Spatially-explicit Gross Cell Product (GCP) time series: past observations (1850-2000) harmonized with future projections according to the Shared Socioeconomic Pathways (2010-2100)

10.5880/pik.2017.007 Geiger, Tobias Tobias Geiger 0000-0002-8059-8270 Potsdam Institute for Climate Impact Research, Potsdam, Germany Daisuke, Murakami Murakami Daisuke Department of Statistical Modeling, Institute of Statistical Mathematics, Tachikawa, Japan Frieler, Katja Katja Frieler 0000-0003-4869-3013 Potsdam Institute for Climate Impact Research, Potsdam, Germany Yamagata, Yoshiki Yoshiki Yamagata Center for Global Environmental Studies, National Institute for Environmental Studies, Tsukuba, Japan Spatially-explicit Gross Cell Product (GCP) time series: past observations (1850-2000) harmonized with future projections according to the Shared Socioeconomic Pathways (2010-2100) GFZ Data Services 2017 Gross Domestic Product Shared Socioeconomic Pathways statistical downscaling Gross Cell Product EARTH SCIENCE > HUMAN DIMENSIONS > POPULATION EARTH SCIENCE > HUMAN DIMENSIONS > SOCIOECONOMICS EARTH SCIENCE > HUMAN DIMENSIONS > SOCIOECONOMICS > PURCHASING POWER economic growth Geiger, Tobias Tobias Geiger 0000-0002-8059-8270 Potsdam Institute for Climate Impact Research, Potsdam, Germany Daisuke, Murakami Murakami Daisuke Department of Statistical Modeling, Institute of Statistical Mathematics, Tachikawa, Japan Frieler, Katja Katja Frieler 0000-0003-4869-3013 Potsdam Institute for Climate Impact Research, Potsdam, Germany Yamagata, Yoshiki Yoshiki Yamagata Center for Global Environmental Studies, National Institute for Environmental Studies, Tsukuba, Japan Geiger, Tobias Potsdam Institute for Climate Impact Research, Potsdam, Germany 1850/2100 Dataset 10.5880/pik.2017.003 http://themasites.pbl.nl/tridion/en/themasites/hyde/ 10.5880/pik.2017.011 CC BY 4.0 We here provide spatially-explicit economic time series for Gross Cell Product (GCP) with global coverage
in 10-year increments between 1850 and 2100 with a spatial resolution of 5 arcmin. GCP is based on a
statistcal downscaling procedure that among other predictors uses national Gross Domestic Product (GDP)
time series and gridded population estimates as input. Historical estimates until 2000 are harmonized
with future socio-economic projections from the Shared Socioeconomic Pathways (SSPs) according to
SSP2 from 2010 onwards.

We further provide a mapping file with identical spatial resolution to associate GCP values with specifc
countries. Based on this mapping we provide nationally aggregated GDP estimates between 1850-2100 in
a separate csv-file.

Additionally, we provide a mapping file with identical spatial resolution providing national assets-GDP
ratios, that can be used to transform GCP to asset values based on 2016 estimates from Credit Suisse’s
Global Wealth Databook 2016.

This dataset has already been used to create a global and spatially-explicit dataset for tropical cyclone
exposure (TCE-DAT), for details see Geiger et al (2017; http://doi.org/10.5880/pik.2017.011).

Files included in the zip folder:
(1) GCP_PPP-2005_1850-2100.nc: GCP in 10-year increments between 1850 and 2100 with a resolution of 5 arcmin.
(2) National_GDP_PPP-2005_1850-2100.csv: nationally-aggregated GDP estimates (as used for GCP downscaling) in 10-year increments between 1850 and 2100.
(3) ISO-country-map.nc: Map for grid cell to ISO 3166 country code mapping with a resolution of 5 arcmin.
(4) GDP2Asset_converter_5arcmin.nc: Map for grid cell GDP to Asset mapping with a resolution of 5 arcmin based on 2016 estimates from Credit Suisse’s Global Wealth Databook 2016.