Mapping the option space for carbon sequestration, food and biodiversity in Great Britain

Mapping the option space for carbon sequestration, food and biodiversity in Great Britain | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Mapping the option space for carbon sequestration, food and biodiversity in Great Britain Sarah Gall, Tom Harwood, Michael Obersteiner, Jim Hall This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6091509/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Sep, 2025 Read the published version in Communications Earth & Environment → Version 1 posted You are reading this latest preprint version Abstract Due to the various negative environmental consequences of current land-use, and land’s importance for climate mitigation, environmental conservation and food security, there is a growing and urgent interest in reforming land-use in many countries. Policy objectives for tree planting to sequester carbon and the creation of protected areas to protect biodiversity require land reallocation. This leads to inevitable trade-offs between land-uses, requiring careful place-based policy design. Here, we evaluate the trade-offs between three objectives for rural land: agricultural/forestry production, carbon sequestration and biodiversity, by calculating metrics for these three objectives on a 500mx500m grid covering Great Britain (GB). We use a multi-objective optimisation to identify the land allocations that satisfy different weightings between the three objectives for given total areas of land-use conversation. Our results show that the current land-use in GB is far from optimal for any combination of objectives. We also find that it is possible to significantly improve carbon sequestration and biodiversity, even with a relatively small proportion of the land being converted to other uses, without compromising overall agricultural production, provided conversions are located carefully. Earth and environmental sciences/Climate sciences/Climate change/Climate-change mitigation Earth and environmental sciences/Ecology/Ecosystem services Land-use change Trade-offs Agriculture Biodiversity Climate mitigation Land-use optimisation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Climate change and biodiversity loss are two of the biggest ecological problems of our time 1 . Both challenges are closely linked to how land is used 2 – 4 . Currently, 70% of the global land mass is under human use 4 . This anthropogenic appropriation of land has had dramatic effects on the natural environment, with agriculture being the leading cause of global biodiversity loss 5 , 6 and a major source of greenhouse gas (GHG) emissions 7 . There are now worldwide political ambitions to confront both challenges, ratified in the Paris Agreement and the Kunming-Montreal Global Biodiversity Framework. To meet the objectives defined in those frameworks, an urgent need for more sustainable land-use has been identified 4 , 8 . This includes sustainable land-management practices and sufficient areas of land allocated for biodiversity and climate mitigation 5 , 9 . At the same time, it is crucial to consider the interactions between biodiversity, land-based climate mitigation and food production, and manage trade-offs between these objectives 10 . This combined challenge of reversing biodiversity and avoiding climate change while providing enough food has been described as the ‘triple challenge’ by the WWF 11 . To tackle this challenge, new approaches combining food security, ecosystem services and biodiversity, as well as climate mitigation and adaptation, need to be considered when developing new land-use strategies 12 – 15 . Due to the heterogeneous character of landscapes, there is not one universally valid solution for sustainable land-use decisions, but instead, context-specific, integrated and multi-dimensional transformation strategies must be developed 16 . Like many other countries, the UK has acknowledged the need to transition to more sustainable land-use. The government has set a vision for environmental conservation 17 aiming “to be the first generation to leave the natural environment of England in a better state than it inherited.” 18 and has also set the goal to achieve net zero greenhouse gas emissions by 2050 19 . To achieve this goal, there is significant emphasis upon afforestation 20 (the Climate Change Committee suggests at least 17% woodland cover by 2050 21 ; the Woodland Trust suggests up to 19% woodland cover to achieve carbon neutrality by 2050 22 ) and sustainable land management as well as rethinking the livestock production sector 23 , 24 . Currently, the land-use sector is responsible for about 12% of total UK GHG emissions 21 . For biodiversity, a range of different objectives has been defined by the UK and its devolved nations, including the creation and restoration of significant new areas of wildlife-rich habitats 25 , 26 and "reversing biodiversity loss by 2030", as stated in the Leader’s Pledge for Nature and formalised in the Environment Act 2021. At the same time, there is an increasing emphasis on domestic food production and self-sufficiency 27 , recognising climate and other risks to agricultural production globally and hence the insecurity of food imports, which account for 46% of consumption 28 . This coincides with the UK leaving the EU’s Common Agricultural Policy (CAP), which has a significant effect on regulations and agricultural subsidies and, therefore, farms in the UK. The Agricultural Act 2020 and the Environmental Land Management (ELM) scheme provide a legislative framework to replace the ‘direct payments’ scheme under the EU’s Common Agricultural Policy with a ’payments for public goods’ scheme 29 . It forms the basis for phasing out direct payments over an agricultural transition period of seven years until the end of 2027 30 . Following these changes in agricultural subsidies, big shifts can be expected in the UK’s agricultural sector. In particular, some smaller livestock farms may go out of business 31 , leaving behind numerous areas that could be converted economically and ecologically beneficially to other land-use types. Those shifts entail the need to reorganise land and consider the most efficient and beneficial land-uses. This provides a unique opportunity for policymakers to rethink land-use policy in the UK and to design and implement new environmental land management instruments to achieve environmental objectives like climate mitigation and adaptation 32 . At the same time, the evidence needed to guide new land-use policies is missing 33 . Land-use modelling can help deliver the evidence needed to design new strategies and policies. There is a range of existing models and approaches for modelling land-use. A common approach is the use of calculators based on exogenously specified land-use scenarios, which evaluate the consequences of those scenarios with respect to different metrics, such as the FABLE calculator 34 or the CCC land-use scenarios 21 , 32 . These models give essential insights for land-use target setting but are usually not spatially explicit and do not allow the exploration of spatial trade-offs between different land-use objectives. Additionally, they consider a few predefined scenarios, and, therefore, cannot explore the full range of possibilities of future land-use patterns. Another common approach for analysing land-use change is the use of agent-based models 35 – 37 , which consider different groups of agents and their interactions with each other and with external drivers. While this approach can give an interesting insight into how agents might react to new policy interventions, it does not allow us to identify the land conversions that may be desirable in the first place. Integrated land-use models, which are another common tool in land-use modelling, combine economic and environmental aspects and are often spatially explicit 38 . Examples are GLOBIOM, which is an integrated global model of land-use competition between agriculture, timber production and energy crops 39 , or MagPIE, a global land-use allocation model based on economic conditions and land and water availability 40 . The downside of these integrated models is that they usually have a very coarse spatial resolution and consider aggregated economic regions rather than individual countries 38 , making them less suitable for national policy-making and simulating landscape-scale features. Additionally, many of the large-scale integrated agricultural and land-use models do not incorporate effects on local biodiversity, even though some have published biodiversity scenarios as contributions to the ‘bending the curve’ discussion 41 . Another integrated model that considers many different sectors, including biodiversity, is the NEVO tool (Natural Environment Valuation Online tool), which optimises land allocation based on the market value of the considered ecosystem services 42 . By assigning a monetary value to all considered benefits (including water quality, recreational purposes, biodiversity, etc.), it implicitly assigns weightings that drive the optimisation outcome. Spatial trade-off modelling is more appropriate for evaluating the land-use interdependencies between different objectives from an explicitly spatial perspective. This type of modelling is used in many contexts to identify optimal locations for specific land-uses and spatial trade-offs. A typical application considers ecosystem service trade-offs 43 – 46 , though methodologies vary and are usually applied in a particular context on a regional or local scale. The focus is generally more on the local interactions between the services than evaluating the trade-offs for policymaking on a national scale. Another study by Felix et al. (2022) 47 explored trade-offs between agriculture and ecosystem services under different agricultural management trajectories for Europe. Similar studies have been conducted on a global scale for trade-offs between food, water and carbon 48 ; carbon storage, biodiversity, water use, and food supply 49 ; or biodiversity, carbon, and water 50 , which are very insightful. However, spatially explicit country-level analysis is still needed to provide more insights for national policy measures and actual decision-making. Existing models do not provide a spatial overview of trade-offs for national policymaking that includes the full range of land-use possibilities without limiting the outputs by only exploring a handful of scenarios or influencing the outcomes with predefined weightings. Therefore, we present a spatially explicit approach targeted at decision-makers in GB that shows the entire decision space for the full range of potential priorities while pointing out synergies and trade-offs that need to be considered. In this paper, we assess the full range of land-use trade-offs and synergies along the dimensions of carbon sequestration, biodiversity and (food and timber) production at high geographic resolution. This allows us to identify the most beneficial place-specific land-use choices for achieving those objectives. Our analysis is implemented on a 500mx500m grid covering Great Britain (GB), entailing 814,004 grid cells. For each grid cell and each of the four land-use categories (arable, pasture, plantation forest, and semi-natural habitat), we quantify the potential location-specific benefits for carbon sequestration, production and biodiversity of maintaining a land-use or converting the land to each of the other land-use categories (see Methods). By analysing the full range of possible land-use conversions in the country, we enable decision-makers to explore the entire options space. Furthermore, we recognise that significant land-use changes (as a percentage of the overall national land area) are politically challenging to implement. To this end, we explore a range of land-use change ‘budgets’ (i.e. the total area over which land-use changes can take place), providing stakeholders with the flexibility to explore the implications of different percentages of land-use change. Finally, we identify the most robust land conversions to changing priority weightings. Results Pareto-optimal land-uses For each grid cell, we identified the land-use conversions that maximise and minimise each objective. Aggregating these maximum and minimum performances over the entire country gives the maximum and minimum potential outcomes for unlimited land conversion (see Table 1 ). Henceforth, we normalise this range, allocating a performance of 0 to the minimum and 1 to the maximum for each of the three objectives. Next, we examine all of the possible trade-offs between the three objectives. We discretise the continuous weighting combinations into a step size of five, where 100-0-0 would fully prioritise the first objective while 35-35-30 would consider all three objectives nearly equally, resulting in 231 vectors of 3-way weightings for each cell. For each of these combinations of objectives, the best category of conversion is identified (including the possibility of retaining the current land-use) and the corresponding benefit is recorded for the three objective metrics. This enables us to identify the optimal land-uses for every grid cell in the country for any combination of objectives and to plot the pareto frontier of possible performance (Fig. 1 ). The shape of the pairwise-pareto curves for each combination of objectives (see orange lines in Fig. 1 ) indicate clear trade-offs between production and carbon sequestration as well as between production and biodiversity. The nearly straight line that forms the frontier between production and biodiversity indicates direct trade-offs, meaning the increase in one benefit comes with a proportional decrease in the other benefit 51 . The slightly concave shape of the pareto frontier between production and carbon sequestration implies that while there are clear trade-offs, there are scenarios in the centre of the curve where one benefit can be increased with a relatively small decrease of the other objective 51 . This means that increasing carbon sequestration from 0.8 to 0.9 will create a more substantial reduction in production than seen when increasing carbon sequestration from 0.4 to 0.5, as well as the other way around. These differences show how crucial spatial targeting of land conversions is to minimise trade-offs. Looking at the performance of the scenarios in terms of carbon sequestration and biodiversity in Fig. 1 c, clear synergies can be seen with both objectives increasing simultaneously. The frontier of pareto efficient scenarios for these two objectives is much shorter than for the other combinations. These strong synergies are caused by the significant benefits that natural broadleaved forests offer for carbon sequestration and biodiversity. A comparison of the scenarios with a 100% weighting for carbon sequestration and a 100% weighting for biodiversity shows that they agree in 78.7% of the convertible cells on the same land conversions, which are mostly to natural broadleaved forest (in 78.3% of convertible cells) and to coniferous plantation forests (in 0.4% of the convertible cells). Current land-use and strictly better scenarios Current land-use in GB achieves 0.62 of the potential land productivity in GB if production was the only objective. The current performances in biodiversity and carbon sequestration are 0.34 and 0.36 of what would be possible if land-use was targeted to maximise those objectives alone. To identify the inefficiency of the current land-use in relation to the pareto frontier, the distance between the normalised current performance and the frontier was measured parallel to each of the three axes. The point where the pareto frontier is met when increasing production without changing the other two objectives has a production value of 0.77 and is, therefore, 0.15 higher than the current (see Figs. 1 a & b). This corresponds to a potential increase of production by 23.6% without decreasing biodiversity or carbon sequestration, compared to the current scenario. For the other two objectives, there is no intersection with the pareto frontier when increasing one objective while keeping the other two constant. Instead, when increasing carbon sequestration while keeping production constant, biodiversity will increase as well. When increasing carbon sequestration as much as possible without decreasing production, the intersection point with the frontier is at 0.66, which is 0.28 better than the current and comes with an improvement of biodiversity of 0.12 to a value of 0.46 (see Figs. 1 a & c). This corresponds to a + 128.9% improvement of carbon sequestration, meaning shifting from significant carbon emissions to a relatively low level of carbon sequestration. When increasing biodiversity as much as possible without decreasing production, the intersection point with the frontier is at 0.51, which is 0.17 better than the current biodiversity performance, which corresponds to a 14.2% increase and comes with an improvement of carbon sequestration of 0.16 to 0.52 (see Figs. 1 b & c). The distances between the current performance and the points on the pareto frontier are visualised in Fig. 1 . To arrive at scenarios on the pareto frontier from the current land-use, the conversion rates are between 45.0% and 72.0%, where the conversion rate is defined as the share of grid cells that are converted. The most common conversion is from pasture to semi-natural habitat, which occurs in 17.6% of all cells over all scenarios. In 6.9% of all scenarios, the most common change is from pasture to arable land; in 63.2%, the most common change is from pasture to semi-natural habitat; and in 29.9% of scenarios, we see a conversion of semi-natural grasslands to pasture. 8 out of the 231 weighting combinations perform better or equal to the current state for all three objectives, so in these scenarios, none of the three objectives would decrease. The conversion rate in those strictly better scenarios lies between 52.5 and 60.0%, which is a substantial amount of land conversions. The strictly better scenarios score between 0.45 and 0.63 of what would be possible on the land if carbon sequestration was the only objective (-34.65 million t CO 2 -eq·yr − 1 to 9.27 million t CO 2 eq·yr − 1 ) compared to 0.36 (-55.76 million t CO 2 -eq·yr − 1 ) under the current scenario. For production, the strictly better scenarios score between 0.65 and 0.76 of what would be possible if production was the only objective (16.61 and 19.25 billion £·yr − 1 ), compared to 0.62 (15.8 billion £·yr − 1 ) under the current scenario. For biodiversity, the strictly better scenarios score between 0.34 and 0.45 of the maximum possible biodiversity score (225,000 and 245,000) compared to 0.34 (225,000) under the current scenario. The eight strictly better scenarios contain the same land conversions in 33.2% of the convertible cells (see Figs. 2 a & b). These include conversions to arable land (35.5% of the common conversions) focused in south-east England (East and West Sussex, Kent and Surrey), south-west England (Somerset, Wiltshire and parts of Dorset) and the West Midlands. All these areas are currently used for pasture and could offer medium to high arable yields. Common conversions to pasture account for 37.6% of the common conversions and occur mostly on semi-natural grassland (mostly acid grassland and heather grassland) and are seen in areas with very low potential arable yields and semi-natural habitat in areas that score comparatively lower on biodiversity, such as central Wales (Gwynedd and west Powys) and parts of Scotland (in the Highlands, northern Perthshire, western Aberdeenshire, and northern Angus). Most of these areas are most likely already used for extensive grazing on acid grassland, heather and heather grassland. Therefore, maintaining and expanding livestock grazing in these areas would come with relatively small disadvantages for biodiversity and carbon targets compared to the current pasture in the south of England, which could offer valuable benefits as arable land or natural habitat. It is important to remember that the subset of strictly better scenarios does not allow a decrease in agricultural production and, therefore, also includes conversions to arable land and pasture where sensible. Looking at the clear current prioritisation of agriculture over nature and the ambitious environmental objectives, the strictly better scenarios that do not allow any decrease in overall production might be considered unambitious and slightly more biodiversity and carbon-focused changes might be considered appropriate. Conversions to managed conifer forests make up only 2.32% of common changes and are mostly suggested on small areas of pastures in parts of Wales (eastern Powys, Anglesey and Denbighshire) and small areas of semi-natural grassland in Cumbria and Perthshire in Scotland. The biodiversity and carbon sequestration benefits of natural mixed and broadleaved forests exceed the benefits from coniferous plantations in most areas. Therefore, plantation forests are mostly seen in areas where a future semi-natural habitat type other than broadleaved forest is predicted. In 24.6% of the common conversions, land is converted to semi-natural habitat, mostly natural broadleaved forest. These conversions can be seen on small patches of pasture and less profitable arable land in England, especially in the north (Cumbria, Northumberland, Durham and Lancashire) and south-west (parts of Devon and south-east Cornwall). In Wales, most of the former pasture would be converted to natural broadleaved woodlands and in Scotland, especially in the south (South Dumfries and Galloway and the eastern part of the Scottish borders) and east (Angus, Fife and Lothian). Semi-natural habitats other than natural broadleaved forests are not seen in the common changes of the strictly better scenarios because they mainly benefit biodiversity but do not deliver any synergies for production or significant carbon sequestration. At the same time, all strictly better scenarios agree on keeping the existing land-use in the same 31.2% of the convertible cells (see Fig. 2 c). These areas are mostly arable land, semi-natural habitats and already existing conifer plantations; 44.3%, 39.7% and 14.3% of the areas that remain unchanged in all strictly better scenarios, respectively. Pasture that remains untouched in all strictly better scenarios barely exists (1.65% of cells remain unchanged). Arable land stays the same in most areas of England with comparatively high yields, especially in the East (Norfolk, Suffolk, parts of Cambridgeshire, Lincolnshire, Nottinghamshire and East Yorkshire), the West Midlands and the eastern parts of southwest England (Oxfordshire, West Berkshire, Wiltshire and Hampshire). Existing conifer forests mostly stay untouched all over GB. For semi-natural habitat, areas that remain unchanged include all existing land with fen and bog, all existing broadleaved forests in England, Wales and Scotland, and heather and acid grassland in central Scotland (Perthshire, Stirling and Highland). Conversion budgets - what can be achieved with a limited number of land conversions? This analysis was carried out by limiting land-use change to a budget of change, meaning only a certain share of the land classified as convertible can be converted. For each combination of weights, we identify the ranked list of conversions across the entire country, based on the weighted sum of their performance. We then identify how many of those ranked conversions can be implemented within the total ‘budget’ of permitted conversions. The analysis was done for 11 budgets, starting with a budget of 1% and then stepping in increments of 10%, up to 100% of the rural land available for conversion. The resulting pairwise-pareto frontiers for the budgets in intervals of 10% are shown in Fig. 3 . If, for a weighting, there is no further benefit for the full budget of land conversions because it would be best to keep the existing land-use, then the actual conversion rate can be lower than the budget. Even with a budget of 100%, the highest conversion rates come to 72.0%. Up to a budget of 40%, the full budget is used for all scenarios. From the 50% budget to 100%, the lowest conversion rate is only 45.0%. The shape of the frontiers differs between the three objective pairings as well as through the set of budgets. In Fig. 3 a, the frontiers, showing the relationship between carbon sequestration and production, almost all have a slightly convex shape; only the 10% budget frontier is nearly linear, with a majority of each frontier pointing towards increased carbon sequestration. A reduction of carbon sequestration of more than 4% is only seen in budgets of 25% and above. The number of scenarios where both objectives improve (or do not decrease) is comparatively high even for the lower budgets, with 72 out of 110, 64/95, and 71/101 scenarios for a budget of 1, 5, and 10% of land conversion, respectively. The pairwise-pareto frontiers for production and biodiversity (see Fig. 3 b) have a similar linear shape and gradient for all budgets, so the trade-offs when only considering these two objectives, stay nearly the same independent of the chosen budget and location on the pareto frontiers. This can be explained by the lack of synergies between the two objectives - the best choice for production will not be beneficial for biodiversity and the other way around. Compared to Figs. 3 a & c, the frontiers throughout the range of budgets are much closer together, meaning that an increase in budget is not as beneficial for managing the trade-offs between production and species occurrence as for the other two combinations. Therefore, the number of scenarios where both objectives improve (or stay the same) is limited for all budgets, especially the lower budgets, with only 6 out of 149 scenarios, 5/157, and 4/133 scenarios for a budget of 1%, 5%, and 10% of land conversion. The number of pairwise-strictly better scenarios is much smaller than for carbon sequestration and production. This shows that the trade-offs between production and biodiversity are much bigger, and land conversion choices that benefit both objectives rarely exist. In Fig. 3 c, the curves describing the relation between carbon sequestration and biodiversity are very short and, therefore, show only minor trade-offs and almost no trade-offs up to a budget of 20%. For budgets of 60, 70, 80, 90, and 100%, the curves are much closer to each other, and the additional benefit is, therefore, a lot smaller. For all budgets, all scenarios that are pareto efficient for carbon sequestration and biodiversity are also strictly better than the current situation for these two objectives. The number of strictly better scenarios for all three objectives is very limited, with six out of 153 pareto-efficient scenarios, 5/160 scenarios, 5/167 scenarios, and 5/152 scenarios for the budgets 1%, 5%, 10% and 15% and eight strictly better scenarios for the 50% − 100% budgets. This shows that, especially when change is supposed to be kept low, the careful choice of land conversions is crucial to avoid trade-offs. For all three objective pairings, the curves become increasingly long for the higher budgets, meaning policymakers have a much bigger decision space to choose from. Identifying hotspots for change To further evaluate the homogeneity of the scenarios and pinpoint hotspots where conversion would be most beneficial, the frequency of change of each cell over the range of Pareto-efficient scenarios was analysed. To identify priority areas for land conversion, the scenarios for the 231 priority weightings are combined. Cells converted under a broader range of weighting combinations can be considered beneficial while showing comparatively smaller trade-offs. The more often a conversion occurs throughout the scenarios, the more robust it is to changing priorities. Figure 4 shows the relative frequency of change in each cell and the frequency of change to each land-use category: arable land, pasture, plantation forest, and semi-natural habitats. The most robust conversions to arable land can be seen on pasture areas in Southwest England (Somerset and North Somerset), Southeast England (East & West Sussex and Kent) and the West Midlands, with shares of 37–69% of the scenarios being converted in at least a quarter of the cells in each region. The most robust conversions to pasture occur in Wales (Gwynedd & Powys) and Scotland, where a quarter of the cells are converted in 33–59% of the scenarios. The most robust conversions to plantation forests occur in South Scotland, East Wales (Powys, Denbighshire and Wrexham) and Northwest England (especially Cumbria), where a quarter of the cells are converted in 4–100% of scenarios and the most robust 10% of cells are converted in 71–100% of scenarios. Conversions to semi-natural habitats happen all over England, with a share of 61–87% of scenarios being converted in the 25% most robust cells in each region, with lower values of 15–45% in areas with high arable yields or already existing plantation forests. In the southwest of Wales (Pembrokeshire, Carmarthenshire and Ceredigion) shares of 68–75% of scenarios are converted in the most robust 25% of cells in each shire. Around the Scottish-English border and in the East of Scotland (Aberdeenshire, Angus, and Fife), even higher robustness values with shares of 68–100% of the scenarios being converted to natural habitat in the most robust 25% of cells. These are the areas in Scotland where broadleaved forests had been predicted as natural habitats. Priority grouping analysis To summarise the broad range of scenarios and make them more intelligible, scenarios were classified into four categories. Scenarios that prioritise carbon sequestration (with a weighting > = 50 for carbon sequestration), scenarios that prioritise production (with a weighting > = 50 for production), scenarios that prioritise biodiversity (with a weighting > = 50 for biodiversity) and ’balanced’ scenarios (where all weightings are < = 50). All four groups include 66 scenarios. Scenarios where either objective is weighted 50 are included in both the balanced and the objective-focused group (see Fig. 8). The frequency of conversions in each cell over all scenarios in each group is shown in Fig. 5. In the group with carbon prioritising weightings, shown in Fig. 5a, most changes are to natural habitat in the form of natural unmanaged woodland and peatland restoration. The most common changes (in 90–98% of the scenarios) are conversions for restoration of lowland peat in eastern England (in Lincolnshire and Cambridgeshire), which is a particularly interesting area because it provides some high-quality agricultural land while at the same time in its drained form is a significant source of greenhouse gases. Equally common changes are conversions for the restoration of natural broadleaved woodlands in many parts of Wales (such as Pembrokeshire, Carmarthenshire, Ceredigion, Monmouthshire, Anglesey and North-east Powys), northern Devon, around the English-Scottish border (Dumfries and Galloway, Cumbria and Northumberland) and in East Scotland. Additionally, conversion to plantation forest appears in 68–100% of all scenarios in the 10% most robust cells in each region in similar areas as broadleaved forest in Wales, South Scotland and Northwest England, where the predicted natural habitat is not broadleaved forest. Conversions to arable land are seen in a minority of scenarios (5–40%) all over England, with slightly higher values (14–53% of scenarios were converted in the 25% most robust cells in each shire) in East & West Sussex, and central and North Somerset. Conversions seen in all carbon prioritising scenarios (100%, dark blue shade in Fig. 5a) are limited to 2.17% of the convertible cells, with 2.04% of cells being changed to semi-natural habitat and 0.12% to managed plantation woodlands. Very similar changes can be seen in the biodiversity prioritising group in Fig. 5c. The areas prioritised for conversion to semi-natural habitats in the emissions-focused group also show high conversion rates (76–100% in the 25% most robust cells in each region) in the biodiversity-prioritizing group. Only the conversion rate of the arable land on drained peatland in Cambridgeshire does not increase as much compared to the emissions-focused scenarios and has values of 85–95%. Conversions that all scenarios in the biodiversity group have in common are conversions to semi-natural habitat in 20.7% of convertible cells. In Wales and Scotland, those include most areas where broadleaved forest is projected as rewilded habitat (in Pembrokeshire, Carmarthenshire, and Anglesey in Wales and along the eastern coast of Scotland and the English-Scottish border). In England, areas with lower expected arable yields would be converted in the north along the Scottish border and the east coast (Northumberland, Durham and North Yorkshire) and in small patches in central England (parts of Gloucestershire, Northamptonshire, Bedfordshire, Hertfordshire and Northwest Essex), and the south-west (Cornwall, Devon and parts of Dorset). Changes seen in the production group (Fig. 5b) mainly include conversions to arable land in most areas of England and conversions to and expansion of pasture in Wales, Scotland and northern England. Here, all scenarios agree on changes in 30.8% of the convertible cells. The same 12.7% of convertible cells are converted to arable land, especially in the south-west (Somerset, Dorset and Wiltshire), south-east (Sussex, Kent and Surrey) and in the West Midlands. The same 17.5% of convertible cells are converted to pasture, focusing on Powys and Gwynedd in Wales, parts of Northumberland, Cumbria, Durham and North Yorkshire in North England, and all over Scotland. A small share of 0.66% of convertible cells, mostly small patches in Cumbria and central and East Scotland, is converted to plantation forest in all production-focused scenarios. Looking at the balanced group (Fig. 5d), the share of convertible cells converted to the same land-use in all scenarios amounts to only 0.40% and includes small patches of plantation woodland and semi-natural habitat in Scotland. Changes to arable land in up to 83% of the scenarios occur in the south-east of England (Sussex, Kent and Surrey) and up to 86% in the southern part of Northwest England around Manchester (parts of Cheshire, Calderdale, Bradford and north Derbyshire). The most robust conversions to pasture occur in Scotland (close to the border to England and along the East and North coast), Central and North Wales, Northwest Yorkshire and North England (Cumbria, Northumberland and Lancashire) with shares of up to 70–77% of scenarios showing conversions. Conversions to coniferous plantation forests in more than 53–100% of scenarios in the 10% most robust cells per region are primarily seen in central Wales, North England and in Scotland along the English-Scottish border. The most common type of land conversion in the balanced group is conversions to semi-natural habitat, mostly natural broadleaved forest. These conversions occur in most of Wales (except central Wales, where acid grasslands are the projected natural habitat type), both in South and East Scotland and along both sides of the English-Scottish border. Additionally, many smaller patches of broadleaved forest with slightly lower conversion robustness occur all over England, especially in Southwest England (Devon and Cornwall), the East Midlands (Leicestershire and Northamptonshire) and the southern part of East England (Hertfordshire and Essex). The relatively lower arable yields in these locations seem to be a more decisive aspect than differences in forest growth rates. Changes seen in the scenarios in the ’prioritising carbon’ group Changes seen in the scenarios in the ’prioritising production’ group Changes seen in the scenarios in the ’prioritising biodiversity’ group Changes seen in the scenarios in the ’balanced’ group Figure 5 Frequency of change to each land-use seen per cell in each of the priority weighting groups Frequency each cell was changed in general, to arable land, to pasture, to plantation forest, and to semi-natural habitat in (a) the ’prioritising carbon’, (b) the ’prioritising production’, (c) the ’prioritising biodiversity’ and (d) the ’balanced’ group Discussion The multi-objective optimisation shown in this paper enables stakeholders to explore the full range of possibilities without any bias from predetermined (implicit) weightings. We thereby add to the literature of land-use scenario studies, which present a small number of optimised scenarios. The comparison of all scenarios and determining the most common conversions enables us to identify beneficial land conversions that are robust to changing priorities. Optimising for a range of budgets helps to identify the most effective land conversions that should be prioritised if only a limited amount of land-use change is aimed for or if the success of policies encouraging change is uncertain. We find that the current land-use achieves 0.62 of the production possible on the land if this was the only objective while scoring 0.34 for biodiversity and 0.36 for carbon sequestration if land-use was optimised to only those objectives, respectively. Using the distance between the current scenario and the pareto frontier as a measure of the efficiency of current land-use, we find that the performance for each of the three objectives could be improved significantly. When maximising production without compromising biodiversity or carbon sequestration, we find a potential increase of 23.6%. When maximising carbon sequestration or biodiversity without decreasing any of the other benefits, increases of 128.9% and 14.2%, respectively, are possible. We find that the current land-use is far from the pareto frontier and, therefore, far from optimal, independent of which objective weighting is considered. A set of strictly better scenarios was identified, which all show conversions from pasture to arable land in England and to semi-natural habitats in Wales and Scotland while keeping existing arable land in most of England as well as all existing plantation and semi-natural forests and peatlands. The conversion rate in those strictly better scenarios lies between 52.5 and 60.0% of all of the rural land area in GB, which is relatively high and another indication of how far from optimal current land-use is. However, some scenarios, including the strictly better ones, show conversions from semi-natural grasslands to pasture, which might result from not including rough grazing on semi-natural grasslands in the analysis. If rough grazing was included, the conversion rates might be slightly lower, and current production might score higher than 0.62. The pareto-frontiers show strong synergies between biodiversity and carbon sequestration and near linear trade-offs between production and biodiversity. The convex shape of the frontier between production and carbon sequestration shows that spatial targeting of land conversions is necessary to minimise trade-offs. The most robust land conversion is the planting of natural broadleaved woodland in current grassland locations that show relatively low potential arable yields. This is caused by its significant benefits for carbon sequestration and biodiversity. The strong dominance of natural broadleaved forests compared to plantation forests, which are used for timber production, is mainly caused by the lower value for biodiversity caused by the lack of diversity in plantations and the life cycle of timber products. In this study, it is assumed that only 48.2% of carbon sequestered by timber plantations is stored long-term (based on recreating the calculations of Peng et al. (2023) 52 for the UK), which makes coniferous plantation forests less beneficial for carbon sequestration than natural forests, notwithstanding conifer plantations’ faster growth. Changes in the life cycle and usage of timber could change that and make plantations more valuable for long-term carbon sequestration. A reduction of land for food production, which occurs in many scenarios other than the most production-oriented, including the production of animal products, without a change in diets, can, to some degree, be compensated by closing the gap in efficiency. However, after this gap is closed, any further reduction will lead to an increase in imports, potentially from countries less committed to environmental targets or richer biodiversity and, therefore, outsourcing of emissions and biodiversity degradation. Most land-use studies base their more sustainable scenarios on significant dietary changes 34 , 53 , which is a desirable objective and an essential step towards sustainability but still uncertain. The National Food Strategy aims to tackle the “environmental damage caused by intensive agriculture” 54 , but until then, an approach like the one presented in this paper can help focus land conversion on areas where trade-offs and losses in food production are minimised. Additionally, it needs to be pointed out how the ratio between arable land and pasture, and therefore the share of animal products in the overall production, differs throughout the scenarios. The current scenario has a ratio of 1.2 between land for arable production and pasture, while the pareto-efficient scenarios show ratios between 0.11 and 3.0, with a mean value of 1.11. Based on our results, several land-use interventions for GB can be identified. Overall, we find that biodiversity and carbon sequestration can both be improved significantly through land-use change without any decline in (food) production (see Fig. 1 ). However, if only a small amount of change is planned, it must be targeted at specific locations to avoid trade-offs. The conversion of current pasture in many parts of Wales and England plays a key role in achieving improved biodiversity and carbon sequestration without compromising food production. If increasing arable production would be of interest, land in the southeast of England that is currently used for pasture could offer land sparing high yield production reserves (see Fig. 2 ). The best option for carbon sequestration and biodiversity is the planting of natural broadleaved forests, given the strong synergies between the objectives. If this is done in areas with relatively low potential arable yields, trade-offs are minimised. The methodology chosen for this study aims to explore the trade-offs between carbon sequestration, production, and biodiversity from a biophysical perspective. While the locations and changes we identified are the most beneficial from a biophysical perspective, local needs such as jobs or access to nature and cultural preferences must be considered in subsequent more detailed studies aiming to design concrete policy implementation. As for most models, the results depend on the data input for the benefit calculations, and benefit estimates can differ depending on the chosen indicators. In this study, wherever possible, national datasets were used to increase the accuracy; no such dataset was available for arable yields, so a global dataset had to be used instead. Especially for biodiversity, using different indicators can lead to differing results. For computational reasons, it was impossible to include habitat connectivity in the biodiversity indicator, which might have led to prioritising other areas for habitat creation. Additionally, it is worth noting that in order to maintain independence between cells for the optimisation process, biodiversity was represented on a cellwise basis. As such, the measure does not consider the relative degradation of different broad ecological systems or non-linearities arising from large-scale condition changes that interact with this at the landscape scale. This limitation is not unique to this study. Grouping all semi-natural habitats in one category without allowing conversions between them leads to lower conversion rates to semi-natural habitats in areas with habitat types with comparatively lower benefits. The habitat sub-category within the semi-natural habitat class that a land cell would be converted to was chosen based on a nearest-neighbour algorithm combined with a prioritisation of peatlands on peaty soils. Allowing the conversion of these habitats to better-performing habitat types, especially natural broadleaved forests, would have led to slightly higher conversion rates to semi-natural habitats and lower conversion rates to plantation forests. At the same time, converting a majority of existing semi-natural habitats to broadleaved forests is not the right approach for biodiversity either since a broad range of different semi-natural habitats is needed. We have assumed urban land-use to remain unchanged, and it has not been evaluated for any of its benefits. However, urban expansion and housing development are predicted to take up additional land in the UK 55 , and some are arguing that this should happen, especially on the Green Belts around existing urban areas 56 . That will require the conversion of current rural areas and natural land. These conversions will put additional pressure on the land and are currently not part of the model but can be included with urban expansion scenarios in a future version. Additionally, land suitability could change under future climate change scenarios, especially for agricultural production 57 . These potential future changes have not been included in this static analysis, which uses data on current land suitability. Another aspect that could be included in future work is the differentiation of arable land (extensive or intensive) and land management practices, which might mitigate trade-offs, such as organic farming, agroforestry, etc. This also applies to other adjustments of land management and agricultural practices, including how livestock is managed and if it is primarily grass-fed or if arable land is used for animal feed production. This is also in line with the discussions about land sharing versus land-sparing, which could be explored as well, even though a study by Law et al. (2015) 58 done in Asia suggests that land allocation might have a more significant influence on achieving a set of ecosystem services and food production than sharing vs sparing. Land ownership needs to be considered to assess the feasibility of land-use change in a cell, which can be explored in future work. Conclusion To address the issue of the triple challenge of climate mitigation, biodiversity and food production, a multi-objective analysis has been implemented to explore land-use trade-offs and synergies between (food and forestry) production, carbon sequestration and biodiversity and identify the most effective and robust place-specific land conversions. Investigating a wide range of priority weightings allows stakeholders to explore the full range of possible land-use changes in GB, avoiding the potential biases associated with evaluating only a handful of scenarios or applying an implicit weighting by translating the benefits into one common unit (such as the monetary value). Complementing the presentation of trade-offs in the form of pareto frontiers with a representation of changes seen throughout all the scenarios identifies the most critical locations for land-use conversion. Running the model with different total areas of permitted conversion (conversion ‘budgets’) allowed identification of the locations that should be targeted if only a small amount of rural land-use conversion is politically feasible. The same concept and methodology could be beneficial for exploring land-use trade-offs from all kinds of land conversions, including other land-use types such as energy crops or the development of new housing and suburban areas. With sufficient data, it can be done in any country or scale, from regional to global. Our results show strong synergies between carbon sequestration and biodiversity and clear trade-offs between food production and biodiversity. Nevertheless, it is possible to significantly increase biodiversity and carbon sequestration from today's land-use configuration without adverse effects on food production. However, the number of scenarios where all three objectives increase or stay the same is limited, so it will require very targeted policies and incentives. The best choice for improving biodiversity and carbon sequestration while exploiting synergies between both is by creating natural broadleaved woodlands in the right locations. If this is done in places with low potential arable yields, trade-offs with production can be minimised. Methods A land allocation and optimisation approach for different budgets of land conversion was used to explore the decision space for change and identify trade-offs between the three objectives carbon sequestration, biodiversity, and production in GB. A resolution of 500m x 500m was chosen to allow the representation of landscape characteristics while keeping the computational and data requirements reasonable. This results in our model containing a total of 814,004 convertible grid cells. Each grid cell can be converted into any of four land-use types: arable land, pasture, plantation forest, and semi-natural habitat. These types represent the following distribution of primary land-use types of convertible land: 25.77%, 31.59%, 6.49% and 36.15%, respectively. We assume that conversions between different semi-natural habitats are less relevant when evaluating land-use trade-offs, therefore, they were grouped under one category. The categories are comprised of a combination of sub-classes based on the UKCEH land cover map categories (see Table 2 ). The associated potential benefits of land conversions were pre-calculated based on the characteristics of each cell’s location, resulting in benefit potential maps. Based on these potential benefits, the optimal conversions and scenarios for a range of priority weightings were calculated using a weighted sum multi-objective optimisation (see Fig. 6 ). The resulting pareto-frontier of non-dominated scenario performances and the corresponding spatial scenarios were then used to analyse the trade-offs between the three objectives and identify the location-specific conversions that offer the most synergies. Calculation of Benefits Static maps were created showing the potential benefit of converting each land cell to each of the four land-use classes, arable land, pasture, plantation forest and semi-natural habitat, and for each of the three performance indicators respectively. For each combination of objective weightings, the optimal land conversions were chosen for each cell based on the potential benefit in the location. For each created scenario, the optimised benefit values of all cells were added up to calculate the overall performance of the land configuration. The Land Cover Map 2020 (25m rasterised land parcels, GB) published by UKCEH 59 was aggregated to a resolution of 500m using a nearest-neighbour resampling approach, which allowed to maintain the original shares of land-use classes. This was used as a baseline scenario for potential land conversions. Cells with the following land cover types in the UKCEH land cover map 59 are not available for potential land conversion and were therefore excluded: inland rock, salt- & freshwater, supralittoral rock & sediment, littoral rock & sediment, saltmarsh as well as urban & suburban areas. The remaining cells, which are considered available for potential land conversion, were categorised as either arable land, pasture, plantation forest or semi-natural habitats based on the current land cover. An exclusion of land for conversion based on land classifications such as national parks, battlefields, etc., was not considered to explore the full range of possibilities. This leaves 86.82% of the land area available for potential change. For each cell, the benefit in terms of emissions, production and biodiversity benefits was calculated for each potential conversion using the data shown in Table 3 and captured in 12 benefit maps (4 land-use categories for conversion x 3 benefits). To keep the computational effort feasible, benefits from each land cover type for each cell were assumed to be fixed instead of using a dynamic model, and where appropriate (e.g. for forestry), benefits were aggregated over a lifecycle. Therefore, a benefit for GHG sequestration, production, and biodiversity was calculated for each cell and each possible change of land-use in that cell. Carbon benefits. For calculating the carbon sequestration benefit of potential land conversions, carbon storage values for soil and vegetation were used from Cantarello et al. (2011) 60 . Each of the included land cover types from the UKCEH land cover map was assigned the corresponding value from Cantarello et al. (2011) 60 . The emissions or sequestration from each possible conversion was computed by calculating the difference between the current carbon storage and the potential future storage. For the semi-natural habitats, a more differentiated approach was chosen to consider the differences in carbon storage between different habitat types. To project the most likely habitat type for each cell that would be selected when restoring nature, a nearest neighbour analysis was used (explained in more detail below). For arable land emissions from fertilisers, an average of 1460.67 g N 2 O-N·ha - 1 was assumed based on the emissions reported by Bell et al. (2015) 61 , which converts to 626.63 kg CO 2 eq·ha - 1 . To calculate the emissions from pasture, the average livestock density per meadow and pasture area between 2016 and 2020 was taken from FAOSTAT 62 and converted using the livestock unit coefficients used by Eurostat 63 . Shares for beef and dairy cows from the UK gov livestock statistics 64 were applied. Emission values for beef cows, dairy cows, and sheep for CH 4 and N 2 O 65 were averaged and converted to CO 2 -eq using GWP-100 conversion values 66 . Emissions from livestock per ha on pasture sum up to 7.62 t CO 2 eq·ha − 1 yr − 1 , assuming an average livestock mix on all pasture areas. To include the significant influence of drained and intact peatlands, areas with peaty soils were identified using data on existing peatland and peaty soils for England 67 , 68 , Scotland 69 and Wales 70 . Then, emissions from Gregg et al. (2021) 71 were assigned according to the peatland’s current and potential future state. Drained peatlands used for farming are a significant source of GHG emissions, which decreases when bogs and fens are rewetted. Undrained, near-natural fen presents an important carbon store that also sequesters small amounts of carbon. To calculate the sequestration and emissions from forest plantations and natural broadleaved forests, tree species occurrence maps from the European Forest Institute 72 , 73 were combined with location-specific yield maps from the Forest Research Ecological Site Classification 74 and the carbon sequestration values per yield class from the woodland carbon code 75 . For managed plantation forests, felling times were assumed based on the tree and yield class specific age of maximum mean annual volume increment 76 . The time scale of carbon sequestration and the resulting differences between already established forests and new conversions to forests are included by considering the tree age distribution 77 , the species and yield specific growth rates and for plantation forests the resulting time until felling. To estimate the ratio of long-term carbon storage in harvested wood products, the global wood product model by Peng et al. (2023) 52 was recreated for the UK using the FAOSTAT Forestry Production and Trade data for the UK in 2022 78 and the Forest Research Forestry Statistics 2023 79,80 . Average yearly numbers for carbon sequestration/emissions from plantation woodlands and natural broadleaved forests calculated over the period 2020–2050. Agricultural and forestry production benefits. Production includes food production from arable land and pasture as well as timber production from forest plantations and is calculated as average yearly revenue from 2020–2050. For priority habitats, no production is assumed. For estimating the food production on arable land, harvested and physical areas of production for eight different crop groups (wheat, barley, rapeseed, sugar beet, potatoes, vegetables, other cereals and other pulses) were obtained from the MapSPAM dataset 81 . Since the overall reported areas did not always fully match the arable land area in this model, the area shares of each crop group in each location were calculated. Attainable crop yields for eleven crops (wheat, barley, rapeseed, oats, beans, potatoes, sugar beet, peas, carrots, cabbage and onion) under rainfed conditions from FAO GAEZv4 82 were converted from dry weight to wet weight using the conversion factors from the model documentation 82 and matched with the MapSPAM crop groups. Food prices for each crop group were computed by calculating the mean FAOSTAT producer prices in £·ha - 1 over 2017–2021 83 and weighting them by the percentual share of that crop of the total harvested area 84 . Extreme outliers > 1.5 IQR were removed and replaced with the mean value of the surrounding cells. For food production from pasture, the average production of milk and meat from cattle, as well as meat and skin from sheep per hectare, was calculated based on calculated livestock densities 62 , shares of beef and dairy cows 64 and yields 84 and multiplied with the average producer prices over 2017-2021 83 . To identify areas that are unsuitable for pasture, the FAO pasture suitability 85 was used and all areas with a suitability index under 20 were assumed to bring no returns of production 86 . For timber production, the forest model described above was used. Since the Woodland carbon code data does not include wood biomass production, the carbon sequestration data was converted into tree mass using carbon densities for the considered tree species 87 . Timber harvesting was assumed to happen at the age of maximum mean annual volume increment 76 and the timber price was calculated using the price average over the years 2018–202388. Significantly lower returns from freshly planted woodlands were considered by including the location- and species-specific rate of forest growth and, based on it, the time until the first harvest. Biodiversity & habitat benefits. To project the habitat type that each cell would be converted to were it to be restored to semi-natural habitat, a nearest neighbour analysis was performed. To ensure that peatland areas were included on all peaty soils but only on these, peatlands were excluded in the nearest neighbour analysis. Then, existing peatland and peaty soils for England 67 , 68 , Scotland 69 and Wales 70 , bog and fen from the UKCEH land cover map 59 were added to the layer. Therefore, peatlands were prioritised over the results of the nearest neighbour analysis since peatland restoration is crucial for biodiversity and climate mitigation. Then, cells classified as one of the semi-natural habitats in the UKCEH land cover map were added to the map. Here, we used a combination of species occurrence probabilities from a species distribution model (SDM) 89 and a land-use specific area-equivalent habitat condition score derived from the PREDICTS database 90 , 91 . The species distribution model was done in line with Croft et al. (2017) 92 using 88 species that have also been used in the SDM run as part of the NEVO model 93 . The species occurrence data was downloaded from the NBN atlas database 94 , which has some limitations due to it not being systematically surveyed but is the most comprehensive dataset available. As environmental variables, 19 bio-climatic variables were calculated in R using the biovars function in the R package dismo. Therefore, monthly rainfall, maximum temperature and minimum temperature data from 1999 to 2021 from the HadUK Climate Observation data were downloaded from the Centre for Environmental Data Analysis (CEDA) Archive 95 , 96 with a resolution of 1km x 1km. Land-use based environmental variables were included as a share of each land cover type within a 1km x 1km cell based on the UKCEH land cover map 59 . The SDM was run using the Boosted Regression Tree and the Random Forest models from the JNCC SDM suite ensemble (available on https://github.com/jncc/sdms ). The best-performing model for each species was chosen based on the area under the curve (AUC) measure. After training the model, the response to land cover change was simulated by keeping the climate variables constant but changing the land cover shares in each cell. The resulting occurrence probabilities for all species were summed up to an overall indicator, as suggested by Calabrese et al. (2014) 97 . The resulting indicators were then normalised over all land-use categories to values between 0 and 1. To incorporate the inherent value of natural habitats that might naturally be less species-rich, land-use specific habitat condition scores consistent with those used in the global Biodiversity Habitat Index 98 , 99 were attributed to each land-use class. For each land-use, the proportion of native species 100 was extracted from the PREDICTS database 91 , 101 and rescaled using the species-area relationship 102 , 103 . The resultant condition metric is scaled from fully intact (1) to fully degraded (0)) in units of effective area of habitat, which can be summed over a region. Arable land was classified with a condition value of 0.08, pasture with 0.1, plantation forest with 0.23, grasslands with 0.3 and all other natural habitats, including broadleaved forests (secondary) with values between 0.33 and 0.7 depending on the type of habitat and if it already exists or would be newly established. The higher habitat condition values given to established secondary semi-natural habitats (0.38–0.7 depending on the habitat type) compared to newly created ones (0.33) ensures that the increased ecological value of a well-established natural area and the lower ecological value of a freshly converted habitat are reflected in the model. The species occurrence projection was normalised to values between 0–1, and then both indicators were combined by calculating their geometric mean. Optimisation & analysis The resulting benefit maps were used as the basis for a multi-objective optimisation. Optimisation techniques are a common approach for land allocation & decision-making problems 104 – 106 . Instead of expressing all objectives in one unit, such as monetising them or finding one ideal solution by weighting the objectives, multi-objective optimisation produces a pareto-frontier of nondominated scenarios. Pareto-efficient or non-dominated means it is impossible to improve one of the objectives further without decreasing one of the other objectives as a consequence. As the aggregate performance with respect to the three objectives is a linear combination of the performances at every grid cell, an exhaustive search on a regular grid can be used to calculate the full range of possible objective weightings to cover the full pareto frontier. Therefore, 231 weight combinations with a step size of 5 were used, where 100-0-0 would fully prioritise the first objective while 35-35-30 would consider all three objectives nearly equally. For each constellation of weights, the following steps were implemented to create a scenario maximizing the overall weighted benefit: With \(\:{w}_{O}\) as the weight of each objective O in the scenario, \(\:O=C,P,B\) (carbon, production, biodiversity) where \(\:\sum\:_{O}{w}_{O}=1\) . It was calculated how high each of the land-use types \(\:k\) scores in each cell n in terms of the overall weighted benefit b : $$\:{b}_{n,k}={\stackrel{-}{b}}_{n,k}^{C}\times\:{w}_{C}+{\stackrel{-}{b}}_{n,k}^{P}\times\:{w}_{P}+{\stackrel{-}{b}}_{n,k}^{B}\times\:{w}_{B}$$ ( 1 ) Where \(\:{\stackrel{-}{b}}_{n,k}^{O}\) is the normalised benefit to objective O . For each cell \(\:n\) the LU type \(\:k\) that yields the maximum weighted benefit is chosen: $$\:{k}_{n}={argmax}_{k}{b}_{n,{k}_{n}}$$ ( 2 ) k 1 , k 2 , k 3 , ..., k M together define a scenario which maximizes the overall weighted benefit \(\:{\sum\:}_{n}{b}_{n,{k}_{n}}\) . For each of the created scenarios, the benefits for carbon sequestration, production and biodiversity were calculated, and the pareto efficient scenarios were conserved. The trade-off between the three objectives (normalising benefits to 0–1) was assessed by identifying the overall shape of the three-dimensional frontier and the gradient and curvature of the curves describing the pareto frontiers between each combination of two objectives. The shape of the resulting pareto frontier can be analysed to understand the relationship between the objectives. While a straight line represents a direct trade-off where the increase of one benefit leads to a proportional decrease of another benefit, a concave curve implies that while there are trade-offs, it is possible to increase one benefit without seeing a proportional decrease in the other service 51 . Additionally, the non-dominated scenarios were compared to assess the importance of specific locations when looking at trade-offs from land conversion. Constrained number of land-use change conversions. Realistically, not all of rural land considered in this model could be converted to a new land-use type. Converting land to new land-use comes with many challenges, and planning with high rates of change might be politically infeasible. Therefore, an approach that allows the exploration of the potential benefits of limited amounts of land conversion and the marginal benefits of increased percentages for the area converted is needed. To identify the areas that should be prioritised when converting land and designing land-use policies, scenarios were calculated for a range of ‘budgets’ of change starting from 1 to 100% of the available area. This approach chooses the land conversions first that offer the highest cellwise benefits per area and are, therefore, the most efficient. Additionally, it allows us to explore the decision space for change, demonstrate how much change will be necessary to attain certain objectives, and identify a threshold where additional change adds only minimal additional benefits. Therefore, the same weighted approach as described above was used, but cells are converted successively, from most to least beneficial, until the change budget is met. Where more cells than comprised in the budget were equally beneficial, cells for conversion were selected randomly. Analysing maps. To spatially identify trade-offs and synergies, all 231 scenarios were compared, and the percentage of scenarios where each cell was converted to a specific land-use cover was calculated and compared between budgets and land-use types. If certain changes came up in a broader range of scenarios, and therefore priority weightings, these conversions were considered more robust to changing priorities of decision-makers and had fewer trade-offs. If, in contrast, the suggested conversion for an area differed a lot depending on the chosen priority weightings, the conversions were not considered robust and are likely to show higher trade-offs. While comparing the maps for different priority weightings can help identify conversions that are robust to changing priority weightings, looking at the budgets facilitates identifying the most effective land conversions. To help summarise the findings and make use of the full range of scenarios, they were grouped according to their weighting as either ’carbon prioritising’ (carbon sequestration weighting > = 50), ’production prioritising’ (production weighting > = 50), ’biodiversity prioritising (biodiversity weighting > = 50) or balanced (none of the three objectives is weighted > 50) as shown in Fig. 7 . The scenarios along the boundaries, with a weighting of 50 for one of the objectives, were included in the balanced and objective-focused group. Then, the four priority groups were compared, and the changes most common in the range of scenarios were analysed. Table 1 Minimum and maximum possible performance against each objective, with no limits on the area of land-use changes Carbon sequestration (million t CO 2 -eq·yr − 1 ) Food and forestry production (billion £·yr − 1 ) Biodiversity (species occurrence based indicator, see Methods) Minimum performance across all possible land-use changes (normalized performance of 0) -143.39 0.062 159884 Maximum performance across all possible land se changes (normalized performance of 1) 98.25 25.42 349740 Table 2 Land cover categories used in the model and the corresponding UKCEH land cover subcategories Land-use category UKCEH Land cover types Arable Arable and horticulture Pasture Improved grassland Plantation forest Coniferous woodland Semi-natural habitat Neutral grassland Calcareous grassland Acid grassland Heather grassland Fen marsh & swamp Heather Bog Broadleaved woodland Table 3 Data used to create the benefit maps for the three objectives, carbon sequestration, production, and biodiversity, as well as the four land cover categories: arable land, pasture, plantation forest, and other semi-natural habitats. Carbon sequestration Production Biodiversity Arable land - Carbon storage of & flows between land cover classes 60 - Emissions from fertilizers 61 - Crop distribution (physical & harvested areas) 81 - Agricultural yields 82 - FAO stats producer prices 83 - FAO stats production areas 84 - UKCEH land cover map 59 - Species occurrence data 94 - Climatic data 96 - JNCC Species distribution model ensemble - Habitat condition values based on PREDICTS biodiversity data 101 Pasture - Carbon storage of & flows between land cover classes 60 - Emissions from livestock 65 - FAO stats livestock patterns 62 - Shares of beef & dairy cows 64 - FAO stats producer prices 83 - Livestock yields 84 - FAO pasture suitability indicator 85 Plantation forests - Carbon storage of land cover classes 60 - Woodland species composition 72 , 73 - Location-specific yield classes 74 - Woodland carbon sequestration 75 - Tree age distribution 77 - Wood products data 78 – 80 - Woodland species composition 72 , 73 - Location-specific yield classes 74 - Woodland carbon sequestration 75 - Tree age distribution 77 - Tree carbon densities 87 - - Timber prices 88 Semi-natural habitats - Carbon storage of land cover classes 60 - Carbon fluxes for different states of peatland 71 - peaty soils for England 67 , 68 , Scotland 69 and Wales 70 - (for broadl. woodlands, the same data sources as for plantation forests were used, except 78 – 80 ) - No production is assumed Declarations Ethics declarations The authors declare no conflict of interest. Acknowledgements This work is supported in part by funds from the Engineering and Physical Sciences Research Council (EPSRC) and the Livestock, Environment and People project of the Oxford Martin School funded by the Wellcome Trust. Code Availability The code used to generate the results is freely accessible and available at https://github.com/sarahgall/CarbonFoodNature_TradeOffs . The code used for the species distribution model was obtained from the JNCC SDM ensemble and can be downloaded via https://github.com/jncc/sdms Data Availability All datasets used in this study are cited in the relevant sections. The land cover raster can be downloaded from the UK Centre for Ecology & Hydrology via the EDINA Environment Digimap service ( https://digimap.edina.ac.uk/environment ) or https://www.ceh.ac.uk/data/ukceh-land-cover-maps . Spatial data for drained peatlands and peaty soils were downloaded for England from Natural England via https://www.data.gov.uk/dataset/9d494f48-f0d7-4333-96f0-8b736ac8fb18/peaty-soils-location1 and https://www.data.gov.uk/dataset/b12f420a-d9f1-4966-aa3e-0f6e680e3875/moorland-deep-peat-ap-status1 , for Wales from UKCEH via https://catalogue.ceh.ac.uk/documents/58139ce6-63f9-4444-9f77-fc7b5dcc00d8 and for Scotland from Scottish Natural Heritage via https://opendata.nature.scot/datasets/snh::carbon-and-peatland-2016-map/explore . Tree species maps for European forests were downloaded from the European Forest Institute via https://efi.int/knowledge/maps/treespecies . Tree species and location-specific yield class potential data can be obtained from the Forest Research Ecological site classification tool via http://www.forestdss.org.uk/geoforestdss/esc4.jsp . Yield class specific carbon sequestration data was obtained from the Woodland carbon code Lookup tables in the Carbon calculation spreadsheet, which can be downloaded via https://www.woodlandcarboncode.org.uk/landowners-apply/template-documents . Forestry production and trade data was downloaded from FAOSTAT and found here: https://www.fao.org/faostat/en/#data/FO . The MapSPAM raster data on crop production areas was downloaded via https://mapspam.info/ . Location-specific attainable arable yields can be downloaded from FAO GAEZv4 via https://gaez.fao.org/pages/data-viewer . Statistics on livestock patterns can be downloaded from FAOSTAT via https://www.fao.org/faostat/en/#data/EK . Producer prices for agricultural products can be downloaded from FAOSTAT via https://www.fao.org/faostat/en/#data/PP . Total harvested area and average livestock yields can be downloaded from the Crops and livestock products dataset from FAOSTAT via https://www.fao.org/faostat/en/#data/QCL . The Suitability of global land area for pasture (FGGD) raster data can be downloaded from FAO via https://data.apps.fao.org/catalog/iso/2b357400-891a-11db-b9b2-000d939bc5d8 . Data on carbon densities for different tree species can be downloaded from the Global wood densities database via https://datadryad.org/stash/dataset/doi: 10.5061/dryad.234 . The species occurrence data was downloaded from the NBN atlas database via https://nbnatlas.org/ . Rainfall and temperature data from the HadUK-Grid Gridded Climate Observations data can be downloaded from the Centre for Environmental Data Analysis (CEDA) Archive via https://catalogue.ceda.ac.uk/uuid/bbca3267dc7d4219af484976734c9527/ . Data on the proportion of native species for the habitat condition calculations can be downloaded from the PREDICTS database from the data portal of the Natural History Museum via https://data.nhm.ac.uk/dataset/the-2016-release-of-the-predicts-database-v1-1 . References Rockström, J. et al. 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Identifying trade-offs between ecosystem services, land use, and biodiversity: A plea for combining scenario analysis and optimization on different spatial scales. Curr. Opin. Environ. Sustain. 5 , 458–463 (2013). Verhagen, W., van der Zanden, E. H., Strauch, M., van Teeffelen, A. J. A. & Verburg, P. H. Optimizing the allocation of agri-environment measures to navigate the trade-offs between ecosystem services, biodiversity and agricultural production. Environ. Sci. Policy 84 , 186–196 (2018). Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Published Journal Publication published 29 Sep, 2025 Read the published version in Communications Earth & Environment → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-6091509","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":429992387,"identity":"3139c594-85c2-4080-b33e-202968b099c1","order_by":0,"name":"Sarah Gall","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYJACZhDBx858AFkwgbAWNmY2FFVEaeExIE6LfPvZh58LGGrlgVo+fy6oYZAzOMD88ANjWxpOLQZn0o2lZzAcN2xj5t0mPeMYg7FkA5uxBGNbDm4tDGkM0jwMxxhBWph5GxgS+xkYzBgY2ypwO6z/GfNvoBb7Nmaex59BWtoY2L/h1cJwI40NaEtNIlALgzTEFh6QLXgcduMZmzWPwYHkNmY2M6BfJIwlm3mKJRLO4fa+fH8a822eijrbfvbmx8AQs5EzON6+8cOHsmTcDoPYdRhMASNIAhJNCQQ0AEEdTMsoGAWjYBSMAkwAALMJQaAJr5mJAAAAAElFTkSuQmCC","orcid":"","institution":"University of Oxford","correspondingAuthor":true,"prefix":"","firstName":"Sarah","middleName":"","lastName":"Gall","suffix":""},{"id":429992388,"identity":"22993bcd-4370-425c-91d6-e70875dbf703","order_by":1,"name":"Tom Harwood","email":"","orcid":"","institution":"University of Oxford","correspondingAuthor":false,"prefix":"","firstName":"Tom","middleName":"","lastName":"Harwood","suffix":""},{"id":429992389,"identity":"29ebbbde-cfeb-4946-8a54-09fe45eaae51","order_by":2,"name":"Michael Obersteiner","email":"","orcid":"https://orcid.org/0000-0001-6981-2769","institution":"University of Oxford","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Obersteiner","suffix":""},{"id":429992390,"identity":"36d7a53d-e8c4-40bf-8d68-448234c9572c","order_by":3,"name":"Jim Hall","email":"","orcid":"https://orcid.org/0000-0002-2024-9191","institution":"University of Oxford","correspondingAuthor":false,"prefix":"","firstName":"Jim","middleName":"","lastName":"Hall","suffix":""}],"badges":[],"createdAt":"2025-02-23 17:55:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6091509/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6091509/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s43247-025-02728-w","type":"published","date":"2025-09-29T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81629240,"identity":"be3f00fb-5969-4968-b5a7-e7a243d7442a","added_by":"auto","created_at":"2025-04-29 11:06:03","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":57376,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTwo-dimensional pairwise pareto efficient scenarios and the resulting pareto frontiers \u003c/strong\u003ePareto efficient points with their performance in terms of (a) carbon sequestration and production, (b) biodiversity and production, and (c) biodiversity and carbon sequestration with the normalised performance of the third objective is shown with a colour gradient. The frontier of pairwise-pareto efficient scenarios, when only considering the combination of two of the scenarios, is shown in orange, and the performance of the current state is shown with the red star. The arrows visualise the distance from the current scenario to the pareto frontier along the three axes.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6091509/v1/850ad9f0234a15de5dea6003.jpg"},{"id":81629939,"identity":"1072094d-2e52-44bf-8485-e063e10a6baf","added_by":"auto","created_at":"2025-04-29 11:14:03","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":97289,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCommon land-use conversions in all strictly better scenarios\u003c/strong\u003e The eight scenarios that are strictly better than the current scenario all have the same changes on 33.24% of the convertible land \u0026nbsp;(a) from the original land-use (b) to a new land-use in common, and (c) the same areas that remain unconverted in 31.19% of the convertible cells\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6091509/v1/78b6b4afa6964588d7420379.jpg"},{"id":81629241,"identity":"c3c1ffba-460c-4d65-a425-cdb7bdc20fa2","added_by":"auto","created_at":"2025-04-29 11:06:03","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":59879,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTwo-dimensional pairwise pareto efficient scenarios under a range of conversion budgets\u003c/strong\u003e Two-dimensional pairwise-pareto contours for land conversion budgets from 1.0% up to 100% for trade-offs between normalised benefits for (a) carbon sequestration and production, (b) production and biodiversity, and (c) biodiversity and carbon sequestration. The colour scale indicates increasing conversion budgets, and the red star represents the current scenario\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6091509/v1/87350af047ea9307b878d258.jpg"},{"id":81629243,"identity":"9346c15d-8ec5-4009-807a-68099ac95970","added_by":"auto","created_at":"2025-04-29 11:06:03","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":77423,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFrequency of change to each land-use seen per cell when overlaying all 231 scenarios\u003c/strong\u003e. The maps show the overall changes that happened in each cell and the share of scenarios where each cell is converted to arable land, pasture, plantation forest and semi-natural habitat, respectively\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6091509/v1/e15a802b5bf39165c40614df.jpg"},{"id":81629940,"identity":"9d9e3efb-e9e7-4a35-9751-f6aad90300b1","added_by":"auto","created_at":"2025-04-29 11:14:03","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":235968,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFrequency of change to each land-use seen per cell in each of the priority weighting groups\u003c/strong\u003e Frequency each cell was changed in general, to arable land, to pasture, to plantation forest, and to semi-natural habitat in (a) the ’prioritising carbon’, (b) the ’prioritising production’, (c) the ’prioritising biodiversity’ and (d) the ’balanced’ group\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6091509/v1/08920444d45e2803b78b6a44.jpg"},{"id":81629246,"identity":"c6131004-7cb0-4d38-a6a2-3976ba13341e","added_by":"auto","created_at":"2025-04-29 11:06:03","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":75769,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of the steps in the methodology\u003c/strong\u003e. For the current land-use, the benefits of all possible conversions are calculated against three objectives: GHG emissions, agricultural/forestry production and biodiversity. For every weighted combination of objectives, the optimal land-use conversion is identified.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6091509/v1/b01067a8c76f9e1449912f8a.jpg"},{"id":81629247,"identity":"7a695f9d-c95b-47d3-a3ab-cdd4388dc27d","added_by":"auto","created_at":"2025-04-29 11:06:03","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":40679,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSummarizing scenarios in four categories based on their objective weightings\u003c/strong\u003e Scenarios are grouped based on their priority weighting under \u0026nbsp;’Carbon prioritising’ (carbon sequestration weighted \u0026gt;= 50), ’Production prioritising’ (production weighted \u0026gt;= 50), ’Biodiversity prioritising’ (biodiversity weighted \u0026gt;= 50), and ’Balanced’ (all three objectives weighted \u0026lt;= 50)\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6091509/v1/2e11f663f2a9ac6603565a4b.jpg"},{"id":92475397,"identity":"f0ade6d2-b261-4bbb-8324-ee130128a811","added_by":"auto","created_at":"2025-09-30 07:16:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1852843,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6091509/v1/da61ed08-db82-4850-a6c9-804d24eb637e.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Mapping the option space for carbon sequestration, food and biodiversity in Great Britain","fulltext":[{"header":"Introduction","content":"\u003cp\u003eClimate change and biodiversity loss are two of the biggest ecological problems of our time\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Both challenges are closely linked to how land is used\u003csup\u003e\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Currently, 70% of the global land mass is under human use\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. This anthropogenic appropriation of land has had dramatic effects on the natural environment, with agriculture being the leading cause of global biodiversity loss\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e and a major source of greenhouse gas (GHG) emissions\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. There are now worldwide political ambitions to confront both challenges, ratified in the Paris Agreement and the Kunming-Montreal Global Biodiversity Framework. To meet the objectives defined in those frameworks, an urgent need for more sustainable land-use has been identified\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. This includes sustainable land-management practices and sufficient areas of land allocated for biodiversity and climate mitigation\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. At the same time, it is crucial to consider the interactions between biodiversity, land-based climate mitigation and food production, and manage trade-offs between these objectives\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. This combined challenge of reversing biodiversity and avoiding climate change while providing enough food has been described as the \u0026lsquo;triple challenge\u0026rsquo; by the WWF\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. To tackle this challenge, new approaches combining food security, ecosystem services and biodiversity, as well as climate mitigation and adaptation, need to be considered when developing new land-use strategies\u003csup\u003e\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Due to the heterogeneous character of landscapes, there is not one universally valid solution for sustainable land-use decisions, but instead, context-specific, integrated and multi-dimensional transformation strategies must be developed\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eLike many other countries, the UK has acknowledged the need to transition to more sustainable land-use. The government has set a vision for environmental conservation\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e aiming \u0026ldquo;to be the first generation to leave the natural environment of England in a better state than it inherited.\u0026rdquo;\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e and has also set the goal to achieve net zero greenhouse gas emissions by 2050\u003csup\u003e19\u003c/sup\u003e. To achieve this goal, there is significant emphasis upon afforestation\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e (the Climate Change Committee suggests at least 17% woodland cover by 2050\u003csup\u003e21\u003c/sup\u003e; the Woodland Trust suggests up to 19% woodland cover to achieve carbon neutrality by 2050\u003csup\u003e22\u003c/sup\u003e) and sustainable land management as well as rethinking the livestock production sector\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Currently, the land-use sector is responsible for about 12% of total UK GHG emissions\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. For biodiversity, a range of different objectives has been defined by the UK and its devolved nations, including the creation and restoration of significant new areas of wildlife-rich habitats\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e and \"reversing biodiversity loss by 2030\", as stated in the Leader\u0026rsquo;s Pledge for Nature and formalised in the Environment Act 2021. At the same time, there is an increasing emphasis on domestic food production and self-sufficiency\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, recognising climate and other risks to agricultural production globally and hence the insecurity of food imports, which account for 46% of consumption\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis coincides with the UK leaving the EU\u0026rsquo;s Common Agricultural Policy (CAP), which has a significant effect on regulations and agricultural subsidies and, therefore, farms in the UK. The Agricultural Act 2020 and the Environmental Land Management (ELM) scheme provide a legislative framework to replace the \u0026lsquo;direct payments\u0026rsquo; scheme under the EU\u0026rsquo;s Common Agricultural Policy with a \u0026rsquo;payments for public goods\u0026rsquo; scheme\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. It forms the basis for phasing out direct payments over an agricultural transition period of seven years until the end of 2027\u003csup\u003e30\u003c/sup\u003e. Following these changes in agricultural subsidies, big shifts can be expected in the UK\u0026rsquo;s agricultural sector. In particular, some smaller livestock farms may go out of business\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, leaving behind numerous areas that could be converted economically and ecologically beneficially to other land-use types. Those shifts entail the need to reorganise land and consider the most efficient and beneficial land-uses. This provides a unique opportunity for policymakers to rethink land-use policy in the UK and to design and implement new environmental land management instruments to achieve environmental objectives like climate mitigation and adaptation\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. At the same time, the evidence needed to guide new land-use policies is missing\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eLand-use modelling can help deliver the evidence needed to design new strategies and policies. There is a range of existing models and approaches for modelling land-use. A common approach is the use of calculators based on exogenously specified land-use scenarios, which evaluate the consequences of those scenarios with respect to different metrics, such as the FABLE calculator\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e or the CCC land-use scenarios\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. These models give essential insights for land-use target setting but are usually not spatially explicit and do not allow the exploration of spatial trade-offs between different land-use objectives. Additionally, they consider a few predefined scenarios, and, therefore, cannot explore the full range of possibilities of future land-use patterns.\u003c/p\u003e \u003cp\u003eAnother common approach for analysing land-use change is the use of agent-based models\u003csup\u003e\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, which consider different groups of agents and their interactions with each other and with external drivers. While this approach can give an interesting insight into how agents might react to new policy interventions, it does not allow us to identify the land conversions that may be desirable in the first place.\u003c/p\u003e \u003cp\u003eIntegrated land-use models, which are another common tool in land-use modelling, combine economic and environmental aspects and are often spatially explicit\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Examples are GLOBIOM, which is an integrated global model of land-use competition between agriculture, timber production and energy crops\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, or MagPIE, a global land-use allocation model based on economic conditions and land and water availability\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. The downside of these integrated models is that they usually have a very coarse spatial resolution and consider aggregated economic regions rather than individual countries\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, making them less suitable for national policy-making and simulating landscape-scale features. Additionally, many of the large-scale integrated agricultural and land-use models do not incorporate effects on local biodiversity, even though some have published biodiversity scenarios as contributions to the \u0026lsquo;bending the curve\u0026rsquo; discussion\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Another integrated model that considers many different sectors, including biodiversity, is the NEVO tool (Natural Environment Valuation Online tool), which optimises land allocation based on the market value of the considered ecosystem services\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. By assigning a monetary value to all considered benefits (including water quality, recreational purposes, biodiversity, etc.), it implicitly assigns weightings that drive the optimisation outcome.\u003c/p\u003e \u003cp\u003eSpatial trade-off modelling is more appropriate for evaluating the land-use interdependencies between different objectives from an explicitly spatial perspective. This type of modelling is used in many contexts to identify optimal locations for specific land-uses and spatial trade-offs. A typical application considers ecosystem service trade-offs\u003csup\u003e\u003cspan additionalcitationids=\"CR44 CR45\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e, though methodologies vary and are usually applied in a particular context on a regional or local scale. The focus is generally more on the local interactions between the services than evaluating the trade-offs for policymaking on a national scale.\u003c/p\u003e \u003cp\u003eAnother study by Felix et al. (2022)\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e explored trade-offs between agriculture and ecosystem services under different agricultural management trajectories for Europe. Similar studies have been conducted on a global scale for trade-offs between food, water and carbon\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e; carbon storage, biodiversity, water use, and food supply\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e; or biodiversity, carbon, and water\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, which are very insightful. However, spatially explicit country-level analysis is still needed to provide more insights for national policy measures and actual decision-making.\u003c/p\u003e \u003cp\u003eExisting models do not provide a spatial overview of trade-offs for national policymaking that includes the full range of land-use possibilities without limiting the outputs by only exploring a handful of scenarios or influencing the outcomes with predefined weightings. Therefore, we present a spatially explicit approach targeted at decision-makers in GB that shows the entire decision space for the full range of potential priorities while pointing out synergies and trade-offs that need to be considered.\u003c/p\u003e \u003cp\u003eIn this paper, we assess the full range of land-use trade-offs and synergies along the dimensions of carbon sequestration, biodiversity and (food and timber) production at high geographic resolution. This allows us to identify the most beneficial place-specific land-use choices for achieving those objectives. Our analysis is implemented on a 500mx500m grid covering Great Britain (GB), entailing 814,004 grid cells. For each grid cell and each of the four land-use categories (arable, pasture, plantation forest, and semi-natural habitat), we quantify the potential location-specific benefits for carbon sequestration, production and biodiversity of maintaining a land-use or converting the land to each of the other land-use categories (see Methods). By analysing the full range of possible land-use conversions in the country, we enable decision-makers to explore the entire options space. Furthermore, we recognise that significant land-use changes (as a percentage of the overall national land area) are politically challenging to implement. To this end, we explore a range of land-use change \u0026lsquo;budgets\u0026rsquo; (i.e. the total area over which land-use changes can take place), providing stakeholders with the flexibility to explore the implications of different percentages of land-use change. Finally, we identify the most robust land conversions to changing priority weightings.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003ePareto-optimal land-uses\u003c/p\u003e \u003cp\u003eFor each grid cell, we identified the land-use conversions that maximise and minimise each objective. Aggregating these maximum and minimum performances over the entire country gives the maximum and minimum potential outcomes for unlimited land conversion (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Henceforth, we normalise this range, allocating a performance of 0 to the minimum and 1 to the maximum for each of the three objectives.\u003c/p\u003e \u003cp\u003eNext, we examine all of the possible trade-offs between the three objectives. We discretise the continuous weighting combinations into a step size of five, where 100-0-0 would fully prioritise the first objective while 35-35-30 would consider all three objectives nearly equally, resulting in 231 vectors of 3-way weightings for each cell. For each of these combinations of objectives, the best category of conversion is identified (including the possibility of retaining the current land-use) and the corresponding benefit is recorded for the three objective metrics. This enables us to identify the optimal land-uses for every grid cell in the country for any combination of objectives and to plot the pareto frontier of possible performance (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe shape of the pairwise-pareto curves for each combination of objectives (see orange lines in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) indicate clear trade-offs between production and carbon sequestration as well as between production and biodiversity. The nearly straight line that forms the frontier between production and biodiversity indicates direct trade-offs, meaning the increase in one benefit comes with a proportional decrease in the other benefit\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. The slightly concave shape of the pareto frontier between production and carbon sequestration implies that while there are clear trade-offs, there are scenarios in the centre of the curve where one benefit can be increased with a relatively small decrease of the other objective\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. This means that increasing carbon sequestration from 0.8 to 0.9 will create a more substantial reduction in production than seen when increasing carbon sequestration from 0.4 to 0.5, as well as the other way around. These differences show how crucial spatial targeting of land conversions is to minimise trade-offs. Looking at the performance of the scenarios in terms of carbon sequestration and biodiversity in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec, clear synergies can be seen with both objectives increasing simultaneously. The frontier of pareto efficient scenarios for these two objectives is much shorter than for the other combinations. These strong synergies are caused by the significant benefits that natural broadleaved forests offer for carbon sequestration and biodiversity. A comparison of the scenarios with a 100% weighting for carbon sequestration and a 100% weighting for biodiversity shows that they agree in 78.7% of the convertible cells on the same land conversions, which are mostly to natural broadleaved forest (in 78.3% of convertible cells) and to coniferous plantation forests (in 0.4% of the convertible cells).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCurrent land-use and strictly better scenarios\u003c/p\u003e \u003cp\u003eCurrent land-use in GB achieves 0.62 of the potential land productivity in GB if production was the only objective. The current performances in biodiversity and carbon sequestration are 0.34 and 0.36 of what would be possible if land-use was targeted to maximise those objectives alone.\u003c/p\u003e \u003cp\u003eTo identify the inefficiency of the current land-use in relation to the pareto frontier, the distance between the normalised current performance and the frontier was measured parallel to each of the three axes. The point where the pareto frontier is met when increasing production without changing the other two objectives has a production value of 0.77 and is, therefore, 0.15 higher than the current (see Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea \u0026amp; b). This corresponds to a potential increase of production by 23.6% without decreasing biodiversity or carbon sequestration, compared to the current scenario. For the other two objectives, there is no intersection with the pareto frontier when increasing one objective while keeping the other two constant. Instead, when increasing carbon sequestration while keeping production constant, biodiversity will increase as well. When increasing carbon sequestration as much as possible without decreasing production, the intersection point with the frontier is at 0.66, which is 0.28 better than the current and comes with an improvement of biodiversity of 0.12 to a value of 0.46 (see Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea \u0026amp; c). This corresponds to a\u0026thinsp;+\u0026thinsp;128.9% improvement of carbon sequestration, meaning shifting from significant carbon emissions to a relatively low level of carbon sequestration. When increasing biodiversity as much as possible without decreasing production, the intersection point with the frontier is at 0.51, which is 0.17 better than the current biodiversity performance, which corresponds to a 14.2% increase and comes with an improvement of carbon sequestration of 0.16 to 0.52 (see Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb \u0026amp; c). The distances between the current performance and the points on the pareto frontier are visualised in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eTo arrive at scenarios on the pareto frontier from the current land-use, the conversion rates are between 45.0% and 72.0%, where the conversion rate is defined as the share of grid cells that are converted. The most common conversion is from pasture to semi-natural habitat, which occurs in 17.6% of all cells over all scenarios. In 6.9% of all scenarios, the most common change is from pasture to arable land; in 63.2%, the most common change is from pasture to semi-natural habitat; and in 29.9% of scenarios, we see a conversion of semi-natural grasslands to pasture.\u003c/p\u003e \u003cp\u003e8 out of the 231 weighting combinations perform better or equal to the current state for all three objectives, so in these scenarios, none of the three objectives would decrease. The conversion rate in those strictly better scenarios lies between 52.5 and 60.0%, which is a substantial amount of land conversions. The strictly better scenarios score between 0.45 and 0.63 of what would be possible on the land if carbon sequestration was the only objective (-34.65\u0026nbsp;million t CO\u003csub\u003e2\u003c/sub\u003e-eq\u0026middot;yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e to 9.27\u0026nbsp;million t CO\u003csub\u003e2\u003c/sub\u003e eq\u0026middot;yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) compared to 0.36 (-55.76\u0026nbsp;million t CO\u003csub\u003e2\u003c/sub\u003e-eq\u0026middot;yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) under the current scenario. For production, the strictly better scenarios score between 0.65 and 0.76 of what would be possible if production was the only objective (16.61 and 19.25\u0026nbsp;billion \u0026pound;\u0026middot;yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), compared to 0.62 (15.8\u0026nbsp;billion \u0026pound;\u0026middot;yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) under the current scenario. For biodiversity, the strictly better scenarios score between 0.34 and 0.45 of the maximum possible biodiversity score (225,000 and 245,000) compared to 0.34 (225,000) under the current scenario.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe eight strictly better scenarios contain the same land conversions in 33.2% of the convertible cells (see Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea \u0026amp; b). These include conversions to arable land (35.5% of the common conversions) focused in south-east England (East and West Sussex, Kent and Surrey), south-west England (Somerset, Wiltshire and parts of Dorset) and the West Midlands. All these areas are currently used for pasture and could offer medium to high arable yields. Common conversions to pasture account for 37.6% of the common conversions and occur mostly on semi-natural grassland (mostly acid grassland and heather grassland) and are seen in areas with very low potential arable yields and semi-natural habitat in areas that score comparatively lower on biodiversity, such as central Wales (Gwynedd and west Powys) and parts of Scotland (in the Highlands, northern Perthshire, western Aberdeenshire, and northern Angus). Most of these areas are most likely already used for extensive grazing on acid grassland, heather and heather grassland. Therefore, maintaining and expanding livestock grazing in these areas would come with relatively small disadvantages for biodiversity and carbon targets compared to the current pasture in the south of England, which could offer valuable benefits as arable land or natural habitat. It is important to remember that the subset of strictly better scenarios does not allow a decrease in agricultural production and, therefore, also includes conversions to arable land and pasture where sensible. Looking at the clear current prioritisation of agriculture over nature and the ambitious environmental objectives, the strictly better scenarios that do not allow any decrease in overall production might be considered unambitious and slightly more biodiversity and carbon-focused changes might be considered appropriate.\u003c/p\u003e \u003cp\u003eConversions to managed conifer forests make up only 2.32% of common changes and are mostly suggested on small areas of pastures in parts of Wales (eastern Powys, Anglesey and Denbighshire) and small areas of semi-natural grassland in Cumbria and Perthshire in Scotland. The biodiversity and carbon sequestration benefits of natural mixed and broadleaved forests exceed the benefits from coniferous plantations in most areas. Therefore, plantation forests are mostly seen in areas where a future semi-natural habitat type other than broadleaved forest is predicted. In 24.6% of the common conversions, land is converted to semi-natural habitat, mostly natural broadleaved forest. These conversions can be seen on small patches of pasture and less profitable arable land in England, especially in the north (Cumbria, Northumberland, Durham and Lancashire) and south-west (parts of Devon and south-east Cornwall). In Wales, most of the former pasture would be converted to natural broadleaved woodlands and in Scotland, especially in the south (South Dumfries and Galloway and the eastern part of the Scottish borders) and east (Angus, Fife and Lothian). Semi-natural habitats other than natural broadleaved forests are not seen in the common changes of the strictly better scenarios because they mainly benefit biodiversity but do not deliver any synergies for production or significant carbon sequestration.\u003c/p\u003e \u003cp\u003eAt the same time, all strictly better scenarios agree on keeping the existing land-use in the same 31.2% of the convertible cells (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). These areas are mostly arable land, semi-natural habitats and already existing conifer plantations; 44.3%, 39.7% and 14.3% of the areas that remain unchanged in all strictly better scenarios, respectively. Pasture that remains untouched in all strictly better scenarios barely exists (1.65% of cells remain unchanged). Arable land stays the same in most areas of England with comparatively high yields, especially in the East (Norfolk, Suffolk, parts of Cambridgeshire, Lincolnshire, Nottinghamshire and East Yorkshire), the West Midlands and the eastern parts of southwest England (Oxfordshire, West Berkshire, Wiltshire and Hampshire). Existing conifer forests mostly stay untouched all over GB. For semi-natural habitat, areas that remain unchanged include all existing land with fen and bog, all existing broadleaved forests in England, Wales and Scotland, and heather and acid grassland in central Scotland (Perthshire, Stirling and Highland).\u003c/p\u003e \u003cp\u003eConversion budgets - what can be achieved with a limited number of land conversions?\u003c/p\u003e \u003cp\u003eThis analysis was carried out by limiting land-use change to a budget of change, meaning only a certain share of the land classified as convertible can be converted. For each combination of weights, we identify the ranked list of conversions across the entire country, based on the weighted sum of their performance. We then identify how many of those ranked conversions can be implemented within the total \u0026lsquo;budget\u0026rsquo; of permitted conversions. The analysis was done for 11 budgets, starting with a budget of 1% and then stepping in increments of 10%, up to 100% of the rural land available for conversion. The resulting pairwise-pareto frontiers for the budgets in intervals of 10% are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIf, for a weighting, there is no further benefit for the full budget of land conversions because it would be best to keep the existing land-use, then the actual conversion rate can be lower than the budget. Even with a budget of 100%, the highest conversion rates come to 72.0%. Up to a budget of 40%, the full budget is used for all scenarios. From the 50% budget to 100%, the lowest conversion rate is only 45.0%.\u003c/p\u003e \u003cp\u003eThe shape of the frontiers differs between the three objective pairings as well as through the set of budgets. In Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, the frontiers, showing the relationship between carbon sequestration and production, almost all have a slightly convex shape; only the 10% budget frontier is nearly linear, with a majority of each frontier pointing towards increased carbon sequestration. A reduction of carbon sequestration of more than 4% is only seen in budgets of 25% and above. The number of scenarios where both objectives improve (or do not decrease) is comparatively high even for the lower budgets, with 72 out of 110, 64/95, and 71/101 scenarios for a budget of 1, 5, and 10% of land conversion, respectively.\u003c/p\u003e \u003cp\u003eThe pairwise-pareto frontiers for production and biodiversity (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb) have a similar linear shape and gradient for all budgets, so the trade-offs when only considering these two objectives, stay nearly the same independent of the chosen budget and location on the pareto frontiers. This can be explained by the lack of synergies between the two objectives - the best choice for production will not be beneficial for biodiversity and the other way around. Compared to Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea \u0026amp; c, the frontiers throughout the range of budgets are much closer together, meaning that an increase in budget is not as beneficial for managing the trade-offs between production and species occurrence as for the other two combinations. Therefore, the number of scenarios where both objectives improve (or stay the same) is limited for all budgets, especially the lower budgets, with only 6 out of 149 scenarios, 5/157, and 4/133 scenarios for a budget of 1%, 5%, and 10% of land conversion. The number of pairwise-strictly better scenarios is much smaller than for carbon sequestration and production. This shows that the trade-offs between production and biodiversity are much bigger, and land conversion choices that benefit both objectives rarely exist.\u003c/p\u003e \u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec, the curves describing the relation between carbon sequestration and biodiversity are very short and, therefore, show only minor trade-offs and almost no trade-offs up to a budget of 20%. For budgets of 60, 70, 80, 90, and 100%, the curves are much closer to each other, and the additional benefit is, therefore, a lot smaller. For all budgets, all scenarios that are pareto efficient for carbon sequestration and biodiversity are also strictly better than the current situation for these two objectives.\u003c/p\u003e \u003cp\u003eThe number of strictly better scenarios for all three objectives is very limited, with six out of 153 pareto-efficient scenarios, 5/160 scenarios, 5/167 scenarios, and 5/152 scenarios for the budgets 1%, 5%, 10% and 15% and eight strictly better scenarios for the 50% \u0026minus;\u0026thinsp;100% budgets. This shows that, especially when change is supposed to be kept low, the careful choice of land conversions is crucial to avoid trade-offs. For all three objective pairings, the curves become increasingly long for the higher budgets, meaning policymakers have a much bigger decision space to choose from.\u003c/p\u003e \u003cp\u003eIdentifying hotspots for change\u003c/p\u003e \u003cp\u003eTo further evaluate the homogeneity of the scenarios and pinpoint hotspots where conversion would be most beneficial, the frequency of change of each cell over the range of Pareto-efficient scenarios was analysed. To identify priority areas for land conversion, the scenarios for the 231 priority weightings are combined. Cells converted under a broader range of weighting combinations can be considered beneficial while showing comparatively smaller trade-offs. The more often a conversion occurs throughout the scenarios, the more robust it is to changing priorities. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the relative frequency of change in each cell and the frequency of change to each land-use category: arable land, pasture, plantation forest, and semi-natural habitats.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe most robust conversions to arable land can be seen on pasture areas in Southwest England (Somerset and North Somerset), Southeast England (East \u0026amp; West Sussex and Kent) and the West Midlands, with shares of 37\u0026ndash;69% of the scenarios being converted in at least a quarter of the cells in each region. The most robust conversions to pasture occur in Wales (Gwynedd \u0026amp; Powys) and Scotland, where a quarter of the cells are converted in 33\u0026ndash;59% of the scenarios. The most robust conversions to plantation forests occur in South Scotland, East Wales (Powys, Denbighshire and Wrexham) and Northwest England (especially Cumbria), where a quarter of the cells are converted in 4\u0026ndash;100% of scenarios and the most robust 10% of cells are converted in 71\u0026ndash;100% of scenarios.\u003c/p\u003e \u003cp\u003eConversions to semi-natural habitats happen all over England, with a share of 61\u0026ndash;87% of scenarios being converted in the 25% most robust cells in each region, with lower values of 15\u0026ndash;45% in areas with high arable yields or already existing plantation forests. In the southwest of Wales (Pembrokeshire, Carmarthenshire and Ceredigion) shares of 68\u0026ndash;75% of scenarios are converted in the most robust 25% of cells in each shire. Around the Scottish-English border and in the East of Scotland (Aberdeenshire, Angus, and Fife), even higher robustness values with shares of 68\u0026ndash;100% of the scenarios being converted to natural habitat in the most robust 25% of cells. These are the areas in Scotland where broadleaved forests had been predicted as natural habitats.\u003c/p\u003e \u003cp\u003ePriority grouping analysis\u003c/p\u003e \u003cp\u003eTo summarise the broad range of scenarios and make them more intelligible, scenarios were classified into four categories. Scenarios that prioritise carbon sequestration (with a weighting\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;50 for carbon sequestration), scenarios that prioritise production (with a weighting\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;50 for production), scenarios that prioritise biodiversity (with a weighting\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;50 for biodiversity) and \u0026rsquo;balanced\u0026rsquo; scenarios (where all weightings are \u0026lt;\u0026thinsp;=\u0026thinsp;50). All four groups include 66 scenarios. Scenarios where either objective is weighted 50 are included in both the balanced and the objective-focused group (see Fig.\u0026nbsp;8). The frequency of conversions in each cell over all scenarios in each group is shown in Fig.\u0026nbsp;5. In the group with carbon prioritising weightings, shown in Fig.\u0026nbsp;5a, most changes are to natural habitat in the form of natural unmanaged woodland and peatland restoration. The most common changes (in 90\u0026ndash;98% of the scenarios) are conversions for restoration of lowland peat in eastern England (in Lincolnshire and Cambridgeshire), which is a particularly interesting area because it provides some high-quality agricultural land while at the same time in its drained form is a significant source of greenhouse gases. Equally common changes are conversions for the restoration of natural broadleaved woodlands in many parts of Wales (such as Pembrokeshire, Carmarthenshire, Ceredigion, Monmouthshire, Anglesey and North-east Powys), northern Devon, around the English-Scottish border (Dumfries and Galloway, Cumbria and Northumberland) and in East Scotland. Additionally, conversion to plantation forest appears in 68\u0026ndash;100% of all scenarios in the 10% most robust cells in each region in similar areas as broadleaved forest in Wales, South Scotland and Northwest England, where the predicted natural habitat is not broadleaved forest. Conversions to arable land are seen in a minority of scenarios (5\u0026ndash;40%) all over England, with slightly higher values (14\u0026ndash;53% of scenarios were converted in the 25% most robust cells in each shire) in East \u0026amp; West Sussex, and central and North Somerset. Conversions seen in all carbon prioritising scenarios (100%, dark blue shade in Fig.\u0026nbsp;5a) are limited to 2.17% of the convertible cells, with 2.04% of cells being changed to semi-natural habitat and 0.12% to managed plantation woodlands.\u003c/p\u003e \u003cp\u003eVery similar changes can be seen in the biodiversity prioritising group in Fig.\u0026nbsp;5c. The areas prioritised for conversion to semi-natural habitats in the emissions-focused group also show high conversion rates (76\u0026ndash;100% in the 25% most robust cells in each region) in the biodiversity-prioritizing group. Only the conversion rate of the arable land on drained peatland in Cambridgeshire does not increase as much compared to the emissions-focused scenarios and has values of 85\u0026ndash;95%. Conversions that all scenarios in the biodiversity group have in common are conversions to semi-natural habitat in 20.7% of convertible cells. In Wales and Scotland, those include most areas where broadleaved forest is projected as rewilded habitat (in Pembrokeshire, Carmarthenshire, and Anglesey in Wales and along the eastern coast of Scotland and the English-Scottish border). In England, areas with lower expected arable yields would be converted in the north along the Scottish border and the east coast (Northumberland, Durham and North Yorkshire) and in small patches in central England (parts of Gloucestershire, Northamptonshire, Bedfordshire, Hertfordshire and Northwest Essex), and the south-west (Cornwall, Devon and parts of Dorset).\u003c/p\u003e \u003cp\u003eChanges seen in the production group (Fig.\u0026nbsp;5b) mainly include conversions to arable land in most areas of England and conversions to and expansion of pasture in Wales, Scotland and northern England. Here, all scenarios agree on changes in 30.8% of the convertible cells. The same 12.7% of convertible cells are converted to arable land, especially in the south-west (Somerset, Dorset and Wiltshire), south-east (Sussex, Kent and Surrey) and in the West Midlands. The same 17.5% of convertible cells are converted to pasture, focusing on Powys and Gwynedd in Wales, parts of Northumberland, Cumbria, Durham and North Yorkshire in North England, and all over Scotland. A small share of 0.66% of convertible cells, mostly small patches in Cumbria and central and East Scotland, is converted to plantation forest in all production-focused scenarios.\u003c/p\u003e \u003cp\u003eLooking at the balanced group (Fig.\u0026nbsp;5d), the share of convertible cells converted to the same land-use in all scenarios amounts to only 0.40% and includes small patches of plantation woodland and semi-natural habitat in Scotland. Changes to arable land in up to 83% of the scenarios occur in the south-east of England (Sussex, Kent and Surrey) and up to 86% in the southern part of Northwest England around Manchester (parts of Cheshire, Calderdale, Bradford and north Derbyshire). The most robust conversions to pasture occur in Scotland (close to the border to England and along the East and North coast), Central and North Wales, Northwest Yorkshire and North England (Cumbria, Northumberland and Lancashire) with shares of up to 70\u0026ndash;77% of scenarios showing conversions. Conversions to coniferous plantation forests in more than 53\u0026ndash;100% of scenarios in the 10% most robust cells per region are primarily seen in central Wales, North England and in Scotland along the English-Scottish border. The most common type of land conversion in the balanced group is conversions to semi-natural habitat, mostly natural broadleaved forest. These conversions occur in most of Wales (except central Wales, where acid grasslands are the projected natural habitat type), both in South and East Scotland and along both sides of the English-Scottish border. Additionally, many smaller patches of broadleaved forest with slightly lower conversion robustness occur all over England, especially in Southwest England (Devon and Cornwall), the East Midlands (Leicestershire and Northamptonshire) and the southern part of East England (Hertfordshire and Essex). The relatively lower arable yields in these locations seem to be a more decisive aspect than differences in forest growth rates.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eChanges seen in the scenarios in the \u0026rsquo;prioritising carbon\u0026rsquo; group\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eChanges seen in the scenarios in the \u0026rsquo;prioritising production\u0026rsquo; group\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eChanges seen in the scenarios in the \u0026rsquo;prioritising biodiversity\u0026rsquo; group\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eChanges seen in the scenarios in the \u0026rsquo;balanced\u0026rsquo; group\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eFigure 5\u003c/em\u003e \u003cb\u003eFrequency of change to each land-use seen per cell in each of the priority weighting groups\u003c/b\u003e \u003cem\u003eFrequency each cell was changed in general, to arable land, to pasture, to plantation forest, and to semi-natural habitat in (a) the \u0026rsquo;prioritising carbon\u0026rsquo;, (b) the \u0026rsquo;prioritising production\u0026rsquo;, (c) the \u0026rsquo;prioritising biodiversity\u0026rsquo; and (d) the \u0026rsquo;balanced\u0026rsquo; group\u003c/em\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe multi-objective optimisation shown in this paper enables stakeholders to explore the full range of possibilities without any bias from predetermined (implicit) weightings. We thereby add to the literature of land-use scenario studies, which present a small number of optimised scenarios. The comparison of all scenarios and determining the most common conversions enables us to identify beneficial land conversions that are robust to changing priorities. Optimising for a range of budgets helps to identify the most effective land conversions that should be prioritised if only a limited amount of land-use change is aimed for or if the success of policies encouraging change is uncertain. We find that the current land-use achieves 0.62 of the production possible on the land if this was the only objective while scoring 0.34 for biodiversity and 0.36 for carbon sequestration if land-use was optimised to only those objectives, respectively. Using the distance between the current scenario and the pareto frontier as a measure of the efficiency of current land-use, we find that the performance for each of the three objectives could be improved significantly. When maximising production without compromising biodiversity or carbon sequestration, we find a potential increase of 23.6%. When maximising carbon sequestration or biodiversity without decreasing any of the other benefits, increases of 128.9% and 14.2%, respectively, are possible. We find that the current land-use is far from the pareto frontier and, therefore, far from optimal, independent of which objective weighting is considered.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eA set of strictly better scenarios was identified, which all show conversions from pasture to arable land in England and to semi-natural habitats in Wales and Scotland while keeping existing arable land in most of England as well as all existing plantation and semi-natural forests and peatlands. The conversion rate in those strictly better scenarios lies between 52.5 and 60.0% of all of the rural land area in GB, which is relatively high and another indication of how far from optimal current land-use is. However, some scenarios, including the strictly better ones, show conversions from semi-natural grasslands to pasture, which might result from not including rough grazing on semi-natural grasslands in the analysis. If rough grazing was included, the conversion rates might be slightly lower, and current production might score higher than 0.62.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe pareto-frontiers show strong synergies between biodiversity and carbon sequestration and near linear trade-offs between production and biodiversity. The convex shape of the frontier between production and carbon sequestration shows that spatial targeting of land conversions is necessary to minimise trade-offs. The most robust land conversion is the planting of natural broadleaved woodland in current grassland locations that show relatively low potential arable yields. This is caused by its significant benefits for carbon sequestration and biodiversity. The strong dominance of natural broadleaved forests compared to plantation forests, which are used for timber production, is mainly caused by the lower value for biodiversity caused by the lack of diversity in plantations and the life cycle of timber products. In this study, it is assumed that only 48.2% of carbon sequestered by timber plantations is stored long-term (based on recreating the calculations of Peng et al. (2023)\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e for the UK), which makes coniferous plantation forests less beneficial for carbon sequestration than natural forests, notwithstanding conifer plantations\u0026rsquo; faster growth. Changes in the life cycle and usage of timber could change that and make plantations more valuable for long-term carbon sequestration.\u003c/p\u003e \u003cp\u003eA reduction of land for food production, which occurs in many scenarios other than the most production-oriented, including the production of animal products, without a change in diets, can, to some degree, be compensated by closing the gap in efficiency. However, after this gap is closed, any further reduction will lead to an increase in imports, potentially from countries less committed to environmental targets or richer biodiversity and, therefore, outsourcing of emissions and biodiversity degradation. Most land-use studies base their more sustainable scenarios on significant dietary changes\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e, which is a desirable objective and an essential step towards sustainability but still uncertain. The National Food Strategy aims to tackle the \u0026ldquo;environmental damage caused by intensive agriculture\u0026rdquo;\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e, but until then, an approach like the one presented in this paper can help focus land conversion on areas where trade-offs and losses in food production are minimised. Additionally, it needs to be pointed out how the ratio between arable land and pasture, and therefore the share of animal products in the overall production, differs throughout the scenarios. The current scenario has a ratio of 1.2 between land for arable production and pasture, while the pareto-efficient scenarios show ratios between 0.11 and 3.0, with a mean value of 1.11.\u003c/p\u003e \u003cp\u003eBased on our results, several land-use interventions for GB can be identified. Overall, we find that biodiversity and carbon sequestration can both be improved significantly through land-use change without any decline in (food) production (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). However, if only a small amount of change is planned, it must be targeted at specific locations to avoid trade-offs. The conversion of current pasture in many parts of Wales and England plays a key role in achieving improved biodiversity and carbon sequestration without compromising food production. If increasing arable production would be of interest, land in the southeast of England that is currently used for pasture could offer land sparing high yield production reserves (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The best option for carbon sequestration and biodiversity is the planting of natural broadleaved forests, given the strong synergies between the objectives. If this is done in areas with relatively low potential arable yields, trade-offs are minimised.\u003c/p\u003e \u003cp\u003eThe methodology chosen for this study aims to explore the trade-offs between carbon sequestration, production, and biodiversity from a biophysical perspective. While the locations and changes we identified are the most beneficial from a biophysical perspective, local needs such as jobs or access to nature and cultural preferences must be considered in subsequent more detailed studies aiming to design concrete policy implementation.\u003c/p\u003e \u003cp\u003eAs for most models, the results depend on the data input for the benefit calculations, and benefit estimates can differ depending on the chosen indicators. In this study, wherever possible, national datasets were used to increase the accuracy; no such dataset was available for arable yields, so a global dataset had to be used instead. Especially for biodiversity, using different indicators can lead to differing results. For computational reasons, it was impossible to include habitat connectivity in the biodiversity indicator, which might have led to prioritising other areas for habitat creation. Additionally, it is worth noting that in order to maintain independence between cells for the optimisation process, biodiversity was represented on a cellwise basis. As such, the measure does not consider the relative degradation of different broad ecological systems or non-linearities arising from large-scale condition changes that interact with this at the landscape scale. This limitation is not unique to this study.\u003c/p\u003e \u003cp\u003eGrouping all semi-natural habitats in one category without allowing conversions between them leads to lower conversion rates to semi-natural habitats in areas with habitat types with comparatively lower benefits. The habitat sub-category within the semi-natural habitat class that a land cell would be converted to was chosen based on a nearest-neighbour algorithm combined with a prioritisation of peatlands on peaty soils. Allowing the conversion of these habitats to better-performing habitat types, especially natural broadleaved forests, would have led to slightly higher conversion rates to semi-natural habitats and lower conversion rates to plantation forests. At the same time, converting a majority of existing semi-natural habitats to broadleaved forests is not the right approach for biodiversity either since a broad range of different semi-natural habitats is needed.\u003c/p\u003e \u003cp\u003eWe have assumed urban land-use to remain unchanged, and it has not been evaluated for any of its benefits. However, urban expansion and housing development are predicted to take up additional land in the UK\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e, and some are arguing that this should happen, especially on the Green Belts around existing urban areas\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. That will require the conversion of current rural areas and natural land. These conversions will put additional pressure on the land and are currently not part of the model but can be included with urban expansion scenarios in a future version.\u003c/p\u003e \u003cp\u003eAdditionally, land suitability could change under future climate change scenarios, especially for agricultural production\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. These potential future changes have not been included in this static analysis, which uses data on current land suitability. Another aspect that could be included in future work is the differentiation of arable land (extensive or intensive) and land management practices, which might mitigate trade-offs, such as organic farming, agroforestry, etc. This also applies to other adjustments of land management and agricultural practices, including how livestock is managed and if it is primarily grass-fed or if arable land is used for animal feed production. This is also in line with the discussions about land sharing versus land-sparing, which could be explored as well, even though a study by Law et al. (2015)\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e done in Asia suggests that land allocation might have a more significant influence on achieving a set of ecosystem services and food production than sharing vs sparing. Land ownership needs to be considered to assess the feasibility of land-use change in a cell, which can be explored in future work.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eTo address the issue of the triple challenge of climate mitigation, biodiversity and food production, a multi-objective analysis has been implemented to explore land-use trade-offs and synergies between (food and forestry) production, carbon sequestration and biodiversity and identify the most effective and robust place-specific land conversions. Investigating a wide range of priority weightings allows stakeholders to explore the full range of possible land-use changes in GB, avoiding the potential biases associated with evaluating only a handful of scenarios or applying an implicit weighting by translating the benefits into one common unit (such as the monetary value). Complementing the presentation of trade-offs in the form of pareto frontiers with a representation of changes seen throughout all the scenarios identifies the most critical locations for land-use conversion. Running the model with different total areas of permitted conversion (conversion \u0026lsquo;budgets\u0026rsquo;) allowed identification of the locations that should be targeted if only a small amount of rural land-use conversion is politically feasible. The same concept and methodology could be beneficial for exploring land-use trade-offs from all kinds of land conversions, including other land-use types such as energy crops or the development of new housing and suburban areas. With sufficient data, it can be done in any country or scale, from regional to global.\u003c/p\u003e \u003cp\u003eOur results show strong synergies between carbon sequestration and biodiversity and clear trade-offs between food production and biodiversity. Nevertheless, it is possible to significantly increase biodiversity and carbon sequestration from today's land-use configuration without adverse effects on food production. However, the number of scenarios where all three objectives increase or stay the same is limited, so it will require very targeted policies and incentives. The best choice for improving biodiversity and carbon sequestration while exploiting synergies between both is by creating natural broadleaved woodlands in the right locations. If this is done in places with low potential arable yields, trade-offs with production can be minimised.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eA land allocation and optimisation approach for different budgets of land conversion was used to explore the decision space for change and identify trade-offs between the three objectives carbon sequestration, biodiversity, and production in GB. A resolution of 500m x 500m was chosen to allow the representation of landscape characteristics while keeping the computational and data requirements reasonable. This results in our model containing a total of 814,004 convertible grid cells. Each grid cell can be converted into any of four land-use types: arable land, pasture, plantation forest, and semi-natural habitat. These types represent the following distribution of primary land-use types of convertible land: 25.77%, 31.59%, 6.49% and 36.15%, respectively. We assume that conversions between different semi-natural habitats are less relevant when evaluating land-use trade-offs, therefore, they were grouped under one category. The categories are comprised of a combination of sub-classes based on the UKCEH land cover map categories (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe associated potential benefits of land conversions were pre-calculated based on the characteristics of each cell’s location, resulting in benefit potential maps. Based on these potential benefits, the optimal conversions and scenarios for a range of priority weightings were calculated using a weighted sum multi-objective optimisation (see Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The resulting pareto-frontier of non-dominated scenario performances and the corresponding spatial scenarios were then used to analyse the trade-offs between the three objectives and identify the location-specific conversions that offer the most synergies.\u003c/p\u003e \u003cp\u003eCalculation of Benefits\u003c/p\u003e \u003cp\u003eStatic maps were created showing the potential benefit of converting each land cell to each of the four land-use classes, arable land, pasture, plantation forest and semi-natural habitat, and for each of the three performance indicators respectively. For each combination of objective weightings, the optimal land conversions were chosen for each cell based on the potential benefit in the location. For each created scenario, the optimised benefit values of all cells were added up to calculate the overall performance of the land configuration.\u003c/p\u003e \u003cp\u003eThe Land Cover Map 2020 (25m rasterised land parcels, GB) published by UKCEH\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e was aggregated to a resolution of 500m using a nearest-neighbour resampling approach, which allowed to maintain the original shares of land-use classes. This was used as a baseline scenario for potential land conversions. Cells with the following land cover types in the UKCEH land cover map\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e are not available for potential land conversion and were therefore excluded: inland rock, salt- \u0026amp; freshwater, supralittoral rock \u0026amp; sediment, littoral rock \u0026amp; sediment, saltmarsh as well as urban \u0026amp; suburban areas. The remaining cells, which are considered available for potential land conversion, were categorised as either arable land, pasture, plantation forest or semi-natural habitats based on the current land cover. An exclusion of land for conversion based on land classifications such as national parks, battlefields, etc., was not considered to explore the full range of possibilities. This leaves 86.82% of the land area available for potential change. For each cell, the benefit in terms of emissions, production and biodiversity benefits was calculated for each potential conversion using the data shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and captured in 12 benefit maps (4 land-use categories for conversion x 3 benefits). To keep the computational effort feasible, benefits from each land cover type for each cell were assumed to be fixed instead of using a dynamic model, and where appropriate (e.g. for forestry), benefits were aggregated over a lifecycle. Therefore, a benefit for GHG sequestration, production, and biodiversity was calculated for each cell and each possible change of land-use in that cell.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCarbon benefits.\u003c/b\u003e For calculating the carbon sequestration benefit of potential land conversions, carbon storage values for soil and vegetation were used from Cantarello et al. (2011)\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Each of the included land cover types from the UKCEH land cover map was assigned the corresponding value from Cantarello et al. (2011)\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. The emissions or sequestration from each possible conversion was computed by calculating the difference between the current carbon storage and the potential future storage. For the semi-natural habitats, a more differentiated approach was chosen to consider the differences in carbon storage between different habitat types. To project the most likely habitat type for each cell that would be selected when restoring nature, a nearest neighbour analysis was used (explained in more detail below). For arable land emissions from fertilisers, an average of 1460.67 g N\u003csub\u003e2\u003c/sub\u003eO-N·ha\u003csup\u003e-\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e was assumed based on the emissions reported by Bell et al. (2015)\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e, which converts to 626.63 kg CO\u003csub\u003e2\u003c/sub\u003e eq·ha\u003csup\u003e-\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo calculate the emissions from pasture, the average livestock density per meadow and pasture area between 2016 and 2020 was taken from FAOSTAT\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e and converted using the livestock unit coefficients used by Eurostat\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. Shares for beef and dairy cows from the UK gov livestock statistics\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e were applied. Emission values for beef cows, dairy cows, and sheep for CH\u003csub\u003e4\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e were averaged and converted to CO\u003csub\u003e2\u003c/sub\u003e-eq using GWP-100 conversion values\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. Emissions from livestock per ha on pasture sum up to 7.62 t CO\u003csub\u003e2\u003c/sub\u003e eq·ha\u003csup\u003e− 1\u003c/sup\u003eyr\u003csup\u003e− 1\u003c/sup\u003e, assuming an average livestock mix on all pasture areas.\u003c/p\u003e \u003cp\u003eTo include the significant influence of drained and intact peatlands, areas with peaty soils were identified using data on existing peatland and peaty soils for England\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e,\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e, Scotland\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e and Wales\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. Then, emissions from Gregg et al. (2021)\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e were assigned according to the peatland’s current and potential future state. Drained peatlands used for farming are a significant source of GHG emissions, which decreases when bogs and fens are rewetted. Undrained, near-natural fen presents an important carbon store that also sequesters small amounts of carbon.\u003c/p\u003e \u003cp\u003eTo calculate the sequestration and emissions from forest plantations and natural broadleaved forests, tree species occurrence maps from the European Forest Institute\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e,\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e were combined with location-specific yield maps from the Forest Research Ecological Site Classification\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e and the carbon sequestration values per yield class from the woodland carbon code\u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e. For managed plantation forests, felling times were assumed based on the tree and yield class specific age of maximum mean annual volume increment\u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. The time scale of carbon sequestration and the resulting differences between already established forests and new conversions to forests are included by considering the tree age distribution\u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e, the species and yield specific growth rates and for plantation forests the resulting time until felling. To estimate the ratio of long-term carbon storage in harvested wood products, the global wood product model by Peng et al. (2023)\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e was recreated for the UK using the FAOSTAT Forestry Production and Trade data for the UK in 2022\u003csup\u003e78\u003c/sup\u003e and the Forest Research Forestry Statistics 2023\u003csup\u003e79,80\u003c/sup\u003e. Average yearly numbers for carbon sequestration/emissions from plantation woodlands and natural broadleaved forests calculated over the period 2020–2050.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAgricultural and forestry production benefits.\u003c/b\u003e Production includes food production from arable land and pasture as well as timber production from forest plantations and is calculated as average yearly revenue from 2020–2050. For priority habitats, no production is assumed. For estimating the food production on arable land, harvested and physical areas of production for eight different crop groups (wheat, barley, rapeseed, sugar beet, potatoes, vegetables, other cereals and other pulses) were obtained from the MapSPAM dataset\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e. Since the overall reported areas did not always fully match the arable land area in this model, the area shares of each crop group in each location were calculated. Attainable crop yields for eleven crops (wheat, barley, rapeseed, oats, beans, potatoes, sugar beet, peas, carrots, cabbage and onion) under rainfed conditions from FAO GAEZv4\u003csup\u003e82\u003c/sup\u003e were converted from dry weight to wet weight using the conversion factors from the model documentation\u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e and matched with the MapSPAM crop groups. Food prices for each crop group were computed by calculating the mean FAOSTAT producer prices in £·ha\u003csup\u003e-\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e over 2017–2021 \u003csup\u003e83\u003c/sup\u003e and weighting them by the percentual share of that crop of the total harvested area\u003csup\u003e\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e. Extreme outliers \u0026gt; 1.5 IQR were removed and replaced with the mean value of the surrounding cells.\u003c/p\u003e \u003cp\u003eFor food production from pasture, the average production of milk and meat from cattle, as well as meat and skin from sheep per hectare, was calculated based on calculated livestock densities\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e, shares of beef and dairy cows\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e and yields\u003csup\u003e\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e and multiplied with the average producer prices over 2017-2021\u003csup\u003e83\u003c/sup\u003e. To identify areas that are unsuitable for pasture, the FAO pasture suitability\u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e was used and all areas with a suitability index under 20 were assumed to bring no returns of production\u003csup\u003e\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFor timber production, the forest model described above was used. Since the Woodland carbon code data does not include wood biomass production, the carbon sequestration data was converted into tree mass using carbon densities for the considered tree species\u003csup\u003e\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e. Timber harvesting was assumed to happen at the age of maximum mean annual volume increment\u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e and the timber price was calculated using the price average over the years 2018–202388. Significantly lower returns from freshly planted woodlands were considered by including the location- and species-specific rate of forest growth and, based on it, the time until the first harvest.\u003c/p\u003e \u003cp\u003e \u003cb\u003eBiodiversity \u0026amp; habitat benefits.\u003c/b\u003e To project the habitat type that each cell would be converted to were it to be restored to semi-natural habitat, a nearest neighbour analysis was performed. To ensure that peatland areas were included on all peaty soils but only on these, peatlands were excluded in the nearest neighbour analysis. Then, existing peatland and peaty soils for England\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e,\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e, Scotland\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e and Wales\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e, bog and fen from the UKCEH land cover map\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e were added to the layer. Therefore, peatlands were prioritised over the results of the nearest neighbour analysis since peatland restoration is crucial for biodiversity and climate mitigation. Then, cells classified as one of the semi-natural habitats in the UKCEH land cover map were added to the map.\u003c/p\u003e \u003cp\u003eHere, we used a combination of species occurrence probabilities from a species distribution model (SDM)\u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e and a land-use specific area-equivalent habitat condition score derived from the PREDICTS database\u003csup\u003e\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e,\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u003c/sup\u003e. The species distribution model was done in line with Croft et al. (2017)\u003csup\u003e\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u003c/sup\u003e using 88 species that have also been used in the SDM run as part of the NEVO model\u003csup\u003e\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e\u003c/sup\u003e. The species occurrence data was downloaded from the NBN atlas database\u003csup\u003e\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e\u003c/sup\u003e, which has some limitations due to it not being systematically surveyed but is the most comprehensive dataset available. As environmental variables, 19 bio-climatic variables were calculated in R using the biovars function in the R package dismo. Therefore, monthly rainfall, maximum temperature and minimum temperature data from 1999 to 2021 from the HadUK Climate Observation data were downloaded from the Centre for Environmental Data Analysis (CEDA) Archive\u003csup\u003e\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e,\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e\u003c/sup\u003e with a resolution of 1km x 1km. Land-use based environmental variables were included as a share of each land cover type within a 1km x 1km cell based on the UKCEH land cover map\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. The SDM was run using the Boosted Regression Tree and the Random Forest models from the JNCC SDM suite ensemble (available on \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/jncc/sdms\u003c/span\u003e\u003cspan address=\"https://github.com/jncc/sdms\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The best-performing model for each species was chosen based on the area under the curve (AUC) measure. After training the model, the response to land cover change was simulated by keeping the climate variables constant but changing the land cover shares in each cell. The resulting occurrence probabilities for all species were summed up to an overall indicator, as suggested by Calabrese et al. (2014)\u003csup\u003e\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e\u003c/sup\u003e. The resulting indicators were then normalised over all land-use categories to values between 0 and 1.\u003c/p\u003e \u003cp\u003eTo incorporate the inherent value of natural habitats that might naturally be less species-rich, land-use specific habitat condition scores consistent with those used in the global Biodiversity Habitat Index\u003csup\u003e\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e,\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e\u003c/sup\u003e were attributed to each land-use class. For each land-use, the proportion of native species\u003csup\u003e\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e\u003c/sup\u003e was extracted from the PREDICTS database\u003csup\u003e\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e,\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e\u003c/sup\u003e and rescaled using the species-area relationship\u003csup\u003e\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e,\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e\u003c/sup\u003e. The resultant condition metric is scaled from fully intact (1) to fully degraded (0)) in units of effective area of habitat, which can be summed over a region. Arable land was classified with a condition value of 0.08, pasture with 0.1, plantation forest with 0.23, grasslands with 0.3 and all other natural habitats, including broadleaved forests (secondary) with values between 0.33 and 0.7 depending on the type of habitat and if it already exists or would be newly established. The higher habitat condition values given to established secondary semi-natural habitats (0.38–0.7 depending on the habitat type) compared to newly created ones (0.33) ensures that the increased ecological value of a well-established natural area and the lower ecological value of a freshly converted habitat are reflected in the model. The species occurrence projection was normalised to values between 0–1, and then both indicators were combined by calculating their geometric mean.\u003c/p\u003e \u003cp\u003eOptimisation \u0026amp; analysis\u003c/p\u003e \u003cp\u003eThe resulting benefit maps were used as the basis for a multi-objective optimisation. Optimisation techniques are a common approach for land allocation \u0026amp; decision-making problems\u003csup\u003e\u003cspan additionalcitationids=\"CR105\" citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e–\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e\u003c/sup\u003e. Instead of expressing all objectives in one unit, such as monetising them or finding one ideal solution by weighting the objectives, multi-objective optimisation produces a pareto-frontier of nondominated scenarios. Pareto-efficient or non-dominated means it is impossible to improve one of the objectives further without decreasing one of the other objectives as a consequence.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs the aggregate performance with respect to the three objectives is a linear combination of the performances at every grid cell, an exhaustive search on a regular grid can be used to calculate the full range of possible objective weightings to cover the full pareto frontier. Therefore, 231 weight combinations with a step size of 5 were used, where 100-0-0 would fully prioritise the first objective while 35-35-30 would consider all three objectives nearly equally.\u003c/p\u003e \u003cp\u003eFor each constellation of weights, the following steps were implemented to create a scenario maximizing the overall weighted benefit:\u003c/p\u003e \u003cp\u003eWith \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{w}_{O}\\)\u003c/span\u003e\u003c/span\u003e as the weight of each objective \u003cem\u003eO\u003c/em\u003e in the scenario, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:O=C,P,B\\)\u003c/span\u003e\u003c/span\u003e (carbon, production, biodiversity) where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sum\\:_{O}{w}_{O}=1\\)\u003c/span\u003e\u003c/span\u003e. It was calculated how high each of the land-use types \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:k\\)\u003c/span\u003e\u003c/span\u003e scores in each cell \u003cem\u003en\u003c/em\u003e in terms of the overall weighted benefit \u003cem\u003eb\u003c/em\u003e:\u003c/p\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{b}_{n,k}={\\stackrel{-}{b}}_{n,k}^{C}\\times\\:{w}_{C}+{\\stackrel{-}{b}}_{n,k}^{P}\\times\\:{w}_{P}+{\\stackrel{-}{b}}_{n,k}^{B}\\times\\:{w}_{B}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003e( 1 )\u003c/h3\u003e\n\u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\stackrel{-}{b}}_{n,k}^{O}\\)\u003c/span\u003e\u003c/span\u003e is the normalised benefit to objective \u003cem\u003eO\u003c/em\u003e. For each cell \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:n\\)\u003c/span\u003e\u003c/span\u003e the LU type \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:k\\)\u003c/span\u003e\u003c/span\u003e that yields the maximum weighted benefit is chosen:\u003c/p\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{k}_{n}={argmax}_{k}{b}_{n,{k}_{n}}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003e( 2 )\u003c/h3\u003e\n\u003cp\u003e \u003cem\u003ek\u003c/em\u003e \u003csub\u003e1\u003c/sub\u003e, \u003cem\u003ek\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e, \u003cem\u003ek\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e, ..., \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eM\u003c/em\u003e\u003c/sub\u003e together define a scenario which maximizes the overall weighted benefit \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sum\\:}_{n}{b}_{n,{k}_{n}}\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eFor each of the created scenarios, the benefits for carbon sequestration, production and biodiversity were calculated, and the pareto efficient scenarios were conserved. The trade-off between the three objectives (normalising benefits to 0–1) was assessed by identifying the overall shape of the three-dimensional frontier and the gradient and curvature of the curves describing the pareto frontiers between each combination of two objectives. The shape of the resulting pareto frontier can be analysed to understand the relationship between the objectives. While a straight line represents a direct trade-off where the increase of one benefit leads to a proportional decrease of another benefit, a concave curve implies that while there are trade-offs, it is possible to increase one benefit without seeing a proportional decrease in the other service\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Additionally, the non-dominated scenarios were compared to assess the importance of specific locations when looking at trade-offs from land conversion.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConstrained number of land-use change conversions.\u003c/b\u003e Realistically, not all of rural land considered in this model could be converted to a new land-use type. Converting land to new land-use comes with many challenges, and planning with high rates of change might be politically infeasible. Therefore, an approach that allows the exploration of the potential benefits of limited amounts of land conversion and the marginal benefits of increased percentages for the area converted is needed. To identify the areas that should be prioritised when converting land and designing land-use policies, scenarios were calculated for a range of ‘budgets’ of change starting from 1 to 100% of the available area. This approach chooses the land conversions first that offer the highest cellwise benefits per area and are, therefore, the most efficient. Additionally, it allows us to explore the decision space for change, demonstrate how much change will be necessary to attain certain objectives, and identify a threshold where additional change adds only minimal additional benefits. Therefore, the same weighted approach as described above was used, but cells are converted successively, from most to least beneficial, until the change budget is met. Where more cells than comprised in the budget were equally beneficial, cells for conversion were selected randomly.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAnalysing maps.\u003c/b\u003e To spatially identify trade-offs and synergies, all 231 scenarios were compared, and the percentage of scenarios where each cell was converted to a specific land-use cover was calculated and compared between budgets and land-use types. If certain changes came up in a broader range of scenarios, and therefore priority weightings, these conversions were considered more robust to changing priorities of decision-makers and had fewer trade-offs. If, in contrast, the suggested conversion for an area differed a lot depending on the chosen priority weightings, the conversions were not considered robust and are likely to show higher trade-offs. While comparing the maps for different priority weightings can help identify conversions that are robust to changing priority weightings, looking at the budgets facilitates identifying the most effective land conversions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo help summarise the findings and make use of the full range of scenarios, they were grouped according to their weighting as either ’carbon prioritising’ (carbon sequestration weighting \u0026gt; = 50), ’production prioritising’ (production weighting \u0026gt; = 50), ’biodiversity prioritising (biodiversity weighting \u0026gt; = 50) or balanced (none of the three objectives is weighted \u0026gt; 50) as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The scenarios along the boundaries, with a weighting of 50 for one of the objectives, were included in the balanced and objective-focused group. Then, the four priority groups were compared, and the changes most common in the range of scenarios were analysed.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMinimum and maximum possible performance against each objective, with no limits on the area of land-use changes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCarbon sequestration (million t CO\u003csub\u003e2\u003c/sub\u003e-eq·yr\u003csup\u003e− 1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFood and forestry production\u003c/p\u003e \u003cp\u003e(billion £·yr\u003csup\u003e− 1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBiodiversity\u003c/p\u003e \u003cp\u003e(species occurrence based indicator, see Methods)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum performance across all possible land-use changes (normalized performance of 0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-143.39\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e159884\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum performance across all possible land se changes (normalized performance of 1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e98.25\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.42\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e349740\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLand cover categories used in the model and the corresponding UKCEH land cover subcategories\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand-use category\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUKCEH Land cover types\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArable\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArable and horticulture\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePasture\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImproved grassland\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlantation forest\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConiferous woodland\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSemi-natural habitat\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeutral grassland\u003c/p\u003e \u003cp\u003eCalcareous grassland\u003c/p\u003e \u003cp\u003eAcid grassland\u003c/p\u003e \u003cp\u003eHeather grassland\u003c/p\u003e \u003cp\u003eFen marsh \u0026amp; swamp\u003c/p\u003e \u003cp\u003eHeather\u003c/p\u003e \u003cp\u003eBog\u003c/p\u003e \u003cp\u003eBroadleaved woodland\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eData used to create the benefit maps for the three objectives, carbon sequestration, production, and biodiversity, as well as the four land cover categories: arable land, pasture, plantation forest, and other semi-natural habitats.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCarbon sequestration\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProduction\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBiodiversity\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eArable land\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e- Carbon storage of \u0026amp; flows between land cover classes\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e \u003c/p\u003e \u003cp\u003e- Emissions from fertilizers\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- Crop distribution (physical \u0026amp; harvested areas)\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e- Agricultural yields\u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e- FAO stats producer prices\u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e- FAO stats production areas\u003csup\u003e\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e- UKCEH land cover map\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e- Species occurrence data\u003csup\u003e\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e- Climatic data\u003csup\u003e\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e\u003c/sup\u003e \u003c/p\u003e \u003cp\u003e- JNCC Species distribution model ensemble\u003c/p\u003e \u003cp\u003e- Habitat condition values based on PREDICTS biodiversity data\u003csup\u003e\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePasture\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e- Carbon storage of \u0026amp; flows between land cover classes\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e- Emissions from livestock\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- FAO stats livestock patterns\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e- Shares of beef \u0026amp; dairy cows\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e- FAO stats producer prices\u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e- Livestock yields\u003csup\u003e\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e- FAO pasture suitability indicator\u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlantation forests\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e- Carbon storage of land cover classes\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e- Woodland species composition\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e,\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e- Location-specific yield classes\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e- Woodland carbon sequestration\u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e- Tree age distribution\u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e- Wood products data\u003csup\u003e\u003cspan additionalcitationids=\"CR79\" citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e–\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- Woodland species composition\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e,\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e- Location-specific yield classes\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e- Woodland carbon sequestration\u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e- Tree age distribution\u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e- Tree carbon densities\u003csup\u003e\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e- - Timber prices\u003csup\u003e\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSemi-natural habitats\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e- Carbon storage of land cover classes\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e \u003c/p\u003e \u003cp\u003e- Carbon fluxes for different states of peatland\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e \u003c/p\u003e \u003cp\u003e- peaty soils for England\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e,\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e, Scotland\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e and Wales\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e- (for broadl. woodlands, the same data sources as for plantation forests were used, except \u003csup\u003e\u003cspan additionalcitationids=\"CR79\" citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e–\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- No production is assumed\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics declarations\u003c/h2\u003e \u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThis work is supported in part by funds from the Engineering and Physical Sciences Research Council (EPSRC) and the Livestock, Environment and People project of the Oxford Martin School funded by the Wellcome Trust.\u003c/p\u003e\n\u003ch3\u003eCode Availability\u003c/h3\u003e\n\u003cp\u003eThe code used to generate the results is freely accessible and available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/sarahgall/CarbonFoodNature_TradeOffs\u003c/span\u003e\u003cspan address=\"https://github.com/sarahgall/CarbonFoodNature_TradeOffs\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The code used for the species distribution model was obtained from the JNCC SDM ensemble and can be downloaded via \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/jncc/sdms\u003c/span\u003e\u003cspan address=\"https://github.com/jncc/sdms\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003ch3\u003eData Availability\u003c/h3\u003e\n\u003cp\u003eAll datasets used in this study are cited in the relevant sections. The land cover raster can be downloaded from the UK Centre for Ecology \u0026amp; Hydrology via the EDINA Environment Digimap service (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://digimap.edina.ac.uk/environment\u003c/span\u003e\u003cspan address=\"https://digimap.edina.ac.uk/environment\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) or \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ceh.ac.uk/data/ukceh-land-cover-maps\u003c/span\u003e\u003cspan address=\"https://www.ceh.ac.uk/data/ukceh-land-cover-maps\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Spatial data for drained peatlands and peaty soils were downloaded for England from Natural England via \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.data.gov.uk/dataset/9d494f48-f0d7-4333-96f0-8b736ac8fb18/peaty-soils-location1\u003c/span\u003e\u003cspan address=\"https://www.data.gov.uk/dataset/9d494f48-f0d7-4333-96f0-8b736ac8fb18/peaty-soils-location1\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.data.gov.uk/dataset/b12f420a-d9f1-4966-aa3e-0f6e680e3875/moorland-deep-peat-ap-status1\u003c/span\u003e\u003cspan address=\"https://www.data.gov.uk/dataset/b12f420a-d9f1-4966-aa3e-0f6e680e3875/moorland-deep-peat-ap-status1\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, for Wales from UKCEH via \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://catalogue.ceh.ac.uk/documents/58139ce6-63f9-4444-9f77-fc7b5dcc00d8\u003c/span\u003e\u003cspan address=\"https://catalogue.ceh.ac.uk/documents/58139ce6-63f9-4444-9f77-fc7b5dcc00d8\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e and for Scotland from Scottish Natural Heritage via \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://opendata.nature.scot/datasets/snh::carbon-and-peatland-2016-map/explore\u003c/span\u003e\u003cspan address=\"https://opendata.nature.scot/datasets/snh::carbon-and-peatland-2016-map/explore\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Tree species maps for European forests were downloaded from the European Forest Institute via \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://efi.int/knowledge/maps/treespecies\u003c/span\u003e\u003cspan address=\"https://efi.int/knowledge/maps/treespecies\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Tree species and location-specific yield class potential data can be obtained from the Forest Research Ecological site classification tool via \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.forestdss.org.uk/geoforestdss/esc4.jsp\u003c/span\u003e\u003cspan address=\"http://www.forestdss.org.uk/geoforestdss/esc4.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Yield class specific carbon sequestration data was obtained from the Woodland carbon code Lookup tables in the Carbon calculation spreadsheet, which can be downloaded via \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.woodlandcarboncode.org.uk/landowners-apply/template-documents\u003c/span\u003e\u003cspan address=\"https://www.woodlandcarboncode.org.uk/landowners-apply/template-documents\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Forestry production and trade data was downloaded from FAOSTAT and found here: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fao.org/faostat/en/#data/FO\u003c/span\u003e\u003cspan address=\"https://www.fao.org/faostat/en/#data/FO\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The MapSPAM raster data on crop production areas was downloaded via \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mapspam.info/\u003c/span\u003e\u003cspan address=\"https://mapspam.info/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Location-specific attainable arable yields can be downloaded from FAO GAEZv4 via \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gaez.fao.org/pages/data-viewer\u003c/span\u003e\u003cspan address=\"https://gaez.fao.org/pages/data-viewer\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Statistics on livestock patterns can be downloaded from FAOSTAT via \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fao.org/faostat/en/#data/EK\u003c/span\u003e\u003cspan address=\"https://www.fao.org/faostat/en/#data/EK\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Producer prices for agricultural products can be downloaded from FAOSTAT via \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fao.org/faostat/en/#data/PP\u003c/span\u003e\u003cspan address=\"https://www.fao.org/faostat/en/#data/PP\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Total harvested area and average livestock yields can be downloaded from the Crops and livestock products dataset from FAOSTAT via \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fao.org/faostat/en/#data/QCL\u003c/span\u003e\u003cspan address=\"https://www.fao.org/faostat/en/#data/QCL\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The Suitability of global land area for pasture (FGGD) raster data can be downloaded from FAO via \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.apps.fao.org/catalog/iso/2b357400-891a-11db-b9b2-000d939bc5d8\u003c/span\u003e\u003cspan address=\"https://data.apps.fao.org/catalog/iso/2b357400-891a-11db-b9b2-000d939bc5d8\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Data on carbon densities for different tree species can be downloaded from the Global wood densities database via https://datadryad.org/stash/dataset/doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5061/dryad.234\u003c/span\u003e\u003cspan address=\"10.5061/dryad.234\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The species occurrence data was downloaded from the NBN atlas database via \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://nbnatlas.org/\u003c/span\u003e\u003cspan address=\"https://nbnatlas.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Rainfall and temperature data from the HadUK-Grid Gridded Climate Observations data can be downloaded from the Centre for Environmental Data Analysis (CEDA) Archive via \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://catalogue.ceda.ac.uk/uuid/bbca3267dc7d4219af484976734c9527/\u003c/span\u003e\u003cspan address=\"https://catalogue.ceda.ac.uk/uuid/bbca3267dc7d4219af484976734c9527/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Data on the proportion of native species for the habitat condition calculations can be downloaded from the PREDICTS database from the data portal of the Natural History Museum via \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.nhm.ac.uk/dataset/the-2016-release-of-the-predicts-database-v1-1\u003c/span\u003e\u003cspan address=\"https://data.nhm.ac.uk/dataset/the-2016-release-of-the-predicts-database-v1-1\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRockstr\u0026ouml;m, J. \u003cem\u003eet al.\u003c/em\u003e A safe operating space for humanity. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e461\u003c/strong\u003e, 472\u0026ndash;475 (2009).\u003c/li\u003e\n\u003cli\u003eCrippa, M. \u003cem\u003eet al.\u003c/em\u003e Food systems are responsible for a third of global anthropogenic GHG emissions. \u003cem\u003eNat. 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Policy\u003c/em\u003e \u003cstrong\u003e84\u003c/strong\u003e, 186\u0026ndash;196 (2018).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Land-use change, Trade-offs, Agriculture, Biodiversity, Climate mitigation, Land-use optimisation","lastPublishedDoi":"10.21203/rs.3.rs-6091509/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6091509/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDue to the various negative environmental consequences of current land-use, and land\u0026rsquo;s importance for climate mitigation, environmental conservation and food security, there is a growing and urgent interest in reforming land-use in many countries. Policy objectives for tree planting to sequester carbon and the creation of protected areas to protect biodiversity require land reallocation. This leads to inevitable trade-offs between land-uses, requiring careful place-based policy design. Here, we evaluate the trade-offs between three objectives for rural land: agricultural/forestry production, carbon sequestration and biodiversity, by calculating metrics for these three objectives on a 500mx500m grid covering Great Britain (GB). We use a multi-objective optimisation to identify the land allocations that satisfy different weightings between the three objectives for given total areas of land-use conversation. Our results show that the current land-use in GB is far from optimal for any combination of objectives. We also find that it is possible to significantly improve carbon sequestration and biodiversity, even with a relatively small proportion of the land being converted to other uses, without compromising overall agricultural production, provided conversions are located carefully.\u003c/p\u003e","manuscriptTitle":"Mapping the option space for carbon sequestration, food and biodiversity in Great Britain","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-29 10:57:59","doi":"10.21203/rs.3.rs-6091509/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-earth-and-environment","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsenv","sideBox":"Learn more about [Communications Earth and Environment](https://www.nature.com/commsenv/)","snPcode":"","submissionUrl":"","title":"Communications Earth \u0026 Environment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"98e5abeb-a20b-484d-beea-dba37c8108a2","owner":[],"postedDate":"April 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":45795627,"name":"Earth and environmental sciences/Climate sciences/Climate change/Climate-change mitigation"},{"id":45795628,"name":"Earth and environmental sciences/Ecology/Ecosystem services"}],"tags":[],"updatedAt":"2025-09-30T07:16:46+00:00","versionOfRecord":{"articleIdentity":"rs-6091509","link":"https://doi.org/10.1038/s43247-025-02728-w","journal":{"identity":"communications-earth-and-environment","isVorOnly":false,"title":"Communications Earth \u0026 Environment"},"publishedOn":"2025-09-29 04:00:00","publishedOnDateReadable":"September 29th, 2025"},"versionCreatedAt":"2025-04-29 10:57:59","video":"","vorDoi":"10.1038/s43247-025-02728-w","vorDoiUrl":"https://doi.org/10.1038/s43247-025-02728-w","workflowStages":[]},"version":"v1","identity":"rs-6091509","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6091509","identity":"rs-6091509","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}