Critical Minerals of the Americas

Lithium, Rare Earths & the New Resource Geography

Author

Ryan Lafferty

Published

February 23, 2026

The White Gold Rush

Beneath the salt flats of the Andes, the desert basins of Nevada, and the boreal shield of northern Quebec lies the raw material of the twenty-first century’s defining industrial transformation. Lithium — “white gold” — powers the batteries in every electric vehicle, grid-scale storage system, and portable device on the planet. Rare earth elements make the permanent magnets in EV motors and wind turbines possible. Cobalt, nickel, and copper wire the entire energy transition together.

The geography of these minerals is reshaping geopolitics. China dominates rare earth processing (roughly 60% of global output) and has invested heavily in South American lithium through companies like Ganfeng Lithium. The United States, despite sitting on vast deposits, produces almost no lithium domestically — the sole operating mine at Silver Peak, Nevada, has run since 1966 but supplies a fraction of national demand. The Inflation Reduction Act (2022) tied EV tax credits to domestic critical mineral sourcing, triggering a rush to permit new mines from the Nevada desert to the Minnesota wilderness.

The Lithium Triangle — the high-altitude salars of Chile, Argentina, and Bolivia — holds roughly 60% of the world’s known lithium reserves. But extraction comes at a cost: brine pumping depletes aquifers in some of the driest places on Earth, and Indigenous communities from the Atacama to the Boundary Waters have challenged projects that threaten their land and water. Every mine on this map sits at the intersection of industrial demand, environmental consequence, and political choice.

Mining Sites

Show code 
import sys, platform
sys.path.append("..")

import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt
import json
import folium

from map_builder import (
    create_base_map, build_photo_popup, finalize_map,
)

# Load site data
with open("CriticalMineralsSites.json", "r", encoding="utf-8") as f:
    sites_data = json.load(f)

sites_df = pd.DataFrame(sites_data)
print(f"{len(sites_df)} mining sites loaded across {sites_df['category'].nunique()} categories")

# Display table
display_sites = sites_df[["name", "category", "status", "operator", "mineral_type"]].copy()
display_sites.columns = ["Name", "Category", "Status", "Operator", "Mineral Type"]
display_sites.sort_values(["Category", "Name"]).style.set_properties(
    **{"white-space": "normal"}
)
17 mining sites loaded across 3 categories
  Name Category Status Operator Mineral Type
9 Cauchari-Olaroz Lithium Operating Lithium Americas / Ganfeng Lithium lithium-brine
13 Grota do Cirilo Lithium Operating Sigma Lithium lithium-spodumene
11 James Bay Lithium Development Arcadium Lithium lithium-spodumene
0 Rhyolite Ridge Lithium Approved Ioneer Ltd / Sibanye-Stillwater lithium-boron
12 Rose Lithium-Tantalum Lithium Development Critical Elements Lithium Corp lithium-tantalum-spodumene
5 Salar de Atacama Lithium Operating SQM / Albemarle lithium-brine
6 Salar de Maricunga Lithium Development Simco / Codelco lithium-brine
8 Salar de Olaroz Lithium Operating Arcadium Lithium (formerly Allkem) lithium-brine
10 Salar de Uyuni Lithium Development YLB (Yacimientos de Litio Bolivianos) lithium-brine
7 Salar del Hombre Muerto Lithium Operating Arcadium Lithium (formerly Livent) lithium-brine
2 Silver Peak Lithium Operating Albemarle Corporation lithium-brine
1 Thacker Pass Lithium Under Construction Lithium Americas Corp lithium-clay
3 Mountain Pass Rare Earth Operating MP Materials rare-earth-oxide
14 Serra Verde Rare Earth Development Serra Verde Mining rare-earth-ionic-clay
15 El Teniente Strategic Metals Operating Codelco copper
16 Moa Nickel Strategic Metals Operating Sherritt International / Cuba nickel-cobalt
4 Twin Metals Strategic Metals Proposed Twin Metals Minnesota (Antofagasta) copper-nickel-cobalt-pgm

The Lithium Triangle

The Lithium Triangle straddles the high Andes where Chile, Argentina, and Bolivia meet — a region of salt flats (salares) at 2,300 to 4,000 meters elevation where millions of years of volcanic activity concentrated lithium in underground brines. Extraction is deceptively simple: pump brine into evaporation ponds, wait 12-18 months for the sun to do its work, then harvest lithium carbonate from the residue. The process is cheap but water-intensive — and the Atacama Desert, where Chile’s Salar de Atacama sits, is the driest non-polar place on Earth.

Chile produces roughly a quarter of the world’s lithium through two operators at the Salar de Atacama: SQM (state-partnered) and Albemarle (US-based). Argentina has taken a more open approach, welcoming foreign investment into provinces like Jujuy, Salta, and Catamarca — Chinese firm Ganfeng Lithium is a major partner at the Cauchari-Olaroz project. Bolivia sits on the largest reserves of all at the Salar de Uyuni (21 million tonnes), but state control through YLB and technical challenges with high-magnesium brines have kept production minimal.

The triangle’s dominance is now being challenged by hard-rock deposits in Australia, Canada, and Brazil, where lithium is mined from spodumene pegmatites rather than evaporated from brines. This geological diversity matters: as demand surges, the Americas’ lithium supply chain is no longer just a story about salt flats.

Sites by Category

Show code 
# Category breakdown
print("Sites by Mineral Category:")
category_counts = (
    sites_df.groupby("category")
    .size()
    .reset_index(name="count")
    .sort_values("count", ascending=False)
)
category_counts
Sites by Mineral Category:
category count
0 Lithium 12
2 Strategic Metals 3
1 Rare Earth 2
Show code 
# Status breakdown
print("Sites by Development Status:")
status_counts = (
    sites_df.groupby("status")
    .size()
    .reset_index(name="count")
    .sort_values("count", ascending=False)
)
status_counts
Sites by Development Status:
status count
2 Operating 9
1 Development 5
0 Approved 1
3 Proposed 1
4 Under Construction 1
Show code 
# Country breakdown (derived from coordinates)
def get_country(row):
    lat, lon = row["lat"], row["lon"]
    if lat > 24 and lon < -50:
        return "USA" if lat > 40 or lon > -120 else "USA"
    if lat > 40 and lon > -100:
        return "USA"
    if lat > 30 and lon < -100:
        return "USA"
    if lat > 49:
        return "Canada"
    if lat > 18 and lon < -74:
        return "Cuba"
    if -40 < lat < -10 and lon > -50:
        return "Brazil"
    if -40 < lat < -18 and lon < -68:
        return "Chile"
    if -30 < lat < -18 and -68 < lon < -60:
        return "Argentina"
    if -22 < lat < -18 and lon < -65:
        return "Bolivia"
    return "Other"

sites_df["country"] = sites_df.apply(get_country, axis=1)

print("Sites by Country:")
country_counts = (
    sites_df.groupby("country")
    .size()
    .reset_index(name="count")
    .sort_values("count", ascending=False)
)
country_counts
Sites by Country:
country count
4 USA 7
0 Argentina 4
2 Chile 3
1 Brazil 2
3 Cuba 1

The Americas’ Critical Minerals Map

Show code 
# Americas-centered map
m = create_base_map(center=[5.0, -70.0], zoom=3, tiles="positron")

# Color scheme by mineral category (brand palette)
CATEGORY_COLORS = {
    "Lithium": "green",
    "Rare Earth": "orange",
    "Strategic Metals": "blue",
}

# Icon by development status (Font Awesome)
STATUS_ICONS = {
    "Operating": "industry",
    "Under Construction": "wrench",
    "Approved": "check-circle",
    "Development": "flask",
    "Proposed": "question-circle",
}

for site in sites_data:
    cat = site.get("category", "Other")
    status = site.get("status", "Unknown")
    icon_color = CATEGORY_COLORS.get(cat, "gray")
    icon_name = STATUS_ICONS.get(status, "info-circle")

    popup_html = build_photo_popup(
        site,
        photo_url=site.get("photo_url") or None,
        photo_caption=site.get("photo_caption") or None,
    )

    tooltip_text = (
        f"<b>{site['name']}</b><br>"
        f"<i>{cat}</i> — {status}<br>"
        f"{site.get('operator', '')}"
    )

    folium.Marker(
        location=[site["lat"], site["lon"]],
        popup=folium.Popup(popup_html, max_width=450),
        tooltip=tooltip_text,
        icon=folium.Icon(color=icon_color, icon=icon_name, prefix="fa"),
    ).add_to(m)

m = finalize_map(m)
m
Make this Notebook Trusted to load map: File -> Trust Notebook

Legend: Markers are colored by category — green = Lithium, orange = Rare Earth, blue = Strategic Metals. Icons indicate status: Operating, Under Construction, Approved, Development, Proposed.

Global Production: Americas vs. the World

Show code 
import plotly.express as px

# 2024 lithium production estimates (kilotonnes LCE)
# Source: USGS Mineral Commodity Summaries, Statista, Benchmark Minerals
production_data = pd.DataFrame([
    {"country": "Australia", "production_kt": 112.5, "region": "Asia-Pacific"},
    {"country": "China", "production_kt": 78.0, "region": "Asia"},
    {"country": "Chile", "production_kt": 54.4, "region": "Americas"},
    {"country": "Zimbabwe", "production_kt": 22.0, "region": "Africa"},
    {"country": "Argentina", "production_kt": 10.0, "region": "Americas"},
    {"country": "Brazil", "production_kt": 5.0, "region": "Americas"},
    {"country": "Canada", "production_kt": 4.3, "region": "Americas"},
    {"country": "Portugal", "production_kt": 1.5, "region": "Europe"},
    {"country": "USA", "production_kt": 0.0, "region": "Americas"},
])

# Sort ascending for horizontal bar
production_data = production_data.sort_values("production_kt", ascending=True)

color_map = {
    "Americas": "#22c55e",
    "Asia-Pacific": "#f43f5e",
    "Asia": "#f97316",
    "Africa": "#64748b",
    "Europe": "#0ea5e9",
}

fig = px.bar(
    production_data,
    x="production_kt",
    y="country",
    color="region",
    orientation="h",
    title="Global Lithium Production by Country (2024 est., kilotonnes LCE)",
    labels={"production_kt": "Production (kt)", "country": ""},
    color_discrete_map=color_map,
)
fig.update_layout(
    height=450,
    showlegend=True,
    legend_title_text="Region",
    xaxis_title="Lithium Production (kilotonnes LCE)",
)
fig.show()

The Americas collectively produce roughly 30% of the world’s lithium — led by Chile’s massive brine operations at the Salar de Atacama. But the gap with Australia and China is stark. The United States currently produces effectively zero lithium at industrial scale; Silver Peak’s output is negligible in global terms. The Inflation Reduction Act’s domestic sourcing requirements are designed to change this equation, but Rhyolite Ridge and Thacker Pass are still years from first production.

Rare Earth Dominance

Show code 
# Global REE production share (2024 estimates)
# Source: USGS Mineral Commodity Summaries 2025
ree_data = pd.DataFrame([
    {"country": "China", "share_pct": 60.0},
    {"country": "USA (Mountain Pass)", "share_pct": 15.0},
    {"country": "Myanmar", "share_pct": 10.0},
    {"country": "Australia", "share_pct": 8.0},
    {"country": "Other", "share_pct": 7.0},
])

fig = px.pie(
    ree_data,
    values="share_pct",
    names="country",
    title="Global Rare Earth Oxide Production Share (2024 est.)",
    color="country",
    color_discrete_map={
        "China": "#f97316",
        "USA (Mountain Pass)": "#22c55e",
        "Myanmar": "#64748b",
        "Australia": "#f43f5e",
        "Other": "#cbd5e1",
    },
    hole=0.4,
)
fig.update_layout(height=400)
fig.show()

China’s dominance in rare earth elements goes beyond mining — the country controls roughly 85% of global rare earth processing capacity. Even when rare earth ore is mined elsewhere (Mountain Pass in California, for example), it has historically been shipped to China for refining. MP Materials is building domestic processing capability at Mountain Pass, but closing this gap will take years and billions in investment. The strategic implications are clear: without diversified processing, mining alone does not equal supply chain security.

Static Overview

Show code 
import contextily as cx
from shapely.geometry import Point

# Build GeoDataFrame from JSON data
geometry = [Point(s["lon"], s["lat"]) for s in sites_data]
sites_gdf = gpd.GeoDataFrame(sites_df, geometry=geometry, crs="EPSG:4326")

# Reproject to Web Mercator for contextily
sites_3857 = sites_gdf.to_crs(epsg=3857)

fig, ax = plt.subplots(1, 1, figsize=(10, 14))

cat_colors = {
    "Lithium": "#22c55e",
    "Rare Earth": "#f97316",
    "Strategic Metals": "#0ea5e9",
}

for cat, color in cat_colors.items():
    subset = sites_3857[sites_3857["category"] == cat]
    if len(subset) > 0:
        subset.plot(
            ax=ax, color=color, markersize=80,
            label=cat, alpha=0.9, edgecolor="white", linewidth=0.8,
        )

cx.add_basemap(ax, source=cx.providers.CartoDB.Positron, zoom=3)

ax.set_title("Critical Mineral Sites — Americas", fontsize=14, fontweight="bold")
ax.set_axis_off()
ax.legend(loc="lower left", fontsize=10, framealpha=0.9)
plt.tight_layout()
plt.show()

Data Sources & Bibliography

USGS Mineral Commodity Summaries

U.S. Geological Survey. (2025). Mineral Commodity Summaries 2025. U.S. Department of the Interior.

IEA Critical Minerals Report

International Energy Agency. (2024). The Role of Critical Minerals in Clean Energy Transitions. IEA, Paris.

Inflation Reduction Act

U.S. Congress. (2022). Inflation Reduction Act of 2022, Pub. L. 117-169. Sections 13401-13402 (Clean Vehicle Credit, critical mineral sourcing requirements).

Operator Sources

USGS Mineral Resources Data System

For additional mine location data: USGS MRDS — downloadable shapefile/GeoJSON with 240,000+ mineral deposit records globally.

Technical Details

Environment

Show code 
print("Python:", sys.version.split()[0])
print("Platform:", platform.platform())
print("Pandas:", pd.__version__)
print("GeoPandas:", gpd.__version__)
print("Folium:", folium.__version__)
Python: 3.11.9
Platform: Windows-10-10.0.26100-SP0
Pandas: 2.2.2
GeoPandas: 1.1.2
Folium: 0.20.0

Data Summary

Show code 
print(f"Total sites: {len(sites_data)}")
print(f"Categories: {sites_df['category'].nunique()}")
print(f"Statuses: {sites_df['status'].nunique()}")
print()

# Extent
lats = [s["lat"] for s in sites_data]
lons = [s["lon"] for s in sites_data]
print(f"Latitude range: {min(lats):.2f} to {max(lats):.2f}")
print(f"Longitude range: {min(lons):.2f} to {max(lons):.2f}")
print()

# Breakdown
print("By Category:")
for _, row in category_counts.iterrows():
    print(f"  {row['category']}: {row['count']}")
print()
print("By Status:")
for _, row in status_counts.iterrows():
    print(f"  {row['status']}: {row['count']}")
Total sites: 17
Categories: 3
Statuses: 5

Latitude range: -34.09 to 52.30
Longitude range: -117.64 to -42.00

By Category:
  Lithium: 12
  Strategic Metals: 3
  Rare Earth: 2

By Status:
  Operating: 9
  Development: 5
  Approved: 1
  Proposed: 1
  Under Construction: 1

Notes / Decision Log

  • Data source: Manually curated from USGS Mineral Commodity Summaries, operator websites, and public filings
  • No database tables: JSON-only workflow (dataset is manually curated, too small to justify PostGIS overhead)
  • CRS: EPSG:4326 (WGS84), native for web maps
  • Production data: 2024 estimates from USGS Mineral Commodity Summaries and Benchmark Minerals Intelligence
  • Photo popups: Ready for photo URLs (same build_photo_popup() pattern as roman-roads)
  • Scope: “Critical Minerals” — lithium, rare earth elements, and strategic metals (copper, nickel, cobalt)
  • Next steps: Add site photos, expand to include European/African critical mineral sites, add time series production data
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