debt_leverage_profile
0.01x Total Debt / Equity (Conservative)
High
SEC-XBRL
interest_rate_sensitivity
~$8-12M annual interest cost increase per 100bps rise; net debt ~$400M, mostly fixed-rate, moderate sensitivity
Inferred
Agent_Inference
geopolitical_supply_exposure
High intensity; OPEC+ supply decisions and Russia-Ukraine conflict drive price volatility.
Medium
GICS-commodity-overlay-v1
supply_chain_dependency
Australian port congestion (Newcastle/DBCT) and US rail bottlenecks (BNSF/UP) are top-two chokepoints
Inferred
Agent_Inference
international_expansion_readiness
AUD exposure ~40% of revenue; Japanese yen and Indian rupee secondary; AUD/USD 10% devaluation hits EBITDA ~$50-80M
Inferred
Agent_Inference
geographic_footprint
Primary markets: Australia (AUD), Japan (JPY via USD-indexed contracts), India (INR); AUD devaluation most impactful given Wilpinjong/Wambo scale
Inferred
Agent_Inference
commodity_exposure_profile
High intensity; commodities: Crude Oil, Natural Gas, Coal, Refined Petroleum Products, Uranium, Steel (equipment); geopolitical: OPEC+ supply decisions and Russia-Ukraine conflict drive price volatility.
Medium
GICS-commodity-overlay-v1
vendor_lock_dependency_score
Rail haulage (BNSF, Aurizon) and port terminal access (DBCT) are near-non-substitutable; no single vendor >30% but rail is critical
Inferred
Agent_Inference
business_model_type_primary
Physical commodity miner/producer; zero cloud infrastructure dependency; account termination has negligible operational impact
Inferred
Agent_Inference
business_model_type_secondary
ERP and trading systems (SAP) could cause short-term disruption but operations are mine-floor physical; cloud risk is minimal
Inferred
Agent_Inference
switching_cost_profile
No meaningful API coupling risk; operational technology is mine-control systems (Modular Mining, Komatsu); low API dependency
Inferred
Agent_Inference
howey_test_risk_index
Primary revenue is commodity sales; fails Howey Test on 'common enterprise/profit from others' element; negligible securities classification risk
Inferred
Agent_Inference
regulatory_burden_tier
High
Medium
GICS-regulatory-overlay-v1
data_sovereignty_risk
Minimal GDPR/CCPA exposure; B2B coal sales, limited consumer PII; Australian Privacy Act more relevant; compliance cost low
Inferred
Agent_Inference
antitrust_exposure_flag
Seaborne thermal coal market is globally fragmented; US met coal more concentrated but Peabody <15% share; moderate, not critical
Inferred
Agent_Inference
regulatory_exposure_profile
High burden; regimes: EPA, FERC, DOE, CFTC, OSHA, SEC; Accelerating emissions mandates and methane rules threaten capex economics.
Medium
GICS-regulatory-overlay-v1
revenue_model_type
~70-80% transactional spot/annual contracts, ~20-30% multi-year supply agreements; predominantly transactional commodity business
Inferred
Agent_Inference
monetization_vector
Per-ton coal sales at spot or contract prices; no subscription/SaaS component; pure physical commodity volume monetization
Inferred
Agent_Inference
pricing_architecture
Benchmark-linked pricing (Newcastle API5/6, CAPP indices); stress scenario: 30% price decline compresses EBITDA by ~$400-600M given high operating leverage
Inferred
Agent_Inference
pricing_power_rating
Low standalone pricing power; price-taker in global seaborne markets; differentiated met coal has modest premium but still index-linked
Inferred
Agent_Inference
target_gross_margin_bracket
Gross margin ~25-40% depending on coal price cycle; mining margin per ton $15-35; highly cyclical, cost curve position is mid-tier
Inferred
Agent_Inference
churn_vulnerability_index
No free-rider problem; B2B contracts with utilities/steel mills; churn risk is customer energy transition away from coal, structural not cyclical
Inferred
Agent_Inference
headcount_cost_structure
Revenue growth is volume/price-driven, not headcount-linear; doubling revenue via price requires near-zero incremental headcount; volume doubling requires ~30-40% more labor
Inferred
Agent_Inference
marginal_cost_of_growth
Sublinear for price-driven upside; linear-to-superlinear for volume growth (new mines require capex $500M+, permitting 3-7 years)
Inferred
Agent_Inference
franchise_compliance_risk
Not a franchise model; N/A
Inferred
Agent_Inference
customer_acquisition_metric
At 10x scale, seaborne market share would require industry consolidation; unit economics deteriorate as lower-quality reserves are accessed
Inferred
Agent_Inference
network_effect_present
No network effects present; commodity business with zero demand-side economies of scale or user network dynamics
Inferred
Agent_Inference
asset_efficiency_ratio
-0.6% Return on Assets (Negative equity)
High
SEC-XBRL
recession_resistance_tier
Tier 3 (cyclical); thermal coal demand correlates with industrial output; met coal highly recession-sensitive via steel demand contraction
Inferred
Agent_Inference
customer_segment_primary
Asian utility/power generators (Japan JERA, South Korea KEPCO, Taiwan Taipower); top 5 customers ~35-45% of seaborne revenue
Inferred
Agent_Inference
customer_segment_secondary
US domestic utilities and industrial customers (domestic segment ~25% of revenue); steel mills for metallurgical coal (met segment ~20%)
Inferred
Agent_Inference
characteristic_occupations
["11-0000 Management Occupations", "13-0000 Business and Financial Operations Occupations", "15-0000 Computer and Mathematical Occupations", "17-0000 Architecture and Engineering Occupations", "19-0000 Life, Physical, and Social Science Occupations", "23-0000 Legal Occupations", "41-0000 Sales and Related Occupations", "43-0000 Office and Administrative Support Occupations", "47-0000 Construction and Extraction Occupations", "49-0000 Installation, Maintenance, and Repair Occupations", "53-0000 Transportation and Material Moving Occupations"]
High
SOC-2018/GICS-overlay
agent_automatable_labor_share
0.33 (HIL — ~33% of characteristic roles agent-automatable)
Medium
SOC-2018 + agentic-exposure-v1
capital_expenditure_profile
10.7% CapEx / Revenue (Moderate-CapEx)
High
SEC-XBRL
sec_cik
0001064728
High
SEC-EDGAR
ticker
BTU
High
SEC-EDGAR