A Strategic Framework for Net-Zero Transitions: Integrating Fuzzy Logic and the DICE Model for Optimizing Ontario’s Energy Future

In response to the urgent threat of climate change and the drivers of high greenhouse gas emissions, countries worldwide are adopting policies to reduce their carbon emissions, with net-zero emissions targets. These targets vary by region, with Canada aiming to achieve net-zero emissions by 2050. In response to the Independent Electricity System Operator’s (IESO’s) “Pathways to Decarbonization” report, which evaluates a proposed moratorium on new natural gas generating stations, this study presents a methodology to support energy transitions in Ontario by using a modified Dynamic Integrated Climate-Economy (DICE) model, which focuses on replacing fossil fuel power plants (FFPPs) with clean energy sources, including nuclear, solar, wind, and hydro. This research expands on our prior work that used the DICE model to evaluate the potential for replacing FFPPs with Small Modular Reactors (SMRs) on a global scale. This study includes solar, wind, hydro, and SMRs to provide a diversified clean energy portfolio and integrates fuzzy logic to optimize construction rates and address uncertainties. The study uses Ontario as a case study, aligning with IESO’s objectives for Ontario’s energy transition. The IESO’s projections for net zero by 2050 are applied. The study is extended to 2100 to assess the longer-term implications of sustained energy transition efforts beyond the immediate goals set by the IESO. This approach is scalable to other regions and countries with similar energy transition challenges. The study results indicate that to meet Ontario’s 2050 net-zero target, approximately 183 SMR units, 1527 solar units, 289 wind units, and 449 hydro units need to be constructed. For the 2100 target, the required number of units is slightly higher due to the longer time frame, reflecting a gradual ramp-up in construction. The optimization of construction rates using fuzzy logic shows that the pace of deployment is influenced by critical factors such as resource availability, policy support, and public acceptance. This underscores the need for accelerated clean energy deployment to meet long-term emissions reduction goals. The findings highlight the complexities of transitioning to a low-carbon energy system and the importance of addressing uncertainties in planning. Policymakers are urged to integrate these insights into strategic energy planning to ensure the successful deployment of clean energy technologies. This study provides valuable recommendations for optimizing energy transitions through a robust, flexible framework that accounts for both technological and socio-economic challenges.