Comparing expert elicitation and model-based probabilistic technology cost forecasts for the energy transition.

We conduct a systematic comparison of technology cost forecasts produced by expert elicitation methods and model-based methods. Our focus is on energy technologies due to their importance for energy and climate policy. We assess the performance of several forecasting methods by generating probabilistic technology cost forecasts rooted at various years in the past and then comparing these with observed costs in 2019. We do this for six technologies for which both observed and elicited data are available. The model-based methods use either deployment (Wright's law) or time (Moore's law) to forecast costs. We show that, overall, model-based forecasting methods outperformed elicitation methods. Their 2019 cost forecast ranges contained the observed values much more often than elicitations, and their forecast medians were closer to observed costs. However, all methods underestimated technological progress in almost all technologies, likely as a result of structural change across the energy sector due to widespread policies and social and market forces. We also produce forecasts of 2030 costs using the two types of methods for 10 energy technologies. We find that elicitations generally yield narrower uncertainty ranges than model-based methods. Model-based 2030 forecasts are lower for more modular technologies and higher for less modular ones. Future research should focus on further method development and validation to better reflect structural changes in the market and correlations across technologies.

Regional water implications of reducing oil imports with liquid transportation fuel alternatives in the United States.

The Renewable Fuel Standard (RFS) is among the cornerstone policies created to increase U.S. energy independence by using biofuels. Although greenhouse gas emissions have played a role in shaping the RFS, water implications are less understood. We demonstrate a spatial, life cycle approach to estimate water consumption of transportation fuel scenarios, including a comparison to current water withdrawals and drought incidence by state. The water consumption and land footprint of six scenarios are compared to the RFS, including shale oil, coal-to-liquids, shale gas-to-liquids, corn ethanol, and cellulosic ethanol from switchgrass. The corn scenario is the most water and land intense option and is weighted toward drought-prone states. Fossil options and cellulosic ethanol require significantly less water and are weighted toward less drought-prone states. Coal-to-liquids is an exception, where water consumption is partially weighted toward drought-prone states. Results suggest that there may be considerable water and land impacts associated with meeting energy security goals through using only biofuels. Ultimately, water and land requirements may constrain energy security goals without careful planning, indicating that there is a need to better balance trade-offs. Our approach provides policymakers with a method to integrate federal policies with regional planning over various temporal and spatial scales.