Presentation on theme: "1 ASIF approach and Fuel Price Elasticity Final Seminar of COST355 WATCH "Changing Behaviour towards a more Sustainable Transport System" Les Balcons."— Presentation transcript:
1 ASIF approach and Fuel Price Elasticity Final Seminar of COST355 WATCH "Changing Behaviour towards a more Sustainable Transport System" Les Balcons du Lac d'Annecy, May 26th 2008 Dr Jean-Loup Madre and Christophe Rizet (INRETS-DEST) Prof. Dirk Zumkeller and Peter Ottmann (IFV University of Karlsruhe)
2 Content 1- Adding a Loading factor to the ASIF approach: from ASIF to DSLI a)Demand b)modal Share c)the Loading factor d)Fuel Intensity 2- The Elasticity of Fuel Consumption to Fuel Price a)for Passengers (WG2) b)for Freight on the Road (WG1) 3- Data Needs (WG3) 4- Outlook
3 The ASIF approach For the analysis of energy consumption, the conventional ASIF approach [Fulton and Eads, 2004] is: –Activities (in Tkm or passkm or vehkm) ; –S for modal Share (in %); –I Intensity (in litres/km); –F Fuel mix (in CO2/litre) CO2 = tkm (= Tkm road + Tkm rail +...) * litres/tkm * CO2/litre
4 Adding a Loading Factor: from ASIF to DSFLIE Let us add L (Loading factor) = D (Demand or occupancy, in tkm or passkm) / C (Capacity in veh*km) An alternative approach would be: –D for Demand (in tkm or passkm), which is more precise than "Activities" ; –SF for modal Share combined with Fuel mix (in %) ; –L for load rate (tkm/vehkm or passkm/vehkm) ; –I for Intensity (litres/vehkm) ; –E for CO2 Emission factor (in CO2/litre); in France 1 l diesel = 2.62 kg CO2 and 1 l gasoline = 2.32 kg CO2. Thus, for freight: CO2 = tkm * vehkm/tkm * litres/vehkm * CO2/litre = D * SF * I * E / L with: tkm = tkm diesel truck + tkm electric train + tkm diesel train +... and for passengers: CO2 = passkm * vehkm/passkm * litres/vehkm * CO2/litre = D * SF * I * E / L with: passkm = passkm petrol car + passkm diesel car + passkm electric train + passkm diesel train +...
5 Figure 1: Modal Share for Domestic Passenger Transport (Billions of passkm) in France ( ) Sources: MEDAD-SESP, UTP, RATP, SNCF, DGAC
6 Figure 2: Development of Modal Split (based on Mileage) in Germany Source: German Mobility Panel [Zumkeller et al., 2006]
7 Figure 3: Evolution of average distance per tonne for French heavy duty vehicles Source: computed from TRM survey (MEDAD-SESP French Ministry of Transport)
8 Figure 4: Road share in freight transport (% tkm) in several EU countries Source: Eurostat
9 L (Loading factor) = D (Demand or occupancy, in tkm or passkm) / T (Traffic in veh*km) 1) Different Measurements a) Average Number of Persons per Vehicle * France: 1.56 in , 1.57 in , ? in , *Germany: around 1.4 from 1995 to 2005 * USA: 1.9 in 1995, 1.6 in 2001 (despite car pooling and HOV lanes) orb) % of Occupied Seats * French Private Car: 35.5 % in (from 31% under 8 km to 44% over 200 km) * PT in Paris Region: 23% in 1975, 17% in 1995 (although all passengers are not "seated") * TGV and long distance trains: 75% * Air France: 62% in 1980, 79% in ) Weighted or not by distance ? e.g. for Private Cars in France Loading Factor for Passengers Not WeightedWeighted
10 Figure 5: Development of Car Occupancy Rate in Germany Source: German Mobility Panel [Zumkeller et al., 2006]
11 Table 1: Loading Factor of Private Cars in France according to Trip Distance (1/2) Trip Distance Persons/CarSeats/CarPersons/Seat km % 8-19 km % km % km % km % All % Sources: National Travel Surveys and
12 Table 1: Loading Factor of Private Cars in France according to Trip Distance (2/2) N.B. Like in Germany, these data were collected through a 7-days diary; thus, journeys with more than one week away from home (75% of mileage for holidays and half for business) are excluded. Comment: There are more persons per car for longer trips. The only significant changes of the average number of persons per car between and were: - a slight increase in the band "8-19 km", - a slight decrease in the band "20-49 km", corresponding to an important urban sprawl during this period, - resulting in a slight overall increase, mainly due to a longer average distance per trip (from 10.2 km in to 11.1 km in ). Larger cars are used for longer trips, but there is not much difference in the average number of seats per car according to trip distance. The resulting proportion of occupied seats varies from 31% for trips under 8 km to 44% for trips over 200 km.
13 Loading Factor for Freight or rate of capacity used L = tkm/(vehkm*max load) = load rate for loaded trips * (1+ rate of empty running) with: - max load (e.g. from 41 t to 44 t in the UK) Contrary to passenger cars (5 seats), the distribution of trucks according to their maximum load has an influence on the overall loading factor. - load rate for loaded trips = (ton / max load) - empty running = emptyKM / loaded KM) Returning to base carrying packaging, containers or roll cages, is it empty or loaded? In France, empty running is more important for OA (34%) than for H&R (22%). Few figures available for the other modes: - best guest estimates for sea container is 50%; - air freight is estimated to be very well loaded (100% in volume).
14 Table 2: Empty Running in different countries for Freight Road Transport in 2006 (% of veh-km) Source: EUROSTAT;
15 Figure 6: Real fuel consumption (liters per 100 kilometers) Source: MEDAD-SESP (French National Accounts for Transport)
16 Elasticity of Fuel Consumption to Fuel Price for Automobile –for the SHORT RUN: about -0.1 (mainly mileage) –for the LONG RUN: about -0.7, due: marginally (for about -0.1) to a slower increase in the number of cars, for about -0.2 to the annual mileage per car, and more substantially (for about the half) to a better fuel efficiency (km/litre). High fuel price pushes car manufacturers and consumers to produce and choose more efficient vehicles, rather than limiting traffic growth. It prepares motorists for a future in which fuel will be scarcer, while at the same time delaying the onset of that future.
17 What about the Loading Factor ? Elasticities: Demand Change in % after 1% Fuel Price Increase Car as driverCar as passengerPublic TransportWalking / Bike The elasticity for car passengers is much weaker than for car drivers. with increasing fuel prices, load factor increases as well. However, the occupancy rate is pushed downward by structural factors: - smaller household size, more people living alone, - and shifts towards more trips to work alone Time and Fuel Cost of carpooling >> Saved Fuel And carpools arise among people living in dense surroundings, i.e. where mass transit is likely to serve. Carpooling has been promoted more in the US (e.g. High Occupancy Vehicles lines on motorways), while Europeans are in favour of modal shift. Table 3: Fuel Price Elasticities for Different Modes (adapted from [Hautzinger et al., 2004]) in Germany
18 Elasticity of Fuel Consumption to Fuel Price for French Heavy Goods Vehicles In terms of tkm: * for Own Account transport (OA) * for Hire and Reward (H&R) Promoting efficient logistics is easier for transport firms (by optimising loading, avoiding empty running, etc.) than for firms from other sectors implementing their own transport. The elasticity of traffic (in veh-km) to fuel price is (almost the same for H&R and for OA); its sensitivity is increasing over time, because –fuel represents a growing share of transport cost (up to 25%), –and hauliers are more and more aware that fuel price will continue to rise. Thus, when fuel price is increasing, the market share of H&R is also increasing: e.g. between 1998 and 2007 * from 71.7% to 74.8% in terms of tkm * and from 83.4% to 85.4% in terms of vehkm.
19 Loading Factor for Trucks For trucks registered in France, the loading factor is in 2007: * 5.8 tons/veh for OA * 11.4 tons/veh for H&R Between 1994 and 2005, fuel efficiency of French trucks has increased less (by only 2%) than the loading factor (+7% for tons/veh). Thus by optimising logistics, it seems possible to moderate road traffic without reducing transport demand, which would play against economic growth.
20 Data Needs White spots still exist, both at national and European level. a) Freight Surveys on Road Freight Transport are harmonised by EUROSTAT, but they don't always provide information on energy consumption. Time series are split mode by mode, but information is lacking on inter-modality. In the US: –the vehicle-based Road Freight Transport survey is stopped, –while the Commodity Flow Survey (a shipment survey not so heavy as in France) is conducted every 5 years. b) Passengers Most of EU countries are collecting traffic counts and have conducted a National Travel Survey, but they are not harmonised; DATELINE: first attempt on long distance travel Continuous surveys would be useful, e.g. to analyse the relationship between changes in fuel price and in the rate of occupancy of private cars. (exist only in the Netherlands, the U.K., Germany and Denmark)
21 Outlook (1/2) Sustainability issues discussed in the WGs of COST355 (e.g. decoupling economic growth from increasing transport demand). Empty running applies to both freight and passenger: if loading factors can be increased, the same quantities of passengers and goods can be shipped with less emissions. For road traffic, long term effects of fuel price increase seem quite different for passengers and for freight: –for automobile with an energy efficiency improving by about 1% per year and no evidence for the loading factor (passkm/seatkm), –and for trucks with very slow improvement of energy efficiency, but a notable increase of the loading factor. There is still a lot of potential to increase loading factors and thus Efficiency and sustainability of the transport system.
22 Outlook (2/2) The largest potential, but maybe also the most difficult one to realize, are empty runs on short distances. On the passenger side, many activities related to short trips are undertaken individually, while long-distance trips are more often undertaken in company. Local freight transport is often very specific and therefore needs special vehicles, while long-distance transports tend to be more standardized (containers). WG1 has analysed different improvements in urban freight, mainly through innovations in deliveries