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This special issue is the outcome of a workshop held at Purdue University in April 2022. It comprises thematic syntheses of five overarching dimensions of the Global-to-Local-to-Global (GLG) challenge to ensuring the long-term sustainability of land and water resources. These thematic dimensions include: climate change, ecosystems and biodiversity, governance, water resources and cyberinfrastructure. In addition, there are eight applications of GLG analysis to specific land and water sustainability challenges, ranging from environmental stress in the Amazon River Basin to groundwater depletion in the United States. Based on these papers, we conclude that, without fine-scale, local analysis, interventions focusing on land and water sustainability will likely be misguided. But formulating such policies without the broader, national/global context is also problematic – both from the point of view of the global drivers of local sustainability stresses, as well as to capture unanticipated spillovers. In addition, because local and global systems are connected to – and mediated by – meso-scale processes, accounting for key meso-scale phenomena, such as labor market functioning, is critical for characterizing GLG interactions. We also conclude that there is great scope for increasing the complexity of GLG analysis in future work. However, this carries significant risks. Increased complexity can outstrip data and modeling capabilities, slow down research, make results more difficult to understand and interpret, and complicate effective communication with decision-makers and other users of the analyses. We believe that research guidance regarding appropriate complexity is a high priority in the emerging field of Global-Local-Global analysis of sustainability.

 

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.

 

The scientific and policy needs to assess and manage climate change impacts have spawned new coupled, multi-scale integrated assessment model (IAM) frameworks that link global climate and economic processes with high-resolution data and models of human-environmental systems at local and meso scales (Fisher-Vanden and Weyant 2020Annu. Rev. Resour. Econ.12471–87). A central challenge is in accounting for the fundamental interdependence of people, firms, and economic activities across space at multiple scales. This requires modeling approaches that can incorporate the relevant spatial details at each scale while also ensure consistency with spatially varying feedbacks and interactions across scales—a condition economists refer to as spatial equilibrium. In this paper, we provide an overview of how economists think about and model spatial interactions, particularly those at the local level. We describe challenges and recent progress in accounting for greater spatial heterogeneity at individual (field, agent) scales and incorporating heterogeneous spatial interactions and dynamics into consistent IAM frameworks. We conclude that the most notable progress is in advancing global IAMs with spatial heterogeneity and dynamics embedded in spatial equilibrium frameworks and that less progress has been made in incorporating features of spatial equilibrium into highly detailed multi-scale IAMs.

 

We develop the first spatially integrated economic-hydrological model of the western Lake Erie basin explicitly linking economic models of farmers' field-level Best Management Practice (BMP) adoption choices with the Soil and Water Assessment Tool (SWAT) model to evaluate nutrient management policy cost-effectiveness. We quantify tradeoffs among phosphorus reduction policies and find that a hybrid policy coupling a fertilizer tax with cost-share payments for subsurface placement is the most cost-effective, and when implemented with a 200% tax can achieve the stated policy goal of 40% reduction in nutrient loadings. We also find economic adoption models alone can overstate the potential for BMPs to reduce nutrient loadings by ignoring biophysical complexities. Key Words: Integrated assessment model; agricultural land watershed model; water quality; cost-share; conservation practice; nutrient management JEL Codes: H23, Q51, Q52, Q53 

Abstract The Earth's population will become more than 80% urban during this century. This threshold is often regarded as sufficient justification for pursuing urban ecology. However, pursuit has primarily focused on building empirical richness, and urban ecology theory is rarely discussed. The Baltimore Ecosystem Study (BES) has been grounded in theory since its inception and its two decades of data collection have stimulated progress toward comprehensive urban theory. Emerging urban ecology theory integrates biology, physical sciences, social sciences, and urban design, probes interdisciplinary frontiers while being founded on textbook disciplinary theories, and accommodates surprising empirical results. Theoretical growth in urban ecology has relied on refined frameworks, increased disciplinary scope, and longevity of interdisciplinary interactions. We describe the theories used by BES initially, and trace ongoing theoretical development that increasingly reflects the hybrid biological–physical–social nature of the Baltimore ecosystem. The specific mix of theories used in Baltimore likely will require modification when applied to other urban areas, but the developmental process, and the key results, will continue to benefit other urban social–ecological research projects.