Gradient effect of road transportation on economic development in different geomorphic regions

The economic benefits of transport infrastructure investment have been widely accepted.However,the varying influence of road transport development across vertical space has rarely been discussed.Taking Sichuan province in China as case study area where the landform is diverse and complex,administrative counties were categorized into 4 main types:plain counties,hill counties,mountain counties,and plateau counties.Using statistical data during 2006-2014,theperformanceofeconomic development and transport construction level in the four types of counties are discussed.Subsequently,the heterogeneous effect of each grade road on economy was calculated by local regression model(GWR).The results indicate that plain counties largely surpassed the other geomorphic counties in economic development level,while the gradient gap among them was on the decline.Similarly,distribution of transport infrastructure presented a decreasing trend from the low plain counties to high plateau counties.Regional imbalances were mainly reflected in the County road and Village road.Regarding the changes of regional gaps,National&Provincial roads and County roads were constantly expanding,whereas the disparity of Village road was slowly narrowing over time.Particularly noteworthy was the non-stationary economic influence of traffic factors across vertical gradients.On average,National&Provincial roads generated higher benefits in the high elevation regions than the lowlands.In contrast,County road and Village road were found to be more effective in promoting economic development in plains.With regard to local estimates of traffic factors,coefficients in mountain counties exhibited larger fluctuation ranges than other geomorphic units.The conclusions provide a basis for government decisionmaking in a more reasonable construction arrangement of road facilities and sustainable economic development.

Inter-annual variations in vegetation and their response to climatic factors in the upper catchments of the Yellow River from 2000 to 2010

To understand the variations in vegetation and their correlation with climate factors in the upper catchments of the Yellow River, China, Normalized Difference Vegetation Index（NDVI） time series data from 2000 to 2010 were collected based on the MOD13Q1 product. The coefficient of variation, Theil–Sen median trend analysis and the Mann–Kendall test were combined to investigate the volatility characteristic and trend characteristic of the vegetation. Climate data sets were then used to analyze the correlation between variations in vegetation and climate change. In terms of the temporal variations, the vegetation in this study area improved slightly from 2000 to 2010, although the volatility characteristic was larger in 2000–2005 than in 2006–2010. In terms of the spatial variation, vegetation which is relatively stable and has a significantly increasing trend accounts for the largest part of the study area. Its spatial distribution is highly correlated with altitude, which ranges from about 2000 to 3000 m in this area. Highly fluctuating vegetation and vegetation which showed a significantly decreasing trend were mostly distributed around the reservoirs and in the reaches of the river with hydropower developments. Vegetation with a relatively stable and significantly decreasing trend and vegetation with a highly fluctuating and significantly increasing trend are widely dispersed. With respect to the response of vegetation to climate change, about 20–30% of the vegetation has a significant correlation with climatic factors and the correlations in most areas are positive： regions with precipitation as the key influencing factor account for more than 10% of the area; regions with temperature as the key influencing factor account for less than 10% of the area; and regions with precipitation and temperature as the key influencing factors together account for about 5% of the total area. More than 70% of the vegetation has an insignificant correlation with climatic factors.