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Engineering-economic simulations of sustainable transport policies

Engineering-economic simulations of sustainable transport policies. Theodoros Zachariadis Economics Research Centre, University of Cyprus P.O. Box 20537, 1678 Nicosia, Cyprus t.zachariadis@ucy.ac.cy COST 355 meeting Prague, October 2006.

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Engineering-economic simulations of sustainable transport policies

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  1. Engineering-economic simulations of sustainable transport policies Theodoros Zachariadis Economics Research Centre, University of Cyprus P.O. Box 20537, 1678 Nicosia, Cyprus t.zachariadis@ucy.ac.cy COST 355 meeting Prague, October 2006

  2. 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)

  3. “Engineering approach” for assessment of vehicle emission abatement strategies • Experimental determination of emissions (chassis/engine dynamometer, exhaust gas analysers, mass balances) • Emission factors (gpollutant/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

  4. 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

  5. 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

  6. 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

  7. 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

  8. Utility tree for non-urban passenger transport

  9. Model development – 3 • Aim: Maximise U subject to budget constraint Y • Solution for CES utility/cost function assuming llevels 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

  10. 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

  11. 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

  12. 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

  13. Road accidents/fatalities indicator – 1 Number of road accidents: with ACCroad injury accidents in thousands vkmbillion road vehicle kilometres a,b country-specific parameters (estimated from statistics of the period 1980-2000) ntype of area studied (built-up or non-built-up)

  14. 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.

  15. 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

  16. 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

  17. 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

  18. Vehicle fuel/size groups

  19. Allocation of vehicle stock into vintages • 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

  20. 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

  21. 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

  22. 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

  23. Cost of passenger transport –1

  24. Cost of freight transport –1

  25. Policy exercises applied • Subsidies to CNG and fuel cell vehicles (50% of their pre-tax purchase cost) • Double tax on automotive diesel fuel for cars/trucks • Advanced emission standards from 2006 onwards (‘Euro V’), but at 40% higher purchase costs • Double investment expenditure for road infrastructure (current figures: 55 billion Euros’00 in 2000, 69 billion Euros’00 in 2010) • Subsidies to public transport fares (50% lower fares) • Road pricing: 3 Euros for each urban trip on average • Subsidies for scrapping old cars: 50% lower purchase cost for each new car replacing an old one • Combination of policies 3 & 6 • Combination of policies 1, 3 & 6 • Combination of policies 3, 5 & 6

  26. Impact of policy exercise4(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

  27. Cumulative impact of selected policies – 1

  28. Cumulative impact of selected policies – 2

  29. 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-economicmodels • Evaluation of costs (direct and indirect) is crucial

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