AI’s Environmental Footprint: Balancing Energy Use, Climate Action, and Sustainable Solutions

Climate Action: How AI Use Impacts the Environment and What You Can Do About It

Artificial intelligence (AI) and the data centres supporting it consume significant amounts of energy and water. While public discussions often focus on AI’s cybersecurity and ethical challenges, its environmental impact deserves equal attention.

AI's demand for resources grows alongside its adoption. However, the technology also offers tools to combat climate change. Organizations can take practical steps to align AI strategies with sustainability goals, balancing resource use with environmental responsibility.

AI’s Growing Appetite for Energy and Water

Cloud computing and digital services have pushed data centres to improve energy efficiency, using better hardware and cooling systems. Still, AI’s energy consumption outpaces these gains. For instance, a single ChatGPT query uses about five times more electricity than a typical web search. Training large language models like GPT-3 consumes as much electricity as powering 130 US homes for a year.

AI image generation demands even more energy. Generating text for 1,000 prompts uses roughly 16% of a smartphone’s full charge, but producing an image requires about 100%. Data centres also rely heavily on water for cooling. GPT-3 is estimated to use a 16-ounce bottle of water for every 10 to 50 responses. With billions of queries processed, annual water withdrawal for AI could reach 6.6 billion cubic metres by 2027.

Beyond operational consumption, building and maintaining data centres contributes up to two-thirds of their total lifetime emissions. Manufacturing IT equipment and chips also add to AI’s environmental footprint. The information and communications technology sector, which includes AI, accounts for at least 1.7% of global greenhouse gas emissions today. This figure will likely grow with increasing digital penetration, cloud storage expansion, and emerging technologies like blockchain.

Mitigating the Impact of AI Energy Consumption

Leading tech companies are taking steps to reduce AI’s environmental impact:

• Amazon matches 100% of its global energy consumption with renewable sources.
• Microsoft requires suppliers to switch to 100% carbon-free electricity by 2030 and aims to meet the same goal internally.
• Google targets carbon-free electricity by 2030 and works on improving AI model efficiency and data centre energy use.
• OpenAI invested in solar-powered solutions for its data centres.
• Salesforce advocates for regulations requiring companies to report AI emissions and efficiency standards.

These examples show that investment in sustainable infrastructure and policies can make a difference. Organizations can draw inspiration from these efforts to improve their own energy management and emissions reduction.

Reducing AI Energy Consumption

Businesses can adopt several practical measures to reduce AI’s environmental impact:

• Measure emissions and track progress: Use tools to monitor and report emissions. Collaborate with suppliers to understand the energy costs of AI usage.
• Optimize AI models and workflows: Use pre-trained models, APIs, compress models, and apply batch processing to reduce energy demand.
• Follow ISO 42001 standards: Incorporate climate change considerations into AI management systems and conduct impact assessments that include environmental effects.
• Choose sustainable vendors: Partner with cloud and AI providers committed to renewable energy and certified in sustainability (ISO 14001, ISO 42001).
• Build a sustainability culture: Educate employees about AI’s environmental impact. Encourage sustainability initiatives and create dedicated groups to lead efforts. Engage customers and suppliers to collaborate on emissions reduction.

AI’s Positive Environmental Impact

Despite its resource demands, AI also has strong potential to support climate action. It can process large datasets quickly, providing valuable insights into climate trends and the effectiveness of emissions reduction strategies.

Key ways AI can help include:

• Enhancing understanding of extreme weather risks
• Modeling climate scenarios for better planning
• Improving resource management and utilization
• Accelerating development of sustainable, energy-efficient materials
• Forecasting demands and optimizing operations to reduce carbon footprints
• Generating accurate product life-cycle assessments to refine supply chain emissions calculations

Maintaining transparency on emissions and energy use through regular reporting and third-party verification builds trust with investors, suppliers, and customers. Achieving certifications like ISO 14001 and ISO 42001 demonstrates a commitment to sustainability and ethical AI practices.

Staying informed about evolving sustainability and AI regulations is essential for compliance and leadership in responsible AI deployment.

Taking Action Today

AI’s energy and water consumption challenge is significant and growing. From startups to global enterprises, every organization has a role in reducing AI-driven environmental impact.

Learning from industry leaders and adopting practical strategies can help you align your AI initiatives with climate goals. The opportunity to make a positive difference is now.

To explore practical AI courses that include sustainable AI practices, visit Complete AI Training.