The International Monetary Fund (IMF) has observed that the economic benefits of Artificial Intelligence (AI) are likely to outweigh its environmental costs, especially for countries integrating renewable energy into AI development. This is particularly relevant for India as it expands its digital and AI infrastructure.

Economic Growth Potential of AI in India

 Economic Value Creation:

• AI’s Potential: Google projects AI could add ?33.8 lakh crore (~$500 billion) to India’s economy by 2030.

• Digital Economy: AI is central to India’s goal of achieving a $1 trillion digital economy by 2028, potentially contributing 20% of GDP.

• Employment Impact: While automating routine jobs, AI is expected to create new high-skilled roles, particularly in data analytics, robotics, and AI model development.

Sectoral Impact:

• Agriculture:

  • AI Tools: Crop prediction, disease detection, precision irrigation using satellite imagery and ML.

  • Project Farm Vibes: 40% increase in yield, 50% water savings, 25% cost reduction in fertilizers.

• Manufacturing:

  • Companies like Tata Steel use AI for predictive maintenance, energy efficiency, and quality control.

  • Aligns with the ‘Make in India’ initiative.

• Financial Inclusion:

  • OnFinanceAI helps onboard unbanked citizens using mobile/transaction data.

  • AI enhances UPI’s reach, improving digital financial services in rural areas.

• Governance and Public Services:

  • Tools like Bhashini enhance multilingual access to digital services.

  • India is pioneering AI-driven Digital Public Infrastructure (DPI).

Environmental Footprint of AI

 Energy Demand:

• AI model training is energy-intensive, relying on data centres powered by electricity, largely from fossil fuels.

• One AI query = 10x energy of a Google search.

• Data Centre Consumption:

  • 2024: 415 TWh (1.5% of global electricity)

  • Projected 2030: 945 TWh, > Japan’s current national usage.

  • US electricity prices may rise by 9% due to AI demand.

 Carbon Emissions:

• AI infrastructure contributes ~1% of global GHG emissions, expected to double by 2026.

• Countries without clean energy infrastructure face higher carbon penalties.

 Water Usage:

• AI cooling systems consume massive freshwater.

• Training GPT-3: 700,000 liters = water for 320 Tesla EVs.

• Global AI water demand could soon match 6x Denmark’s usage.

• Raises water stress, especially in semi-arid regions like India.

 Resource Depletion:

• AI servers and chips require rare-earth minerals like lithium, cobalt, and neodymium.

  • Making 2 kg of computer = 800 kg of raw material extraction.

  • Leads to deforestation, soil degradation, and ecological damage.

 E-Waste Generation:

• Rapid hardware obsolescence adds to hazardous e-waste.

• Contains toxic elements like mercury and lead, threatening public health.

AI as a Tool for Environmental Sustainability

 Smart Solutions Using AI:

• Air Pollution Control: IBM’s Green Horizon predicts pollution trends and supports urban planning.

• Climate Modeling: Google’s GenCast improves extreme weather forecasting.

• Forest Surveillance: AI + satellites track illegal logging and support reforestation.

• Ocean Monitoring:

  • AI tracks marine life and pollutants.

  • Fishial.AI develops the world’s largest fish image database.

India's Strategy: Aligning AI with Green Energy

 Renewable Energy Integration:

• Under the IndiaAI Mission, India is promoting AI + Renewable Energy integration.

• NITI Aayog advocates AI adoption with environmental sustainability in mind.

• At the Paris AI Summit, India emphasized green AI as a global priority.

 Exploring Nuclear and Hydrogen:

• Small Modular Reactors (SMRs) considered for powering AI data clusters.

• Hydrogen fuel cells proposed for green backup in data centers.

 Infrastructure & Green Practices:

• India encourages 100% renewable-powered data centers (e.g., Google & Microsoft centers in Hyderabad & Pune).

• Green Energy Corridors support renewable integration into power grids.

Challenges in Green AI Transition