Ecological and economic onshore wind turbine siting trade-offs in Norway

Ecological and economic onshore wind turbine siting trade-offs in Norway | 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 Ecological and economic onshore wind turbine siting trade-offs in Norway Muhammad Shahzad Javed, Dafna Gilad, Roel May, Thomas Kvalnes, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9167324/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 Expanding wind energy to meet net-zero targets increasingly conflicts with biodiversity and landscape conservation, creating contested geographies where socio-ecological values compete. Existing spatial analyses identify ecologically preferable sites but lack representation of system constraints and trade-offs, while most energy system models optimize technology deployment under cost constraints by specifying regional capacity limits, rarely encoding which specific sites become available under different ecological priorities. We address this disconnect through a spare-share scenario framework coupling high-resolution biodiversity (247 bird species, 5 bat species) and infrastructure datasets with electricity system optimization for Norway's 2050 net-zero pathway. Spatial allocation strategies spanning infrastructure-proximate concentration (spare) to ecologically guided dispersion (share) including collision risk minimization, habitat protection, and infrastructure concentration are examined. Analysis reveals that only 7% of Norwegian land area remains consistently available across spare-share scenarios, while 67% exhibits flexible availability depending on ecological criteria prioritization, and 26% faces universal restrictions. Ecological restrictions reduce onshore wind capacity potential 11-fold, increasing electricity generation costs by 19–21% per MWh while investment and operational costs rise up to 6%. Despite targeting divergent ecological priorities, moderately restricted scenarios cluster within less than 1% cost increase variation, reflecting biodiversity hotspot overlap that creates functionally equivalent constraints while achieving better system integration. Findings demonstrate that for 67% of land, which is sensitive to ecological priorities, spatial allocation decisions are not a technical optimization problem but require inclusive stakeholder engagements to navigate competing ecological and infrastructure values. The framework provides transferable methodological insights for regions balancing renewable expansion with biodiversity preservation. Renewable Resources Energy Engineering onshore wind siting biodiversity conservation spare-share scenarios electricity system optimization net-zero pathways Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction The transformation of energy systems toward renewable sources represents a cornerstone of global decarbonization efforts [ 1 , 2 ]. Wind energy, with its substantial technical potential and low costs [ 3 ], has emerged as a critical technology for achieving climate targets, particularly as electrification extends into transportation, industry, and heating sectors [ 4 , 5 ]. The European Union aims to more than double installed wind capacity to 440 GW by 2030 [ 6 ], while globally, wind generation must expand from less than 2000 TWh to approximately 12,000 TWh by 2050 [ 7 ] to meet climate targets. However, the urgency of climate action increasingly collides with equally pressing concerns about biodiversity conservation, landscape preservation, and ecosystem integrity. With habitat change identified as the primary threat to diverse species across multiple regions [ 8 , 9 ], recent assessments emphasize that nature loss may prove as consequential as climate change itself, with the two crises mutually reinforcing [ 9 , 10 ]. Norway exemplifies this tension while offering a particularly instructive case for examining these trade-offs. The country aims to reduce greenhouse gas emissions by 50% relative to 1990 levels by 2030 and by 90% by 2050 [ 4 , 5 ], necessitating substantial expansion of the electricity system from 127 TWh in 2023 to potentially 250 TWh by 2050 [ 11 ]. This increase stems from development of new energy-intensive industries to move away from oil and gas income and ongoing electrification of transport and industry. The nation possesses extensive onshore wind resources [ 12 – 14 ], substantial existing hydropower capacity for balancing variable generation [ 15 , 16 ], and well-documented ecological data enabling species-specific impact assessment [ 17 , 18 ]. Yet with large hydropower development constrained by environmental limits since 2001 [ 19 ], onshore wind became the primary expansion technology, growing rapidly between 2017 and 2022 from 1.7% to 10% of electricity generation [ 20 ]. This growth ground to a near halt after 2020 [ 16 , 21 ], catalyzed by mounting opposition over landscape disruption, biodiversity impacts, and procedural justice concerns [ 22 , 23 ]. The 2021 Supreme Court ruling on the Fosen wind farms, which found operations violated Sámi rights under international conventions [ 24 ], crystallized broader conflicts surrounding wind energy development. Procedural conflicts have effectively stalled new wind deployment [ 21 , 25 ], leading to potential electricity deficits already by 2027 [ 11 , 26 ], while growing recognition of biodiversity vulnerabilities [ 17 , 27 – 29 ] increasingly constrains where future expansion can occur—placing Norways’ energy transition at a crossroads. The lessons from this experience—balancing climate imperatives against ecological protection and social acceptance—extend beyond national borders to inform renewable energy (RE) transitions across regions facing analogous pressures. These conflicts manifest in planning processes worldwide. Quantitative evidence demonstrates that wind proposals in scenic areas face higher rejection rates [ 30 , 31 ], while empirical assessments reveal that existing Norwegian installations have failed to avoid sites with elevated bird impacts [ 27 ]. Wind energy development affects biodiversity through habitat loss, disturbance, collision mortality, and barrier effects [ 32 – 34 ], with impacts varying substantially by species characteristics and site conditions [ 17 , 35 , 36 ]. Tracking data from over 1,700 mortality events across 45 migratory bird species reveals that energy infrastructure—including power lines and wind turbines—accounts for nearly half of all documented human-induced bird mortality in the African-Eurasian flyway [ 37 ]. Recent pan-European risk assessments identify collision hotspots concentrated in regions including Northern Europe [ 35 ], while system-wide analyses quantify how hydropower, wind installations, and transmission infrastructure collectively shape biodiversity footprints in Norway [ 28 ]. Addressing these intertwined challenges requires frameworks that integrate spatial heterogeneity in environmental impacts of renewable energy siting with system-level operational outcomes. Spatial studies have advanced understanding of where environmental impacts occur [ 27 , 35 , 38 ], while energy system models optimize technology deployment under cost and demand constraints [ 26 , 39 ]. However, spatial analyses typically lack representation of system flexibility requirements, grid-level system bottlenecks, and economic trade-offs beyond siting decisions. In contrast, energy system models frequently treat space as abstract capacity limits specifying how much generation potential exists in a region with rarely encoding the geographic details of which specific sites become available under different environmental priorities. This abstraction prevents models from capturing how the spatial configuration of deployment affects operational efficiency: whether capacity concentrates near existing infrastructure (i.e. sparing nature) or disperses across remote high-wind zones (i.e. sharing with nature) fundamentally alters system operational patterns, temporal balancing requirements, and the relative economics of transmission expansion versus storage investment [ 40 – 42 ]. Recent efforts have begun incorporating ecological and biodiversity considerations [ 26 , 39 ] into energy system modeling, yet comprehensive analysis of how divergent spatial allocation strategies affect operational outcomes remains limited. Broader sustainability research emphasizes that reversing biodiversity declines requires integrated strategies that transform production systems rather than relying on conservation measures alone [ 43 ], a principle equally applicable to RE deployment. This study develops an integrated spatial-economic framework to quantify how different geographic allocation strategies for onshore wind deployment can shape Norway's 2050 electricity system. We address three interconnected questions: First, how do varying ecological constraints affect the geographic availability of suitable wind sites? Second, how do these spatial constraints translate into system-level techno-economic outcomes? And third, how do spatial constraints reshape system balancing and technology substitution patterns? We contrast scenarios spanning a spectrum from infrastructure-concentrated ("spare") to ecologically-dispersed ("share") siting strategies, incorporating species-level biodiversity data [ 17 , 27 ], proximity to existing infrastructures [ 44 ], collision sensitivity frameworks [ 36 ], protected areas [ 45 ] and techno-economic criteria. These scenarios are coupled with the high temporal and spatial resolution electricity system (highRES) optimization model to assess technology deployment, spatio-temporal flexibility requirements, and system-level operational efficiencies [ 46 ]. This approach extends recent work quantifying near-term feasibility constraints under social-environmental restrictions [ 26 ] and monetized externality trade-offs [ 39 ] by analyzing net-zero electricity system operational dynamics. Rather than identifying a single optimal deployment, we employ scenario diversity to reveal trade-off spaces where different ecological priorities yield distinct but viable pathways, recognizing that socio-ecological futures are inherently uncertain yet partially shapeable through present decisions [ 47 ]. 2. Data and methods 2.1 Spare-share scenarios: balancing economic and ecological interests The siting of RE infrastructure reflects competing priorities that cannot be simultaneously optimized: infrastructure proximity may reduce grid extension costs but sacrifices access to higher-quality wind resources, while ecological protection often requires development away from high-biodiversity zones that frequently coincide with superior wind resources [ 30 , 31 ]. Biodiversity encompassing diverse life forms and associated ecosystems is represented here through species richness maps for birds and bats that quantify their spatial distribution based on occurrence data and habitat suitability models [ 48 ]. To develop scenarios, we employ the spare-share framework, which distinguishes between strategies that spare ecologically sensitive areas by concentrating development near existing infrastructure versus strategies that share impacts more broadly across landscapes [ 38 ] (Fig. 1 ). Conserving biodiversity and landscapes for future generations is deeply rooted in Norwegian culture and politics [ 49 ], while visual effects on landscapes and noise have been identified as primary concerns in wind farm opposition in Norway [ 26 , 50 ]. The Norwegian wind farm licensing framework evolved from municipal land-use control (pre-2008) to centralized authority with substantial decision making power. The Norwegian Water Resources and Energy Directorate (NVE) proposed a national framework 1 in 2019 for mapping best suitable areas for onshore wind development. Widespread municipal opposition to this framework underscored the need for scenario-based analysis that accommodates diverse environmental priorities rather than technocratic siting optimization [ 21 ]. Therefore, our scenarios integrate ecologically-guided dispersion with infrastructure-proximate concentration to leverage existing grid connections and road networks. Rather than identifying a single optimal strategy, we developed the following scenarios along the continuum of species richness and infrastructure proximity dimensions (Fig. 1 , Table A1 ). Production maximization (PM) serves as the permissive baseline, excluding only strictly protected areas (IUCN categories Ib and II: wilderness areas and national parks, respectively) while maximizing onshore wind potential to establish economic and operational reference point. Notably, we do not exclude the Ia areas (strict nature reserves—the most stringent IUCN protection level) to reflect historical permissive licensing practices as some existing Norwegian wind farms are sited within this category 2 . Production co-location (PC) restricts development to sites within 5 km of existing infrastructure with the exclusion of protected areas, reflecting concerns about grid extension costs and wilderness fragmentation, as in Norway, undisturbed nature areas (“inngrepsfrie naturområder” in Norwegian), are defined as wilderness areas that are over five kilometers away from human infrastructure. Undisturbed nature areas are divided into three zones: 5 kilometers, 3 to 5 kilometers, and 1 to 3 kilometers away from infrastructure 3 . Collision sensitivity avoidance (CA) prioritizes sites with lower collision risk for vulnerable species—specifically bats, raptors, and gulls—by excluding areas with the highest species concentrations (classes 3–5) and redlist species 4 areas with established buffer zones, while avoiding protected areas (Ia, Ib, and IV - habitat or species management area). The taxonomic groups—bats, raptors, and gulls—are treated as separate datasets due to their elevated vulnerability to turbine blade strikes (Table 3, Ref. [ 17 ]). Morphological characteristics of raptors and gulls exhibit flight traits that increase strike probability, while bats face additional challenges due to attraction to turbine structures [ 38 , 51 ]. This reflects the precautionary principle applied to areas with high collision-sensitive bird and bat concentrations despite limited evidence of population-level impacts [ 27 , 29 , 37 ]. Ecological footprint minimization (EF) imposes strict infrastructure proximity requirements (< 1 km from existing infrastructure) to concentrate development in already-modified landscapes, while excluding high-concentration areas for all bird and bat species (classes 3–5), protected areas (Ia, Ib, II, IV), and redlist species zones, making it the most constrained scenario. Sensitive development (SD) and sustainable co-location (SC) represent balanced share and spare scenarios, respectively. SD balances protection of highly collision-sensitive species (bats, raptors, gulls—more moderate criteria (classes 4–5) than CA) with development feasibility, avoiding protected areas (Ia, Ib, IV) and redlist species zones. SC moderates EF's strict proximity requirement by excluding only the highest species richness areas (all species, classes 4–5) within 3 km of existing infrastructure, alongside high-risk protected areas (Ia, Ib) and redlist species. Finally, sustainable development (SO1, SO2) reflects the balanced development focus with balanced siting strategy where thresholds are applied to the geometric mean of combined species richness and infrastructure datasets rather than individual restrictions: SO1 excludes classes 3–5 (stricter), while SO2 excludes classes 4–5 (moderate), both maintaining protected area exclusions (Ia, Ib) and redlist protections. This geometric mean approach integrates multiple dimensions simultaneously, exploring whether combined metrics yield viable alternatives to dimension-specific restrictions. We applied Jenks natural breaks optimization to partition values into five classes that maximize within-class homogeneity and between-class variance [ 52 ]. This classification approach aligns with Environmental Impact Assessment (EIA) frameworks, where environmental values are categorized from negligible (class 1) to very high significance (class 5) [ 27 ]. The five-class structure enables differentiation between moderate restrictions (excluding only classes 4–5) and stringent restrictions (excluding classes 3–5), as specified in Table A1 . Jenks breaks were selected over arbitrary thresholds because they identify natural discontinuities in the statistical distribution of species richness, providing ecologically meaningful boundaries rather than percentile-based cutoffs that may bisect homogeneous habitat zones [ 26 ]. Our scenarios are not predictions of future policy but rather analytical constructs for exploring diverse plausible futures under socio-environmental uncertainty [ 53 ]. The scenario positions along biodiversity stringency and infrastructure dependence axes (Fig. 1 ) span the range of viable positions articulated in Norwegian planning debates [ 22 – 24 , 26 , 28 , 39 , 54 , 55 ]. Detailed criteria thresholds, data sources, and spatial implementation methods are specified in Table A1 and described in Section 2.2 . 2.2 Datasets and tools Species richness data were derived from the validated life-cycle assessment models [ 17 , 48 ], which developed spatially explicit distributions for diverse bird and bat species across Norway using their occurrence records from the Global Biodiversity Information Facility (GBIF, 2010–2019) and MaxEnt habitat suitability modeling [ 17 , 27 ]. The datasets encompass 247 bird species and 5 bat species [ 27 ], with species richness calculated as the number of species multiplied by their probability of presence at 1km × 1km resolution and rescaled to 100 m × 100 m resolution. Protected areas were obtained from the World Database of Protected Areas (WDPA) [ 45 ], categorized by IUCN management classifications (Ia, Ib, II, IV, and V - protected landscape). The infrastructure index—quantifying proximity to existing settlements—was calculated following [ 56 ] (see table 2, page 19), with values > 1.8 indicating areas with substantial infrastructure presence [ 56 ]. All spatial datasets were harmonized to the ETRS89-LAEA coordinate system (EPSG:3035) at 100 m resolution. Scenario-specific availability rasters were constructed by combining biodiversity layers with protected area and infrastructure constraints using the ‘atlite’ geospatial toolbox (version 0.2.13) [ 57 ]. This tool performs spatial union operations to generate composite availability masks across multiple raster layers. For example, the EF scenario excludes areas where any of the following conditions are met: species richness for bats, raptors, gulls, or all birds falls within classes 3–5 (highest richness values); protected areas Ia, Ib, II, IV; redlist species presence; or infrastructure index indicating locations > 1 km from existing settlements. These high-resolution (100 m) scenario-specific availability rasters enabled detailed spatial analysis across Norway to identify national and regional patterns of land availability (Section 3.1 ) . These rasters were subsequently integrated with technical constraints (i.e., slope, height, physical infrastructures) and weather data for capacity deployment optimization in the highRES energy system model, as described in Section 2.3 . 2.3 Power system modelling framework We employ the open-source highRES model [ 46 ], designed to assess electricity systems with high shares of variable RE and explore flexibility options. highRES is a linear optimization model implemented in the General Algebraic Modeling System (GAMS) to minimize total system costs (annualized investment and operational expenditures) by optimizing the spatial allocation of new technology capacities, hourly dispatch decisions, transmission expansion, and energy storage deployment under perfect foresight with hourly temporal resolution. We adapt highRES to represent the Norwegian power system across 15 administrative regions (revised 2024 boundaries). The model represents a hybrid greenfield framework where existing solar and wind capacities are excluded, while existing hydropower and transmission lines are included and remain fixed at current capacities. Existing wind capacity (~ 5 GW) is excluded due to limited operational lifespan by 2050 and to enable endogenous determination of optimal deployment patterns under scenario-specific ecological constraints. Technologies available for capacity expansion include onshore wind, offshore wind (bottom-mounted in waters < 70 m depth and floating turbines in deeper waters [ 26 ]), solar photovoltaics, and energy storage. All scenarios employ identical techno-economic parameters, 2050 demand projections, weather data, and exclusions for solar PV and offshore wind; the sole variation lies in onshore wind availability masks reflecting the ecological and biodiversity priority configurations detailed in Section 2.2 . A detailed description of highRES model, assumptions, technical constraints, and data sources is provided in the Appendix , and the model code with full replication data is openly available at GitHub 5 . 3. Results 3.1 National and regional spatial patterns of available sites A 7% of the total Norwegian land area is commonly available across the scenarios, revealing rare safe onshore wind development zones (Fig. 2 a). In contrast, 67% exhibits availability in some scenarios but restricted in others, suggesting that the majority of Norway's landscape availability is sensitive to which ecological and infrastructure priorities guide spatial allocation decisions (Fig. 2 c). The dominance of contested geography over consensus zones illustrates Norway's spatial complexity: biodiversity hotspots, landscape values, infrastructure constraints, and ecological sensitivity overlap substantially across the territory. Although the majority of unavailable areas across spare-share scenarios ( unavail ) concentrate in Western and Northern Norway (~ 50% of total unavail ), the eastern counties exhibit significant internal restrictions due to anthropogenic impacts, such as urban footprints and existing developments (Fig. 2 b). Notably, while approximately half of these small urban counties are universally restricted, when combined, they account for only ~ 4% of the total always unavailable area. Scenario correlations reveal the extent to which different restriction frameworks agree on geographic suitability—a high positive correlation indicates the likelihood of sharing flexible area geographies. In contrast, a negative correlation indicates that scenarios access different geographies (Fig. 2 e). Despite both targeting similar biodiversity conservation, the moderate correlation between CA and SD (0.51) suggests that half the area SD permits is restricted by higher CA’s restrictions, with additional divergence arising from heterogeneous spatial distributions of diverse species groups [ 27 ]. This geographic divergence is reflected in the distribution of flexible areas across scenarios (Fig. 2 d), where most flexible zones cluster around 4–5 scenarios, indicating that varying severity levels of similar habitat priorities fragment geographic outcomes. When biodiversity restrictions are combined with proximity constraints in EF (1 km) and SC (3 km) scenarios, the correlation further drops to 0.21, suggesting heterogeneity in balancing spare and share siting strategies for land-based wind farms, with not many emerging as suitable across the restriction frameworks. Due to targeting different geographies, spare-and-share scenarios exhibit negative to very low correlations with one another. The most conservative scenario, EF, exhibits correlations ranging from − 0.08 to 0.21 with other scenarios, indicating that high infrastructure proximity restriction combined with high ecological restrictions creates spatial patterns distinct from either restriction applied independently (Fig. 2 e). Negative correlations between economy (PC) and share scenarios (CA vs PC: -0.12, SD vs PC: -0.12) demonstrate that economy-focused infrastructure concentration and ecology-focused infrastructure concentration target opposite geographies. Moreover, negative or low correlations between economic siting near infrastructure (PC) and ecologically acceptable sites near infrastructure (EF, SC, SO1, SO2) preclude the spatial optimization of wind sites that considers both criteria simultaneously. Notably, highly restricted scenarios correlate more strongly with each other (EF-CA: 0.20) than with moderately restricted scenarios (EF-SD: 0.10; SC-SD: 0.42 vs SC-CA: 0.31), indicating that extreme restrictions—whether ecological or combined—converge on the same limited geography of least-sensitive areas, while moderate thresholds access broader, more diverse landscapes. Three large northern/central counties (Innlandet, Trøndelag, Finnmark) dominate both the flexible area (accounting for ~ 52% of the total) and the unavailable area (33% of the total), indicating that large, geographically diverse counties contain the diverse restriction spectrum. Most existing wind farm locations are in flexible zones, with only ~ 10% in always-available zones (Fig. 2 a, 2 c). 28% of existing wind farm locations appear in only one scenario—the most permissive PM scenario—and most of these single-scenario installations concentrate in Northern Norway (light blue markers, Fig. 2 c). Similar to the overall scenario distribution across flexible zones, most existing wind farm locations appear in 4–6 scenarios, revealing that historical development already navigated moderate ecological contestation but avoided the most sensitive zones. Ecological restrictions reduce onshore wind capacity potential from 418 GW (PM) to 37 GW (EF)—an 11-fold reduction ( Fig. A1 in the appendix ) . The levelized cost of energy (LCOE) of onshore wind increases up to 19% to 21% across scenarios to meet all the new electricity demand for 2050 (Fig. A1 (b); vertical red lines) [ 11 ]. The onshore potential under EF (83 TWh maximum) remains insufficient to meet upper demand projections alone. Regional capacity factor analysis reveals that six counties experience a mean increase in capacity factor, as restrictions preferentially exclude low-quality sites (marked green). Conversely, seven counties exhibit declines in mean capacity factor due to the exclusion of good-quality wind sites (marked red). Notably, three counties suffer on average the steepest degradation of high-quality sites (Finnmark − 10.7%, Akershus − 11%, Telemark − 5.2%), while moderate-quality central regions (Innlandet + 3.4%, Trøndelag + 3.3%) gain relative attractiveness, and some remain largely unaffected (i.e., Møre og Romsdal). Spatial availability patterns ( Fig. A1 (a) in the appendix ) demonstrate that northern capacity depends critically on infrastructure proximity criteria, as these areas' capacity collapses by 70% to 90% (i.e., EF, SC, SO1). Eastern regions exhibit inverse sensitivity as high biodiversity thresholds disproportionately restrict these areas (SC, CA, SD, EF) under species richness frameworks. 3.2 System cost implications of different land use patterns Ecological restriction-related system cost increases are non-linear, driven by both capital and operational costs (Fig. 3 ). Both spare and share scenarios under expandable transmission exhibit higher cost increases because expandable PM has already optimally deployed transmission capacity (reducing baseline costs by 1.8% with + 1.2 GW transmission capacity from PM fixed transmission), leaving minimal further transmission optimization potential for other scenarios, which must then absorb costs through technology substitution (Fig. 3 a,c). Despite divergent ecological priorities, moderately restricted scenarios (SO1, SO2, SC, SD) cluster at 1.5–2.5% cost increases, illustrating that geographic overlap of different ecological and biodiversity hotspots leads to produce similar economic outcomes [ 28 ]. Generation upkeep costs contribute 50–80% of total cost increase across all scenarios due to two reasons. First, Norway's extensive existing hydropower capacity contributes substantially to upkeep costs without associated capital expenditure. Second, ecological restrictions shift the technology portfolio toward higher operational cost alternatives (i.e., offshore increases from ~ 3 GW to ~ 10 GW from PM) with reduced generation efficiency (2,180 vs 2,390 TWh/GW), disproportionately increasing upkeep costs. This is similar to other Norway-based studies [ 26 , 58 ]. Storage investments reflect the economics of balancing between spatial and temporal resources: the system invests in storage when transmission is constrained and reduces investment in storage as transmission expands ( Fig. A3 in the appendix , Section 3.3 ) . EF presents an exception—despite transmission expansion, storage investment increases by 13% of total cost change, demonstrating that strict ecological restrictions create temporal mismatches that neither transmission-alone nor storage-only substitution can fully resolve. Negligible transmission capital investments (< 2% of cost increases) across all expandable scenarios from the base PM-expandable (where already + 1.5 GW transmission capacity is added) illustrate that land-use flexibility constitutes most of the binding economic constraint. Economic impact of coupling spare strategy with economy-focused objective (i.e. PC) virtually imposes negligible cost penalty (Fig. 3 a), demonstrating that infrastructure-proximate deployment is economically optimal when unconstrained by ecological restrictions. However, when spare strategy pairs with ecological constraints (EF, SC), the costs are increased, highlighting the strong influence of ecological constraints on the system-preferred onshore sites, regardless of their infrastructure proximity. 3.3 Capacity mix and regional deployment patterns The interplay between transmission mode, ecological restriction severity, and storage affects how the system substitutes for the constrained onshore wind (Fig. 4 ). Although ecological restrictions shift the technology mix and system substitutes for the constrained onshore wind differently, transmission expansion does not significantly reduce the total system generation capacity across the scenarios. The decrease is between 2 and 4 GW, except for EF, which shows a ~ 2 GW increase in total capacity, as solar substitution partially offsets onshore wind constraints ( Fig. A2 in the appendix ) . Although transmission expansion enables better substitution flexibility, it does not overcome the impact of ecological restrictions on the onshore wind deployment capacity as the onshore wind reduction pattern remains relatively the same regardless of transmission configuration. The most restricted scenario (EF) shows ~ 20 GW of onshore wind reduction from the PM baseline across transmission scenarios, while moderately restricted scenarios (CA/SC/SD) show ~ 5–10 GW reductions. Changes in solar capacity are driven by siting strategy rather than transmission configuration. Spare scenarios (PC, SC) exhibit marginal solar reductions (0.1–1.1 GW) as concentration near existing infrastructure enables better utilization of nearby onshore wind resources. Conversely, share scenarios (CA, SD) show marginal increases (0.1–0.6 GW) as broader spatial deployment creates opportunities for complementary solar integration. This spare-share pattern holds consistently across both transmission configurations, though absolute solar capacities are higher under fixed transmission than expandable (Fig. A2 in the appendix ) . High ecological restrictions in the EF lead to an exception in which the system compensates for constrained wind with high solar deployment (+ 7 GW) rather than costly offshore wind. Offshore wind remains the least sensitive to transmission configurations, varying by 0.5–0.7 GW within scenarios across transmission categories. This demonstrates that offshore deployment in Norway responds to land-based availability constraints rather than to transmission expansion (i.e., grid flexibility), enabling better utilization of available capacity but not fundamentally altering the national technology mix. Floating offshore wind becomes economically competitive only under very high land-based restrictions (i.e., EF), demonstrating that transmission expansion marginalizes floating offshore's economic competitiveness except when land-based options are critically constrained. 3.4 Regional deployment patterns New storage deployment is geographically concentrated in southwestern regions (Møre og Romsdal, Telemark, Agder), primarily driven by temporal balancing of high solar saturation (Fig. A4 in the appendix ). Under fixed transmission, storage is located near generation sites where temporal balancing is most economical—Møre og Romsdal and Trøndelag each host up to 15 GWh despite their distance from demand centers in the south and east. However, new storage shifts toward demand-proximate southern regions (Telemark, Agder) with transmission expansion, reflecting the economic advantage of temporal balancing near load centers when transmission can efficiently connect distant generation. Western regions (Rogaland, Vestland) deploy negligible new storage across all scenarios, given existing hydropower storage, eliminating the need for additional temporal balancing capacity. Spatial deployment patterns reveal stable, region-specific technology structures across transmission configurations. Telemark, Vestfold, and Buskerud inflexibly hold solar allocation across all scenarios regardless of transmission mode, suggesting these regions possess optimal solar resource quality and demand proximity unaffected by the level of constrained onshore wind (Fig. 5 ). Similarly, Agder, Trøndelag, Nordland, and Troms maintain onshore wind deployment across all scenarios. Østfold exhibits high sensitivity to ecological restrictions, where the capacity of 5.1 GW onshore wind (PM) is substituted by 4.1 GW solar, leading to accelerated marginal cost of EF restrictions (Fig. 6 ). Finnmark demonstrates similar vulnerability; even transmission expansion did not make the available areas in the north economically attractive. Offshore expansion mostly occurs in the southwestern regions (Agder, Rogaland). Transmission expansion does not significantly alter regional technology portfolios but shifts the magnitude of capacity (i.e., Agder, Innlandet), demonstrating that transmission flexibility enables capacity optimization rather than technology diversification, with spatial technology allocation determined primarily by resource availability and ecological constraints rather than by grid configuration (i.e., transmission). 3.5 Economic cost of different protection levels The economic pressure of environmental restrictions on system optimization is quantified by shadow prices (dual variables) derived from the model's binding area capacity constraints (for further explanation, see Ref. [ 59 ]). Shadow prices represent the marginal rate at which the total system cost objective function would decrease if the area constraint were relaxed by one unit—mathematically, ∂(total system cost)/∂(available area). These dual values indicate the difficulty of satisfying area constraints under each scenario, revealing the instantaneous economic value of additional developable land at the current optimum. Critically, shadow prices are local derivatives evaluated at a specific solution and therefore serve as comparative indicators of relative economic pressure across scenarios rather than predictive estimates of cost savings from discrete capacity additions. It enables assessment of how varying degrees of ecological restriction severity create economic opportunity costs when limiting access to available wind sites. The analysis reveals substantial variation in economic opportunity cost across scenarios, ranging from 500 million NOK/GW (PM, PC) to 4,5 billion NOK/GW (EF)—a 9-fold escalation. This large range indicates that the marginal economic value of additional developable areas substantially depends on the restrictive frameworks. Despite this extreme range, moderately restricted scenarios (CA, SC, SD) cluster around the median, demonstrating that different ecological and biodiversity priorities yield similar economic opportunity costs when geographic overlap among ecological hotspots creates functionally equivalent restrictions on available onshore wind sites (Fig. 6 , vertical green line). Although transmission modestly affects economic cost (-14% to + 18% change from fixed to expandable, except in the SC scenario, where it is + 45%), it does not alter the scenario ordering, where the opportunity cost of EF remains highest. In contrast, moderate scenarios remain clustered around the median. This persistence reveals two takeaways: first, transmission capacity is not the primary bottleneck for onshore wind deployment under these ecological scenarios; and second, the spatial availability of suitable sites constitutes the binding constraint, suggesting a focus on alternative grid flexibility measures, such as demand response. Unused capacity represents the difference between available capacity potential (after ecological exclusions) and deployed capacity—quantifying additional development potential that remains accessible but economically unviable under system optimization. The relationship between unused capacity and opportunity costs exhibit diminishing returns of permissiveness. Despite having up to three times more unused capacity than moderate scenarios (380 GW PM vs 120 GW SO1), economic opportunity cost decreased by ~ 30% (excluding EF's extreme position), indicating that beyond a threshold, increasing available area yields progressively smaller reductions in the economic value of additional capacity. This non-linearity reflects system-level bottlenecks—grid flexibility limitations, temporal generation-demand mismatches, and regional transmission constraints—that prevent full utilization of available capacity regardless of land-use permissiveness. The trade-off between ecological restrictions and efficient system utilization is distinctly non-linear. Our analysis reveals that moderate restriction scenarios (CA, SC, SD) achieve better system integration compared to extreme scenarios (EF, PM), with a curtailment rate of ~ 100 TWh/GW versus ~ 120 TWh/GW for extreme scenarios (PM, EF)—representing 20% higher curtailment intensity at both ends of the restriction spectrum. Two mechanisms explain this: First, maximum available areas (PM) enable deployment in optimal locations, but system oversupply at peak generation hours and grid flexibility constraints limit absorption capacity. Second, high restrictions (EF) force deployment in locations with lower generation efficiency (2,180 vs 2,390 TWh/GW in PM) and proximity requirements (< 1 km), creating temporal mismatches between generation and demand. This spatial-temporal mismatch is amplified by EF's geographic concentration: as demonstrated in spatial availability analysis (Section 3.1 , Fig. A3 in the appendix ), EF's low correlation with other scenario (-0.08-0.21) concentrates deployment in southwestern counties where infrastructure proximity requirements are met, but these regions exhibit relatively high solar saturation and lower wind resource compared to the geographically diverse, higher-quality northern sites (Trøndelag, Finnmark) accessible under moderate scenarios. This pattern demonstrates that realized capacity utilization does not correlate linearly with land-use restriction severity—moderate restrictions optimize the balance between siting and system integration better. 3.6 Hourly electricity cost distribution Hourly electricity costs are calculated using the hourly electricity shadow prices (i.e., dual variables) and demand, reflecting the marginal cost of meeting demand in each hour. To assess how ecological restrictions affect typical electricity system operation, extreme hours, and operational cost patterns, we analyze three metrics: median (central tendency of typical hours), mean (accounting for extremes), and coefficient of variation (CoV) (measuring relative volatility). Ecological restrictions do not necessarily increase hourly electricity cost volatility. The most restrictive scenario (EF) exhibits lower volatility (CoV: 5.84 fixed, 6.78 expandable) across ecological scenarios and transmission configurations (Fig. 7 ). This connects to the spatial availability findings (Section 3.1 ): limited and fixed geographic availability (i.e., the southwest for EF) constrains dispatch options to a narrow set of locations, and even grid flexibility cannot overcome spatial restrictions on where generation exists. Moreover, lower CoV reflects a relatively less supply-demand mismatch at these limited available sites. Meanwhile, higher cost volatility in other scenarios compared to the base scenario reveals operational outcomes depend critically on whether favorable renewable conditions occur at available or excluded sites—when conditions align with accessible locations, costs remain moderate, but when optimal conditions occur at restricted sites, the system must dispatch expensive alternatives, creating larger cost swings. This median-mean divergence reveals how ecological restrictions primarily affect extreme hours and typical operation hours. The variation in median hourly costs remains < 3% (fixed) and < 6% (expandable), while mean costs increase up to ~ 12% across scenarios. For example, EF shows a higher mean (39.12M NOK) with a similar median (24.76M NOK) to PM (24.86M NOK), indicating that most hours experience comparable costs, but rare extreme hours (< 1% of hours, exceeding 1,000M NOK) become more costly under restrictions. Conversely, SD exhibits a lower mean (34.57M NOK) but a higher median (25.21M NOK) than the base case across transmission configurations, suggesting fewer costly extreme hours but elevated typical operational-hour costs. This pattern demonstrates that the economic impact of restrictions manifests through amplified tail behavior—more frequent or severe extreme-cost hours—rather than across-the-board operational cost increases. Transmission expansion increases hourly cost volatility across all scenarios. This volatility increase reflects enhanced operational flexibility: expandable transmission enables dynamic inter-county dispatch optimization, in which the system routes power hour by hour based on instantaneous electricity production and demand patterns. This creates more variable dispatch patterns than in fixed transmission, where constrained grids force local balancing with predictable local dispatchable generation (i.e., storage). While this flexibility reduces average costs (the mean drops ~ 8% with transmission expansion), it introduces operational variability, as optimal dispatch varies more substantially across hours depending on renewable generation conditions. 4. Discussion Energy system models increasingly recognize that spatial allocation decisions fundamentally shape system outcomes [ 26 , 39 ]. Our analysis demonstrates how spatial prioritization strategies—concentrating development near existing infrastructure (spare) or dispersing capacity to avoid biodiversity hotspots (share)—create distinct investment and operational patterns beyond simple capacity allocation. The integration of species-level biodiversity data with electricity system modelling reveals that system flexibility—through transmission, technology substitution, storage— enables biodiversity protection while maintaining system viability. Our spatial availability analysis divides Norwegian land into three distinct categories: 7% ‘go-to’ areas, 26% ‘no-go’ areas and 67% contested areas requiring priority-setting (Fig. 2 ). This geographic breakdown quantifies what previous studies have described qualitatively: wind energy siting involves extensive contested space requiring explicit priority-setting [ 27 , 28 ]. The policy-sensitive 67% represents neither a technical problem with a single optimal solution nor an intractable conflict, but rather a decision space in which different societal values yield distinct but defensible outcomes. The western counties dominate always available areas while eastern counties concentrate always unavailable zones. This regional pattern suggests that these areas share internally consistent ecological and infrastructure characteristics regardless of the restriction framework. The spare-share spatial strategies manifest technology-specific capacity expansion patterns regardless of grid extension. A spare approach reduces the solar composition in the national capacity mix, while a share approach increases due to broader spatial coverage, creating opportunities for complementarity with onshore wind (Fig. 4 , Fig. A2 ) [ 60 ]. Offshore wind also remains the least sensitive to grid expansion and responds to different ecological restrictions, where it ranges from ~ 3.5 GW to ~ 10 GW depending on the severity of land-based restrictions. Most of the new offshore capacity additions remain in the southwest of Norway due to its proximity to demand centers and optimal use of the established grid rather than north or central Norway, where the system would require new transmission capacity additions. Moreover, these spatial strategies reinforce regional distribution patterns of deployed capacities. Despite varying ecological priorities and transmission configurations, some regions appear to adopt a similar technology. Southern and southeastern counties inflexibly hold the solar deployment ranging between 3–17 GW, while central counties invariably hold onshore wind between 1–10 GW each. Østfold county only appears to be very sensitive to ecological restrictions, completely substituting onshore wind for solar (Fig. 5 ). We note a sustained onshore wind capacity reduction from the base case in northern Norway (Tromsø & Finnmark), due to increased ecological and infrastructure constraints, from ~ 8 GW to ~ 3 GW. The choice between spatial and temporal balancing depends on both siting strategy and the transmission configuration ( Fig. A3 , Fig. A4 in the appendix ) where fixed transmission with share approach and spare approach with expandable transmission exacerbates the role of storage. With fixed transmission, share scenarios exhibit greater storage increases for two reasons: first, deployment away from existing infrastructure necessitates localized temporal balancing due to limited grid connectivity; second, increased solar share in the capacity mix leads to higher temporal mismatch, as solar is only available during the daytime. Conversely, spare scenarios (EF, PC, SC) show minimal storage increases due to a reduction in solar saturation and concentration near demand centres, enabling the maximum use of the established grid rather than installing solar in other low-demand regions with high solar potential ( Fig. A4 in the appendix ). However, this pattern got reversed with transmission expansion. Spare scenarios see storage increases as the system deploys maximum new capacities in the proximity of existing infrastructure, even at high temporal-mismatch locations, requiring storage in addition to transmission to provide spatial redistribution capacity. Meanwhile, storage needs are reduced in the share scenarios because transmission expansion connects distant high-quality sites directly to demand centres, substituting spatial balancing for temporal storage. Recent work on Norwegian flexibility options confirms that spatial distribution of renewable capacity fundamentally affects temporal smoothing requirements [ 60 ], but our results illustrate that beyond a certain threshold grid expansion cannot substitute for spatial availability: even with 1.2 GW of cost-effective transmission additions, system costs increase 1.5-6% under ecological restrictions, driven primarily by operational inefficiencies from constrained siting rather than insufficient grid capacity. Combined with Gilad et al.'s demonstration that transmission infrastructure carries its own biodiversity costs, our findings suggest that spatial planning frameworks constitute the primary policy lever for balancing biodiversity preservation with system economics, with grid expansion serving as a necessary but insufficient complement [ 28 , 38 ]. Our spare-share strategy reveals that the economics of temporal versus spatial balancing hinge on the interplay among ecological constraints, proximity to infrastructure, and grid extension. Notably, no scenario deployed the costly underground subsurface power line despite availability, as overhead expansion met the spatial balancing requirements even under severe land-use constraints. Economic opportunity cost of onshore wind (i.e., marginal value of additional developable land) escalates 3–9 fold from baseline regardless of transmission configuration. This illustrates that although transmission expansion enables access to areas under restrictions, it cannot overcome severe spatial constraints as the system requires access to suitable sites, not merely the ability to connect distant generation to demand centres. Similar observations were quantified by Roithner et al. [ 26 ] for Norway’s 2030 system design: grid investment provides relief, but land availability remains binding. Furthermore, despite targeting divergent ecological priorities, convergence of moderately restricted scenarios (CA, SC, SD) marginal value cost around median illustrate geographic overlap of biodiversity priorities—collision-sensitive species habitats, high-richness areas, protected zones— creating functionally equivalent constraints on available sites regardless of which ecological criteria drive restrictions. The medium restricted scenarios (CA, SC, SD) demonstrate performance advantages beyond cost minimization. The least curtailment rates (95–100 TWh/GW) suggest moderate spatial restrictions do not need to compromise operational efficiency compared to scenarios of both extreme ends (Fig. 6 , Fig. A5 ). Operation cost patterns further demonstrate these findings, illustrating ecological restrictions primarily affect extreme hours rather than typical operation. The median hourly costs remain largely unaffected (3%-6%) across scenarios and hourly cost volatility remains less than 15% from base case, indicating that ecological restrictions did not substantially affect the volatility of hourly costs. This convergence of multiple metrics on moderate restriction scenarios suggest going beyond binary framing of siting decisions. The policy-sensitive 67% of Norwegian territory does not represent intractable conflict, but rather a decision space where different socio-environmental values (collision risk minimization versus habitat protection, infrastructure concentration versus spatial dispersion) yield distinct but comparably viable pathways. The challenge lies not in identifying a single optimal solution, but in establishing participatory processes for explicit priority-setting among defensible alternatives. Historical wind farm deployment in Norway concentrated in contested geography (north, southwest, east; Fig. 2 ), prioritizing wind resource quality over ecological sensitivity rather than selecting lower-quality but ecologically safer locations. Our results validates May et al.'s [ 27 ] finding that " current practice has not succeeded in avoiding sites with higher impacts ", but adds crucial spatial specificity: contested installations cluster in regions where high wind resources coincide with ecological sensitivity, forcing explicit rather than avoided trade-offs. Our scenario analysis reveals that historically developed locations see the steepest onshore wind capacity restrictions under ecological restrictions: northern onshore wind declines from approximately ~ 8 GW to ~ 3 GW, while high ecological restrictions (i.e., EF) reduce southwestern capacity by ~ 10–12 GW (Fig. 2 , Fig. 5 ). This pattern reveals that future expansions cannot access equally productive sites with lower ecological conflicts, as the best combination of resource quality and relative ecological permissiveness has already been performed. Future onshore wind developments will require comparatively harder trade-offs between high-quality wind sites and ecologically sensitive areas. The scenario diversity approach operationalizes what Grimsrud et al. [ 39 ] advocated: explicit acknowledgment that siting decisions reflect societal values and that energy system models can inform but not determine. Future deployment requires not only technical solutions for integrating restricted capacity, but participatory governance frameworks for democratic settlement of contested space where multiple defensible priorities conflict. Future research could expand the analytical scope in three directions: broader taxonomic coverage (terrestrial mammals, marine species), social dimensions (proximity to settlements, noise impacts, landscape effects), and additional renewable energy technologies for more comprehensive assessment of socio-ecological trade-offs. The analytical scenario framework employed here could be complemented by participatory co-design processes engaging diverse stakeholders including local communities, conservation organizations, and energy developers to establish restriction criteria through workshops or deliberative methods [ 53 ], thereby enhancing social legitimacy and ownership of spatial planning decisions. Although developed for the Norwegian context, the spare-share framework offers transferable insights for regions worldwide confronting similar tensions between renewable energy expansion, landscape preservation, and biodiversity conservation [ 30 , 43 ], particularly as countries accelerate decarbonization while navigating competing environmental and social priorities. 5. Conclusion Achieving net-zero electricity systems requires substantial expansion of renewable energy, yet this imperative increasingly collides with biodiversity conservation and landscape preservation priorities. Norway exemplifies this challenge where extensive wind resources coincide with ecologically sensitive zones and culturally valued landscapes. We developed an integrated spare-share scenario framework that couples high-resolution biodiversity (247 bird species, 5 bat species) and infrastructure datasets with electricity system optimization to quantify the system-level techno-economic implications of alternative onshore wind siting configurations spanning infrastructure-proximate concentration (spare) to ecologically-guided dispersion (share) under 2050 net-zero projections. Spatial analysis reveals that only 7% of Norwegian land area remains consistently available across spare-share scenarios, while 67% exhibits flexible availability depending on which ecological criteria receive priority, and 26% faces universal restrictions. This geographic fragmentation demonstrates that wind energy siting involves extensive contested space rather than clear consensus zones. The 67% flexible geography represents neither a technical optimization problem nor an intractable conflict, but rather a decision space where different societal values—collision risk minimization, habitat protection, infrastructure concentration, spatial dispersion—yield distinct but defensible outcomes. Western and northern regions dominate both flexible areas (52%) and unavailable areas (33%), reflecting their geographic diversity. 90% of existing wind farm locations concentrate in contested zones, with 28% viable only under the most permissive conditions, demonstrating that historical development prioritized wind resource quality over ecological consensus. Future expansion cannot replicate these spatial patterns under strict ecological criteria, as the favorable combinations of resource quality and relative ecological permissiveness have been exploited. Ecological restrictions reduce Norway’s onshore wind capacity potential from 418 GW to 37 GW with system costs increasing up to ~ 6% depending on restriction severity. Moderately restricted scenarios cluster within 1% cost-increase variation despite targeting divergent ecological priorities, demonstrating that geographic overlap among biodiversity hotspots creates functionally equivalent constraints. Moreover, these scenarios exhibit operational advantages: 20% lower curtailment intensity and hourly cost volatility within 15% of baseline. Land availability constitutes the binding economic constraint rather than transmission capacity. Marginal economic value of additional developable land ranges from 500 million to 4.5 billion NOK/GW (9-fold escalation), while transmission capital investments contribute < 2% of cost increases, demonstrating that grid expansion cannot overcome geographic exclusions due to system-level bottlenecks (i.e., grid flexibility limitations, higher temporal generation-demand mismatches, and regional transmission constraints). The 5–20 GW onshore wind reduction in the national capacity mix is broadly substituted by offshore wind (up to ~ 7 GW increase) with complementary solar adjustments (± 1.5 GW). These findings demonstrate that determining spatial allocation priorities requires participatory processes rather than model-determined optimization. Energy system models can quantify trade-off implications but cannot determine which trade-offs societies should accept. Although applied to Norway, the spare-share scenario framework offers transferable insights for regions confronting analogous challenges between renewable expansion, biodiversity conservation, and landscape preservation, as decarbonization accelerates globally. Declarations CRediT authorship contribution statement Muhammad Shahzad Javed: conceptualisation, methodology, data curation, formal analysis, visualisation, writing—original draft; Dafna Gilad: conceptualisation, data curation, methodology, writing—review & editing; Roel May: conceptualisation, methodology, data curation, writing—review & editing; Thomas Kvalnes: conceptualisation, data curation, writing—review & editing; Marianne Zeyringer: conceptualisation, methodology, data curation, visualisation, writing—review & editing, project administration. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability highRES version adopted for this work and related input data necessary to replicate the results is openly available at Github: https://github.com/JavedMS/highRES-Norway/tree/highres_NINA. Acknowledgments This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. The authors gratefully acknowledge the Norwegian Institute for Nature Research (NINA) for providing the biodiversity datasets that enabled this analysis. We thank Oskar Vågerö and Guillermo Valenzuela-Venegas for valuable input on spatial analysis and integration of high-resolution datasets into the highRES electricity system model. 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Bioinformatics 28:2520–2522. https://doi.org/10.1093/bioinformatics/bts480 Footnotes https://www.nve.no/media/19307/nasjonalramme-for-vindkraft.pdf https://wimby.eu/resource/d1-3-land-and-sea-use-and-change-maps/ https://www.miljodirektoratet.no/ansvarsomrader/overvaking-arealplanlegging/naturkartlegging/Inngrepsfrie-naturomrader/ https://kartkatalog.geonorge.no/metadata?text=endangered%20species https://github.com/JavedMS/highRES-Norway/tree/highres_NINA Additional Declarations The authors declare no competing interests. Supplementary Files Appendix.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9167324","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":608762733,"identity":"15a024d2-eabb-469d-b959-8c7b6ad5a6b5","order_by":0,"name":"Muhammad Shahzad Javed","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYLACCRBxvLEBRPGQoOXMQbAeIrWAwY0ExgaiFOo2sD9gsKg5LMd383H7wx8Mh2X4GXgPPsCnxewAjwGDxLHDxpK3ExubeRgO80g28CUbENAC9AtbWuIGkBYGoBaDAzxmEvi1AB0m8S+tfsPNg42NP4jTwmDAINlmk2Bwg7GxgYcoLSA1kn02hjPPJDbO5jFI55Fs5jHG75fj7Q8fS3yTkOc7fvzBxx8V1vb87D2GD/BpYWAGehnhDgOICEHA+IEIRaNgFIyCUTCCAQB980SuVyYJsAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-5729-2356","institution":"University of Oslo","correspondingAuthor":true,"prefix":"","firstName":"Muhammad","middleName":"Shahzad","lastName":"Javed","suffix":""},{"id":608764560,"identity":"2c877c48-c99e-458f-b924-a5a1fa80c853","order_by":1,"name":"Dafna Gilad","email":"","orcid":"","institution":"Norwegian Institute for Nature Research (NINA)","correspondingAuthor":false,"prefix":"","firstName":"Dafna","middleName":"","lastName":"Gilad","suffix":""},{"id":608764561,"identity":"7b5b5e8e-7b7a-4619-b6ba-97b69c3245b9","order_by":2,"name":"Roel May","email":"","orcid":"","institution":"Norwegian Institute for Nature Research (NINA)","correspondingAuthor":false,"prefix":"","firstName":"Roel","middleName":"","lastName":"May","suffix":""},{"id":608764562,"identity":"7185f42e-aaa2-4147-806a-035ca22810ac","order_by":3,"name":"Thomas Kvalnes","email":"","orcid":"","institution":"Norwegian Institute for Nature Research (NINA)","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Kvalnes","suffix":""},{"id":608764564,"identity":"6279f285-b823-4f57-8a09-8cdd2bfd0a38","order_by":4,"name":"Marianne Zeyringer","email":"","orcid":"","institution":"University of Oslo","correspondingAuthor":false,"prefix":"","firstName":"Marianne","middleName":"","lastName":"Zeyringer","suffix":""}],"badges":[],"createdAt":"2026-03-19 09:06:25","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9167324/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9167324/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105038505,"identity":"9000cf6a-0ad5-4b62-9e7c-7750fc144b08","added_by":"auto","created_at":"2026-03-20 07:43:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1140592,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAn overview of integrated methodological framework for assessing ecological-economic siting trade-offs in onshore wind deployment.\u003c/strong\u003e Scenario-specific spatial constraints apply only to onshore wind, while uniform spatial constraints apply to all technologies. For scenario definitions, see \u003cstrong\u003eSection 2.1; Table A1\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9167324/v1/3fef60efb30db7338ccc2612.png"},{"id":105038502,"identity":"b628b371-e252-4a34-a759-7588fca3d6e6","added_by":"auto","created_at":"2026-03-20 07:43:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1085472,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNational and regional spatial availability patterns. \u003c/strong\u003eThe figure shows the tripartite division of Norway's land area. \u003cstrong\u003e(a)\u003c/strong\u003e Always-available areas \u003cem\u003e(Avail)\u003c/em\u003e (7% of Norway) represent sites unrestricted across all scenarios, with blue dots indicating existing wind farms located in always-available areas (~10%); \u003cstrong\u003e(b)\u003c/strong\u003eAlways-unavailable areas \u003cem\u003e(Unavail)\u003c/em\u003e (26% of Norway) indicate sites universally restricted across all scenarios, concentrated in Western and Northern Norway; \u003cstrong\u003e(c) \u003c/strong\u003ePolicy discussion sites (67% of Norway) represent flexible areas \u003cem\u003e(Flex)\u003c/em\u003e where availability depends on scenario choice. Red and blue color intensities indicate the number of scenarios the given location appears; \u003cstrong\u003e(d)\u003c/strong\u003e Distribution of flexible area across scenarios, showing most clusters around 4-5 scenarios; \u003cstrong\u003e(e)\u003c/strong\u003e Correlation matrix between scenarios illustrating how likely two scenarios share the same available area in Fig. c;\u003cstrong\u003e (f) \u003c/strong\u003eExisting wind farm location distribution across scenarios—28% appear only in one scenario. Circled counties show the highest share of their area in each category. For example, in Fig. b, Oslo exhibits the highest internal restriction rate (50.5%) but contributes minimally to the total unavailable area (0.5%) [\u003cstrong\u003eSee Table A1\u003c/strong\u003e].\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9167324/v1/5c82c1f35f5e1c8c658d39c8.png"},{"id":105039390,"identity":"072eb50e-7773-447b-8799-8e92cf62b616","added_by":"auto","created_at":"2026-03-20 07:46:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":431378,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSystem cost implications for ecological restrictions.\u003c/strong\u003e(a) Total system costs for net-zero 2050 system designs under fixed and expandable transmission configurations across scenarios. Production maximization (PM) scenario serves as the base case for change in costs (dashed lines in (a) with right y-axis) and cost compositions in (b) and (c). Contribution of each system cost component to total system cost decrease or increase. Negative values reduction in relative cost component from base case component, while positive values show increases. Red vertical lines mark the total system cost increase from the base case. \u003cem\u003eCapex: upfront capital cost; upkeep: fixed \u0026amp; variable operation and maintenance cost.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9167324/v1/3a964c3e0c344f167f1064f4.png"},{"id":105039299,"identity":"185742f3-d1c5-43f5-afbe-d78029a6e913","added_by":"auto","created_at":"2026-03-20 07:45:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":593497,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTechnology capacity deployment patterns under ecological restrictions. (a)\u003c/strong\u003e Fixed and \u003cstrong\u003e(b)\u003c/strong\u003eexpandable transmission scenario results showing installed generation capacity (top panels) and storage capacity (bottom panels). The left panels show baseline PM scenario capacities, while the right panels show changes from PM across scenarios. Negative values indicate reductions and positive values show increases relative to PM. Total installed capacities are presented in Fig. A2.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9167324/v1/4367d6b4a8bfaf3ff5769a59.png"},{"id":105040031,"identity":"0c47d06a-39f3-4c94-b302-b9e870f3b829","added_by":"auto","created_at":"2026-03-20 07:47:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1105589,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRegional technology deployment patterns across Norway.\u003c/strong\u003e \u003cstrong\u003e(a)\u003c/strong\u003e Fixed transmission and \u003cstrong\u003e(b)\u003c/strong\u003e expandable transmission scenarios showing county-level capacity deployment across environmental scenarios. Pie charts represent the installed generation capacity mix in each county, while pie chart sizes are proportional to total county deployment. Transmission infrastructure is shown as HVAC 400kV lines (overhead) and HVDC subsurface power lines. Transmission expansion shifts capacity magnitudes within regions but does not alter county-level technology portfolios, demonstrating that regional comparative advantages in resource quality and environmental restriction drive technology allocation rather than transmission flexibility.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9167324/v1/fb03ef9eb2ac9b8b689aeaef.png"},{"id":105039258,"identity":"cec6d057-4a2a-466e-80a3-84d6857b1e42","added_by":"auto","created_at":"2026-03-20 07:45:33","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":295257,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEconomic opportunity cost and system efficiency across ecological scenarios for onshore wind. (a)\u003c/strong\u003eFixed transmission and \u003cstrong\u003e(b)\u003c/strong\u003e expandable transmission results showing the relationship between economic opportunity cost (million NOK/GW) and unused available capacity (GW), and curtailment rates (circle size, TWh/GW). Economic opportunity cost represents the marginal economic value of relaxing area constraints at the current optimum. Green and red dashed lines indicate the median across scenarios. The transmission expansion, though it slightly shifts the magnitudes, did not impact the diminishing returns of the permissiveness relationship between unused capacity and opportunity costs. See \u003cstrong\u003eFig. A5\u003c/strong\u003ein the appendix for offshore wind.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9167324/v1/74103fb18d129ae2fd37755f.jpeg"},{"id":105038702,"identity":"b599247f-d00d-46f4-8504-546fb3d3cb6a","added_by":"auto","created_at":"2026-03-20 07:44:18","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1299428,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHourly electricity cost distribution and volatility. Distribution of hourly electricity costs for fixed (upper) and expandable (lower) transmission configurations.\u003c/strong\u003eCoefficient of variation (CoV = σ/μ) values on the right quantify relative volatility for each scenario, with lower values indicating more consistent costs relative to the mean. Transmission expansion increases volatility across all scenarios, reflecting enhanced dispatch flexibility.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-9167324/v1/2bb95d3b9da3d6e93f05fe9f.png"},{"id":105040602,"identity":"6dd1f0d3-9d31-4c7e-a569-2c0408426b91","added_by":"auto","created_at":"2026-03-20 07:50:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7180863,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9167324/v1/0919ef2c-8cf8-4678-aa2f-c8abccde4b9d.pdf"},{"id":105038234,"identity":"71667d6e-b3f6-4b99-aafd-5c821fa6f46c","added_by":"auto","created_at":"2026-03-20 07:42:58","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3155794,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-9167324/v1/4e3da718da4dd85ac79a47b9.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eEcological and economic onshore wind turbine siting trade-offs in Norway\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe transformation of energy systems toward renewable sources represents a cornerstone of global decarbonization efforts [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Wind energy, with its substantial technical potential and low costs [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], has emerged as a critical technology for achieving climate targets, particularly as electrification extends into transportation, industry, and heating sectors [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The European Union aims to more than double installed wind capacity to 440 GW by 2030 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], while globally, wind generation must expand from less than 2000 TWh to approximately 12,000 TWh by 2050 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] to meet climate targets. However, the urgency of climate action increasingly collides with equally pressing concerns about biodiversity conservation, landscape preservation, and ecosystem integrity. With habitat change identified as the primary threat to diverse species across multiple regions [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], recent assessments emphasize that nature loss may prove as consequential as climate change itself, with the two crises mutually reinforcing [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNorway exemplifies this tension while offering a particularly instructive case for examining these trade-offs. The country aims to reduce greenhouse gas emissions by 50% relative to 1990 levels by 2030 and by 90% by 2050 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], necessitating substantial expansion of the electricity system from 127 TWh in 2023 to potentially 250 TWh by 2050 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This increase stems from development of new energy-intensive industries to move away from oil and gas income and ongoing electrification of transport and industry. The nation possesses extensive onshore wind resources [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], substantial existing hydropower capacity for balancing variable generation [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and well-documented ecological data enabling species-specific impact assessment [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Yet with large hydropower development constrained by environmental limits since 2001 [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], onshore wind became the primary expansion technology, growing rapidly between 2017 and 2022 from 1.7% to 10% of electricity generation [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. This growth ground to a near halt after 2020 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], catalyzed by mounting opposition over landscape disruption, biodiversity impacts, and procedural justice concerns [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The 2021 Supreme Court ruling on the Fosen wind farms, which found operations violated S\u0026aacute;mi rights under international conventions [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], crystallized broader conflicts surrounding wind energy development. Procedural conflicts have effectively stalled new wind deployment [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], leading to potential electricity deficits already by 2027 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], while growing recognition of biodiversity vulnerabilities [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] increasingly constrains where future expansion can occur\u0026mdash;placing Norways\u0026rsquo; energy transition at a crossroads. The lessons from this experience\u0026mdash;balancing climate imperatives against ecological protection and social acceptance\u0026mdash;extend beyond national borders to inform renewable energy (RE) transitions across regions facing analogous pressures.\u003c/p\u003e \u003cp\u003eThese conflicts manifest in planning processes worldwide. Quantitative evidence demonstrates that wind proposals in scenic areas face higher rejection rates [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], while empirical assessments reveal that existing Norwegian installations have failed to avoid sites with elevated bird impacts [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Wind energy development affects biodiversity through habitat loss, disturbance, collision mortality, and barrier effects [\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], with impacts varying substantially by species characteristics and site conditions [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Tracking data from over 1,700 mortality events across 45 migratory bird species reveals that energy infrastructure\u0026mdash;including power lines and wind turbines\u0026mdash;accounts for nearly half of all documented human-induced bird mortality in the African-Eurasian flyway [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Recent pan-European risk assessments identify collision hotspots concentrated in regions including Northern Europe [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], while system-wide analyses quantify how hydropower, wind installations, and transmission infrastructure collectively shape biodiversity footprints in Norway [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAddressing these intertwined challenges requires frameworks that integrate spatial heterogeneity in environmental impacts of renewable energy siting with system-level operational outcomes. Spatial studies have advanced understanding of where environmental impacts occur [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], while energy system models optimize technology deployment under cost and demand constraints [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. However, spatial analyses typically lack representation of system flexibility requirements, grid-level system bottlenecks, and economic trade-offs beyond siting decisions. In contrast, energy system models frequently treat space as abstract capacity limits specifying how much generation potential exists in a region with rarely encoding the geographic details of which specific sites become available under different environmental priorities. This abstraction prevents models from capturing how the spatial configuration of deployment affects operational efficiency: whether capacity concentrates near existing infrastructure (i.e. sparing nature) or disperses across remote high-wind zones (i.e. sharing with nature) fundamentally alters system operational patterns, temporal balancing requirements, and the relative economics of transmission expansion versus storage investment [\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Recent efforts have begun incorporating ecological and biodiversity considerations [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] into energy system modeling, yet comprehensive analysis of how divergent spatial allocation strategies affect operational outcomes remains limited. Broader sustainability research emphasizes that reversing biodiversity declines requires integrated strategies that transform production systems rather than relying on conservation measures alone [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], a principle equally applicable to RE deployment.\u003c/p\u003e \u003cp\u003eThis study develops an integrated spatial-economic framework to quantify how different geographic allocation strategies for onshore wind deployment can shape Norway's 2050 electricity system. We address three interconnected questions: First, how do varying ecological constraints affect the geographic availability of suitable wind sites? Second, how do these spatial constraints translate into system-level techno-economic outcomes? And third, how do spatial constraints reshape system balancing and technology substitution patterns?\u003c/p\u003e \u003cp\u003eWe contrast scenarios spanning a spectrum from infrastructure-concentrated (\"spare\") to ecologically-dispersed (\"share\") siting strategies, incorporating species-level biodiversity data [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], proximity to existing infrastructures [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], collision sensitivity frameworks [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], protected areas [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] and techno-economic criteria. These scenarios are coupled with the high temporal and spatial resolution electricity system (highRES) optimization model to assess technology deployment, spatio-temporal flexibility requirements, and system-level operational efficiencies [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. This approach extends recent work quantifying near-term feasibility constraints under social-environmental restrictions [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and monetized externality trade-offs [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] by analyzing net-zero electricity system operational dynamics. Rather than identifying a single optimal deployment, we employ scenario diversity to reveal trade-off spaces where different ecological priorities yield distinct but viable pathways, recognizing that socio-ecological futures are inherently uncertain yet partially shapeable through present decisions [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e"},{"header":"2. Data and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Spare-share scenarios: balancing economic and ecological interests\u003c/h2\u003e \u003cp\u003eThe siting of RE infrastructure reflects competing priorities that cannot be simultaneously optimized: infrastructure proximity may reduce grid extension costs but sacrifices access to higher-quality wind resources, while ecological protection often requires development away from high-biodiversity zones that frequently coincide with superior wind resources [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Biodiversity encompassing diverse life forms and associated ecosystems is represented here through species richness maps for birds and bats that quantify their spatial distribution based on occurrence data and habitat suitability models [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. To develop scenarios, we employ the spare-share framework, which distinguishes between strategies that spare ecologically sensitive areas by concentrating development near existing infrastructure versus strategies that share impacts more broadly across landscapes [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConserving biodiversity and landscapes for future generations is deeply rooted in Norwegian culture and politics [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], while visual effects on landscapes and noise have been identified as primary concerns in wind farm opposition in Norway [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. The Norwegian wind farm licensing framework evolved from municipal land-use control (pre-2008) to centralized authority with substantial decision making power. The Norwegian Water Resources and Energy Directorate (NVE) proposed a national framework\u003csup\u003e1\u003c/sup\u003e in 2019 for mapping best suitable areas for onshore wind development. Widespread municipal opposition to this framework underscored the need for scenario-based analysis that accommodates diverse environmental priorities rather than technocratic siting optimization [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Therefore, our scenarios integrate ecologically-guided dispersion with infrastructure-proximate concentration to leverage existing grid connections and road networks. Rather than identifying a single optimal strategy, we developed the following scenarios along the continuum of species richness and infrastructure proximity dimensions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003eA1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eProduction maximization (PM)\u003c/b\u003e serves as the permissive baseline, excluding only strictly protected areas (IUCN categories Ib and II: wilderness areas and national parks, respectively) while maximizing onshore wind potential to establish economic and operational reference point. Notably, we do not exclude the Ia areas (strict nature reserves\u0026mdash;the most stringent IUCN protection level) to reflect historical permissive licensing practices as some existing Norwegian wind farms are sited within this category\u003csup\u003e2\u003c/sup\u003e. \u003cb\u003eProduction co-location (PC)\u003c/b\u003e restricts development to sites within 5 km of existing infrastructure with the exclusion of protected areas, reflecting concerns about grid extension costs and wilderness fragmentation, as in Norway, undisturbed nature areas (\u0026ldquo;inngrepsfrie naturomr\u0026aring;der\u0026rdquo; in Norwegian), are defined as wilderness areas that are over five kilometers away from human infrastructure. Undisturbed nature areas are divided into three zones: 5 kilometers, 3 to 5 kilometers, and 1 to 3 kilometers away from infrastructure\u003csup\u003e3\u003c/sup\u003e. \u003cb\u003eCollision sensitivity avoidance (CA)\u003c/b\u003e prioritizes sites with lower collision risk for vulnerable species\u0026mdash;specifically bats, raptors, and gulls\u0026mdash;by excluding areas with the highest species concentrations (classes 3\u0026ndash;5) and redlist species\u003csup\u003e4\u003c/sup\u003e areas with established buffer zones, while avoiding protected areas (Ia, Ib, and IV - habitat or species management area). The taxonomic groups\u0026mdash;bats, raptors, and gulls\u0026mdash;are treated as separate datasets due to their elevated vulnerability to turbine blade strikes (Table\u0026nbsp;3, Ref. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]). Morphological characteristics of raptors and gulls exhibit flight traits that increase strike probability, while bats face additional challenges due to attraction to turbine structures [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. This reflects the precautionary principle applied to areas with high collision-sensitive bird and bat concentrations despite limited evidence of population-level impacts [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. \u003cb\u003eEcological footprint minimization (EF)\u003c/b\u003e imposes strict infrastructure proximity requirements (\u0026lt;\u0026thinsp;1 km from existing infrastructure) to concentrate development in already-modified landscapes, while excluding high-concentration areas for all bird and bat species (classes 3\u0026ndash;5), protected areas (Ia, Ib, II, IV), and redlist species zones, making it the most constrained scenario. \u003cb\u003eSensitive development (SD)\u003c/b\u003e and \u003cb\u003esustainable co-location (SC)\u003c/b\u003e represent balanced share and spare scenarios, respectively. SD balances protection of highly collision-sensitive species (bats, raptors, gulls\u0026mdash;more moderate criteria (classes 4\u0026ndash;5) than CA) with development feasibility, avoiding protected areas (Ia, Ib, IV) and redlist species zones. \u003cb\u003eSC\u003c/b\u003e moderates EF's strict proximity requirement by excluding only the highest species richness areas (all species, classes 4\u0026ndash;5) within 3 km of existing infrastructure, alongside high-risk protected areas (Ia, Ib) and redlist species. Finally, \u003cb\u003esustainable development\u003c/b\u003e (SO1, SO2) reflects the balanced development focus with balanced siting strategy where thresholds are applied to the geometric mean of combined species richness and infrastructure datasets rather than individual restrictions: SO1 excludes classes 3\u0026ndash;5 (stricter), while SO2 excludes classes 4\u0026ndash;5 (moderate), both maintaining protected area exclusions (Ia, Ib) and redlist protections. This geometric mean approach integrates multiple dimensions simultaneously, exploring whether combined metrics yield viable alternatives to dimension-specific restrictions.\u003c/p\u003e \u003cp\u003eWe applied Jenks natural breaks optimization to partition values into five classes that maximize within-class homogeneity and between-class variance [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. This classification approach aligns with Environmental Impact Assessment (EIA) frameworks, where environmental values are categorized from negligible (class 1) to very high significance (class 5) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The five-class structure enables differentiation between moderate restrictions (excluding only classes 4\u0026ndash;5) and stringent restrictions (excluding classes 3\u0026ndash;5), as specified in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003eA1\u003c/span\u003e. Jenks breaks were selected over arbitrary thresholds because they identify natural discontinuities in the statistical distribution of species richness, providing ecologically meaningful boundaries rather than percentile-based cutoffs that may bisect homogeneous habitat zones [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Our scenarios are not predictions of future policy but rather analytical constructs for exploring diverse plausible futures under socio-environmental uncertainty [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. The scenario positions along biodiversity stringency and infrastructure dependence axes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) span the range of viable positions articulated in Norwegian planning debates [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Detailed criteria thresholds, data sources, and spatial implementation methods are specified in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003eA1\u003c/span\u003e and described in Section \u003cspan refid=\"Sec4\" class=\"InternalRef\"\u003e2.2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Datasets and tools\u003c/h2\u003e \u003cp\u003eSpecies richness data were derived from the validated life-cycle assessment models [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], which developed spatially explicit distributions for diverse bird and bat species across Norway using their occurrence records from the Global Biodiversity Information Facility (GBIF, 2010\u0026ndash;2019) and MaxEnt habitat suitability modeling [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The datasets encompass 247 bird species and 5 bat species [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], with species richness calculated as the number of species multiplied by their probability of presence at 1km \u0026times; 1km resolution and rescaled to 100 m \u0026times; 100 m resolution. Protected areas were obtained from the World Database of Protected Areas (WDPA) [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], categorized by IUCN management classifications (Ia, Ib, II, IV, and V - protected landscape). The infrastructure index\u0026mdash;quantifying proximity to existing settlements\u0026mdash;was calculated following [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e] (see table 2, page 19), with values\u0026thinsp;\u0026gt;\u0026thinsp;1.8 indicating areas with substantial infrastructure presence [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. All spatial datasets were harmonized to the ETRS89-LAEA coordinate system (EPSG:3035) at 100 m resolution.\u003c/p\u003e \u003cp\u003eScenario-specific availability rasters were constructed by combining biodiversity layers with protected area and infrastructure constraints using the \u003cem\u003e\u0026lsquo;atlite\u0026rsquo;\u003c/em\u003e geospatial toolbox (version 0.2.13) [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. This tool performs spatial union operations to generate composite availability masks across multiple raster layers. For example, the EF scenario excludes areas where any of the following conditions are met: species richness for bats, raptors, gulls, or all birds falls within classes 3\u0026ndash;5 (highest richness values); protected areas Ia, Ib, II, IV; redlist species presence; or infrastructure index indicating locations\u0026thinsp;\u0026gt;\u0026thinsp;1 km from existing settlements. These high-resolution (100 m) scenario-specific availability rasters enabled detailed spatial analysis across Norway to identify national and regional patterns of land availability (Section \u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003e3.1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. These rasters were subsequently integrated with technical constraints (i.e., slope, height, physical infrastructures) and weather data for capacity deployment optimization in the highRES energy system model, as described in Section \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003e2.3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Power system modelling framework\u003c/h2\u003e \u003cp\u003eWe employ the open-source highRES model [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], designed to assess electricity systems with high shares of variable RE and explore flexibility options. highRES is a linear optimization model implemented in the General Algebraic Modeling System (GAMS) to minimize total system costs (annualized investment and operational expenditures) by optimizing the spatial allocation of new technology capacities, hourly dispatch decisions, transmission expansion, and energy storage deployment under perfect foresight with hourly temporal resolution. We adapt highRES to represent the Norwegian power system across 15 administrative regions (revised 2024 boundaries). The model represents a hybrid greenfield framework where existing solar and wind capacities are excluded, while existing hydropower and transmission lines are included and remain fixed at current capacities. Existing wind capacity (~\u0026thinsp;5 GW) is excluded due to limited operational lifespan by 2050 and to enable endogenous determination of optimal deployment patterns under scenario-specific ecological constraints. Technologies available for capacity expansion include onshore wind, offshore wind (bottom-mounted in waters\u0026thinsp;\u0026lt;\u0026thinsp;70 m depth and floating turbines in deeper waters [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]), solar photovoltaics, and energy storage. All scenarios employ identical techno-economic parameters, 2050 demand projections, weather data, and exclusions for solar PV and offshore wind; the sole variation lies in onshore wind availability masks reflecting the ecological and biodiversity priority configurations detailed in Section \u003cspan refid=\"Sec4\" class=\"InternalRef\"\u003e2.2\u003c/span\u003e. A detailed description of highRES model, assumptions, technical constraints, and data sources is provided in the \u003cspan refid=\"Sec15\" class=\"InternalRef\"\u003e\u003cb\u003eAppendix\u003c/b\u003e\u003c/span\u003e, and the model code with full replication data is openly available at \u003cb\u003eGitHub\u003c/b\u003e\u003csup\u003e5\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 National and regional spatial patterns of available sites\u003c/h2\u003e \u003cp\u003eA 7% of the total Norwegian land area is commonly available across the scenarios, revealing rare safe onshore wind development zones (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). In contrast, 67% exhibits availability in some scenarios but restricted in others, suggesting that the majority of Norway's landscape availability is sensitive to which ecological and infrastructure priorities guide spatial allocation decisions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). The dominance of contested geography over consensus zones illustrates Norway's spatial complexity: biodiversity hotspots, landscape values, infrastructure constraints, and ecological sensitivity overlap substantially across the territory.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAlthough the majority of unavailable areas across spare-share scenarios (\u003cem\u003eunavail\u003c/em\u003e) concentrate in Western and Northern Norway (~\u0026thinsp;50% of total \u003cem\u003eunavail\u003c/em\u003e), the eastern counties exhibit significant internal restrictions due to anthropogenic impacts, such as urban footprints and existing developments (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Notably, while approximately half of these small urban counties are universally restricted, when combined, they account for only\u0026thinsp;~\u0026thinsp;4% of the total always unavailable area.\u003c/p\u003e \u003cp\u003eScenario correlations reveal the extent to which different restriction frameworks agree on geographic suitability\u0026mdash;a high positive correlation indicates the likelihood of sharing flexible area geographies. In contrast, a negative correlation indicates that scenarios access different geographies (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). Despite both targeting similar biodiversity conservation, the moderate correlation between CA and SD (0.51) suggests that half the area SD permits is restricted by higher CA\u0026rsquo;s restrictions, with additional divergence arising from heterogeneous spatial distributions of diverse species groups [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This geographic divergence is reflected in the distribution of flexible areas across scenarios (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed), where most flexible zones cluster around 4\u0026ndash;5 scenarios, indicating that varying severity levels of similar habitat priorities fragment geographic outcomes. When biodiversity restrictions are combined with proximity constraints in EF (1 km) and SC (3 km) scenarios, the correlation further drops to 0.21, suggesting heterogeneity in balancing spare and share siting strategies for land-based wind farms, with not many emerging as suitable across the restriction frameworks.\u003c/p\u003e \u003cp\u003eDue to targeting different geographies, spare-and-share scenarios exhibit negative to very low correlations with one another. The most conservative scenario, EF, exhibits correlations ranging from \u0026minus;\u0026thinsp;0.08 to 0.21 with other scenarios, indicating that high infrastructure proximity restriction combined with high ecological restrictions creates spatial patterns distinct from either restriction applied independently (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). Negative correlations between economy (PC) and share scenarios (CA vs PC: -0.12, SD vs PC: -0.12) demonstrate that economy-focused infrastructure concentration and ecology-focused infrastructure concentration target opposite geographies. Moreover, negative or low correlations between economic siting near infrastructure (PC) and ecologically acceptable sites near infrastructure (EF, SC, SO1, SO2) preclude the spatial optimization of wind sites that considers both criteria simultaneously. Notably, highly restricted scenarios correlate more strongly with each other (EF-CA: 0.20) than with moderately restricted scenarios (EF-SD: 0.10; SC-SD: 0.42 vs SC-CA: 0.31), indicating that extreme restrictions\u0026mdash;whether ecological or combined\u0026mdash;converge on the same limited geography of least-sensitive areas, while moderate thresholds access broader, more diverse landscapes. Three large northern/central counties (Innlandet, Tr\u0026oslash;ndelag, Finnmark) dominate both the flexible area (accounting for ~\u0026thinsp;52% of the total) and the unavailable area (33% of the total), indicating that large, geographically diverse counties contain the diverse restriction spectrum.\u003c/p\u003e \u003cp\u003eMost existing wind farm locations are in flexible zones, with only\u0026thinsp;~\u0026thinsp;10% in always-available zones (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). 28% of existing wind farm locations appear in only one scenario\u0026mdash;the most permissive PM scenario\u0026mdash;and most of these single-scenario installations concentrate in Northern Norway (light blue markers, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Similar to the overall scenario distribution across flexible zones, most existing wind farm locations appear in 4\u0026ndash;6 scenarios, revealing that historical development already navigated moderate ecological contestation but avoided the most sensitive zones.\u003c/p\u003e \u003cp\u003eEcological restrictions reduce onshore wind capacity potential from 418 GW (PM) to 37 GW (EF)\u0026mdash;an 11-fold reduction \u003cb\u003e(\u003c/b\u003eFig. \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003eA1\u003c/span\u003e \u003cb\u003ein the\u003c/b\u003e \u003cspan refid=\"Sec15\" class=\"InternalRef\"\u003e\u003cb\u003eappendix\u003c/b\u003e\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The levelized cost of energy (LCOE) of onshore wind increases up to 19% to 21% across scenarios to meet all the new electricity demand for 2050 (Fig. \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003eA1\u003c/span\u003e (b); vertical red lines) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The onshore potential under EF (83 TWh maximum) remains insufficient to meet upper demand projections alone. Regional capacity factor analysis reveals that six counties experience a mean increase in capacity factor, as restrictions preferentially exclude low-quality sites (marked green). Conversely, seven counties exhibit declines in mean capacity factor due to the exclusion of good-quality wind sites (marked red).\u003c/p\u003e \u003cp\u003eNotably, three counties suffer on average the steepest degradation of high-quality sites (Finnmark\u0026thinsp;\u0026minus;\u0026thinsp;10.7%, Akershus\u0026thinsp;\u0026minus;\u0026thinsp;11%, Telemark\u0026thinsp;\u0026minus;\u0026thinsp;5.2%), while moderate-quality central regions (Innlandet\u0026thinsp;+\u0026thinsp;3.4%, Tr\u0026oslash;ndelag\u0026thinsp;+\u0026thinsp;3.3%) gain relative attractiveness, and some remain largely unaffected (i.e., M\u0026oslash;re og Romsdal). Spatial availability patterns \u003cb\u003e(\u003c/b\u003eFig.\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003eA1\u003c/span\u003e \u003cb\u003e(a) in the\u003c/b\u003e \u003cspan refid=\"Sec15\" class=\"InternalRef\"\u003e\u003cb\u003eappendix\u003c/b\u003e\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e demonstrate that northern capacity depends critically on infrastructure proximity criteria, as these areas' capacity collapses by 70% to 90% (i.e., EF, SC, SO1). Eastern regions exhibit inverse sensitivity as high biodiversity thresholds disproportionately restrict these areas (SC, CA, SD, EF) under species richness frameworks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 System cost implications of different land use patterns\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eEcological restriction-related system cost increases are non-linear, driven by both capital and operational costs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Both spare and share scenarios under expandable transmission exhibit higher cost increases because expandable PM has already optimally deployed transmission capacity (reducing baseline costs by 1.8% with +\u0026thinsp;1.2 GW transmission capacity from PM fixed transmission), leaving minimal further transmission optimization potential for other scenarios, which must then absorb costs through technology substitution (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea,c). Despite divergent ecological priorities, moderately restricted scenarios (SO1, SO2, SC, SD) cluster at 1.5\u0026ndash;2.5% cost increases, illustrating that geographic overlap of different ecological and biodiversity hotspots leads to produce similar economic outcomes [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGeneration upkeep costs contribute 50\u0026ndash;80% of total cost increase across all scenarios due to two reasons. First, Norway's extensive existing hydropower capacity contributes substantially to upkeep costs without associated capital expenditure. Second, ecological restrictions shift the technology portfolio toward higher operational cost alternatives (i.e., offshore increases from ~\u0026thinsp;3 GW to ~\u0026thinsp;10 GW from PM) with reduced generation efficiency (2,180 vs 2,390 TWh/GW), disproportionately increasing upkeep costs. This is similar to other Norway-based studies [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStorage investments reflect the economics of balancing between spatial and temporal resources: the system invests in storage when transmission is constrained and reduces investment in storage as transmission expands \u003cb\u003e(\u003c/b\u003eFig. \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003eA3\u003c/span\u003e \u003cb\u003ein the\u003c/b\u003e \u003cspan refid=\"Sec15\" class=\"InternalRef\"\u003e\u003cb\u003eappendix\u003c/b\u003e\u003c/span\u003e, Section \u003cspan refid=\"Sec9\" class=\"InternalRef\"\u003e3.3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. EF presents an exception\u0026mdash;despite transmission expansion, storage investment increases by 13% of total cost change, demonstrating that strict ecological restrictions create temporal mismatches that neither transmission-alone nor storage-only substitution can fully resolve. Negligible transmission capital investments (\u0026lt;\u0026thinsp;2% of cost increases) across all expandable scenarios from the base PM-expandable (where already\u0026thinsp;+\u0026thinsp;1.5 GW transmission capacity is added) illustrate that land-use flexibility constitutes most of the binding economic constraint.\u003c/p\u003e \u003cp\u003eEconomic impact of coupling spare strategy with economy-focused objective (i.e. PC) virtually imposes negligible cost penalty (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea), demonstrating that infrastructure-proximate deployment is economically optimal when unconstrained by ecological restrictions. However, when spare strategy pairs with ecological constraints (EF, SC), the costs are increased, highlighting the strong influence of ecological constraints on the system-preferred onshore sites, regardless of their infrastructure proximity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Capacity mix and regional deployment patterns\u003c/h2\u003e \u003cp\u003eThe interplay between transmission mode, ecological restriction severity, and storage affects how the system substitutes for the constrained onshore wind (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Although ecological restrictions shift the technology mix and system substitutes for the constrained onshore wind differently, transmission expansion does not significantly reduce the total system generation capacity across the scenarios. The decrease is between 2 and 4 GW, except for EF, which shows a\u0026thinsp;~\u0026thinsp;2 GW increase in total capacity, as solar substitution partially offsets onshore wind constraints \u003cb\u003e(\u003c/b\u003eFig. \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003eA2\u003c/span\u003e \u003cb\u003ein the\u003c/b\u003e \u003cspan refid=\"Sec15\" class=\"InternalRef\"\u003e\u003cb\u003eappendix\u003c/b\u003e\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAlthough transmission expansion enables better substitution flexibility, it does not overcome the impact of ecological restrictions on the onshore wind deployment capacity as the onshore wind reduction pattern remains relatively the same regardless of transmission configuration. The most restricted scenario (EF) shows\u0026thinsp;~\u0026thinsp;20 GW of onshore wind reduction from the PM baseline across transmission scenarios, while moderately restricted scenarios (CA/SC/SD) show\u0026thinsp;~\u0026thinsp;5\u0026ndash;10 GW reductions.\u003c/p\u003e \u003cp\u003eChanges in solar capacity are driven by siting strategy rather than transmission configuration. Spare scenarios (PC, SC) exhibit marginal solar reductions (0.1\u0026ndash;1.1 GW) as concentration near existing infrastructure enables better utilization of nearby onshore wind resources. Conversely, share scenarios (CA, SD) show marginal increases (0.1\u0026ndash;0.6 GW) as broader spatial deployment creates opportunities for complementary solar integration. This spare-share pattern holds consistently across both transmission configurations, though absolute solar capacities are higher under fixed transmission than expandable (Fig. \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003eA2\u003c/span\u003e \u003cb\u003ein the\u003c/b\u003e \u003cspan refid=\"Sec15\" class=\"InternalRef\"\u003e\u003cb\u003eappendix\u003c/b\u003e\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. High ecological restrictions in the EF lead to an exception in which the system compensates for constrained wind with high solar deployment (+\u0026thinsp;7 GW) rather than costly offshore wind.\u003c/p\u003e \u003cp\u003eOffshore wind remains the least sensitive to transmission configurations, varying by 0.5\u0026ndash;0.7 GW within scenarios across transmission categories. This demonstrates that offshore deployment in Norway responds to land-based availability constraints rather than to transmission expansion (i.e., grid flexibility), enabling better utilization of available capacity but not fundamentally altering the national technology mix. Floating offshore wind becomes economically competitive only under very high land-based restrictions (i.e., EF), demonstrating that transmission expansion marginalizes floating offshore's economic competitiveness except when land-based options are critically constrained.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Regional deployment patterns\u003c/h2\u003e \u003cp\u003eNew storage deployment is geographically concentrated in southwestern regions (M\u0026oslash;re og Romsdal, Telemark, Agder), primarily driven by temporal balancing of high solar saturation (Fig. \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003eA4\u003c/span\u003e \u003cb\u003ein the\u003c/b\u003e \u003cspan refid=\"Sec15\" class=\"InternalRef\"\u003e\u003cb\u003eappendix\u003c/b\u003e\u003c/span\u003e). Under fixed transmission, storage is located near generation sites where temporal balancing is most economical\u0026mdash;M\u0026oslash;re og Romsdal and Tr\u0026oslash;ndelag each host up to 15 GWh despite their distance from demand centers in the south and east. However, new storage shifts toward demand-proximate southern regions (Telemark, Agder) with transmission expansion, reflecting the economic advantage of temporal balancing near load centers when transmission can efficiently connect distant generation. Western regions (Rogaland, Vestland) deploy negligible new storage across all scenarios, given existing hydropower storage, eliminating the need for additional temporal balancing capacity.\u003c/p\u003e \u003cp\u003eSpatial deployment patterns reveal stable, region-specific technology structures across transmission configurations. Telemark, Vestfold, and Buskerud inflexibly hold solar allocation across all scenarios regardless of transmission mode, suggesting these regions possess optimal solar resource quality and demand proximity unaffected by the level of constrained onshore wind (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Similarly, Agder, Tr\u0026oslash;ndelag, Nordland, and Troms maintain onshore wind deployment across all scenarios. \u0026Oslash;stfold exhibits high sensitivity to ecological restrictions, where the capacity of 5.1 GW onshore wind (PM) is substituted by 4.1 GW solar, leading to accelerated marginal cost of EF restrictions (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Finnmark demonstrates similar vulnerability; even transmission expansion did not make the available areas in the north economically attractive. Offshore expansion mostly occurs in the southwestern regions (Agder, Rogaland). Transmission expansion does not significantly alter regional technology portfolios but shifts the magnitude of capacity (i.e., Agder, Innlandet), demonstrating that transmission flexibility enables capacity optimization rather than technology diversification, with spatial technology allocation determined primarily by resource availability and ecological constraints rather than by grid configuration (i.e., transmission).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Economic cost of different protection levels\u003c/h2\u003e \u003cp\u003eThe economic pressure of environmental restrictions on system optimization is quantified by shadow prices (dual variables) derived from the model's binding area capacity constraints (for further explanation, see Ref. [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]). Shadow prices represent the marginal rate at which the total system cost objective function would decrease if the area constraint were relaxed by one unit\u0026mdash;mathematically, \u0026part;(total system cost)/\u0026part;(available area). These dual values indicate the difficulty of satisfying area constraints under each scenario, revealing the instantaneous economic value of additional developable land at the current optimum. Critically, shadow prices are local derivatives evaluated at a specific solution and therefore serve as comparative indicators of relative economic pressure across scenarios rather than predictive estimates of cost savings from discrete capacity additions. It enables assessment of how varying degrees of ecological restriction severity create economic opportunity costs when limiting access to available wind sites.\u003c/p\u003e \u003cp\u003eThe analysis reveals substantial variation in economic opportunity cost across scenarios, ranging from 500\u0026nbsp;million NOK/GW (PM, PC) to 4,5\u0026nbsp;billion NOK/GW (EF)\u0026mdash;a 9-fold escalation. This large range indicates that the marginal economic value of additional developable areas substantially depends on the restrictive frameworks. Despite this extreme range, moderately restricted scenarios (CA, SC, SD) cluster around the median, demonstrating that different ecological and biodiversity priorities yield similar economic opportunity costs when geographic overlap among ecological hotspots creates functionally equivalent restrictions on available onshore wind sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, vertical green line).\u003c/p\u003e \u003cp\u003eAlthough transmission modestly affects economic cost (-14% to +\u0026thinsp;18% change from fixed to expandable, except in the SC scenario, where it is +\u0026thinsp;45%), it does not alter the scenario ordering, where the opportunity cost of EF remains highest. In contrast, moderate scenarios remain clustered around the median. This persistence reveals two takeaways: first, transmission capacity is not the primary bottleneck for onshore wind deployment under these ecological scenarios; and second, the spatial availability of suitable sites constitutes the binding constraint, suggesting a focus on alternative grid flexibility measures, such as demand response.\u003c/p\u003e \u003cp\u003eUnused capacity represents the difference between available capacity potential (after ecological exclusions) and deployed capacity\u0026mdash;quantifying additional development potential that remains accessible but economically unviable under system optimization. The relationship between unused capacity and opportunity costs exhibit diminishing returns of permissiveness. Despite having up to three times more unused capacity than moderate scenarios (380 GW PM vs 120 GW SO1), economic opportunity cost decreased by ~\u0026thinsp;30% (excluding EF's extreme position), indicating that beyond a threshold, increasing available area yields progressively smaller reductions in the economic value of additional capacity. This non-linearity reflects system-level bottlenecks\u0026mdash;grid flexibility limitations, temporal generation-demand mismatches, and regional transmission constraints\u0026mdash;that prevent full utilization of available capacity regardless of land-use permissiveness.\u003c/p\u003e \u003cp\u003eThe trade-off between ecological restrictions and efficient system utilization is distinctly non-linear. Our analysis reveals that moderate restriction scenarios (CA, SC, SD) achieve better system integration compared to extreme scenarios (EF, PM), with a curtailment rate of ~\u0026thinsp;100 TWh/GW versus ~\u0026thinsp;120 TWh/GW for extreme scenarios (PM, EF)\u0026mdash;representing 20% higher curtailment intensity at both ends of the restriction spectrum. Two mechanisms explain this: First, maximum available areas (PM) enable deployment in optimal locations, but system oversupply at peak generation hours and grid flexibility constraints limit absorption capacity. Second, high restrictions (EF) force deployment in locations with lower generation efficiency (2,180 vs 2,390 TWh/GW in PM) and proximity requirements (\u0026lt;\u0026thinsp;1 km), creating temporal mismatches between generation and demand. This spatial-temporal mismatch is amplified by EF's geographic concentration: as demonstrated in spatial availability analysis (Section \u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003e3.1\u003c/span\u003e, Fig. \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003eA3\u003c/span\u003e \u003cb\u003ein the\u003c/b\u003e \u003cspan refid=\"Sec15\" class=\"InternalRef\"\u003e\u003cb\u003eappendix\u003c/b\u003e\u003c/span\u003e), EF's low correlation with other scenario (-0.08-0.21) concentrates deployment in southwestern counties where infrastructure proximity requirements are met, but these regions exhibit relatively high solar saturation and lower wind resource compared to the geographically diverse, higher-quality northern sites (Tr\u0026oslash;ndelag, Finnmark) accessible under moderate scenarios. This pattern demonstrates that realized capacity utilization does not correlate linearly with land-use restriction severity\u0026mdash;moderate restrictions optimize the balance between siting and system integration better.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Hourly electricity cost distribution\u003c/h2\u003e \u003cp\u003eHourly electricity costs are calculated using the hourly electricity shadow prices (i.e., dual variables) and demand, reflecting the marginal cost of meeting demand in each hour. To assess how ecological restrictions affect typical electricity system operation, extreme hours, and operational cost patterns, we analyze three metrics: median (central tendency of typical hours), mean (accounting for extremes), and coefficient of variation (CoV) (measuring relative volatility).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eEcological restrictions do not necessarily increase hourly electricity cost volatility. The most restrictive scenario (EF) exhibits lower volatility (CoV: 5.84 fixed, 6.78 expandable) across ecological scenarios and transmission configurations (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). This connects to the spatial availability findings (Section \u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003e3.1\u003c/span\u003e): limited and fixed geographic availability (i.e., the southwest for EF) constrains dispatch options to a narrow set of locations, and even grid flexibility cannot overcome spatial restrictions on where generation exists. Moreover, lower CoV reflects a relatively less supply-demand mismatch at these limited available sites. Meanwhile, higher cost volatility in other scenarios compared to the base scenario reveals operational outcomes depend critically on whether favorable renewable conditions occur at available or excluded sites\u0026mdash;when conditions align with accessible locations, costs remain moderate, but when optimal conditions occur at restricted sites, the system must dispatch expensive alternatives, creating larger cost swings.\u003c/p\u003e \u003cp\u003eThis median-mean divergence reveals how ecological restrictions primarily affect extreme hours and typical operation hours. The variation in median hourly costs remains\u0026thinsp;\u0026lt;\u0026thinsp;3% (fixed) and \u0026lt;\u0026thinsp;6% (expandable), while mean costs increase up to ~\u0026thinsp;12% across scenarios. For example, EF shows a higher mean (39.12M NOK) with a similar median (24.76M NOK) to PM (24.86M NOK), indicating that most hours experience comparable costs, but rare extreme hours (\u0026lt;\u0026thinsp;1% of hours, exceeding 1,000M NOK) become more costly under restrictions. Conversely, SD exhibits a lower mean (34.57M NOK) but a higher median (25.21M NOK) than the base case across transmission configurations, suggesting fewer costly extreme hours but elevated typical operational-hour costs. This pattern demonstrates that the economic impact of restrictions manifests through amplified tail behavior\u0026mdash;more frequent or severe extreme-cost hours\u0026mdash;rather than across-the-board operational cost increases.\u003c/p\u003e \u003cp\u003eTransmission expansion increases hourly cost volatility across all scenarios. This volatility increase reflects enhanced operational flexibility: expandable transmission enables dynamic inter-county dispatch optimization, in which the system routes power hour by hour based on instantaneous electricity production and demand patterns. This creates more variable dispatch patterns than in fixed transmission, where constrained grids force local balancing with predictable local dispatchable generation (i.e., storage). While this flexibility reduces average costs (the mean drops\u0026thinsp;~\u0026thinsp;8% with transmission expansion), it introduces operational variability, as optimal dispatch varies more substantially across hours depending on renewable generation conditions.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eEnergy system models increasingly recognize that spatial allocation decisions fundamentally shape system outcomes [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Our analysis demonstrates how spatial prioritization strategies\u0026mdash;concentrating development near existing infrastructure (spare) or dispersing capacity to avoid biodiversity hotspots (share)\u0026mdash;create distinct investment and operational patterns beyond simple capacity allocation. The integration of species-level biodiversity data with electricity system modelling reveals that system flexibility\u0026mdash;through transmission, technology substitution, storage\u0026mdash; enables biodiversity protection while maintaining system viability.\u003c/p\u003e \u003cp\u003eOur spatial availability analysis divides Norwegian land into three distinct categories: 7% \u0026lsquo;go-to\u0026rsquo; areas, 26% \u0026lsquo;no-go\u0026rsquo; areas and 67% contested areas requiring priority-setting (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This geographic breakdown quantifies what previous studies have described qualitatively: wind energy siting involves extensive contested space requiring explicit priority-setting [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The policy-sensitive 67% represents neither a technical problem with a single optimal solution nor an intractable conflict, but rather a decision space in which different societal values yield distinct but defensible outcomes. The western counties dominate always available areas while eastern counties concentrate always unavailable zones. This regional pattern suggests that these areas share internally consistent ecological and infrastructure characteristics regardless of the restriction framework.\u003c/p\u003e \u003cp\u003eThe spare-share spatial strategies manifest technology-specific capacity expansion patterns regardless of grid extension. A spare approach reduces the solar composition in the national capacity mix, while a share approach increases due to broader spatial coverage, creating opportunities for complementarity with onshore wind (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig. \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003eA2\u003c/span\u003e) [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Offshore wind also remains the least sensitive to grid expansion and responds to different ecological restrictions, where it ranges from ~\u0026thinsp;3.5 GW to ~\u0026thinsp;10 GW depending on the severity of land-based restrictions. Most of the new offshore capacity additions remain in the southwest of Norway due to its proximity to demand centers and optimal use of the established grid rather than north or central Norway, where the system would require new transmission capacity additions.\u003c/p\u003e \u003cp\u003eMoreover, these spatial strategies reinforce regional distribution patterns of deployed capacities. Despite varying ecological priorities and transmission configurations, some regions appear to adopt a similar technology. Southern and southeastern counties inflexibly hold the solar deployment ranging between 3\u0026ndash;17 GW, while central counties invariably hold onshore wind between 1\u0026ndash;10 GW each. \u0026Oslash;stfold county only appears to be very sensitive to ecological restrictions, completely substituting onshore wind for solar (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). We note a sustained onshore wind capacity reduction from the base case in northern Norway (Troms\u0026oslash; \u0026amp; Finnmark), due to increased ecological and infrastructure constraints, from ~\u0026thinsp;8 GW to ~\u0026thinsp;3 GW.\u003c/p\u003e \u003cp\u003eThe choice between spatial and temporal balancing depends on both siting strategy and the transmission configuration \u003cb\u003e(\u003c/b\u003eFig. \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003eA3\u003c/span\u003e, Fig. \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003eA4\u003c/span\u003e \u003cb\u003ein the\u003c/b\u003e \u003cspan refid=\"Sec15\" class=\"InternalRef\"\u003e\u003cb\u003eappendix\u003c/b\u003e\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e where fixed transmission with share approach and spare approach with expandable transmission exacerbates the role of storage. With fixed transmission, share scenarios exhibit greater storage increases for two reasons: first, deployment away from existing infrastructure necessitates localized temporal balancing due to limited grid connectivity; second, increased solar share in the capacity mix leads to higher temporal mismatch, as solar is only available during the daytime. Conversely, spare scenarios (EF, PC, SC) show minimal storage increases due to a reduction in solar saturation and concentration near demand centres, enabling the maximum use of the established grid rather than installing solar in other low-demand regions with high solar potential \u003cb\u003e(\u003c/b\u003eFig. \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003eA4\u003c/span\u003e \u003cb\u003ein the\u003c/b\u003e \u003cspan refid=\"Sec15\" class=\"InternalRef\"\u003e\u003cb\u003eappendix\u003c/b\u003e\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e However, this pattern got reversed with transmission expansion. Spare scenarios see storage increases as the system deploys maximum new capacities in the proximity of existing infrastructure, even at high temporal-mismatch locations, requiring storage in addition to transmission to provide spatial redistribution capacity. Meanwhile, storage needs are reduced in the share scenarios because transmission expansion connects distant high-quality sites directly to demand centres, substituting spatial balancing for temporal storage.\u003c/p\u003e \u003cp\u003eRecent work on Norwegian flexibility options confirms that spatial distribution of renewable capacity fundamentally affects temporal smoothing requirements [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], but our results illustrate that beyond a certain threshold grid expansion cannot substitute for spatial availability: even with 1.2 GW of cost-effective transmission additions, system costs increase 1.5-6% under ecological restrictions, driven primarily by operational inefficiencies from constrained siting rather than insufficient grid capacity. Combined with Gilad et al.'s demonstration that transmission infrastructure carries its own biodiversity costs, our findings suggest that spatial planning frameworks constitute the primary policy lever for balancing biodiversity preservation with system economics, with grid expansion serving as a necessary but insufficient complement [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Our spare-share strategy reveals that the economics of temporal versus spatial balancing hinge on the interplay among ecological constraints, proximity to infrastructure, and grid extension. Notably, no scenario deployed the costly underground subsurface power line despite availability, as overhead expansion met the spatial balancing requirements even under severe land-use constraints.\u003c/p\u003e \u003cp\u003eEconomic opportunity cost of onshore wind (i.e., marginal value of additional developable land) escalates 3\u0026ndash;9 fold from baseline regardless of transmission configuration. This illustrates that although transmission expansion enables access to areas under restrictions, it cannot overcome severe spatial constraints as the system requires access to suitable sites, not merely the ability to connect distant generation to demand centres. Similar observations were quantified by Roithner et al. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] for Norway\u0026rsquo;s 2030 system design: grid investment provides relief, but land availability remains binding. Furthermore, despite targeting divergent ecological priorities, convergence of moderately restricted scenarios (CA, SC, SD) marginal value cost around median illustrate geographic overlap of biodiversity priorities\u0026mdash;collision-sensitive species habitats, high-richness areas, protected zones\u0026mdash; creating functionally equivalent constraints on available sites regardless of which ecological criteria drive restrictions.\u003c/p\u003e \u003cp\u003eThe medium restricted scenarios (CA, SC, SD) demonstrate performance advantages beyond cost minimization. The least curtailment rates (95\u0026ndash;100 TWh/GW) suggest moderate spatial restrictions do not need to compromise operational efficiency compared to scenarios of both extreme ends (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, Fig. \u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003eA5\u003c/span\u003e). Operation cost patterns further demonstrate these findings, illustrating ecological restrictions primarily affect extreme hours rather than typical operation. The median hourly costs remain largely unaffected (3%-6%) across scenarios and hourly cost volatility remains less than 15% from base case, indicating that ecological restrictions did not substantially affect the volatility of hourly costs. This convergence of multiple metrics on moderate restriction scenarios suggest going beyond binary framing of siting decisions. The policy-sensitive 67% of Norwegian territory does not represent intractable conflict, but rather a decision space where different socio-environmental values (collision risk minimization versus habitat protection, infrastructure concentration versus spatial dispersion) yield distinct but comparably viable pathways. The challenge lies not in identifying a single optimal solution, but in establishing participatory processes for explicit priority-setting among defensible alternatives.\u003c/p\u003e \u003cp\u003eHistorical wind farm deployment in Norway concentrated in contested geography (north, southwest, east; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), prioritizing wind resource quality over ecological sensitivity rather than selecting lower-quality but ecologically safer locations. Our results validates May et al.'s [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] finding that \"\u003cem\u003ecurrent practice has not succeeded in avoiding sites with higher impacts\u003c/em\u003e\", but adds crucial spatial specificity: contested installations cluster in regions where high wind resources coincide with ecological sensitivity, forcing explicit rather than avoided trade-offs. Our scenario analysis reveals that historically developed locations see the steepest onshore wind capacity restrictions under ecological restrictions: northern onshore wind declines from approximately\u0026thinsp;~\u0026thinsp;8 GW to ~\u0026thinsp;3 GW, while high ecological restrictions (i.e., EF) reduce southwestern capacity by ~\u0026thinsp;10\u0026ndash;12 GW (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This pattern reveals that future expansions cannot access equally productive sites with lower ecological conflicts, as the best combination of resource quality and relative ecological permissiveness has already been performed. Future onshore wind developments will require comparatively harder trade-offs between high-quality wind sites and ecologically sensitive areas. The scenario diversity approach operationalizes what Grimsrud et al. [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] advocated: explicit acknowledgment that siting decisions reflect societal values and that energy system models can inform but not determine. Future deployment requires not only technical solutions for integrating restricted capacity, but participatory governance frameworks for democratic settlement of contested space where multiple defensible priorities conflict.\u003c/p\u003e \u003cp\u003eFuture research could expand the analytical scope in three directions: broader taxonomic coverage (terrestrial mammals, marine species), social dimensions (proximity to settlements, noise impacts, landscape effects), and additional renewable energy technologies for more comprehensive assessment of socio-ecological trade-offs. The analytical scenario framework employed here could be complemented by participatory co-design processes engaging diverse stakeholders including local communities, conservation organizations, and energy developers to establish restriction criteria through workshops or deliberative methods [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], thereby enhancing social legitimacy and ownership of spatial planning decisions. Although developed for the Norwegian context, the spare-share framework offers transferable insights for regions worldwide confronting similar tensions between renewable energy expansion, landscape preservation, and biodiversity conservation [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], particularly as countries accelerate decarbonization while navigating competing environmental and social priorities.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eAchieving net-zero electricity systems requires substantial expansion of renewable energy, yet this imperative increasingly collides with biodiversity conservation and landscape preservation priorities. Norway exemplifies this challenge where extensive wind resources coincide with ecologically sensitive zones and culturally valued landscapes. We developed an integrated spare-share scenario framework that couples high-resolution biodiversity (247 bird species, 5 bat species) and infrastructure datasets with electricity system optimization to quantify the system-level techno-economic implications of alternative onshore wind siting configurations spanning infrastructure-proximate concentration (spare) to ecologically-guided dispersion (share) under 2050 net-zero projections.\u003c/p\u003e \u003cp\u003eSpatial analysis reveals that only 7% of Norwegian land area remains consistently available across spare-share scenarios, while 67% exhibits flexible availability depending on which ecological criteria receive priority, and 26% faces universal restrictions. This geographic fragmentation demonstrates that wind energy siting involves extensive contested space rather than clear consensus zones. The 67% flexible geography represents neither a technical optimization problem nor an intractable conflict, but rather a decision space where different societal values\u0026mdash;collision risk minimization, habitat protection, infrastructure concentration, spatial dispersion\u0026mdash;yield distinct but defensible outcomes. Western and northern regions dominate both flexible areas (52%) and unavailable areas (33%), reflecting their geographic diversity. 90% of existing wind farm locations concentrate in contested zones, with 28% viable only under the most permissive conditions, demonstrating that historical development prioritized wind resource quality over ecological consensus. Future expansion cannot replicate these spatial patterns under strict ecological criteria, as the favorable combinations of resource quality and relative ecological permissiveness have been exploited.\u003c/p\u003e \u003cp\u003eEcological restrictions reduce Norway\u0026rsquo;s onshore wind capacity potential from 418 GW to 37 GW with system costs increasing up to ~\u0026thinsp;6% depending on restriction severity. Moderately restricted scenarios cluster within 1% cost-increase variation despite targeting divergent ecological priorities, demonstrating that geographic overlap among biodiversity hotspots creates functionally equivalent constraints. Moreover, these scenarios exhibit operational advantages: 20% lower curtailment intensity and hourly cost volatility within 15% of baseline. Land availability constitutes the binding economic constraint rather than transmission capacity. Marginal economic value of additional developable land ranges from 500\u0026nbsp;million to 4.5\u0026nbsp;billion NOK/GW (9-fold escalation), while transmission capital investments contribute\u0026thinsp;\u0026lt;\u0026thinsp;2% of cost increases, demonstrating that grid expansion cannot overcome geographic exclusions due to system-level bottlenecks (i.e., grid flexibility limitations, higher temporal generation-demand mismatches, and regional transmission constraints). The 5\u0026ndash;20 GW onshore wind reduction in the national capacity mix is broadly substituted by offshore wind (up to ~\u0026thinsp;7 GW increase) with complementary solar adjustments (\u0026plusmn;\u0026thinsp;1.5 GW).\u003c/p\u003e \u003cp\u003eThese findings demonstrate that determining spatial allocation priorities requires participatory processes rather than model-determined optimization. Energy system models can quantify trade-off implications but cannot determine which trade-offs societies should accept. Although applied to Norway, the spare-share scenario framework offers transferable insights for regions confronting analogous challenges between renewable expansion, biodiversity conservation, and landscape preservation, as decarbonization accelerates globally.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMuhammad Shahzad Javed: conceptualisation, methodology, data curation, formal analysis, visualisation, writing\u0026mdash;original draft; Dafna Gilad: conceptualisation, data curation, methodology, writing\u0026mdash;review \u0026amp; editing; Roel May: conceptualisation, methodology, data curation, writing\u0026mdash;review \u0026amp; editing; Thomas Kvalnes: conceptualisation, data curation, writing\u0026mdash;review \u0026amp; editing; Marianne Zeyringer: conceptualisation, methodology, data curation, visualisation, writing\u0026mdash;review \u0026amp; editing, project administration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ehighRES version adopted for this work and related input data necessary to replicate the results is openly available at Github: \u0026nbsp;https://github.com/JavedMS/highRES-Norway/tree/highres_NINA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. The authors gratefully acknowledge the Norwegian Institute for Nature Research (NINA) for providing the biodiversity datasets that enabled this analysis. We thank Oskar V\u0026aring;ger\u0026ouml; and Guillermo Valenzuela-Venegas for valuable input on spatial analysis and integration of high-resolution datasets into the highRES electricity system model.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eIntergovernmental Panel on Climate Change (IPCC) (2023) Climate Change 2022 \u0026ndash; Impacts, Adaptation and Vulnerability: Working Group II Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. 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Existing spatial analyses identify ecologically preferable sites but lack representation of system constraints and trade-offs, while most energy system models optimize technology deployment under cost constraints by specifying regional capacity limits, rarely encoding which specific sites become available under different ecological priorities. We address this disconnect through a spare-share scenario framework coupling high-resolution biodiversity (247 bird species, 5 bat species) and infrastructure datasets with electricity system optimization for Norway's 2050 net-zero pathway. Spatial allocation strategies spanning infrastructure-proximate concentration (spare) to ecologically guided dispersion (share) including collision risk minimization, habitat protection, and infrastructure concentration are examined. Analysis reveals that only 7% of Norwegian land area remains consistently available across spare-share scenarios, while 67% exhibits flexible availability depending on ecological criteria prioritization, and 26% faces universal restrictions. Ecological restrictions reduce onshore wind capacity potential 11-fold, increasing electricity generation costs by 19\u0026ndash;21% per MWh while investment and operational costs rise up to 6%. Despite targeting divergent ecological priorities, moderately restricted scenarios cluster within less than 1% cost increase variation, reflecting biodiversity hotspot overlap that creates functionally equivalent constraints while achieving better system integration. Findings demonstrate that for 67% of land, which is sensitive to ecological priorities, spatial allocation decisions are not a technical optimization problem but require inclusive stakeholder engagements to navigate competing ecological and infrastructure values. The framework provides transferable methodological insights for regions balancing renewable expansion with biodiversity preservation.\u003c/p\u003e","manuscriptTitle":"Ecological and economic onshore wind turbine siting trade-offs in Norway","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-20 07:27:12","doi":"10.21203/rs.3.rs-9167324/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":"b2af8c9c-4d81-4305-93b4-e9bd86531810","owner":[],"postedDate":"March 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":64779309,"name":"Renewable Resources"},{"id":64779310,"name":"Energy Engineering"}],"tags":[],"updatedAt":"2026-03-20T07:27:12+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-20 07:27:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9167324","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9167324","identity":"rs-9167324","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}