Hydrogen-based fuels are potential candidates to help international shipping achieve net-zero greenhouse gas (GHG) emissions by around 2050. This paper quantifies the environmental impacts of liquid hydrogen, liquid ammonia, and methanol used in a Post-Panamax container ship from 2020 to 2050. It considers cargo capacity changes, electricity decarbonization, and hydrogen production transitions under two International Energy Agency scenarios: the Stated Policies Scenario (STEPS) and the Net Zero Emissions by 2050 Scenario (NZE). Results show that, compared to the existing HFO ship, hydrogen-based propulsion systems can decrease cargo weight capacity by 0.3 % to 25 %. In the NZE scenario, hydrogen-based fuels can reduce GHG emissions per tonne-nautical mile by 48 %-65 % compared to heavy fuel oil by 2050. Even with fully renewable hydrogen-based fuels, 18 %-31 % of GHG emissions would still remain. Using hydrogen-based fuels in internal combustion engines requires attention to minimize environmental trade-offs.

The demand for mineral raw materials for the energy transition is expected to increase greatly in the coming years, raising concerns about the environmental impacts associated with their value chains. Prospective Life Cycle Assessment (pLCA) can be applied to explore such impacts under alternative future scenarios. However, pLCA studies in the mineral value chain often lack consistency, as they use different modelling approaches and a heterogeneous set of evolving parameters. This study develops a systematic approach to guide prospective life cycle inventory (LCI) modelling and the consistent use of parameters in the mineral value chain. The approach builds on the SIMPL approach for scenario-based inventory modelling for pLCA by adding extensions specific to mineral value chains, resulting in the SIMPL-Minerals (SIMPL-M) approach. These extensions include defining stages and processes of mineral value chains, identifying parameters that connect to these stages, and linking parameters to macro-level factors that influence their future development. The identified parameters were validated through semi-structured interviews with experts. Furthermore, the SIMPL-M approach is demonstrated through a case study on future global nickel sulfate supply for battery production. The SIMPL-M identifies 19 relevant parameters that should be considered in pLCA studies of mineral value chain. These parameters depend on deposit characteristic, technology, and geography, and cover both primary and secondary value chains, from ore extraction or waste collection to final raw material product. The parameters are connected to a generic inventory model blueprint that shows to which stage they apply. The parameters are also linked to macro-level factors, enabling consistent scenario generation and coherent assumptions about future development of parameters. Applying the SIMPL-M approach in the nickel sulfate case study shows that including parameters beyond standard LCI background data significantly affects impact assessment results, as those result in a 35–38

Road transportation is responsible for one fifth of European Union's total greenhouse gases (GHGs), besides other environmental concerns. Thus, Life Cycle Assessment (LCA) is increasingly used in the automotive sector to guide environmental strategies and policy compliance, emphasizing the importance of methodological choices and their standardization. This study examines three recent and influential LCA guidelines in Europe developed through major harmonization initiatives: TranSensus LCA, Catena-X, and the United Nations Economic Commission for Europe Automotive LCA guidelines. A qualitative comparison of methodological choices and assumptions in these guidelines was conducted to identify areas of overlap, divergences, and flexibilities within each guideline. The analysis showed broad alignment across guidelines, with divergences mainly in electricity modeling and addressing multifunctionality problems, where also degrees of freedom within guidelines remain. Applied to a battery electric vehicle LCA, a quantitative comparison across guidelines (based on a basic expected application of each guideline) demonstrated less than a 10 % difference in most impact categories. Furthermore, the intra-guideline choices (flexibilities) were tested in the LCA model, showing larger variations relative to the basic application of each guideline (e.g., -27 % and +11 % change in climate change impacts when shifting to the Circular Footprint Formula (CFF) and static electricity modeling, respectively, in UNECE guidelines). These findings suggest that horizontal harmonization across guidelines is well advanced, but vertical harmonization within guidelines requires improvement. Future improvements could include more detailed guidance in some parts like CFF application to reduce subjectivity, automation of application, and comprehensiveness in impact categories and life cycle stages coverage.

Waste Electrical and Electronic Equipment (WEEE) is the EU's fastest-growing waste stream. Rapid innovation and changing product types result in highly variable material compositions, complicating collection, pretreatment, and recovery. We analyze the outflow quantity of 59 temporally changing elements across 54 products in the EU-27 from 2006 to 2021, quantifying country- and product-specific contributions. We estimate post-use element losses in collection, sorting, and recovery and find that 33 million tons (56%) of the cumulative element mass is not collected, making insufficient formal collection the main driver of losses. A further 9% cumulative quantity is removed during pre-treatment, and overall only 28% are recovered. Specialty elements exhibit the lowest recovery rates, mainly due to missing recycling technologies. Sixteen products, such as flat panel monitors, desktop PCs, and household luminaires, contain particularly high element quantities. Prioritizing recovery from these products could reduce EU geopolitical dependence, close circular economy gaps, and lower environmental emissions.

Hydrogen-based fuels are expected to support maritime shipping in reaching net-zero climate targets. However, the complexity of hydrogen-based fuel supply, propulsion system deployment, and fleet composition make their full life cycle decarbonization potential unclear. A comprehensive fleet-level assessment of their decarbonization potential is thus essential. Here, we evaluate the life cycle climate change impact of global container shipping using hydrogen-based fuels from 2020 to 2050, considering fuel mix, propulsion system, ship size and transport demand. By integrating energy scenarios from the International Energy Agency with socio-economic scenarios from the Shared Socioeconomic Pathways and the Organization for Economic Co-operation and Development, we explore three scenarios that represent different levels of ambition for the future hydrogen production transition, hydrogen-based fuel use, and corresponding transport demand: the Less Ambitious, Ambitious and Very Ambitious scenarios. Our findings indicate that container shipping's greenhouse gas (GHG) emissions per tonne-nautical mile could decrease from 22 g CO2-eq in 2020 to 21 g, 9 g, and 3 g CO2-eq by 2050 under the Less Ambitious, Ambitious, and Very Ambitious scenarios, respectively. Cumulative GHG emissions from global container shipping could reach 9-12 Gt, 7-10 Gt, and 4-5 Gt CO2-eq between 2020 and 2050 across these scenarios, accounting for 1-3% of the global carbon budget required to achieve the worldwide net-zero target. The substitution of heavy fuel oil with hydrogen-based fuels does not always lead to a reduction in GHG emissions: in the Less Ambitious scenario, cumulative emissions increase by 0.4-0.6 Gt CO2-eq due to the slow decarbonization in hydrogen production, whereas in the Ambitious and Very Ambitious scenarios, they decline by 1-2 Gt and 3-5 Gt CO2-eq, respectively. Deep decarbonization of maritime shipping requires overcoming key bottlenecks in renovating the fleet, scaling up ammonia production and electrolyzer capacity, and ensuring sufficient renewable electricity supply. This highlights the need for coherent policies to foster multi-sectoral coordination among maritime shipping, hydrogen-based fuel production, and power generation to maximize their decarbonizing potential.

Integrated Assessment Models (IAMs) are increasingly used to generate prospective Life Cycle Inventory (pLCI) databases for prospective Life Cycle Assessment (pLCA). This offers advantages, such as reducing temporal mismatches and representing sector-wide mitigation. However, studies often show limited awareness of associated limitations. We aim to raise awareness of these and provide practice-oriented recommendations for the informed and responsible use of IAM-based pLCI databases in pLCA. Drawing on literature from both the IAM and LCA communities and recent conceptual discussions on the limitations of IAMs in the context of pLCA, we identify key characteristics, and limitations of IAMs relevant for pLCA practice. Based on the authors’ experience and literature, we derive practice-oriented recommendations for the informed and responsible use of IAM-based pLCI databases. We identify nine key IAM characteristics that pLCA users need to be aware of, including sectoral perspective, focus on climate change, weak representation of material cycles, techno-optimism, and narrow economic paradigms. We offer four main recommendations: using IAM-based pLCI databases selectively and purposefully; focussing on a limited set of diverse scenarios; interpreting results critically; and ensuring transparency and reproducibility. We emphasise treating IAM-based pLCI databases as exploratory reasoning tools rather than predictive models by acknowledging the limitations, using systematic approaches to scrutinise results, and exercising caution when interpreting. IAM-based pLCI databases are valuable for exploring future environmental impacts but require careful, informed application. Their limitations must be explicitly acknowledged, and results interpreted as conditional insights rather than predictions. When used responsibly and transparently, these tools support sustainable decision-making. Future research should evaluate which IAMs provide sufficient technical detail for pLCI integration and expand the diversity of economic paradigms represented. Furthermore, efforts should focus on developing formal decision rules linking IAM characteristics to concrete modelling choices, ensuring a more systematic application of IAM data in pLCA.

Prospective life cycle assessment (pLCA) is increasingly used to assess the potential environmental impacts of product systems under future scenarios. However, addressing uncertainties arising from future variability in multifunctional processes remains a methodological challenge. For example, waste heat that is considered nonfunctional at the laboratory scale may become functional at larger scales. While some pLCA case studies have addressed these issues, a systematic and comprehensive approach for identifying and handling multifunctional processes remains lacking. This paper introduces a structured, stepwise guidance that builds upon and complements existing methods for addressing multifunctionality under future scenario development in pLCA. Focusing on the uncertainty due to choices and epistemic uncertainties, the guidance covers the identification of relevant inventory parameters such as flow types, the number of functional flows, substituted products, substitution ratios, and allocation factors. Applying this framework to a case study on novel incinerator bottom slag valorization reveals that pLCA results are sensitive to future assumptions regarding potential variability of these inventory parameters. The proposed approach allows for a more comprehensive and transparent assessment of multifunctionality in pLCA, thereby supporting consistent environmental decision-making.

Fuel cells have the potential to reduce greenhouse gas (GHG) emissions from deep-sea shipping. To fully understand the environmental impacts of integrating fuel cells into deep-sea ships, this study evaluates the life cycle environmental impacts from 2020 to 2050 for two leading fuel cell systems: liquid hydrogen with proton exchange membrane fuel cells (liquid-H2 PEMFC) and liquid ammonia with solid oxide fuel cells (liquid-NH3 SOFC). The study covers various factors, including changes in cargo capacity, operation modes, developments in hydrogen production and electricity decarbonization. We examine two energy scenarios developed by the International Energy Agency: the Stated Policies Scenario (STEPS) and the Net Zero Emissions by 2050 Scenario (NZE). Our findings reveal that, under different ranges and speeds, the liquid-H2 PEMFC results in a 2% increase to a 10% decrease in cargo weight, while the liquid-NH3 SOFC leads to a 4%-23% decrease. By 2050, under the NZE scenario, liquid-H2 PEMFC and liquid-NH3 SOFC can reduce GHG emissions per tonne-nautical mile by 69%-75% and 65%-71%, respectively, compared to traditional ships. The use of fuel cells also introduces environmental trade-offs. This assessment can help policymakers gain a more comprehensive understanding of the role of fuel cells in reducing GHG emissions in deep-sea shipping and underscores the potential environmental challenges associated with their large-scale deployment in the future.

Machine learning (ML) offers considerable opportunities for advancing life cycle assessment (LCA), yet a consolidated overview of ML models with potential for prospective LCA (pLCA) is still missing. This systematic review identifies ML models suited to pLCA's data needs and assesses the transferability of retrospective LCA models to pLCA. We apply keyword-based and automated forward/backward searches to identify studies based on algorithm type, training datasets, input/output variables, and performance metrics. We identify 50 publications and classify them into four ML application groups: (1) streamlined LCA, (2) life cycle inventory data prediction, (3) impact category prediction, and (4) characterization factor prediction. Among these, predicting life cycle inventory (LCI) data and characterization factor estimation are directly applicable to pLCA. Life cycle inventory data prediction and streamlined LCA represent the largest body of literature, offering benefits such as estimating missing unit process data, leveraging large external datasets, advanced simulation of process parameters, identifying molecular substitutes, or the employment of rapid screening tools. While characterization factor prediction shows potential, it requires further research for practical implementation. Overall, ML is rarely applied directly to pLCA. However, many ML models developed for retrospective LCA have the potential to be transferred to pLCA, either directly or with adaptations to data structures. This is most evident for predicting missing or technology-specific life cycle inventory data, where similarity-based learning, simulation-enhanced models, and large datasets offer scalable ways to estimate future inventory parameters. Across all groups, uncertainty is insufficiently assessed, with only a minority of studies addressing model, parameter, or scenario uncertainty. Overall, ML holds potential for pLCA when applied with methodological care and attention to underlying uncertainties.

Purpose Displacement factors (DFs) quantify climate benefits of wood-based products relative to non-wood alternatives. Existing DFs are typically static, focus on semi-finished materials, and lack harmonized, transparently documented modelling assumptions. Economy-wide decarbonization motivates a forward-looking assessment. Methods This study provides an open-access, unit-process-based database of future-oriented DFs for 81 wood-product-material-variants across six end-use categories. It uses time-explicit life cycle assessment (LCA), which links each process to the technology landscape at the time it occurs, under three decarbonization scenarios for 2020–2070. Results Wood-based products showed positive DFs in 2020, with weighted averages ranging from near-zero for furniture to 1.04 kg C/kg C for chemicals and textiles, though generally lower than previous estimates. DFs declined over time for all products except chemicals, most steeply under climate-ambitious scenarios. Wood sustained the highest benefit in the least ambitious scenario. The DF decline was driven by faster decarbonization of non-wood products, which contain proportionally more fossil-intensive components. Time-explicit LCA yielded lower DFs for long-lived products as end-of-life processes were assigned to more decarbonized future years. Energy-related DFs approached near-zero by 2070. Conclusion The findings support redirecting wood flows from energy toward long-lived material applications and caution against harvest intensification justified on displacement arguments alone.

Decarbonization targets for the maritime sector until 2050 require greenhouse gas (GHG) mitigation options like alternative fuels and on-board carbon capture (OCC). This study assesses GHG mitigation and side-effects of OCC via Life Cycle Assessment of two LNG-fueled ships, the large crane ship Sleipnir and an LNG carrier, and their distinct operational profiles. A large bandwidth of 32%-55% GHG emissions mitigation is achievable, depending on the ship, operation and design. Non-Climate impacts increase without raising major environmental concerns for the monoethanolamine-solvent OCC systems, based on the measured emissions from pilot operation, despite the limited applicability of current impact pathway modelling on open-sea context. With lower GHG mitigation potential compared to alternative fuels but potentially better availability due to less competition from other sectors, including OCC into the portfolio of intermediate decarbonization options for the marine sector is recommended, emphasizing case-by-case evaluation of life cycle emissions, especially fuel supply and methane slip affected by the operational profile.

Premise has been quickly adopted as a major tool for performing prospective LCAs (pLCA). Premise databases are generated by combining the ecoinvent database and data from climate change (CC) scenarios implemented in Integrated Assessment Models (IAMs). Both the extent of changes made with Premise and the focus on CC raise a question on how they affect LCA results. We aim to give practitioners better insight into how changes may affect results when using Premise databases. Our analysis of Premise consists of several steps. We begin by generating three Premise databases, one for 2020, and two for 2050 assuming different future changes: one that represents a continuation of current trajectories and policies (Base) and one that represents assuming ambitious decarbonization strategies (RCP 2.6). We analyze these databases based on 9 industrial products: Electricity, Heat, Road Transport, Steel, Aluminium, Copper, Concrete, Organic Chemical and Inorganic Chemical. We calculate results for each of them for 16 impact categories, although we focus on CC as the main driver of changes in these databases. We then assess impact change the different databases for each product. We do a contribution analysis where we analyze the contribution of 15 large groups of processes to these products. For CC, we find that impact changes for most products are related to impact reductions in electricity production and increased use of biomass and Carbon Capture and Storage. We find that Electricity, Heat, Road Transport, Steel and Concrete are primary contributors to their own respective impacts. Other products are instead mainly affected by these groups. Furthermore, the contribution from the Ore Minerals, Energy resources, Chemicals and Transport product groups is much higher when we include Indirect contributions (contributions ‘connected through’ another group, e.g. production of fuel for Transport), these product groups connect to impacts elsewhere in the system. In other impact categories we find much more varied results, though the uncertainty around these results is high, specifically ‘land use’ shows large changes in impact. Our approach can highlight products or product groups of interest in large databases like Premise, which can aid in modeling and finding errors. We suggest Premise to be used primarily for assessments of climate change impacts and practitioners clearly communicate versions and settings to make their work reproduceable. Despite us taking a critical stance toward some aspects of Premise, we still recommend using it for pLCA.

Building stock modeling can be used to identify trajectories that do not exceed the remaining carbon budget and support science-based pathways. A systematic approach is used from the field of prospective life-cycle assessment, which is based on systems thinking, to develop scenarios for the Austrian building stock that consider life-cycle greenhouse gas emissions. The influential parameters of the model are identified; their interactions are classified; quantitative future assumptions are adopted; and five scenario narratives are created. A maximum emission reduction of 90% from 2023 to 2050 is revealed. In comparison, leaving current policies in place would lead to a trajectory that reduces emissions by only 66%. Three additional scenarios achieve emission reductions between 84 and 86% by 2050, which may be compatible with the 2 °C carbon budget using an equal-per-capita approach. These scenarios represent different societal choices based on ambitious sufficiency (e.g., behavioral change), technological measures (e.g., a change in the industry), or both, with less effort from all actors. To ensure that Austria contributes to staying within the remaining carbon budget, policy makers are urged to systematically and quickly incorporate sufficiency into their policies and enable the necessary investments in carbon dioxide removal technologies.

Life-cycle assessment (LCA) is essential for sustainable chemical process design. However, current integrations treat LCA as a top-level environmental assessment tool, risking superficial integration and perpetuating conventional process design assumptions. This contribution reviews how LCA is integrated with model-based chemical process design. It focuses on current practices, challenges in goal and scope, modelling, computational, and interpretation integration, and discusses the integration of LCA's core features alongside profit-driven assumptions.The contribution identified more than 100 articles via a hybrid search method and reviewed 53 based on a saturation curve, resulting in 25 metrics to assess process design and LCA integration. To assist practitioners, a comprehensive classification of computational integrations is provided. The review highlights the following gaps: most studies (74%) focus only on cradle-to-gate phases, neglect use and end-of-life phases (89%) and do not define the function (92%). However, including a user function perspective could enhance the integration of use and end-of-life scenarios, supporting circular strategies and sufficiency measures. Additionally, environmental externalities are systematically excluded during model linkage, and most studies concentrate on energy utilities (75%) and material inputs (70%), whereas emissions (26%), waste, and wastewater (25%) are frequently overlooked, which emphasises the dominance of economic factors in current design studies.

While previous research has focused on developing prospective LCI databases that build upon projections from integrated assessment models (IAMs), until now only attributional databases have been developed. To construct consequential LCI databases, a novel approach is required that can be applied consistently on a large scale. To this end, the heuristic approach from Bo Weidema was selected as a basis for this study. This approach has been validated before with historical data and was adapted in this study to identify the marginal suppliers in a prospective context. The different steps within the approach were analyzed and alternative techniques for each step within the heuristic method were proposed. The techniques were tested out on the future electricity sector using projections from two IAMs (IMAGE and REMIND). Results showed how sensitive the results are to which technique is selected in each step. The most sensitive step is the selection of the time interval, with even small changes resulting in a noticeable difference. In addition the results also showed a substantial difference between the IAM projections. The relevance and goals of the alternative techniques for each step were discussed to guide users on forming the heuristic method for their study.

The European Union recently adopted ReFuelEU Aviation as a regulation to stimulate the use of alternative aviation fuels. We explore how this affects the climate impact of European aviation and its alignment with notions of successful climate change mitigation. Using stock-and-flow modelling and lifecycle assessment, we analyse the role of hydrogen in decarbonising the aviation system. We find that the adoption of alternative fuels alone does not guarantee successful mitigation, since the resulting temperature change can vary widely (2.2-8.9 millikelvin estimated by 2070) and most scenarios exceed CO2-based targets. Although alternative fuels can greatly reduce CO2 emissions and non-CO2 impacts-with hydrogen-powered aircraft yielding the largest reductions-persistent air traffic growth drives the near-term use of fossil resources and the long-term scale of non-CO2 effects. Therefore, we recommend reassessing aviation climate targets, including the consideration of non-CO2 effects in budget-based targets and stronger incentives to reduce near-term fossil kerosene use.

Lignin-based asphalt pavements are increasingly recognized for their potential to mitigate climate impacts through biogenic carbon sequestration, unlike degradable bio-products like wooden beams. However, oversimplifying lignin's temporal dynamics could lead to an overestimation of these benefits. We employ both static and dynamic life cycle assessments (LCAs) to evaluate the climate impacts of conventional and four types of lignin-based asphalt pavements, incorporating stochastic uncertainties through Monte Carlo simulations and examining various scenarios influenced by factors like tree rotation periods and pavement lifetimes. Our results indicate that lignin-based pavements generally have lower climate impacts than conventional ones, with dynamic LCA showing up to an 81.7 % increase in impacts due to temporal factors. Using recycled lignin further enhances environmental benefits. Strategic adoption of lignin-based asphalt could potentially offset over 2.0 Gt CO2-eq globally. Sensitivity analysis suggests optimizing lignin sources and extending pavement lifetimes as effective strategies for reducing climate impacts.

Building energy renovation mitigates carbon emissions but often increases material demand and financial costs. This work addresses this problem by investigating the carbon, material, and economic footprints of various renovation scenarios in the Dutch residential sector from 2015 to 2050. Results show that, compared to the baseline, façade refurbishment could lower cumulative lifecycle emissions by up to 0.3%, while raising material use by 21-25% and costs by 2-6%. Sensitivity analysis indicates that refurbishing the heating system offers greater potential for reducing carbon emissions. Rebuilding could cut emissions by up to 17% under an ambitious energy transition, though this would triple material use and construction costs. Circularity strategies could offset up to 89% of the material footprint and reduce carbon emissions by up to 23%. Nonetheless, considerable cost increases from renovations remain inevitable, even with advanced material circulation systems, suggesting circular renovation strategies with enhanced incentives as concerted action.

LCA is increasingly applied to the electromobility sector, in academic and industrial literature, and in policies. While there is awareness on the existence of specific gaps and lack of harmonization in LCA of electromobility, there is however still no common understanding on these and on their relevance. This study intends to develop this common understanding and to further explore potential ways forward. The analysis of gaps and lack of harmonization starts from, and expands the analysis of, the extensive review of state-of-the-art academic and industrial literature, and of existing guidelines and standards, performed by Eltohamy et al. (2024). It further builds on four additional actions: (i) review of position papers; (ii) workshops, gathering LCA experts from industry and research; (iii) in-depth survey on the LCA practice of selected industrials; and (iv) iterative, document-based, approach towards common identification and analysis of gaps. This study accordingly compiles and analyzes the gaps and lack of harmonization in LCA of the electromobility sector per LCA phase. It discusses how these limits alter LCA accuracy, comparability and comprehensiveness and their associated relevance as function of the study context. The identified gaps and lack of harmonization relate to (i) the LCA general method, and more specific rules regarding functional unit, approach to address multi-functionality, system boundaries, electricity modelling, normalization and weighting, sensitivity and uncertainty analysis, etc.; (ii) models used to support LCA, sometimes not accurately representing the real world (e.g. models for data gap filling, prospective EoL modelling, impact characterization); (iii) data, both in terms of (incomplete and imprecise) secondary datasets and of lack of primary data; and (iv) difficulties in implementation. The path forward to overcome these limitations is expected to rely firstly on research and development, particularly crucial to address actual gaps in models and data, and secondly on guidelines setting and consensus building, both key regarding harmonization of method-related issues. This study highlights, classifies and analyzes the existing gaps and lack of harmonization in LCA of the electromobility sector. It flags the key challenges and opportunities to be overcome in the path forward, e.g. regarding trade-offs in accuracy, comprehensiveness and simplicity, or alignment with comparable harmonization initiatives in sectors other than the electromobility one. We now encourage the LCA community to use this study as a blueprint towards improved practices, whether harmonization is key, such as for comparative LCAs of vehicles or parts, or of lower importance, such as in some research contexts.

Cellulose nanocrystals (CNC) are versatile nanomaterials of exceptional strength with many applications ranging from packaging to medical uses. While typically produced from Kraft pulp, novel experimental pathways have been developed to obtain CNC from by-products; however, their environmental impacts have not been assessed at a future industrial scale. In this article, a prospective life cycle assessment (pLCA) is conducted for CNC production in two routes coupling acetosolv pulping and acid hydrolysis, with alternative feedstocks: mango seed shells (MSS) and oil palm mesocarp fibers (OPMF). The study is conducted from cradle to gate for 1 kg CNC (solids, in suspension) and follows an adapted version of the "SIMPL" approach for scenario-based prospective life cycle inventory (pLCI) modeling, while incorporating a pLCI database for 2040 (based on the "premise" framework). Upscaling assumptions based on process calculations, extrapolation, and expert advice are integrated into scenarios with consistency checks. Impact assessment covers climate change, freshwater eutrophication, and fossil resource depletion. A current technology (2024) is compared with future (2040) scenarios with varying degrees of technological progress and climate policy ambition. The MSS route consistently outperforms the OPMF route, with climate change impacts of 18.9-79.6 kg CO2 eq (MSS) vs. 26.1-127.8 kg CO2 eq (OPMF). However, the MSS route shows higher impacts than Kraft pulp-CNC (11.2 kg CO2 eq), mainly due to energyintensive acetosolv solvent recovery. Drastic modifications to the pre-treatments are necessary for MSS-CNC to compete with Kraft pulp-CNC. Insights to reduce CNC impacts and recommendations for upscaling bioprocesses are provided.