Indirect Water Footprint

The indirect water footprint (or Scope 3 water footprint) represents the total volume of water associated with the goods and services acquired by an organisation or individual that is consumed or polluted outside their direct operations. It includes the virtual water used in raw-material extraction, energy generation, component manufacturing and intermediate transport. In most manufacturing companies, it accounts for 60% to 90% of the total water footprint.

Breakdown of the indirect footprint

• Agricultural raw materials: blue and green water for cotton, maize, coffee, etc.
• Mineral raw materials: blue and grey water associated with lithium, copper or bauxite mining.
• Purchased energy: water for cooling in thermal power plants or for producing biofuels.
• Components and semi-manufactured goods: steel, plastics, chemicals.
• External services: data centres, logistics, industrial cleaning.

Step-by-step calculation methodology

1. Supply-chain mapping: identify priority inputs based on expenditure or water risk (ABC Analysis + AWARE).
2. Assign water-footprint factors: use WaterStat, Ecoinvent v3.9, FAO AQUASTAT or supplier primary data.
3. Volumetric calculation: multiply factors (m³/unit) by purchased quantities.
4. Source classification: separate blue, green and grey water.
5. Impact weighting: apply AWARE or Water Stress Index to prioritise critical basins.
6. Verification: triangulate with water bills and external auditors.

Key indicators

• Absolute indirect water footprint (m³/year).
• Indirect water footprint per product unit (m³/unit).
• Indirect share (%) = (Indirect WF / Total WF) × 100.
• Water intensity per monetary value (m³/€ purchased).
• Share in high-stress basins (% of volume with AWARE > 10).

Sectoral benchmark (2024)

• Fast-fashion textile: 88% of the water footprint is indirect, dominated by cotton and polyester.
• Consumer electronics: 72% linked to lithium extraction and chip production.
• Food and beverage: 82% associated with agricultural raw materials (sugar, grains, oils).

Case study: Sports footwear company

• Total water footprint: 1,200 Gm³.
• Indirect: 975 Gm³ (81%).
  • Bovine leather (Argentina): 320 Gm³
  • Synthetic rubber (Thailand): 210 Gm³
  • Electricity (China): 145 Gm³

Actions: partial substitution of leather with recycled microfibre and relocation of production to a solar-powered plant in Spain.
Result: 27% reduction in indirect footprint and drop in the Water Dependency Index from 65% to 44% in two years.

Reduction strategies

• Reformulate products using materials with lower footprints (BCI cotton, recycled polymers).
• Contract renewable energy (PPAs) for suppliers with high electricity consumption.
• Water-efficiency programmes at source: co-finance drip irrigation and treatment at farms.
• Geographic diversification: shift procurement to basins with AWARE < 5.
• Water clauses in supplier contracts: annual reduction KPIs for Tier 1 and Tier 2.
• In-situ water compensation when substitution is not feasible.

Integration with ESG frameworks and regulations

• GRI 303: reports water use across the supply chain.
• CDP Water: scores reduction of indirect footprint and transparency.
• CSRD/ESRS E3: will require disclosure of upstream and downstream water impacts.
• ESPR Regulation: digital product passports will include virtual-water data.

Connection with other indicators

• Water Dependency Index: % of total footprint met externally.
• Footprint Displacement Index: aggregates all externalised environmental footprints.
• Sectoral water-FDI draws directly on indirect water-footprint data.
• Corporate water neutrality: replenishment must cover part of the indirect footprint weighted by scarcity.

Barriers and limitations

• Incomplete traceability in Tier-3+ suppliers.
• Heterogeneous regional datasets and conversion factors.
• Commercial confidentiality limiting access to primary data.

The indirect water footprint is the critical link for understanding an organisation’s real water impact and building resilient, water-neutral supply chains. Reducing it requires deep supplier collaboration, material and energy innovation, and continuous monitoring supported by big-data tools and digital twins.