Is XBRL tagging for sustainability reports "archaic nonsense" — or a chance for digital reporting and AI? If you are an NLP researcher and want to discuss this with us, join NLP4Climate on 12 August.
Climate and societal risk under climate change is inherently uncertain, still under active research, and hampered by real gaps in prediction data. Institutions have often answered with single 'scores' — Decision Making under Deep Uncertainty (DMDU) offers a different approach: test many futures, see which policies survive.
Poor science communication is still damaging climate politics today. We think the fix — building science-aware societies and citizen scientists — is a logical next step. But science itself has a democratic-access problem, much like climate risk does. We're researching whether art, which faces a parallel funding bottleneck, can help open both up.
Public climate data is abundant but coarse. Asset-level risk analysis needs resolution public data can't provide, so a private, well-funded market has emerged to close that gap with proprietary, often AI-driven methods. We think that market's structure — who can afford fine-grained data, and who can't — is itself a climate justice problem, made worse when the data isn't even reliably accurate.
There is no global crisis of trust in science — but there is a real, well-documented gap in who feels invited into it. We think closing that gap, building science-aware societies and citizen scientists, is the logical next step after the climate-communication lesson.
