AI’s Hidden Carbon Cost: Why the Environmental Impact of Algorithms Needs Policy Attention

Artificial Intelligence (AI) is widely discussed as a transformative tool — from improving health diagnostics to boosting agricultural productivity. Far less attention, however, is paid to the environmental footprint of developing and deploying AI systems. Recent international reports suggest that behind the promise of “smart” technologies lies a growing and largely unaccounted ecological cost, raising questions that are particularly relevant for countries like India that are rapidly expanding their digital and AI ambitions.

Why AI’s environmental impact is now under scrutiny

A working paper by the OECD on measuring the environmental impacts of AI compute highlights a fundamental concern: advanced AI models require enormous computational power, which in turn demands electricity, water and physical infrastructure. The global information and communication technology (ICT) sector — including devices such as televisions and servers — is already estimated to account for roughly 1.8% to 2.8% of global greenhouse gas emissions, with some studies placing the figure even higher.

Isolating the precise carbon footprint of AI is difficult because companies rarely disclose detailed energy-use data. This opacity has led to contested claims. For instance, a 2025 report by Google suggested that a single text-based AI prompt consumes just 0.24 watt-hours of electricity. Critics argue that such figures, while technically accurate at the user level, obscure the cumulative energy costs of training, maintaining and cooling large-scale AI systems.

The full life cycle costs: energy, water and emissions

Looking beyond individual queries to the entire AI life cycle paints a starker picture. An issue note by the United Nations Environment Programme warned that by 2027, AI servers could consume between 4.2 and 6.6 billion cubic metres of water annually, primarily for cooling data centres — intensifying water stress in already vulnerable regions.

Carbon emissions are equally significant. Studies suggest that training a single large language model can generate nearly 300,000 kilograms of carbon emissions. Earlier research on deep learning in natural language processing found that developing one large AI model could emit over 626,000 pounds of carbon dioxide — comparable to the lifetime emissions of five cars. These figures underline that AI’s environmental costs extend well beyond everyday electricity use.

Why popular AI tools amplify the concern

The rapid adoption of consumer-facing AI tools has further intensified the debate. According to a UNEP study published in July 2024, a single request made through ChatGPT can consume up to ten times more energy than a conventional Google search. While such tools improve productivity and access to information, their scale of use means that even marginal increases in per-query energy consumption translate into substantial aggregate emissions.

Global policy responses: ethics, regulation and disclosure

Recognising these risks, international organisations have begun to frame AI governance through an environmental lens. In 2021, UNESCO adopted its Recommendation on the Ethics of Artificial Intelligence, explicitly acknowledging AI’s potential negative impacts on societies and the environment. Though non-binding, it was endorsed by around 190 countries.

More concrete regulatory moves are emerging in advanced economies. The United States has proposed the Artificial Intelligence Environmental Impacts Act, while the European Union has incorporated environmental considerations into its harmonised AI regulatory framework. The EU has gone further by mandating disclosure of emissions linked to data centres and high-compute activities under its Corporate Sustainability Reporting Directive.

Where India stands — and what is missing

In India, discussions on AI and climate change largely focus on how AI can help monitor pollution, optimise energy grids or improve climate modelling. The environmental cost of building and deploying large AI models themselves receives little policy attention. This is a significant gap, given India’s rapid expansion of data centres and ambitions to become a global AI hub.

One starting point could be measurement. India already mandates Environmental Impact Assessments (EIA) under the EIA Notification, 2006, for projects such as dams and industrial plants. The scope of such assessments could be expanded to include large-scale AI development and data infrastructure, especially where energy and water use is substantial.

Towards standards, data and accountability

Developing meaningful policy requires reliable data. The government could initiate a multi-stakeholder exercise involving technology companies, research institutions, civil society organisations and environmental experts to establish common metrics for assessing AI’s environmental footprint. Indicators could include greenhouse gas emissions, energy consumption, water use and impacts on land and freshwater ecosystems.

Such data could eventually feed into corporate disclosure norms. There is growing scope to integrate AI-related environmental metrics into environmental, social and governance (ESG) reporting overseen by the Securities and Exchange Board of India and the Ministry of Corporate Affairs, drawing lessons from European reporting frameworks.

Making AI part of the sustainability solution

Addressing AI’s environmental footprint does not mean slowing innovation. Instead, it requires steering it. Practices such as using pre-trained models instead of training from scratch, powering data centres with renewable energy, improving hardware efficiency and transparently reporting AI-specific emissions can significantly reduce impact.

As AI becomes embedded in everyday governance and economic activity, its environmental costs can no longer remain an afterthought. For India, recognising and regulating these hidden costs will be essential if AI is to support — rather than undermine — broader sustainability and climate goals.