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Background: Small airway dysfunction (SAD) plays a critical role in the management of severe asthma, particularly in patients at risk of requiring biological therapies (BTs). Short-term studies have shown that switching to single-inhaler triple therapy (SITT) with extrafine beclomethasone–formoterol–glycopyrronium improves outcomes and helps achieve quiet asthma, a state marked by symptom control, no exacerbations or oral steroids, reduced inflammation, and better small airway function. This study investigated whether, over one year, patients could maintain this state as Steady Quiet Asthma (SQA) and whether baseline measures could predict this sustained response. Methods: Twenty-six patients with severe asthma and SAD were transitioned from open triple-inhaler therapy to a closed, single-inhaler triple therapy containing extrafine beclomethasone–formoterol–glycopyrronium. Assessments at baseline (T0) and at one-year follow-up (T12) included clinical evaluations, spirometry, and impulse oscillometry, with a focus on Fres as a predictor for the need for BT. When prescribed, biologic therapies included mepolizumab, benralizumab, and dupilumab. Results: Of the 26 patients, 9 (34.6%) achieved SQA and did not require biologic therapy at the one-year follow-up, while 17 patients (65.4%) initiated biologic treatment. At T0, patients who required biologics had significantly higher median Fres (21 (19.47; 24.58) vs. 17.61 (15.82; 20.63); p = 0.049) compared to those who remained biologic-free. They also exhibited higher residual volume to total lung capacity ratio (%RV/TLC) values and lower forced expiratory volume in one second/forced vital capacity ratios (FEV₁/FVC). At T12, patients spared from BT showed significant reductions in Fres (p = 0.014) and improvements in small airway function (difference in airway resistance between 5 Hz and 20 Hz (R5–20), forced expiratory flow between 25% and 75% of FVC (%FEF25–75), and better asthma control (ACT). In contrast, patients on BT demonstrated less favorable changes in these parameters. Conclusions: Baseline Fres, FEV1/FVC ratio, and %FEV25–75 are valuable predictors of achieving Steady Quiet Asthma (SQA) and sparing biologic therapy. These findings support the use of SITT in severe asthma and highlight the importance of early functional assessments to guide personalized management. Full article

Biodiversity conservation and sustainable development in high-density forest urban areas have attracted growing attention and are increasingly recognized as critical for achieving the Sustainable Development Goals (SDGs). University campus forests, functioning as ecological islands, possess unique acoustic characteristics and play a vital role in supporting urban biodiversity. In this case study, acoustic monitoring was conducted over the course of a full year to objectively reveal the ecological patterns across temporal scales of the campus sound environment, by combining acoustic indices’ visualization combined with statistical analysis. The findings indicate (1) the existence of ecological sound patterns across different temporal scales, closely associated with phenological cycles; (2) the identification of the specific timing affected by the different species‘ activities, such as the breeding season of birds, the chirping time of cicadas and other insects, as well as the fluctuations in the intensity of human activities, and (3) the development of a methodological framework integrating a visualization technique with statistical analysis to enhance the understanding of long-term ecological dynamics. The results offer a foundation for promoting the sustainable conservation of campus biodiversity in high-density urban settings. Full article

Forest tenure functions as a critical institutional mechanism globally for curbing deforestation and degradation and advancing sustainable forest administration, ultimately underpinning the provision of vital ecosystem services. However, research on robust forest tenure system models both globally and within China remains underdeveloped, hindering their potential as an effective administration tool. The study addresses this gap by conceptualizing China’s forest tenure system through the lens of systems thinking and proposing a Forest Cadastral System based on the Land Administration Domain Model (LADM). We conduct a comprehensive review of the evolution of China’s forest tenure system and an in-depth analysis of the current “person–right–land” configuration. Subsequently, we construct an integrated forest cadastral model structured around three core LADM-compliant packages: party, administrative, and spatial unit. The model is then tested in Ningbo’s forested highlands: trials confirm its efficacy in reconciling tenure security with ecological governance. The findings offer valuable insights for policymakers and practitioners engaged in forest tenure reform and administration, while advancing the academic discourse on leveraging land administration systems for ecosystem service outcomes through robust institutional mechanisms. Full article

Species and phylogenetic diversity play vital roles in sustaining the structure, function, and resilience of plant communities, particularly in tropical rainforests. However, the mechanisms according to which environmental filtering and competitive exclusion influence diversity across forest layers remain insufficiently understood. In this study, we investigated the species and phylogenetic diversity patterns in two representative tropical rainforest sites—Bawangling and Jianfengling—within Hainan Tropical Rainforest National Park, China, focusing on communities associated with the endangered species Hopea hainanensis. We employed a one-way ANOVA and Pearson’s correlation analyses to examine the distribution characteristics and interrelationships among diversity indices and used Mantel tests to assess the correlations with environmental variables. Our results revealed that the plant community in Jianfengling exhibited a significantly higher species richness at the family, genus, and species levels (a total of 288 plant species have been recorded, belonging to 82 families and 183 genera) compared to that in Bawangling (a total of 212 plant species, belonging to 75 families and 162 genera). H. hainanensis held the highest importance value in the middle tree layer across both sites (IV(BWL) = 12.44; IV(JFL) = 5.73), while dominant species varied notably among other forest layers, indicating strong habitat specificity. Diversity indices, including the Simpson index, the Shannon–Wiener index, and Pielou’s evenness, were significantly higher in the large shrub layer of Jianfengling, whereas Bawangling showed a relatively higher Shannon–Wiener index in the middle shrub layer. Phylogenetic diversity (PD) and the phylogenetic structure indices (NRI and NTI) displayed distinct vertical stratification patterns between sites. Furthermore, the PD in Bawangling’s large shrub layer was positively correlated with total phosphorus in the soil, while community evenness was influenced by soil organic carbon and total nitrogen. In Jianfengling, species richness was significantly associated with soil bulk density, altitude, and pH. These findings enhance our understanding of the ecological and evolutionary processes shaping biodiversity in tropical rainforests and highlight the importance of incorporating both species and phylogenetic metrics into the conservation strategies for endangered species such as Hopea hainanensis. Full article

The goals of the European Union towards the energy transition imply profound changes in the energy field, so as to promote sustainable energy development while fostering economic growth. To achieve these changes, the incorporation of sustainable technologies supporting decentralisation, energy efficiency, renewable energy production, and demand flexibility is of vital importance. Blockchain has the potential to change energy services towards this direction. To optimally exploit blockchain, innovative business models need to be designed, identifying the opportunities emerging from unmet needs, while also considering potential risks so as to take action to overcome them. In this context, the scope of this paper is to examine the opportunities and the risks that emerge from the adoption of blockchain in four innovative business models, while also identifying mitigation strategies to support and accelerate the energy transition, thus proposing optimal approaches of exploitation of blockchain in energy services. The business models concern Energy Performance Contracting with P4P guarantees, improved self-consumption in energy cooperatives, energy efficiency and flexibility services for natural gas boilers, and smart energy management for EV chargers and HVAC appliances. Firstly, the value proposition of the business models is analysed and results in a comprehensive SWOT analysis. Based on the findings of the analysis and consultations with relevant market actors, in combination with the examination of the relevant literature, risks are identified and evaluated through a qualitative assessment approach. Subsequently, specific mitigation strategies are proposed to address the detected risks. This research demonstrates that blockchain integration into these business models can significantly improve energy efficiency, reduce operational costs, enhance security, and support a more decentralised energy system, providing actionable insights for stakeholders to implement blockchain solutions effectively. Furthermore, according to the results, technological and legal risks are the most significant, followed by political, economic, and social risks, while environmental risks of blockchain integration are not as important. Strategies to address risks relevant to blockchain exploitation include ensuring policy alignment, emphasising economic feasibility, facilitating social inclusion, prioritising security and interoperability, consulting with legal experts, and using consensus algorithms with low energy consumption. The findings offer clear guidance for energy service providers, policymakers, and technology developers, assisting in the design, deployment, and risk mitigation of blockchain-enabled business models to accelerate sustainable energy development. Full article

Coal serves as the primary energy source for China, with production anticipated to reach 4.76 billion tons in 2024. However, the mining process generates a significant amount of gangue, with approximately 800 million tons produced in 2023 alone. Currently, China faces substantial gangue stockpiles, characterized by a low comprehensive utilization rate that fails to meet the country’s ecological and environmental protection requirements. The environmental challenges posed by the treatment and disposal of gangue are becoming increasingly severe. This review employs bibliometric analysis and theoretical perspectives to examine the latest advancements in gangue utilization, specifically focusing on the application of computational chemistry to elucidate the structural features and interaction mechanisms of coal gangue, and to collate how these insights have been leveraged in the literature to inform its potential utilization routes. The aim is to promote the effective resource utilization of this material, and key topics discussed include evaluating the risks of spontaneous combustion associated with gangue, understanding the mechanisms governing heavy metal migration, and modifying coal byproducts to enhance both economic viability and environmental sustainability. The case studies presented in this article offer valuable insights into the gangue conversion process, contributing to the development of more efficient and eco-friendly methods. By proposing a theoretical framework, this review will support ongoing initiatives aimed at the sustainable management and utilization of coal gangue, emphasizing the critical need for continued research and development in this vital area. This review uniquely combines bibliometric analysis with computational chemistry to identify new trends and gaps in coal waste utilization, providing a roadmap for future research. Full article

This study explores the role of solidarity finance in promoting local development and the empowerment of marginalized communities through financial inclusion and access to community credits. It focuses on how solidarity-based financial mechanisms provide accessible credit with fewer barriers, fostering productive activities and economic resilience. This study employed a quantitative and exploratory design, analyzing data from 51 community funds in Ecuador out of a total of 220 through a self-administered online survey, validated by auditing professionals and answered by community representatives. The 25-item questionnaire gathered data on organizational dynamics, financial practices, and perceptions of sustainability. Descriptive analysis was complemented with an analysis of variance to test hypotheses concerning associativity, self-management, and organizational performance. The results show that while associativity, self-management, and organizational management are perceived as institutional strengths, aspects such as autonomy and solidarity received lower evaluations, suggesting critical areas for strategic improvement. Notably, significant differences emerged between self-management–organization and solidarity–organization groups, emphasizing the importance of associativity (collaboration) in enhancing the sustainability of solidarity finance, which proves to be a vital mechanism for community empowerment and local development; however, its long-term sustainability depends on strengthening internal dimensions, particularly autonomy and solidarity, and reinforcing associativity as a core driver of organizational resilience. Full article

The accurate prediction of significant wave height presents a complex yet vital challenge in the fields of ocean engineering. This capability is essential for disaster prevention, fostering sustainable development and deepening our understanding of various scientific phenomena. We explore the development of a comprehensive predictive methodology for wave height prediction by integrating novel Takagi–Sugeno–Kang fuzzy models within a multiresolution analysis framework. The multiresolution analysis emerges via wavelets, since they are prominent models characterised by their inherent multiresolution nature. The maximal overlap discrete wavelet transform is utilised to generate the detail and resolution components of the time series, resulting from this multiresolution analysis. The novelty of the proposed model lies on its hybrid training approach, which combines least squares with AdaBound, a gradient-based algorithm derived from the deep learning literature. Significant wave height prediction is studied as a time series problem, hence, the appropriate inputs to the model are selected by developing a surrogate-based wrapped algorithm. The developed wrapper-based algorithm, employs Bayesian optimisation to deliver a fast and accurate method for feature selection. In addition, we introduce a projection step, to further refine the approximation capabilities of the resulting predictive system. The proposed methodology is applied to a real-world time series pertaining to spectral wave height and obtained from the Poseidon operational oceanography system at the Institute of Oceanography, part of the Hellenic Center for Marine Research. Numerical studies showcase a high degree of approximation performance. The predictive scheme with the projection step yields a coefficient of determination of $0.9991$, indicating a high level of accuracy. Furthermore, it outperforms the second-best comparative model by approximately $\mathrm{49\%}$ in terms of root mean squared error. Comparative evaluations against powerful artificial intelligence models, using regression metrics and hypothesis test, underscore the effectiveness of the proposed methodology. Full article

Wind power has gained attention as a vital renewable energy source capable of reducing emissions and serving as an effective alternative to fossil fuels. Floating wind farms could significantly enhance the energy capacities of Mediterranean countries. However, location selection for offshore wind farms (OWFs) is a challenge for renewable energy policy and marine spatial planning (MSP). To address these issues, this study considers the marine space of Greece to propose a GIS-based multi-criteria decision-making (MCDM) framework employing the Analytic Hierarchy Process (AHP) to identify suitable sites for OWFs. The approach assesses 19 exclusion criteria encompassing legislative, environmental, safety, and technical constraints to determine the eligible areas. Subsequently, 10 evaluation criteria are weighted to determine the selected areas’ level of suitability. The study considers baseline conditions (1981–2010) and future climate scenarios based on RCP 4.5 and RCP 8.5 for two horizons (2011–2040 and 2041–2070), integrating projected wind velocities and sea level rise to evaluate potential shifts in suitable areas. Results indicate the central and southeastern Aegean Sea as the most suitable areas for OWF deployment. Climate projections indicate a modest increase in suitable areas. The findings serve as input for climate-resilient MSP seeking to promote sustainable energy development. Full article

With the advance of digital transformation, the assessment of the environmental impact of digital tools and technologies grows more relevant. Considering the inflated expectations of environmental responsibility in higher education, this study analyses how websites of Polish universities conform to sustainable web design criteria. The sustainability audit employed a methodology encompassing carbon emissions measurement, technical website analysis, and SEO evaluation. The author analysed 63 websites of public universities in Poland using seven independent audit tools, including an original AI Custom GPT agent preconfigured in the ChatGPT ecosystem. The results revealed a substantial differentiation in CO₂ emissions and website optimisation, with an average EcoImpact Score of 66.41/100. Nearly every fourth website exhibited a significant carbon footprint and excessive component sizes, which indicates poor asset optimisation and energy-intensive design techniques. The measurements exposed considerable variability in emission intensities and resource intensity among the university websites, suggesting the need for standardised digital sustainability practices. Regulations on the carbon footprint of public institutions’ websites and mobile applications could become vital strategic components for digital climate neutrality. Promoting green hosting, “Green SEO” practices, and sustainability audits could help mitigate the environmental impact of digital technologies and advance sustainable design standards for the public sector. The proposed auditing methodology can effectively support the institutional transition towards sustainable management of digital infrastructure by integrating technical, sustainability, and organisational aspects. Full article

This study investigates the role of Big Data Analytics Capabilities (BDACs) in ambidexterity explorative innovation (EXPLRI) and exploitative (EXPLOI) innovation for achieving a sustainable performance (SP) in the manufacturing sector of a resource-constrained developing economy. While a BDAC has been widely linked to innovation in developed economies, its effectiveness in developing contexts shaped by indigenous innovation practices like Jugaad remains underexplored. Anchored in the Resource-Based View (RBV) and Dynamic Capabilities (DC) theory, we propose a model where the BDAC enhances both EXPLRI and EXPLOI, which subsequently leads to an improved sustainable performance. We further examine the Jugaad capability as a cultural moderator. Using survey data from 418 manufacturing firms and analyzed via Partial Least Squares Structural Equation Modeling (PLS-SEM), results confirm that BDA capabilities significantly boost both types of innovations, which positively impact sustainable performance dimensions. Notably, Jugaad positively moderates the relationship between EXPLOI and financial, innovation, and operational performance but negatively moderates the link between EXPLRI and innovation performance. These findings highlight the nuanced influence of culturally embedded innovation practices in BDAC-driven ecosystems. This study contributes by extending the RBV–DC framework to include cultural innovation capabilities and empirically validating the contingent role of Jugaad in enhancing or constraining innovation outcomes. This study also validated the Jugaad capability measurement instrument for the first time in the context of Pakistan. For practitioners, aligning data analytics strategies with local innovative cultures is vital for sustainable growth in emerging markets. Full article

With the global economic restructuring and the consequent population mobility, urban shrinkage has become a common phenomenon. The Qinling–Daba Mountains, a zone with a key ecological function in China, have long experienced population decline and functional degradation. Clarifying the dynamics and influencing factors of urban shrinkage plays a vital role in supporting the sustainable development of the region. This study, using permanent resident population growth rates and nighttime light data, classified cities in the region into four spatial patterns: expansion–growth, intensive growth, expansion–shrinkage, and intensive shrinkage. It further examined the spatial characteristics of shrinkage across four periods (2005–2010, 2010–2015, 2015–2020, and 2020–2022). A Geographically and Temporally Weighted Regression (GTWR) model was applied to examine core influencing factors and their spatiotemporal heterogeneity. The results indicated the following: (1) The dominant pattern of urban shrinkage in the Qinling–Daba Mountains shifted from expansion–growth to expansion–shrinkage, highlighting the paradox of population decline alongside continued spatial expansion. (2) Three critical indicators significantly influenced urban shrinkage: the number of students enrolled in general secondary schools (X₅), the per capita disposable income of urban residents (X₇), and the number of commercial and residential service facilities (X₁₂), with their effects exhibiting significant spatiotemporal heterogeneity. Temporally, X₁₂ was the most influential factor in 2005 and 2010, while in 2015, 2020, and 2022, X₅ and X₇ became the dominant factors. Spatially, X₇ significantly affected both eastern and western areas; X₅’s influence was most pronounced in the west; and X₁₂ had the greatest impact in the east. This study explored the patterns and underlying drivers of urban shrinkage in underdeveloped areas, aiming to inform sustainable development practices in regions facing comparable challenges. Full article

In the past three decades, localized research has highlighted shifts in rainfall patterns and temperature trends in central Ethiopia, a region vital for agriculture and economic activities and heavily dependent on climate conditions to sustain livelihoods and ensure food security. However, comprehensive analyses of long-term climate data remain limited for this area. Understanding local climate trends is essential for enhancing agricultural resilience in the study area, a region heavily dependent on rainfall for crop production. This study analyzes historical rainfall and temperature patterns over the past 30 years and projects future climate conditions using downscaled CMIP6 models under SSP4.5 and SSP8.5 scenarios. Results indicate spatial variability in rainfall trends, with certain areas showing increasing rainfall while others experience declines. Temperature has shown a consistent upward trend across all seasons, with more pronounced warming during the short rainy season (Belg). Climate projections suggest continued warming and moderate increases in annual rainfall, particularly under SSP8.5 by the end of the 21st century. It is concluded that both temperature and rainfall are projected to increase in magnitude by 2080, with higher Sen’s slope values compared to earlier periods, indicating a continued upward trend. These findings highlight potential breaks in agricultural calendars, such as shifts in rainfall onset and cessation, shortened or extended growing seasons, and increased risk of temperature-induced stress. This study highlights the need for localized adaptation strategies to safeguard agriculture production and enhance resilience in the face of future climate variability. Full article

Urban green infrastructure plays a vital role in the sustainable development of cities. As urban areas expand, green spaces are increasingly affected. The pressure from new developments leads to a reduction in vegetation and raises new public health risks. Addressing this challenge requires effective planning, maintenance, and continuous monitoring. To enhance traditional approaches, remote sensing is becoming a vital tool for city-wide observations. Publicly available large-scale data, combined with machine learning models, can improve our understanding. We explore the potential of Sentinel-2 to classify and extract meaningful features from urban landscapes. Using advanced machine learning techniques, we aim to develop a robust and scalable framework for classifying urban environments. The proposed models will assist in monitoring changes in green spaces across diverse urban settings, enabling timely and informed decisions to foster sustainable urban growth. Full article

Microbes perform diverse and vital functions in animals, plants, and humans, and among them, plant-associated microbiomes, especially endophytes, have attracted growing scientific interest in recent years. Numerous plant species thriving in diverse environments have been shown to host endophytic microbes. While endophytic bacteria commonly colonize plant tissues such as stems, roots, and leaves, seed-associated endophytes generally exhibit lower diversity compared to those in other plant compartments. Nevertheless, seed-borne microbes are of particular importance, as they represent the initial microbial inoculum that influences a plant’s critical early developmental stages. The seed endophytic microbiome is of particular interest due to its potential for vertical transmission and its capacity to produce a broad array of phytohormones, enzymes, antimicrobial compounds, and other secondary metabolites. Collectively, these functions contribute to enhanced plant biomass and yield, especially under abiotic and biotic stress conditions. Despite their multifaceted roles, seed microbiomes remain underexplored in plant ecology, and their potential benefits are not yet fully understood. This review highlights recent advances in our understanding of the diversity, community composition, mechanisms of action, and agricultural significance of seed endophytic microbes. Furthermore, it synthesizes current insights into how seed endophytes promote plant health and productivity and proposes future research directions to fully harness their potential in sustainable agriculture. Full article