When Artificial Intelligence Meets the Environment

Artificial intelligence is arguably one of the most powerful tools humans have ever built. It can spot methane leaks from space, predict deadly storms, and help governments respond faster to climate threats. But the same technology promising to protect the planet is also applying more pressure on our environment.

Described in a 2024 Forbes report as “the genius that never sleeps”, AI processes information at digital speed and solves many complex problems far faster than humans. Its rise marks a major technological inflection point, reshaping fields from healthcare and finance to environmental protection. Studies show AI can support climate action, environmental monitoring, and sustainability planning in ways never before possible. Yet as its influence grows, so do the environmental costs behind the scenes, creating a tension between AI’s promise and its footprint.

AI as a Tool for Environmental Protection

One of the most convincing uses of AI in environmental governance can be found in initiatives led by the United Nations Environment Programme (UNEP). UNEP supports the International Methane Emissions Observatory (IMEO), a global platform that uses AI to monitor methane emissions and help in their reduction. According to a 2022 report by UNEP, IMEO’s technology allows for the integration of diverse methane data streams at an unprecedented level of accuracy. By leveraging AI, the platform connects data with science, transparency, and policy, enabling governments and stakeholders to make data-driven decisions, significantly strengthening global climate action.

Another UNEP co-founded initiative, developed in partnership with IQAir, is the Global Environment Monitoring System (GEMS) Air Pollution Platform, the world’s largest air quality information network. IQAir aggregates data from over 25,000 monitoring stations across more than 140 countries and applies AI to analyze real-time air quality trends (see how-artificial-intelligence-helping-tackle-environmental-challenges). These initiatives help governments and health agencies design effective air pollution and public health protection measures.

Going beyond emissions and air quality, AI has also transformed hazard prediction and disaster preparedness. According to a 2018 research report by Columbia Climate School, AI systems can identify tropical cyclones, atmospheric rivers, and weather fronts with 89–99 percent accuracy, phenomena that are often difficult for humans to detect in time (see artificial-intelligence-climate-environment/). This improved forecasting helps reduce loss of life, strengthen emergency response, and improve climate resilience.

AI also supports ecosystem and wildlife monitoring. Its rapid processing capacity enables near real-time satellite analysis to detect illegal logging, monitor biodiversity loss, assess water quality, detect underground leaks in water systems, and predict infrastructure maintenance needs. In disaster management, AI can simulate extreme weather events, identify vulnerabilities, and coordinate real-time response efforts.

Given all these applications, AI is increasingly viewed as a solution to many human and environmental problems. However, this optimistic narrative tells only half the story.

The Hidden Environmental Cost of AI

Building a world powered by AI requires enormous physical infrastructure, data centers, servers, cooling systems, and vast energy supplies. While AI enables better environmental monitoring, the process of training and deploying AI systems is itself environmentally expensive.

According to a 2025 report by the World Resources Institute, AI systems at scale require vast amounts of electricity and water. The International Energy Agency (IEA) reports that a typical AI-focused data center consumes as much electricity as 100,000 households, while the largest data centers currently under construction are expected to consume up to 20 times more. In the United States alone, peak electricity demand is projected to rise by 128 gigawatts by 2029, largely driven by AI-related data centers (see insights/ai-promise-and-peril-for-planet).

This surge in energy demand directly translates into higher greenhouse gas emissions, particularly in regions still dependent on fossil fuels (oil, gas, coal). At the same time, water-intensive cooling systems place additional pressure on already stressed water supplies, especially in drought-prone areas. Rising electricity prices and water scarcity further burden households that are already struggling to make ends meet.

UN officials have also warned that the broader information and communications technology (ICT) sector accounts for approximately 3–4 percent of global emissions, with data centres being major contributors (see ai-promise-and-peril-for-planet). While different initiatives by the UN such as the CODES Action Plan for a Sustainable Planet in the Digital Age, aim to reduce this footprint, the environmental cost of AI still remains substantial.

Another overlooked consequence of AI expansion is electronic waste (e-waste). As AI hardware becomes obsolete, discarded servers, chips, and electronic devices accumulate. According to the UN Global E-waste Monitor, only 17.4 percent of global e-waste is currently recycled in an environmentally sound manner. Alarmingly, global e-waste is projected to reach nearly 75 million metric tonnes by 2030 (see how-artificial-intelligence-helping-tackle-environmental-challenges). Without effective recycling systems and circular economy strategies, the AI revolution risks accelerating environmental degradation through toxic waste, resource depletion, and pollution.

So, What Is the Way Forward?

Every development trajectory comes with trade-offs, and artificial intelligence is no exception. In a world where AI is rapidly becoming the backbone of innovation, the critical question is no longer whether AI can help the environment, but how its benefits can be achieved without worsening environmental harm.

Solar-powered AI is increasingly discussed as a practical way to reduce the environmental footprint of artificial intelligence systems. Instead of relying solely on grid electricity, often generated from fossil fuels, AI infrastructure can be partially or fully powered by on-site or off-site solar energy, particularly for data centres and edge computing systems.

In regions with high solar potential, data centres can integrate large-scale solar farms and rooftop solar installations to offset a significant portion of their electricity demand. While solar power alone may not yet meet the constant, 24-hour energy needs of large AI systems, it can meaningfully reduce dependence on carbon-intensive grids, especially during peak daylight hours.

Also, addressing e-waste requires enforceable producer responsibility. Manufacturers and large AI firms can be required to participate in take-back, refurbishment, and recycling schemes, especially for servers and high-performance computing equipment. Extending hardware lifecycles through reuse will be better for the environment than replacement with new systems.

With the right research, laws, regulations, and energy choices, AI can become part of the climate solution rather than part of the problem.

Also see: Are AI Chatbots Biased on Climate?