electricsheepafrica/africa-synth-critical-minerals-reserves-niger

⚠️ Synthetic dataset — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference.

African Critical Minerals Reserves

Comprehensive synthetic dataset modeling critical mineral reserves, production, trade flows, governance indicators, and supply chain dynamics across 20 African countries, 10 critical minerals, and 3 demand/supply scenarios spanning 2020-2030.

Designed for EV/battery companies, mining investors, policy analysts, and supply chain risk modelers evaluating Africa's role in the global energy transition.

Dataset Summary

Dimension	Value
Total records	~27,000+ (9,000+ per scenario)
Countries	20 (DRC, Zambia, Zimbabwe, South Africa, Namibia, Mozambique, Madagascar, Tanzania, Ghana, Mali, Guinea, Botswana, Morocco, Egypt, Kenya, Uganda, Rwanda, Burundi, Angola, Niger)
Minerals	10 (lithium, cobalt, manganese, graphite, nickel, copper, rare earths, platinum group metals, vanadium, chromium)
Scenarios	3 (baseline_demand, ev_acceleration, supply_chain_disruption)
Year range	2020-2030
Columns	31
Regions	Central, Southern, Eastern, West, North Africa

Research Sources & Calibration

Parameters are calibrated to peer-reviewed and industry sources:

• USGS Mineral Commodity Summaries 2024-2025 - Reserve estimates, production volumes, grade ranges, global market shares for cobalt, lithium, nickel, copper, manganese, graphite, PGMs, chromium, vanadium, rare earths
• IEA Global Critical Minerals Outlook 2024 - EV demand trajectories, battery chemistry demand shares, recycling rates, substitution risk assessments
• African Mining Vision (AMV) 2024 - Critical minerals policy frameworks, domestic value addition baselines, artisanal mining prevalence
• Benchmark Mineral Intelligence 2024 - Battery-grade mineral pricing, processing capacity constraints, supply chain concentration metrics (HHI)
• S&P Global Market Intelligence 2024 - Mine-level production data, extraction cost curves, investment flow estimates
• National critical minerals strategies - DRC mining code 2018, Zimbabwe lithium export ban 2023, Zambia debt restructuring impacts, Namibia critical minerals policy 2023, South Africa Critical Minerals and Metals Strategy 2025

Key Domain Facts Embedded

Fact	Source
DRC produces ~74% of global cobalt (170kt of 230kt in 2023)	USGS MCS 2025
South Africa holds ~90% of global PGM reserves (63kt of 70kt)	USGS MCS 2025, WPIC
Zimbabwe lithium reserves ~2.7Mt with 55kt production in 2023	USGS MCS 2025, APRI
Mozambique graphite reserves ~25Mt, world's 2nd largest	Benchmark Minerals 2024
South Africa manganese production ~6.5Mt (31% of global)	USGS MCS 2025
DRC copper production ~2.6Mt (Copperbelt)	USGS MCS 2025
Chinese investment dominates DRC (65%), Zimbabwe (55%), Guinea (50%)	S&P Global 2024
Zimbabwe banned raw lithium exports (Feb 2023)	National policy

Scenarios

baseline_demand

Current EV adoption trajectory with 12-18% CAGR in battery mineral demand through 2030. Assumes gradual policy support, steady supply chain development, and moderate price appreciation. Production grows at historical rates, ESG scores improve slowly (2%/yr), and geopolitical risk remains stable.

ev_acceleration

Aggressive global EV policies: EU ICE ban by 2035, US IRA expansion, China NEV mandate strengthening. Battery mineral demand CAGR jumps to 22-28%. Massive investment inflows (2.2x baseline), but supply constraints drive 60% price premiums. Processing capacity expands rapidly (12%/yr), ESG improves faster (3.5%/yr), but geopolitical risk rises as competition intensifies.

supply_chain_disruption

Major trade restrictions and geopolitical shocks: DRC raw cobalt export ban expansion, Zimbabwe lithium export ban enforcement, supply chain fragmentation. Production growth drops to 60% of baseline, investment falls to 70%, but prices spike 80%. Export volumes decline (-2%/yr), artisanal mining increases, and geopolitical risk rises sharply (5%/yr trend).

Variables

Identifiers

Variable	Type	Description
record_id	int	Unique sequential identifier
country	string	Full country name
country_code	string	ISO 3166-1 alpha-3 code
region	string	African region (Central/Southern/Eastern/West/North)
mineral	string	Mineral name
scenario	string	Scenario name
year	int	Observation year (2020-2030)

Mining & Production

Variable	Type	Description
mine_type	string	Mining method (open_pit, underground, alluvial_artisanal, laterite, brine, pegmatite)
project_status	string	Project lifecycle stage (operating, development, feasibility, exploration, care_maintenance, closed)
reserve_estimate_tonnes	int	Estimated mineral reserves in metric tonnes
production_volume_tonnes	int	Annual production volume in metric tonnes
grade_percent	float	Ore grade/quality metric (varies by mineral: % metal for base metals, % C for graphite, g/t for PGMs)

Economics

Variable	Type	Description
extraction_cost_usd_per_tonne	float	All-in sustaining cost per tonne (grade-adjusted, infrastructure-adjusted)
market_price_usd_per_tonne	float	Simulated market price with scenario and volatility adjustments
processing_capacity_tonnes	int	Domestic processing/refining capacity in tonnes
domestic_processing_rate_percent	float	Percentage of production processed domestically (0-100)
export_volume_tonnes	int	Volume available for export after domestic processing
investment_flows_usd_millions	float	Annual mining sector investment inflows in USD millions

Governance & Risk

Variable	Type	Description
chinese_investment_share_percent	float	Estimated share of investment from Chinese entities (0-100)
esg_compliance_score	float	ESG compliance score (0-100, higher = better)
artisanal_production_percent	float	Share of production from artisanal/small-scale mining (0-100)
geopolitical_risk_index	float	Composite geopolitical risk (0-100, higher = riskier)
infrastructure_quality_index	float	Infrastructure quality index (0-100)
corruption_perception_index	float	Corruption perception index (0-100, based on Transparency International scale)
political_stability_index	float	Political stability index (0-100)

Market Dynamics

Variable	Type	Description
battery_chemistry_use	string	Primary battery/technology application
ev_demand_share_percent	float	Percentage of demand driven by EV/battery sector
recycling_rate_percent	float	Current global recycling rate for the mineral
substitution_risk_index	float	Risk of substitution by alternative materials (0-100)
supply_concentration_hhi	float	Herfindahl-Hirschman Index of global supply concentration (0-100)
data_source	string	Primary data source reference

Usage

from datasets import load_dataset

# Load specific scenario
ds = load_dataset("electricsheepafrica/african-critical-minerals-reserves", "baseline_demand")

# Load all scenarios
ds = load_dataset("electricsheepafrica/african-critical-minerals-reserves")

# Access as pandas
df = ds["all"].to_pandas()

# Filter for DRC cobalt under EV acceleration
drc_cobalt = df[
    (df["country"] == "Democratic Republic of Congo") &
    (df["mineral"] == "cobalt") &
    (df["scenario"] == "ev_acceleration")
]

# Analyze lithium supply risk by country
lithium_risk = df[df["mineral"] == "lithium"].groupby("country").agg({
    "reserve_estimate_tonnes": "sum",
    "production_volume_tonnes": "sum",
    "geopolitical_risk_index": "mean",
    "esg_compliance_score": "mean",
}).sort_values("geopolitical_risk_index", ascending=False)

Reproduce

pip install -r requirements.txt
python generate_dataset.py
python validate_dataset.py

File Structure

african-critical-minerals-reserves/
├── README.md
├── generate_dataset.py          # Dataset generation script
├── validate_dataset.py          # Validation script
├── requirements.txt             # Python dependencies
└── data/
    ├── african_critical_minerals_baseline_demand.csv
    ├── african_critical_minerals_ev_acceleration.csv
    ├── african_critical_minerals_supply_chain_disruption.csv
    └── african_critical_minerals_all.csv

Limitations

• This is a synthetic dataset calibrated to published sources, not primary survey data
• Reserve estimates include geological uncertainty (lognormal distribution)
• Production projections assume no major new discoveries beyond known deposits
• Investment flows are modeled estimates, not actual committed capital
• ESG and governance scores are composite indices based on publicly available indicators
• Prices include volatility but do not model extreme tail events (e.g., pandemics, wars)
• Artisanal mining data is inherently uncertain; figures represent best estimates

License

CC-BY-4.0

Citation

@dataset{african_critical_minerals_reserves_2024,
  title={African Critical Minerals Reserves Dataset},
  author={Electric Sheep Africa},
  year={2024},
  url={https://huggingface.co/datasets/electricsheepafrica/african-critical-minerals-reserves},
  note={Calibrated to USGS MCS 2024-2025, IEA GCMO 2024, Benchmark Minerals, S&P Global}
}