Based on panel data from 1991, 2000 and 2010 at the county level in China, this study analyzed the coupling characteristics and spatio-temporal patterns of agricultural labor changes and economic development under rapid urbanization using quantitative and GIS spatial analysis methods. Three primary conclusions were obtained. (1) During 1991-2010, China’s agricultural labor at the county level showed a decreasing trend, down 4.91% from 1991 to 2000 and 15.50% from 2000 to 2010. In spatial distribution, agricultural labor force has evolved by decreasing eastward and increasing westward. (2) During 1991-2010, China’s agricultural economy at the county level showed a sustained growth trend, with a total increase of 140.13%, but with clear regional differences. The proportion of agricultural output in national GDP gradually decreased, characterized by decreases in eastern China and increases in western China. (3) The coupling types of economic-labor elasticity coefficient are mainly growth in northwest China, for both the agricultural economy and labor, and are intensive in southeast China, with growth of the agricultural economy and reduction of agricultural labor. Regions with lagged, fading, and declining coupling types are generally coincident with the high incidence of poverty in China. However, different coupling types had a positive developing trend for 1991-2010. Finally, based on the coupling types and spatial distribution characteristics of economic-labor elasticity coefficients, some policy suggestions are proposed to promote the integration of the primary, secondary, and tertiary industries and the vitalization of rural economies.
Landforms are an important factor determining the spatial pattern of cropland through allocation of surface water and heat. Therefore, it is of great importance to study the change in cropland distribution from the perspective of geomorphologic divisions. Based on China’s multi-year land cover data (1990, 1995, 2000, 2005, 2010 and 2015) and geomorphologic regionalization data, we analyzed the change in cropland area and its distribution pattern in six geomorphologic regions of China over the period of 1990-2015 with the aid of GIS techniques. Our results showed that the total cropland area increased from 177.1 to 178.5 million ha with an average increase rate of 0.03%. Cropland area decreased in southern China and increased in northern China. Region I (Eastern hilly plains) had the highest cropland increase rate, while the cropland dynamic degree of Region IV (Northwestern middle and high mountains, basins and plateaus) was significantly higher than that of other regions. The barycenter of China’s cropland shifted from northern China to the northwest over the 25-year period. Regions IV and I were the two regions with the greatest increase of cropland. Region II (Southeastern low and middle mountains) and Region V (Southwestern middle and low mountains, plateaus and basins) were the main decreasing cropland regions. The area of cropland remained almost unchanged in Region III (Northern China and Inner Mongolia eastern-central mountains and plateaus) and Region VI (Tibetan Plateau). The loss of cropland occurred mostly in Regions I and II as a result of growing industrialization and urbanization, while the increase of cropland occurred mainly in Region IV because of reclamation of grassland and other wasteland. These analyzing results would provide fundamental information for further studies of urban planning, ecosystem management, and natural resources conservation in China.
China is now experiencing rapid urbanization. Powerful tools are required to assess its urban spatial policies before implemented toward a more competitive and sustainable development paradigm. This study develops a Land Use Transport Interaction (LUTI) model to evaluate the impacts of urban land-use policies on urban spatial development. The model consists of four sub-models, i.e., transport, residential location, employment location and real estate rent sub-models. It is then applied to Beijing metropolitan area to forecast the urban activity evolution trend based on the land-use policies between 2009 and 2013. The modeling results show that more and more residents and enterprises in the city choose to agglomerate on outskirts, and new centers gradually emerge to share the services originally delivered by central Beijing. The general trend verifies the objectives of the government plan to develop more sub-centers around Beijing. The proposed activity-based model provides a distinct tool for the urban spatial policy makers in China. Further research is also discussed at the end.
Based on land use classification data of remote sensing images, using kernel density, the minimal cumulative resistance model of road traffic accessibility, and a logistic regression model, the characteristics of the spatial pattern and the main factors influencing it were quantitatively examined in Guangdong Province from 1990 to 2013. The framework of the research concerning rural settlement evolution and its effect mechanisms were also discussed and generalized for the future. The results are as follows: (1) The spatial distribution of rural settlements showed spatial directivity of low altitude, low slope, and adjacent to rivers, as well as to villages and towns; thus a special pattern was formed, which was dense on the plains, sparse in mountainous areas, and included two core high density regions of rural settlements in the Chaoshan plain in the east and the Zhanjiang plain tableland region in the west. The spatial distribution of rural settlements was located along the rivers, valleys, and roads with traffic in the mountainous regions surrounding the Pearl River Delta region. (2) In addition to the spatial orientation of the open road, it was important to show that the accessibility of road traffic to the township has had the greatest influence on the spatial distribution of the rural settlements. The connected transport network between towns and villages is significant for rural transformation as a comprehensive increase in township production and service capacity will be the key to optimizing the town-village system in rural areas. (3) Elevation and slope were two basic but influential factors that have affected the distribution, scale, and form of rural settlements. The attributes of the physical geography are the first elements in optimizing village layout and planning spatial reconstruction. (4) In the current Internet and social media era, the reconstruction of market network system orders connects with the global market network system in rural areas. The rural life service circle will be constructed with the township at its core to explore the theory and practice of spatial reconstruction, including its production, life and ecology, and socio-cultural heritage and protection. It will also allow for exploration of the rural settlements’ evolution, rural spatial production, rural social networks, group behavior, social autonomy, and social and cultural fields, which will be the core focus of China’s rural spatial reconstruction research against a background of globalization.
In this study, we adopt kernel density estimation, spatial autocorrelation, spatial Markov chain, and panel quantile regression methods to analyze spatial spillover effects and driving factors of carbon emission intensity in 283 Chinese cities from 1992 to 2013. The following results were obtained. (1) Nuclear density estimation shows that the overall average carbon intensity of cities in China has decreased, with differences gradually narrowing. (2) The spatial autocorrelation Moran’s I index indicates significant spatial agglomeration of carbon emission intensity is gradually increasing; however, differences between regions have remained stable. (3) Spatial Markov chain analysis shows a Matthew effect in China’s urban carbon emission intensity. In addition, low-intensity and high-intensity cities characteristically maintain their initial state during the transition period. Furthermore, there is a clear “Spatial Spillover” effect in urban carbon emission intensity and there is heterogeneity in the spillover effect in different regional contexts; that is, if a city is near a city with low carbon emission intensity, the carbon emission intensity of the first city has a higher probability of upward transfer, and vice versa. (4) Panel quantile results indicate that in cities with low carbon emission intensity, economic growth, technological progress, and appropriate population density play an important role in reducing emissions. In addition, foreign investment intensity and traffic emissions are the main factors that increase carbon emission intensity. In cities with high carbon intensity, population density is an important emission reduction factor, and technological progress has no significant effect. In contrast, industrial emissions, extensive capital investment, and urban land expansion are the main factors driving the increase in carbon intensity.
High concentrations of PM2.5 are universally considered as a main cause for haze formation. Therefore, it is important to identify the spatial heterogeneity and influencing factors of PM2.5 concentrations for regional air quality control and management. In this study, PM2.5 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 PM2.5 concentration in China were evaluated. The main results are as follows. (1) In general, the average concentration of PM2.5 in China increased quickly and reached its peak value in 2006; subsequently, concentrations remained between 21.84 and 35.08 μg/m3. (2) PM2.5 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 PM2.5 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 PM2.5. (3) The center of gravity of PM2.5 has generally moved northeastward, which indicates an increasingly serious haze in eastern China. High-value PM2.5 concentrations have moved eastward, while low-value PM2.5 has moved westward. (4) Spatial autocorrelation analysis indicates a significantly positive spatial correlation. The “High-High” PM2.5 agglomeration areas are distributed in the Huang-Huai-Hai Plain, Fenhe-Weihe River Basin, Sichuan Basin, and Jianghan Plain regions. The “Low-Low” PM2.5 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 PM2.5 concentration. Geographical location, population density, automobile quantity, industrial discharge, and straw burning are the main driving forces of PM2.5 concentration in China.
The formation mechanism and influencing factors identification of soil erosion are the core and frontier issues of current research. However, studies on the multi-factor synthesis are still relatively lacked. In this study, the simulation of soil erosion and its quantitative attribution analysis have been conducted in different geomorphological types in a typical karst basin based on the RUSLE model and the geodetector method. The influencing factors, such as land use type, slope, rainfall, elevation, lithology and vegetation cover, have been taken into consideration. Results show that the strength of association between the six influencing factors and soil erosion was notably different in diverse geomorphological types. Land use type and slope were the dominant factors of soil erosion in the Sancha River Basin, especially for land use type whose power of determinant (q value) for soil erosion was much higher than other factors. The q value of slope declined with the increase of relief in mountainous areas, namely it was ranked as follows: middle elevation hill> small relief mountain> middle relief mountain. Multi-factors interactions were proven to significantly strengthen soil erosion, particularly for the combination of land use type with slope, which can explain 70% of soil erosion distribution. It can be found that soil erosion in the same land use type with different slopes (such as dry land with slopes of 5° and above 25°) or in the diverse land use types with the same slope (such as dry land and forest with a slope of 5°), varied much. These indicate that prohibiting steep slope cultivation and Grain for Green Project are reasonable measures to control soil erosion in karst areas. Based on statistics of soil erosion difference between diverse stratifications of each influencing factor, results of risk detector suggest that the amount of stratification combinations with significant difference accounted for 55% at least in small relief mountain and middle relief mountainous areas. Therefore, the spatial heterogeneity of soil erosion and its influencing factors in different geomorphological types should be investigated to control karst soil loss more effectively.
Rice (Oryza sativa L.) is the most important staple crop of China, and its production is related to both natural condition and human activities. It is fundamental to comprehensively assess the influence of terrain conditions on rice production to ensure a steady increase in rice production. Although many studies have focused on the impact of one or several specific factors on crop production, few studies have investigated the direct influence of terrain conditions on rice production. Therefore, we selected Hunan Province, one of the major rice-producing areas in China, which exhibits complex terrain conditions, as our study area. Based on remote sensing data and statistical data, we applied spatial statistical analysis to explore the effects of terrain factors on rice production in terms of the following three aspects: the spatial patterns of paddy fields, the rice production process and the final yield. We found that 1) terrain has a significant impact on the spatial distribution of paddy fields at both the regional scale and the county scale; 2) terrain controls the distribution of temperature, sunlight and soil, and these three environmental factors consequently directly impact rice growth; 3) compared with the patterns of paddy fields and the rice production process, the influences of terrain factors on the rice yield are not as evident, with the exception of elevation; and 4) the spatial distribution of paddy fields mismatched that of production resources due to terrain factors. Our results strongly suggest that managers should scientifically guide farmers to choose suitable varieties and planting systems and allocate rice production resources in the northern plain regions to ensure food security.
The anastomosing river that is present within the First Great Bend of the Yellow River is different from other sand-bedded rivers of this type because it contains gravel-bedded materials. It is therefore important to determine whether, or not, the specific characteristics of this anastomosing river are similar to those seen in sand-bedded forms, including the characteristics of erosion and deposition, and the stability of channel and interchannel wetlands. Four Landsat images from 1990, 2001, 2013, and 2016 alongside two Google Earth (GE) images from 2011 and 2013 were utilized in this study in tandem with field sampling and observations to select a 12 km main channel length section of the Qihama reach anastomosing river. This section was then used to determine variations in channel planform and sedimentary characteristics over a 26 year period. The results of this study show that this gravel-bedded anastomosing river has exhibited a high degree of stability overall, and that there has been no obvious channel and wetland bank erosion and deposition. Data also show that over the 26 years of this study, anastomosing belt area increased by 2.43%, while the ratio of land to water area remained almost equal. The number of wetlands has also increased along this river section at a rate as high as 62.16% because of the fragmentation of some small interchannel examples, while the talweg has alternately migrated to either the left or right over long periods of time at a relatively stable rate. Indeed, as a result of the migration of this line, there has been significant turnover in the number of islands within the main channel while bank shift has occurred at a rate of about 5 m/yr. The numerous anastomosing channels within this river section remained very stable over the course of this study, characterized by a mean annual migration rate of just 1 m/yr, while the sediments in bank columnar sections are mainly composed of fine sands or silts with a relatively high clay content. The sediment grain-size distribution curve for this river section contains multiple peaks, distinct from the muddy sediments within bank columnar sections from sand-bedded anastomosing rivers. The dense vegetation within riparian and interchannel wetlands alongside this river reach has also protected anastomosing channels from erosion and maintained their stability, a key feature of this gravel-bedded system.
