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.
While the impacts of global drivers such as international trade, population growth, technological development or climate change on local-level pricing, decision making, biodiversity and ecosystem services (BES) have received strong and increasing attention over the recent decades, relatively few studies have examined how impacts on local BES due to human activities or how local responses targeted to improve BES outcomes, can propagate to regional, national and global scale. We discuss the challenges that frequently arise in global-to-local-to-global frameworks when modelling policies aimed at improving land-use change while also maximising the associated benefits from the state of biodiversity and the provision of ecosystem services. We present four complexities associated with case studies that describe approaches to protecting BES in diverse landscapes and contexts within the proposed framework: heterogeneity in local markets; additionality; spillover and leakage effects; and unintended consequences. Our study calls for filling these gaps in our understanding through interdisciplinary, open-source research characterizing the local-to-global biodiversity and ecosystem services linkages in future.
The purchase and sale of assets such as housing will increasingly be affected by forces related to a changing climate. This article considers decisions over assets as a neurobiological process in which an associative memory with pattern completion informs choices. We develop these neuroeconomic explanations and analyze their implications for climate change-related shocks in asset markets, and discuss these effects in the context of both individual experiences as well as community-driven remembering. These neuroeconomic models provide mechanistic explanations for behavioral responses to more easily accessed information (the “representativeness” and “availability” heuristics, “framing” and “priming”). Understanding the links from neuroscience to economics is critical to building policies and institutions capable of coping with and adjusting to disasters affecting real assets that are increasing in frequency and scope due to climate change.
Reforestation is an important strategy for nature-based climate solutions and identifying carbon storage potential of different locations is critical to its success. Applying average carbon values from forest inventories ignores the spatial heterogeneity in forest carbon and the effects of forest edges on carbon storage degradation. Here we show how spatially-explicit, predictive carbon modeling, that leverages satellite, social and biogeophysical datasets, can be used to identify more efficient restoration opportunities for climate mitigation than area-based carbon stock averages. Accounting for regeneration of forest edges, in addition to reforestation, boosts estimates of potential carbon gains by more than 20%. The total potential carbon gain that could be achieved through reforestation at the level indicated by the Bonn Challenge (350Mha) is 51 Gt CO2-eq, but the "missing carbon" in our current forests accounts for 64.6 Gt CO2-eq globally; the greatest potential carbon gains are found in areas of high fragmentation.
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