The rapid growth of artificial intelligence (AI) worldwide is bringing new sustainability concerns into sharp focus. According to a recent report by the United Nations University Institute for Water, Environment and Health (UNU INWEH), AI-related electricity use is linked to significant environmental footprints—not just carbon, but also water and land. In 2025, data centers consumed about 448 terawatt-hours (TWh) of electricity globally, with AI workloads representing roughly 20% of that demand. Both these numbers are expected to rise sharply by 2030, raising critical questions about the hidden environmental costs behind AI’s expansion.
Why this matters
Environmental sustainability often focuses on greenhouse gas emissions, but the underlying resource demands of technology—water use, land occupation, and mineral extraction—are equally vital. AI depends on vast server farms and sophisticated hardware, which require continuous electricity, cooling, and physical space. These needs can exacerbate water stress, land degradation, and uneven energy burdens, especially in vulnerable communities where governance is weak and natural resources are limited. Understanding AI’s multi-dimensional environmental impact can help prevent a repeat of past unsustainable energy transitions, where environmental harm disproportionately affected marginalized populations.
What is happening
The UNU INWEH report highlights that AI’s environmental footprint spans beyond just carbon emissions associated with electricity use. Data centers alone used an estimated 448 TWh in 2025, with AI workloads accounting for approximately 20% of that electricity. Projections suggest total data center electricity consumption could nearly double to 945 TWh by 2030, with AI-related demand potentially doubling its share to 40%. This growth not only increases carbon emissions but also magnifies water consumption and land use consequences due to the cooling systems, energy infrastructure, and hardware supply chains needed. Moreover, the source of electricity is pivotal. Low-carbon energy sources, such as hydropower or nuclear, may still have large water or land footprints, meaning that clean energy does not automatically mean low environmental impact across all resource dimensions.
The report emphasizes that without transparent environmental accounting and equitable policies, the burden of AI’s expansion risks falling excessively on communities already experiencing water scarcity, land pressures, and energy insecurity. These dynamics threaten to replicate historic patterns of environmental injustice related to technological development and energy production. It calls for lifecycle responsibility, improved design efficiency, and effective governance to mitigate these sustainability risks.
What readers can take away
- AI’s environmental impact goes beyond carbon: water use and land effects are significant and must be part of sustainability discussions.
- Energy sources powering AI data centers matter—not all low-carbon electricity options reduce water or land footprints equivalently.
- The rapid growth of AI demands stricter transparency and environmental justice safeguards to avoid burdening vulnerable communities disproportionately.
- Consumers and organizations should encourage lifecycle and resource-efficient AI hardware design to curb escalating environmental costs.
- Policymakers should promote infrastructure and energy systems that balance carbon reduction with sustainable water and land management.
What to watch next
As AI’s footprint is projected to grow considerably by 2030, key developments will likely revolve around governments and industries establishing comprehensive metrics for AI’s environmental costs. This includes better tracking of energy consumption sources and embedded water and land use. Policy frameworks addressing environmental justice in technological infrastructure siting and operations may gain traction. Innovations in AI hardware efficiency and data center cooling technologies could also shape the evolving sustainability landscape for AI.
FAQ
Why is AI’s environmental impact more than just its carbon emissions?
AI depends on powerful data centers that require electricity to run and coolant to prevent overheating. This electricity production often demands water, either for cooling power plants or data centers directly, and involves land use for facilities and mining of minerals for hardware. Thus, AI’s impact includes water consumption and land footprint, not just greenhouse gases.
How much electricity do AI workloads consume now and in the future?
In 2025, AI-related activities accounted for about 20% of the 448 terawatt-hours used by global data centers. By 2030, overall data center consumption could rise to 945 TWh, with AI’s share doubling to roughly 40%, reflecting increased use and more complex AI models.
Can using renewable energy completely eliminate AI’s environmental costs?
Not necessarily. While renewable energy can reduce carbon emissions, some low-carbon power sources consume significant water or require large areas of land. Therefore, AI’s environmental costs also depend on the balance of resources used in electricity generation and infrastructure.
Original source: read more here.
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