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Those concerned with climate governance will want to keep watching what is happening in AI governance. Far from unrelated, the two parallel one another in terms of how fractions of capital—whether within fossil fuel or tech sectors—call for legislating in the face of crisis or for voluntary pledges. In truth, both may be said to be forms of self‐governance. Climate and AI intersect firstly in how they are imagined: dominant climate and AI discourses are both symptoms of Anthropocene thinking and 'capitalist realism'. They also intersect in as much as 'AI for Good' initiatives propose that AI is ethical because it can help to address climate change. What seems missing, however, is any consideration of this climate AI as a procedure—is its knowledge valid, what knowledges does it displace or exclude, what biases are reproduced?—and consideration for its consequences, including harms. Does it actually result in climate mitigation and/or adaptation in a given context? What 'maladaptive' outcomes might it drive? What alternatives does it foreclose? These sorts of questions are ones where geographers will continue to have a lot to say. Those concerned with climate governance will want to keep watching what is happening in AI governance. Far from unrelated, the two parallel one another in terms of how fractions of capital—whether within fossil fuel or tech sectors—call for legislating in the face of crisis or for voluntary pledges. They also intersect in as much as 'AI for Good' initiatives propose that AI is ethical because it can help to address climate change. [ABSTRACT FROM AUTHOR]

• Environmental and climate crises are grist for tech solutions. • AI technologies require computational resources that are environmentally embedded. • Disasters become "shocks" that the AI industry capitalizes on. • Climate AI shapes material investment flows and landscapes. • Data-driven approaches to climate reproduce injustices faced by marginalized communities. Emerging narratives around artificial intelligence (AI) and machine learning place great faith in these technologies' ability to ameliorate threats posed by climate change. They promise the capacity to analyze vast amounts of more precise and real-time data, improving how decision-makers predict, respond, and adapt. Yet scholars in political ecology have long observed that technocentric approaches typically reduce complex human-environment relationships in ways that fail to account for social relations and power dynamics. This paper charts the emerging political economy of "climate AI" – the philanthropies, NGOs, private consultancies, and tech giants investing in data-driven climate initiatives. Mapping out two case studies, we show that environmental and climate crises are grist for tech solutions and find that many climate AI actors are interested in it for surveillance, greenwashing, and commodifying algorithms. We pay special attention to how neocolonial and racialized power structures manifest in climate AI and outline three ways for political ecologists and digital geographers to research its socio-materiality: how computational resources are environmentally embedded, how disasters become "shocks" that the AI industry capitalizes on, and how climate AI shapes material investment flows and landscapes. Highlighting how data-driven approaches to climate crises reproduce injustices already faced by marginalized communities, our analysis contributes to research on environmental data justice. [ABSTRACT FROM AUTHOR]

Algorithms – instructions for acting on data, executed by code – are increasingly being enrolled into climate policy governance via the prediction of policy outcomes, the evaluation of climate mitigation and adaptation strategies, and the design of practitioner actions. Yet the political implications of these technological changes in environmental governance are only just beginning to be theorised. In this paper, we examine one particular facet of this emerging politics: the relationship between thinking algorithmically and hegemonic power. Drawing from Laclau and Mouffe's theorisation of hegemony we argue that algorithmic forms of reasoning lend themselves towards producing hegemonising knowledge regimes, with important implications for a democratic politics of climate change. Recognising that algorithms stand for wider socio‐technical assemblages that structure and create knowledge, we call for greater attention to the reliance on algorithms within climate governance – less for the algorithms themselves than for their particular epistemic commitments that create algorithmic ways of thinking, with associated claims to power. Through a critical review of scholarship at the intersection of critical digital studies and environmental governance, we first identify three key epistemic commitments involved in thinking algorithmically: induction, abstraction, and optimisation. We then examine the correspondence between these key features of algorithmic thinking and the conditions that Laclau and Mouffe propose form the grounds for hegemony: objectivity, universality, and necessity. Better understanding what "thinking algorithmically" entails, and the forms of knowing and acting that it affords and excludes, is vital, we argue, to begin naming the political implications and transformative potential of new forms of climate governance. [ABSTRACT FROM AUTHOR]