Discovering the Multisectoral Impacts of Global Energy Sector Outcomes through Multiple Ensemble Aggregation Measures

Key Points:
• Choice of ensemble aggregation measure has important consequences in analyzing global change scenario ensembles
• Benchmarking from a perceived “neutral scenario” can distort the interpretation
of the outcomes
• Energy system uncertainty influences global and regional water, energy, and food sector outcomes, each with different characteristics
 

Abstract
Understanding complex human-Earth system interactions often involves analyzing large scenario ensembles that encompass a wide range of plausible futures. These ensembles often require aggregation to summarize information based on specific criteria or conditions. However, previous research using global change scenario ensembles has largely overlooked how the choice of aggregation method influences the interpretation of results. 

To address this gap, we leverage a large ensemble dataset designed to capture broad energy system dynamics generated using the Global Change Analysis Model. We first explore how energy-related uncertainties are propagated to both global and regional water-energy food sectors. We then conduct a rank correlation analysis across seven ensemble aggregation measures and demonstrate the need to consider multiple measures in global change scenarios. 

Our results suggest that global water and food sector outcomes in the 21st century vary widely depending on different scenario assumptions. The global energy productivity is projected to improve by the end of the century across all scenarios. Moreover, regions facing water scarcity challenges in 2100 do not always overlap with those facing extreme energy and food sector outcomes. Although rank correlations across seven aggregation measures are relatively stable across sectors, we identify cases where relying on a single measure leads to losing critical information in the full ensemble. Reliance on a single aggregation measure can distort the interpretation of global change scenario outcomes. Instead, adopting multiple ensemble aggregation measures provides a more holistic understanding of global change scenario ensembles.
 

Plain Language Summary
Studying how humans and the environment interact requires looking at many future scenarios. Because these scenarios create a lot of data, researchers use different methods to combine and summarize the results. However, past studies have not fully considered how different ways of summarizing the scenarios can affect conclusions. In this study, we use a large dataset of possible future scenarios and analyze global and regional water, energy, and food system. We compare seven different ways to summarize the data. Our findings show that while the future of global water and food systems depend on different possible scenarios, the energy productivity is expected to improve by 2100. We also find that areas with water scarcity in the future are not always the same as those
with energy and food problems. Though alternative methods for summarizing scenarios often result in similar conclusions, there are also critical differences in some cases which can lead to misinterpreting the scenarios. Using only one combining method can miss important details in the data or result in wrong conclusions. We suggest using multiple summary measures when evaluating future scenarios to gain more reliable insights from the scenarios.