Life Cycle Assessment of Sustainable Materials: A Comprehensive Analysis of Methodological Asymmetries and Environmental Trade-Offs

Abstract

Comparative Life Cycle Assessments (LCAs) of bio-based materials are highly influenced by methodological choices, so the term “bio-based” does not necessarily imply a low environmental impact. This review analyzes over 50 peer-reviewed LCAs (2010–2024) to quantify how four methodological pillars—(i) attributional versus consequential modeling, (ii) timing and storage of biogenic carbon, (iii) Direct Land-Use Change (LUC) and Indirect Land-Use Change (ILUC), and (iv) allocation in multifunctional systems—drive variability across long-life construction and short-life packaging/composites; adding regionalized perspectives (e.g., water scarcity according to the AWARE initiative, and relevant inventories for the MENA region) and ex-ante LCA guidance aligned with technology readiness levels. Methods included systematic selection from Web of Science/Scopus databases, standardized functional units, system boundaries, impact methods (ReCiPe/EF/TRACI/AWARE), biogenic carbon conventions (GWP100, dynamic/GWPbio), LUC/ILUC handling, allocation rules, and end-of-life scenarios, followed by qualitative meta-synthesis. Results show ~85% of studies used attributional approaches; consequential models typically report higher climate impacts when ILUC is included. In the building applications, bio-based alternatives—particularly wood—reduced cradle-to-critical-state global warming potential (GWP) by 30–70%; a “negative GWP” only emerged when storage balances or dynamic characterization were applied. For bioplastics, climate benefits are context-dependent and can disappear once ILUC and agricultural inputs are considered; acidification and eutrophication frequently increase. We conclude that environmental performance is subject to methodological choices rather than bio-based origin; systematic trade-offs persist between reducing GWP, increasing eutrophication/acidification, and increasing pressure on water/biodiversity.

1. Introduction

1.1. Context: The Bioeconomy and Climate Targets

Human-induced climate change, primarily driven by fossil fuel combustion and land-use changes, is accelerating continuously, disrupting the global carbon cycle and other biogeochemical processes and necessitating a radical restructuring of material and energy metabolism. The Intergovernmental Panel on Climate Change (IPCC) has affirmed that achieving the 1.5 °C target requires rapid and sustained reductions in greenhouse gas emissions across all sectors, including the decarbonization of the electricity, industrial, construction, and transport sectors, along with a rapid transition to low-carbon circular material economies based on efficiency, substitution, reuse, and recycling [1]. In this context, the European Commission defines “bioeconomy” as the sustainable production and conversion of renewable biological resources, including residual biomass and waste, into value-added products, such as food, feed, bio-based products, and bioenergy. Consequently, the bioeconomy has become a cornerstone of sustainable development strategies [2,3].

Bio-based materials, including lignocellulose fiber-reinforced composites, polylactic acid (PLA), polyhydroxyalkanoates (PHAs), and starch-based polymers, are increasingly replacing conventional petrochemical polymers and energy-intensive building materials such as steel and concrete. Following the COVID-19 pandemic stagnation in 2020, the overall global plastic production resumed growth in 2021. The production capacity of bioplastics (Figure 1) is expected to increase from approximately 2.21 million tonnes in 2022 to approximately 6.3 million tonnes by 2027 [4]. The environmental justification for bio-based materials relies on two principal mechanisms: (i) the partial decoupling of material supply from the depletion of limited fossil feedstocks through the use of renewable biomass, and (ii) the integration of biogenic carbon into product life cycles, where end-of-life CO₂ emissions can, under specified carbon-accounting conventions, be offset by the CO₂ assimilated during biomass growth. Realizing these benefits depends on systemic conditions—including sustainable feedstock sourcing and land-use governance, process energy mix, functional performance and durability, and end-of-life management (mechanical/chemical recycling, controlled industrial composting, or energy recovery)—to prevent burden shifting (e.g., methane formation due to uncontrolled biodegradation or biodiversity impacts). Accordingly, claims of “carbon neutrality” for bio-based materials must be supported by a cradle-to-grave life cycle assessment that explicitly considers biogenic carbon fluxes, their temporal dynamics, and allocation rules (e.g., ISO 14040/44, ISO 14067), and cannot be presumed solely on the basis of their bio-based origin [5]. We interpreted the results within the framework of ISO 14040/44 for LCA and ISO 14067 for product carbon footprint, highlighting, in construction-related discussions, the implications of EN 15804 + A2 concerning biogenic carbon fluxes and modulus D [6,7,8,9,10,11,12,13,14,15,16].

1.2. The Problem Statement: Environmental Advantages Are Context-Dependent

Despite strong policy interest, the environmental superiority of bio-based materials is not fundamental. Equating “bio-based” with “green” overlooks systemic determinants of impact, including agricultural inputs, the energy intensity of processes, and the uncertain end-of-life pathways. Biomass cultivation often requires large amounts of land, water, and agricultural chemicals (nitrogen, phosphorus, and potassium fertilizers; and pesticides), which may shift the impact of the greenhouse effect to other midpoint categories such as eutrophication, acidification, water scarcity, and biodiversity loss [17,18,19,20].

Moreover, the assumption of “carbon neutrality” of bio-based products remains dependent on carbon accounting conventions and temporal dynamics. Biogenic carbon is sequestered during biomass growth over years or even decades (e.g., forestry products), but can be rapidly released at end-of-life (e.g., during incineration), leading to temporal variability that can be misinterpreted by constant and time-invariant global warming factors. Therefore, an accurate assessment requires explicit consideration of biogenic carbon fluxes (including buffering capacity and deferred emissions), the potential for methane production during anaerobic disposal, and alternative end-of-life scenarios (recycling, controlled composting, energy recovery) using dynamic or time-differentiated characterization approaches rather than assuming neutrality by origin [21,22,23].

Accordingly, a comprehensive, multi-criteria product life cycle assessment, from cradle to grave, is essential to identify trade-offs and avoid burden transfer. This assessment must: (i) integrate multiple impact categories (e.g., global warming—including bio-CO₂ with time factors—eutrophication, acidification, water consumption and scarcity, as well as relevant biodiversity indices); (ii) consider impacts related to land use and land change; (iii) include extreme-consequence scenarios for which substantial commercial alternatives exist; and (iv) represent uncertainty through sensitivity analyses and Monte Carlo simulations. Only under these systemic conditions can the performance of bioresource management be reliably compared with petroleum- and mineral-based alternatives, and can targeted interventions be identified to ensure a genuine net environmental benefit.

1.3. Life Cycle Assessment (LCA) as the Diagnostic Tool

Lifecycle assessment (LCA), standardized under ISO 14040 and ISO 14044, provides an internationally recognized framework for determining the environmental impacts of product systems from cradle to grave by compiling Life Cycle Inventories (LCIs) of energy, material inputs, and emissions, and translating these fluxes into potential impacts through a Life Cycle Impact Assessment (LCIA) [24,25].

Applying LCA to bio-based materials is fraught with methodological inconsistencies and heterogeneities. Compared to fossil fuel-based systems that fully cover life cycle inventory (LCI) databases (such as Ecoinvent and GaBi), bio-based studies often suffer from data gaps, particularly regarding land-use change, biogenic carbon modeling, and allocation procedures in multi-output bioreactors [26,27]. These choices, along with target and scope definition (functional unit, reference flow, system boundaries), baseline dataset selection, life cycle assessment methodology, and assumptions regarding end-of-life options (mechanical/chemical recycling, controlled composting, landfilling, or incineration with energy recovery), are key factors in the differences between studies. For example, reported global warming potential (GWP) values for bioplastics may vary considerably depending on whether indirect land-use change is included within system boundaries and how biocarbon timing is managed [28,29].

1.4. Objectives and Scope of the Review

This paper offers a comprehensive and critical review of the state-of-the-art regarding the LCA of bio-based materials. Going beyond simple comparative statistics, this review aims to analyze the methodological choices that influence environmental performance outcomes.

The specific objectives include:

• Systematic Critical Analysis: The critical analysis evaluates how variations in system boundaries (cradle-to-gate vs. cradle-to-grave), functional units, and allocation methods influence the outcome of the lifecycle analysis for bio-based materials.
• Biogenic Carbon and Land-Use Nexus: Synthesizing current scientific relationships and differences regarding the accounting of biogenic carbon storage and land-use change.
• Sectoral Performance Assessment: The performance evaluation provides a comparative meta-analysis of LCA results across three key sectors: construction (wood, hempcrete), packaging (bioplastics), and textiles/composites.
• Identifying trade-offs: To systematically identify the environmental trade-offs between mitigating the effects of climate change and the degradation of ecosystem quality.

This review brings together the results of more than 50 peer-reviewed studies published between 2010 and 2024, with a focus on high-impact literature addressing both inventory data quality and bioeconomic impact assessment methodologies.

2. Methods: Literature Search, Eligibility, and Representativeness

2.1. Information Sources and Search Strategy

We conducted a structured search of Web of Science and Scopus for LCA studies published between January 2010 and December 2024. Searches were executed in October 2025. We combined controlled terms and free text for bio-based materials and LCA constructs, for example: (bio-based OR “biobased” OR “bio-composite” OR PLA OR PHA OR “bio-PE”) AND (“life cycle assessment” OR LCA OR “carbon footprint” OR LCIA OR “environmental footprint”). The exported records were deduplicated and managed in a screening spreadsheet.

2.2. Eligibility Criteria

We included peer-reviewed journal articles that (i) report an attributional or consequential LCA of a bio-based material used in construction, packaging/polymers, or textiles/composites; (ii) declare a functional unit and system boundary (cradle-to-gate or cradle-to-grave); and (iii) report at least one LCIA method with GWP100. We excluded conference abstracts, non-peer-reviewed reports, generic reviews without primary LCA results, and studies missing a declared FU or LCIA method. Language was limited to English.

2.3. Data Extraction and Coding

For each study we extracted: sector; geography (feedstock and manufacturing region); feedstock type (starch/sugar, oil-crop, lignocellulosic, residues/wastes); product/application; LCA type (attributional vs. consequential); LCIA method (e.g., ReCiPe, EF/ILCD, TRACI, AWARE); biogenic-carbon treatment (GWP100-only vs. dynamic/GWPbio; storage credits); direct and/or indirect land-use change (LUC/ILUC); allocation approach (system expansion, mass/energy, economic); end-of-life (EoL) scenarios and whether modeled as realistic mixes vs. best-case; regionalization of inventories; technology readiness level (TRL); and presence of uncertainty/sensitivity analysis [2,30,31,32,33].

Regional LCI Data Sources and Examples (Non-EU & MENA)

• EPD Hub for the MENA Region: The CLC EPD MENA platform of the international EPD System covers Saudi Arabia, the United Arab Emirates, Kuwait, Oman, Iraq, Qatar, Jordan, Morocco, Bahrain, and Lebanon. It facilitates the acquisition of EPDs compliant with ISO 14025/EN 15804 standards for regional supply chains.
• Gulf programs: Qatar’s Gulf Organisation for Research & Development (GORD) manages the International Green Mark (IGM) EPD program and has signed a memorandum of understanding with EPD International. Examples include EPDs for reinforcing bars (Qatar Steel) in the Gulf countries.
• Saudi Arabia: Saudi Arabia’s EPD (licensee of the International EPD^® System) and company-level portfolios (e.g., Saudi Readymix’s 50+ mix-specific EPDs) support Kingdom of Saudi Arabia–specific background data.
• United Arab Emirates: We draw electricity generation and desalination parameters from national statistics (e.g., EWEC 2023 Statistical Report) and regional energy sources to parameterize cradle-to-gate models.
• Lebanon: In the absence of a national life cycle analysis, we refer to relevant EPDs for Lebanon (e.g., Weber-Sodamco production in Lebanon), IEA energy parameters, and the recent JICA survey on the electricity sector to avoid default EU reference data.
• North Africa (example: Morocco): We refer to Morocco-specific and publicly published LCAs for cement manufacturing processes (e.g., calcined marl vs. OPC/LC3) where applicable.
• Agriculture (multiregional): Where national LCAs are limited, Agri-footprint (63+ countries) provides datasets on crops, fertilizers, and animals covering several geographical areas of the MENA region.
• Regionalized water scarcity: We apply the AWARE 2.0 country/basin characterization factors (subnational, where available) to reflect irrigation water scarcity in arid contexts.

2.4. Representativeness Framework

Because LCA outcomes for bio-based materials depend on sector, region, and methodological choices, we assessed coverage across the following strata: (i) sectors (construction; packaging/polymers; textiles/composites); (ii) geographies (Europe, North America, Asia-Pacific, and other regions); (iii) methodological diversity (attributional and consequential LCAs; multiple LCIA methods; allocation approaches; biogenic-carbon conventions; inclusion of LUC/ILUC); (iv) feedstock types (starch/sugar, oil-crop, lignocellulosic, residues/wastes); and (v) TRL (pilot/demonstration: 5–7; commercial: 8–9).

2.5. Biogenic Carbon Reporting and End-of-Life Modelling

When studies differed in biogenic carbon conventions, we normalized narrative comparisons to net cradle-to-grave GWP100 and annotated where dynamic metrics (e.g., GWPbio) or temporary storage credits were applied.

End-of-life outcomes were modeled as probabilistic mixes derived from municipal/regional waste-management statistics and installed treatment capacities (recycling, incineration with energy recovery, landfilling, composting). For each region, we converted the reported treatment shares and facility capacities into pathway probabilities pi, adjusted for collection and sorting efficiencies, and calculated the end-of-life impact as a weighted average ${Impact}_{EoL}= \sum _i{p}_i{I}_i.$

A Monte Carlo analysis (10,000 iterations) was used to sample the uncertainty of the pathway probabilities p_i and the pathway impacts I_i to produce 95% confidence intervals and identify the infrastructure parameters that most influence the results. An illustrative calculation (40% recycling, 35% incineration, 25% landfill) is provided in the Supplementary Information to show how the weighted end-of-life impact is obtained.

2.6. Policy Application: Procurement Scorecard

To facilitate purchasing decisions, we recommend presenting a minimum set of comparable indicators for each product: LCC (net discounted cost over the declared lifetime), LCA (100-year global warming potential and a relevant indicator such as eutrophication or water scarcity), and S-LCA (three priority social subcategories, e.g., working conditions, local employment, and community health). Each indicator should be normalized on a scale of 0 to 1, and a weighted procurement score should be computed as follows:

S = w_LCC . LCC_norm + w_LCA . LCA_norm + w_S . S-LCA_norm    (weights w sum to 1)

The 60% LCC/25% LCA/15% S-LCA distribution used in the example is purely illustrative and reflects a cost-oriented procurement approach; it is not prescriptive. National authorities must set the final weightings based on local political priorities and stakeholder input.

3. Methodological Frameworks and Critical Challenges

The application of LCA to bio-based materials differs from its application to inert fossil fuel-based systems due to the biological complexity of feedstock production and the temporal dynamics of the carbon cycle. This section critically analyzes the four methodological pillars that contribute to the variability in current literature: (1) attributional vs. consequential LCA, (2) Biogenic carbon accounting, (3) Land-Use Change modeling, and (4) allocation procedures in multi-output biorefining.

3.1. Attributional vs. Consequential LCA Approaches

Literature distinguishes between two fundamental epistemological approaches to LCA, each yielding different results for bio-based systems.

Attributional LCA focuses on describing the environmentally relevant physical flows to and from a lifecycle and its subsystems [34]. It considers the direct physical supply chain, typically using average data (such as the average network mix). The majority of reviewed studies on bio-based materials (approximately 85%) use attributional LCA due to its compliance with ISO 14040 standards and its suitability for product declaration (EPDs).

In contrast, consequential LCA seeks to identify how environmentally relevant flows change in response to potential choices [35]. Since increased demand for biomass can disrupt agricultural markets, consequential LCA is particularly relevant for bio-based materials.

• Market Effects: For example, increased demand for polylactic acid (PLA) produced from maize may lead to maize substitution in animal feed, driving up prices and encouraging crop diversification.
• Marginal Technologies: Instead of using average data, consequential LCA relies on “marginal” data (such as a power plant commissioned to meet increased demand).

Critical Synthesis: Because consequential LCA incorporates indirect market effects, such as indirect land-use change, Plevin et al. (2014) demonstrated that consequential LCA generally produces higher impact estimates for bio-based materials compared to attributional LCA [36]. However, there is considerable uncertainty regarding the economic modeling parameters in consequential LCA. The choice between consequential LCA and attributional LCA boils down to a trade-off between comprehensiveness (consequential LCA) and accuracy (attributional LCA).

3.2. The Biogenic Carbon Accounting Debate

The question of how to manage biocarbon, the carbon that plants absorb during photosynthesis and release at the end of their life cycle, remains one of the most controversial topics in research.

3.2.1. Reader’s Guide: Plain Language Explanations of GWP100 and GWPbio

What is GWP100? It is the IPCC’s standard metric that converts each greenhouse gas into its CO₂-equivalent by adding up its impact on global warming over 100 years (CO₂ = 1 by definition). Due to its simplicity and consistency, most LCAs and carbon footprints report climate change results using GWP100.

What is “biogenic” carbon? Plants absorb CO₂ from the air during their growth; this carbon can be stored in a bio-based product for a certain period and then released later (e.g., at the end of its life cycle). The standard GWP100 totals warming over 100 years but does not allow for precise tracking of the timing of absorption and release.

What is GWPbio? GWPbio is a simple factor (ranging from 0 to 1) used in some studies to reflect the timing of biogenic CO₂ uptake and release. Fast-growing/short-rotation biomass often has lower GWPbio values (greater short-term uptake), while slow-growing biomass may have higher values.

In this review, we apply this factor to ensure comparability of climate results based on GWP100. We clearly indicate studies that used dynamic accounting (e.g., GWPbio or temporary storage credits), as these choices can alter the apparent results.

3.2.2. The “Carbon Neutrality” Assumption

In the past, many LCA studies relied on the assumption of “carbon neutrality,” which assigns zero greenhouse gas potential to biocarbon emissions, based on the equation of uptake and emission (uptake = −1 kg CO₂; emission = +1 kg CO₂; net = 0). However, recent research challenges this assumption as an oversimplification. Johnson (2009) argues that this approach ignores the efficiency of carbon transport, that processing energy is often derived from fossil fuels, and that the collected biomass is not entirely converted into a product (some of the waste decomposes) [37].

3.2.3. Timing and Temporary Storage (Dynamic LCA)

Longer-lasting bio-based materials (such as wooden building materials and natural fiber insulation) absorb and release carbon over time, providing a greater climate benefit by sequestering carbon from the atmosphere. The results of the conventional GWP100 lifecycle assessment are constant and aggregate the radioactive effect over 100 years, effectively ignoring the timing of emission.

• Life cycle Dynamics Assessment: Levasseur et al. (2010) also proposed a lifecycle dynamics assessment that calculates the radiative response based on the timing of emissions [22].
• Global Biogenic Carbon Warming: Cherubini et al. (2011) introduced the “GWPbio” index to measure the imperfect climate impact of biogenic carbon dioxide [21]. They showed that for slow-recirculating biomass (such as slow-growing forests), the biogenic carbon dioxide is not neutral, and it has a greenhouse effect ranging from 0.0 to 0.43, a value higher than the baseline value and dependent on the cycle.

3.2.4. Land-Use and Land-Use Change (LUC)

How ILUC Was Quantified?

A. 

Approaches encountered

• Modeling of economic equilibrium (market-mediated). These modeling studies demand shocks for a crop or commodity and quantify global land transitions using computable general equilibrium (CGE) models (e.g., GTAP-BIO) or partial equilibrium models (e.g., GLOBIOM). California’s LCFS couples GTAP with AEZ-EF for carbon accounting.
• Applications based on factors (default values from policies/literature). Some studies apply default ILUC factors from regulatory sources (e.g., ICAO CORSIA global ILUC values) or literature, with an amortization horizon indicated.

B. 

Common calculation steps

1.

Demand shock and Simulation: Introduce additional demand for bio-feedstock raw materials; simulate the equilibrium to obtain the area change (ΔArea) of land-use transitions based on land cover and location (accounting for changes in yield and co-product production).

2.

Carbon-stock Balance: For each transition i, estimate emissions from (i) living biomass, (ii) soils, and (iii) unexploited carbon sequestration potential relative to counterfactual land cover.

3.

Amortization: Annualize point changes in stocks over a policy-defined horizon (e.g., 20 years according to aviation standards).

4.

Normalization: Divide annual ILUC emissions by annual product output to express the results in gCO₂e per MJ (or per declared functional unit).

C. 

Formula

$${ILUC}_{FU}={\displaystyle \frac{\left[\frac{\sum {∆Area}_i \times {EF}_i}{A}\right]}{Output}}$$

ΔArea_i: modeled (or assumed) area change by land-use transition i (ha)

EF_i: transition-specific emission factor including vegetation, soil, and foregone sequestration (tCO₂e/ha)

A: amortization period (years)

Output: annual energy or mass in the functional unit (MJ, kg, etc.)

The environmental burden of the agricultural phase often dominates the life cycle of bio-based materials. This burden is categorized into direct and indirect land-use change.

• Direct Land-Use Change: This occurs when land is converted for raw material cultivation (e.g., from pasture to maize fields). The loss of soil organic carbon during this conversion can result in a “carbon debt” that requires decades of accumulated material savings to repay [38].
• Indirect Land-Use Change: This occurs when the need for raw materials drives current production to other locations, potentially leading to the conversion of carbon-rich peatlands or deforestation.

Due to a lack of agreement on modeling frameworks, indirect land-use change is often excluded from traditional life cycle assessments, even as direct land-use change is increasingly included (in accordance with IPCC Level 1 recommendations). However, the reduction in greenhouse gas emissions from bio-based materials can be fully offset by considering indirect land-use change, as is often the case in product life cycle assessment studies. Searchinger et al. (2008) provided key work on this topic, indicating that corn ethanol could nearly double greenhouse gas emissions if indirect land-use change were properly accounted for [28].

3.2.5. Allocation in Biorefineries

Bio-based materials are rarely produced in isolation; rather, they are byproducts of integrated bioreactors that also provide heat, energy, and animal feed. The way environmental burdens are distributed among the various byproducts (allocation) has a significant impact on the outcomes.

ISO 14044 provides a hierarchy for managing multifunctionality:

• Avoidance: Using system expansion (substitution) or the splitting process.
• Physical Allocation: Splitting based on mass or energy content.
• Economic Allocation: Splitting based on market value.

The Conflict:

• Mass Allocation: Bio-based products are often penalized. When a process produces 1 kg of high-value bioplastic and 10 kg of low-value waste biomass, mass allocation artificially reduces the plastic impact by allocating approximately 90% of the load to waste.
• Economic Allocation: As the economic driver of production, this approach is favored by industry and recommended by EU environmental impact assessment standards. However, this approach introduces volatility; the estimated environmental impact fluctuates with price changes, which contradicts scientific logic [39].

According to a review by Spierling et al. (2018), 32% of bio-plastic LCA studies used economic distribution, while 43% used mass distribution, making direct comparisons between studies virtually impossible without recalculating the results [40].

4. Comparative LCA Results by Sector: Empirical Evidence of Environmental Performance

Table 1. Numerical Models and Modeling Options (2010–2024).

Modeling Domain	Models/Options Seen in the Literature (2010–2024)	How These Choices Drive Divergent Results	Frequent Pitfalls Noted	Our Recommendation (Based on the 2010–2024 Corpus)
LCA modeling approach	Attributional LCA (ALCA) for declarations/benchmarking; Consequential LCA (CLCA) for change-oriented questions (ISO 14044).	CLCA tends to raise GHG when ILUC is included; ALCA is sensitive to allocation/cut-off & background representativeness.	Treating ALCA as universally “more accurate”; not justifying the approach by goal & scope.	Fit-for-purpose: report an ALCA core result for comparability and add a targeted CLCA sensitivity when market effects (e.g., ILUC) are material.
Biogenic carbon/timing	Static GWP100 vs. dynamic LCA (time-dependent CFs) and GWPbio for biogenic CO2.	Dynamic metrics can change outcomes for long-lived wood products by reflecting storage/delay; static GWP100 can mask timing.	Applying storage credits without explicit timing/EoL; mixing static and dynamic results without labeling.	Dual reporting: always provide GWP100 and a dynamic result (e.g., GWPbio/dynamic LCA) when storage/timing are material.
ILUC/LUC modeling	GTAP-BIO (CGE) + AEZ-EF carbon-stock module (e.g., LCFS); GLOBIOM (partial equilibrium); CORSIA defaults reference both.	Including ILUC can negate climate advantages for short-rotation systems; results hinge on marginal oils, coproducts, and carbon of converted land.	Using a single ILUC value as universal; outdated AEZ/carbon factors, and opaque parameters.	Report with/without ILUC and provide parametric ranges (marginal oils, coproduct credits, carbon stocks); cite version/date of defaults.
Allocation (multifunctionality)	ISO 14044 hierarchy: system expansion/substitution → physical (mass/energy) → economic; EF method adds CFF guidance.	Choice shifts burdens markedly (e.g., biorefinery coproducts), driving cross-study divergence.	Volatile economic allocation without justification; inconsistent application.	Declare the primary rule and apply consistently; provide allocation sensitivity where feasible; align with ISO/EF rules.
LCIA method	ReCiPe 2016; EF 3.0/3.1 (PEF/EN 15804 alignment); TRACI 2.1/2.2 (US); AWARE/AWARE2.0 (water scarcity).	Different methods shift eutrophication, toxicity, and water scarcity results, explaining inconsistencies beyond climate.	Mixing vintages (e.g., EF 3.0 vs. 3.1; TRACI eutrophication factors) without stating versions; non-regionalized water scarcity.	Name method and version; use AWARE/AWARE2.0 at basin/month scale where water matters; follow EN 15804 + A2/EF for EU EPDs; specify TRACI vintage for US EPDs.
End-of-Life (EoL) & Module D	Scenario mixes (landfill/incineration/recycling/composting); EN 15804 + A2 requires C-modules and Module D.	Bio-based packaging results are highly EoL-sensitive; optimistic composting/recycling shares bias results; Module D credits vary.	Declaring single “compost all” or “recycle all” scenarios; mixing A1/A2 EPD vintages.	Use probabilistic, region-realistic EoL mixes; disclose Module D logic and substitution choices.
Background data/databases	ecoinvent v3 (system models for ALCA/CLCA; expanded regionalization/water balance from v3.1).	System model choice (cut-off vs. consequential) and regionalization materially affect results; version mixing shifts outcomes.	Unstated system model; EU defaults used for non-EU contexts.	State database and system model; prioritize regionalized inventories (energy, agriculture, water).
Prospective/ex-ante assessment	Prospective LCA for emerging tech (TRL 5–7 to 8–9).	Pilot-to-mature comparisons can inflate impacts; scale-up assumptions narrow gaps.	Using lab/pilot data as if industrial; ignoring learning curves.	Run ex-ante LCA with scale-up/learning curves and report ranges.

synthesizes numerical models and modeling options reported in LCAs of bio-based materials (2010–2024) and provides recommendations to reduce cross-study divergence (method/version transparency, dual reporting for biogenic carbon timing, ILUC sensitivities, regionalized inventories, and realistic end-of-life mixes).

Applying the LCA framework to bio-based materials yields a complex set of results that depend heavily on the system’s defined boundaries and the intensity of raw material use in agriculture. This section analyzes the comparative performance of bio-based materials across critical economic sectors and provides a quantitative synthesis of key findings from previous studies.

4.1. Building Materials: Long-Term Carbon Storage

In the building sector, climate gains stem primarily from two mechanisms: (1) biogenic carbon stored in long-lived wood-based elements throughout their life cycle; and (2) avoided emissions through the substitution of polluting materials (e.g., steel, cement). Concrete may also absorb CO₂ through carbonation during its use and after demolition, when exposure permits. Therefore, our results focus on the magnitude and conditions under which these mechanisms prevail, and we avoid repeating basic definitions.

The long lifespan of bio-based building materials (50–100+ years) enables them to benefit from temporary carbon storage in the climate. Compared to conventional, energy-intensive alternatives such as steel, aluminum, and Portland cement-based concrete, research on wood, bamboo, and bio composites used in building structures shows a significant reduction in Global Warming Potential.

• Wood and its Products: According to a meta-analysis by Oh et al. (2023), replacing reinforced concrete in residential buildings with structural timber reduces embedded energy and greenhouse gas emissions by 30% to 70% [41]. This is mainly due to two mechanisms: (1) carbon stored in timber during its lifetime is the reason for the negative effect of global warming potential [42]; and (2) substitution of energy-intensive materials (cement and steel).
• Insulation/Composites from Natural Fiber: Compared to mineral wool or expanded polystyrene (EPS), insulation materials such as hempcrete, straw piles, and wood fibers require less energy to produce. Bruno (2025) found that while conventional expanded polystyrene has a positive global warming potential, largely due to the energy used in petrochemical feedstocks and blowing agents, insulation made from hemp fibers exhibits a negative global warming potential, largely due to its carbon sequestration capacity [43]. However, the agricultural stage of natural fibers must be carefully assessed. For example, hemp cultivation in poorly managed systems can lead to high costs due to the potential for soil acidification and fertilization from the use of nitrogen fertilizers.

4.2. Polymers and Packaging: Short-Term Trade-Offs

Biopolymers, also known as bioplastics (such as polyhydroxyalkanoates (PHAs), polylactic acid (PLA), and bio-polyethylene (Bio-PE)), are primarily used in disposable goods, films, and packaging. The end-of-life scenario is a critical factor in determining the environmental performance of this industry.

In studies that explicitly quantified ILUC for sugar/starch or oil-crop feedstocks (e.g., PLA/PHAs), ILUC often dominated cradle-to-grave GWP and, in some cases, negated the apparent climate advantage versus fossil polymers.

• Fossil-derived polymers vs. PLA: Studies comparing PLA, often derived from corn or sugarcane, with conventional polymers such as polyethylene terephthalate (PET) and polypropylene (PP) have yielded mixed results. Given the energy mix used in the polymerization process and the reliance on biocarbon, Vink et al. (2010) found that corn-based PLA has a lower global warming potential than PET [44,45]. However, the source of raw materials significantly influences this advantage.
• End-of-Life Sensitivity: For polymers like PLA, their biodegradability is often cited as an environmental advantage. However, several LCAs (such as Hermann et al., 2011) show that PLA performs only slightly better or even worse than PET when disposed of in landfills (due to inadequate industrial composting infrastructure), thus negating the potential benefit of organic recycling [44]. Furthermore, bioplastics may create environmental burdens by reducing recycling efficiency if they contaminate fossil plastic recycling processes.
• Bio-PE: This polymer is made from sugarcane (by bioethanol) and is chemically equivalent to fossil polyethylene. The main conclusion is that, due to the removal of carbon dioxide during the feedstock phase, bio-PE often has a lower global warming potential than fossil polyethylene. However, because sugarcane requires large amounts of fertilizer and pesticides, it causes problems such as eutrophication, water scarcity, and human toxicity [46].

4.3. Illustrative Procurement Case: Packaging (PLA vs. rPET)

We apply the procurement scorecard to a simplified comparison between PLA and recycled PET (rPET) packaging using normalized indicator values:

PLA: LCC = 0.70; LCA (GWP) = 0.60; and S-LCA = 0.40.

rPET: LCC = 0.80; LCA (GWP) = 0.50; and S-LCA = 0.70.

Using the illustrative weights of 60% for LCC, 25% for LCA, and 15% for S-LCA (cost-focused stance) yields S_PLA scores of 0.63 and S_rPET scores of 0.71, making rPET preferable. It is important to emphasize that the 60/25/15 split is an example chosen to reflect a procurement approach that prioritizes LCC while considering environmental and social factors; it is not a universal prescription. When a bio-based option is attractive from a cost or climate perspective, but performs poorly on social indicators (e.g., pressure on land use or labor risks), the procurement process should require mitigation measures (due diligence on suppliers, sourcing from waste, contractual social safeguards) rather than automatic rejection [43].

4.4. Synthesis: The Climate Change and Ecosystem Quality Dichotomy

A meta-analysis of the biomaterials sector, in both the long and short term, confirms that there is a direct trade-off between harming the local ecosystem and mitigating the effects of climate change, which is supported by published bio-based materials LCA studies. Bio-based materials regularly display:

• Positive global warming factor: This is the primary factor influencing its use. Biogenic carbon sequestration (for building materials) and avoiding fossil-based energy and material feedstocks are considered optimal ways to reduce greenhouse gas emissions.
• Negative indicators of ecosystem quality: The initial agricultural stage often bears the greatest burden. Ammonia (NH₃) and nitrous oxide (N₂O) emissions resulting from high fertilizer application rates lead to significant increases in:

  o

  Eutrophication Potential: This is primarily caused by phosphate and nitrate (NO₃⁻) runoff.

  o

  Acidification Potential: This is caused by ammonia (NH₃) emissions associated with fertilizers.

  o

  Toxicity: This toxicity results from the use of pesticides and herbicides and particularly affects freshwater and human health [17,40].

The magnitude of this trade-off often depends on geographic location. For example, compared to intensive, high-input systems, bio-based feedstock produced in high-yield areas using low-impact farming methods shows a significantly lower likelihood of Acidification and Eutrophication Potentials [36]. This underscores the need to use regional life cycle inventory data rather than generalized European or global averages.

Comparisons prioritize GWP100 and report, where available, acidification, eutrophication, human toxicity, and water scarcity using each study’s declared method (ReCiPe 2016, ILCD/EF, TRACI, or AWARE). Cross-study ranges are interpreted qualitatively when methods differ. The cross-study direction-of-change and key conditions for each system are summarized in

Table 2. Mini-synthesis of core results.

System/Comparison	Climate (GWP100)	Other Midpoints (Acidification, Eutrophication, Human Toxicity)	Water Scarcity (AWARE)	End-of-Life Sensitivity (Real-World Routes)	Key Method Caveats
Timber buildings vs. mineral/steel	↓ (clear reduction); may appear “negative” only with storage credits or dynamic characterization (GWPbio)—otherwise positive but reduced	↔/slight ↑ possible (upstream energy mix, preservatives, logistics), generally not dominant vs. climate gains	Context-dependent (regionalized AWARE factors; supply-chain geography matters)	High: demolition wood split (energy recovery/recycling-reuse/landfill) and Module D benefits materially change totals; use realistic country/operator shares	Separate biogenic flows; “negative GWP” is conditional (storage/dynamic). Construction EPD rules: EN 15804 + A2, ISO 21930. LCIA typically ReCiPe 2016 or EF.
PLA/PHAs packaging vs. fossil plastics	↓/↔ (often lower if feedstock & energy are favorable); can flip if ILUC is included	↑ likely (fertilizers, agriculture, chemicals) in acidification, eutrophication, sometimes human toxicity	↑ Risk for sugar/starch-based feedstocks; strong regional dependence	Very high: EU-27 plastic packaging ~41% recycling on average; industrial composting access limited; avoid best-case “compost-all” assumptions	Indicator mix differs (ReCiPe/EF/TRACI); interpret cross-method ranges qualitatively. Include ILUC sensitivity when available.
Bio-PE packaging vs. PE	↓/↔ (often similar to PE; reductions depend on biogenic carbon and energy mix)	↔/slight ↑ (agri inputs can increase some categories; processing often similar to PE)	Context-dependent (feedstock geography + AWARE)	High: behaves like PE in sorting; contamination can lower real recycling credits; energy recovery vs. landfill shares matter	Method alignment (EF/ReCiPe/TRACI) and regional inventories drive comparability; report End-of-Life probabilities, not idealized routes.

Legend: ↓ = reduction vs. baseline; ↑ = increase vs. baseline; ↔ = mixed/no consistent direction. Baselines: mineral/steel for buildings; fossil plastics for packaging.

.

5. Critical Analysis and Prospects for Future Research

In line with our representativeness framework, when studies focus on supply chains in the MENA region, we prioritize regional EPD/LCA sources (e.g., CLC EPD MENA; GORD/IGM; KSA and UAE repositories) and national energy statistics (e.g., IEA/JICA for Lebanon) to avoid European defaults values and to reflect local electricity and water conditions in the reference models.

Evidence across comparative LCA studies indicates that bio-based materials can contribute meaningfully to climate-change mitigation, particularly in long-lived applications; however, these outcomes are contingent on methodological choices and may coincide with burden shifting to other impact categories associated with agricultural production. To inform a rigorous and transparent transition to a sustainable bioeconomy, this section (i) critically appraises recurrent limitations in current LCA practice—heterogeneity in goal and scope definitions (attributional vs. consequential), inconsistent treatment of biogenic carbon dynamics and temporary storage, sparse or non-harmonized modelling of direct and indirect land-use change (LUC/ILUC), non-standard allocation in multifunctional biorefineries, limited regionalization of inventories and water-scarcity metrics, and optimistic end-of-life assumptions—and (ii) proposes methodological improvements, including prospective/ex-ante LCA at relevant technology readiness levels, explicit and dual reporting of biogenic-carbon accounting (with/without storage credits and with timing assumptions), probabilistic End-of-Life mixes aligned with real infrastructure, harmonized allocation procedures, incorporation of biodiversity-relevant land-use indicators and regionalized water scarcity, and systematic uncertainty and sensitivity analysis.

5.1. The Challenge of Data Quality and Technology Readiness Level (TRL)

A major source of non-comparability in the literature is the disparity in data quality between mature fossil systems and emerging bio-based systems.

5.1.1. Data Maturity Asymmetry

• Large-scale, efficient industrial processes are advantageous for fossil-based products (such as Portland cement and polyethylene), and their Life Cycle Inventory (LCI) data, derived from decades of production, is typically reliable and available through extensive databases (like Ecoinvent). On the other hand, many innovative bio-based materials (such as PHAs and engineered wood products) are presently produced at pilot or demonstration scale (TRL 5–7).
• TRL Bias: LCAs frequently unintentionally contrast an immature, high-energy bio-based pilot process with the environmental profile of an optimized, mature fossil process [47]. This comparison frequently overstates the bio-based alternative’s environmental costs (such as energy consumption and waste streams), which could result in poor investment choices.
• Prospective LCA: to predict the environmental profile of bio-based technologies once they reach maturity, future research must increasingly rely on prospective LCA (also known as ex-ante assessment), which makes use of process modeling and scale-up assumptions (TRL 8–9). For a fair comparison with well-established conventional systems, this change is necessary [48,49,50].

5.1.2. Regional Specificity of LCI Data

The utilization of spatially relevant LCI data is required due to the substantial correlation between the performance of bio-based materials and the agricultural phase. The variability present in global supply chains is not captured by generic LCI data for crop yields, fertilizer efficiency, and regional energy mixes. For example, sugarcane farmed in arid places has a very different water shortage footprint than sugarcane grown in rain-fed tropical regions [46]. Moving beyond crude global averages, future LCA studies must give priority to regionalized approaches that take into account site-specific environmental factors, such as water stress indices and local biodiversity measurements.

5.2. Including Bio-Based Materials into a Circular Economy

End-of-life allocation and carbon accounting are directly impacted by the complex circularity potential of bio-based materials, which include both mechanical recycling and biodegradation.

5.2.1. Carbon Accounting and Cascading Use

The ecological ideal for lignocellulosic materials is the cascade use principle, which involves reusing the material for progressively lower-value applications until burning for energy recovery. By doing this, the duration of carbon storage is maximized. However, because it necessitates complex system expansion to account for the various functional units produced by the material during its whole lifespan, adding cascading usage into LCA is methodologically difficult [51]. The impact of producing new fiberboard is avoided when a wooden beam is recycled into fiberboards; this impact must be appropriately attributed to the life cycle assessment (LCA) of the original product.

5.2.2. The Infrastructure Gap and End-of-Life Realism

The discrepancy between the real End-of-Life infrastructure and the potential End-of-Life scenarios for biodegradable bioplastics (industrial composting, anaerobic digestion) is frequently highlighted in the literature [44]. The value of biogenic carbon is immediately lost if “compostable” materials are burned instead. The probabilistic End-of-Life split based on the availability of real municipal waste management infrastructure must be included in research, rather than just providing the best possibilities.

5.3. Broadening the Scope of Sustainability: From Life Cycle Assessment to Triple Bottom Line

An LCA that focuses just on the environment gives a partial picture of sustainability. Future studies must integrate environmental effects with social and economic performance measures in order to adequately capture the consequences of the bioeconomy.

• Coupling S-LCA and LCC: A comprehensive evaluation of the triple bottom line is made possible by combining LCA results with Social Life Cycle Assessment (S-LCA) and Life Cycle Costing (LCC). Indicators, including fair salaries, labor conditions, land tenure rights, and community welfare—all of which are especially pertinent for agricultural feedstocks—are the focus of S-LCA’s evaluation of social consequences along the value chain [52,53,54].
• Economic Viability: Compared to mature fossil counterparts, bio-based products frequently have greater initial capital costs or higher variable costs (such as feedstock price volatility). LCC offers important context about the economic viability of bio-based materials [55]. To identify truly sustainable and commercially viable materials, companies and policymakers need a decision matrix that integrates E-LCA, S-LCA, and LCC.

Dynamic LCA and biogenic carbon tracking are particularly relevant for decision-making when product lifetimes and storage time significantly influence radiative forcing (e.g., long-life timber construction systems). For formal declarations, EPD frameworks align with the GWP100 with explicit biogenic fluxes; dynamic indicators are optional and should be reported as supplementary information to maintain comparability. For regulatory compliance and product carbon footprints (ISO 14067), GWP100 remains the mandatory reference; dynamic accounting can be used as a sensitivity analysis or decision tool, but does not replace the mandatory GWP100 result. For ecodesign, supplier selection, and R&D—particularly for long-life construction products—we recommend applying dynamic LCA or GWPbio in addition to GWP100 to reveal short- and long-term climate performance that static factors may obscure. Therefore, we advocate dual reporting: (i) primary GWP100 (with transparent biogenic flows) to meet standards and enable benchmarking; and (ii) complementary dynamic results to capture timing and storage effects when relevant to decision-making.

6. Conclusions

This review confirms that LCA is essential for evaluating bio-based materials: their environmental performance depends far more on methodological choices than on their bio-based origin. Two main findings emerge. First, methodological asymmetries—particularly the choice between attributional and consequentialist LCA, the timing and storage of biogenic carbon, the modeling of indirect land-use changes, and their distribution in multi-output biorefineries—lead to significant and often inconsistent differences between studies. Second, environmental trade-offs are systematic: bio-based options frequently reduce global warming potential (GWP) through carbon sequestration and fossil fuel substitution, but these gains are generally accompanied by greater upstream impacts (eutrophication, acidification, water stress, and risks to biodiversity).

To enable robust and relevant public policy decisions, we recommend five priorities for future work and reports:

(i)

Prospective/ex-ante LCAs at TRL 8–9;

(ii)

Regionalized LCAs including indicators of water scarcity and biodiversity;

(iii)

Transparent accounting for biogenic carbon (presentation of results with and without carbon sequestration credits and indication of timing assumptions);

(iv)

Probabilistic end-of-life mixes based on actual municipal/regional infrastructure;

(v)

Integrated E-LCA with S-LCA and LCC to identify and manage trade-offs.