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Socio-economic statistical dataset of the Zhangye (2001 - 2012)

Some economic data of Zhangye City from 2001 to 2012 include: per capita GDP, GDP, the proportion of fiscal revenue to GDP, per capita fiscal revenue, industrial contribution rate, the proportion of town population to total population, the proportion of added value of tertiary industry to GDP, the proportion of added value of secondary industry to GDP, industrial comprehensive benefit index, contribution rate of total assets, contribution rate of fixed assets, social labor productivity, G DP growth rate


Provincial macro social-economic indicators of Heihe River Basin

It includes the social and economic data of Gansu, Qinghai and Inner Mongolia from 2000 to 2012. The specific indicators include GDP, income, population, employment, medical care, education, land area, finance and a series of social and economic indicators;


Input-output table of Heihe River basin (2012)

Input output table of 11 districts and counties in Heihe River Basin in 2012


Scenario analysis of social and economic development over Heihe River Basin (2020 & 2030)

Data analysis method: macroeconomic development forecast Space scope: Sunan County, Ganzhou District, Minle County, Linze County, Gaotai County, Shandan County, Jinta County, Ejina, Suzhou District, Jiayuguan Time frame: 2020, 2030 Data: GDP (1 million yuan), GDP growth rate, primary production (1 million yuan), primary production growth rate, secondary production (million yuan), secondary production growth rate, tertiary production (million yuan), tertiary production growth rate, primary production rate Second rate, third rate


Scenario data of social and economic development in Heihe River Basin

Input output table of 11 districts and counties in Heihe River Basin in 2012


Socio economic database of the middle reaches of Heihe River Basin

1. Data overview Based on the collected statistical yearbooks and survey data of counties and districts in Zhangye City in the middle reaches of Heihe River, the social and economic database in the middle reaches is constructed to reflect the basic situation of regional social economy. 2. Data content The database includes two data sets: (1) statistical yearbook data; (2) survey data of human factors in river basin. The statistical yearbook data mainly includes a number of relevant statistical data such as the gross product, financial revenue, construction of villages and towns, industrial output value, grain output, etc. of Zhangye City and its towns. The survey data of human factors in Heihe River Basin mainly include the survey data of social capital, cultural theory, happiness index and sustainable consumption in Heihe River Basin. 3. Time and space The statistical yearbook data is the statistical data of Ganzhou District, Linze County, Gaotai County, Sunan County, Shandan County, Minle county and towns under the jurisdiction of each county from 1990 to 2010. The survey data of human factors in the basin is the corresponding survey data of counties in the upper, middle and lower reaches in 2005.


SAM for Gaotai (2012)

The social accounting matrix, also known as the national economy comprehensive matrix or the national economy circulation matrix, uses the matrix method to connect the various accounts of the national economy systematically, represents the statistical index system of the national economy accounting system, and reflects the circulation process of the national economy operation. It uses the matrix form to arrange the national accounts orderly according to the flow and stock, domestic and foreign. The data reflects the balanced value of social accounting matrix in Gaotai County.


The dataset of community statistics of each county in Three-River-Source National Park (2017)

This data set contains statistical tables on the community situation of each county in Three-River-Source National Park. The specific contents include: Table 1 includes: number of administrative villages, number of natural villages, number of households, population, number of rural labor force, total value of primary and secondary industries, net income per capita, and number of livestock. Table 2 includes: the ethnic composition of the population (population of each ethnic group), education-related statistics (number of primary and secondary schools and number of students), health-related statistics (number of hospitals, health rooms and medical personnel), and statistics on the education level of the population (number of people with different education levels); Table 3 includes: the grassland (total grassland area, usable grassland area, moderately degraded area and grassland vegetation coverage), woodland (total area, arbor forest area, shrub forest area and sparse forest area), water area (total area, river area, lake area, glacier area, snowy mountain area and wetland area). A total of four counties were designed: Maduo, Qumalai, Zaduo and Zhiduo. This data comes from statistics of government departments.


Economy statistics of Qing-Tibet Plateau

This data contains part of the economic indicators of Qinghai province and Tibet Autonomous Region. The data statistics based on provinces can be used to construct the evaluation index system for the coupling coordination relationship between urbanization and eco-environment on the Tibetan Plateau. The data of the Tibet Autonomous Region contains seven indicators, including the gross domestic product (GDP), the primary, secondary and tertiary industries, industry, construction industry, and the per capita GDP, the time span is 1951-2016. The time span of the data set of Qinghai province is from 1952 to 2015, besides the above seven indicators, there is one more indicator of Qinghai province called agriculture forwdtry animal husbandry and fishery. All data are derived from the statistical yearbook, which is calculated at current prices. The gross domestic product (GDP) for 2005-2008 has been revised based on data from the second economic census.


Population, urbanization, GDP and industrial structure forecast scenario data of the Urmuqi River Basin (Version 1.0) (2010-2050)

Taking 2005 as the base year, the future population scenario prediction adopted the Logistic model of population, and it not only can better describe the change pattern of population and biomass but 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 each industry’s output value was predicted. The trend of changes in industrial structure in China and the research area lagged behind the growth of GDP, and, therefore, it was adjusted according to the need of the future industrial structure scenarios of the research area.