# Life cycle assessment of algal products: A step-by-step guide to application

Léa Braud, Juan Gallardo Rodriguez, Andriamahefasoa Rajaonison, Nora Schelte, Silvio Mangini, Olivier Bernard, Igor Pedra, Laura Monteiro, Luis Costa, Lais Speranza, et al.

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Léa Braud, Juan Gallardo Rodriguez, Andriamahefasoa Rajaonison, Nora Schelte, Silvio Mangini, et al. Life cycle assessment of algal products: A step-by-step guide to application. European Algae Biomass Association. 2025. hal-05462389

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# Life cycle assessment of algal products: A step-by-step guide to application

## EXECUTIVE SUMMARY

This information paper provides an accessible introduction to Life Cycle Assessment (LCA) for researchers, industry professionals, and policymakers in the algae sector, with limited or no experience in the methodology. LCA evaluates the environmental impacts of a product, process, or service across its entire life cycle, from raw material extraction to end-of-life.

Developed collaboratively by LCA experts and non-specialists, this information paper outlines key concepts, applications, and best practices for assessing the environmental performance of algae-based products.

LCA is now a common component of EU-funded algae projects, with its range of applications expanding from the assessment of biofuels to high-value compounds and complex production systems. It plays a central role in corporate sustainability, policy development, and in evaluating algae's contribution to the bioeconomy.

However, applying LCA to algae production technologies and algae-based products presents unique challenges such as system variability, data availability, and methodological choices that can strongly influence results and limit comparability. Raising awareness of these issues within the algae community is essential to ensure that LCA outcomes are interpreted meaningfully and used effectively.

This information paper supports newcomers in understanding key terminology and practices related to LCA in algae systems, enabling more informed decision-making as well as the development of innovative and sustainable algae-based products.
EABA LCA TASK FORCE CONTRIBUTORS

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Document coordinated by:

EUROPEAN ALGAE BIOMASS ASSOCIATION

Léa Braud

Innovation Manager

Vítor Verdelho

General Manager

Silvio Mangini

Regulatory and Standards Advisor

Monique Ras

Scientific Advisor

Jean-Paul Cadoret

Vice-President

Carlos Unamunzaga

President

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GREENCOLAB, PORTUGAL (member)

Lais Speranza

Sustainability Group Leader

UNIVERSITY OF LATVIA, LATVIA

Karīna Bāliņa

Researcher

DECERNA, ENGLAND

Tom Bradley

Director

CORBION, THE NETHERLANDS (member)

Ana Morão

Corporate Scientist Sustainability

Saskia Kliphuis

Public Affairs Manager

OLMIX GROUP, FRANCE (member)

Pi Nyvall

Scientific Director

Mathilde Jamois-Piquet

CSR Manager

VIRIDE, GERMANY (member)

Stefan Schmid

Co-founder and CEO

Ismail Chami

Sustainability Management

BIOREA, FRANCE (member)

Carole Perignon

R&D Project Manager

CENTRE D'ÉTUDE ET DE VALORISATION DES ALGUES (CEVA), FRANCE (member)

Ronan Pierre

Department Manager

Innovation & Products

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Corbion

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With key contributions from:

UNIVERSITY OF ALMERIA, SPAIN (member)

Juan Gallardo Rodriguez

Associate Professor

ARMINES | MINES PARIS - PSL, FRANCE

Andriamahefasoa Rajaonison

Postdoctoral Researcher

GHENT UNIVERSITY, BELGIUM (member)

Nora Schelte

Doctoral Researcher

INRIA, FRANCE (member)

Olivier Bernard

Research Director

A4F, PORTUGAL (member)

Igor Pedra

Sustainability Junior Project Manager

Laura Monteiro

Senior R&D Project Manager

Luis Costa

Chief Scientific and Sustainability Officer

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EXTERNAL REVIEWERS

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INRAE, FRANCE (member)

Pierre Jouannais

Permanent Researcher

AALBORG UNIVERSITY, DENMARK

Massimo Pizzol

Professor at the Department of Sustainability and Planning

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PSL★

MINES PARIS - PSL, FRANCE

Paula Pérez-López

Associate Research Scientist at the Centre O.I.E

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# EUROPEAN ALGAE BIOMASS ASSOCIATION

The European Algae Biomass Association (EABA) was founded in Florence in June 2009 as the European association representing both research and industry in the field of algal technologies. It represents and serves organizations and individuals interested in both macro- and microalgal biomass.

The general objective of the EABA is to promote mutual interaction and cooperation in the field of algae biomass production, transformation, and use for the whole range of algae applications. It aims at creating, developing, promoting, and maintaining solidarity, contact, interaction, and collaboration among its members and at defending their scientific and commercial interests at European and international levels. Its main target is to act as a catalyst for fostering synergies among scientists, industrials, and decision makers in order to promote the development of research, technology, and industrial capacities in the field of algology.

The EABA maintains a technology-neutral stance, ensuring that no specific production method, processing technique, or application of algae biomass is prioritised over others. This approach reflects the interdependence of all technologies and uses within the algae sector. In line with these principles, EABA members share accurate, non-confidential information to support the sustainable growth of the algae biomass industry (adapted from EABA statutes).

## EABA LCA TASK FORCE

The EABA LCA task force is a collaborative initiative that brings together academic and industry experts to tackle both practical and methodological challenges of LCA in the algae sector. Its main objective is to provide clear, actionable recommendations for a wide audience including LCA practitioners, algae entrepreneurs, researchers working on areas such as environmental assessment, process modelling, and algae cultivation - as well as policymakers.

The task force focuses on improving understanding of LCA methodologies while promoting transparency and reproducibility in studies of algae-based products, with the aim of supporting better decision-making, more efficient process design, and the setting of meaningful environmental improvement targets. Expected outcomes include information papers and comprehensive databases of LCA studies and algae-specific datasets, aimed at aligning the algae sector with the broader bio-based industry. By harmonising approaches and leveraging insights from the wider bioeconomy, the task force seeks to enable more consistent and robust assessments of the environmental sustainability of algae-based products.

## OPENING REMARKS

### WHO IS THIS FOR?

This information paper is intended for individuals in the algae sector - academics, industry professionals, or policymakers - with limited or no experience in LCA. Whether the goal is to interpret LCA results or to gain a foundational understanding of the methodology, this document provides a comprehensive introduction to best practices for assessing the environmental sustainability of algae-based products. Developed collaboratively by both LCA experts and non-practitioners, this information paper is written in an accessible and structured manner to serve the broader algae community, ensuring that even those new to LCA can grasp its key principles and applications.

## WHY THIS PAPER?

This information paper is based on established LCA standards and guidelines, and provides practical insights to support further engagement with additional resources (see selected references). Given ongoing debates about LCA methodologies, it is important to note that there is rarely a single "correct" approach. Instead of offering rigid guidance, this document introduces LCA best practices focused on enhancing transparency, comparability, and the meaningful interpretation of results.

A key motivation for this information paper is also to highlight the heterogeneity observed in algae LCAs which complicates the comparability of results, particularly between algae-based products and conventional alternatives. This heterogeneity arises from technological diversity (e.g., cultivation systems) and context-dependent variability (e.g., climatic conditions). While this document identifies and discusses several of these contributing factors, methodological variation introduced through practitioner choices will be addressed in greater detail in a forthcoming information paper.

These challenges have been recognised by the EABA since the early stages of algae LCA development. In collaboration with the European Commission, EABA organised a series of European workshops between 2012 and 2015 to address methodological challenges and promote harmonisation. These efforts have been instrumental in fostering stakeholder collaboration and advancing more consistent and transparent LCA practices tailored to the algae sector. This paper builds on that foundation to further support meaningful and transparent algae LCA practices.

## WHAT TO EXPECT?

This information paper provides an introductory overview of LCA methodologies, illustrated through a simplified case study. While LCA can be used to assess environmental, social, and economic impacts, this document focuses specifically on environmental LCA, which remains the most widely applied approach in algae research to date. It offers foundational knowledge on the topic and directs readers to additional resources for deeper exploration. Although some key challenges are addressed, a more in-depth examination of methodological issues will be presented in a subsequent information paper.

## CONTENTS

1. Introduction

2. Fundamentals of LCA

3. Algae LCAs: From theory to practice

4. No one-size-fits-all: Heterogeneity in algae LCAs

5. Conclusions

6. Selected references

## ABBREVIATIONS

**EPD**, Environmental Product Declaration; **EU**, European Union; **FU**, Functional Unit; **GHG**, Greenhouse Gas; **ILCD**, International Life Cycle Data; **ISO**, International Standard Organization; **LCA**, Life Cycle Assessment; **LCI**, Life Cycle Inventory; **LCIA**, Life Cycle Impact Assessment; **LCSA**, Life Cycle Sustainability Assessment; **PBR**, Photobioreactor; **PEF(CRs)**, Product Environmental Footprint (Category Rules).
# 1. INTRODUCTION

## WHAT IS LCA?

LCA is a methodology used to evaluate the environmental impacts of a product, process, or service across its entire life cycle (ISO 14040/44). This includes everything from raw material extraction to production, distribution, use, and disposal or end-of-life.

By examining every stage of the process, LCA helps to answer crucial sustainability questions, such as: *What are the environmental impacts of producing algal products? How does one algae cultivation system compare to another in terms of environmental impacts? What are the trade-offs between different algal biomass processing methods? Are algal products more sustainable than their conventional alternatives?*

Importantly, a life cycle perspective helps to avoid burden shifting, where improvements in one stage of the product system or impact category may lead to unintended increases in environmental impacts elsewhere.

## HISTORY OF THE EABA AND LCA

The EABA has been actively engaged with LCA for over a decade. This involvement began under the leadership of Prof. Mario Tredici, then EABA president, and Vítor Verdelho who supported early harmonisation efforts across three major European projects. These collaborations led to the launch of an annual European LCA workshop, bringing together project representatives to address the methodological challenges of applying LCA to algae-based products - initially focused on biofuels (Bradley et al., 2015).

Since then, LCA has become a core component of nearly all algae projects funded by the European Union (EU), typically embedded in dedicated sustainability work packages. As the field has advanced, LCA methodologies have adapted to reflect technological developments and emerging environmental priorities. Today, LCA is increasingly used to assess not only environmental, but also economic and social dimensions of sustainability. However, this document focuses specifically on environmental LCA, in line with its predominant application in algae research.

Academia and industry have both played key roles in expanding the knowledge base and refining LCA methodologies. The EABA has continued to lead in this space, organising webinars and workshops to support knowledge-sharing, problem-solving, and capacity-building. In recognition of the growing complexity of these efforts, the EABA recently launched a dedicated task force to address emerging methodological challenges and guide future development of algae-specific LCA practices.

## APPLICATIONS OF LCA

LCA is a cornerstone of sustainability efforts across both private and public sectors. In industry, it informs sustainability reporting and decision-making by providing a comprehensive view of a product's impacts throughout its life cycle. It helps companies reduce environmental impacts, compare alternatives, comply with regulations, and substantiate sustainability claims.

In policy, LCA plays a central role in shaping EU environmental strategies. It underpins key initiatives such as the Circular Economy Action Plan, EcoDesign requirements, and the EU Green Deal, informing product standards, promoting resource efficiency, and guiding carbon reduction strategies. LCA also supports Environmental Product Declarations (EPDs) and green procurement efforts.

At the same time, LCA remains a fast-evolving research field. Advances in modelling, data integration, and open-source tools are enhancing its precision and relevance, especially for dynamic, complex systems such as algae cultivation and biomass processing. As sustainability challenges intensify, methodologies are evolving to integrate environmental with economic and social dimensions, cementing LCA's role in the transition towards a more sustainable and resilient bioeconomy.

## EVOLUTION AND EXPANSION OF LCA IN THE ALGAE SECTOR

Over the past decade, the use of LCA in the algae sector has expanded significantly in both scope and complexity. Between 2009 and 2022, the number of algae-related LCA studies increased significantly. Early work primarily focused on algal biofuels - such as biodiesel, biogas, and bioethanol - which accounted for over half of all studies (Braud et al., 2023). From 2014 onward, the scope expanded to include high-value compounds such as pigments, polyunsaturated fatty acids, and lactic acid, reflecting both shifting commercial priorities and a broader recognition of algae's cross-sector potential.

The diversity of macro- and microalgae species studied also grew, with microalgal species dominating the literature (ibid). Meanwhile, LCA goals broadened from basic product comparisons to include different cultivation systems, growing conditions, conversion technologies, and production scales. This evolution highlights the growing interest of the algal sector in LCA.

## PRACTITIONERS, SOFTWARE, AND DATA

LCA studies should be conducted by trained practitioners - whether they are in-house experts, external consultants, or academic researchers - following internationally recognized standards to ensure consistency and reliability. Common software tools include openLCA, SimaPro, and LCA for Experts (previously known as Gabi), while more advanced frameworks such as Brightway and its graphical interface Activity Browser support advanced modelling (e.g. dynamic, prospective) of complex systems.

LCA relies on two main types of data: primary data, which come from direct measurements (e.g., energy use in a photobioreactor or centrifuge), and secondary data, sourced from databases, literature, and industry reports. Secondary data are used when direct measurements are unavailable or to represent upstream processes outside the practitioner's control, such as raw material extraction. Many studies rely on a mix of both. Access to licensed datasets is often necessary for detailed and context-specific analysis.

Together, these data correspond to the inventory, which is the foundation of any LCA. However, available data are often incomplete, outdated, or not fully representative. As a result, practitioners need to make assumptions or approximations, which introduce uncertainty into the results. To address this, sensitivity and uncertainty analyses are common practice. Verification, as required flexibly by standards, can further enhance reliability (see Section 2).

Sensitivity analysis, Monte Carlo simulations, and the application of pedigree matrices are employed to assess the robustness of results and to identify critical parameters that affect the assessment's confidence levels. Incorporating these analyses ensures that LCA findings are transparent and credible, providing a solid foundation for informed decision-making and comparative evaluations.
## ALIGNING LCA METHODOLOGIES IN THE BIO-BASED INDUSTRY

Several EU-funded projects are advancing LCA methodologies to enhance the environmental sustainability of bio-based industries. These projects aim to refine and harmonise LCA frameworks to improve the consistency, transparency, and reliability of environmental assessments for bio-based products. Key projects include:

*   **ALIGNED (2022–2025):** Focuses on harmonising LCA methodologies across sectors including bio-based chemicals, algae, textiles, and construction. It addresses variability in LCA results and aims to provide a shared framework and database of case studies, supporting more consistent and credible environmental assessments for a sustainable and circular bioeconomy.

*   **CALIMERO (2022–2025):** Improves Life Cycle Sustainability Assessment (LCSA) frameworks by integrating environmental, social, and economic indicators. CALIMERO focuses on identifying opportunities for pollution reduction and efficiency improvements, supporting stakeholders in adopting more sustainable practices across bio-based industries.

*   **LCA4BIO (2024–2026):** Aims to develop reliable, harmonised LCA methodologies for the bio-based sector. By refining tools to assess both environmental and socio-economic impacts, it seeks to enhance the credibility of LCA studies and ensure fair comparisons between bio-based and conventional products.

The work of the EABA LCA task force reinforces these EU initiatives, particularly ALIGNED, which explicitly includes algae as one of its target sectors.

This shared focus on harmonising LCA approaches and improving data transparency presents strong opportunities for collaboration. By engaging with these initiatives, the task force contributes to a broader, coordinated effort to advance consistent and robust sustainability assessments across the bio based industry.

## OVERVIEW OF EXISTING LCA STANDARDS AND GUIDELINES

Assessing the environmental sustainability of algae-based products requires LCA methods that are reliable, transparent, and consistent. While general LCA standards provide a foundational framework, the specific characteristics of algae systems - such as high variability in cultivation systems, multifunctionality due to multiple products, data scarcity and data gaps, and scaling challenges - necessitate tailored approaches. Several international standards, European directives, and emerging initiatives offer guidance relevant to LCA practitioners working in the algae sector. Table 1 summarises the key standards and guidelines currently applicable.

### IMPORTANT NOTE

This information paper is a practical introduction to environmental LCA for stakeholders in the algae sector including researchers, industry professionals, and policymakers, with limited prior experience. Developed collaboratively by LCA experts and non-specialists, it outlines key concepts, common challenges, and best practices to support transparent and comparable assessments of algae-based products. It focuses exclusively on the environmental dimension of sustainability; economic and social aspects will be addressed in future information papers.

<table><caption>**Table 1:** Summary of LCA standards and guidelines relevant to the algae sector (non-exhaustive list).</caption><thead><tr><td>STANDARD/GUIDELINE</td><td>DESCRIPTION</td><td>RELEVANCE TO THE ALGAE SECTOR</td></tr></thead><tbody><tr><td>ISO 14040/44</td><td>Core international standards outlining LCA principles and requirements.</td><td>Foundational structure for conducting algae LCAs. Not algae-specific.</td></tr><tr><td>International Life Cycle Data (ILCD) Handbook</td><td>European Commission's detailed LCA technical guidance.</td><td>Provides quality criteria and methodological clarity, applicable across bio-based sectors.</td></tr><tr><td>Product Environmental Footprint (PEF) method</td><td>EU method for consistent, multi-impact life cycle assessment of products using standardised category rules (PEFCRs).</td><td>Offers a harmonised LCA framework; adaptable to algae products despite no algae-specific PEFCR yet.</td></tr><tr><td>EN ISO 14067: 2018</td><td>Focuses on quantifying and communicating product carbon footprints.</td><td>Useful for algae LCAs with a focus on greenhouse gas emissions (GHG).</td></tr><tr><td>RED II (EU Renewable Energy Directive)</td><td>Sets GHG calculation methods and sustainability criteria for biofuels.</td><td>Applies to algae-derived fuels under EU sustainability schemes.</td></tr><tr><td>EN 16751: 2016</td><td>Bio-based products - Sustainability criteria. Currently under revision in CEN/TC 411 WG4 to include more detailed, quantitative indicators.</td><td>Sets sustainability criteria applicable to the bio-based part of all bio-based products, excluding food, feed and energy, covering environmental, social, and economic aspects.</td></tr><tr><td>EN 16760: 2015</td><td>Bio-based products - Life cycle assessment</td><td>Provides specific LCA requirements and guidance for bio-based products derived wholly or partly from biomass.</td></tr><tr><td>ISO 14068-1: 2023</td><td>Climate change management - Transition to net zero Part 1: Carbon neutrality</td><td>Provides a structured approach for carbon neutrality claims.</td></tr><tr><td>EN 17983: 2024</td><td>Specifies measurement methods for renewable algal raw material for energy and non-energy applications.</td><td>Directly relevant to algae products in energy and non-energy applications. Provides metrics for data collection from farming.</td></tr><tr><td>NPR-CEN/TR 16957</td><td>Guidelines for Life Cycle Inventory (LCI) of the end-of-life phase for bio-based products.</td><td>Relevant for assessing end-of-life stages of algae-based products.</td></tr><tr><td>EN 18027: 2025</td><td>LCA guidelines for comparing the life cycles of bio-based and fossil-based products.</td><td>Offers additional requirements for comparing algae-based products with fossil-based alternatives.</td></tr></tbody></table>
## 2. FUNDAMENTALS OF LCA

This section introduces the fundamentals of LCA aligned with ISO 14040/44 standards and adapted to include examples and considerations specific to the algae sector. For detailed methodological guidance, readers are encouraged to consult the original ISO standards and the additional references listed.

### 2.1. METHODOLOGICAL FRAMEWORK

LCA follows a standardised four-phase framework established by the International Organization for Standardization (ISO 14040 and 14044) to ensure consistency and reliability (see Figure 1).

These phases are:

#### GOAL AND SCOPE DEFINITION

Defines the purpose of the LCA study, the product or service being analysed, modelling details, and data requirements. Decisions made in this step must ensure the LCA is methodologically robust, transparent, and aligned with its intended purpose.

#### LIFE CYCLE INVENTORY (LCI)

Involves collecting and processing data on inputs (e.g., raw materials, energy) and outputs (e.g., products, emissions, waste) throughout the product life cycle, with the aim to quantify all emissions and consumptions of elementary substances.

#### LIFE CYCLE IMPACT ASSESSMENT (LCIA)

Translates inventory data into environmental impacts, such as climate change, water consumption, and resource depletion.

#### INTERPRETATION

Involves synthesising results, assessing sensitivity and robustness, identifying limitations, and deriving key sustainability insights to support decision-making.

LCA is an iterative process, meaning each phase can be refined as new information becomes available. For instance, if data gaps are identified during the LCI phase, the goal and scope may need to be revisited and adjusted accordingly.

<figure><img src="image_21.png" /><figcaption>Figure 1. The four interrelated phases of LCA, adapted from ISO 14040.</figcaption></figure>

The following subsections present each of the four phases of LCA, highlighting the key elements to include based on ISO 14040/44 standards, further elaborated by the ILCD guidance (Wolf et al. 2012). While full compliance with the ISO standards requires including all items, achieving 100% compliance is rarely possible in practice - such as in scientific papers with limited space. Nonetheless, LCA practitioners should strive for full adherence to these standards.

### 2.2. Goal and scope definition

#### 2.2.1. Purpose

*What is the reason for conducting the study? What is the research question or hypothesis? Why is this study important or necessary?*

This section informs the reader of the final report and reminds practitioners working on the study of the reasons the client wanted the work to be done.

The purpose should be clearly and unambiguously stated. If most aspects of an LCA evolve throughout the study, the reason(s) for conducting it should generally remain unchanged during the course of the assessment.

#### EXAMPLE OF PURPOSES

* Product development

* Marketing

* Environmental reporting

* Environmental labelling

* Procurement/choice of supplier

* Learning/awareness raising

* Support policy development

* Support legislation

#### 2.2.2. Intended application

*How are the results of the study intended to be used?*

This section informs the reader of the final report and reminds practitioners working on the study of how the results will be used. It also determines the methodological choices.

It is important to distinguish between the reasons (why?) and the intended application (how?) of the study.

#### EXAMPLE OF APPLICATIONS

* Environmental assessments & understanding

* Ranking of process steps in terms of environmental impact

* Identification of data/knowledge gaps

* Environmental product certification

* Business decision-making

* Product and process development

* Identification of Key Environmental Performance Indicators

* Weak point analysis of a specific product

* Eco design / Design-for-recycling analysis

* Comparison of specific goods or services

#### 2.2.3. Intended audience

*Who is the study intended to inform? To whom will the results be communicated?*

This section tells the reader of the final report and reminds practitioners working on the study who the study was aimed at.

It also determines how the report is written (e.g., method to handle data confidentiality).
### 2.2.4. Comparison

*Will the results be used to support comparative assertions disclosed to the public?*

This section tells the reader of the final report and reminds practitioners working on the study whether the study will be used for public disclosure of results that can be used to compare different products with the same function.

It also determines how the report is written (e.g., stand-alone versus comparative LCA study).

#### COMPARATIVE LCA

*Type of questions:*

* Determine impact and/or establish baseline of a single product or service.

* Identify "hotspots" and improvement opportunities.

*Analysed unit:*

* Quantified definition of the product or process.

#### STAND-ALONE LCA

*Type of questions:*

* Compare different products or services with similar functions.

*Analysed unit:*

* Quantified measure of the function of the product/service.

* The products/services must have the same functional unit.

### 2.2.5. Product system and product

A product system consists of a set of interconnected processes and flows that performs one or more functions, and which models the full life cycle of a product. This product may be any good (e.g., algae-based food ingredient, biofertiliser) or service (e.g., hour of consultation).

This section defines how the products are delivered, detailing both the core function of the product system and its technical configuration:

* What the system does (its function).

* How it performs that function.

* Fundamental specifications.

* Inputs needed (upstream; background).

* How the product is made (foreground).

* Processing, use, disposal (downstream).

### 2.2.6. Foreground versus background system

To illustrate the distinction between foreground and background systems in LCA, the iceberg metaphor is helpful (see Figure 2).

The foreground system, the visible tip of the iceberg, represents the specific system under study and receives most of the attention. Its processes are directly modelled by the LCA practitioner and usually, the practitioner can directly collect data for this such as the electricity consumption of an algae

cultivation system (e.g., photobioreactor).

Beneath the surface lies the much larger background system, which supports the foreground by supplying data on upstream industrial processes (e.g., energy, materials, chemicals) via LCI databases such as ecoinvent and AgriFootprint. Many of the system's inputs originate from these upstream processes, which are often well documented in existing databases, such as 1 kilowatt hour (kWh) of electricity in a specific country.

Distinguishing between foreground and background systems clarifies where practitioners focus their data collection efforts and which parts of the model depend on existing datasets.

<figure><img src="image_22.png" /><figcaption>**Figure 2.** Iceberg metaphor illustrating the distinction between foreground and background systems in LCA.</figcaption></figure>

### 2.2.7. Function

*What does the product, process, or service do?*

This section defines the intended role or purpose of the system under study.

A clear understanding of the function is essential for determining the functional unit.
## EXAMPLES OF FUNCTIONS

An algae cultivation system produces biomass for further processing, an algae-based wastewater treatment unit removes contaminants while generating usable biomass, and an algae-derived fertiliser provides nutrients to enhance soil health and crop productivity.

If a product, process, or service delivers more than one function, the issue of multifunctionality arises (see Section 2.2.11).

### 2.2.8. Functional unit

The functional unit (FU) is a measure of the function, performance, or valuable output of a system.

* The FU quantifies the function of the system and serves as the reference for all LCA calculations. Inputs and outputs are related to the FU. For instance, emissions may be reported in kg of CO₂ per FU. Environmental impacts are then calculated from these emissions and expressed as climate change impacts measured in kg CO₂-equivalents per FU (carbon footprint) or primary energy use in MJ per FU.

* A unit of measure such as kg or m³ should be used for the FU whenever possible although counts or pieces may be more suitable in certain cases depending on the product and goal of the LCA study.

For comparative LCAs, the FU must enable a fair comparison of equivalent functions.

## EXAMPLES OF FUNCTIONAL UNITS

A typical FU for microalgae could be 1 kilogram of protein. To improve comparability, this can be refined using metrics such as the Weight Nutrient Density Score (WNDS), which account for the broader nutritional value of the biomass by considering components such as vitamins, minerals, and unsaturated fatty acids. See Table 2 for more examples.

### 2.2.9. System boundaries

*Which life cycle stages are included in the system under study, and which are excluded?*

Defines the scope of the system being assessed, including temporal coverage (temporal boundaries). Without boundaries, the system would include the entire world.

Typically, system boundaries include one or more of the following life cycle stages:

* Raw material extraction / acquisition

* Manufacturing

* Use / reuse / maintenance

* Recycling / waste management

The chosen boundaries must be clearly described and illustrated with a system diagram (see Figure 3).

### 2.2.10. Modelling approach

In LCA, two main modelling approaches exist: attributional and consequential. These influence key methodological choices, such as the selection of background data and the handling of challenges such as multifunctionality.

<figure><img src="image_23.png" /><figcaption>**Figure 4.** Attributional and consequential LCA modelling approaches, adapted from Weidema (2003).</figcaption></figure>

* **Attributional LCA (ALCA)** quantifies the environmental impacts of a product, process, or service based on its existing life cycle. It typically uses average data to describe the current state of the system and allocates impacts to products based on properties of product flows such as their mass, price, or energy content. The results represent a share of the global environmental burdens and provide a snapshot of the system's current environmental footprint.

* **Consequential LCA (CLCA)**, on the other hand, uses marginal data to assess the environmental consequences of changes in demand and system interactions (see Figure 4). It often addresses multifunctionality through substitution.

<figure><img src="image_24.png" /><figcaption>**Figure 3.** Simplified system diagram of an algal system. The asterisk (*) indicates that additional processes may be included depending on the specific system under study (e.g., formulation of a product after extracting the active ingredient from the biomass).</figcaption></figure>
<table><caption>**Table 2:** Examples of functional units and associated research questions (non-exhaustive list), adapted from Ahlgren et al. (2015)</caption><thead><tr><th>Category</th><th>Examples of FUs</th><th>Examples of associated research questions</th></tr></thead><tbody><tr><td rowspan="3">Use of feedstock</td><td>1 hectare of algae cultivation facility</td><td>How does seasonal variability affect the environmental impacts per hectare of algae cultivation facility? What are the environmental impacts associated with the use of 1 hectare of land for algae cultivation compared to alternative land uses? How does land productivity (biomass yield per hectare) influence the environmental sustainability of algae cultivation?</td></tr><tr><td>Production of 1 kg of algal biomass</td><td>What downstream conversion process offers the lowest environmental impact per kilogram of algal biomass? How do different algal biomass types (e.g. species, cultivation methods) compare in terms of their environmental performance per kilogram produced?</td></tr><tr><td>Treatment of 1 cubic meter of wastewater</td><td>Which algae-based treatment pathway results in the lowest environmental impact per cubic meter of wastewater treated? What are the environmental benefits and trade-offs of valorising wastewater through algae-based processes (e.g. nutrient recovery, biomass production)?</td></tr><tr><td rowspan="2">Single algal product</td><td>Production of 1 kg of algae-based product</td><td>What are the environmental impacts associated with producing 1 kilogram of a specific algae-based product, and how do they compare to conventional alternatives?</td></tr><tr><td>Production of 1 megajoule of algae-based product</td><td>What are the environmental impacts associated with the use of algae-based products as an energy source, per megajoule delivered?</td></tr><tr><td rowspan="2">Multifunctional algal system</td><td>1 algal biorefinery</td><td>What are the key environmental hotspots within the algal biorefinery system? Which processes within an algal biorefinery offer the greatest potential for reducing environmental impacts? How does the environmental performance of an algal biorefinery compare to conventional single-product systems?</td></tr><tr><td>Combination of biorefinery products</td><td>What are the individual and combined environmental impacts of multiple products generated within an algal biorefinery (e.g., biodiesel, animal feed, biochar)? Does integrated co-production in an algal biorefinery offer environmental advantages over producing each product in standalone systems?</td></tr></tbody></table>

Marginal data refers to information describing the environmental impacts associated with a small change, typically an increase or decrease, in the production or consumption of a product or service. Rather than reflecting the share of the global impacts of all units produced, marginal data specifically represents the consequences of producing one additional (or one fewer) unit. The choice between ALCA and CLCA is further illustrated in the ILCD Handbook (Wolf et al., 2012).

The choice between ALCA and CLCA shapes the scope, assumptions, and interpretation of an LCA study.

### EXAMPLE ALCA VERSUS CLCA

Consider the production of algal proteins. Attributional LCA will assess the typical impact of producing 1 kg of algal protein in a Spirulina farm, assuming that the rest of the world is not affected by this production. Consequential LCA will account for the effects introduced by a change in demand for algal proteins, for example a corresponding increase in algal production activities.

### 2.2.11. Multifunctionality

Multifunctionality arises when a process delivers more than one product or function. ISO 14044 provides a stepwise approach to address this issue. The preferred solutions are to avoid multifunctionality through subdivision - splitting the process into separate single-function processes - or through system expansion or substitution - broadening the system boundaries to include all co-products and their functions.

If these approaches are not feasible, inputs and outputs can be distributed among the products using allocation based on a physical relationship (e.g., mass or energy content). If no suitable physical basis exists, an economic allocation, based on the relative market value of the products, may be applied.

### EXAMPLE OF MULTIFUNCTIONAL SYSTEM

Producing algal bioplastic from wastewater using flue gas would also result in the treatment of wastewater and the capture of emissions and may generate biofertiliser as a co-product. This means the system delivers two services and one co-product, all of which must be properly accounted for (see Figure 5).

<figure><img src="image_25.png" /><figcaption>**Figure 5.** Simplified diagram of a multifunctional algal bioplastic production system.</figcaption></figure>

### 2.2.12. Impact assessment

The choice of impact assessment method and impact categories depends on the goal of the LCA study.

LCIA translates the inputs and outputs of a system, known as elementary flows, into environmental impacts. Elementary flows refer to material or energy exchanges between the technosphere and the environment, such as emissions to air or extraction of resources.
There are two levels of assessment: midpoint and endpoint (see Figure 6).

**Midpoint indicators** look at environmental problems in the middle of the cause-effect chain, such as greenhouse gas emissions. These indicators are more detailed and cover more categories.

**Endpoint indicators** group midpoint indicators into broader areas of concern: Human Health, Ecosystem Quality, and Natural Resources.

Different LCIA methods include different sets of impact categories. Common ones include climate change, acidification, eutrophication, ozone depletion, photochemical oxidant formation, particulate matter, human and eco-toxicity, resource and energy use, land and water use.

The impact categories chosen should reflect the key environmental issues relevant to the system being studied, and they are defined during the goal and scope phase of the LCA.

It is recommended to select a broad range of impact categories rather than a limited few. This approach ensures a more comprehensive assessment of environmental impacts, minimising the risk of omitting significant factors. A more inclusive selection of categories provides a more complete understanding of the system under study.

### 2.2.13. Data requirements

Defines the types and sources of data to be used, including their required precision and acceptable levels of uncertainty.

Data requirements must align with the goal and scope of the study:

* For studies intended for public communication or product comparisons, high-quality data is essential; estimates, assumptions, or generic averages should be avoided for key processes.

* For internal use or early-stage decisions, less detailed (rough-and-ready") data may be acceptable.

* The criticality of the decision informs how accurate and reliable the data needs to be.

Not all inputs and outputs need to be included, only those relevant to the impact categories of interest. The goal of the study determines which environmental impacts are important, and these impacts, in turn, determine which data must be collected.

Collecting data when the process is not yet built at full scale, but only in a laboratory or pilot scale can be an issue. This is a recurrent difficulty with algae systems, when developing new products or services. This means that extrapolation must be carried out, which opens the door to subjectivity and requires that specific scenarios be validated.

## 2.3. LIFE CYCLE INVENTORY

The LCI phase is where **data collection** and **system modelling** take place. It involves gathering detailed information on inputs and outputs in line with the defined goal and scope, and typically demands the greatest time and effort.

### 2.3.1. Foreground data collection

**Foreground data** refers to information directly related to the specific system under study.

It is typically gathered through:

* On-site measurements using specialised instruments (e.g., power and water consumption meters, scales for weighing) and document retrieval (e.g. official documents such as invoices, certificates of analysis, registers, permits).

* Secondary sources, such as scientific publications, company sustainability reports, sector-specific studies, or government data.

* Questionnaires or surveys, where data is provided by relevant personnel or external stakeholders.

<figure><img src="image_26.png" /><figcaption>**Figure 6.** Schematic steps from inventory to category endpoints in life cycle impact assessment, adapted from the ILCD Handbook (Wolf et al., 2012)</figcaption></figure>
Key data types include energy and material inputs, emissions to air, water, and soil, product and co-product outputs, and waste generation. However, many uncertainties remain in algae cultivation, particularly regarding direct emissions from the culture to the atmosphere, especially CO₂, NH₃ and N₂O.

This topic will be addressed in greater depth in a forthcoming technical information paper.

Scaling up data remains a major challenge in the LCA of algae-based products. Algae cultivation and biomass processing techniques are still under development, with only a limited number of commercial scale facilities currently in operation worldwide. As a result, much of the available LCI data is derived from laboratory or pilot scale experiments, which do not reflect the efficiencies, integration, and economies of scale achievable in large scale production systems.

Various methods exist to address this gap, including the use of process simulation software such as Aspen Plus to model scaled-up operations more realistically (López-Herrada et al., 2023). In this context, prospective LCA is particularly well suited for algae systems, as it allows for the consideration of future technological developments (Cucurachi et al., 2022).

### 2.3.2. Background data collection

Background data represents the broader industrial systems supporting the foreground processes. It is usually obtained from:

* LCI databases, such as ecoinvent, Agribalyse, Environmental Footprint, GaBi, BEES, ELCD, and Industry Data 2.0.

#### EXAMPLE OF DATABASES

<figure><img src="image_27.png" /></figure>

* Published sources, including scientific literature, governmental and NGO reports, industry datasets, and sector studies.

### 2.3.3. Data validation

Collected data must be verified to ensure consistency, accuracy, and reliability. Several validation approaches include:

* Mass and energy balance checks to confirm input-output consistency.

* Recalculation and cross-checking to identify errors or inconsistencies.

* Comparison with alternative data sources or theoretical estimates.

To address data gaps, the following approaches can be applied:

* Assigning a zero value, if clearly justified (e.g., negligible or intentionally excluded).

* Using estimated values based on similar technologies or proxy data from literature.

### 2.3.4. Relating data to unit processes

Collected data must be aligned with the unit process level of the system model. For instance, if raw data are reported per unit of time (e.g., emissions per year, material use per year), these values must be:

* Converted to the FU of the system (e.g., per kg of product), using the appropriate reference flow.

* Expressed using SI units to maintain consistency.

* Adjusted to reflect the correct spatial and temporal context (e.g., regional energy mixes, current-year data).

* Scaled and validated through methods such as recalculation, cross-checking, and comparison as noted in the data validation step.

Where multiple technologies or products are available (e.g., different types of packaging), choose either:

* A specific technology when detailed, project-specific data are available (foreground).

* A market average or representative mix when using general, secondary data (background).

### 2.3.5. Data aggregation

Once data have been collected and validated, they are aggregated into a LCI table. This involves summing all relevant environmental inputs and outputs across the system. The inventory table includes only environmental flows (e.g. emissions, resource use) and does not contain any economic data.

Data can be aggregated according to various criteria, such as:

* Process type (e.g., transportation, energy supply, raw material extraction)

* Life cycle stage (e.g., production, use phase, end-of-life)

* Geographic region (e.g., country-specific or regional differences)

Aggregation helps organise and interpret the inventory data and supports the subsequent impact assessment phase.

#### IMPORTANT NOTE

While system modelling, addressing multifunctionality, and calculating LCI results are integral parts of the LCI stage, they are not covered in detail in Section 2.3. Modelling perspective and methods to handle multifunctionality were described in Sections 2.2.10 and 2.2.11, respectively. For comprehensive guidance on these topics, readers are encouraged to consult the ILCD Handbook (Wolf et al., 2012).
## 2.4. LIFE CYCLE IMPACT ASSESSMENT

The LCIA is the phase during which the inputs and outputs (elementary flows) that have been collected and reported in the LCI are translated into environmental impacts.

### 2.4.1. Selection of LCIA method

After completing the inventory analysis but before proceeding with impact calculations, LCA practitioners should conduct a systematic validation of their initially selected LCIA methods. This validation serves as a critical checkpoint within the iterative LCA framework outlined in ISO 14040/44.

LCIA methods are used to associate the LCI results with environmental impact categories and indicators in four steps: classification and characterisation, which are mandatory; and normalisation and weighting, which are optional.

LCIA methods differ regarding the included impact categories, the considered indicator level (endpoint vs. midpoint) and the applied models to describe the cause-effect chain and therefore, have a significant influence on results.

The selection of LCIA methods must be made and justified prior to the inventory analysis as part of the goal and scope definition. It is recommended to use LCIA methods that provide a complete set of indicators rather than combining LCIA methods to cover different indicators. Moreover, methods should be scientifically valid and, if possible, endorsed by the governmental body of the relevant region as described in the ILCD Handbook.

The European Commission (EC) recommends the Environmental Footprint (EF) v.3 method for studies that are implemented in accordance with the EF rules (European Commission, 2021). It is important to note that EF includes Global Warming Potential with a 100-year time horizon (GWP100) only, excluding GWP20. The latter is increasingly recognised as an additional impact category in LCA and carbon studies and will be addressed in a forthcoming, more technical information paper. Other LCIA methods often applied in the algae sector are ReCiPe, CML, IPCC, CED, and TRACI (Braud et al., 2023).

### 2.4.2. Selection of impact categories

Each LCIA method includes specific impact categories, but generally, the midpoint impact categories can be classified as acidification, climate change, eutrophication, ecotoxicity, ozone depletion, ionising radiation, photochemical oxidant formation, particulate matter formation, human toxicity, resource use (minerals and metals as well as fossil fuels), land use, and water use. The endpoint level usually includes Human Health, Ecosystem Quality, and Natural Resources (see Figure 6).

Each study should include a comprehensive set of indicators that are relevant for the specific product system in alignment with the study goal. ILCD recommends that by default all of the above impact categories should be checked for relevance but if existing studies of similar systems show that some default impact categories are of little overall relevance, these might be excluded. Any gaps shall be justified in the goal and scope definition, documented, and be explicitly discussed in the results interpretation.

For LCA studies in the algae sector it is recommended to consider a wide range of impact categories beyond climate change to highlight potential trade-offs between different impact areas (Amponsah et al., 2024), such as water use and eutrophication.

The European Commission recommendations point out that relevant environmental impacts of a product might go beyond the proposed impact categories such as impacts on biodiversity. Therefore, it is recommended to report additional information if feasible and relevant, based on the application of additional impact categories or qualitative descriptions.

### 2.4.3. Validation of LCI data against LCIA method

Before conducting the impact assessment, it is essential to validate the compatibility between the LCI data and the selected LCIA method. This ensures methodological coherence and meaningful interpretation of results.

Key validation steps include:

* **Inventory completeness check:** Evaluate whether the collected LCI data aligns with the impact categories covered by the initially selected LCIA method.

* **Geographical relevance assessment:** Verify that the spatial scope of the collected inventory data matches the geographical applicability of the LCIA method. Some methods are region-specific (e.g., water use) while others have global applicability (e.g., climate change).

* **Technical coverage evaluation:** Confirm that all significant elementary flows identified in the inventory have corresponding characterisation factors in the selected LCIA method.

* **Midpoint vs. endpoint reconsideration:** Reassess whether midpoint indicators, endpoint indicators, or both will provide the most meaningful interpretation for the identified significant flows and processes.

### 2.4.4. Calculation of environmental impacts

In this step, also called "characterisation", LCI data are transformed into impact scores using characterisation factors provided by the selected LCIA method. This allows results to be aggregated within each impact category, producing an LCA score per category. The reliability of these results depends on the chosen method, underlying assumptions, and data quality. Given the variation in complexity, regional relevance, and data availability across impact categories, a trade-off often exists between model simplicity and accuracy. These calculations can be performed using a range of software tools, from open-source platforms to professional licensed solutions.

### 2.4.5. Optional elements of the LCIA phase

In addition to core LCIA steps, optional elements such as normalisation, grouping, and weighting may be applied depending on the study's goal and scope. These methods help interpret impact results more effectively but involve value-based choices and external references.

Their use must be clearly documented, transparent, and consistent with the defined objectives of the LCA.

These optional elements are not covered in this information paper and readers are referred to the ISO 14040/44 standards and the ILCD Handbook for comprehensive guidance on these topics.
# EXAMPLES OF SOFTWARE FOR LCA CALCULATIONS

## Open-source software (non-exhaustive):

* **OpenLCA:** A widely used, free, open-source LCA software with comprehensive tools for sustainability assessment and life cycle modelling. While openLCA itself is free, access to comprehensive databases through openLCA nexus, an online repository for LCA data, may involve additional costs (e.g., ecoinvent).

* **Brightway:** An open-source Python library specific to LCA calculations that allows for advanced modelling such as global sensitivity analysis and prospective LCA. It supports transparency and reproducibility by allowing users to share their workflows using scripts and notebooks. While the tool itself is freely available, it does not give access to proprietary databases which involves additional costs.

* **Activity Browser:** The Activity Browser is a graphical interface built on Brightway that maintains its full functionality while offering a more user-friendly, visual environment. It enables users to perform prospective LCA studies without needing to write code, making advanced analysis more accessible to a broader audience.

## Licensed software (non-exhaustive):

* **SimaPro:** A LCA software for detailed modelling and scenario analysis, widely used by experts and organisations for its robust databases and compliance with ISO standards.

* **GaBi (Sphera LCA for Experts):** Known for its comprehensive datasets and advanced modelling capabilities, GaBi is favored by industries needing integration with ERP/PLM systems and detailed scenario analysis.

## 2.5. INTERPRETATION

The life cycle interpretation phase integrates results from the inventory and impact assessment to identify significant issues, evaluate data quality and consistency, and draw conclusions with recommendations. This phase frames the study alongside the goal and scope definition, ensuring a comprehensive understanding of the LCA findings.

### 2.5.1. Identification of significant issues

This step involves organising and analysing results from the LCI and LCIA phases to identify **key environmental issues** relevant to the study's goal and scope. It considers methodological choices, assumptions, and value judgments made earlier in the LCA, such as methods to handle multifunctionality and selection of impact categories.

Significant issues may include specific inventory data (e.g., energy use, emissions), important impact categories (e.g., climate change, resource depletion), or critical life cycle stages (e.g., cultivation, biomass drying). To determine their significance, findings are combined with data quality information and stakeholder perspectives, ensuring a comprehensive and context-aware evaluation.

### 2.5.2. Evaluation

The evaluation phase aims to enhance confidence in the LCA or LCI results by systematically reviewing significant issues in line with the study's goal and scope. This includes **completeness**, **sensitivity**, and **consistency** checks, supported by uncertainty and data quality analyses to ensure reliability and transparency.

**Uncertainty and sensitivity analyses** are particularly valuable for assessing the robustness of results. For instance, they help evaluate the influence of data quality and methodological choices, and support iterative improvements as needed.

**Completeness checks** ensure that all necessary information is included, and **consistency checks** verify that methods and assumptions are applied uniformly and align with the study's objectives. Together, these elements support the development of clear, meaningful conclusions tailored to the intended use of the study.

The LCA process is inherently iterative, both within and across its four phases, allowing for ongoing refinement as system understanding improves and as new data become available.

## 3. ALGAE LCA: FROM THEORY TO PRACTICE

This section offers a practical walkthrough of how to carry out a LCA study step-by-step, using a simplified case study of a pilot scale Spirulina biorefinery. Designed as an educational tool, this example guides readers through each phase of an LCA study - from defining the goal and scope to compiling inventory data, assessing environmental impacts, and interpreting the results.

While the case study is based on operational data from the EU funded project "SpiralG" (Braud et al., 2025), it has been significantly simplified to ensure clarity and accessibility. As such, the results presented here are illustrative only and should not be treated as definitive measures of environmental performance.

The aim of this case study is to assist readers, especially those new to LCA in the algae sector, in understanding how environmental assessments of algal systems are structured and conducted. Along the way, we highlight key methodological challenges commonly encountered in algae LCAs. These issues will be explored in greater detail in a forthcoming, more technical information paper.

### 3.1. GOAL AND SCOPE DEFINITION

#### 3.1.1. Description of the biorefinery

The Spirulina biorefinery is designed to produce approximately 10 metric tons of dried Spirulina biomass and 8 metric tons of food-grade phycocyanin annually. These figures serve as the baseline for the LCA study. Assuming that the cultivation and processing facilities operate 330 days per year, the daily production equates to about 30.30 kg of dried Spirulina biomass, typically in the form of dried spaghettini. Given that these spaghettini contain approximately 5% moisture (i.e., 95% dry weight), the daily output corresponds to 28.78 kg of dry weight equivalent (DW-eq).

Cultivation takes place in Italy, in six open raceway ponds sheltered within a greenhouse covering 4,230 m². The ponds are inoculated annually and maintained using Zarrouk medium, with sodium bicarbonate (NaHCO₃) serving as the inorganic carbon source. Paddle wheels ensure continuous mixing, and temperature is regulated seasonally - warm water circulates beneath the ponds during winter to maintain 25-30 °C, while in summer, the greenhouse walls are opened to enhance airflow.

Daily harvesting involves vibrating filters that separate the biomass from the culture medium, which is recirculated.
<figure><img src="image_28.png" /><figcaption>**Figure 7.** Schematic diagram of the Spirulina biorefinery, adapted from Braud et al. (2025).</figcaption></figure>

The collected biomass is concentrated into a slurry (~10% dry weight), further dewatered into a paste (~23% DW), shaped into spaghettini, and dried overnight to achieve a final product with 95% DW. Following packaging, the dried Spirulina spaghettini are transported to France for downstream processing. After maceration and centrifugation, two main product streams are recovered and refined: a blue extract containing phycocyanin and co-product A. After processing and purification, these products have applications in the food, feed, cosmetics, and agricultural sectors (see Figure 7).

The following subsections address specific elements of the ISO 14040/44 standards on LCA, as outlined in Section 2.

### 3.1.2. Purpose of the study

The objective of this LCA study is to assess the environmental impacts of the pilot scale Spirulina biorefinery producing 28.78 kg DW-eq of dried spaghettini per day (as described previously) and to identify the key processes that contribute most significantly to those impacts, often referred to as environmental hotspots.

### 3.1.3. Intended application

This example serves as an educational case for individuals in the broader algae sector who are new to LCA. The original, detailed study was intended for internal use within the scope of the SpiralG project, aiming to improve understanding of the biorefinery's environmental sustainability and to inform strategies for impact mitigation.

### 3.1.4. Intended audience

The primary audience for the original, detailed study includes the industrial partners involved in the SpiralG project, who seek a better understanding of the environmental performance of the Spirulina biorefinery. A secondary audience includes broader stakeholders in the algae sector who are interested in learning more about the potential environmental impacts of Spirulina-based biorefineries. For this simplified version, the target audience is individuals in the broader algae community seeking to learn more about LCA.

### 3.1.5. Comparison

The outcomes of this study are not intended for public comparison or for making comparative assertions between products. Any comparative use of the results remains internal and should be interpreted within the specific context of the SpiralG project. It is important to note that the results presented here are illustrative only and should not be treated as definitive measures of environmental performance.

### 3.1.6. System under study

The Spirulina biorefinery produces dried biomass, a blue extract containing phycocyanin, and a co-product A concentrate (see Figure 7). Inputs for algal cultivation and biomass processing include water, energy, nutrients, chemicals, and diverse materials (e.g., cellulose filters, construction materials). End-use and disposal are excluded.

### 3.1.7. Functional unit

The Spirulina biorefinery produces both raw biomass and two valuable products. Since the algal system generates multiple outputs, several FUs can be defined to capture the environmental impacts of the different outputs. Here, three FUs are defined:

* FU1: 1kg DW-eq of Spirulina biomass.

This FU focuses on the raw biomass produced and provides insights into the environmental impacts of producing Spirulina biomass.

* FU2: 1kg DW-eq of blue extract.

This FU focuses on the main refined product i.e., the blue extract containing phycocyanin. It assesses the environmental impacts associated with producing this specific product.

* FU3: 28.78kg DW-eq of Spirulina spaghettini and processing into 5.70kg DW-eq of blue extract and 3.46kg DW-eq of CPAc.

This FU reflects the daily output of the Spirulina biomass, which is further processed into 5.70kg of blue extract and 3.46kg of CPAc (co-product A concentrate).
<figure><img src="image_29.png" /><figcaption>**Figure 8.** Simplified system diagram of the pilot scale Spirulina biorefinery, adapted from Braud et al. (2025).</figcaption></figure>

This approach of defining several FUs addresses the complexity of biorefinery systems and introduces the concept of multifunctionality in LCA (see Section 2.2.11). Distributing the environmental impacts between several products is a key challenge, and will be discussed further in a forthcoming, more technical information paper.

### 3.1.8. System boundaries

The Spirulina biorefinery is divided into three main subsystems (see Figure 7): Spirulina cultivation and biomass pre-processing (S1), phycocyanin extraction (S2), and co-product A treatment (S3). All processes from cultivation to the production of blue extract and co-product A concentrate are included in the LCA study. However, as data on the use and disposal of final consumer products were unavailable and considering that these last steps are out of control of the operators of the plant and thus present limited opportunities of improvement of the process, the system boundaries focus on the biorefinery itself, following a "cradle-to-gate" approach (see Figure 8).

The analysis includes the construction and operation of the cultivation facilities in Italy. Infrastructure related to phycocyanin extraction and co-product A treatment in France was excluded as it contributed minimally to the overall operations. Equipment (e.g., centrifuges, ultrafiltration units) was also excluded for simplicity (although secondary data sources could be used).

The geographical scope spans France and Italy, with data collected in 2022 (Braud et al., 2025). Transport of dried Spirulina spaghettini from S1 to S2 is accounted for, as well as the operation of the Spirulina cultivation facility (e.g., general cleaning and maintenance of the buildings).

### 3.1.9. Modelling approach and multifunctionality

An attributional modelling approach was used in this LCA study to identify hotspots in the Spirulina biorefinery. Since ALCA analyses the environmental impacts of current systems without capturing the effects of potential changes in demand and system interactions, it is particularly suitable for hotspot identification (see Section 2.2.10).

The multifunctionality issue arises when choosing FU2 ("1 kg DW-eq of blue extract"), as the environmental impacts must be distributed between the blue extract and co-product A to express the LCA results per kg DW-eq of blue extract. For illustration, three approaches are applied to handle multifunctionality, as subdivision cannot be used: mass allocation, economic allocation, and substitution. It is important to note that the use of substitution in ALCA is controversial and that some LCA practitioners argue that it makes more sense to be used in CLCA (Schaubroeck et al., 2021).

FU3 corresponds to the enlargement approach, where all products and functions of the system are included in the FU. In contrast, FU1 does not involve any multifunctionality issue, as only one product - Spirulina dry spaghettini - is generated by S1 (see Figure 9).

The choice of method for handling multifunctionality has been shown to influence LCA results (Sills et al., 2020) and will be further discussed in a forthcoming information paper.

### 3.1.10. Impact assessment methods

The environmental impacts of the Spirulina biorefinery were assessed using the EF v3.0 LCIA method.

The analysis covered impact categories such as acidification, climate change, ecotoxicity, energy resources, eutrophication, human toxicity, ionising radiation, land use, material resources, ozone depletion, particulate matter formation, photochemical ozone depletion, and water use (see Table 3). This broadens the focus beyond common categories such as climate change, eutrophication, and acidification, which are typical in algae LCAs, and highlights additional impacts specific to algae systems.

The LCA was conducted using the open-source software package Brightway (Mutel, 2017), which is written in the Python programming language. The use of Brightway enhances transparency and reproducibility compared to traditional LCA software. The code used to perform the LCA study is available on an open source Github repository (https://github.com/eaba-org/lca).
<figure><img src="image_30.png" /><figcaption>**Figure 9.** Solving and avoiding multifunctionality through allocation (partitioning) or substitution for FU2, and enlargement for FU3. The grey boxes indicate the product of interest considered in FU2 i.e., 1 kg DW-eq of blue extract.</figcaption></figure>

<table><caption>**Table 3:** Summary of the Environmental Footprint v.3.0. impact categories used in the LCA study.</caption><thead><tr><td>IMPACT CATEGORY - INDICATOR</td><td>ABBREVIATION</td><td>UNIT</td></tr></thead><tbody><tr><td>Acidification - Accumulated Exceedance (AE)</td><td>A</td><td>mol H<sup>+</sup>-eq</td></tr><tr><td>Climate change - Radiative forcing as Global Warming Potential, 100 years time horizon (GWP100)</td><td>GWP</td><td>kg CO<sub>2</sub>-eq</td></tr><tr><td>Ecotoxicity: freshwater - Comparative Toxic Unit for ecosystems (CTUe)</td><td>Etox</td><td>CTUe</td></tr><tr><td>Resource use, fossil - Abiotic resource depletion, fossil fuels (ADP-fossil)</td><td>ADPf</td><td>MJ</td></tr><tr><td>Eutrophication: freshwater - Fraction of nutrients reaching freshwater end compartment (P)</td><td>FE</td><td>kg P-eq</td></tr><tr><td>Eutrophication: marine - Fraction of nutrients reaching marine end compartment (N)</td><td>ME</td><td>kg N-eq</td></tr><tr><td>Eutrophication: terrestrial - Accumulated Exceedance (AE)</td><td>TE</td><td>mol N-eq</td></tr><tr><td>Human toxicity: non-carcinogenic - Comparative Toxic Unit for humans (CTUh)</td><td>HTnc</td><td>CTUh</td></tr><tr><td>Human toxicity: carcinogenic - Comparative Toxic Unit for humans (CTUh)</td><td>HTc</td><td>CTUh</td></tr><tr><td>Ionising radiation: human health - human exposure efficiency relative to u235</td><td>IR</td><td>kBq U235-eq</td></tr><tr><td>Land use - Soil quality index</td><td>LU</td><td>-</td></tr><tr><td>Resource use, minerals and metals - Abiotic resource depletion (ADP ultimate reserves)</td><td>ADPur</td><td>kg Sb-eq</td></tr><tr><td>Ozone depletion - Ozone Depletion Potential (ODP)</td><td>ODP</td><td>kg CFC-11-eq</td></tr><tr><td>Particulate matter - Human health effects associated with exposure to PM2.5.</td><td>PM</td><td>Disease incidences</td></tr><tr><td>Photochemical oxidant formation: human health - Tropospheric ozone concentration increase</td><td>POF</td><td>kg NMVOC-eq</td></tr><tr><td>Water use - User deprivation potential (deprivation-weighted water consumption)</td><td>WU</td><td>m<sup>3</sup> world eq. deprived</td></tr></tbody></table>
### 3.1.11. Data collection

Data for this study were collected on-site at three pilot scale facilities located in Italy and France, covering the entire Spirulina biorefinery system. While the data were detailed, confidentiality concerns required aggregation at the process level to protect proprietary information. Where data gaps occurred, such as in the absence (or malfunctioning) of power or water consumption meters, assumptions and mass balance calculations were applied to ensure completeness. Although this introduced a degree of uncertainty, the overall data quality was sufficient to meet the study's objectives.

It is important to note that no scaling procedures were applied to estimate environmental impacts at commercial scale. The challenges and methodological considerations associated with scaling up algae-based systems will be addressed in a forthcoming, more technical information paper.

## 3.2. LIFE CYCLE INVENTORY

The foreground system was modeled using primary data collected in 2022. A summary of the inventory data used in this LCA study is presented in Table 4 while a detailed version is available in the original publication (Braud et al., 2025).

For the background system, LCI data from the ecoinvent 3.9.1 cut-off database were used for construction materials, energy, water, nutrients, and treatment of solid wastes and wastewater (Wernet et al. 2016). The "allocation, cut-off by classification" system was applied, where the producer is responsible for waste disposal, meaning the environmental impacts of waste treatment

are assigned to the activity that produced the waste. For this reason, wastes are listed as negative inputs in the inventory table (see Table 4). In the ecoinvent database, "Market for" refers to a process that transfers a product from supplier to consumer, essentially acting as a distribution channel. In simpler terms, a market dataset represents the average way a product is used (consumed) within a given area. It should be noted, however, that the consequential version of the ecoinvent database is generally more appropriate when modelling systems using substitution, as it explicitly accounts for marginal changes in supply and demand.

Where possible, country-specific datasets for Italy (IT) and France (FR) were used - for example, electricity mix and waste treatment processes. When these were unavailable, European average (RER) datasets were used, and if needed, global average (GLO) datasets served as a fallback. Proxy datasets were used in cases where exact matches were not available in the database - for example, to represent the construction materials used in building the Spirulina cultivation and pre-processing facilities.

Several simplifying assumptions were made. For instance, while the construction of the Spirulina cultivation facility was included in the model, fuel consumption by construction machinery (such as tractors) was excluded. This assumption was considered reasonable given that the Spirulina pre-processing facility was established within an existing building - a former cattle stable - thus minimising new construction impacts.

The quantities of construction materials were estimated from architectural blueprints and scaled to a 30-year building lifespan. These values were then scaled to the functional unit defined for the study.

<table><caption>**Table 4:** Simplified life cycle inventory of the Spirulina biorefinery expressed per FU3 i.e., 28.78 kg DW-eq of Spirulina spaghettini processed into 5.70 kg of blue extract and 3.46 kg of CPAc.</caption><thead><tr><th>Name</th><th>Amount</th><th>Unit</th><th>Proxy dataset from the ecoinvent v.3.9.1 cut-off database</th></tr></thead><tbody><tr><td colspan="4"><strong>Infrastructures</strong></td></tr><tr><td colspan="4"><strong>Inputs</strong></td></tr><tr><td>Polystyrene</td><td>1.07</td><td>kg</td><td>Market for polystyrene foam slab for perimeter insulation (GLO)</td></tr><tr><td>Polyvinylchloride</td><td>1.96</td><td>kg</td><td>Market for polyvinylchloride, emulsion polymerised (GLO)</td></tr><tr><td>Steel</td><td>0.13</td><td>kg</td><td>Market for steel, low-alloyed (GLO)</td></tr><tr><td>Polyurethane</td><td>0.39</td><td>kg</td><td>Market for polyurethane, rigid foam (RER)</td></tr><tr><td>Pumice</td><td>0.81</td><td>kg</td><td>Market for pumice (GLO)</td></tr><tr><td>Concrete blocks</td><td>1.21</td><td>kg</td><td>Market for concrete block (DE)</td></tr><tr><td>Ceramic tiles</td><td>0.42</td><td>kg</td><td>Market for ceramic tile (GLO)</td></tr><tr><td>Sand</td><td>3.54</td><td>kg</td><td>Market for sand (CH)</td></tr><tr><td>Polyethylene</td><td>0.99</td><td>kg</td><td>Market for polyethylene terephthalate, granulate, amorphous (GLO)</td></tr><tr><td>Polypropylene</td><td>0.093</td><td>kg</td><td>Market for polypropylene, granulate (GLO)</td></tr><tr><td>Polyethylene, high density</td><td>0.68</td><td>kg</td><td>Market for polyethylene, high density, granulate (GLO)</td></tr><tr><td>Polycarbonate</td><td>0.084</td><td>kg</td><td>Market for polycarbonate (GLO)</td></tr><tr><td colspan="4"><strong>Outputs</strong></td></tr><tr><td>Cultivation &amp; processing facility</td><td>9.1.10-5</td><td>unit</td><td>-</td></tr></tbody></table>
<table><thead><tr><th>Name</th><th>Amount</th><th>Unit</th><th>Proxy dataset from the ecoinvent v.3.9.1 cut-off database</th></tr></thead><tbody><tr><td colspan="4"><strong>Operation</strong></td></tr><tr><td colspan="4"><strong>Inputs</strong></td></tr><tr><td>Electricity</td><td>38.20</td><td>kWh</td><td>Market for electricity, medium voltage (IT)</td></tr><tr><td>Ground water</td><td>3460.00</td><td>L</td><td>Tap water production, underground water without treatment (Europe w/o Switzerland)</td></tr><tr><td>Sodium hypochlorite</td><td>0.009</td><td>kg</td><td>Market for sodium hypochlorite, without water, in 15% solution state (RER)</td></tr><tr><td colspan="4"><strong>Outputs</strong></td></tr><tr><td>Waste plastic</td><td>-0.22</td><td>kg</td><td>Market for waste plastic, mixture (IT)</td></tr><tr><td>Waste paperboard</td><td>-0.08</td><td>kg</td><td>Market for waste paperboard (IT)</td></tr><tr><td>Solid waste</td><td>-0.22</td><td>kg</td><td>Market for municipal solid waste (IT)</td></tr><tr><td colspan="4"><strong>Spirulina Cultivation</strong></td></tr><tr><td colspan="4"><strong>Inputs</strong></td></tr><tr><td>Spirulina inoculum</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Sodium bicarbonate</td><td>39.87</td><td>kg</td><td>Market for sodium bicarbonate (GLO)</td></tr><tr><td>Potassium nitrate</td><td>45.53</td><td>kg</td><td>Market for potassium nitrate (RER)</td></tr><tr><td>Magnesium sulfate</td><td>0.68</td><td>kg</td><td>Market for magnesium sulfate (GLO)</td></tr><tr><td>Chelated iron</td><td>0.31</td><td>kg</td><td>Market for EDTA, ethylenediaminetetraacetic acid (GLO)</td></tr><tr><td>Ammonium phosphate</td><td>0.51</td><td>kg</td><td>Monoammonium phosphate production (RER)</td></tr><tr><td>Electricity</td><td>94.96</td><td>kWh</td><td>Market for electricity, medium voltage (IT)</td></tr><tr><td>Ground water</td><td>12858.29</td><td>L</td><td>Tap water production, underground water without treatment (Europe w/o Switzerland)</td></tr><tr><td colspan="4"><strong>Outputs</strong></td></tr><tr><td>Spirulina broth</td><td>53.31</td><td>kg DW-eq</td><td>-</td></tr><tr><td>Waste plastic</td><td>-0.12</td><td>kg</td><td>Market for waste plastic, mixture (IT)</td></tr><tr><td colspan="4"><strong>Harvesting & Dying</strong></td></tr><tr><td colspan="4"><strong>Inputs</strong></td></tr><tr><td>Spirulina broth</td><td>53.31</td><td>kg DW-eq</td><td>-</td></tr><tr><td>Electricity</td><td>204.96</td><td>kWh</td><td>Market for electricity, medium voltage (IT)</td></tr><tr><td>Ground water</td><td>1671.49</td><td>L</td><td>Tap water production, underground water without treatment (Europe w/o Switzerland)</td></tr><tr><td colspan="4"><strong>Outputs</strong></td></tr><tr><td>Spirulina dry spaghettini (95% DW)</td><td>28.78</td><td>kg DW-eq</td><td>-</td></tr><tr><td>Wastewater</td><td>-0.84</td><td>m³</td><td>Treatment of wastewater, average, wastewater treatment (Europe w/o Switzerland)</td></tr><tr><td colspan="4"><strong>Packaging</strong></td></tr><tr><td colspan="4"><strong>Inputs</strong></td></tr><tr><td>Spirulina dry spaghettini (95% DW)</td><td>28.78</td><td>kg DW-eq</td><td>-</td></tr><tr><td>Polyethylene</td><td>0.35</td><td>kg</td><td>Market for polyethylene terephthalate, granulate, amorphous (GLO)</td></tr><tr><td colspan="4"><strong>Outputs</strong></td></tr><tr><td>Spirulina dry spaghettini, packaged</td><td>29.13</td><td>kg</td><td>-</td></tr></tbody></table>
<table><thead><tr><th>Name</th><th>Amount</th><th>Unit</th><th>Proxy dataset from the ecoinvent v.3.9.1 cut-off database</th></tr></thead><tbody><tr><td colspan="4"><strong>Transport</strong></td></tr><tr><td>Transport by car</td><td>0.38</td><td>km</td><td>Market for transport, passenger car, large size, petrol, EURO 3 (GLO)</td></tr><tr><td>Transport by ship</td><td>16.87</td><td>ton.km</td><td>Market for transport, freight, sea, container ship (GLO)</td></tr><tr><td>Transport by lorry</td><td>0.63</td><td>ton.km</td><td>Market for transport, freight, lorry, unspecified (RER)</td></tr><tr><td>Co-product A</td><td>10.57</td><td>kg DW-eq</td><td>-</td></tr><tr><td>Waste plastic</td><td>-0.21</td><td>kg</td><td>Market for waste plastic, mixture (FR)</td></tr><tr><td>Wastewater</td><td>-10.07</td><td>m³</td><td>Treatment of wastewater, average, wastewater treatment (Europe w/o Switzerland)</td></tr><tr><td>Waste paperboard</td><td>-22.48</td><td>kg</td><td>Market for waste paperboard (FR)</td></tr></tbody></table>

## Phycocyanin extraction

### Inputs

<table><tr><td>Electricity</td><td>739.10</td><td>kWh</td><td>Market for electricity, medium voltage (FR)</td></tr><tr><td>Tap water</td><td>15513.23</td><td>L</td><td>Market for tap water (Europe w/o Switzerland)</td></tr><tr><td>Sodium hydroxide</td><td>10.88</td><td>kg</td><td>Market for sodium hydroxide, without water, in 50% solution state (GLO)</td></tr><tr><td>Hydrogen peroxide</td><td>0.17</td><td>kg</td><td>Market for hydrogen peroxide, without water, in 50% solution state (RER)</td></tr><tr><td>Nitric acid</td><td>9.92</td><td>kg</td><td>Market for nitric acid, without water, in 50% solution state (RER)</td></tr><tr><td>Ultrapure water</td><td>26.57</td><td>kg</td><td>Market for water, ultrapure (RER)</td></tr><tr><td>Cellulose filters</td><td>22.48</td><td>kg</td><td>Market for sulfate pulp, bleached (RER)</td></tr><tr><td>Natural gas</td><td>414.65</td><td>m³</td><td>Market for natural gas, high pressure (FR)</td></tr></table>

### Outputs

<table><tr><td>Blue extract (50% phycocyanin)</td><td>5.70</td><td>kg DW-eq</td><td>-</td></tr><tr><td>Co-product A</td><td>10.57</td><td>kg DW-eq</td><td>-</td></tr><tr><td>Waste plastic</td><td>-0.21</td><td>kg</td><td>Market for waste plastic, mixture (FR)</td></tr><tr><td>Wastewater</td><td>-10.07</td><td>m³</td><td>Treatment of wastewater, average, wastewater treatment (Europe w/o Switzerland)</td></tr><tr><td>Waste paperboard</td><td>-22.48</td><td>kg</td><td>Market for waste paperboard (FR)</td></tr></table>

## Co-product A treatment

### Inputs

<table><tr><td>Co-product A</td><td>10.57</td><td>kg DW-eq</td><td>-</td></tr><tr><td>Electricity</td><td>269.42</td><td>kWh</td><td>Market for electricity, medium voltage (FR)</td></tr><tr><td>Tap water</td><td>2115.62</td><td>L</td><td>Market for tap water (Europe w/o Switzerland)</td></tr><tr><td>Sulfuric acid</td><td>2.31</td><td>kg</td><td>Market for sulfuric acid (RER)</td></tr><tr><td>Ultrapure water</td><td>2.86</td><td>L</td><td>Market for water, ultrapure (RER)</td></tr><tr><td>Sodium hydroxide</td><td>0.84</td><td>kg</td><td>Market for sodium hydroxide, without water, in 50% solution state (GLO)</td></tr><tr><td>Potassium hydroxide</td><td>1.15</td><td>kg</td><td>Market for potassium hydroxide (GLO)</td></tr></table>

### Outputs

<table><tr><td>Co-product A concentrate</td><td>3.46</td><td>kg DW-eq</td><td>-</td></tr><tr><td>Wastewater</td><td>-2.02</td><td>m³</td><td>Treatment of wastewater, average, wastewater treatment (Europe w/o Switzerland)</td></tr></table>
### 3.3. LCIA & RESULTS INTERPRETATION

The results show that Spirulina cultivation and phycocyanin extraction are the two life cycle stages contributing the most to the overall environmental impacts of the biorefinery across all categories assessed (see Figure 10).

<figure><img src="image_31.png" /></figure>

**Figure 10.** Relative contribution of each life cycle stage to the overall environmental impacts of the Spirulina biorefinery. These impacts were calculated using FU3. The impact categories from EF 3.0 are detailed in Table 3.

The contribution analysis by input category (e.g., energy, nutrients, chemicals, and waste) indicates that energy and nutrients are the main contributors to most of the assessed environmental impact categories. An exception is observed for land use, where material inputs, particularly cellulose filters used for phycocyanin purification, account for a significant share of the impacts (see Figure 11). Waste treatment processes, including the management of solid waste and wastewater, contribute substantially to freshwater and marine eutrophication.

<figure><img src="image_32.png" /></figure>

**Figure 11.** Relative contribution of each category of inputs to the overall environmental impacts of the Spirulina biorefinery. These impacts were calculated using FU3. The impact categories from EF 3.0 are detailed in Table 3.

Using FU1 enables a detailed assessment of the environmental impacts associated with Spirulina cultivation and biomass pre-processing. For example, the total climate change impact amounts to 10.4 kg CO₂-eq per kg DW-eq of Spirulina spaghetti, with cultivation accounting for 59% of the total impact, harvesting and drying for 28%, operation for 6%, and infrastructure for 7% (see Jupyter Notebook for details).

Finally, the LCA results were expressed per FU2 i.e., per kg DW-eq of blue extract. In this case, the environmental impacts were allocated between CPAc and the blue extract using mass allocation. In a sensitivity analysis, the results obtained were compared with allocation using economic values for both products as well as the use of the substitution approach.

In the case of mass allocation, 62% of the environmental impacts were attributed to the blue extract and 38% to CPAc, based on mass balance. For economic allocation, market values of €170/kg DW-eq for the blue extract and €3/kg DW-eq for CPAc were applied according to Kingsley et al. (2023), resulting in allocation factors of 99% and 1%, respectively.

This large difference reflects the higher market value of the blue extract, which is used in high-value applications such as food and cosmetics, whereas CPAc is primarily a lower-value co-product. These allocation factors are based on 2023 price assumptions; market prices in 2025 may differ depending on evolving demand and specific applications, and could therefore result in different allocation values. Under the substitution approach, CPAc was assumed to replace conventional inorganic fertiliser at a 1:1 ratio (as a simplification), and 0.61 kg DW-eq CPAc are produced per kg DW-eq of blue extract.

<figure><img src="image_33.png" /></figure>

**Figure 12.** Climate change impacts associated with the production of 1 kg DW-eq of blue extract. These impacts were calculated using FU2 i.e., 1 kg DW-eq of blue extract and different approaches for multifunctionality.

**Figure 12** presents the climate change impacts associated with the production of 1 kg DW-eq of blue extract. Three approaches for addressing multifunctionality are compared: mass allocation, economic allocation, and substitution. The results demonstrate substantial variability depending on the chosen method, particularly between mass and economic allocation, and when applying substitution.

This highlights a key methodological challenge that will be further addressed in a forthcoming information paper focusing on methodological challenges in algae LCAs. The interpretation of LCA results is influenced by methodological choices made in the goal and scope of the study. Such variability complicates the comparison of results across different LCA studies, highlighting the importance of transparency in reporting methodological assumptions.
## 4. NO ONE-SIZE-FITS-ALL: HETEROGENEITY IN ALGAE LCAs

Each algal system is unique, with its own set of biological, technical, and regional characteristics. As a result, any LCA study must be specifically tailored to reflect the particularities of the system being assessed. In this information paper, we use a simplified case study of a Spirulina biorefinery to illustrate LCA concepts and best practices. However, it is important to emphasise that this example is not representative of all algae-based systems.

This section highlights the heterogeneity of algae systems, covering both technological diversity and variability due to regional and climatic factors, and how these differences influence LCA results (see Table 5). Understanding this heterogeneity is essential for interpreting LCA outcomes accurately and avoiding generalisations that may not apply to other systems.

### 4.1. REGIONAL AND TEMPORAL VARIATIONS

Regional and temporal variations significantly influence environmental impacts by affecting resource use, emissions, and system performance. Accounting for these factors is essential to ensure accurate interpretation and meaningful comparison of LCA results across different studies.

#### EXAMPLE OF REGIONAL AND TEMPORAL VARIATIONS

* Electricity sources differ widely between countries; some rely heavily on renewables, while others depend on fossil fuels, directly affecting climate change impact results.

* Water scarcity and land availability vary between Mediterranean and Nordic regions, influencing resource use and system feasibility.

* Algal growth and harvesting cycles are strongly seasonal, impacting productivity and the timing of resource inputs.

* Infrastructure conditions and transport modes vary regionally, affecting associated emissions throughout the supply chain.

### 4.2. ECONOMIC SUSTAINABILITY CHALLENGES

Despite their environmental potential, many algae cultivation and processing technologies remain costly and resource-intensive. High operational costs, especially for energy, water, and nutrients, often make algae products more expensive than conventional alternatives.

Capital investment requirements for infrastructure such as photobioreactors, harvesting equipment, and processing units can be substantial, with long payback periods and uncertain return on investment. Most algae systems have not yet achieved economies of scale, which means that production costs remain high per unit of output, particularly in pilot or demonstration projects.

Market uncertainty further complicates economic sustainability, as prices for algae-derived products such as proteins, pigments, and biofertilisers are often volatile or not yet established in mature markets. Moreover, many algae business models depend on policy support mechanisms, including subsidies, carbon credits, or incentives for renewable energy and circular economy solutions. If such support is reduced or removed, economic viability may be compromised.

For a more complete picture of sustainability, LCA can be complemented by Life Cycle Costing (LCC), which estimates the total cost of a product over its life cycle. This helps reveal potential trade-offs, such as systems that perform well environmentally but are economically unfeasible.

<table><caption>**Table 5:** Key sources of heterogeneity in algae LCAs and their implications on system modelling and environmental impacts.</caption><thead><tr><td>SOURCE OF HETEROGENEITY</td><td>COMMENTS</td><td>CHANGES IN LCA STUDY</td></tr></thead><tbody><tr><td>Cultivation system</td><td>Open vs. closed / Onshore vs. offshore</td><td><ul><li>Quantity and type of materials for construction and related energy consumption.</li><li>Land and water use.</li><li>Interactions with the environment (captures and losses).</li><li>Requirement of extra processes, such as cooling.</li></ul></td></tr><tr><td>Source of nutrients</td><td>Fertilisers, waste streams, open environment</td><td><ul><li>Environmental impacts from sourcing can be positive or negative depending on the origin of the inputs.</li></ul></td></tr><tr><td>Infrastructure required</td><td>Location and available facilities</td><td><ul><li>Quantity and type of materials for construction and related energy consumption.</li></ul></td></tr><tr><td>Productivity of the cultivation system</td><td>Due to environment, location, and algal strain</td><td><ul><li>Biomass produced that directly affects input and output flows.</li></ul></td></tr><tr><td>Vulnerability of the system</td><td>Crashing in the system (environmental and biological contaminants)</td><td><ul><li>Time without production affects time dependent flows.</li></ul></td></tr><tr><td>Harvesting method</td><td>Manual/automated and technology used</td><td><ul><li>Materials and related energy consumption.</li></ul></td></tr><tr><td>Processing</td><td>Drying and biorefinery processes</td><td><ul><li>Variation in inputs, such as energy, water, and other consumables.</li><li>Various products can be generated and therefore allocation may be required.</li></ul></td></tr><tr><td>Level of the system</td><td>Automation and scale</td><td><ul><li>Inputs efficiency and energy consumption.</li></ul></td></tr><tr><td>Cleaning</td><td>Process and frequency of cleaning</td><td><ul><li>Quantity and type of chemicals used and associated energy consumption.</li></ul></td></tr></tbody></table>
## 4.3. DIVERSITY IN ALGAL SYSTEMS

There are many differences across algal production systems including cultivation methods (e.g., open ponds, photobioreactors, fermenters, offshore platforms), species selection, and downstream conversion technologies. These variations can significantly influence the environmental impacts reported in an LCA. For this reason, comparing results across different LCA studies can be challenging, and sometimes misleading, if the system-specific context is not fully considered.

### EXAMPLE OF VARIATIONS IN MACROALGAE SYSTEMS

Although macroalgae production systems are often compared to microalgae systems, especially in the context of LCA, their environmental impacts differ significantly. When macroalgae are cultivated using open ponds, the LCA modelling approach can be similar to that of microalgae grown in open ponds. However, when macroalgae are cultivated offshore there are many differences in application of LCA. Consequently, two types of offshore seaweed systems can be identified:

#### Wild harvesting

Wild harvesting can include manual cutting by foot, manual cutting by boat and rake method, and mechanical cutting by macroalgae trawler. This pathway would include tools for harvesting (knives, rakes, rope) and boat with fuel use (calculated based on traveling distance and fuel efficiency).

#### Offshore cultivation

Offshore cultivation include steps such as hatchery, cultivation site equipment (anchors, buoys, ropes) and use of transportation fuel. Multi-use and integrated multi-trophic aquaculture (IMTA) systems can include macroalgae cultivation to balance the system in terms of nutrient flows and optimise resource use.

## 4.4. CHOICE OF THE CULTIVATION SUBSTRATE

The use of waste streams as substrates in algae cultivation introduces both opportunities and challenges for LCA and is a key factor driving variability between studies. Waste streams such as municipal wastewater, flue gas, digestate, and food industry effluents can provide essential nutrients and carbon sources at low or no cost, and their use is often associated with environmental benefits, such as nutrient recovery, waste reduction, and avoided emissions. However, how these benefits are modeled in LCA depends heavily on the assumptions made about the origin and treatment of the waste stream. In some studies, waste is treated as having zero environmental burden up to the point it enters the algae system, which can make the algae product appear particularly sustainable. In others, a portion of the upstream impacts is allocated to the algae system, especially if the waste stream has alternative uses or economic value. In addition, pre-treatment requirements such as filtration, pH adjustment, or energy input for pumping and mixing can add environmental burdens that must be included in the assessment. The choice of allocation method, the treatment of avoided impacts (e.g. reduced eutrophication or displacement of synthetic fertilisers), and regional differences in waste composition and regulation can all influence LCA results. This makes direct comparison between studies difficult unless assumptions are clearly documented. This topic will be explored in greater detail in a forthcoming information paper.

# 5. CONCLUDING REMARKS

This information paper has outlined a step-by-step approach to LCA for algae-based products, grounded in established standards and illustrated with a simplified case study.

Several key considerations are highlighted for the interpretation and application of LCA:

### DATA SELECTION

Primary data from operations under direct control should be prioritised for the foreground system, while secondary data sources, such as ecoinvent, GaBi, or Agri-footprint, are recommended for background processes. Comprehensive documentation of data sources and methodological assumptions is essential to ensure transparency and reproducibility.

### REFERENCE MATERIALS

Peer-reviewed scientific literature, official standards (e.g., ISO 14040/44), and guidance documents from recognised organisations provide a reliable foundation for LCA studies. The references included in this paper offer further direction for robust and trustworthy sources.

### INTERPRETATION OF RESULTS

LCA outcomes are influenced by methodological choices, system boundaries, data quality etc. Results should be interpreted within the context of these parameters, with sensitivity and uncertainty analyses employed to evaluate the robustness of findings.

### COMPARABILITY

Direct comparison of results between LCA studies requires alignment of system boundaries, functional units, and data sources. Methodological transparency is critical for meaningful comparisons.

### RELIABILITY

The reliability of LCA studies is supported by transparency, reproducibility, and the use of high-quality, up-to-date data. Adherence to international standards and, where applicable, independent verification further enhance reliability.

### UPDATES AND REVISION

LCA results may be affected by updates to databases and background data over time. It is important to specify the versions of databases and software used, and to periodically review and revise assessments as new information becomes available.

Ongoing collaboration within the EABA LCA task force and broader EU initiatives remains essential for harmonising methodologies, improving data quality, and enhancing transparency in algae LCAs.

While this information paper addresses foundational aspects, forthcoming papers will delve deeper into specific methodological challenges associated with algae LCAs, such as allocation procedures and the consideration of biogenic carbon. Together, these efforts will strengthen the scientific basis for LCA and support the sustainable development of algae-based value chains.
SELECTED REFERENCES

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Braud, L., Gallardo Rodríguez, J., Rajaonison, A., Schelte, N., Mangini, S., Bernard, O., Pedra, I., Monteiro, L., Costa, L., Speranza, L., Bāliņa, K., Bradley, T., Morão, A., Kliphuis, S., Nyvall, P., Jamois-Piquet, M., Schmid, S., Chami, I., Perignon, C., Pierre, R., Ras, M., Verdelho, V., Cadoret, J., & Unamunzaga, C. (2025). Life cycle assessment of algal products: A step-by-step guide to application. Zenodo. https://doi.org/10.5281/zenodo.16270293