AI and its Environmental Impact

Artificial Intelligence (AI) and Its Environmental Impact is an emerging challenge. AI today represents a digital-ecological paradox; it enables sustainability solutions while simultaneously stressing natural resources. Read here to learn more.

Artificial Intelligence (AI) is reshaping economies, governance, and national security worldwide.

However, beneath its digital surface lies a resource-intensive physical infrastructure, energy-hungry data centres, water-intensive cooling systems, rare-earth-dependent hardware, and rapidly growing e-waste.

As India prepares to host the AI Impact Summit 2026 and champion the “Planet Sutra”, the country stands at a defining crossroads: can AI-led growth be reconciled with ecological sustainability and climate resilience?

India’s AI Push and the Environmental Question

India’s AI ecosystem has expanded rapidly under initiatives such as:

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• IndiaAI Mission (2024): building compute capacity, datasets, and indigenous models
• BharatGen (2025): sovereign large language models for Indian languages
• India Semiconductor Mission (ISM): domestic chip fabrication and design

Yet, this digital ambition is colliding with hard ecological limits.

Key Data Signals:

• The global ICT sector contributes up to 3.9% of global GHG emissions
• India’s data-centre capacity expected to reach 2,073 MW by 2027 (85% growth)
• 50% of Indian data centres are located in extremely water-stressed regions
• One LLM training run can emit ~626,000 pounds of CO₂
• A single ChatGPT query consumes 10× more electricity than a Google search

AI and Its Environmental Impact

1. Massive Electricity Consumption

AI workloads require continuous, high-density power for:

• Model training
• Real-time inference
• Cloud-based deployment

Indian Context

• Mumbai and Chennai data-centre clusters rely heavily on coal-based baseload power
• Diesel backup generators further inflate carbon emissions

Hence, AI weakens India’s decarbonisation trajectory unless grid greening accelerates.

2. Severe Water Stress

Data centres depend on water-intensive cooling systems.

Examples

• Bengaluru data centres consume over 26 million litres annually
• April 2024 water crisis exposed the conflict between urban survival and digital infrastructure

AI growth competes directly with drinking water security.

3. Carbon Emissions from AI Training

• Large Language Models require thousands of GPUs
• Repeated retraining increases Scope-2 emissions

Indian Tech Hubs

• Sovereign AI models in 2025 sharply increased emissions in Bengaluru, Hyderabad, and Pune

Emissions are rising even as AI improves efficiency elsewhere.

4. E-Waste and Hardware Obsolescence

• AI chips have a lifecycle of 2–3 years
• India generated 1.6 million tonnes of e-waste (2024)

Challenges

• Limited advanced recycling facilities
• The informal sector causes soil and groundwater contamination

AI’s material footprint undermines circular-economy goals.

5. Natural Resource Depletion

• Chip manufacturing requires:
  • Rare earth minerals
  • Ultrapure water
• Semiconductor fabs risk groundwater depletion in host regions

Technological self-reliance may create ecological vulnerability.

Structural Challenges in Regulating AI’s Environmental Impact

1. Data-Opacity Problem

• No mandatory disclosure of AI-model-wise energy and water use
• ESG reports obscure real ecological costs

2. Infrastructure-Cooling Paradox

• In tropical climates, cooling consumes nearly as much power as computing
• AI scaling multiplies energy and water demand non-linearly

3. Regulatory Vacuum

• EIA Notification 2006 excludes data centres
• GPU clusters operate without environmental clearance

4. Weak Hardware Lifecycle Governance

• Limited rare-earth recovery infrastructure
• Toxic AI waste leaks into informal recycling chains

5. Coal-Dependent Power Grid

• AI requires a 24×7 baseload
• Renewable intermittency forces fallback to fossil fuels

Global Response: Emerging Norms

• US AI Environmental Impacts Act (2024): Mandatory disclosure of energy and water use
• EU CSRD Framework: ESG transparency for tech firms
• UNESCO AI Ethics Recommendations: Recognise AI’s environmental harm
• Shift from “Red AI” to “Green AI” globally

Way Forward

India’s Planet Sutra seeks to embed sustainability into AI governance.

1. Regulatory Reforms

• Amend EIA Notification 2006 to include data centres >5 MW
• Mandatory environmental clearance for hyperscale AI infrastructure

2. Transparency & ESG

• SEBI and MCA to mandate:
  • Carbon Usage Effectiveness (CUE)
  • Power Usage Effectiveness (PUE)
  • Water Usage Effectiveness (WUE)

3. Green AI Adoption

• Prioritise:
  • Smaller, energy-efficient models
  • Transfer learning over brute-force training
  • Edge AI to reduce cloud load

4. Renewable Integration

• 100% renewable mandates for data centres
• Storage-linked renewable PPAs
• Resource mapping inspired by Haryana Water Resource Atlas (2025)

5. Circular Economy for AI Hardware

• Expand formal e-waste recycling
• Incentivise rare-earth recovery
• Extended Producer Responsibility (EPR) for AI chips

Strategic Significance for India

• Aligns AI growth with India’s climate commitments
• Protects urban water and energy security
• Positions India as norm-setter for the Global South
• Integrates SDG-9 (Industry), SDG-12 (Sustainable Consumption), SDG-13 (Climate Action)

Conclusion

AI must no longer be viewed as a purely digital industry. It is a resource-intensive industrial ecosystem with deep environmental consequences.

As India hosts the AI Impact Summit 2026, the Planet Sutra offers a chance to redefine global AI governance, where innovation respects ecological limits.

By embedding sustainability into the IndiaAI Mission, India can demonstrate that technological leadership and planetary responsibility are not contradictory, but complementary.

Related articles:

• Carbon footprint of digitalization
• AI and ethics
• International AI Treaty