https://www.ijumes.com/ International Journal of Urban Management and Energy Sustainability en daily 1 Wed, 01 Jul 2026 00:00:00 +0330 Wed, 01 Jul 2026 00:00:00 +0330 https://www.ijumes.com/article_738909.html The advent of big data in the architecture, engineering and construction industry has shifted the role of information from a purely representational instrument to a generative driver of the design process. Nevertheless, a deep operational gap persists between abstract data analytics and the physical realization of spatial relationships in architecture. This study introduces the notion of “data-driven value” as a mediating layer intended to bridge this gap, and proposes a conceptual model for translating structured physical and behavioral data into spatial qualities. The study introduces the D2V model, a framework for the conceptual integration of semantic-web technologies with space-syntax logic. To validate the model’s components, a three-round Delphi panel of fifteen experts in architecture, urban design, spatial analysis, building information modelling and data science was convened. The Content Validity Ratio and Content Validity Index were computed for every component. The results indicate that “data-driven value” is realized when data act upon the spatial logic of the building and thereby enhance legibility, functional performance and prediction accuracy. The Delphi panel confirmed that all D2V components possess satisfactory content validity. The components “prediction accuracy” and “spatial legibility” attracted the greatest expert consensus, and Kendall’s coefficient of concordance reached 0.85 by the third round, indicating strong agreement among the experts. The proposed D2V model redefines the architect’s role from “author of form” to “designer of semantic systems.” By realizing data-driven value, architects can reduce ambiguity in design and ensure that data-driven methodologies produce function-oriented, human-centered environments rather than mere geometric complexity. https://www.ijumes.com/article_740733.html With the increasing integration of renewable energy sources, enhancing microgrid efficiency has become a fundamental priority in power systems engineering. However, the stochastic and unpredictable nature of solar and wind energy presents significant challenges for energy management and system stability. In this research, a comprehensive intelligent energy management framework with demand-side flexibility is proposed to improve the efficiency of a hybrid microgrid equipped with photovoltaic systems and small-scale wind turbines. To this end, an integrated model of all microgrid components, including generation units, energy storage systems, and flexible loads such as electric vehicles, has been developed in the MATLAB/Simulink environment. The main innovation of this research lies in the application of a novel geometric method for Maximum Power Point Tracking in solar cells and a robust nonlinear controller for power management of the grid-connected inverter. This approach enables the synchronization of variable load charging intervals with peak renewable energy generation periods. Simulation results demonstrate that the proposed method significantly improves the technical and economic indices of the microgrid and increases the utilization factor of renewable sources. Furthermore, intelligent load management effectively reduces voltage fluctuations, improves power quality, and ensures system stability under various atmospheric conditions. The findings confirm the effectiveness of the proposed integrated framework in coordinating production and consumption, positioning it as a scalable infrastructure for future smart distribution networks. https://www.ijumes.com/article_741004.html In large, spatially complex commercial complexes, the time required to evacuate during an emergency is a decisive determinant of occupant safety. Beyond the physical configuration of exits, the ease with which occupants can read and understand the space its spatial legibility and the social behavior patterns that emerge in crowds jointly shape egress performance. This study assesses the influence of spatial legibility on the reduction of emergency egress time, with an emphasis on the mediating role of social behavior patterns, in the context of commercial complexes. Adopting a quantitative, social-science research design, the study operationalizes spatial legibility, social behavior patterns and emergency egress performance through a structured 5-point Likert questionnaire. The sample size was determined by the Cochran formula for an unlimited population, yielding 384 respondents drawn from visitors of a large commercial complex such as the Kourosh Complex in Tehran city. In the case study, the measurement model showed acceptable reliability and validity, and factor analysis recovered the hypothesized structure. The structural model indicated that spatial legibility significantly reduced emergency egress time, significantly strengthened adaptive social behavior patterns and that social behavior in turn reduced egress time, the indirect effect was ≈ −0.17 and the total effect ≈ −0.59. mean egress time fell from about 196 s in low-legibility conditions to about 121 s in high-legibility conditions. Model-fit indices were within accepted thresholds. All of these values are illustrate the framework’s behavior. The design shows how spatial legibility can shorten emergency egress time both directly and indirectly through social behavior patterns, and it provides commercial-complex designers with an evidence-oriented, testable framework and a ready-to-use instrument. Populating the pipeline with real field data would yield validated, venue-specific conclusions. https://www.ijumes.com/article_741408.html The present study aims to present a population-growth-based model for the optimal disposal of urban waste in the city of Qaemshahr, Iran, so that demographic change and consumption patterns can be linked to waste-management planning. The research is applied in terms of purpose, exploratory in terms of data collection and mixed in terms of the nature of the data. In the qualitative part, data were collected through interviews and analysed using thematic analysis in the quantitative part, a questionnaire and structural equation modelling with the partial-least-squares approach were used. The thematic analysis identified 48 initial codes, organised into seven components under the main theme “effective waste management”: identifying consumption patterns, developing waste-management infrastructure, raising public awareness, social participation, the use of new technologies, creating economic incentives, and regulating laws and regulations. The PLS structural model confirmed that all seven components have a positive and significant effect on urban waste management; developing waste-management infrastructure had the strongest effect, followed by identifying consumption patterns and regulating laws and regulations. The results show a direct, significant relationship between population growth and waste generation, and that the optimal management of urban waste requires a comprehensive, multidimensional approach combining infrastructural, social, technological, economic and regulatory measures. The proposed model can serve as an effective, novel approach for optimising urban-waste management and improving citizens’ quality of life in Qaemshahr and other cities with similar conditions. https://www.ijumes.com/article_741467.html Climatic conditions are among the key factors influencing environmental planning, energy management, and the formulation of architectural design strategies. The De Martonne aridity index, which relies on the two variables of temperature and precipitation, is one of the empirical methods widely used for climate classification. This study aimed to classify the climate of Iran using the De Martonne index and to interpret the results from the perspective of climate-responsive architectural design. For this purpose, the mean annual temperature and precipitation of 201 stations across the country over a 30-year period were collected, and the De Martonne index was computed; the climatic classes and their spatial distribution were then determined and examined for their implications for architectural design. The results showed that 59% of Iran's area falls within the arid class and 34% within the semi-arid class, while 2% is semi-Mediterranean, 0.5% semi-humid, 1.5% humid, and 3% very humid. Arid climates dominate the east, south-east, north-east, and the Persian Gulf margin, whereas semi-arid climates prevail in the mountainous west, north-west, and south-west; humid and very humid climates are concentrated along the Caspian Sea. The prevalence of arid and semi-arid climates and the marked climatic contrasts between zones underline the need for climate-adapted architectural strategies including building orientation, solar-radiation control, opening regulation, natural ventilation, and reduced energy use and support the periodic updating of climatic zoning as a basis for climate-responsive design across Iran's different regions. https://www.ijumes.com/article_727077.html Buildings are significant energy consumers, and there is growing concern regarding the environmental repercussions of energy usage because of global warming and an increase in extreme weather occurrences. This study investigates the necessity for sustainable energy management and improved thermal comfort in educational buildings, especially in classrooms at the Iran University of Science and Technology (IUST) in Tehran, Iran. The study addresses optimizing energy consumption and occupant comfort through optimal daylighting solutions in response to increasing energy demands and the growing climate issue. A multi-objective optimization method was employed to balance the selection of glazing materials and the design of shading devices to enhance daylight advantages. Employing Rhinoceros 3D and Grasshopper with the Ladybug, Honeybee, and Wallacei X plugins, simulations utilized Tehran's climatic data to evaluate the effects on energy consumption and thermal comfort. The research examined two classroom orientations: south-facing (Model A) and north-facing (Model B). Results indicated substantial enhancements: in Model A, the Predicted Mean Vote (PMV) and Predicted Percentage of Dissatisfied (PPD) measures improved by 40% and 19%, respectively, while energy usage reduced from 220 kWh to 190.54 kWh. In Model B, PMV and PPD enhanced by 17% and 15%, respectively, while energy consumption decreased from 228.78 kWh to 209.03 kWh. The findings indicate that optimal daylighting, along with window and shading devices, can significantly improve energy efficiency and comfort in educational environments. https://www.ijumes.com/article_738241.html The location of new housing is among the most carbon-consequential decisions a city makes, because where people live largely determines how far and by what mode they must travel and how much energy their daily lives consume. The objective of this research is therefore to develop and validate an operational model for the optimal location of desirable housing in Tabriz under a zero-carbon approach, by integrating an Artificial Neural Network of the multilayer-perceptron type with a Geographic Information System spatial database. Adopting an analytical–applied design within a post-positivist paradigm, eight carbon-relevant location criteria capturing walkable service access, public-transport proximity, mixed-use intensity, green-space access, education proximity, residential infill, industrial-emission buffering, and river/flood buffering were derived directly from an official municipal land-use layer through GIS spatial operations and used to train a back-propagation network, with performance evaluated against held-out validation and test data and benchmarked against a conventional weighted-linear baseline. Findings indicate that the trained network achieved a coefficient of determination of 0.951 and a root-mean-square error of 0.040 on the validation set, that public-transport proximity, mixed-use intensity, and green-space access were the most influential criteria, and that the highest-suitability housing sites concentrated in the compact, transit- and service-rich central core of the city. The study concludes that ANN–GIS integration under a zero-carbon framing constitutes a robust, interpretable, and transferable instrument for steering housing development toward low-carbon urban form in Iranian metropolitan contexts. https://www.ijumes.com/article_741827.html The Urmia–Serow corridor is a strategic border route in northwestern Iran facing road-based transport pressure, land-use transformation, and regional connectivity challenges. This study assesses the potential role of rail-oriented planning in supporting more coordinated spatial development along this corridor. The research uses a descriptive-analytical and scenario-based approach based on documentary sources, available transport records, field observations, visual interpretation of aerial and satellite images, and illustrative road-based fuel and energy scenario calculations. Existing road-oriented conditions are compared with a proposed rail-oriented planning scenario. The findings indicate that the corridor is affected by road dependence, land-use conversion pressure, and limited spatial coordination. A rail-oriented scenario may support better land-use organization, could help reduce part of the reliance on road-based movement under modal-shift assumptions, and support regional and cross-border connectivity, depending on demand, feasibility, and institutional coordination. Rail-oriented planning is presented as a potential planning strategy, not a confirmed causal solution. Further feasibility studies, updated official data, environmental assessment, and cross-border coordination are required before implementation. https://www.ijumes.com/article_741521.html Optimizing the microclimatic behavior of outdoor spaces in high-density residential fabrics is one of the most effective strategies for mitigating human thermal stress and enhancing urban sustainability in hot and dry climates. Aiming to analyze the impacts of geometry, scale, and spatial organization on outdoor thermal comfort, this study evaluates 18 layout scenarios generated by combining four central courtyard typologieswithin a high-density residential complex in Shiraz. The evaluations were conducted under the most critical summer conditions using numerical microclimate simulations. The analytical findings reveal that transitioning from the large-scale, single-courtyard reference model to fragmented and decentralized spatial configurations consistently leads to a reduction in air temperature and mean radiant temperature (Tmrt), thereby improving the Physiological Equivalent Temperature (PET) index across all scenarios. Analyzing the thermodynamic mechanisms governing the spaces indicates that the radiative load (Tmrt) is the primary driver of thermal perception in this climate. The fine-grained square and medium-sized rectangular typologies exhibit the most successful performance in mitigating solar radiative energy and lowering the PET index, primarily due to the reduction of the Sky View Factor (SVF) and the creation of self-shading by the physical built form. Conversely, spacious courtyards transform into thermal traps owing to their low degree of enclosure. Regarding wind flow and relative humidity components, although elongated geometries offer better aerodynamic potential for airflow passage, the minor reduction in wind velocity in fine-grained fabrics due to physical roughness is entirely negligible when compared to the positive effects of shading on mitigating the radiative load. Ultimately, this research emphasizes that the smart combination of compact and linear fine-grained layouts represents the most effective design strategy for achieving sustainable outdoor thermal comfort in residential complexes within hot and dry regions.