By Zonash Amanullah (UK/Pakistan)
Climate change is no longer an abstract debate held in courtrooms, conferences, or academic journals. It is a lived reality measured in record-breaking heat, unprecedented floods, raging wildfires, and the quiet collapse of livelihoods. As a law graduate trained to think in terms of responsibility, regulation, and accountability, I find it impossible to view the climate crisis as merely a scientific or technological problem. It is, at its core, a governance challenge. And increasingly, Artificial Intelligence (AI) is becoming one of the most consequential tools shaping how we respond to it.
Until recently, the idea that machines could meaningfully assist in understanding the Earth’s climate system sounded like science fiction. Today, it is operational policy. Across Europe, a “digital twin” of the Earth is already being built an ultra-high-resolution simulation of the planet’s atmosphere, oceans, and land systems. This model can track pollution, predict urban heat waves, and anticipate natural disasters with a level of precision unimaginable just a decade ago. With resolution down to a few kilometers, it represents a fundamental shift: climate governance informed not by hindsight, but by foresight.
This matters because law and policy have always struggled with time. Regulations are often reactive, responding to harm after it has already occurred. AI-driven climate modelling disrupts this pattern by offering early warnings and predictive insights. In Hong Kong, for example, AI-based weather models now forecast heavy rainfall and thunderstorms hours in advance with significantly higher accuracy than traditional systems. A marginal improvement in prediction may seem technical, but legally and ethically it can mean the difference between preventable loss of life and effective disaster preparedness.
Perhaps the most compelling impact of AI, however, is not found in data centres or satellites, but in fields and villages. In southern Malawi, farmers devastated by Cyclone Freddy are now using AI chatbots via WhatsApp to receive localized agricultural advice, what to plant, when to harvest, and how to adapt to changing weather patterns. This is climate adaptation at its most human level. It demonstrates that AI is not only a tool for scientists and policymakers, but also for those on the frontline of climate vulnerability.
Critics are right to raise concerns. AI is not environmentally neutral. Data centres consume vast amounts of energy and water, and without clean energy sources, their carbon footprint is significant. Some projections suggest that widespread expansion of AI infrastructure could add tens of millions of tonnes of CO₂ emissions annually.
Yet the broader evidence tells a more complex story. When deployed responsibly, AI has the potential to reduce emissions on a scale that far outweighs its costs. Studies indicate that applying existing AI technologies across energy, transport, and industrial sectors could cut global emissions by over a billion tons of CO₂ by 2035 equivalent to removing hundreds of millions of cars from the road. AI systems are already identifying methane leaks from oil and gas infrastructure using satellite imagery, allowing rapid intervention before vast quantities of this highly potent greenhouse gas escape into the atmosphere.
AI is also transforming how we understand land use one of the most legally contentious and environmentally damaging drivers of emissions. Projects using machine learning can now detect deforestation, urban expansion, and ecosystem degradation in near real time. For regulators, courts, and international bodies, this kind of evidence could fundamentally reshape enforcement of environmental law and accountability for ecological harm.
NASA and other research institutions are likewise using AI to analyse decades of satellite data, revealing patterns in glacier retreat, river shifts, and ecosystem change that once took years to identify. The result is not just better science, but better grounds for policy decisions, litigation, and international cooperation.
But there is a critical caveat. AI is only as good as the data it is trained on. Much of the world particularly in the Global South lacks consistent, high-quality climate data. Models trained on European or North American datasets may perform poorly in rural Africa or Southeast Asia. This raises an uncomfortable legal and ethical question: will AI-driven climate solutions deepen global inequality rather than reduce it?
Bias in data leads to bias in outcomes. Addressing this requires more than better algorithms; it demands investment in global climate infrastructure, open data-sharing frameworks, and inclusive governance. From a legal perspective, it also calls for international standards that ensure transparency, accountability, and equitable access to AI-driven climate tools.
AI is not a silver bullet for the climate crisis. But it is rapidly becoming one of the most powerful instruments we have to understand risk, prevent harm, and adapt to a changing planet. For lawyers, policymakers, and institutions, the challenge is clear: to ensure that this technology is governed wisely, deployed fairly, and aligned with the urgency that the climate emergency demands.
Time is not on our side. If AI can help us see the future more clearly, the law must ensure we act on it faster.
Author is a lawyer and student of LLM at University of Law, The UK.
