Search Results (2,473)

This study introduces a comprehensive framework combining building information modeling (BIM), project management body of knowledge (PMBOK), and machine learning (ML) to optimize energy efficiency and reduce environmental impacts in Riyadh’s construction sector. The suggested methodology utilizes BIM for dynamic energy simulations and design visualization, PMBOK for integrating sustainability into project-management processes, and ML for predictive modeling and real-time energy optimization. Implementing an integrated model that incorporates building-management strategies and machine learning for both commercial and residential structures can offer stakeholders a thorough solution for forecasting energy performance and environmental impact. This is particularly essential in arid climates owing to specific conditions and environmental limitations. Using a simulation-based methodology, the framework was evaluated based on two representative case studies: (i) a commercial complex and (ii) a residential building. The neural network (NN), reinforcement learning (RL), and decision tree (DT) were implemented to assess performance in energy prediction and optimization. Results demonstrated notable seasonal energy savings, particularly in spring (15% reduction for commercial buildings) and fall (13% reduction for residential buildings), driven by optimized heating, ventilation, and air conditioning (HVAC) systems, insulation strategies, and window configurations. ML models successfully predicted energy consumption and greenhouse gas (GHG) emissions, enabling targeted mitigation strategies. GHG emissions were reduced by up to 25% in commercial and 20% in residential settings. Among the models, NN achieved the highest predictive accuracy (R² = 0.95), while RL proved effective in adaptive operational control. This study highlights the synergistic potential of BIM, PMBOK, and ML in advancing green project management and sustainable construction. Full article

Urban flooding, intensified by climate change, poses significant threats to sustainable development, necessitating accurate precipitation projections for effective risk management. This study utilized Bayesian Model Averaging (BMA) to optimize CMIP6 multi-model ensemble precipitation projections for Shanghai, integrating Delta statistical downscaling with observational data to enhance spatial accuracy and reduce uncertainty. After downscaling, RMSE values of daily precipitation for individual models range from 10.158 to 12.512, with correlation coefficients between −0.009 and 0.0047. The BMA exhibits an RMSE of 8.105 and a correlation coefficient of 0.056, demonstrating better accuracy compared to individual models. The BMA-weighted projections, coupled with Soil Conservation Service Curve Number (SCS-CN) hydrological model and drainage capacity constraints, reveal spatiotemporal flood risk patterns under Shared Socioeconomic Pathway (SSP) 245 and SSP585 scenarios. Key findings indicate that while SSP245 shows stable extreme precipitation intensity, SSP585 drives substantial increases—particularly for 50-year and 100-year return periods, with late 21st century maximums rising by 24.9% and 32.6%, respectively, compared to mid-century. Spatially, flood risk concentrates in peripheral districts due to higher precipitation exposure and average drainage capacity, contrasting with the lower-risk central urban core. This study establishes a watershed-based risk assessment framework linking climate projections directly to urban drainage planning, proposing differentiated strategies: green infrastructure for runoff reduction in high-risk areas, drainage system integration for vulnerable suburbs, and ecological restoration for coastal zones. This integrated methodology provides a replicable approach for climate-resilient urban flood management, demonstrating that effective adaptation requires scenario-specific spatial targeting. Full article

This research investigates the responsiveness of climate and low-carbon (green) stock returns to oil prices and conventional energy stock returns, focusing on both contemporaneous and causal relationships, during an oil crisis. Two methodologies are used: vector auto-regressive (VAR) for testing the causal relationship, and ordinary least squares (OLS) for investigating the contemporaneous relationship. The main empirical results suggest that green stocks have a bidirectional positive contemporaneous relationship with oil prices and energy stock returns but no significant bidirectional causal relationship. The results reveal that oil prices and energy stock returns play a larger role in contemporaneous than causal relationships with green stock returns. In addition, green stock returns seem to have a stronger positive relationship with energy stock return than oil prices. Full article

The rapid growth of e-commerce has created substantial environmental impacts, driving the need for advanced optimization models to enhance supply chain sustainability. As consumer preferences shift toward environmental responsibility, organizations must adopt robust quantitative methods to reduce ecological footprints while ensuring operational efficiency. This study develops a novel hybrid multi-criteria decision-making (MCDM) model to evaluate and prioritize green suppliers under uncertainty, integrating the rough-Dombi best–worst method (BWM) and an improved Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The proposed model addresses two key challenges: (1) inconsistency in expert judgments through rough set theory and Dombi aggregation operators and (2) ranking instability via an enhanced TOPSIS formulation that mitigates rank reversal. Mathematically, the rough-Dombi BWM leverages interval-valued rough numbers to model subjective expert preferences, while the Dombi operator ensures flexible and precise weight aggregation. The modified TOPSIS incorporates a dynamic distance metric to strengthen ranking robustness. A case study of five e-commerce suppliers validates the model’s effectiveness, with results identifying cost, green competitiveness, and external environmental management as the dominant evaluation dimensions. Key indicators—such as product price, pollution control, and green design—are rigorously prioritized using the proposed framework. Theoretical contributions include (1) a new rough-Dombi fusion for criteria weighting under uncertainty and (2) a stabilized TOPSIS variant with reduced sensitivity to data perturbations. Practically, the model provides e-commerce enterprises with a computationally efficient tool for sustainable supplier selection, enhancing resource allocation and green innovation. This study advances the intersection of uncertainty modeling, operational research, and sustainability analytics, offering scalable methodologies for mathematical decision-making in supply chain contexts. Full article

This study aims to assess the role of headwater systems (HS) in enhancing ecological connectivity and supporting Green Infrastructure in the Centre Region of Portugal. Specifically, it identifies restoration opportunity areas within HS by analysing land-use changes over the past 70 years, modelling land-use scenarios to promote ecological resilience, and evaluating connectivity between HS and Natura 2000 sites. The methodology integrates spatial analysis of historical land-use data with connectivity modelling using least-cost path approaches. Results show substantial transformation in HS areas, notably the expansion of eucalyptus plantations and a decline in agricultural land. Approximately 58% of the HS are identified as requiring restoration, including areas within the Natura 2000 network. The connectivity assessment reveals that HS can function as effective ecological corridors, contributing to improved water regulation, soil conservation, gene flow, and wildfire mitigation. A total of 61 potential ecological linkages between Natura 2000 sites were identified. These findings highlight the strategic importance of integrating HS into regional and national Green Infrastructure planning and supporting the implementation of the EU Biodiversity Strategy for 2030. The study recommends prioritising headwater restoration through multi-scale planning approaches and active involvement of local stakeholders to ensure sustainable land-use management. Full article

Opuntia stricta var. dillenii (OPD) fruits are rich in betalains and phenolic compounds, which are recognized for their potential health-promoting properties. This study focuses on the optimization of pulsed electric field (PEF)-assisted solid–liquid green extraction (SLE) from OPD whole fruit, using response surface methodology (RSM) experimental design to obtain green extracts rich in bioactive compounds. The optimal PEF pre-treatment conditions (electric field strength and energy input) were determined based on the cell disintegration index (Zp), followed by optimizing SLE conditions (temperature, time, and ethanol content). High-performance liquid chromatography (HPLC-DAD-ESI-Qtof) was used to characterize the individual bioactive compound profile of the obtained OPD green extracts. Results showed that optimal PEF pre-treatment conditions were at 10.5 kJ/kg and 5 kV/cm, followed by SLE at 35 °C for 165 min, using water as the solvent. Conventional optimal SLE conducted at 45 °C, 8% ethanol, and 128 min was applied as the control process. The combined PEF-assisted SLE process enhanced total betalain and phenolic compound yields by 61% and 135%, respectively. Antioxidant activities (DPPH by 145%, FRAP by 28%) and anti-inflammatory potential (hyaluronidase inhibition by 19%) were also significantly improved. This study underscores the potential use of a PEF pre-treatment to improve obtaining green extracts rich in bioactive compounds with high biological activities from OPD whole fruits, using water as a solvent. Full article

With increasing wastewater treatment demands and decarbonization goals, synergistic reduction in pollutants and green house gas (GHG) emissions is crucial. High process emissions like N₂O pose significant challenges, yet optimized carbon reduction strategies for conventional plants are lacking. This study developed three mathematical models to quantify the impact of dissolved oxygen (DO), influent salinity, and C/N ratio on direct emissions (CH₄, N₂O) and indirect emissions. Response Surface Methodology (RSM) optimized these factors to minimize GHG emissions under three accounting scenarios: (1) plants with CH₄ reuse systems: salinity = 0.5 g L⁻¹, DO = 3.67 mg L⁻¹, C/N = 12.75; (2) plants focusing solely on direct emissions: salinity = 0.5 g L⁻¹, DO = 3.35 mg L⁻¹, C/N = 3; and (3) plants assessing total emissions: salinity = 0.5 g L⁻¹, DO = 2.5 mg L⁻¹, C/N = 7.18. Key findings indicated that increasing salinity exacerbated greenhouse gas emissions. Elevated DO levels in the aerobic stage reduced N₂O emissions but increased indirect emissions in the A²/O process. Higher C/N ratios promoted anaerobic CH₄ production, but sufficient carbon reduced N₂O by enabling complete heterotrophic denitrification. A 60−day continuous GHG emissions monitoring campaign was conducted at a WWTP to validate the actual emission reductions achievable under the identified optimal control conditions. An analysis and comparison of operational and economic costs were also performed. The findings provide practical insights into sustainable GHG emission management and offer potential solutions to advance the synergistic reduction in GHG emissions and pollutants. Full article

Climate change and rapid urbanization are underscoring the need for urban green spaces that offer a wide range of ecosystem services, which can provide irreplaceable benefits to residents. Cultural services are the ones that affect visitation patterns the most and may be the easiest to influence via investment or neglect. The main aim of this research was to evaluate and cluster the urban green spaces of a Hungarian city, Szeged, based on their potential cultural ecosystem service values, to uncover their investment and management differences. Regarding the methodology, we performed three field observations on each of the selected 19 sample areas, assessing their potential cultural ecosystem services and visitation patterns. The green spaces were evaluated on a total of 36 criteria, which we analysed using principal component analysis, factor analysis, and cluster analysis. As a result of our research, we defined four main urban green space clusters: city centre squares, suburban playgrounds, central parks, and informal green spaces. The differences in their potential cultural ecosystem service values significantly affect their usage patterns and are indicators of investment inequities. Understanding and tackling the uncovered environmental injustices requires a complex assessment of the local urban fabric along with its usage and management practices. Full article

Non-urbanized areas (NUAs), including residual urban green areas, urban parks, agricultural, natural and semi-natural areas, are a fundamental part of the green infrastructure. They are essential in sustaining life and future development, providing a series of ecosystem services (ESs) vital to human society. However, the rapid expansion of urban areas has led to a significant reduction in green spaces. Land-take, reducing available land resources, impacts ecosystem functionality, making it crucial to preserve high-quality territories and the relative ESs provided. In this context, the aim of this study was to evaluate the reduction in ESs due to the land-take having occurred in the last 20 years in the Province of Monza–Brianza, the Italian province with the highest land-take. To achieve this goal, authors used the official data of land use/cover of the Lombardy Region, with three time thresholds (T0: 1999–2003, T1: 2012–2013, T2: 2021) and applied a methodology for ESs assessment originally developed for the municipal level, adapting it to the provincial scale. The study analyzes trends in land-take and land-use changes and assesses how these changes have led to variations in ES provision. The approach involves calculating multiple indices reflecting different ESs provided by NUAs: provisioning ESs coming from agriculture, regulating ESs provided by natural resources, cultural ESs provided by landscape. Findings reveal that urban expansion has decreased provisioning ESs coming from agriculture, while ESs provided by landscape and natural resources have remained stable or improved, respectively. The natural quality index has improved due to conservation policies, despite the high land-take recorded. Anyway, although regional policies have mitigated some negative effects, the overall reduction in green spaces remains a critical issue. Full article

In recent years, the green and low-carbon agenda has gained importance across various economic sectors, including the construction sector, which encompasses both the development of infrastructure and buildings, as well as the production of construction materials. The purpose of this study is to demonstrate that the effectiveness of green integration is achieved by balancing the collective capital of all participants in forming green value chains. The authors propose a methodology for evaluating the integration capital, which enables the assessment of both joint capital accumulation and the resulting added green value. A system of indicators is proposed to evaluate participants in green integration and determine the maturity levels of their integration capital. The methodology is tested using a case study reflecting green integration in the construction sector covering the erection of buildings and the production of building materials. The authors introduce a three-dimensional model (triangular prisms) to visualize the potential and the integration capital of the involved actors. The study’s findings are applicable to scenario modeling, particularly in developing strategic trajectories for participants in green integration. Full article

Fruit pomace, generated as a by-product of juice processing, is a valuable source of bioactive compounds but requires sustainable extraction approaches to enable its valorisation. Supercritical CO₂ extraction (SFE-CO₂) represents a promising green technology due to its efficiency, solvent-free character, and tuneable selectivity. In this study, the response surface methodology (RSM) was applied to evaluate the effects of pressure, temperature, and time on the recovery of fat, protein, and total phenolic compounds (TPCs) from blackcurrant (Ribes nigrum) and redcurrant (Ribes rubrum) pomace subjected to conventional- and freeze-drying. The highest protein content (14.5%) was obtained in freeze-dried blackcurrant at 400 bar, 60 min, and 30 °C, while the maximum TPCs (24.60 mg GAE/g d.w.) was reached at 500 bar, 60 min, and 40 °C. The redcurrant samples consistently showed lower extractable values across all the responses. Pressure and time were identified as the most influential process variables, enhancing the solvent density and mass transfer during extraction. These results demonstrate that both the drying pre-treatment and raw material type significantly affect the SFE efficiency and confirm the potential of optimised SFE-CO₂ as a viable strategy for converting fruit pomace into functional ingredients for food, nutraceutical, and cosmetic applications. Full article

To address the issues of poor thermal stability, inadequate salt tolerance, and environmental risks in conventional gel systems for the development of high-temperature, high-salinity heterogeneous reservoirs, a triple-synergy gel system comprising anionic polyacrylamide (APAM), polyethyleneimine (PEI), and phenolic resin (SMP) was developed in this study. The optimal synthesis parameters—APAM of 180 mg/L, PEI:SMP = 3:1, salinity of 150,000 ppm, and temperature of 110 °C—were determined via response surface methodology, and a time–viscosity model was established. Compared with existing binary systems, the proposed gel exhibited a mass retention rate of 93.48% at 110 °C, a uniform porous structure (pore size of 2–8 μm), and structural stability under high salinity (150,000 ppm). Nuclear magnetic resonance displacement tests showed that the utilization efficiency of crude oil in 0.1–1 μm micropores increased to 21.32%. Parallel dual-core flooding experiments further confirmed the selective plugging capability in heterogeneous systems with a permeability contrast of 10:1: The high-permeability layer (500 mD) achieved a plugging rate of 98.7%, while the recovery factor of the low-permeability layer increased by 13.6%. This gel system provides a green and efficient profile control solution for deep, high-temperature, high-salinity reservoirs. Full article

While technologies like renewable energy and low-carbon transportation are known to mitigate carbon emissions from urban expansion, achieving carbon neutrality during this process remains a critical unresolved challenge. This issue is particularly pressing for developing countries striving to balance urbanization with carbon reduction. Taking Qionglai City as a case study, this study simulated the territorial spatial functional patterns (TSFPs) and carbon emission distribution for 2025 and 2030. Based on the key drivers of carbon emissions from urban expansion identified through the Geographical and Temporal Weighted Regression (GTWR) model, carbon-neutral pathways were designed for two scenarios: urban expansion scenarios under historical evolution patterns (Scenario I) and urban expansion scenarios optimized under carbon neutrality targets (Scenario II). The results indicate that (1) urban space is projected to expand from 6094.73 hm² in 2020 to 6249.77 hm² in 2025 and 6385.75 hm² in 2030; (2) total carbon emissions are forecasted to reach 1.25 × 10⁶ t (metric tons) and 1.40 × 10⁶ t in 2025 and 2030, respectively, exhibiting a spatial pattern of “high in the central-eastern regions, low in the west”; (3) GDP, Net Primary Productivity (NPP), and the number of fuel vehicles are the dominant drivers of carbon emissions from urban expansion; and (4) a four-pronged strategy, optimizing urban green space vegetation types, replacing fuel vehicles with new energy vehicles, controlling carbon emissions per GDP, and purchasing carbon credits, proves effective. Scenario II presents the optimal pathway: carbon neutrality in the expansion zone can be achieved by 2025 using the first three measures (e.g., optimizing 66.73 hm² of green space, replacing 800 fuel vehicles, and maintaining emissions at 0.21 t/10⁴ CNY per GDP). By 2030, carbon neutrality can be achieved by implementing all four measures (e.g., optimizing 67.57 hm² of green space, replacing 1470 fuel vehicles, and achieving 0.15 t/10⁴ CNY per GDP). This study provides a methodological basis for local governments to promote low-carbon urban development and offers practical insights for developing nations to reconcile urban expansion with carbon neutrality goals. Full article

The Zigui Basin, located in the Three Gorges Reservoir Area, has developed numerous landslides due to its interlayering of sandstone and mudstone, geological structure, and reservoir operations. This study identifies a fourth type of landslide failure mode: an oblique-dip slope wedge (OdSW) landslide, based on the Wanshuitian landslide. Following four heavy rainfall events from 3 to 13 July 2024, this landslide exhibited significant deformation on the 17th and was completely destroyed within 40 min. The dimensions of the landslide were 350 m in length, 160 m in width, and 20 m in thickness, with a volume estimated at 8.0 × 10⁵ m³. The characteristics of landslide deformation and the changes in moisture content within the shallow slide body were ascertained using unmanned aerial vehicles, moisture meters, and mobile phone photography. The landslide was identified to have occurred within the weathered residual layer of mudstone, situated between two sandstone layers, with the eastern boundary defined by an inclined rock layer. Upon transitioning into the accelerated deformation stage, the landslide initially exhibited uniform overall sliding deformation, culminating in accelerated deformation destruction. The dip structure created terrain disparities, resulting in a step-like terrain on the left bank and gentler slopes on the right bank, with interbedded soil and rock in a shallow layer, because the interlayered soft and hard geological conditions caused varied weathering and erosion patterns on the riverbank slopes. The interbedded weak–hard stratum layer fostered the development of the oblique-dip slope wedge landslide. Based on the improved Green–Ampt model, we developed a stability prediction methodology for an oblique-dip slope wedge landslide and determined the rainfall infiltration depth threshold of the Wanshuitian landslide (9.8 m). This study aimed not merely to sharpen the evolution mechanism and stability prediction of the Wanshuitian landslide but also to formulate more effective landslide-monitoring strategies and emergency management measures. Full article

The increasing recognition of historical emissions and uneven financial capacities among developed and developing nations has highlighted the need to look for equity and fairness in global climate action. This study aims to present a revised method that enables mapping the current state of fairness in the global energy transition, addressing both the contribution to the climate crisis and the burden that different countries face in coping with the climate disasters resulting from it. For this purpose, we revise various methods and indices used to measure the progress of energy transition efforts, as well as existing methodologies to appraise the responsibility for climate change and the resulting financial capacity. We propose changes to the existing methods to allow for a clearer analysis of the fairness of the global energy transition. An exemplary use of the proposed modified methodology is applied to six countries that represent developing and developed countries using publicly available data from renowned sources such as IRENA, EM-DAT, and the World Bank, showing the applicability of the method. The main trends in the results highlight the added value of the proposed method. The progress in the energy transition is evaluated in terms of fairness as a transition index by taking into account historical responsibility and financial capacity. Damage from climate-induced disasters and contribution towards climate financing are added as contextual considerations. The country’s historical emissions, GDP, NDC, financial costs of climate-induced disaster, and financing from the Green Climate Fund are used as the basis for the analysis. The findings underscore the differences in energy transition achievement, as well as the differences in pledged and deposited funds among various types of countries. The results demonstrate a disproportionate burden experienced by lower-income nations and depict the ongoing challenges in translating principles of “common but differentiated responsibilities” into concrete outcomes. This study provides an open-source and data-driven perspective that highlights the need for change in global policy discourse and also advocates for the creation of more nuanced, just, and effective approaches to accelerate the clean energy transition worldwide. Full article