All Gone is a series of experiments with AI that build on existing collections of climate fiction to create much-needed new climate imaginaries. As the climate crisis is also a “crisis of imagination” (Ghosh, 2016), this project turns to the art genre that is best at forecasting and imagining alternative futures: science fiction. Using collections of ‘cli-fi’ novels, in which science fiction meets natural disaster or heavy weather, algorithms are trained until they are able to render new climate imaginaries in textual and visual form. The edited texts and curated images are further developed into audio stories and a tarot deck as tools for reflection on present and future living with a changing climate.
MULTIFILE
We summarize what we assess as the past year's most important findings within climate change research: limits to adaptation, vulnerability hotspots, new threats coming from the climate–health nexus, climate (im)mobility and security, sustainable practices for land use and finance, losses and damages, inclusive societal climate decisions and ways to overcome structural barriers to accelerate mitigation and limit global warming to below 2°C.
MULTIFILE
The climate crisis is an urgent and complex global challenge, requiring transformative action from diverse stakeholders, including governments, civil society, and grassroots movements. Conventional top-down approaches to climate governance have proven insufficient (e.g. UNFCCC, COP events), necessitating a shift towards more inclusive and polycentric models that incorporate the perspectives and needs of diverse communities (Bliznetskaya, 2023; Dorsch & Flachsland, 2017). The independent, multidisciplinary approach of citizen-led activist groups can provide new insights and redefine challenges and opportunities for climate governance and regulation. Despite their important role in developing effective climate action, these citizen-led groups often face significant barriers to decision-making participation, including structural, practical, and legal challenges (Berry et al., 2019; Colli, 2021; Marquardt et al., 2022; Tayler & Schulte, 2019).
