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Superhydrophobic magnetic nanomaterials (SHMNMs) are emerging as multifunctional platforms for efficient oil–water separation due to their combination of extreme water repellency, strong oil affinity, and external magnetic responsiveness. This review presents a comprehensive analysis of recent advances in the design, synthesis, and environmental application of SHMNMs. The theoretical foundations of superhydrophobicity and the physicochemical behavior of magnetic nanoparticles are first outlined, followed by discussion of their synergistic integration. Key fabrication techniques—such as sol–gel synthesis, electrospinning, dip-coating, laser-assisted processing, and the use of biomass-derived precursors—are critically assessed in terms of their ability to tailor surface morphology, chemical functionality, and long-term durability. The review further explores the mechanisms of oil adsorption, magnetic separation, and material reusability under realistic environmental conditions. Special attention is paid to the scalability, mechanical resilience, and environmental compatibility of SHMNMs in the context of water treatment technologies. Current limitations, including reduced efficiency in harsh media, potential environmental risks, and challenges in material regeneration, are discussed. This work provides a structured overview that could support the rational development of next-generation superhydrophobic materials tailored for sustainable and high-performance separation of oil and organic pollutants from water. Full article

Economic systems face critical challenges, including widening income inequality, unemployment driven by automation, mounting public debt, and environmental degradation. This study introduces Economocracy as a transformative framework aimed at addressing these systemic issues by integrating democratic principles into economic decision-making to achieve social equity, economic efficiency, and environmental sustainability. The research focuses on two core mechanisms: Economic Productive Resets (EPRs) and Economic Periodic Injections (EPIs). EPRs facilitate proportional redistribution of resources to reduce income disparities, while EPIs target investments to stimulate job creation, mitigate automion-related job displacement, and support sustainable development. The study employs a theoretical and analytical methodology, developing mathematical models to quantify the impact of EPRs and EPIs on key economic indicators, including the Gini coefficient for inequality, unemployment rates, average wages, and job displacement due to automation. Hypothetical scenarios simulate baseline conditions, EPR implementation, and the combined application of EPRs and EPIs. The methodology is threefold: (1) a mathematical–theoretical validation of the Cycle of Money framework, establishing internal consistency; (2) an econometric analysis using global historical data (2000–2023) to evaluate the correlation between GNI per capita, Gini coefficient, and average wages; and (3) scenario simulations and Difference-in-Differences (DiD) estimates to test the systemic impact of implementing EPR/EPI policies on inequality and labor outcomes. The models are further strengthened through tools such as OLS regression, and Impulse results to assess causality and dynamic interactions. Empirical results confirm that EPR/EPI can substantially reduce income inequality and unemployment, while increasing wage levels, findings supported by both the theoretical architecture and data-driven outcomes. Results demonstrate that Economocracy can significantly lower income inequality, reduce unemployment, increase wages, and mitigate automation’s effects on the labor market. These findings highlight Economocracy’s potential as a viable alternative to traditional economic systems, offering a sustainable pathway that harmonizes growth, social justice, and environmental stewardship in the global economy. Economocracy demonstrates potential to reduce debt per capita by increasing the efficiency of public resource allocation and enhancing average income levels. As EPIs stimulate employment and productivity while EPRs moderate inequality, the resulting economic growth expands the tax base and alleviates fiscal pressures. These dynamics lead to lower per capita debt burdens over time. The analysis is situated within the broader discourse of institutional economics to demonstrate that Economocracy is not merely a policy correction but a new economic system akin to democracy in political life. Full article

With the rapid development of the social economy, human activities have increasingly disrupted water environments, and the continuous input of pollutants poses significant challenges for water environment management. Taking the Xiaoxingkai Lake basin as the study area, this paper develops a social–economic–water environment model based on the system dynamics methodology, incorporating subsystems for population, agriculture, and water pollution. The model focuses on four key indicators of pollution severity, namely, total nitrogen (TN), total phosphorus (TP), chemical oxygen demand (COD), and ammonia nitrogen (NH₃-N), and simulates the changes in pollutant loads entering the river under five different scenarios from 2020 to 2030. The results show that agricultural non-point sources are the primary contributors to TN (79.5%) and TP (73.7%), while COD primarily originates from domestic sources (64.2%). NH₃-N is mainly influenced by urban domestic activities (44.7%) and agricultural cultivation (41.2%). Under the status quo development scenario, pollutant loads continue to rise, with more pronounced increases under the economic development scenario, thus posing significant sustainability risks. The pollution control enhancement scenario is most effective in controlling pollutants, but it does not promote socio-economic development and has high implementation costs, failing to achieve coordinated socio-economic and environmental development in the region. The dual-reinforcement scenario and moderate-reinforcement scenario achieve a balance between pollution control and economic development, with the moderate-reinforcement scenario being more suitable for long-term regional development. The findings can provide a scientific basis for water resource management and planning in the Xiaoxingkai Lake basin. Full article

Seeds of Cucurbitaceae crops represent a promising yet underexplored source of bioactive compounds with potential applications beyond nutrition, particularly in the cosmetics industry. This review examines the seeds of Citrullus lanatus (watermelon), Cucumis melo (melon), and Cucurbita pepo (pumpkin), focusing on their biochemical composition and evaluating their functional value in natural cosmetic development. Although these fruits are widely consumed, industrial processing generates substantial seed by-products that are often discarded. These seeds are rich in polyunsaturated fatty acids, proteins, carbohydrates, and phytochemicals, positioning them as sustainable raw materials for value-added applications. The incorporation of seed-derived extracts into cosmetic formulations offers multiple skin and hair benefits, including antioxidant activity, hydration, and support in managing conditions such as hyperpigmentation, acne, and psoriasis. They also contribute to hair care by improving oil balance, reducing frizz, and enhancing strand nourishment. However, challenges such as environmental instability and low dermal permeability of seed oils have prompted interest in nanoencapsulation technologies to improve delivery, stability, and efficacy. This review summarizes current scientific findings and highlights the potential of Cucurbitaceae seeds as innovative and sustainable ingredients for cosmetic and personal care applications. Full article

This research contributes to advance the sustainable production of biofuels and provides insights into the energy and exergy assessment of bio-oil, which is essential for developing environmentally friendly energy production solutions. Energy and exergy analyses were performed to evaluate the pyrolysis of beech wood biomass at 500 °C in an Auger reactor. To improve the quality of the obtained bio-oil, its catalytic deoxygenation was performed within an in-line fluidized catalytic bed reactor using a catalyst based on HZSM5 zeolite modified with 5 wt.% Iron (5%FeHZSM-5). A thermodynamic analysis of the catalytic and non-catalytic pyrolysis system was carried out, as well as a comparative study of the calculation methods for the energy and exergy evaluation for bio-oil. The required heat for pyrolysis was found to be 1.2 MJ/kg_biomass in the case of non-catalytic treatment and 3.46 MJ/kg_biomass in the presence of the zeolite-based catalyst. The exergy efficiency in the Auger reactor was 90.3%. Using the catalytic system coupled to the Auger reactor, this efficiency increased to 91.6%, leading to less energy degradation. Calculating the total energy and total exergy of the bio-oil using two different methods showed a difference of 6%. In the first method, only the energy contributions of the model compounds, corresponding to the major compounds of each chemical family of bio-oil, were considered. In contrast, in the second method, all molecules identified in the bio-oil were considered for the calculation. The second method proved to be more suitable for thermodynamic analysis. The novelties of this work concern the thermodynamic analysis of a coupled system of an Auger biomass pyrolysis reactor and a fluidized bed catalytic deoxygenation reactor on the one hand, and the use of all the molecules identified in the oily phase for the evaluation of energy and exergy on the other hand. Full article

Over the past 30 years, the City of Portland, Oregon, USA, has emerged as a national leader in green stormwater infrastructure (GSI). The initial impetus for implementing sustainable stormwater infrastructure in Portland stemmed from concerns about flooding and water quality in the city’s two major rivers, the Columbia and the Willamette. Heavy rainfall often led to combined sewer overflows, significantly polluting these waterways. A partial solution was the construction of “The Big Pipe” project, a large-scale stormwater containment system designed to filter and regulate overflow. However, Portland has taken a more comprehensive and long-term approach by integrating sustainable stormwater management into urban planning. Over the past three decades, the city has successfully implemented GSI to mitigate these challenges. Low-impact development strategies, such as bioswales, green streets, and permeable surfaces, have been widely adopted in streetscapes, pathways, and parking areas, enhancing both environmental resilience and urban livability. This perspective highlights the history of the implementation of Portland’s GSI programs, current design and performance standards, and challenges and lessons learned throughout Portland’s recent history. Innovative approaches to managing runoff have not only improved stormwater control but also enhanced green spaces and contributed to the city’s overall climate resilience while addressing economic well-being and social equity. Portland’s success is a result of strong policy support, effective integration of green and gray infrastructure, and active community involvement. As climate change intensifies, cities need holistic, adaptive, and community-centered approaches to urban stormwater management. Portland’s experience offers valuable insights for cities seeking to expand their GSI amid growing concerns about climate resilience, equity, and aging infrastructure. Full article

The resource utilization of construction waste and industrial solid waste is a crucial aspect in promoting global urbanization and sustainable development. This study focuses on the preparation of mine backfill materials using construction waste in combination with various industrial solid wastes—ground granulated blast furnace slag (GGBFS), fly ash (FA), silica fume (SF), phosphorus slag (PS), fly ash–phosphorus slag–phosphogypsum composite (FA-PS-PG), and fly ash–phosphorus slag–β-phosphogypsum composite (FA-PS-βPG)—under different substitution rates (50%, 55%, 60%) as control parameters. A total of 19 mix proportions were investigated, evaluating their slump, dry density, compressive strength, uniaxial compressive stress–strain relationship, micromorphology, and phase composition. The results indicate that, compared to backfill materials prepared with pure cement, the incorporation of industrial solid wastes improves the fluidity of the backfill materials. At 56 days, the constitutive model parameter a increased to varying degrees, while parameter b decreased, indicating enhanced ductility. The compressive strength was consistently higher with PS at all substitution rates. The FA-PS-PG mixture with a 50% substitution rate achieved the highest 56-day compressive strength of 8.02 MPa. These findings can facilitate the application of construction waste and industrial solid waste in mine backfilling projects, delivering economic, environmental, and resource-related benefits. Full article

With the growth of the global population and the increasing scarcity of arable land, traditional agricultural production is confronted with multiple challenges, such as efficiency improvement, precision operation, and sustainable development. The progressive advancement of artificial intelligence (AI) technology has created a transformative opportunity for the intelligent upgrade of agricultural equipment. This article systematically presents recent progress in computer vision, machine learning (ML), and intelligent sensing. The key innovations are highlighted in areas such as object detection and recognition (e.g., a K-nearest neighbor (KNN) achieved 98% accuracy in distinguishing vibration signals across operation stages); autonomous navigation and path planning (e.g., a deep reinforcement learning (DRL)-optimized task planner for multi-arm harvesting robots reduced execution time by 10.7%); state perception (e.g., a multilayer perceptron (MLP) yielded 96.9% accuracy in plug seedling health classification); and precision control (e.g., an intelligent multi-module coordinated control system achieved a transplanting efficiency of 5000 plants/h). The findings reveal a deep integration of AI models with multimodal perception technologies, significantly improving the operational efficiency, resource utilization, and environmental adaptability of agricultural equipment. This integration is catalyzing the transition toward intelligent, automated, and sustainable agricultural systems. Nevertheless, intelligent agricultural equipment still faces technical challenges regarding data sample acquisition, adaptation to complex field environments, and the coordination between algorithms and hardware. Looking ahead, the convergence of digital twin (DT) technology, edge computing, and big data-driven collaborative optimization is expected to become the core of next-generation intelligent agricultural systems. These technologies have the potential to overcome current limitations in perception and decision-making, ultimately enabling intelligent management and autonomous decision-making across the entire agricultural production chain. This article aims to provide a comprehensive foundation for advancing agricultural modernization and supporting green, sustainable development. Full article

Addressing climate change and advancing clean energy transitions demand holistic approaches that capture complex, interconnected system behaviors. This review focuses on the application of causal loop diagrams (CLDs) as a core systems-thinking methodology to understand and manage dynamic feedback within environmental, social, and technological domains. CLDs visually map the reinforcing and balancing loops that drive climate risks, clean energy adoption, and sustainable development, offering intuitive insights into system structure and behavior. Through a synthesis of empirical studies and case examples, this paper demonstrates how CLDs help identify leverage points in renewable energy policy, carbon management, and ecosystem resilience. Despite their strengths in simplifying complexity and enhancing stakeholder communication, challenges remain—including data gaps, model validation, and the integration of diverse knowledge systems. The review also examines recent innovations that improve CLD effectiveness, such as hybrid modeling approaches and digital tools that enhance transparency and decision support. By emphasizing CLDs’ unique capacity to reveal feedback mechanisms critical for climate action and energy planning, this study provides actionable recommendations for researchers, policymakers, and practitioners seeking to leverage systems thinking for transformative, sustainable solutions. Full article

Continuous progress in the sector of additive manufacturing has drastically aided the design and fabrication of urban furniture, offering high levels of customization and adaptability. This work looks into the potential of 3D printing to transform urban public spaces by allowing for the creation of functional, aesthetically pleasing, and user-centered furniture solutions. Through additive manufacturing processes, urban furniture can be tailored to meet the unique needs of diverse communities, allowing for the extended usage of sustainable materials, modular designs, and smart technologies. The flexibility of 3D printing also promotes the fabrication of complex, intricate designs that would be difficult or cost-prohibitive using traditional methods. Additionally, 3D-printed furniture can be optimized for specific environmental conditions, providing solutions that enhance accessibility, improve comfort, and promote inclusivity. The various advantages of 3D-printed urban furniture are examined, including reduced material waste and the ability to rapidly prototype and iterate designs alongside the potential for on-demand, local production. By embedding sensors and IoT devices, 3D-printed furniture can also contribute to the development of smart cities, providing real-time data for urban management and improving the overall user experience. As cities continue to encourage and adopt sustainable and innovative solutions, 3D printing is believed to play a crucial role in future urban infrastructure planning. Full article

Nitrous oxide (${\mathrm{N}}_2\mathrm{O}$) emissions from agriculture are rising due to increased fertilizer use and intensive farming, posing a major challenge for climate mitigation. This study introduces a novel reinforcement learning (RL) framework to optimize farm management strategies that balance crop productivity with environmental impact, particularly ${\mathrm{N}}_2\mathrm{O}$ emissions. By modeling agricultural decision-making as a partially observable Markov decision process (POMDP), the framework accounts for uncertainties in environmental conditions and observational data. The approach integrates deep Q-learning with recurrent neural networks (RNNs) to train adaptive agents within a simulated farming environment. A Probabilistic Deep Learning (PDL) model was developed to estimate ${\mathrm{N}}_2\mathrm{O}$ emissions, achieving a high Prediction Interval Coverage Probability (PICP) of 0.937 within a 95% confidence interval on the available dataset. While the PDL model’s generalizability is currently constrained by the limited observational data, the RL framework itself is designed for broad applicability, capable of extending to diverse agricultural practices and environmental conditions. Results demonstrate that RL agents reduce ${\mathrm{N}}_2\mathrm{O}$ emissions without compromising yields, even under climatic variability. The framework’s flexibility allows for future integration of expanded datasets or alternative emission models, ensuring scalability as more field data becomes available. This work highlights the potential of artificial intelligence to advance climate-smart agriculture by simultaneously addressing productivity and sustainability goals in dynamic real-world settings. Full article

The increasing demand for food necessitates that agri-food systems adopt innovative techniques to enhance food production while optimizing the use of limited resources, such as water. In agriculture, hydrogels are being increasingly used to enhance water retention and reduce irrigation requirements. However, most of these materials are based on synthetic polymers that are not biodegradable. This raises serious environmental and health concerns, highlighting the urgent need for sustainable, biodegradable alternatives. Biomass-derived from agro-industrial waste presents a substantial potential for producing hydrogels, which can effectively function as water collectors and suppliers for crops. This review article provides a comprehensive overview of recent advancements in the application of agro-industrial waste for the formulation of hydrogels. Additionally, it offers a critical analysis of the development of hydrogels utilizing natural and compostable materials. Agro-industrial and food waste, which are rich in hemicellulose and cellulose, have been utilized to enhance the mechanical properties and water absorption capacity of hydrogels. These biomaterials hold significant potential for the development of effective hydrogels in agricultural applications; they can be either hybrid or natural materials that exhibit efficacy in enhancing seed germination, improving water retention capabilities, and facilitating the controlled release of fertilizers. Natural hydrogels derived from agro-industrial waste present a sustainable technological alternative that is environmentally benign. Full article

In an era of rapid technological advancement—particularly with the accelerated development of artificial intelligence and digital technologies—entrepreneurship enables firms to dynamically adjust their strategies in response to environmental uncertainty and helps them maintain sustainable competitive advantages over time. As a key concept in entrepreneurship research, entrepreneurial orientation (EO) has long attracted scholarly attention. However, existing studies on EO have primarily focused on its specific outcomes, while insufficient attention has been paid to its antecedents from the perspective of internal threats. Under the threat of performance shortfalls, firms’ strategic choices are influenced not only by resource constraints but also by managerial cognitive biases. Drawing on Behavioral Theory of the Firm, we explore the moderating roles of managerial overconfidence and myopia in the relationship between performance shortfalls and EO. This study aims to uncover the cognitive “black box” behind why some firms are more likely to trigger entrepreneurial behavior in adverse situations. Based on panel data from 2822 A-share listed companies in China spanning the period from 2009 to 2020, and using a fixed-effects regression model, our findings indicate that both historical and social performance shortfalls have significant positive effects on EO. Further analysis reveals that the positive impact of performance shortfalls on EO is attenuated under conditions of heightened managerial overconfidence and myopia. By enriching the boundary conditions of EO from a cognitive perspective, this study provides a theoretical explanation for how firms can engage in entrepreneurial behavior under threat by reducing cognitive biases, thereby offering both theoretical and managerial insights into how firms can maintain sustainable development under crisis conditions. 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

This study analyzes the strategic and institutional frameworks that precede the formulation of trade agreements, with a focus on the European Union’s external action and its link to the Sustainable Development Goals. Based on a documentary research design, this study examines official documents from the EU and the United Nations, as well as the academic literature indexed in Scopus and Web of Science. The methodological process involved four phases: systematic search, selection and classification, inductive content coding, and interpretative analysis. Through this process, this study identifies discursive patterns, normative tensions, and policy orientations that reveal the EU’s evolving approach to sustainable trade governance. The findings highlight the existence of a growing institutional alignment between trade policy and sustainable development frameworks, yet also expose persistent gaps in coherence and implementation. This article contributes to the academic debate by offering a critical and structured analytical lens to understand how trade agreements are politically and institutionally prefigured before their negotiation phase. Full article