Leveraging Power-Law Distributions to Map Impact Origins in Product Supply Chains

Tracing a single product's environmental footprint—greenhouse gas emissions, water consumption, toxic releases, ecosystem damage—requires tracking thousands of processes across global supply chains to their fundamental environmental interactions: ore extraction, agricultural inputs, industrial water use spanning continents and ecosystems. Life Cycle Assessment (LCA) quantifies these impacts comprehensively. However, this comprehensive scope comes at a cost: impacts are aggregated into locationless scores, obscuring where damage likely occurs. Full spatial resolution has remained impractical: regionalizing all processes would require millions of location-specific datasets. This limitation prevents targeted mitigation, especially for spatially-dependent impacts like freshwater ecotoxicity, where the same emission generates orders-of-magnitude different consequences depending on ecosystem sensitivity and baseline contamination. We find that environmental impacts follow power-law distributions across diverse product systems: fewer than 2% of processes account for over 95% of total impact (mean Gini coefficient 0.982, n=449 product-impact combinations). This concentration enables selective spatialization—focusing analytical resources on only critical processes. We present Impact Origin Mapping, a framework combining network-based pathway tracing with AI-assisted geographic attribution to map impacts to affected ecosystems. Applied to lithium carbonate production, the framework traces freshwater ecotoxicity from a Nevada clay deposit to specific water bodies across multiple US states, transforming aggregated LCA scores into ecosystem-level impact maps that enable targeted interventions.