Motlaghzadeh, Kasra2025-12-152025-12-152025-12-152025-12-15https://hdl.handle.net/10012/22738This study responds to the insufficient understanding of the uncertainties surrounding the demand, and the techno-economic and socio-political feasibility of deploying Direct Air Capture (DAC)—a Carbon Dioxide Removal (CDR) technology that may be essential for Canada’s net-zero target and potential post-net-zero obligations. Limited tools are available to analyze such uncertainties and their interactions to support adaptive decision-making under deep uncertainty. These gaps are addressed through three interconnected, systems-based approaches: Integrated Assessment Modeling (IAM) for quantitative scenario modeling, Cross-Impact Balance (CIB) analysis for qualitative scenario discovery, and Adaptation Pathways (AP) for decision-support under deep uncertainty. First, existing IAM studies were systematically reviewed and a national-scale IAM analysis with the Global Change Analysis Model (GCAM) was conducted to quantify key uncertainties shaping DAC deployment in Canada. The following factors are found to strongly influence Canada’s potential reliance on DAC: Socio-economic pathways, fossil-fuel dependence, international CDR obligations grounded in burden-sharing principles, and DAC cost trajectories. Second, the CIB scenario discovery method is employed to examine how these uncertainties—and additional socio-political factors not represented in quantitative models—interact based on expert elicitation with DAC specialists. The CIB analysis produces 15 internally consistent futures and identifies public acceptance and policy coherence as critical bottlenecks if they evolve unfavourably. Third, CIB scenarios are used to parameterize GCAM to quantify DAC demand under four internally consistent, CIB-informed futures. This integrated approach shows that Canada’s DAC requirements could range from 0 to 300 MtCO₂/year by 2075, with narrative explanations linking each scenario’s structural components to its resulting DAC trajectory. Finally, the AP framework is applied to DAC policy, mapping flexible and dynamic strategies across three key dimensions: economic feasibility, electricity supply, and CO₂ transport and storage. APs identify low-regret near-term actions (e.g., electricity grid expansion), reveal thresholds where strategies fail, and indicate when strategic shifts are needed. Methodologically, this thesis demonstrates (1) how semi-quantitative tools such as CIB can validate and enrich IAM scenarios with socio-political dynamics, and (2) how APs can translate uncertainty-rich futures into robust, actionable policy pathways for DAC deployment in Canada. Together, these methods provide a comprehensive decision-support framework for navigating the deep uncertainties surrounding DAC deployment in Canada.endeep uncertaintyclimate changedirect air capturecarbon dioxide removaladaptation pathwaysintegrated assessment modelscenario discoverydecision-makingDoctoral Thesis