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

Abstract

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.

1. Introduction

The second industrial revolution, which occurred during the 19th and early 20th centuries, led to large-scale manufacturing, significantly increasing energy demand due to the high energy consumption of these operations [1]. To meet production demands and support economic growth (GDP), many countries primarily relied on fossil fuels (FFs), which resulted in substantial greenhouse gas (GHG) emissions and contributed to climate change [2,3]. Greenhouse gases contribute to the greenhouse effect by trapping more energy than they can reflect, thereby exacerbating global warming [4]. The combustion of fossil fuels releases various pollutants, including CO_2, methane (CH₄), nitrous oxide (N₂O), and particulate matter, all of which intensify the greenhouse effect [5]. These emissions pose a serious threat to sustainability [6]. The Intergovernmental Panel on Climate Change (IPCC) confirmed that GHGs, particularly CO₂ emissions, are the primary drivers of global warming and stressed the need for clean energy solutions to combat greenhouse gas emissions [7,8]. To address rising energy demand and reduce GHG emissions, there is a growing shift toward replacing fossil fuels with zero-net or clean energy sources, such as renewable energies (hydropower, solar, wind) and nuclear energy, for electricity generation. Clean energy in this context is defined as energy sources with a carbon intensity of less than 20 g of CO₂ per kilowatt-hour (g CO₂/kWh), ensuring that all energy sources meet strict emission standards.

The urgency of addressing climate change has spurred global efforts to transition towards sustainable and clean energy solutions, emphasizing reducing carbon emissions that drive global warming and extreme weather events. Climate change remains one of the most pressing challenges of our time, and rising concentrations of greenhouse gases, particularly CO₂, are accelerating climate disruptions. In response to these threats, nations are adopting policies to reduce their carbon footprints, and net-zero emissions targets have become a central focus. Canada, for instance, has committed to achieving net-zero emissions by 2050 [9], signalling a critical moment in the transition to a cleaner energy future.

Ontario, the most populous province in Canada, faces a complex challenge in reducing its greenhouse gas emissions while maintaining a reliable and affordable energy supply. The province’s energy generation currently relies on a mix of nuclear, hydro, and limited renewable energy sources, such as wind and solar. Recent data indicates that Canada’s CO₂ electricity emissions were approximately 126 g per kilowatt-hour in 2022 [10]. Ontario needs to reach a carbon intensity of less than 20 g per kilowatt-hour to meet the net-zero emissions target. While the province has made progress through nuclear refurbishment and using natural gas as a transitional fuel, additional efforts are needed to substantially reduce emissions and meet the long-term climate goals outlined by the IESO [11,12]. Integrated Assessment Models (IAMs) offer a way to evaluate potential climate change scenarios and inform policymakers [13]. These models are widely utilized by climatologists and researchers due to their simplicity and flexibility and combine multiple sectors, including society, economics, and climate change, into a unified framework [14].

In this study, we employ the DICE model developed by W.D. Nordhaus at Yale University to simulate climate change by integrating economic and environmental data to mitigate global warming [15,16,17,18,19]. For this research, four SMR nuclear power plant, solar, hydro, and wind energy sub-models were created and integrated into the original DICE model using VENSIM version 8.0.9, a dynamic modeling and simulation software platform. VENSIM provides a graphic user interface (GUI), offering easy access to all simulation elements and enabling the integration of additional algebraic models. In the newly developed clean energy sub-models, all existing fossil fuel power plants, which are Natural Gas Power Plants (NGPP) for Ontario, are replaced by SMRs, hydro, solar, and wind energy sources. Using IESO projections for the contribution of each energy source by 2050, this substitution helps mitigate the ongoing accumulation of CO₂ in the atmosphere.

While the DICE model is widely recognized for evaluating long-term policy impacts by incorporating economic and environmental factors such as climate change effects, it does not fully account for the uncertainties surrounding socio-economic factors that influence clean energy construction rates. To address this limitation, our study incorporates fuzzy logic into the DICE model to analyze these uncertainties, enhancing the evaluation of clean energy transitions. This approach provides a more nuanced understanding of how factors like public acceptance, resource availability, and supply chain constraints influence the deployment of clean energy technologies.

Fuzzy logic, which mimics human reasoning, is an essential tool for addressing uncertainties in energy systems [20]. It allows for the representation of uncertainty and vagueness in decision-making, offering a flexible framework for analyzing complex, dynamic systems [21,22,23]. By integrating fuzzy logic into the DICE model, we optimize construction rates for various clean energy sources—including SMRs, wind, solar, and hydro—while considering four key variables: public acceptance (PA), land availability (LA), supply chain efficiency (SC), and human resource availability (HR). This enhanced model accounts for the complex factors that influence energy infrastructure development, providing more accurate predictions of the feasibility and timing of deploying these technologies.

This paper builds on our prior research [24], which focused on replacing FFPPs with SMR units by introducing a clean energy portfolio that includes solar, wind, and hydro sources. In line with IESO’s objectives for Ontario’s energy transition [12], we use the modified DICE model to calculate the construction rates required to meet the clean energy contributions for 2050, as forecasted by the IESO. The model evaluates the construction rates needed to achieve the 2050 and 2100 targets, with 2050 selected to align with IESO’s near-term goals and 2100 included to evaluate the long-term impact of sustained transition efforts. The comparison of construction rates required for these two target years reveals both challenges and opportunities for achieving net-zero emissions while balancing short-term objectives with long-term sustainability. Furthermore, by integrating fuzzy logic, the study improves the DICE model’s capacity to handle uncertainties and optimize construction rates for each energy source, considering socio-economic factors such as public acceptance, land availability, supply chain capacity, and human resource availability.

1.1. Climate Change and Ontario’s Path to Net-Zero

Climate change refers to long-term shifts in environmental factors such as temperature, rainfall, and wind patterns across the Earth. Global climate change affects the planet’s overall conditions, often manifesting through extreme weather events [25,26], which are driven by both natural factors (e.g., solar energy fluctuations, volcanic activity) and human activities (e.g., fossil fuel combustion, deforestation) [27,28]. The primary human-induced contributor to climate change is the emission of greenhouse gases (GHGs), especially from burning fossil fuels for electricity and heat production [29,30].

A critical factor in this crisis is the rising concentration of greenhouse gases, particularly CO₂ and CH₄, which are accelerating global warming. Comprehensive studies, including reports from the IPCC, emphasize the clear and growing link between human activities and the escalating levels of these gases in the atmosphere [7,8]. Among the most significant contributors to GHG emissions is electricity generation from fossil fuel power plants [31]. The scientific literature further highlights the consequences of these emissions, such as rising temperatures, extreme weather events, and disruptions to ecosystems [7,8]. Despite growing awareness and mitigation efforts, carbon emissions from fossil fuels remain high, as noted by Achakulwisut [32]. This ongoing challenge reinforces the critical need to decarbonize electricity generation, one of the largest sources of GHG emissions. Furthermore, with the increasing electrification of sectors like transportation, electricity demand is rising, underscoring the need for a swift transition to low-carbon electricity [33].

In Ontario, the IESO plays a key role in this transition, laying out a clear energy future roadmap. The IESO’s “Pathways to Decarbonization” report emphasizes the province’s commitment to achieving net-zero emissions by 2050 as part of the broader goal to mitigate climate change. This objective involves phasing out fossil fuel power plants and replacing them with clean energy sources like nuclear, wind, solar, and hydropower. Achieving this transition requires a balanced energy mix, ensuring Ontario’s electricity system remains reliable, affordable, and sustainable while meeting the growing demand for low-carbon electricity.

Achieving net-zero emissions in the future requires a strategic and diverse clean energy portfolio with reduced dependence on fossil fuels. This portfolio should include large-scale nuclear energy, SMRs, wind, solar, and hydro, each of which plays a key role in decarbonizing Ontario’s electricity system. Among these, nuclear energy, particularly SMRs, stands out as a reliable and low-carbon power source. As highlighted by Zaman, nuclear energy is critical for achieving environmental goals by significantly reducing carbon emissions [34].

SMRs offer scalable and flexible solutions, capable of meeting varying energy demands while substantially lowering emissions [35]. These modern nuclear fission reactors are designed with modular components and systems that can be pre-fabricated in factories and transported to construction sites. SMRs are smaller in both size and power compared to traditional large reactors, with power outputs ranging from 3 MWe to 300 MWe, as defined by the IAEA [36]. Key advantages of SMRs include their modularity, lower financial risk, load-following design, simplified factory production and assembly, and suitability for off-grid applications. In remote areas with limited skilled labor and high shipping costs, SMRs provide an ideal solution. Their ability to generate electricity and heat based on demand, along with their compact design and passive safety features, enhances safety and minimizes the need for on-site refueling. Some designs even allow for remote monitoring, significantly reducing staffing requirements. The introduction of SMRs helps address the need for flexible power solutions across various applications, overcoming financial and safety barriers that traditional nuclear power plants face [37,38,39,40,41,42,43,44,45].

Wind and solar technologies are also crucial for renewable energy generation. These technologies not only reduce environmental impacts but also enhance grid stability. Their increasing competitiveness makes them viable and cost-effective solutions for clean energy production [46,47]. Expanding the capacity of wind and solar power capacity will be essential for Ontario to meet its long-term energy goals, which align with the IESO’s objectives, and reduce reliance on fossil fuels. Hydroelectric power continues to play a foundational role in a sustainable energy mix, as recognized in the IESO’s decarbonization pathway. Hydro’s reliability and flexibility complement intermittent renewable sources like wind and solar, ensuring a balanced and resilient grid [48].

This study aligns with the IESO’s goal of achieving net-zero emissions by 2050. It examines the potential of a diversified energy portfolio integrating SMRs, hydro, wind, and solar power. The IESO’s objectives focus on reducing fossil fuel dependency while maintaining grid reliability and affordability. This combination of energy sources provides a sustainable solution for phasing out fossil fuel power plants and ensures that Ontario can meet its decarbonization targets efficiently and cost-effectively.

1.2. The Research Gap and the Contribution of This Study

This research addresses the critical need for a robust, flexible framework to support energy transitions capable of handling the complexities and uncertainties of scaling clean energy technologies. By integrating fuzzy logic with the DICE model, this study proposes a novel method for evaluating the construction rates of various clean energy sources, including SMRs, wind, solar, and hydro. The approach considers key socio-economic and environmental factors, allowing for better management of uncertainties related to land availability, supply chain efficiency, and public acceptance. This results in more accurate predictions of the feasibility and timing of clean energy deployment, addressing both short-term and long-term challenges.

The significance of this study lies in its methodological contribution of integrating fuzzy logic with the DICE model and its potential to influence policy-making. The study provides actionable insights for optimizing clean energy transitions, focusing on Ontario’s goal of achieving net-zero emissions by 2050. The findings offer valuable recommendations for improving construction rates and integrating multiple clean energy sources. Additionally, this research demonstrates the flexibility of the fuzzy logic-enhanced DICE model, making it scalable and adaptable for application in other regions or countries with similar energy transition challenges, offering a global solution for decarbonization efforts. Fuzzy logic enables the creation of flexible membership functions that adjust to new data, ensuring the system remains accurate and adaptable as conditions evolve.

This study addresses several research gaps not covered by existing methods. First, the DICE model is modified to include construction rates for multiple clean energy sources such as SMRs, wind, solar, and hydro, addressing a key limitation in the original model. While our previous research primarily focused on nuclear energy implications on a global scale [24], this study expands the analysis to include a more diversified clean energy portfolio, demonstrating the applicability of the modified model to a broader range of technologies. Second, the study incorporates fuzzy logic optimization to account for uncertainties in construction rates, public acceptance, supply chain efficiency, and land availability, overcoming the limitations of earlier models that did not handle such uncertainties. Although the model is applied to Ontario as a case study, the methodology is scalable and can be adapted to other regions with differing socio-economic and environmental conditions. Finally, this paper includes policy recommendations to accelerate the deployment of clean energy technologies by addressing critical factors such as resource availability, public acceptance, and supply chain efficiencies, particularly with respect to SMRs and other renewable energy sources.

The paper is structured as follows: Section 1 introduces the study, outlining the research context and objectives. Section 2 describes the methodology, focusing on the application of the DICE model and fuzzy logic. Section 3 presents the modified DICE model simulation results, including the outcomes from optimizing construction rates by integrating fuzzy logic with the DICE model for clean energy power plant projects. Section 4 discusses the findings, and Section 5 concludes the paper by highlighting the key implications for energy policy moving forward.

2. Methodology: DICE Model and Fuzzy Logic Applications

This section outlines the methodology employed in this study, emphasizing the models and frameworks used to analyze the clean energy transition. The DICE model is first introduced, highlighting its original structure and the specific modifications made to tailor it to the needs of this research. These adjustments were necessary to accommodate the integration of clean energy technologies such as SMRs, wind, solar, and hydro while ensuring compatibility with the IESO’s energy transition goals for Ontario.

Next, the application of fuzzy logic is discussed. Fuzzy logic enhances the model’s ability to account for uncertainties and complexities inherent in climate-economic forecasts. It optimizes the construction rates for clean energy technologies by incorporating four key factors: public acceptance, land availability, supply chain efficiency, and human resource availability. This integration allows for more accurate and flexible decision-making in addressing the socio-economic and environmental uncertainties surrounding deploying clean energy sources. The modified DICE model and fuzzy logic framework provide a comprehensive analysis of Ontario’s energy transition, offering insights into the feasibility of achieving net-zero emissions by 2050 and 2100.

The study employed VENSIM version 8.0.9 for DICE modeling and MATLAB version R2020b for fuzzy logic optimization, integrating these tools to enhance the analysis and provide a more comprehensive understanding of the energy transition dynamics. These tools enabled the modification and optimization of the DICE model to accommodate various clean energy sources, including SMRs, solar, wind, and hydro, while accounting for critical uncertainties.

2.1. Dynamic Integrated Climate-Economy Model (DICE) and Modification

The DICE model, developed by William Nordhaus, is a foundational tool in evaluating climate policies’ economic and environmental implications, particularly the transition to clean energy. Initially developed using VENSIM version 8.0.9 simulation software by researchers at the University of Idaho and MIT [49], it remains an essential framework for understanding the interplay between climate change, economic growth, and energy policy. VENSIM is a visual modeling tool that allows for the conceptualization, documentation, and analysis of dynamic systems [50]. The schematic of the DICE model simulated through VENSIM is shown in Figure 1.

In the DICE model, the atmospheric concentration of CO₂ is treated as “natural capital” due to its impact on the global average surface temperature, negatively affecting overall economic output. Therefore, DICE is useful for providing stakeholders and decision-makers with a clear view of the costs and benefits, enabling more balanced economic decisions when addressing CO₂ emissions. The various sections of the DICE model are color-coded, as shown in Figure 1. Within the VENSIM developer window, the algebraic relationships are displayed beneath the graphical user interface. Structurally, the DICE model consists of four primary sections: the climate (pink), carbon or emission production (green), indices (blue), and the economy (red). While the DICE model does not specify individual energy sources, its general framework is highly effective in modeling the complexities of achieving net-zero emissions. It provides valuable insights into various energy transition pathways’ economic and climate outcomes, including incorporating clean energy sources.

Previous work by Nordhaus (2008) and Nordhaus and Sztorc (2013) employed the DICE model to assess the economic implications of climate policies, including the transition from FFPPs to clean energy sources such as nuclear, wind, solar, and hydro [25,26]. This research offers insights into the potential of large-scale shifts towards low-carbon energy systems. Additionally, research by Holechek and others has examined how clean energy can be integrated into climate policy scenarios, offering a useful framework for understanding the challenges and economic implications of replacing fossil fuels with a diversified clean energy portfolio [51]. Furthermore, researchers like Anthoff and Tol have focused on optimizing clean energy mixes within the FUND framework to achieve climate goals more efficiently [52].

Table 1 presents the key inputs and outputs for the color-coded macroeconomic areas [24], as depicted in Figure 1. Additional variables can be sourced from existing literature on the VENSIM DICE model [53].

Table 1. Major inputs and outputs for the color-coded macroeconomic areas [24].

Sections in DICE Model	Major Inputs to DICE Model	Major Outputs of DICE Model
The carbon or emission production (green) section	Time period Target year reduction of CO2 (This is the output from the modified DICE model) Initial CO2 concentration in the atmosphere	CO2 radiation coefficient CO2 radiation forcing Fraction of cost for GHG reduction
The climate (pink) section	Time period Fraction of cost for GHG reduction Upper ocean heat capacity Deep ocean heat capacity Initial temperature of the atmosphere and upper ocean Initial temperature of the atmosphere and deep ocean	Climate damage scale Reference temperature
The economy (red) section	Time period Investment Population size Growth rate	Reference output Consumption Capital-output ratio Depreciation rate Economic output

Table 1. Major inputs and outputs for the color-coded macroeconomic areas [24].

Sections in DICE Model	Major Inputs to DICE Model	Major Outputs of DICE Model
The carbon or emission production (green) section	Time period Target year reduction of CO2 (This is the output from the modified DICE model) Initial CO2 concentration in the atmosphere	CO2 radiation coefficient CO2 radiation forcing Fraction of cost for GHG reduction
The climate (pink) section	Time period Fraction of cost for GHG reduction Upper ocean heat capacity Deep ocean heat capacity Initial temperature of the atmosphere and upper ocean Initial temperature of the atmosphere and deep ocean	Climate damage scale Reference temperature
The economy (red) section	Time period Investment Population size Growth rate	Reference output Consumption Capital-output ratio Depreciation rate Economic output

The original DICE model predicts atmospheric CO₂ concentrations. H. Shen modified this section [53] to express CO₂ levels in parts per million (ppm), a widely used measure. Shen calibrated the atmospheric CO₂ concentration by incorporating “CO₂ sequestration” into the model. CO₂ concentrations from 1992 to 2011, calculated using the original DICE model, were compared with actual levels recorded by the Mauna Loa Observatory in Hawaii. CO₂ sequestration was then adjusted to ensure the predicted difference did not exceed 3% from available data [53].

To study the impact of replacing FFPPs with nuclear power plants to reduce CO₂ emissions, this research uses a nuclear sub-model of DICE proposed by H. Shen [53]. This sub-model calculates a ratio to replace the existing FFPP units with SMR, solar, hydro, and wind units based on the average power output of a single clean energy unit compared to its fossil fuel counterpart. For this study, the term “unit” refers to the energy output (in MWh) of each type of clean energy plant, not a generic unit. The construction rate for each technology is determined by the corresponding energy capacity: 337,260 MWh for nuclear (80 MWe SMR unit), 231,052 MWh for hydro, 34,352 MWh for solar, and 228,751 MWh for wind. This distinction is crucial for understanding how construction rates are applied in the simulation, particularly when comparing energy sources with different capacities [54].

The assumption in these sub-models is the complete replacement of FFPPs with clean energy units, based on the IESO projections for 2050, to reduce GHG emissions and verify the reduction or mitigation of CO₂ emissions. This assumption arises from the fact that GHG emissions from clean energy sources are much lower than those from FFPPs [55]. The modified DICE model calculates the reduction in GHG emissions based on various construction rates, and this data is incorporated into the Nordhaus portion of the DICE model to predict total CO₂ emissions over time. The proposed sub-models are illustrated in Figure 2, Figure 3, Figure 4 and Figure 5.

This study extends previous DICE analyses by incorporating four sub-models, allowing a more comprehensive evaluation of clean energy transitions. Building on the work of Elaheh and Huan [24,53,56,57], which focused on replacing fossil fuel power plants with SMR units; this research modifies the DICE model’s carbon sections to incorporate a diversified energy portfolio. The new sub-models assess the impact of different target year scenarios (2050 and 2100), aligning with the IESO’s objectives for Ontario’s energy transition. The clean energy portfolio considered here includes SMRs, hydro, wind, and solar power. A key feature of this approach is calculating a power ratio, which represents the average power output of one clean energy unit compared to the corresponding fossil fuel unit.

To clarify the definition of “unit” when discussing the construction rates of clean energy technologies, it is important to note that the simulation considers energy output in MWh for each technology [58,59,60], rather than referring to a generic unit. Specifically, the construction rate for nuclear is defined per 337,260 MWh, which corresponds to the capacity of an 80 MWe SMR unit. For hydro, the construction rate is defined per 231,052 MWh, representing the capacity of a hydro facility. Similarly, for solar, the construction rate is defined per 34,352 MWh, corresponding to the capacity of a solar facility, and for wind, the construction rate is defined per 228,751 MWh, representing the capacity of a wind plant. Thus, when referring to the construction rate for each clean energy source, the term “unit” specifically refers to the energy output (in MWh) for each type of plant (SMR, hydro, solar, wind). This distinction is critical for understanding how the construction rates are applied in the simulation, particularly when comparing different energy sources with varying capacities.

In these sub-models, the existing FFPPs are replaced by SMRs, solar, wind, and hydro units, reducing both greenhouse gas and CO₂ emissions. The modified DICE model calculates these emission reductions based on varying construction rates for each energy source. These reductions are then integrated into the “Nordhaus portion” of the DICE model, which evaluates the economic impacts of CO₂ emissions, atmospheric CO₂ levels, and their effects on global temperatures and other environmental factors. Figure 2, Figure 3, Figure 4 and Figure 5 illustrate the sub-models for SMRs, wind, solar, and hydro, with each model structured similarly but parameterized to reflect the unique characteristics and impacts of the respective energy sources.

Two key inputs drive the analysis: (i) the “target year to reach CO₂ emissions”, which introduces variability in the sub-models by setting different target years (2050 and 2100), and (ii) the clean energy contribution percentage, as defined by the IESO’s objectives. Each scenario calculates the total number of clean energy units required and the corresponding construction rates per year. Additionally, CO₂ emission reductions are estimated based on the target years selected.

The model incorporates both historical and current data for nuclear power plants (NPPs), solar photovoltaic power stations (SPPs), hydropower plants (HPPs), and FFPPs in Ontario. The next phase involves replacing these FFPPs with clean energy sources, utilizing a construction ratio. The primary focus is the replacement of natural gas power plants, with power capacity data sourced from the IESO for 2019 [61]. Moreover, the power capacities for SMRs, wind, solar, and hydro used in the model are also sourced from the IESO [61]. The parameters for each sub-model—wind, solar, hydro, and nuclear—detail power production, efficiency, average MWh, and initial values for existing facilities in Ontario.

The model then calculates the number of NGPP units to be decommissioned and replaced by clean energy units, considering the average power output of each clean energy source relative to the NGPPs. This calculation estimates the CO₂ emissions reductions from NGPP decommissioning, which are subsequently integrated into the DICE model’s CO₂ emission variable.

Table 2. Power output and CO₂ emissions from each type of power plant in Ontario.

Power Plant	Average Power	CO2 Emission (Ton/MWh)
NPP (SMR Core unit of 80 MWe)	337,260 (MWh/core unit)	0.012
Solar	34,352 (MWh/facility)	0.055
Wind	228,751 (MWh/facility)	0.012
Hydro	231,052 (MWh/facility)	0.026
Natural Gas	189,522 (MWh/facility)	0.44

presents the average power output for each clean energy source [61], while

Table 3. Clean energy contribution in Ontario by 2050 as per IESO.

Clean Energy Contribution	Nuclear	Hydro	Solar	Wind
Forecasted Capacity for 2050 according to IESO	43.7%	16.5%	10.3%	29.3%

shows the CO₂ production associated with each power plant type considered in this research [12].

This study also examines the IESO’s forecasted solar, wind, hydro, and nuclear energy capacity in Ontario by 2050 and 2100 [12]. According to the IESO’s current energy mix, Ontario’s electricity generation comprises 46.9% nuclear, 1.4% solar, 0.8% wind, and 50.7% hydro [12]. The forecasted contributions of these energy sources to Ontario’s electricity grid by 2050 are shown in Table 2. Comparing these forecasted values with today’s energy mix highlights the shifts needed for a more sustainable and balanced portfolio. This analysis emphasizes the importance of strategic planning to meet Ontario’s future energy demands and environmental goals.

By applying the DICE model, we evaluate the construction rates necessary to meet the projected energy mix, assessing the feasibility of achieving Ontario’s long-term energy goals. This proactive approach emphasizes the need to reduce greenhouse gas emissions and enhance the sustainability of Ontario’s energy landscape. The insights derived from this analysis will guide policymakers in making informed decisions to ensure a successful and timely energy transition.

Once the SMR, solar, wind, and hydro sub-models are integrated into the DICE model, a series of simulations are run with a one-year time interval. The scenarios for 2050 and 2100 are analyzed to achieve net-zero CO₂ emissions. These simulations focus on construction rates for each energy source and their impact on CO₂ emissions. These simulations aim to show how replacing FFPPs with clean energy sources can reduce global CO₂ emissions and estimate the rate of this reduction over time.

2.2. Application of Fuzzy Logic-Integrating Fuzzy Logic into DICE Modeling for Optimization

To complement the DICE model’s analysis, this study incorporates a fuzzy logic framework designed to optimize the construction rates of clean energy sources. The integration of fuzzy logic helps address uncertainties and complexities associated with socio-economic factors, which are crucial for the deployment of various clean energy technologies such as SMRs, hydroelectric, wind, and solar power [62,63,64]. The fuzzy logic application examines the impacts of four key factors—Public Acceptance (PA), Land Availability (LA), Supply Chain Efficiency (SC), and Human Resource Availability (HR)—that significantly influence the construction rates of these energy sources.

By integrating fuzzy logic outputs into the DICE model, we enhance the model’s ability to refine predictions of construction rates for target years, offering a more nuanced view of potential bottlenecks and providing precise adjustments to the DICE scenarios. This integration establishes a basis for comparing the construction rate derived from fuzzy logic with that of the DICE model for both 2050 and 2100 target years. Understanding the impact of fuzzy logic on DICE model projections is crucial for assessing how these two modeling techniques work together to inform energy policy and planning, ultimately ensuring effective strategies for achieving net-zero emissions.

2.3. Selection of Key Factors Impacting Construction Rate

Several factors influence construction rates in developing clean energy projects, broadly categorized into environmental, economic, technical, and socio-political dimensions. While regulatory frameworks, technological advancements, capital investment, and environmental impact assessments play a crucial role, this research identifies four pivotal factors: supply chain efficiency (SC), human resource availability (HR), public acceptance (PA), and land availability (LA) [39]. These factors are selected due to their direct influence on the construction rate of nuclear, wind, solar, and hydro projects. Below are the reasons for their inclusion:

• Supply Chain Efficiency (SC): SC is critical due to its direct impact on project timelines and costs. The complexity of sourcing specialized components for clean energy projects and the logistical challenges involved dictate the pace of development. Efficient SCs are particularly vital for nuclear projects, where regulatory compliance for nuclear materials and technologies is stringent;
• Human Resource Availability (HR): The availability of skilled labor is crucial, as the construction and operation of clean energy facilities require specialized skills that are often in short supply. Nuclear and hydro projects, in particular, demand more skilled labor, significantly impacting the construction rate when such expertise is scarce;
• Public Acceptance (PA): PA is vital for obtaining the social license to operate. Clean energy projects, especially wind and solar, often face public scrutiny due to their impacts on local communities and landscapes. Nuclear projects face even greater challenges with safety concerns. Thus, PA can significantly affect project initiation and development, influencing the construction rate;
• Land Availability (LA): LA is a fundamental factor due to the large physical space required for clean energy installations, particularly for wind and solar projects. Hydroelectric projects also require access to water bodies, further complicating land acquisition. While nuclear plants require less land per unit of energy produced, their location must meet rigorous safety and environmental standards, making LA a critical factor in determining the construction rate.

While factors like regulatory policies and environmental approvals also play a role, they often intersect with the selected factors. For instance, PA can influence regulatory decisions, and capital investment can be tied to SC and HR, where an efficient supply chain lowers capital costs and available human resources drive economic growth, attracting further investment.

The inclusion of SC, HR, PA, and LA is justified by their direct and substantial impact on construction rates across clean energy projects’ planning and operational phases. These factors represent critical constraints and enablers for project success. This research aims to provide actionable insights for policymakers and industry stakeholders by focusing on these variables.

2.4. Fuzzy Logic Model Development

As shown in Figure 6, the fuzzy logic model developed for this study processes qualitative inputs about PA, LA, SC, and HR to produce a quantifiable output representing the construction rate.

The following components were defined to develop the fuzzy logic system:

• Inputs and Membership Functions: Each input variable is associated with membership functions to translate quantitative values into qualitative descriptors. The membership functions for each input are defined as Low, Medium, and High. The membership functions for the input variables are as follows:

  ○

  Public Acceptance (PA): Gaussian membership functions;

  ○

  Land Availability (LA): Trapezoidal membership functions;

  ○

  Supply Chain Efficiency (SC): Triangular membership functions;

  ○

  Human Resource Availability (HR): Sigmoid membership functions.

• Output: The construction rate (CR) has four potential levels: Zero, Low, Medium, and High. These levels represent the range of construction activity, from no construction to high levels of construction;
• Rules: As shown in

  Table 4. Fuzzy rules.

  Rules#	Rules Description				Decision
  	PA	LA	HR	SC	CR
  1	L	L	L	L	Zero
  2	L	L	L	H	Zero
  3	L	L	H	L	Zero
  4	L	L	H	H	Zero
  5	L	H	L	L	Zero
  6	L	H	L	H	Zero
  7	L	H	H	L	Zero
  8	L	H	H	H	Zero
  9	H	L	L	L	Zero
  10	H	L	L	H	Zero
  11	H	L	H	L	Zero
  12	H	L	H	H	Zero
  13	H	H	L	L	L
  14	H	H	H	L	L
  15	H	H	L	H	L
  16	H	H	H	H	H
  17	M	M	M	M	M

  , a set of fuzzy rules was formulated to reflect the relationships between the input variables and their corresponding outputs. The key rules are as follows:

This is the summary of the above-mentioned rules:

(1)

If PA is High, SC is High, HR is High, and LA is High, then CR is High;

(2)

If PA is Low or LA is Low, then CR is Zero;

(3)

If SC is Low, then CR is Low;

(4)

If PA is Medium, SC is Medium, HR is Medium, and LA is Medium, then CR is Medium.

• Defuzzification: The centroid method was selected for defuzzification, providing a crisp output that balances the input factors and yields a representative construction rate. The centroid method is particularly suitable for cases where the output fuzzy set is asymmetric or has multiple peaks, as it considers the entire fuzzy set rather than just the peaks.

Through this fuzzy logic approach, we optimize the construction rates of SMRs, hydro, wind, and solar power projects, accounting for critical factors such as public acceptance, land availability, supply chain efficiency, and human resource availability. This enhancement to the DICE model provides more accurate and reliable predictions of construction rates, helping guide Ontario’s transition to a sustainable energy future.

3. Simulation and Results

This section presents the simulations conducted to evaluate the effectiveness of the proposed energy transition strategies for Ontario. The chapter is divided into two main parts: the DICE simulation and the fuzzy logic simulation. In the DICE simulation section, we outline the methodology used to assess the required construction rates to achieve net-zero CO₂ emissions by the target years of 2050 and 2100. This includes replacing NGPPs in Ontario with clean energy sources, such as SMRs, solar, wind, and hydro, based on the forecasted contributions outlined by the IESO [12]. The fuzzy logic simulation section then discusses how fuzzy logic is incorporated into the DICE model to optimize these construction rates, accounting for four key socio-economic factors: public acceptance, land availability, supply chain efficiency, and human resource availability. The results from both simulations are presented and analyzed, providing insights into the potential pathways for phasing out natural gas and achieving a sustainable energy future in Ontario.

3.1. Modified DICE Model Simulation and Results

This section presents the simulations’ results by integrating sub-models representing SMRs, solar, wind, and hydro into the DICE model. These simulations aim to assess the feasibility of achieving net-zero CO₂ emissions by 2050 and 2100 based on the forecasted contributions of clean energy sources, as outlined by the IESO [12]. The primary goal of these simulations is to evaluate the reduction in global annual CO₂ emissions resulting from the decommissioning of all operational FFPPs in Ontario and to determine the associated reduction rate.

To clarify, the term “unit” in this study refers to the energy output (in MWh) for each type of clean energy plant rather than a generic unit. The construction rate for each technology is defined by the corresponding energy capacity: 337,260 MWh for nuclear (80 MWe SMR unit), 231,052 MWh for hydro, 34,352 MWh for solar, and 228,751 MWh for wind. This distinction is important for understanding construction rates applied in the simulation, especially when comparing energy sources with varying capacities.

The simulations are conducted over the period from 2019 to 2100, departing from the original DICE model’s reference period of 1965 to 2300. Two target year scenarios—2050 and 2100—are explored. The 2050 target aligns with the IESO’s energy transition objectives for Ontario, while the 2100 target is included to assess the long-term implications of sustained energy transition efforts. These scenarios focus on deploying SMRs, solar, wind, and hydropower plants in Ontario, evaluating the required construction rates to achieve net-zero CO₂ emissions. The short-term climate impacts of these scenarios are thoroughly analyzed.

The model assumes that all existing FFPPs in Ontario will be replaced with clean energy alternatives, and no new FFPP units will be added starting in 2019. This assumption establishes a baseline for projecting the reduction of CO₂ emissions through the replacement of FFPPs. While the model explores the replacement of existing FFPPs with clean energy sources, it does not consider adding new FFPP units beyond 2019. The table included in this section outlines the number of clean energy units required to replace existing FFPPs and meet the additional energy demand arising from population growth.

It is anticipated that replacing the existing FFPP units will lead to a reduction in global annual cumulative CO₂ emissions. Reaching net-zero emissions is a key objective, and one of the primary solutions to achieve this goal is replacing FFPPs with clean energy sources. The modified DICE model calculates CO₂ emissions by replacing FFPPs with clean energy alternatives and determining the construction rates and total predicted units for each clean energy type accordingly. Based on the clean energy contribution projections provided by the IESO for 2050 (Table 3), the DICE model estimates that approximately 183 SMR units, 1527 solar units, 289 wind units, and 449 hydro units will be required in Ontario to replace all operational NGPPs and accommodate the additional energy demand driven by population growth, aiming to reach net-zero emissions by 2050. As

Table 5. Clean energy number of units per target year 2050 and 2100 scenarios.

Power Plant	Number of Operating Units in 2019	Total Number of Units Predicted by DICE Model for 2050 Scenario	Total Number of Units Predicted by DICE Model for 2100 Scenario
NPP	20	183	244
Hydro	224	449	539
Solar	32	1527	2130
Wind	56	289	380

shows, the number of operating units in 2019, along with the total number of units predicted by the DICE model for both the 2050 and 2100 scenarios, highlights the significant increase in the number of units needed to achieve net-zero emissions by 2050, in alignment with IESO projections. By comparing the current number of operating units in Ontario with the number predicted by the DICE model, it is evident that a much larger number of clean energy units will be required to meet the 2050 target. Furthermore, there is a notable increase in the number of units needed beyond 2050, as reflected in the 2100 scenario. This underscores the importance of adopting a comprehensive, long-term strategy for the energy transition—one that goes beyond the immediate 2050 target and aligns with IESO’s decarbonization objectives.

Figure 7 and Figure 8 illustrate the projected number of SMR, solar, wind, and hydro units required to replace all NGPPs in Ontario to reach net-zero emissions by the target years of 2050 and 2100. These figures demonstrate an upward trend in the number of required units starting from 2019, aligning with the growing need for clean energy infrastructure. The curves in both figures represent different energy types: the blue curve indicates SMR units, the orange curve represents solar units, the yellow curve shows wind unit construction and the grey curve indicates hydro unit construction. As can be seen from the figures, the need for clean energy units escalates significantly over time, reflecting both the replacement of existing FFPPs and the additional energy demand driven by population growth. The DICE model projections estimate the number of units required to meet net-zero emissions goals for 2050 and 2100. This highlights the critical role of scaling up clean energy infrastructure to meet long-term decarbonization goals.

In summary, Figure 7 and Figure 8 underscore the importance of early action in the energy transition, with a clear need for substantial increases in the construction of SMR, solar, wind, and hydro units in the coming decades to meet Ontario’s net-zero emissions targets.

Table 6. Average construction rate for target years 2050 and 2100 (unit/year).

Target Year	Average CR of SMR Units (Unit/Year)	Average CR of Hydro Units (Unit/Year)	Average CR of Solar Units (Unit/Year)	Average CR of Wind Units (Unit/Year)
2050	5.2	7.2	48.2	7.5
2100	2.7	3.8	25.8	3.9

presents the average construction rates for each clean energy source required to meet net-zero emissions by the target years 2050 and 2100. It outlines the annual number of units for both scenarios to be constructed for SMRs, solar, wind, and hydro in Ontario. The data in this table provides valuable insights into the scale of construction efforts needed to replace fossil fuel power plants and accommodate rising energy demand.

The construction rates in Table 6 reflect the adjustments needed to achieve Ontario’s long-term energy goals, as outlined by the IESO, and highlight the differences between the target years. Specifically, the 2100 scenario requires a higher construction rate due to the extended timeline, which factors in population growth and increasing energy demand. In contrast, the 2050 target necessitates more immediate action, requiring the rapid deployment of clean energy units and emphasizing the acceleration of construction rates in the shorter term.

This detailed breakdown of construction rates is crucial for assessing the feasibility of meeting net-zero targets. It highlights the necessary pace of deployment, helping to identify potential challenges in the transition process. By analyzing these rates, policymakers and stakeholders can better understand the magnitude of effort required, the impact of various factors influencing construction, and the timelines needed to achieve a sustainable, low-carbon energy system in Ontario.

Figure 9 and Figure 10 illustrate the decommissioning curves for NGPPs in Ontario for the target years 2050 and 2100. These graphs show a consistent reduction in the number of NGPP units over time, emphasizing the necessary steps to achieve net-zero emissions. At the outset, the total numbers of nuclear, hydro, solar, and wind capacities required to replace NGPPs are similar in both the 2050 and 2100 scenarios. However, as time progresses, the rate of clean energy construction slows down in the 2100 scenario compared to 2050. This is due to the transition being spread over a longer period, resulting in a slower buildup of capacity and a lower construction rate in later years. Additionally, this extended timeline leads to a lower decommissioning rate for NGPPs in the 2100 scenario. The consistency in the “internal ratio” of Nuclear, Solar, Hydro, and Wind capacities over time stems from the energy distribution derived from the IESO data. From day one, the simulation follows this distribution, which dictates how energy sources are balanced throughout the timeline. As a result, the relative shares of these energy sources remain relatively stable as the transition unfolds. By 2050, the total accumulated CO₂ mass is projected to decrease from 0.658314 trillion tons of carbon to 0.616886 trillion tons, with a further reduction in the 2100 scenario, where the accumulated CO₂ mass reaches 0.562623 trillion tons. These reductions highlight the significant impact of transitioning to clean energy sources and underscore the importance of early decommissioning fossil fuel power plants in reducing CO₂ buildup in the atmosphere. This reinforces the need for timely action in reducing emissions, ensuring that the energy transition remains on track to meet global climate goals.

Figure 11 shows the projected CO₂ emissions reductions for two target-year scenarios, specifically 2050 and 2100. The results demonstrate that setting an earlier target year results in a higher cumulative reduction in CO₂ emissions over time. The light blue bars in the figure represent the reduction of CO₂ emissions for the 2050 target year scenario, while the dark blue bars represent the same for the 2100 target year scenario.

As we can see from the figure, emissions reductions begin as early as 2019 in both scenarios, with significant reductions occurring over the following decades. By 2050, a substantial decline in emissions is observed in the orange bars, which corresponds to the 2050 target year scenario. This suggests that earlier action on reducing CO₂ emissions produces a more immediate and impactful decrease in concentrations, laying the groundwork for meeting net-zero goals.

The CO₂ accumulation results indicate that by 2050, CO₂ concentrations can be reduced by 6.1%, while a more substantial reduction of 14.5% can be achieved by 2100. These reductions highlight the effectiveness of setting earlier targets in mitigating emissions and moving towards a cleaner energy future. Thus, the figure underscores the importance of taking proactive steps in the near term to accelerate the transition to low-carbon energy systems, ultimately helping to meet long-term climate goals and reducing the adverse effects of climate change.

The DICE simulation results highlight the potential for reducing global CO₂ emissions by replacing fossil fuel power plants with clean energy sources such as SMRs, solar, wind, and hydro. The findings emphasize the importance of early decommissioning of NGPPs to meet the net-zero emissions targets set for 2050 and 2100. The projected reductions in CO₂ concentrations across both target years underline the effectiveness of this strategy. However, the results also reveal challenges in meeting these long-term energy transition goals within the specified timeframes, highlighting the necessity of optimizing construction rates to overcome these hurdles.

To address these challenges, the next section introduces fuzzy logic to refine and optimize construction rates. By incorporating fuzzy logic into the DICE model, we account for uncertainties and variabilities in key factors such as land availability, public acceptance, human resources, and supply chain efficiency—each influencing the pace of clean energy deployment. This fuzzy logic optimization improves the accuracy of the DICE model’s predictions, offering a more detailed and actionable analysis of the transition process. Through this enhanced modeling approach, we can identify more efficient pathways to achieving net-zero emissions and develop strategies that better align with Ontario’s long-term energy and climate goals.

3.2. Fuzzy Logic Results

In this section, we expand the analysis by incorporating fuzzy logic into the DICE model to optimize the construction rates for clean energy sources. This approach considers critical socio-economic factors such as public acceptance, land availability, supply chain efficiency, and human resource availability, which play significant roles in the pace of energy transition. By integrating fuzzy logic, we can address the uncertainties associated with these factors, offering more realistic and practical predictions for achieving net-zero emissions.

The fuzzy logic integration enhances the DICE model’s ability to simulate more feasible and adaptable pathways to net-zero emissions by refining the construction rates for SMRs, wind, solar, and hydro units. Three surface plots were generated to better understand the interactions and influences of these factors on the construction rate. These plots visualize how public acceptance (PA) interacts with land availability (LA), supply chain efficiency (SC), and human resource availability (HR) in shaping the construction rate (CR).

• PA (Public Acceptance) vs. LA (Land Availability) (Figure 12):

This plot reveals a non-linear relationship between PA and LA and their impact on CR. The plot shows that as both PA and LA increase, the construction rate also rises, suggesting that higher public acceptance and greater land availability positively influence the pace of clean energy deployment. A saturation effect is observed at higher values of PA and LA, where the surface plot curve flattens, indicating diminishing returns from further increases in these factors. This suggests that additional investments in public acceptance and land availability will have little effect on further accelerating the construction rate beyond a certain optimal point. Notably, the construction rate increases more sharply in response to changes in public acceptance than in land availability, indicating that PA has a stronger influence on the construction rate within the context of this model. The color gradient represents the CR, with blue indicating lower values and yellow indicating higher values.

• PA (Public Acceptance) vs. HR (Human Resource) Plot (Figure 13):

This plot highlights the crucial role of Human Resources (HR) in determining the construction rate (CR), demonstrating a more consistent impact across the entire range of HR compared to Land Availability (LA). The construction rate significantly increases as HR improves, even at lower Public Acceptance (PA) levels. While PA also contributes to increased CR, HR appears to have a more sustained and robust influence. The plot suggests that a higher CR is achieved when both PA and HR are high, indicating a synergistic effect where the combined impact of these two factors is more substantial than their individual effects. The color gradient represents the CR, with blue indicating lower values and yellow indicating higher values.

• PA (Public Acceptance) vs. SC (Supply Chain Efficiency) Plot (Figure 14):

This plot demonstrates that an increase in PA correlates with a faster construction rate as the surface plot rises with PA. Similarly, the construction rate also increases with Supply Chain Efficiency (SC), but the influence of SC is less pronounced than PA. The steepest rise in CR occurs when PA increases from 0 to 0.5, suggesting that early improvements in public acceptance have a considerable impact on accelerating construction. For SC, the CR increases more gradually across the entire range, indicating that even incremental improvements in supply chain efficiency positively affect the construction rate. There is no noticeable saturation effect for SC, suggesting that continuous improvements in SC benefit the CR without diminishing returns. However, for PA, there is a threshold beyond which further increases yield diminishing returns, signaling a point where additional efforts to boost public acceptance may not yield as significant a return. The color gradient represents the CR, with blue indicating lower values and yellow indicating higher values.

Implications for Project Planning:

The comparative analysis of the three surface plots provides several insights for project planning and strategic decision-making:

• Strategic Focus on Public Acceptance (PA): PA stands out as the most influential factor across all plots, emphasizing the importance of engaging with the public and key stakeholders early in the process. Project planning should prioritize outreach, education, and communication efforts to foster public support, which substantially impacts the construction rate;
• Balanced Approach to Land and Supply Chain Management (LA and SC): While both Land Availability (LA) and Supply Chain Efficiency (SC) are important, their influence on CR is not as pronounced as that of PA or HR. Project managers should ensure robust land acquisition strategies and optimize the supply chain, but should also be aware of diminishing returns in these areas. Efforts in these areas should be balanced to avoid overspending where additional investments yield minimal returns;
• Prioritization of Human Resources (HR): HR emerges as another critical factor that consistently impacts CR. Investments in workforce development, training, and partnerships with educational institutions are essential to ensure that sufficient skilled labor is available for the construction and operation of clean energy projects;
• Resource Allocation: Given the saturation effect observed in the PA vs. LA plot, it is important to allocate resources effectively. Rather than investing excessively in areas with diminishing returns, resources should be redirected toward HR development, where benefits continue to rise with increasing investment;
• Responsive Policies: The insights derived from these surface plots can guide policymakers in designing incentive structures and regulatory frameworks that support the critical factors of PA and HR. Additionally, policies should streamline land acquisition and improve supply chain efficiency to enhance the overall pace of the transition;
• Risk Mitigation: The analysis also helps identify potential risks. For instance, even if HR is high, low PA may still lead to delays or cancellations, suggesting the need for risk management strategies that address public concerns and ensure timely project completion.

In conclusion, while all four factors—PA, LA, SC, and HR—are significant, PA and HR emerge as the most influential on the construction rate. Focusing on these two factors will maximize the construction rate and accelerate the transition to net-zero emissions. The analysis informs strategic decision-making and underscores these factors’ interconnectedness, suggesting that a holistic approach is necessary for successful clean energy project planning. This approach will ensure a smooth, timely, and cost-effective transition to a sustainable energy future.

Defuzzification Output

In this study, the construction rate (CR) for Nuclear energy was evaluated using the example input values [0.6, 0.7, 0.5, 0.8], representing Public Acceptance (PA), Land Availability (LA), Supply Chain Efficiency (SC), and Human Resource Availability (HR), respectively. The Centroid method converted the fuzzy output into a crisp value, enabling clearer and more defined decision-making. This method allows for a nuanced interpretation of the factors influencing construction rates, providing a robust mechanism to integrate qualitative assessments into the quantitative forecasts of the DICE model. The resulting CR of 0.16 indicates a relatively low construction rate, even with moderate PA and SC, despite high LA and HR values.

Similarly, example inputs were proposed for Ontario’s solar, wind, and hydro construction rates to assess their viability in the province’s energy landscape. For solar, the inputs [0.7, 0.6, 0.6, 0.7] reflect generally favorable public acceptance and adequate workforce availability, though moderate supply chain efficiency and limited land availability in urban areas may affect construction rates. For wind, the inputs [0.5, 0.7, 0.5, 0.7] capture a mixed public sentiment and challenges with land availability, although Ontario’s efficient supply chain and skilled workforce for wind energy provide some balance. For hydro, the inputs [0.6, 0.3, 0.3, 0.8] highlight Ontario’s strong supply chain infrastructure and human resources, but limited land availability and environmental concerns around water ecosystems may hinder construction growth.

These values serve as starting points for analysis in a fuzzy logic model or similar decision-making tools. The construction rate outputs for each energy source—0.1687 for SMR, 0.2418 for Hydro, 0.4895 for Solar, and 0.1568 for Wind—illustrate the nuanced interplay between various factors impacting energy project development in Ontario.

The relatively low CR of 0.1687 for SMRs emphasizes that while certain conditions are favorable (notably high HR at 0.8 and relatively high LA at 0.7), challenges in public acceptance (0.6) and supply chain efficiency (0.5) present significant constraints. Similarly, the CR for Hydro at 0.2418 reflects strong HR availability (0.8) but is hindered by limited land availability (0.3) and moderate public acceptance (0.6). Solar, with a higher CR of 0.4895, benefits from favorable public acceptance (0.7), adequate HR (0.7), and LA (0.6), though moderate supply chain efficiency (0.6) suggests potential for improvement. Wind, with the lowest CR of 0.1568, faces significant challenges in public acceptance (0.5) and land availability (0.5) despite having a skilled workforce (0.7).

These insights are valuable for identifying potential areas for improvement and tailoring project planning strategies to address the constraints limiting the construction rate for each energy source. The fuzzy logic optimization thus provides a clearer picture of the interdependencies and factors that must be addressed to maximize the potential of each clean energy technology in Ontario’s transition to a net-zero future.

As shown in

Table 7. CR starting point values for analysis in a fuzzy logic model.

Factors	SMR Value	Hydro Value	Solar Value	Wind Value
Public Acceptance (PA)	0.6	0.6	0.7	0.5
Land Availability (LA)	0.7	0.3	0.6	0.7
Supply Chain Efficiency (SC)	0.5	0.3	0.6	0.5
Human Resource Availability (HR)	0.8	0.8	0.7	0.7
Output (CR)	0.16	0.24	0.48	0.15

, these CR values highlight specific areas where improvements can be made within each energy sector. For SMRs and Hydro, enhancing public acceptance through community engagement and optimizing supply chain efficiency could positively influence construction rates. In the case of Solar, improvements to supply chain processes could further increase its CR, making it more efficient. Wind energy, with the lowest CR, would benefit greatly from focused initiatives to improve public acceptance and increase land availability. These results emphasize the dynamic and interconnected nature of the factors influencing project development, as captured in the fuzzy logic model. Applying fuzzy rules in this context provides valuable insights into how these factors can be quantified and improved, offering actionable recommendations for refining Ontario’s clean energy construction strategy.

3.3. Integration with DICE Model

The output from the fuzzy logic model was integrated into the DICE model to enhance the accuracy of construction rate predictions for the target years of 2050 and 2100. By providing a more nuanced understanding of potential bottlenecks and areas requiring improvement, fuzzy logic allows for more precise adjustments to the DICE model’s scenarios. As shown in

Table 8. Optimized construction rate for target year scenario of 2050 and 2100.

Target Year	CR of SMR Units (Unit/Year)		CR of Hydro Units (Unit/Year)		CR of Solar Units (Unit/Year)		CR of Wind Units (Unit/Year)
	Predicted CR from DICE	Optimized CR Using FUZZY	Predicted CR from DICE	Optimized CR Using FUZZY	Predicted CR from DICE	Optimized CR Using FUZZY	Predicted CR from DICE	Optimized CR Using FUZZY
2050	5.2	0.16 × 5.2 = 0.8	7.2	0.24 × 7.2 = 1.7	48.2	0.48 × 48.2 = 23.1	7.5	0.15 × 7.5 = 1.1
2100	2.7	0.16 × 2.7 = 0.4	3.8	0.24 × 3.8 = 0.9	25.8	0.48 × 25.8 = 12.3	3.9	0.15 × 3.9 = 0.5

, the optimized construction rates derived from fuzzy logic suggest that, under current conditions, the realistic expectation for clean energy construction rates will be considerably lower than the original projections from the DICE model. This discrepancy highlights the slower pace at which clean energy projects are likely to progress compared to initial expectations, underscoring the need for revised strategies and expectations to meet the net-zero emissions targets.

The gap between the original DICE projections and the adjusted construction rates emphasizes the areas where intervention, optimization, and additional resources are crucial. Bridging this gap will be essential for achieving net-zero emissions within the selected timeframes. Specifically, targeted efforts to address the barriers identified in the fuzzy logic analysis—particularly in public acceptance, land availability, supply chain efficiency, and human resource availability—are necessary to accelerate the transition.

Incorporating fuzzy logic into the DICE model enhances the overall analysis by accounting for socio-economic and environmental uncertainties. This refined decision-making framework provides more accurate modeling and offers deeper insights into the critical factors influencing clean energy construction. By addressing these factors strategically, it becomes possible to optimize construction rates and ensure that Ontario’s net-zero emissions targets are achieved on time.

A common basis for comparison and adaptation is required to effectively integrate the construction rates derived from the fuzzy logic model with the DICE model. This involves translating the fuzzy logic output into a format that directly adjusts the DICE model’s construction rate predictions. The fuzzy logic-derived construction rate can be used as a modifier for the DICE model’s original construction rate predictions. The relationship between these two data sets will be illustrated in the following table, providing a clear comparison and adjustment mechanism for optimizing clean energy deployment strategies.

4. Discussion

This study explores Ontario’s transition toward achieving net-zero CO₂ emissions, using a modified DICE model integrated with fuzzy logic to optimize construction rates for clean energy technologies such as SMRs, wind, solar, and hydro. The results from the simulations, conducted for both the 2050 and 2100 target years, underscore the critical need for a carefully planned energy transition strategy that aligns with the energy transition objectives set by the IESO. This study addresses the technical feasibility of the transition and accounts for the socio-economic and environmental complexities that influence the construction rates of clean energy units.

Integrating fuzzy logic into the DICE model is a significant enhancement, providing a more accurate and adaptable approach to predicting energy infrastructure development. By incorporating four key factors—Public Acceptance (PA), Land Availability (LA), Supply Chain Efficiency (SC), and Human Resource Availability (HR)—this research addresses the inherent uncertainties in energy infrastructure planning, offering a more realistic and actionable assessment of the timeframes needed to meet the net-zero targets. The optimized construction rates derived from the fuzzy logic model are notably lower than the projections from the DICE model, indicating that the assumptions regarding resource availability, public acceptance, and logistical challenges may delay the energy transition. This gap between the original DICE projections and the fuzzy logic optimization highlights the need for policy adjustments, resource reallocation, and strategic interventions to overcome these barriers.

An important aspect of this study is the scalability of the fuzzy logic-enhanced DICE model. While this research focuses on Ontario’s transition to net-zero emissions, the modified DICE model with integrated fuzzy logic can be extended to other regions or countries with different energy profiles. The model’s ability to incorporate fuzzy logic allows for adapting the membership functions based on local conditions, including varying levels of resource availability, social acceptance, and environmental factors. As new data becomes available over time, the model’s flexibility can be utilized to modify the membership functions, thus providing more accurate predictions for energy transition planning in diverse regions. This adaptability and scalability make the fuzzy logic-enhanced DICE model a valuable tool for global energy transition efforts.

The simulation results for both 2050 and 2100 reveal the substantial increase in clean energy infrastructure required to replace existing fossil fuel power plants in Ontario. By 2050, approximately 183 SMR units, 1527 solar units, 289 wind units, and 449 hydro units will be needed to meet energy demand and reduce GHG emissions. These figures emphasize the scale of the challenge, particularly as Ontario strives not only to meet its immediate net-zero targets but also to ensure long-term sustainability and energy security. The 2100 target scenario further emphasizes the importance of long-term planning, with the required construction rates continuing to rise due to population growth and the increasing electrification of sectors such as transportation.

The findings also underscore the importance of early decommissioning of fossil fuel power plants in reducing global CO₂ emissions. By 2050, the total accumulated CO₂ will decrease substantially, from 0.658314 trillion tons to 0.616886 trillion tons, with a further reduction to 0.562623 trillion tons in the 2100 scenario. This reflects the impact of transitioning to clean energy sources and reinforces the importance of taking early action to minimize CO₂ accumulation in the atmosphere. However, despite these reductions, achieving net-zero emissions in both the short and long term requires continued investment in clean energy technologies and an accelerated pace of deployment.

While this study assesses four key attributes—land availability, supply chain efficiency, human resource availability, and public acceptance—it is important to note that hydropower may be further constrained by geographic factors, which are not fully captured in the analysis. Hydro energy generation depends heavily on the availability of suitable sites with adequate water resources and favorable topography, which limits its deployment to specific regions. Unlike other clean energy sources, such as wind, solar, and nuclear, which are more flexible in terms of site selection, the feasibility of hydro plants is directly influenced by geographic and environmental conditions. Therefore, while land availability is one of the assessed attributes, hydro’s expansion potential is more constrained by geography than the other energy sources in the model.

Nuclear energy plays a central role in this analysis, particularly through the deployment of SMRs and large-scale nuclear power. Nuclear, particularly SMRs, offers a flexible, scalable solution to Ontario’s energy needs, providing a low-carbon energy source that can complement intermittent renewable sources such as wind and solar. Integrating SMRs into Ontario’s energy mix would be pivotal in meeting the 2050 and 2100 target scenarios. However, while SMRs contribute significantly to emission reductions, the projected construction rates for nuclear energy are far below the required pace. In fact, the current global nuclear construction rate, as indicated by the IAEA [65], is approximately 0.005 units per year during 2021–2022, substantially lower than the 5.2 units/year required by 2050 in our model and 2.7 units/year for 2100. This disparity highlights the urgent need to accelerate nuclear construction rates to meet future energy demand and achieve global climate targets. A diversified energy portfolio, including wind, solar, and hydro, is necessary to ensure grid stability and meet energy demand, especially as the share of renewable energy in the grid continues to grow.

The discrepancies between the DICE projections and the optimized fuzzy logic projections further highlight the challenges in scaling up clean energy infrastructure within the specified timelines. While the DICE model serves as a valuable baseline for understanding the economic and environmental implications of different policy scenarios, integrating fuzzy logic allows for a more nuanced understanding of the factors influencing construction rates. These factors include land availability, the capacity of the supply chain to meet demand for materials and equipment, and the social acceptance of new energy technologies. The results suggest that these factors cannot be overlooked in the planning process, as they directly affect the feasibility and pace of energy transition efforts.

Additionally, the findings highlight the critical importance of government policies and regulatory frameworks that support the development of clean energy technologies. The IESO’s objectives for Ontario’s energy transition provide a foundational framework for this research, but achieving these targets will require strong political will, clear regulatory incentives, and a collaborative approach across sectors. The integration of fuzzy logic-based optimization in the modeling process underscores the need for adaptive policymaking, which can accommodate the evolving challenges of the energy transition and ensure that construction rates align with long-term net-zero goals.

This study reinforces the urgency of a comprehensive and adaptive approach to achieving net-zero emissions, particularly in Ontario’s electricity sector. Integrating fuzzy logic with the DICE model has provided new insights into the complexities of clean energy infrastructure deployment, highlighting the importance of addressing socio-economic uncertainties and resource constraints. While the findings suggest that Ontario can meet its net-zero targets by 2050 and 2100 through a diversified clean energy portfolio, the pace of transition must be accelerated, and strategic interventions are necessary to optimize construction rates and overcome existing barriers. This research provides a roadmap for policymakers, energy planners, and stakeholders to understand the dynamics of clean energy transitions and guide future efforts to build a sustainable, low-carbon energy future.

In light of the results from our DICE model simulations and the fuzzy logic optimization, several policy recommendations emerge to accelerate the energy transition towards net-zero emissions while addressing the factors that impact construction rates. These four critical factors—land availability, public acceptance, supply chain efficiency, and human resource availability—are central to the speed and success of clean energy deployments. Moreover, considering the technological advancements and evolving requirements needed to achieve net-zero emissions, it is crucial for policymakers to act decisively in the following areas:

• Land Use Optimization and Siting Policies: The results from our model indicate that the availability of suitable land for clean energy projects is a key constraint. This limitation significantly impacts construction timelines, particularly for large-scale solar, wind, and SMR plants. Policymakers should focus on optimizing land use by revising zoning laws and offering incentives to repurpose underused or industrial land for clean energy projects. By ensuring that suitable land is more readily available, the pace of clean energy deployment can be significantly increased, as highlighted by the uncertainties and delays identified in the fuzzy logic optimization;
• Fostering Technological Advancements and Innovation: Solar, wind, and hydro are mature technologies with incremental material quality and efficiency improvements. In contrast, SMRs are still in the developmental phase and require significant advancements before they can be deployed at scale [39,66]. While solar, wind, and hydro can be rapidly expanded, SMRs need focused resources to accelerate their development. Policymakers should prioritize advancing SMRs while scaling up existing mature technologies. Policymakers can help mitigate the slower construction rates identified in the fuzzy logic model by encouraging the adoption of innovations that reduce both time and cost for large-scale projects. The need for focused investments in technological innovation, particularly in SMRs, alongside the scaling up of existing renewable technologies, will be key to meeting global climate goals and reducing carbon emissions;
• Skilled Workforce Development: The shortage of skilled labor to support the growing clean energy sector is another significant factor contributing to slower deployment. Our results indicate that the capacity to meet Ontario’s energy transition goals depends on a skilled workforce capable of managing and executing complex energy projects. To address this, policymakers should focus on expanding education and training programs in clean energy sectors. The fuzzy logic analysis reflects how variations in human resource availability directly affect construction timelines, emphasizing the need for more training and development in the energy sector;
• Enhancing Grid Integration and Flexibility: Our findings show that integrating clean energy into the grid and the need for flexible grid infrastructure plays a pivotal role in maintaining reliability as the share of clean energy increases. To support this transition, policies should encourage the development of energy storage solutions and modernized grid systems. This will enhance grid flexibility, a crucial element in mitigating the intermittent nature of clean energy sources, as demonstrated by our fuzzy logic model, which accounts for the uncertainty and variability of clean energy generation;
• Incentivizing Private Investment: The fuzzy logic optimization and DICE model simulations show that financial constraints are another challenge slowing down the deployment of clean energy technologies. To address this, policymakers should create an enabling environment for private investment in the clean energy sector. This can be achieved through tax incentives, subsidies, and other financial mechanisms that support clean energy developers and facilitate faster project implementation;
• Streamlining Regulatory Processes: The results from our study indicate that regulatory delays significantly hinder the construction pace of clean energy projects. Policymakers should prioritize streamlining permitting and approval processes for clean energy projects to reduce bottlenecks. Simplifying regulatory requirements, especially for SMR deployment, would help address the time delays revealed in our fuzzy logic analysis, thus speeding up the transition to net-zero emissions;
• Long-Term Strategic Energy Planning: The need for clear, long-term energy planning is highlighted by the DICE model simulations, which emphasize the importance of strategic, data-driven decision-making to meet both short-term and long-term net-zero goals. Policymakers should establish clear energy transition roadmaps incorporating the uncertainties and variabilities revealed by our fuzzy logic model, enabling the system to adapt to changing circumstances and new data over time.

In summary, the findings of this study underline the importance of addressing these key policy areas to mitigate the uncertainties and challenges that slow the transition to a net-zero energy system. Policymakers are encouraged to incorporate these recommendations into strategic energy planning to facilitate a faster, more efficient deployment of clean energy technologies. By addressing these critical factors, Ontario—and other regions facing similar challenges—can achieve its long-term emissions reduction goals, as illustrated by our fuzzy logic-enhanced DICE model approach.

5. Conclusions and Future Research Opportunities

This study investigates Ontario’s transition to net-zero CO₂ emissions by 2050 and 2100, using a modified DICE model integrated with fuzzy logic to optimize construction rates for clean energy technologies, including SMRs, wind, solar, and hydro. By incorporating fuzzy logic, this research accounts for socio-economic uncertainties and environmental factors influencing the deployment of these clean energy sources. The results emphasize the need for scaling up clean energy infrastructure to replace FFPPs and meet the increasing demand for electricity.

The findings highlight the crucial role of nuclear energy, particularly SMRs, in achieving Ontario’s long-term net-zero goals. However, a comparison between global nuclear construction rates and our modified DICE model projections reveals a significant gap. According to the IAEA, the global average nuclear construction rate was approximately 0.005 units annually from 2021 to 2022. In contrast, our model projects a required construction rate of 5.2 units per year by 2050 and 2.7 units per year by 2100. This indicates that to meet net-zero goals, the pace of nuclear deployment must accelerate approximately 1040 times faster by 2050 and 540 times faster by 2100 than the current global rate.

Additionally, the study stresses the importance of early decommissioning of FFPPs in reducing CO₂ emissions. Timely decommissioning is essential for limiting the accumulation of CO₂ in the atmosphere, which remains a significant challenge in meeting global climate targets. Continued CO₂ reduction hinges on prompt action, especially in phasing out fossil fuels and scaling up clean energy technologies.

The research also underscores the need for adaptive and strategic policymaking to overcome the challenges of scaling up clean energy infrastructure. The fuzzy logic integration provides a more realistic understanding of construction rates and factors such as public acceptance, land availability, supply chain efficiency, and human resource availability. Policymakers must address these factors to ensure an efficient and effective energy transition.

In conclusion, achieving net-zero emissions by 2050 and 2100 is feasible but will require significant efforts from all stakeholders. This study offers valuable insights into the construction rates needed for a successful energy transition and lays the foundation for future research and policy development. Successful transitions will depend on strategic investments in infrastructure, technological advancements, and policies promoting public engagement, supply chain optimization, and workforce development. Ultimately, this approach will build Ontario’s sustainable, low-carbon energy future.

To successfully transition to net zero, various technological advancements are essential. As outlined in the study, solar, wind, and hydro are considered mature technologies that continue to improve incrementally in terms of material quality and efficiency. These technologies can be rapidly expanded to meet the energy demand. However, SMRs, while promising, are still in the developmental phase. Significant advancements are required for SMRs to be deployed at scale, making them a critical area for future research and development.

Policymakers must prioritize advancing SMR technologies, given their potential role in decarbonizing the energy sector. In parallel, scaling up the mature solar, wind, and hydro technologies will be necessary to accelerate the transition. Furthermore, fostering innovation in SMR design, construction, and operation is crucial to overcoming the slower construction rates identified in the fuzzy logic model. By encouraging the adoption of innovations that reduce both time and cost for large-scale projects, governments can facilitate faster deployment and ensure the transition to net-zero emissions is achievable. The need for focused investments in technological innovation, particularly in SMRs, alongside the scaling up of existing renewable technologies, will be key to meeting global climate goals and reducing carbon emissions. In addition, policymakers must prioritize the acceleration of clean energy deployment by focusing on key factors such as land availability, supply chain improvements, and public acceptance. This can be achieved through targeted policies that streamline processes, such as simplifying construction permits. In addition, improving supply chains is urgent to ensure that necessary materials and technologies are available on time, enabling faster deployment. Furthermore, A flexible and adaptive approach to policy development is essential to address the uncertainties in energy transition processes, and fuzzy logic-based models can guide these decisions by offering more accurate predictions and optimizing construction rates. Last but not least, utilizing the research on SMR deployment, it is recommended that policymakers adopt measures to ensure that SMRs and other clean energy technologies are rapidly scaled up to meet the 2050 and 2100 net-zero targets.

This study provides a comprehensive framework for optimizing clean energy transitions but also has limitations. The fuzzy logic model incorporated into the DICE framework relies on assumptions and approximations of complex socio-economic and environmental factors, which may vary in real-world applications. Additionally, the DICE model, while valuable, may not fully account for all dynamic changes in energy markets and policies over time. Future research should focus on refining these models and incorporating additional variables, such as technological advancements, energy storage solutions, and cross-sectoral dependencies. Future studies should also expand the scope of this research to include more regions, particularly those with different energy profiles and challenges. The scalability of the fuzzy logic-integrated DICE model could provide valuable insights into energy transitions globally, offering practical guidance to policymakers worldwide as they strive to meet net-zero emissions targets. By filling these gaps and building upon the foundations laid in this study, future research can contribute significantly to understanding the pathways for achieving a sustainable, low-carbon energy future.