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.
Achieving sustainable development requires understanding how human behavior and the environment interact across spatial scales. In particular, knowing how to manage tradeoffs between the environment and the economy, or between one spatial scale and another, necessitates a modeling approach that allows these different components to interact. Existing integrated local and global analyses provide key insights, but often fail to capture ‘meso-scale’ phenomena that operate at scales between the local and the global, leading to erroneous predictions and a constrained scope of analysis. Meso-scale phenomena are difficult to model because of their complexity and computational challenges, where adding additional scales can increase model run-time exponentially. These additions, however, are necessary to make models that include sufficient detail for policy-makers to assess tradeoffs. Here, we synthesize research that explicitly includes meso-scale phenomena and assess where further efforts might be fruitful in improving our predictions and expanding the scope of questions that sustainability science can answer. We emphasize five categories of models relevant to sustainability science, including biophysical models, integrated assessment models, land-use change models, earth-economy models and spatial downscaling models. We outline the technical and methodological challenges present in these areas of research and discuss seven directions for future research that will improve coverage of meso-scale effects. Additionally, we provide a specific worked example that shows the challenges present, and possible solutions, for modeling meso-scale phenomena in integrated earth-economy models.
more » « less- Award ID(s):
- 2020635
- NSF-PAR ID:
- 10483110
- Publisher / Repository:
- IOP Publishing
- Date Published:
- Journal Name:
- Environmental Research Letters
- Volume:
- 18
- Issue:
- 2
- ISSN:
- 1748-9326
- Page Range / eLocation ID:
- 025007
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract 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 2020
Annu. Rev. Resour. Econ. 12 471–87). 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. -
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Sustainable development requires jointly achieving economic development to raise standards of living and environmental sustainability to secure these gains for the long run. Here, we develop a local-to-global, and global-to-local, earth-economy model that integrates the Global Trade Analysis Project (GTAP)-computable general equilibrium model of the economy with the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model of fine-scale, spatially explicit ecosystem services. The integrated model, GTAP–InVEST, jointly determines land use, environmental conditions, ecosystem services, market prices, supply and demand across economic sectors, trade across regions, and aggregate performance metrics like GDP. We use the integrated model to analyze the contribution of investing in nature for economic prosperity, accounting for the impact of four important ecosystem services (pollination, timber provision, marine fisheries, and carbon sequestration). We show that investments in nature result in large improvements relative to a business-as-usual path, accruing annual gains of $100 to $350 billion (2014 USD) with the largest percentage gains in the lowest-income countries. Our estimates include only a small subset of ecosystem services and could be far higher with inclusion of more ecosystem services, incorporation of ecological tipping points, and reduction in substitutability that limits economic adjustments to declines in natural capital. Our analysis highlights the need for improved environmental–economic modeling and the vital importance of integrating environmental information firmly into economic analysis and policy. The benefits of doing so are potentially very large, with the greatest percentage benefits accruing to inhabitants of the poorest countries.
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ABSTRACT 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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Abstract Modeling and simulation is transforming modern materials science, becoming an important tool for the discovery of new materials and material phenomena, for gaining insight into the processes that govern materials behavior, and, increasingly, for quantitative predictions that can be used as part of a design tool in full partnership with experimental synthesis and characterization. Modeling and simulation is the essential bridge from good science to good engineering, spanning from fundamental understanding of materials behavior to deliberate design of new materials technologies leveraging new properties and processes. This Roadmap presents a broad overview of the extensive impact computational modeling has had in materials science in the past few decades, and offers focused perspectives on where the path forward lies as this rapidly expanding field evolves to meet the challenges of the next few decades. The Roadmap offers perspectives on advances within disciplines as diverse as phase field methods to model mesoscale behavior and molecular dynamics methods to deduce the fundamental atomic-scale dynamical processes governing materials response, to the challenges involved in the interdisciplinary research that tackles complex materials problems where the governing phenomena span different scales of materials behavior requiring multiscale approaches. The shift from understanding fundamental materials behavior to development of quantitative approaches to explain and predict experimental observations requires advances in the methods and practice in simulations for reproducibility and reliability, and interacting with a computational ecosystem that integrates new theory development, innovative applications, and an increasingly integrated software and computational infrastructure that takes advantage of the increasingly powerful computational methods and computing hardware.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1088/1748-9326/acb503
