Search Results (148)

Willow stands (Salix spp.) are an essential part of riparian ecosystems, as they sustain biodiversity and provide bioenergy solutions. The present review synthesizes the global scientific literature about the management of willow stands. In order to achieve this goal, we used a dual approach combining bibliometric analysis with traditional literature review. As such, we consulted 416 publications published between 1978 and 2024. This allowed us to identify key species, ecosystem services, conservation strategies, and management issues. The results we have obtained show a diversity of approaches, with an increase in short-rotation coppice (SRC) systems and the multiple roles covered by willow stands (carbon sequestration, biomass production, riparian restoration, and habitat provision). The key trends we have identified show a shift toward topics such as climate resilience, ecological restoration, and precision forestry. This trend has become especially pronounced over the past decade (2014–2024), as reflected in the increasing use of these keywords in the literature. However, as willow systems expand in scale and function—from biomass production to ecological restoration—they also raise complex challenges, including invasive tendencies in non-native regions and uncertainties surrounding biodiversity impacts and soil carbon dynamics over the long term. The present review is a guide for forest policies and, more specifically, for future research, linking the need to integrate and use adaptive strategies in order to maintain the willow stands. Full article

Flocculants are indispensable in water and wastewater treatment, enabling the aggregation and removal of suspended particles, colloids, and emulsions. However, the conventional development and application of flocculants rely heavily on empirical methods, which are time-consuming, resource-intensive, and environmentally problematic due to issues such as sludge production and chemical residues. Recent advances in machine learning (ML) have opened transformative avenues for the design, optimization, and intelligent application of flocculants. This review systematically examines the integration of ML into flocculant research, covering algorithmic approaches, data-driven structure–property modeling, high-throughput formulation screening, and smart process control. ML models—including random forests, neural networks, and Gaussian processes—have successfully predicted flocculation performance, guided synthesis optimization, and enabled real-time dosing control. Applications extend to both synthetic and bioflocculants, with ML facilitating strain engineering, fermentation yield prediction, and polymer degradability assessments. Furthermore, the convergence of ML with IoT, digital twins, and life cycle assessment tools has accelerated the transition toward sustainable, adaptive, and low-impact treatment technologies. Despite its potential, challenges remain in data standardization, model interpretability, and real-world implementation. This review concludes by outlining strategic pathways for future research, including the development of open datasets, hybrid physics–ML frameworks, and interdisciplinary collaborations. By leveraging ML, the next generation of flocculant systems can be more effective, environmentally benign, and intelligently controlled, contributing to global water sustainability goals. Full article

This study developed a model to predict ships with high deficiency risk under Port State Control (PSC) through machine learning techniques, particularly the Random Forest algorithm. The study utilized actual ship inspection data from the Port of Singapore, comprehensively considering various operational and safety indicators of ships, including but not limited to flag state, ship age, past deficiencies, and detention history. By analyzing these factors in depth, this research enhances the efficiency and accuracy of PSC inspections, provides decision support for port authorities, and offers strategic guidance for shipping companies to comply with international safety standards. During the research process, I first conducted detailed data preprocessing, including data cleaning and feature selection, to ensure the effectiveness of model training. Using the Random Forest algorithm, I identified key factors influencing the detention risk of ships and established a risk prediction model accordingly. The model validation results indicated that factors such as ship age, tonnage, company performance, and flag state significantly affect whether a ship exhibits a high deficiency rate. In addition, this study explored the potential and limitations of applying the Random Forest model in predicting high deficiency risk under PSC, and proposed future research directions, including further model optimization and the development of real-time prediction systems. By achieving these goals, I hope to provide valuable experience for other global shipping hubs, promote higher international maritime safety standards, and contribute to the sustainable development of the global shipping industry. This research not only highlights the importance of machine learning in the maritime domain but also demonstrates the potential of data-driven decision-making in improving ship safety management and port inspection efficiency. It is hoped that this study will inspire more maritime practitioners and researchers to explore advanced data analytics techniques to address the increasingly complex challenges of global shipping. Full article

Heilongjiang Province, as China’s principal gateway for Russian timber imports, faces structural inefficiencies in the localization of wood-processing enterprises—characterized by ecological sensitivity, resource–industry mismatches, and uneven spatial distribution. To address these challenges, this study proposes a two-stage site selection framework that integrates Geographic Information Systems (GIS) with an enhanced Non-dominated Sorting Genetic Algorithm II (NSGA-II). The model aims to reconcile ecological protection with industrial efficiency by identifying optimal facility locations that minimize environmental impact, reduce construction and logistics costs, and enhance service coverage. Using spatially resolved multi-source datasets—including forest resource distribution, transportation networks, ecological redlines, and socioeconomic indicators—the GIS-based suitability analysis (Stage I) identified 16 candidate zones. Subsequently, a multi-objective optimization model (Stage II) was applied to minimize carbon intensity and cost while maximizing service accessibility. The improved NSGA-II algorithm achieved convergence within 700 iterations, generating 124 Pareto-optimal solutions and enabling a 23.7% reduction in transport-related CO₂ emissions. Beyond carbon mitigation, the model spatializes policy constraints and economic trade-offs into actionable infrastructure plans, contributing to regional sustainability goals and transboundary industrial coordination with Russia. It further demonstrates methodological generalizability for siting logistics-intensive and policy-sensitive facilities in other forestry-based economies. While the model does not yet account for temporal dynamics or agent behaviors, it provides a robust foundation for informed planning under China’s dual-carbon strategy and offers replicable insights for the global forest products supply chain. Full article

Balancing ecological conservation and rural livelihoods in protected areas remains a global challenge, particularly under strict land use regulations and economic development constraints. Territorial spatial planning (TSP) in an ecological function area (EFA) faces constraints such as land use restrictions, ecological redlines, and economic development limits. This study investigates how ecological land resources influence farmers’ incomes in ecological function areas (EFAs), with a focus on the moderating role of advanced livelihoods (ALI). Using an integrated Fixed-Effects–SVM–Genetic Algorithm framework, we quantify nonlinear policy-livelihood interactions and simulate multi-scenario governmental interventions (e.g., ecological investment, returning farmland to forest/RFF) across Beijing’s EFA, which can obtain the key findings as follows: (a) Ecological land resources have a significant positive effect on farmers’ incomes due to production-manner adjustment guided by governmental green strategy and corresponding TSP in an ecological restoration area of an EFA, while they have a non-significant impact in the core ecological reserve areas on account of the strict environmental protection restrictions on economic activities. (b) Differences in financial support between lower and higher economic development zones can bring about adverse impact results on farmers’ incomes in an EFA. (c) ALI significantly amplifies the positive impact of ecological land use on farmers’ incomes, demonstrating its critical role in bridging ecological and economic goals. (d) Sensitivity analysis results under RFF, targeted government investment, and ALI can maximize income gains through policy interaction from the government and farmer sides jointly. The above obtained results are beneficial to balance ecological protection and economic interests of farmers’ sustainably in an EFA. Full article

This study examines the establishment of a Hub for Circular Economy and Industrial Symbiosis (HUB-CEIS) centred on a forest biomass waste plant in Fundão, Portugal, presenting an innovative model for rural industrial symbiosis, circular economy governance, and sustainable waste management. Designed as a strategic node within a reverse supply chain, the hub facilitates the conversion of solid waste into renewable energy and high-value co-products, including green hydrogen, tailored for industrial and agricultural applications, with an estimated 120 ktCO₂/year reduction and 60 direct jobs. Aligned with the United Nations (UN) Sustainable Development Goals (SDGs) and the Paris Agreement, this initiative addresses global challenges such as decarbonization, resource efficiency, and the energy transition. Employing a mixed research methodology, this study integrates a comprehensive literature review, in-depth stakeholder interviews, and comparative case study analysis to formulate a governance framework fostering regional partnerships between industry, government, and local communities. The findings highlight Fundão’s potential to become a benchmark for rural industrial symbiosis, offering a replicable model for circularity in non-urban contexts, with a projected investment of USD 60 M. Special emphasis is placed on the green hydrogen value chain, positioning it as a key enabler for regional sustainability. This research underscores the importance of cross-sectoral collaboration in achieving scalable and efficient waste recovery processes. By delivering practical insights and a robust governance structure, the study contributes to the circular economy literature, providing actionable strategies for implementing rural reverse supply chains. Beyond validating waste valorization and renewable energy production, the proposed hub establishes a blueprint for sustainable rural industrial development, promoting long-term industrial symbiosis integration. Full article

This study provides a bibliometric analysis of life cycle assessments (LCAs) to explore the sustainability potential of mass timber buildings, focusing on glulam. The analysis highlights regional differences in carbon footprint performance within the ISO 14040 and EN 15978 frameworks. LCA results from representative countries across six continents show that wood buildings, compared to traditional materials, have a reduced carbon footprint. The geographical distribution of forest resources significantly influences the carbon footprint of glulam production. Europe and North America demonstrate optimal performance metrics (e.g., carbon sequestration), attributable to advanced technology and investment in long-term sustainable forest management. Our review research shows the lowest glulam carbon footprints (28–70% lower than traditional materials) due to clean energy and sustainable practices. In contrast, Asia and Africa exhibit systemic deficits, driven by resource scarcity, climatic stressors, and land-use pressures. South America and Oceania display transitional dynamics, with heterogeneous outcomes influenced by localized deforestation trends and conservation efficacy. Glulam buildings outperformed concrete and steel across 11–18 environmental categories, with carbon storage offsetting 30–47% of emissions and energy mixes cutting operational impacts by up to 67%. Circular strategies like recycling and prefabrication reduced end-of-life emissions by 12–29% and cut construction time and costs. Social benefits included job creation (e.g., 1 million in the EU) and improved well-being in wooden interiors. To further reduce carbon footprint disparities, this study emphasizes sustainable forest management, longer building lifespans, optimized energy mixes, shorter transport distances, advanced production technologies, and improved recycling systems. Additionally, the circular economy and social benefits of glulam buildings, such as reduced construction costs, value recovery, and job creation, are highlighted. In the future, prioritizing equitable partnerships and enhancing international exchanges of technical expertise will facilitate the adoption of sustainable practices in glulam buildings and advance decarbonization goals in the global building sector. Full article

The accelerated penetration of digital technology into natural ecosystems has led to the digital transformation of forest ecological spaces. Smart forestry, as a key pathway for digital-intelligence-enabled ecological governance, plays an important role in global sustainable development and multi-level governance. However, due to differences in functional positioning, resource capacity, and policy translation mechanisms, semantic shifts and disconnections arise between central policies, local policies, and practical implementation, thereby affecting policy execution and governance effectiveness. Fujian Province has been identified as a key pilot region for smart forestry practices in China, owing to its early adoption of informatization strategies and distinctive ecological conditions. This study employed the Latent Dirichlet Allocation (LDA) topic modeling method to construct a corpus of smart forestry texts, including central policies, local policies, and local media reports from 2010 to 2025. Seven potential themes were identified and categorized into three overarching dimensions: technological empowerment, governance mechanisms, and ecological goals. The results show that central policies emphasize macro strategy and ecological security, local policies focus on platform construction and governance coordination, and local practice features digital innovation and ecological value transformation. Three transmission paths are summarized to support smart forestry policy optimization and inform digital ecological governance globally. Full article

In recent years, forest education has become a critical element in global environmental governance. This study employed the CiteSpace tool to systematically analyze 2917 titles of the forest education literature from the Web of Science Core Collection. The goal was to explore the spatial and temporal trends, thematic evolution, and emerging research directions in this field. The research shows that in recent years, the annual number of published papers on forest education has been on a continuous upward trend, and the attention to the subject has increased significantly. The research field mainly takes the United States in North America as the core center, with the joint participation of scholars from Europe and Asia. The development trajectory of the discipline shows a trend of gradual expansion toward multidisciplinary intersections and multidisciplinary integration based on traditional forestry and environmental sciences. Research hotspots mainly focus on core issues such as forest management, climate change, ecosystem services, and biodiversity. In recent years, they have expanded to include willingness to pay, prevalence, and student groups. It is expected that the research focus in the coming years will be on the cross-cutting issues of integrating forests with the economy, social public health, environmental protection, and sustainable development. Full article

The Valencia region exemplifies the intricate interplay of climate, urbanization, and human interventions in managing hydrological systems amidst increasing environmental challenges. This study explores the escalating risks posed by flood events, emphasizing how anthropogenic factors—such as urban expansion, sediment exploitation, and inadequate land use—amplify the vulnerabilities to extreme weather patterns driven by abnormal Greenhouse Gas (GHG) concentration. Nature-based solutions (NBS) like floodplain restoration and dam removal are analyzed for their benefits in enhancing ecosystem resilience and biodiversity but are critiqued for unintended consequences, including accelerated river flow and sedimentation issues. This study further examines the impacts of forest fires, exacerbated by land abandonment and insufficient management practices, on soil erosion and runoff. A critical evaluation of global policies like the Sustainable Development Goals (SDGs) reveals the tension between aspirational targets and practical, locally-driven implementations. By advocating historical insights, ecological restoration practices, and community engagement, the findings highlight the importance of adaptive strategies to harmonize global frameworks with local realities through modeling and scaling simulations, offering a replicable model for sustainable flood mitigation and resilience building in Mediterranean contexts and beyond. Full article

This literature review critically examines the development and optimization of sustainable energy and exergy analysis software specifically designed for offshore wind farms, emphasizing the transformative role of machine learning (ML) in overcoming operational challenges. Offshore wind energy represents a cornerstone in the global transition to low-carbon economies due to its scalability and superior energy yields; however, its complex operational environment, characterized by harsh marine conditions and logistical constraints, necessitates innovative analytical tools. Traditional deterministic methods often fail to capture the dynamic interactions within wind farms, thereby underscoring the need for ML-integrated approaches that enhance precision in energy forecasting, fault detection, and exergy analysis. This PRISMA-ScR review synthesizes recent advancements in ML techniques, including Random Forest, Long Short-Term Memory networks, and hybrid models, demonstrating significant improvements in predictive accuracy and operational efficiency. In addition, it critically identifies current gaps in existing software tools, such as inadequate real-time data processing and limited user interface design, which hinder the practical implementation of ML solutions. By integrating theoretical insights with empirical evidence, this study proposes a unified framework that leverages ML algorithms to optimize turbine performance, reduce maintenance costs, and minimize environmental impacts. Emerging trends, such as incorporating digital twins and Internet of Things (IoT) technologies, further enhance the potential for real-time system monitoring and adaptive control. Overall, this review provides a comprehensive roadmap for the next generation of software tools to revolutionize offshore wind farm management, thereby aligning technological innovation with global renewable energy targets and sustainable development goals. Full article

With global urbanization on the rise, urban and peri-urban forests (UPFs) have emerged as a critical source of green infrastructure. This study conducts a comprehensive analysis of soil conservation (SC) services provided by UPFs across European Union (EU) member states. Utilizing an erosion modeling approach and open access earth observation (EO) data, the distribution and magnitude of SC services within UPFs are evaluated. Significant disparities in SC service supply among EU countries are revealed, with Mediterranean nations exhibiting higher values compared to central and northern European counterparts. The study underscores the pivotal role of UPFs as nature-based solutions (NbSs) in enhancing ecosystem service (ES) provision for citizen well-being. By integrating SC and ES concepts into forest management strategies, UPFs can effectively contribute to achieving Sustainable Development Goals (SDGs) and improving citizen well-being. This research provides valuable insights for EU policymakers and stakeholders, laying the groundwork for integrated UPF management strategies. Through prioritizing SC measures and adopting integrated approaches, policymakers can ensure the resilience and ecological integrity of UPFs, enhancing their capacity to provide vital ecosystem services in Europe’s urbanized landscapes. Full article

The Henan section of the Yellow River Basin (3.62 × 10⁴ km², 21.7% of Henan Province), a vital agro-industrial and politico-economic hub, faces frequent rainfall-induced geohazards. The 2021 “7·20” Zhengzhou disaster, causing 398 fatalities and CNY 120.06 billion loss, highlights its vulnerability to extreme weather. While machine learning (ML) aids geohazard assessment, rainfall-induced geological hazard susceptibility assessment (RGHSA) remains understudied, with single ML models lacking interpretability and precision for complex disaster data. This study presents a hybrid framework (IVM-ML) that integrates the Information Value Model (IVM) and ML. The framework uses historical disaster data and 11 factors (e.g., rainfall erosivity, relief amplitude) to calculate information values and construct a machine learning prediction model with these quantitative results. By combining IVM’s spatial analysis with ML’s predictive power, it addresses the limitations of conventional single models. ROC curve validation shows the Random Forest (RF) model in IVM-ML achieves the highest accuracy (AUC = 0.9599), outperforming standalone IVM (AUC = 0.7624). All models exhibit AUC values exceeding 0.75, demonstrating strong capability in capturing rainfall–hazard relationships and reliable predictive performance. Findings support RGHSA practices in the mid-Yellow River urban cluster, offering insights for sustainable risk management, land-use planning, and climate resilience. Bridging geoscience and data-driven methods, this study advances global sustainability goals for disaster reduction and environmental security in vulnerable riverine regions. Full article

The growing global interest in mitigating climate change implies an increased importance of sustainable development to achieve greenhouse gas emission reductions. The paper analyses the impact of key economic and environmental factors, such as the share of renewable energy, gross domestic product (GDP), fossil fuel consumption, final energy consumption in households and industry, and forest area, on greenhouse gas (GHG) emissions in the European Union (consisting of 27 members) and Poland for comparison—for the period from 1990 to 2023. The study fills a gap in identifying the cross-sectoral determinants of greenhouse gas emissions in the EU, focusing specifically on Poland and the whole EU region since the beginning of the European Union. The research involved the implementation of statistical analyses, dynamic analyses, correlation analyses, and regression analyses. The results showed an increase in the share of renewable energy, GDP, and forest area, which was negatively correlated with the volume of GHG emissions. In contrast, final fossil fuel consumption and final energy consumption in industry and households (to a slightly lesser extent) were also significantly but positively correlated. It is worth noting that the strength of calculated relationships differed for the EU and Poland. The study revealed trends and correlations that affect GHG and are relevant to policy implications for EU climate goals. Considering the various determinants of GHG emissions and Poland’s unique situation (high dependence on coal and a large share of heavy industry), conclusions were formulated for Poland’s and the EU’s climate policies in the context of the European Green Deal. Full article

Space heating consumption prediction is critical for energy management and efficiency, directly impacting sustainability and efforts to reduce greenhouse gas emissions. Accurate models enable better demand forecasting, promote the use of green energy, and support decarbonization goals. However, existing models often lack precision due to limited feature sets, suboptimal algorithm choices, and limited access to weather data, which reduces generalizability. This study addresses these gaps by evaluating various Machine Learning and Deep Learning models, including K-Nearest Neighbors, Support Vector Regression, Decision Trees, Linear Regression, XGBoost, Random Forest, Gradient Boosting, AdaBoost, Long Short-Term Memory, and Gated Recurrent Units. We utilized space heating consumption data from the European Central Bank Headquarters office as a case study. We employed a methodology that involved splitting the features into three categories based on the correlation and evaluating model performance using Mean Squared Error, Mean Absolute Error, Root Mean Squared Error, and R-squared metrics. Results indicate that XGBoost consistently outperformed other models, particularly when utilizing all available features, achieving an R² value of 0.966 using the weather data from the building weather station. This model’s superior performance underscores the importance of comprehensive feature sets for accurate predictions. The significance of this study lies in its contribution to sustainable energy management practices. By improving the accuracy of space heating consumption forecasts, our approach supports the efficient use of green energy resources, aiding in the global efforts towards decarbonization and reducing carbon footprints in urban environments. Full article