Bridging Affordability, Nutrition, and Sustainability: A Systematic Review of Microsimulation Approaches in Food Security Research¹

Food security remains a critical global issue, shaped by rising populations, climate uncertainty, biodiversity loss, and widening socio-economic inequalities (Aborisade and Bach, 2014; Godfray et al., 2010; Lindgren et al., 2018). Ensuring access to affordable, nutritious, and sustainable diets requires a systems-based perspective that considers economic, nutritional, and environmental dimensions together (Abu and Oldewage-Theron, 2019). Microsimulation models are increasingly recognized for their ability to navigate this complexity. These models simulate household and individual responses to interventions, such as taxes, subsidies, dietary shifts, and climate impacts producing detailed insights into distributional impacts and policy trade-offs (Boulanger et al., 2022; Breisinger and Ecker, 2014; Clapp et al., 2022; Diao et al., 2022). This paper reviews microsimulation approaches used in the food security-nutrition-environmental nexus.

Traditional food security models have relied heavily on macro-level frameworks and static indicators, offering high-level insights into dietary patterns and environmental footprints but often failing to account for household-level heterogeneity and dynamic feedbacks (Behrens et al., 2017; Green et al., 2015; Heller et al., 2018). These aggregate approaches obscure how interventions affect diverse socio-economic groups, limiting their value for designing equitable, context-specific policies (Béné, 2020; Leeuwis et al., 2021). Microsimulation modelling offers a breakthrough by simulating individual and household responses to policy interventions, such as taxes, subsidies, dietary shifts, and climate shocks, while accounting for demographic diversity, behavioural variability, and spatial differentiation (Boulanger et al., 2022; Diao et al., 2022; O’Donoghue, 2014). Unlike aggregate models, it enables granular analysis of distributional impacts and policy trade-offs within interconnected food systems.

Despite their growing use in food policy research, most microsimulation models remain fragmented, addressing nutrition, affordability, or environmental sustainability in isolation. Many studies simulate the health and economic impacts of interventions such as sugar-sweetened beverage taxes, nutrient reformulation, or food labeling, showing improvements in diet quality and reductions in chronic disease risk (Lee et al., 2020; Pearson-Stuttard et al., 2018; Shangguan et al., 2021). Others assess food assistance programs like SNAP or produce prescriptions, demonstrating gains in nutritional adequacy and reduced disparities for low-income households (Choi et al., 2021; Mozaffarian et al., 2018; Wang et al., 2023). However, these models often pursue a single policy objective such as improving health or reducing costs without linking outcomes to environmental or equity dimensions.

More recent efforts have extended microsimulation to broader domains, including fiscal and agricultural policy. Simulations of VAT reforms, fertilizer subsidies, and market interventions have examined their effects on poverty, prices, and household welfare (Boulanger et al., 2022; O’Donoghue, 2014; Ramos et al., 2022), while others have explored food system shocks such as COVID-19 and the Ukraine war (Ayaz et al., 2022; Headey et al., 2020; Nechifor et al., 2021). A smaller body of work models carbon and sin taxes on food, highlighting their potential for emissions reduction and dietary improvements (Caillavet et al., 2019; Kehlbacher et al., 2016; Klenert et al., 2023). Yet, even the most advanced models typically evaluate outcomes in parallel rather than as interconnected elements. Building on this, recent reviews have explored how different modelling approaches address these interconnected challenges, offering insights into current strengths and limitations.

Recent reviews underline the growing consensus that microsimulation modelling is essential for advancing food security research amid its inherent complexity and multidimensionality. Mertens et al. (2022) position microsimulation as a methodological breakthrough, offering the ability to capture individual-level heterogeneity and dynamic feedback within food systems. Building on this, Dave et al. (2023) argue that incorporating nutrient bioavailability and food biodiversity into microsimulation models would further enhance their realism, enabling more accurate simulation of real-world dietary choices and constraints. Mui et al. (2025) also point to the utility of microsimulation within broader systems science simulation modelling (SSSM) frameworks, especially for analysing equity in the retail food environment. However, they critique current models for often overlooking social and political contexts and failing to meaningfully engage stakeholders. Complementary reviews stress that fragmented or oversimplified indicators (Jones et al., 2013; Manikas et al., 2023) and the proliferation of disparate metrics (Teeuwen et al., 2022) obscure crucial systemic trade-offs and dimensions such as nutrition, equity, and environmental sustainability. Li et al. (2023) and McKay et al. (2019) further call for holistic, multidimensional approaches that integrate both objective and subjective measures. Collectively, this literature highlights the urgent need for microsimulation-driven frameworks capable of unifying diverse dimensions of food security, capturing distributional and equity impacts, and ultimately guiding more effective and nuanced policy design.

This review is intentionally broad, reflecting the diversity of microsimulation modelling approaches in food policy, and aims to guide the design of microsimulation models across complex and interdisciplinary domains. It specifically asks:

• How have microsimulation models been used to assess food security outcomes at household and individual levels?

• To what extent do these models incorporate multidimensional drivers, including nutritional adequacy, economic affordability, and environmental impacts?

• What methodological innovations and limitations emerge from the literature, and what are the priorities for advancing microsimulation in food security research?

By mapping existing applications and identifying critical gaps, the review offers a roadmap for using microsimulation to support equitable, adaptive, and integrated food policy design.

The paper is structured to comprehensively address the topic. Section 2, Theoretical Framework, introduces fundamental concepts and microsimulation models. Section 3, Methodology, outlines the hybrid review process. Section 4, Results, presents key findings on food security dimensions and the use of microsimulation models. Section 5, Discussion, highlights key takeaways, research gaps, and future research implications. Finally, Section 6, Conclusion, summarizes the analysis and overall conclusions.

In this section we develop, a theoretical framework for analysing complex aspects like affordability, nutrition, and environmental sustainability aspects of food security research (Simelane and Worth, 2020). Ravitch and Riggan (2016) emphasize the framework’s role in explaining and forecasting the interrelations among different concepts and variables. It helps classify numerous concepts found in the literature, showing their interconnectedness in achieving household food and nutrition security (Kivunja, 2018).

Given the complexities of food security, we have developed a theoretical framework for our review. This framework explores how factors like food access, affordability, environment, nutrition, human behaviour, and health are interconnected. Our aim is to offer practical suggestions for building affordable, healthy, and sustainable food systems worldwide, ultimately improving global food security and well-being.

Figure 1 presents an overview of the theoretical framework for examining food security using value chain and microsimulation modelling, inspired by Pingault et al. (2017). This framework provides a foundation for understanding the holistic processes within food security. While Sam et al. (2019) highlighted the interplay between household adaptive capacity, drought risk, and food security, and Ogundari (2017) expanded on socio-economic and demographic factors influencing rural food security, these frameworks lack the depth provided by microsimulation or CGE-micro models. Our review addresses this gap by using microsimulation modelling to dissect the complex interplay within food security dimensions, informing targeted policy development.

Figure 1

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2.7. Policy instruments

Figure 2 in our review showcases the role of microsimulation models in analysing the impacts of diverse policies on key food security aspects like affordability, nutrition, and environmental sustainability. By leveraging models such as EUROMOD (Immervoll and O’Donoghue, 2009), we explore the efficacy of social welfare initiatives in improving household food affordability, which in turn enhances overall food security (Penne and Goedemé 2021). Similarly, models like CVD PREDICT highlight the significance of dietary interventions in mitigating nutrition-related health risks (Liu et al., 2020). Additionally, household microsimulation approaches provide a broader perspective on the interplay between agricultural policies, economic factors, and nutritional programs, offering comprehensive insights into their collective influence on food security (Viera, 2023).

Figure 2

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The diagram further expands our understanding by delineating various policy approaches, including market-based mechanisms, support-focused measures, and dietary guidelines, each contributing uniquely to food security. From carbon pricing strategies and agricultural subsidies to nutritional guidelines and food assistance programs, these interventions illustrate the spectrum of policy tools available to tackle food security challenges. This layered analysis, underpinned by the detailed insights from microsimulation models and the holistic view presented in Figure 2, underscores the complexity of formulating policies that effectively address the multifaceted nature of food security. Through this integrative approach, our review aims to guide the formulation of informed, sustainable, and equitable policies that pave the way for a global food-secure future.

The review’s methodology focuses on the interplay between food security aspects affordability, nutrition, and environmental sustainability using microsimulation models to inform sustainable food policies. Different literature review approaches, ranging from qualitative textual analysis to quantitative bibliometric analysis, have their own benefits and drawbacks (Snyder, 2019). Bibliometric studies offer broad overviews but lack depth in understanding model details (Ellegaard and Wallin, 2015), while traditional reviews provide detailed insights but struggle with extensive literature coverage (Boell and Cecez-Kecmanovic, 2014). SLRs, which utilize content analysis, offer a thorough and detailed examination of the subject matter but can be time-consuming and limited in the number of papers reviewed (Bartolucci and Hillegass, 2010; Petticrew and Roberts, 2008). Given the complexity and interdisciplinary nature of microsimulation in food policy, a hybrid approach was adopted one that combines the breadth of bibliometric mapping with the analytical depth of SLRs (Turnbull et al., 2023).

Although this paper is not a systematic review in the strict sense, we adopted elements of the PRISMA framework (Page et al., 2021) to ensure a transparent, structured, and replicable review process. PRISMA’s guidelines provided a strong foundation for documenting search strategies, screening procedures, and inclusion criteria, thereby enhancing methodological clarity (Moher et al., 2015). However, due to the interdisciplinary and evolving nature of microsimulation modelling in the food system domain, we applied a hybrid approach that balances structure with flexibility. To expand coverage beyond standard database searches, we integrated a citation-based snowballing strategy (Greenhalgh and Peacock, 2005), allowing us to identify additional influential studies and policy-relevant work not captured through initial keyword searches. This combined method enabled a comprehensive yet focused review of the literature.

The review began by defining a clear research question, followed by creating a theoretical framework to structure the study on microsimulation models for food security. Google Scholar was used for its broad coverage of both academic and grey literature, including reports and conference papers, to capture the full spectrum of existing knowledge and innovations in the field (Adams et al., 2016). This strategy provided a comprehensive view of the field, incorporating insights from diverse sources.

3.1. Search Strategy

The literature search targeted peer-reviewed articles and grey literature published between 2013 and 2023, a period marked by significant advancements in microsimulation techniques and heightened focus on food security driven by the Sustainable Development Goals (SDGs), climate action, and improvements in data and computational methods.

Searches were conducted using Google Scholar for its broad coverage of academic and grey literature, complemented by manual screening of references from key articles to identify additional studies (Adams et al., 2016). The search string combined relevant terms to capture the intersection of microsimulation and food security:

("Food security" OR "Food insecurity" OR “Food Consumption”) AND (“Household” OR “Individuals”) AND ("Microsimulation models" OR “Microsimulations” OR “Microsimulation” OR "Micro-simulation") AND ("Affordability" OR "Economic access" OR "Income" OR "Environment" OR "Sustainability" OR "Climate change" OR "Nutrition" OR "Dietary patterns" OR "Diet" OR "Health")

This search retrieved 2,810 records from Google Scholar and an additional 30 studies through citation tracking, resulting in a total of 2,840 records. Following the removal of 58 duplicates (43 duplicates and 15 irrelevant records), 2,752 records proceeded to screening. Titles and abstracts were screened to exclude studies that clearly did not meet the eligibility criteria, resulting in the exclusion of 2,658 records. Of the remaining 94 studies, three could not be retrieved in full, leaving 91 articles for detailed eligibility assessment. An additional 30 articles identified through citation searches were also assessed.

Eligibility screening applied the following inclusion criteria: studies published in English between 2013 and 2023 that employed microsimulation modelling (including standalone and CGE-linked approaches) to assess food security dimensions, namely affordability, nutrition, and environmental sustainability at the individual or household level. Studies were excluded if they were reviews or reports, lacked household-level heterogeneity, focused solely on farm-level modelling, or were qualitative or non-modelling based.

After full-text assessment, 38 studies from the database search were excluded for reasons such as lacking household-level heterogeneity (n=15), insufficient focus on food systems or insufficient methodological detail (n=10), farm-level modelling only (n=7), or being review articles (n=6). From the citation search, 22 studies were excluded due to qualitative approaches (n=11), lack of household-level heterogeneity (n=9), or being reviews (n=2). Ultimately, 60 studies were included in the final synthesis, as illustrated in Figure 3 (PRISMA flow diagram).

Figure 3

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Data from these studies were extracted, focusing on the type of microsimulation model, the scope of food security dimensions addressed, geographic and population coverage, and the policy interventions simulated (taxes, subsidies, dietary shifts, etc.). The synthesis combined quantitative mapping of study characteristics with qualitative analysis to identify methodological advances, limitations, and future opportunities for leveraging microsimulation in food security research.

Our results distil key patterns in the food security literature, covering environmental, nutritional, health, and economic dimensions. We examine carbon modelling and emissions across the food system, along with how nutrient types and volumes are assessed. The review also highlights health–nutrition links, behavioural responses, and policy impacts. Definitions of food insecurity and intra-household dynamics are discussed, as well as the integration of microsimulation with macroeconomic models. Together, these findings offer a clear picture of how food security is currently modelled.

4.1. Application

Understanding the analytical focus of microsimulation studies is critical for informing policy design that balances environmental sustainability, nutritional adequacy, and affordability. Table 1 shows that all 60 reviewed studies applied microsimulation modelling (100%) and addressed at least one core dimension environment, affordability, or nutrition. Affordability was the most frequently examined dimension (95%), followed by nutrition (68%), and environment (20%). Only 46% of studies considered both affordability and nutrition jointly, while just 8% assessed both environment and nutrition. A mere 16% of papers integrated all three dimensions, highlighting a persistent gap in multidimensional policy evaluation. Existing literature underscores that environmental concerns remain underrepresented in affordability-focused models (Drew et al., 2020), and most current frameworks lack the integration necessary for assessing cross-dimensional trade-offs. These findings point to the urgent need for more holistic modelling approaches that can support the design of coherent, cross-sectoral food policies.

Table 1

Analytical Focus	Number of Papers	Probability
Microsimulation Models	60	100%
At Least One Dimension	60	100%
Environment	12	20%
Affordability	57	95%
Nutrition	41	68%
Environment affordability	3	5%
Environment Nutrition	5	8%
Affordability Nutrition	27	45%
All Dimensions Discussed	10	16%

Food security is a global issue, requiring comprehensive research across different regions. Figure 4 shows that Africa was the most researched region with 26 studies. Asia followed with 9 studies, covering nations like Bangladesh, Pakistan, and Myanmar. Europe and North America had 7 and 14 studies respectively, often focusing on policy impacts and health outcomes associated with dietary patterns. Additionally, 1 study was in Australia/Oceania, one in South America and two multiple where developing countries included (Asia/Africa).

Figure 4

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Table 2 shows the distribution of applications across geographical regions, highlighting distinct focus areas between developed and developing contexts. In Africa, the majority of applications focused on agricultural development (Aragie et al., 2023), social protection and food security (6), and economic policies (5), reflecting priorities to strengthen rural systems and poverty alleviation. Asia similarly emphasized social protection (4) and climate change and food production (2). In contrast, North America focused primarily on food assistance programs (5) and health and diet quality (6), targeting nutrition and vulnerable populations. Europe’s studies focused on environmental sustainability (2) and taxation and health economics (3), while Australia/Oceania had only one study, which addressed health and diet quality. South America also showed minimal representation, with one study on social protection and food security. These patterns indicate that developing regions prioritize foundational issues such as agricultural policy and food security, while developed regions focus more on targeted themes like nutrition, health, and sustainability.

Table 2

Application Category	Africa	Asia	North America	Europe	Australia/Oceania	South America	Cross Continent (Asia/ Africa)
Food Assistance Programs/Food Stamps	0	1	5	0	0	0	0
Environmental Sustainability	1	0	1	2	0	0	0
Social Protection and Food Security	6	4	1	1	0	1	1
Economic Policies and Fiscal Studies	5	2	0	1	0	0	0
Agricultural Development and Policies	10	0	0	0	0	0	0
Health Impact and Diet Quality	1	0	6	0	1	0	1
Climate Change and Food Production	2	2	0	0	0	0	0
Taxation and Health Economics	1	0	1	3	0	0	0
Total	26	9	14	7	1	1	2

4.2. Characteristics of included studies

Microsimulation modelling in food security has gained significant traction over the past decade, reflecting its growing relevance in addressing complex, multidimensional policy challenges. Figure 5 illustrates this trajectory across three dimensions: unit of analysis, publication trends, and types of populations studied. This analysis not only maps the evolution of the field but also highlights methodological patterns and gaps that have implications for the design and interpretation of food security policies.

Figure 5

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Panel (a) defines the unit of analysis as the level at which data is modelled either households or individuals. Among the reviewed studies, 73% (n=43) employed households as their unit of analysis, while only 27% (n=17) focused on individuals. This dominance of household-level analysis likely reflects the widespread availability and practicality of household surveys in capturing consumption and expenditure data. However, it also reveals a gap in modelling intra-household dynamics and individual-level nutritional adequacy, which are critical for understanding disparities in food access and dietary outcomes (Naska et al., 2001).

Panel (b) shows the distribution of studies over time, revealing a marked increase in publications in recent years. Between 2013 and 2016, 12 studies were published, increasing slightly to 16 from 2017–2019. Notably, there was a dramatic surge to 32 studies between 2020–2023. This acceleration coincides with global crises such as the COVID-19 pandemic, which exposed vulnerabilities in food systems and spurred interest in using microsimulation to model shocks and policy responses (Ecker et al., 2023; Nechifor et al., 2021). The trend underscores the field’s growing responsiveness to real-world disruptions and its potential for informing adaptive policymaking under uncertainty.

Panel (c) categorizes the populations modelled in these studies. The majority target the general population (n=45), followed by low-income households (n=38), rural farm households (n=32), and urban non-farm households (n=18). While the focus on vulnerable groups such as rural and low-income populations is commendable, the relative underrepresentation of urban households is noteworthy given rapid urbanization and its implications for food security dynamics (Cohen and Garrett, 2010). This suggests a need for more urban-focused microsimulation research to capture the specific challenges of food access in densely populated settings.

4.4. Impact analysis

A robust impact analysis that integrates economic, nutritional, and environmental dimensions is essential for informing effective food security policy. As shown in Figure 6, economic indicators are the most extensively covered: income distribution (52 studies), expenditure levels (57 studies), and food prices (49 studies) are foundational to modelling affordability and access. Nutrition-related impacts, including dietary diversity, nutrient adequacy, and health outcomes, were addressed in 43 studies, reflecting growing interest in linking food policies to human wellbeing.

Figure 6

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In contrast, environmental outcomes remain underexplored. Only 10 studies considered climate-related factors, and a mere 5 examined carbon emissions. These gaps are consistent with broader critiques in the literature that emphasize the lack of environmental integration in food affordability and nutrition studies. Overall, this analysis underscores the need for a more holistic approach that balances social equity, dietary quality, and ecological sustainability within microsimulation frameworks.

4.7. Nutritional modelling

Nutrient modelling plays a critical role in designing dietary interventions that address deficiencies and promote public health. As illustrated in Figure 7, research has heavily focused on modelling macronutrients, calories, protein, fat, and carbohydrates given their fundamental role in energy provision and physiological maintenance. Micronutrients such as iron, vitamin A, calcium, and folate are also frequently included due to their significance in metabolic function and disease prevention.

Figure 7

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Microsimulation models have proven effective in capturing how dietary patterns and policy interventions influence nutrient intake and health outcomes. They are used to evaluate the nutritional consequences of various fiscal and regulatory measures, often targeting specific nutrients like sodium, protein, and iron. For example, Lee et al. (2020) and Pearson-Stuttard et al. (2018) simulated the impact of U.S. sodium reformulation and calorie labelling laws, respectively, demonstrating how regulatory measures shape health risks. Ecker et al. (2023) assessed beverage taxes and subsidies across multiple micronutrients, while Bizimana et al. (2020) examined nutrient adequacy and food security in Ethiopia. Other studies, such as Basu et al. (2020) on CSA programs and Viera (2023) on fiscal policy in Mexico using the GENUS model, further highlight the flexibility of microsimulation to model nutrient transitions in different contexts. Hybrid approaches, like Louhichi et al. (2020) combine CGE and microsimulation models to link economic shocks to dietary outcomes.

A key strength of the current literature lies in its emphasis on region-specific and socioeconomic targeting. For instance, Pauw et al. (2018) in Malawi and Basu et al. (2018) among Palestinian refugees underscore the value of tailoring microsimulation models to address localized nutritional vulnerabilities. This contextual precision enhances policy relevance, particularly for populations at risk of undernutrition.

However, three methodological limitations consistently constrain the nutritional fidelity of existing models. First, most simulate nutrients in isolation, overlooking well-established biochemical interactions. Pearson-Stuttard et al. (2018), for example, modeled the sodium–blood pressure–cardiovascular disease pathway without accounting for nutrient synergies, such as vitamin C’s enhancement of non-heme iron absorption, or antagonisms like calcium’s inhibition of zinc uptake. Similar omissions appear in Basu et al. (2018), which assessed iron deficiency absent vitamin C co-consumption, and Ecker et al. (2023), which neglected the effect of polyphenols on iron bioavailability. Second, many models assume uniform nutrient absorption, failing to distinguish between total intake and bioavailable fractions. Heme iron is absorbed at rates of 15–35%, while non-heme iron ranges from 2–20%, depending on dietary context. Processing methods such as milling can reduce folate content by up to 80%, while traditional practices like fermentation and nixtamalization enhance micronutrient uptake. Yet, even advanced frameworks such as GENUS (Dominguez Viera, 2023) apply static absorption rates, omitting these cultural and biological variations. Third, these technical limitations are compounded by concerns over the validity of the underlying dietary intake data. Large-scale nutrition surveys like NHANES and FFQs rely heavily on self-reported consumption, which is prone to underreporting and measurement error. Archer et al. (2013) found that NHANES caloric intake data often fell outside physiologically plausible ranges, raising questions about the reliability of these datasets for accurately modelling diet-disease relationships.

4.13. Policy instruments and analysis

Microsimulation has become central to evaluating food policy impacts on household welfare, nutrition, and equity. It enables detailed modelling of how interventions, such as food subsidies (32 studies), input support (23), and cash transfers (24) affect consumption and affordability across income groups as shown in Figure 8. By leveraging household survey data and consumption patterns, microsimulation reveals distributional impacts that aggregate models often miss. Basu et al. (2013) modelled SNAP benefit increases to assess calorie adequacy. Bundled policy simulations, like Ecker et al. (2023) work on fortification with income support, further demonstrate microsimulation’s value in testing realistic, multi-pronged reforms.

Figure 8

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Its strength lies in flexibility. Microsimulation incorporates price elasticities, nutrient needs, and behavioral responses to project outcomes across nutrition, fiscal costs, and environmental effects. Studies on dietary guidelines (18), VAT reforms (Harris et al., 2018; O’Donoghue et al., 2018), and warning labels (An et al., 2022) use behavioral data to simulate policy uptake. Broader reforms including trade policies (15) are also captured, revealing how market shifts cascade to household diets. Abuelhaj (2018) applied microsimulation to assess taxation and subsidy policies in the MENA region, emphasizing nutrition-sensitive outcomes. Caillavet et al. (2019) demonstrated that pairing taxes with revenue recycling can mitigate but not eliminate equity concerns, highlighting microsimulation’s capacity to quantify persistent trade-offs under real-world constraints.

Yet, major limitations remain. Most models are static, overlooking behavioral adaptation and long-term effects. Fertilizer policy simulations rarely consider evolving market prices, and transfer programs often ignore changes in household participation over time. Carbon and sin taxes, though modeled in a handful of studies, are rarely validated against real-world outcomes. Geographic granularity is limited, with rural-urban divides and informal food systems often absent. Strengthening microsimulation requires dynamic feedback, finer spatial resolution, and validation against observed responses to ensure MSM not only informs but also reflect policy reality.

Food system MSM faces a pressing imperative: to simultaneously address environmental sustainability, nutritional adequacy, and economic affordability. While microsimulation (MSM) and integrated macro–micro (CGE-MSM) models have advanced understanding of each domain, they are often applied in isolation, limiting their ability to reflect real-world interdependencies. Nutritional MSM tend to overlook cultural dietary norms and nutrient bioavailability; environmental assessments frequently omit critical stages like processing and food waste; and economic MSM often reduce complex diets to calorie-based metrics. Notably, the environmental dimension remains comparatively understudied in food policy MSM, with limited integration of greenhouse gas emissions, biodiversity impacts, and land-use trade-offs. This fragmentation risks generating counterproductive policies for instance, carbon taxes that reduce emissions yet worsen malnutrition by making nutrient-rich foods less affordable for low-income households.

Integrating these dimensions reveals both synergies and trade-offs (Al-Masbhi et al., 2026). Well-designed environmental policies, such as emissions-based subsidies, can support climate-resilient, nutrient-dense crops like pulses and agroforestry species, simultaneously enhancing soil health and household nutrition. Conversely, poorly calibrated interventions may undermine health and equity. Broad-based meat taxes, while effective in reducing emissions, risk exacerbating zinc and iron deficiencies in vulnerable populations. Similarly, efforts to decarbonize supply chains can increase the cost of perishable vegetables, limiting access to essential micronutrients. The risk of carbon leakage where production shifts to less-regulated regions further compounds the problem by increasing reliance on imported, ultra-processed foods that are low in nutritional value and high in emissions (OECD, 2025).

To navigate these complexities, hybrid modelling approaches are essential. On the environmental front, combining detailed Life Cycle Assessment (LCA) with economy-wide Input–Output (IO) analysis enables both product-level specificity and system-wide coverage, supporting standardized GHG accounting across the full value chain (Hagenaars et al., 2025). Nutritional MSM should go beyond static nutrient counts to incorporate bioavailability, dietary diversity, and cultural relevance (Farfán et al., 2017). On the economic side, fiscal policies such as carbon taxes or VAT reforms must be accompanied by redistributive mechanisms like targeted food subsidies or cash transfers to shield vulnerable households from regressive impacts. Empirical evidence from Caillavet et al. (2019) illustrates how recycling carbon revenues to subsidize low-emission, protein-rich foods like legumes can enhance both dietary equity and environmental outcomes.

Model design choices critically influence policy relevance. MSM excels at capturing household-level heterogeneity, including differences in consumption patterns, poverty exposure, and nutritional risk. CGE-MSM frameworks extend this by linking micro-outcomes to macroeconomic dynamics such as price volatility and climate shocks. However, most CGE-MSM models adopt a top-down structure transmitting aggregate shocks to households while often neglecting bottom-up feedback, including behavioural responses and informal coping mechanisms that can meaningfully affect macro outcomes. Consequently, many models continue to rely on static price elasticities and assume average household behaviour, overlooking behavioural adaptation, informal food economies, and localized vulnerabilities. This limits their capacity to represent the complexity of food system dynamics. A layered modelling strategy starting with tractable assumptions and incrementally adding complexitycan help identify the most influential variables, such as price changes, yield shocks, and consumer substitution, while enhancing transparency and contextual relevance.

Data quality remains a fundamental constraint. Dietary intake is often derived from food frequency questionnaires or 24-hour recalls, both of which suffer from recall bias, seasonal variability, and systematic underreporting especially among low-income populations (Harrison et al., 2000). These tools rarely account for food consumed outside the home or shared within households, leading to underestimation of true nutrient intake and inequality in access (Shim et al., 2014). Furthermore, national datasets frequently record food purchases rather than actual consumption, overlooking losses from spoilage, cooking, and intra-household distribution. Nutrient composition databases further obscure accuracy by assuming uniformity across food types and preparation methods, disregarding bioavailability differences. Addressing these gaps requires more frequent and disaggregated dietary surveys, harmonized data systems, and better integration across nutrition, expenditure, and environmental metrics.

Ultimately, advancing food system MSM means constructing frameworks that reflect the complex interplay of health, climate, and inequality. This calls for standardized emissions accounting protocols, context-sensitive nutrient modelling, and affordability mechanisms embedded into fiscal design. It also demands modular modelling tools, robust behavioural assumptions, and interdisciplinary collaboration across public health, environmental science, and economic policy. Only by confronting these layers of complexity directly can MSM inform actionable, equitable, and sustainable transformations in food systems.

We argued at the beginning of this article that food security must be understood as a multidimensional challenge shaped by the interplay of affordability, nutrition, environmental sustainability, and behaviour. This review, based on 60 studies using MSM, reveals that current literature often treats these dimensions in isolation. This fragmented approach limits the effectiveness of policy design and fails to reflect the systemic trade-offs inherent in real-world food systems.

The findings underscore three critical gaps: the marginal treatment of environmental impacts, the underrepresentation of behavioural dynamics, and the persistent separation of policy domains. While affordability and nutrition dominate research and policy agendas, environmental sustainability and consumer behaviour are too often sidelined. This is especially problematic in light of climate change and evolving dietary patterns. By synthesizing studies that integrate simulation and systems-based methods, this review identifies opportunities to build models that better capture interdependencies across domains particularly in contexts of carbon pricing, dietary transition, and environmental metrics.

One notable challenge lies in the quality and harmonization of data. Most studies rely on household-level data from budget surveys, which offer useful expenditure information but lack granularity on actual intake and behaviour. Individual dietary surveys provide richer insights but suffer from issues like recall bias and limited coverage. These limitations highlight the need for improved survey design, cross-national comparability, and greater access to harmonized microdata to support robust MSM.

This review contributes a novel perspective by tracing the evolution of food security MSM across economic, environmental, and health dimensions. It calls for greater interdisciplinarity, deeper attention to behavioural factors, and integrated data infrastructures. If policymakers were to take these findings seriously, it could support the shift from siloed interventions to system-based strategies that align hunger reduction, health promotion, and climate goals moving us closer to a sustainable and equitable food future.

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Author details

1. Ghassan Al-Masbhi

   University of Galway, Galway, Ireland

   For correspondence

   g.al-masbhi@universityofgalway.ie

   "This ORCID iD identifies the author of this article:" 0000-0001-8538-9710

2. Cathal O’Donoghue

   For correspondence

   cathal.odonoghue@universityofgalway.ie

   "This ORCID iD identifies the author of this article:" 0000-0003-3713-5366

3. Denisa M Sologon

   Luxembourg Institute of Socio-Economic Research, Esch-sur-Alzette, Luxembourg

   "This ORCID iD identifies the author of this article:" 0000-0002-0309-5952