27-February -2025-Special-Article

ENVIRONMENTAL IMPACT OF ARTIFICIAL INTELLIGENCE (AI) AND MITIGATION STRATEGIES

Artificial Intelligence (AI) has seen widespread adoption across industries, contributing significantly to economic growth. However, concerns are growing about its environmental impact, particularly due to high energy consumption, greenhouse gas emissions, and e-waste generation.

What is Artificial Intelligence (AI)?

AI refers to the capability of machines to mimic human intelligence, including tasks such as learning, decision-making, problem-solving, and perception.

Global Market: The AI industry is valued at approximately USD 200 billion and is projected to contribute USD 15.7 trillion to the world economy by 2030.

India’s AI Initiatives:

• India aims to develop its own large language model (LLM) to compete with global AI models like DeepSeek and ChatGPT.
• The ‘AI for India 2030’ initiative focuses on ethical, inclusive, and responsible AI development to position India as a leader in AI innovation.

AI Life Cycle:

• Encompasses the process of designing, developing, deploying, and maintaining AI models to achieve efficient outcomes.

AI Data Centers and Their Role

• Definition: AI data centers are specialized facilities that support AI model training and execution by providing high computing power, storage, and networking.
• Key Features:

• High-Performance Computing (HPC): Uses advanced processors like GPUs for efficient AI training.
• Large Storage Capacity: Handles massive datasets required for AI models.
• High-Speed Networking: Facilitates fast data processing and transmission.
• Energy Management: Incorporates advanced cooling technologies and renewable energy to reduce power consumption.

Environmental Challenges of AI

1. Greenhouse Gas (GHG) Emissions

• AI-driven data centers rely heavily on electricity, mostly derived from fossil fuels.
• AI-related activities contribute approximately 1% of global GHG emissions, expected to double by 2026.
• Example: Training a single large language model (LLM) generates around 300,000 kg of CO₂, equivalent to the lifetime emissions of five cars.

2. Rising Power Consumption

• AI models require extensive computing resources, leading to increased energy use.
• Generative AI models consume 10–100 times more power than previous AI versions.
• Example: A single AI query consumes 2.9 watt-hours, compared to 0.3 watt-hours for a standard internet search.

3. Electronic Waste (E-Waste)

• AI hardware, including processors and storage devices, leads to significant e-waste accumulation.
• Generative AI alone could contribute up to 5 million metric tons of e-waste by 2030.

4. Resource Depletion and Water Usage

• AI chips require rare earth elements (REEs), obtained through environmentally harmful mining.
• AI data centers consume significant amounts of water for cooling purposes, straining water resources.

Global Efforts to Reduce AI’s Environmental Impact

1. International Agreements

• COP29 (UNFCCC 2024): Highlighted the urgency of implementing sustainable AI practices.
• UNESCO’s Ethical AI Guidelines: Adopted by over 190 countries, emphasizing energy-efficient AI development.

2. Legislative Measures

• European Union (EU AI Act, 2024): Establishes energy-efficiency standards for AI systems.
• United States (Artificial Intelligence Environmental Impacts Act, 2024): Mandates AI companies to reduce their carbon footprint.

3. AI Action Summit 2025

• Urges global AI developers to optimize algorithms to reduce energy consumption.
• Promotes AI integration into smart grids to enhance energy efficiency.

4. United Nations Environment Programme (UNEP) Recommendations

• Proposes five key strategies to minimize AI’s environmental footprint:

• Use renewable energy for AI operations.
• Optimize AI models for efficiency.
• Implement strict sustainability regulations.
• Enhance transparency in AI-related emissions.
• Invest in AI-driven climate solutions.

Way Forward:

1. Adoption of Renewable Energy

• AI companies should power their data centers with renewable sources like solar and wind.
• Data centers should be located in regions with abundant renewable energy to minimize carbon emissions.
• Example: Google’s DeepMind AI is used to optimize wind energy production.

2. Energy-Efficient AI Models

• Using smaller, specialized AI models can reduce energy consumption.
• Optimized algorithms and specialized hardware can lower energy use by 100 to 1,000 times.
• Businesses should prefer pre-trained models instead of training AI from scratch to conserve computational resources.

3. Responsible E-Waste Management

• Develop recycling programs for AI hardware components.
• Promote circular economy practices in AI chip manufacturing.

4. Transparency and Accountability

• Companies must establish clear sustainability goals and track AI emissions.
• Standardized frameworks for AI-related carbon footprints should be adopted globally.

Conclusion

While AI is a powerful tool for innovation and economic growth, its environmental costs cannot be ignored. Sustainable AI practices, such as the use of renewable energy, optimization of AI models, and responsible e-waste management, are essential to balance technological advancement with environmental protection. Collaborative global efforts and policy interventions will be crucial in ensuring AI’s growth does not come at the cost of the planet.

Mains Question

1. Discuss the environmental impact of artificial intelligence (AI) and suggest measures to make AI development more sustainable. (150 WORDS)