Earth and Environmental Sciences

Abstract North America is retreating from carbon pricing at the precise moment when its necessity is most acute. In March 2025, Canada eliminated its federal consumer carbon tax, and the United States has dismantled the regulatory architecture for greenhouse gas regulation, including revoking the foundational 2009 EPA endangerment finding. This paper asks three questions: (1) How does the North American retreat constitute an ethical failure when evaluated through ethical security economics? (2) What does international comparative evidence reveal about the conditions under which carbon pricing succeeds? (3) What structural reforms would render carbon pricing ethically grounded and politically durable? We apply ethical security economics, a values-based framework grounding economic evaluation in five interrelated principles (sustainability, justice, peace, compassion, and authenticity with accuracy), to analyse these concurrent policy reversals. We contrast North America’s retreat with European jurisdictions where carbon pricing has been associated with substantial emissions reductions alongside economic growth, and find that the retreat constitutes a comprehensive ethical failure across all five dimensions. We introduce the concept of reactive economics, situated within established scholarship on carbon lock-in and fiscal path dependency, to characterise the structural pattern of subsidising harm-generating activities while dismantling instruments designed to address their consequences. The paper concludes with institutional recommendations, including an independent Carbon Price Board mechanism, for reintegrating carbon pricing within values-explicit governance frameworks.

This study offers a new approach to probabilistic earthquake hazard assessment (PEHA) in the densely populated regions of Southern Sumatra and West Java, Indonesia. While much attention is given to powerful, offshore megathrust earthquakes, this research focuses on a different yet equally dangerous threat: shallow, moderate-magnitude earthquakes (4.5 ≤ Mw ≤ 6.5) that occur on land. These events, often caused by unmapped faults, pose a significant risk due to their proximity to major cities and infrastructure. To develop a more reliable model, a best-fit earthquake rate model was estimated using declustered shallow earthquake events as a reference. This model enhances existing methods by offering a more precise depiction of where these shallow, damaging earthquakes are likely to occur. We accomplished this by analyzing a comprehensive probability of exceedance (PoE) of earthquakes with magnitudes up to 6.5 and depths up to 50 km that occurred between 1963 and 2022, mapping and modeling both the known active faults and the historical seismic activity in the region, and using advanced statistical methods to create a highly reliable, integrated seismicity rate model. The final product, the Integrated Most Reliable Spatial Seismicity Rate Model (ModelIMRSSR), is proposed as a useful tool for government authorities and urban planners. It can be used to create detailed seismic hazard maps that highlight areas of highest risk, especially those with unmapped faults. By guiding development away from these high-risk zones and identifying specific locations for physical reinforcement, this research provides a framework for sustainable investment. The proactive use of these findings can lead to more resilient communities and a significant reduction in potential damage and loss of life from future earthquakes.

Accurate large-scale mapping of coastal dune types is critical for coastal management but remains challenging due to the dunes’ high spatial heterogeneity and the spectral complexity in coastal environments. Therefore, this study aims to develop a coastal dune mapping approach and demonstrate its applicability across Australia and New Zealand. Firstly, we leveraged geological maps to delineate the spatial extent of coastal dune fields, effectively masking out non-dune areas. Secondly, we applied an object-based image analysis (OBIA), followed by a random forest classifier that integrated multi-source features to classify six coastal dune types within the delineated coastal dune fields, including active dunes, foredunes, sandy beaches, semi-stabilized dunes, stabilized dunes, and wetlands (dune slacks). Based on this approach, we generated a 10 m fine classification map of coastal dune types across Australia and New Zealand, covering a total area of 27,564 km 2 . Accuracy assessment yielded an overall classification accuracy of 90.68 %. Most categories achieved satisfactory performance, with stabilized dunes showing the highest accuracy (96.81 %). We also found that approximately 498 km 2 of coastal dune area has been converted to urban development land. However, this development pressure was mitigated by conservation efforts, as 15,781.80 km 2 (59.44 %) of the total coastal dune area was situated within protected areas. This study provides a replicable methodology for large-scale fine classification of coastal dune types. The resulting map offers scientific support for monitoring coastal dune systems state and evolution under human intervention.

Phytoplankton and zooplankton dynamics in the western Ross Sea are strongly shaped by interactions among sea-ice retreat, water-column structure, and spatial environmental gradients. During the ROSSMIZE expedition (November 1994–January 1995), a multidisciplinary survey across four contrasting regions of the western Ross Sea captured the transition from early-season ice influence to summer stratification. Mesozooplankton were sampled using a sensor-equipped BIONESS system, enabling high-resolution coupling of biological patterns with temperature, salinity, fluorescence, and depth. The region emerged as a mosaic of subsystems structured by latitude, ice history, and hydrographic conditions. Depth was the dominant driver of community composition and diversity: generalized additive models indicated that diversity peaked at intermediate depths, reflecting a balance between surface-driven production and deeper, more stable water masses. Temperature, salinity, and fluorescence further modulated these patterns, underscoring the sensitivity of pelagic communities to fine-scale physical gradients. Together, these results demonstrate that spatial structuring of zooplankton in the western Ross Sea is governed not only by seasonal ice dynamics but also by depth-dependent habitat features and latitudinal environmental transitions during the spring–summer period.

With an ever-growing recognition of Land Surface Temperature (LST) as a key Essential Climate Variable (ECV), it becomes utmost important to have such a variable at the fine spatial and temporal scales of urban spaces and dynamics. Sentinel-3 provides coarse LST (1 km, daily) based on thermal imagery acquired by its Sea and Land Surface Temperature Radiometer (SLSTR) as well as fine Spectral Directional Reflectances (SDRs, 300 m, every two days) synergically inferred from both SLSTR and the Ocean and Land Colour Instrument (OLCI), which gives the opportunity for using the latter as a predictor in the downscaling of the former. Herein, two scale-invariance-based architectures were developed: a single-timestamp (STS) model, trained with coarse data of the timestamp whose fine target it infers; and a multi-timestamp (MTS) one, trained with multiple timestamps. Note that while several Machine Learning (ML) models besides Linear Regression (LR) were considered for the MTS architecture, only LR was used for the STS one due to the limited amount of available data which the former require for hyperparameter tuning. The models were developed over four Danish Functional Urban Areas (FUAs) using SRD-derived indices and seasonal and geospatial predictors and validated against Landsat data. While Gradient Boosting (GB) achieved the best coarse-scale performance it corresponded to the worst fine-scale performer together with Random Forest (RF), indicating scale invariance breakdown. Tree-based models performed poorly due to extrapolation limitations, whereas Neural Net (NN) and LR proved more robust. After residual correction, single-timestamp LR achieved the best fine-scale performance, making it the most reliable and recommended architecture for operations. The overall results showed that, although ML models may better predict the target at their training scale, their performance may not significantly generalise at others, therefore revealing scale specificity. Furthermore, the results suggested that usage of the more general multi-timestamp architecture instead of the single one may deteriorate performance.

Quantifying landscape ecological risk (LER) using multi-period land use data and ecological indicators is essential for understanding regional ecological dynamics. However, LER assessment is sensitive to spatial delineation, introducing uncertainty. This study developed an integrated LER model that incorporates the remote sensing ecological index and abundance index, and evaluated spatial unit effects through comparative analyses of fishnet, hexagonal, sub-watershed, and county units. LER dynamics in the Shandong Peninsula Urban Agglomeration (SPUA) from 2004 to 2024 were analyzed, and a boosted regression trees model was applied to quantify the relative importance of influencing factors and their nonlinear effects. The results indicate that: (1) sub-watershed units showed greater robustness and stability across multiple evaluation indicators, supporting their suitability for LER assessment; (2) LER in the SPUA exhibited a fluctuating but overall slightly increasing trend over the past two decades, with a persistent west-low and east-high spatial pattern; and (3) relief degree (29.13%) and nighttime light (17.78%) were the dominant factors shaping LER, showing an inverted U-shaped response and a saturating nonlinear increase, respectively. This study supports the use of sub-watershed units as an appropriate spatial unit for LER assessment and provides insights into terrain-sensitive conservation and sustainable land-use management in urbanizing regions.

In optical remote sensing 3D reconstruction, high-resolution satellite stereo matching is a critical task, yet it is challenged by extreme imaging geometries, texture-less and repetitive patterns, occlusions, and scene variations caused by spatio-temporal heterogeneity. To address these issues, we propose IFMA-Stereo, an innovative binocular disparity estimation method that leverages a monocular depth foundation model. Our approach constructs a multi-scale spatial information pyramid to jointly integrate the foundation model with a disparity extraction network. At the feature level, an attention interaction mechanism captures multi-dimensional contextual dependencies and transforms general scene understanding priors into long-range associative features suitable for stereo cost volume construction. At the pixel level, a cyclic iterative refinement module embeds depth information from the foundation model throughout the iteration process and performs joint optimization, enhancing the model’s adaptability in geometrically complex regions. Experiments on the US3D and GaoFen-7 datasets demonstrate that IFMA-Stereo achieves superior performance in challenging areas (texture-less regions, disparity discontinuities, repetitive patterns) and effectively mitigates prediction errors caused by spatio-temporal heterogeneity, albeit at the cost of increased inference time compared to baseline methods. Quantitatively, the method achieves an end-point error (EPE) of 1.347 and a D1 error of 7.26% on the US3D dataset, and an EPE of 1.585 and a D1 error of 13.41% on the GaoFen-7 dataset. Notably, the method also yields precise predictions for unseen urban areas, indicating strong generalization. These results confirm that IFMA-Stereo achieves state-of-the-art accuracy in remote sensing disparity estimation.

Accelerated biodiversity loss due to anthropogenic factors has led some researchers to suggest Earth may be approaching a 6 th mass extinction event. While artificial intelligence (AI) cannot counteract this biodiversity loss directly, AI has seen an increased adoption as a powerful tool for marine biodiversity monitoring, particularly by enabling the rapid and efficient collection and processing of large volumes of highly accurate biodiversity data. Such monitoring can, in turn, inform conservation strategies and evidence-based marine policy. This review aims to increase the accessibility of AI-based imaging techniques for marine biodiversity research and to help researchers make informed decisions on how these tools can effectively be applied to marine biodiversity studies. This review is intentionally framed as an introductory primer rather than a novel conceptual synthesis, aiming to consolidate and clarify foundational AI imaging concepts for a marine biodiversity audience. We compare traditional survey methods with AI-assisted approaches to marine data collection, explain the fundamental principles behind AI imaging workflows, and discuss common limitations and sources of bias associated with their implementation. By establishing this baseline knowledge, we review current applications of AI imaging in marine biodiversity monitoring, and outline practical pathways for integrating these systems into marine research programs. Ultimately, we aim for readers to gain a deeper understanding not only of how complex AI systems support marine biodiversity monitoring, but how best to deploy them responsibly and effectively to address the growing data challenges facing marine science today.

This study explores women’s subjective resilience to climate change in informal settlements in Nairobi, Kenya, focusing on the lived experiences of women who face heightened vulnerability. Informal settlements, characterized by overcrowding, inadequate infrastructure, and insecure tenure, are disproportionately affected by extreme weather events such as flooding and heatwaves. While existing literature highlights climate resilience at the socio-ecological systems level, there is limited attention on women’s personal experiences and adaptive strategies. This research fills that gap by investigating how women perceive and respond to climate challenges, contributing valuable insights into how individual experiences of resilience interact with broader systems of adaptation. Using qualitative methods, the study examines the roles women play in household and community-level adaptation, emphasizing their agency and the systemic barriers they encounter, including poverty, political marginalization, and limited access to resources. The findings reveal that women’s resilience is shaped by interactions between personal assets and strategies and external resources at every level of the social-ecology. These interactions both reinforce and challenge broader socio-ecological resilience frameworks, highlighting the need for integrated, context-specific climate adaptation. The study calls for more inclusive approaches to climate adaptation that build on mutual aid and community-level initiatives in informal settlements to recover, adapt and transform in the face of climate change. Ultimately, this research offers a foundation for designing more effective, community-driven climate strategies that center women’s experiences and promote sustainable, system-level resilience.

Geological lithology interpretation in multi-source remote sensing imagery still faces challenges due to the spatial heterogeneity of multi-source features and the fragmented spatial distribution of geological elements. To address these issues, we propose the complementary feature self-integration network (CFSI-Net), a unified framework for multi-source feature fusion and lithology interpretation. To mitigate spatial heterogeneity in multi-source features, we developed the cross-domain feature extraction and fusion module (CDFEFM), a dual-branch structure that adaptively extracts and fuses complementary features across channels while reducing redundant lithology information. Furthermore, we introduced the frequency-spatial feature integration module (FSFIM) and a segmentation module (SegM) to alleviate the problem of boundary ambiguity in segmentation. FSFIM captures boundary structural details and spatial contextual dependencies from the frequency and spatial domains, whereas SegM further refines spatial dependencies across channels, enriching textural details and enhancing the discrimination of adjacent elements. Experimental results on the CUG-LithXZ dataset demonstrate that CFSI-Net consistently outperforms state-of-the-art models, achieving an OA of 80.13% and mIoU of 66.18% in Geo-GZ, and an OA of 72.49% and mIoU of 50.26% in Geo-LZ. These results confirm that the synergistic utilization of frequency and spatial information provides richer details for accurate geological lithology interpretation in complex terrains.

Temperate phages play crucial ecological roles in engineered microbial communities, yet their adaptive strategies under antibiotic stress remain unclear. Here, metagenomic analysis was used to investigate how temperate phages facilitate host adaptation in activated sludge acclimated to chloramphenicol (CAP). Antibiotic stress markedly reshaped bacterial and temperate phage communities, with dominant degraders (e.g., Sphingomonas and Caballeronia ) reaching relative abundances of 6.5–42.0%. Temperate phages exhibited specific adaptive responses by significantly enriching antibiotic resistance genes, including multidrug ( arlR and mtrA ) and peptide ( bcrA ) resistance genes, resulting in a 1.56–4.15-fold increase in the phage-derived resistome relative to the control. They also mediated general adaptive responses by encoding auxiliary genes involved in oxidative stress mitigation, DNA repair, biofilm formation, and antiviral defense. Host-phage linkage prediction identified 1045 phage–bacteria interactions, including 11 ARG-harboring viral operational taxonomic units associated with dominant CAP-degrading hosts. Collectively, our findings reveal that temperate phages facilitate microbial resilience in antibiotic-stressed environments by delivering mutualistic genetic traits, encompassing both specific (antibiotic resistance genes) and general (antiviral defense, metabolic, and stress mitigation) adaptive responses, highlighting their ecological significance and potential for enhancing the stability and performance of wastewater treatment systems under pharmaceutical stress.

Few-shot remote sensing scene classification aims to accurately recognize unseen scene categories using only a scarce number of labeled samples, which has emerged as a research hotspot in the field of remote sensing image interpretation. However, remote sensing images intrinsically suffer from large intra-class variations, high inter-class similarities, and complex background interferences. Traditional few-shot learning methods typically perform feature metric learning in Euclidean space, making it difficult to capture the non-Euclidean geometric distribution characteristics of remote sensing features, and they often neglect the spatial structural information embedded in feature maps. To address these issues, this paper proposes a novel few-shot remote sensing scene classification method, termed ZSFS-RGBN, which integrates a Zigzag Scanning Feature Sequence with a Riemannian Geometric Barycenter Network. Specifically, ResNet12 is first employed as the backbone to extract deep convolutional feature maps from both the support and query sets. Second, a Zigzag scanning strategy is introduced to reorganize the two-dimensional feature maps into one-dimensional feature sequences, thereby effectively preserving the spatial locality and structural continuity of the features. Third, an autoregressive moving average (ARMA) model is constructed to characterize the spatial dependencies of the feature sequences, and its state parameters are mapped onto a symmetric positive definite (SPD) matrix manifold, enabling the deep semantic representations of remote sensing scenes in a non-Euclidean geometric space. Finally, a Riemannian geometric barycenter network is designed to learn the Riemannian barycenter of each category on the SPD manifold, where a joint loss function is introduced to simultaneously optimize intra-class compactness and inter-class separability. Comprehensive experiments are conducted on three public remote sensing scene datasets: NWPU-RESISC45, UC Merced Land-Use, and WHU-RS19. Experimental results demonstrate that the proposed method consistently outperforms several representative state-of-the-art approaches under both 5-way 1-shot and 5-way 5-shot settings. Furthermore, ablation studies verify the effectiveness of each component within the proposed framework.

With the increasing diversity of remote sensing image acquisition conditions and imaging scenarios, building extraction models often experience significant performance degradation in cross-dataset applications due to variations in sensors and scene characteristics. Improving their cross-domain generalization ability has therefore become a critical research problem. To address the challenges of appearance style discrepancy and feature distribution shift in cross-domain building extraction, this paper proposes WCMNet, a wavelet-guided and CNN–Mamba hybrid network for unsupervised domain adaptation in building extraction. Specifically, a Mamba Wavelet Alignment (MWA) module is designed to align low-frequency style information in the wavelet domain while preserving directional high-frequency edge structures, thereby mitigating cross-domain appearance discrepancies and reducing structural degradation during domain translation. In addition, a Global–Local Mamba Block (GLMB) is developed to jointly model local textures and global semantic dependencies. In GLMB, the CNN branch captures fine-grained local details and boundary cues, while the Mamba branch models long-range contextual information; an adaptive gated fusion mechanism further integrates the two types of features. Experimental results on six cross-domain transfer tasks across the WHU, Massachusetts, and Potsdam datasets demonstrate that WCMNet consistently outperforms existing state-of-the-art domain adaptation methods. In particular, WCMNet achieves an average IoU of 65.13% and an average BIoU of 74.80% across all transfer settings, with improvements of up to 27.35 percentage points in IoU and 38.32 percentage points in BIoU compared with the strongest competing methods. These results demonstrate that the proposed MWA and GLMB effectively improve building completeness, boundary delineation accuracy, and cross-domain robustness.

Global food security is increasingly threatened by population growth, climate change, and declining wild fish stocks, positioning aquaculture as a critical solution to meet rising fish demand. Effective breeding in captive fish is hindered by reproductive dysfunctions, such as incomplete oocyte maturation and insufficient sperm production, due to absent natural environmental cues. Induced breeding through hormonal manipulation, targeting the hypothalamus-pituitary-gonad (HPG) axis with gonadotropin-releasing hormone analogs (GnRHa) and recombinant gonadotropins, has significantly improved spawning outcomes in species. Emerging technologies, including CRISPR-Cas9, artificial intelligence, and multiomics profiling, offer precision and sustainability in reproductive management. However, challenges persist, including species-specific responses, scalability, environmental risks from hormone effluents, and ethical concerns. This paper proposes a precision endocrinology framework integrating omics, automation practices to optimize reproductive efficiency and ensure sustainable aquaculture growth addressing food security while minimizing environmental impact.

This study evaluated the potential of VIS–NIR–SWIR leaf spectroscopy to predict N, P, and K contents in sugarcane, considering spectral variability across two growing seasons (2023/2024 and 2024/2025), phenological stages, and five varieties. Spectral signatures (350–2500 nm) were acquired using a FieldSpec 3 spectroradiometer and processed with MSC, smoothing, and the first Savitzky–Golay derivative. Diagnostic bands were identified by Spearman correlation, and prediction was performed using partial least squares regression (PLSR). PCA showed that spectral variability was driven mainly by seasonal and phenological factors, whereas varietal effects were secondary. In 2023/2024, greater spectral homogeneity was associated with lower predictive performance. In 2024/2025, greater spectral heterogeneity was associated with improved prediction for N (R2 = 0.83; RMSE = 0.78 g kg−1) and P (R2 = 0.80; RMSE = 0.10 g kg−1). Potassium remained the most challenging nutrient to predict (maximum R2 = 0.25), mainly due to its ionic nature and the resulting lack of significant correlation with specific VIS, NIR, and SWIR spectral features. These results indicate strong potential for predicting N and P in fresh sugarcane leaves, although model robustness depends on the extent of spectral variability in the dataset.

Abstract Situated in the Cabo Verde Archipelago, Fogo is among the most active oceanic volcanoes in the Atlantic, hosting frequent eruptions some of which were highly explosive and at least one gravitational flank collapse in the last 100 kyr. This study presents new volcanic glass shard geochemical data with high spatial distribution from 54 sites comprising samples from both pre- and post-collapse times. The analyzed glasses comprise basanites, tephrites, and foidites with a subset extending into the phonolite field. The glass compositions complement bulk rock data particularly in the range between 6 and 1 wt.% MgO, thus enabling more detailed inter-dataset comparison. Major and trace element data reveal five geochemical Groups characterized by incompatible element contents partly delineating rock series. Compositional diversity is primarily controlled by fractional crystallization, and to lesser extents by mantle-source heterogeneity and degrees of partial melting. A geochemical framework is established for investigating provenance of ash and volcaniclastics in the Cabo Verde region and beyond by integrating new glass data with published bulk rock and tephra records. We validate the classification scheme through comparison with primary marine tephra deposits, demonstrating robust sample assignment to the observed compositional Groups. The new provenance data go beyond simple attribution to Fogo by resolving distinct compositional Groups, enabling improved discrimination within Fogo-derived material. Group assignment and characterization of differentiation trends provide a useful first-order indication of pre- versus post-collapse affinity. The glass dataset serves as a reference for future provenance and source correlation studies across the Cabo Verde Archipelago.

Barrier islands along Florida’s Atlantic coast are increasingly threatened by sea-level rise, intensified hurricanes, shoreline armoring, and rapid coastal development. This study examined how beach and dune configurations varied in relation to coastal elevation patterns, NDVI-based surface greenness, and stakeholder perceptions across the East Central Florida Atlantic coast. Light Detection and Ranging (LiDAR) elevation datasets (2016, 2022, 2024), National Agriculture Imagery Program (NAIP)-derived Normalized Difference Vegetation Index (NDVI) analyses (2015, 2019, 2023), and stakeholder survey data from two coastal resilience workshops conducted in Volusia County in November 2024 were assessed to evaluate geomorphic change, vegetation-greenness patterns, and public perceptions of shoreline management strategies. Results showed descriptive differences among shoreline-type groups. Seawall-backed sites experienced the greatest net elevation loss (−0.529 m averaged over two sites) and a small negative mean transect-level NDVI change (−0.034) between 2015 and 2023, while natural dune sites showed an overall elevation gain (0.255 m averaged over three sites), despite some site-level loss after the 2022 hurricanes, and no net mean transect-level NDVI change (0.000) over the same NDVI period. Because the LiDAR and NDVI datasets are not temporally matched, these patterns are interpreted as complementary rather than causal lines of evidence. Stakeholder survey responses demonstrated that most respondents recognized the importance of dunes and coastal vegetation for resilience, but also expressed concerns about effectiveness, long-term maintenance, and cost of natural or hybrid solutions. Overall, the findings suggest that natural and minimally armored shorelines may retain greater capacity for elevation and vegetation-greenness recovery than hardened coastal systems, while also emphasizing the need for adaptive, conservation-based coastal management strategies that account for both physical shoreline conditions and stakeholder concerns.

Satellite Synthetic Aperture Radar (SAR) is an established technology for studying and monitoring archaeological landscapes, providing insights into surface morphology and the presence of near subsurface features. However, its application in large-scale archaeological prospection is limited by the lack of robust, automated methods for SAR data analysis. This study introduces a novel Deep Learning pipeline to automatically detect and segment archaeological settlement mounds, known as tells, in central Iraq on satellite SAR data. The pipeline leverages a state-of-the-art supervised method for instance segmentation, YOLOv8-Seg, and medium-resolution satellite SAR products, specifically the Copernicus Sentinel-1 Interferometric Wide Swath Mode Ground Range Detected and Copernicus Global 30-m Digital Elevation Model products. The model identifies tell sites with an Average Precision of 0.495±0.010 and a pixel-wise Intersection over Union of 0.361±0.048 over the test areas. Archaeological interpretation of the model’s inferences confirms its reliability in locating and segmenting archaeological sites, leading also to the identification of previously unmapped potential sites. After a main test in central Iraq, the proposed workflow demonstrates promising transferability to a nearby test area in Iran, although with a need for regional fine-tuning to account for inherent variations in feature morphology and environmental context. This research establishes a baseline for future Deep Learning applications in Synthetic Aperture Radar-based archaeological prospection.