Urban green space can bring various ecosystem benefits to diverse social groups. Among those ecosystem benefits, intangible social values are often neglected but highly relevant to human welfare. Existing research on the social values of urban green space often focusses on the perspective of urban inhabitants rather than tourists, even though tourists are also major beneficiaries. By combining different data sources into a comprehensive source about green-space social values, we investigated the disparity between inhabitants' and tourists' perceptions about space-associated social values, and further explored the underlying environmental conditions in the East Lake scenic area, Wuhan. For this, we collected 347 questionnaires through an on-site survey and 11,908 photos uploaded by 2165 social media users (Sina Blog), and we used SolVES (Social Value for Ecosystem Services) to uncover the spatial patterns of social values and the relationships between social value indicators and natural surroundings. Social-value hotspots occurred near water and trails. Perceptions differed, however, between inhabitants and tourists. Inhabitants perceived a larger scale of social values and could benefit more from recreation and economic values. Tourists, on the other hand, showed greater appreciation for aesthetic and cultural values. Environmental features were associated with social values to differing extent; distance to water and land use/cover exerted significantly influence. These findings should be taken into consideration to improve urban spatial planning and to optimize green infrastructures for human welfare.

Urban green space can help to reduce PM_2.5 concentration by absorption and deposition processes. However, few studies have focused on the historical influence of green space on PM_2.5 at a fine grid scale. Taking the central city of Wuhan as an example, this study has analyzed the spatiotemporal trend and the relationship between green space and PM_2.5 in the last two decades. The results have shown that: (1) PM_2.5 concentration reached a maximum value (139 μg/m³) in 2010 and decreased thereafter. Moran’s I index values of PM_2.5 were in a downward trend, which indicates a sparser distribution; (2) from 2000 to 2019, the total area of green space decreased by 25.83%. The reduction in larger patches, increment in land cover diversity, and less connectivity led to fragmented spatial patterns of green space; and (3) the regression results showed that large patches of green space significantly correlated with PM_2.5 concentration. The land use/cover diversity negatively correlated with the PM_2.5 concentration in the ordinary linear regression. In conclusion, preserving large native natural habitats can be a supplemental measure to enlarge the air purification function of the green space. For cities in the process of PM_2.5 reduction, enhancing the landscape patterns of green space provides a win-win solution to handle air pollution and raise human well-being.

Urban green space (UGS) provides a wide range of ecosystem services for human beings. The fairness of UGS distribution has important implications for sustainable urban development and human well-being. However, many current studies on green space fairness are limited to within specific cities, and UGS fairness in rapidly developing metropolitan areas is still largely neglected. This study quantifies the change in green space fairness in the Wuhan metropolitan area from 2000 to 2020 based on a long time series of land cover and population spatial distribution products. We incorporated green space types and population distribution into the fairness evaluation framework, quantified the value of green space ecosystem services available to people through nested buffers, and analyzed the spatial and temporal variation of green space fairness in the study area based on the Gini coefficient of each city. The results show that the supply of green space services in most cities has shown a trend of “decreasing and then increasing” in the past 20 years. The fairness of green space in three cities has shown an improving trend, and the Gini coefficient in four cities is still above 0.50 in 2020. Based on the current green space and related indicators in each city, we propose future greening policies for each city. The methodology and conclusions of this study will help improve future green space planning recommendations in metropolitan areas and urban agglomerations, thus promoting regional public infrastructure coproduction and sharing.

This study aims to investigate the spatial and temporal dynamics of housing prices associated with the urban infrastructure in Wuhan, China. The relationship between urban infrastructure and housing prices during rapid urbanization has drawn popular concerns. This article takes 619 residential communities during the period 2010 to 2018 in Wuhan’s main urban area as research units, and uses the geographically and temporally weighted regression (GTWR) model to study the spatial-temporal differentiation in the effects of urban infrastructure on housing prices. The results show that: 1) From 2010 to 2018, housing prices in Wuhan’s main urban area were generally on the rise, but the increment speed has shown an obvious periodic characteristic, the spatial distribution of housing prices has shown an obvious core and periphery distribution and the peak value area shifted from Hankou to Wuchang. 2) The influential factors of housing prices have significant spatiotemporal non-stationarity, while the impact, direction and intensity of the influential factors varies in time and space. Spatially, the influence factors show different differentiation rules for spatial distribution, and the influencing direction and strength of the urban infrastructure on housing prices are closely related to the spatial location, distribution density and the type of urban infrastructure. Temporally, the influencing strength of various urban facilities varies. This research will benefit both urban planners for optimizing urban facilities and policy-makers for formulating more specific housing policies, which ultimately contributes to urban sustainability.

Most arable land in rural areas of China is highly fragmented, and promoting large-scale operation by means of farmland transfer is of great significance to both increase farm income and improve the ecological stability of farmland. At present, crayfish are widely popular with the consumer market, and the rice–crayfish cultivation pattern has considerable industrial advantages for water-rich plain areas. However, farmers do not have a sufficient understanding of the new model, and its application is not common enough, failing to realize the advantages of the region’s special industries. The purpose of this paper is to analyze the current situation of farmland transfer in rice–crayfish cultivating regions of Hunan and Hubei, China, and employ the logistic regression model to explore the impacts of operation scale and transfer experience on farmers’ willingness toward farmland transfer-in, in order to promote the sustainable development of rice–crayfish cultivating. The results showed that: Firstly, both operation scale and transfer experience have a significant positive relevance with farmers’ willingness toward farmland transfer-in. Secondly, there are obvious differences in the impact path among large-scale, medium-scale, and small-scale rice or rice–crayfish farmers. Thirdly, the transfer-out experience is inhibitory or not significantly associated with the willingness toward farmland transfer-in. Fourthly, the transfer experience intensifies the positive influence of the expansion of operation scale on the farmers’ willingness toward farmland transfer-in. This study can provide some reference and basis for the local government to make differentiated measures according to the farmland transfer preferences of farmers with different scales and operation types, so as to promote farmers’ willingness to operate at a large scale and realize the sustainable development of the regional rice–crayfish cultivating model.

Manufacturing agglomeration (MA) is an important way to achieve high-quality industrial development and promote land use efficiency in China. However, there is a lack of research on the relationship between MA and the green use efficiency of industrial land (GUEIL). Based on the panel data of 279 prefecture-level cities in China, from 2004 to 2019, this study analyzes the spatial and temporal differentiation characteristics of MA and GUEIL, then empirically analyzes the impact of MA on GUEIL. The results show that: (1) during the study period, the national MA levels showed a slight decline, followed by a small increase. In addition, the inter-regional differences are mainly characterized as eastern region > central region > northeast region > western region. (2) The national and regional GUEIL showed a trend of increasing, slightly decreasing, and then increasing again. The overall regional differences in efficiency show the characteristics of: eastern region > central region > western region > northeastern region. (3) At the national scale, MA had a “U-shaped” relationship with GUEIL; at the regional scale, MA had a significant “U-shaped” effect on GUEIL in the northeast, central and western regions, while having a single negative effect in the eastern region. Finally, this study provides suggestions for optimizing the manufacturing structure and improving the GUEIL.