Carbon-costed diets highlight redistribution needs: evidence from 165 countries

Carbon-costed diets highlight redistribution needs: evidence from 165 countries | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Carbon-costed diets highlight redistribution needs: evidence from 165 countries Hongyi Cai, Oliver Taherzadeh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9224832/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Food-system greenhouse gas emissions (GHGE) impose large social costs that are rarely reflected in food prices. The affordability impacts of internalising these costs remain unclear. Carbon pricing is a key instrument for internalising environmental costs, yet its application to food systems has been constrained by concerns over food affordability and distributional equity. Here, we integrate dietary intake, country-specific environmental footprints, food prices, and income distributions with MATILDA (Micro–Macro Assessment Tool to Identify Low-impact Dietary Actions) to estimate market and carbon-inclusive dietary costs across 165 countries and socio-demographic groups. We examine how internalising the social cost of diet-related GHGE alters dietary affordability globally and regionally. Our results reveal profound inequalities in the economic and environmental burdens of food consumption. Across regions, the inclusion of GHGE costs to food prices raises the cost of diets by 8–32%, limiting food affordability for an additional 397 million people worldwide. Low-income populations in sub-Saharan Africa and Southern Asia are particularly vulnerable, while full-pricing of diets in high-income regions has minimal impact on food affordability. These findings underscore the importance of coupling dietary transitions with redistributive and environmental pricing policies to ensure that food system decarbonisation does not exacerbate global nutrition inequities. Environmental Policy Environmental Economics Carbon pricing Dietary affordability Food system emissions Social cost of carbon Distributional equity Figures Figure 1 Figure 2 Figure 3 Figure 4 Highlights Internalising the social cost of diet-related greenhouse gas emissions raises dietary costs by 8–32% across 165 countries Carbon-inclusive food pricing renders diets unaffordable for an additional 397 million people globally Low-income populations in sub-Saharan Africa and Southern Asia face the greatest affordability risks from dietary carbon pricing High-income regions experience minimal affordability impacts when full environmental costs are reflected in food prices Effective food system decarbonisation requires redistributive policies to prevent exacerbating global nutrition inequities 1. Introduction Greenhouse gas emissions (GHGE) linked to food production contribute to climate change, biodiversity loss, and adverse health impacts 1 , yet these externalities are rarely reflected in market prices. As a consequence, unpriced carbon emissions distort food consumption and production decisions and generate hidden environmental, health, and social costs of around $ 15 trillion annually 2 . In response, carbon pricing has been widely implemented across energy, transport, and industrial systems, with evidence showing that a $ 40 per-ton CO₂ tax covering about 30% of emissions can reduce emissions by 4–6% 3 , while global carbon pricing instruments have mobilised over $ 100 billion 4 . Despite the food system accounting for approximately 30% of global GHGE 5 , carbon pricing remains largely absent from food consumption and agricultural production 6 . In the absence of an effective price signal, incentives to shift towards lower-emission diets remain weak 7 , yet modelling indicates that food price signals could cut food-related impacts by 5–6%, including about 63 Mt CO₂-Eq. (6%) of GHGE in Europe 8 . Understanding the social cost of diet-related GHGE is therefore critical for policy design, as higher food taxes would raise average annual EU household food spending by about €109, but recycling revenues through subsidies or transfers reduces net costs to around €26 per year, protecting vulnerable groups while incentivising lower-impact consumption 9 . Yet despite this evidence, food carbon pricing has gained limited traction, with equity concerns, particularly the risk of pricing low-income households out of nutritious diets, cited as a primary barrier to implementation 10 . Two structural gaps limit existing knowledge. First, existing research has not yet provided an integrated assessment of how internalising the social cost of diet-related GHGE would affect the affordability of the diets people actually consume 11 , 12 . Most studies examining diet costs and affordability focus on healthy or recommended dietary patterns benchmarked against normative dietary guidelines or reference food baskets 13 , approaches that are informative for assessing nutritional adequacy but do not capture prevailing consumption patterns or the emissions embedded within current diets 14 . While prior work has begun to estimate the global costs of sustainable dietary patterns and demonstrated that low-carbon diets can reduce ecological and health costs 15 , these analyses rarely link environmental cost internalisation to food affordability outcomes across countries and population groups 16 . In parallel, studies have examined food-related environmental pricing scenarios, including carbon taxes and fiscal reforms, to assess impacts on consumption, emissions, and welfare 17 , 18 . However, existing estimates are commonly based on national averages, obscuring substantial heterogeneity by socio-demographic characteristics such as urban–rural residence, age, sex, or educational level 19 . Furthermore, current estimates of the carbon cost of diets remain limited in scope and resolution. Existing studies typically rely on global average Life Cycle Assessment (LCA) impact factors that overlook country-specific emissions footprints of production systems, trade structures, and agricultural practices 20 . Collectively, these gaps mean that the true affordability cost of dietary carbon pricing remains unquantified for the populations at most risk. Addressing these gaps requires an integrated framework that links dietary consumption, environmental footprints of food supply chains, and income distributions in a consistent and comparable manner across countries. As a point of departure, we quantify price effects of dietary carbon pricing based on country-specific food emission footprint modelling, current dietary patterns, and across socio-demographic groups. Here, we build on the MATILDA (Micro–Macro Assessment Tool to Identify Low-impact Dietary Actions) framework 21 and extend it to a cost-focused application, referred to here as MATILDA-Cost, to estimate both market and carbon-inclusive costs of current diets across 165 countries and multiple sociodemographic subgroups. MATILDA is a globally harmonised dietary impact model that integrates food consumption microdata from the Global Dietary Database (GDD) with supply-chain environmental footprints from the Food and Agriculture Biomass Input–Output (FABIO) model, bridging micro-level dietary intake data with macro-level food production and trade systems across diverse socio-demographic subgroups and countries worldwide. MATILDA-Cost integrates harmonised dietary intake data with country-specific environmental footprint coefficients, food price information, and population income distributions. By jointly analysing current dietary patterns and supply-chain emissions, this study quantifies how incorporating environmental costs reshapes the economic burden of diets across countries and sociodemographic groups. The results identify population groups that may require complementary policies such as targeted subsidies, revenue recycling, or social protection to safeguard equitable access to diets during climate mitigation. 2. Methods and data The MATILDA-Cost framework links harmonised dietary intake data with country-specific food prices, environmental footprint coefficients, and income distributions to assess how internalising diet-related greenhouse gas emissions reshapes dietary affordability across countries and sociodemographic groups. A schematic overview of the model framework is provided in Supplementary Fig. 1. Dietary intake data and energy intake rescaling Dietary intake data from the GDD are standardised to 2,000 kcal per person per day. To improve the accuracy of dietary cost estimates, we rescaled dietary intakes to country- and sociodemographic group-specific energy intake levels using anthropometry-based energy requirements estimated by Springmann (2025) 22 , differentiated by age, sex and physical activity level. We combined the rescaled food group–specific dietary energy intakes with corresponding food group prices derived from the HDB. Dietary costs were calculated for each country and sociodemographic subgroup (i.e., age, sex, or educational level, and urban–rural residence) included in the GDD. Estimation of dietary cost Dietary costs were estimated by integrating food group–specific price information from the World Bank Food Prices for Nutrition Data Hub 23 with dietary intake data from the GDD 24 . We obtained country-specific cost estimates for the six food groups defined in the Healthy Diet Basket (HDB) 25 : starchy staples; animal-source foods; legumes, nuts and seeds; vegetables; fruits; and oils and fats (Supplementary Table 1). The HDB has been validated as a global dietary standard that is not only nutritionally adequate but also associated with relatively lower environmental impacts compared with current consumption patterns 26 . These data report the minimum cost of acquiring the least expensive locally available foods required to meet dietary energy and HDB requirements, expressed in purchasing power parity (PPP) dollars per person per day at 2,330 kcal. Food group costs were converted to per-unit prices (PPP dollars per kcal) by dividing reported daily costs by the caloric quantities assigned to each food group in the HDB, yielding harmonised, country-specific unit prices. All cost data are expressed in international dollars at 2017 PPP prices and were converted to 2018 values using World Bank PPP conversion factors to align with the GDD reference year. Social cost of diet-related GHGE Estimating the social cost of carbon (SCC), the monetised damage caused by one additional tonne of CO₂ emissions, is central to environmental cost internalisation. Published SCC estimates vary widely depending on discount rate, damage function, and socioeconomic scenario. We adopt the social cost of diet-related GHGE estimates reported by Rennert et al. (2022), based on an ensemble of models, which provide a mean value of $ 185 ton/CO₂ (5th–95th percentile range: $ 44–413 ton/ CO₂, in 2020 United States Dollar, USD) 27 . This corresponds to $ 0.185 kg/CO₂. To ensure consistency with the dietary cost calculations, we adjust these estimates for inflation and PPP to express them in 2018 PPP USD, resulting in social cost of diet-related GHGE of $ 0.143 kg/CO₂ and an uncertainty range of $ 0.034–0.320 kg/ CO₂. These adjusted values are then applied to compute the social costs of dietary GHGE for each demographic subgroup within the GDD, based on country- and product-specific GHG estimates in MATILDA. Income and population distribution data Income and population distribution data were obtained from the Poverty and Inequality Platform (PIP) of the World Bank 28 , which reports per-capita consumption or income aggregates and corresponding population shares across 100 income bins at 1% increments for each country. These aggregates are based on either consumption expenditure or income, depending on data availability in each country. Where both measures were available, income-based aggregates were prioritised, as they directly capture monetary receipts. In our analysis, 64 countries used income-based aggregates, predominantly high-income countries, while 82 countries used consumption expenditure, predominantly low- and middle-income countries. For each country, we selected the observation closest to the reference year 2018. Income levels were scaled to 2018 values using country-specific annual real GDP per capita growth rates. Dietary affordability assessment Country-level dietary cost estimates were derived from harmonised food price and consumption data, yielding two minimum dietary cost thresholds for each country: (i) food-only dietary cost and (ii) a full-price dietary cost, which incorporates the monetised social cost of diet-related GHGE. Country-level dietary cost thresholds were aggregated to the regional and global levels by taking unweighted means across countries within each region. Within each country, population-weighted income distributions were constructed by ordering income bins in ascending order of per-capita income and calculating cumulative population shares. Regional- and global-level affordability analysis were also conducted by pooling all country-level income bins within the same region and deriving cumulative distribution functions (CDFs) of income, weighted by their respective population sizes. Dietary affordability was assessed by comparing per-capita income with country-specific dietary cost estimates. A diet is considered unaffordable when its cost exceeds the share of income available for food expenditure. Following Smith and Subandoro (2007) 29 , country-specific food expenditure shares were assigned based on the World Bank’s income group classification: Low-income = 0.75; Lower-middle-income = 0.70; Upper-middle-income = 0.575; High-income = 0.50. These shares represent the proportion of household income that can reasonably be reserved for food, after accounting for essential non-food expenditures such as housing, transport, education, health and farm inputs 30 . For each income bin, individuals were classified as unable to afford a given dietary scenario if their effective food budget, defined as the per-capita income multiplied by the corresponding food expenditure share, fell below the national average dietary cost estimate. Dietary cost estimates were calculated at the group level, accounting for differences in dietary composition across socio-demographic subgroups. However, due to the lack of income distribution data disaggregated by socio-demographic characteristics, the affordability analysis was conducted using average national dietary costs for each scenario. For each region and for the global population, the share of the population unable to afford the food-only dietary cost, the share unable to afford the full-price dietary cost was calculated, revealing the additional population rendered unable to afford a healthy diet once external costs are internalised to national food costs. 3. Results Accounting for the social cost of diet-related GHGE increases the absolute cost of maintaining current average diets globally by a median of 15.5% (IQR 11.3%–22.0%), with substantial cross-country heterogeneity, as country-level increases range from 4.8% to 62.6%. Across 165 countries, the estimated food-only dietary cost (2018 PPP, per capita per day) ranged from $1.58 to $12.19, with a median of $4.20 (IQR $3.48–$5.43). The social cost of diet-related GHGE ranged from $0.20 to $3.03 per person per day, with a median of $0.70 (IQR $0.50–$0.98). Countries with the highest food-only dietary costs included Croatia ($12.19/day), Uzbekistan ($11.80/day), and Bosnia and Herzegovina ($11.46/day), whereas the lowest costs were observed in Guinea ($1.58/day), Senegal ($1.62/day), and Gambia ($1.75/day). Regional heterogeneity in the relative contribution of GHGE costs to total dietary costs was also pronounced. The median relative contribution of the social cost of diet-related GHGE to dietary costs was highest in South America (+33.0%) and Middle Africa (+27.4%), followed by Eastern Africa (+23.6%) and Western Africa (+22.6%). In contrast, relative increases were lowest in Northern America (+8.8%), Eastern Europe (+8.8%), and Western Europe (+9.5%). Substantial regional heterogeneity was observed in both the level and composition of dietary costs, as well as in the contribution of the social cost of diet-related GHGE (Figure 2). The highest median full-priced dietary costs were observed in Polynesia ($6.43/person/day), followed by Central Asia ($5.96) and Oceania ($5.91), whereas the lowest full-priced dietary cost was found in Western Africa ($3.02) and Melanesia ($3.13) (Figure 2). Cost composition also differed markedly across regions. Animal-source foods accounted for a high fraction of full-priced dietary costs in Northern Europe (62.3%), Eastern Europe (61.3%), and Western Europe (58.5%), whereas plant-based items dominated in Polynesia, where fruits, vegetables, legumes, and nuts/seeds together represented 72.0% of food-only costs. Dairy products accounted for the largest share of full-priced dietary costs in more than half of the regions, contributing most to Northern Europe (41.9%) and Oceania (38.3%). The relative contribution of the social cost of diet-related GHGE to full-price dietary costs varied substantially (median 14.7%, IQR 10.6–19.2%; range 8.4–26.4%). The largest shares were observed in South America (26.4%), Middle Africa (21.3%), and Central America (19.8%). In contrast, the social cost share was lowest in Eastern Europe (8.7%) and Northern America (8.4%). Dietary costs exhibited marked differences between socio-demographic groups and based on plant-based dietary food share (Figure 3). Using full-price dietary cost as an example, within the same decile of plant-based dietary share, urban populations consistently incurred higher dietary costs than rural populations, with a mean difference of $0.70 per day. Higher educational level was associated with higher dietary costs across most deciles, particularly in urban settings, with differences of approximately $0.69 to $1.71 per day between high- and low-education groups. Sex-related differences in dietary costs ranged from approximately $0.55 to $1.70 per day across regions, with males generally exhibiting higher costs than females. Age-related differences in dietary costs ranged from approximately $0.25 to $1.25 per day across regions, with younger adults (15–34 years) generally exhibiting higher costs than older adults (55+ years), particularly in lower plant-based deciles. Both food-only and full-price dietary costs displayed a clear declining trend across deciles of plant-based food proportion. For full-price dietary costs, the median cost across all population subgroups decreased from approximately $5.6 per day in the lowest plant-based decile to $3.3 per day in the highest decile (PPP, 2018). Including environmental costs further increases existing regional inequalities in dietary affordability. Globally, we estimate 3.61 billion people (51%) are unable to afford the food-only dietary cost (i.e. current cost) of the national average diet (Figure 4). Once the social cost of GHGE is added, 4.01 billion people (57% of the global population), an increase of approximately 397 million people, are unable to afford their diet. Marked regional disparities were observed. In sub-Saharan Africa (Eastern, Middle, Southern, and Western Africa), unaffordability levels were the highest globally, with a mean of 75.9% of the population unable to afford the food-only dietary cost of the national average diet. In Eastern Africa, 87% (311 million) of the population could not afford the food-only dietary cost, increasing to 91% (325 million) under a full-priced diet. Similarly, in Middle Africa, unaffordability rose from 82% (160 million) for food-only costs to 87% (171 million) for the full-price dietary cost, while in Western Africa, the corresponding shares increased from 71% (266 million) to 79% (298 million). By contrast, unaffordability remained comparatively low in high-income regions. In Western Europe, only 1% (1.9 million) of the population could not afford the full-price dietary cost, while in Northern America, the corresponding share was 3% (9.8 million) (Supplementary Table 2). 4. Discussion This study provides a global, demographically resolved assessment of how internalising the social cost of diet-related greenhouse gas emissions reshapes the economic burden of current diets. While carbon pricing is widely discussed as a cost-effective climate policy, its application to food systems remains contested due to concerns over affordability, equity, and the essential nature of food consumption 31 . By integrating dietary intake data, region-specific environmental footprints, and income distributions within a harmonised modelling framework, we identify three key findings. First, internalising GHGE costs increases the cost of diets in all regions, with a median relative increase of 15.5% across 165 countries, but with marked cross-country variability. Second, these increases translate into substantial affordability consequences. Globally, the share of people unable to afford diets rises from 51% under food-only costs to 57% under total costs, corresponding to approximately 397 million additional people falling into unaffordability (exceeding the population size of the United States). Third, the distributional burden is highly uneven: the largest absolute and relative affordability impacts are concentrated in low-income regions, particularly sub-Saharan Africa and South Asia, while high-income regions tend to exhibit comparatively low unaffordability despite higher per-capita environmental costs, reflecting a better capacity to absorb increased dietary prices. Our findings further show that an estimated 397 million additional people globally become unable to afford current diets once environmental costs are internalised. These regressive impacts reflect the interaction between two structural asymmetries. First, food systems in low-income regions tend to be more emissions-intensive per unit of dietary energy, owing to lower agricultural efficiency, higher reliance on extensive livestock production, and greater land-use change emissions embedded in supply chains. Second, baseline dietary costs already absorb a large share of household income in these regions, leaving limited capacity to absorb additional carbon-related cost increases. These dynamics differ markedly from high-income regions, where more efficient supply chains keep per-unit carbon costs lower as a share of diet cost, and where higher incomes buffer any residual impact. These findings are consistent with Springmann et al. (2021), who show that the global costs of healthy and sustainable diets are highest in low-income countries relative to per-capita income, and with Lucas et al. (2023), who demonstrate that low-carbon diets could reduce both health and ecological costs but require structural changes to food supply chains that are currently most constrained in low- and middle-income settings 20 , 32 . Our findings are consistent with previous research showing that carbon pricing and environmental cost internalisation are often regressive without redistribution, especially in low-income settings 33 . For example, distributional analyses of carbon pricing in the European Union show that, at the aggregate level, carbon taxes can have regressive effects because lower-income countries face a relatively higher burden than high-income countries 34 . Consistent patterns have also been documented at the household level, where lower-income households experience larger welfare losses in the absence of targeted transfers 35 . 4.1 Policy recommendation Several policy instruments are available to mitigate the unequal impacts that arise from full-pricing of diets to support a sustainable dietary transition. Recycling revenues from environmental pricing offers a direct way to offset affordability losses. Based on the carbon price applied in this study ( $ 0.143 per kg CO₂), the social cost of diet-related GHGE across the 165 countries studied could generate approximately $ 2.25– $ 2.36 billion (2018 PPP) annually. While modest in global terms, these costs represent a meaningful share of national income in low- and middle-income countries: the social cost of diet-related GHGE amounts to 49.5% of GDP in the Central African Republic, 14.2% in Mozambique, and 13.5% in Yemen (Supplementary Table 3). This finding aligns with previous evidence that carbon pricing can generate around 10–12% of total government revenue from carbon taxes alone and over 20% when combined with fossil fuel subsidy removal 36 . When recycled through cash transfers, these revenues can raise average household incomes by around 3%, thereby partially compensating for affordability losses 36 . Targeting equal per-capita revenue redistribution in our framework to populations unable to afford diets reduces global dietary unaffordability from 51% to 48%, with the largest improvements observed in Western Africa, where the share falls from 71% to 62% (Supplementary Fig. 2). Such targeted transfers or equal per-capita refunds (i.e. lump-sum refunds distributed equally across the population) 37 can substantially reduce regressive effects. For example, analyses covering 16 Latin American and Caribbean countries representing more than 540 million people show that recycling carbon pricing revenues through cash transfers can fully offset cost increases for low-income households 38 , while EU-wide simulations indicate that a consumption-based carbon tax generating around €200 billion annually would raise costs for the lowest-income groups by up to 6–9% in the absence of compensation but can completely neutralise inequality impacts when revenues are redistributed as lump-sum transfers 39 . Similar approaches could be applied to food systems through existing social protection 40 or food assistance programmes 41 . Targeted food subsidies provide a complementary tool. Empirical evidence shows that subsidising nutrient-rich foods by 10–25% increases fruit and vegetable intake by approximately 0.4–0.6 servings per day, and that a 20% price reduction leads to a 16–17% increase in purchases 42 . When combined with pricing of high-emission foods, such subsidies can promote healthier diets while cushioning affordability impacts 43 , provided that lower-impact alternatives are readily available in domestic markets to avoid exacerbating national or household food insecurity. Policy needs also differ by income level. In high-income countries, environmental pricing may encourage shifts towards lower-impact diets with limited affordability consequences, as aligning diets in such countries with national dietary guidelines has been shown to reduce diet-related GHGE by around 20–50% without compromising nutritional adequacy or affordability 44 . By contrast, in low-income regions, protecting access to nutritious diets requires safeguards that prevent environmental costs from being passed on to consumers. Evidence shows that in food systems at earlier stages of transition, a nutritious diet remains unaffordable for a large share of the population, with only around 15% of people able to afford recommended diets in rural and informal systems 45 . In these contexts, policy measures such as exemptions for staple foods, gradual phase-in of pricing, or compensatory transfers are essential 46 . 4.2 Strengths and limitations This study has several key strengths. First, it provides the first global, harmonised assessment of the true cost of current diets by jointly integrating food prices, dietary intake, greenhouse gas footprints, and income distributions across 165 countries. Second, MATILDA captures socio-demographic heterogeneity, revealing distributional impacts hidden in national averages and informing equity-focused policy design. Third, dietary GHGE estimates are linked to country-specific production systems and trade structures, improving consistency between consumption-based footprints and economic valuation. This study has several limitations that should be acknowledged. First, while we estimate the full-priced cost of diets by internalising the social cost of diet-related GHGE, we do not model behavioural responses to higher food prices. As a result, our analysis does not capture potential changes in consumption patterns, substitution effects, or feedbacks along the food supply chain 47 that could arise under carbon-inclusive pricing. Consequently, the estimated emissions reductions associated with full pricing remain uncertain. Future work could integrate price elasticities of demand or diet optimisation frameworks to assess how consumers might adjust food choices in response to carbon-inclusive food prices 48 , and to quantify the resulting environmental and health outcomes. Second, dietary cost estimates rely on World Bank food price data, which reflect the lowest-cost items within each food group. While this approach ensures global comparability, it may underestimate actual food expenditures faced by consumers, particularly in settings with limited market access, high price dispersion, or strong preferences for branded or processed foods 49 . Incorporating more detailed retail price data or household expenditure surveys would improve the representation of real-world food purchasing conditions 50 . Third, the social cost of diet-related GHGE is derived from global estimates of the social cost of carbon and applies a uniform carbon price across countries and food groups. This approach does not capture potential regional heterogeneity in climate damages, differences in vulnerability to climate impacts, or local environmental externalities 51 , such as biodiversity loss, water pollution, or land degradation. Future research could explore region-specific carbon cost estimates or multi-dimensional environmental pricing schemes that better reflect local environmental risks. Finally, although dietary costs are estimated at the socio-demographic group level, affordability is assessed using national income distributions applied to average national diets, due to the lack of income data disaggregated by socio-demographic characteristics. As a result, the affordability analysis captures cross-country and regional differences in income distributions but does not reflect within-country variation in affordability across population subgroups. Future research should therefore prioritise improved data linking income, food prices, and dietary patterns at the subgroup level, and model behavioural responses to carbon-inclusive food prices. 5. Conclusion This study provides evidence that carbon pricing applied to food systems carries profound distributional consequences, disproportionately burdening populations in low-income regions who bear the least responsibility for global emissions. Our analysis shows that without deliberate redistribution mechanisms, including targeted transfers, food subsidies, and social protection calibrated to local income and dietary conditions, pricing dietary GHGE risks deepening the nutritional inequities it ostensibly seeks to resolve. The revenues generated by such pricing represent a meaningful and underutilised policy resource. Realising the dual goals of food system decarbonisation and equitable nutrition access will require embedding environmental pricing within broader social protection frameworks, particularly in low- and middle-income country contexts. Declarations CRediT authorship contribution statement Hongyi Cai: Writing – review & editing, Writing – original draft, Visualization, Validation, Methodology, Formal analysis, Data curation. Oliver Taherzadeh: Writing – review & editing, Writing – original draft, Supervision, Project administration, Conceptualization. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements This work was funded by a Kiem seed funding grant from Leiden University and the Dutch Research Agenda (NWA) programme ‘Transition to a sustainable food system’ (project number NWA.1235.18.201), which is financed by the Dutch Research Council (NWO). Data availability The data underlying this article are derived from multiple publicly available sources. Dietary intake data were obtained from the Global Dietary Database (GDD; https://globaldietarydatabase.org/). Food price data were sourced from the World Bank Food Prices for Nutrition Data Hub (Healthy Diet Basket; https://www.worldbank.org/en/programs/icp/brief/foodpricesfornutrition). Environmental footprint coefficients were drawn from the Food and Agriculture Biomass Input–Output (FABIO) model (https://doi.org/10.5281/zenodo.2577067). Income and population distribution data were obtained from the World Bank Poverty and Inequality Platform (PIP; https://pip.worldbank.org/). The MATILDA-Cost model code used in this study will be made available on request. References Zurek, M., Hebinck, A. & Selomane, O. Climate change and the urgency to transform food systems. Science 376 , 1416–1421 (2022). Lord, S. Hidden Costs of Agrifood Systems and Recent Trends from 2016 to 2023 . (2023). Metcalf, G. E. & Stock, J. H. The Macroeconomic Impact of Europe’s Carbon Taxes. Am. Econ. J. 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Food 6 , 1176–1185 (2025). Rennert, K. et al. Comprehensive evidence implies a higher social cost of CO2. Nature 610 , 687–692 (2022). The World Bank. Poverty and Inequality Platform (PIP). (2023). Smith, L. & Subandoro, A. Measuring Food Security Using Household Expenditure Surveys. Int. Food Policy Res. Inst. IFPRI Food Secur. Pract. Tech. Guide Ser. (2007). Nzayiramya, S., Muhammad, A. & Baffoe-Bonnie, A. A global assessment of food and non-food spending: evidence from 173 countries and implications for food security. Agric. Food Secur. 14 , 20 (2025). Isbasoiu, A., Jayet, P.-A. & De Cara, S. Increasing food production and mitigating agricultural greenhouse gas emissions in the European Union: impacts of carbon pricing and calorie production targeting. Environ. Econ. Policy Stud. 23 , 409–440 (2021). Springmann, M., Clark, M. A., Rayner, M., Scarborough, P. & Webb, P. The global and regional costs of healthy and sustainable dietary patterns: a modelling study. Lancet Planet. Health 5 , e797–e807 (2021). Wang, Q., Hubacek, K., Feng, K., Wei, Y.-M. & Liang, Q.-M. Distributional effects of carbon taxation. Appl. Energy 184 , 1123–1131 (2016). Feindt, S., Kornek, U., Labeaga, J. M., Sterner, T. & Ward, H. Understanding regressivity: Challenges and opportunities of European carbon pricing. Energy Econ. 103 , 105550 (2021). Williams, R. C., Gordon, H., Burtraw, D., Carbone, J. C. & Morgenstern, R. D. THE INITIAL INCIDENCE OF A CARBON TAX ACROSS INCOME GROUPS. Natl. Tax J. 68 , 195–213 (2015). Timilsina, G. R. & Sebsibie, S. Economic and Distributional Impacts of Selected Carbon Pricing Policies for the Arab Republic of Egypt. Dev. Res. https://documents1.worldbank.org/curated/en/099430006042429299/pdf/IDU13edd46ce13af614f4a1a76e193fcc82b1034.pdf (2024). Budolfson, M. et al. Climate action with revenue recycling has benefits for poverty, inequality and well-being. Nat. Clim. Change 11 , 1111–1116 (2021). Missbach, L., Steckel, J. C. & Vogt-Schilb, A. Cash transfers in the context of carbon pricing reforms in Latin America and the Caribbean. World Dev. 173 , 106406 (2024). Maier, S., De Poli, S. & Amores, A. F. Carbon Taxes on Consumption: Distributional Implications for a Just Transition in the EU . https://www.econstor.eu/handle/10419/306595 (2024). Berejena, T., Malongane, F. & Metsing, T. I. Social Protection Programs and Their Support for Promoting Access to Nutrient-Dense Foods for Vulnerable Communities in South Africa. Curr. Dev. Nutr. 9 , 107452 (2025). FAO, IFAD, UNICEF, WFP & WHO. The State of Food Security and Nutrition in the World 2024 . (FAO ; IFAD ; UNICEF ; WFP ; WHO ;, 2024). Huangfu, P. et al. Impact of price reductions, subsidies, or financial incentives on healthy food purchases and consumption: a systematic review and meta-analysis. Lancet Planet. Health 8 , e197–e212 (2024). An, R. Effectiveness of subsidies in promoting healthy food purchases and consumption: a review of field experiments. Public Health Nutr. 16 , 1215–1228 (2013). Steenson, S. & Buttriss, J. L. Healthier and more sustainable diets: What changes are needed in high-income countries? Nutr. Bull. 46 , 279–309 (2021). Ambikapathi, R. et al. Global food systems transitions have enabled affordable diets but had less favourable outcomes for nutrition, environmental health, inclusion and equity. Nat. Food 3 , 764–779 (2022). Westbury, S. et al. The influence of the urban food environment on diet, nutrition and health outcomes in low-income and middle-income countries: a systematic review. BMJ Glob. Health 6 , (2021). Davis, K. F., Downs, S. & Gephart, J. A. Towards food supply chain resilience to environmental shocks. Nat. Food 2 , 54–65 (2021). Habib, M. et al. Carbon pricing and the food system: Implications for sustainability and equity. Trends Food Sci. Technol. 150 , 104577 (2024). Headey, D., Hirvonen, K. & Alderman, H. Estimating the cost and affordability of healthy diets: How much do methods matter? Food Policy 126 , 102654 (2024). Lusk, J. L. Consumer Research with Big Data: Applications from the Food Demand Survey (FooDS). Am. J. Agric. Econ. 99 , 303–320 (2017). Briggs, A. D. M., Kehlbacher, A., Tiffin, R. & Scarborough, P. Simulating the impact on health of internalising the cost of carbon in food prices combined with a tax on sugar-sweetened beverages. BMC Public Health 16 , 107 (2016). Additional Declarations The authors declare no competing interests. Supplementary Files SMfigure1.tif Overview of the MATILDA-Cost framework. Sfigure2popcostlogdistributionW1394H1150.tiff Cumulative population income distributions before and after equal per-capita revenue recycling, and the share of the population unable to afford the food-only dietary cost, by world region. SupplementarymaterialCarboncosteddietsrequireredistributiontoensureequitableaccesstosustainablediets.docx Carbon-costed diets highlight redistribution needs: evidence from 165 countries Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9224832","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":612119101,"identity":"da610e4d-5487-4096-9f00-8f70ca49f25d","order_by":0,"name":"Hongyi Cai","email":"","orcid":"https://orcid.org/0000-0003-3767-0465","institution":"Institute of Environmental Sciences (CML), Leiden University, PO Box 9518, Leiden 2300 RA, The Netherlands","correspondingAuthor":false,"prefix":"","firstName":"Hongyi","middleName":"","lastName":"Cai","suffix":""},{"id":612119102,"identity":"4a06f4d0-8c24-41ce-afa5-4e967b683a40","order_by":1,"name":"Oliver Taherzadeh","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0001-6144-9483","institution":"Institute of Environmental Sciences (CML), Leiden University, PO Box 9518, Leiden 2300 RA, The Netherlands","correspondingAuthor":true,"prefix":"","firstName":"Oliver","middleName":"","lastName":"Taherzadeh","suffix":""}],"badges":[],"createdAt":"2026-03-25 15:12:41","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9224832/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9224832/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105579578,"identity":"c7471353-0a21-40d0-bbd0-ca81329375a2","added_by":"auto","created_at":"2026-03-27 14:18:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":35545,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelative increase in dietary costs after accounting for the social cost of diet-related GHGE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCountry-level estimates are expressed as percentage changes compared with baseline food-only costs. Countries with missing data are shown in grey.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"figure1worldmap.png","url":"https://assets-eu.researchsquare.com/files/rs-9224832/v1/1a335866578557f4040d3fa9.png"},{"id":107479684,"identity":"4f45edac-8580-4ee4-ae1f-2b6846357847","added_by":"auto","created_at":"2026-04-22 01:44:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":47667,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRegional composition of dietary costs and the social cost of diet-related GHGE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStacked bars show the per-capita daily dietary cost by food group across regions in 2018 PPP $. Food-group components represent food-only dietary costs, while the blue bar segment indicates the social cost of diet-related GHGE.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"figure2proportionfoodgroup.png","url":"https://assets-eu.researchsquare.com/files/rs-9224832/v1/b941925eafc6e121f2ee0f3b.png"},{"id":105751855,"identity":"d9718b90-5f5b-40d2-8fc5-3064d5b2ee31","added_by":"auto","created_at":"2026-03-30 15:48:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":90165,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDietary cost gradients across deciles of plant-based dietary composition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFood-only and full-price dietary costs across deciles of plant-based food proportion by socio-demographic subgroup. The left y-axis shows deciles of plant-based food proportion, with 1 indicating the lowest and 10 indicating the highest proportion of plant-based foods. Colours represent dietary cost (USD/day, PPP, 2018). Percentages shown on the right-hand side indicate the corresponding plant-based food proportion for each decile. Population subgroups are defined by sex, age, urban–rural residence, and educational attainment.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"figure3heatmapW900H1150.png","url":"https://assets-eu.researchsquare.com/files/rs-9224832/v1/59ce91160ca97a6ed4bcf2ea.png"},{"id":105728758,"identity":"24e9ccd1-c5fe-4135-9b0e-3655e9dc8755","added_by":"auto","created_at":"2026-03-30 11:12:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":137014,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRegional affordability of food-only and full-price dietary costs across income distributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCurves represent cumulative distribution functions of population income for each region. Vertical dashed and dotted lines indicate the thresholds for food-only dietary cost and full-price dietary cost, respectively, while their intersections with the income \u003c/em\u003ecumulative distribution functions\u003cem\u003e (CDFs) denote the population share unable to afford each dietary cost.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"figure4popcostlogdistributionW1394H1150.png","url":"https://assets-eu.researchsquare.com/files/rs-9224832/v1/857698c9209cef590a5bd96f.png"},{"id":107704794,"identity":"0eb42a38-aad5-4177-9beb-4b30de5158b2","added_by":"auto","created_at":"2026-04-24 08:58:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":499927,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9224832/v1/7417c3dc-3975-45b4-a0fe-60d8efe90ce3.pdf"},{"id":105579579,"identity":"1a8b2585-8473-4ade-8f83-c9025439e90d","added_by":"auto","created_at":"2026-03-27 14:18:26","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":469626,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of the MATILDA-Cost framework.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"SMfigure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-9224832/v1/e8aa99c15a967d1dc2ce29ac.tif"},{"id":105579584,"identity":"62089721-9b63-44cd-8023-cbc3d453f45e","added_by":"auto","created_at":"2026-03-27 14:18:26","extension":"tiff","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":5817948,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCumulative population income distributions before and after equal per-capita revenue recycling, and the share of the population unable to afford the food-only dietary cost, by world region.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Sfigure2popcostlogdistributionW1394H1150.tiff","url":"https://assets-eu.researchsquare.com/files/rs-9224832/v1/0de1d432392d554f21fc72a7.tiff"},{"id":105728147,"identity":"1e417744-8005-473e-90a5-64918a5df2e0","added_by":"auto","created_at":"2026-03-30 11:10:13","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":708921,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCarbon-costed diets highlight redistribution needs: evidence from 165 countries\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"SupplementarymaterialCarboncosteddietsrequireredistributiontoensureequitableaccesstosustainablediets.docx","url":"https://assets-eu.researchsquare.com/files/rs-9224832/v1/7ae116add9fdb8435b4db0a9.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eCarbon-costed diets highlight redistribution needs: evidence from 165 countries\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Highlights","content":"\u003cul type=\"disc\"\u003e\n \u003cli\u003eInternalising the social cost of diet-related greenhouse gas emissions raises dietary costs by 8\u0026ndash;32% across 165 countries\u003c/li\u003e\n \u003cli\u003eCarbon-inclusive food pricing renders diets unaffordable for an additional 397 million people globally\u003c/li\u003e\n \u003cli\u003eLow-income populations in sub-Saharan Africa and Southern Asia face the greatest affordability risks from dietary carbon pricing\u003c/li\u003e\n \u003cli\u003eHigh-income regions experience minimal affordability impacts when full environmental costs are reflected in food prices\u003c/li\u003e\n \u003cli\u003eEffective food system decarbonisation requires redistributive policies to prevent exacerbating global nutrition inequities\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eGreenhouse gas emissions (GHGE) linked to food production contribute to climate change, biodiversity loss, and adverse health impacts\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, yet these externalities are rarely reflected in market prices. As a consequence, unpriced carbon emissions distort food consumption and production decisions and generate hidden environmental, health, and social costs of around \u003cspan\u003e$\u003c/span\u003e15 trillion annually\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. In response, carbon pricing has been widely implemented across energy, transport, and industrial systems, with evidence showing that a \u003cspan\u003e$\u003c/span\u003e40 per-ton CO₂ tax covering about 30% of emissions can reduce emissions by 4\u0026ndash;6%\u003csup\u003e3\u003c/sup\u003e, while global carbon pricing instruments have mobilised over \u003cspan\u003e$\u003c/span\u003e100 billion\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite the food system accounting for approximately 30% of global GHGE\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, carbon pricing remains largely absent from food consumption and agricultural production\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. In the absence of an effective price signal, incentives to shift towards lower-emission diets remain weak\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, yet modelling indicates that food price signals could cut food-related impacts by 5\u0026ndash;6%, including about 63 Mt CO₂-Eq.\u0026nbsp;(6%) of GHGE in Europe\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Understanding the social cost of diet-related GHGE is therefore critical for policy design, as higher food taxes would raise average annual EU household food spending by about \u0026euro;109, but recycling revenues through subsidies or transfers reduces net costs to around \u0026euro;26 per year, protecting vulnerable groups while incentivising lower-impact consumption\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Yet despite this evidence, food carbon pricing has gained limited traction, with equity concerns, particularly the risk of pricing low-income households out of nutritious diets, cited as a primary barrier to implementation\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTwo structural gaps limit existing knowledge. First, existing research has not yet provided an integrated assessment of how internalising the social cost of diet-related GHGE would affect the affordability of the diets people actually consume\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Most studies examining diet costs and affordability focus on healthy or recommended dietary patterns benchmarked against normative dietary guidelines or reference food baskets\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, approaches that are informative for assessing nutritional adequacy but do not capture prevailing consumption patterns or the emissions embedded within current diets\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. While prior work has begun to estimate the global costs of sustainable dietary patterns and demonstrated that low-carbon diets can reduce ecological and health costs\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, these analyses rarely link environmental cost internalisation to food affordability outcomes across countries and population groups\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. In parallel, studies have examined food-related environmental pricing scenarios, including carbon taxes and fiscal reforms, to assess impacts on consumption, emissions, and welfare\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. However, existing estimates are commonly based on national averages, obscuring substantial heterogeneity by socio-demographic characteristics such as urban\u0026ndash;rural residence, age, sex, or educational level\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Furthermore, current estimates of the carbon cost of diets remain limited in scope and resolution. Existing studies typically rely on global average Life Cycle Assessment (LCA) impact factors that overlook country-specific emissions footprints of production systems, trade structures, and agricultural practices\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCollectively, these gaps mean that the true affordability cost of dietary carbon pricing remains unquantified for the populations at most risk. Addressing these gaps requires an integrated framework that links dietary consumption, environmental footprints of food supply chains, and income distributions in a consistent and comparable manner across countries. As a point of departure, we quantify price effects of dietary carbon pricing based on country-specific food emission footprint modelling, current dietary patterns, and across socio-demographic groups. Here, we build on the MATILDA (Micro\u0026ndash;Macro Assessment Tool to Identify Low-impact Dietary Actions) framework\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e and extend it to a cost-focused application, referred to here as MATILDA-Cost, to estimate both market and carbon-inclusive costs of current diets across 165 countries and multiple sociodemographic subgroups. MATILDA is a globally harmonised dietary impact model that integrates food consumption microdata from the Global Dietary Database (GDD) with supply-chain environmental footprints from the Food and Agriculture Biomass Input\u0026ndash;Output (FABIO) model, bridging micro-level dietary intake data with macro-level food production and trade systems across diverse socio-demographic subgroups and countries worldwide.\u003c/p\u003e \u003cp\u003eMATILDA-Cost integrates harmonised dietary intake data with country-specific environmental footprint coefficients, food price information, and population income distributions. By jointly analysing current dietary patterns and supply-chain emissions, this study quantifies how incorporating environmental costs reshapes the economic burden of diets across countries and sociodemographic groups. The results identify population groups that may require complementary policies such as targeted subsidies, revenue recycling, or social protection to safeguard equitable access to diets during climate mitigation.\u003c/p\u003e"},{"header":"2. Methods and data","content":"\u003cp\u003eThe MATILDA-Cost framework links harmonised dietary intake data with country-specific food prices, environmental footprint coefficients, and income distributions to assess how internalising diet-related greenhouse gas emissions reshapes dietary affordability across countries and sociodemographic groups. A schematic overview of the model framework is provided in Supplementary Fig.\u0026nbsp;1.\u003c/p\u003e \u003cp\u003e \u003cem\u003eDietary intake data and energy intake rescaling\u003c/em\u003e \u003c/p\u003e \u003cp\u003eDietary intake data from the GDD are standardised to 2,000 kcal per person per day. To improve the accuracy of dietary cost estimates, we rescaled dietary intakes to country- and sociodemographic group-specific energy intake levels using anthropometry-based energy requirements estimated by Springmann (2025)\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, differentiated by age, sex and physical activity level. We combined the rescaled food group\u0026ndash;specific dietary energy intakes with corresponding food group prices derived from the HDB. Dietary costs were calculated for each country and sociodemographic subgroup (i.e., age, sex, or educational level, and urban\u0026ndash;rural residence) included in the GDD.\u003c/p\u003e \u003cp\u003e \u003cem\u003eEstimation of dietary cost\u003c/em\u003e \u003c/p\u003e \u003cp\u003eDietary costs were estimated by integrating food group\u0026ndash;specific price information from the World Bank Food Prices for Nutrition Data Hub\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e with dietary intake data from the GDD\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. We obtained country-specific cost estimates for the six food groups defined in the Healthy Diet Basket (HDB)\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e: starchy staples; animal-source foods; legumes, nuts and seeds; vegetables; fruits; and oils and fats (Supplementary Table\u0026nbsp;1). The HDB has been validated as a global dietary standard that is not only nutritionally adequate but also associated with relatively lower environmental impacts compared with current consumption patterns\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. These data report the minimum cost of acquiring the least expensive locally available foods required to meet dietary energy and HDB requirements, expressed in purchasing power parity (PPP) dollars per person per day at 2,330 kcal.\u003c/p\u003e \u003cp\u003eFood group costs were converted to per-unit prices (PPP dollars per kcal) by dividing reported daily costs by the caloric quantities assigned to each food group in the HDB, yielding harmonised, country-specific unit prices. All cost data are expressed in international dollars at 2017 PPP prices and were converted to 2018 values using World Bank PPP conversion factors to align with the GDD reference year.\u003c/p\u003e \u003cp\u003e \u003cem\u003eSocial cost of diet-related GHGE\u003c/em\u003e \u003c/p\u003e \u003cp\u003eEstimating the social cost of carbon (SCC), the monetised damage caused by one additional tonne of CO₂ emissions, is central to environmental cost internalisation. Published SCC estimates vary widely depending on discount rate, damage function, and socioeconomic scenario. We adopt the social cost of diet-related GHGE estimates reported by Rennert et al. (2022), based on an ensemble of models, which provide a mean value of \u003cspan\u003e$\u003c/span\u003e185 ton/CO₂ (5th\u0026ndash;95th percentile range: \u003cspan\u003e$\u003c/span\u003e44\u0026ndash;413 ton/ CO₂, in 2020 United States Dollar, USD)\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. This corresponds to \u003cspan\u003e$\u003c/span\u003e0.185 kg/CO₂. To ensure consistency with the dietary cost calculations, we adjust these estimates for inflation and PPP to express them in 2018 PPP USD, resulting in social cost of diet-related GHGE of \u003cspan\u003e$\u003c/span\u003e0.143 kg/CO₂ and an uncertainty range of \u003cspan\u003e$\u003c/span\u003e0.034\u0026ndash;0.320 kg/ CO₂. These adjusted values are then applied to compute the social costs of dietary GHGE for each demographic subgroup within the GDD, based on country- and product-specific GHG estimates in MATILDA.\u003c/p\u003e \u003cp\u003e \u003cem\u003eIncome and population distribution data\u003c/em\u003e \u003c/p\u003e \u003cp\u003eIncome and population distribution data were obtained from the Poverty and Inequality Platform (PIP) of the World Bank\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, which reports per-capita consumption or income aggregates and corresponding population shares across 100 income bins at 1% increments for each country. These aggregates are based on either consumption expenditure or income, depending on data availability in each country. Where both measures were available, income-based aggregates were prioritised, as they directly capture monetary receipts. In our analysis, 64 countries used income-based aggregates, predominantly high-income countries, while 82 countries used consumption expenditure, predominantly low- and middle-income countries. For each country, we selected the observation closest to the reference year 2018. Income levels were scaled to 2018 values using country-specific annual real GDP per capita growth rates.\u003c/p\u003e \u003cp\u003e \u003cem\u003eDietary affordability assessment\u003c/em\u003e \u003c/p\u003e \u003cp\u003eCountry-level dietary cost estimates were derived from harmonised food price and consumption data, yielding two minimum dietary cost thresholds for each country: (i) food-only dietary cost and (ii) a full-price dietary cost, which incorporates the monetised social cost of diet-related GHGE. Country-level dietary cost thresholds were aggregated to the regional and global levels by taking unweighted means across countries within each region.\u003c/p\u003e \u003cp\u003eWithin each country, population-weighted income distributions were constructed by ordering income bins in ascending order of per-capita income and calculating cumulative population shares. Regional- and global-level affordability analysis were also conducted by pooling all country-level income bins within the same region and deriving cumulative distribution functions (CDFs) of income, weighted by their respective population sizes.\u003c/p\u003e \u003cp\u003eDietary affordability was assessed by comparing per-capita income with country-specific dietary cost estimates. A diet is considered unaffordable when its cost exceeds the share of income available for food expenditure. Following Smith and Subandoro (2007)\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, country-specific food expenditure shares were assigned based on the World Bank\u0026rsquo;s income group classification: Low-income\u0026thinsp;=\u0026thinsp;0.75; Lower-middle-income\u0026thinsp;=\u0026thinsp;0.70; Upper-middle-income\u0026thinsp;=\u0026thinsp;0.575; High-income\u0026thinsp;=\u0026thinsp;0.50. These shares represent the proportion of household income that can reasonably be reserved for food, after accounting for essential non-food expenditures such as housing, transport, education, health and farm inputs\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. For each income bin, individuals were classified as unable to afford a given dietary scenario if their effective food budget, defined as the per-capita income multiplied by the corresponding food expenditure share, fell below the national average dietary cost estimate.\u003c/p\u003e \u003cp\u003eDietary cost estimates were calculated at the group level, accounting for differences in dietary composition across socio-demographic subgroups. However, due to the lack of income distribution data disaggregated by socio-demographic characteristics, the affordability analysis was conducted using average national dietary costs for each scenario. For each region and for the global population, the share of the population unable to afford the food-only dietary cost, the share unable to afford the full-price dietary cost was calculated, revealing the additional population rendered unable to afford a healthy diet once external costs are internalised to national food costs.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003eAccounting for the social cost of diet-related GHGE increases the absolute cost of maintaining current average diets globally by a median of 15.5% (IQR 11.3%\u0026ndash;22.0%), with substantial cross-country heterogeneity, as country-level increases range from 4.8% to 62.6%. Across 165 countries, the estimated food-only dietary cost (2018 PPP, per capita per day) ranged from $1.58 to $12.19, with a median of\u0026nbsp;$4.20 (IQR $3.48\u0026ndash;$5.43). The social cost of diet-related GHGE ranged from $0.20 to $3.03 per person per day, with a median of $0.70 (IQR $0.50\u0026ndash;$0.98). Countries with the highest food-only dietary costs included Croatia ($12.19/day), Uzbekistan ($11.80/day), and Bosnia and Herzegovina ($11.46/day), whereas the lowest costs were observed in Guinea ($1.58/day), Senegal ($1.62/day), and Gambia ($1.75/day).\u003c/p\u003e\n\u003cp\u003eRegional heterogeneity in the relative contribution of GHGE costs to total dietary costs was also pronounced. The median relative contribution of the social cost of diet-related GHGE to dietary costs was highest in South America (+33.0%) and Middle Africa (+27.4%), followed by Eastern Africa (+23.6%) and Western Africa (+22.6%). In contrast, relative increases were lowest in Northern America (+8.8%), Eastern Europe (+8.8%), and Western Europe (+9.5%).\u003c/p\u003e\n\u003cp\u003eSubstantial regional heterogeneity was observed in both the level and composition of dietary costs, as well as in the contribution of the social cost of diet-related GHGE (Figure 2). The highest median full-priced dietary costs were observed in Polynesia ($6.43/person/day), followed by Central Asia ($5.96) and Oceania ($5.91), whereas the lowest full-priced dietary cost was found in Western Africa ($3.02) and Melanesia ($3.13) (Figure 2). Cost composition also differed markedly across regions. Animal-source foods accounted for a high fraction of full-priced dietary costs in Northern Europe (62.3%), Eastern Europe (61.3%), and Western Europe (58.5%), whereas plant-based items dominated in Polynesia, where fruits, vegetables, legumes, and nuts/seeds together represented 72.0% of food-only costs. Dairy products accounted for the largest share of full-priced dietary costs in more than half of the regions, contributing most to Northern Europe (41.9%) and Oceania (38.3%). The relative contribution of the social cost of diet-related GHGE to full-price dietary costs varied substantially (median 14.7%, IQR 10.6\u0026ndash;19.2%; range 8.4\u0026ndash;26.4%). The largest shares were observed in South America (26.4%), Middle Africa (21.3%), and Central America (19.8%). In contrast, the social cost share was lowest in Eastern Europe (8.7%) and Northern America (8.4%).\u003c/p\u003e\n\u003cp\u003eDietary costs exhibited marked differences between socio-demographic groups and based on plant-based dietary food share (Figure 3). Using full-price dietary cost as an example, within the same decile of plant-based dietary share, urban populations consistently incurred higher dietary costs than rural populations, with a mean difference of $0.70 per day. Higher educational level was associated with higher dietary costs across most deciles, particularly in urban settings, with differences of approximately $0.69 to $1.71 per day between high- and low-education groups. Sex-related differences in dietary costs ranged from approximately $0.55 to $1.70 per day across regions, with males generally exhibiting higher costs than females. Age-related differences in dietary costs ranged from approximately $0.25 to $1.25 per day across regions, with younger adults (15\u0026ndash;34 years) generally exhibiting higher costs than older adults (55+ years), particularly in lower plant-based deciles. Both food-only and full-price dietary costs displayed a clear declining trend across deciles of plant-based food proportion. For full-price dietary costs, the median cost across all population subgroups decreased from approximately $5.6 per day in the lowest plant-based decile to $3.3 per day in the highest decile (PPP, 2018).\u003c/p\u003e\n\u003cp\u003eIncluding environmental costs further increases existing regional inequalities in dietary affordability. Globally, we estimate 3.61 billion people (51%) are unable to afford the food-only dietary cost (i.e. current cost) of the national average diet (Figure 4). Once the social cost of GHGE is added, \u0026nbsp;4.01 billion people (57% of the global population), an increase of approximately 397 million people, are unable to afford their diet. Marked regional disparities were observed. In sub-Saharan Africa (Eastern, Middle, Southern, and Western Africa), unaffordability levels were the highest globally, with a mean of 75.9% of the population unable to afford the food-only dietary cost of the national average diet. In Eastern Africa, 87% (311 million) of the population could not afford the food-only dietary cost, increasing to 91% (325 million) under a full-priced diet. Similarly, in Middle Africa, unaffordability rose from 82% (160 million) for food-only costs to 87% (171 million) for the full-price dietary cost, while in Western Africa, the corresponding shares increased from 71% (266 million) to 79% (298 million). By contrast, unaffordability remained comparatively low in high-income regions. In Western Europe, only 1% (1.9 million) of the population could not afford the full-price dietary cost, while in Northern America, the corresponding share was 3% (9.8 million) (Supplementary Table 2).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study provides a global, demographically resolved assessment of how internalising the social cost of diet-related greenhouse gas emissions reshapes the economic burden of current diets. While carbon pricing is widely discussed as a cost-effective climate policy, its application to food systems remains contested due to concerns over affordability, equity, and the essential nature of food consumption\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. By integrating dietary intake data, region-specific environmental footprints, and income distributions within a harmonised modelling framework, we identify three key findings. First, internalising GHGE costs increases the cost of diets in all regions, with a median relative increase of 15.5% across 165 countries, but with marked cross-country variability. Second, these increases translate into substantial affordability consequences. Globally, the share of people unable to afford diets rises from 51% under food-only costs to 57% under total costs, corresponding to approximately 397\u0026nbsp;million additional people falling into unaffordability (exceeding the population size of the United States). Third, the distributional burden is highly uneven: the largest absolute and relative affordability impacts are concentrated in low-income regions, particularly sub-Saharan Africa and South Asia, while high-income regions tend to exhibit comparatively low unaffordability despite higher per-capita environmental costs, reflecting a better capacity to absorb increased dietary prices.\u003c/p\u003e \u003cp\u003eOur findings further show that an estimated 397\u0026nbsp;million additional people globally become unable to afford current diets once environmental costs are internalised. These regressive impacts reflect the interaction between two structural asymmetries. First, food systems in low-income regions tend to be more emissions-intensive per unit of dietary energy, owing to lower agricultural efficiency, higher reliance on extensive livestock production, and greater land-use change emissions embedded in supply chains. Second, baseline dietary costs already absorb a large share of household income in these regions, leaving limited capacity to absorb additional carbon-related cost increases. These dynamics differ markedly from high-income regions, where more efficient supply chains keep per-unit carbon costs lower as a share of diet cost, and where higher incomes buffer any residual impact. These findings are consistent with Springmann et al. (2021), who show that the global costs of healthy and sustainable diets are highest in low-income countries relative to per-capita income, and with Lucas et al. (2023), who demonstrate that low-carbon diets could reduce both health and ecological costs but require structural changes to food supply chains that are currently most constrained in low- and middle-income settings\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur findings are consistent with previous research showing that carbon pricing and environmental cost internalisation are often regressive without redistribution, especially in low-income settings\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. For example, distributional analyses of carbon pricing in the European Union show that, at the aggregate level, carbon taxes can have regressive effects because lower-income countries face a relatively higher burden than high-income countries\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Consistent patterns have also been documented at the household level, where lower-income households experience larger welfare losses in the absence of targeted transfers\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Policy recommendation\u003c/h2\u003e \u003cp\u003eSeveral policy instruments are available to mitigate the unequal impacts that arise from full-pricing of diets to support a sustainable dietary transition. Recycling revenues from environmental pricing offers a direct way to offset affordability losses. Based on the carbon price applied in this study (\u003cspan\u003e$\u003c/span\u003e0.143 per kg CO₂), the social cost of diet-related GHGE across the 165 countries studied could generate approximately \u003cspan\u003e$\u003c/span\u003e2.25\u0026ndash;\u003cspan\u003e$\u003c/span\u003e2.36\u0026nbsp;billion (2018 PPP) annually. While modest in global terms, these costs represent a meaningful share of national income in low- and middle-income countries: the social cost of diet-related GHGE amounts to 49.5% of GDP in the Central African Republic, 14.2% in Mozambique, and 13.5% in Yemen (Supplementary Table\u0026nbsp;3). This finding aligns with previous evidence that carbon pricing can generate around 10\u0026ndash;12% of total government revenue from carbon taxes alone and over 20% when combined with fossil fuel subsidy removal\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhen recycled through cash transfers, these revenues can raise average household incomes by around 3%, thereby partially compensating for affordability losses\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Targeting equal per-capita revenue redistribution in our framework to populations unable to afford diets reduces global dietary unaffordability from 51% to 48%, with the largest improvements observed in Western Africa, where the share falls from 71% to 62% (Supplementary Fig.\u0026nbsp;2). Such targeted transfers or equal per-capita refunds (i.e. lump-sum refunds distributed equally across the population)\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e can substantially reduce regressive effects. For example, analyses covering 16 Latin American and Caribbean countries representing more than 540\u0026nbsp;million people show that recycling carbon pricing revenues through cash transfers can fully offset cost increases for low-income households\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, while EU-wide simulations indicate that a consumption-based carbon tax generating around \u0026euro;200\u0026nbsp;billion annually would raise costs for the lowest-income groups by up to 6\u0026ndash;9% in the absence of compensation but can completely neutralise inequality impacts when revenues are redistributed as lump-sum transfers\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Similar approaches could be applied to food systems through existing social protection\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e or food assistance programmes\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Targeted food subsidies provide a complementary tool. Empirical evidence shows that subsidising nutrient-rich foods by 10\u0026ndash;25% increases fruit and vegetable intake by approximately 0.4\u0026ndash;0.6 servings per day, and that a 20% price reduction leads to a 16\u0026ndash;17% increase in purchases\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. When combined with pricing of high-emission foods, such subsidies can promote healthier diets while cushioning affordability impacts\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e, provided that lower-impact alternatives are readily available in domestic markets to avoid exacerbating national or household food insecurity.\u003c/p\u003e \u003cp\u003ePolicy needs also differ by income level. In high-income countries, environmental pricing may encourage shifts towards lower-impact diets with limited affordability consequences, as aligning diets in such countries with national dietary guidelines has been shown to reduce diet-related GHGE by around 20\u0026ndash;50% without compromising nutritional adequacy or affordability\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. By contrast, in low-income regions, protecting access to nutritious diets requires safeguards that prevent environmental costs from being passed on to consumers. Evidence shows that in food systems at earlier stages of transition, a nutritious diet remains unaffordable for a large share of the population, with only around 15% of people able to afford recommended diets in rural and informal systems\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. In these contexts, policy measures such as exemptions for staple foods, gradual phase-in of pricing, or compensatory transfers are essential\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Strengths and limitations\u003c/h2\u003e \u003cp\u003eThis study has several key strengths. First, it provides the first global, harmonised assessment of the true cost of current diets by jointly integrating food prices, dietary intake, greenhouse gas footprints, and income distributions across 165 countries. Second, MATILDA captures socio-demographic heterogeneity, revealing distributional impacts hidden in national averages and informing equity-focused policy design. Third, dietary GHGE estimates are linked to country-specific production systems and trade structures, improving consistency between consumption-based footprints and economic valuation.\u003c/p\u003e \u003cp\u003eThis study has several limitations that should be acknowledged. First, while we estimate the full-priced cost of diets by internalising the social cost of diet-related GHGE, we do not model behavioural responses to higher food prices. As a result, our analysis does not capture potential changes in consumption patterns, substitution effects, or feedbacks along the food supply chain\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e that could arise under carbon-inclusive pricing. Consequently, the estimated emissions reductions associated with full pricing remain uncertain. Future work could integrate price elasticities of demand or diet optimisation frameworks to assess how consumers might adjust food choices in response to carbon-inclusive food prices\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e, and to quantify the resulting environmental and health outcomes.\u003c/p\u003e \u003cp\u003eSecond, dietary cost estimates rely on World Bank food price data, which reflect the lowest-cost items within each food group. While this approach ensures global comparability, it may underestimate actual food expenditures faced by consumers, particularly in settings with limited market access, high price dispersion, or strong preferences for branded or processed foods\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Incorporating more detailed retail price data or household expenditure surveys would improve the representation of real-world food purchasing conditions\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThird, the social cost of diet-related GHGE is derived from global estimates of the social cost of carbon and applies a uniform carbon price across countries and food groups. This approach does not capture potential regional heterogeneity in climate damages, differences in vulnerability to climate impacts, or local environmental externalities\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e, such as biodiversity loss, water pollution, or land degradation. Future research could explore region-specific carbon cost estimates or multi-dimensional environmental pricing schemes that better reflect local environmental risks.\u003c/p\u003e \u003cp\u003eFinally, although dietary costs are estimated at the socio-demographic group level, affordability is assessed using national income distributions applied to average national diets, due to the lack of income data disaggregated by socio-demographic characteristics. As a result, the affordability analysis captures cross-country and regional differences in income distributions but does not reflect within-country variation in affordability across population subgroups. Future research should therefore prioritise improved data linking income, food prices, and dietary patterns at the subgroup level, and model behavioural responses to carbon-inclusive food prices.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study provides evidence that carbon pricing applied to food systems carries profound distributional consequences, disproportionately burdening populations in low-income regions who bear the least responsibility for global emissions. Our analysis shows that without deliberate redistribution mechanisms, including targeted transfers, food subsidies, and social protection calibrated to local income and dietary conditions, pricing dietary GHGE risks deepening the nutritional inequities it ostensibly seeks to resolve. The revenues generated by such pricing represent a meaningful and underutilised policy resource. Realising the dual goals of food system decarbonisation and equitable nutrition access will require embedding environmental pricing within broader social protection frameworks, particularly in low- and middle-income country contexts.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eHongyi Cai:\u0026nbsp;\u003c/strong\u003eWriting \u0026ndash; review \u0026amp; editing, Writing \u0026ndash; original draft, Visualization, Validation, Methodology, Formal analysis, Data curation.\u003cstrong\u003e\u0026nbsp;Oliver Taherzadeh:\u0026nbsp;\u003c/strong\u003eWriting \u0026ndash; review \u0026amp; editing, Writing \u0026ndash; original draft, Supervision, Project administration, Conceptualization.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThis work was funded by a Kiem seed funding grant from Leiden University and the Dutch Research Agenda (NWA) programme \u0026lsquo;Transition to a sustainable food system\u0026rsquo; (project number NWA.1235.18.201), which is financed by the Dutch Research Council (NWO).\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe data underlying this article are derived from multiple publicly available sources. Dietary intake data were obtained from the Global Dietary Database (GDD; https://globaldietarydatabase.org/). Food price data were sourced from the World Bank Food Prices for Nutrition Data Hub (Healthy Diet Basket; https://www.worldbank.org/en/programs/icp/brief/foodpricesfornutrition). Environmental footprint coefficients were drawn from the Food and Agriculture Biomass Input\u0026ndash;Output (FABIO) model (https://doi.org/10.5281/zenodo.2577067). Income and population distribution data were obtained from the World Bank Poverty and Inequality Platform (PIP; https://pip.worldbank.org/). The MATILDA-Cost model code used in this study will be made available on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZurek, M., Hebinck, A. \u0026amp; Selomane, O. 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Simulating the impact on health of internalising the cost of carbon in food prices combined with a tax on sugar-sweetened beverages. \u003cem\u003eBMC Public Health\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 107 (2016).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Leiden University","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":"Carbon pricing, Dietary affordability, Food system emissions, Social cost of carbon, Distributional equity","lastPublishedDoi":"10.21203/rs.3.rs-9224832/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9224832/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFood-system greenhouse gas emissions (GHGE) impose large social costs that are rarely reflected in food prices. The affordability impacts of internalising these costs remain unclear. Carbon pricing is a key instrument for internalising environmental costs, yet its application to food systems has been constrained by concerns over food affordability and distributional equity.\u003c/p\u003e \u003cp\u003eHere, we integrate dietary intake, country-specific environmental footprints, food prices, and income distributions with MATILDA (Micro\u0026ndash;Macro Assessment Tool to Identify Low-impact Dietary Actions) to estimate market and carbon-inclusive dietary costs across 165 countries and socio-demographic groups. We examine how internalising the social cost of diet-related GHGE alters dietary affordability globally and regionally.\u003c/p\u003e \u003cp\u003eOur results reveal profound inequalities in the economic and environmental burdens of food consumption. Across regions, the inclusion of GHGE costs to food prices raises the cost of diets by 8\u0026ndash;32%, limiting food affordability for an additional 397\u0026nbsp;million people worldwide. Low-income populations in sub-Saharan Africa and Southern Asia are particularly vulnerable, while full-pricing of diets in high-income regions has minimal impact on food affordability.\u003c/p\u003e \u003cp\u003eThese findings underscore the importance of coupling dietary transitions with redistributive and environmental pricing policies to ensure that food system decarbonisation does not exacerbate global nutrition inequities.\u003c/p\u003e","manuscriptTitle":"Carbon-costed diets highlight redistribution needs: evidence from 165 countries","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-27 14:18:21","doi":"10.21203/rs.3.rs-9224832/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":"937c9f2e-706e-42c3-9dc4-b10d43cf5193","owner":[],"postedDate":"March 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":65131982,"name":"Environmental Policy"},{"id":65131983,"name":"Environmental Economics"}],"tags":[],"updatedAt":"2026-03-27T14:18:21+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-27 14:18:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9224832","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9224832","identity":"rs-9224832","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}