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Small is beautiful: climate-change science as if people mattered

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Rodrigues, R. R. and Shepherd, T. G. orcid id iconORCID: https://orcid.org/0000-0002-6631-9968 (2022) Small is beautiful: climate-change science as if people mattered. PNAS Nexus, 1 (1). pgac009. ISSN 2752-6542 doi: 10.1093/pnasnexus/pgac009

Abstract/Summary

There is a widely accepted gap between the production and use of climate information. It is also widely accepted that at least part of the reason for this situation lies in the challenge of bridging between what may be characterized as ‘‘top-down’’ approaches to climate information on the global scale, and local decision contexts, which necessarily take a ‘‘bottom-up’’ perspective, in which climate change is just one factor among many to consider. We here reflect on the insights provided in a different context—that of economics—by E.F. Schumacher in his celebrated book Small is Beautiful (1973), to see what light they might shed on this challenge, with a focus on climate-change science for adaptation. Schumacher asked how economics might look if it was structured ‘‘as if people mattered’’. We ask the same question of climate-change science, and find many parallels. One is the need to grapple with the complexity of local situations, which can be addressed by expressing climate knowledge in a conditional form. A second is the importance of simplicity when dealing with deep uncertainty, which can be addressed through the use of physical climate storylines. A third is the need to empower local communities to make sense of their own situation, which can be addressed by developing ‘‘intermediate technologies’’ that build trust and transparency. Much of climate-change science is necessarily big science. We argue that in order to make climate information useable for adaptation, it is also necessary to discover the beauty of smallness.

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Item Type Article
URI https://reading-clone.eprints-hosting.org/id/eprint/103943
Item Type Article
Refereed Yes
Divisions Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher Oxford University Press
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