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Sustainability

Sustainability is an international, peer-reviewed, open-access journal on environmental, cultural, economic, and social sustainability of human beings, published semimonthly online by MDPI. The Canadian Urban Transit Research & Innovation Consortium (CUTRIC), International Council for Research and Innovation in Building and Construction (CIB) and Urban Land Institute (ULI) are affiliated with Sustainability and their members receive discounts on the article processing charges.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, SCIE and SSCI (Web of Science), GEOBASE, GeoRef, Inspec, RePEc, CAPlus / SciFinder, and other databases.
Journal Rank: JCR - Q2 (Environmental Studies) / CiteScore - Q1 (Geography, Planning and Development)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 17.9 days after submission; acceptance to publication is undertaken in 3.6 days (median values for papers published in this journal in the second half of 2025).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Testimonials: See what our editors and authors say about Sustainability.
Companion journals for Sustainability include: World, Sustainable Chemistry, Conservation, Future Transportation, Architecture, Standards, Merits, Bioresources and Bioproducts, Accounting and Auditing and Environmental Remediation.
Journal Cluster of Environmental Science: Sustainability, Land, Clean Technologies, Environments, Nitrogen, Recycling, Urban Science, Safety, Air, Waste, Aerobiology and Toxics.

Impact Factor: 3.3 (2024); 5-Year Impact Factor: 3.6 (2024)

Imprint Information Journal Flyer Open Access ISSN: 2071-1050

Latest Articles

25 pages, 757 KB

Open AccessSystematic Review

Emerging Contaminants in Water Resources: Monitoring Gaps, Treatment Limitations and Governance Challenges with Insights from Portugal

by Pedro Esperanço, Teresa Leal, André Almeida, António Canatário Duarte, Luísa Cruz-Lopes, José Manuel Gonçalves and Margarida Oliveira

Sustainability 2026, 18(10), 5086; https://doi.org/10.3390/su18105086 (registering DOI) - 18 May 2026

This study provides a comprehensive overview of emerging contaminants in water resources. It includes a global perspective with specific insights from Portugal. Following PRISMA 2020 guidelines, peer-reviewed studies published between 2020 and 2025 were critically assessed to identify patterns of contamination, monitoring gaps [...] Read more.

This study provides a comprehensive overview of emerging contaminants in water resources. It includes a global perspective with specific insights from Portugal. Following PRISMA 2020 guidelines, peer-reviewed studies published between 2020 and 2025 were critically assessed to identify patterns of contamination, monitoring gaps and technological readiness levels. Results indicate frequently detected emerging contaminants including pesticides, antibiotics and antidepressants in surface water, groundwater and wastewater systems. Advanced analytical methods, particularly liquid chromatography coupled with high-resolution mass spectrometry, stands out as the main detection technique, allowing the identification of trace levels of contaminants. These techniques also support the identification of pollution patterns associated with agriculture, urban and industrial effluents. However, significant asymmetries persist between international and Portuguese research. Particularly evident in systematic monitoring networks and integrated risk assessment approaches. Conventional water/wastewater treatment plants show limited removal efficiency, while advanced oxidation processes, adsorption technologies and microalgae-based systems demonstrate promising but variable performance depending on scale and operational maturity. The findings highlight gaps between scientific advances and regulatory implementation, emphasizing the need for strengthened monitoring frameworks and technology scale-up strategies. They also call for improved integration between science, governance, and sustainability policies to ensure resilient water resource management in line with the Sustainable Development Goals. Full article

(This article belongs to the Special Issue Towards Sustainable Wastewater Solutions: Innovations, Challenges, and Economic Opportunities)

20 pages, 776 KB

Open AccessArticle

Application of the Quantitative Strategic Planning Matrix in Evaluating Startup Development Strategies

by Aija Vonoga, Anda Zvaigzne, Inta Kotāne, Aija Čerpinska, Baiba Mistre and Nina Wieda

Sustainability 2026, 18(10), 5085; https://doi.org/10.3390/su18105085 (registering DOI) - 18 May 2026

At the outset, startups need to choose a strategy that will enable them to operate in the long term. A proper strategy for startups ensures the development and overall viability of their products or services. The present research was conducted to identify the [...] Read more.

At the outset, startups need to choose a strategy that will enable them to operate in the long term. A proper strategy for startups ensures the development and overall viability of their products or services. The present research was conducted to identify the most suitable development strategies for startups in the Baltic States. Accordingly, the research aims to evaluate alternative startup strategies, considering external and internal factors. Research methods: monographic, SWOT analysis, and the quantitative strategic planning matrix (QSPM). Experts were selected using the targeted sampling method, based on the following selection criteria: experience in startup management or active involvement in the startup ecosystem in the Baltic States. The research involved 12 experts from Latvia, Lithuania, and Estonia. The research was conducted between November 2025 and January 2026. The geographical coverage was Latvia, Lithuania, and Estonia. First limitation of the research was that it provided insight into the five proposed startup strategies for the Baltic States, while other alternative strategies were not considered, given the limited scope and the need for in-depth analysis. The second limitation was that the sample includes 12 experts, including representatives from all three Baltic ecosystems, but they are not in the same numerical ratio. The main conclusion was that the intensive strategy was the most significant one. The second most important alternative strategy was the financial strategy. The least important alternative strategy was the sustainability strategy. The results showed that startups in the Baltic States focused on intensive market entry and product development, as well as strategic financial management, to develop in the long term. Full article

27 pages, 6506 KB

Open AccessArticle

Spatiotemporal Dynamics of Wetland Landscape Pattern and Its Driving Mechanisms in the Poyang Lake Region (2000–2020)

by Xiaoyan Duan, Yiwei Jin, Hong Xu and Minghui He

Sustainability 2026, 18(10), 5084; https://doi.org/10.3390/su18105084 (registering DOI) - 18 May 2026

Poyang Lake represents China’s largest freshwater wetland. The wetland landscape has undergone substantial changes driven by climate change and intensive human activities. Nevertheless, long-term classified analyses of wetland evolution and quantitative assessments of its driving factors remain scarce in the region. Based on [...] Read more.

Poyang Lake represents China’s largest freshwater wetland. The wetland landscape has undergone substantial changes driven by climate change and intensive human activities. Nevertheless, long-term classified analyses of wetland evolution and quantitative assessments of its driving factors remain scarce in the region. Based on 21 Landsat images from 2000 to 2020, this study systematically examined the spatiotemporal dynamics of the wetland landscape. Analyses incorporated land-use dynamic degree, landscape metrics, transfer matrices, and standard deviational ellipses, with key driving forces identified via Pearson correlation and structural equation modeling. Results indicate a 3029.63 km² reduction in wetland area, exhibiting contrasting trends between natural and artificial wetlands. The wetland centroid shifted 7.4 km southwestward. Connectivity of lake increased and fragmentation declined, whereas paddy field fragmentation intensified. Wetland evolution was predominantly driven by socioeconomic factors, whereas climate primarily influenced natural wetlands. The study elucidates the coupled effects of anthropogenic and natural factors, offering insights into wetland restoration and ecological security in the middle and lower Yangtze River. The findings suggest prioritizing natural wetland connectivity, controlling wetland-to-non-wetland conversion, and incorporating long-term remote-sensing monitoring into regional wetland restoration planning. Full article

(This article belongs to the Section Environmental Sustainability and Applications)

33 pages, 7890 KB

Open AccessArticle

Real-Time Small Floating Object Detection from Dynamic Water Surfaces Using YOLO11-MCN for Sustainable Aquatic Monitoring

by Anchuan Wang, Ling Qin, Qing Huang and Qun Zou

Sustainability 2026, 18(10), 5083; https://doi.org/10.3390/su18105083 (registering DOI) - 18 May 2026

Reliable perception of small floating objects is critical for the management of aquatic environments and supports key applications, including the autonomous navigation of Unmanned Surface Vehicles (USVs), waterborne debris monitoring, and Search and Rescue (SAR) operations. However, accurate detection in dynamic water-surface environments [...] Read more.

Reliable perception of small floating objects is critical for the management of aquatic environments and supports key applications, including the autonomous navigation of Unmanned Surface Vehicles (USVs), waterborne debris monitoring, and Search and Rescue (SAR) operations. However, accurate detection in dynamic water-surface environments remains a significant challenge, as targets are frequently obscured by high-frequency wave clutter, and feature distributions are destabilized by covariate shifts caused by illumination. To address these limitations, this study proposes YOLO11-MCN, a real-time detection framework that integrates two architectural components specifically designed for water-surface monitoring. The Multi-Scale Contextual Attention (MSCA) module distinguishes target signatures from background noise by aggregating contextual information across heterogeneous receptive fields, thereby suppressing false positives generated by waves. The Channel Normalization Attention Mechanism (CNAM) addresses illumination instability through feature statistic calibration based on Group Normalization, effectively mitigating covariate shifts induced by extreme lighting variations. Furthermore, these components are complemented by a high-resolution P2 detection head, which recovers the geometric details of small-scale targets typically lost during downsampling. Extensive experiments conducted on a dataset of 5812 images demonstrate that YOLO11-MCN achieves an mAP@0.5 of 92.7%, outperforming the YOLO11n baseline by 5.9 percentage points. Robustness evaluations confirm that MSCA and CNAM significantly reduce missed detections under severe wave clutter and backlighting conditions. With a recall of 90.5%, an inference speed of 94 FPS on desktop hardware, and a compact footprint of 3.89M parameters and 14.8 GFLOPs, the proposed framework offers a robust and efficient solution for intelligent water-surface surveillance systems within the single-class detection paradigm evaluated in this study, with strong potential for edge-device deployment following platform-specific optimization. Full article

22 pages, 517 KB

Open AccessArticle

Environmental Provisions in Trade Agreements and Corporate Green Innovation Theory and Mechanisms

by Xinheng Liu, Xue Peng, Lei Li, Qi Ban and Zheng Xue

Sustainability 2026, 18(10), 5082; https://doi.org/10.3390/su18105082 (registering DOI) - 18 May 2026

Green innovation is vital for the sustainable economic development of countries. One effective way to protect the environment and promote sustainable economic development is through corporate green innovation. This paper uses data from Chinese listed companies and the Trade and Environment Database, which [...] Read more.

Green innovation is vital for the sustainable economic development of countries. One effective way to protect the environment and promote sustainable economic development is through corporate green innovation. This paper uses data from Chinese listed companies and the Trade and Environment Database, which covers the period from 2013 to 2024, to examine how environmental regulatory provisions in trade agreements affect green innovation among Chinese enterprises and the underlying mechanisms. The study finds that environmental provisions in trade agreements can greatly encourage firms to innovate in a green way. This conclusion remains unchanged after a series of robustness tests. Analysis of the mechanisms reveals that environmental provisions in trade agreements inhibit firms’ green innovation through compliance costs, while promoting it through innovation compensation effects. However, the latter outweighs the former. Analysis of heterogeneity suggests that environmental legislation has a greater impact on green innovation within highly innovative, heavily polluting, non-state-owned, unauthorised firms. Full article

33 pages, 2356 KB

Open AccessArticle

Sustainable Valorization of Blast Furnace Slag into NaA Zeolite via Selective Acetic Acid Leaching for Efficient Heavy Metal Adsorption

by Yifei Lv, Xinyue Lv, Mengyao Zhao, Jingyu Zhao, Jiayong Qiu, Yingjiang Wen, Kai Zhao, Junru Zhu, Yuhan Ge, Xinzhe Lu and Yongjia Dou

Sustainability 2026, 18(10), 5081; https://doi.org/10.3390/su18105081 (registering DOI) - 18 May 2026

Sustainable management of industrial solid waste is critical for a circular economy. This study presents a novel approach for valorizing blast furnace slag (BFS) into NaA zeolite through selective acetic acid leaching followed by hydrothermal crystallization. The leaching step selectively extracts Ca²⁺ [...] Read more.

Sustainable management of industrial solid waste is critical for a circular economy. This study presents a novel approach for valorizing blast furnace slag (BFS) into NaA zeolite through selective acetic acid leaching followed by hydrothermal crystallization. The leaching step selectively extracts Ca²⁺ and Mg²⁺ while efficiently retaining silicon and aluminum in the solid residue, producing a reactive aluminosilicate precursor that facilitates zeolite nucleation and growth. The effects of the silicon-to-aluminum molar ratio (n(Si)/n(Al)), crystallization temperature, and duration on the phase evolution and morphology were systematically investigated. The results demonstrate that phase-pure NaA zeolite with high crystallinity and a uniform cubic morphology can be obtained from precursor gels with n(Si)/n(Al) ratios of 0.5–1.25. Optimal synthesis conditions were identified as n(Na):n(Si):n(Al):n(H₂O) = 6:1:1:240 at 373 K for 8 h. The resulting zeolites exhibit a BET specific surface area of 52.1 m²/g, a micropore volume of 0.016 cm³/g, an average adsorption pore size of 4.7 nm, and an external specific surface area of 12.8 m²/g. It achieved near-complete removal of Cu²⁺ and high adsorption efficiencies for Pb²⁺ (77.78%) and Ni²⁺ (71.79%) from 250 mg/L solutions at 298 K with a dosage of 4.0 g/L, following the affinity sequence Cu²⁺ > Pb²⁺ > Ni²⁺, with all pairwise differences statistically significant at p < 0.001, using one-way ANOVA and Tukey’s HSD tests. The adsorption of three metal ions was most accurately described by the Freundlich isotherm and pseudo-second-order kinetic models, indicating heterogeneous multilayer chemisorption. The theoretical maximum monolayer adsorption capacities (q_max) were 307.67 mg/g for Cu²⁺, 246.09 mg/g for Pb²⁺, and 173.79 mg/g for Ni²⁺, whereas the kinetic equilibrium adsorption capacities (q_e) reached 62.69, 48.85 and 41.69 mg/g, respectively. This study demonstrates a value-added strategy for valorizing BFS into a micro-mesoporous adsorbent, advancing both circular resource utilization and environmental remediation. Full article

(This article belongs to the Section Waste and Recycling)

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13 pages, 28456 KB

Open AccessArticle

Magnetizing Pre-treatment of Diaspore Bauxite with Bamboo Powder: Simultaneous Iron Recovery and Red Mud Reduction in Alumina Production

by Kai Shi, Xingzhong Huang, Weizhen Liu, Zhang Lin, Hao Jiang and Xiaoqin Li

Sustainability 2026, 18(10), 5080; https://doi.org/10.3390/su18105080 (registering DOI) - 18 May 2026

The red mud generated during the alumina production process is a highly alkaline solid waste, with a global stockpile exceeding 4 billion tons. Reducing red mud at the source and enhancing its comprehensive utilization are significant global challenges. Herein, we propose a pre-treatment [...] Read more.

The red mud generated during the alumina production process is a highly alkaline solid waste, with a global stockpile exceeding 4 billion tons. Reducing red mud at the source and enhancing its comprehensive utilization are significant global challenges. Herein, we propose a pre-treatment method utilizing co-roasting of bamboo powder and diaspore bauxite. Characterization techniques, including XRD, SEM-EDS, and TESCAN Integrated Mineral Analyzer (TIMA), have demonstrated that roasting modifies the composition and structure of the bauxite. After roasting, diaspore undergoes dehydroxylation and transforms into alumina, while hematite is transformed into magnetite by reducing gases such as CO and CH₄ produced from the pyrolysis of bamboo powder. Simultaneously, the roasting process created cracks in the alumina, resulting in an increased specific surface area and leaching rate of alumina. Under the optimal roasting conditions (diaspore bauxite to bamboo powder ratio of 30:1, 650 °C, 25 min), the roasted bauxite can be subjected to Bayer digestion at 260 °C with the addition of 4% calcium oxide, achieving a relative leaching rate of alumina of 98.8% and reducing red mud production by 17.3% at the source. Magnetic separation enabled the recovery of iron resources from red mud, with the iron concentrate obtained exhibiting a grade of 58.8% and an iron recovery rate of 85.6%, and the final red mud production was reduced by 63.6%, which is beneficial to the sustainable development of the alumina industry. Full article

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20 pages, 2733 KB

Open AccessArticle

Sustainable On-Road Energy Harvesting: A CFD Study on Wind Turbine System Integrated with Electric Vehicles

by Jaidon Jibi Kurisinkal, Taimoor Asim and Muhammad Younas

Sustainability 2026, 18(10), 5079; https://doi.org/10.3390/su18105079 (registering DOI) - 18 May 2026

Electric vehicles (EVs) are playing a crucial role in decarbonising the transportation industry by cutting down on toxic emissions from vehicles. Increasing the range of EVs is still a major hurdle in the widespread adoption of such vehicles, and serious efforts are underway [...] Read more.

Electric vehicles (EVs) are playing a crucial role in decarbonising the transportation industry by cutting down on toxic emissions from vehicles. Increasing the range of EVs is still a major hurdle in the widespread adoption of such vehicles, and serious efforts are underway across the globe in order to address this issue. A potential solution to this is the integration of small wind turbines with EVs to extract wind power and help charge the batteries. However, serious efforts in this regard are severely lacking in the published literature. This study aims to bridge this gap through systematic numerical investigations on a drag-based vertical-axis wind turbine (VAWT) installed on top of an EV. Utilising Computational Fluid Dynamic (CFD)-based solvers, the flow fields associated with the turbine are analysed in detail. Instantaneous and average power produced by the turbine have been critically evaluated over its entire operational range and at different vehicle speeds. The results obtained show that the VAWT has a peak power coefficient (Cp) of 0.46 at a tip speed ratio (λ) of 0.55. The average power produced by the VAWT at 30 mph, 50 mph, and 70 mph is about 160 W, 700 W, and 2 kW, respectively. Full article

(This article belongs to the Section Energy Sustainability)

26 pages, 6889 KB

Open AccessArticle

GPU-Accelerated High-Resolution Dam-Break Flood Simulation Using 0.5 m Airborne LiDAR for Sustainable Disaster Risk Reduction in Ageing Reservoirs: Application to Geumosan Reservoir, South Korea

by Seung-Jun Lee, Jisung Kim and Hong-Sik Yun

Sustainability 2026, 18(10), 5078; https://doi.org/10.3390/su18105078 (registering DOI) - 18 May 2026

Ensuring the sustainability of ageing water-storage infrastructure is an increasingly urgent challenge under climate-driven hydrological extremes. In the Republic of Korea, approximately 18,000 small and medium-sized agricultural reservoirs—many several decades old—pose escalating risks to downstream communities and threaten progress toward SDGs 6, 11, [...] Read more.

Ensuring the sustainability of ageing water-storage infrastructure is an increasingly urgent challenge under climate-driven hydrological extremes. In the Republic of Korea, approximately 18,000 small and medium-sized agricultural reservoirs—many several decades old—pose escalating risks to downstream communities and threaten progress toward SDGs 6, 11, and 13. This study presents a 0.5 m airborne LiDAR-based, GPU-accelerated two-dimensional shallow-water simulation of a hypothetical breach of the Geumosan Reservoir, South Korea, using a MUSCL + HLL solver verified against the Ritter (1892) and Stoker (1957) analytical dam-break solutions. Two scenarios are compared: Run A with a uniform Manning coefficient (n = 0.035) and Run B with spatially variable roughness derived from the Korean Ministry of Environment land-cover map (mean n = 0.0711). Mass conservation is preserved to within 0.01% during the closed-domain phase. Spatially variable roughness expands the total inundated area by 8.5% (3.05 → 3.31 km²) while reducing the Extreme-hazard zone, defined by the DEFRA hazard rating HR = h(v + 0.5), by 24% (1.49 → 1.14 km²); arrival times in the downstream urban corridor are delayed by up to 30 min. Uniform Manning assumptions therefore systematically overestimate extreme-hazard extents while underestimating the broader shallow-inundation footprint—biases comparable in magnitude to breach-parameter uncertainty. By delivering reproducible, georeferenced hazard, arrival-time, and damage-class maps for emergency action planning, the proposed framework supports risk-informed and sustainable management of ageing reservoir infrastructure and community-level disaster resilience aligned with the Sendai Framework and SDGs 6, 11, and 13. Full article

27 pages, 1005 KB

Open AccessArticle

Industrial Symbiosis Readiness of Small- and Medium-Sized Enterprises: A Cross-Country Comparative Analysis and a Digital Waste-to-Resource Network Model

by Esra Atabay, Hasan Volkan Oral, Radu Godina, Kader Öz, Aleksandar Erceg, Fahmi Abu Al-Rub and Sara Abu Al-Rub

Sustainability 2026, 18(10), 5077; https://doi.org/10.3390/su18105077 (registering DOI) - 18 May 2026

The transition toward a circular economy has made industrial symbiosis an important approach for improving resource efficiency and reducing environmental impact, especially for small- and medium-sized enterprises (SMEs). However, the extent to which SMEs can adopt these practices differs across countries. This study [...] Read more.

The transition toward a circular economy has made industrial symbiosis an important approach for improving resource efficiency and reducing environmental impact, especially for small- and medium-sized enterprises (SMEs). However, the extent to which SMEs can adopt these practices differs across countries. This study aims to explore the readiness of SMEs for industrial symbiosis in Türkiye, Jordan, Portugal, and Croatia, and to propose a digital model that can support this transition. The research is based on a qualitative, literature-driven comparative analysis examining institutional structures, technological capacity, sectoral characteristics, and collaboration networks in each country. The findings indicate that, despite contextual differences, all four countries face similar challenges, such as limited data sharing, insufficient digital infrastructure, and weak inter-firm cooperation. While EU member states demonstrate more developed policy frameworks, implementation gaps remain evident across cases. Building on these insights, the study introduces the Digital Recycling and Material Network (DREAM) model, a digital platform that connects waste-generating firms, recycling companies, and businesses that use secondary raw materials. The model enables real-time data sharing and supports sustainability-oriented matching mechanisms. Overall, the study suggests that digital platforms like DREAM can play a key role in strengthening industrial symbiosis practices and supporting SMEs in their transition toward circular production systems. Full article

(This article belongs to the Section Economic and Business Aspects of Sustainability)

37 pages, 5577 KB

Open AccessArticle

Machine Learning-Based Forecasting of Waste Generation Proxies Under Data-Limited Conditions for Supporting Adaptive and Sustainable Citarum River Management

by Sudradjat Supian, Sukono, Riaman, Hafizan Juahir, Tubagus Robbi Megantara, Indra, Astrid Sulistya Azahra, Dede Irman Pirdaus and Moch Panji Agung Saputra

Sustainability 2026, 18(10), 5076; https://doi.org/10.3390/su18105076 (registering DOI) - 18 May 2026

This study addresses the prediction of daily waste generation dynamics under data-limited conditions in a strategic watershed serving over 25 million residents. A machine learning framework is developed using daily proxies reconstructed from annual data (2019–2024) through an additive seasonal stochastic disaggregation approach, [...] Read more.

This study addresses the prediction of daily waste generation dynamics under data-limited conditions in a strategic watershed serving over 25 million residents. A machine learning framework is developed using daily proxies reconstructed from annual data (2019–2024) through an additive seasonal stochastic disaggregation approach, while maintaining consistency with official SIPSN records. Statistical analysis identifies the 2023 annual total as anomalous (+127.06% YoY) using the IQR method, while sensitivity tests to various parameter configurations indicate that the baseline setting (α = 0.95; σ_frac = 0.08) provides stable estimates. Four models—Random Forest, Support Vector Regression (SVR), XGBoost, and Long Short-Term Memory (LSTM)—are evaluated using strict chronological partitioning to maintain temporal integrity. Results indicate that the evaluation reflects the model’s ability to reproduce synthetic proxies, rather than direct field observations. SVR performed best (R² = 0.8157; RMSE = 881.43 t/day), outperforming the persistence baseline by +32.2%. After data leakage correction, XGBoost’s performance decreased significantly (R² = 0.1591). Feature analysis confirmed the dominance of short-term statistical indicators, while the hierarchical bootstrap approach produced more comprehensive uncertainty estimates, with SVR remaining the most stable across seasons. Full article

42 pages, 1443 KB

Open AccessArticle

Design of a Training Water Network Plant for Vocational Education in the Urban Water Cycle: A Case Study in Spain

by Albert Canut-Montalva, Carlos Rizo-Maestre, Joaquín Martínez-López and Joaquín Solbes-Llorca

Sustainability 2026, 18(10), 5075; https://doi.org/10.3390/su18105075 (registering DOI) - 18 May 2026

In the context of increasing water scarcity, the new paradigm in efficient water management relies on the digitalisation of water infrastructure to optimise resource use. One of the key factors in addressing the new challenges facing urban water cycle companies is the shortage [...] Read more.

In the context of increasing water scarcity, the new paradigm in efficient water management relies on the digitalisation of water infrastructure to optimise resource use. One of the key factors in addressing the new challenges facing urban water cycle companies is the shortage of qualified technical staff. This context highlights the new training needs of technical personnel required by companies in the urban water cycle sector due to the increasing digitalisation of tools and the new technological requirements of jobs which are not yet sufficiently reflected in the existing training offer. Companies express their dissatisfaction with how poorly existing training programs meet their current needs. Vocational training has a fundamental role to play in providing high-quality, technically up-to-date training that is aligned with the needs of water management companies. This mission involves the adoption of innovative teaching strategies and methods and the development of innovative teaching resources. This paper presents the design of a bench-scale plant specifically designed as a teaching resource at a Spanish vocational training centre that offers intermediate-level training in water networks and treatment plants and advanced-level training in water management. The plant, occupying a footprint of 4 × 5 m, simulates a drinking water distribution network, from the intake to the distribution network via a pumping station with two pumps (1 + 1) of 0.75 kW each that provide a flow range of 4–12 m³/h with a range of 22–10 m water column and a regulating reservoir of 1 m³ located above the water network. The plant is equipped with sensors that allow operational data to be monitored: pressures, flow rates, consumption and levels, enabling multiple operational scenarios to be simulated: leaks, sectorisation, pressure and flow management, etc. Its design has focused on facilitating the acquisition by students of the skills and learning outcomes required in the curricula of the different professional modules that make up the aforementioned studies, through learning based on multidisciplinary collaborative projects. Full article

(This article belongs to the Special Issue Water and Wastewater Treatment Futures: Sustainable Solutions for a Circular Economy)

43 pages, 3244 KB

Open AccessArticle

Designing Low-Carbon Gardens: A Sustainable Approach in Landscape Architecture

by Margot Dudkiewicz-Pietrzyk

Sustainability 2026, 18(10), 5074; https://doi.org/10.3390/su18105074 (registering DOI) - 18 May 2026

This manuscript addresses the challenge of designing low-carbon and climate-neutral landscapes. While gardens and green spaces are commonly perceived as environmentally beneficial, they may generate significant greenhouse gas emissions throughout their life cycle. Despite the widespread application of carbon footprint assessment in building [...] Read more.

This manuscript addresses the challenge of designing low-carbon and climate-neutral landscapes. While gardens and green spaces are commonly perceived as environmentally beneficial, they may generate significant greenhouse gas emissions throughout their life cycle. Despite the widespread application of carbon footprint assessment in building design, its integration into landscape architecture remains limited. The aim of this study is to systematize the concept of the garden carbon footprint and to develop a coherent framework for its evaluation. The research adopts a conceptual synthesis approach based on an interdisciplinary literature review, supported by a simplified Life Cycle Assessment (LCA) methodology. A component-based model is proposed, integrating embodied carbon, operational emissions, and carbon sequestration. The results demonstrate that the carbon performance of designed landscapes varies significantly depending on design strategies and management approaches. Importantly, the findings confirm that climate neutrality may be possible under specific conditions, particularly at larger spatial scales. The proposed framework contributes to the integration of carbon footprint assessment into landscape design processes and supports the development of low-emission, climate-resilient solutions. Full article

(This article belongs to the Topic Sustainable Built Environment, 2nd Volume)

33 pages, 1931 KB

Open AccessArticle

Built Environment, Safety, and Urban Economic Contexts in Shaping Urban Park Visitation for Sustainable Urban Development: Evidence from a Multi-Method Analysis of Las Vegas

by Zheng Zhu, Shuqi Hu, Xinyue Shen and Xiwei Shen

Sustainability 2026, 18(10), 5073; https://doi.org/10.3390/su18105073 (registering DOI) - 18 May 2026

Urban park use is a key indicator of sustainable urban development, reflecting the accessibility and social value of urban green infrastructure. However, existing studies often struggle to distinguish stable spatial differences from short-term temporal dynamics. Using monthly data for 125 urban parks in [...] Read more.

Urban park use is a key indicator of sustainable urban development, reflecting the accessibility and social value of urban green infrastructure. However, existing studies often struggle to distinguish stable spatial differences from short-term temporal dynamics. Using monthly data for 125 urban parks in Las Vegas from 2022 to 2024, this study examines how park visitation is shaped by spatial, temporal, and contextual factors. It addresses three objectives: identifying cross-park determinants of visitation, examining within-park monthly dynamics, and assessing spatial variation in key relationships. Park visitation is measured using observed visit counts, with dwell time and travel distance used as alternative behavioral outcomes for robustness tests. To address these research questions, this study asks: (1) what structural and contextual factors explain cross-park differences in park visitation; (2) how park visitation responds to changing contextual conditions within parks over time at the monthly scale; and (3) whether the relationships between park visitation and its key determinants vary across space. To answer these questions, the analysis combines annual cross-sectional ordinary least squares (OLS) regression, monthly panel models, Random Forest analysis, robustness tests, and geographically weighted regression. This study employs a triangulated analytical framework combining cross-sectional ordinary least squares (OLS) regression monthly fixed-effects (FE) panel models, and Random Forest (RF) analysis. These factors function as stable support for sustainable park use. Crime exposure shows no stable global linear effect, but its association with visitation appears conditional on temporal and spatial context. Overall, the findings suggest that park visitation is shaped by the interaction of physical design, safety conditions, and urban context. By explicitly separating cross-sectional spatial and economic inequalities from within-park temporal dynamics, this study offers policy-relevant evidence for urban planners and park managers seeking to promote more inclusive, efficient, and sustainable urban park systems through integrated design, economic activation, and safety-oriented interventions. Full article

18 pages, 847 KB

Open AccessArticle

Emotional Well-Being in Tourism Experiences on Pathways: Evidence from User-Generated Content

by Alessandra Marasco and Valentina Marchi

Sustainability 2026, 18(10), 5072; https://doi.org/10.3390/su18105072 (registering DOI) - 18 May 2026

Within the contemporary debate on tourism, sustainability and well-being, increasing attention has been devoted to the role of cultural and nature-based tourism experiences on pathways for human transformation and well-being. This study contributes to this emerging area of research by analyzing the emotional [...] Read more.

Within the contemporary debate on tourism, sustainability and well-being, increasing attention has been devoted to the role of cultural and nature-based tourism experiences on pathways for human transformation and well-being. This study contributes to this emerging area of research by analyzing the emotional well-being associated with travellers’ experiences on Italian pathways through an automated text-mining approach based on their online reviews on TripAdvisor. Through an analysis of reviews relating to 10 pathways, it improves the understanding of how emotional well-being emerges from reviews, their linguistic structure and how the main experiential factors are associated with positive and negative emotions experienced by travellers. The findings provide evidence of the association of these experiences with emotional well-being, show how well-being emerges in relation to the dimensions of challenge, community and escape during the journey on the pathway, and identify distinct linguistic styles in reviews associated with well-being. Full article

(This article belongs to the Special Issue Sustainable Nature-Based Tourism)

36 pages, 3694 KB

Open AccessArticle

Review of Sustainable Finance: Key Trends and Research Agenda

by Magdalena Zioło, Elżbieta Szaruga and Anna Spoz

Sustainability 2026, 18(10), 5071; https://doi.org/10.3390/su18105071 (registering DOI) - 18 May 2026

The study presents a comprehensive approach to reviewing achievements in sustainable finance, identifying research trends and directions, key problem areas, the geographical origins of publications, keyword clusters, and the research methods applied by scholars in this field. The analysis is based on a [...] Read more.

The study presents a comprehensive approach to reviewing achievements in sustainable finance, identifying research trends and directions, key problem areas, the geographical origins of publications, keyword clusters, and the research methods applied by scholars in this field. The analysis is based on a dataset of 9218 publications indexed in the Web of Science and Scopus databases covering the period 2012–2025. The methodological framework combines bibliometric analysis, text mining, and topic modelling techniques, including the Latent Dirichlet Allocation (LDA) method, supported by visualization tools. These methods enable the identification of publication dynamics, the geographic distribution of research activity, thematic clusters, and the dominant research methods used in the literature. The results indicate a significant increase in academic interest in sustainable finance, particularly after 2018. The main research trends identified include green finance, climate finance, renewable energy financing, ESG investing, sustainability reporting, and the development of green financial instruments. The analysis also highlights the growing role of innovation and financial institutions in supporting sustainability transitions. In addition, the study identifies emerging research areas such as blue finance and green banking, providing a broader perspective on the evolving structure of sustainable finance research. To the best of our knowledge, this is the only review paper that approaches sustainable finance in such a comprehensive manner, particularly by incorporating the emerging concept of blue finance. Full article

(This article belongs to the Special Issue Energy Markets and Sustainable Development Goals (SDGs): Challenges and Prospects)

23 pages, 758 KB

Open AccessArticle

A Multi-Criteria Decision-Support Framework for Sustainable Asphalt Mixtures: Integrating Mechanical Performance and Environmental Impacts Through Structural Normalisation

by Caroline F. N. Moura, Hugo M. R. D. Silva and Joel R. M. Oliveira

Sustainability 2026, 18(10), 5070; https://doi.org/10.3390/su18105070 (registering DOI) - 18 May 2026

Sustainability assessment of road pavements requires the combined consideration of environmental and mechanical performance, since conventional mass-based Life Cycle Assessment (LCA) may lead to misleading conclusions. This study proposes a multi-criteria decision-support framework that integrates LCA results with key mechanical indicators through structural [...] Read more.

Sustainability assessment of road pavements requires the combined consideration of environmental and mechanical performance, since conventional mass-based Life Cycle Assessment (LCA) may lead to misleading conclusions. This study proposes a multi-criteria decision-support framework that integrates LCA results with key mechanical indicators through structural normalisation, enabling the comparison of asphalt mixtures on an equivalent structural basis. Three sustainable asphalt mixtures were analysed, namely Hot Recycled Mix Asphalt (HRMA), Half-Warm Mix Asphalt (HWMA), and Cold Recycled Mixture (CRM), and compared with a reference Hot Mix Asphalt (HMA). Environmental impacts were quantified using a cradle-to-gate LCA, while mechanical performance was characterised through stiffness, fatigue resistance, rutting, and moisture susceptibility. These indicators were integrated into a Structural Contribution index and a Material Environmental Impact Ratio. The results show that, although CRM benefits from cold production and high recycling rates, its lower structural performance reduces its advantage when equivalent thickness is considered. HWMA emerges as the most favourable compromise within the adopted framework, combining lower environmental impacts with competitive structural performance, while HRMA offers the greatest structural contribution with competitive environmental performance. Sensitivity analysis confirms the robustness of the framework under realistic variations in weighting assumptions. The study demonstrates that incorporating structural performance into environmental assessment is essential to avoid misleading conclusions and to support more reliable decision-making in sustainable pavement design. Full article

(This article belongs to the Section Sustainable Materials)

26 pages, 3384 KB

Open AccessArticle

The Impact of Green Credit on Agricultural Carbon Emissions: Spatial Spillover Effects and Channels in China

by Yuzhen Deng, Zhicheng Yang, Litian Yang, Yuping Wen and Kaixi Chen

Sustainability 2026, 18(10), 5069; https://doi.org/10.3390/su18105069 (registering DOI) - 18 May 2026

Reducing agricultural carbon emissions is an important component of China’s efforts to achieve its carbon peaking and carbon neutrality goals. As an important policy oriented financial instrument, green credit can facilitate lower agricultural carbon intensity by directing resources more efficiently across regions and [...] Read more.

Reducing agricultural carbon emissions is an important component of China’s efforts to achieve its carbon peaking and carbon neutrality goals. As an important policy oriented financial instrument, green credit can facilitate lower agricultural carbon intensity by directing resources more efficiently across regions and encouraging low carbon transformation in agriculture. Using panel data for 30 Chinese provinces from 2005 to 2022, this study measures agricultural carbon emission intensity (ACEI) from six sources. It then examines the spatial spillover effects, transmission channels, and nonlinear characteristics associated with green credit by using a spatial Durbin framework, mediation analysis, and panel threshold model. The results indicate that: (1) green credit development is significantly associated with lower ACEI; (2) green credit exhibits significant spatial spillover effect, being associated with lower ACEI both within a province and in neighboring provinces; (3) green credit exhibits marked regional heterogeneity in its impact on ACEI: it shows both direct and spillover effects in the eastern region, only spillover effects in the central region, and only direct effects without effective diffusion in the western region; (4) green credit is associated with lower ACEI through industrial structure upgrading and lowering agricultural energy consumption intensity; (5) green credit has a single threshold effect on ACEI based on its own development level. After crossing the threshold, the emission intensity reduction effect weakens but remains significant. These results offer empirical evidence for refining green credit arrangements and advancing coordinated agricultural emission reduction across regions. Full article

(This article belongs to the Special Issue Agricultural Economic Transformation and Sustainable Development: 2nd Edition)

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26 pages, 4052 KB

Open AccessArticle

Optimizing Anchorage Safety Under Typhoons: Key Factor Identification and Dynamic Tiered Management via SEM–fsQCA Hybrid Modeling

by Tifang Li, Zihao Weng, Jin Yan, Lijun Wang, Ronghui Li and Wei Wang

Sustainability 2026, 18(10), 5068; https://doi.org/10.3390/su18105068 (registering DOI) - 18 May 2026

Identifying and optimizing core factor configurations for anchorage operational safety under typhoon scenarios is critical to enhancing anchorage operational resilience and sustainable port development. This study develops a complementary hybrid SEM–fsQCA framework: key factors are identified via literature review and expert interviews; SEM [...] Read more.

Identifying and optimizing core factor configurations for anchorage operational safety under typhoon scenarios is critical to enhancing anchorage operational resilience and sustainable port development. This study develops a complementary hybrid SEM–fsQCA framework: key factors are identified via literature review and expert interviews; SEM quantifies factor correlations and contribution weights and corrects expert-evaluated anchorage capacity; six core factors are extracted, three typhoon types (heavy-rainfall, strong-wind, complex-track) are defined, and a coupled anchorage–typhoon case dataset is constructed. Subsequently, fsQCA performs necessary condition analysis and identifies causal configurations driving safety effectiveness. Based on these configurations, we establish a dynamic three-tier risk classification framework for refined anchorage management. Validated using 36 coupled cases (12 anchorages × 3 typhoon types) from Huizhou Port, a core hub in the Guangdong–Hong Kong–Macao Greater Bay Area, this framework enables adaptive vessel traffic scheduling throughout the entire typhoon cycle through dynamic tiered management. The proposed “identification-intervention-feedback” closed-loop governance model delivers theoretical rigor and operational implementation ability for coastal port typhoon risk mitigation. Full article

(This article belongs to the Special Issue Sustainable Strategies for Building More Resilient Coastal Communities in a Changing Climate)

25 pages, 8184 KB

Open AccessSystematic Review

Artificial Intelligence for Energy Optimization in Educational Buildings in Saudi Arabia: A Systematic Review of Design Variables and Decision-Support Approaches in Hot-Arid Climates

by Malaz Khalid Hamzah, Hatem El Shafie and Mohanned Althobaiti

Sustainability 2026, 18(10), 5067; https://doi.org/10.3390/su18105067 (registering DOI) - 18 May 2026

This study systematically reviews the role of Artificial Intelligence (AI) and Machine Learning (ML) in supporting design decisions to improve energy efficiency in educational buildings, with particular emphasis on Saudi Arabia’s hot-arid climate. A PRISMA-based Systematic Literature Review was conducted using Google Scholar, [...] Read more.

This study systematically reviews the role of Artificial Intelligence (AI) and Machine Learning (ML) in supporting design decisions to improve energy efficiency in educational buildings, with particular emphasis on Saudi Arabia’s hot-arid climate. A PRISMA-based Systematic Literature Review was conducted using Google Scholar, ScienceDirect, ResearchGate, and the Saudi Digital Library for studies published between 2020 and 2025. Eligible studies included peer-reviewed articles and high-quality conference papers addressing AI/ML applications in building energy performance, optimization, or design decision-making in educational or comparable buildings. Studies published before 2020, non-peer-reviewed sources, irrelevant studies, papers focused solely on non-educational buildings without transferable findings, and studies lacking full-text access were excluded. The search identified 594 records, of which 37 studies met the eligibility criteria, resulting in a final sample of 37 reviewed sources. The review shows that ML models, hybrid methods, and multi-objective optimization techniques are increasingly used to improve energy performance and support early-stage design. The most influential variables include envelope properties, glazing, shading, lighting efficiency, HVAC systems, and renewable energy integration. However, major gaps remain, particularly the limited application of AI-driven optimization in Saudi educational buildings and the lack of real-world validation in hot-arid settings. This review provides a concise foundation for future AI-assisted design strategies aligned with sustainable educational building development and Saudi Vision 2030. Full article

(This article belongs to the Special Issue Sustainable Optimization for Energy and Environmental Systems Engineering)

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