# Large-scale development of biogas production in France: Improved climate benefits over the long term compared to biomass burial

N. Malet, Sylvain Pellerin, T. Nesme

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N. Malet, Sylvain Pellerin, T. Nesme. Large-scale development of biogas production in France: Improved climate benefits over the long term compared to biomass burial. Science of the Total Environment, 2025, 1002, pp.180601. 10.1016/j.scitotenv.2025.180601. hal-05314658

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# Large-scale development of biogas production in France: Improved climate benefits over the long term compared to biomass burial

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N. Malet<sup>a,b,*</sup> ◐, S. Pellerin<sup>a</sup>, T. Nesme<sup>a</sup>

## HIGHLIGHTS

* Net GHG emissions of AD vs. biomass burial assessed at fine spatial resolution in France.

* AD more efficiently mitigated GHG net emissions than direct burial.

* AD mitigated GHG emissions by 10.8 Mt. CO₂ eq/year in the “Regulatory CC” scenario.

* Expanding cover crops increased AD's mitigation to 17.1 Mt. CO₂ eq/year.

* Soil carbon benefits of AD and burial converged after 30 years, favoring AD long-term.

## GRAPHICAL ABSTRACT

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## ARTICLE INFO

**Keywords:**

Anaerobic digestion

Biomass burial

Greenhouse gas emissions

France

Modelling

Soil carbon sequestration

## ABSTRACT

Numerous studies have highlighted anaerobic digestion's (AD) role in mitigating greenhouse gas (GHG) emissions, but no one has thoroughly considered its long-term soil organic carbon effects nor compared these emissions to the most frequent alternative biomass use, i.e. soil burial. Using a spatially explicit modelling approach combining an anaerobic digestion and a soil carbon model, we assessed the long-term impact on GHG net emissions of agricultural biomass AD vs. direct soil burial at the national scale of France. We found that AD more efficiently mitigated GHG net emissions than direct burial, primarily through fossil fuel substitution and lowered manure storage emissions. Interestingly, we found that while AD initially reduced soil carbon storage compared to direct burial, both usages converged in the long-term. Biogas grid injection could save 10.8 Mt. CO₂ eq/year at the national scale of France, potentially reaching 17.1 Mt. CO₂ eq/year with widespread cover crops (amounting to respectively 2.6 and 4.1 % of national GHG emissions). This study provides valuable perspectives for fostering sustainable development within the AD sector as well as a methodology that can be adapted to diverse agricultural and geographical contexts.

**Abbreviations:** AD, Anaerobic Digestion; C, Carbon; CC, Cover-Crop; CHP, Combined Heat and Power; CH₄, Methane; CO₂ eq, Carbon dioxide equivalent; CV, Coefficient of variation; C/N, Carbon-to-Nitrogen Ratio; DM, Dry matter; GHG, Greenhouse Gas; k1, Isohumic Coefficient; LCA, Life Cycle Assessment; LHV, Low Heating Value; Mt., Megaton; N, Nitrogen; NUTS, Nomenclature of Territorial Units for Statistics; N₂O, Nitrous Oxide; Std dev, Standard deviation; tC, Ton of carbon.

* Corresponding author at: INRAE, UMR 1391 ISPA, CS 20032, 33882, Villenave d'Ornon, France.

E-mail address: nicolas.malet@gmail.com (N. Malet).

<footnote><sup>a</sup> INRAE, Bordeaux Sciences Agro, UMR 1391 ISPA, 33882, Villenave d'Ornon, France</footnote>

<footnote><sup>b</sup> French Environment and Energy Management Agency (ADEME), 20 avenue du Grésillé, BP 90406, 49004, Angers Cedex 01, France</footnote>
# 1. Introduction

Anaerobic digestion (AD) is a well-established technology to produce biogas from a wide range of organic substrates (e.g. livestock manure, crop residues, energy crops, agro-industrial waste, municipal waste, etc.) thereby improving the security of energy supply and the defossilization of the energy sector. In Europe, biomethane production has grown rapidly over the last two decades from 47 TWh in 2005 to 171 TWh in 2020 (EurObserv'ER online database, n.d.). In order to achieve carbon neutrality by 2050 and to reduce fossil fuel dependency, the European Green Deal plans to reduce gross energy consumption and to massively develop the share of renewable energy (European Commission, 2019). Accordingly, biogas production is expected to contribute significantly to the achievement of these objectives (Scarlat et al., 2018). France is currently the third largest producer of biogas in Europe, after Germany and Italy (EurObserv'ER online database, n.d.). It is also the country with the highest biomethane production potential in Europe by 2050 (Gas for Climate, 2022) especially because of its high agricultural biomass resource.

Besides energy production, biogas plants are promoted in order to mitigate greenhouse gas (GHG) emissions (Bacenetti et al., 2016, 2013; Bacenetti and Fiala, 2015; Hamelin et al., 2014; Siddiqui et al., 2020; Styles et al., 2016; Vasco-Correa et al., 2018). However, although massive development of AD is pushed by many countries, the actual contribution of such strategies to the reduction of GHG emissions remains highly uncertain due to the large variability in net GHG emission of AD compared to alternative substrate biomass use (Bacenetti et al., 2016, 2013; Bacenetti and Fiala, 2015; Bachmaier et al., 2013; Malet et al., 2023a; Siddiqui et al., 2020; Vasco-Correa et al., 2018). By substituting fossil fuels, biomethane production from AD results in emission reductions from fossil fuel extraction, distribution and combustion. In addition, AD opens opportunities for better management of organic waste and livestock manure by recovering the CH₄ emitted during their storage. Furthermore, the so-called digestate resulting from biomass anaerobic digestion can be used as an organic fertilizer that substitutes synthetic mineral fertilizers and that helps sequester carbon in soils (Brockmann et al., 2018; Frøseth et al., 2014; Møller et al., 2009; Møller, 2015; Tambone et al., 2010). However, the operation of biogas plants is also associated with GHG emissions during biomass transport, energy consumption of the AD process, fugitive biomethane emissions, and biogas & digestate recovery.

In addition, agricultural biomasses such as livestock manure or crop residues – the main substrate resources for AD – can also be directly applied to soils, thereby providing both nitrogen and carbon to agricultural soils. Although the fertilizing value of digestates is widely acknowledged, its actual contribution to carbon storage compared to the direct land application of raw substrates remains unclear and poorly quantified. The major uncertainties lie in the fact that AD results in less carbon being returned to soils – compared to direct biomass burial – because part of the substrate carbon is directed toward biogas with efficiencies varying across substrate types. In addition, the digestate carbon is often considered as more recalcitrant than that contained in the corresponding substrates (Bodilis et al., 2015; Thomsen et al., 2013), thereby affecting soil carbon dynamics in both the short and the long-term. As a result, the actual contribution of biogas production to the net reduction in GHG emissions requires considering both positive and negative GHG emissions from carbon and nitrogen cycles, and to compare them with net emissions resulting from soil direct burial of agricultural biomasses, a caveat of many studies (Anselmo Filho and Badr, 2004; Burg et al., 2018; Chinnici et al., 2015; Crawford et al., 2016; Fiorese and Guariso, 2010; Guerini Filho et al., 2019; Höhn et al., 2014; Huopana et al., 2013; Sun et al., 2021; Wang et al., 2017; Zubaryeva et al., 2012).

This study aims at filling this knowledge gap by estimating the net GHG emissions benefits related to AD of agricultural substrates in a context of large AD development. Using a fine spatial resolution (NUTS4

sub-national region), the whole study relied on a spatially explicit modelling approach to estimate and compare the net GHG emissions of two scenarios at the national scale of France: either massive AD development (hereafter called “Anaerobic digestion scenario”, where biomethane is used for cogeneration or gas-grid injection) or burial of the same biomasses in agricultural soils (hereafter called “Direct biomass burial scenario”). Both scenarios underwent comprehensive evaluations that encompassed the entirety of their respective processes, from the initial stages of biomass management to the ultimate utilization or burial of resulting by-products (Malet et al., 2023a), with specific attention to soil carbon storage.

# 2. Materials and methods

The whole study relied on a spatially explicit modelling approach to simulate the net GHG emissions of a large-scale AD development scenario in France compared to those resulting from a scenario in which the same biomasses would be directly buried in soils. System boundaries of each scenario are presented in Section 2.1. We focused on the most important agricultural biomass including livestock manure, crop residues, and cover crops, as well as sewage sludges. Simulations were performed at the canton scale (NUTS4 sub-national region of 148 km² on average, see Section 2.2), i.e. at the finest resolution for which agricultural biomass data were available. We developed a specific model to simulate both carbon and nitrogen fluxes and related GHG emission for each of the two considered scenarios. To do so, our tool combined an anaerobic digestion and a soil carbon models to simulate the carbon and nitrogen transformations in both the digester and soils, while respecting mass conservation laws (Section 2.3). In each canton (n = 3705 at the national scale of France, of which 3528 were simulated), we simulated a notional anaerobic digestion plant and an average agricultural soil with characteristics specific to the region. The methodology used to estimate GHG emissions was derived from extensive literature survey, as described in Section 2.4. We draw upon a previous study that specifically estimated net GHG emissions from anaerobic digestion, using the same spatially explicit modelling approach (Malet et al., 2023a).

## 2.1. Systems & boundaries definition

For both scenarios, we considered GHG emissions as soon as biomass management differed, i.e. after their agricultural production (Fig. 1). GHG emission assessment ended after biogas use in the AD scenario and after digestate or raw biomass application to soils with related cultivated soil management emissions.

### 2.1.1. Anaerobic digestion scenario

We considered the following process in the AD scenario, with related GHG emissions. First, we considered that substrates were stored on the AD site after their delivery, thereby potentially emitting CH₄ and N₂O. Substrates were then conveyed to the digester in which the *stricto sensu* AD took place. This resulted in the production of biogas and digestate. Part of this biogas was considered lost due to leakage while another fraction was considered flared (e.g. during maintenance periods). Biogas could be transformed to produce both heat and electricity through the well-known combined heat and power (CHP) cogeneration process. Alternatively, biogas could also be purified as biomethane and injected into natural gas-grid network. We considered that each of these end-use energies replaced the same amount of energy likely provided by fossil sources. Note however that the self-consumed fraction of the different energies has been deducted. Finally, the digestate was considered as stored after having undergone phase separation, transported and spread on agricultural soils thereby contributing to soil organic carbon sequestration. Since several authors reported that emissions from construction and decommissioning of AD plants were not significant (Bacenetti et al., 2013; Bacenetti and Fiala, 2015; Siddiqui et al., 2020), we did not consider them.
### 2.1.2. Direct biomass burial scenario

In the direct burial scenario, we assumed that exactly the same amounts of biomass that entered the digester would be directly applied to soils. We considered that livestock manure was first stored and then transported and spread on soils. Similarly, crop residues and catch and cover-crops were considered as buried directly in soils.

## 2.2. Substrate resources

The amounts of available biomass were estimated using the same approach as in Malet et al. (2023b). Manure and crop residues were extracted from the ELBA database which provided exploitable resources for each canton in France, using statistical data (mostly from agricultural census) and technical parameters (e.g., excretion ratios, allometric

coefficients) (Marsac et al., 2018). The different forms of manure (slurry, solid manure, droppings) were provided for cattle, poultry, equines, pigs, sheep, and goats. Residues of straw cereals, maize, rapeseed and sunflower were accounted for. The amounts of crop residues were corrected to account for harvest and handling losses as well as straw requirements for animal bedding, in line with the methodology detailed in Malet et al. (2023b). Cover-crop biomass was derived from the processing of Launay et al. (2021) data, as explained in Malet et al. (2023b). Launay et al. (2021) conducted a high-resolution modelling of cover-cropping at the national scale of France. We used the same data by simulating cover-crop (CC) production either in regions where it is mandatory by law ("Regulatory CC") or in a hypothetical option of massive generalization of cover-crops across arable cropping systems ("Generalized CC"). Finally, sewage sludge resources were estimated

<figure><img src="image_7.png" /><figcaption>Fig. 1. Boundaries of the anaerobic digestion scenario and its corresponding raw biomass direct burial scenario.</figcaption></figure>
from the wastewater database of the French Ministry of Ecological Transition (available at https://www.assainissement.developpement-durable.gouv.fr).

As mentioned, we considered two levels of cover crop biomass production. For each of these levels, the resources from the other substrate categories were added to cover-crop biomass estimates. Finally, we optimized the amounts of substrates to be anaerobically digested by considering a series of AD process constraints. For this purpose, we implemented an optimization approach in each sub-region to maximize biomethane production while maintaining the C/N ratio of substrate mixture below 30 and its dry matter (DM) content below 30 %. This led us to exclude part of the crop residue resource because of their too high dry matter or C/N ratio (see Malet et al. (2023b) for further details).

Overall, the considered substrate resources amounted to 31.2 million t DM and 45 million t DM in the “Regulatory CC” and “Generalized CC” options respectively (after mixture optimization based on C/N ratio and dry matter content of substrate mix, see Fig. 2). Note that the generalization of cover crops allowed for more crop residues to be digested thanks to dilution effect in the mixtures. Our spatially explicit approach enabled to consider the variability of substrate availability and technical feasibility of mixtures across cantons (see Fig. S1).

## 2.3. C & N balance calculation

Biogas production was simulated with the SYS-Metha biophysical model (Bareha et al., 2021). SYS-Metha predicted the carbon and nitrogen fluxes within the biogas systems as well as the amounts and

(Bareha et al., 2021; Malet et al., 2023a).

Soil carbon storage was simulated in both scenarios with the AMG model that simulates the changes in soil organic carbon stock on an annual time step, in response to cropping practices. In AMG, for each biomass applied to soils, only a part of the carbon supplied is stabilized and feeds the soil's active carbon pool (humification process, function of the isohumic coefficient k1, detailed for each biomass in Table S1). The remaining (1-k1) is directly mineralized and released as CO₂ to the atmosphere. The active soil carbon pool also undergoes a so-called secondary mineralization process. The rate of secondary mineralization (coefficient k2) depends on both the soil type (pH, clay content, lime content, C/N) and the climatic context (precipitation, evapotranspiration, temperature). Note that we considered a single 0.66 value for the k1 parameter of digestates (Levavasseur et al., 2020). AMG has been widely calibrated for French soil-climate and agricultural contexts (Andriulo et al., 1999; Clivot et al., 2019; Levavasseur et al., 2020).

For each canton, we estimated an average k2 using soil data from the GlobalSoilMap project (Mulder et al., 2016) – by assuming a C/N ratio of 12 for soil organic matter – as well as climate data from the Meteo-France database at 8 km resolution for the recent past climate (1990 to 2020). In this work, the changes in soil organic carbon stocks were simulated over a 31-year period, using canton- and year-specific values for k2 parameter. We also assumed that initial soil organic carbon stocks were similar in both scenarios and that substrate supply were at steady-state. We defined the net soil carbon storage as the annual change in soil organic carbon stock, as detailed in Eq. (1).

$$ \text{Net carbon storage}_{\text{Canton.Year.Scenario}} = \sum_{\text{Biomass}} \left( \text{Carbon flux applied to the soil}_{\text{Canton.Year.Scenario.Biomass}} \times k1_{\text{Biomass}} \right) - \left( \text{Soil Organic Carbon Stock}_{\text{Canton.Year}-1, \text{Scenario}} \times k2_{\text{Canton.Year}} \right) \quad (1) $$

characteristics of both biogas and digestate as well as digestate phase separation. The model has been evaluated on full-scale anaerobic digestion plants and demonstrated good predictive performances

Because the soil carbon and nitrogen cycles are in fact interrelated, we quantified the net amount of N available in soils by considering the

<figure><img src="image_8.png" /><figcaption>Fig. 2. Biomass resource for anaerobic digestion and direct biomass return to soil by substrate category and for both options of cover-crop (CC) production. Biomass resources are presented after mixture optimization based on C/N ratio and DM content of substrate mix.</figcaption></figure>
gross N input from soil biomass application and removing the amount of N immobilized in soil organic matter during humification. We also took into account that soil organic matter secondary decomposition releases nitrogen (see Eq. (2)).

practices in biogas plants aiming to minimize methane emissions and preserve the methanogenic potential of substrates. In the direct soil burial scenario, we assumed an average storage duration of 90 days for raw manure, which corresponds to standard practices in France. This value reflects a balance between longer durations due to field inaccessibility or legal restrictions during winter, and shorter durations in spring when spreading is possible shortly after manure collection. For digestates, we did not assume a specific storage duration but used generic IPCC emission factors (2019a). We considered best available management practices during digestate storage, including phase separation and covered storage with biomethane recovery. We also assumed that the CH4 leakage rate from the digester *stricto sensu* was 1 % according to the IPCC guidelines (IPCC, 2019a). We considered that digestates underwent phase separation and were covered during storage with residual biomethane recovery. Concerning the land application of digestates or raw biomasses, we assumed that they were buried/incorporated in soils in both scenarios. We hypothesized that the amount of energy generated or the net amount of nitrogen supplied fully displaced the equivalent amount supplied by the corresponding fossil-based product (i.e. substitution level = 100 %).

$$ \text{Net nitrogen supply}_{\text{Canton, Year, Scenario}} = \sum_{\text{Biomass}} \left( \begin{array}{l} \text{Nitrogen flux applied to the soil}_{\text{Canton, Year, Scenario, Biomass}} \\ + \text{Nitrogen flux secondary mineralization of soil organic matter}_{\text{Canton, Year, Scenario}} \\ - \text{Nitrogen flux immobilized during humification}_{\text{Canton, Year, Scenario}} \end{array} \right) \qquad (2) $$

With:

$$ \text{Nitrogen flux secondary mineralization of soil organic matter}_{\text{Canton, Year, Scenario}} = \frac{\text{Soil Organic Carbon Stock}_{\text{Canton, Year}-1, \text{Scenario}} \times k2_{\text{Canton, Year}}}{C_{/N} \text{ soil}} $$

$$ \text{Nitrogen flux immobilized during humification}_{\text{Canton, Year, Scenario}} = \frac{\sum_{\text{Biomass}} (\text{Carbon flux applied to the soil}_{\text{Canton, Year, Scenario, Biomass}} \times k1_{\text{Biomass}})}{C_{/N} \text{ soil}} $$

When net nitrogen supply was negative (i.e. when nitrogen supply from biomass application to soils was lower than nitrogen immobilized during humification), we considered that a mineral nitrogen supply was performed to avoid restricting carbon storage, thus accounting for GHG emissions from the production and application of these fertilizers. When nitrogen supply was not limiting (i.e. when nitrogen supply from biomass application to soils was higher than nitrogen immobilized during humification), nitrogen in excess was considered substituted to mineral fertilizer use. Due to the decomposition of soil organic matter, we also took into account dynamically the associated environmental losses of nitrogen (through nitrification/denitrification, volatilization and leaching).

## 2.4. GHG emission calculations

For each of the considered scenarios, we estimated the net GHG emissions as the sum of gross, negative and avoided emissions. Gross emissions were related to biomass storage and spreading, methane leakage from the digesters and to fossil energy use (for transport, technical heating, etc.). Negative emissions corresponded to net removal of CO2 from the atmosphere during carbon storage in soils. Avoided emissions corresponded to reductions in GHG emissions made possible by implementation of lower GHG-emitting practices for the supply of the same good or services (compared to a reference scenario). In our study, avoided emissions corresponded to the substitution of biogas-derived energy produced to fossil sources and the substitution of biomass-derived nitrogen to synthetic mineral fertilizers. Emission factors related to effluent and digestate management, cultivated soils and anaerobic digestion were gathered from well-referenced studies or from classical LCA databases (Base Carbone®, ADEME, n.d.; EMEP/EEA, 2016; Esnouf et al., 2021; IPCC, 2019a, 2019b; Koch and Salou, 2020; Vargas-Gonzalez et al., 2015; Wernet et al., 2016), as done in Malet et al. (2023a). Emission factors, assumptions, parameters and sources used are summarized in the Table S.2.

Note that we simulated optimal management practices for biogas plants. In the anaerobic digestion scenario, we assumed that livestock manures were stored for 8 days before entering the digester, following Vargas-Gonzalez et al. (2015). This short storage duration reflects good

sibility or legal restrictions during winter, and shorter durations in spring when spreading is possible shortly after manure collection. For digestates, we did not assume a specific storage duration but used generic IPCC emission factors (2019a). We considered best available management practices during digestate storage, including phase separation and covered storage with biomethane recovery. We also assumed that the CH4 leakage rate from the digester *stricto sensu* was 1 % according to the IPCC guidelines (IPCC, 2019a). We considered that digestates underwent phase separation and were covered during storage with residual biomethane recovery. Concerning the land application of digestates or raw biomasses, we assumed that they were buried/incorporated in soils in both scenarios. We hypothesized that the amount of energy generated or the net amount of nitrogen supplied fully displaced the equivalent amount supplied by the corresponding fossil-based product (i.e. substitution level = 100 %).

Soil carbon and nitrogen dynamics were estimated over 31 years, together with the associated carbon storage, direct and indirect soil N2O emissions and substitution of synthetic mineral fertilizers, and were presented either on a pluri-annual basis or as mean annual values. The other emission sources were considered unchanged over 31 years.

Emissions were converted to CO2 eq using the classical global warming potentials of 1 for CO2, 28 for CH4 and 265 for N2O (IPCC, 2013). Results were presented in absolute values (kt CO2 eq/year or Mt. CO2 eq/year) or in kg CO2 eq/ton of digested carbon per year, a relevant functional unit for analyzing GHG mitigation efficiency.

In order to determine the climate performance of AD compared to direct biomass burial, we calculated the difference in net GHG emissions between those of the AD scenario minus those of the biomass burial scenario. A negative difference meant that AD mitigated GHG emissions compared to direct burial of biomass in soils.

For comparisons in the discussion section, we provide an emission factor for biomethane (expressed in g CO2 eq/kWh injected) as the difference in net GHG emissions - for which we did not include substitution to fossil energy in the AD scenario - divided by the amount of biomethane injected into the grid.

## 3. Results

### 3.1. Anaerobic digestion is more effective in mitigating GHG emissions than direct biomass burial

Our results showed that biogas production performed more efficiently in mitigating GHG than biomass burial (Fig. 3). In the “Regulatory CC” option, biomass digestion with biomethane injection into gas-grid would reduce GHG emissions by 10.8 Mt. CO2 eq/year compared to direct burial in soils corresponding to 2.6 % of national GHG emissions or 13.3 % of agricultural emissions in France (considering annual GHG emissions of 418 Mt. CO2 eq and agricultural sector emissions of 81.2 Mt. CO2 eq (CITEPA, 2022)). Cogeneration performed less effectively than direct biogas injection into gas-grid, notably because of the highly nuclear, decarbonized electricity mix in France. Heat rate recovery was a
<figure><img src="image_9.png" /><figcaption>**Fig. 3.** Annual difference in net GHG emissions (in Mt. CO₂eq/year) between the AD minus the biomass burial scenarios at the national scale of France according to biogas end-use.</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>**Fig. 4.** Greenhouse gas net emissions from AD scenario compared to the direct burial scenario, for the biogas direct injection and the “Regulatory CC” options. Emissions have been standardized per ton of carbon entering each scenario. The crosses correspond to the net emissions for each scenario, i.e., the sum of gross positive, negative and avoided emissions.</figcaption></figure>

major determinant of GHG emissions for cogeneration systems. If heat is used efficiently (i.e. at 61 %), AD would mitigate GHG emissions by 9.0 Mt. CO₂eq/year. Low heat use, as observed in a sample of anaerobic digesters surveyed in France, would halve the climate benefits of AD

compared to direct biogas injection.

When expressed per t of carbon processed, the standardized difference in net GHG emission ranged from -679 kg CO₂eq/tC for direct injection (Fig. 4) to -348 kg CO₂eq/tC for cogeneration with low heat
recovery (see Fig. S.2). The lower GHG emissions observed in AD compared to direct biomass burial were due to (i) large substitution of fossil fuels (–766 kg CO₂ eq/tC in direct injection and –499 kg CO₂ eq/tC in cogeneration with 61 % heat used) (ii) and significantly lower CH₄ emissions during livestock effluent storage in the AD scenario. Note that these attenuated emissions were partly offset by AD process emissions (248 kg CO₂ eq/tC for injection and 94 kg CO₂ eq/tC for cogeneration) mainly as CH₄. In addition, we found lower carbon storage in soils in the AD scenario compared to direct biomass burial. As a result, more N was immobilized in soil organic matter in the direct burial scenario, thus limiting the N available to plants and the associated N fertilizer substitution effects compared to the AD scenario. Note however that these two opposite effects compensated each other and led to no difference in climatic benefit from (digested or not) biomass soil application between both scenarios.

Significant improvement in climate benefits from AD would arise if

cover-crop production were generalized, as in the “Generalized CC” option. The difference in net GHG emissions between AD minus the direct burial scenario would amount to 17.1 Mt. CO₂ eq/year in injection or 14.0 Mt. CO₂ eq in cogeneration, corresponding to 4.1 and 3.4 % of national GHG emissions, respectively (i.e. 21.0 and 17.3 % of agricultural sector emissions). Cover crop generalization would therefore help to mitigate 58 % more GHG emissions at the national level compared to CC production limited to regions where it is mandatory by law, as illustrated by the “Regulatory CC” option. The benefit of CC generalization would be achieved by (i) a seven-fold increase in cover-crop biomass and (ii) a slightly higher mitigation efficiency of AD (–751 kg CO₂ eq/tC compared to –679 kg CO₂ eq/tC in the “Regulatory CC” option, for injection, see details in Fig. S.3). In the “Generalized CC” option, the standardized CH₄ production was higher and the agronomic benefits related to the soil application of digestates vs. raw biomass were improved compared to those observed in the “Regulatory CC” option (i).

<figure><img src="image_11.png" /><figcaption>**Fig. 5.** Annual difference in net GHG emissions between the AD minus the direct burial scenario by canton, with biomethane injection, according to cover-crop generalization (panels A & B) and distribution of the difference by canton (panels C & D). For panels C and D, the vertical dashed blue line corresponds to the arithmetic average of the difference in net GHG emissions per canton.</figcaption></figure>
e. difference in C storage lowered and difference in substitution of mineral N fertilizers increased). However, this was partly balanced by a lesser benefit of AD in terms of emission abatement during biomass storage (the latter being due to livestock manure dilution in a vegetal-rich substrate mix).

## 3.2. A significant difference in net GHG emissions in both livestock- and crop-oriented farming systems depending on the level of cover crop production

Our results also showed that the difference in net GHG emission exhibited a strong spatial variability across cantons (std dev<sub>Regulatory CC</sub> = 3.88 kt CO<sub>2</sub> eq/canton [CV = 121 %] and std. dev<sub>Generalized CC</sub> = 5.25 kt CO<sub>2</sub> eq/canton [CV = 105 %] for biomethane injection). This variability

was mainly driven by the amount of substrate carbon processed per canton and by the intensity of GHG mitigation through anaerobic digestion compared to direct biomass burial standardized to the amount of carbon processed.

Our study showed that in the “Regulatory CC” option, GHG emission mitigation was significant in livestock regions, such as the west of France representing 54 % of the difference in net GHG emissions at the French level (Fig. 5A). The difference in net GHG emission was much greater in livestock-oriented cantons compared to crop-oriented cantons (Fig. S.4), with values reaching up to –2029 kg CO<sub>2</sub> eq/tC due to efficient mitigation of CH<sub>4</sub> emissions during manure storage in livestock regions (Fig. S.5). In contrast, in the “Generalized CC” option, AD climate benefits would clearly appear in the cereal-oriented regions of the Parisian basin due to their high cover-crop biomass production potential

<figure><img src="image_12.png" /><figcaption>**Fig. 6.** Net soil organic carbon storage in the AD and the direct biomass burial scenarios (panels A and B) and difference in net GHG emissions between the AD minus the direct burial scenarios over time (panels C and D), for both the Regulatory (panels A and C) and the Generalized (panels B and D) cover-crop options. The shaded areas around the curves correspond to ±the standard deviation around the mean.</figcaption></figure>
(Fig. 5B). Cumulatively, these crop-oriented cantons contributed to 64% of the evolution in net GHG emission difference at the national level between the “Regulatory CC” and the “Generalized CC” options.

The strong spatial structure of differences in net GHG emissions due to the high specialization of French agricultural systems was also identified when differences are expressed per hectare of arable land by canton, thereby removing the effect of the sub-national region surface variability (Fig. S.6).

### 3.3. The difference in carbon storage fades over time

Both biomass use scenarios resulted in net soil carbon storage (Fig. 6, panels A and B). However, our results showed that net carbon storage decreased over time and reached almost zero after 30 years, regardless of whether biomass was used for AD or for direct burial. Put differently, our simulations showed that soil organic carbon stocks reach new equilibriums in both biomass use scenarios, although not at the same level. Such equilibriums result from increased organic carbon stocks over time due to repeated carbon inputs, compensated by increased and carbon stock-proportional carbon losses through secondary mineralization. As a result, in the long term, no more soil carbon storage is expected for both biomass use scenario.

Our results also showed that, cumulated over 31 years and over the whole national territory, carbon storage was lower in the AD scenario compared to direct biomass burial. For instance, in the “Regulatory CC” option, AD would sequester 359 Mt. CO₂ eq in comparison to 445 Mt. CO₂ eq for the direct burial scenario. In the “Generalized CC” option, AD would store 449 Mt. CO₂ eq vs. 547 Mt. CO₂ eq for the direct biomass burial scenarios, with values significantly higher than under the “Regulatory CC” option. The difference in soil carbon storage between scenarios was mainly observed in livestock regions (Fig. S.7). As large fraction of manure carbon is stabilized in soils (due to higher potential to stabilize in soils compared to crop residues), direct burial of manure is more profitable for soil carbon storage than AD, which partly degrades the carbon to produce biogas.

Note that net carbon storage per ton of carbon input was slightly lower in the “Generalized CC” compared to the “Regulatory CC” option (Fig. 6, panels A vs B) due to higher share of cover-crop biomass in substrate mix, resulting in more biodegradable biomass returned to soils.

Finally, the difference in total net GHG emissions widened in favor of AD over time (Fig. 6, panels C and D), from -530 to -789 kg CO₂ eq/tC in the “Regulatory CC” option and from -631 to -838 kg CO₂ eq/tC in the “Generalized CC” option. This results from the fact that soil carbon storage - the main benefit of the direct burial scenario - is finite over time, unlike fossil fuel substitution. Over the long-term, biogas production is likely to provide substitution effects to fossil fuels, while the difference in soil carbon storage will tend to zero between both biomass use scenarios.

## 4. Discussion

Using a spatially explicit modelling approach coupled with fine estimation of agricultural substrate resources available in France, we compared net GHG emissions from agricultural biomass anaerobic digestion vs. direct burial in agricultural soils. To our knowledge, this is the first study (i) that has focused on such a large spatial scale while accounting for spatial soil and climate variability and (ii) that relied on both anaerobic digestion and soil organic matter carbon and nitrogen modelling.

Our study showed that biogas production was overall more effective in mitigating GHG emissions than direct biomass burial. Put differently, we found that biomethane resulted in 25.8–39.2 g CO₂ eq/kWh<sub>Low Heating</sub> Value if injected depending on cover crop availability - a reduction of 83 to 89 % compared to natural gas. Our results are lower than the 44.6 g CO₂ eq/kWh of biomethane estimated by Adelt et al. (2011), even

though their estimated 82 % savings compared to natural gas are consistent with our estimates or the 44 g CO₂ eq/kWh LHV provided by the French emission factor database (Base Carbone®, ADEME, n.d.). However, our biomethane emission factor was higher than the 23.4 g CO₂ eq/kWh LHV estimated for France by Vargas and Maurice (2020). The variability among studies in GHG emission savings achieved by AD reflects the large differences existing in methodology, assumptions, and data inputs. For instance, in the study of Adelt et al. (2011), biogas production is not compared to direct biomass burial in soils since authors only consider crops exclusively produced for energy purposes. Or again, the emission factor provided by the Ademe Base Carbone is derived from an attributional LCA, focusing only on the energy production function, i.e. not considering the impacts induced and avoided by the biogas sector compared to conventional biomass management.

The overall lower emissions in the AD scenario compared to direct biomass burial was mainly achieved by (i) substitution of biogas to fossil energy sources and (ii) lower CH₄ losses from manure during storage, a result often reported (Chadwick et al., 2011; Hijazi et al., 2016; Miranda et al., 2015). At the French national level, the wide variation among regions in differences in net emissions between scenarios was driven by the capacity to mitigate emissions during manure storage. This means that the more substrate mix is manure rich, the more AD can achieve GHG emissions savings by reducing storage duration. This important result was found thanks to our spatially explicit approach, without which we could not have identified the variability in GHG mitigation efficiency provided by AD (compared to direct biomass burial) within the French national territory and its main driver. Nevertheless, CH₄ losses from AD process remained a major gross emission source, partly offsetting the savings in emissions from livestock manure storage. Finally, we showed that AD scenario would lead to lower soil organic carbon storage than the biomass direct burial scenario. This result is not consistent with the few empirical studies that reported similar soil carbon storage between both scenarios (Béghin-Tanneau et al., 2019; Pastorelli et al., 2021; Thomsen et al., 2013; Zicker et al., 2020). Most of them are conducted in laboratory from incubation and show a stronger recalcitrance of digestates to mineralization compared to undigested substrates. However, they focus on specific substrates (corn residues (Chen et al., 2012), corn silage and sugar-cane sucrose (Béghin-Tanneau et al., 2019)) and on a limited number of soils. Results from medium to long-term field experiments show more contrasting results (Pastorelli et al., 2021). Such variability can be explained by the diversity of the pedoclimatic contexts in which the trials are conducted and the variety of substrates and their chemical compositions. Also, in these field or laboratory studies, one aspect remains unclear concerning the respect of mass balances, i.e. whether the amount of digestate (and more particularly of C) applied to soil corresponds to the amount that would have been strictly produced from the corresponding substrate(s). This inaccuracy is likely to affect the observed difference in soil C storage, a limitation that has been overcome in our study due to its strict C & N mass balance conservation. Finally, our results highlighted that the difference in net carbon storage between both scenarios fades in the long term. As a result, the climate benefits of AD are likely to improve over time due to the permanence of its energy substitution effects unlike finite carbon storage. To our knowledge, this result has never been reported in the literature, in particular because empirical or modelling studies about long-term effects of digestate application compared to undigested biomasses remain limited (Zicker et al., 2020).

Numerous implications can be drawn from this study. The findings contribute to better understanding of the impact of AD on GHG emissions in order to ensure sustainable development of the sector. This becomes even more necessary in the context of the bio-economy, which is leading to an increase in the use of biomass and the diversification of its use. This study has resulted in spatially explicit maps and datasets that can be used for local policy-making to support the AD plant investments at the local level. The resulting maps helped to identify the most favorable regions for the installation of biogas plants, i.e., those in
which it would be possible to produce significant energy amounts and to
massively reduce GHG emissions. However, these potentials will only be
reached if agricultural biomass is accessible, a major technical and
economical issue. From a methodological perspective, this study sug-
gests modelling of carbon and nitrogen transformation in both the
digester and in soils and especially the consideration of long-term pro-
cess dynamics. In this regard, this study provides a reproducible meth-
odology to evaluate exploratory scenarios of anaerobic digestion
massive development at large spatial scales.

Although we based our methodology on vast and widely used data-
bases as well as on validated models, we acknowledge that our work
suffers from limitations.

First, we address limitations arising from assumptions regarding
agricultural practices and biomass management. (i) Beyond theoretical
biomass availability, several operational and technological constraints
may hinder the effective collection of crop residues. Harvest and con-
ditioning of straw are highly sensitive to weather conditions, and rainfall
during the harvest period can prevent baling or cause significant delays.
Time constraints related to the cropping calendar — such as the need to
quickly sow the following crop or to comply with regulatory deadlines
for cover crop establishment — can reduce the capacity to mobilize
residues. Technical limitations also exist: for crops such as maize,
rapeseed, or sunflower, whose residues are typically left on the field,
appropriate machinery and harvesting logistics are not systematically
available at the farm level. In some cases, the technologies required to
recover and condition these types of residues are not yet mature or
economically viable. These factors must be considered when designing
realistic and resilient supply chains for anaerobic digestion. (ii) Addi-
tionally, our modelling approach deliberately assumed incorporation of
all organic amendments, including digestates, raw manures, and sewage
sludges, in both scenarios, as part of a “best technical practice” frame-
work. This allowed a like-for-like comparison, ensuring that observed
differences between the two pathways could be attributed to the
intrinsic characteristics of the biogas processes themselves, rather than
to variations in field application techniques, which are known to influ-
ence nitrogen losses and greenhouse gas emissions. By standardizing the
application method, we minimized potential confounding factors and
provided a clearer assessment of the effect of the biogas pathway on
nutrient cycling and emissions. This approach also reflects common
practices in French arable systems, where incorporation of digestates is
generally applied for agronomic efficiency and regulatory compliance.
(iii) Moreover, we acknowledge that transport distances may vary
locally within each region, especially where biomass is highly concen-
trated near biogas plants. In our study, however, we applied a region-
specific average round-trip distance calculated as the average distance
from the periphery to the centroid of each NUTS4 region. Although this
does not fully capture local heterogeneity, this conservative assumption,
using maximal average distances, results in transport-related green-
house gas emissions that remain very low. This is consistent with find-
ings from multiple life cycle assessments in agricultural supply chains
where transport emissions are often minor compared to total emissions.

Second, we highlight constraints associated with the assumptions
embedded in our modelling framework. (i) In our model, although ni-
trogen substitution effects were accounted for based on net nitrogen
inputs to the soil, nitrogen use efficiency (NUE) was not explicitly
considered. This simplification was motivated by the considerable
variability in NUE values across different fertilizer types, both organic
and mineral, as well as the lack of robust, context-specific data for
various nitrogen forms, including digestates. Explicitly integrating NUE
into emission models remains challenging due to its inherent variability
and the uncertainty it may introduce into emission estimates. We
acknowledge that this limitation could affect the precision of net
greenhouse gas emission calculations related to different nitrogen
sources. Future research should aim to refine these estimates by incor-
porating crop models or empirical NUE coefficients tailored to fertilizer
types and agronomic conditions, thereby enhancing the accuracy and

reliability of nitrogen-related emission assessments.

(ii) We also showed that carbon storage was a key variable in the
difference in net GHG emissions and its dynamics over time. However,
uncertainties remain on the biomass-specific isohumic coefficient 'k1',
especially for digestates, whose amending potential has been studied
more recently. The refinement of such coefficients would allow more
accurate GHG balance estimates to be provided.

Another limitation is that we did not account for soil carbon satu-
ration. When the mineral phase saturation is reached, most organic
carbon will accumulate as particulate organic matter instead of mineral-
associated organic matter, the former being less protected from miner-
alization than the latter. Therefore, it is likely that we overestimated
carbon storage in soils close to saturation. However, most French
cropland soils remain far from saturation (Martin et al., 2021), sug-
gesting that this overestimation remained limited. In addition, AD
remain more advantageous when carbon storage decreases. (iii) Finally,
although widely accepted, we assumed that energy from biogas would
fully substitute fossil energies. However, data on energy consumption
show that renewable energies do not displace fossil energies in their
entirety, but rather add to them at least partly (Marques et al., 2018;
York and Bell, 2019). In the “Regulatory CC” option, a partial or even
non-existent substitution of the biomethane produced to natural gas
would strongly reduce or even negate the climatic interest of biogas
production. With natural gas substitution levels of 50 % and 0 %, the
difference in net GHG emissions would be –295.8 and +87 kg CO₂ eq/tC
respectively, compared to –678.8 kg CO₂ eq/tC with full substitution.
More research efforts are needed to determine the degree to which
renewable energies substitute to fossil energy sources. These results also
showed that to be sustainable, the development of biogas sector must be
combined with effective measures to promote actual substitution rather
than additional use of the produced energy.

Third, we outline aspects not explicitly considered in the analysis. We did not account for emissions related to AD plant construction and dismantling. However, several studies have reported that these emissions are in fact negligible over the AD plant lifetime (Bacenetti and Fiala, 2015; Siddiqui et al., 2020). Lastly, we did not consider the potential valorization of the CO₂ fraction in biogas such as agricultural use for crops under greenhouses and algae, use in the food industry (gasi-fication, cooling), use in chemistry or its conversion into CH₄ by methanation, notably because these valorization options are at different stages of development (Batlle-Vilanova et al., 2019; Voelklein et al., 2019; Wu et al., 2021; Xu et al., 2021). These additional ways of using CO₂ from AD are likely to improve the climatic benefits provided by biogas production.

5. Conclusion

While the role of AD in defossilizing the energy sector is widely
acknowledged, its impact on GHG mitigation compared to direct
biomass burial remains poorly documented. Using a spatially explicit
modelling approach, we found that AD was overall more effective in
mitigating GHG emissions than burying biomass directly into soils. This
climate benefit from AD resulted mostly from fossil fuel substitution and
lower emissions during manure storage, the latter being prominent in
livestock regions. Cover crop generalization would significantly increase
biomethane production and mitigate larger amount of GHG emissions
due to the increased mobilization of biomass. Finally, we found that soil
carbon sequestration would be lower in the AD scenario compared to
direct biomass burial but this difference is likely to fade out over long-
term period. Because substitution effects to fossil energy will last, AD
is likely to hold long-term climatic benefits. Future research should
clarify the effects of AD on other impact categories such as water use,
biodiversity and soil quality.
### CRediT authorship contribution statement

N. Malet: Writing – review & editing, Writing – original draft, Visualization, Software, Resources, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. S. Pellerin: Writing – review & editing, Supervision, Project administration, Methodology, Funding acquisition, Conceptualization. T. Nesme: Writing – review & editing, Supervision, Project administration, Methodology, Funding acquisition, Conceptualization.

### Funding

This work was supported by the French Environment and Energy Management Agency (ADEME) and Bordeaux Sciences Agro (Univ. Bordeaux).

### Declaration of competing interest

The authors declare no competing interests.

### Acknowledgment

We warmly thank Romain Girault and Florent Levavasseur for their help in parameterizing the SYS-Metha and AMG models respectively. We gratefully acknowledge Camille Launay and Sylvain Marsac for their support in the use and processing of the agricultural substrate resource datasets. We would also like to thank Fabrice Béline and his collaborators for discussions about AD process constraints elaboration at national scale. This work was supported by the French Environment and Energy Management Agency (ADEME) and Bordeaux Sciences Agro (Univ. Bordeaux).

### Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.scitotenv.2025.180601.

### Data availability

Data will be made available on request.

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