Environmental footprint initiative : agricultural working group milestone 5 : biodiversity impacts in LCA.
Date: 2025-10-01
Institutions: Joint Research Centre, Listorti, Guilia
Subjects: EU environmental policy, adaptation to climate change, common agricultural policy, evaluation method, reduction of gas emissions, report

ISSN 1831-9424 

Environmental Footprint Initiative - 
Agricultural Working Group Milestone 5 

Biodiversity impacts in LCA 

Garcia Herrero, L., De Laurentiis, V., Sinkko, T., Guerrieri, 
V., Biganzoli, F., Listorti, G., Sala, S. 

2025 

 EUR 40471 

 
  
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JRC142437 

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ISBN 978-92-68-32059-4   ISSN 1831-9424   doi:10.2760/9118063  

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How to cite this report: Garcia Herrero, L., De Laurentiis, V., Sinkko, T., Guerrieri, V., Biganzoli, F. et al., Environmental Footprint Initiative - 
Agricultural Working Group Milestone 5 - Biodiversity impacts, Publications Office of the European Union, Luxembourg, 2025, 
https://data.europa.eu/doi/10.2760/9118063, JRC142437. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Contents 

Abstract ....................................................................................................................................................................................................... 2 

Acknowledgements ............................................................................................................................................................................... 3 

Authors ................................................................................................................................................................................................. 3 

1.  The Environmental Footprint methods ............................................................................................................................... 4 

2.  Introduction ........................................................................................................................................................................................ 6 

3.  Overview of evaluated methods and selected criteria ............................................................................................. 7 

3.1 The Environmental Footprint methods and working groups.......................................................................... 7 

3.2 Selection of evaluation criteria and methods and models to evaluate .................................................. 7 

3.2 Testing and validation ......................................................................................................................................................... 9 

3.3 Normalisation and Weighting for the EF in Biodiversity Impact Assessment ..................................... 9 

4.  Summary of the evaluation per impact category ..................................................................................................... 10 

5.  Options resulting from the evaluation ............................................................................................................................ 14 

5.1 Option A) Adopt an ENDPOINT method covering several impact categories .................................... 14 

5.1.1 Impact World+ ........................................................................................................................................................... 14 

5.1.2 ReCiPe2016 ................................................................................................................................................................. 15 

5.1.3 LC-IMPACT .................................................................................................................................................................... 16 

5.2 Option B) Adopt a MIDPOINT method focused on Land Use impacts ................................................... 17 

5.3 Option C) Developing a biodiversity indicator through normalisation and weighting .................. 18 

5.4 Hybrid approach ................................................................................................................................................................... 22 

6.  Testing the selected options ................................................................................................................................................. 23 

7.  Selection of the most preferable option ........................................................................................................................ 27 

8.  Conclusions ..................................................................................................................................................................................... 29 

References .............................................................................................................................................................................................. 30 

List of abbreviations and definitions....................................................................................................................................... 33 

List of figures ....................................................................................................................................................................................... 34 

List of tables ......................................................................................................................................................................................... 35 

Annex I ...................................................................................................................................................................................................... 36 

Annex II ..................................................................................................................................................................................................... 37 

1 

 
 
Abstract 

The Environmental Footprint (EF) methods, recommended by the European Commission, offer a 
framework for assessing the environmental impact of various sectors. This report outlines the 
findings from the Agricultural Working Group (AWG), Milestone 5, focusing on the evaluation of 
methods to better reflect biodiversity impacts within the EF. It addresses the need to integrate 
biodiversity as a distinct category due to its critical importance in ecosystem stability and provision 
of services. The proposed options include adopting endpoint or midpoint methods and developing a 
biodiversity indicator through normalisation and weighting. The report highlights the importance of 
capturing multiple drivers of biodiversity loss, such as land use and climate change, to provide a 
holistic understanding of environmental impacts. The policy relevance of this work is reflected in its 
potential to improve EF methods, ensuring they remain scientifically robust and aligned with 
sustainability goals, thereby supporting better decision-making in environmental policy and 
management. 

2 

 
 
 
Acknowledgements  

This report is a deliverable of the project “Technical support for the Environmental Footprint and the 
Life Cycle Data Network (EF5)", Administrative Arrangement (AA) N° 36372 between DG 
Environment and the Joint Research Centre (JRC). 

We are grateful for technical support, feedback and comments received during numerous 
discussions from the members of the Agricultural Working Group, Milestone 5, including Marc-
Andree Wolf, Rafael Horn, Jan Lindner, Rafael Horn, Nicole Unger, An De Schryver, and Lisanne De 
Weert, as well as from the Environmental Footprint Technical Advisory Board. Also to Rana Pant, 
and Mauro Cordella from DG ENVIRONMENT. The use of GPT@JRC (GPT 4-o), a Generative Artificial 
Intelligence tool developed by the Joint Research Centre of the European Commission, is also 
acknowledged, as a means of further improving the quality of the text. 

Authors 

García-Herrero Laura (European Commission, Joint Research Centre (JRC), Ispra, Italy) 

De Laurentiis Valeria (European Commission, Joint Research Centre (JRC), Ispra, Italy) 

Sinkko Taija (European Commission, Joint Research Centre (JRC), Ispra, Italy) 

Guerrieri Valentina (University of Bologna, Italy) 

Biganzoli Fabrizio (Unisystems Srl, Italy) 

Listorti Giulia (European Commission, Joint Research Centre (JRC), Ispra, Italy) 

Sala Serenella (European Commission, Joint Research Centre (JRC), Ispra, Italy) 

3 

 
 
 
 
 
 
 
 
1.  The Environmental Footprint methods 

The Environmental Footprint (EF) methods, recommended by the European Commission 
(European Commission, 2021), are a set of standardised metrics designed to measure the 
environmental performance of products, organisations, and sectors across the European 
Union. These methods are part of a broader initiative to promote sustainable consumption 
and production, aiming to provide reliable and comparable information about the 
environmental impacts associated with goods and services. The EF methods encompass two 
main methods: the Product Environmental Footprint (PEF) and the Organisation 
Environmental Footprint (OEF). The PEF and OEF are life cycle assessment-based approaches 
that consider 16 environmental impacts, including carbon footprint, water use, land use, and 
resource depletion, among others. By using a comprehensive life cycle perspective, these 
methods help identify hotspots in supply chains and encourage improvements. The EF 
methods adopt a multi-criteria approach, assessing a wide array of environmental impacts 
rather than focusing solely on carbon emissions, which allows for a more holistic 
understanding of environmental performance. 

In order to bring the last scientific and practical knowledge to the EF, the role of the 
Agricultural Working Group (AWG) is key. The AWG is tasked with ensuring that the EF 
methods are applicable and relevant to the agricultural sector and sometimes beyond 
agriculture, such as the issue of water modelling, or how to better reflect biodiversity impacts 
in the EF. The AWG is composed of experts from various fields, including agronomy, 
environmental science, and sustainability, who collaborate to refine the methodologies and 
ensure they are scientifically robust, practical, and aligned with the realities of agricultural 
production. The work conducted under the AWG is presented at the Technical Advisory Board 
(TAB), which oversees the scientific and technical rigor of the EF methods. The TAB is 
responsible for providing expert advice and guidance on methodological developments, 
ensuring consistency and reliability across different sectors. It evaluates proposals for new 
methods, updates existing guidelines, and addresses technical challenges that arise during 
the implementation of the EF methods. The TAB acts as a bridge between scientific research 
and practical application, ensuring that the EF methods remain at the forefront of 
environmental assessment practices.  

This report shows the main outcome of the work done between 2021-2024 under the sub-
group of the AWG called “Milestone 5”, and presented at the TAB. 

4 

 
 
Figure 1. Overview of the milestones (sub-groups) of the AWG 

Source: Authors’ own elaboration 

5 

 
 
 
 
2.  Introduction 

The unprecedented rate of biodiversity loss due to human activities poses a critical threat to the 
stability and resilience of ecosystems worldwide (IPBES 2019). Biodiversity, encompassing the 
variety of all life forms on Earth, plays an essential role in ecosystem function and the provision of 
services indispensable to human well-being, such as food security, clean water, and climate 
regulation. This is particularly relevant in agriculture, which is a significant cause of biodiversity 
loss and is itself highly affected by biodiversity loss, especially under intensive farming systems 
(Abbass et al., 2022; Crenna et al., 2019). Therefore, addressing the impacts of our consumption 
and production is urgently needed. 

Among the most recognised methods to evaluate environmental impacts is life cycle assessment 
(LCA) (ISO, 2006a, 2006b). This method has been used to inform stakeholders throughout the 
policy cycle, and in particular, to assess the environmental impacts of food systems (Sala et al., 
2021; Boschiero et al., 2023). A widely used LCA method falls under the Environmental Footprint 
(EF) umbrella, which includes two methods: the Product Environmental Footprint (PEF) and the 
Organisational Environmental Footprint (OEF), recommended by the European Commission (EC, 
2021). Current EF methodologies, designed to measure the environmental performance of products 
and organisations, have been criticised for their inadequacy in capturing the complex and 
multifaceted nature of biodiversity impacts (Hélias et al., 2022). These methods include 16 impact 
categories, such as climate change, water use, and acidification. However, these categories often 
fail to encompass the full spectrum of biodiversity impacts, particularly those arising from land use 
and management practices. This gap is of significant concern, given the acknowledged rates of 
biodiversity loss and the high demand from stakeholders for decision-making (Hélias et al., 2022; 
Pedersen et al., 2019; van Der Werf et al., 2020). 

Considering the EF governance, composed of several groups of experts, this work, presented in this 
report was designed to address the biodiversity gap currently present in EF methods by highly 
engaging actors from a wide variety of sectors and disciplines to reach a consensus.  Therefore, 
this document address the EF biodiversity gap by showing the evaluation of shortlisted biodiversity 
assessment methods and their testing in different contexts. It emphasises the importance of 
adopting a nuanced approach that considers multiple drivers of biodiversity loss, such as land use 
change and climate change, to provide a more holistic understanding of environmental impacts.  

6 

 
 
 
3.  Overview of evaluated methods and selected criteria 

3.1 The Environmental Footprint methods and working groups 

The EF methods adopt a multi-criteria approach, assessing a wide array of environmental impacts 
rather than focusing solely on carbon emissions, which allows for a more holistic understanding of 
environmental performance. In order to bring the last scientific and practical knowledge to the EF, 
the EC has set seven groups of experts from the industry, academia, NGOs, and government, 
named Agricultural Working Group (AWG) and Data Working Group (DWG) coordinated by the JRC.  

The EF framework also has a Technical Advisory Board (TAB) composed by some members of 
working groups and other experts in LCA and policymaking, which oversees the scientific and 
technical rigor of the EF methods. TAB members are responsible for providing expert advice and 
guidance on methodological developments, ensuring consistency and reliability across different 
sectors. In this work, the AWG was tasked to support the JRC in finding the way to better reflect 
biodiversity impacts in the EF.  

The AWG dealing with this task was composed of experts from various fields, including agronomy, 
environmental science, and sustainability, who collaborated to refine the methodologies and ensure 
they are scientifically robust, practical, and aligned with the realities of agricultural production. The 
work conducted under the AWG frame was presented at the TAB at different moments to get their 
feedback and approval. Figure 2 illustrates the methodological process of this work, including the 
different moments where experts’ feedback was provided. In this paper, when we refer to “experts” 
we mean EC, AWG, and TAB members. 

Figure 2. Overview of the method. 

Source: Authors’ own elaboration 

3.2 Selection of evaluation criteria and methods and models to evaluate 

The selection of models and methods followed a comprehensive review of existing literature (annex 
I), including key works by Damiani et al. (2023) and Sanyé Mengual et al. (2023), which informed 
the main methods to be discussed at the AWG. After the discussion and presentation at TAB, 14 
methods were assessed. Some methods included several models (e.g. in the ReCiPe2016 method, 
we evaluated the 7 models underpinning the key impact categories affecting biodiversity). Life 
Cycle Impact Assessment (LCIA) methods were selected based on their capacity to cover multiple 
impact categories, ecosystems, and taxonomic groups, among other characteristics. The AWG 

7 

 
 
members  decided to focus on LCA methods as the LCA community is well advanced in evaluating 
biodiversity loss impacts from the main biodiversity drivers, namely climate change, and land use; 
as Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) 
indicates. The list of methods and models evaluated are provided in figure 3. The full description of 
each model is presented in García-Herrero et al. (forthcoming). We evaluated the underpinning 
models tackling biodiversity impacts from different angles including land use, water, climate 
change, toxicity, eutrophication, acidification, photochemical ozone formation, and when applicable, 
ionising radiation. Some stakeholders emphasised the need for alignment with similar debates in 
the LCA community, such as the ongoing discussions within the Global Guidance for Life Cycle 
Impact Assessment Indicators and Methods (GLAM) community, which are supported by entities like 
the European Commission and followed by scientists from the EC-JRC. Due to differing time 
frames, the method proposed by the GLAM community was not evaluated, and although it was 
closely monitored, it was not fully completed (including proper testing) for implementation in the 
next EF recommendations. 

Figure 3. Overview of models evaluated per impact category. 

Note that order in the list and in the description below does not imply any preference. 

Source: Authors’ own elaboration 

An evaluation framework was designed to evaluate different selected LCA methods and models. 
The framework was drafted by the JRC and discussed with the AWG members. It was presented at 
TAB for feedback.  

The methods and models were assessed against eight macro-criteria, namely: documentation, 
completeness, environmental relevance, robustness, temporal and spatial representativeness, EF 
method compatibility, and availability of characterisation factors (CFs). The evaluation involved a 
systematic approach to scoring each model based on its performance across the defined criteria. In 
total, we evaluated 28 criteria (annex II). Each criterion was described to facilitate the evaluation 
and rated on a scale from 1 to 5 (from low to high value), with thresholds established for each to 
ensure methodological rigor. The selection process also considered the transparency and 
accessibility of documentation, the availability of characterisation factors (CFs), and the extent of 
testing and application across different sectors. Details on the evaluation framework are available 
in García-Herrero et al. (forthcoming).  

8 

 
 
3.2 Testing and validation 

After discussing the results of the evaluation with the AWG members and the TAB, selected models 
underwent further testing to validate their applicability within the EF framework. This involved 
applying the models to EF datasets to assess their operational feasibility and the consistency of 
results across different impact categories. Additional correlation analyses were conducted to 
compare the ranking of products based on their biodiversity impact using different endpoint 
methods. In particular we tested: 3342 EF datasets, 50 EF Representative Products, and 128 
products of the Consumption Footprint. A correlation between ranking of 128 products with EF-EQ, 
and the Land Use impact categories from LCImpact, IW+, ReCiPe as also conducted. 

3.3 Normalisation and Weighting for the EF in Biodiversity Impact 

Assessment 

Weighting and normalisation are critical steps in the LCA process, particularly within the EF (EC, 
2021). They enable the aggregation of diverse environmental impacts into a single score or sub-
scores that can inform decision-making. These processes are essential for integrating biodiversity 
impacts as a distinct category within the EF. 

Normalisation involves scaling impact category results by a reference value, often representing the 
total or average environmental impact within a specific region or globally. This process facilitates 
the comparison of different environmental impacts on a common scale. In the context of 
biodiversity, normalization allows for the assessment of biodiversity impacts relative to a baseline, 
providing a clearer understanding of the significance of these impacts in a broader environmental 
context. 

In the proposed EF framework modifications, normalisation was used to adjust biodiversity impacts 
to reflect the average contributions to ecosystem quality, human health, and natural resources. This 
adjustment ensures that the biodiversity impacts are accurately represented in relation to other 
environmental pressures, enabling a balanced assessment that considers the interconnectedness of 
various impact categories. In the case of this research, normalisation was conducted as described 
in Farago et al. (2019). 

Weighting assigns relative importance to different impact categories based on their significance to 
the areas of protection: human health, ecosystem quality, and natural resources. This process 
involves stakeholder input and scientific evidence to determine the appropriate weights for each 
category, reflecting societal values and policy priorities. In the case of this research weighting was 
conducted as described in Cerutti et al. (2018), as the relevance of the inclusion of this information 
in future EF developments was already foreseen. 

9 

 
 
 
4.  Summary of the evaluation per impact category 

In the following lines there is a short summary of the evaluated methods per impact category 
provided in alphabetical order. 

Analysis per impact category following EF impact categories connected with biodiversity impacts:  

Table 1. Impact category and short summary of the evaluation 

Impact 
Acidification 

Climate Change 

Short summary of the evaluated methods 
The evaluation of this impact category has been assessed through four distinct 
models, each embedded in ReCiPe, LC-Impact, Impact World+, and Stepwise2006. As 
it is often the case with many impact categories, Stepwise2006 consistently garnered 
the lowest scores across various criteria pertaining to documentation and 
transparency. In contrast, the other methods share several common characteristics, 
such as compatibility with the EF, extensive testing, comprehensive documentation, 
and a relatively broad spatial coverage, ranking slightly above the defined threshold. 
However, these models exhibit weaknesses when assessed in terms of robustness, 
temporal representativeness, resolution, and time horizon, especially when compared 
to how other impact categories perform in these specific criteria. The primary 
distinguishing factor among these models lies in the scope of environmental 
coverage. Specifically, ReCiPe2016 and LC-Impact limit their scope to terrestrial 
ecosystems and vascular plants when considering taxonomic classification. On the 
other hand, Impact World+ offers a broader perspective by encompassing terrestrial, 
freshwater, and marine ecosystems. In addition, it covers a wider array of taxonomic 
groups, including terrestrial vascular plants, freshwater bony fishes, and marine 
organisms such as Annelida, Arthropoda, Bryozoa, Chlorophyta, Cnidaria, Dinophyta, 
Echinodermata, Foraminifera, Haptophyta, Heterokontophyta, Mollusca, and 
Rhodophyta. 
Taking into account the evaluations of these various methods, it is worth noting that 
LC-Impact consistently emerges as the top-ranking model within this category. This is 
primarily attributable to its extensive ecosystem and taxonomic group coverage, 
which provides a more comprehensive and nuanced understanding of the 
environmental impacts associated with the analysed processes or products 
The assessment of this impact category was conducted across six distinct models, 
namely ReCiPe2016, LC-Impact, Impact World+, Stepwise2006, GLOBIO, and the 
Water Temperature Impact on Fish model. Each model provides unique insights into 
the environmental impacts associated with this category. 
The Water Temperature Impact on Fish model, while fairly comprehensive in its 
approach, exhibits limitations in its scope. It focuses on one ecosystem affected by 
climate change and one specific taxonomic group, bony fishes. This narrow focus 
distinguishes it from the other methods, which offer broader coverage. Additionally, 
the lack of CF availability, despite requests made to model developers, and the 
restricted testing conducted within the model's report/publication further underscore 
its limitations. 
GLOBIO, on the other hand, presents restraints in terms of ecosystem and taxonomic 
coverage, encompassing only terrestrial ecosystems and vascular plants.  
Stepwise2006 follows a pattern similar to its performance in the acidification impact 
category, characterised by insufficient documentation to facilitate a clear 
understanding of its functioning in calculating climate change at the endpoint. 
Impact World+ model, which serves as the underpinning model for climate change, 
boasts thorough documentation and robust testing. However, it falls short in terms of 
ecosystem coverage, focusing solely on terrestrial ecosystems. This limitation is 
important, particularly in an impact category that affects multiple ecosystems, and it 
constrains the model's overall recommendation. 
Among the evaluated models, ReCiPe2016 and LC-Impact consistently rank highest 
across most criteria, including those related to EF compatibility. While ReCiPe2016 is 

10 

 
Impact 

Ecotoxicity 

Eutrophication 

Ionising Radiation 

Short summary of the evaluated methods 
marginally weaker in terms of environmental coverage, it compensates with well-
documented testing records.  
This impact category has undergone evaluation through four distinct methods: 
ReCiPe2016, LC-Impact, Impact World+, and Stepwise2006. However, it is crucial to 
highlight the limitations of the Stepwise2006 model. Unfortunately, its underpinning 
model lacked essential information, as the scientific work was not open-access, and 
CF were not accessible. Consequently, it cannot be recommended as a reliable choice 
for assessing this impact category. 
When we consider the other three models analysed, they share several key 
characteristics. All three models, ReCiPe2016, LC-Impact, and Impact World+, exhibit 
similar profiles in terms of ecosystem coverage, encompassing freshwater, marine, 
and terrestrial environments. Furthermore, they offer global spatial resolution, making 
them highly adaptable for implementation in various Life Cycle Assessment (LCA) 
software platforms, all operating under the Environmental Footprint (EF) method. 
While these models have their own unique features, such as their approaches to 
addressing uncertainty (with LC-Impact generally excelling in this aspect and 
ReCiPe2016 having room for improvement), the primary differentiating factor lies in 
the method they employ for assessing toxicity, notably the use of UseTox. Both LC-
Impact and Impact World+ utilize UseTox, which results in several shared 
characteristics, including the availability of a single temporal horizon. 
Conversely, ReCiPe2016 builds upon the work of Van Zelm et al. (2019) and 
incorporates three distinct time horizons (20 years, 100 years, and infinite). This 
multiplicity of time horizons plays a critical role in influencing the variability of final 
results, particularly when assessing non-degradable elements like metals in soils. 
Consequently, for cases where the temporal dimension holds significant importance 
and metals are a central concern, ReCiPe2016 emerges as the preferred choice due to 
its more comprehensive temporal assessment. 
The assessment of this impact category was performed using four foundational 
models: ReCiPe2016, LC-Impact, Impact World+, and Stepwise2006. All of these 
models have been extensively documented and rigorously tested across various 
products and sectors. 
However, it is important to note that Stepwise2006 lags behind the others in terms of 
environmental relevance, robustness, and spatial resolution criteria. When examining 
the associated sub-criteria, it consistently ranks lower compared to the other models 
we have analysed, making it the less robust option among them. It is worth 
mentioning that the ReCiPe2016 model does score lower in one specific sub-criterion, 
which is the consideration of risk levels in characterizing 
species/population/community vulnerability, threat status, and rarity, as information 
on vulnerability is not provided. 
When assessing this impact category, the three models - ReCiPe2016, Impact World+, 
and Stepwise2006 - generally behave in a similar manner, with no significant 
disparities in the evaluation outcomes.  
This specific impact category is unique as it is identified solely within the Impact 
World+ method. The Characterisation Factors (CF) for ecosystems within this method 
are derived from the approach developed by Garnier-Laplace et al. (Garnier-Laplace 
et al. 2008). Importantly, this approach aligns with the ecotoxicity assessment 
method employed in USEtox, in accordance with the recommendations outlined in the 
International Reference Life Cycle Data System (ILCD) Handbook (Margni et al. 2008). 
This method includes CFs for human health as well, although this aspect falls outside 
the scope of the current analysis. Furthermore, the native geographical resolution 
scale is not regionalised, suggesting that the model does not differentiate impacts at 
a regional level. The method encompasses 29 elementary flows, which contribute to a 
limited assessment of the environmental impact within the specified category. 
Instead, the model facilitates the calculation of both exposure/response factors and 
severity factors, offering a more nuanced understanding of the potential 
consequences associated with the assessed environmental interventions. 

11 

 
Impact 

Land Use 

Terrestrial Biodiversity Impacts of Land Inundation (Dorber et al., 2020) 
ECOSCARCITY 2013/2021 (de Baan, 2013) 

Land Use Impacts on Functional Plant Diversity (FPD) 
Habitat Conversion and Fragmentation (HCF) 
Biodiversity Multi-Scale Assessments of Product Systems (BioMAPS) 
Land Use Intensity Specific Biodiversity Footprint (LUIS, based on Chaudhary 

Short summary of the evaluated methods 
The main limitation of this model is the publication date (2008), since then, we did 
not find any update. 
The assessment of this particular impact category was undertaken across 11 distinct 
models. It is noteworthy that this impact category holds significant relevance and has 
been subjected to in-depth scrutiny when compared to various other impact 
categories. This impact category covers only impacts connected to terrestrial 
ecosystem and covers a low range of taxonomic groups. However, similar to other 
impact categories, the documentation of Stepwise2006 was insufficient to enable a 
comprehensive evaluation. 
Among the 11 analysed models, six were specifically tailored to address land use 
impact categories. These models included: 
1. 
2. 
3. 
4. 
and Brooks 2018) 
5. 
6. 
Of these models, those utilizing Chaudhary and Brooks (2018) and de Baan et al. 
(2013) have undergone extensive testing across various products and sectors, 
affirming their reliability and applicability. De Baan et al. (2013) is a model perceived 
outdated by the experts compared with latest developments on land use, and it has 
limited information on biodiversity risk level as well as on the temporal resolution of 
the characterisation factors. Chaudhary and Brooks (2018) instead is well known, and 
tested.  
In contrast, the remaining four models did not undergo a sufficiently rigorous testing 
process to meet the criteria for recommendation. This limited testing primarily relied 
on the information provided within the respective model's report or scientific 
publication. It is important to note that, in some instances, these methods do offer 
significant detail regarding the information required, particularly related to location 
and land use management practices. For example, the methods used in FPD, BioMAPS, 
and LUIS are notable for their comprehensive data requirements in these areas. 
BioMAPS is a new method developed under the LANCA team which allows a midpoint 
integration and could co-exist with current Land Use EF impact category. Although the 
use of a midpoint impact category or subcategory was not considered at the 
beginning of the AWG MS5 discussion, it emerged in the last meeting and therefore it 
will be discussed in this report. 
On the other hand, the models integrated into ReCiPe2016, LC-Impact, GLOBIO, and 
Impact World+ demonstrated a high level of performance during the evaluation. While 
ReCiPe2016 draws upon Elshout et al. (2014) and de Baan et al. (2013), albeit with 
some adjustments, it does not completely resolve the limitations highlighted 
previously. This last is also the model behind Impact World+. LC-Impact on the other 
hand, build upon the foundation laid by Chaudhary et al. (2015). There is an updated 
version of this model in Chaudhary and Brooks 2018. The main differentiating factor 
lies in the fact that the 2018 work aimed to address five specific aspects that had 
been identified by UNEP-SETAC as areas in need of improvement. These five key 
aspects encompass: 1) Expanding Land Use Classes: The models should be able to 
differentiate between biodiversity damages caused by different land uses, such as 
distinguishing between pasture and cropland, 2) Inclusion of CFs for Plants in the 
impact assessment, 3) Reducing Uncertainty in CFs, 4) Assessing model-predicted 
species loss against the global rate of extinction and observed species loss within the 
studied systems, 5) Furthermore, case studies should be conducted to examine the 
robustness and the model's ability to rank alternative product systems in terms of 
their biodiversity impacts. GLOBIO, on the other hand, presents a discrete evaluation, 
and a notable challenge lies in the unit of measure it utilises, which is "mean species 
abundance per hectare." This unit of measure can pose difficulties when it comes to 

12 

 
Impact 

Photochemical 
Ozone Formation 

Water Use 

Short summary of the evaluated methods 
integrating GLOBIO's results with other impact categories that rely on a different unit, 
such as PDF. 
The assessment of this particular impact category was conducted using three distinct 
models: ReCiPe2016, LC-Impact, and Stepwise2006. Each of these models provides 
valuable insights into the environmental impact being evaluated. 
Commonly, all the underpinning models integrated into these methods share strong 
documentation and have undergone extensive testing across various products and 
services. This ensures their reliability and robustness in assessing environmental 
impacts. 
However, Stepwise2006 ranks lower when compared to the other two methods for a 
few key reasons. Firstly, it is limited by the spatial resolution defined in the 
Environmental Design of Industrial Products (EDIP) 2003 methodology, which does 
not provide resolution beyond the global level. Additionally, it features a higher 
temporal resolution, calculating impacts annually, whereas the models used for 
comparison provide information on a monthly basis. Despite these limitations, 
Stepwise2006 distinguishes itself by offering broader ecosystem coverage, including 
both terrestrial and freshwater environments, whereas the other models focus 
primarily on terrestrial ecosystems. 
When comparing the recommendations derived from ReCiPe2016 and LC-Impact, the 
latter marginally outperforms the former due to its superior handling of data input 
uncertainty, characterisation of model uncertainty, and sensitivity to normative 
choices.  
The evaluation of this impact category encompassed a thorough analysis of six 
distinct models, namely ReCiPe2016, LC-Impact, Impact World+, GLOBIO, Habitat 
Change Potential, and Water Consumption Impacts on Fish Species Richness. Each of 
these models provides unique insights into the assessment of this particular impact 
category. 
In terms of sub-criteria related to the documentation and transparency of the models, 
all six models earned commendable scores, as they are well-documented and offer 
clear and comprehensive descriptions of their methodologies. However, when it comes 
to evaluating the temporal representativeness, resolution, and time horizon, most 
models received lower rankings, signifying potential room for improvement in these 
areas. One notable exception is the Habitat Change Potential model, which excels in 
these aspects. It's worth mentioning, though, that this model primarily covers 
European CF and is limited to midpoint impacts rather than endpoint impacts. 
Notably, the Habitat Change Potential model, Water Consumption Impacts on Fish 
Species Richness, and the underpinning model for water use in GLOBIO displayed 
some limitations in terms of EF compatibility and ecosystem coverage, with a primary 
focus on freshwater ecosystems. Additionally, these models exhibited a relative lack 
of testing, partly attributed to their status as more recently developed models. 
Among the evaluated models, the LC-Impact model for water use received the 
highest ranking. It is closely followed by Impact World+ and ReCiPe2016. The LC-
Impact model is particularly lauded for its robust assessment of water use impact, 
even though it exhibits lower ecosystem and taxonomic coverage compared to Impact 
World+ and ReCiPe2016. On the other hand, Impact World+ and ReCiPe2016 offer 
slightly broader coverage in terms of ecosystems and taxonomic groups. However, 
LC-Impact stands out for its stronger EF compatibility compared to the other two 
models. 

Source: Authors’ own elaboration 

General consideration. The method Stepwise2006 for most of impact categories showed a lack 
of information about its application, therefore, it was often excluded due to the impossibility of 
evaluating the characteristics of the model behind the impact categories. This is a limitation 
(mainly identified in Stepwise2006 but also in other methods) as some criteria could be ranked 
higher, but the absence of clarity did not allow to give a better scoring.  

13 

 
 
5.  Options resulting from the evaluation 

Based on the outcome of the evaluation presented in the previous section, the following three 
options were identified as a possible way forward to integrate impacts on biodiversity in the EF 
method.   

5.1 Option A) Adopt an ENDPOINT method covering several impact categories 

In this option, an indicator expressing the impact on biodiversity calculated with an endpoint 
approach, taken from an existing endpoint life cycle impact assessment method, would be added to 
the EF impact assessment as a separate indicator, to be considered in addition to the single score. 

Out of the evaluated methods, five of them stand out for their coverage of more than three impact 
categories. These methods are: ReCiPe2016, LC-Impact, Impact World+, Stepwise2006, and 
GLOBIO. Of these, the ones ranking higher, thanks to their coverage of impact categories, 
ecosystems, and taxonomic groups – as well as transparency of documentation available – are: 
Impact World+, ReCiPe2016, and LC-Impact. As a result, these three methods (reported together 
with the metrics used in Table 1) were identified as promising options, and, in case option A were 
selected as the preferred option, further evaluation and testing would be needed to select one out 
of the three.  

In the following, an overview of the main advantages and disadvantages identified for each of the 
three methods is provided. 

Table 2. Metrics used at endpoint in the aggregated methods. 

Method 
Impact World+/Damage category: Ecosystem quality 
ReCiPe2016/Damage category: Ecosystems 
LC-Impact/Damage category: Ecosystem quality 

Metric 
PDF.m2.yr 
Species.yr 
PDF.yr 

Source: Authors’ own elaboration 

5.1.1 Impact World+ 

The endpoint damage category under consideration is "Ecosystem quality." This method has 
demonstrated strong performance across all the assessed impact categories. Notably, it stands as 
the sole method that takes into account ionizing radiation's potential harm to ecosystems. It is the 
method covering the highest number of taxonomic groups (15 taxonomic groups) and marine, 
freshwater, and terrestrial ecosystems. In this method, as shown under IW in Figure 4, land use has 
the highest weight followed by climate change and acidification.  

Among the aggregated methods chosen, it represents the most recent one. It is comprehensively 
outlined in the work of Bulle et al. in 2019, and it has been fully incorporated into both commercial 
software solutions and OpenLCA. In the context of this evaluation, the method was assessed using 
Impact World+ (version 1). However, it is worth noting that a more recent version 2.0 is now 
available. While this updated version has not yet been integrated into SIMAPRO, it is publicly 
available1, and the possibility of implementing it should be explored and considered for 
recommendation. Experts concur that land use is a primary driver of biodiversity loss. Hence, any 
comprehensive approach must incorporate a robust and up-to-date model underpinning land use. 

1 https://zenodo.org/records/7348580 

14 

 
 
The endorsement of adopting Impact World+ is contingent upon a shift in this impact category, 
substituting it with Chaudhary and Brooks 2018.  

It is important to highlight the disparity in metrics; Impact World+ employs units in PDF.m2 .y, 
whereas Chaudhary and Brooks use PDF.y. The advancement of this recommendation hinges on the 
practicality of converting these metrics. The JRC team has initiated contact with both method 
developers to solicit feedback on potential conversion methods. The results of testing the methods 
in EF data, along with a correlation analysis between the two methods, will be shared during the 
TAB meeting. 

5.1.2 ReCiPe2016 

The ReCiPe2016 method offers clear guidelines for transitioning from midpoint to endpoint 
assessment. This transition can greatly facilitate the incorporation of the biodiversity impact 
category into assessments. The method provides instructions for calculating both the midpoint and 
endpoint impacts. Table 3 provides a reference for the timing considerations in the EF method, 
serving as a valuable guide for practitioners when using ReCiPe2016 as endpoint to calculate the 
biodiversity impact (named Ecosystems damage category). The timing considerations for other 
impact categories will be provided in case this method is selected to capture biodiversity impacts. 

Table 3. Impact categories in ReCiPe2016 and reference time to be used. 

Impact category 
Acidification 
Climate change 
Ecotoxicity  
Eutrophication (freshwater) 
Land Use 
Photochemical Ozone Formation 
Water Use  

Reference (*) 
No difference 
100Y 
Infinite 
No difference 
No difference 
No difference 
No difference 

(*) “No difference” indicates that Characterisation Factors do not differ from different time horizons, or just one time 
horizon is provided. 

Source: Authors’ own elaboration 

This pre-selection is based on the method's robustness and clear guidance, making it a practical 
choice for effectively assessing biodiversity impact across various categories. This method has also 
some limitations, highlighted below and which did not allow the AWG MS5 to recommend.  

Lack of Recent Models: It is essential to note that the ReCiPe2016 endpoint method, while well-
established and widely recognised, has not been updated since then. This temporal aspect presents 
a limitation, despite the method's extensive testing and application. 

Limited Geographic Specificity: The ReCiPe2016 endpoint method may not consistently account for 
regional or geographic disparities in environmental conditions and impacts (e.g. land use is global, 
while acidification or water use is country specific). Environmental conditions can vary significantly 
from one location to another, and this method may not adequately capture these regional 
distinctions.  

15 

 
5.1.3 LC-IMPACT 

In this case the recommendation is to focus on Ecosystem Quality damage category. This choice 
brings coherence to the units of measurement, making it easier to assess and compare impacts. 
The method under consideration has demonstrated impressive performance, with high scores in 
various impact categories, highlighting its overall effectiveness in capturing environmental 
consequences.  

One major limitation is the exclusion of the "Ecotoxicity" impact category. This omission is not 
acceptable within the AWG. It stems from the method treating Ecotoxicity as a standalone damage 
category with different units. This creates a gap in the assessment, potentially missing critical 
environmental impacts. The attempt to include Ecotoxicity by adapting it to the same units as 
Ecosystem Quality reveals significant uncertainty, as indicated in Figure 4. This figure shows a 
comparison between the contribution of different impact categories to the damage on ecosystem 
quality across various methods, when applied to a case study assessing the impacts of EU 
consumption, as presented in Sanyé Mengual et al. (2023). In Figure 4a, the ecosystem quality 
damage indicator in the LC-Impact method was calculated taking into account also the effects of 
ecotoxicity, while Figure 4b shows the same results after excluding ecotoxicity from the LC-Impact 
damage indicator. It is possible to see that in the second case, the results obtained with the LC-
Impact method are well aligned with those of other LCIA methods (showing climate change and 
land use as main drivers of impacts of biodiversity), while in the first case, LC-Impact diverges 
substantially from the other methods as the impact on biodiversity is mostly driven by impacts on 
ecotoxicity. This uncertainty can introduce ambiguity into the overall assessment and may limit the 
method's reliability.  

In summary, the adoption of the LC-Impact method focusing on Ecosystem Quality has several 
strengths, including its high performance in various impact categories and its consistency with well-
known assessment methods. However, its exclusion of Ecotoxicity and the uncertainty associated 
with trying to incorporate it into the analysis are notable weaknesses that should be carefully 
considered when evaluating the method's suitability for a given biodiversity impact assessment. 

16 

 
 
 
 
 
 
 
 
 
 
 
 
Figure 4. Contribution of midpoint impact categories to overall biodiversity impact, by LCIA method or model 

 a) Highlighted in yellow LC-Impact and role of ecotoxicity when included in the calculation of the damage 
indicator. b) LC-Impact considering only the Ecosystem Quality damage category without the inclusion of 
ecotoxicity 

Source: adapted from Sanyé Mengual et al. (2023) 

5.2 Option B) Adopt a MIDPOINT method focused on Land Use impacts 

In this option, a new midpoint impact category expressing the impact of land use activities on 
biodiversity would be added to the 16 impact categories currently part of the EF method. The 
17thimpact category, defined “land use, biodiversity” would then be integrated in the single score by 
assigning it a weight, after performing a recalibration of the EF weighting set (currently composed 
of 16 weights adding up to 100).  

The BioMAPS model was identified as promising for this purpose, despite being very recent and 
having undergone limited testing (at the time of discussing these options). This was due to the fact 
that it provides operational characterisation factors at different geographical scales (from grid cell 
level to national and global scales), compatible with the EF list of land occupation and 
transformation flows (therefore it can be easily integrated in the EF method), shows a good 
coverage of land use intensity levels, and presents flexibility to be expanded in the future to better 
take into account the effect of certain agricultural practices (described in D2.3 LCSA methodology 
to be implemented in WP4 demonstrations from ORIENTING project 2and Maier 2023). 

To integrate this new impact category in the single score, a recalibration of the weighting set was 
performed. This allowed to assign a new weight to the 17th midpoint impact category and to 
recalculate the remaining weights accordingly. This recalibration allowed also to update the 
evaluation of robustness (of the underpinning models and of the normalisation inventory) - an 
intermediate step in the definition of the weighting set – to account for the developments in the EF 
method that had taken place since the development of the original weighting set. This was done by 
repeating the approach followed in 2017 (presented in Cerutti et al. (2018)) to define the EF 
weighting set, but applying some changes to reflect the fact that the impacts of land use activities 
were now covering two midpoints (i.e. land use soil and land use biodiversity) (Cerutti et al., 2018). 
It should be noted that for the purpose of this exercise, no new data was collected compared to 
what had been collected in 2017 through surveys and targeted consultations. On this occasion, it 

2 https://orienting.eu/wp-content/uploads/2022/12/D2.3-LCSA-methodology-implemented-WP4-demonstrations.pdf 

17 

 
 
 
 
was possible to calculate a new weighting set by introducing a small change in the calculation 
approach, still leveraging on the same data. Table 4 shows both the current and the recalibrated 
weighting sets, and the relative change between the two. For ten impact categories, the suggested 
new weighting factor is lower than the current one, as a result of the addition of a new impact 
category and, in some cases, a decrease in the associated robustness factor. For the remaining six 
impact categories, a small increase in the associated weighting factor can be seen: this means that 
the robustness factor increases (reflecting updates either in the impact assessment model or in the 
normalisation inventory) compensated the reduction caused by the inclusion of an additional 
impact category. Nevertheless, no major changes can be observed.  

Table 4. Result of the recalibration of weighting factors 

 Impact category 

Climate Change 
Ozone Depletion 
Human Toxicity, Cancer 
Human Toxicity, Non-
Cancer 
Particulate Matter 
Ionizing Radiation, Human 
Health 
Photochemical Ozone 
Formation—Human Health 
Acidification 
Eutrophication, Terrestrial 
Eutrophication, Freshwater 
Eutrophication Marine 
Ecotoxicity, Freshwater 
Land Use - Soil 
Water Use 
Resource Use, Fossils 
Resource Use, Minerals 
And Metals 
Land Use Biodiversity 

Weighting factors 
in EF3.1  [%] - A 
21.06 
6.31 
2.13 

Suggested new weighting 
factors  [%] - B 
19.52 
5.74 
2.34 

1.84 

8.96 

5.01 

4.78 

6.2 
3.71 
2.8 
2.96 
1.92 
7.94 
8.51 
8.32 

7.55 

- 

2.02 

9.47 

3.25 

3.82 

5.08 
3.09 
2.60 
3.08 
2.09 
7.14 
10.07 
7.58 

6.87 

6.24 

 B-A   [%] 

-1.5 
-0.6 
0.2 

0.2 

0.5 

-1.8 

-1.0 

-1.1 
-0.6 
-0.2 
0.1 
0.2 
-0.8 
1.6 
-0.7 

-0.7 

- 

Source: Authors’ own elaboration 

5.3 Option C) Developing a biodiversity indicator through normalisation and 

weighting  

This option builds on Option B, but in addition to introducing a new impact category to assess the 
impacts of land use activities on biodiversity it foresees the development of three indicators, one 
for each Area of Protection (AoP), forming part of the single score. For this reason, the indicators 
are referred to as sub-scores. 

Similarly to the single score, the three sub-scores are calculated by means of normalisation and 
weighting. To this end, a dedicated set of weights was calculated for the impact categories relevant 
for each AoP, following a two stepped approach. 

In a first step, each impact category was connected to one or more AoP, based on the relationships 
between midpoint and endpoint indicators established by existing LCIA methods (i.e. Recipe2016, 
GLAM, IW+, and LC-Impact) and defined in Sala et al. (2017). The AoP considered were: human 

18 

 
health, ecosystem quality, and natural resources and ecosystem services. A midpoint impact 
category was connected to an AoP, when a link was established by at least two out of the five 
sources consulted, as illustrated in table 5. 

Table 5. Connection between midpoint impact categories and areas of protection.  

Impact category 

Climate Change 
Ozone Depletion 
Human Toxicity, Cancer 
Human Toxicity, Non-Cancer 
Particulate Matter 

Assigned 
to Human Health 
x 
x 
x 
x 
x 

Assigned 
to Ecosystem Quality 
x 

Assigned to 
Natural Resources 

x (1,4) 

Ionizing Radiation, Human Health 

Photochemical 
Ozone Formation—Human Health 
Acidification 
Eutrophication, Terrestrial 
Eutrophication, Freshwater 
Eutrophication Marine 
Ecotoxicity, Freshwater 
Land Use - Soil 
Water Use 
Resource Use, Fossils 
Resource Use, Minerals And 
Metals 
Land Use - Biodiversity 

x 

x 

x (4) 

x (2, 5) 

x (2,3,4,5) 

x 
x 
x 
x 
x 

x 

x 

x 
x (1,4) 
x 

x 

Legend= 1: Sala et al. 2018; 2: Recipe2016; 3: GLAM; 4: IW+; 5: LC-impact 

Source: authors’ own elaboration 

Note: when all five sources establish a connection only a black cross is displayed, when 2 to 4 sources 
establish a connection a black cross is displayed and the sources are listed in brackets, when only one source 
establishes a connection a grey cross is displayed and the source is provided in brackets. In the last case, the 
midpoint impact category is not linked to that area of protection for the purpose of this exercise. 

In a second step, for all those impact categories that were linked to two or three areas of 
protection, their associated weight in the EF weighting set (after the above-mentioned 
recalibration) was split across the relevant AoPs (e.g. in the case of climate change, the weight of 
18.7% was subdivided in three weights, one for each AoP, respectively 10.1% 5.4% and 3.2%). This 
was done by performing an optimisation exercise aimed at assigning to each area of protection a 
total weight (calculated as the sum of all the weights assigned to each impact category for that 
AoP) as close as possible to the weight assigned to the same AoP inside the GLAM LCIA method 
(Bayazıt et al., 2024). The outcome of this process is illustrated in Table 6 and Figure 5. 

As a result, when communicating results of a PEF study, the biodiversity score can be singled out 
next to the overall EF single score that includes it.  

19 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 6. Recalibration on weighing and association to each area of protection. HH: human health; EQ: 
ecosystem quality; NR&ES: natural resources and ecosystem services. 

Impact categories 

Weighting 
factors 
(%WF) 

AOP 

Effect 
on HH 

Effect 
on EQ 

Effect 
on 
NR&ES 
0.17 

AOP 

WF 
HH 

WF 
EQ 

WF 
NR&ES 

3.18 

10.13  5.40 
5.51 
3.74 
3.23 

9.08 
3.12 

0.29 

0.54 
1 
1 
1 

1 
1 

18.7 
5.5 
3.7 
3.2 

3.7 

9.1 
3.1 

Climate Change 
Ozone Depletion 
Human Toxicity, Cancer 
Human Toxicity, Non-
Cancer 
Particulate Matter 
Ionizing Radiation, 
Human Health 
Photochemical 
Ozone Formation—
Human Health 
Acidification 
7.9 
Eutrophication, Terrestrial  3.0 
2.5 
Eutrophication, 
Freshwater 
Eutrophication Marine 
Ecotoxicity, Freshwater 
Land Use - Soil 
Water Use 
Resource Use, Fossils 
Resource Use, 
Minerals And Metals 
Land Use – Biodiversity 
(BioMAPS) 
SUM 

3.0 
3.3 
6.8 
9.7 
7.3 
6.6 

6.0 

GLAM endpoints 

0.80 

0.20 

2.93 

0.73 

1 
1 
1 

1 
1 

4.87 
2.96 
2.49 

2.95 
3.33 

0.43 

0.30 

4.13 

2.92 

0.27 
1 
1 

6.84 
2.61 
7.27 
6.58 

1 

5.98 

41.9 
42.2 

31.6  26.5 
26.5 
31.3 

Source: Authors’ own elaboration 

20 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 5. Overview of the calculation approach of the three AoP sub-scores and single score. 

Source: Authors’ own elaboration 

21 

 
 
 
5.4 Hybrid approach 

The discussion within the group touched upon the concept of a hybrid approach, aiming to 
amalgamate the highest-ranking models for each evaluated impact category. However, this option 
appears to pose substantial challenges for implementation, primarily for the following reasons: 

⎯  Transition from Midpoint to Endpoint: One critical challenge is that not all models may support 

the transition from midpoint to endpoint assessments, as proposed in option 1. 

⎯  Uniformity of Units: To create a consolidated indicator, it is imperative that the units used are 
consistent across all selected models. Unfortunately, such uniformity is not always achievable, 
given the inherent differences in units adopted by each method. 

⎯  Scale Variability: The results generated by different models could be expressed on varying 

scales, and this issue might not be resolved in a medium-term perspective. 

Within the context of this hybrid approach, discussions have arisen about the potential 
simplification of impact categories, focusing on land use, climate change, and ecotoxicity due to 
their established relevance as drivers of biodiversity loss. Even with a reduced number of impact 
categories, the primary limitations associated with the highest-scoring models persist. Although 
numerous limitations identified in the methods described above could be mitigated through this 
approach, the lack of operational feasibility within a medium-term perspective discourages its 
endorsement. 

22 

 
 
 
6.  Testing the selected options 

After initial discussion with the members of the AWG MS5, further testing needs for evaluating the 
effectiveness of option C in delivering a sound indicator on impacts on ecosystem quality were 
identified. Furthermore, in this context, the JRC ran a test to assess whether the recalibration of 
weights required by options B and C would bring significant changes to the EF method. 

Overall, the following tests were conducted: 

1.  Comparison between the relative contribution of impact categories to the single score 
obtained across 3342 EF datasets with the EF3.1 method version, and with a version 
modified by adding a new impact category (land use biodiversity) and recalibrating the 
weighting factors (as presented in Option B). 

2. 

Investigating the relative contribution of impact categories to the ecosystem quality sub-
score (presented in Option C), across 3342 EF datasets. 

3.  Calculating the impact on biodiversity for 128 products of the Consumption Footprint 

(Sanyé Mengual and Sala, 2023) with the EF ecosystem quality sub-score (presented in 
Option C) as well as with three other endpoint methods (i.e. LC-Impact, ReCiPe2016 , and 
Impact World+), and performing a correlation analysis between the ranking of products in 
terms of their impact on biodiversity with the four approaches. 

4.  Comparing the relative contribution of different impact categories to the ecosystem quality 

impact obtained for an EU citizen when using the EF ecosystem quality sub-score 
(presented in Option C) and the abovementioned three endpoint methods. 

The results of test number 1 are illustrated in Figure 6a displaying the distribution obtained across 
the 3342 EF datasets of the relative contribution of each impact category to the single score with 
the EF3.1 method, and Figure 6b, showing the same analysis with the modified method. The main 
outcome of this comparison is that the introduction of a new impact category and the recalibration 
of the weights only brings minor changes to the overall contribution of impact categories to the 
single score, implying that the suggested refinements do not cause any significant disruption to the 
method. 

23 

 
 
Figure 6. Box and whisker plots visualising the distribution across 3342 EF datasets of the relative 
contribution of each impact category to the single score obtained with the EF3.1 method (6a) and with the 
version of the method suggested by Options B and C (considering an additional impact category and 
recalibrated weighting factors (6b)). 

Notes. CC = climate change, ODP = ozone depletion, HTOX-c = human toxicity cancer, HTOX-nc = human toxicity non-

cancer, PM = particulate matter, IR = ionising radiation, POF = photochemical ozone formation, AC = acidification, TEU 
= terrestrial eutrophication, FEU = freshwater eutrophication, MEU = marine eutrophication, ECOTOX = ecotoxicity 
freshwater, LU_S = land use, soil, LU_BIO = land use, biodiversity, Water = water use, MRD = resource use, mineral 
and metals, FRD = resource use, fossils 

Source: Authors’ own elaboration 

The results of test number 2, investigating the relative contribution of impact categories to the 
ecosystem quality sub-score (presented in Option C), across 3342 EF datasets, show that the main 
contributing impact categories are on average climate change and ecotoxicity. This in a way 
deviates from what was seen in endpoint methods (as discussed in Sanyé Mengual et al. (2023) and 
illustrated in Figure 7), where the main contributing impact categories to the impacts on biodiversity 
are most often climate change and land use (the relative order varies across methods). This was 

24 

 
 
seen as a problematic outcome by some members of the AWG (reflecting the fact that land use 
impacts are not given enough relevance in defining the ecosystem quality impacts with this 
approach), and for this reason further testing was conducted. 

Figure 7. Box and whisker plots visualising the distribution across 3342 EF datasets of the relative 
contribution of each impact category to the ecosystem quality sub-score suggested by Option C. 

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

CC

POF

AC

TEU

FEU

MEU

ECOTOX

LU_BIO

WU

Notes. CC = climate change,  POF = photochemical ozone formation, AC = acidification, TEU = terrestrial eutrophication, 
FEU = freshwater eutrophication, MEU = marine eutrophication, ECOTOX = ecotoxicity freshwater, LU_S = land use, 
soil, LU_BIO = land use, biodiversity, Water = water use 

Source: Authors’ own elaboration 

In test number 3 the impact on biodiversity for 128 products of the Consumption Footprint was 
calculated with the EF ecosystem quality sub-score (presented in Option C) as well as with three 
other endpoint methods (i.e. LC-Impact, ReCiPe2016 , and Impact World+), and a correlation 
analysis between the ranking of products in terms of their impact on biodiversity with the four 
approaches was performed. The results of the correlation analysis are reported in Table 7. It is 
possible to see that the ranking of products obtained with the EF ecosystem quality sub-score is 
strongly correlated to the one obtained by applying ReCiPe2016 and Impact World+, and also 
correlated with the one obtained with LC-Impact (the latter method shows lower correlation with 
the other two endpoint methods, which are instead strongly correlated with each other). This 
supports the use of the approach presented in option C, despite the findings of test 2. 

Table 7. Correlation between the ranking of products according to their impact on ecosystem quality obtained 
with the EF approach (Option C) and the ReCiPe2016, LC-Impact, and Impact World+ endpoint methods. 

ReCiPe2016 

LC-Impact 

Impact World + 

1.00 
ReCiPe2016 
0.74 
LC-Impact 
0.89 
Impact World + 
EF – EQ subscore  0.96 

0.74 
1.00 
0.61 
0.73 

0.89 
0.61 
1.00 
0.87 

Source: Authors’ own elaboration 

EF – EQ sub-
score 
0.96 
0.73 
0.87 
1.00 

25 

 
 
 
 
In test number 4, the exercise performed in Sanyé Mengual et al., (2023) was repeated including 
the results obtained with the EF ecosystem quality sub-score in order to compare the relative 
contribution of different impact categories to the ecosystem quality impact obtained for a EU 
citizen when using the EF ecosystem quality sub-score (presented in Option C) with the ones 
obtained with the three endpoint methods investigated in test number 3. The results are displayed 
in Figure 8, and partly support the findings of test number 2, as, also when performing this analysis 
with the Consumption Footprint model, the contribution of land use to the damage on ecosystem 
quality is lower compared to what was reported by the endpoint methods, while ecotoxicity presents 
a larger contribution (it should be mentioned that ecotoxicity is not considered in the LC-Impact 
method, for the reasons explained in section 4). This is a direct consequence of the fact that while 
endpoint methods are calculating the potential damage of ecosystem quality deriving from each 
midpoint impact category through the cause-effect chain, the approach here suggested by Option C 
is instead obtaining this sub-score via normalisation and weighting. Therefore, it cannot be expected 
that the relative contribution of impact categories to the score thus obtained can reflect the extent 
to which each impact category is a driver of biodiversity loss.  

Figure 8. Contribution of midpoint impact categories to overall biodiversity impact obtained with the EF 
ecosystem quality sub-score (option C) and the ReCiPe2016, LC-Impact, and Impact World+ endpoint methods 

Notes: (*) LC-impact results are considering only the Ecosystem Quality damage category without the inclusion of 

ecotoxicity 

Source: Authors’ own elaboration 

26 

 
 
 
 
 
 
 
 
7.  Selection of the most preferable option 

The three options presented above, together with the results of the tests described in section 5, 
were discussed thoroughly with the AWG MS5 members and were then presented to the TAB 
members. On this occasion, a discussion took place to identify advantages and disadvantages 
associated to each option, as summarised in table 8. 

Both the AWG and the TAB agreed that option A presented significant disadvantages, leading to its 
exclusion. This was mostly due to the inconsistency introduced by mixing the EF method, 
characterised by its own modelling of the impact pathway from pressures to midpoint impacts, with 
an endpoint indicator taken from a different method, following a different modelling of the impact 
pathway. 

Although acknowledging the limitations of Option B, mostly related to the fact that it allowed to 
explicitly communicate biodiversity impacts caused by only one driver of biodiversity loss (namely 
land use), this was seen as more promising thanks to its complementarity with the EF method. The 
main drawback of Option B was overcome by the solution proposed in Option C. This option was the 
preferred one by the AWG and TAB members, as it allowed to introduce in the short term in the EF 
method a sub-score reflecting the impacts of a product on biodiversity, comprehensively addressing 
all the drivers of biodiversity loss currently included in the EF method (plus the newly introduced 
one focusing on land use). It was acknowledged that this option suffers from some limitations, as 
shown by the results of the testing exercise, in the way in which different impact categories drive 
the ecosystem quality sub-score. Nevertheless, due to the good correlation found with the main 
existing endpoint methods, these limitations were seen as acceptable. As a result, the TAB agreed 
with the proposed approach, and it is expected that this method will be included in the next EF 
recommendations. 

Table 8. Advantages and disadvantages of the three options resulting from the discussion with the AWG and 
the TAB members. 

Option  Advantages 

Disadvantages 

A 

⎯  Science-based midpoint to endpoint 

⎯  Potential overlapping and inconsistencies with 

modelling approach 

single score 

⎯  The resulting method would be a combination 
of midpoint (single score) and endpoint 
approaches 

⎯  Problematic for decision support, as two 

different scores would need to be considered 
when comparing product A and product B 

⎯  Alternatively, an additional aggregation would 
need to be performed to combine the single 
score and the biodiversity endpoint 

⎯  Only one driver of biodiversity loss would be 
explicitly communicated by this new impact 
category (land use impacts on biodiversity) 

⎯  No overlapping with other midpoint 

impact categories 

⎯  The new impact category can be 
included in the single score 

⎯  No overlapping with other midpoint 

⎯  The scores calculated for each Area of 

impact categories 

Protection would not be based on a science-

27 

B 

C 

 
Option  Advantages 

Disadvantages 

based midpoint to endpoint modelling 
approach, but would instead be the result of 
normalisation and weighting 

⎯  The new impact category can be 
included in the single score 

⎯  Development of a score assessing 
biodiversity loss leveraging on all 
drivers of biodiversity loss captured 
by the midpoint indicators of the EF 
method 

⎯  Addressing the three Areas of 
Protection and thus setting the 
ground to move towards an endpoint 
approach 

Source: Authors’ own elaboration 

It is worth noting that this milestone group is cognisant of ongoing initiatives aimed at enhancing 
biodiversity assessment within the framework of LCA methodology, such as the UNEP-GLAM3 
project, the Sustainability Finance Directive activities, and the Taskforce on Nature-related Financial 
Disclosures4, among others. New methodologies will be subject to future evaluation, as well as 
those already assessed but still in the early stages of implementation, given their potential for 
evolution and maturation to a point where medium-term recommendations may become 
appropriate. 

3 https://www.lifecycleinitiative.org/activities/life-cycle-assessment-data-and-methods/global-guidance-for-life-cycle-impact-assessment-

indicators-and-methods-glam/ 

4 https://tnfd.global/ 

28 

 
 
 
 
8.  Conclusions 

The urgent need to incorporate biodiversity considerations into environmental impact assessments 
is increasingly recognized as critical for sustainable development. In this context, improving the 
Environmental Footprint (EF) framework to effectively capture biodiversity impacts represents a 
significant step forward in aligning environmental assessments with the pressing challenges of 
biodiversity conservation. 

This report reflects the comprehensive evaluation conducted to different impact assessment 
methods, with a focus on their capacity to address biodiversity impacts within the EF framework. 
The analysis considered a variety of methods, including ReCiPe2016, LC-Impact, Impact World+, and 
the BioMAPS model, among others. Each method was examined based on criteria such as 
comprehensiveness, environmental relevance, and compatibility with existing EF methodologies. The 
findings revealed both strengths and limitations in current approaches, underscoring the complexity 
of assessing biodiversity impacts in a manner that is both scientifically robust and practically 
applicable within the EF framework. 

One of the key insights from this evaluation is the potential of the BioMAPS model to become a 
dedicated biodiversity impact category within the EF framework. The model's ability to address land 
use impacts on biodiversity, along with its alignment with the LANCA framework, makes it a good 
candidate for integration. This approach is particularly relevant given the increasing attention to 
land use change and its impacts on biodiversity at both local and global scales, while it only covers 
land use and further work should be done to better reflect biodiversity impacts. 

The recommendations outlined in this report highlight the importance of collaborative efforts 
among researchers, policymakers, and stakeholders to advance the integration of biodiversity into 
the EF framework. Such collaboration is essential for refining methodologies, establishing 
consensus on key indicators, and ensuring that assessments are aligned with the latest scientific 
understanding and policy objectives. Additionally, fostering dialogue between the EF community and 
other related initiatives, such as the Global Guidance for Life Cycle Impact Assessment Indicators 
and Methods (GLAM), could facilitate the sharing of best practices and enhance the robustness of 
biodiversity assessments. 

As the world faces the challenges of environmental damage and loss of biodiversity, it is key to 
have strong and reliable ways to assess these issues. Ongoing research including stakeholders’ 
engagement are key to keeping biodiversity at the heart of environmental impact assessments. This 
work is not over, as addressing uncovered aspects by selected methodology will be conducted. 

29 

 
 
 
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32 

 
 
List of abbreviations and definitions  

Abbreviations 
AoP 
AWG  
BDP  
BioMAPS 
CFs 
EC  
EDIP 
EF  
EQ  
EU  
FPD  
GLAM  

HCF  
HH  
IPBES  

IW  
JRC  
LCA  
LCIA  
LUIS  
MSA  
NR&ES  
OEF  
PDF  
PEF  
PDFFD  
TAB  
TNFD  

Definitions 
Areas of Protection 
Agricultural Working Group 
Biodiversity Damage Potential 
Biodiversity Multi-Scale Assessments of Product Systems 
Characterisation Factors 
European Commission 
Environmental Design of Industrial Products 
Environmental Footprint 
Ecosystem Quality 
European Union 
Functional Plant Diversity 
Global Guidance for Life Cycle Impact Assessment Indicators 
and Methods 
Habitat Conversion and Fragmentation 
Human Health 
Intergovernmental Science-Policy Platform on Biodiversity 
and Ecosystem Services 
Impact World 
Joint Research Centre 
Life Cycle Assessment 
Life Cycle Impact Assessment 
Land Use Intensity Specific Biodiversity Footprint 
Mean Species Abundance 
Natural Resources and Ecosystem Services 
Organisation Environmental Footprint 
Potentially Disappeared Fraction 
Product Environmental Footprint 
Potential Damage Fraction of Functional Diversity 
Technical Advisory Board 
Taskforce on Nature-related Financial Disclosures 

33 

 
 
 
 
 
List of figures 

Figure 1. Overview of the milestones (sub-groups) of the AWG .............................................................................. 5 

Figure 2. Overview of the method. ............................................................................................................................................ 7 

Figure 3. Overview of models evaluated per impact category. ................................................................................. 8 

Figure 4. Contribution of midpoint impact categories to overall biodiversity impact, by LCIA method 
or model ................................................................................................................................................................................................... 17 

Figure 5. Overview of the calculation approach of the three AoP sub-scores and single score. ........ 21 

Figure 6. Box and whisker plots visualising the distribution across 3342 EF datasets of the relative 
contribution of each impact category to the single score obtained with the EF3.1 method (6a) and 
with the version of the method suggested by Options B and C (considering an additional impact 
category and recalibrated weighting factors (6b)). ......................................................................................................... 24 

Figure 7. Box and whisker plots visualising the distribution across 3342 EF datasets of the relative 
contribution of each impact category to the ecosystem quality sub-score suggested by Option C. .. 25 

Figure 8. Contribution of midpoint impact categories to overall biodiversity impact obtained with 
the EF ecosystem quality sub-score (option C) and the ReCiPe2016, LC-Impact, and Impact World+ 
endpoint methods ............................................................................................................................................................................... 26 

34 

 
 
List of tables 

Table 1. Impact category and short summary of the evaluation .......................................................................... 10 

Table 2. Metrics used at endpoint in the aggregated methods. ............................................................................. 14 

Table 3. Impact categories in ReCiPe2016 and reference time to be used. ................................................... 15 

Table 4. Result of the recalibration of weighting factors .......................................................................................... 18 

Table 5. Connection between midpoint impact categories and areas of protection. ................................. 19 

Table 6. Recalibration on weighing and association to each area of protection. HH: human health; 
EQ: ecosystem quality; NR&ES: natural resources and ecosystem services. .................................................... 20 

Table 7. Correlation between the ranking of products according to their impact on ecosystem 
quality obtained with the EF approach (Option C) and the ReCiPe2016, LC-Impact, and Impact 
World+ endpoint methods.............................................................................................................................................................. 25 

Table 8. Advantages and disadvantages of the three options resulting from the discussion with the 
AWG and the TAB members. ......................................................................................................................................................... 27 

Table 9. List of methods and evaluated per impact category. ............................................................................... 36 

Table 10. Overview of criteria, sub-criteria and description. ................................................................................... 38 

35 

 
 
 
 
 
Annex I 

Table 9 provides the list with the methods evaluated per impact category. 

Table 9. List of methods and evaluated per impact category. 

Method 
ReCiPe2016 
Huijbregts et al. (2016; 2017) 

LC-Impact  
Verones et al. (2016; 2020)  

Impact World+ 
Bulle et al. (2009) 

Stepwise 2006 
Weidema et al. (2008; 2009) 

GLOBIO 
Schipper et al. (2017); Janse et 
al. (2016) 

EcoScarcity Frischknecht & 
Knopfel (2013); FOEN (2021) 
Land Use Impacts on 
Functional Plant Diversity 
(FPD) 
Scherer et al. (2020) 
BioMAPS 
Maier (2023)  
Biodiversity Multi-Scale 
Assessments of Product 
Systems – the BioMAPS 
Method 

Impact Category 
Climate change 

Water Use 

Land Use 

Ecotoxicity 

Eutrophication 

Acidification 

Photochemical 
Ozone Formation 
Climate change 

Water Use 
Land Use 
Ecotoxicity 
Eutrophication 
Acidification 
Photochemical 
Ozone Formation 
Climate change 
Water Use 
Land Use 
Ecotoxicity 
Eutrophication 
Acidification 
Ionising Radiation 
Climate change 
Land Use 
Ecotoxicity 
Eutrophication 
Acidification 
Photochemical 
Ozone Formation 
Climate change 
Water Use 
Land Use 
Eutrophication 
Land Use 

Land Use 

Metrics (from Damiani et al. 2023) 

Species·yr/m3 
consumed
Species∙yr/kg emitted 
Species∙yr /m2

 annual crop land 

consumed

PDF·yr/m3 
PDF/°C 
PDF∙yr/kg emitted 
PDF∙yr/m2
PDF/m2
 land occupation 
PDF∙yr/m2 land transformation 

 soil erosion ∙yr 

dissipated 

PDF·m2·yr/kg emitted 
PDF·m2·yr/m3 
PDF·m2·yr/m3 
PDF·m2·yr/m3 
PDF·m2·yr/m2 
PDF·m2·yr/m2 

cooling water 

turbined water 

transformed 
occupied·yr 

BAHY 

MSA ·m3 
MSA ·km2 

BDP ·m2 settlement area-eq 

PDFFD 

Land Use 

PBR ·m² ·year (potential biodiversity risk) 

36 

 
 
 
Method 
HCF (Habitat conversion and 
fragmentation) 
Kuipers et al. (2021) 
Terrestrial biodiversity impacts 
of land inundation (LI) 
Dorber et al. 2020 
Land use intensity specific 
biodiversity footprint (LUIS) 
Chaudhary & Brooks 2018 
Water temperature impacts on 
fish  
Li et al. 2022 
Water consumption impacts on 
fish species richness (FSR) 
Pierrat et al. 2023 
HCP (Habitat Change Potential) 
Damiani et al. 2021 

Impact Category 
Land Use 

Metrics (from Damiani et al. 2023) 
PDF/m2 
PDF∙yr/m2 

Land Use 

PDF/m2 

Land Use 

PDF/m2 

Climate Change 

PDF 

Water Use 

PDF/m3/year 

Water Use 

Habitat Change Potential (HCP m2· s/m3) 

Source: Authors’ own elaboration 

In the food sector, fertiliser emissions are amongst the main drivers of several environmental 
impact categories, such as eutrophication, acidification, climate change and others. Decreasing the 
emissions from fertiliser use, by reducing the amount of fertiliser applied, improving the nutrient 
uptake efficiency of plants and decreasing the loss of nutrients, represents one of the main 
challenges that agricultural systems face. 

Available LCIA methods and Life Cycle Inventory (LCI) databases, including the Environmental 
Footprint (EF) method and database, often lack the good accuracy and/or consistency of fertilisers’ 
modelling. 

The goal of this milestone is to provide operational recommendations to improve fertiliser 
modelling in EF at the life cycle inventory phase of crop systems. 

Crop systems are here defined as crops or crop sequences cultivated on agricultural fields over a 
period of time. Annual and perennial crops as well as grassland are included while greenhouse 
cultivation, soilless cultivation (such as hydroponic, aquaponic, aeroponic, soiless substate culture, 
and vertical farming), and pasture are excluded. Aquatic plants cultivation through aquaculture and 
algae culture are excluded as well. 

In the case of greenhouse cultivation, practitioners are invited to follow the modelling proposed in 
the dedicated PEFCR. 

This milestone focuses on both inorganic and organic fertiliser application on agricultural field. 
Storage and handling of organic fertilisers from animal husbandry at the farm level, including 
losses during these stages, are covered by Milestone 3. The topics of the boundary 
technosphere/ecosphere at the field and regionalisation/subcompartments are part of Milestone 7 
(Wolf et al., 2025).   

Annex II 

Table 10 shows the criteria and sub-criteria used in the evaluation. Those sub criteria with a yellow 
background are considered as mandatory to reach the threshold proposed. 

37 

 
Table 10. Overview of criteria, sub-criteria and description. 

CRITERIA 

SUB-CRITERIA 

DESCRIPTION 

Documentation, transparency, 
reproducibility 

Accessibility of the 
characterisation model's 
documentation 
Modelling assumptions 
and value choices 

Availability of the input 
data 
Availability of the 
characterisation factors 
Impact pathway 
completeness 

Completeness of the scope 

Ecosystem coverage  

Environmental relevance 

Robustness 

Geographic coverage of 
characterisation factors 

Unit of measure 

Essential Biodiversity 
Variable classes (genetic 
composition, species 
populations, species 
traits, community 
composition, ecosystem 
function, ecosystem 
structure) 
Degree to which risk 
level is considered 

Taxonomic coverage 
Scientific robustness of 
the model and modelling 
choices 

Scientific robustness of 
midpoint to endpoint 
modelling 

38 

How accessible the documentation about 
the model is 

Clear specification of the assumptions 
and value choices in the documentation. 
Assumptions include also estimation 
methods such as data scaling, 
extrapolation, etc. 
How available the input data of the model 
are 
Are characterisation factors available 

Coverage of the impact pathway from 
disturbance to damage on ecosystems 
(i.e. inventory model, early midpoint, 
midpoint, early endpoint, endpoint) 
Coverage of ecosystem types (terrestrial, 
marine, freshwater), not applicable for all 
impact categories, in that case 3 
Geographical coverage of the model in 
relation to the availability of 
characterisation factors 
Ability of the indicator(s) to describe 
relevant aspects of ecosystems dynamics 
and biodiversity 
Consideration of one or more essential 
biodiversity variable (EBV) classes in the 
developing process of the indicator(s) or 
EBV descriptive power of the indicator(s) 

Characterisation of 
species/population/community 
vulnerability, threat status, rarity 
Coverage of taxonomic groups 
The model reflects up to date and 
validated knowledge on the environmental 
mechanisms. Value choices (e.g. on 
aggregation, data gaps, proxies) are 
detailed and robust 
If midpoint and endpoint indicators are 
used, robustness of conversion between 
midpoint and endpoint indicators. If 
midpoint indicators are not used, 
robustness in linking the environmental 
mechanism with endpoint indicators. 

If no endpoint indicator is used, a value of 
3 shall be assigned, which means that 
midpoint indicators are preferred over 
end-point indicators with low scientific 
robustness 

 
 
CRITERIA 

SUB-CRITERIA 

DESCRIPTION 

Definition and relevance 
of characterisation's 
reference state 

Uncertainty and 
sensitivity 

Temporal 
representativeness of 
the underlying 
biophysical models 
Temporal 
representativeness of 
the underlying 
biophysical models 

Temporal representativeness, 
and resolution, time horizon 

Temporal resolution of 
characterisation factors 

Definition and relevance 
of characterisation's 
time horizon 

Spatial 
representativeness of 
the underlying 
biophysical models 
Spatial resolution of the 
input used in the model 

Spatial resolution of 
characterisation factors 

Spatial resolution 

Compatibility with EF method 

Compatibility with LCA 
and EF methodology 

39 

Definition of the reference state of the 
characterisation model. This is important 
if the reference state is relevant for the 
quantification of the impact and 
determines a variability in final results. 

If reference state is not relevant and not 
defined, a value of 3 shall be assigned 
Uncertainty of input data or 
characterisation model and sensitivity to 
normative choices is addressed 
Time period considered and transferability 

Temporal resolution of the input data 
which the characterisation model builds 
upon. The resolution of the input data is 
evaluated instead of model's output 
resolution in case input resolution is lower 
than the model's output (e.g. a model 
producing monthly results, using annual 
input data) 
This criterion complements the criterion 
on "Temporal resolution of the input". 
Here, only the resolution of the 
characterisation factors is considered. 
What is the temporal resolution of the 
characterisation factors? Does it allow for 
a proper representation of the impacts? 
Definition of the time horizon of the 
characterisation model. This is important 
if the time horizon is relevant for the 
quantification of the impact and 
determines a variability in final results. 
If time horizon is not relevant and not 
defined, a value of 3 shall be assigned 
Geographical area(s) considered and 
transferability 

Spatial resolution of the input data which 
the characterisation model builds upon. 
The resolution of the input data is 
evaluated instead of model's output 
resolution in case input resolution is lower 
than the model's output (e.g. a model 
producing results at country level, using 
input data at global level) 
This criterion complements the criterion 
on "Spatial resolution of the input" under 
"Environmental relevance". Does the 
spatial resolution of the characterisation 
factors allow for a proper representation 
of the impacts? 
The model is developed for or compatible 
with the analysis of products and 

 
 
CRITERIA 

SUB-CRITERIA 

DESCRIPTION 

Conform with the EF 
nomenclature and units 
or adaptability 
Spatial resolution of the 
model in relation to the 
spatial resolution of EF 
elementary flows 
Coverage of elementary 
flows 

Usability of 
characterisation factors 
for practitioners 
Tests of the 
characterisation already 
conducted 

organisations life cycles, and suited for 
comparative analysis (e.g. do not include 
safety factors/precautionary principle) 
Conformity with the EF data nomenclature 
or ease of adaptation 

Does the spatial resolution of model 
match with the spatial resolution of the 
EF method 

Percentage of EF flows of the related 
impact category that the model can cover 
(with or without adaptation), including 
additional flows (e.g. additional land use 
intensity classes, flows for new impact 
categories) 

Implementation in the software used by 
LCA practitioners or availability for being 
easily applied 
Case studies/applications where the 
model has been already applied. 
Optimally they refer to relevant products, 
are applied at an adequate scale and are 
peer reviewed. Specify what emerges 
from testing if relevant 

Source: Authors’ own elaboration 

Characterisation factors 

40 

 
 
 
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purposes. The portal also provides access to a wealth of datasets from European countries. 

 
 
 
 
 
 
The Joint Research Centre (JRC) provides 
independent, evidence-based knowledge and 
science, supporting EU policies to positively 
impact society 

EU Science Hub 
Joint-research-centre.ec.europa.eu