The Hindu Editorial Analysis
14 January 2026
India must focus on AI and its environmental impact
(Source – The Hindu, International Edition – Page No. – 8)
Topic : GS Paper – GS-3 : Science & Technology, Environment, Climate Change, Governance
Context
Artificial Intelligence (AI) has rapidly become central to policymaking and economic discourse across sectors ranging from healthcare to agriculture. However, the environmental implications of AI development and deployment have received comparatively limited attention. The editorial highlights growing global evidence that AI systems — particularly large-scale models and data centres — impose significant environmental costs in terms of energy use, carbon emissions, and water consumption. As India accelerates AI adoption, the article argues that these impacts must be acknowledged, measured, and governed through appropriate policy frameworks.
Core Issue
The central issue is whether India’s AI strategy sufficiently accounts for the environmental externalities of AI development and use.
While AI is increasingly framed as a tool to combat climate change, its own contribution to emissions, resource depletion, and ecological stress risks undermining sustainability goals if left unregulated.
Environmental Costs of AI Development
According to OECD and UNEP assessments:
- Development and deployment of AI algorithms increase carbon footprints.
- The global ICT sector contributes 1.8–2.8% of global greenhouse gas emissions, with some estimates as high as 3.9%.
- AI data centres are energy-intensive and often water-intensive, aggravating resource stress.
Studies indicate that:
- Training a single Large Language Model (LLM) can emit hundreds of thousands of kilograms of CO₂.
- AI inference (routine usage) also consumes substantial energy, sometimes many times higher than conventional digital queries.
These findings challenge the perception that AI’s environmental impact is marginal.
Water Stress and Resource Consumption
UNEP projections warn that:
- AI servers could consume 4.2–6.6 billion cubic metres of water by 2027, contributing to water scarcity.
- Cooling of data centres poses particular risks in water-stressed regions such as parts of India.
Beyond energy, AI development also impacts:
- Freshwater availability,
- Land use,
- Natural resource extraction for hardware manufacturing.
These costs extend AI’s environmental footprint beyond electricity consumption alone.
Data Gaps and Transparency Challenges
The editorial cautions against relying on selective or incomplete disclosures:
- Corporate claims of low per-query energy consumption may obscure lifecycle impacts.
- Lack of standardised reporting makes independent verification difficult.
Without transparent and comparable data, policymakers cannot accurately assess AI’s true environmental costs or design effective regulation.
Global Responses: World vs India
Internationally:
- UNESCO’s 2021 Recommendation on the Ethics of Artificial Intelligence recognises AI’s negative environmental impacts.
- The European Union and the United States have moved towards regulatory frameworks addressing AI’s environmental footprint.
- EU initiatives such as harmonised AI rules and sustainability reporting obligations reflect this emerging consensus.
India, by contrast, remains at an early stage of integrating environmental considerations into AI governance.
Measuring AI’s Environmental Impact in India
The editorial argues that India must prioritise measurement before mitigation.
Possible steps include:
- Extending the scope of Environmental Impact Assessment (EIA) to include large-scale AI and data-centre projects.
- Developing metrics to track:
- Energy use,
- GHG emissions,
- Water consumption,
- Natural resource utilisation linked to AI systems.
Such assessment would align AI development with existing environmental governance frameworks.
Role of Standards, Disclosure, and ESG
The government could:
- Establish measuring and reporting standards for AI’s environmental impact.
- Involve stakeholders including technology companies, think tanks, and civil society.
- Integrate AI-related disclosures into ESG frameworks, under regulators such as the Ministry of Corporate Affairs and SEBI.
Drawing inspiration from the EU’s Corporate Sustainability Reporting Directive (CSRD), India could mandate disclosure of emissions and resource use from data centres and high-compute AI activities.
Sustainable AI Practices
The editorial notes that mitigation is possible through:
- Use of renewable energy for data centres,
- Deployment of pre-trained and efficient models instead of repetitive training,
- Optimisation of algorithms to reduce computational demand,
- Transparent reporting of AI-specific environmental estimates.
These measures can reduce AI’s ecological footprint without stifling innovation.
Way Forward
India must shift the AI discourse:
- From viewing AI only as a climate solution,
- To recognising and managing its environmental costs.
A balanced approach that combines innovation with sustainability is essential to prevent AI-driven growth from becoming environmentally counterproductive.
Conclusion
As India advances its AI ambitions, environmental governance must evolve in parallel. Ignoring the ecological costs of AI risks transferring today’s digital gains into tomorrow’s environmental liabilities. By measuring impacts, enforcing transparency, and embedding sustainability into AI policy, India can ensure that technological progress aligns with long-term environmental stewardship.
Responsible AI is not only ethical and inclusive — it must also be environmentally sustainable.