the system dynamics approach: results of scenarios for europe
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Institut für Wirtschaftspolitik und WirtschaftsforschungUniversität Karlsruhe (TH)
The System Dynamics Approach: Results of Scenarios for Europe
Claus Doll Institut für Wirtschaftspolitik und
Wirtschaftsforschung (IWW)Universität Karlsruhe (TH)
REVENUE Seminar 1Brusels, June 9th 2004
Institut für Wirtschaftspolitik und WirtschaftsforschungUniversität Karlsruhe (TH)
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.
Institut für Wirtschaftspolitik und WirtschaftsforschungUniversität Karlsruhe (TH)
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
Institut für Wirtschaftspolitik und WirtschaftsforschungUniversität Karlsruhe (TH)
Task 2.4: Dual model applicationMARS (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.
Institut für Wirtschaftspolitik und WirtschaftsforschungUniversität Karlsruhe (TH)
MARS Model: Some feedback mechanismsTravel speed
Traffic volume
Infrastructure capacity
Infrastructure quality
Available Budget
Congestion pricing revenues
Fund compositionand allocation rules
Budget spendingrules
Time costsWelfaremeasure
Environmentalpricing revenues
Time
Modal share
Relevant feedback loops:
1. Traffic volume – travel speeds – congestion revenues – available budget – infrastructure capacity – travel speeds – time costs – traffic volume: Negative, results in equilibrium or oscillations.
2. Time – (traffic volume) – infrastructure quality – travel speeds – traffic volume – infrastructure quality: Slightly negative dominated by time-dependent deterioration of infrastructure.
3. 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.
Average infrastructureprices
Institut für Wirtschaftspolitik und WirtschaftsforschungUniversität Karlsruhe (TH)
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.
Institut für Wirtschaftspolitik und WirtschaftsforschungUniversität Karlsruhe (TH)
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!
R1: No funds
R2: Network
R3: Area
R4: Intermodal
R5: P.T. sup.
P1: Urban Congestion mill. € -5.709 -10.563 -10.305 -10.563 -10.305 P2: Motorway Toll mill. € -7.984 -7.080 -4.910 -2.447 -2.647 P3: Swiss case mill. € -7.968 -4.151 -5.750 -673 -743 P4: Pure SMCP mill. € -3.275 -8.844 -10.070 -9.922 -10.070 P5: SMCP+mark-ups mill. € 6.311 5.536 5.359 5.739 5.359
Institut für Wirtschaftspolitik und WirtschaftsforschungUniversität Karlsruhe (TH)
Results for pricing scenario P1: Urban congestion charging• Nearly / exactly identical slope of allocation schemes
R2 to R5: Litte excessive funds to distribute.
Results for policy scenario 1Urban congestion pricing
-25'000
-20'000
-15'000
-10'000
-5'000
0
5'000
0 10 20 30 40 50 60 70 80 90 100
Time horizon (years)
Pres
ent v
alue
of u
ser t
ime
cost
s (m
io.
Euro
)
R1: No Funds R2: NetworkR3: Area R4: IntermodalR5: P.T. Support
Institut für Wirtschaftspolitik und WirtschaftsforschungUniversität Karlsruhe (TH)
R2: Network funds R3: Area funds Share of congested traffic
0,0%
2,0%
4,0%
6,0%
8,0%
10,0%
12,0%
14,0%
0 10 20 30 40 50 60 70 80 90 100
Time (years)
Mode 1 Road Inter-urbanMode 2 Road UrbanMode 3 P.T. Inter-UrbanMode 4 P.T. Urban
Share of congested traffic
0,0%
2,0%
4,0%
6,0%
8,0%
10,0%
12,0%
14,0%
0 10 20 30 40 50 60 70 80 90 100
Time (years)
Mode 1 Road Inter-urbanMode 2 Road UrbanMode 3 P.T. Inter-UrbanMode 4 P.T. Urban
Network quality standard index
0%
10%
20%
30%
40%
50%
60%
70%
80%
0 10 20 30 40 50 60 70 80 90 100
Time (years)
Mode 1 Road Inter-urbanMode 2 Road UrbanMode 3 P.T. Inter-UrbanMode 4 P.T. Urban
Network quality standard index
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 10 20 30 40 50 60 70 80 90 100
Time (years)
Mode 1 Road Inter-urbanMode 2 Road UrbanMode 3 P.T. Inter-UrbanMode 4 P.T. Urban
Details for pricing scenario P1
Institut für Wirtschaftspolitik und WirtschaftsforschungUniversität Karlsruhe (TH)
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. Results for policy scenario 2
Motorway tolling
-25.000
-20.000
-15.000
-10.000
-5.000
0
5.000
10.000
0 10 20 30 40 50 60 70 80 90 100
Time horizon (years)
Pres
ent v
alue
of u
ser t
ime
cost
s (m
io. E
uro)
R1: No Funds R2: NetworkR3: Area R4: IntermodalR5: P.T. Support
Institut für Wirtschaftspolitik und WirtschaftsforschungUniversität Karlsruhe (TH)
Some details fore pricing scenario P2R3: Area funds R4: Intermodal fund
Share of congested traffic
0,0%
2,0%
4,0%
6,0%
8,0%
10,0%
12,0%
14,0%
0 10 20 30 40 50 60 70 80 90 100
Time (years)
Mode 1 Road Inter-urbanMode 2 Road UrbanMode 3 P.T. Inter-UrbanMode 4 P.T. Urban
Share of congested traffic
0,0%
2,0%
4,0%
6,0%
8,0%
10,0%
12,0%
0 10 20 30 40 50 60 70 80 90 100
Time (years)
Mode 1 Road Inter-urbanMode 2 Road UrbanMode 3 P.T. Inter-UrbanMode 4 P.T. Urban
Share of interest payments at annual
expenditures (excluding credit pay-back)
0%
20%
40%
60%
80%
100%
120%
0 10 20 30 40 50 60 70 80 90 100
Time (years)
Mode 1 Road Inter-urbanMode 2 Road UrbanMode 3 P.T. Inter-UrbanMode 4 P.T. Urban
Share of interest payments at annual expenditures (excluding credit pay-back)
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 10 20 30 40 50 60 70 80 90 100
Time (years)
Mode 1 Road Inter-urbanMode 2 Road UrbanMode 3 P.T. Inter-UrbanMode 4 P.T. Urban
Institut für Wirtschaftspolitik und WirtschaftsforschungUniversität Karlsruhe (TH)
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
Results for policy scenario 5SRMC pricing with mark-ups
-4.000
-2.000
0
2.000
4.000
6.000
8.000
10.000
0 10 20 30 40 50 60 70 80 90 100
Time horizon (years)
Pres
ent v
alue
of u
ser t
ime
cost
s (m
io. E
uro)
R1: No Funds R2: NetworkR3: Area R4: IntermodalR5: P.T. Support
Institut für Wirtschaftspolitik und WirtschaftsforschungUniversität Karlsruhe (TH)
Some details for pricing scenario P5R1: No fund allocation R4: Intermodal fund
Share of congested traffic
0,0%
2,0%
4,0%
6,0%
8,0%
10,0%
12,0%
0 10 20 30 40 50 60 70 80 90 100
Time (years)
Mode 1 Road Inter-urbanMode 2 Road UrbanMode 3 P.T. Inter-UrbanMode 4 P.T. Urban
Share of congested traffic
0,0%
2,0%
4,0%
6,0%
8,0%
10,0%
12,0%
0 10 20 30 40 50 60 70 80 90 100
Time (years)
Mode 1 Road Inter-urbanMode 2 Road UrbanMode 3 P.T. Inter-UrbanMode 4 P.T. Urban
Network quality standard index
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
0 10 20 30 40 50 60 70 80 90 100
Time (years)
Mode 1 Road Inter-urbanMode 2 Road UrbanMode 3 P.T. Inter-UrbanMode 4 P.T. Urban
Network quality standard index
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
0 10 20 30 40 50 60 70 80 90 100
Time (years)
Mode 1 Road Inter-urbanMode 2 Road UrbanMode 3 P.T. Inter-UrbanMode 4 P.T. Urban
Institut für Wirtschaftspolitik und WirtschaftsforschungUniversität Karlsruhe (TH)
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.
Institut für Wirtschaftspolitik und WirtschaftsforschungUniversität Karlsruhe (TH)
ASTRA modules and main interfaces
Modules:
POP: PopulationMAC: MacroeconomicsREM: Regional economicsFOT: Foreign tradeTRA: TransportENV: EnvironmentVFT: Vehicle fleetWEM: Welfare
Institut für Wirtschaftspolitik und WirtschaftsforschungUniversität Karlsruhe (TH)
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
Institut für Wirtschaftspolitik und WirtschaftsforschungUniversität Karlsruhe (TH)
ASTRA-T Scenario DefinitionCharging regime Revenue allocation policy
Refund by
direct tax reduction
Refund by labour cost reduction
Reinvestment in in mode of charge
collection
Reinvestment by cross-
subsidisation
Congestion charging in urban areas Congestion-DT Congestion-LC Congestion-Road Congestion-Cross
Interurban road user tolls Interurban-DT Interurban-LC Interurban-Road Interurban-Cross
SMCP in all moces (TIPMAC scenarios) SMCP-DT SMCP-LC
• 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).
Institut für Wirtschaftspolitik und WirtschaftsforschungUniversität Karlsruhe (TH)
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.
Total revenues
0
50
100
150
200
250
300
350
400
450
2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020
bill.
Eur
o
Congestion-DTCongestion-LCCongestion-RoadCongestion-CrossInterurban-DTInterurban-LCInterurban-RoadInterurban-CrossSMCP-DTSMCP-LC
Institut für Wirtschaftspolitik und WirtschaftsforschungUniversität Karlsruhe (TH)
Overview of results for 2020Scenario
GDP
Em-ploy-ment
Con-sump-
tion
Invest-ments
Exports
TFP
Tons
Tkm
Trips
Pkm
CO2
Congestion-DT 1.52 -0.03 2.45 7.53 0.16 0.84 0.14 16.12 -0.04 0.24 1.52
Congestion-LC 0.87 -0.12 1.05 4.30 0.11 0.58 0.05 16.10 -0.05 0.21 0.87
Congestion-Road 1.42 0.48 1.08 3.96 0.19 0.76 0.69 16.45 -0.01 0.35 1.42
Congestion-Cross 1.33 0.42 0.95 3.91 0.18 0.66 0.58 16.42 -0.02 0.37 1.33
Interurban-DT -1.19 -0.78 -0.11 0.10 -2.17 -1.79 -2.18 -0.53 0.01 -0.49 -1.19
Interurban-LC -1.79 -0.84 -1.59 -2.78 -2.21 -2.03 -2.18 -0.50 -0.02 -0.54 -1.79
Interurban-Road 0.35 0.47 0.61 2.34 -1.77 -0.70 -0.85 0.20 -0.04 -0.31 0.35
Interurban-Cross 0.17 0.39 0.39 2.06 -1.81 -0.91 -0.98 0.23 -0.04 -0.31 0.17
SMCP-DT 1.13 -0.45 2.99 7.44 -3.03 0.07 -1.91 17.28 -0.42 -2.37 1.13 SMCP-LC -0.62 -0.77 -1.21 -0.57 -3.04 -0.59 -2.08 17.44 -0.44 -2.50 -0.62
Percent change from BAU to policy
Institut für Wirtschaftspolitik und WirtschaftsforschungUniversität Karlsruhe (TH)
Development of GDP (leading indicator)
GDP against BAU
-2,0
-1,5
-1,0
-0,5
0,0
0,5
1,0
1,5
2,0
2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020
[%]
Congestion-DTCongestion-LCCongestion-RoadCongestion-CrossInterurban-DTInterurban-LCInterurban-RoadInterurban-CrossSMCP-DTSMCP-LC
Institut für Wirtschaftspolitik und WirtschaftsforschungUniversität Karlsruhe (TH)
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.
Institut für Wirtschaftspolitik und WirtschaftsforschungUniversität Karlsruhe (TH)
Employment effectsEmployment against BAU
-1,0
-0,8
-0,6
-0,4
-0,2
0,0
0,2
0,4
0,6
0,8
2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020
[%]
Congestion-DTCongestion-LCCongestion-RoadCongestion-CrossInterurban-DTInterurban-LCInterurban-RoadInterurban-CrossSMCP-DTSMCP-LC
• 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.
Institut für Wirtschaftspolitik und WirtschaftsforschungUniversität Karlsruhe (TH)
Effects on total consumption
Consumption against BAU
-2,0
-1,0
0,0
1,0
2,0
3,0
4,0
2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020
[%]
Congestion-DTCongestion-LCCongestion-RoadCongestion-CrossInterurban-DTInterurban-LCInterurban-RoadInterurban-CrossSMCP-DTSMCP-LC
• Most significant stimulation by refund via direct tax reduction• Effect is neutralised in iter-urban tolls due to the reduction of exports
Institut für Wirtschaftspolitik und WirtschaftsforschungUniversität Karlsruhe (TH)
Effects on exportsExports against BAU
-3,5
-3,0
-2,5
-2,0
-1,5
-1,0
-0,5
0,0
0,5
2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020
[%]
Congestion-DTCongestion-LCCongestion-RoadCongestion-CrossInterurban-DTInterurban-LCInterurban-RoadInterurban-CrossSMCP-DTSMCP-LC
• 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.
Institut für Wirtschaftspolitik und WirtschaftsforschungUniversität Karlsruhe (TH)
Investment effectsInvestments against BAU
-4,0
-2,0
0,0
2,0
4,0
6,0
8,0
10,0
2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020
[%]
Congestion-DTCongestion-LCCongestion-RoadCongestion-CrossInterurban-DTInterurban-LCInterurban-RoadInterurban-CrossSMCP-DTSMCP-LC
• Short-run: Positive impulses from direct use of revenues for reinvestment. • Long-run: Better performance of investment stimulation by refunding
Institut für Wirtschaftspolitik und WirtschaftsforschungUniversität Karlsruhe (TH)
Sensitivity analysis
Interurban-Cross
Impact on variable Link transport on GDP Employment Consumption Investment Export TFP tons tkm trips pkm CO2 Consumption -0.381 -0.296 -0.558 -1.106 -0.076 -0.203 -0.344 -0.211 0.057 0.015 -0.230 Employment 0.267 0.386 0.625 0.022 -0.059 0.134 0.235 0.108 0.043 0.121 0.159 Export -0.439 -0.297 -0.576 -0.765 -1.720 -0.268 -1.035 -1.742 0.072 0.019 -0.751 Intermediates -0.005 -0.011 0.199 0.050 -0.088 -0.113 -0.166 -0.311 0.108 0.076 -0.003 Investment 3.031 1.509 3.252 7.925 0.588 1.810 1.809 1.293 0.036 0.369 1.016 Productivity -2.675 -0.680 -3.003 -6.081 -0.723 -2.398 -1.622 -1.287 0.091 -0.120 -0.790
• 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.
• Method: Switching the link of transport to particular measures off. • Performed for three scenarios:
Congestion-DT, Congestion-Cross and Inter-urban-cross. Example:
Institut für Wirtschaftspolitik und WirtschaftsforschungUniversität Karlsruhe (TH)
Development of sensitivities over time• Example: Influence on GDP in Inter-urban-cross scenario
Change of GDP against BAU
-4,000
-3,000
-2,000
-1,000
0,000
1,000
2,000
3,000
4,000
2005 2010 2020
Year
Per
cent
age
chan
ge to
BA
U [%
]
Base Interurban Charge
Exclude Transport Influence onConsumptionExclude Transport Influence onEmploymentExclude Transport Influence onExportExclude Transport Influence onIntermediatesExclude Transport Influence onInvestmentExclude Transport Influence on TFP
Exclude Gov Debt Influence onInvestment
Institut für Wirtschaftspolitik und WirtschaftsforschungUniversität Karlsruhe (TH)
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.
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