1Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
2State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
3Akesu National Station of Observation and Research for Oasis Agro-ecosystem, Akesu, China
4College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
1Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
2State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
3Akesu National Station of Observation and Research for Oasis Agro-ecosystem, Akesu, China
4College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
Received: 12 Jan 2021 – Accepted for review: 24 Jan 2021 – Discussion started: 26 Jan 2021
Abstract. The increasing demand of ScenarioMIP is calling for GDP projections of high resolution for the future Shared Socioeconomic Pathways (SSPs) in both socioeconomic development and in climate change of adaption and mitigation research. While to date the global GDP projections for five SSPs are mainly provided at national scales, and the gridded data set are very limited. Meanwhile, the historical GDP can be disaggregated using nighttime light (NTL) images but the results are not open accessed, making it cumbersome in climate change impact and socioeconomic risk assessments across research disciplines. To this end, we produce a set of spatially explicit global Gross Domestic Product (GDP) that presents substantial long-term changes of economic activities for both historical period (2005 as representative) and for future projections under all five SSPs with a spatial resolution of 30 arc-seconds. Chinese population in SSP database were first replaced by the projections under the two-children policy implemented since 2016 and then used to spatialize global GDP using NTL images and gridded population together as fixed base map, which outperformed at subnational scales. The GDP data are consistent with projections from the SSPs and are freely available at http://doi.org/10.5281/zenodo.4350027 (Wang and Sun, 2020). We also provide another set of spatially explicit GDP using the global LandScan population as fixed base map, which is recommended at county or even smaller scales where NTL images are limited. Our results highlight the necessity and availability of using gridded GDP projections with high resolution for scenario-based climate change research and socioeconomic development that are consistent with all five SSPs.
In this article, the authors have produced a set of spatially explicit global GDP based on SSP scenarios. This could be an interesting publication if the authors can improve the novelty of this approach. The authors can also improve the paper in many other ways. First of all, the authors could provide a better introduction for SSP. For example, what are the five SSP scenarios and why they are significant. In addition, the organization could also be imporved. The authors have discusses different terms and policies, but there is a very poor transition between paragraphs. In addition, there is a lack of novelty for the method. Furthremore, the authors need to check the writing and grammar. For example, in the abstract, "ScenarioMIP" is not explained; line 120 - "with other auxiliary information but are not open accessed."; Line 133 - you used "population count (density)" and in line 158 you separated the terms "population count, population density", any significance?; Line 846 - are the 846 RMSE(s).
We produce a set of spatially explicit global GDP with consideration of two-children policy in China that presents substantial long-term changes for both historical period and for future projections under five SSPs to face the increasing demand of ScenarioMIP of high resolution for future socioeconomic development and climate change of adaption and mitigation research.
We produce a set of spatially explicit global GDP with consideration of two-children policy in...
