Urban Tree Canopy and Environmental Justice: Examining the Distributional Equity of Urban Tree Canopy in Guangzhou, China

Rapid urbanization has led to a series of environmental problems that are harmful to the physical and mental health of residents. Not only can increasing urban tree canopy (UTC) improve sustainable city development, but it can also effectively promote the quality of life for residents; however, the uneven spatial distribution of UTC can also bring about social justice problems. Currently, there are few studies related to the justice of UTC distribution in China. Based on this, the paper adopts object-oriented image classification technology to interpret and extract UTC data from satellite images, and it uses house price to explore the justice of the spatial distribution of UTC in the main urban area of Guangzhou from the perspective of environmental justice by ANOVA, Pearson correlation coefficient, and bivariate local spatial autocorrelation. The results show that: (1) There is a significant positive correlation between UTC and house price in the main urban area of Guangzhou, and there are regional differences in the distribution of UTC, with the UTC in the "very high" house price group being significantly higher than that in other groups. (2) The spatial clustering of UTC and house price in the main urban area of Guangzhou is found to be "low-low" and "high-high"; thus, it can be concluded that the spatial distribution of UTC in the main urban area of Guangzhou is uneven. This is an environmental injustice, as the areas with low UTC are spatially clustered in old residential areas, while the areas with high UTC are spatially clustered in commercial housing estates with high house prices. The study shows that urban tree planting should not only focus on quantitative improvement but also on equitable spatial layouts to promote social equity and justice thereby improving the urban ecological environment and promoting healthy urban development.

The Dynamic Relationship among Bank Credit, House Prices and Carbon Dioxide Emissions in China

This paper explores the dynamic relationship among bank credit, house prices and carbon dioxide emissions in China by systematically analyzing related data from January 2000 to December 2019 with the help of the time-varying parameter vector autoregression with stochastic volatility (TVP-SV-VAR) model and the Bayesian DCC-GARCH model. Empirical results show the expansion of bank credit significantly drives up house prices and increases carbon dioxide emissions in mosttimes. The rise in house prices inhibits the expansion of bank credit but increases carbon dioxide emissions and aggravates environment pollution, and that the increase in carbon dioxide is helpful to stimulate bank credit expansion and house price rise. In addition, bank credit and house prices are most relevant, followed by bank credit and carbon dioxide emissions, then by house prices and carbon dioxide emissions. Therefore, we believe that in order to stabilize skyrocketing house prices, restrain carbon dioxide emissions, and secure a stable and healthy macro-economy, the government should strengthen management of bank credit, and effectively control its total volume.

Are House Prices Affected by PM2.5 Pollution? Evidence from Beijing, China

With the progress of high-quality development in China, residents have begun to focus on the air quality of their residential areas in an effort to reduce the health threats of air pollution. Gradually, the risk associated with air pollution has become an important factor affecting housing prices. To quantitatively analyze the impact of air pollution on house prices, panel data, including data for fine particulate matter (PM2.5) concentrations, house prices and other auxiliary variables from 2009 to 2018, were collected from 16 districts in Beijing, China. Based on this dataset, ordinary least squares (OLS), moderating effect and threshold effect models were constructed for empirical investigation. Within the studied decade, PM2.5 pollution shows a significant decreasing trend of −3.79 μg m−3 yr−1 (p < 0.01). For house prices, the opposite trend was found. The empirical results indicate that PM2.5 pollution has a negative effect on house prices and that every 1% increase in PM2.5 causes an approximately 0.541% decrease in house prices. However, the inhibition of PM2.5 on housing prices is moderated by regional educational resources, especially in areas with high education levels. In addition, per capita disposable income can also cause heterogeneities in the impact of PM2.5 on house prices, whereby the threshold is approximately CNY 101,185. Notably, the endogeneity problems of this study are solved by the instrumental variable method, and the results are robust. This outcome suggests that the coordinated control of air pollution and balanced educational resources among regions are required for the future sustainable development of the real estate market.

The politics of urban trees: Tree planting is associated with gentrification in Portland, Oregon

This study evaluated the hypothesis that urban-tree planting increases neighborhood gentrification in Portland, Oregon. We defined gentrification as an increase in the median sales price of single-family homes in a Census tract compared to other tracts in the city after accounting for differences in the housing stock such as house size and number of bathrooms. We used tree-planting data from the non-profit Friends of Trees, who have planted 57,985 yard and street trees in Portland (1990-2019). We estimated a mixed model of gentrification (30 years and 141 tracts) including random intercepts at the tract level and a first-order auto-regressive residual structure. Tract-level house prices and tree planting may be codetermined. Therefore, to address potential endogeneity of tree planting in statistical modeling, we lagged the number of trees planted by at least one year. We found that the number of trees planted in a tract was significantly associated with a higher tract-level median sales price, although it took at least six years for this relationship to emerge. Specifically, each tree was associated with a $131 (95% CI: $53-$210; p-value=0.001) increase in tract-level median sales price six years after planting. The magnitude of the association between the number of trees planted and median sales price generally increased as the time lag lengthened. After twelve years, each tree was associated with a $265 (95% CI: $151-$379; p-value<0.001) increase in tract-level median sales price. Tree planting was not merely a proxy for existing tree cover, as the percent of tract covered in tree canopy was independently associated with an increase in median sales price. Specifically, each 1-percentage point increase in tree-canopy cover was associated with a $882 (95% CI: $226-$1,538; p-value=0.008) increase in median sales price. In conclusion, tree planting is associated with neighborhood-level gentrification, although the magnitude of the association is modest.

Can bank credit withstand falling house price in China?

This paper examines whether the falling house price causes credit risk or not in China. We note that bidirectional causal relationships exist in several sub-periods using sub-sample rolling window test. Our analysis confirms the option-based model (Foster & Van Order, 1984) that proves the falling house price leads to more defaults of mortgage and increasing credit risk. Meanwhile, the rise in credit risk is likely to be accompanied by the increasing house price. Rising house price has no impact on credit risk. We find banks’ credit expansion may be irrational which leads to the accumulation of credit risk. The tight credit policies and high loan interest rates stimulate the house price to fall. In addition, based on the analysis of the previous periods, we do not think that the credit risk will explode systematically in China with the current situation of mortgage growth slowing down. However, due to the pivotal role of real estate credit in loan structure of banks in China, we still need to be alert to the potential accumulation credit risk caused by the default of individuals and enterprises. It is essential for banks to strengthen the examination of personal credit certificates to prevent speculative loans. Regulators should take into account the impact on bank credit when they formulate policies to control the house price.

[Effects of socioeconomic factors on urban plant diversity of residential areas in Shanghai, China]

Residential area, usually consisting of buildings and vegetation, is one of the dominant land use types and an important kind of habitat in the urban area. Therefore, it plays an important role in urban biodiversity conservation. Previous studies found that plant diversity abroad in urban residential areas was influenced by socioeconomic factors. However, it is not clear whether this result hold for Chinese cities which have completely different urban management regime. In this study, we investigated plant species diversity in 39 residential neighborhoods in Shanghai. Biodiversity indexes, regression analysis, and partial least square regression analysis were employed to estimate the relationships between plant diversity and socioeconomic factors of population density, house price, house age and greenspace coverage. Our results showed that socioeconomic factors did affect plant diversity in urban residential areas in Shanghai. The effects varied with plant species, population density, house price, house age, and greenspace coverage. The house age had the strongest effect on most plant taxa, then followed by population density, house price, and greenspace coverage. We tested the hypotheses of "luxury effect" and "legacy effect", and found that they only partially explained the spatial distribution of plant taxa in Shanghai. These results could provide insights for management and conservation of plant diversity, as well as urban landscape planning and design in Shanghai.