In this week's blog, the Alcea team wanted to dive into a topic that's fiercely debated in tech and environmental circles. It's something we as a team are always trying to educate ourselves on and do our best with: AI and sustainability. Last year, we shared a series of four blogs on the topic, and one of our most widely read was called: AI for sustainable development: How AI tools and technology can help in monitoring the environment
Artificial Intelligence (AI) has transformed industries, enabling efficiencies, innovation, and advancements across diverse fields. However, as the world grapples with the urgency of addressing climate change and reducing environmental impacts, AI’s sustainability or challenges, to be more accurate, has come under scrutiny. Can AI be an ally in achieving sustainability goals, or is it simply contributing to the problem? Let’s explore both sides of the equation.
The Environmental Costs of AI
AI development and deployment require substantial computational power, which translates to high energy consumption. Here are some key factors contributing to AI’s environmental impact:
Energy-Intensive Training Processes
Training large AI models, such as GPT or image recognition algorithms, demands immense computational resources. For example, a single training run of a state-of-the-art AI model can consume as much electricity as multiple households use in a year.
The carbon footprint of training and running these models depends heavily on the energy source. Non-renewable energy sources exacerbate emissions.
Water Usage in Data Centres
Data centres, where AI computations are performed, require extensive cooling systems to prevent overheating. This cooling process often relies on significant water resources, raising concerns about water scarcity in some regions.
E-Waste and Hardware Demand
AI’s reliance on cutting-edge hardware accelerates the production and disposal of electronics, contributing to e-waste. Rare earth materials used in these components are often sourced through environmentally damaging mining processes.
Dive deeper
To read further and wider on this topic, the Alcea team would recommend checking out:
Explained: Generative AI’s environmental impact, by Adam Zewe | MIT News, January 17, 2025. This is the first of two articles looking at generative AI and sustainability.
Unesco: Recommendations on the ethics of Artificial Intelligence
AI has an environmental problem. Here’s what the world can do about that. UN Environment Programme, September 21, 2024. This is a really interesting piece focusing on the practical and answering so many often asked questions, like this one:
"Is anybody doing anything about the environmental impacts of AI?
More than 190 countries have adopted a series of non-binding recommendations on the ethical use of AI, which covers the environment."
How AI Can Help Advance Sustainability
Despite these challenges, AI has significant potential to drive sustainability efforts across industries. Here are some ways AI can contribute:
Optimising Energy Usage
AI systems can analyse energy consumption patterns and optimise the operation of smart grids. For example, AI-driven energy management in buildings can reduce electricity use by dynamically adjusting heating, cooling, and lighting.
Reducing Waste in Supply Chains
Predictive analytics powered by AI can streamline supply chain operations, minimising overproduction and waste. Retailers, for instance, can use AI to forecast demand more accurately, thereby reducing unsold inventory.
Enhancing Renewable Energy Integration
AI algorithms help in managing and integrating renewable energy sources into existing grids. Predictive maintenance for wind turbines and solar farms ensures their efficient operation, while AI models can forecast weather patterns to optimise energy generation.
Conservation and Environmental Monitoring
AI plays a crucial role in monitoring deforestation, tracking wildlife populations, and identifying illegal fishing activities. Satellite imagery analysis powered by AI provides actionable insights for conservation efforts.
Efficient Water Management
AI systems can monitor and predict water usage, detect leaks, and optimise irrigation in agriculture, ensuring that water resources are utilised effectively.
Striking a Balance: Pathways to Sustainable AI
To ensure AI contributes to sustainability rather than detracting from it, proactive measures are essential. Here are some strategies to achieve this balance:
Green Data Centres
Transitioning to renewable energy-powered data centres can significantly reduce the carbon footprint of AI operations. Major tech companies, including Google and Microsoft, have committed to achieving carbon neutrality by using sustainable energy sources.
Efficient Algorithms
Researchers are developing more efficient algorithms that achieve the same results with fewer computational resources. Techniques like model distillation and federated learning can reduce the energy requirements of AI systems.
Carbon Accounting for AI Projects
Organisations can adopt carbon accounting practices to evaluate the environmental impact of their AI projects and make informed decisions to mitigate emissions.
Lifecycle Management of Hardware
Extending the lifespan of AI hardware through refurbishment, recycling, and responsible sourcing of materials can minimise e-waste and reduce reliance on environmentally harmful mining practices.
Transparent Reporting and Collaboration
Open collaboration and transparency among researchers, policymakers, and businesses can lead to industry-wide standards and best practices for sustainable AI development.
Summing Up: AI as a Tool for Positive Change?
AI is neither inherently sustainable nor unsustainable. Its impact depends on how it is designed, developed, and deployed. By addressing its environmental costs and leveraging its potential to drive efficiencies, AI can become a powerful tool for achieving sustainability goals. However, this requires deliberate action, innovative thinking, and global collaboration.
The journey toward sustainable AI is complex but essential. As we move forward, integrating sustainability into AI strategies will help to mitigate its environmental impact and ensure that AI contributes meaningfully to a greener, more sustainable future.
We know this is an ongoing and extremely important topic, so the whole team at Alcea is going to keep coming back to this area; we'll share new news and updates and keep working hard to stay updated with the latest developments. If you'd like to check out some of our earlier articles on this topic check out: AI for Sustainable Development: How AI tools and technology are helping grow the food we need and AI for Sustainable Development: How AI tools and technology can help manage the resources of our planet.
Want to talk? Contact us directly to learn how Alcea can support your AI adoption.
Comments