Governing the Environmental Externalities of Artificial Intelligence: An Emerging Global Challenge

17 Jan Governing the Environmental Externalities of Artificial Intelligence: An Emerging Global Challenge

Posted at 17 Jan 2026 in Current Affairs by Ritik singh 0 Comments

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GS- 3 – Science & Technology- Governing the Environmental Externalities of Artificial Intelligence: An Emerging Global Challenge

FOR PRELIMS

What are the environmental impacts of Artificial Intelligence?

FOR MAINS

What steps are being taken globally to regulate the environmental impact of Artificial Intelligence?

Why in the News?

Artificial Intelligence (AI), particularly Large Language Models (LLMs), is rapidly expanding across governance, industry, finance, healthcare, and defence. While AI is often projected as an efficiency-enhancing digital solution, emerging evidence reveals that its environmental footprint—energy use, carbon emissions, and water consumption—is substantial and largely unaccounted for in public policy. Recent global estimates now provide, for the first time, clearer data on the ecological costs of AI systems, exposing a serious governance gap, especially in developing economies like India.

Scale of AI’s Environmental Footprint

The global Information and Communication Technology (ICT) sector already accounts for about 1.8–2.8% of global greenhouse gas (GHG) emissions, with some assessments placing the figure close to 4%. AI is a rapidly growing contributor within this sector.

Training a single large-scale AI model can emit hundreds of thousands of kilograms of carbon dioxide, comparable to the lifetime emissions of multiple automobiles. Unlike traditional software, AI systems require repeated, compute-intensive training and inference processes, making their carbon intensity structurally high. Further, carbon footprint data remains fragmented, opaque, and largely voluntary, limiting accurate assessment and regulation.

AI and Energy Consumption Patterns

AI workloads are highly energy-intensive due to:
1. Massive computational requirements of model training
2. Continuous inference demand from millions of users
3. Dependence on specialised hardware such as GPUs and TPUs
Individual AI queries may appear insignificant, but at scale they create a large and persistent electricity demand. Aggregate energy use is often underestimated due to narrow metrics that exclude data centre overheads, cooling systems, and upstream energy losses. This creates a mismatch between perceived digital efficiency and actual environmental cost.

Water Consumption: The Emerging Hidden Crisis

Beyond carbon emissions, AI is now emerging as a major driver of industrial water use. Data centres depend heavily on freshwater for cooling high-density servers. Projections suggest that global AI-related data infrastructure could consume billions of cubic metres of water annually within this decade.
This trend is particularly concerning for water-stressed regions, including large parts of India, where competing demands from agriculture, urban consumption, and climate change already strain freshwater availability. AI-led digital growth, if unchecked, could intensify local water insecurity.

Global Governance Responses

1. UNESCO’s AI Ethics Framework (2021) acknowledges AI’s environmental externalities and calls for sustainable AI development.
2. European Union has taken a leadership role by integrating sustainability considerations into its AI Act (2024) and corporate disclosure norms.
3. United States follows a sector-specific approach, regulating energy and environmental impacts through existing climate and industrial laws.

India’s Regulatory and Policy Gap

1. Mandatory assessment of AI’s environmental impact
2. Disclosure norms for emissions and water use from data centres
3. Integration of AI-related externalities into climate policy

Extending Environmental Impact Assessment (EIA) to AI

India’s Environmental Impact Assessment (EIA) Notification, 2006 currently focuses on physical infrastructure. However, AI infrastructure—data centres, cloud computing hubs, and large-scale computing facilities—has comparable ecological consequences.

A reformed approach could include:

Bringing large AI and data infrastructure projects under EIA scrutiny
Lifecycle assessment covering energy use, emissions, and water consumption
Periodic environmental audits of AI systems

Role of Disclosure and ESG Norms

ESG (Environmental, Social, Governance) reporting standards
SEBI-mandated sustainability disclosures for listed companies
Emission reporting from data centres and AI training activities

Sustainable Pathways for AI Development

Greater use of pre-trained and shared models to reduce redundant training
Transition of data centres to renewable energy sources
Adoption of water-efficient cooling technologies
Reporting of AI-specific efficiency metrics

Conclusion

AI has the potential to accelerate economic growth and public service delivery, but its environmental externalities can no longer remain invisible. For India, aligning AI expansion with environmental sustainability is not a constraint on innovation but a prerequisite for long-term resilience. Institutionalising environmental assessment, disclosure frameworks, and sustainable infrastructure will ensure that AI contributes to inclusive and sustainable development rather than becoming a silent ecological burden.

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Prelims question:

Q. With reference to the environmental impact of Artificial Intelligence (AI), consider the following statements:

1. Training large AI models can result in significant carbon emissions due to high computational requirements.
2. AI-driven data centres consume energy but have negligible water requirements.
3. India currently has a mandatory framework to assess the environmental impact of AI systems.
Which of the statements given above is/are correct?
A. 1 only
B. 1 and 2 only
C. 2 and 3 only
D. 1, 2 and 3

Answer: A

Mains Question:

Q. Artificial Intelligence is often viewed as an efficiency-enhancing technology, yet its environmental footprint raises serious sustainability concerns. Discuss the environmental impacts of AI and suggest policy measures India can adopt to ensure sustainable AI development.

(250 words)

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