The System Dynamics Approach: Results of Scenarios for Europe

1
The System Dynamics Approach Results of
Scenarios for Europe
REVENUE Seminar 1 Brusels, June 9th 2004

• Claus Doll
• Institut für Wirtschaftspolitik und
  Wirtschaftsforschung (IWW)
• Universität Karlsruhe (TH)

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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.

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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

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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.
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MARS Model Some feedback mechanisms
Travel speed
Time costs
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.

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Features of the MARS model

• 4 modes and 5 transport funds (urban,
  inter-urban, road, P.T. and intermodal/inter-regio
  nal).
• 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.

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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!

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Results for pricing scenario P1 Urban
congestion charging

• Nearly / exactly identical slope of allocation
  schemes R2 to R5 Litte excessive funds to
  distribute.

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Details for pricing scenario P1
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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.

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Some details fore pricing scenario P2
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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

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Some details for pricing scenario P5
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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.

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

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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.

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Overview of results for 2020
Percent change from BAU to policy
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Development of GDP (leading indicator)
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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.

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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.

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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

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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.

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Investment effects

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

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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.

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Development of sensitivities over time

• Example Influence on GDP in Inter-urban-cross
  scenario

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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.