Earth and Environmental Sciences

Correction on: Maschette D, Wotherspoon S, Murase H, Kelly N, Ziegler P, Swadling KM and Kawaguchi S (2025) Resource potential and maturity estimates of Euphausia superba in East Antarctica. Front. Mar. Sci. 12:1448250. doi: 10.3389/fmars.2025.1448250Wrong content There was a mistake in table 1 as published. The values for 'Max length, 50% selected' and 'Range over which selection occurs' were incorrectly reported as 39mm and 9mm respectively. These values should have been 35mm and 11mm respectively. The corrected table 1 appears below.

No abstract is available for this article.

No abstract is available for this article.

No abstract is available for this article.

ABSTRACT Gathering activities have evolved from survival‐driven practices to predominantly recreational and cultural pursuits, weakening the connection with nature and potentially reducing navigational skills, which may help explain why gathering accounts for 8% of the search operations in Catalonia (Spain). A key focus of this study was the comparison of three classical probability of area (POA) models for locating missing gatherers—the rings, watershed, and walking time cost models—as well as combinations of the watershed model with the other two. The study also provides an overview of gatherers' spatial behavior, risk factors, and vulnerability. The dataset included 248 search operations involving 300 gatherers, occurring between 2010 and 2022 in Catalonia (Spain). Methods included descriptive and regression tree analyses on distance traveled, ordinal regression on health outcomes, and comparative evaluation of POA models using the MapScore framework, complemented by connectivity and high‐probability area analyses. Results indicate that elapsed time strongly influences both distance traveled and health outcomes, while meteorology and spatial features also contribute. Overall, POA models substantially improved search effectiveness compared with random search, and differences between models are discussed.

ABSTRACT Rural housing inequality remains a major challenge for sustainable development in the Global South, yet its assessment has long been constrained by data poverty. Macro‐level statistics fail to capture village‐level variation, field surveys are costly, and conventional computer vision approaches depend heavily on large labeled datasets. To address this problem, this study proposes a Geo‐AI framework that combines VLM‐assisted pairwise labeling, PAIR‐CNN inference, TrueSkill aggregation, and spatial analysis. Using 4065 rural street‐view images from Huaiji County, Guangdong Province, we derived perceived modernization scores for 283 villages. GPT‐4o achieved substantial agreement with a human‐consensus benchmark (Cohen's kappa = 0.68), and the PAIR‐CNN reached an average accuracy of 78.76%. Spatial analysis further reveals a nested pattern of spatial differentiation, with overall inequality driven mainly by within‐township imbalance. The framework demonstrates how VLMs and lightweight vision models can support scalable and interpretable assessment of rural built environments.

ABSTRACT Urban Heat Islands (UHI) represent one of the most significant environmental challenges facing rapidly growing cities worldwide. This study presents an innovative integrated framework combining remote sensing, machine learning, and multi‐index interaction modeling to assess UHI risk patterns. Multi‐temporal Landsat satellite data from 2018–2024 were processed to derive seven key indices: Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Modified Normalized Difference Water Index (MNDWI), Normalized Difference Water Index (NDWI), Normalized Difference Built‐up Index (NDBI), Albedo, and Land Surface Temperature (LST). A supervised Support Vector Machine (SVM) classification was employed for Land Use Land Cover (LULC) mapping, achieving overall accuracies exceeding 85%. The novel contribution of this research lies in the development of a Risk Interaction Model that synthesizes multiple indices into four integrated risk classes: Vegetated & Cool, Bare & Hot, Water‐Buffered, and Urban Heat Hotspot zones. Results reveal distinct UHI patterns in the study area, with mean LST variations ranging from 18.72°C to 33.14°C over the study period. The framework successfully identified critical UHI hotspots concentrated in built‐up areas and provided actionable insights for urban climate adaptation strategies.

ABSTRACT Poverty is a persistent global challenge. However, relying solely on socioeconomic indicators or low‐resolution nighttime light data hinders the precision of poverty identification. Moreover, the spatial characteristics of buildings, as key indicators of socioeconomic development, remain under‐investigated for their potential in identifying impoverished areas. To bridge these gaps, this study combined high‐resolution (10‐m) SDGSAT‐1 nighttime light with three‐dimensional (3D) building indicators to enable precise poverty identification. The XGBoost algorithm was employed to assess the Multidimensional Poverty Index (MPI) for counties within Jiangxi Province, China. A comparison was also made with conventional NPP‐VIIRS nighttime light (500‐m resolution). The findings indicate that the incorporation of 3D building indicators into SDGSAT‐1 data substantially improves model performance, providing a more robust poverty estimation than single‐source datasets. Specifically, the combined model achieved R 2 values of 0.8986 and 0.8540 for SDGSAT‐1 and NPP‐VIIRS, respectively. This represents an information gain of 0.1160 for the high‐resolution SDGSAT‐1 data compared to its baseline value of 0.7826. Additionally, SHAP analysis elucidates that “mean building height”, “sky view factor”, and “standard deviation of pixel light values” are the predominant drivers of the model. In conclusion, combining 3D building indicators with fine‐grained nighttime light data enables a higher precision in identifying poverty across county‐level units. This approach presents a novel methodology for the timely monitoring of poverty across large regions. These findings provide a robust foundation for optimizing resource allocation and informing sustainable poverty alleviation strategies.

Humanity is crossing multiple planetary boundaries while facing rising inequality, democratic fragility, and worsening mental health, exposing the incompatibility of unlimited gross domestic product-driven growth with a finite, socially interdependent planet. Only 17% of the Sustainable Development Goal targets are on track, indicating the need for a deeper transformation rather than faster implementation. Synthesising evidence across disciplines, we argue that human beings are evolutionarily wired for cooperation and relational wellbeing, and not perpetual consumption and status competition. This argument underpins a post-2030 shift in a global development paradigm that places multidimensional wellbeing, of people and the planet, at its core. We outline three mutually reinforcing systemic shifts: deliberative democracy that gives communities real power to shape collective futures; economic democracy that redirects finance, enterprise design, and fiscal policy towards equitable, regenerative outcomes; and transformed land and resource governance that recognises ecological limits and the rights of nature. By aligning institutions with the cooperative nature of humans and the Earth's regenerative capacity, societies can achieve flourishing lives for all within planetary boundaries, offering a scientifically grounded agenda for the decades beyond 2030.

The rapid diffusion of industrial robots is reshaping global production networks, yet we know surprisingly little about how automation affects the sectoral composition of foreign direct investment (FDI). We argue that robot adoption generates a competitive sorting process among advanced economies—attracting technology-intensive capital through two distinct channels while leaving endowment-driven and site-specific sectors structurally insulated. Using a balanced panel of 32 OECD countries over 2013–2024 and System GMM estimation, we find that robot density significantly increases Manufacturing FDI through a direct productivity channel and Services FDI through an indirect complementarity channel, while Construction, Mining, and Agriculture FDI remain invariant to automation intensity. Strikingly, the complementarity effect on services exceeds the productivity effect on manufacturing in magnitude, suggesting that automation's strongest locational pull operates not where robots are deployed but through cross-sectoral spillovers into knowledge-intensive activities. These findings remain robust to alternative variable measurements. We term this pattern the technological upgrading hypothesis: rather than uniformly reshoring investment from developing countries, automation creates within-OECD differentiation by pulling technology-driven FDI toward automation-frontier economies—a mechanism that aggregate analyses cannot detect and that carries significant implications for the geography of investment and the risk of FDI polarisation among advanced economies.

Amidst the interactive effects of demographic structural contraction and frequent natural hazards, the resilience goals of the educational facility system face a critical trade-off between long-term operational efficiency and emergency functional robustness. Previous studies have frequently examined school consolidation or disaster reinforcement in isolation, neglecting the spatiotemporal coupling effects between long-term adaptability and instantaneous robustness. To address this gap, this study draws on Complex Adaptive Systems (CAS) theory to construct a three-dimensional assessment framework evaluating the inherent resilience, adaptability, and robustness of educational facilities. Using Kumamoto City, Japan, as an empirical case, this study quantified the comprehensive performance of the facility system under long-term demographic changes (2020–2070) and multi-hazard scenarios (earthquakes and floods), and identified five typical resilience patterns using K-means clustering. The results indicate that the resilience patterns of educational facilities exhibit significant structural and spatial differentiation characteristics: facilities in the central built-up core face risks of future resource obsolescence due to socio-spatial mismatch, while those in peripheral areas are highly susceptible to functional overload when accommodating transferred evacuation demands during acute shocks. Spatial analysis shows that the distribution of such resilience risks is not homogeneous; rather, it exhibits localized agglomeration that closely aligns with micro-natural geomorphology and demographic evolution patterns. The assessment framework and typology-based intervention strategies developed in this study provide quantitative support for formulating differentiated disaster mitigation strategies and resource integration schemes for public service facilities in shrinking cities.