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Abstract: This Perspective evaluates recent progress in modeling nature–society systems to inform sustainable development. We argue that recent work has begun to address longstanding and often-cited challenges in bringing modeling to bear on problems of sustainable development. For each of four stages of modeling practice—defining purpose, selecting components, analyzing interactions, and assessing interventions—we highlight examples of dynamical modeling methods and advances in their application that have improved understanding and begun to inform action. Because many of these methods and associated advances have focused on particular sectors and places, their potential to inform key open questions in the field of sustainability science is often underappreciated. We discuss how application of such methods helps researchers interested in harnessing insights into specific sectors and locations to address human well-being, focus on sustainability-relevant timescales, and attend to power differentials among actors. In parallel, application of these modeling methods is helping to advance theory of nature–society systems by enhancing the uptake and utility of frameworks, clarifying key concepts through more rigorous definitions, and informing development of archetypes that can assist hypothesis development and testing. We conclude by suggesting ways to further leverage emerging modeling methods in the context of sustainability science.

Authors' Impact/Purpose: This report documents methods used to analyze the economic and environmental impacts of the Inflation Reduction Act (IRA), enacted in August 2022 by the United States Congress. The analysis relies on the U.S. Regional Energy Policy (USREP) economy-wide model developed by researchers at the Massachusetts Institute of Technology (MIT), linked with the Regional Energy Deployment System (ReEDS) electricity sector model developed by researchers at the National Renewable Energy Laboratory (NREL).

Authors' Summary: Solar geoengineering is a proposed set of technologies to help lessen the impacts of climate change by reducing the amount of sunlight received by the Earth. Stratospheric aerosol injection is a method of solar geoengineering that reduces sunlight by increasing the amount of aerosol particles in the stratosphere, a process which can also cause stratospheric ozone depletion. Nearly all studies of stratospheric aerosol injection have focused exclusively on the direct impacts of increased stratospheric aerosol on climate. However, changes in sunlight also alter the rates of chemical reactions throughout the atmosphere, changing the concentrations of greenhouse gases that affect climate like methane and tropospheric ozone.

Our results show that these changes in greenhouse gases due to geoengineering chemical feedbacks can substantially alter the climate effect of geoengineering, especially on regional and seasonal scales. Our results also show that geoengineering-induced stratospheric ozone depletion can lead to net global health benefits, as the impacts on mortality from overall improvements in surface air quality due to chemical feedbacks outweigh those from increases in UV exposure. These same chemical feedbacks can also improve crop yields and overall plant growth.

Our results underscore the risk of surprises that could arise from solar geoengineering.

Authors' Summary: There are many systems involved in energy transitions, which makes it difficult to anticipate which factors are most likely to result in higher renewable energy adoption in the future, and the currently available projections of future renewable shares are highly uncertain.

We focus here on wind and solar energy in particular, and use a model that represents a variety of the different systems involved (including energy, agriculture, land use, and water) to create a set of nearly 4,000 scenarios that span a wide range of possible futures. Each scenario is driven by a combination of different parameter inputs chosen based on factors that we expect to impact wind and solar energy shares.

By analyzing this set of scenarios, we can find the most important drivers, and combinations of drivers, globally and in each of the 32 different regions represented in our model. Additionally, we look at the scenarios that produced the highest fractions of wind and solar energy and identify four different combinations of parameters that can lead to these high renewable fractions. For each of the four paths, we explore the implications in terms of outcomes like water consumption, air pollution, and food prices, and discuss the resulting tradeoffs.

Authors' Summary: The differences in phytoplankton variability through time observed at fixed locations (Eulerian perspective) or following water parcels (Lagrangian perspective) are poorly understood. We created a large set of satellite chlorophyll matched time series pairs in the Eulerian and Lagrangian perspective, using global drifter trajectories as an approximation of how surface ocean currents move.

We found that for most ocean locations, chlorophyll variability measured in Eulerian and Lagrangian perspectives is not different. In high latitude zones, chlorophyll appears to vary similarly over large areas. However, in localized regions of the ocean, such as western boundary currents and upwelling regions, chlorophyll variability in these two perspectives may significantly differ. The causes are linked to the specific ocean dynamics of each area.

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