Engineering-economic simulations of sustainable transport policies Theodoros Zachariadis Economics...

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, Cyprust.zachariadis@ucy.ac.cy

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