A distinct rise of global interest in the role of cultural and creative industries in post-industrial urban development calls for the transformation of our industrial heritage into creative clusters. Despite many factories being removed in deindustrialization, some enterprises still preserve and reuse the former industrial lands. However, the inconclusive judgments on the performances of creative clusters call for an in-depth examination of the underlying mechanism of transforming dilapidated urban industrial spaces. In addition, few empirical studies have revealed the commonalities and differences of market entities employing both the top-down and bottom-up approaches during the redevelopment.

Providing equal geographical access to hospitals, either in the public or private healthcare sector, is vital and will benefit public health in general. Against the background of the partial privatization of the healthcare sector, the impact of private hospitals on equal healthcare access has been a highly neglected issue. We have applied an assessment methodology to study this situation by comparing the status quo scenario with one without private hospitals, based on accessibility analysis and spatial equality measurements. The case study of Beijing, China revealed a double-sided impact. With the presence of private hospitals, the Gini coefficient of spatial accessibility in urban districts was reduced from 0.03391 to 0.03211, while it increased from 0.1734 to 0.1914 in suburban districts. Thus, private hospitals improved spatial equality in urban districts in Beijing but jeopardized it in suburban districts. These research findings should enlighten policymakers to promote healthcare equality but would also need to be repeated in some other big cities.

Looking ahead to the future-stage autonomous transportation system (ATS), personal mobility service (PMS) aims to provide the recommended travel options based on both microscopic individual travel demand and the macroscopic supply system objectives. Such a goal relies on massive heterogeneous data to interpret and predict user travel intentions, facing the challenges caused by prevalent centralized approaches, such as an unbalanced utilization rate between cloud and edge, and data privacy. To fill the gap, we propose a federated logit model (FMXL), for estimating user preferences, which integrates a discrete choice model—the mixed logit model (MXL), with a novel decentralized learning paradigm—federated learning (FL). FMXL supports PMS by (1) respectively performing local and global estimation at the client and server to optimize the load, (2) collaboratively approximating the posterior of the standard mixed logit model through a continuous interaction mechanism, and (3) flexibly configuring two specific global estimation methods (sampling and aggregation) to accommodate different estimation scenarios. Moreover, the predicted rates of FMXL are about 10% higher compared to a flat logit model in both static and dynamic estimation. Meanwhile, the estimation time has been reduced by about 40% compared to a centralized MXL model. Our model can not only protect user privacy and improve the utilization of edge resources but also significantly improve the accuracy and timeliness of recommendations, thus enhancing the performance of PMS in ATS.

Prevailing strategies for commuting efficiency focus predominantly on land-use and transportation policies, whereas research on the environmental benefits of housing reallocation strategies to mitigate excess commuting remains critically underexplored. Here this study proposes an information-enabled housing exchange framework and quantifies the potential reduction of commuting-related carbon emissions it can achieve. Leveraging housing and travel survey data from Beijing (n = 2,032), Munich (n = 3,131) and Singapore (n = 7,418), at the household level, our analysis reveals that commuting distances could be reduced by approximately 10.49%–12.70%, corresponding to CO₂ emissions reductions of 11.33%, 12.09% and 13.42%, respectively. Crucially, strategic relocation of the households with the 5% highest carbon reduction potential could yield more than 50% of total achievable emissions reduction. These results demonstrate that enhancing housing market efficiency through information transparency can generate substantial environmental co-benefits. The integration of conventional policies with this approach would enable a viable pathway to sustainable urban development.