Cost-optimal vs. policy-driven scenarios for a decarbonised European energy system

Cost-optimal vs. policy-driven scenarios for a decarbonised European energy system | 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 Research Article Cost-optimal vs. policy-driven scenarios for a decarbonised European energy system Natasha Frilingou, Dirk-Jan Van de Ven, Jon Sampedro, Alexandre Torné, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7847096/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The European Union’s (EU) climate strategy—anchored in the European Green Deal, the Fit-for-55 package, and updated National Energy and Climate Plans (NECPs)—requires a rapid transformation of the energy system to meet the legally binding target of net-zero greenhouse gas emissions by 2050 and a 55% reduction by 2030 relative to 1990 levels. Yet, how national plans align with EU-wide ambition, alongside the implications for investment, infrastructure, and power-system operation, remain insufficiently assessed. We address this gap by linking an EU-specific implementation of a prominent integrated assessment model with Member State-level disaggregation (GCAM-Europe) with a higher-resolution European electricity system model (EXPANSE). This modelling framework captures national heterogeneity, sectoral detail, and spatiotemporal variability in electricity demand, renewable supply, storage, and grid constraints. We analyse four scenarios representing EU-wide (Fit-for-55) or national (NECP) targets, implemented through either explicit policies ( POLICY ) or cost-optimal carbon caps ( FREE ). Results show that all scenarios achieve the − 55% fossil CO2 reduction by 2030, with the electricity sector driving the largest chunk. Renewable energy nearly doubles, with POLICY scenarios accelerating electrification and heat pump deployment, while FREE scenarios lean more on biofuels. Efficiency targets are only partially met, with POLICY scenarios distributing savings more evenly across Member States compared to concentrated reductions ( FREE ). By 2035, power system transformation diverges strongly: FREE scenarios expand about 1,160 GW of new capacity, concentrated in a few resource-rich regions, while POLICY scenarios reach around 1,680 GW with broader spatial distribution, requiring higher overall investment in renewables and grids. Average wholesale electricity prices are higher and more heterogeneous under POLICY scenarios, reflecting carbon costs, transmission bottlenecks, and reliance on fossil backup. These results highlight trade-offs between economic efficiency and equitable burden-sharing, underscoring the importance of coordinated EU governance, infrastructure planning, and complementary policies to balance cost-effectiveness with political feasibility and social acceptance. Environmental Policy Integrated assessment modelling electricity system modelling national energy and climate plans Fit for 55 European climate policy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction The European Union’s (EU) transition to a low-carbon economy requires balancing collective ambition and national priorities. The European Green Deal was introduced in December 2019 to outline the goal towards climate neutrality by 2050 [ 1 ], followed by the European Climate Law in 2021 [ 2 ] that made the EU’s commitment to net-zero greenhouse gas (GHG) emissions by 2050 and a 55% reduction by 2030, compared to 1990 levels, legally binding. Then came the adoption of the ‘Fit-for-55’ package in 2023: a comprehensive set of legislative measures inter alia including the EU Emissions Trading System (ETS) Directive and the Effort Sharing Regulation (ESR), as well as the Renewable Energy Directive (RED), the Energy Efficiency Directive (EED), and the Land Use, Land Use Change and Forestry (LULUCF) Regulation, altogether intended to enable achieving the bloc’s 2030 climate target [ 3 ]. The policy documents detailing how Member States should individually contribute to this target are the National Energy and Climate Plans (NECPs) [ 4 ], which were first introduced in 2019 and since updated to align with the more ambitious EU targets: the draft updates were published in 2023 and, following an EU-wide and country-specific assessment, most revisions—expected within 2024—were delayed to 2025. By May 2025, 24 final NECPs had been submitted, alongside a new EU-wide assessment (excluding Estonia, Belgium, and Poland) that found the EU is on track to its 2030 emissions target and could achieve an increase of the renewable energy share in final energy consumption to at least 42.5% with the aspiration to achieve 45% [ 5 ]. An ambition gap, however, seemingly remains, particularly for the 2030 energy efficiency objectives for both primary and final energy use, and, to a limited extent, to renewable penetration. Overall, the implied rate of CO 2 emissions cuts is unprecedented and challenging [ 6 ]. Several studies have modelled the implications of NECP or Fit-for-55 targets. Some components of NECPs have been quantitatively assessed for Greece (energy system [ 7 ] and power sector [ 8 ]), Ukraine (power sector) [ 9 ], the UK (heating sector [ 10 ]), Spain (energy system [ 11 ]), and Hungary (power sector [ 12 , 13 ]), while EU-wide assessments have focused on sufficiency [ 12 , 13 ], divergence from reference scenarios [ 14 ], and European power markets implications [ 15 ]. The Fit-for-55 policy package, in turn, with considerations of individual policies and sometimes net-zero targets, have been applied for specific sectors [ 16 – 18 ], single-country case studies [ 19 – 22 ] or at the EU level [ 23 – 25 ], although most studies have focused on parts of the policy package, such as EU-ETS prices [ 26 , 27 ] or renewable electricity generation requirements [ 28 – 31 ], with very few EU-wide takes of the Fit-for-55 across all Member States individually (e.g., [ 32 ]). Critically what is missing in the literature is a comprehensive quantification and comparison of the bottom-up trajectories implied in all individual NECPs with the top-down goals of Fit-for-55, and thus a solid understanding of how each Member State contributes to the collective responsibility, their sectoral burden-sharing, and the implications for equitable allocations among Member States. This can largely be attributed to existing operational capacities to enable such an assessment: few modelling tools feature the geographic disaggregation and technological detail required to sufficiently represent the diversity of Member States and their strategies as well as the sectoral transformations throughout the economy. Integrated assessment models (IAMs) have been widely criticised for their inadequate spatial granularity and sectoral resolution [ 33 ], oversimplification of policy measures, limitations tied onto least cost-optimisation and idealised framework conditions that fail to reflect real-world complexities [ 34 , 35 ]. Regional aggregations, typically found in whole-system global or European models such as IAMs, tend to obscure national heterogeneity in energy infrastructure, governance, resource availability, and socioeconomic conditions [ 36 ]. On the other hand, despite their higher sectoral and policy detail, national energy models typically lack the ability to account for cross-border interactions, regional market integration, and EU-wide spillover effects—all critical factors in assessing EU-wide policy packages like Fit-for-55 or the coherence of NECPs [ 37 ]. Moreover, despite the importance of electrification and sector coupling in mitigation scenarios [ 38 ], much like transformations in other sectors of the economy [ 38 ], IAMs typically fail to offer the deep dive in the electricity sector (e.g., a detailed representation of transmission networks [ 39 ]) that is necessary to effectively support energy transition and climate policy development [ 40 , 41 ]. The EU power sector remains a major source of the bloc’s emissions, accounting for 19% of each CO 2 emissions in 2023 [ 42 ], and is expected to be the first sector to decarbonise towards allowing the required flexibility for other sectors on the way to net-zero by 2050 [ 43 ]. Accurately reflecting the power sector transformation over long time horizons, and the variability of renewable energy, which are strongly dependent on national- and local-scale grid systems [ 44 ], is therefore crucial for a robust assessment of investment needs. These limitations constrain the usefulness of IAMs alone for evaluating how national planning aligns with collective EU targets, or analysing infrastructure needs, such as cross-border electricity flows and shared resource constraints, and as such there is emerging work connecting IAMs with detailed electricity models [ 45 , 46 ]. Our study fills these critical gaps by validating the individual plans vis-à-vis the aggregated policy goals at an EU-wide level in an integrated manner and quantifying broader energy- and electricity-system implications of achieving the bloc’s 2030 targets, using the newly developed GCAM-Europe model [ 47 ] and coupling it with the EXPANSE electricity model [ 48 ]. GCAM-Europe is an expansion of the Global Change Analysis Model (GCAM) [ 49 ], a leading and widely established IAM, with Member State-level granularity and significantly improved spatial resolution, electricity flows, and load segmentation that allows accurately capturing temporal variation in electricity demand and supply. Unlike most current-generation IAMs, which operate at higher geographic levels [ 50 ], GCAM-Europe can reflect the national differences in energy systems, emissions profiles, resource availability, and policy ambitions, allowing for better targeted, policy-relevant, and actionable insights at the country level, without overlooking global developments potentially impacting EU paths. EXPANSE [ 43 , 51 ], on the other hand, is a highly-resolved electricity system model that captures regional electricity demand, generation, storage, and transmission dynamics at the level of 296 NUTS-2 regions in Europe, as well as grid constraints. Building on previous attempts to establishing soft links between IAMs and spatially-detailed electricity system models towards bridging the temporal resolution gap [ 52 – 55 ] that hinders the accurate estimation the optimal level of variable renewable generation [ 56 ], we couple the two models, eventually allowing to derive in detail the energy- and electricity-system transformations required to achieve the Fit-For-55 and NECP targets at both scales. 2. Methodology 2.1. Model framework 2.1.1. GCAM-Europe GCAM-Europe[ 47 ] is an expansion of the Global Change Analysis Model (GCAM), a well-reputed IAM widely used in global scenario analysis [ 49 ]. The model is designed to analyse alternative “what-if” type scenarios within a single computational platform. GCAM includes technology-rich representations of the economy, energy sector, land use, and water linked to a global climate model that can be used to explore climate change mitigation policies, including carbon taxes, carbon trading, regulations and accelerated deployment of energy technology. Regional population and labour productivity growth assumptions drive the energy, land-use, and water systems employing numerous technology options to produce, transform, and provide energy services, as well as to produce agricultural and forest products and to determine land use and land cover. A full description of the GCAM model can be found in its online documentation [ 57 ]. GCAM-Europe[ 58 ] features high-detail geographical disaggregation for the European continent. While core GCAM divides the European continent in five regions (EU-12, EU-15, Europe Eastern, Europe-non-EU, and European Free Trade Association), in GCAM-Europe all European countries are disaggregated into individual model regions ( Figure S1 ). GCAM-Europe replaces the default international data sources for newly defined European countries with Europe-specific datasets wherever possible, prioritising sources such as Eurostat for energy statistics. In cases where country-level data are unavailable in these European sources, GCAM-Europe defaults to broader international datasets, primarily the International Energy Agency. The model enhances sectoral and technological detail, especially in buildings, by adding new end-use categories (e.g., hot water, cooking, appliances) and integrating technologies such as heat pumps. Residential demand is further disaggregated by consumer groups using country-level data. In industry, GCAM-Europe incorporates a European Single Market to simulate intra-EU trade in selected goods. The electricity sector is represented as an integrated grid with regions, load segments, and storage, aligned with ENTSO-E’s National Trends scenario[ 59 ] for temporal resolution. This structure captures electricity flows, flexibility, and price variations across time and regions, supporting analysis of storage and cross-regional trade. In this study, we use GCAM-Europe to project scenario-specific changes in the energy mix and key prices, as its level of detail enables us to explore the country-level effects of European policy packages, without overlooking efforts—and their potential spillover implications—at the global level and in non-energy sectors. 2.1.2. EXPANSE EXPANSE is a spatially explicit, technology-rich, cost optimisation model of the European electricity system in 2035 [ 48 , 53 , 60 ]. It includes 33 countries (EU without Cyprus and Malta, plus Albania, Bosnia and Herzegovina, Montenegro, North Macedonia, Norway, Serbia, Switzerland, and the UK) and considers electricity demand, generation, storage, and transmission. The model operates at a spatially explicit level, with 296 NUTS-2 regions for electricity generation and 128 grid nodes for demand, storage, and transmission [ 48 ]. EXPANSE has been used to assess regional benefits and vulnerabilities linked to the transition of the European electricity sector [ 43 ], investigate air pollution and health co-benefits of low-carbon electricity supply scenarios in the region [ 61 ], analyse employment and skill implications[ 51 ] as well as options for reducing reliance on Russian gas together with other models [ 53 ], and evaluate the regional interdependencies of cost-effectively implementing European targets for renewable electricity and GHG emissions [ 48 ]. Its technology portfolio covers a broad range of electricity generation options, including conventional sources, such as coal- and gas-fired and nuclear power plants, as well as renewable technologies like hydropower, solar photovoltaics (PV), onshore and offshore wind turbines, biogas and woody biomass, along with supporting storage (pumped hydropower storage, batteries, and power-to-hydrogen). In this study, EXPANSE minimises total system costs, including annualised investment costs and marginal costs of electricity generation, storage, and transmission infrastructure, to find optimal regional capacities in 2035. Investment and operation and maintenance cost assumptions stem from long-term estimates for 2035 and include changes in costs from technological learning. Fossil fuel price, as well as carbon prices modelled as a fuel tax come from GCAM-Europe. A weighted average cost of capital (WACC) of 3% is assumed for all technologies and regions. Input assumptions of EXPANSE and its mathematical formulation are available in[ 43 ] and in the Supplementary Materials . Moreover, EXPANSE uses electricity demand, fuel and CO2 prices, and GHG and renewable electricity constraints from GCAM-Europe, instead of previous assumptions. 2.1.3. Model linking Coupling of IAMs with power system models could provide a novel way to explore multiple interacting decision scales that no single model could capture on its own [ 62 ]. Here, for each scenario, we run GCAM-Europe in five-year steps from 2015 to 2050, generating outputs such as country-level electricity demand, required emission reductions, and renewable electricity shares consistent with EU or national targets. These results are used as (maximum or minimum) constraints for EXPANSE, which is used to quantify the configuration of the EU electricity system in 2035, specifically in terms of generation, storage, and transmission capacity needs and investments, and electricity prices. Carbon prices are taken from GCAM-Europe and used as a fuel tax in EXPANSE. As an optimisation model solving for least-cost system configuration, and in certain cases diverging from GCAM-Europe constraints, EXPANSE sometimes pursues more ambitious outcomes, when these are found more cost-efficient. Contrary to GCAM-Europe, EXPANSE does not include negative emissions technologies; meeting EU targets would, therefore, require the CCS levels provided by GCAM-Europe on top of the electricity system configuration described by EXPANSE. EXPANSE assumes frozen nuclear capacity everywhere except for complete phaseout in Belgium and Germany, at least partial phaseout in Switzerland, and possible expansion of nuclear power (by order from most to least) in Poland, the UK, Finland, Bulgaria, France, Slovakia, and Romania. Inputs, outputs, and linkages between GCAM-Europe and EXPANSE are illustrated in Fig. 1 . 2.2 NECP analysis and scenario design We analysed 27 NECPs, 24 of which are final and 3 (Belgium, Estonia, and Poland) draft versions at the time of our analysis, to extract individual national targets on emissions, renewable energy shares in final energy consumption, energy efficiency, as well as stated coal phaseout policies, before verifying the extracted targets with NECP data from Ember [ 63 ]. When available, emissions projections with additional measures (WAM) provided in the NECPs are used for validation; these reflect expected outcomes of policy measures, including those under discussion with high likelihood of adoption after the NECP submission. For missing WAM projections, projections with existing measures (WEM) provided in the NECPs are used instead (see Table S1) . We then explored four scenarios (Table 1 ), each representing a different trajectory toward meeting the EU’s climate objectives. Scenarios are classified as NECP or FF55 depending on whether climate targets are applied at the Member State or EU-wide level. They are further distinguished as POLICY or FREE , depending on whether they follow explicit policy or cost-optimal pathways. Up to 2030, all scenarios reflect the energy taxation on electricity, natural gas, and transport fuels, remaining constant at observed 2024 levels. Post-2030, policies are held constant, while an economy-wide carbon price is applied to drive progress towards net-zero by 2050 (or earlier, where national targets require). Together, these scenarios represent a range of plausible policy narratives that consider and compare prescriptive regulatory approaches at both scales (i.e., national vs EU-wide) and then contrast them with more flexible, least-cost mitigation strategies (i.e., specific policies vs economy-wide carbon caps). While all scenarios are calibrated to achieve the same total reduction in fossil CO 2 emissions at the EU level by 2030, they differ significantly in how mitigation responsibilities are distributed across sectors and Member States. To ensure consistency with official projections, population and GDP assumptions are harmonised with the 2024 edition of the EU Ageing Report [ 64 ] reflecting expected demographic and economic trends across Member States. To isolate the relative price effects of climate policy, all scenarios incorporate existing Member State-specific energy taxes (on electricity, gas, and transport fuels) as of 2024 and fix them through to 2030. Regions outside the EU are assumed to follow their latest Nationally Determined Contributions (NDCs) and long-term targets. The revamped EU-ETS regulates power generation and energy-intensive industries through a declining emissions cap, while ETS2 is introduced for buildings and road transport, reaching a carbon price of €45/tCO 2 (2020 prices) by 2030. Vehicle emissions standards are assumed to tighten progressively, culminating in near-zero emissions from new vehicles by 2035 in line with the EU regulation of internal combustion engine phaseout. Table 1 Description of the policy representation in modelled scenarios with Member-state (pink) or Aggregated EU-wide implementation (blue). Scenarios Description National-level policy implementation NECP_ POLICY Up to 2030, this scenario reflects the implementation of explicit EU-wide policies (ETS, vehicle emission standards), and country-specific coal phaseout, energy efficiency and renewable energy targets as defined in the NECPs. Post-2030, a carbon cap constrains net CO 2 emissions linearly towards zero in 2050, assuming unchanged LULUCF emissions and removals. NECP_ FREE Member States achieve the same level of fossil CO 2 emissions cuts as in the NECP_POLICY scenario through an economy-wide carbon cap, allowing the model to select the cost-optimal pathway for reducing emissions, rather than imposing explicit policies. Post-2030, a carbon cap constrains net CO 2 emissions linearly towards zero in 2050, assuming unchanged LULUCF emissions and removals. EU-level policy implementation FF55_ POLICY Up to 2030, this scenario reflects the implementation of explicit EU-wide policies (ETS, vehicle emission standards). Instead of applying country-specific targets, EU-wide targets in line with Fit-For-55 are applied for renewables (42.5% of final energy) and energy efficiency (11.7% reduction in final energy w.r.t. 2020), allowing the model to optimally allocate efforts for these targets across Member States. Post-2030, a carbon cap constrains net CO 2 emissions linearly towards zero in 2050, assuming unchanged LULUCF emissions and removals. FF55_ FREE The EU achieves the same level of fossil CO 2 emissions cuts as in the FF55_POLICY scenario through an economy-wide carbon cap, allowing the model to select the cost-optimal pathway for reducing emissions. Post-2030, a carbon cap constrains net CO 2 emission linearly towards zero in 2050, assuming unchanged LULUCF emissions and removals. 3. Results 3.1. Fossil CO 2 emissions Explicit national and EU-wide climate policies, represented by the NECP_POLICY and FF55_POLICY scenarios respectively, are projected to significantly reduce CO 2 emissions by 2030 (Fig. 2 ). GCAM-Europe results show that fossil CO 2 emissions across the EU-27 decline steeply from 2015 to 2030 in both policy and cost-optimal pathways, with the electricity sector delivering the largest chunk of emissions reductions, followed by significant decreases in buildings and transport (Fig. 2 A). All four scenarios achieve emissions reductions of over 1 GtCO 2 by 2030 compared to 2015 levels and reach the − 55% target with only minor differences between the POLICY and FREE variants. The additional constraints of explicit policy-driven scenarios yield slightly different sectoral contributions compared to the cost-optimal scenarios, mainly in higher reductions in electricity rather than transport. By 2050, emissions in all scenarios are reduced by ~ 94% compared to 1990; the electricity sector approaches carbon neutrality, having the highest share of emission cuts from 2030 onwards in cost-optimal scenarios. Industrial emissions in the EU are projected to fall by over 130% in 2050, requiring deep transformation of industrial processes and widespread deployment of CCS. Results also highlight how reductions are shared across Member States (Fig. 2 B): although big emitters (Germany, France) drive the bulk of absolute emissions declines, ambitious transformations relative to baselines (in percentage terms) are spearheaded by smaller economies (Portugal, Greece). Overall, results illustrate that the EU’s Fit-for-55 and updated NECP targets are broadly consistent with cost-optimal decarbonisation. Emissions reductions under NECP scenarios are also broadly consistent with WAM projections ( Figure S2 ), validating the credibility of our modelling framework. 3.2. Energy efficiency All policy scenarios lead to a marked decline in final energy consumption by 2030, primarily through reduced use of fossil fuels (Fig. 3 A). Driven by the EU’s binding 11.7% energy efficiency target, POLICY scenarios achieve the lowest energy consumption, with Germany, France, Italy, the Netherlands and Spain contributing the most to these savings (Fig. 3 B); however, they do not reach the 763 Mtoe EED target, at best dropping to 831 Mtoe in the NECP_POLICY scenario. Cost-optimal scenarios ( FREE ), instead, result in nearly half the efficiency gains compared to the POLICY ones (Fig. 3 B), hinting that planned policies are pushing beyond what is least-cost from a modelling perspective, especially in countries with higher energy inefficiency ambitions. In those scenarios, the model reduces energy efficiency contributions of higher-cost regions, primarily Germany, France, and the Balkans (Croatia, Bulgaria, and Hungary). Overall, policy-driven scenarios lead to a broader distribution of energy efficiency gains across Member States, extending well beyond the largest economies, while cost-optimal scenarios concentrate reductions in a few major emitters. 3.3. Renewable energy Renewable energy use across the EU nearly doubles by 2030 under all scenarios, with electricity, solid biomass, and biofuels leading the growth (Fig. 4 A). The FF55_POLICY scenario nearly meets the 42.5% renewable target share, and even cost-optimal variants approach similar levels (~ 40%), demonstrating that renewable energy targets are consistent with cost-effective pathways. While all scenarios nearly double renewable energy use by 2030, fuel mixes differ: POLICY scenarios frontload electrification and heat pump uptake, while FREE scenarios rely more on biofuels. Germany and France are the largest contributors to this expansion, supported by strong uptake in Spain, the Netherlands, Sweden, Greece, and Poland (Fig. 4 B). Results confirm that both EU and national policies are effectively accelerating the transition to renewables, while allowing flexibility for countries to leverage their unique domestic resources and cost advantages. 3.4. Power system investments and new capacities Investment needs in power system technologies vary significantly across cost-optimal or policy-driven scenarios (Fig. 5 ). The FF55_POLICY scenario shows the highest investment requirements, suggesting additional annualised renewable investment of €90 billion primarily for new onshore (€48 billion) and offshore (€23 billion) wind generation capacity, due to stringent EU-wide carbon pricing and renewable energy targets, which promote capital-intensive deployment of new generation capacity. The NECP_POLICY scenario follows, with slightly lower albeit substantial annualised renewable investments reaching €85 billion driven by national-level constraints. In contrast, the cost-optimised FREE scenarios reach similar emissions reductions with far lower investments at €49 billion/year, avoiding prescriptive efficiency and renewable targets and allowing generation and infrastructure deployment where most cost-effective. In terms of infrastructure, all scenarios require similar investments in storage and transmission, but POLICY scenarios show substantial increase in generation (about + 70%) as binding policy constraints trigger earlier and more widespread deployment of renewable technologies regardless of cost efficiency. Cost-optimal pathways minimise overall investment generation needs by exploiting the cheapest options geographically across the EU, while real-world policies reflect broader participation and faster scaling, thereby increasing total system costs from an EU system-wide perspective. By 2035, Europe’s electricity system would need to undergo a major transformation in response to climate policy goals, with new capacity additions dominated by wind and solar PV (Fig. 6 ). Offshore wind expands primarily in the North Sea, solar in Southern Europe, and onshore wind across Central and Eastern Europe, while the deployment of battery storage and hydrogen-based electricity storage are particularly prominent in western and northern Member States. High-voltage transmission expansion plays a central role in linking renewable-rich regions with demand centres, underlining the importance of coordinated planning between generation and grid infrastructure to support system flexibility and integration. In the FREE scenarios, new capacities reach ~ 1,160 GW and concentrated in a limited number of locations, reflecting a purely cost-optimal allocation. By contrast, the POLICY scenarios reach ~ 1,680 GW of capacity, distributed across a wider set of nodes throughout Europe. Based on official EU/ENTSO-E projections, the total installed capacity across technologies in the EU by 2035 is expected to reach approximately 1,800–2,200 GW, depending on scenario and assumptions, which comes close to our POLICY results [ 65 ]. This policy-driven diversification leads to a more geographically balanced system transformation compared to the more centralised pattern in the FREE scenarios. With those capacities in place, the model simulates market operations, which shape the system dispatch, i.e., how technologies are used across time to meet demand (see Figure S2 ). In turn, dispatch outcomes influence the average locational marginal prices (LMPs), since LMPs are determined by marginal generation costs, network constraints, and temporal flexibility [ 66 ]. The large scale of changes in the composition of the grids across European countries by 2035, and in particular the increase in renewable capacity in the scale of 550 GW for the FREE scenarios and 950 GW for the POLICY scenarios, could result in different electricity pricing dynamics from the past [ 15 ]. Here, regional electricity prices are calculated endogenously by EXPANSE and refer to annual averages of locational marginal prices (LMPs) (Fig. 7 ). The POLICY scenarios result in higher and more spatially varied LMPs, especially in Central and Southern Europe, due to high CO 2 prices that are part of the marginal cost of dispatchable fossil fuel-based generation, which determine the market price in many hours of the year when they are still needed, the reliance on this generation during low renewable output periods, and the merit-order effect. In contrast, the FREE scenarios produce lower and more uniform LMPs, particularly in Northern and Eastern Europe, where the absence of CO 2 prices, still abundant renewables, and fewer constraints reduce marginal generation costs. These results highlight that while CO 2 prices act as a decarbonisation signal, they can also raise electricity prices under POLICY scenarios due to the market-clearing process. 4. Discussion and conclusion By examining Fit for 55 vis-à-vis NECP targets using appropriate modelling capacity with Member State granularity and deep dive into the power sector, our study fills an important gap in the EU climate policy research literature. Using an IAM (GCAM-Europe) soft-linked with an electricity system model (EXPANSE), we demonstrated that both EU-wide and Member State-level climate policy targets, as defined in the Fit-for-55 package and the updated NECPs, can lead to deep reductions in CO 2 emissions in the European power sector by 2030. Emissions outcomes under both NECP_POLICY and FF55_POLICY scenarios converge with their cost-optimal counterparts ( NECP_FREE and FF55_FREE ), confirming that current policies are largely in line with efficient mitigation trajectories. Fossil CO 2 emissions in the EU-27 could decline from 3,166 MtCO 2 in 2015 to ~ 1,800 MtCO 2 in 2030, depending on the scenario, corresponding to a reduction of roughly 44%. In terms of energy efficiency, our scenarios underscore the tension between politically binding efficiency targets and system-wide cost efficiency. POLICY scenarios more than double the efficiency gains and distribute them across a much wider set of Member States, reducing energy demand substantially. However, they still fall short of the EU’s EED target, with NECPs altogether coming the closest, and FREE scenarios performing even worse and creating uneven burden-sharing, with the cheapest opportunities (e.g., Nordics, France) bearing most of the burden. This indicates that efficiency improvements may require deliberate policy levers going beyond what ‘least-cost’. Convergence of POLICY and FREE scenarios around near-doubling of renewable energy use by 2030 indicates that renewables are no longer mere policy ambition today but a cost-efficient path to decarbonisation. The variation lies less in whether renewables expand and more in how the mix evolves, as POLICY scenarios accelerate electrification and heat pumps, while cost-optimal pathways lean more heavily on biofuels, highlighting that renewables are a non-regret option for the EU, though the choice of technology mix reflects trade-offs between policy priorities (e.g., faster electrification) and economic efficiency (e.g., reliance on biofuels). From an economic efficiency perspective, the FREE scenarios demonstrate that the EU can achieve comparable emissions reductions with substantially lower investment in the electricity system (annually, roughly €40–45 billion less) when resources are allocated purely on cost optimisation. However, such an approach risks concentration of investments in countries with the highest renewable potentials and cheapest abatement options, potentially sidelining other regions and developing new dependencies for interconnections. Regional electricity prices are higher in POLICY scenarios, as renewables are expanded under binding CO 2 price and policy targets, keeping fossil plants in the margin under high carbon cost. Even with extensive renewable capacity expansions, regions with very high electricity demand and residual reliance on gas and coal still experience the highest prices (e.g., Central and Western Europe). By coupling GCAM-Europe with the spatially and temporally detailed EXPANSE model, we reveal the power system-level implications of reaching these targets. Policy stringency, in the form of binding renewable and efficiency targets, would drive higher overall investment needs, particularly in capital-intensive generation technologies, but also reduce flexibility, as investments are not always made in the lowest-cost locations. Required investments would be driven not only by renewable generation but also by the associated infrastructure—i.e., grid expansion, storage, and hydrogen capacity needed to integrate variable resources and ensure system reliability. Wind and solar seemingly dominate new generation capacity, with large regional variation depending on resource potential. Our findings underscore the need for coordinated infrastructure planning to enable a cost-effective and geographically balanced transition, as well as the added cost and complexity of meeting policy-driven targets when compared to purely cost-optimal pathways, an important consideration for EU institutions aiming to balance ambition, equity, and feasibility in their long-term climate strategies. EU decision-making must carefully navigate the balance between economic efficiency and equitable burden-sharing to sustain political legitimacy, the trade-offs between which are highlighted in our exercise. Whether Fit-for-55- or NECP-driven, POLICY scenarios impose binding targets, yielding significantly higher investment requirements, reflecting the push toward capital-intensive technologies and increases in electricity prices in Central and Southern Europe due to system constraints and reliance on expensive dispatchable generation. In contrast, the FREE scenarios achieve similar emissions reductions with substantially lower annual investment needs in the electricity sector, since resources are allocated cost-optimally by concentrating generation in resource-rich regions without prescriptive technology or efficiency constraints. However, the EU cannot directly mandate where specific technologies should be deployed; decisions must be agreed by the European Parliament and the Council. This points to the need for a stronger governance framework, potentially through an enhanced Energy Union strategy, to better integrate electricity markets and align national choices with collective EU-level objectives Higher locational marginal prices in POLICY scenarios, particularly in Central and Western Europe, underline that the transition may not automatically deliver lower consumer costs, even with higher diffusion of renewables. Carbon prices, reliance on fossil backup, and transmission bottlenecks can keep average prices high. Without revenue recycling, targeted subsidies, or compensation mechanisms, such disparities risk undermining social acceptance of the transition. Grid planning, flexibility mechanisms, and long-term funding instruments must be scaled up to ensure secure, affordable, and equitable transitions. Future policy design must consider these spatial and temporal dynamics, reinforcing the value of coupling long-term system planning with short-term flexibility and targeted national support. Although cost-optimal scenarios can, in principle, achieve comparable decarbonisation outcomes at lower system costs and electricity prices, real-world energy transitions rarely align with idealised optimisation outcomes due to political, institutional, and socioeconomic constraints. Therefore, current policies may require recalibration or complementary instruments to steer investment effectively and reduce prices, despite the associated political and regulatory complexity. Declarations Acknowledgements Authors acknowledge support from the Horizon Europe European Commission Projects IAM COMPACT (grant no. 101056306, including funding from the Swiss State Secretariat for Education, Research, and Innovation), DIAMOND (grant no. 101081179), ACCLIMATE (grant no. 101184374), and PRISMA (grant no.101081604, including funding from the Swiss State Secretariat for Education, Research, and Innovation). The computations at the University of Geneva were performed using Baobab High Performance Computing service. The views and opinions expressed in this paper are those of the authors alone. Data availability statement The datasets generated during, and analysed in, this study are available in Zenodo (DOI 10.5281/zenodo.17258601). GCAM-Europe-7.2 is available for download https://zenodo.org/records/15655568 and maintained in the open-access online repository https://github.com/bc3LC-GCAMEurope/gcam-core. EXPANSE is detailed in an open-access online platform https://iamparis.eu/detailed_model_doc/26. The code to reproduce the figures is publicly available in GitHub https://github.com/NatasjaF/FF55_NECP. Author contribution N.F., D.-J.v.d.V., A.T., and S.M. contributed to the research design. N.F., D.-J.v.d.V., A.T., and J.S. developed the analysis, with contribution of all authors. N.F. wrote the first draft of the paper, and all authors contributed to writing the paper. A.N., E.T., and K.K. notably contributed to the review and editing. Conflict of interests The authors declare no competing interests. References European Commission. COM/2019/640 final: The European Green Deal 2019. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=COM%3A2019%3A640%3AFIN (accessed July 1, 2025). European Parliament. 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08:55:43","extension":"html","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":165493,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7847096/v1/ef9a8c08e9e215e5c05b537a.html"},{"id":93474696,"identity":"be6eeb5f-ba67-4946-892e-f80c03004c64","added_by":"auto","created_at":"2025-10-14 08:55:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":120324,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eModel linking\u003c/strong\u003e. Inputs, outputs, and connectionsbetween the integrated assessment model GCAM-Europe and the electricity system model EXPANSE.\u003c/p\u003e","description":"","filename":"Figure1Modellinking.png","url":"https://assets-eu.researchsquare.com/files/rs-7847096/v1/032213c25c6e9eb412cfdca8.png"},{"id":93474700,"identity":"66fe41a1-aaa2-41af-ad9d-67a8e350afe0","added_by":"auto","created_at":"2025-10-14 08:55:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":865230,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCO\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2 \u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003eemissions [MTCO\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e] by sector, region, and scenario in GCAM-Europe\u003c/strong\u003e. Panel A shows fossil CO\u003csub\u003e2\u003c/sub\u003e emissions at the EU level by sector and scenario, while the panel B shows the contribution to fossil CO\u003csub\u003e2\u003c/sub\u003e emission reduction in 2030 with regards to 2015 by EU Member State and scenario.\u003c/p\u003e","description":"","filename":"Figure2Emissions.png","url":"https://assets-eu.researchsquare.com/files/rs-7847096/v1/6e4bc6f3d151e1aea3e9490a.png"},{"id":93474973,"identity":"519e32c2-4731-4aeb-97dd-c8cb28917850","added_by":"auto","created_at":"2025-10-14 09:03:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":811524,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFinal energy [Mtoe] by fuel, scenario, and Member State in GCAM-Europe\u003c/strong\u003e. Panel A shows the final energy by fuel and scenario at the EU level in 2015, 2030, and 2050, while panel B shows the contribution to energy efficiency in 2030 with regards to 2015 by EU Member State and scenario.\u003c/p\u003e","description":"","filename":"Figure3Efficiency.png","url":"https://assets-eu.researchsquare.com/files/rs-7847096/v1/7d8ddcdd6e756bb7814098c6.png"},{"id":93474975,"identity":"7e3176be-905f-4bcb-bf09-c8fdcecdec0a","added_by":"auto","created_at":"2025-10-14 09:03:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":953718,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRenewable final energy [Mtoe, %] by fuel, scenario and Member State in GCAM-Europe\u003c/strong\u003e. Panel A shows renewable final energy use by fuel (left axis) as well as the share of renewables in final energy (right axis) in 2015, 2030, and 2050 at the EU level while panel B shows the contribution towards renewable energy use in 2030 with regards to 2015 by EU Member State and scenario.\u003c/p\u003e","description":"","filename":"Figure4Renewables.png","url":"https://assets-eu.researchsquare.com/files/rs-7847096/v1/41d2eaa20f391f9888cf144e.png"},{"id":93474706,"identity":"0baf2c32-d434-483d-94c1-ab727a9ab739","added_by":"auto","created_at":"2025-10-14 08:55:42","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":471412,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePower system investments [BEUR/year] from EXPANSE. \u003c/strong\u003ePanel A shows new power system investments in 2035 across technologies and scenarios while panel B shows\u003cstrong\u003e \u003c/strong\u003enew power system investments in 2035 across grid infrastructure and scenarios.\u003c/p\u003e","description":"","filename":"Figure5Investments.png","url":"https://assets-eu.researchsquare.com/files/rs-7847096/v1/f13f9f356863fd93479f1883.png"},{"id":93474980,"identity":"41266d3f-dbbb-4aa6-90e2-9e763dc5c444","added_by":"auto","created_at":"2025-10-14 09:03:42","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3797162,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNew generation, storage, and transmission capacities [GW] from EXPANSE\u003c/strong\u003e. Pies across all scenarios show new generation and storage capacities in 2035 as a difference from 2018; the colours represent different technologies while the sizes of the pies represent the amount of GW added. The lines show new transmission capacities in high voltage DC (green) or AC (pink) in 2035 as a difference from 2018, while the size of the lines represents the amount of GW added.\u003c/p\u003e","description":"","filename":"Figure6Newcapacities.png","url":"https://assets-eu.researchsquare.com/files/rs-7847096/v1/ad8601b6e1f5201510686c5f.png"},{"id":93476284,"identity":"82b9d3b9-88d8-4265-bf65-e1a91bc03f96","added_by":"auto","created_at":"2025-10-14 09:11:42","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2206116,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAverage wholesale locational marginal price (LMP) [EUR/MWh] from EXPANSE. \u003c/strong\u003eThe\u003cstrong\u003e \u003c/strong\u003eprices are shows in 296 sub-national regions (NUTS-2) across all scenarios in 2035 and account for CO\u003csub\u003e2\u003c/sub\u003e prices.\u003c/p\u003e","description":"","filename":"Figure7Averagewholesalelocationalmarginalprices.png","url":"https://assets-eu.researchsquare.com/files/rs-7847096/v1/dcdcf161faa16220d7048c25.png"},{"id":93477569,"identity":"791e61b7-23e5-48da-b364-bc63ee234c8f","added_by":"auto","created_at":"2025-10-14 09:28:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9692436,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7847096/v1/6d5b22a9-fb5a-4c0b-92f2-14043e300d16.pdf"},{"id":93476543,"identity":"60ce9540-f693-43df-b24d-4c9260aac552","added_by":"auto","created_at":"2025-10-14 09:19:42","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":722729,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-7847096/v1/80c9d6c3ca3498a3c607e9f6.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eCost-optimal vs. policy-driven scenarios for a decarbonised European energy system\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe European Union\u0026rsquo;s (EU) transition to a low-carbon economy requires balancing collective ambition and national priorities. The European Green Deal was introduced in December 2019 to outline the goal towards climate neutrality by 2050 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], followed by the European Climate Law in 2021 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] that made the EU\u0026rsquo;s commitment to net-zero greenhouse gas (GHG) emissions by 2050 and a 55% reduction by 2030, compared to 1990 levels, legally binding. Then came the adoption of the \u0026lsquo;Fit-for-55\u0026rsquo; package in 2023: a comprehensive set of legislative measures inter alia including the EU Emissions Trading System (ETS) Directive and the Effort Sharing Regulation (ESR), as well as the Renewable Energy Directive (RED), the Energy Efficiency Directive (EED), and the Land Use, Land Use Change and Forestry (LULUCF) Regulation, altogether intended to enable achieving the bloc\u0026rsquo;s 2030 climate target [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The policy documents detailing how Member States should individually contribute to this target are the National Energy and Climate Plans (NECPs) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], which were first introduced in 2019 and since updated to align with the more ambitious EU targets: the draft updates were published in 2023 and, following an EU-wide and country-specific assessment, most revisions\u0026mdash;expected within 2024\u0026mdash;were delayed to 2025. By May 2025, 24 final NECPs had been submitted, alongside a new EU-wide assessment (excluding Estonia, Belgium, and Poland) that found the EU is on track to its 2030 emissions target and could achieve an increase of the renewable energy share in final energy consumption to at least 42.5% with the aspiration to achieve 45% [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. An ambition gap, however, seemingly remains, particularly for the 2030 energy efficiency objectives for both primary and final energy use, and, to a limited extent, to renewable penetration. Overall, the implied rate of CO\u003csub\u003e2\u003c/sub\u003e emissions cuts is unprecedented and challenging [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSeveral studies have modelled the implications of NECP or Fit-for-55 targets. Some components of NECPs have been quantitatively assessed for Greece (energy system [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and power sector [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]), Ukraine (power sector) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], the UK (heating sector [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]), Spain (energy system [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]), and Hungary (power sector [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]), while EU-wide assessments have focused on sufficiency [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], divergence from reference scenarios [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and European power markets implications [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The Fit-for-55 policy package, in turn, with considerations of individual policies and sometimes net-zero targets, have been applied for specific sectors [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], single-country case studies [\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] or at the EU level [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], although most studies have focused on parts of the policy package, such as EU-ETS prices [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] or renewable electricity generation requirements [\u003cspan additionalcitationids=\"CR29 CR30\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], with very few EU-wide takes of the Fit-for-55 across all Member States individually (e.g., [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]). Critically what is missing in the literature is a comprehensive quantification and comparison of the bottom-up trajectories implied in all individual NECPs with the top-down goals of Fit-for-55, and thus a solid understanding of how each Member State contributes to the collective responsibility, their sectoral burden-sharing, and the implications for equitable allocations among Member States.\u003c/p\u003e\u003cp\u003eThis can largely be attributed to existing operational capacities to enable such an assessment: few modelling tools feature the geographic disaggregation and technological detail required to sufficiently represent the diversity of Member States and their strategies as well as the sectoral transformations throughout the economy. Integrated assessment models (IAMs) have been widely criticised for their inadequate spatial granularity and sectoral resolution [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], oversimplification of policy measures, limitations tied onto least cost-optimisation and idealised framework conditions that fail to reflect real-world complexities [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Regional aggregations, typically found in whole-system global or European models such as IAMs, tend to obscure national heterogeneity in energy infrastructure, governance, resource availability, and socioeconomic conditions [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. On the other hand, despite their higher sectoral and policy detail, national energy models typically lack the ability to account for cross-border interactions, regional market integration, and EU-wide spillover effects\u0026mdash;all critical factors in assessing EU-wide policy packages like Fit-for-55 or the coherence of NECPs [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMoreover, despite the importance of electrification and sector coupling in mitigation scenarios [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], much like transformations in other sectors of the economy [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], IAMs typically fail to offer the deep dive in the electricity sector (e.g., a detailed representation of transmission networks [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]) that is necessary to effectively support energy transition and climate policy development [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The EU power sector remains a major source of the bloc\u0026rsquo;s emissions, accounting for 19% of each CO\u003csub\u003e2\u003c/sub\u003e emissions in 2023 [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], and is expected to be the first sector to decarbonise towards allowing the required flexibility for other sectors on the way to net-zero by 2050 [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Accurately reflecting the power sector transformation over long time horizons, and the variability of renewable energy, which are strongly dependent on national- and local-scale grid systems [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], is therefore crucial for a robust assessment of investment needs. These limitations constrain the usefulness of IAMs alone for evaluating how national planning aligns with collective EU targets, or analysing infrastructure needs, such as cross-border electricity flows and shared resource constraints, and as such there is emerging work connecting IAMs with detailed electricity models [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOur study fills these critical gaps by validating the individual plans vis-\u0026agrave;-vis the aggregated policy goals at an EU-wide level in an integrated manner and quantifying broader energy- and electricity-system implications of achieving the bloc\u0026rsquo;s 2030 targets, using the newly developed GCAM-Europe model [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] and coupling it with the EXPANSE electricity model [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. GCAM-Europe is an expansion of the Global Change Analysis Model (GCAM) [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], a leading and widely established IAM, with Member State-level granularity and significantly improved spatial resolution, electricity flows, and load segmentation that allows accurately capturing temporal variation in electricity demand and supply. Unlike most current-generation IAMs, which operate at higher geographic levels [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], GCAM-Europe can reflect the national differences in energy systems, emissions profiles, resource availability, and policy ambitions, allowing for better targeted, policy-relevant, and actionable insights at the country level, without overlooking global developments potentially impacting EU paths. EXPANSE [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], on the other hand, is a highly-resolved electricity system model that captures regional electricity demand, generation, storage, and transmission dynamics at the level of 296 NUTS-2 regions in Europe, as well as grid constraints. Building on previous attempts to establishing soft links between IAMs and spatially-detailed electricity system models towards bridging the temporal resolution gap [\u003cspan additionalcitationids=\"CR53 CR54\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] that hinders the accurate estimation the optimal level of variable renewable generation [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], we couple the two models, eventually allowing to derive in detail the energy- and electricity-system transformations required to achieve the Fit-For-55 and NECP targets at both scales.\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Model framework\u003c/h2\u003e\u003cdiv id=\"Sec4\" class=\"Section3\"\u003e\u003ch2\u003e2.1.1. GCAM-Europe\u003c/h2\u003e\u003cp\u003eGCAM-Europe[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] is an expansion of the Global Change Analysis Model (GCAM), a well-reputed IAM widely used in global scenario analysis [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. The model is designed to analyse alternative \u0026ldquo;what-if\u0026rdquo; type scenarios within a single computational platform. GCAM includes technology-rich representations of the economy, energy sector, land use, and water linked to a global climate model that can be used to explore climate change mitigation policies, including carbon taxes, carbon trading, regulations and accelerated deployment of energy technology. Regional population and labour productivity growth assumptions drive the energy, land-use, and water systems employing numerous technology options to produce, transform, and provide energy services, as well as to produce agricultural and forest products and to determine land use and land cover. A full description of the GCAM model can be found in its online documentation [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. GCAM-Europe[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e] features high-detail geographical disaggregation for the European continent. While core GCAM divides the European continent in five regions (EU-12, EU-15, Europe Eastern, Europe-non-EU, and European Free Trade Association), in GCAM-Europe all European countries are disaggregated into individual model regions (\u003cb\u003eFigure S1\u003c/b\u003e). GCAM-Europe replaces the default international data sources for newly defined European countries with Europe-specific datasets wherever possible, prioritising sources such as Eurostat for energy statistics. In cases where country-level data are unavailable in these European sources, GCAM-Europe defaults to broader international datasets, primarily the International Energy Agency. The model enhances sectoral and technological detail, especially in buildings, by adding new end-use categories (e.g., hot water, cooking, appliances) and integrating technologies such as heat pumps. Residential demand is further disaggregated by consumer groups using country-level data. In industry, GCAM-Europe incorporates a European Single Market to simulate intra-EU trade in selected goods. The electricity sector is represented as an integrated grid with regions, load segments, and storage, aligned with ENTSO-E\u0026rsquo;s National Trends scenario[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e] for temporal resolution. This structure captures electricity flows, flexibility, and price variations across time and regions, supporting analysis of storage and cross-regional trade. In this study, we use GCAM-Europe to project scenario-specific changes in the energy mix and key prices, as its level of detail enables us to explore the country-level effects of European policy packages, without overlooking efforts\u0026mdash;and their potential spillover implications\u0026mdash;at the global level and in non-energy sectors.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.1.2. EXPANSE\u003c/h2\u003e\u003cp\u003eEXPANSE is a spatially explicit, technology-rich, cost optimisation model of the European electricity system in 2035 [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. It includes 33 countries (EU without Cyprus and Malta, plus Albania, Bosnia and Herzegovina, Montenegro, North Macedonia, Norway, Serbia, Switzerland, and the UK) and considers electricity demand, generation, storage, and transmission. The model operates at a spatially explicit level, with 296 NUTS-2 regions for electricity generation and 128 grid nodes for demand, storage, and transmission [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. EXPANSE has been used to assess regional benefits and vulnerabilities linked to the transition of the European electricity sector [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], investigate air pollution and health co-benefits of low-carbon electricity supply scenarios in the region [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e], analyse employment and skill implications[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] as well as options for reducing reliance on Russian gas together with other models [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], and evaluate the regional interdependencies of cost-effectively implementing European targets for renewable electricity and GHG emissions [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Its technology portfolio covers a broad range of electricity generation options, including conventional sources, such as coal- and gas-fired and nuclear power plants, as well as renewable technologies like hydropower, solar photovoltaics (PV), onshore and offshore wind turbines, biogas and woody biomass, along with supporting storage (pumped hydropower storage, batteries, and power-to-hydrogen).\u003c/p\u003e\u003cp\u003eIn this study, EXPANSE minimises total system costs, including annualised investment costs and marginal costs of electricity generation, storage, and transmission infrastructure, to find optimal regional capacities in 2035. Investment and operation and maintenance cost assumptions stem from long-term estimates for 2035 and include changes in costs from technological learning. Fossil fuel price, as well as carbon prices modelled as a fuel tax come from GCAM-Europe. A weighted average cost of capital (WACC) of 3% is assumed for all technologies and regions. Input assumptions of EXPANSE and its mathematical formulation are available in[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] and in the \u003cb\u003eSupplementary Materials\u003c/b\u003e. Moreover, EXPANSE uses electricity demand, fuel and CO2 prices, and GHG and renewable electricity constraints from GCAM-Europe, instead of previous assumptions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.1.3. Model linking\u003c/h2\u003e\u003cp\u003eCoupling of IAMs with power system models could provide a novel way to explore multiple interacting decision scales that no single model could capture on its own [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Here, for each scenario, we run GCAM-Europe in five-year steps from 2015 to 2050, generating outputs such as country-level electricity demand, required emission reductions, and renewable electricity shares consistent with EU or national targets. These results are used as (maximum or minimum) constraints for EXPANSE, which is used to quantify the configuration of the EU electricity system in 2035, specifically in terms of generation, storage, and transmission capacity needs and investments, and electricity prices. Carbon prices are taken from GCAM-Europe and used as a fuel tax in EXPANSE. As an optimisation model solving for least-cost system configuration, and in certain cases diverging from GCAM-Europe constraints, EXPANSE sometimes pursues more ambitious outcomes, when these are found more cost-efficient. Contrary to GCAM-Europe, EXPANSE does not include negative emissions technologies; meeting EU targets would, therefore, require the CCS levels provided by GCAM-Europe on top of the electricity system configuration described by EXPANSE. EXPANSE assumes frozen nuclear capacity everywhere except for complete phaseout in Belgium and Germany, at least partial phaseout in Switzerland, and possible expansion of nuclear power (by order from most to least) in Poland, the UK, Finland, Bulgaria, France, Slovakia, and Romania. Inputs, outputs, and linkages between GCAM-Europe and EXPANSE are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.2 NECP analysis and scenario design\u003c/h2\u003e\u003cp\u003eWe analysed 27 NECPs, 24 of which are final and 3 (Belgium, Estonia, and Poland) draft versions at the time of our analysis, to extract individual national targets on emissions, renewable energy shares in final energy consumption, energy efficiency, as well as stated coal phaseout policies, before verifying the extracted targets with NECP data from Ember [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. When available, emissions projections with additional measures (WAM) provided in the NECPs are used for validation; these reflect expected outcomes of policy measures, including those under discussion with high likelihood of adoption after the NECP submission. For missing WAM projections, projections with existing measures (WEM) provided in the NECPs are used instead (see \u003cb\u003eTable S1)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eWe then explored four scenarios (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), each representing a different trajectory toward meeting the EU\u0026rsquo;s climate objectives. Scenarios are classified as \u003cem\u003eNECP\u003c/em\u003e or \u003cem\u003eFF55\u003c/em\u003e depending on whether climate targets are applied at the Member State or EU-wide level. They are further distinguished as \u003cem\u003ePOLICY\u003c/em\u003e or \u003cem\u003eFREE\u003c/em\u003e, depending on whether they follow explicit policy or cost-optimal pathways. Up to 2030, all scenarios reflect the energy taxation on electricity, natural gas, and transport fuels, remaining constant at observed 2024 levels. Post-2030, policies are held constant, while an economy-wide carbon price is applied to drive progress towards net-zero by 2050 (or earlier, where national targets require). Together, these scenarios represent a range of plausible policy narratives that consider and compare prescriptive regulatory approaches at both scales (i.e., national vs EU-wide) and then contrast them with more flexible, least-cost mitigation strategies (i.e., specific policies vs economy-wide carbon caps).\u003c/p\u003e\u003cp\u003eWhile all scenarios are calibrated to achieve the same total reduction in fossil CO\u003csub\u003e2\u003c/sub\u003e emissions at the EU level by 2030, they differ significantly in how mitigation responsibilities are distributed across sectors and Member States. To ensure consistency with official projections, population and GDP assumptions are harmonised with the 2024 edition of the EU Ageing Report [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e] reflecting expected demographic and economic trends across Member States. To isolate the relative price effects of climate policy, all scenarios incorporate existing Member State-specific energy taxes (on electricity, gas, and transport fuels) as of 2024 and fix them through to 2030.\u003c/p\u003e\u003cp\u003eRegions outside the EU are assumed to follow their latest Nationally Determined Contributions (NDCs) and long-term targets. The revamped EU-ETS regulates power generation and energy-intensive industries through a declining emissions cap, while ETS2 is introduced for buildings and road transport, reaching a carbon price of \u0026euro;45/tCO\u003csub\u003e2\u003c/sub\u003e (2020 prices) by 2030. Vehicle emissions standards are assumed to tighten progressively, culminating in near-zero emissions from new vehicles by 2035 in line with the EU regulation of internal combustion engine phaseout.\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\u003eDescription of the policy representation in modelled scenarios with Member-state (pink) or Aggregated EU-wide implementation (blue).\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\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eScenarios\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eNational-level policy implementation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNECP_ POLICY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUp to 2030, this scenario reflects the implementation of explicit EU-wide policies (ETS, vehicle emission standards), and country-specific coal phaseout, energy efficiency and renewable energy targets as defined in the NECPs. Post-2030, a carbon cap constrains net CO\u003csub\u003e2\u003c/sub\u003e emissions linearly towards zero in 2050, assuming unchanged LULUCF emissions and removals.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNECP_ FREE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMember States achieve the same level of fossil CO\u003csub\u003e2\u003c/sub\u003e emissions cuts as in the NECP_POLICY scenario through an economy-wide carbon cap, allowing the model to select the cost-optimal pathway for reducing emissions, rather than imposing explicit policies. Post-2030, a carbon cap constrains net CO\u003csub\u003e2\u003c/sub\u003e emissions linearly towards zero in 2050, assuming unchanged LULUCF emissions and removals.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eEU-level policy implementation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFF55_ POLICY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUp to 2030, this scenario reflects the implementation of explicit EU-wide policies (ETS, vehicle emission standards). Instead of applying country-specific targets, EU-wide targets in line with Fit-For-55 are applied for renewables (42.5% of final energy) and energy efficiency (11.7% reduction in final energy w.r.t. 2020), allowing the model to optimally allocate efforts for these targets across Member States. Post-2030, a carbon cap constrains net CO\u003csub\u003e2\u003c/sub\u003e emissions linearly towards zero in 2050, assuming unchanged LULUCF emissions and removals.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFF55_ FREE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe EU achieves the same level of fossil CO\u003csub\u003e2\u003c/sub\u003e emissions cuts as in the FF55_POLICY scenario through an economy-wide carbon cap, allowing the model to select the cost-optimal pathway for reducing emissions. Post-2030, a carbon cap constrains net CO\u003csub\u003e2\u003c/sub\u003e emission linearly towards zero in 2050, assuming unchanged LULUCF emissions and removals.\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"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Fossil CO\u003csub\u003e2\u003c/sub\u003e emissions\u003c/h2\u003e\u003cp\u003eExplicit national and EU-wide climate policies, represented by the \u003cem\u003eNECP_POLICY\u003c/em\u003e and \u003cem\u003eFF55_POLICY\u003c/em\u003e scenarios respectively, are projected to significantly reduce CO\u003csub\u003e2\u003c/sub\u003e emissions by 2030 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). GCAM-Europe results show that fossil CO\u003csub\u003e2\u003c/sub\u003e emissions across the EU-27 decline steeply from 2015 to 2030 in both policy and cost-optimal pathways, with the electricity sector delivering the largest chunk of emissions reductions, followed by significant decreases in buildings and transport (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). All four scenarios achieve emissions reductions of over 1 GtCO\u003csub\u003e2\u003c/sub\u003e by 2030 compared to 2015 levels and reach the \u0026minus;\u0026thinsp;55% target with only minor differences between the \u003cem\u003ePOLICY\u003c/em\u003e and \u003cem\u003eFREE\u003c/em\u003e variants. The additional constraints of explicit policy-driven scenarios yield slightly different sectoral contributions compared to the cost-optimal scenarios, mainly in higher reductions in electricity rather than transport. By 2050, emissions in all scenarios are reduced by ~\u0026thinsp;94% compared to 1990; the electricity sector approaches carbon neutrality, having the highest share of emission cuts from 2030 onwards in cost-optimal scenarios. Industrial emissions in the EU are projected to fall by over 130% in 2050, requiring deep transformation of industrial processes and widespread deployment of CCS. Results also highlight how reductions are shared across Member States (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB): although big emitters (Germany, France) drive the bulk of absolute emissions declines, ambitious transformations relative to baselines (in percentage terms) are spearheaded by smaller economies (Portugal, Greece). Overall, results illustrate that the EU\u0026rsquo;s Fit-for-55 and updated NECP targets are broadly consistent with cost-optimal decarbonisation. Emissions reductions under NECP scenarios are also broadly consistent with WAM projections (\u003cb\u003eFigure S2\u003c/b\u003e), validating the credibility of our modelling framework.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Energy efficiency\u003c/h2\u003e\u003cp\u003eAll policy scenarios lead to a marked decline in final energy consumption by 2030, primarily through reduced use of fossil fuels (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Driven by the EU\u0026rsquo;s binding 11.7% energy efficiency target, \u003cem\u003ePOLICY\u003c/em\u003e scenarios achieve the lowest energy consumption, with Germany, France, Italy, the Netherlands and Spain contributing the most to these savings (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB); however, they do not reach the 763 Mtoe EED target, at best dropping to 831 Mtoe in the \u003cem\u003eNECP_POLICY\u003c/em\u003e scenario. Cost-optimal scenarios (\u003cem\u003eFREE\u003c/em\u003e), instead, result in nearly half the efficiency gains compared to the \u003cem\u003ePOLICY\u003c/em\u003e ones (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), hinting that planned policies are pushing beyond what is least-cost from a modelling perspective, especially in countries with higher energy inefficiency ambitions. In those scenarios, the model reduces energy efficiency contributions of higher-cost regions, primarily Germany, France, and the Balkans (Croatia, Bulgaria, and Hungary). Overall, policy-driven scenarios lead to a broader distribution of energy efficiency gains across Member States, extending well beyond the largest economies, while cost-optimal scenarios concentrate reductions in a few major emitters.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Renewable energy\u003c/h2\u003e\u003cp\u003eRenewable energy use across the EU nearly doubles by 2030 under all scenarios, with electricity, solid biomass, and biofuels leading the growth (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The \u003cem\u003eFF55_POLICY\u003c/em\u003e scenario nearly meets the 42.5% renewable target share, and even cost-optimal variants approach similar levels (~\u0026thinsp;40%), demonstrating that renewable energy targets are consistent with cost-effective pathways. While all scenarios nearly double renewable energy use by 2030, fuel mixes differ: \u003cem\u003ePOLICY\u003c/em\u003e scenarios frontload electrification and heat pump uptake, while \u003cem\u003eFREE\u003c/em\u003e scenarios rely more on biofuels. Germany and France are the largest contributors to this expansion, supported by strong uptake in Spain, the Netherlands, Sweden, Greece, and Poland (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Results confirm that both EU and national policies are effectively accelerating the transition to renewables, while allowing flexibility for countries to leverage their unique domestic resources and cost advantages.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Power system investments and new capacities\u003c/h2\u003e\u003cp\u003eInvestment needs in power system technologies vary significantly across cost-optimal or policy-driven scenarios (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The \u003cem\u003eFF55_POLICY\u003c/em\u003e scenario shows the highest investment requirements, suggesting additional annualised renewable investment of \u0026euro;90\u0026nbsp;billion primarily for new onshore (\u0026euro;48\u0026nbsp;billion) and offshore (\u0026euro;23\u0026nbsp;billion) wind generation capacity, due to stringent EU-wide carbon pricing and renewable energy targets, which promote capital-intensive deployment of new generation capacity. The \u003cem\u003eNECP_POLICY\u003c/em\u003e scenario follows, with slightly lower albeit substantial annualised renewable investments reaching \u0026euro;85\u0026nbsp;billion driven by national-level constraints. In contrast, the cost-optimised \u003cem\u003eFREE\u003c/em\u003e scenarios reach similar emissions reductions with far lower investments at \u0026euro;49\u0026nbsp;billion/year, avoiding prescriptive efficiency and renewable targets and allowing generation and infrastructure deployment where most cost-effective. In terms of infrastructure, all scenarios require similar investments in storage and transmission, but \u003cem\u003ePOLICY\u003c/em\u003e scenarios show substantial increase in generation (about\u0026thinsp;+\u0026thinsp;70%) as binding policy constraints trigger earlier and more widespread deployment of renewable technologies regardless of cost efficiency. Cost-optimal pathways minimise overall investment generation needs by exploiting the cheapest options geographically across the EU, while real-world policies reflect broader participation and faster scaling, thereby increasing total system costs from an EU system-wide perspective.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBy 2035, Europe\u0026rsquo;s electricity system would need to undergo a major transformation in response to climate policy goals, with new capacity additions dominated by wind and solar PV (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Offshore wind expands primarily in the North Sea, solar in Southern Europe, and onshore wind across Central and Eastern Europe, while the deployment of battery storage and hydrogen-based electricity storage are particularly prominent in western and northern Member States. High-voltage transmission expansion plays a central role in linking renewable-rich regions with demand centres, underlining the importance of coordinated planning between generation and grid infrastructure to support system flexibility and integration. In the \u003cem\u003eFREE\u003c/em\u003e scenarios, new capacities reach\u0026thinsp;~\u0026thinsp;1,160 GW and concentrated in a limited number of locations, reflecting a purely cost-optimal allocation. By contrast, the \u003cem\u003ePOLICY\u003c/em\u003e scenarios reach\u0026thinsp;~\u0026thinsp;1,680 GW of capacity, distributed across a wider set of nodes throughout Europe. Based on official EU/ENTSO-E projections, the total installed capacity across technologies in the EU by 2035 is expected to reach approximately 1,800\u0026ndash;2,200 GW, depending on scenario and assumptions, which comes close to our \u003cem\u003ePOLICY\u003c/em\u003e results [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. This policy-driven diversification leads to a more geographically balanced system transformation compared to the more centralised pattern in the \u003cem\u003eFREE\u003c/em\u003e scenarios.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWith those capacities in place, the model simulates market operations, which shape the system dispatch, i.e., how technologies are used across time to meet demand (see \u003cb\u003eFigure S2\u003c/b\u003e). In turn, dispatch outcomes influence the average locational marginal prices (LMPs), since LMPs are determined by marginal generation costs, network constraints, and temporal flexibility [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe large scale of changes in the composition of the grids across European countries by 2035, and in particular the increase in renewable capacity in the scale of 550 GW for the \u003cem\u003eFREE\u003c/em\u003e scenarios and 950 GW for the \u003cem\u003ePOLICY\u003c/em\u003e scenarios, could result in different electricity pricing dynamics from the past [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Here, regional electricity prices are calculated endogenously by EXPANSE and refer to annual averages of locational marginal prices (LMPs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The \u003cem\u003ePOLICY\u003c/em\u003e scenarios result in higher and more spatially varied LMPs, especially in Central and Southern Europe, due to high CO\u003csub\u003e2\u003c/sub\u003e prices that are part of the marginal cost of dispatchable fossil fuel-based generation, which determine the market price in many hours of the year when they are still needed, the reliance on this generation during low renewable output periods, and the merit-order effect. In contrast, the FREE scenarios produce lower and more uniform LMPs, particularly in Northern and Eastern Europe, where the absence of CO\u003csub\u003e2\u003c/sub\u003e prices, still abundant renewables, and fewer constraints reduce marginal generation costs. These results highlight that while CO\u003csub\u003e2\u003c/sub\u003e prices act as a decarbonisation signal, they can also raise electricity prices under \u003cem\u003ePOLICY\u003c/em\u003e scenarios due to the market-clearing process.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion and conclusion","content":"\u003cp\u003eBy examining Fit for 55 vis-\u0026agrave;-vis NECP targets using appropriate modelling capacity with Member State granularity and deep dive into the power sector, our study fills an important gap in the EU climate policy research literature. Using an IAM (GCAM-Europe) soft-linked with an electricity system model (EXPANSE), we demonstrated that both EU-wide and Member State-level climate policy targets, as defined in the Fit-for-55 package and the updated NECPs, can lead to deep reductions in CO\u003csub\u003e2\u003c/sub\u003e emissions in the European power sector by 2030. Emissions outcomes under both \u003cem\u003eNECP_POLICY\u003c/em\u003e and \u003cem\u003eFF55_POLICY\u003c/em\u003e scenarios converge with their cost-optimal counterparts (\u003cem\u003eNECP_FREE\u003c/em\u003e and \u003cem\u003eFF55_FREE\u003c/em\u003e), confirming that current policies are largely in line with efficient mitigation trajectories. Fossil CO\u003csub\u003e2\u003c/sub\u003e emissions in the EU-27 could decline from 3,166 MtCO\u003csub\u003e2\u003c/sub\u003e in 2015 to ~\u0026thinsp;1,800 MtCO\u003csub\u003e2\u003c/sub\u003e in 2030, depending on the scenario, corresponding to a reduction of roughly 44%.\u003c/p\u003e\u003cp\u003eIn terms of energy efficiency, our scenarios underscore the tension between politically binding efficiency targets and system-wide cost efficiency. \u003cem\u003ePOLICY\u003c/em\u003e scenarios more than double the efficiency gains and distribute them across a much wider set of Member States, reducing energy demand substantially. However, they still fall short of the EU\u0026rsquo;s EED target, with NECPs altogether coming the closest, and \u003cem\u003eFREE\u003c/em\u003e scenarios performing even worse and creating uneven burden-sharing, with the cheapest opportunities (e.g., Nordics, France) bearing most of the burden. This indicates that efficiency improvements may require deliberate policy levers going beyond what \u0026lsquo;least-cost\u0026rsquo;.\u003c/p\u003e\u003cp\u003eConvergence of \u003cem\u003ePOLICY\u003c/em\u003e and \u003cem\u003eFREE\u003c/em\u003e scenarios around near-doubling of renewable energy use by 2030 indicates that renewables are no longer mere policy ambition today but a cost-efficient path to decarbonisation. The variation lies less in whether renewables expand and more in how the mix evolves, as \u003cem\u003ePOLICY\u003c/em\u003e scenarios accelerate electrification and heat pumps, while cost-optimal pathways lean more heavily on biofuels, highlighting that renewables are a non-regret option for the EU, though the choice of technology mix reflects trade-offs between policy priorities (e.g., faster electrification) and economic efficiency (e.g., reliance on biofuels).\u003c/p\u003e\u003cp\u003eFrom an economic efficiency perspective, the \u003cem\u003eFREE\u003c/em\u003e scenarios demonstrate that the EU can achieve comparable emissions reductions with substantially lower investment in the electricity system (annually, roughly \u0026euro;40\u0026ndash;45\u0026nbsp;billion less) when resources are allocated purely on cost optimisation. However, such an approach risks concentration of investments in countries with the highest renewable potentials and cheapest abatement options, potentially sidelining other regions and developing new dependencies for interconnections. Regional electricity prices are higher in \u003cem\u003ePOLICY\u003c/em\u003e scenarios, as renewables are expanded under binding CO\u003csub\u003e2\u003c/sub\u003e price and policy targets, keeping fossil plants in the margin under high carbon cost. Even with extensive renewable capacity expansions, regions with very high electricity demand and residual reliance on gas and coal still experience the highest prices (e.g., Central and Western Europe).\u003c/p\u003e\u003cp\u003eBy coupling GCAM-Europe with the spatially and temporally detailed EXPANSE model, we reveal the power system-level implications of reaching these targets. Policy stringency, in the form of binding renewable and efficiency targets, would drive higher overall investment needs, particularly in capital-intensive generation technologies, but also reduce flexibility, as investments are not always made in the lowest-cost locations. Required investments would be driven not only by renewable generation but also by the associated infrastructure\u0026mdash;i.e., grid expansion, storage, and hydrogen capacity needed to integrate variable resources and ensure system reliability. Wind and solar seemingly dominate new generation capacity, with large regional variation depending on resource potential. Our findings underscore the need for coordinated infrastructure planning to enable a cost-effective and geographically balanced transition, as well as the added cost and complexity of meeting policy-driven targets when compared to purely cost-optimal pathways, an important consideration for EU institutions aiming to balance ambition, equity, and feasibility in their long-term climate strategies.\u003c/p\u003e\u003cp\u003eEU decision-making must carefully navigate the balance between economic efficiency and equitable burden-sharing to sustain political legitimacy, the trade-offs between which are highlighted in our exercise. Whether Fit-for-55- or NECP-driven, \u003cem\u003ePOLICY\u003c/em\u003e scenarios impose binding targets, yielding significantly higher investment requirements, reflecting the push toward capital-intensive technologies and increases in electricity prices in Central and Southern Europe due to system constraints and reliance on expensive dispatchable generation. In contrast, the \u003cem\u003eFREE\u003c/em\u003e scenarios achieve similar emissions reductions with substantially lower annual investment needs in the electricity sector, since resources are allocated cost-optimally by concentrating generation in resource-rich regions without prescriptive technology or efficiency constraints. However, the EU cannot directly mandate where specific technologies should be deployed; decisions must be agreed by the European Parliament and the Council. This points to the need for a stronger governance framework, potentially through an enhanced Energy Union strategy, to better integrate electricity markets and align national choices with collective EU-level objectives\u003c/p\u003e\u003cp\u003eHigher locational marginal prices in \u003cem\u003ePOLICY\u003c/em\u003e scenarios, particularly in Central and Western Europe, underline that the transition may not automatically deliver lower consumer costs, even with higher diffusion of renewables. Carbon prices, reliance on fossil backup, and transmission bottlenecks can keep average prices high. Without revenue recycling, targeted subsidies, or compensation mechanisms, such disparities risk undermining social acceptance of the transition. Grid planning, flexibility mechanisms, and long-term funding instruments must be scaled up to ensure secure, affordable, and equitable transitions. Future policy design must consider these spatial and temporal dynamics, reinforcing the value of coupling long-term system planning with short-term flexibility and targeted national support. Although cost-optimal scenarios can, in principle, achieve comparable decarbonisation outcomes at lower system costs and electricity prices, real-world energy transitions rarely align with idealised optimisation outcomes due to political, institutional, and socioeconomic constraints. Therefore, current policies may require recalibration or complementary instruments to steer investment effectively and reduce prices, despite the associated political and regulatory complexity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eAuthors acknowledge support from the Horizon Europe European Commission Projects IAM COMPACT (grant no. 101056306, including funding from the Swiss State Secretariat for Education, Research, and Innovation), DIAMOND (grant no. 101081179), ACCLIMATE (grant no. 101184374), and PRISMA (grant no.101081604, including funding from the Swiss State Secretariat for Education, Research, and Innovation). The computations at the University of Geneva were performed using Baobab High Performance Computing service. The views and opinions expressed in this paper are those of the authors alone. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during, and analysed in, this study are available in Zenodo (DOI 10.5281/zenodo.17258601). GCAM-Europe-7.2 is available for download https://zenodo.org/records/15655568 and maintained in the open-access online repository https://github.com/bc3LC-GCAMEurope/gcam-core. EXPANSE is detailed in an open-access online platform https://iamparis.eu/detailed_model_doc/26. The code to reproduce the figures is publicly available in GitHub https://github.com/NatasjaF/FF55_NECP. \u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eN.F., D.-J.v.d.V., A.T., and S.M. contributed to the research design. N.F., D.-J.v.d.V., A.T., and J.S. developed the analysis, with contribution of all authors. N.F. wrote the first draft of the paper, and all authors contributed to writing the paper. A.N., E.T., and K.K. notably contributed to the review and editing. \u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eConflict of interests\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eEuropean Commission. COM/2019/640 final: The European Green Deal 2019. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=COM%3A2019%3A640%3AFIN (accessed July 1, 2025).\u003c/li\u003e\n\u003cli\u003eEuropean Parliament. 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Cambridge Working Papers in Economics 2023.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"dd59c924-3912-43cf-9539-1255aed78d21","identifier":"10.13039/501100000780","name":"European Commission","awardNumber":"101056306","order_by":0},{"identity":"86557ec0-83f8-4f6c-88ca-455a8ad98141","identifier":"10.13039/501100000780","name":"European Commission","awardNumber":"101081179","order_by":1},{"identity":"d2a94c05-69b1-4725-ad34-4808be49a1db","identifier":"10.13039/501100000780","name":"European Commission","awardNumber":"101184374","order_by":2},{"identity":"5612be5e-3329-4a4d-a91c-697ee73f152d","identifier":"10.13039/501100000780","name":"European Commission","awardNumber":"101081604","order_by":3}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"National Technical University of Athens","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Integrated assessment modelling, electricity system modelling, national energy and climate plans, Fit for 55, European climate policy","lastPublishedDoi":"10.21203/rs.3.rs-7847096/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7847096/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe European Union\u0026rsquo;s (EU) climate strategy\u0026mdash;anchored in the European Green Deal, the Fit-for-55 package, and updated National Energy and Climate Plans (NECPs)\u0026mdash;requires a rapid transformation of the energy system to meet the legally binding target of net-zero greenhouse gas emissions by 2050 and a 55% reduction by 2030 relative to 1990 levels. Yet, how national plans align with EU-wide ambition, alongside the implications for investment, infrastructure, and power-system operation, remain insufficiently assessed. We address this gap by linking an EU-specific implementation of a prominent integrated assessment model with Member State-level disaggregation (GCAM-Europe) with a higher-resolution European electricity system model (EXPANSE). This modelling framework captures national heterogeneity, sectoral detail, and spatiotemporal variability in electricity demand, renewable supply, storage, and grid constraints. We analyse four scenarios representing EU-wide (Fit-for-55) or national (NECP) targets, implemented through either explicit policies (\u003cem\u003ePOLICY\u003c/em\u003e) or cost-optimal carbon caps (\u003cem\u003eFREE\u003c/em\u003e). Results show that all scenarios achieve the \u0026minus;\u0026thinsp;55% fossil CO2 reduction by 2030, with the electricity sector driving the largest chunk. Renewable energy nearly doubles, with \u003cem\u003ePOLICY\u003c/em\u003e scenarios accelerating electrification and heat pump deployment, while \u003cem\u003eFREE\u003c/em\u003e scenarios lean more on biofuels. Efficiency targets are only partially met, with \u003cem\u003ePOLICY\u003c/em\u003e scenarios distributing savings more evenly across Member States compared to concentrated reductions (\u003cem\u003eFREE\u003c/em\u003e). By 2035, power system transformation diverges strongly: \u003cem\u003eFREE\u003c/em\u003e scenarios expand about 1,160 GW of new capacity, concentrated in a few resource-rich regions, while \u003cem\u003ePOLICY\u003c/em\u003e scenarios reach around 1,680 GW with broader spatial distribution, requiring higher overall investment in renewables and grids. Average wholesale electricity prices are higher and more heterogeneous under \u003cem\u003ePOLICY\u003c/em\u003e scenarios, reflecting carbon costs, transmission bottlenecks, and reliance on fossil backup. These results highlight trade-offs between economic efficiency and equitable burden-sharing, underscoring the importance of coordinated EU governance, infrastructure planning, and complementary policies to balance cost-effectiveness with political feasibility and social acceptance.\u003c/p\u003e","manuscriptTitle":"Cost-optimal vs. policy-driven scenarios for a decarbonised European energy system","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-14 08:55:36","doi":"10.21203/rs.3.rs-7847096/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"763ab638-38a3-4a8f-a540-03ef815d3956","owner":[],"postedDate":"October 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56191292,"name":"Environmental Policy"}],"tags":[],"updatedAt":"2025-10-14T08:55:37+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-14 08:55:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7847096","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7847096","identity":"rs-7847096","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}