PNNL

Abstract

Global climate goals require a transition to a deeply decarbonized energy system. Meeting the objectives of the Paris Agreement through countries’ Nationally Determined Contributions and Long-Term Strategies represents a complex problem with consequences across multiple systems shrouded by deep uncertainty. Robust, large-ensemble methods and analyses that can map the wide range of possible future states of the world are needed to help policymakers design effective strategies to meet emissions reduction goals. This study contributes a scenario discovery analysis applied to a large ensemble of 5,760 model realizations generated using a global-scale detailed process integrated assessment model. Eleven categories of energy-related uncertainties sensitivities are systematically varied representing national mitigation pledges, institutional factors, and techno-economic parameters, among others. The resulting ensemble characterizes the impacts of a combinatorial time-evolving uncertainty space using standard energy system metrics to determine how future uncertainties drive national and global pathways toward deep decarbonization. Results show globally consistent but regionally variable outcomes as measured by multiple metrics including electricity costs and stranded assets. Larger economies and developing regions experience more severe economic outcomes across a broad sampling of uncertainty. The scale of CO2 removal globally determines how much the energy system can continue to emit, but the relative role of different CO2 removal options in meeting decarbonization goals varies across regions. Previous studies characterizing parametric uncertainty have typically varied only a few sensitivities, and other large-ensemble work has not (to our knowledge) combined such a framework with national emissions pledges or institutional factors. Our results underscore the value of large-ensemble scenario discovery frameworks for decision support as countries begin to design strategies to meet their goals.