THE CONTEXT: The global AI sector is experiencing unprecedented growth, with investments projected to reach $200 billion by 2025 (Goldman Sachs) and contributions to global GDP estimated at $20 trillion by 2030 (WEF). Major tech players like Meta, Microsoft, and Amazon are racing to build data centers. At the same time, environmental concerns escalated due to AI’s 1% global GHG emissions from data centers (IEA), 552 tonnes CO₂ per GPT-3 training, and freshwater consumption projected to hit 6.6 billion cubic meters by 2027 (UC Riverside).
THE CHALLENGES:
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- Hardware Lifecycle: Data centers’ exponential energy demand clashes with global decarbonization goals. 2% of global electricity (460 TWh in 2022) used by data centers; projected to hit 1,050 TWh by 2026 (IEA). 64% of India’s data centers rely on coal-heavy grids, violating SDG 13 (Climate Action).
- Software Lifecycle: GPT-3 training produces 502–552 tonnes CO₂ (≈112 cars annually), while AI inference now rivals training emissions (UNEP). Microsoft’s 2024 emissions spike was 48% due to AI, exposing greenwashing in ESG pledges.
- E-Waste Crisis & Circular Economy Failure: AI’s hardware churn outpaces recycling capacity, exacerbating toxicity. 5 million metric tons of AI e-waste by 2030 (MIT Tech Review), with 80% ending in informal landfills. 1 GPU contains 3g lead and 0.2g mercury, contaminating groundwater. Unlike the EU’s Right to Repair Act, E-Waste Rules (2023) lack AI-specific provisions.
- Water-Energy Nexus & Regional Inequity: AI’s freshwater demand worsens droughts in climate-vulnerable regions. GPT-3 training evaporates 700,000 liters of fresh water (UC Riverside). Arizona’s data centers consume 1.7 billion liters/year, worsening the Colorado River crisis. Google’s Finland data centers use 97% carbon-free energy vs. 12% in water-stressed Chile.
- Ethical Erosion and algorithmic Colonialism: AI entrenches the Global North’s dominance while exploiting Southern resources. 70% of AI climate models underrepresent Global South data (UNESCO). AI’s “coded gaze” replicates colonial resource extraction patterns.
- Regulatory Fragmentation & Sovereignty Dilemmas: Competing national agendas undermine global climate governance. 190+ UNESCO guidelines lack enforcement; India’s DPDP Act ignores AI emissions. China’s “AI Supremacy 2030” prioritizes compute power over Paris Agreement targets.
- Mitigation Paradox: Green Tech’s Hidden Costs: Sustainable AI solutions inadvertently increase resource consumption. Google’s carbon-aware computing cuts emissions by 20% but requires 30% more rare earth metals. Nvidia’s H100 GPUs: 3x efficiency gains but rely on conflict minerals from Congo.
- Geopolitical Resource Competition: AI fuels a “Green Tech Cold War” over critical minerals. One ton of rare earths is needed per 10,000 GPUs; China controls 90% of the global supply. India’s PLI Scheme: Incentivizes semiconductor hubs but overlooks lithium recycling. US-China Chip War risks derailing COP30 climate targets, per Brookings Institute.
THE WAY FORWARD:
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- Decentralized Renewable Energy Hubs for AI Infrastructure: Establish AI-Cluster Renewable Zones (ACRZs) in renewable-rich states (e.g., Rajasthan, Gujarat) to power data centers via solar/wind, integrating constitutional mandates with climate justice.
- AI-Driven Circular Economy Frameworks: Implement AI-Integrated E-Waste Microfactories under Extended Producer Responsibility (EPR), leveraging blockchain for traceability. Amend E-Waste Rules 2023 to include GPU/TPU recycling quotas (e.g., 30% by 2030), inspired by EU’s Right to Repair Act.
- Sovereign Green AI Models for Emission Reduction: Develop BharatGPT—a 10x more efficient LLM trained on renewable-powered supercomputers, reducing carbon footprints by 90%. Allocate ₹2,000cr for R&D in quantum-AI hybrids, leveraging IISc’s Param Siddhi Supercomputer.
- Constitutionalizing Climate-Centric AI Governance: Amend Environment Protection Act, 1986 to include “Algorithmic Environmental Impact Assessments” (A-EIA) for AI projects. Expand “sustainable development” doctrine to cover AI’s ecological costs.
- Global South Solidarity for Equitable AI Innovation: Launch Global AI Equity Fund at G20, prioritizing tech transfer and green compute access for developing nations. Propose carbon credits for cross-border AI research, aligning with International Solar Alliance objectives.
- Citizen-Centric AI Audits for Participatory Sustainability: Deploy AI Jan Sunwai Portals for public scrutiny of AI projects’ environmental impact, backed by RTI mandates. Amend RTI Act, 2005 TO include “Algorithmic Transparency” clauses, requiring AI firms to disclose energy/water use.
THE CONCLUSION:
AI’s environmental costs demand a systems-thinking approach across its value chain. While innovation drives economic growth, embedding sustainability into AI design (via clean energy, efficient models, and transparency) is vital to avoid long-term ecological harm. India must lead by example, aligning its AI ambitions with global climate commitments.
UPSC PAST YEAR QUESTION:
Q. Introduce the concept of Artificial Intelligence (AI). How does AI help clinical diagnosis? Do you perceive any threat to privacy of the individual in the use of Al in healthcare? 2023
MAINS PRACTICE QUESTION:
Q. The rapid adoption of Artificial Intelligence (AI) technologies poses significant environmental challenges, particularly in terms of energy consumption and e-waste generation.” Critically analyze the ecological risks associated with AI and suggest innovative policy measures to mitigate these challenges.
SOURCE:
https://www.thehindu.com/opinion/op-ed/ai-has-an-environmental-problem/article69262476.ece
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