Promoting or Inhibiting?Influence of Railway Container Transportation on Regional Economic Development

In the context of building a country with a strong transportation network,railway container transportation(RCT)is an important means of reducing costs,increasing efficiency,and adjusting transportation structures.Thus,its impact on regional economic development is important.Based on data from railway container-handling stations and spatial econometric models,this study discusses the differences in the development of RCT and their impact on regional economic development at different leves.This study has three main findings:first,there are significant regional differences in the development of the RCT.The intra-regional differences between the eastern and central regions of China(which do not include Hong Kong,Macao and Taiwan)are gradually narrowing,while the regional differences in the western region are widening.Meanwhile,the intra-regional differences in important economic zones such as Pearl River Delta Economic Zone(PRDEZ),Chengdu-Chongqing Economic Zone(CYEZ),Bohai Rim Economic Zone(BHEZ),and Yangtze River Delta Economic Zone(YRDEZ)are narrowing daily.Second,the development differences of RCT in regional level and important economic regions level show different trends.The unbalanced features of large regions are increasingly evident,whereas the differences in economic regions are decreasing.However,the problem of overlapping RCT remains prominent.Third,the transformation of RCT development mode and fierce competition among transportation modes cause RCT to have a restraining effect on the regional economy at three levels.Rational allocation of resources and other means must be used to guide the transformation from inhibition to promotion,and by formulating targeted policies that will promote the development of RCT,which will improve the transportation structure and help construct a country with a strong transportation system.

Impact of Accessibility on Housing Prices in Dalian City of China Based on a Geographically Weighted Regression Model

This paper studies the relationship between accessibility and housing prices in Dalian by using an improved geographically weighted regression model and house prices, traffic, remote sensing images, etc. Multi-source data improves the accuracy of the spatial differentiation that reflects the impact of traffic accessibility on house prices. The results are as follows: first, the average house price is 12 436 yuan(RMB)/m^2, and reveals a declining trend from coastal areas to inland areas. The exception was Guilin Street, which demonstrates a local peak of house prices that decreases from the center of the street to its periphery. Second, the accessibility value is 33 minutes on average, excluding northern and eastern fringe areas, which was over 50 minutes. Third, the significant spatial correlation coefficient between accessibility and house prices is 0.423, and the coefficient increases in the southeastern direction. The strongest impact of accessibility on house prices is in the southeastern coast, and can be seen in the Lehua, Yingke, and Hushan communities, while the weakest impact is in the northwestern fringe, and can be seen in the Yingchengzi, Xixiaomo, and Daheishi community areas.

地表城市热岛时空演变——对夏季热浪时期的关注

Many cities face heat wave(HW) events, combined with the existent surface urban heat island(SUHI) effects. This places pressure on human settlements and sustainable development. However, few studies have investigated the SUHI effects from the perspective of HWs. In this study, the summer HWs in Beijing from 2001 to 2021 were calculated, and the evolution of HWs and SUHIs was quantitatively analyzed based on the dynamic nature of the urban-rural boundary. Beijing experienced 27 HWs in the 21 years, including 10 instances in June, four in July, and 13 in August. The SUHI varied during HWs, between 2–3℃ in most years. The highest SUHI occurred in 2019, reaching 3.99℃ and covering the largest area(10,887 km^(2)). The fluctuation in HWs and SUHIs generally displayed the same spatiotemporal pattern, and HWs amplified the SUHIs to a certain extent, with the highest correlation coefficient being 0.44. Additionally, impervious surfaces and cropland contributed most to SUHIs,and night light enhanced SUHIs. Observing the co-evolution of HWs and SUHIs will be helpful for ecological maintenance and urban infrastructure optimization and provide theoretical support for reducing heat risk and improving the human settlement environment.