Search Results (1,977)

Hidden hunger and grassland degradation represent interconnected governance challenges in northern China’s pastoral areas. Nutrition-sensitive agriculture (NSA) has been conceptualised largely around crop-based systems, with limited attention to grassland grazing systems, where nutritional value is shaped by ecology, feeding practices, seasonality, local knowledge, and market institutions. Drawing on five rounds of fieldwork (2019–2025) across meadow, typical, and desert steppes in Inner Mongolia, this study employs a multi-case comparative design involving 92 semi-structured interviews, 58 policy documents, and long-term observations. Using reflexive thematic analysis, we develop an ecology–nutrition synergy framework to explain local practices and institutional constraints in nutrition-sensitive livestock farming. Three pathways are identified: grass–livestock nutritional balancing, scientific valorisation of native forage, and market experimentation linking ecological origin to nutritional quality. These pathways operate through three mechanisms: ecological mediation of nutritional quality, endogenous quality fluctuation as an inherent feature, and scientific codification of traditional pastoral knowledge. Four structural dilemmas constrain scaling: incompatibility between natural quality fluctuation and industrial standardisation; absence of institutional trust in nutritional premiums; short-term trade-offs between stocking control and nutritional enhancement; and fragmented cross-sectoral governance. The study extends NSA to grassland systems and offers a framework for integrating ecological protection, livestock quality, and nutrition-oriented governance in arid and semi-arid rangelands. Three theoretical contributions are advanced: (i) extending NSA’s conceptual boundary from cropping systems to natural grassland pastoral systems; (ii) embedding a nutrition-output dimension within Ostrom’s SES framework, thereby creating a triple-nested ecology–nutrition synergy framework; and (iii) specifying three grazing-system-specific mechanisms that distinguish grassland livestock systems from both crop-based and confined animal production systems. Full article

The transition towards carbon-neutral construction materials requires innovative solutions that combine reduced embodied energy, enhanced durability and improved building energy efficiency. This study investigates and compares two novel bioaerated low-density composites—BAAC and BIOAERMAC—developed through biologically driven aeration processes incorporating industrial byproducts. BAAC is produced using Saccharomyces cerevisiae and hydrogen peroxide, replacing conventional aluminum powder and improving safety while enabling the valorization of waste-derived yeast. BIOAERMAC is a gypsum-based composite incorporating synthetic anhydrite, microorganisms, peroxides, and recycled rubber from end-of-life tires. The materials were characterized in terms of hygrothermal behavior and dimensional stability, and compared with commercial autoclaved aerated concrete under equivalent mechanical strength conditions. The results highlight significant differences in moisture transport and shrinkage, primarily governed by pore structure and connectivity. BAAC exhibits behavior comparable to conventional AAC, whereas BIOAERMAC shows reduced capillary and hygroscopic absorption, indicating limited pore connectivity, but higher drying shrinkage. These findings demonstrate the effectiveness of bioaeration in tailoring pore structure and controlling the trade-off between moisture transport, durability, and dimensional stability, highlighting the potential of bioaerated composites for low-carbon and energy-efficient building applications. Full article

The fractional Whitham–Broer–Kaup model governs nonlinear wave propagation in memory-dependent media, including porous structures, viscoelastic fluids, and irregular seabeds, yet the full dynamical spectrum from quasi-periodicity to deterministic chaos, the role of stochastic forcing, and reliable identification from noisy data remains insufficiently explored, particularly how the fractional order $\beta$ influences these regimes. This study addresses these gaps through a comprehensive, multi-method dynamical analysis of a representative nonlinear oscillator embodying key FWBK features. Three-dimensional attractor visualizations, return maps, and surrogate data tests demonstrate a transition from quasi-periodic toroidal attractors to fully developed chaos via torus breakdown, confirming that observed complexity originates from deterministic nonlinearity. Poincaré sections reveal multistability and KAM-type structures, where coexisting attractors depend on initial conditions, while increasing noise progressively disrupts coherent dynamics. The OGY control method effectively stabilizes unstable periodic orbits across chaotic regimes with minimal perturbation, and Lyapunov analysis indicates that stochastic forcing attenuates chaos while enhancing dissipation. The Fokker–Planck framework shows that noise reshapes probability landscapes, driving transitions from unimodal to bimodal distributions. Comparative analysis of SINDy, JMAP and VBA highlights trade-offs in interpretability, computational efficiency, and uncertainty quantification, while an integrated Bayesian–PCE–Sobol approach quantifies parametric uncertainty and reveals time-dependent sensitivity variations. Additionally, the overlapping of soliton solutions extracted via the enhanced modified Sardar sub-equation method reveals structural relationships among soliton families and their stability under interaction. Soliton branches that maintain high overlap under noise correspond to stable regimes, while those losing coherence indicate the onset of chaos. Furthermore, while the reduced dynamics in $\eta$-space are independent of $\beta$, the fractional order controls spatial compression and temporal scaling in physical coordinates, directly influencing observable wave localization. These results imply that fractional effects can modify chaos transitions, support controllability through OGY, and influence noise–instability interactions depending on $\beta$. This framework provides a robust, transferable methodology for analyzing and controlling nonlinear oscillatory systems under deterministic and stochastic conditions, with direct applications to FWBK-based models in coastal engineering, fiber optics, and quantum interference systems. Full article

Smart cities increasingly rely on distributed sensing and edge intelligence to support urban planning, mobility management, environmental monitoring, and critical infrastructure operation. However, large-scale urban Internet-of-Things deployments are constrained by heterogeneous device capabilities, limited energy availability, variable communication conditions, and data-governance requirements. Federated learning offers a data-locality-preserving alternative to centralized model training, but conventional federated learning strategies often assume full, random, or fixed client participation, which can lead to unnecessary energy consumption, communication overhead, or client starvation in resource-constrained urban environments. This paper proposes an Energy- and Communication-Aware Federated Learning strategy, termed ECA-FL, for smart city sensing systems. The main novelty of the work lies in the joint use of residual device energy and communication conditions to guide adaptive client participation and local training effort, providing a tunable resource–performance trade-off rather than an accuracy-maximizing strategy alone. The framework is evaluated through a controlled simulation-based study using a synthetic multi-class urban sensing proxy task distributed across 100 federated clients under strongly non-IID conditions. Compared with full-participation FedAvg, ECA-FL reduces cumulative energy consumption by 82.9% and communication overhead by 64.7%, while maintaining a final accuracy of 0.8124 compared with 0.8319 for FedAvg-full. Compared with rigid fixed-participation strategies, ECA-FL avoids severe learning degradation by adapting participation dynamically instead of excluding clients according to a static rule. A sensitivity analysis further shows that the trade-off parameter controls the balance between learning performance and resource conservation, allowing the framework to be adjusted according to different deployment priorities. The results support the hypothesis that adaptive energy- and communication-aware participation can substantially reduce operational cost while preserving acceptable learning performance within the adopted simulation setting. The study provides practical design insights for sustainable, communication-conscious, and data-locality-preserving federated learning in smart city sensing infrastructures. Full article

This study presents a comparative life cycle assessment (LCA) of a conventional wet flue gas desulfurization (FGD) process and two carbonate-melt-based FGD configurations (CMFGD-H and CMFGD-T), based on a functional unit of 1 kg SO₂ removed. Process-level life cycle inventory (LCI) data were generated using process simulation to ensure consistency and comparability across all systems. The results indicate that both CMFGD configurations significantly reduce environmental impacts in terms of global warming potential (GWP), fine particulate matter formation (PM), and terrestrial acidification (TA) compared to the conventional FGD process. Specifically, GWP decreased from 177.75 kg CO₂ eq to 37.47 and 35.68 kg CO₂ eq for CMFGD-H and CMFGD-T, respectively. Similar reductions were observed for PM and TA, primarily due to the elimination of limestone consumption, the absence of gypsum waste generation, and reduced direct process emissions. Hotspot analysis revealed that direct CO₂ emissions dominate GWP across all configurations, whereas PM and TA are influenced by both direct emissions and upstream energy supply. In the CMFGD systems, environmental burdens shift from direct emissions toward upstream processes, particularly electricity and hydrogen production, highlighting the importance of energy system characteristics. However, a clear trade-off was identified in fossil resource scarcity (FRC), which increased significantly for CMFGD configurations (1.858–1.976 kg oil eq) compared to the conventional process (0.128 kg oil eq). This increase is primarily attributed to greater dependence on upstream energy supply chains, including fossil-based electricity, fuel, and hydrogen production. Sensitivity analysis further indicates that FRC is configuration-dependent, with hydrogen consumption dominating in CMFGD-H and CO utilization playing a more significant role in CMFGD-T. Nevertheless, even with reductions in these key parameters, FRC remains substantially higher than that of the conventional process, indicating that this impact is fundamentally governed by upstream energy dependency rather than individual process variables. The results demonstrate that CMFGD technologies offer substantial environmental benefits in terms of emission-related impacts but may increase resource depletion. These findings highlight that achieving sustainable CMFGD systems requires an integrated approach that combines process optimization with low-carbon and resource-efficient energy supply. Full article

Digital trade reflects the convergence of the new technological revolution and traditional trade. Investigating its effectiveness in pollution control and carbon mitigation (PCCM) is crucial for addressing global environmental challenges. This research exploits the rollout of cross-border e-commerce comprehensive pilot zones (CECPZs) as an exogenous policy shock, leveraging double machine learning (DML) methods to assess the impact of digital trade on PCCM using panel data from 280 Chinese prefecture-level cities from 2011 to 2023. The results reveal that digital trade significantly enhances PCCM, mainly by promoting technological innovation, intelligent industrial transformation, and public participation; government emphasis on new quality productive forces and digital government construction positively moderates the link between digital trade and PCCM, while intensified environmental regulation exerts a counteracting inhibitory effect. Heterogeneous outcomes reveal that the promoting effects of digital trade are more evident in large areas, as well as in cities that are neither traditional industrial bases nor resource-based. Further analysis shows that digital trade can deliver a triple dividend in the form of reduced pollution, lower carbon emissions, and sustained economic growth. These findings provide meaningful guidance for promoting a balanced and sustainable relationship between human activities and the natural environment in the digital era. Full article

Water scarcity in Peru is increasingly shaped by competing sectoral demands, particularly between large-scale mining and agriculture. Both sectors rely heavily on limited freshwater resources in arid coastal and Andean basins, generating complex trade-offs between economic productivity, environmental sustainability, and social equity. This review synthesizes and critically evaluates current knowledge on water footprint (WF) dynamics within mining–agriculture systems, integrating hydrosocial theory, water–energy–food nexus thinking, and sustainability transition frameworks. Mining activities in Peru are characterized by high blue and grey water footprints, associated with intensive extraction processes and contamination risks, while agriculture exhibits diverse water footprints depending on crop type, irrigation efficiency, and climatic conditions. The interaction of these sectors creates hydrosocial conflicts driven by unequal water allocation, environmental degradation, and institutional fragmentation. This paper identifies key drivers of conflict and evaluates emerging pathways for sustainability transitions, including technological innovation, nature-based solutions, and participatory governance mechanisms. An integrative conceptual framework derived from a thematic synthesis of the reviewed literature is proposed. The findings provide actionable insights for policymakers and researchers seeking to reconcile economic development with water sustainability in resource-constrained environments. Full article

The Constrained Application Protocol (CoAP) is widely adopted to ensure end-to-end reliability in resource-constrained Artificial Intelligence of Things (AIoT) and Wireless Sensor Networks (WSNs). However, CoAP’s default retransmission timeout (RTO) mechanism lacks algorithmic responsiveness under volatile channel conditions, and state-of-the-art benchmarks like CoCoA+ and FASOR often suffer from over-conservative backoff states or destabilizing retransmission storms. To overcome these operational bottlenecks, this paper proposes a novel dual-adaptive Dynamic RTO algorithm specifically engineered for heterogeneous IoT deployment scales. The proposed framework dynamically adjusts its parameter inspection cycle (N) based on instantaneous round-trip time (RTT) variance while simultaneously scaling its tuning coefficient (α) in response to real-time packet loss indicators. To rigorously validate the algorithmic resilience, performance evaluations were conducted within a highly volatile network environment governed by the Gilbert–Elliott dynamic loss model across multi-hop linear (1 × 6) and grid (3 × 6, 5 × 6) topologies. Experimental results demonstrate that the proposed Dynamic RTO consistently optimizes the throughput–latency trade-off, achieving a total communication time of 25.92 s in complex grids—outperforming CoCoA+ and FASOR by 14.28% and 8.89%, respectively. Furthermore, the proposed mechanism significantly curtails transmission overhead, restricting the cumulative retransmission footprint to just 59 counts under severe localized impairments, thereby establishing a scalable, resource-efficient, and empirically robust transport-layer solution for next-generation edge-computing infrastructures. Full article

The General Data Protection Regulation (GDPR), adopted by the European Union in 2018, aims to enhance consumer trust and market efficiency by strengthening data protection. The concurrent stringent compliance requirements raise operational costs and could reshape competition by favoring larger firms with greater regulatory capacity. While the GDPR reduces data-related risks and promotes global digital trade through its extraterritorial reach, the potential advantage to larger firms could incentivize strategic responses such as mergers and acquisitions (M&A) to consolidate market power. Given the rapid expansion of Chinese digital firms in e-commerce, social media, and cloud services across the EU, this study examines how the GDPR has affected their cross-border M&A activities between 2014 and 2021. Based on difference-in-difference analysis, the study finds that the GDPR did not have a statistically significant impact on the number or value of mergers and acquisitions by Chinese digital firms in the EU in the short term. This suggests that firms may enhance their institutional adaptability by strengthening their compliance capabilities. However, institutional and cultural differences pose long-term entry barriers for the firms. The study contributes by highlighting how firms adjust internationalization strategies under stringent regulatory regimes, offering policy-relevant insights for governments and regulatory authorities. Full article

Controversy persists on a global scale regarding the trade-offs between greenhouse gas (GHG) emissions, yield, the global warming potential (GWP), and GHG intensity (GHGI) following organic fertilizer substitution within vegetable cropping systems. This study aimed to quantify these effects under diverse conditions and elucidate the direct and indirect drivers governing these outcomes through a meta-analysis and structural equation modeling (SEM). We synthesized 655 paired observations from 69 published studies using random-effects meta-analysis, finding that organic fertilizer substitution significantly increased CH₄ emissions and GWP compared to inorganic fertilizer controls. Although this was the general trend, organic fertilizer could reduce GWP under specific climatic and soil conditions by reducing N₂O emissions, such as mean annual precipitation <400 mm or soil total nitrogen ≥3 g kg⁻¹. These conditions were also associated with substantially higher yield and lower GHGI. Furthermore, SEM demonstrated that field management practices exerted significant direct effects on N₂O emissions, GWP, and GHGI. Reductions in N₂O emissions, GWP, and GHGI could be achieved with fertilizer application duration ≥10 years, total N application rate ≥300 kg ha⁻¹, and field cultivation or plowing. GHGI was also reduced through yield enhancement under a moderate organic substitution rate (33–66%) or irrigation ≥300 mm. Our study provides a scientific basis for moving beyond universal recommendations towards precision organic management, which is essential for optimizing fertilization strategies to mitigate agricultural GHG emissions. Full article

Rural electrification in Sub-Saharan Africa faces a trilemma: cutting carbon emissions, making it economically viable, and achieving fair access to energy for all. This paper develops a multi-objective framework that optimises carbon revenue, net present value (NPV), total energy supply, cooking fuel (firewood and LPG), health costs, and benefit to society. The model uses continuous decision variables: daily energy allocation among four sources (solar, generator, firewood, LPG) to three population groups (men, women, children). The case study is a rural community of 7000 people in Nigeria (Tier 1 energy consumers). Six policy scenarios are considered: baseline, high carbon price, low carbon price, microfinance, government subsidy and community cooperative. This study compared algorithms and identified a hybrid Non-dominated Sorting Genetic Algorithm and Particle Swarm Optimisation II as the most suitable algorithm for solving the formulated optimisation problem. It was found that NPV and unit cost of energy would increase to $175,500 and 26.4 ¢/kWh, respectively, by increasing the price of carbon from $8/ton to $12/ton. Firewood generates health savings and carbon revenue in the range of $4100–$12,270/year. Prices below $8/ton do not induce optimal reconfigurations in the system. The best energy supply (2825 kWh/day) and the lowest unsatisfied demand occur in the government subsidy scenario with the greatest disparity index, displaying an equity-efficiency trade-off. The framework shows that sustainable access to energy can be unlocked using strategic integration of carbon finance, valuation of health benefits and equity constraints. Full article

AI-enabled infrastructure systems increasingly govern access to emergency services, disaster relief, and utility restoration, yet they routinely produce inequitable outcomes even when allocation algorithms apply procedurally neutral rules. The standard explanation locates the cause inside the algorithm. This paper argues instead that inequity arises from the interaction between the algorithm and the physical environment in which it operates: network topology, resource locations, and demand distribution jointly constrain what any policy can achieve, and when those constraints are sufficiently binding, ethical infeasibility is structural rather than algorithmic. We introduce a constraint-based formulation that embeds ethical requirements into the feasible region, and a hierarchical Irreducible Infeasible Subsystem (IIS) procedure that attributes infeasibility to rule design, algorithmic choice, or physical infrastructure. We further establish the Structural Infeasibility Theorem, deriving closed-form bounds on inter-group disparity across all feasible policies. The framework was applied to zone-decomposable infrastructure allocation problems generally, with a metropolitan ambulance-dispatch system serving as a concrete instantiation. The study delivers four findings. First, the minimum-service violation may not be caused by the allocation algorithm itself; rather, it may arise from the physical layout of the infrastructure. Second, the observed efficiency–equity trade-off may not be an unavoidable feature of equitable allocation, but may instead reflect the difficulty of achieving equity within an underbuilt system. Third, before new infrastructure is added, improvements in equity may represent harm redistribution rather than harm reduction. Fourth, the IIS certificate can be translated into a concrete capital-investment requirement, showing what physical change may be needed to restore ethical feasibility. Full article

The measurement and improvement of urban land use efficiency (ULUE) are crucial for sustainable development in China’s Canal Cities (CCCs). Drawing on the theories of production factors, spatial externalities, and agglomeration economy, this study proposes a framework that explicitly addresses the trade-offs and synergies of sustainable land use. A comprehensive ULUE evaluation index system was established. The super-SBM (Slack-Based Measure) and Global Malmquist–Luenberger (GML) index models were employed to assess the green efficiency of urban land use from 2002 to 2023, while Kernel Density Estimation (KDE) and the optimal parameters-based geographical detector (OPGD) model were used to investigate the spatiotemporal evolution and influencing factors of ULUE. The results reveal a distinctive V-shaped trend in efficiency, marked by significant spatial disequilibrium and predominantly technology-driven sustainable growth. Furthermore, ULUE exhibits a spatial distribution characterized by bipolar and multipolar differentiation, accompanied by concurrent concentration and dispersion, with high-value clusters dominating the spatial clustering type. Government regulation emerges as the dominant factor influencing ULUE, underscoring the pivotal role of policy intervention in guiding the sustainable development of land use. The interactions among pairs of influencing factors strengthened over time; notably, the interaction between government regulation and other factors is the strongest. Four-quadrant analysis profoundly reveals the underlying mechanism, distinguishing a high-quality, sustainable development model driven by technological innovation and a resource-dependent economic growth model. The findings provide valuable insights for promoting green development and formulating sustainable land use policies in CCCs. Full article

Under the concurrent advancement of ecological civilization and resource-dependent region transformation, key state-owned forest areas in northeast China have shifted from timber supply to ecosystem protection. However, while the Natural Forest Protection Program has restored forest resources and increased coverage, it has also led to the contraction of traditional industries, reduced employment, population outflow, and a structural tension between weak economic growth and enhanced ecological functions. This study aims to investigate how labor mobility affects the coordinated development of economic and ecological welfare in these regions. To achieve this, we construct economic and ecological welfare indices using entropy weighting and calculate their coupling coordination degree based on panel data from the China Forestry Statistical Yearbook (2000–2017) and the China Forestry and Grassland Statistical Yearbook (2018–2025). Our key scientific contributions are as follows: (1) we reveal a nonlinear and significantly negative impact of labor mobility on coupling coordination; (2) we identify industrial structure as a partial mediating channel; and (3) we uncover significant regional and developmental stage heterogeneity. Methodologically, we employ fixed-effects, mediation, threshold, and spatial panel models to ensure robustness. The findings provide novel insights into labor–environment trade-offs in forest-dependent regions and offer policy implications for optimizing labor allocation, strengthening ecological compensation and industrial synergy, and improving regional governance to achieve coordinated economic–ecological development. Full article

Sewage sludge management is increasingly shifting from a liability-focused “treat-and-dispose” approach toward resource recovery, where digestion residues and their liquid fractions are treated as secondary feedstocks for nutrient, water, and energy recovery. In Europe, the recast Urban Wastewater Treatment Directive strengthens performance and monitoring requirements and reinforces the need for efficient sludge treatment and downstream valorization routes. This review synthesizes evidence on how pretreatment-induced changes in digestate properties translate into membrane performance outcomes and maps practical design implications for selecting pretreatment-membrane trains for nutrient recovery and reclaimed water production. Pressure-driven membrane methods (MF/UF/NF/RO), together with membrane distillation and electrodialysis, are central candidates for producing clarified water streams and concentrating nutrients; however, their performance is governed by digestate rheology, colloidal stability, and the composition of soluble microbial products and inorganic ions, which collectively shape fouling and scaling risks. Pretreatments such as thermal hydrolysis and microwave conditioning can modify floc structure and solubilize organics, with potential benefits for dewaterability and mass transfer, but can also shift particle size distributions toward fines and increase fouling propensity if not coupled with appropriate solid–liquid separation and conservative flux control. Emphasis is placed on mechanisms and operational trade-offs rather than single-point performance claims, highlighting where evidence is robust and where further comparability and full-scale validation remain necessary. Full article