Transportation Research Part D 18 (2013) 78–85

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Transportation Research Part D

journal homepage: www.elsevier .com/ locate / t rd

The implications of carbon pricing in Australia: An industriallogistics planning case study

Behnam Fahimnia a,b,⇑, Mohsen Reisi c, Turan Paksoy d, Eren Özceylan d

a University of South Australia, School of Management, Adelaide, Australiab University of Technology Sydney, UTS Business, School of Management, Australiac University of Newcastle, School of Mathematical and Physical Sciences, NSW, Australiad University of Selçuk, Department of Industrial Engineering, Konya, Turkey

a r t i c l e i n f o

Keywords:Logistics planningEnvironmental sustainabilityCarbon pricingFuel consumption

1361-9209/$ - see front matter Crown Copyright �http://dx.doi.org/10.1016/j.trd.2012.08.006

⇑ Corresponding author at: University of TechnoloE-mail addresses: behnam.fahimnia@uts.edu.au,

a b s t r a c t

This article investigates the cost implications and carbon reduction potentials of the car-bon-pricing scheme in Australia. A non-linear optimization model is developed represent-ing the trade-off between transportation costs and the costs of carbon emission and fuelconsumption. The latter are expressed as functions of vehicle traveling speed and roadroughness. Piecewise functions and tangent plane approximation are adopted to linearizethe developed model for implementation in CPLEX. Empirical findings from model imple-mentation in an actual case study suggest that the current carbon-pricing scheme in Aus-tralia may only make a minor increase in the overall logistics costs that may be inadequateto drive a significant shift in transport behaviors.

Crown Copyright � 2012 Published by Elsevier Ltd. All rights reserved.

1. Introduction

Increased awareness of the environmental issues triggered by climate change and global warming challenges has resultedin the recent shift towards creating green supply chains (GSCs) and environmentally sustainable logistics planning (ESLP).The basic idea behind the development of GSC and ESLP models is to incorporate the environmental concerns in planningand optimization of existing logistics and supply chain (SC) networks. The number of organizations contemplating the intro-duction and integration of environmental issues and concerns into their SC practices is continuously increasing. This trend isup due to both government pressures (i.e. mandatory environmental regulations) and incentives (i.e. promotion of voluntaryenvironmental programs and projects). While prescriptive mandates, such as the carbon-pricing scheme in Australia and theemissions trading scheme in Europe, can force companies to run greener operations, governments can also encourage vol-untary actions to achieve environmental goals.

In a typical logistics planning model, the performance measures may include financial performance, operational effi-ciency, quality, and customer satisfaction. Among these, cost minimization and profit maximization have been the most re-spected performance indicators (Fahimnia et al., 2012). The development of an ESLP model requires the trade-off betweenthe traditional economic performance of a SC and its environmental responsibility. Depending on the current SC practicesand long-term objectives, a variety of indicators can be used to track the organization’s environmental performance. Thesemay include air emission generation, energy consumption, manufacturing waste as well as the rate at which the producedgoods can be reused, recycled, remanufactured and disposed.

Effective from July 2012, Australia will have a carbon price, starting at $23 per ton of greenhouse emissions, for 3 yearsbefore a full emission trading scheme is introduced in 2015. This article is an attempt to demonstrate the effectiveness of thisscheme in the logistics industry through the implementation of a real world case study.

2012 Published by Elsevier Ltd. All rights reserved.

gy Sydney, UTS Business, School of Management, Australia. Tel.: +61 2 9514 3612; fax: +61 2 9514 3602.behnam.fahimnia@unisa.edu.au (B. Fahimnia).

B. Fahimnia et al. / Transportation Research Part D 18 (2013) 78–85 79

2. Problem formulation

The logistics network under investigation includes multiple suppliers, manufacturers (production facilities) and custom-ers in geographically dispersed locations. Manufacturers produce a range of products during multiple time periods to satisfydeterministic market demands at customer locations. Demand forecasts at customer locations can be backordered in oneperiod at a known penalty cost. All market demand, however, must be satisfied by the end of the planning horizon (i.e.no backlogging in the closing period).

Our modeling assumptions include:

� Number, location and capacity of suppliers and plants are known.� Number and location of customers are known.� Material flow is only allowed between two sequential echelons.� Demand is deterministic and aggregate demand for all types of products in the concerned periods is assumed to be known

for the next planning horizon.� Transportation costs are proportional to the transportation distances.� Road roughness degrees are known for the concerned transport routes.� Restrictions on the vehicle traveling speed between two nodes are known (i.e. minimum and maximum traveling speeds).

The environmental measures (cost of air emission and fuel consumption) are expressed as functions of vehicle travelingspeed and road roughness. The model outputs for each period of the planning horizon include the amount of products trans-ported between SC participants, vehicle traveling speeds, generated air emissions, consumed fuel and backlogged products.

The generated air emission is derived from the speed at which a vehicle travels. Fig. 1 illustrates how the rate of carbonemission is calculated for the corresponding traveling speed for light duty trucks (European Commission, 1999; Jost et al.,1994). It is assumed that the traveling characteristics remain unchanged during the trip. These may include road gradient,vehicle load, altitude, degradation of pollution controls, ambient temperature and the use of air conditioning. According toFig. 1, minimum and maximum carbon emission rates are observed when a vehicle travels at 71 km/h and 110 km/h.

The rate of fuel consumption depends on two key factors including traveling speed and road roughness. Fig. 2 illustratesthe fuel efficiency relationship between the amount of fuel consumed (L/100 km) and the traveling speed for a lightweightvehicle (Bektas and Laporte, 2011). In the speed range of 20–50 km/h, the fuel consumption declines gradually as the trav-eling speed increases. A vehicle reaches its optimal fuel economy at around 50 km/h after which the fuel usage tends to risewith an increase in vehicle traveling speed. The similarity between the curves in Figs. 1 and 2 indicates that the rate of car-bon emission is relatively proportional to the rate at which the fuel is consumed.

Improvements in road roughness or surface pavement condition reduce the amount of fuel consumed by vehicles. Roadroughness degree is often measured according to the International Roughness Index (IRI). Various IRI degrees have differentimpacts on the vehicle movement and lead to various fuel consumption rates accordingly. Bureau of Transport and Commu-nications Economics in Australia has investigated the relationship between the national road conditions and fuel consump-tion, greenhouse gas emissions and vehicle operating costs (Bureau of Transport and Communications Economics, 1996,1997). The study found that the ‘‘The lower the roughness degree, the lesser the amount of fuel consumed by vehiclesand the smaller the quantity of greenhouse gases emitted on an end-use basis.’’ (Bureau of Transport and CommunicationsEconomics, 1997). Fig. 3 illustrates the relationship between IRI and fuel cost adjustment multiplier (Sinha and Labi, 2007).According to this figure, larger IRI values (which indicate worse road conditions) would result in increased fuel cost (i.e.greater fuel cost adjustment multiplier). As an example, for a vehicle traveling on a highway with IRI of 2.13 m/km rough-ness, the base fuel cost is multiplied by 1.13. The IRI baseline of 1.26 m/km is where further improvement in the road con-dition would have no impact on the rate of fuel consumption.

The four indices used for the purpose of model formulation include the following: s for suppliers (set of S suppliers), m formanufacturers (set of M manufacturers), c for customers (set of C customers) and t for time periods (set of T time periods).

0.7

0.8

0.9

1.0

1.1

1.2

1.3

40 50 60 70 80 90 100 110

Car

bon

emis

sion

rat

e (k

g/km

)

Traveling speed (km/hr)

Fig. 1. Relationship between the rate of carbon emission and vehicle traveling speed for a light duty truck. Note: Adopted from European Commission(1999) and Jost et al. (1994).

6

7

8

9

10

11

12

13

14

20 30 40 50 60 70 80 90 100 110

Fuel

con

sum

ptio

n (L

/100

km)

Traveling speed (km/hr)

Fig. 2. Fuel consumption rate and vehicle traveling speed for a light duty truck. Note: Adopted from Bektas and Laporte (2011).

80 B. Fahimnia et al. / Transportation Research Part D 18 (2013) 78–85

Input parameters

dct demand of c at t (ton) qst capacity of s at t (ton) �qmt production capacity of m at t (ton) dt unit cost of transportation excluding fuel cost at t ($/ton-h) ct cost of carbon emission (carbon price) at t ($/ton emission) et fuel cost at t ($/L) ksm distance between s and m (km) �kmc distance between m and c (km) lsm road roughness degree between s and m (m/km) �lmc road roughness degree between m and c (m/km) xsm fuel cost adjustment multiplier between s and m �xmc fuel cost adjustment multiplier between m and c Vmax

smt

maximum allowed speed of vehicle traveling between s and m at t (km/h)

Vminsmt

minimum allowed speed of vehicle traveling between s and m at t (km/h)

V 0maxmct

maximum allowed speed of vehicle traveling between m and c at t (km/h)

V 0minmct

minimum allowed speed of vehicle traveling between m and c at t (km/h)

Emaxsmt

maximum allowed carbon emission generated between s and m at t (kg)

E0maxsmt

maximum allowed carbon emission generated between m and c at t (kg)

bct

backlogging (penalty) cost in c at t ($/ton)

The model outputs include a set of continuous and binary decision variables.Continuous decision variables:

Qsmt

amount of products transported from s to m at t (ton) Q 0mct amount of products transported from m to c at t (ton) Bct amount of product backlogged at c at t (ton) Vsmt speed of vehicle traveling from s to m at t (km/h) V 0mct speed of vehicle traveling from m to c at t (km/h) Esmt rate of carbon emission generation between s and m at t (kg/km) E’mct rate of carbon emission generation between m and c at t (kg/km) Fsmt rate of fuel consumption between s and m at t (L/100 km) F 0mct rate of fuel consumption between m and c at t (L/100 km)

Binary decision variables

Gsmt ¼1; If there is transportation between s and m at t0; otherwise

�

G0mct ¼1; If there is transportation between m and c at t0; otherwise

�

B. Fahimnia et al. / Transportation Research Part D 18 (2013) 78–85 81

Using the parameters and decision variables, Eq. (1) presents the MINLP formulation of the ESLP objective function

Min Z ¼X

s

Xm

Xt

ksm

Vsmtdt � Q smt þ

Xm

Xc

Xt

�kmc

V 0mct

dt � Q 0mct þX

c

Xt

bct � Bct þX

s

Xm

Xt

et

100xsm � Fsmt:ksm

þX

m

Xc

Xt

et

100xmc � F 0mct � �kmc þ

Xs

Xm

Xt

ct

1000Esmt � ksm þ

Xm

Xc

Xt

ct

1000E0mct � �kmc ð1Þ

Terms 1 and 2 are the transportation costs, excluding fuel, from suppliers to manufacturers and from manufacturers tocustomer. Term 3 calculates the backlogging/penalty cost incurred when failing to satisfy a customer demand in one period.Terms 4 and 5 formulate the cost of fuel consumed in the first and second SC echelons, expressed as functions of road rough-ness degree and vehicle traveling speed. Terms 6 and 7 represent the cost of generated air emissions from suppliers to man-ufacturers and from manufacturers to customers (expressed as functions of vehicle traveling speed).

The model constraints are presented in Eqs. (2)–(16). These include the supply, production and distribution capacity con-straints, balance/equilibrium equations as well as speed and pollution restrictions.

Supply and production capacity constraints:

Xm

Q smt 6 qst 8s;t ð2ÞX

c

Q 0mct 6 �qmt 8m;t ð3Þ

Demand satisfaction constraint at each period:

Xm

Q 0mct ¼ dct � bct þ bcðt�1Þ 8c;t ð4Þ

Demand satisfaction constraint for the entire planning horizon:

Xm

Xt

Q 0mct ¼X

t

dct 8c ð5Þ

Balance equation at manufacturers:

Xs

Q smt ¼X

c

Q 0mct 8m;t ð6Þ

Constraint on maximum and minimum traveling speeds (constraint on road safety index):

Vminsmt 6 Vsmt 6 Vmax

smt 8s;m;t ð7ÞV 0min

mct 6 V 0mct 6 V 0maxmct 8m;c;t ð8Þ

Pollution generation (versus speed limitation) constraint:

Esmt � ksm 6 Emaxsmt 8s;m;t ð9Þ

E0mct � kmc 6 E0maxmct 8m;c;t ð10Þ

1

1.04

1.08

1.12

1.16

1.2

1.24

1.28

1.32

1.36

1.4

1.26

1.34

1.42

1.50

1.58

1.66

1.74

1.82

1.90

1.98

2.05

2.13

2.21

2.29

2.37

2.45

2.53

2.61

2.69

2.77

2.84

2.92

3.00

3.08

3.16

Fuel

cos

t adj

ustm

ent m

ultip

lier

IRI (m/km)

Fig. 3. Fuel cost adjustment multiplier at various road roughness degrees. Note: adopted from Sinha and Labi (2007).

82 B. Fahimnia et al. / Transportation Research Part D 18 (2013) 78–85

Restriction on continuous decision variables:

Table 1Carbon

Spee

40–450–560–670–780–890–9100–

1�M 6 Q smt �M � Gsmt 6 0 8s;m;t ð11Þ1�M 6 Q 0mct �M � G0mct 6 0 8m;c;t ð12Þ40Gsmt 6 Vsmt 6 110Gsmt 8s;m;t ð13Þ40G0mct 6 V 0mct 6 110G0mct 8m;c;t ð14ÞEsmt; E0mct; Fsmt; F 0mct P 0 8s;m;c;t ð15Þ

Restriction on binary variables:

Gsmt; G0mct ¼ f0;1g 8s;m;c;t ð16Þ

The resulting MINLP model has T[M(4S + 4C) + C] continuous variables and TM(S + C) binary variables. The number of con-straints is C + T[C + S + M(2 + 3S + 3C)] excluding constraints (13) and (14).

2.1. Linearization of the proposed MINLP model

Piecewise linearization and tangent plane approximation are adopted to convert the proposed MINLP model to a mixedinteger linear programming (MILP) model. Piecewise functions are used to find the carbon emission rates (Esmt and E0mct) fromFig. 1 and fuel consumption rate (Fsmt and F 0mct) from Fig. 2 for specific traveling speeds (Vsmt and V 0mct). This would linearizeTerms 4–7 of the objection function presented in Eq. (1). For this, seven speed intervals are defined in Figs. 1 and 2 (between40 km/h and 110 km/h) and piecewise linear approximation is developed for each interval. Eq. (17) calculates the carbonemission rate, Esmt and E0mct , when the vehicle travels at speeds Vsmt and V 0mct . Values for the two carbon emission coefficients(CECs), a and b, are given in Table 1 for the seven speed intervals.

Esmt ¼ a Vsmt þ b and E0mct ¼ a V 0mct þ b 8s;m;c;t ð17Þ

By the same token, Eq. (18) derives the rate of fuel consumption, Fsmt and F 0mct , from traveling speeds Vsmt and V 0mct . Valuesof the fuel consumption coefficients a0 and b0 can be found in Table 1 obtained from the piecewise linearization of Fig. 2.

Fsmt ¼ a0 Vsmt þ b0 and F 0mct ¼ a0 V 0mct þ b0 8s;m;c;t ð18Þ

Linear approximation is used in Terms 4 and 5 of Eq. (1) to derive the value of fuel cost adjustment multiplier (xmc and�xmc) for a specific road roughness degree (lsc and �lmc). Eqs. (19) and (20) use values in Fig. 3 to approximate the values ofxsm and xmc (Sinha and Labi, 2007).

xmc ¼ 0:001½ðlsm=0:0158� 80Þ=10�2 þ 0:018½ðlsm=0:0158� 80Þ=10� þ 0:9991 ð19Þ�xmc ¼ 0:001½ð�lmc=0:0158� 80Þ=10�2 þ 0:018½ð�lmc=0:0158� 80Þ=10� þ 0:9991 ð20Þ

The nonlinear components in Terms 1 and 2 of Eq. (1) include QsmtVsmt

and Q 0mctV 0mct

. We use a mathematical concept, the so-calledTangent Plane method, to approximate the values of these two components. The main idea of tangent plane approximationis that, similar to visualizing the line tangent to a curve at a point in two-space, we can also find the tangent plane to a sur-face in three-space (Voronovich, 1999). In this approach, on the graph of a differentiable function, we zoom in toward a cer-tain point on the graph surface so that the graph becomes indistinguishable from its tangent line and hence the function canbe approximated by a linear function (Voronovich, 2006). Let Z = f(x,y) represent a surface. Here Z can be approximated usingZ0 = fx(x � x0) + fy(y � y0) + z0, where fx and fy are partial derivatives of f with respect to x and y. In this equation, (x0,y0,z0) is apoint on the surface Z, the position of which determines |Z � Z0| that demonstrates the accuracy of the approximation. Basedon this, the Q

V components in Terms 1 and 2 of Eq. (1) are replaced by:

fðQ ;VÞ ¼QVffi �Q 0

V20

ðV � V0Þ þ1

V0ðQ � Q 0Þ þ

Q 0

V0ð21Þ

emission and fuel consumption coefficients obtained from piecewise linearization of Figs. 1 and 2.

d intervals (km/h) CECs FCC

a b a0 b0

9 –0.011274 1.42038 0 7.19 –0.007394 1.22638 0.04 5.19 –0.003007 0.96316 0.07 3.39 0.002001 0.61260 0.10 1.29 0.007670 0.15908 0.10 1.29 0.014023 –0.41269 0.16 –4.2110 0.021064 –1.11679 0.11 0.8

B. Fahimnia et al. / Transportation Research Part D 18 (2013) 78–85 83

where Q0 is the average number of products shipped between two locations and V0 is the mid speed at each speed interval inTable 1, e.g. V0 = 55 for the speed interval [50,60].

3. Case study

The case company (referred to as AFC) is a medium size automotive chemical provider involved in manufacturing anddistribution of types of engine coolants in Australia. Three suppliers (S) provide the required material for the productionof coolants in four manufacturing plants (M = 4). Final products are then distributed from manufacturers to the customers(retailers) in five geographical locations (C). The planning horizon is 1 year comprising 12 1-month periods (T). The objectivefunction (linearized Eq. (1)) in this case study has 1596 continuous variables, 384 binary variables and 1349 constraints.

Australia has introduced a carbon-pricing scheme implementable from July 2012. The starting price for a ton of carbonpollution is set at $23 in 2012 rising to $24.15 in 2013 and $25.40 in 2014. The primary intention of this research is to inves-tigate the effectiveness of this scheme as a way to transition into a low-carbon economy. We designed a set of scenarios forcarbon prices. The scenarios (outlined in Table 2) include a scenario of no carbon-pricing exercised as in 2011 (CPS1), threescenarios for the recently introduced carbon-pricing scheme in 2012–2014 (CPS2-4) and three scenarios designed for com-parison analysis of the current scheme (CPS5-7).

Numerical results from the model implementation (using CPLEX 12) for the seven scenarios are presented in Table 3.While carbon has never been priced officially in the past (CPS1), in 2012 emission cost will constitute about 1.31% of theoverall logistics cost at AFC (CPS2). The contribution will rise to 1.38% in 2013 (CPS3) and 1.44% in 2014. Since carbon pricewould only form a small portion of the overall logistics costs, the optimization model tends to give the higher minimizationpriority to the transportation costs (Terms 1 and 2 of the Eq. (1)). This unbalanced cost distribution would assist the CPLEXmodel draw the optimal solution for the proposed ESLP model in only 5 s without significant trade-off trials required. Theoptimal solution determines the quantity of products transported along the SC, vehicle traveling speeds, generated air emis-sions, consumed fuel, and backlogs.

The results for the scenarios CPS5-7 indicate that the contribution of carbon price in the value of objective function (i.e.overall logistics cost) is not increased corresponding to the rate at which the carbon price is raised. At the price of $46 per ton(i.e. double the 2012 rate), the emission cost is increased by less than 87% (i.e. 2.46% contribution compared to 1.31% in2012—Table 3). Identical figures can be observed for the carbon prices increased by five and ten times its 2012 rates(CPS6 and CPS7). The optimization model would find it more challenging finding the optimal solution as the carbon pricerises (i.e. more trade-off trials required). This can be witnessed in Table 3 by the longer model runtimes in CPS5-7.

One primary intention behind pricing carbon in Australia is to shift behavior in optimizing the logistics operations. Thetransport sector contributes a significant 20% of Australia’s carbon emission (Commonwealth Scientific and Industrial Re-search Organisation, 2008). Fig. 4 shows the amount of carbon emitted by AFC for the seven scenarios outlined in Table 2.Carbon emission is reduced from 848.86 ton in 2011 when no carbon-pricing is exercised to 834.78 ton in 2012. This wouldbring an improvement of about 1.65% followed by further 0.2% and 0.55% in 2013 and 2014. It is evident that more significantemission reductions are achieved at larger carbon prices (refer to CPS5-7). In the best-case scenario, increasing the carboncost by ten times its 2012 rate results in a 29.52% improvement in the carbon emission figure.

Since last year when the idea of a carbon price first emerged in Australia, AFC has included an item to all its external con-tracts, named a ‘change of law’ clause. This would allow the adjustment of selling prices from the time the carbon-pricingscheme is exercised. Fig. 5 indicates that the overall logistics cost in AFC is increased by 1.12% in 2012, rising to 1.18% in2013 and 1.25% in 2014. New pricing provisions will need to be introduced by AFC to address the overall price shift. How-ever, Fig. 5 shows that increases in logistics costs through the introduction of carbon-pricing scheme is unlikely to make sig-nificant changes in the tonnage of carbon emitted by AFC. While carbon is priced to create a financial incentive to shift fuelconsumption behavior, a carbon price of $23 per ton sounds too minor to drive a significant shift. More reasonable emissionreductions can be observed at the carbon price of above $46. In fact, increasing the carbon price to double its 2012 rate wouldresult in about four times improvement in carbon emission. In the best case scenario among seven, carbon emission at AFC isreduced by 30% provided the carbon price is magnified by ten times its 2012 rate.

Finally, we analyze the impact of IRI on AFC’s fuel consumption and overall logistics cost. Fig. 6 illustrates the results fortwo road roughness scenarios (RRSs) in which RRS1 represents the current road conditions and RRS2 is the best case scenario

Table 2Set of carbon-pricing scenarios.

Carbon-pricing scenario Carbon price ($) Description

CPS1 0 No carbon price in 2011CPS2 23 Starting carbon-pricing rate in 2012CPS3 24.15 Carbon-pricing rate in 2013CPS4 25.4 Carbon-pricing rate in 2014CPS5 46 Hypothetical scenario (double the 2012 rate)CPS6 115 Hypothetical scenario (five-times the 2012 rate)CPS7 230 Hypothetical scenario (10-times the 2012 rate)

Table 3Numerical results for various carbon-pricing scenarios.

Carbon-pricing scenarios Overall logistics cost ($) Emission cost ($) Contribution of emission cost (%) Runtime (Sec)

CPS1 1,445,632 0 0.00 3CPS2 1,461,845 19,200 1.31 5CPS3 1,462,759 20,114 1.38 5CPS4 1,463,684 21,039 1.44 5CPS5 1,479,230 36,400 2.46 14CPS6 1,529,497 85,193 5.57 28CPS7 1,551,625 137,606 8.87 66

Fig. 4. Generated emission at each carbon-pricing scenario.

Fig. 5. Reduced carbon emission versus increased logistics costs at each carbon-pricing scenario.

84 B. Fahimnia et al. / Transportation Research Part D 18 (2013) 78–85

(i.e. IRR is equal to 1.26 for all the concerned roads—refer to Fig. 3). Improving the roughness of the road surface has signif-icant impact of up to 18.5% on the fuel consumption (from 112,338 l down to 91,570 l) that would accordingly result in lesscarbon emissions as well as lower fuel and emission costs. Logistics cost saving of about 2.6% can be achieved throughimproving the physical condition of roads. Considering this influence of road roughness on vehicle fuel consumption andoverall logistics cost, road rehabilitation programs may need to be adopted by the local/federal authorities to avoid furtherdeterioration of the road pavements caused by environmental or vehicle actions. Such initiatives at the government level candrive further economic incentive programs encouraging logistics and SC enterprises, such as AFC, to utilize the raised reve-nue for implementing initiative environmental projects and programs.

Fig. 6. Consumed fuel versus overall logistics costs in two RRSs.

B. Fahimnia et al. / Transportation Research Part D 18 (2013) 78–85 85

4. Conclusions

This article examined the potential cost implications and carbon emission benefits of the recently introduced carbon-pric-ing scheme in Australia. An environmentally sustainable logistics planning model was developed in this paper to investigatethe effectiveness of the scheme in a real world case problem. The proposed model incorporates the major economic and envi-ronmental cost elements (i.e. typical transportation costs as well as the costs of carbon emission and fuel consumption) ex-pressed as functions of road roughness and vehicle traveling speed.

The results from a real world case study revealed that the proposed carbon price of $23 per ton is unlikely to add con-siderably to the overall logistics cost. The scheme results in a minor increase of about 1.2% in overall logistics cost thatmay only have a small impact on changing the industry behavior towards running greener logistics. While this does not indi-cate that a shift will not occur, the current pricing can be more effective where the economic impacts on the logistics indus-try are more pronounced, which may occur at the carbon prices of over $46 per ton (double the proposed 2012 rate). It wasalso found that improving the roughness of the road surface through various forms of rehabilitation programs may reducefuel consumption by 18% with according reductions in carbon emissions.

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