Uncertainty assessment of the IMAGE/TIMER B1 CO2 emissions scenario, using the NUSAP method

Abstract

This project implemented a novel approach to uncertainty assessment, known as the NUSAP method (Numeral Unit Spread Assessment Pedigree) to assess qualitative and quantitative uncertainties in the TIMER energy model, part of RIVMs IMAGE Model. We used the IMAGE B1 scenario as case study. We used 5 complementary tools to assess uncertainty: (1) A comprehensive checklist for model quality assurance providing a quick scan to flag major areas of concern and associated pitfalls in the complex mass uncertainties; (2) A meta-level analysis of the results of the six SRES energy models, which gave us some insight in the potential roles of model structure uncertainties. (3) The Morris algorithm for global sensitivity analysis which identified as most sensitive components:. Population levels and economic activity; Intra-sectoral structural change; Progress ratios for technological improvements; Resources of fossil fuels (size and cost supply curves); Autonomous and price-induced energy efficiency improvement; Initial costs and depletion of renewables; Supplemented with expert elicitation Morris served as an efficient selection mechanism to focus the analysis; (4) A NUSAP expert elicitation workshop, which yielded a differentiated insight into parameter strength of sensitive parameters; (5) A diagnostic diagram putting spread and strength together to provide guidance in prioritisation of key uncertainties. Overall, the project demonstrated that the NUSAP method can be applied to complex models in a meaningful way. The method provides a useful means to focus research efforts on the potentially most problematic parameters while it at the same time pinpoints specific weaknesses in these parameters.