The (In)visible Health Risks of Climate Change

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College of Natural Science and Mathematics, Geography and the Environment


Marginalization, Inequity, Justice, Climatic extremes, Governmentality, Healthcare, Bias, Rights


This paper scrutinizes the assertion that knowledge gaps concerning health risks from climate change are unjust, and must be addressed, because they hinder evidence-led interventions to protect vulnerable populations. First, we construct a taxonomy of six inter-related forms of invisibility (social marginalization, forced invisibility by migrants, spatial marginalization, neglected diseases, mental health, uneven climatic monitoring and forecasting) which underlie systematic biases in current understanding of these risks in Latin America, and advocate an approach to climate-health research that draws on intersectionality theory to address these inter-relations. We propose that these invisibilities should be understood as outcomes of structural imbalances in power and resources rather than as haphazard blindspots in scientific and state knowledge. Our thesis, drawing on theories of governmentality, is that context-dependent tensions condition whether or not benefits of making vulnerable populations legible to the state outweigh costs. To be seen is to be politically counted and eligible for rights, yet evidence demonstrates the perils of visibility to disempowered people. For example, flood-relief efforts in remote Amazonia expose marginalized urban river-dwellers to the traumatic prospect of forced relocation and social and economic upheaval. Finally, drawing on research on citizenship in post-colonial settings, we conceptualize climate change as an ‘open moment’ of political rupture, and propose strategies of social accountability, empowerment and trans-disciplinary research which encourage the marginalized to reach out for greater power. These achievements could reduce drawbacks of state legibility and facilitate socially-just governmental action on climate change adaptation that promotes health for all.

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