environmental protection, economic efficiency and intermodal competition in freight transport

Pergamon

Transpn Res.-C, Vol. 4, No. 6, pp. 391406, 1996 Copyright 0 1996 Elsevier Science Ltd

Printed in Great Britain. All rights reserved 0968-090X/96 $15.00 + 0.00

PII: 80%84!30X(%)00021-6

ENVIRONMENTAL PROTECTION, ECONOMIC EFFICIENCY AND INTERMODAL COMPETITION IN FREIGHT TRANSPORT

DOMENICO CAMPISI and MASSIMO GASTALDI Istituto di Analisi dei Sistemi ed Informatica de1 C.N.R., Viale Manzoni 30, 00185, Rome, Italy

Abstract-The awareness of the consequences of a further rise in transport for the environment has not only been a matter of concern for scientific researchers but also for planners and policy- makers. In fact, the environment is now an ever present factor in the new political agenda and issues of excessive traffic congestion and global atmospheric pollution are increasingly attracting administrators’ attention. One of the most important scenarios proposed for the protection of the environment, taking into account the adverse effects of traffic, is the redistribution of freight transport demand. In this paper the Italian situation has been tested, evidencing productive sectors and regions really benefiting from a more effective redistribution of trade flows among existing links on the freight network. This pattern is estimated by evaluating substitution elasticities before and after the introduction of a pollution tax. Numerical simulations, in terms of reduction of pollution emissions and transportation costs, are also provided. Copyright % 1996 Elsevier Science Ltd

1, INTRODUCTION

In recent years the European transport scene has shown relevant changes and peculiar trends. Passenger and freight mobility have drastically increased and, as a consequence, congestion has also increased in all transport modes. Unlike passenger transport, domestic freight profiles greatly differ within the Member States of the European Community (Commission of the European Community, 1991). By considering six transportation modes (rail, road, sea and inland water, air and pipeline), it can be shown that in Belgium, Italy, Portugal, The Netherlands, Luxembourg, Denmark, Greece, the United Kingdom, Ireland and France, goods are mainly transported by road, whereas in Germany they are distributed by roads, railways and waterways; in Spain, goods haulage is divided mainly between road and sea, railways being used only marginally. To ensure an effective transportation plan, one of the basic objectives of the Community transport policy is to secure a greater co-ordination of investments between national transport programmes and to establish a common guideline, in order to develop both national and European transport networks (Button, 1993). Then public authorities need to modify the market structure, considering the interface between transport modes to ensure a rapid and reliable transfer of modal shares so avoiding that the major part of the freight transport growth will fall to the existing dominant mode, road transport, which, together with rail, represents the bulk of European freight transport. Nowadays roads account for 89.7% of the total domestic tonnage moved in European Countries and 59.6% of intra EC-transport followed by rail, which respectively accounts for 5.5 and 24.5%. The historical rise of freight transport by road is due to its good accessibility, to the inadequacy of the rail network in terms of speed, particularly to the peripheral parts of the EC (Southern Italy, Spain, Portugal and Greece), and to the flexibility of the truck service with respect to rail transport in terms of organization and dimension because of the large amount of truck competitors. The increasing awareness of environmental damage caused by transport, and in particular road transport, is creating political pressure for remedial action. Some of these will inevitably come about through the introduction of regulations and standards, or the tightening and more stringent enforcement of existing laws. These actions will lead to rate increases or tax for transport use diminishing the gap between payments and costs.

391

392 Domenico Campisi and Massimo Gastaldi

Thus, transport seems to have a double face nowadays. On the one hand, it is increasingly recognized that growing transport plays a vital role in building an integrated European market economy determining an increasing congestion and, on the other hand, there is a growing awareness of the high, sometimes unacceptable, social costs of transport (area of land use and environmental impact). Freight transport impinges on the environment in several important ways which were not considered a decade or so ago. In particular, it contributes, not only to local environment problems of noise, atmospheric lead pollution, accidents, low level ozone concentration, vibration, visual intrusion and community severance, but particulate emissions also significantly add to transboundary pollution through the emission of nitrogen oxide (NO,) and to global environmental degradation through its contribution to carbon dioxide (CO*) emission. All modes are environmentally intrusive but the nature and scale of their impact widely differ (Button, 1993). In this paper, a contribution to a more efficient functioning of the Italian freight transportation system in terms of competitive technologies for the reduction of congestion and the improvement of environmental quality is presented. The analysis is based on three scenarios. The first depicts the present situation. The second simulates the possibility of switching from freight transportation flows toward rail by means of a cost minimizing procedure. This scenario is simulated by econometrically estimating the substitution and demand elasticities both on the set of ten productive sectors and the twenty Italian regions. This allows highlighting of productive sectors which really benefit from increasing inter-modal competition and the special lay-out of forthcoming interventions in the Italian railway system. The third scenario simulates the introduction of a pollution tax on freight transport. This scenario works in the same way as the second one but explicitly considers the above additional cost. Since the pollution tax is based on the specific pollution levels, it will be shown that further possibilities should be given to increase the (more ecological) rail share. These results are still partial since environmental impacts of production processes are not considered, but they can be considered a starting point for the application of a sustainable development model for delivering production. Notice that environmental taxes are introduced without directly considering the social costs of pollution by the transport sector and therefore the results give an underestimation of this complex phenomenon (for a detailed discussion see Gastaldi et al., 1996).

The paper is organized as follows. Section 2 introduces the econometric model utilized in the analysis. In Section 3 its extension to incorporate the pollution tax is presented. In Section 4 regional and sectoral levels of aggregation and the data source are illustrated whereas in Section 5 the results derived from the numerical experiments on the Italian case are presented. Finally, Section 6 concludes the paper.

2. THE TRANSPORTATION MODEL

In this section a brief introduction to the complex relations between production and costs is outlined. Suppose that the production process is characterized by the following function (Greene, 1990):

Y=AW

The solution to the problem of minimizing the cost of producing a specific output Y given a set of factor prices P = (p1,p2,.... ..,p,) produces the cost minimizing set X= {x1,x2,..., x,;x(= x,(Y,P)} of factor demands. The total cost of production is given by:

C=p,x,(Y, P)+pzx2(Y, P)+ .*..... +pmXm(Y, P) = C(Y, P)

The cost-minimizing factor demands are obtained by applying Shepard’s Lemma (She- pard, 1970), which states that if C(Y,P) gives the minimum cost of production, then the cost-minimizing set of factor demands is:

XT = K( Y, P)/api

Environmental pollution 393

Alternatively, by differentiating logarithmically, we obtain the cost-minimizing factor cost shares:

Si = a In C/a lnpi = (X/apJ(pi/C) = PiXy / C

Recall that factor input i is said to be (Hicks-Allen) a substitute to factorj if

XQ = (axi/api) > 0

whereas it is said a (Hicks-Allen) complement of factor j if xij < 0. Therefore, if a rise in thejth factor price that reduces the use of thejth factor (xii< 0) increases (reduce) the use of the ith factor for each fixed Y, then i is a substitute (complement) for j. Since xii= xii, the above definition is perfectly symmetric. Moreover, the price elasticity of factor demand is defined as:

&ii = (aXilaPjNPjl-4 i, j = 1. 2, . ..m

where .sii is not symmetric. We may also define

0ij = Eq/Sj i, j = 1, 2, . ..m

Coefficients oij are symmetric and are called the (Allen-partial) elasticities of factor substitution. Clearly Oij > 0 (oij < 0) if factors i and j are substitutes (complements) for each other and the sign of oij can then be used to determine whether a particular pair of factors are substitutes or complements.

Following Buckley and Westbrook (1989, 1990) we model the movement of goods from a set of given origins to a set of given destinations. Thus, the output Y simply couples the delivered production (or transportation) of a certain commodity with a given weight W, and the shipping number of kilometres L (the distance from the origin to a destination). Production and transport of a given commodity are the only inputs of our simplified production process. Therefore, the output depends on both the weight of the available commodity and the prices of the possible transportation modes. Then, the cost function in our model is:

C = (P, Q> (1)

In equation 1, Q = ( W,L) is the output vector and P = (P~,p~,p~) is the input price vector where pn, pr are the rates of available transportation modes (R = rail; T = truck) and pp is the production price of the delivered product. Since goods in transit incur inventory costs, transportation rates usually fail to reflect the full shipping costs associated with any movement (Friedlaender and Spady, 1980). To this aim, transportation rates here are utilized explicitly and will depend on the main attributes of the inventory costs: value, length of haul and weight of shipment. Therefore, we can consider a (simplified) three- input model with the following cost function:

C=(PR,~T,PP, W,L) (2)

where pn and pr are respectively the rail and truck rate &/ton), pp is the price of produce delivered (f/ton), W is the weight of the shipment (tons) and L is the length of the haul (km).

In this model, the product being produced is the transportation of some weight of a given commodity; output is measured by a two element vector that includes the tonnnage of the product delivered and the number of kilometres that it was shipped. As far as the pp production price is concerned, we simply assume average production costs in each productive sector; this assumption neglects the specific differentiation between firms in each area of the country but allows to focus the effects of different costs outside the firms. This model differs from previous ones in that the product is included as an input into the production of the delivered product, along with rail and truck transportation. Tests reported in Buckley and Westbrook (1989) confirm that inclusion of the product as an input significantly enhances the performance of the model.


To estimate the Allen elasticities of substitution (AES), we adopt the following procedure:

(i) define the Barnett Translog cost function on the basis of the Shepard duality theory;

(ii) derive a set of input-share functions by means of the Shepard Lemma; (iii) estimate the share function; (iv) measure the AES coefficients in accordance with the standard formula.

Our methodology is based on an input cost function expressed by the Barnett Translog approximation [BTL-Barnett Transcendental Logarithmic; Barnett, 19851 which extends the Translog specification [TL; Christensen et al., 19731 for the computa- tion of the substitution elasticities. The application of the transformation proposed by Barnett improves the translog models’ performances. In fact, while the elasticities are not very different from those obtained applying TL, their standard errors have decreased markedly. Moreover, it can be proved that the three input cost function (2) expressed by a BTL approximation satisfies the regularity conditions (non-negativity of the fitted shares and concavity of the estimated cost equation) required by economic theory (Bianco et al., 1995). Negatively fitted shares obviously produce results with no economic or statistical sense and the observations that produce negatively fitted shares or concavity violations must be considered errors for the estimation and must be omitted. Conditions lJnn*cr~-crrJ > 0 and aan < 0 as defined by Caves and Christensen (1980) ensure concavity of the estimated cost equation (see Appendix for details). We transform the relative prices to improve the concavity of the estimated cost function (Barnett, 1985). In our three-input case this transformation yields:

pi/pp + (pi/pp) + 0, for i = R, T (rail, truck)

where 8i assume negative values bounded by:

- min(pi/pp) < 0, < 0 for i = R, T (rail, truck).

Given these bounds, it is feasible (see Appendix for details) to perform a grid search over (&&), choosing as optimal the values @a*,&*) that minimize the number of concavity violations (maximize the number of used observations).Let

I’i = pi/pp and oi = I’i + 8i with i = R, T

Redefining Lpi= ln(@ for i = R,T (R = rail, T = truck) our BTL cost function becomes

ln(C/pp) = a0 + URLPR + aTLPT + aw h W + a~ h L

+ 1/2aRR(LPR)* + aRTLPRLPT + aRWLPR h w-t aRLLPR h L

+ 1 /hT(LPT)* + aTWLPT h w + aTLLPT h L (3)

+ 1/2aww(ln W)* + clwr In Wln L + Jja,(ln L)*

- bRLP,’ - brLPy’ - 1/2bRRLP,* - bRTLP,‘LP,-’ - 1/2hLP,*

and the cost shares are given by:

sR = (vR/mR) * @R + aRRLPR + aRTLPT + aRW h w+ aRL h L + bRLPR*

+ bRRLPi3 + bRTLPi*LP+)

ST = (VT/W) * (aT + aRTLPR + aITLPT + Ll~w ln w + (ITL hl L + bTLP,-*

+ hLPy3 + bRTLP,-*LP,‘)

Sp=8lnC/6lnpp=l-sR-sr

(4)


Coefficients ao, ai, av, bi and b, for i, j= R,T are parameters to be estimated. Since the Allen-Uzawa elasticities of substitution are defined as oij = CCv/C&‘, where Ci= SC/&pi and C,= ZCi/Spj, for our BTL specification AES are defined as

0~ = (Vi * Vj/wi * tij) * (au - buLP;* LP,T* + UiiOj)/aiaj for i, j = R, T (5)

and

bii = (Vi/wi)* * [aii - 2biLPL3 - 3biiLPi4 - 2byLPL3LP,y’ + S; - si * (Vi/wi)]/$

for i = R, T (6)

for ij= R,T. An expression for oap, crp and upp can also be easily deducted. The conventional demand elasticities can be related to the elasticities of substitution (Allen, 1956); in fact, the following relation holds:

&ii = Sjoq and &ii = sioii for i, j = R, T (7)

which respectively represent the cross-price demand elasticities between rail and truck (~nr,era) and the own-price demand elasticities for rail (ERR) and truck (s-r-r).

3. THE EXTENDED ENVIRONMENTAL MODEL

Various environmental issues associated with transportation have the greatest influence on the development of freight transportation systems. First in importance to policy, to date, is the air pollution caused by noxious gases. Other influences are the contribution of automotive emissions to greenhouse warming (a just emerging but potentially enor- mous effect), traffic congestion, which might not be classified as a pure environmental effect but which has demonstrable adverse psychological effects on health and noise, land impacts and aesthetics are also quite important but do not seem to have such urgent impacts on thinking about public policy. In this paper only air pollution and, in particular, CO2 and NO, emissions are considered. To extend the econometric methodology presented in the previous section, we include pollution tax in the original model and estimate how substitution will be affected by evaluating the possibility of switching road freight transportation to the apparently more ecological rail transportation. Therefore the cost equation 2 now becomes

where

pR' =PR+AR,PT'=PT+AT

AR = pollution tax on rail freight transportation, &/ton

AT = pollution tax on truck freight transportation, &/ton E = pollutant emission, g/km.

AR and AT, then, simulate the introduction of additional costs. Notice that the pre- sence of taxes affect the cost function as shown above. Then, equations 3 and 4 respectively become:

MC/m) = a0 + aRLpR + aTLPG + aw In W + uL In L + aE In E

+ 1/2aRrt(LP~)’ + aRTLpRLpT + aRN?LPR In W+ aRLLPR In L + aRELPR In E

+ 1/2@~rV&)* + aTWLpT In W + OTL LPT In L + aTELPT In E

+ 1/2aww(ln WI* + L~WL In Wln L + i+<(ln L)*

+ aWE ln Wln E + a<< ln L ln E + 1 /2aEE(ln kJ2

- bRLp<’ - brLP‘+-’ - 1/2bRRLP;* - bRrLPk_‘LP;-’ - 1/2brrLP;-*

396

and

Domenico Campisi and Massimo Gastaldi

SR = 6 In c/a In?& = aR + aRRLf$R + aRTJ?T + (IRW h w + aRL h I!. + aRE h E

+ bRLpk-2 + bRRLPi3 + bRTI!&2LP;--’

whereas equations 5, 6 and 7 require no further adjustments.

4. DATA BASE

For our purposes, a large part of the ninety-nine physical commodities classified by the NST/R (Nomenclature Statistique du Traffic provided by the Observatoire Econo- mique Statistique du Traffic) have been aggregated into the following 10 sectors (the numbers of the corresponding commodities of the NST/R classification are reported in parentheses):

l-Agricultural products and livestock (l-9) 2-Forage and foodstuffs (11-18) 3-Solid mineral fuels (21-24) 4-Oil products (3 l-37) 5-Minerals and junk for the metallurgical industry (41, 4447) &Metallurgical products (50-59) 7-Raw and manufactured minerals, building materials (61-65, 69) 8-Compost and chemical fertilizer (71-72) 9-Chemical products (81-84, 89)

1 O-Machinery and vehicles (9 i-99)

The disaggregate inter-regional trade flows data (IV) among the 20 Italian regions have been obtained from a project financed by the Italian National Research Council. The results of this project provide a detailed profile of the inter-regional movement of goods considering the aggregated productive sectors and analyse the internal transport among the 20 Italian regions assumed as production and consumption places in 1985. Notice that air, pipeline, inland waterways and sea transportation are not considered in our frame- work since they only account for a low level of the transport share; furthermore, in the case of inland and sea waterways, data are generally too aggregated and data on shipment characteristics (i.e. vessel type, dimension and capacity) and relative rates are not readily available. This data set allows the comparison of the tonnages shipped by rail and truck for the 10 sectors presented above. In Italy, Sectors 6 (metallurgical products) and 10 (machinery and vehicles) account for more than 2/3 of rail inter-regional transport; then Sector 10 (machinery and vehicles), which alone amounts to about l/4 of the road movement of goods, is followed by Sectors 7 (raw and manufactured minerals, building materials) and 2 (forage and foodstuffs). The rates (pR,pT) have been provided directly from the Italian Confederation of Truck Operators (CONFETRA) and the Italian Railways (FS). Prices of the delivered product Cpr) have been appropriately calculated from the Input- Output table of the same year (Istat, 1987).

5. NUMERICAL RESULTS

This section is devoted to analysis of freight transport in Italy calculating the substitution and elasticity coefficients between rail and truck. The implicit cost of pollution was more difficult to estimate. Table 1 shows that transportation in general is a large contributor to air pollution. Carbon dioxide (CO*) from transportation alone represents


Table 1. Contribution of transport to conventional pollutant emission in Italy, 1985 (1000 tons/year)

co2 co HC NO, SO, SPM

Total I 520 889 5500 800 1500 2000 400 Transport sector 342 200 4510 448 645 140 92 Transport sector % 22.5 82.0 56.0 43.0 7.0 23.0 Passenger transport 197200 3192 N/A 239 45 N/A Freight transport 145000 1318 N/A 406 95 N/A Passenger transport % 57.7 70.8 N/A 37.1 32.1 N/A Freight transport % 42.3 29.2 N/A 62.9 67.9 N/A Rail freight transport 6127 7 N/A 7 II N/A Truck freight transport 126439 1232 N/A 399 84 N/A Rail freight transport %’ 4.2 0.005 N/A 1.7 11.6 N/A Truck freight transport % 87.2 93.5 N/A 98.2 88.4 N/A

-.~ ._._~ .-~ Sources: Faiz (1993) and Ferrovie dello Stato (1993a). Note: ‘Percentages do not add up to one because other transportation modes are not included. N/A: Not available.

Pb

NjA N/A N/A N/A N/A N/A N/A N/A N/A N/A

the 22.5% of total emissions (Faiz, 1993); other sources are carbon monoxide (CO), hydrocarbons (HC), nitrogen oxides (NO,.), sulphur oxides (SO,), suspended particulate matter (SPM) and lead (Pb). Note that when released into the air CO becomes CO,; HC becomes oxidated to aldehydes and eventually becomes CO;?. Table 1 shows the contribution of transportation to man-made emissions in Italy in 1985; in addition, Table 2 shows the transportation emissions in g/km. In a study dedicated to transportation (Fer- rovie dello Stato, 1993a), the European Commission and Greenpeace suggested a pollution tax between 0.01 and 0.06 ECU (18 and E108) per t-km to cover the environmental cost of transport. But, in our analysis, such a sizeable tax would increase freight transportation rates by 5&100%. This proposal was criticized as not being feasible for the short run (5-10 years) since no relevant changes in the infrastructure or technology are possible. Therefore, for simulation purposes only, we choose a modest short-term freight tax of &I 0 per t-km. We distribute this between rail and truck rates proportionally according to the CO* emissions in g/km, the weight and the distance of the shipment for each mode. Our experience with existing data shows that in this way the rates change by less than 1% (1 to 8 per thousand) for rail and 2.5-9.4% for truck. This modest change, which could produce a revenue of about 90 billion Italian Lire, is sufficient to validate our procedure, which could be repeated with different tax levels.

To measure factor substitution possibilities [equations 5, 6 and 71, we estimated the Allen elasticities of substitution (oij) and the price elasticities (E$. Table 3 reports the substitution elasticities between rail and truck (a RT=u-rn), the own-price demand elasticities for rail (ERR) and truck (&r-r), the cross-price demand elasticities between rail and truck (aar and ~a). Coefficients have been calculated at productive sector-specific means of the data. Since oar> ‘0 rail and truck carriers are highly substitutable for all the productive sectors. In the present situation, in the absence of an air pollution tax, coefficients vary between sectors with values that range from 1.084 (Sector 6, metallurgical products) to 22.27 (Sector 10, machinery and vehicles). Notice that higher levels of substitution are related to the movement of goods requiring special trucks and wagons where a large amount of the Italian Railways’ efforts are concentrated (Ferrovie dello Stato, 1993b). The demand own-price elasticities vary considerably between sectors with values ranging, in the case of rail transport, from -0.902 (Sector 8, compost and chemical fertiliser) to -2.412 (sector 3, solid mineral fuels). In the case of truck transportation, the values range from -0.27 (Sector 4, oil products) to - 1.68 (Sector 9, chemical products). Whereas the estimated elasticities of demand for rail services are sufficiently high to sug- gest the efficacy of selective rate cutting, the estimated truck elasticities do not appear to be sufficiently high to warrant this conclusion. In addition, since for all sectors

[ERRI > JQ-TI, rail d emanded quantity is more sensitive to a change in rail price than truck demanded quantity with respect to the truck price, so demonstrating a one-time superiority of the truck services.


Thus, from a sectoral point of view, the estimated substitution and demand elasticities indicate a vigorous inter-modal competition mainly in Sectors 2 (forage and foodstuffs), 8 (compost and chemical fertiliser), 9 (chemical products) and 10 (machinery and vehicles). This pattern is confirmed when a pollution tax is applied; in fact in these sectors the highest substitution coefficient changes are revealed. As far as Sectors 1 (agricultural products and livestock) and 7 (raw and manufactured minerals, building materials) are concerned, notice that substitution elasticities remain low, although change due to the pollution tax is significant ( > 3%). Since in Italy a large amount of goods are delivered by truck, the sectoral results show how the rail carrier could more effectively increase its market share with the appropriate fare policies. This becomes more evident when a pollution tax is applied since the change in own-price elasticities of demand is positive for rail but negative for truck; this is an expected outcome because the trucks create more pollution ihan trains. On the contrary, the lowest levels of inter-modal competition are reached in Sectors 3 (solid mineral fuels), 4 (oil product) and 6 (metallurgical products) which are the core of road transportation.

The cross-elasticities of demand for rail with respect to changes in truck rates ranged from 0.005 (Sector 6, metallurgical products) to 0.64 (Sector 2, forage and foodstuffs) whereas the elasticities of demand for truck with respect to changes in rail rates ranged from 0.001 (Sector 6, metallurgical products) to 0.241 (Sector 10, machinery and vehicles). Notice also that for each productive sector an increase in truck rates produces a larger change in rail demand than vice versa (E~~ > E& with the exception of Sector 5 (minerals and junk for the metallurgical industry), which is a unique sector characterized by W, < WR. This result is more evident when a pollution tax is added; in fact, the change in cross-elasticities of demand underlines that a modest change in fare policy penalizes the mode that pollutes more. The last two columns show that when freight rates for truck increase, the demand becomes more elastic, whereas increasing the rail rates the demand becomes less elastic. Generally, the elasticities of substitution go up for each sector, that is a pollution tax gives an incentive to shift freight between modes. The pollution tax affects all sectors’ substitution and demand elasticities similarly. In fact, the largest change in substitution elasticity is in Sector 10 (machinery and vehicles) with 4.93%, where the shipped weight is the highest for both the transportation modes; the lowest change is in Sector 4 (oil products) with 1.75%. Notice that the bulk of the oil is shipped by waterways (cabotage) and locally oil products are delivered by tank truck where no substitution is possible. Since in Italy the truck is the prevalent mode of freight transportation and the CO;? emission is twenty times higher with respect to rail (see Table 2) the resulting pollution tax obviously impacts truck more than rail transportation.

The first two columns in Table 4 indicate percentage changes in total transportation costs and pollution emissions (AC%,AP%) if a central authority were to force a single freight operator to ship their goods following the results offered by the substitution process. The third and fourth columns report the same results by comparing the marginal effects of the environmental policy (AC’%,AP%). Notice that from the cost point of view, Sectors 3 (solid mineral fuel), 4 (oil products) and 7 (building materials) are marginally affected by the process whereas Sectors 2 (forage and foodstuffs), 8 (compost and chemical fertilizer) and 10 (machinery and vehicles) are the most heavily affected. Notice that higher levels of pollution reduction are depicted in sectors where the substitution elasticities are particularly evident. Then, it seems that freight rates, cost and the corresponding pollution reduction are very sensitive even if a minimal pollution tax of LlO per t-km is considered.

Table 2. Emissions of pollutants in Italy, 1985 (in g/vehicle km)

co2 co HC NO, SO, SPM Pb

Rail 56.13 1 0.3 2 0.3 N/A VA Truck 1158 8 2.8 17.5 1.59 N/A N/A

Sources: Commission of the European Community (1991) and Ferrovie dello Stato (1993a).

Environmental pollution

Table 3. Substitution and demand elasticities-sectoral results

399

Sector 1

Segor 2

Sector 3

Sector 4

Sector 5

Sector 6

Sector 7

Sector 8

Sector 9

Sector IO

without pollution tax 6.01 -1.531 -0.764 0.158 0.028 with pollution tax 6.26 -1.512 -0.791 0.171 0.027 without pollution tax 21.94 -1.401 -0.283 0.640 0.030 with pollution tax 22.60 - 1.373 -0.302 0.680 0.029 without pollution tax 2.59 -2.412 - 1.002 0.075 0.027 with pollution tax 2.65 -2.363 -1.052 0.079 0.027 without pollution tax 2.28 -1.207 -0.270 0.158 0.004 with pollution tax 2.32 -1.189 -0.298 0.162 0.004 without pollution tax 5.40 -1.391 -1.373 0.111 0.172 with pollution tax 5.55 -1.322 -1.461 0.123 0.170 without pollution tax I.08 -1.033 -0.900 0.005 0.001 with pollution tax 1.11 -1.017 -0.920 0.005 0.001 without pollution tax 2.03 -2.216 -1.339 0.067 0.006 with pollution tax 2.10 -2.180 -1.395 0.071 0.006 without pollution tax 20.98 -0.902 -0.489 0.125 0.014 with pollution tax 21.73 -0.890 -0.516 0.137 0.014 without pollution tax 14.16 -2.108 - 1.680 0.207 0.024 with pollution tax 14.80 -2.080 -1.771 0.226 0.023 without pollution tax 22.27 -1.656 -0.619 0.457 0.241 with pollution tax 23.37 -1.631 -0.660 0.502 0.238

DRT &RR El-T &RT ETR

Tables 5 and 6 report the same outcomes as Tables 3 and 4 related to the regional applications, where each region is considered both as place of production (origin) and as place of consumption (destination). The coefficients have been calculated at regional means of the data. The methodology only failed to converge for Val d’Aosta and Molise regions; this is due to the low number of observations ( < 20) considered in the estimation procedure. Since bar > > 0, rail and truck carriers are highly substitutable for all the Ita- lian regions. Notice that, considering a region as a destination or origin of freight flows, the substitution measures are aligned. These coefficients, however, rank differently among regions with values that range from 6.814 (destination) and 6.957 (origin) for Basilicata to 32.83 (destination) and 26.44 (origin) for Lombardia. As far as the demand own-price elasticities are concerned, values vary considerably between regions with values ranging, in the case of rail transport, from -0.409 (destination) and -0.401 (origin) for Puglia to -2.136 (destination) and -2.035 (origin) for Lombardia. In the case of truck transportation, values ranged from -0.201 (destination) and -0.223 (origin) for Puglia to -I. 122 (destination) and -1.078 (origin) for Lombardia. Notice that, as already shown in the sectoral case, ERR > cr+ the implication of this is that the superiority of truck services is confirmed at regional level. Moreover, by applying a pollution tax this pattern becomes more evident because of the higher environmental truck impact.

The cross-elasticities of demand for rail with respect to changes in truck rate range, considering a region as the destination of freight flows, from 0.113 (Marche) to 0.973 (Sicily). Considering a region as the origin of freight flows, values range from 0.101 (Lombardia) to 0.916 (Sicily). The elasticities of demand for truck with respect to changes in rail rates range from 0.014 (destination) and 0.015 (origin) for Marche to 0.460

Table 4. Percentage change in total transportation costs and total emissions-sectoral results

ACR+T AC’R+T

WI $5 w-1 $5

Sector 1 -3.46 -1.06 1.95 -3.86 Sector 2 - 12.99 -4.35 5.63 -13.53 Sector 3 -1.38 -0.59 0.89 -1.47 Sector 4 -1.34 -0.42 0.54 - I .42 Sector 5 -2.21 - 1.25 I .06 -2.84 Sector 6 -0.60 -0.09 0.36 -0.79 Sector 7 -1.19 -0.23 0.41 -1.43 Sector 8 -12.17 -5.01 4.93 -12.11 Sector 9 -8.31 -3.69 3.51 -8.00 Sector IO -12.87 -5.67 3.90 - 12.85


Table 5. Substitution and demand elasticities-regional resuits

ORT ERR ETT &RT &TR

Piemonte

Val d’Aosta

Lombardia

Trentino

Veneto

Friuli

Liguria

Emilia Rom.

Toscana

Umbria

Marche

Lazio

Abruzzo

Molise

Campania

Puglia

destination

origin

destination

origin

destination

origin

destination

origin

destination

origin

destination

origin

destination

origin

destination

without pollution tax with pollution tax without pollution tax with pollution tax without pollution tax with pollution tax without pollution tax with pollution tax without pollution tax with pollution tax without pollution tax with pollution tax without pollution tax with pollution tax without pollution tax with pollution tax without pollution tax with pollution tax without pollution tax with pollution tax without pollution tax with pollution tax without pollution tax with pollution tax without pollution tax with pollution tax without pollution tax with pollution tax without pollution tax with pollution tax without pollution tax with pollution tax without pollution tax with pollution tax without pollution tax with pollution tax without pollution tax with pollution tax without pollution tax with pollution tax without pollution tax with pollution tax without pollution tax with pollution tax without pollution tax with pollution tax without pollution tax with pollution tax without pollution tax with pollution tax without pollution tax with pollution tax without pollution tax with pollution tax without pollution tax with pollution tax without pollution tax with pollution tax without pollution tax with pollution tax without pollution tax with pollution tax without pollution tax with pollution tax

destination

origin

destination

origin

destination

origin

destination

origin

destination

origin

destination

origin

destination

origin

destination

27.48 -1.350 -0.744 0.899 0.233 28.55 - I .320 -0.765 0.958 0.227 24.81 -1.561 -0.699 0.841 0.245 25.63 -1.510 -0.726 0.896 0.239

- - - - - - - -

32.83 34.09 26.44 27.40 14.80 15.18 16.64 17.02 20.99 21.67 21.01 21.78 10.53 10.75 10.66 10.89 16.84 17.19 16.63 16.95 17.53 18.01 17.76 18.19 16.76 17.21 15.38 15.75 12.01 12.21 12.09 12.35 9.69 9.85

10.23 10.37 13.19 13.35 12.85 13.09 8.11 8.21 8.34 8.44

- -

-2.136 -1.122 -2.098 -1.173 -2.035 -1.078 -2.025 -1.109 -0.518 -0.385 -0.509 -0.395 -0.542 -0.335 -0.531 -0.355 -1.536 -0.621 -1.498 -0.669 -I .608 -0.680 -1.553 -0.712 -0.521 -0.213 -0.510 -0.229 -0.501 -0.233 -0.495 -0.246 -0.990 -0.702 -0.972 -0.727 -1.165 -0.604 -1.111 -0.619 - I .463 -0.420 - 1.402 -0.445 -1.429 -0.636 -1.378 -0.689 -1.012 -0.691 -0.980 -0.742 -1.016 -0.539 -0.979 -0.526 -1.028 -0.429 -1.001 -0.409 -1.068 -0.536 -1.041 -0.556 -1.014 -0.822 -1.001 -0.860 - 1.049 -0.779 -1.025 -0.828 -1.289 -0.844 -1.240 -0.899 -1.221 -0.844 -1.198 -0.823 -1.057 -0.661 -0.985 -0.693 -1.097 -0.745 -1.035 -0.784

0.121 0.036 0.132 0.035 0.101 0.023 0.110 0.022 0.505 0.115 0.536 0.114 0.536 0.184 0.571 0.179 0.116 0.097 0.125 0.095 0.132 0.106 0.142 0.104 0.498 0.096 0.516 0.093 0.262 0.077 0.274 0.075 0.596 0.391 0.626 0.379 0.555 0.202 0.586 0.199 0.463 0.088 0.491 0.087 0.396 0.102 0.419 0.101 0.714 0.203 0.756 0.197 0.859 0.242 0.908 0.237 0.431 0.173 0.461 0.169 0.459 0.134 0.484 0.132 0.113 0.014 0.118 0.014 0.136 0.015 0.142 0.015 0.599 0.147 0.628 0.141 0.717 0.179 0.755 0.173 0.523 0.109 0.541 0.106 0.575 0.074 0.598 0.073

- - - - - -

- - - - -

14.83 -1.379 15.10 - 1.298 13.51 -1.113 13.78 -1.069 9.08 -0.409 9.17 -0.395 8.69 -0.401 8.79 -0.389

-0.923 0.851 -0.980 0.900 -0.751 0.752 -0.797 0.793 -0.201 0.318 -0.215 0.326 -0.223 0.279 -0.236 0.288

-

0.195 0.191 0.262 0.255 0.105 0.102 0.062 0.061

-continued opposite

Environmental pollution

Table Scontinued.

401

ORT ERR @l-T '%T ETR

Basilicata

Calabria

Sicilia

Sardegna

without pollution tax with pollution tax without pollution tax with pollution tax without pollution tax with pollution tax without pollution tax with pollution tax without pollution tax with pollution tax without pollution tax with pollution tax without pollution tax with pollution tax without pollution tax with pollution tax

destination

origin

destination

origin

destination

origin

destination

origin

6.81 -0.783 -0.512 0.452 0.060 6.89 -0.750 -0.533 0.466 0.059 6.96 -0.704 -0.482 0.43 I 0.058 7.04 -0.685 -0.512 0.447 0.057

IO.14 -1.391 -0.98 I 0.41 I 0.263 10.29 -1.301 - 1.042 0.430 0.255 10.22 -1.310 - 1.002 0.421 0.280

10.39 -1.256 - 1.098 0.440 0.275 13.12 -1.814 - 1.004 0.973 0.460 13.36 -1.753 - 1.053 I.030 0.451 11.11 -1.713 -0.970 0.916 0.396 11.32 - I .675 - 1.023 0.964 0.389 IO.61 -1.197 -0.503 0.650 0.273 10.75 - 1.068 -0.545 0.685 0.268 10.89 -1.311 -0.563 0.714 0.299

II.04 -I .256 -0.595 0.755 0.292

Table 6. Percentage changes in total transportation costs and total emissions-regional results

Piemonte

Val d’Aosta

Lombardia

Trentino

Veneto

Friuli

Liguria

Emilia Rom

Toscana

Umbria

Marche

Lazio

Abruz.zo

Molise

Campania

Puglia

Basilicata

Calabria

Sicilia

Sardegna

destination origin

destination origin

destination origin

destination origin

destination origin

destination origin

destination origin

destination origin

destination origin

destination origin

destination origin

destination origin

destination origin

destination origin

destination origin

destination origin

destination origin

destination origin

destination origin

destination origin

A~R+T

W)

AC'R+T Ap

WI (%)

-10.61 -4.25 4.25 - 12.35 -9.50 -4.02 4.01 - 10.95

-12.51 -5.23 5.47 -14.80 -10.22 -4.01 5.49 - 12.97

-5.49 - 1.98 1.95 -6.91 -6.30 -2.58 2.35 -8.03 -8.21 -2.12 3.65 -10.69 -8.18 -2.25 3.54 -10.70 -4.07 -1.57 1.99 -5.36 -4.08 -1.52 I .92 -5.43 -6.43 -2.70 2.05 -8.16 -6.24 -2.61 2.09 -8.02 -6.83 -2.36 2.26 -8.60 -6.87 -2.65 2.29 -8.73 -6.53 -2.41 3.01 -8.13 -6.04 -2.35 2.99 -7.30 -4.53 -2.00 2.36 -6.24 -4.69 -1.87 2.95 -6.30 -3.76 -1.01 1.55 -4.85 -3.96 -1.05 1.57 -5.18 -5.1 I --1.25 2.26 -6.97 -4.97 -1.23 I .98 -6.76 -3.12 -0.68 1.20 -4.20 -3.22 -0.62 I .09 -4.74

-5.69 -2.36 2.85 -5.95 -5.11 -2.54 2.96 -5.14 -3.55 -1.11 1.85 -4.49 -3.41 -1.12 1.88 -4.26 -2.67 -0.85 1.33 -3.12 -2.67 -0.84 1.38 -3.20 -3.96 -1.42 1.67 -5.13 -3.95 -I .48 1.96 -5.17 -5.33 -1.98 2.88 -5.96 -4.52 -I .95 2.44 -5.74 -4.25 -1.75 2.05 -4.43 -4.44 -1.71 2.10 -4.61


(destination) and 0.396 (origin) for Sicily. Both in aggregate and disaggregate terms, Tables 5 and 6 complexly show, confirming sectoral results, that the introduction of a pollution tax could positively affect the intermodal switching among the Italian regions. In particular, regions in the Padanian zone could effectively benefit from the proposed lay-out of the East-West railway corridor accompanied by the introduction of an environmental policy. In fact, in this area an estimated vigorous intermodal substitution process could create both a reduction of transportation costs and a relevant decrease of pollution emissions: Padanian regions (Lombardia, Piemonte, Emilia Romagna and Veneto) could benefit from an average pollution reduction of 14%. Notice also that because of the peculiar geomorphic configuration of the Padanian region, a significant reduction in air pollution could help to reduce accidents due to fog (a phenomenon particularly great in this area), and medical and psychological diseases, through a drastic reduction of smog. In addition, the same results indicate the effectiveness of the additional rail links between Milan and Naples.

6. CONCLUSIONS

Measuring the complex relations between rail and truck freight transportation when considering environmental impact is a hard task: the ongoing technologies and existing infrastructures do not allow changes in the short term and we still do not have a generally acceptable measure for environmental impact. Yet, what we learn from our exercise is that there are inter-modal substitution possibilities and that the inclusion of pollution costs of freight shipping, through a pollution tax, can affect modal choice. A number of key vari- ables affect the demand for road and rail freight transport: distance, cost, quality of service, existing alternatives, economic and environmental conditions. In all developed countries rail is the most successful over medium distances and road is more attractive over short distances since it offers a level of flexibility that rail cannot provide. On the contrary, rail freight rates are usually set too high with respect to those of trucks, switching potential “long-distance” customers away from rail. Notice, however, that the introduction of a pollution tax does not invert the above pattern. In addition, customers are not only influenced by rates and speed, but also by the perceived service quality of rival modes of transport. This issue is particularly striking today since new production systems require a more efficient and reliable transport service to function efficiently. From a social point of view, sustainable development requires consideration of the environmental impact for each economic activity. A pollution tax is a partial response to this requirement. Other policies, such as fines, permits, prohibitions and regulations, could be as efficient but this would include more bureaucracy and would be a more punitive approach. As shown here, a pollution tax could influence the choices of transportation mode that could partially affect the environment without significantly hurting the process of economic growth. This in itself satisfies the intrinsic intergenerational trade-off when considering sustainable development (Campisi et al., 1994; Nijkamp, 1994).

From a policy point of view our simulations offer some results which could be further investigated. First we gave a measure of savings deriving from policies aimed at reducing road congestion and consequently pollution emissions. Secondly, with the aim of obtaining a more significant reduction in the emissions level, we simulated the effect of an environment tax. In this case, besides a marginal increase in the transportation cost with respect to the outcomes of the previous policy, authors are still working on the following three items:

(i) the evaluation of the increasing level of an indirect taxation to be utilized for an improvement of transportation and environmental infrastructures;

(ii) the size of the transportation costs reduction with respect to the present trend; (iii) the evaluation of the level of the pollution tax able to cut pollutant emissions to

prefixed levels.

Although additional costs could be passed on to the consumer or absorbed in lower production costs elsewhere and the freight industry is relatively price inelastic, the results


of this study show evidence of the possibility of a relativeiy limited modal transfer. Therefore, the results could help in choosing the modal mix nearest a more balanced position of modern freight transport systems. Figures 1 and 2 summarize the obtained results. The first simulation, at sectoral level, complexly produces the reduction cost by

W Pollution emission

cl Costs

Fig. 1. Costs and pollution reductions (sectoral results)

W Pollution emissions (origin)

WPollution emissions (destination)

w costs (origin)

q Costs (destination)

Fig. 2. Costs and pollution reductions (regional results).


7.16% and gives a corresponding pollution reduction of 2.23%. As a consequence of the second simulation, the marginal transportation cost increases by 3.36% with a relevant decrease in the pollution level of 7.90%. Then, summarizing the sectoral simulation results (simulation 3 in the figures) produces a reduction in pollution (10.13%) and transportation costs (3.80%). At the regional level, the first simulation determines a total reduction in cost of 7.60% (destination) and 6.97% (origin) with a corresponding pollution decrease of, respectively, 2.57 and 2.3 1%; the second policy, besides the cost increase of 3.20% (destination) and 3.01% (origin), gives evidences of a decrease in pollution of 9.29% (destination) and 8.51% (origin). Thus, summarizing the simulation results it is possible to estimate a cost reduction of 4.40% (destination) and 3.98% (origin) and a corresponding pollution fall of 11.86% (destination) and 11.52% (origin).

Acknowledgement-The authors wish to thank two anonymous referees for providing valuable comments and suggestions.

REFERENCES

Allen, R. G. D. (1956). Mathematical Analysis for Economists. Macmillan, London. Bamett, W. A. (1985) The miniflex-Laurent translog flexible functional form. Journal ofEconometrics, 30,334. Bianco, L., Campisi, D. and Gastaldi, M. (1995) Which regions really benefit from rail-truck substitution?

Empirical evidence for Italy. Papers in Regional Science, 74(l), 41-62. Buckley, P. and Westbrook, M. D. (1989) The Importance of Market Definition in the Assessment of the Competitive

Relationship between Rail and Truck Transportation. Mimeo, Georgetown University. Buckley, P. and Westbrook, M. D. (1990) Flexible functional forms and regularity: assessing the competitive

relationship between rail and truck transportation. Review of Economics and Statistics, 72, 623-630. Button, K. (1993) The future of European transport. In The Future of Transportation and Communication (Thord

R., Ed.), pp. 45-83. Campisi, D., Cohen, B. C., Gastaldi, M. and Schachter, G. (1994) Transportation choices for sustainable devel-

opment. In Proceedings of International Symposium on Models of Sustainable Development, Paris, Vol. 1, pp. 531-546.

Caves, D. W. and Christensen, L. R. (1980) Global properties of flexible functional frontiers. Reviews in Economics and Statistics, 70, 422-432.

Christensen, L. R., Jorgenson, D. W. and Lau, L. J. (1973) Transcendental logarithmic production frontiers. Reviews in Economics and Statistics, 55, 2845.

Commission of the European Community (1991) Green paper. Mimeo, Luxembourg. Faiz, A. (1993) Automotive emissions in developing countries-relative implications for global warming,

acidification and urban air quality. Transportation Research -A, 27(3), 167-186. Ferrovie dello Stato (1993a) Linea treno, Vol. 36(8). Rome, Italy. Ferrovie dello Stato (1993b) Libro Bianco. IL trasporto delie merci in Italia, Mimeo, Area Trasporto, Rome,

Italy. Friedlaender, A. and Spady, R. H. (1980) A derived demand function for freight transportation. Reviews in

Economics and Statistics, 62, 432441. Gastaldi, M., Pradayrol, J. P., Quinet, E. and Rega, M. (1996) Valuation of environmental externalities: from

theory to decision-making. Transportation Planning and Technology In press. Greene, W. H. (1990) Econometric Analysis. Macmillan, New York. Istat (1987) Tavola Intersettoriale de1l’Economia Italiana 1985, Rome, Italy. Nijkamp, P. (1994) Roads towards environmentally sustainable transport. Transportation Research -A, 28(4),

261-271. Shepard, R. (1970) The Theory of Cost and Production. Princeton University Press, Princeton, NJ.

(Appendix opposite)


APPENDIX

From a statistical point of view the results show that the sectoral data fit better than the regional ones (all applications have a large proportion of highly significant rr and r-ratios). Although the estimated coefficients are all significant, to verify whether the cost function is well behaved, it is necessary to prove if the non-negativity of the fitted shares and concavity conditions, not assured by construction, both hold. Analysing the extreme ranges assumed by parameters (6,&) and their optimal values (8a*,&*) obtained with a fixed level of discretization Sn = 8r = 5* IO-’ for each sectoral and regional simulation, the authors’ experience shows that the dispersion of the optimal values is more uniform for 6’ whereas the optimal values for 0a* are more concentrated. By normalizing the variation ranges of each parameter dividing the difference between the upper bound and the lower bound into ten equal intervals, the results show that for the sectoral application the optimal values of Ba* and 6,’ have been reached in the 6&70% decile. At the regional level, OR reaches its optimal value in the 8&90% decile in the majority of cases, whereas a more distributed pattern is reached for 0-r; generally, the vast majority of optimal values are concentrated in the variation range defined by the 5CM0, 60-70 and 70-80% deciles. In addition, column 2 in Table Al presents, for each sector, the number of initial observations (A) or the number of observations of the considered sample, in the third column the number of considered observations in the model is shown with @a =&=O without improving regularity (B), whereas in the fourth column the number of considered observations with optimal values of @a and 0, (C) is shown; analogous results are given by including pollution tax in the model (columns B/-C’). The same table is built at regional level Table A2 where, in the first column, we consider the region as the origin or destination of goods movement. In aggregate terms, the competitive relationship between rail and truck, under the alternative specifications and estimation methods provided in Section 2, satisfies the required regularity conditions with a greater degree [notice that the number of considered observations grows from 38% (B) to 87% (C) (without pollution tax) and from 38% (B’) to 88% (C’) (with pollution tax) for the sectoral results and from 39% (B) to 72% (C) (without pollution tax) and from 43% (B’) to 75% (C’) (with pollution tax) for the regional ones].

Table Al. Number of considered observations in sectoral simulations

A B C B’ C’

Sector 1 Sector 2 Sector 3 Sector 4 Sector 5 Sector 6 Sector 7 Sector 8 Sector 9 Sector IO Total

229 90 210 88 209 263 118 I82 II9 184 27 I4 20 14 21

130 46 110 48 II5 60 12 49 12 50

221 95 203 99 202 205 82 I87 80 190

86 22 72 22 75 203 60 186 61 188 276 104 266 106 265

1700 643 1485 649 1499

A, number of initial observations. B, number of observations verifying regularity conditions (1st model). C, number of observations verifying regularity conditions (2st model). B’, number of observations verifying regularity conditions (1st model with environment extension). C’, number of observations verifying regularity conditions (2st model with environment extension).

(Table A2 overleaf)

TR(C) 4/6-D


Table A2. Number of considered observations in regional simulations

A

Piemonte

Val d’Aosta

Lombardia

Trentino

Veneto

Friuli

Liguria

Emilia Rom.

Toscana

Umbria

Marche

Lazio

Abruzzo

Molise

Campania

Pugha

Basilicata

Calabria

Sicilia

Sardegna

Total

destination origin

destination origin

destination origin

destination origin

destination origin

destination origin

destination origin

destination origin

destination origin

destination origin

destination origin

destination origin

destination origin

destination origin

destination origin

destination origin

destination origin

destination origin

destination origin

destination origin

destination origin

I21 123

155 145 57 59

II4 I25 16 78 17 80

138 I50 136 I31 69 61 90 98

123 II9 80 83

II0 102 95

I01 58 56 61 55 83 78 51 50

1700 1700 3400

B C B’ C’

35 95 40 99 36 100 41 IO1

-

46 41 17 22 50 50 30 34 42 43 66 74 49 48 I8 22 43 41 49 41 34 34

- I25 II8 34 40 85 90 42 48 53 52

106 125 107 93 40 45 63 61 92 87 55 54

45 130 48 I25 16 34 22 41 62 86 65 95 35 45 36 50 45 55 48 55 69 109 75 126 50 109 52 98 I9 42 23 45 51 69 45 70 56 96 50 90 39 55 36 55

-

49 97 52 99 48 89 50 90 34 60 42 65 38 63 46 69 I8 35 25 35 I9 36 25 40 25 45 28 49 22 40 25 47 33 60 40 68 29 55 30 50 I9 35 25 30 17 35 I5 29

657 1229 739 1275 671 1231 732 I276

1328 2460 1471 2551

A, number of initial observations. B, number of observations verifying regularity conditions (1st model). C, number of observations verifying regularity conditions (2st model). B’, number of observations verifying regularity conditions (1st model with envrronmentat extension). C’, number of observations verifying regularity conditions (2st model with environmental extension).

environmental protection, economic efficiency and intermodal competition in freight transport

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