Delving into the Black Box: Stochastic Ex-Post Multi-Criteria Evaluation of Net-Zero Scenarios with an Application to Switzerland

Many modeling teams globally have published energy scenarios detailing pathways toward net-zero greenhouse gas emissions. These diverse scenarios are informative but entail a challenge for political decision-makers, utility executives, and citizens. There is limited consensus on how to identify the most important elements in them, interpret, and navigate them. To address this problem, this paper develops a new methodology for comparing energy scenarios based on user-defined decision criteria. Criteria values are first estimated expost using stochastic models, which account for uncertainty regarding future technology costs and impacts. Then, a statistical analysis is applied to understand the criteria values' trends, tradeoffs, and uncertainties. We demonstrate this methodology on Swiss energy scenarios using five sample criteria: investment, domestic land use, material consumption, domestic biodiversity impacts, and social acceptance. The analysis reveals that two classes of technologies drive criteria outcomes: (i) must-have technologies, deployed at large scales in all scenarios, and (ii) emerging technologies, such as hydrogen or biomass, whose role in the energy transition are less well understood. Policies on must-have technologies should enable rapid deployment and reduce uncertainty about costs and impacts. Conversely, policies for emerging technologies should foster further understanding of their impacts and promote public discourse around which impacts are socially desirable. Whilst the method is applied to Switzerland in a case study, it is applicable to other contexts provided sufficient and accurate scenario data. Future work should focus on improving the method's robustness, including the selection and operationalization of criteria.