Prospective Life Cycle Assessment of Hydrogen: A Systematic Review of Methodological Choices

Abstract

This systematic review examines methodological choices in assessing hydrogen production and utilisation technologies using prospective life cycle assessments (LCA) between 2010 and 2022, following PRISMA guidelines. The review analysed 32 peer-reviewed articles identified through Scopus, Web of Science, and BASE. The study reveals a significant gap in the consistent application of prospective LCA methodologies for emerging hydrogen technologies. Most studies employed attributional approaches, often lacking prospective elements in life cycle inventory (LCI) modelling. Although some initiatives to integrate forward-looking components were noted, there was often lack of clarity in defining LCA objectives, technology readiness level (TRL), and upscaling methods. Of the 22 studies that focused on emerging hydrogen technologies, few detailed upscaling methods. Additionally, the review identified common issues, such as the limited use of prospective life cycle impact assessment (LCIA) methods, inadequate data quality evaluation, and insufficient sensitivity and uncertainty analysis. These findings highlight the substantial gaps in modelling low-TRL hydrogen technologies and the need for more robust, comprehensive approaches to assess uncertainties. The review also identified common practices and areas for improvement to enhance the reliability and relevance of hydrogen technology environmental assessments.

1. Introduction

Following the Paris Agreement, several countries have established targets to reduce greenhouse gas emissions and mitigate their effects on climate change [1,2]. In this context, hydrogen (H₂) has gained significant attention due to its potential as a carbon-free energy carrier and its ability to address the intermittency of many sustainable energy sources [3,4]. Moreover, hydrogen can be used to reduce greenhouse gas emissions in various industrial processes, including those in the steel and cement industries [5,6,7]. Furthermore, hydrogen has potential applications as a reactant in the Carbon Capture and Utilization (CCU) sector, where captured carbon dioxide (CO₂) can be converted into fuels like methanol, ethanol and other carbohydrates [8].

To date, most hydrogen production relies on fossil fuels through steam methane reforming, a carbon-intensive production pathway [9]. There are several alternatives for more sustainable hydrogen production. Among them, water electrolysis is a promising alternative hydrogen production route [10]. The most prevalent technologies for water electrolysis include Alkaline Electrolyzer Cells (AECs), Proton Exchange Membranes (PEM), and Solid Oxide Electrolyzer Cells (SOECs). In addition to hydrogen production from water electrolysis, hydrogen production from biomass presents another interesting pathway for sustainable hydrogen production [11]. Most of these technologies are still under development and not available on a large scale [12]. Properly assessing the environmental performance of these technologies before widespread implementation is critical in order to ensure their sustainability [3,13,14].

Life Cycle Assessment (LCA) is a cornerstone methodology for evaluating the environmental burdens associated with a product, process, or activity [15], and it has been traditionally used to assess the environmental impacts of mature technologies ex post. Such assessment entails assessing the environmental impacts after technologies have been commercialised [16].

In LCA, two primary approaches can be employed: attributional and consequential. According to Schaubroeck et al. [17], the attributional approach attempts to provide information on what portion of global environmental burdens can be associated with a product and its life cycle. It focuses on describing the environmentally relevant physical flows to and from a life cycle and its subsystems, providing a static picture of the average environmental impacts. Conversely, the consequential approach attempts to provide information on the environmental burdens that occur as a consequence of a decision, such as changes in product demand. This approach is dynamic and seeks to describe how environmentally relevant flows will change in response to possible decisions, making it particularly useful in understanding the broader system-wide impacts of adopting new hydrogen technologies [17].

Traditional LCA methodologies may not fully capture the complex dynamics of emerging innovations, which require an ex ante approach. Using LCA in an ex ante perspective poses unique challenges related to, for example, lack of data, scale-up, and uncertainty with respect to both how the emerging technology will be deployed and the market conditions in which the technology will be deployed [18]. Scale-up methods refer to the techniques and processes used to transition a technology or process from a smaller, pilot-scale or laboratory-scale operation to a larger, commercial-scale application, addressing challenges such as maintaining efficiency, managing increased material and energy flows, and anticipating larger-scale environmental impacts [19].

To address the complex dynamics associated with emerging technologies, understanding the definition of foreground and background systems in Life Cycle Inventory (LCI) is crucial. The foreground system encompasses the processes, activities, and technologies directly associated with the production system under study, which are within the control of the LCA practitioner [20]. The background system, on the other hand, involves processes and activities that provide inputs to the foreground system, but which are outside the control of the LCA practitioner. These typically include upstream activities like the production of raw materials and energy, as well as downstream processes like waste management and recycling [20].

Prospective LCA tries to address the issues mentioned above and aids in identifying the expected future environmental impact before technologies reach commercialisation [21]. Using prospective LCA is critical in the context of hydrogen technologies, as it mitigates the risk of misestimating the environmental impacts of immature technologies.

Despite its importance, prospective LCA is still in an immature stage, lacking harmonised definitions and procedures for its execution, which leads to various challenges for its application. Hetherington et al. [22] identified four common challenges in studies focusing on early-stage technologies, including defining system boundaries, addressing upscaling issues, ensuring data availability, and managing uncertainties. To address these challenges and enhance comparability among studies, Thonemann et al. [23] suggest critical factors that should be considered, for example, clearly defining the aim of the study, specifying the functionality of the technology, outlining geographical and temporal system boundaries, and employing consistent impact assessment methodologies. Arvidsson et al. [24] emphasise the importance of clearly defining the aim of the study, but also highlight the importance of including the technology readiness level (TRL) of technologies modelled in the production system. Additionally, ensuring a clear scenario definition and a consistent narrative are necessary to avoid the occurrence of temporal and technological mismatch during assessments in future scenarios [25].

A crucial aspect of prospective LCA is the accurate upscaling of data from laboratory or pilot scales to industrial scales, which presents significant challenges given the uncertainties associated with emerging technologies. To address these challenges, Tsoy et al. [26] and Erakca et al. [19] reviewed various upscaling methods that have been employed in ex ante LCA. These methods include process simulation, manual calculations, molecular structure models (MSMs), and the use of proxies.

Furthermore, Moni et al. [18] identified methodological challenges across five major areas, including cross-study comparability, data availability and quality, scale-up issues, uncertainty management, and assessment timeframe, and provided recommendations to address those issues. In a broader perspective, Van der Giesen [16] highlights the importance of involving stakeholders and experts in the assessment. Taking into account the low TRL of clean hydrogen technologies, it is imperative to use LCA following the prospective LCA approach.

This systematic review aims to identify how the methodological issues that need careful attention when LCA is used for evaluating innovative productions have been addressed in the assessment of the environmental impacts of hydrogen technologies. In addition, this systematic review aims to understand if and how the LCA of novel hydrogen technologies complied with the common practices of prospective LCA.

2. Methodology

This systematic review follows the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines [27]. The inclusion criteria for this review were defined to focus on peer-reviewed articles written in English that address the prospective environmental assessment of hydrogen production, storage, and utilisation using LCA. Articles considered suitable for the review specifically dealt with the application or methodological propositions of prospective LCA. All grey literature identified via the search, such as reports and conference papers, were excluded. Review papers were also not considered suitable for the review since they do not represent applications of prospective LCA or methodological propositions for its use. Additionally, articles where hydrogen production or use played a secondary role were excluded. For instance, studies like Morrisey et al. [28], which evaluate the environmental sustainability of a wastewater treatment plant where hydrogen is a by-product, or Tsiklios et al. [29], which assess the environmental impacts of hydrogen transportation focusing on pipelines, were not included. Additionally, articles assessing fuel cell electric vehicles were excluded since, in those, hydrogen production is typically relegated to background modelling or they focus solely on vehicle performance compared to internal combustion or electric ones [30,31,32,33].

A thorough search was conducted across three academic databases: Scopus [34], Web of Science (WoS) core collection [35], and Bielefeld Academic Research Engine (BASE) [36]. Additionally, a backward snowballing approach was used to identify relevant peer-reviewed articles from the reference lists of the initially identified articles [37]. For all the academic databases, the search was performed in January 2024 and considered articles published until December 2023. No specific starting year was defined.

To support the identification of relevant scientific literature addressing the prospective environmental assessment of hydrogen production, storage, and utilisation, the following search string composed of relevant keywords and Boolean operators was defined: (“Hydrogen” OR “power-to” OR “power to”) AND (“Life cycle*” OR “LCA”) AND (“prospective” OR “ex-ante”). The search was performed within article titles, abstracts, and keywords.

The selection process involved an initial removal of duplicate articles with the support of the artificial intelligence tool Rayyan [38]. The prevailing articles underwent a screening by two authors independently, based on the article title, abstract, and keywords, to refine the article selection further. In cases where the initial analysis fell short of determining whether to include an article, a thorough examination of the full text supported the decision on its inclusion. In those cases where the authors disagreed on a publication’s inclusion or exclusion in the full-text screening phase, a third author analysed it until a consensus was reached.

The analysis of the selected studies included a bibliometric analysis and a content-based analysis. The bibliometric analysis of studies is conducted to contextualize the number of publications, distribution by year, and their locations. For the content-based analysis, data were collected qualitatively for each phase of the LCA [39,40]. Specific criteria were defined for each phase (

Table 1. List of criteria used for the review in each Life Cycle Assessment phase of selected articles.

Goal and Scope Definition	Life Cycle Inventory (LCI)	Life Cycle Impact Assessment (LCIA)	Interpretation
Aim of the study	Production system modelling	Inclusion of prospective aspects	Uncertainty and sensitivity analysis (as defined by Igos et al. [41])
Modelling approach
Functional unit definition	Future context typology (as defined by Bisinella et al. [42])		Data quality assessment
Temporal boundaries
Technology maturity	Data sources
Upscaling method
Benchmark maturity

), including the aim of the study, functional unit definition, temporal boundaries, technology maturity, production system modelling, future context typology, inclusion of prospective aspects, data sources, upscaling methods, and data quality assessment. Reviewers worked independently to extract data, ensuring the integrity and consistency of the collected data. Results were compiled in an excel file.

A thematic analysis was conducted to identify recurring patterns in the approaches employed for the environmental assessment of hydrogen technologies using LCA. Visual representations, including figures, were used to illustrate the findings. Variability in study designs, methodologies, and outcomes was assessed to evaluate consistency across studies.

3. Results

The search conducted in the scientific databases returned a total of 337 publications, of which a total of 32 peer-reviewed articles met the inclusion criteria (Figure 1) and were considered suitable for the systematic review. The full results for the literature review are provided in the Supplementary Materials.

Figure 2 presents the distribution of publication year for the 32 articles under consideration for this review, categorised by the country of affiliation of the first author. The majority of these articles originate from Europe (23 articles), with Spain (7 articles) and Germany (7 articles) leading in contributions to prospective LCA studies aiming to assess hydrogen technologies. The oldest article identified was published in 2011 in the Netherlands [43]. Until 2021, the number of articles assessing hydrogen technologies prospectively has been steady and low, with most of the articles coming from the Netherlands and Spain. From 2022, there has been a noticeable increase in the publication trend, leading to an increase in the number of articles over the past two years.

3.1. Goal and Scope Definition—Data Compilation and Results

Figure 3 shows the different TRLs reported by the reviewed articles. Of the 32 articles analysed in this systematic review, 22 consider the assessment of emerging technologies for H₂ production and/or utilisation, i.e., technologies that have not yet reached market maturity when assessed. The remaining 10 articles assess technologies already established in the market.

Regarding the modelling approach, 31 articles adopted an attributional approach, and only 1 article’s study followed a consequential approach. The choice of the functional unit varies across studies, but the kg of H₂ produced is the most used functional unit, with a total of 15 articles. However, the papers exhibit highly variable physical and chemical characteristics, such as differing volume and pressure conditions. Other functional units identified were MJ of H₂, which was used in 9 articles. In 1 article, the functional unit is defined as 1 kg of desired product and no article considers multiple functional units.

Considering the assessment of emerging H₂ production technologies, 3 out of the 6 articles at the research level (TRL 1–3) and 1 article at the development level (TRL 4–6) do not use any upscaling methods to project the impact of the assessed technologies at higher TRL. In total, 5 articles upscale the H₂ production methods using a simplified analysis based on information from secondary data sources. In 1 article, learning curves are used for upscaling and in 1 article, the use of General Morphological Analysis (GMA) is employed to construct scenarios with a focus on specific factors. GMA is defined as “a method for structuring a conceptual problem space and, through a process of existential combinatorics, synthesising a solution space” [44]. Finally, 1 article upscales technologies without specifying the maturity level of the technology.

Another aspect identified is that 28 articles present comparisons of hydrogen production with other technologies, such as steam methane reforming, coal and biomass gasification, PEM, AECs, and solid oxide electrolysis. The definition of a timeframe is not disclosed in 11 articles. Of the remaining articles, the most frequent timeframe used is the consideration of impacts until 2050, with 4 of them using multiple time horizons.

3.2. Life Cycle Inventory—Data Compilation and Results

Roughly two-thirds (20 out of 32) of the reviewed articles modelled certain aspects of the Life Cycle Inventory (LCI) prospectively, either in the background or in the foreground system. Half of them (10) modelled certain aspects of the LCI prospectively in both the background and foreground systems. Concerning foreground LCI compilation, most articles addressed prospective aspects of the technologies focused on changes in their production efficiencies (e.g., faradaic efficiency, material and energy consumption) and lifetime using data from either the literature, simulations, lab-scale data, interviews or a combination of two or more of these sources (Figure 4).

Concerning the background database, prospective modelling was almost exclusively achieved by changing the future energy mix and its related carbon footprint. Those cases represent the sole exception in Integrated Assessment Modelling (IAM) based data that contained information on broader future technological evolution, such as the changes in technological efficiencies. These studies relied on two types of IAM solely: namely, Remind and IMAGE.

LCA databases, sometimes combined with scenario assumptions, are the main source of data for compiling the background data. Only one article does not state the source data used to model the background database (Figure 5). When future scenarios were modelled in the background, they were mostly based on ready-made projections, such as those based on the shared socio-economic pathways (SSP) and national (e.g., from the China’s National Energy Association) or international (e.g., from the Joint Research Centre) future energy projections (Figure 5). In one case, an energy model designed to computationally answer the question about the future power mix has been developed for the study. Concerning the use of SSP, the ready-made projection most widely used in the background database modelling, only the SSP2 has been employed, although in a few different Representative Concentration Pathways (RCPs). Different types of scenarios were used for the foreground database (Figure 4). When both the foreground and the background LCI were modelled prospectively, typically the production efficiencies were changed in the former, and either only the energy mix (five studies) or the energy mix together with other technological parameters based on IAM results (four studies) were changed in the latter.

3.3. Life Cycle Impact Assessment—Data Compilation and Results

Almost no studies conduct prospective methods in the life cycle impact assessment. The only study that adapts the characterisation factors for the prospective assessment is conducted by Lamers et al. [45]. In this study, they include uncertainty for characterisation factors of near-term climate forcers (NTCF), which are used in a Monte Carlo simulation.

3.4. Interpretation—Data Compilation and Results

Of the reviewed papers, only 7 conducted an uncertainty analysis, whereas 19 conducted a sensitivity analysis. Of the 7 articles conducting an uncertainty analysis, 1 article used fuzzy logic, 4 conducted a Monte Carlo simulation, and 2 conducted an uncertainty analysis on an analytical resolution. Regarding the sensitivity analysis, 15 studies conducted a Local Sensitivity Analysis (LSA), where parameters are changed one at a time. Two studies conducted a Method of Elementary Effects (MoEE) sensitivity analysis. Still, in this category, only 1 article conducted a bivariate analysis.

The most frequently variated parameter in the sensitivity analysis was the electricity input to the system (7 articles). This is either done by changing the type of electricity—for example, from a market mix to wind power—or by changing the carbon intensity of the electricity supply. Another parameter commonly analysed in the sensitivity analysis was the efficiency of the hydrogen production process (considered in 10 articles). Finally, 2 articles conducted a sensitivity analysis on the method of dealing with multi-functionality. Only 3 articles assessed the quality of data used in future scenarios; for example, Lamers et al. [45] highlight the effect that more recent data in the LCI can have in the estimation of the results, as well as the uncertainty propagation effect that needs to be considered because of the data quality and completeness.

4. Discussion

4.1. Goal and Scope Definition

From the review, it is evident that most of the LCA studies about the environmental impact of hydrogen technologies deal with technologies that are not yet on the market. Despite this, the lack of a clear identification of the maturity levels (i.e., TRL) of the technologies assessed is common practice. This is a critical shortcoming for two main reasons. Firstly, not knowing the maturity level of the technology does not support the proper comparison of results across the different studies and the different H₂ production technologies. Secondly, the clear identification of the TRL of the technology under study would support a better identification of the most suitable prospective LCA method and the assessment of the suitability of the proposed methods when analysing their findings. The existence of multiple time frames is also a crucial condition for the assessment of impacts in a prospective approach.

The practice of upscaling results, while widespread, is not always implemented in those studies dealing with pre-market technologies, which can clearly impair the reliability and comparability of the results. For the defining of the upscaling methods, most studies consider simplified analyses, which are not homogeneous across studies. For instance, Watanabe et al. [46] employed a simplified extrapolation method to estimate future scenarios. The study used current LCI data and applied straightforward scaling factors to project the impacts of large-scale implementation in 2050. While this approach provides a quick estimation, it may introduce inconsistencies if the technological maturity is not clearly specified.

In contrast, advanced empirical scaling methods like learning curves can offer a more dynamic and accurate projection. For example, Lamers et al. [45] employed learning curves to model the efficiency improvements in power-to-hydrogen (PtH2) technologies as they scale up. By incorporating a learning rate, the study projected material and energy efficiency gains over time.

Process simulation, another recognized upscaling method, is particularly useful for technologies at intermediate TRLs where detailed process data is available but is not yet at an industrial scale. Puig-Samper et al. [47] assessed the prospective environmental performance of hydrogen production from high-temperature electrolysis coupled with concentrated solar power (CSP). The study utilised a detailed process simulation to model the entire system, providing a comprehensive and tailored LCI that highlighted the future potential of the emerging technology.

Delpierre et al. [48] utilised a structured upscaling approach combining several methods. The study began by gathering LCI data from pilot-scale systems, followed by a technology analysis through literature review, expert workshops, and semi-structured interviews. The data was then scaled up to represent a large-scale hydrogen production plant projected for 2050, using scenario creation with General Morphological Analysis (GMA) to explore possible future technological and environmental outcomes. This structured approach ensures that the scaling process is informed by expert knowledge and realistic technological projections, making the resulting LCA more reliable.

These issues become especially critical given that the majority of studies are comparative. Without clarity on whether the compared technologies are at the same maturity level, or without proper upscaling approaches, comparisons can become unfair, leading to misguided decisions. The lack of a standardized upscaling approach across studies exacerbates these challenges, as different methods and assumptions can lead to vastly different outcomes.

A harmonised approach between studies would support a better definition of the future state of development of each H₂ production technology. In addition, upscaling technologies without mentioning the TRLs increases the difficulty in comparing methodological assumptions and results across the different articles.

The reviewed articles also showed high variability in the physicochemical conditions under which H₂ is reported. Clean hydrogen is anticipated to become a fundamental component in securing a clean energy supply and serving as a feedstock across various sectors, each with distinct quality requirements. Defining multiple functional units based on the end use of hydrogen can enhance the comparability of studies. Most studies assessing hydrogen production in future scenarios use hydrogen produced from fossil sources—currently the predominant production method—as a benchmark. However, as the market share of this production path is expected to decrease significantly in future contexts, defining alternative benchmarks, even those not yet at market maturity, could provide deeper insights during the upscaling process. Utilising both benchmarks could yield a more comprehensive understanding of the results.

4.2. Life Cycle Inventory

This study has revealed a significant trend in modelling the LCI within the reviewed literature, with one-third of the articles not incorporating any prospective elements into their LCI modelling. This fact represents an important limitation when assessing a technology not yet on the market. Its impact could easily be misestimated compared with more mature technologies if the specificities of modelling low-TRL technologies are not considered in the LCI building. The focus on addressing the expected changes in production, energy, and material efficiencies in the foreground LCI modelling is nevertheless noteworthy. These are among those factors that are critical in determining the future environmental impact of hydrogen production [49].

The approach to background database modelling is rather uniform across the reviewed articles, with most studies achieving prospective modelling by changing the future energy mix and its related carbon footprint. Using IAM-based data with tools like Premise v2.1.0 [50] is an interesting deviation, offering a more comprehensive approach to modelling future technological changes and broader technological evolutions. However, the limited use of such data indicates a potential area for further development.

Adopting ready-made projections, such as those based on shared socio-economic pathways and national or international energy forecasts, reflects a reliance on existing knowledge structures. While this approach provides a structured framework for scenario development, it may also limit the exploration of alternative, less conventional futures. Therefore, fostering a more diverse range of scenario-building methodologies could enhance the relevance and resilience of prospective LCA results in the face of uncertain environmental and technological landscapes.

In conclusion, the reviewed literature demonstrates a certain effort to integrate prospective elements into the LCI modelling of hydrogen technologies, albeit with varying degrees of comprehensiveness and transparency.

4.3. Life Cycle Impact Assessment

Including prospective methods in the LCIA phase is not common practice in the reviewed articles. Only one study has adapted characterisation factors in their prospective LCA study. A big challenge in prospective LCA arises when newly developed substances, materials, or technologies do not have characterisation factors in current LCIA methods. It is, therefore, important to assess whether new substances and materials used in the life cycle of the emerging technology are represented with characterisation factors and to update these impact assessment methods accordingly.

The effects of hydrogen on climate change and other impact categories are currently still not well defined. In the current Environmental Footprint impact assessment method [51], there are no characterisation factors for hydrogen. A study conducted by Derwent [52] estimates that hydrogen has a Global Warming Potential of 8 ± 2 CO₂-eq over a 100-year time period (GWP100). None of the reviewed articles included characterisation factors for hydrogen. The impact of hydrogen-related technologies could, therefore, be significantly higher due to hydrogen leakages along the value chain [53].

A second aspect not touched upon so far in any study that would be relevant to consider is the timing of when the emissions occur, which might influence the relative impact of that emission. The warming impact of CO₂, for example, depends on the background concentration of this gas in the atmosphere. Due to the logarithmic correlation between radiative forcing and CO₂ concentration levels, the effectiveness of CO₂ in causing radiative forcing—measured as the amount of forcing per unit of CO₂ change against a set baseline concentration—diminishes as the underlying CO₂ concentrations rise [54]. This implies that emitting or sequestering a particle of carbon today has an impact different than it would have in the future. This is particularly relevant considering the high expectation for the production of synthetic chemicals from carbon capture and clean hydrogen production.

4.4. Interpretation

Methodological assumptions and estimates in an LCA study can greatly influence the results. Because of the inherent uncertainties in prospective LCA, it is paramount to reflect on certain modelling decisions, the data quality, and the procedure for adopting the foreground and background models. When conducting a prospective LCA for emerging technologies, such as hydrogen technologies, the variability and uncertainty of input data are particularly significant due to the high level of uncertainty and limited availability of empirical data. Input parameters often rely on expert judgment or sparse scientific literature rather than comprehensive data sets, making it crucial that the LCA practitioner considers these uncertainties in the interpretation of results. According to Lacirignola et al. [55], the variability of inputs can significantly impact the results of global sensitivity analysis (GSA), necessitating careful consideration of the assumptions made during the modelling process.

Uncertainty and sensitivity analysis can be a strong tool for exploring the impact of different parameters on the LCA results. The implementation of uncertainty and sensitivity analysis is, however, not widespread. Most studies conduct a one-at-a-time sensitivity analysis that focuses only on a few hand-picked parameters. This could potentially result in an incomplete or biased interpretation of results. While one-at-a-time sensitivity analyses are common, they often fail to capture the interactions between parameters that can influence LCA outcomes. The use of GSA provides a more robust method for understanding these interactions and the overall variability in results [56]. However, applying GSA in the context of emerging technologies requires additional caution due to the sensitivity of results to input descriptions. Varying the probability distributions of inputs in GSA allows for a more comprehensive understanding of how assumptions impact the final rankings of key parameters and the resulting environmental assessments [55].

While acknowledging the complexity and time-intensiveness of performing uncertainty and sensitivity analysis, this interpretation step can greatly contribute to the robustness of the results and improve their comparability. These additional analyses are valid for all types of LCAs, but in the case of those dealing with immature and/or future technologies and conditions, the uncertainty related to the inventory data can be significant, and a thorough assessment of its impact on the results is advisable in order to make them more robust. Given the high level of uncertainty associated with inventory data for emerging technologies, it is essential to perform a thorough assessment of how this uncertainty impacts the LCA results. This includes not only quantifying the uncertainty but also understanding its propagation throughout the model. Lacirignola et al. [55] propose a methodology for handling these uncertainties by iterating GSA under varying input assumptions, which helps in identifying key parameters and understanding the robustness of the results. This approach is particularly useful when data quality is low, as is often the case with future scenarios or emerging technologies.

The fact that only a minority of studies have evaluated the data quality for future scenarios indicates a potential area for methodological refinement. Ensuring the reliability and validity of such data is crucial for the robustness of LCI models and their implications for environmental assessments.

5. Limitations

The main limitations of this review arise from issues related to scope and selection bias. The narrow selection criteria for the papers may restrict the scope of the study, potentially omitting significant studies and introduce selection bias. With 32 articles included, there is a substantial risk that the sample may not comprehensively represent the entire body of the relevant literature. Furthermore, variability in the application of criteria for evaluating methodological choices by different reviewers could lead to inconsistencies and reduce the reliability of the findings. Temporal variability in data availability and technological advancements also poses a challenge, as older studies may utilise outdated methods, resulting in inconsistencies when compared to more recent research.

Additionally, while the study utilised the artificial intelligence tool Rayyan [38] for duplicates removal, the process of screening, data extraction and thematic analysis was conducted manually. This approach was chosen due to the relatively small sample size and the need for human judgment in evaluating complex methodological nuances. However, this manual process is time-consuming and not easily reproducible, which could limit the scalability of the study. In future reviews, with an increased number of samples, the incorporation of machine learning techniques such as natural language processing (NLP) could be employed to automate data extraction and analysis, thereby enhancing efficiency and reproducibility.

6. Conclusions

The goal of this systematic review was to investigate the methodological approaches employed in the environmental assessment of hydrogen technologies within the scientific literature, specifically from a future-oriented life cycle perspective. By analysing how hydrogen technologies have been assessed from an environmental perspective in the scientific literature, this systematic review contributes to a deeper understanding of the methodologies employed in such assessments.

Although the majority of LCA articles on the environmental impact of hydrogen technologies focus on technologies that have not yet been commercialised, a significant number of articles evaluate the prospective impact of currently available technologies. However, there is a notable lack of clarity regarding the intended objectives of the LCA. Including a clear statement to explicitly address this aspect is imperative for properly interpreting the results and defining the best methodological choices. This approach will, for instance, clarify the need for upscaling techniques and assist in constructing consistent scenarios in the LCI. Similarly, explicitly defining the maturity level of the technology, or the TRL, within the goal and scope definition is essential, along with a clear statement of the upscaling methods employed.

The review also identified challenges related to upscaling and scenario building. Many studies rely on simplified or non-standardized upscaling methods, such as basic extrapolations, which can result in inconsistent and unreliable projections of environmental impacts. To address this, the adoption of more sophisticated and harmonized upscaling techniques, such as process simulations and advanced empirical scaling methods, is recommended. These methods have been shown to offer more dynamic and accurate projections, but are underutilised in the current body of research. Furthermore, while most of the articles benchmark future hydrogen production technologies against current fossil-based ones, defining alternative benchmarks—even if the technology is not mature yet—could generate more insightful results.

The efforts to integrate prospective elements into LCI modelling by including the expected changes in production, energy, and material efficiencies in both foreground and background LCI modelling are notable. There is, nevertheless, room for further development. For example, the diversification of background scenario-building methodologies, which currently rely almost exclusively on ready-made projections, could help improve the relevance of the results by addressing the uncertainties of environmental and technological contexts. Additionally, engaging experts and stakeholders in the scenario-building process supports the creation of more harmonized and consistent future scenarios where temporal evolution and technological development are well-aligned.

Importantly, the environmental impact of hydrogen, particularly its effects on climate change, is still poorly characterised in current impact assessment methods and, consequently, in LCA studies. Hydrogen leakages along the value chain could potentially yield a significantly higher impact than what is estimated currently. A better characterisation of the environmental impact of hydrogen emissions is needed to prevent misguiding results regarding the potential of hydrogen-based technologies.

Uncertainty and sensitivity analyses are powerful tools for providing a comprehensive interpretation of results. To enhance the credibility of prospective LCA studies and obtain a holistic understanding of key system parameters, it is recommended that uncertainty and sensitivity analysis be systematically conducted. Uncertainties can arise at various steps in the LCA model, including in the input parameters, the upscaling of these input parameters within the foreground system, and when developing future scenarios to update the background system. Addressing these inherent uncertainties is essential to ensure robust and reliable environmental assessments. By implementing comprehensive uncertainty and sensitivity analyses—such as GSA—and rigorously evaluating data quality, the interpretation phase can be significantly strengthened. This will not only improve the comparability of results, but also provide clearer guidance for decision-makers considering future technological options.

This review has highlighted that further progress needs to be made to improve both the methodological approaches used to assess hydrogen technologies and the reporting of the results. These improvements would support the comparison of existing study results and provide better recommendations to policymakers, researchers, technology developers, and other relevant stakeholders.