Social Life Cycle Assessment of Multifunctional Bioenergy Systems: Social and Socioeconomic Impacts of Hydrothermal Treatment of Wet Biogenic Residues into Intermediate Bioenergy Carriers and Sustainable Solid Biofuels

Abstract

This study presents a social life cycle assessment (S-LCA) of the F-CUBED Production System (FPS), an innovative process that converts wet biogenic residues—specifically paper biosludge, virgin olive pomace, and fruit and vegetable residues—into intermediate bioenergy carriers via hydrothermal treatment (TORWASH^®), pelletization, and anaerobic digestion. The hydrothermal carbonization of these low-grade, moisture-rich biogenic residues enhances the flexibility and reliability of renewable energy systems while also offering the potential to reduce environmental burdens compared to conventional disposal methods. Through this S-LCA, the study aims to evaluate the cradle-to-gate socioeconomic impacts of the FPS in three European contexts—Sweden, Italy, and Spain—using the 2020 UNEP Guidelines and the Social Hotspots Database (SHDB) and applying quantitative modeling via SimaPro. The functional unit is defined as 1 kWh of electricity produced. The assessment combines SHDB-based modeling with primary data from stakeholder surveys conducted in the three countries. Impact categories are harmonized between SHDB and UNEP typologies, and the results are reported in medium-risk-hour equivalents (mrheq). The results show a heterogeneous social impact profile across case studies. In Sweden, the treatment of paper biosludge delivers substantial benefits with minimal risk. In Spain (orange peel), the introduction of the FPS demonstrated a strong social benefit, particularly in health and safety and labor rights, indicating high institutional performance and good integration with local industry. Conversely, in Italy (olive pomace), the FPS revealed significant social risks, especially in the biopellet production and electricity generation sectors, reflecting regional vulnerabilities in labor conditions and governance. This suggests that targeted mitigation strategies are recommended in contexts like Southern Italy. These findings highlight that the social sustainability of emerging bioenergy technologies is context-dependent and sensitive to sectoral and regional socioeconomic conditions. This S-LCA complements prior environmental assessments and emphasizes the importance of integrating social performance considerations in the deployment and scaling of innovative bioenergy systems.

1. Introduction

To reduce its dependence on external energy sources and achieve carbon neutrality by 2050, the European Union must increasingly rely on renewable biofuels for electricity generation [1]. This need is especially critical, given the growing direct demand for electricity across multiple end-use sectors, as well as for the production of energy carriers such as hydrogen. However, since renewable sources like solar and wind are inherently intermittent due to weather-related fluctuations, they must be complemented with dispatchable energy systems—capable of delivering power reliably at any time of day or year [2]. Furthermore, climate change is placing mounting pressure on natural and human systems, making the development of sustainable energy solutions more critical than ever. In this context, the sustainable valorization of biogenic residues and wastes for bioenergy plays a key role in enhancing the flexibility and stability of renewable energy systems while reducing environmental impacts compared to conventional disposal methods. Despite their potential, these residues are often difficult to use due to low energy density, high moisture content, poor biological stability, material heterogeneity, impurities [3,4,5,6], and social perceptions. Access to residual biomass is also expected to become increasingly constrained [7], with competition for bioenergy uses projected to significantly limit its availability by 2030 and beyond [8]. As such, conventional biomass sources alone are unlikely to meet future energy demands or sustainability goals [6]. A promising alternative lies in producing bioenergy from wet biogenic residue streams—such as sewage sludge, paper biosludge, and agro-industrial residues. Although recovering resources from wet wastes is energy-intensive due to their complex composition and high moisture levels [9,10], it remains a necessary challenge for advancing sustainable waste management and environmental protection.

Despite their technical and environmental potential, the adoption of these technologies is still accompanied by social concerns and uncertainties. This underscores the need for studies that evaluate not only their technical and environmental performances but also their social impacts and levels of public acceptance. In recent years, there has been growing interest in using social life cycle assessment (S-LCA) to evaluate the social performance of energy products such as electricity, hydrogen, solar fuels, and biofuels [11,12]. According to Iribarren et al. [11], the S-LCA methodology, originally developed to evaluate potential social benefits and drawbacks along a product’s supply chain [13], is widely recognized as a pivotal tool in sustainability science and is applicable across both public and private sectors. By identifying potential social impacts along product supply chains, avoiding the transfer of burdens across impact categories and geographic regions, and integrating environmental and economic data, S-LCA results are increasingly recognized as valuable tools for informing policymaking and guiding business strategies. Zarauz et al. [14] indicate in their review paper that technical scientists are more likely than social scientists to use S-LCA to measure social sustainability and that numerous assessments have been carried out to complement earlier environmental results for biobased goods. Furthermore, research that integrated S-LCA with economic and/or environmental evaluations was more likely to incorporate fewer social indicators, indicating that environmental and economic factors were given a higher weight in overall evaluations of bioenergy [15]. The majority of the reviewed studies in the work of Fionnuala and Egle [15] indicates that the S-LCA is carried out following the LCA methodology and the guidelines from Benoît Norris et al. [13]. A potential step in making sure that precise and pertinent social indicators are considered is the inclusion of stakeholder consultations and input in different works [14,15]; however, details on the sample size and the type of stakeholder engagement were frequently lacking. The studies utilize a variety of social indicators, but the most often used ones are employment and working conditions, followed by health and safety [15]. This might be because certain indicators (for example, wages, hours worked, and the number of work-related injuries) are easier to measure than others. Also, Zarauz et al. [14] highlight that the highest percentage of indicators in the literature sources analyzed was associated with workers (48%), followed by the local community (34%), society (9%), consumers (5%), value chain actors (3%), and children (1%). In addition, the selection of appropriate social indicators is widely recognized as a critical component in conducting S-LCA. As noted by Iribarren et al. [11], while biofuels may show strong performance in terms of contributing to economic development, they often score poorly in areas such as forced labor, gender representation in the workforce, health-related expenditures, the promotion of social responsibility, and fair compensation. These findings underscore the importance of developing specific regulatory frameworks to ensure safe and equitable working conditions across increasingly complex global supply chains. In fact, the results reported by Yupanqui et al. [16] indicate that the employment created via the manufacturing of advanced biofuels, which may have positive social effects, is the most pertinent social component of the case study’s findings. This source also reports that the poor working conditions and outsourced risks in global supply chains as a result of rising energy demand are frequently cited as reasons for the bad economic effects of Germany’s forestry and agriculture industries. The risk is raised by longer working hours, even when the actual working conditions are no worse than those associated with fossil fuels.

In any case, 35% of the examined publications by Zarauz et al. [14] used a social database for their assessments, and the majority of studies had a cradle-to-gate (45%) or cradle-to-grave (37%) approach. Moreover, the use of social databases (e.g., SHDB or PSILCA) to evaluate international impacts was predominantly associated with studies conducted in the Global North (95%), revealing a strong alignment between Northern research perspectives and the application of these databases. Concerning the geographical area of S-LCA implementation, despite the fact that human wants are universal, meeting them varies, depending on the situation, as well as the contexts and cultures of stakeholders, which limit their perspectives on human dignity and its preservation. These limitations demonstrated the bioeconomy’s strong regional reliance, particularly in terms of society. Globally, local indicators had more trouble gathering social data related to particular value chains, while national indicators hardly ever provided information on particular social performance as affirmed in [14].

This paper presents the S-LCA results of the research conducted within the F-CUBED (Future Feedstock Flexible Carbon Upgrading to Bio Energy Carriers) project, a Horizon 2020 initiative funded by the European Commission (Grant Agreement No. 884226). The project successfully demonstrated, at a pilot scale (TRL 5), the viability of converting wet biogenic residues into intermediate bioenergy carriers, specifically fuel pellets, with the proven environmental and social benefits of hydrothermal conversion of biomass for the generation of bioenergy and bioproducts at an industrial scale.

Central to the approach is the integration of a mild hydrothermal carbonization process (TORWASH^®, developed by TNO in Petten, The Netherlands), which enables low-temperature biomass conversion and the full valorization of both solid and liquid process streams. The study compares three different feedstocks, from three different industrial sectors and different countries, using data derived from experimental trials and process modeling [17,18,19]. The case studies were chosen to represent diverse European contexts in terms of biogenic residue availability, energy policy frameworks, and socioeconomic conditions—factors that are central to evaluating the scalability and sustainability of the F-CUBED Production System across three European contexts: Sweden, Italy, and Spain. Each country served as a pilot site for testing a specific biomass stream: pulp and paper biosludge (PPB) in Sweden, olive pomace (OP) in Italy, and orange peels (ORP) in Spain. This geographic distribution ensures coverage of the Northern, Southern, and Mediterranean regions of Europe, enhancing the broader relevance of the findings. More detailed information can be found on the official F-CUBED project website.

The FPS incorporates hydrothermal pretreatment and the subsequent densification of the solid fraction (pelletization), enhancing both logistics and supply chain sustainability [6,20], alongside the anaerobic digestion of the liquid fraction. By leveraging combined heat and power (CHP) and biogas conversion technologies, the system facilitates the integration of variable renewable electricity sources into a decarbonized energy infrastructure.

This project does not merely focus on the technological advancements in bioenergy but also deeply engages with the socioeconomic ramifications of its widespread adoption. The primary objective of the LCA study conducted in the F-CUBED project extends beyond assessing the environmental impacts of the FPS, which have already been detailed by Ugolini et al. [21] and seeks to evaluate the social and socioeconomic impacts of the novel technology using a life cycle assessment approach. An S-LCA has been conducted to forecast and preliminary evaluate the future potential social impacts (negative as risk or positive as benefits) for the full-scale applications of the actual TRL5 F-CUBED technology. Adopting an LCA approach provides a holistic understanding of the system’s implications, addressing its broader social dimensions. This focus, combined with the previously published assessment of environmental impacts, highlights the critical importance of integrating both environmental and social dimensions to comprehensively evaluate the sustainability and societal acceptance of the innovative F-CUBED value chain.

The S-LCA methodology, leveraging insights from the Social Hotspots Database (SHDB), provides a robust framework for this analysis. It allows for an in-depth examination of supply chain interactions, labor conditions, community impacts, and broader socioeconomic effects. This approach underscores the project’s commitment to not only advancing bioenergy technology but also ensuring that its deployment enhances socioeconomic conditions, mitigates risks, and fosters a more sustainable and equitable future.

The present study aims to evaluate socioeconomic impacts, through the S-LCA, with particular attention to the potential improvement of social conditions and the overall socioeconomic performance of the novel F-CUBED technology, focusing on key stakeholder categories such as workers, local communities, and value chain actors, involved in the life cycle of the system. In addition, this paper helps address the current research gap concerning the societal impacts of hydrothermal biomass conversion, which have been examined in only a limited number of studies to date [22].

2. Materials and Methods

This section provides an overview of the case studies analyzed within the FPS, along with their respective industrial contexts and the methodological framework of the S-LCA. The S-LCA was conducted in accordance with ISO standards [23,24], following the Environmental Life Cycle Assessment (E-LCA) approach [21]. In this study, the SimaPro 9.1 software tool was employed, utilizing the Ecoinvent 3 (version 3.7) database and the SHDB (version 5) to model the F-CUBED value chain.

According to Benoit Norris and Norris [25], S-LCA combined with the SHDB provides a robust framework for assessing supply chain due diligence. The SHDB (V5) was used to evaluate the socioeconomic aspects of the cradle-to-gate life cycle of F-CUBED products, including pellets, electricity, and heat. However, while the SHDB is a widely recognized resource for assessing social risks across global supply chains, it does encounter known limitations, particularly concerning geographic granularity: the database is built on a Global Input Output (IO) model derived from the Global Trade Analysis Project, which aggregates data at the country and sector level rather than at more granular subnational or local levels [26]. This means that social risk assessments are typically conducted for broad country-specific sectors (CSSs), which, at a finer scale, can obscure significant regional or local variations within countries. To mitigate this, we carefully selected economic sectors that closely correspond with the actual activities and geographic context of each case study (Sweden, Italy, and Spain). Additionally, economic values used in the inventory phase were based on country-specific sectoral data, ensuring that social risks were assessed with a higher degree of contextual and local relevance. Furthermore, these quantitative data were complemented through a stakeholder engagement process, including surveys and interviews, which provided qualitative insights into local socioeconomic dynamics. This integrated approach helped strengthen the contextual relevance and interpretative accuracy of the S-LCA results.

The definition of the study’s goal, scope, and methodological phases underwent several iterative refinements in line with established guidelines. Life cycle modeling was based on conceptual process design and system modeling [18], supported with pilot-scale experimental data [17,19]. Where feasible, allocation was avoided through system expansion, extending system boundaries to include additional processes required to produce equivalent outputs for co-products.

2.1. Case Studies Considered in the LCA for F-CUBED

To rigorously assess the potential socioeconomic implications of the FPS, this study considered several carefully selected case studies. These case studies consist of three distinct wet biogenic residue streams that have been chosen to represent a spectrum of operational conditions, geographical locations, and technological configurations, ensuring that the S-LCA provides a robust and generalizable assessment of the FPS’s performance [21]. The targeted feedstocks include PPB with dry matter content of 3.5%, OP with dry matter content of 19.4%, and fruit and vegetable residues, specifically ORP with dry matter content of 20.0%. The block flow diagram of the FPS is presented in Figure 1, and a detailed description of the FPS is available in a previous publication [21].

The PPB case study is geographically contextualized in Sweden, reflecting the location of the industrial partner participating in the Torwash pilot testing—specifically, Smurfit Kappa. The OP case study is geographically situated in Italy at the Frantoio Oleario Chimienti, a facility affiliated with the APPO farmers’ association, located in Bari in the Apulia Region. Finally, the ORP case study is geographically situated in Spain, in the Delafruit facility, located in La Selva del Camp, Tarragona.

A summary of the case studies included in the S-LCA is presented in

Table 1. Description of the industrial context where FPS has been integrated, generating the case studies considered in the S-LCA [21].

Biogenic Residue Stream	Object of Investigation	Description
Treatment of pulp and paper biosludge (DM 3.5%)	Industrial context	Smurfit Kappa (SK) Kraftliner paper mill in Piteå, Sweden. The mill produces kraftliner as the main product. The wastewater streams from this mill are sent to the wastewater treatment plant (WWTP).
	F-CUBED Production System	Integration of the F-CUBED technology at the site of Smurfit Kappa (Piteå, Sweden) paper mill, for operational application with pulp and paper sludge (biosludge) as feedstock.
Treatment of virgin olive pomace (DM 19.4%)	Industrial context	APPO olive mill in Sannicandro di Bari, Italy. In the mill, the cleaned olives are pressed for the extraction of the extra virgin olive oil. The olive pomace is sent to the AD reactor for biogas generation.
	F-CUBED Production System	Integration of the F-CUBED technology at the site of APPO olive mill for operational applications with virgin olive pomace as a feedstock.
Treatment of orange peel (DM 20%)	Industrial context	Delafruit’s food processing plant in Reus, Spain. At the plant, fresh oranges are squeezed to obtain orange juice, which is used for different purposes. The orange peels are sent to the AD reactor for biogas generation.
	F-CUBED Production System	Integration of the F-CUBED technology at the site of Delafruit’s facility for operational application with orange peels as a feedstock.

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2.2. S-LCA Methodological Approach for the F-CUBED Production System

S-LCA is a methodology used to evaluate the social impacts associated with products and services throughout their life cycle, for example, from raw material extraction to product dispatch within a cradle-to-gate system boundary [13]. Additionally, S-LCA provides a structured assessment framework that integrates both quantitative and qualitative data.

The S-LCA methodology applied to the FPS follows the 2020 UNEP Guidelines for Social Life Cycle Assessment of Products and Organizations [13]. Like Environmental Life Cycle Assessment (E-LCA), these guidelines recommend structuring the assessment into four phases, aligned with ISO standards 14040:2021 (Principles and Framework) [23] and 14044:2021 (Requirements and Guidelines) [24]: (1) goal and scope definition; (2) life cycle inventory (LCI); (3) life cycle impact assessment (LCIA); and (4) interpretation of results.

2.2.1. Goal and Scope Definition

This section outlines and examines the goal, functional unit (FU), system boundaries, and allocation approach adopted for the S-LCA study. The objective of the current assessment is to evaluate the social impacts of the FPS across three selected biogenic residue streams, focusing on both potential benefits and risks for key stakeholders involved throughout the system’s life cycle. The results contribute to the technology development evaluation in social terms, to support the sustainability design forecasting potential hotspots of the products, emissions, and waste. The life cycle stages taken into account in the assessment have been assumed to be general macro-processes belonging to the main economic sectors for the specific EU countries of the biogenic residues streams’ productive sites, i.e., Sweden, Italy, and Spain. This approach allows the unification in a single study of the three stream-flow case studies of residue treatment (PPB, OP, and ORP) and social contexts (different EU State Members).

The target audience of the study also includes members of the agro-food industries and forest-based products, such as the pulp and paper industry. Moreover, this study will be available for the interested public (technical and non-technical), while the findings of the research can serve as valuable information for decision-makers in the above-mentioned industrial sectors.

2.2.2. Functional Unit and System Boundaries

In this study, the functional unit is defined as 1 kWh of electricity produced. This unit provides a measurable reference for evaluating the performance of the production system, serving as the basis for relating all associated inputs and outputs. The functional unit (FU) used, similar to the E-LCA, is an output unit related to and based on a physical attribute (1kWh of produced electricity) that is, for the S-LCA analysis, translated into an economic value form using prices.

Based on the objectives of the S-LCA, the system boundaries are defined using a cradle-to-gate approach, which is an appropriate framework for comparing alternative bioenergy production pathways from different feedstocks [27].

The system is designed to exclude upstream stages such as the biomass field production and the associated logistics of transporting biomass to industrial facilities where residues are generated. The process begins at the point of residue extraction and follows the transformation pathway through hydrothermal treatment, pelletization, and energy conversion, culminating in the final products (pellets, heat, or electricity) potentially dispatchable to end-users. The system includes the following: (1) upstream processes, such as residue extraction and feedstock preconditioning; (2) mainstream processes within the integrated F-CUBED plant, including the TORWASH^® hydrothermal treatment, dewatering, drying, and pelletization; and (3) downstream processes, involving transport to the power plant and biomass-to-energy conversion. The treatment of the liquid fraction via anaerobic digestion is also incorporated into the system boundaries. Figure 2 provides a schematic overview of the entire FPS as applied to each of the feedstock case studies.

With respect to geographical scope, the analysis assumes that the F-CUBED plant is situated in Northern Sweden for the PPB case in Catalonia, Spain, for the ORP case study, and in Southern Italy for the OP case study.

2.2.3. Allocation Approach

According to the International Organization for Standardization (ISO) [23], allocation in life cycle assessment (LCA) involves the distribution of input or output flows from a process or product system among multiple co-products or functions. This is necessary when a process generates more than one product or service, requiring a methodological approach to assign environmental burdens proportionally. The choice of allocation method, whether based on physical properties (e.g., mass or energy content), economic value, or system expansion, can significantly influence the results and interpretation of the LCA and should be carefully justified to ensure methodological consistency and transparency. Although the allocation approach is traditionally more emphasized in environmental LCA (E-LCA), it is highly pertinent to S-LCA theory.

In S-LCA, allocation is needed to distribute social impacts across different products or functions when a process produces multiple outputs, and the partitioning concerns social indicators such as worker well-being, fair wages, human rights, and community development. The choice of allocation method can influence how social burdens or benefits are attributed within a product system, especially in bioenergy contexts where labor conditions, community effects, and stakeholder engagement can vary for different co-products [13]. F-CUBED represents a multifunctional bioenergy production system in which co-products occur as outputs. These co-products include intermediate energy carriers (e.g., pellets), electricity, and biogas. Therefore, the distribution of social impacts within the process must be carefully allocated across these different outputs to ensure an accurate assessment of social sustainability.

Given the complexity of allocating social impacts in bioenergy production systems, the present S-LCA, in compliance with [13,23,24], follows, where feasible, the principle of avoiding allocation by employing system expansion. This approach extends the system boundaries by including secondary social processes, ensuring a holistic evaluation of how different co-products interact with society. For example, in the case of the anaerobic digestion of the filtrate after the dewatering step, the impact assessment extends beyond the bioenergy production facility. It includes the labor conditions, wages, and potential social benefits associated with the management of anaerobic digestion by-products. This approach allows for a more accurate representation of social externalities, particularly when detailed foreground social data is available from project partners.

However, when avoiding allocation is not feasible, a systematic allocation approach is necessary. In this study, social impact allocation is based on the economic values or a physical relationship, such as the mass or energy content of the outputs, ensuring that social burdens are proportionally assigned. An example is the olive stones separated from the feedstock in the upstream processes of the OP case study. Here, the allocation of social risks and benefits follows the distribution of labor and working conditions associated with the separation, processing, and potential economic value of the co-product.

Addressing multi-functionality in S-LCA remains one of the most significant sources of uncertainty, as also highlighted in [28] for E-LCA. The complexity arises from the following:

• The variability of social conditions in different production stages;
• Differences in labor intensity and worker exposure to risks across co-products;
• Unclear boundaries in assessing social spillover effects, such as job creation or loss due to by-product valorization.

2.2.4. Social Life Cycle Inventory

The Social Life Cycle Inventory (S-LCI) is based on a quantitative approach, and it consists of the inventory of all flows of the FPS normalized per functional unit.

During the S-LCI phase, data collection focused on two main components: the social flows and the activity variables. Social flows refer to the social indicators that reflect the potential social impacts linked to the production system. These indicators are connected to the broader socioeconomic system through the activity variables, such as the number of worker hours, which represent quantifiable measures of the effort or input associated with a given process or operation and act as a bridge between the technical aspects of the process and the assessment of its social implications. In this way, collecting accurate data on activity variables enables a meaningful analysis of how a product system interacts with society throughout its life cycle.

S-LCI data used in this study are classified into two main categories: (i) primary or foreground data, which were obtained directly through questionnaires administered to project partners, as well as from on-site measurements conducted during pilot-scale testing of the FPS; and (ii) secondary or background data, which were sourced from a combination of estimations, computational models, established databases (e.g., SHDB), scientific publications, statistical records, and relevant technical reports. This dual data approach enhances the robustness of the assessment by ensuring both context-specific accuracy and comprehensive coverage of supply chain processes.

To validate and enhance the completeness of the S-LCI, a stakeholder engagement process was conducted, primarily through the implementation of a targeted survey. Section 2.2.5 provides details on the stakeholder engagement activity.

Beyond the process-based model approach, the FPS has also been segmented into sectors, which are interconnected through economic flows expressed in a common monetary unit. The economic values of all inputs, originally expressed in euros, pounds, or Swedish krona, have been converted to USD base year 2011, in alignment with the current version of the SHDB, which uses USD 2011 as its reference currency.

In the S-LCA, databases such as the SHDB adopt a specific reference year and currency to ensure consistency and comparability across data. The use of USD of a defined base year, 2011 in this case, as a standard reference provides methodological consistency like a fixed baseline for data collection, capturing a snapshot of socioeconomic conditions that guarantee analytical rigor.

Additionally, several key considerations include control over the distorting effects of inflation and currency fluctuations [29], which can otherwise complicate the analysis of social impacts across different time periods, coherence across datasets, and studies using the SHDB. This methodological alignment is essential for making reliable comparisons between products, processes, or systems, and it enhances the credibility and comparability of findings, allowing researchers and practitioners to draw more robust and meaningful conclusions about social performance over time.

Since the real value of money evolves over time, and social conditions, wages, and purchasing power have changed significantly since 2011, relying on unadjusted financial data can lead to misleading conclusions. Between 2011 and 2023, cumulative inflation has resulted in an approximately 35% increase in price levels in the United States. To ensure methodological consistency with the SHDB reference framework, inventory prices originally expressed in 2023 values (year of the assessment) were adjusted while taking into account the currency and using the inverse inflation factor and thereby converting them into constant 2011 USD.

By converting 2023 values into constant 2011 USD using the historical USD/EUR exchange rate from 2011, part of the inflationary effect is implicitly accounted for. This approach ensures partial correction for inflation while maintaining consistency with the SHDB reference framework. As such, inventory prices originally expressed in 2023 euros were converted using the 2011 exchange rate of 1.33 (on 1 January 2011) to approximate their equivalent in constant 2011 USD. To further mitigate limitations linked to outdated price data, we complemented SHDB background modeling with primary stakeholder survey data and country-specific economic sector modeling across Sweden, Italy, and Spain (see Section 2.2.5). This hybrid approach enhances the localization and contextual accuracy of the social risk assessment and reduces reliance on static global averages. Moreover, we emphasize that updating all data to the currency year corresponding to the actual implementation period of the research (2022–2023) would not necessarily improve the robustness of hotspot identification. This is because current socioeconomic and geopolitical conditions (e.g., in 2024–2025) have evolved significantly even from 2022 to 2023, due to factors such as energy market disruptions, labor market shifts, and geopolitical instability. Thus, the use of more recent prices would still be an imperfect proxy for today’s social risk landscape.

Secondary and primary data for the impact categories and subcategories were collected for any specific economic sectors and sites related to the value chain.

An initial analysis was carried out using the SHDB in conjunction with the SimaPro software to identify social hotspots (SHs) within the product system, as well as to pinpoint specific social issues relevant to the context of the F-CUBED value chain. According to the Guidelines for Social Life Cycle Assessment of Products and Organizations [13], an SH is defined as a unit process or life cycle stage with a high potential for social or environmental impact, significantly contributing to one or more impact subcategories. These hotspots are typically located in specific geographical regions (e.g., countries) where conditions may pose considerable social risks, reflect ongoing challenges to social well-being, or, conversely, present opportunities for social improvement. They highlight critical areas within the supply chain where elevated concerns, such as labor rights violations, inadequate health and safety conditions, or weak governance, may be present, warranting closer scrutiny in the social assessment process and the adoption of appropriate mitigation measures.

Primary data were gathered through direct contact with organizations and companies through questionnaires and surveys, interviews, or assisted questionnaire compilation with stakeholders (e.g., workers, local inhabitants, other target groups). The selected target groups were located in one of the specific countries of interest for the S-LCA (Sweden, Italy, and Spain) and distributed among the categories of stakeholders interested and potentially affected by the development of the novel production system implemented with the F-CUBED project.

The collection of primary data through stakeholder engagement methods (e.g., surveys, questionnaires, and interviews) served a dual purpose. First, it enabled the refinement of the preliminary social hotspot analysis based on generic SHDB data by filling information gaps and enhancing contextual accuracy. Second, it allowed for the validation of identified social risks and the detailed assessment of their significance, with a focus on the most relevant impact subcategories and associated social indicators.

In parallel, secondary data were incorporated to support the analysis of each selected impact category and subcategory. These data were derived from established social inventory indicators, such as average wages, the incidence of workplace accidents, and labor law compliance, which provide quantifiable and context-specific evidence of social performance conditions across sectors and regions [30].

2.2.5. Survey Methodology and Rationale

To improve the assessment of the FPS’s potential social impacts at the local level and enhance the transparency and representativeness of the S-LCI, a targeted stakeholder survey was designed and implemented as part of the S-LCI. The methodological rationale for using surveys is supported by a broad body of socioeconomic and social science literature [31,32,33,34], which recognizes survey-based methods—including questionnaires and interviews—as effective tools for improving data quality and mitigating systemic biases that may arise from the inherent limitations of life cycle modeling.

As noted by Couper [31], the scientific discipline of survey methodology plays a critical role in empirical social analysis. By enabling the collection of stakeholder-specific, nuanced data, surveys contribute to a more robust and representative understanding of social impacts. This is particularly important in S-LCA, where social indicators are often context-dependent and may vary significantly across geographies, economic sectors, and stakeholder groups. Accordingly, the use of stakeholder surveys strengthens the social dimension of S-LCA by facilitating the inclusion of grounded, real-world perspectives, and by validating risk assessments derived from secondary databases such as the SHDB.

The stakeholder engagement process aims to enrich the S-LCI by integrating primary data and contextual insights from stakeholders directly or indirectly involved in the value chain of the FPS to explore how the introduction of the novel production system might influence key dimensions such as quality of life, working conditions, and broader socioeconomic well-being across the different stakeholder groups.

As depicted in Figure 3, the engagement takes place at more than one level.

The method of engagement was adapted to meet the varying needs and expectations of stakeholders, using a combination of tools, including online questionnaires, email interaction, phone calls, and interviews on a web platform, applied flexibly and iteratively, concurrently or sequentially, throughout the data collection process.

In compliance with the 2020 UNEP Guidelines for Social Life Cycle Assessment of Products and Organizations [13], the survey focused on a defined set of social performance indicators, known as impact subcategories, enabling benchmarking against other biomass conversion technologies. Six stakeholder groups were considered across the three case study countries, Italy, Spain, and Sweden: value chain actors, local community, workers, society, consumers, and children.

The questionnaire used to perform the survey had the following layout:

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A 1.5-page introductory section briefly describing the F-CUBED project, the survey objectives, and the questionnaire content.

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A first section (“system phase of interest”) where respondents were requested to indicate in which product system phase they were placed (e.g., residues production, product use, etc.) with respect to the F-CUBED Production System.

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A second section where it was asked which stakeholder category, foreseen in the UNEP Guidelines, was more likely to be affected by the introduction of the technology (including six options ranging from workers to children).

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A third section specifically devoted to the estimate of the social impacts: It required respondents to assess, for a set of distinct impact subcategories, the likely type of impact (positive, negative, or zero/not significant) and its rating (1 to 4, from low to high). The set of impact categories changed between the different stakeholder categories, with workers (11) and local community (9) being the ones with the largest set of categories, and children listing the smallest number (4).

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A fourth section ended the questionnaire asking the respondents to accept some privacy-related conditions concerning the use of the gathered information.

The accompanying letter and the complete survey questionnaire are available as Supplementary Material (codes S1 and S2, respectively).

2.2.6. Social Life Cycle Impact Assessment

The Social Life Cycle Impact Assessment (S-LCIA) represents the phase of the S-LCA dedicated to the quantification, evaluation, and interpretation of potential social impacts associated with a product system throughout its life cycle. The S-LCIA provides a structured approach to assessing how activities across the value chain may affect various stakeholder groups, considering both risks and opportunities. This phase is particularly relevant for emerging technologies or systems that have not yet been fully implemented, as it enables the prospective estimation of potential social impacts based on modeled scenarios and available data. This forward-looking capability is essential for supporting sustainable technology development and informing decision-making during early design or deployment stages.

Potential social impact is defined as the likelihood that a social impact will occur as a result of both the consumption of the product and the actions/behaviors of organizations connected to its life cycle [13]. The impact indicator, in the same way as the impact category potential in E-LCA, reflects the extent of the social impact and belongs to a certain impact (sub)category. The impact category potential, related to a certain characterization factor, is represented in S-LCA with worker hours, related to labor hour intensity factors. These factors allow, used together with the social risk level characterizations, the expression of social risks and opportunities in terms of work hours by sector and country [25].

The integration of S-LCA databases, such as the SHDB, streamlines and automates numerous steps within the S-LCIA phase. Specifically, the procedures associated with the reference scale (RS) approach to S-LCIA are inherently executed within the framework of database-driven analysis [13]. Within the SHDB model architecture, S-LCI data are acquired in their unprocessed quantitative form and subsequently subjected to characterization via S-LCIA procedures. The model estimates the labor intensity of each unit process by calculating the number of worker hours required across the supply chain to meet a defined final demand, typically represented by the functional unit or the delivery of a specific good or service. Social flow data, or “sociosphere flows,” are normalized as worker hours per 2011 USD of economic input, modulated via a context-specific risk indicator. These flows are then converted into medium-risk hour equivalents (mrheq), a unit that quantifies potential social impacts by accounting for both the magnitude of labor inputs and the associated risk levels. Risk characterization in SHDB is operationalized through a weighting scheme that reflects the relative likelihood of occurrence of adverse social conditions, benchmarked against a medium-risk scenario (assigned a reference value of 1.0). This probabilistic scaling enables comparative assessments across sectors, regions, and impact categories.

In this S-LCA, the Social Hotspot 2022 Category Method was used. This method follows the Reference Scale Assessment (formerly known as Type I or RS S-LCIA) and is designed to assess social opportunities or social risk. The Social Hotspot 2022 Category Method includes the characterization of different risk levels within each subcategory, followed by a damage assessment step that aggregates subcategory results to the category level. All subcategories within a category are given equal weight in determining the overall category-level risk. These weights are calibrated to prevent results from being influenced by the number of subcategories included. The method supports the aggregation of work hours across different risk levels, either within a detailed set of up to 30 social risk subcategories or within a broader set of five social risk categories (damage categories), which were considered in the data collection process. In each case, the “characterization step” multiplies the worker hours at a given risk level by a factor that reflects the relative probability of occurrence of the adverse working condition or community condition, for that indicator. The probability levels are expressed relative to the likelihood of the adverse condition occurring when the risk level is medium. As

Table 2. SHDB Impact Assessment Method: Mrh factors.

Scale Level	Color Code	Description	Value (mrheq)
4		Very High risk	10
3		High risk	5
2		Medium risk	1
1		Low risk	0.1

presents, a low risk indicates approximately one-tenth the likelihood of occurrence compared to medium risk; therefore, its characterization factor is 0.1 medium-risk-hour equivalents (mrhe). A Very high risk reflects a likelihood roughly ten times greater than that of medium risk, corresponding to a characterization factor of 10 medium-risk-hour equivalents per very high risk-hour. High risk represents approximately half the likelihood of very high risk, or five times that of medium risk, resulting in a characterization factor of 5 medium-risk-hour equivalents per high risk-hour.

Using these characterization factors enables the user to achieve the following: (1) determine a total quantity of risk (in mrheq) for each indicator, and (2) identify which country-specific sectors and which social inventory flows contribute to the overall risk for each indicator, thereby highlighting social hotspots for each indicator itself.

An ordinal scale with 1 to 4 Performance Reference Points (PRPs), ranging from “low risk” to “very high risk,” serves as the RS for impact assessment in this study. PRPs are context-dependent thresholds, targets, or objectives that define various levels of social risk or performance. They support the estimation of the scope and significance of potential social impacts on target groups within the product system.

These criteria are reflected in the medium-risk hour (mrh) factors used in the SHDB Impact Assessment Method, as outlined in Table 2. When appropriate inventory indicator data is compared to the defined levels, it becomes possible to assess whether the data reflects poor or strong performance.

During the impact assessment phase, there are multiple opportunities for aggregation and weighting. For example, social-subcategory results can be aggregated into broader impact categories to produce a set of stakeholder-level performance outcomes. This process helps synthesize complex phenomena, especially in S-LCIA, enhancing both the understanding and the communication of the findings. Given the significant influence of location-specific factors, aggregation was conducted with great care to avoid misinterpretation or loss of contextual detail. As a result, global supply chains were handled cautiously to ensure that contextual meaning was preserved. To express performance at the impact indicator or subcategory level, weighting is required. The relative significance or influence of each indicator on the performance of a given impact subcategory is expressed through the application of weighting factors. In the SHDB database, weighting reflects the proportionate probability of an unfavorable scenario, based on the assessed level of risk. These relationships between relative probabilities and the medium-risk hour (mrh) level are explicitly expressed [35].

The interpretation of the results is the final phase of an S-LCA, in which the findings from the S-LCIA are thoroughly reviewed and analyzed to support conclusions and recommendations, in alignment with the defined goal and scope.

3. Results and Discussion

This section presents the results of the S-LCI and S-LCIA, aimed at evaluating the extent and relevance of social impacts associated with the FPS when applied to three case studies in Sweden (PPB), Italy (OP), and Spain (ORP). Through the use of characterization factors, the severity of risks or the magnitude of opportunities was quantified across the value chain, allowing for a comprehensive interpretation of results across different social dimensions. In this context, higher impact scores indicate elevated social risk or stress, whereas lower or negative scores reflect positive contributions to social well-being or opportunities for improvement.

3.1. Results and Discussion of the Social Life Cycle Inventory

This section outlines the S-LCI phase conducted for the FPS, focusing on the selected biogenic residue streams. In this study, the S-LCI refers to the SHDB model that was developed based on the existing environmental LCA [21] by identifying the unit processes representative of the FPS, using the most relevant (CSS) available in SHDB.

The S-LCI architecture is delineated in

Table 3. Input production processes selected for the S-LCA and the respective sector of the economy.

Input Process	Sub-Process	Sector of the Economy
Preconditioning	-	Specific industrial sector generating the residues
TORWASH® treatment and dewatering step	-	Other machinery and equipment manufacturing (except transport and electronic equipment)
Biopellets production	-	Lumber and wood products production
Electricity production (PELLETS)	Electricity production	Electricity production
	Avoided heat production	Gas extraction
Electricity production (BIOGAS)	Electricity production	Electricity production
	Avoided heat production	Gas extraction

. The objective of the S-LCI exercise is to quantify, for each of the three case studies under investigation, the economic value (in constant 2011 USD) of inputs sourced from relevant CSS within SHDB that are required to produce the F-CUBED outputs.

Several unit processes included in the S-LCI phase were modeled using SH processes derived from the SHDB. These SH processes were selected based on the country-specific context (i.e., Sweden, Italy, or Spain) and the type of biogenic residue stream involved, namely PPB, OP, or ORP. The inclusion of SH strength in the inventory phase by addressing data gaps, such as the unavailability of specific indicators or their respective weightings, and by improving the completeness and representativeness of the social life cycle dataset. This approach ensures alignment with data quality requirements related to coverage, consistency, and transparency, which are critical for comparative or consequential S-LCA applications.

The economic sectors selected for PPB assessment correspond to key activities in the system, including paper manufacturing, machinery and equipment, wood pellet production, and electricity generation. The economic sectors chosen for the OP case study correspond to the specific industrial sector of vegetable oil production in Italy, machinery and equipment, wood pellets, and electricity generation. The economic sectors for ORP case study refer to the specific industrial sector of vegetables, fruits, and nuts growing in Spain, machinery and equipment, wood pellets, and electricity generation.

The corresponding social LCI datasets applied in the three case studies are detailed in

Table 4. Social LCI datasets for the country-specific economic sectors linked to the case studies: (A) pulp and paper biosludge in Sweden (PPB); (B) olive pomace case study in Italy (OP); and (C) orange peels in Spain (ORP). The unit USD 2011 is referred to for a single ton of residue (USD 2011/t _residue).

Process	Case Study	Co-Products	Economic Sector		Values (USD 2011)
Feedstock pretreatment	A	Enhanced biosludge	Paper products, publishing (ppp)/SWE U	SE	−0.186
	B	Olive pomace destoned and diluted	Vegetable oils and fats (vol)/ITA U	IT	1.51
	C	Orange peels ground and diluted	Vegetables, fruit, nuts (v_f)/ESP U	ES	33.67
TORWASH® pretreatment	A	Solids produced	Other machinery and equipment manufacturing (except transport and electronic equipment)	SE	0.403
	B			IT	5.211
	C			ES	25.05
Biopellets production	A	Biopellets	Lumber and wood products production	SE	0.550
	B			IT	35.05
	C			ES	36.06
Electricity production (PELLETS)	A	Avoided heat production	Gas extraction	SE	8.378
	B			IT	506.68
	C			ES	1033.33
	A	Dispatchable electricity	Electricity production	SE	1.214
	B			IT	281.50
	C			ES	398.82
Electricity production (BIOGAS)	A	Avoided heat production	Gas extraction	SE	2.150
	B			IT	84.31
	C			ES	732.76
	A	Electricity production	Electricity production	SE	1.028
	B			IT	82.18
	C			ES	494.96

. For every case study, the assessment builds upon primary data sourced from the E-LCA, which served as the foundation for developing the S-LCA model. Specifically, the E-LCA [21] provided detailed information on the composition of the supply chain, enabling the identification of all relevant stages of the FPS required to generate electricity from the wet biogenic residue stream of PPB, OP residues, and ORP residues.

The complete data collection for the S-LCI phase of each case study is presented comparatively in

Table 5. Social life cycle inventory of the F-CUBED Production System for the pulp and paper biosludge (PPB) case study in Sweden, the olive pomace case study (OP) in Italy, and the orange peel case study (ORP) in Spain.

Process	Data	SH Unit Process	Units	Case Study
				PPB	OP	ORP
UPSTREAM processes
Feedstock pretreatment	Input	Residues unit process	USD 2011	−0.18615 1	1.51 2	33.67 3
		Residue’s value	EUR/kg	−0.215 4	0.001	0.0065
	Output	Preconditioned residue	kg/tFU	32.9 5	2013.5 6	5180 7
MAIN STREAM processes
TORWASH® pretreatment	Input	Other machinery and equipment manufacturing (except transport and electronic equipment)	USD 2011	0.403	5.211	25.05
		Substitution values of solids	EUR/kg	0.047	0.035	0.07
	Output	Solids from mainstream processes	kg/tFU	11.41	198	476
Biopellet production	Input	Lumber and wood products production	USD 2011	0.55	35.05	36.06
		Substitution values of pellets (bulk)	EUR/kg	0.139	0.37	0.221
	Output	Biopellets	kg/tFU	5.25	126	217
DOWNSTREAM processes
Electricity production (PELLETS)	Input (avoided heat)	Gas extraction	USD 2011	8.38	506.68	1033.33
		Avoided heat scenario 54%	kWh/tFU	41	3860	3799.78
		Price of thermal kWh	p/kWh	23.43	15.05	31.18
		Current exchange rate	EUR/GBP	1.16	1.16	1.16
	Input (electricity)	Electricity production value	USD 2011	1.21	281.50	398.82
		Electricity production	kWh/tFU	13.3	1600	2326.47
		Prices of electricity	EUR/kWh	0.121	0.234	0.228
	Output	Electricity from pellets	p	1	1	1
FILTRATE processing
Electricity production (BIOGAS)	Input (avoided heat)	Gas extraction	USD 2011	2.15	84.31	732.76
		Avoided heat	kWh/tFU	10.52 8	3860 9	2694.50 8
		Price of thermal kWh	p/kWhth	23.43	15.05	31.18
		Current exchange rate	EUR/GBP	1.16	1.16	1.16
	Input (electricity)	Electricity production value	USD 2011	1.03	82.18	494.96
		Electricity production	kWh/tFU	11.26	467.11	2887,28
		Prices of electricity	EUR/kWh	0.121	0.234	0.228
	Output	Electricity from biogas	p	1	1	1

¹ Paper products, publishing (ppp)/SWE U, considering 1.1515 kg/tADp. ² Vegetable oils and fats (vol)/ITA U. ³ Vegetables, fruit, nuts (v_f)/ESP U. ⁴ Disposal cost for landfilling of sewage sludge. ⁵ Biosludge (wb) DM 3.5%, expressed as kg/tADP. ⁶ Olive pomace preconditioned, expressed as kg/tOP. ⁷ Orange peels preconditioned, expressed as kg/tORP. ⁸ Scenario of heat reuse of 54%. ⁹ Scenario of heat reuse of 80%.

, along with a detailed description of the underlying assumptions. These elements collectively provide a comprehensive overview of the inventory modeling approach applied to each case study.

The unit processes defined as outputs of the production system were derived from the LCI of the E-LCA [21], as previously described. Conversely, the input unit processes required for modeling the supply chain were identified from secondary data sources. All economic values used in the S-LCI are expressed in constant 2011 US dollars (USD 2011). For currency conversion, an exchange rate of 1.33 EUR/USD, corresponding to the rate in January 2011, was applied.

In the case of PPB, conventional disposal via landfilling, within defined environmental safety parameters, has been considered the baseline reference case study. Accordingly, an avoided cost was introduced to reflect the economic benefit associated with diverting the residue from landfills. In the OP case study, the economic value attributed to the residue generated via the two-phase olive oil extraction process, which produces wet pomace, was based on the authors’ expertise and reflects typical market values observed in Italy. The economic value assigned to the residue generated from orange processing, specifically during orange juice production, was based on the market value of orange peels used as feed in Ecuador. Nevertheless, it is important to note that the reference supply chain for residue extraction is situated nationally, with operations occurring in Spain.

The economic value attributed to the solid fraction produced through the TORWASH^® hydrothermal treatment and subsequent dewatering was based on the market value of wood chips, selected as a substitutable good due to its functional equivalence in energy applications. Similarly, the economic value of biopellets, manufactured from the F-CUBED solid fraction, was estimated using the market price of wood pellets as the surrogate reference. Where primary data were not available, surrogate values were employed to estimate input costs. The surrogate value represents the monetary value of a substitute good or service that delivers a comparable level of utility to the end-user or performs an equivalent function within the production system. This methodological approach follows the definition proposed by Bonfanti et al. [36], ensuring consistency in the representation of economic flows across the S-LCI.

For the sake of clarity and transparency,

Table 6. F-CUBED Production processes provided via SHDB for the pulp and paper biosludge (PPB) case study in Sweden, the olive pomace (OP) case study conducted in Italy, and the orange peels (ORP) case study in Spain.

Process	Co-Products	Sector of the Economy	Data Source
Pulp and Paper Biosludge Case Study
Preconditioning	Enhanced biosludge	Paper products, publishing (ppp)/SWE U	[25,37]
TORWASH® treatment and dewatering step	Solids produced	Other machinery and equipment manufacturing (except transport and electronic equipment)_SE	Wood fuel and peat prices for heating plants, nominal prices, 192 SEK/MWh (2021); in [38]
Biopellet production	Biopellets	Lumber and wood products production_SE	Price of wood pellets for European industrial wood pellets [39]
Electricity production (PELLETS)	Dispatchable electricity	Electricity production_SE	Electricity price for households, taxes and network price not included [38]
	Avoided heat production	Gas extraction_SE	[40]
Electricity production (BIOGAS)	Dispatchable electricity	Electricity production_SE	Electricity price for households, taxes and network price not included [38]
	Avoided heat production	Gas extraction_SE	[40]
Olive Pomace Case Study
Preconditioning	Olive pomace destoned and diluted	Vegetable oils and fats (vol)/ITA U	[25] and authors expertise in the sector
TORWASH® treatment and dewatering step	Solids produced	Other machinery and equipment manufacturing (except transport and electronic equipment)_IT	Authors expertise in the sector: average price of wood chips M50, 35 EUR/t
Biopellets production	Biopellets	Lumber and wood products production_IT	Price of wood pellets for European industrial wood pellets from [39]
In the sector	Electricity production	Electricity production_IT	[41]
	Avoided heat production	Gas extraction_IT	[40]
Electricity production (BIOGAS)	Electricity production	Electricity production_IT	[41]
	Avoided heat production	Gas extraction_IT	[40]
Orange Peel Case Study
Preconditioning	Orange peels ground and diluted	Vegetables, fruit, nuts /ESP U	[42]
TORWASH treatment and dewatering step	Solids produced	Other machinery and equipment manufacturing (except transport and electronic equipment)_ES	Average price of wood chips P45/G50, 70 EUR/t, from Astillas, precio según tamaño de grano y coste de producción, 2017; in [43]
Biopellets production	Biopellets	Lumber and wood products production_ES	Pellets, precio según el tipo de suministro, 2017; in [42]
Electricity production (PELLETS)	Electricity production	Electricity production_ES	[41]
	Avoided heat production	Gas extraction_ES	[40]
Electricity production (BIOGAS)	Electricity production	Electricity production_ES	[41]
	Avoided heat production	Gas extraction_ES	[40]

presents an overview of the production and unit processes included in the assessment, along with their corresponding economic sectors and data sources used to obtain prices or surrogate values.

The prices of wood chips and densified wood fuels were sourced from official energy statistics published by the Swedish Energy Agency, based on national energy balances presented in Sweden Facts and Figures 2022 [38]. Figure 4 illustrates the reported prices for 2021 are 192 SEK/MWh_th for wood chips and 324 SEK/MWh_th for wood pellets.

To convert these values into euros per ton (EUR/t), the following lower heating values (LHVs) were applied: 2.91 MWh/t for wood chips and 5.12 MWh/t for wood pellets. An exchange rate of 0.084 EUR/SEK was used to complete the conversion.

3.2. Results and Discussion of the Survey on Socioeconomic Aspects

3.2.1. Sample of Stakeholders and Questionnaire Distribution

The survey targeted 44 stakeholders, selected for their relevance to key impact categories and their connection to the FPS in the three pilot countries: Italy, Spain, and Sweden. It was conducted between June and early August 2023, with multiple follow-up rounds to encourage participation. A total of 19 responses were received, corresponding to a 43% response rate. Respondents represented six European countries, with the majority from Italy (8), followed by The Netherlands (4), Ireland (2), Spain (2), Sweden (2), and Germany (1).

Several factors likely contributed to the moderate response rate:

-

The survey period coincided with the summer holiday season, making it harder to reach participants.

-

The nature of the survey required stakeholders to envision the future industrial-scale deployment of the FPS, a scenario outside the typical scope of their daily work—particularly as social impact assessment is not commonly part of their expertise.

-

Some participants felt that the introductory explanation in the questionnaire was not sufficient. Upon request, additional information was provided via phone or email, and stakeholders were also referred to the F-CUBED website.

-

Certain European-level organizations declined to respond, citing insufficient insight into their national members’ views on the technology.

Despite these challenges, the response rate was considered statistically useful, given the complexity and specialized nature of the topic.

3.2.2. Survey Results

The key survey results focus first on identifying the main stakeholder categories perceived as most affected by the implementation of the FPS and second on the internal analysis of the most significant impact subcategories.

As displayed in

Table 7. Survey results about the stakeholder categories.

Stakeholder Category	%
Value chain actors	23.2
Local community	20.6
Workers	19.7
Society	18.2
Consumers	13.9
Children	4.4

, three stakeholder categories were ranked slightly above all the others: “Value chain actors”, “Local community”, and “Workers”. This fits with an intuitive view of which social areas might be more sensitive to the introduction of the F-CUBED technology: all of them can be clearly and directly associated with the introduction or activities of the novel FPS plants.

Children were ranked much lower, largely due to the limited direct relevance of the FPS to this group. Some respondents even noted concerns over potential health risks in this category, highlighting a future area that might warrant closer investigation.

The survey results show a generally positive reception toward the FPS, especially concerning economic development, employment opportunities, and sustainability alignment. Less attention was given to ethical and deep social dimensions, highlighting a possible area for future communication and stakeholder engagement. Several impact subcategories were highlighted more frequently and more strongly by the survey’s respondents. When examining the value chain actors category, the focus was largely on economic implications. Survey participants emphasized the importance of technological advancement (18.3%), new market opportunities (16.1%), and the economic viability of the technology (14.4%). Employment perspectives were also considered relevant (13.9%). However, ethical and social concerns such as fair competition and broader social responsibility were given less importance, suggesting a predominant focus on practical economic outcomes.

In contrast, the local community category received a more balanced evaluation, with both economic and social dimensions rated as important. Economic opportunity (14.4%) and the availability of local resources (13.8%) were ranked highest, followed by environmental factors such as air and water quality and the potential for local job creation, both at 11.9%. Notably, even respondents from environmental NGOs reported no significant concerns regarding environmental impacts, which suggests a broadly favorable perception of the system’s integration into local settings.

The workers category, although ranked third in the survey (Table 7), attracted considerable attention being clearly directly affected by the introduction of the new technology. In this category answers tend to be more balanced (work conditions, 12.4%; career prospects, 12.4%; job satisfaction, 12.4%) because it is also the one with the highest number of options for answering. Finally, aspects such as equal opportunity, job stability, and social benefits were ranked below and not highlighted as major concerns. This result may suggest a generally favorable expectation of the new system’s integration into existing employment structures or a lack of perceived risk among workers.

The society category revealed an optimistic view of more specific, socially-related impact subcategories. Many participants recognized the FPS contribution as aligned with policy and societal interests, and potentially valuable in addressing future social challenges: contribution to sustainable development (23.4%); alignment with societal goals-policies (21.3%); social challenges and energy demands (19.1%); and broader social acceptance (19,1%). Despite this, explicitly ethical concerns such as societal values were less frequently cited, indicating a possible gap between technological promise and its perceived ability to influence ethical behavior or broader cultural shifts.

Although the consumer category was not highly ranked, the responses pointed to high expectations in terms of service quality. The reliability of bioenergy products (23.4%) and the affordability of energy (21.5%) emerged as important concerns, along with the accessibility of bioenergy products (20.6%) and the perception of technology benefits and drawbacks (19.6%). This means that consumers’ expectations remain critical to its perceived success.

In summary, regarding the internal analysis of every main stakeholder category, the survey results indicate that, in overall terms, economic-related issues tend to be considered more important by the respondents than socially or ethics-related ones. This suggests that the new technology is expected to provide clear benefits in this area, while there is more uncertainty about the other two.

Feedback from the stakeholder survey revealed a generally high level of optimism regarding the social potential of FPS, with respondents anticipating positive contributions across multiple social dimensions.

The stakeholder engagements provided a consistent validation of the methodological choices that have been done in the development of the S-LCA both in the selection of the most relevant subcategories to analyze and in the interpretation of the final results.

In future assessments, repeating a similar survey, with an improved, more comprehensive description of the novel technology and a clearer, more user-friendly guide for respondents, might enhance the quality and depth of stakeholder-derived insights providing even more robust results.

3.3. Results and Discussion of Social Life Cycle Impact Assessment

This section illustrates the results provided via the S-LCIA based on two main methodological adjustments: (1) harmonization between the impact categories of the SHDB database and UNEP 2020 Guidelines and (2) the selection of the SHDB subcategories that are most representative and relevant for the FPS.

The SHDB impact assessment methodology organizes social performance indicators into five principal impact categories: labor rights and decent work, health and safety, human rights, local community, and governance. This categorization is broadly consistent with the updated 2020 UNEP Guidelines for Social Life Cycle Assessment of Products and Organizations [13]. However, key discrepancies remain due to the partial alignment of the SHDB with the subcategories recommended in the guidelines. As a result, harmonization efforts are needed to ensure a comprehensive and coherent application across assessments [25].

In the SHDB framework, these five impact categories are derived through the aggregation of 30 distinct subcategories, forming the core of the S-LCIA phase. In contrast, the 2020 UNEP Guidelines propose a broader structure comprising six stakeholder-related impact categories: human rights, working conditions, health and safety, cultural heritage, governance, and socioeconomic repercussions, subdivided into a total of 40 subcategories.

This structural divergence implies that, while SHDB offers a practical and operational framework for early-stage or large-scale social risk screening, it may require supplementary subcategory-level analysis and mapping to fully conform with the UNEP Guidelines in more comprehensive S-LCA studies. Therefore, careful methodological alignment and correspondence mapping are essential when applying SHDB within studies adhering to UNEP’s normative framework.

Table 8. Social categories investigated in the S-LCIA of the FPS, selected SHDB impact subcategories, and proposed correspondence with the UNEP Guidelines subcategories.

Social Impact Categories	Subcategories	SHDB ID	UNEP 2020 Harmonization
Labor rights and decent work	Wage assessment	1A	Career prospects Employment prospects
	Workers in poverty	1C	Economic opportunities
	Forced labor	1E	Work conditions
	Excessive working time	1F	Work conditions
	Social benefits	1I	Job satisfaction
	Labor laws/convs	1J	Training requirements
	Unemployment	1L	Job stability
Health and safety	Occupational health and safety (Occ Tox and Haz)	2A	Children, health, and well-being Children, exposure to pollutants or hazardous substances
Society	Poverty and inequality	3F	Local employment Broader social acceptance Social challenges and energy demands
	State of Env sustainability	3G	Availability of local resources Contribution to sustainable development
Governance	Legal system	4A	Market opportunities Alignment with societal goals and policies
	Corruption	4B	Future prospects
Community	Access to drinking water	5A	Air and water quality
	Access to sanitation	5B	Alignment with societal goals and policies
	Children out of school	5C	Children, health, and well-being
	Access to hospital beds	5D	Alignment with societal goals and policies
	Smallholder vs. commercial farms	5E	Economic viability and market opportunities
	Access to electricity	5F	Energy affordability Accessibility of bioenergy products Perceptions of technology and its benefits or drawbacks
	Property rights	5G	Technological advancements Reliability of bioenergy products

presents the social impact categories assessed during the S-LCIA of the FPS, alongside the selected subcategories derived from stakeholder survey outcomes, as detailed in Section 3.2. This table also includes a preliminary harmonization map between the subcategories adopted by the SHDB and those recommended in the UNEP Guidelines for Social Life Cycle Assessment of Products and Organizations and Methodological Sheets for Subcategories in S-LCA [13,44].

To ensure a more comprehensive and context-relevant analysis, two additional subcategories were incorporated into the original list of the SHDB:

• Injuries and fatalities (2B): This subcategory is critical for assessing occupational health and safety, particularly in relation to labor intensity and exposure to risk factors throughout the value chain. Its inclusion strengthens the representativeness of the Working Conditions impact category.
• Democracy and freedom of speech (4C): In the current geopolitical context, where energy system resilience is increasingly influenced by global supply dependencies, this subcategory becomes especially relevant. It allows for the assessment of systemic risks associated with countries where severe restrictions on civil liberties, such as freedom of expression and peaceful assembly, may signal broader governance and human rights concerns.

The extended set of subcategories enables a more holistic assessment of social risk and opportunity, aligned with both SHDB’s operational structure and UNEP’s normative S-LCA framework.

Building upon these methodological foundations, a comprehensive and multilayered data visualization approach was implemented to effectively communicate the S-LCIA findings. Four key types of visualization were developed: (1) aggregate social impact by category; (2) disaggregated impact by economic sector; (3) subcategory-level analysis; and (4) risk characterization. Together, these visualizations introduce a progressively deeper understanding, enabling a more comprehensive and detailed interpretation of the S-LCIA results.

3.3.1. Aggregate Social Impact by Impact Category

This analysis provides a synthesized overview of the social risks or benefits associated with each harmonized impact category. It integrates risk characterization results across economic sectors and life cycle stages, allowing for high-level comparisons across case studies and facilitating initial prioritization of areas of concern. As detailed in

Table 9. Single score social impacts by impact category expressed in medium-risk hour equivalents (mrheq) for pulp and paper biosludge (PPB), olive pomace (OP), and orange peel (ORP) case studies.

Damage Category	Social Impact Indicator Damage Assessment (mrheq)
	PPB	OP	ORP
(1) Labor rights and decent work	−0.103	3.661	−108.217
(2) Health and safety	−0.180	5.907	−161.077
(3) Society	−0.061	2.933	−85.303
(4) Governance	−0.149	4.405	−130.811
(5) Community	−0.054	2.589	−79.562
Total	−0.546	19.496	−564.970

, the social footprint of the FPS was assessed by aggregating the social impacts associated with each country-specific sector (CSS) into a consolidated score for each damage category.

In this context, a damage category refers to an “area of protection”—a conceptual construct that represents domains considered to hold intrinsic or societal value (e.g., human well-being, community cohesion, institutional stability) and which are intended to be preserved or enhanced through sustainability-oriented interventions. These categories represent the final aggregation level (the least detailed) in the impact assessment. The aggregated social impact results for each case study represent the cumulative risk contributions of each economic sector across all life cycle stages of the FPS. These results offer a high-level synthesis of the social risk landscape, supporting comparative assessments and guiding stakeholder-specific mitigation strategies, capturing the potential long-term consequences of social risks across the product system’s life cycle.

The results from the S-LCIA of the FPS applied to three biogenic residue streams reveal a nuanced picture of how social impacts unfold across different European industrial contexts.

In the case of PPB, treated within the Swedish industrial setting, the FPS demonstrates an overall beneficial social footprint. All five categories register slightly negative mrheq values, meaning the implementation of the technology contributes to a net reduction in social risks. The most significant improvements are observed in the areas of health and safety and governance. These results are in line with Sweden’s generally strong institutional frameworks and well-enforced labor standards. The production stages that influence these outcomes the most are the electricity generation phases, both from pellets and biogas, where robust occupational health and safety standards and good labor practices significantly mitigate risks. Furthermore, the governance category benefits from the high regulatory compliance and low corruption indices characteristic of the Swedish industrial and energy sectors.

A starkly different situation emerges with OP in the Italian context. Here, the FPS generates the highest social impact values, positive in sign, and thus indicative of risk, among the three case studies. The most critical areas are health and safety and labor rights with health and safety presenting the most concerning figure. This can be attributed to the relatively higher exposure to occupational hazards in the energy generation phases, as well as the labor-intensive nature of pellet production in small and medium-sized enterprises in Southern Italy. The sector’s reliance on seasonal or informal labor, together with disparities in enforcement of workplace safety regulations, contributes to these results. In terms of governance, the risk reflects persistent concerns over regulatory efficacy, bureaucratic inefficiencies, and localized issues of transparency and accountability. The community category also reveals areas of vulnerability, particularly in relation to access to infrastructure and utilities, although some mitigating effects are observed due to the involvement of smallholders and the promotion of local employment. Overall, the Italian case study portrays a complex scenario where economic opportunities generated via the FPS, especially in terms of job creation and valorization of agricultural by-products, coexist with persistent structural challenges that elevate social risk.

In contrast, the ORP case study, centered in Spain, presents an exceptionally positive social profile. All five impact categories display strongly negative mrheq values, signifying substantial social benefits. The standout performance lies in the categories of health and safety and labor rights where the introduction of the FPS appears to markedly reduce social risks. These findings suggest that the Spanish agri-food and renewable energy sectors involved in this case are well regulated and characterized by relatively safe and stable employment conditions. The positive outcomes also reflect the effective integration of the F-CUBED production chain into the existing industrial ecosystem, which benefits from high levels of automation and technological maturity. Governance is another area where the Spanish case study performs exceptionally well, with strong institutions and adherence to EU labor and environmental norms reinforcing the social sustainability of the system. Community-level benefits are also evident, driven by improved access to services, employment, and a cleaner environmental footprint. The scale of these benefits suggests that the FPS not only fits seamlessly within the Spanish socioeconomic framework but actively enhances it, making this case a model of socially sustainable bioenergy valorization.

These outcomes reflect effective corporate policies, responsible value chain management, and alignment with international social standards. In particular, the health and safety impact category presents the most favorable condition, strong performance in ensuring workplace safety, likely with good prevention measures and low accident rates. The governance score also indicates effective governance practices, such as transparency, anti-corruption, and regulatory compliance.

3.3.2. Disaggregated Impact by Economic Sector

This analysis presents the sector-specific contributions to overall social impact both numerically and through bar charts, detailing the relative weight of each economic activity (e.g., TORWASH^® treatment, pelletization, biogas generation) within the value chain. This visualization is critical for identifying sectoral hotspots and informs targeted intervention strategies.

The detailed breakdown of the social impacts for each F-CUBED case study (i.e., PPB, OP, and ORP) across various production phases and impact categories offers a nuanced picture of how the FPS interacts with the socioeconomic contexts in which it is deployed.

Table 10. Social impacts of the F-CUBED Production System (FPS) for the pulp and paper biosludge, olive pomace, and orange peel case studies, disaggregated by economic sector. Values are expressed in medium-risk hour equivalents (mrheq). Values corresponding to a medium-risk level, aligned to the scale level of 2 PRPs, are highlighted in blue.

Case Study	Economic Sector/ Production Phase	Labor Rights and Decent Work	Health and Safety	Society	Governance	Community
Pulp and paper biosludge	1-Enhanced biosludge	−0.002	−0.003	−0.001	−0.002	−0.001
	2-Torwash and dewatering	0.005	0.010	0.004	0.006	0.004
	3-Biopellets	0.009	0.016	0.006	0.012	0.006
	4-Electricity from pellets	−0.095	−0.167	−0.057	−0.134	−0.052
	5-Electricity from biogas	−0.021	−0.036	−0.012	−0.030	−0.010
	Total	−0.103	−0.180	−0.061	−0.149	−0.054
Olive pomace	1-Preconditioning	0.048	0.075	0.036	0.040	0.036
	2-Torwash and dewatering	0.056	0.091	0.040	0.063	0.036
	3-Biopellets	1.710	2.630	1.325	1.941	1.204
	4-Electricity from pellets	1.339	2.258	1.122	1.718	0.961
	5-Electricity from biogas	0.509	0.853	0.410	0.642	0.351
	Total	3.661	5.907	2.933	4.405	2.589
Orange peels	1-Preconditioning	0.538	0.894	0.427	0.514	0.427
	2-Torwash and dewatering	0.267	0.436	0.194	0.305	0.175
	3-Biopellets	0.920	1.415	0.713	1.044	0.648
	4-Electricity from pellets	−66.778	−99.570	−52.594	−80.402	−48.959
	5-Electricity from biogas	−43.163	−64.253	−34.042	−52.273	−31.854
	Total	−108.217	−161.077	−85.303	−130.811	−79.562

provides a visualization of social impacts’ disaggregated production phase. Each production phase of the FPS is linked to its corresponding economic sector. The impacts are reported across five social impact categories, labor rights and decent work, health and safety, society, governance, and community, and they are expressed in medium-risk hour equivalents (mrheq). These figures capture both risks and benefits, with negative values reflecting social benefits and positive values indicating social risks.

For the PPB case study, the most substantial positive contributions stem from the electricity generation phases—particularly electricity from pellets and biogas, which produce significantly negative scores across all five impact categories. This suggests that these steps contribute to reducing social risks, likely due to Sweden’s advanced energy infrastructure, strong enforcement of labor and safety standards, and relatively low social tensions around energy production. For instance, the electricity from the pellet phase alone exhibits notably beneficial values in labor rights and governance, indicating not only job quality and safety but also institutional robustness. The upstream steps, including enhanced biosludge treatment and the Torwash and dewatering phases, show slight positive scores, suggesting minimal risks introduced via these technologies. However, their impact is largely offset by the stronger benefits downstream. The pellet production step introduces minor social risks across the board, possibly reflecting labor intensity or supply chain dependencies that are less socially optimized.

Figure 5 illustrates the contribution of each process stage to the total social impact in the Swedish case study.

The histogram shows that electricity production from biopellets alone accounts for a dominant share of the total benefit, ranging from –90% to –97%, depending on the impact category. This is a strong indication that the Swedish energy sector, characterized by high regulatory standards, strong worker protections, and integrated energy efficiency strategies like heat recovery, transforms what might otherwise be neutral processes into socially advantageous operations.

Conversely, biopellet production and the Torwash and dewatering steps are the primary contributors to the remaining social risks, accounting for approximately 8–11% and 4–7% of the total impact, respectively. Although these figures are modest in absolute terms, their recurring presence across multiple impact categories suggests localized, sector-specific risks, potentially linked to labor intensity or technical labor demands in machinery operation. The minimal contributions from the enhanced biosludge treatment process further support the conclusion that upstream operations in Sweden present limited social risk, in line with the country’s overall strong socioeconomic baseline.

In contrast, the OP case study, centered in the Italian region of Apulia, reveals a markedly different social profile. Every phase of the production chain, from preconditioning to final energy conversion, contributes positively to the overall social risk profile, culminating in substantial total impacts across all categories (Table 10). The most striking findings relate to the biopellet production and electricity generation from pellets, which contribute disproportionately to the total social risks. These stages alone are responsible for large increases in social impact values, particularly in labor rights and health and safety.

Figure 6 depicts the social impact distribution for the Italian case study (OP). The figure reveals a more risk-intensive profile, dominated by biopellet production and electricity generation from pellets, that together contribute between 80% and 86% of the total social impact across all categories. Specifically, biopellet production accounts for 44–47%, while electricity generation contributes 36–39%. Meanwhile, phases like preconditioning and Torwash and dewatering have a minimal social impact, each contributing only around 1–1.5% of the total in most categories. Their limited contribution aligns with lower labor intensity and mechanization during these earlier stages. However, the fact that even these steps are not entirely impact-neutral reinforces the systemic nature of the social risks embedded in this value chain.

This scenario reflects structural vulnerabilities within the local production environment, which may include precarious employment arrangements, the variable enforcement of occupational safety regulations, and limitations in governance mechanisms The medium risk identified for labor rights and decent work, together with the health and safety impact category, in the Italian case stems from sector-specific data gaps. In particular, the agricultural sector is affected by the “caporalato” system, an illegal labor recruitment practice that disproportionately involves migrant workers and is associated with conditions resembling forced labor. This context also implies reduced workplace safety, increased exposure to hazardous substances, and limitations on individual rights. The high scores in the governance category suggest challenges not only in transparency and institutional trust but also in ensuring equitable access to resources and public infrastructure. This pattern paints a picture of a technologically promising system being deployed in a socioeconomic environment that lacks the resilience or safeguards to fully capitalize on its benefits without introducing significant social burdens. While the economic opportunities associated with the FPS may be present, the current deployment context in Southern Italy appears to exacerbate existing socioeconomic and societal vulnerabilities, rather than alleviate them.

This holistic view signals the need for targeted social safeguards and potentially a revision of workforce management practices, particularly in downstream energy operations and pellets manufacturing.

The ORP case study, implemented in Spain, offers a compelling contrast, standing out as a noticeable example of how favorable socioeconomic conditions can transform the same technology into a highly impactful and socially beneficial intervention. As Table 10 reports, the initial stages of preconditioning and TORWASH^® treatment introduce moderate social risks, particularly in labor rights health and safety, and governance. These results are likely influenced by the agricultural and food processing sectors’ typical labor patterns, which may involve seasonal or low-paid work. However, once the system moves into the biopellets and energy generation phases, the impact profile shifts dramatically. The electricity generation phases—both from pellets and from biogas—exhibit extremely negative mrheq values across all categories, indicating large social benefits. These values suggest not only the displacement of higher-risk energy production methods but also the presence of mature regulatory environments, decent labor conditions, and robust local infrastructure. The significant negative scores, particularly in health and safety and governance, reflect the ability of the Spanish system to convert industrial bioenergy production into a source of social value—improving working conditions, enhancing transparency, and providing community-level benefits. Moreover, these gains go beyond process efficiency and touch on broader structural benefits: for example, domestic renewable electricity production contributes to energy sovereignty, reduces reliance on potentially unstable or undemocratic energy exporting regions, and thus strengthens national governance frameworks. This creates a virtuous cycle where energy policy, labor standards, and community stability align to generate robust social benefits. Unlike in the Italian case, where the system appears to amplify risks, the Spanish case demonstrates the potential for F-CUBED to deliver wide-reaching social co-benefits when embedded in a supportive context.

Figure 7 effectively reinforces the numerical data presented in Table 10 and illustrates the relative percentage contributions of each production phase to the total social impact across the impact categories.

The chart shows that electricity production from biopellets emerges as the most socially beneficial phase, contributing between −62% and −40% to the total social risk reduction in several categories. This substantial benefit reflects the efficiency, maturity, and social responsibility embedded in Spain’s renewable energy infrastructure.

The biopellet production and preconditioning phases, by contrast, contribute only marginally to the overall impact, 0.8% and 0.5%, respectively, rendering them virtually negligible in the system’s social risk profile. These low-risk values suggest effective risk management and limited worker exposure in these phases. The Torwash and dewatering steps follow a similar pattern, contributing minimally to social risks, indicating that Spain’s machinery and equipment sector operates within acceptable risk thresholds for labor rights and safety.

3.3.3. Subcategory-Level Analysis

Subcategories—mapped from SHDB to the UNEP typology—were examined to understand their distribution across economic sectors. The disaggregated results of the S-LCIA by subcategory provide a more detailed picture of the social sustainability performance of the FPS across the three investigated case studies.

Table 11. Contribution analysis of economic sectors to the total social impacts by impact subcategory for the case studies of pulp and paper biosludge (PPB) in Sweden, olive pomace (OP) in Italy, and orange peels (ORP) in Spain. Values corresponding to a medium-risk level, aligned to the scale level of 2 PRPs, are highlighted in blue.

Social Impact Category	Social Impact Subcategory	Impact Assessment by Social Hotspot 2022 Category Method (mrheq)
		PPB	OP	ORP
Labor rights and decent work	1A Wage assessment	−0.202	4.316	−128.981
	1C Workers in poverty	−0.060	4.478	−154.812
	1E Forced labor	−0.155	5.122	−155.865
	1F Excessive working time	−0.151	4.681	−149.980
	1I Social benefits	−0.064	4.141	−18.567
	1J Labor laws/convs	−0.039	0.663	−21.892
	1L Unemployment	−0.060	4.125	−145.715
Health and safety	2A Occ Tox and Haz	−0.136	5.665	−156.115
	2B Injuries and fatalities	−0.224	6.149	−166.040
Society	3F Poverty and inequality	−0.097	4.642	−148.193
	3G State of Env sustainability	−0.104	5.185	−154.280
Governance	4A Legal system	−0.149	5.012	−150.366
	4B Corruption	−0.053	2.457	−81.214
	4C Democracy and freedom of speech	−0.245	5.747	−160.855
Community	5A Access to drinking water	−0.032	1.481	−23.008
	5B Access to sanitation	−0.064	4.009	−140.005
	5C Children out of school	−0.075	3.172	−91.019
	5D Access to hospital beds	−0.069	2.845	−87.493
	5E Smallholder vs. commercial farms	−0.057	0.955	−1.101
	5F Access to electricity	−0.018	1.700	−69.430
	5G Property rights	−0.062	3.962	−144.880

, combined with Figure 8, Figure 9 and Figure 10, illustrates both the absolute and relative contributions to social risk, enabling a detailed analysis of specific social dimensions, expressed in medium-risk hour equivalents (mrheq).

This approach facilitates the identification of sector-specific social risks and co-benefits, enabling stakeholders to more precisely target interventions aimed at improving social sustainability. It also strengthens the interpretive depth of the S-LCIA by linking aggregated results to underlying social dynamics, such as the enforcement of labor rights or the inclusiveness of value chains. These results underscore stark contrasts between contexts, highlighting how deeply the local socioeconomic and institutional landscape shapes the social consequences of technological deployment.

Data from the PPB case study (Table 11) show a consistent pattern of low to moderate social benefits across almost all subcategories: negative mrheq values throughout the board signal a reduction in social risks (low level of risk). In the labor rights category, subcategories like wage assessment (1A), workers in poverty (1C), forced labor (1E), and excessive working time (1F) all display slight to moderate negative values, confirming the overall strength of the Swedish labor system. These conditions reflect a labor market characterized by fair wages, strong union presence, and adherence to international labor standards. Occupational safety indicators (2A, 2B) reinforce this perspective: both exposure to toxic substances (2A) and risk of injuries and fatalities (2B) are associated with small but tangible benefits, reflecting Sweden’s rigorous health and safety regulations, especially in energy and industrial sectors. On the community level, additional benefits are observed in access to services like sanitation (5B), healthcare (5D), and water (5A), factors which, although marginal in their relative scores, indicate the presence of strong social infrastructure. The scores for governance, particularly regarding legal systems (4A) and democracy (4C), are also negative, suggesting that the implementation of F-CUBED in this region does not encounter systemic fragilities. Overall, while the absolute values may be limited, they accumulate into a coherent picture of a socially stable and beneficial deployment context with minimal risk amplification across any social dimension.

Among the assessed subcategories, the greatest net social benefits are observed for democracy and freedom of speech (4C), injuries and fatalities (2B), and wage assessment (1A). This scenario is supported by Figure 8, which depicts the disaggregated impacts across individual subcategories and provides a clearer visualization of how the specific social themes of the subcategories are influenced by each production phase.

The benefits are predominantly associated with the electricity from the biopellets phase, which is linked to Sweden’s highly regulated and socially robust electricity sector. This phase accounts for approximately –90% to –105% of the total social risk across most subcategories, with an average value of –94%. In contrast, the biopellets production and Torwash with dewatering phases show slight positive contributions to social risk, particularly within subcategories 1A (wage assessment), 2B (injuries and fatalities), and 4C (freedom of expression and democratic rights). The contributions from these phases range from 2% to 13% (average 6%) and from 8% to 15% (average 10%), respectively. These results reflect the labor- and process-intensive nature of these operations, which are associated with economic sectors that exhibit relatively lower social performance indicators compared to those in energy generation.

In sharp contrast, the OP case study in Italy reveals substantial social risks across almost every subcategory (Table 11). The data show high positive mrheq values, indicating elevated risk levels, particularly in the labor rights health, governance, and community categories.

The labor-related subcategories, such as wage assessment (1A), worker poverty (1C), forced labor (1E), and excessive working time (1F), stand out significantly, with values ranging from approximately 4 to over 5 mrheq, suggesting precarious employment conditions, likely exacerbated by informal or seasonal labor patterns in the agricultural and pellet production sectors of Southern Italy. The scores on social benefits (1I) and Unemployment (1L) similarly point toward systemic weaknesses in the social safety net, contributing to a context of economic vulnerability and limited career stability.

The health and safety subcategories (2A, 2B) also reveal severe risks: both occupational toxics and hazards (2A) and the incidence of workplace injuries and fatalities (2B) present the highest values within their category, indicating that workers in the OP supply chain face significant exposure to hazardous conditions, insufficient protective measures, and possible shortcomings in enforcement. This paints a concerning picture of operational safety in both the processing and energy production phases.

In fact, according to the European Statistics on Accidents at Work (ESAW) administrative data collection exercise [45], Italy shows, as fatal accidents at work in 2019, an incidence rate (per 100,000 persons employed) of 2.1 against the average of 1.7 in EU. Moreover, at the national level, the National Institute for Occupational Accident Insurance (INAIL) reports that, as of 31 December 2022, the number of accidents that occurred in 2022 was 697,773, an increase of 25.7% compared to 2021 and of 25.9% compared to 2020. At the national level, the data show, in particular, an increase compared to 2021 both of the cases occurred at work (+28.0%) and those in transit, that is, occurred on the return journey between home and work (+11.9%) [46].

From a societal standpoint, poverty and inequality (3F) emerge as acute problems, reinforcing the socioeconomic fragility in the region. Meanwhile, Environmental Sustainability scores (3G) suggest challenges in aligning industrial innovation with broader ecological goals and assessing the potential environmental risks related to supply chains. This subcategory relates to the Environmental Performance Index (EPI) indicator [35] used to rank 180 countries on environmental health and ecosystem vitality and provide a gauge at a national scale of how close countries are to established environmental policy targets.

In terms of governance, both the effectiveness of the legal system (4A) and corruption risks (4B) are highlighted as major concerns, pointing to the limited institutional capacity to ensure the fair and transparent implementation of new technologies.

At the community level, democracy and freedom of speech (4C) registers the highest score in this category, underscoring latent socio-political tensions, while essential services such as sanitation (5B), healthcare (5D), education (5C), and electricity (5F) all reflect medium to high social risks. Access to land and property rights (5G) is also a concern, likely reflecting land tenure issues or unbalanced development dynamics between smallholders and larger commercial operators (5E).

The subcategory 4C, democracy and freedom of speech, addresses a core aspect of human rights, specifically the right to freedom of expression as enshrined in Article 19 of the Universal Declaration of Human Rights. The assessment of risks associated with this subcategory is based on the integration of three well-established indices: the Economist Intelligence Unit’s Democracy Index, Freedom House’s Freedom in the World Index, and the Global State of Democracy Indices by the International Institute for Democracy and Electoral Assistance (IDEA). These indices collectively evaluate the status of democratic governance globally, using criteria such as the electoral process and political pluralism, government functionality, political participation, civic culture, and the protection of civil liberties [35].

The analysis focuses on five key attributes of democracy: representative government, fundamental rights, checks on government power, impartial administration, and participatory engagement. Based on these dimensions, countries are classified into three categories: free, partly free, and not free. Italy, based on current international assessments, is unequivocally categorized as a free country. A likely explanation, in this view, is the European experience of local communities and energy cooperatives, which demonstrate that energy democracy is the route to resolving a number of socioeconomic concerns and addressing climate change [42]. Cities and local communities around the globe have been reclaiming public services or redesigning them to meet people’s needs, realize their rights, and jointly address social and environmental concerns [47,48]. In Italy, although the introduction of the free market in the energy sector, ENEL is still the main producer of electricity detaching a share of 33.8% and ENI is the main producer of natural gas with a share of 62.6%, based on data provided via ARERA [49,50].

This scenario is illustrated in Figure 9, which presents the disaggregated impacts by individual subcategories.

Biopellet production and the electricity generation stages from pellets are the most significant contributors: biopellet production typically comprises 37–85% (average value 47%) of total risk across most subcategories, and electricity from biopellets adds an average contribution of about 36%, while upstream steps (preconditioning and Torwash) usually contribute less than 3% (only the subcategory 5E, smallholder vs. commercial farms in the preconditioning step rise up to 6.4%). This breakdown confirms that downstream interventions are essential to improve the social performance of the FPS in the OP pathway. These include targeted strategies in energy-related supply chains, improved labor practices, and attention to local community infrastructure and legal protections.

In summary, the cumulative interpretation is that the FPS, while promising technologically, is deeply embedded in a context of socioeconomic fragility in the Italian case study, and without careful mitigation, it risks reinforcing or even exacerbating existing social inequalities.

The ORP case study, implemented in Spain, is markedly different and positive in social terms (Table 11). Here, the impact scores across all subcategories are substantially negative signifying robust social benefits. In the labor rights category, every subcategory from wages (1A) to labor law compliance (1J) to unemployment (1L) reflects a significant risk reduction. Forced labor (1E) and excessive working time (1F), two critical global labor concerns, show particularly large negative values (−155.8 and −149.9, respectively), suggesting that Spain’s institutional environment for labor governance is not only functional but also able to turn potentially vulnerable labor-intensive sectors into socially secure and compliant workplaces. Health and safety indicators (2A, 2B) follow the same trend. These subcategories occupational toxics and hazards (2A) and injuries and fatalities (2B) both show extremely negative values (−156.1 and −166.0, respectively), highlighting high occupational safety performance, particularly in the energy generation and biomass handling processes.

Societal indicators (3F, 3G) reinforce this pattern, with poverty and inequality (3F) registering considerable improvements, likely reflecting the integration of marginalized residues (such as orange peels) into a productive and economically beneficial value chain. The category for the state of environmental sustainability (3G) also shows strong performance, underscoring how the valorization of agri-food residues can contribute to broader sustainable development goals in practice, rather than theory.

Governance-related scores (4A, 4B) are also impressively positive. The legal system (4A) subcategory and the indicator for corruption (4B) both show strong risk reductions (−150.3 and −81.2, respectively), affirming institutional reliability and transparency. These institutional strengths are further complemented by dramatic positive values in community-related subcategories (4C, 5A–5G), especially in democracy and freedom of speech (4C, −160.8). These results are highly relevant in today’s geopolitical context, where domestic renewable energy production can be a lever to promote not only environmental resilience but also democratic integrity. Improved access to basic services, such as electricity (5F), water (5A), and healthcare (5D), further reinforces the positive narrative. The benefit extends even to more context-specific indicators like property rights (5G) and the balance between smallholder and commercial agriculture actors (5E), suggesting that this specific bioenergy pathway does not marginalize smaller producers or generate land-use conflict. Instead, it appears to operate in a socially integrative and structurally sound manner.

Although the total social performance score of the overall system is strongly negative, indicating a net social benefit—particularly due to substantial risk reductions in downstream electricity generation—the biopellet production stage remains the relatively significant contributor to residual social risk within the main production chain (average values of 2.24%). In the biopellet production phase of the ORP case study, a moderate social risk level is observed across nine subcategories: 1A (wage assessment), 1E (forced labor), 1F (excessive working time), 2A (occupational health and safety), 2B (injuries and fatalities), 3F (poverty and inequality), 3G (environmental sustainability), 4A (legal system), and 4C (freedom of expression and democratic rights). This performance is primarily attributable to the classification of this phase under the “Lumber and wood products production” sector in the Social Hotspot Database (SHDB), which represents the activities involved in the transformation of the TORWASH^® treated and dewatered solid fraction into densified bioenergy carriers (i.e., biopellets).

The moderate risk levels in this stage likely reflect structural challenges commonly associated with the biomass processing sector, such as exposure to physically demanding labor, incomplete labor protections, and variability in governance indicators within national industrial contexts.

To further enhance the social sustainability of the FPS, in the Spanish context of ORP valorization, targeted improvements in this sector are advisable. These could include stronger enforcement of occupational safety regulations, proactive oversight of working time compliance, wage transparency, and actions to improve institutional governance and worker representation. Such interventions would not only mitigate remaining social risks but also reinforce the robustness and replicability of the system in other socioeconomic settings.

The visual format of the data in Figure 10 highlights at a glance the areas of strongest performance and those where potential improvements may be targeted.

The results of the ORP (orange peel) case study in the S-LCA assessment show a striking pattern across all analyzed subcategories: strong social benefits emerge primarily from the electricity generation phases. Indeed, the most negative contributions (representing strong social benefits or opportunities) are concentrated in the electricity from pellets and electricity from biogas. These production stages of the FPS provide score ranges between −60.9% –134% (average value: −64%) and −40% –36% (average value: −39%), respectively. These stages benefit from low-risk profiles typically associated with regulated energy sectors in Spain.

In contrast, upstream and main processing steps contribute minimally and positively: higher social risks, though still within favorable ranges given the negative scoring system, are associated with the preconditioning (average value 1.3%) and Torwash and dewatering stages (average value 0.53%). This can be attributed to the relatively higher labor intensity and upstream supply chain dependencies of these processes, which may involve material or labor inputs from higher-risk sectors.

3.3.4. Risk Characterization

This analysis describes the risk characterization of the numeric impact scores into performance levels (low, medium, high, very high risk) using SHDB’s ordinal PRP system as reported in Table 2. These models apply PRP and severity-based weighting factors to translate raw impact values into qualitative risk levels. It represents a fundamental step to consolidate category and subcategory-level assessments across geographic contexts, supporting the prioritization of social risk mitigation and stakeholder engagement actions. The characterization thresholds and algorithms used in this analysis are transparently documented in the SHDB framework and have been detailed in Section 2.2.6.

For the PPB case study in Sweden, based on the S-LCIA, all subcategories, even those with the highest absolute impacts, are classified as “Low Risk”, indicating that the observed social issues fall within an acceptable range from a sustainability perspective.

These results demonstrate a generally low-risk social profile across all evaluated subcategories and align with Sweden’s robust social protection frameworks, high labor standards, and democratic institutions.

The Electricity from pellets phase consistently shows the strongest social benefits and supports the earlier finding that in Sweden, bioenergy valorization contributes positively not only to environmental goals but also to social sustainability, particularly when embedded in a well-regulated energy economy.

The upstream processes (preconditioning/enhanced biosludge) and Torwash show almost neutral or marginally positive values, expected given their limited exposure to systemic labor or governance risks. The biopellet phase, while showing some low positive values, still falls within safe limits and does not indicate any critical hotspot. These characterization results confirm that the implementation of the FPS in the Swedish pulp and paper sector is socially low-risk and institutionally aligned. Social impacts are well managed across all process steps, and the most mature downstream sectors, particularly electricity generation, contribute to risk reduction. This makes the Sweden-based biosludge case study a benchmark for best practice social integration in circular bioenergy systems.

For the OP case study in Italy, despite all processes being situated within the same national context, the social risk levels vary significantly, depending on the production phase, with downstream activities, especially biopellets and electricity generation from pellets, emerging as the most socially vulnerable. The most critical social risks are concentrated in the phases of biopellet production and electricity generation from biopellets, which consistently reach medium-risk level across the key subcategories: forced labor (1E), occupational toxics and hazards (2A), injuries and fatalities (2B), environmental sustainability (3G), and democracy and freedom of speech (4C).

Preconditioning, Torwash and dewatering, and electricity from biogas remain generally low in risk, showing that upstream and more mechanized or closed-loop phases pose minimal social concern. The medium risk is not an extreme score, but its repetition across categories and concentration in specific phases suggest clear hotspots for social performance improvement, especially worker safety and protective measures, formalization and transparency in labor contracts, and stakeholder engagement in local environmental and governance issues. In addition to the social risks identified in the subcategories already discussed (i.e., 2A, 2B, and 4C) for the Italian case study, some comments have to be added for the forced labor (1E) and occupational toxics and hazards (2A). The subcategory forced labor (1E), according to Benoit Norris et al. [35], constitutes a violation of fundamental human rights. It deprives societies of developing skills and human resources and educating children for the future labor market. The ILO Conventions also stipulate that forced labor shall be punishable as a penal offense [35]. Here, the occurrence of a medium-risk level in the economic sectors of the biopellet production and electricity sector, respectively, requires further investigations and accuracy in monitoring these production steps of the F-CUBED value chain in Italy. The existence and effective application of a comprehensive anti-trafficking law and criminal accountability are essential elements that have to be looked upon.

The medium-risk level in the subcategory 2A is also a relevant issue. The subcategory of Occupational Toxics and Hazards deals with the exposure of humans to various risks, such as hazardous noise levels, carcinogenic substances, and airborne particles that may cause respiratory or other health diseases. Therefore, it means that these economic sectors of the F-CUBED value chain in Italy do not comply with the average level of risk of Europe.

Nevertheless, in the whole picture of the OP Case Study, the two subcategories of smallholder vs. commercial farms (5E) and labor laws (1J), showing low risk in all the involved economic sectors, can be read as opportunities. In particular, the smallholder vs. commercial farms impact subcategory is noteworthy. Smallholder farms should be considered a unit within the local economy, community, and agricultural environment, contributing significantly to economic growth, poverty reduction, and the local population’s food security when supported with initiative from their local governments and communities. This translates into the potential of the FPS to represent a theoretical alternative technical solution deployable at the mill level (or associated with mills) differently from the conventional olive pomace exploitation involving a third-party industrial entity, such as olive pomace mills. Therefore, the low risk level reflects the likelihood of the existence and prosperity of smallholders.

This characterization reinforces the need for context-aware, phase-specific social risk mitigation strategies, particularly in regions where economic precarity overlaps with industrial innovation. Addressing these risks early will strengthen both the social license to operate and the broader sustainability credentials of the F-CUBED technology in Italy. Mitigation strategies tailored to the olive pomace context are recommended, such as emphasizing the enhancement of supply chain transparency through blockchain traceability, which could reduce corruption risks by 30–40% [51]. Additionally, strengthening labor rights enforcement and promoting stakeholder engagement are proposed as key measures to address the identified social risks. These strategies align with the broader goal of improving social sustainability within the FPS while maintaining compliance with relevant international standards.

In the ORP case study implemented in Spain, the FPS demonstrates overall favorable social performance, with all impact subcategories showing negative scores—ranging from −1.1 to −166.0 medium-risk-hour equivalents (mrheq)—indicating widespread social benefits. These benefits are particularly pronounced in the electricity generation phases (both from pellets and biogas), which consistently deliver negative impact scores across all categories, likely due to Spain’s high regulatory standards and modernized energy infrastructure. These phases serve as exemplars of socially responsible energy conversion within the FPS.

However, the biopellet production phase emerges as the primary contributor to residual social risks. It is the only production stage classified as medium risk across all evaluated subcategories, including wage assessment (1A), forced labor (1E), excessive working time (1F), occupational health and safety (2A), injuries and fatalities (2B), poverty and inequality (3F), environmental sustainability (3G), legal system (4A), and freedom of expression and democratic rights (4C).

This performance reflects broader structural challenges within the “Lumber and wood products production” sector in the SHDB, which represents the activities involved in the transformation of the TORWASH^® treated and dewatered solid fraction into densified bioenergy carriers (i.e., biopellets).

This sector encompasses several risks, such as exposure to physically demanding labor, incomplete labor protections, and variability in governance indicators within national industrial contexts. Addressing these risks presents a clear opportunity to enhance the social sustainability of the FPS. Recommended actions include stricter enforcement of occupational safety standards, improved wage transparency, compliance monitoring for working hours, and strengthened institutional governance. Additionally, stakeholder engagement strategies aimed at improving community benefit-sharing and worker representation would support a more equitable value chain. By implementing these targeted interventions, the already high-performing ORP pathway could become a best practice model for socially sustainable bioenergy systems across the EU.

The risk landscape of the ORP pathway confirms that social risks are highly phase-dependent, and in this case, they are isolated primarily to the biopellets production sector. Addressing these risks through policy, monitoring, and stakeholder engagement would further strengthen the social sustainability of the FPS in Spain, turning an already high-performing value chain into a best practice benchmark for biobased innovation in the EU.

In conclusion, the social performance of the FPS varies significantly, depending on the socioeconomic and institutional context in which it is deployed. While the technology itself remains constant, its social outcomes diverge markedly across different settings, as demonstrated by the three case studies. In Sweden, the FPS delivers modest but consistent social benefits within an already favorable and well-regulated environment. These benefits, although limited in absolute value due to the high baseline of social performance, confirm the social viability of the system, particularly in the pulp and paper sector. The Swedish model demonstrates how strong governance, high labor standards, and safety regulations can support the stable integration of sustainable technologies. In this case study, the potential benefits are more likely to reinforce existing institutional strengths rather than to produce transformative social improvements.

In contrast, the Italian case illustrates how the deployment of innovative systems without adequate alignment with local socioeconomic conditions and mitigation strategies can exacerbate pre-existing vulnerabilities, particularly in labor-intensive and regulatory-weak phases such as biopellet production and electricity generation at the local scale. This case highlights considerable potential for improvement but also reveals systemic vulnerabilities, especially in areas such as labor rights, public health, governance, and community infrastructure. Nonetheless, the introduction of the FPS presents a significant opportunity to shift the sector toward circular economy principles, improving worker safety and sustainability outcomes over time.

The Spanish case, by comparison, demonstrates the most successful integration of technology and social sustainability. Despite minor medium-risk areas in a few sectors, the deployment of the FPS in Spain demonstrates its potential to deliver notable positive contributions across several key social impact categories. It acts as a net contributor to social welfare, delivering wide-ranging and tangible benefits across all assessed dimensions. This outcome is facilitated by a supportive policy landscape and a sectoral composition conducive to positive social change. The alignment of the FPS with Spain’s broader socioeconomic objectives, such as regional employment generation, compliance with advanced labor legislation, and the promotion of industrial diversification, highlights its potential as a context-sensitive innovation. This showcases the capacity of the technology to act as a catalyst for social sustainability when embedded within a supportive socioeconomic ecosystem.

Taken together, the comparative analysis, supported by both numerical data and layered visualizations, underscores the critical role of local context in shaping social outcomes. This highlights the strategic importance of context-aware, locally tailored implementation approaches.

These findings reinforce that the effectiveness of FPS as a socially sustainable innovation depends not only on its technical and environmental performances but also on the socio-institutional landscape into which it is introduced requiring a proactive adaptation of strategies to local needs.

4. Conclusions and Outlook of the S-LCA

The findings of this study underscore the critical importance of tailoring deployment strategies to local social conditions, particularly when introducing novel technologies such as the FPS. In fact, the socioeconomic context into which a technology is embedded plays a decisive role in shaping its social performance, influencing whether it mitigates existing risks, reinforces structural inequities, or catalyzes inclusive development.

The cross-case S-LCA of the FPS, applied to the valorization of wet biogenic residues in Sweden, Italy, and Spain, offers valuable insights into the social sustainability performance of advanced bioenergy technologies. Although the core technological configuration, comprising TORWASH^® hydrothermal treatment, pelletization, electricity generation, and anaerobic digestion, remains constant across all three contexts, the resulting social outcomes vary significantly. These differences highlight the need for context-sensitive deployment strategies that account for national and regional socioeconomic conditions, institutional capacities, and labor market dynamics.

This divergence emphasizes the critical role of aligning technological innovation with supportive social infrastructure. Accordingly, future efforts to replicate or scale the system should consider not only technical and environmental feasibility but also the social readiness and resilience of local ecosystems. Measures such as worker training, participatory planning, and regulatory reform could be pivotal in transforming high-risk scenarios into opportunities for inclusive, sustainable development.

To maximize the social performance of the FPS, it is essential to implement practical, context-specific mitigation strategies. They are particularly important in contexts such as the Italian case study, where impact subcategories, especially within the electricity generation and biopellets production sectors, indicate a moderate social risk. These measures should guide the future deployment of the technology and ensure that the system contributes positively across diverse socioeconomic contexts. Moreover, they serve as actionable recommendations for policymakers, industry stakeholders, and local communities.

The following strategies are proposed:

• Develop sector-specific mitigation plans in regions exhibiting medium risk, particularly in labor and governance-related categories.
• Strengthen labor protections and promote formalization by enforcing existing labor standards, encouraging formal employment relationships, and improving occupational safety—especially in the biomass and renewable energy sectors, which often rely on seasonal or informal labor. Foster local partnerships with cooperatives or social enterprises to support fair labor practices and engage local communities through participatory governance.
• Implement traceability mechanisms, such as blockchain-based tools and certification schemes, particularly in sectors prone to corruption, informality, or forced labor. These instruments contribute to the continuous monitoring of ethical compliance across value chains.
• Encourage public–private collaboration to deliver training and upskilling programs, with a focus on rural and economically marginalized areas. Such initiatives empower local workers and enhance the social resilience of bioenergy systems.
• Collaborate with NGOs, research institutions, and public authorities to monitor social performance, gather stakeholder feedback, and iteratively refine mitigation strategies, in alignment with the dynamic nature of the S-LCA framework.

These strategies should be implemented in close coordination with relevant stakeholders, including workers, local governments, technology adopters, and civil society actors to ensure an adaptive, inclusive, and accountable rollout of the FPS.

Beyond its case-specific findings, this study yields two broader contributions to the field of social sustainability assessment. First, it advances methodological practice by operationalizing the integration of the SHDB with the UNEP’s S-LCA Guidelines, specifically adapting this combined framework to the circular economy and bioenergy context of the FPS. Second, it underscores the value of participatory methods: stakeholder engagement was instrumental not only in validating the selection of relevant subcategories but also in contextualizing and interpreting the results.

Based on the detailed S-LCA of the FPS in Sweden (PPB), Italy (OP), and Spain (ORP), several favorable subcategories emerged. These reflect social benefits closely aligned with the Sustainable Development Goals (SDGs), as mapped in the UNEP 2021 Methodological Sheets for Subcategories in S-LCA. Notably, in Spain and Sweden, the most positively impacted goals include SDG 8 (Decent Work and Economic Growth), SDG 3 (Good Health and Well-Being), and SDG 16 (Peace, Justice and Strong Institutions), confirming that worker safety, labor rights and democratic values are critical indicators of successful implementation. In Italy, despite its medium-risk profile, the case presents opportunities for improvement, particularly through smallholder inclusion and labor reform, which align with SDG 2 (Zero Hunger).

In conclusion, technological innovation in bioenergy must be accompanied, not only by environmental impact assessments but also by socially responsive deployment strategies. The results presented here advocate for a shift from “one-size-fits-all” technological diffusion to locally tailored implementation models that optimize both environmental and social performances. Such an approach is indispensable to achieving the dual objectives of climate mitigation and social equity in the context of the European Green Deal and global sustainability agendas.

The comparative analysis demonstrates that FPS would be best suited to integrated applications within forestry-based and agro-industrial clusters, where organic wet residues are abundant and where there is a push for circularity, decarbonization, and local energy resilience—aligning with national climate and waste directives and EU cohesion funding mechanisms. Such promising avenues for future deployment of the FPS could include countries such as Portugal, France, Greece, and Poland, where institutional readiness, biomass availability, and alignment with sustainable energy goals offer favorable conditions for scaling up this innovative solution. In these countries, longitudinal studies assessing the socioeconomic impacts of FPS deployment at the commercial scale are critical to validate prospective findings and support evidence-based policymaking. They may demonstrate the contribution of FPS to broader socio-technical transitions, such as rural revitalization, the decarbonization of decentralized energy systems, and circular economy strategies, and reveal its role in shaping a just and inclusive energy future.