Uncertainties arising from future decisions driving the makeup of the energy system globally affect multiple sectors in the human-Earth system on diverse spatiotemporal scales. The complex interplay between sectors requires a thorough examination of these uncertainties, usually conducted through large scenario ensembles encompassing a wide range of potential futures. However, previous efforts have overlooked the methodological choice of aggregation measures across the ensemble, despite potential consequences. In this study, we leverage a large ensemble dataset that captures the uncertainties associated with the energy system generated using the Global Change Analysis Model. Using the ensemble, we first explore how energy-related uncertainties are propagated to both the global and regional water-energy-food sectors. We then conduct a rank correlation analysis across diverse cross-ensemble aggregation measures that are used to aggregate ensemble members for further analysis and highlight the potential downsides arising from relying on a single measure. Our results highlight that the influences that arise from low-carbon transitions can increase the uncertainties of all sectors at the end of the century, each with its unique dynamic. Moreover, the most severe outcomes in the majority of regions take place under scenarios with extreme socioeconomic assumptions in combination with low-carbon transitions. Our findings emphasize that threshold-based classification measures that have been frequently adopted to identify critical outcomes in multisectoral systems may overlook the dynamics embedded in the scenario ensemble. As an alternative, using appropriate cross-ensemble aggregation measures in order to derive robust insights from the outcomes holds promise.

Land scarcity is increasing over time, driven by complex multi-sector dynamics. The impacts of land scarcity on the economy and environment are multi-faceted and regional, so any action to convert land will contain inherent tradeoffs. These impacts are complicated by the deeply uncertain evolution of the various sectors influencing land scarcity. A need therefore exists to provide multi-metric and multi-sector assessments that are robust to myriad uncertainties. Land conservation effectively limits the supply of productive land, while biofuel consumption increases the demand and competition for that land, and how these dynamics individually and jointly propagate to economic and environmental impacts is an important open question. To address this, we adopt the Global Change Analysis Model (GCAM) that has representations of various important sectors including the climate, land-use economy, energy systems, agriculture, and water resources. Scenarios of increased land demand (from biofuels) and decreased land supply (from conservation) under various socioeconomic pathways drawn from the SSPs were simulated using GCAM. We find that while biofuel consumption and land conservation reduce carbon emissions, this comes at the cost of higher food prices, reduced crop production, and increased water withdrawals. Additionally, some regions experience these tradeoffs more severely than others and are more heavily impacted from the same biofuel mandate or by an additional percent of protected land. These and other findings highlight the importance of multi-sector modeling frameworks that capture many cross-sector linkages, and acknowledge the important uncertainties confronting the human-Earth system when making any analysis of the land scarcity impacts.