Growing Interconnections Between Energy and AI Worldwide

Syllabus: GS3/Environment

Context

• In a 2024 report, the International Energy Agency (IEA) highlighted the growing interconnections between energy and AI worldwide.

Global Data Centre Energy Outlook

• Rising Demand: Data centre electricity demand projected to more than double by 2030 to 945 TWh.
  • AI-optimised data centres demand to quadruple by 2030.
• Global Share: Data centres currently consume 1–2% of total power, expected to rise to 3–4% by 2030.
  • Comparison: Steel industry consumes 7% of total power.
  • Water Stress: Rising freshwater use for cooling servers is a parallel concern.
• IEA Projection: Renewables and natural gas to dominate supply due to cost-competitiveness and availability.

India’s Data Centre Landscape

• Current & Future Growth: The demand is projected to rise from 1.2 GW (2024) to 4.5 GW (2030) (McKinsey Report).
  • AI-driven data centres alone will consume 40–50 TWh annually by 2030.
• Regional Distribution: Mumbai – 41%, Chennai – 23%, NCR – 14% of total capacity.
• Energy Mix: India is the 3rd-largest energy consumer, dominated by coal, crude oil, and natural gas.

How AI adoption world wide impacts the Environment?

• High Energy Demand: Training large AI models (like GPT, image generators) consumes massive electricity, often concentrated in data centres.
  • Global data centre demand could triple by 2030, with AI as the main driver. If powered by fossil fuels, this raises CO₂ emissions.
• Water and Resource Use: Data centres require huge water volumes for cooling — sometimes millions of litres per day.
• E-Waste and Hardware Turnover: Frequent upgrades of GPUs/TPUs for AI accelerate electronic waste, adding pressure on waste management systems.

Positive Impacts of AI on Environment

• Optimising Energy Systems: AI helps forecast solar and wind better, enabling higher renewable integration and reducing curtailment.
  • AI-managed smart grids, batteries, and demand-response systems reduce energy wastage.
• Climate Modelling & Adaptation: Enhances climate predictions, extreme weather forecasting, and precision agriculture to cut fertilizer/water use.
  • AI supports disaster risk management and climate-resilient infrastructure planning.
• Efficiency in Industries: AI-driven optimization reduces emissions in transport (fuel routing, logistics), buildings (smart HVAC), and manufacturing (process automation).
• Policy Framework: Energy Conservation Building Code & National Energy Efficiency Roadmap integrate AI in renewable energy, and sustainable design.
• Smart Real Estate: AI-driven solutions such as smart lighting, predictive Heating, Ventilation, and Air Conditioning (HVAC), automated building controls energy savings up to 25%.

Way Ahead

• The rapid adoption of AI worldwide is set to transform both the energy sector and the environment. 
• AI-driven data centres are projected to multiply electricity and water demand—raising concerns of higher CO₂ emissions, resource stress, and e-waste.
• AI also offers powerful solutions by enabling energy efficiency, renewable integration, and climate adaptation. 
• The challenge ahead lies in ensuring that AI’s energy-hungry growth is powered by clean, sustainable sources while leveraging its potential to build a greener, more resilient future.

Source: TH