Search Results (257)

Higher education institutions increasingly rely on digital infrastructures, but these systems also contribute to carbon emissions through their digital carbon footprint. Institutional websites are an important part of this footprint, serving both as sources of emissions and as platforms for communicating sustainability initiatives. This study evaluates the carbon footprints of the homepages of the 12 member and associate university websites of the UNITA Universitas Montium Alliance, a European Universities Initiative focused on digital transformation and sustainability. Using the Website Carbon Calculator (v4), an audit conducted in June 2026 measured CO₂e emissions per page view, estimated annual emissions under standardized traffic, hosting energy use, and the number of trees required to offset emissions. Results showed considerable variation, with ratings ranging from B to F and emissions between 0.08 and 2.38 g CO₂e per page view. Five institutions already use green hosting. The same 12 homepages were re-measured under an identical protocol on 20 July and 17 August 2026; eleven returned identical values in all three rounds and the twelfth varied by 0.05 g CO₂e per page view. These findings extend a technical carbon-footprint auditing methodology previously validated only within single-country university systems to a comparative, multi-country alliance setting, and are consistent with website design and hosting choices, not national electricity grids, playing the dominant role in the observed differences between institutions. For UNITA, this points to a concrete opportunity to coordinate action through its existing Green Sustainability and Virtual Campus work packages, aligning the alliance’s digital infrastructure with the sustainability standards it already promotes elsewhere. Full article

This study examines how social media environmental engagement (ESME) is associated with college students’ organizational citizenship behavior for the environment (OCBE) through green self-identity (GSI), and whether this indirect association varies across levels of perceived pro-environmental organizational climate (PEOC). Although digital environmental engagement has received growing attention, less is known about the identity-based mechanism linking such engagement to students’ offline pro-environmental citizenship behavior and the institutional conditions under which this mechanism may differ. Drawing on social identity theory, we propose that ESME is positively associated with GSI, which, in turn, is associated with OCBE. We further propose that PEOC moderates the association between ESME and GSI. Using a three-wave time-lagged self-report survey of 481 college students in China, we tested the proposed moderated mediation model using regression and bootstrap analyses. The results provide evidence consistent with the proposed model: GSI mediated the positive association between ESME and OCBE, and this identity-based association was stronger when perceived institutional environmental support was relatively weak. This study contributes to research on digital environmental engagement and university sustainability education by clarifying the joint roles of online engagement, green self-identity, and perceived campus climate. The findings suggest that universities may benefit from connecting online environmental engagement with supportive and visible campus sustainability practices. Full article

Against the backdrop of global sustainable development, university campuses are typical high-energy-consuming entities and important carriers for practicing green and low-carbon concepts. However, China’s unified assessment standard for green campus (GB/T 51356-2019) lacks hot-humid specific indicators, limiting scientific guidance for South China. Adopting a multilevel framework and comparative analysis approach, this study analyzes four standards issued by China, Singapore, and India regarding their frameworks, categories, weights, and scoring methods, focusing on adaptability to hot and humid environments. Results reveal significant disparities: Singapore emphasizes energy efficiency (47% weight) with streamlined certification; Taiwan of China focuses on operational performance and practical feasibility of indicators; India attaches greater importance to process verification and quantitative evaluation rationality; the assessment standard for green campus shows cumbersome procedures and low-energy weight (11.2%) and lacks hot-humid indicators. The study recommends streamlining certification, establishing incentives, increasing energy efficiency weight, and supplementing hot-humid adaptive indicators such as sponge campus construction, building orientation design, and natural ventilation optimization. The research results can provide theoretical support for the revision of the assessment standard for green campus and the formulation of regional evaluation frameworks and offer a reference for sustainable campus development in hot-humid areas worldwide. Full article

With the continued advancement of low-carbon campus transformation and the increasing penetration of renewable energy, campus integrated energy systems have become key infrastructure for green campus development. However, the highly random nature of student behavior causes dynamic fluctuations in electricity, heating, and cooling loads, creating major challenges for real-time supply-demand balance and economic system scheduling. To address this problem, this paper takes student behavior uncertainty as the core disturbance factor and proposes a flexible architecture-driven autonomous adaptation and multi-energy complementary optimization strategy. A closed-loop operation paradigm of signal–response–complementarity–regulation is established, in which dynamic electricity price signals, comfort-oriented guidance, and campus functional energy-zone division are combined to form a multi-level autonomous response chain. To improve solution efficiency, the electromagnetic wave propagation algorithm is further enhanced, and a Multi-Objective Electromagnetic Wave Propagation Algorithm (MEMWPA) is developed. Wave-impedance matching and energy-flux-density feedback mechanisms are introduced to strengthen convergence performance in complex multi-objective optimization problems. Comparative case studies show that the proposed strategy can effectively smooth the net load curve, reduce the campus peak load by 26.73%, and increase the load factor by 14.533 percentage points, thereby improving both operational flexibility and energy efficiency. Full article

Against the background of green campus development and building decarbonization, photovoltaic (PV) systems in university research buildings involve not only energy performance but also campus character, architectural form, and spatial experience. To address the limited attention paid to such multidimensional adaptability, this study derives candidate indicators through a literature review and practical project analysis, refines them through two rounds of Delphi expert consultation, and determines their relative weights using the analytic hierarchy process (AHP). The resulting evaluation framework comprises five criteria and eighteen indicators covering planning pattern coordination, architectural form integration, building physical environmental performance, landscape character integration, and technical performance and innovation. Architectural form integration received the highest criterion weight (0.240), while power generation efficiency had the highest comprehensive indicator weight (0.142). Applied to the research building clusters at Zhejiang University Zijingang Campus, the framework yielded a comprehensive adaptability score of 87.3/100, corresponding to the excellent level. By translating multidimensional considerations into a unified quantitative result, the framework provides a structured basis for scheme assessment and adaptability classification at the design stage. Full article

Higher education institutions are increasingly expected to reduce greenhouse gas emissions while maintaining reliable educational, laboratory, and administrative operations. This challenge is particularly relevant in transition economies, where university campuses often depend on fossil-fuel-based electricity systems, natural-gas heating, and aging infrastructure. This study presents a campus-scale decarbonization assessment for the Tashkent Institute of Chemical Technology in Uzbekistan. The quantified inventory covered Scope 1 emissions from natural-gas combustion and Scope 2 emissions from purchased electricity. Paper use, digital services, behavioural measures, and campus greening were assessed as supplementary institutional indicators and were excluded from the quantified total because consistent pre- and post-intervention activity data were unavailable. The assessment combined institutional utility records for 2023–2025 with information on renewable-energy deployment, heating modernization, digital transformation, sustainability awareness, and campus greening. A 300 kW solar photovoltaic system comprising 666 modules was commissioned in May 2023, with a documented annualized generation potential of approximately 520,000 kWh. Purchased grid electricity amounted to 711,402, 745,947, and 749,060 kWh in 2023, 2024, and 2025, respectively, while annual natural-gas consumption was 144,775, 161,200, and 142,031 m³. Using a conservative standard-based net calorific value of 31.8 MJ/m³ together with IPCC stationary-combustion factors, annual Scope 1 and Scope 2 emissions were estimated at 637.53, 685.29, and 652.65 tCO₂-eq, respectively. The 2025 total was 4.76% below the 2024 value but 2.37% above the 2023 value. The annualized PV technical potential corresponds to a theoretical maximum Scope 2 displacement of 276.64 tCO₂-eq/year under 100% self-consumption. This value does not represent measured generation or a realized emission reduction and was not included in the quantified inventory. Digitalization, behavioural engagement, and greening were evaluated as complementary measures rather than assigned separate emission-reduction credits. The study provides a transparent and regionally relevant framework for universities in transition economies seeking to strengthen campus carbon management under incomplete data conditions. Full article

Urban green spaces play a fundamental role in maintaining ecological stability, supporting biodiversity, regulating urban microclimates, and improving human wellbeing. Because photosynthetic activity is a key indicator of vegetation functionality and environmental adaptation, this study evaluated the photosynthetic performance of vegetation across eight urban green spaces within the campus of the University of Life Sciences “King Michael I” in Timișoara, Romania. Vegetation structure, spatial organization, and estimated photosynthetic rates of selected ornamental species were comparatively analyzed under the temperate climatic conditions of the study area. Two-factor analysis of variance (ANOVA) revealed that the spatial characteristics of the investigated green spaces had a highly significant effect on photosynthetic variability (F(7, 42) = 6.783; p = 0.000021), explaining approximately 46% of the total variance, whereas species identity accounted for only 13.3% and showed no statistically significant influence (p = 0.052863). The highest photosynthetic performance was recorded in structurally diverse green spaces characterized by dense tree canopy and balanced woody–herbaceous vegetation, whereas the lowest values occurred in highly urbanized areas with limited vegetation cover. These findings demonstrate that the structural organization and vegetation composition of urban green spaces strongly influence photosynthetic performance and support the use of integrated ecological assessment as a decision-support tool for sustainable and climate-resilient urban green infrastructure planning. Full article

Educational buildings account for approximately 17% of the building sector’s energy consumption, making them critical for zero-emission building (ZEB) strategies. With students spending over 50% of their time indoors, life-cycle carbon assessments and targeted retrofitting are essential for realizing UN sustainability goals on campuses. This study develops a comprehensive life-cycle carbon assessment framework using a Calibrated Building Energy Simulation to simultaneously evaluate embodied and operational carbon emissions in campus facilities. This research evaluates two distinct university buildings for analysis: a historical 1970 educational building in Cosenza, Italy (Mediterranean climate) and a modern 2009 building in Ankara, Türkiye (semi-arid/continental climate). To minimize the total carbon footprint, seven distinct retrofitting scenarios are systematically simulated and compared: adding photovoltaic (PV) panels, integrating solar films on windows, applying internal and external insulations, implementing green wall applications, applying psychological-adaptive HVAC control and decreasing the heating set-point temperature. Results indicate that psychological-adaptive HVAC control is the most effective, achieving approximately 19.3% operational carbon savings across both cases with a low embodied carbon penalty. Conversely, the green wall application was the least effective, with a carbon payback period of 53.55 years. Ultimately, this study provides actionable engineering pathways for transforming campus buildings into net-zero emission educational facilities. Full article

In response to issues such as disordered airflow distribution and prominent local hotspots in campus non-standard data centers, this study took a non-standard raised-floor air-supply data center at a university in Hangzhou as the research object, and used a combination of on-site measurements and computational fluid dynamics (CFD) numerical simulation to investigate the optimization of the thermal environment. The temperature and air velocity of the data center were measured using a handheld hot-wire anemometer, and a standard k-ε turbulence model was established on the 6SigmaDC platform (now Cadence Reality DC Design Pro, version 2024.1). Model accuracy was confirmed through grid independence verification with three mesh levels and statistical error metrics (MAE, MBE, RMSE) across multiple measurement zones. The results show that the mean absolute error of temperature does not exceed 0.9 °C in all zones and the mean absolute error of air velocity does not exceed 0.20 m/s, indicating that the model effectively reproduces the airflow distribution and thermal environment of the data center. On this basis, to address the uneven airflow distribution in the underfloor plenum, an optimization strategy was proposed that involved the installation of composite baffles and the coordinated adjustment of variable floor tile openings. Eight representative simulation scenarios were designed, with the coefficient of variation and air supply uniformity index as evaluation indicators. Results indicate that the combined effect of perforated baffles and variable floor tile openings is the optimal strategy, reducing the range of net airflow among air supply outlets from 0.100 to 0.077 m³/s, decreasing the coefficient of variation from 12.8% to 10.8%, and increasing the air supply uniformity index by 10.7%. Whole-room thermal environment verification shows that the optimal scheme reduces the supply heat index (SHI) from 0.42 to 0.35, with an estimated PUE reduction of about 0.03, achieving both airflow uniformity improvement and energy-saving benefits. By improving the cooling efficiency and reducing the PUE, this retrofit strategy contributes to the sustainable operation of small-to-medium-sized campus data centers, supporting energy efficiency and carbon footprint reduction goals under green campus and low-carbon initiatives. Full article

The growing implementation of the United Nations Sustainable Development Goals (SDGs) has increased the importance of sustainable transformation in higher education institutions. Universities are increasingly expected to integrate environmental management, digital technologies, governance, education, and community engagement into unified sustainability frameworks. However, integrated sustainability management models for technical universities in emerging economies remain insufficiently explored. This study analyzes the long-term transformation of the EKTU Green Campus project at D. Serikbayev East Kazakhstan Technical University (Kazakhstan) during 2018–2025. The research is based on methodological triangulation combining institutional statistical data, official UI GreenMetric methodologies and indicators, university sustainability reports, and verified ranking results. The study examines the interaction between Smart Campus technologies, digital governance, sustainability-oriented education, environmental management, and campus infrastructure modernization. The results indicate substantial progress across multiple dimensions of institutional sustainability, including energy efficiency, digital sustainability governance, renewable energy implementation, sustainable transportation, circular economy practices, and education and research indicators. The findings suggest that Smart Campus technologies and digital monitoring systems can function as core instruments of institutional sustainability management rather than solely as technical infrastructure solutions. The EKTU Green Campus experience provides a practical reference for sustainability transformation in technical universities in Central Asia and other developing regions, while acknowledging that its applicability may depend on local institutional and socio-economic contexts. The study is limited by its single-case design and its reliance on institutional and ranking data, which may affect the generalizability of the findings. Full article

Green exposure in public spaces plays a critical role in promoting mental health and well-being, especially for populations experiencing elevated psychological pressure in academically demanding environments such as university campuses. However, existing research remains largely limited to coarse-scale assessments, often overlooking the nuanced and nonlinear interactions between visual environmental characteristics and restorative quality across diverse spatial typologies. Addressing this gap, this study proposes a fine-scale, data-driven framework that integrates computer vision (CV) with perception-based evaluations using Shandong Jianzhu University as a case study. A semantic segmentation model (Mask2Former) was employed to extract 14 visual environmental elements from 193 campus photographs, while 426 students provided 4260 image-based evaluations of Perceived Restorative Quality (PRQ). Ridge regression optimized through cross-validation was used to identify key environmental correlates of PRQ, K-means clustering was applied to classify Campus Restorative Space (CRS) typologies, and Generalized Propensity Score Matching (GPSM) was employed to examine nonlinear dose–response relationships between sensory dimensions and restorative quality. The results indicate that natural elements, particularly vegetation and water-related features, are generally associated with higher PRQ scores; however, restorative responses do not consistently follow linear patterns. Several sensory dimensions exhibited threshold effects and diminishing marginal returns, suggesting that restorative benefits depend on balanced environmental composition rather than the simple accumulation of green elements. Furthermore, three CRS typologies—Greenery-Dominant Spaces, Open Landscape Spaces, and Courtyard Spaces—demonstrated distinct restorative pathways, indicating that the effects of environmental characteristics vary across spatial contexts. These findings support a context-dependent understanding of restorative environments and highlight the importance of spatial typology, environmental composition, and nonlinear responses in shaping restorative experiences. The study proposes a transferable analytical framework for assessing campus restorative environments and offers practical implications for evidence-based campus planning and design. Full article

Campus sustainability assessments increasingly rely on broad indicator systems, yet their empirical foundation remains heavily dependent on institutional self-reports, administrative records, and checklist-based scoring. These data types capture governance and educational activities effectively. However, they are less suitable for evaluating spatially heterogeneous or temporally dynamic environmental conditions, such as greenspace distribution, land use composition, and thermal environments. Such conditions are difficult to verify through self-reporting alone. Remote sensing and GIS offer potential solutions by providing externally verifiable and cross-campus comparable measurements, but their integration into formal assessment standards remains underdeveloped. The literature reveals three persistent gaps: an evidence gap (over-reliance on self-reports), a spatial gap (limited inclusion of spatially explicit indicators), and a standardization gap (case studies without transferable protocols). These gaps are relevant in China, where diverse campus forms intensify comparability challenges. To address these issues within the Chinese context, this review proposes a conceptual two-level framework. The framework comprises a comprehensive assessment structure covering governance, education, operations, environment, and participation. It is paired with a separate spatial evidence module based on remote sensing and GIS. This framework is a conceptual reference and an operational pathway, rather than an empirically validated or calibrated tool. The framework intends to guide the systematic integration of spatial evidence into future Chinese green campus standards. However, threshold values, weighting schemes, and contextual adjustment factors would require further empirical testing and policy deliberation. Full article

Environmental education is crucial for promoting individual green and low-carbon lifestyles and constructing sustainable societies. However, it is still unclear about the internal mechanism and external boundary conditions of how school environmental education affects university students’ green and low-carbon behavior. From an educational perspective, this paper develops an integrated model of students’ green and low-carbon behavior grounded in social cognitive theory to explore the impacts of two dimensions of school environmental education: teacher-led environmental education and campus low-carbon climate. Structural equation modeling with bootstrapping estimation and moderating effects was performed using a representative sample of 763 respondents from Sichuan Province, China. The results show that teacher-led environmental education not only directly influences students’ green and low-carbon behavior, but also indirectly affects it through mediating effects and serial mediating effects of low-carbon knowledge and attitude. The total indirect mediating effect of teacher-led environmental education is larger than its direct effect. The mediating effect of low-carbon knowledge is 3.3 times that of attitude. Thus, the primary target of teacher-led environmental education is promoting low-carbon knowledge. Moreover, campus low-carbon climate significantly moderates students’ intention–behavior relationship. The higher the low-carbon climate students perceive, the more likely their behavioral intentions are to translate into actual behaviors. Full article

The rapid growth of express packaging waste (EPW) is creating increasing challenges for urban waste management and high-quality resource recovery, while reliable automated sorting remains difficult because of heterogeneous materials, deformable packaging, random stacking, and frequent partial occlusion. This study develops a system-oriented RGB-D sensing framework to improve the reliability of EPW sorting in conveyor-belt recycling operations. A synchronized and spatially registered RGB-D paired dataset, MEPWaste, was constructed containing 4528 paired samples (9056 RGB/depth images) from 10 representative categories of inner and outer packaging under multiple levels of stacking and occlusion. Based on this dataset, a multimodal detection framework was designed to improve feature reliability within each modality, reinforce spatial consistency between modalities, and enhance detection stability in densely stacked scenes with fragmented visible evidence. The proposed framework achieved 89.1% ${mAP}_{50}$ and 69.3% ${mAP}_{50:95}$, exceeding the RGB-only model by 5.3 and 5.8 percentage points, respectively, and the RGB-D baseline by 2.7 and 3.4 percentage points, with recall reaching 85.1%. A closed-loop sensing–inference–execution pipeline integrating synchronized RGB-D acquisition, real-time detection, robotic grasping, and PLC-based control was further implemented to examine engineering feasibility. Overall, the study provides a practical technical pathway for improving EPW sorting reliability and supporting intelligent resource recovery in waste management systems, with potential applicability to green campus management scenarios characterized by high-frequency parcel consumption and concentrated packaging waste generation. Full article

Accurate distance measurement in outdoor environments remains a challenging problem for mobile augmented reality (AR) systems due to sensor noise, environmental variability, and the limitations of single-modality approaches. Existing consumer AR solutions often prioritize usability over metric robustness, leading to performance degradation in large-scale or heterogeneous outdoor scenarios. This work presents EfMAR, an adaptive framework for outdoor mobile AR-based geospatial measurements that integrates multiple sensing modalities through a structured sensor fusion architecture. EfMAR combines visual SLAM, inertial sensing, depth information, and global positioning cues to improve robustness and consistency in distance estimation across diverse outdoor conditions. Beyond implementation, the framework formalizes a reusable architectural model for adaptive multi-sensor fusion, supporting reproducibility and future comparative research. A dedicated dataset is described, comprising 584 unique real-world evaluation instances collected across representative outdoor scenarios. External literature-derived data were utilized strictly as calibration baselines for modeled operational degradation profiles, maintaining methodological transparency. Performance evaluation focuses on analyzing relative behavior, stability, and variability across sensing approaches rather than establishing absolute accuracy benchmarks. Comparative results across multiple distance ranges and environments indicate that hybrid sensor fusion strategies exhibit more stable and consistent performance trends compared to single-modality solutions, particularly in challenging urban contexts. Dispersion analysis further highlights the influence of environmental factors such as lighting conditions and spatial scale on measurement variability. Overall, the results position EfMAR as a flexible and adaptive framework designed to enhance robustness in outdoor AR-based geospatial measurement tasks. By emphasizing consistency, transparency, and architectural generalization, this work contributes a practical foundation for future research and development in mobile AR sensing for real-world outdoor applications. Full article