Traditionally, environmental, economic, and social impact assessments of technological innovations have been conducted retrospectively, which means assessing the present or past impacts of products and services. However, for the evaluation of future aspects of technological developments, alternative assessment methods are needed. Prospective assessment is a future-oriented method that can be used to assess environmental, economic, and social impacts. Prospective assessments, like retrospective assessments, provide guidance to decision-makers, including technology developers, policymakers, and manufacturers. Despite the benefits offered by such assessments, a standard method to follow when conducting a prospective assessment presently does not exist.
This section focuses on the methodological challenges of prospective assessments for the evaluation of the impacts of emerging technologies, with a particular focus on emerging battery technologies. Four key challenges of prospective assessments are defined and discussed, being data availability and quality, scaling issues, uncertainty management and variability, and comparability. Each of these challenges is described, and existing methods are suggested to mitigate the challenges. The section concludes by emphasising the need for harmonised and standardised methods when communicating results related to prospective LCAs. In addition, studies need to address the key challenges identified to improve the wider acceptance of results amongst stakeholders and decision-makers.
12.1 Introduction
Environmental and social impact assessments have been developed from an environmental accounting perspective to identify the impacts and hotspots of a present or past technology retrospectively [33]. The increased use of those assessment methodologies as an engineering tool calls for alternative assessment methods that enable guiding innovations towards sustainability. This is the case for emerging technologies that are at an early stage of the development process [2]. Emerging technologies include both innovative products and processes that are not yet on the market, such as novel battery technologies, as well as novel advances to products and technologies that already exist on the market [4]. To evaluate the impacts of these technologies, predictions of future markets or future production technologies must be made that account for changes in economic, environmental, and social conditions over time [18].
Prospective assessment is a future-oriented method that can be used to assess environmental, economic, and social impacts of emerging technologies as well as mature technologies that are predicted to change significantly in the future [6]. Prospective assessment is also sometimes referred to as ex ante, future-oriented, early-stage, anticipatory, explorative, and scenario-based assessment, although the definitions of these methods, and whether they refer to the same method or slight variations of a method, are inconsistently reported across literature [4, 6, 9]. For this section, the definition of prospective assessment is taken as the systematic assessment of a future point in time, considering developments in society, technology, economy, and policy, that potentially influences the technology, its societal conditions, and its environmentally relevant flows [2, 18, 48].
Anzeige
A key benefit of prospective assessment is that decisions can be made proactively, at an early stage of the development process, to minimise or even prevent future potential adverse environmental, economic, and social impacts of the technology being assessed [22, 31, 50]. Prospective assessments, like retrospective assessments, provide guidance to decision-makers, including technology developers, policymakers, and manufacturers. The results of prospective assessments can be used in multiple ways depending on the goal and scope of the study. For example, results can inform technology developers of valuable changes that can be implemented at an early stage of development, support stakeholders in terms of investment opportunities that can enable further research and technological development, inform policymakers of recommendations based on assessed policy implementation scenarios, and guide manufacturers towards assessed applications of a technology that are considered relatively more sustainable [7, 9]. Therefore, the future potential and future challenges of the innovative technology are defined prior to the technology being implemented at larger production scales [50].
Prospective assessment is therefore valuable for assessing emerging battery technologies that have not yet penetrated the market and are still in the development phase. By assessing these technologies at an early stage, mitigation measures can be implemented more readily and influence the production technology as it is upscaled to industrial production. A standard method for conducting a prospective assessment of emerging battery technologies, or for other technologies, presently does not exist. This leads to challenges when using the results in a decision-making context [4, 17]. Although such challenges also exist for retrospective assessments, Hetherington et al. argue that challenges in prospective assessment are more prominent due to the time sensitivity to apply the results within the technology development timeframe [19]. These challenges are largely related to data availability, scaling issues, uncertainty, and comparability of results [19].
This section focuses on the challenges for applying prospective assessment to emerging battery technologies. Within the following sections, four key challenges of prospective assessments are defined and discussed. These challenges include data availability and quality (Sect. 12.2), scaling issues (Sect. 12.3), uncertainty management (Sect. 12.4), and comparability (Sect. 12.5). Section 12.6 concludes with an outlook on research and development in prospective assessment of emerging battery technologies.
12.2 Data Availability and Quality
The availability of data is limited and often of lower quality than preferred to meet the goal of prospective assessments [9]. Primary data, or measured raw data, are often scarce and/or confidential. While this is also a known challenge for retrospective LCA, key difference for prospective assessments is that data is simply not yet available or based on a laboratory scale [33]. Material input and energy consumption data are difficult to collect at this scale since production conditions are not yet optimised and the production processes themselves are still under investigation for their feasibility. Measurements on laboratory scale typically lead to energy consumptions that are not representative of production conditions at larger scales and material consumptions that suffer from high input quantities and poor product yields [11, 15].
Anzeige
The availability and quality of data is therefore largely related to the technology readiness level (TRL) and manufacturing readiness level (MRL) of the technology being assessed. The TRL indicates the level from 1 (concept development) to 9 (small-scale production) that addresses the maturity and functional readiness of a technology, whereas the MRL indicates the manufacturing maturity level of the technology’s components and subsystems [15]. For emerging technologies, the TRL ranges from 2 (concept feasible) to 5 (laboratory-scale production validated), with corresponding MRLs from 2 (new manufacturing concepts identified) to 5 (prototype production simulated) [31]. Gavankar et al. found that the environmental burden per unit output is likely to reduce significantly with increased technology and manufacturing maturity levels [15].
In terms of battery technologies, Greenwood et al. [16] have criticised the TRL for being too generalised for consistent application to batteries. They have taken inspiration from the TRL, MRL, and other readiness levels, to develop the Battery Component Readiness Level (BC-RL) framework. In the BC-RL, three types of technologies are defined depending on the extent to which the technology can use existing production and cell assembly processes, and nine stages are identified, from laboratory production (stage 1) to commercialisation (stage 9).
As highlighted in Table 12.1, on each BC-RL stage, different data sources can be used for assessments of battery components and cells. For example, in the theoretical concept development phase, data availability is insufficient to conduct a full LCA. In this case, streamlined LCA approaches (e.g. ignoring some up- or downstream processes, mixing qualitative and quantitative data, etc.) are more suitable and applied for screening purposes [3, 12]. Similarly, in phase 3, lab-produced coin cell prototypes can be used to establish simplified LCAs of novel technologies. When using simplified LCA however, complex challenges arise with emerging technologies that should be understood by all stakeholders to enable effective development decisions to be made [19]. For example, the function of the product might not yet be clearly defined (such as for nanomaterials), systems change when scaled up – lack of knowledge on how they will function at larger scales, processing stages might not be fully defined, coproduct use is unclear, and end-of-life treatment is unknown. Additional techniques (e.g. proxies, process simulation) are therefore used to fill data gaps and assess the technology in a production-scale environment (addressed further in Sect. 12.3).
Table 12.1
Battery Component Readiness Levels by Greenwood et al. [16] and examples of assessments for battery components and cells
Battery Component Readiness Level
Assessment example
1. Theoretical concept development
Environmental screening of nanomaterials for batteries [13]
2. Determination of fundamental component properties
Estimation of material cost for fictive dual-graphite cells [37]
3. Determination of electrochemical properties for small-format full cells – system-level proof of concept
LCA based on lab-scale coin cell with a sulphide solid electrolyte [55]
4. Determination of electrochemical properties for commercial-scale cells – system-level prototype
LCA based on a lab-produced pouch cells with a Sn0.9Mn0.1O2 anode powering a remote-controlled vehicle [5]
5. Development of proof of concept for scalable component production
Combination of pilot and lab-scale data to predict the environmental impact of processing battery-grade cobalt sulphate [38]
6. Development of industrial-scale component production processes
Cost and energy estimates of industrial production of LiPF6 based on process modelling [43]
7. Development of proof of concept for scalable cell production
LCA for a cell based on a pilot-scale production line [11]
8. Development of industrial-scale cell production processes
Gate-to-gate GHG emissions of a 7 GWh cell production line based on calculations [10]
9. Establishment of commercial plants for component and cell production
Top-down LCA of an automotive lithium-ion battery pack produced in an existing 30 GWh factory [42]
For prospective assessments, the background data is also of significance [8, 17, 35]. Foreground data is data related to the technology being assessed, including its relevant components and processes, whereas background data refers to data for processes further upstream and downstream of the technology being assessed. For example, background changes in the electricity mix can influence the future impacts of electric vehicles and battery technologies [30, 35, 54]. Therefore, changes in the electricity mixes must also be included in the background data used for the assessments in order to more accurately capture the future impacts of battery electric vehicles [8].
The available data and quality determine the extent to which results can be applied for comparative assertion or hotspot assessments [56]. The goal of the study needs to be transparent and adapted to the data quality. It is furthermore essential to consider both the quality level and the time required for assessment, as well as the timing of the results needed to impact effectively the design and development stage.
12.3 Scaling Issues and Modelling Choices
Additional modelling choices are made for prospective assessments to determine the expected impacts of an emerging product system, including defining the functional unit, system boundary, allocation methods, and scaling factors. These challenges are not unique to prospective assessments, but due to data gaps and several unknowns such as the future application of the product system and how the production processes will be implemented at larger scales, these challenges become more prominent in prospective assessment [19].
The reliance on laboratory-scale data to perform prospective LCAs presents a significant challenge. It requires the upscaling of foreground inventory data. In a typical scaled-up process, the process is first optimised in the lab, followed by several steps to upscale the technology before building a large-scale facility [45]. This includes preliminary validation of lab-scale process and constructing a mini-plant, followed by a pilot plant to validate processes and finally simulate industrial-scale production. Due to the time-intensive nature of actual upscaling, prospective assessments utilise various data projection techniques to predict the future implementation of technology on an industrial scale [47]. In the case of emerging batteries, there are different methods for scaling products, and the scales defined for this context are at the level of electrodes, cells, or packs.
12.3.1 Upscaling at the Product Level (Cells and Packs)
Upscaling at the product level includes the tasks of determining the material composition of the product to which other material and energy flows are related. For incremental innovations that build on existing technology, for example, an increase of the electrode thickness, the already existing cell design needs to be adapted. Performance-mass models are the most used tools to calculate the effects of the changed cell design to the material composition. Those types of models are typically spreadsheet based and are available for a variety of different cell chemistries and cell formats as they are often used within cost assessments. Prominent examples for conventional Li-ion technology are the BatPaC model [32], the CellEst model [52], or the mass model developed by Schünemann [39].
Prospective assessments on new technology for batteries are typically based on lab-produced prototype cells (e.g. coin cells or small pouch) used for experimental purposes. However, prototype cells produced on a lab scale are not always comparable with those used in industry [27] and using these as a base of assessments might result in under- or overestimations of impacts. For example, lab-scale-produced solid-state cells typically contain a relatively thick solid electrolyte (typically 80–200 μm for solid polymer and composite polymer and up to 1 mm for inorganic electrolytes) but need to be reduced to below 25 μm to realise high energy densities [58]. Zhang et al. [55] illustrate how such thick inorganic solid electrolyte (1 mm) in a lab-produced coin cell is responsible for 97.1% of the total manufacturing energy consumption and has roughly double the global warming potential impact compared to a conventional LIB cell.
Different methods can be applied to upscale novel battery technologies from lab-scale or theoretical conceptual cells in prospective assessments on new technology. These include the use of basic calculation project cells produced in a lab environment, adapted spreadsheet-type models, and more advanced electrochemical models. The first approach uses basic (linear) calculations to estimate how the lab-produced prototype cells might scale in the future. For example, Zhang et al. [55] linearly scale the 1-mm-thick solid electrolyte used in a coin cell down to thickness of 20 μm to quantify the environmental benefits. Similarly, Wolff et al. [53] use a set of linear equations to scale a lithium-sulphur lab-produced coin cell to represent a 50 kWh automotive battery. While such simple scaling is useful for approximations, they typically neglect the technical complexity of batteries. To include such technical complexity, more advanced models are used to simulate specific cell designs used as an input to the inventory data. Due to the structural similarity of different battery types, existing spreadsheet-based models can be adapted for new cell chemistries. Peters et al. [34], for example, use a modified BatPaC model to obtain the inventory data and related life cycle environmental impacts of different sodium-ion battery types based on the electrochemical parameters of different sodium-ion active materials.
The two discussed types of models are important to develop the LCI for the raw materials, the production, and the end-of-life life cycle stage. For a holistic analysis as is foreseen in LCA, the use phase of the battery needs to be understood. This task requires more advanced electrochemical models to understand the aging mechanisms and the performance of the battery over its life cycle. Electrochemical models have been linked to LCA to enable a more systematic evaluation of battery design parameters (e.g. electrode thickness, porosity, and ambient temperature) and operating conditions on energy density and life cycle and related environmental impacts of lithium-ion batteries [24, 25]. Such coupling between detailed electrochemical models and system analysis models such as LCA or cost assessments has only recently been proposed and is presently not widely adopted or available.
12.3.2 Upscaling at the Unit Process Level
In the context of the production life cycle stage, an upscaling method for the unit process level can be defined as the procedure to project how a new process presently available at a low MRL might function at a higher MRL [47]. The goal of this upscaling process is to generate the potential material and energy flows of the unit process as well as potential emissions and waste flows. Following Parvatker and Eckelman [33] and van der Giesen et al. [48], a range of general LCI generation methods can be identified (see Fig. 12.1). The choice of method is largely dependent on the data and time available for the assessment and the MRL of the assessed technology. Scaling up the data from lower TRLs and MRLs to commercial scale also impacts the accuracy and introduces uncertainty into the results (discussed in more detail in Sect. 12.4).
A reversible triangle depicts L C I data upscaling methods in low to high uncertainty, high to low accuracy, availability, and M R L. The methods are getting a plant database, process simulation, advanced process calculations, stoichiometry, and other models on the left panel. The battery example of L C I data upscaling methods is on the right panel.
Fig. 12.1
Methods to generate life cycle inventory data with respect to uncertainty, accuracy, data availability, and manufacturing readiness level (MRL). (Adapted from van der Giesen et al. [48] and Parvatker and Eckelman [33])
×
When inventory data cannot be directly obtained from a commercial plant or LCI database, the first LCI generation method choice is the use of process simulation. Process simulation refers to steady-state and dynamic simulation models using process simulation software (e.g. Aspen Plus, CHEMCAD, or HSC Sim) and is commonly used in chemical and process engineering to analyse, design, and improve production processes. As such models typically require detailed process operational parameters, the manufacturing readiness level of the process has to be relatively high. Following process simulation, the second method is manual calculation [47] which includes advanced process calculations, basic process calculations, and calculation based on stoichiometric relations. As opposed to basic process calculations, advanced process calculations include more details such as production scale, equipment efficiencies, equipment sizing, and calculation of the energy requirement of each piece of equipment used in a production process [33]. On the lowest MRL, molecular structure-based models and proxies can be used to generate process inventory data. Molecular structure-based models are not widely applied to generate inventory data.
Amongst the presented methods, process calculations are mostly adopted for scaling calculations for the production life cycle stage. In many cases, the process design can be anticipated with the help of production engineering knowledge. If the production processes are similar to existing ones, dimensional analysis can be a powerful tool to scale LCI [57]. This is particularly the case for chemical production systems, for which several scale-up frameworks based on process calculations have been developed [36, 40]. However, as the battery production process chain includes process engineering, manufacturing, and electrical processes, a transfer of the scale-up frameworks comes with additional challenges. In scaling the LCI from a lab- or pilot-scale to large-scale production, the following factors describe the situation at low MRL [11]:
Significantly lower material efficiencies
Lower process efficiencies, energy efficiency, and throughput of the processes
Overvaluation of technical building services like the dry room
Lack of systemic efficiencies within the production system like the use of waste heat, energy recovery, or solvent recovery
Unoptimised product design compared to industrial-scale batteries
Taking these factors into consideration, the scale-up of unit processes needs to include the production system perspective. In the battery production cell system, the dry room plays a key role for the energy demand. Modelling approaches for the dry room have become increasingly accurate but at the same time more complex [1, 49]. During scaling, it can be a viable option to use dry room area related key parameters, such as presented by Vogt et al. [49]. Depending on the location of the dry room, the energy demand is typically within a range of 0.85–0.975 kWh h−1 m−2 and 0.05–0.7 kg CO2-eq h−1 m−2.
The presented LCI generation and scaling approaches can be applied to most life cycle stages. However, the use phase of batteries requires different scaling approaches, for example, electrochemical models which were explained in the previous subsection. Additionally, learning rate or experience curves can be applied to estimate the future development of technical performance parameters [44]. While these have been applied in techno-economic assessments, the application of learning rates to generate LCI data for foreground systems is a relatively new concept.
12.4 Uncertainty Management
Prospective LCA of emerging technologies has been identified as a challenging area within the field of LCA due to the lack of empirical data available to perform assessments. This entails the need for assumptions to be made about future developments. The absence of empirical data, combined with the high degree of uncertainty in the future scenarios being evaluated, can lead to uncertainties in the results between studies performing a prospective LCA [48]. This can make it challenging for decision-makers to use the results of the assessment in a meaningful way. Due to such challenges, the management of uncertainty becomes a critical aspect in the prospective LCA of emerging technologies.
To address these challenges, it is important to have robust and transparent methods for uncertainty management in prospective LCA of emerging technologies. This will ensure that the results of the assessment are reliable and that decision-makers can have confidence in the results. Several methods have been proposed for uncertainty management in prospective LCA, including sensitivity analysis, scenario analysis, and Monte Carlo simulation [45]. These methods can be used to identify the key sources of uncertainty in the results and to assess their impact on the results of the assessment. In addition, several types of uncertainties exist, including parameter uncertainty, scenario uncertainty, and model uncertainty [23]. This section focuses on parameter and scenario uncertainty, which are the most found ones in prospective LCAs of emerging technologies.
Parameter uncertainty refers to the variability and unreliability of the input data used in an LCA study [29]. This type of uncertainty is prevalent in every type of product assessed using an LCA, but it is especially pronounced at emerging technologies. Input data can be uncertain due to a lack of information or due to variability in the data collected. There are several factors that can cause variability in the input data. Some of the most common sources of variability include data collection, data quality, model uncertainty, and measurement uncertainty. For example, data on energy consumption, emissions, or material use can be subject to measurement error, variability in production processes, or changes in the market energy mixes. To account for parameter uncertainty, LCA practitioners often use sensitivity analyses to examine how changes in input parameters affect the overall results of an LCA. The Monte Carlo method is another commonly used approach to account for parameter uncertainty, where the input data is modelled using probability distributions and then simulated multiple times to account for variability.
The uncertainty in LCA scenarios arises from the choices made in the modelling process, such as the assumptions made in the goal and scope phase or the upscaling calculations [45]. This type of uncertainty is especially prevalent in prospective LCAs of emerging technologies, where information is limited and there is a high degree of uncertainty about the future development of the technology [23]. For instance, when making assumptions about the development of the electricity grid in the background datasets, the results of the LCA can be highly uncertain due to the selection made. To mitigate scenario uncertainty, researchers frequently employ sensitivity analyses to investigate how changes in key assumptions or boundary conditions impact the overall results of the LCA [26]. Additionally, prospective LCAs can be used to explore extreme-case scenarios (or anticipatory LCAs) or to assess the robustness of the results under different boundary conditions [51].
Several studies have been conducted to address uncertainty in prospective LCAs, including Cooper and Gutowski’s approach for selecting probability distributions [7], Lacirignola et al.’s procedure for examining the robustness of global sensitivity analysis results [23], Marini and Blanc’s method for identifying parameters that contribute to uncertainty using the Sobol indices [26], Ravikumar et al.’s statistical test for significant differences in LCA results (Ravikumar et. al. 2018), and Wender et al.’s development of anticipatory LCAs [51].
In conclusion, uncertainty management is a critical aspect of prospective LCA of emerging technologies. Applying the existing methods to document the uncertainties in the results of LCA will aid in wider acceptance of the results. Regardless of the choice of method used to mitigate uncertainty, considering uncertainty is essential to ensure the reliability and credibility of the results of a prospective LCA study. Failing to consider uncertainty can result in unreliable and misleading results, making it difficult to make informed decisions about the sustainability of emerging technologies.
12.5 Comparability
LCA has become a widely used method for evaluating the environmental impact of emerging technologies. However, comparability of results between studies can be challenging due to several factors, and they can be related to the aim of the LCA study, the functionality of the LCA, system boundaries, and specified life cycle impact assessment methodologies [45].
12.5.1 Aim of the Study
In prospective life cycle assessments, the aim of the study plays a crucial role in determining the comparability of results. The aim of the study can be generally categorised into (i) comparisons of technologies or (ii) identifications of hotspots, both of which are predominantly used in retrospective assessments. Unlike retrospective assessments, prospective assessments require differentiation between comparisons at different technology readiness levels (TRLs), such as laboratory-scale compared to industrial-scale production, or comparisons at a similar TRL but different maturity readiness level (MRL), such as laboratory-produced Li-ion battery cells compared to laboratory-produced solid-state battery cells. Assessing technologies at different TRLs can prove to be the most challenging task and often result in less accurate comparisons. To improve the comparability and interpretability of results, it is essential to communicate the readiness levels and production scale of the technology as part of the prospective assessment and clearly state them in the study [15]. The use of various upscaling methods, as discussed in Sect. 12.2, can also aid in comparing technologies with different TRLs.
The aim of the LCA study also plays a crucial factor in determining the comparability of results between studies. For example, a study that aims to compare the environmental impact of different materials used in a product will have a different system boundary than a study that aims to compare the life cycle impact of different production processes for a specific material. The choice of system boundary affects the results of the study and, therefore, the comparability of results between studies with different aims [6].
Finally, the goal of the LCA study can also influence the functional unit, which is a crucial aspect of the LCA methodology. The functional unit is the unit of measurement that allows for the comparison of different technologies or products. The choice of functional unit must be clearly defined and consistent between studies for results to be comparable [46].
12.5.2 Functionality
The functional unit is a crucial component in conducting an LCA as it quantifies the performance of a product system. The challenge in defining the functional unit lies in the fact that the future function of emerging technologies may not be fully known. Systems at an early stage are susceptible to change, and additional functionality may develop as the product matures. This was evident in a study by Hischier et al. who found that the main factor for variations in the LCA outcome depends on a well-defined functional unit [20].
To overcome this challenge, it is necessary to either define ranges for the functional unit or to consider multiple functional units. A framework described by Simon et al. [41] that includes the functional analysis of a lab-scale process can be useful in defining the system functions. The authors must also be aware of the issues concerning the definition of the functional unit and should investigate the effects of different functional units to analyse the full function along the life cycle. However, it is important to note that defining the functional unit can be challenging, especially when the application of the product is not yet apparent. This can lead to inaccurate results due to the uncertainty in upscaling and a potential change or decrease in functionality.
For example, different functional units, such as one battery pack or cell, 1 kilogram of battery, 1 kilowatt hour of storage capacity, or 1 kilometre driven, may be used to evaluate variations when assessing the environmental impact of electric vehicle batteries [28]. In this case, normalising the LCAs based on a common functional unit, such as 1 watt hour of capacity, can help facilitate comparison of the assessment results of different emerging battery technologies. By including multiple functional units in the assessment and analysing the sensitivity of the functional unit choice, a more in-depth understanding of the environmental performance can be obtained.
12.5.3 System Boundary
The system boundary is a critical factor in the comparability of results between LCA studies. The system boundary determines the extent of the life cycle to be included in the study, and the choice of system boundary can have a significant impact on the results of the study. For example, a study with a narrow system boundary may not include all environmental impacts associated with a technology or product, while a study with a broad system boundary may include environmental impacts that are not related to the technology or product [6].
The choice of system boundary can also impact the level of detail and accuracy of the results. For example, a narrow system boundary might provide a detailed analysis of specific processes within the technology but may not fully capture the overall environmental impact of the technology. On the other hand, a broad system boundary might provide a comprehensive view of the technology but may not provide the level of detail necessary to evaluate specific processes or impacts. Therefore, it is essential to indicate the extent of inclusion or exclusion of the processes in the system boundary. Studies comparing similar systems should attempt to include the necessary processes required for a fair comparison and evaluation with existing technologies.
12.5.4 Life Cycle Impact Assessment
The choice of life cycle impact assessment methodology is another key factor in the comparability of results between LCA studies. Different methodologies have different strengths and weaknesses, and the choice of methodology can have a significant impact on the results of the study. For example, some methodologies may be more suitable for assessing the impact of emerging technologies, while others may be better suited for comparing the impact of different production processes [45].
In the impact assessment phase, the methodologies used to calculate the impacts can also vary, leading to different results. Some methodologies have additional regionalised impact categories, while others use global impact categories, which can result in significant differences in the results. Therefore, an appropriate choice of methodology representing the impacts for the system is necessary [33]. In addition, to improve comparability with previous and upcoming studies, it would be beneficial to include as many impact categories and methodologies as possible.
Using outdated LCIA methods can lead to incorrect conclusions as emerging technologies can cause unknown impacts in the future that are not captured by existing LCIA categories. Additionally, different characterisation factors are available for various impact categories, and it is suggested to perform the LCA with different characterisation factors [20].
Data availability is often limited in early-design-stage assessments, making it difficult to determine all impact categories [11]. Moreover, the potential environmental impacts of new substances may be overlooked, due to missing LCIA categories, insufficient LCI data, or a lack of knowledge about new impacts. Therefore, there is a need for using a standardised LCIA method when performing prospective LCA of emerging technologies.
12.6 Conclusion and Outlook
This section discussed the definition of prospective assessment as well as the use of its results to inform technology developers, policymakers, and manufacturers. The section also highlighted that there is presently no standard method for conducting a prospective assessment. Several challenges related to data availability, scaling issues, uncertainty, and comparability were identified and discussed. These challenges are highly interconnected and largely linked to the availability of data and choice of upscaling methods, leading to increased uncertainties and difficulty to compare results with existing technologies.
The use of prospective assessments for emerging technologies, such as electric vehicle batteries, poses significant challenges that must be transparently reported and understood by all stakeholders. The lack of transparency in LCA results, especially in a prospective context, requires clear descriptions and justifications of all assumptions made during the assessment process. The results of prospective assessments can provide valuable insights in identifying the environmental impacts of emerging technologies, and results can be used to envisage mitigation strategies.
However, it is important to note that prospective LCAs do not predict the future but rather explore a range of possible scenarios that define the space in which the technology may operate. This allows for a fair comparison of emerging technologies with incumbent technologies and the verification of design options that could steer the technology towards a preferred future state.
In conclusion, the use of prospective LCA in evaluating emerging technologies has the potential to provide valuable insights into the future development of these technologies. By exploring various scenarios and implementing systematic uncertainty management, prospective LCA can support decision-makers in steering the technology towards a preferred future state. It is crucial that the results of these assessments are transparently reported and understood by all stakeholders, including technology developers, policymakers, industry decision-makers, and society, to ensure their effective use in decision-making.
Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
