Sustainable Cities and Society最新文献

A WRF-CFD coupled model with high-temporal resolution is employed to investigate pollutant dispersions during a sea-land breeze (SLB) day in an identical building block configuration at three locations in Shanghai, China, and the blocks are set at the coast (L1), downtown (L2) and inland (L3). The results show that the localized wind speed drops below 1 m/s during the sea-land-breeze collision period (SLBCP), which leads to pollutant accumulation. The closer the block is to the coast, the earlier occurrence and longer duration of SLBCP, and Blocks L1, L2, and L3 experience SLBCPs during the morning peak traffic period (MPTP), low-traffic-volume period (at midday), and evening peak traffic period (EPTP), with durations of 2.5 h, 2 h, and 1 h, respectively. Due to the low wind speeds of both land breezes and sea breezes during the overlap of MPTP and SLBCP, the pollutant concentration in Block L1 is significantly elevated, and the peak concentration is two times higher than that in the non-coastal blocks (L2 and L3). Block L2 shows a peak concentration during the midday low-traffic-volume period, while no evident peak concentration is found in L3 in EPTP. The mean concentrations in Blocks L1, L2, and L3 during EPTP are 72 %, 57 %, and 31 % lower than those during MPTP, respectively. This suggests that SLB has significantly different effects on wind fields and pollutant dispersion in building blocks with different distances from the coast and can provide reference data for transport planning in coastal cities.

In this study, we propose a methodological framework to identify and evaluate cost-effective pathways for enhancing resilience in large-scale interdependent infrastructure systems, considering decision-makers’ risk preferences. We focus on understanding how decision-makers with varying risk preferences perceive the benefits from infrastructure resilience investments and compare them with upfront costs in the context of high-impact low-probability (HILP) events. First, we compute the costs of interventions as the sum of their capital costs and maintenance costs. The benefits of the interventions include the reduction in physical damage costs and business disruption losses resulting from the improved resilience of the network. In the final stage, we develop statistical models to predict the perceived net benefits of different network resilience configurations in power, water, and transport networks. These models are employed in an optimization framework to identify optimal resilience investment pathways. By incorporating Cumulative Prospect Theory (CPT) in the optimization framework, we show that decision-makers who assign higher weights to low probability events tend to allocate more resources towards post-disaster recovery strategies leading to increased resilience against HILP events, like earthquakes. We illustrate the methodology using a case study of the interdependent infrastructure network in Shelby County, Tennessee.

Since COVID-19, the focus on urban resilience has intensified, particularly on cities' ability to adapt and recover while maintaining essential functions and liveability; however, few studies have examined the resilience of urban vibrancy during such health crises. This study investigates urban vibrancy resilience in Inner London during the COVID-19 pandemic using multi-sourced social media data (geo-tagged Twitter and Flickr). We propose an analytical framework based on space-time permutation scan statistics (STPSS) to identify spatiotemporal urban areas of interest (ST-AOIs), examining their spatial, temporal, and contextual characteristics. Our findings show that central neighbourhoods with transport hubs, educational and healthcare facilities, eateries, and financial centres exhibit greater resilience. These areas adapt by shifting active periods in response to disruptions. Additionally, we assess the varying resilience capacities of different types of points of interest. This research provides actionable insights for urban planners and policymakers by demonstrating how identifying characteristics of robust urban vibrancy can contribute to the resilience of cities and communities, particularly under normal conditions after COVID-19. The findings offer concrete strategies for integrating social media data into urban planning processes, enabling more responsive and adaptive governance that meets the dynamic needs of urban populations.

Green and blue spaces are vital for mitigating urban heat island impacts but are poorly studied in arid regions. In this study, we quantify monthly and seasonal cooling for five contrasting types of green and blue infrastructure (GBI): rivers, lakes, "captured" agricultural areas, urban parks, and golf courses in the Cairo and Giza provinces of Egypt. Using Landsat-8 images of Land Surface Temperature (LST) we assessed change in LST along bisecting transects and in circle plots for three replicates of each GBI type, in each of four seasons. Cooling was greatest in summer for all GBI types. Cooling differentials of LST were greater for water bodies than for green spaces. Ordered by increasing cooling potential (May LST cooling) they were: Agricultural areas (3.3 °C), Golf courses (4.3 °C), Parks (4.4 °C), Lakes (8.2 °C) and Rivers (12.2 °C). The cooling effects extending into adjacent buffer areas were greatest for blue spaces like rivers and lakes. This paper provides the first data for cooling by less-studied GBI types in arid regions, such as golf courses and urban agriculture. It provides information to support city planners to embrace green and blue spaces within metropolitan areas and to protect them from urban sprawl.

District-level building energy systems play a significant role in urban energy networks in the future. Understanding the key distributive features of district electricity use profiles is essential for the optimal planning and design of energy networks. Due to the diversity of building electricity use characteristics, the district-level electricity use profile exhibits a prominent “peak staggering effect.” Current physics-based and statistical models cannot fully represent realistic distributions and the uncertainties of district profiles. Thus, it is critical to quantitatively investigate the changing patterns and distributive features of electricity use profiles at various district levels. This paper proposes a novel approach for district building electricity use profile simulation. Probability distribution inference methods integrating Gaussian Mixture Model (GMM)/lognorm distribution fitting, singular value decomposition (SVD)-based feature transformation, and distribution addition theorems have been proposed to generate the feature parameters of electricity use profiles at various district scales, thus generating simulated district electricity use profiles. The performance of the proposed model was validated using engineering-informed metrics, including peak demands, load duration curves, and standard deviations of the load parameters. The results of the case study suggest that the average relative error of the 99 % peak demand is reduced from 17.60 % in the baseline model to 3.48 % in the proposed model, the average relative error of the duration of 2Q_m reduced from 40.82 % in the baseline model to 0.99 % in the proposed model, and the average relative error of the standard deviation of load parameters was reduced from >100 % in the baseline model to <35 % in the proposed model. The results indicate a better quantification of district electricity use distributions and uncertainties, providing practical tools to support the capacity design and optimization of integrated district energy systems.

Embracing low-carbon transition (LCT) is crucial for achieving sustainable development goals. This study theoretically analyzes the impact of LCT on corporate sustainability from a risk-taking perspective. By constructing a multi-period difference-in-differences (DID) model of low-carbon city pilot policy (LCCP). We find that LCT promotes corporate risk-taking (CRT). Heterogeneity analysis shows that LCT significantly improves CRT in state-owned enterprises (SOEs), high-risk-taking enterprises (HRTs), and central and western China. Mechanism analysis shows that LCT will motivate enterprises to improve technological innovation and increase CRT through the innovation compensation effect, however, it will increase the financing constraints (SA) of enterprises and reduce CRT through the cost-push effect. Further analysis reveals that equity equalization (Bala) amplify the positive impact of LCT on CRT. In contrast, the shareholding of institutional investors (Inst) and corporate competitive culture(Cult) will weaken this positive effect. This paper provides the theoretical basis and practical reference for the research on pilot policies and CRT.

Deep learning (DL) has proven to be an effective tool for predicting the daylighting performance of buildings on individual rooms or standalone buildings by utilizing a few straightforward design parameters as input variables for analysis. In addition to existing studies, exploring methods to characterize larger objects with spatial relationships may contribute to understanding the impact of layout on the overall daylighting performance of buildings. In this study, a DL model based on the framework of “Autoencoder-Based Feature Extraction with Artificial Neural Network (AE-ANN)” has been developed to predict the daylighting performance of the layout of teaching building clusters. In order to efficiently extract the layout characteristics and improve the model's generalization capabilities, an autoencoder (AE) was pre-trained to encode the planar layout images of teaching building clusters into feature vectors, which were then employed for training an ANN model. In the testing dataset, the AE-ANN model demonstrated impressive accuracy, achieving R² values of 0.946 for sDA and 0.853 for ASE, alongside MSE values of 0.312 and 0.656. This research investigated the feasibility of the AE-based model for predicting daylighting performance of large-scale scenarios, highlighting its potential as a fundamental model for the development of more intricate daylighting prediction models.

As urban populations grow and cities expand, the challenge of managing urban heat and its environmental impacts becomes increasingly critical. Traditional methods for analyzing urban temperature dynamics often fall short in precisely capturing the complexity of urban landscapes. This paper introduces the 3D Landscape Clustering (3LC) framework, a new tool designed to analyze urban temperature dynamics by factoring in landscape variables. It clusters landscapes into homogeneous groups using high-resolution 3D land cover maps. The 3LC adopts a clustering mechanism to enhance flexibility and objectivity in landscape categorization, thereby enhancing the depth and accuracy of urban climate studies and moving beyond traditional classification frameworks such as the Local Climate Zone (LCZ). Case studies demonstrate its capability to provide detailed insights into the relationships between urban landscape features and temperature variations. Additionally, the paper details how the framework can excel in multi-city analyses and outlines advanced analytical techniques. Promising research opportunities and limitations are identified. This research reshapes our approach to landscape categorization, advancing our understanding of the interactions between landscape and climate dynamics, and contributing to more sustainable, climate-resilient cities.

Resource-based cities (RBCs) encounter numerous challenges in terms of ecological environment (EE) protection and socio-economy (SE) upgrading. This exerts pressure on the advancement of sustainable development. A benign relationship between EE and SE is a crucial factor in promoting RBCs to realize sustainable development. Using Sichuan RBCs as a case study, this study determined the EE and SE resilience levels, examined the degree of coupling coordination between the two levels of resilience, and investigated the specific pathways in which RBCs might reach a high coupling state. The level of resilience in Sichuan RBCs is dropping for EE, stable for SE, and steadily increasing overall. The degree of coupling coordination is suboptimal, as shown by a D-value of 0.260. However, there is an overall trend towards improved coordination. There 8 cities with a moderate state of dysfunction. In the high state development scenario, the coupling coordination degree of the cities rises significantly. Guang'an, Luzhou, Nanchong, and Zigong should prioritize addressing the issue of EE pollution emission and work to strengthen the regulation and treatment of industrial pollutants. Moreover, to enhance the influx of talent and population, the cities of Dazhou, Guangyuan, Panzhihua, and Ya'an should implement human-centered urban design and layout.

Global warming and the risk of preterm birth are both major factors that impact population health. This study investigated the impact of environmental temperature during different stages of pregnancy on the probability of preterm birth (PTB) in Wuhan, China through 2014 to 2016. The results revealed that temperature exposure throughout the entire pregnancy exhibited a U-shaped relationship with the risk of PTB; when temperature exposure during the entire pregnancy was below 14 °C or above 20 °C, the risk of PTB increased. Early pregnancy exposure to temperatures below 7 °C or above 22 °C, and late pregnancy exposure to temperatures below 7 °C or above 26 °C, were associated with an increased risk of PTB. Additionally, elevated PM_2.5 exposure increased PTB risk, while O₃ exposure exhibited a U-shaped relationship with preterm birth. Compared to non-high-risk pregnancies, high-risk pregnancies exhibited a higher risk of preterm birth across all stages of pregnancy. Notably, when late pregnancy temperature exposure exceeded 28 °C, the risk of PTB rapidly increased for non-high-risk pregnancies. This research has significant implications for improving maternal and new-born health by future sustainable city planning and the optimization of temperature forecast warning systems, particularly under the dual pressures of rapid urbanization and climate change.