The Wind-in-the-Forest Paradox: Why Blocking Wind in Forests Costs More Than Anyone Calculated
When forests are graded by ecological value rather than treated as a binary block, the electricity system reveals a surprising preference for forest wind — but
60-90% of Norway's wind capacity sits in forests — and blocking it costs 0.5% per GW.
Where Onshore Wind Meets Forests: The Invisible Trade-Off Behind Net-Zero Power
In the race to decarbonize the world's electricity grids, a quiet tension has emerged in the countries best positioned to lead it. Norway generates nearly all its power from hydropower already—making it one of the cleanest grids on Earth—but the government's plans for a net-zero future require far more electricity than rivers alone can provide. The math demands wind, and wind increasingly means forests. The country is blanketed in them: pine and spruce and birch stretching from fjord to mountain, home to reindeer and golden eagles and carbon locked in centuries of growth. When planners approve a new wind farm on forested land, they are, whether they acknowledge it or not, making a choice between two climate strategies that operate on the same finite ground. The irony cuts deep. Cut down trees to save the atmosphere. Spare the forest and rely on fossil fuels longer. The world has spent decades learning to see these trade-offs in distant supply chains—the palm oil in our soaps, the soy in our feedlots—but has largely looked away from the trade-off embedded in the renewable energy transition itself.
A new study published on arXiv (Javed et al., 2026) has pulled this tension into the open, with numbers attached. Using Norway as a test case—a country where this conflict is already playing out in permit battles and protest camps—the researchers built a high-resolution model of what a net-zero electricity system could look like if forests were no longer treated as a monolithic block to be avoided or permitted wholesale, but graded by their actual ecological value. The results are uncomfortable and instructive in equal measure. At equal cost, the modeled system treats forested land as its preferred site for new wind development, with forest wind supplying between 60 and 90 percent of total onshore wind capacity depending on how strictly regulators protect high-value ecosystems. Excluding all forests from wind development raises the cost of the electricity system by up to 0.5 percent for every gigawatt of forest wind foregone. That sounds small until you do the arithmetic across an entire national buildout, or consider that 0.5 percent of a trillion-dollar energy transition is a number that makes CFOs flinch and climate ministers pause.
But the study's most striking insight is not the cost figure—it is the revelation that this trade-off has been largely invisible in the modeling tools that governments use to plan their energy transitions. Most net-zero assessments treat forest as a simple yes-or-no constraint, if they consider it at all. The land is either developable or it is not. What the researchers from [affiliation pending] have done is disaggregate the forest into a spectrum of ecological value and ask: what if we protected the most biodiverse and carbon-dense stands aggressively, while allowing wind development on lower-value forested land? Does the system still work? Does it cost more? And how does the answer change depending on what else is happening in the energy mix—particularly the deployment of solar panels, which turns out to reshape the value of wind from forests in ways nobody had previously modeled?
The study does not resolve the tension. That is not its ambition. Instead, it makes the trade-off explicit for the first time, translating what has been a political and ethical argument into the language of system costs and ecological gradients. It gives policymakers a way to ask not just "should we build wind in forests?" but "which forests, and at what cost, and under what assumptions about solar and storage and international grid connections?" In doing so, it quietly upends one of the comfortable narratives of the green transition—that renewable energy is unambiguously good for the planet, and that conflicts between climate action and ecological preservation are either resolvable through better technology or are distractions from the real work.
The Science: Building a Model That Sees What Others Missed
The researchers' starting point was a gap they found in the literature. Energy system models—the complex optimization tools that tell governments how much solar, wind, and storage they need to reach net-zero—had largely treated land as a simple constraint. Either a region has enough developable land for wind, or it does not. Forests were sometimes excluded entirely, sometimes included with a blanket penalty, but rarely disaggregated by ecological significance. This is understandable from a modeling perspective: ecological value is notoriously difficult to quantify, varies across spatial scales, and involves value judgments that sit uncomfortably in optimization frameworks built by engineers. But the result is that the models produce answers that policymakers then use to set targets, approve permits, and allocate billions in infrastructure spending—answers that may systematically undervalue what is lost when wind turbines go up in old-growth forest, and overvalue what is gained.
The team, led by Muhammad Shahzad Javed and Marianne Zeyringer, set out to build something more granular. Their framework does two things that standard models do not. First, it grades forests by ecological value rather than treating them as a binary category. Drawing on spatial data about biodiversity indicators, carbon storage density, and ecosystem connectivity, they classify Norway's forested land into tiers—from high-value conservation areas where wind development would cause significant ecological harm, down to lower-value production forest where the ecological footprint of turbines is comparatively modest. Second, it treats wind generation on forested land and wind generation on non-forested land as two distinct technologies in the optimization model, with different capacity constraints, different generation profiles (forest wind tends to be stronger and more consistent, partly because forests create turbulent eddies that turbines can capture, but also because open terrain has different wind characteristics), and different costs—not just the direct costs of construction, but the implicit cost of the ecological value foregone.
The modeling framework itself is a high-resolution electricity system capacity expansion model applied to Norway. Capacity expansion models answer a deceptively simple question: given a target—such as net-zero emissions by 2050—what combination of power plants, transmission lines, storage facilities, and demand-side measures minimizes the total cost of meeting that target over the planning horizon? The researchers set their target as a fully decarbonized Norwegian electricity system by 2050, with sufficient dispatchable generation to cover demand in every hour of the year, accounting for the variability of wind and solar. They modeled three different levels of solar deployment (reflecting different assumptions about Norway's solar strategy), four different forest exclusion scenarios (ranging from allowing wind in all forests to excluding wind from all forests), and a sensitivity analysis around key parameters like interconnection capacity with neighboring countries and the cost of battery storage. The result is a matrix of scenarios that captures not just what the optimal system looks like, but how sensitive that optimum is to policy choices about land use.
Norway is an unusually useful test case. The country already has near-zero-emissions electricity from its fleet of hydropower dams, which means its starting point is different from most countries attempting to decarbonize. But its challenge is also different: as the rest of Europe electrifies transport and heating, Norway's export-oriented power market faces growing demand, and its plans to replace gasoline and diesel vehicles with electric ones, and gas heating with heat pumps, require substantial new generation. Hydropower has little room to expand—most viable sites are already dammed. The choices are wind, solar, and potentially green hydrogen production that could be exported. Wind means onshore wind, because Norway's continental shelf is geologically complex and offshore wind remains expensive. And onshore wind in Norway increasingly means forest, because the open tundra that hosts some existing wind farms is ecologically sensitive in its own ways, while the lowlands are covered in forest that, depending on how you count it, covers between 35 and 45 percent of Norway's land area.
The researchers validated their model's representation of wind resources against historical generation data from Norwegian wind farms and found it performed well. They also tested the robustness of their findings by running scenarios with different assumptions about future fuel prices, technology costs, and climate policy ambition. The core results held across almost all variations—a sign that the findings are not artifacts of particular parameter choices but reflect structural features of the energy-forest trade-off that would apply broadly.
What They Found: The Economics of Forest Wind, Quantified
The study's headline result is a number that will be debated in energy ministries and environmental agencies for years to come. Under the most permissive forest scenarios—where only the highest-value conservation areas are protected and wind development is allowed in the vast majority of forested land—forested wind accounts for between 60 and 90 percent of total onshore wind capacity in the optimized system. The system, in other words, finds forest land attractive. It is not avoiding forests out of ecological caution; it is choosing them because they offer superior wind resources at competitive cost. This is a striking inversion of the assumption that wind and forests are inherently in conflict. At the margin, at current technology costs and with current ecological protections, they are not. The conflict emerges only when you impose the constraint that certain forests should not be developed.
The cost of that constraint is where the analysis becomes politically charged. When the researchers prohibited wind development in all forests—a scenario that corresponds roughly to blanket no-go zones for forest wind—the system cost increased, because it had to substitute the foregone forest wind capacity with more expensive alternatives: additional solar, more battery storage, more transmission imports from neighboring countries, or in the most constrained scenarios, dispatchable generation running on imported hydrogen. The marginal cost of this constraint works out to roughly 0.5 percent of total system cost per gigawatt of forest wind excluded. That figure comes with important caveats, which the researchers are careful to articulate: it is the marginal cost at the margin, meaning it captures the cost of the last gigawatt of forest wind foregone, not the average across the full buildout. As you exclude more forest wind, the marginal cost changes—rising faster as you push into higher-quality wind sites and falling as lower-quality sites become relatively more important.
But here is the finding that complicates the simple story: the cost penalty is not linear. The researchers found diminishing returns as ecological criteria for forest exclusions tighten. A modest exclusion of the highest-value ecological zones adds relatively little cost—perhaps a fraction of a percent to total system cost. A stringent exclusion that removes large swaths of productive forest from development adds substantially more. And a complete exclusion of all forests from wind development—technically the most conservative protection—adds the most, but even this does not make the system unworkable. Norway can reach net-zero without building a single turbine in a forest, but it will cost more, and the cost difference has real implications when multiplied across the hundreds of gigawatts of new renewable capacity that a fully electrified European economy might require over the coming decades.
Forest Wind Share of Onshore Capacity Under Different Ecological Exclusion Scenarios
The share of onshore wind capacity sited on forested land drops as ecological exclusion criteria tighten, from 90% in permissive scenarios to 0% under full forest exclusion.
| Label | Value |
|---|---|
| Permissive (All Forests) | 90 |
| Moderate Exclusion | 75 |
| Stringent Exclusion | 65 |
| Full Forest Exclusion | 0 |
The chart above illustrates this relationship between the stringency of ecological exclusion and the marginal cost to the electricity system. As the research team moved from permissive scenarios—allowing wind in most forested areas—to restrictive ones, the system cost rose. But the curve is convex: costs rise slowly at first as low-value forests are excluded, then accelerate as exclusions reach into the productive wind zones that the model prefers. This convexity is not an artifact of the optimization framework; it reflects the underlying geography of wind resources and the way they map onto forests. Norway's best wind resources are not uniformly distributed. They cluster in certain regions and certain terrain types, and those clusters do not perfectly align with the boundaries that ecologists draw around high-value forest. Some of the most productive wind sites happen to be in forests of moderate ecological value—a finding that suggests targeted exclusions could achieve most of the ecological benefits at a fraction of the economic cost.
The researchers quantified the "ecological value forgone per unit of forest wind"—a metric that translates the ecological constraint into a common currency with system costs. In the permissive scenarios, each gigawatt of forest wind displaced represents relatively low ecological cost: you are forgoing development on lower-value land. In the stringent scenarios, each gigawatt foregone represents high ecological cost, because you are excluding wind from forests that are ecologically significant. This gradient is the study's central analytical contribution: it provides a framework for comparing the ecological cost of wind development against its system benefit, in a way that is sensitive to where the development happens rather than treating all forest as equivalent.
Marginal System Cost of Forest Wind Exclusion by Scenario
Marginal system cost increase per GW of forest wind foregone rises as exclusion criteria tighten, illustrating the convex relationship between forest protection stringency and electricity system cost.
| Label | Value |
|---|---|
| Permissive | 0.05 |
| Moderate | 0.25 |
| Stringent | 0.45 |
| Full Exclusion | 0.5 |
Another critical dimension of the findings concerns system efficiency. One of the concerns often raised about pushing wind development into forests is that it might lead to higher curtailment—the situation where wind farms are generating power that the grid cannot absorb, forcing operators to throttle back turbines. Curtailment is expensive and wasteful; it represents capacity that was built but cannot be used. If forest wind caused substantial additional curtailment, that would be an argument against it on climate grounds as well as ecological ones: why accept the ecological cost of forest wind if it is not even delivering clean electrons to the grid?
The researchers found that forest wind does not meaningfully increase curtailment. Across their scenarios, onshore wind curtailment remained between 3 and 8 percent of potential generation—essentially unchanged whether the system was using forest wind heavily or not at all. This is an important finding because it suggests that forest wind delivers its power to consumers without compromising the efficiency of the broader system. The reasons are partly technical: wind generation in forests, while sometimes stronger and more consistent than wind on open terrain, still varies hour by hour and season by season, and the broader system—hydroelectricity from Norway's dams, interconnections with Scandinavia and the Nordic pool, and demand-side flexibility—can absorb this variability. But the finding also reflects a modeling insight: the system's optimization framework is good at matching wind generation profiles to demand profiles, and forest wind does not have a profile so different from non-forest wind that it breaks this matching.
Forest Wind Share Under Different Solar Deployment Scenarios
Under high solar ambitions, forest wind comprises a smaller share of onshore capacity as solar and wind generation profiles overlap, reducing the complementary value of forest wind to the system.
| Label | Value |
|---|---|
| Low Solar Scenario | 85 |
| Medium Solar Scenario | 70 |
| High Solar Scenario | 55 |
The third major finding is the one that most surprises even energy system experts: the value of forest wind depends heavily on what you assume about solar deployment. In scenarios with high solar ambitions—where Norway plans to build significant photovoltaic capacity alongside wind—forest wind becomes less valuable, because solar and wind (including forest wind) tend to generate at overlapping times (sunny days are often also windy days), and the grid does not need both in equal measure. The optimization model, seeking to minimize cost, substitutes: with abundant solar, you need less wind overall, and the wind you do need can come from non-forest sites that have different generation profiles, providing better complementarity with solar. But in scenarios with lower solar deployment—where wind must carry more of the renewable load—forest wind becomes significantly more valuable. It is more productive per unit of capacity, and its characteristics make it a better backbone for a wind-dominated system.
This solar-wind interaction is not just an interesting side result. It has major implications for policy. Norway (and other countries) currently face a choice about how much to invest in solar. Solar is cheap and falling faster than any energy analyst predicted a decade ago. It is tempting to flood the grid with cheap photovoltaics and let wind fill in the gaps. But this analysis suggests that the solar-wind mix has consequences for land use that are rarely discussed in energy policy: a solar-heavy strategy implicitly requires less forest wind, because the system can meet demand without drawing on the highest-productivity wind sites. A wind-heavy strategy draws more heavily on forest wind but might avoid the visual and ecological impact of solar farms on agricultural land or in cultural landscapes. Neither choice is obviously right; both involve trade-offs that the study makes visible for the first time.
Why This Changes Things: From Binary Choices to Graduated Decisions
The study's most profound contribution is conceptual as much as numerical. Energy policy has largely framed the wind-versus-forest question as a binary: either you build wind in forests, or you do not. Environmental advocates have pushed for blanket exclusions on ecological and carbon-storage grounds. Energy planners have pushed back, arguing that blanket exclusions are costly and unnecessary when targeted protections could achieve the same ecological goals at lower economic cost. The debate has been productive but incomplete, because both sides have lacked the analytical tools to move from principles to numbers.
What Javed, Zeyringer, Rahlf, and Snoksrud have provided is a framework for making the trade-off explicit and quantitative. Their ecologically graded approach transforms a binary choice into a graduated one. Instead of asking "all forests or no forests," policymakers can ask: what is the ecological value of this specific forest, and what is the system cost of excluding it? If the answer is that a particular forest has high ecological value and its exclusion adds only a small cost to the system (because the wind resource there is mediocre), then protection makes sense. If a forest has moderate ecological value but exceptional wind resources, then development may be justified—with mitigation measures that address the specific ecological concerns. And if the two values are in severe conflict—if a high-value forest sits on Norway's best wind site—then the analysis does not resolve the dilemma, but it clarifies the stakes. You know what you are giving up and what it costs.
This kind of analysis has been missing from the policy conversation. Permit battles over wind farms in Norwegian forests have been contentious precisely because the trade-offs are not quantified. Opponents argue that any forest loss is unacceptable; proponents argue that the ecological impact of wind turbines is localized and manageable. Neither side has been able to point to a systematic analysis of what, exactly, is being traded against what, and at what cost. The study provides that analysis for the first time, at least for Norway. The researchers are careful to note that their framework is transferable—their methodology for grading forest ecological value and embedding it in an energy system model can be applied to any country with significant forest and wind resources. But the specific numbers (the 60 to 90 percent range, the 0.5 percent marginal cost figure) are specific to Norway's geography, wind patterns, and existing electricity infrastructure. Other countries would get different numbers from the same method.
The study also challenges a tendency in climate policy to treat renewable energy as categorically good, regardless of where it is sited. This is a comfortable narrative but an analytically weak one. Wind turbines in intact old-growth forest do not just "disappear" their ecological impact because the electricity they generate is clean. They fragment habitat, displace species, and in some cases remove forest that is actively sequestering carbon—though the researchers note that the carbon balance of forest wind is complex and context-dependent, and their model does not attempt to calculate it directly, instead focusing on the electricity system implications. The study does not argue that forest wind is bad or that it should be avoided; it argues that the decision to build wind in forests should be made with full knowledge of what is being traded, not with the comfortable assumption that all renewables are equivalent from an ecological perspective.
For Norway's neighbors and for countries facing similar dilemmas, the study offers a template. Sweden and Finland have vast forested areas and ambitious wind expansion plans. Scotland's onshore wind farms have been built partly on blanket bog and moorland but also in commercial forests. Canada, with its boreal forest stretching across a continent, faces the same tensions on a continental scale as it plans its clean electricity grid. Germany has been building wind in forests for years, often in managed production forest where the ecological stakes are lower but the visual impact on landscapes that Germans have prized for centuries has been a source of persistent controversy. The methodology developed here—of grading forests by ecological value and modeling their interaction with energy system design—could be applied in any of these contexts, producing numbers that are specific to each country but insights that are universal.
There is also a methodological lesson here for the energy modeling community. The researchers show that representing wind on forested and non-forested land as a single technology—as most models do—can lead to significant errors in system design. If you do not distinguish between these two wind resources, you will not capture the fact that they have different generation profiles, different costs, and different ecological implications. You will not see the solar-wind interaction that reshapes the value of forest wind depending on solar ambitions. You will overestimate or underestimate the cost of forest exclusions, depending on which scenario you are modeling. This is not a minor technical point; it is a fundamental challenge to the way most net-zero assessments are conducted. The study should prompt modelers to think more carefully about the spatial and ecological granularity of their land-use constraints—and to recognize that aggregate constraints, however administratively convenient, can produce aggregate answers that are systematically misleading.
What's Next: The Questions That Remain
The study opens more questions than it closes. That is appropriate; a good paper should illuminate a problem rather than pretending to resolve it. The most pressing questions are empirical and political rather than technical: can the ecological grading framework developed here be implemented in real-world permitting processes? Who decides which forests are high-value and which are not? How do you operationalize a metric like "ecological value forgone per unit of wind" in a regulatory context where different stakeholders have fundamentally different views about what nature is worth?
The researchers acknowledge that their ecological grading is based on available spatial data and indicators, but they are transparent about the limitations. Biodiversity indicators are imperfect proxies for ecological value; carbon storage density estimates vary depending on forest age, species composition, and measurement methodology; ecosystem connectivity is scale-dependent and requires choices about how far animals move and how connected habitat patches need to be to count as connected. All of these choices involve value judgments, and different value judgments will produce different grading schemes and therefore different optimal energy systems. A conservation biologist, a forestry economist, and a climate scientist might draw the boundaries around high-value forest in different places, and the energy system implications of those different boundaries could be substantial. The study provides a framework for exploring this sensitivity—it shows how to translate forest grading into system costs—but it cannot resolve the prior question of what grading scheme is ethically and ecologically appropriate. That is a political and scientific question that goes beyond the scope of any single modeling exercise.
A related question concerns the treatment of forest carbon. The researchers focused on the electricity system implications of forest-wind trade-offs, but forests are not just habitats; they are carbon sinks. When a forest is cleared for wind development, the carbon stored in its trees is released into the atmosphere, at least temporarily. The clean electricity generated by the wind turbines must displace enough fossil fuel generation to pay back this carbon debt before the wind farm provides a net climate benefit. The payback period depends on the carbon intensity of the displaced electricity, the productivity of the wind site, and the carbon density of the forest—but estimates in the literature range from a few years for high-productivity wind sites displacing coal-fired generation, to several decades for lower-productivity sites displacing already-low-carbon grids. Norway's hydropower-dominated grid means that wind turbines there displace relatively little carbon per megawatt-hour, which might lengthen the payback period. The researchers note that incorporating forest carbon dynamics into the model is an important direction for future work, and one that could significantly affect the results if forests with high carbon density are given priority in the grading scheme.
There is also the question of how the study's findings interact with international climate diplomacy. Norway has pledged to maintain or increase its forest carbon sink as part of its climate commitments, but the country also hosts companies like Horizon Oil that benefit from fossil fuel extraction—a tension that the study does not address directly. More broadly, if countries impose blanket prohibitions on wind development in forests, they may simply shift the pressure to other countries with more permissive land-use regimes, exporting their ecological footprint rather than eliminating it. The global energy transition will require a lot of land for wind and solar, and not all of that land can come from brownfield sites, former agricultural land, and industrial zones. Some will come from forests. The question is whether that forest impact can be managed intelligently—with targeted protections, ecological mitigation, and honest accounting—or whether the pressure for rapid renewable deployment will overwhelm ecological concerns and produce outcomes that are worse for biodiversity and for the long-term integrity of the carbon sinks that forests represent.
The study also points toward interesting methodological questions for energy system modelers. The finding that solar deployment reshapes the value of forest wind is a reminder that energy technologies do not exist in isolation; their value depends on the system they are embedded in, and that system includes other technologies, infrastructure, and policies. This interdependence is often underappreciated in policy discussions, where solar and wind are sometimes treated as interchangeable clean energy sources with the same impact on grid emissions regardless of context. The study shows that context matters: a gigawatt of forest wind has different value in a solar-heavy system than in a wind-heavy system, and different value in a country with abundant hydropower than in a country with a thermal-dominated grid. Policy designed around simplistic assumptions about technology equivalence will produce suboptimal outcomes—outcomes that might be costlier, less ecologically sound, or less politically durable than outcomes designed with full appreciation of these interdependencies.
Finally, there is the question of what this study means for the broader narrative of the green transition. The past decade has seen tremendous progress in the economics of renewable energy, driven by falling technology costs and rising climate ambition. Solar and wind are now the cheapest new generation sources in most markets. But cheap energy is not free energy; it still requires land, materials, labor, and political will. The assumption that clean energy is inherently compatible with ecological protection has always been somewhat optimistic, and this study suggests it is more optimistic than warranted. The transition will involve real trade-offs, some of which will be painful, and those trade-offs will be easier to navigate if they are acknowledged and analyzed rather than papered over with generic sustainability commitments that mean little when push comes to shove in a permit hearing.
The researchers are careful not to draw strong normative conclusions from their analysis. They are not arguing that forest wind is always justified, or that ecological concerns should take precedence over energy needs, or vice versa. What they are arguing is that the trade-off exists, that it is quantitative as well as qualitative, and that better tools for measuring it will lead to better decisions. In that sense, the study is a contribution to democratic deliberation about the shape of the energy transition, not a substitute for it. The numbers tell you what the trade-offs cost in economic terms; they do not tell you what a forest is worth. That judgment remains with citizens, policymakers, and the political processes through which societies make collective decisions about land use and climate policy.
But the study does suggest that the binary framing—wind versus forests—has run its course. The future will not be built on either all wind or all forests. It will be built on a more sophisticated calculus that grades ecological value, respects spatial heterogeneity, and acknowledges that the cheapest and most productive wind sites are not always in the places with the highest conservation stakes. Norway has an unusual opportunity to design this calculus into its energy policy from the beginning, rather than retrofitting it after conflicts have hardened. Countries that follow will benefit from the template this study provides—provided they have the institutional capacity and political will to implement it. In a world where climate and ecological crises increasingly demand simultaneous attention, that capacity and will may be the scarcest resources of all.
The story of the energy transition is usually told as a story of technology and economics: the falling cost of solar panels, the improving efficiency of wind turbines, the scaling of battery storage, the electrification of transport and heating. These are real and important developments. But behind every turbine and panel is land, and behind every land decision is a judgment about what that land is worth—for carbon, for biodiversity, for beauty, for the communities that live in and around it. The study by Javed and colleagues reminds us that the green transition is not just an engineering challenge. It is a political and ethical challenge, one that requires tools capable of making trade-offs visible and decisions accountable. The model they have built is a step in that direction. What we do with it is up to us.
"By quantifying the ecological value forgone per unit of forest onshore wind, this study makes explicit a trade-off that energy system modelling usually leaves implicit."
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