Search Results (1,074)

Polyurethane (PU) mixtures are a promising high-strength, rapid-curing alternative to conventional asphalt, but their widespread application is hindered by slow curing rates and sensitivity to ambient moisture. To address these limitations, this study systematically evaluated the efficacy of three additives—lignin-based fiber, Glauber’s salt, and green vitriol—in regulating the curing behavior and performance of PU mixtures. Marshall stability, volumetric properties, and moisture resistance were measured under both outdoor and controlled laboratory curing conditions. Lignin fiber uniformly accelerates early-stage curing by enhancing moisture distribution via capillary action. Glauber’s salt releases crystalline water, drastically boosting early-age strength (by 162.4% after 2 days) but at the cost of an increased air void content (up to 8.1%) and reduced long-term water stability (residual stability <80%). Green vitriol acts through Fe²⁺ catalysis and crystalline water release, with its effectiveness being highly temperature- and delay-time-dependent. Combining fiber with Glauber’s salt yields the highest early strength but the shortest construction window (<1 h) and the most severe volumetric deterioration beyond the optimal delay time. All mixtures achieved high ultimate strength after sufficient curing (7 days), but the improvement varied significantly with additive type—ranging from 52.2% (fiber alone) to 162.4% (Glauber’s salt alone). Moreover, even under ideal curing, incomplete –NCO conversion persisted, indicating intrinsic cross-linking limitations. The residual stability of all mixtures fell below the 80% specification for conventional asphalt, suggesting that this metric alone is insufficient for assessing the moisture resistance of high-strength PU mixtures. This study demonstrates that while additives significantly enhance early-age performance, their application requires carefully optimized dosage, delay time, and temperature control to balance early strength gains with long-term volumetric integrity and durability. The findings provide revised evaluation metrics and practical guidelines for implementing PU mixtures in rapid pavement construction and repair. Full article

The electrification of public transport, particularly Bus Rapid Transits (BRT), is a significant step toward achieving sustainable urban mobility and reducing dependency on fossil fuels. However, rapid adoption of Electric Vehicles Smart Charging (EVSC) infrastructure presents grid stability, economic and environmental concerns. The rising demand for electric cars, particularly in developing nations such as Nigeria, highlights the urgent need for a sustainable hybrid renewable energy charging infrastructure for BRT systems. This study presents a techno-economic assessment of an off-grid hybrid systems that use photovoltaic (PV), wind turbines (WTs), hydrogen (H₂), fuel cell (FC) and battery technologies to power Electric Vehicles Smart Charging within Bus Rapid Transits networks. The Lagos BRT charging system at City Mall Station (CMS) serves as a case study, with hourly renewable resources obtained from National Aeronautics and Space Administration database (NASA). Using the HOMER pro-optimization tool, a multi-criteria analysis is performed to evaluate system viability, with special focus on key metrics such as levelized cost of energy (LCOE), net present cost (NPC), renewable energy fraction (REF), and greenhouse gas (GHG) emissions. The simulation results demonstrate that the hybrid PV/wind/FC/battery configuration is exceptionally economical, with an LCOE as low as $0.222/kWh, $2.03M NPC, 51.3% REF, and 159,209 kg of carbon dioxide emissions per year compared to grid-dependent charging. The study shows that integrated renewable-hydrogen systems are not only financially feasible, but also provide significant insights for policymakers, transportation authorities, and energy planners seeking to accelerate the transition to green public transportation infrastructure through innovative hybrid energy schemes. Full article

Urban drought pressure is increasing the operational risk and cost of maintaining municipal green infrastructure. Irrigation is still widely managed through fixed routines and fragmented information. To address this challenge, the study develops an integrated operational analysis by combining water consumption records, maintenance data and a GIS inventory for twenty municipal green spaces. System characterisation and performance screening were carried out using hourly meter readings to distinguish typical scheduled irrigation peaks from non-standard consumption patterns. To move from monitoring to control, irrigation needs were estimated using evapotranspiration (ET₀) and a garden-coefficient logic adapted to urban planting conditions and compared with measured consumption. The comparison indicates a potential reduction of 29–61% through improved scheduling and system adjustment. Based on the diagnosis, technical intervention scenarios were defined and assessed using techno-economic metrics, including ground-cover redesign and Mediterranean-adapted planting strategies. To support implementation, options were organised into intervention priorities using a multicriteria tool that balances water savings, costs and feasibility under municipal operations. Coimbra, Portugal is used as a case study, and a pilot application in a city garden, supported by 797 user surveys, clarifies practical constraints for scaling beyond isolated pilots. Turf-free scenarios indicate a 53.4% reduction in water use and a 60.5% reduction in operational costs, with a payback period below three years. The results highlight the potential of data-driven irrigation management to support more resilient, cost-effective and water-efficient municipal green infrastructure across diverse urban contexts. Full article

In the context of urban distribution, given the complexity of express delivery and the variability of distribution conditions, vehicle routing problems with time-dependent characteristics have received increasing attention. This study incorporates a cross-period travel time estimation method for road segments that accounts for temporal and weather-dependent variations in vehicle speed. Building upon this foundation, this study establishes an multi-objective optimization model for the green vehicle routing problem that systematically incorporates intricate constraints, including time-varing vehicle speed, fuel consumption, carbon emissions, and customer servive time windows. This model aims to achieve three primary objectives: (1) minimizing the fleet size, (2) minimizing the overall delivery expenses, which include fuel consumption and carbon emissions, and (3) maximizing the average customer satisfaction. To solve this model, we develop an improved Non-Dominated Sorting Genetic Algorithm III (INSGA-III). To effectively prevent the algorithm from becoming trapped in local optima, we propose a dual-criteria selection mechanism. Meanwhile, we introduce a destroy-and-repair variable neighborhood search strategy to enhance the algorithm’s optimization capability under complex constraints. Experimental evaluations conducted on Solomon benchmark instances as well as real-world case studies indicate that the proposed INSGA-III algorithm surpasses widely utilized multi-objective optimization methods across all assessed performance metrics. This highlights the significant potential of the presented INSGA-III algorithm for practical applications in urban delivery scenarios, which is closely linked to the sustainable development of cities. Full article

Teas processed from specialty-colored tea leaves possess distinctive quality profiles shaped by their volatile and non-volatile compounds, which serve as critical metrics for evaluating tea cultivars. In this study, we comprehensively characterized the quality attributes of flat green teas produced from three tea cultivars—green-leaved ‘FDDB’, yellow-leaved ‘ZH2’, and purple-leaved ‘ZJ’—using an integrated analytical approach including sensory evaluation, widely targeted metabolomics, GC-E-nose, and HS-SPME-GC-MS. Sensory evaluation revealed distinct sensory characteristics among teas processed from the three cultivars with different leaf colors. GC-E-nose analysis further confirmed that the aroma profiles of these tea samples could be clearly distinguished based on leaf color. Metabolomic analysis identified a total of 2050 non-volatile compounds, among which 18 amino acids, 5 phenolic acids, and 4 flavonoids were pinpointed as key contributors to the unique taste profiles of infusions from ZH2 and ZJ teas. Additionally, a total of 1100 volatile compounds were detected, with 94, 75, and 90 key aroma-active compounds identified in FDDB, ZH2, and ZJ teas, respectively. Collectively, in this study, systematic analysis revealed significant differences in both volatile and non-volatile chemical compositions across the three tea cultivars. These findings provide a scientific foundation for understanding the processing suitability and quality formation mechanisms of tea cultivars with distinct leaf colors. Full article

With the rapid growth of the digital economy, the application of artificial intelligence (AI) technology has injected new momentum into persistent green innovation. Using data on Chinese A-share listed companies from 2010 to 2023, this article aims to investigate whether senior executives’ adoption of AI technology influences companies’ persistent green innovation and to identify the specific mechanisms underlying this relationship. To improve measurement accuracy, this paper employs the BERT model to conduct an in-depth analysis of corporate annual report texts to construct an executive AI adoption metric. The findings reveal that executive AI adoption significantly promotes corporate persistent green innovation, and this effect is primarily achieved through enhanced data factor allocation capabilities. Moreover, strategic agility positively moderates the relationship between executive AI adoption and corporate persistent green innovation. Specifically, the higher the level of strategic agility, the stronger the mediating role of data factor allocation in the relationship between executive AI adoption and corporate persistent green innovation. In particular, executive AI adoption plays a more significant role in fostering persistent green innovation among firms with higher total factor productivity and those facing intense market competition. Full article

Climate change necessitates a global transition toward green and low-carbon development, underscoring the critical importance of energy efficiency. Buildings account for a substantial portion of urban energy consumption and carbon emissions, with central air-conditioning systems representing the largest energy-consuming component. This study focuses on optimizing equipment selection—including chillers, pumps, and cooling towers—for the refrigeration plant of a commercial complex in Xiamen. Following theoretical optimization, the operational performance of the implemented system was empirically analyzed using long-term monitoring data from 2024 to 2025. The results demonstrate an energy efficiency ratio (EER) of 5.44 in 2024 and 5.28 in 2025, surpassing the Grade I efficiency threshold (5.2) stipulated by the Chinese standard T/CRAAS 1039-2023. Monthly EER values consistently remained above 5.06 throughout the cooling season. Detailed performance analysis of individual equipment further confirmed that actual operational performance of chillers, pumps, and cooling towers closely matched or even exceeded rated performance metrics, with chiller efficiency deviations controlled within 5%. This study integrates optimized equipment selection at the design stage with empirical performance analysis based on actual operation, providing a validated approach for improving the energy efficiency of refrigeration plants in commercial buildings and offering valuable references for the revision of relevant energy efficiency standards. Full article

Urban blue-green spaces play an important role in mitigating thermal environmental stress, yet their long-term configuration and relative thermal environmental benefits remain insufficiently understood at the metropolitan scale. This study examined Beijing from 2000 to 2020 by integrating Landsat time-series imagery, land-cover data, landscape metrics, land surface temperature retrieval, Geodetector analysis, and a configuration-oriented Blue-Green Environmental Benefit Index (BGEBI). The results showed that Beijing’s blue-green spaces experienced three stages: rapid decline during 2000–2003, gradual recovery during 2004–2012, and rapid expansion during 2013–2020. Spatially, low-temperature zones were mainly concentrated in the northwestern ecological conservation areas, whereas high-temperature zones were mainly distributed in the southeastern core and plain areas. Green-space landscape indicators, especially forest-related metrics, showed stronger explanatory associations with LST spatial heterogeneity than most wetland-related indicators at the metropolitan scale. The BGEBI results indicated an overall increase in relative thermal environmental benefits from 2000 to 2020, with high-value areas mainly located in the northwestern and central-western mountainous regions and low-value areas mainly distributed in southeastern urbanized areas. These findings suggest that blue-green space planning in high-density megacities should pay greater attention to landscape configuration, spatial connectivity, and scale-sensitive management. The proposed BGEBI framework provides a relative spatial-prioritization tool for identifying areas where blue-green configuration optimization may support thermal-environment improvement. Full article

The present study aimed to establish a robustness modeling framework for the determination of cannabidiol (CBD) and Δ⁹-tetrahydrocannabinol (THC) in cannabis extract using a multivariate approach. A two-level full factorial design was implemented to examine four critical analytical factors, including methanol concentration (80–85% v/v), flow rate (0.8–1.2 mL/min), column temperature (23–27 °C), and detection wavelength (208–212 nm). Seven analytical responses for each compound were assessed, including peak area, retention time, resolution, asymmetry factor, number of theoretical plates, capacity factor, and peak area difference relative to the reference method. Statistical analysis demonstrated that both main effects and interaction effects significantly influenced the measured responses. Design space construction was performed based on predefined acceptance criteria to ensure method robustness: resolution > 1.5, asymmetry < 1.5, number of theoretical plates > 2000, capacity factor > 2, and peak area difference within −5% to 5%. Predictive performance of the developed models was verified by comparing predicted and experimental results. Good agreement was observed under most conditions, whereas deviation was noted for THC quantification at a detection wavelength of 212 nm. Furthermore, CBD and THC contents determined under three selected operating conditions within the established design space were statistically comparable to those obtained using the reference method, except for the condition employing 212 nm detection. The Analytical GREEnness Metric Approach (AGREE) assessment indicated moderate greenness performance of the analytical procedure. Overall, the multivariate two-level full factorial design proved to be an effective tool for robustness modeling of the HPLC method for simultaneous quantification of CBD and THC. Full article

Transition finance has emerged as a critical instrument for facilitating brown firms’ sustainable transformation, yet its heterogeneous effects across different stages of corporate development remain underexplored. This study develops a novel green value metric using a regression coefficient weighting approach and employs a difference-in-differences (DID) model to investigate how transition finance influences corporate green value through innovation persistency, based on a sample of Chinese listed brown firms from 2011 to 2022. The empirical results show that transition finance is significantly associated with an enhancement in corporate green value. Specifically, brown firms receiving transition finance exhibit a 61.6% higher green value than non-recipient firms. This effect is most pronounced during the maturity stage, where the additional green value premium for mature-stage firms is approximately 15.3% higher than for decline-stage firms. Mechanism analysis reveals that innovation persistency serves as the fundamental channel; mature-stage firms exhibit superior capacity to sustain consistent R&D investments and translate these persistent efforts into market-recognized green value premiums. These findings provide actionable insights for policymakers: transition finance frameworks should incorporate lifecycle-sensitive mechanisms rather than applying uniform standards, and incentive structures should prioritize sustained innovation commitment over one-off technological upgrades to maximize long-term sustainability outcomes. Full article

Climate finance has become a major means of fostering sustainability in the construction industry, which encounters higher pressures to mitigate its environmental footprint without sacrificing economic viability. In line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this study employs a hybrid approach, integrating a systematic literature review (SLR) and bibliometric analysis, to provide a comprehensive overview of the role and mechanisms of climate finance for sustainable practices in the construction industry. From 2019 to 2025, 176 papers were identified in the Scopus (73) and Web of Science (103) databases. The SLR enables both systematic collection and qualitative analysis of financial instruments, policy frameworks, and sustainability performance metrics, and bibliometric analysis provides a report of publication behavior, geographic distribution, and thematic network. Findings suggest intense clustering of research in countries, with India, China, and the United States as key focus areas, and that construction firms predominantly accessed climate finance on instruments including green bonds, sustainability-linked loans, public–private partnerships, and multilateral climate funds. Sustainability performance is commonly assessed using indicators such as carbon emissions, energy efficiency, lifecycle costs, and environmental, social, and governance (ESG) metrics. The findings also highlight the critical role of public policies, such as green procurement, carbon pricing, and fiscal incentives, in enabling sustainable construction practices. From a theoretical perspective, this study contributes to the understanding of how financial mechanisms, policy frameworks, and sustainability metrics interact to drive sectoral transformation. Future research should focus on standardizing sustainability metrics, evaluating financing impacts, and expanding studies in emerging economies. Full article

Traditional urban landscape evaluations have primarily relied on either objective spatial metrics, such as the Green View Index (GVI), or subjective human surveys, often failing to capture the complex mechanisms of human environmental perception. This study proposes a novel Explainable Artificial Intelligence (XAI) framework that integrates objective physical configuration with subjective cognitive assessment to predict human landscape preference. Utilizing 159 urban landscape images, we extracted physical features via semantic segmentation (SegFormer) and psychological perceptions via a zero-shot vision-language model (CLIP). Our hybrid Random Forest model successfully bridged these dimensions, achieving moderate yet promising predictive performance (Rsquare = 0.442). SHAP (Shapley Additive exPlanations) analysis revealed that psychological perceptions—specifically Safety (0.104), Fascination (0.096), and Tranquility (0.080)—outperformed traditional objective metrics like GVI (0.067) in determining overall preference, while sub-model interpretation linked these psychological responses to specific physical elements such as buildings, sky openness, low vegetation, and water bodies. The findings suggest that urban green space design should move beyond maximizing greenery quantity and instead prioritize spatial compositions that induce psychological security, visual interest, and restoration. The proposed framework offers a scalable and interpretable tool for human-centered landscape assessment, while acknowledging limitations related to sample size, cultural generalizability, pretrained model bias, and reliance on static two-dimensional imagery. Full article

Plant diversity is a key indicator of ecosystem structure, function, and restoration status, yet its rapid assessment remains challenging in sandy ecosystems where vegetation is sparse, spatially heterogeneous, and strongly affected by exposed soil backgrounds. In such environments, conventional greenness-based spectral indices may not adequately capture species-level variation because plant communities are controlled not only by photosynthetic biomass but also by soil moisture, micro-topography, and dune-related habitat heterogeneity. This study evaluated the potential of Sentinel-2-derived spectral indices for estimating plant α-diversity in the Hunshandak Sandland, northern China. Based on field observations from 888 plots collected during 2017–2024, four α-diversity metrics—species richness, Shannon–Wiener index, Simpson index, and Pielou evenness index—were calculated and compared with 21 spectral indices using correlation analysis, partial least squares regression (PLSR), and random forest (RF) models. The results showed that model performance varied substantially among diversity metrics. Species richness was estimated with the highest accuracy, whereas Shannon–Wiener, Simpson, and Pielou indices showed weaker predictability, indicating that remotely sensed spectral indices were more sensitive to species number than to abundance distribution and evenness. Moisture- and soil-background-sensitive indices, including the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Bare Soil Index (BSI/BRI), and Chlorophyll Absorption Ratio Index (CARI), showed relatively stable relationships with plant diversity across different vegetation gradients. Although the overall explanatory power was moderate rather than high, the results demonstrate the practical value of Sentinel-2 spectral indices for regional screening of plant diversity patterns in sandy ecosystems. This study provides empirical evidence for biodiversity monitoring and ecological restoration assessment in semi-arid sandy landscapes and highlights the need to integrate environmental covariates, multi-source remote sensing, and phenological information in future studies. Full article

Existing research on the landscape patterns of urban parks and green spaces demonstrates a disproportionate focus across tiers within China’s urban hierarchy. Numerous studies have concentrated on economically developed first-tier cities, such as Beijing, Shanghai, and Guangzhou. In contrast, medium-to-large non-first-tier cities, especially provincial capitals and emerging cities within the first- and second tiers, have been relatively understudied, although they have received increasing attention in recent years. This bias extends regionally, with studies predominantly examining cities in the more developed central and eastern regions, while less-developed areas and lower-tier cities receive significantly less attention. This study tracks changes in park quantity, spatial concentration, patch structure and driver associations at three planning-related time points. Shenyang provides a distinct cold-region and old industrial city case, shaped by long winters, industrial renewal and outward urban growth. Furthermore, to inform park and green-space planning in Northeast China’s cold-climate cities, exemplified here by Shenyang, a major metropolis with a monsoon-influenced humid continental climate (Köppen Dwa), long cold winters, and relatively short warm summers, we document a shift in park distribution from the urban core to peripheral areas. Based on park vector layers reconstructed from planning documents, remote sensing interpretation and field verification, this study combined spatial analysis, landscape metric calculation and driver-association modeling. ArcGIS Pro was used to identify changes in distribution centers, directional extension and local clustering; FRAGSTATS 4.2 was used to calculate park landscape metrics; and SIMCA-P 14.1 was used to examine the statistical associations between selected landscape indicators and potential driving variables. The results show that the number and total area of parks in central Shenyang increased substantially from 2000 to 2024. Spatially, park distribution became less concentrated in the traditional inner city, while new clusters gradually appeared in peripheral districts and newly developed urban areas. The old urban core remained important, but its dominance weakened as park provision expanded outward. The landscape metric results further indicate that park expansion was accompanied by more irregular patch forms, stronger fragmentation and declining structural continuity. The driver association analysis suggests that climate conditions, population change, industrial restructuring, real estate investment, road construction and urban greening policies were related to different aspects of park landscape change. These associations should be interpreted as statistical relationships rather than direct causal effects. Overall, this study clarifies the spatial restructuring of park green spaces in a cold-region old industrial city and provides planning evidence for improving park connectivity, coordinating green space expansion with urban construction and supporting sustainable park system development in Northeast China. Full article

In medicine and dentistry, bone-loss treatment often uses hydroxyapatite combined with collagen membranes. The biocompatibility of these biomaterials depends on their composition and physical/mechanical properties. In this study, a graft composed of synthetic hydroxyapatite nanoparticle (Blue Bone^®) and a bovine type I collagen membrane (Green Membrane Perio^®) was developed compared with commercial Bio-Oss^® graft and Mucograft^® membrane. The materials were characterized by roughness, wettability, tensile testing, DSC, SEM, and TEM. In vivo, temporoparietal bone defects were created in 40 Wistar rats divided into five groups (n = 8): sham (no biomaterial); Bio-Oss^®; Bio-Oss^® + Mucograft^®; Blue-Bone^®; and Blue-Bone^® + Green Membrane Perio^®. Immunohistochemistry showed Green Membrane Perio^® was made of thin, well-organized type I collagen fibers and was free of contaminants. Immunohistochemistry, histology, and immunohistochemical analyses indicated that Blue Bone^® and Green Membrane Perio^® were biocompatible and supported tissue regeneration. The Blue Bone^® groups demonstrated higher collagen content than the Bio-Oss^® + Mucograft^® group. Quantitative and qualitative outcomes included morphological, thermal, mechanical, and surface property measurements, as well as cellular compatibility testing. The results showed comparable wettability and surface roughness, adequate membrane tensile strength, osteoconductive nanoparticle morphology, no adverse inflammatory reactions, and similar new bone formation metrics compared with controls. In conclusion, the combination of synthetic hydroxyapatite nanoparticles (Blue Bone^®) and a bovine type I collagen membrane (Green Membrane Perio^®) showed good performance when compared to established products and was considered safe and biocompatible for bone repair applications. Full article