Sustainable Cities and Society最新文献

Polycentric urban structures are widely regarded as solutions to mitigating urban heat islands (UHI). However, few studies clarified the influences of morphological and functional polycentrism on UHI. Using Chongqing as the case study, we revealed the spatial pattern of UHI based on ECOSTRESS images and morphological/functional polycentrism based on multi-source data. Then, we quantified the spatial and nonlinear effects of polycentric structures on UHI using spatial regressions and XGBoost-SHAP methods. The results showed that polycentric structures exhibit spillover and nonlinear effects on local heat variation. Building volume alleviated UHI in the urban core, signifying a substantial shadowing effect in a mountainous setting. Population density positively but weakly affected local UHI among urban centers. Facility mixture positively affected UHI mainly in facility-abundant subcenters within the effective range between 0.5 and 1.0. As a functional factor, functional linkages exerted positive effects mainly in the urban cores where the degree centrality is larger than 1,500 while exhibiting negative correlation in the peripheral subcenters, suggesting the cooling effects by dispersing functions in the urban core. These findings help mitigate UHI by targeting measures for optimizing polycentric structures.

The New Urban Agenda was conceived as a global endeavor to transpose the Sustainable Development Goals (SDGs) of the 2030 Agenda into urban areas. Both initiatives were merged into the Spanish Urban Agenda (SUA), which includes 10 strategic and 30 specific goals, among which leading and promoting digital innovation toward the development of smart cities stand out. As Urban Digital Twins (UDTs) are a technology that arises to enhance smart cities, this research aims at assessing how they may contribute to the achievement of the SUA. A framework was accordingly built to associate UDTs with the SUA to then be applied to the Barcelona digital twin as case study. A combination of an in-depth literature review on UDTs and the Delphi technique was used as methodological approach. More than 70 % of the strategic and specific goals included in the SUA, as well as the four sustainability dimensions are represented in the new instrument. The case study reflected a limited contribution in pursuing the SUA. Nevertheless, the proposed framework offers potential lines of action to reinforce the role of the Barcelona digital twin towards the SUA.

Green infrastructure provides multifarious benefits, improving urban resilience and sustainability amid increasing climate change and urbanization. Traditional green infrastructure planning studies were based on the spatial-equity principle, which usually neglected residents’ aggregation pattern, leading to the conflict with equitable green exposure. Using the sponge city of Zhengzhou as a case, this study proposes a novel priority ranking strategy, namely accessibility equity. We first spatially quantified the regional socioecological conditions on 0.25 km² grids as green infrastructure planning drivers, including stormwater management, urban thermal environment, air quality, habitat maintenance and water purification. Subsequently we integrated these planning drivers with population density to conduct spatial autocorrelation analysis, priority ranking, and grid clustering. While most planning drivers are positively correlated with population density, except air quality, some areas show opposite trends in terms of local perspective, suggesting that the spatial equity may lead to mismatch between residents’ demand and GI priority. The priority order based on accessibility equity, rises in the city center and falls in suburbs and industrial zones. The study area is divided into four categories using k-means clustering, and we propose the corresponding adaptive green infrastructure development strategy. The framework can be a practical tool for guiding green infrastructure and sponge city projects.

China has implemented a Centralized Rural Policy since 2004 to enhance energy efficiency. However, this has led to the potential creation of a Rural Heat Island (RHI) effect, which could diminish outdoor thermal comfort and increase building energy consumption during hot summers. While most studies on heat island effect focus on spatiotemporal variations and heat mitigation measures, there is limited research on rural areas, particularly the special layout of rural residences. Additionally, most studies only consider the outdoor environment, overlooking indoor thermal comfort and building energy consumption. Therefore, in order to investigate the RHI effect and assess the efficacy of localized heat mitigation measures, this study analyzed 22 types of courtyard layout patterns in a typical centralized village in northern China through detailed field measurements and performance simulations. The results show an obvious heat island effect in the rural centralized residences, where residential zones recorded average temperatures of 1.6 °C higher than those of rural boundaries. Courtyards featuring a south wing significantly alleviated outdoor thermal stress, reducing the discomfort time of extreme Physiological Equivalent Temperature (PET) by 1.5 h compared to those courtyards with a wall. Among the four localized heat mitigation measures examined, the featured black fabric shade performs best for its effectiveness in mitigating outdoor thermal stress, capable of reducing the courtyard's maximum Mean Radiant Temperature (T_mrt) by 21.5 °C and decreasing the duration of extreme PET by 2 h. Photovoltaic modules installed on the roof not only generate energy but also alleviate outdoor thermal stress, reducing the maximum T_mrt by 12.9 °C and lowering 23 % to 28 % daily energy demand, making them highly suitable for deployment in rural areas with high rates of energy poverty. The simulated results indicate that these localized heat mitigation measures mutually reinforce each other in reducing the RHI effect. The combination of four heat mitigation measures can reduce PET by up to 20 % and EUI by up to 44 % compared to the original courtyard. Incorporating these localized strategies into planning practice enables rural planners and policymakers to develop effective interventions against the RHI effect.

Spatial interaction centrality reflects the relative importance of population mobility within a location in urban population mobility. Population mobility networks visually represent urban population mobility, with mobility features and network topology contributing to the quantification of spatial interaction centrality of locations (i.e., geographical nodes). However, existing centrality measures rarely consider mobility features and network topology simultaneously. Centrality quantification also ignores the differences in distance effects between long- and short-distance trips. These factors have led to the inaccurate quantification of centrality. We propose an algorithm called k-dis-weight-shell that quantifies the spatial interaction centrality of geographical nodes at different spatiotemporal scales. Considering the different effects of distance on long- and short-distance trips, we use a spatial continuous wavelet transformation to estimate the radiation radius of geographical nodes. Then, by combining network topology with mobility features (mobility distance and flow), the algorithm transforms them into a ranked order of spatial interaction centrality. Tested in Wuhan and Chengdu, our algorithm outperforms six existing benchmarks. For cases in urban planning and epidemic management, results show that k-dis-weight-shell effectively distinguishes similarities and differences between the distribution of population mobility's spatial interaction centrality and the urban center hierarchy at a coarse spatiotemporal scale. Additionally, it reveals a double wave phenomenon of spatiotemporal correlation between population mobility and COVID-19 transmission before and after lockdown at a fine spatiotemporal scale.

In permafrost regions, where climate change poses significant challenges to infrastructure stability, understanding the thermal behaviour of buildings is crucial. This study conducts a detailed investigation into the thermal performance of elevated buildings in permafrost regions within the context of a changing climate. High-resolution regional climate simulation-informed computational fluid dynamics (CFD) models were developed for northern buildings. The findings indicate that the presence of elevated buildings can disrupt the permafrost’s natural thermal equilibrium in the future. This disturbance can extend vertically and horizontally, potentially leading to altered ground temperature gradients and increased air and ground temperatures by 4.25% and 3.85%, respectively. The research findings also highlight a 12.75% reduction in wind speed beneath the study building when transitioning from the local scale (i.e., single-building) to the neighbourhood scale (i.e. with surrounding buildings). These results underscore the critical significance of exploring the neighbourhood scale in building design and planning within permafrost regions, emphasizing the need for comprehensive assessment tools to inform effective strategies and decisions. The holistic approach adopted in this study, sets out a clear vision to guide northern adaptation initiatives that address some of the climate change issues in the buildings’ design by utilizing integrated climate system-built environment modelling.

Shared mobility services, such as bike sharing, have gained immense popularity and emerged as an integral part of sustainable urban mobility solutions. The planning of such systems requires forecasting the potential origin–destination (OD) flows between mobility sites (e.g., bike sharing stations) within the proposed network. Existing methods primarily focus on mobility flows between large regions, and do not generalize well to detailed planning applications due to the high spatial resolution required, with increased uncertainty and data sparsity. This study proposes a zero-inflated negative binomial graph neural network (ZINB-GNN) to generate sparse OD flows while capturing complex spatial dependencies. To reflect sparsity, OD flows are modeled as following ZINB distributions parameterized via feed-forward networks. To capture spatial dependencies, localized graphs are constructed to represent proximity between OD pairs, with spatial features encoded using GNNs. ZINB-GNN is validated through a case study of the bike sharing system in New York City. The results verify its prowess in both prediction accuracy and uncertainty quantification under real-world network expansion scenarios. We also demonstrate its interpretability by revealing important factors affecting OD flows. These findings can directly inform the planning of bike sharing systems, and the methodology may be adapted for other shared mobility systems.

Green space accessibility is a crucial environmental justice issue, which is influenced by socio-economic factors and may lead to disparate health outcomes among individuals. While numerous scholars have examined the association between green space accessibility and socio-economic status or health separately, research on the pathways linking these three dimensions remains relatively sparse. Using Statistical Area Level 1 (SA1) data from eight Australian capital cities, this study explores the pathways from socio-economic status to green space accessibility and subsequently to diverse health outcomes. The findings indicate the limited green space accessibility in some socio-economically disadvantaged neighborhoods, particularly among ethnic minorities and in larger cities. Additionally, a significant association is observed between increased green space accessibility and reduced chronic diseases in the national capital and major global cities. Regarding spatial proximity, the study suggests that green spaces do not need to be immediately adjacent to residential areas but can be planned within reasonable walking distances to confer significant health benefits. These findings can inform the development of equitable green space planning policies and contribute to improved human health.

Emerging modes of shared mobility like bike-sharing may significantly contribute to increasing the share of sustainable travel in medium- and small-sized cities. However, research exploring the bike-sharing usage determinants specifically for them has been severely underrepresented in the literature. To address this gap, we conducted an analysis of bike-sharing rentals for over a one-year period in Milton Keynes, a medium-sized city of 288,000 residents. Being the largest of the planned settlements built under the UK government “New Towns” programme, Milton Keynes despite having the infrastructure to offer opportunities for modal shift has been repeatedly characterised as a car-centric city. The paper examines temporal variation by comparing the average number of bike rentals in different seasons and months. Analysis of peak rentals and travel directions suggests that throughout the year bikes are mainly used to travel to work. By contrast, recreational travel appears to happen mostly during the summer months. Regression analysis shows that the number of public transport stops, offices and schools are associated with a higher number of bike rentals, supporting the view that utilitarian travel is the main usage pattern. Our research also highlights the bike-sharing's potential to be adopted as a first/last-mile option that will complement and support public transit.

The Urban Heat Island (UHI) effect exacerbates the sustainability and well-being challenges of extreme heat events. While city planning and design measures have been shown to mitigate UHI severity, the complex interaction among these measures has limited the ability of previous research to assess their impact holistically and across urban scales. To investigate the cross-scalar effectiveness of multiple UHI mitigation measures, this study applies sensitivity analysis (SA) to nine parameters in an urban generative model. Previously unstudied planning parameters, land parcel area and road network density, are included in the analysis. From the SA of 21,000 model solutions for a 100 ha case study site in Singapore, building density, podium density, and land parcel area are found to have greatest impacts on UHI. This finding supports a hypothesis that urban planning parameters have a high potential for UHI mitigation. Key findings include that a high green plot ratio (>50 %) combined with a low site coverage ratio (<50 %) permits even high-density model solutions (gross plot ratio >4) to maintain annual UHI below 0.89 °C. The conclusion discusses the implications of the findings for heat-resilient city planning and demonstrates that performance-based evaluation of generative urban models can improve upon prescriptive planning approaches.