Modelling policy scenarios: refocussing the model-policy logic for the case of German passenger transport.

Background: National energy and climate scenarios are typically simulated or optimised using sectoral or energy system models, which include a large number of model settings and scenario assumptions. However, their realisation is contingent upon framework conditions and policy settings, which are often included in accompanying narrative scenarios. This paper therefore proposes refocussing the model-policy logic towards directly modelling policy effects. Applying this approach to the case of German passenger transport, I focus on demand-side policies and use open-source databases and models to develop a module for the translation of policies into model parameters. Results: Separate model runs were used to test a ceteris paribus policy reference scenario for 2035, the marginal impacts of modelled single policy effects, and a joint policy package scenario. Relative to the reference, demand-side policies show significant impacts: an annual reduction of 355 bn person-kilometres (30%) and a reduction of car-owning households from 95 to 90% in rural areas and from 76 to 64% in urban areas. The resulting mode shift decreases car-driven kilometres by 400 bn and increases public transport by 45 bn per year. This may reduce GHG emissions by an additional 30 Mt (or 33%) relative to the reference in 2035. Conclusions: Transport demand policies can significantly mitigate GHG, calling for a stronger policy focus beyond the much-studied shift to electric vehicles. While further research and model development are needed, the feasibility of policy scenario modelling increases its utility for policy-making. [ABSTRACT FROM AUTHOR]