Search Results (9,609)

Rapid urbanization, uncoordinated land-use growth, and insufficient integration of public transport have led to severe traffic congestion and declining mobility in Peshawar, Pakistan, even after the implementation of a Bus Rapid Transit (BRT) system. The core research problem addressed in this study is the mismatch between growing travel demand and the limited capacity, coverage, and operational efficiency of the existing urban transport network. This research aims to evaluate the current performance of Peshawar’s transport system and to identify integrated, evidence-based strategies to alleviate congestion and enhance urban mobility. Specifically, the objectives are to assess roadway level of service on major corridors, examine public transport user satisfaction with the BRT system, and propose targeted infrastructure and operational improvements. A mixed-methods approach was employed, combining traffic volume and level-of-service (LOS) analysis, public transport user surveys, and field observations at critical intersections. The findings indicate that several key arterial roads operate at LOS E–F during peak hours, and future traffic projections indicate widespread capacity failures under existing road geometries. Survey results reveal significant dissatisfaction with the BRT system, particularly due to limited spatial coverage, inadequate feeder routes, overcrowding, and excessive travel times. Based on these results, the study proposes integrated interventions, including road widening and auxiliary lanes, geometric and signalized junction improvements, expansion of BRT feeder services, development of new arterial and ring roads, and enhanced pedestrian and parking infrastructure. This study links quantitative traffic performance measures with user-perceived service deficiencies. It provides practical, data-driven guidance for policymakers and planners to support a more efficient, accessible, and sustainable urban transport system in Peshawar. Full article

In the context of rapid urbanization, the traditional spatial fabric and cultural connotations of historic districts are increasingly threatened, leading to growing problems such as architectural homogenization and weakened public identity. As an important dimension linking spatial form and public cognition, urban memory has gradually become a key entry point for the study of historic district conservation and renewal. At the same time, the large volume of user-generated content accumulated on social media provides a new data foundation and research pathway for architectural and urban memory studies. Taking the Sajinqiao area in Xi’an as the study area, this study uses Weibo texts containing the keyword “Sajinqiao” from 2018 to 2025 as the basic dataset. A Chinese-RoBERTa pretrained language model was employed to identify and screen high-focus Weibo samples, and a classification framework of five types of memory elements was constructed, including roads, areas, nodes, business units, and food entities. On this basis, memory elements were extracted, standardized, and quantified in terms of memory intensity to analyze their evolutionary characteristics. The results show that, first, urban memory in the Sajinqiao area exhibited marked stage-based fluctuations during the study period. Second, business- and consumption-related elements remained dominant in the type structure over the long term. Third, core urban memory was primarily supported by local food entities and related business units, indicating that public memory gradually shifted from experience-oriented memory to destination-oriented memory. This study provides an operational framework for the identification, quantification, and dynamic assessment of urban memory in historic districts, and offers empirical support for memory-oriented conservation and renewal strategies in the Sajinqiao area and similar historic districts. Full article

Against the global imperative to address climate change and accelerate energy transitions, the rapid growth of the electric vehicle (EV) industry has turned public charging infrastructure into a key foundation of urban operations, driven by carbon peaking and carbon neutrality goals. However, effective supply depends not only on scale but on deep association with urban functional spaces. This study compares Gusu District (a historic preservation district) and Industrial Park District (a new development district) in Suzhou. The goal is to reveal how public electric vehicle charging stations (EVCSes) associate with functional spaces under different urban development models. The study employs Standard Deviational Ellipse (SDE), Kernel Density Estimation (KDE), Bivariate Spatial Autocorrelation, and Coupling Coordination Degree (CCD) models to compare layout patterns, clustering features and functional association. The research findings are as follows: (1) The EVCS layout in Gusu District shows strong dependence on roads and administrative boundaries, while EVCSes in Industrial Park District show clear planning intervention, less constrained by its administrative boundary. (2) KDE analysis confirms that Gusu District has continuous clustering centered on the ancient city, but Industrial Park District shows a multi-center layout. (3) Bivariate Spatial Autocorrelation reveals different priorities in facility allocation. In Gusu District, spatial association is mainly driven by high-mobility nodes, while in Industrial Park District, EVCSes are more deeply embedded in social services and daily life scenarios. (4) CCD analysis reveals that the coordination in Gusu District forms a monocentric, spatially continuous gradient centered on the ancient city, whereas in Industrial Park District it displays a polycentric but fragmented pattern, with high coordination areas confined to planned cores. This comparative study reveals the EVCS spatial layout, which is shaped by both administrative boundaries and policy constraints, and the heterogeneity in spatial association between two districts. It provides scientific evidence and decision support for different spatial governance and facility optimization in various types of urban areas. Full article

Amid tightening ecological constraints, accelerating urbanization transition, and increasingly complex spatial governance, the coordinated evolution of Urban Green Space (UGS) and New-Type Urbanization (NTU) has become central to green transition and high-quality development in major river basins. Drawing on city-level panel data for the Yangtze River Economic Belt (YREB) and the Yellow River Basin (YRB) from 2006 to 2022, this study integrates a Coupling Coordination Degree (CCD) model, spatial statistical analysis, and interpretable machine learning to investigate UGS-NTU coordination, with emphasis on spatiotemporal evolution, spatial differentiation, and nonlinear contribution pathways. The findings indicate that: (1) UGS and NTU levels rise in both basins, but their spatial trajectories differ substantially. The YREB exhibits river-oriented expansion and gradient diffusion, whereas the YRB features nodal agglomeration and discontinuous expansion. (2) The CCD improves overall in both basins, with downstream areas leading, the middle reaches following, and the upper reaches lagging behind; UGS lag is widespread in the middle and upper reaches. (3) The YRB shows stronger spatial agglomeration, more pronounced regional differentiation, and more persistent low-value clustering, while the YREB is characterized by stable high-value clustering in the Yangtze River Delta. (4) The YREB is mainly associated with green space system optimization, whereas the YRB is more closely associated with improvements in the foundational capacities of NTU. Both associations exhibit clear nonlinear characteristics. This study provides empirical support for differentiated green transition and high-quality development strategies in major river basins. Full article

Sustainable and resilient road infrastructure requires the early identification of pavement deterioration mechanisms that emerge under complex urban traffic conditions, particularly at signalized intersections where repeated stop–go operations, queue persistence, and lane-wise freight concentration generate highly nonuniform structural loading. However, most existing intelligent transportation studies emphasize crash prediction, traffic-state estimation, or mobility optimization, while the infrastructure-performance consequences of freight-dominant interrupted flow remain insufficiently addressed. To support proactive pavement management and resilient urban road operation, this study proposes a traffic simulation-driven deep learning framework for predicting lane-level pavement deterioration under freight loading and stop–go urban traffic conditions. A high-resolution PTV Vissim 2024 microscopic simulation environment was developed for a four-leg signalized urban intersection, and a structured multi-scenario design was used to generate progressively increasing operational stress regimes, ranging from baseline flow to freight-dominant oversaturated operation. The resulting lane-wise dataset integrates direct traffic variables with pavement-oriented descriptors, including the Lane Freight Loading Index (LFLI), Stop–Go Severity Index (SGSI), ESAL proxy, queue persistence, and Loading Asymmetry Index (LAI). To learn the complex relationship between traffic operation and infrastructure degradation, a new Freight-Aware Lane Interaction Transformer Network (FLIT-Net) is introduced. The proposed model combines feature embedding, lane-interaction self-attention, freight-aware gating, residual refinement, and multi-task regression to jointly predict rutting risk, fatigue-cracking risk, and the Pavement Deterioration Index (PDI). Experimental results show that FLIT-Net outperforms MLP, CNN, LSTM, Bi-LSTM, and generic Transformer baselines, achieving RMSE/MAE/$R^2$ values of 0.041/0.032/0.9687 for rutting risk, 0.044/0.034/0.9635 for fatigue-cracking risk, and 0.031/0.024/0.9824 for PDI. Sensitivity and scenario-wise analyses further confirm that deterioration increases monotonically with freight intensity, stop–go severity, and queue persistence, highlighting the importance of lane-resolved deterioration intelligence for sustainable maintenance prioritization. The proposed framework bridges traffic microsimulation, pavement-oriented feature engineering, and freight-aware deep learning, providing a decision-support basis for improving the performance, safety, and resilience of urban pavement infrastructure. Full article

Traditional approaches to urban real estate green value assessment rely heavily on single structured data sources. Such methods often provide limited interpretability and fail to capture multidimensional green attributes accurately. To address these limitations, this study constructs a multimodal assessment framework that integrates image, text, and spatial information. A housing price prediction model is developed based on a Multi-Layer Perceptron architecture. Results show that the proposed method is superior to traditional models (such as the Hedonic pricing model, Ridge regression, and eXtreme Gradient Boosting, as well as single-modality control models). The core evaluation metric, mean squared error, reaches 0.0505 ± 0.0021. SHapley Additive exPlanations analysis shows that the text modality provides the largest contribution to model prediction, accounting for 51.45% of the global contribution. However, this dominance reflects the model’s dependence on textual green signals rather than the establishment of causal relationships. The result may also be influenced by marketing language bias and symbolic sustainability signals. The image modality contributes 38.48%, while the spatial modality contributes 10.07%, indicating a complementary relationship among the three modalities. Green premium analysis confirms that the model achieves higher prediction accuracy for high-priced residences and effectively captures differences in green premium across housing price tiers. This study provides a new technical pathway for real estate green value assessment. Full article

Rapid urbanization, the pursuit of a higher quality of life, increasing energy and fuel demands, and remaining dependence on traditional fossil fuels, along with an increasingly pronounced negative impact on the environment, encourage society to seek new, lower-emission solutions. Among them, the biomass-to-biofuels process shows a promising pathway. Biomass, a renewable energy source, can be converted into a variety of biofuels in gas, liquid, or solid forms (e.g., syngas, bio-oil, biochar). There are two primary methods for converting biomass into biofuels: biochemical and thermochemical conversion. The latter is considered more flexible and efficient than biochemical conversion. Thus, this review aims to provide a summary of the most recent results from experimental research on biomass conversion to biofuels using various thermochemical conversion methods. Additionally, this review covers fundamentals and highlights the main evaluation parameters of thermochemical conversion systems, helping readers better understand the results reported by the researchers discussed in this article. This review also briefly addresses prospects and projections for future biofuel production, as well as measures to promote biofuel production. Full article

Contemporary post-war cities increasingly require adaptive urban systems capable of addressing climate vulnerability, infrastructural instability, environmental degradation, and human well-being simultaneously. This study develops an interdisciplinary framework for adaptive biophilic infrastructure and resource governance within the context of sustainable post-war reconstruction in Ukraine. The research combines literature analysis, comparative urban assessment, and experimental evaluation of eco-modified construction materials. Particular attention is given to vertical greening systems, adaptive underground infrastructure, daylight-integrated public environments, multifunctional urban systems, and environmentally responsive concrete composites incorporating porous minerals and plant-based biomass. Comparative examples from Montreal, New York, Seoul, and Singapore are examined alongside differentiated Ukrainian urban contexts, including Kyiv, Kharkiv, Dnipro, Odesa, Kherson, Lviv, and Uzhhorod. The findings demonstrate that adaptive biophilic infrastructure may improve urban microclimates, strengthen thermal and acoustic regulation, enhance infrastructural adaptability, and support psycho-emotional comfort within dense and post-conflict urban environments. The study further indicates that underground and layered urban systems increasingly function as multifunctional socio-ecological infrastructures integrating mobility continuity, environmental regulation, public accessibility, emergency protection, and human-centered spatial resilience. The experimental assessment demonstrates that eco-modified materials contribute to moisture stabilization, thermal buffering, acoustic moderation, and passive environmental regulation within adaptive urban systems. The incorporation of porous mineral additives and plant biomass improved the environmental responsiveness of the investigated composites while supporting more resource-efficient construction approaches. The study concludes that sustainable post-war reconstruction requires a transition from fragmented technological interventions toward integrated socio-ecological urban frameworks capable of combining environmental regulation, infrastructural resilience, resource efficiency, adaptive governance, and human-centered spatial design within long-term urban sustainability strategies. Full article

The performance of FastEddy, a GPU-based large eddy simulation model, in simulating building-induced turbulent flow in an operating airport is studied for the first time through four examples, including a super typhoon case at Hong Kong International Airport (HKIA) and a real case of low-level wind effect. The simulation results are quantitatively compared with wind observations from Light Detection and Ranging (LIDAR) systems for selected cases, and with aircraft data and pilot reports in one example of low-level wind effect. The FastEddy model is found to perform reasonably well through these case studies, even for the radial component of the winds exceeding 20 m/s in a highly turbulent airflow simulation of a typhoon, as well as turbulent airflow features in a building complex at and around HKIA. The building-induced turbulent flow as observed by the LIDARs and the aircraft are largely reproduced. The scatter plots of the model-simulated and the observed Doppler velocities have good correlation in terms of the slope of the best-fit linear equation, correlation coefficient and root-mean-square difference. Moreover, for the case of low-level wind effect, FastEddy simulation is found to provide useful insight into the turbulent flow arising from the new terminal building over the northeastern part of HKIA (near 22.325° N 113.918° E) under construction. Further research directions for studying the performance of FastEddy are also discussed, such as considering more complex urban environments, comparison with in situ measurements of anemometers, and direct output of the eddy dissipation rate (EDR) from the model for comparing with LIDAR and anemometer-based measurements. Full article

In the context of SDG 7 and SDG 13 of the 2030 Sustainable Development Agenda, a new performance indicator has started to gain momentum in scientific research: renewable energy productivity. Understanding the drivers and the challenges of green energy productivity could help add on to the classical focus of renewable energy research on infrastructure, technical and economic feasibility, and environmental and social impacts, by considering the performance indicators in this field more. Only very few studies have explored the influencing factors of renewable energy productivity. Thus, this research aims to reveal the impact of social, economic, energy, and environmental variables on green energy productivity. The methodological approach involves bibliometric analyses of the literature on green energy productivity (GEP) and panel data regression models involving 16 independent variables. The main findings indicate positive effects of green taxes, female participation in the workforce, and highly educated people on GEP, pointing out the importance of green taxation, education, and gender equality in sustainable development. On the other hand, negative relationships of green energy productivity with economic growth, traditional energy variables, and air pollution were found for the European Union’s member states over 2007 and 2023. The results suggest that the analyzed European countries based their economic growth on traditional resources, with less importance given to renewable resources and green technologies, as the share of renewable resources of GDP was also negatively correlated. While private financial resources increase green energy productivity, questions about research and development investments, urbanization, and diversity index are still debatable. Full article

Recently, numerous studies have exploited satellite Aerosol Optical Depth (AOD) to estimate near-surface particulate matter (PM) concentrations, with the aim of overcoming the limited spatial and temporal coverage of ground-based air quality monitoring networks. Despite significant progress, the relationship between AOD and PM remains highly uncertain, mainly due to the inadequate representation of local aerosol microphysical properties and of hygroscopic growth effects. In particular, satellite AOD is retrieved at ambient relative humidity, whereas standard PM measurements are performed under dry conditions. This study proposes a physics-informed, semi-empirical approach that overcomes these limitations by directly relating satellite AOD to PM measured at ambient humidity. Co-located measurements, from a Light Optical Aerosol Counter (LOAC) in the urban area of Bologna (Po Valley, Italy) during 2023, are used. This study is designed as a pilot application to evaluate the physical consistency of the proposed framework under well-characterised observational conditions, including spatial co-location, temporal matching to satellite overpasses, and exclusion of precipitation and desert dust events. The LOAC provides particle number size distribution and particle-type classification, which are used to estimate key aerosol properties controlling the AOD–PM theoretical relationship, including the Effective Radius, Extinction Efficiency, and aerosol Mass Density. These quantities, together with Mixing Layer Height, are combined within a theoretical framework linking PM and AOD, allowing for the derivation of a physically based scaling coefficient without relying on empirical hygroscopic growth corrections. The results show that using ambient PM_2.5 alone already yields a moderate linear correlation with AOD normalized by Mixing Layer Height (Pearson’s R = 0.56) whereas no meaningful correlation is found when using standard dry PM_2.5. When aerosol microphysical properties derived from LOAC measurements are incorporated, the correlation substantially improves (R = 0.76), with regression slopes close to unity and reduced errors, independently of the season. These results demonstrate that explicitly accounting for aerosol size and optical properties enhances the physical consistency and robustness of satellite-based PM estimates. The proposed framework also provides a pathway to indirectly derive aerosol hygroscopic growth factors by coupling ambient PM estimates from satellite observations with conventional dry PM measurements. This opens new perspectives for characterizing aerosol–humidity interactions from space and for improving air quality monitoring in regions lacking of dense in situ networks. Full article

The rapidly urbanizing peripheries of the Global South face significant demographic pressures, leading to governance deficits that often neglect the long-term structural safety of new buildings. While regulatory frameworks predominantly emphasize initial construction quality, they frequently overlook the critical “post-occupancy” phase, during which distinct structural risks accumulate. This study introduces a reproducible, open-data risk identification framework designed to trace theoretical “windows of vulnerability” in Çekmeköy, a peripheral district of Istanbul. By triangulating temporal, spatial, and demographic municipal administrative records from 2018 to 2024, we illustrated how low-cost data can serve as proxies for prioritizing structural risk assessments. The findings demonstrate that a 103% population increase between 2008 and 2023, coupled with a 21% reduction in the average household size, has generated urgent housing demand that outpaces supply. We hypothesize that these conditions create high-probability zones for “informality creep,” where demographic pressures induce informal practices, such as unauthorized structural modifications within ostensibly formal high-rise settings. The primary contribution is a transferable algorithmic tool, the Weighted Post-Occupancy Vulnerability Index (POVI). Rather than serving as a deterministic building-level diagnostic, this framework operates much like an epidemiological screening process; it acts as a macroscopic prioritization heuristic that allows resource-constrained municipalities to proactively direct their inspection efforts. By mathematically quantifying the conditions under which post-occupancy risks develop, this framework provides an essential resource for enhancing urban resilience during reactive urbanism planning. Full article

Decommissioned airfields are increasingly recognized as strategic sites for ecological regeneration, climate adaptation, and the creation of new public spaces. However, research on their transformation has predominantly focused on the environmental performance of Nature-based Solutions (NBS), often overlooking the role of inherited spatial morphology in structuring regeneration processes and outcomes. This paper proposes an AI-assisted, morphology-based proto-ontological framework for analyzing and designing post-airfield architecture. The framework was developed through the inductive and comparative analysis of a corpus of 32 urban post-airfield regeneration projects, from which recurrent inherited morphologies, transformation actions, spatial devices, and NBS were identified and structured into a relational sequence. The framework was then applied to two contrasting case studies: Maurice Rose Airfield Park (Frankfurt) and Xuhui Runway Park (Shanghai); these were selected for their different transformation logics. The results show that similar airfield morphologies can generate markedly different climatic, ecological, social, and memory-related outcomes depending on how they are transformed and linked to NBS. The study demonstrates that inherited airfield morphologies are not passive remnants but operative spatial structures, and that NBS should be understood as spatially embedded and form-generating design components. The proposed proto-ontology offers a transferable analytical model and a basis for future computational and generative design applications. Full article

Amid China’s transition from rapid urbanization to high-quality development, quantifying urban building metabolism is crucial for building resilient resource management systems. However, current research predominantly focuses on eastern cities, largely overlooking non-residential buildings. Here, we apply dynamic material flow analysis (dMFA) to quantify the material stocks of residential and non-residential buildings in two major economic hubs in western China, Xi’an and Chengdu. The stock patterns from 1950 to 2050 and the underlying drivers are further clarified. Model projections suggest that material stocks in both cities will peak around 2040, reaching 2.2 billion tons in Chengdu and 1.08 billion tons in Xi’an, under the intensive scenario. Chengdu reaches stock saturation 2 to 3 years earlier than Xi’an, and the total stocks are approximately twice those of Xi’an. Reinforced concrete and steel structures dominate future building development and increase the accumulation of cement and steel. Sand and gravel still account for the majority of building materials. Demand for new construction materials shows a pronounced double-peak pattern, occurring in 2016 and 2026. Construction waste is projected to rise sharply by mid-century; scenario analysis indicates that an 80% material recovery rate has the potential to largely offset new material demand. Sensitivity analysis identifies building lifetime extension and construction technology improvement as the strategies with the greatest potential for mitigating future waste generation. This study expands the scope of urban building material metabolism research and provides a scientific basis for low-carbon urban planning and construction waste management in China. Full article

The construction industry is characterised by high raw materials consumption and large waste generation. Upcycling waste construction materials offers an opportunity to reduce embodied carbon emissions while creating community assets. This paper examines how integrated design supports the effective reuse of waste materials from a modular building factory through the design of a community garden pavilion. Using Whole Lifecycle Assessment, the carbon impacts of three scenarios were evaluated. Case 1, the baseline scenario, represented the traditional temporary accommodation system using new materials with a hybrid steel–timber structure. Case 2 adopts new materials for the timber frame structure, combined with reused wooden pallets for the envelope. Case 3 represents an upcycling scenario where structural and envelope materials are reused from the modular building factory’s waste streams. Results show that the whole-life carbon emissions were 15,892.32 kgCO₂e for Case 1, 4293.25 kgCO₂e for Case 2, and 3044.99 kgCO₂e for Case 3, representing reductions of 73% and 81%, respectively, compared with the baseline. The findings demonstrate that integrated design and industrial material reuse can significantly reduce embodied carbon across a building’s life cycle. Recommendations for applying modular factory waste in community-led urban projects are provided. Full article