# Evaluation of global biotic resource consumption against absolute boundaries

Gonzalo Puig-Samper, Natacha Gondran, Julie Clavreul, Anne Prieur-Vernat, Mikolaj Owsianiak

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Gonzalo Puig-Samper, Natacha Gondran, Julie Clavreul, Anne Prieur-Vernat, Mikolaj Owsianiak. Evaluation of global biotic resource consumption against absolute boundaries. Resources, Conservation and Recycling, 2026, 226, pp.108651. 10.1016/j.resconrec.2025.108651. hal-05333749

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# Evaluation of global biotic resource consumption against absolute boundaries

Gonzalo Puig-Samper<sup>a,b,*</sup> <sup> ID </sup>, Natacha Gondran<sup>a ID</sup>, Julie Clavreul<sup>b</sup>, Anne Prieur-Vernat<sup>b</sup>, Mikołaj Owsianiak<sup>c,d ID</sup>

## ARTICLE INFO

### Keywords:

Absolute environmental sustainability assessment (AESA)

Input-output modeling

Life-cycle assessment (LCA)

biotic resources

Biomass

## ABSTRACT

Human activities rely on biotic natural resources to provide products and services necessary to meet human needs. This instrumental value is captured in life-cycle assessment through the “Natural resources” Area of Protection. Although several absolute boundaries have been proposed to safeguard biotic resources, it remains unclear whether these resources are currently used at a sustainable rate. This study addresses this question by evaluating global biotic resource consumption from 1995 to 2011 against suggested biotic resource boundaries, relying on Exiobase projections to assess the evolution beyond 2011. The assessment couples absolute boundaries with life-cycle impact assessment (LCIA) methods, enabling evaluation using consistent LCIA metrics. Five absolute boundaries and four LCIA methods were adapted to the Exiobase multiregional input-output model. Results show that most of existing boundaries are already transgressed, regardless of whether mass-based or LCIA-based control variables are applied. The wide range and normative nature of existing boundaries emphasize the need for harmonized, science-based boundaries to ensure the sustainable use of biotic resources.

## 1. Introduction

Biotic natural resources contribute to human welfare by providing services related to nourishment, energy, health, shelter, and culture (Cardinale et al., 2012). The reliance on biotic resources is expected to increase due to the urgent need to lower greenhouse gas emissions (Daioglou et al., 2019) and ensure food security for nearly 10 billion people by 2050 (Willett et al., 2019). Overexploitation of certain biotic resources led to their reduced availability in some regions (Shelton et al., 2006), but the impact of increasing global demand for biotic resources against absolute boundaries for biotic resources remains unknown. This work aims to address this gap by evaluating global consumption of biotic resources from 1995 to 2011 against absolute boundaries proposed for these resources. The period 2011 to 2022 is included relying on Exiobase projections.

The concept of absolute boundaries for natural resources is rooted in the need to safeguard their instrumental value for humans, as natural

resources are a necessary condition for humans to meet their basic needs (Rao and Min, 2018). Introducing these boundaries in absolute environmental sustainability assessment (AESA) aims to determine whether the consumption levels of natural resources are low enough to guarantee their continued instrumental role (Bjørn et al., 2025, 2019). Although several boundaries have been proposed for natural resources, there is no consensus on their values, resource coverage, indicators used, or underlying safeguard values (Dudka et al., 2025; Thomassen et al., 2024). Because of these gaps and the unclear environmental relevance of existing boundaries, we use the term absolute boundary to denote fixed and quantifiable sustainability thresholds, acknowledging they are not necessarily biophysically derived like carrying capacities. Most proposed boundaries focus on natural resources, assuming substitutability of abiotic and biotic resources (Bringezu, 2015). However, protecting the instrumental value of resources requires addressing the processes that regulate the availability of individual natural resources (Thomassen et al., 2024), and recognizing the limited substitutability between

<footnote><sup>a</sup> Mines Saint-Etienne, CNRS, Univ Jean Monnet, Univ Lumière Lyon 2, Univ Lyon 3 Jean Moulin, ENS Lyon, ENTPE, ENSA Lyon, UMR 5600 EVS, Institut Henri Fayol, F-42023, Saint-Etienne, France</footnote>

<footnote><sup>b</sup> ENGIE Lab CRIGEN, 93240 Stains, France</footnote>

<footnote><sup>c</sup> Section for Quantitative Sustainability Assessment, Department of Environmental and Resource Engineering, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark</footnote>

<footnote><sup>d</sup> Centre for Absolute Sustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: g.puigsamper@emse.fr (G. Puig-Samper).</footnote>
abiotic and biotic resources (Ehrlich and Mooney, 1983). The current debate mainly revolves around setting boundaries for abiotic resources (Dudka et al., 2025; Thomassen et al., 2024), with very few studies addressing boundaries for biotic resources (Bringezu, 2015).

Introducing absolute boundaries for biotic resources in AESA can build on previous efforts in the field of life-cycle assessment (LCA), where the instrumental value of natural resources for humans is represented by the “Natural Resources” Area of Protection (AoP) (Life Cycle Initiative, 2019). Using life-cycle impact assessment (LCIA) methods to quantify absolute boundaries for biotic resources allows absolute boundaries and environmental impacts to be expressed at the same location in the impact pathway, making them comparable (Bjørn et al., 2019). LCIA-based control variables enable consideration of the processes controlling biotic resources provision, such as their renewability (Crenna et al., 2018). The use of LCIA-based control variables to quantify absolute boundaries for resources was previously explored for abiotic resources using the Environmental Footprint LCIA method (Sala et al., 2020). More recently, the literature has discussed the limited applicability of LCIA methods for abiotic resources in AESA (Bendahmane et al., 2024) and the infancy of available absolute boundaries (Dudka et al., 2025). Less attention has been paid to biotic resources, possibly due to their lower inclusion in LCA studies (Klinglmair et al., 2014).

Biotic resources are directly used by human activities and support environmental processes useful to humans (Swart et al., 2015). In LCA, biotic resources generally refer to those harvested from nature, in contrast to those obtained from human-made systems (Klinglmair et al., 2014). The interpretation of biotic resources varies among LCIA methods, which locate differently the frontier between biosphere and technosphere, leading to various resource coverage. For instance, some methods argue that inputs from nature, such as soil, are the biotic resources to be protected when producing resources in the technosphere (Sonderegger et al., 2017). The diverse services provided by biotic resources has led to the development of several LCIA methods with different perspectives (Schulze et al., 2020). Their different considerations and modeled impact pathways underscore the need to further explore the characterization of biotic resources in LCIA and their coupling with absolute boundaries in AESA. Harmonizing absolute boundaries and environmental impacts by using a common control variable enables their comparison, paving the way for their integration into AESA.

The objective of this work is to evaluate global biotic resource consumption in relation to proposed boundaries for biotic resources. For this purpose, we first assessed the suitability of existing LCIA indicators as control variables for biotic resources consumption in AESA. We focused on combining existing boundary proposals with LCIA methods to derive quantitative boundary estimates using various control variables. Global biotic resource consumption from 1995 to 2022 was analyzed using the Exiobase multi-regional input-output (MRIO) model, which enabled the harmonization of absolute boundaries and environmental impacts. The results of this study show the exceedance of most of the existing boundaries, regardless of the used control variable. The findings also highlight the need for a clearer definition of boundary safeguard values, and the integration of control variables that represent the impact pathways leading to a threat of the selected safeguard value.

## 2. Methodology

### 2.1. Overview of the methodological approach

Coupling absolute boundaries with LCIA methods requires disaggregating absolute boundaries into covered resources to subsequently map them to characterization factors (CF) used in LCIA methods. However, absolute boundaries often lack quantitative detail for each resource and do not clearly specify their resource coverage.

The first step of the methodological approach involved reviewing

available absolute boundaries for biotic resources and disaggregating them into covered resources (Fig. 1). The review was conducted using a framework to define environmental boundaries in AESA (Vea et al., 2020). The identified resources were then mapped to Exiobase v3.8.2 satellite accounts (Stadler et al., 2018). Exiobase was chosen because both it and the boundaries are based on statistics from the Food and Agriculture Organization (FAO) (Food and Agriculture Organization of the United Nations, 2024).

Disaggregating absolute boundaries into resources allowed filtering of the retrieved LCIA methods to ensure adequate coverage of the resources protected by the boundaries (step ii). The retained LCIA methods were adapted to Exiobase by mapping LCA elementary flows to Exiobase satellite accounts (step iii).

In step iv, global annual biotic resource consumption from 1995 to 2022 was quantified. Adapted LCIA methods were applied to calculate the impact using various indicators, ensuring alignment between biotic resource coverage in LCIA methods and absolute boundaries.

In step v, absolute boundaries were quantified using mass-based indicators and characterized using the available LCIA methods. This approach is the same used to build normalization factors for LCA (Crenna et al., 2019), but exclusively based on Exiobase. Applying LCIA methods to mass-based absolute boundaries captures changes in supply chains since some CFs are spatially resolved. Therefore, absolute boundaries were modeled in Exiobase maintaining the proposed mass-based extraction levels of biotic resources while considering more updated supply chains.

In step vi, the impact of global annual biotic resource consumption was related to characterized absolute boundaries. This comparison was conducted annually, considering implemented boundaries and LCIA methods. The comparison was made using the absolute sustainability ratio (ASR), as commonly applied in AESA studies. The ASR is defined as the ratio between the environmental impacts of a system and its assigned share of the boundary (Ryberg et al., 2018).

### 2.2. Review and disaggregation of boundaries (step i)

Table 1 presents an overview of absolute boundaries retrieved from the literature. The screening of boundaries, including biotic resources and defined at a global level, was based on previous literature reviews (Bringezu, 2015; Dudka et al., 2025). These boundaries were evaluated using a framework for defining environmental boundaries, which assesses five dimensions: environmental sustainability objective of the boundary, boundary principle, impact and accepted levels, scientific estimate, and uncertainty principle (Vea et al., 2020). Table S.26 in the Supplementary Information (SI-1) provides the complete assessment.

The retrieved boundaries reflect three types of environmental sustainability objectives: (i) protect nature to ensure human welfare (Bringezu, 2015; UNEP, 2011), (ii) present various safeguard values related to access to resources, human welfare and environmental sustainability (Dittrich et al., 2012), or (iii) argue that Holocene-like conditions should be protected (Buczko et al., 2016). Protecting nature to ensure human welfare underscores the instrumental value of biotic resources (Bringezu, 2015; UNEP, 2011). However, the other environmental sustainability objectives are either disconnected from protecting biotic resources for humans (Buczko et al., 2016), or present various safeguard values whose relationship remains unclear (Dittrich et al., 2012).

The retrieved boundaries share a common rationale regarding the boundary principle and scientific estimate. Boundaries set limits to the annual mass extraction of biotic resources in a target year based on resource consumption in a reference year. This principle does not align with any of the classified perceptions of the Earth system (Vea et al., 2020), but rather constitutes a normative reduction. Whether these consumption levels are sufficient to protect biotic resource instrumental role remains unanswered. In addition to the global limits proposed, all boundaries consider the convergence of per capita biotic resource
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Overview of the followed methodological approach. *Includes the databases used by literature sources to quantify original boundaries (Krausmann et al., 2009; UNEP IRP, 2024). Databases are indicated once for simplicity. LCIA: Life-cycle impact assessment, UN: United Nations.</figcaption></figure>

consumption. Global limits are either distributed equally among the future global population (Bringezu, 2015; Dittrich et al., 2012), or adjusted to match the per capita consumption levels of industrialized countries at the reference year (UNEP, 2011). For instance, the boundary proposed by the UNEP Half scenario is based on halving 2000 per capita consumption in industrialized countries.

Regarding scientific estimates, all boundaries use mass-based control variables such as raw material consumption (RMC) or total material consumption (TMC). All boundaries aggregate biotic and abiotic resources into a single estimate, except Bringezu (2015), which provides a separate boundary for biotic resources. Only RMC is modelled in this work to maintain consistency with the other boundaries. One of the sources (Dittrich et al., 2012) additionally proposed boundaries based on direct consumption using the domestic material consumption (DMC). These were excluded from Table 1 because they are explicitly based on equality and improvements in resource productivity.

Boundaries cover biotic resources from nature (biosphere) as well as those obtained from human-made systems (technosphere). However, wild fish and marine animal catches are not considered in one of the boundaries (UNEP, 2014, 2011). The boundary proposed by Buczko et al. (2016) was excluded from the methodological approach since it does not specify covered biotic resources. The disaggregation of the boundaries was based on information provided by the sources and referenced databases (Section S3.1, SI-1).

## 2.3. Review and filtering of LCIA methods (step ii)

LCIA methods assessing the “Natural Resources” AoP and including biotic resources were gathered from literature reviews (Crenna et al., 2018; Klinglmair et al., 2014; Sonderegger et al., 2017; VanderWilde and Newell, 2021) and LCIA manuals (Finkbeiner, 2016; Lindeijer et al., 2002; Swart et al., 2015). The complete list of the 19 identified methods is available in Table S.27, SI-1. The reviewed LCIA methods use indicators located at different stages of the impact pathway, ranging from resource aggregation on a common basis to surplus cost or energy required for extraction. The indicator location is used to classify LCIA

methods into: resource accounting methods (RAM), midpoint, and endpoint (Swart et al., 2015).

Although RAM methods are close to inventory results, they aggregate resources extraction based on a common unit, such as energy or exergy. RAM methods offer the broadest flow coverage but are less relevant as indicators for quantifying the boundary since they are located at the very beginning of the impact pathway (Persson et al., 2022; Vea et al., 2020). These methods aggregate natural resources (biotic and abiotic) without accounting for the actual vulnerability of the covered biotic resources or their substitutability (Klinglmair et al., 2014).

Midpoint indicators capture these characteristics by modeling two impact pathways leading to biotic resource depletion: (i) reduction of biotic resources stocks, and (ii) decreased biotic production potential (Alvarenga et al., 2015). The first pathway is represented by LCIA methods based on use-to-availability ratios, such as BIRD (Bach et al., 2017), distance-to-target approaches, such as the Ecological scarcity method (Frischknecht et al., 2021, 2009), and the joint consideration of renewability, exploitation and vulnerability in the naturally occurring biotic resources method (Beylot et al., 2020; Crenna et al., 2018). Decreased biotic production potential represents changes in net primary productivity (NPP) due to land use. It is assessed by LCIA methods comparing NPP to potential NPP (Langlois et al., 2014), human appropriation of the net primary productivity (HANPP) from land occupation (Alvarenga et al., 2015; Préat et al., 2018), and changes in soil organic carbon (Brandão and Milà i Canals, 2013).

## 2.4. Mapping and adaptation of LCIA methods to Exiobase (step iii)

LCIA methods were mapped to absolute boundaries to ensure they can be used as metrics to protect the environmental sustainability objective of the boundary (Bjørn et al., 2019). The mapping between LCIA methods and boundaries was made considering methods' theoretical coverage, as some resources can be covered by LCIA methods through other types of flows, such as land occupation flows (Dewulf et al., 2007).

Selected LCIA methods must: (i) follow the biosphere-technosphere
<table><caption>**Table 1**
Overview of the identified absolute boundaries for biotic resources.</caption><thead><tr><th>Source</th><th>Reference and target year</th><th>Change relative to reference year</th><th>Principle</th><th>Biotic resources coverage</th></tr></thead><tbody><tr><td>(UNEP 2011, 2014)</td><td>Ref.: 2000 Targ.: 2050</td><td>BaU scenario: Freeze RMC per capita at 2000 levels<br>Half scenario: Reduce 2000 RMC per capita by half<br>Tough scenario: Freeze 2000 global extraction</td><td>Contraction and convergence of RMC per capita.<br>Tough scenario based on global reduction</td><td>Crops, crop residues, wood, and grazed biomass and fodder crops according to the used database (Krausmann et al., 2009)</td></tr><tr><td>(Dittrich et al., 2012)</td><td>Ref.: 1992<br>Targ.: 2030</td><td>Rio scenario: Freeze global extraction at 1992 levels</td><td>Rio scenario based on global reduction. Convergence considered.</td><td>Crops, crop residues, wood, grazed biomass and fodder crops, and fishery (wild catch) according to the used database (UNEP IRP, 2024)</td></tr><tr><td>(Bringezu, 2015)</td><td>Ref.: 2000<br>Targ.: 2050</td><td>Freeze RMC and TMC<sup>a</sup> at 2000 levels</td><td>Global reduction of biotic resources extraction. Convergence considered.</td><td>Crops, crop residues, grazed biomass and fodder crops, forestry and fishery (wild catch) according to the used database (UNEP IRP, 2024)</td></tr><tr><td>(Buczko et al., 2016)</td><td>Ref.: 1970<br>Targ.: 2030</td><td>Limit global TMC to 45 Gt (biotic and abiotic)</td><td>Half current TMC to return to 1970's level</td><td>Not explicitly stated</td></tr></tbody></table>

BaU: Business-as-usual, DMC: Domestic material consumption, Ref: Reference year, RMC: Raw material consumption, Targ: Target year, TMC: Total material consumption. <sup>a</sup> RMC is used to quantify used biotic resources, and TMR includes used and unused biotic resources. Although the author mentions an additional boundary based on halving 2000 RMC, it is discarded in the article's discussion.

<table><caption>**Table 2**
Selected LCIA methods for the assessment of biotic resources.</caption><thead><tr><th>LCIA method</th><th>Description</th><th>Indicator</th><th>Category</th><th>Coverage in Exiobase<sup>a</sup></th></tr></thead><tbody><tr><td>CEENE<br>(Dewulf et al., 2007)</td><td>Quantifies the cumulative exergy that is no longer available to sustain ecosystem processes</td><td>Cumulative exergy extracted from the natural environment (CEENE)</td><td>RAM</td><td>Cropland (13), and infrastructure land (1)</td></tr><tr><td>Solar Energy Demand (Rugani et al., 2011)</td><td>Quantifies the solar energy required to produce a good or a service</td><td>Solar Energy Demand (SED)</td><td>RAM</td><td>Cropland (13), forest area (2), infrastructure land (1), other land use (1), permanent pastures (3)</td></tr><tr><td>NPP-based or CEENE 2.0<br>(Alvarenga et al., 2013)</td><td>Quantifies the exergy extracted from the natural system, and deprived exergy in human-made systems due to land occupation</td><td>Exergy extracted from the biosphere +<br>Deprived net primary productivity (NPP) for land use at human-made systems</td><td>RAM</td><td>Cropland (13), fishery (3), grazed biomass (1), infrastructure land (1), and wood (4)</td></tr><tr><td>HANPP<sub>LUC</sub>-based<br>(Alvarenga et al., 2015)</td><td>Evaluates the impacts of land use on biomass production by accounting for the change in NPP caused by land use</td><td>Midpoint: HANPP<sub>LUC</sub> (land use change);<br>Endpoint: Backup technology for seaweed production (cost)</td><td>Midpoint and endpoint</td><td>Cropland (13), infrastructure land (1), and permanent pastures (3)</td></tr></tbody></table>

<sup>a</sup> Number of Exiobase satellite accounts for which non-null characterization factors are provided by the method. CEENE: Cumulative exergy extracted from the natural environment, EPS: Environmental Priority Strategies, HANPP<sub>LUC</sub>: Human appropriation of net primary productivity due to land occupation, NPP: Net primary productivity, RAM: Resource accounting method.

frontier definition of the boundary, (ii) not be limited to a specific type of biotic resource, and (iii) provide a minimum number of CFs for resources covered by the boundary. The biosphere-technosphere frontier definition refers to whether the LCIA method and absolute boundary have the same perspective regarding the coverage of biotic resources harvested from nature and obtained from human-made systems (Alvarenga et al., 2015). Resource coverage is a challenge for biotic resources because some LCIA methods provide CFs for elementary and intermediate flows (i.e., within the technosphere) (Heijungs, 2024), which are not yet available in life-cycle inventory databases or MRIO models (Crenna et al., 2018). The lack of inventoried flows led to a prevalence of RAM methods being implemented in this work (Table 2).

Details on the mapping and implementation of LCIA methods in Exiobase are provided in **Section S2, SI-1. Region-specific, biomass-generic** CFs were implemented for the HANPP<sub>LUC</sub>-based method. However, the influence of region-specific, biomass-specific CFs was tested for boundaries characterization (**Section S4, SI-1**). This sensibility analysis responds to the high variability among crops observed in the original CFs (Alvarenga et al., 2015).

## 2.5. Quantification of global biotic resource consumption (step iv)

The global biotic resource consumption from 1995 to 2022 was quantified using the input-output methodology (Leontief, 1970) via the product-by-product version of Exiobase (Stadler et al., 2018). The python package pymrio v0.5.4 was used to manipulate Exiobase (Stadler, 2021). Biotic resource consumption using a consumption-based accounting approach (D<sup>CBA</sup>) was quantified as a the sum of the resource

use induced by a final demand (y) and the resource use allocated to final consumption (F<sup>y</sup>)(Eq. (1)).

$$D^{CBA} = SLy + F^{y} \qquad (1)$$

where L denotes the Leontief matrix, y the final demand matrix, and S the matrix containing the factors of production coefficients. Satellite accounts can refer to environmental burdens, resource use, or other socioeconomic extensions. The product SLy represents the direct and indirect biotic resource consumption induced by a given final demand.

The production (E<sup>PBA</sup>) and consumption-based impact (E<sup>CBA</sup>) of resource consumption were calculated as given by Eq. (2) and Eq. (3), respectively. Production-based impacts were calculated as the element-wise product of the characterization matrix (C) and the satellite account matrices representing sectoral (F) and final consumption (F<sup>y</sup>) burdens. Consumption-based impacts were derived by applying CFs to the total biotic resource use estimated in Eq. (1).

$$E^{PBA} = C \odot (F + F^{y}) \qquad (2)$$

$$E^{CBA} = C \odot D^{CBA} \qquad (3)$$

## 2.6. Quantification of boundaries (step v)

After disaggregation into covered biotic resources, absolute boundaries were quantified using Exiobase. Eq. (4) estimates the global biotic resource consumption at the reference year of boundaries (D<sup>CBA</sup><sub>ref</sub>).
$$D_{\text{ref}}^{\text{CBA}} = S_{\text{ref}}L_{\text{ref}}y_{\text{ref}} + F_{\text{ref}}^{y} \quad (4)$$

where $L_{\text{ref}}$ is the Leontief matrix, $y_{\text{ref}}$ the final demand matrix, $S_{\text{ref}}$ the matrix containing the factors of production coefficients, and $F_{\text{ref}}^{y}$ the matrix of biotic resources extraction linked to final consumption at the reference year ref. The matrices used correspond to the Exiobase system at the boundary references years, either 2000 or 1992. The value for 1992 was extrapolated from a linear regression between estimates from Exiobase and the original study (Dittrich et al., 2012). All Exiobase estimates were validated by comparison with the boundary values gathered from the literature (Table S.19, SI-1).

The retrieved absolute boundaries are based on the convergence of per capita resource consumption between regions projected for 2030 or 2050. Boundary values, calculated in Eq. (4), were distributed among Exiobase regions based on proposed convergence pathways and population projections (United Nations, Department of Economic and Social Affairs, Population Division, 2024). These pathways promote a shift in consumption towards regions with low per capita consumption in the reference year and high expected population growth. Methodological details are provided in Section S3.1, SI-1.

The distributed regional boundaries were used to model absolute boundaries based on 2011 supply chains – the modeling year – since it is the latest year based on observations in Exiobase v3.8.2. The scenario reflects boundary mass extraction values, while considering convergence pathways, and the 2011 supply chain structure. Since boundaries are set for 2030 and 2050, their characterized values may evolve with future changes in consumption patterns, trade, and technological advancements (e.g., crop yield gains), which were not considered in this modeling. As such, the obtained boundary values and compliance estimates should be interpreted within the context of 2011 supply chain conditions.

Eq. (5) models this “what if” scenario in Exiobase (Wiebe et al., 2018), where $y'$ is the modified final demand matrix, $F^{y}$ the matrix of resource use due to the modified final demand, and $D_{m}^{\text{CBA}}$ the matrix of total biotic resource consumption in the modeling year $m$. The sum of all elements in $D_{m}^{\text{CBA}}$ corresponds to the mass-based absolute boundary for biotic resources.

$$D_{m}^{\text{CBA}} = S_{m}L_{m}y' + F^{y} \quad (5)$$

Absolute boundaries were characterized using the selected LCIA methods to enable comparison with characterized impacts. The characterized absolute boundary ($E_{m}^{\text{CBA}}$) was calculated using the characterization matrix (C) of each LCIA method, as shown in Eq. (6).

$$E_{m}^{\text{CBA}} = C \odot D_{m}^{\text{CBA}} \quad (6)$$

## 2.7. Comparison of impacts to boundaries (step vi)

Annual impacts of global biotic resource consumption were compared to the characterized boundaries over the assessment period 1995–2022. The ASR, defined as the ratio of the impact over the absolute boundary, was calculated per year. Each year, an ASR value was calculated for every combination of boundary and LCIA method, resulting in five ASR values per boundary per year.

# 3. Results and discussion

## 3.1. Evolution of global biotic resource consumption

Fig. 2a shows the evolution of biotic resources consumption during the period 1995–2022 relative to UNEP boundaries, using UNEP’s resource coverage. Global consumption of biotic resources increased by a factor of 1.3 between 1995 and 2011, and by 1.7 when including Exiobase projections to 2022. By 2011, consumption had already exceeded the most ambitious 2050 boundary, with projections

indicating transgression of the Half scenario boundary by 2022.

Consumption is dominated by China, India, and the Africa rest-of-the-world region (WF), which together accounted for 36 % of global consumption in 2011 from a consumption-based accounting perspective. This is mainly driven by population growth, as per capita consumption remains significantly lower than in industrialized countries (Fig. 2b, 2c). For example, in 2011, per capita consumption ranged from 1.6 t/capita in India to 15.3 t/capita in Luxembourg.

Post-2011 projections suggest decreasing differences in median per capita consumption, but widening the overall range. The ratio between the lowest and highest per capita consumption doubled from 11 in 2011 to 22 in 2022. Comparing these values against UNEP’s convergence-based targets (Table S.20, SI-1) illustrates the principle of common but differentiated responsibilities. For instance, Luxembourg would need to reduce its 2022 per capita consumption by a factor of 5 to meet the least ambitious boundary (4.83 t/capita) by 2050, while the WF region consumed only 34 % of that target.

Boundaries convergence pathways and population forecasts, imply increased consumption in some developing regions, while industrialized countries must reduce theirs. Similar patterns were found for other boundaries (Table S.21, SI-1), highlighting regional disparities in responsibility to meet global limits.

## 3.2. Assessment considering alternative control variables

LCIA-based control variables agree with mass-based consumption in showing boundary transgression. The most ambitious boundaries were already exceeded in 2011, regardless of the indicator (Fig. 3). Projections suggest that the boundary proposed by the Half scenario was crossed in 2022, except for HANPP$_{LUC}$, while the boundary proposed by the BaU scenario remains respected.

All control variables show a gradual increase in ASR until 2011. The similar trend observed among LCIA-based control variables is attributed to the prevalence of exergy-based methods, which are variably correlated with land use. Post-2011 impacts rise sharply based on Exiobase projections, which rely on trade and macroeconomic data (EXIOBASE Consortium, 2021). These results should be interpreted cautiously and used to assess biotic resource consumption trends.

The ranking of control variables used to calculate the ASR varies by boundary (Fig. 3) due to differences in convergence pathways. These pathways reshape final demand across regions, altering product demand and boundary values. For instance, reduced demand for flows with high CFs leads to lower boundary values. This highlights the sensitivity of LCIA-based boundaries to resource type and origin, and the limitations of current modeling, which does not consider future changes in consumption, trade, or technology advancements.

## 3.3. Comparison of LCIA-based control variables

Introducing LCIA-based control variables shift environmental impacts from Asia (China, India, WA region), as observed for extracted mass, to the WF region. All LCIA methods identify the WF region as a hotspot in 2011 from a production-based approach (Fig. 4), though regional distributions vary due to methodological differences, particularly in resource coverage and position of the biosphere-technosphere frontier.

NPP-based and CEENE methods show similar distributions, emphasizing cropland occupation in WF, WA, India, and the US. However, regionalized CFs based on natural NPP alter impact shares for regions with low and high natural NPP. For example, the Middle East RoW region (WM) contribution drops 70 % compared to CEENE due to low natural NPP (Table S.9, SI-1), while Brazil’s share rises from 5 % to 9 %. The NPP-based method inclusion of biotic resources harvested from the biosphere, specifically grazed biomass, also contributes to rise the share in certain regions. This effect is principally observed for the America RoW region (WL), where the 52 % increase primarily comes from
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Evolution of biotic resource consumption per Exiobase region (a) and per capita (b, c) compared to UNEP boundaries. The comparison is made using UNEP flow coverage and clusters for regions (Table S.1, SI-1). Data points correspond to the median per capita consumption of biotic resources. The lower and upper tops of the whiskers are drawn at the 25th and 75th percentiles, respectively. Post-2011 results are based on Exiobase projections. WA: RoW Asia-Pacific, WE: RoW Europe, WF: RoW Africa, WL: RoW America, WM: RoW Middle East.</figcaption></figure>

grazing (75 %).

The HANPP<sub>LUC</sub>-based method yields a different distribution across regions by considering actual NPP. It reduces impacts in regions with low natural NPP and high actual NPP, such as the WM region, due to the low or even negative CFs in these regions (Table S.17, SI-1). This explains the negative contribution observed for the WM region. The same effect appear for China and the EU, although negative contributions are offset by other elementary flows, or positive CFs for some countries in the case of the EU. Conversely, the WF region shows high impacts (28 %) due to high natural and low actual NPP.

The SED method outlines a more heterogeneous distribution due to its non-differentiation between natural and human-made systems, being completely correlated with land use. Forest and other land uses

represent 61 % of the global impact, with rising impacts in Norway and Canada. Still, environmental impacts largely occur in WF, WA, WL regions and Russia.

Negative results from the HANPP<sub>LUC</sub>-based method indicate that land produces more biomass than in its reference state, implying a positive environmental impact. However, this evidences a key limitation of selected LCIA methods: focusing on availability overlooks biotic resource-specific aspects (e.g., vulnerability), potentially favoring intensive land use over conservation. Consequently, failing to protect the instrumental role of biotic resources, as vulnerable resources may be depleted, while the indicator still reflects a positive environmental impact.

These limitations become more evident when applying region-
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Annual global biotic resource consumption compared to proposed boundaries using five different control variables. Dashed lines from 2011 onwards indicate results based on Exiobase projections. Horizontal dashed lines represent the most ambitious boundary of each source (ASR=1). CEENE: Cumulative exergy extracted from the natural environment, HANPP<sub>LUC</sub>: Human appropriation of the net primary productivity, NPP: Net primary productivity, SED: Solar energy demand, UNEP: United Nations Environment Programme.</figcaption></figure>

specific, biomass-specific CFs to quantify boundaries under the HANP-
P<sub>LUC</sub>-based method (Section S4, SI-1). The predominantly negative CFs
yield negative boundaries, implying that biotic resource extraction is
limited by a positive environmental impact (Table S23, SI-1). For
example, sugar cane shows the highest environmental benefits, para-
doxically having been identified as a key driver of deforestation
(Obidzinski et al., 2015). Such findings challenge the suitability of
RAM-derived control variables for protecting the instrumental role of
biotic resources.

**3.4. Influence of accounting approaches and product contributions**

Using a consumption-based approach redistributes regional re-
sponsibilities. Across LCIA methods, except SED, the EU becomes the
second largest contributor in 2011 after WF (Fig S.3, SI-1). Its contri-
bution to global impact ranges between 6 % (SED) to 12 % (CEENE). The
comparison of accounting approaches also highlights China’s contribu-
tion from a consumption perspective, while India and the US remain
significant in both approaches, as shown by their proximity to the y = x
line (Fig S.3, SI-1).

Some regions show sharp increases in impact under a consumption-
based accounting approach, notably Taiwan and South Korea, indicating
significant environmental impacts abroad (Fig S.4, SI-1). Japan,
Switzerland, United Kingdom, and the WM region also show substantial
environmental impacts abroad. In contrast, Australia, Canada and the
Europe RoW region (WE) consistently show the lowest consumption-to-
production impact ratios.

Regional distributions by satellite account remain consistent from a
consumption-based approach (Fig S.5, SI-1), except for the HANPP<sub>LUC</sub>-
based method, where negative contributions from China, WM region,
United Kingdom, and Taiwan disappear due to the large share of their
impacts occurring abroad. This removes the effect of negative CFs for
these regions (Table S.15, SI-1).

Food-related products are the major contributor to environmental impacts from a consumption-based perspective across all LCIA methods (Fig S.6, SI-1). Their contribution ranges from 64 % in CEENE to 29 % in SED on a global scale, primarily driven by cropland and grazing. SED assigns more impact to final demand (37 %), mainly due to land occupation in WF and Russia linked to the Forest area – Marginal use satellite account, which has a null CF in the other LCIA methods. Table S.24, SI-1 lists the ten Exiobase product categories with the largest impact across methods (Fig. 4).

Food-related products also dominate regional environmental im-
pacts in NPP-based, CEENE, and HANPP<sub>LUC</sub>-based methods. For
instance, they represent 53–62 % of EU impacts. Switzerland is the only
country where the category of *Services* equals or slightly exceeds food-
related products contribution. The focus of SED on land use shifts the
distribution of impacts within regions towards *Final demand* and *Shelter*.

3.5. *Limitations*

This study involves several assumptions to interpret proposed
boundaries relative to current consumption. Boundaries were translated
into LCIA-based equivalents using 2011 supply chains, assuming uni-
form reductions in final demand across products. While regional varia-
tions were considered, characterized boundaries remain sensitive to
shifts in consumption and trade, and future technological improve-
ments. Future work should explore alternative scenarios to assess the
influence on characterized boundary values, and enable a fairer com-
parison between future impacts and boundaries (Wood et al., 2018).

The results of the study are influenced by a series of uncertainties
linked to Exiobase projections beyond 2011, aggregation of CFs, MRIO
data, and boundaries. MRIO and Exiobase uncertainties were not
addressed and require further study. Uncertainty in MRIO data could be
explored using other MRIO tables or introducing uncertainty estimates,
while newer versions of Exiobase provide more updated time horizons.
Aggregating CFs can significantly affect results (Davin et al., 2024),
especially for RoW regions, which were identified as hotspots. This
finding highlights the need to apply MRIO tables with a higher spatial
resolution, such as Exiobase 3rx (Bjelle et al., 2020).

Uncertainty in boundaries includes both parameter uncertainty in their quantification, and epistemological uncertainty concerning their environmental relevance. Parameter uncertainty was partially addressed in these work by comparing different estimates, and various tools for their quantification, namely: Exiobase and the databases references in the literature. With regard to the environmental relevance of boundaries, this work is limited by the normative nature of the reviewed thresholds, which are not biophysically derived. This limits the interpretation of boundary exceedance, as it remains uncertain whether such transgression signals ecological risk or deviation from policy targets.

The exploration of control variables was limited by the lack of CFs and inventoried flows, leading to the prevalence of control variables derived from resource accounting methods. To overcome this limitation, it is recommended to introduce species-specific and geographical data into boundary quantification, inventories and LCIA methods (Crenna et al., 2018). These data would allow to employ control variables that are located downstream of the impact pathway, closer to the damage to the selected safeguard value.

**4. Implications and perspectives**

This study shows an increasing pressure on biotic resources and
transgression of most of existing boundaries. However, due to the
normative nature of existing boundaries, it remains unclear whether this
transgression threatens the instrumental value of biotic resources. This
limitation prevents the integration of existing boundaries into AESA,
and highlights the need for defining science-based boundaries. The
proposed harmonizing approach can support future incorporation of
biotic resource boundaries into AESA.
<figure><img src="image_6.png" /><figcaption>Fig. 4. Production-based contribution to global environmental impacts in 2011 broken down by region and satellite account. “LU” and “EM” indicate resources were characterized using satellite accounts for land use and extracted mass, respectively. CEENE: Cumulative exergy extracted from the natural environment, HANPP<sub>LUC</sub>: Human appropriation of the net primary productivity, NPP: Net primary productivity, SED: Solar energy demand. Regions acronyms are specified in Table S1, SI-1.</figcaption></figure>

Absolute boundaries for biotic resources need to be defined based on the reason to protect biotic resources, and quantified according to science-based criteria. Both research gaps were identified for mineral resources, underscoring the arbitrary nature of existing boundaries (Dudka et al., 2025). The reason to protect biotic resources, also referred to as role of resources (Schulze et al., 2020), implies clearly defining the instrumental value of resources to be protected, the systems in which biotic resources are valued and the biotic resources concerned. Current definitions of biotic resources vary widely across boundaries and LCIA

methods, highlighting the need for a consistent definition and systematic characterization (Crenna et al., 2018).

Quantifying absolute boundaries following a science-based approach requires the use of a control variable that aligns with the value to be protected (Dudka et al., 2025). Mass-based control variables were shown to provide limited information since they do not represent the impact pathways leading to damage to the instrumental role of biotic resources. Their use to propose boundaries for biotic and abiotic resources disregards fundamental differences in regeneration rates
between these types of resources, and assumes substitutability between them. The implemented RAM-derived control variables also overlook the differences between biotic resources, and their focus on availability challenges their suitability to protect the instrumental role of biotic resources. Introducing dissipation-based boundaries could make the definition of boundaries for abiotic and biotic more consistent, shifting the focus from availability to accessibility (Beylot et al., 2020). We recommend developing biotic-specific boundaries and exploring alternative control variables that better capture varying impacts across biotic resources and geographies to the safeguard value of the boundary.

The results underscore the need to redistribute per capita biotic resource consumption to allow developing countries to catch up, while staying within global boundaries. LCIA-based control variables reinforce this by showing the impact of industrialized regions from a consumption-based perspective. LCIA-based control variables offer an opportunity to redefine supply chains, directing them towards regions with lower potential environmental impacts on biotic resources.

## Funding sources

This work was supported by the French Ministry of Higher Education, Research and Innovation through the CIFRE individual fellowship [grant number 2022/0710].

## Glossary

C: Characterization matrix

F: Satellite accounts associated with sectors

F': Satellite accounts linked to final consumption

F<sup>y</sup><sub>ref</sub>: Satellite accounts linked to the modified final consumption at the reference year

F': Satellite accounts linked to the modified final consumption

D<sup>CBA</sup>: Consumption-based biotic resource consumption

D<sup>CBA</sup><sub>m</sub>: Consumption-based biotic resource consumption at the modeling year

D<sup>CBA</sup><sub>ref</sub>: Consumption-based biotic resource consumption at the reference year

E<sup>CBA</sup>: Consumption-based impact biotic resource consumption

E<sup>PBA</sup>: Production-based impact biotic resource consumption

E<sup>CBA</sup><sub>m</sub>: Consumption-based impact biotic resource consumption at the modeling year m: Modeling year

L: Leontief matrix

L<sub>m</sub>: Leontief matrix at the modeling year

L<sub>ref</sub>: Leontief matrix at the reference year ref: Reference year

S: Matrix of coefficients of production

S<sub>m</sub>: Matrix of coefficients of production at the modeling year

S<sub>ref</sub>: Matrix of coefficients of production at the reference year

y: Final demand matrix

y<sub>m</sub>: Final demand matrix at the modeling year

y<sub>ref</sub>: Final demand matrix at the reference year

y': Modified final demand matrix

AESA: Absolute environmental sustainability assessment

AoP: Area of protection

ASR: Absolute sustainability ratio

BaU: Business-as-usual

CEENE: Cumulative exergy extracted from the natural environment

CExD: Cumulative exergy demand

CF: Characterization factor

DMC: Domestic material consumption

EPS: Environmental Priority Strategies

EU: European Union

FAO: Food and Agriculture Organization

HANPP: Human appropriation of net primary productivity

LCA: Life-cycle assessment

LCIA: Life-cycle impact assessment

LPY: Lost potential yield

MRIO: Multiregional input-output table

MSY: Maximum sustainable yield

NPP: Net primary productivity

RAM: Resource accounting method

RMC: Raw material consumption

RoW: Rest-of-the-World

TMC: Total material consumption

SED: Solar energy demand

SOC: Soil organic carbon

UN: United Nations

WA: RoW Asia-Pacific

WE: RoW Europe

WF: RoW Africa

WL: RoW America

WM: RoW Middle East

## CRediT authorship contribution statement

**Gonzalo Puig-Samper:** Writing – original draft, Visualization, Software, Methodology, Investigation, Formal analysis, Conceptualization. **Natacha Gondran:** Writing – review & editing, Supervision, Resources, Methodology, Funding acquisition, Formal analysis, Conceptualization. **Julie Clavreul:** Writing – review & editing, Supervision, Funding acquisition, Conceptualization. **Anne Prieur-Vernat:** Supervision, Project administration, Funding acquisition. **Mikolaj Owsianiak:** Writing – review & editing, Supervision, Resources, Methodology, Formal analysis, Conceptualization.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.resconrec.2025.108651.

## Data availability

One supplementary file containing used data has been submitted with this article.

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