Regional impacts of electricity system transition in Central Europe until 2035

The study addresses how meeting national electricity targets in Central Europe by 2035 will redistribute benefits and burdens across sub-national regions, potentially creating regional winners and losers. In the context of the European Green Deal’s decarbonization and equity goals, rapid deployment of renewables promises air quality and employment benefits but may increase system costs, land use, and create local burdens. Deployment to date has been uneven across regions, suggesting uneven impacts. The research quantifies regional impacts (system costs, employment, greenhouse gas and particulate matter emissions, land use) for multiple technically feasible electricity system futures and evaluates how evenly these impacts are distributed across 650 NUTS-3 regions. It explicitly explores trade-offs among three societal aims: minimizing total system costs, maximizing regional equality (evenness of system costs), and maximizing renewable electricity generation.

Prior European electricity system modeling has focused largely on technical and economic least-cost allocations, weather effects on costs and renewable generation, or regional economic impacts. Only a few studies consider regional impacts from an equity lens, such as equitable allocation of solar and wind in Germany or equitable renewable allocation in Switzerland, and electricity access equity in sub-Saharan Africa. Existing studies often lack a holistic quantification of employment, land use, and health-related impacts and frequently neglect full technical system feasibility (hourly operation, transmission, storage). This study extends the literature by providing a comprehensive, spatially explicit quantification of multiple regional impacts with system feasibility and an explicit assessment of regional equality using the Gini index.

The analysis soft-links two optimization models: EXPANSE (spatially explicit, bottom-up, technology-rich, single-year model for 2035 with annual resolution over 650 NUTS-3 regions) and PyPSA (hourly electricity system model with 100 aggregated grid nodes). Using modeling to generate alternatives (MGA), EXPANSE computes 100 maximally different near-optimal spatial allocations of generation capacities with scenario-specific slack randomly varied between 0% and 20% above cost-optimal total levelized generation costs. For each EXPANSE scenario, capacities are aggregated to 100 grid nodes and passed to PyPSA, which optimizes hourly dispatch and investment in storage (battery, hydrogen, pumped hydro) and transmission (HVAC/HVDC) to minimize annualized total system costs. A “frozen generation capacity” scenario fixes 2035 capacities to 2018 levels as a reference. All scenarios use exogenous 2035 electricity demand (total +8% vs. today) and enforce that each country’s annual demand is met by indigenous generation (hourly imports/exports allowed but not counted toward annual demand). Country-level generation targets for MGA scenarios: Austria 100% renewable electricity; Denmark 100% renewable electricity; France <50% nuclear generation; Germany >70% renewable generation; Poland <40% coal/lignite generation; Switzerland >11.4 TWh year−1 renewables excluding hydropower. Technology portfolio includes onshore/offshore wind, rooftop/open-field PV, large/small hydro, biomass (biogas, woody, waste), geothermal, nuclear, hard coal, lignite, gas, oil; storage: battery, hydrogen, pumped hydro; transmission: HVAC/HVDC. Costs (CAPEX/OPEX), potentials, and hourly resource availability are compiled from published datasets (e.g., Renewables Ninja, PRIMES, JRC ENSPRESO, PyPSA-Eur). Total system costs include annualized capital and variable costs of generation, storage, and transmission with a 5% WACC; no taxes/subsidies modeled. Regional impacts are computed by multiplying modeled regional capacities or generation with technology-specific impact factors: employment (direct jobs in construction, installation, O&M, decommissioning; plus fuel extraction/transport for biomass/coal/lignite), greenhouse gas emissions (direct CO₂-eq from combustion), particulate matter (PM10-eq from combustion), and land use (direct). Regional equality is quantified using 1 − Gini index for system costs per capita, jobs per 1000 inhabitants, emissions per capita, and land use share of area. Four focal scenarios are examined in-depth: minimum total system costs; maximum regional equality of system costs; maximum renewable electricity generation; and frozen generation capacity (2018 mix in 2035).

System configuration ranges (100 MGA scenarios vs. frozen capacity):

• Generation shifts: Offshore wind 33–192 TWh/y (vs 27); onshore wind 233–320 (vs 115); open-field PV 37–53 (vs 10); rooftop PV 164–231 (vs 36). Nuclear 216–371 (vs 588); hard coal 2–230 (vs 300); lignite 65–188 (vs 231); gas 0–90 (vs 57). Oil ~0.
• Storage: Up to +7 GW hydrogen and +50 GW battery across scenarios. Frozen and minimum-cost scenarios add no storage; maximum equality adds ~0.675 GW battery and 0.075 GW hydrogen; maximum renewables requires ~45 GW battery and ~2 GW hydrogen.
• Transmission: Maximum renewables needs ~33 TWkm expansion; minimum-cost ~25 TWkm (−24% vs max renewables); maximum equality ~19 TWkm (−42% vs max renewables).
• Costs (MGA ranges): Generation 112–120 B€/y; storage 7–15 B€/y; transmission 5–6 B€/y; total 125–137 B€/y. Frozen: 100, 7, 5, 112 B€/y respectively. • Minimum-cost scenario: Generation 112 B€/y, storage 7, transmission 6, total 125. • Maximum equality: Generation 119, storage 7, transmission 5, total 132. • Maximum renewables: Generation 113, storage 15, transmission 6, total 134.
• Employment: MGA scenarios 376–417 thousand jobs; frozen 274. Minimum-cost 387, maximum equality 384, maximum renewables 415. Targets increase jobs; maximizing renewables yields the highest employment.
• Emissions: GHG 144–324 MtCO₂-eq/y (vs frozen 448). Minimum-cost ~304; max equality ~260; max renewables ~144 (lowest). PM10 76–122 kt/y (vs frozen 135): minimum-cost 118; max equality 100; max renewables 77 (lowest).
• Land use: MGA 2486–2882 km² (vs frozen 1719). Minimum-cost ~2557; max equality ~2833; max renewables ~2824. Higher land use is associated with equality and renewables; lower with cost minimization. Distinct scenario characteristics:
• Minimum-cost: High centralized hard coal (~190 TWh/y), lignite (~160), offshore wind (~161), large hydro dams (~86), large run-of-river (~63), biomass waste (~35); no added storage; substantial transmission expansion. Spatially concentrated coastal wind/PV in sunny south; retains some coal/nuclear; gas used for balancing.
• Maximum equality: High decentralized onshore wind (~287), rooftop PV (~226), open-field PV (~48), woody biomass (~57), biogas (~45), small hydro (~22), geothermal (~12), gas (~84); low offshore wind; less coal in DE/PL; more gas in FR/PL/CH; some battery/hydrogen in DK; moderate transmission; more even regional distribution of costs and jobs.
• Maximum renewables: Renewable generation ~1037 TWh/y; retains significant nuclear (~366) and some lignite (~150) to keep costs near-optimal; very high battery (+45 GW) and transmission (+33 TWkm) requirements; widespread PV/wind both coastal and inland; minimal fossil balancing except residual lignite; Switzerland relies on pumped hydro. Regional impacts and equality:
• Meeting targets decreases GHG and PM most in current coal regions (e.g., Spree-Neisse, Radomski, Sosnowiecki); some gas-driven increases (e.g., Neuss, Görlitz). Land use increases in most regions due to wind/PV/biomass buildout; equality scenario can reach up to ~0.65% of land in Weser-Ems.
• Gini-based equality: Frozen has least even system costs and employment. Minimum-cost scenario shows relatively uneven distributions (higher inequality). Maximum equality has the most even distributions; maximum renewables also achieves above-average equality, particularly for land use and employment compared to minimum-cost. Trade-offs:
• Minimum-cost vs maximum equality: Equality improves by ~18% (52% vs 44% equality score), renewables +4% (928 vs 892 TWh/y), but total costs +6% (132 vs 125 B€/y). Emissions lower (GHG −14%, PM −8%), land use higher (+11%), employment ~−1%.
• Maximum equality vs maximum renewables: Renewables +12% (1037 vs 928 TWh/y), total costs +2% (134 vs 132 B€/y), equality −8% (48% vs 52%), GHG −45%, PM −29%, land use similar, employment +8%. Overall: The three aims cannot be optimized simultaneously. Cost minimization yields lower land use but higher emissions and lower jobs with concentrated regional impacts; equality distributes impacts more evenly but raises costs and land use; maximizing renewables lowers emissions strongly and increases jobs, with higher costs and land use yet tends to reduce regional inequalities relative to frozen and minimum-cost baselines.

The findings demonstrate that achieving national electricity targets by 2035 entails substantial reconfiguration of generation, storage, and transmission with distinct spatial patterns depending on societal aims. Minimizing system costs concentrates new wind/PV in high-resource coastal/southern regions, retains coal/nuclear where cost-effective, and relies on transmission expansion, leading to uneven regional impacts and higher GHG/PM relative to other target-achieving scenarios. Maximizing regional equality spreads decentralized renewables and gas more evenly inland, requires modest storage and less transmission, and improves the evenness of system costs, jobs, and environmental impacts, albeit with higher total costs and land use. Maximizing renewables combines widespread deployment with high storage and transmission needs (except where pumped hydro suffices), delivering the lowest GHG and PM and high employment, and also tends to improve regional equality compared to cost-minimizing pathways. These results highlight unavoidable trade-offs among cost efficiency, equality, environmental outcomes, and land use, informing policymakers that choices about targets and deployment strategies will materially shape who bears costs and who benefits across regions.

This study provides a holistic, NUTS-3–level quantification of regional technical, economic, social, health, and environmental impacts of electricity system transitions in six Central European countries by 2035. Using 100 MGA scenarios with soft-linked EXPANSE–PyPSA modeling, it shows that minimizing costs, maximizing regional equality, and maximizing renewables are mutually conflicting aims with distinct infrastructure pathways and regional impact profiles. Compared to 2018 capacities, meeting 2035 targets increases regional equality of system costs by roughly 18–43% and renewable generation by 97–140%, with total system costs rising by 12–22%. Policymakers should recognize that equality-oriented or renewables-oriented strategies reduce emissions and, in the case of renewables, increase employment, at the expense of higher total costs and land use, while cost-minimizing strategies concentrate impacts and yield poorer environmental outcomes. Future research should further explore policy instruments’ effects on regional distributions of costs/benefits, expand to whole-energy-system analyses, and evaluate longer-term horizons beyond 2035.

Key limitations include: (1) Policies (subsidies, taxes, carbon pricing) are not explicitly modeled; their effects are only implicitly captured through exploring near-optimal alternatives, and distributional changes due to policies are not quantified. (2) Each country is constrained to meet annual demand with indigenous generation, limiting international cooperation effects and possibly bounding lower equality values, though main conclusions on trade-offs remain. (3) The scope is the electricity sector only, not a full energy system; cross-sectoral couplings are not captured. (4) The analysis horizon is 2035, aligned with national targets; technology build-rate constraints and longer-term transitions (e.g., complete phase-outs of lignite/nuclear) may differ beyond this period.