Water electrolysis technologies in the future – projection of environmental impacts and levelized costs until 2045

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Recently, during the United Nations Climate Change Conference COP28, the participants agreed on the beginning of the end of the fossil fuel era. Hydrogen, when produced using renewable energy, can be a substitute for fossil fuel carriers and enables the storage of the renewable energy, leading into a post-fossil age. This paper presents environmental impacts as well as levelized costs along the life cycle of water electrolysis technologies for hydrogen production. Methods The applied methodological approaches are Life Cycle Assessment (LCA) and Life Cycle Costing (LCC), both life cycle-oriented and based on consistent data sources and detailed assessments of prospective technological developments and their effects on environmental and economic indicators. The considered technological developments include electricity and critical raw material demand decreases on the one hand and lifetime as well as electrolysis capacity increases on the other hand. The objectives of the investigations are AEC, PEMEC, and SOEC as the currently most mature water electrolysis technologies for hydrogen production. Results The environmental impacts and life cycle costs provoked by the hydrogen production will significantly decrease in the long term (up to 2045). For the case of Germany, worst-case climate change results for 2022 are 27.5 kg CO 2eq. /kg H 2 . Considering technological improvements, electrolysis operation with wind power and a clean heat source, a reduction to 1.33 kg CO 2eq. /kg H 2 can be achieved by 2045 in the best-case. The electricity demand of the electrolysis technologies is the main contributor to environmental impacts and levelized costs in most considered cases. Conclusions A unique combination of possible technological, environmental, and economic developments in the production of green hydrogen up to the year 2045 is presented. Based on a comprehensive literature research, several research gaps, like a combined comparison of all three technologies by LCA and LCC, were identified and research questions were posed and answered. Consequently, prospective research should not be limited to one water electrolysis but should be carried out with an openness to all three technologies. Furthermore, it is shown that data from the literature for the LCA and LCC of water electrolysis technologies differ considerably in some cases. Therefore, extensive research into the material inventories for plant construction is needed, but also into the energy and mass balances of plant operation, for a corresponding analysis. Even for today’s plants, the availability and transparency of literature data is still low and must be expanded. Life Cycle Assessment Life Cycle Costing Green Hydrogen Water Electrolysis Critical Raw Materials Levelized Costs Climate Change Alkaline Water Electrolysis Proton Exchange Membrane Electrolysis High-temperature Solid Oxide Electrolysis Cell Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Background According to the Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC) the global temperature has increased by about 1.07 K since 1850 [1]. One main reason is the anthropogenic use of fossil energy. Consequently, the reduction and substitution of fossil energy is a major challenge and is consequently addressed by current energy transformation approaches. A main outcome of the recent United Nations Climate Change Conference, COP28, is an agreement on the beginning of the end of the fossil fuel era. Hydrogen produced by water electrolysis technologies can substitute fossil energy carriers when renewable energy is used. Furthermore, hydrogen is interesting for energy storage and a wide range of applications [2, 3]. Thus, it is a key enabler of a transition to a post-fossil fuel age. The three most mature and predominant water electrolysis technologies are the objects of this study. These technologies are namely alkaline electrolysis cells (AEC), polymer electrolyte membrane electrolysis cells (PEMEC), and solid oxide electrolysis cells (SOEC). These labels mirror their fundamental cell concepts [3]. Within this study, environmental impacts as well as costs along the life cycle of these water electrolysis technologies are assessed by Life Cycle Assessment (LCA) and Life Cycle Costing (LCC). Knowledge of the key technological, economic, and environmental development potentials is of great importance for today’s technology roll-out as well as the future development of the hydrogen economy. Within this chapter, relevant literature as well as identified research gaps around these technologies are presented. This is followed by a goal definition and the formulation of research questions for this study. Subsequently, technological principles and differences of these technologies are described. Previous relevant studies and identified research gaps The three water electrolysis technologies have several similarities, like the requirement of water and electricity for their operation and hydrogen as output. However, they differ in their characteristics, making technology-specific assessments necessary. A recent review by Wilkinson et al. [4] on LCAs for hydrogen production reveals that several LCA publications consider only two different water electrolysis technologies. However, no study was identified that included a comparison of all three technologies. Also, a review by Koj et al. [5] of 32 studies, including water electrolysis technologies and further Power-to-X (PtX) technologies, illustrated the scarcity of electrolysis technology comparisons in LCAs. Though not included in the review studies, some studies with environmental assessments of all three electrolysis technologies has been published. Tenhumberg and Büker [6] conducted an environmental comparison of AEC, PEMEC, and SOEC. Their study is limited to a consideration of the climate change impact results of these technologies and does not completely represent an assessment according to the ISO 14040 and 14044 standards for LCA [7, 8]. Consequently, the study has to be considered as a carbon footprint assessment and not an LCA of these technologies. In addition to the carbon footprint, hydrogen production costs are analyzed. Conditions between the years 2018 and 2030 were taken into account. Furthermore, the LCA by Zhao et al. [9] compares the manufacturing and construction processes of the three technologies, but leaves out an assessment of their operation phases. Also, prospective conditions and costs were not analyzed. Two LCA articles published by Gerloff consider all three electrolysis technologies [10, 11]. In the first article published by Gerloff [11] the main focus is on Power-to-Methane plants. However, environmental results for electrolysis are identifiable as part of the overall results. Gerloff [10] compared the three electrolysis technologies using an environmental assessment study, which can be regarded as an LCA. In addition to the climate change impact category, up to seven other environmental impact categories were analyzed in one part of the analysis. In addition to conditions for the year 2019, future scenarios for the years 2030 and 2050 were also considered. However, the only prospective variation that takes place is about the composition of the national electricity mix. Variations of important technological parameters, such as electricity demand and the service life of the stacks, do not appear to have taken place. The study by Gerloff [10] does not include an LCC or any other form of economic analysis. Compared to the first article, the second article included several identical approaches (e.g., assessments of the years 2019, 2030, and 2050 and same impact categories) and assumptions regarding the electrolysis (e.g. the electricity demand). The most recent environmental assessment publication considering all three water electrolysis technologies was published by Zhang et al. [12]. The study can be considered as an LCA and takes water electrolysis with onshore and offshore wind power into account. Changes in parameters over time, economic aspects or hydrogen production using the electricity grid mix are not considered. Table A 1 in the Appendix summarizes several characteristics of these previous LCA studies compared to the present LCA study. In addition to LCA studies, LCC and its interaction with LCA is of interest. The LCA review by Wilkinson et al. [4] also includes information on whether economic and/or technological aspects are considered alongside environmental aspects. According to this, 15% of the studies dealt with economic in addition to environmental aspects, 10% considered economic, technological, and environmental aspects in parallel. However, such combined analyses usually focus only on one electrolysis technology. In addition, prospective analyses that include both LCC and LCA results are extremely scarce. A closer look at studies that can be regarded as LCC studies of hydrogen production using water electrolysis in a corresponding review study by Nicita et al. [13] shows a clear focus on PEMEC electrolysis technology. SOEC was only considered in one LCC study by Bekel and Pauliuk [14]. The authors of this study are aware of only one publication comparing the LCC results of all three electrolysis technologies, which was published as part of the center of excellence "Virtual Institute - Power to Gas and Heat" project and which serves as the basis for the present work [15]. Objectives and research questions To address the existing research gaps, this study aims to investigate various technological, economic, and environmental aspects considering advancements of the hydrogen production from AEC, PEMEC, and SOEC until 2045. The study aims at pointing out the development of relevant influencing technological factors and their impact on environmental and economic results. Special attention is paid to following factors: different electricity sources (wind power vs. electricity mix), development of the demand for electricity, development of the demand for critical raw/construction materials, development of lifetimes. Furthermore, the study has to answer some fundamental research questions to achieve its aims: How do the electrolysis technologies differ from each other regarding different environmental impact categories and compared to a reference technology? How do the life cycle costs differ when using different water electrolysis technologies? How do the results differ for the years 2022 and 2045? Do the environmental and economic results show a positive or opposite dependency compared to each other? To answer these research questions, technological, environmental, and economic sub-models are implemented and presented in the chapter "Methods". Technology description The splitting of water using water electrolysis is an electrochemical reaction. This requires an energy supply in the form of direct current [ 16 ] as well as heat [ 17 ]. The reaction occurs in electrolysis cells, and Eq. ( 1 ) describes the overall reaction: $${\text{H}}_{2}\text{O} \to {\text{H}}_{2}+{\frac{1}{2}\text{O}}_{2} \varDelta {\text{H}}_{\text{R}}^{0}=+ 286\frac{\text{k}\text{J}}{\text{m}\text{o}\text{l}}$$ 1 Despite the same overall reaction, the three electrolysis technologies differ. This can already be seen in the differences in the cell structure and partial reactions. The schematic representations of the cell concepts on which the three electrolysis technologies are based, as well as partial reactions, can be seen in Fig. 1 . The AEC is characterized by two chambers separated by a diaphragm. These chambers contain a liquid electrolyte, a solution of water and potassium hydroxide (KOH). At the cathode the splitting of water into H 2 and OH − ions occurs [ 19 ]. So far, nickel and nickel alloys are preferably used as electrode materials [ 20 ]. Composite materials, such as Zirfon® consisting of zirconium oxide and polysulfone, are currently mostly used for the diaphragm [ 21 ]. In PEMEC, a proton-conducting polymer membrane, usually NAFION®, is used as the electrolyte [ 19 ]. In these cells, the water is split on the anode side. From there, the protons flow through the membrane. Hydrogen is then formed at the cathode. In this technology, the membrane is directly connected to the electrodes, as no liquid electrolyte is used [ 19 ]. In addition to the membrane material mentioned above, the following materials are particularly relevant for PEMEC: Platinum as the anode material and iridium or ruthenium as possible cathode materials [ 20 ]. The central element of the SOEC is a solid oxide layer, which acts as the electrolyte. At the anode, the water vapor used in this high-temperature technology is split into H 2 and O 2− ions. The O 2− ions can reach the anode with the help of vacancy diffusion and react there to form O 2 [ 19 ]. Typically, the electrolyte or the solid oxide layer consists of zirconium oxide (ZrO 2 ) doped with yttrium oxide (Y 2 O 3 ) [ 20 ]. Nickel is used as the catalyst [ 19 ]. The most advanced [ 22 – 26 ] and most common [ 27 ] electrolysis technology to date is the AEC system, which allows realizing large plant capacities at the lowest investment costs to date for water electrolysis technologies [ 22 – 24 , 26 , 27 ]. It should be noted that minor impurities and an associated product purity of ≥ 99.5% may still be present before the final gas treatment [ 28 ]. As mentioned before, several materials are required for the manufacturing and construction of electrolysis cell stacks. Regarding the used life cycle inventories for these cell stacks, which can be found in the "Methods" section, the following materials for electrolysis technologies are considered as critical by the EU list of critical raw materials [ 29 ]. For the construction of AEC stacks graphite and nickel are typically used. Titanium as well as the PGMs iridium and platinum are used for the construction of PEMEC stacks. Small amounts of titanium can also be used for the construction of SOEC systems. Furthermore, cobalt, nickel and the rare earth elements lanthanum and yttrium are also used for SOEC construction. More detailed information about the assumed materials and their amounts can be found in the chapter "Methods". The main methodological aspects of LCA and LCC are first explained before the specific methodological selection for this study is presented. Methods Methodological approach LCA is characterized by standardization, based on ISO standards 14040 and 14044 [ 7 , 8 ]. Within LCA, environmental aspects and impacts throughout the life cycle, ranging from raw material extraction to disposal are examined. Due to its comprehensive and multi-layered analysis possibilities, LCA is used in this study as the environmental assessment method. The economic aspects of water electrolysis systems can be analyzed and compared using various methodological concepts. Techno-economic analysis is a very common approach, using selected economic indicators based on a technical analysis. LCC is an alternative to this. In methodological terms, LCC and LCA are similar and can be based mostly on the same data. This method is based on the system boundaries, the functional unit (FU), and the phases of classic LCA. Due to its proximity to the LCA approach and the resulting data consistency, the LCC approach is used in this study. Goal and scope of LCA and LCC As described in more detail in the "Background" chapter, this study aims to investigate various technological, economic, and environmental aspects as well as advancements in hydrogen production from AEC, PEMEC, and SOEC until 2045. For the present LCA and LCC study, a mass-related FU is selected with "1 kg H 2 ". Furthermore, this specification is supplemented by the specification of the physical property, in this case the pressure, which is assumed to be 10 bar. The technologies examined for the product hydrogen are thus directly comparable in terms of their environmental impacts and life cycle costs. All three water electrolysis technologies, AEC, PEMEC, and SOEC, are analyzed. Germany is chosen as the geographical framework. In addition to current conditions in 2022, future developments, especially including technological improvements and a decarbonizing electricity grid mix, are also analyzed. As Germany is aiming for greenhouse gas neutrality by 2045, this year is also of particular interest and is analyzed in this paper. For both years, a time horizon of plant operation and accompanying hydrogen production over 20 years is considered. Modeling approach, system boundary, software, and databases An attributive cradle-to-gate LCA approach is chosen for this study. Typical cradle-to-gate assessments begin with the extraction of raw materials, through the construction of the plants, energy supply and conversion, and end with the provision of hydrogen (at the factory gate). A possible subsequent use of the hydrogen, e.g., as fuel, lies outside these system boundaries. A schematic representation of the main system boundaries is given in Fig. 2 . Furthermore, the recycling and end-of-life of the electrolysis systems are not yet standardized and, consequently, not considered in the LCA part of this study. The LCA software, openLCA version 1.10.3, is used. The LCA database ecoinvent (version 3.7.1) in the "cut-off by classification" system model is used to provide background data for the Life Cycle Inventory [ 30 ]. The information on the foreground data used for LCA and LCC for AEC, PEMEC and SOEC is discussed in the later sections "Common data for LCA and LCC", "Data for LCA", and "Data for LCC". For the LCC analyses, an own Excel tool including numerous literature-based economic parameters of the technology options under consideration is used. As a variant of LCC, environmental life cycle costing was chosen. The LCC Excel tool developed also contains key formulas for the LCOH calculation. Environmental impacts (LCIA indicators) The synthesis of existing LCIA methods in the European context in the form of the Environmental Footprint (EF) framework [ 31 ] in version 3.0 was used for this study. The mid-point impact indicator values selected are considered to be scientifically more robust than end-point indicators [ 32 ]. Table 1 contains a list of the environmental categories and indicators selected on this basis, as well as the associated units and abbreviations used. Table 1 Environmental indicators selected for LCIA EF impact category Impact category indicator Abbre-viation Unit Climate change Global warming potential GWP 100 kg CO 2eq Ozone depletion Ozone depletion potential ODP kg CFC11 eq Particulate matter Impact on human health PM-ihh disease incidence Ionising radiation Human exposure efficiency relative to U 235 IR-hee kBq U-235 eq Photochemical ozone formation Tropospheric ozone concentration increase POF-toci kg NMVOC eq Acidification Accumulated exceedance A-ae mol H + eq Eutrophication, terrestrial Accumulated exceedance EP-ter-ae mol N eq Eutrophication, freshwater Fraction of nutrients reaching freshwater end compartment (P) EP-fw-p kg P eq Eutrophication, marine Fraction of nutrients reaching marine end compartment (N) EP-mar-n kg N eq LCC indicator and learning curve approach The choice of indicators is also relevant for the LCC. For this study, particular attention was therefore paid to the selection of indicators within existing hydrogen-related publications. An overview study [ 33 ] shows that previous LCC calculations of hydrogen production systems have most frequently used the following indicators: Levelized Costs of Hydrogen (LCOH), capital expenditures/plant costs (CapEx), and plant operating expenditures (OPEX or O&M costs). Levelized Costs concepts are considered to be fundamental approaches for techno-economic comparison of competing technologies and/or production sites as well as technology assessments in general [ 34 , 35 ]. The LCOH reflects the total costs over the lifetime of the systems under consideration. Furthermore, according to Kuckshinrichs & Koj the LCOH can be understood as a break-even value, that indicates a price required as revenue over the lifetime of a technology in order to justify an investment [ 34 ]. The CapEx and OpEx indicators can be considered separately but are also components of the production costs. As the LCOH is based on CapEx and OpEx and is more meaningful and relevant, OpEx and CapEx are not treated separately as part of the LCC calculations in this study. Based on its advantages and the establishment of its use, LCOH is selected as the only indicator for LCC in this study. In its simplest form, the LCOH represents the following mathematical relationship: the sum of CapEx and OpEx is divided by the total energy yield of the plant under consideration over its lifetime and discounted to the reference year [ 36 ]. In addition, sub-categories and further categories can be included in the calculation. Examples are decommissioning costs, taxes, or external costs [ 34 ]. As described by Kuckshinrichs & Koj [ 34 ] LCOH assessments can consider a private (or synonymously business) or social perspective. In this study, a private perspective is used. The differentiation between these two perspectives is not described in detail here but can be found in Kuckshinrichs & Koj [ 34 ]. Eq. (2) takes different previously published LCOH formulations for this private perspective into account [ 29 , 34 , 37 , 38 ]. $$LCOH= \frac{{I}_{0}+ {\sum }_{t=1}^{n}\frac{{WC}_{t}+ {EC}_{t}+ {HC}_{t}+{RC}_{t}+{AC}_{t}+ {OFC}_{t}}{{(1+i)}^{t}}}{{\sum }_{t=1}^{n}\frac{{MHydrogen}_{t}}{{(1+i)}^{t}}}$$ (2) In Eq. (2), I 0 stands for the sum of initial investment costs (CapEx). The unit of the investment costs is € 2022 . In addition, several fixed (operation-related) and variable (demand-related) cost components are taken into account. The variable costs, which are based on the amount of hydrogen produced, include water costs per year (WC t ), electricity costs per year (EC t ), and heat costs per year (HC t ). Furthermore, the costs of the cell stack replacement (RC t ) are of relevance. The fixed (operation-related) costs include administration costs (AC t ), insurance costs (IC t ) and other fixed operating costs (OFC t ). All cost components are considered in real terms, meaning that inflation is not considered. The entire service life of the water electrolysis system is recorded with n, while t indicates the respective year under consideration. The variable i represents the interest rate used for discounting. MHydrogen t indicates the annual amount of hydrogen provided in kWh. As for the LCA recycling and end-of-life of the systems are not considered for LCC in this study. This is also a common approach in many other studies on the calculation of LCOH. The unit for the variables WC t , EC t , HC t , RC t , AC t , IC t , and OFC t is € 2022 /year for annual production, while the unit for MHydrogen t is kWh/year (or MWh/year). The two parameters I 0 and i are of particular interest, as value assumptions for these parameters are particularly intensely debated in science and beyond, e.g. the debate on Cost of Capital [ 39 – 41 ]. Furthermore, these parameters go along with uncertainties, as they can change over time and vary depending on location. Consequently, an established and multi-layered approach is chosen to determine future capex values. In addition, both latter parameters are subjected to a sensitivity analysis in the result chapter. To extrapolate the CapEx values to the year 2045, a learning curve approach is used. The basic learning curve concept was developed by Wright and published in 1936 [ 42 ]. It analyzed the costs of technologies and their development over a selected period. Additionally, these learning curves combine technological improvements of manufacturing processes over time with cost developments. Thus, for this study, the learning curves were selected for a consistent assessment of prospective technological and LCC developments, by describing the relationship between the production or cumulative capacity increase of a good and the reduction of its costs [ 43 ]. From the different configuration possibilities of learning curves, Eq. (3) is chosen for this study: $${C}_{t}={C}_{0}{\left(\frac{{X}_{t}}{{X}_{0}}\right)}^{-\beta } \left(3\right)$$ Within Eq. (3) 𝐶 0 stands for the costs at time t = 0. X 0 stands for the cumulative capacities of technologies at time t = 0, while X t stands for the cumulative capacities at a prospective time t. The applied learning parameter is given by β and can be calculated with a logarithmic equation based on a learning rate. As an example, an economic learning rate of 15% means that the costs decrease by 15% when the cumulative installed capacity doubles. [ 44 ] To calculate prospective CapEx values for the water electrolysis technologies, it is important to know about the identified learning rates for water electrolysis technologies. For electrolysis, different learning rates between 8% [ 45 ] and 18 ± 13% [ 46 ] were identified by literature research. Table A 2 in the Appendix lists values from the literature according to the level of learning rates. The highest learning rates were identified in the distant past of the last century, when only AEC technology was available and less mature. Consequently, newer values are lower and tend to be higher for PEMEC and SOEC compared to the most mature AEC technology. To take the range of values and different developments into account and present more current conditions, three different learning rates for electrolysis systems are taken into account for the own calculations within this study: 7%, 10%, and 13%. As mentioned before learning curve calculations also requires values of production volumes (leading to cumulative installed capacities). So far, several projections of total water electrolysis capacities have been published. However, a differentiation of capacities according to the different electrolysis technologies is given only very rarely. Publications by Boehm et al. [ 43 , 47 ] are an exception in this regard. In the first publication by Boehm et al. [ 43 ] starting values and projections of the global cumulative electrolysis capacities up to the year 2050 are included. The entire globally assumed annual increase in electrolysis capacity is then multiplied by the share of the respective electrolysis technologies, as contained in the publication by Boehm et al. [ 47 ]. Based on the annual capacity expansion and the initial values, the cumulative installed capacity can be calculated. Furthermore, Boehm et al. differentiate between variants of high and low capacity expansion. This differentiation of "high" and "low" developments of installed capacities from the year 2022 until the year 2045 is also considered in this study and shown in Fig. 3 . Figure 3 shows that based on the assumptions by Boehm et al. the highest absolute capacity increases are expected for PEMEC systems. Until the year 2045, higher capacities are expected for AEC compared to SOEC systems. Nevertheless, stronger increases in SOEC capacities are assumed from 2035 in particular, which leads to a noticeable approximation of the results. Furthermore, the highest absolute increases are assumed for the distant future, particularly from 2040 onwards. By contrast, the highest rates of capacity multiplication are already projected for the period between 2025 and 2030. The chosen values for the cost components in this study are listed in the section "Data for LCC". Common data for LCA and LCC For a fair comparison of technology options, it is important to use a data source that is as consistent as possible. Such a common data source is seen in the “State-of-the-Art and Targets” of the US Department of Energy (DOE), which were published separately for the three technologies [ 48 – 50 ]. These documents contain data on the status (State-of-the-Art) of 2022, targets for the year 2026, and ultimate targets for several key performance indicators (KPIs). For the present study especially assumptions for the electricity demand, lifetime, critical raw material content as well as capital cost are relevant. Additionally, the heat demand can be derived from data for the SOEC. Electricity and heat demand, as well as lifetime, are important for both, LCA and LCC. The material content is relevant for LCA, and the capital cost is used for LCC. Within the present study it is assumed that the ultimate targets are applicable for the year 2045. Regarding these technical targets, no restriction to the US market is discernible. The information contained in the DOE documents is therefore considered to be globally applicable and also usable for the German analysis framework. Additionally, important data relevant for LCA and LCC were supplemented by literature data on water and KOH demand, as well as our own assumptions on nominal load and full load hours (FLH). A nominal load is also assumed for 2045 to ensure objective comparability and as there are no economies of scale for the stacks due to their modular design. Operation with the electricity mix is based on the assumption of very even operation over a long period of time. The FLH assumed for this are therefore much higher than for the connection to fluctuating electricity-generating wind turbines. Table 2 lists common data for LCA and LCC assumed in this study. Table 2 Common data assumptions for LCA and LCC Unit 2022 2045 Primary source Typ of electrolysis - AEC PEMEC SOEC AEC PEMEC SOEC Nominal load MW el 1 1 1 1 1 1 own assumption Lifetime stack h 60,000 40,000 20,000 80,000 80,000 80,000 [ 48 – 50 ] FLH (Electricity mix (M) / Wind (W)) h/a 7,000 (M) 2,000 (W) 7,000 (M) 2,000 (W) 7,000 (M) 2,000 (W) 7,000 (M) 2,000 (W) 7,000 (M) 2,000 (W) 7,000 (M) 2,000 (W) own assumption Electricty demand (system) kWh/kg H 2 55 55 38 48 46 35 [ 48 – 50 ] Heat demand (system) kWh/kg H 2 9 7 [ 48 – 50 ] Water demand kg H 2 0/kg H 2 8.9 8.9 8.9 8.9 8.9 8.9 [ 51 ] KOH demand kg KOH/kg H 2 8.5 E-04 - - 8.5 E-04 - - [ 15 ] Data for LCA When selecting the Life Cycle Inventory (LCI) data for the cells and cell stacks, it is important to ensure that not only transparent LCI models are used, but that these also enable a fair comparison with each other. For this reason, the following LCI models were selected for the stack, as these LCI models also consider stack components made of steel. The model from Lotric et al. was used for PEMEC, the inventory of Koj et al. for AEC, and LCI data published by Schreiber et al. for SOEC [ 52 – 54 ]. The LCI model from Bareiß et al. [ 55 ] is otherwise frequently used for LCAs with PEMEC technology. However, this only takes into account a very small amount of steel for screws and bolts. The Lotric LCI model used in this work [ 53 ] is also characterized by more material information and a high degree of transparency compared to other known PEMEC LCI models like those published by Bareiß et al. and Schmidt Rivera et al. [ 55 , 56 ]. Based on the DOE's technical targets, however, it is noticeable that the estimate of the required platinum group metal (PGM) quantity differs significantly from the State-of-the-Art determined by the DOE. Therefore, the value determined by the DOE is used in the PEMEC LCI model for the year 2022 in this study, instead of the original value. Additionally, the energy required for the manufacturing and construction of the three electrolysis technologies should be considered. This aspect and accompanying data are neglected partly in the previously mentioned LCI model publications. Consequently, this additional energy input is considered within the LCI models of this study by the consistent consideration of only one publication. For this purpose, data for all three electrolysis technologies on manufacturing and construction energy published by Gerloff [ 10 ] is taken into account. As these assumptions basically rely on the manufacturing of small or micro plants, they were scaled up according to the scaling assumptions mentioned by Gerloff [ 10 ]. With regard to the German electricity mix for 2022, statistical data [ 57 ] was used and combined into an electricity mix LCI model using own assumptions and available ecoinvent data sets. A study of several research institutes [ 58 ] was used for the electricity mix in 2045 and a model was also created, taking into account own assumptions and ecoinvent data sets. The resulting LCI table of assumed German electricity mixes for 2022 and 2045 can be found in Table A 3. Additionally, LCI data on the construction of the electrolyzers and their components can be found in Table A 4 - Table A 9 in the Appendix . Regarding future cell stacks, it can be assumed that the use of materials decreases over time in consequence of advancing manufacturing and construction processes and improving material properties. This applies also to the use of raw materials that are considered to be potentially critical. The DOE's ultimate target is to reach an electrode PGM loading of 0.03 g/kW, while 0.8 g/W is regarded as State-of-the-Art for PEMEC. This corresponds to a reduction in specific material requirements of 96.25%. In the European context, there are also targets for the KPIs of electrolysis technologies that are comparable to the DOE technical targets, but do not reflect the status quo in 2022. These are the targets published by the Clean Hydrogen Joint Undertaking (CHJU) or Clean Hydrogen Partnership [ 59 ]. The CHJU targets assume a reduction in the total demand for critical raw materials as catalysts for PEMEC electrolysis from 2.5 to 0.25 g/kW, i.e., by 90%, between 2020 and 2030. In the case of alkaline electrolysis, the demand for critical raw materials a reduction from 0.6 g/kW in 2020 to 0 g/kW in 2030 is assumed. No clear targets are specified for SOEC in the DOE and CHJU documents. Nevertheless, it can be assumed that the use of critical raw materials will also be significantly reduced in the future for this technology. Based on this, a simplifying and cross-technology assumption of a 96.25% reduction until 2045 compared to the original values (also for the AEC, though a reduction of 100% is mentioned above) is made in this work. Regarding the AEC, this is a rather conservative estimate compared to the CHJU target values. All three electrolysis technologies are compared with an established reference technology, in this case steam reforming with natural gas/methane (SMR). The applied LCI data for SMR are based on publications by Wulf [ 60 , 61 ]. The authors describe that data can be considered for the years 2030 and 2032 for this reference technology. As no SMR LCI literature sources extending further into the future could be identified, the model is used for both points in time in this study. The applied LCI model of the German electricity grid mix and used LCI data for SMR can be found in Table A 10, Table A 11, and Table A 12 in the Appendix . Data for LCC Data for the own LCC model to calculate LCOH were collected with the intention of being as consistent as possible and to take current conditions into account. Thus, most values are taken from the techno-economic publications by Boehm et al. [ 47 ]. Many of the values used to determine LCOH are expressed as a percentage of the CapEx. The CapEx, which is developing over time, is therefore of particular importance. For this reason, the DOE publications already used for the consideration of other electrolysis data [ 48 – 50 ] are used as the starting points (values for 2022) and as a basis for the CapEx projections. The DOE values describe the uninstalled CapEx of whole electrolysis systems. The starting values are calculated using the average exchange rate between the Euro and US dollar for 2022 of 1.05 $ /€ [ 62 ]. For AEC, the starting value in 2022 is 476.19 €/kW, for PEMEC 952.38 €/kW and for SOEC 2,380.95 €/kW. To obtain CapEx values for the year 2045, the already described learning curve approach is used. Considering the three electrolysis technologies, different learning rates (7%, 10%, and 13%), and two different capacity scenarios (low and high increase), the CapEx development values of AEC, PEMEC, and SOEC can be found in Fig. 4 . The upper whiskers of the respective boxplots (max value) indicates starting values in the year 2022, as assumed by the DOE documents [ 48 – 50 ]. In contrast, the lower whisker limit (min value) stands for the calculated CapEx values for the year 2045. The circles represent the CapEx results in five-year increments. Additionally, the center line inside the box marks the median value. The x-marker within the boxplots in Fig. 4 represents the arithmetic mean of all data points. For each electrolysis technology, the upper limit of the whiskers boxplots in Fig. 4 represents the starting values. The learning curve analysis in Fig. 4 shows significantly decreasing CapEx values for all three electrolysis technologies. As illustrated, the CapEx of AEC systems can be reduced from 476 to 186 €/kW el in the best and to 313 €/kW el in the worst of the considered cases. The projected relative reductions range from 34–61%. For PEMEC systems Fig. 4 reveals CapEx reductions from 952 to 195 €/kW el as the best and to 446 €/kW el as the worst case. These decreases range from 53–80%. CapEx of SOEC systems can be reduced from 2381 €/kW el to 363 €/kW el (best case) and 960 €/kW el (worst case). Furthermore, significantly stronger effects are provoked by the learning rate variations than by different capacity scenarios. Higher CapEx starting values are given for PEMEC systems, but by 2045 this technology can reach the level of AEC systems in the best case. For the own LCC model the obtained best (BC) and worst case (WC) results from the learning curve analysis for the year 2045 were considered as CapEx values for each technology. To keep the LCOH calculations as consistent as possible, further relevant data were taken from the Supplementary Material to a paper published by Boehm et al. [ 47 ]. The publication includes parameters from 2020 to 2050. As no exact figures were available for the years 2022 and 2045, values for 2020 and 2050 were taken from the publication. In particular, the assumptions regarding the costs of electricity and heat can be discussed critically, as the data published by Boehm et al. [ 47 ] could not already consider more recent developments with effects on energy markets. However, the development of these prices will remain subject to considerable uncertainty in the future. For this reason, these assumptions are initially used here as a consistent basic assumption and the effects of other prices are shown later in a sensitivity analysis. The final choice of assumptions with exclusive relevance for the LCC calculations can be found in Table 3 . Table 3 Data for LCC (LCOH) calculations and the years 2022 and 2045 unit 2022 2045 prim. source Electrolysis technology - AEC PEMEC SOEC AEC PEMEC SOEC - Spec. Invest (CapEx) € 2022 / kW el 476.19 952 2381 186.3 (BC) 313.2 (WC) 195.4 (BC) 445.52 (WC) 362.5 (BC) 959.9 (WC) [ 48 – 50 ] and own calcul. Stack share on CapEx % 50 60 30 44 36 10 [ 47 ] OpEx fixed % of CAPEX 4 4 4 2 2 2 [ 47 ] Insurance costs % of CAPEX 0.5 0.5 0.5 0.5 0.5 0.5 [ 47 ] Administration costs % of CAPEX 2 2 2 2 2 2 [ 47 ] Electricity supply costs €ct/kWh el 3.5 (Mix) 3.5 (Mix) 3.5 (Mix) 4 (Wind) 8 (Mix) 4 (Wind) 8 (Mix) 4 (Wind) 8 (Mix) [ 47 ] Heat supply costs €ct/kWh th 5.5 5.5 [ 47 ] Interest rate % 4 4 4 4 4 4 [ 47 ] Results LCIA-Results As part of the life cycle impact assessment, the absolute GWP 100 results of hydrogen production using electrolysis technologies are first compared with the reference technology. Subsequently, a contribution analysis shows the different reasons for the results. Causes for the GWP 100 results of different cell stack variants are determined. Finally, additional impact categories are investigated and compared with the reference technology. Figure 5 first shows the absolute GWP 100 results for different electrolysis technologies, points in time and power supply variants in comparison with the reference technology, steam methane reforming (SMR). Figure 5 clearly illustrates the great potential for reducing the GWP 100 of hydrogen production through operation with wind power compared to the use of grid electricity (electricity mix). By using wind power, reductions of almost 93% can be achieved for AEC and PEMEC systems, while a decrease of 81% is possible for SOEC. Electrolysis based on the German electricity mix in 2045, which is assumed to be completely renewable, provokes still significantly higher results in GWP 100 values than for wind power supplied systems (35.7–41.2%), but the gap between the values is narrowing. Compared to SMR, the water electrolysis technologies can achieve up to 87.8% lower values for the GWP 100 indicator when using wind power. The results are converging across the technologies over time. AEC and PEMEC are already at a very comparable level in 2022, which is due to the identical electricity consumption assumptions. The different contributions to the overall environmental impacts of hydrogen production are discussed in detail in the contribution analysis. Figure 6 illustrates the relative contributions to the results of hydrogen production for the GWP 100 indicator. As can be seen in Fig. 6 , the energy sources, electricity and, in the case of SOEC also steam/heat, are responsible for most of the GWP 100 results. The contribution of the electricity supply to the environmental impacts is most pronounced in the case of electricity mix use. The contributions shown can be allocated to the life cycle phases of manufacturing and construction as well as operation. Plant operation dominates over manufacturing and construction across all technologies. Manufacturing and construction include the cells, cell stacks, and the BoP components. Additionally, a replacement is considered if the number of hours of hydrogen production exceeds the service life. For SOEC, a combined view of the last two figures shows that the clear prospective reduction in GWP 100 results is primarily due to the assumed more environmentally friendly heat supply. Figure 7 shows the results of the GWP 100 indicator for the different electrolysis technologies and for different years and underlying LCI models. The addition A indicates the respective original LCI model. The suffix B describes the consideration of assumptions on production from the publication by Gerloff [ 10 ] as used in this study and explained in the section "Data for LCA". For each technology variant, the five materials (Top 5) with the highest influence on the GWP 100 indicator are considered. The remainder (Rest) always includes all contributions that cannot be assigned to these respective Top 5 contributions. Some of the material designations are abbreviated and not mentioned before. ABS is an acronym for acrylonitrile-butadiene-styrene, (P)TFE is the abbreviation of (poly)tetrafluoroethylene, and NMP stands for N-methyl-2-pyrrolidone. Figure 7 illustrates that the results of the different LCI stack manufacturing and construction models are heterogeneous. The PEMEC electrolysis stacks, whose production in 2022 is still associated with the highest results, induce the lowest results in 2045. For PEMEC this is primarily provoked by the high contributions of the critical raw materials, PGMs and titanium, in the year 2022. This is also because the mining and provision of platinum and iridium are particularly energy- and emissions-intensive. According to the International Renewable Energy Agency (IRENA), one kilogram of these materials, including their supply, contributes around 10,000 kg CO 2eq to climate change [ 63 ]. As a significant decrease in the specific use of these materials is expected and assumed in this study, also the climate change results are strongly declining. The AEC and SOEC also show significant reductions in the GWP 100 results for the year 2045 compared to those for the year 2022. However, their results are not determined to the same extent by critical raw materials. The contributions of manufacturing energy in the LCI models for 2022 differ significantly. This manufacturing energy assumption based on the publication by Gerloff [ 10 ] leads to significantly higher results than in the original models by Koj et al. for AEC, Lotric et al. for PEMEC, and Schreiber et al. for SOEC [ 52 – 54 ]. While the calculated GWP 100 results for the "B" LCI models are around 47% higher for AEC and PEMEC systems the results of SOEC are even 89% higher than for model "A". The data from Gerloff [ 10 ] on manufacturing and construction energy that is used for the "B" LCIA for additional impact categories and comparison with the reference technology The environmental analyses in this study are not limited to the GWP 100 indicator. Further indicators listed in the "Methods" chapter are included in the analysis and the electrolysis technologies are analyzed in comparison with each other and with SMR, using spider diagrams. The presentation is based on a decadal logarithmic scale and the results are shown relative to the environmental impacts of SMR. The gray area (100% values) indicates the calculated environmental impacts of SMR for each impact category. For greater clarity and comprehensibility, the analyses for 2022 and 2045 are shown in separate diagrams. Figure 8 shows the results for the year 2022. Figure 8 reveals that the advantages of certain technology variants determined for GWP 100 do not apply equally to all additional environmental impacts considered. Furthermore, Fig. 8 illustrates clear differences between the technology variants that produce hydrogen with the German electricity mix in 2022 and those that do so with wind power also for the other impact categories. The variants using the electricity mix have a significantly higher environmental impact. In the most extreme case of the eutrophication potential of fresh water, the values for operation with the electricity mix are even up to 115 times higher compared to SMR. The main reason for this is coal-fired power generation as a component of grid electricity (electricity mix). Large amounts of the energy- and emission-intense produced materials steel, aluminum, and copper are required for these kinds of power plants. These electricity mix contributions also have a high impact on several other environmental indicators. In contrast, electrolysis using wind power already achieves significantly lower results in 2022 compared to SMR regarding the GWP 100 and ODP indicators and comparable results with regard to the EP-mar-n, EP-ter-ae, A-ae and POF-toci indicators. The ODP results of electrolysis technologies supplied by wind power are 61–86% lower, and the GWP 100 results are 63–82% lower than SMR. However, regarding EP fw, IR, and PM, water electrolysis with wind power does not achieve the environmental performance of SMR. The main reason for this is the environmental impact caused by the upstream processes of the steel components required for the cell stacks. The results of the electrolysis technologies compared to SMR for 2045 are presented in Fig. 9 . As illustrated in Fig. 9 for the year 2045, the electricity mix variants are significantly more competitive in terms of their environmental performance compared to the reference technology and the present. This is a result of the completely renewable electricity mix. Thus, the values clearly improve against 2022. Depending on the technology, the variants with wind power perform better than the reference technology for five or six indicators (POF-toci, ODP, GWP 100 , A-ae, EPter-ae and EP-mar-n). Advantages that are given for both the variants with wind and with the mix are shown for the indicators ODP, GWP 100 , and POF-toci. Clear disadvantages with up to five times higher environmental impacts compared to the reference technology are only given for the EP fw indicator. The other indicator for which significantly higher results are available for all electrolysis variants considered, up to 160% higher, is PM-ihh. The use of steel for cell stacks and for constituents of the electricity provision is also of great importance for these indicators, as high environmental impacts are associated with the energy- and consequently emission-intense upstream processes of steel. LCC results Based on the assumptions and calculated CapEx values in the chapter "Methods" the LCOH of the electrolysis technologies is calculated for the years 2022 and 2045. On the one hand, the LCOH resulting from operation with the electricity mix in the year 2022 is determined. On the other hand, the costs of electrolysis operation with wind power in 2045 are analyzed. Extreme cases are thus taken into account. For 2045, the WC is given for the lowest learning rates and capacity increases within the assessed range. Contrary to this, the BC is given for highest learning rates and capacity increases within the range. The resulting LCOH for water electrolysis technologies, given in € 2022 /kg H 2 , is illustrated in Fig. 10 . A wide range and significant influencing factors that change over time can be identified in Fig. 10 . In 2022, there are still clear differences in LCOH results for the three electrolysis technologies. The LCOH is lowest for AEC systems. This is due to higher CapEx and higher costs for replacing the stacks given for PEMEC and SOEC systems. With these two systems, more frequent stack replacements occur due to lower lifetime expectations and the high assumed operating time when operating with the electricity mix. Analyses for the year 2045 show a strong convergence of the LCOH. The calculated range reaches 2.3–3.8 € 2022 /kg H 2 . Higher lifetimes have a reducing effect on the LCOH as no replacement costs will occur. Reductions in LCOH will additionally be provoked by a prospective CapEx decrease. In contrast, the assumed electricity supply costs increase from 2022 to 2045, along with the accompanying specific cost contribution. This cost-increasing effect outweighs cost-reducing effects (especially CapEx reductions) if the AEC systems are operated in 2045 and the WC. Consequently, the LCOH increase in this special case. For the SOEC systems, there is an additional cost reduction potential if waste heat from a neighboring plant could be used free of charge or at a low cost. Sensitivity analyses In the following section, separate sensitivity analyses for LCA and LCC are presented. The sensitivity analyses start with an assessment of the GWP 100 results. Based on an effort to carry out analyses that are as similar and consistent as possible, the same parameters are used wherever possible. Four parameters were considered in the LCA sensitivity analyses and mentioned subsequently. In line with the results presented above, the parameter of electricity demand revealed outstanding importance. Additionally, the variation of the parameters full load hours, lifetime, and time horizon are also examined. Beside the used process data, these parameters are relevant and variable parameters within the underlying LCI models of this study. The full load hours and the time horizon are each included in the calculation of the amount of hydrogen produced. Consequently, these parameters lead to changes in the amount of cell stacks and cells considered producing a fixed amount of hydrogen. The lifetime assumption, on the other hand, is not included in the balancing of the amount of hydrogen generated. This parameter only takes into account whether components need to be replaced during the period under consideration. The sensitivity analyses for the four parameters are applied to one of the electrolysis technologies under consideration. PEMEC technology was selected because it has become the electrolysis technology that has received the most attention in recent years. This can be seen from a dataset of global hydrogen projects provided by the International Energy Agency (IEA) [ 64 ]. Within the current version of this dataset, corrected in January 2024, more than 340 projects are related to PEMEC and 263 projects are related to AEC. In addition, the sensitivity analysis is limited to operation with wind power and thus to the production of green hydrogen. The results are illustrated in Fig. 11 . A variation in electricity demand leads to significant changes of GWP 100 results. This is valid for both points in time considered. A variation of electricity demand by ± 10% also leads to changes in the GWP 100 results of approximately ± 10%. A reduction of FLH provokes a lower hydrogen production during the considered time horizon, which causes an increase in GWP 100 per specific amount of hydrogen by 1.7% in 2022. An increase in FLH causes a higher hydrogen production and would lead to decreasing GWP 100 results if assessed without stack replacement requirements. However, a reduction in specific GWP 100 results is counteracted if stack replacements are required. The stack lifetime of 40,000 hours and assumed full load hours per year in the base case in 2022 imply that stack replacement is not yet necessary. A reduction of the lifetime below 40,000 hours, however, makes a stack replacement necessary. In all other cases of varying the lifetime, the GWP 100 results remain unchanged compared to the base case. In these cases, the lifetime is high enough to enable an operation without stack replacement. Consequently, no changes are illustrated. The effects due to time horizon variations are very similar to those of the FLH. An increase in the time horizon from 20 to 22 years leads to a stack swap with the lifetime assumptions of 2022, which would be accompanied by an increase in the GWP 100 results. Due to the observed outstanding importance of electricity demand on the GWP 100 results, its variation by ± 10% is assessed for all technologies and points of time. The results of this sensitivity analysis are illustrated in Fig. 12 . For PEMEC and AEC systems, the effects of varying the electricity demand on the GWP 100 results in Fig. 12 are comparably high for both points in time. In 2022, the effect of varying the electricity demand is significantly greater for these technologies than for SOEC systems, due to their significantly lower electricity demand than the alternative electrolysis technologies. The reason is the considerably lower share of electricity demand on the total GWP 100 results for SOEC systems (see also Fig. 7 and Fig. 8 ), especially for 2022. The GWP 100 results for SOEC systems change by 3.4 to 8.8% for 2022 and by 9.1–9.4% for 2045 if the electricity demand varies by ± 10%. When looking at the PEMEC and AEC systems, GWP 100 results change linearly by around 10% for both 2022 and 2045 if a ± 10% variation is assessed. When operating with wind power, there is a tendency towards lower results compared to the variants with an electricity mix. This is due to the slightly lower contribution of the operating phase when using wind power compared to the electricity mix. Following the previous presentation of the environmental sensitivity analyses, the following section is dedicated to the sensitivity analysis relating to the LCC. Besides electricity demand and FLH, which are also considered for LCA, additionally, the parameters CapEx and interest rate are assessed. Especially the latter two parameters are of interest, due to before mentioned debate in science and beyond, e.g., on Cost of Capital [ 39 – 41 ], and associated uncertainties. Consequently, the inclusion of both parameters in the sensitivity analyses helps to quantify the degree of uncertainty caused by varying these assumptions. Figure 13 illustrates the effects on the LCOH results for all three electrolysis technologies and the variation of the four parameters by ±10%. As shown by Fig. 13 , AEC and PEMEC reveal the highest effects of the variation of the parameter electricity demand. This result reflects the dominant influence of electricity demand on the LCOH already shown in Fig. 10 . The highest relative change observed for electricity demand variations by ± 10% was 7.6%. For the remaining cases, a variation of the FLH parameter is usually the parameter that has the greatest influence on the LCOH results. The highest relative change of LCOH determined for the FLH parameter variation was 5.7%. However, it can also be determined that the FLH variations in one direction or the other lead to different values. For other parameters, the amount is the same in both directions. This shows that the relationship between FLH and LCOH is not linear, while other parameters change linearly. An increase of 10% in FLH leads to a smaller proportional change in hydrogen production costs than a 10% decrease. The variations in the CapEx assumptions by ± 10% also have a noticeable effect on the results, causing changes in the range of 2.4–5.3%. Though the effects of varying the interest rate parameter are comparatively small (0.6–1.2%), these changes are still not negligible. Discussion A key finding of the analyses presented is that the production of hydrogen using water electrolysis technologies will be accompanied by decreasing environmental impacts in the long term (up to 2045). This finding confirms the fundamental outcome of previous publications on the prospective environmental impacts of hydrogen production and delivers new insights for the considered case study. In the period under consideration, the highest GWP 100 results are 27.5 kg CO 2eq /kg H 2 and the lowest are 1.33 kg CO 2eq /kg H 2 . Compared to the production of green hydrogen with low CO 2 emissions, as achieved using AEC and PEMEC systems in the year 2022, reductions of up to almost a quarter are possible by technological improvement until 2045. The origin and demand of electricity is the most significant factor for the environmental impacts of all the electrolysis variants considered. While the considered German electricity mix in 2022 provokes 497 g CO 2eq /kWh el , the assumed mix in 2045 provokes 54 g CO 2eq /kWh el . The GWP 100 value (30 g CO 2eq /kWh el ) related to the considered wind electricity dataset, is once again well below the current and future grid mix levels. Even with the use of wind electricity, electricity demand remains a determining factor in the environmental results. Consequently, its prospective reduction, which is a common assumption in the literature and used in this study, is particularly relevant in terms of environmental improvements. The additional expected reduction in the use of construction materials as well as increasing lifetimes can also be expected to reduce the environmental impacts. In the case of SOEC systems, the results are particularly dependent on assumptions regarding heat supply. For the year 2022, this study assumes a heat supply that is still largely based on fossil fuels. In the event of a particularly low-emission heat supply in the future, SOEC systems have the potential to produce hydrogen with a very low environmental impact due to their particularly high efficiency. A comparison of the electrolysis technologies shows a convergence of the environmental impact results, to that extent as this is not already the case. Under the assumptions made and depending on the electrolysis technology, the LCOH can be reduced from a maximum of 5.6 €/kg H 2 in 2022 to a minimum of 2.3 €/kg H 2 in 2045. As for the environmental results, the electricity demand and its reduction are of the greatest importance for the LCOH of AEC and PEMEC. In 2022, the LCOH results of the three technologies diverge stronger than the environmental indicators. The analyses until 2045 show that the LCOH will also converge to a comparable level in the future. As the learning rates for the technologies are likely to differ between the technologies, due to the different degrees of their maturity, even further convergence is conceivable. With SOEC systems, a high learning rate is more likely than with already more mature AEC systems. It is therefore possible for the learning rate of AEC systems not to be significantly higher than assumed for the WC calculations. In the long term more noticeable differences between these technologies will be noted regarding materials used for manufacturing and especially about the type of used critical raw materials and their quantities. Due to the diminishing differences in environmental and economic performance and the possibility of diversifying the use of critical raw materials, there is a strong argument for the combined use of these three technologies in the future. In the literature, there are numerous assessments of the current State-of-the-Aart and potential target values for electrolysis technologies. Due to the breadth of usable data and its consistency, a key database selected in this study is that of the DOE on the status quo and the target values of the three electrolysis technologies. Compared to the existing literature, some assumptions within the DOE documents [ 48 – 50 ] as essential data sources of this study can be critically discussed. On the one hand, sources like those published by Boehm et al. or Chatenet et al. [ 43 , 47 , 65 ] do not see such large differences of CapEx values between AEC and PEMEC systems for the year 2022. On the other hand, regarding the operation phase, within other publications [ 51 , 52 , 66 , 67 ] there is a tendency for lower electricity demand values for AEC (48–52 kWh/kgH 2 ) compared to PEMEC. Thus, the overall LCOH results based on the assumptions of this study are in a realistic range and without preference for one technology option. Furthermore, the learning curve approach applied to CapEx developments is based on assumptions regarding the capacity developments of water electrolysis systems. The number of publications on differentiated forecasts of capacity developments for the three technologies examined over time is still very low. However, these assumptions determine the possible future CapEx developments, so that significantly different assumptions on capacity developments would also influence the overall LCOH results. Regarding the interest rates, a range between 3.6% and 4.4% is assessed within the sensitivity analyses. However, some literature on interest rates assumes significantly different percentages. For the interest rate, which is highly dependent on location, time and actor perspective, exemplary assumptions of between 5.5 and 10% can be found for Germany [ 37 , 68 ]. Interest rates varying by several percentage points would result in LCOH deviations of several percents. The database used for LCA contains almost exclusively data sets that can be used as background data, which correspond to the status quo and are not extrapolated into the future. This is why, for example, the data records for materials such as steel or copper are also used in the analyses for the year 2045 in this study. However, it is likely that such processes will change in the future. This will additionally tend to lead to lower environmental impacts. Consequently, the background data used in this study is associated with higher environmental impacts than could be the case in the future because of process optimization. The specific results of this study can be transferred to locations outside Germany only with restrictions. Differences between locations are mainly caused by the operation phase of the water electrolysis, due to different environmental and economic properties of the electricity supply. There are locations outside Germany and outside Europe where renewable electricity can be generated with significantly lower levelized costs due to better availability of renewable energy sources. In some regions, not only favorable production costs arise for individual renewable energy sources, but also significantly lower costs for grid electricity. In addition, interest rates can vary from country to country. For such regions, the cost component shares on the LCOH would differ strongly from those determined in this study for Germany. Consequently, previous studies on production costs or especially those on LCOH point out significantly lower costs for hydrogen imports to Germany compared to its domestic production. A review by Breuer et al. [ 69 ] points out domestic hydrogen production costs between 3.3 and 7.3 €/kg H 2 assumed for Germany in the year 2050 within previous publications. Furthermore, the review found costs between 1.4 and 2 €/kg H 2 for imports to Germany in the year 2050. Thus, the LCOH values obtained in this study for the year 2045 and domestic production in Germany can be considered very low compared to the values of the review. Possible reasons are potential considerations of taxes, overhead costs, decommissioning costs, or other cost components in the studies considered within the review. Regarding LCA results, a review by Wilkinson et al. [ 4 ] identified values mainly below 5 kg CO 2eq /kg H 2 for this kind of water electrolysis configurations. However, the review states that in earlier publications on hydrogen production by electrolysis in Germany, even GWP values below 0.9 kg CO 2eq /kg H 2 were determined. Thus, the calculated GWP values within this study are in the range of values from previous studies on water electrolysis technologies using renewable electricity. Conclusions This study provides a particularly far-reaching, differentiated, transparent, and consistent comparison for the three electrolysis technologies AEC, PEMEC, and SOEC. A unique combination of possible technological, environmental, and economic developments in the production of green hydrogen up to the year 2045 is presented. Based on a comprehensive literature research, several research gaps were identified, and research questions were posed and answered, e.g., how the results differ for the years 2022 and 2045. Still, the current study reveals a need for subsequent research. As a consequence of the presented findings, prospective research should not be limited to one type of water electrolysis but should be carried out with an openness to all three technologies. It is shown that the data from the literature that can be used for the LCA and LCC of water electrolysis technologies differ considerably in some cases. There is, therefore, still a need for extensive research into the material inventories for plant construction, but also into the energy and mass balances of plant operation, i.e., the foreground data. Even for current plants, the availability and transparency of literature data are still low and can be expanded. Recent research activities on the adaptation of background data for prospective LCA should be intensified. Future overall systemic developments would thus be better reflected in prospective LCA studies. In some cases, there is also the possibility of material substitution in the manufacturing of electrolysis technologies in some cases. As one example, a possible ban on per- and polyfluoroalkyl substances (PFAS) is discussed at the EU level and would require alternative materials for components like PTFE-containing gaskets. The material substitution topic offers R&D potential, especially for materials and raw materials research and for manufacturers. New knowledge gained in this way should be made available to experts and for research in the field of LCA to provide this research with the best possible data. About the environmental impacts considered, some particularly robust indicators beyond the GWP were selected for this LCA study. This provides a more diverse range of knowledge about the various environmental impacts. Nevertheless, there are other indicators and methodologies that can be used in future assessments to gain further insights into the environmental impacts and life cycle costs of water electrolysis technologies. There has been some recent research on the recycling and disposal of water electrolysis technologies. However, clear standardizations or regulations in this regard could not be identified during a literature search for this study and were therefore not taken into account for the sake of simplicity. As clarity in this regard increases, future research should also include corresponding data and its possible further development, e.g., in the form of increasing recycling rates of individual raw materials [ 15 ]. Due to its outstanding importance on LCA and LCC results the electricity demand assumptions must also be confirmed by future research or, if necessary, modified. Regarding LCC in general and LCOH calculation in particular the inclusion of recycling or commissioning costs would be an interesting complement. Also, further indicators, e.g. levelized revenue/profit, could contribute to new LCC insights about electrolysis technologies. Future research on LCC of electrolysis technologies should also take the newest CapEx and interest rate developments into account. From a technological perspective, there is also a particular need for research into emerging water electrolysis technologies, which are currently at a significantly lower stage of development than the options under consideration (e.g., anion exchange membrane technology, AEM). The present work provides a particularly broad and transparent database that can be used as a basis for the previously listed research opportunities. Abbreviations ABS: Acrylonitrile-butadiene-styrene AC t : Administration costs AP: Acidification AEC: Alkaline electrolysis cells AEM: Anion exchange membrane AR6: Sixth Assessment Report of the Intergovernmental Panel on Climate Change BC: Best case CHJU: Clean Hydrogen Joint Undertaking or Clean Hydrogen Partnership COP28: The 28 th Conference of the Parties to the UN Framework Convention on Climate Change DIN EN ISO: Deutsches Institut für Normung, European norm, International Organization for Standardization DOE: Department of Energy EC t : Electricity costs per year EP fw: Eutrophication, freshwater EP mar: Eutrophication, marine EP ter: Eutrophication, terrestrial FU: Functional unit GWP 100 : Global warming potential (GWP) over a 100-year time horizon HC t : Heat costs per year I: Interest rate IC t : Insurance costs IEA: International Energy Agency IPCC: Intergovernmental Panel on Climate Change IR: Ionising radiation KPIs: Key performance indicators LCA: Life Cycle Assessment LCC: Life Cycle Costing LCI: Life Cycle Inventory MHydrogen t : Annual amount of hydrogen provided in kWh n: service life of the water electrolysis systems NMP: N-methyl-2-pyrrolidone ODP: Ozone depletion OFC t : Fixed operating costs OP EFRE/ERDF NRW: Operational Program for the promotion of investments in growth and employment for North Rhine- Westphalia from the European fund for regional development PEMEC: Polymer electrolyte membrane electrolysis cells PGMs: Platinum Group Metals PM: Particulate matter POCP: Photochemical ozone creation potential PTFE: Polytetrafluoroethylene PtX: Power-to-X RC t : Cell stack replacement SMR: Steam reforming of methane SOEC: Solid oxide electrolysis cells t: year under consideration. TFE: Tetrafluoroethylene US: United States WC: Worst case WC t : Water costs per year Declarations Acknowledgements Funding of the center of excellence "Virtual Institute - Power to Gas and Heat" (EFRE-0400151) by the “Operational Program for the promotion of investments in growth and employment for North Rhine-Westphalia from the European fund for regional development” (OP EFRE NRW) through the Ministry of Economic Affairs, Innovation, Digitalization and Energy of the State of North Rhine-Westphalia is gratefully acknowledged. The authors would also like to thank Freia Harzendorf for the co-development of the economic model used in this study. Data Availability Statement: The datasets supporting the conclusions of this article are included within the article and its appendix. Author Contribution "J.C.K. was responsible for the conceptualization and methodology, wrote the main manuscript text, and prepared all figures. P.Z., C.W., K. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3958723","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":274424990,"identity":"30a13711-2529-404a-818d-7dbbfa93de86","order_by":0,"name":"Jan Christian Koj","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIie2QsWrDMBRFrzDIi6Hr89L8wjOGlC7xr8hoDW0gS4cOAoMz9gdK/6GLZ0HAkz/CUMjsMUOGyHFn2WMgOmi4CB3e1QMCgTtEGAnQx7OLFugZCuOZV7p8UtQSBXCKqPMpKyxQIiPL/vWHC4gu69Vu84ZY27liR04bjoAuZ8V6j+TkHyNMXFPaXGRhuzUptqWhLS9QvjlxU17Ok/I+zBVrKTVMTlnjf4rXgKikZmrZdWn3rpgu6+TkL5Yd6uyPPt3G6Pg7DJdN+RXr3q9UbtE0JrLTjfTXAlZjudt3n8zc20AgEHhUrmUaQSB3Y0eNAAAAAElFTkSuQmCC","orcid":"","institution":"Forschungszentrum Jülich","correspondingAuthor":true,"prefix":"","firstName":"Jan","middleName":"Christian","lastName":"Koj","suffix":""},{"id":274424991,"identity":"acec8012-275b-431a-bb31-94e02c9fa6ca","order_by":1,"name":"Petra Zapp","email":"","orcid":"","institution":"Forschungszentrum Jülich","correspondingAuthor":false,"prefix":"","firstName":"Petra","middleName":"","lastName":"Zapp","suffix":""},{"id":274424992,"identity":"c1501f85-b1c4-4fe8-8852-493ba8f47d34","order_by":2,"name":"Christoph Wieland","email":"","orcid":"","institution":"University of Duisburg-Essen","correspondingAuthor":false,"prefix":"","firstName":"Christoph","middleName":"","lastName":"Wieland","suffix":""},{"id":274424993,"identity":"91f7d930-0a60-4210-a5af-d2b9ddc80c69","order_by":3,"name":"Klaus Görner","email":"","orcid":"","institution":"Gas- und Wärme-Institut Essen","correspondingAuthor":false,"prefix":"","firstName":"Klaus","middleName":"","lastName":"Görner","suffix":""},{"id":274424994,"identity":"95dd71bc-15e1-4dd4-aed1-efc946c77660","order_by":4,"name":"Wilhelm Kuckshinrichs","email":"","orcid":"","institution":"Forschungszentrum Jülich","correspondingAuthor":false,"prefix":"","firstName":"Wilhelm","middleName":"","lastName":"Kuckshinrichs","suffix":""}],"badges":[],"createdAt":"2024-02-15 13:07:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3958723/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3958723/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13705-024-00497-6","type":"published","date":"2024-12-05T15:57:19+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":51662746,"identity":"5aa1164b-5c3d-401c-bd36-175fb946f2ae","added_by":"auto","created_at":"2024-02-26 20:12:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":103338,"visible":true,"origin":"","legend":"\u003cp\u003eAEC, PEMEC, and SOEC: schematic illustration of cell concepts and reactions – based on Steinmüller et al. and Liu et al. [3, 18]\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3958723/v1/3b6cffafbf0849a7a184cc10.png"},{"id":51663091,"identity":"ba36c26d-ff01-41b1-9c40-b101c0837ab6","added_by":"auto","created_at":"2024-02-26 20:20:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":139541,"visible":true,"origin":"","legend":"\u003cp\u003eSystem boundaries of cradle-to-gate hydrogen production; based on [DISS 150]\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3958723/v1/8e6aaa2014e50e79296606a8.png"},{"id":51662747,"identity":"99dd2191-01be-4fff-ba25-3d0e52d2f59c","added_by":"auto","created_at":"2024-02-26 20:12:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":65310,"visible":true,"origin":"","legend":"\u003cp\u003eAssumed cumulative installed capacities of AEC, PEMEC, and SOEC until the year 2045 based on [43, 44, 47]\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3958723/v1/b268b895f3bdb4f9d322bdf1.png"},{"id":51662749,"identity":"2de2de2e-fc96-4073-8094-de6e2abb7ca2","added_by":"auto","created_at":"2024-02-26 20:12:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":274102,"visible":true,"origin":"","legend":"\u003cp\u003eCapEx of electrolysis technologies based on learning curve analysis - taking into account different learning rates and capacity increases\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3958723/v1/fa38891cf111f5b5abcd71a5.png"},{"id":51662748,"identity":"1f950b3e-5312-45c1-b64a-df2ee3ac20bc","added_by":"auto","created_at":"2024-02-26 20:12:31","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":92738,"visible":true,"origin":"","legend":"\u003cp\u003eGWP\u003csub\u003e100\u003c/sub\u003e results of hydrogen production by AEC, PEMEC, and SOEC compared to SMR\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3958723/v1/a555432255b232bb8b41ca23.png"},{"id":51662753,"identity":"6b23b1cd-9f7b-4b86-a136-947b39b73ebb","added_by":"auto","created_at":"2024-02-26 20:12:31","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":372309,"visible":true,"origin":"","legend":"\u003cp\u003eContribution analysis for AEC, PEMEC, and SOEC for the indicator GWP\u003csub\u003e100\u003c/sub\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-3958723/v1/7bec6c6cbacd0885db1ed5a6.png"},{"id":51662750,"identity":"8fb40611-923c-4795-b877-439f5ecf03d6","added_by":"auto","created_at":"2024-02-26 20:12:31","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":196253,"visible":true,"origin":"","legend":"\u003cp\u003eGWP\u003csub\u003e100\u003c/sub\u003e results for different LCI stack manufacturing and construction models\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-3958723/v1/4ca46b74538e191ca3c701d1.png"},{"id":51662752,"identity":"8908fdee-8c57-4a0b-8303-731427bc2ed4","added_by":"auto","created_at":"2024-02-26 20:12:31","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":127274,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of AEC, PEMEC, and SOEC with SMR for the year 2022 using several environmental indicators\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-3958723/v1/fda88afe88f44df1d0801c55.png"},{"id":51662751,"identity":"e6526107-f902-4809-a713-3be85e8dc670","added_by":"auto","created_at":"2024-02-26 20:12:31","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":111543,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of AEC, PEMEC, and SOEC with SMR for the year 2045 using several environmental indicators\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-3958723/v1/aced36ec87c4657373b0472e.png"},{"id":51662755,"identity":"945f15b9-c2d4-4c41-8a92-5088f2ec09b0","added_by":"auto","created_at":"2024-02-26 20:12:31","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":69665,"visible":true,"origin":"","legend":"\u003cp\u003eLCOH results of AEC, PEMEC, and SOEC for the years 2022 and 2045\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-3958723/v1/12da61efe37c3f837cee41fc.png"},{"id":51662754,"identity":"d2ed84ad-4ff5-453d-8f9d-bc83b40aa7f8","added_by":"auto","created_at":"2024-02-26 20:12:31","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":47772,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of four parameter variations by ±10% on the GWP\u003csub\u003e100\u003c/sub\u003e results for PEMEC\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-3958723/v1/e4ecc4107b9f348a674b7b40.png"},{"id":51663092,"identity":"695bf3e1-043c-439a-8e24-9ceb9770d3d2","added_by":"auto","created_at":"2024-02-26 20:20:31","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":52437,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of electricity demand variations by ±10% on the GWP\u003csub\u003e100\u003c/sub\u003e results for three electrolysis technologies\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-3958723/v1/d39c64c975b13ae150228992.png"},{"id":51662757,"identity":"f9c2415c-16a1-4dde-920f-6bef8ef7e904","added_by":"auto","created_at":"2024-02-26 20:12:32","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":48358,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of four parameter variations by ±10% for three electrolysis technologies on the LCOH results for three electrolysis technologies - including prospective best cases (BC) and worst cases (WC)\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-3958723/v1/4f4ec6cf8e8776040ac68c64.png"},{"id":70965388,"identity":"d692efd6-7ed0-46f8-b55f-080c956d8a8d","added_by":"auto","created_at":"2024-12-09 16:19:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2506134,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3958723/v1/e354e8c3-1914-41aa-9447-b342f9c4c042.pdf"},{"id":51662744,"identity":"571cae51-c1c2-4954-8f5b-b502cc9ea8d9","added_by":"auto","created_at":"2024-02-26 20:12:31","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":49904,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-3958723/v1/7d7af6c13f56ebcb49bfd59b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Water electrolysis technologies in the future – projection of environmental impacts and levelized costs until 2045","fulltext":[{"header":"Background","content":"\u003cp\u003eAccording to the Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC) the global temperature has increased by about 1.07 K since 1850\u0026nbsp;[1]. One main reason is the anthropogenic use of fossil energy. Consequently, the reduction and substitution of fossil energy is a major challenge and is consequently addressed by current energy transformation approaches. A main outcome of the recent United Nations Climate Change Conference, COP28, is an agreement on the beginning of the end of the fossil fuel era. Hydrogen produced by water electrolysis technologies can substitute fossil energy carriers when renewable energy is used. Furthermore, hydrogen is interesting for energy storage and a wide range of applications\u003cem\u003e\u0026nbsp;\u003c/em\u003e[2, 3]. Thus, it is a key enabler of a transition to a post-fossil fuel age.\u003c/p\u003e\n\u003cp\u003eThe three most mature and predominant water electrolysis technologies are the objects of this study. These technologies are namely alkaline electrolysis cells (AEC), polymer electrolyte membrane electrolysis cells (PEMEC), and solid oxide electrolysis cells (SOEC). These labels mirror their fundamental cell concepts\u0026nbsp;[3].\u003c/p\u003e\n\u003cp\u003eWithin this study, environmental impacts as well as costs along the life cycle of these water electrolysis technologies are assessed by Life Cycle Assessment (LCA) and Life Cycle Costing (LCC). Knowledge of the key technological, economic, and environmental development potentials is of great importance for today\u0026rsquo;s technology roll-out as well as the future development of the hydrogen economy. Within this chapter, relevant literature as well as identified research gaps around these technologies are presented. This is followed by a goal definition and the formulation of research questions for this study. Subsequently, technological principles and differences of these technologies are described.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrevious relevant studies and identified research gaps\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe three water electrolysis technologies have several similarities, like the requirement of water and electricity for their operation and hydrogen as output. However, they differ in their characteristics, making technology-specific assessments necessary.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA recent review by Wilkinson et al.\u0026nbsp;[4]\u0026nbsp;on LCAs for hydrogen production reveals that several LCA publications consider only two different water electrolysis technologies. However, no study was identified that included a comparison of all three technologies. Also, a review by Koj et al.\u0026nbsp;[5]\u0026nbsp;of 32 studies, including water electrolysis technologies and further Power-to-X (PtX) technologies, illustrated the scarcity of electrolysis technology comparisons in LCAs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThough not included in the review studies, some studies with environmental assessments of all three electrolysis technologies has been published. Tenhumberg and B\u0026uuml;ker\u0026nbsp;[6]\u0026nbsp;conducted an environmental comparison of AEC, PEMEC, and SOEC. Their study is limited to a consideration of the climate change impact results of these technologies and does not completely represent an assessment according to the ISO 14040 and 14044 standards for LCA\u0026nbsp;[7, 8]. Consequently, the study has to be considered as a carbon footprint assessment and not an LCA of these technologies. In addition to the carbon footprint, hydrogen production costs are analyzed. Conditions between the years 2018 and 2030 were taken into account. Furthermore, the LCA by Zhao et al.\u0026nbsp;[9]\u0026nbsp;compares the manufacturing and construction processes of the three technologies, but leaves out an assessment of their operation phases. Also, prospective conditions and costs were not analyzed. Two LCA articles published by Gerloff consider all three electrolysis technologies\u0026nbsp;[10, 11]. In the first article published by Gerloff\u0026nbsp;[11]\u0026nbsp;the main focus is on Power-to-Methane plants. However, environmental results for electrolysis are identifiable as part of the overall results. Gerloff\u0026nbsp;[10]\u0026nbsp;compared the three electrolysis technologies using an environmental assessment study, which can be regarded as an LCA. In addition to the climate change impact category, up to seven other environmental impact categories were analyzed in one part of the analysis. In addition to conditions for the year 2019, future scenarios for the years 2030 and 2050 were also considered. However, the only prospective variation that takes place is about the composition of the national electricity mix. Variations of important technological parameters, such as electricity demand and the service life of the stacks, do not appear to have taken place. The study by Gerloff\u0026nbsp;[10]\u0026nbsp;does not include an LCC or any other form of economic analysis. Compared to the first article, the second article included several identical approaches (e.g., assessments of the years 2019, 2030, and 2050 and same impact categories) and assumptions regarding the electrolysis (e.g. the electricity demand). The most recent environmental assessment publication considering all three water electrolysis technologies was published by Zhang et al.\u0026nbsp;[12]. The study can be considered as an LCA and takes water electrolysis with onshore and offshore wind power into account. Changes in parameters over time, economic aspects or hydrogen production using the electricity grid mix are not considered.\u0026nbsp;Table A 1\u0026nbsp;in the Appendix summarizes several characteristics of these previous LCA studies compared to the present LCA study.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eIn addition to LCA studies, LCC and its interaction with LCA is of interest. The LCA review by Wilkinson et al.\u0026nbsp;[4]\u0026nbsp;also includes information on whether economic and/or technological aspects are considered alongside environmental aspects. According to this, 15% of the studies dealt with economic in addition to environmental aspects, 10% considered economic, technological, and environmental aspects in parallel. However, such combined analyses usually focus only on one electrolysis technology. In addition, prospective analyses that include both LCC and LCA results are extremely scarce.\u003c/p\u003e\n\u003cp\u003eA closer look at studies that can be regarded as LCC studies of hydrogen production using water electrolysis in a corresponding review study by Nicita et al.\u0026nbsp;[13]\u0026nbsp;shows a clear focus on PEMEC electrolysis technology. SOEC was only considered in one LCC study by Bekel and Pauliuk\u0026nbsp;[14].\u003c/p\u003e\n\u003cp\u003eThe authors of this study are aware of only one publication comparing the LCC results of all three electrolysis technologies, which was published as part of the center of excellence \u0026quot;Virtual Institute - Power to Gas and Heat\u0026quot; project and which serves as the basis for the present work\u0026nbsp;[15].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjectives and research questions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo address the existing research gaps, this study aims to investigate various technological, economic, and environmental aspects considering advancements of the hydrogen production from AEC, PEMEC, and SOEC until 2045. The study aims at pointing out the development of relevant influencing technological factors and their impact on environmental and economic results. Special attention is paid to following factors:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cem\u003edifferent electricity sources (wind power vs. electricity mix),\u003c/em\u003e\u003c/li\u003e\n \u003cli\u003e\u003cem\u003edevelopment of the demand for electricity,\u0026nbsp;\u003c/em\u003e\u003c/li\u003e\n \u003cli\u003e\u003cem\u003edevelopment of the demand for critical raw/construction materials,\u0026nbsp;\u003c/em\u003e\u003c/li\u003e\n \u003cli\u003e\u003cem\u003edevelopment of lifetimes.\u003c/em\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFurthermore, the study has to answer some fundamental research questions to achieve its aims:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cem\u003eHow do the electrolysis technologies differ from each other regarding different environmental impact categories and compared to a reference technology?\u003c/em\u003e\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eHow do the life cycle costs differ when using different water electrolysis technologies?\u003c/em\u003e\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eHow do the results differ for the years 2022 and 2045?\u003c/em\u003e\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eDo the environmental and economic results show a positive or opposite dependency compared to each other?\u003c/em\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo answer these research questions, technological, environmental, and economic sub-models are implemented and presented in the chapter \u0026quot;Methods\u0026quot;.\u003c/p\u003e\n\u003ch3\u003eTechnology description\u003c/h3\u003e\n\u003cp\u003eThe splitting of water using water electrolysis is an electrochemical reaction. This requires an energy supply in the form of direct current [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] as well as heat [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The reaction occurs in electrolysis cells, and Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) describes the overall reaction:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$${\\text{H}}_{2}\\text{O} \\to {\\text{H}}_{2}+{\\frac{1}{2}\\text{O}}_{2} \\varDelta {\\text{H}}_{\\text{R}}^{0}=+ 286\\frac{\\text{k}\\text{J}}{\\text{m}\\text{o}\\text{l}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eDespite the same overall reaction, the three electrolysis technologies differ. This can already be seen in the differences in the cell structure and partial reactions.\u003c/p\u003e \u003cp\u003eThe schematic representations of the cell concepts on which the three electrolysis technologies are based, as well as partial reactions, can be seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe AEC is characterized by two chambers separated by a diaphragm. These chambers contain a liquid electrolyte, a solution of water and potassium hydroxide (KOH). At the cathode the splitting of water into H\u003csub\u003e2\u003c/sub\u003e and OH\u003csup\u003e\u0026minus;\u003c/sup\u003e ions occurs [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. So far, nickel and nickel alloys are preferably used as electrode materials [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Composite materials, such as Zirfon\u0026reg; consisting of zirconium oxide and polysulfone, are currently mostly used for the diaphragm [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn PEMEC, a proton-conducting polymer membrane, usually NAFION\u0026reg;, is used as the electrolyte [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In these cells, the water is split on the anode side. From there, the protons flow through the membrane. Hydrogen is then formed at the cathode. In this technology, the membrane is directly connected to the electrodes, as no liquid electrolyte is used [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In addition to the membrane material mentioned above, the following materials are particularly relevant for PEMEC: Platinum as the anode material and iridium or ruthenium as possible cathode materials [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe central element of the SOEC is a solid oxide layer, which acts as the electrolyte. At the anode, the water vapor used in this high-temperature technology is split into H\u003csub\u003e2\u003c/sub\u003e and O\u003csup\u003e2\u0026minus;\u003c/sup\u003e ions. The O\u003csup\u003e2\u0026minus;\u003c/sup\u003e ions can reach the anode with the help of vacancy diffusion and react there to form O\u003csub\u003e2\u003c/sub\u003e [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Typically, the electrolyte or the solid oxide layer consists of zirconium oxide (ZrO\u003csub\u003e2\u003c/sub\u003e) doped with yttrium oxide (Y\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Nickel is used as the catalyst [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe most advanced [\u003cspan additionalcitationids=\"CR23 CR24 CR25\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and most common [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] electrolysis technology to date is the AEC system, which allows realizing large plant capacities at the lowest investment costs to date for water electrolysis technologies [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. It should be noted that minor impurities and an associated product purity of \u0026ge;\u0026thinsp;99.5% may still be present before the final gas treatment [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs mentioned before, several materials are required for the manufacturing and construction of electrolysis cell stacks. Regarding the used life cycle inventories for these cell stacks, which can be found in the \"Methods\" section, the following materials for electrolysis technologies are considered as critical by the EU list of critical raw materials [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. For the construction of AEC stacks graphite and nickel are typically used. Titanium as well as the PGMs iridium and platinum are used for the construction of PEMEC stacks. Small amounts of titanium can also be used for the construction of SOEC systems. Furthermore, cobalt, nickel and the rare earth elements lanthanum and yttrium are also used for SOEC construction. More detailed information about the assumed materials and their amounts can be found in the chapter \"Methods\".\u003c/p\u003e \u003cp\u003eThe main methodological aspects of LCA and LCC are first explained before the specific methodological selection for this study is presented.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMethodological approach\u003c/h2\u003e \u003cp\u003eLCA is characterized by standardization, based on ISO standards 14040 and 14044 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Within LCA, environmental aspects and impacts throughout the life cycle, ranging from raw material extraction to disposal are examined. Due to its comprehensive and multi-layered analysis possibilities, LCA is used in this study as the environmental assessment method.\u003c/p\u003e \u003cp\u003eThe economic aspects of water electrolysis systems can be analyzed and compared using various methodological concepts. Techno-economic analysis is a very common approach, using selected economic indicators based on a technical analysis. LCC is an alternative to this. In methodological terms, LCC and LCA are similar and can be based mostly on the same data. This method is based on the system boundaries, the functional unit (FU), and the phases of classic LCA. Due to its proximity to the LCA approach and the resulting data consistency, the LCC approach is used in this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eGoal and scope of LCA and LCC\u003c/h2\u003e \u003cp\u003eAs described in more detail in the \"Background\" chapter, this study aims to investigate various technological, economic, and environmental aspects as well as advancements in hydrogen production from AEC, PEMEC, and SOEC until 2045.\u003c/p\u003e \u003cp\u003eFor the present LCA and LCC study, a mass-related FU is selected with \"1 kg H\u003csub\u003e2\u003c/sub\u003e\". Furthermore, this specification is supplemented by the specification of the physical property, in this case the pressure, which is assumed to be 10 bar. The technologies examined for the product hydrogen are thus directly comparable in terms of their environmental impacts and life cycle costs.\u003c/p\u003e \u003cp\u003eAll three water electrolysis technologies, AEC, PEMEC, and SOEC, are analyzed. Germany is chosen as the geographical framework. In addition to current conditions in 2022, future developments, especially including technological improvements and a decarbonizing electricity grid mix, are also analyzed. As Germany is aiming for greenhouse gas neutrality by 2045, this year is also of particular interest and is analyzed in this paper. For both years, a time horizon of plant operation and accompanying hydrogen production over 20 years is considered.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eModeling approach, system boundary, software, and databases\u003c/h2\u003e \u003cp\u003eAn attributive cradle-to-gate LCA approach is chosen for this study. Typical cradle-to-gate assessments begin with the extraction of raw materials, through the construction of the plants, energy supply and conversion, and end with the provision of hydrogen (at the factory gate). A possible subsequent use of the hydrogen, e.g., as fuel, lies outside these system boundaries. A schematic representation of the main system boundaries is given in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurthermore, the recycling and end-of-life of the electrolysis systems are not yet standardized and, consequently, not considered in the LCA part of this study. The LCA software, openLCA version 1.10.3, is used. The LCA database ecoinvent (version 3.7.1) in the \"cut-off by classification\" system model is used to provide background data for the Life Cycle Inventory [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe information on the foreground data used for LCA and LCC for AEC, PEMEC and SOEC is discussed in the later sections \"Common data for LCA and LCC\", \"Data for LCA\", and \"Data for LCC\".\u003c/p\u003e \u003cp\u003eFor the LCC analyses, an own Excel tool including numerous literature-based economic parameters of the technology options under consideration is used. As a variant of LCC, environmental life cycle costing was chosen. The LCC Excel tool developed also contains key formulas for the LCOH calculation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEnvironmental impacts (LCIA indicators)\u003c/h2\u003e \u003cp\u003eThe synthesis of existing LCIA methods in the European context in the form of the Environmental Footprint (EF) framework [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] in version 3.0 was used for this study. The mid-point impact indicator values selected are considered to be scientifically more robust than end-point indicators [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e contains a list of the environmental categories and indicators selected on this basis, as well as the associated units and abbreviations used.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEnvironmental indicators selected for LCIA\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEF impact category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImpact category indicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAbbre-viation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClimate change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlobal warming potential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGWP\u003csub\u003e100\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ekg CO\u003csub\u003e2eq\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOzone depletion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOzone depletion potential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eODP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ekg CFC11 eq\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticulate matter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImpact on human health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePM-ihh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003edisease incidence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIonising radiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHuman exposure efficiency relative to U\u003csup\u003e235\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIR-hee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ekBq U-235 eq\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhotochemical ozone formation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTropospheric ozone\u003c/p\u003e \u003cp\u003econcentration\u003c/p\u003e \u003cp\u003eincrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePOF-toci\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ekg NMVOC eq\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcidification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAccumulated\u003c/p\u003e \u003cp\u003eexceedance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA-ae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003emol H\u003csup\u003e+\u003c/sup\u003eeq\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEutrophication, terrestrial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAccumulated\u003c/p\u003e \u003cp\u003eexceedance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEP-ter-ae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003emol N eq\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEutrophication, freshwater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFraction of nutrients\u003c/p\u003e \u003cp\u003ereaching freshwater\u003c/p\u003e \u003cp\u003eend compartment (P)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEP-fw-p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ekg P eq\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEutrophication, marine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFraction of nutrients\u003c/p\u003e \u003cp\u003ereaching marine end\u003c/p\u003e \u003cp\u003ecompartment (N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEP-mar-n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ekg N eq\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eLCC indicator and learning curve approach\u003c/h2\u003e \u003cp\u003eThe choice of indicators is also relevant for the LCC. For this study, particular attention was therefore paid to the selection of indicators within existing hydrogen-related publications. An overview study [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] shows that previous LCC calculations of hydrogen production systems have most frequently used the following indicators: Levelized Costs of Hydrogen (LCOH), capital expenditures/plant costs (CapEx), and plant operating expenditures (OPEX or O\u0026amp;M costs). Levelized Costs concepts are considered to be fundamental approaches for techno-economic comparison of competing technologies and/or production sites as well as technology assessments in general [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The LCOH reflects the total costs over the lifetime of the systems under consideration. Furthermore, according to Kuckshinrichs \u0026amp; Koj the LCOH can be understood as a break-even value, that indicates a price required as revenue over the lifetime of a technology in order to justify an investment [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The CapEx and OpEx indicators can be considered separately but are also components of the production costs. As the LCOH is based on CapEx and OpEx and is more meaningful and relevant, OpEx and CapEx are not treated separately as part of the LCC calculations in this study. Based on its advantages and the establishment of its use, LCOH is selected as the only indicator for LCC in this study. In its simplest form, the LCOH represents the following mathematical relationship: the sum of CapEx and OpEx is divided by the total energy yield of the plant under consideration over its lifetime and discounted to the reference year [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In addition, sub-categories and further categories can be included in the calculation. Examples are decommissioning costs, taxes, or external costs [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. As described by Kuckshinrichs \u0026amp; Koj [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] LCOH assessments can consider a private (or synonymously business) or social perspective. In this study, a private perspective is used. The differentiation between these two perspectives is not described in detail here but can be found in Kuckshinrichs \u0026amp; Koj [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Eq.\u0026nbsp;(2) takes different previously published LCOH formulations for this private perspective into account [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$LCOH= \\frac{{I}_{0}+ {\\sum }_{t=1}^{n}\\frac{{WC}_{t}+ {EC}_{t}+ {HC}_{t}+{RC}_{t}+{AC}_{t}+ {OFC}_{t}}{{(1+i)}^{t}}}{{\\sum }_{t=1}^{n}\\frac{{MHydrogen}_{t}}{{(1+i)}^{t}}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e(2)\u003c/h2\u003e \u003cp\u003eIn Eq.\u0026nbsp;(2), I\u003csub\u003e0\u003c/sub\u003e stands for the sum of initial investment costs (CapEx). The unit of the investment costs is \u0026euro;\u003csub\u003e2022\u003c/sub\u003e. In addition, several fixed (operation-related) and variable (demand-related) cost components are taken into account. The variable costs, which are based on the amount of hydrogen produced, include water costs per year (WC\u003csub\u003et\u003c/sub\u003e), electricity costs per year (EC\u003csub\u003et\u003c/sub\u003e), and heat costs per year (HC\u003csub\u003et\u003c/sub\u003e). Furthermore, the costs of the cell stack replacement (RC\u003csub\u003et\u003c/sub\u003e) are of relevance. The fixed (operation-related) costs include administration costs (AC\u003csub\u003et\u003c/sub\u003e), insurance costs (IC\u003csub\u003et\u003c/sub\u003e) and other fixed operating costs (OFC\u003csub\u003et\u003c/sub\u003e). All cost components are considered in real terms, meaning that inflation is not considered. The entire service life of the water electrolysis system is recorded with n, while t indicates the respective year under consideration. The variable i represents the interest rate used for discounting. MHydrogen\u003csub\u003et\u003c/sub\u003e indicates the annual amount of hydrogen provided in kWh. As for the LCA recycling and end-of-life of the systems are not considered for LCC in this study. This is also a common approach in many other studies on the calculation of LCOH. The unit for the variables WC\u003csub\u003et\u003c/sub\u003e, EC\u003csub\u003et\u003c/sub\u003e, HC\u003csub\u003et\u003c/sub\u003e, RC\u003csub\u003et\u003c/sub\u003e, AC\u003csub\u003et\u003c/sub\u003e, IC\u003csub\u003et\u003c/sub\u003e, and OFC\u003csub\u003et\u003c/sub\u003e is \u0026euro;\u003csub\u003e2022\u003c/sub\u003e/year for annual production, while the unit for MHydrogen\u003csub\u003et\u003c/sub\u003e is kWh/year (or MWh/year).\u003c/p\u003e \u003cp\u003eThe two parameters I\u003csub\u003e0\u003c/sub\u003e and i are of particular interest, as value assumptions for these parameters are particularly intensely debated in science and beyond, e.g. the debate on Cost of Capital [\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Furthermore, these parameters go along with uncertainties, as they can change over time and vary depending on location. Consequently, an established and multi-layered approach is chosen to determine future capex values. In addition, both latter parameters are subjected to a sensitivity analysis in the result chapter.\u003c/p\u003e \u003cp\u003eTo extrapolate the CapEx values to the year 2045, a learning curve approach is used. The basic learning curve concept was developed by Wright and published in 1936 [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. It analyzed the costs of technologies and their development over a selected period. Additionally, these learning curves combine technological improvements of manufacturing processes over time with cost developments. Thus, for this study, the learning curves were selected for a consistent assessment of prospective technological and LCC developments, by describing the relationship between the production or cumulative capacity increase of a good and the reduction of its costs [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. From the different configuration possibilities of learning curves, Eq.\u0026nbsp;(3) is chosen for this study:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$${C}_{t}={C}_{0}{\\left(\\frac{{X}_{t}}{{X}_{0}}\\right)}^{-\\beta } \\left(3\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWithin Eq.\u0026nbsp;(3) \u0026#119862;\u003csub\u003e0\u003c/sub\u003e stands for the costs at time t\u0026thinsp;=\u0026thinsp;0. X\u003csub\u003e0\u003c/sub\u003e stands for the cumulative capacities of technologies at time t\u0026thinsp;=\u0026thinsp;0, while X\u003csub\u003et\u003c/sub\u003e stands for the cumulative capacities at a prospective time t. The applied learning parameter is given by β and can be calculated with a logarithmic equation based on a learning rate. As an example, an economic learning rate of 15% means that the costs decrease by 15% when the cumulative installed capacity doubles. [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eTo calculate prospective CapEx values for the water electrolysis technologies, it is important to know about the identified learning rates for water electrolysis technologies. For electrolysis, different learning rates between 8% [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] and 18\u0026thinsp;\u0026plusmn;\u0026thinsp;13% [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] were identified by literature research. Table A 2 in the \u003cspan refid=\"Sec24\" class=\"InternalRef\"\u003eAppendix\u003c/span\u003e lists values from the literature according to the level of learning rates. The highest learning rates were identified in the distant past of the last century, when only AEC technology was available and less mature. Consequently, newer values are lower and tend to be higher for PEMEC and SOEC compared to the most mature AEC technology. To take the range of values and different developments into account and present more current conditions, three different learning rates for electrolysis systems are taken into account for the own calculations within this study: 7%, 10%, and 13%.\u003c/p\u003e \u003cp\u003eAs mentioned before learning curve calculations also requires values of production volumes (leading to cumulative installed capacities). So far, several projections of total water electrolysis capacities have been published. However, a differentiation of capacities according to the different electrolysis technologies is given only very rarely. Publications by Boehm et al. [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] are an exception in this regard. In the first publication by Boehm et al. [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] starting values and projections of the global cumulative electrolysis capacities up to the year 2050 are included. The entire globally assumed annual increase in electrolysis capacity is then multiplied by the share of the respective electrolysis technologies, as contained in the publication by Boehm et al. [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Based on the annual capacity expansion and the initial values, the cumulative installed capacity can be calculated. Furthermore, Boehm et al. differentiate between variants of high and low capacity expansion. This differentiation of \"high\" and \"low\" developments of installed capacities from the year 2022 until the year 2045 is also considered in this study and shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows that based on the assumptions by Boehm et al. the highest absolute capacity increases are expected for PEMEC systems. Until the year 2045, higher capacities are expected for AEC compared to SOEC systems. Nevertheless, stronger increases in SOEC capacities are assumed from 2035 in particular, which leads to a noticeable approximation of the results. Furthermore, the highest absolute increases are assumed for the distant future, particularly from 2040 onwards. By contrast, the highest rates of capacity multiplication are already projected for the period between 2025 and 2030.\u003c/p\u003e \u003cp\u003eThe chosen values for the cost components in this study are listed in the section \"Data for LCC\".\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCommon data for LCA and LCC\u003c/h2\u003e \u003cp\u003eFor a fair comparison of technology options, it is important to use a data source that is as consistent as possible. Such a common data source is seen in the \u0026ldquo;State-of-the-Art and Targets\u0026rdquo; of the US Department of Energy (DOE), which were published separately for the three technologies [\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. These documents contain data on the status (State-of-the-Art) of 2022, targets for the year 2026, and ultimate targets for several key performance indicators (KPIs). For the present study especially assumptions for the electricity demand, lifetime, critical raw material content as well as capital cost are relevant. Additionally, the heat demand can be derived from data for the SOEC. Electricity and heat demand, as well as lifetime, are important for both, LCA and LCC. The material content is relevant for LCA, and the capital cost is used for LCC. Within the present study it is assumed that the ultimate targets are applicable for the year 2045. Regarding these technical targets, no restriction to the US market is discernible. The information contained in the DOE documents is therefore considered to be globally applicable and also usable for the German analysis framework.\u003c/p\u003e \u003cp\u003eAdditionally, important data relevant for LCA and LCC were supplemented by literature data on water and KOH demand, as well as our own assumptions on nominal load and full load hours (FLH). A nominal load is also assumed for 2045 to ensure objective comparability and as there are no economies of scale for the stacks due to their modular design. Operation with the electricity mix is based on the assumption of very even operation over a long period of time. The FLH assumed for this are therefore much higher than for the connection to fluctuating electricity-generating wind turbines. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e lists common data for LCA and LCC assumed in this study.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCommon data assumptions for LCA and LCC\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e2045\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePrimary source\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyp of electrolysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePEMEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSOEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePEMEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSOEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNominal load\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMW\u003csub\u003eel\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eown assumption\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLifetime stack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e80,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e80,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e80,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFLH\u003c/p\u003e \u003cp\u003e(Electricity mix (M) / Wind (W))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eh/a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,000 (M) 2,000 (W)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7,000 (M) 2,000 (W)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7,000 (M) 2,000 (W)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7,000 (M) 2,000 (W)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7,000 (M) 2,000 (W)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7,000 (M) 2,000 (W)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eown assumption\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElectricty demand (system)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ekWh/kg H\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeat demand (system)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ekWh/kg H\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater demand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ekg H\u003csub\u003e2\u003c/sub\u003e0/kg H\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKOH demand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ekg KOH/kg H\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003cp\u003eE-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.5 \u003c/p\u003e \u003cp\u003eE-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eData for LCA\u003c/h2\u003e \u003cp\u003eWhen selecting the Life Cycle Inventory (LCI) data for the cells and cell stacks, it is important to ensure that not only transparent LCI models are used, but that these also enable a fair comparison with each other. For this reason, the following LCI models were selected for the stack, as these LCI models also consider stack components made of steel. The model from Lotric et al. was used for PEMEC, the inventory of Koj et al. for AEC, and LCI data published by Schreiber et al. for SOEC [\u003cspan additionalcitationids=\"CR53\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. The LCI model from Barei\u0026szlig; et al. [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] is otherwise frequently used for LCAs with PEMEC technology. However, this only takes into account a very small amount of steel for screws and bolts. The Lotric LCI model used in this work [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] is also characterized by more material information and a high degree of transparency compared to other known PEMEC LCI models like those published by Barei\u0026szlig; et al. and Schmidt Rivera et al. [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Based on the DOE's technical targets, however, it is noticeable that the estimate of the required platinum group metal (PGM) quantity differs significantly from the State-of-the-Art determined by the DOE. Therefore, the value determined by the DOE is used in the PEMEC LCI model for the year 2022 in this study, instead of the original value. Additionally, the energy required for the manufacturing and construction of the three electrolysis technologies should be considered. This aspect and accompanying data are neglected partly in the previously mentioned LCI model publications. Consequently, this additional energy input is considered within the LCI models of this study by the consistent consideration of only one publication. For this purpose, data for all three electrolysis technologies on manufacturing and construction energy published by Gerloff [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] is taken into account. As these assumptions basically rely on the manufacturing of small or micro plants, they were scaled up according to the scaling assumptions mentioned by Gerloff [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. With regard to the German electricity mix for 2022, statistical data [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e] was used and combined into an electricity mix LCI model using own assumptions and available ecoinvent data sets. A study of several research institutes [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e] was used for the electricity mix in 2045 and a model was also created, taking into account own assumptions and ecoinvent data sets. The resulting LCI table of assumed German electricity mixes for 2022 and 2045 can be found in Table A 3. Additionally, LCI data on the construction of the electrolyzers and their components can be found in Table A 4 - Table A 9 in the \u003cspan refid=\"Sec24\" class=\"InternalRef\"\u003eAppendix\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eRegarding future cell stacks, it can be assumed that the use of materials decreases over time in consequence of advancing manufacturing and construction processes and improving material properties. This applies also to the use of raw materials that are considered to be potentially critical. The DOE's ultimate target is to reach an electrode PGM loading of 0.03 g/kW, while 0.8 g/W is regarded as State-of-the-Art for PEMEC. This corresponds to a reduction in specific material requirements of 96.25%. In the European context, there are also targets for the KPIs of electrolysis technologies that are comparable to the DOE technical targets, but do not reflect the status quo in 2022. These are the targets published by the Clean Hydrogen Joint Undertaking (CHJU) or Clean Hydrogen Partnership [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. The CHJU targets assume a reduction in the total demand for critical raw materials as catalysts for PEMEC electrolysis from 2.5 to 0.25 g/kW, i.e., by 90%, between 2020 and 2030. In the case of alkaline electrolysis, the demand for critical raw materials a reduction from 0.6 g/kW in 2020 to 0 g/kW in 2030 is assumed. No clear targets are specified for SOEC in the DOE and CHJU documents. Nevertheless, it can be assumed that the use of critical raw materials will also be significantly reduced in the future for this technology. Based on this, a simplifying and cross-technology assumption of a 96.25% reduction until 2045 compared to the original values (also for the AEC, though a reduction of 100% is mentioned above) is made in this work. Regarding the AEC, this is a rather conservative estimate compared to the CHJU target values.\u003c/p\u003e \u003cp\u003eAll three electrolysis technologies are compared with an established reference technology, in this case steam reforming with natural gas/methane (SMR). The applied LCI data for SMR are based on publications by Wulf [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. The authors describe that data can be considered for the years 2030 and 2032 for this reference technology. As no SMR LCI literature sources extending further into the future could be identified, the model is used for both points in time in this study. The applied LCI model of the German electricity grid mix and used LCI data for SMR can be found in Table A 10, Table A 11, and Table A 12 in the \u003cspan refid=\"Sec24\" class=\"InternalRef\"\u003eAppendix\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eData for LCC\u003c/h2\u003e \u003cp\u003eData for the own LCC model to calculate LCOH were collected with the intention of being as consistent as possible and to take current conditions into account. Thus, most values are taken from the techno-economic publications by Boehm et al. [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Many of the values used to determine LCOH are expressed as a percentage of the CapEx. The CapEx, which is developing over time, is therefore of particular importance. For this reason, the DOE publications already used for the consideration of other electrolysis data [\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] are used as the starting points (values for 2022) and as a basis for the CapEx projections. The DOE values describe the uninstalled CapEx of whole electrolysis systems. The starting values are calculated using the average exchange rate between the Euro and US dollar for 2022 of 1.05 \u003cspan\u003e$\u003c/span\u003e/\u0026euro; [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. For AEC, the starting value in 2022 is 476.19 \u0026euro;/kW, for PEMEC 952.38 \u0026euro;/kW and for SOEC 2,380.95 \u0026euro;/kW. To obtain CapEx values for the year 2045, the already described learning curve approach is used. Considering the three electrolysis technologies, different learning rates (7%, 10%, and 13%), and two different capacity scenarios (low and high increase), the CapEx development values of AEC, PEMEC, and SOEC can be found in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The upper whiskers of the respective boxplots (max value) indicates starting values in the year 2022, as assumed by the DOE documents [\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. In contrast, the lower whisker limit (min value) stands for the calculated CapEx values for the year 2045. The circles represent the CapEx results in five-year increments. Additionally, the center line inside the box marks the median value. The x-marker within the boxplots in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e represents the arithmetic mean of all data points.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor each electrolysis technology, the upper limit of the whiskers boxplots in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e represents the starting values. The learning curve analysis in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows significantly decreasing CapEx values for all three electrolysis technologies. As illustrated, the CapEx of AEC systems can be reduced from 476 to 186 \u0026euro;/kW\u003csub\u003eel\u003c/sub\u003e in the best and to 313 \u0026euro;/kW\u003csub\u003eel\u003c/sub\u003e in the worst of the considered cases. The projected relative reductions range from 34\u0026ndash;61%. For PEMEC systems Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e reveals CapEx reductions from 952 to 195 \u0026euro;/kW\u003csub\u003eel\u003c/sub\u003e as the best and to 446 \u0026euro;/kW\u003csub\u003eel\u003c/sub\u003e as the worst case. These decreases range from 53\u0026ndash;80%. CapEx of SOEC systems can be reduced from 2381 \u0026euro;/kW\u003csub\u003eel\u003c/sub\u003e to 363 \u0026euro;/kW\u003csub\u003eel\u003c/sub\u003e (best case) and 960 \u0026euro;/kW\u003csub\u003eel\u003c/sub\u003e (worst case). Furthermore, significantly stronger effects are provoked by the learning rate variations than by different capacity scenarios. Higher CapEx starting values are given for PEMEC systems, but by 2045 this technology can reach the level of AEC systems in the best case.\u003c/p\u003e \u003cp\u003eFor the own LCC model the obtained best (BC) and worst case (WC) results from the learning curve analysis for the year 2045 were considered as CapEx values for each technology.\u003c/p\u003e \u003cp\u003eTo keep the LCOH calculations as consistent as possible, further relevant data were taken from the Supplementary Material to a paper published by Boehm et al. [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. The publication includes parameters from 2020 to 2050. As no exact figures were available for the years 2022 and 2045, values for 2020 and 2050 were taken from the publication. In particular, the assumptions regarding the costs of electricity and heat can be discussed critically, as the data published by Boehm et al. [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] could not already consider more recent developments with effects on energy markets. However, the development of these prices will remain subject to considerable uncertainty in the future. For this reason, these assumptions are initially used here as a consistent basic assumption and the effects of other prices are shown later in a sensitivity analysis. The final choice of assumptions with exclusive relevance for the LCC calculations can be found in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eData for LCC (LCOH) calculations and the years 2022 and 2045\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eunit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e2045\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eprim. source\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElectrolysis technology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePEMEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSOEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePEMEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSOEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpec. Invest (CapEx)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026euro;\u003csub\u003e2022\u003c/sub\u003e/\u003c/p\u003e \u003cp\u003ekW\u003csub\u003eel\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e476.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e186.3 (BC)\u003c/p\u003e \u003cp\u003e313.2 (WC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e195.4\u003c/p\u003e \u003cp\u003e(BC)\u003c/p\u003e \u003cp\u003e445.52 (WC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e362.5 (BC) 959.9 (WC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] and own calcul.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStack share on CapEx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpEx fixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% of CAPEX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsurance costs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% of CAPEX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdministration costs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% of CAPEX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElectricity supply costs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026euro;ct/kWh\u003csub\u003eel\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003cp\u003e(Mix)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003cp\u003e(Mix)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003cp\u003e(Mix)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e(Wind)\u003c/p\u003e \u003cp\u003e8\u003c/p\u003e \u003cp\u003e(Mix)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e(Wind)\u003c/p\u003e \u003cp\u003e8\u003c/p\u003e \u003cp\u003e(Mix)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e(Wind)\u003c/p\u003e \u003cp\u003e8\u003c/p\u003e \u003cp\u003e(Mix)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeat supply costs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026euro;ct/kWh\u003csub\u003eth\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterest rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eLCIA-Results\u003c/h2\u003e\n \u003cp\u003eAs part of the life cycle impact assessment, the absolute GWP\u003csub\u003e100\u003c/sub\u003e results of hydrogen production using electrolysis technologies are first compared with the reference technology. Subsequently, a contribution analysis shows the different reasons for the results. Causes for the GWP\u003csub\u003e100\u003c/sub\u003e results of different cell stack variants are determined. Finally, additional impact categories are investigated and compared with the reference technology.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e first shows the absolute GWP\u003csub\u003e100\u003c/sub\u003e results for different electrolysis technologies, points in time and power supply variants in comparison with the reference technology, steam methane reforming (SMR).\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e clearly illustrates the great potential for reducing the GWP\u003csub\u003e100\u003c/sub\u003e of hydrogen production through operation with wind power compared to the use of grid electricity (electricity mix). By using wind power, reductions of almost 93% can be achieved for AEC and PEMEC systems, while a decrease of 81% is possible for SOEC.\u003c/p\u003e\n \u003cp\u003eElectrolysis based on the German electricity mix in 2045, which is assumed to be completely renewable, provokes still significantly higher results in GWP\u003csub\u003e100\u003c/sub\u003e values than for wind power supplied systems (35.7\u0026ndash;41.2%), but the gap between the values is narrowing. Compared to SMR, the water electrolysis technologies can achieve up to 87.8% lower values for the GWP\u003csub\u003e100\u003c/sub\u003e indicator when using wind power. The results are converging across the technologies over time. AEC and PEMEC are already at a very comparable level in 2022, which is due to the identical electricity consumption assumptions. The different contributions to the overall environmental impacts of hydrogen production are discussed in detail in the contribution analysis. Figure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e illustrates the relative contributions to the results of hydrogen production for the GWP\u003csub\u003e100\u003c/sub\u003e indicator.\u003c/p\u003e\n \u003cp\u003eAs can be seen in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, the energy sources, electricity and, in the case of SOEC also steam/heat, are responsible for most of the GWP\u003csub\u003e100\u003c/sub\u003e results. The contribution of the electricity supply to the environmental impacts is most pronounced in the case of electricity mix use. The contributions shown can be allocated to the life cycle phases of manufacturing and construction as well as operation. Plant operation dominates over manufacturing and construction across all technologies. Manufacturing and construction include the cells, cell stacks, and the BoP components. Additionally, a replacement is considered if the number of hours of hydrogen production exceeds the service life. For SOEC, a combined view of the last two figures shows that the clear prospective reduction in GWP\u003csub\u003e100\u003c/sub\u003e results is primarily due to the assumed more environmentally friendly heat supply.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e shows the results of the GWP\u003csub\u003e100\u003c/sub\u003e indicator for the different electrolysis technologies and for different years and underlying LCI models. The addition A indicates the respective original LCI model. The suffix B describes the consideration of assumptions on production from the publication by Gerloff [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e] as used in this study and explained in the section \u0026quot;Data for LCA\u0026quot;. For each technology variant, the five materials (Top 5) with the highest influence on the GWP\u003csub\u003e100\u003c/sub\u003e indicator are considered. The remainder (Rest) always includes all contributions that cannot be assigned to these respective Top 5 contributions. Some of the material designations are abbreviated and not mentioned before. ABS is an acronym for acrylonitrile-butadiene-styrene, (P)TFE is the abbreviation of (poly)tetrafluoroethylene, and NMP stands for N-methyl-2-pyrrolidone.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e illustrates that the results of the different LCI stack manufacturing and construction models are heterogeneous. The PEMEC electrolysis stacks, whose production in 2022 is still associated with the highest results, induce the lowest results in 2045. For PEMEC this is primarily provoked by the high contributions of the critical raw materials, PGMs and titanium, in the year 2022. This is also because the mining and provision of platinum and iridium are particularly energy- and emissions-intensive. According to the International Renewable Energy Agency (IRENA), one kilogram of these materials, including their supply, contributes around 10,000 kg CO\u003csub\u003e2eq\u003c/sub\u003e to climate change [\u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e]. As a significant decrease in the specific use of these materials is expected and assumed in this study, also the climate change results are strongly declining. The AEC and SOEC also show significant reductions in the GWP\u003csub\u003e100\u003c/sub\u003e results for the year 2045 compared to those for the year 2022. However, their results are not determined to the same extent by critical raw materials. The contributions of manufacturing energy in the LCI models for 2022 differ significantly. This manufacturing energy assumption based on the publication by Gerloff [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e] leads to significantly higher results than in the original models by Koj et al. for AEC, Lotric et al. for PEMEC, and Schreiber et al. for SOEC [\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e]. While the calculated GWP\u003csub\u003e100\u003c/sub\u003e results for the \u0026quot;B\u0026quot; LCI models are around 47% higher for AEC and PEMEC systems the results of SOEC are even 89% higher than for model \u0026quot;A\u0026quot;.\u003c/p\u003e\n \u003cp\u003eThe data from Gerloff [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e] on manufacturing and construction energy that is used for the \u0026quot;B\u0026quot;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003eLCIA for additional impact categories and comparison with the reference technology\u003c/h2\u003e\n \u003cp\u003eThe environmental analyses in this study are not limited to the GWP\u003csub\u003e100\u003c/sub\u003e indicator. Further indicators listed in the \u0026quot;Methods\u0026quot; chapter are included in the analysis and the electrolysis technologies are analyzed in comparison with each other and with SMR, using spider diagrams. The presentation is based on a decadal logarithmic scale and the results are shown relative to the environmental impacts of SMR. The gray area (100% values) indicates the calculated environmental impacts of SMR for each impact category. For greater clarity and comprehensibility, the analyses for 2022 and 2045 are shown in separate diagrams. Figure \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e shows the results for the year 2022.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e reveals that the advantages of certain technology variants determined for GWP\u003csub\u003e100\u003c/sub\u003e do not apply equally to all additional environmental impacts considered. Furthermore, Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e illustrates clear differences between the technology variants that produce hydrogen with the German electricity mix in 2022 and those that do so with wind power also for the other impact categories. The variants using the electricity mix have a significantly higher environmental impact. In the most extreme case of the eutrophication potential of fresh water, the values for operation with the electricity mix are even up to 115 times higher compared to SMR. The main reason for this is coal-fired power generation as a component of grid electricity (electricity mix). Large amounts of the energy- and emission-intense produced materials steel, aluminum, and copper are required for these kinds of power plants. These electricity mix contributions also have a high impact on several other environmental indicators.\u003c/p\u003e\n \u003cp\u003eIn contrast, electrolysis using wind power already achieves significantly lower results in 2022 compared to SMR regarding the GWP\u003csub\u003e100\u003c/sub\u003e and ODP indicators and comparable results with regard to the EP-mar-n, EP-ter-ae, A-ae and POF-toci indicators. The ODP results of electrolysis technologies supplied by wind power are 61\u0026ndash;86% lower, and the GWP\u003csub\u003e100\u003c/sub\u003e results are 63\u0026ndash;82% lower than SMR. However, regarding EP fw, IR, and PM, water electrolysis with wind power does not achieve the environmental performance of SMR. The main reason for this is the environmental impact caused by the upstream processes of the steel components required for the cell stacks.\u003c/p\u003e\n \u003cp\u003eThe results of the electrolysis technologies compared to SMR for 2045 are presented in Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eAs illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e for the year 2045, the electricity mix variants are significantly more competitive in terms of their environmental performance compared to the reference technology and the present. This is a result of the completely renewable electricity mix. Thus, the values clearly improve against 2022. Depending on the technology, the variants with wind power perform better than the reference technology for five or six indicators (POF-toci, ODP, GWP\u003csub\u003e100\u003c/sub\u003e, A-ae, EPter-ae and EP-mar-n). Advantages that are given for both the variants with wind and with the mix are shown for the indicators ODP, GWP\u003csub\u003e100\u003c/sub\u003e, and POF-toci. Clear disadvantages with up to five times higher environmental impacts compared to the reference technology are only given for the EP fw indicator. The other indicator for which significantly higher results are available for all electrolysis variants considered, up to 160% higher, is PM-ihh. The use of steel for cell stacks and for constituents of the electricity provision is also of great importance for these indicators, as high environmental impacts are associated with the energy- and consequently emission-intense upstream processes of steel.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003eLCC results\u003c/h2\u003e\n \u003cp\u003eBased on the assumptions and calculated CapEx values in the chapter \u0026quot;Methods\u0026quot; the LCOH of the electrolysis technologies is calculated for the years 2022 and 2045. On the one hand, the LCOH resulting from operation with the electricity mix in the year 2022 is determined. On the other hand, the costs of electrolysis operation with wind power in 2045 are analyzed. Extreme cases are thus taken into account. For 2045, the WC is given for the lowest learning rates and capacity increases within the assessed range. Contrary to this, the BC is given for highest learning rates and capacity increases within the range. The resulting LCOH for water electrolysis technologies, given in \u0026euro;\u003csub\u003e2022\u003c/sub\u003e/kg H\u003csub\u003e2\u003c/sub\u003e, is illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eA wide range and significant influencing factors that change over time can be identified in Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e. In 2022, there are still clear differences in LCOH results for the three electrolysis technologies. The LCOH is lowest for AEC systems. This is due to higher CapEx and higher costs for replacing the stacks given for PEMEC and SOEC systems. With these two systems, more frequent stack replacements occur due to lower lifetime expectations and the high assumed operating time when operating with the electricity mix. Analyses for the year 2045 show a strong convergence of the LCOH. The calculated range reaches 2.3\u0026ndash;3.8 \u0026euro;\u003csub\u003e2022\u003c/sub\u003e/kg H\u003csub\u003e2\u003c/sub\u003e. Higher lifetimes have a reducing effect on the LCOH as no replacement costs will occur. Reductions in LCOH will additionally be provoked by a prospective CapEx decrease. In contrast, the assumed electricity supply costs increase from 2022 to 2045, along with the accompanying specific cost contribution. This cost-increasing effect outweighs cost-reducing effects (especially CapEx reductions) if the AEC systems are operated in 2045 and the WC. Consequently, the LCOH increase in this special case. For the SOEC systems, there is an additional cost reduction potential if waste heat from a neighboring plant could be used free of charge or at a low cost.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003eSensitivity analyses\u003c/h2\u003e\n \u003cp\u003eIn the following section, separate sensitivity analyses for LCA and LCC are presented. The sensitivity analyses start with an assessment of the GWP\u003csub\u003e100\u003c/sub\u003e results. Based on an effort to carry out analyses that are as similar and consistent as possible, the same parameters are used wherever possible. Four parameters were considered in the LCA sensitivity analyses and mentioned subsequently. In line with the results presented above, the parameter of electricity demand revealed outstanding importance. Additionally, the variation of the parameters full load hours, lifetime, and time horizon are also examined. Beside the used process data, these parameters are relevant and variable parameters within the underlying LCI models of this study. The full load hours and the time horizon are each included in the calculation of the amount of hydrogen produced. Consequently, these parameters lead to changes in the amount of cell stacks and cells considered producing a fixed amount of hydrogen. The lifetime assumption, on the other hand, is not included in the balancing of the amount of hydrogen generated. This parameter only takes into account whether components need to be replaced during the period under consideration. The sensitivity analyses for the four parameters are applied to one of the electrolysis technologies under consideration. PEMEC technology was selected because it has become the electrolysis technology that has received the most attention in recent years. This can be seen from a dataset of global hydrogen projects provided by the International Energy Agency (IEA) [\u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e]. Within the current version of this dataset, corrected in January 2024, more than 340 projects are related to PEMEC and 263 projects are related to AEC. In addition, the sensitivity analysis is limited to operation with wind power and thus to the production of green hydrogen. The results are illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eA variation in electricity demand leads to significant changes of GWP\u003csub\u003e100\u003c/sub\u003e results. This is valid for both points in time considered. A variation of electricity demand by \u0026plusmn;\u0026thinsp;10% also leads to changes in the GWP\u003csub\u003e100\u003c/sub\u003e results of approximately\u0026thinsp;\u0026plusmn;\u0026thinsp;10%.\u003c/p\u003e\n \u003cp\u003eA reduction of FLH provokes a lower hydrogen production during the considered time horizon, which causes an increase in GWP\u003csub\u003e100\u003c/sub\u003e per specific amount of hydrogen by 1.7% in 2022. An increase in FLH causes a higher hydrogen production and would lead to decreasing GWP\u003csub\u003e100\u003c/sub\u003e results if assessed without stack replacement requirements. However, a reduction in specific GWP\u003csub\u003e100\u003c/sub\u003e results is counteracted if stack replacements are required. The stack lifetime of 40,000 hours and assumed full load hours per year in the base case in 2022 imply that stack replacement is not yet necessary. A reduction of the lifetime below 40,000 hours, however, makes a stack replacement necessary. In all other cases of varying the lifetime, the GWP\u003csub\u003e100\u003c/sub\u003e results remain unchanged compared to the base case. In these cases, the lifetime is high enough to enable an operation without stack replacement. Consequently, no changes are illustrated.\u003c/p\u003e\n \u003cp\u003eThe effects due to time horizon variations are very similar to those of the FLH. An increase in the time horizon from 20 to 22 years leads to a stack swap with the lifetime assumptions of 2022, which would be accompanied by an increase in the GWP\u003csub\u003e100\u003c/sub\u003e results.\u003c/p\u003e\n \u003cp\u003eDue to the observed outstanding importance of electricity demand on the GWP\u003csub\u003e100\u003c/sub\u003e results, its variation by \u0026plusmn;\u0026thinsp;10% is assessed for all technologies and points of time. The results of this sensitivity analysis are illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eFor PEMEC and AEC systems, the effects of varying the electricity demand on the GWP\u003csub\u003e100\u003c/sub\u003e results in Fig. \u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003e are comparably high for both points in time. In 2022, the effect of varying the electricity demand is significantly greater for these technologies than for SOEC systems, due to their significantly lower electricity demand than the alternative electrolysis technologies. The reason is the considerably lower share of electricity demand on the total GWP\u003csub\u003e100\u003c/sub\u003e results for SOEC systems (see also Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e), especially for 2022. The GWP\u003csub\u003e100\u003c/sub\u003e results for SOEC systems change by 3.4 to 8.8% for 2022 and by 9.1\u0026ndash;9.4% for 2045 if the electricity demand varies by \u0026plusmn;\u0026thinsp;10%. When looking at the PEMEC and AEC systems, GWP\u003csub\u003e100\u003c/sub\u003e results change linearly by around 10% for both 2022 and 2045 if a\u0026thinsp;\u0026plusmn;\u0026thinsp;10% variation is assessed. When operating with wind power, there is a tendency towards lower results compared to the variants with an electricity mix. This is due to the slightly lower contribution of the operating phase when using wind power compared to the electricity mix.\u003c/p\u003e\n \u003cp\u003eFollowing the previous presentation of the environmental sensitivity analyses, the following section is dedicated to the sensitivity analysis relating to the LCC. Besides electricity demand and FLH, which are also considered for LCA, additionally, the parameters CapEx and interest rate are assessed. Especially the latter two parameters are of interest, due to before mentioned debate in science and beyond, e.g., on Cost of Capital [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e], and associated uncertainties. Consequently, the inclusion of both parameters in the sensitivity analyses helps to quantify the degree of uncertainty caused by varying these assumptions.\u003c/p\u003e\n \u003cp\u003eFigure 13 illustrates the effects on the LCOH results for all three electrolysis technologies and the variation of the four parameters by \u0026plusmn;10%.\u003c/p\u003e\n \u003cp\u003eAs shown by Fig. \u003cspan class=\"InternalRef\"\u003e13\u003c/span\u003e, AEC and PEMEC reveal the highest effects of the variation of the parameter electricity demand. This result reflects the dominant influence of electricity demand on the LCOH already shown in Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e. The highest relative change observed for electricity demand variations by \u0026plusmn;\u0026thinsp;10% was 7.6%. For the remaining cases, a variation of the FLH parameter is usually the parameter that has the greatest influence on the LCOH results. The highest relative change of LCOH determined for the FLH parameter variation was 5.7%. However, it can also be determined that the FLH variations in one direction or the other lead to different values. For other parameters, the amount is the same in both directions. This shows that the relationship between FLH and LCOH is not linear, while other parameters change linearly. An increase of 10% in FLH leads to a smaller proportional change in hydrogen production costs than a 10% decrease. The variations in the CapEx assumptions by \u0026plusmn;\u0026thinsp;10% also have a noticeable effect on the results, causing changes in the range of 2.4\u0026ndash;5.3%. Though the effects of varying the interest rate parameter are comparatively small (0.6\u0026ndash;1.2%), these changes are still not negligible.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eA key finding of the analyses presented is that the production of hydrogen using water electrolysis technologies will be accompanied by decreasing environmental impacts in the long term (up to 2045). This finding confirms the fundamental outcome of previous publications on the prospective environmental impacts of hydrogen production and delivers new insights for the considered case study. In the period under consideration, the highest GWP\u003csub\u003e100\u003c/sub\u003e results are 27.5 kg CO\u003csub\u003e2eq\u003c/sub\u003e/kg H\u003csub\u003e2\u003c/sub\u003e and the lowest are 1.33 kg CO\u003csub\u003e2eq\u003c/sub\u003e/kg H\u003csub\u003e2\u003c/sub\u003e. Compared to the production of green hydrogen with low CO\u003csub\u003e2\u003c/sub\u003e emissions, as achieved using AEC and PEMEC systems in the year 2022, reductions of up to almost a quarter are possible by technological improvement until 2045. The origin and demand of electricity is the most significant factor for the environmental impacts of all the electrolysis variants considered. While the considered German electricity mix in 2022 provokes 497 g CO\u003csub\u003e2eq\u003c/sub\u003e/kWh\u003csub\u003eel\u003c/sub\u003e, the assumed mix in 2045 provokes 54 g CO\u003csub\u003e2eq\u003c/sub\u003e/kWh\u003csub\u003eel\u003c/sub\u003e. The GWP\u003csub\u003e100\u003c/sub\u003e value (30 g CO\u003csub\u003e2eq\u003c/sub\u003e/kWh\u003csub\u003eel\u003c/sub\u003e) related to the considered wind electricity dataset, is once again well below the current and future grid mix levels. Even with the use of wind electricity, electricity demand remains a determining factor in the environmental results. Consequently, its prospective reduction, which is a common assumption in the literature and used in this study, is particularly relevant in terms of environmental improvements. The additional expected reduction in the use of construction materials as well as increasing lifetimes can also be expected to reduce the environmental impacts. In the case of SOEC systems, the results are particularly dependent on assumptions regarding heat supply. For the year 2022, this study assumes a heat supply that is still largely based on fossil fuels. In the event of a particularly low-emission heat supply in the future, SOEC systems have the potential to produce hydrogen with a very low environmental impact due to their particularly high efficiency. A comparison of the electrolysis technologies shows a convergence of the environmental impact results, to that extent as this is not already the case.\u003c/p\u003e \u003cp\u003eUnder the assumptions made and depending on the electrolysis technology, the LCOH can be reduced from a maximum of 5.6 \u0026euro;/kg H\u003csub\u003e2\u003c/sub\u003e in 2022 to a minimum of 2.3 \u0026euro;/kg H\u003csub\u003e2\u003c/sub\u003e in 2045. As for the environmental results, the electricity demand and its reduction are of the greatest importance for the LCOH of AEC and PEMEC. In 2022, the LCOH results of the three technologies diverge stronger than the environmental indicators. The analyses until 2045 show that the LCOH will also converge to a comparable level in the future. As the learning rates for the technologies are likely to differ between the technologies, due to the different degrees of their maturity, even further convergence is conceivable. With SOEC systems, a high learning rate is more likely than with already more mature AEC systems. It is therefore possible for the learning rate of AEC systems not to be significantly higher than assumed for the WC calculations.\u003c/p\u003e \u003cp\u003eIn the long term more noticeable differences between these technologies will be noted regarding materials used for manufacturing and especially about the type of used critical raw materials and their quantities. Due to the diminishing differences in environmental and economic performance and the possibility of diversifying the use of critical raw materials, there is a strong argument for the combined use of these three technologies in the future.\u003c/p\u003e \u003cp\u003eIn the literature, there are numerous assessments of the current State-of-the-Aart and potential target values for electrolysis technologies. Due to the breadth of usable data and its consistency, a key database selected in this study is that of the DOE on the status quo and the target values of the three electrolysis technologies. Compared to the existing literature, some assumptions within the DOE documents [\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] as essential data sources of this study can be critically discussed. On the one hand, sources like those published by Boehm et al. or Chatenet et al. [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e] do not see such large differences of CapEx values between AEC and PEMEC systems for the year 2022. On the other hand, regarding the operation phase, within other publications [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e] there is a tendency for lower electricity demand values for AEC (48\u0026ndash;52 kWh/kgH\u003csub\u003e2\u003c/sub\u003e) compared to PEMEC. Thus, the overall LCOH results based on the assumptions of this study are in a realistic range and without preference for one technology option. Furthermore, the learning curve approach applied to CapEx developments is based on assumptions regarding the capacity developments of water electrolysis systems. The number of publications on differentiated forecasts of capacity developments for the three technologies examined over time is still very low. However, these assumptions determine the possible future CapEx developments, so that significantly different assumptions on capacity developments would also influence the overall LCOH results. Regarding the interest rates, a range between 3.6% and 4.4% is assessed within the sensitivity analyses. However, some literature on interest rates assumes significantly different percentages. For the interest rate, which is highly dependent on location, time and actor perspective, exemplary assumptions of between 5.5 and 10% can be found for Germany [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Interest rates varying by several percentage points would result in LCOH deviations of several percents.\u003c/p\u003e \u003cp\u003eThe database used for LCA contains almost exclusively data sets that can be used as background data, which correspond to the status quo and are not extrapolated into the future. This is why, for example, the data records for materials such as steel or copper are also used in the analyses for the year 2045 in this study. However, it is likely that such processes will change in the future. This will additionally tend to lead to lower environmental impacts. Consequently, the background data used in this study is associated with higher environmental impacts than could be the case in the future because of process optimization.\u003c/p\u003e \u003cp\u003eThe specific results of this study can be transferred to locations outside Germany only with restrictions. Differences between locations are mainly caused by the operation phase of the water electrolysis, due to different environmental and economic properties of the electricity supply. There are locations outside Germany and outside Europe where renewable electricity can be generated with significantly lower levelized costs due to better availability of renewable energy sources. In some regions, not only favorable production costs arise for individual renewable energy sources, but also significantly lower costs for grid electricity. In addition, interest rates can vary from country to country. For such regions, the cost component shares on the LCOH would differ strongly from those determined in this study for Germany. Consequently, previous studies on production costs or especially those on LCOH point out significantly lower costs for hydrogen imports to Germany compared to its domestic production. A review by Breuer et al. [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e] points out domestic hydrogen production costs between 3.3 and 7.3 \u0026euro;/kg H\u003csub\u003e2\u003c/sub\u003e assumed for Germany in the year 2050 within previous publications. Furthermore, the review found costs between 1.4 and 2 \u0026euro;/kg H\u003csub\u003e2\u003c/sub\u003e for imports to Germany in the year 2050. Thus, the LCOH values obtained in this study for the year 2045 and domestic production in Germany can be considered very low compared to the values of the review. Possible reasons are potential considerations of taxes, overhead costs, decommissioning costs, or other cost components in the studies considered within the review.\u003c/p\u003e \u003cp\u003eRegarding LCA results, a review by Wilkinson et al. [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] identified values mainly below 5 kg CO\u003csub\u003e2eq\u003c/sub\u003e/kg H\u003csub\u003e2\u003c/sub\u003e for this kind of water electrolysis configurations. However, the review states that in earlier publications on hydrogen production by electrolysis in Germany, even GWP values below 0.9 kg CO\u003csub\u003e2eq\u003c/sub\u003e/kg H\u003csub\u003e2\u003c/sub\u003e were determined. Thus, the calculated GWP values within this study are in the range of values from previous studies on water electrolysis technologies using renewable electricity.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study provides a particularly far-reaching, differentiated, transparent, and consistent comparison for the three electrolysis technologies AEC, PEMEC, and SOEC. A unique combination of possible technological, environmental, and economic developments in the production of green hydrogen up to the year 2045 is presented.\u003c/p\u003e \u003cp\u003eBased on a comprehensive literature research, several research gaps were identified, and research questions were posed and answered, e.g., how the results differ for the years 2022 and 2045. Still, the current study reveals a need for subsequent research. As a consequence of the presented findings, prospective research should not be limited to one type of water electrolysis but should be carried out with an openness to all three technologies.\u003c/p\u003e \u003cp\u003eIt is shown that the data from the literature that can be used for the LCA and LCC of water electrolysis technologies differ considerably in some cases. There is, therefore, still a need for extensive research into the material inventories for plant construction, but also into the energy and mass balances of plant operation, i.e., the foreground data. Even for current plants, the availability and transparency of literature data are still low and can be expanded.\u003c/p\u003e \u003cp\u003eRecent research activities on the adaptation of background data for prospective LCA should be intensified. Future overall systemic developments would thus be better reflected in prospective LCA studies.\u003c/p\u003e \u003cp\u003eIn some cases, there is also the possibility of material substitution in the manufacturing of electrolysis technologies in some cases. As one example, a possible ban on per- and polyfluoroalkyl substances (PFAS) is discussed at the EU level and would require alternative materials for components like PTFE-containing gaskets. The material substitution topic offers R\u0026amp;D potential, especially for materials and raw materials research and for manufacturers. New knowledge gained in this way should be made available to experts and for research in the field of LCA to provide this research with the best possible data.\u003c/p\u003e \u003cp\u003eAbout the environmental impacts considered, some particularly robust indicators beyond the GWP were selected for this LCA study. This provides a more diverse range of knowledge about the various environmental impacts. Nevertheless, there are other indicators and methodologies that can be used in future assessments to gain further insights into the environmental impacts and life cycle costs of water electrolysis technologies.\u003c/p\u003e \u003cp\u003eThere has been some recent research on the recycling and disposal of water electrolysis technologies. However, clear standardizations or regulations in this regard could not be identified during a literature search for this study and were therefore not taken into account for the sake of simplicity. As clarity in this regard increases, future research should also include corresponding data and its possible further development, e.g., in the form of increasing recycling rates of individual raw materials [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDue to its outstanding importance on LCA and LCC results the electricity demand assumptions must also be confirmed by future research or, if necessary, modified.\u003c/p\u003e \u003cp\u003eRegarding LCC in general and LCOH calculation in particular the inclusion of recycling or commissioning costs would be an interesting complement. Also, further indicators, e.g. levelized revenue/profit, could contribute to new LCC insights about electrolysis technologies. Future research on LCC of electrolysis technologies should also take the newest CapEx and interest rate developments into account.\u003c/p\u003e \u003cp\u003eFrom a technological perspective, there is also a particular need for research into emerging water electrolysis technologies, which are currently at a significantly lower stage of development than the options under consideration (e.g., anion exchange membrane technology, AEM).\u003c/p\u003e \u003cp\u003eThe present work provides a particularly broad and transparent database that can be used as a basis for the previously listed research opportunities.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eABS: Acrylonitrile-butadiene-styrene\u003c/p\u003e\n\u003cp\u003eAC\u003csub\u003et\u003c/sub\u003e: Administration costs\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAP: Acidification\u003c/p\u003e\n\u003cp\u003eAEC: Alkaline electrolysis cells\u003c/p\u003e\n\u003cp\u003eAEM: Anion exchange membrane\u003c/p\u003e\n\u003cp\u003eAR6: Sixth Assessment Report of the Intergovernmental Panel on Climate Change\u003c/p\u003e\n\u003cp\u003eBC: Best case\u003c/p\u003e\n\u003cp\u003eCHJU: Clean Hydrogen Joint Undertaking or Clean Hydrogen Partnership\u003c/p\u003e\n\u003cp\u003eCOP28: The 28\u003csup\u003eth\u003c/sup\u003e Conference of the Parties to the UN Framework Convention on Climate Change\u003c/p\u003e\n\u003cp\u003eDIN EN ISO: Deutsches Institut f\u0026uuml;r Normung, European norm, International Organization for Standardization\u003c/p\u003e\n\u003cp\u003eDOE: Department of Energy\u003c/p\u003e\n\u003cp\u003eEC\u003csub\u003et\u003c/sub\u003e: Electricity costs per year\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEP fw: Eutrophication, freshwater\u003c/p\u003e\n\u003cp\u003eEP mar: Eutrophication, marine\u003c/p\u003e\n\u003cp\u003eEP ter: Eutrophication, terrestrial\u003c/p\u003e\n\u003cp\u003eFU: Functional unit\u003c/p\u003e\n\u003cp\u003eGWP\u003csub\u003e100\u003c/sub\u003e: Global warming potential (GWP) over a 100-year time horizon\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHC\u003csub\u003et\u003c/sub\u003e: Heat costs per year\u003c/p\u003e\n\u003cp\u003eI: Interest rate\u003c/p\u003e\n\u003cp\u003eIC\u003csub\u003et\u003c/sub\u003e: Insurance costs\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIEA: International Energy Agency\u003c/p\u003e\n\u003cp\u003eIPCC: Intergovernmental Panel on Climate Change\u003c/p\u003e\n\u003cp\u003eIR: Ionising radiation\u003c/p\u003e\n\u003cp\u003eKPIs: Key performance indicators\u003c/p\u003e\n\u003cp\u003eLCA: Life Cycle Assessment\u003c/p\u003e\n\u003cp\u003eLCC: Life Cycle Costing\u003c/p\u003e\n\u003cp\u003eLCI: Life Cycle Inventory\u003c/p\u003e\n\u003cp\u003eMHydrogen\u003csub\u003et\u003c/sub\u003e: Annual amount of hydrogen provided in kWh\u003c/p\u003e\n\u003cp\u003en: service life of the water electrolysis systems\u003c/p\u003e\n\u003cp\u003eNMP: N-methyl-2-pyrrolidone\u003c/p\u003e\n\u003cp\u003eODP: Ozone depletion\u003c/p\u003e\n\u003cp\u003eOFC\u003csub\u003et\u003c/sub\u003e: Fixed operating costs\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOP EFRE/ERDF NRW: Operational Program for the promotion of investments in growth and employment for North Rhine- Westphalia from the European fund for regional development PEMEC: Polymer electrolyte membrane electrolysis cells\u003c/p\u003e\n\u003cp\u003ePGMs: Platinum Group Metals\u003c/p\u003e\n\u003cp\u003ePM: Particulate matter\u003c/p\u003e\n\u003cp\u003ePOCP: Photochemical ozone creation potential\u003c/p\u003e\n\u003cp\u003ePTFE: Polytetrafluoroethylene\u003c/p\u003e\n\u003cp\u003ePtX: Power-to-X\u003c/p\u003e\n\u003cp\u003eRC\u003csub\u003et\u003c/sub\u003e: Cell stack replacement\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSMR: Steam reforming of methane\u003c/p\u003e\n\u003cp\u003eSOEC: Solid oxide electrolysis cells\u003c/p\u003e\n\u003cp\u003et: year under consideration.\u003c/p\u003e\n\u003cp\u003eTFE: Tetrafluoroethylene\u003c/p\u003e\n\u003cp\u003eUS: United States\u003c/p\u003e\n\u003cp\u003eWC: Worst case\u003c/p\u003e\n\u003cp\u003eWC\u003csub\u003et\u003c/sub\u003e: Water costs per year\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding of the center of excellence \u0026quot;Virtual Institute - Power to Gas and Heat\u0026quot; (EFRE-0400151) by the \u0026ldquo;Operational Program for the promotion of investments in growth and employment for North Rhine-Westphalia from the European fund for regional development\u0026rdquo; (OP EFRE NRW) through the Ministry of Economic Affairs, Innovation, Digitalization and Energy of the State of North Rhine-Westphalia is gratefully acknowledged.\u003c/p\u003e\n\u003cp\u003eThe authors would also like to thank Freia Harzendorf for the co-development of the economic model used in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets supporting the conclusions of this article are included within the article and its appendix.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e\"J.C.K. was responsible for the conceptualization and methodology, wrote the main manuscript text, and prepared all figures. P.Z., C.W., K. G., and W.K. supervised the work and reviewed draft versions of the manuscript. K.G. was responsible for the project administration and funding acquisition of the underlying project. All authors reviewed the manuscript.\"\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eH. Lee, K. Calvin, D. Dasgupta, G. Krinner, A. Mukherji, P. Thorne, C. Trisos, J. Romero, P. Aldunce, K. Barrett, G. Blanco, W.W.L. Cheung, S.L. Connors, F. Denton, A. Diongue-Niang, D. Dodman, M. Garschagen, O. Geden, B. Hayward, C. Jones, F. Jotzo, T. Krug, R. Lasco, J.-Y. Lee, V. Masson-Delmotte, M. Meinshausen, K. Mintenbeck, A. Mokssit, F.E.L. Otto, M. Pathak, A. Pirani, E. Poloczanska, H.-O. P\u0026ouml;rtner, A. Revi, D.C. Roberts, J. Roy, A.C. Ruane, J. Skea, P.R. Shukla, R. Slade, A. Slangen, Y. Sokona, A.A. S\u0026ouml;rensson, M. Tignor, D.v. Vuuren, Y.-M. Wei, H. Winkler, P. Zhai, Z. 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B\u0026ouml;ttcher, S. Braungardt, V. B\u0026uuml;rger, V. Cook, L. Emele, K. G\u0026ouml;ckeler, W.K. G\u0026ouml;rz, F. Hacker, K. Hennenberg, W. J\u0026ouml;r\u0026szlig;, P. Kasten, K. Kreye, S. Ludig, F.C. Matthes, L. Moosmann, C. Nissen, J. Reise, M. Scheffler, K. Schumacher, K. Wiegmann, A. Zerrahn, H. Brugger, T. Fleiter, T. Mandel, M. Rehfeldt, C. Rohde, J.S. Jana Deurer, Klimaschutzinstrumente-Szenario 2030 (KIS-2030) zur Erreichung der Klimaschutzziele 2030 CLIMATE CHANGE 30/2023, Berlin, 2023.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"energy-sustainability-and-society","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"esso","sideBox":"Learn more about [Energy, Sustainability and Society](https://energsustainsoc.biomedcentral.com/)","snPcode":"13705","submissionUrl":"https://submission.nature.com/new-submission/13705/3","title":"Energy, Sustainability and Society","twitterHandle":"@OpenEnviron","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Life Cycle Assessment, Life Cycle Costing, Green Hydrogen, Water Electrolysis, Critical Raw Materials, Levelized Costs, Climate Change, Alkaline Water Electrolysis, Proton Exchange Membrane Electrolysis, High-temperature Solid Oxide Electrolysis Cell","lastPublishedDoi":"10.21203/rs.3.rs-3958723/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3958723/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo limit climate change and reduce further harmful environmental impacts the reduction and substitution of fossil energy carriers is a main challenge for the next decades. Recently, during the United Nations Climate Change Conference COP28, the participants agreed on the beginning of the end of the fossil fuel era. Hydrogen, when produced using renewable energy, can be a substitute for fossil fuel carriers and enables the storage of the renewable energy, leading into a post-fossil age. This paper presents environmental impacts as well as levelized costs along the life cycle of water electrolysis technologies for hydrogen production.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe applied methodological approaches are Life Cycle Assessment (LCA) and Life Cycle Costing (LCC), both life cycle-oriented and based on consistent data sources and detailed assessments of prospective technological developments and their effects on environmental and economic indicators. The considered technological developments include electricity and critical raw material demand decreases on the one hand and lifetime as well as electrolysis capacity increases on the other hand. The objectives of the investigations are AEC, PEMEC, and SOEC as the currently most mature water electrolysis technologies for hydrogen production.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe environmental impacts and life cycle costs provoked by the hydrogen production will significantly decrease in the long term (up to 2045). For the case of Germany, worst-case climate change results for 2022 are 27.5 kg CO\u003csub\u003e2eq.\u003c/sub\u003e/kg H\u003csub\u003e2\u003c/sub\u003e. Considering technological improvements, electrolysis operation with wind power and a clean heat source, a reduction to 1.33 kg CO\u003csub\u003e2eq.\u003c/sub\u003e/kg H\u003csub\u003e2\u003c/sub\u003e can be achieved by 2045 in the best-case. The electricity demand of the electrolysis technologies is the main contributor to environmental impacts and levelized costs in most considered cases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA unique combination of possible technological, environmental, and economic developments in the production of green hydrogen up to the year 2045 is presented.\u003c/p\u003e\n\u003cp\u003eBased on a comprehensive literature research, several research gaps, like a combined comparison of all three technologies by LCA and LCC, were identified and research questions were posed and answered. Consequently, prospective research should not be limited to one water electrolysis but should be carried out with an openness to all three technologies. Furthermore, it is shown that data from the literature for the LCA and LCC of water electrolysis technologies differ considerably in some cases. Therefore, extensive research into the material inventories for plant construction is needed, but also into the energy and mass balances of plant operation, for a corresponding analysis. Even for today’s plants, the availability and transparency of literature data is still low and must be expanded.\u003c/p\u003e","manuscriptTitle":"Water electrolysis technologies in the future – projection of environmental impacts and levelized costs until 2045","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-26 20:12:26","doi":"10.21203/rs.3.rs-3958723/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-12T11:44:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-12T10:20:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-09T17:39:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"2354b8f1-8072-4c77-9576-392261cb4a65","date":"2024-03-25T14:02:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"19b74d4b-e7c2-4e4c-ba6b-48176d3c3003","date":"2024-03-19T16:41:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-01T12:51:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-27T10:41:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-02-23T04:49:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Energy, Sustainability and Society","date":"2024-02-15T13:02:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"energy-sustainability-and-society","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"esso","sideBox":"Learn more about [Energy, Sustainability and Society](https://energsustainsoc.biomedcentral.com/)","snPcode":"13705","submissionUrl":"https://submission.nature.com/new-submission/13705/3","title":"Energy, Sustainability and Society","twitterHandle":"@OpenEnviron","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d0014c1a-44af-41ae-9e61-b19c15a659b4","owner":[],"postedDate":"February 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-09T16:13:58+00:00","versionOfRecord":{"articleIdentity":"rs-3958723","link":"https://doi.org/10.1186/s13705-024-00497-6","journal":{"identity":"energy-sustainability-and-society","isVorOnly":false,"title":"Energy, Sustainability and Society"},"publishedOn":"2024-12-05 15:57:19","publishedOnDateReadable":"December 5th, 2024"},"versionCreatedAt":"2024-02-26 20:12:26","video":"","vorDoi":"10.1186/s13705-024-00497-6","vorDoiUrl":"https://doi.org/10.1186/s13705-024-00497-6","workflowStages":[]},"version":"v1","identity":"rs-3958723","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3958723","identity":"rs-3958723","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}