Scales of inequality: The role of spatial extent in environmental justice analysis

Although environmental justice scholars have been addressing spatial scale for at least thirty years, one of its components remains largely overlooked, namely extent. In this paper, we investigate the effects of extent variation in environmental (in)justice patterns by analyzing the statistical associations between socio-economic marginality and environmental vulnerability at three different spatial scopes: metropolitan areas, municipalities, and boroughs. Using census and geospatial data, our case study focuses on the relations between income and color/race, on the one hand, and susceptibility to flooding and landslides, on the other, in Brazil’s two largest cities (São Paulo and Rio de Janeiro). We constructe an integrated, census tract-level database using areal weighting interpolation combining census with susceptibility data to perform k-means multivariate clustering analysis. Although our results show that the most vulnerable to landslides and flooding are non-white, low-income people – confirming common environmental justice claims – they also suggest that spatial extent impinges on statistical patterns. While the clusters are very similar in the metropolitanAlthough environmental justice scholars have been addressing spatial scale for at least thirty years, one of its components remains largely overlooked, namely extent. In this paper, we investigate the effects of extent variation in environmental (in)justice patterns by analyzing the statistical associations between socio-economic marginality and environmental vulnerability at three different spatial scopes: metropolitan areas, municipalities, and boroughs. Using census and geospatial data, our case study focuses on the relations between income and color/race, on the one hand, and susceptibility to flooding and landslides, on the other, in Brazil’s two largest cities (São Paulo and Rio de Janeiro). We constructe an integrated, census tract-level database using areal weighting interpolation combining census with susceptibility data to perform k-means multivariate clustering analysis. Although our results show that the most vulnerable to landslides and flooding are non-white, low-income people – confirming common environmental justice claims – they also suggest that spatial extent impinges on statistical patterns. While the clusters are very similar in the metropolitan and municipal scales, pointing to a fractal-like structure, they differ significantly at the borough scale. This smaller scale reveals a different picture, one in which well-to-do white people are just as exposed to hazards (especially flooding) as non-white, middle- to lower-middle-class people. We argue this is a result of elite self-exposure to risk, which is supported by socio-spatially unjust distribution of risk-mitigation infrastructures. Policymakers should pay attention to such scale-dependent complexities in devising ways to cope with the increasing inequities brought about by climate change. and municipal scales, pointing to a fractal-like structure, they differ significantly at the borough scale. This smaller scale reveals a different picture, one in which well-to-do white people are just as exposed to hazards (especially flooding) as non-white, middle- to lower-middle-class people. We argue this is a result of elite self-exposure to risk, which is supported by socio-spatially unjust distribution of risk-mitigation infrastructures. Policymakers should pay attention to such scale-dependent complexities in devising ways to cope with the increasing inequities brought about by climate change.