Search Results (1,092)

The development of agricultural new productive forces (ANPFs) represents a vital pathway to overcoming the bottlenecks of agricultural modernization and reshaping agricultural competitiveness. As sustainable development and green transformation have become global priorities, the formation of ANPFs is increasingly viewed as a key engine for promoting resource-efficient agriculture, low-carbon production, ecological protection, and resilient food systems. Using panel data from 16 prefecture-level cities in Anhui Province, China, spanning the period 2010–2023, this study employs the entropy-weighted TOPSIS method to measure the levels of ANPFs and sustainable agricultural development (SAD). A panel data model is then applied to examine the impact of ANPFs on SAD, while a mediation-effect model is used to test the underlying transmission mechanisms. Finally, a spatial econometric model is employed to assess the spatial spillover effects between ANPFs and SAD. The results reveal that ANPFs exert a significant and robust positive impact on Anhui’s SAD, with the strength of this effect decreasing gradually from central to southern and northern regions. Further analysis indicates that the driving influence of ANPFs operates through three key mediating pathways: the improvement of new-type infrastructure, the enhancement of agricultural scientific and technological innovation, and the advancement of agricultural digital transformation. Moreover, ANPFs demonstrate a positive spatial spillover effect, suggesting that the development of new productive forces in one region promotes agricultural modernization in neighboring areas. These findings demonstrate that ANPFs not only enhance productivity but also contribute to sustainable agricultural development. Accordingly, strengthening ANPFs development can serve as an effective strategy for promoting long-term agricultural sustainability, indicating that central Anhui should be prioritized as a core hub for fostering ANPFs, enabling the gradient diffusion of infrastructure, innovation capacity, and digital services toward southern and northern Anhui. Strengthening regional coordination mechanisms will further amplify the spatial spillover of ANPFs, thereby advancing high-quality agricultural development across the province. This study provides new evidence for how ANPFs can support sustainable agricultural transformation, offering policy insights for green growth, food security, and rural revitalization. Full article

This study examines the relationship between national open educational resource (OER) policies and Sustainable Development Goal 4 (SDG4) outcomes across 187 countries between 2015 and 2024, with particular attention to the moderating role of artificial intelligence (AI) readiness. Despite widespread optimism about digital technologies as catalysts for universal education, systematic evidence linking formal OER policy frameworks to measurable improvements in educational access and completion remains limited. The analysis employs fixed effects and difference-in-differences estimation strategies using an unbalanced panel dataset comprising 435 country-year observations. The research investigates how OER policies associate with primary completion rates and out-of-school rates while testing whether these relationships depend on countries’ technological and institutional capacity for advanced technology deployment. The findings reveal that AI readiness demonstrates consistent positive associations with educational outcomes, with a ten-point increase in the readiness index corresponding to approximately 0.46 percentage point improvements in primary completion rates and 0.31 percentage point reductions in out-of-school rates across fixed effects specifications. The difference-in-differences analysis indicates that OER-adopting countries experienced completion rate increases averaging 0.52 percentage points relative to non-adopting countries in the post-2020 period, though this estimate remains statistically imprecise (p equals 0.440), preventing definitive causal conclusions. Interaction effects between policies and readiness yield consistently positive coefficients across specifications, but these associations similarly fail to achieve conventional significance thresholds given sample size constraints and limited within-country variation. While the directional patterns align with theoretical expectations that policy effectiveness depends on digital capacity, the evidence should be characterized as suggestive rather than conclusive. These findings represent preliminary assessment of policies in early implementation stages. Most frameworks were adopted between 2019 and 2022, providing observation windows of two to five years before data collection ended in 2024. This timeline proves insufficient for educational system transformations to fully materialize in aggregate indicators, as primary education cycles span six to eight years and implementation processes operate gradually through sequential stages of content development, teacher training, and institutional adaptation. The analysis captures policy impacts during formation rather than at equilibrium, establishing baseline patterns that require extended longitudinal observation for definitive evaluation. High-income countries demonstrate interaction coefficients between policies and readiness that approach marginal statistical significance (p less than 0.10), while low-income subsamples show coefficients near zero with wide confidence intervals. These patterns suggest that OER frameworks function as complementary interventions whose effectiveness depends critically on enabling infrastructure including digital connectivity, governance quality, technical workforce capacity, and innovation ecosystems. The results carry important implications for how countries sequence educational technology reforms and how international development organizations design technical assistance programs. The evidence cautions against uniform policy recommendations across diverse contexts, indicating that countries at different stages of digital development require fundamentally different strategies that coordinate policy adoption with foundational capacity building. However, the modest short-term effects and statistical imprecision observed here should not be interpreted as evidence of policy ineffectiveness, but rather as confirmation that immediate transformation is unlikely given implementation complexities and temporal constraints. The study contributes systematic cross-national evidence on aggregate policy associations while highlighting the conditional nature of educational technology effectiveness and establishing the need for continued longitudinal research as policies mature beyond the early implementation phase captured in this analysis. Full article

In the context of China’s dual-carbon agenda and the Digital China initiative, elucidating the role of digital literacy in shaping consumption-based household carbon emissions (HCE) is essential for advancing low-carbon urban living and supporting a broader green transition. Existing research has rarely examined, at the individual level, how digital capability shapes household consumption decisions and the structure of carbon emissions. Accordingly, this study draws on matched household-individual microdata from the China Family Panel Studies (CFPS). We employ a two-way fixed effects model, kernel density analysis, and qualitative comparative analysis. We test the nonlinear effect of digital literacy on household consumption-related carbon emissions and examine its heterogeneity. We also examined the mediating role of perceived environmental pressure, social trust and income level. The research results show that: (1) The net impact of digital literacy on carbon emissions related to household consumption shows an inverted U-shaped curve, rising first and then falling. When digital literacy is low, it mainly increases emissions by expanding consumption channels, reducing transaction costs and improving convenience. Once digital literacy exceeds a certain threshold, the mechanism will gradually turn to optimize the consumption structure, so as to support the low-carbon transformation of individuals. (2) The impact of digital literacy on HCE is structurally different in different types of consumption. In terms of transportation and communication expenditure, the emission reduction effect is the most significant, and with the improvement in digital literacy, this effect will become more and more obvious. For housing-related consumption, the turning point appeared the earliest. With the improvement in digital literacy, its effect will enter the emission reduction stage faster. (3) Digital literacy can reduce carbon emissions related to household consumption by enhancing residents’ perception of environmental pressure and strengthening social trust. However, it may also increase emissions by increasing residents’ incomes, because it will expand the scale of consumption, which will lead to an increase in carbon emissions related to household consumption. (4) The heterogeneity analysis shows that as digital literacy improves, carbon emissions increase more strongly among rural residents, people with low human capital, low-income households, and women. However, the turning-point threshold for emission reduction is relatively lower for women and rural residents. (5) Low-carbon transitions in household consumption are shaped by dynamic interactions among multiple factors, and multiple pathways can coexist. Digital literacy can work with environmental responsibility to endogenously promote low-carbon consumption behavior. It can also, under well-developed infrastructure, empower households and amplify the emission-reduction effects of technology. Full article

The increasing digitization of critical infrastructure and the increasing use of Industrial Internet of Things (IIoT) systems are leading to a significant increase in the exposure of operating systems to cyber threats. The integration of information (IT) and operational (OT) layers, characteristic of today’s industrial environments, results in an increase in the complexity of system architecture and the number of security events that require ongoing analysis. Under such conditions, classic approaches to monitoring and responding to incidents prove insufficient, especially in the context of systems with high reliability and business continuity requirements. The aim of this article is to analyze the possibilities of using Large Language Models (LLMs) in the protection of industrial IoT systems operating in critical infrastructure. The paper analyzes the architecture of industrial automation systems and identifies classes of cyber threat scenarios characteristic of IIoT environments, including availability disruptions, degradation of system operation, manipulation of process data, and supply-chain-based attacks. On this basis, the potential roles of large language models in security monitoring processes are examined, particularly with respect to incident interpretation, correlation of heterogeneous data sources, and contextual analysis under operational constraints. The experimental evaluation demonstrates that, when compared to a rule-based baseline, the LLM-based approach provides consistently improved classification of incident impact and attack vectors across IT, DMZ, and OT segments, while maintaining a low rate of unsupported responses. These results indicate that large language models can complement existing industrial IoT security mechanisms by enhancing context-aware analysis and decision support rather than replacing established detection and monitoring systems. Full article

Citizen-centric smart city construction (SCC) has been the crucial mode for enhancing citizens’ well-being with rapid urbanization. While smart cities are constructed to improve urban operational safety, the concomitant low resilience of infrastructure, data breaches, and other issues also lead to physical, financial, and legal consequences, which therefore have the complicated the impact on citizens’ security perception of smart city construction (CSPSCC). To achieve sustainable smart city construction, it is important to clarify the influencing factors on CSPSCC. Although the enhancement of CSPSCC needs the joint efforts of citizens, government, and social organizations, the previous studies mostly focus on influencing factors from the single stakeholder. To address this gap, the theory of planned behavior was expanded to examine factors influencing CSPSCC from the perspective of participatory governance. Taking Nanjing as a case, hypotheses testing, mediating testing, and heterogeneity analysis were carried out for this theoretical model. The results show that the security governance of citizens, the government, and social organizations all had a positive impact on CSPSCC, with citizens’ behavioral intention being the most significant influencing factor. In addition, CSPSCC is also significantly affected by the citizens’ age, educational level, and usage frequency of smart city services. Full article

Background: Driven by rapid urbanization, infrastructural development, socio-economic growth, and population increase, noise pollution has become a major public health and environmental policy challenge in Moroccan cities. However, current legislation and enforcement mechanisms remain insufficient to address rising exposure levels and associated health risks. Methods: This systematic review followed PRISMA guidelines to examine urban noise levels, health implications, the regulatory frameworks, and policy actions related to noise pollution in Morocco. Various databases were systematically searched (Scopus, Web of Science, Google Scholar), along with reports from international organizations and government bodies for studies published between 2003 and 2025. Eligible documents included peer-reviewed publications and official reports directly addressing Moroccan noise pollution, legislation, urban impacts, or health outcomes. Results: Twenty-three Moroccan studies and additional regional, European, and legislative sources were included. Findings show that average noise levels in Moroccan urban centers generally exceed international safety thresholds and are associated with cardiovascular risks, sleep disturbances, and psychological stress. The regulatory framework suffers from weak enforcement, limited monitoring protocols, and an absence of noise mapping. Tangier, Béni Mellal, Témara, Marrakech, and Casablanca exhibit significant environmental inequalities, particularly in low-income districts. Conclusions: Morocco’s current noise-management system is inadequate to address the growing health and environmental impacts of urban noise. Urgent actions are needed, including a dedicated noise-control law, systematic monitoring, noise mapping, and integration of public-health considerations into environmental governance. Policy reforms must prioritize vulnerable populations and align with international best practices. Full article

Odour emissions from wastewater infrastructure represent a significant environmental and social challenge in urban areas. This study evaluates the odour impact of a municipal wastewater pumping station using an integrated field-based approach that combines sensory observations, chemical measurements and meteorological data. Field olfactometry and on-site gas monitoring were applied over a two-year campaign covering different operational and seasonal conditions. The results indicate that odour perception is strongly influenced by hydrogen sulphide concentration, air temperature and wind speed, with short-term high-intensity episodes playing a disproportionate role in odour nuisance. To support integrated interpretation, a Synthetic Odour Index (SOI) was developed to consolidate chemical, sensory and microclimatic information into a single numerical indicator, extending existing odour indices by explicitly integrating field-based sensory and meteorological data. The SOI showed a moderate but statistically significant association with odour intensity (r ≈ 0.3) and effectively differentiated low- and high-nuisance conditions. The proposed methodology demonstrates the value of combining field measurements with integrated data analysis for assessing and managing odour emissions from urban wastewater pumping stations and provides a practical basis for operational monitoring and odour mitigation strategies. Full article

Distributed energy systems (DESs) are crucial for renewable deployment, but decentralised generation substantially increases flexibility requirements. Flexibility is framed as a system property that emerges from the coordinated operation of demand, storage and dispatchable generation across multi-energy carriers. Demand response schemes and demand-side management can provide flexibility, but their effective potential is constrained by user participation. Sector-coupling strategies and energy storage systems enable temporal and cross-sector decoupling between renewable generation and demand. Electrochemical batteries are technically mature and well suited for short-term balancing, but costs and environmental impacts are significant. Power-to-Heat with heat pumps and thermal energy storage is a cost-effective solution, especially when combined with low-temperature district heating. Electric vehicles, when operated under smart-charging and vehicle-to-grid schemes, can shift large charging demands feeding energy into the grid, facing battery degradation and infrastructure costs. Power-to-Gas and Power-to-X use hydrogen and electrofuels as long-term storage but are penalised by low round-trip efficiencies and significant capital costs if power-to-power with fuel cells is applied. On the supply side, micro-CHP can provide dispatchable capacity when fuelled by renewable fuels and combined with seasonal storage. Costs and efficiencies are strongly scale-dependent, and markets, regulation, digital infrastructure and social acceptance are key enablers of flexibility. 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

The health and operational continuity of emergency responders are fundamental pillars of sustainable and resilient disaster management systems. These personnel operate in high-risk environments, exposed to intense physical, environmental, and psychological stress. This makes it crucial to monitor their health to safeguard their well-being and performance. Traditional methods, which rely on intermittent, voice-based check-ins, are reactive and create a dangerous information gap regarding a responder’s real-time health and safety. To address this sustainability challenge, the convergence of the Internet of Things (IoT) and wearable biosensors presents a transformative opportunity to shift from reactive to proactive safety monitoring, enabling the continuous capture of high-resolution physiological and environmental data. However, realizing a field-deployable system is a complex “system-of-systems” challenge. This review contributes to the field of sustainable emergency management by analyzing the complete technological chain required to build such a solution, structured along the data workflow from acquisition to action. It examines: (1) foundational health sensing technologies for bioelectrical, biophysical, and biochemical signals; (2) powering strategies, including low-power design and self-powering systems via energy harvesting; (3) ad hoc communication networks (terrestrial, aerial, and space-based) essential for infrastructure-denied disaster zones; (4) data processing architectures, comparing edge, fog, and cloud computing for real-time analytics; and (5) visualization tools, such as augmented reality (AR) and heads-up displays (HUDs), for decision support. The review synthesizes these components by discussing their integrated application in scenarios like firefighting and urban search and rescue. It concludes that a robust system depends not on a single component but on the seamless integration of this entire technological chain, and highlights future research directions crucial for quantifying and maximizing its impact on sustainable development goals (SDGs 3, 9, and 11) related to health, sustainable cities, and resilient infrastructure. Full article

Due to the high mobility, flexibility, and low cost, unmanned aerial vehicles (UAVs) can provide an efficient way for provisioning data communication and computing offloading services for massive Internet of Things (IoT) devices, especially in remote areas with limited infrastructure. However, current transmission schemes for unmanned aerial vehicle-assisted Internet of Things (UAV-IoT) predominantly employ polling scheduling, thus not fully exploiting the potential multiuser diversity gains offered by a vast number of IoT nodes. Furthermore, conventional opportunistic scheduling (OS) or opportunistic beamforming techniques are predominantly designed for downlink transmission scenarios. When applied directly to uplink IoT data transmission, these methods can incur excessive uplink training overhead. To address these issues, this paper first proposes a low-overhead multi-UAV uplink OS framework based on channel reciprocity. To avoid explicit massive uplink channel estimation, two scheduling criteria are designed: minimum downlink interference (MDI) and the maximum downlink signal-to-interference-plus-noise ratio (MD-SINR). Second, for a dual-UAV deployment scenario over Rayleigh block fading channels, we derive closed-form expressions for both the average sum rate and the asymptotic sum rate based on the MDI criterion. A degrees-of-freedom (DoF) analysis demonstrates that when the number of sensors, K, scales as ${\rho }^{\alpha }$, the system can achieve a total of $2\alpha$ DoF, where $\alpha \in \left[0,1\right]$ is the user-scaling factor and $\rho$ is the transmitted signal-to-noise ratio (SNR). Third, for a three-UAV deployment scenario, the Gamma distribution is employed to approximate the uplink interference, thereby yielding a tractable expression for the average sum rate. Simulations confirm the accuracy of the performance analysis for both dual- and three-UAV deployments. The normalized error between theoretical and simulation results falls below 1% for K > 30. Furthermore, the impact of fading severity on the system’s sum rate and DoF performance is systematically evaluated via simulations under Nakagami-m fading channels. The results indicate that more severe fading (a smaller m) yields greater multiuser diversity gain. Both the theoretical and simulation results consistently show that within the medium-to-high SNR regime, the dual-UAV deployment outperforms both the single-UAV and three-UAV schemes in both Rayleigh and Nakagami-m channels. This study provides a theoretical foundation for the adaptive deployment and scheduling design of UAV-assisted IoT uplink systems under various fading environments. Full article

In the macro context of promoting sustainable development and achieving net zero emissions, the role of green technology innovation, renewable energy utilization and environmental policy is crucial. However, there is still a lack of consistent empirical evidence regarding the combined emission reduction effect of these three factors in OECD countries. This study aims to empirically examine the combined impact of green technology innovation (GTI), renewable energy consumption (REC), and environmental taxes (ETAX) on carbon dioxide emissions. We expect that the former two will effectively reduce emissions, while the effect of environmental taxes depends on their design. Based on the panel data of 35 OECD economies from 1990 to 2019, this study adopts the augmented mean group (AMG) as the main estimation method, and uses the common correlation mean group (CCEMG) for the robustness test. To control potential endogenous issues, the difference generalized method of moments (GMM) is also employed for estimation. The causal relationship between variables is tested using the Dumitrescu–Herlin method. The results show that, as expected, GTI and REC have a significant negative impact on carbon dioxide reduction. However, ETAX is positively correlated with carbon emissions and does not have statistical significance, which deviates from the ideal policy effect and suggests that there may be efficiency bottlenecks in the current tax design. The causality test further reveals that there is a significant two-way causal relationship between CO₂ emissions and GTI, REC, ETAX, GDP, and fossil fuel consumption (FEC). Therefore, it is recommended that OECD countries give priority to expanding investment in green technologies and renewable energy infrastructure and re-evaluate and optimize environmental tax policies to effectively promote the transition to a low-carbon economy. 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

Background: Fintech companies have revolutionized the financial services industry in India in recent years. This is especially true for the growth of digital payment methods. India’s unbanked are being introduced to banking by fintech companies. Despite the country’s strong banking system, many residents find it difficult to get government financial services. This is particularly true for rural or low-income people. This vacuum has been addressed by fintech solutions including digital banking, micro-lending applications, mobile wallets, and UPI platforms. Objectives: to study the impact of financial technology businesses on increasing financial inclusion for India’s underbanked and unbanked population and Challenges encountered by financial technology enterprises in their endeavors to access unbanked populations, encompassing concerns of infrastructure with special reference to western Uttar Pradesh. Method: This mixed-methods study examines how FinTech is narrowing the financial gap for unbanked people using quantitative econometric analysis and qualitative case study assessments. Results: Digital financial innovation and regulatory support encourage inclusive growth in underdeveloped economies, whereas rich nations benefit from sophisticated banking institutions. This is indicated by the small influence of GDP per capita (β = 0.22–0.32, p < 0.05). Findings: The study found that inclusive finance is revolutionized when FinTech is used with the help of robust regulatory frameworks and digital infrastructure. Policymakers should prioritize cybersecurity, public-private partnerships to improve digital literacy, and rural connection if they want more people to take part in the digital financial ecosystem. Implications: FinTech can remove obstacles to accessing financing. The proper coordinated improvements in regulatory frameworks, digital infrastructure and financial literacy among the people are necessary to achieve full financial inclusion. Full article

Drones have emerged as critical infrastructure for enhancing logistics efficiency in the emerging low-altitude economy, particularly in collaborative vehicle–drone research. However, existing research often neglects the impact of fair task allocation on workload balance among formations in large-scale routing scenarios, which compromises service quality. To address this gap, we introduce the Multi-vehicle with drones Collaborative Routing Problem with Large-scale Packages (MCRPLP), formulated as a bi-objective model aiming to minimize both operational cost and workload imbalance. A Hybrid Strategy-assisted Multi-objective Optimization Algorithm (HSMOA) is developed to overcome the limitations of existing methods, which struggle with balancing solution quality and computational efficiency in solving large-scale routing. Based on a Non-dominated Sorting Genetic Algorithm (NSGA-II), the HSMOA integrates a heuristic task assignment strategy that greedily reassigns packages between adjacent clusters. Then, by integrating a Pareto-front superiority evaluation model, an elite individual supplement strategy is designed to dynamically prune sub-optimal solution subspaces while enhancing the search within high-quality Pareto-front subspaces in HSMOA. Extensive experiments demonstrate the effectiveness of HSMOA in terms of solution quality and computational efficiency compared to multiple state-of-the-art methods. Further sensitivity analysis and managerial insights derived from a real-world case are also provided to support practical logistics implementation. Full article