A groundbreaking report from the United Nations University Institute for Water, Environment and Health (UNU-INWEH) unveils the extensive environmental footprint underpinning artificial intelligence (AI) across carbon emissions, water usage, and land occupation, exposing complexities beyond the often-cited surge in electricity consumption. This comprehensive study paints a sobering picture of the physical infrastructure, resource demands, and environmental justice implications accompanying the explosive growth of AI technologies worldwide.
At the heart of this investigation lies the understanding that AI’s environmental impact extends well beyond energy consumption and carbon footprints. The report emphasizes the intricate supply chains and physical systems supporting AI: sprawling data centers, semiconductor fabrication, cooling mechanisms, and resources extracted for critical minerals. These components introduce significant water withdrawals, land use for energy infrastructure, and the escalating challenge of electronic waste management. In doing so, the report marks a crucial shift from the conventional carbon-centric discussions toward a holistic environmental perspective.
The scale of AI’s operational energy demands is staggering. Projections estimate that data centers, the backbone of AI computing, will consume 448 terawatt-hours of electricity in 2025—an amount equivalent to the national consumption of France, ranking them as the 11th largest global electricity user if considered a country. Notably, AI workloads account for roughly 20% of this power use, a share predicted to rise to 40% by 2030. Should current growth trajectories persist, the energy consumption attributed to AI could nearly triple by 2030, corresponding to around 945 terawatt-hours annually and equating to nearly 3% of worldwide electricity usage. This prodigious demand alone could sustain the energy needs of 1.3 billion people living in Sub-Saharan Africa for over five years—a demographic particularly vulnerable to energy scarcity.
Beyond energy, the water footprint of AI infrastructure poses an underappreciated risk to global freshwater resources. Data centers currently utilize an estimated 9.3 trillion liters of water, sufficing for the drinking requirements of the global population for approximately 1.6 years. The report underscores that water withdrawals, especially in arid or depleted regions, can severely stress aquatic ecosystems and groundwater reserves, even when some of this water is eventually returned. Moreover, land requirements for electricity generation related to AI’s growth are poised to surpass 14,000 square kilometers by 2030, roughly the size of Northern Ireland, presenting additional challenges for land management and biodiversity conservation.
Training state-of-the-art AI models such as ChatGPT-5 demands colossal energy inputs, consuming around 100 gigawatt-hours of electricity—comparable to the annual residential energy consumption of 770,000 individuals in Sub-Saharan Africa. The corresponding water and land footprints—1 billion liters and 1.5 square kilometers respectively—highlight the significant spatial and resource components embedded within AI’s developmental phase. However, the report pivots attention toward the AI’s ubiquitous daily use, which far exceeds the energy footprint of training alone. For instance, ChatGPT processes roughly 2.5 billion prompts daily, translating into annual electricity use of about 383 gigawatt-hours and water consumption sufficient for half a million people’s domestic needs annually, reflecting the enormous cumulative resource drain of AI services.
The environmental cost per AI interaction varies significantly by technology and usage context. For example, Google handles approximately 5 trillion search queries each year, where a traditional search requires around 0.3 watt-hours, but AI-enhanced generative searches inflate this figure to up to 3 watt-hours—a tenfold increase. Additionally, AI-generated video content emerges as a looming environmental crisis. A single high-resolution video clip may demand more than 415 watt-hours of energy, outstripping the energy required for producing hundreds of static AI-generated images. Given that energy requirements rise quadratically with resolution and frame count, the burgeoning prevalence of AI video generation could rapidly escalate infrastructure strain.
Crucially, the report explores the intricate trade-offs between carbon, water, and land footprints in AI energy sourcing. Transitioning from coal to bioenergy production can reduce carbon emissions by an average of 72%, yet simultaneously inflates water consumption more than thirtyfold and enlarges land use by a factor of one hundred. This nuance dismantles simplistic narratives around “green” or “renewable-powered” data centers and compels stakeholders to weigh multifaceted environmental impacts in energy procurement and infrastructure siting. The geographic variance in electricity supply further complicates the notion of universal sustainability metrics.
The environmental and social implications extend deeply into the realm of mineral extraction and electronic waste. AI infrastructure relies on minerals often mined under conditions that disproportionately harm communities in the Global South, exacerbating environmental degradation and social injustices. By 2030, AI-related hardware waste could reach 2.5 million metric tons annually—equivalent to discarding a quarter of a million Eiffel Towers—posing severe challenges for hazardous material management and pollution control. The report calls for robust lifecycle governance spanning from resource acquisition through responsible disposal to mitigate these burdens on vulnerable populations.
Disparities in AI infrastructure distribution exacerbate global inequalities. Currently, 90% of specialized AI cloud infrastructure capacity is concentrated in just two countries—the United States and China—with only 32 nations worldwide hosting such facilities at all. The vast majority of over 150 countries remain dependent consumers of AI services, bearing metal extraction and e-waste costs disproportionately while reaping scant strategic benefits. This digital divide manifests not only as an economic disparity but as an environmental justice concern demanding urgent attention and coordinated global action.
Ireland stands as a cautionary exemplar of the perils of unregulated AI infrastructure growth. Data centers now consume 21% of the country’s total metered electricity—a sharp rise from 5% in 2015—exceeding the energy used by all urban households combined. The national grid operator’s decision to pause new data center approvals until 2028 encapsulates the critical need for integrative energy planning and sustainable infrastructure development, highlighting the risks that other nations might encounter without proactive governance.
The report presents a compelling call to action and a roadmap for responsible AI governance framed around six foundational principles: transparency in environmental impact reporting; efficiency engineered at the design phase; equity and environmental justice considerations; lifecycle accountability; international collaboration; and sustainable use practices. It addresses varied stakeholders—from governments integrating AI into energy and land-use policy, to industry prioritizing footprint-aware model development, to users selecting appropriate computational scales—emphasizing governance as a collective, multilevel imperative.
Finally, the report recognizes user interface design and behavioral choices as potent instruments for environmental stewardship. For instance, adopting a “concise mode” in AI interactions, which avoids unnecessary politeness or verbosity, can reduce token output by 30%, saving significant electricity—estimated at 87 to 98 gigawatt-hours annually. This reduction parallels the residential energy usage of 760,000 individuals in Sub-Saharan Africa, illustrating how seemingly small efficiency gains in user interactions and product defaults can cascade into substantial sustainability dividends.
In its starkest summary, UNU-INWEH’s report declares that AI’s environmental footprint is neither fixed nor inevitable; it is the product of cumulative engineering, usage, and policy decisions rooted in physical realities. Confronting AI’s rapid expansion with holistic, transparent, and just frameworks offers the only viable path to ensuring that technological progress advances human well-being within planetary boundaries. Without systemic and cooperative stewardship, the opportunity for AI to be a force for sustainable innovation risks being eclipsed by escalating environmental costs and intensifying inequalities.
Subject of Research: Environmental impacts of AI infrastructure and usage, including energy, carbon, water, land footprints, and associated social justice concerns.
Article Title: Environmental Cost of AI’s Energy Use: Carbon, Water and Land Footprints
News Publication Date: 2026
Web References:
https://unu.edu/inweh/collection/environmental-cost-of-AIs-Enrgy-Use-Carbon-water-and-land-footprints
References:
Aczel, M., Chamanara, S., Matin, M., Farsi, A., Marwala, T., Madani, K. (2026). Environmental Cost of AI’s Energy Use: Carbon, Water and Land Footprints. United Nations University Institute for Water, Environment and Health (UNU-INWEH), Richmond Hill, Ontario, Canada. doi: 10.53328/INR26RMA002
Image Credits: United Nations University Institute for Water, Environment and Health (UNU-INWEH)
Keywords
Artificial intelligence, AI energy consumption, carbon emissions, water footprint, land footprint, environmental justice, data centers, AI infrastructure, e-waste, sustainable AI, mineral extraction, global digital divide
