Search Results (2,674)

As urban built-up areas are the main generators of carbon emissions, scientific and accurate estimation of carbon emission levels in urban built-up areas is an important method to help implement the carbon neutrality target. Nowadays, developing a spatial data–based carbon emission estimation model that reduces dependence on energy consumption data, shortens the estimation cycle, and enhances its applicability to urban spatial development remains an urgent challenge. In this study, we developed a spatial data-based carbon emission estimation model for urban built-up areas using data from five winter cities in China over a 15-year period as an example. The estimation model not only strengthens the connection between carbon emission results and urban spatial elements, but also gets rid of the over-reliance on energy data, which in turn greatly shortens the estimation cycle of urban carbon emissions. We also used the model to investigate the distribution of carbon emissions in urban built-up areas. Compared with the traditional carbon emission estimation model based on energy consumption, the correlation coefficient between the two models is greater than 0.95, and the error between the two models is extremely small, indicating that this model has important practical value. On this basis, we propose applications for this model. We apply the model to Harbin, China, to estimate built-up area carbon emissions without using energy consumption data, thereby improving estimation efficiency. We also assess how the current urban planning strategy influences low-carbon construction. Additionally, we use the SHAP method to rank each spatial element’s contribution to carbon emissions. Based on these results, we propose low-carbon optimization strategies for winter cities in China. Full article

To enhance the design and construction efficiency of artificial ground freezing (AGF) in water-rich sandy strata, this study takes the No. 2 cross-passage of Zhengzhou Metro Line 8 as a case study and conducts an integrated analysis combining field monitoring and numerical simulation. During the freezing process, a sensor network was deployed to capture real-time data on temperature distribution and pore water pressure evolution. Based on the collected measurements, a three-dimensional hydrothermal coupled model was developed using COMSOL Multiphysics 6.1 and validated against field data. The results demonstrate a distinct multi-stage evolution in the formation of the frozen curtain, with the highest heat exchange rate observed at the initial phase. Under a 50-day freezing schedule, increasing the average coolant temperature by 4 °C still yielded a frozen wall that meets the design thickness requirement. Additionally, several cost-effective freezing schemes were explored to accommodate varying construction timelines. This study supports sustainable urban infrastructure development by minimizing energy consumption during artificial ground freezing (AGF) processes. Full article

We present a CFD-driven surrogate modeling framework that integrates a Convolutional Autoencoder (CAE) with a Deep Neural Network (DNN) for the rapid prediction of urban wind environments and their subsequent use in UAV trajectory planning. A Reynolds-Averaged Navier–Stokes (RANS) CFD database is generated, parameterized by boundary-condition descriptors, to train the surrogate for velocity magnitude and turbulent kinetic energy (TKE). The CAE compresses horizontal flow fields into a low-dimensional latent space, providing an efficient representation of complex flow structures. The DNN establishes a mapping from input descriptors to the latent space, and flow reconstructions are obtained through the frozen decoder. Validation against CFD demonstrates that the surrogate captures velocity gradients and TKE distributions with mean absolute errors below 1% in most of the domain, while residual discrepancies remain confined to near-wall regions. The approach yields a computational speed-up of approximately 4000× relative to CFD, enabling deployment on embedded or edge hardware. For path planning, the domain is discretized as a k-Non-Aligned Nearest Neighbors (k-NANN) graph, and an A* search algorithm incorporates heading constraints and surrogate-based TKE thresholds. The integrated pipeline produces turbulence-aware, dynamically feasible trajectories, advancing the integration of high-fidelity flow predictions into urban air mobility decision frameworks. Full article

As an effective energy storage solution, gas reservoir compressed air energy storage (CAES) can efficiently utilize curtailed wind power to meet urban electricity demands. Well pattern optimization enables rational design and adjustment of well layouts to maximize productivity, efficiency, and economic benefits while reducing energy losses and operational costs. To address limitations in conventional optimization methods—including oversimplified constraints, neglect of reservoir heterogeneity, and insufficient consideration of complex flow regimes—this study proposes an innovative multi-constraint well pattern optimization method incorporating productivity, energy conversion efficiency, drainage area, and economic performance for quantitative evaluation of well configurations. First, the reservoir flow domain was partitioned based on two flow regimes (Darcy and non-Darcy flow) near wells. Mathematical flow equations accounting for reservoir heterogeneity were established and solved using the rectangular grid method to determine productivity and formation pressure distributions for vertical and horizontal wells. Second, a drainage radius prediction model was developed based on pressure drop superposition principles to calculate gas drainage areas. Finally, an optimization function F, integrating productivity models and drainage radius calculations through ratio optimization criteria, was formulated to quantitatively characterize well pattern performance. An optimization workflow adhering to inter-well interference minimization principles was designed, culminating in a comprehensive CAES well pattern optimization framework. Case studies and sensitivity analyses on the depleted Mabei Block 8 CAES reservoir demonstrated the following: The quantitative optimization metric w decreases with increasing reservoir heterogeneity. w exhibits a unimodal relationship with production pressure differential, peaking at approximately 2.5 MPa. Optimal configuration was achieved with 3 horizontal wells and 23 vertical wells. Full article

Transport is among the fastest-growing contributors to carbon dioxide (CO₂) emissions in the Gulf Cooperation Council (GCC) region, where rapid urbanization, population growth, and high mobility demand continue to shape energy use. This study aims to quantify the extent to which economic growth and urbanization drive transport-related CO₂ emissions across Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates between 2012 and 2022. Using sector-specific data from the International Energy Agency and World Bank, we apply panel and country-level log–log regression models to estimate long-run and short-run elasticities of transport CO₂ emissions with respect to GDP and urban population. The analysis also includes robustness checks excluding the COVID-19 pandemic year to isolate structural effects from temporary shocks. Results show that transport emissions remain strongly correlated with GDP in most countries, indicating emissions-intensive growth, while the influence of urbanization varies: positive in Kuwait and Saudi Arabia, where expansion is car-dependent, and negative in Oman and Qatar, where compact urban forms and transit investments mitigate emissions. The findings highlight the importance of differentiated policy responses—fuel-pricing reform, vehicle efficiency standards, electrification, and transit-oriented planning—to advance low-carbon mobility. By integrating elasticity-based diagnostics with decoupling analysis, this study provides the first harmonized empirical framework for the GCC to assess progress toward transport-sector decarbonization. Full article

Rio de Janeiro’s favelas house over 20% of the city’s population in just 5% of its territory, with Rio das Pedras emerging as a critical case study: ranking as Brazil’s fifth most populous favela and its most vertically intensified. This study quantifies how uncontrolled vertical growth in informal settlements disrupts microclimate dynamics, directly impacting thermal comfort. Using high-resolution geospatial analytics, we integrated digital surface models (DSMs) derived from LiDAR and photogrammetric data (2013, 2019, and 2024) with microclimatic simulations to assess urban morphology changes and their thermal effects. A spatiotemporal cadastral analysis tracked vertical expansion (new floors) and demolition patterns, while ENVI-met simulations mapped air temperature anomalies across decadal scenarios. Results reveal two key findings: (1) rapid, unregulated construction has significantly altered local airflow and surface energy balance, exacerbating the urban heat island (UHI) effect; (2) microclimatic simulations consistently recorded elevated temperatures, with the most pronounced impacts in densely built zones. These findings underscore the need for public policies to mitigate such negative effects observed in informal settlement areas. Full article

Against the accelerating of global climate change and carbon neutrality transitions, energy price volatility exerts complex effects on the employment dimension of economic sustainability through both industrial and agricultural channels as intermediaries. This study employed a mixed-frequency vector autoregression model to statistically analyze the weekly prices of four major industries and 24 sub-markets in China. The main outcome was the urban unemployment rate in China, and it was verified against the urban unemployment rates in 31 cities and the unemployment rates by age group (YUR/LUR). The study investigated the employment dimension of economic sustainability. Energy and energy metal prices represent the energy transition, while food and industrial goods prices characterize the intermediary linkages. Unemployment rates serve as the employment dimension of economic sustainability. The findings reveal bidirectional interactions and heterogeneous transmission mechanisms between prices and unemployment: energy prices exhibit weaker direct links to unemployment, partly influenced by demand inelasticity and policy adjustments; agricultural products face more persistent impacts, reflecting policy interventions and demand constraints; chemical products demonstrate the highest sensitivity and fastest response to unemployment shocks; metals show significant internal variations, with sub-market-level impacts being more pronounced yet shorter-lived. These insights advance climate and energy economics by guiding low-carbon transition policies, optimizing resource allocation, and managing energy market risks for resilient economic sustainability. Full article

This paper introduces a control-oriented Linear Parameter Varying (LPV) model of an energy-efficient electric vehicle, enhanced to account for wind-induced disturbances. The proposed model structure is designed to support model-based control strategies focused on minimizing energy consumption. In addition to core control inputs—such as torque reference and cornering radius—the model integrates a simulated representation of wind effects on the vehicle’s longitudinal dynamics. To manage the underlying nonlinearities of the vehicle dynamics, a trajectory-based linearization approach was employed to construct the baseline LPV model without wind effects. The accuracy of the extended model was validated using real-world speed profile data. Owing to its modular and control-compatible design, the model provides a solid foundation for testing and developing energy-saving control strategies, making it especially applicable to the design and operation of energy-efficient electric vehicles. The proposed model holds significant potential for further reducing energy consumption, particularly in urban transportation scenarios. Full article

The storage of different forms of energy is becoming increasingly important in the energy system sector, due to the significant fluctuations that renewable energy sources influence on urban energy systems. Nowadays, these sources have been promoted for the transition towards modern energy systems at different scales, due to their reduced emissions of greenhouse gases. Yet, many doubts remain about their efficacy in urban settlements worldwide. For this reason, to promote the fast implementation of renewable energy technologies around the world, it is of great importance to design and develop free-access and user-friendly tools to help stakeholders in the planning and management of urban energy districts. The present study has proposed an evaluation tool to model decentralised energy storage systems using Urban Building Energy Models, including an optimisation method to size the best capacity in each building of a district. The developed models simulate two storage technologies: battery power banks and heated water tanks. To present the outcomes of the tool, these models have been tested in two scenarios of Portugal, located in a densely populated area and the most isolated region of the country. Among the most important findings of the results are their ability to evaluate the performance of individual buildings by group archetype and total district metrics, using different temporal periods in a single model to identify the buildings taking most advantage of storage technologies. In addition, the optimisation algorithm efficiently estimated the ideal size of each storage technology, reducing the need of unnecessary capacity. Full article

This study aims to evaluate the operational efficiency of electric vehicle (EV) charging stations and explore optimization strategies to enhance resource utilization and service performance. A systematic review approach was first applied to identify the main evaluation indicators and influencing factors from existing studies. Subsequently, a super-efficiency Data Envelopment Analysis (DEA) model was used to assess the efficiency of six EV charging stations in a certain City, China. The robustness analysis was carried out, and the output variables were replaced, and the evaluation results did not change. The results show substantial disparities in efficiency across stations: C1 exhibits the highest operational efficiency, while C3 performs the lowest. The inefficiencies primarily result from supply–demand mismatches and redundant capacity investment. Based on these findings, the study proposes both overall and localized optimization strategies to improve operational performance. The results provide valuable insights for urban energy infrastructure planning and contribute to the enhancement of high-quality, low-carbon transportation development in China. Full article

As climate change and urbanization accelerate, urban flooding poses an increasingly severe threat to urban residents and their properties, creating an urgent need for effective solutions to achieve sustainable urban disaster management. While physically based hydrodynamic models can accurately simulate urban floods, they are data- and computational-resource-demanding. Meanwhile, artificial intelligence models driven by data often lack generalizability across different urban areas. To address these challenges, integrating spiking neural networks, graph convolutional networks (GCNs), and particle swarm optimization (PSO), a novel PSO-enhanced spiking graph convolutional neural network (P-SGCN) is proposed. The model is trained on a self-constructed dataset based on social media data, incorporating six representative Chinese cities: Beijing, Shanghai, Shenzhen, Wuhan, Hangzhou, and Shijiazhuang. These cities were selected for their diverse urban and flood characteristics to enhance model generalizability. The P-SGCN significantly outperforms baseline models such as GCN and long short-term memory, achieving an accuracy, precision, recall, and F1 score of 0.846, 0.847, 0.846, and 0.846, respectively. These results indicate our model’s capability to effectively handle data from six cities while maintaining high accuracy. Meanwhile, the model improves single-city performance through transfer learning and offers extremely fast inference with minimal energy consumption, making it suitable for real-time applications. This study provides a scalable and generalizable solution for urban flood risk management, with potential applications in disaster preparedness and urban planning across varied geographic and socioeconomic contexts. Full article

The Energy–Economy–Environment (3E) nexus within basin economic zones has received significant scholarly attention. As a major river basin economic belt in China, the Yangtze River Economic Belt (YREB) serves as an important case for examining the status and drivers of coordinated 3E development. The findings of this study may also offer valuable insights for promoting sustainable development in river basin economies globally. Encompassing 11 provinces and municipalities, the YREB represents not only a vital socioeconomic region in China but also one of the nation’s largest energy consumers, facing considerable environmental pressures. Using panel data spanning 2009–2019, this study applies the coupling coordination degree (CCD) model, spatial Durbin model, and Moran’s I to assess the coordination level of the 3E system in the YREB. The main findings are as follows: (1) The CCD demonstrated a trend that was fluctuating but generally on the rise throughout the study period. Higher values were observed in eastern provinces and lower ones in western provinces, which reveals a distinct east–west spatial gradient. (2) A significantly positive spatial correlation was observed in provincial 3E coordination, although this correlation fluctuated and showed a slowly weakening trend over time. Local spatial clustering patterns also shifted, marked by the persistence of high-high clusters, an increase in low-low clusters, and the emergence of low-high outliers. (3) Estimates from the spatial Durbin model indicate that urbanization, automobile consumption, and foreign trade exert positive overall effects on the CCD, whereas industrial structure exerts a negative overall effect. Environmental policy is not statistically significant in the static model but shows a negative overall effect when the CCD is lagged by one period. Full article

Urban trees play a critical role in mitigating climate change by capturing atmospheric CO₂ and providing multiple co-benefits, including cooling urban environments, reducing building energy demand, and enhancing citizens’ physical and psychological well-being. This study presents the Co Carbon Trees Measurement project, a citizen science initiative implemented in the city of Viladecans, Spain, involving 658 students, local administration, and academia, three components of the EU mission’s quadruple helix governance model. Over one year, 1274 urban trees were measured for trunk diameter and height to quantify annual CO₂ sequestration using a direct measurement approach combining field data collection with a mobile application for a height assessment and a flexible measuring tape for diameter. Results indicate that carbon fixation increases with tree size, displaying a parabolic function with larger trees sequestering significantly more CO₂. A range between 10 and 20 kg of CO₂ is sequestered by the urban trees in the period 2024–2025. The study also highlights the broader benefits of urban trees, including shading, mitigation of the urban heat island effect, and positive impacts on mental health and social cohesion. While the total CO₂ captured in Viladecans (≈810 tons/year) is small relative to city emissions (≈170,000 tons/year), the methodology demonstrates a scalable, replicable approach for monitoring progress toward climate neutrality and integrating urban trees into planning and climate action strategies. This approach positions green infrastructure as a central component of sustainable and resilient urban development. Full article

Geological models form the basis for scientific investigations of both the surface and subsurface of urban environments. Urban cover, however, usually prohibits the collection of new subsurface data. Therefore, models depend on existing subsurface datasets that are often of poor quality and have an uneven spatial and temporal distribution, introducing significant uncertainty. This research proposes a novel method to mitigate uncertainty caused by clusters of uncertain data points in kriging-based geological modelling. This method estimates orientations from clusters of uncertain data and randomly selects points for geological interpolation. Unlike other approaches, it relies on the spatial distribution of the data and translating geological information from points to geological orientations. This research also compares the proposed approach to locally changing the accuracy of the interpolator through data-informed local smoothing. Using the Ouseburn catchment, Newcastle upon Tyne, UK, as a case study, results indicate good correlation between both approaches and known conditions, as well as improved performance of the proposed methodology in model validation. Findings highlight a trade-off between model uncertainty and model precision when using highly uncertain datasets. As urban planning, water resources, and energy analyses rely on a robust geological interpretation, the modelling objective ultimately guides the best modelling approach. Full article

As one of the world’s primary energy sources, coal has driven economic development but has also led to severe surface subsidence. Currently, many regions around the world face significant ground deformation risks due to ongoing or legacy mining activities. Accurate monitoring and trend prediction are critical for enhancing subsidence early-warning capabilities and urban resilience. The northern region of Huainan City exhibits a spatial pattern characterized by the coexistence of mining areas, urban areas, and decommissioned mining sites, among which the mining areas show more pronounced surface deformation due to prolonged mining activities. To fully understand the subsidence evolution characteristics and differences across various regions, an LSTM–Transformer prediction model was constructed based on SBAS-InSAR monitoring technology to predict the surface subsidence processes in the three types of areas separately. The results indicated that the subsidence rate and cumulative subsidence in the mining areas were significantly greater than those in the urban and decommissioned areas, demonstrating more intense deformation activity. The average subsidence rates for the mining areas, urban areas, and decommissioned mining sites were −57.42 mm/yr, −5.37 mm/yr, and −3.21 mm/yr, respectively. The model’s prediction results demonstrated good accuracy across different regions, with the root mean square errors (RMSEs) for the mining areas, urban areas, and decommissioned mining sites being 2.16 mm, 1.03 mm, and 0.22 mm, respectively. The study shows that the constructed LSTM–Transformer hybrid model not only possesses strong capability in fitting subsidence trends but will also provide a scientific basis for future monitoring and early warning of surface subsidence hazards. Full article