Artificial Intelligence and Climate Change: More than meets the eye

Many in the climate and sustainability community are concerned about the implications of the AI revolution on the global warming issue. These concerns are understandable – while artificial intelligence enables amazing work and creative tools, there’s a high energy consumption price as running AI models requires more data centers, electricity, and cooling infrastructure. AI-attributable energy consumption is estimated to grow at an annual rate of 30% by 2028, potentially surpassing Iceland’s total electricity consumption.

Another concern is the potential misuse of AI capabilities from an environmental perspective. Artificial intelligence can increase consumer consumption by enhancing targeted advertising and can assist fossil fuel companies in increasing their output. Artificial intelligence can be detrimental just like any technology.

The climate community believes that answers to the climate crisis will come from deep technology solutions based on physics, chemistry, biology, and hardware, backed by massive capital investments, but AI-based optimizations are perceived as nice-to-have additions.

However, I see the situation as more complex, where AI will play a significant role in addressing environmental challenges. This stems from accelerated technological developments in several areas: First, the availability of artificial intelligence models and cloud computing infrastructures has increased dramatically. Second, the distribution of ”smart” edge devices connected to the network, across a variety of industries, has expanded. Fast and cheap computing capabilities at the edge (Edge computing), enabled by advanced chips, have become more widely available. Third, access to large databases in many fields, such as satellite imagery (with improved resolution and reduced costs) and open-source weather data, has increased.

The combination of these technologies can accelerate the adoption of new solutions by significantly reducing implementation time and costs. Let’s explore some examples of how AI can contribute to addressing environmental challenges:

In the AgTech sector, more companies are employing artificial intelligence models that integrate diverse layers of information, including satellite images, weather data, and various sensors, to provide farmers with insights on increasing efficiency and adopting environmentally friendly practices. AI can also significantly reduce the use of water and pesticides while increasing crop yields.

Artificial intelligence algorithms are also used to monitor entire water and sewage networks, detecting pipeline failures, and tracking contamination events in real-time. These capabilities, supported by a growing network of smart devices connected to central systems, can help save millions of cubic meters of water per year, maintain the quality of wastewater treatment, and prevent emergencies while reducing energy and repair costs.