Drained organic soils in agricultural use in the European Union (EU) contribute approximately 80% of Cropland and Grassland greenhouse gas (GHG) emissions released to the atmosphere, which makes their restoration highly relevant for achieving climate change mitigation targets in the EU. However, the cost-effectiveness of different restoration measures and their synergies with economic incentives remain poorly understood. Here, we provide an EU-wide assessment of the economic potential of restoring drained organic soils used for agriculture through 2050 using the economic land-use model GLOBIOM-EU. We investigate the climate benefits of three restoration measures-full rewetting, rehabilitation, and full rewetting with paludiculture-and evaluate their cost-effectiveness by developing marginal abatement cost curves (MACCs). Our results indicate that under a GHG price of 100 EUR tCO2e-1, 38.2-44.4 MtCO2e yr-1 could be mitigated in 2050. Demand for paludiculture products would substantially improve the attractivity of full rewetting, enabling up to 2 Mha of drained organic soils to be restored even without additional climate policy incentives, delivering 17 MtCO2e yr-1 mitigation in 2050. In addition, meeting the 2050 targets of the EU Nature Restoration Regulation (NRR) alone could mitigate emissions by 20-26 MtCO2e yr-1, equivalent to 23%-29% of current emissions from drained agricultural organic soils in the EU. These findings demonstrate that restoring drained organic soils represents a substantial mitigation opportunity for the EU agricultural sector, with synergies for bioeconomy development and nature restoration. An integrated approach to policy design is needed to ensure efficiency in delivering multiple co-benefits.

There is currently a lack of high-resolution pan-European information on land use management, especially in terms of how intensively and extensively cropland and grassland are managed. This is partly due to the lack of ground-based information, which is needed to downscale these types of management practices (some of which are captured in different types of agricultural censuses and surveys) as well as the inability of remote sensing to capture different kinds of land use. This type of information is needed for economic land use modelling and for assessing policy impacts, such as the latest reforms from the Common Agricultural Policy (CAP) and other European Union (EU) Green Deal targets. These types of analyses are undertaken using economic land use models such as GLOBIOM and CAPRI, which is one of the main aims of the Horizon Europe funded LAMASUS project (https://www.lamasus.eu/). This presentation will provide an overview of the ongoing developments in creating high-resolution spatially explicit layers on agricultural and grassland management for Europe to support the LAMASUS project. The proposed cropland and grassland management classes will be outlined along with the methodology for how they have been implemented using existing data layers from remote sensing, statistical data from Eurostat, the Joint Research Centre of the EU, agricultural ministries, and other sources. One of the key challenges is ensuring that the high-resolution data matches official statistics at the national (and NUTS2 level where available) so that they can be used by the economic land use models in LAMASUS. A method will be presented for how this is achieved using priors in the form of integrated layers of cropland and grassland probability created from existing high-resolution remotely sensed input layers.

Climate change significantly affects forest dynamics in Europe and is projected to intensify further, posing challenges to policy goals and ecosystem integrity. This requires changes in forest management that anticipate impacts and act to minimize negative consequences on forest functioning while maintaining forests’ contribution to the bio-based economy and decarbonization targets. However, it is still unclear how alternative forest management strategies can support biodiversity, green growth, and mitigation targets under changing environmental conditions. This study uses an integrated modeling framework to address this issue and assess forest management adaptation in the European Union, considering conservation goals and emerging biomass demands. The results show that climate policies will be a major driver of forest management until mid-century, with climate impacts shaping management decisions thereafter. Productivity changes vary regionally, with temperature-limited ecosystems benefiting and water-limited forests declining in growth. These biophysical impacts may displace harvests from Mediterranean and temperate forests to the boreal zone, requiring changes in management practices. Adaptive forest management will, therefore, be crucial for achieving policy goals in Europe under future climate scenarios.

This chapter discusses the connections between two-stage stochastic optimization (STO) and robust statistical estimation. The main question related to statistical predictions is how to use the predictions to optimize the overall decisions and how current decisions can affect predictions. In general, problems of decision making, feasible solutions, concepts of optimality and robustness are characterized from the context of decision-making situations, that is, systems structure, goals, security constraints, safety norms, supply–demand relationships and thresholds. Robust statistical approaches can be effectively combined with disciplinary or interdisciplinary models, for example, land use model Global Biosphere Management Model (GLOBIOM), for effective decision making in the conditions of uncertainty, increasing interdependencies and systemic risks.

Transforming the global food system is essential to avoid exceeding Earth’s environmental limits. A robust evidence base is crucial to assess the scale and combination of interventions required for a sustainable transformation. We developed a risk assessment framework, underpinned by an evidence synthesis of global food system modeling studies, to quantify the potential of individual and combined interventions to mitigate the risk of exceeding global environmental limits for agricultural area, greenhouse gas (GHG) emissions, surface water flows, and nutrient cycles by 2050. GHG emissions and nutrient cycles are the most difficult limits to avoid exceeding and are conditional on shifts toward diets with a low proportion of animal-source foods; steep reductions in emissions intensity; substantial improvements in nutrient management, feed-conversion ratios, and crop yields; and efforts to limit overconsumption and food waste. Ambitious actions across the global food system are needed to ensure the required level of risk mitigation.

Eating a lot of meat and dairy puts pressure on nature. It leads to deforestation, water pollution, and climate change. By 2050, there will be over 9 billion people on Earth. Without any change in the way we eat, environmental damage will only get worse. What if we could eat foods that taste like meat and milk, but without hurting the planet as much? Novel plant-based foods, like plant-based burgers or oat milk, are made to taste and feel like animal products while using less land, water, and producing fewer greenhouse gases. Traditional plant-based foods, like tofu, lentils, and chickpeas are also healthy, tasty, and better for the planet. If everyone gradually replaced half of their meat and dairy with plant-based options we could save forests, reduce emissions, and make food more affordable. Even small changes in diet can make a big difference for the planet—and our future.

Monitoring and estimating spatially resolved changes in soil organic carbon (SOC) stocks are necessary for supporting national and international policies aimed at assisting land degradation neutrality and climate change mitigation, improving soil fertility and food production, maintaining water quality, and enhancing renewable energy and ecosystem services. In this work, we report on the development and application of a data-driven, quantile regression machine learning model to estimate and predict annual SOC stocks at plow depth under the variability of climate. The model enables the analysis of SOC content levels and respective probabilities of their occurrence as a function of exogenous parameters such as monthly temperature and precipitation and endogenous, decision-dependent parameters, which can be altered by land use practices. The estimated quantiles and their trends indicate the uncertainty ranges and the respective likelihoods of plausible SOC content. The model can be used as a reduced-form scenario generator of stochastic SOC scenarios. It can be integrated as a submodel in Integrated Assessment models with detailed land use sectors such as GLOBIOM to analyze costs and find optimal land management practices to sequester SOC and fulfill food–water–energy–-environmental NEXUS security goals.

Most ambitious climate change mitigation pathways indicate multifold bioenergy expansion to support the en-ergy transition, which may trigger increased biomass imports from major bioenergy-consuming regions. How-ever, the potential global land-use change and sustainability trade-offs alongside the bioenergy trade remain poorly understood. Here, we apply the Global Biosphere Management Model (GLOBIOM) to investigate and compare the effects of different increasing bioenergy import strategies in line with the 1.5 degrees C-compatible bio-energy demand in China, which is projected to represent 30% of global bioenergy consumption by the middle of the century. The results show that sourcing additional bioenergy from different world regions could pose het-erogeneous impacts on the local and global land systems, with implications on food security, greenhouse gas emissions, and water and fertilizer demand. In the worst cases under strict trade settings, relying on biomass import may induce up to 25% of unmanaged forests converted to managed ones in the supplying regions, while in an open trade environment, increasing bioenergy imports would drastically change the trade flows of staple agricultural or forestry products, which would further bring secondary land-use changes in other world regions. Nevertheless, an economically optimized biomass import portfolio for China has the potential to reduce global overall sustainability trade-offs with food security and emission abatement. However, these benefits vary with indicator and time and are conditional on stricter land-use regulations. Our findings thus shed new light on the design of bioenergy trade strategies and the associated land-use regulations in individual countries in the era of deep decarbonization.

Bioenergy with carbon capture and storage, among other negative-emission technologies, is required for China to achieve carbon neutrality—yet it may hinder land-based Sustainable Development Goals. Using modelling and scenario analysis, we investigate how to mitigate the potential adverse impacts on the food system of ambitious bioenergy deployment in China and its trading partners. We find that producing bioenergy domestically while sticking to the food self-sufficiency ratio redlines would lower China’s daily per capita calorie intake by 8% and increase domestic food prices by 23% by 2060. Removing China’s food self-sufficiency ratio restrictions could halve the domestic food dilemma but risks transferring environmental burdens to other countries, whereas halving food loss and waste, shifting to healthier diets and narrowing crop yield gaps could effectively mitigate these external effects. Our results show that simultaneously achieving carbon neutrality, food security and global sustainability requires a careful combination of these measures.

Plant-based animal product alternatives are increasingly promoted to achieve more sustainable diets. Here, we use a global economic land use model to assess the food system-wide impacts of a global dietary shift towards these alternatives. We find a substantial reduction in the global environmental impacts by 2050 if globally 50% of the main animal products (pork, chicken, beef and milk) are substituted-net reduction of forest and natural land is almost fully halted and agriculture and land use GHG emissions decline by 31% in 2050 compared to 2020. If spared agricultural land within forest ecosystems is restored to forest, climate benefits could double, reaching 92% of the previously estimated land sector mitigation potential. Furthermore, the restored area could contribute to 13-25% of the estimated global land restoration needs under target 2 from the Kunming Montreal Global Biodiversity Framework by 2030, and future declines in ecosystem integrity by 2050 would be more than halved. The distribution of these impacts varies across regions-the main impacts on agricultural input use are in China and on environmental outcomes in Sub-Saharan Africa and South America. While beef replacement provides the largest impacts, substituting multiple products is synergistic.

While nitrogen inputs are crucial to agricultural production, excess nitrogen contributes to serious ecosystem damage and water pollution. Here, we investigate this trade-off using an integrated modelling framework. We quantify how different nitrogen mitigation options contribute to reconciling food security and compliance with regional nitrogen surplus boundaries. We find that even when respecting regional nitrogen surplus boundaries, hunger could be substantially alleviated with 590 million fewer people at risk of hunger from 2010 to 2050, if all nitrogen mitigation options were mobilized simultaneously. Our scenario experiments indicate that when introducing regional N targets, supply-side measures such as the nitrogen use efficiency improvement are more important than demand-side efforts for food security. International trade plays a key role in sustaining global food security under nitrogen boundary constraints if only a limited set of mitigation options is deployed. Policies that respect regional nitrogen surplus boundaries would yield a substantial reduction in non-CO 2 GHG emissions of 2.3 GtCO 2 e yr −1 in 2050, which indicates a necessity for policy coordination.

Delaying climate mitigation action and allowing a temporary overshoot of temperature targets require large-scale carbon dioxide removal (CDR) in the second half of this century that may induce adverse side effects on land, food and ecosystems. Meanwhile, meeting climate goals without global net-negative emissions inevitably needs early and rapid emission reduction measures, which also brings challenges in the near term. Here we identify the implications for land-use and food systems of scenarios that do not depend on land-based CDR technologies. We find that early climate action has multiple benefits and trade-offs, and avoids the need for drastic (mitigation-induced) shifts in land use in the long term. Further long-term benefits are lower food prices, reduced risk of hunger and lower demand for irrigation water. Simultaneously, however, near-term mitigation pressures in the agriculture, forest and land-use sector and the required land area for energy crops increase, resulting in additional risk of food insecurity. Delaying climate mitigation requires large-scale carbon dioxide removal (CDR) in the second half of this century, with possible adverse effects. Under scenarios with no dependence on CDR technologies, this study examines the short- and long-term implications of climate mitigation for land-use and food systems.

Concerns regarding the impact of climate change, food price volatility, and weather uncertainty have motivated users of simulation models to consider uncertainty in their simulations. One way to do this is to integrate uncertainty components in the model equations, thus turning the model into a problem of numerical integration. Most of these problems do not have analytical solutions, and researchers, therefore, apply numerical approximation methods. This article presents a novel approach to conducting an uncertainty analysis as an alternative to the computationally burdensome Monte Carlo-based (MC) methods. The developed method is based on the degree three Gaussian quadrature (GQ) formulae and is tested using three large-scale simulation models. While the standard single GQ method often produces low-quality approximations, the results of this study demonstrate that the proposed approach reduces the approximation errors by a factor of nine using only 3.4% of the computational effort required by the MC-based methods in the most computationally demanding model.

Global emissions scenarios play a critical role in the assessment of strategies to mitigate climate change. The current scenarios, however, are criticized because they feature strategies with pronounced overshoot of the global temperature goal, requiring a long-term repair phase to draw temperatures down again through net-negative emissions. Some impacts might not be reversible. Hence, we explore a new set of net-zero CO2 emissions scenarios with limited overshoot. We show that upfront investments are needed in the near term for limiting temperature overshoot but that these would bring long-term economic gains. Our study further identifies alternative configurations of net-zero CO2 emissions systems and the roles of different sectors and regions for balancing sources and sinks. Even without net-negative emissions, CO2 removal is important for accelerating near-term reductions and for providing an anthropogenic sink that can offset the residual emissions in sectors that are hard to abate. Current emissions scenarios include pathways that overshoot the temperature goals set out in the Paris Agreement and rely on future net negative emissions. Limiting overshoot would require near-term investment but would result in longer-term economic benefit.

Land‐based climate mitigation measures have gained significant attention and importance in public and private sector climate policies. Building on previous studies, we refine and update the mitigation potentials for 20 land‐based measures in >200 countries and five regions, comparing “bottom‐up” sectoral estimates with integrated assessment models (IAMs). We also assess implementation feasibility at the country level. Cost‐effective (available up to $100/tCO 2 eq) land‐based mitigation is 8–13.8 GtCO 2 eq yr −1 between 2020 and 2050, with the bottom end of this range representing the IAM median and the upper end representing the sectoral estimate. The cost‐effective sectoral estimate is about 40% of available technical potential and is in line with achieving a 1.5°C pathway in 2050. Compared to technical potentials, cost‐effective estimates represent a more realistic and actionable target for policy. The cost‐effective potential is approximately 50% from forests and other ecosystems, 35% from agriculture, and 15% from demand‐side measures. The potential varies sixfold across the five regions assessed (0.75–4.8 GtCO2eq yr −1 ) and the top 15 countries account for about 60% of the global potential. Protection of forests and other ecosystems and demand‐side measures present particularly high mitigation efficiency, high provision of co‐benefits, and relatively lower costs. The feasibility assessment suggests that governance, economic investment, and socio‐cultural conditions influence the likelihood that land‐based mitigation potentials are realized. A substantial portion of potential (80%) is in developing countries and LDCs, where feasibility barriers are of greatest concern. Assisting countries to overcome barriers may result in significant quantities of near‐term, low‐cost mitigation while locally achieving important climate adaptation and development benefits. Opportunities among countries vary widely depending on types of land‐based measures available, their potential co‐benefits and risks, and their feasibility. Enhanced investments and country‐specific plans that accommodate this complexity are urgently needed to realize the large global potential from improved land stewardship.

Global emissions scenarios play a critical role in the assessment of strategies to mitigate climate change and their related societal transformations. The current generation of scenarios, however, are criticized because they rely heavily on net negative CO2 emissions (NNCE) that result from allowing temperature limits to be temporarily exceeded. In this study we present a new set of emissions scenarios that exclude NNCE. We show that such scenarios require a more rapid near-term transformation with significant long-term gains for the economy (even without considering the benefits of avoided climate impacts). Scenarios that avoid temperature overshoot and NNCE are thus not only economically more attractive over the long term, they also involve lower climate risks. Our study further identifies possible alternative configurations of net-zero CO2 emissions systems and the distinct roles of different sectors and regions in order to balance emissions sources and sinks.

Achieving climate neutrality in the European Union (EU) by 2050 will require substantial efforts across all economic sectors, including agriculture. At the same time, an ambitious unilateral EU agricultural mitigation policy is likely to have adverse effects on the sector and may have limited efficiency at global scale due to emission leakage to non-EU regions. To analyse the competitiveness of the EU's agricultural sector and potential non-CO2 emission leakage conditional on mitigation efforts outside the EU, we apply three economic agricultural sector models. We find that an ambitious unilateral EU mitigation policy in line with efforts needed to achieve the 1.5 °C target globally strongly affects EU ruminant production and trade balance. However, since EU farmers rank among the most greenhouse gas efficient producers worldwide, if the rest of the world were to start pursuing agricultural mitigation efforts too, economic impacts of an ambitious domestic mitigation policy get buffered and EU livestock producers could even start to benefit from a globally coordinated mitigation policy.

Abstract Allowing delayed climate mitigation actions and overshoot of temperature targets requires large scale negative carbon emissions that may induce adverse side-effects on land, food and ecosystems in the second half of this century. Meanwhile, meeting climate goals without net negative emissions inevitably needs the implementation of early and quick emissions reduction measures, which also brings challenges in the near-term. Here we identify the implications of scenarios without a dependence on net-negative carbon emissions on land-use and food systems. We find that early climate actions without the reliance on net-negative carbon emissions have multiple benefits and trade-offs in land-use and food systems, and avoid the need for drastic (mitigation-induced) shifts in land-use in the long term. Further long-term benefits are lower food prices, a reduced risk of hunger and lower water scarcity. At the same time, however, near-term mitigation pressure in the AFOLU sectors and the required land area for energy crops increases, resulting in additional agricultural intensification.