The development and changes in international situation after September 11 have exerted complicated influence on China’s external security environment. Therefore, how to judge current international security environmen...The development and changes in international situation after September 11 have exerted complicated influence on China’s external security environment. Therefore, how to judge current international security environment that China faces has become a very hot subject for discussion.展开更多
Taking the aquaculture area, the number of farming boats and that of aquaculturist as input variables, the aquaculture production as desirable output variable and polluted economic loss as undesirable output variable,...Taking the aquaculture area, the number of farming boats and that of aquaculturist as input variables, the aquaculture production as desirable output variable and polluted economic loss as undesirable output variable, this paper conducts SBM model to evaluate the aquaculture efficiency based on the data of 16 aquaculture-developed provinces in China from 2004 to 2011. The results show the efficiency in China has not changed much in recent years with the efficiency values mainly between 0.39 and 0.53, and the efficiency of marine-aquaculture-dominated provinces is generally higher than that of freshwater-aquaculture-dominated ones. To analyze the difference under the efficiency, the panel Tobit model is used with education level factor, training factor, technology extension factor, technical level factor, scale factor and species factor as the efficiency influencing factors. The results show that technology extension factor and technical level factor have significant positive influence.展开更多
China has become a major investor and constructor of electrical power plants in developing countries.However,the impacts of China's overseas power stations(COPSs)on the developing countries hosting them are poorly...China has become a major investor and constructor of electrical power plants in developing countries.However,the impacts of China's overseas power stations(COPSs)on the developing countries hosting them are poorly understood.Here,a novel method is proposed to evaluate the influence of COPSs in 80 host countries.First,their electricity consumption from 1971 to 2017 was estimated using data provided by the World Bank,International Energy Agency,and World Resources Institute.Regression analysis was then used to predict consumption from 2018 to 2025.Finally,three parameters were used to evaluate the influences of COPSs.The results show that:1)COPSs significantly increased the total installed capacity of 35 of the host countries by>20%.2)The power generated by COPSs is greater than the growing demands of 32 of the host countries.3)COPSs will increase the per capita electricity consumption of all 80 host countries.4)Among the 437 COPSs existing in 2000–2019,renewable power plants(including hydropower)were most numerous,accounting for 51.3%.This proportion increased significantly after 2013 and renewable plants will continue to dominate as China will no longer invest in new coal-fired power stations after 2021.展开更多
High concentrations of PM_(2.5) are universally considered as a main cause for haze formation. Therefore, it is important to identify the spatial heterogeneity and influencing factors of PM_(2.5) concentrations for re...High concentrations of PM_(2.5) are universally considered as a main cause for haze formation. Therefore, it is important to identify the spatial heterogeneity and influencing factors of PM_(2.5) concentrations for regional air quality control and management. In this study, PM_(2.5) data from 2000 to 2015 was determined from an inversion of NASA atmospheric remote sensing images. Using geo-statistics, geographic detectors, and geo-spatial analysis methods, the spatio-temporal evolution patterns and driving factors of PM_(2.5) concentration in China were evaluated. The main results are as follows.(1) In general, the average concentration of PM_(2.5) in China increased quickly and reached its peak value in 2006; subsequently, concentrations remained between 21.84 and 35.08 μg/m3.(2) PM_(2.5) is strikingly heterogeneous in China, with higher concentrations in the north and east than in the south and west. In particular, areas with relatively high PM_(2.5) concentrations are primarily in four regions, the Huang-Huai-Hai Plain, Lower Yangtze River Delta Plain, Sichuan Basin, and Taklimakan Desert. Among them, Beijing-Tianjin-Hebei Region has the highest concentration of PM_(2.5).(3) The center of gravity of PM_(2.5) has generally moved northeastward, which indicates an increasingly serious haze in eastern China. High-value PM_(2.5) concentrations have moved eastward, while low-value PM_(2.5) has moved westward.(4) Spatial autocorrelation analysis indicates a significantly positive spatial correlation. The "High-High" PM_(2.5) agglomeration areas are distributed in the Huang-Huai-Hai Plain, Fenhe-Weihe River Basin, Sichuan Basin, and Jianghan Plain regions. The "Low-Low" PM_(2.5) agglomeration areas include Inner Mongolia and Heilongjiang, north of the Great Wall, Qinghai-Tibet Plateau, and Taiwan, Hainan, and Fujian and other southeast coastal cities and islands.(5) Geographic detection analysis indicates that both natural and anthropogenic factors account for spatial variations in PM_(2.5) concentration. Geographical location, population density, automobile quantity, industrial discharge, and straw burning are the main driving forces of PM_(2.5) concentration in China.展开更多
Ecological land rent is the excess profit produced by resource scarcity, and is also an important indicator for measuring the social and economic effects of resource scarcity. This paper, by calculating the respective...Ecological land rent is the excess profit produced by resource scarcity, and is also an important indicator for measuring the social and economic effects of resource scarcity. This paper, by calculating the respective ecological land rents of all the provinces in China for the years 2002 and 2007, and with the assistance of the software programs ArcGIS and GeoDA, analyzes the spatial differentiation characteristics of ecological land rent; then, the influencing factors of ecological land rent differentiation among the provinces are examined using the methods of traditional regression and spatial correlation analysis. The following results were obtained： First, ecological land rent per unit of output in China shows stable dis- tribution characteristics of being low in the southwestern and northeastern provinces, and high in Hebei and Henan provinces. There is also an increasing tendency in the central and western provinces, and a decreasing one in the eastern provinces. In general, the spatial distribution of ecological land rent per unit of output in China is quite scattered. Second, the total ecological land rent shows significant spatial aggregation characteristics, in particular the provinces in China possessing high total amounts of ecological land rent tend to be adjacent to one another, as do those with low total amounts, and the spatial difference characteristics of the eastern, central and western provinces are distinguished. The Bohai Rim, Yangtze River Delta and Pearl River Delta are shown to be highly clustering regions of total ecological land rent, while the western provinces have very low ecological land rent in terms of total amount. Third, population distribution, economic level and industrial structure were all im- portant influencing factors influencing ecological land rent differentiation among provinces in China. Furthermore, population density, urbanization level, economic density, per capita consumption level and GDP per capita were all shown to be positively related to total eco- logical land rent, which indicates that spatial clustering exists between ecological land rent and these factors. However, there was also a negative correlation between ecological land rent and agricultural output percentage, indicating that spatial scattering exists between ecological land rent and agricultural output percentage.展开更多
文摘The development and changes in international situation after September 11 have exerted complicated influence on China’s external security environment. Therefore, how to judge current international security environment that China faces has become a very hot subject for discussion.
基金supported by the Major State Basic Research Development Program of China: the Research on the Key Technology of Clean and High Efficient Mariculture Pond (Grant Nos. 2011BAD 13B03)Promotive Research Fund for Excellent Young and Middle-Aged Scientists of Shandong Province: High Efficiency and Low Carbon Development Research of Shandong Mariculture Industry (Grant Nos. BS2012HZ 024)the Research of Chinese Mariculture Industry High Efficiency and Low Carbon Development Model Implementation Mechanism Funded by the Marine Development Institute of Ocean University of China Humanities and Social Science Key Research Base of Ministry of Education (Grant Nos. 2012JDZS02)
文摘Taking the aquaculture area, the number of farming boats and that of aquaculturist as input variables, the aquaculture production as desirable output variable and polluted economic loss as undesirable output variable, this paper conducts SBM model to evaluate the aquaculture efficiency based on the data of 16 aquaculture-developed provinces in China from 2004 to 2011. The results show the efficiency in China has not changed much in recent years with the efficiency values mainly between 0.39 and 0.53, and the efficiency of marine-aquaculture-dominated provinces is generally higher than that of freshwater-aquaculture-dominated ones. To analyze the difference under the efficiency, the panel Tobit model is used with education level factor, training factor, technology extension factor, technical level factor, scale factor and species factor as the efficiency influencing factors. The results show that technology extension factor and technical level factor have significant positive influence.
基金supported by the[Strategic Priority Research Program of the Chinese Academy of Sciences]under Grant[number XDA19030304].
文摘China has become a major investor and constructor of electrical power plants in developing countries.However,the impacts of China's overseas power stations(COPSs)on the developing countries hosting them are poorly understood.Here,a novel method is proposed to evaluate the influence of COPSs in 80 host countries.First,their electricity consumption from 1971 to 2017 was estimated using data provided by the World Bank,International Energy Agency,and World Resources Institute.Regression analysis was then used to predict consumption from 2018 to 2025.Finally,three parameters were used to evaluate the influences of COPSs.The results show that:1)COPSs significantly increased the total installed capacity of 35 of the host countries by>20%.2)The power generated by COPSs is greater than the growing demands of 32 of the host countries.3)COPSs will increase the per capita electricity consumption of all 80 host countries.4)Among the 437 COPSs existing in 2000–2019,renewable power plants(including hydropower)were most numerous,accounting for 51.3%.This proportion increased significantly after 2013 and renewable plants will continue to dominate as China will no longer invest in new coal-fired power stations after 2021.
基金The Strategic Priority Research Program of the Chinese Academy of Sciences,No.XDA19040401China Postdoctoral Science Foundation,No.2016M600121+1 种基金National Natural Science Foundation of China,No.41701173,No.41501137The State Key Laboratory of Resources and Environmental Information System
文摘High concentrations of PM_(2.5) are universally considered as a main cause for haze formation. Therefore, it is important to identify the spatial heterogeneity and influencing factors of PM_(2.5) concentrations for regional air quality control and management. In this study, PM_(2.5) data from 2000 to 2015 was determined from an inversion of NASA atmospheric remote sensing images. Using geo-statistics, geographic detectors, and geo-spatial analysis methods, the spatio-temporal evolution patterns and driving factors of PM_(2.5) concentration in China were evaluated. The main results are as follows.(1) In general, the average concentration of PM_(2.5) in China increased quickly and reached its peak value in 2006; subsequently, concentrations remained between 21.84 and 35.08 μg/m3.(2) PM_(2.5) is strikingly heterogeneous in China, with higher concentrations in the north and east than in the south and west. In particular, areas with relatively high PM_(2.5) concentrations are primarily in four regions, the Huang-Huai-Hai Plain, Lower Yangtze River Delta Plain, Sichuan Basin, and Taklimakan Desert. Among them, Beijing-Tianjin-Hebei Region has the highest concentration of PM_(2.5).(3) The center of gravity of PM_(2.5) has generally moved northeastward, which indicates an increasingly serious haze in eastern China. High-value PM_(2.5) concentrations have moved eastward, while low-value PM_(2.5) has moved westward.(4) Spatial autocorrelation analysis indicates a significantly positive spatial correlation. The "High-High" PM_(2.5) agglomeration areas are distributed in the Huang-Huai-Hai Plain, Fenhe-Weihe River Basin, Sichuan Basin, and Jianghan Plain regions. The "Low-Low" PM_(2.5) agglomeration areas include Inner Mongolia and Heilongjiang, north of the Great Wall, Qinghai-Tibet Plateau, and Taiwan, Hainan, and Fujian and other southeast coastal cities and islands.(5) Geographic detection analysis indicates that both natural and anthropogenic factors account for spatial variations in PM_(2.5) concentration. Geographical location, population density, automobile quantity, industrial discharge, and straw burning are the main driving forces of PM_(2.5) concentration in China.
基金National Natural Science Foundation of China, No.41001382 No.41201386
文摘Ecological land rent is the excess profit produced by resource scarcity, and is also an important indicator for measuring the social and economic effects of resource scarcity. This paper, by calculating the respective ecological land rents of all the provinces in China for the years 2002 and 2007, and with the assistance of the software programs ArcGIS and GeoDA, analyzes the spatial differentiation characteristics of ecological land rent; then, the influencing factors of ecological land rent differentiation among the provinces are examined using the methods of traditional regression and spatial correlation analysis. The following results were obtained： First, ecological land rent per unit of output in China shows stable dis- tribution characteristics of being low in the southwestern and northeastern provinces, and high in Hebei and Henan provinces. There is also an increasing tendency in the central and western provinces, and a decreasing one in the eastern provinces. In general, the spatial distribution of ecological land rent per unit of output in China is quite scattered. Second, the total ecological land rent shows significant spatial aggregation characteristics, in particular the provinces in China possessing high total amounts of ecological land rent tend to be adjacent to one another, as do those with low total amounts, and the spatial difference characteristics of the eastern, central and western provinces are distinguished. The Bohai Rim, Yangtze River Delta and Pearl River Delta are shown to be highly clustering regions of total ecological land rent, while the western provinces have very low ecological land rent in terms of total amount. Third, population distribution, economic level and industrial structure were all im- portant influencing factors influencing ecological land rent differentiation among provinces in China. Furthermore, population density, urbanization level, economic density, per capita consumption level and GDP per capita were all shown to be positively related to total eco- logical land rent, which indicates that spatial clustering exists between ecological land rent and these factors. However, there was also a negative correlation between ecological land rent and agricultural output percentage, indicating that spatial scattering exists between ecological land rent and agricultural output percentage.
