Context

• Artificial Intelligence (AI) has become an indispensable force in modern society, revolutionising industries, economies, and daily life.
• With recent advancements in computing power and data availability, AI adoption has surged, driving economic value at an unprecedented scale.
• The global AI market, currently valued at $200 billion, is projected to contribute up to $15.7 trillion to the global economy by 2030.
• However, while AI offers immense economic potential, its rapid expansion also raises critical concerns, particularly regarding its environmental footprint.

AI’s Environmental Impact Across Stages

• Hardware Production and Infrastructure
  • Raw Material Extraction and Manufacturing
    • The manufacturing of AI hardware requires rare earth metals like lithium, cobalt, and nickel, which are mined under environmentally damaging conditions.
    • Mining operations contribute to deforestation, habitat destruction, and significant carbon emissions.
    • Additionally, the extraction of these materials often involves unethical labour practices in some regions.
  • Energy-Intensive Production
    • The fabrication of semiconductors and other AI hardware involves complex chemical processes and high-temperature treatments, consuming vast amounts of energy.
    • The semiconductor industry alone accounts for a notable share of global industrial emissions.
  • E-Waste Crisis
    • As AI-driven systems demand more computing power, the lifecycle of AI hardware shortens, contributing to a growing electronic waste (e-waste) problem.
    • Many GPUs and TPUs become obsolete within a few years, leading to discarded electronic components that contain hazardous substances like lead, mercury, and cadmium, which pollute the environment when not properly recycled.
• Data Centre Operations: The Backbone of AI
  • Energy Consumption
    • Data centres are responsible for approximately 1% of global greenhouse gas emissions, according to the International Energy Agency (IEA).
    • This figure is expected to double by 2026 as AI applications become more widespread.
    • AI models, particularly generative AI models like ChatGPT and DeepSeek, require significantly higher computing power than traditional algorithms, further escalating energy demand.
  • Water Usage for Cooling
    • AI data centres generate immense heat due to their continuous operations, necessitating efficient cooling systems.
    • Many large-scale data centres rely on water-based cooling systems, which consume millions of litres of water annually.
    • This exacerbates water scarcity in regions where such facilities are located.
  • Location-Based Carbon Footprint
    • The environmental impact of data centres is also influenced by their geographical location.
    • Data centres in regions powered by coal and fossil fuels have a much higher carbon footprint than those situated in areas using renewable energy.
    • Companies that fail to strategically place their infrastructure contribute more to global emissions.
• AI Model Life Cycle Emissions
  • Training AI Models
    • Training state-of-the-art AI models is an extremely energy-intensive process.
    • For instance, GPT-3’s training process emitted approximately 552 tonnes of carbon dioxide equivalent (CO₂-e), comparable to the emissions from nearly 125 gasoline-powered cars over a year.
    • Advanced models like GPT-4 require even more computational resources, escalating their environmental impact.
  • Inferencing and Continuous Operation
    • Once AI models are deployed, they require substantial computational power to process user queries and make real-time predictions.
    • This is known as inferencing, which can sometimes consume 10–100 times more energy than earlier AI models.
    • Since these models run continuously on cloud servers, their energy consumption compounds over time.
  • Data Storage and Retrieval
    • AI models rely on massive datasets that require ongoing storage and retrieval, further increasing energy usage.
    • Maintaining these vast datasets involves constant processing and updating, which contributes to sustained power consumption.
  • Model Retirement and Re-training
    • Unlike traditional software that can run for years with periodic updates, AI models often require retraining as new data becomes available.
    • Each retraining cycle demands significant computational resources, leading to recurring carbon emissions.

The Global Response to AI’s Environmental Challenges

• As awareness of AI’s environmental impact grows, global discussions on sustainable AI practices have gained momentum.
• At COP29, the International Telecommunication Union emphasised the need for greener AI solutions, urging businesses and governments to integrate sustainability into their AI strategies.
• More than 190 countries have adopted ethical AI recommendations that address environmental concerns, and legislative efforts in the European Union and the U.S. aim to curb AI’s carbon footprint.
• However, despite these initiatives, concrete policies remain scarce.
• Many national AI strategies primarily focus on economic growth and technological innovation, often overlooking the role of the private sector in reducing emissions.

Strategies for Sustainable AI Development

• Need to Strike a Balance
  • Achieving a balance between AI-driven innovation and environmental responsibility requires a multi-faceted approach.
  • A key step in this direction is investing in clean energy sources. Companies can reduce AI’s carbon footprint by transitioning to renewable energy and purchasing carbon credits to offset emissions.
  • Additionally, locating data centres in regions with abundant renewable resources can help alleviate energy strain and minimise environmental damage.
  • AI itself can contribute to sustainability by optimizing energy grids.
  • For instance, Google’s DeepMind has successfully applied machine learning to improve wind energy forecasting, enabling better integration of wind power into the electricity grid.
• Hardware Efficiency
  • Hardware efficiency is another critical factor in reducing AI’s environmental impact.
  • The development of energy-efficient computing components and regular maintenance of hardware can significantly lower emissions.
  • Moreover, optimising AI models can lead to substantial energy savings. Smaller, domain-specific models designed for particular applications require less computational power while delivering comparable results.
  • Research suggests that the carbon footprint of large language models (LLMs) can be reduced by a factor of 100 to 1,000 through algorithmic optimisation, specialized hardware, and energy-efficient cloud computing.
  • Businesses can also reduce resource consumption by adapting pre-trained models rather than training new models from scratch.
• Transparency and Accountability
  • Transparency and accountability are essential to driving sustainability efforts.
  • Organisations must measure and disclose the environmental impact of their AI systems to gain a comprehensive understanding of life cycle emissions.
  • Establishing standardised frameworks for tracking and comparing emissions across the AI industry will promote consistency and encourage companies to adopt greener practices.

Conclusion

• Sustainability must be embedded into the core design of AI ecosystems to ensure their long-term viability.
• While AI presents groundbreaking opportunities for economic growth and technological progress, it is crucial to address the environmental costs associated with its expansion.
• By investing in renewable energy, optimising hardware and software efficiency, and developing transparency in emissions tracking, we can achieve a sustainable AI future.