Value of urban views in a bay city: Hedonic analysis with the spatial multilevel additive regression (SMAR) model

This study estimates the impact of the introduction of New York’s High Line, an elevated walkway in Manhattan by relating the increase in housing values to the issue of eco-gentrification in urban areas. Studies have shown that increasing the amount of green space can lead to gentrification: rising property values, displacement of existing residents, and a large in-migration of wealthy populations. We utilize Geographic Information Systems (GIS) to spatially model the effects of the High Line on residential property values. We exploit spatial and temporal differences of the High Line’s opening to estimate its impact on residential home values. We find that homes closest to the High Line experienced a 35.3% increase in housing values. We estimate the driving force behind this premium by using a triple difference approach by analyzing elevation levels and find that homes with relatively the same height as the High Line receive the largest premium. This study serves to inform policy makers regarding potential inequality of green space provisions. We provide policy recommendations for minimizing the housing price impact of green space creation.

Floor level is an important factor influencing housing price. However, most existing literature just treated floor level as a control variable and simply obtained a linear relationship between floor level and housing price. To fill this gap, this study uses the hedonic price model to deeply investigate floor level premium. The emphasis of this study is on landscape proximity and also the interactive effect of floor level and landscapes. A better understanding between floor level, landscape proximity and housing price is provided. Contrary to most existing studies, the findings suggest that the relationship between floor level and housing price is non-linear and is different in multi-storey buildings and high-rise buildings. As for the interactive effect between floor level and landscape proximity, we find that the amenity value of landscapes shows vertical heterogeneity at different floor levels within a building. On the other hand, landscape proximity significantly affects the relationship between floor level and housing price. This study addresses issues that were not studied before but are crucial to estimate the value of floor level and landscapes. It provides important implications for a better living environment and urban planning.

Urban studies attempt to identify the geographic areas with restricted access to healthy and affordable foods (defined as food deserts in the literature). While prior publications have reported the socioeconomic disparities in healthy food accessibility, little evidence has been released from developing countries, especially in China. This paper proposes a geo-big data approach to measuring transit-varying healthy food accessibility and applies it to identify the food deserts within Shenzhen, China. In particular, we develop a crawling tool to harvest the daily travel time from each community (8117) to each healthy food store (102) from the Baidu Map under four transport modes (walking, public transit, private car, and bicycle) during 17:30–20:30 in June 2016. Based on the travel time calculations, we develop four travel time indicators to measure the healthy food accessibility: the minimum, the maximum, the weighted average, and the standard deviation. Results show that the four accessibility indicators generate different estimations and the nearest service (minimum time) alone fails to reflect the multidimensional nature of healthy food accessibility. The communities within Shenzhen present quite different typology with respect to healthy food accessibility under different transport modes. Multilevel additive regression is further applied to examine the associations between healthy food accessibility and nested socioeconomic characteristics at two geographic levels (community and district). We discover that the associations are divergent with transport modes and with geographic levels. More specifically, significant social equalities in healthy food accessibility are identified via walking, public transit, and bicycle in Shenzhen. Based on the associations, we finally map the food deserts and propose corresponding planning strategies. The methods demonstrated in this study should offer deeper spatial insights into intra-urban foodscapes and provide more nuanced understanding of food deserts in urban settings of developing countries.

The greenhouse effect caused by CO₂ emissions leads to global warming, glacial melting, sea–level rise and frequent outbreaks of extreme weather, which seriously threatens the safety of human life. China is currently the biggest emitter of carbon dioxide in the world, and the iron and steel industry is the largest contributor to China's CO₂ emissions. Therefore, investigating the main driving forces of the growth in CO₂ emissions in the iron and steel industry is very necessary and urgent. Interestingly, ignoring the nonlinear relationships between economic variables, most of the existing studies use linear methods to examine the industry's CO₂ emissions. Based on 30 provincial panel data from 2000 to 2013, this study uses the nonparametric additive regression model to examine the key driving forces of CO₂ emissions in China's iron and steel industry. The estimation results show that the nonlinear effect of economic growth on CO₂ emissions is consistent with the Environmental Kuznets Curve (EKC) hypothesis. Energy efficiency improvement follows an inverted "U–shaped" pattern in relation to CO₂ emissions because of the difference in R&D funding and R&D personnel investments at different times. An inverted "U–shaped" effect of energy structure is due to the optimization of energy consumption. However, the impact of urbanization exhibits a positive "U–shaped" pattern since fixed asset investment and private car population are much larger at the later stage. Hence, the differential nonlinear effects of these driving forces at different times should be taken into consideration, when discussing reduction of CO₂ emissions in China's iron and steel industry.