapes consistently emerge as the most accepted policies across all five countries, preferences reflect public acceptance of CCS deployment against market-based and regulatory policies. The results reveal strong policy implications for integrating CCS into a broader portfolio of decarbonization strategies while accounting for within- and cross-country variations. Chapter 2 investigates the public willingness to support large-scale CCS deployment by examining how individuals evaluate trade-offs between the climate benefits and the associated deployment costs. As Chapter 1 provided evidence that CCS is recognized as a part of a broader portfolio of climate policies, understanding society’s perspective of finding the right balance between climate benefits and safety concerns is crucial for ensuring its successful implementation. Using a discrete choice experiment, this study finds that public preferences are more strongly influenced by the presence of rigorous and transparent monitoring procedures for seismic incidents than by the climate change mitigation potential of CCS. Although this preference pattern is observed across all five countries, each nation would face unique challenges in fostering CCS deployment, as public preferences vary in distinct ways depending on the national context. Chapter 3 examines individuals’ stated preferences for alternatives that scale up CCS technologies to identify different population classes in Canada. Individuals are diverse in their perceptions of climate change, CCS risks and benefits, social issues, etc., leading them to perceive CCS deployment in different dimensions. As a result, large scale deployment of CCS remains a controversial topic, despite its growing recognition for meeting net-zero goals. With known evidence for diversity of public perspectives on CCS, Canada presents a compelling case of this divide. Using a latent class analysis, this study identifies three distinct classes. All classes value rigorous monitoring for CCS deployment. Class membership is largely influenced by individuals’ political orientation, perceived benefits and risks of CCS, and environmental and social concerns. Identifying distinct class profiles can offer valuable insights for policymakers, enabling the development of more targeted and effective strategies that promote acceptance of CCS across diverse public segments while helping to minimize controversy and opposition. Together, these three chapters provide valuable insights into the ongoing debate on the successful implementation of CCS, underscoring the importance of developing more responsive, transparent, and inclusive climate policies that reflect public preferences and concerns.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,011 |
| Communication savante | 0,006 | 0,010 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 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 source (Gemma direct ou Codex distillé), 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 ».