Linking the LCA of Forest Bio-Based Products for Construction, Ecosystem Services, and Sustainable Forest Management

Abstract

The multifunctional role of forests in supplying renewable biomaterials and delivering ecosystem services (ESs) is often overlooked in standard life cycle assessment (LCA) methodologies, despite its relevance for sustainable construction. This study developed the BioCons Impact Compensation Model (ICM), which integrates ES into life cycle inventory (LCI) databases and quantifies proprietary BioCons Mitigation Indicators, capturing additional environmental information, ensuring transparency, and preventing greenwashing. Using structural Scots pine in Spain as a case study, the GWP_-luluc-roots indicator was found to be 226.84 kg CO_2-eq/FU, representing 36% of the biogenic carbon (616.45 kg CO_2-eq/FU), highlighting the contribution of root-derived carbon to long-term soil carbon storage. The BioCons Mitigation Indicators demonstrate that mitigation generally exceeds environmental impacts, except for HTP-nc-inorganics, with surplus ES available as biocredits to offset emissions in other life cycle stages. Integrating these indicators into environmental product declarations (EPDs) provides a transparent and accurate view of environmental performance. The results validate the hypothesis that forest bio-based construction products (FBCPs) act as carriers of ESs embedded in derived products, supporting more comprehensive and robust sustainability assessments.

1. Introduction

The construction sector plays a crucial role in the European Union’s economy, generating 25 million jobs and accounting for 10% of its added value. However, it is also responsible for 40% of energy consumption, 30% of waste generated, and 10% of the domestic environmental footprint [1]. The EU’s goal of climate neutrality by 2050 is based on a deep transformation of the sector with sustainable technologies and materials. The international trend toward increasing adoption of timber construction has a significant contribution to global decarbonization targets [2]. The use of industrialized mass timber in Spain, aligned with the global trend, represents between 0.5% and 1.5% of new buildings, and it is projected to reach approximately 3% by 2026 [3].

Despite the forestry sector playing a fundamental role in providing resources in the bioeconomy [4], natural and semi-natural forests are not only key primary production systems of raw materials. They are also critical for delivering multiple ecosystem services (ESs) [5], defined as ecological processes that contribute, directly or indirectly, to human well-being through economic, environmental, or social benefits [6,7,8,9]. The condition of an ecosystem, defined as its capacity to supply ESs according to its structural and functional characteristics, determines the quantity and quality of the ESs it can provide [10]. This capacity is maintained through sustainable forest management (SFM), which ensures the long-term functionality and resilience of forest ecosystems.

Land-use-intensive sectors, such as construction, have a high demand for water and natural raw materials, depending on the ability of ecosystems to meet these needs. This requires maintaining and enhancing multiple ecological functions to preserve atmospheric, water, and soil quality, thereby preventing disruptions in manufacturing processes. Therefore, the prosperity and long-term sustainability of economic sectors, industries, and processes rely on the ESs provided by forests [10]. Nevertheless, these activities can exert pressure on ecosystems, potentially compromising their capacity to deliver such services [8,11,12].

Life cycle assessment (LCA) approaches are internationally standardized tools used to assess environmental impacts in production processes through their entire life cycle (LC) and are, thus, also suitable for the sustainability assessment of forest bio-based construction materials and products (FBCMPs) [10,11]. One of the main applications of an LCA is in the development of environmental product declarations (EPDs). An EPD communicates verifiable, accurate, and non-misleading environmental information regarding construction products and their applications.

The existing set of regulations and standards governing LCA and EPDs of construction products consists of the EN 15804: 2012+A2: 2019 standard [12], “Sustainability of construction works. Environmental product declarations. Core rules for the product category of construction products”, and it is further developed by the EN 14685: 2014 standard [13], “Round and sawn timber. Environmental product declarations. Product category rules for wood and wood-based products for use in construction”. These standards comprise several impact categories (ICs) and their corresponding indicators, which are quantified by gathering all elementary input and output flows entering and leaving the product system in a so-called life cycle inventory (LCI). The quantification is based on an impact assessment method (IAM), which defines reference units and calculation rules for each indicator.

Those regulations make no distinction between indicators for products of biological and non-biological origin, even though their impacts and mitigation potentials differ substantially. This discrepancy stems from the biological nature of wood, which exhibits entirely distinct characteristics compared with non-biological construction materials. These differences arise from the formation processes occurring during the tree’s life. FBCMs embody not only the net carbon stored throughout the tree’s lifetime but also other GHGs such as NO₂ and SO₂. Moreover, they contribute to the removal of particulate matter from the atmosphere through leaf deposition and support the regulation of carbon, water, and nitrogen cycles, among other ecological processes. These ESs are intrinsic to FBCMs and help offset pollutants emitted during the LC of corresponding FBCPs. Therefore, restricting the assessment to embodied ESs—represented by the indicators global warming potential–biogenic (GWP_-biogenic) and use of renewable primary energy resources used as raw materials, as defined in current standards—constitutes an undue simplification of the environmental performance of biomaterials. It is, thus, both consistent and imperative to integrate these ESs as intrinsic properties of FBCMs, recognizing FBCPs as carriers of such services. This perspective enables a more comprehensive and scientifically robust assessment of their contribution to sustainability and climate change mitigation.

Over the last decade, studies on LCA have shown a clear trend toward integrating ESs, although each study has addressed this goal from a different perspective (see

Table 1. Previous methodological approaches: type, topic, novelty, limitations, and challenges.

Type/ Topic	Novelty	Limitations	Challenges	Study (Year)
A/ Pinus sylvestris sawn timber production	− First attempt to integrate the natural system into LCA.	− Lack of ES data and quantification models; − Impacts and remediation not differentiated.	− Integrating temporal scale in ES quantification; − Advancing on quantification of damages and remediation.	[14] 2013
RL/ Principles for modeling LU impact	− Guiding principles for the development of IAMs for biodiversity and ES assessment.	− Reduced local applicability; − Limited ESs and biodiversity indicators, local data, and CF and weighting factor availability.	− Development of regional models, CFs, and weighting for biodiversity and ES quantification.	[15] 2013
A/ Biodiesel manufacturing (TES framework)	− Considering the supply capacity of ecosystems; − Application of spatially explicit models.	− Own metric; − Multiple spatial scales; − ES allocation across all activities.	− Weighting factors for ES allocation; − Missing LCA metrics.	[16] 2015
A/ Bioplastics manufacturing	− Spatial modeling of LULUC; − TES framework.	− Limited ESs under consideration and local data and spatial model availability; − Highly demanding of technical knowledge and computational resources.	− Improving local data and models; − Creating user-friendly tools.	[17] 2017
A/ Coal mining and electricity generation	− Computational framework to operationalize TESs in LCA, explicitly incorporating ESs in the assessment boundaries.	− Limited ES data and quantification models; − ES allocation across all activities; − Own metric; − ES supply at the serviceshed scale.	− Development of ES data and quantification models; − Development of a systematic methodology to identify the activities demanding ESs in the serviceshed.	[18] 2018
A/ Biofuel life cycle	− First multi-scale integration of ESs into LCA.	− ES allocation based on land ownership; − Limited ES data and quantification models, and computational structure availability.	− Developing ES supply data to be included in conventional LCI databases.	[19] 2019
A/ Organic vs. non-organic farming	− Impact allocation across products and ESs supplied by croplands.	− Limited ES data and allocation factor availability.	− Developing data and impact allocation factors.	[20] 2019
RL/ ESs as endpoint indicators	− ESs as endpoint indicators of their own AoPs.	− ES quantification model complexity; data-intensive and computationally demanding.	− Allocation of ESs between several product systems and indicators; − Weighting factors from mid- to endpoint indicators; − Database with regionalized CFs across a range of scales for background processes.	[21] 2022

A: applied; RL: review-like; CFs: characterization factors; AoPs: areas of protection; TESs: techno-ecological synergies; LULUC: land use and land use change.

).

In general, all these studies focused on how emissions affect ecosystems. The first attempt to integrate ESs into the LCA framework [14] introduced the concept of incorporating ES flows into the LCI database with negative values. Later studies [21] refined and expanded this approach, applying it to the calculation of endpoint indicators to define an area of protection specific to ESs. An additional significant step in integrating ESs into LCA was the inclusion of the ecosystems’ capacity to supply such services, as explored in the studies by [16,18,19]. Other approaches have explored the allocation of agricultural system impacts not only to the product but also to the ES generated [20], and the integration of ESs into LCA through a multiscale spatial framework [17]. Furthermore, several guidelines have been developed to facilitate the development and application of these frameworks, such as [15], which focus on land use impacts.

Previous approaches, except [12,19], have primarily focused on the negative environmental impacts of a product’s life cycle on ecosystems, largely neglecting the potential of ecosystem services (ESs) to offset these impacts and mitigate their consequences. This represents a critical gap in the life cycle assessment (LCA) methodology, limiting both the academic understanding and practical application of ESs in the environmental evaluation of forest-based construction materials and products (FBCMPs).

Building on this context, the present study advances the existing methodology by explicitly incorporating ES gains and losses into a framework tailored for FBCMPs, addressing these methodological and regulatory challenges. The resulting BioCons Impact Compensation Model (BioCons ICM) treats forest-based materials (FBMs) as carriers of multiple ESs, recognizing the ecological benefits occurring throughout the life of a tree beyond the two ESs typically accounted for in current regulations: biogenic carbon storage and calorific energy.

The BioCons ICM provides an integrative framework that incorporates ES elementary flows into life cycle inventories (LCIs), enhancing the environmental information conveyed by indicators and complementing existing environmental product declarations (EPDs) for FBCMPs. By treating ESs as inherent material properties, the model enables calculations under standardized rules, ensuring validity, comparability, and consistent application across all FBCMPs. Furthermore, it allows for the inclusion of ES flows into LCIs while maintaining regulatory alignment with [12] and the International Reference Life Cycle Data System (ILCD) guidelines.

The academic goals of this study are as follows:

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To advance the LCA methodology by expanding system boundaries to include ES flows as intrinsic properties of materials.

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To provide a standardized, quantifiable framework for integrating ESs into LCI databases.

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To critically evaluate the strengths and limitations of current indicators for representing ES mitigation in FBCMPs.

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To enhance the scientific rigor and practical applicability of EPDs by incorporating additional environmental information.

To define and develop the BioCons ICM, the research framework applied in this study is summarized in

Table 2. BioCons research framework: alignment of hypothesis, specific objectives, and research questions.

General Objective: Develop the BioCons ICM to Integrate the ESs Carried by FBMs into the LCA of FBCPs. Main Research Question: Can the Product System of LCA of FBCMs be Expanded to Integrate the ESs They Carry?
Hypothesis: Treating forest-based construction materials as carriers of ecosystem services systematically improves the environmental performance assessment of construction products. This approach provides an academic contribution by linking in situ (building scale) and ex situ (forest scale) impacts through a quantifiable, scientifically grounded framework aligned with sustainable forest management (SFM).
Specific Objectives	Secondary Research Questions
S.O.1: Define the framework for integrating ES elementary flows into LCI databases.	S.R.Q.1: Can the ES elementary flows be systematically quantified and included in LCI databases?
S.O.2: Identify strengths and weaknesses of actual indicators to represent the mitigation of impacts by ES flows.	S.R.Q.2: Are the current indicators adequate to incorporate ES flows in the LCA of FBCMPs?
S.O.3: Analyze the role of the additional environmental information from the BioCons ICM in EPDs of FBCPs.	S.R.Q.3: How can the additional environmental information from the BioCons ICM be communicated in EPDs?

.

In summary, this study provides a methodological and academic contribution by offering a replicable approach to incorporate ESs into LCA, improving both the environmental assessment of construction products and the scientific understanding of ecosystem service integration in product life cycles.

2. Materials and Methods

Building on the hypothesis that FBCMs act as carriers of ESs that, further along the value chain, become embedded within FBCPs, this section describes the methodological approach used to address each research question listed in Table 2. Responding to the main research question—how the LCA of FBCMPs can be modelled to partially offset their impacts through the ESs provided during a tree’s lifetime—requires a further subdivision into specific components.

Primarily, the BioCons IAM seeks to address, at least in part, the gaps identified in the previous section while also advancing solutions to the associated challenges. It is important to emphasize that this is undertaken within the context of FBCMPs, considering both regulatory requirements and the specific characteristics of the entire value chain. Section 2.1, “Facing Gaps and Challenges,” reviews each of these aspects, describing how they are systematically addressed within the BioCons IAM framework. Finally, the section concludes by presenting the main challenge inherent to the BioCons methodology itself, highlighting an important issue that must be overcome to fully implement this approach.

Finally, in Section 2.2, “Implementing BioCons ICM in Structural Scots pine (P. sylvestris) Timber Production in Spain: A Case Study”, the BioCons ICM is presented and described in detail. This section outlines the required expansion of the product system boundaries to incorporate ESs, elaborating on the methodological aspects of their calculation and on the challenges involved in integrating them into the LCI database as complementary elementary flows. In addition, it quantitatively illustrates the application of the BioCons ICM to a real-world scenario, using inventory data collected by the authors in previous studies, alongside ES data calculated from the actual conditions of the geographic regions where Scots pine is harvested for timber in Spain. These data include meteorological conditions, growth parameters, wood density, and yields of material, enabling a realistic and applied demonstration of the methodology.

2.1. Facing Challenges

The evolution of the studies reviewed reflects a conceptual continuity in integrating nature and technology, accompanied by methodological and applied diversification. Despite this progress, key challenges persist, including data availability constraints, the scalability of product systems across sectors and spatial scales, allocation issues, and the lack of standardization across studies. The subsequent subsections describe the methodological choices and procedures adopted to address them.

2.1.1. Data Availability Constraints

The limitation of data is not specific to the integration of ESs but is a general challenge for LCA. In addition to the scarcity of freely available and open-access LCI databases, commercial databases often provide data corresponding to products and/or processes that are not geographically, technologically, or temporally contextualized to the product system under analysis. These data should maximize the representativeness of the specific product system and can be obtained from specialized literature based on representative case studies, machinery catalogues, and actual factory consumption records. An illustrative example of an LCI for glulam of Scot Pine in Spain is provided in [22].

2.1.2. Sectoral, Temporal, and Spatial Scalability

Addressing the quantification of ES flows for their inclusion in LCA in a general manner is overly ambitious and may even lead to ambiguity. The products and/or processes involved in each sector often present such substantial differences that a common methodology for incorporating ESs cannot be applied across them (e.g., LCA of urban green infrastructures versus wood-based products for construction). Therefore, it is necessary to adjust the goals and scope definition phase to the specific characteristics of the sector, and the value chain to which the product system belongs, to ensure the consistency of the results. The BioCons ICM is specifically contextualized to FBCMPs, spanning the forest, industrial, and construction sectors and their corresponding value chains.

Regarding the selection of the proper temporal scale for quantifying ESs, the rotation period is used in the case of FBCMs. This approach allows for the quantification of ESs accumulated over the lifetime of a tree, including inter- and intra-annual variations. The ESs calculated in this way are considered embedded in and carried by the biomaterial itself.

The difficulty of defining an appropriate spatial scale for quantifying ESs represents one of the main challenges highlighted by several authors. This issue arises because ESs are generated and perceived across multiple spatial levels—such as the serviceshed, ecosystem, or forest—each involving different ecological processes, management contexts, and data requirements. Consequently, many studies propose complex multi-scale approaches to capture these spatial interactions accurately, although their complexity limits their applicability in practice. In the case of FBCMs, many ESs result from biological processes at the scale of the individual tree, such as photosynthesis, and should, consequently, be accounted for in Module A1. These ESs can be readily scaled from the individual tree to the FU by applying raw material yields (Figure 1). Although some ESs require models at the stand or serviceshed scale (e.g., water availability, land use, and land-use change), these can be extrapolated to the individual tree level using forest mensuration techniques.

2.1.3. Allocation Challenges

The allocation of impacts and benefits is a fundamental aspect that influences all other challenges and research gaps. Some of the authors reviewed [16,18,19,20,21] have addressed impact allocation as the distribution of the total ESs provided among all activities that depend on them. This perspective is demand-oriented and resembles a territorial LCA.

In contrast, the BioCons ICM approach focuses on the benefits generated by the tree, considering them inherent characteristics of the biomaterial. In this framework, ESs are allocated to the process and/or product that incorporates the material, independently of other processes or products that may rely on the same ES to mitigate their impacts but do not embody them in their material composition. Consequently, this approach ensures a fairer allocation of impacts and helps prevent greenwashing.

It encompasses processes ranging from forest planting and regeneration of forests to the end-of-life stage of the building and Module D, which accounts for benefits and loads beyond the system boundary, such as those associated with material reuse, recovery, or recycling. However, the integration of ESs occurs in Module A1 (raw material extraction and supply), since this is where they are generated, as established by current regulations. This reinforces the notion that ESs are intrinsically linked to the biomaterial, which acts as a carrier and embeds them throughout the entire LC.

The process of allocating impacts and benefits to forest bioproducts is as straightforward as applying the material yields required to manufacture one FU of the final product. Consequently, it becomes possible to establish the number of trees that must be felled, logged, transported, and processed to obtain one FU of the final product. Yield data must come from reliable and scientifically supported sources. For tree-to-stem and stem-to-log yields, experimental studies conducted at the appropriate spatial scale (local, national, etc.) can provide an excellent source of information. In the case of Spain, the work of [23] compiles a substantial amount of relevant data for the 32 most important forest species. Log-to-FU yields should be obtained from empirical data provided by manufacturers or from experimental studies. Figure 1 illustrates the relationship between the different raw material yields.

2.1.4. Operationalization and Standardization of ES Indicators

Standardizing the calculation of ESs through a single or limited set of fixed models and data sources is not feasible due to the heterogeneity of the issues discussed in this section. Moreover, one of the main strengths of LCA lies in its flexibility, which allows it to be applied to any process, product, or service, even though calculation rules must be adapted for each product category. This flexibility would be significantly reduced if calculation models were standardized across different product categories.

The resolution of these gaps is based on addressing the methodological aspects discussed above and applying the corresponding standards. Accordingly, in the BioCons ICM, the impact category indicators are quantified following the calculation models and reference units established in [12,13]. Since only carbon storage is currently accounted for within the GWP_-biogenic and global warming potential–land use and land use change (GWP_-LULUC) indicators, the BioCons ICM proposes representing the mitigating ES through a set of additional environmental indicators included in the Additional Environmental Information section of EDPs.

For this study, the removal of CO, CO₂, SO₂, and NO₂ from the atmosphere by trees, as well as the deposition of PM on leaves, were consided. For their quantification, the proposed reference unit is expressed as “unit of ES per functional unit” (ES/FU), in accordance with LCA principles. The material yield flow illustrated in Figure 1 enables reference unit conversion when required.

Due to the selection of these ESs, the impact category indicators included in this study are acidification potential (AP), global warming potential–biogenic and –land use and land use change (GWP_-biogenic and GWP_-luluc), ecotoxicity potential–freshwater–inorganic (ETP-fw-inorganic), eutrophication potential–marine and –terrestrial (EP-marine and EP-terrestrial), human toxicity potential–non-cancerogenic–inorganic (HTP-nc-inorganic), photochemical ozone formation potential (POCP), and particulate matter (PM). These ESs and indicators were selected for reasons of simplicity, as this study represents the first application of the BioCons ICM framework. The following section details the calculation procedures for each selected ES and indicator, including the models and data sources applied within the BioCons ICM framework.

2.1.5. The BioCons ICM—Specific Challenge

The BioCons ICM poses its own challenge by aiming to include both traditional LCI data (emission elementary flows) and ES elementary flows within the same inventory database. These two data sources are not homogeneous in terms of reference units or taxonomy, making it necessary to harmonize them (Figure 2).

The EN 15804: +A2 IAM adheres to the International Reference Life Cycle Data System (ILCD), which aims to ensure consistency and comparability in LCA studies. It sets the standard for elementary flows classification and taxonomy and the exchange of LCI data [24]. It provides a standardized list of elementary flows that represent exchanges with the environment, including inputs from and outputs to the natural environment. In the case of ESs, their assessment, comparing and communicating results, also requires their previous categorization and description in a harmonized way. The Common International Classification of ESs (CICES) goes beyond a simple classification as it is based on the framework of the cascade model, and its tiered structure allows the addition of new ES classes that are relevant for specific studies [25]. It was designed to help measure, account for, and assess final ESs and has been used for designing indicators [26]. As the CICES aims to classify final ESs, they can be considered outputs of the natural system boundaries that enter the social and economic systems, which, in the LCA context, correspond to inputs into the product system (Figure 2). The CICES, V5.2, and ILCD+EPD were used in this study.

Once the ES elementary flows, classification systems, and metrics are selected, comparing the ESs and emission flows enables a better understanding of the relationship between pollutant-emitting activities and the biological processes providing the ESs that mitigate them (

Table 3. Relationship between LCA indicators, emissions, and mitigation sources in BioCons framework.

Indicator (Impact Driver)	Emission Sources	ILCD Flows	ES Mitigation Sources	CICES Class
AP (NO2 and SO2)	High-temperature and S-containing fossil fuel combustion	Nitrogen dioxide	Gas exchange (stomata)	2.3.6.1 *1
		Sulfur dioxide
GWP-biogenic (CO and CO2)	Combustion of fossil fuels and decomposition of biomass	Carbon dioxide, biogenic	Gas exchange (stomata)	2.3.6.1
		Carbon monoxide, biogenic
GWP-luluc (GHGs)	GHG emissions and removals by soil bio-processes, LULUC	Carbon dioxide, biogenic	Organic matter storage by bioprocesses	2.3.4.2 *3
		Carbon dioxide, to soil or BS *2
		Carbon monoxide, biogenic
		Carbon monoxide, from soil or BS
ETP-fw-inorganic (CO)	Combustion of fossil fuels and biomass	Carbon monoxide, biogenic	Passive diffusion (stomata)	2.3.5.1 *4
		Carbon monoxide, from soil or BS
		Carbon monoxide, land transformation/occupation
EP-marine (NO2)	High-temperature combustion	Nitrogen dioxide	PM deposition on leaves	2.3.5.1
EP-terrestrial (NO2)	High-temperature combustion	Nitrogen dioxide	PM deposition on leaves	2.3.6.1
HTP-nc-inorganic (CO)	Combustion of fossil fuels and biomass	Carbon monoxide, biogenic	Passive diffusion (stomata)	2.3.5.1
		Carbon monoxide, from soil or BS
POCP (NO2 and SO2)	High-temperature and S-containing fossil fuel combustion	Nitrogen dioxide	Gas exchange (stomata)	2.3.6.1
		Nitrogen dioxide
PM (NO2, SO2, PM2.5, and PM10 *5)	Combustion and industrial dust High-temperature and S-containing fossil fuel combustion	Nitrogen dioxide	PM deposition on leaves	2.3.6.1
		Sulfur dioxide
		Particulates, <2.5 um
		Particulates, <10 um

*¹ Regulation of chemical composition of atmosphere and oceans, and the maintenance of continental atmospheric/ocean circulation patterns; *² BS: biomass stock; *³ decomposition and fixing processes and their effects on soil quality; *⁴ regulation of chemical conditions of macronutrients in freshwater by living processes; *⁵ PM_2.5 and PM₁₀ are PM smaller than 2.5 microns and 10 microns.

). This approach clarifies the equivalence between ES mitigation sources (CICES) and emission sources (ILCD).

2.2. Implementing BioCons ICM in P. sylvestris Structural Timber Production in Spain: A Case Study

This case study was conducted to detail the methodological steps, calculations, and data requirements necessary to implement the BioCons ICM (Figure 3), while illustrating its scientific robustness and practical applicability. Furthermore, the case study highlights the improvements offered by this approach compared with traditional LCA methods, particularly in capturing the environmental contributions of ESs and providing more comprehensive product sustainability profiles.

The following paragraphs provide a detailed explanation of these procedures and illustrate their application to a real-world case using inventory and ES data for Scots pine harvested in Spain.

Key points include the expansion of product system boundaries to incorporate ESs, the calculation of the selected impact category indicators (AP, GWP_-biogenic, GWP_-luluc, ETP-fw-inorganic, EP-marine, EP-terrestrial, HTP-nc-inorganic, POCP, and PM), and the integration of ESs into the LCI database as complementary elementary flows. Since current regulations only allow carbon storage offsets to be included in the GWP_-biogenic and GWP_-LULUC indicators, the BioCons ICM introduces a set of additional indicators—referred to as BioCons Mitigation Indicators—to represent ES offsets that might be included in the additional environmental information section of the EPDs. For this case study, acidification mitigation potential (AMP), ecotoxicity–freshwater–inorganic–mitigation potential (ET-fw-inorganic-MP), eutrophication–marine–mitigation potential (E-marine-MP), eutrophication–terrestrial–mitigation potential (E-terrestrial-MP), human toxicity–non-cancer–inorganic–mitigation potential (HT-nc-inorganic-MP), photochemical ozone creation–mitigation potential (POCP-MP), particulate matter mitigation potential (PMMP), and global warming potential–biogenic–roots (GWP_{-biogenic-roots}) are described and quantified in

Table 4. BioCons Mitigation Indicators: definitions and CFs.

AoA	BioCons	Mitigation Indicators	Calculations -ES Flowi × CFi *1
FQ SQ	AP-MP	Potential of NO2 and SO2 removal via stomatal gas exchange contributing to the AP indicator	-NO2 × 5.20 × 10−2
			-SO2 × 1.08 × 10−1
FQ	GWP-luluc-roots	Potential of CO2 and CO addition to soil through root decomposition to mitigate emissions contributing to the GWP-biogenic indicator	-CO × 1.57
			CO2 × −1 × 0.3 *2
FQ	ETP-fw-inorganic-MP	Potential impact of emissions of toxic inorganic substances on freshwater ecosystems, such as heavy metals and metalloids; CO also has a residual contribution	-CO × 0.0228
FQ	EP-marine-MP	Enrichment of marine ecosystems with anthropogenic N emissions, and the corresponding increment in primary production	-NO2 × 0.389
SQ HH	EP-terrestrial-MP	Enrichment of a terrestrial ecosystem with anthropogenic N emissions and the corresponding increment of primary production	-NO2 × 0.877
HH	HTP-nc-inorganics-MP	Impacts on non-carcinogenic human health caused by the emissions of inorganic substances, particularly heavy metals and toxic gases; although CO is an inorganic compound, it also has a low contribution due to its capacity to interfere with oxygen transport	-CO × 1.08 × 10−6
HH	PM-MP	Potential of NO2, SO2, and PM removal via leaf deposition and stomatal gas exchange, contributing to the PM indicator Potential of CO2 and CO addition to soil through root decomposition to mitigate emissions contributing to the GWP-biogenic indicator	-NO2 × 1.60 × 10−6
			-SO2 × 8.08 × 10−6
			-PM2.5 × 2.39 × 10−4
			-PM10 × 5.49 × 10−5
HH	POCP-MP	Capacity of volatile organic compound emissions and, to a lesser extent, of secondary precursors like CO, NO2, SO2 and CH4 to form tropospheric ozone	-NO2 × 1
			-SO2 × 0.0811

^*1 CF_i: characterization factor for ES flow_i from EN 15804+A2 IAM. *² Humification coefficient is set at 30% for Scots pine in Spain. The positive sign of this quantity arises because the corresponding ILCD flow represents the incorporation of CO₂ emissions into soil; consequently, the associated conversion factor is negative.

.

To relate midpoint indicators with widely recognized metrics used for communicating results to non-expert audiences, they are grouped into areas of affection (AoAs), analogous to the AoPs in the ReCiPe and IMPACT 2000+ endpoint indicators. The AoAs are ecosystem health (EH) and human health (HH), reflecting the environmental effects of the corresponding emissions. EH is further subdivided into air quality (AQ), freshwater quality (FQ), and soil quality (SQ), depending on the affected environmental compartment and the relevant ES flows.

Each AoA is influenced by biological processes linked to proper ecosystem functioning. Therefore, the relationships among indicators, emission flows, and AoAs form the basis for connecting them to the ES flows that counteract each impact. Table 1 summarizes the indicators, their definitions relative to emissions, and the AoAs to which they belong. In brief, HH includes HTP-nc-inorganics, PM, and POCP; AQ includes GWP-biogenic and GWP-luluc; FQ includes AP, EP-marine, and ETP-fw-inorganics; and SQ includes AP, EP-terrestrial, and LU/SQI.

The FU was defined as 1 m³ of structural timber of Pinus sylvestris intended for use as a pillar or beam, at the factory gate. Accordingly, the system boundaries were limited to the product stage (Modules A1–A3) of the LCA, excluding the manufacturing and maintenance of tools, machinery, and vehicles used in the processes and assuming that kiln drying was powered by biomass-based energy.

The LCI was designed to maximize data representativeness. For the quantification of ES elementary flows in Spain, the province of Burgos (Castilla and León) was selected as the most representative region for Scots pine. The ESs included in this study were carbon storage and the removal of SO₂, CO, NO₂, PM₁₀, and PM_2.5. Except for carbon storage, these values were calculated using version 6.1.49 of the i-Tree Eco software [27] from the USDA Forest Service, following the models described in [28]. The carbon storage allocation among tree compartments was based on modular biomass values and carbon contents reported in [23]. Biogenic carbon was accounted for using the ±1 method, as outlined in [13]. A conventional rotation period of 80 years was applied.

Two product systems were analysed in this study. The first corresponded to the conventional product system, strictly applying the [12] requirements. The second was the BioCons product system, which expands the boundaries of the conventional system to allow the quantification of ESs and extends the impact category indicators in the additional environmental information section of the EPDs. This approach enables a comprehensive assessment of the environmental performance of FBCMPs, including the mitigation potential provided by ESs.

Apart from the ES flows, all other inventory data were identical across the systems under study, including the tools, machinery, and vehicles used, as well as their efficiencies and consumption rates. Background data for fuels, electricity, and lubricants were obtained from the Agribalyse V3.01 database (downloaded on 27 May 2021), which is freely accessible. Data related to the specific value chain of FBCMPs were taken from the BioCons database developed by the authors, which integrates real operational data from experimental plots and forestry companies [22,29]. In addition, the technical specifications of the machinery involved were considered. This study was carried out in OpenLCA using the methodologies established in [12,13].

Regarding the GWP_-luluc-roots indicator, the humification coefficient must be determined. This coefficient represents the fraction of root-derived carbon that becomes stabilized in soil organic matter during decomposition, a process extensively investigated through both empirical studies and modeling approaches. Global-scale biogeochemical models such as CENTURY and RothC [30,31] parameterize humification coefficients between 0.2 and 0.35, indicating that roughly 30% of decomposed organic carbon is transferred to stable soil pools, while the remainder is mineralized to CO₂. Empirical evidence [32,33] supports these modeling assumptions, reporting that root-derived carbon stabilization efficiencies range from 20% to 40%. Taken together, these converging lines of evidence justify the adoption of a mean stabilization coefficient of 0.30 (30%) for root carbon in soil carbon dynamics modeling, particularly under temperate and Mediterranean forest conditions.

Although this is basic knowledge, the calculations required to estimate the number of trees to be felled per FU, and the corresponding carbon in soil from humification of roots are presented below (Equations (1)–(7)). The data for these calculations are provided in

Table 5. Data on material yield, basic density, and partitioning of volume among tree parts, with total yield and stem/root volume calculations per functional unit (FU) for P. sylvestris. BG: belowground.

Variable/Parameter	Reference Unit		Source
Stem biomass	kg dry matter/tree	484.10	[23]
BG biomass	kg dry matter/tree	176.90	[23]
C content	%	50.9	[23]
Humification coefficients	%	30.0	[30,31,32,33]
Basic density	kg dry matter/m3	430.00	[34]
Steam-to-FU yield	m3 stem/FU	2.33	Manufacturers

.

Vstem = 484.1 kg dry matter/tree% 430 kg dry matter/m³ = 1.13 m³/tree

(1)

Trees per FU = 2.33 m³ stem/FU% 1.13 m³ stem/tree = 2 trees/FU

(2)

V_-roots = (176.9 kg dry matter/tree% 430 kg dry matter/m³) × 2 trees/FU = 0.82 m³/FU

(3)

Biomass_-roots = 0.82 m³/FU × 430 kg dry matter/m³ = 352.6 kg dry matter/FU

(4)

C_-roots = 0.59 kg C/kg dry matter × 352.6 kg dry matter/FU = 207.68 kg C/FU

(5)

CO_2-roots = 207.68 kg C/UF × 3.64 kg CO_2-eq/kg C = 755.96 kg CO_2-eq/UF

(6)

CO_{-in soil} = 0.30 × 755.96 kg CO_2-eq/UF = 226.79 kg CO₂/UF

(7)

3. Results

The following paragraphs present the results obtained, which address the research questions posed in the Introduction.

3.1. Life Cycle Inventory Data

First, the difficulty of systematically quantifying ES elementary flows and incorporating them into conventional LCI databases was highlighted. The Materials and Methods Section explained how models and yield data can be used to derive flows per FU. The absolute values of each ES flow quantified for the case study are shown in

Table 6. ES elementary flows carried by P. sylvestris roundwood.

ES Elementary Flow		kg/UF
CO	Carbon monoxide, biogenic	−0.38
	Carbon monoxide, from soil or BS *1	−0.03
CO2	Carbon dioxide, biogenic	−615.85
	Carbon dioxide, to soil or BS *2	226.79
NO2	Nitrogen dioxide	−5.52
SO2	Sulfur dioxide	−1.84
PM	Particulates, <2.5 µm	−0.84
	Particulates, <10 µm	−9.53

*¹ BS: biomass stock. *² The positive sign of this quantity arises because the corresponding ILCD flow represents the incorporation of CO₂ emissions into soil.

, differentiating between particle size in the case of PM (due to its influence on human health) and the carbon cycle compartment in the case of CO and CO₂, specifically between the carbon stored in the stem wood (biogenic) and in the soil after root decomposition (to soil or biomass stock). The values of the latter case have an impact on the GWP_-LULUC indicator, whose current calculation model penalizes forestry and forest use. Therefore, the compensation applied in this study plays a very important role, as it significantly reduces this unjustified impact. This aspect will be further addressed in the Results and Future Research Sections.

3.2. Quantification of ES Mitigation Potential

The second secondary research question addresses whether ecosystem service (ES) elementary flows can be integrated into the calculation of current standard indicators. As discussed in the Materials and Methods Section, the standard only allows the mitigation of impacts through ESs in the GWP_-biogenic and GWP_-LULUC indicators. Therefore, a series of BioCons Mitigation Indicators is proposed (

Table 7. BioCons Mitigation Indicators (by elementary flow and total values) for the manufacturing of P. sylvestris structural timber in Spain.

ILCD Elementary Flows	AP-MP (molH+eq)	GWP-luluc-roots (kg CO2-eq)	ETP-fw-Inorganic-MP (CTUe)	EP-Marine-MP (kg Neq)	EP-terrestrial-MP (kg Neq)	HTP-nc-inorganics-MP (CTUh)	PM-MP (Disease Inc.)	POCP-MP (kg NMVOCeq)
Carbon monoxide, biogenic			−3.64 × 10−3			−1.72 × 10−7
Carbon monoxide, from soil or BS *1		−0.05	−2.80 × 10−4			−1.31 × 10−8
Carbon dioxide, to soil or BS		−226.79
Nitrogen dioxide	−4.37			−1.85	−12.19		−1.77 × 10−5	−4.74
Sulfur dioxide	−2.61						−2.94 × 10−5	−0.13
Particulates, <2.5 µm							−2.00 × 10−4
Particulates, <10 µm							−5.20 × 10−4
Total	−6.98	−226.62	−3.92 × 10−3	−1.85	−12.19	−1.73 × 10−6	−5.87 × 10−4	−4.87

*¹ BS: biomass stock.

), which might be reflected in the additional environmental information section of EPDs. In this way, the third research question is also addressed, aiming to identify how the additional environmental information provided by the BioCons mitigation indicators can be communicated in EPDs.

It is worth highlighting the value obtained for GWP_-luluc-roots, −226.79 kg CO_2-eq, corresponding to the CO₂-to-soil or -biomass stock elementary flow. This result reflects a substantial contribution to carbon sequestration in soil and/or biomass, an aspect that the conventional LCA approach does not account for. Therefore, including this component enables a more comprehensive assessment of the net climate impact, emphasizing the importance of considering carbon fluxes associated with root-derived organic matter.

Table 8. Life cycle assessment balance for product stage (Modules A1–A3) of emission impacts and ESs carried by P. sylvestris structural timber under the conventional and BioCons ICM approaches. Indicators that include uptake flows according to [12] are highlighted in orange, while those proposed by the authors to encompass uptake flows are highlighted in blue. All quantities are expressed per functional unit.

Life Cycle Inventory				Impact Assessment
Elementary Flows			kg	AP (molH+eq)	GWPbiogenic (kg CO2-eq)	GWPLULUC (kg CO2-eq)	ETP-fw-inorganic (CTUe)	EP-marine (kg Neq)	EP-terrestrial (kg Neq)	HTP-nc-inorganics (CTUh)	POCP (kg NMVOCeq)	PM (disease inc.)
Ecosystem Services ES	CO	Biogenic	−0.16		−0.60		−3.64 × 10−3			−1.72 × 10−7
		From soil or BS *1	−0.01			−0.05	−2.80 × 10−4			−1.31 × 10−8
	CO2	Biogenic	−615.85		−615.85
		To soil or BS *2	47.04			−226.79
	NO2	Nitrogen dioxide	−2.37	−4.37				−1.85	−12.19		−4.88	−1.77 × 10−5
	SO2	Sulfur dioxide	−0.79	−2.61							−0.13	−2.94 × 10−5
	PM	Particulates, <2.5 um	−0.36									−2.00 × 10−4
		Particulates, <10 um	−4.09									−5.20 × 10−4
Emissions	Market for electricity *3		67.27	0.24	0.09	0.21	13.49	0.31	0.38	1.89 × 10−8	0.01	5.98 × 10−7
	Market for diesel		18.80	0.08	0.02	1.98 × 10−3	144.82	1.02 × 10−2	0.11	3.60 × 10−8	0.05	2.91 × 10−7
	Diesel combustion		18.80	0.61	0	0	0.05	0.31	3.35	1.10 × 10−7	0.84	4.37 × 10−7
	Market for lubricating oil		3.78	0.04	−0.02	4.19 × 10−3	30.36	5.27 × 10−3	0.06	1.73 × 10−8	0.11	2.67 × 10−7
	Ecosystem services			−6.98	−616.45	−226.84	−3.93 × 10−3	−1.85	−12.19	−1.85 × 10−7	−5.01	−5.87 × 10−4
	Emissions			0.97	−616.45	0.22	188.72	0.63	3.90	1.82 × 10−7	1.01	1.59 × 10−6
	Balance			−6.01	0	−226.62	188.71	−1.22	−8.28	−0.03 × 10−7	−4.00	−5.85 × 10−4

*¹ BS: biomass stock. *² The positive sign of this quantity arises because the corresponding ILCD flow represents the incorporation of CO₂ emissions into soil; consequently, the associated conversion factor is negative. *³ Low voltage, kWh/UF.

presents the emission impact values, the ES supply, and the ES mitigation results (balance) for structural timber from Pinus sylvestris. Only the GWP_-biogenic and GWP_-luluc indicators (highlighted in orange) currently account for ES flows, specifically carbon removal. In the conventional LCA framework, root-derived carbon is not included. However, in the BioCons model, this contribution is explicitly represented by the GWP-luluc-roots indicator, which more accurately reflects the carbon dynamics associated with root systems. For the comparison, the contribution of roots to soil carbon is included in GWP_-luluc, as this indicator comprises carbon removal to both biomass stock and soil. All the calculated ES values contribute to offsetting the impacts identified in the case study and, moreover, generate a remaining surplus that could serve as a credit to compensate for impacts occurring in other life cycle stages of sawn P. sylvestris wood not considered in this analysis. The only impact category that is not fully compensated is the human toxicity potential (HTP).

3.3. Verification of Research Hypothesis Through Comparison of EN 15804 and BioCons Approaches

Finally, the comparison between the conventional EN 15804 and the BioCons product systems confirms the research hypothesis, showing that the environmental profile of FBCMPs is more accurately represented when FBCMs are considered carriers of ESs. This study demonstrates that ES flows beyond carbon storage on the stem—such as those related to NO₂, SO₂, PM, and root-derived carbon—can also effectively mitigate environmental impact, as reflected in the BioCons Mitigation Indicators values (Table 7). Overall, these quantitative advantages are framed within the principles of SFM, which seeks to balance the productivity of raw material supply while ensuring the conservation and enhancement of forest ecosystem conditions.

4. Discussion

This section presents a detailed discussion of this study’s findings in light of its overarching objective: the development of a BioCons ICM predicated on the hypothesis that FBCMs function as carriers of ESs, which are subsequently embedded within the FBCPs derived from them.

The discussion addresses the research questions through the results obtained, encompassing several key aspects. First, it considers the systematic quantification of ESs and their integration into conventional LCI databases. Second, it examines the potential of normative indicators to incorporate this additional environmental information. Finally, it explores how this additional environmental information can be communicated within EPDs, providing a pathway to reflect ES contributions in a standardized and transparent manner.

First, the results presented in Table 6 show the values of ES flows per FU quantified for Pinus sylvestris growing under average conditions in Spain, as proof of systematic quantification of ESs. The need for high-quality, context-specific ES data for LCI databases has already been highlighted by [12,19]. With this study, the authors provide original inventory data that can be considered representative mean values for standing timber, harvesting, transportation, and manufacturing of Scots pine sawn wood in Spain. ES and emission elementary flows can be systematically calculated based on those data and dendrometry techniques, as described in the Materials and Methods Section.

The incorporation of emission and mitigation elementary flows into LCI databases is performed by identifying the equivalences between CICES classes and ILCD flows, and subsequently applying the sign assigned to each flow—positive for emissions, according to the standard, and negative for mitigation—a perspective previously proposed by [14,21] but not implemented previously. The values in Table 6 can, therefore, be incorporated into LCI databases as representative and high-quality reference data for use in other studies, given their reliability and the provenance of the data. The ability of such an LCI database to distinguish clearly between emissions and ES elementary flows enables the later distinction of impacts and mitigation, helping to prevent greenwashing.

Second, building on the limitations of standard [12] environmental indicators, the opportunity to develop and define the BioCons Mitigation Indicators is fully exploited (Table 7), enabling a more comprehensive assessment of the environmental contributions of FBCPs.

Other methodologies have proposed their own indicators, such as the TES Sustainability Metric [19] or the endpoint indicators of [21]. The TES Sustainability Metric provides a relative assessment of the demand for ESs compared with the ecosystem’s capacity to supply them, allowing us to distinguish situations where demand is lower, equivalent, or exceeds the natural supply capacity. The TES-LCA framework expands product system boundaries to include ecosystems themselves, thus enabling the identification of novel ecological solutions that enhance ES supply. An illustration of their approach is that, since trees sequester carbon and regulate air quality, restoration activities can directly increase ES supply capacity. The authors do not discuss the potential greenwashing implications of this method, leaving this aspect open for further examination. As emphasized throughout this paper, for FBRMs, such supply capacity is secured by SFM, but the BioCons Mitigation Indicators avoid greenwashing.

The ongoing debate regarding the use of midpoint versus endpoint indicators for ESs is examined in [21]. The [12] cautions that reliance on midpoint indicators reduces interpretability for decision makers. Indeed, endpoint indicators are generally more suitable and accessible for non-experts, particularly decision makers and the general public, because midpoint indicators rely on biophysical concepts and units that require specialist expertise for proper analysis. Yet, aggregating midpoint indicators into endpoints inevitably leads to a loss of critical information needed to fully characterize the behaviour of the entire value chain, undermining realistic and precise assessment. Given that LCA can be applied to any product, service, or process, it follows that expert knowledge is indispensable for both execution and interpretation. Conducting an LCA without the necessary expertise increases uncertainty in the results, particularly during the inventory and modelling phases, where a thorough understanding of the value chain and its underlying concepts is indispensable.

In the particular case of FBCMPs, entrusting the execution of an LCA to non-experts in the forestry sector appears inadvisable, as practitioners must not only be familiar with harvesting and processing procedures to identify the required flows in the LCI, but also possess the capacity to analyse, interpret, and evaluate the biological processes occurring within the ecosystems that supply raw materials and ESs. Nevertheless, it is both logical and appropriate that the information generated from midpoint indicators be translated into a form and set of results that are more readily communicable to non-expert audiences (including decision makers), thereby enabling them to better understand the implications of these values.

The BioCons Mitigation Indicators are both congruent and complementary with those defined in the [12] standard, being specific to FBCPs while remaining comparable to other construction products whose impacts are calculated according to the standard. These indicators retain the analytical strengths of midpoint approaches while making explicit the final effects on the environment and human well-being, as expressed through the defined areas of protection.

Regarding the incorporation of BioCons Mitigation Indicators, the additional environmental information of EPDs provides a more precise and transparent representation of the environmental profile of FBCPs, while ensuring that LC emissions remain visible and traceable, thus avoiding any risk of greenwashing that often affects previous methodological approaches.

Taken together, these results demonstrate that the overall main research objective of establishing a comprehensive BioCons framework has been achieved, and the findings support the acceptance of the research hypothesis, confirming that FBCPs act as carriers of ESs, which are subsequently embedded in the derived products.

Finally, the case study illustrates these findings through the data presented in Table 8, which displays the values of the BioCons Mitigation Indicators and the corresponding [12] Environmental Indicators separately, together with the resulting balance between mitigation and impact. Except for GWP_-biogenic, which is already compensated in [12], and ETP-fw-inorganic, which exhibits a high value primarily linked to fossil fuel consumption, all other indicators show negative balances. This outcome suggests that the mitigation potential provided by the ESs associated with the production of the wood needed to obtain one FU of structural Scots pine sawn timber in Spain exceeds the environmental burdens generated, highlighting the strong net-positive environmental contribution of this FBCM. The surplus ESs act as biocredits, available to compensate for emissions generated in other stages of the product’s LC not covered in this assessment, thereby extending the mitigation potential beyond the system boundaries considered. However, the development of a standardized protocol for the allocation and certification of ESs in biomaterials remains a task for future research, as such a framework would enable these materials to transfer the environmental benefits they carry to downstream products, ensuring a transparent and traceable recognition of their mitigation potential within LC assessment systems.

The impact results from the case study reported by [14] reveal that the impact mitigation potential of the Scots pine stand within the production chain of structural sawn wood in Belgium is significant, accounting for nearly all resource use, the final remediation effect on human health, and the estimated biodiversity loss due to land occupation. However, the implementation of the BioCons ICM demonstrates that the mitigation potential associated with ESs surpasses these impacts, with the exception of HTP-nc_inorganics, as previously discussed. This outcome is largely attributable to the inventory data employed and the application of the research hypothesis. These findings support the research hypothesis that FBCPs act as carriers of ESs, which are subsequently embedded in derived products, and highlight the added value of the BioCons Mitigation Indicators in capturing environmental benefits that are otherwise overlooked in conventional LCA frameworks.

It is particularly important to emphasize the relevance of the GWP_-luluc-roots indicator. Conventional LCA approaches generally account for soil-related emissions but neglect the carbon sequestration and long-term storage potential of root systems. As evidenced in this study, the root component represents 36% of the GWP_-biogenic value (616.45 vs. 226.84 kg CO_2-eq/FU for biogenic and root components, respectively), revealing a significant enhancement in the overall carbon storage capacity when belowground biomass dynamics are properly represented. It is also important to consider that, throughout the tree’s rotation period, root systems continuously sequester carbon, a portion of which remains stored after harvesting and decomposes gradually over decades. As detailed in the Materials and Methods Section, roughly 30% of this carbon is incorporated into stable soil carbon pools, potentially persisting for centuries. Beyond carbon storage, this process enhances soil physical properties, improving structure and thereby positively affecting the hydrological cycle, nitrogen dynamics, biodiversity, and the regenerative potential of the forest stand, among other critical ESs.

An important avenue for future research is to explore the linkages between the GWP_-luluc-roots indicator and land use (LU) and soil quality (SQI) indicators. These indicators, currently among the least developed within conventional LCA frameworks, play a crucial role in capturing impacts on other key environmental compartments, such as biodiversity, soil health, and water resources. The [12] addresses water impacts in a rather limited and non-explicit way, failing to fully capture both quantity and quality aspects, which highlights the need to consider additional indicators when evaluating the broader environmental contributions of FBCPs.

5. Conclusions

This study demonstrates the feasibility and relevance of systematically quantifying ESs associated with FBCPs and integrating them into LCI databases. The inventory data generated for structural Scots pine in Spain are representative and can serve as high-quality reference values for future LCA studies.

The BioCons Mitigation Indicators capture the mitigation potential of ESs typically overlooked by conventional LCA indicators. In the case study, these indicators demonstrate that mitigation largely surpasses emissions, except for HTP-nc-inorganics. The surplus ES acts as biocredits, available to offset emissions in other life cycle stages, providing a transparent and quantifiable view of environmental benefits.

The GWP_-luluc-roots indicator highlights the crucial role of root-derived carbon in carbon sequestration, accounting for 226.84 kg CO₂ eq/FU, which represents 36% of the biogenic carbon (616.45 kg CO₂ eq/FU). Roots sequester carbon throughout the tree rotation period, and approximately 30% of this carbon is stabilized in soil over centuries. It is well-known that this enhances carbon storage and improves soil structure, hydrological cycles, nitrogen dynamics, biodiversity, and forest regeneration, thereby emphasizing the need to include belowground biomass dynamics in LCA frameworks.

Incorporating BioCons Mitigation Indicators into the additional environmental information of EPDs allows for a more precise and transparent representation of the environmental profile of FBCPs, while ensuring that LC emissions remain visible and traceable, thus preventing greenwashing.

The results also validate the research hypothesis that FBCPs act as carriers of ESs, which are subsequently embedded in derived products, highlighting the added value of the BioCons Mitigation Indicators in capturing environmental benefits that conventional LCA frameworks overlook.

Future research should focus on developing a standardized protocol for the allocation and certification of ESs in biomaterials, enabling these materials to transfer environmental benefits to downstream products. Further investigation is also needed on the relationship between GWP_-luluc-roots, LU, and SQI indicators, particularly for capturing biodiversity and water-related impacts, which remain underrepresented in conventional LCA standards, such as [12].