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Engineering-economic simulations Engineering-economic simulations of sustainable transport policiesof sustainable transport policies
Theodoros Zachariadis
Economics Research Centre, University of Cyprus
P.O. Box 20537, 1678 Nicosia, [email protected]
COST 355 meetingPrague, October 2006
Environmental impact of energy systems: the “engineering approach”
• Emphasis on technological dimension
• “Bottom-up” approach
• Detailed simulation of physical/chemical processes (flows, chemical reactions, mass/energy/momentum balances)
and/or
experimental determination of system properties
• Evaluation of future technologies based on their technical potential (ΒΑΤ – Best Available Technology)
“Engineering approach” for assessment of vehicle emission abatement strategies
• Experimental determination of emissions (chassis/engine dynamometer, exhaust gas analysers, mass balances)
• Emission factors (g pollutant / km) as a function of average vehicle speed/acceleration
• Extra emissions per vehicle due to engine/catalyst cold start & fuel evaporation
• Future evolution of basic variables (vehicle population, distance travelled per vehicle, average driving speed) are simulated phenomenologically
• Evaluation of future technologies on the basis of research results & engineering knowledge of their technical potential
However, decision-making requires to know:
• Cost constraints – Current costs (investment, operation & maintenance, fuel)– Economies of scale– Learning processes– Infrastructure development costs– Subjective costs (e.g. discomfort)
• Consumer/producer behaviour– Disposable income– Substitution effects– Inertia & myopia– Rebound effects
• Overall economic background (e.g. GDP, fuel prices, taxes/subsidies)
Simulations are necessary that account for fundamental (micro)economic principles
A long-term engineering-economic model for the EU transport sector
- Model was developed: at the National Technical University of Athens, within the
MINIMA-SUD project (Methodologies to Integrate Impact Assessment in the Field of Sustainable Development) funded by the EC (5th Framework Programme)
for each EU 15 country
for all transport sectors (passenger/freight, road/rail/air/sea)
- Runs year by year up to 2030
- Is calibrated so as to fit official statistics in base year and partly reproduce existing forecasts
- Calculates transportation energy consumption, pollutant & greenhouse gas emissions + noise, congestion & road fatalities indicators
Model development – 1
• Total expenditure on transport depends on private income (for passenger transport) or weighted industrial+agricultural value added (for freight transport) and average user price of transport
• A microeconomic optimisation framework is assumed for the allocation of total expenditure between transport modes:
– Maximisation of consumer utility for passenger transport
– Minimisation of transport costs for freight transport
Model development – 2
• Consumer and producer choices are described as a series of separable choices, which create a nesting structure (decision tree).
• Utility/cost functions at each level of the decision trees are Constant Elasticity of Substitution (CES) functions:
q: quantity (pkm/tkm), σ: elasticity of substitution, Y: income,
p: generalised price (Euro’00 per pkm/tkm), αi: share parameter
1
1
1 1
11
1
11
1
, )( , n
i
n
iiiii
n
ii pPqpYqU
Utility tree for non-urban passenger transport
l=0
l=1
l=2
l=3
l=4
l=5
l=6
l=7
σ = 0.4σ = 0.4σ = 0.4
σ = 1.8σ = 1.5
σ = 0.2 σ = 0.8
σ = 0.5
σ = 0.4
σ = 1.1
σ = 0.3
Transport
Consumer Utility
Other goods & services
Motorised Non-Motorised & Motorcycles
Non-Motorised Motorcycles
Rural Highways
Low-speed High-speed
Air High-speed railLand Water
Private Public
Buses Rail
Rural HighwaysRural Highways
Big cars Small cars
Rural Highways
σ = 0.6
σ = 0.8
Model development – 3
• Aim: Maximise U subject to budget constraint Y
• Solution for CES utility/cost function assuming l levels of utility tree:
• σl available from TREMOVE
• Model calibration: determination of αi
• From exogenous reference case, qi, pi are available
αi are calculated model can reproduce reference case and perform scenario runs
021
1,
0,1,
1,
2,1,
,
1,,
0,, ...
i
ii
li
lili
li
lili
ili p
p
p
p
p
p
p
Yq
ll
Generalised price concept
• Generalised price reflects monetary + time costs, i.e.:
– Vehicle purchase costs– Registration and circulation taxes– Maintenance costs– Insurance costs– Fuel costs– Public transport fares– Time costs = [(travel time)+(waiting time)] /
(avg. distance travelled) * (value of time)
• (Travel time) = (speed)-1 [min]
• Value of Time (Euro’00 per passenger/tonne per hour): different for each transport mode, road type, peak/off-peak travel
Generalised price concept – 2
• Congestion function:
with
invex investment expenditure in road infrastructure
parkex investment expenditure in parking space
m vehicle type, b in the baseline, s in a scenario
r1,r2,r3 adjustment factors
LF load factors
PCU passenger car units
p,f indices for passenger and freight transport
ff
tf
tf
pp
tp
tpt
i
bt
st
h
bt
st
g
m
tmmtm
PCULF
tkmPCU
LF
pkmvkm
parkex
parkexr
invex
invexr
vkm
vkmrtraveltimetraveltime
,
,
,
,
,
,3
,
,2
2000,
,12000,, ;
Congestion
• Congestion-related sustainability indicator: Total travel time (hours spent travelling in a vehicle per year, by road type)
with
kmv average distance travelled annually per vehicle of each type
)(m
,, tmtmt kmvtraveltimeTTT
Road accidents/fatalities indicator – 1
Number of road accidents:
with
ACC road injury accidents in thousands
vkm billion road vehicle kilometres
a,b country-specific parameters (estimated from statistics of the period 1980-2000)
n type of area studied (built-up or non-built-up)
n btn
stn
btn
stn
b
mtnmt speed
speed
invex
invexvkmaACC
,,
,,
,,
,,,,
Road accidents/fatalities indicator – 2
Road fatalities:
with
F number of deaths in road accidents
af,bf country-specific parameters estimated from statistics
of the period 1970-2000
t time in years, with t=0 for 1970.
)exp( tbaACCF fftt
Noise indicator • Like air pollution, noise annoyance is addressed through an
‘emissions’ approach, i.e. emitted sonar energy
• Most common indicator: A-weighted equivalent noise level Leq, expressed in db(A)
• Base year noise emissions come from the TRENDS project (Keller et al., 2002)
• Future emissions calculated with UBA Vienna approach:
with
Leq noise emissions level in db(A)
MSV total vehicle kilometres driven
p share of heavy duty vehicles in traffic
v average driving speed
1
2
1
2
1
212 log20
05301
05301 log10 log10
v
v
p.
p.
MSV
MSV - LL eq,eq,
Running a scenario
• In a scenario (evaluation of a policy instrument), some transport demand quantities or prices in the model change
• This changes also generalised prices / demand quantities / congestion
• This will feed back to a further change in quantities / prices / congestion
• After some iterations, the new equilibrium prices and quantities are determined for each year; this is the model solution for that scenario
Calculation of road vehicle stock
• pkm/tkm and prices available from model solution
• Annual vehicle mileage by vehicle size/road type evolves as a function of income and oil prices
• Occupancy rates of cars decrease with time as a result of rising income and declining household size
• With the aid of the above assumptions, vehicle stock is calculated for several fuel/size groups
Vehicle fuel/size groupsPassenger cars Buses and coaches Heavy duty trucks (contd.)
gasoline, < 1400 cc diesel 7.5-16 t GVWgasoline, 1400-2000 cc LPG dieselgasoline, > 2000 cc CNG LPGdiesel, < 2000 cc electric CNGdiesel, > 2000 cc methanol electricLPG ethanol methanolCNG fuel cell ethanolelectric Light duty trucks fuel cellmethanol gasoline 16-32 t GVWethanol diesel dieselfuel cell CNG LPG
Powered Two Wheelers electric CNGmopeds methanol electricmotorcycles 50-250 cc ethanol methanolmotorcycles 250-750 cc fuel cell ethanolmotorcycles > 750 cc Heavy duty trucks fuel cell
3.5-7.5 t GVW > 32 t GVWdiesel dieselLPG LPGCNG CNGelectric electricmethanol methanolethanol ethanolfuel cell fuel cell
GVW: Gross Vehicle Weight
• Vehicle stock is decomposed into age cohorts, according to – an initial age distribution in base year – assumptions on evolution of scrapping rates
• Scrapping is simulated through a modified Weibull function:
with φ(k) survival probability, k age in years, b,T parameters
with C the total lifetime cost of a new car,b in the baseline, s in a scenario
1)0( ; exp)(
b
T
bkk
Allocation of vehicle stock into vintages
bt
st
t
t
kktt C
C
INCOME
INCOME
k
kSTOCKSCRAP
,
,
1
29
11,1 )1(
)(1
Determination of technology shares
• Choice of technology in road transport is driven by
– Emissions legislation (within the same fuel/size group)
– Relative user prices, determined from vehicle, maintenance and fuel costs
• The model includes the 113 technology classes of the COPERT III methodology + alternative vehicle technologies/ fuels: CNG, methanol, ethanol, fuel cells, electricity
• Simpler approach for non-road transport modes
• New registrations change average technical and economic properties of each vehicle fuel/size group
For subsequent years, technical and economic data are updated with new technology shares
• Emissions calculated: NOx, NMVOC, SO2, PM, Pb, CO2
Major data sources for the transport model – 1
• Eurostat (NewCronos database): energy balances, vehicle stock data, macroeconomic data, energy prices & taxes
• DG TREN Statistical Pocketbook ‘Energy and Transport in Figures’: pkm/tkm data, total vehicle stock, road fatalities
• Eurostat/EEA (TERM report): vkm data for all transport modes
• ECMT/UNECE/Eurostat Pilot Survey on the Road Vehicle Fleet in 55 countries
• EC TRACE project (1999): data on value of time by country, vehicle type and road type
• UITP (International Public Transport Union): fares for buses, tram & metro
• AEA (Association of European Airlines): air transport fares
Major data sources for the transport model – 2 • TREMOVE base case results of Auto-Oil II application:
vehicle costs, evolution of traffic activity by fuel/size group up to 2020, urban/non-urban split, peak/off-peak split up to 2020
• COPERT III methodology & computer model: emission factors and overall calculation scheme for road vehicle emissions (conventional technologies/fuels only)
• TRENDS database: age & technology distribution of road vehicles in base year, emission and fuel consumption factors for non-road vehicles
• MEET project: emission and fuel consumption factors for alternative vehicle technologies/fuels and for future non-road vehicles
• Other studies for costs and fuel consumption of alternative vehicle technologies/fuels
Cost of passenger transport –1
0.0
0.2
0.4
0.6
0.8
1.0
1.2
Eu
ro'0
0 /
pk
m
2000 2005 2010 2015 2020 2025 2030
Medium sized gasoline cars, urban peak driving
time costfuel costvehicle cost
Cost of freight transport – 1
0.0
0.5
1.0
1.5
2.0
2.5
Eu
ro'0
0 /
tk
m
2000 2005 2010 2015 2020 2025 2030
Diesel trucks 3.5-7.5 t, urban peak driving
time costfuel costvehicle cost
Policy exercises applied
1. Subsidies to CNG and fuel cell vehicles (50% of their pre-tax purchase cost)
2. Double tax on automotive diesel fuel for cars/trucks
3. Advanced emission standards from 2006 onwards (‘Euro V’), but at 40% higher purchase costs
4. Double investment expenditure for road infrastructure (current figures: 55 billion Euros’00 in 2000, 69 billion Euros’00 in 2010)
5. Subsidies to public transport fares (50% lower fares)
6. Road pricing: 3 Euros for each urban trip on average
7. Subsidies for scrapping old cars: 50% lower purchase cost for each new car replacing an old one
8. Combination of policies 3 & 6
9. Combination of policies 1, 3 & 6
10. Combination of policies 3, 5 & 6
Impact of policy exercise 4 (investment expenditure for roads)
• Total time spent in urban driving declines by 6% • Driving becomes somewhat cheaper (by ~4% in urban
areas and by <1% in motorways) • Impact not very remarkable because of ‘rebound effect’:
improved congestion makes car travel more attractive road pkm/tkm & energy intensity increase
• Largest benefit for freight transport due to higher share of time costs
• Pollutant emissions change by ±3% • Negligible impact on accidents• Some increase in noise levels
Cumulative impact of selected policies – 1
80
90
100
110
120
130
140
150
160
170
2000 2010 2020 2030
Transportation energy demand (2000=100)
Baselineemission stds
road pricing
altern. fuels
Cumulative impact of selected policies – 2
25
50
75
100
2000 2010 2020 2030
Transportation urban NOx emissions (2000=100)
Baselineroad pricing
altern. fuelsemission stds
Synopsis
• For the formulation of effective sustainable development strategies it is necessary to combine and reconcile:Engineering approaches
(detailed evaluation of technical measures)Economic approaches (costs, international economic
context, consumer/producer behaviour, feedback mechanisms)
Development of engineering-economic models
Evaluation of costs (direct and indirect) is crucial