Algorithmic Climate Politics?

Authors: Ruth Machen*, Newcastle University, Eric Nost*, University of Guelph
Topics: Political Geography, Cultural and Political Ecology, Human-Environment Geography
Keywords: climate change, digital, ways of knowing, politics
Session Type: Paper
Day: 4/3/2019
Start / End Time: 9:55 AM / 11:35 AM
Room: Empire Room, Omni, West
Presentation File: No File Uploaded

Climate change is increasingly known and governed algorithmically. Algorithms are instructions for acting on data, executed by sets of code (or software), and have been central to modelling the climate, predicting climatic change, and envisioning future scenarios. They are also increasingly enrolled in new ways to implement and assess mitigation and adaptation strategies. Whilst recognising that algorithms often stand for wider sociotechnical assemblages that frame, structure and create knowledge – assemblages in which delimiting the algorithm and its specific effects may be misleading (Gillespie and Seaver 2016, Kitchin 2016) - we argue for revisiting the algorithm itself, and its particular epistemological commitments, as a way to name the political implications of knowing and governing climate change algorithmically. Towards this goal, this paper first maps out some of the key ways in which algorithms and climate change governance intersect – reviewing existing scholarship in political ecology, digital geographies and environmental governance to examine the sorts of promises that algorithms are seen to afford, the futures that become envisaged, and the forms of reasoning and notions of politics that become central. Second, we set out four key epistemological premises that are common to these algorithmic modalities – induction, association, patterning and optimisation. Thirdly, using these epistemic commitments as a framework, we examine the effects that emerge within the domain of climate governance. We conclude with a number of nascent questions that return to trouble algorithmic climate politics as important areas for future research.

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