The local costs of global climate change: spatial GDP downscaling under different climate scenarios

Massimiliano Rizzati, Gabriele Standardi, Gianni Guastella, Ramiro Parrado, Francesco Bosello and Stefano Pareglio

Spatial Economic Analysis, 2023, vol. 18, issue 1, 23-43

Abstract: We present a tractable methodology to estimate climate change costs at a 1 × 1 km grid resolution. Climate change costs are obtained as projected gross domestic product (GDP) changes, under different global shared socio-economic pathway–representative concentration pathway (SSP-RCP) scenarios, from a regional (multiple European NUTS levels) version of the Intertemporal Computable Equilibrium System (ICES) model. Local costs are obtained by downscaling projected GDP according to urbanized area estimated by a grid-level model that accounts for fixed effects, such as population and location, and spatially clustered random effects at multiple hierarchical administrative levels. We produce a grid-level dataset of climate change economic impacts under different scenarios that can be used to compare the cost – in terms of GDP loss – of no adaptation and the benefits of investing in local adaptation.

Date: 2023
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DOI: 10.1080/17421772.2022.2096917

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