AI and Climate Resilience: Promise and Pitfalls in India's Adaptation Push
AI offers powerful tools for climate adaptation, yet data gaps, costs, and ethical risks require careful integration with traditional knowledge and policy.
As extreme weather events become more frequent and intense, artificial intelligence (AI) is emerging as a key tool in the fight to build climate resilience. From early warning systems that predict floods and heatwaves to algorithms that optimise energy use and manage natural resources, AI can process vast datasets far faster than conventional methods. This capability makes it invaluable for climate modelling, risk assessment, and community-level engagement, helping cities become safer and more adaptable.
However, the promise of AI is not without its limitations. High-quality, localised data are often scarce in developing regions, and AI models trained on incomplete or biased datasets can produce unreliable forecasts. The energy consumption of large AI systems themselves adds to carbon emissions, creating a paradox. Moreover, over-reliance on technology can sideline indigenous knowledge and community-led practices that are crucial for adaptive responses.
A critical analysis shows that AI is not a silver bullet. Its effectiveness depends on integrating it with interdisciplinary approaches—combining climate science, social research, policy design, and grassroots participation. For India, this means investing in data infrastructure, ensuring equitable access to AI tools, and building human capacity to interpret and act on AI-driven insights.
The way forward lies in a balanced strategy: deploy AI where it adds clear value, but pair it with robust governance, transparency, and a commitment to sustainability. Only then can AI truly enhance climate resilience without deepening existing vulnerabilities.