The dark side of AI: New study shows its environmental costs threaten water, land, and climate

Data centres, the global infrastructure powering AI, could consume 945 terawatt-hours of electricity annually by 2030 – nearly triple the combined annual electricity use of Pakistan, Bangladesh, and Nigeria, countries collectively home to more than 650 million people.

However, this is just the tip of the iceberg. On top of the carbon footprint, every unit of electricity used by data centres also carries a “water footprint” for cooling and energy production, and a “land footprint” associated with power generation and supply chains.

Rethinking how sustainability is measured

According to a new study from UN University (UNU), AI-related water consumption could equal the basic annual domestic needs of 1.3 billion people by the end of the decade, while its land footprint may exceed 14,500 square kilometres — roughly twice the size of the Jakarta metropolitan area.

The report highlights a critical gap in how AI’s environmental impact is measured. Greenhouse gas emissions, particularly those linked to training large models, tend to be prioritised, but this approach overlooks other environmental costs.

Solutions seen as “green” in one sense may worsen pressures in others, particularly in regions already facing resource scarcity. For example, switching to certain renewable energy sources may reduce carbon emissions but can significantly increase water consumption and land use.

Daily use of AI is the main culprit

Public debate has largely centred on the energy required to train advanced AI models, but the study finds that day-to-day usage accounts for roughly 80 to 90 per cent of total energy demand. 

The scale is striking: one widely used AI service is estimated to process around 2.5 billion prompts per day, consuming hundreds of gigawatt-hours of electricity each year.

Energy use also varies widely depending on the task. Generating a single AI image can require more than a thousand times the energy of simple text classification, while video generation demands even greater resources.

Efficiency improvements alone are unlikely to offset these rising demands. The report points to the so-called rebound effect, in which lower costs and improved performance drive higher usage, ultimately increasing total resource consumption.

Local burdens, global benefits

The environmental impacts of AI infrastructure are not evenly distributed. While the benefits of the technology are global, its costs are often concentrated in specific regions.

In some countries, data centres already account for a significant share of national electricity consumption, placing pressure on energy systems. In others, expanding facilities are drawing heavily on water supplies, sometimes amid drought conditions.

At the same time, the report warns of a growing electronic waste challenge, with AI infrastructure projected to generate up to 2.5 million tonnes of e-waste annually by 2030. Much of this burden is likely to fall on lower-income countries with limited capacity for safe disposal.

The production of critical minerals needed for AI hardware also raises concerns about environmental degradation and social inequities in extraction regions.

A widening digital and environmental divide

The expansion of AI infrastructure is also creating new disparities in access and influence. According to the report, more than 90 per cent of AI-specialised computing capacity is concentrated in just two countries – the United States and China. At the same time, over 150 nations lack significant domestic AI infrastructure.

This imbalance not only limits economic opportunities but also raises questions of environmental justice, as some countries bear the environmental costs without sharing in the benefits of AI-driven growth. 

Towards responsible AI

Despite the stark findings, UNU researchers stress that the report is not an argument against AI itself. Rather, it calls for urgent action to ensure that the technology develops within planetary limits.

The study outlines a framework for a “responsible AI ecosystem”, built on principles including transparency, efficiency by design, equity, lifecycle responsibility, global cooperation and sustainable use.

Governments are urged to integrate AI infrastructure into energy, water and land-use planning, while companies are encouraged to design systems that minimise resource consumption. Users, too, have a role to play by choosing lower-impact applications where possible.

Ultimately, the report argues that the future of AI will depend not only on technological innovation but also on governance choices made today.