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Strategic metals ranking in the automobile sector
Abel Ortego1
CIRCE - Research Centre for Energy Resources and Consumption
Alicia Valero†, Antonio Valero††, Guiomar Calvo††, Mar Villacampa†††, Marta Iglesias†††
†CIRCE - Research Centre for Energy Resources and Consumption
††Universidad de Zaragoza
†††SEAT, S.A.
ABSTRACT
A conventional passenger vehicle demands more than 50 different types of metals, some of them
such as tantalum, indium, niobium or rare earths elements (REE), are considered critical by the
European Commission. Besides this, their functional recycling is practically absent. Moreover, the
transition to fully electric vehicles will require more electrical and electronic devices, motors and
batteries that will need an increasing amount of critical metals.
With the aim to identify possible future metal supply constraints, an own methodology has been
developed and applied to the automobile manufacturing industry. This approach defines a variable
called Strategic Metal Index (SMI) which is calculated for each metal. The SMI is the result of
combining the following parameters: (1) Automobile sector demand with respect to world
production; (2) Available reserves; (3) Known resources; (4) Metal production capacity; (5)
Economic importance and (6) Supply risk. Together with another methodology called
thermodynamic rarity developed by the authors, they should provide a holistic decision support
tool for raw material strategic planning in the automobile sector.
The SMI has been applied to 50 metals used by different types of vehicle powertrains. The
assessment covers metal demand from 2018 to 2050 according to vehicle sales projections for five
different scenarios.
This assessment reflects as main possible constraints: Ni, Li, Co and Mn (batteries); Nd and Dy
(permanent magnets); Pt (catalytic converters); Tb (lighting and fuel injectors); Sb (steel alloys
and paintings); Au, Ag and Ta (electronics); In (screens); Te (steel alloys and electronics) and Se
(sensors and glasses).
KEYWORDS
Material bottlenecks; Strategic raw materials; Resource efficiency; Electric vehicle; Passenger
vehicles; Strategic decision tool.
1 Corresponding author: Abel Ortego, CIRCE, Research Centre for Energy Resources and
Consumption, Mariano Esquillor nº15, Zaragoza 50018, Spain. Email: aortego@fcirce.es
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INTRODUCTION
The vehicle manufacturing sector is one of the largest raw material consumers and the tendency is
that this demand will continue growing in the future [1]. Global vehicle sales have doubled in the
last 30 years [2] and as a result vehicle world fleet has grown exponentially [3]. For instance, only
in the European Union, passenger car fleet has grown by 5% annually in the last 5 years [4] and
projections indicate that it will keep growing in the coming years [5]. This evolution in vehicle
sales has caused in parallel an increase in raw material demand to manufacture them [6].
Besides, automobile manufacturers are directing their efforts to comply with the environmental
protection legislation [7] because stricter regulations are continuously being introduced [8]. As an
example, according to the European transport White Paper, before 2050 urban mobility will not
be allowed to conventional fueled vehicles [9] and some cities such as Paris, Barcelona, Milan or
London have even committed to more ambitious targets to procure that a major area of their cities
is zero emissions by 2030 [10]. These cleaner vehicles need more different types of metals and as
a consequence in the last years not only the quantity of vehicles has grown but also the number of
different raw materials necessary to manufacture them has changed [11]. Some examples of this
change is the fact that some steel parts such as engine head, suspension arms or wheels are being
substituted by aluminum alloys for light weighting purposes [12–14]. It is also remarkable that
more permanent magnets such as neodymium or dysprosium are being demanded to manufacture
hybrid or fully electric powertrains [15,16]. Moreover, some metals such as lithium, cobalt or rare
earths are being used to manufacture batteries [17] and others like silver, indium, tantalum or
lanthanum to make electronic components [18]. As a result, nowadays a conventional vehicle
needs around 50 different types of metals [11]. Furthermore, some of these metals such as light
rare earth elements, cobalt, gallium, indium, magnesium, niobium, tantalum, or vanadium are
considered critical due to potential supply risk problems and economic importance [19–25] or have
other associated problems such as the case of rare earths production and the associated thorium
accumulation [26].
One solution to guarantee a sustainable use of critical metals is the improvement of recycling
processes at the end of life of the product. In the case of ELV, the EU Directive 2000/53/EC sets
strict targets to promote the reusing, recycling and recovering of materials (from 2015, the total
mass percentage of materials reused and recovered with respect to the average car’s weight must
be equal to 95%, of which at least 85% must come from reuse and recycling). However ELV
recycling operations are mainly focused on recycling major metals such as steel and aluminum
alloys [18] and many others are not functionally recycled [11]. Nevertheless, these scarce materials
may be recycled, but in reality only a very small proportion is ever recycled back into cars. The
vast majority are downcycled into less technically demanding applications or indeed simply
thrown away [27]. As it was demonstrated by Andersson et al [18], from a total of 17 metals
investigated, only Pt from catalytic converters is functionally recycled. Other examples are the
cases of nickel, chromium and molybdenum, which approximately 60% unintentionally ending as
the iron source in steel-making process [28].
It is also remarkable the case of nickel, as only 40% of its content in automobiles is reused for its
nickel content in steel plate rolls, being the rest downcycled or ending up in landfills [29]. As a
result current functional recycling rates of some of these materials are almost negligible because
sometimes recycling processes are more expensive than primary raw material costs, as it happens
in the cases of indium, gallium, cadmium and tellurium [30]. Indeed, and even if it has been
demonstrated that recycling has a huge improving potential by including pre-recycling processes
to recover the metals, current recycling rates are still very low [31]. For instance, less than 3 % of
the lithium contained in a battery is currently recycled [32] and only 42 % of the total battery waste
mass can be recycled with current available technology [33]. As a consequence, the concern
regarding raw material availability is becoming an important issue for countries which aim to
guarantee their sustainability [22,34–37].
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This is why a number of studies have analyzed this specific issue in the car industry. Focusing on
the components that use critical metals, Field et al. [38] assessed the use of several critical and
minor metals used in a Ford Fiesta, Ford Focus and F-150. They realized that strategic metals were
mainly used in electrical, drivetrain and suspension systems. Du et al. [39] quantified the use of
critical metals in conventional vehicles by means of an hybrid approach (input and output) and
compared the results of previous studies for 25 metals. As a main conclusion it was stated that the
comparison among different studies is quite difficult because there are no standard nomenclatures
to define vehicle subsystems. Restrepo et al. [40] evaluated the use of critical metals in electronic
components used in passenger vehicles. It was demonstrated that REE are mainly found in electric
motors (alternator, starters, steering motor, etc…) and drive motors (in hybrid and electric ones).
This is why they suggest dismantling strategies for these components before entering shredding
processes.
Henbler et al. [41] presented the ESSENZ method that compared a Mercedes-Benz C-Class with
different powertrains (petrol, diesel and plug-in hybrid). The method shows that more different
materials are used in PHEV and that combustion engines perform better than PHEV in the category
of abiotic resource depletion of metals.
Regarding the future availability of raw materials, Simon et al. [17] researched the impact of
different types of lithium ion batteries in lithium, cobalt, nickel and manganese supply. The
assessment was limited to European passenger vehicle fleets and metal resources. It was identified
that a possible shortage in European lithium and nickel reserves might be expected around 2025.
Grandell et al. [35] assessed the role of critical metals in clean technologies, including electric
vehicles. The paper identified several constraints in the future for Ag, Te, In, Dy, La, Co, Pt and
Rh.
In this respect, a new methodology called Thermodynamic Rarity was proposed to assess the use
of scarce and critical metals from a thermodynamic perspective [42]. Rarity measures in exergy
terms, the impact of materials contained in a component, considering the scarcity of these materials
in Nature, by means of the ore grade in mines and the exergy required to extract them from a
hypothetical bare rock, to post-beneficiation conditions. Thermodynamic Rarity can be considered
as a new dimension to assess metal criticality and it complements other common non-physical
assessment dimensions like supply risk or economic importance which are used by the European
Commission [43]. This approach has been applied to identify more critical vehicle components for
different powertrain vehicles [11] and to calculate the loss of mineral capital in current End of Life
Vehicle (ELV) recycling processes [44]. The application of the rarity approach, allows not only to
recognize the physical value of materials with a low contribution in mass terms, but also to quantify
their specific importance in the vehicle as a whole. This is because scarce and difficult to obtain
metals have a higher rarity than abundant ones (i.e. platinum vs. iron). This approach is objective
and universal because it is only based on physical parameters. However it is well known that there
are non-physical factors that could also provoke metal supply constraints to a given industry such
as the specific dependency for these metals, the unavailability of substitution alternatives, the
existence of other industries that also demand or compete for the same metal, the future expected
demand for emerging technologies or the concentration of mining activity in politically unstable
countries. For these reasons the physical point of view must be also complemented with another
method that also considers socio-economic parameters.
To advance in scientific knowledge regarding metal availability and importance for the automobile
industry, this paper proposes a complementary index to thermodynamic rarity to identify possible
raw material supply constraints in the vehicle manufacturing industry until 2050. This method is
based on: (1) expected material demand; (2) available reserves; (3) known resources; (4) metal
capacity production; (5) supply risks and (6) economic importance. It must be noted that the
author’s intention is not to propose a new critical metal list but to make a list of strategic metals
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and to show what vehicle components demand these metals and hence are advised to be eco-
designed to avoid future raw material supply risks.
MATERIAL AND METHODS
Strategic Metal Index
An index named Strategic Metal Index (SMI) has been defined in order to rank raw materials in
the automobile sector according to different criteria. The SMI index ranges from 0 to 100 and is
calculated considering the following variables:
A: Automobile manufacturing sector demand of each metal with respect to total
production. It is calculated by dividing the cumulative demand (2018 – 2050) of automobile
manufacturing sector and the total cumulative demand (2018 – 2050) of all sectors for each
studied metal. It gives an idea of the importance of the automobile sector in the world
capacity production of this metal.
B: Available reserves with respect to cumulative demand (from 2018 to 2050) of this metal.
It is calculated by dividing the total cumulative demand (2018 – 2050) and the available
reserves. It gives an idea of the directly geological availability of this commodity.
C: Metal known resources with respect to cumulative demand (from 2018 to 2050) of this
metal. It is calculated by dividing the total cumulative demand (2018 to 2050) and the
current known resources. As in the previous case it gives an idea of the geological
availability of each commodity but based on resources instead of reserves (i.e. not
necessarily economically feasible at the time of determination).
D: Production capacity and annual demand ratio for each metal (from 2018 to 2050). It is
useful because it compares for each studied year the expected demand with the production
capacity. To do it, the production capacity is modeled by means of the Hubbert theory. This
model is explained in section 2.3.
E: Economic Importance. Value taken from the Critical Raw Material report published by
the European Commission [45]. This index ranges from 0 to 10 and so it can be used in
SMI by extrapolating it to a 0 to 100 scale. This variable complements the A variable, and
offers an idea of the economic dependency of a given metal.
F: Supply risk. Value taken from the Critical Raw Material report published by the
European Commission [45]. This index ranges from 0 to 10 and so to use it in the SMI
index it is extrapolated to a 0 to 100 scale. It offers a geopolitics vision of dependency for
each commodity.
The SMI is calculated as the sum of the six described variables (A – F) by means of using
weighting coefficients for each one, as follows (equation 1):
SMI = α*A + β*B + γ*C + δ*D + ε*E + ζ*F Eq. 1
Where: α + β + γ + δ + ε + ζ = 1
Once the SMI is assessed for each metal, all of them can be ranked. Nevertheless at this stage the
challenge is to calculate the required variables. In the next sections the assessment methods to
calculate reserves data, resources data, metal production capacity and metal demand for each year
from 2018 to 2050 are explained.
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Reserves and Resources
As extraction is ultimately limited by the amount of minerals present in the crust with sufficient
concentration, it is important to know raw material availability in terms of reserves and resources.
Resources (RES) are concentrations of naturally occurring materials on the Earth’s crust in such
form that economic extraction is feasible, currently or at some future time. Reserves (RSV) in turn
are the portion of resources which can be economically extracted or produced at the time of
determination. Reserves are thus lower than resources and more dynamic, since identified
resources can be reclassified as reserves when commodity prices rise or a decrease in production
costs takes place. Different sources have been compared and those considered more accurate have
been used for the methodology [46–50]. Information regarding reserves and resources of rare earth
elements (REE) comes from [51] and [46].
Variables B and C from equation 1 are calculated by means of dividing resources and reserves by
cumulative demand respectively. Appendix A shows the Reserves and Resources values used.
Metal production
As annual production rates need to be synchronized with the rising demand of materials,
projections regarding future raw material production are equally required. In this paper it is
assumed that material production will follow the Hubbert peak model. Hubbert [52,53] showed
that trends in fossil fuels production always followed the same pattern. The curve of production
started slowly before rising steeply and tending towards an exponential increase over time. This
trend goes on until reaching an inflection point, upon which the curve starts to decrease, generating
a bell-shaped curve of normal distribution. The area below the curve depends on the combination
of the available reserves or resources and the historic production data of the commodity.
Production of commodity pa in year t is given by Equation 2:
√
2
Eq. 2
Where R are the reserves (RSV) or resources (RES) of the commodity and the parameters b0 and
t0 are the unknowns. The function's maximum is given by parameter t0, and it verifies that:
√
2 Eq. 3
With this approach, the maximum production peak of the commodity can be obtained, meaning
the year when production starts to decrease. Additionally, future yearly projections of production
can be obtained using a business-as-usual scenario. It is presumed that production will continue
rising with an exponential trend, as it has been the case for most commodities. Yet geological
availability in form of reserves or resources prevents at some point this continuing growth.
The variable D from equation 1 is calculated by means of identifying the possible year in which
metal production can be smaller than metal expected demand. It is assessed by means of a lineal
interpolation, where a bottleneck in 2050 or later has null value and a bottleneck in 2018 has a
figure of 100. The model has uncertainty, for this reason the coefficient of determination (R2) for
each metal production capacity has been calculated. This coefficient is used to correct the value of
the lineal interpolation always extending the maximum capacity year and thus keeping the criteria
of a best case scenario. By means of this method those values with a small reliability will not
contribute too much in variable D from equation 1.
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Metal demand
Metal demand for each type of vehicle has been assessed. Metal composition data for combustion
vehicles (petrol and diesel) comes from an automobile manufacturer and in the cases of PHEV and
BEV from a scientific paper revision. The metal compositions for each type of vehicle and the
assumptions are included in Appendix B. Regarding material demand, it was considered that a
certain amount of raw materials comes from secondary sources (recycling processes). As the
available information on recycling rates is usually very aggregated or general, the recycling rates
used in this study come from the United Nations Environmental Program [54], and are listed in
Appendix C. Eq. 4 shows how material demand in the studied vehicles is calculated for a given
year for each commodity:
_ ∗ ∗1 Eq. 4
Where da_tv is the quantity of primary material a demanded for the analyzed type of vehicle (tv)
during a given year; N is the number of yearly manufactured units of each type of vehicle; M is
the quantity of material a demanded by each type of vehicle to manufacture one functional unit -
FU (FU=1 vehicle); r is the share of material which comes from recycling.
The impact of recycling on primary production is assumed as one to one displacement because
reprocessing does not change material properties. It should be stated though that as Geyer et al.
demonstrated [55], this approach is not a rule because rebound effects in demand could appear, so
robust models to predict the impact of recycling in primary demand must be yet developed. As the
projections presented in this study go until 2050 and the vehicle lifetime is lower, material demand
from fleet renovation must be considered. This effect is taken into consideration using Equation 5.
Eq. 5
Where Nnv is the number of new vehicles which are added to the global fleet and Nrv is the number
of vehicle to renew old ones.
Nevertheless, it must be also considered that automobile manufacturing industry will need to
compete for materials with many sectors, such as construction, chemicals, metal industry or
electronics and with other disruptive sectors such as renewable energy technologies.
Unfortunately, for the rest of the sectors the information on material consumption is scattered and
in many cases unavailable. This is why in this paper it is assumed that material demand for other
sectors (_) will be kept constant until 2050 and equal to the difference between total material
production in 2018 (Pa)2018 and material demand for vehicles for the same year _ (Eq. 6).
Obviously, this is a very conservative assumption as historically material demand for other sectors
has usually increased year by year. That said, this assumption allows us to identify the lower bound
for potential material constraints regarding material competition of automobile manufacturing
sector with other sectors.
_
_ Eq. 6
Accordingly, total demand for a given commodity a in year t _is calculated by means of Eq.
7, whereas the total cumulative material demand for commodity a (_from 2018 to 2050 is
obtained through Eq. 8.
_
__ Eq. 7
_
_
Eq. 8
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RESULTS AND DISCUSSION
Vehicle stock and sales projections
The demand projections for each type of vehicle are represented in Figure 1. In the case of annual
sales (left) it must be taken into consideration that fleet renovation effect is included. The lifetime
considered for all types of vehicles is 17 years according to data published by the Spanish ELV
recycling management system [56]. Projections of sales and world fleet evolution are built using
data from International Energy Agency roadmaps and the International Organization of
Automobile Manufacturers [2,57].
Figure 1: World vehicle sales projection (left) and world fleet evolution (right)
According to the above, world fleet would increase to more than 2,200 million of vehicles in 2050
from which 26 %, 53 % and 19 % will be ICEV, PHEV and BEV respectively. On the other hand,
it stands that PHEV and BEV sales would surpass those from ICEV in 2032 and 2044 respectively.
Regarding sales it can be observed that beyond 2020 ICEV world sales will decrease.
Metal demand, resources and reserves
Through sales projections, annual material demand by type of vehicle and demand for the rest of
sectors for each metal is assessed. These demands are compared to resources and reserves data to
calculate B and C parameters of equation 1. In Figure 2, demand, reserves and resources data for
each metal are shown.
It can be highlighted that cumulative demand exceeds reserves values for silver, arsenic, gold,
cadmium, cobalt, copper, gallium, mercury, indium, nickel, lead, antimony, tin, strontium,
tellurium and zinc. However, when comparing the demand with resources it only happens in the
case of tellurium. It is important to note that reserves and resources data for tellurium are usually
inaccurate or incomplete as it is a byproduct mainly produced during the copper and lead-zinc
refining processes.
0.0E+00
2.0E+07
4.0E+07
6.0E+07
8.0E+07
1.0E+08
1.2E+08
1.4E+08
2018 2023 2028 2033 2038 2043 2048
ICEV PHEV BEV
0.0E+00
5.0E+08
1.0E+09
1.5E+09
2.0E+09
2.5E+09
2018 2023 2028 2033 2038 2043 2048
ICEV PHEV BEV
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Figure 2: Total demand, resources and reserves for each metal
Annual capacity production comparison with annual demand
Through a combination of metal capacity production and metal demand explained in sections 2.3
and 2.4, respectively, the data of maximum production peaks have been calculated. Table 1 shows
those metals where demand may exceed capacity production before 2050, with their regression
factors R2 of the Hubbert model applied. The information concerning possible bottlenecks
represents the latest possible year considering the reliability or R2.
Table 1: Metals where demand may exceed production before 2050
Metal R2 Peak production Possible bottleneck
A
g
0.71 2026 ≤ 2040
Au 0.78 2018 ≤ 2042
B 0.91 2030 ≤ 2047
Co 0.90 >2050 ≤ 2026
Dy 0.95 >2050 ≤ 2020
Li 0.94 >2050 ≤ 2020
Mn 0.84 2027 ≤ 2048
Nd 0.96 >2050 ≤ 2021
Ni 0.94 2029 ≤ 2025
Pt 0.95 >2050 ≤ 2023
Sb 0.71 2018 ≤ 2040
Se 0.95 2028 ≤ 2040
Tb 0.95 >2050 ≤ 2019
1.E+00
1.E+01
1.E+02
1.E+03
1.E+04
1.E+05
1.E+06
1.E+07
1.E+08
1.E+09
1.E+10
1.E+11
1.E+12
Ag
Al
As
Au
B
Ba
Be
Bi
Cd
Ce
Co
Cr
Cu
Dy
Eu
Fe
Ga
Gd
Ge
Hg
In
Ir
La
Li
Mg
Mn
Mo
Nb
Nd
Ni
Pb
Pd
Pr
Pt
Rh
Ru
Sb
Se
Sm
Sn
Sr
Ta
Tb
Te
Ti
V
W
Yb
Zn
Zr
tonnes
Total demand Reserves Resources
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It can be observed that for Co, Dy, Li, Nd, Pt and Tb the demand and production intersections are
achieved before the metal production peak. This means that for these metals the problem is not
availability but an excessive expected demand because production and demand intersect along the
growing tendency. Tellurium, despite a bottleneck being identified, is not included in the table
because R2 is too low to be considered reliable (only 0.35).
Figure 1 presents the case of selenium. According to the Hubbert methodology, a possible
production peak may be achieved in 2028. Considering the evolution of the expected demand, the
possible bottleneck could arrive in 2035. If the reliability of the model is considered through the
R2 value (i.e. 0.95), the maximum capacity production increases and hence the bottleneck is
displaced to 2040.
Figure 3: Estimated production and demand for selenium case
Metal strategic Ranking
As different variables are needed to calculate the SMI, several scenarios are presented to assess it
under different possible situations. The following scenarios have been defined:
Geo: The higher weight (0.6 over 1) is given to metal geological availability variables (B,
C and D). The rest of variables are equitably weighted.
EU: The higher weight (0.6 over 1) is given to the variables defined by the European
Commission (E and F). The rest of variables are equitably weighted.
Ams: The higher weight (0.6 over 1) is given to the automobile demand with respect to
total demand (A). The rest of variables are equitably weighted.
Equi: All variables have the same weight.
Exp: It is a scenario based on the common criteria between the authors of this paper and
the car manufacturer.
2,000
2,100
2,200
2,300
2,400
2,500
2,600
2,700
2,800
2,900
3,000
2018 2022 2026 2030 2034 2038 2042 2046 2050
tonnes
demand Hubbert max Hubbert ave
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The values used for the weighting coefficients are presented in Table 2.
Table 2: Weighting coefficients used for the different scenarios
Scenario α Β
γ
δ Ε
ζ
G
eo 0.13 0.2 0.2 0.2 0.13 0.13
EU 0.1 0.1 0.1 0.1 0.3 0.3
Ams 0.6 0.08 0.08 0.08 0.08 0.08
E
q
ui 0.16 0.16 0.16 0.16 0.16 0.16
Ex
p
0.3 0.1 0.1 0.1 0.2 0.2
The selection of these scenarios allows us to have a range of values in the SMI calculation. Figure
4 represents the SMI ranking and the uncertainty range for each case. Metals are ranked through
the SMI value calculated for an Average scenario. The average scenario is calculated as the
average SMI value obtained for each metal from the scenarios shown in Table 2. The SMI for the
Average scenario is represented with crosses. Color scale means: Red (SMI ≥ 35); Orange (35 >
SMI ≥ 20); Green (SMI < 20). The most critical metals under the SMI approach (those which SMI
≥ 50) are Ni (61), Li (57), Tb (56), Co (55), Dy (51) and Sb (51). These metals are followed by Nd
(47), Pt (44), Au (40), Ag (40) and Te (40).
Figure 4: Metal strategic ranking
0
10
20
30
40
50
60
70
80
Ni
Li
Tb
Co
Dy
Sb
Nd
Pt
Au
Ag
Te
Mn
Ta
In
Se
V
W
Zn
Cr
Cu
Nb
Ga
B
Sn
Pr
Pd
Mo
Fe
Yb
Ba
Rh
Al
Ge
Bi
La
Ru
Ir
Hg
Ce
Sm
Eu
Pb
Mg
Cd
Ti
Gd
Be
As
Sr
Zr
SMI
max min base
Ave
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Considering the possible range of results among the studied metals and scenarios, the lowest
corresponds to the Ams scenario for most cases. This scenario assigns the highest impact to the
automobile sector demand with respect to the total demand. The opposite case corresponds for
Geo and EU scenarios. In Appendix D all SMI values for each metal and scenario are shown.
It must be underlined that the SMI should only serve as a strategic ranking tool for metals in cars.
It is not a criticality list of metals because, contrary to general criticality lists such as the one by
the European Commission, the SMI reflects material shortages from the perspective of the
automobile industry. Obviously, certain metals that are critical by the EC but do not appear in cars
are not considered in this approach, i.e. tungsten, hafnium or scandium. However, this does not
mean that they might appear in the future, if these metals are demanded in future cars.
Most strategic metals and vehicle applications
The next step is to identify the most critical parts considering the metals with an SMI greater than
35 for the Exp scenario. Table 3 contains this information, showing that the most affected
components are batteries, which mainly use Ni, Li Co and Mn, all of these metals with an average
SMI value higher than 40 and in the case of Ni, Li and Co even higher than 55.
Moreover, the case of Tb stands out, which is mainly used in lightings and fuel injectors. Lighting
equipment can be subject to Se shortages, an element that is also used for glazing manufacturing.
Permanent magnets are also included among the most affected components. Dy and Nd have SMI
values of 51 and 47, respectively. High strength steel alloys can be also affected, as Sb and Te are
ranked between the most strategic metals. Catalytic converters used to treat combustion gases will
be also at risk due to their critical metals content. This is mainly because of their Pt content, which
has an average SMI of 44. Finally, electronic components that demand Au and Ag for contacts and
welding can be subjected also to the possible shortage of Ta to manufacture capacitors or In, which
is mainly used in the screens of combi instruments and infotainment units.
Table 3: Main strategic metals and vehicle applications
Metal SMI range Applications
Ni From 51.4 to 73.1 Batteries (NMC and NCA) and steel alloys
Li From 45.2 to 75.3 Batteries (NMC)
Tb From 47.5 to 74.1 Lighting and fuel injectors
Co From 50.0 to 60.3 Batteries (NMC and NCA) and steel alloys
Dy From 43.4 to 64.9 Permanent magnets
Sb From 29.3 to 65.5 Steel alloys and paintings
Nd From 43.9 to 53.9 Permanent magnets
Pt From 42.3 to 45.7 Catalytic converters
Au From 26.7 to 55.9 Electronics (contacts coating)
Ag From 26.4 to 53.5 Electronics (contacts coating)
Te From 22.7 to 51.7 Steel and lead alloys and electronics
Mn From 22.6 to 48.1 Batteries (NMC) and steel alloys
Ta From 32.3 to 42.3 Electronics (capacitors)
In From 21.2 to 45.3 Screens
Se From 19.9 to 45.1 Lighting sensors and glasses
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CONCLUSIONS
The SMI is presented as a useful index to rank materials demanded to manufacture vehicles
according to their possible future strategic importance to the sector and so guide in the formulation
of possible ecodesign alternatives. The SMI is calculated through a holistic approach considering
physical variables such as reserves and resources and non-physical ones such as supply risks and
economic importance of raw materials within and outside the sector.
The SMI should not be understood as a quantitative variable for measuring metal scarcity or
criticality. If the SMI value of metal A doubles that of metal B, it does not mean that the former is
twice as critical or scarce as the latter. This is for instance the case of aluminum and germanium
where the SMI for Al (21.2) is slightly higher than that of Ge (20.4). But in fact germanium is
scarcer in the crust than aluminum, and contrary to the latter, due to its global economic importance
and supply risks, germanium is considered critical by the European Commission. The SMI rather
reflects how scarcity and criticality of a given metal may affect the automotive sector.
Moreover, and contrary to the Thermodynamic Rarity indicator proposed by the authors in
previous studies (which uses exergy as the unit of measure to value minerals according to their
specific physical features in the crust and mining energy intensities), the assumptions considered
implicitly in this method, make that the SME cannot be considered either as a universal numeraire
of metal sustainability in the automobile sector. Such assumptions are: (1) the vehicle composition
is considered constant throughout the analyzed years; (2) future mineral reserves discoveries are
not considered; (3) the possible growth in the metal demand of other sectors is not considered; (4)
Metal production capacity is modeled using a Hubbert approach, which is theoretical; (5) Supply
risk and economic importance by the EC might change over time (6) the weighting factors used
for each category composing the SMI is arbitrary.
Nevertheless, the SMI complements the thermodynamic approach because it provides a different
dimension for the potential identification of raw material shortages in the automotive sector. This
dimension incorporates non-physical parameters such as supply risk, sector dependency or
economic importance which are indeed key for the automobile industry.
Particularly, in this paper we have obtained through the SMI that the main identified shortages are
those concerning the manufacturing of batteries in electric vehicles (Ni, Co, Li and Mn), permanent
magnets for motors (Nd and Pr), electronic components (Ag, Au, Ta, Te and In), catalytic
converters (Pt), fuel injectors (Tb) and paintings (Sb). In fact these same components were also
identified as the most relevant when the assessment was carried out from a thermodynamic point
of view in a previous research [11] what ensures that the thermodynamic approach at least in the
automobile case can be considered as self-sufficient. Yet this may not be the case for other sectors
and this is why both approaches are always recommended to have a holistic view of the problem.
For the identified components, automobile manufactures should encourage ecodesign strategies to
reduce the demand of these strategic metals, to find substitutes or to increase their functional
recyclability. This is why, based on the obtained results, in a future paper, the authors will propose
specific ecodesign measures in vehicles.
ACKNOWLEDGMENTS
This study has been carried out under the framework of EXCITE project (EXergy approach to
encourage CIrcular economy pracTices in vEhicles), funded by SEAT, S.A. under the agreement
NºOFE-01115-L5G1R8 and ENE2017-85224-R project, financed by the Spanish Ministry of
Economy, Industry and Competitiveness.
0006-12
13
NOMENCLATURE
BEV: Battery Electric Vehicle
CRM : Critical raw materials
d: material demand
D: cumulative material demand
ELV: End of Life Vehicle
FU: Functional Unit
GHG: Greenhouse gas
ICEV: Internal Combustion Engine Vehicle
m: studied technologies
N: manufactured units
Nns: new units added to the global market
Nrn: units manufactured to renew
installations
PHEV: Plug Hybrid Electric Vehicle
R: reserves or resources
r: material share from recycling
Re: recycling quote
REE: Rare Earth Element
RES: resources
RSV: reserves
SMI: Strategic Metal Index
t: studied years
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APPENDIX A – RESERVES AND RESOURCES DATA
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Table A. 1: Reserves and Resources data (in tonnes)
Metal Reserves Resources Metal Reserves Resources
Ag 570,000 1,308,000
Mn 690,000,000 1,030,000,000
Al 28,000,000,000 75,000,000,000 Mo 15,000,000 19,400,000
As 730,000 11,000,000
Nb 4,300,000 14,328,483
Au 57,000 100,000
Nd 8,750,000 16,700,000
B 380,000,000 410,000,000 Ni 78,000,000 130,000,000
Ba 320,000,000 2,000,000,000 Pb 88,000,000 2,000,000,000
Be 400,000 400,000
Pd 33,000 46,000
Bi 370,000 370,000
Pr 2,000,000 4,800,000
Cd 500,000 3,600,000
Pt 33,000 50,000
Ce 31,700,000 31,700,000
Rh 5,000 5,000
Co 7,000,000 145,000,000
Ru 6,000 6,000
Cr 500,000,000 12,000,000,000 Sb 1,500,000 4,300,000
Cu 720,000,000 6,350,000,000 Se 100,000 172,000
Dy 2,600,000 2,980,000
Sm 2,900,000 2,900,000
Eu 244,333 244,333
Sn 4,700,000 76,200,000
Fe 160,000,000,000 800,000,000,000 Sr 6,800,000 1,000,000,000
Ga 5,200 1,000,000
Ta 100,000 317,060
Gd 1,235,000 3,622,143
Tb 566,104 566,104
Ge 12,500 440,000
Te 11,080 25,000
Hg 94,000 600,000
Ti 794,000,000 2,000,000,000
In 11,000 47,100
V 19,000,000 63,000,000
Ir 2,000 2,000
W 3,100,000 7,000,000
La 6,000,000 22,600,000
Yb 1,900,000 1,900,000
Li 14,000,000 40,000,000
Zn 220,000,000 1,900,000,000
Mg 2,400,000,000 12,000,000,000 Zr 75,000,000 235,029,851
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18
APPENDIX B – VEHICLE COMPOSITION
Table B. 1: Compilation of materials used in passenger car vehicles PHEV and BEV (in g) per unit of
vehicle with the exception of Al, Cast Iron, Cu and Steel which are in kg.
[60] [35] [61] [62]
PHEV2 PHEV BEV3 BEV PHEV
Al (kg) 200
Cast Iron (kg) 20
Ce 0.31
Co
Cu (kg) 60 150 67.5
Dy 129.66
210 336
Er 0.18
Eu <0.01
Gd <0.01
Ga 0.57
1.05 1.68
Ge
0.05 0.08
In 0.08
0.05 0.08
La 6.68
Li 6,256.55
Mo
Nd 531.88
360 576
Nb 109.14
Pd 1.81
0.12
Pt 5.51
Pr 4.01
120 192
Rh <0.01
Sa 1.4
Sc
Ag 50
6 9.6
Steel (kg) 790
Sr
Ta 10.83
Te 19.86
21 34
For the assessment of materials used in batteries, it has been considered that current battery market
situation is led by Li:ion batteries as demonstrated by the fact that both Nissan and Tesla are
currently using Li:ion batteries in their vehicles [63,64]. Even if Toyota used NiMH and Li:ion
batteries in their vehicles, last Prius PHEVs version is already using Li:ion depending on the
equipment level [65]. This is why materials considered to be demanded for batteries in vehicles
are those demanded by Li:ion batteries.
2 With medium equipment level.
3 Original values are published for a 50 kW motor. In the present study, values are adapted for 50 kW and 80 kW
motors in PHEV and BEV, respectively.
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19
Table B. 2: Material demand by type of battery in g. Values adapted to a battery autonomy of 200 km.
Authors compilation
[66] [67] [36] Average
Li 9.01 7.2 9.3 8.50
Ni 57.40 46.5 58 53.97
Co 10.91 9 12 10.34
Table B. 3 contains the metal composition for each type of vehicle. In ICEV petrol and diesel cases,
information comes from SEAT Leon model (segment C).
Table B. 3: Metal composition (in gr) for each type of vehicle
ICEV Diesel ICEV Petrol PHEV BEV
Ag 10.19 19.47 28 29.80
Al 61,103 78,343 141,370 200,000
As 0.14 1.30 0 0
Au 3.15 3.65 0.20 0.32
B 23.76 26.37 0 0
Ba 832.55 777.66 0 0
Be 0.03 0.02 0 0
Bi 8.81 8.84 0 0
Cd 0.15 0.12 0 0
Ce 2.67 0.37 49.67 0.15
Co 9.72 8.06 2,712 9,330
Cr 5,041 5,566 6,510 6,031
Cu 15,584 15,376 59,166 150,000
Dy 0.19 0.48 13.81 18.73
Eu 0 0.0001 0.23 0.23
Fe 701,095 653,524 806,140 746,945
Ga 0.27 0.27 0.81 1.12
Gd 0.0005 0.0005 0.17 0.17
Ge 0.0036 0.003 0.05 0.08
Hf 0.0027 0.008 0 0
Hg 0.047 0.001 0 0
In 0.216 0.21 0.38 0.38
Ir 0.018 0 0 0
La 0.341 0.40 7.38 7.38
Li 22.06 4.63 2,242 7,709
Mg 13,622 3,565 0 0
Mn 4,333 4,211 5,968 5,530
Mo 240.03 187.97 260 260
Nb 154.20 145.57 426.30 426.30
Nd 23.71 18.84 552.79 749.30
Ni 1,590 2,993 16,049.57 55,724
Pb 12,527 11,535 9,750 9,750
Pd 1.99 1.84 0.94 0
Pr 0.066 0.08 51.48 98
Pt 3.79 0.13 5.51 0
0006-19
20
Rh 0.12 0.09 0.01 0
Ru 0.012 0.013 0 0
Sb 15.70 35.36 0 0
Se 0.013 0.02 0 0
Sm 0.21 0.33 2.32 3.15
Sn 208.53 234.61 0 0
Sr 148.84 144.08 0 0
Ta 4.65 6.53 10.83 10.83
Tb 0.01 0.02 13.62 26.93
Te 0.20 0.18 0 0
Ti 541.41 536.41 0 0
V 92.81 86.62 852.61 790
W 9.24 3.17 0 0
Y 0.07 0.13 0.41 0.41
Yb 0.0003 0.0002 0.08 0.16
Zn 6,614 6,502 0 0
Zr 12.50 78.42 0 0
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APPENDIX C – METAL RECYCLING VALUES
Table C. 1: Metal recycling values [54]
Metal Rec
y
clin
g
rate Metal Rec
y
clin
g
rate
Ag 30% Mn 37%
Al 36% Mo 33%
As 1% Nb 50%
Au 30% Nd 5%
B 0% Ni 29%
Ba 1% Pb 51%
Be 17.5% Pd 50%
Bi 0% Pr 5%
Cd 25% Pt 50%
Ce 1% Rh 40%
Co 32% Ru 55%
Cr 20% Sb 17%
Cu 30% Se 5%
Dy 10% Sm 1%
Eu 1% Sn 22%
Fe 50% Sr 0%
Ga 25% Ta 17.5%
Gd 5% Tb 1%
Ge 35% Te 1%
Hf 0% Ti 52%
Hg 37.5% V 0.5%
In 37.5% W 46%
Ir 17.5% Y 0%
La 5% Yb 0%
Li 1% Zn 22.5%
Mg 33% Zr 5%
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APPENDIX D – STRATEGIC METAL INDEX AND SCENARIOS
Table D. 1: SMI values for each metal and scenario
Exp EU Geo Equi Ams
Ag 35.60 39.82 53.55 47.95 26.39
Al 21.79 29.18 20.84 22.04 12.13
As 11.44 11.42 22.83 18.95 9.19
Au 34.60 37.33 55.92 48.99 26.70
B 27.87 34.46 38.14 35.43 17.02
Ba 19.91 24.36 27.52 25.46 12.44
Be 16.98 25.11 11.99 14.66 7.15
Bi 14.80 13.66 26.94 22.68 13.88
Cd 12.46 12.46 24.91 20.68 9.97
Ce 19.31 26.98 14.82 17.26 9.99
Co 54.67 50.07 56.99 55.65 60.35
Cr 31.32 40.93 35.32 35.10 17.71
Cu 30.82 33.94 34.23 33.44 25.97
Dy 54.29 47.88 43.42 46.35 64.99
Eu 19.09 26.35 13.43 16.22 10.55
Fe 22.89 30.47 23.97 24.50 12.57
Ga 30.25 35.79 32.84 32.62 22.35
Gd 17.44 25.63 12.30 15.02 7.55
Ge 20.70 27.91 20.83 21.64 11.07
Hg 14.05 14.05 28.10 23.33 11.24
In 32.99 39.96 45.30 41.94 21.24
Ir 19.21 26.73 17.17 18.76 9.59
La 19.64 27.71 16.41 18.47 9.54
Li 57.94 45.26 55.63 54.91 75.30
Mg 18.18 25.86 14.25 16.50 8.73
Mn 35.48 43.28 48.12 44.75 22.56
Mo 25.22 30.47 28.80 28.21 17.49
Nb 31.66 35.82 31.39 31.97 26.16
Nd 48.01 43.98 44.92 45.49 53.98
Ni 57.09 58.95 73.17 67.92 51.40
Pb 14.45 12.03 23.25 19.97 15.91
Pd 28.08 30.32 22.58 24.72 26.21
Pr 29.33 31.24 21.43 24.34 28.37
Pt 43.93 42.37 45.42 44.73 45.72
Rh 22.71 27.86 18.64 20.63 16.65
Ru 19.19 26.77 17.28 18.83 9.47
Sb 45.77 55.13 65.55 59.94 29.33
Se 32.04 39.13 45.14 41.53 19.96
Sm 19.71 26.37 13.27 16.25 12.13
Sn 27.25 34.22 32.98 31.86 16.81
Sr 10.55 10.31 20.53 17.11 8.89
Ta 38.49 36.55 32.35 34.21 42.31
Tb 59.57 50.41 47.55 50.55 74.18
Te 34.25 40.26 51.69 46.47 22.75
Ti 15.98 21.62 16.79 17.18 8.31
0006-22
23
Exp EU Geo Equi Ams
V 36.77 37.42 25.45 29.38 38.17
W 32.34 43.35 35.18 35.52 17.10
Yb 25.56 31.96 15.85 19.91 18.98
Zn 31.13 39.89 37.17 36.16 18.24
Zr 9.16 9.12 18.22 15.13 7.39
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