**Bridging Tools to Better Understand Environmental Performances and Raw Materials Supply of Traction Batteries in the Future EU Fleet**

**Silvia Bobba 1,\*, Isabella Bianco 2, Umberto Eynard 1,2,3, Samuel Carrara 4, Fabrice Mathieux <sup>1</sup> and Gian Andrea Blengini 1,2**


Received: 10 April 2020; Accepted: 12 May 2020; Published: 15 May 2020

**Abstract:** Sustainable and smart mobility and associated energy systems are key to decarbonise the EU and develop a clean, resource efficient, circular and carbon-neutral future. To achieve the 2030 and 2050 targets, technological and societal changes are needed. This transition will inevitably change the composition of the future EU fleet, with an increasing share of electric vehicles (xEVs). To assess the potential contribution of lithium-ion traction batteries (LIBs) in decreasing the environmental burdens of EU mobility, several aspects should be included. Even though environmental assessments of batteries along their life-cycle have been already conducted using life-cycle assessment, a single tool does not likely provide a complete overview of such a complex system. Complementary information is provided by material flow analysis and criticality assessment, with emphasis on supply risk. Bridging complementary aspects can better support decision-making, especially when different strategies are simultaneously tackled. The results point out that the future life-cycle GWP of traction LIBs will likely improve, mainly due to more environmental-friendly energy mix and improved recycling. Even though second-use will postpone available materials for recycling, both these end-of-life strategies allow keeping the values of materials in the circular economy, with recycling also contributing to mitigate the supply risk of Lithium and Nickel.

**Keywords:** Life Cycle Assessment (LCA); Material Flow Analysis (MFA); Criticality; traction batteries; forecast; supply

#### **1. Introduction**

Sustainable and smart mobility, when articulated with appropriate energy systems, is a key asset to decarbonise the EU and develop a clean, resource efficient and carbon-neutral future. This is confirmed by several policy initiatives, among others: the European Green Deal [1] and the EC COM(2019) 22 [2]. In addition, the transition towards a low-carbon mobility contributes to the United Nations Sustainable Development Goals (UN SGDs), for instance Goal 7—ensure access to affordable, reliable, sustainable and modern energy for all [1].

To achieve the 2030 and 2050 targets, technological and societal changes concerning mobility are needed. To improve circular economy and resource efficiency in the automotive sector, the technological aspects should cover the whole value chain of vehicles, from their design to their End-of-Life (EoL),

e.g., new vehicles, light-weighting materials, easier reusability and recyclability of materials, recycled content in vehicles and transport infrastructure [2,3]. In addition, the behaviour of consumers should change toward choices oriented to more environmental-friendly practices, e.g., increase of the occupancy rate of vehicles and the adoption of public transport, sharing of vehicles, teleworking options [3,4]. Automation of vehicles is already proceeding but further efforts are required in the research field to reduce emission and avoid rebound effects (e.g., higher demand for mobility) [4].

This transition will inevitably change the composition of the EU fleet in the future: the increasing share of electric vehicles (xEVs) is already occurring and this trend is expected to accelerate in the next decade [3,5,6]. Meanwhile, new technologies can have an important role in the next future, even though nowadays they are at a very early stage, e.g., fuel cells electric vehicles [7]. Several scenarios are already available in the literature, considering new technologies appearing in the EU market and their evolution according to various parameters (environmental targets, consumers' lifestyles and behaviour, electric driving ranges, economic factors, etc.) (e.g., [6,8]). Such forecasts are quite complex to compare due to the adoption of different assumptions behind the models. Although there is no consensus in the scientific literature on a specific model to adopt in forecasting the uptake of xEVs, the trend is quite clear: xEVs will rapidly increase and will have a very important share on the EU fleet up to 2050, with a consequent increase of traction batteries required for such vehicles.

Among the traction batteries, the most common and most promising chemistry currently used in the EU market are the Li-ion batteries (LIB). The characteristics of LIBs are suitable for their use in different type of xEVs, especially battery electric vehicles (BEVs) and plug-in electric vehicles (PHEVs) [5]. Note that According to the scope of the analysis, since HEVs need relatively small batteries and will have a lower impact on the LIBs market, HEVs are not included in the following analysis.

When traction LIBs reach about 70–80% of their capacity, they are usually extracted from xEV and they have to be properly collected and recycled according to the in-force Directives (2000/53/EC and 2006/66/EC) [9]. However, pilots and research projects are demonstrating that the residual capacity of extracted batteries could be even used in less energy-demanding applications, e.g., residential buildings [9–11]. Even though the second-use of LIB is an industrial practice, there is potential for the creation of a business case beyond 2030 [5]. The second-use of batteries, before their recycling, is an example of circular economy practice where the value of batteries (and therefore embedded materials) is retained within the economy and the resource efficiency is maximised. On the other hand, the flow of batteries available for recycling is delayed in time, which means that also the availability of Secondary Raw Materials (SRMs) from waste batteries is postponed in time due to their higher lifetime [12]. Therefore, an in-depth knowledge of the processes along the value chain of complex products as LIBs is fundamental to assess the effects and the trade-offs of different strategies, e.g., the EoL options.

The New Circular Economy Action Plan [13] and the New Industrial Strategy for Europe (EC, 2020), two of the main building blocks of the European Green Deal [1], stress the role of Critical Raw Materials (CRMs) to achieve a climate-neutral, circular and competitive economy. Future sustainability requirements for batteries could in the future consider, for instance, the carbon footprint of battery manufacturing, ethical sourcing of raw materials and facilitating reuse, repurposing and recycling. Secure and sustainable supply of both primary and secondary raw materials for key technologies such as e-mobility is hence a prerequisite to achieve climate neutrality. With the transition of Europe's industry to climate-neutrality, the reliance on available fossil fuels could be replaced with reliance on non-energy raw materials, many of which are sourced from abroad and for which global competition is becoming more intense [14]. According to OECD forecasts, global demand for raw materials will more than double by 2060, making diversified sourcing essential to increase Europe's security of supply. CRMs are also crucial for markets such as e-mobility, batteries, renewable energies, pharmaceuticals, aerospace, defence and digital applications [15].

The performance of LIBs is related to various aspects, in particular to which materials are used in their cathodes and anodes [5]. The combination of the increasing demand for xEVs and the dynamics related to battery chemistries translate into a growing and diversified demand for raw materials; among the key materials for the manufacturing of high-performant LIBs, some have been classified as CRMs in the EU and/or worldwide [16], e.g., cobalt, natural graphite and lithium. The EU heavily relies on imports for many materials used in batteries. According to the EC Raw Materials Information System (RMIS—https://rmis.jrc.ec.europa.eu/), as the future demand of materials for strategic sectors is concerned, the EU will continue to be almost entirely dependent on third countries, in particular for traction batteries. In fact, the EU produces only 1% of all battery raw materials overall. Materials needed for the batteries manufacturing are mainly extracted in China (32%), Africa and Latin America (both 21%), but also the manufacturing process is mainly occurring in Asian countries. In this framework, some EU initiatives are focusing on improving the EU capacity in manufacturing batteries and improving the EU value chains (e.g., European Battery Alliance and Strategic Action Plan on Batteries).

Due to high cost of specific cathodes, e.g., Li-Co-based such as NMC (nickel-manganese-cobalt) or NCA (nickel-cobalt-aluminium), chemistries with lower cobalt content are already available [17,18]. For instance, the NMC111 is already replaced by cathodes with lower Co content, e.g., NMC811 or NMC9.5.5, which means a strong reduction of the cobalt and manganese content and an increase of nickel. Increased collection of end-of-life vehicles (ELVs) and boosting recycling could increase the amount of recovered materials that can be potentially recirculated in the economic system, i.e., their value is retained within the EU and the EU dependency can reduce [2,14]. The recovery of such materials is particularly relevant for the EU since they are materials with a high supply risk and high economic importance [19]. Note that nickel needed for the batteries manufacturing should belong to CLASS I nickel, i.e., 99% pure nickel [20,21], which should be considered when assessing the recirculation of recovered nickel from recycling processes.

Recycling of the battery at its EoL can be advantageous from both resource conservation and environmental perspectives [22]. Recycling can provide SRMs that according to their quality can be used by the battery sector (e.g., cobalt) or by other industries [23], avoid the extraction of virgin raw materials and generally have lower environmental impacts.

Directive 2006/66/EC14 defines the minimum recycling rates of batteries: 45% of LIBs at their EoL have to be collected and at least 50% of the average weight of LIBs should be recycled, excluding energy recovery. Nevertheless, as underlined by Ellingsen et al. (2018) [22], this Directive often incentivises batteries industries to recover base metals, which are massively used and relatively abundant in nature and available in commodity markets (such as iron and copper). On the other hand, it is known that the market value of metals contained in batteries is an important economic driver for battery recycling. In particular, the higher prices of cobalt and nickel relatively can explain why recycling processes are currently focusing on these metals [22].

To assess the potential contribution of batteries in decreasing the environmental burdens of the EU mobility in the future, several aspects should be included in the assessment. Focusing on the environmental impacts of batteries along their life-cycle, in the literature Life-Cycle Assessment (LCA) studies are already available (e.g., [24–26]). However, "*guidelines or harmonized approaches do not yet exist*" [27] and some issues emerging from the available literature still need to be addressed [28], which make it even more challenging to capture the environmental performances of different type of traction batteries in xEVs, especially in relation to the adoption of energy mix and taking into account different EoL options. Again, a single assessment tool does not likely provide a complete overview of such a complex system; in fact, some aspects are not captured through LCA, e.g., resource efficiency of some EoL options, hence LCA should be integrated with other assessment tools [29]. To obtain a more complete understanding of products' status [30], different assessment tools should be combined [29,31,32]. There are already some studies demonstrating the added value of combining LCA and (dynamic) Material Flow Analysis (MFA), which identify and quantify the stocks and flows of products/materials along their whole value chain. Bridging tools and the complementary use of their results support a more prospective decision-making, especially when different strategies are

assessed, e.g., waste strategies [31]. Finally, the relevance of specific materials in terms of availability and vulnerability of a system, can be captured by the criticality assessments, providing relevant information of supply disruption and mitigation measures, taking into account flows of both primary and secondary materials [16].

Well established tools can be adapted and combined according to the product/system' characteristics to provide a wider understanding of the environmental performances in a life-cycle perspective. Hence, it is possible to capture different aspects of the assessed system/product and provide information according to the interests of the specific stakeholders. In addition, a flexible model and a common structure of the data collection (e.g., identification of best available sources, common data when possible) eases the update of the assessment according to the availability of data/information. Finally, consistency of input for different analyses is improved and the comparability of results is strengthened.

For a sustainable management of traction batteries, in an exponentially growing market and with a life-cycle perspective, some key questions need to be answered:


#### *Aim and Structure of the Paper*

This paper builds on a toolbox of existing assessment methodologies and bridges them to assess the environmental performances of complex systems. The adoption of different methodologies providing different type of information of the potential effects of the rapid evolution of a key sector for the EU is the core of the paper. For this reason, well established assessment tools are integrated: (1) to consider specific characteristics of the assessed LIBs; (2) to provide a more holistic and comprehensive understanding of traction LIBs; and (3) to build structured and comprehensive responses to relevant questions made by different stakeholders of the whole value chain of traction LIBs (e.g., manufacturers, recyclers, consumers and policy makers).

The authors believe that bridging tools and a more structured use of their results, as well as a mutual informing among the tools, is an added value in improving the knowledge of complex system and can support decision-making. The main focus of the performed assessment is the environmental performances of traction batteries (mainly LIBs) in decreasing the environmental burdens of the EU fleet up to 2050. In particular, LCA, MFA and criticality assessments, with emphasis on supply risk, are the three tools to bridge and use in a complementary manner. As mentioned in the Introduction, for this paper, the criticality of materials is used as filter to prioritise materials. The criticality of materials is used as filter to prioritise materials. In particular, among the key materials for the future development of batteries in the EU, as defined by the SWD (2018) 45, criticality of materials is used to identify those to be firstly studied. LCA is used to understand the environmental performance of traction batteries in the current and future EU fleet. MFA is used to trace flows in anthropogenic flow cycles, to understand the current and future demand of (C)RMs, including where they are stocked, when they will be available for recycling, etc. In a mutual fashion, criticality is used to prioritise the materials that undergo LCA and MFA, while LCA and MFA provide information to assess potential effects of variation of primary/secondary materials in terms of supply risk in the future (e.g., potential decrease of supply risk due to higher recycling).

The study also aimed at identifying synergies in gathering data and information to be used in the three methodologies and applied them to a specific case study.

A concise literature review about the main aspects affecting the environmental performances of traction batteries (in particular LIBs) is provided in Section 2. A short description of the methodologies is reported in Section 3 and the application to traction LIBs in the current and future EU fleet is described in Section 4. Section 5 summarises the main outcomes of the analysis, highlighting also the main limitations of the study. The main conclusions and further research needs are illustrated in Section 6.

#### **2. Literature Review: Main Aspects A**ff**ecting the Environmental Assessment of LIBs in the Future EU Fleet**

The research on xEV batteries is currently significantly active and rapidly evolving, as proved by the annual number of publications focusing on EV increased since the 1990s [33,34] and the increasing number of patents worldwide focusing on EV [35]. This section reports the main outcomes of the performed literature review, mostly referred to scientific publications between 2016 and 2019, with some exceptions for particularly relevant data published during 2010-2015. The critical review was carried according to the main research questions presented in the Introduction.

Traction batteries are recognised as key components for the future uptake of xEV and for the decrease of the environmental impacts of the future EU mobility system [13,17,36]. Forecasts and market trends of LIBs are available in the literature from both policy and research studies and manufacturers declarations. Most of the consulted studies are characterised by an exponential increase of both BEVs and PHEVs in EU, although other studies suggest that the uptake of new technologies can be described through an S-curve [6,37]. The modelling of such a curve entails the definition of the saturation level, which depends on technological/economic/social aspects, e.g., increase of occupancy rate of cars, price of new vehicles, concept of mobility and social acceptance of new technologies (see Alonso Raposo et al. [3]). Due to the different scopes, but also modelling, boundaries and assumptions of the explored studies, the comparability of the future EU fleet is quite complex.

Many recent studies have focused on the life-cycle impacts of LIBs, recognising in LIBs the main element differentiating xEVs from ICEVs [38,39]. Even though LCA is a standardised (ISO 14040-44) [40] and mature methodology, more methodological efforts to quantify the life-cycle impact of LIBs are needed [9]. In 2018, the Product Environmental Footprint Category Rules (PEFCR) provided a "detailed and comprehensive technical guidance on how to conduct a PEF study" [41]; among the application fields covered by the document, traction LIBs are included. Despite the guidance, available results from different LCA studies on LIBs end up being very heterogeneous and it is still difficult to clearly define the environmental battery performances [42,43]. These discrepancies are due to different factors: lack, in many cases, of primary data; necessary simplifications and assumptions of the LCA model; and different chemistries of LIBs and therefore different performances [42]. In this context, Peters and Weil [44] started a deep work of review, selection of data and unification of the Life Cycle Inventories (LCIs) of LCA studies that were, until that moment, published on the manufacturing stage of batteries. Peters and Weil [44] analysed 79 studies developed between 2010 and 2016, but just five of these studies used exclusively own primary inventories and clearly disclosed the data [24–26,38]. Other LCA studies provide detailed LCI of LMO-NCM battery based on primary data [45], of NCA cell from its dismantling [46], of Li-Sulphur batteries integrating lab experimentations and theoretical modelling [47], and of lithium manganese batteries (LMO) and lithium iron phosphate batteries (LFP) collecting data from a manufacturer [48]. Among the above-mentioned LIBs, NMC could become the

most used Li-ion battery chemistry in 2030, followed by LFP and NCA with a 40% combined market share [5].

Because of the growth of LIBs, the demand of raw materials for manufacturing will increase according to their content in different LIBs chemistries. An increasing number of studies aimed at quantifying the future demand of materials for LIBs, especially if such materials are critical (e.g., cobalt). MFA is often used to quantify the stocks and flows of materials in and between processes along the value chain of LIBs. Among the consulted studies, the majority adopt a global approach, while few focus on the EU value chain; in addition, the analyses often focus on the demand of primary raw materials without including the contribution deriving from SRMs. In addition, more circular options than recycling are arising in the EU. This is the case of extending the lifetime of LIBs, e.g., through their second-use in less energy-demanding applications [9,49]. Thus, a proper management of EoL can have benefits in terms of environmental impacts and supply of SRMs, as well as effects and trade-offs between different EoL options should be further explored to provide information to be used in properly manage waste batteries.

Focusing on the environmental impacts of reuse and second-use of batteries, relevant aspects to be considered in assessing the impacts of LIBs were identified. As the reuse is concerned, key aspects to be considered are electricity mix [50–52], efficiency losses of batteries [50,52] and the characteristics of both the battery and the second-use application [9,45,53–55]. Due to the novelty of the topic and the scarce availability of data, input data are often based on warranties of LIBs and assumptions [9].

As far as concern recycling, guidelines for the impact calculation of LIBs recycling are provided in the PEFCR on batteries [41]. Bobba et al. [12], using information from industries [56], assessed the impacts of different EoL options, mainly focusing of the materials assessment, i.e., through a dynamic MFA. R&D projects and industrial companies are currently investing some efforts to improve the recovery of materials embedded in batteries to increase the sustainability of LIBs and tackle with economic barriers, which in some cases are important obstacles to the development of recycling at industrial scale, e.g., in the case of lithium recycling. For that reason, the amount of SRMs available in the future is expected to increase and contribute to partially cover the demand of raw materials for LIBs.

Available studies assessing criticality of raw materials were critically reviewed by Schrijvers et al. [16]. In this study, it is highlighted that different methods have been developed to identify criticality assessment factors and indicators at different levels (global, country or region, company, technology or specific products) [16]. In addition, data availability is recognised as a key factor that limits the evaluation of criticality. Proxies are needed to overcome this lack of data. Furthermore, data quality, including both data uncertainty and data representativeness, is rarely addressed in the interpretation and communication of results [16]. Focusing on the EU, the list of CRMs and the criticality methodology are a key instrument in the context of the EU raw materials policy, a precise commitment of the Raw Material Initiative [57]. Since the publication of the first list in 2011 and subsequent updates in 2014 and 2017, the EC criticality methodology responded to the needs of governments and industry to better monitor raw materials and inform decision makers on how security of supply can be achieved through diversification of supply, resource efficiency, recycling and substitution. To prioritise needs and actions at the EU level, the list of CRMs supports in negotiating trade agreements, challenging trade distortions and in programming the research and innovation funding. The EC methodology [58] defines CRMs as the combination of high economic importance (EI) for the EU and high risk of supply disruption (SR, supply risk). The assessment in essentially based on past data, e.g., the 2017 list was based on the five-year average (i.e., 2010-2014). Demand growth is often considered by technology-oriented methods, but not always considered by studies focusing on a national economy. This makes this exercise suitable to describe current economic situation, disregarding the future development of the economy [16].

The performed literature review highlights the complexity of the topic and the fact that several aspects should be taken into account to provide valuable and complete information on the environmental performances of traction LIBs in the EU in the future. In this framework, the integration of LCA and MFA to better understanding complex systems and environmental impacts is an added value [29,30,32]. Studies combining LCA and MFA of products are already available in the literature [31,59] and synergies between LCA and criticality were already proposed by Mancini et al. [60]. Specific consideration of future availability and demand of primary/secondary (critical) raw materials for traction LIBs in the EU were explored by Golroudbary et al. [61] and Pillot [62]. Song et al. [63] developed a detailed study on dynamic MFA of the CRMs for the Chinese LIB industry combining both the MFA and a CRMs evaluation model based on Blengini et al. [58] considering future scenarios up to 2025. Studies on the criticality of raw materials embedded in LIBs were investigated by Olivetti et al. [18] and Helbig et al. [64]. The results of criticality assessment and LCA were combined by Gemechu et al. [65]. However, the results do not provide specific information related to the potential variation of the supply risk due to the potential improvement of specific circular strategies and related environmental impacts. Finally, synergies among LCA, MFA and potential supply risk should be further improved at inventory level since some input data can serve all (e.g., processes efficiency and materials content).

The literature review confirms that, to the authors' knowledge, there are no studies specifically addressing traction batteries in the EU along their whole value chain integrating information provided by all LCA, MFA and supply risk considerations together. In addition, a future-oriented approach requires more scientific efforts in terms of methodology, to develop models taking into account key aspects of foreseeable future, e.g., physical scarcity or the future development of the economy [16].

#### **3. Methodology: Modelling Flows and Impacts of LIBs in the EU Fleet**

The integration of assessment tools able to analyse different but complementary aspects is a key feature to improve an in-depth and comprehensive knowledge of the EU fleet. In the following paragraphs, the main features of performed assessment of the environmental impact of traction LIBs in the EU fleet are illustrated.

A Life-Cycle Thinking (LCT) approach is adopted to include in the assessment all the relevant aspects along the whole value chain of products, taking also into account external aspects affecting environmental performances of products, e.g., socioeconomic aspects.

#### *3.1. LCA of Traction LIBs*

The developed LCA follows the (ISO 14040-44) and the PEFCR for batteries [41]. The LCA tool provides the necessary background information on environmental impacts of products/services under analysis along their whole value chain. Considering the potential development of technology and the complexity of some products, the development of modular LCAs and the adoption of parameters make the LCA model flexible: (1) to update according to available input data; (2) to speed-up the LCAs of different products; and (3) to enlarge the analysis (e.g., new materials and/or components).

According to PEFCR for batteries, the functional unit (F.U.) for rechargeable batteries can be defined as *1 kWh of the total energy provided over the service life by the battery system*. Nevertheless, this functional unit requires referring to the expectancy life of the battery, which is often hard to estimate because it is affected by many different parameters [10,42]. Moreover, since the majority of available LCA studies on batteries show impact results for 1 kg of battery or for 1 Wh of storage capacity, the developed LCA tool provides results for both 1 kWh of energy provided and 1 kg of battery pack. The LCA tool provides information on the specific LCI datasets used for the analysis, with reference to Environmental Footprint (EF) and Ecoinvent databases (see Supplementary Materials for details). Datasets are connected to the related impacts, evaluated with the EF method; this enables the user to easily assess the batteries for all the impact categories available within this method. In this manuscript, attention is however focused on the Global Warming Potential (GWP), as one of the most robust categories and of high societal and policy interest [66]. Results for the other EF impact categories are reported in the Supplementary Materials.

The unified database of Peters and Weil [44] is used to assess the environmental impacts of different LIBs chemistries according to available inventories in the literature. In addition, this analysis updates and extends the unified database with recent data on the manufacturing of LIBs data [45,46] and on other stages of batteries life-cycle (use and EoL stages). Table 1 lists the main characteristics of the analysed batteries and the selected source of the LCI data.


**Table 1.** Main characteristics of the analysed batteries and the selected source of the LCI data.

The use stage is defined by the energy losses due to the battery and charger efficiency [41]. The model was built in a way that the total energy used by a xEV before replacing the traction LIB is obtained by multiplying the distance covered by vehicles and the average fuel economy (energy necessary to cover the distance of 1 km). The change of the energy mix along time is considering through an increasing share of renewables in the energy mix, based on EC projections [69,70].

The EoL stage includes dismantling of components, the conversion into recycled material, other operations and credits connected to the re-availability of material after the recycling process [41]. The LCI of the EoL stage was unified with reference to data provided by the PEFCR on batteries, with exception of input/output data of lithium, nickel, manganese, cobalt, graphite, copper and aluminium. For these latter, quantities of recycled material (and consequent credits) were calculated considering the amount of materials available in each battery and the recovery of the same material after the recycling process.

To make the LCA replicable and updatable, the LCI of each LIB chemistry is provided in a spreadsheet (included in the Supplementary Materials), where each input/output flow of material, energy, waste and emission is related to 1 kg of battery pack. Each flow is connected to the related impacts, enabling the automatic calculation of the impact of the battery life-cycle. The changing of input/output quantities or parameter values allows the evaluation of different scenarios, as shown in Section 5. Moreover, the modularity of the model allows quick and consistent comparisons between environmental performances of different batteries. Additionally, due to the fast development of the technology, the modularity of the LCA model allows enlarging it, adding e.g., new materials and/or components.

#### *3.2. MFA of traction LIBs in the EU*

MFA is used to better understand the value chain of products through the representation of the main processes along the value chain but also to quantify the stocks and flow of products/materials over time [71].

According to Bobba et al. [12], the adoption of parameters in the MFA model makes it customisable and flexible to assess different scenarios and identifying e.g., circular economy aspects and/or effects of EoL options along the whole value chain of LIBs. In addition, the modularity of the model allows easily adding/updating modules within the MFA model in case of new/more data would be available. In case some modules are not of interest of the assessment (e.g., second-use of specific LIBs' chemistries or in addressing some future EoL scenarios), parameters allow simply not considering these modules for the quantification of stocks and flows. In addition, different aspects of LIBs can be assessed, i.e., stocks and flows of both batteries, materials embedded in LIBs and storage energy capacity.

Figure 1 shows the MFA model created to estimate the stocks and flow of the EU fleet in the future. Differently from Bobba et al. [12], the model was enlarged to also include the recirculation of SRMs in the system and incineration/landfilling of LIBs. Due to the difficulty in collecting all the needed data, some flows were estimated through the adoption of a parameter which was made varying in-time. In line with the goal of the study, the model is used to quantify the stocks and flows of traction LIBs, lithium, nickel and the energy storage capacity for various LIBs chemistries and applications (i.e., PHEV and BEVs).

**Figure 1.** Modelling of stocks and flows of LIBs in the EU (adapted from Bobba et al. [12]).

#### Where:


#### *3.3. Criticality and Supply Risk of LIBs Raw Materials*

According to Schrijvers et al. [16], "the raw material criticality is the field of study that evaluates the economic and technical dependency on a certain material, as well as the probability of supply disruptions, for a defined stakeholder group within a certain time frame". In line with the system boundary of the study, the analysis on materials supply risk is applied at EU level, focusing on battery's raw materials. As mentioned in Section 2, the EC methodology [58] defines CRMs as the combination of high economic importance (EI) for the EU and high risk of supply disruption (SR, supply risk).

In this paper, we focus on the contribution of recycling as a supply risk mitigation factor, according to the EC methodology [58]. Recycling of end-of-life products is in fact considered a more secure source of raw materials, in comparison to primary production [58].

The battery materials initially prioritised in this study include cobalt, lithium, natural graphite and nickel; manganese is considered too, although less often identified as CRMs [16,72] due to the lower supply concentration and subsequent lower supply risk. Among the batteries' materials, technologies for recycling Li are currently available in the EU, even though not yet at industrial scale [73,74]. Concerning nickel, it is to be considered that nickel used for manufacturing traction LIBs needs to be of high quality; in fact, Nickel Class I has a Ni content higher than 99% [20]. Despite some examples of high-quality nickel recovered from batteries (e.g., [75]), most of the nickel recycled nowadays is not suitable for the manufacturing of new LIBs [76]. In the EU, stainless steel is the biggest user of primary and scrap nickel, which already uses nickel scrap recycling for stainless steel production and will increase by 2025.

Despite limitations, data provided by MFA, in particular amount of materials recovered from traction LIBs recycling, are used to estimate the potential reduction of the SR for both Li and Ni in relation to the expected evolution of the EU fleet.

#### *3.4. Common Inventory and Data Gaps*

Availability of data is a key point for all the above illustrated assessment methodologies. In some cases, the same data can serve two or three components, which is an added value for an already difficult data collection. Moreover, the adoption of the same data and/or information improve the consistency of the obtained results and ease the replicability of the assessment in case new data will be available in the future. Finally, a common inventory facilitates the clear definition of the needed assumptions for the three components.

Table 2 provides an overview of the data and assumptions included in the study that are common to at least two out of the three components. However, it is highlighted that the adoption of the same data for multiple components requires more efforts in terms of data quality and geographical/temporal representativeness.


**Table 2.** Overview of possible synergies between LCA, MFA and Criticality (supply risk) in terms of inventory data.


**Table 2.** *Cont.*

#### **4. Case-Study: Traction Batteries in the Future EU Fleet**

The selected methodologies were applied to traction LIBs in the EU fleet between 2015 and 2030 to assess the environmental contribution of traction LIBs to the potential decrease of the impacts of the EU fleet in the next decades, but also the role of the key materials embedded in batteries. In this section, a brief description of the case-study is provided; to assess the potential added value of the coordinated approach, different scenarios were considered.

#### *4.1. Description of the Case-study and the Assessed Scenarios*

The environmental assessment focuses on traction batteries used in both PHEVs and BEVs, especially on LIBs embedding Ni and Li. Note that, even though some LIBs are already used for HEVs, this technology was excluded from the analysis since the main power source is a combustion engine. A Base-Case Scenario was created to capture most of the aspects mentioned in the research questions (Section 1); in addition, the effects of some key aspects (i.e., EoL management and change of energy mix) are considered through the creation of ad-hoc scenarios hereinafter described (see Table 3).

#### 4.1.1. "Base-Case Scenario"

The evolution of BEVs and PHEVs in EU between 2015 and 2030 is based on the EU Long-Term Strategy (LTS 1.5◦C Technical), which assumes a reduction of the EU greenhouse gas emissions for 2030 of about 50% and zero emissions in 2050 [4].

The case-study is mainly focused on traction NMC and NCA chemistries, which are expected to dominate the traction LIBs EU market in the future (Section 2); LMO/NMC chemistry is also included in the analyses of current scenarios, while it is excluded in the calculations of future scenarios because of the scarce availability of data on possible trends for these chemistries. For the use stage of LIBs, the default amount for energy density is 9.6 kWh/kg (aligned with the PEFCR for batteries) and fixed for all the LIBs. Default values for the battery use are: 100,000 km for the driven distance (in line with the warranty generally given for batteries by BEV producers) and a fuel economy of 0.2 kWh/km (according to Fuel Economy data provided by EPA). For the EoL, no remanufacturing and second-use are assumed to develop at industrial scale in the EU. The recuperation of materials is calculated as the product of the collection rate, the dismantling rate and recycling rate. A collection rate of 95% is assumed, according to the PEFCR [41], and the recycling rate is based on Lebedeva et al. [74] and Cusenza et al. [45]. These rates are parameters which can be easily modified for assessing future scenarios.

The specific energy mix considered in the study is based on literature data [69,70]. The estimation of the stocks and flows of LIBs/energy storage capacity/embedded materials is adapted from [12], with additional modules and flows; the value chain of batteries in the EU (Figure 2) is assumed to mainly maintain the current characteristics. This means that the manufacturing of batteries is assumed in the EU, according to Peters and Weil [44].

Concerning the assessment of Li and Ni, as representative of key materials in the battery sector for the EU, their content in LIBs is assumed to vary in time according to available roadmaps (e.g., [5,72,74,77]). Currently, the recirculation of Li and Ni in a closed loop (i.e., to manufacture new LIBs) is almost null. The recovery of Li is not yet developed at industrial scale in the EU, mainly due to economic reasons [73,74]; therefore, it is assumed a current recycling efficiency equal to 1% in 2018 [41], linearly increasing in the future thanks to the ongoing research activities (up to 3% in 2030). On the other hand, the recycling rate of Ni is already quite high, i.e., 96% [41]; however, to be used for manufacturing new cathodes, the purity of Ni has to be very high, and, according to the authors' knowledge, there are few examples of companies that are using secondary Ni to manufacture new cathode (e.g., [78]).

#### 4.1.2. Scenario A: Extension of the LIBs Lifetime Through Their Second-use

Second-use of batteries in less energy demanding applications is an EoL option that can reduce the environmental impacts and boost resource efficiency [79]. Despite not yet occurring at large scale in the EU, the ongoing pilots and research activities have demonstrated that this option is valid and can increase in the next decades, if also supported by an adequate regulatory framework. Therefore, based on the Base-Case Scenario, Scenario A was created to assess the environmental effects of extending the lifetime of batteries. In particular, the life-cycle impacts include both the first- and second-use, the energy storage capacity of LIBs is further exploited before their recycling and the embedded materials are locked in the in-use stock for longer compared to the Base-Case Scenario.

The total amount of energy that is provided by the battery during its first and eventually second life is quite complex, since it depends on different connected variables, e.g., depth of discharge (DoD), charging-rate and operation temperature [10,42], and the environmental benefits depends on both the LIB's and the system's characteristics [9]. For this study, Scenario A considers a default value of 20,000 kWh provided by the xEV battery first life and a second life providing 5143 kWh, according to Bobba et al. [9,12]. A linear increase of second-use of batteries from 0% (current situation) to 10% in 2030 and 30% in 2050 is assumed.

#### 4.1.3. Scenario B: Improved EoL Extension of the LIBs Lifetime Through Their Second-use and Improvement of Recycling

Recovery of key materials is essential to decrease the dependency of the EU from third countries (Section 3.3). Then, the effects of an improvement in the management of EoL practices in terms of both second-use of LIBs and improved recycling efficiency is assessed in Scenario B. The trend of LIBs in second-use applications is the same as in Scenario A. In addition, it is assumed that the recycling technology allows higher recycling efficiency of Li (15% in 2030 and 2050) and higher level of purity for covered Ni, which can be used again to manufacture LIBs (linear increase of the closed loop of Ni, from 0% to 25% between 2018 and 2030).

#### 4.1.4. Scenario C: Renewable Energy for the Manufacturing Stage

The amount and the source of energy used in the life cycle of LIBs can highly influence its environmental performance [24,80–82]. Currently, there are examples of producers of LIBs' cells that are using high share of renewables in the production process, e.g., Tesla and Northvolt Ett. In particular, Tesla claims to design its Gigafactory 1 in Nevada to be completely powered by solar array installed on its roof and wind turbine installed nearby, while Northvolt Ett declares that its plant in Sweden will rely on clean electricity from wind and hydroelectric power [83].

Hence, to observe the effects of a more environmental-friendly energy mix in the life-cycle of LIBs, Scenario C assumes that electricity for battery manufacturing is equally provided by photovoltaic panels, wind turbines and hydroelectric plants. All other input/output flows and related variables follow the Base-Case Scenario.



#### **5. Results and Discussion**

Section 5 reports the main outcomes of the assessment. In particular, for the LCA analysis, results are reported on GWP impacts provided by the LCA tool for 1 kWh of the total energy provided by the battery during its entire life-cycle. Note that, as previously discussed, this latter F.U. is influenced by the battery lifetime, which is by default set to 20,000 kWh for all the analysed batteries. In addition, results reported in Section 5 mainly refer to the NMC and NCA chemistries as most of the consulted sources provide information of future trends of these two chemistries and almost no data about the uptake of LMO/NMC batteries are available.

#### *5.1. Results*

The Life-Cycle Impact Assessment (LCIA) of the assessed LIBs shows that the current life-cycle GWP is on average 0.16 kg CO2 eq/kWh. The impact of NMC batteries results slightly higher than the NCA one. This difference could be partly due to the different material composition and partly to the higher mass of NCM batteries. The contribution analysis confirmed that the manufacturing stage highly contributes to the life-cycle GWP. In addition, the EoL recycling can reduce the impact of the battery by 22% on average. Among the LIBs' chemistries, the NMC 111 battery manufacturing has the highest impacts per kWh of provided energy. At the same time, NMC shows the highest benefit from its recycling, mainly related to the credits due to the availability of secondary raw materials after the battery recycling, mainly copper, nickel sulphate and cobalt sulphate. In the Base-Case Scenario, it is highlighted that the change of materials in different LIBs (e.g., from NMC111 to NMC811) and the increase of renewable energy share lead to a decrease of GWP in time (Figure 2). The manufacturing impacts of NMC batteries will decrease by 22% and 31% in 2030 and 2050 compared to the NMC nowadays in the market (2010-2018), while the reduction is lower (9% and 15%) for NCA batteries manufacturing. High reductions are observed for the use stage: about 42% in 2030 and 56% in 2050 for both chemistries.

**Figure 2.** Life-cycle Global Warming Potential (GWP) of NMC and NCA chemistries in the Base-Case Scenario for different years. Labels at the top of each column indicate the respective values of the life-cycle GWP.

Focusing on the MFA, the results of the Base-Case Scenario show that the increasing demand of LIBs in the EU will not significantly affect the energy capacity storage and materials flows in the EU until 2030; then, waste flows start to be relevant in terms of quantities, especially recycling flows. Figure 3 shows an example of stocks and flows of energy capacity and materials in the studied system (for interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article). Combining the above-illustrated GWP and energy storage capacity of LIBs placed on the EU market, it is estimated that the GWP of traction LIBs entering in EU fleet in 2015 is about 12 kt of CO2 eq., and it will increase up to 35 kt in 2030 and 95 kt in 2050.

**Figure 3.** Flows of energy capacity storage in the EU in 2050.

Focusing on materials embedded in batteries, is observed that the amount of Li required for traction LIBs demand in 2030 and 2050 is, respectively, five and seven times higher than the 2020 demand (Figure 4). For Ni, these values increase to 7 and 14 times. Once extracted from EVs, the amount of Li/Ni entering in the recycling process can potentially provide 7% of the Li/Ni demand in 2030 and 26% in 2050. However, the recovered Li in 2030 is lower than 40 tonnes in 2030 and about 400 tonnes in 2050, mainly due to the lack of recycling processes at industrial scale; similarly, the recovered Ni in 2030 is about 900 tonnes and 11,000 tonnes in 2050. Note that almost all the recovered Ni is recycled in an open-loop, hence not used for manufacturing new LIBs. Secondary Li is lower than 0.5% of the Li demand in 2030, while secondary Ni is 0.4% of Ni demand for LIBs; these values slightly increase up to 1% for Li and to 2.5% for Ni in 2050.

**Figure 4.** Li and Ni embedded in traction LIBs placed on the EU market (POM), available for recycling and recovered in different years.

If second-use will develop in the EU (Scenario A), LIBs and embedded materials will last longer within the system, decreasing the life-cycle impacts and postponing the amount of materials available for recycling. Assuming a second-life providing additional 5143 kWh, the GWP impact per kWh of provided energy necessarily decreases. With the assumption that the percentage of reused batteries is of 10% in 2030 and 30% in 2050, the LCIA results show an average GWP reduction, respectively, of 3% and 8% (Figure 5). If, in addition to the reuse of batteries, the recycling efficiency of LIBs will increase in time (Scenario B), reductions of 11% and 17% are observed for NMC in 2030 and 2050, respectively. The reduction is lower for the NCA chemistry: 4% and 9% in 2030 and 2050, respectively (Figure 5).

The adoption of a completely renewable energy mix for the NMC and NCA manufacturing (Scenario C) decreases the GWP of the manufacturing stage for both the current and future scenarios (see also Supplementary Materials). Benefits of manufacturing plants powered by renewables become progressively less evident over time, because of the enhanced sustainability of the European energy mix in 2030 and 2050. Compared to the Base-Case Scenario, Scenario C shows a GWP reduction of 23% in the current situation, 15% in 2030 and 12% in 2050.

**Figure 5.** Life-cycle Global Warming Potential (GWP) of NMC and NCA batteries for Base-Case Scenario, Scenario A and Scenario B in different years. Percentages indicates the reduction of GWP impacts with reference to the Base-Case Scenario for the same year.

The development of second-use of LIBs (Scenario A) confirmed a delay in availability of Li and Ni for recycling and the consequent decrease of secondary Li and Ni in the EU: in 2050 about 400 tonne of secondary Li and 9,500 tonne of Ni will be available (about 0.9% and 2% of the Li and Ni demand in 2050) (yellow bars in Figure 6). Difference of secondary Li and Ni compared to the Base-Case Scenario are almost null since the stock of LIBs in second-use application will become more relevant around 2040. Finally, from the analysis of stocks and flows of materials, it emerged that the increase of recycling efficiency will result in a significant flow of SRMs (green bars in Figure 6). In this case, the secondary Li and Ni in 2050 will be respectively 7% and 9% of the Li and Ni demand in 2050, i.e., 3000 and 38,000 tonnes.

**Figure 6.** Li and Ni embedded in traction LIBs placed on the EU market (POM) and recovered from recycling processes for the Base-Case Scenario, Scenario A and Scenario B in different years.

Finally, Figure 7 reports the EOL-RIR and the EOL-RR [58] calculated for all the assessed scenarios. The increasing recovery of Li as SRMs (Scenario B, green bars) significantly increases both indices; a similar result is observed if the technological development of recycling Ni will allow reaching a high level of purity, i.e., >99%, in order to use Ni as SRMs for manufacturing new LIBs' cathodes. Note that the amount of materials stocked in second-use application is not importantly affecting the indices since second-use is still a limited EoL option (Scenario A, yellow bars).

**Figure 7.** EOL-RIR and EOL RR of Li and Ni according to the Base-Case Scenario, Scenario A and Scenario B in different years.

The EOL-RIR of Li and Ni is used to better understand the role of recycling in mitigating the SR in 2050 according to the expected evolution of the EU fleet. The current and future EoL-RIR is estimated by the MFA tool (average 2015–2020) and in the future (2050), based on the assessed scenarios.

The supply risk reduction of Ni is < 1% in the Base-Case Scenario (2015-2020) and 6.3% in Scenario B (2050). Ni used to manufacture LIBs requires a higher grade (Class I) than Ni used in, e.g., steel applications, which is its main use according to the Ni Institute [21] and the overall EoL-RIR is estimated to be about 34% [84]. Even though the recycling rate (RR) of Ni from LIBs is quite high

and its trend is rising, the required grade is higher to feed the input for batteries with SRM. Thus, the EoL-RIR can slightly reduce the supply risk.

The figures are quite similar for Li, with a supply risk reduction <1% in the Base-Case Scenario (2015-2020) and 5% in Scenario B (2050).

Moreover, it is well known that the supply risk for Ni is much lower than that of Li according to several criticality assessments run internationally [16], thus it appears that recycling as a risk mitigation factor seems even more important for Li than for Ni. Unfortunately, at present and in the future, recycling, alone, does not seem a relevant mitigation factor for the supply risk of neither Li nor Ni (all other factors being equal, and taking into account the limits of the model).

#### *5.2. Discussion*

The complexity of batteries and the assessment of their (environmental) performances is certainly a major challenge in this and in other studies. A single assessment tool cannot provide a complete overview of the impacts of LIBs and can unlikely capture all the effects of different EoL options [29,85]. This is particularly relevant for those new options that have shown potential to develop in the next future (e.g., second-use of batteries), as well as in case of raw materials with high supply risk (e.g., Li).

LCA and MFA are applied to traction LIBs in the current and future EU fleet, to assess the life-cycle GWP of specific LIBs chemistries currently available in the market and LIBs entering in the EU fleet up to 2050 (Section 3.1); in addition, stocks and flows of LIBs/storage capacity/embedded materials were quantified along the EU value chain of LIBs for different scenarios (Section 3.2). The criticality of materials in this study was used to filter the raw materials to firstly focus on and to assess the supply risk of such materials using data provided by the MFA results. Among the key materials for the future development of batteries [72], those selected for the study are Li and Ni embedded (Section 3.3). Due to the uncertainty of input data related to the fast evolution of the technology, the complexity of the LIBs' components, the globalised market and different assumptions behind available LCA and MFA studies, assessment models are built using modules and parameters. This makes the model flexible and updatable according to available data (e.g., in case of new components/processes/stocks) and addresses uncertainty through the creation of different scenarios (e.g., change in energy mix and increase/decrease of processes efficiency). Obtained results confirm the added value of the adoption of different assessment tools to improve the knowledge of complex systems, as traction LIBs [29,30,32].

Since the vehicle electrification certainly plays an important role in the decarbonisation of mobility, the investigation on the environmental performance of traction batteries is of crucial importance, being the main element that differentiates ICEVs and EVs. The lack of reliable data highlighted by several authors (e.g., Peters et al. [42] and Zackrisson et al. [25]), and therefore the difficulty in consistent comparison between LCIA results is addressed in the study through the adoption of the unified database proposed by Peters and Weil [44], used to assess the GWP of NMC, NCA and LMO/NMC chemistries. To bypass the barriers for information sharing and reuse often caused by the only partial interoperability between the main LCA software and database currently available [86], to ease the replicability of results and to further enlarge the proposed analysis, the LCA tool is provided in the Supplementary Materials as a spreadsheet file. LCIA results obtained for 1 kg of battery pack (provided in the Supplementary Material) are aligned with the available literature [24,26,44]. The results expressed for the F.U. of 1 kWh provided by the battery are not easily comparable with previous literature since most authors did not provide results with this F.U. In addition, it is necessary to underline that LCIA results for 1 kWh of provided energy directly depend on the lifetime of the battery. This latter is fixed in this paper to 20,000 total kWh provided, but can vary among different batteries. This parameter can however be easily modified in the LCA tool provided in the Supplementary Material and according to available data, its variation is recommended to understand its relevance in line to the goal of the analysis. The change of the energy mix plays a key role in decreasing both the manufacturing impacts and the life-cycle impacts of LIBs in 2030 and 2050. In fact, manufacturing LIBs with renewable energy sources (Scenario C) reduces the impacts of LIBs placed on market in 2020 of almost 4000 tonnes of

CO2 compared to the impacts of LIBs placed on market the same year and manufactured with the current EU energy mix. In addition, the increased share of renewables (Base-Case Scenario) is the most important factor in reducing the life-cycle GWP of NMC and NCA chemistries: respectively, by 24% and 22% in 2030 and by 32% and 30% in 2050. To better understand the contribution of LIBs in decreasing the impacts of the EU mobility system in the future, a wider analysis should be performed to include the life-cycle impacts of different types of vehicles, even though this means a further level of uncertainty related to needed assumptions and simplifications, e.g., losing the detail on different chemistries and performances due to the high amount of information to be processed. In addition, electricity mix importantly affects the life-cycle impacts of EV vehicles, thus is a key aspect requiring more in-depth analysis, especially in the case of future energy mix [28]. The developed spreadsheet file can be used to model the contribution of LIBs taking into account key parameters and without losing details about performances of batteries (see Supplementary Information).

In 2030, 32 kt of Li and 202 kt of Ni will be required according to the targets established by the EU Long-Term Strategy [4]. These values increase up to 44 kt and 423 kt in 2050. The assessment of different trend of xEVs uptake, as discussed in Section 2, is recommended. In fact, input data concerning materials content in LIBs are currently poor and should be updated with new available data, and it is expected that new technologies will appear in the market, like fuel cell EVs [3,7].

The results of the analysis confirm that the growth of LIBs in the future EU fleet corresponds to an increasing flow of energy storage capacity, which is better exploited through second-use of LIBs [12]. As a consequence of the extension of LIBs' lifetime, Li and Ni are locked in the second-use stock, and therefore they cannot be available for recycling and potentially be recovered as SRMs.

Even though LIBs are not second-used, current recycling of Li is quite poor [73,74] and the secondary Ni is not pure enough to be used again for LIBs manufacturing [20,21]. In fact, without any improvements in recycling processes (Base-Case Scenario), the contribution of secondary Li and Ni to the EU materials demand is and will remain quite limited (1% of the Li demand and 2.5% of the Ni demand in 2050). Even though the availability of Li and Ni is delayed in time according to the extended lifetime of LIBs in second-use applications (Scenario A), the potential increase of recycling (Scenario B) of Li and Ni entails environmental benefits in terms of GWP (17% and 9% of the life-cycle GWP for, respectively, NMC and NCA chemistries in 2050, compared to the GWP of currently LIBs in the market). Moreover, this improvement also means an increasing amount of SRMs, hence a slight mitigation of the supply risk in the future (2050). In fact, although supply risk of Ni is relatively low at present and the recycling could lead to a more sustainable production, the overall risk would not be affected significantly. The supply risk of Li is very high due to the high concentration of EU sourcing in Chile and completely reliant on import from third countries. Despite a rising trend of Li in the future, EoL RIR will not be able to mitigate this important supply risk. It can be concluded that, with the aim of pursuing strategies of mitigation, fostering European domestic production and diversifying the EU suppliers seems the best option to reduce the supply risk, rather than the way of recycling.

Batteries require specific grade of materials for their chemistry and a sectorial level assessment would draw a more precise picture for their risk of supply disruption [63]. In addition, the EU consumption of raw materials used in batteries is extremely low compared to other sectors, e.g., Li is mostly consumed by the glass and ceramics industries, and only 1% feeds the battery industry. Ni is mainly used to produce different stainless and alloy steels, which is the biggest user of primary and scrap Ni [21]. The Ni consumption in the EU battery industry is negligible compared to the other sectors. Within this context, the competition among sectors which use the same raw materials for different purpose could play an important role to define the supply risk and/or economic vulnerability of battery raw materials. According to Nassar et al. [87], the industry's relative vulnerability can be quantified by calculating "the ratio of an industry's expenditures for a given commodity relative to that industry's profitability". This approach seems to fit well to a sectorial level such as battery manufacturing, although it requires a well-structured breakdown of economic sectors and data availability.

It is worth noting that assumptions about, e.g., the share of materials recovered in a closed/open-loop, evolution of recycling efficiency, extended lifetime of LIBs, materials content, variation of LIBs capacity, supply data, etc., are based on available literature and often refer to past and current situation. Modularity and parameters in the developed models are used: (1) to make the application of selected tools flexible and updatable according to available data; and (2) to address uncertainty through the creation of different scenarios. Relevant parameters identified in the performed analysis are energy mix, recycling efficiency and supply risk. More efforts are needed to further explore the effects of, e.g., improved lifetime, one of the key parameters identified in the literature [10,42] and future supply risk.

Finally, the proposed approach could in the future be used to enlarge the performed analysis covering more aspects, e.g., different deployment scenarios, improved EoL practices and new materials. An interesting example on new materials expected to be used in manufacturing future traction LIBs is Niobium [88]. Niobium, which belongs to the CRMs list [19], is mainly produced in Brazil and Canada, even though exploration activities are taking place in several countries [89]. Currently, niobium is not massively used for manufacturing batteries, but its application has some potential, e.g., for anodes [88,90,91], which means a potential increase of demand. Very few studies on niobium in batteries are available and environmental impacts are almost unknown [92]. According to the authors' knowledge, the GWP of ferro-niobium were provided by Dolganova [93], who used primary data provided by CBMM (Companhia Brasileira de Metalurgia e Mineração). Such gap of data requires more efforts in assessing the environmental impacts of niobium for batteries, but also in investigating various aspects affecting both MFA and its supply, e.g., niobium content in anodes, which materials it will substitute (see Table 2).

Overall, the combination of information provided by different tools and experts in different fields, as well as the assessment of different scenarios, is recommended to identify key aspects that can contribute to decrease the environmental impacts of a strategic sector for the EU, such as mobility.

#### **6. Conclusions**

LCA, MFA and criticality (supply risk) considerations are contrasted and discussed in a mutually interactive manner, with focus on the environmental impacts of traction LIBs and some of their constituting materials in the current and future EU fleet from different perspectives. Modules and parameters used in both the LCA and MFA models allow identifying relevant aspects in terms of impacts and assessing different scenarios, including different possible EoL options. Criticality mainly contributes to prioritise materials to be studied and improve knowledge on the potential contribution of SRMs in the supply risk for specific materials.

The Supplementary Materials support the replicability of the assessment, as well as future studies. Users can easily access to relevant information on both the LCI and LCIA results, being able to: (1) directly compare input/output flows of the life-cycle of different LIBs; (2) further enlarge the assessment; and (3) modify and/or add input/output flows. The dynamic MFA tool describes the value chain of traction LIBs in the EU including all EoL options, and it allows quantifying the stocks and flows of LIBs/energy storage capacity/embedded materials in the EU for different scenarios and under different assumptions. Criticality assessments run worldwide suggested to initially focus on Li and Ni as key raw materials embedded in LIBs. The paper provided information of the role of recycling as mitigation factor of the supply risk of both Li and Ni used in LIBs.

Results point out that the life-cycle GWP of traction LIBs will likely improve in the future, mainly due to more environmental-friendly energy mix and improved LIBs' recycling. Even though second-use will postpone the availability of materials for recycling, recycling improvement can importantly increase the flows of SRMs, boosting resource efficiency and keeping the values of materials in the EU. In addition, recycling can further contribute to reducing the supply risk of both Li and Ni in 2050. Such enhancements are related to both the development of recycling technologies at large scale (in case of Li) and the higher grade of recovered materials (in case of Ni).

Lack of knowledge about the LIBs value chain, lack of robust data as input for the assessments and adoption of data based on past and current trends are importantly reflected in the obtained results, thus suggesting to intensify research efforts. Obviously, further methodological work, concerning, e.g., scenario setting, uptake and impacts of future technologies, adoption of consequential LCA, substitution of materials, effects of stocks and flows, etc. would also be advisable to appropriately support more prospective decision-making.

Both the literature review and the performed analysis confirmed the added-value of performing a multi-criteria analysis, especially when addressing complex systems. Involvement of different expertise and running scenarios varying relevant parameters are keys in updating both the modelling and the input data in order to provide reliable information to identify circular economy aspects that support decision making to properly manage the whole value chain of a strategic sector for the EU, such as batteries for e-mobility.

**Supplementary Materials:** The following tables in excel file that provides the main assumptions of the assessment reported in this paper are available online at http://www.mdpi.com/1996-1073/13/10/2513/s1. Moreover, to make the LCA replicable and updatable, the LCI of each LIB chemistry is provided in the spreadsheets of this excel file, where each input/output flow of material, energy, waste and emission is related to 1 kg of battery pack.Each flow is connected to the related impacts, enabling the automatic calculation of the impact of the battery life-cycle. The LCIA for all the impact categories included in the assessment are reported in the last spreadsheet ("Impact\_calculation").

**Author Contributions:** Conceptualization, S.B., G.A.B. and I.B.; methodology, S.B., G.A.B., I.B. and U.E.; validation, G.A.B. and F.M.; investigation, S.B., G.A.B., I.B. and U.E.; resources, F.M.; data curation, S.B., I.B., U.E. and S.C.; writing—original draft preparation, S.B., I.B. and U.E.; writing—review and editing, G.A.B, F.M. and S.C.; visualization, S.B., I.B. and U.E.; supervision, G.A.B. and F.M.; project administration, G.A.B. and F.M. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.

**Disclaimer:** The views expressed in the article are personal and do not necessarily reflect an official position of the European Commission.

#### **References**


© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## *Article* **Chronological Transition of Relationship between Intracity Lifecycle Transport Energy E**ffi**ciency and Population Density**

#### **Shoki Kosai 1,2, Muku Yuasa <sup>2</sup> and Eiji Yamasue 2,\***


Received: 27 March 2020; Accepted: 18 April 2020; Published: 22 April 2020

**Abstract:** Interests in evaluating lifecycle energy use in urban transport have been growing as a research topic. Various studies have evaluated the relationship between the intracity transport energy use and population density and commonly identified its negative correlation. However, a diachronic transition in an individual city has yet to be fully analyzed. As such, this study employed transport energy intensity widely used for evaluating transport energy efficiency and obtained the transport energy intensity for each transportation means including walk, bicycle, automobile (conventional vehicles, electric vehicles, hybrid vehicles, and fuel cell vehicles), bus and electric train by considering the lifecycle energy consumption. Then, the intracity lifecycle transport energy intensity of 38 cities in Japan in 1987–2015 was computed, assuming that the cause of diachronic transition of intracity transport energy efficiency is the modal shifting and electricity mix change. As a result, the greater level of population density was associated with the lower intracity transport energy intensity in Japanese cities. The negative slope of its regression line increased over time since the intracity lifecycle transport energy intensity in cities with low population density continuously increased without any significant change of population density. Finally, this study discussed the strategic implications particularly in regional areas to improve the intracity lifecycle transport energy efficiency.

**Keywords:** metropolitan area; in-city; transport energy intensity; well to wheel; material structure

#### **1. Introduction**

In the last few decades, the global energy landscape in cities has dramatically changed due to the expansion of the global population and heavy industrialization [1]. Sustainable urban development is difficult to be achieved in view of current climate change and damages to the ecological environment [2]. Given the utmost importance of developing sustainable cities with high energy efficiency for policy makers [3], interests in evaluating energy consumption in cities have increasingly been growing as a research topic [4,5].

In particular, due to the rapid motorization and urbanization, the energy consumption in the city transport sector has been growing in the fastest manner in all energy-intensive sectors [6,7]. The transport sector is considered the most complex sector in which to reduce emissions [8] and the fuel use in the road transportation in 2050 is expected to increase by two times compared to that in 2010 [9]. Therefore, the improvement of intracity transport energy efficiency through the analysis of energy consumption in urban transport is required [10,11].

Various studies evaluated the relationship in the urban transport sector between the energy consumption and the city-related variables [12–26]. Some studies employed the top-down approach at the macro level by using cross-section data in cities (e.g., [16]), while other studies conducted the

bottom-up approach by collecting the individual trip data (e.g., [19]). Energy-related indicators include transport energy consumption (e.g., gasoline consumption) and carbon dioxide emissions. Meanwhile, the city-related variables cover various perspectives including the urban form such as population, density, area as well as the social form such as gender, age, income, and job.

Although these relationships have been reviewed and summarized in detail in many studies (see [12,22,24]), in particular, a negative correlation between intracity transport energy consumption and population density was commonly identified in earlier studies.

However, a diachronic transition of energy-related value and urban form in an individual city has yet to be fully analyzed. Some papers assessed the transport energy consumption in a certain time range and identified the growth rate of transport energy consumption and land form in the provincial areas [15,20,26], whereas the transition of an individual city was not considered.

In addition, lifecycle thinking of energy consumption in the city transportation is of significant importance. One of the major lifecycle approaches accounts for the entire fuel consumption covering from well to wheel (WTW) [27]. The WTW fuel consumption has been analyzed for various transportation modes including automobiles [28] (conventional vehicles [29], hybrid vehicles [30], electric vehicles [31] and fuel cell vehicles [32]), trains [33] and bicycles [34]. The other lifecycle approach accounts for the energy consumption during all lifecycle phases including manufacture, operation, maintenance, and end-of-life [35]. These lifecycle approaches need to be integrated in the transport energy consumption in cities for an encompassing understanding of city transport.

One of the approaches for measuring the intracity transport energy efficiency is the consideration of modal split in a given area [36]. By using modal split, Schipper and his colleagues have analyzed the national transport energy intensity as a form of transport energy efficiency in Spain, Australia, France, United Kingdom, Japan and United States [37,38]. While various studies attempted to classify the determinants of urban modal choice from the perspectives of socio demography, spatial characteristics and socio psychology to further explore modal split [39,40], this study uses the modal split in the calculation to evaluate the intracity transport energy efficiency.

This study focuses on the passenger transport energy efficiency in Japanese cities. Limited studies on the urban transport energy consumption in Japan have been reported so far. Waygood et al., evaluated CO2 emissions in the transport sector in Osaka city considering the modal share [41]. Since Yang et al. pointed out the difficulty of applying the empirical studies in one country to another in this topic [26], the focus on Japanese cities would assist in the exploration of new spatial scope.

As such, the objective of this study is to analyze the chronological transition of relationship between intracity transportation energy efficiency and population density in Japanese cities by considering the lifecycle energy consumption of various transportation means. This study assumes that the cause of diachronic transition of intracity transport energy efficiency is the modal shifting and electricity mix change.

This study is structure as follows: Section 2 illustrates the methodology consisting of boundary of transport energy consumption, introduction of intracity transportation mode, the calculation of intracity transport energy efficiency, and data collection. The transport energy efficiency of each transportation mode is presented in Section 3. Section 4 presents the chronological relationship between intracity transport energy efficiency and population density. Section 5 discusses the potential implications of the current city transportation in Japan based on the obtained results and highlights the research limitations and future perspectives. Section 6 concludes the paper.

#### **2. Materials and Methods**

#### *2.1. Boundary of Energy Consumption*

A lifecycle phase of transportation means includes the material structure, assembly, operation, maintenance, and disposal. Most existing studies on the transport energy consumption in cities have focused on the tank to wheel (TTW) energy consumption during the operational phase. Meanwhile, the significant impact of energy consumption during the manufacturing phase, especially material structure, has been widely highlighted [28,42–44]. As such, this study considers energy consumption during the phases of material structure and operation among all lifecycle stages. The other phases are out of scope.

In addition, this study extends its boundary from TTW to WTW during the operational phase. In particular, energy consumption from well to tank (WTT) represents the energy input for the fuel production, which is the same range of energy consumption for material structure during the phase of manufacturing.

Although energy input for the development of transport infrastructure is significant and varies depending on the transportation modes, this study does not consider the energy input for the infrastructure.

#### *2.2. Transporation Mode*

Transportation modes in Japanese cities are mainly comprised of road, and urban rail. The transportation means and their corresponding fuel types in each of the transportation modes are summarized in Table 1.


**Table 1.** Transportation modes, means and fuel types.

#### *2.3. Intracity Transport Energy E*ffi*ciency*

This study employs the transport energy intensity which has been widely utilized for evaluating the transport energy efficiency [45,46]. The transport energy intensity of transportation means, referring to TEI (J/kg/m), is represented by energy consumption per distance per weights of both passengers and means.

The detailed steps for obtaining transport energy intensity are presented as follows. Subscripts and abbreviations used in this paper are summarized in Table 2.

First, energy consumption for the material structure was calculated based on the weight and rate of the composition of the transportation means and the energy consumption rate of composition, using the following equation:

$$Q\_{material} = \sum (Mc\_i \mathbf{e}\_i) \tag{1}$$

Energy consumption for the material structure accounts for the energy input of material production required for a vehicle body. Given that the inclusion of the manufacturing phase causes a change in the transport energy efficiency with operational duration, energy consumption during the operational phase was calculated on the basis of time series. TTW energy consumption during the operational phase per year was obtained by using the following equation:

$$Q\_{operation}^{\text{TTW}} = \frac{Ld}{k} \tag{2}$$

TTW energy consumption during the operational phase was multiplied with the fuel production rate to obtain the well-to-tank (WTT) energy consumption during the operational phase using the following equation:

$$Q\_{\text{operation}}^{\text{WTT}} = p\_f Q\_{\text{operation}}^{\text{TFW}} \tag{3}$$

WTW energy consumption during the operational phase was calculated using the following equation:

$$Q\_{\text{operation}} = Q\_{\text{operation}}^{\text{WTT}} + Q\_{\text{operation}}^{\text{TTW}} \tag{4}$$


**Table 2.** Summary of subscripts and abbreviations.

This study considered the entire weight of transportation by summing the weights of both the passengers and means. The average weight of a passenger was assumed to be 60 kg and the average occupancy rate was used for the calculation. The entire weight was obtained using the following equation:

$$m = M + \Theta 0 Pr\_{\text{ave}}.\tag{5}$$

Then, the transport energy intensity for each of transportation means was calculated using the following equation:

$$\text{TEI}\_{t,\ x} = \frac{Q\_{\text{material, x}} + tQ\_{\text{partial}, x}}{m\_{\text{x}}tL\_{\text{x}}} \tag{6}$$

For the calculation of transport energy intensity in this study, the use duration of transportation means is a major parameter.

Finally, the intracity transport energy intensity was obtained by using the following equation.

$$\text{CTEI}\_{\text{y}} = \sum\_{\mathbf{x}} \text{TEI}\_{\text{y}, \mathbf{x}} s\_{\mathbf{y}, \mathbf{x}} \tag{7}$$

38 cities in Japan are selected, and the chronological range assessed in this study is 1987–2015. In addition, for the calculation of intracity transport energy intensity, this study uses the TEI in the case of use duration of lifetime. In addition, since the assessed year range is 1987–2015, this study uses the TEI of conventional vehicles (CV) as a representative value of an automobile.

Two assumptions must be noted. Technical features of each transportation means are assumed to be identical through the assessed years. The WTT energy consumption of gasoline, light diesel and each power generation type is also assumed to be identical during the assessed years. Meanwhile, the transition of WTT energy consumption of electricity with time is calculated on a basis of electricity mix in Japan in each of assessed years. In other words, this study assumes that the cause of diachronic transition of intracity transport energy efficiency is the modal shifting and electricity mix change.

#### *2.4. Data Collection*

The energy consumption of material structure was obtained from MiLCA (version 2, Japan EnvironmentalManagement Association for Industry, Tokyo, Japan) [47]. TheWTT energy consumption including gasoline, light diesel and each power generation type were obtained from Japan Automobiles Research Institute [48].

In the case of roadways, Corolla Fielder (Toyota), LEAF (Nissan), PRIUS (Toyota) and MIRAI (Toyota) represent CV, electric vehicles (EV), hybrid vehicles (HV) and fuel cell vehicles (FCV), respectively. The data for each type was taken from the authors' previous research [28]. ERGA (Isuzu) represents buses and data was obtained from Kudo's work [49]. In the case of railways, Yamanote line 205 system (JR-EAST) data represents electric trains and data was taken from the report of Institute of Energy Economics, Japan [50].

Modal split and population density in 38 Japanese cities in 1987, 1992, 1999, 2005, 2010, and 2015 were taken from Ministry of Land, Infrastructure and Transport [51], and Ministry of Internal Affairs and Communications [52]. Thirty-eight Japanese cities include Sapporo, Hirosaki, Morioka, Sendai, Shiogama, Yuzawa, Koriyama, Utsunomiya, Tokorozawa, Chiba, Matsudo, Tokyo, Yokohama, Kawasaki, Yamanashi, Kanazawa, Gifu, Nagoya, Kasugai, Kyoto, Uji, Osaka, Sakai, Kobe, Nara, Kainan, Matsue, Yasugi, Hiroshima, Kure, Tokushima, Imabari, Kochi, Nankoku, Kitakyusyu, Fukuoka, Kumamoto, and Hitoyoshi.

#### **3. Transport Energy Intensity for Each of the Transportation Means**

The energy utilization at the assessed phases is computed and summed to present the transport energy intensity for each of the transportation means. Parametric analysis is executed by changing the use duration.

First, the result in 2015 in the case of use duration of lifetime is shown in Figure 1. Transport energy intensity decreases in the order of automobiles, buses, electric trains, bicycles and walks. For small-scale transportation means including bicycles and automobiles, energy consumption for the material structure has a great contribution to determining the TEI. Meanwhile, the material structure hardly affects the transport energy intensity for large-scale transportation means including buses and electric trains.

In the various types of automobile, its TEI increases in the order of HV, FCV, EV and CV. The CV presents the greatest TTW fuel consumption in the TEI, whereas the EV and FCV have a higher impact of energy consumption for material structure. The installation of additional equipment such as motor and battery and the substituted material for the body such as aluminum replaced with iron in the EV and FCV [53] requires the greater volume of energy input for the material structure. Consequently, the difference of the TEI between CV and EV appears to be slight. Notably, whether to include the weight of vehicle body in the calculation of intensity brings upon different trends of outcomes. The previous author's study [28], in which the transport energy efficiency of these automobile types was computed without considering the weight of vehicle body, showed the EV is the least efficient vehicle type.

In particular, due to the current electricity configuration in Japan, the WTT fuel consumption for the electricity production is significant. Given the lesser amount of energy input required for electricity production by renewables compared to fossil fuels [48], the increase in the share of renewables in the energy mix would contribute to the mitigation of the TEI of EV and electric trains.

**Figure 1.** Transport energy intensity for each means of transportation in 2015 in the case of lifetime use duration.

Notably, the virgin materials are only taken into account and the recycled materials are not considered in the calculation of energy consumption under the manufacturing stage. The use of recycled materials would highly contribute to the reduction of energy consumption. For example, the recycling process could mitigate 97% and 65% of energy consumption for producing the virgin Al [54] and virgin Fe [55], respectively. Meanwhile, the recycling rate of Al in Japan has already reached 92.5% in 2017 [54]. The appropriate distribution of virgin and recycled Al is important to meet its incremental demand in the vehicle industry.

Then, the transport energy intensity in 2015, with the change of use duration, is monitored. The result is presented in Figure 2. The transport energy intensity of all of the transportation means decreases in inverse proportion to use duration. Particularly, for automobiles and bicycles, there remains room to improve the transport energy intensity by increasing in the use duration. If the lifetime is extended for another 5 years, the transport energy intensity of bicycle and automobile potentially increases by 38% and 6%, respectively. For electric trains, due to the negligibly small impact of material consumption on the transport energy intensity, its TEI almost remains the same after the three months of operation. This is because the dominator in Equation (6) is significantly greater than the energy consumption for material structure of electric train. The much heavier weight and longer movement distance of electric train compared with other transportation means would highly affect this difference. Given the longer lifetime of railway rolling stock compared with automobiles and buses, electric train would be an appropriate mean for urban transport.

Given that the lifetime extension of both bicycles and automobiles improve the transport energy intensity, promotions of domestic reuse and improvements of material durability are of utmost importance. While the various business models are involved in shipping used bicycles towards Asia and Middle East, its domestic reuse has yet to be fully operated [56]. On the other hand, improvement of material durability needs to be carefully taken into account. Particularly for automobiles, the material structure has been altered to the lightweight design [57]. While the reduction of vehicle weight leads to the improvement of fuel economy, there is a possibility that the energy requirement for a material structure is not mitigated due to the greater energy input for producing the replaced materials. Furthermore, the transition of material composition provokes the issue of raw material criticality. For example, replacement of steel with aluminum mitigates the environmental impacts [58] and relieves the concern of reserve [59] but deteriorates net import reliance [59]. Given that the reliance on raw materials in other countries is highly associated with security of supply and criticality issue [60], the transition of material structure affects not only the transport energy intensity of transportation means but also the criticality of product manufacturing.

**Figure 2.** Transport energy intensity in 2015 with the change of use duration.

#### **4. Relationship between Intracity Transport Energy Intensity and Population Density by Year**

This Section presents the relationship between intracity transport energy intensity and population density. The obtained intracity transport energy intensities in 38 Japanese cities are presented in Appendix A.

First, the relationship between intracity transport energy intensity and population density in 1987, 1992, 1999, 2005, 2010, and 2015 is illustrated in Figure 3 and its regression model is presented in Table 3. In 1987–2015, the greater level of population density is associated with the lower intracity transport energy intensity in Japanese cities. This negative trend matches the global trend which has been widely reported (e.g., see the most well-known research work relevant to its trend conducted by Newman and Kenworthy [14]). Given the high score of R2, the high level of negative correlation in the range of 1987–2015 could be seen in Japan's case. Meanwhile, it is seen that the regression coefficient becomes lower with time, which means the negative slope of regression line increases in the course of year.

**Figure 3.** Intracity transport energy intensity and population density.


**Table 3.** Regression model of CTEI vs. population density.

Why did the regression coefficient increasingly grow with time? A more in-depth analysis is required to shed light on the mechanism of its relationship in Japan.

After the descriptive characteristics of the relationship between the intracity transport energy intensity and population density by year, a visual inspection with a focus on the transition of each of the assessed cities is executed to reveal the mechanism of its relationship.

The intracity transport energy intensity in the course of change in population density by city is presented in Figure 4. To show the trend of each city, the regression line is presented as well.

**Figure 4.** Intracity transport energy intensity and population density.

As for the group of low population density (~2500 persons/km2), in most of cities intracity transport energy intensity has been increasingly growing with almost no change of population density in 1987–2015. Some cities (e.g., Imabari, Kure, and Yamanashi) drastically decrease in population density while continuously increase in intracity transport energy intensity. The slope of regression line of most cities in this group indicates nearly 80◦–90◦.

As for the group of medium population density (2500 persons/km2 ~ 7000 persons/km2), the degree of increase in intracity transport energy intensity is moderated while the population density has slightly grown in some cities (Uji, Kobe, Kasugai, Nagoya, Matsudo) after 2000. The slope of regression line of these cities indicates 45◦–65◦. In addition, in some cities (Chiba, Fukuoka, Tokorozawa), the slight increase in intracity transport energy intensity is presented, while the population density has continuously grown due to population influx from regional areas. The slope of regression line of these cities indicates 10◦–30◦.

As for the group of high population density (7000 persons/km2~), the slope of regression line in all of cities (Yokohama, Kawasaki, Osaka, and Tokyo) indicates less than 1◦. In Kawasaki, Osaka, Tokyo, the intracity transport energy intensity has almost remained the same or even has decreased, while the population density has continuously grown. Meanwhile, the U-shaped figure is observed in Yokohama. It must be noted that the intracity transport energy intensity in Kawasaki and Tokyo has increased from 2010 to 2015 although the modal split has not changed much. This might be because the fossil fuel contributes to the greater share in electricity mix after the Fukushima nuclear accident [61] and this causes the increase in the transport energy intensity of electric train.

Therefore, the main cause of the decrease in regression coefficient with time in 1987–2015 presented in Table 3 would be because the intracity transport energy intensity of each city continuously increased under the case of low population density while it slightly increased under the case of medium population density and remained almost the same in the case of high population density.

#### **5. Discussion**

The negative correlation between intracity transport energy intensity and population density was observed in the case of Japan. It was indicated that the negative slope of its regression line increases in the course of time in 1987–2015. In particular, the intracity transport energy intensity in cities with low population density has continuously increased without any significant change of population density. This would be because the reliance on automobiles has increased due to the difficulties in the development and extension of public transport systems from a financial perspective. For example, it is not considerably worthwhile from the financial perspective to invest money on extending the existing urban rail for a few settlements in less densely populated areas. In fact, transportation by rail in regional areas has declined and the operation of the railway in some regional areas has eventually been abandoned due to fiscal deficit in Japan. Meanwhile, the intracity transport energy intensity has slightly increased in cities with medium population density and almost remained the same in cities with high population density. This would be because the public transport system has already been well developed and the modal share of public transport (bus and electric train) has been consistently high.

Japanese population in 2050 is anticipated to decrease by 20% compared to 2012 [62]. Especially, population in cities of regional areas is projected to decrease by more than 20% due to the population influx as previously mentioned [63]. Considering the negative correlation presented in this study, there is a possibility that the intracity transport energy intensity will increase particularly in regional cities.

To address this issue in regional areas, the improvement of transport energy intensity is of significant importance for not only the technical aspects such as fuel economy and service life but also the social and regulatory aspects.

As for technical aspects, the transition of automobile type is important. According to Figure 1, the replacement of CVs with EVs may not significantly change the trend of intracity transport energy efficiency among 38 Japanese cities, whilst its replacement with HVs and FCVs would improve the intracity transport energy efficiency, particularly in regional areas. Considering various challenges in the development of hydrogen infrastructure for FCVs, improving the diffusion of HVs in less densely populated areas would be the first choice.

One of the promising social and regulatory approaches is to introduce the system of ridesharing. It must be noted that the transport energy intensity of automobiles in Japan have a significant room for improvement by increasing the occupancy rate. The number of vehicles privately owned in regional areas has increased in recent decades [64], which may potentially cause a decrease in a number of carried passengers for each of transportation means by trip and a drop in the transport energy intensity of automobiles. The promotion of ridesharing systems might contribute to an increase in occupancy rate. Still, ridesharing is an emerging concept in Japan. Although ridesharing was recently introduced in two underpopulated areas in Japan empirically, the taxi industry staunchly opposes the legalization of ridesharing. In addition, 70% of Japanese people are not willing to make use of ridesharing because of uncomfortableness of sharing vehicles with strangers [65]. To the contrary of negative impressions, ridesharing could deliver various advantages for passengers including reduction of fee and waiting time and free from commute stress [66]. Control of vehicle ownership by taxation [67], establishment of legal system and briefing of ridesharing concept for public hearing would be necessary to increase automobile occupancy rate.

Other conceivable social and regulatory approach would be to obligate elderly drivers to return the driver license. Following a series of frequent fatal traffic accidents caused by elderly drivers, its suitability for making compulsory has been heavily debated. The National Police Agency in Japan has only advised drivers 65 and older to consider surrendering their driver license of their own accord, although it has yet to be legalized [68]. The findings in this research may imply its necessity from the perspectives of transport energy efficiency. The population aging rate in 2050 will increase by 40% compared with 2012 [69]. The automobile modal split for people aged 65 or older has increased from 15% in 1987 to 37.3% in 2010 in three major metropolitan areas as well as from 18.5% in 1987 to 57.6% in 2010 in regional urban areas [70]. Particularly in cities with low population density, this trend would highly affect the increase in the CTEI. Considering that the super-aging society in Japan will potentially exacerbate problems with intracity transport energy efficiency on the basis of the current trend, to obligate elderly drivers to return the driver license would contribute to its improvement.

Notably, this obligation would raise a concern about deteriorating the quality of life for the elderly in regional cities where public transportation is not well developed. People tend to stay at the attached city where they have lived even in an inconvenient area [63]. The establishment of transportation systems such as ridesharing as previously mentioned and small-scale public transportation (e.g., minibus) with financial support as well as city compactification without a forced relocation, would be important.

Future perspectives of this study need to be mentioned.

The chronological transition in technical features in each transportation means and the energy consumption for the production of gasoline, light diesel and each power generation type will be further investigated to aid in more accurate calculation of intracity transport energy intensity.

Other lifecycle stages including maintenance and end-of-life need to be included in the future research. The next generation vehicles such as EV, in particular, need to replace the battery due to the degradation of its quality, and its replacement requires energy to some extent during the maintenance stage. The lifespan of battery used in the next generation vehicles is around 8–10 years, and the battery needs to be replaced even before exhausting the lifetime of vehicle. Besides that, the energy consumption for the infrastructure development for each of the transportation means will be addressed. In spite of the argument in favor of electric trains in urban railways in this study, the inclusion of energy consumption for the development of rail infrastructure might change the result. The extension of the boundary for energy consumption in the transport sector would provide more comprehensive information on the transport energy intensity for each transportation means.

The energy consumption under the operation stage used in this study was on the basis of average fuel economy given in the data lists of representative transport means in a top-down approach, whereas the energy efficiency in the transport sector is somewhat dependent on the local urban environment and driving patterns [71]. The empirical examination in a bottom-up approach would assist in a detailed investigation of the relationships between energy use and city form.

#### **6. Conclusions**

This study has first obtained the transport energy intensity for each of the transportation means including walk, bicycle, automobile (CV, EV, HV, FCV), bus and electric train by considering the lifecycle energy consumption. Based on the modal split, the intracity transport energy intensity of 38 cities in Japan in 1987–2015 has been computed. Then, the chronological relationship between intracity transport energy intensity and population density by year has been presented. Finally, to reveal the mechanism of its relationship, the transition of each of assessed cities has been visually monitored. Findings are as follows:

• Transport energy intensity decreases in the order of automobiles, buses, electric trains, bicycles and walks.


• The negative slope of its regression line between intracity transport energy intensity and population density increases over time in 1987–2015.

The main cause of the decrease in the regression coefficient with time in Japan in 1987–2015 between intracity transport energy intensity and population density could be that, in cities with low population density, the intracity transport energy intensity continuously increases without any significant change of population density while, with increasing population density slightly increases in cities with a medium population density and almost remains the same in cities with a high population density.

**Author Contributions:** Conceptualization, S.K., M.Y. and E.Y.; methodology, S.K., M.Y. and E.Y.; validation, S.K., M.Y. and E.Y.; formal analysis, S.K. and M.Y.; investigation, M.Y.; resources, S.K. and M.Y.; data curation, M.Y.; writing—original draft preparation, S.K.; writing—review and editing, E.Y.; visualization, S.K.; supervision, E.Y.; project administration, E.Y.; funding acquisition, E.Y. All authors have read and agreed to the published version of the manuscript.

**Funding:** This study was partly supported by research funds from KAKENHI Grants (26281056, and 19H04329) and from the Environment Research and Technology Development Fund (S-16).

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

#### **Appendix A**


**Table A1.** Intracity transport energy intensity for each of 38 Japanese cities#.

#### **References**


© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

### *Review* **Exergetic Life Cycle Assessment: A Review**

#### **Martin N. Nwodo \* and Chimay J. Anumba**

College of Design, Construction and Planning, University of Florida, Gainesville, FL 32611, USA; anumba@ufl.edu

**\*** Correspondence: nwodomartin@ufl.edu

Received: 8 April 2020; Accepted: 22 May 2020; Published: 26 May 2020

**Abstract:** Exergy is important and relevant in many areas of study such as Life Cycle Assessment (LCA), sustainability, energy systems, and the built environment. With the growing interest in the study of LCA due to the awareness of global environmental impacts, studies have been conducted on exergetic life cycle assessment for resource accounting. The aim of this paper is to review existing studies on exergetic life cycle assessment to investigate the state-of-the-art and identify the benefits and opportunity for improvement. The methodology used entailed an in-depth literature review, which involved an analysis of journal articles collected through a search of databases such as Web of Science Core Collection, Scopus, and Google Scholar. The selected articles were reviewed and analyzed, and the findings are presented in this paper. The following key conclusions were reached: (a) exergy-based methods provide an improved measure of sustainability, (b) there is an opportunity for a more comprehensive approach to exergetic life cycle assessment that includes life cycle emission, (c) a new terminology is required to describe the combination of exergy of life cycle resource use and exergy of life cycle emissions, and (d) improved exergetic life cycle assessment has the potential to solve characterization and valuation problems in the LCA methodology.

**Keywords:** exergy; life cycle assessment; sustainability; LCA; review

#### **1. Introduction**

Exergy analysis is useful to rationally and meaningfully assess and compare processes and systems [1]. These capabilities are reflected in the two key features of exergy analysis: (a) efficiency to provide a real evaluation of how actual performance tends to or deviates from the ideal, and (b) exergy loss to identify more clearly than energy analysis the types, causes, and locations of thermodynamic losses [1]. Exergy efficiency was included in the 2001 Swiss canton of Geneva as a new parameter for characterizing the energy performance of buildings [2]. As exergy describes the work potential of energy, exergy-based analysis is used in system design or process optimization [3]. Exergy is used for assessment, design, analysis, and improvement of systems; an example is an application of exergy analysis to integrated energy systems such as biomass, geothermal, and steam power systems [1]. Table 1 summarizes the importance of exergy, classified by topic. In life cycle analysis, exergy-based approach is relevant to quantify energy and material resources, determine consumption and depletion of natural resources, and as an indicator of resource utilization efficiency [4–9]. In production processes, exergy method is used to keep inventory of exergy losses and efficiencies on a single scale [10–12]. Exergy has been used in technological processes to achieve sustainability to reflect extent of use of renewable resources, to account for technological efficiency and conversion of waste products into neutral or harmless products [13]. Exergy is used to deepen the understanding of the built environment to develop low-exergy systems for the future [14,15] and for a scientifically based sustainable building assessment tool [16,17]. Exergy analysis optimizes the efficiencies of energy systems [18–21] and in this way, reduces global impacts [22,23].


**Table 1.** Summary of the importance of exergy classified by topic area.

Although, most uses of exergy are in the area of metallurgical and chemical process analysis, thermal system design, and energy conversion system design [24], the use of exergy in life cycle analysis is currently emerging. The emergence of exergetic life cycle assessment is probably because of the importance of wholistic analysis of a system, a product or a process for resource accounting over a life span. Szargut et al. [25] first analyzed life cycle of a system based on exergy by developing the concept of cumulative exergy demand. Cornelissen and Hirs [4] further proposed the inclusion of the concept of exergy into Life Cycle Assessment (LCA) as exergetic life cycle assessment. They described exergetic life cycle assessment as a method to measure the depletion of natural resources in LCA and as a tool to evaluate the efficiency of resource use. There are also other studies on exergetic life cycle assessment [5,26–30]. The aim of this paper is to review existing studies on exergetic life cycle assessment to investigate the state-of-the-art and identify opportunities for improvement of the approach. Following the introduction, this paper describes exergy and exergy-based methods, introduces the use of exergy in LCA, analyzes studies on exergetic life cycle assessment, and provides discussion and conclusions from the findings.

#### **2. Description of Exergy**

#### *2.1. Definition of Exergy*

The capacity of doing work has been accepted as a measure of the quality of energy [31]. Energy quality in a system can be grouped into either available energy or unavailable energy. Exergy is a measure of the possible maximum useful work before a system reaches equilibrium with its environment [32]. Szargut [31] defined exergy as the work obtainable for a matter to be brought to a state of thermodynamic equilibrium with the common elements of the natural surroundings by mechanism of reversible processes, which involves interchange only with the elements of nature. According to Bejan et al. [33], exergy is available when an idealized system (called an environment) interacts to equilibrium with another system of interest, while heat transfer occurs only with the environment. The following can be deduced from these definitions:


The choice and definition of the reference environment or state is necessary for exergy analysis. This is because the sensitivity of the results to different choices of the reference state might vary with the operative conditions of the system analyzed [20]. Correspondingly, when the state is significantly different from that of the chosen base conditions, its flows are not overly sensitive to the definition of the reference environment. This is the case, for instance, in the analysis of power plants. In turn, when the properties are close to those of the base conditions, results from the analysis have great variations depending on the definition of the chosen base state. This is the case of the analysis of space heating and cooling in buildings.

#### *2.2. Brief Historical Background on Exergy*

The historical background on exergy can be traced to the first and second laws of thermodynamics. According to Szargut et al. [25], in the 1840s, James Joule proved that there was an exact numerical equivalence between work and heat (also known as the conservation of energy), in accordance with the first law of thermodynamics. While the second law was based on Carnot's experiments, which demonstrated that the limitations of heat-to-work conversion depend on the temperature at which the heat is available or the 'quality' of the heat. Consequently, the internal energy and entropy functions were defined, followed by the enthalpy, the Helmholtz function, and Gibbs Free Energy [25]. These functions increased the capacity to understand the effects of the laws and their use to effectively solve practical problems. According to Szargut et al. [25], exergy function is introduced to improve our comprehension of thermal and chemical processes by enabling the investigation of processes, whether complex or not, to determine the theoretically most efficient way by which that process could be performed within the environment.

#### *2.3. Relationship between Exergy and Other Energy Qualities*

The terms entropy, exergy, and emergy help to articulate the important qualities of energy [32]. While exergy depicts the amount of work a system can exert on its environment, unavailable energy, also known as entropy, cannot be converted into work. According to Shukuya and Hammache [34], exergy measures the potential of dispersion of energy and matter in their environment while entropy quantifies the state of dispersion or the extent of dispersion of the energy and matter (Figure 1). In other words, exergy is used within the process or system to produce entropy. The terms exergy and entropy generation minimization [35] are common in the second law of thermodynamics.

Exergy is related to emergy in that the latter reflects all the exergy retrieved and used from the original state to the present state of a process. Odum [36] defined emergy as: "the available energy of single kind previously used directly and indirectly to make a product. Its unit is the emjoule (ej)" Solar emjoules (sej) are the solar energy equivalents required directly or indirectly to make a product with energy content. Bastianoni et al. [37] stated that emergy can be expressed as a function of exergy, although the goals and boundaries of the reference state differ. While the former retraces the embodied solar energy in a product, the latter assesses the quantity of primary resources that goes into that product. In addition, in the former, the system comprises the whole biosphere, with solar energy as the basic input. In the latter, however, the analyst defines the control volume, according to the goal of the study [37]. Among the major energy qualities, exergy is preferred for this study because of its flexibility in the choice of primary resources for analysis, and its capability to quantify the potential of dispersion of energy and matter into the environment of study.

**Figure 1.** Illustration of energy, exergy, and entropy flow in and out of a building envelope system [34].

#### **3. Exergy-Based Methods and Life Cycle Assessment**

#### *3.1. Exergy-Based Methods*

Morosuk et al. [8] consider "exergy-based methods" as a general term that includes the conventional and advanced exergetic, exergoeconomic, and exergoenvironmental analyses and evaluations. Exergoeconomic analysis is a technique that combines both exergy and economic analyses to evaluate the costs of inefficiencies in a process, and it is referred to by other names such as thermoeconomics, second-law costing, and cost accounting [38]. Exergoenvironmental analysis combines exergy analysis and life cycle assessment, which is conducted at the component level, to identify the location, magnitude, and the causes of environmental impact [39,40]. Tsatsaronis and Morosuk [41] stated that exergy-based methods describe the situation, the quantity and the sources of inefficiencies, costs, and environmental impacts; and enable analysts to study the interconnections between them. In generic terms, the major exergy-based methods include the following:

#### 3.1.1. Cumulative Exergy Demand (CExD)

The Cumulative Exergy Demand (CExD) was proposed by Szargut et al. [25]. It is defined as the sum of exergy of all supplies required to produce a product or provide a service [7]. CExD is related to Cumulative Energy Demand (CED), but unlike CED, it can account for materials and quality of energy inputs. In addition to this advantage, CExD analysis can provide insight into potential improvements and for comparing alternative products, by accounting for exergy use throughout the life cycle.

#### 3.1.2. Thermo-Ecological Cost (TEC)

The Thermo-Ecological Cost (TEC) method accounts only for the cumulative consumption of non-renewable primary exergy resources. It is expressed in exergy units and not in monetary units. This method was developed with the premise that it is essential to determine and reduce the depletion of non-renewable natural materials in the field of ecological applications of exergy [42].

#### 3.1.3. Cumulative Exergy Extraction from Natural Environment (CExENE)

The Cumulative Exergy Extraction from Natural Environment (CExENE) method is an extension of the boundaries of CExD to include land use. CExD accounts for energetic supplies and materials traditionally considered non-energetic such as mineral, water, and metal, but ignores land use. CED only accounts for materials, which may be used as energy carriers [43]. Therefore, CExENE is quantitatively the most comprehensive resource indicator of the three, because it evaluates energy carriers, non-energetic supplies, and land occupation. Conceptually and qualitatively, CExENE differs from CExD and therefore leads to a different evaluation. CExD measures the exergy that is transferred into the technological system from nature, while CExENE accounts for total exergy that is deprived of the natural system [43] which may or may not be transferred into the technological system.

#### 3.1.4. Industrial/Ecological Cumulative Exergy Demand (ICExD/ECExD)

The Industrial/Ecological Cumulative Exergy Demand (ICExD/ECExD) method is an extension of the CExD method to emphasize on industrial and ecological processes, respectively [44,45]. ICExD reports the exergy of natural wealth consumed by each industrial sector both directly and indirectly, while ECExD reports the exergy used up in ecological systems to produce each natural wealth [46]. For a production chain, ECExD analysis improves on ICExD analysis by including exergy losses in the industrial as well as ecological stages [46]. This method defines the mathematical form of economic and ecological systems through fiscal and physical input-output tables. Like the economic input-output model, the main advantage of this method is probably the availability of the necessary macroeconomic data for each sector. However, there is lack of details of individual processes in these sectors, which can lead to aggregation error [46].

#### 3.1.5. Extended Exergy Accounting (EExA)

As proposed by Sciubba [47], the Extended Exergy Accounting (EExA) method is used to compute a commodity value based on its resource equivalent value instead of its fiscal cost. This method is based on two essential assumptions: (a) that the cumulative exergy content of a product or service is the sum of the exergies of the product's constituents, in addition to a weighted sum of the exergies of the production process of the product, and (b) that non-energetic costs such as labor, capital, and environmental emissions can be reformulated in terms of exergy from global system balances. While (a) is a paraphrase of Szargut's CExD, (b) is the original contribution of EExA. A theoretical and practical advance in EExA can be found in Dai et al. [48].

#### 3.1.6. Comparison of the Exergy-Based Methods

This section presents a comparison of the exergy-based methods. The comparison is based on deductions from the description of the exergy-based methods. Table 2 compares the identified exergy-based methods in terms of scope and limitations. The comparison was based on a desk study.



While the scope column emphasizes the focus of each of the methods, the limitations column emphasizes their shortcomings.

According to Dewulf et al. [49], the CExD is by far the most applied method as a measure for environmental impacts. The limitation of CExD method, the exclusion of exergy losses in ecological system, is accommodated in ECExD method but the latter is limited to only production processes. On the other hand, although CExENE method appears to extend boundaries of CExD to include land use, the CExENE method is not designed to track exergy transferred into the technological system. An ideal exergy-method could be an extension of the CExD method to cover ecological systems.

#### *3.2. Life Cycle Assessment*

Life Cycle Assessment (LCA) is defined as the aggregation and approximation of inputted and outputted resources, and the potential environmental impacts of a product system, in addition to their processes and designs, over its life cycle [50,51]. This section will focus on the description of LCA methodology. A recent review of articles on "LCA of buildings" can be found in Nwodo and Anumba [52]. The methodology includes the goal and scope definitions, inventory analysis, impact assessment, and interpretation of results [50].

#### 3.2.1. Goal and Scope Definition

The goal and scope definitions entail the aim of conducting the LCA study and expected application, the target audience, the results use, and the specification of system boundary and functional units [53]. During the scope definition process, the system boundary and functional units are determined, indicating the included and excluded processes and quantification of the product's function [53,54]. For example, the system boundary for LCA of a building may consist of either cradle-to-grave process (i.e., raw material production phase to end-of-life phase), cradle-to-gate process (i.e., raw material production phase to construction phase), or gate-to-gate process (i.e., within construction phase).

#### 3.2.2. Life Cycle Inventory Analysis

The Life Cycle Inventory (LCI) is the calculation of the inputted and outputted resources such as energy, materials, carbon emissions, and wastes for each of the stages in the service life of a product [55]. An LCI analysis requires extensive non-duplicated data collection. Finnveden et al. [56] observed that setting up inventory data could be one of the most difficult stages of an LCA. According to ISO [50], the goal and scope definition of a study sets the plan for implementing the LCI phase. For example, with the specified system boundary, the data for each unit process are collected to be included in the inventory. The collected data are utilized to quantify the inputted and outputted resources of a unit process. The operational steps outlined in Figure 2 are performed when executing the plan for an LCI analysis, although some iterative steps are not shown [50]. Over the decades, several national and international databases have evolved, including Swedish SPINE@CPM database, the German ProBas database, the Japanese JEMAI database, the US NREL database, the Australian LCI database, the Swiss ecoinvent database, and the European Life Cycle Database [56].

#### 3.2.3. Life Cycle Impact Assessment

Life Cycle Impact Assessment (LCIA) is the classification and characterization of the LCI results based on environmental impacts or human effects [53–55]. The main impact categories from these classification and characterization include human health impacts such as respiratory organics, respiratory inorganics, global warming potential, and climate change; ecosystem quality impacts such as eutrophication potential, acidification potential, and land use; and resources impacts such as energy and material or mineral use [57–60]. A simplified LCI model for a specific type LCA can be adapted from existing databases such as ecoinvent, Athena, Gabi, and OpenLCA, for calculating LCIA. Figure 3 illustrates the elements of an LCIA while Figure 4 illustrates a simple example of LCA calculation process [61].

**Figure 2.** Simplified procedures for Life Cycle Assessment (LCA) inventory analysis [50].

3.2.4. Life Cycle Interpretation

The last process in standard LCA methodology is the interpretation of results. During the interpretation, LCA results are reported in such a way as to evaluate the need and opportunities to reduce the impact of a product on the environment [55]. The life cycle interpretation process comprises elements such as a highlight of essential issues based on LCI and LCIA results; an evaluation of comprehensiveness, sensitivity analysis, and consistency check; and conclusions, limitations, and recommendations. The findings of the interpretation can take the form of conclusions and recommendations to decision-makers, which is consistent with the goal and scope of the study [50]. The interpretation reflects the fact that the LCIA results are based on a relative approach, meaning they indicate likely environmental effects and do not claim to predict actual impacts. The interpretation framework may involve a review and revision of the scope of the LCA in an iterative way, as well as data quality in a manner that is consistent with the goal and scope definition [50]. Validation of results and sensitivity analysis may also be conducted during this process [54]. However, differences in the units of conventional LCA results make life cycle interpretation a difficult process. To avert this challenge, weighting, although optional, is usually employed, which introduces subjectivity to LCA results interpretation.

**Figure 3.** Illustration of the elements of a Life Cycle Impact Assessment (LCIA) [50].

**Figure 4.** Simple example of LCA calculation process [61].

#### *3.3. Use of Exergy-Based Method in Life Cycle Assessment*

An exergy-based method is relevant in LCA since the latter also measures life cycle resource use and corresponding life cycle environmental impacts. De Meester et al. [5] opined that exergy enables the natural energy and material resources to be simultaneously quantified. According to Finnveden et al. [6], a thermodynamic approach based on exergy can be used to measure the use of resources in LCA and in other sustainability assessment methods because the approach can account for energy resources, metal ores, and other materials using their chemical exergies, which are expressed in the same unit. Exergy can complement LCA as an additional impact category indicator [14]. Cornelissen and Hirs [4] demonstrated that, besides LCA, life cycle exergy analysis has value in quantification of the environmental issue of natural resources being depleted.

As mentioned previously, the method of cumulative exergy demand can be used to express the summation of exergies of natural resources expended in all the stages of a technological system or process. Unlike cumulative energy demand, it also measures the chemical exergy of the non-energetic raw materials, which are extracted from the environment. As a result, the CExD method has the capacity to be used in LCA. Currently, CExD method is used in exergetic life cycle assessment for resource accounting by applying any of the following three techniques [25]:


The following section provides a review of previous studies on exergetic life cycle assessment.

#### **4. A Review of Exergetic Life Cycle Assessment**

#### *4.1. Methodology for Articles Selection*

The procedure involved an analysis of systematically selected articles to investigate the extent of exergy use in LCA and in other sustainability assessments. The reviewed articles were selected from a set of journal articles, which were published for a specified duration between 1990 and December 2018, in addition to recent articles (published since 2019 till date) collected from Google Scholar and/or Scopus. The Web of Science Core Collection is maintained by Clarivate Analytics, and it is a multiple-database platform that includes SCI-Expanded, SSCI, A&HCI, and ESCI. This platform holds more than 12,000 high-impact international journals and it is frequently used by researchers throughout the world [62]. A design was created to search for the articles. To retrieve the articles, three title "TI" record fields and one topic (TS) record field were created as follows:


The first record field found articles, which have in their titles, the precise phrase "exergy life cycle assessment". Similarly, the second query found articles in which the precise phrase "exergetic life cycle assessment" shows in the title. The third query has a broader scope and found articles that contain at least one of the inputted keyword terms in the title. The "exerg\*" term found different forms of that term such as exergy and exergetic. Finally, the fourth record field has the broadest scope and found articles that contain at least one of the inputted keyword terms in the topic field. Table 3 presents an overview of the input data used to search and collect the articles from Web of Science Core Collection database. The combination of the search result sets using the "OR" Boolean operator amounted to 43 articles set for further sorting and manual check. During sorting, some of the articles were found to be irrelevant to the subject and removed, in addition to manually seek for and add relevant cross-referenced articles. A total of 25 articles were finally selected for the tabular analysis.



#### *4.2. Analysis of the Selected Articles*

The 25 articles that discussed the use of exergy or its method in LCA and in other sustainability assessments were analyzed. Table 4 shows the summary of the analysis in terms of aim, method, result/discussion, and relevant conclusion. The result/discussion and conclusion from the collected articles are not limited to those shown in Table 4. However, those presented are deemed the most pertinent to identify the state-of-the-art and relevance of exergy in life cycle analysis. The presented articles essentially investigated the application of exergy methods to sustainability assessments for which LCA is a state-of-the-art technique.


**Table 4.** Analysis of articles on use of exergy methods in sustainability assessments.


**Table 4.** *Cont.*



*4.3. Findings from the Reviewed Studies*

The following is a summary of the findings from the reviewed articles:

• There is a solid scientific basis for thermodynamic approach based on exergy [6,70];


In addition, the following were observed while studying the bibliometrics of the reviewed articles in Web of Science:


#### **5. Discussion**

#### *5.1. Exergetic Life Cycle Assessment and Benefit*

Currently, exergetic life cycle assessment, as a thermodynamic approach based on exergy, is utilized to measure resource use in LCA and in other sustainability assessment methods [6]. In other words, exergetic life cycle assessment supplements LCA with a deeper life cycle environmental impact assessment by including the impact of non-energy resources such as mineral ores. One of the categories of environmental impacts that needs consideration is resource use [50], especially, non-energy resources. Exergetic life cycle assessment solves the problem of characterization of non-energy resources in conventional LCA. The exergy-based methods, which were described in Section 3.1, are employed to characterize and quantify the impact of resource use. A review of the current methods to characterize resource use category in LCA can be found in Finnveden et al. [6]. The following two paragraphs state the resource use characterization problem in conventional LCA and the benefit of exergetic life cycle assessment to overcome this methodological problem in LCA.

The resource use characterization problem is encountered in conventional LCA at the LCIA stage in the estimation of environmental impacts for each material and process. Essentially, factors are assigned to each material unit depending on the environmental impact category in consideration. For example, as illustrated in Figure 4, the characterization factor of steel for the global warming potential category is 0.43 per unit mass of steel. Similarly, the characterization factor of glass for the global warming potential category is 1.064 per unit mass of glass. However, these characterization factors mainly depend, amongst others, on the following conditions:


The calculation of characterization factors used in databases for LCIA requires that systematic modeling procedure be followed, which includes multimedia fate and exposure models, in addition to derivation of resulting impacts from experimental toxicity data. Calculation of these factors are localized and complex, even for a controlled study. Exergetic life cycle assessment can be an alternative solution to bypass the complex modeling needed to calculate these characterization factors. As already stated, resource use can be energy or non-energy resources such as mineral ores and materials. For a given energy source, the exergetic life cycle assessment, in form of cumulative exergy demand, can be calculated from the cumulative energy demand and gross calorific exergy-to-energy ratio [24]. For each material (e.g., aluminum, brick, concrete, steel, etc.), exergetic life cycle assessment, in the form of material exergy demand, utilizes the unit exergy of the material to estimate the resource depletion (such as chemical elements, compounds, and ores) due to the material use. Unit exergy of a material is a cumulative of the standard unit chemical exergies of the substances that make up the material. Standard unit chemical exergy of a substance is the unit chemical exergy of the substance when the reference environment is composed of air at 298.15 K of temperature and 101.325 kPa of pressure. Table of values for standard chemical exergies of substances can be found in literature [25].

#### *5.2. Opportunity for a More Comprehensive Exergetic Life Cycle Assessment*

Exergetic life cycle assessment describes and measures resource use in LCA and in other sustainability assessment methods. However, in LCA, environmental impacts of both life cycle resource use and life cycle emissions are estimated or predicted. There is, therefore, the need to advance the exergetic life cycle assessment to include the impact of life cycle emissions. The reasoning is that since exergy is a measure of the degree of disequilibrium between a substance (in this case, emission) and its environment [78], then exergy of emissions can also be a measure of the environmental impact potential [24].

In this paper, the term "Exergy-based life cycle assessment (Exe-LCA)" is proffered to describe a measure of both life cycle resource use and life cycle emissions. Full development of the method for Exe-LCA is beyond the scope of this review paper and is discussed elsewhere [79]. Like material exergy demand, which was discussed in Section 5.1, the exergy of life cycle emissions can be estimated using emitted substance, instead. This is the exergy that is lost to the environment due to the emission of substances that cause environmental impacts during the life span of a product or process. Exergy of life cycle emission is a function of emission mass, standard chemical exergy of emission, and molar mass. For example, global warming potential is a measure of the potential of greenhouse gas emissions, such as carbon dioxide and methane, to cause global warming environmental impact. Therefore, the exergy of life cycle emissions that cause global warming environmental impact is a cumulative of the exergies of life cycle carbon dioxide emission, life cycle methane emission, and those of the other greenhouse gas emissions. The same procedure can be followed for other environmental impact categories.

In conventional LCA, the environmental impacts are quantified relative to one selected emission per category. For instance, global warming potential is in carbon dioxide equivalence, and acidification potential is in sulfur dioxide equivalence, etc. Such representations do not portray the contributions of the other underlying emissions e.g., contributions of methane and nitrous oxide to global warming, and the contributions of nitric and hydrochloric acids to acidification. One main advantage of using exergy of life cycle emissions is that the contributions of all the identified chemical emissions during the inventory analysis can fully be included as a cumulative. In addition, exergy of life cycle emissions, as well as exergy of life cycle resource use, express their measurements in the same unit of exergy. This unification capacity results in ease of comparison and other benefits that can make the exergy-based method suitable to achieving life cycle sustainability assessment [80], in addition to being a viable solution to a major methodological problem in LCA.

#### **6. Summary and Conclusions**

A review of exergetic life cycle assessment was conducted, using a systematic approach, to investigate the state-of-the-art and relevance of exergy in LCA, and to identify opportunity for improvement of the method. The paper was structured to describe exergy, introduce exergy-based

methods and LCA, review and analyze studies on exergetic life cycle assessment, and to discuss the benefits of exergetic life cycle assessment and highlight opportunities for improvement. The methodology involved a literature review, which entailed a systematic selection of journal articles through search of major databases such as Web of Science Core Collection, Scopus, and Google Scholar. The databases were assumed to be comprehensive enough for the collection of the most relevant articles in exergetic life cycle assessment.

The review has shown that exergy-based method can improve on the conventional LCA method. Both exergy and LCA can be used to assess resource consumption and environmental impacts. However, exergy-based LCA goes deeper to assess the quality of resource consumption and environmental impacts. For instance, exergy assesses efficiency of resource use, resource recovery factor, and/or emission rate. Additionally, characterization factors, which are developed using exergy-based method, will be more accurate and robust than that from the conventional LCA method. This is because the former is based on standard thermodynamic properties (e.g., of temperature, and pressure) while the latter is dependent on subjective factors such as fate, exposure, and effects.

In addition, both exergy-based method and LCA can estimate potential environmental impacts from life cycle emissions but report or interpret them in different ways. In conventional LCA, each impact category is reported relative to one reference emission e.g., carbon dioxide equivalence for global warming potential, while in exergy-based method, each emission can uniquely be quantified and summed up. The advantage is that both absolute and relative values can be obtained using exergy-based method for a more robust comparative analysis and decision-making opportunity. It is recommended that exergy-based LCA method, instead of the conventional LCA, be utilized especially in cases where quality of evaluation, single objective values, combined environmental and economic assessment, robust inventory of characterization factors, and benchmarking are required. The critical issues in performing exergy-based LCA include the assumptions in determination of the exergies of the resources and emissions such as standard thermodynamic conditions, pure state of resources and emissions, and difficulty in determination of the individual emission mass. These issues should not be confused with those unique issues in performing exergy analysis of energy systems such as sensitivity of reference environments, choice of exergy efficiency type, unavoidable nature of irreversibility, boundary definition, and choice between steady state and dynamic state conditions, as reported in [20,81].

The following conclusions are deduced from the study:


**Author Contributions:** Conceptualization, M.N.N. and C.J.A.; methodology, M.N.N.; resources, C.J.A.; writing—original draft preparation, M.N.N.; writing—review and editing, C.J.A.; funding acquisition, M.N.N. and C.J.A. All authors have read and agreed to the published version of the manuscript.

**Funding:** The authors acknowledge the research sponsorship from The Petroleum Technology Development Fund (PTDF) (grant number: PTDF/ED/PHD/NMA/88/16), Nigeria.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


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