Accelerating Small Modular Reactor Deployment and Clean Energy Transitions: An Algebraic Model for Achieving Net-Zero Emissions

Elaheh Shobeiri (), Filippo Genco, Daniel Hoornweg and Akira Tokuhiro
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Elaheh Shobeiri: Department of Energy and Nuclear Engineering, Ontario Tech University, 2000 Simcoe Street North, Oshawa, ON L1G 0C5, Canada
Filippo Genco: Department of Energy and Nuclear Engineering, Ontario Tech University, 2000 Simcoe Street North, Oshawa, ON L1G 0C5, Canada
Daniel Hoornweg: Department of Energy and Nuclear Engineering, Ontario Tech University, 2000 Simcoe Street North, Oshawa, ON L1G 0C5, Canada
Akira Tokuhiro: Department of Energy and Nuclear Engineering, Ontario Tech University, 2000 Simcoe Street North, Oshawa, ON L1G 0C5, Canada

Sustainability, 2025, vol. 17, issue 8, 1-36

Abstract: This study addresses the urgent need for transitioning to clean energy systems to achieve net-zero emissions and mitigate climate change. It introduces an algebraic modeling framework inspired by the nuclear fission six-factor formula to optimize the construction rates of clean power plants, with a focus on Small Modular Reactors (SMRs). The framework integrates four key factors affecting SMR deployment: Public Acceptance (PA), Supply Chain Readiness (SC), Human Resource (HR) Availability, and Land Availability (LA), including their associated sub-factors. The proposed algebraic formula optimizes projections from the existing Dynamic Integrated Climate-Economy (DICE) model. By capturing socio-economic and environmental constraints, the model enhances the accuracy of clean energy transition scenarios. In the case of Ontario’s pathway to achieving net-zero emissions, the results indicate that incorporating the algebraic formula reduces the SMR construction rate projected by the DICE model from 5.2 to 3.7 units per year by 2050 and from 2.7 to 1.9 units per year by 2100. This reduction highlights the need for accelerated readiness in key deployment factors to avoid delays in reaching net zero targets, reinforcing the importance of strategic investments in PA, SC, HR, and LA. Validation against historical nuclear deployment data from the U.S., Japan, and Canada confirms the model’s ability to reflect real-world trends, with PA and SC emerging as the most influential factors. In addition to informing SMR planning, this approach offers a structured tool for prioritizing policy actions and can be adapted to other clean technologies, enhancing strategic decision making in support of net-zero goals.

Keywords: climate change; modified DICE model; SMRs; mathematical modeling; algebraic equations (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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