Search Results (6,512)

Reducing agricultural emissions is vital for climate mitigation, yet evidence on green finance’s potential to facilitate agricultural decarbonization—particularly in China—remains scarce. Leveraging China’s Green Finance Reform and Innovation Pilot Zones as a quasi-natural experiment, this study employs a staggered difference-in-differences design and complementary Callaway-Sant’Anna estimates. Using a balanced panel of 282 prefecture-level and above cities spanning 2012–2022—a window covering five pre-policy years before the initial 2017 pilot rollout and sufficient post-policy years to capture dynamic effects for the 2017, 2019, and 2022 cohorts—this study assesses the policy impact on agricultural carbon emission intensity. The findings reveal that the pilot policy reduces emission intensity by approximately 9.2% on average. This result is robust across event-study analyses, placebo tests, PSM-DID, policy interference checks, and alternative outcome specifications. Channel-consistent evidence suggests that the effect operates through three mechanisms: greener credit allocation, stronger green technological innovation, and lower-carbon adjustment of the agricultural production structure. The effect is larger in eastern China, major grain-producing regions, and cities with higher levels of financial development, and exhibits a strengthening trend over time. By analyzing China’s city-based pilot approach, this study demonstrates how financial policy can support agricultural decarbonization in settings characterized by dispersed emitters, imperfect environmental monitoring, and strong food-security constraints. The findings extend beyond China to inform other developing economies seeking non-price-based pathways to greener agriculture. Full article

Despite recent advances in artificial intelligence for agriculture, reliable crop recommendation remains constrained by limited access to soil diagnostics, insufficient integration of environmental context, and the absence of transparent, quantitative evaluation frameworks. This study addresses the research question: How can we integrate multiple indicators to generate accurate, explainable, and context-sensitive crop recommendations? To this end, we propose a multimodal decision-support framework that combines image-based soil texture classification with geospatial, and climatic information. A convolutional neural network was trained on a curated dataset of 3250 soil images aggregated from four publicly available sources, covering four primary soil texture classes, alongside tabular soil and nutrient data. The model was evaluated using 5-fold stratified cross-validation, achieving an average classification accuracy of 99.30% (standard deviation ≈ 0.66), and was further validated on an independent hold-out test set to assess generalization performance. To enhance practical applicability, the framework incorporates elevation, rainfall, temperature, and major soil nutrients, and employs a large language model to generate user-oriented, interpretable justifications for each recommendation. Crop recommendations were quantitatively evaluated using a novel Agronomic Suitability Score (ASS), which measures alignment across soil compatibility, climatic suitability, seasonal alignment, and elevation tolerance. Across six geographically diverse case studies, the framework achieved mean ASS values ranging from 3.76 to 4.96, with five regions exceeding 4.45, demonstrating strong agronomic validity, robustness, and scalability. A Streamlit-based application further illustrates the system’s ability to deliver accessible, location-aware, and explainable agronomic guidance. The results indicate that the proposed approach constitutes a scalable decision-support tool with significant potential for sustainable agriculture and food security initiatives. Full article

Over the past decade, road safety research has experienced rapid development due to the rapid expansion of large crash databases, the adoption of artificial intelligence techniques, and the demand for proactive and predictive safety solutions. This study conducts a data-driven review of recent research trends in transport safety. It focuses on main domains including crash severity analysis, human factors, vulnerable road users (VRUs), spatial modeling, and artificial intelligence applications. A systematic search of the Scopus database identified 15,599 relevant scientific papers published between 2016 and 2025. After constructing this corpus, titles, abstracts, and keywords were preprocessed using a natural language pipeline. The analysis employed BERTopic, a transformer-based topic modeling framework. The analysis identified 29 distinct research topics, further synthesized into five major thematic areas: (1) crash severity and injury analysis, (2) driver behavior and human factors, (3) vulnerable road users, (4) artificial intelligence, machine learning, and computer vision in intelligent transportation systems, and (5) spatial analysis and hotspot detection. A notable increase in publications related to artificial intelligence and machine learning has been evident since 2020. The results show a transition from descriptive, post-crash studies to integrated, multimodal, predictive analysis. Overall, the findings reveal a paradigm shift in the field. This study also identifies ethical and economic issues associated with the use of artificial intelligence in intelligent transportation systems, including data management, infrastructure requirements, system security, and model transparency. The results signify a transition from intuition-based models to explainable, spatially explicit, and data-intensive models, ultimately facilitating proactive risk assessment and informed decision-making. Full article

The application of quantum machine learning (QML) to security-relevant problems has attracted growing attention, yet its practical behavior in realistic workloads remains insufficiently characterized. This paper investigates the feasibility and limitations of variational quantum classifiers (VQCs) for identifying dummy power traces in side-channel analysis (SCA). A controlled benchmarking framework is developed to evaluate training stability, sensitivity to key design parameters, and resource–performance trade-offs under realistic constraints. To move beyond idealized simulation, hardware-relevant factors, including finite measurement budgets and device noise, are incorporated, and inference robustness under degraded operating conditions is assessed. The results show that VQCs can capture meaningful discriminative patterns in structured side-channel data, although robustness and performance depend strongly on encoding strategy, circuit depth, and measurement conditions. These findings provide an empirical assessment of the potential and limitations of QML for side-channel security and offer practical guidance for future research. Full article

The rapid integration of artificial intelligence (AI) into digital platforms has raised critical questions about how AI’s ethical declarations influence this sector. This study adopts a mixed-methods approach. First, a descriptive content analysis examined 54 declarations, including 45 national declarations across Africa, Asia, Europe, and the Americas, and 9 from major global actors (MGAs) such as the OECD, G7, and the EU. Ethical principle frequency was examined, and a benchmarking index was developed to compare “dominant principles” cited in over 50% of regional declarations with those cited in over 50% of MGA declarations. The analysis reveals universal adoption of societal well-being, fairness, accountability, and privacy (100%), while transparency and security show regional variation (75%). Second, a semi-systematic literature review following PRISMA guidelines identified four opportunities (e.g., global participation) and seven limitations (e.g., lack of standard frameworks, definitional ambiguities, implementation challenges, and legal enforcement difficulties). The implications of these limitations for digital platforms are then examined, leading to the identification of two dimensions for responsible platform governance: assessment mechanisms (e.g., UNESCO’s Ethical Impact Assessment) and governance implementation structures. The study further distinguishes three tiers of enforceability: declarative, procedural, and institutionalized ethics, bridging normative declarations and operational practice in platform governance. Full article

Unmanned Aerial Vehicle (UAV)-assisted hierarchical federated learning (HFL) has emerged as a promising architecture for Internet of Things (IoT)-based smart agriculture, which enables scalable model training over large and sparse farmlands. In this setting, UAVs act as mobile edge servers, aggregating local updates from distributed agricultural IoT devices and relaying them to the cloud server. While HFL improves scalability and reduces communication overhead, it still faces critical security threats due to its reliance on public wireless channels and the vulnerability of model aggregation to malicious updates. In this paper, we propose a secure authentication scheme that integrates anomaly detection with elliptic curve cryptography (ECC)-based mutual authentication to protect both the communication and training phases. In the proposed scheme, UAVs authenticate participating clients before receiving their local models, then perform anomaly detection to identify and exclude malicious participants. If a client is found to be malicious, its identity credentials are revoked and broadcast by the cloud server to prevent future participation. The security of the proposed scheme is formally verified using Burrows–Abadi–Needham (BAN) logic, the Real-or-Random (RoR) model, and the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool, along with informal security analysis. The performance evaluation includes comparisons of security features, computation cost, and communication cost with other related schemes, and an experimental assessment of anomaly detection performance. The results demonstrate that our scheme provides strong security guarantees, low overhead, and effective malicious client detection, making it well suited for UAV-assisted HFL in smart agriculture. Full article

Against the backdrop of climate change, forest fires increasingly undermine ecosystem stability and reshape species distributions in Heilongjiang Province. Therefore, quantifying the drivers of fire occurrence and conducting long-term fire risk forecasting holds critical value for regional ecological security. Centered on the forested regions of Heilongjiang Province, this study systematically assessed the relative contributions of multi-source factors—including topography, vegetation, and meteorological conditions—to fire occurrence and compared the predictive performance of three models: Deep Neural Network with Residual Connections (ResDNN), Artificial Neural Network (ANN), and Support Vector Machine (SVM). Modeling results based on historical fire records indicated that the ResDNN model achieved the highest accuracy (85.6%). Owing to its robust nonlinear mapping capability, it performed better in capturing complex feature interactions than ANN and SVM. These results demonstrate its strong applicability to forest fire prediction in Heilongjiang Province. Building on these findings, the study employed the best-performing ResDNN model in conjunction with CMIP6 multi-model climate projections to simulate and map the spatiotemporal probability of forest fire occurrence from 2030 to 2070. The results provide an intuitive representation of long-term fire-risk trajectories under future climate scenarios and offer scientific support for regional fire prevention, monitoring, early-warning systems, and forest management under climate change. Full article

Remote sensing images acquired by UAVs under nighttime or low-illumination conditions suffer from insufficient illumination, leading to degraded image quality, detail loss, and noise, which restrict their application in public security and disaster emergency scenarios. Although existing machine learning-based enhancement methods can recover part of the missing information, they often cause color distortion and texture inconsistency. This study proposes an improved low-illumination image enhancement method based on a Weakly Paired Image Dataset (WPID), combining the Hierarchical Deep Convolutional Generative Adversarial Network (HDCGAN) with a low-rank image fusion strategy to enhance the quality of low-illumination UAV remote sensing images. First, YCbCr color channel separation is applied to preserve color information from visible images. Then, a Low-Rank Representation Fusion Network (LRRNet) is employed to perform structure-aware fusion between thermal infrared (TIR) and visible images, thereby enabling effective preservation of structural details and realistic color appearance. Furthermore, a weakly paired training mechanism is incorporated into HDCGAN to enhance detail restoration and structural fidelity. To achieve objective evaluation, a structural consistency assessment framework is constructed based on semantic segmentation results from the Segment Anything Model (SAM). Experimental results demonstrate that the proposed method outperforms state-of-the-art approaches in both visual quality and application-oriented evaluation metrics. Full article

In the context of the coordinated development of high-proportion renewable energy integration and alternating current/direct current (AC/DC) hybrid transmission, the sending-end power grid faces challenges such as decreased system strength, contracted stability boundaries, and difficulties in covering high-risk operating conditions. This paper proposes a new renewable energy scale impact analysis method that integrates “typical scenario construction-scale ladder comparison–prediction-driven time series injection” in response to the operational constraints of AC/DC transmission. In terms of method implementation, firstly, a two-layer typical scenario system is constructed under unified transmission constraints and fixed grid boundaries: A regular benchmark scenario covers the main operating range, and a set of high-risk scenarios near the boundaries is obtained through multi-objective intelligent search, which is then refined through clustering to form a computable stress-test scenario library. Here, the boundary scenarios are generated by a multi-objective search that simultaneously drives multiple key section load rates towards their limits, subject to AC power-flow feasibility and operational constraints, and the resulting Pareto candidates are reduced into a compact stress-test library by clustering. Secondly, a ladder scenario with increasing renewable energy scale is constructed, and cross-scale comparisons are carried out within the same scenario system to extract the scale effect and critical laws of key safety indicators. Finally, data resampling and Gated Recurrent Unit multi-step prediction are introduced to generate wind power output time series, enabling the temporal mapping of prediction results to scenario injection quantities, and constructing a closed-loop input interface of “prediction–scenario–grid indicators”. The results demonstrate that the proposed hierarchical framework, under unified AC/DC export constraints, can effectively construct a compact stress-test scenario library with enhanced boundary-risk coverage and can reveal how transient voltage security evolves across renewable expansion scales. By coupling boundary-oriented scenario construction, cross-scale comparable assessment, and forecasting-driven time series injection, the framework improves engineering interpretability and practical applicability compared with conventional scenario sampling/reduction workflows. For the forecasting module, the Gated Recurrent Unit (GRU) model achieves MAPE = 8.58% and RMSE = 104.32 kW on the test set, outperforming Linear Regression (LR)/Random Forest (RF)/Support Vector Regression (SVR) in multi-step ahead prediction. Full article

Background and Objectives: Population ageing is a major challenge of the 21st century and is associated with declining physical and mental abilities, increased disease burden, and higher mortality. Latvia has the lowest healthy life expectancy in the European Union. Social well-being is an important component of healthy and active ageing and may be associated with older adults’ quality of life (QoL). This study aimed to assess the relationship between social well-being, as a component of health, and QoL, including its components (control, autonomy, self-realisation and pleasure), among adults aged 50 and older in Latvia. Materials and Methods: Data from 1643 Latvian participants in wave 9 of the Survey of Health, Ageing, and Retirement in Europe (2022) were analysed using linear regression. QoL was measured using the 12-item Control, Autonomy, Self-Realisation, and Pleasure (CASP-12) scale. Social well-being factors included household composition, education, employment status, financial capacity, living area, social network (SN) characteristics, and received help, based on self-reported questionnaires. Results were considered statistically significant if the p-value was less than 0.05. Results: The factors positively associated with overall QoL were being employed, better financial capacity, greater satisfaction with SN, larger SN, participation in social activities, and higher educational attainment. Being employed and the ability to make ends meet easily were positively associated with all QoL components. Higher satisfaction with the SN and participation in social activities were positively related to the control, autonomy, pleasure, and self-realisation components. Conclusions: These findings underscore the importance of social and economic resources for QoL in later adulthood, suggesting that both the quality of social relationships and material security play a central role in shaping overall QoL and its components among older adults. Full article

Background/Objectives: Healthy food incentive programs (HFIP), such as fruit and vegetable voucher incentives, can supplement other nutrition assistance programs to support food and nutrition security. However, little is known about the prevalence of HFIP, particularly at the municipal level. This study examines the prevalence of HFIP in a nationally representative sample of U.S. municipalities and the association between the availability of HFIP and municipal characteristics. Methods: Using data from the CDC’s 2021 National Survey of Community-Based Policy and Environmental Supports for Healthy and Active Living (n = 1982 municipalities), a weighted bivariate analysis and multivariable logistic regression analysis were conducted to estimate the prevalence of HFIP overall and by municipal characteristics, and to assess the relationship between municipal characteristics and HFIP. Results: Only 7.8% of municipalities reported offering HFIP in 2021. The odds of having an HFIP were higher in municipalities with a food policy council (aOR 2.8; 95%CI: 1.9, 3.9) compared to those without. Larger communities (size ≥ 50,000 reported 24.6%) and those with a higher prevalence of residents living in poverty were also more likely to offer HFIP. Conclusions: Few municipalities reported offering HFIP. Results suggested that engaging institutions and individuals (e.g., via food access coalitions) may be strategies that could support municipalities initiating and implementing HFIP to improve diet quality and reduce chronic disease risk. Full article

Energy storage plays a key role in the energy transition by enabling the effective integration of variable renewable energy sources such as solar and wind power and by supporting the stability and flexibility of modern energy systems. The rapid development of energy storage technologies has become one of the pillars of sustainable energy management; however, it simultaneously raises environmental, material, and systemic challenges. This review analyses the environmental implications of energy storage development using an integrative perspective that combines technological, environmental, and system-level analysis. The paper examines major classes of energy storage technologies, including electrochemical, mechanical and physical, thermal energy storage, and chemical pathways within Power-to-X, with particular emphasis on their technical characteristics, maturity, and life cycle environmental performance. Lithium-ion battery systems typically achieve round-trip efficiencies of 85–92% and cycle lifetimes exceeding 5000 cycles, while flow batteries may exceed 10,000 cycles under stationary operating conditions. Mechanical storage technologies such as pumped hydro provide efficiencies of approximately 70–85% with operational lifetimes exceeding several decades. Key challenges related to critical raw material availability, recycling, end-of-life management, and ecosystem impacts are discussed, highlighting the importance of sustainable production and recovery strategies in supporting the circular economy. In addition, the review addresses the consequences of insufficient reuse of secondary materials and the growing relevance of digitisation and cyber resilience of energy storage systems as indirect contributors to environmental risk. The review also considers geopolitical aspects related to critical material supply chains and the cyber security of energy storage infrastructure, emphasising their growing importance for the resilience and environmental sustainability of future energy systems. The analysis indicates that further development of energy storage technologies will significantly influence not only power systems but also transport, industry, and heat sectors. The results emphasise that sustainable deployment of energy storage requires hybrid system architectures and policy frameworks that account for environmental performance, system flexibility, and long-term resilience in line with the principles of sustainable development. Full article

Kazakhstan is one of the world’s major wheat producers and exporters, playing an important role in regional and global food security. However, increasing quality requirements in domestic and export markets have exposed limitations in the country’s capacity to consistently supply high-quality spring bread wheat (Triticum aestivum L.). This review aims to assess the current status of spring wheat grain quality in Northern Kazakhstan, identify the main factors driving its variation, and outline pathways for quality improvement. The analysis is based on published literature, official statistics, national quality standards, and recent data on wheat production, grading, breeding systems, agronomic practices, and trade patterns. The review reveals that wheat production is dominated by medium-quality grain (primarily class 3), while high-quality classes suitable for premium and improver markets represent a small share. Compared with major exporters such as Canada, the United States, and Australia, Kazakh wheat is generally inferior across key quality parameters. Structural constraints include the limited integration of quality assessments within breeding programs, insufficient laboratory infrastructure, weak agroecological zoning by quality classes, and suboptimal agronomic management, particularly regarding nitrogen use. Environmental heterogeneity and climate change further influence the yield–quality balance. Overall, the findings suggest that improving wheat grain quality in Kazakhstan will require coordinated advances in breeding, agronomy, institutional capacity, and market alignment, enabling a gradual shift toward a more competitive, quality-oriented wheat production system. Full article

Against the backdrop of dietary structure evolution and the “big food concept” strategy, there has been a shift from the traditional grain-centric perspective toward a diversified supply system. Taking Hubei Province—a major grain-producing region in China—as a case study, this research establishes a multi-criteria evaluation system and conducts analysis using statistical yearbooks and land survey data. By integrating natural conditions, economic benefits, and production capacity, the suitability of cultivated land for growing grain crops, cash crops, and forage crops is assessed. Concurrently, landscape pattern indices were applied to quantify the degree of farmland fragmentation. Employing a self-organizing mapping (SOM) neural network model, we synthesized suitability and fragmentation data to delineate differentiated farmland conservation zones. The results revealed significant spatial heterogeneity in crop suitability and fragmentation levels. High-suitability zones for grain crops were concentrated in the Jianghan Plain, while forage crops exhibited higher suitability in northeastern and southeastern Hubei. Farmland fragmentation showed a spatial pattern of lower levels in central Jianghan Plain, gradually increasing toward surrounding hilly and mountainous areas. SOM clustering effectively partitioned farmland into six functional zones: multifunctional agricultural zones, mixed farming zones, grain crop zones, cash crop zones, forage crop zones, and production improvement zones. This multi-source geographic and statistical data-driven zoning framework provides scientific basis for targeted policy interventions. It enables the quantitative management, quality enhancement, and spatial optimization of farmland resources, thereby operationalizing the big food concept to strengthen regional food security. Full article

Energy security represents a fundamental pillar of economic sustainability, being defined as a state’s ability to ensure continuous, reliable, and affordable access to energy resources. In the context of recent geopolitical shifts, such as worldwide military conflicts, the vulnerabilities of energy systems have become evident, highlighting the interdependence between energy security and economic stability. Analyzing energy security involves assessing the diversification of sources, supply routes, critical infrastructure, and the degree of dependence on imports. The transition to renewable sources, in line with the objectives established by the European Union, contributes to reducing the risks associated with fossil market volatility and to strengthening economic resilience. At the same time, the integration of digital technologies and the development of storage capacities increase the flexibility of energy systems. Evaluating energy security must include indicators regarding price accessibility, environmental sustainability, and institutional capacity for crisis management. By aligning energy policies with macroeconomic and climate strategies, states can prevent major energy crises, mitigate the impact of external shocks, and ensure long-term sustainable economic development. The study highlights and brings to light Romania’s energy security situation by conducting an in-depth analysis of the Romanian Power System and assessing the most severe vulnerabilities and risks that could jeopardize the proper functioning of the system and the supply to electricity consumers. Based on these findings, various strategies for the safety, security, and resilience of the Romanian Power System have been developed. Full article