Accounting for AI Inference in Corporate GHG Inventories: A Four-Tier Methodology for Scope 3 Category 1 Reporting

A new four-tier methodology standardizes how companies should account for AI inference emissions under corporate sustainability regulations, addressing a critical gap where current practices either ignore the category or overestimate emissions by up to 40x. The framework uses direct token-based physical calculations where data exists, cascading to spend-based proxies for opacity, revealing that AI inference compliance is methodologically complex but typically low-magnitude for most organizations.

The Corporate Sustainability Reporting Directive (CSRD) mandates Scope 3 Category 1 disclosure starting January 2024, yet enterprises lack standardized guidance for quantifying AI inference emissions—creating regulatory ambiguity and inconsistent reporting. Current approaches either omit AI services entirely or apply blanket ICT-sector economic input-output factors that wildly overestimate actual physical emissions, introducing both compliance risk and misleading ESG metrics.

This four-tier framework addresses a genuine market failure in ESG tooling. By grounding estimates in peer-reviewed GPU energy benchmarks and regional grid carbon data, organizations gain methodological defensibility while avoiding false precision claims. The research demonstrates that for a typical 200-person European firm, total AI inference emissions remain below 1 tCO2e—suggesting the compliance burden stems from methodological uncertainty rather than material environmental impact.

The framework's practical significance lies in enabling proportionate carbon accounting: high-transparency customers gain token-level precision using ML.ENERGY Leaderboard data, while low-transparency SaaS subscriptions fall back to spend-based EEIO without systematic overestimation. This tiered approach reduces gaming incentives.

Critically, the research surfaces a water-carbon trade-off invisible to current ESG platforms: Sweden's renewable-heavy grid minimizes carbon footprint but maximizes water consumption relative to alternatives. This finding directly influences data centre location strategy for companies optimizing across multiple environmental dimensions, suggesting that simplistic carbon-only metrics may drive suboptimal infrastructure decisions.

• →AI inference emissions are typically 10-40x smaller than standard ICT sector proxies estimate, making current ESG reporting tools systematically misleading.
• →A four-tier methodology calibrated to GPU benchmarks and regional grids enables proportionate carbon accounting without false precision or major estimation error.
• →Compliance timelines and methodology standardization gaps create near-term reporting risk for enterprises with significant AI service consumption.
• →Nordic hydro-powered data centres minimize carbon but maximize water use, introducing trade-offs that single-metric ESG frameworks fail to capture.
• →For most organizations, AI inference represents sub-1-tCO2e impact, reframing the compliance challenge as methodological rather than magnitude-driven.