摘要：One of the challenges in globally consistent assessmentsof physical climate risks is the fact that asset exposure data are eitherunavailable or restricted to single countries or regions. We introduce aglobal high-resolution asset exposure dataset responding to this challenge.The data are produced using “lit population” (LitPop), a globallyconsistent methodology to disaggregate asset value data proportional to acombination of nightlight intensity and geographical population data. Bycombining nightlight and population data, unwanted artefacts such asblooming, saturation, and lack of detail are mitigated. Thus, thecombination of both data types improves the spatial distribution ofmacroeconomic indicators. Due to the lack of reported subnational assetdata, the disaggregation methodology cannot be validated for asset values.Therefore, we compare disaggregated gross domestic product (GDP) per subnational administrativeregion to reported gross regional product (GRP) values for evaluation. Thecomparison for 14 industrialized and newly industrialized countries showsthat the disaggregation skill for GDP using nightlights or population dataalone is not as high as using a combination of both data types. Theadvantages of LitPop are global consistency, scalability, openness,replicability, and low entry threshold. The open-source LitPop methodologyand the publicly available asset exposure data offer value for manifold usecases, including globally consistent economic disaster risk assessments andclimate change adaptation studies, especially for larger regions, yet atconsiderably high resolution. The code is published on GitHub as part of theopen-source software CLIMADA (CLIMate ADAptation) and archived in the ETHData Archive with the link https://doi.org/10.5905/ethz-1007-226(Bresch et al., 2019b). The resulting asset exposuredataset for 224 countries is archived in the ETH Research Repository withthe link https://doi.org/10.3929/ethz-b-000331316(Eberenz et al., 2019).
