Scenario Discovery Analysis of Drivers of Solar and Wind Energy Transitions Through 2050

Abstract

Deep human‐Earth system uncertainties and strong multi‐sector dynamics make it difficult to anticipate which conditions are most likely to lead to higher or lower adoption of renewable energy, and models project a broad range of future solar and wind energy shares across future scenarios. To elucidate these dynamics, we explore a large data set of scenarios simulated from the Global Change Analysis Model (GCAM), and use scenario discovery to identify the most significant factors affecting solar and wind adoption by mid‐century. We generated a data set of over 4,000 scenarios from GCAM by varying 12 different socioeconomic factors at high and low levels, including assumptions about future energy demand, resource costs, and fossil fuel emissions paths, as well as specific technology assumptions including wind and solar backup requirements and storage costs. Using scenario discovery, we assess the most important factors globally and regionally in creating high fractions of solar and wind energy and explore interconnected effects on other systems including water and non‐CO2 emissions. Globally and regionally, we found that solar and wind‐related technology costs were the primary drivers of high wind and solar energy adoption, though a few regions depend heavily on other parameters like carbon capture and storage costs, population and gross domestic product trajectories, and fossil fuel costs. We also identify four key paths to high solar and wind energy by mid‐century and discuss their tradeoffs in terms of other outcomes.

Large ensemble exploration of global energy transitions under national emissions pledges

“…Scenario discovery can refer to any methodology aimed at identifying areas of interest within the outcome space of a model via a systematic exploration of deep uncertainties, with the ultimate goal of connecting critical drivers (model parameters and structural forms, exogenous uncertainties, policy levers) to outcome metrics and narrative storylines to inform decision-making (Lempert et al, 2008;Bryant and Lempert, 2010;Lempert et al, 2003). This approach is used widely in human-earth systems modeling (McJeon et al, 2011;Shortridge and Guikema, 2016;Lamontagne et al, 2018;Moksnes et al, 2019;Dolan et al, 2022;Birnbaum et al, 2022;Morris et al, 2022;Guivarch et al, 2022;Woodard et al, 2023) using a variety of statistical, machine learning, and data mining techniques (Lempert et al, 2008;Kwakkel and Jaxa-Rozen, 2016;Kwakkel and Cunningham, 2016;Jafino and Kwakkel, 2021;Steinmann et al, 2020). In this study, we apply scenario discovery to GCAM, an actively developed and widely used multisector model for large ensemble analyses; refer to Section 3.1 for more details.…”