Search Results (5,248)

Sustainable livelihoods for farmers are crucial to achieving the United Nations Sustainable Development Goals (SDGs). In particular, the sustainability of farmers’ livelihoods in plain agricultural areas is strategically important for poverty alleviation, food security, and the reduction in urban–rural disparities. Drawing on data from a sample survey of farmers and field interviews conducted in the Jianghan Plain, this study evaluates farmers’ sustainable livelihoods in plain agricultural areas. The results indicate the following: (1) In 2022, the sustainable livelihood level of farmers in the Jianghan Plain was relatively low; the average Sustainable Livelihood Index (SLI) of the sampled farmers was 0.3462, with livelihood capital and livelihood outcomes accounting for 50.58% and 49.42% of the SLI, respectively. (2) A significant bidirectional positive interaction existed between farmers’ livelihood capital and livelihood outcomes. Specifically, the coefficient of the promotion effect of livelihood capital on livelihood outcomes was 0.1312, whereas that of the feedback effect of livelihood outcomes on livelihood capital was 1.0128. (3) The coupling coordination degree between farmers’ livelihood capital and livelihood outcomes was moderately unbalanced, with livelihood capital identified as the primary obstacle to farmers’ sustainable livelihood development. The average obstacle degree of livelihood capital was 0.7305, indicating a high degree of constraint. The cumulative obstacle degree of social capital (0.2707), human capital (0.1876), and natural capital (0.1460) reached 60.43%, indicating that these three factors collectively constituted the key obstacles. These findings provide valuable insights into promoting farmers’ livelihood sustainability and rural revitalization in plain agricultural areas. Full article

Achieving low-carbon agriculture in major grain-producing areas hinges on balancing emission reduction imperatives with the need to maintain food security. However, previous studies have largely examined agricultural carbon emissions (ACE) at administrative scales, potentially overlooking heterogeneous agricultural functions and emission pathways within urban agglomerations. This study integrates ACE accounting, spatial inequality and autocorrelation analyses, and Logarithmic Mean Divisia Index (LMDI) decomposition to investigate the spatiotemporal dynamics, spatial differentiation, and driving mechanisms of ACE in the Main Grain-Producing Areas of the Middle Reaches of the Yangtze River, China, using panel data from 31 prefecture-level cities during 2013–2023. The results show that ACE experienced expansion, decline, and subsequent stabilization, while agricultural carbon intensity decreased by 37.29%, indicating gradual decoupling between agricultural development and carbon emissions. Significant spatial heterogeneity and positive spatial dependence were observed, with intra-agglomeration disparities contributing more to overall inequality than inter-agglomeration differences. The three urban agglomerations also exhibited distinct emission characteristics associated with their agricultural functions and emission structures. LMDI decomposition showed that agricultural economic development was the main factor increasing ACE, whereas improvements in production efficiency and changes in rural population mitigated emissions. Overall, the results reveal that similar regional emission trends can arise from heterogeneous agricultural functions, emission structures, and development trajectories. This highlights the need for differentiated low-carbon transition pathways rather than uniform emission-reduction strategies and provides a basis for coordinating food security with low-carbon agricultural development. Full article

Heavy metal contamination of agricultural soils and urban green spaces has become a major environmental concern, threatening ecosystem functioning, food safety, and sustainable land management. Silicon-based growing media have emerged as an environmentally friendly approach for reducing metal mobility while enhancing plant establishment in contaminated environments. Despite growing research interest, a comprehensive evaluation of the mechanisms, effectiveness, and practical applications of silicon-amended growing media across diverse plant systems remains lacking. This systematic review addresses this gap by synthesizing current evidence following the PRISMA 2020 framework. A systematic search of Web of Science, Scopus, PubMed, ResearchGate and Google Scholar identified 247 publications published between 2010 and 2025, of which 32 peer-reviewed studies met the predefined inclusion criteria for qualitative analysis. The reviewed literature demonstrates that silicon incorporation into growing media improves substrate functionality by modifying physicochemical properties, immobilizing heavy metals, regulating metal transport within plants, strengthening antioxidant and osmo-protective defense systems, preserving photosynthetic activity, and improving nutrient acquisition and water-use efficiency. Furthermore, silicon influences molecular signaling pathways and promotes beneficial rhizosphere interactions that collectively enhance plant resilience under metal stress. Among the evaluated materials, silicon nanoparticles consistently exhibited greater remediation efficiency than conventional silicon sources because of their higher surface reactivity and improved bioavailability. Overall, silicon-based substrate engineering represents a multifunctional and sustainable strategy for mitigating heavy metal contamination while improving the performance of agricultural crops and urban vegetation. Future research should focus on validating these findings under long-term field conditions, optimizing silicon formulations for different substrate types and contamination scenarios, evaluating environmental safety, and integrating silicon-based technologies into climate-resilient agricultural practices and urban green infrastructure. Full article

Wetlands monitoring is essential for sustainable watershed biodiversity conservation, and climate resilience. This study performs a spatiotemporal wetland change analysis for the Sangamon River Watershed, Illinois between 2000 and 2025, using Landsat 5 Thematic Mapper (TM), Sentinel–2 Surface Reflectance (SR), Synthetic Aperture Radar (SAR), Gray–Level Co–occurrence Matrix (GLCM) and terrain data through cloud–based processing in Google Earth Engine (GEE), Google Colab and ArcGIS Pro 3.6.0. We conducted a comparative assessment of deep learning (Deep Neural Network [DNN], U-Net, Attention U-Net, and SegFormer), and machine learning models (Random Forest [RF], Gradient Tree Boosting [GTB], and Support Vector Machines [SVM]) through pixel–based and object–based methods. National Land Cover Database (NLCD) was used for training and validation using stratified random sampling for five categories namely wetlands, forest, agriculture/grassland/barren land, urban/developed and water. A proportion of 54.85% (860.68 km²) of wetlands extent was lost to other land uses, particularly agriculture, urban and forest, along with 46.51% (694.88 km²) forest and 36.80% (66.13 km²) water bodies loss. Agriculture/grassland/barren and urban/developed witnessed increases of 8.56% (820.62 km²) and 72.92% (799.52 km²), respectively. For Landsat–based and Sentinel–based classifications, SegFormer outperformed all ML and DL classifiers with (OA = 94%, Kappa = 0.89, mean F1 = 0.80, mean IoU = 0.70 and OA = 96%, Kappa = 0.92, mean F1 = 0.95, mean IoU = 0.73, respectively) with excellent wetland delineation (PA = 0.99, UA = 0.97, F1 = 0.98, IoU = 0.97 and PA = 0.99, UA = 0.99, F1 = 0.98, IoU = 0.99, respectively). Sentinel–based classifications had improved performance than Landsat, while object–based models consistently outperformed pixel–based methods. The Digital Elevation Model (DEM) and slope were the most influential predictors for RF models, while GLCM and SAR produced negligible influence. The integrated and comparative GeoAI framework provides a robust methodology for watershed–scale wetland monitoring and supports evidence–based conservation, restoration prioritization, climate resilience, and sustainable land–use planning, while offering strong potential for application in other agricultural watersheds following regional validation. Full article

Shallow wetlands retain metals and metalloids from industrial, mining, agricultural, urban, and atmospheric sources; however, shallow water columns, active sediment–water exchange, and recurrent wetting–drying also favor remobilization. This critical narrative review evaluates physical, chemical, biological, and ecological engineering approaches for metal-contaminated shallow wetlands, focusing on the hydrological and biogeochemical conditions governing performance. Evidence was differentiated among natural or semi-natural wetlands, engineered wetland analogues, and transferable studies of contaminated sediments, soils, or wastewaters. Physical interventions rapidly control localized sediment inventories but can disturb habitats and redistribute particles. Chemical amendments reduce porewater concentrations and bioavailability, although durability depends on pH, redox conditions, dissolved organic matter, and competing ions. Biological and ecological engineering approaches can support ecological function recovery, but performance depends on plant traits, microbial processes, hydroperiod, and maintenance. No intervention was consistently superior across site conditions and outcome domains. Within this framework, sustainability is evaluated through durable risk reduction, ecological function recovery, life-cycle feasibility, responsible residual management, and accountable long-term stewardship. The reviewed mechanistic and conceptual evidence supports considering site-specific treatment trains, although direct comparative field evidence demonstrating their superiority over individual interventions remains limited. Future studies should prioritize hydrologically realistic field validation, standardized flux and bioavailability endpoints, mixed-contaminant scenarios, life-cycle assessment, and explicit measurement of ecological function recovery. Full article

This study aims to systematically assess urban Chinese college students’ awareness and depth of understanding of the Sustainable Development Goals (SDGs), examine how digital media exposure and academic discipline shape SDG-related cognitive development, and provide empirical evidence to inform the design of sustainable development education in higher education institutions. A questionnaire survey was administered in China. Descriptive statistics, multinomial logistic regression, path analysis, and correlation analysis were employed to examine the relationships among digital media use, urban context, disciplinary background, and SDG cognition. The results are as follows. Digital media exposure and city tier were both associated with variations in SDG cognition, and a significant nonlinear threshold was observed at approximately two hours of daily media use. A clear threshold effect emerges, with students using digital media for more than two hours per day demonstrating markedly higher cognitive levels. Although overall awareness is relatively high, knowledge remains broad but superficial, with weaker understanding of ecological and collaborative goals. Disciplinary differences are evident—medical and agricultural students show stronger comprehension, while arts and humanities students lag behind—yet support for interdisciplinary collaboration is substantial. Participation in SDG-related learning activities significantly enhances attitudinal and behavioral change. This study advances the literature by integrating digital media exposure, urban hierarchy, and academic discipline into a unified analytical framework to explain variations in SDG cognition among Chinese urban college students. It identifies a significant media use threshold effect (over two hours per day) that enhances cognitive accumulation, revealing a nonlinear mechanism rarely examined in prior research. By uncovering disciplinary heterogeneity and the relative weakness in ecological and collaborative goal awareness, the study moves beyond descriptive assessments to provide targeted, evidence-based implications for curriculum design and interdisciplinary sustainable development education in higher education institutions. Full article

Processing quality in chestnut (Castanea spp.) is a complex trait jointly determined by fruit development, postharvest metabolic changes, and responses to processing. However, its genetic basis and regulatory networks remain poorly understood. This review provides an integrated framework linking product-specific processing requirements with their biochemical basis, candidate genes, and molecular breeding strategies. Starch composition and fine structure primarily determine cooked texture, storage hardening, and digestibility; starch degradation and sugar metabolism affect sweetness and thermally induced flavor formation; and phenolic substrates, together with oxidative enzymes, determine browning potential and color stability. We review the biochemical basis underlying these traits and summarize candidate genes and regulatory pathways involved in starch synthesis and structural modification, starch-to-sugar conversion, enzymatic browning, flavor formation, and the accumulation of nutritional and bioactive compounds. Nevertheless, stable quantitative trait loci, favorable haplotypes, and causal genes associated with chestnut processing quality remain insufficiently validated. Future research should develop product-oriented, standardized phenotyping systems and integrate multi-environment genetic analyses, multi-omics network dissection, marker-assisted selection, genomic selection, and gene editing to elucidate the genetic mechanisms underlying chestnut processing quality and enable the precision breeding of processing-specific cultivars. Full article

This article presents a scoping review of the literature on digital twins (DTs) for sustainable groundwater resources management, which constitutes a very recent and rapidly expanding research field, with literature moving quickly from conceptual frameworks to application-oriented systems. The literature, selected through the PRISMA 2020 methodology, is organized into seven sub-topics: AI and ML foundations, digital twin architectures and frameworks, aquifer-scale DT applications, agricultural and water–energy–food (WEF) nexus DTs, basin and urban water DTs, sensing, monitoring and IoT infrastructures, and governance, resilience and socio-hydrology. This structure shows how the field is shifting from monitoring and prediction toward integrated decision support, where process-based models, machine learning surrogates, real-time sensing and optimization are combined to support drought mitigation, saltwater intrusion control, irrigation management, climate adaptation and basin-scale planning. Across the reviewed studies, the most recurrent contributions are the construction of hybrid model architectures, the use of DTs to close the loop between observation and control, and the growing recognition that groundwater management must incorporate governance, stakeholder decision-making and socio-hydrological feedbacks. At the same time, the literature still faces key limitations, especially uncertainty quantification, interoperability between models and data streams, transferability to data-scarce settings and limited validation under real operational conditions. Future research should therefore focus on physics-informed and explainable AI, federated and scalable DT architectures, stronger coupling with socio-hydrological and governance frameworks, and more field-tested implementations that can demonstrate robust performance across diverse hydrogeological and institutional contexts. Full article

With accelerating population aging and retirement-policy reforms, post-retirement work participation and its health effects have attracted increasing attention. Using data from the 2013–2020 waves of the China Health and Retirement Longitudinal Study (CHARLS), this study examined the effect of working beyond retirement on changes in cognitive function among the Retirement-Transition Population using a staggered difference-in-differences approach. Cognitive function was decomposed into episodic memory and executive function, and robustness checks were conducted using parallel trend tests and propensity score matching combined with difference-in-differences (PSM-DID). The results showed that working beyond retirement was positively associated with improved cognitive function. This effect was mainly observed in episodic memory, whereas its effect on executive function was limited. Heterogeneity analyses indicated that the cognitive effects were more pronounced among men and urban residents. Individuals without chronic diseases, without depressive symptoms, and with better self-rated health exhibited greater cognitive benefits. Among individuals with work-type change, those experiencing an agricultural-to-non-agricultural transition showed more pronounced cognitive benefits. The findings suggest that the cognitive effects of working beyond retirement are dimension-specific and heterogeneous across population groups. Future delayed retirement policies should recognize the health value of work participation, incorporate health benefits into policy considerations, and promote age-friendly employment through health assessment, job matching, and follow-up service mechanisms. Full article

Coal-resource-exhausted cities face severe spatial conflicts between mining land redevelopment, urban sprawl, agricultural expansion, and ecological restoration, necessitating optimized spatial planning of forest-dominated ecological landscapes. This study constructs an ecology-prioritized identification and multi-scenario optimization framework for forest landscapes in resource-depleted urban fringes, taking Xinqiu District, Fuxin City, China, as a representative case. Coupling the Analytic Hierarchy Process and Entropy Weight Method, a mining-adapted Ecological Protection Importance (EPI) evaluation system was established. Results indicated that geological hazard sensitivity exerted the highest weighting dominance ($w_6=0.2322$) in EPI, establishing an Ecological Priority Control Zone covering 47.86% (6782.04 ha) of the district. Spatial dual-conflict mapping revealed a conflict footprint of 1493.82 ha, heavily dominated by Ecological-Urban/Mining Conflicts (1156.95 ha). Multi-scenario simulation via Fragstats 4.2 demonstrated that Option A (ecology priority-agriculture secondary) achieved the highest landscape score (0.8870), effectively maximizing network cohesion (COHESION = 99.3911) and restoring natural fractal boundary geometry (PAFRAC = 1.4760). The “southern conservation, central remediation, and river corridor connectivity” zonal strategy provides a science-based decision tool for forest network reconstruction in resource-exhausted cities. Full article

Urban green spaces play an important role in supporting ecosystem functions, landscape connectivity, and urban resilience; however, their potential contribution may be constrained by fragmentation, inadequate maintenance, and limited integration among spatial planning instruments. This study develops a preliminary green infrastructure (GI) planning framework for the municipality of Noicattaro (Bari, Apulia, Italy) by integrating planning provisions with the observed condition and spatial distribution of existing public green spaces. A GIS-based spatial analysis, complemented by field surveys, assessed 89 public green spaces for compliance with planning requirements and maintenance status. The analysis also considered the spatial structure of the surrounding peri-urban landscape, including the karstic dry valleys (Lame), agricultural land uses, and relevant landscape and hydrogeological planning constraints. The results show that only 20.2% of the surveyed green spaces met both planning and maintenance criteria, whereas 78.4% fell into categories characterised by planning non-compliance, inadequate maintenance, or both. These findings highlight potential gaps between formal planning provisions and the observed condition and management of the municipal green system. The analysis of the Lame further revealed a predominance of agricultural land over natural vegetation, emphasising the importance of considering the wider agricultural and peri-urban landscape when identifying potential ecological connections. Based on the combined spatial and field assessment, a preliminary GI network framework was developed around core ecological areas, buffer zones, and strategically located green spaces, with the aim of identifying potential spatial connections and areas for further investigation. Overall, the study demonstrates the potential of integrating planning analysis, GIS-based assessment, and field observations as a screening and decision-support approach for more context-sensitive GI planning in peri-urban Mediterranean landscapes. The proposed framework should be regarded as a preliminary planning proposal requiring further validation through biodiversity, ecosystem-service, accessibility, and implementation assessments. Full article

Spent coffee grounds (SCGs), a ubiquitous and renewable agricultural waste, have emerged as a promising biosorbent for environmental remediation. This review provides a comprehensive overview of the application of SCG-derived materials in wastewater treatment and gas purification. We systematically summarize their physicochemical characteristics, adsorption performance towards diverse contaminants, and underlying mechanisms. Specifically, modification strategies of raw SCGs are discussed in detail, including chemical modifications (e.g., degreasing/alkali/acid/organic solvent/metal oxide treatment), thermochemical conversions (e.g., pyrolysis, hydrothermal carbonization, and activation), and the fabrication of composites with natural or synthetic materials such as chitosan, clay minerals, and agricultural/industrial wastes. These approaches effectively optimize pore structure, enrich surface functionalities, and enhance selectivity and adsorption capacity. Particular attention is devoted to SCG-derived activated carbon and composites for capturing gaseous pollutants (e.g., CO₂, H₂S, PH₃, and VOCs). Techno-economic analysis of SCG-derived materials production is discussed to evaluate their commercial viability and overall sustainability. Finally, critical research gaps are identified, and future perspectives are proposed, emphasizing the elucidation of adsorption mechanisms, rational material design, and rigorous techno-economic and life-cycle assessments. This review underscores the potential of SCG-based materials as low-cost, high-performance alternatives to conventional adsorbents, aligning with the principles of a circular economy and environmental sustainability. Full article

Chemical pollution from industrial, agricultural, and urban activities represents a major threat to freshwater ecosystems and aquatic organisms. This study evaluated hepatic biomarker responses in Oreochromis niloticus (Nile tilapia) maintained under chronic environmental exposure to water from the Iguaçu River, one of the most polluted urban rivers in Brazil. Juvenile fish were kept in cages at three sites along the river, and hepatic biomarkers were assessed after 15 and 22 months of environmental exposure. Fish showed severe histopathological liver lesions, activation of antioxidant defenses, and oxidative stress responses accompanied by increased DNA damage. Immunological responses, particularly melanomacrophage proliferation and granuloma formation, were also observed across the monitored exposure scenarios. Alterations in plasma biochemical parameters, including AST, ALT, LDH, albumin, and globulin, were consistent with changes in hepatic function. Overall, the integrated biomarker responses revealed distinct patterns of biological alteration among the monitored exposure scenarios and were consistent with chronic exposure to complex environmental contaminant mixtures. These findings are consistent with alterations in liver integrity in fish maintained under long-term environmental exposure in the Iguaçu River. The study highlights the sensitivity of Nile tilapia as a bioindicator species for aquatic biomonitoring and provides valuable information to support environmental monitoring, risk assessment, and conservation strategies for the Iguaçu River Basin. Full article

Agriculture is pivotal to China’s dual-carbon goals, yet the nonlinear and spatiotemporally heterogeneous relationships between agricultural carbon emissions (ACE) and their associated socioeconomic, agricultural-production, public-investment, and climatic factors remain poorly understood. Here, we compile a multi-source provincial ACE inventory covering cropland use, rice cultivation, and livestock production for 2000–2023 and combine spatial trend and autocorrelation diagnostics with explainable machine learning using XGBoost–SHAP and geographically and temporally weighted regression (GTWR). We find that national ACE increased from 254.48 to 270.25 Mt, while emission intensity fell by 59.6%, indicating that the carbon efficiency of agricultural production improved substantially, although total emissions did not achieve an absolute decline. ACE exhibited persistent spatial imbalance, significant spatial clustering, and gradual diffusion of high-emission areas. Model benchmarking showed that XGBoost achieved the best overall performance, with a mean cross-validated R² of 0.9131 and an independent temporal-test R² of 0.7056. SHAP importance aggregated across repeated cross-validation identified agricultural public investment (21.3%) and urbanization rate (17.9%) as the two leading factors, followed by precipitation, temperature, agricultural industrial structure, and industrial agglomeration level, with the same six factors consistently identified by the three tree-based models. SHAP dependence plots further revealed nonlinear relationship patterns and approximate transition locations around an agricultural public investment level of CNY 11.84 billion, an urbanization rate of 54.69%, annual precipitation of 698.4 mm, and annual temperature between 13.01 and 22.17 °C. GTWR provided stronger statistical evidence of spatiotemporal nonstationarity for urbanization rate, temperature, and industrial agglomeration level, whereas the coefficient patterns of agricultural public investment, agricultural industrial structure, and, particularly, precipitation received more limited local statistical support. These findings provide support for region-specific agricultural carbon mitigation by improving agricultural input-use efficiency, optimizing the allocation of public investment, facilitating the transition toward low-emission crop and livestock management, and integrating climate adaptation with sustainable agricultural production and food security. Full article

Urbanisation has become a significant driver of ecological transformation, resulting in the degradation of urban green and blue spaces (UGSs and UBSs) and a subsequent decrease in ecosystem services (ESs). It is imperative to evaluate the spatio-temporal dynamics of UGS and UBS in order to develop sustainable urban planning strategies, particularly in the swiftly expanding cities of the Global South. In the Siliguri Planning Area (SPA), India, this study examines the long-term variations in UGS and UBS and their associated ecosystem service values (ESVs) from 1991 to 2021. The Normalised Difference Vegetation Index (NDVI) and Modified Normalised Difference Water Index (MNDWI) were employed to delineate UGS and UBS using multi-temporal Landsat imagery, respectively). The benefit transfer method was employed to quantify ESV, and sensitivity analysis was conducted to assess the valuation’s reliability. Also, adjusted value coefficients were employed. The findings indicated that landscapes such as tea garden (58.74% reduce) and agricultural land (46.09 increase) have undergone a substantial transformation as a result of urbanisation, with the built-up areas increasing from 5798.61 ha in 1991 to 8500.23 ha in 2021 (46.59% increase). Simultaneously, UGS decreased (by 40.26%) from 12,533.04 ha (47.81% oftotal area)in 1991 to 7486.29 ha (28.55% oftotal area) in 2021, while UBS decreased (by 79.47%) from 255.69 ha (0.94% oftotal area) in 1991 to 52.48 ha (0.19% oftotal area) in 2021. Subsequently, the ESV of UGS and UBS fell significantly from 1183.05 crores (INR) to 706.67 crores (INR) and 34.72 crores (INR) to 7.12 crores (INR), respectively. This confirms the elasticity of the valuation estimates, as the sensitivity coefficients remained below one. The study contributes to understanding the crucial role of urban planners, private property owners, and builders in promoting green–blue infrastructure conservation, wetland restoration, and ecological zoning. Such ecosystem service-based planning is essential for achieving sustainable development in rapidly urbanising regions and enhancing urban ecological resilience. Full article