The fate of Chinese urban villages (chengzhongcun) has recently attracted both research and policy attention. Two important unaddressed questions are: what are the sources of informality in otherwise orderly Chinese cities; and, will village redevelopment policy eliminate informality in the Chinese city? Reflecting on the long-established study of informal settlements and recent research on informality, it is argued that the informality in China has been created by the dual urban–rural land market and land management system and by an underprovision of migrant housing. The redevelopment of chengzhongcun is an attempt to eliminate this informality and to create more governable spaces through formal land development; but since it fails to tackle the root demand for unregulated living and working space, village redevelopment only leads to the replication of informality in more remote rural villages, in other urban neighbourhoods and, to some extent, in the redeveloped neighbourhoods.

Cellular automata (CA) simulation has become a popular method of exploring the behaviour of all kinds of self-organising systems. The city may clearly be viewed as such a system but one with a particularly complex set of transition rules. Many natural processes such as the spread of fire or vegetation can be modelled by a simple set of local rules. Insofar as the development of a piece of land depends on the neighbourhood situation as well as on the characteristics of a site, urban evolution can be treated in much the same way, with transition rules translating the evaluation of the location into a land conversion outcome. If this modelling paradigm is to be used to gain insight into real-world urban development processes, there is a need to discover ways of capturing the richness of land conversion behaviour in the simplifying mechanisms of CA. Our paper contributes to this research agenda by integrating multicriteria evaluation (MCE) into a CA simulation in order to define nondeterministic, multidimensional, and multilevel transition rules. An analytical hierarchy process is used to implement MCE-derived transition rules. The integrated MCE – CA model may be used in a gaming mode to explore how urban form evolves under different development regimes caricatured by the set of multicriteria weights. We use it to test loosely hypotheses about the nature of the regimes that have governed the expansion of a fast-growing southern Chinese city.

The United Nations specified the need for “providing universal access to greenspace for urban residents” in the 11th Sustainable Development Goal. Yet, how far we are from this goal remains unclear. Here, we develop a methodology incorporating fine-resolution population and greenspace mappings and use the results for 2020 to elucidate global differences in human exposure to greenspace. We identify a contrasting difference of greenspace exposure between Global South and North cities. Global South cities experience only one third of the greenspace exposure level of Global North cities. Greenspace exposure inequality (Gini: 0.47) in Global South cities is nearly twice that of Global North cities (Gini: 0.27). We quantify that 22% of the spatial disparity is associated with greenspace provision, and 53% is associated with joint effects of greenspace provision and spatial configuration. These findings highlight the need for prioritizing greening policies to mitigate environmental disparity and achieve sustainable development goals.

Increased urbanisation and the associated reduced contact of individuals with natural environments have led to a rise in mental disorders, including depression. Residential greenness, a fundamental component of urban design, has been shown to reduce the public health burden of mental disorders. The present study investigates the association between residential green exposure and prevalence of major depressive disorders using a large and diverse cross-sectional dataset from the UK Biobank. In this cross-sectional, observational, associational study, we used baseline data from the UK Biobank cohort of participants aged 37-73 years from across the UK. Environmental exposure data were derived from a modelled and linked built environment database. Residential greenness was assessed with a 0·5 m resolution Normalised Difference Vegetation Index, which is derived from spectral reflectance measurements in remotely sensed colour infrared data and measured within geocoded dwelling catchments. Other environment metrics included street-level movement density, terrain, and fine particulate exposures. A series of logistic models examined associations between residential greenness and odds of major depressive disorder after adjusting for activity-influencing environments and individual covariates. Of 122 993 participants with data on major depressive disorder, the study analytical sample comprised 94 879 (77·1%) participants recruited across ten UK Biobank assessment centres between April 29, 2009, and Oct 1, 2010. A protective effect of greenness on depression was consistently observed, with 4·0% lower odds of major depressive disorder per interquartile increment in Normalised Difference Vegetation Index greenness (odds ratio 0·960, 95% CI 0·93-0·99; p=0·0044). Interaction analyses indicated that the beneficial effects of greenness were more pronounced among women, participants younger than 60 years, and participants residing in areas with low neighbourhood socioeconomic status or high urbanicity. The results point to the benefits of well designed green environments on mental health. Further longitudinal studies are needed to decipher causal pathways. In the UK, policies aimed at optimising allocation and design of green spaces might help preserve psychological ecosystem services, thereby, improving the mental wellbeing of populations and enhancing the mental capital of cities. University of Hong Kong, UK Biobank, and the UK Economic & Social Research Council.