How Structural Differences Influence Cross-Model Consistency: An Electric Sector Case Study

Multiple models are often employed to examine a range of possible outcomes for one or more scenarios, yielding insights into causal relationships and their uncertainties. Electric sector capacity expansion scenarios are commonly explored in such efforts due to the electric sector's overall economic importance, but multi-model results typically span a broad solution space despite efforts to harmonize input assumptions, making decision implications difficult to discern. This study investigates the relationship between efforts to harmonize input assumptions across multiple models and the resulting consistency of model outputs under different electric sector scenarios. We compare cross-model consistency between two electric sector capacity expansion models (GCAM-USA and ReEDS) for six electric sector scenarios with alternate assumptions about fossil fuel prices, technology innovation, and economy-wide transitions under four harmonization configurations that vary model representations of electricity demand, fuel prices, renewable resources, and capacity retirements. These comparisons reveal that cross-model consistency can vary across scenarios under a given harmonization configuration, suggesting that harmonization efforts must often be scenario-specific to achieve comparable cross-model consistency. Harmonization does not always improve consistency due to complex interactions between input assumptions, harmonized features, and unharmonized features. However, thorough harmonization can reduce uncertainty bounds and reveal insights into what drives cross-model consistency between two or more models. This work provides researchers and decision makers with a framework to carefully consider tradeoffs between effort and impact of harmonization efforts within multi-model analysis activities.