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

Abstract

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.