AI Drives Data-Center Energy and Water Demand

Forbes reports that global AI infrastructure expansion drove a 17% rise in data-center electricity demand in 2025, with data centers consuming 1.5% of global electricity in 2024. Cooling could require 1.2 trillion liters of water annually by 2030, but current GHG Protocol Scope 3 guidance lacks explicit categorization for AI, creating a reporting gap that hampers accurate environmental accounting.

What happened Forbes contributor Vaishali Nigam Sinha reports that the global expansion of AI infrastructure is driving rapid growth in data-center energy demand. Forbes reports data centers consumed about 1.5% of global electricity in 2024 and that demand rose 17% in 2025, outpacing overall electricity growth. Forbes cites projections that data-center cooling could consume 1.2 trillion liters of water annually by 2030. Forbes also reports that current GHG Protocol Scope 3 guidance technically covers AI as a purchased service but lacks explicit, consistent categorization and reliable activity data, creating a reporting "ghost room". Editorial analysis - technical context Industry-pattern observations: measurement gaps matter for practitioners because energy and water intensity for AI workloads depend on multiple variables not routinely disclosed, including server utilization, workload mix, cooling technology, and regional grid carbon intensity. Companies and cloud providers that publish granular utilization metrics, PUE equivalents, and water-use metrics materially improve downstream Scope 3 accounting, while lack of standardization forces estimators to rely on approximations. Context and significance the article places the reporting gap amid a broader shift where AI is increasingly treated as foundational infrastructure rather than a discrete SaaS feature. That shift raises questions about lifecycle boundaries and shared responsibility across hardware manufacturers, hyperscalers, and enterprise users. Standard setters such as the GHG Protocol currently provide a framework but, according to Forbes, do not yet offer a clear, uniform taxonomy or data templates for AI-specific infrastructure impacts. What to watch For practitioners: observers should watch for three developments: emergence of sector-specific reporting templates from standard setters, more granular disclosures from major cloud providers on energy intensity and water use per workload, and third-party tools that translate utilization telemetry into Scope 3 inputs. These indicators will determine whether reporting evolves from proxy-based estimates to measurement-driven accounting. Key Points - 1Rapid data-center growth has measurable environmental demands, creating a reporting gap that hampers accurate Scope 3 accounting. - 2Inconsistent disclosure of utilization, cooling, and grid carbon data forces estimators to use proxies, reducing comparability across organizations. - 3Standardized, workload-aligned metrics from providers and templates from standard setters would enable more accurate, auditable sustainability claims. Scoring Rationale Forbes contributor analysis on AI's growing data-center energy and water footprint, with key stats corroborated by IEA data. Addresses a real practitioner concern Scope 3 accounting gaps but is a single-contributor opinion piece rather than breaking news or primary research. IEA figures confirm the energy growth claims. Practice interview problems based on real data 1,625 SQL & Python problems across 15 industry datasets — the exact type of data you work with. Try 250 free problems /problems