Sustainable Cities and Society最新文献

Carbon Capture, Utilization, and Storage (CCUS) technology is vital for achieving global carbon reduction targets. However, the uncertainties in technology and economic viability are influenced by location. To promote the sustainable development of CCUS technology, the study proposes a data-driven framework for optimizing location decisions. Firstly, the framework considers multiple factors, including geospatial data on resources, risks, power production, transportation, and environment. It also evaluates qualitative and quantitative data across economic, social, environmental, and technological dimensions. Secondly, the two-stage model is conducted as follows: Using Geographic Information System (GIS) technology, the first stage identifies suitable regions for CCUS projects, while the second stage prioritizes these regions using the TODIM method. Further, validated in China, the Junggar Basin, Tarim Basin, Ordos Basin, Sichuan Basin, and Bohai Rim Basin are identified as suitable for CCUS deployment. The Huaneng Luohuang Power Plant is the most conducive location for CCUS projects as pilot demonstrations. Final sensitivity analysis, scenario analysis, and comparative analysis have respectively affirmed the stability, dynamism, and reliability of the model. These analyses have also been instrumental in elucidating the final preferred outcomes under various decision-making preferences and strategic orientations. The framework for decision-making and data-driven priority model for CCUS projects layout proposed in the study can provide technical support and practical evidence for decision-makers in planning CCUS projects and formulating supportive policies.

Exploring the modifiable areal unit problem (MAUP) in land surface temperature (LST) and its influencing factors is crucial for understanding LST variation patterns and quantifying the factors' impact. Neglecting the MAUP could lead to an incomplete understanding of LST changes and their driving mechanisms. This study used optimal parameters-based geographical detector and gradient boosting regressor models to investigate the MAUP in LST and its influencing factors. The analysis covered 87 cities across seven climate zones in China, examining MAUP in 12 spatial scales to discern the scale and zoning effects on LST and its influencing factors. The research findings were as follows: (1) The sensitivity of LST influencing factors to spatial scales exhibited both spatial and temporal heterogeneity. Significant differences in the q-values of LST influencing factors were observed across various climate zones and periods (daytime and nighttime), with human factors, particularly those related to residents' work, buildings, life, and rest, showing higher spatial scale dependency than natural factors. (2) Zoning effects significantly impacted the q-values of LST influencing factors and were closely linked to the discretization methods and quantities used, which could alter the trends of these q-values. (3) Across the 12 spatial scales, more than 67.34 % of LST influencing factor interaction types were classified as bi-variable enhancement types. The q-values for LST influencing factor interactions were higher and more stable than those of single factors. LST influencing factor interactions in transitional climate zones exhibited high sensitivity to spatial scales. This research enhances our understanding of LST variations, providing valuable insights for urban climate adaptability planning and the development of climate-resilient cities.

Efficient ventilation systems play a crucial role in reducing occupants' exposure to indoor contaminants, including particles potentially carrying viruses like SARS-CoV-2. Displacement ventilation systems have demonstrated their effectiveness in improving indoor air quality during cooling modes. However, traditional displacement ventilation systems often struggle to achieve satisfactory distribution of contaminant concentrations during heating modes. To address this issue, this study focused on enhancing the ventilation performance of a dual-coil displacement-induction unit. Through a combination of experimental measurements and computational fluid dynamic (CFD) techniques, the study examined airflow and contaminant concentration distributions in an environmental chamber conditioned by these units. The results demonstrated good agreement between measured and simulated data, validating the CFD model. Further evaluation in a 25-occupant classroom under cold outdoor conditions showed that the dual-coil unit could achieve satisfactory ventilation performance in heating modes with proper design, comparable to traditional displacement ventilation in cooling modes. Additionally, the unit's versatility allows it to accommodate a wide range of air conditioning applications, from heating to cooling, making it a promising solution for displacement ventilation in various environments.

The safety, comfort, and sustainability of the nighttime lighting environment in residential areas become increasingly the focus of social concern. This study integrates remote sensing observations, ground measurements, perception evaluations, and grading maps to compare the data of different residential areas, achieving perceptual performance assessment and energy-saving optimization of the lighting environment. The main researches include (1) Comparison of the Feeling of Safety (FoS) and Perceived Lighting Quality (PLQ) in the nighttime light environments of Dalian's residential areas by contrasting characteristics between high and low illuminated neighborhoods, new and old neighborhoods, open and enclosed neighborhoods, and internal and peripheral roads, (2)Establishment of a lighting environment perceptual evaluation model, proposing illuminance ranges for overall residential areas (6.67–17.97 lx), peripheral roads (8.79–24.50 lx), and internal roads (6.45–16.21 lx), (3) Construction a ground horizontal illuminance inversion model for Dalian and generate FoS and PLQ perception grading maps, (4) Within the scope of the study, the residential areas with insufficient, moderate, and excessive lighting account for 24 %, 56 %, and 20 %, respectively. This study provides effective strategies for reducing energy consumption, ensuring nighttime safety, and enhancing the comfort of living and helps to timely identify potential risk points that do not meet the perceptual standards.

How ventilation corridors affect urban climate is attracting researchers' attention. Taking the inland Chinese city of Wuhan as an example, this paper first uses remote sensing image technology to evaluate the urban thermal environment. Additionally, based on the GIS/RS spatial analysis method, the ventilation corridors in the central urban area are identified and constructed. Finally, the mesoscale meteorological model WRF-UCM is used to simulate four cases with different corridor forms to explore the impact of different corridors on the climate environment in Wuhan during the summer. The results indicate that: (1) The WRF-UCM model, when coupled with LCZ classification, can significantly improve the accuracy of mesoscale urban canopy meteorological field simulations. (2) The water corridors located in the central urban area can effectively regulate the temperature and wind environment during summer. In the high-temperature period of the day, the average temperature in the central city decreases by 0.3–0.4 °C, the heat island proportion index decreases by 1.61 %, and the strong heat island proportion index decreases by 1.89 % in the afternoon. During the period of low temperature, the average wind speed in the central urban area increased by 0.05 m/s increase, and even increased by 0.1 m/s. (3) The specific humidity value of the green corridor is reduced by 0.0000136 kg/kg in comparison to the construction land in the corridor, while the water corridor can increase by 0.000133 kg/kg. If the two kinds of surface, water and green land, are organically combined in the corridor, it will be able to improve the hot and humid conditions in Wuhan in summer. (4)Low-rise and low-density construction land as the corridors in the central urban area can not improve the urban thermal and wind environment. Through an attempt to conduct a complete workflow of urban heat island analysis, ventilation corridor identification and setting, urban climate simulation, analysis and summary, the authors believe that it is an effective set of working methods in sustainable urban planning, design, and policy-making. The implementation of pertinent research findings in the domain of urban planning and design demonstrates its universal applicability and has the potential to extend to analogous research and practice.

Understanding and describing how urban heat islands evolve is important, given the noticeable impact they have on people living in cities. This paper considers the London heat island from gridded values with one-arcminute spatial resolution over a 33-year period, from 1990 to 2022. Among the available variables in the database, maximum and minimum air temperatures were used. A cold island was not observed, since temperatures in the city centre were higher than those in the surroundings during the day and at night. However, the urban heat island extension was higher for the maximum temperature, whereas this island was limited to the city centre for the minimum temperature, in line with the area delimited by the congestion charge. Lamb weather types were determined, and it was found that the anticyclonic type prevailed, followed by southwest, west, and cyclonic types. The difference between both temperatures was about 6.8 °C in the city centre, and was particularly defined for anticyclonic and cyclonic types. Moreover, anticyclonic situations were linked with the highest urban heat island intensities for minimum temperature. Finally, the temperature trend was similar for both temperatures –about 0.2–0.3 °C/10 years in the city centre– thereby offering a possible quantification of climate change.

Declining urban air quality affects socioeconomic stability, public health, and ecosystems and is demanding attention of the administration to address environmental sustainability goals. Given the effects of ozone, a greenhouse gas, on local climate and health, this study introduces a Unified Spectro-Spatial Graph Neural Network (USS-GNN) designed for simultaneous 24-hour forecasting of ozone and its precursor, nitrogen-dioxide, while addressing their chemical interactions and spatiotemporal dynamics. This model exploits the graph structure of atmospheric dynamics and mines high-level spatial, spectral, and physical features from atmospheric data through a Dot Product Edge Attention mechanism and a location-aware graph feature rewiring technique. The proposed model is developed for Indian capital city New Delhi, utilizes hourly observations for the years 2021 and 2022 and achieved $R^2$ values of 0.650 and 0.618, RMSE of 13.950 and 16.120 $\mu {\mathrm{g/m}}^3$, MAE of 10.730 and 12.930 $\mu {\mathrm{g/m}}^3$ for ozone and nitrogen-dioxide respectively, outperforming state-of-the-art models. The model’s forecast analysis identified error-prone areas, effects of local meteorology, and pollutant interdependencies. An ablation study further detailed the impacts of graph operations on forecasts. Moreover, this study promotes the utility of bivariate modeling frameworks in improving urban pollution monitoring and supporting sustainable city management through data-driven policy implementations.

We develop two-stage stochastic programming models for generator winterization that enhance power grid resilience while incorporating social equity. The first stage in our models captures the investment decisions for generator winterization, and the second stage captures the operation of a degraded power grid, with the objective of minimizing load shed and social inequity. To incorporate equity into our models, we propose a concept called adverse effect probability that captures the disproportionate effects of power outages on communities with varying vulnerability levels. Grid operations are modeled using DC power flow, and equity is captured through mean or maximum adverse effects experienced by communities. We apply our models to a synthetic Texas power grid, using winter storm scenarios created from the generator outage data from the 2021 Texas winter storm. Our extensive numerical experiments show that more equitable outcomes, in the sense of reducing adverse effects experienced by vulnerable communities during power outages, are achievable with no impact on total load shed through investing in winterization of generators in different locations and capacities.

This paper proposes a new framework for Smart Distribution Networks (SDN) operation by leveraging data centers' spatial–temporal flexibility. Combining this flexibility with Battery Energy Storage Systems (BES) capabilities can create a more robust and practical solution for real-world grid management challenges. Reducing the power exchange with the upstream network during peak hours is critical to reduce total losses. The proposed framework shows a 24.61 % reduction in power exchange with the upstream network during peak hours, resulting in a 39.71 % reduction in total loss compared to normal operation. In addition, managing the uncertainty of renewable energy resource generation and load demand using the robust optimization method has been one of the main goals of this work. Finally, the study utilizes sensitivity analysis to identify the most optimal placement of data centers and BES units within the power grid, maximizing the benefits of this integrated approach. Sensitivity analysis on data centers and BES locations identified optimal locations on buses 15, 25, 33 and 2, 8, 23, 28, respectively. The results show that this strategic placement will reduce total losses by 12 % more than the proposed framework.

Street trees are vital in mitigating urban heat islands, with their cooling effect significantly influenced by the urban layout. Past studies explored how urban canyon characteristics—aspect ratio, building coverage—affect tree cooling, yet seldom analyzed the impact of distance between trees and buildings. Addressing this, our study evaluates tree cooling effects concerning their proximity to shaded and sunlit walls. The findings highlight that cooling effectiveness varies with the ratio of distance from the shaded wall to building height ($D_{sha}$:H), peaking within a ratio range of 0.55 to 0.7. Below a ratio of 0.3, effectiveness decreases to 9–29 %, emphasizing the importance of strategic planting distances. The result shows that when planted at an optimal distance from buildings, small trees can produce similar radiation mitigation effects to those of larger trees. This discovery advocates for thoughtful tree placement in high-density areas, optimally leveraging their shade and evapotranspiration benefits. The study provides actionable insights for urban planners and landscape architects, suggesting that careful consideration of tree placement relative to building shadows can significantly improve urban climates, offering a strategic approach to deploying green infrastructure in high-rise complexes for enhanced climate resilience.