The Ecological Impact of AI: From Hidden Costs to Sustainable Opportunities

As an AI strategist and innovation expert, my mission is to guide organizations in using AI in ways that are not only responsible but also sustainable. In this blog, I’ll shed light on the environmental costs of AI and show how your organization can make conscious, sustainable choices.

Training large AI models requires tremendous amounts of energy. Generative AI, like ChatGPT, is a prime example. Every single prompt consumes about 10 times more energy than the average Google search. With billions of prompts generated daily, the ecological tab adds up quickly.

“The energy needed to train just one large language model can equal multiple transatlantic flights and that’s even before deployment.” - Dr. Sasha Luccioni, AI Researcher at Hugging Face

A lesser-known but equally pressing issue is water consumption. Servers in data centers require cooling, often using vast quantities of fresh groundwater. In 2022 alone, Google used over 15.8 billion liters of water for its data centers in the U.S. Looking ahead, AI is projected to consume 6 billion cubic meters of water annually by 2027 six times Denmark’s yearly water usage.

Training a single chatbot can emit as much CO₂ as 125 round-trip flights between New York and Beijing. Moreover, the hardware used powerful GPUs quickly becomes obsolete, contributing to e-waste. These devices are filled with rare earth metals, difficult to recycle and potentially harmful to soil and air quality.

Dr. Luccioni advocates for greater transparency and measurement. She develops tools that reveal the carbon footprint of AI models, enabling developers to make more informed, sustainable choices.

According to Henrik Skaug Sætra, author of AI for the Sustainable Development Goals, AI has a dual impact: it can accelerate progress toward the SDGs or undermine them if implemented without care.

Positive Impacts

• SDG 6 – Clean Water and Sanitation: AI improves water quality monitoring, leak detection, and water management. Smart irrigation systems can drastically reduce water use in agriculture.

• SDG 7 – Affordable and Clean Energy: AI-driven optimization of energy grids enables better matching of supply and demand, promoting renewable sources like solar and wind.

• SDG 11 – Sustainable Cities and Communities: AI optimizes traffic flows, predicts air quality, and reduces energy use in buildings via smart systems—creating cleaner, more livable urban environments.

• SDG 13 – Climate Action: AI supports climate modeling, early disaster detection (e.g., floods or wildfires), and strategies to reduce industrial CO₂ emissions.

• SDG 14 & 15 – Life Below Water & Life on Land: AI helps analyze biodiversity data, track endangered species, and combat illegal fishing or deforestation using satellite imagery and drones.

Negative Impacts

• Energy-Intensive Models (SDG 7): High electricity consumption for training and running AI models strains energy grids and increases greenhouse gas emissions when sourced from fossil fuels.

• Electronic Waste (SDG 12): Short AI hardware life cycles contribute to e-waste, containing rare metals that are often not reused—posing risks to ecosystems and human health.

• Water Consumption (SDG 6): Intensive cooling needs threaten local water supplies, especially in arid regions where AI infrastructure is strategically located.

Sætra stresses the importance of a holistic, strategic approach to evaluating AI’s environmental impact. Both benefits and risks must be carefully weighed in AI development and deployment.

Under the EU’s Corporate Sustainability Reporting Directive (CSRD), effective from January 2024, and in the context of the upcoming EU AI Act, there is currently no specific requirement to measure or report AI’s ecological footprint something urgently needed.

The CSRD requires companies to disclose detailed information about their environmental, social, and governance (ESG) impact. AI plays a dual role:

• Subject of Reporting: Companies must transparently disclose the environmental impact of their AI systems, including energy use and carbon emissions.

• Tool for Reporting: AI can enhance CSRD reporting by automating data analysis and streamlining the reporting process.

By responsibly and transparently integrating AI into both operations and reporting, organizations can comply with CSRD requirements while contributing to sustainable development.

The ecological impact of AI is not a future threat—it’s a present reality. Fortunately, there are steps your organization can take today:

• Make sustainability a core element of your AI strategy, not an afterthought.

• Include energy use as a key metric when developing models or selecting cloud infrastructure.

• Use AI to improve CSRD reporting, leveraging it for transparency, not just performance.

• Raise internal awareness through training, workshops, and dialogue on sustainable AI.

• Partner with organizations committed to sustainability, from your own ESG teams to strategic tech partners.

AI doesn’t have to be a climate threat on the contrary, it can be a driver of sustainability. With the right decisions, AI can support energy optimization, climate modeling, and the circular economy. Let’s build a future where AI and sustainability go hand in hand not head to head.