# Harmonizing life cycle assessment studies of emerging technologies: The case of virgin and recycled carbon fibers

A Kamal Kamali, Javid Isayev, Bertrand Laratte, Guido Sonnemann

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A Kamal Kamali, Javid Isayev, Bertrand Laratte, Guido Sonnemann. Harmonizing life cycle assessment studies of emerging technologies: The case of virgin and recycled carbon fibers. Resources, Conservation and Recycling, 2025, 220, pp.108323. 10.1016/j.resconrec.2025.108323. hal-05070733

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# Harmonizing life cycle assessment studies of emerging technologies: The case of virgin and recycled carbon fibers

A.Kamal Kamali<sup>a, *</sup> <sup> ID </sup>, Javid Isayev<sup>a</sup> <sup> ID </sup>, Bertrand Laratte<sup>b, c</sup> <sup> ID </sup>, Guido Sonnemann<sup>a</sup> <sup> ID </sup>

## ARTICLE INFO

### Keywords:

LCA

Emerging

Technologies

Carbon

Fibers

Harmonization

Life cycle inventory

## ABSTRACT

The use of carbon fibers has expanded beyond aerospace to renewable energy and automotive sectors, driving demand for low-cost, eco-friendly alternatives to energy-intensive PAN-based production. This study identified 28 Life cycle assessment (LCA) articles, encompassing 56 inventories for virgin and recycled carbon fibers. Following a screening process, 10 inventories representing distinct technologies were harmonized by aligning functional units, system boundaries, and background systems for meaningful comparison. Supercritical hydrolysis, a promising alternative, showed the lowest environmental impact, while energy-autonomous pyrolysis exhibited negative greenhouse gas emissions but produced fibers with 80 % of virgin tensile strength. This study represents the first attempt to harmonize LCAs of emerging technologies, addressing incomparability issues in published research to enable meaningful comparisons. It evaluates the reproducibility of LCA studies and offers recommendations for improvement. Additionally, it provides insights into the environmental impacts of emerging carbon fiber production and recycling technologies.

## 1. Introduction

Carbon fibers (CFs) were first used in the 19th century for incandescent lamp filaments but lost favor to tungsten (Morgan, 2005a). Modern CF development began in 1958 when Roger Bacon accidentally produced them while experimenting with carbon's triple point. Early CFs, made from rayon, had poor mechanical properties, leading British and Japanese researchers to develop polyacrylonitrile (PAN)-based CFs. By 1971, Toray Industries had begun large-scale PAN-based CF production (Zhang et al., 2023). This milestone marked the maturation of PAN-based CFs, which continue to dominate the global market (Das et al., 2016; Grand View Research, 2023). Since then, gradual improvements were attained in terms of physical properties and economic viability of CFs, leading to their broader industrial use.

Global CF demand is projected to reach 285 kilotons by 2025, tripling the demand from just a decade ago. Initially, CFs were predominantly used in leisure and sports equipment, but their adoption expanded significantly in the aerospace sector, particularly after the 1973 oil crisis. CF-reinforced polymers (CFRP) now account for about 50 % of the weight in aircraft such as the Boeing 787 and Airbus A380,

contributing to significant fuel savings. It wasn't until 2007 that CFs began to see broader use in other industries, including renewable energy, automotive, and construction (Morgan, 2005b; Zhang et al., 2023). This expansion into non-aerospace sectors was largely driven by increasing concerns over greenhouse gas emissions (Zhang et al., 2023). For example, CFs have improved wind turbine blade performance, enabling rotor diameters up to 240 m for offshore installations, where strong, lightweight materials are essential (Morgan, 2005b; Zhang et al., 2023). Similarly, electric vehicle manufacturers are working to replace metal chassis, body, and other components with CFRP to reduce overall vehicle weight (Morgan, 2005b; Wazeer et al., 2023; Zhang et al., 2023). However, the high cost and limited recyclability of CFs remain major barriers to broader use beyond the aerospace sector, driving ongoing efforts to develop lower-cost alternatives.

Recently, efforts to develop more economical and environmentally friendly methods for producing CFs have gained momentum. These emerging approaches include the use of renewable precursors and the advancement of recycling technologies. Virgin CFs (vCFs), particularly PAN-based, offer unmatched mechanical properties but come at a high cost, limiting their use to high-end applications such as in the aerospace

<footnote><sup>a</sup> Univ. Bordeaux, CNRS, Bordeaux INP, ISM, UMR 5255, Groupe Analyse du Cycle de Vie et Chimie Durable (CyVi), 351 Cours de la Libération, F-33405 Talence, Cédex, France</footnote>

<footnote><sup>b</sup> Laval University, Department of Wood and Forest Sciences, 2425 De La Terrasse Street, Québec City, QC G1V 0A6, Canada</footnote>

<footnote><sup>c</sup> Arts et Métiers Institute of Technology, University of Bordeaux, CNRS, Bordeaux INP, INRAE, I2M Bordeaux, F-33400 Talence, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: ahmad-kamali.kamali@u-bordeaux.fr (A.Kamal Kamali).</footnote>
industry (Zhang et al., 2023). In contrast, lower-cost alternatives like recycled CFs (rCFs) are increasingly being used in non-aerospace sectors. Researchers have explored alternative precursor materials to PAN, such as lignin, textile-grade PAN, and polyethylene, in an effort to reduce production costs. Additionally, recycling technologies have been developing to address the need for low-cost CFs while managing the growing volume of production and end-of-life waste. These recycling technologies can be categorized into three main types: mechanical (e.g., shredding, grinding), thermal (e.g., pyrolysis, microwave pyrolysis, fluidized bed processes), and chemical recycling (e.g., subcritical solvolysis, supercritical solvolysis). Each method varies in its suitability for different waste streams, scalability, and the quality of the resulting rCFs (Giorgini et al., 2020; Sukanto et al., 2021; Zöllner et al., 2024).

Both vCFs and rCFs have been subjected to environmental assessments using the life cycle assessment (LCA) method. These assessments have examined various production and recycling technologies at different scales. Researchers conducting these studies have relied on data from diverse sources with varying quality and have made different assumptions. Additionally, differences in modeling choices—such as system boundaries, functional units, life cycle impact assessment methods, and geographical scopes—create challenges in comparing the results of these LCA studies. To date, no single study has provided a harmonized comparison of all recycling and primary production technologies. In this work, a harmonization procedure, inspired by previous research on bottled wines (Jourdaine et al., 2020), has been applied to LCA case studies focused on both vCFs and rCFs. All available life cycle inventories (LCIs) are collected, screened, and harmonized to enable a direct comparison. The value of harmonization studies lies in their ability to: (1) comprehensively compare technologies, pinpointing those with high environmental benefits and areas for improvement; (2) identify the best-available life cycle inventories (LCIs) using criteria detailed in Section 2.2; (3) assess the reproducibility of published LCA research; and (4) provide a robust benchmark for future studies.

## 2. Materials and methods

The harmonization procedure involves selecting relevant case studies with available LCIs. Next, modeling parameters of selected case studies are identified and summarized as described in Section 2.1. These inventories are further screened to exclude those that do not align with the criteria detailed in Section 2.2. These parameters are then harmonized across all case studies to ensure a correct and logical comparison of product systems that serve the same function. To achieve this, both the goal and scope definition stage and the life cycle inventory stage are repeated. At the goal and scope stage, the system boundary, functional unit, and life cycle impact assessment method are harmonized, as described in Sections 2.4.2, 2.4.3, 2.4.4. During the life cycle inventory stage, LCIs are extracted from the selected case studies, each focusing on a specific technology. Discrepancies in both the foreground and background data are then identified and resolved, as described in Section 2.5, ensuring a consistent and logical comparison. This harmonization addresses a significant challenge in LCA studies: the lack of standardized modeling approaches, which often results in incomparable outcomes. Researchers have repeatedly highlighted this issue as a barrier to comparability in LCA studies (Moni et al., 2020; Thonemann et al., 2020). By conducting a harmonization study, different technologies for producing vCFs and rCFs can be benchmarked, enabling the identification of promising technologies and supporting decision-making.

### 2.1. Articles selection

To identify and review relevant LCA case studies in the domain of CFs, a search was conducted using the Scopus database with the keywords: "LCA" OR "life cycle assessment" OR "life cycle analysis" AND "carbon fiber*". This search included only articles published before October 2024 and yielded 183 results listed in Table S1. Among these,

157 articles applied LCA to some form of CFRP or related products, such as wind turbines and automobile parts. However, only 28 articles shared inventories covering 56 distinct methods of CF production or recycling. Table S2 presents a detailed description of relevant modelling parameters of these 28 articles.

These methods can be categorized as follows:

* Primary Production:

■ 17 articles investigated PAN-based CFs.

■ 2 articles investigated lignin-based CFs.

* Recycling Technologies:

■ 14 articles investigated pyrolysis.

■ 8 articles investigated sub-critical solvolysis.

■ 6 articles investigated supercritical solvolysis.

■ 9 articles investigated other recycling technologies.

### 2.2. Life cycle inventories' screening

Identified LCIs undergo an additional screening process before remodeling. Inventories may be excluded for two main reasons: they are either duplicates or they describe early-stage technologies without addressing the potential for scale-up. This screening step eliminates inventories that have been remodeled and republished without significant changes from earlier studies. It also filters out inventories developed for lab-scale or pilot-scale operations, prioritizing those that emulate industrial-scale processes—whether based on experimental primary data or theoretical scaled-up models. Table 1 lists the data sources, technology scales, CF tensile strength, CF tensile modulus, and rCF properties retention percentage, for all 56 inventories. Those selected for further harmonization, remodeling, and comparison are highlighted in yellow. Additionally, network charts (Figures S1 and S2) are provided for each production and recycling technology (e.g., PAN-based, pyrolysis) to illustrate the relationships between original and duplicate inventories, identify which studies are derivatives of others, and indicate the technology scale associated with each inventory. Studies that have conducted assessments on an industrial scale and have specified the properties of CFs have been preferred to facilitate a meaningful comparison. Out of 56 inventories, 8 research articles are selected containing 10 inventories for 9 unique technologies to produce either vCF or rCF, (Benitez et al., 2021; Cheng et al., 2022; Hermansson et al., 2023; Kawajiri and Kobayashi, 2022; Kulas et al., 2021; Liu et al., 2021; Meng et al., 2018; Pillain et al., 2019). Based on this screening, we can recommend the use of these inventories in future research on CFs as they provide the highest data quality available.

### 2.3. Investigated product systems

The following provides a summary of nine selected technologies for manufacturing vCFs or rCFs, essential for remodeling inventories accurately. While many variations exist for each technology, the description focuses on the remodeled versions.

PAN-based CF technology relies on PAN, a synthetic polymer, to produce vCFs. The process assessed by (Benitez et al., 2021) with polymerizing acrylonitrile through suspension polymerization, achieving an 85 % conversion rate. PAN is dissolved in dimethyl sulfoxide (DMSO) and extruded into filaments during spinning. These fibers are washed, stretched, and aligned to improve their mechanical properties. The next step, stabilization, involves heating the fibers in air at 220–280 °C, converting them into a stable thermoset structure. The stabilized fibers are then carbonized in a nitrogen atmosphere, first at 750 °C, then at 1500 °C, transforming them into pure carbon. Finally, surface treatment (e.g., electrolysis) and epoxy coating enhance bonding in composite applications.

Solvolysis is a chemical recycling method using a solvent and catalyst to break down the polymer matrix in CFRP. In the remodeled process, (Kawajiri and Kobayashi, 2022), CFRP scrap is soaked in a solution of
<caption>**Table 1**</caption>

A list of the 56 identified inventories, their data source, technology scale, and carbon fibers' mechanical properties. Selected inventories are highlighted in bold.

<table><thead><tr><th>Authors</th><th>Data Source</th><th>Technology Scale</th><th>Tensile Strength [MPa]</th><th>Tensile Modulus [GPa]</th><th>Properties Retention [%]</th><th>Comments</th></tr></thead><tbody><tr><td colspan="7"><strong>PAN-Based</strong></td></tr><tr><td>(Atescan-Yuksek et al., 2024)</td><td>(Khalil, 2017)</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>(Bayram et al., 2024)</td><td>(Karuppannan Gopalraj et al., 2021)</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>(Prenzel et al., 2023)</td><td><strong>Primary Data</strong></td><td>Industrial</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>(Hermansson et al., 2023)</td><td>(Das, 2011; Hermansson et al., 2022b; Romaniw, 2013)</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>(Cheng et al., 2022)</td><td>(Liu et al., 2021; Meng et al., 2017; Nunes et al., 2018; Song et al., 2009)</td><td>Industrial Scale</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>(Sommer et al., 2022)</td><td>(Pillain et al., 2019)</td><td>Unspecified</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>(Karuppannan Gopalraj et al., 2021)</td><td>(Das, 2011; Scheepens et al., 2020)</td><td>Unspecified</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>(Akbar and Liew, 2021)</td><td>(Griffing and Overcash, 2010; Pillain et al., 2019)</td><td>Unspecified</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>(La Rosa et al., 2021)</td><td>(Das, 2011)</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>(Liu et al., 2021)</td><td>(Meng et al., 2017)</td><td>Unspecified</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>(Ghosh et al., 2021)</td><td>(Das, 2011; Johnson and Sullivan, 2014; Nunna et al., 2019)</td><td>Unspecified</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>(Benitez et al., 2021)</td><td><strong>Primary Data</strong></td><td>Industrial Scale</td><td>4900</td><td>240</td><td>-</td><td>CF type: Toray T700 G</td></tr><tr><td>(Forcellese et al., 2020)</td><td>(Khalil, 2017)</td><td>Literature</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>(Pillain et al., 2019)</td><td>(Griffing and Overcash, 2010)</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>(Nunes et al., 2018)</td><td>(Griffing and Overcash, 2010)</td><td>-</td><td>-</td><td>-</td><td>-</td><td>Properties are provided for the composite material</td></tr><tr><td>(Meng et al., 2017)</td><td>(Scelsi et al., 2011; Song et al., 2009; Witik et al., 2012)</td><td>Industrial scale</td><td>-</td><td>230</td><td>-</td><td>-</td></tr><tr><td>(La Rosa et al., 2016)</td><td>Primary data + (Das, 2011) for energy consumption</td><td>Lab scale</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td colspan="7"><strong>Lignin based</strong></td></tr><tr><td>(Hermansson et al., 2023)</td><td>(Das, 2011; Hermansson et al., 2022b; Moncada et al., 2018; Romaniw, 2013)</td><td>Industrial Scale (Scaled up)</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>(Kulas et al., 2021)</td><td>Experimental data, process simulation software, and literature</td><td>Industrial Scale (Scaled up)</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td colspan="7"><strong>Pyrolysis</strong></td></tr><tr><td>(Wu et al., 2023)</td><td>(Krauklis et al., 2021; Meng et al., 2018; Pakdel et al., 2021)</td><td>Unspecified</td><td>-</td><td>202.4</td><td>88.0 %</td><td>Properties reference: (Meng et al., 2018)</td></tr><tr><td>(Kawajiri and Kobayashi, 2022)</td><td><strong>Primary data (Carbon Fiber Recycle Co.)</strong></td><td>1800 ton/year</td><td>2000</td><td>-</td><td>80.0 %</td><td>vCF tensile strength = 2500 Mpa</td></tr><tr><td>(Cheng et al., 2022)</td><td>(Nunes et al., 2018)</td><td>Pilot Scale</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>(Sommer et al., 2022)</td><td>(Pillain et al., 2019)</td><td>Unspecified</td><td>-</td><td>-</td><td>78.0 %</td><td>Properties are based on (Liu et al., 2019; Pillain et al., 2019)</td></tr><tr><td>(Hermansson et al., 2022a)</td><td>(Dong et al., 2018)</td><td>Unspecified</td><td>-</td><td>-</td><td>82.0 %</td><td>Properties were assumed</td></tr><tr><td>(Karuppannan Gopalraj et al., 2021)</td><td><strong>Primary data</strong></td><td>Lab-Scale</td><td>-</td><td>-</td><td>-</td><td>Properties are provided for the composite material</td></tr><tr><td>(Akbar and Liew, 2021)</td><td>(Dong et al., 2018; Khalil, 2018; Song et al., 2009; Witik et al., 2012)</td><td>Unspecified</td><td>3790</td><td>237</td><td>-</td><td>Properties are taken from the recycler</td></tr><tr><td>(Liu et al., 2021)</td><td>(Nunes et al., 2018)</td><td>Industrial</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>(Pillain et al., 2019)</td><td>(Howarth et al., 2014; Witik et al., 2012)</td><td>Industrial Scale (Scaled up)</td><td>-</td><td>-</td><td>58.0 %</td><td>Properties are based on (Oliveux et al., 2015)</td></tr><tr><td>(Nunes et al., 2018)</td><td><strong>Primary data</strong></td><td>Pilot scale</td><td>-</td><td>-</td><td>80.0 %</td><td>Properties are based on (Boulanghien et al., 2015)</td></tr><tr><td>(Meng et al., 2018)</td><td>Primary and secondary data</td><td>Industrial Scale (Scaled up)</td><td>-</td><td>202.4</td><td>88.0 %</td><td>vCF tensile modulus = 230 GPa</td></tr><tr><td>(Khalil, 2018)</td><td>(Howarth et al., 2014; Khalil, 2017; Meng et al., 2017; Morin et al., 2012a; Pickering, 2006; Witik et al., 2012; Yang et al., 2012)</td><td>Unspecified</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>(Dong et al., 2018)</td><td>(Witik et al., 2012)</td><td>Unspecified</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>(Cheul-Kyu et al., 2010)</td><td><strong>Primary data</strong></td><td>Lab-scale</td><td>3138.128</td><td>-</td><td>95.5 %</td><td>vCF tensile strength = 3285.227 Mpa</td></tr><tr><td>(Wu et al., 2023)</td><td>(La Rosa et al., 2021, 2018)</td><td>Unspecified</td><td>-</td><td>204.7</td><td>89.0 %</td><td>Properties are based on (Meng et al., 2018)</td></tr></tbody></table>

(continued on next page)
<table><caption>**Table 1 (continued)**</caption><thead><tr><th>Authors</th><th>Data Source</th><th>Technology Scale</th><th>Tensile Strength [MPa]</th><th>Tensile Modulus [GPa]</th><th>Properties Retention [%]</th><th>Comments</th></tr></thead><tbody><tr><td>(Kawajiri and Kobayashi, 2022)</td><td>Hitachi Chemical</td><td>Industrial Scale (Scaled up)</td><td>3198</td><td>-</td><td>99.9 %</td><td>vCF tensile strength = 3200 MPa</td></tr><tr><td>(La Rosa et al., 2021)</td><td>(La Rosa et al., 2018)</td><td>Plant Level</td><td>-</td><td>-</td><td>-</td><td>rCF are assumed to match vCF quality</td></tr><tr><td>(Shanmugam et al., 2019)</td><td>(La Rosa et al., 2018, 2016)</td><td>Unspecified</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>(Meng et al., 2018)</td><td>(La Rosa et al., 2016)</td><td>Lab Scale</td><td>-</td><td>204.7</td><td>89.0 %</td><td>vCF tensile modulus = 230 GPa</td></tr><tr><td>(La Rosa et al., 2018)</td><td>Primary Data (Connora Chemical)</td><td>Industrial Scale</td><td>-</td><td>-</td><td>-</td><td>rCF are assumed to match vCF quality</td></tr><tr><td>(La Rosa et al., 2016)</td><td>Primary Data</td><td>Lab scale</td><td>-</td><td>-</td><td>-</td><td>rCF are assumed to match vCF quality</td></tr><tr><td>(Cheul-Kyu et al., 2010)</td><td>Primary Data</td><td>Lab-scale</td><td>3187.161</td><td>-</td><td>97.0 %</td><td>vCF = 3285.227 MPa</td></tr><tr><td colspan="7"><strong>Supercritical Solvolysis</strong></td></tr><tr><td>(Sommer et al., 2022)</td><td>(Pillain et al., 2019)</td><td>Unspecified</td><td>-</td><td>-</td><td>95.0 %</td><td>Properties are based on (Liu et al., 2019; Pillain et al., 2019)</td></tr><tr><td>(Hermansson et al., 2022a)</td><td>(Dong et al., 2018)</td><td>Unspecified</td><td>-</td><td>-</td><td>98.0 %</td><td>Properties were assumed</td></tr><tr><td>(Pillain et al., 2019)</td><td>Primary data</td><td>Industrial Scale (Scaled up)</td><td>-</td><td>-</td><td>-</td><td>rCF are assumed to match vCF quality according to (Morin et al., 2012b)</td></tr><tr><td>(Khalil, 2018)</td><td>(Dauguet et al., 2015; Henry et al., 2016; Howarth et al., 2014; Khalil, 2017; Knight, 2013; La Rosa et al., 2016; Meng et al., 2017; Morin et al., 2012a; Piñero-Hernanz et al., 2008)</td><td>Unspecified</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>(Dong et al., 2018)</td><td>(Knight, 2013)</td><td>Unspecified</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>(Liu et al., 2021)</td><td>Primary and secondary data</td><td>Industrial Scale (Scaled up)</td><td>144.41</td><td>2.76</td><td>95.17-93.17 %</td><td>-</td></tr><tr><td colspan="7"><strong>Mechanical</strong></td></tr><tr><td>(Wu et al., 2023)</td><td>136 - 2018 Meng</td><td>Unspecified</td><td>-</td><td>-</td><td>-</td><td>Assumed to replace glass fibers</td></tr><tr><td>(Meng et al., 2018)</td><td>Unspecified</td><td>Industrial Scale</td><td>-</td><td>-</td><td>-</td><td>Assumed to replace glass fibers</td></tr><tr><td colspan="7"><strong>Electrodynamic Fragmentation</strong></td></tr><tr><td>(Sommer et al., 2022)</td><td>(Mativenga et al., 2016)</td><td>Unspecified</td><td>-</td><td>-</td><td>83.0 %</td><td>Properties are based on (Liu et al., 2019; Morin et al., 2012b)</td></tr><tr><td>(Pillain et al., 2019)</td><td>Primary Data</td><td>Industrial Scale (Scaled up)</td><td>-</td><td>-</td><td>83.0 %</td><td>Properties are based on (Roux et al., 2017)</td></tr><tr><td colspan="7"><strong>Fluidized Bed</strong></td></tr><tr><td>(Sommer et al., 2022)</td><td>(Meng et al., 2018)</td><td>Unspecified</td><td>-</td><td>-</td><td>75.0 %</td><td>Properties are based on (Liu et al., 2019; Meng et al., 2018)</td></tr><tr><td>(Meng et al., 2018)</td><td>Primary and secondary data</td><td>Industrial Scale (Scaled up)</td><td>-</td><td>218.04</td><td>94.8 %</td><td>vCF tensile modulus = 230.00 GPa</td></tr><tr><td colspan="7"><strong>Thermally Activated Oxide Semiconductors</strong></td></tr><tr><td>(Cheng et al., 2022)</td><td>Primary and secondary data</td><td>Industrial Scale (Scaled up)</td><td>-</td><td>-</td><td>97.0 %</td><td>Properties are measured</td></tr><tr><td colspan="7"><strong>Combined Pyrolysis and Solvolysis</strong></td></tr><tr><td>(Merlo-Camuñas et al., 2024)</td><td>Primary Data (Innovation Tree company)</td><td>Industrial scale</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>(Dong et al., 2018)</td><td>(AKESSON et al., 2013; Lester et al., 2004; Suzuki and Takahashi, 2005)</td><td>Unspecified</td><td>-</td><td>-</td><td>-</td><td>-</td></tr></tbody></table>

benzyl alcohol (solvent) and K3PO4 (catalyst) at 190 °C for 2 h, dissolving the resin and freeing the CFs. The fibers are rinsed in water, and the solvent is recovered via distillation. A heat treatment at 350 °C for 1 hour removes any remaining fillers, yielding fully reclaimed CFs.

Supercritical solvolysis uses supercritical fluids to break down the polymer matrix. In supercritical hydrolysis, water is heated above 374 °C and pressurized to 25 MPa, creating supercritical conditions that hydrolyze the resin. A heat exchanger recovers energy from the outgoing water, while methane valorization captures methane from the

wastewater (Pillain et al., 2019). In supercritical n-butanol solvolysis, the system is heated to 330 °C at 6.5 MPa for 60 min, decomposing the resin. The CFs are cleaned with acetone, and n-butanol is transformed into a liquid product, which is burned for energy (Liu et al., 2021).

Fluidized bed recycling is a thermal process that breaks down the polymer matrix in a heated silica sand bed. The reactor operates at 450–550 °C, decomposing the matrix into volatile gases while the CFs remain intact. A cyclone separates the fibers from the gas stream, and the gases are oxidized to recover energy. The fibers are then ready for
reuse, and the recovered energy makes the process less energy-intensive (Meng et al., 2018).

Thermally activated oxide semiconductor (TASC) employs metal oxides, such as chromium oxide (Cr₂O₃), as catalysts to degrade the resin in CFRP. The system is heated to 500 °C, with an oxygen-nitrogen gas mixture controlling the flow rate. The resin decomposes, leaving the CFs intact after 25 min (Cheng et al., 2022).

Pyrolysis is a thermal process for CFRP recycling, where the polymer matrix is decomposed in an oxygen-free environment. The CFRP scrap is heated to 550 °C for 3.7 h, converting 90 % of the epoxy resin into coal tar, while the remaining 10 % is treated at the same temperature for an additional 2.2 h. The CFs are then air-cooled for 0.5 h, resulting in long, clean fibers. Energy generated from coal tar gasification helps sustain the process (Kawajiri and Kobayashi, 2022).

Electrodynamic fragmentation is a mechanical process using high-voltage electric pulses in water to separate fibers from CFRP. The CFRP sample is immersed in deionized water between electrodes, and electric pulses generate shock waves, fracturing the resin and releasing the CFs. The fibers are separated from the water and resin residue. Depending on conditions, some resin may remain, requiring further treatment (Pillain et al., 2019).

Lignin-based CF technology produces CFs from lignin, a renewable byproduct of biomass. Two methods are used: one extracts lignin from wood chips, and the other from corn stover (Hermansson et al., 2023; Kulas et al., 2021). Purified lignin undergoes melt spinning, where it is heated and extruded into fibers. Stabilization occurs at lower temperatures than in PAN-based processes, requiring less energy. Carbonization in an inert atmosphere results in CFs emitting biogenic CO₂ and methane.

## 2.4. Goal and scope harmonization

### 2.4.1. The aim of the study

This study serves two main purposes. First, it aims to conduct a meaningful assessment of the environmental impacts and energy demands of technologies for producing both vCF and rCF. This is done by identifying LCIs that accurately represent real-world processes by adhering to the criteria outlined in Section 2.2. Once these inventories are identified, any modelling discrepancies that impede comparison are resolved, such as the functional unit, system boundary, and background data selection. Second, it provides a benchmark that future researchers can use to compare their specific technology for producing vCF or rCF against those documented in the literature. This is done by sharing all inventories including ecoinvent datasets corresponding to each input and output to achieve good model reproducibility.

### 2.4.2. Functional unit

In selected LCA literature on CFs, see Table S2, three main types of functional units are commonly used:

* Application-Based: Defined as a single unit or piece of an aircraft or automotive part.

* Waste Treatment-Based: Defined by the mass of composite waste to be treated.

* Product-Based: Defined as one kilogram of CF produced.

However, these functional units do not fully address all the qualitative and quantitative aspects recommended by LCA guidelines (Bjørn et al., 2018b), which is addressed by answering the following questions: "what?", "how much?", "for how long/how many times?", "where?", and "how well?". For instance, defining the functional unit as one kilogram of CF addresses the "what?" and "how much?" questions but fails to consider the others. The question "for how long/how many times?" relates to the product's lifespan. This is sometimes included in application-based functional units but is often missing in the other types (Benitez et al., 2021; Hermansson et al., 2022a; Shanmugam et al., 2019). The "where?"

question, which refers to the geographical scope, is usually determined in the scope definition section. Additionally, the last question, "how well?", pertains to the quality of the product, and this is frequently ignored. For example, when that mass of composites waste to be treated is selected as the functional unit, the quality of the products resulting from recycling, landfilling, or incineration is not considered. In some LCAs, rCFs are assumed to have the same quality as vCFs to enable comparison (La Rosa et al., 2021, 2018; Pillain et al., 2019). However, these studies often fail to specify exact mechanical properties or even provide a range of values. This omission is significant because some studies compare rCFs produced by pyrolysis or other recycling technologies with PAN-based CFs using one kilogram of fibers as the benchmark (Bayram et al., 2024). This approach ignores the fact that rCFs obtained through pyrolysis have inferior mechanical properties and cannot directly replace PAN-based CFs. Failing to account for this difference undermines the validity of such comparisons.

In this study, we use 1 kg of carbon fiber produced as the functional unit. This unit explicitly defines "what?" as carbon fibers and "how much?" as 1 kg, while indirectly addressing "how well?" by classifying CFs into three tiers based on equivalent mechanical performance. It also considers "for how long?" by assuming that CFs within the same tier will have similar lifespans when used in the same application. Since the exact lifetime of CFs depends on their specific application, we assume that fibers of similar quality will have comparable durability. Finally, the geographic scope of the study is limited to CFs produced and recycled in Europe, addressing the question of "where?". Accordingly, background processes, such as electricity mixes, have been selected to align with the European region whenever possible.

We classify technologies into three tiers based on the quality of the fibers they produce, see Table 1 and Table 2. This tiered classification allows for technologies yielding CFs with equivalent functionality to be compared, while CFs from different tiers are not. First-tier technologies, such as PAN-based processes, supercritical solvolysis, TASC, supercritical hydrolysis, fluidized bed, and solvolysis, retain at least 94 % of the mechanical properties (i.e., tensile strength or tensile modulus) compared to vCF. Second-tier technologies, including pyrolysis and electrodynamic fragmentation, produce rCF with mechanical properties ranging between 80 % and 94 % of vCF. Finally, third-tier technologies are limited to lignin-based CFs, which exhibit relatively low modulus and strength, with values of 172 GPa and 1720 MPa, respectively (Chen, 2014; Souto et al., 2018). This classification is driven by the quality of the CFs produced by each technology.

### 2.4.3. System boundaries

A cradle-to-gate approach is applied in the assessment and comparison of vCFs to rCFs. In other words, recycling technologies are considered as alternatives to primary production. This approach includes all processes during material acquisition and processing while excluding those in the use and end-of-life stages. Notably, there is no standardized set of stages across the published inventories, as the processes vary depending on the technology, especially in emerging recycling technologies under continuous development. Some inventories include the impact arising from shredding or transportation (Cheng et al., 2022; Liu et al., 2021; Pillain et al., 2019), these steps have been excluded as shredding CFRP will likely become unnecessary at industrial scale and transportation has been considered by using "market for" ecoinvent datasets.

<caption>**Table 2**</caption>

Classification of CFs into three tiers based on the retention of mechanical properties relative to virgin PAN-based CFs.

<table><thead><tr><td>CFs Tiers</td><td>Properties Retention [ %]</td></tr></thead><tbody><tr><td>First Tier</td><td>100–94</td></tr><tr><td>Second Tier</td><td>94–80</td></tr><tr><td>Third Tier</td><td>Well Below 80 %</td></tr></tbody></table>
The cut-off allocation approach is consistently applied when modeling recycled materials, assuming that composite waste is treated as a burden-free material. This approach, adopted by all selected inventories, does not attribute any environmental credit to primary production technologies, offering no incentive for manufacturers to recycle. We adhere to this approach because many recycling technologies do not yet produce rCF of equal quality to vCF (Zhang et al., 2023), as recommended by the British Standards Institute in PAS 2050:2011 (British Standards Institute, 2011; Hermansson et al., 2022a). This approach also allows for a direct comparison between vCFs and rCFs, without artificially inflating or deflating the environmental impacts associated with their production.

If environmental credits are granted, they should be distributed between fiber manufacturers and composite manufacturers. CF recyclability is determined not only by fiber producers but also by composite manufacturers, as the recovery rate and the fibers' ability to retain mechanical properties after recycling often influenced by the matrix material (Zhang et al., 2020). To permit the sharing of environmental credits, the system boundary should be expanded to include the whole composite material, although this lies outside the scope of the current study. This approach would promote collaboration between fiber and composite producers in designing fibers and matrices that result in recyclable composites

### 2.4.4. Life cycle impact assessment method and database

The life cycle impact assessment was performed using ReCiPe v1.03, midpoint (H), with Cumulative Energy Demand (CED) included as an additional metric. ReCiPe (Huijbregts et al., 2017), adapted to the European geographical scope, classifies environmental impacts into 18 midpoint indicators related to ecosystems, human health, and resources. CED, (Frischknecht et al., 2007), is particularly relevant due to the high energy consumption associated with CF production (Das, 2011), making it crucial for identifying energy usage across different technologies and pinpointing hotspots for improvement. Modeling was based on the ecoinvent 3.9.1 cut-off database (Wernet et al., 2016), and calculations carried out in Microsoft Excel.

## 2.5. Life cycle inventory harmonization

### 2.5.1. Foreground data inventory

The foreground system consists of all processes required to deliver the flows and services contributing to the investigated product output. This includes physically embodied flows, supporting service flows, and flows essential for infrastructure production and maintenance—although infrastructure-related flows are often excluded from system boundaries of product LCAs. These flows can originate or end up in either the Technosphere (e.g., transformed materials, waste, and energy) or the Ecosphere (e.g., naturally available materials and emissions) (Bjørn et al., 2018a). All data points identified as part of the foreground system are detailed in tables referred to as the inventory of inputs and outputs, which are organized into discrete stages and available in the SI. These inputs and outputs are linked to background processes using LCI databases, specifically ecoinvent in this study.

Foreground data may come from various sources, such as secondary data from research articles and patents or primary data obtained from on-site measurements and interviews with professionals. The most reliable data is primary data from industrial or pilot-scale operations, which were preferred during the LCI screening process, Section 2.2. Some of the selected inventories rely on primary data from industrial-scale processes, while others used pilot- or lab-scale data due to the current state of technological maturity, Table 1. In these instances, the original authors have scaled-up their inventories to emulate industrial production using theoretical energy calculations and assumptions about material recyclability. This approach ensures the highest quality of published inventories available at the time of this study. However, it should be noted that CF technologies vary between developers,

including differences in process parameters and the quality of CFs, which can result in variations in environmental impacts. Therefore, it is important to recognize that an inventory based on one type of technology—even mature ones, such as PAN-based CFs—may not be representative of other practices. This is especially true for emerging recycling technologies like supercritical solvolysis, where continued research and development will expand our knowledge of the technologies, leading to potentially different outcomes.

### 2.5.2. Background data selection

Background data refers to data points not specific to the investigated product output, often shared across many products and sectors. Examples include solvents, water, electricity and other common materials. In this study, background data is sourced from ecoinvent, a leading LCI database.

The selection of background processes has been harmonized across the 10 selected inventories. Flows of the same nature—such as electricity and tap water—were linked to different datasets corresponding to the geographical scope of the original study. As part of this harmonization, datasets specific to the European geography were chosen for all inventories. In some cases, certain inventories did not specify which ecoinvent datasets were used, which undermines reproducibility, see Table 4. For these instances, the most appropriate dataset was selected based on the input/output descriptions. All ecoinvent datasets used for remodeling are provided in the supplementary information, facilitating future efforts for remodeling and comparison.

Proxies were selected for six background processes due to the absence of identical representations of these activities in the ecoinvent database. Three of these proxies were used in the reproduced inventory for lignin production (Hermansson et al., 2023). Although the authors documented the ecoinvent datasets used to model the inputs, they did not document how the outputs are modelled. As a result, we selected the most suitable datasets based on the description of the outputs. For hemicellulosic sugars, C6 sugars, and unspecified waste, we used the dataset “market for biowaste | ROW.” This choice is justified by the organic nature of these materials (e.g., wood chips) and their potential for energy production, especially through the fermentation of hemicellulosic and C6 sugars (Ardolino et al., 2018; da Conceição Gomes et al., 2019). Another proxy was chosen in the reproduced inventory for the solvolysis recycling technology, specifically for tripotassium phosphate (Kawajiri and Kobayashi, 2022). Although an ecoinvent dataset exists for trisodium phosphate, it was deemed unsuitable for representing the impacts of tripotassium phosphate due to the lower environmental impact of the reactants used to produce trisodium phosphate (Wernet et al., 2016). Instead, we selected potassium carbonate as a proxy. This material is assumed to be recycled into the process with a 95 % efficiency. As such, any error introduced by the proxy selection has a minimal effect on the overall technology’s impact. The final two proxies were used in the reproduced inventory for the TASC technology (Cheng et al., 2022). Nitrocellulose was represented by the dataset “market for cellulose fibre | RoW,” and cordierite, a silicate mineral, was represented by “market for feldspar | GLO.” While these proxies are not ideal for accurately reflecting the impacts of nitrocellulose and cordierite, the quantities involved (1.08 g per 1 kg of rCFs) are so small that their impact is negligible, particularly when considering the electricity consumption and other process flows.

## 3. Results and discussion

### 3.1. Climate change impact (CCI)

The CCI category is frequently discussed in LCA studies, expressed in terms of kg CO₂-equivalent. Stages are shown for PAN-based, supercritical n-butanol, and solvolysis while for the other technologies it shows the input material/process, as only three authors have specified technology stages in their assessment. Notably, all emissions, such as
carbon dioxide, carbon monoxide, among others have been grouped under emissions.

Starting with first-tier technologies (Fig. 1), PAN-based CFs have the highest CCI, emitting 69.33 kg CO₂-eq. Supercritical n-butanol ranks second, emitting 41.34 kg CO₂-eq, followed by TASC, fluidized bed, solvolysis, and supercritical hydrolysis, which emit 5.38, 2.38, 1.95, and 0.05 kg CO₂-eq, respectively.

The high CCI of PAN-based technology is largely due to the polymerization of acrylonitrile into polyacrylonitrile, as well as the carbonization and stabilization of fibers. When linked to materials and processes, over 90 % of PAN-based CFs' high CCI is attributable to emissions, electricity, heat, and acrylonitrile production.

For supercritical n-butanol, the CF reclamation stage, which involves the transformation of n-butanol into a liquid product that is later incinerated to recover energy, contributes significantly to its CCI. While energy recovery reduces n-butanol's impact from 47.29 to 36.71 kg CO₂-eq (−10.58 kg CO₂-eq attributed to energy recovery), the impact remains substantial.

In the case of TASC and supercritical hydrolysis, energy consumption is the primary contributor to their environmental impacts. For fluidized bed technology, emissions play a major role. In solvolysis, the high impact occurs during the depolymerization stage, but this is offset during the distillation stage, where 95 % of the solvent, benzyl alcohol, is recovered.

Notably, in both supercritical solvolysis technologies, the authors assume that energy will be recovered. In the case of n-butanol, energy recovered from the liquid product (29.73 kWh) exceeds the total electricity used in the process (19.53 kWh). When water is used as a solvent, energy recovery reaches 99 %. However, in both cases, the solvents are not recyclable.

For the second-tier technologies (Fig. 2), the pyrolysis of CFRP is an energy-autonomous process, recovering 12.59 MJ for every 1.76 MJ consumed. This makes pyrolysis the only recycling technology with a negative CCI of −0.14 kg CO₂-eq. In contrast, the CCI of electrodynamic fragmentation is 0.73 kg CO₂-eq, with 82 % of this impact attributed to electricity consumption, as high-voltage electric pulses are generated in water to separate fibers from CFRP.

For third-tier technologies, such as lignin-based CFs, the choice of raw material significantly affects the process and its corresponding CCI. Producing 1 kg of CFs from corn stover emits 16.82 kg CO₂-eq, compared to 21.21 kg CO₂-eq when produced from wood chips. This difference is primarily due to the processes used to convert these materials into lignin. Corn stover requires large amounts of steam and heat to be transformed into a useful product, while wood chips result in high amounts of hemicellulose sugars, C6 sugars, and unspecified organic waste, modeled as biowaste (see Section 2.5.2). Notably, the LCIA method used does not account for the contribution of biogenic carbon to climate change.

## 3.2. Other environmental impact categories

Most environmental impact categories align closely with the trends observed in the CCI. However, exceptions exist, as illustrated in Fig. 3 and detailed in Table 3. Table 3 provides quantified results for all 18 impact categories assessed under the ReCiPe method, along with cumulative energy demand. This data highlights potential trade-offs between impact categories, clearly quantifying environmental advantages (e.g., reduced carcinogenic human toxicity) and disadvantages (e.g., increased resource depletion) to inform decision-making.

For first-tier technologies, PAN-based vCFs have the highest impact across most categories, except for non-renewable energy resource consumption and carcinogenic human toxicity. These two unrelated exceptions are observed in two distinct technologies. In supercritical n-butanol, the consumption of fossil fuels is higher than that of PAN-based vCFs, as n-butanol is produced from fossil fuels. Specifically, 22.13 kg oil-eq is consumed in supercritical n-butanol recycling, compared to 19.14 kg oil-eq for PAN-based vCFs. However, burning the liquid byproduct from n-butanol to recover energy has a positive effect on ionizing radiation, as fossil energy substitutes nuclear power in European electricity grid. In fact, producing 1 kg of rCF through supercritical n-butanol solvolysis results in −2.47 kBq Co-60-Eq in ionizing radiation. Carcinogenic human toxicity is highest for the TASC technology, primarily due to the use of chromium trioxide, which is modeled as chromium oxide. Although only about 0.02 kg of chromium trioxide is used

<figure><img src="image_3.png" /><figcaption>Fig. 1. Climate change impact comparison of first-tier technologies, including a contribution analysis based on different stages or materials/processes involved.</figcaption></figure>
Second Tier: 94-80% of Virgin
Carbon Fibers Tensile Strength

<figure><img src="image_4.png" /><figcaption>Fig. 2. Climate change impact comparison of second- and third-tier technologies, with a contribution analysis based on the materials and processes involved.</figcaption></figure>

per kilogram of rCF produced, it significantly impacts carcinogenic human toxicity, resulting in 2.76 kg 1,4-DCB-Eq, compared to 2.12 kg for PAN-based production.

For second-tier technologies, namely pyrolysis and electrodynamic fragmentation, pyrolysis shows higher impacts in four out of 19 categories: particulate matter formation, photochemical oxidant formation (both human health and terrestrial ecosystems), and material resource use (metals/minerals), see Table 3. This is mainly due to the formation of coal tar during the pyrolysis process. While pyrolysis has a positive impact on CCI, half of the remaining categories (9 out of 19) show impacts that are approximately 60 % of those for electrodynamic fragmentation.

In the case of third-tier technologies, lignin-based vCFs, eight impact categories follow the same trend as the CCI, see Table 3. The environmental impacts of corn stover-based CFs range from 40 % to 80 % of those for wood chips-based CFs. Additionally, corn stover CFs exhibit positive impacts in three categories: land use (–73.1 %), ozone depletion (–46.1 %), and water use (–0.2 %). These benefits arise because wheat bran, a byproduct of corn stover processing, offsets some of the environmental burdens.

All the environmental impacts discussed in this paragraph are influenced by the process parameters. This is especially true for emerging technologies like pyrolysis, solvolysis, and supercritical solvolysis, where a wide range of parameters are being tested, as shown in Table S3.

4. Reproducibility

Harmonizing published LCIs requires the ability to reproduce the
original results through remodeling and recalculating the environmental
impacts. This process highlights the challenges of reproducibility and
the potential for errors. In Section 2.1, we identified the available

Third: Well Below 80% of Virgin
Carbon Fibers Tensile Strength

<figure><img src="image_5.png" /></figure>

inventories—those that included the bare minimum of information such
as inputs, outputs, and their corresponding values. However, simply
providing this information does not guarantee perfect reproducibility.
Other critical parameters include the functional unit, the reference flow,
the LCIA method, the database used, the exact datasets selected to model
each flow, and the results for not only the main product system but also
its components and materials.

Out of the 28 articles that provided inventories, only one article did not specify the LCIA method, and seven did not identify the database used (see Table S2). All articles defined a functional unit (see Table S2), but 13 of them did not report specific environmental impacts related to CFs (see Table 4), as their focus was on the products rather than CFs specifically. A summary of information presented in Table 4 is available for the selected articles in Table 1.

The reproducibility errors, defined as the percentage difference be-
tween the impact calculated by the original authors and the impact
reproduced in this study, are presented in Table 4. When articles fail to
specify the datasets used to model input/output flows, the reproduc-
ibility error can reach as high as ±400 % (Kawajiri and Kobayashi,
2022; Meng et al., 2018). This is especially true for inventories with only
a few inputs/outputs, where selecting a different dataset than the one
used by the original authors can result in drastically different outcomes.
Of the 28 articles, eight specified both the database and the datasets used
for modeling (Bayram et al., 2024; Benitez et al., 2021; Hermansson
et al., 2023, 2022b; Karuppannan Gopalraj et al., 2021; La Rosa et al.,
2021; Sommer et al., 2022; Wu et al., 2023), and five of these also
provided environmental impacts specific to a given mass of CF pro-
duced, see Table 4. Unfortunately, three of these five articles referenced
datasets that could not be found in the database they claimed to have
used (Karuppannan Gopalraj et al., 2021; La Rosa et al., 2021; Sommer
et al., 2022), leading to reproducibility errors of up to 204 %.

Only two inventories were reproduced with high precision: the
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** A comparison of Thermally Activated Oxide Semiconductor (TASC) and Supercritical N-Butanol recycling technologies with traditional PAN-based carbon fiber production illustrating lower environmental impact in terms of carcinogenic human toxicity and non-renewable energy consumption.</figcaption></figure>

inventories by (Benitez et al., 2021) and (Bayram et al., 2024) for PAN-based CFs. Bayram's inventory was reproduced with a relative error of just 0.25 %, likely due to rounding errors in a word file where numbers were limited to two decimal places. Benitez's inventory, on the other hand, showed a relative error of 11.15 %, mainly due to uncertainties in how certain outputs were modeled—whether as waste or byproducts—because the appropriate datasets were not specified. Additionally, Benitez used an older version of ecoinvent (v3.3) compared to the v3.9.1 used in this study, which contributed to the error.

For optimal reproducibility, it is recommended to communicate the following clearly: the functional unit, the reference flow, the LCIA method, the database used, the exact datasets selected to model each flow, and the results for not only the main product system but also its components and materials. This is particularly important when modeling these elements as part of the published LCA.

## 5. Conclusions

This paper is motivated by prior harmonization efforts in the context of bottled wine, it explores harmonization in emerging technologies, focusing on CFs. By reproducing high-quality, industrial-scale inventories, it enables meaningful comparisons between CFs with similar mechanical properties. The harmonization process involved standardizing the steps considered across selected inventories, which led to the exclusion of shredding and transportation from certain cases. Additionally, ecoinvent datasets specific to European geography were used to ensure consistency.

It is important to note that all environmental impacts presented in this work are probable estimates, inherently tied to the data used for their calculation. As the assessed technologies continue to develop, these estimates may evolve and could deviate from the scaling-up assumptions used to model their environmental impact at a larger scale. Therefore, the reported potential environmental impacts should be considered provisional rather than definitive.

It was found that not all CF technologies could be compared directly, as often CFs vary in quality. To address this, three tiers were established to compare CFs with similar mechanical properties and potential for similar applications. Supercritical hydrolysis emerged as the first-tier technology with the lowest CCI, while pyrolysis was classified as a second-tier technology and the only one with a positive CCI. However, it is crucial to consider the modeling assumptions underlying these results, as both processes assumed significant energy recovery. Lignin-based CFs were categorized as third-tier technology, as they produce CFs of lower quality compared to PAN-based CFs.

This work demonstrates the value of harmonization studies, particularly in emerging fields where numerous studies exist, but comparability is limited due to inconsistent modelling choices. Examples of such fields include natural fibers, perovskite solar cells, etc. Additionally, the study reviewed the state of reproducibility in LCA research within the CF field and provided recommendations for improving reproducibility. Future research could leverage these harmonized inventories to compare with other CFs production and recycling technologies.
CRediT authorship contribution statement

**A.Kamal Kamali:** Writing – review & editing, Writing – original draft, Visualization, Software, Methodology, Data curation, Conceptualization. **Javid Isayev:** Visualization, Software, Methodology, Data curation. **Bertrand Laratte:** Writing – review & editing, Supervision. **Guido Sonnemann:** Writing – review & editing, Validation, Supervision.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

A. Kamal Kamali reports financial support was provided by French National Research Agency. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.resconrec.2025.108323.

Data availability

Data is available in the supporting information

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