Search Results (281)

Academic institutions represent significant energy consumers, not only due to the magnitude and variability of their energy demand over time but also because of their institutional responsibility to promote sustainable practices. Despite this relevance, the scientific literature still lacks comprehensive benchmark indicators specifically tailored to the energy behavior of universities, thereby hindering the development of effective energy planning strategies in this sector. This study helps to address this gap by analyzing key energy performance indicators, with a focus on electricity consumption, across a representative experimental dataset. The dataset comprises 156 consumption units from ten Italian universities, selected to capture a broad spectrum of climatic zones, urban environments, energy systems, functional uses of spaces, and levels of utility availability. The analysis revealed an average electricity consumption of approximately 60 kWh/m²/year, with significantly higher values in warmer regions, mainly due to the widespread adoption of fully electric thermal systems. A baseline consumption level of around 35 kWh/m²/year was identified. Furthermore, electricity consumption normalized by Heating Degree Days reached values of approximately 500 kWh/HDD/year, particularly in centers with a prevalence of laboratories. The findings offer relevant insights for stakeholders (including designers, facility and energy managers, and policymakers), supporting data-driven decision making in the energy planning processes of academic environments. Full article

This study proposes a comprehensive artificial intelligence (AI)-based framework to predict, disaggregate, and optimize energy consumption and associated CO₂ emissions across a multi-building university campus. Leveraging real-world data from 27 buildings at the University of Valladolid (Spain), six AI models—artificial neural networks (ANN), radial basis function (RBF), autoencoders, random forest (RF), XGBoost, and decision trees—were trained on heat exchanger performance metrics and contextual building parameters. The models were validated using an extensive set of key performance indicators (MAPE, RMSE, R², KGE, NSE) to ensure both predictive accuracy and generalizability. The ANN, RBF, and autoencoder models exhibited the highest correlation with actual data (R > 0.99) and lowest error rates, indicating strong suitability for operational deployment. A detailed analysis at building level revealed heterogeneity in energy demand patterns and model sensitivities, emphasizing the need for tailored forecasting approaches. Forecasts for a 5-year horizon further demonstrated that, without intervention, energy consumption and CO₂ emissions are projected to increase significantly, underscoring the relevance of predictive control strategies. This research establishes a robust and scalable methodology for campus-wide energy planning and offers a data-driven pathway for CO₂ mitigation aligned with European climate targets. Full article

Against the backdrop of growing concerns over university students’ mental health worldwide, campus environments play a crucial role not only in shaping spatial experiences but also in influencing psychological well-being. However, the psychosocial mechanisms through which campus landscapes affect well-being remain insufficiently theorized. Drawing on survey data from 500 students across two Chinese universities, this study employs structural equation modeling (SEM) and interpretable machine learning techniques (XGBoost-SHAP) to systematically examine the interrelations among landscape perception, place attachment, perceived social acceptance, school belonging, and psychological well-being. The results reveal the following: (1) campus landscapes serve as the primary catalyst for fostering emotional identification (place attachment) and social connectedness (perceived social acceptance and school belonging), thereby indirectly influencing psychological well-being through these psychosocial pathways; (2) landscape perception emerges as the strongest predictor of well-being, followed by school belonging. Although behavioral variables such as the green space maintenance quality, visit frequency, and duration of stay contribute consistently, their predictive power remains comparatively limited; (3) significant nonlinear associations are observed between core variables and well-being. While the positive effects of landscape perception, place attachment, and school belonging exhibit diminishing returns beyond certain thresholds, high levels of perceived social acceptance continue to generate sustained improvements in well-being. This study advances environmental psychology by highlighting the central role of campus landscapes in promoting mental health and provides actionable strategies for campus planning. It advocates for the design of balanced, diverse, and socially engaging landscape environments to maximize psychological benefits. Full article

As global climate disasters become frequent, colleges and universities in disaster-prone areas are facing problems in disaster response and post-disaster recovery. Based on the theory of urban resilience, we case-studied nine universities in Conghua District, Guangzhou City, China, using the Delphi method and the analytic hierarchy process (AHP). We constructed a multi-criteria evaluation model for campus disaster prevention resilience under extreme climate conditions. By identifying 4 facets and 16 criteria, 9 colleges were ranked. The distance of the college from the city center, the terrain and natural environment of the college, the level of the college, and the ownership of the college affected their ranking The results of this study help campus managers and planners integrate campus resilience plans into campus planning, institutional regulations, campus site selection, and campus construction in the future. Full article

Background/Objectives: College and undergraduate students around the world struggle with stress, anxiety, and depression, which have a significant negative influence on their academic performance, social interactions, and general well-being. Creating successful preventative and intervention plans requires an understanding of the many, multi-level factors that contribute to psychological discomfort. The objective of this scoping review was to use the Socio-Ecological Model (SEM) to map the determinants of psychological distress among college students in a comprehensive manner. Methods: A total of 15 review publications published between 2015 and 2024, including narrative reviews, systematic reviews, meta-analyses, and umbrella reviews, were analyzed under the guidance of PRISMA ScR. These studies synthesized evidence across various countries, including China, Iran, India, Canada, Egypt, Nigeria, Saudi Arabia, and the United States. Results: Academic pressure, financial stress, poor sleep, unhealthy coping mechanisms, and pre-existing mental health issues were all individual-level concerns, with female and minority students being more vulnerable. Strong familial ties and friendships served as protective interpersonal support. Heavy academic workloads, strict grading guidelines, a lack of mental health resources, and unwelcoming campus environments were among the institutional factors. Stigma and socioeconomic disparities are examples of community-level variables that make mental health issues worse. Conclusions: Student mental health is shaped by interrelated factors across all SEM levels. Integrated, multi-level strategies are essential to fostering supportive campuses, strengthening community networks, and implementing inclusive policies that promote mental health equity. Full article

This article presents a case of strategic and participatory institutional innovation in higher education, focused on developing teacher digital competence (TDC) as a key enabler of sustainable digital transformation. In response to the post-pandemic challenges faced by the National University of Cuyo (UNCuyo), a large and multi-campus public university in Argentina, the European CUTE methodology was adapted and implemented to align professional development with institutional planning. Grounded in the DigCompEdu framework, this action-oriented process moved beyond individual initiatives to create a coordinated, multi-level strategy involving educators, department leaders, and university authorities. Through a research-based design that included context analysis, participatory diagnosis, and co-designed interventions, the project built a shared understanding of digital teaching needs and institutional readiness. The implementation highlights how locally adapted frameworks, collaborative structures, and iterative decision-making can drive meaningful change across a complex university system. This case contributes to the international conversation on how higher education institutions can operationalize innovation at scale by investing in teacher competence, inclusive processes, and strategic alignment. Lessons learned from this experience are relevant for universities seeking to build institutional capacity for digital transformation in diverse educational contexts with potential downstream benefits for student learning and inclusion. Full article

Electric bikes (e-bikes) offer a low-carbon, space-efficient solution for campus mobility, yet their sustainable potential is increasingly challenged by patterns of non-compliant use, including speeding, informal parking, and unauthorized charging. This study integrates the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM) to examine the cognitive and contextual factors that shape such behaviors among university students. Drawing on a survey of 408 e-bike users and structural equation modeling, the results show that non-compliance is primarily driven by perceived usefulness, ease of action, and behavioral feasibility, with affective and normative factors playing indirect, reinforcing roles. Importantly, actual behavior is influenced not only by intention but also by students’ perceived capacity to act within low-enforcement environments. These findings highlight the need to align behavioral perceptions with sustainability goals. The study contributes to sustainable mobility governance by clarifying key psychological pathways and offering targeted insights for designing perception-sensitive interventions in campus transport systems. Furthermore, by promoting compliance-oriented campus mobility, this research highlights a pathway toward enhancing the resilience of transport systems through behavioral adaptation within semi-regulated environments. Full article

This research proposes an integrated lighting and solar shading strategy to improve energy efficiency and user comfort in a retrofit project in a temperate-humid climate. The study examines a future library addition to an existing faculty building in Bursa, featuring highly glazed façades (77% southwest, 81% northeast window-to-wall ratio), an open-plan layout, and situated within an unobstructed low-rise campus environment. Trade-offs between daylight availability, heating, cooling, lighting energy use, and visual and thermal comfort are evaluated through integrated lighting (DIALux Evo), climate-based daylight (CBDM), and energy simulations (DesignBuilder, EnergyPlus, Radiance). Fifteen solar shading configurations—including brise soleil, overhangs, side fins, egg crates, and louvres—are evaluated alongside a daylight-responsive LED lighting system that meets BS EN 12464-1:2021. Compared to the reference case’s unshaded glazing, optimal design significantly improves building performance: a brise soleil with 0.4 m slats at 30° reduces annual primary energy use by 28.3% and operational carbon emissions by 29.1% and maintains thermal comfort per ASHRAE 55:2023 Category II (±0.7 PMV; PPD < 15%). Daylight performance achieves 91.5% UDI and 2.1% aSE, with integrated photovoltaics offsetting 129.7 kWh/m² of grid energy. This integrated strategy elevates the building’s energy class under national benchmarks while addressing glare and overheating in the original design. Full article

This study focuses on the multi-objective coordination problem in university campus planning and design, proposing an optimized methodology integrating an improved multi-objective decision-making framework. A five-dimensional objective system—comprising energy efficiency, spatial quality, economic cost, ecological benefits, and cultural expression—was established, alongside the identification and standardization of 29 key variables to construct mapping relationships among objective functions. On the algorithmic level, an adapted NSGA-III was implemented on the MATLAB platform (version R2022b), introducing a dynamic reference point mechanism and hybrid constraint-handling strategy to enhance convergence and solution diversity. Taking the northern residential area of the western campus of Dalian University of Technology as a case study, multiple Pareto-optimal solutions were generated. Five representative alternatives were selected and evaluated through the AHP–TOPSIS method to determine the optimal scheme. The results indicated significant improvements in energy, economic, spatial, and ecological dimensions, while also achieving quantifiable control over cultural expression. On this basis, an integrated optimization strategy targeting “form–function–environment–culture” was proposed, offering data-informed support and procedural reference for systematic campus planning. This study demonstrates the effectiveness, adaptability, and practical value of the proposed approach in addressing multi-objective conflicts in university planning. Full article

Universities in severely cold regions face the dual challenge of adapting to seasonal climate variations while enhancing outdoor thermal comfort in outdoor leisure plazas. This study takes a university in Hohhot as a case study. Through field investigations conducted in summer and winter, thermal benchmarks were established. Based on this, an orthogonal experimental design was developed considering greenery layout, plant types, and surface albedo. ENVI-met was used to simulate and analyze the seasonal regulatory effects of landscape elements on the microclimate. The results show that: (1) the lower limit of the neutral PET range in Hohhot in winter is −11.3 °C, and the upper limit in summer is 31.3 °C; (2) the seasonal contribution of landscape elements to PET ranks as follows: plant types > greenery layout > surface albedo; and (3) the proposed optimization plan achieved a weighted increase of 6.0% in the proportion of activity area within the neutral PET range in both summer and winter. This study is the first to construct outdoor thermal sensation categories for both summer and winter in Hohhot and to establish a thermal comfort optimization evaluation mechanism that considers both diurnal and seasonal weightings. It systematically reveals the comprehensive regulatory effects of landscape elements on the thermal environment in severely cold regions and provides a nature-based solution for the climate-responsive design of campus plazas in such areas. Full article

This paper investigates integrating a sensory data model for managing an existing 50-year-old building. A primary challenge in retrofitting older structures is the optimal deployment of high-quality sensors, systematic data acquisition, and subsequent data management. To address this, the study implemented a network of over 50 sensors connected via 270 m of wired infrastructure, deliberately avoiding wireless transmission to ensure data reliability. This configuration generates 5568 data points daily, which are archived on a dedicated server. The data is planned for integration into the Campus Geographical Information System (GIS), enabling private and public access. A methodology was employed, involving the strategic placement of sensors based on building use patterns, continuous data monitoring, and iterative sensor performance evaluation. The findings from the study indicate that integrating sensory data through this structured approach significantly enhances building management capabilities. Specifically, the results demonstrate improved energy efficiency and environmental performance, which is particularly relevant for public and educational facilities. The research highlights that a data-driven, monitoring-based management system can optimize operational functions and inform future retrofitting strategies for aging buildings. Full article

This paper combines Christopher Alexander’s pattern language with generative AI into a hybrid design framework. The result is a narrative synthesis that can be useful for informed project design. Advanced large language models (LLMs) enable the real-time synthesis of design patterns, making complex architectural choices accessible and comprehensible to stakeholders without specialized architectural knowledge. A lightweight, web-based tool lets project teams rapidly assemble context-specific subsets of Alexander’s 253 patterns, reducing a traditionally unwieldy 1166-page corpus to a concise, shareable list. Demonstrated through a case study of a university department building, this method results in environments that are psychologically welcoming, fostering health, productivity, and emotional well-being. LLMs translate these curated patterns into vivid experiential narratives—complete with neuroscientifically informed ornamentation. LLMs produce representative images from the verbal narrative, revealing a surprisingly traditional design that was never input as a prompt. Two separate LLMs (for cross-checking) then predict the pattern-generated design to catalyze improved productivity as compared to a standard campus building. By bridging abstract design principles and concrete human experience, this approach democratizes architectural planning grounded on Alexander’s human-centered, participatory ethos. Full article

Air pollution poses a growing threat to public health and the environment, highlighting the need for continuous and precise urban air quality monitoring. The aim of this study was to implement and evaluate an autonomous air quality monitoring platform developed by the University of Ruse, “Angel Kanchev”, under Bulgaria’s National Recovery and Resilience Plan (project BG-RRP-2.013-0001), co-financed by the European Union through the NextGenerationEU initiative. The system, based on Libelium’s mobile sensor technology, was installed at a height of two meters on the university campus near Rodina Boulevard and operated continuously from 1 March 2024 to 30 March 2025. Every 15 min, it recorded concentrations of CO, CO₂, NO₂, SO₂, PM₁, PM_2.5, and PM₁₀, along with meteorological parameters (temperature, humidity, and pressure), transmitting the data via GSM to a cloud-based database. Analyses included a distributional assessment, Spearman rank correlations, Kruskal–Wallis tests with Dunn–Sidak post hoc comparisons, and k-means clustering to identify temporal and meteorological patterns in pollutant levels. The results indicate the high operational stability of the system and reveal characteristic pollution profiles associated with time of day, weather conditions, and seasonal variation. The findings confirm the value of combining calibrated IoT systems with advanced statistical methods to support data-driven air quality management and the development of predictive environmental models. Full article

This study aims to address the understudied evaluation of public space performance in renovated multi-functional university buildings, with a special focus on university complexes based on integrated industry–research–education models. While existing literature emphasizes outdoor campus environments, few studies have systematically assessed the internal public spaces that support interdisciplinary collaboration. Using the Center for Balanced Architecture at Zhejiang University as a case study, we employed a mixed-methods approach that combined Depthmap software for spatial integration and visual integration analyses with user satisfaction surveys. Our results reveal significant post-renovation improvements in spatial accessibility, particularly in terms of First Floor Plan connectivity. However, they also uncover persistent issues: despite high objective integration scores, user satisfaction with wayfinding systems remains low, pointing to a cognitive efficiency gap. Furthermore, disparities in satisfaction with acoustics, privacy, and social spaces across different user groups highlight the importance of balancing openness with individual needs. These findings provide empirical evidence to help optimize future renovation designs and enhance spatial experience and performance. Full article

Urban disaster risks are escalating, and university campus emergency shelters (UCESs) are key to alleviating the supply–demand imbalance in emergency shelter services (ESSs) within high-density central urban areas. However, existing studies lacked the measurement of UCES service effectiveness from a regional supply–demand perspective, limiting the ability to guide planning practices. Therefore, we focused on the capacity of UCESs to improve regional supply–demand relationships and developed a service effectiveness evaluation framework for UCESs in the central urban area of Tianjin under an earthquake scenario. We identified emergency shelter spaces within the campuses and developed a campus–city collaborative shelter capacity model to determine their service supply capacity. Then we quantified regional service demand driven by seismic risk. Finally, we quantified the service effectiveness of each UCES by constructing a service effectiveness evaluation model. Results showed that (1) the total shelter capacity and service coverage of 13 UCESs accounted for approximately 32.1% of the central district’s population and 67.5% of its land area, indicating their strong potential to provide large-scale ESSs. (2) Average seismic risk values ranged from 0.200 to 0.260, exhibiting the characteristic of being higher in the south and lower in the north. (3) Service effectiveness was classified into three levels—higher (1.150–1.257), medium (0.957–0.988), and lower (0.842–0.932)—corresponding to planning interventions that can be implemented based on them. This study aims to reveal differences between different UCESs to improve regional supply–demand relationships by evaluating their service effectiveness and supporting refined emergency management and planning decisions. Full article