Elucidating the complex mechanism between urbanization, economic growth, carbon dioxide emissions is fundamental necessary to inform effective strategies on energy saving and emission reduction in China. Based on a balanced panel data of 31 provinces in China over the period 1997-2010, this study empirically examines the relationships among urbanization, economic growth and carbon dioxide (CO2) emissions at the national and regional levels using panel cointegration and vector error correction model and Granger causality tests. Results showed that urbanization, economic growth and CO2 emissions are integrated of order one. Urbanization contributes to economic growth, both of which increase CO2 emissions in China and its eastern, central and western regions. The impact of urbanization on CO2 emissions in the western region was larger than that in the eastern and central regions. But economic growth had a larger impact on CO2 emissions in the eastern region than that in the central and western regions. Panel causality analysis revealed a bidirectional long-run causal relationship among urbanization, economic growth and CO2 emissions, indicating that in the long run, urbanization does have a causal effect on economic growth in China, both of which have causal effect on CO2 emissions. At the regional level, we also found a bidirectional long-run causality between land urbanization and economic growth in eastern and central China. These results demonstrated that it might be difficult for China to pursue carbon emissions reduction policy and to control urban expansion without impeding economic growth in the long run. In the short-run, we observed a unidirectional causation running from land urbanization to CO2 emissions and from economic growth to CO2 emissions in the eastern and central regions. Further investigations revealed an inverted N-shaped relationship between CO2 emissions and economic growth in China, not supporting the environmental Kuznets curve (EKC) hypothesis. Our empirical findings have an important reference value for policy-makers in formulating effective energy saving and emission reduction strategies for China.
The urban vulnerability poses a serious challenge to achieving sustainable development. With the concentration of the population and the economy, cities must manage the higher frequencies and risks of various hazards and are becoming more vulnerable. Research on the assessment and regulatory control of urban vulnerability is of great significance for both urbanization quality improvement and sustainable development in China or other countries in the world. Because of the complexity of cities and vulnerability concepts, existing studies have focused on different aspects of urban vulnerability. And the research content of urban vulnerability is scattered and relatively independent, leading to a lack of comparability among the research data and resulting in tremendous difficulties in summarizing the conclusions through comparison of independent research data. Therefore the goal of this study was to construct urban vulnerability index (UVI) from the perspective of sustainable development that could assess urban vulnerability comprehensively. In this study, we selected 10 subindexes involving 36 specific parameters from four aspects (resources, eco-environmental systems, economics, and social development) to construct a comprehensive index system. We also established the standard values of measurements. Then we take 288 prefecture-level cities in China as a study area and evaluate its overall urban vulnerability and its spatial differentiation. Results indicate that urban vulnerability of China has a remarkable spatial differentiation of both “gradient distribution” and “clustered distribution”; the extent of urban vulnerability corresponds to city size, the bigger the city, the lower its vulnerability; resource-based cities are more vulnerable than comprehensive cities; a city’s economic growth rate does not reflect the extent of its urban vulnerability. Further, we offer a few suggestions to cope with urban vulnerability in China.
Spatially explicit modeling techniques recently emerged as an alternative to monitor land use changes. This study adopted the well-known CLUE-S (Conversion of Land Use and its Effects at Small regional extent) model to analyze the spatio-temporal land use changes in a hot-spot in Northeast China (NEC). In total, 13 driving factors were selected to statistically analyze the spatial relationships between biophysical and socioeconomic factors and individual land use types. These relationships were then used to simulate land use dynamic changes during 1980-2010 at a 1 km spatial resolution, and to capture the overall land use change patterns. The obtained results indicate that increases in cropland area in NEC were mainly distributed in the Sanjiang Plain and the Songnen Plain during 1980-2000, with a small reduction between 2000 and 2010. An opposite pattern was identified for changes in forest areas. Forest decreases were mainly distributed in the Khingan Mountains and the Changbai Mountains between 1980 and 2000, with a slight increase during 2000-2010. The urban areas have expanded to occupy surrounding croplands and grasslands, particularly after the year 2000. More attention is needed on the newly gained croplands, which have largely replaced wetlands in the Sanjiang Plain over the last decade. Land use change patterns identified here should be considered in future policy making so as to strengthen local eco-environmental security.
Since 1979, the Pearl River Delta (PRD) of China has experienced rapid socio- economic development along with a fast expansion of construction area. Affected by both natural and human factors, a complex interdependency is found among the regional changes in construction area, GDP and population. A quantitative analysis of the four phases of the regional land use data extracted from remote sensing images and socioeconomic statistics spanning 1979 to 2009 demonstrates that the proportion of construction area in the PRD increased from 0.5% in 1979 to 10.8% in 2009, accompanied with a rapid loss of agricultural land. An increase of one million residents was associated with an increase of GDP of approximately 32 billion yuan before 2000 and approximately 162 billion yuan after 2000. Because the expansion of construction area has approached the limits of land resource in some cities of the PRD, a power function is found more suitable than a linear one in describing the relationship between GDP and construction area. Consequently, the Logistic model is shown to provide more accurate predictions of population growth than the Malthus model, particularly in some cities where a very large proportion of land resource has been urbanized, such as Shenzhen and Dongguan.
Farmland abandonment is a type of land use change in the mountain region, and this change is under rapid development. Whether farmland transfer can prevent this process and promote the effective allocation of land resources or not is a question worth studying and discussion. With the help of the previous research findings, the objective of this paper was to find out the role of farmland transfer on preventing farmland abandonment, by using the methods of multiple view with two factors, and single factor correlation analysis. The results showed that: (1) At village level, a significant negative correlation between farmland transfer and farmland abandonment existed in the study site, with R2 = 0.7584. This correlation of farmland with high grade farming conditions presented more outstandingly. The fitted curve for the farmland at Level I had the largest R2 at 0.288, while that for the farmland at Level IV had the smallest R2 at 0.103. Which indicated that farmland transfer could prevent the abandonment of farmland with high grade farming conditions? (2) At plot level, the abandonment rate of farmland with high grade farming conditions was significantly lower than that of farmland with poor grade farming conditions. It was the lowest at 10.49% for the farmland with Level I farming conditions, whereas the farmland with Level I farming conditions was 26.21%. Abandoned farmland was mainly contributed by farmland with Level IV farming conditions in the study site. (3) At village level, the role of farming conditions on farmland abandonment was insignificant. The univariate correlation analysis revealed that the abandonment ratio was negatively correlated with the proportions of farmland at Levels I and II and their accumulated proportion; however, their R2 were small at 0.194, 0.258, and 0.275, respectively. The abandonment of farmland with high farming conditions still existed. The abandonment ratios of farmland at Levels I and II were high at 9.96% and 10.60%, respectively. This presented that farmland transfer on behalf of the land rental market was still not developed. (4) However, the village possessed the high rate of farmland transfer, and its rate of farmland abandonment with high grade farming conditions was all lower. When the transfer ratios of farmland were over 20%, the abandonment ratios of farmland at Levels I and II were 6.47% and 6.92%, respectively. Farmland abandonment was still controlled by the improvement of land rental market. And the functions of land rental market optimizing the utilization of farmland resources have been presented to a certain degree. (5) To further improve the marketing degree of land rental, the probability of farmland abandonment could be reduced. Especially, their function to farmland with high grade farming conditions was very obvious, and could avoid the waste of farmland resources with high grade farming conditions.
To understand historical human-induced land use/cover change (LUCC) and its climatic effects, it is essential to reconstruct historical land use/cover changes with explicit spatial information. In this study, based on the historically documented cropland area at county level, we reconstructed the spatially explicit cropland distribution at a cell size of 1 km × 1 km for the Songnen Plain in the late Qing Dynasty (1908 AD). The reconstructions were carried out using two methods. One method (hereafter, referred to as method I) allocated the cropland to cells ordered from a high agricultural suitability index (ASI) to a low ASI, but they were all within the domain of potential cropland area. The potential cropland area was created by excluding natural woodland, swamp, water bodies, and mountains from the study area. The other method (hereafter, method II) allocated the cropland to cells in the order from high ASI to low ASI within the domain of cropland area in 1959. This method was based on the hypothesis that the cropland area domain in 1959 resulted from enlargement of the cropland area domain in 1908. We then compared these two reconstructions. We found that the cropland distributions reconstructed by the two methods exhibit a similar spatial distribution pattern. Both reconstructions show that the cropland was mostly found in the southern and eastern parts of the Songnen Plain. The two reconstructions matched each other for about 68% of the total cropland area. By spatially comparing the unmatched cropland cells of the two reconstructions with the settlements for each county, we found that unmatched cropland cells from method I are closer to settlements than those from method II. This finding suggests that reconstruction using method I may have less bias than reconstruction with method II.
Accurate estimation of evapotranspiration (ET), especially at the regional scale, is an extensively investigated topic in the field of water science. The ability to obtain a continuous time series of highly precise ET values is necessary for improving our knowledge of fundamental hydrological processes and for addressing various problems regarding the use of water. This objective can be achieved by means of ET data assimilation based on hydrological modeling. In this paper, a comprehensive review of ET data assimilation based on hydrological modeling is provided. The difficulties and bottlenecks of using ET, being a non-state variable, to construct data assimilation relationships are elaborated upon, with a discussion and analysis of the feasibility of assimilating ET into various hydrological models. Based on this, a new easy-to-operate ET assimilation scheme that includes a water circulation physical mechanism is proposed. The scheme was developed with an improved data assimilation system that uses a distributed time-variant gain model (DTVGM), and the ET-soil humidity nonlinear time response relationship of this model. Moreover, the ET mechanism in the DTVGM was improved to perfect the ET data assimilation system. The new scheme may provide the best spatial and temporal characteristics for hydrological states, and may be referenced for accurate estimation of regional evapotranspiration.
The ice shelves in the northern Antarctic Peninsula are highly sensitive to variations of temperature and have therefore served as indicators of global warming. In this study, we estimate the velocities of the ice shelves in the northern Antarctic Peninsula using co-registration of optically sensed images and correlation module (COSI-Corr) in the Environment for Visualizing Images (ENVI) based on Moderate Resolution Imaging Spectroradiometer (MODIS) images during 2000-2012, from which we conclude that the ice ?ow directions generally match the peninsulas pattern and the crevasse, ice flows mainly eastward into the Weddell Sea. The spatial pattern of velocity field exhibits an increasing trend from the western grounding line to the maximum at the middle part of the ice shelf front on Larsen C with a velocity of approximately 700 ma-1, and the velocity field shows relatively higher values in its southerly neighboring ice shelf (e.g. Smith Inlet). Additionally, ice ?ows are relatively quicker in the outer part of the ice shelf than in the inner parts. Temporal changes in surface velocities show a continuous increase from 2000 to 2012. It is worth noting that, the acceleration rate during 2000-2009 is relatively higher than that during 2009-2012, while the ice movement on the southern Larsen C and Smith Inlet shows a deceleration from 2009 to 2012.
