Mains Practice Questions

Introduction

Artificial Intelligence (AI) is revolutionizing industries and economies, but its growing environmental footprint is a critical concern. From high energy consumption in data centers to e-waste from AI hardware, unchecked AI expansion can contribute to ecological degradation. Balancing technological progress with sustainability is essential for AI’s long-term viability.

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Environmental Challenges of AI:

• High Energy Consumption: AI data centers require enormous electricity, increasing pressure on power grids and raising greenhouse gas emissions.
  • According to the International Energy Agency (IEA), energy demand from data centers is expected to double by 2026.
• Carbon Emissions from Model Training: Training advanced AI models requires intensive computational power, leading to high CO₂ emissions.
  • For instance, training GPT-3 emits 552 tonnes of CO₂, equivalent to the annual emissions of dozens of cars.
• Growing E-Waste: Frequent hardware upgrades, driven by AI's computational demands, contribute to electronic waste.
  • The rapid expansion of AI infrastructure increases obsolete computing equipment, exacerbating the global e-waste crisis.
• Water Consumption for Cooling: AI data centers require significant water resources to cool high-performance computing systems.
  • Major AI hubs, like Google’s data centers, consume millions of liters of water annually for cooling operations.
• Material Extraction for Hardware: AI chip manufacturing relies on rare earth metals, leading to environmental degradation from mining.
  • The extraction of minerals like lithium and cobalt for GPUs and semiconductors harms ecosystems and depletes natural resources.
• Inefficiency in Model Training: Large, general AI models use excessive computational resources compared to smaller, specialized models.
  • Generative AI models such as ChatGPT require 10–100 times more computing power than earlier AI versions, worsening the environmental footprint.
• Lack of Environmental Regulations: Most AI governance frameworks focus on ethics and security but overlook sustainability.

Conclusion

AI drives innovation and economic growth, but its environmental impact must be addressed. Sustainable AI requires clean energy, optimized models, and responsible e-waste management. Aligning AI progress with ecological preservation is an ethical imperative.