Risk assessment — Critical Materials Atlas

Three established ways to score supply risk — each letting the data or the criticality literature, not the weights I chose, decide.

The three methods

1 · Entropy-weighted TOPSIS (data-driven weights). Shannon entropy gives each criterion a weight by how much it varies across the 32 materials, then TOPSIS ranks each by closeness to the worst case (Hwang & Yoon 1981; Achzet & Helbig 2013). The weights it produces: origin gap 0.42China share 0.36trade HHI 0.10mine HHI 0.04refining conc. 0.03hard to substitute 0.02governance risk 0.01no recycling 0.00. The ranking diverges moderately from my fixed-weight index (Spearman ρ 0.63) — the data load most weight on China-share and origin-gap, pushing tungsten, magnesium and magnets up.

2 · GeoPolRisk (Gemechu et al. 2016) — stage concentration × the governance risk of the producers, for the mine and trade stages.

3 · Monte-Carlo supply-at-risk — each producer fails with a governance-derived probability and a random severity; over 20,000 draws I report the mean loss (ESaR) and the 95% tail (VaR/CVaR) — the shock scenarios made probabilistic.

Caveat: entropy rewards dispersion, not importance, so it nearly zeroes near-uniform criteria like recycling — read TOPSIS as a data-driven complement to the fixed index, not a verdict. All on public data, deterministic (fixed seed); disruption probabilities are governance-derived assumptions, not forecasts.

Material	TOPSIS	fixed	GPR mine	GPR trade	ESaR	CVaR95
Beryllium, unwrought	100	76	38	73	19%	71%
Strontium carbonate	86	55	25	25	17%	36%
Magnesium, unwrought	85	61	86	47	14%	44%
Rare-earth permanent magnets	80	52	50	36	26%	70%
Tungsten, unwrought	78	46	81	31	16%	48%
Lithium carbonate	74	62	17	38	19%	52%
Aluminium ores / bauxite	67	46	23	57	16%	38%
Silicon, < 99.99%	57	46	64	15	27%	77%
Hafnium, unwrought	55	34	25	9	18%	60%
Phosphorus	54	62	53	32	27%	73%
Natural graphite	53	52	76	19	27%	79%
Arsenic	52	28	31	11	17%	39%
Titanium, unwrought	51	39	30	11	12%	30%
Tantalum, unwrought	45	33	41	10	33%	68%
Gallium	45	57	90	9	27%	86%
Germanium	44	44	76	9	27%	80%
Ferro-vanadium	44	47	53	8	17%	43%
Fluorspar, >97% CaF2	39	48	52	23	28%	72%
Phosphate rock	36	34	38	15	22%	56%
Antimony, unwrought	31	38	49	18	13%	31%
Nickel, unwrought	30	35	54	5	14%	37%
Baryte	28	16	31	10	16%	38%
Manganese ore	25	46	29	30	23%	56%
Ferro-niobium	25	63	73	49	16%	48%
Feldspar	24	25	30	20	8%	19%
Cobalt oxides & hydroxides	23	43	100	15	30%	64%
Natural borates	21	42	38	39	15%	39%
Coking coal	19	24	21	14	20%	55%
Refined copper cathodes	16	23	27	9	13%	31%
Platinum, unwrought	12	40	62	12	24%	67%
Helium	9	22	26	12	19%	60%
Palladium, unwrought	5	24	40	11	15%	36%

Computed by build_riskmethods.py from data.json + flows_2024.json + wgi.json → riskmethods.json. Tail risk highest: Gallium, Germanium, Natural graphite, Silicon, < 99.99%. A screening panel — the Monte-Carlo probabilities are transparent governance-based assumptions, not predictions.

Three ways to score the risk — letting the data, not me, decide