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  1. Abstract

    Meeting the United Nation’ Sustainable Development Goals (SDGs) calls for an integrative scientific approach, combining expertise, data, models and tools across many disciplines towards addressing sustainability challenges at various spatial and temporal scales. This holistic approach, while necessary, exacerbates the big data and computational challenges already faced by researchers. Many challenges in sustainability research can be tackled by harnessing the power of advanced cyberinfrastructure (CI). The objective of this paper is to highlight the key components and technologies of CI necessary for meeting the data and computational needs of the SDG research community. An overview of the CI ecosystem in the United States is provided with a specific focus on the investments made by academic institutions, government agencies and industry at national, regional, and local levels. Despite these investments, this paper identifies barriers to the adoption of CI in sustainability research that include, but are not limited to access to support structures; recruitment, retention and nurturing of an agile workforce; and lack of local infrastructure. Relevant CI components such as data, software, computational resources, and human-centered advances are discussed to explore how to resolve the barriers. The paper highlights multiple challenges in pursuing SDGs based on the outcomes of several expert meetings. These include multi-scale integration of data and domain-specific models, availability and usability of data, uncertainty quantification, mismatch between spatiotemporal scales at which decisions are made and the information generated from scientific analysis, and scientific reproducibility. We discuss ongoing and future research for bridging CI and SDGs to address these challenges.

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  2. Abstract

    The rapid depletion of US groundwater resources and rising number of dying wells in the Western US brings attention to the significance of groundwater governance and sustainability restrictions. However, such restrictions on groundwater withdrawals are likely to generate spillover effects causing further environmental stresses in other locations and adding to the complexity of sustainability challenges. The goal of this paper is to improve our understanding of the implications of growing global food demand for local sustainability stresses and the implications of local sustainability policies for local, regional, and global food production, land use, and prices. We employ SIMPLE-G-US (Simplified International Model of agricultural Prices, Land use, and the Environment—Gridded version for the United States) to distangle the significance or remote changes in population and income for irrigation and water resources in the US. Then we examine the local-to-global impacts of potential US groundwater sustainability policies. We find that developments in international markets are significant, as more than half of US sustainability stresses by 2050 are caused by increased commodity demand from abroad. Furthermore, a US sustainable groundwater policy can cause overseas spillovers of US production, thereby potentially contributing to environmental stresses elsewhere, even as groundwater stress in the US is alleviated. These unintended consequences could include deforestation due to cropland expansion, as well as degradation in water quality due to intensification of production in non-targeted areas.

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    Sustainability science seeks to understand human–nature interactions behind sustainability challenges, but has largely been place-based. Traditional sustainability efforts often solved problems in one place at the cost of other places, compromising global sustainability. The metacoupling framework offers a conceptual foundation and a holistic approach to integrating human–nature interactions within a place, as well as between adjacent places and between distant places worldwide. Its applications show broad utilities for advancing sustainability science with profound implications for global sustainable development. They have revealed effects of metacoupling on the performance, synergies, and trade-offs of United Nations Sustainable Development Goals (SDGs) across borders and across local to global scales; untangled complex interactions; identified new network attributes; unveiled spatio-temporal dynamics and effects of metacoupling; uncovered invisible feedbacks across metacoupled systems; expanded the nexus approach; detected and integrated hidden phenomena and overlooked issues; re-examined theories such as Tobler's First Law of Geography; and unfolded transformations among noncoupling, coupling, decoupling, and recoupling. Results from the applications are also helpful to achieve SDGs across space, amplify benefits of ecosystem restoration across boundaries and across scales, augment transboundary management, broaden spatial planning, boost supply chains, empower small agents in the large world, and shift from place-based to flow-based governance. Key topics for future research include cascading effects of an event in one place on other places both nearby and far away. Operationalizing the framework can benefit from further tracing flows across scales and space, uplifting the rigor of causal attribution, enlarging toolboxes, and elevating financial and human resources. Unleashing the full potential of the framework will generate more important scientific discoveries and more effective solutions for global justice and sustainable development.

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  4. Free, publicly-accessible full text available September 1, 2024
  5. Free, publicly-accessible full text available August 1, 2024
  6. Due to the exponential growth of scientific publications on the Web, there is a pressing need to tag each paper with fine-grained topics so that researchers can track their interested fields of study rather than drowning in the whole literature. Scientific literature tagging is beyond a pure multi-label text classification task because papers on the Web are prevalently accompanied by metadata information such as venues, authors, and references, which may serve as additional signals to infer relevant tags. Although there have been studies making use of metadata in academic paper classification, their focus is often restricted to one or two scientific fields (e.g., computer science and biomedicine) and to one specific model. In this work, we systematically study the effect of metadata on scientific literature tagging across 19 fields. We select three representative multi-label classifiers (i.e., a bag-of-words model, a sequence-based model, and a pre-trained language model) and explore their performance change in scientific literature tagging when metadata are fed to the classifiers as additional features. We observe some ubiquitous patterns of metadata’s effects across all fields (e.g., venues are consistently beneficial to paper tagging in almost all cases), as well as some unique patterns in fields other than computer science and biomedicine, which are not explored in previous studies. 
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  7. This dataset includes empirically estimated cropland supply elasticities for more than 75,000 grid cells over the continental United States calculated for years around 2010. The data is provided in NetCDF, GeoTIFF, CSV, and HAR file formats. 
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  8. New estimates of downscaled gridded water supply elasticity are provided for 75,651 grid cells (at 5 arc-min resolution) for the United States agriculture given the groundwater irrigation and recharge rates around year 2010. 
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