Capturing Carbon, Weighing Choices: Essays in Climate Policy, Willingness to Pay, and Heterogeneous Preferences
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".