Search Results (1,463)

This paper presents the design and fabrication of a triboelectric nanogenerator based on a Quadruple Parallel-cavity Helmholtz Resonator (4C–HR TENG) for the efficient harvesting of noise energy in marine engine room environments. The device utilizes sound waves to drive periodic contact and separation between polytetrafluoroethylene (PTFE) particles in the resonant cavity and the vibrating diaphragm as well as the upper electrode plate, thereby converting sound energy into mechanical energy and finally into electrical energy. The device consists of an acoustic waveguide with a length of 350 mm and both width and height of 60 mm, along with a Helmholtz Resonator with a diameter of 60 mm and a height of 40 mm. Experimental results indicate that under resonance conditions with a sound pressure level of 109.8 dB and a frequency of 110 Hz, the device demonstrates excellent output performance, achieving a peak output voltage of 250 V and a current of 4.85 μA. We analyzed and investigated the influence mechanism of key parameters (filling ratio, sound pressure level, the height between the electrode plates, and particle size) on the output performance. Through COMSOL Multiphysics simulation analysis, the sound pressure enhancement effect and the characteristic of concentrated diaphragm center displacement at the first-order resonance frequency were revealed, verifying the advantage of the four-cavity structure in terms of energy distribution uniformity. In practical applications, the minimum responsive sound pressure level corresponding to the operating frequency range of the 4C–HR TENG was determined. The output power reaches a maximum of 0.27 mW at a load resistance of 50 MΩ. At a sound pressure level of 115.1 dB, the device can charge a 1 μF capacitor to 4.73 V in just 32 s and simultaneously illuminate 180 LEDs in real-time, demonstrating its potential for environmental noise energy harvesting and micro-energy supply applications. This study provides new insights and experimental evidence for the efficient recovery of noise energy. Full article

To maintain power system stability and supply quality when integrating doubly fed induction generator (DFIG)-based wind energy conversion systems (DFIG-WECSs), regulators regularly update grid codes specifying low voltage ride-through (LVRT) requirements. This paper presents a systematic literature review (SLR) on the use of STATCOMs to enhance LVRT capability in DFIG-WECSs. Objectives included a structured literature search, bibliographic analysis, thematic synthesis, trend identification, and proposing future research directions. A PRISMA-based methodology guided the review, utilising PRISMA 2020 for Abstracts in the development of the abstract. The final search was conducted on Scopus (31 March 2025). Eligible studies were primary research in English (2009–2014) where STATCOM was central to LVRT enhancement; exclusions included non-English studies, duplicates, reviews, and studies without a STATCOM focus. Quality was assessed using an adapted Critical Appraisal Skills Programme (CASP) tool. No automation or machine learning tools were used. Thirty-eight studies met the criteria and were synthesised under four themes: operational contexts, STATCOM-based schemes, control strategies, and optimisation techniques. Unlike prior reviews, this study critically evaluates merits, limitations, and practical challenges. Trend analysis shows evolution from hardware-based fault survival strategies to advanced optimisation and coordinated control schemes, emphasising holistic grid stability and renewable integration. Identified gaps include cyber-physical security, techno-economic assessments, and multi-objective optimisation. Actionable research directions are proposed. By combining technical evaluation with systematic trend analysis, this review clarifies the state of STATCOM-assisted LVRT strategies and outlines pathways for future innovation in DFIG-WECS integration. Full article

With the advancement of the “dual carbon” goal, the power system is accelerating its transition towards a clean and low-carbon structure, with a continuous increase in the penetration rate of renewable energy generation (REG). However, the volatility and uncertainty of REG output pose severe challenges to power grid operation. Traditional distribution networks face immense pressure in terms of scheduling flexibility and power supply reliability. Active distribution networks (ADNs), by integrating energy storage systems (ESSs), soft open points (SOPs), and demand response (DR), have become key to enhancing the system’s adaptability to high-penetration renewable energy. This work proposes a DR-aware scheduling strategy for ESS-integrated flexible distribution networks, constructing a bi-level optimization model: the upper-level introduces a price-based DR mechanism, comprehensively considering net load fluctuation, user satisfaction with electricity purchase cost, and power consumption comfort; the lower-level coordinates SOP and ESS scheduling to achieve the dual goals of grid stability and economic efficiency. The non-dominated sorting genetic algorithm III (NSGA-III) is adopted to solve the model, and case verification is conducted on the standard 33-node system. The results show that the proposed method not only improves the economic efficiency of grid operation but also effectively reduces net load fluctuation (peak–valley difference decreases from 2.020 MW to 1.377 MW, a reduction of 31.8%) and enhances voltage stability (voltage deviation drops from 0.254 p.u. to 0.082 p.u., a reduction of 67.7%). This demonstrates the effectiveness of the scheduling strategy in scenarios with renewable energy integration, providing a theoretical basis for the optimal operation of ADNs. Full article

This article is aimed at assessing energy–economy models with a focus on their ability to capture the dynamic structural changes of economic systems and the related energy supply chains. A narrative literature review approach was employed, synthesizing relevant peer-reviewed research. The search yielded 229 publications spanning from 2015 to 2024. After applying screening criteria based on methodological transparency, quantitative modelling, and explicit energy–economy integration, 120 articles were retained, from which 23 representative modelling frameworks were selected. The review identifies five key dimensions shaping the realism and applicability of integrated models: geographical and temporal scope, technological detail, modelling approach, and the degree of micro- and macroeconomic realism. Results show a growing adoption of multi-scale modelling and a gradual shift toward hybrid structures combining technological and macroeconomic components. However, significant gaps remain: only 26% of the models move beyond equilibrium assumptions; 17% incorporate behavioural or heterogeneous agents; and almost half rely on exogenous technological change. Moreover, the representation of policy instruments—particularly performance standards, sectoral benchmarks, and public investment mechanisms—remains incomplete across most frameworks. Overall, this analysis highlights the need for more transparent coupling strategies, enhanced behavioural realism, and improved representation of financial and transition risks. These findings inform the methodological development of next-generation models and indicate priority areas for future research aimed at improving the robustness of policy-relevant transition assessments. Full article

In the rapidly evolving digital economy, the expansion of business-to-business e-commerce ecosystems has compelled traditional industries to integrate into digital supply chains to achieve sustainable development. Industrial e-commerce is no longer limited to online transactions but extends to the digital transformation of backend operations. Drawing upon the perspective of the digital business ecosystem, this study investigates how digital supply chain integration, manifested through digital transformation, impacts energy efficiency. By utilizing a panel fixed effects model and advanced text mining techniques on a dataset of 721 listed firms in the resource-intensive sectors of China spanning from 2011 to 2023, this research constructs a novel index to quantify corporate digital maturity based on semantic analysis. The empirical results demonstrate that digital transformation significantly enhances energy efficiency by facilitating optimized resource allocation and data-driven decision making required by modern digital markets. Mechanism analysis reveals that green innovation functions as a pivotal mediator that bridges the gap between digital investments and environmental performance. Furthermore, this relationship is found to be contingent upon corporate social responsibility strategies, ownership structures, and the scale of the firm. This study contributes to the electronic commerce literature by elucidating how traditional manufacturers can leverage digital technologies and green innovation to navigate the twin transition of digitalization and sustainability, offering theoretical implications for platform governance in industrial sectors. Full article

With the acceleration of digital transformation, open-source software has become a fundamental component of modern smart grids and other critical infrastructures. However, the complex dependency structures of open-source ecosystems and the continuous emergence of vulnerabilities pose substantial challenges to software supply chain security. In power information networks and cyber–physical control systems, vulnerabilities in open-source components integrated into Supervisory Control and Data Acquisition (SCADA), Energy Management System (EMS), and Distribution Management System (DMS) platforms and distributed energy controllers may propagate along the supply chain, threatening system security and operational stability. In such application scenarios, large language models (LLMs) often suffer from limited semantic accuracy when handling domain-specific security terminology, as well as deployment inefficiencies that hinder their practical adoption in critical infrastructure environments. To address these issues, this paper proposes KD-SecBERT, a domain-specific semantic bidirectional encoder optimized through multi-level knowledge distillation for open-source software supply chain security in smart grid applications. The proposed framework constructs a hierarchical multi-teacher ensemble that integrates general language understanding, cybersecurity-domain knowledge, and code semantic analysis, together with a lightweight student architecture based on depthwise separable convolutions and multi-head self-attention. In addition, a dynamic, multi-dimensional distillation strategy is introduced to jointly perform layer-wise representation alignment, ensemble knowledge fusion, and task-oriented optimization under a progressive curriculum learning scheme. Extensive experiments conducted on a multi-source dataset comprising National Vulnerability Database (NVD) and Common Vulnerabilities and Exposures (CVE) entries, security-related GitHub code, and Open Web Application Security Project (OWASP) test cases show that KD-SecBERT achieves an accuracy of 91.3%, a recall of 90.6%, and an F1-score of 89.2% on vulnerability classification tasks, indicating strong robustness in recognizing both common and low-frequency security semantics. These results demonstrate that KD-SecBERT provides an effective and practical solution for semantic analysis and software supply chain risk assessment in smart grids and other critical-infrastructure environments. Full article

Smart cities aim to integrate technological, infrastructural, and socio-environmental systems in order to improve urban sustainability and quality of life. To qualify as both smart and sustainable, a city is generally expected to pursue self-sufficiency through the adoption of sustainable practices in energy production, water supply, and food systems. Such cities also seek to reduce operational costs for both private operators and municipalities, while aiming to enhance the quality of life of their residents. Within this context, the relevance of a web-based application becomes particularly apparent. An application equipped with predefined indicators can provide a structured and measurable framework for assessing the current status of a city or town in relation to smart and sustainable development. This framework allows for the evaluation of the extent to which a city aligns with established criteria associated with smart and sustainable urban models. This paper introduces a Python-based web application, developed using Python version 3.10, designed to assess or support the self-assessment of a city’s alignment with identified smart and sustainable development indicators. This study does not claim empirical validation or benchmarking performance; the proposed system is presented as a proof-of-concept framework. The work does not propose new smart city indicators. Rather, it presents an integrative system that seeks to operationalise existing smart and sustainable city indicators within a unified and modular web-based assessment framework, designed to support cross-domain evaluation and citizen-accessible self-assessment. Full article

This paper presents an Interoperable User-Centred Digital Twin (I-UCDT) framework for sustainable energy system management, addressing the growing complexity of energy generation, storage, demand, and grid interaction across industrial and community-scale systems. The proposed framework provides a unified environment for the visual representation and management of interconnected energy components, supporting informed decision-making among diverse stakeholder groups. The I-UCDT framework adopts a modular plug-and-play architecture based on the Functional Mock-up Interface (FMI) standard, enabling scalable and interoperable integration of heterogeneous energy models from platforms such as Modelica, MATLAB/Simulink, and EnergyPlus. A standardised data layer processes and structures raw model inputs, while an interactive visualisation layer translates complex energy flows into intuitive, user-accessible insights. By applying human–computer interaction principles, the framework reduces cognitive load and enables users with varying technical backgrounds to explore supply–demand balancing, decarbonisation pathways, and optimisation strategies. It supports the full lifecycle of energy system design, planning, and operation, offering flexibility for both industrial and community-scale applications. A case study demonstrates the framework’s potential to enhance transparency, usability, and energy efficiency. Overall, this work advances digital twin research for energy systems by combining technical interoperability with explicitly formalised user-centred design characteristics (C1–C10) to promote flexible and sustainable energy system management. Full article

Environmental, Social, and Governance (ESG) factors have become central to international trade, transforming how firms, industries, and governments engage in global markets. This study conducts a systematic literature review to synthesize current knowledge on the ESG–trade nexus. Using content analysis, three key thematic clusters were identified: (i) ESG in supply chains and logistics, (ii) ESG in export performance and international competitiveness, and (iii) ESG and trade within geopolitics, energy, and resource security. The synthesis reveals that ESG has evolved from a voluntary corporate initiative into a structural determinant of global competitiveness, resilience, and legitimacy. Building on these findings, the study proposes an integrative ESG–Trade framework, which conceptualizes ESG as a multidimensional governance ecosystem comprising (i) institutional and regulatory, (ii) technological and operational, and (iii) geopolitical and strategic dimensions. This framework explains how sustainability regulations, digital transformation, and global political economy dynamics co-evolve to shape trade flows and industrial upgrading. The study highlights the need for greater regulatory coherence and strategic ESG integration while offering a foundation for future interdisciplinary and empirical research on sustainable trade governance. Full article

In recent years, with the growing intensity of extreme weather events, imbalances in energy supply and demand, and frequent regional conflicts, the stability of our energy systems faces increasing challenges. Against this backdrop, the green innovation ecosystem can optimize the energy system’s structure and operational efficiency by promoting multi-actor interaction and multi-element synergy, thereby enhancing its resilience. Accordingly, this study aims to reveal how the green innovation ecosystem drives improvements in energy resilience (ER) through factor configurations and to identify the pathways leading to high-ER outcomes. To address this, this study constructs a research framework of the “core layer–environmental layer–supporting layer” for the green innovation ecosystem, and selects seven conditional variables, namely dual green innovation, multidimensional environmental regulation, green finance, and digital infrastructure. Based on official Chinese statistics, panel data from 30 provinces were compiled, and the dynamic qualitative comparative analysis (QCA) method was used to analyze how multiple factors interacted from 2016 to 2022 to achieve high ER from a spatiotemporal perspective. The results show that: (1) There is no single necessary condition for achieving high ER. (2) Dual green innovation and public participation in environmental regulation play a universal role in achieving high ER. They are combined with green finance, market-based environmental regulation, and digital infrastructure, forming three configuration pathways for achieving high ER. (3) No significant time effect is observed. (4) Pronounced spatial heterogeneity exists. The eastern region focuses on the green finance-enabled pathway, the central region has a high coverage of all three pathways, and the western region has relatively weak overall adaptability. Based on these findings, this study argues that enhancing ER depends on the coordinated allocation of multiple factors, and there is no single optimal pathway. Policymakers should adopt a configurational mindset and select appropriate combinations of elements in light of regional development conditions to enhance ER. Full article

Achieving sustainable development in the low-carbon transition requires securing critical raw materials (CRMs) while reducing environmental burdens and strengthening industrial resilience (SDGs 7, 9, 12, 13). This review synthesizes 2016–2025 evidence on how the European Union’s policy package—the Critical Raw Materials Act (CRMA), the Batteries Regulation, the Ecodesign for Sustainable Products Regulation (ESPR) with Digital Product Passports (DPPs), and the recast Waste Shipments Regulation (WSR)—shapes markets for secondary supply in battery-relevant metals such as lithium, cobalt, nickel, copper, aluminum, and rare earths. We apply a structured scoping review protocol to map the state of the art across policy instruments (EPR, ecodesign/DPP, recycled content mandates, recovery targets, shipment controls) and value chain stages (collection, preprocessing, refining, manufacturing). The analysis highlights benefits, including clearer investment signals, improved traceability, and emerging opportunities for industrial symbiosis, but also identifies drawbacks such as heterogeneous standards, compliance costs, and trade frictions. Evidence gaps remain, especially in causal ex post assessments, price pass-through, and interoperability of MRV/DPP systems. The paper contributes by (i) providing an integrative framework linking policy instruments, value chain stages, and investment signals for secondary CRM supply, and (ii) outlining a research agenda for rigorous ex post evaluation, improved MRV/DPP data architectures, and better alignment between EU trade rules, circularity, and a just energy transition. Full article

Since the Industrial Revolution, the increase in greenhouse gas emissions has led to a significant rise in global temperatures compared to the pre-industrial period. This development has heightened the importance of carbon pricing policies in combating climate change. This study aims to examine the effects of carbon pricing instruments, carbon taxes and emissions trading systems (ETS) on carbon dioxide (CO₂) emissions in OECD countries. A panel data analysis covering the period 2002–2023 was conducted, taking into account structural differences across countries as well as shared economic dynamics. The findings indicate that both carbon taxes and ETS mechanisms are effective in reducing CO₂ emissions in the long run. Moreover, while increased industrial activity contributes to higher emissions, a greater share of renewable and nuclear sources in the energy mix is found to support emission reduction. The study demonstrates that carbon pricing policies exert limited short-term effects but generate structural and lasting impacts in the long term. The findings are consistent with the existing literature and theoretical framework. Achieving permanent reductions in emissions requires a comprehensive policy approach that not only implements carbon pricing, but also strengthens energy efficiency and fuel substitution in the industrial sector while continuously increasing the share of clean sources in the energy supply. The analysis shows that carbon taxes and emissions trading systems (ETS) are effective in reducing emissions over the long run in OECD countries, and that their success varies depending on countries’ energy profiles and policy designs. These results underline that a well-designed and complementary carbon pricing framework is critical for achieving a sustainable transition. Full article

This study was conducted to investigate potential regulatory mechanisms of hybridization increased the meat production performance in sheep. Thirty-six 3-month-old male lambs of Suffolk sheep (SFK, n = 12), Hu sheep (HH, n = 12), and their F₁ hybrids (SH, n = 12) were selected and raised in individual pens under identical nutritional supply and husbandry management regimes over a 95-day (including a 15-day pre-trial period) experimental period. At the end of the feeding trial, six sheep closest to the average body weight were selected from each group for the subsequent trial, involving the collection of Longissimus dorsi samples and the determination of production performance, muscle fiber characteristic and transcriptomic and metabolomic analysis. The results showed that the SH sheep had significantly higher pre-slaughter live weight and carcass weight than the HH sheep, while lower than those of the SFK sheep (p < 0.05). The muscle fiber density of the SH group was significantly higher than that of the parental groups, while the muscle fiber diameter and cross-sectional area were significantly smaller (p < 0.05). The collagen fiber content of the SH group was intermediate between the two parental groups and significantly higher than that of the SFK group (p < 0.05). Transcriptomic analysis identified 2920 differentially expressed genes (DEGs), which were mainly enriched in the AMPK, PI3K-Akt, and PPAR signaling pathways. Metabolomic analysis detected 1617 differential metabolites (DMs), which were enriched in the fatty acid degradation and steroid hormone biosynthesis pathways. Integrated analysis revealed that core genes SESN3 and metabolites (malate, testosterone) enhance energy supply capacity through AMPK pathway, thereby promoting muscle fiber proliferation and increasing meat yield in the hybrid sheep. In conclusion, the heterosis of the SH group originates from the remodeling of muscle fiber structure and the synergistic regulation of related pathways, which provides a theoretical basis for sheep crossbreeding. 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

The endogenous money creation paradigm posits that banks generate money through lending, with deposits serving as a byproduct. This study investigates the mechanism driving the “credit–deposit paradox” during Poland’s high-interest-rate environment, introducing innovative methodological approaches to quantify systemic monetary impairment. Using comprehensive monthly data from 2006 to 2024, we employ a mixed-methods framework featuring: (1) Bayesian vector autoregression with Minnesota priors to test dynamic interdependencies; (2) a novel money shortage indicator (MSI) that operationalizes credit–deposit decoupling through three theoretically grounded components; (3) Markov regime-switching analysis to identify persistent monetary stress regimes. Key findings reveal a structural decoupling between deposit growth and credit creation, with robust evidence that exogenous money inflows accumulate as idle deposits rather than stimulating lending. The economy experienced significant periods of money shortage conditions, with the most severe impairment occurring during recent high-stress periods. The analysis confirms the dominance of cost-push inflation from energy and food prices, while monetary factors played a limited role. High interest rates amplified credit demand suppression, creating conditions consistent with endogenous money creation disruption. Methodologically, this study enables three key advances: (1) systematic measurement of monetary transmission breakdowns; (2) empirical identification of structural factors disrupting credit–deposit dynamics; (3) temporal characterization of monetary stress persistence patterns. These contributions advance the endogenous money framework by demonstrating its vulnerability to behavioral, policy-induced, and exogenous disruptions during high-stress periods. Practically, the MSI offers policymakers a real-time diagnostic tool for identifying monetary transmission breakdowns, while the regime analysis informs targeted countercyclical measures. Specific policy recommendations include developing sector-specific liquidity facilities, coordinating fiscal transfers with monetary policy to prevent deposit–loan decoupling, and prioritizing supply-side interventions during cost-push inflation episodes. By integrating post-Keynesian theory with empirical evidence from Poland, this study contributes to understanding money creation mechanisms in highly stressed economic environments. Full article