Modeling nuclear’s role in climate change: optimized pathways to net-zero emissions

Abstract

A key focus of this research is the role of nuclear energy, particularly Small Modular Reactors (SMRs), in supporting the transition away from fossil-fueled power plants to achieve net-zero emissions. This study introduces the DICE Nuclear Deployment Model (DNDM), a novel extension of the Nordhaus Dynamic Integrated Climate Economy (DICE) model that incorporates fuzzy logic and an algebraic optimization framework to evaluate feasible nuclear construction strategies under real-world constraints. The fuzzy logic component addresses uncertainty in critical transition variables—Public Acceptance (PA), Supply Chain Readiness (SC), Human Resources (HR), and Land Availability (LA)—while the algebraic model quantifies their interactions to estimate construction rates. The algebraic formulation introduces nonlinear coupling terms to simulate the compound effect of transition constraints, enabling calibration with historical deployment patterns. This integrated approach enables scenario-based decision-making that accounts for socio-technical complexity and regional variability. The model is applied to long-term clean energy transition pathways in Ontario, simulating net-zero scenarios for the years 2050 and 2100. The simulations explore how nuclear energy—when combined with wind, solar, and hydro—can contribute to emission reduction under feasibility constraints. While the model does not include renewables-only scenarios, grid reliability analysis, or a full life cycle or cost-benefit assessment, it offers a robust framework to explore policy-aligned transition pathways using integrated, constraint-aware modeling. Additionally, high-level considerations such as nuclear waste management are acknowledged as important limitations that merit further interdisciplinary exploration in future research. This thesis provides a scalable tool for analyzing nuclear deployment within broader clean energy strategies. It contributes quantitative insights for policy, investment, and planning discussions, while acknowledging that future work should address operational, economic, and lifecycle dimensions to further enhance modeling depth.