Critical Materials Atlas
Method · robustness

Do the findings survive a harder test?

The Trends page says concentration is rising for a set of materials. That rests on a Mann-Kendall test which assumes each year is independent — but concentration series are serially correlated, which overstates significance. Here the same claims are re-run under an autocorrelation-robust test and split across the 2017 data-vintage join. What survives, we can stand behind; what doesn’t, we say so.

Does “concentration is rising” survive a tougher test? I re-run every trend two harder ways and keep only what passes both.
The two harder tests

1 · Autocorrelation-robust (Hamed-Rao, 1998). A plain trend test assumes each year is independent; concentration series aren’t, which flatters significance. This correction demands a stronger signal before a trend counts.

2 · Across the data-vintage splice. Each trend is re-estimated separately on 2002–2016 and on 2017–2024; one that keeps the same sign in both halves isn’t an artifact of the 2017 join.

Both screens control the false-discovery rate (Benjamini-Hochberg) across all 32 materials. Computed by build_robustness.py.

The rising-concentration materials, under the harder tests

Materials flagged with a significant rising export-HHI by the standard test, then re-checked. “Var ×” is the Hamed-Rao variance inflation from positive autocorrelation (≥1; this is a deliberately conservative one-sided correction — serial correlation may only make the test harder, never easier). A value of 1.00 means the series had no significant positive autocorrelation to penalise.

MaterialSen slope/yrMK FDRHamed-Rao FDRvar ×survives?splice-consistent?

Hamed & Rao (1998), A modified Mann-Kendall trend test for autocorrelated data, J. Hydrology. Series from trends.json (export-HHI, 2002–2024) → robustness.json. This is an exploratory screen on 23 annual points, not a confirmatory test.