Search Results (339)

Against the backdrop of heightened global economic uncertainty and increasingly frequent risks in agricultural supply chains, enhancing agricultural supply chain resilience has become a critical issue for safeguarding national food security and promoting high-quality agricultural development. As key actors within agricultural supply chains, the impact of financialization—defined as the shift of resources to non-core financial assets—among agricultural listed firms on supply chain resilience warrants systematic examination. Using panel data from 165 Chinese agricultural listed firms (2010–2022), this study empirically investigates the impact of corporate financialization on agricultural supply chain resilience and its underlying mechanisms. An entropy-weighted composite index based on 16 parameters is used to assess agricultural supply chain resilience. It is composed of three dimensions: resistance capability, recovery capacity, and renewal capacity. The results show that: Financialization significantly undermines supply chain resilience, with the most substantial negative effect on recovery capacity, followed by renewal capacity, and the weakest on resistance capacity. Heterogeneity analyses show more pronounced negative effects among non-state-owned enterprises, non-primary sector firms, and capital-intensive enterprises. Financing constraints and capital expenditures partially mediate the negative relationship between financialization and resilience, while profitability persistence exacerbates the crowding-out effect. These findings suggest that policymakers should strike a compromise between reducing excessive financialization and strengthening agricultural supply chains. While prudently guiding agricultural firms’ financial asset allocation, greater emphasis should be placed on developing a diverse and coordinated industrial support system, thereby diverting financial capital away from crowding out core operations and toward effectively serving the real economy, ultimately contributing to national food security and agricultural modernization. Full article

Long-duration energy storage (LDES) can deliver system-wide flexibility and decarbonization benefits, yet investment is often hindered because these benefits are diffuse and not fully monetized under conventional market structures. A public-asset-oriented valuation and cost-allocation framework is proposed for LDES. First, LDES externality benefits are quantified through a system-level optimization-based simulation on a stylized aggregated regional network, with key indicators including thermal generation cost, carbon penalty, renewable curtailment cost, involuntary load shedding, and end-user electricity expenditures. Second, LDES investment costs are allocated among thermal generators, renewable operators, grid entities, and end users via a benefit-based Nash bargaining mechanism. In the case study, introducing LDES reduces thermal generation cost by 3.92%, carbon penalties by 5.59%, and renewable curtailment expenditures by 7.07%, while eliminating load shedding. The resulting cost shares are 46.9% (renewables), 28.7% (end users), 22.4% (thermal generation), and 0.5% (grid entity), consistent with stakeholder-specific benefit distributions. Sensitivity analyses across storage capacity and placement further show diminishing marginal returns beyond near-optimal sizing and systematic shifts in cost responsibility as benefit patterns change. Overall, this framework offers a scalable, economically efficient, and equitable strategy for cost redistribution, supporting accelerated LDES adoption in future low-carbon power systems. Full article

District heating networks (DHNs) are a key technology in the transition toward sustainable heat supply, increasingly integrating renewable sources and thermal energy storage. High-temperature aquifer thermal energy storage (HT-ATES) can enhance DHN efficiency by shifting heat production over time, potentially reducing both costs and greenhouse gas emissions. However, most life cycle assessments (LCAs) remain static, rely on average data, and neglect temporal dispatch dynamics and marginal substitution among heat sources for environmental evaluation. This study introduces a dynamic life cycle inventory framework that explicitly links HT-ATES-operation scheduling in DHNs with marginal life cycle data. The framework expands system boundaries to capture time-varying changes in heat composition, combines a district heating merit-order representation (distinguishing must-run and flexible capacities) with linear programming to determine least-cost dispatch, and translates marginally displaced technologies into environmental and economic consequences. Foreground inputs are derived from an existing third-generation DHN (heat demand, generation assets, efficiencies) and publicly available energy carrier cost data and are linked to consequential background inventory datasets (ecoinvent). The framework is demonstrated for one year of operation for an HT-ATES concept with 50 GWh of injected heat. Hourly resolved results identify the marginally displaced technologies and indicate annual reductions of 5.86 kt CO₂e alongside cost savings of EUR 1.09 M. A comparison of alternative operation schedules shows strong sensitivity of both economic and environmental performance to operational strategy. Overall, the proposed framework provides a replicable and adaptable basis for consequential assessment of HT-ATES operation in DHNs and supports strategic decision-making on seasonal thermal storage deployment in low-carbon heat systems. Full article

In an era where economies increasingly rely on knowledge and innovation, sustaining long-term growth depends on understanding how education drives productivity beyond conventional measures. Yet, existing studies on the education–growth nexus remain fragmented, often focusing narrowly on schooling attainment while overlooking the complementary roles of lifelong learning and public investment in human capital. Addressing this critical gap, the present study adopts a multidimensional approach to evaluate how educational attainment, adult learning participation, and government expenditure on education collectively shape economic performance across the 27 European Union (EU) member states. Drawing on an unbalanced Eurostat panel dataset (2013–2022), the study employs a fixed-effects regression model with White cross-section robust standard errors to account for heteroskedasticity and serial correlation. The empirical results reveal that all three educational dimensions exert positive and statistically significant effects on GDP, with government educational expenditure emerging as the most influential driver, followed by adult learning participation, underscoring the transformative role of continuous skill renewal in dynamic labor markets. These findings advance Human Capital Theory by framing education not merely as an individual asset but as an interactive, systemic driver of national productivity and resilience. The study offers actionable insights for policymakers, calling for integrated strategies that align formal education, lifelong learning systems, and sustained public investment to foster inclusive, knowledge-driven, and sustainable economic growth across the EU. Full article

Urban rivers are critical ecological and cultural assets facing accelerating biodiversity loss. This study examines the integrated redevelopment of the Jadro River spring in Solin, Croatia, where a protected ichthyological reserve intersects layered heritage and urban edges to enhance conservation and public value. Using a single-case study design that combines archival project documentation, participant observation by the architect–authors, and a post-occupancy review three years after completion, the analysis synthesizes ecological, social, and design evidence across planning, delivery, and operation phases. The project delivered phased visitor and interpretation centers, accessible paths and bridges, habitat-compatible materials, and formalized access management that relocated parking from riverbanks, reduced episodic pollution sources, and prioritized inclusive, low-impact use. Governance and programming established a municipal management plan, curriculum-ready interpretation, and carrying capacity monitoring, transforming an underused picnic area into an educational, recreational, and conservation-oriented public landscape while safeguarding sensitive habitats. A transferable design protocol emerged, aligning blue green infrastructure, heritage conservation, adaptive reuse, and social–ecological system (SES)-informed placemaking to protect the endemic soft-mouth trout and strengthen a sense of place and community stewardship. The case supports SES-based riverpark renewal in which conservative interventions within protected cores are coupled with consolidated services on resilient ground, offering a replicable framework for ecologically constrained urban headwaters. Full article

Technical constraints to be faced in microgrids have become more frequent with high renewable integration. In this context, Thermal Energy Storage (TES) has emerged as a promising solution to enable consumers’ flexibility to contribute to the solution of such operational issues. This paper examines the integration of the novel system ECHO-TES (a Thermal Energy Storage System developed within the European Project ECHO) in microgrids to address technical constraints, utilizing OpenDSS and Python simulations. Building on that, the Efficient Compact Modular Transaction Simulation System (ECHO-TSS) adds a layer of virtual automated transactions, coordinating multiple ECHO-TES assets to simulate not only energy flows and electricity consumption, but also the associated economic interactions. The study explores the critical role of TES in enhancing microgrid efficiency, flexibility, and sustainability, particularly when coupled with renewable energy sources. By analyzing diverse demand scenarios, the research aims to assess its impact on grid stability and management. The paper highlights the importance of advanced modeling tools like OpenDSS in simulating complex microgrid operations, including the dynamic behavior of TES systems. It also investigates demand-side management strategies and the potential of TES to mitigate challenges associated with renewable energy variability. The findings contribute to the development of robust, adaptive microgrid systems and support the global transition towards sustainable energy infrastructure. Full article

South Africa’s persistent electricity shortages and recurrent load shedding remain among the most pressing challenges to national economic growth and social stability. This paper presents a techno-economic framework to assess how optimized deployment of photovoltaic (PV) and battery energy storage systems (BESSs) can mitigate these disruptions under realistic grid and regulatory constraints. Despite recent operational improvements at Eskom—including a 10-month period without load shedding in 2024—energy insecurity persists due to aging coal assets, limited transmission capacity, and slow renewable integration. Using hourly demand and solar-resource data for 2023, combined with Eskom’s load-reduction records, a Particle Swarm Optimization (PSO) model identifies cost-optimal hybrid system configurations that minimize the Levelized Cost of Electricity (LCOE) while maximizing coverage of unserved energy. Three deployment scenarios are analyzed: (i) constrained regional grid capacity, (ii) flexible redistribution of capacity across six provinces, and (iii) unconstrained national deployment. Results indicate that constrained deployment covers about 86% of curtailed load at 1.88 USD kWh⁻¹, whereas flexible and unconstrained scenarios achieve over 99% coverage at ≈0.58 USD kWh⁻¹. The findings demonstrate that targeted PV–BESS expansion, coupled with selective grid reinforcement, can effectively eliminate load shedding and accelerate South Africa’s transition toward a resilient, low-carbon electricity system. Full article

As an emerging sports tourism event, Guizhou’s “Village Super League” injects new vitality into the optimization of human–land relationships and the development of household livelihoods in traditional villages of ethnic mountainous regions. Studying five affected traditional tourism villages from an “event–actor–capital” perspective using mixed methods, this research finds the following: (1) The composite average score of household livelihood capital is 0.3177, indicating a medium–low level, which suggests that households’ livelihood structure still requires significant enhancement despite the tourism boost from the “Village Super League”. (2) There is an imbalance in development among the villages. The livelihoods of households under the influence of the “Village Super League” exhibit distinct characteristics, being “driven by external flows, led by social capital, supported by the material foundation, and coordinated with other forms of capital.” (3) The evolution of household livelihoods follows a pathway of “event-driven supplementation, endogenous renewal of actors, capital integration and synergy.” By constructing shared event memory markers, the livelihoods of villages at different stages of tourism development demonstrate differentiated dynamic mechanisms. The findings deepen the theoretical understanding of livelihoods in traditional villages under event-driven development. Consequently, this study recommends that policymakers and community stewards channel transient social capital and external flows into durable physical and financial assets to ensure livelihood sustainability beyond the initial event boom. Full article

As a pivotal global commodity, crude oil price volatility directly impacts economic stability and strategic security. Being the most widely traded asset worldwide, it also serves as a key financial barometer and a critical transition fuel in the shift towards renewable energy. Nevertheless, accurate forecasting of crude oil prices remains challenging due to three persistent challenges: (1) the lack of a systematic method to filter out redundant and noisy features for deep learning models; (2) the limited ability of existing models to simultaneously capture both local bidirectional dependencies and global periodic patterns; and (3) the non-adaptive nature of conventional attention mechanisms, which restricts their capacity to dynamically focus on the most informative historical periods. To bridge these gaps, this study introduces a novel forecasting framework with three key contributions. First, we introduce a hierarchical feature selection paradigm based on LightGBM to systematically eliminate data redundancy and noise, thereby constructing an optimal feature subset for subsequent deep modeling. Second, an improved Autoformer encoder, integrated with Bidirectional GRUs, is designed to simultaneously capture local bidirectional dependencies and global periodic patterns, enabling a more comprehensive multi-scale temporal representation. Third, a dynamic fusion mechanism is incorporated to adaptively recalibrate the significance of historical timesteps. This enables the model to focus on periods rich in information, enhancing contextual awareness in predictions. Future research aims to enhance forecasting capabilities by achieving a deeper integration of local and global temporal representations, potentially through exploring advanced gating or sparse attention mechanisms. Full article

Sub-hourly operational optimization of Power-to-X (PtX) hydrogen systems remains largely unexplored, despite their growing importance as flexible assets in renewable-dominated energy systems. Existing models typically assume hourly market resolution and linear process behavior, overlooking how intra-hour price volatility and non-linear electrolyzer efficiencies shape operational costs, flexibility, and emissions. This study pioneers a data-driven optimization framework that integrates synthetic 15 min electricity-price generation, agent-based simulation, and mixed-integer quadratically constrained programming (MIQCP) to evaluate hydrogen-production strategies under the forthcoming European 15 min market regime. Using a Danish PtX facility with on-site wind and solar generation as a case study, the framework quantifies how adaptive scheduling compares with non-adaptive baselines across multiple volatility scenarios. The results show that dynamic 15 min optimization reduces hydrogen-production costs by up to 40% relative to hourly scheduling, and that extending the objective function to include electricity-sales revenue improves net profitability by approximately 11%. Although adaptive scheduling slightly increases CO₂ intensity due to altered renewable utilization, it substantially enhances flexibility and cost efficiency. Scientifically, this study introduces the first reproducible synthetic-data approach for sub-hourly optimization of non-linear electrolyzer systems, bridging a critical gap in the demand-side-management and sector-coupling literature. Practically, it provides evidence-based guidance for PtX operators and regulators on designing adaptive, volatility-responsive control strategies aligned with Europe’s transition to high-frequency electricity markets and net-zero objectives. Full article

Phosphate rock is a vital natural resource classified by the European Commission as a critical raw material (CRM), extensively mined for its agricultural, industrial, and technological applications. While primarily used in fertilizer production, phosphate deposits also contain significant concentrations of trace metals, notably rare earth elements (REE), which are essential for renewable energy, electronics, and defense technologies. In response to growing demand, the recovery of REE from phosphate ores and processing by-products, particularly phosphogypsum (PG), has gained international attention. This review provides a comprehensive analysis of the global phosphate industry, examining production trends, market dynamics, and the environmental implications of phosphate processing. Special focus is placed on the geochemical behavior and mineralogical associations of REE within phosphate ores and industrial residues, namely PG and purification sludge. Although often treated as waste, these by-products represent underexplored secondary resources for REE recovery. Technological advancements in hydrometallurgical, solvometallurgical, and bioleaching methods have demonstrated promising recovery efficiencies, with some pilot-scale studies exceeding 70%–80%. However, large-scale implementation remains limited due to economic, technical, and regulatory constraints. The circular economy framework offers a pathway to enhance resource efficiency and reduce environmental impact. By integrating innovative extraction technologies, strengthening regulatory oversight, and adopting sustainable waste management practices, phosphate-rich countries can transform environmental liabilities into strategic assets. This review concludes by identifying key knowledge gaps and suggesting future research directions to optimize REE recovery from phosphate deposits and associated by-products, contributing to global supply security, economic diversification, and environmental sustainability. Full article

This study explores the dynamic relationship between industrial robots, education, and environmental sustainability in the United States, emphasizing their role in reducing CO₂ emissions. The research aims to quantify how automation, human capital, and the energy transition contribute to carbon mitigation within a data-driven, AI-oriented policy framework. Quarterly data spanning 2011Q1–2024Q4 were analyzed using the advanced Quantile-on-Quantile Autoregressive Distributed Lag (QQARDL) model, which captures heterogeneous long- and short-run effects across emission distributions. Results reveal that industrial robot adoption, education, and renewable energy transition significantly reduce emissions, with the strongest effects occurring at both high- and low-emission quantiles. Economic growth and financial development also support decarbonization when complemented by green finance and innovation, while urbanization increases emissions unless aligned with compact urban design and clean energy systems. The findings imply that AI-driven industrial robotics and education jointly foster sustainability through efficiency, innovation, and awareness. Policymakers are encouraged to integrate automation strategies, renewable energy incentives, and sustainability education into climate policy. This study provides empirical evidence supporting the Resource-Based View, highlighting human capital and intelligent automation as strategic assets for achieving long-term carbon neutrality. Full article

The integration of a high proportion of renewable energy has significantly reduced the grid inertia level and markedly increased the risk of transient frequency instability in power systems. Meanwhile, the large-scale integration of diverse heterogeneous resources—such as wind power, photovoltaics, energy storage, and high voltage direct current (HVDC) transmission systems—has considerably enriched the portfolio of frequency regulation assets in modern power grids. However, the marked disparities in the dynamic response characteristics and actuation speeds among these resources introduce significant nonlinearity and high-dimensional complexity into the system’s transient frequency behavior. As a result, conventional methods face considerable challenges in achieving accurate and timely prediction of such responses. However, the substantial differences in the frequency regulation characteristics and response speeds of these resources have led to a highly nonlinear and high-dimensional complex transient frequency response process, which is difficult to accurately and rapidly predict using traditional methods. To address this challenge, this paper proposes an online prediction method for transient frequency response that deeply integrates physical principles with data-driven approaches. First, a frequency dynamic response analysis model incorporating the frequency regulation characteristics of multiple resource types is constructed based on the Single-Machine Equivalent (SME) method, which is used to extract key features of the post-fault transient frequency response. Subsequently, information entropy theory is introduced to quantify the informational contribution of each physical feature, enabling the adaptive weighted fusion of physical frequency response features and Wide-Area Measurement System (WAMS) data. Finally, a physics-guided machine learning framework is proposed, in which the weighted physical features and the complete frequency curve predicted by the physical model are jointly embedded into the prediction process. An MLP-GRU-Attention model is designed as the data-driven predictor for frequency response. A physical consistency constraint is incorporated into the loss function to ensure that predictions strictly adhere to physical laws, thereby enhancing the accuracy and reliability of the transient frequency prediction model. Case studies based on the modified IEEE 39-bus system demonstrate that the proposed method significantly outperforms traditional data-driven approaches in terms of prediction accuracy, generalization capability under small-sample conditions, and noise immunity. This provides a new avenue for online frequency security awareness in renewable-integrated power systems with multiple heterogeneous frequency regulation resources. Full article

The power system is evolving with the integration of new technologies, including electronic devices and renewable energy sources, which are increasingly used to support new applications, reduce dependence on fossil fuels, and drive system innovation. However, this shift brings a significant drawback: a reduction in power quality (PQ). The literature extensively discusses the impact of poor PQ on electrical assets and explores potential solutions to this new challenge. Building on this foundation, this paper introduces new PQ indices derived from existing metrics and validated on both synthetic and real signals to assess their effectiveness. The aim is to provide researchers and system operators with simple and efficient tools for the clear identification of PQ issues in monitored networks. These new indices are designed to be flexible and independent of acquisition conditions, making them suitable for a wide range of frequencies (e.g., 50 Hz–150 kHz) and applications. After an overview of the PQ landscape, the paper demonstrates the use of these indices on various voltage waveforms, including a case study from a measurement campaign. The promising results indicate that, when combined with existing indices, these new metrics can form a strong foundation for a deeper understanding and more accurate classification of PQ issues in power networks. Full article

Industrial heritage is increasingly becoming an important resource for sustainable urban renewal. With the acceleration of deindustrialization and urban transformation, Adaptive Reuse (AR) is regarded as the core path connecting heritage protection and functional renewal. Balancing the diverse value dimensions of AR has also become a key research focus. However, existing research mostly focuses on financial returns and investment efficiency, ignoring the long-term impact of community space and cultural dimensions on economic feasibility; at the same time, culture is often simplified into a tool for asset appreciation and urban branding, lacking a systematic model that reveals the structural role of culture in economic feasibility. Therefore, this study constructs a multi-attribute decision-making framework that integrates economic performance, community space, and cultural value. Using Guangzhou Guanggang New City as a representative case, the Fuzzy Delphi Method (FDM), Analytic Network Process (ANP), and Grey Relational Analysis (GRA) were employed to screen and rank the highest-priority reuse schemes. The results show that the economic dimension holds the highest overall weight, followed by the community and cultural dimensions. This suggests that economic feasibility remains a key prerequisite for industrial heritage renewal, while cultural and community factors play an important supporting role in achieving long-term sustainability. This study provides a quantifiable assessment path for the adaptive reuse of industrial heritage and offers a basis for decision making in other cities seeking a balance between economic rationality and cultural sustainability. Full article