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ABSTRACT In a series of highly cited papers over the period of 2009–2023, earth system scientists have identified a set of nine planetary boundaries that must not be breached if we wish to avoid catastrophic consequences for nature and humanity. These range from well‐mixed, global boundaries, such as climate‐altering atmospheric greenhouse gas concentrations, to localized limits on freshwater availability and reactive nitrogen entering the environment. Recent estimates suggest that six of the nine planetary boundaries have already been breached. The food system is a key driver of these exceedances and, therefore, must play a key role in any solutions. However, the establishment of these boundaries and the analysis of potential solutions have often been devoid of economic considerations. Furthermore, in the case of several of these planetary boundaries, limited attention has been given to the economic policies that might allow society to address them, as well as the likely synergies and tradeoffs across economic policies targeted to individual objectives. This paper seeks to bring further economic analysis to bear on the quantitative assessment of global and local economic policies aimed at respecting these planetary boundaries, concluding with seven lessons to inform future research on this topic. 

Abstract Domestic attempts to advance the Sustainable Development Goals (SDGs) in a country can have synergistic and/or trade-off effects on the advancement of SDGs in other countries. Transboundary SDG interactions can be delivered through various transmission channels (e.g., trade, river flow, ocean currents, and air flow). This study quantified the transboundary interactions through these channels between 768 pairs of SDG indicators. The results showed that although high income countries only comprised 14.18% of the global population, they contributed considerably to total SDG interactions worldwide (60.60%). Transboundary synergistic effects via international trade were 14.94% more pronounced with trade partners outside their immediate geographic vicinity than with neighbouring ones. Conversely, nature-caused flows (including river flow, ocean currents, and air flow) resulted in 39.29% stronger transboundary synergistic effects among neighboring countries compared to non-neighboring ones. To facilitate the achievement of SDGs worldwide, it is essential to enhance collaboration among countries and leverage transboundary synergies. 

Abstract Several species of vector mosquitoes (eg Culex pipiens (Linnaeus, 1758), Aedes albopictus (Skuse, 1895)) complete juvenile development in artificial containers. Rain barrels are green infrastructure tools used to conserve rainwater for outdoor use, though they may also serve as a source of mosquito habitat in residential neighborhoods. To identify rain barrel features, maintenance habits, and other conditions associated with the presence of juvenile mosquitoes (ie month), we conducted periodic inspections of rain barrels at 53 households in central Illinois, USA between June and September 2016. Additionally, we administered a questionnaire to the household study participants. In the first month of the study, a diversity of mosquito species was detected in household rain barrels, but from July to September juveniles of Ae. albopictus were predominant. More than half of inspected households contained at least one mosquito-positive rain barrel within the study period. Using stepwise model selection, the strongest predictors of whether or not mosquito juveniles were detected in rain barrels were the use of a preventative measure (eg Bti, chlorine, goldfish), the presence of a mesh covering on the lid of the barrel, and the month of the year. Additionally, the participant questionnaire revealed that the majority of respondents were aware of immediate elimination methods, but few were aware of the need for long-term preventative maintenance of rain barrels against larval mosquito colonization. These findings provide valuable insight into best practices for mosquito prevention in green infrastructure and highlight the importance of proper maintenance and education to minimize juvenile mosquito habitat. 

Abstract Cities and companies have great potential to reduce pressures on Earth system boundaries. Science-based target setting has emerged as a powerful tool to help achieve the potential, but its uptake has been limited. Moreover, cities and companies usually develop their targets separately, even though many are co-located. Focusing on the top 200 cities and 500 companies by greenhouse gas emissions, we analyse the current state and potential of adopting science-based targets for climate. Of these key actors, 110 cities with existing net-zero targets and 22 companies with existing science-based targets could together eliminate up to 3.41 GtCO₂e of annual emissions. We argue that this reduction potential could increase by as much as 67% (to 5.70 GtCO₂e) if the cities and companies that already have targets bring their co-located counterparts on board to keep abreast of their ambitions. Using freshwater as another example, we discuss entry points for addressing interrelated Earth system boundaries through city–company collaborations. Our findings elucidate previously untapped potentials that could accelerate transformations for operating within Earth system boundaries. 

Abstract Maintaining educational resources and training materials as timely, current, and aligned with the needs of students, practitioners, and other users of geospatial technologies is a persistent challenge. This is particularly problematic within CyberGIS, a subfield of Geographic Information Science and Technology (GIS&T) that involves high‐performance computing and advanced cyberinfrastructure to address computation‐ and data‐intensive problems. In this study, we analyzed and compared content from two open educational resources: (1) a popular online web resource that regularly covers CyberGIS‐related topics (GIS Stack Exchange) and (2) existing and proposed content in the GIS&T Body of Knowledge. While current curricula may build a student's conceptual understanding of CyberGIS, there is a noticeable lack of resources for practical implementation of CyberGIS tools. The results highlight discrepancies between the attention and frequency of CyberGIS topics according to a popular online help resource and the CyberGIS academic community. 

Abstract Understanding species distributions is a global priority for mitigating environmental pressures from human activities. Ample studies have identified key environmental (climate and habitat) predictors and the spatial scales at which they influence species distributions. However, regarding human influence, such understandings are largely lacking. Here, to advance knowledge concerning human influence on species distributions, we systematically reviewed species distribution modelling (SDM) articles and assessed current modelling efforts. We searched 12,854 articles and found only 1,429 articles using human predictors within SDMs. Collectively, these studies of >58,000 species used 2,307 unique human predictors, suggesting that in contrast to environmental predictors, there is no ‘rule of thumb’ for human predictor selection in SDMs. The number of human predictors used across studies also varied (usually one to four per study). Moreover, nearly half the articles projecting to future climates held human predictors constant over time, risking false optimism about the effects of human activities compared with climate change. Advances in using human predictors in SDMs are paramount for accurately informing and advancing policy, conservation, management and ecology. We show considerable gaps in including human predictors to understand current and future species distributions in the Anthropocene, opening opportunities for new inquiries. We pose 15 questions to advance ecological theory, methods and real-world applications. 

Abstract With increasing demands for precise water resource management, there is a growing need for advanced techniques in mapping water bodies. The currently deployed satellites provide complementary data that are either of high spatial or high temporal resolutions. As a result, there is a clear trade‐off between space and time when considering a single data source. For the efficient monitoring of multiple environmental resources, various Earth science applications need data at high spatial and temporal resolutions. To address this need, many data fusion methods have been described in the literature, that rely on combining data snapshots from multiple sources. Traditional methods face limitations due to sensitivity to atmospheric disturbances and other environmental factors, resulting in noise, outliers, and missing data. This paper introduces Hydrological Generative Adversarial Network (Hydro‐GAN), a novel machine learning‐based method that utilizes modified GANs to enhance boundary accuracy when mapping low‐resolution MODIS data to high‐resolution Landsat‐8 images. We propose a new non‐saturating loss function for the Hydro‐GAN generator, which maximizes the log of discriminator probabilities to promote stable updates and aid convergence. By focusing on reducing squared differences between real and synthetic images, our approach enhances training stability and overall performance. We specifically focus on mapping water bodies using MODIS and Landsat‐8 imagery due to their relevance in water resource management tasks. Our experimental results demonstrate the effectiveness of Hydro‐GAN in generating high‐resolution water body maps, outperforming traditional methods in terms of boundary accuracy and overall quality. 

Instead of relying on human-annotated training samples to build a classifier, weakly supervised scientific paper classification aims to classify papers only using category descriptions (e.g., category names, category-indicative keywords). Existing studies on weakly supervised paper classification are less concerned with two challenges: (1) Papers should be classified into not only coarse-grained research topics but also fine-grained themes, and potentially into multiple themes, given a large and fine-grained label space; and (2) full text should be utilized to complement the paper title and abstract for classification. Moreover, instead of viewing the entire paper as a long linear sequence, one should exploit the structural information such as citation links across papers and the hierarchy of sections and paragraphs in each paper. To tackle these challenges, in this study, we propose FuTex, a framework that uses the cross-paper network structure and the in-paper hierarchy structure to classify full-text scientific papers under weak supervision. A network-aware contrastive fine-tuning module and a hierarchyaware aggregation module are designed to leverage the two types of structural signals, respectively. Experiments on two benchmark datasets demonstrate that FuTex significantly outperforms competitive baselines and is on par with fully supervised classifiers that use 1,000 to 60,000 ground-truth training samples. 

The widespread availability of geotagged data combined with modern map services allows for the accurate attachment of data to spatial networks. Applying statistical analysis, such as hotspot detection, over spatial networks is very important for precise quantification and patterns analysis, which empowers effective decision-making in various important applications. Existing hotspot detection algorithms on spatial networks either lack sufficient statistical evidence on detected hotspots, such as clustering, or they provide statistical evidence at a prohibitive computational overhead. In this paper, we propose efficient algorithms for detecting hotspots based on the network local K-function for predefined and unknown hotspot radii. The K-function is a widely adopted statistical approach for network pattern analysis that enables the understanding of the density and distribution of activities and events happening within the spatial network. However, its practical application has been limited due to the inefficiency of state-of-the-art algorithms, particularly for large-sized networks. Extensive experimental evaluation using real and synthetic datasets shows that our algorithms are up to 28 times faster than the state-of-the-art algorithms in computing hotspots with a predefined radius and up to more than four orders of magnitude faster in identifying hotspots without a predefined radius. Additionally, to address dynamic changes in the spatial network, we propose an incremental hotspot detection approach that efficiently updates hotspot computations by leveraging prior results as new events are added.