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

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.