New York: Europe and the Arabs

The environmental impact of artificial intelligence (AI) extends beyond causing alarming greenhouse gas emissions that contribute to global warming. The technology's ecological footprint is also expanding at a rate that could deplete the planet's natural resources.

Data centers powering AI systems could consume up to 945 terawatt-hours of electricity annually by 2030, equivalent to three times the combined annual electricity consumption of Pakistan, Bangladesh, and Nigeria—countries with a combined population of over 650 million.

However, this is just the tip of the iceberg. In addition to the carbon footprint, every unit of electricity consumed by data centers has a "water footprint" for cooling and power generation, and a "land footprint" related to power generation and supply chains. According to a UN daily news report, a copy of which we received, an article titled "Rethinking the Sustainability Scale" states:

According to a new study by the United Nations University, AI-related water consumption could equal the annual basic household needs of 1.3 billion people by the end of this decade, while its land footprint could exceed 14,500 square kilometers, roughly five times the size of Cairo Governorate.

The report highlights a fundamental gap in how the environmental impact of AI is measured. The focus is often on greenhouse gas emissions, particularly those associated with training large models, while other environmental costs are overlooked.

Solutions considered "green" from one perspective may exacerbate pressures in other areas, especially in regions already suffering from resource scarcity. For example, while switching to certain renewable energy sources may reduce carbon emissions, it could also lead to a significant increase in water consumption and land use. Everyday AI Use
Public debate has largely focused on the energy required to train advanced AI models, but the study revealed that everyday usage accounts for 80 to 90 percent of total energy demand—a staggering figure.

It is estimated that one widely used AI service processes around 2.5 billion instructions daily, consuming hundreds of gigawatt-hours of electricity annually.

Energy consumption also varies significantly depending on the task. Generating a single image using AI can require more than a thousand times the energy needed to classify a simple text, while generating video requires far greater resources.

Efficiency improvements alone are unlikely to offset this increased demand. The report highlighted a "rebound effect," where lower costs and improved performance lead to increased usage rates, ultimately resulting in a further increase in overall resource consumption.

Local Burdens and Global Benefits
The environmental impacts of AI infrastructure are not evenly distributed. While the benefits of this technology are global, its costs are often concentrated in specific regions. In some countries, data centers already account for a significant share of national electricity consumption, placing a strain on power grids. In others, expanding facilities are significantly depleting water resources, even during periods of drought.

Meanwhile, the report warns of a growing challenge posed by e-waste. AI infrastructure is projected to generate up to 2.5 million tons of e-waste annually by 2030. A large portion of this burden is likely to fall on low-income countries that lack the capacity to safely dispose of this waste.

The production of essential minerals for AI devices also raises concerns about environmental degradation and social inequality in the regions where extraction takes place.

A widening digital and environmental divide: The expansion of AI infrastructure is also creating new inequalities in access and influence. According to the report, more than 90 percent of the world’s AI computing power is concentrated in just two countries: the United States and China. In contrast, more than 150 countries lack a tangible domestic AI infrastructure. This imbalance not only limits economic opportunities but also raises questions about environmental justice, as some countries bear the environmental costs without reaping the benefits of AI-driven growth.

Towards Responsible AI

Despite these stark findings, researchers at the United Nations University emphasized that the report is not intended to oppose AI per se, but rather calls for urgent action to ensure that this technology develops within the planet's environmental limits.

The study proposed a framework for a "responsible AI ecosystem," based on principles including transparency, efficiency from the design stage, equity, lifecycle responsibility, global cooperation, and sustainable use.

It urged governments to integrate AI infrastructure into energy, water, and land-use plans and encouraged companies to design systems that minimize resource consumption. Users also have a role to play by choosing applications with the lowest environmental impact whenever possible.

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