Search

Governments use metrics made possible by new data technologies to allocate budgets, manage pandemics, valorize ecosystems, and demonstrate how these actions are legitimate. Big data is pointed to as providing objective answers that emerge untampered from observations of the world as it is – a view from nowhere. While data is more valued than ever in environmental governance, so too are arrangements that seek stakeholders' input and otherwise address their subjective interests – a view from everywhere. Different kinds of metrics perform state actors as accountable in both registers: metrics that are responsive to dynamic conditions; that account for specific stakeholders; that can be prioritized against one another in interactive data visualization tools. Louisiana, USA's Coastal Master Plan is an attempt to stem wetlands loss through fine-scale modeling of large volumes of data and calculation of these kinds of social and environmental metrics. State actors there make accountability claims that appear contradictory: their decisions are legitimate because they are driven by the best available coastal science and technology, while their data tools 'didn't make decisions for us.' As state actors deploy environmental big data and metrics to make sense of it, we should be able to explain these apparently contradictory stances and the controversies that result. STS theory on metrics in environmental governance benefits from characterizing how 'modes of authorized seeing' are given expression by different metrical forms and what brings modes into contact and conflict. [ABSTRACT FROM AUTHOR]

Conservationists around the world advocate for "data-driven" environmental governance, expecting data infrastructures to make all relevant and actionable information readily available. But how exactly is data to be infrastructured and to what political effect? I show how putting together and maintaining environmental data for decision-making is not a straightforward technical task, but a practice shaped by and shaping politico-economic context. Drawing from the US state of Louisiana's coastal restoration planning process, I detail two ways ecosystem modelers manage fiscal and institutional "frictions" to "infrastructuring" data as a resource for decision-making. First, these experts work with the data they have. They leverage, tweak, and maintain existing datasets and tools, spending time and money to gather additional data only to the extent it fits existing goals. The assumption is that these goals will continue to be important, but building coastal data infrastructure around current research needs, plans, and austerity arguably limits what can be said in and done with the future. Second, modelers acquire the data they made to need. Coastal communities have protested the state's primary restoration tool: diversions of sediment from the Mississippi River. Planners reacted by relaxing institutional constraints and modelers brought together new data to highlight possible winners and losers from ecological restoration. Fishers and other coastal residents leveraged greater dissent in the planning process. Political ecologists show that technocentric environmental governance tends to foreclose dissent from hegemonic socioecological futures. I argue we can clarify the conditions in which this tends to happen by following how experts manage data frictions. As some conservationists and planners double down on driving with data in a "post-truth" world, I find that data's politicizing effects stem from what is asked of it, not whether it is "big" or "drives." [ABSTRACT FROM AUTHOR]

Abstract: Non-profit, consumer, and government advocates are working to expand access to locally-produced foods as a way of addressing major social and ecological issues. Some activists, however, suggest that farms “scaling-up” production and distribution may lose sight of the movement's aims by circumventing a direct exchange between growing and consumer and by delivering “local” shares long distances. I argue that in order to answer whether scaling-up is misguided, we first have to understand how farms come to scale differently. I describe the varied practices that three community supported agriculture (CSA) farms in the Midwest perform in order to give their products market value as embedded in a specific socio-ecological context. I focus on three key moments of CSA that advocates are concerned about in scaling-up: 1) the employment of different kinds of labor; 2) operating within the seasons; 3) the management of sharer expectations about produce quantity and quality. As what I call commodity practice, farm decisions about these factors produce differently scaled local food commodities. These different practices are not necessarily incongruent with the aims of the movement. My main point in this approach is to advance an awareness of hybridity in local food institutions. As CSAs and other institutions like food hubs grow and evolve, a look at commodity practices can shed light on and confound some of the apparent contradictions in scaling-up. [Copyright &y& Elsevier]