Taking 2005 as the base year, the future population scenario prediction adopted the Logistic model of population; not only is it better able to describe the change pattern of population and biomass, but it is also widely applied in the economic field. The urbanization rate was predicted using the urbanization Logistic model. Based on the existing urbanization horizontal sequence value, the prediction model was established by acquiring the parameters in the parametric equation applying nonlinear regression. The urban population was calculated by multiplying the predicted population by the urbanization rate. The Logistic model was used to predict the future gross national product of each county (or city), and then according to the economic development level of each county (or city) in each period (in terms of real GDP per capita), the corresponding industrial structure scenarios in each period were set, and the output value of each industry was predicted. The trend of changing industrial structure in China and the research area lagged behind the growth of GDP and was therefore adjusted according to the need of the future industrial structure scenarios of the research area.

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Yang, L., Zhong, F. (2018). Population, urbanization, GDP and industrial structure forecast scenario data of the Yerqiang River Basin (Version 1.0) (2010-2050). A Big Earth Data Platform for Three Poles, DOI: 10.11888/Socio-econ.tpe.0000002.file. CSTR: 18406.11.Socio-econ.tpe.0000002.file. (Download the reference： RIS | Bibtex )

Related Literatures:

1. Climate Change Response Division of National Development and Reform Commission,The Administrative Center for China's Agenda 21 (ACCA21). (2012). Impact assessment of Climate Change on China and Its Countermeasure: sea level rise and melting glaciers[M]. Beijing: Science Press.( View Details | Bibtex)

2. Zhang, J.T., He, X.J., Shangguan, D.H., Zhong, F.L., &Liu, S.Y. (2012). Impact of Intensive Glacier Ablation on Arid Regions of Northwest China and Its Countermeasure. Journal of Glaciology and Geocrylogy, 34(4), 848-854.( View Details | Bibtex)

Using this data, the data citation is required to be referenced and the related literatures are suggested to be cited.

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