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Accepted Paper:
Paper short abstract:
I analyse ethnographically how humans & non-humans are reconfigured in a corporate carbon accounting machinery to perform smooth environments, removing erroneous environments. The company bets on specific human bots to sustain the machinery. Dream&datascapes are configured for capitalist purposes.
Paper long abstract:
Capitalist promises of reconciling nature, technology and markets rely profoundly on the imaginary of datafied environments that are readily readable for decision-makers, figured as centralised elites, managers or decentralised consumers. Corporate carbon accounting purports to render legible a company's impacts on the environment. Using ethnographic engagement with a multinational Fortune 50 company's Corporate Responsibility Team, I experiment with a four-fold story. First, I consider the corporate carbon machinery, its heterogeneous configuration, including the presence of human bodies and their ontic practices. Second, I show how doing carbon within this machinery effects, what I call, erroneous environments. Third, I complicate the politics of bodies, showing how they are both used in bets to repair breakdowns of the machinery and how betting on making some bodies absent promises a sustainable machinery. Fourth, I suggest that the ideal human body in the imaginary of a sustainable machinery of carbon accounting is figured like a bot. My figure of the bot employs data do achieve social and political effects. I offer this four-fold story to question the imaginary that "nature" increasingly becomes apprehended exclusively through "data". The story I tell foregrounds a "through 'data'" in which the focus is on the "through": in the company, it matters to datafy environments such that these environments can be overseen; and by overseeing, some environments are not only performed as managed or governed but they are also overlooked, ignored. The company configures environments so that erroneous environments disappear. What matters is a clean and green dreamscape.
Meeting (in) data
Session 1