Assessing Mineral and Metal Resources in Life Cycle Assessment: An Overview of Existing Impact Assessment Methods

Abstract

Mineral resources and metals are integral to modern society, with growing demand driven by recent technological advancements. Life cycle assessment (LCA) provides a valuable framework for assessing resource use, and numerous methodologies have been developed to address both the midpoint and endpoint levels of life cycle impact assessment (LCIA). This review aims to provide a comprehensive overview of the existing LCIA methodologies related to minerals and metals, with a focus on recent developments, progress made, and potential future directions. It examines these LCIA methods in terms of resources considered, underlying assumptions, data sources, and identified limitations. According to the nature of the underlying considerations, the various methods are grouped into different families. In addition, the novelty of this article is to place raw material criticality considerations alongside LCA characterization methods; however, only one class of critical raw materials, rare earth elements (REEs), is considered. These REEs are mainly used in electrical and electronic components (e.g., electric vehicle motors) and in various renewable energy technologies (e.g., wind turbines) due to their unique properties that make them difficult to substitute. However, their supply is constrained by limited global reserves and their concentration in a few countries. This situation highlights the need for more reliable and accurate data on resource production and recycling. Additionally, this review presents case studies that apply LCIA methods to real-world scenarios, illustrating current capabilities as well as areas where further research and refinement are needed.

Full text

Academic Editor: Rajesh Kumar
Jyothi
Received: 11 December 2024
Revised: 12 February 2025
Accepted: 13 February 2025
Published: 18 February 2025
Citation: Jabara, M.; Wu, J.; De
Franceschi, S.; Manzardo, A. Assessing
Mineral and Metal Resources in Life
Cycle Assessment: An Overview of
Existing Impact Assessment Methods.
Sustainability 2025,17, 1692. https://
doi.org/10.3390/su17041692
Copyright: © 2025 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
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Review
Assessing Mineral and Metal Resources in Life Cycle
Assessment: An Overview of Existing Impact
Assessment Methods
Marco Jabara 1,2 , Junzhang Wu 2, Saverio De Franceschi 1,2 and Alessandro Manzardo 2,*
1Department of Industrial Engineering (DII), University of Padova, 35122 Padova, Italy;
marco.jabara@phd.unipd.it (M.J.); saverio.defranceschi@phd.unipd.it (S.D.F.)
2CESQA, Department of Civil, Environmental, and Architectural Engineering (ICEA), University of Padova,
35122 Padova, Italy; junzhang.wu@unipd.it
*Correspondence: alessandro.manzardo@unipd.it
Abstract: Mineral resources and metals are integral to modern society, with growing
demand driven by recent technological advancements. Life cycle assessment (LCA) pro-
vides a valuable framework for assessing resource use, and numerous methodologies have
been developed to address both the midpoint and endpoint levels of life cycle impact
assessment (LCIA). This review aims to provide a comprehensive overview of the existing
LCIA methodologies related to minerals and metals, with a focus on recent developments,
progress made, and potential future directions. It examines these LCIA methods in terms
of resources considered, underlying assumptions, data sources, and identified limitations.
According to the nature of the underlying considerations, the various methods are grouped
into different families. In addition, the novelty of this article is to place raw material
criticality considerations alongside LCA characterization methods; however, only one class
of critical raw materials, rare earth elements (REEs), is considered. These REEs are mainly
used in electrical and electronic components (e.g., electric vehicle motors) and in various
renewable energy technologies (e.g., wind turbines) due to their unique properties that
make them difficult to substitute. However, their supply is constrained by limited global
reserves and their concentration in a few countries. This situation highlights the need for
more reliable and accurate data on resource production and recycling. Additionally, this
review presents case studies that apply LCIA methods to real-world scenarios, illustrating
current capabilities as well as areas where further research and refinement are needed.
Keywords: life cycle assessment; life cycle impact assessment; resource depletion; minerals;
metals; rare earth elements
1. Introduction
Mineral resources have long been fundamental to human society, and their importance
continues to grow. Minerals and metals are found in varying quantities on the earth but all
are susceptible to depletion, and their extraction has intensified significantly in recent years.
This escalation is driven in part by the rising demand for materials essential to emerging
technologies related to IT equipment, clean energy (e.g., photovoltaic panels and wind
turbines), electric vehicles, semiconductors, and batteries. Special attention must be given
to raw materials classified as critical by the European Union (EU) and various national
governments. For instance, a 2023 study on critical raw materials for the EU [1] identified
34 critical resources, while the United States Geological Survey (USGS) published 50 critical
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Sustainability 2025,17, 1692 2 of 33
minerals in 2022 [
2
]. The criticality of resources is influenced by multiple factors, including
economic and strategic importance, potential supply chain disruption, geopolitical risk,
and a lack of viable substitutes. Consequently, understanding the Environmental, Social,
and Governance (ESG) impacts associated with the extraction, processing, and consump-
tion of these resources is crucial in advancing the United Nations’ 2030 Agenda and its
17 Sustainable Development Goals (SDGs). The mining industry, in particular, is directly
or indirectly linked to several SDGs, such as SDG 6 (Clean Water and Sanitation), SDG 7
(Affordable and Clean Energy), SDG 8 (Work and Economic Growth), SDG 9 (Industry,
Innovation, and Infrastructure), SDG 11 (Sustainable Cities and Communities), SDG 12
(Responsible Production and Consumption), SDG 13 (Climate Action), and SDG 15 (Life
on Land) [
3
]. As such, responsible mineral resource management is essential to achieving
global sustainability objectives.
This paper focuses on the potential impacts of using mineral and metallic resources
in products and systems from a life cycle assessment (LCA) perspective. LCA is based
on the International Standards ISO 14040 [
4
] and ISO 14044 [
5
] and follows an iterative
process comprising four steps: goal and scope definition; life cycle inventory (LCI); life
cycle impact assessment (LCIA); and interpretation. In particular, the study explores the
characterization phase of LCIA concerning the mineral and metallic resources category.
Currently, there is no scientific consensus on the best method for accounting for resource use
in LCA. However, the UNEP-GLAM initiative, particularly in its second phase (2017–2019),
involved different stakeholders in an effort to provide practical guidance on characterizing
mineral and metal use. Building on this initiative, Sonderegger et al. [
6
] reviewed the
existing literature and identified four main families of methods for characterizing resource-
use impacts: “depletion methods”; “future efforts methods”; “thermodynamic accounting
methods”; and “supply risk methods”. Resource depletion refers to the decline in reserves
and is closely linked to accessibility. Future efforts will be driven by changes in resource
quality resulting from current use, both from a geological and economic perspective.
Thermodynamic methods assess cumulative energy or exergy consumption associated with
resource extraction. Supply risk evaluates the criticality of raw materials based on social,
economic, and environmental factors. Extending on Sonderegger et al.’s work [
6
], Berger
et al. [
7
] identified the most appropriate method for quantifying resource use in product
systems depending on the specific aspect under consideration.
The first section of this document, building on previous work [
6
,
7
], provides an
overview of the characterization models and corresponding characterization factors (CFs)
developed over the past 30 years. It highlights key modeling choices, assumptions, and
reference data. Seven groups of models were identified based on the criteria used to
characterize resources: depletion, dissipation, exergy, economic, scarcity, supply risk, and
price-related aspects.
The second part presents key findings from recent studies on new mining projects
and Material Flow Analysis (MFA)—at different geographical levels—of the rare earth
elements (REEs). This is preparatory to showing an example of using these analyses to
build a characterization model that considers REEs as distinct elements.
REEs are included in the most recent lists of critical raw materials for the EU and the
U.S. [
1
,
2
] and are also classified as strategic mineral resources by China [
8
]. Demand for
these elements is expected to continue rising beyond 2030, driven by their essential role in
energy transition technologies, including permanent magnets in electric vehicles and wind
turbines [
9
]. In China, for example, as reported in [
10
], it is projected to reach 380,000 tons
by 2035, a significant increase from the 35,000 tons in 2018.
The primary objective of this analysis is to provide an overview of mineral and metal
resource characterization models in LCA, extending the work of Sonderegger et al. [
6
] to

Sustainability 2025,17, 1692 3 of 33
encompass the latest advancements. To support LCA practitioners, this paper also presents
examples of applications developed by researchers cited in this review. Additionally, it
explores key issues related to REE management, in the context of LCA resource charac-
terization. In particular, the importance of mapping existing and future mining projects
is emphasized, as well as regional and global flows to obtain data for more precise and
accurate LCA characterization models. Material Flow Analysis (MFA) is a widely used
technique for the mapping of material flows, based on mass balances, which enables the
identification of inputs and outputs across various product systems while considering
temporal and geographical boundaries.
Regarding mining projects, an illustrative example is provided by a research group [
11
]
that analyzed active mines and ongoing projects worldwide. In the context of MFA, an
analysis of Neodymium (Nd) in China in 2016 is presented [
12
]. Furthermore, a 2019
study [
13
] proposing a set of characterization factors (CFs) for individual REEs is discussed.
Finally, the importance of recycling in LCA characterization is considered, with reference
to a study by Zhao et al. [14] that evaluates the anthropogenic in-use stock of REEs across
several Chinese provinces to assess the recycling potential of nine REE-containing products.
2. Overview of Characterization Models and Factors for Minerals and
Metals: Past, Current, and Future Developments
In the following sections, we focus on the main characterization models for assessing
the use, depletion, scarcity, and criticality of mineral and metallic resources. Since the 1990s,
researchers have increasingly integrated the concept of mineral resources into life cycle
assessment (LCA). The characterization models and their factors developed by different au-
thors are described below, emphasizing their mathematical formulation, key assumptions,
data sources, and limitations. Table 1lists the characterization factors (CFs) examined and
the number of elements considered, while Figure 1outlines the boundaries of the analysis,
categorizing the methodologies into distinct families.
Table 1. Overview of the main characterization factors developed to account for the use of mineral
resources in LCA and the numbers of the elements to which these are associated.
Characterization Factor Year N◦of Elements Authors
ADP (Abiotic Depletion Potential) 1995 84 Guinée and Heijungs [15]
ADP—first update 2002 41 + 14 aggregates van Oers et al. [16]
AADP (Anthropogenic Abiotic Depletion
Potential) 2011 10 Schneider et al. [17]
AADP—update 2015 35 Schneider et al. [18]
ADP—second update 2020 76 van Oers et al. [19]
TADP (temporally explicit abiotic depletion
potential) 2022 6 Yokoi et al., 2022 [20]
EDP (environmental dissipation potential) 2020 76 van Oers et al. [21]
LPST (lost potential service time) 2021 18
Charpentier Poncelet et al. [
22
]
ADR (average dissipation rate) 2021 18
Charpentier Poncelet et al. [
22
]
LPST and ADR update 2022 61
Charpentier Poncelet et al. [
23
]
EVDP (economic value dissipation
potential) 2023 15 Santillàn-Saldivar et al. [24]
CExD (Cumulative Exergy Demand) 2007 107 Bösch et al. [25]
CEENE (Cumulative Exergy Extraction
from the Natural Environment) 2007 79 Dewulf et al. [26]
MDP (Mineral Depletion Potential) 2009 20 Goedkop et al. [27]
SCP (surplus cost potential) 2016 12 elements + 1
group of elements Vieira et al. [28]
UCCS (User Cost Country-Specific) 2024 29 Yokoi et al., 2024 [29]

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Table 1. Cont.
Characterization Factor Year N◦of Elements Authors
SOP (surplus ore potential) 2017 18 Vieira et al. [30]
CSP (crustal scarcity potential) 2020 76 Arvidsson et al. [31]
ESP (economic scarcity potential) 2014 17 Schneider et al. [32]
ESP—update 2019 19 elements + 1
group of elements Pell et al. [33]
GSP (geopolitical supply risk potential) 2022 4 Santillàn-Saldivar et al. [34]
GSP—update 2024 46 Koyamparambath et al. [35]
VLP (value loss potential) 2023 66 Ardente et al. [36]
Sustainability 2025, 17, x FOR PEER REVIEW 4 of 32
CSP (crustal scarcity potential) 2020 76 Arvidsson et al. [31]
ESP (economic scarcity potential) 2014 17 Schneider et al. [32]
ESP—update 2019
19 elements + 1 group
of elements Pell et al. [33]
GSP (geopolitical supply risk potential) 2022 4 Santillàn-Saldivar et al. [34]
GSP—update 2024 46 Koyamparambath et al. [35]
VLP (value loss potential) 2023 66 Ardente et al. [36]
Figure 1. Framework of characterization models considered in this review, grouped into families
and developed by several authors.
2.1. Abiotic Resource Depletion
One of the earliest and most significant families of characterization models concerns
abiotic resource depletion, developed since the mid-1990s. The following paragraphs de-
scribe the original model and its subsequent updates and refinements.
Figure 1. Framework of characterization models considered in this review, grouped into families and
developed by several authors.

Sustainability 2025,17, 1692 5 of 33
2.1. Abiotic Resource Depletion
One of the earliest and most significant families of characterization models concerns
abiotic resource depletion, developed since the mid-1990s. The following paragraphs
describe the original model and its subsequent updates and refinements.
2.1.1. Original Characterization Model
In a 1995 paper, Guinée and Heijungs [
15
] first introduced the concept of resource
depletion in LCA by developing equivalence factors per unit extracted. They distinguished
between biotic and abiotic resources—biotic factors are living components of an ecosystem
(plants, trees, bacteria, etc.), while abiotic factors include non-living factors that affect an
ecosystem (e.g., water, minerals, and metals). To quantify depletion, they proposed a simple
model that aggregates equivalence factors weighted by extraction amounts. For abiotic
resources, these equivalence factors—termed Abiotic Depletion Potentials (ADPs)—are
expressed by Equation (1) [14].
ADPi=Pi
(Ri)2·Rre f 2
Pre f "kgSb−eq
kg #(1)
where
Pi
and
Ri
, respectively, represent the production and the reserves of resource “i”
over a given period; and
Pre f
and
Rre f
represent the production and reserve of the reference
substance, antimony (Sb). In Equation (1), the reserve term is squared to give greater weight
to elements with lower reserves for the same reserve-to-production ratio.
Several definitions of reserves exist:
•
“Reserve base”: part of an identified resource that meets certain minimum physical and
chemical criteria related to current mining and production practices, including grade,
quality, thickness, and depth [2];
•
“Economic reserve”: part of the reserve base that can be extracted or produced economi-
cally at the time of determination [2];
•
“Ultimate reserves”: reserves estimated by multiplying the concentrations of chemical
elements in the Earth’s crust by its mass. It is also possible to add reserves in the
oceans and in the atmosphere to cover all primary means of extraction [15];
•“Ultimately extractable reserves”: technically extractable reserves [15].
Guinée and Heijungs [
15
] used “ultimate reserves” in their assessment, calculated by
considering the mass and the concentration of the Earth’s crust, the volume and concen-
trations of the oceans, and the mass and concentration of the atmosphere. The source of
the element production data is a 1993 report by the U.S. Department of the Interior [
37
],
although specific data for some elements were missing. Due to missing data for some
elements, rhenium (Re) was used as a proxy. For platinum group metals (PGMs), a single
aggregate value was distributed evenly among different elements. Finally, for compound
mineral resources (e.g., TiO
2
and B
2
O
3
), the production data were allocated to the indi-
vidual constituent elements according to their relative molecular weights. Despite its
simplicity, it has been widely applied to characterize the potential environmental impacts
of various products in terms of resource depletion; however, it relies on strong assumptions,
prompting several methodological updates.
2.1.2. First Update of Characterization Model
A study commissioned by the Dutch Ministry of Transport [
16
] introduced an
improved inventory for abiotic resource depletion assessment. Key updates include
the following:

Sustainability 2025,17, 1692 6 of 33
•
Calculation of characterization factors (CFs) using different estimates of extractable re-
sources;
•Calculation of CFs for aggregates (such as lime, gypsum, etc.);
•Distinction between element depletion and fossil fuel depletion;
•Consideration of multi-output processes in metal refining;
•New data sources for reserve estimates.
This update provided CFs for individual elements in the case of “ultimate reserves”, the
“reserve base”, and reserves using 1999 as the base year. The estimation of “ultimate reserves”
was performed similarly to Guinée [
38
], while USGS data (1999) were used for the “reserve
base” and reserves. In the 1999 USGS report [
39
], extraction and reserve values of various
aggregates were given and it was, therefore, possible to derive a CF for them. However, due
to the absence of extraction rate data for certain elements, Guinée (1995) assumed they were
extracted at the same rate as Re; these elements were not included in the update analysis.
The ADPs obtained when considering the three different resource types differ. Significant
differences were found between the reserves and the“ultimate reserves”, while similarities
between the reserves and the “reserve base” also existed. Given the uncertainty about which
type of reserve is most appropriate for inclusion in LCA studies, the authors suggested
considering multiple characterization models and conducting a sensitivity analysis to
explore the impact on the results. The problem of moving from elemental CFs to mineral
ones and vice versa has also been considered. Since ore may contain several elements,
the problem arises of allocating the various energy and material inputs to the various
outputs of the mining and refining processes. The allocation can be based on physical
quantities (e.g., mass) or it can be economic. Another key finding was the recognition of
anthropogenic resource stocks (e.g., elements in landfills) that could be recycled, thereby
reducing primary resource extraction. This could be performed in the development of new
models for assessing depletion in LCA.
This update of the original model has made some improvements and highlighted
concepts for future implementation. Although the number of CFs is reduced compared to
previous study—which might be seen as a limitation—the current approach employs more
robust data without relying on strong assumptions.
2.1.3. Second Update of Incorporating the Anthropogenic Stock Concept
A subsequent update [
17
] introduced characterization factors incorporating anthro-
pogenic stock, i.e., the total quantity of an element present in society regardless of its
chemical form [40].
First, resources were considered in the formulation of ADPs instead of “ultimate
reserves”, defined by the 2010 USGS report as follows: “concentrations of naturally occurring
solid, liquid, or gaseous material in or on the Earth’s crust in such form and quantity that economic
extraction of a commodity is currently or potentially feasible” [40].
ADPi,resource =ERi
(Resi)2·Resre f 2
ERre f "kgSb−eq
kg #(2)
where
ERi
and
ERre f
represent the extraction rate of the element “i” and of antimony,
respectively; and
Resi
and
Resre f
are the resource of the element “i” and antimony, respectively.

Sustainability 2025,17, 1692 7 of 33
Once the ADPs for the resource had been formulated, the anthropogenic stock was
added to them, resulting in the following relationship of new CFs, the Anthropogenic
Stock-Extended Abiotic Depletion Potential (AADP) (see Equation (3) [17]).
AADPi,resource =ERi
(Resi+Anth_stocki)2·Resre f +Anth_stockre f 2
ERre f "kgSb−eq
kg #(3)
Material Flow Analysis (MFA) is commonly used to estimate anthropogenic stock.
However, at the time of this update, MFA data were available for only a limited number
of elements. To address this, anthropogenic stocks were estimated based on cumulative
extraction rates from 1990 to 2008 based on data from the USGS (2010). The assumption
that pre-1900 extractions were negligible was also made.
Within the Technosphere, different types of anthropogenic stock can be distinguished:
in-use, hibernating, deposited, or dissipated. The dissipated part of the stock, defined as
the fraction lost through chemical reactions or leaching [
41
], should be subtracted from
the total anthropogenic stock. In this study, the dissipate flow is neglected based on the
results for copper (Cu) from a previous study [
41
]; however, because elements other than
copper have different properties that may render their dissipated quantities non-negligible,
an element-specific evaluation of dissipation is advisable for future implementations of
the model. Furthermore, elements with a higher anthropogenic stock contribute less to
depletion than those with low stock values. To compare the values of ADPs and AADPs,
a fictitious inventory containing 1 kg of each metal considered has been proposed. The
introduction of the anthropogenic stock has improved the overall assessment of depletion
but a major limitation is the development of CFs for only a few elements (ten elements).
A subsequent study [
18
] expanded the model by revising “ultimately extractable reserves”
and considering mining depth. Estimates assumed that 0.01% of the total continental crust
to a depth of 3 km was available for metals, while 0.001% was assumed for co-elements [
42
].
Having calculated the volume of the continental crust to a depth of 3 km and knowing the
crustal abundance, it is possible to calculate the amount of each element [
43
]. Although new
factors have been developed, further improvements are possible, including the following:
•Consideration of resource dissipation;
•Distinction between base metals and precious metals;
•
Integration of the concept of quality loss of recovered materials in the definition of
anthropogenic resource stocks.
2.1.4. Integrating Time-Series Production Data and Refining “Ultimate Reserves” Estimates
A 2020 study [
19
] further refined the ADP-based characterization model by incorpo-
rating historical production data and updating global reserve estimates.
To update the production data of different elements, reference was made to the
1900–2015 time
series of the USGS (2018) [
44
] and the 1970–2016 time series of the British
Geological Survey (BGS, 2018) [
45
]. For most elements, the USGS was used; when USGS
data were unavailable, those from the BGS report were considered. In the case of cerium
(Ce), hafnium (Hf), ruthenium (Ru), and scandium (Sc), the reference is a European doc-
ument developed in collaboration with Deloitte [
46
]. Compared to previous studies, a
step forward was taken by considering disaggregated data for rare earth element (REE)
production based on assumptions proposed in a 2019 paper [
13
]. Here, 11 large REE
deposits in different geographical settings were considered, covering more than 80% of
the total of these resources. The final reserves were derived using the composition of the
upper continental crust in terms of major elements, as reported in Rudnick and Gao [
47
];

Sustainability 2025,17, 1692 8 of 33
their document—considered the standard in geological science—also indicates the different
thicknesses of the layers that make up the Earth’s crust.
The innovative contribution proposed by van Oers et al. [
19
] concerns the consid-
eration of a fluctuation in ADP values due to variations in the production of individual
elements. Two alternatives have been proposed to take account of fluctuations in produc-
tion: one based on a moving average, and another on the cumulative production up to
a given year. In the case of the moving average, instead of the annual production of an
element, a 5-year average production is used, expressed as Equation (4) [19]:
Pi,t(moving average) = 1
m·
t
∑
s=t−m+1
Pi,skg
yea r (4)
where
m
is the number of years considered in the average. The choice of a period of 5 years
is a compromise between reducing fluctuations and identifying a trend. Using a longer
period would solve the problem of fluctuations but no trend line would be identified.
The proposed relationship for cumulative production up to a given year is shown as
Equation (5) [19]:
Pi,t(cumulative) =
t
∑
u=1
Pi,u[kg](5)
They also proposed a calculation of the ADPs of individual elements by considering
the production of a single year (2015), the average production over 5 years (up to 2015),
and a cumulative production of 46 years (from 1970 to 2015). Comparing the ADP values
for 2015 to those of the 5-year moving average, the latter are always lower or at most equal
(only in the case of two elements: antimony and barium), with a maximum percentage
reduction of 35.3% for mercury (Hg). A comparison of the ADPs for 2015 to those of the
cumulative production shows a maximum reduction of 75.3% in the case of gallium (Ga)
and a maximum percentage increase of 84.6% for arsenic (As).
In this update, the set of elements with an associated CF has been extended to include
strategic commodities such as REEs. However, to derive the different ADPs, certain
assumptions have been made regarding both the extraction and the “ultimate reserves” of
the different elements. To further improve the accuracy and completeness of the data used
to construct factors, the assumptions should be reduced as much as possible or at least
tested for validity after a certain period.
2.1.5. Temporally Explicit Abiotic Depletion Potential
A more recent approach by Yokoi et al. [
20
] introduced the temporally explicit abiotic
depletion potential (TADP), incorporating emerging economies’ growth and technological
development trends. TADP factors were derived using Material Flow Analysis (MFA) and
expressed by Equation (6) [20].
TADPi,T=ERi,T
(Resi)2·Resre f 2
ERre f,T"kgFe−eq
kg #(6)
where terms
ERi,T
and
ERre f,T
represent the average extraction rate of resource “i” and
of the reference substance (iron) up to the target year “T”;
Resi
and
Resre f
are—as in the
case of ADP—the natural reserves of resource “i” and the reference resource, respectively.
Average extraction rates are calculated from the sum of annual extraction of resource ”i”
over a period between the base year (2010) and the target year (2050 or 2100). To calculate
the extractions in future years necessary to obtain the average extraction rate up to the
target year “T”, Yokoi et al. [
20
] used the MFA technique. The calculation of the TADPs

Sustainability 2025,17, 1692 9 of 33
was performed for six metals in the two target periods (2050 and 2100) and in the case of
five future socio-economic scenarios. These CFs can, therefore, be seen as an extension of
the ADPs with a view to the future. However, given the difficulty of mapping the flows
of different resources, the model has been applied to a small number of elements and
should be extended to other important resources. The authors suggest that a possible future
implementation of the model should also consider future ESG risks to achieve an integrated
assessment of the potential impacts of resource use.
2.2. Resource Dissipation
Resource dissipation in LCA was first introduced by Rolf Frischknecht at the 55th
Discussion Forum on Life Cycle Assessment (Zurich, Switzerland) in 2014 [
48
]. A fun-
damental question raised by scientists concerns the appropriate abiotic resource flows
to be considered in LCA. One approach is to separately assess the resources extracted
from the natural environment and those used in a dissipative manner. To illustrate this
distinction in the impact assessment phase of LCA, an example was considered where
aluminum is used only once and a case where it is recycled [48]. ADPs have been applied
to dissipative resource use, determined as the difference between extracted resources and
those effectively recycled. An unresolved issue remains in the differentiation between
borrowing and dissipative use, though an economic feasibility approach has been proposed
for recovering resources from waste.
Borrowing refers to the possibility of future use of a resource once it has been extracted
through recovery and recycling operations, whereas dissipative use is definitive.
Ciacci et al. [
49
] assessed resource losses from the design of various products for
56 metals and metalloids, distinguishing between materials “dissipated in use”, “currently
non-recyclable”, “potentially recyclable”, and “unspecified”. A model based on economic
allocation (global market share) to four material streams was proposed to measure the
simultaneous loss of elements in the four identified groups. This approach further refined
the concept of dissipation and extended its application to multiple elements using Material
Flow Analysis (MFA). The MFA approach to dissipative resource use was also applied
in a 2016 study on products such as photovoltaic cells, catalysts, and thermal barrier
coating made in Germany [
50
]. The methodology accounted for material inputs and
outputs across all life cycle stages, integrating industry data, the literature, and expert
assessment. Key element flows were analyzed for both a past (2012) and a future (2030)
scenario, mapping dissipative flows of indium (In) and gallium (Ga) for photovoltaic cells;
germanium (Ge) for catalysts; and yttrium (Y) for thermal barrier coatings. This analysis
identified dissipation hotspots and provided recommendations for mitigation. However,
integrating dissipation into LCA databases presents challenges due to the need to quantify
resource losses throughout a product’s life cycle and to identify contaminants in waste
streams. In response, the European Joint Research Centre (JRC) developed a technical
report in 2017 [
51
] to evaluate the feasibility of incorporating dissipative resource use into
LCA. Addressing dissipation in LCA requires action at two levels: life cycle inventory
(LCI) and life cycle impact assessment (LCIA). Five inventory alternatives were proposed,
outlined below:
1.
Identification of total, partial, and null dissipation classes with possible partial dissi-
pation subdivided into high, medium, and low sub-classes;
2. Consideration of resources in a binary “dissipated–non-dissipated” (0–1) model;
3.
A “net approach” that avoids the consideration of input and output flows for all
unit processes;

Sustainability 2025,17, 1692 10 of 33
4.
Determination of an average dissipated share and an average non-dissipated
share—in
practice, a simplification of the first alternative, where intermediate classes
are not taken into account;
5.
Identification of only dissipated flows with a net approach combined with a
0–1 approach.
Each approach has advantages and drawbacks but the fifth option was selected for
the feasibility analysis due to its practicality and its synthesis of the first four approaches.
Following this selection, case studies were conducted. One study examined global copper
stocks and flows in 2010, defining dissipation rates to construct an inventory based on life
cycle phases (from extraction to end-of-life). This inventory was compared to a “classical”
depletion approach, which only considered mined copper and recycled copper at the end
of its life. Both inventories applied ADP (“ultimate reserves”) factors, revealing significant
differences. The dissipative model provided a more precise identification by life cycle phase
but did not indicate negative impacts compared to the depletion model. The report focused
exclusively on inventory without considering the impact assessment phase. Although this
paper does not delve into the impact assessment phase, an initial overview of the flow anal-
ysis was presented to clarify the dissipation concept. The following sections will examine
the primary characterization methods developed to account for the dissipative effect.
2.2.1. Environmental Dissipation
The first approach to developing a comprehensive characterization method for as-
sessing dissipative flow impacts was conducted by a large research group in 2020 [
21
].
Their primary goal was to evaluate the future impact of current resource use over two time
horizons: short-term (25 years), and very long-term (500 years, from 2020 to 2520). The
proposed system analyzed naturally occurring stocks in nature and in the Technosphere,
along with the material flows between them. Natural stocks include resources in the Earth’s
crust, oceans, and atmosphere, while Technosphere stocks are categorized as “in use”
(within products) and “in hibernation” (e.g., in landfills, processing waste, or abandoned
products). The innovative aspect of the developed characterization model concerns the
variation in accessibility caused by current resource use. The proposed characterization
factor (CF) follows a similar construction to the ADP, using copper as a reference element,
and considers two variables: variation in the total accessible stock, and severity of resource
inaccessibility. The first variable is expressed by Equation (7) [21].
Ct,T,i=Et,T,i
Pt,i
(7)
where the term in the numerator represents the total global emission of a resource “i” at
the time “t” for the time-horizon “T” considered; the term in the denominator is the total
primary extraction added to the secondary supply of resource “i” in the year “t”.
The severity of the consequences resulting from the inaccessibility of a resource is
presented by Equation (8) [21].
St,T,i=Pt,i
R2
t+T,i1
kg·yea r (8)
where the denominator is the total accessible resource stock in the environment and Tech-
nosphere in the year “t + T” (i.e., the final year of the period considered).

Sustainability 2025,17, 1692 11 of 33
By combining Equations (7) and (8), the CF for environmental dissipation—called
environmental dissipation potential (EDP)—is obtained [21]:
EDPt,T,i=Ct,T,i·St,T,i
Ct,T,re f ·St,T,r e f
= Et,T,i
R2
t+T,i!· R2
t+T,re f
Et,T,re f !(9)
In the very long-term case, it is possible to set
Et,T,i
equal to the current primary
extraction (of 2020) and
Rt+T,i
equal to the continental crust content of the resource “i”.
This approach makes EDP analogous to ADP, with the distinction that EDP considers total
emissions to the environment, whereas ADP considers total extraction. If the same reference
substance were used, the two factors would be comparable, with a proportionality factor
of 36.41. However, no CFs were proposed for the short-term (25 years) horizon due to
the extensive data required (e.g., accessible stocks in nature and in the Technosphere and
global flows of emitted, hibernated, and occupied elements). Future updates to the model
could address this gap. Finally, in [
21
], a case study of two European copper companies is
presented considering 1 kg of copper cathode as the functional unit and system boundaries
from cradle to gate. Both ADP and EDP factors were applied, revealing different relative
contributions of elements. The depletion method attributed nearly all of the impact to
copper, while the dissipation approach highlighted significant contributions from other
elements, such as platinum, cadmium, and silver. Therefore, the two methods provide
different perspectives: depletion focuses on resource extraction for product manufactur-
ing, while dissipation considers emissions throughout the entire production process (e.g.,
platinum contributes significantly to dissipation because it is used in explosives for mining).
With the aim of filling the gap related to the short-time horizon (25 years), some
authors who initially proposed EDP as CF [
21
] extended their work using essentially the
same assumptions and mathematical formulation [
52
]. The main challenge in obtaining
data suitable for constructing CFs over the short term lies in estimating the flows of
elements to and from the Technosphere and nature to determine currently accessible
stocks. One potential approach to estimating these flows is Substance Flow Analysis
(SFA), although for short-term modeling it requires continuous updating, which presents
significant difficulties. By making several assumptions and simplifications, van Oers
et al. [
52
] developed a model to obtain CFs from a short-term perspective, for fifty five
elements, incorporating key aspects such as the recycling rate and the functional evaluation
of the elements. Despite its effectiveness, this model could be further refined by minimizing
the number of proxies used.
2.2.2. Lost Potential Service Time and Average Dissipation Rate
A significant advancement in integrating the dissipative flow of elements into LCIA
methods was made in 2021 by Charpentier Poncelet et al. [
22
]. In their work, they proposed
two methods based on service time (ST), defined as follows: “the service provided by a resource
as a part of the stocks in use in the economy, after its extraction from nature and until its dissipation
after one or more applications”. The total expected ST is the anthropogenic lifetime as reported
by Helbig et al. [53].
The first method proposed by Charpentier Poncelet et al. [
22
], called lost potential
service time (LPST), quantifies the missed opportunity to use resources once extracted
relative to a target: the Optimum Service Time (OST). The choice of time horizon is crucial to
accommodate the interests of the various stakeholders and to enable comparison with other
characterization methods. For this reason, three different time horizons were analyzed:
25, 100, and 500 years. In the case of the short-time horizon, a comparison with depletion
potentials measured in terms of “economic reserves” may be of interest, while in the case of

Sustainability 2025,17, 1692 12 of 33
the long-time horizon, a comparison with depletion potentials in terms of “ultimate reserves”
may be useful.
The second method [
22
], represented by the average dissipation rate (ADR) indicator,
refers to the overall annual dissipation rate of various metals independent of any specific
time horizon. To calculate CFs in the two cases, data from 1997 to 2015 were screened,
considering twenty nine end-use sectors for various metals, the yields of the main processes
in each life cycle phase, and dissipative uses. Charpentier Poncelet et al. [
22
] assumed
that yields remained constant over time and equal for all sectors. The mathematical
formulations of CFs in the two methods are detailed below, and starting with LPST, the
factors are expressed with respect to a reference substance (iron) and refer to a precise time
horizon (t.h.) [22].
LPSTi,t.h.=OSTi,t.h.−STi,t.h.
OST Fe,t.h.−STFe,t.h."kgFe−eq
kg #(10)
The ST of a metal is calculated by summing its mass ratio in service over time within
a given time horizon and relative to one kg of extraction. The OST represents the service
time under theoretically optimal conditions, i.e., with perfect yields.
In the case of the ADR method, CFs are calculated as the inverse of the expected total
service time of the metal “i” (see Helbig et al. [
53
]), assuming an infinite time horizon
(which was 1000 years) and using iron as the reference substance [53].
ADRi=STtot,Fe
STtot,i"kgFe−eq
kg #(11)
These two sets of CFs, calculated for eighteen metals, provide different interpretations
of total dissipation following extraction. One considers the lost opportunity to use one kg of
metal from the stocks in use in the economy compared to a theoretical target of perfect yield
(zero dissipation), and the other focuses on general extraction rates without considering a
specific time horizon. The relative ranking of metals is essentially the same in both cases.
The methods just described, apart from referring to a limited number of elements, are based
on assumptions, some of which are very strong. For example, the yields of the different
substances are not differentiated according to the production chains and applications. In
addition, from the analysis of numerical factor values, it is not possible to distinguish the
contributions of different dissipative processes along the life cycle of a product system. To
overcome the problem of the limited number of resources considered, the same group of
authors extended the number of metals characterized by dissipative flows, for both LPST
and ADR factors, in a 2022 paper [
23
]. A further step in the work led to the assessment of
the potential impact of resource dissipation in the socio-economic sphere since the value
and utility of a resource can be represented by its average price over a given time interval.
Two new CFs were introduced at the damage level. These CFs multiply each resource’s
relative LPST and ADR by its average price over different time periods. The strength of the
latter two factors is the ability to obtain disaggregated data, with hypotheses, for ten rare
earth elements.
2.2.3. Dissipation Related to Economic Value
In the context of a framework for the development of LCIA methodologies—which is
part of the broader SUPRIM project [
54
]—it is possible to include a new method, developed
in 2023, focusing on the economic functions of abiotic resources [
24
]. This dissipative
approach, centered on a short-term perspective (less than ten years), considers two func-
tions: the recoverability function and value function. The first is expressed as a function
of two variables and leads to the estimation of potentially dissipated resources, while the

Sustainability 2025,17, 1692 13 of 33
second determines the potential loss of value due to dissipation by assigning an economic
value to the dissipated flows. The corresponding CFs are developed by combining the two
functions, which depend on the analyzed resource “a” and on the resource “x” in a given
dissipative compartment [24].
EVDPa,x=Ra,x·VaUSDeq
kg (12)
The design of the recoverability function involved multiple steps and the analysis of
several data sources. First, an overall average minimum grade was defined as the minimum
concentration of the resource based on statistical analysis of data from the Standard and
Poor’s database [
55
]. The minimum resource grade varies depending on the dissipative
compartment, which includes soil (Earth’s crust and upper continental crust), urban soil,
air, water, and landfill. The resulting recoverability function is dimensionless, ranging from
0 (complete recovery) to 1 (complete dissipation).
When it comes to the value of a good, there is no consensus in economics on which
quantitative indicator best represents it. For the structure of the value function, the price of
a resource (understood as the exchange value) and the economic importance—as defined
by the European Commission’s 2020 criticality study [
56
]—were considered. Economic
importance (EI) depends on the share of a resource used (RS) in an economic sector, the
Added Value (AV) provided by the sector, and the Substitution Index (SI), as shown
below [24].
EI =∑
S
RSS·AVS·S I (13)
EI
values vary between 0 (irrelevance of a resource for the EU economy) and 10
(irreplaceability for the EU). Once economic importance has been defined, it is possible
to construct a value function that associates potential dissipation in mass terms with the
potential dissipation in monetary units [24].
Va,y=APa,y·EIa,y
GUSDeq
kg (14)
where “a” is the analyzed resource; “y” is the year considered;
APa,y
is the annual average
price of a resource “a” (taken from the USGS report of 2021 [
57
]); and Gis a scaling factor.
A criticality threshold, based on expert judgment, is set at an EI of 2.8. The Gfactor is
precisely 2.8 to ensure that the potential dissipative loss of a resource with an EI of 2.8 is
equal to its price. Sàntillan-Saldivar et al. [
24
] combined these functions to develop a set of
specific dissipation factors for environmental compartments, applying them to a case study
on hydrometallurgical recycling of a lithium-ion battery. The work relates to a limited
number of resources (fifteen resources) and is based on various hypotheses, particularly
regarding resource recoverability. In fact, the study assumes that the main quantity for
evaluating the potential recoverability of a resource is its concentration in a compartment
and that recovery from a secondary source mirrors primary extraction (mines). Further
limitations include the geographical focus (EU), the need for data updates to account for
annual price fluctuations, and the potential revision of economic importance indicators in
future criticality studies.
2.3. Exergy-Related Characterization Models
In thermodynamics, the term exergy first appeared in the mid-20th century, rooted
in the concept of available energy. Over subsequent years, its potential applications were
explored, particularly in assessing the environmental impact of resource exploitation
through thermodynamic parameters. In 2007, some researchers [
25
] proposed using exergy

Sustainability 2025,17, 1692 14 of 33
as an indicator of resource-energy quality and as a means to quantify the energy taken
from the environment during product manufacturing. Exergy is inherent in resources
and, like energy, can exist in chemical, thermal, kinetic, potential, nuclear, or radiative
forms. For mineral resources and metals, which often consist of multiple chemical elements,
only chemical exergy is considered. When the composition is known, chemical exergy can
be calculated from the Gibbs free energy of formation and the exergy of the constituent
elements. The CFs proposed for minerals and metals correspond to chemical exergy,
expressed in energy per kg of substance “i”. In the case of ores—which often contain several
extractable metals—an allocation factor is applied, leading to the following formulation, as
shown in Equation (15) [25].
CExDores,i=Exch,i·a(r,m),i
ciMJeq
kg (15)
where
Exc h,i
is the chemical exergy;
ci
the mass fraction of the metal “i” in the ore; and
a(r,m),i
denotes the allocation factor for the element “i” (letters in brackets refer to the
type of allocation performed: rstands for economic allocation based on revenue, while
mstands for allocation based on mass). The main limitation of this methodology stems
from assumptions made to address the lack of data on resource composition and to account
for substantial variability in metal concentrations. An average composition was assumed
for all ores, represented by the median exergy value of 0.63 MJ per kg of material. Thus,
differences between the CFs of metals contained in ores primarily reflect variations in
metal concentrations.
Meanwhile, another characterization model associated with the concept of exergy has
been developed, known as CEENE [
26
]. This model integrates exergy analysis results with
product life cycle inventories from the Ecoinvent databases. It is intended as an extension
of the previous CExD method, incorporating both qualitative and quantitative variations.
The main distinction between the two models lies in the subtracted energy flow considered.
CExD accounts for the energy taken from the natural environment and transferred to a
technological system, whereas CEENE assesses the energy flow deprived from nature. In
the specific case of metals, the various literature references are considered for the energy
calculations. Quantitatively, the first method considers the entire metal ore entering the
Technosphere, while the second accounts only for ores containing metal fractions. CEENE
considers one hundred and eighty four extraction flows from the natural environment (of
which seventy nine relate to minerals and metals), categorized into fossil fuels, nuclear
energy, renewable energies (wind, hydro, and solar), metals, minerals and their aggregates,
atmospheric resources, water resources, and land use. The developed CF is defined as
the exergy content per unit of specific flow (MJ
ex
/unit of flux). For some elements (e.g.,
aluminum), the total exergy involved in the extraction process is not fully accounted for as
the materials are not completely pure and often consist of several chemical species. The
different mineral species per element were obtained from industrial chemistry, extractive
metallurgy handbooks, Ecoinvent databases, and reports.
2.4. Economic-Related Characterization Models
Extraction from a deposit typically yields multiple elements due to the simultaneous
presence of different minerals. Mining operations, related to ore and metals extraction, also
encompass economic considerations. To assess the potential impact of the depletion of a
resource, previous studies [
27
] proposed a method focusing on the additional discounted
cost that society incurs as a result of a mining operation. CFs were obtained for 20 ele-

Sustainability 2025,17, 1692 15 of 33
ments, per unit mass extracted, by combining various economic quantities, as shown in
Equation (16) [27].
CFend point,i=MCIi·Pi·NPV∆TUSD
kg (16)
where
MCIi
represents the marginal cost increase in USD/kg
2
, which is the ratio of the
cost increase to the mass extracted that triggered the price rise;
Pi
denotes the amount of
resources produced over a given period; and
NPV∆T
is the net present value, accounting
for a specific time interval
∆
T. Since the discount rate is a key factor in calculating the
net present value, CFs have been proposed for four different discount rates: 2%, 3%,
4%, and 5%. In addition to the definition of the CFs at the damage or endpoint level
(Equation (16)), the authors also introduced midpoint-level CFs—termed mineral depletion
potential (MDP)—related to the change in grade of a mineral, intended as a decrease in
the concentration of a mineral caused by extraction [
27
]. As with most midpoint CFs, a
reference substance was used (iron in this case). Due to the mathematical complexity of the
model underlying these midpoint factors, we do not present the equations here and instead
refer readers to the original document for a detailed discussion. This complexity, along
with the extensive data required on global mineral deposits, explains why these factors
have been developed for a limited number of elements.
Another economic perspective on resource depletion considered co-production and
the specific differences in costs between mines. In a 2016 study, Vieira et al. [
28
] reasoned
about the average cost increase associated with future mining, noting that the first mines
to be exploited are typically those with the lowest costs. To quantify the future economic
scarcity of a resource associated with its extraction, the following factors were defined in
Equation (17) [28].
SCPi=RMM Ei
CMEi,tot Ci(MEi)dMEi
MMEi−C MEi,tot USD2013
kg (17)
where
Ci
is the determined operating cost derived from the cost–tonnage curve of metal
“i” as a function of its extraction
MEi
;
MMEi
denotes the global maximum tonnage of
metal “i” ever extracted; and
CMEi,tot
corresponds to the current cumulative tonnage of
metal “i” extracted. The cost–tonnage relationship can be approximated by a log–logistic
distribution and applies to the period 2000–2012 or 2000–2013 depending on the metal. For
this simplification, the authors followed their previous work [
58
] where the log–logistic
distribution was applied to the grade–tonnage of copper.
The data required to derive the characterization factors for twelve individual elements
and one group of elements (platinum group metals) were sourced from a wide variety of
references, including the following: historical statistics of minerals in the U.S., reports from
the Nuclear Energy Agency, reports from the U.S. Geological Survey, and a UNEP docu-
ment on long-term global metal stocks. Analysis of the CFs for the different metals showed
a maximum variation of six orders of magnitude, with iron exhibiting the lowest value
and rhodium the highest. The application of these factors, however, is subject to several
restrictions—mainly related to the non-consideration of parameters such as variations in
mine types and minerals—as well as a lack of consideration for recent technological ad-
vancements. Additional weaknesses include the limited number of characterized resources
and the use of a single aggregate value for platinum group metals (PGM).
An additional economic model has recently been developed by a group of authors [
29
]
based on the “user cost model” [
59
]. User costs represent the costs that users (in this case,
future resource extractors) would need to bear for utilizing capital assets. Based on this
cost concept, the authors [
29
] developed a country-specific and year-specific CF for a set of
twenty nine mineral resources. For simplicity, this factor will be referred to in this review

Sustainability 2025,17, 1692 16 of 33
as the ”User Cost Country-Specific” (UCCS), although this terminology does not appear in
the original article. Mathematically, the UCCS is expressed by Equation (18) [29].
UCCSi,j,t=UCi,j,t
Pi,j,t
=1
1+r
Ri,j,t
Pi,j,t·Vi,t·Pi,j,t
Pi,j,t
=1
1+r
Ri,j,t
Pi,j,t
·Vi,tUSD
kg (18)
where
UCi,j,t
is the specific user cost for resource “i” in country “j” in year “t”;
Pi,j,t
is the
annual mining production of the same resource in the same country;
Ri,j,t
is the reserve
of a resource “i” in a country “j” at year “t”;
Vi,t
is the market price of refined metals;
and
r
is the discount rate, which is set at a default value of 3%. A total of 193 countries
were considered with 2020 selected as the reference year. Mineral production and reserve
data were obtained from a 2022 USGS report [
60
]. By calculating CFs based on user cost,
the authors in [
29
] found a correlation with market price. Specifically, resources with
the highest CFs were found to be precious metals, which also exhibit the highest market
prices. The characterization model requires relatively limited data for its development;
however, the assumption that resource availability is solely determined by reserves is an
oversimplification. A potential refinement of the model could include additional socio-
economic and environmental factors to provide a more comprehensive assessment of
resource availability. It is also important to note that the study looks at current resource
management without considering future trends. Consequently, it does not account for
price volatility resulting from shifts in resource demand, technological advancements, or
reserves decreasing.
2.5. Scarcity-Related Characterization Models
In the literature, the terms scarcity and depletion are often used indistinctly [
61
] despite
referring to distinct concepts: scarcity is more closely associated with the economic sphere,
whereas depletion pertains to the geological domain. A 2010 study, conducted by the Hague
Centre for Strategic Studies (a study center in The Netherlands) [
62
], sought to clarify the
concept of mineral scarcity by linking it not to the depletion of existing stocks but rather
to the quantity extracted that becomes profitable in current market conditions. Scarcity,
therefore, implies a dynamic nature of mineral reserves and its potential manifestation in the
future depends on various interconnected factors, including supply and demand, mining
technology, and mineral prices. Following this clarification, the main characterization
methods proposed in the LCA field for assessing resource scarcity are presented.
The first method relates to the concept of mineral grade, i.e., the concentration of
desired material within it, and has been treated by Vieira et al. [
30
]. The developed CF
considers the decrease in future grade after several extractions of a resource; therefore, to
obtain the same amount of material in the future, additional ore extraction will be required
compared to the past. The expression of the factor—called surplus ore potential (SOP)—is
completely analogous to that of the surplus cost potential (SCP) factor illustrated above,
except that the cost–tonnage relationship is replaced by the grade–tonnage relationship,
calculated by Equation (19) [30].
SOPi=RM REi
CREi,tot OMi(REi)dREi
MREi−CREi,tot kgore
kg (19)
where
OMi(REi)
is the concentration of ore extracted for a certain amount of extracted
resource
REi
;
MREi
represents the maximum quantity to be extracted of a resource “i”; and
CREi,tot
is the currently known cumulative quantity (in tons) of the resource “i” extracted
globally. As in the case of other CFs, a reference substance can be selected. Copper was

Sustainability 2025,17, 1692 17 of 33
chosen due to its historical application in the log–logistic distribution for the grade–tonnage
relationship [30].
SOP∗
i=CFi(SOP)
CFCu (SOP)"kgCu−eq
kg #(20)
Using this methodology, eighteen resources were characterized. Given the existence
of several reserve definitions, two sets of factors were developed. The first set was based
on global reserves, as specified by the USGS (2014), while the second referred to “ultimate
recoverable resources”, defined by the UNEP as 0.01% of the total amount of resources
within the Earth’s crust, considering a depth of 3 km. In both cases, the final analysis
revealed differences between elements of five orders of magnitude, with manganese and
iron exhibiting the lowest values and gold the highest. For the calculation of the mineral
extracted for each resource, economic allocation was applied based on revenues, intended
as the average price of resources over a five-year period. This time frame was chosen to
minimize the influence of price variability. Among the various points that can be improved,
there is certainly the limited coverage of resources and the consideration of the grade alone
as a determining quantity for the exploitation of a mine. Additional constraints are inherent
in the selection of the resource types considered.
A further study on scarcity, conducted by Arvidsson et al. [
31
], focused on the con-
centrations of various resources within the Earth’s crust to develop a proxy for long-term
global scarcity. The significance of crustal content lies in its stability over time and its
association with various measures of reserves and deposits. For these reasons, crustal
concentration was selected as the only variable in the mathematical formulation of the
crustal scarcity potential (CSP) factors, as shown in Equation (21) [31].
CSPi=CSi
Ci"kgSi−eq
kg #(21)
Silicon was selected as the reference substance due to its abundance among the ma-
terials considered. A straightforward relationship such as Equation (21) offers several
advantages, including the ability to encompass a wide range of materials with minimal
effort in terms of data collection and information retrieval. It was possible to obtain disag-
gregated data for two important groups of metals: rare earth elements (REEs) and platinum
group metals (PGMs). This is of fundamental importance for LCA practitioners conduct-
ing assessments of products containing these elements. However, the simplicity of the
modeling approach also presents limitations as it does not account for all aspects related
to resource utilization. Furthermore, the long-term perspective may overlook potential
short-term fluctuations in resource extraction driven by technological advancements and
economic shifts.
2.6. Supply Risk-Based Characterization Models
The issue of supply insecurity related to certain strategic raw materials has long been
a subject of interest for both companies and governments worldwide. Since the early
2000s, numerous researchers have attempted to quantify this risk by proposing various
indicators. In a 2013 review, Achzet and Helbig [
63
] examined twenty different indicators,
both qualitative and quantitative, developed for assessing supply risk. The ranking of
these indicators was based on their frequency of use, with the “country concentration”,
measured by the Herfindahl–Hirschman Index (HHI), being the most prominent. This
concentration index ranges from 0 to 1 and serves as a metric of market competition. A
value of 0 indicates perfect competition, while a value of 1 suggests a monopoly. Other

Sustainability 2025,17, 1692 18 of 33
commonly used indicators include recycling potential, substitutability, dependence on
imports, and commodity prices.
In the context of LCA, the first risk-based approach to resource supply was developed
by Schneider et al. [
32
]. Unlike the conventional geological assessment of resource deple-
tion, integrating economic aspects provides a more comprehensive analysis of industrial
production, enabling the identification of potential disruptions in the supply chain. In
contrast to the characterization factors (CFs) previously examined, the economic scarcity
potential (ESP) is a dimensionless aggregate indicator, comprising a combination of ten
distinct indices. These indices span geological, economic, social, and governance domains,
with each index having a defined critical threshold. If the value of an indicator exceeds its
respective threshold, it signals a potential supply risk. The supply risk is thus expressed by
Equation (22) [32] for each resource at the global average level.
ESPi=
10
∏
j=1
max

 indicator valuei,j
critical valuei,j!2
, 1


(22)
where subscript “i” refers to the various resources considered; and subscript “j” corresponds
to the ten indices associated with the different domains. The inclusion of multiple areas
in the evaluation is one of the strengths of this methodology. However, it is important to
highlight the limitations related to the definition of critical values and the need for periodic
updates of the data due to the highly dynamic nature of global structures. In this analysis,
equal importance is assigned to each of the indices during aggregation. While this approach
is standard, it may be of interest to practitioners to assign different weights to the indices;
however, such a modification would introduce additional uncertainty. Considering these
limitations, and placing particular emphasis on rare earth elements (REEs) due to their
economic significance and the critical risks associated with potential supply disruptions,
scholars [
33
] have analyzed and updated the various ESP factors for certain metals. The
proposed methodology closely follows that of Schneider et al. [
32
] with the main difference
being the inclusion of a different set of elements in the analysis. Moreover, more up-
to-date data sources were utilized wherever possible in order to better reflect current
global realities. The results, assuming equal weighting for the indices, revealed that
REEs and PGMs dominated the ranking, outpacing elements such as gold, copper, iron,
and lithium. The effect of altering the weighting of the indices was also investigated.
Specifically, greater weight was assigned to economic importance, accounting for half of
the final ESP score. In this revised scenario, the ranking of the elements shifted compared
to the base case of equal weighting. This underscores the importance of selecting an
appropriate weighting scheme in the decision-making process. However, it should be
noted that the calculation of economic significance is quite simplified, incorporating a
limited number of variables. Furthermore, the production chain of the final elements is
significantly streamlined, omitting certain processing stages that would be valuable to
explore in greater depth.
Supply risk can also be analyzed from a purely geopolitical perspective. In this
context, various characterization methods have been proposed. A pioneering approach to
integrating the geopolitical dimension into life cycle assessment (LCA) was introduced by
Gemechu et al. [
64
]. The developed methodology falls within the broader framework of life
cycle sustainability assessment (LCSA), which—while beyond the scope of this review—is
essential for understanding subsequent models. A method for calculating geopolitical risk
was proposed, distinguishing risk at the country level and based on import data rather
than global material production. To quantify this risk, a factor was introduced expressed

Sustainability 2025,17, 1692 19 of 33
as the product of the Herfindahl–Hirschman Index (HHI) and a risk mitigation factor, as
described in Equation (23) [64].
SRc,i=H HIj+MFi,j= n
∑
j=1
s2
j!· n
∑
j=1
GIj·ISc,j!(23)
HHI for the country “j” is evaluated by squaring each country’s market share of
the global production of a specific commodity and then adding the resulting values. The
mitigation factor, on the other hand, is expressed by summing the products of each country’s
indicators of political instability GIj(derived from the World Bank’s WGI) by ISc,j, which
is the import share of country “c” in the country’s market “j”. Equation (23) was applied to
twelve individual elements and two groups of elements (REEs and PGMs), considering
the main global economies and the so-called emerging countries. The work of Gemechu
et al. [
64
] was the starting point for the alignment of the geopolitical risk method with other
typical midpoint indicators used in LCA, with which a mass flow can be associated.
In 2022, scholars [
34
] operated this association with the aim of harmonizing geopoliti-
cal risk assessment and LCA practices. Being the first attempt in this direction, a model
was developed starting from a case study relating to four elements contained within a
particular system produced: a lithium-ion battery. Equation (22) has been appropriately
modified by introducing both considerations relating to recycling as a risk mitigation factor
and a new term, the average annual price in dollars of a certain raw material. As a result
of these changes, the new CF—called geopolitical supply risk potential (GSP)—has been
expressed as a function of resource “i” and a country “c” in a given year as presented in
Equation (24) [34].
GSPc,i=HH Ii·∑
j
GIj·ISc,j,i
DPc,i·TIc,i
·piUSD
kg (24)
where
HHIi
is the Herfindahl–Hirschman for raw material “i”;
GIj
the geopolitical instabil-
ity of country “j”;
ISc,j,i
the import share of the resource “i” from country “j” to country “c”;
DPc,i
the domestic production (with an estimate of recycled material included) of resource
“i” in the country “c”;
TIc,i
represents the total import of resource “i” into country “c”;
and
pi
is the average annual market price of a given raw material “i”. The calculation of
these factors was carried out on a very limited number of resources and within a geograph-
ical context limited to the European Union. Moreover, as in the case of ESP, a periodic
update of the data is essential due to the dynamic nature of geopolitical and economic
conditions worldwide. However, the structure of the geopolitical risk method makes it
particularly versatile, allowing it to provide valuable insights tailored to specific locations
and time periods.
A further step in updating the geopolitical risk method was achieved by researchers in
a paper presented in July 2024 [
35
]. The primary objective of the study was to demonstrate
the method’s potential by expanding the number of resources characterized from both
spatial and temporal perspectives. To align the GSPs with other CFs implemented, copper
was chosen as the reference to which the other elements were compared. In this way,
GSPs are expressed in kg Cu
eq
/kg of resource, as in the case of the SOP. After clarifying
the methodological choices, the study presented its findings in terms of the percentage
contributions of individual elements to the overall geopolitical risk within a photovoltaic
laminate. The analysis revealed that certain elements, despite being present in relatively
small quantities within the product, could significantly dominate the potential geopolitical
risk. With this latest methodological refinement, the assessment of supply chain disruption
risks within LCA has reached a more advanced stage of development; however, ongoing
research remains essential for continuous improvement. Two key directions for future

Sustainability 2025,17, 1692 20 of 33
studies could include evaluating the uncertainty of the method and investigating the
variations in the impact resulting from different data sources.
2.7. Price-Based Characterization Model
In this section, a methodology for assessing future accessibility to mineral and metallic
resources will be presented, developed by three authors [
36
], and not precisely attributable
to any of the previous families. This method considers the value linked to the functions
of minerals and metals within man-made products, assuming that the market price is
a good approximation of this functional value. This assumption significantly simplifies
the calculation of characterization factors (CFs), making the method applicable to a wide
range of resources. Mathematically, the model is relatively straightforward as it relates
the average price of a given resource, over a specified time period, to that of a reference
substance (such as copper), as expressed in Equation (25) [36].
VLPi=Priceavergae,i
Priceavera ge,re f .sub."kgre f .sub
kg #(25)
Resource prices fluctuate in the short term due to a multitude of factors that are not
necessarily linked to their intrinsic usefulness. To minimize these fluctuations, the study
considered multiple time horizons spanning several decades and conducted a sensitivity
analysis to assess variations in the results. When copper is chosen as the reference element
and a 50-year time frame is adopted, the highest factors are observed for precious metals
(such as gold and silver) and elements of the platinum group. A comparative analysis
between value loss potentials (VLPs) and Abiotic Depletion Potentials (ADPs), based on
“ultimate reserves”, revealed a weak correlation. This discrepancy arises because the two
indicators capture different aspects of resource availability. For instance, germanium has
a very low ADP due to its relative abundance in the Earth’s crust; however, it exhibits a
high VLP as its market price is significantly influenced by the strong demand for strategic
applications such as photovoltaic cells, optical fibers, and special glass. Although this
methodology is based on limited assumptions and requires minimal data input, it presents
notable limitations concerning data completeness, uncertainty, and potential uninvestigated
effects. Finally, given the model’s recent development and its limited application in case
studies, additional critical issues may yet be identified.
To identify the main strengths and weaknesses of the different characterization models
examined in this review, the following table (Table 2) highlights the improvements already
made and provides possible ideas for future updates.
Table 2. Strengths and weaknesses of the models under consideration in this review.
Characterization Model Strengths Weaknesses
Abiotic resource depletion
model—original [15]
First LCA resource depletion approach
Geological focus only, without
consideration of the socio-economic and
geopolitical implications of
resource management
Simple mathematical formulation
Many assumptions, even strong, on data
to derive CFs of considered elements
Abiotic resource depletion
model—first update [16]
Extended set of modeled resources Compared to the original model,
reduction in characterized elements
Consideration of different types
of reserves
New reserve data sources
Maintenance of various assumptions for
the calculation of CFs
Consideration of the various refining
outputs and their allocation

Sustainability 2025,17, 1692 21 of 33
Table 2. Cont.
Characterization Model Strengths Weaknesses
Abiotic resource depletion
model—second update [17]
Extent of reserves considered: not only
natural but also anthropogenic
Limited number of elements included in
the model
Lack of consideration of
resource dissipation
Adoption of MFA for anthropogenic
stock assessment
Lack of consideration of quality loss
related to recovered materials
Abiotic resource depletion
model—time series [19]
Updating data sources for production
and reserve elements Various assumptions, particularly on
extraction rates and “ultimate reserves”
Consideration of fluctuations in the
production data of individual resources
Temporally explicit abiotic
depletion model [20]
Adding economic and technological
considerations to the purely geological
ones of previous models
Very small number of characterized
elements
Resource use based on consequences for
future generations
Environmental dissipation
model [21]
Introduction to the concept of
dissipation in LCA Lack of a set of CFs for
short-time horizon
Simple mathematical formulation
Large number of resources characterized
Various assumptions and simplifications
in the calculation of CFs for a
long-term perspective
Consideration of different time horizons
for the assessment of the impact of
current resource use on
future generations
Lost potential service time
model [22]
Computation of the method for time
horizons of 25, 100, and 500 years
Small number of resources considered
Various underlying assumptions
Relatively complex
mathematical formulation
Average dissipation rate
model [22]
Consideration of life cycle phase yields
of products in twenty nine
end-use sectors
Small number of resources considered
Simple mathematical formulation Various underlying assumptions
No consideration of any time horizon
Economic value dissipation
model [24]
Integration of raw material criticality
studies into LCA
Limited number of resources considered
Resource price considerations included
Important hypothesis on
resource recoverability
Geographical limitation: only the
EU considered
Need for updates due to changes in
resource prices and when new criticality
studies are published
Exergy-related models [25,26]
Model simplicity
Exergy-only considerations for modeling
Small amount of data required to
implement the model
Lack of information on the composition
of resources
Wide range of characterized resources Assumption of an average composition
for all ores
Surplus cost model [28]
Consideration of mining cost differences
Model complexity
Outlook resource scarcity from an
economic point of view
Small number of characterized resources
Several data sources needed to build CFs

Sustainability 2025,17, 1692 22 of 33
Table 2. Cont.
Characterization Model Strengths Weaknesses
User cost model [29]
Combination of socio-economic and
geological considerations
Model complexity and needing multiple
data sources
Focus on current use of resources and
lack of future vision
Broad geographical coverage (one
hundred and ninety three countries)
Non-inclusion of price volatility due to
changing demand for resources,
technological advances, and
reserves reduction
Surplus ore model [30]
Integration of the physical phenomenon
of decreasing ore quality Limited resource coverage
Focus on future implications of current
resource use
Model complexity and needing multiple
data sources
Crustal scarcity model [31]
Model simplicity based only on
crustal content
Focuses only on the geological aspect of
resource management
Constant crustal content over time
Long-term view of scarcity without
considering possible
short-term variations
Economic scarcity
model—original [32]
Consideration of geological, economic,
social, and governance spheres
Model complexity (combination of ten
indicators to obtain CFs) and periodic
updates required
Ability to identify barriers in the
resource supply chain
Small number of characterized resources
Economic scarcity
model—update [33]Extension of characterized elements Several assumptions for
resource extension
Geopolitical supply risk
model—original [34]
Geopolitical risk assessment and
LCA combined Only four resources modeled
Comprehensive view of
resource management
Model complexity and periodic
updates required
Geopolitical supply risk
model—update [35]Extension of characterized elements Lack of model uncertainty assessment
Value loss model [36]
Model simplicity based only on market
price of resources
Focuses only on the economic aspect of
resource management
Selecting appropriate time horizons to
minimize price volatility
High number of characterized resources
and few data sources
3. Focus on Rare Earth Elements
Rare earth elements (REEs) are classified as critical raw materials by the European
Union and various national governments due to potential supply constraints resulting from
limited availability and geopolitical instability. Additionally, their high economic impor-
tance stems from their strategic role in high-tech applications. To define and monitor these
critical raw materials, extensive efforts have been made at the global level. Organizations
such as the European Union and the U.S. Geological Survey have periodically published
lists identifying these materials. The most recent updates were released in a 2023 European
Commission report [
1
] and a 2022 U.S. Department of the Interior report [
65
]. As illustrated
in Figure 2, several elements appear on both lists, highlighting shared concerns regarding
resource security.

Sustainability 2025,17, 1692 23 of 33
Sustainability 2025, 17, x FOR PEER REVIEW 22 of 32
Geopolitical supply risk
model—update [35] Extension of characterized elements Lack of model uncertainty assessment
Value loss model [36]
Model simplicity based only on market
price of resources
Focuses only on the economic aspect of
resource management
Selecting appropriate time horizons to
minimize price volatility
High number of characterized resources and
few data sources
3. Focus on Rare Earth Elements
Rare earth elements (REEs) are classified as critical raw materials by the European
Union and various national governments due to potential supply constraints resulting
from limited availability and geopolitical instability. Additionally, their high economic
importance stems from their strategic role in high-tech applications. To define and
monitor these critical raw materials, extensive efforts have been made at the global level.
Organizations such as the European Union and the U.S. Geological Survey have
periodically published lists identifying these materials. The most recent updates were
released in a 2023 European Commission report [1] and a 2022 U.S. Department of the
Interior report [65]. As illustrated in Figure 2, several elements appear on both lists,
highlighting shared concerns regarding resource security.
Figure 2. List of critical raw materials by the EU, 2023 [1], and by the USGS, 2022 [65], showing
common elements.
Among the essential elements for numerous high-tech applications, rare earth
elements (REEs) play a crucial role. They are widely used in technologies such as
permanent magnets for electric motors, consumer electronics, baeries, LED screens, and
other high-performance materials. REEs consist of 17 chemical elements that are generally
Figure 2. List of critical raw materials by the EU, 2023 [
1
], and by the USGS, 2022 [
65
], showing
common elements.
Among the essential elements for numerous high-tech applications, rare earth elements
(REEs) play a crucial role. They are widely used in technologies such as permanent
magnets for electric motors, consumer electronics, batteries, LED screens, and other high-
performance materials. REEs consist of 17 chemical elements that are generally classified
into two groups based on atomic weight: light rare earth elements (LREE) and heavy
rare earth elements (HREE). The first group includes scandium (Sc), lanthanum (La),
cerium (Ce), praseodymium (Pr), neodymium (Nd), promethium (Pm), samarium (Sa), and
europium (Eu), while the second group includes yttrium (Y), gadolinium (Gd), terbium
(Tb), dysprosium (Dy), holmium (Ho), erbium (Er), thulium (Tm), ytterbium (Yb), and
lutetium (Lu). These materials possess a unique combination of chemical and physical
properties, making them difficult to replace in many advanced applications. However, their
strategic importance is accompanied by high extraction and processing costs, which have
historically led to a geographical concentration of refining activities in Asia, particularly
in China. In terms of global reserves, China holds the largest share, followed by Vietnam,
Brazil, Russia, India, and Australia. As shown in Figure 3, based on data from the 2024
USGS report [
2
], countries such as Brazil, Russia, and Vietnam possess substantial reserves
but have relatively low production levels due to the lack of suitable processing technologies.
In contrast, the United States ranks second in global REE production, despite having only
one operational mine.

Sustainability 2025,17, 1692 24 of 33
Sustainability 2025, 17, x FOR PEER REVIEW 23 of 32
classified into two groups based on atomic weight: light rare earth elements (LREE) and
heavy rare earth elements (HREE). The first group includes scandium (Sc), lanthanum
(La), cerium (Ce), praseodymium (Pr), neodymium (Nd), promethium (Pm), samarium
(Sa), and europium (Eu), while the second group includes yrium (Y), gadolinium (Gd),
terbium (Tb), dysprosium (Dy), holmium (Ho), erbium (Er), thulium (Tm), yerbium (Yb),
and lutetium (Lu). These materials possess a unique combination of chemical and physical
properties, making them difficult to replace in many advanced applications. However,
their strategic importance is accompanied by high extraction and processing costs, which
have historically led to a geographical concentration of refining activities in Asia,
particularly in China. In terms of global reserves, China holds the largest share, followed
by Vietnam, Brazil, Russia, India, and Australia. As shown in Figure 3, based on data from
the 2024 USGS report [2], countries such as Brazil, Russia, and Vietnam possess substantial
reserves but have relatively low production levels due to the lack of suitable processing
technologies. In contrast, the United States ranks second in global REE production, despite
having only one operational mine.
Figure 3. Annual production and reserves of REEs in the world, as reported in [2].
To justify the focus on rare earth elements, the European Commission’s 2023 report
on critical raw materials [1] is considered. This document outlines the methodology
adopted for criticality assessment, which is based on defining threshold values for two
key parameters: supply risk (SR) and economic importance (EI). A raw material is
classified as critical if it exceeds both thresholds—1 for SR and 2.8 for EI. As detailed in
[66], both parameters are derived from a combination of various indices, including the
Herfindahl–Hirschman Index, Import Reliance, Substitution Index, recycling rate, and the
Value Added of raw materials produced in the EU by the economic sector. Table 3 shows
the values of SR and EI, present in [1], including copper and nickel due to their strategic
importance. Although these metals do not exceed the SR threshold and, therefore, strictly
speaking, should not be classified as critical materials, Table 3 highlights that the highest
SR values (marked in red) are recorded for rare earth elements (REEs). However, in terms
of EI, only a subset of REEs—dysprosium, terbium, neodymium, praseodymium, and
samarium—stand out, while palladium and rhodium (both PGMs) and tungsten exhibit
Figure 3. Annual production and reserves of REEs in the world, as reported in [2].
To justify the focus on rare earth elements, the European Commission’s 2023 report on
critical raw materials [
1
] is considered. This document outlines the methodology adopted
for criticality assessment, which is based on defining threshold values for two key parame-
ters: supply risk (SR) and economic importance (EI). A raw material is classified as critical
if it exceeds both thresholds—1 for SR and 2.8 for EI. As detailed in [
66
], both parameters
are derived from a combination of various indices, including the
Herfindahl–Hirschman
Index, Import Reliance, Substitution Index, recycling rate, and the Value Added of raw
materials produced in the EU by the economic sector. Table 3shows the values of SR and
EI, present in [
1
], including copper and nickel due to their strategic importance. Although
these metals do not exceed the SR threshold and, therefore, strictly speaking, should not
be classified as critical materials, Table 3highlights that the highest SR values (marked in
red) are recorded for rare earth elements (REEs). However, in terms of EI, only a subset
of REEs—dysprosium, terbium, neodymium, praseodymium, and samarium—stand out,
while palladium and rhodium (both PGMs) and tungsten exhibit the highest EI values
overall. When considering both SR and EI together, rare earth elements emerge as the
most critical materials, with dysprosium and neodymium ranking as the top two in terms
of criticality.
Table 3. Supply risk (SR) and economic importance (EI) of critical raw materials [1].
Raw
Material
Supply
Risk (SR)
Economic
Importance (EI) SRI Raw
Material
Supply
Risk (SR)
Economic
Importance (EI) SREI
Aluminum 1.2 5.8 6.96 Cerium 4 4.9 19.6
Antimony 1.8 5.4 9.72 Lanthanum 3.5 2.9 10.15
Arsenic 1.9 2.9 5.51 Neodymium 4.5 7.2 32.4
Baryte 1.3 3.5 4.55
Praseodymium
3.2 7 22.4
Beryllium 1.8 5.4 9.72 Samarium 3.5 7.7 26.95
Bismuth 1.9 5.7 10.83 Magnesium 4.1 7.4 30.34

Sustainability 2025,17, 1692 25 of 33
Table 3. Cont.
Raw
Material
Supply
Risk (SR)
Economic
Importance (EI) SRI Raw
Material
Supply
Risk (SR)
Economic
Importance (EI) SREI
Boron 3.6 3.9 14.04 Manganese 1.2 6.9 8.28
Cobalt 2.8 6.8 19.04 Natural
graphite 1.8 3.4 6.12
Coking coal 1 3.1 3.1 Niobium 4.4 6.5 28.6
Feldspar 1.5 3.2 4.8 Iridium 3.9 6.4 24.96
Fluorspar 1.1 3.8 4.18 Palladium 1.5 8.1 12.15
Gallium 3.9 3.7 14.43 Platinum 2.13 6.9 14.7
Germanium 1.8 3.6 6.48 Rhodium 2.4 8.6 20.64
Hafnium 1.5 4.3 6.45 Ruthenium 3.8 5.5 20.9
Helium 1.2 2.9 3.48 Phosphate
rock 1 6.4 6.4
Dysprosium 5.6 7.8 43.68 Copper 0.1 4 0.4
Erbium 5.6 3.5 19.6 Phosphorus 3.3 4.7 15.51
Europium 5.6 3.3 18.48 Scandium 2.4 3.7 8.88
Gadolinium 3.3 3.3 10.89 Silicon metal 1.3 4.9 6.37
Holmium 5.6 3.2 17.92 Strontium 2.6 6.5 16.9
Lutetium 5.6 5 28 Tantalum 1.3 4.8 6.24
Terbium 4.9 6.4 31.36 Titanium
metal 1.6 6.3 10.08
Thulium 5.6 3.2 17.92 Tungsten 1.2 8.7 10.44
Ytterbium 5.6 3.2 17.92 Vanadium 2.3 3.9 8.97
Yttrium 3.5 2.9 10.15 Nickel 0.5 5.7 2.85
Lithium 1.9 3.9 7.41
Colors indicate the criticality of each element, considering only the SR, only the EI or its product: light green
indicates the least criticality, while deep red indicates the most critical.
3.1. Key Technologies and the Supply Chain of Rare Earth Elements
As previously discussed, rare earth elements are strategic elements in numerous
key technological applications, making an analysis of their future supply crucial for all
nations. To address this issue, the European Joint Research Centre (JRC) conducted a
study in 2023 [
67
] forecasting the supply chain of various materials within the European
Union. The study began by identifying fifteen EU-relevant technologies across five strategic
sectors. For each technology, the authors mapped out the key supply chain stages and
associated supply risks. They then examined the materials involved and developed two
future demand scenarios based on economic and technological growth projection: “High
Demand Scenario” (HDS) and “Low Demand Scenario” (LDS). Rare earth elements are
present in several of these technologies, including wind turbines and traction motors for
electric mobility. REEs are widely used in wind turbines, forming permanent magnets
inside the generators that produce electricity. Similarly, REEs are a key component of the
permanent magnets in the rotors of electric vehicle traction motors. To better understand the
fundamental importance of rare earth elements, their global demand projections up to 2030
and 2050 in the two scenarios HDS and LDS proposed by the JRC study [
67
] are presented in
the following figures. Figure 4shows that the demand trend for terbium (Tb), dysprosium
(Dy), neodymium (Nd), and praseodymium (Pr) in wind turbines is similar across both
scenarios, differing only in absolute values. In the LDS scenario, demand for these elements
in 2030 is even lower than in 2020 but increases by 2050. In the case of traction motors, as
shown in Figure 5, the situation is different for dysprosium (Dy) and neodymium (Nd).
For both elements, however, there is an increase in demand in 2030 and 2050 in the two
scenarios. Future demand is based on the estimated number of electric vehicles on the
market, the raw materials content per vehicle, and several other assumptions.

Sustainability 2025,17, 1692 26 of 33
Sustainability 2025, 17, x FOR PEER REVIEW 25 of 32
2030 and 2050 in the two scenarios HDS and LDS proposed by the JRC study [67] are
presented in the following figures. Figure 4 shows that the demand trend for terbium (Tb),
dysprosium (Dy), neodymium (Nd), and praseodymium (Pr) in wind turbines is similar
across both scenarios, differing only in absolute values. In the LDS scenario, demand for
these elements in 2030 is even lower than in 2020 but increases by 2050. In the case of
traction motors, as shown in Figure 5, the situation is different for dysprosium (Dy) and
neodymium (Nd). For both elements, however, there is an increase in demand in 2030 and
2050 in the two scenarios. Future demand is based on the estimated number of electric
vehicles on the market, the raw materials content per vehicle, and several other
assumptions.
Figure 4. Global demand for REEs in 2030 and 2050 in the two HDS and LDS scenarios for wind
turbines, as reported in [67].
Figure 4. Global demand for REEs in 2030 and 2050 in the two HDS and LDS scenarios for wind
turbines, as reported in [67].
Sustainability 2025, 17, x FOR PEER REVIEW 26 of 32
Figure 5. Global demand for REEs in 2030 and 2050 in the two HDS and LDS scenarios for traction
motors, as reported in [67].
The substitution of critical raw materials is a key mitigation strategy to address
potential supply disruptions. In the case of electric vehicle (EV) traction motors, three
types of permanent magnets are commonly used, as follows: metallic magnets, rare earth
magnets, and ferrite magnets [68]. One potential approach to reducing future demand for
rare earth elements in permanent magnets is replacing them with alternative materials
such as ferrites. A recent study [68] explored advances in preparation methods and
enhancements in the magnetic properties of ferrite permanent magnets. However, for
substitution to be technically and economically viable, several additional factors must be
carefully evaluated, including economic feasibility, environmental impact, and regulatory
compliance.
3.2. LCA and Rare Earth Elements
As discussed in the previous sections, most resource characterization models in life
cycle assessment (LCA) either exclude REEs or present aggregated data. To address this
limitation, it is crucial to map new mining projects and the flow of materials, even at a
regional level. In this regard, several recent studies have been conducted both globally
and regionally. One such study was carried out by a team of researchers from Peking
University [12], which analyzed the flow of neodymium (Nd) in China in 2016. The study
considered not only primary production but also imports, exports, and consumption, as
well as resource loss during processing. The aim was to quantify changes in the stock of
Nd. Additionally, the researchers sought to disaggregate the aggregated data from rare
earth mining statistics, focusing specifically on Nd. This was made possible by leveraging
data on the composition of REEs in various Chinese mineral deposits and the annual
production figures from these sites.
In 2023, another group of researchers [11] conducted an analysis of mining projects
reported by listed companies and governments, distinguishing between active mines and
projects in an advanced stage of development. The global distribution of resources is
highly diversified in terms of deposit quality and tonnage. Major sites, such as Bayan Obo
in China, Olympic Dam in Australia, and Mountain Pass in the U.S., contribute to more
than half of the total rare earth resources. While advanced rare earth projects are spread
across various regions, Africa stands out as a continent with significant potential due to
its rich carbonatite deposits, which could be a major source of REE. If these projects are
Figure 5. Global demand for REEs in 2030 and 2050 in the two HDS and LDS scenarios for traction
motors, as reported in [67].

Sustainability 2025,17, 1692 27 of 33
The substitution of critical raw materials is a key mitigation strategy to address
potential supply disruptions. In the case of electric vehicle (EV) traction motors, three types
of permanent magnets are commonly used, as follows: metallic magnets, rare earth magnets,
and ferrite magnets [
68
]. One potential approach to reducing future demand for rare earth
elements in permanent magnets is replacing them with alternative materials such as ferrites.
A recent study [
68
] explored advances in preparation methods and enhancements in
the magnetic properties of ferrite permanent magnets. However, for substitution to be
technically and economically viable, several additional factors must be carefully evaluated,
including economic feasibility, environmental impact, and regulatory compliance.
3.2. LCA and Rare Earth Elements
As discussed in the previous sections, most resource characterization models in life
cycle assessment (LCA) either exclude REEs or present aggregated data. To address this
limitation, it is crucial to map new mining projects and the flow of materials, even at a
regional level. In this regard, several recent studies have been conducted both globally
and regionally. One such study was carried out by a team of researchers from Peking
University [
12
], which analyzed the flow of neodymium (Nd) in China in 2016. The study
considered not only primary production but also imports, exports, and consumption, as
well as resource loss during processing. The aim was to quantify changes in the stock of Nd.
Additionally, the researchers sought to disaggregate the aggregated data from rare earth
mining statistics, focusing specifically on Nd. This was made possible by leveraging data
on the composition of REEs in various Chinese mineral deposits and the annual production
figures from these sites.
In 2023, another group of researchers [
11
] conducted an analysis of mining projects
reported by listed companies and governments, distinguishing between active mines and
projects in an advanced stage of development. The global distribution of resources is highly
diversified in terms of deposit quality and tonnage. Major sites, such as Bayan Obo in
China, Olympic Dam in Australia, and Mountain Pass in the U.S., contribute to more than
half of the total rare earth resources. While advanced rare earth projects are spread across
various regions, Africa stands out as a continent with significant potential due to its rich
carbonatite deposits, which could be a major source of REE. If these projects are successfully
developed, they could lead to a diversification of the rare earth supply chain, reducing its
current dependence on China as the dominant player in the market.
The two cases presented above could serve as a starting point for the update and
implementation of existing CFs related to individual REEs in LCA studies. In fact, an
initial attempt was made in 2019 [
13
], where various data sources—including total reserves,
extraction rates, and average grades—were explored to produce estimates for each element.
The analysis focused on eleven giant rare earth deposits, with the aim of deriving more
accurate ADP values based on weaker assumptions compared to those used, for instance,
by Guinée [15] in the original depletion model.
Another crucial factor to consider in rare earth depletion impact assessment studies
within the LCA context is recycling. Recycling can play a significant role in meeting
the growing demand for REEs and in diversifying the supply chain. By quantifying the
potential recoverable resources, import-dependent countries could enhance their autonomy,
reducing their exposure to market volatility. Zhao et al. [
14
] evaluated the anthropogenic
stock of REEs across thirty-one Chinese provinces, aiming to quantify the end-of-life
recycling potential of nine products containing REEs. These old products, such as wind
turbines, electric vehicles, smartphones, and e-bikes, are referred to as urban mines. The
recovery of materials from these urban mines could reduce the need for primary extraction,
yielding a range of environmental, economic, and social benefits. The concept of urban

Sustainability 2025,17, 1692 28 of 33
mining refers to the process of recovering critical materials from the stock in use, i.e., from
old technological products, to reduce primary extraction and mitigate the environmental
impacts associated with raw material production. However, a major barrier to realizing
this potential is the lack of economically feasible technologies for recycling critical raw
materials such as REEs.
4. Conclusions
This paper provides a review of methodologies for assessing resource depletion, dissi-
pation, scarcity, and criticality within the life cycle assessment (LCA) framework. Over the
years different approaches have been developed, evolving from traditional depletion mod-
els based on “ultimate reserves” to more holistic methods integrating economic, geopolitical,
and thermodynamic perspectives. These advancements reflect the increasing recognition
of the complexity surrounding resource sustainability challenges and the growing need
for more accurate, dynamic, and policy-relevant indicators. A key strength of recent de-
velopments is the introduction of anthropogenic stocks, dissipative flows, and supply risk
factors, providing a more complete understanding of resource availability. The increasing
attention to geopolitical risks has also improved the alignment between LCA practices
and real-world concerns related to supply security and market volatility. Despite these ad-
vancements, several limitations persist. Firstly, many methodologies still rely on simplified
assumptions due to the lack of comprehensive, high-quality global datasets. For instance,
estimates of “ultimate reserves”, dissipation rates, and anthropogenic stock dynamics often
involve strong approximations that could introduce uncertainty into the results. Secondly,
while some models have extended their coverage to a wider range of elements—including
rare earth elements (REEs) and platinum group metals (PGMs)—many remain limited
to a small subset of resources, reducing their applicability across different materials and
products. Another challenge is the difficulty of integrating diverse methodologies into a
unified framework. Approaches like exergy-based calculations, economic assessments, and
scarcity evaluations often use different units, reference substances, and temporal scopes,
making direct comparisons difficult. Furthermore, many depletion models assume static
reserve estimates, even though technological advancements, recycling innovations, and
market shifts continuously reshape resource accessibility.
An increasingly used technique to support the construction of CFs is Material Flow
Analysis (MFA). By systematically evaluating material flows and inventories through
mass balances and accounting for spatial and temporal limitations, MFA could provide
more granular and regionally specific data acquired both at regional and provincial levels.
This enhanced detail is crucial for accurately reflecting current conditions and guiding
the sustainable management of mineral and metal resources. Such efforts align with the
UN SDGs, underscoring the growing significance of both MFA and LCA in quantifying
environmental impacts and identifying strategic intervention points.
Furthermore, this review examines the issue of raw materials classified as critical
by the EU and the U.S., specifying those considered in both contexts. Criticality is dy-
namic, influenced by market demand, supply constraints, and the availability of suitable
substitutes. Among the critical raw materials, this review selects REEs as a focal point
given their increasingly widespread use in many technologies, including permanent mag-
nets in electric motors and wind turbine generators, consumer electronics, batteries, and
high-performance materials for aerospace applications. Rapid growth in the production of
electrical and electronic equipment in recent years has led to higher end-of-life concentra-
tions of REEs in urban environments, prompting interest in “urban mining.” However, the
economic feasibility of REE recovery from secondary sources remains limited, representing
a significant barrier to unlocking their full circular economy potential.

Sustainability 2025,17, 1692 29 of 33
In conclusion, strengthening data quality, exploring integrative indicators, and im-
proving recovery technologies for critical resources like REEs are essential steps toward
more sustainable and resilient resource management. Through continuous refinement and
innovation, LCA complemented with MFA can more accurately capture resource dynamics
and guide policy, research, and industry toward long-term global sustainability. The in-
novative contribution of this review lies in combining the concept of resource criticality
with resource characterization methods in LCA to identify potential improvements in data
collection, thus offering a possible direction for future research. However, only rare earth
elements have been considered, and it could be valuable to expand the scope to include
other critical resources. Another limitation of this work is that it has mainly considered
papers in which mineral resource management is linked to LCA, with little insight into
other areas of research that could provide a more comprehensive view and further advance
the development of new characterization models within LCA.
Author Contributions: Conceptualization, M.J. and A.M.; methodology, M.J.; validation, J.W.,
S.D.F. and A.M.; formal analysis, J.W.; data curation, M.J.; writing—original draft preparation,
M.J.;
writing—review
and editing, J.W., S.D.F. and A.M.; supervision, J.W.; and A.M.; project admin-
istration, A.M.; funding acquisition, A.M. All authors have read and agreed to the published version
of this manuscript.
Funding: This research was funded by the National Recovery and Resilience Plan (NRRP).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Conflicts of Interest: The authors declare no conflicts of interest.
List of Abbreviations
AADP Anthropogenic Stock-Extended Abiotic Depletion Potential
ADP Abiotic Depletion Potential
ADR Average dissipation rate
BGS British Geological Survey
CEENE Cumulative Exergy Extraction from the Natural Environment
CExD Cumulative Exergy Demand
CF Characterization factor
CSP Crustal scarcity potential
EDP Environmental dissipation potential
EI Economic importance
ESG Environmental, Social, and Governance
ESP Economic scarcity potential
EU European Union
EVDP Economic value dissipation potential
GSP Geopolitical supply risk potential
HHI Herfindahl–Hirschman Index
HREEs Heavy rare earth elements
JRC Joint Research Center
LCI Life cycle inventory
LCIA Life cycle impact assessment
LCSA Life cycle sustainability assessment

Sustainability 2025,17, 1692 30 of 33
LPST Loss potential service time
LREEs Light rare earth elements
MCI Marginal cost increase
MDP Mineral depletion potential
MFA Material Flow Analysis
NPV Net present value
OST Optimum Service Time
PGMs Platinum group metals
REEs Rare earth elements
SCP Surplus cost potential
SDGs Sustainable Development Goals
SFA Substance Flow Analysis
SI Substitution Index
SOP Surplus ore potential
SR Supply risk
ST Service time
TADP Temporally explicit abiotic depletion potential
UCCS User Cost Country-Specific
USGS United States Geological Survey
VA Value Added
VLP Value loss potential
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