{"paper_id":"11fc0fdb-342b-4603-b4af-8414fd685eda","body_text":"Decarbonization Pathways for Canada’s Federated Energy System Using a Subnational Integrated Assessment Model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Decarbonization Pathways for Canada’s Federated Energy System Using a Subnational Integrated Assessment Model Muhammad Awais, Deven Azevedo, Madeleine McPherson This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7378724/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Amid growing climate risks and energy security challenges, Canada's path to Net Zero emissions by 2050 hinges on regionally differentiated transformations across its energy system. This study presents a detailed scenario-based analysis using MESSAGEix-Canada, the country's first open-source, sub-national integrated assessment model. We explore how energy system transitions evolve across provinces and sectors under varying policy pathways. Results from the Net Zero scenario indicate a 65% reduction in fossil fuel extraction, an eight-fold increase in electricity supply, and a tenfold growth in low-emissions hydrogen, achieved without significantly increasing total energy system investments relative to the Legislated pathway. Instead, capital shifts away from oil and gas production toward renewables, storage, and grid expansion. Electrification of end-use sectors, alongside carbon capture and clean hydrogen deployment, drives emissions reductions. Spatial analysis reveals Alberta, Saskatchewan, and Newfoundland and Labrador face steep structural changes in resource extraction, while provinces like Ontario and Quebec become hubs of electrification and clean energy infrastructure. The analysis highlights that achieving Net Zero is technically feasible, but demands urgent, coordinated, and province-specific strategies. Policymakers in resource-intensive provinces must plan for a managed fossil phase-out and support economic diversification. In contrast, electricity-rich provinces must scale transmission and hydrogen capacity to meet cross-sector demand. MESSAGEix-Canada provides a transparent and flexible platform to co-design such transitions with stakeholders—supporting policy alignment, investment targeting, and just transition planning within Canada's federated climate governance landscape. Earth and environmental sciences/Climate sciences Scientific community and society/Energy and society Physical sciences/Energy science and technology Earth and environmental sciences/Environmental social sciences Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Context Canada plays a critical role in global climate policy, balancing its vast energy reserves with ambitious commitments to reducing greenhouse gas (GHG) emissions. Although it contributes approximately 1.5% of global CO₂ emissions (IEA, 2023 ), its position as the fourth-largest global oil producer and a key player in energy markets makes its significance extend beyond direct emissions (Canada Energy Regulator, 2023 ). This dual role creates a complex policy landscape where economic growth, energy security, and climate commitments must be carefully managed. The country’s energy system is highly heterogeneous, with provinces exhibiting significant disparities in resource availability, energy infrastructure, and policy priorities. This diversity underscores the need for regionally tailored climate policies that reconcile economic dependencies with decarbonization goals (Rhodes et al., 2022 ). Despite Canada’s commitment to achieve Net Zero emissions by 2050, it continues to face structural challenges rooted in its fossil fuel-dependent economy. Per capita CO₂ emissions remain among the highest globally, driven by energy-intensive industries, oil sands development, and widespread reliance on natural gas. With a highly decentralised system of government, Canada’s provinces and territories hold considerable jurisdiction over energy policy and regulation, making co-ordination across provinces and with the federal government an essential element to successful energy transition outcomes (IEA, 2022 ). Provinces such as Quebec, British Columbia, and Manitoba benefit from abundant hydropower resources, resulting in low-carbon electricity grids and comparatively lower per capita emissions. In contrast, Alberta and Saskatchewan rely heavily on fossil fuels for electricity generation and industrial energy use, leading to substantially higher emissions intensities. However, regional differences extend beyond electricity supply. Energy demand across transport, buildings, and industrial sectors also varies widely and remains predominantly fossil-fuel based, even in provinces with clean power systems. Figure 1 highlights this subnational heterogeneity by visualizing key energy and emissions indicators across provinces and territories. While high shares of renewable electricity are observed in most regions, particularly through legacy hydropower, these have not consistently translated into lower overall emissions. Emissions per capita remain elevated in resource-intensive provinces, while emissions per unit of GDP are high in regions with carbon-intensive economic activity. This suggests that clean electricity alone is insufficient to deliver economy-wide decarbonization. Instead, structural transformations in end-use sectors, particularly through electrification and energy efficiency are required to achieve substantial and equitable emissions reductions nationwide. Given these challenges, robust analysis of policy options is needed to identify the interventions which will have the highest benefit to cost ratio. Energy models are crucial tools in this type of analysis as they capture complex supply and demand dynamics and are designed to allow the comparison of policy measures such as carbon pricing, renewable energy incentives, and efficiency standards. By simulating sectoral interactions, these models help policymakers identify cost-effective strategies for reducing emissions while maintaining economic stability and energy security (Plazas-Niño et al., 2022 ). Scenario modeling further supports long-term planning, allowing governments to adapt policies in response to evolving technological advancements and market conditions. Despite the central role of energy models in supporting climate policy, Canada lacks an open-source peer-reviewed model with full provincial and territorial resolution. Existing tools operate at the national or at specific aggregated level, such as OSeMOSYS (Howel(Kuling et al., 2022 )ls et al., 2013) and the Energy Policy Simulator (Energy Innovation, 2025) or are proprietary and closed-source, limiting their transparency, reproducibility and ability to answer stakeholder driven scenarios. Models like NATEM (based on the TIMES framework) (Vaillancourt et al., 2017), gTech (Navius Research Inc., 2025), and AD-MERGE (Bailie et al., 2023) are widely used by the stakeholders in Canada but they restrict the possibility to reproduce the results and understand different transformation pathways by varying the assumptions. These limitations underscore the need for an integrated modeling framework for Canada that adheres to the FAIR scientific principles: Findability, Accessibility, Interoperability, and Reusability, to enhance transparency, collaboration, and scientific reproducibility in climate policy analysis (McPherson et al., 2022 ; Wilkinson et al., 2016 ). Informing Climate Ambition Through Systemic Boundaries Across National and Provincial Scales Achieving Canada's Net Zero targets necessitates more than analyzing aggregate national transition pathways, and it requires a detailed understanding of subnational energy transformation dynamics. While Canada's 2035 Nationally Determined Contribution (NDC) submission to UNFCC and advisories from the Net Zero Advisory Body (NZAB) of Canada set a clear national direction, realizing these goals depends on aligning provincial and territorial energy transitions with national targets (NZAB, 2023 ). This paper addresses the gap by answering the question: What is the gap between Canada's current policy trajectory and its stated climate goals? And critically, how do provincial pathways contribute to closing that gap? To answer these questions, we used MESSAGEix-Canada, an open-source, subnational integrated assessment model developed to evaluate energy-economy-emissions transitions at the provincial and territorial scale (see Methods). Rather than assess feasibility alone, our objective is to quantify the system-level emissions gap under existing policies and explore additional efforts needed to align with Canada's Net Zero commitments. The novelty of this study lies in its ability to combine provincial and national perspectives within one modelling framework, allowing for a coherent assessment of how subnational actions collectively align, or fail to align with national targets. This enables tracking and comparing provincial emissions reduction trajectories alongside the aggregated national pathway, highlighting regional disparities, sectoral challenges, and opportunities for coordinated policy design. MESSAGEix-Canada also provides a framework to assess the impact of emerging policies, emerging technologies, and shifting geopolitical contexts on Canada’s decarbonization pathways. While we developed the framework to be able to analyze a suite of scenarios, including variants with alternative technology adoption rates, carbon pricing levels, and sectoral policy mixes but for this study, we keep the focus on comparing two core scenarios: the legislated-policy case, representing the current policy environment at both federal and provincial levels, and a Net Zero-aligned scenario incorporating deep decarbonization across all sectors, including hard-to-abate segments such as transport and industry (see Methods for scenario details). Beyond scenario exploration, MESSAGEix-Canada is designed to interface with sector-specific models through structured boundary condition coupling. Outputs from MESSAGEix-Canada, such as, electricity capacity by technology, fuel-specific energy demand in transport, space heating fuel shares in buildings, and industrial fuel use disaggregated by subsector, serve as consistent inputs and system constraints for downstream models such as; Hourly power dispatch and capacity expansion models that incorporate reliability and grid integration; Detailed transport models analyzing modal shifts, EV adoption, and hydrogen infrastructure; End-use building models assessing heating technology adoption; Sectoral industry models evaluating carbon capture, electrification, or material circularity. This systems integration enables a multi-model ecosystem that sets credible boundary conditions, while detailed sectoral tools assess operational and behavioral responses. In doing so, the model supports both high-level pathway exploration and more granular policy design, offering a flexible and modular foundation for exploring climate action pathways across Canada's diverse energy landscape. Achieving Net Zero in Canada by 2050 requires steep emission reductions in Alberta and sectoral shifts in industry and transport While Canada's existing climate policies reflect essential progress, our analysis shows that under the Legislated scenario, additional measures are required to achieve the country's Net Zero target by 2050. In contrast, the Net Zero scenario demonstrates that national emissions can decline from 503.3 MtCO₂/year in 2025 to 59.4 MtCO₂/year in 2050, through a combination of end-use efficiency improvements, fuel switching, large-scale deployment of zero-carbon supply technologies, and targeted carbon dioxide removal. Our analysis does not include additional removals from Direct Air Capture and thus represents a conservative estimate of the mitigation potential under this pathway. Emissions reductions are observed across all provinces and territories, with variation in timing and magnitude indicating differences in energy system structures, baseline emissions, and decarbonization opportunities. Alberta, which accounts for the highest emissions in 2025 (217.9 MtCO₂/year), shows the most considerable absolute reduction (170.3 MtCO₂) by 2050 in the model results. This is associated with transformations in both the electricity and industrial sectors, including reduced reliance on fossil-based generation and the introduction of carbon capture and storage (CCS). Ontario and Quebec also contribute substantially to national mitigation, with modeled cumulative reductions of 106.6 MtCO₂ and 51.0 MtCO₂, respectively, enabled mainly by electrification and demand-side transitions. British Columbia achieves a 36.4 MtCO₂ reduction, leveraging its low-carbon electricity mix to support further end-use decarbonization. Several smaller provinces and territories also exhibit meaningful changes. Atlantic Canada achieves a combined modeled reduction of 35.1 MtCO₂. Notably, Yukon, Northwest Territories, and Nova Scotia show emissions reductions exceeding 100% relative to their 2025 levels, indicating modeled negative emissions or major structural shifts in their supply systems. In contrast, emissions increase in Nunavut (+ 134.4%) and Prince Edward Island (+ 117.0%), though from low initial baselines, suggesting the importance of tailored mitigation approaches in emerging systems. Sectorally, the most significant sources of emissions in 2025 are modeled in energy demand from industry (168.7 MtCO₂), transportation (158.9 MtCO₂), and electricity supply (60.2 MtCO₂). By 2050, the most significant absolute reductions will occur in transportation (145.9 MtCO₂, 91.9%) and industry (132.3 MtCO₂, 78.4%), driven by electrification, modal shifts, and efficiency improvements. Emissions from electricity supply decline by 89.9 MtCO₂ (149.2%), reflecting a transition to non-emitting sources and the emergence of negative emissions in select regions. Emissions from oil and gas supply decrease by over 100 MtCO₂, indicating significant upstream decarbonization and reduced fossil fuel demand in the modeled pathway. Carbon dioxide removal, particularly through CCS, plays a transitional role. Deployment peaks around 2030, primarily in Alberta and Saskatchewan, and declines thereafter as fossil-based activities are phased out. Bioenergy with carbon capture and storage (BECCS) is modeled with minimal contribution, suggesting limited reliance on engineered removals in the scenario. Residual emissions in 2050 are modest, with approximately 17 MtCO₂ from fuel combustion and 60 MtCO₂ from remaining industrial and demand-side processes. Depending on accounting frameworks, this level may require balancing through natural carbon sinks, land-use strategies, or international offsets to achieve Net Zero. Overall, the Net Zero scenario illustrates a technically feasible and spatially differentiated decarbonization trajectory for Canada. The results highlight the importance of regionally adaptive mitigation strategies that reflect differences in resource endowments, infrastructure, and sectoral profiles. Provinces with emissions-intensive systems demonstrate the potential for steep reductions, while those with hydro-dominated supply systems play a key role in supporting electrification and system integration. These findings underscore the need for coordinated multi-level policy frameworks to support an effective and equitable energy transition. Energy demand transformation through electrification and hydrogen as energy carriers The Net Zero scenario demonstrates that energy demand transformation and reduction are feasible across all Canadian provinces and territories, primarily through end-use efficiency improvements, electrification, and fuel switching. The pathway contrasts significantly with outcomes under the Legislated scenario, where energy demand continues to grow in most provinces due to limited structural change. While both scenarios reflect some level of transition, only the Net Zero pathway achieves the scale of reduction aligned with Canada's climate targets. Our results indicate that by 2050, Canadian final energy demand will decline by approximately 23 percent under Net Zero, whereas demand increases under the Legislated case. Ontario and Alberta show the most substantial contributions in absolute terms. In Ontario, energy demand falls from 2665.7 petajoules in 2025 to 2067.3 petajoules in 2060 under the Net Zero pathway, a 22 percent reduction. Under the Legislated scenario, however, demand grows by nearly 9 percent. Alberta sees a similarly large transformation, with demand decreasing by 12 percent under Net Zero, compared to a 13 percent increase under Legislated. These reductions are enabled by systemic changes, including electrification of transport and industry, indicating switching to fuel efficient technologies. Smaller provinces and territories also experience sharp percentage reductions. The Northwest Territories shifts from a 28 percent increase under Legislated to a 9 percent decline under Net Zero, representing the most significant difference in scenario outcomes across regions. New Brunswick, Prince Edward Island, and Nova Scotia all exhibit demand reductions exceeding 24 percent under the Net Zero scenario, compared to moderate growth under current policy. These differences emphasize the importance of ambition in provincial strategies and the potential for tailored interventions. Quebec, which already benefits from a low-emission electricity grid, still achieves a 22 percent decline in final energy demand under Net Zero, suggesting that energy efficiency and electrification offer mitigation opportunities even in relatively decarbonized jurisdictions. Saskatchewan presents a different profile: while its demand still increases under Net Zero, the growth is limited to under 9 percent compared to more than 37 percent under Legislated, due to improved efficiency in its industrial base and electrification of transport. Sectoral trends show that the most significant reductions occur in transportation, followed by buildings. Transportation demand falls by over 50 percent under Net Zero, driven by widespread vehicle electrification, modal shifts, and behavioral change. Buildings sector demand declines by over 26 percent, primarily due to building retrofits and the replacement of fossil-based heating systems with electric alternatives. In contrast, industrial energy use remains relatively stable, with a modest 10 percent reduction under Net Zero compared to continued growth under Legislated. This reflects ongoing economic expansion in industrial sectors, which face more gradual decarbonization pathways due to process complexity and infrastructure turnover. These sectoral transitions are supported by increasing shares of electricity and hydrogen in final energy use, particularly in transport and buildings. Figure 4 illustrates the evolution of electricity and hydrogen shares across provinces and sectors from 2025 to 2050. In the transportation sector, the mean share of electricity increases from 32 percent in 2025 to over 73 percent by 2050, with British Columbia and Quebec approaching maximum shares above 85 percent. Similarly, residential and commercial electricity shares increase by 15 percentage points on average over the same period, reflecting a shift toward electric heating and appliances. In contrast, industry sees only a marginal increase in electricity share, from 22.7 to 22.9 percent, suggesting more limited electrification potential in industrial processes. Hydrogen adoption, though starting from a near-zero baseline, also expands modestly under Net Zero. By 2050, average hydrogen shares reach 24% percent in industrial sector. The most significant increases are observed in Yukon's industrial sector and Saskatchewan's buildings sector, where hydrogen provides niche decarbonization options in hard-to-electrify end uses. These trends suggest that hydrogen remains a complementary solution to electrification, with adoption concentrated in remote regions with favorable infrastructure or specific decarbonization needs. The results suggest that while all provinces can reduce final energy demand under a Net Zero pathway, the magnitude and drivers of change differ by region. The comparison with Legislated policies illustrates the extent to which current commitments fall short of supporting deep reductions in demand. Most importantly, the Net Zero scenario demonstrates that significant demand reductions are technically achievable across a diverse set of provincial contexts without sacrificing energy services. This underscores the need for accelerated and coordinated efforts between federal and provincial governments to align policy frameworks with long-term climate goals. Table 1 Regional structural shifts in final energy use under the Net Zero scenario by 2050. Qualitative thresholds for change: Electrification and liquid fuel reduction are classified as high (>130 PJ), moderate (50–129 PJ), low (10–49 PJ), and minimal (<10 PJ). For hydrogen uptake: high (≥100 PJ), moderate (30–99 PJ), low (5–29 PJ), and minimal (<5 PJ). Gas phaseout is described qualitatively based on modeled reductions in fossil gas use in the buildings sector. Province/Territory Electrification Surge Hydrogen Uptake Gas Phaseout Liquid Reduction Sectoral Notes Alberta High in industry, buildings, transport High in industry Full in buildings High in transport Major industrial fuel switching and EV uptake Ontario High in buildings and transport Low in industry Full in buildings Very high in transport Strong electrification across all sectors Quebec Moderate in buildings Low in industry Full in buildings Low in transport Heat and hydrogen supplement industrial decarbonization Saskatchewan Low in buildings Moderate in industry Partial Moderate in transport Growth in bio-based fuels and hydrogen British Columbia Low in buildings and transport Low overall Full Moderate in transport Aggressive modal shifts and electricity uptake Atlantic provinces Moderate to high in buildings Mixed across provinces Mostly phased out Moderate in transport Sector coupling with district heat and biomass Territories Low but consistent across sectors Low to moderate in industry Rapid transitions Low in transport Small systems with supportive policy-driven shifts Reallocating Capital and fossil phase out in the energy supply infrastructure Results from the Net Zero scenario indicate a profound transformation of Canada’s energy supply system, underpinned by shifts in investment allocation, contraction of fossil fuel extraction, large-scale expansion of clean electricity generation, and a reconfiguration of hydrogen production. These systemic changes reflect the structural requirements of aligning the national energy system with a Net Zero objective. While the scenario excludes interprovincial or international trade dynamics, infrastructure constraints, and firm-level behavioral responses, it offers valuable insights into directional changes required to achieve deep decarbonization. Investments under the Net Zero pathway reflect an early and strategic redirection of capital toward low-carbon infrastructure. By mid-century, cumulative investments amount to $ 1.12 trillion, marginally lower than in the Legislated pathway. This is not due to lower ambition but rather reflects avoided investment in fossil fuel infrastructure and greater efficiency in supply-side systems. The results show that Net Zero does not necessarily require more capital overall but instead demands smarter capital allocation. Regionally, the investment landscape changes considerably. In Alberta, cumulative investment is 22% lower relative to the Legislated case, reflecting sharp declines in oil and gas infrastructure. By contrast, Ontario sees investment increase by 20%, driven by grid expansion, electrification, and hydrogen-related infrastructure. British Columbia more than doubles its energy-related investment under the Net Zero pathway, pointing to its growing role in renewable integration and export-oriented energy services. By sector, results show that the Net Zero scenario leads to greater capital flows into renewable electricity, energy storage, and grid integration technologies. Investment in wind power increases by over $ 10 billion, while energy storage investment rises sharply from $ 78.5 billion to $ 113.3 billion, indicating the critical importance of flexibility and reliability in a high-renewables system. Investments in fossil extraction and processing, in contrast, decline by nearly 90%, highlighting a transition away from legacy energy systems. These investment shifts are mirrored in the supply of primary energy resources, where results indicate a dramatic reduction in fossil fuel extraction. By 2050, total extraction falls to 668,900 PJ, a 65% decline from levels projected under current policies. The sharpest reductions are observed in Alberta, Saskatchewan, and British Columbia, where extraction activities contract by 65%, 61%, and 73%, respectively. Extraction is nearly phased out in Ontario, Nova Scotia, and the territories. The results suggest a marked contraction in domestic fossil fuel supply under Net Zero conditions. In particular, oil extraction is projected to decline by 88% relative to the Legislated scenario by 2050, and gas extraction by 54%, with coal extraction eliminated entirely. These trends underscore the supply-side consequences of deep decarbonization and the need for economic diversification in fossil fuel–dependent regions. Electricity plays a central role in enabling decarbonization across sectors. Under the Net Zero scenario, total electricity generation increases by 15% compared to the Legislated pathway, reaching 51,230 PJ/year by 2050. This growth supports electrification in end-use sectors, expansion of hydrogen production, and system-wide decarbonization. The generation mix shifts decisively toward variable renewables, with wind power growing to 8,597 PJ (26% higher than Legislated), and solar PV expanding to 599 PJ. Modest additions in geothermal and hydropower support system balancing, while natural gas generation without CCS drops by over 75%, and biomass without CCS declines substantially. Provincial dynamics reveal how Net Zero pathways are spatially differentiated. Alberta experiences the largest absolute increase in electricity generation under Net Zero, followed by Ontario. However, British Columbia, Nova Scotia, and the Northwest Territories show the highest percentage increases, driven by renewables deployment and diesel displacement. For instance, wind generation in Nova Scotia grows by more than 200%, while solar PV more than doubles in Prince Edward Island and Saskatchewan. These regional variations suggest a need for coordinated planning to match resource endowments with system demand. Hydrogen production also undergoes a fundamental shift in the Net Zero scenario. While overall hydrogen output is slightly lower by 2050 compared to the Legislated case, the results show that low-carbon hydrogen dominates the supply mix. Electrolytic hydrogen expands nearly 230-fold to 227 PJ by 2050, supported by clean electricity. Production from coal and gas with carbon capture and storage (CCS) also rises substantially (e.g., coal + CCS reaches 220 PJ, compared to 0 PJ under current policy). At the same time, unabated fossil hydrogen drops by over 75%, indicating that decarbonization of hydrogen supply is a core requirement for Net Zero alignment. Provincially, Alberta remains the largest hydrogen producer, but the Net Zero scenario shows accelerated growth in Newfoundland and Labrador (+ 17,021%), Manitoba (+ 5,674%), and British Columbia (+ 3,657%), suggesting the emergence of new regional hydrogen hubs. Quebec also expands significantly, reflecting the role of hydroelectricity in enabling low-cost green hydrogen. In contrast, Ontario and Saskatchewan reduce production relative to Legislated, pointing to a shift in industrial geography and energy infrastructure. Conclusion This study presents the development and application of MESSAGEix-Canada, the first open-source, sub-national integrated assessment model tailored to Canada's energy transition. Designed to follow FAIR principles, the model enables spatially explicit and policy-relevant analysis of decarbonization pathways, addressing longstanding gaps in transparency and regional granularity in Canadian energy modeling. Through the scenario analysis, the study provides new evidence on how supply-side systems must transform to align with national Net Zero targets, highlighting critical regional and sectoral dynamics. The results demonstrate that achieving Net Zero emissions by 2050 requires a fundamental reallocation of investments from fossil-based infrastructure toward clean electricity, energy storage, and low-carbon hydrogen. Cumulative energy supply investment under the Net Zero scenario is comparable to the Legislated pathway, indicating that decarbonization is financially viable if capital is redirected early and strategically. Fossil fuel extraction declines by approximately 65% nationally, with the sharpest reductions in Alberta, Saskatchewan, Newfoundland and Labrador, and British Columbia. In contrast, provinces with abundant renewable resources, such as Quebec, Manitoba, and British Columbia, emerge as critical nodes for electricity and hydrogen supply. At the same time, Ontario sees significant investment growth driven by electrification and infrastructure expansion. Electricity generation under Net Zero increases by over 6,600 PJ relative to the current policy case, with wind and solar contributing the most significant gains. At the same time, natural gas and biomass without carbon capture and storage (CCS) decline significantly, and nuclear remains broadly stable. These changes reflect a significant shift toward variable renewable supply and underscore the need for enhanced system flexibility, enabled through investments in storage, smart grids, and interprovincial transmission. Hydrogen production also plays a central role in decarbonizing industry and transport, with a shift from unabated fossil hydrogen toward electricity- and CCS-based production. Electrolytic hydrogen expands nearly 230-fold by 2050, and new substantial output emerges in provinces such as Newfoundland and Labrador, Manitoba, and British Columbia. These findings also underscore the importance of regionally tailored policy responses. Policymakers in Alberta must prioritize economic diversification and phase down oil and gas extraction, while simultaneously supporting hydrogen deployment with CCS and electrification of industrial demand. In Ontario, grid modernization, flexibility investment, and targeted electrification are essential to manage rising electricity demand. Quebec and Manitoba are well-positioned to lead in green hydrogen production, leveraging their hydroelectric capacity to support domestic decarbonization and potential export markets. British Columbia should continue to scale wind power and electricity storage while developing integrated clean energy hubs. Saskatchewan and Newfoundland and Labrador face deeper structural transitions and will require focused support for clean energy alternatives, bioenergy development, and labor market transitions. At the federal level, the results support the urgent need for a national just transition strategy that aligns the federal Net Zero target with provincial realities. This includes targeted financial support, regulatory alignment, and mechanisms for infrastructure coordination across provinces. System planners and utilities should invest in interprovincial transmission, long-duration storage, and other flexibility-enabling infrastructure to manage spatial and temporal variability. For investors and technology developers, the results identify clear opportunities in provinces undergoing rapid transformation—particularly in electricity, hydrogen, and carbon capture technologies. In sum, the Net Zero scenario illustrates that Canada's transition to Net Zero is not only technically feasible but can be achieved without significantly higher investment than under current policies—provided that strategic, early-stage decisions are made to reorient capital flows and system planning. MESSAGEix-Canada delivers an open, extensible platform to support this transition, enabling co-learning and collaborative decision-making among policymakers, researchers, utilities, and civil society. Future developments will further enhance its capacity by integrating macroeconomic modeling, short-term power system reliability, and trade dynamics. As a public resource, MESSAGEix-Canada is poised to serve as a cornerstone of evidence-based energy and climate policy in Canada, helping to translate long-term ambition into regionally grounded, actionable pathways. Methods We have developed MESSAGEix-Canada, an open-source, sub-national integrated assessment model specifically tailored to capture Canada’s energy system dynamics across provinces and territories. The MESSAGEix-Canada model is an engineering-economic optimization framework built on the MESSAGEix framework (Huppmann et al., 2019 ), a linear programming (LP) formulation designed for strategic energy planning and integrated assessment of energy-engineering-economy-environment (E4) systems. This model facilitates long-term energy system optimization by incorporating future projections and constraints to determine cost-effective pathways for achieving energy and climate objectives. The objective function minimizes the total discounted system costs, encompassing energy supply, transformation, and demand, while also integrating emissions costs and temporal-spatial constraints to explore different policy scenarios. The mathematical formulation of MESSAGEix-Canada is implemented in GAMS, while scenario definition, input data processing, and result analysis are conducted through Python-based API packages. The model structure and solution methodologies align with the MESSAGEix framework (Huppmann et al., 2019 ). The adaptation of MESSAGEix to the Canadian context is maintained in a version-controlled Gitlab repository to ensure modularity and transparency. Reference Energy System MESSAGEix-Canada provides a structured representation of the Canadian energy system by integrating energy supply, conversion, and end-use consumption across all provinces. The model follows an engineering-economic optimization framework that determines cost-effective energy system transitions while incorporating emissions constraints and policy objectives. The reference energy system, as shown in Fig. 6 , defines the key components of energy supply and demand captured by the model. The model captures primary energy resources such as fossil fuels, renewables and biomass. Energy conversion technologies, including power generation, refining, hydrogen production, and storage, are explicitly represented to assess the trade-offs between pathways. The model disaggregates energy flows into final consumption sectors, allowing for a detailed evaluation of sector-specific energy demands and how end use technology choices affect upstream sectors. Fossil fuel extraction technologies are categorized into conventional and unconventional sources, including oil sands, hydraulic fracturing and enhanced recovery. The model accounts for associated emissions from extraction, transportation and refining. The electricity system includes all major power generation technologies. Over 20 thermal power generating technologies are included, capturing various coal, gas and biomass powerplants with and without carbon capture and storage. For renewables, solar photovoltaic, concentrated solar power, onshore and offshore wind, and hydropower are incorporated accounting for provincial differences in resource availability and accounting for system integration costs. The model represents multiple hydrogen production technologies, including electrolysis, steam methane reforming, biomass gasification, and coal gasification, with or without carbon capture and storage. Hydrogen infrastructure, including liquefaction and transportation, is accounted for to assess the role of hydrogen as part of the broader energy transition. The model determines the share of hydrogen in industrial, transport, and power sector applications based on cost, emissions constraints, and technology competitiveness. Final energy consumption is categorized into residential and commercial, industrial, and transportation sectors, where technology choices and energy efficiency measures are determined endogenously. The model incorporates demand-side efficiency improvements through conservation cost curves, allowing for dynamic shifts in energy consumption patterns across these end use sectors. The following sections provide a detailed overview of the data assumptions and sources used across the reference energy system, covering key aspects of energy supply, conversion, and final consumption. Adaptation to the Canadian Context MESSAGEix-Canada has been developed to reflect the specific energy system characteristics, policy landscape, and economic conditions of Canada. The development process involved integrating key assumptions (e.g., Canadian energy policies), input data (e.g., oil and gas reserves), and techno-economic parameters (e.g., provincial GDP growth), tailored to the Canadian energy sector. The initial adaptation was based on the MESSAGEix-GLOBIOM global integrated assessment model (IAM), specifically utilizing the North American (NAM) region. Energy efficiency parameters, technology cost assumptions, and sectoral transformation constraints were derived from this global framework to construct a preliminary single-node Canadian prototype. This model was subsequently expanded to Canada’s 13 provinces and territories, accounting for each province and territories’ existing energy infrastructure, different resource availability and unique import / export potential. Calibration The calibration process ensures that the model aligns with historical energy balances, sectoral energy trends, and economic drivers in each Canadian province and territory. This step integrates population and GDP projections, energy demand estimation, power sector capacity calibration, and oil and gas production constraints to ensure the model realistically captures Canada’s possible energy transition pathways. Population and GDP projections were sourced from Statistics Canada and extended using regression-based methods to provide long-term projections up to 2060 (see model documentation for more detail). Figure 3 presents the projected population and GDP growth trajectories at the provincial and territorial levels. Since credible, publicly available GDP projections by province and territory do not exist, GDP projections were scaled from national to sub-regional levels assuming historical provincial GDP shares remain constant into the future. Energy demand estimation was based on a downscaling approach, where energy service demands from the North American region in MESSAGEix-GLOBIOM were allocated to the Canadian context. Historical energy consumption data at the provincial level was used to calibrate demand patterns, ensuring consistency with (Statistics Canada, 2025) energy balances and reports from the (Canada Energy Regulator, 2023 ). Future demands are projected by scaling historical final-energy consumption using provincial population and GDP growth rates and then reconciling any year-to-year discrepancies via a minimax Linear Programming (LP) formulation. Specifically, we introduce a variable (M) representing the largest absolute deviation between population-driven and GDP-driven growth rates over the entire projection period and minimize (M), thereby enforcing consistency between these two socioeconomic drivers. Existing electricity generation capacity was taken from the Canadian Open Data for Energy Resources (CODERS) dataset, which provides plant-level data on installed capacity, fuel type, and generation by province (Hendriks et al., 2023 ). Variable renewable potentials are also taken from CODERS’ gridded capacity factor data. Renewable energy was categorized into eight distinct resource bins to represent regional differences in wind, solar, and hydroelectric generation capabilities. Existing oil and gas production capacity was calibrated using historical extraction data from the Canada Energy Regulator (Canada Energy Regulator, 2023 ). Conventional and unconventional oil and gas reserves were inputted for each province and territory, drawing primarily on data from Natural Resources Canada and the Canada Energy Regulator (see model documentation for more details). The model accounts for upstream emissions from extraction and processing, as well as downstream emissions from refining and final consumption. The calibration process ensures that fossil fuel production pathways are consistent with existing production capacity and available resources. The integration of calibrated energy demand, power generation capacity, and fossil fuel production constraints ensures that MESSAGEix-Canada represents Canada’s energy system dynamics leveraging as much public data as is available. By aligning model assumptions with historical data and applying region-specific constraints, the calibration process establishes a robust analytical foundation for evaluating energy transition pathways, technology deployment strategies, and policy impacts. Table 1 Summary of data used for calibration. All the techno-economic input data and assumptions such as technological efficiencies, lifetime, technology costs are also available in the visualization dashboard and available in the GitLab repository Description References Population projections for Canada, provinces and territories (Statistics Canada, 2025d ) Gross domestic product (GDP) by province and territory (Statistics Canada, 2025e ) Historical Energy balances (supply and demand of primary and secondary energy) (Statistics Canada, 2025e ) Electricity generation by type (Hendriks et al., 2023 ; Statistics Canada, 2025b ) Oil, gas, and coal production (Statistics Canada, 2025a ) Imports and exports of energy (Statistics Canada, 2025c ) Emission Factors (IPCC, 1996 ) Technological Change (growth rates, learning rates based on Shared Socioeconomic Pathways (SSP2) (International Energy Agency, 2014 ; Riahi et al., 2012 ) Renewable Potentials and Capacity Factors (Hendriks et al., 2023 ) Power Plants Capacity (Hendriks et al., 2023 ) Modularity & Scenario Design Policy Modularity The model is designed with a modular policy framework that allows for the inclusion of evolving federal and provincial climate policies. Given the dynamic nature of energy and climate policy in Canada, this modular approach ensures that policies can be updated, refined, or replaced as new measures are introduced. Policies are implemented as separate, independently connected modules, which allows for flexibility in scenario development and policy assessment. The modular structure enables policymakers and researchers to activate or modify policy instruments depending on the analysis required. This flexibility is essential for examining the impacts of existing, proposed, and hypothetical policies on Canada’s energy system. Policies are implemented using different mechanisms, including: Hard constraints on technological activity, such as mandated phaseouts of specific technologies, that are consistent with regulatory commitments. Share constraints, which enforce minimum or maximum shares of specific energy sources, such as renewable portfolio standards. Investment tax credits, which reduce capital costs for technologies benefiting from financial incentives. Carbon pricing mechanisms, which apply costs to emissions-intensive technologies or a group of technologies and can be conditional on whether a technology exceeds an emissions standard (e.g., in the case of Canada’s Output-Based Pricing System). Technology Modularity Alongside policy modularity, the model incorporates technology modularity, allowing for representation of rapidly integrating emerging energy technologies. This is achieved through a structured template where new technologies can be introduced by defining their techno-economic parameters, including capital and operational costs, efficiency levels, fuel input-output mapping, diffusion constraints, and expected learning rates. The documentation provides a standardized framework for adding new technologies, ensuring that innovations such as advanced nuclear reactors, next-generation energy storage, synthetic fuels, or emerging carbon capture techniques can be incorporated and assessed within the model. This capability enables policymakers and stakeholders in their understanding of how new technologies could shape Canada’s long-term energy transition and evaluate their role under different policy and market conditions. Scenario Design The model includes a range of federal and provincial policies that shape Canada's energy transition. These policies can be easily turned on and off in the model, allowing for detailed policy analysis. Canada’s most consequential climate policy has been projected to be industrial pricing policies, which is represented in MESSAGEix-Canada through both provincial systems and the federal Output-Based Pricing System (OBPS) (CCI, 2024 ). The model captures the policy’s cost impact by adding a carbon cost to the variable cost of each covered technology. This is calculated in two steps: Calculating the payable emissions intensity as the difference between the emission intensity of a given technology in MESSAGEix-Canada and the corresponding standard defined in the industrial pricing regulations. Multiplying the payable emission intensity by the applicable carbon credit price. The applied carbon costs from industrial pricing to each covered technology vary across provinces due to the differences in the output-based standards and tightening rates that each province has adopted. MESSAGEix-Canada’s industrial pricing implementation employs a simplified approach to credit allocation; since revenues are not explicitly represented in MESSAGEix-Canada, credits are accounted for using a negative payable emission intensity (same calculation as the payable emissions intensity mentioned above), which reduces the cost per unit of energy produced for technologies with an emission intensity lower than the OBPS standard. To find a reasonable equilibrium between the supply and demand of OBPS credits, the model tracks both payable and receivable emissions across technologies and regions. If imbalances arise, model iterations are conducted by increasing or decreasing the assumed credit price until a rough balance is achieved. This iterative approach ensures that the representation of industrial pricing within MESSAGEix-Canada remains realistic while leaving flexibility to account for credit banking and use of offsets. For the provincial policies, the industrial pricing implementation applies the carbon intensity and carbon prices based on provincial definitions of output-based standards and tightening rates. Three scenarios have been designed to validate and calibrate the model: a No Policy scenario, Legislated and a balanced Net Zero pathway. These scenarios serve as a basis for model validation and policy assessment, providing insights into how Canada’s energy system responds to existing policies and long-term Net Zero targets. In future analyses, the model will explore additional sensitivities and specific policy questions. Table 2 summarizes key assumptions in both scenarios. Table 2 Overview of scenario architecture used in the analysis. Scenario Design Legislated Net Zero Includes the major implemented energy policies and regulations in Canada. Pathway scenarios which include a trajectory aligned with national and provincial Net Zero emissions commitments by 2050. The two Net Zero scenarios include different demand and lifestyle assumption. Carbon pricing via the Output-Based Pricing System (OBPS) , which applies an aggregate cost to emissions exceeding intensity limits for each technology and commodity. X X Phased coal phase-out by 2030 , except for facilities with carbon capture and storage (CCS). X X Investment tax credits , reflecting federal commitments to incentivize clean energy and industrial decarbonization. X X Clean Electricity Regulations reflecting the emission intensity standards one electricity genration technologies X X Net Zero emissions constrained at national level , allowing provincial pathways to optimize their net zero pathways consistent with the Canada wide targets. X Stakeholder Validation The MESSAGEix-Canada model was developed following FAIR scientific principles, ensuring that all model components are Findable, Accessible, Interoperable , and Reusable . The full model codebase, input datasets, and scenario assumptions are version-controlled and publicly available in an open-source repository to facilitate reproducibility, peer review, and collaborative extension. To validate the modeling framework and scenario logic, we engaged with Canadian energy and climate policy stakeholders through the Energy Modelling Hub’s national model comparison initiative. As part of this exercise, MESSAGEix-Canada scenario results were presented and discussed in a multi-model forum alongside outputs from Navius, ECCC, and Canada Energy Regulator models. Stakeholder feedback from these sessions was used to refine policy representations, improve transparency, and benchmark outputs—ensuring policy relevance and analytical credibility. Declarations Author Contribution M.A. led the model development, scenario analysis, manuscript writing, and figure preparation. D.A. and M.M. contributed to the conceptualization of the analysis and provided critical review and feedback on the manuscript. M.M. acquired funding and provided overall supervision. All authors reviewed and approved the final version of the manuscript. Acknowledgement The authors would like to acknowledge Brendan Danaher, and the software development team of the Sustainable Energy Systems Integration & Transition (SESIT) Research Group at the University of Victoria for their continuous support in maintaining and improving the model choices and software infrastructure. Their efforts in ensuring compliance with best practices for open-source development have been instrumental in enhancing the model's usability and transparency.We also gratefully acknowledge funding support from the Open Insights Project, which has contributed to the development and refinement of this research. Data Availability The input data and model code used in this study are publicly available at https://gitlab.com/sesit/message-ix. Comprehensive documentation, including model structure, scenario definitions, and implementation guidelines, can be accessed at https://sesit.gitlab.io/message-ix/. Additionally, the model results and input datasets can be visualized through an interactive visualization platform at https://message.sesit.ca/, providing stakeholders with a transparent and user-friendly interface for exploring scenario outputs and energy system pathways. References Canada Energy Regulator. (2023). Canada’s Energy Future 2023: Energy Supply and Demand Projections to 2050 . https://www.cer-rec.gc.ca/en/data-analysis/canada-energy-future/2023/canada-energy-futures-2023.pdf CCI. (2024). Industrial carbon pricing the top driver of emissions reductions, new analysis shows - Canadian Climate Institute . https://climateinstitute.ca/news/industrial-carbon-pricing-the-top-driver-of-emissions-reductions-new-analysis-shows/ Hendriks, R. , Monroe, J. , Cusi, T. , Ahnaf, A. , Aldana, D. , Griffiths, K. , Dorman, T. , Chhina, A. , & McPherson M. (2023). Canadian Open-Source Database for Energy Research and Systems-Modelling (CODERS) (1.0). Energy Modelling Hub (EMH). https://coders.cme-emh.ca/ Huppmann, D., Gidden, M., Fricko, O., Kolp, P., Orthofer, C., Pimmer, M., Kushin, N., Vinca, A., Mastrucci, A., Riahi, K., & Krey, V. (2019). The MESSAGEix Integrated Assessment Model and the ix modeling platform (ixmp): An open framework for integrated and cross-cutting analysis of energy, climate, the environment, and sustainable development. Environmental Modelling and Software , 143–156. https://doi.org/10.1016/j.envsoft.2018.11.012 IEA. (2022). Canada 2022 – Analysis . https://www.iea.org/reports/canada-2022 IEA. (2023). IEA, Greenhouse Gas Emissions from Energy Highlights . International Energy Agency. (2014). World Energy Outlook 2014 . http://www.worldenergyoutlook.org/weo2014/ IPCC. (1996). Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories: The Workbook (Volume 2) . http://www.ipcc-nggip.iges.or.jp/public/gl/invs5a.html Kuling, K., Barnes, T., Shivakumar, A., Brinkerink, M., & Niet, T. (2022). Applying the open-source climate, land, energy, and water systems (CLEWs) model to Canada. Energy Strategy Reviews , 44 , 100929. https://doi.org/https://doi.org/10.1016/j.esr.2022.100929 McPherson, M., Monroe, J., Jurasz, J., Rowe, A., Hendriks, R., Stanislaw, L., Awais, M., Seatle, M., Xu, R., Crownshaw, T., Miri, M., Aldana, D., Esfahlani, M., Arjmand, R., Saffari, M., Cusi, T., Toor, K. S., & Grieco, J. (2022). Open-source modelling infrastructure: Building decarbonization capacity in Canada. Energy Strategy Reviews , 44 , 100961. https://doi.org/10.1016/J.ESR.2022.100961 NZAB. (2023). Compete and Succeed in a Net Zero Future . https://cdn.prod.website-files.com/64ef3fd141170da059cb6d80/65172c0363ff6814d9e13c2f_e2d538e427a10f032f50859e216a2293_NZAB_2022_Annual_Report_Final_-_EN-corr.pdf Plazas-Niño, F. A., Ortiz-Pimiento, N. R., & Montes-Páez, E. G. (2022). National energy system optimization modelling for decarbonization pathways analysis: A systematic literature review. Renewable and Sustainable Energy Reviews , 162 , 112406. https://doi.org/10.1016/J.RSER.2022.112406 Rhodes, E., Hoyle, A., McPherson, M., & Craig, K. (2022). Understanding climate policy projections: A scoping review of energy-economy models in Canada. Renewable and Sustainable Energy Reviews , 153 , 111739. https://doi.org/10.1016/J.RSER.2021.111739 Riahi, K., Dentener, F., Gielen, D., Grubler, A., Jewell, J., Klimont, Z., Krey, V., McCollum, D., Pachauri, S., Rao, S., van Ruijven, B., van Vuuren, D. P., & Wilson, C. (2012). Chapter 17 - Energy Pathways for Sustainable Development. In Global Energy Assessment - Toward a Sustainable Future (pp. 1203–1306). Cambridge University Press and International Institute for Applied Systems Analysis. http://www.globalenergyassessment.org Statistics Canada. (2025a). Crude oil and equivalent production, monthly . Statistics Canada. (2025b). Electric power generation, monthly generation by type of electricity . Statistics Canada. (2025c). Imports and exports of energy, annual . Statistics Canada. (2025d). Population Projections for Canada, Provinces and Territories, 2024 to 2074 (Issues 91-520–X). https://www150.statcan.gc.ca/n1/en/catalogue/91-520-X Statistics Canada. (2025e). Supply and demand of primary and secondary energy in terajoules, annual . Wilkinson, M. D., Dumontier, M., Aalbersberg, Ij. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J. W., da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., … Mons, B. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data 2016 3:1 , 3 (1), 1–9. https://doi.org/10.1038/sdata.2016.18 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 12 Nov, 2025 Reviews received at journal 10 Oct, 2025 Reviewers agreed at journal 15 Sep, 2025 Reviewers agreed at journal 02 Sep, 2025 Reviewers invited by journal 27 Aug, 2025 Editor assigned by journal 26 Aug, 2025 Submission checks completed at journal 21 Aug, 2025 First submitted to journal 15 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-7378724\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":509200440,\"identity\":\"efdccf76-d507-4fbf-af4d-5781ac61cdf9\",\"order_by\":0,\"name\":\"Muhammad Awais\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYJCCAwwVEjykajmDpIU4zYxtSByCWvhnJD88+HOehQx//+EHDD9qGOTtCWmRuJFmcJh3mwQPiMHYc4zBsIego24nGBxmBGphuMFgwMDbwMBIUIv87fQPB3/OkeCRP3/8A+PfBgZ7gloMbucYHOBtkOAxOJBjwAy0JZGgFsP7bwoO8xyT4DG8kVNwWOaYRHLPAQJa5M4c3/zxR02dvdz54xsfvqmxsW1vIGQNMgCaL0GK+lEwCkbBKBgFuAAAXO0+IyR+NtcAAAAASUVORK5CYII=\",\"orcid\":\"\",\"institution\":\"International Institute for Applied Systems Analysis\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Muhammad\",\"middleName\":\"\",\"lastName\":\"Awais\",\"suffix\":\"\"},{\"id\":509200441,\"identity\":\"2d8f30e5-9d99-472c-bf0b-f9dbbdc43740\",\"order_by\":1,\"name\":\"Deven Azevedo\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Victoria\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Deven\",\"middleName\":\"\",\"lastName\":\"Azevedo\",\"suffix\":\"\"},{\"id\":509200442,\"identity\":\"97e69455-251d-4708-96b3-fb0febdcc58c\",\"order_by\":2,\"name\":\"Madeleine McPherson\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Victoria\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Madeleine\",\"middleName\":\"\",\"lastName\":\"McPherson\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-08-15 06:08:15\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-7378724/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-7378724/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":90498947,\"identity\":\"9f80549c-968d-4111-aa34-b62b1a0ec5ab\",\"added_by\":\"auto\",\"created_at\":\"2025-09-03 11:18:31\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":91502,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003epresents key provincial energy-economy-emission indicators\\u003c/strong\\u003e for population, GDP (in 2020 USD), GHG emissions, final energy demand by sector, and the share of renewable electricity (including hydro, wind, solar, and biomass, as % of total generation). Data are sourced from Statistics Canada and the Canada Energy Regulator (CER).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7378724/v1/017663c119d4679c11e822bb.png\"},{\"id\":90499730,\"identity\":\"bebff9f6-f892-414a-b283-4a8af2f15827\",\"added_by\":\"auto\",\"created_at\":\"2025-09-03 11:26:31\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":250313,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cem\\u003e\\u003cstrong\\u003eIllustrative schematic of the MESSAGEix-Canada framework and its systemic boundary conditions\\u003c/strong\\u003e\\u003c/em\\u003e\\u003cem\\u003e.\\u003c/em\\u003e Under global climate targets, the model incorporates emission constraints and solves for least-cost transformation pathways, providing solutions at provincial and territorial scales. While the linkage to sectoral and macroeconomic models is not part of this study, MESSAGEix-Canada serves as a foundation for future integrations that will enhance policy relevance and regional robustness. The integrated assessment modeling framework is calibrated with provincial energy balances and generates a solution space that spans technology options and pathways, enabling analysis of technology uptake, diffusion, and phase-out over time, and informing high-level decarbonization strategies for policymakers.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7378724/v1/b72984f927ef144f4ea53cbd.png\"},{\"id\":90498949,\"identity\":\"7471ebcc-223e-4f16-8d15-2f29e982a558\",\"added_by\":\"auto\",\"created_at\":\"2025-09-03 11:18:31\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":220543,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ea) \\u003cstrong\\u003eNational greenhouse gas (GHG) emissions trajectories under different policy scenarios.\\u003c/strong\\u003e\\u003cbr\\u003e\\n This figure compares emissions trajectories from three key sources. The shaded range reflects legislated policy scenarios from Navius Research, using publicly available data from the \\u003ca href=\\\"https://www.naviusresearch.com/canada_energy_dashboard/\\\"\\u003eCanada Energy Dashboard\\u003c/a\\u003e, which explores a range of techno-economic and behavioral assumptions. The Canada Energy Regulator (CER), a federal agency, provides a single reference projection based on current policies. MESSAGEix-Canada presents both a Legislated and a Net Zero scenario. The assumptions underlying MESSAGEix-Canada scenarios are summarized in the adjacent text boxes. Canada’s official 2035 Nationally Determined Contribution (NDC) target is shown for reference. \\u003cstrong\\u003eb)\\u003c/strong\\u003e \\u003cstrong\\u003eCumulative emission reduction by sectors for all provinces and territories.\\u003c/strong\\u003e Supply categories include liquids (biomass, coal, natural gas, oil, and transport fuels) representing combustion emissions from fuel production, processing, and transport; and “other” sources, representing emissions from fuel extraction and minor non-fuel supply activities. Demand categories are shown by sub-sectors (industry, buildings, transport, etc.).. Detailed results across provinces and disaggregated emission sources and gases can be explored in the interactive visualization dashboard (Emissions tab)\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7378724/v1/01deea0db71d61d7c953ce6d.png\"},{\"id\":90500038,\"identity\":\"e8757e3f-44ab-44a7-aaf7-3270ca3f14bf\",\"added_by\":\"auto\",\"created_at\":\"2025-09-03 11:34:31\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":405684,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eShare of electricity and hydrogen in final energy use across Canadian provinces and territories. The shaded range represents the 2050 share (minimum to maximum across provinces). These shares illustrate the pace and extent of the energy transition, indicating which sectors and regions are most cost-optimal to electrify or transition to low-carbon hydrogen in end-use applications. Results show that in industry, electrification is challenging beyond ~50%, with hydrogen uptake occurring concurrently. In contrast, both transport and buildings sectors demonstrate significantly higher electrification potential, reflecting greater technical and economic feasibility for deep decarbonization\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7378724/v1/bf464381089d81828766cecf.png\"},{\"id\":90499732,\"identity\":\"5d60afac-b70f-44cc-bcfe-d4fa119d1cd0\",\"added_by\":\"auto\",\"created_at\":\"2025-09-03 11:26:31\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":713960,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSupply-side transformations. a) Energy supply cumulative investments from 2025 by province, showing the distribution of capital allocation across electricity, fossil, and other infrastructure categories in 2050 across provinces b) \\u0026nbsp;Secondary energy supply by source for electricity (top row) and hydrogen (bottom row) in the Net Zero scenario, aggregated into three regional groups: Western Provinces (British Columbia, Alberta, Saskatchewan, Manitoba), Eastern Provinces (Ontario, Québec), and Atlantic Provinces (Nova Scotia, New Brunswick, Prince Edward Island, Newfoundland and Labrador). The detailed results can be visualized in the visualization dashboard (Secondary Energy \\u0026amp; Investments tab).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7378724/v1/9531ecf134ffe3394ee74bde.png\"},{\"id\":90499734,\"identity\":\"2272538e-6021-4ea1-868e-cfee6e2920c4\",\"added_by\":\"auto\",\"created_at\":\"2025-09-03 11:26:31\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":202580,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eReference Energy System (RES) in MESSAGEix-Canada. The figure represents energy flows from primary resource extraction to conversion, transmission, and final consumption across end-use sectors. The numbered components indicate technologies represented in the model, each characterized by techno-economic parameters such as costs, efficiencies, lifetime, growth constraints, and diffusion rates, enabling a dynamic assessment of energy system transitions.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7378724/v1/b4bc903f46233098a3532ccd.png\"},{\"id\":90501026,\"identity\":\"46d5b752-ba92-4fa7-9fa2-c43de5276715\",\"added_by\":\"auto\",\"created_at\":\"2025-09-03 11:42:34\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2713965,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7378724/v1/257bd4ad-99e9-4494-be7f-6a432eec9619.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Decarbonization Pathways for Canada’s Federated Energy System Using a Subnational Integrated Assessment Model\",\"fulltext\":[{\"header\":\"Context\",\"content\":\"\\u003cp\\u003eCanada plays a critical role in global climate policy, balancing its vast energy reserves with ambitious commitments to reducing greenhouse gas (GHG) emissions. Although it contributes approximately 1.5% of global CO₂ emissions (IEA, \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e), its position as the fourth-largest global oil producer and a key player in energy markets makes its significance extend beyond direct emissions (Canada Energy Regulator, \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). This dual role creates a complex policy landscape where economic growth, energy security, and climate commitments must be carefully managed. The country\\u0026rsquo;s energy system is highly heterogeneous, with provinces exhibiting significant disparities in resource availability, energy infrastructure, and policy priorities. This diversity underscores the need for regionally tailored climate policies that reconcile economic dependencies with decarbonization goals (Rhodes et al., \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eDespite Canada\\u0026rsquo;s commitment to achieve Net Zero emissions by 2050, it continues to face structural challenges rooted in its fossil fuel-dependent economy. Per capita CO₂ emissions remain among the highest globally, driven by energy-intensive industries, oil sands development, and widespread reliance on natural gas. With a highly decentralised system of government, Canada\\u0026rsquo;s provinces and territories hold considerable jurisdiction over energy policy and regulation, making co-ordination across provinces and with the federal government an essential element to successful energy transition outcomes (IEA, \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eProvinces such as Quebec, British Columbia, and Manitoba benefit from abundant hydropower resources, resulting in low-carbon electricity grids and comparatively lower per capita emissions. In contrast, Alberta and Saskatchewan rely heavily on fossil fuels for electricity generation and industrial energy use, leading to substantially higher emissions intensities. However, regional differences extend beyond electricity supply. Energy demand across transport, buildings, and industrial sectors also varies widely and remains predominantly fossil-fuel based, even in provinces with clean power systems. Figure\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e highlights this subnational heterogeneity by visualizing key energy and emissions indicators across provinces and territories. While high shares of renewable electricity are observed in most regions, particularly through legacy hydropower, these have not consistently translated into lower overall emissions. Emissions per capita remain elevated in resource-intensive provinces, while emissions per unit of GDP are high in regions with carbon-intensive economic activity. This suggests that clean electricity alone is insufficient to deliver economy-wide decarbonization. Instead, structural transformations in end-use sectors, particularly through electrification and energy efficiency are required to achieve substantial and equitable emissions reductions nationwide.\\u003c/p\\u003e\\u003cp\\u003eGiven these challenges, robust analysis of policy options is needed to identify the interventions which will have the highest benefit to cost ratio. Energy models are crucial tools in this type of analysis as they capture complex supply and demand dynamics and are designed to allow the comparison of policy measures such as carbon pricing, renewable energy incentives, and efficiency standards. By simulating sectoral interactions, these models help policymakers identify cost-effective strategies for reducing emissions while maintaining economic stability and energy security (Plazas-Ni\\u0026ntilde;o et al., \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Scenario modeling further supports long-term planning, allowing governments to adapt policies in response to evolving technological advancements and market conditions.\\u003c/p\\u003e\\u003cp\\u003eDespite the central role of energy models in supporting climate policy, Canada lacks an open-source peer-reviewed model with full provincial and territorial resolution. Existing tools operate at the national or at specific aggregated level, such as OSeMOSYS (Howel(Kuling et al., \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e)ls et al., 2013) and the Energy Policy Simulator (Energy Innovation, 2025) or are proprietary and closed-source, limiting their transparency, reproducibility and ability to answer stakeholder driven scenarios. Models like NATEM (based on the TIMES framework) (Vaillancourt et al., 2017), gTech (Navius Research Inc., 2025), and AD-MERGE (Bailie et al., 2023) are widely used by the stakeholders in Canada but they restrict the possibility to reproduce the results and understand different transformation pathways by varying the assumptions. These limitations underscore the need for an integrated modeling framework for Canada that adheres to the FAIR scientific principles: Findability, Accessibility, Interoperability, and Reusability, to enhance transparency, collaboration, and scientific reproducibility in climate policy analysis (McPherson et al., \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Wilkinson et al., \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\"},{\"header\":\"Informing Climate Ambition Through Systemic Boundaries Across National and Provincial Scales\",\"content\":\"\\u003cp\\u003eAchieving Canada's Net Zero targets necessitates more than analyzing aggregate national transition pathways, and it requires a detailed understanding of subnational energy transformation dynamics. While Canada's 2035 Nationally Determined Contribution (NDC) submission to UNFCC and advisories from the Net Zero Advisory Body (NZAB) of Canada set a clear national direction, realizing these goals depends on aligning provincial and territorial energy transitions with national targets (NZAB, \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). This paper addresses the gap by answering the question: What is the gap between Canada's current policy trajectory and its stated climate goals? And critically, how do provincial pathways contribute to closing that gap?\\u003c/p\\u003e\\u003cp\\u003eTo answer these questions, we used MESSAGEix-Canada, an open-source, subnational integrated assessment model developed to evaluate energy-economy-emissions transitions at the provincial and territorial scale (see Methods). Rather than assess feasibility alone, our objective is to quantify the system-level emissions gap under existing policies and explore additional efforts needed to align with Canada's Net Zero commitments.\\u003c/p\\u003e\\u003cp\\u003eThe novelty of this study lies in its ability to combine provincial and national perspectives within one modelling framework, allowing for a coherent assessment of how subnational actions collectively align, or fail to align with national targets. This enables tracking and comparing provincial emissions reduction trajectories alongside the aggregated national pathway, highlighting regional disparities, sectoral challenges, and opportunities for coordinated policy design. MESSAGEix-Canada also provides a framework to assess the impact of emerging policies, emerging technologies, and shifting geopolitical contexts on Canada’s decarbonization pathways.\\u003c/p\\u003e\\u003cp\\u003eWhile we developed the framework to be able to analyze a suite of scenarios, including variants with alternative technology adoption rates, carbon pricing levels, and sectoral policy mixes but for this study, we keep the focus on comparing two core scenarios: the legislated-policy case, representing the current policy environment at both federal and provincial levels, and a Net Zero-aligned scenario incorporating deep decarbonization across all sectors, including hard-to-abate segments such as transport and industry (see Methods for scenario details).\\u003c/p\\u003e\\u003cp\\u003eBeyond scenario exploration, MESSAGEix-Canada is designed to interface with sector-specific models through structured boundary condition coupling. Outputs from MESSAGEix-Canada, such as, electricity capacity by technology, fuel-specific energy demand in transport, space heating fuel shares in buildings, and industrial fuel use disaggregated by subsector, serve as consistent inputs and system constraints for downstream models such as; Hourly power dispatch and capacity expansion models that incorporate reliability and grid integration; Detailed transport models analyzing modal shifts, EV adoption, and hydrogen infrastructure; End-use building models assessing heating technology adoption; Sectoral industry models evaluating carbon capture, electrification, or material circularity. This systems integration enables a multi-model ecosystem that sets credible boundary conditions, while detailed sectoral tools assess operational and behavioral responses. In doing so, the model supports both high-level pathway exploration and more granular policy design, offering a flexible and modular foundation for exploring climate action pathways across Canada's diverse energy landscape.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\n\"},{\"header\":\"Achieving Net Zero in Canada by 2050 requires steep emission reductions in Alberta and sectoral shifts in industry and transport\",\"content\":\"\\u003cp\\u003eWhile Canada's existing climate policies reflect essential progress, our analysis shows that under the Legislated scenario, additional measures are required to achieve the country's Net Zero target by 2050. In contrast, the Net Zero scenario demonstrates that national emissions can decline from 503.3 MtCO₂/year in 2025 to 59.4 MtCO₂/year in 2050, through a combination of end-use efficiency improvements, fuel switching, large-scale deployment of zero-carbon supply technologies, and targeted carbon dioxide removal. Our analysis does not include additional removals from Direct Air Capture and thus represents a conservative estimate of the mitigation potential under this pathway.\\u003c/p\\u003e\\u003cp\\u003eEmissions reductions are observed across all provinces and territories, with variation in timing and magnitude indicating differences in energy system structures, baseline emissions, and decarbonization opportunities. Alberta, which accounts for the highest emissions in 2025 (217.9 MtCO₂/year), shows the most considerable absolute reduction (170.3 MtCO₂) by 2050 in the model results. This is associated with transformations in both the electricity and industrial sectors, including reduced reliance on fossil-based generation and the introduction of carbon capture and storage (CCS). Ontario and Quebec also contribute substantially to national mitigation, with modeled cumulative reductions of 106.6 MtCO₂ and 51.0 MtCO₂, respectively, enabled mainly by electrification and demand-side transitions. British Columbia achieves a 36.4 MtCO₂ reduction, leveraging its low-carbon electricity mix to support further end-use decarbonization.\\u003c/p\\u003e\\u003cp\\u003eSeveral smaller provinces and territories also exhibit meaningful changes. Atlantic Canada achieves a combined modeled reduction of 35.1 MtCO₂. Notably, Yukon, Northwest Territories, and Nova Scotia show emissions reductions exceeding 100% relative to their 2025 levels, indicating modeled negative emissions or major structural shifts in their supply systems. In contrast, emissions increase in Nunavut (+ 134.4%) and Prince Edward Island (+ 117.0%), though from low initial baselines, suggesting the importance of tailored mitigation approaches in emerging systems.\\u003c/p\\u003e\\u003cp\\u003eSectorally, the most significant sources of emissions in 2025 are modeled in energy demand from industry (168.7 MtCO₂), transportation (158.9 MtCO₂), and electricity supply (60.2 MtCO₂). By 2050, the most significant absolute reductions will occur in transportation (145.9 MtCO₂, 91.9%) and industry (132.3 MtCO₂, 78.4%), driven by electrification, modal shifts, and efficiency improvements. Emissions from electricity supply decline by 89.9 MtCO₂ (149.2%), reflecting a transition to non-emitting sources and the emergence of negative emissions in select regions. Emissions from oil and gas supply decrease by over 100 MtCO₂, indicating significant upstream decarbonization and reduced fossil fuel demand in the modeled pathway.\\u003c/p\\u003e\\u003cp\\u003eCarbon dioxide removal, particularly through CCS, plays a transitional role. Deployment peaks around 2030, primarily in Alberta and Saskatchewan, and declines thereafter as fossil-based activities are phased out. Bioenergy with carbon capture and storage (BECCS) is modeled with minimal contribution, suggesting limited reliance on engineered removals in the scenario.\\u003c/p\\u003e\\u003cp\\u003eResidual emissions in 2050 are modest, with approximately 17 MtCO₂ from fuel combustion and 60 MtCO₂ from remaining industrial and demand-side processes. Depending on accounting frameworks, this level may require balancing through natural carbon sinks, land-use strategies, or international offsets to achieve Net Zero.\\u003c/p\\u003e\\u003cp\\u003eOverall, the Net Zero scenario illustrates a technically feasible and spatially differentiated decarbonization trajectory for Canada. The results highlight the importance of regionally adaptive mitigation strategies that reflect differences in resource endowments, infrastructure, and sectoral profiles. Provinces with emissions-intensive systems demonstrate the potential for steep reductions, while those with hydro-dominated supply systems play a key role in supporting electrification and system integration. These findings underscore the need for coordinated multi-level policy frameworks to support an effective and equitable energy transition.\\u003c/p\\u003e\"},{\"header\":\"Energy demand transformation through electrification and hydrogen as energy carriers\",\"content\":\"\\u003cp\\u003eThe Net Zero scenario demonstrates that energy demand transformation and reduction are feasible across all Canadian provinces and territories, primarily through end-use efficiency improvements, electrification, and fuel switching. The pathway contrasts significantly with outcomes under the Legislated scenario, where energy demand continues to grow in most provinces due to limited structural change. While both scenarios reflect some level of transition, only the Net Zero pathway achieves the scale of reduction aligned with Canada's climate targets.\\u003c/p\\u003e\\u003cp\\u003eOur results indicate that by 2050, Canadian final energy demand will decline by approximately 23 percent under Net Zero, whereas demand increases under the Legislated case. Ontario and Alberta show the most substantial contributions in absolute terms. In Ontario, energy demand falls from 2665.7 petajoules in 2025 to 2067.3 petajoules in 2060 under the Net Zero pathway, a 22 percent reduction. Under the Legislated scenario, however, demand grows by nearly 9 percent. Alberta sees a similarly large transformation, with demand decreasing by 12 percent under Net Zero, compared to a 13 percent increase under Legislated. These reductions are enabled by systemic changes, including electrification of transport and industry, indicating switching to fuel efficient technologies.\\u003c/p\\u003e\\u003cp\\u003eSmaller provinces and territories also experience sharp percentage reductions. The Northwest Territories shifts from a 28 percent increase under Legislated to a 9 percent decline under Net Zero, representing the most significant difference in scenario outcomes across regions. New Brunswick, Prince Edward Island, and Nova Scotia all exhibit demand reductions exceeding 24 percent under the Net Zero scenario, compared to moderate growth under current policy. These differences emphasize the importance of ambition in provincial strategies and the potential for tailored interventions.\\u003c/p\\u003e\\u003cp\\u003eQuebec, which already benefits from a low-emission electricity grid, still achieves a 22 percent decline in final energy demand under Net Zero, suggesting that energy efficiency and electrification offer mitigation opportunities even in relatively decarbonized jurisdictions. Saskatchewan presents a different profile: while its demand still increases under Net Zero, the growth is limited to under 9 percent compared to more than 37 percent under Legislated, due to improved efficiency in its industrial base and electrification of transport.\\u003c/p\\u003e\\u003cp\\u003eSectoral trends show that the most significant reductions occur in transportation, followed by buildings. Transportation demand falls by over 50 percent under Net Zero, driven by widespread vehicle electrification, modal shifts, and behavioral change. Buildings sector demand declines by over 26 percent, primarily due to building retrofits and the replacement of fossil-based heating systems with electric alternatives. In contrast, industrial energy use remains relatively stable, with a modest 10 percent reduction under Net Zero compared to continued growth under Legislated. This reflects ongoing economic expansion in industrial sectors, which face more gradual decarbonization pathways due to process complexity and infrastructure turnover.\\u003c/p\\u003e\\u003cp\\u003eThese sectoral transitions are supported by increasing shares of electricity and hydrogen in final energy use, particularly in transport and buildings. Figure\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e illustrates the evolution of electricity and hydrogen shares across provinces and sectors from 2025 to 2050. In the transportation sector, the mean share of electricity increases from 32 percent in 2025 to over 73 percent by 2050, with British Columbia and Quebec approaching maximum shares above 85 percent. Similarly, residential and commercial electricity shares increase by 15 percentage points on average over the same period, reflecting a shift toward electric heating and appliances. In contrast, industry sees only a marginal increase in electricity share, from 22.7 to 22.9 percent, suggesting more limited electrification potential in industrial processes.\\u003c/p\\u003e\\u003cp\\u003eHydrogen adoption, though starting from a near-zero baseline, also expands modestly under Net Zero. By 2050, average hydrogen shares reach 24% percent in industrial sector. The most significant increases are observed in Yukon's industrial sector and Saskatchewan's buildings sector, where hydrogen provides niche decarbonization options in hard-to-electrify end uses. These trends suggest that hydrogen remains a complementary solution to electrification, with adoption concentrated in remote regions with favorable infrastructure or specific decarbonization needs.\\u003c/p\\u003e\\u003cp\\u003eThe results suggest that while all provinces can reduce final energy demand under a Net Zero pathway, the magnitude and drivers of change differ by region. The comparison with Legislated policies illustrates the extent to which current commitments fall short of supporting deep reductions in demand. Most importantly, the Net Zero scenario demonstrates that significant demand reductions are technically achievable across a diverse set of provincial contexts without sacrificing energy services. This underscores the need for accelerated and coordinated efforts between federal and provincial governments to align policy frameworks with long-term climate goals.\\u003c/p\\u003e\\u003ctable border=\\\"0\\\" cellspacing=\\\"3\\\" cellpadding=\\\"0\\\" class=\\\"fr-table-selection-hover\\\"\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eTable 1 Regional structural shifts in final energy use under the Net Zero scenario by 2050.\\u0026nbsp;\\u003c/strong\\u003e\\u003cem\\u003eQualitative thresholds for change: Electrification and liquid fuel reduction are classified as high (\\u0026gt;130 PJ), moderate (50–129 PJ), low (10–49 PJ), and minimal (\\u0026lt;10 PJ). For hydrogen uptake: high (≥100 PJ), moderate (30–99 PJ), low (5–29 PJ), and minimal (\\u0026lt;5 PJ). Gas phaseout is described qualitatively based on modeled reductions in fossil gas use in the buildings sector.\\u003c/em\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eProvince/Territory\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eElectrification Surge\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHydrogen Uptake\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eGas Phaseout\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eLiquid Reduction\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eSectoral Notes\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003eAlberta\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003eHigh in industry, buildings, transport\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003eHigh in industry\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003eFull in buildings\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003eHigh in transport\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003eMajor industrial fuel switching and EV uptake\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003eOntario\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003eHigh in buildings and transport\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003eLow in industry\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003eFull in buildings\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003eVery high in transport\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003eStrong electrification across all sectors\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003eQuebec\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003eModerate in buildings\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003eLow in industry\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003eFull in buildings\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003eLow in transport\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003eHeat and hydrogen supplement industrial decarbonization\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003eSaskatchewan\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003eLow in buildings\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003eModerate in industry\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003ePartial\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003eModerate in transport\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003eGrowth in bio-based fuels and hydrogen\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003eBritish Columbia\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003eLow in buildings and transport\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003eLow overall\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003eFull\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003eModerate in transport\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003eAggressive modal shifts and electricity uptake\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003eAtlantic provinces\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003eModerate to high in buildings\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003eMixed across provinces\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003eMostly phased out\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003eModerate in transport\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003eSector coupling with district heat and biomass\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003eTerritories\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003eLow but consistent across sectors\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003eLow to moderate in industry\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003eRapid transitions\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003eLow in transport\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003ctd\\u003e\\n \\u003cp\\u003eSmall systems with supportive policy-driven shifts\\u003c/p\\u003e\\n \\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/table\\u003e\"},{\"header\":\"Reallocating Capital and fossil phase out in the energy supply infrastructure\",\"content\":\"\\u003cp\\u003eResults from the Net Zero scenario indicate a profound transformation of Canada\\u0026rsquo;s energy supply system, underpinned by shifts in investment allocation, contraction of fossil fuel extraction, large-scale expansion of clean electricity generation, and a reconfiguration of hydrogen production. These systemic changes reflect the structural requirements of aligning the national energy system with a Net Zero objective. While the scenario excludes interprovincial or international trade dynamics, infrastructure constraints, and firm-level behavioral responses, it offers valuable insights into directional changes required to achieve deep decarbonization.\\u003c/p\\u003e\\u003cp\\u003eInvestments under the Net Zero pathway reflect an early and strategic redirection of capital toward low-carbon infrastructure. By mid-century, cumulative investments amount to \\u003cspan\\u003e$\\u003c/span\\u003e1.12 trillion, marginally lower than in the Legislated pathway. This is not due to lower ambition but rather reflects avoided investment in fossil fuel infrastructure and greater efficiency in supply-side systems. The results show that Net Zero does not necessarily require more capital overall but instead demands smarter capital allocation.\\u003c/p\\u003e\\u003cp\\u003eRegionally, the investment landscape changes considerably. In Alberta, cumulative investment is 22% lower relative to the Legislated case, reflecting sharp declines in oil and gas infrastructure. By contrast, Ontario sees investment increase by 20%, driven by grid expansion, electrification, and hydrogen-related infrastructure. British Columbia more than doubles its energy-related investment under the Net Zero pathway, pointing to its growing role in renewable integration and export-oriented energy services.\\u003c/p\\u003e\\u003cp\\u003eBy sector, results show that the Net Zero scenario leads to greater capital flows into renewable electricity, energy storage, and grid integration technologies. Investment in wind power increases by over \\u003cspan\\u003e$\\u003c/span\\u003e10\\u0026nbsp;billion, while energy storage investment rises sharply from \\u003cspan\\u003e$\\u003c/span\\u003e78.5\\u0026nbsp;billion to \\u003cspan\\u003e$\\u003c/span\\u003e113.3\\u0026nbsp;billion, indicating the critical importance of flexibility and reliability in a high-renewables system. Investments in fossil extraction and processing, in contrast, decline by nearly 90%, highlighting a transition away from legacy energy systems.\\u003c/p\\u003e\\u003cp\\u003eThese investment shifts are mirrored in the supply of primary energy resources, where results indicate a dramatic reduction in fossil fuel extraction. By 2050, total extraction falls to 668,900 PJ, a 65% decline from levels projected under current policies. The sharpest reductions are observed in Alberta, Saskatchewan, and British Columbia, where extraction activities contract by 65%, 61%, and 73%, respectively. Extraction is nearly phased out in Ontario, Nova Scotia, and the territories. The results suggest a marked contraction in domestic fossil fuel supply under Net Zero conditions.\\u003c/p\\u003e\\u003cp\\u003eIn particular, oil extraction is projected to decline by 88% relative to the Legislated scenario by 2050, and gas extraction by 54%, with coal extraction eliminated entirely. These trends underscore the supply-side consequences of deep decarbonization and the need for economic diversification in fossil fuel\\u0026ndash;dependent regions.\\u003c/p\\u003e\\u003cp\\u003eElectricity plays a central role in enabling decarbonization across sectors. Under the Net Zero scenario, total electricity generation increases by 15% compared to the Legislated pathway, reaching 51,230 PJ/year by 2050. This growth supports electrification in end-use sectors, expansion of hydrogen production, and system-wide decarbonization. The generation mix shifts decisively toward variable renewables, with wind power growing to 8,597 PJ (26% higher than Legislated), and solar PV expanding to 599 PJ. Modest additions in geothermal and hydropower support system balancing, while natural gas generation without CCS drops by over 75%, and biomass without CCS declines substantially.\\u003c/p\\u003e\\u003cp\\u003eProvincial dynamics reveal how Net Zero pathways are spatially differentiated. Alberta experiences the largest absolute increase in electricity generation under Net Zero, followed by Ontario. However, British Columbia, Nova Scotia, and the Northwest Territories show the highest percentage increases, driven by renewables deployment and diesel displacement. For instance, wind generation in Nova Scotia grows by more than 200%, while solar PV more than doubles in Prince Edward Island and Saskatchewan. These regional variations suggest a need for coordinated planning to match resource endowments with system demand.\\u003c/p\\u003e\\u003cp\\u003eHydrogen production also undergoes a fundamental shift in the Net Zero scenario. While overall hydrogen output is slightly lower by 2050 compared to the Legislated case, the results show that low-carbon hydrogen dominates the supply mix. Electrolytic hydrogen expands nearly 230-fold to 227 PJ by 2050, supported by clean electricity. Production from coal and gas with carbon capture and storage (CCS) also rises substantially (e.g., coal\\u0026thinsp;+\\u0026thinsp;CCS reaches 220 PJ, compared to 0 PJ under current policy). At the same time, unabated fossil hydrogen drops by over 75%, indicating that decarbonization of hydrogen supply is a core requirement for Net Zero alignment.\\u003c/p\\u003e\\u003cp\\u003eProvincially, Alberta remains the largest hydrogen producer, but the Net Zero scenario shows accelerated growth in Newfoundland and Labrador (+\\u0026thinsp;17,021%), Manitoba (+\\u0026thinsp;5,674%), and British Columbia (+\\u0026thinsp;3,657%), suggesting the emergence of new regional hydrogen hubs. Quebec also expands significantly, reflecting the role of hydroelectricity in enabling low-cost green hydrogen. In contrast, Ontario and Saskatchewan reduce production relative to Legislated, pointing to a shift in industrial geography and energy infrastructure.\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eThis study presents the development and application of MESSAGEix-Canada, the first open-source, sub-national integrated assessment model tailored to Canada's energy transition. Designed to follow FAIR principles, the model enables spatially explicit and policy-relevant analysis of decarbonization pathways, addressing longstanding gaps in transparency and regional granularity in Canadian energy modeling. Through the scenario analysis, the study provides new evidence on how supply-side systems must transform to align with national Net Zero targets, highlighting critical regional and sectoral dynamics.\\u003c/p\\u003e\\u003cp\\u003eThe results demonstrate that achieving Net Zero emissions by 2050 requires a fundamental reallocation of investments from fossil-based infrastructure toward clean electricity, energy storage, and low-carbon hydrogen. Cumulative energy supply investment under the Net Zero scenario is comparable to the Legislated pathway, indicating that decarbonization is financially viable if capital is redirected early and strategically. Fossil fuel extraction declines by approximately 65% nationally, with the sharpest reductions in Alberta, Saskatchewan, Newfoundland and Labrador, and British Columbia. In contrast, provinces with abundant renewable resources, such as Quebec, Manitoba, and British Columbia, emerge as critical nodes for electricity and hydrogen supply. At the same time, Ontario sees significant investment growth driven by electrification and infrastructure expansion.\\u003c/p\\u003e\\u003cp\\u003eElectricity generation under Net Zero increases by over 6,600 PJ relative to the current policy case, with wind and solar contributing the most significant gains. At the same time, natural gas and biomass without carbon capture and storage (CCS) decline significantly, and nuclear remains broadly stable. These changes reflect a significant shift toward variable renewable supply and underscore the need for enhanced system flexibility, enabled through investments in storage, smart grids, and interprovincial transmission. Hydrogen production also plays a central role in decarbonizing industry and transport, with a shift from unabated fossil hydrogen toward electricity- and CCS-based production. Electrolytic hydrogen expands nearly 230-fold by 2050, and new substantial output emerges in provinces such as Newfoundland and Labrador, Manitoba, and British Columbia.\\u003c/p\\u003e\\u003cp\\u003eThese findings also underscore the importance of regionally tailored policy responses. Policymakers in Alberta must prioritize economic diversification and phase down oil and gas extraction, while simultaneously supporting hydrogen deployment with CCS and electrification of industrial demand. In Ontario, grid modernization, flexibility investment, and targeted electrification are essential to manage rising electricity demand. Quebec and Manitoba are well-positioned to lead in green hydrogen production, leveraging their hydroelectric capacity to support domestic decarbonization and potential export markets. British Columbia should continue to scale wind power and electricity storage while developing integrated clean energy hubs. Saskatchewan and Newfoundland and Labrador face deeper structural transitions and will require focused support for clean energy alternatives, bioenergy development, and labor market transitions.\\u003c/p\\u003e\\u003cp\\u003eAt the federal level, the results support the urgent need for a national just transition strategy that aligns the federal Net Zero target with provincial realities. This includes targeted financial support, regulatory alignment, and mechanisms for infrastructure coordination across provinces. System planners and utilities should invest in interprovincial transmission, long-duration storage, and other flexibility-enabling infrastructure to manage spatial and temporal variability. For investors and technology developers, the results identify clear opportunities in provinces undergoing rapid transformation\\u0026mdash;particularly in electricity, hydrogen, and carbon capture technologies.\\u003c/p\\u003e\\u003cp\\u003eIn sum, the Net Zero scenario illustrates that Canada's transition to Net Zero is not only technically feasible but can be achieved without significantly higher investment than under current policies\\u0026mdash;provided that strategic, early-stage decisions are made to reorient capital flows and system planning. MESSAGEix-Canada delivers an open, extensible platform to support this transition, enabling co-learning and collaborative decision-making among policymakers, researchers, utilities, and civil society. Future developments will further enhance its capacity by integrating macroeconomic modeling, short-term power system reliability, and trade dynamics. As a public resource, MESSAGEix-Canada is poised to serve as a cornerstone of evidence-based energy and climate policy in Canada, helping to translate long-term ambition into regionally grounded, actionable pathways.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cp\\u003eWe have developed MESSAGEix-Canada, an open-source, sub-national integrated assessment model specifically tailored to capture Canada\\u0026rsquo;s energy system dynamics across provinces and territories. The MESSAGEix-Canada model is an engineering-economic optimization framework built on the MESSAGEix framework (Huppmann et al., \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e), a linear programming (LP) formulation designed for strategic energy planning and integrated assessment of energy-engineering-economy-environment (E4) systems. This model facilitates long-term energy system optimization by incorporating future projections and constraints to determine cost-effective pathways for achieving energy and climate objectives. The objective function minimizes the total discounted system costs, encompassing energy supply, transformation, and demand, while also integrating emissions costs and temporal-spatial constraints to explore different policy scenarios.\\u003c/p\\u003e\\u003cp\\u003eThe mathematical formulation of MESSAGEix-Canada is implemented in GAMS, while scenario definition, input data processing, and result analysis are conducted through Python-based API packages. The model structure and solution methodologies align with the MESSAGEix framework (Huppmann et al., \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). The adaptation of MESSAGEix to the Canadian context is maintained in a version-controlled Gitlab repository to ensure modularity and transparency.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eReference Energy System\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eMESSAGEix-Canada provides a structured representation of the Canadian energy system by integrating energy supply, conversion, and end-use consumption across all provinces. The model follows an engineering-economic optimization framework that determines cost-effective energy system transitions while incorporating emissions constraints and policy objectives.\\u003c/p\\u003e\\u003cp\\u003eThe reference energy system, as shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e, defines the key components of energy supply and demand captured by the model. The model captures primary energy resources such as fossil fuels, renewables and biomass. Energy conversion technologies, including power generation, refining, hydrogen production, and storage, are explicitly represented to assess the trade-offs between pathways. The model disaggregates energy flows into final consumption sectors, allowing for a detailed evaluation of sector-specific energy demands and how end use technology choices affect upstream sectors.\\u003c/p\\u003e\\u003cp\\u003eFossil fuel extraction technologies are categorized into conventional and unconventional sources, including oil sands, hydraulic fracturing and enhanced recovery. The model accounts for associated emissions from extraction, transportation and refining.\\u003c/p\\u003e\\u003cp\\u003eThe electricity system includes all major power generation technologies. Over 20 thermal power generating technologies are included, capturing various coal, gas and biomass powerplants with and without carbon capture and storage. For renewables, solar photovoltaic, concentrated solar power, onshore and offshore wind, and hydropower are incorporated accounting for provincial differences in resource availability and accounting for system integration costs.\\u003c/p\\u003e\\u003cp\\u003eThe model represents multiple hydrogen production technologies, including electrolysis, steam methane reforming, biomass gasification, and coal gasification, with or without carbon capture and storage. Hydrogen infrastructure, including liquefaction and transportation, is accounted for to assess the role of hydrogen as part of the broader energy transition. The model determines the share of hydrogen in industrial, transport, and power sector applications based on cost, emissions constraints, and technology competitiveness.\\u003c/p\\u003e\\u003cp\\u003eFinal energy consumption is categorized into residential and commercial, industrial, and transportation sectors, where technology choices and energy efficiency measures are determined endogenously. The model incorporates demand-side efficiency improvements through conservation cost curves, allowing for dynamic shifts in energy consumption patterns across these end use sectors.\\u003c/p\\u003e\\u003cp\\u003eThe following sections provide a detailed overview of the data assumptions and sources used across the reference energy system, covering key aspects of energy supply, conversion, and final consumption.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\n\\u003ch3\\u003eAdaptation to the Canadian Context\\u003c/h3\\u003e\\n\\u003cp\\u003eMESSAGEix-Canada has been developed to reflect the specific energy system characteristics, policy landscape, and economic conditions of Canada. The development process involved integrating key assumptions (e.g., Canadian energy policies), input data (e.g., oil and gas reserves), and techno-economic parameters (e.g., provincial GDP growth), tailored to the Canadian energy sector.\\u003c/p\\u003e\\u003cp\\u003eThe initial adaptation was based on the MESSAGEix-GLOBIOM global integrated assessment model (IAM), specifically utilizing the North American (NAM) region. Energy efficiency parameters, technology cost assumptions, and sectoral transformation constraints were derived from this global framework to construct a preliminary single-node Canadian prototype. This model was subsequently expanded to Canada\\u0026rsquo;s 13 provinces and territories, accounting for each province and territories\\u0026rsquo; existing energy infrastructure, different resource availability and unique import / export potential.\\u003c/p\\u003e\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eCalibration\\u003c/h2\\u003e\\u003cp\\u003eThe calibration process ensures that the model aligns with historical energy balances, sectoral energy trends, and economic drivers in each Canadian province and territory. This step integrates population and GDP projections, energy demand estimation, power sector capacity calibration, and oil and gas production constraints to ensure the model realistically captures Canada\\u0026rsquo;s possible energy transition pathways.\\u003c/p\\u003e\\u003cp\\u003ePopulation and GDP projections were sourced from Statistics Canada and extended using regression-based methods to provide long-term projections up to 2060 (see model documentation for more detail). Figure\\u0026nbsp;3 presents the projected population and GDP growth trajectories at the provincial and territorial levels. Since credible, publicly available GDP projections by province and territory do not exist, GDP projections were scaled from national to sub-regional levels assuming historical provincial GDP shares remain constant into the future.\\u003c/p\\u003e\\u003cp\\u003eEnergy demand estimation was based on a downscaling approach, where energy service demands from the North American region in MESSAGEix-GLOBIOM were allocated to the Canadian context. Historical energy consumption data at the provincial level was used to calibrate demand patterns, ensuring consistency with (Statistics Canada, 2025) energy balances and reports from the (Canada Energy Regulator, \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Future demands are projected by scaling historical final-energy consumption using provincial population and GDP growth rates and then reconciling any year-to-year discrepancies via a minimax Linear Programming (LP) formulation. Specifically, we introduce a variable (M) representing the largest absolute deviation between population-driven and GDP-driven growth rates over the entire projection period and minimize (M), thereby enforcing consistency between these two socioeconomic drivers.\\u003c/p\\u003e\\u003cp\\u003eExisting electricity generation capacity was taken from the Canadian Open Data for Energy Resources (CODERS) dataset, which provides plant-level data on installed capacity, fuel type, and generation by province (Hendriks et al., \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Variable renewable potentials are also taken from CODERS\\u0026rsquo; gridded capacity factor data. Renewable energy was categorized into eight distinct resource bins to represent regional differences in wind, solar, and hydroelectric generation capabilities.\\u003c/p\\u003e\\u003cp\\u003eExisting oil and gas production capacity was calibrated using historical extraction data from the Canada Energy Regulator (Canada Energy Regulator, \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Conventional and unconventional oil and gas reserves were inputted for each province and territory, drawing primarily on data from Natural Resources Canada and the Canada Energy Regulator (see model documentation for more details). The model accounts for upstream emissions from extraction and processing, as well as downstream emissions from refining and final consumption. The calibration process ensures that fossil fuel production pathways are consistent with existing production capacity and available resources.\\u003c/p\\u003e\\u003cp\\u003eThe integration of calibrated energy demand, power generation capacity, and fossil fuel production constraints ensures that MESSAGEix-Canada represents Canada\\u0026rsquo;s energy\\u003c/p\\u003e\\u003cp\\u003esystem dynamics leveraging as much public data as is available. By aligning model assumptions with historical data and applying region-specific constraints, the calibration process establishes a robust analytical foundation for evaluating energy transition pathways, technology deployment strategies, and policy impacts.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eSummary of data used for calibration. All the techno-economic input data and assumptions such as technological efficiencies, lifetime, technology costs are also available in the visualization dashboard and available in the GitLab repository\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"2\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDescription\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eReferences\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePopulation projections for Canada, provinces and territories\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e(Statistics Canada, \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2025d\\u003c/span\\u003e)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eGross domestic product (GDP) by province and territory\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e(Statistics Canada, \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2025e\\u003c/span\\u003e)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHistorical Energy balances (supply and demand of primary and secondary energy)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e(Statistics Canada, \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2025e\\u003c/span\\u003e)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eElectricity generation by type\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e(Hendriks et al., \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Statistics Canada, \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2025b\\u003c/span\\u003e)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eOil, gas, and coal production\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e(Statistics Canada, \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2025a\\u003c/span\\u003e)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eImports and exports of energy\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e(Statistics Canada, \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2025c\\u003c/span\\u003e)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eEmission Factors\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e(IPCC, \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e1996\\u003c/span\\u003e)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eTechnological Change (growth rates, learning rates based on Shared Socioeconomic Pathways (SSP2)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e(International Energy Agency, \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e; Riahi et al., \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eRenewable Potentials and Capacity Factors\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e(Hendriks et al., \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePower Plants Capacity\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e(Hendriks et al., \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\n\\u003ch3\\u003eModularity \\u0026 Scenario Design\\u003c/h3\\u003e\\n\\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003ePolicy Modularity\\u003c/h2\\u003e\\u003cp\\u003eThe model is designed with a modular policy framework that allows for the inclusion of evolving federal and provincial climate policies. Given the dynamic nature of energy and climate policy in Canada, this modular approach ensures that policies can be updated, refined, or replaced as new measures are introduced. Policies are implemented as separate, independently connected modules, which allows for flexibility in scenario development and policy assessment.\\u003c/p\\u003e\\u003cp\\u003eThe modular structure enables policymakers and researchers to activate or modify policy instruments depending on the analysis required. This flexibility is essential for examining the impacts of existing, proposed, and hypothetical policies on Canada\\u0026rsquo;s energy system. Policies are implemented using different mechanisms, including:\\u003c/p\\u003e\\u003cp\\u003e\\u003cul\\u003e\\u003cli\\u003e\\u003cp\\u003eHard constraints on technological activity, such as mandated phaseouts of specific technologies, that are consistent with regulatory commitments.\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003eShare constraints, which enforce minimum or maximum shares of specific energy sources, such as renewable portfolio standards.\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003eInvestment tax credits, which reduce capital costs for technologies benefiting from financial incentives.\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003eCarbon pricing mechanisms, which apply costs to emissions-intensive technologies or a group of technologies and can be conditional on whether a technology exceeds an emissions standard (e.g., in the case of Canada\\u0026rsquo;s Output-Based Pricing System).\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/ul\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eTechnology Modularity\\u003c/h2\\u003e\\u003cp\\u003eAlongside policy modularity, the model incorporates technology modularity, allowing for representation of rapidly integrating emerging energy technologies. This is achieved through a structured template where new technologies can be introduced by defining their techno-economic parameters, including capital and operational costs, efficiency levels, fuel input-output mapping, diffusion constraints, and expected learning rates.\\u003c/p\\u003e\\u003cp\\u003eThe documentation provides a standardized framework for adding new technologies, ensuring that innovations such as advanced nuclear reactors, next-generation energy storage, synthetic fuels, or emerging carbon capture techniques can be incorporated and assessed within the model. This capability enables policymakers and stakeholders in their understanding of how new technologies could shape Canada\\u0026rsquo;s long-term energy transition and evaluate their role under different policy and market conditions.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eScenario Design\\u003c/h2\\u003e\\u003cp\\u003eThe model includes a range of federal and provincial policies that shape Canada's energy transition. These policies can be easily turned on and off in the model, allowing for detailed policy analysis.\\u003c/p\\u003e\\u003cp\\u003eCanada\\u0026rsquo;s most consequential climate policy has been projected to be industrial pricing policies, which is represented in MESSAGEix-Canada through both provincial systems and the federal Output-Based Pricing System (OBPS) (CCI, \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). The model captures the policy\\u0026rsquo;s cost impact by adding a carbon cost to the variable cost of each covered technology. This is calculated in two steps:\\u003c/p\\u003e\\u003cp\\u003e\\u003col\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003eCalculating the \\u003cem\\u003epayable emissions intensity\\u003c/em\\u003e as the difference between the emission intensity of a given technology in MESSAGEix-Canada and the corresponding standard defined in the industrial pricing regulations.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003eMultiplying the payable emission intensity by the applicable carbon credit price.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003c/ol\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe applied carbon costs from industrial pricing to each covered technology vary across provinces due to the differences in the output-based standards and tightening rates that each province has adopted.\\u003c/p\\u003e\\u003cp\\u003eMESSAGEix-Canada\\u0026rsquo;s industrial pricing implementation employs a simplified approach to credit allocation; since revenues are not explicitly represented in MESSAGEix-Canada, credits are accounted for using a negative payable emission intensity (same calculation as the payable emissions intensity mentioned above), which reduces the cost per unit of energy produced for technologies with an emission intensity lower than the OBPS standard.\\u003c/p\\u003e\\u003cp\\u003eTo find a reasonable equilibrium between the supply and demand of OBPS credits, the model tracks both payable and receivable emissions across technologies and regions. If imbalances arise, model iterations are conducted by increasing or decreasing the assumed credit price until a rough balance is achieved. This iterative approach ensures that the representation of industrial pricing within MESSAGEix-Canada remains realistic while leaving flexibility to account for credit banking and use of offsets.\\u003c/p\\u003e\\u003cp\\u003eFor the provincial policies, the industrial pricing implementation applies the carbon intensity and carbon prices based on provincial definitions of output-based standards and tightening rates.\\u003c/p\\u003e\\u003cp\\u003eThree scenarios have been designed to validate and calibrate the model: a No Policy scenario, Legislated and a balanced Net Zero pathway. These scenarios serve as a basis for model validation and policy assessment, providing insights into how Canada\\u0026rsquo;s energy system responds to existing policies and long-term Net Zero targets. In future analyses, the model will explore additional sensitivities and specific policy questions. Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e summarizes key assumptions in both scenarios.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eOverview of scenario architecture used in the analysis.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"3\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eScenario Design\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eLegislated\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eNet Zero\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eIncludes the major implemented energy policies and regulations in Canada.\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003ePathway scenarios which include a trajectory aligned with national and provincial Net Zero emissions commitments by 2050. The two Net Zero scenarios include different demand and lifestyle assumption.\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eCarbon pricing via the Output-Based Pricing System (OBPS)\\u003c/b\\u003e, which applies an aggregate cost to emissions exceeding intensity limits for each technology and commodity.\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eX\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eX\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003ePhased coal phase-out by 2030\\u003c/b\\u003e, except for facilities with carbon capture and storage (CCS).\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eX\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eX\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eInvestment tax credits\\u003c/b\\u003e, reflecting federal commitments to incentivize clean energy and industrial decarbonization.\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eX\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eX\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eClean Electricity Regulations reflecting \\u003cb\\u003ethe emission intensity standards one electricity genration technologies\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eX\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eX\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eNet Zero emissions constrained at national level\\u003c/b\\u003e, allowing provincial pathways to optimize their net zero pathways consistent with the Canada wide targets.\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eX\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eStakeholder Validation\\u003c/h2\\u003e\\u003cp\\u003eThe MESSAGEix-Canada model was developed following FAIR scientific principles, ensuring that all model components are \\u003cem\\u003eFindable, Accessible, Interoperable\\u003c/em\\u003e, and \\u003cem\\u003eReusable\\u003c/em\\u003e. The full model codebase, input datasets, and scenario assumptions are version-controlled and publicly available in an open-source repository to facilitate reproducibility, peer review, and collaborative extension.\\u003c/p\\u003e\\u003cp\\u003eTo validate the modeling framework and scenario logic, we engaged with Canadian energy and climate policy stakeholders through the Energy Modelling Hub\\u0026rsquo;s national model comparison initiative. As part of this exercise, MESSAGEix-Canada scenario results were presented and discussed in a multi-model forum alongside outputs from Navius, ECCC, and Canada Energy Regulator models. Stakeholder feedback from these sessions was used to refine policy representations, improve transparency, and benchmark outputs\\u0026mdash;ensuring policy relevance and analytical credibility.\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\u003cp\\u003eM.A. led the model development, scenario analysis, manuscript writing, and figure preparation. D.A. and M.M. contributed to the conceptualization of the analysis and provided critical review and feedback on the manuscript. M.M. acquired funding and provided overall supervision. All authors reviewed and approved the final version of the manuscript.\\u003c/p\\u003e\\u003ch2\\u003eAcknowledgement\\u003c/h2\\u003e\\u003cp\\u003eThe authors would like to acknowledge Brendan Danaher, and the software development team of the Sustainable Energy Systems Integration \\u0026amp; Transition (SESIT) Research Group at the University of Victoria for their continuous support in maintaining and improving the model choices and software infrastructure. Their efforts in ensuring compliance with best practices for open-source development have been instrumental in enhancing the model's usability and transparency.We also gratefully acknowledge funding support from the Open Insights Project, which has contributed to the development and refinement of this research.\\u003c/p\\u003e\\u003ch2\\u003eData Availability\\u003c/h2\\u003e\\u003cp\\u003eThe input data and model code used in this study are publicly available at https://gitlab.com/sesit/message-ix. Comprehensive documentation, including model structure, scenario definitions, and implementation guidelines, can be accessed at https://sesit.gitlab.io/message-ix/. Additionally, the model results and input datasets can be visualized through an interactive visualization platform at https://message.sesit.ca/, providing stakeholders with a transparent and user-friendly interface for exploring scenario outputs and energy system pathways.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eCanada Energy Regulator. (2023). \\u003cem\\u003eCanada\\u0026rsquo;s Energy Future 2023: Energy Supply and Demand Projections to 2050\\u003c/em\\u003e. https://www.cer-rec.gc.ca/en/data-analysis/canada-energy-future/2023/canada-energy-futures-2023.pdf\\u003c/li\\u003e\\n\\u003cli\\u003eCCI. (2024). \\u003cem\\u003eIndustrial carbon pricing the top driver of emissions reductions, new analysis shows - Canadian Climate Institute\\u003c/em\\u003e. https://climateinstitute.ca/news/industrial-carbon-pricing-the-top-driver-of-emissions-reductions-new-analysis-shows/\\u003c/li\\u003e\\n\\u003cli\\u003eHendriks, R. , Monroe, J. , Cusi, T. , Ahnaf, A. , Aldana, D. , Griffiths, K. , Dorman, T. , Chhina, A. , \\u0026amp; McPherson M. (2023). \\u003cem\\u003eCanadian Open-Source Database for Energy Research and Systems-Modelling (CODERS)\\u003c/em\\u003e (1.0). Energy Modelling Hub (EMH). https://coders.cme-emh.ca/\\u003c/li\\u003e\\n\\u003cli\\u003eHuppmann, D., Gidden, M., Fricko, O., Kolp, P., Orthofer, C., Pimmer, M., Kushin, N., Vinca, A., Mastrucci, A., Riahi, K., \\u0026amp; Krey, V. (2019). The MESSAGEix Integrated Assessment Model and the ix modeling platform (ixmp): An open framework for integrated and cross-cutting analysis of energy, climate, the environment, and sustainable development. \\u003cem\\u003eEnvironmental Modelling and Software\\u003c/em\\u003e, 143\\u0026ndash;156. https://doi.org/10.1016/j.envsoft.2018.11.012\\u003c/li\\u003e\\n\\u003cli\\u003eIEA. (2022). \\u003cem\\u003eCanada 2022 \\u0026ndash; Analysis\\u003c/em\\u003e. https://www.iea.org/reports/canada-2022\\u003c/li\\u003e\\n\\u003cli\\u003eIEA. (2023). \\u003cem\\u003eIEA, Greenhouse Gas Emissions from Energy Highlights\\u003c/em\\u003e.\\u003c/li\\u003e\\n\\u003cli\\u003eInternational Energy Agency. (2014). \\u003cem\\u003eWorld Energy Outlook 2014\\u003c/em\\u003e. http://www.worldenergyoutlook.org/weo2014/\\u003c/li\\u003e\\n\\u003cli\\u003eIPCC. (1996). \\u003cem\\u003eRevised 1996 IPCC Guidelines for National Greenhouse Gas Inventories: The Workbook (Volume 2)\\u003c/em\\u003e. http://www.ipcc-nggip.iges.or.jp/public/gl/invs5a.html\\u003c/li\\u003e\\n\\u003cli\\u003eKuling, K., Barnes, T., Shivakumar, A., Brinkerink, M., \\u0026amp; Niet, T. (2022). Applying the open-source climate, land, energy, and water systems (CLEWs) model to Canada. \\u003cem\\u003eEnergy Strategy Reviews\\u003c/em\\u003e, \\u003cem\\u003e44\\u003c/em\\u003e, 100929. https://doi.org/https://doi.org/10.1016/j.esr.2022.100929\\u003c/li\\u003e\\n\\u003cli\\u003eMcPherson, M., Monroe, J., Jurasz, J., Rowe, A., Hendriks, R., Stanislaw, L., Awais, M., Seatle, M., Xu, R., Crownshaw, T., Miri, M., Aldana, D., Esfahlani, M., Arjmand, R., Saffari, M., Cusi, T., Toor, K. S., \\u0026amp; Grieco, J. (2022). Open-source modelling infrastructure: Building decarbonization capacity in Canada. \\u003cem\\u003eEnergy Strategy Reviews\\u003c/em\\u003e, \\u003cem\\u003e44\\u003c/em\\u003e, 100961. https://doi.org/10.1016/J.ESR.2022.100961\\u003c/li\\u003e\\n\\u003cli\\u003eNZAB. (2023). \\u003cem\\u003eCompete and Succeed in a Net Zero Future\\u003c/em\\u003e. https://cdn.prod.website-files.com/64ef3fd141170da059cb6d80/65172c0363ff6814d9e13c2f_e2d538e427a10f032f50859e216a2293_NZAB_2022_Annual_Report_Final_-_EN-corr.pdf\\u003c/li\\u003e\\n\\u003cli\\u003ePlazas-Ni\\u0026ntilde;o, F. A., Ortiz-Pimiento, N. R., \\u0026amp; Montes-P\\u0026aacute;ez, E. G. (2022). National energy system optimization modelling for decarbonization pathways analysis: A systematic literature review. \\u003cem\\u003eRenewable and Sustainable Energy Reviews\\u003c/em\\u003e, \\u003cem\\u003e162\\u003c/em\\u003e, 112406. https://doi.org/10.1016/J.RSER.2022.112406\\u003c/li\\u003e\\n\\u003cli\\u003eRhodes, E., Hoyle, A., McPherson, M., \\u0026amp; Craig, K. (2022). Understanding climate policy projections: A scoping review of energy-economy models in Canada. \\u003cem\\u003eRenewable and Sustainable Energy Reviews\\u003c/em\\u003e, \\u003cem\\u003e153\\u003c/em\\u003e, 111739. https://doi.org/10.1016/J.RSER.2021.111739\\u003c/li\\u003e\\n\\u003cli\\u003eRiahi, K., Dentener, F., Gielen, D., Grubler, A., Jewell, J., Klimont, Z., Krey, V., McCollum, D., Pachauri, S., Rao, S., van Ruijven, B., van Vuuren, D. P., \\u0026amp; Wilson, C. (2012). Chapter 17 - Energy Pathways for Sustainable Development. In \\u003cem\\u003eGlobal Energy Assessment - Toward a Sustainable Future\\u003c/em\\u003e (pp. 1203\\u0026ndash;1306). Cambridge University Press and International Institute for Applied Systems Analysis. http://www.globalenergyassessment.org\\u003c/li\\u003e\\n\\u003cli\\u003eStatistics Canada. (2025a). \\u003cem\\u003eCrude oil and equivalent production, monthly\\u003c/em\\u003e.\\u003c/li\\u003e\\n\\u003cli\\u003eStatistics Canada. (2025b). \\u003cem\\u003eElectric power generation, monthly generation by type of electricity\\u003c/em\\u003e.\\u003c/li\\u003e\\n\\u003cli\\u003eStatistics Canada. (2025c). \\u003cem\\u003eImports and exports of energy, annual\\u003c/em\\u003e.\\u003c/li\\u003e\\n\\u003cli\\u003eStatistics Canada. (2025d). \\u003cem\\u003ePopulation Projections for Canada, Provinces and Territories, 2024 to 2074\\u003c/em\\u003e (Issues 91-520\\u0026ndash;X). https://www150.statcan.gc.ca/n1/en/catalogue/91-520-X\\u003c/li\\u003e\\n\\u003cli\\u003eStatistics Canada. (2025e). \\u003cem\\u003eSupply and demand of primary and secondary energy in terajoules, annual\\u003c/em\\u003e.\\u003c/li\\u003e\\n\\u003cli\\u003eWilkinson, M. D., Dumontier, M., Aalbersberg, Ij. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J. W., da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., \\u0026hellip; Mons, B. (2016). The FAIR Guiding Principles for scientific data management and stewardship. \\u003cem\\u003eScientific Data 2016 3:1\\u003c/em\\u003e, \\u003cem\\u003e3\\u003c/em\\u003e(1), 1\\u0026ndash;9. https://doi.org/10.1038/sdata.2016.18\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"npj-climate-action\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"npjclimataction\",\"sideBox\":\"Learn more about [npj Climate Action](https://www.nature.com/npjclimataction)\",\"snPcode\":\"44168\",\"submissionUrl\":\"https://submission.springernature.com/new-submission/44168/3\",\"title\":\"npj Climate Action\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"NPJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7378724/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7378724/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eAmid growing climate risks and energy security challenges, Canada's path to Net Zero emissions by 2050 hinges on regionally differentiated transformations across its energy system. This study presents a detailed scenario-based analysis using MESSAGEix-Canada, the country's first open-source, sub-national integrated assessment model. We explore how energy system transitions evolve across provinces and sectors under varying policy pathways.\\u003c/p\\u003e\\u003cp\\u003eResults from the Net Zero scenario indicate a 65% reduction in fossil fuel extraction, an eight-fold increase in electricity supply, and a tenfold growth in low-emissions hydrogen, achieved without significantly increasing total energy system investments relative to the Legislated pathway. Instead, capital shifts away from oil and gas production toward renewables, storage, and grid expansion. Electrification of end-use sectors, alongside carbon capture and clean hydrogen deployment, drives emissions reductions. Spatial analysis reveals Alberta, Saskatchewan, and Newfoundland and Labrador face steep structural changes in resource extraction, while provinces like Ontario and Quebec become hubs of electrification and clean energy infrastructure.\\u003c/p\\u003e\\u003cp\\u003eThe analysis highlights that achieving Net Zero is technically feasible, but demands urgent, coordinated, and province-specific strategies. Policymakers in resource-intensive provinces must plan for a managed fossil phase-out and support economic diversification. In contrast, electricity-rich provinces must scale transmission and hydrogen capacity to meet cross-sector demand. MESSAGEix-Canada provides a transparent and flexible platform to co-design such transitions with stakeholders\\u0026mdash;supporting policy alignment, investment targeting, and just transition planning within Canada's federated climate governance landscape.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Decarbonization Pathways for Canada’s Federated Energy System Using a Subnational Integrated Assessment Model\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-09-03 11:18:27\",\"doi\":\"10.21203/rs.3.rs-7378724/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2025-11-12T08:32:35+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-10-10T15:06:21+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"134634154233157507968147874355958107409\",\"date\":\"2025-09-15T07:51:21+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"172479739264094477019620128001190586895\",\"date\":\"2025-09-02T15:21:18+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-08-27T09:28:31+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2025-08-26T08:57:23+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2025-08-21T18:26:26+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"npj Climate Action\",\"date\":\"2025-08-15T05:59:09+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"npj-climate-action\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"npjclimataction\",\"sideBox\":\"Learn more about [npj Climate Action](https://www.nature.com/npjclimataction)\",\"snPcode\":\"44168\",\"submissionUrl\":\"https://submission.springernature.com/new-submission/44168/3\",\"title\":\"npj Climate Action\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"NPJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"69fe152b-fb4a-469f-8202-ca92958c83bc\",\"owner\":[],\"postedDate\":\"September 3rd, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[{\"id\":54080403,\"name\":\"Earth and environmental sciences/Climate sciences\"},{\"id\":54080404,\"name\":\"Scientific community and society/Energy and society\"},{\"id\":54080405,\"name\":\"Physical sciences/Energy science and technology\"},{\"id\":54080406,\"name\":\"Earth and environmental sciences/Environmental social sciences\"}],\"tags\":[],\"updatedAt\":\"2026-02-16T11:25:46+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-09-03 11:18:27\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7378724\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7378724\",\"identity\":\"rs-7378724\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}