This comprehensive review on AI's role in climate resilience strikes a chord with the current state of tech-enabled environmental action—full of potential but facing practical hurdles. It’s encouraging to see a wide lens applied: nine sectors, both adaptation and mitigation, and a global perspective highlighting where AI research is booming and where it’s barely scratching the surface. Asia leading research efforts makes sense given its diversity of climate challenges and tech ecosystems, but the glaring underrepresentation of regions like Africa and South America reminds us that climate AI solutions can’t be one-size-fits-all—or one-region-dominant.
The dominance of classical machine learning over deep learning probably reflects a pragmatic choice: simpler, interpretable models still rule in high-stakes environmental decisions where explainability matters for trust and policy adoption.
Yet, the call for better data accessibility, ethical frameworks, and interdisciplinary collaboration is where the rubber meets the road. After all, AI is just a tool cracked open from a trove of data and human judgment; without inclusive data and ethical guardrails, it risks widening inequalities rather than easing them.
For those of us cheering on innovation, this paper is a timely reminder to keep our feet grounded in real-world problems—pushing AI not just for flashy breakthroughs, but for sustainable, equitable resilience that respects local contexts and global imperatives alike. Source: Frontiers | Enhancing System Resilience to Climate Change through Artificial Intelligence: A Systematic Literature Review
