debt_leverage_profile
Net debt ~$7.5B USD; net debt/EBITDA ~6-7x; a 20bp rate rise adds ~$15M annual interest cost on floating-rate debt
Inferred
Agent_Inference
interest_rate_sensitivity
~30-40% of debt is floating-rate; 20bp increase pressures FFO by ~$15-20M; refinancing risk elevated given 2024-2026 maturities
Inferred
Agent_Inference
geopolitical_supply_exposure
High intensity; European gas dependency on Russia exposed structural energy security vulnerabilities.
Medium
GICS-commodity-overlay-v1
supply_chain_dependency
Solar panel manufacturing concentrated in China/Southeast Asia; wind turbine components reliant on European and Chinese steel supply chains
Inferred
Agent_Inference
international_expansion_readiness
Operates in Chile (CLP), Brazil (BRL), and Bermuda (USD-pegged); CLP and BRL devaluation risk material, representing ~10-15% of consolidated revenue
Inferred
Agent_Inference
geographic_footprint
Primary markets: USA (~75%), Canada (~10%), Chile and other LatAm (~10%); CLP and BRL sovereign devaluation pose moderate consolidated earnings risk
Inferred
Agent_Inference
commodity_exposure_profile
High intensity; commodities: Natural Gas, Coal, Uranium, Crude Oil, Copper (grid), Lithium (storage); geopolitical: European gas dependency on Russia exposed structural energy security vulnerabilities.
Medium
GICS-commodity-overlay-v1
vendor_lock_dependency_score
No single vendor exceeds 30% of operational input; however, GE and Vestas collectively supply majority of wind turbine components, creating moderate substitution risk
Inferred
Agent_Inference
business_model_type_primary
Physical utility and power generation infrastructure; cloud provider termination has negligible operational impact — assets are regulated physical plants
Inferred
Agent_Inference
business_model_type_secondary
Regulated utility and contracted renewable energy; cloud disruption affects back-office systems only, not core revenue-generating infrastructure
Inferred
Agent_Inference
switching_cost_profile
Minimal API coupling risk; operational systems are largely legacy SCADA and ERP platforms, not cloud-native API-dependent architectures
Inferred
Agent_Inference
howey_test_risk_index
Revenue model (regulated utility tariffs + long-term PPAs) does not constitute an investment contract; Howey Test risk is effectively zero
Inferred
Agent_Inference
regulatory_burden_tier
Very High
Medium
GICS-regulatory-overlay-v1
data_sovereignty_risk
Limited GDPR/CCPA exposure; customer data is primarily utility billing data in regulated jurisdictions; compliance cost is modest relative to revenue base
Inferred
Agent_Inference
antitrust_exposure_flag
Low antitrust risk; operates as regulated monopoly in utility segments with rate-setting oversight by regulators; renewable merchant segment is competitive
Inferred
Agent_Inference
regulatory_exposure_profile
Very High burden; regimes: FERC, NERC, EPA, NRC, State PUCs, DOE; Rate-case lag and clean-energy mandates compress returns on regulated asset base.
Medium
GICS-regulatory-overlay-v1
revenue_model_type
~85-90% recurring via regulated tariffs and long-term PPAs (10-25 year contracts); transactional/merchant power sales represent ~10-15%
Inferred
Agent_Inference
monetization_vector
Regulated cost-of-service utility rates plus contracted renewable PPA payments; revenue is formulaic and regulator/contract-determined
Inferred
Agent_Inference
pricing_architecture
Prices set by regulators (utility) or locked in PPAs; minimal discretionary pricing power; stress scenario is regulatory rate freezes or PPA counterparty default
Inferred
Agent_Inference
pricing_power_rating
Low independent pricing power; regulated rate base earns allowed ROE (~8-10%); PPA escalators (CPI-linked) provide modest inflation pass-through
Inferred
Agent_Inference
target_gross_margin_bracket
Gross margin ~40-50%; EBITDA margin ~45-55%; regulated utilities compress margins but provide stability; renewable segment margins higher
Inferred
Agent_Inference
churn_vulnerability_index
No free-rider leakage; utility customers are captive by regulation; PPA offtakers are contractually bound; effective churn rate near zero
Inferred
Agent_Inference
headcount_cost_structure
Revenue growth is sublinear to headcount; capital-intensive not labor-intensive; doubling generation capacity requires minimal proportional headcount increase
Inferred
Agent_Inference
marginal_cost_of_growth
Marginal growth cost is capital (turbines, solar, grid infrastructure), not labor; incremental EBITDA margins on new assets ~50-55% once operational
Inferred
Agent_Inference
franchise_compliance_risk
Not a franchise model; operates under utility regulatory licenses and renewable energy contracts; regulatory compliance drift risk is moderate given multi-jurisdictional exposure
Inferred
Agent_Inference
customer_acquisition_metric
At 10x scale, CAC is irrelevant — customers are captive utility ratepayers or institutional PPA counterparties; growth driven by asset development, not customer acquisition
Inferred
Agent_Inference
network_effect_present
No network effects present; utility and renewable generation assets are standalone; value does not increase with additional users or participants
Inferred
Agent_Inference
asset_efficiency_ratio
AI displacement risk is low for core operations; SCADA/grid management could benefit from AI optimization but physical asset base is not disintermediable by AI
Inferred
Agent_Inference
recession_resistance_tier
Tier 1 recession-resistant; electricity demand is largely inelastic; regulated utility revenue is guaranteed; PPAs insulate against spot market downturns
Inferred
Agent_Inference
customer_segment_primary
Regulated utility residential and commercial ratepayers; captive, fragmented, no single customer >5% of utility segment revenue
Inferred
Agent_Inference
customer_segment_secondary
Institutional PPA offtakers (municipalities, utilities, corporate renewable buyers); concentration risk exists if top 3 PPA counterparties represent ~25-30% of renewable revenue
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", "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"]
High
SOC-2018/GICS-overlay
agent_automatable_labor_share
0.34 (HIL — ~34% of characteristic roles agent-automatable)
Medium
SOC-2018 + agentic-exposure-v1
capital_expenditure_profile
Capital is being redeployed from legacy thermal/utility assets toward renewable energy growth (wind, solar, hydro); company actively divesting regulated utility assets to fund clean energy transition
Inferred
Agent_Inference
sec_cik
0001174169
High
SEC-EDGAR
ticker
AQN
High
SEC-EDGAR