1. Quantitative modelling of the STOA scenarios with ASTRA: potentials and limits Angelo Martino TRT Trasporti e Territorio

2. STOA workshop – Brussels, 7 th May 2013 2 The ASTRA model  Tool for strategic assessment of transport policy and trends  Integration of transport – environment – technology – economy  Long record of experiences in European and national projects  A new version of the model is currently under development on behalf of DG MOVE Unit A3 (as part of the ASSIST project)  Capability to simulate several different policy measures, e.g.:  Technological improvements  Pricing and charging  Economic incentives (e.g. subsidies, feebate)  Targets (e.g. emission standards)  Regulation (e.g. speed limits)

3. STOA workshop – Brussels, 7 th May 2013 3 The ASTRA model methodology  Based on System Dynamics (implemented with Vensim®)  Through computer simulation, SD allows to understand how systems change over time  The behaviour of systems is primary determined by its feedback mechanisms  System is continuously evolving: unlike CGE models or transport assignment alghoritms equilibrium is never reached  Time horizon: 1995 – 2050  Spatial coverage: EU29 (EU27 + Norway and Switzerland)  Geographical detail: mostly country level  www.astra-model.eu www.astra-model.eu

4. STOA workshop – Brussels, 7 th May 2013 4 ASTRA model structure

5. STOA workshop – Brussels, 7 th May 2013 5 Modelling technology improvements  The implementation of technology measures affects:  Fuel/energy consumption of vehicles  Cost of vehicles (investment and operating costs)  Investments in R&D  Implementing technology measures in ASTRA means:  Changing fuel/energy consumption factors  Changing CO2 emission factors (linked to fuel consumption)  Changing cost of vehicle for each fuel category (and size)  Adding investments to develop innovation

6. STOA workshop – Brussels, 7 th May 2013 6 Modelling technology improvements Investments Vehicle cost Fleet size and composition Transport demand GDP Energy consumption Operating cost Fuel duties CO2 emissions Government revenues Unitary energy consumption Learning curves

7. STOA workshop – Brussels, 7 th May 2013 7 Modelling policies  Changes generated by policy measures are reflected in three main areas:  User cost  Additional cost due to charges or taxes (road pricing or fuel taxes)  Variable operating costs (reduced fuel consumption/eco-driving)  Vehicle purchase cost (feebate)  User time (speed limits, reserved lanes, etc.)  New regulation (speed limits)  New facilities (reserved lanes)  Authority expenditure  Subsidies (to reduce public transport fares)  Increased public investment (new transport facilities)  Increased expenditure for public activities (managing cordon pricing)

8. STOA workshop – Brussels, 7 th May 2013 8 Modelling policies Infrastructure User travel time User generalised cost by mode Transport demand Public expenditure Aggregated demand GDP CO2 emissions Public revenues Cordon pricing User travel cost Transport demand by mode Energy consumption

9. STOA workshop – Brussels, 7 th May 2013 9 GHG-TransPoRD reference scenario  The GHG-TransPoRD Reference Scenario was based on two main sources:  “EU energy trends to 2030 — update 2009” (PRIMES model, used for assessment of the EC White Paper)  ADAM project reference scenario (after 2030)  Key assumptions:  Moderate economic growth  Transport demand growing more or less like economy  Some improvement of transport energy efficiency  Transport CO2 emissions increasing as efficiency gains are more than offset by demand growth  No penetration of innovative cars in the EU fleet  The project was concluded in early 2012: http://www.ghg-transpord.eu http://www.ghg-transpord.eu

10. STOA workshop – Brussels, 7 th May 2013 10 ASTRA results explained - 1  Scenario 1 - Cleaner modes: fuel cell cars climb to 61% of the fleet  Vehicle fleet composition in ASTRA develops as new cars enter and old cars are scrapped  New cars market shares (by technology) are modelled by a choice algorithm based on (purchase and operating) cost, filling station network availability and consumer preferences  Parameters are calibrated on observed fleet development in the past  Observed data on new technologies does not exist: validity of parameters is a professional judgment

11. STOA workshop – Brussels, 7 th May 2013 11 ASTRA results explained - 2  Scenario 2 – Changing the modal split: car share falls only from 64% to 58% and truck share from 57% to 50%  In ASTRA the mode split is computed by a logit algorithm applied separately to several demand segments (distance band, purpose, freight type…)  The algorithm is based on cost, time and other parameters reflecting “alternative specific constants”  The algorithm is calibrated to replicate observed mode split  In last 15 years mode split has not changed dramatically  “Alternative specific constants” parameters reflect this situation and do not allow a major mode shift  Also capacity constraints prevent large shift from car to other modes

12. STOA workshop – Brussels, 7 th May 2013 12 ASTRA results explained - 3  Scenario 3 - Reducing growth rates: despite halved transport demand, the gain in CO2 emission reduction is much lower  Technological improvements become less effective because are applied to a lower amount of traffic: the reduction of energy consumption per km is applied to less km and then the total cut is lower  Neither mode split nor fleet composition is drastically changed with respect to AFS  Innovative vehicles penetration rate is higher because of extremely high oil price which pushes the convenience of alternative fuels  Reduced freight volumes are modelled by reducing intra-EU trade and this lead to negative economic output

13. STOA workshop – Brussels, 7 th May 2013 13 Thanks for your attention www.astra-model.eu martino@trt.it