Sustainable Cities and Society最新文献

China's urbanization has reshaped landscapes, economies, and societies nationwide at unprecedented paces, yet inconsistent march among these processes has resulted in insufficient and unbalanced urban development. Here, we constructed a Population-Land-Economic-Social urbanization (PLESU) system with multiple elements and interaction flows, and explored the spatiotemporal dynamics and influencing factors of the coupling coordination degree (CCD) among PLESU system in 110 prefectural-level cities of Yangtze River Economic Belt (YREB) from 2000 to 2020. Results indicated that the CCD evolution exhibited significant path dependence during the past two decades, with considerable room for improvement. A key finding was that the low-quality development of land urbanization and social urbanization subsystems was the main factor for the lack of synergy among PLESU system, with land finance dependence and municipal investment confirmed to have significant impact on this. Furthermore, results also highlighted narrowing inter-regional differences in the CCD among downstream, midstream and upstream, revealing clustering of CCD in urban agglomerations, yet differing in their internal differentiation patterns and drivers. Exploring the synergy among urban systems will raise policymakers’ awareness of the necessity for embracing a holistic approach toward better sustainability by considering the insufficient and unbalanced nature of urbanization.

Subsurface heat islands induce an underground climate change in urban areas, which can threaten public comfort and health, subsurface ecosystems, transportation infrastructure, and civil infrastructure. Meanwhile subsurface heat islands harbor a marked energy recovery potential. Despite increasing investigations, the understanding of subsurface heat islands remains limited and suffers from the lack of expedient and accurate simulation approaches. Here we explore the use of machine learning to accurately and expediently simulate subsurface heat islands in terms of ground temperature and deformation anomalies. Using the Chicago Loop district as a case study, we identify a series of physical features to establish a relationship between central drivers and effects of subsurface heat islands. We incorporate these features into a random forest model to simulate underground climate change with variable training datasets. The results indicate that ground temperature and deformation anomalies across an entire city district can be predicted based on data extracted solely from a handful of buildings. The proposed approach achieves comparable accuracy to current simulation methods but boasts a calculation speed that can be over a hundred times faster, promising to advance fundamental science while effectively informing engineering and decision-making in the mitigation of underground climate change.

High technology and artificial intelligence (AI) are crucial for achieving urban Dual Carbon Goals. This study proposes a heterogeneous deep learning framework with analysis and prediction phases to explore AI technology's impact on urban carbon emissions. In the analysis phase, fixed effect models address differences in AI development and time heterogeneity among cities. In the prediction phase, an Attention Deep & Cross Network (ADCN) model leveraging feature interactions is proposed to enhance prediction precision and robustness. The Shapley Additive Explanations (SHAP) method quantifies each feature's contribution to ADCN's predictions, elucidating factors' impacts on carbon emissions. This study investigates AI development levels and other variables across 275 Chinese cities to test model performance and uncover the AI-carbon emissions relationship. Results show that fixed effects models significantly improve prediction accuracy, with ADCN outperforming statistical and machine learning models (RMSE: 646.262, MAE: 474.818, R²: 0.993). SHAP analysis reveals that AI technology level (11.85 %), smart city (12.35 %), energy consumption (11.60 %), population (9.38 %), urbanization rate (8.89 %), and GDP (8.40 %) significantly influence carbon emissions. Especially, the interaction between AI technology and smart city or intelligent manufacturing proportion increases their carbon reduction by 1.059 × 10²¹ or 4.992 × 10¹⁹ tons. AI technology moderates the impact of increasing energy consumption and urbanization, reducing their potential emissions by 20 % and 1 %. The framework offers high accuracy and scalability, providing valuable insights for strategy development.

Urban heat islands (UHIs) have been investigated from various perspectives. However, little is known about UHI-mitigation strategies in terms of UHI networks and the overall connectivity. Therefore, we developed a research framework to construct a UHI network from a connectivity perspective in a typical “furnace city”—Fuzhou city, China. Initially, morphological spatial patterns, mean standard deviations, and landscape connectivity were analyzed to identify UHI sources and assess their importance. Subsequently, six natural and socioeconomic factors were integrated into the model to create a combined resistance surface for thermal diffusion. Finally, circuit theory was applied to build a UHI network and pinpoint key nodes. Our results show that the combined resistance increased from the center of the study area to the periphery. In addition, 38 UHI sources, 84 thermal corridors, 30 heating nodes, and 21 cooling nodes were identified. The UHI sources and key nodes were primarily distributed in an uneven manner in the nuclear and northwestern regions of the research area. Furthermore, cooling measures were developed for UHI networks to reduce network connectivity. Our research framework offers a new perspective for promoting healthy urban development and climate-adaptation planning.

The shading from surrounding buildings significantly affects the energy and daylighting performance of transparent insulation materials (TIM) systems. In previous studies, the performance of TIM systems was primarily discussed in ideal situations without considering the influence of surrounding buildings. However, this is not realistic in actual urban scenarios. This study presents a case study conducted in Changsha to evaluate and compare the energy and daylighting performance of the window with transparent insulation slats (WTIS) and normal double glazing (NDG). The study considers the varying degrees of building shading effects. The results show that windows facing west exhibit the best energy efficiency, while windows facing south have the worst. WTIS achieves a higher Useful Daylight Illuminance (UDI) when building shading effects are not significant, whereas NDG achieves a higher UDI when building shading effects are significant. Despite increasing lighting energy consumption by 69.8 % to 84.3 %, WTIS consistently outperforms NDG in terms of total energy savings. Furthermore, strategically utilizing or deactivating WTIS according to recommended periods during winter can enhance the total solar gain for the building by approximately 22.3 %. This study provides valuable recommendations for the application of WTIS systems and the design of buildings in the hot-summer and cold-winter zone.

The Photovoltaic-Green Roof (PV-GR) system, which integrates rooftop photovoltaics and green roofing, has significant potential for sustainable urban development and climate change mitigation. However, the specific effects of PV-GR are not yet clear. This paper employs methodologies including Geographic Information Systems (GIS), Denitrification-Decomposition(DNDC) Model, and solar simulation. Combined with ecological balance calculations, these methods assess PV-GR's carbon reduction benefits and its potential to mitigate climate change. Using Xiamen City as a case study, research shows that Xiamen has about 54 km² of rooftops suitable for PV-GR. Annually, PV-GR can produce about 5.931×10³ tons of biomass and generate 7,427 GWh of electricity, meeting about 22.13 % of Xiamen's annual electricity demand. The annual carbon reduction from Xiamen's PV-GR is estimated at about 5.131×10⁶ t CO_2-eq, offsetting around 29.28 % of the city's annual carbon emissions. Over a 30-year lifecycle, PV-GR's carbon emissions and reduction benefits amount to 2.274×10⁷ t CO_2-eq and 1.539×10⁸ t CO_2-eq, respectively. The ecological footprint of deploying PV-GR in Xiamen is 6.709×10⁴ Gha, while the biocapacity reaches 4.542×10⁵ Gha. The global ecological balance stands at 3.872×10⁵ Gha, suggesting that PV-GR can significantly contribute to mitigating climate change.

Urban parks serve as key nature-based solutions to alleviate the urban heat island phenomenon. Studies have examined park cooling effects (PCEs) from various perspectives. However, the diurnal impacts of environmental factors and their contributions to PCEs, specifically the cooling indicators that comprehensively characterize PCEs, are not well understood. To fill the gap, we constructed a new PCE index, park cooling composite index, based on principal components analysis and six cooling indicators. We selected 68 parks to explore the diurnal variations of PCEs in a “furnace city” using multi-source geospatial data and an optimal parameters-based geographical detector (OPGD) model. PCEs were not affected by park spatial distribution, and urban parks typically exhibited enhanced PCEs in daytime. Correlations between environmental factors and PCEs varied diurnally, with variations among PCEs. Park area and park perimeter were significantly correlated with all PCEs at nighttime. The OPGD revealed that the majority of the internal and external interactive factors of parks enhanced PCEs, regardless of the time. Additionally, balancing strategies for daytime and nighttime PCEs of different park types were developed. These findings provide a comprehensive understanding of the daily variations in PCEs, aiding in the design and planning of parks to adapt to extreme heat.

High-speed rail is often viewed as a green transportation mode, substituting for carbon-intensive vehicular and aviation trips. However, its potential to induce additional travel demand and transport-sector carbon emissions has been largely overlooked. Here, we empirically examine the impacts of high-speed rail accessibility, the ease with which one city can reach other cities via the high-speed rail network, on transport-sector carbon emissions across 315 Chinese cities between 2010 and 2020, using a two-stage least squares model. Contrary to the anticipated emission-reduction effect, our analysis suggests a net positive impact of high-speed rail on transport-sector carbon emissions. Specifically, a 1% increase in high-speed rail accessibility leads to a 0.18% increase in transport-sector carbon emissions in the long run, as carbon emissions generated from induced travel demand have outweighed the carbon savings from substitution for carbon-intensive inter-city trips. This high-speed rail-induced increase in carbon emissions is observed in the road transport subsector, while the aviation subsector exhibits a compensatory reduction. Moreover, we find that integrating the high-speed rail network into local subway systems can curb the emission-intensifying effect, highlighting the importance of joint public transit planning. This study reveals an unexpected environmental impact of high-speed rail, conveying valuable insights into transport-sector decarbonization.

Water scarcity is becoming serious with economic growth, causing water competition across various sectors. Previous studies have mostly explored water use in specific sectors, yet little is known about the water reallocation between urban and rural areas. Here, we investigate urban-rural water use from the production (agriculture and industry) and domestic (urban and rural household) perspectives during 2000–2022 in the Beijing-Tianjin-Hebei urban agglomeration, and identify their potential drivers. We find that urban water use changes little due to the offset of industrial and urban domestic use, while rural water use decreases significantly with the trend of 0.387 ± 0.026 billion m³/yr. Water use changes derive from the joint effects of accelerated human activities and decelerated water use intensity. Urbanization explains more variability in water use changes than water resource endowment. Population urbanization, accompanied with rural-to-urban water reallocation, is a primary cause for enlarged urban-rural gap in water use. Urban-rural gap in water use intensity is narrowing, mainly due to the greater decline in agriculture. This study concludes that urban system often withdraws the neighbor agricultural water when the local water availability cannot meet its growing demand, and our findings offer references for regional water resource management and urban-rural environmental justice.

Investing in energy-efficient retrofitting of existing buildings requires a robust decision-making framework. This study develops a multi-objective optimization technique to assist designers in minimizing payback time and maximizing energy savings within a specified initial investment. The proposed method utilizes a novel metaheuristic, the Modified Supply-Demand-Based Optimization Algorithm (MSDOA), to achieve optimal decisions. The model was tested on nine case studies involving buildings with various facilities, demonstrating its effectiveness. For example, an investment of $190,000 resulted in a payback period of less than three years and energy savings of over 10 % of the baseline consumption. The model considers initial investment, net present value (NPV), payback period, and energy targets as constraints. To evaluate the model's robustness, a sensitivity analysis was performed, examining the impact of varying initial investments, energy savings miscalculations, auditing errors, changes in electrical power costs, and interest rates. The results indicate that higher investments consistently lead to increased energy savings, though the payback period may vary. The MSDOA showed superior convergence speed compared to other algorithms, ensuring more reliable and accurate optimization outcomes. This study confirms the validity of the proposed design and highlights its potential for significant energy savings and financial benefits in building retrofitting projects.