# Joint assessment of the environmental impacts and resource criticality of French food consumption scenarios in 2050 from a regionalised life cycle perspective

Lazare Deteix, Thibault Salou, Eléonore Loiseau

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Lazare Deteix, Thibault Salou, Eléonore Loiseau. Joint assessment of the environmental impacts and resource criticality of French food consumption scenarios in 2050 from a regionalised life cycle perspective. Sustainable Production and Consumption, 2025, 55, pp.37-50. 10.1016/j.spc.2025.02.005. hal-04964985

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# Joint assessment of the environmental impacts and resource criticality of French food consumption scenarios in 2050 from a regionalised life cycle perspective

Lazare Deteix, Thibault Salou, Eléonore Loiseau*

ITAP, Univ Montpellier, INRAE, Institut Agro, Montpellier, France

## ARTICLE INFO

Editor: Dr. Kuishuang Feng

**Keywords:**

Prospective LCA

Impact regionalisation

Trade-offs

Vulnerability

Supply risk

Sensitivity analysis

## ABSTRACT

Five prospective scenarios combining changes in diets, supply chains, and agricultural practices have been proposed for French food consumption in 2050, including a business-as-usual (BAU) situation. This study aims to perform a joint assessment of their environmental impacts and resource criticality according to a regionalised life cycle perspective.

Food consumption is modelled through 46 representative products. The life cycle inventories (LCI) of these products were parameterized to be consistent with each scenario, considering factors such as diets, agricultural practices, origin and volume of food imports, and energy mixes. The IMPACT World+ method is used to compute regionalised impacts, and Supply Risk Potential (SRP) indices are quantified for mineral resources, land, water and agricultural products based on the Joint Research Centre criticality method.

The results reported that three of the four scenarios have lower environmental impacts than the BAU scenario for all impact categories. For climate change, impacts could be reduced by between 25 % and 45 %. Similar results were observed for resource criticality. These results were mainly explained by the change in diets, with lower levels of overall consumption, and less animal products in proportion.

Phosphate and potassium fertilisers use, strongly influenced by agricultural practices, was the primary contributors to mineral resources criticality. Depending on the share of meat products in diets, pastures contributed significantly to land SRP. In the same way, meat consumption influenced agricultural SRP through animal feed. Finally, water SRP was mainly driven by hydropower production for electricity.

The sensitivity analysis highlighted potential trade-offs between environmental damage and resource criticality for mineral resources and agricultural products (higher environmental impacts lead to lower SRP). Furthermore, the regionalisation of impacts can lead to variations of up to 50 % in impact results for a given scenario. Additional efforts are thus needed for considering local conditions and practices in agricultural LCIs.

## 1. Introduction

Global demand for food is increasing, due to population growth and rising living standards. By 2050, the food demand is expected to increase by 35 to 56 % (in kcal consumed), in comparison with 2010 (van Dijk et al., 2021). These socio-economic trends are increasing the pressure on ecosystems and natural resources which food systems depend on to feed humanity (Westhoek et al., 2016), such as land, water, minerals and energy.

Food consumption is also responsible for a large number of environmental impacts, contributing to the transgression of several

planetary boundaries (Rockström et al., 2020). Overall, food systems are estimated to be responsible for 70 % of freshwater consumption (Mekonnen and Hoekstra, 2014), 26 % of greenhouse gas emissions (Poore and Nemecek, 2018), 64 % of deliberate nitrogen fixation and 80 % of phosphorus leakage flows (Springmann et al., 2018; Steffen et al., 2015). Food systems are therefore one of the primary causes of biodiversity loss (Benton et al., 2021).

In the face of these challenges, a number of solutions have been proposed for feeding a growing population and ensuring food security, while reducing the environmental impact of food consumption in a resource-constrained world. These include changing diets, the reduction

<footnote>* Corresponding author at: 2 place Pierre Viala, 34000 Montpellier, France.</footnote>

<footnote>E-mail address: eleonore.loiseau@inrae.fr (E. Loiseau).</footnote>
of food waste or evolving agricultural practices (Godfray et al., 2010).

Many countries and regions are proposing policies to face these challenges. The European Union's Farm to Fork Strategy is one example. It aims to accelerate the transition to a sustainable food system and help reduce climate change impacts, reverse biodiversity loss and ensure food security. France aims to strengthen its food sovereignty by reducing dependence on agricultural inputs and adapting agriculture to climate change. In addition, the Russo-Ukrainian war has highlighted the interdependence of European food systems and agricultural inputs, and threatens the food security of many countries on a global scale (Bertolozzi-Caredio et al., 2023; Lin et al., 2023).

Prospective scenarios for feeding the population at a global scale have been developed, and their environmental impacts have been assessed over a limited number of categories, such as climate change, biodiversity loss, land use or water consumption (Gerten et al., 2020; Springmann et al., 2018; Willett et al., 2019). However, these studies are limited to the environmental impact of agricultural production, while the impact of the downstream stages in the food supply chain are unaccounted for. Furthermore, they do not consider the specific features of food consumption within different countries, such as differences in diets or agricultural practices.

At a national level, for example in France, prospective studies were carried out in order to define alternative production and consumption patterns involving the food sector (ADEME, 2021). However, the prospective modelling of food consumption in these scenarios was mainly focused on broad food categories such as “meat” or “fruits and vegetables”. Barbier et al. (2022) concentrated on food consumption in 2050 by downscaling the French scenarios (ADEME (2021) specifically for the food sector, and assessing their environmental impacts on climate change only. Potential trade-offs between environmental issues were therefore ignored.

Life Cycle Assessment (LCA) is a reference method for the environmental multicriteria impact assessment of products and services, but also of broader-scale systems such as a country or lifestyle and consumption patterns (Hellweg and Milà i Canals, 2014). It has been successfully applied to agri-food systems with applications at different scales, i.e. products (Poore and Nemecek, 2018; Roy et al., 2009), diet (Nemecek et al., 2016), consumption patterns (Notarnicola et al., 2017b; Nuss et al., 2023; Sanyé-Mengual et al., 2023), cities (Genta et al., 2022; Loiseau et al., 2023), and territories (Lulovicova and Bouissou, 2023, 2024).

Prospective LCA is defined as “LCA that models the product system at a future point in time relative to the time at which the study is conducted” (Arvidsson et al., 2023). The two key aspects of prospective LCA are the future state of the system modelled, as well as the low technological maturity of the studied technology (Arvidsson et al., 2023; Langkau et al., 2023; Thonemann et al., 2020). Prospective LCA has been focused on emerging technologies such as nanomaterial, chemical production or energy systems, and four studies examined future food production technologies (Thonemann et al., 2020). Recently, Lulovicova and Bouissou (2024) applied territorial LCA to assess the environmental impacts of the food production and consumption of the French region of Brittany under several prospective trajectories. Nevertheless, to the authors' knowledge, no LCA of prospective food consumption scenarios exist at a national scale.

Furthermore, LCA should address food security issues (Notarnicola et al., 2017a; van der Werf et al., 2014) and food system vulnerability in order to identify potential trade-offs between system efficiency and resilience (Berardy et al., 2020; Garnett, 2014; Soussana, 2014). To address resource vulnerability, the resource criticality framework has begun to be a part of LCA (Santillán-Saldivar et al., 2021; Sonnemann et al., 2015). Criticality methods assess the economic and technical dependency on a certain materials, as well as the probability of supply disruptions, for a defined stakeholder group within a certain time frame (Schrijvers et al., 2020). In particular, Deteix et al. (2024a) characterised food product criticality for land, water and mineral resources within

LCA on a bread case study. To date however, no criticality assessment has yet been performed regarding food consumption.

This paper therefore aims to assess both the environmental impacts and the resource criticality of food consumption scenarios in France in 2050 using the LCA framework. Among existing criticality assessment methods, three have been recommended for LCA purposes (Berger et al. (2020); Sonderegger et al. (2020); Hackenhaar et al. (2022)), i.e. ESSENZ (Bach et al., 2016), GeoPolRisk (Santillán-Saldivar et al., 2022) and the Joint Research Centre approach (Blengini et al., 2017b). The Joint Research Centre criticality method was originally designed for mineral resources (Blengini et al., 2017b), but was then later extended to land (Deteix et al., 2023), water (Deteix et al., 2024a) and even raw agricultural products (Deteix et al., 2024b), which are key resources for the food sector. For the first time, the use of the Joint Research Centre method was proposed, including the above-mentioned new extensions at the scale of the entire food consumption of a country.

In order to address the issue of LCA regionalisation for agri-food systems, particular attention is given to the definition of prospective scenarios integrating the specificities of the French context in terms of agri-food production and logistics chains (Berardy et al., 2020; van der Werf et al., 2014). These characteristics are included in both the life cycle inventory (LCI) and life cycle impact assessment (LCIA) phases. Many impact pathways are strongly location dependent, such as water scarcity, land use, or eutrophication, as well as resource Supply Risk Potentials (SRP) for land, water and agricultural products. It is therefore crucial to regionalise LCIA computations (Mutel et al., 2019).

Section 2 first describes the five prospective food consumption scenarios investigated, based on the scenarios of (ADEME, 2021) and (Barbier et al., 2022). Then, the four phases of the LCA are conducted. The prospective LCI is built using a parametric approach where different diet compositions, supply chain importation levels, agricultural practices, and energy mixes are considered. Finally the method for a full regionalised LCIA is further described, as well as integration of the resource criticality assessment.

Section 3 presents the results, comparing regionalised environmental impacts and criticality impacts for the five prospective scenarios, as well as contribution analysis per food groups. In this section, the results are also compared with other studies, and the effect of regionalisation as well as the main contributions and limitations of the study are discussed.

## 2. Methods

This section first provides an overview of each of the prospective scenarios studied. Particular attention was paid to model the type and quantities of food products consumed in each scenario using the study of Barbier et al. (2022). Then, the methodology used followed the 4 standard LCA phases (ISO, 2006), i.e. i) goal and scope definition, ii) life cycle inventory (LCI), iii) life cycle impact assessment (LCIA), and iv) interpretation with a global sensitivity analysis. LCIs relied on a parametric approach to take into account the specific features of each scenario (share of organic products, import levels, energy mix). The LCIA phase was regionalized at foreground and background level, following the approach of Mutel and Hellweg (2023). Other indicators related to resource criticality were also computed during this phase, by using resources Supply Risk as characterisation factors.

### 2.1. Prospective scenario descriptions

Five prospective scenarios were developed, deliberately based on contrasting economic, technical and social options. In addition to an S0 “Business-as-usual” (BAU) scenario, which was the reference, the other four scenarios originated from the work of the French Agency for Ecological Transition (ADEME, 2021), covering all economic sectors for a carbon-neutral French society in 2050. These scenarios were specifically adapted to the food sector by Barbier et al. (2022). The S1 scenario (“Frugal Generation”) supposes that transition is mainly driven by
frugality, i.e. overall consumption is reduced, international trade is contracted and preference is given to local production, recycling and low tech use. Scenario S2 (“Regional Cooperation”) relies on a society transformed by shared governance such as territorial cooperation and repopulations of medium-sized towns, while Scenario S3 (“Green Technologies”) relies on decarbonised energy systems through technological innovation. Finally scenario S4 (“Restauration Gamble”) bets on the ability of a society to decarbonise energy systems and develop carbon capture and storage, as well as technological innovation to tackle climate and biodiversity crises (ADEME, 2021). In all scenarios, population growth follows the French National Institute of Statistics and Economic Studies (INSEE) that considers a low fertility scenario, with 69.7 million people expected in 2050, compared with 67 million in 2018 (Barbier et al., 2022; INSEE, 2021).

According to the work of (Barbier et al., 2022) and LCA studies on food products (Notarnicola et al., 2017b; Nuss et al., 2023; Poore and Nemecek, 2018), four key factors, that significantly influence the LCI parameters but are not part of the inventory model (Langkau et al., 2023), were identified to construct the five scenarios assessed in this study. These included diet composition (quantities of food consumed and the types of food products consumed), agricultural practices (conventional or organic), trade levels (agricultural products origins), and electricity and gas mixes.

From a nutritional point of view, these scenarios optimise 6 different diets within the French population (small, medium, large meat consumers, pesco-vegetarians, vegetarians and vegans), the proportions of which vary according to the scenarios (Barbier et al., 2022). For this reason, the scenarios are not based on a single reference menu, such as the EAT-Lancet menu (Willett et al., 2019).

Fig. 1 reports the five scenarios using different values for the key

factors (estimates build on data from Barbier et al., 2022).

S1 demonstrated the lowest average per capita food consumption, mainly based on plant products and low meat consumption. No more conventional agriculture was practiced, and more than 85 % of consumed food was produced in France. French electricity generation heavily relied on wind and solar technologies, and more than 80 % of gas came from renewable energy. S4, on the contrary, was characterised by the same amount of consumed meat and similar trade levels as for the BAU scenario, as well as 70 % of conventional agriculture. The electricity mix was composed of more than 20 % nuclear, 40 % of wind (onshore and offshore) and 20 % solar. Only 50 % of gas was renewable. S2 and S3 lie between S1 and S4. For all scenarios, the food waste rate in 2050 was 50 % lower than its present value (Barbier et al., 2022).

## 2.2. Goal and scope

The aim of the LCA study was to evaluate the environmental performance and resource criticality of food consumption by an average French inhabitant in 2050, under five contrasting prospective scenarios. The present study examines the impacts and resource dependency of food consumption. The functional unit selected for analysis is the total quantity of food consumed by a French inhabitant over one year, which is considered the most adapted. This functional unit has been selected in other studies that focus on consumer footprints such as the works of the Joint Research Centre for European consumers (Genta et al., 2022).

The criticality assessment covered mineral, land, and water resources, as well as raw agricultural products. The system boundaries ranged from cradle to the point of sale, i.e. the study included the different stages of agricultural production and food processing, as well as intermediary logistical stages. However, it excluded the consumer's

<figure><img src="image_3.png" /><figcaption>Fig. 1. Visual description of the 4 key factors in the five prospective scenarios.</figcaption></figure>
journey (last mile) as well as the preparation of meals at home for final consumption. At each stage of the life cycle, the waste management generated by packaging, inedible food parts (e.g. peelings, bones, etc.), and food losses during logistic phases (e.g. retailing) was taken into account. However, the management of waste generated by food waste at home remained outside the system boundaries. The functional unit was the annual food consumption of an average French inhabitant in 2050, at the point of sale.

## 2.3. Life cycle inventory modelling approach

The food consumption LCI was built in five stages, as illustrated in Fig. 2. These stages are described in the following sections.

### 2.3.1. Consumption intensity calculation

In the first stage, the food consumption intensity is determined. The food consumption intensity corresponds to the average food quantities bought in one year by one French inhabitant. It is calculated based on two data sources.

First, the National Individual Food Consumption Survey (INCA3) in France, undertaken by the French Agency for Food, Environmental and Occupational Health & Safety (Anses, 2017) provides estimates of the nutrient intakes of the French population in 2014. This study provides the food quantities ingested of 43 food groups by one inhabitant today, representative of one inhabitant annual total food consumption. Then, the study of (Barbier et al., 2022) provides the estimated food quantities ingested of 15 food categories by the French population in 2050 in each scenario, taking into account demographic evolutions.

To get the consumption intensity of the 43 food groups in 2050 per inhabitant, two steps were performed, i.e. 1) translating data from food ingested to food bought and 2) extrapolating actual consumption to 2050 consumption.

To trace the quantities ingested back to the quantities purchased, the quantities of food waste, food inedible parts (e.g. spelling for vegetables, shell for eggs ...) and the changes in mass due to cooking were taken into account, according to Eq. (1):

$$q_{group.bought} = \frac{q_{group.ing}}{R2C_{group} * (1 - Ined_{group}) * (1 - FW_{group.original} * FW_{reduction\ 2050})} \quad (1)$$

With:

* $q_{group.bought}$: quantities bought

* $q_{group.ing}$: quantities ingested

* $R2C_{group}$: raw/cooked ratio

* Ined$_{group}$: share of inedible part of the product

* TheFW$_{group.original}$: food waste rate

* FW$_{reduction\ 2050}$: food waste reduction rate in 2050

The Food Waste rates come from the study of (De Laurentiis et al., 2023) who estimated food waste rates for European countries across 11 food groups based on Material Flow Analysis. It is the ratio of food wasted at household level over food entering household level. To avoid annual anomaly, the average of the year 2018, 2019 and 2020 have been used. These three years have been chosen to be consistent with the Business as Usual scenario from Barbier et al. (2022), which was not given in a fixed year, but around the years 2018, 2019 and 2020. The food waste reduction rate in 2050 is 2 times the current level, in line with France's objectives (Ministère de l'agriculture, 2013).

Eq. (2) shows how the consumption intensity in 2050 is obtained from the current consumption intensity:

$$q_{group\ 2050} = q_{group\ 2017} * \left( 1 + \frac{\frac{q_{\text{categorie\ 2050}}}{pop_{2050}} - \frac{q_{\text{categorie\ ref}}}{pop_{ref}}}{\frac{q_{\text{categorie\ ref}}}{pop_{ref}}} \right) \quad (2)$$

With:

* $q_{group\ 2050}$: quantities of food group consumed by one inhabitant in 2050

* $q_{group\ 2017}$: quantities of food group consumed by one inhabitant in 2017 from INCA3 study (Anses, 2017)

<figure><img src="image_4.png" /><figcaption>Fig. 2. Approach developed to compute the regionalised prospective Life Cycle Inventory. LCI: Life Cycle Inventory.</figcaption></figure>
* *q*<sub>categorie 2050</sub>: quantities of food group consumed by the French population currently from (Barbier et al., 2022)

* *q*<sub>categorie ref</sub>: quantities of food group consumed by the French population currently (Barbier et al., 2022)

* pop<sub>2050</sub>: French population in 2050

* pop<sub>ref</sub>: French population currently

The quantities of food group consumed in 2050 (*q*<sub>group 2050</sub> in Eq. (2)) vary with each scenario. The key factor “diet composition” therefore entails the set of food groups consumed quantities. Table S1 from Supplementary Information (SI) recaps all the data sources for the terms of Eqs. (1) and (2).

### 2.3.2. Basket of representative food products and matching with life cycle inventories

In the second stage, the food groups were divided into 46 representative products (see Eq.(S1) from SI). The representative products were selected for being both most widely consumed and with the greatest environmental impact (Notarnicola et al., 2017b).

In the third stage, the representative food products were matched with food product LCIs from the Agribalyse 3.1.0 (Koch and Salou, 2020) LCA database. Agribalyse is a French LCA database providing food product LCIs including agricultural production, processing, transport, packaging and consumption, but excluding the final consumer journey. The database and the associated research program covers 2500 food items and supports French environmental food labelling policies (Colomb et al., 2015). The food products from Agribalyse were selected at the “supermarket level”, according to the chosen system boundaries. The amount of food product consumed in each scenario and the list of products and their corresponding LCI are available in Tables S2 and S3 from SI.

Stage four was focussed on the adaptation and parametrisation of the LCIs so they may be consistent with the different prospective scenarios, and stage five concerned the regionalisation of the agricultural LCIs. These latter two stages are detailed in the next section.

### 2.3.3. Food supply chains life cycle inventories parametrisation

In order to reflect food consumption according to the five prospective scenarios, food product LCIs were parameterised at all stages of the food supply chains, i.e. agricultural production, food processing, transport and point of sale. At the point of sale, and during the food processing stage, the French electricity and gas mixes were parametrised. These mixes then varied according to the “energy mix” key factor (see Fig. 1).

At the agricultural production stage, the first parametrisation concerned the countries of origin of agricultural products. Each agricultural

product was defined as the consumption mix of products from different countries (see Fig. 3), similarly to the approach of Patouillard et al. (2021). For example, the tomato consumption mix was a mix of French, Spanish and Moroccan tomatoes. The initial countries of origin and their proportion per agricultural products were defined using data from Agribalyse and FAOSTAT. The proportion of each country in each agricultural product final consumption mix then varied with the “trade level” key factor (see Fig. 1). The LCIs for imported agricultural products were chosen from the Agribalyse database if available, and then from the Ecoinvent LCA database (version 3.8). However, the Agribalyse and Ecoinvent databases do not cover all LCIs for foreign and/or organic agricultural products. Therefore, a new LCIs dataset had to be created. This involved copying the process LCIs for the corresponding French agricultural products and changing the process location to the new country. Furthermore, the fertiliser, irrigation and electricity mixes were also adapted to the new country by extracting the actual country mixes from the Ecoinvent 3.8 database. Finally, distances and modes of transport to France were also modified according to the new country of origin, based on the Agribalyse transport matrix (Asselin-Balençon et al., 2022).

The second parametrisation of the LCIs at the agricultural stage was the differentiation of agricultural products between conventional and organic practices within each country. Each agricultural product in a country was thus an average between conventional and organic farming (see Fig. 3). The proportions of organic and conventional agriculture were defined for both French and international agricultural products, allowing the respective proportions to vary according to the “agricultural practices” key factor (see Fig. 1).

The parametrisation was performed by following the parametric approach of Douziech et al. (2023), where the parametrisation of both foreground and background inventory can be performed through the lca_algebraic python library (Jolivet et al., 2021).

## 2.4. Life cycle impact assessment

### 2.4.1. Regionalised life cycle impact assessment

The regionalisation of LCA impacts remains an operational challenge (Frischknecht et al., 2019), even though this is crucial for differentiating the impacts of agricultural products with regionalised LCIs (Peng and Pfister, 2024). Although LCIA method developers are progressively proposing regionalised characterisation factors (Mutel et al., 2019), LCA studies that use regionalised characterisation factors remain rare and limited to a few impact categories, most often water scarcity or land use. One of the key issues is the matching between LCI elementary flows and regionalised characterisation factors (Frischknecht et al., 2019). Each

<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Life Cycle Inventory model of agricultural product consumption mixes. p: share of each country of origin, s: share of agricultural practices type.</figcaption></figure>
impact category is defined by its appropriate native scale, e.g. water is defined at sub-watershed level, or land use at ecoregion level. Spatial scales can therefore be very different depending on the type of impact, giving rise to greater or lesser sensitivity to spatial variability (Bulle et al., 2019; Mutel et al., 2019; Verones et al., 2020). In addition, the effect of regionalisation also depends on the sector of activity, and is particularly important for the agricultural sector (Patouillard, 2019; Peng and Pfister, 2024).

In this study, LCIA regionalisation was performed following the approach of Mutel and Hellweg (2023) where both foreground and background LCIA regionalisation are performed by introducing regionalisation matrixes that match LCI elementary flows and characterisation factors locations. This approach was operationalised with the bright-way2regional python package, one step above the brightway2 LCA python software for regionalised impact assessment (Mutel, 2017).

Among the most recent LCIA methods, Impact World + (Bulle et al., 2019) and LC-Impact (Verones et al., 2020) have both been explicitly designed for regionalisation. Impact World+ proposes regionalised impacts for water scarcity, land occupation & transformation, marine & freshwater eutrophication and terrestrial & freshwater acidification, as well as regionalised impacts at the endpoint level for the Human Health and Ecosystem Quality Area of Protection. This method was chosen because it is the only method that provides characterisation factors at the country level that can be used for operational purposes.

Mutel and Hellweg (2023) proposed four approaches for LCIA regionalisation. Here the first approach, “Shared Spatial Scale”, was used, since most of the elementary flows from agricultural products were regionalised at the country scale, which is the same scale as for the regionalised characterisation factors from Impact World+. Elementary flows with infra-country location were associated with the corresponding country location (e.g. “US-country x” becomes “US”), and elementary flows with upper-country locations (e.g. “Europe”) were given global location and were hence matched with the global default characterisation factors.

### 2.4.2. Supply risk characterisation factors

The LCIA Supply Risk model was based on the model developed by Terlouw et al. (2019) and Santillán-Saldivar et al. (2021, 2022). Indeed, in this case the importance of resource use is quantified by the physical amount of resource flows recorded in LCIs. The approach of (Deteix et al., 2024a), based on the Joint Research Centre method, was used to derive Supply Risk characterisation factors from the criticality method for mineral, land and water resources, and match them with the food consumption LCI.

Hence, the criticality assessment requires a characterisation of the flows of occupied land and of extracted minerals and water. Concerning water flows, these differ from the water flows that are traditionally accounted for in LCA. Indeed, the latter are defined as consumed flows (water withdrawn minus water released), while criticality rather focusses on the access to the resource, and not on the environmental consequences of its consumption (Deteix et al., 2024a).

For agricultural product Supply Risk characterisation, Supply Risk indexes were computed at the country level according to the Joint Research Centre criticality method, and were used directly as characterisation factors. However, agricultural products are considered as goods in LCA, and not as natural resources directly extracted from the environment (Klinglmair et al., 2014; Notarnicola et al., 2017a). As a result, there are no specific elementary flows (i.e., extracted natural resources or pollutant emissions) dedicated to these products in the LCI datasets. To apply the Supply Risk characterisation factors to agricultural products, new elementary flows corresponding to harvested agricultural products at the farm gate were defined in the LCI datasets for agricultural products consumed within each scenario. Furthermore, the agricultural products Supply Risk factors were adapted to account for these products features by including parameters related to exposure to extreme weather events, or commodities price volatilities (Deteix et al.,

2024b). The Supply Risk characterisation factors for raw agricultural products were then applied to these elementary flows. They were applicable to all agricultural products, regardless of their uses, whether for food or feed (e.g. for maize). Concerning meat products, the Supply Risk characterisation factors were applied to the carcass and not to the live animal, (i.e. at the slaughterhouse gate) which is the stage at which they were calculated.

Finally, four midpoint impact categories relating to resource SRP were obtained.

Table 1 reports the Supply Risk characterisation factor sets, as well as the elementary flows accounted for per resource for the LCA criticality assessment.

Excluding mineral resources, the Supply Risk impacts were regionalised at the country level. Therefore, the same impact regionalisation approach was used as for environmental impact assessment (see Section 2.3.1).

For water, land and agricultural products, the global median values of the Supply Risk input parameters replaced the missing values, thus allowing for all countries to be characterised.

### 2.5. Sensitivity analysis

In order to analyse the effect of the four key factors (see Fig. 1), additional scenarios were simulated and assessed. These were based on multiple combinations of the different modalities for each key factor (see Table 2), thus resulting in 225 simulations.

The potential synergies or trade-offs between impacts were analysed by carrying out a Principal Component Analysis on the LCA results including the Impact World + endpoint impact categories and the four SRP midpoint impact categories.

These scenario simulations also allowed for a Global Sensitivity Analysis (GSA) to be performed.

## 3. Results and discussion

The results first reported the regionalised environmental impacts of the food consumption of a French inhabitant in 2050, according to the five prospective scenarios. Secondly, the resource SRP of all the scenarios was analysed. Thirdly, a joint analysis of environmental damages and resource SRs was carried out on the 225 simulations in order to identify potential trade-offs between these two dimensions.

Finally, the significance of regionalisation of the impacts was discussed by comparing the results with global or regionalised characterisation factors from two contrasting scenarios in terms of food origins (low or high imports).

### 3.1. Environmental impacts of the scenarios

The environmental impacts of the five prospective scenarios for all midpoint impact categories can be compared in Fig. 4.

In general, the impact was stronger for the S0 reference scenario than for scenarios S1, S2 and S3, while it was lower than for scenario S4. The high impact values of Scenario S0 were due to its highest consumption intensity and because it had one of the highest meat consumption levels.

Regarding the scenarios S1 to S4, a similar trend for all impact categories was observed, i.e. the scenarios from the least to the most impacting were as follows: S1, S2, S3 and S4. This could be mainly explained by the increase in consumption intensity and diet variations from S1 to S4. Meat consumption levels gradually increased, while fruit and vegetable intake decreased proportionally. For instance, these mechanisms explain the observed effects on climate change, whether over a short or long term.

However, for the land transformation impact, scenario S3 presented a greater impact than S4, despite a lower consumption intensity. The Agribalance database does not adequately represent the land transformation flows observed in France, thus limiting the interpretability of
<table><caption>**Table 1**
Summary description of the four characterisation factors set.</caption><thead><tr><th>Resource type</th><th>Mineral</th><th>Land</th><th>Water</th><th>Agricultural products</th></tr></thead><tbody><tr><td>Supply Risk index reference</td><td>(Blengini et al., 2017a)</td><td>(Deteix et al., 2023)</td><td>(Deteix et al., 2024a)</td><td>(Deteix et al., 2024b)</td></tr><tr><td>Number of resources covered</td><td>82</td><td>1</td><td>1</td><td>108</td></tr><tr><td>Spatial coverage</td><td>European Union</td><td>World</td><td>World</td><td>World</td></tr><tr><td>Spatial resolution</td><td>European Union as a whole</td><td>Countries (230)</td><td>Countries (276)</td><td>Countries (150)</td></tr><tr><td>Strategy to fill data gaps</td><td>NC</td><td>World median</td><td>World median</td><td>World median</td></tr><tr><td>Elementary flow characterised</td><td>Minerals mass flow</td><td>Land occupied</td><td>Water extracted</td><td>Product harvested</td></tr><tr><td>Elementary flow unit</td><td>kg</td><td>m<sup>2</sup>·year<sup>-1</sup></td><td>m<sup>3</sup></td><td>kg</td></tr><tr><td>Supply Risk data availability</td><td>(European Commission, 2020)</td><td>doi:10.57745/RALP5G</td><td>doi:10.57745/U8TLHN</td><td>doi:10.57745/SKHTL7</td></tr></tbody></table>

<table><caption>**Table 2**
Key factor modalities for scenario simulation and sensitivity analysis.</caption><thead><tr><th>Key factor</th><th>Modalities</th></tr></thead><tbody><tr><td>Diet composition</td><td><ul><li>S0 diet composition</li><li>S1 diet composition</li><li>S2 diet composition</li><li>S3 diet composition</li><li>S4 diet composition</li></ul></td></tr><tr><td>Agricultural practices</td><td><ul><li>100 % of organic agriculture</li><li>50 % of organic agriculture</li><li>0 % of organic agriculture</li></ul></td></tr><tr><td>Trade level</td><td><ul><li>-90 % importations</li><li>same importation level</li><li>+ 90 % importations</li></ul></td></tr><tr><td>Energy mixes</td><td><ul><li>S0 energy mixes</li><li>S1 energy mixes</li><li>S2 energy mixes</li><li>S3 energy mixes</li><li>S4 energy mixes</li></ul></td></tr></tbody></table>

the resulting impacts. In the Agribalyse database, organic products in France are responsible for the conversion of secondary forestland into arable land, whereas conventional products have no such land transformation flows. These data also explain why scenario S2 presented a similar impact on land transformation to S0, despite a much lower consumption intensity (see Fig. 4).

Regarding land occupation, S2 and S3 presented impact values close to S0 although less food was consumed. This was due to a higher proportion of organic products, which have slightly lower yield than conventional products in Agribalyse, especially for field crops. Therefore, a kilo of products required more land, thus explaining the rather high levels of land occupation.

For ionising radiation and fossil and nuclear energy uses, S4 impact was much lower than for S0. Regarding these two impact categories, the significantly lower impact values for all scenarios could be explained by the lower proportion of nuclear electricity in the French electricity grid in 2050 compared to the reference scenario.

For ozone layer depletion, the low impacts for all scenarios compared with the reference scenario could be explained by their significantly lower consumption of seafood, which was the major contributor to this impact category due to emissions from diesel combustion in marine engines.

In all scenarios, metals were the primary source of toxicity and ecotoxicity impacts, due to their high characterisation factor values (Pizzol et al., 2011). However, for all scenarios, these impacts were related to ecoinvent data from background processes for animal feed (shrimp, beef). These results therefore raise the question of the robustness of using several LCA databases simultaneously.

## 3.2. Scenario resource supply risk potentials

The SRP of mineral, land and water resources, as well as agricultural products for the five scenarios can be compared in Fig. 5.

The SRP results for the four groups of resources presented similar trends to the environmental impacts, i.e. scenario S0 had the highest SRP

compared to all the other scenarios, while the scenarios from the most to the least impacting were ranked as follows: S4, S3, S2, and S1. The scenarios S1 to S3 exhibited a lower impact than the reference scenario S0. In contrast, scenario S4 had a SRP that was almost identical to that of scenario S0 for mineral resources and agricultural products, while exhibiting a very slight increase in the land SRP and a slight decrease in the water SRP (see Fig. 5).

For mineral resources, whatever the scenario, the majority of SRP were due to phosphate and potash, which are used as plant fertiliser, thus accounting for 57 % to 68 % of total SRP. Bauxite, used for aluminium production, and iron, used for steel production, are required for infrastructure activities and thus represented a constant contribution to the SRP across all scenarios (see Fig. 5-A).

Concerning land, cropland (annual and perennial) consistently accounted for the majority of impacts across all scenarios. The contribution of pasture was significant for the S0 scenario and increased from S1 to S4, concomitantly with beef consumption. Finally, forests brought a significant contribution in all scenarios, since they are exploited for the production of cardboard in food packaging (see Fig. 5-B).

For agricultural products, the SRP was more evenly distributed across the products. In all scenarios, wheat, vegetables (tomatoes and carrots) potatoes, beans and grapes (through wine consumption) each contributed to more than 10 % of the impacts. The significant contribution of beans in all scenarios could be attributed to the notable increase in legume consumption across all scenarios, as a substitute for meat. Barley was an important contributor in all scenarios except for S0, as it is a vital cereal for animal feed, and represented an even greater proportion of rations in organic farming. Conversely, soya was only a major contributor in scenarios S3 and S4, still for animal feed, where it accounted for a large proportion of rations in conventional farming. Results suggest that meat was not a primary contributor to agricultural SRP, but that it rather contributed indirectly through the Supply Risk associated with animal feed (see Fig. 5-C).

The water SRP was exclusively due to water withdrawals for hydroelectricity production, in agreement with observations by (Deteix et al., 2024a). On one hand, hydropower involved the withdrawal of enormous volumes compared with volumes required for other uses, such as irrigation. On the other hand, the amount of water consumed remained low, because it was all discharged later on, unlike irrigation, where a large proportion of the water withdrawn was consumed (see Fig. 5-D).

## 3.3. Potential trade-offs between environmental impacts and supply risk potentials

Fig. 6 illustrates the environmental damage and resources SRP for the 225 scenarios simulated from all the key factor combinations. Results are clustered per diet composition (Fig. 6-A) and agricultural practices (Fig. 6-B).

Depending on the type of resource considered, results indicate either trades-offs, where scenarios have high environmental damages and low SRPs, or synergies, where scenarios have high environmental damages and high SRPs (see Fig. 6).

In this study, the Agribalyse processes used to model organic
<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Scenario impacts for the midpoint impact category. The red dashed line indicates S0 impact values, i.e. of the reference scenario. LT: Long term, ST: Short term, C: Cancer, NC: Non cancer.</figcaption></figure>

agricultural products showed slightly higher environmental impacts on land use than conventional products, due to slightly lower yields. These results were transmitted to the damage level for Ecosystem Quality in particular, because of the significant weight of land use impact on this area of protection compared with the other impact categories (Bulle et al., 2019). However, the most recent studies revealed that the impact differences between organic and conventional products were highly dependent on crop types, farming practices and impact categories (Boschiero et al., 2023).

Regarding mineral resources, a trade-off was observed between the environmental impact and SRP. Scenarios with the highest environmental damages had the lowest SRP (Fig. 6). Sharing the same diet, the High Organic scenarios tended to produce slightly higher environmental damages, but the lowest values for mineral SRP. Indeed, the mineral SRP is mainly related to mineral fertilisers, whereas organic systems use only organic fertilisers, thus explaining their lower mineral SRP. This result was emphasized by Deteix et al. (2024a). Therefore, as evidenced by the GSA, after the diet key factor, the agricultural practices key factor was the second most significant factor that affected the mineral SRP, (see Fig. 1 from SI).

Regarding land resources, a strong correlation could be observed between environmental damages and SRPs (Fig. 6). For food system LCAs, the greatest impact on the environment was observed for the

agricultural stage (Castellani et al., 2019; Poore and Nemecek, 2018). In the case of food consumption, the environmental impact was therefore very sensitive to the yield of agricultural processes (Garnett, 2014; van der Werf et al., 2014, 2020). As the land SRP was based on land use flows, it was also influenced by agricultural yields, which explains its correlation with damages.

The water SRP was almost exclusively related to water withdrawals for hydroelectricity. It had a moderate correlation with environmental damages (see Fig. 6) and was rather related to energy consumption. Therefore, in agreement with the GSA, for this resource, both the diet and the energy mix key factors explained the SRP (see Fig. 1 from SI).

For agricultural products, a trade-off was observed between environmental damages and SRP, although it was less pronounced than for minerals. The variation in agricultural SRP according to the proportion of organic products is due to changes in the composition of animal feed. Organic feed includes less oilcake and more grazing for beef, for example, which does not require agricultural products, thus explaining the lower agricultural SRP.

### 3.4. Data and modelling choices

#### 3.4.1. *Life cycle impact assessment regionalisation*

Fig. 7 reports the spatial distribution of the impacts for the seven
<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Contribution to agricultural and mineral Supply Risk Potential impacts for five food consumption prospective scenarios.</figcaption></figure>

midpoint impact categories that were regionalised in IW+. The site-generic and regionalised version of the impacts were compared for two variants of the S0 scenario, one with a 90 % reduction in the level of imports (“Low import”), the other with a 90 % increase in the level of imports (“High Import”).

The differences in impacts between regionalised and site-generic impacts can vary up to 50 % (water scarcity and freshwater eutrophication). Regionalisation of the impacts could amplify differences between scenarios, as is the case with water scarcity and freshwater acidification (see Fig. 7). Conversely, regionalisation could also attenuate differences between scenarios for some impact categories, as was observed with land transformation. Furthermore, regionalisation of impacts might also alter conclusions drawn from comparisons of scenarios. For example, regarding land occupation and marine eutrophication, the “High Import” scenario had the greatest impact when site-generic impacts were considered, whereas the “Low Import” scenario had the greatest impact when regionalised impacts were considered (see Fig. 7).

Finally, contribution profiles also differed when impacts were regionalised or not. For example, water scarcity impacts in Brazil and Côte d'Ivoire were significant in the site-generic impact assessment yet they represented a minimal impact when the impact was regionalised. The same situation was observed for the impact occurring in Canada for freshwater acidification (see Fig. 7).

It is therefore crucial to take the regionalisation of impacts into account in order to estimate a more precise value for the impact of a system, but also to be able to conclude when systems are compared and when the different contributions to the total impact are analysed.

Yet, comparison of the impact categories also indicated that certain impact categories such as water scarcity, freshwater eutrophication or land transformation were more sensitive to regionalisation than others, such as land occupation or terrestrial acidification.

From the inventory point of view, the low sensitivity to regionalisation might have been due to the majority of elementary flows taking place within a single country. For example, for terrestrial acidification and land occupation, the majority of impacts take place in France. This low sensitivity to regionalisation might also have been related to the distribution of characterisation factors values, which were more or less extended according to the impact category. For land occupation, characterisation factors values ranged from 0 to 1.15, while they ranged from 0 to 100 for water scarcity.

For further investigation, an uncertainty analysis taking into account the uncertainties of the characterisation factors could be carried out. This could make it possible to determine the significance of the differences between the regionalised impact assessment and the site-generic impact assessment.

A new method has been operationalised for impact regionalisation and has demonstrated the importance and feasibility of regionalising impacts for LCAs of agri-food systems. The regionalisation of agricultural inventories takes into account the location of emissions and resource consumption, as well as the countries' fertiliser, irrigation and electricity mixes. Obviously, these approaches could be implemented at finer scales than countries, provided that data and characterisation factors at finer scales are available.

### 3.4.2. Agricultural LCI modelling

In this study, the Agribalyse processes used to model organic agricultural products led to slightly higher environmental impacts on land use than for conventional products, due to slightly lower yields. These results were transferred to the damage level for the impact on Ecosystem Quality in particular, because of the significant weight of land use compared with the other impact categories (Bulle et al., 2019). Modeling the environmental impacts of specific agricultural practices still represents an avenue of research, which should be improved as further
<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Relationship between the environmental impact at the endpoint level and Supply Risk Potential of food consumption of a French citizen for all key factor combinations. Results are distinguished according to the key factors “diet composition” (A) and “agricultural practices distribution” (B). SRP: Supply Risk Potential.</figcaption></figure>

versions of the Agribalyse database are developed.

Furthermore, the LCIs in the Agribalyse database were based on either statistical data representative of average farming practices or experimental data that were less representative. This was particularly the case for organic products, thus limiting the generalisability of the results in terms of the influence of agricultural practices on the impact of food consumption. As reported by Boone et al. (2016), local conditions and practices can greatly influence agricultural LCA results. They distinguished four types of variability factors, i.e. environmental policy, farm management, annual weather conditions and innovation. These factors notably affect the levels of inputs used, such as fertilisers and pesticides, as well as yields. Using generic data can hide these variabilities, and can lead to impacts being underestimated if yields are overestimated, or, on the contrary, to impacts being overestimated if the quantities of inputs reported are greater than in reality.

Three avenues can be explored to improve the regionalisation of agricultural LCI datasets. Firstly, the modelling of agricultural practices could be better regionalised by constructing LCI archetypes based on primary data from on-site measurements or field surveys. Secondly, LCIs could be developed using parameters that are sensitive to regionalisation, such as field emission models sensitive to local climate and soil conditions. Finally, regionalised LCI databases could be developed using

local and regional agricultural statistics on the type and amount of inputs, labour intensity or yields (Sinisterra-solís et al., 2023).

For further insight, and in line with the regionalisation of impacts at country level, the specific agricultural practices of each country should also be taken into account. This means adapting the types and quantities of inputs to each country, as well as the yields. Approaches such as MEXALCA for adapting agricultural inventories (Guillaume et al., 2024; Roches et al., 2010), or the use of non-LCA databases such as the HESTIA database (Hestia Database, n.d.), are potential options. In addition, some agricultural emission models that currently take into account soil and climate conditions (Koch and Salou, 2022) could also be used for calculating the quantities of elementary flows emitted by agricultural processes in each country.

### 3.5. Policy implications

The work carried out in this study allows both the environmental impacts and resource dependency of prospective food supply scenarios to be quantified. These aspects are important for supporting the framing of public policies on food systems, such as the environmental labelling of food products in France, or the European Farm to Fork strategy (Gerten et al., 2020; Guillaume et al., 2024). In addition, at European level,
<figure><img src="image_9.png" /><figcaption>Fig. 7. Country contribution for two scenarios (high and low imports) with regionalised or site-generic impact assessment methods.</figcaption></figure>

increasing attention has been paid to ensuring food supply and security and to putting in place effective initiatives to manage risks such as the COVID19 pandemic, the Russo-Ukrainian war, or extreme weather events (Bertolozzi-Caredio et al., 2023). These initiatives can cover different aspects of food supply, in particular reducing the proportion of animal products in diets, relocating part of agricultural production, or reducing food waste, with targets for 2050 being set at the European (–30 % of food waste) and French (–50 % of food waste) level.

The results highlighted that modifications to dietary habits exerted a synergistic effect on environmental impacts and resource supply risk, attributable to the quantities of the different food products consumed in each scenario. However, for a given food product, local conditions and farming practices could lead to potential trade-offs between environmental impacts or resource supply risk. Agricultural production is not adapted to all soil and climate conditions, and importing food products can generate less environmental impact than sourcing them locally. For instance, certain vegetables can be grown in greenhouses to reduce their sensitivity to the climate. These infrastructures help to maintain yields, but require more resources. Consequently, food supply policies should both take into account the potential and constraints of their territory for agricultural production, and ensure that they diversify their sources of supply to make them more resilient. To support them, the modelling approach used provides a scientific basis for raising awareness about potential undesirable effects. Yet, in addition to the environmental and vulnerability aspects, it is important to consider social and economic factors to ensure win-win strategies and their acceptability by stakeholders.

Furthermore, the methodological developments implemented are appropriate for assessing the environmental sustainability of other economic sectors. The studied critical resources, i.e. land, water and agricultural products, are key for food systems, but also for other strategic sectors related to ecological and energy transitions. For example, energy systems require water (hydroelectricity, nuclear power, hydrogen production), land (photovoltaic panels, wind power) or even agricultural products (renewable gas, biofuels). Biotic resources are key

to the chemical sector, electricity for mobility and industry in general.

## 4. Conclusion

This study provides quantitative information to decision-makers when comparing different food consumption prospective scenarios based on both environmental criteria and resource vulnerability criteria. This is the first time that such an assessment has been carried out jointly from a life cycle perspective.

According to the impact category, the environmental gains of the different scenarios were more or less significant, with some presenting even higher impacts than for the reference scenario.

Concerning the impact of food consumption on climate change, the food consumption of a French inhabitant in 2050 in three scenarios generated lower impacts than the business-as-usual situation. However, the impact was higher in the scenario S4 due to higher food consumption and higher proportion of meat based product consumption. Furthermore, the prospective consumption scenarios had impacts that were more or less similar to the reference situation, depending on the impact category. This highlights the importance of carrying out multi-criteria assessments of so-called low-carbon scenarios.

The joint analysis of environmental impacts and resource vulnerability using criticality methods in LCA demonstrated the existence of potential synergies and trade-offs between these two dimensions. Overall, the least impacting scenarios were those where the quantities consumed were the lowest and where the least meat was consumed. These scenarios also had the lowest SRP for land resources, thus exhibiting a synergy for this resource. Nevertheless, there were also trade-offs between environmental impacts and resource Supply Risks. For instance, scenarios with the highest environmental impacts also tended to present the lowest mineral and agricultural products SRP.

The comparison with the results from other prospective exercises and a sensitivity analysis of the levers established to reduce impacts both confirmed the importance of changing diets. They also highlighted the crucial role that modifications in agricultural production practices may
play in both reducing environmental impacts and resource vulnerability. This study can thus support the design of public food policies that seek to ensure food security, and promote more sustainable food systems, as these policies are on the agenda of the European Union. The results also emphasized the value of regionalising LCA impacts for enhanced decision-making support.

However, the prospective assessments of food systems still require improvements in the quality of LCI data, particularly relative to the impacts of different agricultural practices and to the regionalisation of LCIs. These aspects should be prioritised. The further development of prospective impact and Supply Risk factors would enhance the significance of results, as these remain open avenues of research.

## CRediT authorship contribution statement

**Lazare Deteix:** Writing – original draft, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Thi-bault Salou:** Writing – review & editing. **Eléonore Loiseau:** Writing – review & editing, Validation, Supervision, Resources, Project adminis-tration, Methodology, Investigation, Funding acquisition, Conceptualization.

## Declaration of Generative AI and AI-assisted technologies in the writing process

During the preparation of this work the authors used DeepL in order to improve readability and language. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

## 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

The authors acknowledge Joséphine Ras for the English proofreading.

This work was supported by the French National Research Agency (ANR grant ANR-20-CE03-0006).

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.spc.2025.02.005.

## Data availability

The links to the datasets have been specified in the article.

The scripts are available upon reasonable demand.

Additional data are available in Supplementary Material.

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