Balancing land use for conservation, agriculture, and renewable energy

Domain: chemistry
Published: 2026-03-07
Editorial outcome: major_revision

Abstract
Growing demand for food coupled with climate commitments to reduce emissions will result in more land
development for agriculture and renewable energy. Simultaneously, conserving land for biodiversity and nature’s
contributions to people (NCP) is imperative for achieving international climate, sustainable development, and
biodiversity goals. Meeting these interconnected objectives requires efficient land allocation across sectors. Here, we
present a flexible, multiple-objective framework for strategically allocating land to mitigate threats to biodiversity and
NCP under climate change while supporting development. Application of this framework at a global scale through
country-level targets shows that if future development is planned without consideration of nature, demands for land
could impact nearly 1 million km2 of high-priority conservation areas. Multi-sector planning can mitigate potential
conflict, reducing carbon loss and species exposure. Our findings underscore the need to conserve critical areas for
nature, reduce land demand for food and energy, and intentionally coordinate land use across sectors.

Introduction
Climate change presents an urgent challenge, with the policy decisions, technological advancements, and sustainable
practices we adopt today shaping the future of both human society and the natural environment (IPCC, 2023 [1]). The
path to a net-zero emissions future requires an immediate focus on changes in land use and energy development
across sectors: Opportunities for climate change mitigation lie in transforming agricultural systems and diets,
implementing natural climate solutions like reforestation and avoided deforestation, and swiftly transitioning to
renewable energy sources (Barthelmie & Pryor, 2021 [2]; Creutzig et al., 2017 [3]; Gielen et al., 2019 [4]; Griscom et
al., 2017 [5]; Roe et al., 2021 [6]; Searchinger et al., 2018 [7]). Climate change solutions can benefit and include
biodiversity (Pacifici et al., 2015 [8]; Griscom et al., 2017 [5]); however, without coordinated planning, popular forms
of mitigation such as renewable energy development can further exacerbate the ongoing biodiversity crisis
(Hernandez et al., 2015 [9]; Köppel et al., 2014 [10]; Levin et al., 2023 [11]; Wu et al., 2023 [12]; Condon et al., 2025
[13]). The international community has recognized the urgent need for greater investment in climate and biodiversity
through the Kunming-Montreal Global Biodiversity Framework and the Paris Climate Agreement. Global
commitments include protecting 30% of the planet’s important areas for nature, halting the loss of any additional
areas through integrated, participatory spatial planning (CBD, 2022 [14]), and limiting global warming to well below
2°C (UNFCCC, 2017 [15]).

Growing demand for food and energy associated with rising population and per-capita consumption increases the
urgency of efficient land use planning. Projections indicate that demand for cropland and energy will grow across a
range of future climatic and socioeconomic scenarios, driving an expansion of land conversion (Popp et al., 2017 [16];
Riahi et al., 2017 [17]; Boakes et al., 2024 [18]). Often overlooked, energy generation constitutes a significant form of
land use. Land use for energy generation will increase in the coming decades, with increases rivaling those associated
with agriculture (Johnson et al., 2021 [19]; Trainor et al., 2016 [20]). Renewable energy sources have large land use
requirements, with cropland for bioenergy exceeding other sources in projected land use (both in total area and per
unit of power), followed by wind energy, hydropower, and solar energy (Fthenakis & Kim, 2009 [21]; McDonald et al.,
2009 [22]; Trainor et al., 2016 [20]; Nøland et al., 2022 [23]; Lovering et al., 2022 [24]). While large areas will be
required for future renewable energy development, renewable sources can continue generating energy over time
within their footprint, whereas extractive energy sources such as coal, oil, and gas, must continually open new drilling
or mining sites to maintain output, resulting in comparable land use requirements across certain renewable and fossil
technologies (Dai et al., 2023 [25]). While land use competition from solar and wind projects in both rural areas
(Spangler et al., 2024 [26]; Nilson & Stedman, 2023 [27]; Rand et al., 2025 [28]) and natural habitats (Katzner et al.,
2025 [29]; Levin et al., 2023 [11]) have intensified, siting strategies that avoid conflict (Wu et al., 2023 [12]) or
increase land use compatibility like agrivoltaics (Swanson et al., 2025 [30]; Merheb et al., 2025 [31]) have begun to
emerge.

Siloed land-use planning could lead to patterns of human infrastructure that harm natural ecosystems and wildlife. To
balance increased development demands and climate solutions with minimizing extinctions, it is crucial to address the
existing impacts on biodiversity driven by habitat loss and fragmentation and to avoid further loss (Isbell et al., 2023
[32]; Jaureguiberry et al., 2022 [33]; Pörtner et al., 2021 [34]; Smith et al., 2022 [35]). Meeting humans’ growing
demands while addressing the urgency of biodiversity conservation and climate change mitigation will require
strategic, collaborative planning to optimize land use across sectors (Fastré et al., 2021 [36]; Kiesecker et al., 2010
[37]). Specific attention to the dynamics of shifting climatic suitability for species and crops will be essential to ensure
that resources are used effectively and land conserved is suitably located (Hannah et al., 2020 [38]; Hannah et al.,
2020 [39]).

Previous research has examined global geographic priorities for biodiversity, carbon, and other natural assets that
deliver key benefits to people (Chaplin-Kramer et al., 2022 [40]; Jung et al., 2021 [41]; Neugarten et al., 2024 [42];
Strassburg et al., 2020 [43]), with priorities for biodiversity including those with consideration of climate change
(Hannah et al., 2020 [39]) and the intersection with future land uses such as agriculture (Kehoe et al., 2017 [44];
Williams et al., 2021 [45]) and solar and wind power (Dunnett et al., 2022 [46]; Kiesecker et al., 2019 [47]; Rehbein et
al., 2020 [48]). These advances explore the components of future planning individually, leaving underexplored the
potential for multi-sector planning to inform the intersection of future conservation, agriculture, and renewable
energy. Here, we present a framework for strategically allocating land under climate change to mitigate threats to
biodiversity, carbon, and nature’s other contributions to people (NCP) while supporting development. Our approach
builds on previous studies (Hannah et al., 2020 [39]; Neugarten et al., 2024 [42]; Johnson et al., 2021 [19]) to offer a
spatially-explicit examination of this multifaceted problem and propose solutions through an integrative, cross-sector
method. Importantly, our framework allows the exploration of multiple scenarios with relatively low computational
power, enabling usage by a broader audience than in larger models (e.g., integrated assessment models). In this
study, we hypothesize that strategic allocation can be a critical solution to help address the converging challenges
facing our ecosystems and development landscapes.

Results
Scenario framework
We developed three planning scenarios to evaluate how differing priorities affect optimal land allocation for
conservation, agriculture, and renewable energy (Fig. 1). The ‘Production-First’ planning scenario allocates land to
meet future demand for development (for energy and food) based on potential yield, siting constraints, and siting
feasibility (e.g., distance to roads and powerlines), and the remaining land is then available to be optimally allocated
for conservation. The ‘Nature-First’ planning scenario first allocates land for conservation and the remaining land is
allocated for development. The ‘Multi-Sector’ planning scenario allocates land for conservation and development
concurrently. This innovative scenario framework allows planners to explicitly examine tradeoffs by facilitating a
comparison of areas that would be allocated differently depending on planning priorities.

Land for development is allocated to meet targets for renewable energy (photovoltaic solar power, concentrated
solar power, wind power, hydroelectric power, and biofuel crops) and food crops. The targets for each region and
sector were retrieved from the Shared Socioeconomic Pathways (SSP) database, defined as future sector-specific
demand for energy in gigawatts (PV solar, CSP solar, hydro, and onshore wind) and tonnes of dry matter (crops for
fuel and crops for food). Projections for food and energy reflect the SSP 1 “Sustainability” narrative for the year 2050,
aligned with ambitious global sustainability goals that seek to mitigate climate change and promote responsible
consumption (Riahi et al., 2017 [17]). The region-level (see regions in Fig. S1) development targets were attributed to
each country based on market share (see Methods) to run the analysis at the country level. We assume that
additional conservation and development come from the conversion of available land, with available land excluding
current dense development (more than 80% human modification, see Methods). We are using a pixel-based approach
that allocates each pixel to one sector and does not allow for co-location of multiple sectors (for example, wind
turbines in future agricultural lands). This decision is based on current available data and the practical, context-
specific considerations that are needed at local scales to co-locate different land uses. Accordingly, our results
represent upper bounds on additional land requirements in this scenario; realized outcomes will depend on local
technical, agronomic, and biodiversity constraints.

In all scenarios, land for conservation is allocated to meet targets for biodiversity, carbon, and other NCP. For
biodiversity, we focus on 7,199 terrestrial vertebrate species categorized by the IUCN Red List (IUCN, 2021 [49];
BirdLife International, 2020 [50]) as Near Threatened and threatened (Vulnerable, Endangered, and Critically
Endangered). Each species’ habitat is established based on the overlap of their present habitat (Area of Habitat; see
Methods) and future climatic niche (Hannah et al., 2020 [39]). Biodiversity targets were defined at the species level
based on global range size (Hanson et al., 2020 [51]; Rodrigues et al., 2004 [52]). To implement these targets at the
country level, the percentage targets were used in combination with the country-level habitat. For example, if a
species’ global target was to conserve 20% of its habitat, each country with that species would have a conservation
target set at 20% of the habitat in that country. Carbon includes vulnerable terrestrial carbon, as described by Noon
et al., 2021 [111]. NCP build on the ecosystem services concept to recognize the role of culture in the linkage between
people and nature (Díaz et al., 2018 [53]), operationalized here with the use of spatially-explicit models that combine
ecological supply of benefits with the populations they benefit (Chaplin-Kramer et al., 2022 [40]), including those for
nitrogen retention, sediment retention, coastal risk reduction, pollination, and access to nature. Targets for
conserving carbon and NCP were set to 90% of the current levels of each NCP to align with the critical natural assets
concept from Chaplin-Kramer et al., 2022 [40].

Land allocation for each target is spatially optimized using mixed integer linear programming (ILP; Hanson et al., 2023
[54]). ILP combines mathematical modeling with computational algorithms to find the best possible solutions (e.g.,
where to place a conservation area to represent the most species) given constraints provided (e.g., budget limits or
land availability). ILP solvers can outperform other approaches in reaching cost-effective solutions for conservation
problems, both in terms of the optimality of solutions and in computational efficiency (Beyer et al., 2016 [55];
Schuster et al., 2020 [56]). For each scenario, we ran the analyses in two ways: first, constraining the analysis to
allocate up to 30% of land per country to conservation (including existing conservation areas), and second, permitting
the percentage of land allocated to conservation to be as high as needed to meet individual targets for species,
carbon, and NCP, which is often higher than 30% (although it can be lower).

Our framework provides several ways to accounting for climate change in land-use planning. First, conservation
priority areas are determined at the species level based on the overlap between each species’ current habitat and
their projected future climatic suitability. By accounting for the projected changes in species’ climatic suitability,
conservation initiatives can adapt and allocate resources strategically, facilitating vulnerable species' long-term
survival and improving the overall effectiveness of conservation efforts (Hannah et al., 2020 [39]). Second, our
allocation for agricultural land is based on future, rather than solely current, climatic suitability for crops. Growing
conditions for crops are shifting, providing both new opportunities for agriculture and reducing the suitability of
existing cropland, resulting in environmental tradeoffs that are important to consider (Hannah et al., 2020 [38]).
Lastly, we base our projections for climatic shifts (i.e., related to species and crop suitability) on a moderate climate
change scenario, while our estimates of demand for renewable energy are based on what is needed to meet a low
emissions scenario. This distinction allows planners to prepare for the worst impacts of climate change while working
toward mitigating those impacts in a way that aligns with global emissions reduction targets. Our decision to use a
moderate emissions scenario for yields also aims to account for uncertainties in climate sensitivity and other non-
linear processes in the climate system that the climate models did not account for.

Land allocation for conservation, agriculture, and renewable energy
The ability to meet multiple objectives depends on the degree of coordination across sectors and the scale at which
conservation objectives are implemented. In the Multi-Sector planning scenario with national 30% conservation
targets, 2.0 million km2 of currently undeveloped land (2% of total land area, equivalent to the land area of Mexico) is
converted to agriculture globally by 2050, with 56% dedicated to food crops and 44% to biofuel crops. An additional
4.5 million km2 (3% of land) is allocated for renewable energy generation: 76% for wind power (including spacing
between turbines); 19% for solar power, and 5% for hydroelectric power. In addition to existing conservation areas,
20.4 million km2 (15% of land) is allocated for conservation to meet 30% of land conserved per country (Fig. 2; see
Figs. S2 and S3 for other scenarios). Aligned with SSP projections, regions with the largest allocation for development
include eastern and southern Asia, most notably India, China, and South Korea, northern coastal Africa in Algeria and
Morocco; the central United States; and western Europe. Land allocation for all countries and scenarios can be found
in Supplementary Data 1.

Our results indicate that if the increase in development to meet future demand relies solely on additional land
conversion without co-locating sectors (e.g., wind energy on agricultural land), it is not possible to meet the country-
level targets for conservation and development needs. Conservation and development targets met varied by scenario,
with the Nature-First planning scenario generally being more effective in meeting conservation targets, Production-
First being more effective at meeting development targets, and Multi-Sector falling in between (Fig. 3A).

The extent to which conservation targets were achieved primarily depended on whether land allocations were
constrained to meet a 30% target for conservation and, to a lesser extent, on the planning scenario. For biodiversity,
targets are only met for slightly less than half of species when constrained to 30% land conservation. Removing the
constraint led to targets being achieved for nearly all species in Nature-First and Multi-Sector and 88% in Production-
First. For NCP, when constrained to 30% of land for conservation, targets are met in 3% of countries in the
Production-First planning scenario and 9% in Multi-Sector and Nature-First. Removing the 30% land conservation
constraint allows NCP targets to be met in 96% of countries in the Nature-First planning scenario, 84% in Multi-Sector,
and 62% in Production-First. Without the area-based constraint, we found that 56% of all land is needed for
conservation globally, which is 41% above what is already conserved (Fig. S5). Nearly all countries require more than
30% of land conserved to meet conservation targets (Fig. S6 and see Supplementary Data 1), with variation across SSP
regions: Latin America (LAM) required 69%, Organization for Economic Co-operation and Development countries
(OECD) 58%, Asia (ASIA) 57%, Middle East and Africa (MAF) 52%, and the countries from reforming economies of
Eastern Europe and the former Soviet Union (REF) 48%.

The percentage of development targets met was 94% in the Production-First planning scenario, 74% in Nature-First,
and 87% in Multi-Sector. Production targets achieved also varied by sector. For example, in the Nature-First scenario,
approximately half of hydropower and wind targets were achieved, whereas over 80% of targets were achieved for
energy crops, food crops, and solar energy. Land efficiency for development, meaning the amount of land allocated
per tonne of crop dry matter or gigawatt of energy generated, varied by both planning scenario and by sector. For
example, hydropower land efficiency ranged more than two-fold, from 64 km2/gigawatt (GW) in Production-First to
148 km2/GW in Nature-First. This finding suggests that hydropower may be difficult to efficiently site when avoiding
conservation areas because areas suitable for hydropower are more constrained. Conversely, solar power land
efficiency was not as dependent on the planning scenario, ranging only between 34-36 km2/GW across all scenarios.
This result aligns with previous findings that solar power is relatively land efficient (Bolinger & Bolinger, 2022 [57];
Fthenakis & Kim, 2009 [21]) and flexible in siting (Baruch-Mordo et al., 2019 [58]).

Our multi-objective optimization framework can enable decision-makers to address tradeoffs in the case of
potentially conflicting objectives explicitly. In our global application, we examined differences in land efficiency (Fig.
3B), and the extent to which species and carbon were exposed to the development footprint (i.e., the spatial overlap
of carbon or over 10% of a species’ habitat [Figs. 3C and 3D]). Per unit of development, the Production-First scenario
was the most land efficient, yet it consistently led to greater exposure for species and NCPs. This tradeoff
demonstrates spatial overlap between the areas most suitable for development and those for conservation,
consistent with previous studies (Neugarten et al., 2024 [42]). Total land allocated per sector, which may be indicative
of cost, did not vary substantially across scenarios: mean land allocated ranged from 5,134-5,172 km2 per target met
for conservation and from 7,154-8,254 km2 for production (Table S1, and see Supplementary Data 1 for country-level
land allocation). Given varying costs and priorities, land-use planners can benefit from explicit consideration of
tradeoffs between production output and conservation goals in conflict areas. To exemplify a potential method for
doing so, we have assessed the Pareto frontier reflecting countries’ individual optimizations to provide a comparison
of the severity of tradeoffs across scenarios (Figs. S6-7).

Under the Production-First planning scenario, 674 Near Threatened and threatened species (including 214
endangered species) were exposed to development, compared to 572 (172 endangered) in Multi-Sector and 510 (148
endangered) in Nature-First (Fig. 3C). The size of impact varied by species and scenario, with development
overlapping, on average, 4.3% of each endangered species habitat in the Nature-First planning scenario compared to
6.0% in Production-First (see the range of species-specific impacts in Fig. S9). Carbon exposure also varied
considerably: in Production-First, development overlapped with 21,462 Megatonnes (Mt) of carbon, compared to
17,392 Mt in Multi-Sector, and 17,060 Mt in Nature-First. Because the difference across planning scenarios is likely
partially due to differences in development targets met, species and carbon exposed are reported per unit of
development in Figs. 3B-D.

Independent single-use allocations for photovoltaic (PV) solar and wind energy indicate measurable overlap with land
allocated for crops for food and fuel. Globally, 2.7% of land allocated for PV solar overlapped with land allocated for
total crop expansion (or 1.1% of crop expansion overlapped with that for PV), primarily in Europe and India (Fig. S10),
and 2.1% of land allocated for wind energy overlapped with that for crop expansion (or 3.5% of crop expansion
overlapped with that for wind), primarily in Europe and China (Fig. S11). These results highlight where, if technically
feasible, co-location could reduce the need for additional land conversion.

Potential conflict between important areas for conservation and development
To identify land that could be allocated for development or conservation depending on planners’ priorities and
therefore identify areas of potential conflict between conservation and development, we overlaid land allocation for
development from the Production-First planning scenario and land allocation for conservation from the Nature-First
scenario (Fig. 4). This revealed several regions where important areas for development coincide with those critical for
future conservation, including eastern and southern Asia, western Europe, and northern Africa (Fig. 4 insets). The
ASIA and OECD regions had the largest area of overlap at 439K km2 and 270K km2, primarily driven by wind
overlapping with conservation, followed by the MAF region at 229K km2, primarily driven by food crops overlapping
with conservation (Fig. S12). MAF had the highest proportion of development that overlapped with the 30%
conservation allocation at 16%, followed by ASIA and OECD at 14% and 12%. Countries with the most significant areas
of overlap for conservation and development priorities included China (189K km2), India (106K km2), and the United
States (75K km2) (Fig. S13). While potential conflict areas signal that the land is compatible for multiple land uses, they
do not necessarily signal that the identified land uses themselves are compatible. Existing empirical work suggests
that biodiversity will fare poorly on developed lands (Kumara et al., 2022 [59]; Conkling et al., 2022 [60]) and that
targeting low conflict areas, particularly for energy development, can result in better outcomes (Kiesecker et al., 2024
[61]).

While our framework would allow development to be sited in areas with potential food or energy yield (Oakleaf et al.,
2019 [62]), the model is designed to avoid siting development in current conservation areas by imposing an additional
cost (see Methods). Despite the higher cost, development was sometimes allocated within current conservation areas
(Fig. S14), signaling potential risk to biodiversity and NCP in those areas. Countries with the most notable overlap
between land allocated for future development and current conservation areas included Mongolia (7,200 km2), China
(5,150 km2), Japan (5,025 km2), Italy (4,975 km2), and Niger (4,150km2).

Overlap with conservation varied by development sector (Fig 5). While hydropower has the smallest total footprint by
area, it was the most likely to overlap with land allocated for conservation, likely because areas that are riparian and
of high topographic variability have high biodiversity and provide significant levels of NCP in the form of water quality
and sediment regulation. Further, the Multi-Sector planning scenario was less effective in mitigating conflict for
hydropower than for other sectors: 18% of hydropower development overlapped with conservation areas in Multi-
Sector compared to less than 7% in all other sectors. In Production-First, land allocated for hydropower had the
highest overlap with conservation areas (35%), followed by cropland for food (23%) and energy (18%), solar (14%) and
wind power (13%). The higher the overlap between cropland and conservation may be because cropland relies on
higher land productivity (for example, soil quality), which is not necessary for solar and wind power. Overlap between
solar power and conservation priorities was most effectively reduced with the Multi-Sector planning scenario. In
Production-First, the 14% of development overlapping with conservation dropped to 2% in Multi-Sector.

Methods
Overview
We used integer linear programming with the spatial conservation prioritization R package ‘prioritizr’ (Hanson et al.,
2023 [54]) and Gurobi optimization software (version 10) (Gurobi Optimization, LLC., 2022 [99]) to strategically
allocate land for conservation and six development sectors: solar PV, concentrated solar power, onshore wind power,
hydropower, crops for energy (Oakleaf et al., 2019 [62]), and crops for food (Fischer et al., 2021 [100]). By identifying
areas with the greatest conservation value and those with high development potential, we determined the allocation
of land use that benefits conservation and food and energy development. All prioritizations were solved to within 1%
of the optimal solution.

All spatial raster data were prepared at 5km using the R package, ‘terra’ (Hijmans, 2023 [101]). For categorical data,
resampling and aggregation was based on nearest neighbor (mode), and for continuous data, aggregation was based
on bilinear interpolation (mean). Data resolution affects the outcomes of prioritization (Arponen et al., 2012 [91]); we
aimed to strike a balance between computation efficiency, the resolution of available data, and the resolution at
which we felt it was responsible to interpret future species and crop climatic niche data.

We performed prioritization analyses for development and conservation using the data and parameters detailed in
the following sections and in Table S2. All prioritizations were performed at the country level to more realistically
allocate renewable energy production and align with the scale at which conservation policies are often implemented.
Given our aim to evaluate the amount of land needed for development without using highly modified landscapes, we
omitted land that has existing intensive human activities from what land was available to the prioritization algorithms.
In all prioritizations, we used the human modification (HM) dataset (Kennedy et al., 2019 [102]) to define available
land. HM quantifies the proportion of each square km modified for human settlement, production, and
transportation, with values ranging from unmodified at 0 to completely modified at 1. After aggregation to 5km, we
excluded areas with a more than 80% degree of human modification (aligned with methods in Johnson et al., 2021
[19]). For some problems, it was not possible to meet development and/or conservation targets within the available
land (7 of 160 countries). In cases that were verified, the potential for development was the limiting factor. To
address this, we first attempted to meet 100% of targets, but if that failed, the development targets were
incrementally decreased until we could successfully solve the problem. All solutions were evaluated against the
original, ideal targets post-hoc to understand what portion could be possibly met, meaning the results reflect the
cases in which targets could not be met. We considered promoting spatial ‘clustering’ of the solutions with prioritizr’s
option for a ‘boundary penalty’ but opted not use this approach due to the high sensitivity of the results to the value
selected and the lack of a standardized approach for determining a consistent boundary penalty value across country-
level prioritizations. Further, the boundary penalty approach can lead to solutions that are highly dissimilar to others
and may result in solutions that don’t effectively provide functional connectivity (Hanson et al., 2022 [103]).

All data were cropped and masked to each country using boundaries from Natural Earth data
(www.naturalearthdata.com) using the ‘rnaturalearth’ R package (Massicotte & South, 2023 [104]). Country names
between the SSP regions and Natural Earth were crosschecked and aligned manually, which for some countries with
contested boundaries included combining Natural Earth borders to reflect the countries reported in the database.
Once the spatial data were prepared, all layers were converted into sparse matrices to facilitate faster and more
efficient prioritizations.

All data analysis and non-spatial visualizations were completed with R (version 4.1.2; R Core Team, 2021 [105]) and
final maps were created with R and ArcGIS Pro (version 3.0.2).

Scenarios
We developed three planning scenarios to represent different priorities in land-use planning (Fig. 1). The first is
‘Production-First’, in which land is allocated for food and energy development concurrently based on potential yield
and feasibility to meet future demand in one prioritizr analysis (or ‘problem’), then the remaining land is allocated for
conservation to meet targets for biodiversity and Nature’s Contributions to People (NCP) in a second problem.
Technically, we did this with a ‘locked out’ constraint, meaning that the land from the first problem’s solution was not
available in the second problem. The second planning scenario is ‘Nature-First’, in which the problems are completed
in the opposite direction: first, land is allocated for conservation to meet as many targets as possible, and then that
solution is locked out from the subsequent development problem. Finally, the third planning scenario is ‘Multi-
Sector’, in which all conservation and development sectors are allocated concurrently in one prioritizr problem. This
was completed technically using prioritizr ‘management zones’, in which each sector is a separate zone.

For each planning scenario in each country, we performed the prioritization in two ways: to get as close to meeting
conservation targets as possible 1) within 30% of land area and 2) within all land area. 30% was selected as a
threshold based on recent country-level commitments aiming to align with the global 30x30 goal outlined in the
Kunming-Montreal Global Biodiversity Framework. In the analyses for which conservation area is constrained to 30%,
a minimum set objective is used for all problems, meaning that the cost of the solution (detailed below) is minimized
while ensuring that all targets are met. Because all conservation targets often could not be met within 30% of land
area, the percentage of the targets aimed to be met are incrementally increased until 30% of land area is met. In the
analyses for which there was no constraint, a minimum set objective is used for the conservation problem in the
Nature-First planning scenario and the development problem in Production-First. In Multi-Sector, conservation in
Production-First, and development in Nature-First, a minimum shortfall objective is used with a budget that is larger
than the cost of the solution, simulating an infinite budget. This objective minimizes the overall shortfall for as many
targets as possible. This approach allowed us to evaluate what targets can be met in all remaining land after the land
was optimally selected for one sector.

Prioritizations for development
We performed the development prioritization to identify areas for agriculture and renewable energy development for
the six sectors. These sectors are allocated concurrently (with each other) using prioritizr’s management zones. For
each energy sector (including bioenergy crops), the features used were sector-level potential yields from Oakleaf et
al., 2019 [62]. For food crops, potential yield is calculated as the maximum potential yield under a 2050 RCP 6.0
scenario (Fischer et al., 2021 [100]) across four food crops that occupy the most land globally: maize, rice, soy, and
wheat (FAOSTAT, 2016 [106]). We base our projections for climatic shifts on the RCP 6.0 scenario, versus our demand
for food and renewable energy are based on what is needed to meet a low emissions scenario. This distinction allows
planners to prepare for the worst impacts of climate change while working toward mitigating those impacts in a way
that aligns with global emissions reductions targets.

The targets for each sector were retrieved from the SSP database. Food crops are those for human consumption; land
for pasture is not considered as a development category. The SSP database provides demand at the regional level,
which we allocated to each country based on their projected market share. For wind and solar power sectors, the
projected market share was calculated from Jacobson et al., (2019) projections. Jacobson et al., 2019 [107] does not
include projections for all countries; thus, the market share for countries not considered in their analyses and
projections for market share for hydropower were calculated based on current market share (IRENA, 2023 [108]).
Country-level market share for cropland is calculated from change in cropland between 2050 and 2020 in an SSP 1
scenario (Chen et al., 2022 [109]). The projected market share for cropland was used for bioenergy as well, as Chen et
al., 2022 [109] do not differentiate between whether cropland is used for food or energy crops.

The cost of allocating land for development includes three equally weighted components. Two layers are used in all
scenarios: a ubiquitous land cost (value = 1) and feasibility (0-1), defined as the inverse of the sector-specific
development potential index (DPI) (Oakleaf et al., 2019 [62]). The DPI is a global suitability index at 1-km resolution
(with standardized values ranging from 0 to 1) that indicates the potential for development based on spatially-explicit
available resources, siting constraints (i.e., land cover, slope), and siting feasibility (i.e., major roads, rails, ports,
powerlines, access to demand centers). The third cost layer varies by scenario. While in the Nature-First and Multi-
Sector planning scenarios, current conservation areas are excluded from available land (‘locked out’), in Production-
First, there is an additional cost for current conservation areas (value = 1) (UNEP-WCMC and IUCN, 2021 [49]) to
reflect the economic, political, and social costs of developing in conservation areas and allow the results to reveal
potential risk to those areas. In Nature-First and Multi-Sector, the third cost layer for development reflects value for
conservation: this cost reflects the nature features data used in this analysis, biodiversity, carbon, and NCP, each
weighted equally. Specifically, the cost layer includes range-size rarity of climate change overlaps for terrestrial
vertebrates (Hannah et al., 2020 [39]), vulnerable carbon (Noon et al., 2021 [111]), and a sum of five layers of NCP
(Chaplin-Kramer et al., 2022 [40]). More details on the natural features data are below.

Prioritizations for conservation
We performed the conservation prioritization to allocate land to meet targets for biodiversity and NCP. We
performed the prioritizations in two ways for each planning scenario: 1) to meet feature targets; 2) to meet the
greatest proportion of targets possible within 30% of land within each country. To reach 30% within the prioritizr
problem, we lowered all the conservation feature targets to 10% of their original target, then increased the targets by
percentage increments until 30% of land area was met.

Current conservation areas were compiled from the World Database on Protected Areas (WDPA) and the World
Database on Other Effective Area-Based Conservation Measures (WDOECM) (UNEP-WCMC & IUCN, 2021 [49]). The
data were prepared for analysis using the ‘wdpar’ R package (Hanson, 2022 [112]; Hanson et al., 2020 [51]), then the
shapefile data were rasterized to align with all other layers as binary based on the majority of a pixel. The
conservation prioritizations are always built on current conservation areas, meaning that when the 30% constraint
was used, if 30% or more of land was already protected, no additional land was allocated for conservation.

Natural features data included those for biodiversity, carbon, and NCP. To map biodiversity distributions, we utilized
current habitat maps for 7,199 Near Threatened and threatened species, including 2,395 birds, 1,478 mammals, 2,259
amphibians, and 1,067 reptiles, and created layers that only included where there was overlap between their current
habitat (IUCN, 2021 [49]; BirdLife International, 2020 [50]) with their future climatic suitability (Hannah et al., 2020
[39]). Biodiversity is represented as the species-level overlap between “Area of Habitat” (AOH; IUCN, 2020; BirdLife
International, 2020 [50]) and future climatic suitability in an SSP 3, RCP 7.0 2050 scenario (Hannah et al., 2020 [39]).
This means only the portions of the AOH that remain within a species’ projected climatic niche under the SSP 3, RCP
7.0 scenario are considered, an approach that conservatively identifies climate refugia while allowing for limited
movement under climate change. Although it would be ideal to align with the RCP 6.0 scenario used for food crops,
this scenario was not available. AOH is derived from Red List expert range maps that have been filtered for suitable
habitat based on up-to-date satellite data and known elevation limits (Brooks et al., 2019 [113]). AOH data used here
are based on IUCN and BirdLife International spatial data for terrestrial mammals, birds, reptiles, and amphibians.
They are filtered for habitat using a previously described land cover to species habitat table (Santini et al., 2019 [114])
and ESA land cover classes for 2018. Spatial data presence codes ‘Extant’ (1), ‘Extinct’ (5) and origin codes ‘Native’ (1),
‘Reintroduced’ (2), and ‘Assisted Colonization’ (6) were used to define each species’ Red List range used to produce
the AOH. Species included Near Threatened and threatened (Vulnerable, Endangered, and Critically Endangered)
birds (n = 2,395), mammals (n = 1,478), reptiles (n = 1,067), and amphibians (n = 2,259). For species that did not have
an overlap between their current AOH and their future climatic suitability, their current AOH was used.

Carbon is represented by vulnerable terrestrial carbon storage, which represents the spatially explicit, above-ground,
and below-ground carbon that could be lost in a typical disturbance event (Noon et al., 2021 [111]). NCP features
included six data layers that were most relevant to area-based conservation efforts and were publicly available from
Chaplin-Kramer et al., 2022 [40]: nitrogen retention for water quality regulation, sediment retention for water quality
regulation, coastal risk reduction, pollinator habitat sufficiency for pollination-dependent crops, and access to nature
(urban and rural). The NCP data includes those “realized” by beneficiaries, including people downstream of habitats
providing water quality benefits, populations protected from storm surge, or people within a certain travel time of
natural areas.

Potential conflict
To generate the potential conflict map (Fig. 4), we compared land allocation outcomes across four sets of results: 1)
Production-first with the 30% conservation target (Fig. S2), 2) Nature-first with a 30% conservation target (Fig. S3), 3)
Production-first with no conservation constraint (Fig. S4), and 4) Nature-first with no conservation constraint (Fig. S5).
Conflict areas represent planning units where the allocation of land diverged across these sets of results, reflecting
areas where there may be competing demands between development and conservation.

Comparison of independent allocations for select development
To assess the potential for co-location within our single-use framework, we independently prioritized land for crops
(food and energy), PV solar energy, and wind energy. We used the same data, constraints, and cost structure as the
main analysis, but created independent vectors for each sector’s data rather than utilizing prioritizr’s management
zones. We then analyzed the overlap between the results for 1) land for crops and for PV solar energy and 2) land for
crops and for wind energy and summarized and mapped global results.

Data availability
Input datasets and key spatial maps generated in this study have been deposited in Figshare at
https://figshare.com/articles/software/BalancingLandUse_Files_zip/26133865.

Code availability
The code used to run the prioritizations analyses is available on Figshare:
https://figshare.com/articles/software/BalancingLandUse_Files_zip/26133865.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The authors make a case for multi-functional landscapes by demonstrating that such multi-sector planning can lead to benefical reductions in number of threatedned species and carbon loss, while without such multi-functional landscapes it is impossible to meet both conservation and development targets. I enjoyed reading the paper, and found the analysis to be novel and insightful. I recommend the paper be published with some modifications (however with the caveat that an unsuitable answer to comment 1 will lead to a change in my evaluation):\n1. The authors mention `tradeoffs` multiple times in the paper, but do not show an actual tradeoff curve or pareto optimality frontier that can be traversed by a policy maker in order to identify the varied solution set and associated costs available to them. The cost in this case would be how many grid cells need to be transitioned from their current land use to a new one. Just because the integer-programming approach can find an optimal or near-optimal solution does not mean that the cost will not be so prohibitively large as to be impractical. if the authors analysis shows that the costs are impractical, then I will no longer be able to recommend publication.\n2. I found figure 4 confusing, can you add what each color denotes specifically (the same way you did with black) in this figure?\n3. In figure 2, most current cropland area has been transitioned to some other land-use without compensating in other regions. E.g. U.S Midwest seems to have no cropland; India has minimal cropland in a couple of states; No agriculture around the Nile etc. How is this realistic even in a future projection?\n4. Going back to figure 4, what percentage of land under potential conflict is already belonging to the same land use class as either the Nature-first or the production first-scenario?\n5. Does the `prioritizr` package allow clustering such that we do not get pixelated results i.e. one grid cell is conservation and the very next grid cell is development (e.g. a chess board pattern)? If so, how did the authors ensure contiguity of land use classes?", "reviewer_2": "This manuscript presents a framework for allocating land uses to mitigate threats to biodiversity, carbon, and ecosystem services while supporting development. The authors used a combination of linear programming, spatial prioritization, and scenarios to show how differing priorities affect optimal land allocation. The study highlights the need for colocation of multiple land use sectors to meet conservation and development goals. The paper is novel, interesting, and ambitious, and should be of interest to a broad international community. I do have several questions that require better explanations and justifications, particularly on the methods, and on the spatial patterns in the maps (some of which were surprising, at least to me).\nOn the methods:\nPlease explain a little more the basis of mixed integer linear programming and how it works. Also, the citations in lines 90-92, used to justify the approach, are not in the references section.\nPlease explain the rationale for not allowing expansion in areas with human footprint value <0.2\nLine 320 says \"For some problems, it was not possible to meet development and/or conservation targets within the available land.\" Please explain more on how many countries were affected by this problem, if this issue may have resulted in inconsistencies in the results, and how these inconsistencies could have affected the comparisons and/or the overall findings.\nPlease provide more information on how species' exposure to development was measured, and on the magnitude of that exposure. My understanding is that if a species was projected to see development within its predicted habitat (i.e., habitat loss), then that species counts as impacted by development, correct? Then, does this mean that 1 pixel of habitat loss is enough to count the species as impacted by development? Is that ecologically meaningful? To get a better understanding on the impact of development on species, it would be useful to report not only how many species are impacted, but how large, on average, are those impacts (i.e. the proportion of the species' predicted habitat affected by development). That will give a better idea of the ecological meaning of the impact, as e.g. 5% habitat loss is not the same as e.g. 40% habitat loss.\nOther comments:\nFigure 4 identifies areas of potential conflict between conservation and development around the globe, representing a key result of this paper. Some of the spatial patterns, however, seem surprising, at least to me. For instance, the Chaco region, in Paraguay and northern Argentina, is known for being an important area for biodiversity and a global deforestation hotspot. Although the figure is a little blurry, I was surprised that this region appears with no conflicts between conservation and development. More surprising, agricultural expansion in Argentina is projected to happen far away in the drier, western region of Argentina, which is not the typical region for agriculture there. Thus, the question here is: how well is the model at allocating the land uses within each country? In other words, how do we know that land uses are projected in the right places? I understand that the aim of this paper is not to try to predict the future, but the model should be accurate at predicting whether/where two land uses compete for the same spot of land. Ultimately, the findings of this paper rely on how and where the model allocates land uses. More information would be helpful.\nThe text in pages 5 and 6 is not totally clear, with an excess of numbers reported, making it sometimes difficult to follow and retain the main messages.\nFig 2 shows land allocated for conservation and development in the multi-sector planning scenario. Please include the maps for the other scenarios as well, including under the two conservation options. These could be included in the appendix.\nIn the discussion, please explicitly answer the hypothesis stated in line 58.\nFinally, this is an open question, but could the use of a different land use model approach provide a drastically different answer on what we need to meet development and conservation goals?", "reviewer_3": "The abstract is concise and well-structured, but it should mention the method used. The reference to a framework is less sounding and generalist.\nIn my opinion, the manuscript's structure should be improved, placing the section on the methods after the main text. This led the reader to understand how scenarios framework has been developed, the tools used and the assumptions adopted.\nAuthors provide a framework based on an integrative spatial planning approach and ability to integrate multiple objectives and assess tradeoffs. The inclusion of multiple objectives in land-use planning is not new, but a certain noteworthy could be fine in how they combine this approach with country-level data, local and Indigenous perspectives (Heiner et al., 2019; Kennedy et al., 2023), and ongoing efforts to limit land conversion (such as through zero-deforestation commitments; Garrett et al., 2019).\nIn this sense, the study is relevant to the field, but its significance needs to be disclosed compared to established literature. Authors mention previous research shortly when should be more comprehensive. A more exhaustive description of the literature is crucial to understand the novelty and justify some scientific assumptions as for example: why authors choose Nature´s Contribution to People (NCP) and not Nature Futures Framework (NFF)? Authors adopts integer linear programming with the spatial conservation prioritization R package ‘prioritizr. Many other tools could be used to develop conservation scenarios (i.e Zonation or MARXAN): why do R package ‘prioritizr has been the choice?\nI would refer the same rationale of justify assumption to the choice of NCP. A consistent literature review exists on NCP but no citation about this topic. Showing that this study articulates NCP with prioritization R package ‘prioritizr is relevant, even more when articulated to SSP framework.\nSuch reinforcement of the literature review and comparison with other study is strongly recommend also to improve the conclusions. Insights from this study are not only for land-use planning: they could be extremely relevant to orient Internacional and nacional policies. That aspect is missing in the texts. Indeed, conclusion are very poor and generalist and as such, need to be reformulated. In do that, I suggest authors to reflect on what stakeholders could be use the results from this study but also think about what the limitations of this approach (i.e resolution? Spatial scales? Others?). The calibration of a mathematical model and projections from a database could provide some results that are not real: for example, I am curious to ask authors what they think about the crops and food area in northern coastal Africa in Algeria and Morocco (fig.2) and its relationship with water scarcity, soil quality and population migrations. I suggest introducing some commentary about this point.\nThe following general typos have also to be reviewed:\nThe reference to Hansen et all, 2023 has to be review to Hanson et all, 2023.\nCheck all the references in the bibliography: many authors are on the list but are not referred to in the text.\nI recommend authors to review the work to meet the expected standards of the journal and the field itself which is complex and relevant."}, "author_response": "We appreciate all the reviewers’ valuable suggestions and comments on our manuscript, they have been instrumental in improving and revising our work and we are greatly appreciative. We have carefully reviewed all suggestions and made corrections accordingly with point-by-point responses.\nWe would like to highlight the following overarching updates:\n• We have evaluated and discussed tradeoffs, including with Pareto optimality (Figs. S7-8) and further examination of land efficiency under the scenarios (Table S1), based on feedback from Reviewer 1.\n• We have expanded upon key analytical decisions in the Methods, including regarding usage of a boundary penalty and the human modification index based on feedback from Reviewers 1 and 2.\n• We have reorganized and streamlined our results section (pages 5 and 6, specifically) to be clearer to the reader, based on feedback from Reviewer 2.\n• We have analyzed and reported species-level exposure to land allocated for development under each scenario (Fig. S8) based on feedback from Reviewer 2.\n• We have elaborated on the need for careful consideration of the scale of implementation of this framework based on feedback from Reviewers 2 and 3.\n• We have clarified and elaborated on the methodology in the Main Text to improve accessibility and to better define the parameters of the analysis based on feedback from Reviewer 3.\nIn the following pages, the comments from reviewers are in blue italics and our responses are in black. Our resubmission package includes Manuscript files with and without tracked changes; please note that line numbers are referring to the clean manuscript which has revisions hidden.\nThank you for your invaluable feedback."}, {"round": 2, "reviewer_comments": {"reviewer_2": "The authors did a great job at incorporating the comments, and I appreciate the time and effort at explaining an clarifying the information presented, as well as at expanding the caveats.\nMy only remaining (minor) comment, is that the results for the Chaco region are still surprising, as local studies projecting future land use changes for the Chaco region seem to show substantially higher rates of deforestation (and thus, potential conflicts with conservation) compared to this study (e.g. https://doi.org/10.1007/s10113-022-01965-5; https://doi.org/10.1088/1748-9326/ad44b6). At a discretion of the authors, I do wonder if it is worth adding in the limitations that locally calibrated land use models may give very different results, at least for some regions.", "reviewer_4": "The approach of the study to compare global \"production-first\" and \"nature-first\" land use maps, and then try to find an \"optimal\" map combining both targets, is original and a meaningful contribution to literature. While I recognise the many assumption choices that need to be made in such an exercise, I would need to flag several issues I encounter in the methodology and the related conclusions. While there are several minor points, my main worry is that the different land uses in the study (productive and natural ones) are assumed to be entirely exclusive, while current reality already shows that is often not the case.\nSee below my chronological comments on the received manuscript.\nIntroduction:\n- While the rest of the document nicely outlines specific impacts for different energy uses (wind, solar, water, energy crops), the second paragraph of the introduction piles up the literature on this topic in one sentence (lines 37-39 \"Land use .. with agriculture\"). While I understand that the format in Nature journals require only a short introduction, the land-use reality, and also existing literature, is quite varying for each variable. The same sentence could be more distinguished by renewable energy source, providing the appropriate literature for each.\nScenario framework:\n- line 79, \"within ... 2050\": Why SSP1? SSP1 projections for food are significantly lower than those of other SSPs, as behavioural change is assumed to lower meat and therefore crop demand. Also, the sentence lets the reader think that SSP1 is also used for the energy demand projections, but the Methods indicate that Jacobson et al (2019) is used for this.\n- line 84, \"(for example, ... lands)\": This example is precisely one of the combinations that is quite viable and already happening currently. Only the base of the tower, which is an insignificant amount of land, is exclusive of other uses.\nResults:\n- line 136-137, \"76% ... turbines)\": Again, this is a huge area in which the spacing areas can easily be used for other purposes, like agriculture and solar, and to a certain extent even natural purposes. In Figure 2, is this entire blue area in Northeastern China assumed to be for wind power only?\n- line 171, \"approximately ... achieved\": Important doubt: since hydropower is assumed to follow its \"current market share\" in the calculated targets, according the Methods, fulfilling only half of this target could mean that current hydropower is being removed. This would most likely not make sense from an overall sustainability perspective, as hydro reservoirs can also be used for natural purposes, and the main ecological damage from creating a hydro reservoir is probably at the time of construction, hence reverting the land use may be hardly valuable (and even worse, if hydropower is allocated elsewhere as a result). This also leads to the overall question of how the model treats areas that already have renewables installed nowadays, are these current installations entirely subject to change in your methodology?\n- Fig 3, panel B: Using stacked columns is not meaningful for efficiency values.\n- Lines 221-222, \"The ASIA ... by wind\": This is a crucial part of the results as it strengthens my point above that wind power is quite compatible with other land uses, which apparently would quite strongly affect the overall results of the study and hence the conclusions.\n- lines 249-250, \"land allocated .. areas (35%)\": Again an important question here is whether current hydropower is included in this figure (see also next comment). If so, that would confirm that conservation areas are compatible with hydro reservoirs in the real world.\n- Fig 5: While the upper panel figure says to only refer to additional production, it is not clear whether current production adds to the overlap in the lower panel.\nDiscussion & conclusions:\n- line 271, (Ortiz et al, 2022): There is likely much more evidence for this claim, certainly in the realm of bioenergy, even between those references you already cited in the introduction.\n- lines 275-276, \"While our .. Ven et al., 2021)\": It would be good if this claim could be backed up by some data, or even a table making such comparisons with IAM outcomes.\n- lines 287-289, \"Multi-functional .. improve yields\": I see this important critic on my end coming back as a policy recommendation here. While it indeed could be a meaningful recommendation for land-based solar power, which is hardly combined with other land uses nowadays, wind power is already consistently co-located with other land uses nowadays, so more than an outcome from the paper, I see it as a lack in the methodology.\n- lines 342-343, \"Our framework .. energy technologies\": Honestly surprised by this statement. I would say that conventional energy technologies (conventional is commonly including fossil and nuclear power plants) are much more flexible in terms of citing, as they are not bound to meteorological circumstances.\nMethods:\n- lines 422-424, \"This distinction .. reduction targets\": While I understand the concept of robustness in this method choice, what if these impacts do not occur, as the renewable energy targets are attempting to accomplish? Wouldn´t the allocation of crop production be significantly different? Probably worth doing such a sensitivity.\n- line 426, \"land for pasture is not considered\": And what happens with this enormous share of land? Is it assumed to stay constant, or deemed available or other land uses? Currently unclear in manuscript.\n- line 466, \"future .. scenario\": Also here, it would be useful to see if allocation of natural areas would drastically change when using a climate scenario more aligned with the renewable energy objectives followed.", "reviewer_5": "Please see attached document."}, "author_response": "Response to Reviewer Comments\nWe appreciate all the reviewers’ valuable suggestions and comments on our manuscript, they have been instrumental in improving and revising our work and we are greatly appreciative. We have carefully reviewed all suggestions and made corrections accordingly with point-by-point responses.\nWe would like to highlight the following overarching updates:\n• We have evaluated and discussed tradeoffs, including with Pareto optimality (Figs. S7-8) and further examination of land efficiency under the scenarios (Table S1), based on feedback from Reviewer 1.\n• We have expanded upon key analytical decisions in the Methods, including regarding usage of a boundary penalty and the human modification index based on feedback from Reviewers 1 and 2.\n• We have reorganized and streamlined our results section (pages 5 and 6, specifically) to be clearer to the reader, based on feedback from Reviewer 2.\n• We have analyzed and reported species-level exposure to land allocated for development under each scenario (Fig. S8) based on feedback from Reviewer 2.\n• We have elaborated on the need for careful consideration of the scale of implementation of this framework based on feedback from Reviewers 2 and 3.\n• We have clarified and elaborated on the methodology in the Main Text to improve accessibility and to better define the parameters of the analysis based on feedback from Reviewer 3.\nIn the following pages, the comments from reviewers are in blue italics and our responses are in black. Our resubmission package includes Manuscript files with and without tracked changes; please note that line numbers are referring to the clean manuscript which has revisions hidden.\nThank you for your invaluable feedback."}, {"round": 3, "reviewer_comments": {"reviewer_4": "While the authors have adapted the manuscript in several places to address my comments, have have unfortunately chosen to not adapt their methodology and the analysis itself, despite the clear flaws layed out in the previous review round. The paper primarily focuses on land allocation between different future uses, though they still ignore the fact that certain land uses can be, and typically are, co-located in the same area. This flaw unfortunately has a significant impact on their final results and message, meaning that I can not support publication in a prestigious journal like Nature Communications in its current form.\nSpecifically, the authors keep argumenting against allowing coexistence of wind power and agricultural land (the large majority of todays wind power is installed in cropland or pasture; Maguire et al, 2024) in their methodology. Specifically, they seem to give a lot of weight Trainor et al (2016) in their argument, a relatively old paper in Plos One (IF~3) that introduce the concept of \"land-scape level impact\" as an alternative to the direct area footprint. While I do not argue in that wind power has wider landscape impacts, the focus of the paper is clearly on land allocation, and the wind that blows over that land is indeed important for wind power, but does not prevent it from being co-located with at least agricultural land, and also to a certain extend natural land types (while I understand the constraints, and the context-specific nature of the co-location with natural land). The authors also mention \"limitations in available global data and the challenges of adequately capturing tradeoffs to biodiversity and ecosystem function in multi-use landscapes\" in their argument, which is understandable, but since the gross of the argument focuses on the landscape impact of wind, it is doubtful if indeed nothing could have been done methodologically (at least allowing for the obvious potential coexistence between wind power and agriculture) or whether the authors preferred not to re-run the analysis. Additionally, methodological constraints are not necessarily a reason to accept flawed results.\nOn the impact of the abovementioned flaw on the consequential anomalies in the results (i.e. huge areas for \"only wind\" in China and the USA), the authors provide the answer: \"Our global map is intended to highlight global patterns and areas of potential convergence in land-use demands; their purpose is not to prescribe precise local land-use decisions but to inform where more detailed, context-specific planning and coordination will be most critical. The wind production areas in northeastern China and the central United States are prime examples of where there is a need to integrate knowledge across scales\". The issue is that in the context of integrating wind power with agriculture, this need does not really exist, as it is already common practice. While I understand it is not focused on prescribing precise local land use decisions, the authors do take some important conclusions from it, such as \"Our results indicate that if the increase in development to meet future demand relies solely on additional land conversion, it is not possible to meet the country-level targets for conservation and development needs\", a conclusion which may be affected for many countries when properly reflecting potentials for co-locating land uses. Also the results in Figure 5 are an important output of the paper, and strongly affected by the choice of not accounting for co-locating at least wind and agricultural land, which could free up significant land areas and reduce overlap.\nFinally, while the authors seem well up-to-date on the literature on land conservation, the newly added sentences in the introduction alleviate they are less informed about the latest research on the energy-land nexus (only a few and very dated papers are cited), which is a critical topic in the context of their paper. For the purpose of the introduction itself, but also for avoiding obvious flaws in the results, I recommend the authors to dig a bit deeper into more recent papers about land use competition and co-existence of renewable energy with other land uses.", "reviewer_5": "The authors have done a good job in addressing the issues raised. I do not wish to raise further comments at this stage."}, "author_response": "Response to Reviewer Comments\nReviewer #4 (Remarks to the Author):\nWhile the authors have adapted the manuscript in several places to address my comments, have have unfortunately chosen to not adapt their methodology and the analysis itself, despite the clear flaws layed out in the previous review round. The paper primarily focuses on land allocation between different future uses, though they still ignore the fact that certain land uses can be, and typically are, co-located in the same area. This flaw unfortunately has a significant impact on their final results and message, meaning that I can not support publication in a prestigious journal like Nature Communications in its current form.\nResponse\nWe agree that co-location, especially between wind energy and cropland, is an important pathway to reduce land use. Our study’s optimization intentionally used a single-use, pixel-based allocation because globally consistent data on the ecological and technical conditions needed to support sustainable co-location are not yet available (see lines 316-324; all line numbers reference clean manuscript).\nHowever, to more directly address the reviewer’s concern with regard to colocation of future cropland and future renewable energy production, we have added an analysis which compares independent allocations for select development sectors using our existing data and framework. We ran independent, single-use prioritizations for wind, photovoltaic (PV) solar, and cropland (includes both food and energy crops) and then analyzed the overlap among the land allocated for renewable energy and cropland. See the new Methods section, Comparison of independent allocations for select development on lines 534-539.\nThe additional analysis indicates that globally, 2.7% of land allocated for PV solar overlapped with land allocated for crops (or 1.1% of crop expansion overlapped with that for PV), primarily in Europe and India (new Fig. S10). 2.1% of land allocated for wind energy overlapped with that for crops (or 3.5% of crop expansion overlapped with that for wind), primarily in Europe and China, followed by eastern Brazil and Tanzania (new Fig. S11). These results highlight opportunities where co-location could reduce additional land conversion. We discuss these results as indicative of where co-location could reduce future land demand while noting that biodiversity outcomes and technical feasibility may vary. We summarize these results in the Results (lines 228-233) and now state upfront that our primary single-use allocation should be interpreted as an upper bound on land demands when co-location is not explicitly modeled for our climate and socio-economic scenario (lines 96-98).\nSpecifically, the authors keep argumenting against allowing coexistence of wind power and agricultural land (the large majority of todays wind power is installed in cropland or pasture; Maguire et al, 2024) in their methodology. Specifically, they seem to give a lot of weight Trainor et al (2016) in their argument, a relatively old paper in Plos One (IF~3) that introduce the concept of \"land-scape level impact\" as an alternative to the direct area footprint. While I do not argue in that wind power has wider landscape impacts, the focus of the paper is clearly on land allocation, and the wind that blows over that land is indeed important for wind power, but does not prevent it from being co-located with at least agricultural land, and also to a certain extend natural land types (while I understand the constraints, and the context-specific nature of the co-location with natural land). The authors also mention \"limitations in available global data and the challenges of adequately capturing tradeoffs to biodiversity and ecosystem function in multi-use landscapes\" in their argument, which is understandable, but since the gross of the argument focuses on the landscape impact of wind, it is doubtful if indeed nothing could have been done methodologically (at least allowing for the obvious potential coexistence between wind power and agriculture) or whether the authors preferred not to re-run the analysis. Additionally, methodological constraints are not necessarily a reason to accept flawed results.\nResponse\nAs introduced in our response to the previous comment, the new comparison of independent allocations analyses between wind and cropland and PV solar and cropland show that there are areas that may be well suited for both renewable energy and cropland, illustrating that if it were technically feasible for co-location to occur there, additional conversion could be reduced (see 228-233 and Figs. S10 and S11, described in the previous response). However, the potential is a small percentage of the total allocated land for wind power (2.1%), suggesting that endogenously including co-location within the model would only result in minor changes to the overall results. Additionally, see lines 307-313 in the Discussion in which we acknowledge co-location and cite work on wind-agriculture and solar-agriculture coexistence (e.g., Maguire et al., 2024; Ravi et al., 2016; Miskin et al., 2019; Deshmukh et al., 2019; Hernandez et al., 2019). The new analysis strengthens this point by identifying where multi-functional landscapes may be productive while emphasizing that technical feasibility of co-location and outcomes for biodiversity and NCP are not assessed.\nOn the impact of the abovementioned flaw on the consequential anomalies in the results (i.e. huge areas for \"only wind\" in China and the USA), the authors provide the answer: \"Our global map is intended to highlight global patterns and areas of potential convergence in land-use demands; their purpose is not to prescribe precise local land-use decisions but to inform where more detailed, context-specific planning and coordination will be most critical. The wind production areas in northeastern China and the central United States are prime examples of where there is a need to integrate knowledge across scales\". The issue is that in the context of integrating wind power with agriculture, this need does not really exist, as it is already common practice. While I understand it is not focused on prescribing precise local land use decisions, the authors do take some important conclusions from it, such as \"Our results indicate that if the increase in development to meet future demand relies solely on additional land conversion, it is not possible to meet the country-level targets for conservation and development needs\", a conclusion which may be affected for many countries when properly reflecting potentials for co-locating land uses. Also the results in Figure 5 are an important output of the paper, and strongly affected by the choice of not accounting for co-locating at least wind and agricultural land, which could free up significant land areas and reduce overlap.\nResponse\nWe thank the reviewer for their feedback. We would like to provide two clarifications. First, we do not necessarily limit development to only natural areas; rather, we only exclude highly modified areas with a human modification index of above 0.8 (see Methods lines 408-410). When allocating land, this methodology does allow for allocation on some agricultural land (e.g., wind in the U.S. Midwest) and thus assumes a degree of colocation of existing cropland with other sectors. Second, our results portray additional potential future allocation for each sector. This means the areas indicated as wind in the results reflect places selected to meet future demand under a single-use assumption; it not a claim that wind cannot co-locate with cropland or other sectors in those areas. While we acknowledge that wind and cropland can be (and often are) co-located, the expansion of each does not necessarily occur simultaneously.\nWe have added clarification to the way we discuss our results in specifying that we are assuming co-location does not occur in future expansion (lines 165, 307). We have adjusted the line the reviewer specifically mentions to “Our results indicate that if the increase in development to meet future demand relies solely on additional land conversion without co-locating sectors (e.g., wind energy on agricultural land), it is not possible to meet the country-level targets for conservation and development needs” (lines 164-166). We also added the following specification to the Fig. 1 caption: “Expansion of multiple sectors in one pixel was not considered” (line 127).\nWe agree that the magnitude of additional land implied by single-use allocation would decline where co-location is feasible, and this is highlighted in lines 17, 227-228, and 307-309. We have added specification that our single-use allocations represent an upper bound on land needs (lines 96-98) and the new comparison of independent allocations analyses results provide quantitative context.\nFinally, while the authors seem well up-to-date on the literature on land conservation, the newly added sentences in the introduction alleviate they are less informed about the latest research on the energy-land nexus (only a few and very dated papers are cited), which is a critical topic in the context of their paper. For the purpose of the introduction itself, but also for avoiding obvious flaws in the results, I recommend the authors to dig a bit deeper into more recent papers about land use competition and co-existence of renewable energy with other land uses.\nResponse\nWe appreciate the suggestion to strengthen our coverage of recent energy-land nexus research and we agree that including more up-to-date references improves the manuscript. We have now expanded the Introduction to summarize current evidence on relative land use intensity of different energy technologies and the extent of land use conflicts with solar and wind energy, using global analysis where available (though, we acknowledge that most social science research is from the U.S.). We also reference studies that provide solutions to these conflicts through avoidance or dual use. These additions update our framing while remaining consistent with our global, single-use optimization scope.\nReviewer #5 (Remarks to the Author):\nThe authors have done a good job in addressing the issues raised. I do not wish to raise further comments at this stage.\nResponse\nWe thank the reviewer and greatly appreciate their feedback."}, {"round": 4, "reviewer_comments": {"reviewer_4": "The authors have done a proper job in recognising co-location of future development, which is relatively smaller than expected from their initial results. With these changes, I would accept publication in its current form."}, "author_response": "Response\nWe thank the reviewer for their helpful feedback."}]

Decision letter