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Background Scenario design is currently not a standardised process. The formulation of storylines representing different dimensions (for example economic or societal developments) demands an investigation of assumption compatibility, coherence, and consistency. Scenario techniques that use expert opinion as the sole information source are particularly appropriate for personal decisions. Contexts where scenarios serve as decision support on a societal level—for example in political decision-making—benefit from unbiased, fact-depicting, multi-dimensional information that is available in statistical data. Methods The presented approach uses the well-established method of Bayesian model averaging for the formulation of consistent, transparent, and intuitively understandable quantitative scenario assumptions. These assumptions are used in quantitative models to produce outlooks and forecasts. Illustrated by the example of quantitative energy models used to investigate developments of the energy system by scenario technique, the approach contrasts with other scenario methods. Bayesian model averaging (BMA) is a method that allows for an evaluation of both system relation stability in terms of observable co-evolvement of phenomena in the past and of future system states of interest based on expert opinion where past evolvements serve as a point of reference. Results The results are scenarios assessable with respect to (1) the consistency of scenario assumptions in terms of statistical confirmation, (2) the suitability of a quantitative model to represent the scenario, and (3) the statistical uncertainty of the scenario for a given quantitative model. A transparent scenario construction process results in traceable assumption documentation (an exemplary communication is provided in the Appendix). Perhaps, the most important novelty of the approach is the possibility of communicating to decision-makers the associated uncertainty in easily understandable terms. The distinction between provable possible assumptions (based ...

Background Energy models are used to illustrate, calculate and evaluate energy futures under given assumptions. The results of energy models are energy scenarios representing uncertain energy futures. Methods The discussed approach for uncertainty quantification and evaluation is based on Bayesian Model Averaging for input variables to quantitative energy models. If the premise is accepted that the energy model results cannot be less uncertain than the input to energy models, the proposed approach provides a lower bound of associated uncertainty. The evaluation of model-based energy scenario uncertainty in terms of input variable uncertainty departing from a probabilistic assessment is discussed. Results The result is an explicit uncertainty quantification for input variables of energy models based on well-established measure and probability theory. The quantification of uncertainty helps assessing the predictive potential of energy scenarios used and allows an evaluation of possible consequences as promoted by energy scenarios in a highly uncertain economic, environmental, political and social target system. Conclusions If societal decisions are vested in computed model results, it is meaningful to accompany these with an uncertainty assessment. Bayesian Model Averaging (BMA) for input variables of energy models could add to the currently limited tools for uncertainty assessment of model-based energy ...

Abstract Background Energy models are used to illustrate, calculate and evaluate energy futures under given assumptions. The results of energy models are energy scenarios representing uncertain energy futures. Methods The discussed approach for uncertainty quantification and evaluation is based on Bayesian Model Averaging for input variables to quantitative energy models. If the premise is accepted that the energy model results cannot be less uncertain than the input to energy models, the proposed approach provides a lower bound of associated uncertainty. The evaluation of model-based energy scenario uncertainty in terms of input variable uncertainty departing from a probabilistic assessment is discussed. Results The result is an explicit uncertainty quantification for input variables of energy models based on well-established measure and probability theory. The quantification of uncertainty helps assessing the predictive potential of energy scenarios used and allows an evaluation of possible consequences as promoted by energy scenarios in a highly uncertain economic, environmental, political and social target system. Conclusions If societal decisions are vested in computed model results, it is meaningful to accompany these with an uncertainty assessment. Bayesian Model Averaging (BMA) for input variables of energy models could add to the currently limited tools for uncertainty assessment of model-based energy scenarios. JEL: C520, C32.