Dynamic transformation toward resilient and circular food systems: A decision support framework for food security

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The rationale for developing a dynamic decision support system is the growing debate over the necessity of (re-)regionalization policies in food supply chains that incorporate circularity and resilience strategies. This paper explores the trade-offs and synergies between decisions made at the strategic, tactical, and operational levels, global and local food systems, climate mitigation and adaptation strategies, as well as sustainability and profitability/technology, while evaluating the strategic combinations of various macroeconomic, socio-technical, and political measures. To achieve this ubiquitous goal, we use a mixed-methods approach, combining quantitative and qualitative analysis through interviews and a holistic System Dynamics (SD) model to simulate the Shared Socioeconomic Pathways (SSP) as narratives. The simulation quantifies the SSPs to find a balanced transformation pathway for a resilient and sustainable food system, depending on global trade policies, transport and logistics, economic development, consumption patterns, and technological development. This holistic approach to food systems at various levels is unique and provides a clear decision-support framework that assess measures and their combinations into a set of strategies. Food Security Dynamic Interaction System Dynamics Supply Chain Resilience Shared Socio-Economic Pathways Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 INTRODUCTION Global societal welfare, ecological productivity, and food security are drastically changing, driven by climate change impacts on agricultural systems (Challinor et al., 2017 ), rapid urbanization (Satterthwaite et al., 2010 ), demographic developments and migration patterns (Andrews, 2020 ), resource overconsumption (Hoekstra & Wiedmann, 2014 ), shifts in consumption behavior (Kakaei et al., 2022 ), geopolitical instabilities (Browning & Brassett, 2023 ), and pandemics (Bretschger, 2020). Specifically, food security relies on both stable food production and consumption systems, in which sufficient food quantity (both locally produced and imported), access, and utilization play a central role. Because food systems are directly linked to ecosystems through provisioning (water, timber, and genetic resources), regulatory (floods, diseases), and supporting (soil formation, water, and nutrient cycles) services, their resilience is threatened by climate change (Khoury, 2014; Campbell et al., 2016 ). Specifically, the modeling of climate change impacts on food systems has improved significantly, largely due to advances in parameterization and model complexity. Thus, decision-making under uncertainty has been well-explored, which helps inform transformative actions, such as the WILIAM Model (Mediavilla et al., 2025 ). Yet, research on food security dynamics from a systemic perspective, encompassing all dimensions of food security, such as socio-technical advances, the demand side, macroeconomic perspectives, and infrastructure, remains limited (IPCC, 2014; Campbell et al., 2016 ). Increased food insecurity and extended global inequalities result from several climate mitigation pathways across various generations of IPCC scenarios, assuming that developed countries will continue to sustain higher GDP levels per capita, consumption, and energy use, relying on fossil fuels (Kanitkar et al., 2024 ). This provocative finding aligns with that of Hasegawa et al. (2018), who found that stringent climate change mitigation measures, such as afforestation, CO 2 pricing, and bioenergy crop production, will have a more significant negative impact on food insecurity than climate change itself. Fujimori et al. ( 2022 ) calculated that 2°C stabilization pathways will put an additional 79,4 mln people at risk of hunger by 2100. The negative effect of climate change mitigation on food security can be attributed to the fact that scenario estimations are based on impact assessment models, which typically focus on cost-optimizing mitigation pathways, thereby neglecting societal aspects (Jaiswal et al., 2024 ). Apart from climate-related vulnerabilities, challenges in food supply chains include higher energy and transportation costs, demand volatility, inventory management of perishable goods, and marketplaces driven by the growth of e-food businesses and circularity approaches. These challenges became more transparent due to supply disruptions caused by the COVID-19 pandemic and geopolitical tensions, underscoring the need to introduce sustainability and circularity practices within food systems to enhance resilience. Sustainability practices in food supply chains, including circular food systems, are well-explored primarily at the local operational level (Schroeder et al., 2018 ; Principato et al., 2023 ). At the system level, agricultural macroeconomics, resilient food supply chains, and circular systems are well analyzed from a strategic perspective, providing indices for monitoring sustainable, circular, and resilient performance (Miranda et al., 2021 ). However, systematic approaches and comprehensive frameworks at global and local scales are still lacking to establish synergies between circular and resilient food supply chains, on the one hand, and between global and local food systems, on the other, while balancing sustainability, profitability, and technological advances at strategic, tactical, and operational levels. Thus, the present paper provides a holistic decision-support framework to systematically integrate measures relevant at various levels, estimate strategies across global and local food systems, as well as circular and resilient food supply chains, driven by climate mitigation and adaptation. Using a System Dynamics (SD) model prior to interviews conducted in Germany, we develop interconnected linkages among macroeconomic parameters, climate futures, and socio-economic-technical innovations relevant to sustainable and resilient food supply chains. Following IPCC’s Shared Socioeconomic Pathways (SSP) narratives, various indicators such as population growth, demographic change, urbanization, economic development and technological process, as well as collaboration among national economies are systematically integrated into the SD model. The SSP scenarios for food security are summarized in Fig. 1 , relying on frameworks by Bauer et al. ( 2017 ) and Mitter et al. ( 2020 ). Each author team describes key scenario drivers, including global markets, short versus complex supply chains, circularity strategies, sustainable lifestyles, technological developments, and macroeconomic parameters. These narratives are then turned into scenarios with quantitative projections of energy use, land use, and greenhouse gas emissions. The present scenarios are based on the forecastings of the Representative Concentration Pathways (RCP), which include more consistent short-lived gases and land-use changes. However, they are not necessarily more capable of representing future developments from macroeconomic, demographic, and technological perspectives (IPCC, 2024). The scenarios are parametrized in detail for four SSPs in order to model the output parameters, as GDP in the food sector, investments in the agricultural sector, international climate financing, climate resilience of food systems, consumer food prices, number of people at food poverty risk, and total greenhouse gas (GHG) emissions from the food sector. Figure 1 : The Eur-Agri-SSPs (based on O’Neill et al., 2017, O’Neill et al., 2017). Based on the theoretical concepts and parameterization of all relevant factors, we apply a mixed-methods approach that includes interviews, fieldwork, and participatory modelling techniques. Simulation runs were conducted for the SSPs, providing clear, measurable transformation pathways for food systems towards more resilient, circular systems. The SSPs are parameterized based on literature and verified with stakeholders. The research design is presented in the Fig. 2 , which implies the conceptual framework of the paper, as an innovative one. In some cases, where the parameters are rather qualitative (such as connected/disconnected global markets), we assign school notes ranging from 0 to 3 during the workshops. While the scenario development process is outside the scope of the paper, the paper focuses on the parametrization of the SSP narratives. The only difference between the scenarios presented in Fig. 1 and the original framework developed by Mitter et al. ( 2020 ) is the neglect of SSP2 (Middle Way), to reduce the complexity of the SD simulations, without losing any context. Figure 2 : Conceptual Framework of the paper. RESEARCH CONTEXT Globalized trade has fundamentally transformed the global food system, underscoring the increasing importance of supply security (Norton et al., 2014). Complex market structures, demand uncertainties, increased economic competition, consumer pressures, and stricter political regulations have increased the need for transformation toward sustainable, resilient supply chains (Negri et al., 2020). Global supply chains and their physical distribution systems proved fragile to external shocks and crises, revealing their vulnerability within complex global production networks and across industries and regions (Sarkis, 2020). As a result of the COVID-19 pandemic and the Russian-Ukrainian war, international production and supply chains have been severely disrupted, leading to acute shortages of components and end products. The supply chain disruption effects are, among others, more significant in the agri-food sector, as the risk of food insecurity has risen substantially in the international context, from both strategic and operational perspectives, driven by regional climate change and restrictions on material and people movement (Kazancoglu et al., 2021). The Russian-Ukrainian war significantly disrupted global food production and supply, as Russia and Ukraine accounted for approximately 30% and 20% of global wheat and corn exports, respectively. Ukraine was the world’s leading exporter of sunflower oil, followed by Russia. With wheat and grain exports totaling 50 million tons, Ukraine was also the largest contributor to the UN World Food Program (Xu et al., 2020). Supply problems for seeds, fertilizers, and pesticides, as well as logistical bottlenecks, deepened the problem. In particular, the logistics industry, as the integrator and enabler of supply chains, has suffered from disruptions caused by the COVID-19 pandemic (UNICEF, 2020) and the Russian-Ukrainian war over the past years. Congested ports and longer detention times for containers caused severe delays, jeopardizing the survival of shipping companies. Besides monetary damages, shipping companies faced challenges related to standoff regulations, quarantine requirements, employee vaccination requirements, climate-related infrastructure disruptions, and general employee shortages. The disruption factors caused by the pandemics and political instabilities come in addition to the already well-known factors, such as climate-related impacts (droughts, flood, hurricanes) as well as funding crises (management shortages in natural resources), regional and global geopolitical instabilities, population dynamics, transport and logistics (Biehl et al., 2017). Thus, analyzing the factors relevant to resilience across various stages of the food supply chain and systematically integrating them into a comprehensive decision-making framework increases transparency and the efficiency of supply chain governance at both global and local scales. Specifically, this paper examines the tensions between local and global, sustainable, resilient and circular food systems, identifying the mechanisms for balancing climate mitigation and adaptation strategies. RESILIENT AND CIRCULAR FOOD SYSTEMS Resilient Food Systems Ecologically resilient, self-organized, or local food systems are considered most resilient (Worstell & Green, 2017). The current issues related to climate change, degraded agro-ecological conditions, unstable governments, poorly functioning institutions, and the effects of COVID-19 and the Russian-Ukrainian war underscore the urgent need to transform food security policies for more resilient systems (Devereux et al., 2020). Schipansky et al. (2016) define resilient food systems as the coping of the actors with interacting and cumulative forces that undermine food access and equity, while defining the sources of vulnerability to be the sudden shocks (e.g., catastrophic weather events), intermittent shocks (e.g., price volatility), and gradual pressures (e.g., climate change and shifting human diets). To operationalise resilience in food systems, several indices have been developed. For example, the Food and Agricultural Organization (FAO) defines the food resilience index to be dependent on household income and food access, access to basic services, (non) agricultural assets, agricultural practice and technology, social safety, climate change, enabling institutional environments, sensitivity, and adaptive capacity (FAO, 2014). The Baseline Resilience Indicators for Communities apply indices of socio-economic, institutional, infrastructural, community, and environmental capacities (Cutter et al., 2010). The Region Resilience Index comprises indicators of regional economic, socio-demographic, and community connectivity attributes (Pendall et al., 2010). The Livelihood Vulnerability Index, integrates socio-demographic profiles, social networks, health factors, water availability, natural disasters, and climate variability (Hahn et al., 2009). Barman et al. (2021) investigated the resilience of food supply chains against disruptions like COVID-19, shocks, and outbreaks utilizing conceptual, empirical, and descriptive methods, as well as optimization-based modeling to simulate household food consumption, national food production, food exports and imports, as well as food retail and volatility prices. Poo et al. (2024) explored overall resilience in the food supply chain by developing the Integrated Food Supply Chain Resilience Framework to estimate, using simulation techniques, descriptive factors such as production-to-supply ratio, food demand, water availability, national food resilience, and economic losses due to food insecurity. These indices cover practical, temporal, and financial aspects of the disruptions at operational level, but provide no decision framework for dealing with uncertainty, which is addressed in this paper. Yet the paper draws on some of the parameters from the literature on food resilience indices, thus combining the strategic and operational levels. Circular Food Systems Circular food systems fundamentally differ from conventional food supply chains (Bruel et al., 2019). Circularity in food systems is described as a spectrum of practices that go beyond end-of-life waste recovery to include design for circular flows, valorization of by-products, and systemic reconfiguration of production–consumption–waste networks. This conceptualization shows the need for indicators that reach across contexts and stages of the food chain. They should capture aspects such as material loops, by-product utilization, as well as governance and ecosystem interactions (Chiaraluce et al., 2021; Rodino et al., 2023). Clear ways to measure the circularity impacts are necessary to understand the systemic effects in different places, e.g., cities vs. rural areas, richer vs. poorer regions, core vs. peripheral economies (Borrello et al., 2020). In contrast to other products or industries (e.g., rare earths), food cycles should be closed locally at the operational level rather than aiming for a global solution, meanwhile adopting and implementing strategic decisions at global levels (Ibn-Mohammed et al., 2021). To analyze circularity in food systems, Angili et al. (2022) and Nikkhah et al. (2021) have utilized Life Cycle Assessment (LCA) to measure material and energy efficiency, CO 2 emissions, and waste reduction, focusing on the ecological impacts of material and energy usage. Falcone et al. (2022), Ribeiro et al. (2022), and Saba et al. (2022) integrated Environmental Life Cycle Costing and the Material Circularity Indicator to measure circularity, thereby expanding the LCA technique to include the economic dimension. The identified parameters included are e.g, the use of virgin raw materials, non-recoverable waste production, waste management efficiency, waste valorization processes, and the amount of saved water. Morales and Lhuillery (2021) included value chain and resource allocation efficiency as critical parameters. However, a more holistic and dynamic approach is needed to integrate all relevant parameters into a decision-support system for food security and its various dimensions, enabling informed, systemic, inclusive, and predictive decisions. RESEARCH DESIGN Scenario planning is widely used in academic literature as a valuable method for developing and evaluating probable future scenarios. However, most studies have conventionally concentrated on forecasting models (Lim & McAleer, 2002), econometric models (Croes & Vanegas, 2005), and neural networks (Palmer et al., 2006). These models operate under the fundamental assumption of linearity, neglecting the dynamics and interactions of the diverse feedback mechanisms within these systems (Jamal et al., 2004). In contrast, SD elucidates relationships by correlating system behavior with feedback loops inherent within the system, thereby surpassing the notion of linearity, in contrast to alternative methodologies (Sterman, 2000 ). Especially in cases relevant to the food system (Rejeski, 1998 ; Sterman, 2000 ), the SD approach is based on accumulating stocks and flows, as well as feedback control, to evaluate complex situations through mathematical simulations, on the premise that any complex situation can be described in terms of simple elements. SD assumes that things are interconnected in complex patterns and that nonlinearities and time delays are paramount to the system behavior arising from its structure (Sterman, 2000 ). SD primarily focuses on determining the system’s structure (Sterman, 2000 ), followed by ex-ante estimates that the “future system states are replicated from the SD model” (Winz et al., 2009 ). The modeling procedure adopted in this study is based on Sterman ( 2000 )’s process, which involved five stages: Problem articulation : The current problem for decision-makers is the extent of (re-) regionalization in food supply chains, whilst considering climate mitigation, adaptation, and circularity strategies. This problem arises from the lack of measured trade-offs and synergies to support decisions on strategic, tactical, and operational levels of food systems. Dynamic hypotheses : Trade in the era of globalization has not only increased efficiency but also brought fragility to food supply chains. In the wake of disruptions, responses such as localization, diversification, and circular practices have come to the forefront. Thus, there is a systemic oscillation between efficiency-driven globalization vs. resilience-driven localization, where strengthening feedback through policy, innovation, and governance enhances long-term food security and sustainability in food supply networks. Formulation : This stage involves developing the causal loop diagram (CLD) and stock and flow diagram (SFD). To develop the CLD, we used the Participatory Systems Mapping (PSM) framework in Germany (see Fig. A- 1 in the Appendix , which is based on the theory by Lopez & Videira, 2015). The model assumptions and the parameterization process are explained in detail in the supplementary information (Table S.-2). The interviews helped to derive the structure of the causal diagram by confirming recurring patterns and feedback mechanisms. In addition, qualitative statements were interpreted quantitatively when literature values were missing or when data were widely varying. With this approach, the interviews ensured that the model was not based solely on assumptions from the literature, but rather realistically reflected regional dynamics, bottlenecks, and opportunities for action. Data collection protocol (Fig. A.-2), interviewee profiles (Table A.-1), and general information of interviews (kew questions, product types, key statements) (Table A.-2) are included in Appendix . Next, an SFD was created to emphasize the system’s underlying physical structure. Testing : As the process involves quasi-quantitative variables, validation is a critical component of any model-based methodology. Following Barlas (1994), the model was tested for robustness through both structural and behavioral validity tests. While behavioral validity tests assess whether the model’s behavior closely aligns with dominant patterns observed in a real system, structural validity tests examine if significant structural errors exist within the model. One of the simplest approaches is using the model check and unit check functions available in Vensim. The qualitative integration of the interviews strengthened both structural validity (model architecture) and behavioral validity (plausibility of system responses), in line with standard SD methodology. Policy formulation and evaluation : Once sufficient confidence in the model structure and behavior is achieved, it is used for policy design and evaluation, presented in Discussion. The third stage (formulation) with PSM (Fig. A.-1 in Appendix ) was considered particularly crucial for enhancing the model’s reliability by integrating various group process techniques, such as qualitative interviews, workshops, discussions, brainstorming sessions, and fieldwork, with SD modeling (Sterman, 2000 ; Hanson et al., 2005; Stave et al., 2017). A total of 41 semi-structured telephone interviews were conducted with 68 agricultural and food-processing businesses organized within the Genussregion Niederrhein association in the Lower Rhine Region, Germany. The interviews were conducted according to a guideline covering six topics: business structure, production, sales channels, logistics, sustainability/circularity practices, as well as cooperation and digitization interests. The interviews provided empirical information about real operational structures, challenges, and decision-making logic. They tested the plausibility of the assumptions formulated in the model and ensured that the modeled feedback reflected the actual dynamics in regional food networks. The interviews identified key operational and structural drivers, which were later reflected in the model variables (see Tables A.-1 and A.-2 in Appendix ). The interviews reveal several recurring patterns across all businesses that are central to the modeling. Direct marketing continues to dominate sales, while online retail is largely unused and generates only low revenue. As a result, businesses remain heavily dependent on regional markets. In logistics, fresh produce logistics pose major challenges. Many businesses report high transport costs and cold chain-related restrictions; 14 interview partners express concrete interest in joint logistics solutions. In the area of marketing, most businesses have a website but rarely or never use social media. Online shops exist but play hardly any economic role. Sustainability is generally seen as important, but businesses often lack the financial resources to implement certifications or investments in circular practices. Finally, the business structure shows that most are micro-enterprises with very few employees, which limits their organizational resilience and capacity for innovation. SYSTEM DYNAMICS MODEL By assessing cascading effects and dynamic interactions, the SD model provides descision support system to the policymakers to balance climate mitigation targets with structured transformation strategies toward a sustainable, circular, and resilient food network. The SD model is developed, simulated, and tested for Germany, given the availability of trustworthy data from the interview analysis and statistical service. To create a better understanding of the prevailing feedback loops in this complex model, the model has been subdivided into four divisions: population dynamics (black); macroeconomics of food dynamics including geopolitics (blue), transportation and energy dynamics (orange), as well as climate resilience and natural production potential coupled with environmental (natural) resources (green) (see Fig. 3 ). Figure 3 : Holistic SD model of Sustainable Food System in Germany The operationalization of the model is derived from the parameters along all dimensions of food security, food production and consumption and their influential factors, as well as strategic areas of food security. These factors are systematically brought together with the main topic areas of the SSPs, which are global markets, trade restrictions, environmental standards, resource degradation, sustainable lifestyles, political regulations and technological advances. All the parameters and their values are summarized in Table A.-3 in the Appendix . Population dynamics The population dynamics part-model shows the change in the net population (the sum of populations across age groups) of Germany from 2020 to 2040. Immigration and emigration are considered as key indicators of population dynamics in Germany, driven by the Russian-Ukrainian war. During the ‘war scenario’, the war factor drops down to a lower value (below 0.5) till the duration of the war (decided by varying the variable “war duration to”), indicating the period of instability caused due to the outbreak of the war. In the event of the war lasting longer than 2028, it is evident that there will be large-scale immigration from war-affected countries to Germany. Macroeconomic dynamics The categories of food considered in this model are winter wheat, dairy products, and meat, which are the most vital food sources for human nutrition. Land conversion, water and soil quality, yield production, and labor in the agricultural sector describe aspects of food production, whereas food prices and nutrition requirements describe aspects of food consumption. Moreover, the export and import of food also strongly depend on the food price index and geopolitical instability. Germany was heavily dependent on imports of winter wheat from Ukraine/Russia; thus, the war significantly increased procurement costs, affecting commodity prices. To meet the increasing food requirements, one approach is to increase the number of cultivation cycles. While this directly impacts local production, it causes soil nutrients to be depleted at much higher rates, rendering the soil non-arable in the long run if not adequately maintained. Furthermore, with each production cycle, the marginal yield of the land tends to decrease, thereby reducing the overall yield potential of the farmlands. Increased energy and fertilizer prices, climate-driven parameters, and the shortage of farm workers are limiting factors. Another loop in the model focuses on population growth, the impact on arable land, and the land requirements for residential purposes. This impacts the conversion of the land, accompanied by increasing urbanization levels. This overall situation significantly reduces the total available land in Germany. Transportation and energy dynamics Food production, consumption, and distribution change at both local and global levels due to increased transportation demands. This increases carbon emissions and a country’s total energy requirements, which in turn leads to higher energy prices. It is noted that transportation and logistics costs rise as the amount of food transported per person and energy prices increase. This impacts the overall cost of food supplies, making the system unsustainable and highly vulnerable to external disruptions in the long run. The energy prices have been significantly impacted by the Russian-Ukrainian war, reflected in increased transportation costs and last-mile delivery costs. Therefore, Germany needs to become self-sufficient in renewable energy production, which is highly relevant not only to food production systems but also to the logistics sector, supporting the adoption of electric vehicles (EVs), reducing the dependency on fossil fuels. This situation also impacts the adoption of renewable energy production mechanisms, making a modest contribution to the country’s overall energy needs. Beyond electrifying fleets, another food logistics strategy is to drastically shorten food supply chains, introducing the farm-to-fork strategy (direct distribution of food from producers to consumers). We described this parameter as binary (0 – no direct distribution, 1 – implementation of a farm-to-fork strategy). SIMULATION RESULTS The simulations were conducted in Vensim® PLE 8.2.1 (64-bit version) application. The simulation was run between 2022 and 2040 with a Time step of 1 year. Macroeconomics in the food sector Figure 4 shows that GDP gets its highest values for SSP4 in the beginning of the sumilation runs, yet within the end of the next decade it reaches the highest values for SSP1, explained by the high investments in agricultural sector, advanced technological developments, trade liberalization, strict policy in environmental regulations, shortening of food supply chains (farm-to-fork strategy with local food sourcing), increased circularity strategies, as well as sustainable lifestyles. Noteworthy is that focusing on adaptation (SSP4) rather than on mitigation strategies leads to a higher increase in GDP (SSP2), which is a striking result and might depend on the relatively short simulation period, given that mitigation strategies require a longer period of impact. Another interpretation of this result is higher need of climate insurance policies, especially in the food sector. Figure 4 : GDP in Food Sector and Investments in Agriculture for the BaU and SSP scenarios A sharp increase in GDP in the SSP4 scenario may also be explained by further market globalization and a significant rise in food imports (Fig. 5 a). The correlation between GDP and food imports supports this hypothesis (Fig. 5 c). Moreover, high GDP in the food sector and globally connected markets (SSP1 and SSP4) will lead to higher funding availability for global climate financing (Fig. 5 b). This might increase investments in technological development in the food sector (including new agricultural and farming practices) and investments in more efficient adaptation strategies. An interesting observation here is that, in SSP1 and SSP4, Germany has higher international climate financing possibilities (Fig. 5 b). In SSP4, this might be explained by indirect financial flows driven by food imports (Fig. 5 a). Figure 5 : Development of the Food imports (a) for various scenarios, International climate financing (b), and the correlation between Food imports and GDP in the food sector for the SSP4 (c). Consumer food prices The increase in GDP and demand for food will lead to a sharp rise in consumer food prices (Fig. 6 ). It is forecasted that the average price of food will rise to Euros 4,534 per ton from an initial average of Euros 3,288 per ton (37.9% increase). The influx of population due to geopolitical instabilities, combined with fuel and winter wheat supply restrictions, put enormous pressure on food sustainability and total food consumption. To meet the increase in food consumption from 12 to almost 20 mln tons by 2040, agricultural lands must be expanded from the current 166.45 km² to 201.43 km² by 2040. This represents an apparent tension in land competition, as Germany simultaneously witnesses a strong urbanization trend (from the current 36,996 thousand km² to 76,996 thousand km² by 2040). Figure 6 : Consumer Food Price and number of people at the risk of food poverty for all the scenarios. As shown in Fig. 6 , consumer food prices exhibit almost no fluctuating trend for BaU. However, the number of people at risk of food poverty increases to the same level as in the SSP4 scenario. This finding highlights that globally connected markets, advanced technologies, or increases in GDP will reduce the risk of food poverty, thereby increasing food security. On the contrary, the only way to significantly decrease the risk of food poverty is to proceed with the SSP1 scenario, pursuing sustainable lifestyles, circularity strategies (with a strong reduction in food waste), and shortening the food supply chains (a farm-to-fork strategy with local sourcing). Environmental parameters Total GHG emissions across the food supply chains (including food transport), as well as climate change resilience (considering irrigation, land conversion), are presented in Fig. 7 . Along with SSP1, SSP2 exhibits higher local climate resilience, dependent on resource-efficient technologies, primarily used for mitigation activities, compared to the high-tech path. Yet, even though significant progress has been made in cutting these emissions, the simulation shows that total GHG emissions might almost double over the next decade, reaching 200 to 350 million tons of CO 2eq by 2040 under BaU. Almost 4% of these emissions are generated from food waste, resulting in 27.47 million tons of CO 2eq by 2022. Yet, in contrast to total GHG emissions, emissions from food waste are expected to almost triple, reaching 60 million tons of CO 2eq by 2040. As shown in Fig. 7 , GHG emissions are lowest in SSP1. This observation is provocative because it indicates that slow technological development in SSP2 and SSP3 doesn’t affect emissions, whereas resource overexploitation does. Figure 7 : Total GHG emissions along the food supply chains (bottom diagram) and climate change resilience index (bottom diagram) Summary The most striking results are that, in the SSP4 scenario with globally connected supply chains and a strong reliance on technological developments, GDP in the food sector rises significantly from its current level of 160 mln Euros to nearly 450 mln Euros by 2040. Yet, the consumer food price index also rises significantly from its current level of 132 to 135 by 2040. Hence, the risk of food poverty increases, placing an additional 2 mln people at risk. Moreover, focusing on adaptation strategies in SSP4 leads to a higher increase in GDP than focusing on mitigation strategies, as seen in SSP2. With a high GDP in the food sector and globally connected markets resulting from SSP1 and SSP4, this increases funding availability for global climate financing, supporting technological advances in adaptation strategies at the global level. The most stable scenario for both locally increased social resilience and ecologically safe food systems is the SSP1, where the markets are open, yet, food supply chain is short and transparent (farm to folk strategy, where direct marketing and distribution of food from producers to consumers takes pace) as well as society is aware of sustainability following sustainable lifestyles with less food waste. DISCUSSION The development and implications of circularity and resilience in food systems require a joint, multi-level framework that integrates indicators of circular flows while measuring adaptive capacity at different scales. The literature consistently calls for: (i) theory-based models of circularity; (ii) context-appropriate indicator sets that cover material, economic, governance, and ecosystem aspects; (iii) resilience metrics that track exposure, adaptation, and recovery at the household, regional, and national levels; and (iv) pathways informed by governance and data that turn measurements into actionable decisions and policy (Agyemang and Kwofie, 2021; Chaudhary et al., 2018). Thus, integrating circularity and resilience into a single, clear decision-making system is paramount. Our model addresses the need for harmonized, but flexible indicators that drive concrete action and allow comparisons across different contexts, meaning that indicator sets can be tailored to subsectors (e.g., wheat) and then generalized to broader agri-food contexts with careful standardization (Borrello et al., 2020). Addressing these literature-based observations, our simulation model has demonstrated that positive effects are reflected in resilient food strategies and efficient circular food systems, which will have a greater impact on food transformation. The circular food market is expected to continue growing, despite the population’s diet habits unlikely to change significantly (average food alternative consumption fluctuates due to cultural differences among the migrant population). However, food waste is expected to further increase, leading to a significant rise in GHG emissions from the food sector (and in total). This is concerning since reducing food waste and valorizing food byproducts are primary goals of circular food systems, which are crucial for enhancing sustainability. Food waste significantly contributes to environmental degradation. Effective waste management strategies can mitigate the environmental impact of food production. This would require classifying food waste as a resource for novel products derived from underutilized biomass, which consumers are willing to purchase (McCarthy et al., 2019 ). Reducing food waste at downstream stages of the supply chain can lower the demand for inputs in upstream processes, such as in food processing. These interconnections highlight the importance of managing food waste not as an isolated issue but as part of a broader system encompassing all stages of the food supply chain (Kumar et al., 2022). While food alternative consumption fluctuates in our results, a shift towards circular food systems may lead to a transformation in dietary patterns. A circular approach to food production can support healthier consumption patterns by emphasizing plant-based foods with enhanced nutritional value while reducing reliance on processed foods. It is assumed that the share of people who reduce their per capita consumption of meat and animal-based products increases annually by 1% (Rabès et al., 2020 ). Thus, we assumed that 10% of people will have a vegetarian diet by 2040 (currently about 6 mln). This requires consumer awareness and a willingness to substitute animal-sourced foods for environmental goals (van Selm et al., 2022 ), thereby further stimulating growth in the circular food market. Finally, policies that promote resilience in food systems often emphasize the importance of diversity and local sourcing. More local and regional food production can enhance supply chain resilience by reducing dependence on single sources of supply, thereby improving food availability during crises (Marusak et al., 2021 ). Governance strategies For example, local governments may adopt sustainability strategies to address immediate challenges, such as disaster risk management, while pursuing comprehensive approaches that aim for broader environmental and social changes within communities (Ji & Darnall, 2020). Additionally, effective governance strategies positively impact the achievement of the SDGs. Their effects can be cumulative and long-lasting if they are continuously refined and adapted to new challenges, emphasizing the necessity of integrated and coordinated sustainable policies that account for additional variables. In particular, the population growth rate highly influences the relationship between sustainable governance and sustainable development indicators, indicating an inverse relationship between population growth and sustainable development (Gündoğdu & Ayteki̇n, 2022). These observations indicate that sustainable governance should not be viewed as static, but rather as a dynamic process that can adapt to changing circumstances, necessitating regular assessment and adjustment to remain effective. Food prices It was shown that the average prices of food (winter wheat, dairy, and meat) rose to Euros 4,232 per ton, initially from an average of Euros 3,243 per ton (a 30.5% increase). Labor shortages and increased costs of commodities and fertilizers also pressure food prices, which are amplified by geopolitical tensions (Adjemian, 2023). Related policy responses have led to reduced food production and international trade, which in turn have destabilized food prices and caused food crises in various regions (Bai et al., 2022 ). The implications of rising food prices extend beyond economic factors. They also pose significant risks to food security and social stability. Accordingly, volatile food prices can lead to severe food insecurity, forcing households to compromise between food and other essential needs. Food Logistics The farm-to-fork concept (EU, 2020) ensures the direct marketing and distribution of regional food through shortened supply chains and environmentally aware consumption. This is directly linked to the reduced transportation costs and the lower carbon footprint achieved due to the shorter distance traveled within the region (Poo et al., 2024 ). It is interesting to note that, despite the involvement of the EVs and implementation of the farm-to-fork concept, the cost of energy is not reduced and stays at the same level (increased from an average of Euros 1.4 per gallon to Euros 7.6 per gallon). While a sustainable lifestyle may help reduce fuel dependency, the simulation results are counterintuitive. This could be because, while the farm-to-fork model will result in savings in the upstream supply chain (bulk orders at low frequency), it might fail in the downstream (small orders at high frequency). This increase in downstream transportation shadows the savings that could be realized through using EVs for transportation. Regarding CO 2 emissions, the production of winter wheat and the adoption of a farm-to-fork model may not be feasible, as it is associated with high outbound logistics costs and low shipment sizes compared to imported resources. CONCLUSION Our model effectively captured the complex relationships among global and local variables that have a substantial impact on the food system. The developed SD model comprehensively captures these complexities on a global scale, but it could also consider fundamental properties of resilience and circularity for sustainability, which can help establish resilient systems and incorporate diversity strategies, adaptability, cohesion, and eco-efficiency, thereby developing a new narrative for the food economic system. In this vein, there are numerous interrelationships between strategies, processes, and stakeholders in global food systems that mutually affect one another. Our model tries to capture these dynamics and serves as a tool to mitigate future disruptions in food supply chains. The following points summarize the key takeaways for (political) decision-makers from this study: Strict political regulations, glocalization strategies (global value chains, yet local and transparent food supply chains), increased circularity strategies, and sustainable lifestyles contribute to high GDP in the food sector. High GDP values are achieved when focusing on adaptation strategies (SSP4) rather than mitigation strategies (SSP2), resulting in higher climate resilience for a short time of period. If GDP doubles (SSP4) (through increased technologies in adaptation, high investments in agriculture, and increased global trade), climate financing funds will increase, yet, in general, climate financing doesn’t help the reduction of CO 2 locally; in fact, it increases 4 times. Even though the consumer food prices are relatively low (due to increased food availability through imports), the local poverty risks will increase, because local production will decrease coming along with resource depletion, increased energy consumption, less regenerative energies, and increased food loss. GDP must increase four times to escape the negative effects (SSP1), but this is highly sensitive to policy regulation, technology, consumer behavior, and short local food supply, even though markets are open and globally connected. GDP is not a good indicator of food security. For an overall increase in food security, policy plays a less significant role than consumer behavior and a focus on technology for mitigation and increased climate resilience. Global supply chains contribute to economic efficiency, but do not reduce the risk of food poverty. Declarations Conflict of Interest Statement: The authors declare no conflict of interest. Author Contribution Ani Melkonyan-Gottschalk is the corresponding author, mainly setting the paper design, idea, research focus, innovation. She also coordinated the entire paper writing process, extracted the results.Vasanth Kamath has mainly designed the System Dynamics model and ran the simualations. 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06:31:23","extension":"html","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":170896,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8321026/v1/855bc8f0a8ceff8d57d69f3b.html"},{"id":100947386,"identity":"504ae12f-1d44-4ab1-a12c-366c16065a35","added_by":"auto","created_at":"2026-01-23 06:31:23","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":195049,"visible":true,"origin":"","legend":"\u003cp\u003eThe Eur-Agri-SSPs (based on \u003ca href=\"https://www.sciencedirect.com/science/article/pii/S0959378020307421#b0320\"\u003eO’Neill et al., 2017\u003c/a\u003e, \u003ca href=\"https://www.sciencedirect.com/science/article/pii/S0959378020307421#b0320\"\u003eO’Neill et al., 2017\u003c/a\u003e).\u003c/p\u003e","description":"","filename":"Folie1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8321026/v1/521bee09d5017b0e69e90e43.jpg"},{"id":100951640,"identity":"3c5818f3-955e-43ab-995b-fe20cb90ed47","added_by":"auto","created_at":"2026-01-23 07:11:02","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":162665,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual Framework of the paper.\u003c/p\u003e","description":"","filename":"Folie2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8321026/v1/f59a1a6ac2c8f9a7a76ec211.jpg"},{"id":100952437,"identity":"ac3a38aa-11ce-4b33-8fc3-a0b925d2594a","added_by":"auto","created_at":"2026-01-23 07:16:12","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":156083,"visible":true,"origin":"","legend":"\u003cp\u003eHolistic System Dynamics model of Sustainable Food System in Germany\u003c/p\u003e","description":"","filename":"Folie3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8321026/v1/ef1e400cee8da84b75d70399.jpg"},{"id":100951558,"identity":"8d40d418-6105-422d-96c4-d865c90da8a8","added_by":"auto","created_at":"2026-01-23 07:10:52","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":116900,"visible":true,"origin":"","legend":"\u003cp\u003eGDP in Food Sector and Investments in Agriculture for the BaU and SSP scenarios\u003c/p\u003e","description":"","filename":"Folie4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8321026/v1/037a909d70fe755058827743.jpg"},{"id":100952297,"identity":"1dc14224-c268-49b4-aed9-d0d8934967a4","added_by":"auto","created_at":"2026-01-23 07:12:35","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":105790,"visible":true,"origin":"","legend":"\u003cp\u003eDevelopment of the Food imports (a) for various scenarios, International climate financing (b) and the correlation between Food imports and GDP in the food sector for the SSP4 (c).\u003c/p\u003e","description":"","filename":"Folie5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8321026/v1/d6b67830466636799c57055e.jpg"},{"id":100947366,"identity":"16c652ac-eebc-4176-aa1b-90b06dccccd0","added_by":"auto","created_at":"2026-01-23 06:31:22","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":113464,"visible":true,"origin":"","legend":"\u003cp\u003eConsumer Food Price and number of people at the risk of food poverty for all the scenarios.\u003c/p\u003e","description":"","filename":"Folie6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8321026/v1/0c4576b210050a8d006bc700.jpg"},{"id":100951547,"identity":"3e93c49a-b232-4355-bc11-908dd792a43f","added_by":"auto","created_at":"2026-01-23 07:10:49","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":82073,"visible":true,"origin":"","legend":"\u003cp\u003eTotal GHG emissions along the food supply chains (bottom diagram) and climate change resilience index (upper diagram)\u003c/p\u003e","description":"","filename":"Folie7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8321026/v1/98e9c38f64817c415ef504b5.jpg"},{"id":104758490,"identity":"c4657e0d-4055-43ac-99d1-0e1d14585c66","added_by":"auto","created_at":"2026-03-16 23:54:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1596281,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8321026/v1/9bf5bcfe-ecdd-4fbc-95e6-0eda00c792ad.pdf"},{"id":100947358,"identity":"e39db8ed-81ed-489f-a68b-c60e577d7dca","added_by":"auto","created_at":"2026-01-23 06:31:22","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":96768,"visible":true,"origin":"","legend":"","description":"","filename":"supportinginformationtemplatenewfinal1.doc","url":"https://assets-eu.researchsquare.com/files/rs-8321026/v1/25af5c04506214220543bf8a.doc"},{"id":100951056,"identity":"966c0f3d-c6b8-490f-97cd-7781d709c694","added_by":"auto","created_at":"2026-01-23 07:09:55","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":343007,"visible":true,"origin":"","legend":"","description":"","filename":"APPENDIX.docx","url":"https://assets-eu.researchsquare.com/files/rs-8321026/v1/0540e8072dadaba392aaa767.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dynamic transformation toward resilient and circular food systems: A decision support framework for food security","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eGlobal societal welfare, ecological productivity, and food security are drastically changing, driven by climate change impacts on agricultural systems (Challinor et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), rapid urbanization (Satterthwaite et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), demographic developments and migration patterns (Andrews, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), resource overconsumption (Hoekstra \u0026amp; Wiedmann, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), shifts in consumption behavior (Kakaei et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), geopolitical instabilities (Browning \u0026amp; Brassett, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and pandemics (Bretschger, 2020). Specifically, food security relies on both stable food production and consumption systems, in which sufficient food quantity (both locally produced and imported), access, and utilization play a central role. Because food systems are directly linked to ecosystems through provisioning (water, timber, and genetic resources), regulatory (floods, diseases), and supporting (soil formation, water, and nutrient cycles) services, their resilience is threatened by climate change (Khoury, 2014; Campbell et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Specifically, the modeling of climate change impacts on food systems has improved significantly, largely due to advances in parameterization and model complexity. Thus, decision-making under uncertainty has been well-explored, which helps inform transformative actions, such as the WILIAM Model (Mediavilla et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Yet, research on food security dynamics from a systemic perspective, encompassing all dimensions of food security, such as socio-technical advances, the demand side, macroeconomic perspectives, and infrastructure, remains limited (IPCC, 2014; Campbell et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIncreased food insecurity and extended global inequalities result from several climate mitigation pathways across various generations of IPCC scenarios, assuming that developed countries will continue to sustain higher GDP levels per capita, consumption, and energy use, relying on fossil fuels (Kanitkar et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This provocative finding aligns with that of Hasegawa et al. (2018), who found that stringent climate change mitigation measures, such as afforestation, CO\u003csub\u003e2\u003c/sub\u003e pricing, and bioenergy crop production, will have a more significant negative impact on food insecurity than climate change itself. Fujimori et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) calculated that 2\u0026deg;C stabilization pathways will put an additional 79,4 mln people at risk of hunger by 2100. The negative effect of climate change mitigation on food security can be attributed to the fact that scenario estimations are based on impact assessment models, which typically focus on cost-optimizing mitigation pathways, thereby neglecting societal aspects (Jaiswal et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eApart from climate-related vulnerabilities, challenges in food supply chains include higher energy and transportation costs, demand volatility, inventory management of perishable goods, and marketplaces driven by the growth of e-food businesses and circularity approaches. These challenges became more transparent due to supply disruptions caused by the COVID-19 pandemic and geopolitical tensions, underscoring the need to introduce sustainability and circularity practices within food systems to enhance resilience. Sustainability practices in food supply chains, including circular food systems, are well-explored primarily at the local operational level (Schroeder et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Principato et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). At the system level, agricultural macroeconomics, resilient food supply chains, and circular systems are well analyzed from a strategic perspective, providing indices for monitoring sustainable, circular, and resilient performance (Miranda et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, systematic approaches and comprehensive frameworks at global and local scales are still lacking to establish synergies between circular and resilient food supply chains, on the one hand, and between global and local food systems, on the other, while balancing sustainability, profitability, and technological advances at strategic, tactical, and operational levels.\u003c/p\u003e \u003cp\u003eThus, the present paper provides a holistic decision-support framework to systematically integrate measures relevant at various levels, estimate strategies across global and local food systems, as well as circular and resilient food supply chains, driven by climate mitigation and adaptation. Using a System Dynamics (SD) model prior to interviews conducted in Germany, we develop interconnected linkages among macroeconomic parameters, climate futures, and socio-economic-technical innovations relevant to sustainable and resilient food supply chains. Following IPCC\u0026rsquo;s Shared Socioeconomic Pathways (SSP) narratives, various indicators such as population growth, demographic change, urbanization, economic development and technological process, as well as collaboration among national economies are systematically integrated into the SD model.\u003c/p\u003e \u003cp\u003eThe SSP scenarios for food security are summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, relying on frameworks by Bauer et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Mitter et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Each author team describes key scenario drivers, including global markets, short versus complex supply chains, circularity strategies, sustainable lifestyles, technological developments, and macroeconomic parameters. These narratives are then turned into scenarios with quantitative projections of energy use, land use, and greenhouse gas emissions. The present scenarios are based on the forecastings of the Representative Concentration Pathways (RCP), which include more consistent short-lived gases and land-use changes. However, they are not necessarily more capable of representing future developments from macroeconomic, demographic, and technological perspectives (IPCC, 2024). The scenarios are parametrized in detail for four SSPs in order to model the output parameters, as GDP in the food sector, investments in the agricultural sector, international climate financing, climate resilience of food systems, consumer food prices, number of people at food poverty risk, and total greenhouse gas (GHG) emissions from the food sector.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e: \u003cem\u003eThe Eur-Agri-SSPs (based on O\u0026rsquo;Neill et al., 2017, O\u0026rsquo;Neill et al., 2017).\u003c/em\u003e\u003c/p\u003e \u003cp\u003eBased on the theoretical concepts and parameterization of all relevant factors, we apply a mixed-methods approach that includes interviews, fieldwork, and participatory modelling techniques. Simulation runs were conducted for the SSPs, providing clear, measurable transformation pathways for food systems towards more resilient, circular systems. The SSPs are parameterized based on literature and verified with stakeholders. The research design is presented in the Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, which implies the conceptual framework of the paper, as an innovative one.\u003c/p\u003e \u003cp\u003eIn some cases, where the parameters are rather qualitative (such as connected/disconnected global markets), we assign school notes ranging from 0 to 3 during the workshops. While the scenario development process is outside the scope of the paper, the paper focuses on the parametrization of the SSP narratives. The only difference between the scenarios presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and the original framework developed by Mitter et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) is the neglect of SSP2 (Middle Way), to reduce the complexity of the SD simulations, without losing any context.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e: \u003cem\u003eConceptual Framework of the paper.\u003c/em\u003e\u003c/p\u003e"},{"header":"RESEARCH CONTEXT","content":"\u003cp\u003eGlobalized trade has fundamentally transformed the global food system, underscoring the increasing importance of supply security (Norton et al., 2014). Complex market structures, demand uncertainties, increased economic competition, consumer pressures, and stricter political regulations have increased the need for transformation toward sustainable, resilient supply chains (Negri et al., 2020). Global supply chains and their physical distribution systems proved fragile to external shocks and crises, revealing their vulnerability within complex global production networks and across industries and regions (Sarkis, 2020). As a result of the COVID-19 pandemic and the Russian-Ukrainian war, international production and supply chains have been severely disrupted, leading to acute shortages of components and end products. The supply chain disruption effects are, among others, more significant in the agri-food sector, as the risk of food insecurity has risen substantially in the international context, from both strategic and operational perspectives, driven by regional climate change and restrictions on material and people movement (Kazancoglu et al., 2021). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Russian-Ukrainian war significantly disrupted global food production and supply, as Russia and Ukraine accounted for approximately 30% and 20% of global wheat and corn exports, respectively. Ukraine was the world\u0026rsquo;s leading exporter of sunflower oil, followed by Russia. With wheat and grain exports totaling 50 million tons, Ukraine was also the largest contributor to the UN World Food Program (Xu et al., 2020). Supply problems for seeds, fertilizers, and pesticides, as well as logistical bottlenecks, deepened the problem. In particular, the logistics industry, as the integrator and enabler of supply chains, has suffered from disruptions caused by the COVID-19 pandemic (UNICEF, 2020) and the Russian-Ukrainian war over the past years. Congested ports and longer detention times for containers caused severe delays, jeopardizing the survival of shipping companies. Besides monetary damages, shipping companies faced challenges related to standoff regulations, quarantine requirements, employee vaccination requirements, climate-related infrastructure disruptions, and general employee shortages.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe disruption factors caused by the pandemics and political instabilities come in addition to the already well-known factors, such as climate-related impacts (droughts, flood, hurricanes) as well as funding crises (management shortages in natural resources), regional and global geopolitical instabilities, population dynamics, transport and logistics (Biehl et al., 2017). Thus, analyzing the factors relevant to resilience across various stages of the food supply chain and systematically integrating them into a comprehensive decision-making framework increases transparency and the efficiency of supply chain governance at both global and local scales. Specifically, this paper examines the tensions between local and global, sustainable, resilient and circular food systems, identifying the mechanisms for balancing climate mitigation and adaptation strategies.\u003c/p\u003e"},{"header":"RESILIENT AND CIRCULAR FOOD SYSTEMS","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eResilient Food Systems\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEcologically resilient, self-organized, or local food systems are considered most resilient (Worstell \u0026amp; Green, 2017). The current issues related to climate change, degraded agro-ecological conditions, unstable governments, poorly functioning institutions, and the effects of COVID-19 and the Russian-Ukrainian war underscore the urgent need to transform food security policies for more resilient systems (Devereux et al., 2020). Schipansky et al. (2016) define resilient food systems as the coping of the actors with interacting and cumulative forces that undermine food access and equity, while defining the sources of vulnerability to be the sudden shocks (e.g., catastrophic weather events), intermittent shocks (e.g., price volatility), and gradual pressures (e.g., climate change and shifting human diets).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo operationalise resilience in food systems, several indices have been developed. For example, the Food and Agricultural Organization (FAO) defines the food resilience index to be dependent on household income and food access, access to basic services, (non) agricultural assets, agricultural practice and technology, social safety, climate change, enabling institutional environments, sensitivity, and adaptive capacity (FAO, 2014). The Baseline Resilience Indicators for Communities apply indices of socio-economic, institutional, infrastructural, community, and environmental capacities (Cutter et al., 2010). The Region Resilience Index comprises indicators of regional economic, socio-demographic, and community connectivity attributes (Pendall et al., 2010). The Livelihood Vulnerability Index, integrates socio-demographic profiles, social networks, health factors, water availability, natural disasters, and climate variability (Hahn et al., 2009). Barman et al. (2021) investigated the resilience of food supply chains against disruptions like COVID-19, shocks, and outbreaks utilizing conceptual, empirical, and descriptive methods, as well as optimization-based modeling to simulate household food consumption, national food production, food exports and imports, as well as food retail and volatility prices. Poo et al. (2024) explored overall resilience in the food supply chain by developing the \u003cem\u003eIntegrated Food Supply Chain Resilience Framework\u003c/em\u003e to estimate, using simulation techniques, descriptive factors such as production-to-supply ratio, food demand, water availability, national food resilience, and economic losses due to food insecurity. These indices cover practical, temporal, and financial aspects of the disruptions at operational level, but provide no decision framework for dealing with uncertainty, which is addressed in this paper. Yet the paper draws on some of the parameters from the literature on food resilience indices, thus combining the strategic and operational levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCircular Food Systems\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCircular food systems fundamentally differ from conventional food supply chains (Bruel et al., 2019). Circularity in food systems is described as a spectrum of practices that go beyond end-of-life waste recovery to include design for circular flows, valorization of by-products, and systemic reconfiguration of production\u0026ndash;consumption\u0026ndash;waste networks. This conceptualization shows the need for indicators that reach across contexts and stages of the food chain. They should capture aspects such as material loops, by-product utilization, as well as governance and ecosystem interactions (Chiaraluce et al., 2021; Rodino et al., 2023). Clear ways to measure the circularity impacts are necessary to understand the systemic effects in different places, e.g., cities vs. rural areas, richer vs. poorer regions, core vs. peripheral economies (Borrello et al., 2020). In contrast to other products or industries (e.g., rare earths), food cycles should be closed locally at the operational level rather than aiming for a global solution, meanwhile adopting and implementing strategic decisions at global levels (Ibn-Mohammed et al., 2021).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo analyze circularity in food systems, Angili et al. (2022) and Nikkhah et al. (2021) have utilized Life Cycle Assessment (LCA) to measure material and energy efficiency, CO\u003csub\u003e2\u003c/sub\u003e emissions, and waste reduction, focusing on the ecological impacts of material and energy usage. Falcone et al. (2022), Ribeiro et al. (2022), and Saba et al. (2022) integrated Environmental Life Cycle Costing and the Material Circularity Indicator to measure circularity, thereby expanding the LCA technique to include the economic dimension. The identified parameters included are e.g, the use of virgin raw materials, non-recoverable waste production, waste management efficiency, waste valorization processes, and the amount of saved water. Morales and Lhuillery (2021) included value chain and resource allocation efficiency as critical parameters. However, a more holistic and dynamic approach is needed to integrate all relevant parameters into a decision-support system for food security and its various dimensions, enabling informed, systemic, inclusive, and predictive decisions.\u0026nbsp;\u003c/p\u003e"},{"header":"RESEARCH DESIGN","content":"\u003cp\u003eScenario planning is widely used in academic literature as a valuable method for developing and evaluating probable future scenarios. However, most studies have conventionally concentrated on forecasting models (Lim \u0026amp; McAleer, 2002), econometric models (Croes \u0026amp; Vanegas, 2005), and neural networks (Palmer et al., 2006). These models operate under the fundamental assumption of linearity, neglecting the dynamics and interactions of the diverse feedback mechanisms within these systems (Jamal et al., 2004). In contrast, SD elucidates relationships by correlating system behavior with feedback loops inherent within the system, thereby surpassing the notion of linearity, in contrast to alternative methodologies (Sterman, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Especially in cases relevant to the food system (Rejeski, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Sterman, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), the SD approach is based on accumulating stocks and flows, as well as feedback control, to evaluate complex situations through mathematical simulations, on the premise that any complex situation can be described in terms of simple elements. SD assumes that things are interconnected in complex patterns and that nonlinearities and time delays are paramount to the system behavior arising from its structure (Sterman, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). SD primarily focuses on determining the system\u0026rsquo;s structure (Sterman, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), followed by ex-ante estimates that the \u0026ldquo;future system states are replicated from the SD model\u0026rdquo; (Winz et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe modeling procedure adopted in this study is based on Sterman (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2000\u003c/span\u003e)\u0026rsquo;s process, which involved five stages:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eProblem articulation\u003c/b\u003e: The current problem for decision-makers is the extent of (re-) regionalization in food supply chains, whilst considering climate mitigation, adaptation, and circularity strategies. This problem arises from the lack of measured trade-offs and synergies to support decisions on strategic, tactical, and operational levels of food systems.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eDynamic hypotheses\u003c/b\u003e: Trade in the era of globalization has not only increased efficiency but also brought fragility to food supply chains. In the wake of disruptions, responses such as localization, diversification, and circular practices have come to the forefront. Thus, there is a systemic oscillation between efficiency-driven globalization vs. resilience-driven localization, where strengthening feedback through policy, innovation, and governance enhances long-term food security and sustainability in food supply networks.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eFormulation\u003c/b\u003e: This stage involves developing the causal loop diagram (CLD) and stock and flow diagram (SFD). To develop the CLD, we used the Participatory Systems Mapping (PSM) framework in Germany (see Fig. A-\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e in the \u003cspan refid=\"Sec21\" class=\"InternalRef\"\u003eAppendix\u003c/span\u003e, which is based on the theory by Lopez \u0026amp; Videira, 2015). The model assumptions and the parameterization process are explained in detail in the supplementary information (Table S.-2). The interviews helped to derive the structure of the causal diagram by confirming recurring patterns and feedback mechanisms. In addition, qualitative statements were interpreted quantitatively when literature values were missing or when data were widely varying. With this approach, the interviews ensured that the model was not based solely on assumptions from the literature, but rather realistically reflected regional dynamics, bottlenecks, and opportunities for action. Data collection protocol (Fig. A.-2), interviewee profiles (Table A.-1), and general information of interviews (kew questions, product types, key statements) (Table A.-2) are included in \u003cspan refid=\"Sec21\" class=\"InternalRef\"\u003eAppendix\u003c/span\u003e. Next, an SFD was created to emphasize the system\u0026rsquo;s underlying physical structure.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTesting\u003c/b\u003e: As the process involves quasi-quantitative variables, validation is a critical component of any model-based methodology. Following Barlas (1994), the model was tested for robustness through both structural and behavioral validity tests. While behavioral validity tests assess whether the model\u0026rsquo;s behavior closely aligns with dominant patterns observed in a real system, structural validity tests examine if significant structural errors exist within the model. One of the simplest approaches is using the model check and unit check functions available in Vensim. The qualitative integration of the interviews strengthened both structural validity (model architecture) and behavioral validity (plausibility of system responses), in line with standard SD methodology.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePolicy formulation and evaluation\u003c/b\u003e: Once sufficient confidence in the model structure and behavior is achieved, it is used for policy design and evaluation, presented in Discussion.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThe third stage (formulation) with PSM (Fig. A.-1 in \u003cspan refid=\"Sec21\" class=\"InternalRef\"\u003eAppendix\u003c/span\u003e) was considered particularly crucial for enhancing the model\u0026rsquo;s reliability by integrating various group process techniques, such as qualitative interviews, workshops, discussions, brainstorming sessions, and fieldwork, with SD modeling (Sterman, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Hanson et al., 2005; Stave et al., 2017). A total of 41 semi-structured telephone interviews were conducted with 68 agricultural and food-processing businesses organized within the \u003cem\u003eGenussregion Niederrhein\u003c/em\u003e association in the Lower Rhine Region, Germany. The interviews were conducted according to a guideline covering six topics: business structure, production, sales channels, logistics, sustainability/circularity practices, as well as cooperation and digitization interests. The interviews provided empirical information about real operational structures, challenges, and decision-making logic. They tested the plausibility of the assumptions formulated in the model and ensured that the modeled feedback reflected the actual dynamics in regional food networks. The interviews identified key operational and structural drivers, which were later reflected in the model variables (see Tables A.-1 and A.-2 in \u003cspan refid=\"Sec21\" class=\"InternalRef\"\u003eAppendix\u003c/span\u003e). The interviews reveal several recurring patterns across all businesses that are central to the modeling. Direct marketing continues to dominate sales, while online retail is largely unused and generates only low revenue. As a result, businesses remain heavily dependent on regional markets. In logistics, fresh produce logistics pose major challenges. Many businesses report high transport costs and cold chain-related restrictions; 14 interview partners express concrete interest in joint logistics solutions. In the area of marketing, most businesses have a website but rarely or never use social media. Online shops exist but play hardly any economic role. Sustainability is generally seen as important, but businesses often lack the financial resources to implement certifications or investments in circular practices. Finally, the business structure shows that most are micro-enterprises with very few employees, which limits their organizational resilience and capacity for innovation.\u003c/p\u003e"},{"header":"SYSTEM DYNAMICS MODEL","content":"\u003cp\u003eBy assessing cascading effects and dynamic interactions, the SD model provides descision support system to the policymakers to balance climate mitigation targets with structured transformation strategies toward a sustainable, circular, and resilient food network. The SD model is developed, simulated, and tested for Germany, given the availability of trustworthy data from the interview analysis and statistical service. To create a better understanding of the prevailing feedback loops in this complex model, the model has been subdivided into four divisions: population dynamics (black); macroeconomics of food dynamics including geopolitics (blue), transportation and energy dynamics (orange), as well as climate resilience and natural production potential coupled with environmental (natural) resources (green) (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e: \u003cem\u003eHolistic SD model of Sustainable Food System in Germany\u003c/em\u003e\u003c/p\u003e \u003cp\u003eThe operationalization of the model is derived from the parameters along all dimensions of food security, food production and consumption and their influential factors, as well as strategic areas of food security. These factors are systematically brought together with the main topic areas of the SSPs, which are global markets, trade restrictions, environmental standards, resource degradation, sustainable lifestyles, political regulations and technological advances. All the parameters and their values are summarized in Table A.-3 in the \u003cspan refid=\"Sec21\" class=\"InternalRef\"\u003eAppendix\u003c/span\u003e.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePopulation dynamics\u003c/h2\u003e \u003cp\u003eThe population dynamics part-model shows the change in the net population (the sum of populations across age groups) of Germany from 2020 to 2040. Immigration and emigration are considered as key indicators of population dynamics in Germany, driven by the Russian-Ukrainian war. During the ‘war scenario’, the war factor drops down to a lower value (below 0.5) till the duration of the war (decided by varying the variable “war duration to”), indicating the period of instability caused due to the outbreak of the war. In the event of the war lasting longer than 2028, it is evident that there will be large-scale immigration from war-affected countries to Germany.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMacroeconomic dynamics\u003c/h3\u003e\n\u003cp\u003eThe categories of food considered in this model are winter wheat, dairy products, and meat, which are the most vital food sources for human nutrition. Land conversion, water and soil quality, yield production, and labor in the agricultural sector describe aspects of food production, whereas food prices and nutrition requirements describe aspects of food consumption. Moreover, the export and import of food also strongly depend on the food price index and geopolitical instability. Germany was heavily dependent on imports of winter wheat from Ukraine/Russia; thus, the war significantly increased procurement costs, affecting commodity prices. To meet the increasing food requirements, one approach is to increase the number of cultivation cycles. While this directly impacts local production, it causes soil nutrients to be depleted at much higher rates, rendering the soil non-arable in the long run if not adequately maintained. Furthermore, with each production cycle, the marginal yield of the land tends to decrease, thereby reducing the overall yield potential of the farmlands. Increased energy and fertilizer prices, climate-driven parameters, and the shortage of farm workers are limiting factors. Another loop in the model focuses on population growth, the impact on arable land, and the land requirements for residential purposes. This impacts the conversion of the land, accompanied by increasing urbanization levels. This overall situation significantly reduces the total available land in Germany.\u003c/p\u003e\n\u003ch3\u003eTransportation and energy dynamics\u003c/h3\u003e\n\u003cp\u003eFood production, consumption, and distribution change at both local and global levels due to increased transportation demands. This increases carbon emissions and a country’s total energy requirements, which in turn leads to higher energy prices. It is noted that transportation and logistics costs rise as the amount of food transported per person and energy prices increase. This impacts the overall cost of food supplies, making the system unsustainable and highly vulnerable to external disruptions in the long run. The energy prices have been significantly impacted by the Russian-Ukrainian war, reflected in increased transportation costs and last-mile delivery costs. Therefore, Germany needs to become self-sufficient in renewable energy production, which is highly relevant not only to food production systems but also to the logistics sector, supporting the adoption of electric vehicles (EVs), reducing the dependency on fossil fuels. This situation also impacts the adoption of renewable energy production mechanisms, making a modest contribution to the country’s overall energy needs. Beyond electrifying fleets, another food logistics strategy is to drastically shorten food supply chains, introducing the farm-to-fork strategy (direct distribution of food from producers to consumers). We described this parameter as binary (0 – no direct distribution, 1 – implementation of a farm-to-fork strategy).\u003c/p\u003e "},{"header":"SIMULATION RESULTS","content":"\u003cp\u003eThe simulations were conducted in Vensim® PLE 8.2.1 (64-bit version) application. The simulation was run between 2022 and 2040 with a Time step of 1 year.\u003c/p\u003e\u003ch2\u003eMacroeconomics in the food sector\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows that GDP gets its highest values for SSP4 in the beginning of the sumilation runs, yet within the end of the next decade it reaches the highest values for SSP1, explained by the high investments in agricultural sector, advanced technological developments, trade liberalization, strict policy in environmental regulations, shortening of food supply chains (farm-to-fork strategy with local food sourcing), increased circularity strategies, as well as sustainable lifestyles. Noteworthy is that focusing on adaptation (SSP4) rather than on mitigation strategies leads to a higher increase in GDP (SSP2), which is a striking result and might depend on the relatively short simulation period, given that mitigation strategies require a longer period of impact. Another interpretation of this result is higher need of climate insurance policies, especially in the food sector.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e: \u003cem\u003eGDP in Food Sector and Investments in Agriculture for the BaU and SSP scenarios\u003c/em\u003e\u003c/p\u003e\u003cp\u003eA sharp increase in GDP in the SSP4 scenario may also be explained by further market globalization and a significant rise in food imports (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). The correlation between GDP and food imports supports this hypothesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). Moreover, high GDP in the food sector and globally connected markets (SSP1 and SSP4) will lead to higher funding availability for global climate financing (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). This might increase investments in technological development in the food sector (including new agricultural and farming practices) and investments in more efficient adaptation strategies. An interesting observation here is that, in SSP1 and SSP4, Germany has higher international climate financing possibilities (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). In SSP4, this might be explained by indirect financial flows driven by food imports (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea).\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e: \u003cem\u003eDevelopment of the Food imports (a) for various scenarios, International climate financing (b), and the correlation between Food imports and GDP in the food sector for the SSP4 (c).\u003c/em\u003e\u003c/p\u003e\u003ch2\u003eConsumer food prices\u003c/h2\u003e\u003cp\u003eThe increase in GDP and demand for food will lead to a sharp rise in consumer food prices (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). It is forecasted that the average price of food will rise to Euros 4,534 per ton from an initial average of Euros 3,288 per ton (37.9% increase). The influx of population due to geopolitical instabilities, combined with fuel and winter wheat supply restrictions, put enormous pressure on food sustainability and total food consumption. To meet the increase in food consumption from 12 to almost 20 mln tons by 2040, agricultural lands must be expanded from the current 166.45 km² to 201.43 km² by 2040. This represents an apparent tension in land competition, as Germany simultaneously witnesses a strong urbanization trend (from the current 36,996 thousand km² to 76,996 thousand km² by 2040).\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e: \u003cem\u003eConsumer Food Price and number of people at the risk of food poverty for all the scenarios.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, consumer food prices exhibit almost no fluctuating trend for BaU. However, the number of people at risk of food poverty increases to the same level as in the SSP4 scenario. This finding highlights that globally connected markets, advanced technologies, or increases in GDP will reduce the risk of food poverty, thereby increasing food security. On the contrary, the only way to significantly decrease the risk of food poverty is to proceed with the SSP1 scenario, pursuing sustainable lifestyles, circularity strategies (with a strong reduction in food waste), and shortening the food supply chains (a farm-to-fork strategy with local sourcing).\u003c/p\u003e\u003ch2\u003eEnvironmental parameters\u003c/h2\u003e\u003cp\u003eTotal GHG emissions across the food supply chains (including food transport), as well as climate change resilience (considering irrigation, land conversion), are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Along with SSP1, SSP2 exhibits higher local climate resilience, dependent on resource-efficient technologies, primarily used for mitigation activities, compared to the high-tech path. Yet, even though significant progress has been made in cutting these emissions, the simulation shows that total GHG emissions might almost double over the next decade, reaching 200 to 350\u0026nbsp;million tons of CO\u003csub\u003e2eq\u003c/sub\u003e by 2040 under BaU. Almost 4% of these emissions are generated from food waste, resulting in 27.47\u0026nbsp;million tons of CO\u003csub\u003e2eq\u003c/sub\u003e by 2022. Yet, in contrast to total GHG emissions, emissions from food waste are expected to almost triple, reaching 60\u0026nbsp;million tons of CO\u003csub\u003e2eq\u003c/sub\u003e by 2040. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, GHG emissions are lowest in SSP1. This observation is provocative because it indicates that slow technological development in SSP2 and SSP3 doesn’t affect emissions, whereas resource overexploitation does.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e: \u003cem\u003eTotal GHG emissions along the food supply chains (bottom diagram) and climate change resilience index (bottom diagram)\u003c/em\u003e\u003c/p\u003e\u003ch2\u003eSummary\u003c/h2\u003e\u003cp\u003eThe most striking results are that, in the SSP4 scenario with globally connected supply chains and a strong reliance on technological developments, GDP in the food sector rises significantly from its current level of 160 mln Euros to nearly 450 mln Euros by 2040. Yet, the consumer food price index also rises significantly from its current level of 132 to 135 by 2040. Hence, the risk of food poverty increases, placing an additional 2 mln people at risk. Moreover, focusing on adaptation strategies in SSP4 leads to a higher increase in GDP than focusing on mitigation strategies, as seen in SSP2. With a high GDP in the food sector and globally connected markets resulting from SSP1 and SSP4, this increases funding availability for global climate financing, supporting technological advances in adaptation strategies at the global level. The most stable scenario for both locally increased social resilience and ecologically safe food systems is the SSP1, where the markets are open, yet, food supply chain is short and transparent (farm to folk strategy, where direct marketing and distribution of food from producers to consumers takes pace) as well as society is aware of sustainability following sustainable lifestyles with less food waste.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe development and implications of circularity and resilience in food systems require a joint, multi-level framework that integrates indicators of circular flows while measuring adaptive capacity at different scales. The literature consistently calls for: (i) theory-based models of circularity; (ii) context-appropriate indicator sets that cover material, economic, governance, and ecosystem aspects; (iii) resilience metrics that track exposure, adaptation, and recovery at the household, regional, and national levels; and (iv) pathways informed by governance and data that turn measurements into actionable decisions and policy (Agyemang and Kwofie, 2021; Chaudhary et al., 2018). Thus, integrating circularity and resilience into a single, clear decision-making system is paramount. Our model addresses the need for harmonized, but flexible indicators that drive concrete action and allow comparisons across different contexts, meaning that indicator sets can be tailored to subsectors (e.g., wheat) and then generalized to broader agri-food contexts with careful standardization (Borrello et al., 2020).\u003c/p\u003e \u003cp\u003eAddressing these literature-based observations, our simulation model has demonstrated that positive effects are reflected in resilient food strategies and efficient circular food systems, which will have a greater impact on food transformation. The circular food market is expected to continue growing, despite the population\u0026rsquo;s diet habits unlikely to change significantly (average food alternative consumption fluctuates due to cultural differences among the migrant population). However, food waste is expected to further increase, leading to a significant rise in GHG emissions from the food sector (and in total). This is concerning since reducing food waste and valorizing food byproducts are primary goals of circular food systems, which are crucial for enhancing sustainability. Food waste significantly contributes to environmental degradation. Effective waste management strategies can mitigate the environmental impact of food production. This would require classifying food waste as a resource for novel products derived from underutilized biomass, which consumers are willing to purchase (McCarthy et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Reducing food waste at downstream stages of the supply chain can lower the demand for inputs in upstream processes, such as in food processing. These interconnections highlight the importance of managing food waste not as an isolated issue but as part of a broader system encompassing all stages of the food supply chain (Kumar et al., 2022).\u003c/p\u003e \u003cp\u003eWhile food alternative consumption fluctuates in our results, a shift towards circular food systems may lead to a transformation in dietary patterns. A circular approach to food production can support healthier consumption patterns by emphasizing plant-based foods with enhanced nutritional value while reducing reliance on processed foods. It is assumed that the share of people who reduce their per capita consumption of meat and animal-based products increases annually by 1% (Rab\u0026egrave;s et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Thus, we assumed that 10% of people will have a vegetarian diet by 2040 (currently about 6 mln). This requires consumer awareness and a willingness to substitute animal-sourced foods for environmental goals (van Selm et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), thereby further stimulating growth in the circular food market. Finally, policies that promote resilience in food systems often emphasize the importance of diversity and local sourcing. More local and regional food production can enhance supply chain resilience by reducing dependence on single sources of supply, thereby improving food availability during crises (Marusak et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eGovernance strategies\u003c/h2\u003e \u003cp\u003eFor example, local governments may adopt sustainability strategies to address immediate challenges, such as disaster risk management, while pursuing comprehensive approaches that aim for broader environmental and social changes within communities (Ji \u0026amp; Darnall, 2020). Additionally, effective governance strategies positively impact the achievement of the SDGs. Their effects can be cumulative and long-lasting if they are continuously refined and adapted to new challenges, emphasizing the necessity of integrated and coordinated sustainable policies that account for additional variables. In particular, the population growth rate highly influences the relationship between sustainable governance and sustainable development indicators, indicating an inverse relationship between population growth and sustainable development (G\u0026uuml;ndoğdu \u0026amp; Ayteki̇n, 2022). These observations indicate that sustainable governance should not be viewed as static, but rather as a dynamic process that can adapt to changing circumstances, necessitating regular assessment and adjustment to remain effective.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eFood prices\u003c/h2\u003e \u003cp\u003eIt was shown that the average prices of food (winter wheat, dairy, and meat) rose to Euros 4,232 per ton, initially from an average of Euros 3,243 per ton (a 30.5% increase). Labor shortages and increased costs of commodities and fertilizers also pressure food prices, which are amplified by geopolitical tensions (Adjemian, 2023). Related policy responses have led to reduced food production and international trade, which in turn have destabilized food prices and caused food crises in various regions (Bai et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The implications of rising food prices extend beyond economic factors. They also pose significant risks to food security and social stability. Accordingly, volatile food prices can lead to severe food insecurity, forcing households to compromise between food and other essential needs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eFood Logistics\u003c/h2\u003e \u003cp\u003eThe farm-to-fork concept (EU, 2020) ensures the direct marketing and distribution of regional food through shortened supply chains and environmentally aware consumption. This is directly linked to the reduced transportation costs and the lower carbon footprint achieved due to the shorter distance traveled within the region (Poo et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). It is interesting to note that, despite the involvement of the EVs and implementation of the farm-to-fork concept, the cost of energy is not reduced and stays at the same level (increased from an average of Euros 1.4 per gallon to Euros 7.6 per gallon). While a sustainable lifestyle may help reduce fuel dependency, the simulation results are counterintuitive. This could be because, while the farm-to-fork model will result in savings in the upstream supply chain (bulk orders at low frequency), it might fail in the downstream (small orders at high frequency). This increase in downstream transportation shadows the savings that could be realized through using EVs for transportation. Regarding CO\u003csub\u003e2\u003c/sub\u003e emissions, the production of winter wheat and the adoption of a farm-to-fork model may not be feasible, as it is associated with high outbound logistics costs and low shipment sizes compared to imported resources.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eOur model effectively captured the complex relationships among global and local variables that have a substantial impact on the food system. The developed SD model comprehensively captures these complexities on a global scale, but it could also consider fundamental properties of resilience and circularity for sustainability, which can help establish resilient systems and incorporate diversity strategies, adaptability, cohesion, and eco-efficiency, thereby developing a new narrative for the food economic system. In this vein, there are numerous interrelationships between strategies, processes, and stakeholders in global food systems that mutually affect one another. Our model tries to capture these dynamics and serves as a tool to mitigate future disruptions in food supply chains. The following points summarize the key takeaways for (political) decision-makers from this study:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eStrict political regulations, glocalization strategies (global value chains, yet local and transparent food supply chains), increased circularity strategies, and sustainable lifestyles contribute to high GDP in the food sector. High GDP values are achieved when focusing on adaptation strategies (SSP4) rather than mitigation strategies (SSP2), resulting in higher climate resilience for a short time of period.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIf GDP doubles (SSP4) (through increased technologies in adaptation, high investments in agriculture, and increased global trade), climate financing funds will increase, yet, in general, climate financing doesn\u0026rsquo;t help the reduction of CO\u003csub\u003e2\u003c/sub\u003e locally; in fact, it increases 4 times.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eEven though the consumer food prices are relatively low (due to increased food availability through imports), the local poverty risks will increase, because local production will decrease coming along with resource depletion, increased energy consumption, less regenerative energies, and increased food loss.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eGDP must increase four times to escape the negative effects (SSP1), but this is highly sensitive to policy regulation, technology, consumer behavior, and short local food supply, even though markets are open and globally connected. GDP is not a good indicator of food security.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFor an overall increase in food security, policy plays a less significant role than consumer behavior and a focus on technology for mitigation and increased climate resilience.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eGlobal supply chains contribute to economic efficiency, but do not reduce the risk of food poverty.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of Interest Statement:\u003c/h2\u003e \u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAni Melkonyan-Gottschalk is the corresponding author, mainly setting the paper design, idea, research focus, innovation. She also coordinated the entire paper writing process, extracted the results.Vasanth Kamath has mainly designed the System Dynamics model and ran the simualations. He also described the research methods.Denis Daus carried out literature review on resilient and circular food systems, partially has written the Theoretical part and took care of references.Tim Gruchmann reviewed the paper, aligned with the needs of the jounal and has written the Discussion.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe analysis is available upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eThe present study is part of a research project approved by the University of S\u0026atilde;o Paulo\u0026apos;s Institute of Psychology Ethics Committee (CAAE: 58516616.0.0000.5561).\u0026rdquo;\u003c/li\u003e\n \u003cli\u003eAdjemian, M. K., Arita, S., Meyer, S., \u0026amp; Salin, D. (2024). 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Circular business models for sustainable development: A \u0026ldquo;waste is food\u0026rdquo; restorative ecosystem. \u003cem\u003eBusiness Strategy and the Environment\u003c/em\u003e, 28(2), 274-285.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Food Security, Dynamic Interaction, System Dynamics, Supply Chain Resilience, Shared Socio-Economic Pathways","lastPublishedDoi":"10.21203/rs.3.rs-8321026/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8321026/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe overarching goal of this paper is to support the governance of sustainable transformation pathways for resilient and sustainable food systems, ensuring food security. The rationale for developing a dynamic decision support system is the growing debate over the necessity of (re-)regionalization policies in food supply chains that incorporate circularity and resilience strategies. This paper explores the trade-offs and synergies between decisions made at the strategic, tactical, and operational levels, global and local food systems, climate mitigation and adaptation strategies, as well as sustainability and profitability/technology, while evaluating the strategic combinations of various macroeconomic, socio-technical, and political measures. To achieve this ubiquitous goal, we use a mixed-methods approach, combining quantitative and qualitative analysis through interviews and a holistic System Dynamics (SD) model to simulate the Shared Socioeconomic Pathways (SSP) as narratives. The simulation quantifies the SSPs to find a balanced transformation pathway for a resilient and sustainable food system, depending on global trade policies, transport and logistics, economic development, consumption patterns, and technological development. This holistic approach to food systems at various levels is unique and provides a clear decision-support framework that assess measures and their combinations into a set of strategies.\u003c/p\u003e","manuscriptTitle":"Dynamic transformation toward resilient and circular food systems: A decision support framework for food security","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-23 06:31:17","doi":"10.21203/rs.3.rs-8321026/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7008a47c-6d15-4ac9-9037-67a247d28383","owner":[],"postedDate":"January 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-16T23:54:10+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-23 06:31:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8321026","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8321026","identity":"rs-8321026","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}