Earth and Environmental Sciences

Abstract This article presents a systematic review on child and youth participation in climate assemblies. The objective is to analyse the experiences carried out to date, their evaluation processes and the impacts derived from them. With the PRISMA methodology, 18 documents, published between 2014 and 2025, were identified that describe 22 experiences in government-led, community and school contexts. The results show that most of the initiatives are promoted and run by adults, and the participation of children and young people tends to be consultative in nature. The assessment of the impact of these experiences is mainly based on facilitators’ and participants’ perceptions and testimonies without rigorous frameworks that make it possible to measure real effects at the individual, community, environmental and political levels. This lack of systematicity limits the understanding of the true scope of these initiatives. Therefore, the research highlights the need to develop robust assessment frameworks to measure real social impact and move towards more meaningful, inclusive and transformative youth participation in climate governance processes.

Hyperspectral images (HSIs) simultaneously capture spatial and spectral information of a target. Compared with conventional visible-light images, HSI can offer higher spectral resolution that facilitates more detailed characterization. However, most existing HSI classification methods primarily emphasize low-level and high-level feature interactions while lacking effective encoding of mid-level interactions, which are often more discriminative. Moreover, HSI classification is typically conducted with patch-based inputs; although this approach facilitates the extraction of spatial information surrounding the central pixel, it tends to inadvertently dilute the network’s focus away from the central pixel. To address these challenges, we propose a Large-to-Small Kernel Guided Multi-Level Aggregation network (LSGMA). A novel Multi-Level Aggregation (MLA) module is designed, which enhances the network’s emphasis on mid-level features. It enables the simultaneous extraction of low-, mid-, and high-level features, thereby ultimately improving the classification accuracy of the model. In addition, a Large-to-Small Kernel Guided Focus (LSGF) module is introduced that more effectively captures spatial neighborhood cues while maintaining strong focus on central features. Extensive experiments on four public datasets demonstrate that the proposed LSGMA network achieves superior performance compared with several state-of-the-art methods.

The ingestion of nanoplastics (NPs) poses a growing environmental health threat, yet how intrinsic host factors modulate their intestinal fate remains poorly defined. This study tests the hypothesis that dietary patterns govern NP bioaccumulation by differentially regulating gut motility and barrier integrity. Mice were fed a control (CD), high-fat (HFD), or high-fiber diet (HFib) and exposed to 0, 5, or 25 mg/kg/day of deuterium-labeled polystyrene NPs for 8 weeks. Dietary composition profoundly altered colonic NP accumulation: compared to CD-fed mice, an HFD exacerbated the burden by 2.83-fold (328.6 ± 23.5 μg/g dry weight), whereas a HFib attenuated it to 34% (38.9 ± 7.6 μg/g). This differential accumulation was linked to barrier damage and motility suppression, most severe under HFD. Multiomics analysis revealed that HFD promoted gut dysbiosis and deficiency of short-chain fatty acids, particularly butanoic acid. This metabolic deficit was associated with disrupted enteric nervous system signaling, notably suppressed serotonergic pathways. Integrative path modeling delineated two mechanistic landscapes: a barrier-centric pathogenic cascade driven by HFD and a microbiota-led protective network sustained by HFib. Our findings establish host nutrition as a potent modifier of NP intestinal fate and accumulation, highlighting dietary fiber as a plausible nutritional strategy to enhance intestinal resilience.

Terrestrial laser scanners provide both geometric and radiometric information, and terrestrial laser scanning (TLS) intensity is influenced by surface optical properties and the angle of incidence. This study presents a piecewise polynomial Lambert–Beckmann (PPLB) model to enhance TLS intensity characterization for broad-leaved surfaces. The PPLB model incorporates the Beckmann law, thus enabling a data-driven estimation of transition angles and enhancing the fitting flexibility across diverse leaf surfaces. TLS measurements were conducted on adaxial and abaxial leaf surfaces of five tree species using a RIEGL VZ-400 scanner. The results indicated that the TLS intensity consistently declined as the angle of incidence increased; however, different species and surfaces showed distinct angular response patterns. Across all ten tested species–surface combinations, the PPLB model achieved low root mean square error values of 0.0095–0.0183 and yielded three physically meaningful parameters: specular reflection contribution (ks), surface roughness (m), and transition angle (θT). Excluding the θT = 0° fitted result for the Golden Shower Tree abaxial surface, the estimated nonzero θT values ranged from 30° to 64°, indicating substantial variability in threshold angles among leaf surfaces beyond the commonly assumed value of 45°. These findings highlight the importance of incorporating surface-specific threshold angles for improved leaf characterization.

Abstract With climate change intensifying and growing concerns over greenhouse gas (GHG) emissions, accurate accounting of emissions has become crucial towards identifying hotspots and opportunities for reduction. Higher Education Institutions have taken a large initiative in carbon accounting by reporting scope 1, 2, and 3 emissions. However, scope 3 emissions remain particularly challenging due to its large scope and the diversity of available models and methodologies for calculating. This case study compares several commonly used models (WARM, EIO-LCA, IPCC Waste Tool, and SIMAP) for scope 3 GHG emissions accounting, focusing on GHG protocol category 5: waste generated in operations subcategory. Using a consistent dataset of solid waste metrics from the University of Wisconsin–Madison, differences in results are evaluated to assess the variability across models. Utilizing multiple models and scenarios within those models across a single multi-year data set enables identification of key assumptions and system boundaries that drive variability. The analysis revealed variation in scope 3 estimates across models, with total emissions ranging from approximately metric tons of −6474 to 8642 metric tons of CO 2 equivalents across the 2023 analysis year and models. These findings highlight the need to understand the different implications on projections of environmental impact generated by utilizing different available models with the same dataset and different settings within those models. And highlight the need to understand the different tradeoffs with respect to model assumptions and calculations alongside the effort needed to generate results with each model. This is relevant for considering the comparison among universities’ impacts as well, as these may change significantly based on their choice of model. This is relevant for universities as well when they are comparing their emissions to those of their peers.

ABSTRACT Rural women smallholder farmers (RWSF) play a central role in agriculture in sub‐Saharan Africa (SSA); however, their potentials for adaptation to climate change (CC) remain underused. In this study, we identified and prioritized best practices for including RWSF in climate resilience‐building efforts through the teaching of ecological coping strategies and adaptation to CC. Employing a mixed‐method approach, the study combines a systematic review of 78 articles with participatory workshops that included 100 RWSF, 10 NGOs, and 3 local authorities from a climate‐sensitive district in Benin. The review reported 16 adaptation strategies, including conservation agriculture, climate‐smart approaches, and income generation projects. Furthermore, 13 inclusion practices were identified, including incremental learning, awareness campaigns, land and finance access, and recognition of women as agents of change. The workshops allowed stakeholders to prioritize the reported practices given their local context. The results revealed significant differences in the rankings established by the stakeholders. Although RWSF prioritized incremental learning and land access, NGOs emphasized capacity building and women's development, and local authorities stressed awareness campaigns and access to climate finance. Meaningful inclusion of RWSF in teaching ecological coping and climate adaptation strategies can challenge gender norms, promote socio‐economic empowerment, and enhance environmental sustainability. The study offers evidence‐based recommendations for policymakers, NGOs, and communities to integrate gender‐sensitive approaches into adaptation interventions.

ABSTRACT Loess is a widely distributed and collapsible soil on China's Loess Plateau, and due to its poor dynamic performance under cyclic loads, it poses significant challenges to the stability of infrastructure. However, systematic datasets linking calcium oxalate crystal‐induced modification, microstructural characteristics, and dynamic mechanical responses remain limited. The dataset includes compacted loess specimens prepared at oxalic acid concentrations of 0, 0.5, 1.5 and 2.0 mol/L and target moisture contents of 14%, 17% and 20%. Dynamic damping‐ratio curves were obtained using a hollow cylindrical torsional shear apparatus, while SEM observations and PCAS analysis were used to quantify pore‐size distribution, pore morphology, pore orientation and related microstructural descriptors. Microstructural characterisation was performed using JSM‐6700F scanning electron microscopy combined with the Pore and Crack Analysis System. The image‐based analysis provided quantitative information on pore‐size distribution, pore orientation, pore morphology and the bonding characteristics between crystals and soil particles. This dataset includes the basic physical properties of loess, the dynamic damping ratio curves versus shear strain for calcium oxalate crystal‐modified loess and untreated loess, scanning electron microscopy images of microstructures before and after dynamic shear, quantitative pore structure parameters derived from Pore and Crack Analysis System analysis, and Pearson correlation matrices linking oxalic acid concentration, microstructural parameters and dynamic damping ratios. This dataset provides reusable data for Earth system science and civil engineering research. It can be used to validate numerical models of soil modification, compare eco‐friendly stabilisation methods for collapsible loess and support further assessment of infrastructure performance in loess regions.