Water splitting via water electrolysis is an electrochemical reaction. This requires an energy supply in the form of direct current [17] as well as heat [18]. The reaction occurs in electrolysis cells, with Eq. (1) describing it:
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$${\text{H}}_{2} {\text{O }} \to {\text{H}}_{2} + \frac{1}{2}{\text{O}}_{2}\ { }\Delta {\text{H}}_{{\text{R}}}^{0} = + { }286\frac{{{\text{kJ}}}}{{{\text{mol}}}}$$ (1)Despite the same overall reaction, the three electrolysis technologies differ, which can already be seen in the differences in the cell structure and partial reactions.
Schematic representations of the cell concepts on which the three electrolysis technologies are based, as well as partial reactions, can be found in Fig. 1.
AECs are 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, water splits into H2 and OH− ions [20]. To date, nickel and nickel alloys have been preferentially used as electrode materials [21]. Composite materials, such as Zirfon®, which consists of zirconium oxide and polysulfone, are currently mostly used in the diaphragms [22].
In a PEMEC, a proton-conducting polymer membrane, usually NAFION®, is used as the electrolyte [20]. 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 [20]. In addition to the above-mentioned membrane material, the following materials are particularly relevant for PEMECs: platinum as the anode material and iridium or ruthenium as possible cathode materials [21].
The central element of the SOEC is a solid oxide layer, which acts as the electrolyte. At the anode, the water vapor used for this high-temperature technology is split into H2 and O2− ions. The O2− ions can reach the anode via vacancy diffusion and react to form O2 [20]. Typically, the electrolyte or solid oxide layer consists of zirconium oxide (ZrO2) doped with yttrium oxide (Y2O3) [21]. Nickel is used as the catalyst [20].
The most advanced [23,24,25,26,27] and common [28] electrolysis system to date is AEC, which allows large plant capacities to be realized at the lowest investment costs to date for water electrolysis technologies [23,24,25, 27, 28]. It should be noted that minor impurities and an associated product purity of ≥ 99.5% may still be present before the final gas treatment [29].
As mentioned previously, several materials are required for the manufacturing and construction of electrolysis cell stacks. Regarding the life cycle inventories used for these cell stacks, which can be found in the "Methods" section, the following materials for electrolysis technologies are considered critical in the EU’s list of critical raw materials [30]. For the construction of AEC stacks, graphite and nickel are typically used. Titanium and the platinum group metals (PGMs) iridium and platinum are typically 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 of lanthanum and yttrium are also used for SOEC construction. More detailed information regarding the assumed materials and their quantities can be found in the "Methods" section.
The main methodological aspects of LCA and LCC are first explained before the specific methodological selection for this study is presented.
An LCA is characterized by standardization based on ISO standards and [8, 9]. An LCA examines environmental aspects and impacts throughout the life cycle, ranging from raw material extraction to disposal. Due to its comprehensive and multi-layered analysis capabilities, LCA was 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 for this, in which selected economic indicators are used on the basis of 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 takes into account 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.
As described in detail in the "Background" chapter, this study aims to investigate various technological, economic, and environmental aspects as well as advancements in hydrogen production from AECs, PEMECs, and SOECs through .
For the present LCA and LCC study, a mass-related FU was selected with "1 kg H2". Furthermore, this specification is supplemented by specification of the physical property, in this case, the pressure, which is assumed to be 10 bar. The technologies examined for the production of 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 was chosen as the geographical framework. In addition to its conditions in , future developments, especially those involving technological improvements and a decarbonizing electricity grid mix, are also analyzed. As Germany is aiming for greenhouse gas-neutrality by , this year is 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.
An attributive cradle-to-gate LCA approach was chosen for this study. Cradle-to-gate assessments typically begin with the extraction of raw materials through the construction of plants, energy supply and conversion, and end with the provision of hydrogen (at the factory gate). A possible subsequent use of hydrogen, e.g., as fuel, lies outside these system boundaries. A schematic representation of the main system boundaries is presented in Fig. 2.
Furthermore, the recycling and end-of-life of electrolysis systems have not yet been standardized and, consequently, are not considered in the LCA section of this study. The openLCA software, version 1.10.3, was used. The LCA database ecoinvent (version 3.7.1) in the "cut-off by classification" system model was used to provide background data for the Life Cycle Inventory [31].
Information on the foreground data used for the LCA and LCC for AEC, PEMEC, and SOEC is discussed in the sections, "Common data for LCA and LCC", "Data for LCA", and "Data for LCC".
For the LCC analyses, an own Microsoft Excel tool, which includes numerous literature-based economic parameters of the technology options under consideration, was used. As a variant of LCC, environmental life cycle costing was chosen. The LCC Excel tool developed also contains key formulas for levelized costs of hydrogen (LCOH) calculations.
The synthesis of existing LCIA methods in the European context in the form of the Environmental Footprint (EF) framework [32] in version 3.0 was used for this study. The mid-point impact indicator values selected were considered to be more scientifically robust than end-point indicators [33]. Table 1 contains a list of the environmental categories and indicators selected on this basis, as well as the associated units and abbreviations used. Major reasons for the selection are the classification as more robust (categories I and II) than other indicators (robustness category III) and their good comparability with other studies.
The choice of indicators is also relevant for the LCC. For this study, therefore, particular attention was given to the selection of indicators within existing hydrogen-related publications. An earlier study [34] showed that previous LCC calculations of hydrogen production systems have most frequently used the following indicators: LCOH, capital expenditures/plant costs (CapEx), and plant operating expenditures (OpEx). LCOH concepts are fundamental approaches to techno-economic comparisons of competing technologies and/or production sites, as well as for technology assessments in general [35, 36]. The LCOH reflect the total lifetime costs of the systems under consideration. Furthermore, according to Kuckshinrichs & Koj, the LCOH can be understood as a break-even value that indicates the price required as revenue over the lifetime of a technology to justify an investment [35]. The CapEx and OpEx indicators can be considered separately but are also components of production costs. As the LCOH are based on CapEx and OpEx and are 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, the LCOH indicator was preselected as the first indicator for LCC in this study. A more recent study by Ishimoto et al. [37] presents a literature review of LCC approaches on fuel cell and hydrogen systems. Regarding cost calculation methods and indicators, they found out the levelized cost method and the net present value (NPV) to be the most frequently applied. Although the levelized cost approach reveals specific economic results for a unit of a product (e.g., kg H2), the NPV embodies the absolute economic results of a project. According to Rosłon et al. [38], NPV is the primarily used indicator for assessing the economic efficiency of projects. This economic metric can also be considered to support decision-making by comparing the economic attractiveness of different investment opportunities [39]. Thus, NPV was selected as the second economic indicator in this study because it is also a frequently used and established indicator and provides additional information on the profitability of hydrogen production opportunities.
In its simplest form, the LCOH indicator 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 [40]. In addition, subcategories and further categories can be included in the calculation. Examples include decommissioning costs, taxes, or external costs [35]. As described by Kuckshinrichs & Koj [35], LCOH assessments can consider a private (or synonymously business) or social perspective. In this study, a private perspective is used. The difference between these two perspectives is not described in detail here but can be found in Kuckshinrichs & Koj [35]. Equation (2) takes different previously published LCOH formulations for this private perspective into account [30, 35, 41, 42]:
$$LCOH= \frac{{I}_{0}+ {\sum }_{t=1}^{n}\frac{{WC}_{t}+ {EC}_{t}+ {HC}_{t}+{RC}_{t}+{AC}_{t}+ {OFC}_{t}}{{(1+i)}^{t}}}{{\sum }_{t=1}^{n}\frac{{MHydrogen}_{t}}{{(1+i)}^{t}}}$$ (2)In Eq. (2), I0 represents the sum of the initial investment costs (CapEx). The unit of the investment costs is €. 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 the water costs per year (WCt), the electricity costs per year (ECt), and the heat costs per year (HCt). Furthermore, the costs of the cell stack replacement (RCt) are relevant. The fixed (operation-related) costs include administration costs (ACt), and other fixed operating costs (OFCt). 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, where t indicates the respective year under consideration. The variable i represents the interest rate used for discounting. MHydrogent 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 the LCC in this study. This approach is also common in many other studies for calculating LCOH. The unit for the variables WCt, ECt, HCt, RCt, ACt, ICt, and OFCt is €/year for annual production, whereas the unit for MHydrogent is kWh/year (or MWh/year).
The two parameters I0 and i are of particular interest, as value assumptions for these are particularly intensely debated in academia and beyond, e.g., the debate on the cost of capital [43,44,45]. Furthermore, these parameters are associated with uncertainties, as they can change over time and vary depending on location. Consequently, an established and multi-layered approach was chosen to determine future CapEx values. In addition, both latter parameters were subjected to a sensitivity analysis, which is presented in the "Results" section.
The second considered LCC indicator, NPV, takes into account the initial and potential later investments, the net demolition costs at the end of the lifetime as well as net cash flows (revenues minus expenditure) during the years considered in the planning horizon. There are several different formulas for NPV, which ultimately describe the same basic calculation approach. Equation (3) is based on similar equations that were published by Rosłon et al. and Schoenmaker & Schramade [38, 39]:
$$NPV = \sum_{t=0}^{n}\frac{{NCF}_{n}}{{(1+i)}^{n}}$$ (3)where NCFn stands for the net cash flow, considering the initial point of time (t = 0) and year n at the end of the planning horizon. Again, the variable i represents the interest rate used for discounting.
To extrapolate the CapEx values to the year , a learning curve approach was taken. The basic learning curve concept was developed by Wright and first published in [46]. The study analyzed the costs of technologies and their development over a selected period. In addition, these learning curves combine technological improvements in manufacturing processes over time with cost developments. Thus, for this study, learning curves were selected for a consistent assessment of prospective technological and LCC developments by describing the relationship between the increase in production or cumulative capacity of a good and the reduction of its costs [47]. Based on the different configurations of learning curves, Eq. (4) was chosen for this study:
$${C}_{t}={C}_{0}{\left(\frac{{X}_{t}}{{X}_{0}}\right)}^{-\beta }$$ (4)where C0 represents the costs at time t = 0. X0 represents the cumulative capacities of technologies at time t = 0. Xt represents the cumulative capacities and Ct the costs 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. For example, an economic learning rate of 15% means that the costs decrease by 15% when the cumulative installed capacity doubles [48].
To calculate prospective CapEx values for water electrolysis technologies, it is important to know their identified learning rates. For electrolysis, different learning rates between 8% [49] and 18 ± 13% [50] were identified by literature review. Table A 2 in the Supplementary material 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 greater for PEMEC and SOEC than for 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 were taken into account for our own calculations as part of this study: 7%, 10%, and 13%.
As previously noted, learning curve calculations also require values of production volumes (leading to cumulative installed capacities). Several projections of total water electrolysis capacities have been published to date. However, differentiation of capacities corresponding to different electrolysis technologies has been very rare. Publications by Boehm et al. [47, 51] are an exception in this regard. In the first of these [47], starting values and projections of the global cumulative electrolysis capacities up to the year were included. The entire globally assumed annual increase in electrolysis capacity was then multiplied by the share of the respective electrolysis technologies, as presented in Boehm et al. [51]. Based on the annual capacity expansion and initial values, the cumulative installed capacity could be calculated. Furthermore, Boehm et al. differentiated between variants of high- and low-capacity expansion. This differentiation of "high" and "low" developments of installed capacities from until was also considered in this study and is shown in Fig. 3.
Figure 3 shows that, based on the assumptions of Boehm et al., the highest absolute capacity increases are expected for PEMEC systems. Until , higher capacities are expected for AECs than for SOECs. Nevertheless, stronger increases in SOEC capacities are assumed from in particular, which leads to a noticeable approximation of the results. Furthermore, the highest absolute increases are assumed for the distant future, particularly from onward. In contrast, the highest rates of capacity multiplication are already projected for the period between and .
The chosen values for the cost components in this study are listed in the sub-section "Data for LCC".
For a fair comparison of technology options, it is important to use a data source that is as consistent as possible. One such common data source is seen in the “State-of-the-Art and Targets” of the U.S. Department of Energy (DOE), which have been published separately for the three technologies [52,53,54]. These documents contain data on the status (state-of-the-art) as of , the targets for the year , and the ultimate targets for several key performance indicators (KPIs). For this study, assumptions for electricity demand, lifetime, critical raw material content and capital cost are especially relevant. In addition, the heat demand can be derived from the SOEC data. Electricity and heat demand, as well as lifetime, are important for both the LCA and LCC. The material content is relevant for the LCA, and the capital cost is used for the LCC. In this study, it is assumed that the ultimate targets are applicable to the year . No restriction on the US market is discernible regarding these technical targets. The information contained in the DOE documents is therefore considered to be globally applicable and usable for the German analysis framework.
In addition, 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 to ensure objective comparability, as there are no economies of scale for the stacks due to their modular design. The operation with the electricity mix assumes of a very even operation over a long period of time. The FLH assumed for this purpose are therefore much higher than those assumed for connecting to fluctuating electricity-generating wind turbines. Table 2 lists the common LCA and LCC data assumed in this study.
When selecting Life Cycle Inventory (LCI) data for cells and cell stacks, it is important to ensure that transparent LCI models are used and that these also enable fair comparisons. 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 Lotrič et al. was used for the PEMEC, the inventory from Koj et al. was used for the AEC, and the LCI data published by Schreiber et al. were used for the SOEC [22, 56, 57]. The LCI model from Bareiß et al. [58] is otherwise frequently used for LCAs with PEMEC technology. However, this approach accounts for a very small amount of steel for screws and bolts. The Lotrič LCI model used in this work [56] is also characterized by more material information and a high degree of transparency compared to other known PEMEC LCI models, such as those developed by Bareiß et al. and Schmidt Rivera et al. [58, 59]. Based on the DOE's technical targets, however, it is apparent that the estimate of the required PGM quantity differs significantly from the state-of-the-art quantity determined by the DOE. Therefore, the value determined by the DOE is used in the PEMEC LCI model for in this study instead of the original value. In addition, the energy required for the manufacturing and construction of the three electrolysis technologies should be considered. This aspect and accompanying data are partially neglected in the previously mentioned LCI model publications. Consequently, this additional energy input is considered within the LCI models of this study on the basis of the consistent consideration of only one publication. For this purpose, data for all three electrolysis technologies on manufacturing and construction energy published by Gerloff [11] are taken into account. As these assumptions essentially rely on the manufacturing of small or micro plants, they were scaled up according to the scaling assumptions mentioned by Gerloff [11]. Although the PGM demand within the base Lotrič LCI model was modified as described above, the critical raw material intensity for the base models of AEC and SOEC were considered usable for for the sake of simplicity. With regard to the German electricity mix for , statistical data [60] were used and combined into an electricity mix LCI model using own assumptions and available ecoinvent data sets. A study of several research institutes [61] was used for the electricity mix in , and a model was also created, that took into account our own assumptions and ecoinvent data sets. The resulting LCI table of assumed German electricity mixes for and can be found in Table A 3. In addition, LCI data on the construction of the electrolyzers and their components can be found in Table A 4–Table A 9 in the Supplementary material.
With respect to future cell stacks, it can be assumed that the use of materials decreases over time because of advancing manufacturing and construction processes and improving material properties. This applies in particular 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, whereas 0.8 g/W is regarded as state-of-the-art for PEMEC technology. This corresponds to a reduction in the specific material requirements of 96.25%. In the European context, there are also targets for KPIs for electrolysis technologies that are comparable to the DOE’s technical targets, but do not reflect the status quo in . These targets are published by the Clean Hydrogen Joint Undertaking (CHJU) or Clean Hydrogen Partnership [62]. The CHJU targets assume a reduction in the total specific demand for critical raw materials as catalysts for PEMEC electrolysis from 2.5 to 0.25 g/kW, i.e., by 90%, between and . For AEC and the same decade, the CHJU targets assume a reduction in the total specific demand for critical raw materials from 0.6 g/kW in to 0 g/kW in . 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 for this technology in the future. Based on this, a simplifying and cross-technology assumption of a 96.25% reduction until compared to the original values (also for the AEC, although a reduction of 100% is mentioned above) is made in this work. With respect to the AEC, this is a fairly conservative estimate compared to the CHJU target values. Consequently, reductions in following critical raw materials were considered for the different electrolysis technologies: cobalt (SOEC), graphite (AEC), lanthanum (SOEC), nickel (AEC and SOEC), platinum (PEMEC), titanium (PEMEC and SOEC), and yttrium (SOEC).
All three electrolysis technologies were compared with an established reference technology, in this case, steam reforming with natural gas/methane (SMR). The applied LCI data for SMR were based on publications by Wulf [63, 64]. The authors describe that data can be considered for the years and for this reference technology. As no sources of SMR LCI literature could be identified extending further into the future, the model was used for both points in time in this study. The applied LCI model of the German electricity grid mix and the LCI data used for SMR can be found in Table A 10, Table A 11, and Table A 12 in the Supplementary material.
The data needed for the LCC model in this study to calculate the LCOH were collected with the goal of being as consistent as possible and taking current conditions into account. Thus, most values were taken from the techno-economic publications by Boehm et al. [51]. Many of the values used to determine LCOH are expressed as a percentage of the CapEx. The CapEx, which develops over time, is therefore of particular importance. For this reason, the DOE publications already used for the consideration of other electrolysis data [52,53,54] are used as the starting points (values for ) and as a basis for the CapEx projections. The DOE values describe the uninstalled CapEx of entire electrolysis systems. The starting values were calculated using the average exchange rate between the Euro and US dollar for of 1.05 $/€ [65]. For the AEC, the starting value in was 476.19 €/kWel; for the PEMEC, it was 952.38 €/kWel; and for the SOEC, it was 2,380.95 €/kWel. To obtain CapEx values for the year , the already described learning curve approach is used. Considering the three electrolysis technologies, different learning rates (LR) (7%, 10%, and 13%), and two different capacity scenarios (low and high increase) are calculated. The CapEx development values of the AEC, PEMEC, and SOEC can be found in Fig. 4. The upper whiskers of the respective boxplots (maximum value) indicate starting values in the year , as assumed by the DOE documents [52,53,54]. In contrast, the lower whisker limit (minimum value) represents the calculated CapEx values for . The circles represent the CapEx results in 5-year increments. In addition, the centerline inside the box marks the median value. The x-marker within the boxplots in Fig. 4 represents the arithmetic mean of the data points.
For each electrolysis technology, the upper limits of the whisker’s boxplots in Fig. 4 represent the starting values. The learning curve analysis in Fig. 4 exhibits significantly decreasing CapEx values for all three electrolysis technologies. As is illustrated, the CapEx of AEC systems can be reduced from 476 to 186 €/kWel in the best case and to 313 €/kWel in the worst case. The projected relative reductions ranged from 34 to 61%. For PEMEC systems, Fig. 4 reveals CapEx reductions from 952 to 195 €/kWel as the best case and to 446 €/kWel as the worst case. These decreases ranged from 53 to 80%. The CapEx of SOEC systems can be reduced from €/kWel to 363 €/kWel (best case) and 960 €/kWel (worst case). Furthermore, LR variations have significantly greater effects than different capacity scenarios. Higher CapEx starting values are given for PEMEC systems, but by , this technology could reach the level of AEC systems in the best case.
For the LCC model in this study, the best case (BC) and worst case (WC) results obtained from the learning curve analysis for the year 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 of a paper published by Boehm et al. [51]. The publication includes data from to . As no exact figures were available for the years and , values for and 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. [51] could not take more recent developments regarding effects on energy markets into account. However, the development of these prices will remain subject to considerable uncertainty in the future. For this reason, these assumptions were initially used here as a consistent basic assumption, with the effects of other prices shown later in a sensitivity analysis. The final choice of assumptions with exclusive relevance for the LCC calculations can be found in Table 3.
As part of the life cycle impact assessment, the absolute GWP100 results of hydrogen production using electrolysis technologies were first compared with those of the reference technology. A subsequent contribution analysis revealed the different reasons for these results. The causes of the GWP100 results of different cell stack variants were also determined. Finally, additional impact categories were investigated and compared with those of the reference technology.
Figure 5 first shows the absolute GWP100 results for different electrolysis technologies, points in time and power supply variants in comparison to the reference technology, SMR.
Figure 5 clearly illustrates the great potential for reducing the GWP100 of hydrogen production through operation with wind power compared to the use of grid electricity (electricity mix). Using wind power, reductions of almost 93% can be achieved for AEC and PEMEC systems, whereas a decrease of 81% is possible for an SOEC in .
Electrolysis based on the German electricity mix in , which is assumed to be fully renewable, still provokes significantly higher results in GWP100 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 GWP100 indicator when using wind power. The results converge across the technologies over time. AEC and PEMEC are already at a highly comparable level, 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 (Fig. 6). Figure 6 illustrates the relative contributions to the results of hydrogen production for the GWP100 indicator. The underlying data are presented in Table A 13 and Table A 14 (Supplementary material).
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 GWP100 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 predominates over manufacturing and construction across all technologies. Manufacturing and construction include the cells, cell stacks, and Balance of Plant (BoP) components. In addition, a replacement is considered if the number of hours of hydrogen production exceeds the service life. Within the considered time horizon of plant operation over 20 years after initial installation, AECs require two stack replacements, PEMECs three, and SOECs six. This is based on conditions and operating with the electricity mix. When operating with wind power under the assumptions made, only SOECs require one stack replacement. Due to increasing lifetimes, there is no need for stack replacement in the case of electrolysis with wind power. For electricity mix-based electrolysis, one stack replacement is required in for each technology. For SOEC, a combined view of the last two figures shows that the clear prospective reduction in GWP100 results is primarily due to the assumed more environmentally friendly heat supply.
Figure 7 shows the results of the GWP100 indicator for the different electrolysis technologies and for different years and underlying LCI stack manufacturing and construction models. The suffix "A" indicates the respective original LCI model. The suffix "B" describes the consideration of assumptions regarding manufacturing and construction energy from the paper by Gerloff [11] as used in this study and explained in the "Data for LCA" section. For each technology variant, the five materials (Top 5) with the highest influence on the GWP100 indicator were considered. The remainder (Rest) always includes all contributions that cannot be assigned to these respective Top 5. Some of the material designations are abbreviated and have not been mentioned previously. 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 is still associated with the highest results, feature the lowest in . For PEMEC, this is primarily due to the high contributions of the critical raw materials, PGMs and titanium, in . This is 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 CO2eq to climate change [66]. As a significant decrease in the specific use of these materials is expected and assumed in this study, the climate change results are also strongly declining. The AEC and SOEC also exhibit significant reductions in the GWP100 results for compared to those for . However, their results are not determined to the same extent by critical raw materials. The contributions of manufacturing energy in the LCI models for differ significantly. This manufacturing energy assumption based on the publication by Gerloff [11] leads to significantly higher results than in the original models by Koj et al. for AEC, Lotrič et al. for PEMEC, and Schreiber et al. for SOEC [22, 56, 57]. While the calculated GWP100 results for the "B" LCI models are around 47% higher for AEC and PEMEC systems, the results for SOEC are even 89% higher than for model "A".
The environmental analyses in this study were not limited to the GWP100 indicator. Further indicators listed in the "Methods" chapter are included in the analysis and the electrolysis technologies were analyzed in comparison to each other and with SMR using spider diagrams. The presentation was based on a decadal logarithmic scale and the results are shown relative to the environmental impacts of SMR. The grey area (100% values) indicates the calculated environmental impacts of SMR for each impact category. For greater clarity and comprehensibility, the analyses for and are shown in separate diagrams. Figure 8 shows the results for .
Figure 8 reveals that the advantages of certain technology variants determined for GWP100 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 and those that also do so with wind power 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 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 emissions-intense produced materials steel, aluminum, and copper are required for these types 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 compared to SMR regarding the GWP100 and ODP indicators and comparable results with respect 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 GWP100 results are 63–82% lower, than SMR. However, regarding EP-fw-p, 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.
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The results of the electrolysis technologies compared to SMR for are presented in Fig. 9. As illustrated in Fig. 9 for , the electricity mix variants are significantly more competitive in terms of their environmental performance compared to the reference technology (SMR). This is a result of the fully renewable electricity mix. Thus, the values clearly improve against . Depending on the technology, the variants with wind power perform better than the reference technology for five or six indicators (POF-toci, ODP, GWP100, A-ae, EP-ter-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, GWP100, 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-p indicator. The other indicator for which significantly higher results are available for all electrolysis variants considered, at up to 160% higher, is PM-ihh. The use of steel for cell stacks and for constituents of the electricity provision is of great importance for these indicators, as high environmental impacts are associated with the energy- and consequently emissions-intensive upstream processes of steel.
Based on the assumptions and calculated CapEx values in the "Methods" chapter, the LCOH of the electrolysis technologies was calculated for and . First, the LCOH resulting from operation with the electricity mix in is determined. Then, the costs of electrolysis operation with wind power in were analyzed. Extreme cases were thus taken into account. For , the WC is given for the lowest learning rates and capacity increases within the assessed range. Contrary to this, the BC was given for the highest learning rates and capacity increases within the range. The resulting LCOH for water electrolysis technologies, given in €/kg H2, is illustrated in Fig. 10.
A wide range and significant influencing factors that change over time can be observed in Fig. 10. In , there were still clear differences in the LCOH results for the three electrolysis technologies. The LCOH was the lowest for AEC systems. This is due to the higher CapEx and higher costs of 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 times when utilizing the electricity mix. Analyses for show a strong convergence of the LCOH. The calculated range reaches 2.3–3.8 €/kg H2. A longer lifetime has 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 to , along with the accompanying specific cost contribution. This cost-increasing effect outweighs the cost-reducing ones (especially CapEx reductions) if the AEC systems are operated in and the WC. Consequently, the LCOH increases in this specifical case. For SOEC systems, there is additional cost reduction potential if waste heat from a neighboring plant can be used free of charge or at a low cost.
The following NPV calculation is intended to show the hydrogen prices at which the technology options are economically viable at different points in time. While NPV < 0 expresses economic losses over the period considered, variants with NPV > 0 indicate economic surpluses. A somewhat typical range of 2–5 €/kg H2 is assumed for hydrogen price assumptions, as also considered by Abadie & Chamorro (Abadie & Chamorro, ). The results of the NPV calculations for all three electrolysis technologies for and a BC and WC in are shown in Fig. 11.
Figure 11 also shows that at a hydrogen price of 2 €/kg H2, the initial investment and costs incurred would not be sufficiently covered by revenues in all cases. At a hydrogen price of 3 €/kg H2, financial surpluses could be generated for all AEC system options considered. Due to decreasing CapEx values, PEMEC systems would be economically viable in for both variants at a hydrogen price of 3 €/kg H2. In , however, this would only have been possible for PEMEC systems at a hydrogen price of 4 €/kg H2. The economic viability of the SOEC system at the hydrogen prices considered is even more difficult than for the alternative technologies, especially for . Furthermore, in the WC scenario of there are disadvantages for SOEC systems and a price of 3 €/kg H2 would not be sufficiently economically viable. On the other hand, in the BC scenario of , SOEC systems are as economically viable as the alternative ones. The reason for these different results is that the underlying CapEx value in BC for the SOEC systems is much closer to the level of the alternative technologies than in WC.
The following section presents separate sensitivity analyses for LCA and LCC. Due to the outstanding importance of this indicator in LCA studies the LCA part of the sensitivity analyses start with and is limited to an assessment of the GWP100 results. The variations are compared with the results of the base case (Fig. 5). To ensure that analyses are similar and consistent, the same parameters are used wherever possible. Four parameters were considered in the LCA sensitivity analyses and are subsequently mentioned. In accordance with the results presented above, the parameter of electricity demand was of the highest importance. In addition, the variations in the parameters of FLHs, lifetime, and time horizon were also examined. In addition to the process data, these parameters are relevant and variable parameters within the underlying LCI models of this study. The FLH and time horizon were 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 for producing a fixed amount of hydrogen. Furthermore, variations in these parameters could potentially create the need for component replacements. 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 must be replaced during the period under consideration. GWP100 sensitivity analyses for these four parameters are applied to one of the electrolysis technologies under consideration only. PEMEC was selected because it has become the electrolysis technology that has received the most attention in recent years. This can be seen in a data set of global hydrogen projects provided by the International Energy Agency (IEA) [67]. Within the current version of this data set, corrected in January , more than 340 projects related to the PEMEC, and 263 to the AEC. In addition, the sensitivity analysis was limited to operation with wind power and thus to the production of green hydrogen. The results are illustrated in Fig. 12.
As shown in Fig. 12, a variation in electricity demand leads to significant changes in GWP100 results. This is valid for both points in time considered. A variation in electricity demand of ± 10% also leads to changes in the GWP100 of approximately ± 10% (0.197 kg CO2eq/kg H2).
A reduction in FLH leads to lower hydrogen production during the considered time horizon, which causes an increase in GWP100 per specific amount of hydrogen of 1.6% (0.032 kg CO2eq/kg H2) in . An increase in FLH in leads to higher hydrogen production and to decreasing GWP100 if assessed without stack replacement requirements in . However, for a 10% reduction in FLH, the reduction in specific GWP100 results is counteracted by the need to replace one cell stack, causing a GWP100 increase of 8.97% (0.177 kg CO2eq/kg H2) in . The stack lifetime of 40,000 h and assumed FLH per year in the base case in (see Fig. 5) imply that stack replacements are not necessary. A reduction in the lifetime to less than 40,000 h, however, necessitates one stack replacement, and goes along with a GWP100 increase of 11.3% (0.224 kg CO2eq/kg H2). In all the other cases for which the lifetime is varied, the GWP100 results remain unchanged compared to those of the base case. In these instances, the lifetime is high enough to enable operation without stack replacement. Consequently, no changes are illustrated.
The effects due to time horizon variations are similar to those of the FLH. Thus, a reduction of the time horizon also causes an increase in GWP100 per specific amount of hydrogen of 1.6% (0.032 kg CO2eq/kg H2) in and 0.7% (0.010 kg CO2eq/kg H2) in . An increase in FLH in leads to decreasing GWP100 as there is no stack replacement. However, with the lifetime assumption of an increase in the time horizon from 20 to 22 years leads to one stack replacement, accompanied by an increase in the GWP100 results by 8.97% (0.177 kg CO2eq/kg H2).
Due to the observed outstanding importance of electricity demand on the GWP100 results, its variation of ± 10% and effect on GWP100 is additionally assessed for all electrolysis technologies and points in time. The results of this sensitivity analysis are illustrated in Fig. 13.
For the PEMEC and AEC systems, the effects of varying the electricity demand on the GWP100 (Fig. 13) were comparably high for both points in time. In , the effect of varying the electricity demand was significantly greater for these technologies than for SOEC systems due to the considerably lower share of electricity demand on the total GWP100 results for SOEC systems (see also Fig. 7 and Fig. 8), especially for . The GWP100 results for SOEC systems changed by 3.4–8.8% for and by 9.1–9.4% for if the electricity demand varies by ± 10%. When looking at the PEMEC and AEC systems, the GWP100 results change linearly by approximately ± 10% for both and if a ± 10% variation was assumed. When operating with wind power, there is a tendency towards lower results than when operating 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.
Building on the previous presentation of the environmental sensitivity analyses, the following section is dedicated to a sensitivity analysis relating to the LCC. The influences of various parameters can be shown particularly well with a sensitivity analysis of the LCOH indicator. Due to the similarity of the indicators and the fact that a supplementary sensitivity analysis is not expected to add substantial value, it is not conducted for NPV. In addition to the electricity demand and FLH, which are also considered for LCA, the parameters of CapEx and interest rate are also assessed. In particular, the latter two parameters are of interest due to the previously mentioned debate in academia and beyond on the cost of capital [43,44,45] 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 14 illustrates the effects on the LCOH results for all three electrolysis technologies and the variation in the four parameters by ± 10%. As shown in Fig. 14, the AEC and PEMEC reveal the greatest effects on the variation in the parameter electricity demand (El. dem.). This result reflects the dominant influence of electricity demand on the LCOH, as shown in Fig. 10. The highest relative change observed for electricity demand variation, ± 10%, was 7.8%. For the remaining cases, a variation of the FLH parameter has the greatest influence on the LCOH results. The highest relative change in LCOH determined for the FLH parameter variation was 7.3%. However, it can also be determined that FLH variations in one direction or the other lead to different values. For the other parameters, the amount was the same in both directions. This shows that the relationship between FLH and LCOH is not linear, while the other parameters change linearly. Thus, an increase of 10% in FLH leads to a smaller proportional change in hydrogen production costs than a 10% decrease.
Figure 14 also illustrates a noticeable effect on the results for a variation in the CapEx assumptions of ± 10%, causing changes in the range of 2.1–6.5%. Although the effects of varying the interest rate parameter [variable i in Eq. (2)] are comparatively small (0.5–1.8%), these changes are still not negligible. The cases considered here do not result in any variation in the stack numbers. Accordingly, the stack numbers correspond to the results and description for the base case (Fig. 5).
A key finding of the analyses presented is that the production of hydrogen using water electrolysis technologies will be accompanied by decreasing GWP100 in the long term (up to ). Future improvements are also evident for the results of eight additionally analyzed environmental impact indicators. These findings confirm the fundamental conclusions of previous publications regarding the prospective environmental impacts of hydrogen production and provide new insights into the considered case study. In the period under consideration, the highest GWP100 is 27.5 kg CO2eq/kg H2 and the lowest 1.33 kg CO2eq/kg H2. Compared to the production of green hydrogen with low CO2 emissions, which was achieved using the AEC and PEMEC systems in , technological improvement could reduce CO2 emissions by up to almost a quarter by . The origin and demand for electricity were the most significant factors in the environmental impacts of all of the electrolysis variants considered. Although the considered German electricity mix for provokes 497 g CO2eq/kWhel, the assumed mix in only emits 54 g CO2eq/kWhel. The GWP100 value (30 g CO2eq/kWhel) related to the considered wind electricity data set 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 environmental results. Consequently, its prospective reduction, which is a common assumption in the literature and employed 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 , 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 in the GWP100 results to the extent that 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 5.4 €/kg H2 in to a minimum of 2.3 €/kg H2 in . This is also consistent with the NPV analyses. It can be seen that, under the assumptions made, electrolysis can only be carried out economically at a hydrogen price level of over 2 €/kg H2. As for the LCA results, the electricity demand and its reduction are of the greatest importance for the LCOH of AEC and PEMEC systems. In , the LCOH results for the three technologies diverged more strongly than those for the other economic indicators. Analyses through show that the LCOH will also converge on a comparable level in the future. As the learning rates for the technologies are likely to differ between the technologies due to their different degrees of 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 to not be significantly higher than that assumed for WC calculations.
In the long term, the differences between these technologies will become more apparent regarding the materials used for manufacturing, especially in terms of the type of critical raw materials used 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 to be made for the combined use of these three technologies in the future.
In the literature, there are numerous assessments of the current state-of-the-art 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 literature, some assumptions within the DOE documents [52,53,54] as essential data sources of this study can be critically discussed. On the one hand, sources such as those published by Boehm et al. or Chatenet et al. [47, 51, 68] do not see such large differences in CapEx values between AEC and PEMEC systems for . On the other hand, regarding the operation phase, in other publications [22, 55, 69, 70] there is a tendency towards lower electricity demand values for AEC (48–52 kWh/kgH2) than for PEMEC systems. Thus, the overall LCOH results based on the assumptions of this study are within a realistic range and do not indicate that one technology option is preferred. 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 development for the three technologies examined over time is still very low. However, these assumptions determine the possible future of CapEx developments, such that significantly different assumptions about capacity developments would also influence the overall LCOH results. With respect to interest rates, a range between 3.6% and 4.4% was assessed within the sensitivity analyses. However, some of the publications addressing interest rates assume significantly different percentages. For the interest rate, which is highly dependent on location, time, and actor perspective, typical assumptions of between 5.5 and 10% can be found for Germany [41, 71]. Interest rates varying by several percentage points would result in LCOH deviations of several percent.
The database used for the LCA almost exclusively contains 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 in this study. However, it is likely that such processes will change in the future. This will also tend to lead to lower environmental impacts. Consequently, the background data used in this study are associated with greater environmental impacts than could be the case in the future because of process optimization.
The specific results of this study can only be transferred to locations outside Germany with restrictions. Differences between locations are primarily caused by the operating phase of water electrolysis due to differences in the environmental and economic properties of the electricity supply. There are locations outside Germany and Europe where renewable electricity can be generated with significantly lower levelized costs due to better availability of renewable energy sources. In some regions, favorable production costs arise for individual renewable energy sources, and the costs for grid electricity are significantly lower. In addition, interest rates can vary by country. For such regions, the cost component shares on the LCOH would strongly differ from those determined in this study for Germany. Consequently, previous studies on production costs, especially those on LCOH, point to significantly lower costs for hydrogen imports to Germany than for domestic production. A review by Breuer et al. [72] noted domestic hydrogen production costs between 3.3 and 7.3 €/kg H2 assumed for Germany in in previous publications. Furthermore, the review revealed costs of between 1.4 and 2 €/kg H2 for imports to Germany in . Thus, the LCOH values obtained in this study for and domestic production in Germany could be considered very low compared to the values of the review. Possible reasons for this are potential considerations of taxes, overhead costs, decommissioning costs, or other cost components in the studies considered.
With respect to the GWP100 results, a review by Wilkinson et al. [5] identified values mainly below 5 kg CO2eq/kg H2 for this kind of water electrolysis configuration. However, the review states that in earlier publications on hydrogen production by electrolysis in Germany, even GWP values below 0.9 kg CO2eq/kg H2 were determined. Thus, the calculated GWP values in this study are within the range of values from previous studies on water electrolysis technologies using renewable electricity.
With regard to the products of water electrolysis, it is usually assumed that oxygen is an unintended and non-harmful by-product and therefore all environmental impacts are attributed to hydrogen [69, 73, 74]. This approach is also used in this study.
However, if it is possible to use the oxygen produced by water electrolysis, allocation or substitution could be a way of allocating parts of the environmental impact to oxygen. Analyses by Bargiacchi et al. and de Kleijne et al. [73, 74] suggest that significant environmental impacts could be attributed to oxygen in this case. In addition to reducing the specific environmental impact of hydrogen production, downstream reuse could also have a positive effect on the economics of electrolysis systems. The oxygen produced can be sold or used in addition to hydrogen (e.g., in hospitals or industrial plants) [75,76,77]. Although the use of oxygen in fuel cells and for medical and other applications is expected to increase in the future, it is questionable whether it is possible to fully utilize the quantities of oxygen that are likely to be produced.
Innovation may be once again changing the game in the chemicals industry. Two forces seem to be driving this innovation: a heightened focus on sustainability (from companies, customers, and policymakers) and changing customer preferences. Yet, this potential transformation is taking place amid historic pressure on the industry. First, some companies are pursuing research and development (R&D) and investments against short timelines set by announced sustainability targets from both private and public entities. Second, these capital expenditure decisions, in some cases, must be made even before supply chains for new feedstocks have been secured and project risk can be mitigated by long-term offtake contracts. Furthermore, the current consumer price premium for sustainable products may change as supplies increase.
The chemicals industry seems to be under increasing pressure to reduce emissions, increase recycled inputs to minimize waste, and develop inherently safer chemicals. Pressure may come from across stakeholder groups: local and federal governments, nongovernmental organizations, investors, industry groups, and downstream consumers. Numerous policies, regulations, and targets have been announced over the last few years, with many investors requiring companies to disclose environmental data.1 In fact, brands are driving demand for more sustainable materials to meet their sustainability targets and prepare for the low-carbon, reduced-waste future that policies are pushing toward.2 Today, more than 1,700 companies and financial institutions, globally, have announced net-zero commitments.3 In addition, according to a Deloitte survey, 59% of respondents reported their companies have started using sustainable materials, such as recycled materials and lower-emitting products.4
Over the last two years, several such policies and regulations have been adopted and proposed. The United States passed the Inflation Reduction Act,5 which provides incentives and funding to clean energy production and infrastructure, and proposed climate disclosure rules,6 which could require listed companies to disclose scope 1, scope 2, and some scope 3 emissions. In September , President Joseph Biden signed an executive order creating a National Biotechnology and Biomanufacturing Initiative to advance American biotechnology and biomanufacturing.7 The European Union also proposed the Fit for 55 package8 and the European Green Deal,9 which could promote several initiatives, including clean energy, energy efficiency, and longer-lasting products that can be repaired, recycled, and reused.
The chemical industry's role in reducing emissions and waste will likely be important as the demand for chemicals and materials grows. For instance, global demand for plastics is expected to triple between and from 460 million tons (MT) to 1,231 MT, with increased use in the transportation, construction, and packaging sectors as economic growth drives demand in those sectors.10 It’s important to note that more than 75% of the chemical industry’s emissions are scope 3 (figure 1).11 This has led to an increased focus on decarbonized upstream inputs, low-carbon end uses, and downstream end-of-life options. And meeting targets will likely become increasingly important to brand value as stakeholders pressure brands to demonstrate progress in meeting corporate sustainability commitments. This pressure on brands could inevitably trickle through to original equipment manufacturers and parts and component manufacturers.
Consumer preferences may shift as new products are developed and existing products are improved to solve problems, fulfill needs, and enhance the way we live and work. Shifts could continue to occur due to demographic changes. For instance, one forecast indicates that demand for medical devices could rise by nearly 50% between and as the population ages and the prevalence of chronic disease increases.12 Global demand for electric vehicles (EVs) is forecast to increase eightfold between and (from 3 million to 27.5 million),13 as policy incentives, better performance, and preferences for sustainable products could drive demand in the sector. Shifts could also occur in response to growing public awareness of an issue. For instance, several regions, countries, and states have banned single-use plastics.14 And in March , the resolution to end plastic pollution passed the United Nations Environment Assembly, and a binding United Nations treaty could be signed as early as .15
Each one of these shifts in demand could reverberate through the products’ supply chain. Some shifts may only impact one part or component, but other shifts could impact every part of the supply chain, from feedstocks to end use. This may be especially true when the shift is toward more sustainable goods. Consumers generally want the same (or better) performance and affordability in addition to sustainability. This raises the question of whether it’s more economical for the producer to decarbonize their existing product or start producing a different product that may have slightly different performance metrics but a lower carbon footprint.
This reevaluation of chemical companies’ portfolios is expected to be important as markets continue to evolve because demand for new advanced materials could increase (e.g., lithium-ion batteries for energy storage, graphene for wearable medical devices), while other chemicals and products become outdated (e.g., chlorofluorocarbons [CFCs] after public awareness of their harm to the ozone layer led governments to ban them, and film after the use of digital increased). The difference now is that shifts in demand toward sustainable products could impact all products rather than just a few.
Additionally, while in the past, chemicals companies have generally focused mainly on the volume of product sales, as scope 3 emissions gain importance, chemicals companies may start considering how their products are used downstream. For instance, chemicals companies may choose to sell their products (e.g., plastics, resins) to electric vehicle manufacturers (rather than internal combustion engine vehicles manufacturers) to reduce their downstream scope 3 emissions.
The drivers of shifting demand and sustainability have contributed to today’s innovation in advanced materials, but the current acceleration is likely due in large part to advancements in enabling technologies such as robotics, artificial intelligence (AI) (for more details, see the sidebar “Enabling technologies: Artificial intelligence”), 3D printing, and material informatics (both physics-based machine learning and de novo simulations and eventually quantum computing). Advanced materials research is extensive, incorporating multiple fields, including material science, chemistry, physics, nanotechnology, and biotechnology (for more details, see the sidebar “Enabling technologies: Synthetic biology”), and its development is being accelerated in part by technologies and policies that shorten the time to market. Additionally, initiatives like the Materials Genome Initiative aim to expand the range of advanced materials and accelerate time to market.16
Materials like self-healing concrete and bioresorbable polymers that allow stitches to absorb into the body represent advancements in materials that can improve how we live and work. Scientists can design new, purpose-driven materials engineered to outperform naturally occurring materials. They can also manipulate and create cells, cell-like structures, DNA, and proteins in organic processes.
The applications of these materials are generally even more wide-ranging than the sciences behind them. Considerable improvements in the fields of medicine, electronics, automotive, construction, energy, and agriculture over the last few years have facilitated new applications of biomaterials, semiconductors, smart materials, nanomaterials, and advanced plastics and resins (figure 3).
The ability of companies to reexamine existing products and design new products in response to sustainability and consumer preferences may help determine their future success. Companies should evaluate the entire supply chains of each of their products, from feedstock to part to product. Key considerations to examine will include sustainability, cost structure, and performance characteristics at each stage.
The industry is exploring taking a circular ecosystem approach to reduce waste and emissions throughout the value chain. Companies can reduce scope 1 and scope 2 emissions through abatement solutions such as electrification, renewable energy usage, clean hydrogen usage, and efficiency improvements. Circular ecosystems can further help to abate upstream and downstream scope 3 emissions through solutions such as bio-based organic building blocks, CCU, advanced chemical recycling, and industrial bio-based operations.
For these circular ecosystems to be successful, a few elements are especially important. First, renewable or low-carbon energy should be used whenever possible. IEA estimates that renewable power generation will need to rise by 12% annually through to meet net-zero targets, which is twice the average of –.48 Second, feedstocks that can be reproduced more quickly (e.g., biomass) should be used when possible before more finite resources (like fossil fuels). The full life cycle of these feedstocks should also be taken into consideration. Third, hard-to-abate processes should use carbon capture, utilization, and storage (CCUS) to reduce emissions. Fourth, end-of-life options must be considered (e.g., recyclability, biodegradability, compostability).
But value could be created for companies along the supply chain if circular ecosystems are developed. Today’s linear ecosystem results in most products losing economic value entirely at the end of the product’s life as they end up in a landfill (figure 4). A circular system would preserve economic value as products are reused, refurbished, repaired, or recycled. Additional economic value could be created in several ways. For instance, producers could reach new customer segments with reused, refurbished, repaired, or recycled products, as well as through increased efficiencies, such as a company using its own waste as a low-cost feedstock. Lastly, companies could reduce regulatory, investment, and reputational risks by moving toward sustainable business practices.
To help fully realize this value creation, companies should track and report their sustainability efforts in a way that is wholly transparent. Life-cycle assessments can provide a more comprehensive understanding of the environmental impact of a particular product throughout its entire life cycle, from raw material extraction to disposal. Additionally, accurately and transparently reporting the results to investors and customers is important. Customers that view a company as transparent are 1.5 times more likely to pay more for a product even when a cheaper option is available.49
Bio-based materials seem to be gaining traction as a potentially viable solution for developing more sustainable goods and potentially solving some specific aspects of waste by moving toward biodegradability and compostability as options. Bio-based materials are made from natural feedstocks or inputs extracted from plants or other organic sources, such as starch, cellulose, and proteins, and can further be processed through various biological and chemical reactions. These materials are then transformed into fibers, films, and resins via processes such as extrusion, casting, and molding. They can be classified into categories such as bioplastics, bio-composites, and bio-based chemicals, each with unique properties and applications.67
Four generations of feedstocks are used for manufacturing bio-based materials:
First-generation feedstocks (e.g., corn, sugarcane, and soybeans): These are commonly used to produce biofuels and bioplastics. For instance, some companies make bio-based polymers from corn for use in textiles, carpets, and other applications.68
Second-generation feedstocks (e.g., switchgrass, algae, and agricultural waste): A few bioplastics companies use agricultural waste such as potato peels and other food scraps to make compostable packaging.
Third-generation feedstocks (e.g., microorganisms like bacteria, yeast, and fungi): For instance, algae can be used to produce oils that can be used in food, cosmetics, and biofuels.69 Another example is a biotech company, which uses bacteria to produce self-healing concrete that seals cracks, making special coatings or waterproof membranes unnecessary.70
Fourth-generation feedstocks (e.g., synthetic biology-based feedstocks to design and engineer organisms that produce specific materials or chemicals): One genetic engineering company uses synthetic biology to engineer microbes to produce high-value chemicals such as fragrances, flavors, and medicines.71
Consumer demand for bio-based materials is currently growing and is expected to continue to grow in the future as businesses move toward more sustainable materials. Global bioplastics only account for 1% of total plastics production, but production capacities could potentially increase from around 2.2 MT in to approximately 6.3 MT in , a 23% CAGR.72
But, as with other advanced materials, challenges exist.
Scalability: There can be additional complexity in scaling up the production of materials using bio-based feedstocks rather than traditional chemicals. To help ensure high performance and durability, scaling may need to be done in stages with particular attention to efficient processing and manufacturing and quality control. AI and other technologies could help to streamline the manufacturing of bio-based materials by detecting and removing intermediate, redundant, or wasteful procedures, such as finding the most promising feedstocks or optimizing processing conditions. This can lead to more efficient and cost-effective production processes that help minimize waste and lower the time and cost required to scale up production.
Redesign of products and processes: Nascent infrastructure in the bio-based materials market and supply chain can pose significant challenges for life-cycle analysis and sustainability efforts. For instance, drop-in bio-based materials, which are designed to replace existing materials without significant changes to the manufacturing process, may reduce emissions relative to traditional materials. However, the environmental benefits may be limited if the production of bio-based material relies on fossil fuels elsewhere in the supply chain. On the other hand, “smart drop-in” and carbon-neutral materials can offer significant environmental benefits but may require a redesign of the entire manufacturing process. This can involve significant investments in R&D, as well as changes to existing manufacturing infrastructure, which can be difficult to finance.
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