Synthesized indicator for evaluating security of strategic minerals in China: A case study of lithium

Zhou, Na; Wu, Qunli; Hu, Xiangping; Zhu, Yiqun; Su, Hui; Xue, Shuangjiao

doi:10.1016/j.resourpol.2020.101915

Cited by 14 publications

(14 citation statements)

References 52 publications

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“…Eheliyagoda et al [38], Zhou et al [14], as well as Pell et al [17] applied their approaches on the HDI as conducted for previous criteria: Eheliyagoda et al [38] applied the normalization formula shown in Equation ( 8) on HDI values, Zhou et al [14] normalized according to the observed minimum and maximum values, Pell et al [17] applied their combination of a normalization to a scale from 0 to 1 based on minimum and maximum values and the distance-to-target method. Schneider et al [15] also stuck to their distanceto-target method already performed for A01-A03 with a threshold for the weighted HDI of 0.12.…”

Section: By-product Dependence (E)mentioning

confidence: 99%

“…We conclude that a simple linear transformation of the HHI into a score from 0 to 100 represents their interpretation of the country concentration best. Zhou et al [14] also determine normalized scores by scaling the HHI values linearly, but they use the extreme values observed in their data set as thresholds.…”

Section: Concentration (A)mentioning

confidence: 99%

“…For A04, only two different normalization approaches for the HHI are identified. Zhou et al [14] applied the same curve to A04 as for A01. Li et al [25] decreased the number of levels from four for A01 to three for A04, using thresholds of HHI 1500 and 2500.…”

Section: Concentration (A)mentioning

confidence: 99%

“…Figure 5). Three methods use the observed minimum and maximum WGI scores for normalizing them to supply risk scores: Blagoeva et al [34] and Zhou et al [14] based on the arithmetic mean of all six WGI dimensions, and Nassar et al [13] based on the geometric mean of all six WGI dimensions. DERA [35] and Jasinski et al [36] presented a level-based normalization with three and four levels, respectively.…”

Section: Regulations (D)mentioning

confidence: 99%

“…Equation ( 8)) on the weighted PPI values. Both Zhou et al [14] and Pell et al [17] adopted their previously presented approaches. Zhou et al [14] applied the normalization based on the minimum and maximum observed values of the PPI, whereas Pell et al [17] first applied a rescaling from 0 to 1 according to the minimum and maximum observed values followed by the distance-to-target approach.…”

Section: Regulations (D)mentioning

confidence: 99%

See 4 more Smart Citations

An Overview of Indicator Choice and Normalization in Raw Material Supply Risk Assessments

et al. 2021

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Supply risk assessments are an integral part of raw material criticality assessments frequently used at the country or company level to identify raw materials of concern. However, the indicators used in supply risk assessments to estimate the likelihood of supply disruptions vary substantially. Here, we summarize and evaluate the use of supply risk indicators and their normalization to supply risk scores in 88 methods published until 2020. In total, we find 618 individual applications of supply risk criteria with 98 unique criteria belonging to one of ten indicator categories. The most often used categories of supply risk indicators are concentration, scarcity, and political instability. The most frequently used criteria are the country concentration of production, depletion time of reserves, and geopolitical risk. Indicator measurements and normalizations vary substantially between different methods for the same criterion. Our results can be used for future raw material criticality assessments to screen for suitable supply risk indicators and generally accepted indicator normalizations. We also find a further need for stronger empirical evidence of widely used indicators.

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Section: By-product Dependence (E)mentioning

confidence: 99%

Section: Concentration (A)mentioning

confidence: 99%

Section: Concentration (A)mentioning

confidence: 99%

Section: Regulations (D)mentioning

confidence: 99%

Section: Regulations (D)mentioning

confidence: 99%

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An Overview of Indicator Choice and Normalization in Raw Material Supply Risk Assessments

et al. 2021

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China and Lithium Geopolitics in a Changing Global Market

Altiparmak

2022

Chin. Polit. Sci. Rev.

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The energy market is shifting from fossil fuels to renewables. This transition is creating new geopolitical dynamics. In the past, traditional energy geopolitics focused on the concentrated distribution of fossil fuel resources and the conflicts and dependencies that this created. In contrast, the ‘new’ renewable energy geopolitics emphasises the dispersed distribution or decentralisation of production capacity and the independence of states this generates. However, the market for lithium, which is essential to renewable energy storage through being a key component of lithium-ion (Li-ion) batteries, does not entirely fit theoretical conceptions of the renewable energy market’s dynamics. By focusing on China as a critical case, this article shows that lithium geopolitics has potentially created new (inter)dependencies and opportunities for conflicts, while also paradoxically enhancing state interindependence in renewable technology energy production. Thus, this hybrid form of energy geopolitics necessitates revising conventional energy security explanations to match these new market conditions.

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Measuring the natural gas supply security performance of China's natural gas suppliers: A comprehensive framework using FAHP-Entropy-PROOTHEE method

Zhu

Zhao

et al. 2022

Journal of Cleaner Production

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Synthesized indicator for evaluating security of strategic minerals in China: A case study of lithium

Cited by 14 publications

References 52 publications

An Overview of Indicator Choice and Normalization in Raw Material Supply Risk Assessments

An Overview of Indicator Choice and Normalization in Raw Material Supply Risk Assessments

China and Lithium Geopolitics in a Changing Global Market

Measuring the natural gas supply security performance of China's natural gas suppliers: A comprehensive framework using FAHP-Entropy-PROOTHEE method

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