Sustainable Development Goal 7: Affordable and Clean Energy (47852)

The need for a green, clean, and sustainable economy has pushed Western societies to create a new economic system to help local economies achieve their primary targets. Many scholars claim that the uncertainty of climate change and the contemporary necessity for environmental protection have led most advanced countries to reconsider and redesign their economies, adding environmental management into every reference system. The European Union’s response to that need is the European Green Deal (EGD). The EGD represents one of the European Union’s major strategies to achieve climate neutrality by 2050 and resets the Commission’s commitment to tackling climate and environmental-related challenges. Creating a fair, healthy, and environmentally friendly food system is essential for that effort toward sustainability, which is why agriculture and farmers play an important role in this transition. Understanding farmers’ perspectives on the EGD is essential for successfully implementing its policies. The EU has already launched a strategic dialogue on the future of EU agriculture. This research investigates farmers’ views on the EGD through a focus group approach, providing a qualitative understanding of their perceptions, concerns, and suggestions for policy improvements. In addition, this research will try to present recommendations for future research. Full article

Climate action has been deemed as fundamental to counteract the impacts of rising global temperatures on health which will disproportionately affect low-income populations, racial and ethnic minorities, women, and other historically marginalized groups. Along with poverty reduction, inequality mitigation, gender equality promotion, and public health protection, climate action has been recognized as a fundamental goal for achieving Sustainable Development Goals (SDGs). However, despite growing recognition of the need to align climate action with development goals, there is a knowledge gap regarding how the implementation of climate change mitigation and adaptation policies impacts social inequalities. To address this knowledge gap, this document proposes a scoping review protocol aimed at identifying and synthesizing research that examines the impacts of climate policies on inequalities at the subnational scales, within the transport, infrastructure and health. The objective of this review is to map existing evidence, identify conceptual and empirical gaps and inform policy strategies that promote climate action in line with values of social justice and equality. Full article

To address the conflicting demands of lightweight materials and high load-bearing capacity in high-end fields such as aerospace and biomedical engineering, there is an urgent need to conduct research on the mechanical behavior and response mechanism of porous titanium alloy structures. In this paper, a combination of experimental testing, numerical simulation, and theoretical analysis was employed to conduct the research. A titanium alloy porous structure with different porosities was constructed based on classical three-period minimal surface optimization, and its preparation was completed using advanced selective laser melting technology. A multidimensional characterization experimental device was established to accurately obtain its mechanical performance data. It was found that the mechanical behavior of the structures is insensitive to loading rates, but more sensitive to their structural volume fraction. The quantitative characterization of microstructure damage and fracture morphology, as well as the identification of failure modes, indicates that the microstructure damage of the porous metal exhibits a ductile–brittle synergistic damage characteristic. By combining high-precision numerical simulation technology, the damage modes and damage evolution laws of porous metal structures in titanium alloys were comprehensively elucidated. By analyzing energy dissipation and constructing evaluation indicators for energy absorption efficiency, the energy absorption characteristics of the porous metal structure were elucidated, and the interaction behavior and correlation mode between the platform stress and the structural volume fraction of the porous metal structure were accurately described. Full article

ZnO thin-film ultraviolet photodetectors are widely used in the military, space, environmental protection, medicine, and other fields. Accurate printing of ZnO photoelectric-sensitive films plays a key role in the detection results. Therefore, obtaining printing technology with a simple process and high precision has become a challenge for ZnO photoelectrically sensitive films. By adjusting the distance between the nozzle and the collecting plate, the jet is atomized in a straight line and deposited directly on the collecting plate, which effectively improves the stability and controllability of the jet spraying and deposition processes. ZnO thin films with a uniform distribution of nanoparticles, significantly improved density, and controllable deposition area linewidth were successfully prepared. The effects of different ZnO film structures on the performance of ultraviolet photodetectors were tested. When the ultraviolet light intensity is 500, 1000, and 2500 mW/cm², the $I_{light}$ of the photodetector is 4.62, 9.38, 14.67 mA, The on/off ratio ($I_{light}$/$I_{dark}$) is 20.7, 42.1, 65.8, implying satisfactory photoelectric performance as well as high stability and repeatability, providing an effective technical means for the precise printing application of micro-nano functional devices. Full article

COVID-19 significantly disrupted the progress of the SDGs globally, including in Saudi Arabia. This study explores the progression of SDGs in Saudi Arabia during and after COVID-19, focusing on four dimensions: financial, socioeconomic, health, and environmental. A qualitative approach was employed, involving 19 semi-structured interviews conducted in two rounds (during and post COVID-19). Thematic analysis, conducted using NVivo 14.0, identified four main themes and 16 subthemes, which align with the SDG dimensions. The study revealed significant disruptions across four SDG dimensions during the pandemic. These included economic downturns, increased poverty, strained healthcare systems, and environmental changes. Guided by systems theory as an analytical lens, the study findings indicate that while COVID-19 caused disruptions across SDGs, it also acted as a catalyst for transformational shifts across interconnected SDG domains. The post-pandemic period has shown recovery, including economic growth, enhanced gender equality, improved mental health services, and a renewed focus on sustainability. Six cross-thematic themes emerged: (1) economic recovery and employment, (2) gender equity and education, (3) mental health and healthcare, (4) poverty reduction and food security, (5) environmental sustainability, and (6) digital transformation resilience. Based on these insights, the study provides recommendations for Saudi policymakers to align SDG progress with Saudi Vision 2030 in line with pragmatic sustainability. Full article

Renewable energy’s growing penetration erodes traditional power systems’ inherent dynamic symmetry—balanced inertia, damping, and frequency response. This paper proposes a self-adaptive virtual synchronous generator (VSG) control strategy for a photovoltaic hybrid energy storage system (PV-HESS) based on a radial basis function (RBF) neural network. The strategy establishes a dynamic adjustment framework for inertia and damping parameters via online learning, demonstrating enhanced system stability and robustness compared to conventional VSG methods. In the structural design, the DC-side energy storage system integrates a passive filter to decouple high- and low-frequency power components, with the supercapacitor attenuating high-frequency power fluctuations and the battery stabilizing low-frequency power variations. A small-signal model of the VSG active power loop is developed, through which the parameter ranges for rotational inertia (J) and damping coefficient (D) are determined by comprehensively considering the active loop cutoff frequency, grid connection standards, stability margin, and frequency regulation time. Building on this analysis, an adaptive parameter control strategy based on an RBF neural network is proposed. Case studies show that under various conditions, the proposed RBF strategy significantly outperforms conventional methods, enhancing key performance metrics in stability and dynamic response by 16.98% to 70.37%. Full article

Forests play a central role in climate change mitigation by acting as biogenic carbon reservoirs and providing renewable biomass for energy systems. In Portugal, where fire-prone landscapes and species composition dynamics pose increasing management challenges, understanding the carbon storage potential of forest biomass is crucial for designing effective decarbonization strategies. This study provides a comprehensive characterization of the Portuguese forest and quantifies the biogenic carbon stored in live and dead biomass across the main forest species. Species-specific carbon contents, rather than the conventional 50% assumption widely used in the literature, were applied to National Forest Inventory data, enabling more realistic and representative carbon stock estimates expressed in kilotonnes of CO₂ equivalent. While the approach relies on inventory-based biomass data and literature-derived carbon fractions and is therefore subject to associated uncertainties, it provides an improved representation of species-level carbon storage at the national scale. Results show that Pinus pinaster, Eucalyptus globulus, and Quercus suber together represent the largest share of carbon storage, with approximately 300,000 kilotonnes of CO₂ equivalent retained in living trees. Wood is the dominant carbon pool, but roots and branches also account for a substantial fraction, emphasizing the need to consider both above- and below-ground biomass in carbon accounting. In parallel, a bibliometric analysis based on the systematic evaluation of scientific publications was conducted to characterize the evolution, thematic focus, and geographic distribution of global research on forest-based biogenic carbon. This analysis reveals a rapidly expanding scientific interest in biogenic carbon, particularly since 2020, reflecting its growing relevance in climate change mitigation frameworks. Overall, the results underscore both the strategic importance of Portuguese forests and the alignment of this research with the broader international scientific agenda on forest-based biogenic carbon. Full article

The increasing integration of photovoltaic (PV) systems into modern power grids poses significant operational challenges, including variability in solar generation, fluctuations in demand, degradation of power quality, and reduced reliability under uncertain conditions. Addressing these challenges requires advanced control strategies that can manage uncertainty while coordinating storage, inverter-level actions, and power quality functions. This paper proposes a unified stochastic Model Predictive Control (SMPC) framework for the optimal management of photovoltaic (PV) systems under uncertainty. The approach integrates chance-constrained optimization with Value-at-Risk (VaR) modeling to ensure system reliability under variable solar irradiance and demand profiles. Unlike conventional deterministic MPCs, the proposed method explicitly addresses stochastic disturbances while optimizing energy storage, generation, and power quality. The framework introduces a hierarchical control architecture, where a centralized SMPC coordinates global energy flows, and decentralized inverter agents perform local Maximum Power Point Tracking (MPPT) and harmonic compensation based on the instantaneous power theory. Simulation results demonstrate significant improvements in energy efficiency from 78% to 85%, constraint satisfaction from 85% to 96%, total harmonic distortion reduction by 25%, and resilience (energy supply loss reduced from 15% to 5% under fault conditions), compared to classical deterministic approaches. This comprehensive methodology offers a robust solution for integrating PV systems into modern grids, addressing sustainability and reliability goals under uncertainty. Full article

Modern data centers are becoming increasingly energy-intensive as AI workloads, hyperscale architectures, and high-power processors push power demand to unprecedented levels. This work examines the sources of rising energy consumption, including evolving IT load dynamics, variability, and the limitations of legacy AC-based power-delivery architectures. These challenges amplify the environmental impact of data centers and highlight their growing influence on global electricity systems. The paper analyzes why conventional grid-tied designs are insufficient for meeting future efficiency, flexibility, and sustainability requirements and surveys emerging solutions centered on DC microgrids, high-voltage DC distribution, and advanced wide-bandgap power electronics. The review further discusses the technical enablers that allow data centers to integrate renewable energy and energy storage more effectively, including simplified conversion chains, coordinated control hierarchies, and demand-aware workload management. Through documented strategies such as on-site renewable deployment, off-site procurement, hybrid energy systems, and flexible demand shaping, the study shows how data centers are increasingly positioned not only as major energy consumers but also as key catalysts for accelerating renewable-energy adoption. Overall, the findings illustrate how the evolving power architectures of large-scale data centers can drive innovation and growth across the renewable energy sector. Full article

Agro-industrial facilities host processes and products that are highly sensitive to thermal fluctuations. Given the projected increase in air temperatures in tropical regions due to climate change, improving indoor thermal conditions in these facilities has become critically important. Conventional cooling systems are widely used to maintain adequate indoor temperatures; however, they are associated with high energy consumption. In this context, Ground Source Heat Pump (GSHP) technology emerges as a promising alternative to reduce cooling loads by exchanging heat with the ground. This study evaluates the reductions in cooling energy consumption and the return on investment of a GSHP system integrated with conventional cooling system, considering a prototype agro-industrial room located in two ecotones of the Brazilian Midwest: the Amazon Forest (AF) and Brazilian Savanna (BS). Building energy simulations were performed using EnergyPlus software v. 9 under current climate conditions and climate change scenarios for 2050 and 2080. Initially, the prototype room was conditioned using a conventional HVAC system; subsequently, a GSHP system was integrated to enhance energy efficiency and reduce energy demand. Under current conditions, cooling energy demand in the BS and AF ecotones is projected to increase by 16.5% and 18.3% by 2050, and by 24.5% and 23.5% by 2080, respectively. The payback analysis indicates that the average return on investment improves under future climate scenarios, decreasing from 14.5 years under current conditions to 10.13 years in 2050 and 9.86 years in 2080. The findings contribute to understanding the thermal resilience and economic feasibility of ground-coupled heat exchangers as a sustainable strategy for mitigating climate change impacts in the agro-industrial sector. Full article

This study presents the first comprehensive techno-economic assessment of wind-based green hydrogen production across Senegal, a country highly dependent on fossil fuel imports. Using a novel integrated approach combining 30 years of ERA5 reanalysis data (1993–2023), turbine performance modeling and electrolyzer comparison, it fills an important gap for renewable hydrogen development in West Africa. Wind resources were analyzed at multiple altitudes, revealing strong potential in both coastal and northeastern regions, particularly during the dry season, with higher wind speeds at higher turbine heights. Four turbines (Vestas_150, Goldwind_155, Vestas_126 and Nordex_N100) and two electrolyzer types (alkaline and PEM) were evaluated. The alkaline system performed best. Vestas_150 and Goldwind_155 achieved the highest hydrogen yields of 241 and 183 tons/year and CO₂ reductions of 2951 and 2241 tons/year, generating carbon credits of 0.118 M$ and 0.089 M$, respectively. Their levelized cost of electricity remained low (0.042 and 0.039 $/kWh), while smaller turbines showed higher costs. Vestas_150 also had the shortest payback period of 2.16 years, making it the most competitive option. Sensitivity analyses showed that longer system lifespans and high-performance turbines significantly reduce the levelized cost of hydrogen. Priority investment zones include Saint-Louis, Matam, Louga and Tambacounda, with levelized cost of hydrogen values as low as 3.4 $/kg. Full article

With the rapid development of mobile edge computing (MEC) and unmanned aerial vehicle (UAV) communication networks, UAV-assisted edge computing has emerged as a promising paradigm for low-latency and energy-efficient computation. However, the time-varying nature of air-to-ground channels and the coupling between UAV trajectories and computation offloading decisions significantly increase system complexity. To address these challenges, this paper proposes an Adaptive UAV Edge Intelligence Framework (AUEIF) for joint UAV computation offloading and trajectory optimization under dynamic channels. Specifically, a dynamic graph-based system model is constructed to characterize the spatio-temporal correlation between UAV motion and channel variations. A hierarchical reinforcement learning-based optimization framework is developed, in which a high-level actor–critic module is responsible for generating coarse-grained UAV flight trajectories, while a low-level deep Q-network performs fine-grained optimization of task offloading ratios and computational resource allocation in real time. In addition, an adaptive channel prediction module leveraging long short-term memory (LSTM) networks is integrated to model temporal channel state transitions and to assist policy learning and updates. Extensive simulation results demonstrate that the proposed AUEIF achieves significant improvements in end-to-end latency, energy efficiency, and overall system stability compared with conventional deep reinforcement learning approaches and heuristic-based schemes while exhibiting strong robustness against dynamic and fluctuating wireless channel conditions. Full article

In recent years, diseases, environmental pollution, and food safety issues have seriously threatened global health, generating international concern. Many existing detection strategies used to deal with the above problems have high accuracy and sensitivity, but usually rely on large-sized, complex instruments and professional technicians, which are not suitable for on-site testing. Therefore, it is imperative to develop highly sensitive, rapid, and portable analytical methods. Recently, microfluidic paper-based analytical devices (μPADs) have been recognized as a highly promising microfluidic device substrate to deal with the issues existing in medical, environmental, and food safety, etc., due to their advantages, including environmental-friendliness, high flexibility, low cost, and mature technology. This review comprehensively summarizes the recent advances in μPADs. We first overview the development of paper-based materials and their core fabrication techniques, followed by a detailed discussion on the material selection and detection mechanisms of the devices. The review also provides an assessment of the application achievements of μPADs in medical diagnostics, environmental analysis, and food safety monitoring. Finally, current challenges in the field are summarized and future research directions and prospects are proposed. Full article

A comprehensive assessment of photovoltaic (PV) enhancement technologies requires a metric that incorporates not only performance gains but also economic viability and system compatibility. This paper introduces the Lifespan-, Surface-area-, and Cost-Adjusted Effectiveness factor (${\mathrm{F}}_{\mathrm{L}\mathrm{S}\mathrm{C}\mathrm{A}\mathrm{E}}$), a novel multi-dimensional indicator designed to evaluate the overall effectiveness of PV enhancers—such as passive and active coolers—by jointly accounting for lifespan alignment, spatial integration, and cost-to-performance trade-offs. Unlike conventional performance metrics, ${\mathrm{F}}_{\mathrm{L}\mathrm{S}\mathrm{C}\mathrm{A}\mathrm{E}}$ captures the interdependence between technical and economic parameters by integrating the enhancer’s contribution to net power output, its operational lifespan relative to the PV module, its physical area relative to the PV surface, and the manufacturing cost in relation to the cost per watt of PV power. A series of analytical case studies were conducted involving four PV cooler patterns with varying power outputs, costs, sizes, and lifespans. The findings demonstrate that ${\mathrm{F}}_{\mathrm{L}\mathrm{S}\mathrm{C}\mathrm{A}\mathrm{E}}$ is highly sensitive to power enhancement and cost fluctuations, and it penalizes oversizing or lifespan mismatches. Furthermore, the minimum threshold effectiveness (${\mathrm{F}}_{\mathrm{L}\mathrm{S}\mathrm{C}\mathrm{A}\mathrm{E},\mathrm{m}\mathrm{i}\mathrm{n}}$), derived from the ratio of actual to maximum PV output under standard test conditions, provides a robust baseline for determining system viability. The proposed metric proves to be a reliable and scalable tool for the comparative evaluation of PV enhancement strategies. It enables stakeholders to make informed decisions about design optimization, financial planning, and policy formulation. ${\mathrm{F}}_{\mathrm{L}\mathrm{S}\mathrm{C}\mathrm{A}\mathrm{E}}$ serves as a critical advancement in PV performance analysis, offering a unified framework to assess not only energy gain but also the practicality and sustainability of PV enhancement technologies. Full article

Emerging economies confront the dual challenge of accelerating digital transformation while simultaneously mitigating environmental degradation under conditions of institutional and governance heterogeneity. In this context, this study examines how artificial intelligence (AI) capability influences green innovation efficiency (GIE) in emerging Asian economies and investigates whether environmental, social, and governance (ESG) performance conditions this relationship. Using an unbalanced panel of 59,112 firm-year observations from 4926 publicly listed firms across 15 emerging Asian economies over the period 2011–2022, we employ a comprehensive panel-data econometric framework that accounts for unobserved heterogeneity, dynamic effects, endogeneity, and potential self-selection bias. The empirical results indicate that AI capability is positively and significantly associated with higher green innovation efficiency. More importantly, ESG performance strengthens this relationship, suggesting that robust governance frameworks enhance firms’ ability to translate digital intelligence into environmentally efficient innovation outcomes. These findings underscore that AI adoption alone is insufficient to generate sustainable value; rather, its environmental effectiveness depends critically on complementary governance structures that promote transparency, accountability, and responsible risk management. The results remain robust after correcting for endogeneity concerns, alternative model specifications, and extensive sensitivity and heterogeneity analyses. Overall, this study contributes to the literature on digital transformation and sustainability by providing large-scale, multi-country evidence that highlights the pivotal role of ESG in shaping the sustainability returns to AI adoption in emerging economies. Full article

A major factor in worldwide ecological harm is the large quantity of municipal solid waste generated because of rapid industrialization and population growth. Nowadays, there are numerous mechanical, biological, and thermal waste treatment processes that can reduce the amount of landfilled waste. A variety of analytical tests are conducted to evaluate the potential risks that landfills pose to human health and the environment. Among these, laboratory leaching tests are commonly employed to assess the release of specific waste constituents that may become hazardous to the environment. Municipal solid waste (MSW) management poses significant environmental risks due to leachate contamination in bioreactor landfills, where acidic conditions (pH ≈ 5) can mobilize heavy metals. This study evaluates the reliability of leaching tests for biodried reject MSW from CWMC Marišćina, Croatia, by comparing standard EN 12457-1 and EN 12457-2 methods (L/S = 2 and 10 L/kg) with simulations of aerobic degradation using acetic acid (10 g/L) to maintain pH = 5 over 9 days. Waste composition analysis revealed plastics (35%), paper/cardboard (25%), metals (15%), and glass (10%) as dominant fractions. Although the majority of parameters determined through standard leaching tests remain below the maximum permissible limits for non-hazardous waste, simulations under acidic conditions demonstrated substantial increases in eluate concentrations between days 6 and 9: Hg (+1500%), As (+1322%), Pb (+1330%), Ni (+786%), and Cd (+267%), with TDS rising 33%. These results highlight the underestimation of risks by conventional tests, emphasizing the need for pH-dependent methods to predict in situ leachate behavior in MBO-treated waste and support improved EU landfill regulations for enhanced environmental protection. Full article

The three key design criteria for nearly zero-energy buildings (nZEBs) and climate-neutral buildings are minimizing energy use, ensuring high occupant comfort, and reducing environmental impact. Thermal comfort is one of the main components of indoor environmental quality (IEQ), strongly affecting occupants’ health, well-being, and productivity. As energy-efficiency requirements become more demanding, the appropriate selection of heating systems, their automated control, and the management of solar heat gains are becoming increasingly important. This study investigates the influence of two low-temperature radiant heating systems—underfloor and wall-mounted—and the use of Venetian blinds on perceived thermal comfort in a highly glazed public nZEB building located in a densely built urban area within a temperate climate zone. The assessment was based on the PMV (Predicted Mean Vote) index, commonly used in IEQ research. The results show that both heating systems maintained indoor conditions corresponding to comfort or slight thermal stress under steady state operation. However, during periods of strong solar exposure in the room without blinds, PMV values exceeded 2.0, indicating substantial heat stress. In contrast, external Venetian blinds significantly stabilized the indoor microclimate—reducing PMV peaks by an average of 50.2% and lowering the number of discomfort hours by 94.9%—demonstrating the crucial role of solar protection in highly glazed spaces. No significant whole-body PMV differences were found between underfloor and wall heating. Overall, the findings provide practical insights into the control of thermal conditions in radiant-heated spaces and highlight the importance of solar shading in mitigating heat stress. These results may support the optimization of HVAC design, control, and operation in both residential and non-residential nZEB buildings, contributing to improved occupant comfort and enhanced energy efficiency. Full article

Developing efficient and durable oxygen reduction reaction (ORR) catalysts is crucial for advancing fuel cell technology and sustainable energy conversion. In this study, a scalable strategy was employed to synthesize ZIF-derived nitrogen-sulfur co-doped carbon nanosheets embedded with in situ generated ZnS and Co₉S₈ nanoparticles. The synergistic effect of heteroatom doping and metal sulfide modification effectively modulated the electronic structure, optimized charge transfer pathways, and enhanced structural stability. The optimized catalyst exhibited a half-wave potential of 0.83 V vs. RHE, close to that of commercial 20 wt% Pt/C (0.85 V), excellent 4e⁻ ORR selectivity, and exceptional stability, with only a ~15 mV degradation after 10,000 cycles. These results demonstrate that the combination of nitrogen sulfur co-doping and in situ metal sulfide addition pro-vides an effective approach for designing highly active and durable non-precious metal catalysts for the ORR. This synthetic concept provides practical guidance for the scalable preparation of multifunctional nanomaterial-based catalysts for electrochemical energy applications. Full article

Background: Blockchain has been extensively discussed for enhancing transparency, traceability, and trust in general; however, there is fragmented empirical evidence available with respect to this issue within maritime logistics. The objective is to integrate and categorize peer-reviewed publications concerning applications of blockchain in maritime logistics and related supply chain domains. Methods: A systematic literature review with PRISMA 2020 was performed in Scopus database, and after a process of screening and eligibility, a total of 78 journal articles published mainly from September 2024 were incorporated. Descriptive and bibliometric analyses were conducted, and VOS viewer-based bibliographic coupling were employed to visualize thematic structure. Results: The review identifies seven research priorities for blockchain in maritime logistics: Technological Interoperability, Economic and Operational Impact, Cybersecurity and Privacy, Adoption and Scalability, Decision-Making and Trust, Environmental Sustainability, and Standardization and Regulatory Frameworks. Blockchain’s primary advantages are enhanced data integrity and visibility, whereas key challenges include interoperability, legal/regulatory uncertainty (e.g., e-doc recognition), high costs, scalability ceilings, integration with legacy systems, and data governance fears. Conclusions: The application of blockchain in maritime logistics depends on combined technical and institutional enabling conditions; an Integrated Blockchain Adoption Framework (IBAF) is suggested, and providing practical guides based on standardization, legal convergence, and hybrid governance modes. Full article

As an energy-saving fluid machinery component, the ejector holds significant potential for promoting energy conservation and sustainable transformation in aerospace. This review synthesizes recent progress, identifies persistent challenges, and outlines future directions for ejector technology in this field, addressing a gap in existing reviews. (1) In aero-engine systems, performance faces constraints from high-speed compression effects and flow losses. These systems require optimized design across a wide range of speeds. A mixed configuration incorporating a blade mixer achieved a 5~7% thrust increase under static conditions. (2) In high-altitude test facilities, transient start-up and flow instability under off-design conditions demand more precise models and control strategies. An alternative solution using a second throat exhaust diffuser reduced the start-up time by 50~70%. (3) In rocket-based combined cycle engines, development is limited by thermal choking, mode transition, and combustion-flow coupling issues. Optimization of the rocket layout and geometric throat increased the bypass ratio in ejector mode by 35% and improved the specific impulse by 12.5%. Future efforts should focus on constructing multi-physics coupling numerical simulation models for ejectors, analyzing unsteady flow behavior and thermal effects within ejectors, and developing performance optimization strategies based on intelligent control. These approaches are expected to enhance the engineering applicability and system efficiency of ejector technology in the aerospace field, which is increasingly focused on energy conservation and sustainable transformation. Full article

This study employs CFD simulations carried on ANSYS Fluent 2022 R1 (ANSYS Inc., Canonsburg, PA, USA), to address the design, development, and thermodynamic analysis of a biomass burner, based on mass and energy balances, combustion efficiency, flame temperature, and thermodynamic properties. The prototype incorporates a flow deflector located before the combustion chamber. This component improves the air-fuel mixture to maximise thermal efficiency and minimise pollutant emissions. The burner is specifically designed to use sawdust as fuel and is intended for industrial applications such as heating or drying processes. The integration of the flow deflector results in uniform, complete combustion, achieving 90% thermal efficiency and an adjustable thermal power output of 0–100 kW. Compared to conventional burners, this design reduces CO emissions by 20% and NO_x emissions by 15%, demonstrating significant environmental improvements. The design methodology is based on mass and energy balance equations to evaluate combustion efficiency as a function of the stoichiometric ratio, along with experimental testing. These experimental tests were conducted using an ECOM (America Ltd., Nashua, NH, USA) gas analyser and anemometer. The internal temperature was monitored with a K-type thermocouple (Omega Engineering Inc., Norwalk, CT, USA). The results confirmed the positive influence of the structural design on thermal performance. The proposed burner aims to maximise heat generation in the combustion chamber, offering an innovative alternative for biomass combustion systems. Full article

This study investigates the role of biogas and biomethane in accelerating the decarbonization of district heating systems in Europe. A structured literature review combined with two representative case studies evaluate technological, economic, and environmental performance across different system scales. The Meppel optimization model developed for the Netherlands and the large-scale Backbone energy system modelling framework for Finland are compared to identify methodological synergies and operational insights for integrating bioenergy into heating networks. The results show that biogas-based combined heat and power systems can reduce carbon dioxide emissions by more than 70 percent compared with fossil-based alternatives and significantly improve local energy security, especially when coupled with heat pumps and thermal storage. Large-scale modelling further demonstrates that biomethane and bioenergy resources provide valuable system flexibility, facilitating sector coupling and supporting the balancing of variable renewable electricity production. This study’s main contribution is an integrated comparative assessment at two different scales (local and regional), linking operational data, modelling, and performance results to determine how biogas and biomethane can optimize the energy system in the short and long term for centralized heat supply. The findings confirm that biogas and biomethane are essential, dispatchable renewable resources capable of supporting scalable, low-carbon, and resilient district heating systems across Europe. Full article

Although the literature is rich in studies of indoor thermal comfort, there is a lack of research on outdoor thermal comfort, despite its importance in response to global warming and the rise of urban heat islands. Physics models addressing spatial (urban energy form, green areas) and temporal (climate variability) factors are urgently needed. This study proposes a useful method for outdoor comfort evaluation at a district scale, based on the energy form of built-up areas and hyperlocal climatic conditions. It enables the determination of distributed Physiological Environmental Temperature values at a district scale, assessing the greenery effect and mutual radiative exchanges. Applied to a case study in Florence, Italy, it integrates multiple measurement techniques. The main results highlight the model’s ability to evaluate outdoor thermal perception through the new identified indicator of Virtual Physiological Environmental Temperature (PET*) spread, ranging from 23.5 to 101.0 °C, specifically referring to the worst climatic conditions inside an urban canyon in relation to different real scenarios. The results confirm the method’s effectiveness as a tool for thermodynamics and planning for the well-being of an urban built-up environment. It offers useful support for sustainability and human-centric design, oriented to UHI mitigation and climate change adaptation strategies. Full article

The world faces increasing food, environmental, and human security issues, primarily attributed to an overburdened agricultural sector struggling to keep pace with rising population and demand for food, energy, and fiber. Advances in food production and agriculture, especially with monoculture farming, have continued to meet these demands but at a high price regarding resource depletion and environmental devastation. This is especially severe in developing world areas with rural populations with thin resource margins. Regenerative agriculture has emerged as a solution to provide shielding for food production, ensure environmental protection, and promote social equity while addressing many of these issues. Regenerative agriculture food production aims to restore soils, forests, waterways, and the atmosphere and operate with lower offsite negative environmental and social impacts. This review discusses the fundamental principles and practices of sustainable plant protection for regenerative farming. It focuses on the role of biological and ecological processes, reduces non-renewable inputs, and aims to incorporate traditional ecological knowledge into pest control practices. It offers essential transition strategies, including critical changes from conventional integrated pest management (IPM) to agro-ecological crop protection, focusing on systemic approaches to design agroecosystems. It also reaffirms the importance of a vast diversity of pest control methods that are culturally, mechanistically, physically, and biologically appropriate for regenerative farming practices. Ultimately, the aim is to encourage ecological, economic, and social sustainability for the future of more resilient and controlled agricultural practices. Full article

Microplastics represent a pressing global environmental concern due to their persistence, widespread occurrence, and adverse impacts on aquatic ecosystems and human health. Effective removal of these contaminants from water is essential to safeguard biodiversity and ensure water quality. This work focuses on the pivotal role of membrane-based filtration technologies, including microfiltration, ultrafiltration, nanofiltration, reverse osmosis, membrane bioreactors, and dynamic membranes, in capturing and eliminating microplastics. The performance of these systems depends on key membrane characteristics such as pore size, material composition, hydrophilicity, mechanical strength, and module design, which govern retention efficiency, fouling resistance, and operational stability. Membrane filtration offers a highly effective, scalable, and sustainable approach to microplastic removal, outperforming conventional treatment methods by selectively targeting a wide range of particle sizes and morphologies. By highlighting the critical contribution of membranes and filtration processes, this study underscores their potential in mitigating microplastic pollution and advancing sustainable water treatment practices. Full article

To address the challenge that unmanned surface vehicles (USVs) struggle to effectively balance tracking accuracy, control smoothness, and system energy efficiency under external disturbances, this paper proposes an anti-disturbance path tracking control method integrating quantum-inspired optimization (QIO) and dynamic game theory (GT). The proposed control method consists of a two-layer optimization architecture: the upper layer employs dynamic game theory to optimize the guidance process, modeling the optimization of the look-ahead distance ($Ld$) and switching radius ($R$) in the LOS guidance algorithm as a non-cooperative game, and achieves adaptive adjustment to path variations and environmental disturbances by solving for the Nash equilibrium. The lower layer, based on a quantum-inspired optimization algorithm, enhances the control process by employing quantum bit probability amplitude encoding for the PID parameter space and utilizing a quantum rotation gate mechanism for efficient global search, thereby achieving online self-tuning of PID parameters under environmental disturbances. Simulation results indicate that, under sea conditions with external disturbances, the proposed method achieves a superior balance among tracking accuracy, control smoothness, and system energy efficiency compared to the traditional fixed-parameter PID-LOS approach, enhancing the comprehensive anti-disturbance robustness of the USV. Full article

This study investigates the influence mechanism of key factors on the heating value of syngas during underground coal gasification (UCG) and proposes an optimization path for enhanced energy conversion efficiency based on typical global field test data. Integrating data review and pattern analysis, it systematically explores the influence of core factors, including coal seam characteristics, reactor structure, and gasification agent ratio. It is found that the relationship between syngas heating value and coal rank is not simply linear, with representative heating values ranging from 4.13 to 11.96 MJ/m³. Medium-rank coal, characterized by “medium volatile matter and low ash content”, yields high-heating-value syngas when paired with air/steam as the gasification agent. Shaftless reactor structures demonstrate superior overall performance compared to shaft-based designs, with the representative heating value improving from 3.83 MJ/m³ to 7.8 MJ/m³. The combination of U-shaped horizontal wells with the Controlled Retracting Injection Point (CRIP) technology improves the heating value. Effective control over the syngas heating value can be achieved by optimized composition and ratio of the gasification agent, with representative value of 9.10 MJ/m³ in oxygen-enriched steam gasification compared to 4.28 MJ/m³ in air gasification. Based on an evaluation of data fluctuation characteristics, the significance ranking of the factors is as follows: gasification agent, coal rank, and reactor structure. Consequently, an engineering optimization path for enhancing UCG syngas heating value is proposed: prioritize optimizing the composition and ratio of the gasification agent as the primary means of heating value control; on this basis, rationally select coal rank resources, focusing on process compatibility to mitigate performance fluctuations; and then incorporate advanced reactor structures to construct a synergistic and efficient gasification system. This research can provide theoretical support and data references for engineering site selection, process design, and operational control of UCG projects. Full article

The proliferation of mobile devices has driven significant growth in adaptive mobile applications (AMAs) that dynamically adjust their behavior based on contextual changes. While existing research has extensively studied individual adaptive systems, limited attention has been given to cooperative adaptation—where multiple AMAs coordinate their adaptive behaviors within shared mobile ecosystems. This systematic literature review addresses this research gap by analyzing 95 peer-reviewed studies published between 2010 and 2025 to characterize the current state of cooperative adaptation in mobile applications. Following established systematic review protocols, we searched six academic databases and applied rigorous inclusion/exclusion criteria to identify relevant studies. Our analysis reveals eight critical dimensions of cooperative adaptation: Monitor–Analyze–Plan–Execute–Knowledge (MAPE-K) structure, application domain, adaptation goals, context management, adaptation triggers, aspect considerations, coordination mechanisms, and cooperation levels. The findings indicate that 63.2% of studies demonstrate some form of cooperative behavior, ranging from basic context sharing to sophisticated conflict resolution mechanisms. However, only 7.4% of studies explicitly address high-level cooperative adaptation involving global goal optimization or comprehensive conflict resolution. Energy efficiency (21.1%) and usability (33.7%) emerge as the most frequently addressed adaptation goals, with Android platforms dominating the research landscape (36.8%). The review identifies significant gaps in comprehensive lifecycle support, standardized evaluation methodologies, and theoretical frameworks for multi-application cooperation. These findings establish a foundation for advancing research in cooperative adaptive mobile systems and provide a classification framework to guide future investigations in this emerging domain. Full article

Energy efficiency in hospitals—where continuous operation with high internal gains and strict comfort needs—demands facade strategies tailored to climate. This study quantifies how the window-to-wall ratio (WWR) distribution and city-specific envelope properties affect the annual heating and cooling loads of a four-story, 3000 m² hospital in Turkey. Energy simulations were conducted using DesignBuilder (2021) with EnergyPlus under a controlled modeling framework, following ASHRAE healthcare guidelines for internal loads and TS 825:2024 for envelope compliance. Three locations were selected to span national variability: Bursa (Marmara—temperate/transition), Mersin (Mediterranean—hot–humid), and Kars (humid continental—cold). Scenario 1 (S1) assigned a graduated WWR on the south facade by floor—20%, 30%, 40%, and 50% from ground to top—while the north, east, and west facades were held at 20%, 30%, and 20%. Scenario 2 (S2) preserved the same geometry and WWR values but applied the graduated WWR to the north facade instead, keeping the south at 20%, east at 30%, and west at 20%. Within each city, opaque and glazing properties were kept constant across scenarios to isolate WWR–orientation effects. For every city–scenario combination, annual space-heating and space-cooling loads were computed, and window heat gains and losses were analyzed on the facade with variable WWR to support interpretation of performance mechanisms. The results indicate that S2 outperforms S1 in Mersin, S1 outperforms S2 in Kars, and S2 offers a moderate advantage in Bursa. Full article

The environmental pollution caused by emerging organic contaminants—such as ibuprofen—is becoming increasingly a cause for alarm. New treatments for their removal are currently being developed, but the nature and toxicity of the transformation products (TPs) formed during the processes cannot be readily assessed experimentally. Atomistic simulations are thus of high interest in predicting the chemical structure of these TPs. In this paper, we demonstrate that the transformation of a contaminant molecule under irradiation can be studied using the threshold algorithm combined with the density functional-based tight-binding (DFTB) method. The fragmentation pathways of an ibuprofen molecule under irradiation are studied as a function of the energy added to the system. Specifically, the chemical structures of ibuprofen’s TPs, the paths between them, their stabilities, probabilities of occurrence, and the related mass spectra were obtained as a function of the amount of energy absorbed. We also simulated the evolution of the ibuprofen molecule as a function of the number of pulses, i.e., for a sequence of energy depositions. A dominant fragmentation scheme is identified, where first the OH group is released, followed by the loss of the CO group. The photon energy and the number of pulses are found to be key parameters for the selection of this degradation route among all identified fragmentation pathways. Full article

The global shift towards sustainable energy and stringent climate policies has underscored the need for decarbonizing energy systems, electrifying transportation, and transforming supply chains. In this context, China’s new energy vehicle (NEV) industry, as the largest global producer and consumer of automobiles, is pivotal in advancing energy substitution and achieving carbon reduction goals. This study investigates the energy efficiency and supply chain transformation within China’s NEV sector, leveraging panel data from 12 representative provinces over the period 2017–2023. Employing a robust analytical framework that integrates the DEA-BCC model, Malmquist index, and Tobit regression, the study provides a dynamic and regionally differentiated assessment of NEV industry efficiency. The results reveal significant improvements in total factor energy efficiency, predominantly driven by technological progress. R&D intensity, infrastructure development, and environmental regulation are identified as key enablers of efficiency, while excessive government intervention tends to hinder performance. The findings offer valuable empirical insights and policy recommendations for optimizing China’s NEV industry in the context of energy system transformation and sustainable industrial development. Full article

Promoting synergistic economic–resource–environmental development in resource-based cities (RBCs) is a fundamental requirement for ensuring national energy security and advancing regional sustainable and coordinated development. This study innovatively proposes the theoretical framework of “green transformation resilience (GTR)” based on evolutionary resilience theory, and then empirically explores the GTR of 114 RBCs in China from the perspective of urban development stages using multiple data models. The findings indicate that the GTR demonstrated an overall upward trend, though it remained at a consistently low level. Regenerative RBCs exhibited the highest GTR levels. GTR exhibits an uneven spatial distribution, primarily caused by super-variation density. The factor detection results indicate that factors such as government intervention, income level, and human capital have strong explanatory power for the spatial variation of GTR. Interaction analysis confirmed the significant nonlinear enhancement or bivariate enhancement of all pairs of factors. This study provides a basis for the differentiated development paths of GTR in China’s RBCs. Moreover, through factor interaction testing, it also offers guidance on policy combinations and prioritization for RBCs in different development stages. Full article

Evaluating the effect of recycling Pesticide Packaging Waste (PPW) is essential for improving recycling rates, which plays a crucial role in controlling environmental pollution and optimizing the efficiency of agricultural resources worldwide. Based on the micro-survey data of 1223 farmers in Yunnan and Hainan provinces of China, this study measures the economic effect by the farmers’ annual total household income and the ecological effect by the ecological environment quality of villages. The propensity score matching method (PSM) is employed to empirically test the economic and ecological effects of farmers’ recycling behavior of PPW and their differences. The research findings are as follows: Farmers’ recycling of PPW can generate significant positive economic and ecological effects, which are 116.7% and 4%, respectively. The heterogeneity analysis shows that farmers with a low degree of land fragmentation have a more obvious economic effect from PPW recycling, while farmers with a higher degree of land fragmentation have a more significant ecological effect; farmers with high pesticide costs have more significant economic and ecological effects from PPW recycling. Based on these findings, it is suggested to increase the attention at the policy level, enhance farmers’ environmental awareness and capacity, and focus on the characteristics of different groups. Full article

This article presents a narrative, traditional literature review summarizing current research on the integration of digital technologies in waste management. The study examines how intelligent technologies, including Geographic Information Systems, Big Data analytics, and artificial intelligence, can improve energy efficiency, support sustainable resource use, and enhance the development of low emission and circular waste management systems. The reviewed research shows that the combination of spatial analysis, large-scale data processing, and predictive computational methods enables advanced modeling of waste distribution, the optimization of collection routes, intelligent sorting, and the forecasting of waste generation. Geographic Information Systems support spatial planning, site selection for waste facilities, and environmental assessment. Big Data analytics allows the integration of information from Internet of Things sensors, global positioning systems, municipal databases, and environmental registries, which strengthens evidence-based decision making. Artificial intelligence contributes to automatic classification, predictive scheduling, robotic sorting, and the optimization of recycling and energy recovery processes. The study emphasizes that the integration of these technologies forms a foundation for intelligent waste management systems that reduce emissions, improve operational efficiency, and support sustainable urban development. Full article

Conventional urea nitrogen (N) fertilizers are characterized by low use efficiency, resulting in substantial economic losses and environmental degradation. To address this issue, we developed a novel carbon-based stabilized coated urea by incorporating biochar, the urease inhibitor NBPT, and the nitrification inhibitor DCD through a low-energy in situ coating process. This study evaluated the effects of this fertilizer on N transformation and loss via soil column leaching and ammonia volatilization experiments, as well as its impact on choy sum (Brassica chinensis L.) yield, N use efficiency (NUE), and product quality under field conditions. Results indicated that coatings containing both NBPT and DCD (specifically, formulations with 0.5%NBPT + 1.0%DCD, and 1.0%NBPT + 1.5%DCD) significantly reduced cumulative ammonium-N leaching by 41.5–53.8% and nitrate-N leaching by 45.3–59.4% compared to conventional urea. All coated treatments suppressed ammonia volatilization by over 10%, with the highest inhibition (26.92%) observed in the treatment with 1.0%NBPT + 1.5%DCD. The synergistic coating also modulated key soil enzyme activities involved in N cycling. Field trials demonstrated that the formulations with 0.5%NBPT + 1.0%DCD and 0.5%NBPT + 1.5%DCD increased choy sum yield by 56.1% and 58.1%, respectively, while significantly improving NUE and agronomic efficiency. Moreover, these treatments enhanced vegetable quality by reducing nitrate content and increasing vitamin C and soluble sugar concentrations. In conclusion, this carbon-based stabilized coated urea, which integrates biochar with NBPT and DCD, represents a promising strategy for minimizing N losses, improving NUE, and advancing sustainable vegetable production. Full article

Laos, a resource-rich and politically stable country in Southeast Asia, has experienced rapid economic expansion driven by foreign investments in hydropower, mining, and industrial park development. While these sectors have contributed substantially to national growth, they have also intensified environmental degradation and social pressures. This study critically evaluates the effectiveness of the Environmental and Social Impact Assessment (ESIA) system in Laos within the broader framework of environmental governance and sustainable development. A qualitative research design was employed, combining legal and policy document analysis, review of secondary literature, and case studies of three representative projects. The findings reveal that, although Laos has established a comprehensive ESIA regulatory framework, its implementation remains constrained by weak institutional capacity, overlapping administrative mandates, and limited technical resources. Furthermore, low levels of public participation and transparency reduce the inclusiveness and credibility of ESIA processes. Despite these challenges, recent legal reforms and growing international cooperation demonstrate gradual progress toward more accountable and integrated environmental governance. Strengthening institutional capacity, enhancing inter-ministerial coordination, and incorporating social considerations into project evaluations are essential steps to transform the ESIA framework from a procedural obligation into a robust tool for promoting sustainable and responsible investment in Laos. Full article

Energy security crises have historically been turning points for energy systems, exposing vulnerabilities, reshaping policy priorities, and boosting technological change. However, whether—and to what extent—such crises accelerate low-carbon transitions remains contested. This paper examines the effects of the 2022 energy crisis on the European Union (EU)’s energy transition, using policy analysis combined with a quantitative assessment of renewable energy trends, forecasts, and targets. We analyse the ambition, implementation, and outcomes of the REPowerEU plan, the main response to the crisis. In an unprecedented move, REPowerEU securitised renewable energy as a means to reduce dependence on Russian energy imports. However, the plan only moderately increased earlier renewable energy targets and did not reverse declining subsidies despite more forceful implementation measures. Its effects have been uneven across technologies. Already accelerating solar may overshoot its targets, onshore wind might only slightly accelerate beyond its current steady growth, and offshore wind remains constrained by economic and institutional uncertainties. Despite increased subsidies for fossil fuels, coal continued declining, oil remained stable, and natural gas dropped. Overall, REPowerEU sustained rather than transformed the EU’s low-carbon transition, illustrating both the potential and limits of accelerating decarbonisation under security crises. Full article

Early fault diagnosis in induction motors is important to maintain correct operation in terms of energy and efficiency, as well as to achieve a reduction in costs associated with maintenance or unexpected stoppages in production processes. These motors are widely used in industry due to their reliability, low cost, and great robustness; however, over time, they may be exposed to wear that can affect their performance, endanger the integrity of operators, or cause unexpected shutdowns that generate economic losses. Corrosion in the bearings is one of the most common failures, which is mainly triggered by high humidity in combination with high temperatures. However, despite its relevance, it has not been widely explored as a cause of failure in induction motors. Unlike failures that occur in specific or localized areas, corrosion in bearings does not manifest through specific frequencies associated with the phenomenon, since the corrosion occurs extensively on the surface of the raceway, making early diagnosis difficult with conventional techniques based on spectral analysis. Therefore, this work proposes an approach for the analysis of magnetic stray flux and vibration signals under different levels of corrosion using statistical and non-statistical parameters to capture variations in the dynamic behavior of the motors while employing genetic algorithms to select the most relevant parameters for each signal and optimize the configuration of an ensemble learning algorithm. The classification of the bearing condition is achieved using support vector machines in combination with the bagging method, which increases the robustness and accuracy of the model in the presence of signal variability. A classification accuracy between the healthy state and two gradualities greater than 99% was obtained, indicating that the proposed approach is reliable and efficient for corrosion diagnosis. Full article

The growing use of electric vehicles (EVs) creates challenges in designing charging systems that are smart, dependable, and efficient, especially when environmental conditions change. This research proposes a fuzzy-logic-based PID control strategy integrated into a photovoltaic (PV) powered EV charging system to address uncertainties such as fluctuating solar irradiance, grid instability, and dynamic load demands. A MATLAB-R2023a/Simulink-R2023a model was developed to simulate the charging process using real-time adaptive control. The fuzzy logic controller (FLC) automatically updates the PID gains by evaluating the error and how quickly the error is changing. This adaptive approach enables efficient voltage regulation and improved system stability. Simulation results demonstrate that the proposed fuzzy–PID controller effectively maintains a steady charging voltage and minimizes power losses by modulating switching frequency. Additionally, the system shows resilience to rapid changes in irradiance and load, improving energy efficiency and extending battery life. This hybrid approach outperforms conventional PID and static control methods, offering enhanced adaptability for renewable-integrated EV infrastructure. The study contributes to sustainable mobility solutions by optimizing the interaction between solar energy and EV charging, paving the way for smarter, grid-friendly, and environmentally responsible charging networks. These findings support the potential for the real-world deployment of intelligent controllers in EV charging systems powered by renewable energy sources This study is purely simulation-based; experimental validation via hardware-in-the-loop (HIL) or prototype development is reserved for future work. Full article

This study presents a comprehensive systematic analysis, investigating hardware accelerators specifically designed for real-time cardiovascular signal processing, focusing mainly on Electrocardiogram (ECG), Photoplethysmogram (PPG), and blood pressure monitoring systems. Cardiovascular Diseases (CVDs) represent the world’s leading cause of morbidity and mortality, creating an urgent demand for efficient and accurate diagnostic technologies. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we systematically analysed 59 research papers on this topic, published from 2014 to 2024, categorising them into three main categories: signal denoising, feature extraction, and decision support with Machine Learning (ML) or Deep Learning (DL). A comprehensive performance benchmarking across energy efficiency, processing speed, and clinical accuracy demonstrates that hybrid Field Programmable Gate Array (FPGA)-Application Specific Integrated Circuit (ASIC) architectures and specialised Artificial Intelligence (AI) on Edge accelerators represent the most promising solutions for next-generation CVD monitoring systems. The analysis identifies key technological gaps and proposes future research directions focused on developing ultra-low-power, clinically robust, and highly scalable physiological signal processing systems. The findings provide guidance for advancing hardware-accelerated cardiovascular diagnostics toward practical clinical deployment. Full article

A cobalt-containing zeolitic imidazolate framework (ZIF-67) was carbonized by different routes to composite materials (cZIFs) composed of metallic Co, Co₃O₄, and N-doped carbonaceous phase. The effect of the carbonization procedure on the water pollutant removal properties of cZIFs was studied. Higher temperature and prolonged thermal treatment resulted in more uniform particle size distribution (as determined by nanoparticle tracking analysis, NTA) and surface charge lowering (as determined by zeta potential measurements). Surface-governed environmental applications of prepared cZIFs were tested using physical (adsorption) and electrochemical methods for dye degradation. Targeted dyes were methylene blue (MB) and methyl orange (MO), chosen as model compounds to establish the specificity of selected remediation procedures. Electrodegradation was initiated via an intermediate reactive oxygen species formed during oxygen reduction reaction (ORR) on cZIFs serving as electrocatalysts. The adsorption test showed relatively uniform adsorption sites at the surface of cZIFs, reaching a removal of over 70 mg/g for both dyes while governed by pseudo-first-order kinetics favored by higher mesoporosity. In the electro-assisted degradation process, cZIF samples demonstrated impressive efficiency, achieving almost complete degradation of MB and MO within 4.5 h. Detailed analysis of energy consumption in the degradation process enabled the calculation of the current conversion efficiency index and the amount of charge associated with O₂^•−/^•OH generation, normalized by the quantity of removed dye, for tested materials. Here, the proposed method will assist similar research studies on the removal of organic water pollutants to discriminate among electrode materials and procedures based on energy efficiency. Full article

The photovoltaic–energy storage–direct current–flexibility (PEDF) system provides an integrated pathway for low-carbon and intelligent building energy management by combining on-site PV generation, electrical storage, DC distribution, and flexible load control. This paper reviews recent advances in these four modules and synthesizes quantified benefits reported in real-world deployments. Building-scale systems typically integrate 20–150 kW PV and achieve ~10–18% energy-efficiency gains enabled by DC distribution. Industrial-park deployments scale to 500 kW–5 MW, with renewable self-consumption often exceeding 50% and CO₂ emissions reductions of ~40–50%. Community-level setups commonly report 10–15% efficiency gains and annual CO₂ reductions on the order of tens to hundreds of tons. Key barriers to large-scale adoption are also discussed, including multi-energy coordination complexity, high upfront costs and uncertain business models, limited user engagement, and gaps in interoperability standards and supportive policies. Finally, we outline research and deployment priorities toward open and interoperable PEDF architectures that support cross-sector integration and accelerate the transition toward carbon-neutral (and potentially carbon-negative) built environments. Full article

Driven by the dual imperatives of global energy transition and sustainable development goals, lithium-ion batteries, as critical energy storage carriers, have seen the assessment of their lifecycle reliability and durability become a core issue underpinning the sustainable operation of clean energy systems. Grounded in a multidimensional perspective of sustainable development, this study aims to establish a quantifiable and monitorable battery reliability evaluation framework to address the challenges to lifespan and performance sustainability faced by batteries under complex multi-stress coupled operating conditions. Lithium-ion batteries are widely used across various fields, making an accurate assessment of their reliability crucial. In this study, to evaluate the lifespan and reliability of lithium-ion batteries when operating in various coupling stress environments, a multi-stress collaborative accelerated model(MCAM) considering interaction is established. The model takes into account the principal stress effects and the interaction effects. The former is developed based on traditional acceleration models (such as the Arrhenius model), while the latter is constructed through the combination of exponential, power, and logarithmic functions. This study firstly considers the scale parameter of the Weibull distribution as an acceleration effect, and the relationship between characteristic life and stresses is explored through the synergistic action of primary and interaction effects. Subsequently, a multi-stress maximum likelihood estimation method that considers interaction effects is formulated, and the model parameters are estimated using the gradient descent algorithm. Finally, the validity of the proposed model is demonstrated through simulation, and numerical examples on lithium-ion batteries demonstrate that accurate lifetime prediction is enabled by the MCAM, with test duration, cost, and resource consumption significantly reduced. This study not only provides a scientific quantitative tool for advancing the sustainability assessment of battery systems, but also offers methodological support for relevant policy formulation, industry standard optimization, and full lifecycle management, thereby contributing to the synergistic development of energy storage technology across the economic, environmental, and social dimensions of sustainability. Full article

Isolated microgrids with green hydrogen storage offer a promising solution for supplying electricity to remote communities where conventional grid expansion is infeasible. Designing such systems requires balancing two conflicting objectives: minimizing installation and operation costs while maximizing supply reliability. This paper proposes a multi-objective optimization methodology, based on the Non-dominated Sorting Genetic Algorithm II, to determine the optimal sizing of multiple microgrid components. This sizing explicitly addresses both the power capacities (kW) (for photovoltaic panels, wind turbines, electrolyzers, and fuel cells) and the energy storage capacities (kWh and kg) (for batteries and hydrogen tanks, respectively), aiming to generate Pareto-optimal solutions that explore this trade-off. The proposed method evaluates the trade-off by minimizing two objectives: the Net Present Value, which includes investment, replacement, and maintenance costs, and the total expected interruption hours, derived from an hourly energy balance analysis. The methodology’s effectiveness is validated using four distinct case studies. Three of these are based on real locations with specific load profiles and climate data. To test the method’s robustness, a fourth case study uses a fictitious load profile, designed with pronounced seasonal variations and a clear distinction between weekday and weekend consumption. Our results demonstrate the method’s ability to identify efficient hybrid renewable topologies combining photovoltaic and/or wind generation, batteries, and hydrogen systems (electrolyzer, storage tank, and fuel cell). The obtained cost–reliability curves provide practical decision-support tools for system planners. Full article

Escalating energy prices have positioned households as pivotal agents in advancing demand-side energy efficiency. This study examines three complementary energy-saving strategies among Polish households: (1) habitual, low-cost actions such as switching off unnecessary lighting; (2) capital-intensive investments, including LED lighting and energy-efficient appliances; and (3) time-based and prosumptive strategies linked to dynamic tariffs and photovoltaic systems. The empirical analysis is based on a nationwide survey conducted using the Computer-Assisted Web Interviewing method, involving 401 respondents. The study’s contribution lies in integrating these strategies within a single analytical model and providing the first empirical evidence on their socio-demographic determinants in Central and Eastern Europe, with Poland as a representative case. The results show that older individuals more often adopt everyday habitual practices, whereas higher income and education levels are associated with investment-oriented behaviours. Urban households tend to favour technological solutions, while rural households more frequently adopt time-of-use tariffs and PV systems. Two complementary pathways are identified: a behavioural–habitual path and an investment–technological path. The findings offer guidance for public policy by showing that energy savings increase when financial incentives are combined with clear communication and low-effort decision tools that help households optimise energy use regardless of demographic profile. Full article

Climate change governance has become an essential concern for policymakers, with carbon dioxide (CO₂) emissions representing one of the most pressing challenges to sustainable economic development. In this context, understanding the main drivers of CO₂ emissions is essential for designing effective public policies that support Romania’s transition toward a low-carbon economy. This study investigates the determinants of CO₂ emissions in Romania’s energy sector between 2008 and 2023 using the Logarithmic Mean Divisia Index (LMDI) decomposition method. The analysis considers five key elements: the carbon intensity effect (ΔC), the energy mix effect (ΔM), the energy efficiency effect (ΔL), the economic effect (ΔB), and the population effect (ΔP). The results highlight the need for coherent governance frameworks and targeted policy measures to balance economic expansion with environmental sustainability. The study offers actionable insights for public authorities aiming to strengthen Romania’s climate governance and align national strategies with the objectives of the European Green Deal and climate neutrality by 2050. Full article

The methodological frameworks applied in the Colombian Amazon to study emerging agri-food systems are insufficient, as they often employ linear models that fail to recognize the importance of small-scale producer networks that depend on the integration and cooperation of other actors to form part of a value chain. In this study, the value network (VN) perspective was applied to characterize four Amazonian fruits identified as emerging agricultural economies in rural communities: Seje (Oenocarpus bataua), Asaí (Euterpe precatoria), Moriche (Mauritia flexuosa), and Peach palm (Bactris gasipaes). The research was conducted in the Amazonian department of Guaviare, where economic momentum around value networks of Amazonian fruits has emerged in recent years. The framework proposed by Sprinzer-Heinze was adapted, using the social, economic, institutional, and environmental dimensions instead of linear chains to analyze the value networks (VNs). Data collection combined participatory workshops, surveys, and interviews with key actors involved throughout the value network, and an index was constructed to compare the networks and identify their strengths and weaknesses. The study was complemented with social network analyses to assess the levels of cooperation among key actors across each product’s value network. The findings reveal that value networks have emerged as an institutional attempt to implement a strategy aimed at enhancing rural livelihoods and promoting economic initiatives with a lower impact on deforestation. Nevertheless, further actions are required to strengthen such networks, as their performance remains weak in aspects such as environmental sustainability, technological upgrading, innovation, and institutional and social support. Social network analysis revealed a common structure characterized by interconnections among producers, associations, and institutions across all value networks. However, these actors need to develop a more robust network culture to better coordinate their actions and ensure long-term sustainability. Full article

This paper presents an FPGA-based architecture for real-time spline-based signal reconstruction, targeted at multimedia signal processing applications. Leveraging the multi-resolution properties of B-splines, the proposed design enables efficient upsampling, denoising, and feature preservation for image and video signals. Implemented on a mid-range FPGA, the system supports parallel processing of multiple channels, with low-latency memory access and pipelined arithmetic units. The proposed pipeline achieves a throughput of up to 33.1 megasmples per second for 1D signals and 19.4 megapixels per second for 2D images, while maintaining average power consumption below 250 mW. Compared to CPU and embedded GPU implementations, the design delivers >15× improvement in energy efficiency and deterministic low-latency performance (8–12 clock cycles). A key novelty lies in combining multi-resolution B-spline reconstruction with fixed-point arithmetic and streaming-friendly pipelining, making the architecture modular, compact, and robust to varying input rates. Benchmarking results on synthetic and real multimedia datasets show significant improvements in throughput and energy efficiency compared to conventional CPU and GPU implementations. The architecture supports flexible resolution scaling, making it suitable for edge-computing scenarios in multimedia environments. Full article

The growing demand for personalized healthcare is driving the development of Wireless Body Area Networks (WBANs). These networks enable continuous monitoring of physiological parameters. In WBANs, routing protocols are essential for ensuring reliable data delivery. However, designing efficient protocols is challenging due to the specific environment of the human body. Key issues include limited energy, frequent topology changes caused by movement, and diverse Quality of Service needs. In this review, we investigate, summarize, and analyze state-of-the-art WBAN routing protocols. Specifically, we outline the architecture of WBAN-based eHealth systems and review major design challenges. We then present a categorized survey of recent protocols. Subsequently, we examine the distribution across protocol categories and compare their performance. Finally, we identify open challenges and discuss future research directions. Full article

The rapid urbanization of recent decades has intensified the need for sustainable and adaptive city models that balance economic growth, environmental protection, and social well-being. This study addresses the challenge of assessing the performance of European smart cities by proposing a composite index of urban sustainability based on citizens’ perceptions. Using data from the Quality of Life in European Cities Survey (2023), the research applies a multi-criteria analytical framework grounded in the Benefit-of-the-Doubt (Data Envelopment Analysis) approach, which allows each city to determine optimal indicator weights and eliminates pre-assigned biases. The analysis integrates six dimensions of smart city performance—mobility, living, environment, economy, governance, and people—to evaluate cities’ adaptability to the needs of their residents. Results reveal that cities such as Aalborg (Denmark), Luxembourg (Luxembourg), Cluj-Napoca (Romania), and Zurich (Switzerland) exhibit the highest performance, demonstrating balanced progress across sustainability-oriented domains. The findings suggest that integrating citizens’ evaluations with data-driven weighting provides a more comprehensive and context-sensitive understanding of urban sustainability. The study concludes that the proposed composite index provides a robust methodological framework for benchmarking European smart cities, supporting policymakers in designing targeted strategies for enhancing livability, inclusiveness, and sustainable urban growth. Full article