At COP30, artificial intelligence (AI) was not just a side topic it became a central point of debate about the future of the planet. The discussions split into two camps: one arguing that AI is an unregulated, energy-intensive force contributing to rising emissions through the expansion of data centers and chip production; the other claiming that AI could serve as a powerful tool for tackling climate change, optimizing energy grids, forecasting extreme weather, improving industrial efficiency, and speeding up scientific discoveries that help reduce carbon footprints.
Both perspectives have valid points, but the critical question is: under what rules can AI be harnessed for the benefit of the planet? The debate centers on governance how we regulate and guide the development and use of AI technologies in ways that maximize their benefits while minimizing harmful effects.
One productive way to frame AI in this context is as “infrastructure.” Infrastructure itself is neither inherently good nor bad it is neutral, shaped by how it’s governed. For example, roads can be used to facilitate commerce and trade, but they can also enable the spread of pollution. Electricity can power life-saving medical equipment or be used to fuel destructive weapons. Similarly, AI, as a form of infrastructure, can either exacerbate climate challenges or help solve them. It’s the role of policymakers to ensure that AI development is directed toward public good, shaping incentives and regulations in a way that aligns technological advancement with the urgent need for decarbonization.
This framing of AI as infrastructure pushes us toward specific, practical measures. For instance, transparency in AI model training and energy consumption becomes essential. To ensure AI’s climate benefits are realized, we must have clear visibility into how AI systems are trained, what data they use, and how much energy they consume during their operation. Governments could implement procurement standards for AI technologies, ensuring that the tools they deploy are energy-efficient and aligned with climate goals. Moreover, there is a growing call for climate-aligned reporting for large-scale computing, including the AI systems that drive much of today’s technological progress.
These practical measures go beyond just technological design. They require an understanding of how AI intersects with other regulatory domains like competition policy, digital rights, and technology governance. AI’s role in climate action does not only involve how it’s used to reduce emissions it also raises questions about the power and control over these technologies, who gets access to them, and how they are deployed across industries. The governance of AI, therefore, requires a new kind of thinking that blends climate policy with tech policy, ensuring that the deployment of AI is equitable, transparent, and accountable.
At COP30, climate negotiators faced the reality that addressing climate change now involves grappling with the digital economy. Decarbonization will not be achieved through wind turbines, solar panels, and batteries alone. It will also depend on the effective use of algorithms, data, and computing power tools that have the potential to optimize energy systems, improve efficiency across industries, and drive innovations in areas like carbon capture, sustainable agriculture, and clean energy.
However, this intersection of climate action and digital technology also creates a new set of challenges. As AI systems continue to evolve, their environmental impact, particularly in terms of energy consumption and emissions from data centers, is growing. The energy needed to train and operate large AI models, as well as the hardware infrastructure required to support these technologies, has significant carbon footprints. If not properly regulated, the proliferation of AI could lead to more demand for energy, potentially undoing some of the progress made through other climate solutions.
The key takeaway from the discussions at COP30 is that the fight against climate change is increasingly intertwined with the digital transformation of society. In the coming years, the technologies we use to combat climate change will not only include physical solutions like renewable energy sources but also the digital tools that support them. AI could either be a catalyst for global decarbonization or a contributor to further environmental harm it all depends on how we govern its development and deployment.
The lesson from Belém is clear: AI and the digital economy must be factored into every conversation about the climate crisis. As AI continues to grow in importance, it’s crucial that its role in climate policy is shaped by thoughtful, forward-looking regulations. The future of both AI and climate change mitigation will depend on how effectively we can align these two forces, ensuring that AI becomes a tool for good in the fight against global warming, rather than an unwitting contributor to the problem.
In conclusion, COP30 highlighted that decarbonization is no longer just about physical technologies; it’s also about the algorithms, computing power, and governance frameworks that underpin the digital economy. To achieve meaningful climate progress, AI must be governed in a way that aligns with sustainability goals. By integrating climate-conscious policies into the development of AI, we can ensure that technology becomes an ally in the fight against climate change rather than an obstacle.
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