A Comparative Methodological Framework for Evaluating Distributional and Nutritional Effects of Food Carbon Pricing

Modelling the distributional impacts of food carbon pricing is important for understanding how climate policies affect households. The agri-food system contributes nearly one third of global greenhouse gas (GHG) emissions, with livestock and fertiliser use driving most of this footprint (Crippa et al., 2021). In the EU, agriculture accounts for about 10% of emissions, yet carbon pricing remains focused on energy and industry (Ramstein, 2019). Food systems are often excluded because dietary habits, cultural norms and socio-economic conditions vary widely across countries (Smith and Gregory, 2013). These cross-country differences, ranging from meat heavy diets in Western Europe to higher food budget shares in Eastern Europe, influence how carbon taxes are transmitted to households (Dokova et al., 2022). This paper introduces a microsimulation framework to assess household level impacts of food carbon taxes across eight countries, integrating expenditure, price and emissions data to explore trade-offs between climate objectives and distributional outcomes.

A substantial literature has examined fiscal instruments in food and environmental policy, including health motivated taxes, subsidies and carbon pricing initiatives. Public health studies have analysed the nutritional and expenditure effects of taxes on unhealthy foods or beverages (Segal et al., 2022; Smed et al., 2016; Tiffin et al., 2015), while environmental economics has increasingly focused on carbon pricing as a mitigation tool, including in agriculture (Kebreab et al., 2022; Sørensen et al., 2025). A range of modelling approaches reflects this diversity of objectives. Macroeconomic frameworks such as Computable General Equilibrium (CGE) and Input Output (IO) models capture system wide interactions (Böhringer et al., 2017; Ntombela et al., 2019). Demand system models such as AIDS, QUAIDS and EASI simulate price driven changes in consumption (Caillavet et al., 2019; García-Muros et al., 2017). Agent based models examine dynamic system responses (Roxburgh et al., 2025) and microsimulation models use household level microdata to assess tax incidence and food expenditure patterns (Callan et al., 2009; O’Donoghue, 2014). While each strand contributes useful insights, relatively few studies bring together environmental, nutritional and equity dimensions within a comparable framework across multiple countries.

In a European policy context, cross country differences may be relevant for both the level and the design of food carbon taxation. Policy instruments are increasingly being discussed at the European level as well as within individual Member States, and any coordinated carbon tax is likely to interact with national income distributions, food budget shares and dietary patterns, which can lead to different distributional outcomes even under the same tax rate (Büchs and Schnepf, 2013; Callan et al., 2009). Studies of energy related carbon taxes suggest that countries with similar headline measures of regressivity can reach these outcomes through different underlying mechanisms, which indicates that a uniform European tax might have uneven welfare effects if national structures are not fully considered (Dorband et al., 2019; Klenert et al., 2018). A better understanding of these cross-country differences can therefore help in thinking about policy tools, such as targeted subsidies or revenue recycling schemes, that could alleviate regressive effects while remaining consistent with a broader European climate framework (Sterner, 2012; Varacca et al., 2024).

Despite progress, several gaps remain. Cross-country analyses of food carbon taxation are still scarce, as most studies focus on single-country settings (Varacca et al., 2024). Several modelling approaches concentrate mainly on environmental or economic outcomes, and only a smaller group of contributions considers nutrition, affordability and emissions jointly within one framework (Bonnet et al., 2018; Säll and Gren, 2015; Zhang et al., 2024). When using HBS-based microsimulation to assess equity, a key challenge is the prevalence of zero expenditures due to short recall periods, infrequent purchases and underreporting. If not explicitly addressed, these zeros can bias elasticity estimates and complicate behavioural modelling (Deaton and Irish, 1984; Shonkwiler and Yen, 1999; Peltner and Thiele, 2021). Existing HBS based microsimulation studies often focus on single countries, do not always link food demand to emissions data and only partially account for interactions across food groups, so the links between emissions reduction, nutrition and affordability are not yet fully explored (Al-Masbhi et al., 2026 ; Edjabou and Smed, 2013; Forero-Cantor et al., 2020; Garnett, 2011; Maestre-Andrés et al., 2019; Revoredo-Giha et al., 2018).

These considerations motivate the research questions explored in this paper. First, how does a harmonised food carbon tax affect household food expenditure, diet-related emissions and caloric intake across countries? Second, how do these effects vary across national contexts with distinct dietary patterns, production systems and income distributions? Third, what trade-offs arise between emission reduction, distributional impacts and dietary energy in a comparable tax setting? Examining these questions helps clarify why household-level impacts differ across countries and why national structures matter for the design of food carbon pricing.

This paper develops a cross-country microsimulation framework that links Life Cycle Assessment (LCA) emissions data with Household Budget Survey (HBS) information on food expenditure and caloric content for eight countries: Ireland, France, Spain, Denmark, Poland, Luxembourg, the United Kingdom and Türkiye. By combining emissions intensities (kg CO₂e/kg) nutritional information and household spending patterns, the framework evaluates the distributional and dietary implications of applying a carbon tax to food.¹ The analysis focuses on household-level outcomes food expenditure, diet-related emissions, the cost of dietary energy and the tax burden as a share of income under fixed quantities, without behavioural responses.

Methodologically, the study employs a static microsimulation model that applies a carbon tax to the emissions embodied in food items, assumes full pass-through into retail prices, recalculates post-tax prices and traces changes in household expenditure, tax payments and diet-related emissions across income deciles and countries. The model does not rely on EUROMOD and does not incorporate substitution effects, behavioural elasticities or interactions with benefit systems. Distributional outcomes are summarised using standard inequality and progressivity measures, including pre- and post-tax Gini coefficients, the Kakwani index and the Reynolds–Smolensky index. A sensitivity analysis using alternative LCA datasets tests how assumptions on emissions intensities influence results.

The eight countries in this study (Ireland, France, Spain, Denmark, Poland, Luxembourg, the UK, and Türkiye) offer a varied and policy-relevant context for evaluating food carbon taxation. Empirical studies from Ireland, France, Denmark and the UK show that diet-related greenhouse gas emissions are largely driven by ruminant meat, dairy products and discretionary processed foods, with higher per-capita dietary emissions in central and western European countries than in Mediterranean settings (Alves et al., 2024; Hyland et al., 2017; Murakami and Livingstone, 2018; Rippin et al., 2021; Trolle et al., 2022; Vieux et al., 2012). Poland and Türkiye rely more on cereals, legumes, and vegetables, which lowers average dietary emissions but increases exposure to food price shocks, since low income households spend a larger share of their budgets on food (Gumus et al., 2010; Rejman et al., 2014). Higher consumption of convenience and packaged foods in Ireland and the UK involves more energy-intensive processing, refrigeration and packaging, increasing upstream emissions (Browne et al., 2023; Garnett, 2011). By contrast, Poland and Türkiye make greater use of seasonal produce and shorter domestic supply chains, which can lower transport emissions but heighten vulnerability to climate and market shocks for fresh foods (Aysoy et al., 2015; Drejerska and Sobczak-Malitka, 2023). These contrasts in diets, supply chains and food budget shares make this set of countries well suited to studying how a uniform food carbon tax translates into different distributional and nutritional outcomes.

The remainder of this paper is structured as follows: Section 2 describes the data sources, emissions accounting, and microsimulation model. Section 3 presents the cross-country results and Section 4 concludes and offers directions for future research.

Developing fair and effective carbon pricing in the food sector requires understanding how dietary habits, emissions and income interact. This analysis uses microsimulation to link household consumption with emissions and nutritional outcomes across different carbon tax scenarios (Figari et al., 2015; O’Donoghue, 2014).

This section presents results from the microsimulation model for eight countries. Household food expenditures are converted into quantities and linked to LCA-based emissions and caloric data to estimate tax burdens, food budget shares and the carbon intensity of diets by income decile. We then assess distributional effects using standard inequality and progressivity measures, including pre- and post-tax Gini coefficients, the Kakwani index and the Reynolds–Smolensky index. Finally, a sensitivity analysis examines how alternative LCA emission factors affect the pattern of results across food groups and countries.

3.1. Food Price Patterns

Energy-adjusted food prices provide a useful indication of cross-country differences in affordability and the potential impact of a food carbon tax. Figure 1 shows the average price per 1,000 kJ for each food group. Consistent with earlier work on diet costs (Darmon et al., 2003; Monsivais and Drewnowski, 2007), fish and seafood are generally the most expensive sources of dietary energy, followed by non-ruminant meat and beverages, while grains remain the lowest-cost energy source. Dairy, eggs and fruits and vegetables fall in the middle of the distribution, though their levels vary across countries. These differences reflect cross-country variation in retail price structures, food preferences, and the types of foods most purchased in each category.

Figure 1

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Figure 2 scales these energy-adjusted prices by GDP per capita to indicate how the cost of dietary energy relates to national income levels, following previous work on food affordability (FAO, 2025; Reisch et al., 2013). Once income differences are accounted for, the relative position of countries changes substantially. High-income countries such as Denmark, Luxembourg and Ireland, where households have greater purchasing power, display lower values on this indicator despite having high nominal food prices. By contrast, Poland and Türkiye show higher values, reflecting tighter food budgets relative to income. This combined measure should be interpreted as indicative rather than definitive, but it helps distinguish between food groups that are costly yet more affordable in high-income settings and those that may pose genuine financial pressure for households in lower-income contexts.

Figure 2

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The variation observed across Figures 1 and 2 suggests that the same tax-induced price change could translate into different levels of financial pressure across countries, depending on local purchasing power and the role of specific foods in national diets. In Poland and Türkiye, most food groups have higher GDP-adjusted prices than in Denmark or Luxembourg, indicating that even modest price increases may be more difficult to absorb for households. By contrast, in high-income countries, higher nominal prices coexist with greater purchasing power, which can soften the immediate budget impact of a food carbon tax. Grains remain the most affordable source of dietary energy in all countries, although their budget share varies across the income distribution.

3.2. Expenditure and Consumption Pattern

Understanding how households allocate their food budgets across income groups is important for evaluating the distributional effects of food carbon pricing. Figure 3 shows the composition of household food expenditure, expressed as a share of total food spending, across income deciles in the eight countries. In most cases, lower-income households devote a larger share of their food budget to staples such as grains, bread and cereals, which is in line with Engel’s Law and reflects the importance of low-cost, energy-dense foods for meeting basic needs (Anker, 2011). Higher-income households tend to spend relatively more on discretionary categories, including ruminant meat, beverages and fish, which are typically more expensive and more carbon intensive, and in several countries the share of fruits and vegetables also rises with income, suggesting a shift towards more varied and higher-quality diets.

There are, however, some clear exceptions to the standard Engel pattern. In Türkiye, the budget share for grains and related products increases with income. This may reflect a shift from inexpensive homemade staples, such as unpackaged bread and bulk flour, towards more costly packaged breads, breakfast cereals and processed grain products among higher-income households. In Luxembourg, staple foods are relatively inexpensive compared with income, so their budget share remains almost constant across deciles, while in Denmark and Ireland the gradient is weak because core staples such as rye bread and oats are widely consumed across the income distribution. These cases illustrate how cultural food habits and relative price levels can modify the usual income gradient in staple consumption.

These cross-country trends carry important implications for modelling food-sector carbon taxation. The strong income gradients in discretionary and emissions-intensive categories, such as ruminant meat and beverages, indicate higher price elasticity among wealthier households, offering greater mitigation potential with minimal nutritional risk (Bouyssou et al., 2024). Conversely, staples like grains and non-ruminant meats exhibit relatively flat budget shares across deciles, suggesting limited substitution potential and raising equity concerns if carbon taxes disproportionately impact these categories (García-Muros et al., 2017). Incorporating this heterogeneity in food composition is vital for microsimulation frameworks aiming to assess the nutritional, environmental, and affordability trade-offs of carbon pricing. Without such disaggregation, models risk oversimplifying household responses and misrepresenting both the regressivity and effectiveness of fiscal interventions targeting food systems.

Figure 3

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3.3. Nutritional Implications: carbon intensity of dietary energy by income decile

As governments explore fiscal tools to address food system emissions, understanding who bears the burden of carbon pricing is essential for designing equitable policy. Figure 4 illustrates the energy-based carbon intensity of household food consumption, expressed relative to the national mean, across income deciles in the eight countries. This measure captures average greenhouse gas emissions per kilojoule (kJ) consumed, enabling cross-country comparison of dietary emission patterns by income level (Bonnet et al., 2018; Springmann et al., 2018).

A clear upward gradient is evident in France, Spain, Türkiye, Luxembourg, and the United Kingdom, where higher-income households consume diets with significantly higher emissions per unit of energy. These patterns are consistent with greater consumption of red meat, dairy, and processed foods among wealthier groups (Crippa et al., 2021; Poore and Nemecek, 2018). In contrast, lower-income households typically rely on more affordable, plant-based staples, which are both lower in emissions and cost. Ireland shows a non-linear pattern, with middle-income deciles exhibiting the highest carbon intensity. Denmark displays a much flatter profile, punctuated by a few noticeable spikes. These spikes appear to reflect data features rather than substantive dietary differences. The Danish HBS has a relatively small sample size compared with the other countries, so dividing the data into ten deciles amplifies the influence of a small number of households with unusually high purchases of ruminant meat or cheese (Deaton, 1997). This produces sharp movements between adjacent deciles that are unlikely to reflect systematic behavioural patterns. The overall picture remains one of relatively uniform diets across the income distribution, which is consistent with Denmark’s comparatively compressed income structure (Causa et al., 2016; Heckman and Landersø, 2021). These results align with previous evidence on socio-nutritional gradients in Europe (Caillavet et al., 2019; Moberg et al., 2021) and illustrate how both dietary composition and underlying data structure can shape observed patterns.

Figure 4

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3.4. Carbon Tax Burden

Modelling the distributional effects of food-sector carbon taxation requires careful attention to both inter- and intra-income group disparities. Figure 5 illustrates the carbon tax burden as a share of equivalized disposable income across deciles for eight countries, with boxplots highlighting within-decile variability. To facilitate cross-country comparison, the carbon tax is calibrated to a common reference level of 0.3% of average equivalized disposable income in each country. For every country, we first compute the weighted mean equivalized household income and take 0.3% of this value as the benchmark tax burden for the “average” household. Given baseline food-related emissions, we then choose a country-specific tax rate per kilogram of CO₂e that would generate this benchmark burden for a household with average income and average emissions. This rate is applied uniformly to all food emissions, so households with more carbon-intensive diets pay more in absolute terms, and the tax as a share of income varies across the distribution.

The analysis reveals a consistent regressive pattern across all countries: lower-income households bear a disproportionately higher tax burden relative to disposable income, while higher-income groups, despite contributing more in absolute terms, experience a lower percentage impact as shown in Figure 5. This finding aligns with prior studies on the regressivity of food and energy carbon taxes (García-Muros et al., 2017; Revoredo-Giha et al., 2018). The boxplots also reveal substantial variation within deciles, especially among lower-income households. This heterogeneity matters for distributional analysis: it shapes how households are affected by the tax and cautions against treating income groups as homogeneous. Ignoring these differences can lead to unrealistic assumptions about how households substitute between foods and may mask nutritional vulnerabilities within the same income bracket (Garnett, 2016; Springmann et al., 2018). These findings emphasize the importance of integrating granular household-level data into climate policy modelling frameworks to capture equity dimensions effectively.

Figure 5

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This study develops a harmonized microsimulation framework to assess the distributional and nutritional effects of food carbon taxation across seven EU Member States and Türkiye. By linking LCA emissions intensities with nationally representative HBS, the framework provides a detailed cross-country analysis of how food-sector carbon pricing affects households along the income distribution. The eight countries were selected to reflect a wide range of dietary patterns, income levels and food system structures: northern and western European countries tend to consume more emission-intensive animal products, whereas Türkiye and several southern and eastern Member States rely more on plant-based staples and have higher food budget shares. This diversity enables an assessment of how a common carbon tax interacts with different national contexts.

A key contribution of the study is harmonizing both data structures and dietary profiles across countries. HBS surveys differ in recall periods, survey length and item aggregation, and countries vary in cultural food preferences and food basket composition (Peltner and Thiele (2021)). The harmonization procedure produces a consistent seven-group food classification and a unified link between expenditures, quantities, emissions and dietary energy. This ensures that observed cross-country differences reflect genuine variation in diets and costs rather than survey artefacts. The use of caloric carbon intensity, measured as CO₂e per kilojoule, further strengthens comparison by linking emissions directly to the energy content of diets.

The results show moderate but consistent regressivity: lower-income households bear a larger relative burden because they devote a higher share of income to food and have limited capacity to adjust diets, while higher-income households are affected less in proportional terms. Cross-country variation is pronounced. Türkiye displays the most regressive pattern, consistent with its higher food budget shares and the erosion of real incomes caused by sustained inflation in recent years, both of which weaken households’ ability to absorb price increases (OECD, 2023). In contrast, Denmark has the least negative Kakwani index, reflecting flatter food expenditure gradients across income groups and the cushioning effect of a comprehensive welfare state and higher baseline purchasing power (Andersen, 2025). These structural differences help explain why countries with more emission-intensive diets show higher mitigation potential, whereas those with more plant-based consumption patterns experience smaller relative changes. They also suggest that in a context such as Denmark, where distributional impacts are comparatively mild and social protection systems are strong, the political and social feasibility of introducing agricultural or food-sector carbon pricing may be greater, as reflected in Denmark’s plans to implement a tax on agricultural greenhouse gas emissions (State of Green, 2024). These findings highlight a clear trade-off between environmental effectiveness and distributional fairness. Taxing more emission-intensive foods can generate larger emissions reductions, but it also increases food costs and can be regressive where low-income households spend a large share of income on food or have limited capacity to adjust consumption. Overall, the cross-country differences highlight how dietary structures, socio-economic conditions and institutional contexts shape the equity implications of food carbon taxation.

The political feasibility of food carbon taxation also depends on perceived fairness and the visibility of compensating measures. Evidence on carbon pricing shows that public support increases when low- and middle-income households are clearly protected and when revenue recycling is transparent and easy to understand (Carattini et al., 2018). In this context, options such as lump-sum carbon dividends, targeted transfers to low-income households or subsidies for healthier, low-carbon foods can all help address equity concerns and strengthen acceptability. Framing food carbon taxes in terms of co-benefits for health and climate resilience has also been shown to improve public support (Maestre-Andrés et al., 2019).

The microsimulation is static: quantities are held constant, behavioural adjustments are not modelled, and the tax is assumed to be fully passed through into consumer prices. Empirical studies show that pass-through can be incomplete or over-shifted depending on market structure and policy shocks (Erutku, 2019; Vercammen, 2025; Weyl and Fabinger, 2013). Within these assumptions, the framework captures environmental impacts through changes in diet-related emissions and nutritional impacts through changes in the cost and carbon intensity of dietary energy. Because food is a basic necessity, these distributional and nutritional outcomes are closely linked to public acceptability: perceived unfairness can trigger resistance to carbon pricing even when mitigation benefits are substantial (OECD, 2022). Future work will extend the model by incorporating behavioural elasticities, substitution patterns and empirically grounded pass-through parameters, and by simulating alternative designs such as revenue recycling and targeted food subsidies. It will also decompose the channels through which food carbon taxes affect different income groups, so as to assess how different policy packages can reduce regressivity, account for country-specific drivers and improve acceptability.

This table summarises the sensitivity analysis of food specific emission factors using Denmark, Spain, France, and the United Kingdom, and benchmarks these values against the global average series used in the simulations. For each HBS food category, mean_other is the four-country European average emission coefficient, while IEEmissions reports the corresponding global average coefficient. The columns ci_low and ci_high report the 95 percent confidence interval around the mean using a normal approximation, and ci_low_t and ci_high_t report the corresponding interval using the t distribution to reflect the small number of country sources. The results show that many staple foods, including cereals, bread, pasta, fruit, vegetables, potatoes, oils, milk, yoghurt, and beverages, have relatively narrow confidence intervals, indicating broadly consistent emission factors across the four countries. In contrast, wider intervals are observed for high emission animal-based products such as beef and lamb, as well as for several processed or composite categories including crisps, dried fruit and nuts, confectionery products, and readymade meals, reflecting heterogeneity in production systems, processing intensity, and LCA assumptions. Despite this variation, the relative ordering of food categories by emissions remains stable, with animal-based products consistently exhibiting higher carbon intensities than plant-based staples, suggesting that the main qualitative conclusions are robust to plausible variation in the underlying emission coefficients. More details are found in the Supplementary file 1.

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Author details

1. Ghassan Al-Masbhi

   University of Galway; BiOrbic SFI Research Centre, Galway, Ireland

   For correspondence

   G.Al-Masbhi1@universityofgalway.ie

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

2. Cathal O’Donoghue

   University of Galway; BiOrbic SFI Research Centre, Galway, Ireland

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

3. Zeynep Gizem Can

   University of Galway; BiOrbic SFI Research Centre, Galway, Ireland

   "This ORCID iD identifies the author of this article:" 0000-0002-2852-4995

4. Anna Diop-Christensen

   University College of Northern Denmark, Hjørring, Denmark

   "This ORCID iD identifies the author of this article:" 0000-0001-9043-5195

5. Ana Montes

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

   "This ORCID iD identifies the author of this article:" 0009-0008-0900-8603

6. Emily Paradowska

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

7. Martina Pezer

   Institute of Public Finance, Zagreb, Croatia

   "This ORCID iD identifies the author of this article:" 0000-0003-3291-220X

8. Denisa Sologon

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

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