"One belt, one road" along the lines of risk rating, credit risk rating and Moodie's national sovereignty rating reflects the structure of sovereign risk in every country. The rating of Moodie's national sovereignty is from the highest Aaa to the lowest C level, and there are twenty-one levels. Data source: organized by the author. Data quality is good. The rating level is divided into two parts, including investment level and speculation level. AAA level is the highest, which is the sovereign rating of excellent level. It means the highest credit quality and the lowest credit risk. The interest payment has sufficient guarantee and the principal is safe. The factors that guarantee the repayment of principal and interest are predictable even if they change. The distribution position is stable. C is the lowest rating, indicating that it cannot be used for real investment.
Vulnerability of disaster bearing body is the degree of damage that human social and economic activities may suffer under the disturbance or pressure of natural disasters under a certain social and economic background, that is, the nature that disaster bearing body is vulnerable to damage and loss in the face of natural disasters. Based on the actual scientific research and expert guidance, this data constructs the vulnerability assessment indicators of disaster bearing bodies from the three aspects of exposure, sensitivity and adaptability, and uses the revised serv vulnerability model to calculate the Himalayan surrounding areas (domestic part) and the Asian water tower area. In order to systematically analyze the vulnerability of disaster bearing bodies in the study area, this data selects indicators from six aspects: population, economy, traffic lines, ecological environment, livestock and buildings, and constructs an indicator system of 6 first-class indicators, 18 second-class indicators and 29 third-class indicators. After the obtained vulnerability assessment results of population, economy, traffic lines, ecological environment, livestock and buildings are normalized, the vulnerability assessment maps of Himalayan surrounding areas (domestic part) and Asian water tower area are obtained by vector superposition.
The data set is based on the NPP simulated by 16 dynamic global vegetation models (TRENDY v8) under S2 Scenario (CO2+Climate) and represents the net primary productivity of the ecosystem. Data was derived from Le Quéré et al. (2019). The range of source data is global, and the Qinghai Tibet plateau region is selected in this data set. Original data is interpolated into 0.5*0.5 degree by the nearest neighbor method in space, and the original monthly scale is maintained in time. The data set is the standard model output data, which is often used to evaluate the temporal and spatial patterns of gross primary productivity, and compared with other remote sensing observations, flux observations and other data.
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.
Food consumption is not only an important indicator to determine the carrying capacity of land resources, but also an important basis to reflect residents' living standards. The food consumption data of the Qinghai Tibet Plateau is based on the data of the Tibet statistical yearbook to sort out the main types and consumption of food in urban and rural areas, such as the consumption of grain, meat, eggs and milk; Combined with the questionnaire survey data of typical counties, the type and quantity data of food consumption in typical counties are statistically sorted out. The data set includes: (1) urban and rural food consumption data on the Qinghai Tibet Plateau; (2) Consumption data of typical counties in Qinghai Tibet Plateau. The data can be used to analyze the spatial differences of food consumption in the Qinghai Tibet Plateau, which is of great significance to the study of land carrying capacity in the Qinghai Tibet Plateau.
This data set is the statistical yearbook of Tibet Autonomous Region in different years, mainly including different social and economic statistical contents. The Tibet Statistical Yearbook is mainly based on statistical charts and analysis. It records the annual economic and social development comprehensively, systematically and continuously through highly intensive statistical data. Obtaining statistical data is a necessary prerequisite for economic and social research. With the help of Tibet statistical yearbook, it can provide data support for the social and Economic Research of Tibet Autonomous Region. Due to the lack of data in the Qinghai Tibet Plateau, it is difficult to find detailed socio-economic statistics on the network platform. This data set comes from the statistical departments of the Tibet Autonomous Region, which can provide support for relevant investigation and research.
The data set is based on the GPP simulated by 16 dynamic global vegetation models (TRENDY v8) under S2 Scenario (CO2+Climate) and represents the gross primary productivity of the ecosystem. Data was derived from Le Qu é r é Et al. (2019). The range of source data is global, and the Qinghai Tibet plateau region is selected in this data set. Original data is interpolated into 0.5*0.5 degree by the nearest neighbor method in space, and the original monthly scale is maintained in time. The data set is the standard model output data, which is often used to evaluate the temporal and spatial patterns of gross primary productivity, and compared with other remote sensing observations, flux observations and other data.
This dataset includes the basic information of salt lakes in Qinghai, Xinjiang Uygur Autonomous Region, Tibet Autonomous Region, Inner Mongolia Autonomous Region and other regions (Gansu, Shaanxi, Shanxi, Jilin, Liaoning). It includes yhid, yhbsm, yhmcdm, yhmc, shxlx, lsxian, lsshi, lsshen, cysj, pjss, zdss, MJ, HMGC and Lake area_ Minimum x value (hqfw)_ X1) Lake area_ Maximum x value (hqfw)_ X2) Lake elevation_ Minimum y value (hqfw)_ Y1) Lake area_ Maximum y value (hqfw)_ Y2), development status (kfzg), remarks (BZ) and other information. Mainly through the standardization of historical data and actual field survey data. There are 2607 records in the data set, and they are fully open for sharing.
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.
The data includes the area and attributes of different types of land, such as cultivated land, grassland and woodland, of 1280 households at domestic and abroad, which is used to support the analysis of the natural capital part of sustainable livelihoods. The field survey data is collected by the research group. Before collecting the data, the research group and the invited experts conducted a pretest to improve the questionnaire; before the formal survey, the members participating in the data collection were strictly trained; during the formal survey, each questionnaire could be filed after three times of inspection. The data is of great value to understand the natural capital and land endowment of farmers in the vulnerable areas of environment and economy, and is an important supplement to the national and macro data in this area.