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Decomposing the decoupling of Danish road freight traffic growth and economic growth
ARTICLE IN PRESS
Transport Policy 14 (2007) 39–48 www.elsevier.com/locate/tranpol
Decomposing the decoupling of Danish road freight traffic growth and economic growth Ole Kveiborg, Mogens Fosgerau Danish Transport Research Institute, Knuth-Winterfeldts Alle´, Bygning 116 vest, DK-2800 Kgs. Lyngby, Denmark Available online 31 October 2006
Abstract In recent years many European countries have seen a decoupling of the growth in road freight traffic (vehicle kilometres) from economic growth. A similar decoupling has not been observed in road freight transport (tonne kilometres). In this paper the historical growth in national Danish road freight traffic and transport is attributed to causes using a Divisia index decomposition method. It is demonstrated that overall road freight traffic growth is a consequence of often opposite pointing growth effects in the underlying factors. The observed decoupling of road freight traffic growth from economic growth is mainly the result of use of larger vehicles, increasing average loads, and less empty running. Growth in road freight transport is primarily caused by growth in production. A decrease in the number of tons lifted per tonne produced (the handling factor) is offset by an increase in the tonne kilometres per tonne lifted. r 2006 Elsevier Ltd. All rights reserved. Keywords: Decomposition; Decoupling; Growth; Freight; Transport; Sustainable development
1. Introduction Almost a quarter of man made carbon dioxide (CO2) emissions in Europe were transport related in 2003 and 84% of emissions from transport were caused by road transport in 1998.1 Emissions from transport are closely related to the volume of transport and the energy consumed. Historically, freight transport volumes (tonne kilometres) and economic activities have followed similar trends (see e.g. Stead, 2001 and Tapio, 2005 for evidence from the European Union). The improvements in fuel efficiency have not been sufficient to offset the increase in transport. This has led the European Commission to emphasise the importance of decoupling freight traffic growth from economic growth (CEC, 2001a, b). Decoupling of road freight traffic growth (vehicle kilometres) from economic growth has been observed in the 1980s and 1990s in Denmark. Similar patterns can be observed in Finland, Ireland, Sweden and in the UK, whereas countries like the Netherlands, Germany and Switzerland have Corresponding author. Tel.: +45 45256554. 1
E-mail address: [email protected] (O. Kveiborg). According to Eurostat official tables.
0967-070X/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.tranpol.2006.07.002
experienced the opposite2 development. Similar patterns are not observed for Danish road freight transport (tonne kilometres), but weak decoupling is found in the 1990s in e.g. UK, Ireland, Sweden, Finland and Luxembourg according (Tapio, 2005). In this paper we shall investigate the causes for the observed decoupling of road freight traffic and transport and hereby investigate whether the decoupling is likely to be sustained in the future. We address this issue by a Divisia index decomposition method that enables us to calculate the relative contributions to the growth in road freight traffic and transport from economic activities, the composition of commodities, the weight to value ratios, the handling of the commodities, the average load and trip length. We have obtained a unique data set combining detailed national accounts with the annual Danish freight transport survey. The national accounts data provide information about the economic activity in industries combined with 2 This is based on Eurostat tables for Gross Domestic Product (GDP) and vehicle kilometers with heavy vehicles. The tables were found on the Eurostat webpage in 2004 in the transport section and in the national accounts statistics. The tables for different countries and variables are provided for uneven periods and the definition of which trucks are included is ambiguous.
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O. Kveiborg, M. Fosgerau / Transport Policy 14 (2007) 39–48
commodity groups following the NST/R-24 classification used in European transport statistics over a period ranging from 1981 to 1992. Total production values were available until 1997. Moreover, the data contain the weight of the production by industry and commodity group; this information is based on trade statistics enhanced with information on the weight of traded goods. All production statistics are total accounts based on all registered trading of goods. These statistics contain the value as well as the weight of the goods. We combine the national accounts data with the annual Danish freight transport survey, which provides us with information on transport volumes (tonnes, tonne km, vehicle km, trips, etc.). The transport data were available until 2003. Growth in road freight traffic and transport is caused by several factors. While economic growth of course is very important, other factors are important as well. Our data enable us to analyse the influence of these factors. Central to the discussion of sustaining and increasing the decoupling in the future are issues related to logistical restructuring, trip lengths and average loads. Another result of this paper, also discussed in Fosgerau and Kveiborg (2004), is that changes in the composition of commodities within industries and the weight to value ratios (the inverse value density) do not seem to contribute significantly to the development in road freight transport or road freight traffic. The growth in the factors described in this paper is composed of changes at a lower level of aggregation. For example logistical restructuring is an aggregation of changes in firms’ use of subcontractors, use of distribution centres, changes in the supply chain etc. We do not discuss these underlying changes, but focus on the aggregate measures. A discussion of the various elements that contribute to the changes in the aggregate measures can be found in, e.g., Van de Riet et al. (2004). The link between economic activity and transport is described in many aggregate models. Some examples are Mckinnon and Woodburn (1996), Lakshmanan and Han (1997), NEI et al. (1999) in the REDEFINE project (Jackson et al., 1989), and Steer Davies Gleave (2003). These models use a number of conversion factors to transform economic activity measured in economic terms to transport activity (or energy use) measured in physical terms. Lakshmanan and Han (1997) analyse the factors behind CO2 emissions from transport in the US. They include all types of modes and both passenger and freight transportation. They attribute changes in CO2 emissions to transport intensity per GDP, energy intensity, different types of modes and finally interaction effects. Steer Davies Gleave (2003) applies a similar approach to different European countries. We follow the Lakshmanan and Han (1997) approach, but we extend their analysis by describing the factors included in their transport intensity per GDP measure. In particular we add the value to weight ratios and the average length and load of haul. We thus provide additional insight into the driving forces behind the development in freight traffic and the
energy use of freight transport. In contrast to most of the above-mentioned literature our interest is mainly in road freight transport. We have chosen not to focus on all modes because (1) road transport is the primary mode in national freight transport in Denmark and (2) it is in our view somewhat artificial to compare freight traffic and transport across modes using a common measure such as vehicle kilometres or tonne kilometres. However, modal shift will obviously influence decoupling and it is included indirectly in our handling factor. The paper is organised as follows. In Section 2 we present the basic data sources, the model linking economic activity to freight transport and the Divisia index decomposition methodology. Section 3 describes the results of applying the decomposition methodology to our data. In Section 4 we discuss the decoupling factors. Finally, we sum up our findings in Section 5. 2. The model and the data Our analysis is carried out by decomposing changes in road freight traffic and transport on contributions from independent factors. Decomposition analysis is most often used when data are limited or when data are difficult to compare across different sources, e.g. the combination of economic data and freight transport data. The Divisia index approach is very suited for decomposing time trends into different factors. A good introduction to the method is Boyd et al. (1987). We will return to the specific formulation later. The starting point of the analysis is the model outlined in Fig. 1. The boxes correspond to observables: production first by industry, then by commodity and industry, production in tonnes, tonnes lifted by truck size and owner, and kilometres driven. Arrows between boxes correspond to transformation factors: commodity mix by industries, value density by commodity, handling factor by commodity, distribution on vehicle size and owner of the vehicle by commodity, and
Fig. 1. The conceptual model used in the analysis.
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41
160 150 147
140
137
130
Index
120 110 100 90 80 Production value GDP
70 60
1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 Year Fig. 2. Growth differences between GDP and production values in Denmark between 1981 and 1997.
average load and trip length by commodity, truck size and owner of the truck, which we have calculated. We analyse the change in these factors as the causes for the observable growth in freight traffic and transport. The quantities and factors are described in more detail below. We use the industry production values instead of GDP as our economic measure. The GDP of an industry is the value of its production less the value of inputs other than capital and labour. Thus GDP is a poor measure of the volume of goods transported, since the whole product is transported, not just the part that is added by the industry. To measure the volume of goods traded we therefore use the production value, which includes the value of all inputs. The two economic measures are closely related and follow similar patterns throughout the analysed period as illustrated in Fig. 2. GDP growth was 2.0% and production values grew by 1.7% p.a. from 1981 to 1997. The only difference in growth rates occurred in 1987 and 1988. This was probably the result of a very restrictive fiscal policy in Denmark with a huge cutback in government spending. The analysis here will thus reveal the same patterns irrespective of which of the two economic activity measures is used. The data we use in the model and illustrated by the boxes in Fig. 1 are: Xijt: The production value in fixed prices in industry i of commodity j in year t Mjt: The weight of the production of commodity j in year t Ljklt: The tons lifted of commodity j in truck size k owned by type l in year t Tjklt: Trips with truck size k owned by type l carrying commodity j in year t3
Kjklt: Vehicle km with truck size k owned by type l carrying commodity j in year t K~ t : Vehicle km including miscellaneous goods not classified in the regular P commodity groups in the transport statistics4 ðK~ t ¼ jkl K jklt þ K Misc:;t Þ: Kt: Vehicle km including empty running ðK t ¼ K~ t þ K Empty; t Þ: Vjklt: Tonne km with truck size k owned by l carrying commodity j in year t Vt: Tonne km including miscellaneous goods not classified in the regularPcommodity groups in the transport statistics ðV t ¼ jkl V jklt þ V Misc:; t Þ: The two latter variables are not shown in Fig. 1. They are used in the corresponding model for road freight transport. The difference between this model and the model illustrated in Fig. 1 will be explained below. The data in Xijt give production values in fixed prices for 19 industries and 26 groups of commodities in the Danish economy. Lists of the industries and the commodity groups are provided in the Appendix. This information is given for a period covering 1981–1992 for commodity details, but until 1997 for the industry disaggregation. All values are total accounts based on national make tables. The make tables classify production according to the producing industry, i, and the commodity, j. Similarly use tables classify inputs according to commodity and the using industry. The combination of make and use tables leads to the input–output tables. The make and use tables comprise 2900 commodities at the fundamental level; in our data set this has been aggregated according to the standard 4
3
Used in the calculation of the average trip length and load and not shown in the figure.
The drivers filling out the questionnaire in the transport survey may not always know what they are conveying, e.g., because it is packed in a container etc. The value is not shown in the figure.
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goods classification for transport statistics (NST/R-24), which is used in many European transport statistics. The commodity groups are linked through the make table to 19 aggregate industries, corresponding to those used in the ADAM model of the Danish economy (Dam, 1995), which among other things, forecasts production values by industry. The national accounts data are given in monetary values. We have supplemented with information about the production in industries and of commodities measured in tonnes. These data have been produced by Statistics Denmark (Pedersen, 1999) by looking at each of the 2900 commodities and using available information about the weight of each of these commodities. Often this information is directly available from the national trade statistics, but sometimes assessments have been made, for example of the weight of 100 sq m of carpet etc. This is very timeconsuming work, which is why these data were only constructed for 6 years (1981, 1983, 1985, 1987, 1990 and 1992). We have constructed data for intermediate years by linear interpolation. The data also contain transport volumes (tonnes lifted, trips, tonne kilometres and vehicle kilometres including empty running) by the same types of commodities and by the size of the conveying truck. These data are recorded in an annual survey of a sample of Danish trucks. The recorded transport includes both transport by own-account and hire-and-reward transport, but only domestic trips within Danish borders. This means that trips starting or ending outside Danish borders and cabotage trips by foreign trucks in Denmark are not accounted for in the data. These data are available from 1981 to 1997 at the detailed level and until 2003 at an aggregate level. The transport statistics have been used for official freight transport statistics in Denmark since 1981. Participation in the survey is mandatory for those sampled by Statistics Denmark. The odometer of all Danish motor vehicles has been registered at regular intervals since 1998. This information is used to scale the survey data and has thus improved the already rather high quality of the transport data we have used. We use the data to construct the following variables, which are represented in Fig. 1 as the arrows between the boxes (the variables corresponding to the calculation of tonne kilometres and the mark up factors are not illustrated in the figure): P zt ¼ V t = j V jt a mark-up on tonne km of miscellaneous goods, P xt ¼ K~ t = j K jklt a mark-up on vehicle km of miscellaneous goods, a mark-up on vehicle km of empty wt ¼ K t =K~ t running, bjt ¼ V jt =Ljt the amount of tonne km per tonne lifted by commodity type j, ojklt ¼ K jklt =T jklt the average trip length by commodity type j, truck size k, owned by l,
kjklt ¼ Ljklt =T jklt jklt ¼ Ljklt =Ljkt
sjkt ¼ Ljkt =Ljt ljt ¼ Ljt =M jt gjt ¼ M jt =X jt aijt ¼ X ijt =X it X it ¼
P
j X ijt
the average load per trip by commodity type j, truck size k, owned by l, the share of tons lifted per vehicle size and ownership by commodity type j, truck size k, owned by l, the share of tons lifted per vehicle size, by commodity type j, the handling factor by commodity type j, the weight to value ratio by commodity type j, (the inverse value density) the share of production of commodity j in industry i, production of commodity j by industry i.
We can decompose the growth in traffic on these variables using an approach similar to Jorgenson et al. (1987). The method is known as the Divisia index decomposition (Liu et al, 1992, and Lakshmanan and Han, 1997). This method gives accurate estimates for infinitesimal changes and is a very good approximation for short-term changes (Lakshmanan and Han, 1997). The following expression for vehicle kilometres (Kt) holds by definition: X X ojklt X K t wt xt K jklt xt wt jklt sjkt ljt gjt aijt X it . kjklt I jkl jkl (1) Denoting the growth rate in a variable as Y_ ¼ qlnY =qt we can write K_ t as X K jklt X K jklt _ jklt k_ jklt K_ t w_ t þ x_ t þ o Kt Kt jkl jkl X K jklt X K jkt þ s_ jkt _jklt þ Kt Kt jkl jk X K jt X K jt X K ijt l_ jt þ þ gjt þ aijt Kt Kt Kt j j ij X K ijt þ ðX_ it X_ t Þ þ X_ t . ð2Þ K t ij This means that we can decompose the growth in traffic into the following factors starting from the left: w_ t x_ t P _ K =K o Pjkl jklt t jklt _ K =K k Pjkl jklt t jklt _jklt jkl K jklt =K t P
_ jkt jk K jkt =K t s
P
_
j K jt =K t ljt
growth in empty running (mark-up), growth in traffic with miscellaneous goods (mark-up), growth in average trip length, growth in average load, growth in the share of tons lifted by ownership, growth in the share of tons lifted by vehicle size, growth in the handling factor,
ARTICLE IN PRESS O. Kveiborg, M. Fosgerau / Transport Policy 14 (2007) 39–48
P P P
_ jt j K jt =K t g
weight to value ratio (growth in the inverse value density), growth in commodity mix in the industries, growth in the industries’ production value, and finally growth in overall production value.
ij K ijt =K t aijt
_ X_ t Þ
ij K ijt =K t ðX it
X_ t
The model is formulated in continuous time. Our data are discrete, hence we approximate by Y_ t Y t Y t1 =Y t1 for most of the series and by Y_ t 2ðY t Y t1 Þ=ðY t þ Y t1 Þ when the series change to and from zero; the weights Kjklt/Kt are furthermore replaced by two-year averages in the latter type of series. Most of the series include a continuous positive (or negative) trend. Two-year averages in these cases lead to growth rates below (above) the true averages. When series differ between positive and negative changes the over- and under estimations cancel out. We do not include interaction terms in our decomposition (2). The interaction terms are the joint contribution to growth from the included factors. This can be illustrated by the following example. Assume that the model can be described as x ¼ yz. Then the following decompositions are equivalent: Dx x1 x0 y1 z1 y0 z0 Dyz0 y1 Dz ¼ ¼ ¼ x0 y0 z 0 x0 y0 z 0 Dy Dz DyDz ¼ þ þ . y0 z0 y0 z 0 From this we can se that the final interaction term is not included in the discrete approximation we use. Oosterhaven and Hoen (1998) discuss the differences between different
43
decomposition methods including and excluding the interaction terms. They argue that the interaction terms will only be important for long time intervals between subsequent observations of five years and more. This is supported by Lakshmanan and Han (1997). Since we use a time interval of one year we do not include interaction terms. We can apply the same type of decomposition to growth in transport (tonne kilometres). This model is somewhat simpler as the first factor in (2) is replaced by z_ t and the following four factors by a single factor bjt relating transport performance to tons lifted. This is contained in Eq. (3). V_ t z_ t þ
X V jt j
þ
Vt
X V ijt ij
Vt
b_ jt þ
X V jt j
Vt
l_ jt þ
X V jt j
ðX_ it X_ t Þ þ X_ t .
Total production Production by industry Commodity mix
Accumulated growth in pc
Value density Handling factor Misc. mark-up
0.2
ij
Vt
aijt ð3Þ
The growth in the factors contributing to the growth in freight transport is shown in Fig. 3. Transport and total production are visibly correlated. However, we can see that transport developed a little slower than total production from the early 1990s. The explanations for this tendency are found in the growth in the remaining factors, which we will discuss below. The average growth p.a. for each of the included factors is shown in Table 1. The most important factor was obviously total production (2.43%) as expected, but the composition of the
Tonne km
0.3
X V ijt
3. Decomposing transport and traffic
0.5
0.4
Vt
gjt þ
Tonne km/ton
0.1
0
-0.1
-0.2
-0.3 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 Year Fig. 3. Factors contributing to the growth in freight transport.
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Table 1 Average growth in percent p.a. for the factors explaining growth in freight transport (tonne km), 1981–1997 Factor
Avg. growth percent p.a.
Total production +Production by industry +Commodity mix +Inverse value density +Handling factor +Tonne km/tonne +Misc. mark-up
2.43 0.78 0.05 0.36 1.05 1.05 0.20
+Correction for approximations and missing values
0.33
¼ Tonne km
1.94
Avg. growth index 125 40 3 19 54 54 10 17 100
production across industries was also an important factor (0.78%). The decline in this factor has reduced the growth in road freight transport as the production has shifted towards less transport intensive industries that mainly produce services. Traditional service industries (the last eight industries in the list in the Appendix) have increased production from a share 59.9 to 60.5% of total production; industries that produce physical goods only produce approximately 4% of total service production (this share is slightly declining through the period). The impact from changes in the level of service production is thus mainly reflected in the changes in industry output and not through changes in the value density. Having accounted for the production across industries we can see that the changes in the commodity mix5 within industries only had a minor impact on the development on freight transport (0.05%). This means that within each industry there was little tendency for the mix of products to shift towards more transport intensive products. We similarly find that the value to weight ratio only had a minor influence on the growth (0.36%) and that most of this growth can be attributed to the period 1981–1983 (see Fig. 3). Thus, except for the first years, there is little tendency for products of each commodity type to become heavier per monetary unit. In other words, the production value in fixed prices of an industry was closely related to the physical weight of the production. The handling factor is important in explaining the transport growth. The handling factor has decreased (1.05%), which is an indicator of a smoother logistical 5 We have assumed the same growth in the commodity mix, the value density and the handling factor in the years from 1992 to 1997. This has had some influence on the identity of the calculations, which we have absorbed in the growth factor for the miscellaneous mark up factor. We can check that this has only a minor influence by comparing the sum of the growth factors using the assumed growth rates and compare to the growth in tonne km. We overestimate the growth in the tonne km by a little more than 0.33% (shown in Table 1) by this method, which we believe to be due to a larger negative growth in the handling factor.
chain. The produced goods are involved in fewer trips using trucks, which reduce the amount of road transport. Many things influence this particular factor (see e.g. Cool, 1997) such as the production and the transport logistics with the use of further sub-contractors, distribution centres and the use of other non-road based modes. However, there are also two issues related to the data quality. First, we note that the handling factor combines the two different data sources; national accounts and travel diaries. The first measures the product weight without packaging whereas the latter includes packaging. This affects both the size of the handling factor and the development as the amount of packaging changes. Second, if more goods are conveyed directly from producer to its final destination and cross a border on the way, or if the use of other modes has increased, then these influence the relationship between the two data sources. This is a consequence of the data, which only contain national transport on roads and not transport crossing Danish borders. This influences the size of the handling factor (e.g. a lower handling factor, when some products are transported directly to abroad or on other modes). The penultimate factor in Table 1: tonne kilometres per tonne increased (1.05%) and has thus lead to an increase in transport. The factor is often interpreted as a measure of trip length. However, it is only an approximation for the trip length. Tonne kilometres are found as a calculation per trip (Tonkmtrip ¼ Tontrip nKmtrip ). The total amount of tonne kilometres is P calculated as the sum of tonne kilometres per trip ( trip Tonkmtrip ). Hence, calculating total tonne kilometres divided by total tonnes is not equal to the observed amount of vehicle kilometres P P ð trip Tontrip nKmtrip =Tona trip Kmtrip Þ P nor is it equal to the P average trip distance ð trip Tontrip nKmtrip = P Tona trip Kmtrip = trip 1Þ. Finally, we note that the changes in the transport performance with miscellaneous goods (including the correction mentioned above) did not influence the aggregate transport performance by much since it had an average growth rate of only 0.20% p.a. We noted above that freight transport measured as tonne kilometres is a composite measure combining both weight and distance. Both the total tons lifted and the total distances were increasing so we need to go into more detail to get some indication of the importance of the two factors as explanations for the observed developments in freight transport and traffic. We do this by looking at the vehicle kilometre (road freight traffic). Applying the decomposition in (2) we get the average growth rates p.a. shown in Table 2. The related historical accumulated growth is shown in Fig. 4. The table shows the relevant effects differentiated on the size of the vehicle. This enables us to verify the expected effects of vehicle size on vehicle kilometres by showing that larger vehicles perform a larger share of the kilometres. However, to make Fig. 4 easier to read we have not included this differentiation in the figure.
ARTICLE IN PRESS O. Kveiborg, M. Fosgerau / Transport Policy 14 (2007) 39–48 Table 2 Average growth in percent p.a. for the factors explaining growth in freight traffic (vehicle km), 1981–1997 Factor
Avg. growth percent p.a.
Avg. growth index
Total production +Production by industry +Commodity mix +Inverse Value density +Handling factor +Truck size Small trucks Large trucks +Ownership Own account, small trucks Hire and rew., small trucks Own account, large trucks Hire and rew., large trucks Average load Small trucks Large trucks +Average length Small trucks Large trucks +Miscellaneous mark-up +Empty running mark-up +Correction for approximations and missing values
2.43 0.95 0.22 0.39 1.11 1.09 2.52 1.43 0.52 0.16 0.36 0.42 0.10 1.06 0.93 0.13 2.04 1.78 0.26 0.42 0.28 0.27
301 118 27 49 138 135 312 177 64 20 44 52 13 131 116 16 253 221 32 52 35 34
0.81
100
¼ Vehicle km
Again we find that the total production had a strong influence on the development of road freight traffic. Having accounted for the composition across industries we can see that the composition of the individual commodities within industries only contributed by a small amount to the overall growth.6 We further find that the average growth rates for the handling factor (1.11%), the truck size (1.09%), the average load (1.06%) and the average length of haul (2.04%) were larger than the average growth rate in freight traffic (0.81%). This emphasises the importance of accounting for the influential factors when explanations for the development in road freight traffic (and transport) are to be found. Failing to account for the development in the underlying factors, when they are of this magnitude could result in misleading forecasts and wrong conclusions. We find as expected that the handling factor had a negative impact on the vehicle kilometres because the number of trips per unit of a good is reduced, which leads to fewer kilometres. We also find that the truck size had a large reducing impact on the vehicle kilometres. The factor is calculated as the share of the tonnes lifted by small and large trucks respectively. We see that the influence from the share of tonnes lifted by small trucks was negative while the 6 Again we have assumed the same average growth rate for the four years, where we do not have data. For the analysis of the vehicle kilometre the error coming from this is an underestimation of 0.27 %, which is included in Table 2.
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influence from large trucks is positive. This implies that small trucks lifted relatively fewer tonnes through the observed period. We do not see changes in the growth factor for neither the small nor the large trucks (Fig. 4); they both showed a relatively steady pattern over time with a negative growth rate for small and a positive growth for large trucks. We do not see similar clear patterns with respect to the growth rates in vehicle ownership. There was a small shift from small company owned trucks to small hire-andreward trucks and an opposite shift from large hire-andreward trucks to large company owned trucks. The two remaining influential factors are the average loads and average length of haul. The average load was increasing with a reduction in the vehicle kilometres as the outcome (note that this effect is computed in Table 2 as given by Eq. (2)). However, the increase in the average load was more than offset by the growth in the average trip length where the small trucks contributed by most of this growth. It is interesting to note that the contribution from growth in empty running was negative. Hence, the utilisation of the vehicles was improved, also in this respect. However, this effect was moderate. 4. Decoupling freight traffic Comparison of Table 1 and 2 can help us understand the observed decoupling of freight traffic from economic growth. The main difference between the growth rates in the two measures of transport (vehicle kilometres and tonne kilometres) lies mainly in the factors not included in the decomposition of the tonne kilometres, as the first economic growth factors are similar in size. The utilisation of the vehicles is increasing due to reduced empty running and due to a larger average load. The latter effect is primarily seen within the group of small vehicles. We should note here that the increasing load factor could be the result of a shift towards larger vehicles within the group of small vehicles. We cannot verify this with our data, since we only have two size groups of vehicles (smaller and larger than 16 tonnes total weight). Nevertheless, the outcome is the same, namely that fewer kilometres are registered in order to convey the same amount of goods. Note also that the vehicle size does not have an effect on the tonne kilometres. Hence, the utilisation of the vehicles does not influence the transport performance (tonne km). We further find that the average distance per trip has increased relatively more for small trucks compared to large trucks. This could be an indication of a centralisation of distribution centres or storage facilities, where smaller vehicles are used for local distribution. Fewer distribution centres means increasing average distances and more kilometres driven by small vehicles, which are the type of vehicles that are used for local distribution. The increasing number of vehicle kilometres driven by small trucks
ARTICLE IN PRESS O. Kveiborg, M. Fosgerau / Transport Policy 14 (2007) 39–48
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0.5
0.5
0.3
Vehicle size Avg. trip length Avg. load
0.4 Accumulated growth in pct
Accumulated growth in pct
0.4
Vehicle km Total production Production by industry Handling factor
0.2 0.1 0 -0.1
0.3 0.2 0.1 0 -0.1
-0.2
-0.2
-0.3
-0.3
-0.4
Vehicle km Total production Commodity mix Value density Ownership Misc. mark-up Empty running
-0.4 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997
1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997
Year
Year
Fig. 4. The historical decomposition of freight traffic (vehicle km) 1981–1997.
maintains the relative high impact on the growth in total vehicle kilometres from this category of trucks despite a declining number of tonnes lifted. It is important to note that the decoupling started in 1990. The data cannot in itself tell us why this was the starting point. One could suspect that increased cabotage was the cause. In 1989 the European Community introduced a new regulation allowing foreign vehicles to perform transport in other countries (e.g. cabotage and third country transport). In relation to our analysis only cabotage transport in Denmark is of interest as this is performed within Danish borders by foreign trucks. Our data contain transport within Danish borders on Danish trucks. Third country transport is border crossing and has thus not been included in our data. Hence, any shift from Danish trucks to foreign trucks in border crossing transport will not influence our calculations. We do not have any comparable statistics about foreign cabotage transport in Denmark, but the amount of cabotage transport performed by Danish trucks abroad was about 1% of total transport performed by Danish trucks abroad in 1999 (Statistics Denmark, 2000). As Denmark is a very small country it is not likely that foreign trucks perform a higher amount of cabotage transport in Denmark. Altogether, the impact of the liberalisation in the EC must be expected to be limited and the explanation for the timing of the decoupling must be sought elsewhere. The average growth rate in freight traffic from 1989 to 1997 was negative and strong decoupling can be observed following the Tapio (2005) terminology. However, this negative growth is not sustained. Between 1997 and 2002 freight traffic grew with an average of 2.1% p.a. This was slightly less than the corresponding average economic growth of 2.4% p.a. Following Tapio (2005) this should be characterised as expansive coupling. The change from decoupling and negative growth in vehicle kilometres to increasing growth rates and coupling can be explained by upper limits to capacity utilisation of the vehicles. Due to legislations on allowable vehicle sizes there are upper limits on the size of the average load. There are similar lower limits to the amount of empty running. The observed
average load and empty running are approaching these limits during the period covered by our data. This means that more vehicles must be used to accommodate the increasing demand induced by continuing economic growth and thus that the vehicle kilometres start growing at a faster pace. 5. Conclusion In this paper we have applied the Divisia index decomposition method to a Danish data set in order to analyse factors driving the development in road freight transport and road freight traffic. Our data show a decoupling in road freight traffic, especially strong from 1989 to 1997, but no similar decoupling in road freight transport. This is in line with results obtained for other countries (e.g. Stead, 2001; Tapio, 2005). Growth in transport is primarily a consequence of growth in economic activity, but our results point out several other factors that influence the development. In addition to economic growth there is an effect from goods being conveyed over longer distances. However, a change in production towards more service industry production together with a logistical change towards less handling of the goods diminishes the growth in freight transport. Besides these effects we do not find other relevant factors that contribute to either increased or decreased growth in freight transport. Adding the effects together leads to an average growth rate in freight transport of 1.94% p.a., which is close to the 2.43% growth in total production. We are also interested in knowing the consequences for the infrastructure, energy consumption and emissions of, e.g., CO2, which are closely related to the vehicle kilometres performed by the trucks. The growth in road freight traffic is decoupled from economic growth, which beyond the factors contributing to the growth in road freight transport is caused by an increased use of larger trucks, and better utilisation with increasing average loads on the vehicles and reduction in empty running. However, there are large changes in trip length and average load.
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The increasing average load has a reducing influence on the growth in road freight traffic, whereas the average trip length has the opposite effect. In total we observe a growth in road freight traffic of only 0.81% p.a. Changes in energy consumption and thus also in CO2 emissions will follow the growth rates in vehicle kilometres, because energy efficiency has not changed very much throughout the analysed period. Data on the CO2 emissions per kilometre show almost no improvement between the different environmental vehicle classes introduced by the EU (the so-called EURO classes). We therefore conclude that energy consumption and CO2 emissions follow the same decoupling trend as found in the vehicle kilometres. Emission of other substances will most likely be even further decoupled because large reductions in emissions per kilometre are introduced in new vehicles. These findings are interesting because some of the important factors are closely related to policies influencing freight transport. We have found that the logistical element is quite important through a decreasing handling factor. The handling factor reflects a falling number of loading and un-loading of vehicles at distribution centres and warehouses, etc. This shows that transport is influenced by the existence of e.g. distribution centres and other arterial infrastructure facilities. The policy choice of where and how to provide such infrastructures can be of great importance for the development of freight transport and traffic. Moreover, it may also be considered a task for policy makers to provide instruments that can help transport providers to minimise their empty running. This would call for a central system, where available capacity could be announced and transport buyers could take advantage of this spare capacity. Small-scale systems like this exist, but the large potential can only be explored if a large number of vehicles are included in the system. This would probably need a strong political support. Our results show that there may be an important impact from this. Another important effect is the maximum laden weight. We have observed an increasing average load and an increase in the size of trucks used. Both of these effects reduce freight traffic. However, it is not permitted to use vehicles larger than 48 tonnes total weight (40 tonnes for international traffic) in Denmark. This sets an upper limit to the growth in average loads and also on the effect that can be obtained from using larger vehicles. The current discussion about legalising 60 tonnes vehicles in Denmark can have an impact on the possibility of sustaining the low growth rate in freight traffic (note that freight transport is not affected by the two factors discussed here). However, this is an issue that needs further analysis. Larger vehicles would reduce transport costs and thus lead to increasing demand (induced demand and modal shift). This offsets the potential sustained low growth in road freight traffic. From a methodological point of view, the results show that it is important to distinguish between industries, but that a further distinction between commodities within the industries is less important. The results further indicate
47
that the assumption of identity between industries and goods often made in input–output analysis should not be a cause of great concern to the aggregate type of models. The relatively unimportant changes in the value densities are the result of the price-quantity deflator employed. The Passche index (a typical quantity deflator) is used in the Danish national accounts, which ensures that the deflated fixed prices are good quantity measures. Contrary to the suggestion by De Jong et al. (2004) and RAND Europe and Transek, 2001, it may not be necessary to put huge efforts into the calculation of appropriate value densities. This applies especially to the freight models using input–output tables and to computable general equilibrium modelling as the basis for the demand for freight transport. Such models involve a transformation from trade flows in monetary terms to transport flows in tonnes. This transformation involves both the value densities and the handling factor. Our analysis indicates that it may not be problematic to use a composite transformation factor. In a modelling perspective these findings imply that focus should be put on describing the changes within different industries, which then gives us very good indications of the demand for freight transport. Appendix List of industries and commodity types (sorted in ascending order with respect to transport volume) Note: Industries 12 through 19 produce non-physical services only Industry 1
Commodity type 1
Food
2
Construction materials Food products
2
3
Agriculture
3
4 5
Metal industries Chemical products Petroleum refineries Other manufacturing Beverages and tobacco Transportation equipment Energy extraction Other services Electricity, gas and water Construction
4 5
Miscellaneous goods (used in transport statistics only) Gravel, sand, dirt, stone, and salt Feed and fodder Cement, bricks, limestone
6
Cereals
7
Petrol and mineral oil
8
Wood
9
Machinery
6 7 8 9 10 11 12 13
10 11 12
Furniture, clothes, paper products etc. Live animals Fertilisers
13
Chemical products
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14 15 16 17 18 19
Wholesale and retail trade Sea transport Other transportation Financial services Housing Government services
14
Tar and asphalt
15 16
Semi-products from metal Potatoes
17
Sugar beets
18 19
Metal products Iron ore and scrap of iron
20 21 22
Cellulose and waste of paper Coal Skin and raw materials for textile production Fatty substances Glass and ceramics Crude oil Ore and scrap of other metals Services
23 24 25 26 27
References Boyd, G., McDonald, J.F., Ross, M., Hanson, D.A., 1987. Separating the changing composition of US manufacturing from energy efficiency improvements: a Divisia index approach. Energy Journal 8, 77–96. Commision of the European Communities, 2001a. European Transport Policy for 2020: Time to Decide [COM(2001)370]. http://www. europa.eu.int/comm/energy_transport/en/lb_en.html Commision of the European Communities, 2001b. A Sustainable Europe for a Better World: A European Union Strategy for Sustainable Development. [COM(2001) 264]. http://www.europa.eu.int/comm/ environment/eussd/index.htm Cool, T., 1997. An Estimator for the Road Freight handling factor. Working paper, Transport Research Center (AVV), Ministry of Transport, Rotterdam, The Netherlands. Dam, P.U., 1995. ADAM—A Model of the Danish Economy. Statistics Denmark, Copenhagen, Denmark.
de Jong, G., Gunn, H., Walker, W., 2004. National and International freight transport models: an overview and ideas for future development. Transport Reviews 24 (1), 103–124. Fosgerau, M., Kveiborg, O., 2004. Reviewing some critical assumptions in the relationship between economic activity and freight transport. International Journal of Transport Economics 31 (2), 247–261. Jackson, R.W., Hewings, G.J.D., Sonis, M., 1989. Decomposition approaches to the identification of change in regional economies. Economic Geography 65, 217–231. Jorgenson, D.W., Gollop, F.M., Fraumeni, B.M., 1987. Productivity and US Economic Growth. Harward University Press, Cambridge, MA. Lakshmanan, T.R., Han, X., 1997. Factors underlying transportation CO2 emissions in the USA: a decomposition analysis. Transportation Research D 2 (1), 1–15. Liu, X.Q., Ang, B.W., Ong, H.L., 1992. The application of the Divisia index to the decomposition of changes in industrial energy consumption. The Energy Journal 13 (4), 161–177. Mckinnon, A.C., Woodburn, A., 1996. Logistical restructuring and road freight traffic growth. An Empirical Assessment. Transportation 23, 141–161. NEI, et al., 1999. REDEFINE, relationship between demand for freighttransport and industrial effects. Final report. Netherlands Economic Institute, The Netherlands. Oosterhaven, J., Hoen, A.R., 1998. Preference, technology, trade and real income in the European Union.—An intercountry decomposition analysis for 1975–1985. The Annals of Regional Science 32, 505–524. Pedersen, O.G., 1999. Physical Input–output Tables for Denmark Products and Material 1999 Air Emissions 1990–92. Statistics Denmark, Denmark. RAND Europe and Transek, 2001. Idea Studies for a New Swedish Freight Model, RAND’s Study. SIKA, Stockholm, Sweden. Statistics Denmark, 2000. Statistiske efterretninger: Transport 2001, 20. Stead, D., 2001. Transport intensity in Europe—indicators and trends. Transport Policy 8, 29–46. Steer Davies Gleave, 2003. Freight Transport Intensity of Production and Consumption. Report no EUR 20864EN, Institute for Propesctive Technological Studies. Joint Research Centre, Seville, Spain. Tapio, P., 2005. Towards a theory of decoupling: degrees of decoupling in the EU and the case of road traffic in Finland between 1970 and 2001. Transport Policy 12, 137–151. van de Riet, O., de Jong, G., Walker, W., 2004. Drivers of Freight Transport Demand Worldwide. Paper presented to the European Transport Conference 2004, Strasbourg, France, October 2004.
Transport Policy 14 (2007) 39–48 www.elsevier.com/locate/tranpol
Decomposing the decoupling of Danish road freight traffic growth and economic growth Ole Kveiborg, Mogens Fosgerau Danish Transport Research Institute, Knuth-Winterfeldts Alle´, Bygning 116 vest, DK-2800 Kgs. Lyngby, Denmark Available online 31 October 2006
Abstract In recent years many European countries have seen a decoupling of the growth in road freight traffic (vehicle kilometres) from economic growth. A similar decoupling has not been observed in road freight transport (tonne kilometres). In this paper the historical growth in national Danish road freight traffic and transport is attributed to causes using a Divisia index decomposition method. It is demonstrated that overall road freight traffic growth is a consequence of often opposite pointing growth effects in the underlying factors. The observed decoupling of road freight traffic growth from economic growth is mainly the result of use of larger vehicles, increasing average loads, and less empty running. Growth in road freight transport is primarily caused by growth in production. A decrease in the number of tons lifted per tonne produced (the handling factor) is offset by an increase in the tonne kilometres per tonne lifted. r 2006 Elsevier Ltd. All rights reserved. Keywords: Decomposition; Decoupling; Growth; Freight; Transport; Sustainable development
1. Introduction Almost a quarter of man made carbon dioxide (CO2) emissions in Europe were transport related in 2003 and 84% of emissions from transport were caused by road transport in 1998.1 Emissions from transport are closely related to the volume of transport and the energy consumed. Historically, freight transport volumes (tonne kilometres) and economic activities have followed similar trends (see e.g. Stead, 2001 and Tapio, 2005 for evidence from the European Union). The improvements in fuel efficiency have not been sufficient to offset the increase in transport. This has led the European Commission to emphasise the importance of decoupling freight traffic growth from economic growth (CEC, 2001a, b). Decoupling of road freight traffic growth (vehicle kilometres) from economic growth has been observed in the 1980s and 1990s in Denmark. Similar patterns can be observed in Finland, Ireland, Sweden and in the UK, whereas countries like the Netherlands, Germany and Switzerland have Corresponding author. Tel.: +45 45256554. 1
E-mail address: [email protected] (O. Kveiborg). According to Eurostat official tables.
0967-070X/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.tranpol.2006.07.002
experienced the opposite2 development. Similar patterns are not observed for Danish road freight transport (tonne kilometres), but weak decoupling is found in the 1990s in e.g. UK, Ireland, Sweden, Finland and Luxembourg according (Tapio, 2005). In this paper we shall investigate the causes for the observed decoupling of road freight traffic and transport and hereby investigate whether the decoupling is likely to be sustained in the future. We address this issue by a Divisia index decomposition method that enables us to calculate the relative contributions to the growth in road freight traffic and transport from economic activities, the composition of commodities, the weight to value ratios, the handling of the commodities, the average load and trip length. We have obtained a unique data set combining detailed national accounts with the annual Danish freight transport survey. The national accounts data provide information about the economic activity in industries combined with 2 This is based on Eurostat tables for Gross Domestic Product (GDP) and vehicle kilometers with heavy vehicles. The tables were found on the Eurostat webpage in 2004 in the transport section and in the national accounts statistics. The tables for different countries and variables are provided for uneven periods and the definition of which trucks are included is ambiguous.
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commodity groups following the NST/R-24 classification used in European transport statistics over a period ranging from 1981 to 1992. Total production values were available until 1997. Moreover, the data contain the weight of the production by industry and commodity group; this information is based on trade statistics enhanced with information on the weight of traded goods. All production statistics are total accounts based on all registered trading of goods. These statistics contain the value as well as the weight of the goods. We combine the national accounts data with the annual Danish freight transport survey, which provides us with information on transport volumes (tonnes, tonne km, vehicle km, trips, etc.). The transport data were available until 2003. Growth in road freight traffic and transport is caused by several factors. While economic growth of course is very important, other factors are important as well. Our data enable us to analyse the influence of these factors. Central to the discussion of sustaining and increasing the decoupling in the future are issues related to logistical restructuring, trip lengths and average loads. Another result of this paper, also discussed in Fosgerau and Kveiborg (2004), is that changes in the composition of commodities within industries and the weight to value ratios (the inverse value density) do not seem to contribute significantly to the development in road freight transport or road freight traffic. The growth in the factors described in this paper is composed of changes at a lower level of aggregation. For example logistical restructuring is an aggregation of changes in firms’ use of subcontractors, use of distribution centres, changes in the supply chain etc. We do not discuss these underlying changes, but focus on the aggregate measures. A discussion of the various elements that contribute to the changes in the aggregate measures can be found in, e.g., Van de Riet et al. (2004). The link between economic activity and transport is described in many aggregate models. Some examples are Mckinnon and Woodburn (1996), Lakshmanan and Han (1997), NEI et al. (1999) in the REDEFINE project (Jackson et al., 1989), and Steer Davies Gleave (2003). These models use a number of conversion factors to transform economic activity measured in economic terms to transport activity (or energy use) measured in physical terms. Lakshmanan and Han (1997) analyse the factors behind CO2 emissions from transport in the US. They include all types of modes and both passenger and freight transportation. They attribute changes in CO2 emissions to transport intensity per GDP, energy intensity, different types of modes and finally interaction effects. Steer Davies Gleave (2003) applies a similar approach to different European countries. We follow the Lakshmanan and Han (1997) approach, but we extend their analysis by describing the factors included in their transport intensity per GDP measure. In particular we add the value to weight ratios and the average length and load of haul. We thus provide additional insight into the driving forces behind the development in freight traffic and the
energy use of freight transport. In contrast to most of the above-mentioned literature our interest is mainly in road freight transport. We have chosen not to focus on all modes because (1) road transport is the primary mode in national freight transport in Denmark and (2) it is in our view somewhat artificial to compare freight traffic and transport across modes using a common measure such as vehicle kilometres or tonne kilometres. However, modal shift will obviously influence decoupling and it is included indirectly in our handling factor. The paper is organised as follows. In Section 2 we present the basic data sources, the model linking economic activity to freight transport and the Divisia index decomposition methodology. Section 3 describes the results of applying the decomposition methodology to our data. In Section 4 we discuss the decoupling factors. Finally, we sum up our findings in Section 5. 2. The model and the data Our analysis is carried out by decomposing changes in road freight traffic and transport on contributions from independent factors. Decomposition analysis is most often used when data are limited or when data are difficult to compare across different sources, e.g. the combination of economic data and freight transport data. The Divisia index approach is very suited for decomposing time trends into different factors. A good introduction to the method is Boyd et al. (1987). We will return to the specific formulation later. The starting point of the analysis is the model outlined in Fig. 1. The boxes correspond to observables: production first by industry, then by commodity and industry, production in tonnes, tonnes lifted by truck size and owner, and kilometres driven. Arrows between boxes correspond to transformation factors: commodity mix by industries, value density by commodity, handling factor by commodity, distribution on vehicle size and owner of the vehicle by commodity, and
Fig. 1. The conceptual model used in the analysis.
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41
160 150 147
140
137
130
Index
120 110 100 90 80 Production value GDP
70 60
1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 Year Fig. 2. Growth differences between GDP and production values in Denmark between 1981 and 1997.
average load and trip length by commodity, truck size and owner of the truck, which we have calculated. We analyse the change in these factors as the causes for the observable growth in freight traffic and transport. The quantities and factors are described in more detail below. We use the industry production values instead of GDP as our economic measure. The GDP of an industry is the value of its production less the value of inputs other than capital and labour. Thus GDP is a poor measure of the volume of goods transported, since the whole product is transported, not just the part that is added by the industry. To measure the volume of goods traded we therefore use the production value, which includes the value of all inputs. The two economic measures are closely related and follow similar patterns throughout the analysed period as illustrated in Fig. 2. GDP growth was 2.0% and production values grew by 1.7% p.a. from 1981 to 1997. The only difference in growth rates occurred in 1987 and 1988. This was probably the result of a very restrictive fiscal policy in Denmark with a huge cutback in government spending. The analysis here will thus reveal the same patterns irrespective of which of the two economic activity measures is used. The data we use in the model and illustrated by the boxes in Fig. 1 are: Xijt: The production value in fixed prices in industry i of commodity j in year t Mjt: The weight of the production of commodity j in year t Ljklt: The tons lifted of commodity j in truck size k owned by type l in year t Tjklt: Trips with truck size k owned by type l carrying commodity j in year t3
Kjklt: Vehicle km with truck size k owned by type l carrying commodity j in year t K~ t : Vehicle km including miscellaneous goods not classified in the regular P commodity groups in the transport statistics4 ðK~ t ¼ jkl K jklt þ K Misc:;t Þ: Kt: Vehicle km including empty running ðK t ¼ K~ t þ K Empty; t Þ: Vjklt: Tonne km with truck size k owned by l carrying commodity j in year t Vt: Tonne km including miscellaneous goods not classified in the regularPcommodity groups in the transport statistics ðV t ¼ jkl V jklt þ V Misc:; t Þ: The two latter variables are not shown in Fig. 1. They are used in the corresponding model for road freight transport. The difference between this model and the model illustrated in Fig. 1 will be explained below. The data in Xijt give production values in fixed prices for 19 industries and 26 groups of commodities in the Danish economy. Lists of the industries and the commodity groups are provided in the Appendix. This information is given for a period covering 1981–1992 for commodity details, but until 1997 for the industry disaggregation. All values are total accounts based on national make tables. The make tables classify production according to the producing industry, i, and the commodity, j. Similarly use tables classify inputs according to commodity and the using industry. The combination of make and use tables leads to the input–output tables. The make and use tables comprise 2900 commodities at the fundamental level; in our data set this has been aggregated according to the standard 4
3
Used in the calculation of the average trip length and load and not shown in the figure.
The drivers filling out the questionnaire in the transport survey may not always know what they are conveying, e.g., because it is packed in a container etc. The value is not shown in the figure.
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goods classification for transport statistics (NST/R-24), which is used in many European transport statistics. The commodity groups are linked through the make table to 19 aggregate industries, corresponding to those used in the ADAM model of the Danish economy (Dam, 1995), which among other things, forecasts production values by industry. The national accounts data are given in monetary values. We have supplemented with information about the production in industries and of commodities measured in tonnes. These data have been produced by Statistics Denmark (Pedersen, 1999) by looking at each of the 2900 commodities and using available information about the weight of each of these commodities. Often this information is directly available from the national trade statistics, but sometimes assessments have been made, for example of the weight of 100 sq m of carpet etc. This is very timeconsuming work, which is why these data were only constructed for 6 years (1981, 1983, 1985, 1987, 1990 and 1992). We have constructed data for intermediate years by linear interpolation. The data also contain transport volumes (tonnes lifted, trips, tonne kilometres and vehicle kilometres including empty running) by the same types of commodities and by the size of the conveying truck. These data are recorded in an annual survey of a sample of Danish trucks. The recorded transport includes both transport by own-account and hire-and-reward transport, but only domestic trips within Danish borders. This means that trips starting or ending outside Danish borders and cabotage trips by foreign trucks in Denmark are not accounted for in the data. These data are available from 1981 to 1997 at the detailed level and until 2003 at an aggregate level. The transport statistics have been used for official freight transport statistics in Denmark since 1981. Participation in the survey is mandatory for those sampled by Statistics Denmark. The odometer of all Danish motor vehicles has been registered at regular intervals since 1998. This information is used to scale the survey data and has thus improved the already rather high quality of the transport data we have used. We use the data to construct the following variables, which are represented in Fig. 1 as the arrows between the boxes (the variables corresponding to the calculation of tonne kilometres and the mark up factors are not illustrated in the figure): P zt ¼ V t = j V jt a mark-up on tonne km of miscellaneous goods, P xt ¼ K~ t = j K jklt a mark-up on vehicle km of miscellaneous goods, a mark-up on vehicle km of empty wt ¼ K t =K~ t running, bjt ¼ V jt =Ljt the amount of tonne km per tonne lifted by commodity type j, ojklt ¼ K jklt =T jklt the average trip length by commodity type j, truck size k, owned by l,
kjklt ¼ Ljklt =T jklt jklt ¼ Ljklt =Ljkt
sjkt ¼ Ljkt =Ljt ljt ¼ Ljt =M jt gjt ¼ M jt =X jt aijt ¼ X ijt =X it X it ¼
P
j X ijt
the average load per trip by commodity type j, truck size k, owned by l, the share of tons lifted per vehicle size and ownership by commodity type j, truck size k, owned by l, the share of tons lifted per vehicle size, by commodity type j, the handling factor by commodity type j, the weight to value ratio by commodity type j, (the inverse value density) the share of production of commodity j in industry i, production of commodity j by industry i.
We can decompose the growth in traffic on these variables using an approach similar to Jorgenson et al. (1987). The method is known as the Divisia index decomposition (Liu et al, 1992, and Lakshmanan and Han, 1997). This method gives accurate estimates for infinitesimal changes and is a very good approximation for short-term changes (Lakshmanan and Han, 1997). The following expression for vehicle kilometres (Kt) holds by definition: X X ojklt X K t wt xt K jklt xt wt jklt sjkt ljt gjt aijt X it . kjklt I jkl jkl (1) Denoting the growth rate in a variable as Y_ ¼ qlnY =qt we can write K_ t as X K jklt X K jklt _ jklt k_ jklt K_ t w_ t þ x_ t þ o Kt Kt jkl jkl X K jklt X K jkt þ s_ jkt _jklt þ Kt Kt jkl jk X K jt X K jt X K ijt l_ jt þ þ gjt þ aijt Kt Kt Kt j j ij X K ijt þ ðX_ it X_ t Þ þ X_ t . ð2Þ K t ij This means that we can decompose the growth in traffic into the following factors starting from the left: w_ t x_ t P _ K =K o Pjkl jklt t jklt _ K =K k Pjkl jklt t jklt _jklt jkl K jklt =K t P
_ jkt jk K jkt =K t s
P
_
j K jt =K t ljt
growth in empty running (mark-up), growth in traffic with miscellaneous goods (mark-up), growth in average trip length, growth in average load, growth in the share of tons lifted by ownership, growth in the share of tons lifted by vehicle size, growth in the handling factor,
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P P P
_ jt j K jt =K t g
weight to value ratio (growth in the inverse value density), growth in commodity mix in the industries, growth in the industries’ production value, and finally growth in overall production value.
ij K ijt =K t aijt
_ X_ t Þ
ij K ijt =K t ðX it
X_ t
The model is formulated in continuous time. Our data are discrete, hence we approximate by Y_ t Y t Y t1 =Y t1 for most of the series and by Y_ t 2ðY t Y t1 Þ=ðY t þ Y t1 Þ when the series change to and from zero; the weights Kjklt/Kt are furthermore replaced by two-year averages in the latter type of series. Most of the series include a continuous positive (or negative) trend. Two-year averages in these cases lead to growth rates below (above) the true averages. When series differ between positive and negative changes the over- and under estimations cancel out. We do not include interaction terms in our decomposition (2). The interaction terms are the joint contribution to growth from the included factors. This can be illustrated by the following example. Assume that the model can be described as x ¼ yz. Then the following decompositions are equivalent: Dx x1 x0 y1 z1 y0 z0 Dyz0 y1 Dz ¼ ¼ ¼ x0 y0 z 0 x0 y0 z 0 Dy Dz DyDz ¼ þ þ . y0 z0 y0 z 0 From this we can se that the final interaction term is not included in the discrete approximation we use. Oosterhaven and Hoen (1998) discuss the differences between different
43
decomposition methods including and excluding the interaction terms. They argue that the interaction terms will only be important for long time intervals between subsequent observations of five years and more. This is supported by Lakshmanan and Han (1997). Since we use a time interval of one year we do not include interaction terms. We can apply the same type of decomposition to growth in transport (tonne kilometres). This model is somewhat simpler as the first factor in (2) is replaced by z_ t and the following four factors by a single factor bjt relating transport performance to tons lifted. This is contained in Eq. (3). V_ t z_ t þ
X V jt j
þ
Vt
X V ijt ij
Vt
b_ jt þ
X V jt j
Vt
l_ jt þ
X V jt j
ðX_ it X_ t Þ þ X_ t .
Total production Production by industry Commodity mix
Accumulated growth in pc
Value density Handling factor Misc. mark-up
0.2
ij
Vt
aijt ð3Þ
The growth in the factors contributing to the growth in freight transport is shown in Fig. 3. Transport and total production are visibly correlated. However, we can see that transport developed a little slower than total production from the early 1990s. The explanations for this tendency are found in the growth in the remaining factors, which we will discuss below. The average growth p.a. for each of the included factors is shown in Table 1. The most important factor was obviously total production (2.43%) as expected, but the composition of the
Tonne km
0.3
X V ijt
3. Decomposing transport and traffic
0.5
0.4
Vt
gjt þ
Tonne km/ton
0.1
0
-0.1
-0.2
-0.3 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 Year Fig. 3. Factors contributing to the growth in freight transport.
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Table 1 Average growth in percent p.a. for the factors explaining growth in freight transport (tonne km), 1981–1997 Factor
Avg. growth percent p.a.
Total production +Production by industry +Commodity mix +Inverse value density +Handling factor +Tonne km/tonne +Misc. mark-up
2.43 0.78 0.05 0.36 1.05 1.05 0.20
+Correction for approximations and missing values
0.33
¼ Tonne km
1.94
Avg. growth index 125 40 3 19 54 54 10 17 100
production across industries was also an important factor (0.78%). The decline in this factor has reduced the growth in road freight transport as the production has shifted towards less transport intensive industries that mainly produce services. Traditional service industries (the last eight industries in the list in the Appendix) have increased production from a share 59.9 to 60.5% of total production; industries that produce physical goods only produce approximately 4% of total service production (this share is slightly declining through the period). The impact from changes in the level of service production is thus mainly reflected in the changes in industry output and not through changes in the value density. Having accounted for the production across industries we can see that the changes in the commodity mix5 within industries only had a minor impact on the development on freight transport (0.05%). This means that within each industry there was little tendency for the mix of products to shift towards more transport intensive products. We similarly find that the value to weight ratio only had a minor influence on the growth (0.36%) and that most of this growth can be attributed to the period 1981–1983 (see Fig. 3). Thus, except for the first years, there is little tendency for products of each commodity type to become heavier per monetary unit. In other words, the production value in fixed prices of an industry was closely related to the physical weight of the production. The handling factor is important in explaining the transport growth. The handling factor has decreased (1.05%), which is an indicator of a smoother logistical 5 We have assumed the same growth in the commodity mix, the value density and the handling factor in the years from 1992 to 1997. This has had some influence on the identity of the calculations, which we have absorbed in the growth factor for the miscellaneous mark up factor. We can check that this has only a minor influence by comparing the sum of the growth factors using the assumed growth rates and compare to the growth in tonne km. We overestimate the growth in the tonne km by a little more than 0.33% (shown in Table 1) by this method, which we believe to be due to a larger negative growth in the handling factor.
chain. The produced goods are involved in fewer trips using trucks, which reduce the amount of road transport. Many things influence this particular factor (see e.g. Cool, 1997) such as the production and the transport logistics with the use of further sub-contractors, distribution centres and the use of other non-road based modes. However, there are also two issues related to the data quality. First, we note that the handling factor combines the two different data sources; national accounts and travel diaries. The first measures the product weight without packaging whereas the latter includes packaging. This affects both the size of the handling factor and the development as the amount of packaging changes. Second, if more goods are conveyed directly from producer to its final destination and cross a border on the way, or if the use of other modes has increased, then these influence the relationship between the two data sources. This is a consequence of the data, which only contain national transport on roads and not transport crossing Danish borders. This influences the size of the handling factor (e.g. a lower handling factor, when some products are transported directly to abroad or on other modes). The penultimate factor in Table 1: tonne kilometres per tonne increased (1.05%) and has thus lead to an increase in transport. The factor is often interpreted as a measure of trip length. However, it is only an approximation for the trip length. Tonne kilometres are found as a calculation per trip (Tonkmtrip ¼ Tontrip nKmtrip ). The total amount of tonne kilometres is P calculated as the sum of tonne kilometres per trip ( trip Tonkmtrip ). Hence, calculating total tonne kilometres divided by total tonnes is not equal to the observed amount of vehicle kilometres P P ð trip Tontrip nKmtrip =Tona trip Kmtrip Þ P nor is it equal to the P average trip distance ð trip Tontrip nKmtrip = P Tona trip Kmtrip = trip 1Þ. Finally, we note that the changes in the transport performance with miscellaneous goods (including the correction mentioned above) did not influence the aggregate transport performance by much since it had an average growth rate of only 0.20% p.a. We noted above that freight transport measured as tonne kilometres is a composite measure combining both weight and distance. Both the total tons lifted and the total distances were increasing so we need to go into more detail to get some indication of the importance of the two factors as explanations for the observed developments in freight transport and traffic. We do this by looking at the vehicle kilometre (road freight traffic). Applying the decomposition in (2) we get the average growth rates p.a. shown in Table 2. The related historical accumulated growth is shown in Fig. 4. The table shows the relevant effects differentiated on the size of the vehicle. This enables us to verify the expected effects of vehicle size on vehicle kilometres by showing that larger vehicles perform a larger share of the kilometres. However, to make Fig. 4 easier to read we have not included this differentiation in the figure.
ARTICLE IN PRESS O. Kveiborg, M. Fosgerau / Transport Policy 14 (2007) 39–48 Table 2 Average growth in percent p.a. for the factors explaining growth in freight traffic (vehicle km), 1981–1997 Factor
Avg. growth percent p.a.
Avg. growth index
Total production +Production by industry +Commodity mix +Inverse Value density +Handling factor +Truck size Small trucks Large trucks +Ownership Own account, small trucks Hire and rew., small trucks Own account, large trucks Hire and rew., large trucks Average load Small trucks Large trucks +Average length Small trucks Large trucks +Miscellaneous mark-up +Empty running mark-up +Correction for approximations and missing values
2.43 0.95 0.22 0.39 1.11 1.09 2.52 1.43 0.52 0.16 0.36 0.42 0.10 1.06 0.93 0.13 2.04 1.78 0.26 0.42 0.28 0.27
301 118 27 49 138 135 312 177 64 20 44 52 13 131 116 16 253 221 32 52 35 34
0.81
100
¼ Vehicle km
Again we find that the total production had a strong influence on the development of road freight traffic. Having accounted for the composition across industries we can see that the composition of the individual commodities within industries only contributed by a small amount to the overall growth.6 We further find that the average growth rates for the handling factor (1.11%), the truck size (1.09%), the average load (1.06%) and the average length of haul (2.04%) were larger than the average growth rate in freight traffic (0.81%). This emphasises the importance of accounting for the influential factors when explanations for the development in road freight traffic (and transport) are to be found. Failing to account for the development in the underlying factors, when they are of this magnitude could result in misleading forecasts and wrong conclusions. We find as expected that the handling factor had a negative impact on the vehicle kilometres because the number of trips per unit of a good is reduced, which leads to fewer kilometres. We also find that the truck size had a large reducing impact on the vehicle kilometres. The factor is calculated as the share of the tonnes lifted by small and large trucks respectively. We see that the influence from the share of tonnes lifted by small trucks was negative while the 6 Again we have assumed the same average growth rate for the four years, where we do not have data. For the analysis of the vehicle kilometre the error coming from this is an underestimation of 0.27 %, which is included in Table 2.
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influence from large trucks is positive. This implies that small trucks lifted relatively fewer tonnes through the observed period. We do not see changes in the growth factor for neither the small nor the large trucks (Fig. 4); they both showed a relatively steady pattern over time with a negative growth rate for small and a positive growth for large trucks. We do not see similar clear patterns with respect to the growth rates in vehicle ownership. There was a small shift from small company owned trucks to small hire-andreward trucks and an opposite shift from large hire-andreward trucks to large company owned trucks. The two remaining influential factors are the average loads and average length of haul. The average load was increasing with a reduction in the vehicle kilometres as the outcome (note that this effect is computed in Table 2 as given by Eq. (2)). However, the increase in the average load was more than offset by the growth in the average trip length where the small trucks contributed by most of this growth. It is interesting to note that the contribution from growth in empty running was negative. Hence, the utilisation of the vehicles was improved, also in this respect. However, this effect was moderate. 4. Decoupling freight traffic Comparison of Table 1 and 2 can help us understand the observed decoupling of freight traffic from economic growth. The main difference between the growth rates in the two measures of transport (vehicle kilometres and tonne kilometres) lies mainly in the factors not included in the decomposition of the tonne kilometres, as the first economic growth factors are similar in size. The utilisation of the vehicles is increasing due to reduced empty running and due to a larger average load. The latter effect is primarily seen within the group of small vehicles. We should note here that the increasing load factor could be the result of a shift towards larger vehicles within the group of small vehicles. We cannot verify this with our data, since we only have two size groups of vehicles (smaller and larger than 16 tonnes total weight). Nevertheless, the outcome is the same, namely that fewer kilometres are registered in order to convey the same amount of goods. Note also that the vehicle size does not have an effect on the tonne kilometres. Hence, the utilisation of the vehicles does not influence the transport performance (tonne km). We further find that the average distance per trip has increased relatively more for small trucks compared to large trucks. This could be an indication of a centralisation of distribution centres or storage facilities, where smaller vehicles are used for local distribution. Fewer distribution centres means increasing average distances and more kilometres driven by small vehicles, which are the type of vehicles that are used for local distribution. The increasing number of vehicle kilometres driven by small trucks
ARTICLE IN PRESS O. Kveiborg, M. Fosgerau / Transport Policy 14 (2007) 39–48
46
0.5
0.5
0.3
Vehicle size Avg. trip length Avg. load
0.4 Accumulated growth in pct
Accumulated growth in pct
0.4
Vehicle km Total production Production by industry Handling factor
0.2 0.1 0 -0.1
0.3 0.2 0.1 0 -0.1
-0.2
-0.2
-0.3
-0.3
-0.4
Vehicle km Total production Commodity mix Value density Ownership Misc. mark-up Empty running
-0.4 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997
1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997
Year
Year
Fig. 4. The historical decomposition of freight traffic (vehicle km) 1981–1997.
maintains the relative high impact on the growth in total vehicle kilometres from this category of trucks despite a declining number of tonnes lifted. It is important to note that the decoupling started in 1990. The data cannot in itself tell us why this was the starting point. One could suspect that increased cabotage was the cause. In 1989 the European Community introduced a new regulation allowing foreign vehicles to perform transport in other countries (e.g. cabotage and third country transport). In relation to our analysis only cabotage transport in Denmark is of interest as this is performed within Danish borders by foreign trucks. Our data contain transport within Danish borders on Danish trucks. Third country transport is border crossing and has thus not been included in our data. Hence, any shift from Danish trucks to foreign trucks in border crossing transport will not influence our calculations. We do not have any comparable statistics about foreign cabotage transport in Denmark, but the amount of cabotage transport performed by Danish trucks abroad was about 1% of total transport performed by Danish trucks abroad in 1999 (Statistics Denmark, 2000). As Denmark is a very small country it is not likely that foreign trucks perform a higher amount of cabotage transport in Denmark. Altogether, the impact of the liberalisation in the EC must be expected to be limited and the explanation for the timing of the decoupling must be sought elsewhere. The average growth rate in freight traffic from 1989 to 1997 was negative and strong decoupling can be observed following the Tapio (2005) terminology. However, this negative growth is not sustained. Between 1997 and 2002 freight traffic grew with an average of 2.1% p.a. This was slightly less than the corresponding average economic growth of 2.4% p.a. Following Tapio (2005) this should be characterised as expansive coupling. The change from decoupling and negative growth in vehicle kilometres to increasing growth rates and coupling can be explained by upper limits to capacity utilisation of the vehicles. Due to legislations on allowable vehicle sizes there are upper limits on the size of the average load. There are similar lower limits to the amount of empty running. The observed
average load and empty running are approaching these limits during the period covered by our data. This means that more vehicles must be used to accommodate the increasing demand induced by continuing economic growth and thus that the vehicle kilometres start growing at a faster pace. 5. Conclusion In this paper we have applied the Divisia index decomposition method to a Danish data set in order to analyse factors driving the development in road freight transport and road freight traffic. Our data show a decoupling in road freight traffic, especially strong from 1989 to 1997, but no similar decoupling in road freight transport. This is in line with results obtained for other countries (e.g. Stead, 2001; Tapio, 2005). Growth in transport is primarily a consequence of growth in economic activity, but our results point out several other factors that influence the development. In addition to economic growth there is an effect from goods being conveyed over longer distances. However, a change in production towards more service industry production together with a logistical change towards less handling of the goods diminishes the growth in freight transport. Besides these effects we do not find other relevant factors that contribute to either increased or decreased growth in freight transport. Adding the effects together leads to an average growth rate in freight transport of 1.94% p.a., which is close to the 2.43% growth in total production. We are also interested in knowing the consequences for the infrastructure, energy consumption and emissions of, e.g., CO2, which are closely related to the vehicle kilometres performed by the trucks. The growth in road freight traffic is decoupled from economic growth, which beyond the factors contributing to the growth in road freight transport is caused by an increased use of larger trucks, and better utilisation with increasing average loads on the vehicles and reduction in empty running. However, there are large changes in trip length and average load.
ARTICLE IN PRESS O. Kveiborg, M. Fosgerau / Transport Policy 14 (2007) 39–48
The increasing average load has a reducing influence on the growth in road freight traffic, whereas the average trip length has the opposite effect. In total we observe a growth in road freight traffic of only 0.81% p.a. Changes in energy consumption and thus also in CO2 emissions will follow the growth rates in vehicle kilometres, because energy efficiency has not changed very much throughout the analysed period. Data on the CO2 emissions per kilometre show almost no improvement between the different environmental vehicle classes introduced by the EU (the so-called EURO classes). We therefore conclude that energy consumption and CO2 emissions follow the same decoupling trend as found in the vehicle kilometres. Emission of other substances will most likely be even further decoupled because large reductions in emissions per kilometre are introduced in new vehicles. These findings are interesting because some of the important factors are closely related to policies influencing freight transport. We have found that the logistical element is quite important through a decreasing handling factor. The handling factor reflects a falling number of loading and un-loading of vehicles at distribution centres and warehouses, etc. This shows that transport is influenced by the existence of e.g. distribution centres and other arterial infrastructure facilities. The policy choice of where and how to provide such infrastructures can be of great importance for the development of freight transport and traffic. Moreover, it may also be considered a task for policy makers to provide instruments that can help transport providers to minimise their empty running. This would call for a central system, where available capacity could be announced and transport buyers could take advantage of this spare capacity. Small-scale systems like this exist, but the large potential can only be explored if a large number of vehicles are included in the system. This would probably need a strong political support. Our results show that there may be an important impact from this. Another important effect is the maximum laden weight. We have observed an increasing average load and an increase in the size of trucks used. Both of these effects reduce freight traffic. However, it is not permitted to use vehicles larger than 48 tonnes total weight (40 tonnes for international traffic) in Denmark. This sets an upper limit to the growth in average loads and also on the effect that can be obtained from using larger vehicles. The current discussion about legalising 60 tonnes vehicles in Denmark can have an impact on the possibility of sustaining the low growth rate in freight traffic (note that freight transport is not affected by the two factors discussed here). However, this is an issue that needs further analysis. Larger vehicles would reduce transport costs and thus lead to increasing demand (induced demand and modal shift). This offsets the potential sustained low growth in road freight traffic. From a methodological point of view, the results show that it is important to distinguish between industries, but that a further distinction between commodities within the industries is less important. The results further indicate
47
that the assumption of identity between industries and goods often made in input–output analysis should not be a cause of great concern to the aggregate type of models. The relatively unimportant changes in the value densities are the result of the price-quantity deflator employed. The Passche index (a typical quantity deflator) is used in the Danish national accounts, which ensures that the deflated fixed prices are good quantity measures. Contrary to the suggestion by De Jong et al. (2004) and RAND Europe and Transek, 2001, it may not be necessary to put huge efforts into the calculation of appropriate value densities. This applies especially to the freight models using input–output tables and to computable general equilibrium modelling as the basis for the demand for freight transport. Such models involve a transformation from trade flows in monetary terms to transport flows in tonnes. This transformation involves both the value densities and the handling factor. Our analysis indicates that it may not be problematic to use a composite transformation factor. In a modelling perspective these findings imply that focus should be put on describing the changes within different industries, which then gives us very good indications of the demand for freight transport. Appendix List of industries and commodity types (sorted in ascending order with respect to transport volume) Note: Industries 12 through 19 produce non-physical services only Industry 1
Commodity type 1
Food
2
Construction materials Food products
2
3
Agriculture
3
4 5
Metal industries Chemical products Petroleum refineries Other manufacturing Beverages and tobacco Transportation equipment Energy extraction Other services Electricity, gas and water Construction
4 5
Miscellaneous goods (used in transport statistics only) Gravel, sand, dirt, stone, and salt Feed and fodder Cement, bricks, limestone
6
Cereals
7
Petrol and mineral oil
8
Wood
9
Machinery
6 7 8 9 10 11 12 13
10 11 12
Furniture, clothes, paper products etc. Live animals Fertilisers
13
Chemical products
ARTICLE IN PRESS O. Kveiborg, M. Fosgerau / Transport Policy 14 (2007) 39–48
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14 15 16 17 18 19
Wholesale and retail trade Sea transport Other transportation Financial services Housing Government services
14
Tar and asphalt
15 16
Semi-products from metal Potatoes
17
Sugar beets
18 19
Metal products Iron ore and scrap of iron
20 21 22
Cellulose and waste of paper Coal Skin and raw materials for textile production Fatty substances Glass and ceramics Crude oil Ore and scrap of other metals Services
23 24 25 26 27
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