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X. Chen et al.
30.1 Introduction
The ever increasing consumption of raw materials has led to concern about future
supply in terms of both total requirements for raw materials and their impacts on
economy, society and environment. Forced by the fast development of technology,
demands for raw materials have rapidly grown. However, the material supply cannot
meet this increasing demand in global market (Rosenau-Tornow et al. 2009). Therefore, a secure supply of raw materials becomes a critical issue that influences all the
stakeholders through supply chain. There have been numerous studies to develop
rigorous and comprehensive methodologies for assessing material criticality. For
example, a criticality matrix approach, developed by the U.S. National Research
Council (National Research Council 2008), defines two indicators: supply risk and
economic importance and plots them against each other in the form of risk-matrix to
classify the critical levels (e.g., more critical, less critical) of materials. The European
Commission applied this approach only to separate “critical” and “non-critical” materials through fixed thresholds defined by the Ad hoc Working Group on Defining Critical Raw Materials, appointed by the EU (Commission 2014). Whilst this approach
was based on the two-dimensional matrix approach, a third dimension of “environmental implications” was added to quantify the environmental impacts to human
health and the ecosystem (Graedel et al. 2012; Nassar et al. 2012). To simplify the
comparison of criticality of different materials, the authors also applied a weighting
approach to aggregate indicators to obtain a single overall criticality score. Several
studies have reviewed different approaches for criticality assessment, and found
out although most of criticality assessment studies evaluate supply risk, economic
vulnerability, even environmental implications in common, the choices of indicators
and weighting approaches varies considerably and are arbitrarily determined by the
different stakeholders (e.g., private companies, industrial sectors and governmental
organizations) (Erdmann and Graedel 2011; Achzet and Helbig 2013; Glöser et al.
2015; Lloyd et al. 2012). This diversity is necessary to meet the perspectives and
needs of the different stakeholders, but makes it difficult to reach a consensus in
respect of the results calculated.
The quantification of criticality indicators requires reliable and specific data.
Although various data sources and approaches can be considered for data acquisition,
for example, Life Cycle Assessment databases are generally used to calculate the
environmental impacts (Mancini et al. 2015; Hatayama and Tahara 2018), a rigorous,
transparent and accurate calculation for both indicators is still questionable due to
the complexity of assessment and the extensive effort for data acquisition. Where
experts have difficulty in providing an exact numerical value to express their opinions, linguistic assessment is a more realistic alternative (Duclos et al. 2010; Goddin
2019; Chuu 2011). In this way, the risk metrics regarding criticality can be measured
as linguistic terms, such as “low”, “medium” and “high” and so on. However, the use
of such linguistic terms introduces uncertainty due to verbal subjectivity, named as
linguistic imprecision (Druzdzel 1989). This is because linguistic terms are usually
X. Chen et al.
30.1 Introduction
The ever increasing consumption of raw materials has led to concern about future
supply in terms of both total requirements for raw materials and their impacts on
economy, society and environment. Forced by the fast development of technology,
demands for raw materials have rapidly grown. However, the material supply cannot
meet this increasing demand in global market (Rosenau-Tornow et al. 2009). Therefore, a secure supply of raw materials becomes a critical issue that influences all the
stakeholders through supply chain. There have been numerous studies to develop
rigorous and comprehensive methodologies for assessing material criticality. For
example, a criticality matrix approach, developed by the U.S. National Research
Council (National Research Council 2008), defines two indicators: supply risk and
economic importance and plots them against each other in the form of risk-matrix to
classify the critical levels (e.g., more critical, less critical) of materials. The European
Commission applied this approach only to separate “critical” and “non-critical” materials through fixed thresholds defined by the Ad hoc Working Group on Defining Critical Raw Materials, appointed by the EU (Commission 2014). Whilst this approach
was based on the two-dimensional matrix approach, a third dimension of “environmental implications” was added to quantify the environmental impacts to human
health and the ecosystem (Graedel et al. 2012; Nassar et al. 2012). To simplify the
comparison of criticality of different materials, the authors also applied a weighting
approach to aggregate indicators to obtain a single overall criticality score. Several
studies have reviewed different approaches for criticality assessment, and found
out although most of criticality assessment studies evaluate supply risk, economic
vulnerability, even environmental implications in common, the choices of indicators
and weighting approaches varies considerably and are arbitrarily determined by the
different stakeholders (e.g., private companies, industrial sectors and governmental
organizations) (Erdmann and Graedel 2011; Achzet and Helbig 2013; Glöser et al.
2015; Lloyd et al. 2012). This diversity is necessary to meet the perspectives and
needs of the different stakeholders, but makes it difficult to reach a consensus in
respect of the results calculated.
The quantification of criticality indicators requires reliable and specific data.
Although various data sources and approaches can be considered for data acquisition,
for example, Life Cycle Assessment databases are generally used to calculate the
environmental impacts (Mancini et al. 2015; Hatayama and Tahara 2018), a rigorous,
transparent and accurate calculation for both indicators is still questionable due to
the complexity of assessment and the extensive effort for data acquisition. Where
experts have difficulty in providing an exact numerical value to express their opinions, linguistic assessment is a more realistic alternative (Duclos et al. 2010; Goddin
2019; Chuu 2011). In this way, the risk metrics regarding criticality can be measured
as linguistic terms, such as “low”, “medium” and “high” and so on. However, the use
of such linguistic terms introduces uncertainty due to verbal subjectivity, named as
linguistic imprecision (Druzdzel 1989). This is because linguistic terms are usually