Simulation models of multi-sector systems are increasingly used to understand societal resilience to climate and economic shocks and change. However, multi-sector systems are also subject to numerous uncertainties that prevent the direct application of simulation models for prediction and planning, particularly when extrapolating past behavior to a nonstationary future. Recent studies have developed a combination of methods to characterize, attribute, and quantify these uncertainties for both single- and multi-sector systems. Here we review challenges and complications to the idealized goal of fully quantifying all uncertainties in a multi-sector model and their interactions with policy design as they emerge at different stages of analysis: (1) inference and model calibration; (2) projecting future outcomes; and (3) scenario discovery and identification of risk regimes. We also identify potential methods and research opportunities to help navigate the tradeoffs inherent in uncertainty analyses for complex systems. During this discussion, we provide a classification of uncertainty types and discuss model coupling frameworks to support interdisciplinary collaboration on multi-sector dynamics (MSD) research. Finally, we conclude with recommendations for best practices to ensure that MSD research can be properly contextualized with respect to the underlying uncertainties.

Climate mitigation can bring health co-benefits by improving air quality. Yet, whether mitigation will widen or narrow current health disparities remains unclear. Here we use a coupled climate-energy-health model to assess the effects of a global carbon price on the distribution of ambient fine particulate matter (PM2.5) exposure and associated health risks across an ensemble of nearly 30,000 future scenarios. We find that pricing carbon consistently lowers the PM2.5-attributable death rates in lower-income countries by reducing fossil fuel burning (e.g., China and India). Since these countries are projected to have large ageing populations, the greatest reduction in global average PM2.5-attributable death rate is found in elderly populations, which are more vulnerable to air pollution than the other age groups. In contrast, the health effects in higher-income countries are more complex, because pricing carbon can increase the emissions from bioenergy use and land-use changes, counteracting the mortality decrease from reduced fossil fuel burning. Mitigation technology choices and complex interactions between age structures, energy use, and land use all influence the distribution of health effects. Our results highlight the importance of an improved understanding of regional characteristics and cross-sector dynamics for addressing the interconnected challenges of climate, health, and social inequalities.