The System Dynamics Approach: Results of Scenarios for Europe

1. REVENUE Seminar 1 Brusels, June 9th 2004 The System Dynamics Approach: Results of Scenarios for Europe Claus Doll Institut für Wirtschaftspolitik und Wirtschaftsforschung (IWW) Universität Karlsruhe (TH)

2. Objectives and method of task 2.4 • Goals: • Investigation of the dynamics behind long-term decisions in transport network planning. • Identification of the key drivers behind long-term optimality decisions. • Approach: • Development of a small transport sector specific system dynamics model (MARS), containing several evaluation tools • Application of the ASTRA model to answer general questions concerning the link betwen transport and the rest of economy. • Discussion: Applicability of the framework within the case studies.

3. Contents • System Dynamics and CGE-Models • Revenue Distribution within the Transport Sector: Structure and Results of the MARS model • Revenue allocation within or outside the Transport Sector: Results of the ASTRA-Model for Europe • Conclusions

4. Task 2.4: Dual model application MARS (Multimodal Assessment of Revenue allocation Strategies): Partial analysis of revenue allocation variants within the transport sector by assuming a self-financing system of 4 transport modes. Rough model calibration to Europe and application to 25 combinations out of pricing and fund allocation policies. ASTRA (ASsessment of TRAnsport Policies. System-Dynamics model platform developed during several EC-funded research projects. Covers 14 countries, passenger and freight transport of all modes, trade and production by 25 economic sectors, government activities, environment and traffic safety. The model is used to investigate long-term effects of earmarking pricing revenues in the EU Member States. Brief presentation of model mechanisms and some results. Short presentation of modular model structure and feedback mechanisms. Detailed discussion of scenario results for the EU-15 countries.

5. MARS Model: Some feedback mechanisms Time costs Travel speed Time Welfare measure Modal share Budget spending rules Infrastructure capacity Infrastructure quality Traffic volume Average infrastructure prices Congestion pricing revenues Environmental pricing revenues Fund composition and allocation rules Available Budget • Relevant feedback loops: • Traffic volume – travel speeds – congestion revenues – available budget – infrastructure capacity – travel speeds – time costs – traffic volume: Negative, results in equilibrium or oscillations. • Time – (traffic volume) – infrastructure quality – travel speeds – traffic volume – infrastructure quality: Slightly negative dominated by time-dependent deterioration of infrastructure. • Traffic volume – average infrastructure prices – traffic volume: Positive loop caused by economies of scale in AC-Models; might lead to excessive demand or to crowding out of entire demand.

6. Features of the MARS model • 4 modes and 5 transport funds (urban, inter-urban, road, P.T. and intermodal/inter-regional). • Pricing options: Infrastructure (AC and SMC), congestion, accidents (SMC) and environment (SMC). plus mark-ups. • Assessment of max. 5 revenue spending scenarios for each of max. 5 pricing policy scenarios. • Welfare measure = time costs valued by the „rule of half“ • Self-financing of transport sector with link to capital market. • Stochastic deterioration of networks, by time and traffic load. • User time costs depending of traffic load and network quality.

7. Definition and results of the base scenario • Model calibration for Europe where possible • Time horizon: 30 years. • Results: Mode-specific revenue use recommended in 3 of 5 pricing scenarios • Costs of fund administration and fund allocation rules to be considered!

8. Results for pricing scenario P1: Urban congestion charging • Nearly / exactly identical slope of allocation schemes R2 to R5: Litte excessive funds to distribute.

9. Details for pricing scenario P1

10. Results for pricing scenario P2: Average infrastructrue cost charging on motorways • Much more dynamic than P1 due to more stable excess funds available for redistribution.

11. Some details fore pricing scenario P2

12. Results for pricing scenario P5: Full SRMC + mark-ups • Due to high and stable revenues in each mode no transfer required and positive welfare until year +75

13. Some details for pricing scenario P5

14. Sensitivity analysis for selected key variables • Negative performance of all pricing scenarios in the long run due to the ambitious definition of the reference case. • Time is less critical for the optimality ranking of the revenue allocation schemes than expected. • In general, the model is rather stable against changes of parameters. one of the most sensitive ones is the influence of road quality on speed. • The sensitivity results are to be considered in front of the specific calibration fo the model and might be totally different for other constellations.

15. ASTRA modules and main interfaces Modules: POP: Population MAC: Macroeconomics REM: Regional economics FOT: Foreign trade TRA: Transport ENV: Environment VFT: Vehicle fleet WEM: Welfare

16. Impactchains and their time structure Pricing Abbreviations: GDP: Grossdomestic product GVA: Grossvalue added TPF: Total factorproductivity FD: Freight demand PO: Production output IO: Input-output

17. ASTRA-T Scenario Definition • Fixed allocation of reinvestments to road types (single carriageway roads, motorways) or to rail facilities (network, terminals, rolling stock). • Refund via tax increases: No price increases assumed as indicated by IASON model applications of CGEurope and E3ME). • Refund via social contributions: 50% employers (partly increase of GVA) and 50% for consumers (partial use for increased consumption).

18. Development of total revenues • Outstanding level of TIPMAC SMC-revenues against partial toll regimes. • Lowest level by urban congestion revenues. • No great impact of transport-specific feedback loops on level of revenues.

19. Overview of results for 2020 Percent change from BAU to policy

20. Development of GDP (leading indicator)

21. Explanation • Congestion charge: Generally positive as stimulation of consumption and investments are not deemed by the decrease of exports • Inter-urban toll: First negative development as exports get more expensive. Positive development of reinvestment scenarios due to increased investments and stimulated TFP. No recreation of refund-alternatives. • SMCP and inter-urban tolling show, that the consumption impulse caused by the reduction of direct taxes is superior to the stimulation of employment via the reduction of labour costs. • Road investments seem to perform slightly better than cross-funding, caused by higher time savings achievable in road.

22. Employment effects • Diffuse picture: most positive development of reinvestment scenarios. • Initial peak in SMCP-LC due to high income and consequently high potential to reduce labour costs. But this is not sustainable due to generally high extra load on production costs.

23. Effects on total consumption • Most significant stimulation by refund via direct tax reduction • Effect is neutralised in iter-urban tolls due to the reduction of exports

24. Effects on exports • Clear picture: inter-urban road tolls and SMCP on all modes increase production costs in export-oriented industries and thus reduce the productivity in this sector.

25. Investment effects • Short-run: Positive impulses from direct use of revenues for reinvestment. • Long-run: Better performance of investment stimulation by refunding

26. Sensitivity analysis • Method: Switching the link of transport to particular measures off. • Performed for three scenarios: Congestion-DT, Congestion-Cross and Inter-urban-cross. • Example: • Most significant influence of transport on investments • in case of strong modal shifts in long-distance transport strong influence on TFP. • Strong impact on exports in case of high price increases in long-distance transport.

27. Development of sensitivities over time • Example: Influence on GDP in Inter-urban-cross scenario

28. Conclusions • Considering revenue spending alternatives short- and long-term developments are to be distinguished. • The optimality of particular allocation schemes is driven by the indicators considered and thus by policy preferences. • In general the reinvestment of revenues in the transport sector seems to crease most positive effects via its stimulating impact on investments and factor productivity. • The ASTRA model indicates a better performance of investments in roads compared to rail when considering economic indicators However, ASTRA does not contain a sophisticated capacity model, taking into account local network conditions. This information is to be contributed from the case study level.