Discovering the Multisectoral Impacts of the Global Energy Sector
Outcomes through Diverse Cross-ensemble Aggregation Measures
- Gi Joo Kim,
- Jacob Wessel,
- Abigail Nora Birnbaum,
- George Moraites,
- Abigail C Snyder,
- Jennifer Morris,
- Thomas B Wild,
- Jonathan R. Lamontagne
Thomas B Wild
Joint Global Change Research Institute, Pacific Northwest National Laboratory
Author ProfileAbstract
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.