Sensitivity of global land-based mitigation potential to land-use scenarios and interactions across sectors

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Previous studies identified major trade-offs between these but limited the analysis to fairly few dimensions of uncertainty. Using the SuCCESs model, we quantified global total and land-based mitigation potentials, sensitivities, and interactions between 2020 and 2100 across six key dimensions, resulting in 864 scenarios with different assumptions for drivers of land utilisation: emission pricing, diets, food distribution, conservation, biomaterial demand, and wildfire activity. Mitigation options included actions across land, energy, and material systems, spanning forestation, halting deforestation, adjusting forest rotation and residue management, shifting agricultural and forestry systems, and substituting fossil fuels with bioenergy or BECCS. Limiting warming to 1.5–2°C required the agriculture, forestry, and land-use (AFOLU) sector to contribute 500–1,700 GtCO₂e of mitigation and supply 100–140 EJ of bioenergy annually. Peak AFOLU mitigation occurred under globally vegan diets combined with emission pricing that incentivised afforestation of freed pastureland. Trade-offs between forestation and biomass production remained, with SuCCESs favouring afforestation over bioenergy. Biodiversity conservation and emission pricing targeted overlapping mitigation areas, offering no combined benefit. High timber demand reduced mitigation potential and increased biodiversity loss, while reduced paper demand avoided the most deforestation but offered no climate benefits. Slight variations in wildfire prevalence had little impact. Our findings highlight the need for integrated policies that manage land competition and account for policy interactions to fully unlock land-based mitigation potential. Paris Agreement and land sector combining mitigation strategies biodiversity and climate change biomaterials and climate change land competition for climate action reducing emissions from agriculture and forests for net zero Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Limiting global warming to well below 2°C remains a challenge, as current policies fall short of necessary greenhouse gas ( GHG ) emissions reductions (Ripple et al., 2024; UNEP, 2024). Land-based mitigation of climate change, such as afforestation and reforestation ( A/R ), climate-optimal forest rotations, substitution of fossil-based materials with biomaterials, and bioenergy with carbon capture and storage ( BECCS ), can enhance land-based carbon sequestration while co-benefiting biodiversity conservation and many other vital ecosystem services (Becvarik et al., 2024; Roca-Barceló et al., 2024; Roggero et al., 2023; Aleksandrowicz et al., 2016; Roe et al., 2021). Yet, their large-scale implementation is constrained by the intensifying competition for land (Searchinger et al., 2023a). Allocating land between different uses implies trade-offs between different objectives, such as carbon sequestration, food production, biomass supply, and biodiversity conservation; but doing so in an uninformed manner can lead to unnecessary compromises (Burgess et al., 2012; Zhao et al., 2024). In recent years, researchers have been analysing trade-offs associated with land-based mitigation using integrated assessment models ( IAMs ) and other integrated approaches (Doelman et al., 2018; Searchinger et al., 2018; Frank et al., 2021; Roe et al., 2021). These analyses indicate complex interdependencies in a world with rising food, energy, and material demands (IIASA, 2024). For example, multi-dimensional analyses show that forestation can enhance carbon sequestration and biodiversity (Doelman et al., 2020; Jäger et al., 2024), but typically has an inverse relationship with bioenergy production and food security (Frank et al., 2021; Hirata et al., 2024; Zhao et al., 2024). Numerous studies identify agricultural expansion as a primary driver of global deforestation and forest degradation (Jayathilake et al., 2021; Pendrill et al., 2022; Ma et al., 2023), causing biodiversity loss (Kadoya et al., 2022) and gross land use-change emissions two-thirds the size of the global forest sink (Kruid et al., 2021; Pan et al., 2024). Currently, forests cover 3600 Mha (29%) of global land, of which 42% are managed forests, and provide habitat for over 70% of terrestrial biodiversity (FAO, 2020; Pan et al., 2024; UNEP and FAO, 2020). Agriculture currently occupies 4,800 Mha (38% of global land), with 3,200 Mha (25%) dedicated to livestock pastures and 1,600 Mha (13%) to cropland for food, feed, and modern bioenergy production (Searchinger et al., 2023b). Rising demand for livestock products and bioenergy could expand agricultural land to 6,300 Mha (50%) of global land by 2100, enhancing the risk for continued agricultural deforestation (Fujimori et al., 2017; Winkler et al., 2021; IEA, 2021; IIASA, 2018). Meanwhile, shifts toward “regional preparedness and self-sufficiency” policies often disregard production inefficiencies and require additional agricultural expansion into forests and other habitats to meet regional demands (Beltran-Peña et al., 2020; Rabbi et al., 2023; Zhang et al., 2023). Studies suggest forests hold 50% of future cost-effective land-based mitigation potential (Roe et al., 2021; Mo et al., 2023). However, the true mitigation potential of forests remains uncertain, as climate change may alter forest growth and increase destruction through extreme weather and wildfires (Jäger et al., 2024). Given the constraints on land availability and the interdependencies between different objectives we have for land use, understanding the extent to which land use can contribute to mitigation and climate goals is critically important. Releasing and repurposing land to carbon sequestration may be an important mitigation enabler, and can be achieved through increased production efficiency, optimized land allocation, and shifts in the demand for land-use products. Numerous studies have investigated this from different viewpoints. A global shift to low-meat diets and agroforestry, for example, has the potential to transform as much as 25% of global land with significant carbon sequestration (Hayek et al., 2024, 2021; Zomer et al., 2016) and co-benefits for biodiversity (Henry et al., 2019). Enhancing international food trade could release agricultural land in some regions while addressing undernourishment trans-regionally (Doelman et al., 2018; Janssens et al., 2022). Yet, once land is released, its repurposing remains a challenge due to the intricate interconnections between energy, climate, materials, and land systems that must be carefully assessed (Roe et al., 2021, 2019). While earlier studies have provided invaluable insights into the mitigation potential of dietary changes, for example, the focus on a selected set of dimensions complicates identifying sensitivities, synergies, and trade-offs between all relevant dimensions across studies due to comparability issues in scope, time, and accounting. Additionally, the detailed models used in these studies are typically computationally heavy and only allow for integration of a limited number of connected dimensions, affected systems, or mitigation options (Aleksandrowicz et al., 2016; Doelman et al., 2018; Frank et al., 2021; Henry et al., 2019; Leclère et al., 2020; Searchinger et al., 2018; Zhao et al., 2024). Omissions and dimension reductions are always unavoidable in modelling, often even necessary. However, combining more dimensions, systems, and mitigation options into a unified framework will be crucial for understanding interactions between them, and making better-informed decisions. This study explores the global, long-term mitigation potential of land use under a range of scenario components, covering alternative diets, habitat conservation targets, global food distribution restrictions, material demands and wildfire disruptions, all affecting land available for mitigation. We use the lightweight IAM SuCCESs (Ekholm et al., 2024a, 2024b) and its capabilities to run large scenario ensembles that integrate energy, climate, materials, and land use for different levels of carbon pricing. The model can freely choose between the available mitigation strategies afforestation, reforestation, halting deforestation, altering forest rotation, shifting agricultural and forestry systems, adjusting residue management, and use of modern bioenergy and BECCS. By unifying more land-competing mitigation dimensions into a single, computationally efficient framework, we can capture interactions, sensitivities, synergies, and trade-offs that affect the mitigation potential of land. Our analysis is guided by the following research questions: How large is the global land-based mitigation potential according to the SuCCESs model, and what are the key determinants for it? What would be the optimal land allocation strategy to maximize land-based mitigation while balancing food production, energy generation, biomaterial supply, and biodiversity conservation? What interaction effects (synergies and trade-offs) arise between land-based mitigation and other land uses when combining a growing number of dimensions? By identifying the conditions under which different mitigation strategies are most effective, this research aims to provide insights into how land-use decisions can support ambitious climate goals while addressing food security, biodiversity conservation, and sustainable resource management. 2. Methods 2.1 SuCCESs model We used the lightweight, bottom-up integrated assessment model SuCCESs that integrates the global energy, material, land, and climate systems (Ekholm et al., 2024a). SuCCESs is a demand-driven, intertemporal optimization model that calculates long-term scenarios from 2020 to 2100 by minimising discounted system costs to satisfy global demands for climate-relevant products and services, such as different types of energy, transportation, plant and livestock-based foods, and energy- and emission-intensive materials. Constrained by demand, energy and material balances, technology, and policy (e.g., climate targets, carbon pricing), SuCCESs simulates an efficient-market solution. It calculates CO₂, CH₄, and N₂O emissions from energy, materials, and land use, feeding them into its climate module to estimate atmospheric concentrations, radiative forcing, and global mean surface temperature changes. Other forcing agents, such as aerosols and ozone, follow the average of shared socio-economic pathway 2 ( SSP2 ) and representative concentration pathway 2.6 scenarios (IIASA, 2018) and are given to the model exogenously. Land use is modelled through CLASH, SuCCESs’ land-use module (Ekholm et al., 2024b), which allocates global land to cropland, pastures, forests, urban areas, and natural ecosystems. CLASH tracks food, and woody and crop biomass for energy and materials, along with carbon storage in vegetation and soil. Terrestrial carbon stock changes reflect the net exchange of carbon between land and atmosphere. Decision variables include land allocation, forest harvesting, and production volumes of land-use products. While food and wood-derived products’ demands are exogenous, bioenergy demand is endogenously determined through the market equilibrium across the different modules of SuCCESs. Thus, while the model can freely substitute biomass for fossil fuels, it cannot independently replace conventional materials with biomaterials. For a detailed description of the CLASH land-use model please refer to Ekholm et al. (2024b), and for SuCCESs to Ekholm et al. (2024a). A description of the model modifications made for this study can be found in the supplements . 2.2 Scenarios Each scenario in this paper was a combination of six components (dimensions). The scenario components were selected based on a) high prevalence in recent scientific literature, b) contribution to the global land scarcity problem, c) global scalability of associated mitigation potential, and d) compatibility with the SuCCESs IAM. As a first component, emission pricing penalised unabated emissions and rewarded carbon uptake, providing mitigation incentive for the cost-optimising model. Prices for CO₂, CH₄, and N₂O generally rose over the century, though with fluctuations across time steps, and were applied uniformly to all net emissions, including those from land use and natural land fluxes. Second, we varied the demand for primary animal products ensuing from SSP2 or SSP1 food demands, which directly influenced livestock-related emissions and the need for pastureland and cropland used for feed. Third, to simulate trading barriers by regional self-sufficiency policies on a crude level, we required each biome to produce a minimum of 65% of the food demand within the biome itself. The global food market case lifted this constraint to potentially lower global cropland. Fourth, biodiversity habitat protection restricted the model from converting or harvesting pristine ecosystems or mature forests. Fifth, the demand for wood-derived products, such as paper or construction timber was varied, varying demand pressures on production forests. Lastly, we applied symmetrical uncertainty to the SuCCESs parametrisation’s wildfire prevalence to assess the mitigation potentials’ sensitivity to changes in fire activity. Table 1 , presents an overview of the scenario components, sorted from least to most ecologically sustainable within the six dimensions emission pricing, global diet, global food distribution, biodiversity habitat conservation, biomaterial utilisation, and wildfire activity. Combining all components, one per dimension, yielded 864 scenarios calculated for this study. This number does not include the scenario component Halting Biodiversity Loss which made scenarios infeasible. For a detailed description of all scenario components please refer to the supplements . Table 1 : Description of scenario components affecting land availability. A single scenario consists of one component from each dimension. The baseline scenario contains no emission pricing, an omnivore global diet following SSP2 diets, restricted food distribution across biomes by 65%, no additional habitat protection following SSP2 land-use change, forestry business as usual following SSP2 biomaterial demands, and SuCCESs-Default wildfire activity parametrised from LPJGuess/Simfire-Blaze model. The baseline scenario components are indicated with the comment “(baseline)”. Dimension Component (Case) Description and Sources Emission Pricing None (baseline) No emission penalty. Steady Transition Net unabated emissions are priced. Prices for CO 2 , CH 4 and N 2 O emissions are derived from shadow prices of a model run with a 2.0°C constraint without overshoot. Overshoot The pricing reflects the GHG shadow prices from a baseline model run allowing overshoot as needed, but temperature anomaly must reach equal or below 1.5°C by 2100. Early Decarbonisation As above, but with 1.5°C constraint, without overshoot flexibility. Global Diet Omnivore (baseline) SSP2-level food demand* with high demand for livestock-derived products. Flexitarian SSP1-level food demand* with declining demands for livestock-derived products. Lacto-Ovo SSP2-level milk & egg demands*, meat only from culled livestock. Vegan All animal product demand phased out between 2030 and 2050. Global Food Distribution Restricted food distribution (baseline) 65% of food demand within a biome must be produced within that biome, approximating average national self-sufficiency levels in SuCCESs’ coarse geographical resolution (Economist Impact & Corteva Agriscience, 2022; Jones and O’Neill, 2016). Global Food Market Food production from any region can fulfil food demand in any region. Biodiversity Habitat Conservation No additional habitat protection (baseline) Model default in CLASH (Ekholm et al., 2024b; Hurtt et al., 2020), leading to approximately 20% pristine habitat, consistent with Beyer and Manica, 2020. Pristine Ecosystems Protection Pristine ecosystems are preserved for minimal additional biodiversity loss (Díaz et al., 2020). Maturing Habitats Pristine ecosystems are preserved, and mature secondary forests (80+ years) must double between 2020 and 2100. Biomaterial Utilisation Forestry as Usual (baseline) SSP2-level demands for timber, paper and pulp (IIASA, 2018). Electronic Papers 90% of graphic paper avoided due to shift to electronic formats (FAO, 2021) Timber Cities Half of new urban residential buildings made of wood, decreasing cement demand (Mishra et al., 2022). Wildfire Activity SuCCESs-Default (baseline) Default wildfire activity in SuCCESs, based on Simfire-Blaze model (Ekholm et al., 2024b, 2024a). Less Fires More Fires Future forest fire uncertainty from five IAMs is symmetrically applied to SuCCESs' default parameters (Jäger et al., 2024). * per-capita food demand of MAgPIE model regarding crops, milk, eggs, beef, pork, poultry, and sheep/goat (Dietrich et al., 2019) , multiplied by SSP2 population projections (IIASA, 2024; Samir et al., 2024) . Only the global diet and wildfire activity scenario components directly avoided emissions by reducing ruminant-related and forest burning emissions. The remaining scenario components affected the possibilities of SuCCESs to optimize mitigation decisions based on emission pricing, demand, and land availability. Land-based mitigation options included afforestation or reforestation ( A/R ), avoided deforestation, relocating agriculture and forestry sites, and adjusting forest rotation. Furthermore, the model can substitute fossil fuels in transportation, and metal and chemical industries with bioenergy and biofuel, and other renewables (wind, solar, hydro). The model can also choose to combine bioenergy with carbon capture and storage ( BECCS ), which generates negative emissions by removing the captured CO 2 from the carbon cycle. We analysed the greenhouse gas ( GHG ) emissions CO 2 , CH 4 , N 2 O and their sources, temperature change, energy mix, and land use in the scenarios. We report emissions in CO 2 equivalents, converting with the 100-year global warming potential 28 for CH 4 , and 265 for N 2 O. CO 2 emissions from the AFOLU sector were diagnosed (outside the optimisation routine) using year-2020 carbon densities for forests to have the results conceptually in line with AFOLU emissions calculated with bookkeeping models used in the Global Carbon Project (Friedlingstein et al., 2025) and excluding indirect effects from CO 2 fertilization and climate change (Grassi et al., 2018). We assumed global mean temperature rise above pre-industrial climate (temperature anomaly) in 2020 is 1.37°C. By integrating multiple dimensions of land-competition into a unified, computationally efficient framework, we could quantify each component’s mitigation potential as well as the scenarios’ combined mitigation potentials and associated interaction effects (sensitivities, synergies, and trade-offs). We define a scenario component's effect on a variable (e.g., emissions) as the realised change when added to the baseline (which assumes an omnivore diet, restricted food distribution, no habitat protection, business-as-usual forestry, and default wildfire activity). The interaction effect is the gap between the summed individual components’ effects and the combined scenario’s actual modelled outcome. 3. Results 3.1 Mitigation potentials Using the SuCCESs integrated assessment model, we evaluated the global total and land-based mitigation potential across 864 land availability-affecting scenarios varying in emission pricing, meat amount in diets, biodiversity habitat conservation, global food distribution, biomaterial demands, and wildfire prevalence. The baseline scenario consisted of no emission pricing with all other dimensions following SSP2 scenario projections. The baseline scenario’s total net emissions were 4000 GtCO₂e cumulatively between 2020–2100 (54 GtCO 2 e/year). The associated baseline temperature anomaly reached 2.1°C in 2050 and 3.4°C in 2100 ( supplement Fig. S-F4 ). Scenarios without emission pricing reduced the temperature increase with respect to the baseline, but not enough to keep warming within Paris Agreement limits. Out of the 864 scenarios, 234 scenarios stayed between 1.5°C and 2°C in 2100 with a total mitigation of 1400–2300 GtCO₂e between 2020 and 2100 across all sectors, and 414 ended up below 1.5°C in 2100 with a total mitigation of 2100–3100 GtCO₂e ( supplement Fig. S-F6, top ). The cumulative land-based mitigation contribution of the agriculture, forestry and land-use ( AFOLU ) sector across all scenarios was -2–1700 GtCO 2 e (2020-2100) compared to baseline ( supplement Fig. S-F7, top ). The large range including near-zero mitigation and slight net emissions indicates a high sensitivity of the AFOLU sector’s mitigation potential to incentives (emission pricing) and land demand pressures. Among the scenario dimensions, emission pricing was the largest contributor to emissions and temperature reductions. Introducing emission pricing from baseline reduced emissions by 1,500-2,300 GtCO₂e across all sectors ( Fig. 1 a, coloured bars ). The contribution from the AFOLU sector was roughly 500 GtCO₂e ( Fig. 1 b, coloured bars ). The AFOLU reductions, however, were highly sensitive to the interactions with other scenario components, as shown by the black horizontal ranges in Figure 1 , with mitigation outcomes varying by ±100%. The AFOLU reductions account only for direct emission reductions within the sector, excluding emission reductions from biomass substituting fossil fuels in other sectors. Emission pricing drove many mitigation actions within the AFOLU sector, including up to 37 Mkm² forest expansion, an 80–560 GtC increase in carbon stocks within managed forests and other habitats, and the preservation of up to 12 Mkm² of pristine ecosystems, compared to scenarios without emission pricing. Expanding bioenergy (100-140 EJ/year) and wind and solar power (120–180 EJ/year combined capacity) caused faster coal phase-out, less crude oil and natural gas use more nuclear power ( supplement Fig. S-F8 ), and rapid global electrification of road and rail traffic. This led to reduced fossil fuel extraction, refining, and combustion emissions depending on emission pricing case. Of the remaining scenario components, reducing animal product consumption provided the greatest and most consistent mitigation potential (controlled for the effect of underlying emission pricing) ( Fig. 2 ). The mitigation potential of diets was amplified by rising emission pricing, which incentivized afforestation and bioenergy production in the land-area freed from livestock use, i.e. pastures and croplands for animal feed. The vegan diet enabled the highest mitigation from the AFOLU sector, thanks to avoided emissions from cropland and livestock and the large-scale afforestation of freed pastureland ( Fig. 2 b ). Scenarios combining a vegan diet with emission pricing achieved 1100–1700 GtCO 2 e combined mitigation in the AFOLU sector (14–21 GtCO 2 e/year) ( supplement Fig. S-F7, top ), of which the vegan component contributed 550–990 GtCO 2 e ( Fig. 2 b ). In contrast, the flexitarian diet component only contributed 90-210 GtCO 2 e to the 600–900 GtCO 2 e (8–11 GtCO 2 e/year) combined mitigation in the AFOLU sector with emission pricing, indicating minor freeing-up of pastureland with only slight reductions in meat consumption. However, reduced animal feed production in vegan and vegetarian scenarios also lowered biomass by-products available for bioenergy and BECCS, increasing emissions in non-AFOLU sectors by limiting bioenergy substitution in electricity and industry. As a result, the total mitigation potential ( Fig. 2 a ) peaked with a global vegan diet under early decarbonization pricing at only 770 GtCO₂e, achieving up to 0.7°C cooling with respect to the baseline ( Fig. 2 c ). In contrast, habitat conservation became less effective with rising emission pricing. Without emission pricing, the Maturing Habitats case achieved 650 GtCO 2 e mitigation, whereas under early decarbonisation emission pricing, only 90 GtCO 2 e of mitigation was added by Maturing Habitats . Since emission pricing already provided incentive to avoid deforestation emissions and enhance carbon land sinks, conservation provided minimal added mitigation benefit. Additionally, conservation fully concentrated wood production on unprotected forests in a spill-over effect that greatly intensified short-rotation forestry compared to scenarios without biodiversity habitat conservation. While other scenario components had a considerably smaller effect on cumulative emissions, they had important cross-reaching implications. Global food market reduced overall cropland area, avoiding up to 20 GtCO 2 e cropland emissions (2020-2100). It also enabled land-based mitigation in scenarios where land availability was particularly rigid, such as those combining emission pricing with Omnivore and Flexitarian diets and biodiversity habitat conservation. Bioenergy and BECCS were used by most scenarios to significantly curb electricity generation and cement production emissions. The Timber Cities component, however, caused extensive expansion of short-rotation forestry to provide wood for buildings, greatly limiting biomass availability for energy generation despite gains in logging residue. This shortage reduced the opportunities for bioenergy and BECCS, and in some scenarios even increased fossil fuel use in cement production to compensate for the decreased bioenergy supply. This offset – and in some cases reversed – the emission reductions from lower cement demand. Additionally, the Timber Cities components had negative impacts on global carbon stocks in forests from harvesting mature stands which are also important biodiversity habitats. Wildfire prevalence had the smallest impact on our scenarios, except with Overshoot emission pricing, where heavy BECCS reliance post-overshoot (8–20 EJ/year between 2050 and 2100) made fires more consequential. 3.2 Land use change for mitigation A large part of the land-based mitigation observed in our scenarios arose from changes in land use. To understand this better, Fig. 3 presents the change in land areas between 2020 and 2100 depending on emission pricing and diet, as their combination played a major role in the extent of forest expansion. The remaining variation (height of boxes) was mainly due to the extent of habitat conservation. Without emission pricing, pastureland for Omnivore and Flexitarian diets were reduced by up to 21 Mkm² by moving them to more productive land ( Fig. 3 , first two yellow boxes ). Without emission pricing as incentive, freed pastures were converted to non-forest ( Fig. 3 , other managed land ), to avoid afforestation costs. To satisfy wood product demands, existing managed and pristine forests were harvested with minimal replanting (thus maximal conversion to non-forest) to avoid replanting costs. This greatly expanded non-forest biodiversity habitats but destroyed the most vital forested habitats, compared to introducing emission pricing. With emission pricing, more productive biomes were used to expand forests instead of using them as pastures. Only Omnivore diet required pastureland expansion under emission pricing. For Omnivore and Flexitarian diets under emission pricing, non-forested land was used for afforestation. In Lacto-Ovo and Vegan diets, forests and non-forested land could be expanded on abandoned pastureland while preserving considerably more of pristine ecosystems, compared to no emission pricing. In Overshoot emission pricing scenarios however, rapid BECCS upscaling led to cropland expansion into managed and pristine non-forests, especially if combined with high meat demand, which kept land pressures high. ( Fig. 3 , teal boxes ). 3.3 Synergies and trade-offs from interaction effects To highlight how interactions between the scenario components modify their mitigation effectiveness, Fig. 4 presents the mitigation potentials of individual scenario components when stacked onto others. For instance, the mitigation potential of Global food market contributed 50 GtCO₂e under Flexitarian diet, but 130 GtCO₂e reductions under Vegan diet, as opposed to the baseline case with 65% of food needing to be produced and consumed within the same biome. Likewise, Maturing Habitats had a negligible 10 GtCO₂e mitigation potential in combination with Flexitarian diet and Global Food Market under Early Decarbonisation pricing ( Fig. 4 ), but 170 GtCO₂e under Steady Transition pricing ( supplement Fig. S-F15 ). These interactions highlight the interdependency of land-based mitigation actions, making it difficult to quantify universal mitigation potentials for different mitigation measures. We defined interactions between scenario components as the diminished or added mitigation potential of a multi-component scenario compared to the sum of the individual component’s mitigation potential when added from baseline. We classified six interaction types using K-means clustering: moderate and strong synergies (11% and 16% of scenarios) where combined mitigation exceeded the sum of single mitigation potentials; moderate, strong, and extreme trade-offs (13%, 16%, and 7%) where combined mitigation was lower; and negligible interactions (37%) where effects were roughly additive ( supplement Fig. S-F17 ). We focused our analysis only on the strong interactions and extreme trade-offs. Strong synergy scenarios with 180–440 GtCO₂e additional mitigation potentials consisted of combinations of emission pricing, meat-free diets, and no additional habitat protection components. The meat-free diets avoided large amounts of emissions from livestock and freed vast agricultural land, expanding managed forests by 18–28 Mkm² and non-forest habitats by up to 20 Mkm². Biomass use almost tripled from 2020 to 2040, replacing some fossil fuels in industry processes and electricity generation. Lots of fossil fuels remained in use however, offset by the vast mitigation in the AFOLU sector. Despite no additional biodiversity protection, pristine forests remained intact, and secondary forests expanded and matured, mimicking the Maturing habitats case – except where Timber Cities drove extensive short-rotation forestry. Extreme trade-off scenarios (470–630 GtCO₂e diminished mitigation potential compared to sum of individual component’s mitigation potential) arose from emission pricing scenarios combining Omnivore and Flexitarian diets with biodiversity conservation. These combinations forced a balance between livestock expansion, ecosystem protection and carbon sequestration on minimally flexible land. In the AFOLU sector, these scenarios converted non-forested land to forests and pastures, increasing forest area by 5–12 Mkm 2 but reducing non-forest habitats by 5–15 Mkm². Forest expansion was driven by aggressive tree planting, which doubled newly established forest areas between 2020 and 2030, and intensified short-rotation harvesting mid-century, particularly under Timber Cities cases. Global Food Market and Electronic Papers components reduced land-use pressure slightly, which resulted in larger carbon stock in mature forest. In extreme trade-off scenarios, the maximum achievable total mitigation of 1700–2500 GtCO₂e cumulatively (21–31 GtCO₂e/year) was similar to the strong synergies scenarios with 1800–3100 GtCO₂e (23–39 GtCO₂e/year) total mitigation ( supplement Fig. S-F6, bottom ). However, the mitigation achieved in the AFOLU sector reached only 700–900 GtCO₂e (9–11 GtCO₂e/year), compared to 900–1600 GtCO₂e (11–20 GtCO₂e/year) achieved in strong synergy scenarios ( supplement Fig. S-F7, bottom ). To make up for the lack of land available for mitigation, extreme trade-offs scenarios used more biomass (9500 EJ cumulative or 120 EJ/year on average) than strong synergies scenarios (7500 EJ cumulative or 90 EJ/year on average), mostly from crop farming residues. Additionally, extreme trade-off scenarios required on average 2.6 times higher CO₂ removals via CCS (40–300 GtCO 2 cumulative removal by 2100) than strong synergy scenarios. These results were not sensitive to the considered variations for wildfire activity. 4. Discussion Balancing land use for carbon sequestration, food production, biodiversity conservation, and resource demands on approximately 12,700 Mha of global land is a major challenge. We calculated the mitigation potentials of 864 land availability-affecting scenarios combining emission pricing, meat amount in diet, biodiversity habitat protection, food distribution constraints, wood product demand, and wildfire activity. Our results indicate that keeping warming within 1.5–2°C required cross-sectoral cumulative emission reductions of 250–720 GtCO₂e until 2050 and 1400–2300 GtCO₂e until 2100, compared to the baseline scenario which follows SSP2 product demands and land-use change without emission pricing. The largest achieved mitigation scenario reached 1000 GtCO₂e until 2050 and 3100 GtCO₂e until 2100 compared to baseline, resulting in 0.8°C temperature anomaly in 2100. The Agriculture, Forestry, and Land-use ( AFOLU ) sector contributed on average 500 GtCO₂e, but up to 1700 GtCO₂e, of mitigation between 2020 and 2100, mainly through avoiding land-use-change emissions, and expansion and maturing of forests. Additionally, the AFOLU sector provided 100-140 EJ biomass per year to decarbonise the energy and industry sectors. The contribution of the AFOLU sector was predominantly dependent on incentive and land availability. This made emission pricing and low-meat diets freeing pastureland the most powerful levers that created strong synergies of up to 440 GtCO₂e in additional combined mitigation and helped limit global temperature rise to its minimum across all scenarios: 0.8°C above pre-industrial levels by 2100. Alleviating regional self-sufficiency goals could further release agricultural land from less productive regions for climate mitigation. Yet, trade-offs between A/R and biomass production remained, even in scenarios combining global food market with vegan diets. In those cases, the cost-optimisation favoured afforestation over bioenergy production, leading to less fossil fuel substitution in energy and industry sectors than with less available land. Biodiversity conservation was only beneficial for climate in the absence of other measures and lost relative effectiveness under emission pricing. This was because emission pricing and habitat protection both targeted the prevention of deforestation and other land-use-change emissions, suggesting interchangeability, but no synergistic gains between these two components. Importantly, habitat conservation without emission pricing or requirements to mature forests triggered an intensification of short-rotation forestry on unprotected land, causing net deforestation of up to 8 Mkm 2 between 2020 and 2100. These levels of deforestation were not observed in scenarios without additional habitat protection. In all scenarios, increasing timber demand displayed adverse effects on climate and ecosystems. The extensive expansion of short-rotation forestry to satisfy construction timber demands undermined biomass supply needed for decarbonising energy and industry sectors. Additionally, more mature forests were harvested, diminishing biodiversity habitats and forest carbon stocks. Reducing graphic paper demand by 90% had minimal climate impact but most consistently reduced forestry intensity among all scenario components. Our scenarios were not sensitive to slight variations in wildfire prevalence. Our findings align with and expand upon some previous research on land-based mitigation potential, while also revealing key differences. Frank et al. (2021) previously unified UN sustainable development goals with land-based mitigation potentials using the GLOBIOM land management model. Our study confirmed that sustainability goals (e.g., for biodiversity protection) supported climate mitigation when emission pricing was absent but had little impact once pricing was in place. Likewise, our model reproduced the synergies between emission pricing and agricultural emission cuts (e.g., reducing meat consumption), and like GLOBIOM, used freed pastureland for forestation, which constituted the bulk of global land’s mitigation potential. Zhao et al. (2024) using the GCAM integrated assessment model ( IAM ) projected 510-740 GtCO 2 land-based mitigation potential from pastureland conversions for complying with Paris Agreement targets. This is consistent with our findings for the reduced meat consumption scenarios; however, by accounting for a broader range of combination effects, our study produced higher upper-bound estimates. Furthermore, total forest cover needed in 2050 and 2100 to stay below 1.5°C warming (2400–6100 Mha) aligned closely with Roe et al. (2019) reviewing multiple IAM estimates. Jäger et al. (2024) suggest that IAMs tend to overestimate the mitigation potential of forestation by selecting areas prone to increased future fire activity. SuCCESs, however, explicitly accounts for the spatial, temporal, and climatic dynamics of future wildfires, and optimizes afforestation accordingly, which reduced the sensitivity of the mitigation potential to wildfire prevalence. Reducing paper demand showed negligible climate benefits, reinforcing findings of van Ewijk et al. (2021). Our study did not, however, reproduce the 150 GtCO 2 emission savings from replacing construction cement with wood in 50% of new urban residential buildings observed in Mishra et al. (2022) with the MAgPIE IAM. This is likely due to their study’s model setup producing construction timber more effectively than SuCCESs, while unlike SuCCESs, not taking climate change impacts on forests (e.g., CO 2 fertilization and changes in temperatures, wildfires, and precipitation) into account. The comparability of this study to previous research highlights both the robustness of the lightweight SuCCESs model and the added value of integrating more dimensions into one framework, particularly in capturing component interactions and land-use trade-offs. The strong linkage between emissions and emission pricing underline the importance of incentives to drive large-scale mitigation across sectors. The large variability in the projected AFOLU sector’s contribution to climate change mitigation, however, showcases the varying effectiveness of land-based mitigation strategies depending on 1) the interactions between policies, and 2) land and technology available for fossil fuel-substituting bioenergy. The model’s extensive use of bioenergy in electricity generation and industry indicates that global land availability is a crucial factor in meeting Paris Agreement targets. However, realizing this potential depends on the readiness of bioenergy technologies (with and without CCS) to replace fossil fuels across sectors, such as electricity generation, ammonia and cement production, or biofuels for transportation. Trade-offs between afforestation and bioenergy supply were apparent even in scenarios with vast land availability – with more bioenergy used the less land was available – emphasizing the need to reduce land-use products demands, such as livestock-derived and pulp and paper products, while enhancing carbon storage on land until bioenergy technologies are developed enough to replace a large amount of fossil fuels. This underscores that land-based mitigation cannot be considered in isolation and highlights the importance of integrating multiple dimensions into a unified framework. While models are invaluable tools to analyse multi-dimensional problems, it is important to recognize their limitations. SuCCESs may overlook critical regional variations due to its coarse geographical resolution. For example, the model distributes electricity globally, basing wind and solar variability on European seasonal patterns. This does not account for the demand variations across more and less densely populated or developed areas, nor for the climatic conditions and variabilities, including extreme weather events, for wind and solar energy. Therefore, our results might underestimate the renewable capacity needed to consistently fulfil energy demands globally. Furthermore, the model overlooks land-use impacts of installing wind and solar capacity, as large-scale installations may require land clearance or locking. This effect is expected to be minor, but might have implications for surrounding biodiversity or overall mitigation potential. Conversely, the representation of regional self-sufficiency (“restricted food distribution) remains rudimentary, and global food trade may offer more significant mitigation potential in practice than presented in this study. In addition, some of the mitigation strategies available in the model are still being developed or require significant investment to scale up. These include CCS technologies, bioenergy-based ammonia and cement production, and transitioning from blast furnaces to direct reduced iron. Meanwhile, SuCCESs does not fully incorporate existing, viable land-use strategies such as forest thinning, densification, diversification, carbon soil enhancement strategies, and agroforestry or silvipasture. The land-based mitigation potential may be altered drastically if undeveloped bioenergy options were to be excluded, and more viable land-use strategies to be included. This study applied GHG emission pricing based on shadow prices derived from model runs constrained by temperature targets. This yielded emission prices lower than most estimates in the literature, likely due to (1) SuCCESs’ stronger natural carbon sink and (2) SuCCESs’ low transient climate response to emissions and concentrations, compared to other IAMs (for further details please refer to the discussion in supplements). Both effects lead to higher allowable emissions to reach a given temperature target. However, as the shadow prices from the temperature-constrained runs are fed back as emission penalties to analyse the land-use scenario components’ effects, this does not directly affect the analysis of land-based mitigation potential presented in this paper. Future research could expand upon this study by integrating more blended land use as mitigation options, such as agroforestry that blends forest with cropland or pastures, or urban greening. Furthermore, integrating soil carbon-enrichment practices on different types of land uses might be interesting mitigation options to explore with IAMs. Yet, theoretical mitigation potentials can only be realised in practice if landowners are informed, willing, and economically resilient to transition to different land-use practices. This makes policy instruments essential for enabling land-use transitions. Since A/R and bioenergy plantations remain the primary contributors of land-based mitigation, future forest disturbance, such as species range expansions due to climate change and the introduction of invasive species by human activity, must be considered when planning mitigation portfolios. Although our study found limited wildfire sensitivity, the escalating wildfire activity in certain biomes poses dangers to regional ecosystems and livelihoods, necessitating proactive preparedness. 4.1 Conclusions This study highlights the critical role that land-based mitigation can play in achieving global climate targets, particularly when supported by emission pricing, low-meat diets, and smart land-use allocation. Our findings reveal that, although the AFOLU sector holds a substantial mitigation potential of up to 1700 GtCO₂e by 2100, this potential is highly sensitive to land demand pressures, policy incentives, and interaction effects across mitigation strategies. Emission pricing proved the most influential factor for driving mitigation, both in the AFOLU sector and beyond. It incentivized forest expansion, preservation of ecosystems, and substitution of fossil fuels with renewables and bioenergy. However, its effectiveness was heavily conditioned by other scenario components. When combined with low-meat diets and unrestricted global food distribution, emission pricing unlocked strong synergies, enabling substantial afforestation and biomass availability. In contrast, when coupled with omnivore diets and strict habitat protection, land pressures intensified, reducing flexibility and diminishing total mitigation potential. Trade-offs were particularly stark in scenarios that combined high demands for land-intensive products with conservation goals. The interaction between mitigation components proved to be as important as their individual impacts. For instance, habitat conservation added significant value only when other policies were absent, highlighting its role as a substitute, rather than a complement, to emission pricing. Meanwhile, increased timber demand for construction had adverse effects on climate outcomes, not only by increasing pressure on mature forests but also by diverting biomass away from energy and industry decarbonization efforts. Although reducing demand for pulp and paper had minimal direct climate benefits, it consistently reduced forestry pressure and allowed for carbon stock preservation. These findings underline the importance of considering land-use policies in an integrated manner. Future research should integrate even more nuanced land-use strategies such as agroforestry, soil carbon enhancement, and regional land management to improve realism and inclusivity. Furthermore, real-world implementation will depend not only on biophysical and economic feasibility but also on social and institutional readiness. Policymakers must design flexible, context-specific interventions that consider ecological trade-offs, promote sustainable consumption, and provide incentives for landowners to adopt climate-smart practices. Only then can the full potential of land as a climate solution be realized. Declarations Model and Data Availability The SuCCESs model is openly available on GitHub via github.com/SuCCESsIAM . The model version used in this study, including study-specific model modifications, can be found under DOI 10.57707/fmi-b2share.542b976b966a4ea5a38ac6ecbc7d97cb . The scenario files are openly available in .nc -format via DOI 10.57707/fmi-b2share.c8cd2567c6e441058d91e37235d42851 . Acknowledgements The corresponding author warmly thanks the co-authors, Prof. Annalea Lohila, Dr. Marje Prank, Dr. Liisa Kulmala, as well as Quentin Bell, Arttu Väisänen, and Miguel Aldana for their insight, support, and empathy throughout the research process. Their contributions were invaluable in bringing this work to completion. Funding This research was supported by the Research Council of Finland (grant number 341311). Competing interests The authors have no conflict of interest to declare. Author contributions Following the CRediT taxonomy, the authors contributed to this paper in the following ways. Conceptualization: Ekholm, Freistetter, Partanen; Data curation: Freistetter, Ekholm; Formal analysis: Freistetter, Ekholm, Partanen; Funding acquisition: Ekholm; Investigation: Freistetter; Methodology: Ekholm, Freistetter; Project administration: Ekholm; Resources: Ekholm; Software: Ekholm, Freistetter; Supervision: Ekholm, Partanen; Validation: Ekholm, Freistetter, Partanen; Visualization: Freistetter; Writing original draft: Freistetter; Review and editing: Ekholm, Partanen, Keppo, Freistetter. Supplements The online resource Supplements contains a glossary of abbreviations, additional figures, and further information on the methods. References Aleksandrowicz, L., Green, R., Joy, E.J.M., Smith, P., Haines, A., 2016. The Impacts of Dietary Change on Greenhouse Gas Emissions, Land Use, Water Use, and Health: A Systematic Review. PLOS ONE 11, e0165797. https://doi.org/10.1371/journal.pone.0165797 Becvarik, Z.A., White, L.V., Lal, A., 2024. 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Environmental Epidemiology 8, e288. https://doi.org/10.1097/EE9.0000000000000288 Roe, S., Streck, C., Beach, R., Busch, J., Chapman, M., Daioglou, V., Deppermann, A., Doelman, J., Emmet-Booth, J., Engelmann, J., Fricko, O., Frischmann, C., Funk, J., Grassi, G., Griscom, B., Havlik, P., Hanssen, S., Humpenöder, F., Landholm, D., Lomax, G., Lehmann, J., Mesnildrey, L., Nabuurs, G.-J., Popp, A., Rivard, C., Sanderman, J., Sohngen, B., Smith, P., Stehfest, E., Woolf, D., Lawrence, D., 2021. Land-based measures to mitigate climate change: Potential and feasibility by country. Global Change Biology 27, 6025–6058. https://doi.org/10.1111/gcb.15873 Roe, S., Streck, C., Obersteiner, M., Frank, S., Griscom, B., Drouet, L., Fricko, O., Gusti, M., Harris, N., Hasegawa, T., Hausfather, Z., Havlík, P., House, J., Nabuurs, G.-J., Popp, A., Sánchez, M.J.S., Sanderman, J., Smith, P., Stehfest, E., Lawrence, D., 2019. Contribution of the land sector to a 1.5 °C world. Nat. Clim. Chang. 9, 817–828. https://doi.org/10.1038/s41558-019-0591-9 Roggero, M., Gotgelf, A., Eisenack, K., 2023. Co-benefits as a rationale and co-benefits as a factor for urban climate action: linking air quality and emission reductions in Moscow, Paris, and Montreal. Climatic Change 176, 179. https://doi.org/10.1007/s10584-023-03662-6 Samir, K., Moradhvaj, Potancokova, M., Adhikari, S., Yildiz, D., Mamolo, M., Sobotka, T., Zeman, K., Abel, G., Lutz, W., Goujon, A., 2024. Wittgenstein Center (WIC) Population and Human Capital Projections - 2023. https://doi.org/10.5281/zenodo.10618931 Searchinger, T., Peng, L., Zionts, J., Waite, R., 2023a. The Global Land Squeeze: Managing the Growing Competition for Land. Searchinger, T., Peng, L., Zionts, J., Waite, R., 2023b. The Global Land Squeeze: Managing the Growing Competition for Land. WRIPUB. https://doi.org/10.46830/wrirpt.20.00042 Searchinger, T.D., Wirsenius, S., Beringer, T., Dumas, P., 2018. Assessing the efficiency of changes in land use for mitigating climate change. Nature 564, 249–253. https://doi.org/10.1038/s41586-018-0757-z UNEP, 2024. Emissions Gap Report 2024. United Nations Environment Programme. UNEP, FAO, 2020. The State of the World’s Forests 2020. FAO and UNEP ; van Ewijk, S., Stegemann, J.A., Ekins, P., 2021. Limited climate benefits of global recycling of pulp and paper. Nat Sustain 4, 180–187. https://doi.org/10.1038/s41893-020-00624-z Winkler, K., Fuchs, R., Rounsevell, M., Herold, M., 2021. Global land use changes are four times greater than previously estimated. Nat Commun 12, 2501. https://doi.org/10.1038/s41467-021-22702-2 Zhang, Z., Abdullah, M.J., Xu, G., Matsubae, K., Zeng, X., 2023. Countries’ vulnerability to food supply disruptions caused by the Russia–Ukraine war from a trade dependency perspective. Sci Rep 13, 16591. https://doi.org/10.1038/s41598-023-43883-4 Zhao, X., Mignone, B.K., Wise, M.A., McJeon, H.C., 2024. Trade-offs in land-based carbon removal measures under 1.5 °C and 2 °C futures. Nat Commun 15, 2297. https://doi.org/10.1038/s41467-024-46575-3 Zomer, R.J., Neufeldt, H., Xu, J., Ahrends, A., Bossio, D., Trabucco, A., van Noordwijk, M., Wang, M., 2016. Global Tree Cover and Biomass Carbon on Agricultural Land: The contribution of agroforestry to global and national carbon budgets. Sci Rep 6, 29987. https://doi.org/10.1038/srep29987 Supplementary Files LandMitigationPotentialSupplementsblinded.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revise 06 Feb, 2026 Reviewers agreed at journal 23 Aug, 2025 Reviewers invited by journal 21 May, 2025 Editor assigned by journal 02 May, 2025 First submitted to journal 01 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6573840","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":460081298,"identity":"b35bf28c-db72-452d-9501-2cf577a9d6b4","order_by":0,"name":"Nadine-Cyra Freistetter","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIie3PsWsCMRTH8XcI5xKb9Xe01H8h4iAFoX/LUehk4cYb5Ag9SBfpfP+GdOiaI2CXgKurCJ06XHEVaeSkU6OOHfId3vDgw0uIQqH/mdBucKJIHhcd3ZAgHp8hiTwQTQJEcVo5kqgThFriagkbVL+bPxp1zdxkOWH0UpbbJt8V/Np+P68ywpWH3M0eM1NZwo2tFbQVSF6f5uVEEHx/EXoiTE9RAaSKaiUgbO9940jhJcsvR/YE9NdlU+8F7i1bn76yOlyRjiCSqKW7wlh0hnxmhi2cYKmCXQyTysYDR+Any4e3LZuOge7Hpsmnt5zPOu5huzH60mPacMEmFAqFQpf3A9SgVkiMlHydAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-6467-4624","institution":"Finnish Meteorological Institute: Ilmatieteen Laitos","correspondingAuthor":true,"prefix":"","firstName":"Nadine-Cyra","middleName":"","lastName":"Freistetter","suffix":""},{"id":460081299,"identity":"c882a4b3-b110-47d0-9fab-4a2d9c41c0f8","order_by":1,"name":"Tommi Ekholm","email":"","orcid":"","institution":"Finnish Meteorological Institute: Ilmatieteen Laitos","correspondingAuthor":false,"prefix":"","firstName":"Tommi","middleName":"","lastName":"Ekholm","suffix":""},{"id":460081300,"identity":"f1d316be-f2bd-4483-9467-fd63d7f0faad","order_by":2,"name":"Ilkka Keppo","email":"","orcid":"","institution":"Aalto University School of Engineering: Aalto-yliopisto Insinooritieteiden korkeakoulu","correspondingAuthor":false,"prefix":"","firstName":"Ilkka","middleName":"","lastName":"Keppo","suffix":""},{"id":460081301,"identity":"512da588-8f80-48fc-ad49-fef38f3ff956","order_by":3,"name":"Antti-Ilari Partanen","email":"","orcid":"","institution":"Finnish Meteorological Institute: Ilmatieteen Laitos","correspondingAuthor":false,"prefix":"","firstName":"Antti-Ilari","middleName":"","lastName":"Partanen","suffix":""}],"badges":[],"createdAt":"2025-05-01 19:01:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6573840/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6573840/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84301669,"identity":"15decdbc-6a90-4347-b9bc-5acff9c87025","added_by":"auto","created_at":"2025-06-10 10:52:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":43236,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEmission pricings’ mitigation potential compared to no emission pricing. \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ea)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Total global cumulative emissions (from fossil fuels, industry, and AFOLU) between 2020 and 2100, \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eb)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e global cumulative AFOLU net emissions between 2020 and 2100, \u003c/em\u003e\u003cem\u003e\u003cstrong\u003ec)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e global mean temperature anomaly in 2100. The coloured bars show the effect of introducing emission pricing when all other scenario components are set to their baseline values. The black horizontal lines indicate the variability in mitigation potential when emission pricing is applied alongside other scenario components, highlighting the uncertainty and variability in mitigation potential depending on combination effects.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6573840/v1/286bf05e40ccd2f709eb43aa.png"},{"id":84301672,"identity":"407524f6-6691-48ac-bd49-da41e598e8fa","added_by":"auto","created_at":"2025-06-10 10:52:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":111210,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScenario components’ mitigation potential for different emission pricings, compared to their respective dimension baselines. a)\u003c/strong\u003e Total global cumulative emissions (from fossil fuels, industry, and AFOLU) between 2020 and 2100, \u003cstrong\u003eb)\u003c/strong\u003e global cumulative AFOLU net emissions between 2020 and 2100, \u003cstrong\u003ec)\u003c/strong\u003eglobal mean temperature anomaly in 2100. The coloured bars show the effect of introducing emission pricing indicated by the colour, when all other scenario components are set to their baseline values. The black horizontal lines indicate the variability in mitigation potential when emission pricing is applied alongside other scenario components, highlighting the uncertainty and variability in mitigation potential depending on combination effects.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6573840/v1/fab03428b9ea3f1410d266d3.png"},{"id":84301674,"identity":"e2614a50-a4f5-4d64-b329-b2e40cade511","added_by":"auto","created_at":"2025-06-10 10:52:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":82572,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLand-use change between 2020 and 2100 for global diets under emission pricing. \u003c/strong\u003eColouring represents emission pricing.\u003cstrong\u003e \u003c/strong\u003eSame-coloured neighbouring boxes indicate four global diets (from high to no livestock product consumption) under the same emission pricing. More detailed land use change plots are shown in \u003cstrong\u003esupplement Fig. S-F10 to Fig. S-F13.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6573840/v1/925e7a14a3b4cc3b43a6b33d.png"},{"id":84302578,"identity":"37847925-043f-4979-9195-144f2900b784","added_by":"auto","created_at":"2025-06-10 11:00:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":169922,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStacked mitigation potentials for individual non-baseline scenario components under the Early decarbonisation emission pricing. \u003c/strong\u003eEach column represents a scenario dimension, excluding wildfires due to their minimal impact. Nodes represent scenario components indicated by the label, while edges link all components of a complete scenario (assuming SuCCESs-default wildfire activity). The shape, colour and colour shade of the nodes depend on the additional reduction in total GHG emissions they bring, indicated in Gt CO\u003csub\u003e2\u003c/sub\u003ee cumulatively between 2020 and 2100. Brown colour and upward arrow nodes indicate an increase in emissions when changing from dimension baseline to another case (e.g., from Omnivore to Flexitarian for the diet dimension). Green-coloured downward arrow nodes indicate emission reduction. The round bubble in the rightmost column shows total cumulative net emissions between 2020 and 2100 in the linked scenario. The respective figures for the remaining three emission pricings can be found in the \u003cstrong\u003esupplement Fig. S-F14–16\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6573840/v1/12681350f9701c62dd4b0f39.png"},{"id":84303981,"identity":"e0af7d56-6ad3-4957-8373-1d90502b13ef","added_by":"auto","created_at":"2025-06-10 11:16:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1630900,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6573840/v1/c1d153b3-0d13-418c-a3e7-7c52d61000b3.pdf"},{"id":84301679,"identity":"46eb33b2-bd35-4c78-9efa-f2e90f43459b","added_by":"auto","created_at":"2025-06-10 10:52:38","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":11158259,"visible":true,"origin":"","legend":"","description":"","filename":"LandMitigationPotentialSupplementsblinded.docx","url":"https://assets-eu.researchsquare.com/files/rs-6573840/v1/f9e6ade84985fe1f6f4553bb.docx"}],"financialInterests":"","formattedTitle":"Sensitivity of global land-based mitigation potential to land-use scenarios and interactions across sectors","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLimiting global warming to well below 2\u0026deg;C remains a challenge, as current policies fall short of necessary greenhouse gas (\u003cstrong\u003eGHG\u003c/strong\u003e) emissions reductions (Ripple et al., 2024; UNEP, 2024). Land-based mitigation of climate change, such as afforestation and reforestation (\u003cstrong\u003eA/R\u003c/strong\u003e), climate-optimal forest rotations, substitution of fossil-based materials with biomaterials, and bioenergy with carbon capture and storage (\u003cstrong\u003eBECCS\u003c/strong\u003e), can enhance land-based carbon sequestration while co-benefiting biodiversity conservation and many other vital ecosystem services\u0026nbsp;(Becvarik et al., 2024; Roca-Barcel\u0026oacute; et al., 2024; Roggero et al., 2023; Aleksandrowicz et al., 2016; Roe et al., 2021). Yet, their large-scale implementation is constrained by the intensifying competition for land\u0026nbsp;(Searchinger et al., 2023a).\u003c/p\u003e\n\u003cp\u003eAllocating land between different uses implies trade-offs between different objectives, such as carbon sequestration, food production, biomass supply, and biodiversity conservation; but doing so in an uninformed manner can lead to unnecessary compromises (Burgess et al., 2012; Zhao et al., 2024). In recent years, researchers have been analysing trade-offs associated with land-based mitigation using integrated assessment models (\u003cstrong\u003eIAMs\u003c/strong\u003e) and other integrated approaches (Doelman et al., 2018; Searchinger et al., 2018; Frank et al., 2021; Roe et al., 2021). These analyses indicate complex interdependencies in a world with rising food, energy, and material demands (IIASA, 2024). For example, multi-dimensional analyses show that forestation can enhance carbon sequestration and biodiversity\u0026nbsp;(Doelman et al., 2020; J\u0026auml;ger et al., 2024), but typically has an inverse relationship with bioenergy production and food security\u0026nbsp;(Frank et al., 2021; Hirata et al., 2024; Zhao et al., 2024).\u003c/p\u003e\n\u003cp\u003eNumerous studies identify agricultural expansion as a primary driver of global deforestation and forest degradation (Jayathilake et al., 2021; Pendrill et al., 2022; Ma et al., 2023), causing biodiversity loss (Kadoya et al., 2022) and gross land use-change emissions two-thirds the size of the global forest sink (Kruid et al., 2021; Pan et al., 2024). Currently, forests cover 3600 Mha (29%) of global land, of which 42% are managed forests, and provide habitat for over 70% of terrestrial biodiversity (FAO, 2020; Pan et al., 2024; UNEP and FAO, 2020). Agriculture currently occupies 4,800 Mha (38% of global land), with 3,200 Mha (25%) dedicated to livestock pastures and 1,600 Mha (13%) to cropland for food, feed, and modern bioenergy production (Searchinger et al., 2023b). Rising demand for livestock products and bioenergy could expand agricultural land to 6,300 Mha (50%) of global land by 2100, enhancing the risk for continued agricultural deforestation (Fujimori et al., 2017; Winkler et al., 2021; IEA, 2021; IIASA, 2018). Meanwhile, shifts toward \u0026ldquo;regional preparedness and self-sufficiency\u0026rdquo; policies often disregard production inefficiencies and require additional agricultural expansion into forests and other habitats to meet regional demands\u0026nbsp;(Beltran-Pe\u0026ntilde;a et al., 2020; Rabbi et al., 2023; Zhang et al., 2023). Studies suggest forests hold 50% of future cost-effective land-based mitigation potential\u0026nbsp;(Roe et al., 2021; Mo et al., 2023). However, the true mitigation potential of forests remains uncertain, as climate change may alter forest growth and increase destruction through extreme weather and wildfires\u0026nbsp;(J\u0026auml;ger et al., 2024).\u003c/p\u003e\n\u003cp\u003eGiven the constraints on land availability and the interdependencies between different objectives we have for land use, understanding the extent to which land use can contribute to mitigation and climate goals is critically important. Releasing and repurposing land to carbon sequestration may be an important mitigation enabler, and can be achieved through increased production efficiency, optimized land allocation, and shifts in the demand for land-use products.\u003c/p\u003e\n\u003cp\u003eNumerous studies have investigated this from different viewpoints. A global shift to low-meat diets and agroforestry, for example, has the potential to transform as much as 25% of global land with significant carbon sequestration (Hayek et al., 2024, 2021; Zomer et al., 2016) and co-benefits for biodiversity (Henry et al., 2019). Enhancing international food trade could release agricultural land in some regions while addressing undernourishment trans-regionally (Doelman et al., 2018; Janssens et al., 2022). Yet, once land is released, its repurposing remains a challenge due to the intricate interconnections between energy, climate, materials, and land systems that must be carefully assessed (Roe et al., 2021, 2019).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile earlier studies have provided invaluable insights into the mitigation potential of dietary changes, for example, the focus on a selected set of dimensions complicates identifying sensitivities, synergies, and trade-offs between all relevant dimensions across studies due to comparability issues in scope, time, and accounting. Additionally, the detailed models used in these studies are typically computationally heavy and only allow for integration of a limited number of connected dimensions, affected systems, or mitigation options\u0026nbsp;(Aleksandrowicz et al., 2016; Doelman et al., 2018; Frank et al., 2021; Henry et al., 2019; Lecl\u0026egrave;re et al., 2020; Searchinger et al., 2018; Zhao et al., 2024). Omissions and dimension reductions are always unavoidable in modelling, often even necessary. However, combining more dimensions, systems, and mitigation options into a unified framework will be crucial for understanding interactions between them, and making better-informed decisions.\u003c/p\u003e\n\u003cp\u003eThis study explores the global, long-term mitigation potential of land use under a range of scenario components, covering alternative diets, habitat conservation targets, global food distribution restrictions, material demands and wildfire disruptions, all affecting land available for mitigation. We use the lightweight IAM SuCCESs (Ekholm et al., 2024a, 2024b) and its capabilities to run large scenario ensembles that integrate energy, climate, materials, and land use for different levels of carbon pricing. The model can freely choose between the available mitigation strategies afforestation, reforestation, halting deforestation, altering forest rotation, shifting agricultural and forestry systems, adjusting residue management, and use of modern bioenergy and BECCS. By unifying more land-competing mitigation dimensions into a single, computationally efficient framework, we can capture interactions, sensitivities, synergies, and trade-offs that affect the mitigation potential of land. Our analysis is guided by the following research questions:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eHow large is the global land-based mitigation potential according to the SuCCESs model, and what are the key determinants for it?\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;What would be the optimal land allocation strategy to maximize land-based mitigation while balancing food production, energy generation, biomaterial supply, and biodiversity conservation?\u003c/li\u003e\n \u003cli\u003eWhat interaction effects (synergies and trade-offs) arise between land-based mitigation and other land uses when combining a growing number of dimensions?\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eBy identifying the conditions under which different mitigation strategies are most effective, this research aims to provide insights into how land-use decisions can support ambitious climate goals while addressing food security, biodiversity conservation, and sustainable resource management.\u0026nbsp;\u003c/p\u003e"},{"header":"2. Methods","content":"\u003ch2\u003e2.1 SuCCESs model\u003c/h2\u003e\n\u003cp\u003eWe used the lightweight, bottom-up integrated assessment model SuCCESs that integrates the global energy, material, land, and climate systems (Ekholm et al., 2024a). SuCCESs is a demand-driven, intertemporal optimization model that calculates long-term scenarios from 2020 to 2100 by minimising discounted system costs to satisfy global demands for climate-relevant products and services, such as different types of energy, transportation, plant and livestock-based foods, and energy- and emission-intensive materials. Constrained by demand, energy and material balances, technology, and policy (e.g., climate targets, carbon pricing), SuCCESs simulates an efficient-market solution. It calculates CO₂, CH₄, and N₂O emissions from energy, materials, and land use, feeding them into its climate module to estimate atmospheric concentrations, radiative forcing, and global mean surface temperature changes. Other forcing agents, such as aerosols and ozone, follow the average of shared socio-economic pathway 2 (\u003cstrong\u003eSSP2\u003c/strong\u003e) and representative concentration pathway 2.6 scenarios (IIASA, 2018) and are given to the model exogenously.\u003c/p\u003e\n\u003cp\u003eLand use is modelled through CLASH, SuCCESs\u0026rsquo; land-use module (Ekholm et al., 2024b), which allocates global land to cropland, pastures, forests, urban areas, and natural ecosystems. CLASH tracks food, and woody and crop biomass for energy and materials, along with carbon storage in vegetation and soil. Terrestrial carbon stock changes reflect the net exchange of carbon between land and atmosphere. Decision variables include land allocation, forest harvesting, and production volumes of land-use products. While food and wood-derived products\u0026rsquo; demands are exogenous, bioenergy demand is endogenously determined through the market equilibrium across the different modules of SuCCESs. Thus, while the model can freely substitute biomass for fossil fuels, it cannot independently replace conventional materials with biomaterials.\u003c/p\u003e\n\u003cp\u003eFor a detailed description of the CLASH land-use model please refer to Ekholm \u003cem\u003eet al.\u003c/em\u003e (2024b), and for SuCCESs to Ekholm \u003cem\u003eet al.\u003c/em\u003e (2024a). A description of the model modifications made for this study can be found in the \u003cstrong\u003esupplements\u003c/strong\u003e.\u003c/p\u003e\n\u003ch2\u003e2.2 Scenarios\u003c/h2\u003e\n\u003cp\u003eEach scenario in this paper was a combination of six components (dimensions). The scenario components were selected based on \u003cstrong\u003ea)\u003c/strong\u003e high prevalence in recent scientific literature, \u003cstrong\u003eb)\u003c/strong\u003e contribution to the global land scarcity problem, \u003cstrong\u003ec)\u003c/strong\u003e global scalability of associated mitigation potential, and \u003cstrong\u003ed)\u003c/strong\u003e compatibility with the SuCCESs IAM. As a first component, emission pricing penalised unabated emissions and rewarded carbon uptake, providing mitigation incentive for the cost-optimising model. Prices for CO₂, CH₄, and N₂O generally rose over the century, though with fluctuations across time steps, and were applied uniformly to all net emissions, including those from land use and natural land fluxes. Second, we varied the demand for primary animal products ensuing from SSP2 or SSP1 food demands, which directly influenced livestock-related emissions and the need for pastureland and cropland used for feed. Third, to simulate trading barriers by regional self-sufficiency policies on a crude level, we required each biome to produce a minimum of 65% of the food demand within the biome itself. The global food market case lifted this constraint to potentially lower global cropland. Fourth, biodiversity habitat protection restricted the model from converting or harvesting pristine ecosystems or mature forests. Fifth, the demand for wood-derived products, such as paper or construction timber was varied, varying demand pressures on production forests. Lastly, we applied symmetrical uncertainty to the SuCCESs parametrisation\u0026rsquo;s wildfire prevalence to assess the mitigation potentials\u0026rsquo; sensitivity to changes in fire activity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable \u003cem\u003e1\u003c/em\u003e\u003c/strong\u003e, presents an overview of the scenario components, sorted from least to most ecologically sustainable within the six dimensions emission pricing, global diet, global food distribution, biodiversity habitat conservation, biomaterial utilisation, and wildfire activity. Combining all components, one per dimension, yielded 864 scenarios calculated for this study. This number does not include the scenario component \u003cem\u003eHalting Biodiversity Loss\u003c/em\u003e which made scenarios infeasible. For a detailed description of all scenario components please refer to the \u003cstrong\u003esupplements\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e: Description of scenario components affecting land availability.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eA single scenario consists of one component from each dimension. The baseline scenario contains no emission pricing, an omnivore global diet following SSP2 diets, restricted food distribution across biomes by 65%, no additional habitat protection following SSP2 land-use change, forestry business as usual following SSP2 biomaterial demands, and SuCCESs-Default wildfire activity parametrised from LPJGuess/Simfire-Blaze model. The baseline scenario components are indicated with the comment \u0026ldquo;(baseline)\u0026rdquo;.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eDimension\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eComponent (Case)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eDescription and Sources\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eEmission Pricing\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eNone \u003cem\u003e(baseline)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eNo emission penalty.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eSteady Transition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eNet unabated emissions are priced. Prices for CO\u003csub\u003e2\u003c/sub\u003e, CH\u003csub\u003e4\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO emissions are derived from shadow prices of a model run with a 2.0\u0026deg;C constraint without overshoot.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;Overshoot\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eThe pricing reflects the GHG shadow prices from a baseline model run allowing overshoot as needed, but temperature anomaly must reach equal or below\u0026nbsp;1.5\u0026deg;C by 2100.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eEarly Decarbonisation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eAs above, but with 1.5\u0026deg;C constraint, without overshoot flexibility.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eGlobal Diet\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eOmnivore \u003cem\u003e(baseline)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eSSP2-level food demand* with high demand for livestock-derived products.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eFlexitarian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eSSP1-level food demand* with declining demands for livestock-derived products.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eLacto-Ovo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eSSP2-level milk \u0026amp; egg demands*, meat only from culled livestock.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eVegan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eAll animal product demand phased out between 2030 and 2050.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eGlobal Food Distribution\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eRestricted food distribution\u003cem\u003e\u0026nbsp;(baseline)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e65% of food demand within a biome must be produced within that biome, approximating average national self-sufficiency levels in SuCCESs\u0026rsquo; coarse geographical resolution\u0026nbsp;(Economist Impact \u0026amp; Corteva Agriscience, 2022; Jones and O\u0026rsquo;Neill, 2016).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eGlobal Food Market\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eFood production from any region can fulfil food demand in any region.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eBiodiversity Habitat Conservation\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eNo additional\u0026nbsp;\u003cbr\u003ehabitat protection \u003cem\u003e(baseline)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eModel default in CLASH\u0026nbsp;(Ekholm et al., 2024b; Hurtt et al., 2020), leading to approximately 20% pristine habitat, consistent with\u0026nbsp;Beyer and Manica, 2020.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003ePristine Ecosystems\u0026nbsp;\u003cbr\u003e\u0026nbsp;Protection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003ePristine ecosystems\u003cem\u003e\u0026nbsp;\u003c/em\u003eare preserved for minimal additional biodiversity loss\u0026nbsp;(D\u0026iacute;az et al., 2020).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eMaturing Habitats\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003ePristine ecosystems are preserved, and mature secondary forests (80+ years) must double between 2020 and 2100.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eBiomaterial Utilisation\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eForestry as Usual \u003cem\u003e(baseline)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eSSP2-level demands for timber, paper and pulp\u0026nbsp;(IIASA, 2018).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eElectronic Papers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003e90% of graphic paper avoided due to shift to electronic formats\u0026nbsp;(FAO, 2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eTimber Cities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eHalf of new urban residential buildings made of wood, decreasing cement demand (Mishra et al., 2022).\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eWildfire Activity\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eSuCCESs-Default \u003cem\u003e(baseline)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eDefault wildfire activity in SuCCESs, based on Simfire-Blaze model\u0026nbsp;(Ekholm et al., 2024b, 2024a).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eLess Fires\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMore Fires\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eFuture forest fire uncertainty from five IAMs is symmetrically applied to SuCCESs\u0026apos; default parameters\u0026nbsp;(J\u0026auml;ger et al., 2024).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e* per-capita food demand of MAgPIE model regarding crops, milk, eggs, beef, pork, poultry, and sheep/goat\u0026nbsp;\u003c/em\u003e\u003cem\u003e(Dietrich et al., 2019)\u003c/em\u003e\u003cem\u003e, multiplied by SSP2 population projections\u0026nbsp;\u003c/em\u003e\u003cem\u003e(IIASA, 2024; Samir et al., 2024)\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOnly the global diet and wildfire activity scenario components directly avoided emissions by reducing ruminant-related and forest burning emissions. The remaining scenario components affected the possibilities of SuCCESs to optimize mitigation decisions based on emission pricing, demand, and land availability. Land-based mitigation options included afforestation or reforestation (\u003cstrong\u003eA/R\u003c/strong\u003e), avoided deforestation, relocating agriculture and forestry sites, and adjusting forest rotation. Furthermore, the model can substitute fossil fuels in transportation, and metal and chemical industries with bioenergy and biofuel, and other renewables (wind, solar, hydro). The model can also choose to combine bioenergy with carbon capture and storage (\u003cstrong\u003eBECCS\u003c/strong\u003e), which generates negative emissions\u0026nbsp;by removing the captured CO\u003csub\u003e2\u003c/sub\u003e from the carbon cycle.\u003c/p\u003e\n\u003cp\u003eWe analysed the greenhouse gas (\u003cstrong\u003eGHG\u003c/strong\u003e) emissions CO\u003csub\u003e2\u003c/sub\u003e, CH\u003csub\u003e4\u003c/sub\u003e, N\u003csub\u003e2\u003c/sub\u003eO and their sources, temperature change, energy mix, and land use in the scenarios. We report emissions in CO\u003csub\u003e2\u003c/sub\u003eequivalents, converting with the 100-year global warming potential 28 for CH\u003csub\u003e4\u003c/sub\u003e, and 265 for N\u003csub\u003e2\u003c/sub\u003eO. CO\u003csub\u003e2\u003c/sub\u003e emissions from the AFOLU sector were diagnosed \u0026nbsp;(outside the optimisation routine) using year-2020 carbon densities for forests to have the results conceptually in line with AFOLU emissions \u0026nbsp;calculated with bookkeeping models used in the Global Carbon Project (Friedlingstein et al., 2025) and excluding indirect effects from CO\u003csub\u003e2\u003c/sub\u003e fertilization and climate change (Grassi et al., 2018). \u0026nbsp;We assumed global mean temperature rise above pre-industrial climate (temperature anomaly) in 2020 is 1.37\u0026deg;C.\u003c/p\u003e\n\u003cp\u003eBy integrating multiple dimensions of land-competition into a unified, computationally efficient framework, we could quantify each component\u0026rsquo;s mitigation potential as well as the scenarios\u0026rsquo; combined mitigation potentials and associated interaction effects (sensitivities, synergies, and trade-offs). We define a scenario component\u0026apos;s effect on a variable (e.g., emissions) as the realised change when added to the baseline (which assumes an omnivore diet, restricted food distribution, no habitat protection, business-as-usual forestry, and default wildfire activity). The interaction effect is the gap between the summed individual components\u0026rsquo; effects and the combined scenario\u0026rsquo;s actual modelled outcome.\u0026nbsp;\u003c/p\u003e"},{"header":"3. Results","content":"\u003ch2\u003e3.1 Mitigation potentials\u003c/h2\u003e\n\u003cp\u003eUsing the SuCCESs integrated assessment model, we evaluated the global total and land-based mitigation potential across 864 land availability-affecting scenarios varying in emission pricing, meat amount in diets, biodiversity habitat conservation, global food distribution, biomaterial demands, and wildfire prevalence. The baseline scenario consisted of no emission pricing with all other dimensions following \u003cstrong\u003eSSP2\u003c/strong\u003e scenario projections. The baseline scenario\u0026rsquo;s total net emissions were 4000 GtCO₂e cumulatively between 2020\u0026ndash;2100 (54 GtCO\u003csub\u003e2\u003c/sub\u003ee/year). The associated baseline temperature anomaly reached 2.1\u0026deg;C in 2050 and 3.4\u0026deg;C in 2100 (\u003cstrong\u003esupplement Fig. S-F4\u003c/strong\u003e). Scenarios \u003cem\u003ewithout\u003c/em\u003e emission pricing reduced the temperature increase with respect to the baseline, but not enough to keep warming within Paris Agreement limits. Out of the 864 scenarios, 234 scenarios stayed between 1.5\u0026deg;C and 2\u0026deg;C in 2100 with a total mitigation of 1400\u0026ndash;2300 GtCO₂e between 2020 and 2100 across all sectors, and 414 ended up below 1.5\u0026deg;C in 2100 with a total mitigation of 2100\u0026ndash;3100 GtCO₂e (\u003cstrong\u003esupplement Fig. S-F6, top\u003c/strong\u003e). The cumulative land-based mitigation contribution of the agriculture, forestry and land-use (\u003cstrong\u003eAFOLU\u003c/strong\u003e) sector across all scenarios was -2\u0026ndash;1700 GtCO\u003csup\u003e2\u003c/sup\u003ee (2020-2100) compared to baseline (\u003cstrong\u003esupplement Fig. S-F7, top\u003c/strong\u003e). The large range including near-zero mitigation and slight net emissions indicates a high sensitivity of the AFOLU sector\u0026rsquo;s mitigation potential to incentives (emission pricing) and land demand pressures.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAmong the scenario dimensions, emission pricing was the largest contributor to emissions and temperature reductions. Introducing emission pricing from baseline reduced emissions by 1,500-2,300 GtCO₂e across all sectors (\u003cstrong\u003eFig. 1\u003c/strong\u003e\u003cstrong\u003ea, coloured bars\u003c/strong\u003e). The contribution from the AFOLU sector was roughly 500 GtCO₂e (\u003cstrong\u003eFig. 1\u003c/strong\u003e\u003cstrong\u003eb, coloured bars\u003c/strong\u003e). The AFOLU reductions, however, were highly sensitive to the interactions with other scenario components, as shown by the black horizontal ranges in \u003cstrong\u003eFigure 1\u003c/strong\u003e, with mitigation outcomes varying by \u0026plusmn;100%. The AFOLU reductions account only for direct emission reductions within the sector, excluding emission reductions from biomass substituting fossil fuels in other sectors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEmission pricing drove many mitigation actions within the AFOLU sector, including up to 37 Mkm\u0026sup2; forest expansion, an 80\u0026ndash;560 GtC increase in carbon stocks within managed forests and other habitats, and the preservation of up to 12 Mkm\u0026sup2; of pristine ecosystems, compared to scenarios without emission pricing. Expanding bioenergy (100-140 EJ/year) and wind and solar power (120\u0026ndash;180 EJ/year combined capacity) caused faster coal phase-out, less crude oil and natural gas use more nuclear power (\u003cstrong\u003esupplement Fig. S-F8\u003c/strong\u003e), and rapid global electrification of road and rail traffic. This led to reduced fossil fuel extraction, refining, and combustion emissions depending on emission pricing case.\u003c/p\u003e\n\u003cp\u003eOf the remaining scenario components, reducing animal product consumption provided the greatest and most consistent mitigation potential (controlled for the effect of underlying emission pricing) (\u003cstrong\u003eFig. 2\u003c/strong\u003e). The mitigation potential of diets was amplified by rising emission pricing, which incentivized afforestation and bioenergy production in the land-area freed from livestock use, i.e. pastures and croplands for animal feed. The vegan diet enabled the highest mitigation from the AFOLU sector, thanks to avoided emissions from cropland and livestock and the large-scale afforestation of freed pastureland (\u003cstrong\u003eFig. 2\u003c/strong\u003e\u003cstrong\u003eb\u003c/strong\u003e). Scenarios combining a vegan diet with emission pricing achieved 1100\u0026ndash;1700 GtCO\u003csub\u003e2\u003c/sub\u003ee combined mitigation in the AFOLU sector (14\u0026ndash;21 GtCO\u003csub\u003e2\u003c/sub\u003ee/year) (\u003cstrong\u003esupplement Fig. S-F7, top\u003c/strong\u003e), of which the vegan component contributed 550\u0026ndash;990 GtCO\u003csub\u003e2\u003c/sub\u003ee (\u003cstrong\u003eFig. 2\u003c/strong\u003e\u003cstrong\u003eb\u003c/strong\u003e). In contrast, the flexitarian diet component only contributed 90-210 GtCO\u003csub\u003e2\u003c/sub\u003ee to the 600\u0026ndash;900 GtCO\u003csub\u003e2\u003c/sub\u003ee (8\u0026ndash;11 GtCO\u003csub\u003e2\u003c/sub\u003ee/year) combined mitigation in the AFOLU sector with emission pricing, indicating minor freeing-up of pastureland with only slight reductions in meat consumption. However, reduced animal feed production in vegan and vegetarian scenarios also lowered biomass by-products available for bioenergy and BECCS, increasing emissions in non-AFOLU sectors by limiting bioenergy substitution in electricity and industry. As a result, the total mitigation potential (\u003cstrong\u003eFig. 2\u003c/strong\u003e\u003cstrong\u003ea\u003c/strong\u003e) peaked with a global vegan diet under early decarbonization pricing at only 770 GtCO₂e, achieving up to 0.7\u0026deg;C cooling with respect to the baseline (\u003cstrong\u003eFig. 2\u003c/strong\u003e\u003cstrong\u003ec\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn contrast, habitat conservation became less effective with rising emission pricing. Without emission pricing, the \u003cem\u003eMaturing Habitats\u003c/em\u003e case achieved 650 GtCO\u003csub\u003e2\u003c/sub\u003ee mitigation, whereas under \u003cem\u003eearly decarbonisation\u003c/em\u003e emission pricing, only 90 GtCO\u003csub\u003e2\u003c/sub\u003ee of mitigation was added by \u003cem\u003eMaturing Habitats\u003c/em\u003e. Since emission pricing already provided incentive to avoid deforestation emissions and enhance carbon land sinks, conservation provided minimal added mitigation benefit. Additionally, conservation fully concentrated wood production on unprotected forests in a spill-over effect that greatly intensified short-rotation forestry compared to scenarios without biodiversity habitat conservation.\u003c/p\u003e\n\u003cp\u003eWhile other scenario components had a considerably smaller effect on cumulative emissions, they had important cross-reaching implications. \u003cem\u003eGlobal food market\u003c/em\u003e reduced overall cropland area, avoiding up to 20 GtCO\u003csub\u003e2\u003c/sub\u003ee cropland emissions (2020-2100). It also enabled land-based mitigation in scenarios where land availability was particularly rigid, such as those combining emission pricing with \u003cem\u003eOmnivore\u003c/em\u003e and \u003cem\u003eFlexitarian\u003c/em\u003e diets and biodiversity habitat conservation. Bioenergy and BECCS were used by most scenarios to significantly curb electricity generation and cement production emissions. The \u003cem\u003eTimber Cities\u003c/em\u003e component, however, caused extensive expansion of short-rotation forestry to provide wood for buildings, greatly limiting biomass availability for energy generation despite gains in logging residue. This shortage reduced the opportunities for bioenergy and BECCS, and in some scenarios even increased fossil fuel use in cement production to compensate for the decreased bioenergy supply. This offset \u0026ndash; and in some cases reversed \u0026ndash; the emission reductions from lower cement demand. Additionally, the \u003cem\u003eTimber Cities\u003c/em\u003e components had negative impacts on global carbon stocks in forests from harvesting mature stands which are also important biodiversity habitats. Wildfire prevalence had the smallest impact on our scenarios, except with \u003cem\u003eOvershoot\u003c/em\u003e emission pricing, where heavy BECCS reliance post-overshoot (8\u0026ndash;20 EJ/year between 2050 and 2100) made fires more consequential.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e3.2 Land use change for mitigation\u003c/h2\u003e\n\u003cp\u003eA large part of the land-based mitigation observed in our scenarios arose from changes in land use. To understand this better, \u003cstrong\u003eFig. 3\u003c/strong\u003e presents the change in land areas between 2020 and 2100 depending on emission pricing and diet, as their combination played a major role in the extent of forest expansion. The remaining variation (height of boxes) was mainly due to the extent of habitat conservation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWithout emission pricing, pastureland for \u003cem\u003eOmnivore\u003c/em\u003e and \u003cem\u003eFlexitarian\u003c/em\u003e diets were reduced by up to 21 Mkm\u0026sup2; by moving them to more productive land (\u003cstrong\u003eFig. 3\u003c/strong\u003e\u003cstrong\u003e, first two yellow boxes\u003c/strong\u003e). Without emission pricing as incentive, freed pastures were converted to non-forest (\u003cstrong\u003eFig. 3\u003c/strong\u003e\u003cstrong\u003e, other managed land\u003c/strong\u003e), to avoid afforestation costs. To satisfy wood product demands, existing managed and pristine forests were harvested with minimal replanting (thus maximal conversion to non-forest) to avoid replanting costs. This greatly expanded non-forest biodiversity habitats but destroyed the most vital forested habitats, compared to introducing emission pricing. With emission pricing, more productive biomes were used to expand forests instead of using them as pastures. Only \u003cem\u003eOmnivore\u003c/em\u003e diet required pastureland expansion under emission pricing. For \u003cem\u003eOmnivore\u003c/em\u003e and \u003cem\u003eFlexitarian\u003c/em\u003e diets under emission pricing, non-forested land was used for afforestation. In \u003cem\u003eLacto-Ovo\u003c/em\u003e and \u003cem\u003eVegan\u003c/em\u003e diets, forests and non-forested land could be expanded on abandoned pastureland while preserving considerably more of pristine ecosystems, compared to no emission pricing. In \u003cem\u003eOvershoot\u003c/em\u003e emission pricing scenarios however, rapid BECCS upscaling led to cropland expansion into managed and pristine non-forests, especially if combined with high meat demand, which kept land pressures high. (\u003cstrong\u003eFig. 3\u003c/strong\u003e\u003cstrong\u003e, teal boxes\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e3.3 Synergies and trade-offs from interaction effects\u003c/h2\u003e\n\u003cp\u003eTo highlight how interactions between the scenario components modify their mitigation effectiveness, \u003cstrong\u003eFig. 4\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003epresents the mitigation potentials of individual scenario components when stacked onto others. For instance, the mitigation potential of \u003cem\u003eGlobal food market\u003c/em\u003e contributed 50 GtCO₂e under \u003cem\u003eFlexitarian\u003c/em\u003e diet, but 130 GtCO₂e reductions under \u003cem\u003eVegan\u003c/em\u003e diet, as opposed to the baseline case with 65% of food needing to be produced and consumed within the same biome. Likewise, \u003cem\u003eMaturing Habitats\u003c/em\u003e had a negligible 10 GtCO₂e mitigation potential in combination with \u003cem\u003eFlexitarian\u003c/em\u003e diet and \u003cem\u003eGlobal Food Market\u003c/em\u003e under \u003cem\u003eEarly Decarbonisation\u003c/em\u003e pricing (\u003cstrong\u003eFig. 4\u003c/strong\u003e), but 170 GtCO₂e under \u003cem\u003eSteady Transition\u003c/em\u003e pricing (\u003cstrong\u003esupplement\u003c/strong\u003e \u003cstrong\u003eFig. S-F15\u003c/strong\u003e). These interactions highlight the interdependency of land-based mitigation actions, making it difficult to quantify universal mitigation potentials for different mitigation measures.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe defined interactions between scenario components as the diminished or added mitigation potential of a multi-component scenario compared to the sum of the individual component\u0026rsquo;s mitigation potential when added from baseline. We classified six interaction types using K-means clustering: moderate and strong synergies (11% and 16% of scenarios) where combined mitigation exceeded the sum of single mitigation potentials; moderate, strong, and extreme trade-offs (13%, 16%, and 7%) where combined mitigation was lower; and negligible interactions (37%) where effects were roughly additive (\u003cstrong\u003esupplement Fig. S-F17\u003c/strong\u003e). We focused our analysis only on the strong interactions and extreme trade-offs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStrong synergy scenarios with 180\u0026ndash;440 GtCO₂e additional mitigation potentials consisted of combinations of emission pricing, meat-free diets, and no additional habitat protection components. The meat-free diets avoided large amounts of emissions from livestock and freed vast agricultural land, expanding managed forests by 18\u0026ndash;28 Mkm\u0026sup2; and non-forest habitats by up to 20 Mkm\u0026sup2;. Biomass use almost tripled from 2020 to 2040, replacing some fossil fuels in industry processes and electricity generation. Lots of fossil fuels remained in use however, offset by the vast mitigation in the AFOLU sector. Despite no additional biodiversity protection, pristine forests remained intact, and secondary forests expanded and matured, mimicking the \u003cem\u003eMaturing habitats\u003c/em\u003e case \u0026ndash; except where \u003cem\u003eTimber Cities\u003c/em\u003e drove extensive short-rotation forestry.\u003c/p\u003e\n\u003cp\u003eExtreme trade-off scenarios (470\u0026ndash;630 GtCO₂e diminished mitigation potential compared to sum of individual component\u0026rsquo;s mitigation potential) arose from emission pricing scenarios combining \u003cem\u003eOmnivore\u003c/em\u003e and \u003cem\u003eFlexitarian\u003c/em\u003e diets with biodiversity conservation. These combinations forced a balance between livestock expansion, ecosystem protection and carbon sequestration on minimally flexible land. In the AFOLU sector, these scenarios converted non-forested land to forests and pastures, increasing forest area by 5\u0026ndash;12 Mkm\u003csup\u003e2\u003c/sup\u003e but reducing non-forest habitats by 5\u0026ndash;15 Mkm\u0026sup2;. Forest expansion was driven by aggressive tree planting, which doubled newly established forest areas between 2020 and 2030, and intensified short-rotation harvesting mid-century, particularly under \u003cem\u003eTimber Cities\u003c/em\u003e cases. \u003cem\u003eGlobal Food Market\u003c/em\u003e and \u003cem\u003eElectronic Papers\u003c/em\u003e components reduced land-use pressure slightly, which resulted in larger carbon stock in mature forest. In extreme trade-off scenarios, the maximum achievable total mitigation of 1700\u0026ndash;2500 GtCO₂e cumulatively (21\u0026ndash;31 GtCO₂e/year) was similar to the strong synergies scenarios with 1800\u0026ndash;3100 GtCO₂e (23\u0026ndash;39 GtCO₂e/year) total mitigation (\u003cstrong\u003esupplement Fig. S-F6, bottom\u003c/strong\u003e). However, the mitigation achieved in the AFOLU sector reached only 700\u0026ndash;900 GtCO₂e (9\u0026ndash;11 GtCO₂e/year), compared to 900\u0026ndash;1600 GtCO₂e (11\u0026ndash;20 GtCO₂e/year) achieved in strong synergy scenarios (\u003cstrong\u003esupplement Fig. S-F7, bottom\u003c/strong\u003e). To make up for the lack of land available for mitigation, extreme trade-offs scenarios used more biomass (9500 EJ cumulative or 120 EJ/year on average) than strong synergies scenarios (7500 EJ cumulative or 90 EJ/year on average), mostly from crop farming residues. Additionally, extreme trade-off scenarios required on average 2.6 times higher CO₂ removals via CCS (40\u0026ndash;300 GtCO\u003csub\u003e2\u003c/sub\u003e cumulative removal by 2100) than strong synergy scenarios. These results were not sensitive to the considered variations for wildfire activity.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eBalancing land use for carbon sequestration, food production, biodiversity conservation, and resource demands on approximately 12,700 Mha of global land is a major challenge. We calculated the mitigation potentials of 864 land availability-affecting scenarios combining emission pricing, meat amount in diet, biodiversity habitat protection, food distribution constraints, wood product demand, and wildfire activity. Our results indicate that keeping warming within 1.5\u0026ndash;2\u0026deg;C required cross-sectoral cumulative emission reductions of 250\u0026ndash;720 GtCO₂e until 2050 and 1400\u0026ndash;2300 GtCO₂e until 2100, compared to the baseline scenario which follows SSP2 product demands and land-use change without emission pricing. The largest achieved mitigation scenario reached 1000 GtCO₂e until 2050 and 3100 GtCO₂e until 2100 compared to baseline, resulting in 0.8\u0026deg;C temperature anomaly in 2100.\u003c/p\u003e\n\u003cp\u003eThe Agriculture, Forestry, and Land-use (\u003cstrong\u003eAFOLU\u003c/strong\u003e) sector contributed on average 500 GtCO₂e, but up to 1700 GtCO₂e, of mitigation between 2020 and 2100, mainly through avoiding land-use-change emissions, and expansion and maturing of forests. Additionally, the AFOLU sector provided 100-140 EJ biomass per year to decarbonise the energy and industry sectors. The contribution of the AFOLU sector was predominantly dependent on incentive and land availability. This made emission pricing and low-meat diets freeing pastureland the most powerful levers that created strong synergies of up to 440 GtCO₂e in additional combined mitigation and helped limit global temperature rise to its minimum across all scenarios: 0.8\u0026deg;C above pre-industrial levels by 2100. Alleviating regional self-sufficiency goals could further release agricultural land from less productive regions for climate mitigation. Yet, trade-offs between A/R and biomass production remained, even in scenarios combining global food market with vegan diets. In those cases, the cost-optimisation favoured afforestation over bioenergy production, leading to less fossil fuel substitution in energy and industry sectors than with less available land. Biodiversity conservation was only beneficial for climate in the absence of other measures and lost relative effectiveness under emission pricing. This was because emission pricing and habitat protection both targeted the prevention of deforestation and other land-use-change emissions, suggesting interchangeability, but no synergistic gains between these two components. Importantly, habitat conservation without emission pricing or requirements to mature forests triggered an intensification of short-rotation forestry on unprotected land, causing net deforestation of up to 8 Mkm\u003csup\u003e2\u003c/sup\u003e between 2020 and 2100. These levels of deforestation were not observed in scenarios without additional habitat protection. In all scenarios, increasing timber demand displayed adverse effects on climate and ecosystems. The extensive expansion of short-rotation forestry to satisfy construction timber demands undermined biomass supply needed for decarbonising energy and industry sectors. Additionally, more mature forests were harvested, diminishing biodiversity habitats and forest carbon stocks. Reducing graphic paper demand by 90% had minimal climate impact but most consistently reduced forestry intensity among all scenario components. Our scenarios were not sensitive to slight variations in wildfire prevalence.\u003c/p\u003e\n\u003cp\u003eOur findings align with and expand upon some previous research on land-based mitigation potential, while also revealing key differences. Frank \u003cem\u003eet al.\u003c/em\u003e (2021) previously unified UN sustainable development goals with land-based mitigation potentials using the GLOBIOM land management model. Our study confirmed that sustainability goals (e.g., for biodiversity protection) supported climate mitigation when emission pricing was absent but had little impact once pricing was in place. Likewise, our model reproduced the synergies between emission pricing and agricultural emission cuts (e.g., reducing meat consumption), and like GLOBIOM, used freed pastureland for forestation, which constituted the bulk of global land\u0026rsquo;s mitigation potential. Zhao \u003cem\u003eet al.\u0026nbsp;\u003c/em\u003e(2024) using the GCAM integrated assessment model (\u003cstrong\u003eIAM\u003c/strong\u003e) projected 510-740 GtCO\u003csub\u003e2\u003c/sub\u003e land-based mitigation potential from pastureland conversions for complying with Paris Agreement targets. This is consistent with our findings for the reduced meat consumption scenarios; however, by accounting for a broader range of combination effects, our study produced higher upper-bound estimates. Furthermore, total forest cover needed in 2050 and 2100 to stay below 1.5\u0026deg;C warming (2400\u0026ndash;6100 Mha) aligned closely with Roe \u003cem\u003eet al.\u003c/em\u003e (2019) reviewing multiple IAM estimates.\u0026nbsp;J\u0026auml;ger \u003cem\u003eet al.\u003c/em\u003e (2024)\u0026nbsp;suggest that IAMs tend to overestimate the mitigation potential of forestation by selecting areas prone to increased future fire activity. SuCCESs, however, explicitly accounts for the spatial, temporal, and climatic dynamics of future wildfires, and optimizes afforestation accordingly, which reduced the sensitivity of the mitigation potential to wildfire prevalence. Reducing paper demand showed negligible climate benefits, reinforcing findings of\u0026nbsp;van Ewijk \u003cem\u003eet al.\u003c/em\u003e (2021). Our study did not, however, reproduce the 150 GtCO\u003csub\u003e2\u003c/sub\u003e emission savings from replacing construction cement with wood in 50% of new urban residential buildings observed in\u0026nbsp;Mishra \u003cem\u003eet al.\u003c/em\u003e (2022)\u0026nbsp;with the MAgPIE IAM. This is likely due to their study\u0026rsquo;s model setup producing construction timber more effectively than SuCCESs, while unlike SuCCESs, not taking climate change impacts on forests (e.g., CO\u003csub\u003e2\u003c/sub\u003e fertilization and changes in temperatures, wildfires, and precipitation) into account.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe comparability of this study to previous research highlights both the robustness of the lightweight SuCCESs model and the added value of integrating more dimensions into one framework, particularly in capturing component interactions and land-use trade-offs. The strong linkage between emissions and emission pricing underline the importance of incentives to drive large-scale mitigation across sectors. The large variability in the projected AFOLU sector\u0026rsquo;s contribution to climate change mitigation, however, showcases the varying effectiveness of land-based mitigation strategies depending on 1) the interactions between policies, and 2) land and technology available for fossil fuel-substituting bioenergy. The model\u0026rsquo;s extensive use of bioenergy in electricity generation and industry indicates that global land availability is a crucial factor in meeting Paris Agreement targets. However, realizing this potential depends on the readiness of bioenergy technologies (with and without CCS) to replace fossil fuels across sectors, such as electricity generation, ammonia and cement production, or biofuels for transportation. Trade-offs between afforestation and bioenergy supply were apparent even in scenarios with vast land availability \u0026ndash; with more bioenergy used the less land was available \u0026ndash; emphasizing the need to reduce land-use products demands, such as livestock-derived and pulp and paper products, while enhancing carbon storage on land until bioenergy technologies are developed enough to replace a large amount of fossil fuels. This underscores that land-based mitigation cannot be considered in isolation and highlights the importance of integrating multiple dimensions into a unified framework.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile models are invaluable tools to analyse multi-dimensional problems, it is important to recognize their limitations. SuCCESs may overlook critical regional variations due to its coarse geographical resolution. For example, the model distributes electricity globally, basing wind and solar variability on European seasonal patterns. This does not account for the demand variations across more and less densely populated or developed areas, nor for the climatic conditions and variabilities, including extreme weather events, for wind and solar energy. Therefore, our results might underestimate the renewable capacity needed to consistently fulfil energy demands globally. Furthermore, the model overlooks land-use impacts of installing wind and solar capacity, as large-scale installations may require land clearance or locking. This effect is expected to be minor, but might have implications for surrounding biodiversity or overall mitigation potential. Conversely, the representation of regional self-sufficiency (\u0026ldquo;restricted food distribution) remains rudimentary, and global food trade may offer more significant mitigation potential in practice than presented in this study. In addition, some of the mitigation strategies available in the model are still being developed or require significant investment to scale up. These include CCS technologies, bioenergy-based ammonia and cement production, and transitioning from blast furnaces to direct reduced iron. Meanwhile, SuCCESs does not fully incorporate existing, viable land-use strategies such as forest thinning, densification, diversification, carbon soil enhancement strategies, and agroforestry or silvipasture. The land-based mitigation potential may be altered drastically if undeveloped bioenergy options were to be excluded, and more viable land-use strategies to be included.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study applied GHG emission pricing based on shadow prices derived from model runs constrained by temperature targets. This yielded emission prices lower than most estimates in the literature, likely due to (1) SuCCESs\u0026rsquo; stronger natural carbon sink and (2) SuCCESs\u0026rsquo; low transient climate response to emissions and concentrations, compared to other IAMs (for further details please refer to the discussion in supplements). Both effects lead to higher allowable emissions to reach a given temperature target. However, as the shadow prices from the temperature-constrained runs are fed back as emission penalties to analyse the land-use scenario components\u0026rsquo; effects, this does not directly affect the analysis of land-based mitigation potential presented in this paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFuture research could expand upon this study by integrating more blended land use as mitigation options, such as agroforestry that blends forest with cropland or pastures, or urban greening. Furthermore, integrating soil carbon-enrichment practices on different types of land uses might be interesting mitigation options to explore with IAMs. Yet, theoretical mitigation potentials can only be realised in practice if landowners are informed, willing, and economically resilient to transition to different land-use practices. This makes policy instruments essential for enabling land-use transitions. Since A/R and bioenergy plantations remain the primary contributors of land-based mitigation, future forest disturbance, such as species range expansions due to climate change and the introduction of invasive species by human activity, must be considered when planning mitigation portfolios. \u0026nbsp;Although our study found limited wildfire sensitivity, the escalating wildfire activity in certain biomes poses dangers to regional ecosystems and livelihoods, necessitating proactive preparedness.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e4.1 Conclusions\u003c/h2\u003e\n\u003cp\u003eThis study highlights the critical role that land-based mitigation can play in achieving global climate targets, particularly when supported by emission pricing, low-meat diets, and smart land-use allocation. Our findings reveal that, although the AFOLU sector holds a substantial mitigation potential of up to 1700 GtCO₂e by 2100, this potential is highly sensitive to land demand pressures, policy incentives, and interaction effects across mitigation strategies.\u003c/p\u003e\n\u003cp\u003eEmission pricing proved the most influential factor for driving mitigation, both in the AFOLU sector and beyond. It incentivized forest expansion, preservation of ecosystems, and substitution of fossil fuels with renewables and bioenergy. However, its effectiveness was heavily conditioned by other scenario components. When combined with low-meat diets and unrestricted global food distribution, emission pricing unlocked strong synergies, enabling substantial afforestation and biomass availability. In contrast, when coupled with omnivore diets and strict habitat protection, land pressures intensified, reducing flexibility and diminishing total mitigation potential. Trade-offs were particularly stark in scenarios that combined high demands for land-intensive products with conservation goals.\u003c/p\u003e\n\u003cp\u003eThe interaction between mitigation components proved to be as important as their individual impacts. For instance, habitat conservation added significant value only when other policies were absent, highlighting its role as a substitute, rather than a complement, to emission pricing. Meanwhile, increased timber demand for construction had adverse effects on climate outcomes, not only by increasing pressure on mature forests but also by diverting biomass away from energy and industry decarbonization efforts. Although reducing demand for pulp and paper had minimal direct climate benefits, it consistently reduced forestry pressure and allowed for carbon stock preservation.\u003c/p\u003e\n\u003cp\u003eThese findings underline the importance of considering land-use policies in an integrated manner. Future research should integrate even more nuanced land-use strategies such as agroforestry, soil carbon enhancement, and regional land management to improve realism and inclusivity. Furthermore, real-world implementation will depend not only on biophysical and economic feasibility but also on social and institutional readiness. Policymakers must design flexible, context-specific interventions that consider ecological trade-offs, promote sustainable consumption, and provide incentives for landowners to adopt climate-smart practices. Only then can the full potential of land as a climate solution be realized.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eModel and Data Availability\u003c/h2\u003e\n\u003cp\u003eThe SuCCESs model is openly available on GitHub via \u003cem\u003egithub.com/SuCCESsIAM\u003c/em\u003e. The model version used in this study, including study-specific model modifications, can be found under DOI \u003cem\u003e10.57707/fmi-b2share.542b976b966a4ea5a38ac6ecbc7d97cb\u003c/em\u003e. The scenario files are openly available in \u003cem\u003e.nc\u003c/em\u003e-format via DOI \u003cem\u003e10.57707/fmi-b2share.c8cd2567c6e441058d91e37235d42851\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe corresponding author warmly thanks the co-authors, Prof. Annalea Lohila, Dr. Marje Prank, Dr. Liisa Kulmala, as well as Quentin Bell, Arttu V\u0026auml;is\u0026auml;nen, and Miguel Aldana for their insight, support, and empathy throughout the research process. Their contributions were invaluable in bringing this work to completion.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis research was supported by the Research Council of Finland (grant number 341311).\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors have no conflict of interest to declare.\u003c/p\u003e\n\u003ch2\u003eAuthor contributions\u003c/h2\u003e\n\u003cp\u003eFollowing the CRediT taxonomy, the authors contributed to this paper in the following ways. \u003cstrong\u003eConceptualization:\u003c/strong\u003e Ekholm, Freistetter, Partanen; \u003cstrong\u003eData curation:\u003c/strong\u003e Freistetter, Ekholm; \u003cstrong\u003eFormal analysis:\u003c/strong\u003e Freistetter, Ekholm, Partanen; \u003cstrong\u003eFunding acquisition:\u003c/strong\u003e Ekholm; \u003cstrong\u003eInvestigation:\u003c/strong\u003e Freistetter; \u003cstrong\u003eMethodology:\u003c/strong\u003e Ekholm, Freistetter; \u003cstrong\u003eProject administration:\u003c/strong\u003e Ekholm; \u003cstrong\u003eResources:\u003c/strong\u003e Ekholm; \u003cstrong\u003eSoftware:\u003c/strong\u003e Ekholm, Freistetter; \u003cstrong\u003eSupervision:\u003c/strong\u003e Ekholm, Partanen; \u003cstrong\u003eValidation:\u003c/strong\u003e Ekholm, Freistetter, Partanen; \u003cstrong\u003eVisualization:\u003c/strong\u003e Freistetter; \u003cstrong\u003eWriting original draft:\u003c/strong\u003e Freistetter; \u003cstrong\u003eReview and editing:\u003c/strong\u003e Ekholm, Partanen, Keppo, Freistetter.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSupplements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe online resource \u003cem\u003eSupplements\u003c/em\u003e contains a glossary of abbreviations, additional figures, and further information on the methods.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAleksandrowicz, L., Green, R., Joy, E.J.M., Smith, P., Haines, A., 2016. 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Sci Rep 6, 29987. https://doi.org/10.1038/srep29987\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"climatic-change","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clim","sideBox":"Learn more about [Climatic Change](https://www.springer.com/journal/10584)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/clim/default.aspx","title":"Climatic Change","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Paris Agreement and land sector, combining mitigation strategies, biodiversity and climate change, biomaterials and climate change, land competition for climate action, reducing emissions from agriculture and forests for net zero","lastPublishedDoi":"10.21203/rs.3.rs-6573840/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6573840/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLand-based climate change mitigation can help limit global warming to well below 2°C but requires balancing with food production, nature conservation, and biomaterial supply. Previous studies identified major trade-offs between these but limited the analysis to fairly few dimensions of uncertainty.\u003c/p\u003e\n\u003cp\u003eUsing the SuCCESs model, we quantified global total and land-based mitigation potentials, sensitivities, and interactions between 2020 and 2100 across six key dimensions, resulting in 864 scenarios with different assumptions for drivers of land utilisation: emission pricing, diets, food distribution, conservation, biomaterial demand, and wildfire activity. Mitigation options included actions across land, energy, and material systems, spanning forestation, halting deforestation, adjusting forest rotation and residue management, shifting agricultural and forestry systems, and substituting fossil fuels with bioenergy or BECCS.\u003c/p\u003e\n\u003cp\u003eLimiting warming to 1.5–2°C required the agriculture, forestry, and land-use (AFOLU) sector to contribute 500–1,700 GtCO₂e of mitigation and supply 100–140 EJ of bioenergy annually. Peak AFOLU mitigation occurred under globally vegan diets combined with emission pricing that incentivised afforestation of freed pastureland. Trade-offs between forestation and biomass production remained, with SuCCESs favouring afforestation over bioenergy. Biodiversity conservation and emission pricing targeted overlapping mitigation areas, offering no combined benefit. High timber demand reduced mitigation potential and increased biodiversity loss, while reduced paper demand avoided the most deforestation but offered no climate benefits. Slight variations in wildfire prevalence had little impact.\u003c/p\u003e\n\u003cp\u003eOur findings highlight the need for integrated policies that manage land competition and account for policy interactions to fully unlock land-based mitigation potential.\u003c/p\u003e","manuscriptTitle":"Sensitivity of global land-based mitigation potential to land-use scenarios and interactions across sectors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-10 10:52:33","doi":"10.21203/rs.3.rs-6573840/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revise","date":"2026-02-06T15:57:46+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-08-23T11:31:44+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-21T20:21:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-02T09:32:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"Climatic Change","date":"2025-05-01T14:59:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"climatic-change","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clim","sideBox":"Learn more about [Climatic Change](https://www.springer.com/journal/10584)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/clim/default.aspx","title":"Climatic Change","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"9b10bfb2-3700-4b12-8945-244e1430c8ca","owner":[],"postedDate":"June 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-17T18:02:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-10 10:52:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6573840","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6573840","identity":"rs-6573840","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}