This data set records the statistical data of per capita GDP and growth rate and ranking (2010-2018) of all regions in China, and the data are divided by year. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set contains eight data tables, each of which has the same structure. For example, the data table of 2017-2018 has four fields: Field 1: Region Field 2: quantity Field 3: Rank Field 4: growth rate
Qinghai Provincial Bureau of Statistics
The distribution data of Central Asia desert oil and gas fields are in the form of vector data in ". SHP". Including the distribution of oil and gas fields and major urban settlements in the five Central Asian countries. The data is extracted and cut from modis-mcd12q product. The spatial resolution of the product is 500 m, and the time resolution is 1 year. IGBP global vegetation classification scheme is adopted as the classification standard. The scheme is divided into 17 land cover types, among which the urban data uses the construction and urban land in the scheme. The data can provide data support for the assessment and prevention of sandstorm disasters in Central Asia desert oil and gas fields and green town.
GAO Xin
Grassland actual net primary production (NPPa) was calculated by CASA model. CASA model was calculated with the combination of satellite-observed NDVI and climate (e.g. temperature, precipitation and radiation) as the driving factors, and other factors, such as land-use change and human harvest from plant material, were reflected by the changes of NDVI. CASA NPP was determined by two variables, absorbed photosynthetically active radiation’ (APAR) and the light-use efficiency (LUE). Grassland potential net primary production (NPPp) was calculated by TEM model. TEM is one of process-based ecosystem model, which was driven by spatially referenced information on vegetation type, climate, elevation, soils, and water availability to calculate the monthly carbon and nitrogen fluxes and pool sizes of terrestrial ecosystems. TEM can be only applied in mature and undisturbed ecosystem without take the effects of land use into consideration due to it was used to make equilibrium predications. Grassland potential aboveground biomass (AGBp) was estimated by random forest (RF) algorithm, using 345 AGB observation data in fenced grasslands and their corresponding climate data, soil data, and topographical data.
NIU Ben, ZHANG Xianzhou
It is summarized that the agricultural and socio-economic status of the five Central Asian countries (Kazakhstan, Kyrgyzstan, Tajikistan, Uzbekistan and Turkmenistan) in 2016. This data comes from the statistical yearbook of five Central Asian countries, including six elements: total population, cultivated land area, grain production area, GDP, proportion of agricultural GDP to total GDP, proportion of industrial GDP to total GDP, and forest area. Detailed statistics of the six socio-economic elements of the five Central Asian countries. It can be seen from the statistics that there are different emphases among the six elements of the five Central Asian countries. This data provides basic data for the project, facilitates the subsequent analysis of the ecological and social situation in Central Asia, and provides data support for the project data analysis.
LIU Tie
The GDP per capita growth resilience dataset for countries along the Belt and Road is a comprehensive reflection of the level of GDP per capita growth resilience of each country. The GDP per capita growth resilience dataset was prepared with reference to the World Bank's statistical database, using year-on-year data on GDP per capita (constant 2010 US dollars) for countries along the Belt and Road from 2000 to 2019, and based on sensitivity and adaptability analysis, taking into account the year-on-year changes of each indicator. Through a comprehensive diagnostic, a product on GDP per capita growth resilience was prepared. "The GDP per capita growth resilience dataset for countries along the Belt and Road is an important reference for analysing and comparing the current GDP per capita growth resilience of each country.
XU Xinliang
"The resilience of the domestic economic systems of the countries along the Belt and Road reflects the level of resilience of the domestic economic systems of each country, and the higher the value of the data, the stronger the resilience of the domestic economic systems of the countries along the Belt and Road. The resilience of domestic economic systems includes macroeconomic development resilience, industrial and service sector development resilience, and the data products are prepared with reference to the World Bank statistical database, using GDP per capita, gross fixed capital formation as a percentage of GDP, inflation as measured by GDP deflator, and gross savings as measured by GDP deflator for countries along the Belt and Road from 2000 to 2019. The resilience products of the domestic economic system are prepared through a comprehensive diagnosis based on sensitivity and adaptability analysis, taking into account the year-on-year changes of each indicator, using year-on-year data of six indicators: GDP per capita, gross fixed capital formation as a percentage of GDP, gross savings as a percentage of GDP, industrial value added as a percentage of GDP, and service value added as a percentage of GDP. "The resilience dataset of the domestic economic systems of the countries along the Belt and Road is an important reference for analysing and comparing the resilience of the domestic economic systems of various countries.
XU Xinliang
1. Data overview Take Ganzhou District, Linze County and Gaotai County of Zhangye City in the middle reaches of Heihe River Basin as the research area, and carry out input-output survey on agricultural, industrial and service enterprises and individuals in the research area from May to November 2013. According to the survey data, use the survey method to compile the input-output table of 42 departments in 2012 in this area. 2. The data content Data mainly reflects the input-output of various national economic industries in the process of production, circulation and consumption in ganlingao region in 2012.
XU Zhongmin, SONG Xiaoyu
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.
National Bureau of Statistics
Data content: price index_ Consumer price index (CPI) (2010-2019) Data source and processing method: obtain the economic original data of the third pole (China region) price index from the official website of the world bank and sina.com from 2015 to 2019, and obtain the economic data set of the third pole (China region) price index from 2010 to 2019 through data sorting, screening and cleaning. The data start time is from 2010 to 2019 in Microsoft Excel (xlsx) format.
FU Wenxue
This data set is collected according to the output results of tesim ecological process model, including biomass, plant N and P content, evapotranspiration, NPP and other model output results. Some of the results are obtained by field measurement, some by laboratory analysis of field samples, some by literature.
PENG Hongchun
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
ZHANG Dawei
The dataset includes two parts that are: 1) channel flow, crop pattern, field management, and socio-economy data measured at super-station in 2008, 2010, 2011, 2012 (UTC+8), respectively. 2) irrigation data, crop pattern, and socio-economy data investigated at Daman irrigation district and Yingke irrigation district, respectively. 1.1 Objective of investigation Objectives of investigation for two parts data are to obtain crop pattern and irrigation water volume change with time, and to supply parameter for irrigation water optimal allocation model. 1.2 Investigation spots and items Investigation spots include six water management stations that are Dangzhai, Hua’er, Daman, Xiaoman, Jiantan, and Ershilidun, respectively, at Daman irrigation district. Investigation items comprise water allocation time, branch channel inflow, Dou channel inflow, irrigation area, channel water use efficiency, water price, and water fee. Investigation time is described as followed: 2012.03.16 to 2012.04.04, Spring irrigation; 2012.04.04 to 2012.05.14, Summer irrigation; 2012.05.20 to 2012.06.24, Summer irrigation; 2012.05.16 to 2012.07.06, Summer irrigation; 2012.07.15 to 2012.08.02, Autumn irrigation; 2012.08.10 to 2012.08.26, Autumn irrigation. Investigation spots include eight water management station that are Chang’an, Shangqin, Dangzhai, Liangjiadun, Shimiao, Xiaoman, Xindun, and Yangou, respectively, at Yingke irrigation district. Investigation time and items is described as followed: Year Data items Spots 2008, 2010, 2011 Irrigation data: Irrigation time, water level of Dou channel, channel flow, irrigation area Xiaoman county, Shangtouzha village 2012 Irrigation data: Irrigation time, water level of Dou channel, channel flow, irrigation area Chang’an, Shangqin, Dangzhai, Liangjiadun, Shimiao, Xiaoman, Xindun, Yangou 2012 Well data: Well deep, groundwater abstraction, irrigation area Chang’an, Liangjiadun, Shangqin 2012 Socio-economy data: population, agricultural income, un-agricultural income, water use for living, average residential area, education Chang’an, Xiaoman, Liangjiadun, Shangqin 2012 Field management: fertilizer name, fertilization time, fertilization rate, pesticide name, pesticide rate, time Chang’an, Xiaoman, Liangjiadun, Shangqin 2008, 2010, 2011, 2012 Crop pattern: crop name, seed time, harvest time, crop area, irrigation quota, field water use efficiency, crop yield, crop production value Xiaoman, Chang’an, Liangjiadun, Shangqin 1.3 Data collection Data was collected by cooperating with water management department of Yingke and Daman.
GE Yingchun, Xu Fengying, LI Xin
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.
DENG XiangZheng
Input output table of 11 districts and counties in Heihe River Basin in 2012
DENG XiangZheng
I. Overview This data set contains socio-economic statistics of counties (cities) in the upper reaches of the Yellow River from 2000 to 2005. The data set is divided into basic conditions, comprehensive economics, agriculture, industry and infrastructure, education, health and social security, 4 There are 30 basic categories, including all the socio-economic statistical indicators. Ⅱ. Data processing description The data is stored in excel format, classified by province, with basic socio-economic statistics for each county. Ⅲ. Data content description This data set contains four basic classifications, namely basic situation, comprehensive economy, agriculture, industry and infrastructure, education, health and social security. The basic information includes the administrative area, the number of townships (towns), the number of villagers' committees, the total number of households at the end of the year, the number of rural households, the rural population, the number of employees at the end of the year, the number of rural employees, and the number of agricultural, forestry, animal husbandry and sideline fishermen The total power of agricultural machinery and local telephone users; the total economic categories include: the value added of the primary industry, value added of the secondary industry, revenue within the local fiscal budget, fiscal expenditure, the balance of savings deposits of urban and rural residents, and loans of financial institutions at the end of the year Balance; major categories of agriculture, industry and capital construction include: grain output, cotton output, oil output, total meat output, number of industrial enterprises above designated size, total industrial output value above designated size, and capital investment completed; education, health and social security The major categories include the number of students in ordinary middle schools, the number of students in primary schools, the number of beds in hospitals and health centers, the number of beds in social welfare homes, and the number of beds in social welfare homes. In some remote areas, some data are missing. Ⅳ. Data usage description Through this data set, the socio-economic problems of counties (cities) in the upper reaches of the Yellow River can be analyzed, and the socio-economic driving forces of certain natural processes can be analyzed and researched through this data set.
XUE Xian, DU Heqiang
Data content: Central Bank gold and foreign exchange reserves (2000-2020) and money supply (2000-2017) Data sources and processing methods: the original data of banks and currencies in China (including the third pole) in 2015-2019 are obtained from the official website of the world bank and sina.com, and the bank and currency data sets of China (including the third pole) from 2000 to 2020 are obtained through data sorting, screening and cleaning. The data start time is from 2000 to 2020 in Microsoft Excel (xlsx) format.
FU Wenxue
The data set is the global vegetation productivity data, including Gross Primary Productivity(GPP) and Net Primary Productivity (NPP). It was obtained by the CNRM-CM6-1 mode simulation of CMIP6 under the Historical scenario. The time range of the data covers from 1850 to 2014, the time resolution is a month, and the spatial resolution is about 1.406°×1.389°. For the simulated data details, please go to the following link: http://www.umr-cnrm.fr/cmip6/spip.php?article11.
Program for Climate Model Diagnosis and Intercomparison (PCMDI)
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
WANG Zhongjing
The data set recorded the total investment in fixed assets in Qinghai from 1980 to 2016. The data were derived from the Qinghai Society and Economics Statistical Yearbook and the Qinghai Statistical Yearbook. The accuracy of the data is consistent with that of the statistical yearbook. The table contains 11 fields. Field 1: Year Interpretation: Year of the data Field 2: Total Interpretation: Total investment in fixed assets Unit: 100,000,000 yuan Field 3: State-owned economy Interpretation: State-owned economic investment in fixed assets Unit: 100,000,000 yuan Field 4: Collective Economy Interpretation: Collective economic investment in fixed assets Unit: 100,000,000 yuan Field 5: Individual Economy Interpretation: Individual economic investment in fixed assets Unit: 100,000,000 yuan Field 6: Other types of economy Interpretation: Other economic investment in fixed assets Unit: 100,000,000 yuan Field 7: Total Growth Interpretation: Total growth of investment in fixed assets Unit: % Field 8: State-owned growth Interpretation: Growth of state-owned economic investment in fixed assets Unit: % Field 9: Collective growth Interpretation: Growth of collective economic investment in fixed assets Unit: % Field 10: Individual Growth Interpretation: Growth of individual economic investment in fixed assets Unit: % Field 11: Other growth Interpretation: Growth of other economic investment in fixed assets Unit: %
Qinghai Provincial Bureau of Statistics, Qinghai Provincial Bureau of Statistics
"Hydrological ecological economic process coupling and evolution of Heihe River basin governance under the framework of water rights" (91125018) project data collection - economic and social data of Heihe River 2010 . 1. Data overview: Economic and social data of Heihe River 2010. 2. Data content: Economic and social data of Ganzhou District, Linze County and Gaotai County of Heihe River Basin 2010.
WANG Zhongjing
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