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Cost-benefit analysis of low-noise pavements: dust intothe calculationsKnut Veisten a & Juned Akhtar aa Institute of Transport Economics (TOI), Safety and Environment , Gaustadalleen 21,NO-0349, Oslo, NorwayPublished online: 05 Aug 2010.

To cite this article: Knut Veisten & Juned Akhtar (2011) Cost-benefit analysis of low-noise pavements: dust into thecalculations, International Journal of Pavement Engineering, 12:1, 75-86, DOI: 10.1080/10298436.2010.506537

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Cost-benefit analysis of low-noise pavements: dust into the calculations

Knut Veisten* and Juned Akhtar

Institute of Transport Economics (TOI), Safety and Environment, Gaustadalleen 21, NO-0349 Oslo, Norway

(Received 12 June 2009; final version received 25 June 2010)

This paper presents a cost-benefit analysis (CBA) of roads noise measures in Norway. Low-noise pavement alternativeswere compared to stone mastic asphalt with a maximum aggregate size of 11mm. The low-noise alternatives were expectedto reduce the noise levels by 1–4.5 dB over their lifetime, compared to the reference, but had shorter lifetime and, mostly,higher investment cost. A new element included into our CBA of low-noise asphalts is their property in terms of asphalt-wearing and dust production. Given a relationship between asphalt-wearing and airborne particulate matter, there is apotential health impact of pavement choice. Official valuations of both noise changes and PM10 changes were applied for thebenefit estimations. Thin-layer asphalts obtained higher benefit-cost ratios than porous asphalts, mainly due to small changesin unit costs and technical lifetime compared to the reference. Alterations in dust production had considerable weight in thebenefits, but did not considerably alter the ranking of asphalts compared to analyses not taking dust into account.

Keywords: asphalt-wearing properties; economics; health impact; particulate matter; porous asphalt; risk analysis; thin-layer asphalt

1. Introduction

Noise represents a complex type of external effect from

human activity. Measurement of noise is by itself

intriguing, and human perception of road noise will vary

according to the activity and sensitivity of the individual

(Fyhri and Klæboe 2009). But measurement scales linking

road noise to noise exposure and to annoyance have been

established (Miedema and Oudshoorn 2001, Klæboe

2003). Reduced noise for those living and using the area

adjacent to the roads will yield a reduction in annoyance

costs. This counts as benefits in a cost-benefit analysis

(CBA) of noise control measures (Hanley et al. 1997).

Traditional road noise control measures in Norway

have so far only comprised installations impeding noise

propagation, such as noise barriers and facade insulation.

However, over the last years, there has been increased

focus on noise reduction measures at source, and a recently

concluded project dealt with the optimisation of

environmental properties of road surfaces in order to

achieve target levels of both noise and dust, or particulate

matter (NPRA 2006a, Evensen 2009). Some types of

noise-reducing asphalts are now closer to being qualified

as feasible noise control measures in Nordic countries

(Amundsen and Klæboe 2005, Kropp et al. 2007).

Environment and health impacts, in monetary terms,

should be included in CBA of pavement alternatives

(Cheneviere and Ramdas 2006), as well as CBA of noise

control measures in general (Hanley et al. 1997).

This paper presents a CBA of various low-noise

pavement alternatives. These are compared in terms of

extra costs and extra benefits to a reference, and this

reference is stone mastic asphalt (SMA) with a maximum

aggregate size of 11mm (SMA11). The alternative

asphalts were expected to reduce the noise levels by 1–

4.5 dB over their lifetime, compared to the reference. The

initial noise reduction for some newly laid pavements

could attain 6 dB, but their noise-reducing capability

decays more rapidly compared to SMA11. The low-noise

alternatives also have shorter technical lifetime than the

reference, and most of them also have higher investment

costs (Arnevik 2006b).

The noise reduction represents the main benefit of

alternative low-noise pavements that can be weighed

against the cost increase. However, their property in terms

of wearing and dust production, compared to SMA11, is

also relevant in a CBA. Pavement wear results in airborne

dust pollution and part of the total suspended particles are

of finer sizes that enter the human respiratory tract,

potentially causing adverse health effects (Kunzli et al.

2000, de Kok et al. 2006). The use of studded tyres, which

is common in most parts of Norway, as well as Sweden and

Finland, increases pavement wear and yields higher levels

of airborne dust pollution (Raitanen 2005, Snilsberg

2008). Studded tyres will also generate a higher share of

particles with finer particle size (#2.5mm, that is, PM2.5)

that can be deposited in the lower respiratory tract (Wilson

and Suh 1997). A larger part, in weight percentage, of the

ISSN 1029-8436 print/ISSN 1477-268X online

q 2011 Taylor & Francis

DOI: 10.1080/10298436.2010.506537

http://www.informaworld.com

*Corresponding author. Email: kve@toi.no

International Journal of Pavement Engineering

Vol. 12, No. 1, February 2011, 75–86

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airborne particles produced by pavement wear will be

coarser than PM2.5, but the finer particles will have

relatively more importance in terms of the surface area

they represent (Schwartz et al. 1996, Snilsberg 2008).

Furthermore, also particles less than 10mm in diameter

(PM10) can have negative health effects (Donaldson and

Tran 2002).

Potential impact on health effects from pavement

choice, via changes in airborne particulate matter, would

also imply economic impacts (Viscusi 1993). If the

wearing property is better in low-noise asphalts, compared

to the reference, it adds an additional benefit. If it is worse,

it yields a negative benefit which diminishes the overall

benefits obtained from noise reduction. Although the

relationship between asphalt wearing and air pollution,

especially the particulate matter (which will be referred to

as PM10 in the following), is not precise (Snilsberg 2008),

a measurement, or estimate, is still relevant for an

economic assessment. Official estimations of health

effects from PM10 concentrations (NIPH 2005), as well

as official valuations of both noise changes and PM10

changes (NPRA 2006b), were applied for these benefit

estimations. To our knowledge, the combination of noise

impacts and PM10 impacts has not been assessed before in

an economic analysis of low-noise pavements.

The rest of the paper is arranged as follows: In the

following section, we describe the methodology of CBA

and the specific assumptions we apply; even including air

pollution effects with the noise effects, the analysis may be

considered partial in terms of omitting possible impacts/-

variables. In the third section, we describe the database for

the pavement reference and low-noise alternatives,

including an assessment of input uncertainty. The fourth

section presents the results of the data analysis, including a

comprehensive Monte Carlo-based risk analysis of

estimated benefit-cost ratios. Our findings are discussed

and concluded in the final section.

2. CBA of noise-reducing measures

2.1 Principles

CBA is the main method for economic assessment, at least

within the neo-classical approach (Mishan 1988). Costs of

noise-reducing measures will be compared to the impacts

such measures will yield, where benefits are monetised

values of the noise reduction and other potential impacts

(Navrud 2002).1 Normally, the costs and benefits (that are

either positive or negative, depending on the impact) are

stated as present values, summing costs and benefits over a

project period where future costs and benefits are

discounted to be comparable to values at present. If the

net present value is positive, i.e. the benefits divided by the

costs (benefit-cost ratio) is above unity, the measure is

deemed economically efficient. If several measures are

compared, the alternative with highest benefit-cost ratio is

the best candidate for selection (Mishan 1988).

Ideally, a CBA should include ‘all benefits and costs,

on all people, over all relevant areas and time periods’

(Moore and Pozdena 2004). We will assess if pavement

choice implies different external effects, in addition to

user cost differences, that is delay (time use), vehicle

operating costs and crash costs (Ehlen 1997). In our case,

we focus the effects on noise and air pollution when

changing pavement type and these effects will be

assessed at the benefit side of the CBA. Differences in

delay costs are handled at the cost side of the CBA in our

calculations (Arnevik 2006b). There may be other effects

that we omit, e.g. differences in vehicle operating costs

due to rolling resistance properties, indirect road safety

effects (crash cost differences) due to pavement friction

properties or indirect environmental effects due to

recycling properties (Morgan 2006).2 Thus, our CBA

may be characterised as partial. The error of a partial

analysis will of course increase if the omitted effects are

substantial, and this should be taken into account when

assessing the resulting estimates.

2.2 Pavement alternatives and procedures forestimating costs and benefits

The alternative low-noise pavements comprise two main

types:3

. thin bituminous surfacing (TSF) with a maximum

aggregate size of 8mm, differentiated in one

ordinary tested (TSF8), one including rubber

(TSF8r) and one ‘best possible’, based on European

experiences (TSF8x); and. porous asphalt concrete (PAC), one single-layer

type with a maximum aggregate size of 11mm

(SPAC11) and three double-layer PAC types. One of

the double-layer PAC in our analysis represents an

average of various types, tested at various sites,

where the bottom layer has a maximum aggregate

size of 16mm and the top layer has a maximum

aggregate size of 8mm (DPAC8/16); and another

is an average double-layer PAC where the top

layer has a maximum aggregate size of 11mm

(DPAC11/16); and the third is a ‘best possible’

double-layer PAC where the top layer also has a

maximum aggregate size of 11mm, but differing

from the others in the distribution of aggregate sizes

and in void content (referred to as DPAC11/16x).4

Costs of investments for these seven low-noise

pavements, as well as the reference (SMA11), will be

repeated over the project horizon according to the

expected lifetime of the pavement. Then, a salvage value

representing sum of cost annuities beyond the project

horizon, set to 40 years, will be deducted from the costs.

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No differences in operation costs (salting, cleaning) will

be assumed, which is based on recent test experiences

(Evensen 2009). Norwegian cost figures for low-noise

pavements are framed by the fact that these are still at the

test stage, but the basic relationship is that the unit cost of

TSF is close to the unit cost of SMA, but having a slightly

shorter lifetime. PAC has higher unit costs, particularly

double-layer PAC, and even shorter lifetime than TSF

(Morgan 2006, Arnevik 2006a, 2006b, Evensen 2009).

The economic benefits of a change to some low-noise

alternative arise from the noise reduction and from any

other impacts over which the affected population have

preferences.5 One possible impact is that the selected low-

noise pavement has different wearing properties than the

reference. If this results in changes in emission of

particulate matter (PM10) to the air, the impact can be

valued in monetary terms and enter the calculation as

additional positive or negative benefits. Noise benefit

estimations will be based on the estimated dB changes

over the pavement lifetime (Arnevik 2006a, Veisten and

Akhtar 2008, Evensen 2009), multiplied by a monetised

value per dB decrease per dwelling per year (NPRA

2006b). The dB change is handled as an average for all

affected dwellings, and the monetised dB value is

multiplied by the number of dwellings alongside the

road section (Veisten et al. 2007a). The estimated change

in particulate matter (PM10) is based on abrasion volumes

of particulate matter using the Nordic ball mill test,

simulating the wearing of asphalt by studded tyres.6 The

estimated relative differences in Nordic ball mill test

values, compared to the SMA11 reference (Evensen

2009), are applied to adjust the share of average PM10

emissions, due to asphalt wearing (studded tyres), per

vehicle km on Norwegian roads (SFT 1999).

The official measures of health effects of dust, or

particulate matter, in Norway do not distinguish between

particle sizes below 10mm (NIPH 2005),7 thus official

monetary valuations also relate only to PM10. The PM10

emissions per vehicle km, differentiated between light

(90%) and heavy (10%) vehicles, are valued monetarily

applying the official value per kg emission in densely

populated areas, i.e. Oslo in our case (NPRA 2006b). The

emissions, and, subsequently, the monetary valuations, are

measured as annual averages, yielding averages of winter

conditions with studded tyres and summer conditions

without studded tyres. Applying vehicle km emission

values, total emission (change) on a road section is given

from AADT, the annual average daily traffic (Eriksen

2000). The monetised impact of the PM10 changes on the

particular road section is, as for noise, dependent on the

number of affected households/dwellings. Thus, the benefit

effect from PM10 changes is weighted by the number of

dwellings adjacent to the road (Veisten and Akhtar 2008).

2.3 Sensitivity analysis/uncertainty analysis

We carry out a comprehensive sensitivity/uncertainties

analysis, with simultaneous assessment of various input

uncertainties based on simulations. Although this will be

based on subjective assessments of uncertainty for some

input components, the procedure will provide a probability

distribution of estimated benefit-cost ratios of low-noise

asphalts (O’Brien et al. 1994). A Monte Carlo type of

simulation will be accomplished by the use of the

programme @RISKe for Excel spreadsheets (Palisade

2002). @RISK yields ranking coefficients for the input

components (noise reduction, PM10 change, pavement

lifetime, investment cost) in terms of the effect on net

benefits and benefit-cost ratios. The specific effect of such

a ranking coefficient, bk, where k refers to input, can be

calculated from the following formulae:

bk ¼

change in net benefitsd ðnet benefitÞ

change in input ksd ðinput kÞ

: ð1Þ

Division by the standard deviation normalises

(standardises) the effects from different inputs. The

formulae in Equation (1) can be rewritten in terms of

measuring the change in net benefits from a specific input

change – following traditional sensitivity analysis:

change in net benefit ¼ sd ðnet benefitÞ

£bk £ change in input k

sd ðinput kÞ: ð2Þ

We will apply the default Latin Hypercube simulation

method which requires a lower number of iterations than a

standard Monte Carlo simulation (Palisade 2002).8

However, we do not know the true distribution of the

input variables (noise reduction, PM10 change, pavement

lifetime, investment cost). We apply normal distributions

and represent the uncertainty (risk) as a percentage of

the average value (point estimates), thereby obtaining

standard deviations of the distributions. The assumptions

regarding point estimates and spread for noise reduction,

PM10 emission, pavement lifetime and investment costs

will be based primarily on experiences by the Norwegian

Public Roads Administration (Veisten and Akhtar 2008).

3. Material

We will build our CBA on a main hypothetical, although

realistic, case for a road section with an average speed of

70 km/h, AADT 20,000 and 500 households adjacent to

the road. We apply official PM10 valuations for Oslo

(NPRA 2006b), such that our main case can be considered

an urban ring road, with relatively good traffic flow

conditions, having buildings of several storeys with

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dwellings alongside (Veisten et al. 2007a). The assumed

noise reductions obtained from a change to one of the

seven low-noise pavements, relative to the reference (SMA

11), are presented in Table 1.

The average noise reduction over the lifetime is

multiplied by 476 NOK, or 59.50 EUR, applying an

exchange rate of 8 NOK/EUR. By this, we obtain noise

benefit estimates per year per household/dwelling, based

on the official value of 238 NOK per dB change per

(annoyed) person per year (NPRA 2006b) and an average

household size of ca. 2 persons. Table 2 presents PM10

correction factors (based on estimated wearing parameters

from the Nordic ball mill test) for the low-noise

pavements, relative to SMA11.

In most cases, the estimated PM10 emission due to

studded tyres is the same for the low-noise pavement as for

SMA11, but for two pavements (DPAC8/16 and

DPAC11/16x), a reduction is indicated. For 70 km/h

speed, the PM10 emission due to studded tyres is calculated

as 0.037 g per vehicle km, for light vehicles (0.095 g total

PM10 emission, also including exhaust gas), while it is

calculated as 0.187 g per vehicle km, for heavy vehicles

(0.737 g total PM10 emission, also including exhaust gas),

according to SFT (1999). The economic valuation of

(avoiding) one kilogram PM10 from road traffic is 3680

NOK, or 460 EUR, for Oslo (NPRA 2006b). Table 3

displays pavement cost assumptions.

Total unit costs are slightly lower for the thin surfacing

layers (TSF8, TSF8x) than for the reference (SMA11),

except for the one including rubber (TSF8r). The unit costs

for double PAC are nearly 2.5 times higher than for the

reference. Total investment costs will be further influenced

by technical lifetime of the pavement (given in Table 1),

whereby shorter lifetime implies more frequent repaving

over the project horizon, which is set to 40 years. A

discount rate of 3% will be applied (Bickel et al. 2006).

Operational costs, i.e. winter maintenance/salting,

assumed to be equal for all pavement types, approximately

0.70 EUR per m2 per year, and will thus not enter the CBA

of a change to low-noise pavements (Evensen 2009). All

cost and benefit elements are handled in a common

spreadsheet model – and cost elements are confined to

investment costs (plus delay costs), while benefit elements

comprise monetised values of noise reduction and PM10

changes (Veisten and Akhtar 2008).9

For our comprehensive sensitivity/uncertainty anal-

ysis, we will assume ^30% uncertainty in pavement

investment costs per m2, in pavement lifetime and in

average noise reduction over the lifetime; and ^50%

uncertainty in PM10 change. Table 4 summarises the

assumed uncertainty in input variables that will enter

the simulation applying @RISK with 10,000 iterations and

the default Latin Hypercube simulation method.

The simulated standard deviations follow from the

assumed ^20% uncertainty in point estimates for Table

1.

Assumed

noisereductions(dB)forlow-noisepavem

entalternatives,compared

toSMA11–speedca.70km/h.

TSF8

TSF8x

TSF8r

SPAC11

DPAC8/16

DPAC11/16

DPAC11/16x

Initialreduction(new

lylaid

low-noisepavem

ent)

2dB

4dB

5dB

4dB

7dB

5dB

6dB

Assumed

lifetime–AADT<

20,000(ref

¼6years)

5years

5years

2years

3years

2years

3years

3years

Averagenoisereductionover

lifetime

1.33dB

3dB

3.25dB

3.2dB

5.5dB

4dB

5dB

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pavement investment costs per m2 and PM10 change and

^30% uncertainty in point estimates for pavement

lifetime and average noise reduction over the lifetime.

As indicated from Table 4, the simulated estimates

are nearly identical to the deterministic/plotted input

values.

4. Results

4.1 Net benefits and benefit-cost ratios

Table 5 displays the resulting cost-benefit calculations –

the estimated net benefits (present values for a 25-year

project on 1 km) and benefit-cost ratios. For the simulated

also, estimates the standard deviations, the skewness and

the kurtosis are reported. The standard deviation of the

simulated net benefit enters directly into the @RISK

calculations based on Equation (2) displayed in Section

2.3. The skewness and the kurtosis values indicate

something more about the form of the probability

distributions, if it is symmetric and/or if it is more

peaky/flatter than the normal distribution (Walpole et al.

1998).

The relatively less costly TSF pavements obtain the

highest (deterministic) benefit-cost ratios. The ordinary

tested average type (TSF8) obtains the second-highest

benefit-cost ratio, although the noise reduction and noise

reduction benefits are relatively limited. Higher noise

reductions from porous asphalts hardly outweigh the high

extra costs, as compared to the reference (SMA11). For

two of these (DPAC11/16 and DPAC8/16), the simulated

net benefits are negative. The ‘best possible’ double-layer

PAC from the testing (DPAC11/16x) obtains highest net

benefits, but lower benefit-cost ratio than TSF8. The TSF

including rubber (TSF8r) obtains a much lower benefit-

cost ratio than the other TSF, indicating the importance of

Table 2. Estimated PM10 correction factors for low-noise pavement alternatives, compared to SMA11.

TSF8 TSF8x TSF8r SPAC11 DPAC8/16 DPAC11/16 DPAC11/16x

PM10 correction factor 1 1 1.2 1 0.85 1 0.75

Table 3. Assumed investment costs (EUR) – unit costs and delay/warning costs – for low-noise pavement alternatives, comparedto SMA11.

SMA11 TSF8 TSF8x TSF8r SPAC11 DPAC8/16 DPAC11/16 DPAC11/16x

Cost of paving asphalt (incl. millingand adhesion), per m2

10.13 9.62 9.62 16.25 12.85 25.04 25.02 21.71

Cost of delay and warning, per m2,dual carriageway, AADT ¼ 20,000

0.79 0.19 0.19 0.33 0.33 0.55 0.55 0.55

Total investment cost per m2 10.91 9.81 9.81 16.58 13.19 25.58 25.56 22.26

Table 4. Assumed uncertainty in input variables – for CBA of low-noise pavement alternatives, compared to SMA11.

TSF8 TSF8x TSF8r SPAC11 DPAC8/16 DPAC11/16 DPAC11/16x

Total investment costs per m 2 (EUR)Deterministic 9.92 9.92 16.70 13.30 25.80 25.78 22.48Simulated estimate 9.92 9.92 16.70 13.30 25.80 25.78 22.48Simulated st. dev. 2.98 2.98 5.01 3.99 7.70 7.70 6.74

Pavement lifetime (years)Deterministic 5.0 5.0 2.0 3.0 2.0 3.0 3.0Simulated estimate 5.0 5.0 2.0 3.0 2.0 3.0 3.0Simulated st. dev. 1.5 1.5 0.6 1.0 0.6 1.0 1.0

Noise reduction over lifetime (dB)Deterministic 1.30 3.34 4.25 3.20 5.87 4.00 5.00Simulated estimate 1.30 3.34 4.25 3.20 5.87 4.00 5.00Simulated st. dev. 0.39 1.00 1.28 0.96 1.76 1.20 1.50

PM10 change (correction factor)Deterministic 1.00 1.00 1.20 1.00 0.85 1.00 0.75Simulated estimate 1.00 1.00 1.20 1.00 0.85 1.00 0.75Simulated st. dev. 0.50 0.50 0.60 0.50 0.43 0.50 0.38

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the weakened asphalt wearing property and PM10

deterioration. The share of PM10 valuation in benefits is

relatively large and negative for TSF8, while it is

relatively large and positive for two double-layer PAC

(DPAC11/16x and DPAC8/16).

The simulated benefits come close to the determi-

nistic, except for the two PAC having deterministic

benefit-cost ratios close to 1. The high simulated

standard deviations also indicate the uncertainty of the

benefit-cost ratios for these two pavements (DPAC11/16

and DPAC8/16). Furthermore, high simulated skewness

and kurtosis values indicate that the probability

distribution does not have the symmetric bell shape of

the normal.

4.2 Sensitivity analysis/uncertainty analysis

Since the estimated net benefits and benefit-cost ratios

clearly depend on the assumption of affected house-

holds/dwellings per km – set to 500 in the calculations

above, we have assessed the impact of reducing the

number to 300. Simulated net benefits are displayed in

Figure 1, while (deterministic) benefit-cost ratios are

displayed in Figure 2.

The overall picture and ranking is similar for the case

with 300 dwellings, but net benefits and benefit-cost ratios

are reduced. Only TSF8 and TSF8x obtain ‘robust’

benefit-cost ratios above 2, and for the two most uncertain

porous asphalts (DPAC11/16 and DPAC8/16), the

deterministic benefit-cost ratios fall beneath 1.

Since TSF8 and DPAC11/16 represent averages of

various pavement types, tested at various sites (Berge

2008, Evensen 2009), we limit the extended sensitivity

testing to these two low-noise pavement alternatives. We

apply Equation (2) in Section 2.3, and present three levels

of changes in the four main input values: increases of 5, 25

and 50%. Tables 6 and 7 show the results.

The simulated net benefits of TSF8 were 769,900 EUR

(present values for a 40-year project on 1 km), for the case

of 500 dwellings. A substantial cost increase of 50%

would not yield negative simulated net benefits, but an

increase in relative dust/particle production by 50% would

yield negative estimates. Furthermore, if the noise

reduction were 25% less, net benefits would be negative.

The simulated net benefits of DPAC11/16 were

negative, 25,613 EUR (present values for a 40-year

project on 1 km), for the case of 500 dwellings. It is an

increase in pavement lifetime which will particularly

affect net benefits – a 25% increase would yield positive

net benefits. Further noise reductions would need to be

considerable to yield positive net benefits. A cost

reduction of 25% would also yield positive net benefits.

The cumulative probability distributions for the net

benefits of a change from SMA11 to, respectively, TSF8

and DPAC11/16, are displayed in Figures 3 and 4. WhileTable

5.

Estim

ated

net

benefits(presentvalues

fora25-yearproject

on1km)andbenefit-costratiosofachangeto

low-noisepavem

entalternatives,compared

toSMA11.

TSF8

TSF8x

TSF8r

SPAC11

DPAC8/16

DPAC11/16

DPAC11/16x

Net

benefits

855,859

2,300,779

1,610,476

1,875,662

1,012,183

493,498

2,618,507

Shareofdust(PM

10)reductionbenefits

00

228%

015%

028%

Sim

ulatednet

benefit

769,900

2,213,248

1,533,909

1,609,452

357,108

25613

2,066,786

Sim

ulatedst.dev.

2,321,198

2,526,559

8,868,574

4,782,829

21,444,420

10,642,880

18,986,610

Sim

ulatedskew

ness

20.70

20.58

78.48

29.34

253.77

0.72

272.54

Sim

ulatedkurtosis

50.04

145.6

7760

1085

5916

2283

6731

Benefit-costratio(deterministic)

13.46

34.49

3.94

3.11

1.27

1.21

2.35

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there is a 35.5% overall risk of negative net benefits for

TSF8, there is a 46.4% overall risk of negative net benefits

for DPAC11/16.

5. Discussion and conclusions

We have assessed the economic benefits of a change to

low-noise pavements on urban motorways/ring roads in

Norwegian cities, assuming 500 affected households per

km. Low-noise pavement alternatives that differ only

slightly from existing SMA in terms of unit costs and

technical lifetime will yield the most robust economic

net benefits, as long as there is some noise reduction

without impairing air quality due to increased asphalt

wear. TSF pavements obtain highest benefit-cost ratios.

Thus, these pavements represent the safe low-noise

option, compared to PAC. This conclusion is consistent

with earlier findings based on a much more limited

testing of low-noise pavements in Norway (Veisten et al.

2007a, 2007b).

However, the ordinary tested average type of TSF

pavements yields quite small noise reduction over the

lifetime, compared to the reference alternative (SMA).

Thus, PAC merits further attention and testing. A ‘best

possible’ double-layer PAC obtains fairly robust positive

net benefits, high unit costs and low lifetime notwith-

standing, and yields both considerable noise reduction and

dust reduction. For the ordinary tested average types of

double-layer PAC, net benefits are highly uncertain, with

high risk of negative economic net benefits. However,

future improvements, particularly in technical lifetime,

could make double PAC a more robust option. Yet, also

unit cost reductions are necessary to yield benefit-cost

ratios of PAC comparable to those for TSF.

The combination of noise impacts and dust/particles

(implicitly valued as PM10) impacts in our CBA represents

a novelty in our study. It is important to extend the

economic analyses of noise control measures, like low-

noise pavements, in terms of including all possible impacts

(Morgan 2006). The impact of dust/PM10 on the benefits is

relatively large for those pavement having correction

factors different from unity – constituting up to ca 1/4 of

overall benefits. The measurement of this impact is still

somewhat uncertain, and hinges on an assumed strong

relationship between the amount of dust from asphalt

wearing and the amount of PM10 which may not be met in

reality (Snilsberg 2008). Our analysis does not cover

possible changes in relative shares of finer and coarser

particles, e.g. the changes in particles below 2.5mm in

diameter, when changing pavement, may be different from

the changes in particles between 2.5 and 10mm in

diameter. We have combined the pavement wearing and

Figure 1. Simulated net benefits (present values for a 25-year project on 1 km) for low-noise pavement alternatives, compared toSMA11 – 500 vs. 300 dwellings per km.

Figure 2. Benefit-cost ratios for low-noise pavementalternatives, compared to SMA11 – 500 vs. 300 dwellings per km.

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Table

6.

Sensitivityanalysis–simulatednet

benefits

–TSF8.

Input(k)

Changein

input(%

)Changein

input

SD

ofinput

SD

(net

benefit)

Rankingcoefficient

Changein

net

benefit

Totalinvestm

entcostsper

m2(EUR)

50.50

2.98

2,321,198

20.08

230,908

25

2.48

2.98

2,321,198

20.08

2154,539

50

4.96

2.98

2,321,198

20.08

2309,078

Pavem

entlifetime(years)

50.25

1.50

2,321,198

0.12

46,424

25

1.25

1.50

2,321,198

0.12

232,120

50

2.50

1.50

2,321,198

0.12

464,240

Noisereductionover

lifetime(dB)

50.07

0.39

2,321,198

0.08

30,949

25

0.33

0.39

2,321,198

0.08

154,747

50

0.65

0.39

2,321,198

0.08

309,493

PM

10correctionfactor

50.05

0.50

2,321,198

20.75

2174,090

25

0.25

0.50

2,321,198

20.75

2870,449

50

0.50

0.50

2,321,198

20.75

21,740,899

Notes:@RISK

calculationsenteredinto

Equation(2),Section2.3,for5,25and50%

increasesin

inputvalues.

Table

7.

Sensitivityanalysis–simulatednet

benefits

–DPAC11/16.

Input(k)

Changein

input(%

)Changein

input

SD

ofinput

SD

(net

benefit)

Rankingcoefficient

Changein

net

benefit

Totalinvestm

entcostsper

m2(EUR)

51.29

7.70

21,444,420

20.06

2215,558

25

6.45

7.70

21,444,420

20.06

21,077,791

50

12.90

7.70

21,444,420

20.06

22,155,582

Pavem

entlifetime(years)

50.10

0.60

21,444,420

0.06

214,444

25

0.50

0.60

21,444,420

0.06

1,072,221

50

1.00

0.60

21,444,420

0.06

2,144,442

Noisereductionover

lifetime(dB)

50.29

1.76

21,444,420

0.02

71,522

25

1.47

1.76

21,444,420

0.02

357,610

50

2.94

1.76

21,444,420

0.02

715,220

PM

10correctionfactor

50.04

0.85

21,444,420

20.11

2117,944

25

0.21

0.85

21,444,420

20.11

2589,722

50

0.43

0.85

21,444,420

20.11

21,749,443

Notes:@RISK

calculationsenteredinto

Equation(2),Section2.3,for5,25and50%

increasesin

inputvalues.

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PM10 relationship, from Lerfald (2008), with official

estimations of health effects from PM10 concentrations

(NIPH 2005) and official monetary valuation of these

effects (NPRA 2006b). Since the asphalt-wearing property

differs between pavement alternatives, particulate matter

seems to represent a potential health benefit element of

pavement choice, in addition to noise properties. If the

wearing property is improved and verified in terms of

impact on particulate matter and health effects, it will gain

even stronger importance in the socio-economic assess-

ment of road pavements.

It should also be noted that our analyses are quite

general, even if we apply measures and values that may fit

fairly well to the case of a ring road in Oslo, Norway. In

addition to adjacent dwellings and the number of affected

individuals, also topography, climate and other local

conditions will impact on the final health effects from road

noise and particulate matter (Morgan 2006). Some aspects

of our analysis are peculiar to Nordic conditions: the use of

studded tyres and the short technical lifetime of the

pavements. However, airborne particulatematter from road

transport is more universal, although combustion-gener-

ated fine particles will be more important than pavement

wearing (Harrison 2000, Thorpe and Harrison 2008). Our

analysis shows the potential impact of particulate matter in

societal economic assessment of pavements in aNorwegian

application, but the approach is still applicable to other

settings. It has shown the potential importance of extending

the societal economic analysis of pavement choice, taking

into account more effects in monetary terms. It is a task for

future research to extend economic analysis into other

applications relevant for pavement choice.

Acknowledgements

This study was funded by the Norwegian Public Roads

Administration, through the project ‘Environmentally

Friendly Pavements’ (project number 600740, responsi-

bility 65600). The draft version of the model for cost-

benefit analysis was developed in the project ‘Environ-

mental Noise’ (159459), under the programme ‘Forur-

ensninger: kilder, spredning, effekter og tiltak’ [‘Pollution:

Sources, Dispersion, Effects and Measures’] (PROFO),

funded by the Research Council of Norway. We are very

grateful for the helpful comments from two anonymous

referees to this journal, as well as for the constructive

contributions on pavement tests from Jostein Aksnes, Leif

Jørgen Bakløkk, Ragnar Evensen, Bjørn Ove Lerfald,

Camilla Nørbech and Nils Sigurd Uthus. All remaining

errors and omissions are entirely our own responsibility.

Notes

1. Road noise control can be regarded as a local public good.Noise reduction measures can be portioned out to certainlocalities (e.g. giving a preference to measures in areas withthe wealthier segment of the population), but all those living(or working or performing leisure activities) in these areascannot be denied the benefit of a less noisy environment –the noise reduction is a non-exclusive good to all thosewithin the locality. The noise reduction will also be non-rivalin the sense that any person’s ‘consumption’ of the reducednoise will not reduce other persons’ ‘consumption’ of thesame good within the locality (Hanley et al. 1997).

2. Elvik and Greibe (2005) concluded, from a meta-analysis of18 studies, that there were no statistically significant effectson road safety (and thus, crash costs) of changing to noise-reducing pavements (porous asphalts). Furthermore, regard-ing vehicle operation costs, Bendtsen (2004) did not findevidence for differences in rolling resistance and fuelconsumption between standard dense asphalts and porousasphalts. Also, additional external (environmental) effectsare probably limited and/or going in both directions. Berbeeet al. (1999) and Pagotto et al. (2000) argue that noise-reducing porous asphalt has an adsorption property allowinga more gradual run-off of water (limited peak flows andslower discharge) and a filtering effect. This could yield anadditional benefit that our analysis omits. Yet, the sameporous asphalts may be recycled to a slightly lesser degreethan dense asphalt types (Litzka et al. 1999, Pucher et al.

Figure 3. Cumulative probability function – simulated netbenefits – TSF8.

Figure 4. Cumulative probability function – simulated netbenefits – DPAC11/16.

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2004); and the gradual run-off may also imply morepollutants in the worn asphalts, although Descornet et al.(1998) found that the quantities of pollutants in worn porousasphalt were generally relatively low.

3. Porous asphalt has a high stone content and a grading thatprovides a high void content (.20%) – a feature thatincreases sound absorption. Double-layer porous asphaltconsists of two layers of porous asphalt: a coarse open-graded bottom layer and a finer textured top layer. Thin-layerasphalts are very thin and relatively open layers (,14%) thatare laid on a thick layer of polymer-modified bitumenemulsion (Morgan 2006).

4. The average double-layer porous asphalt (DPAC 11/16)represents different pavement types in terms of aggregatesize and void content in surface layers, but all with similarstone quality. The ‘best possible’ version (DPAC 11/16x)has a larger share of aggregate larger than 4mm and slightlyhigher void content than the other pavement types (Lerfald2008, Evensen 2009).

5. Compared to other measures to control road noise, the greatadvantage of noise-reducing pavements is the reduction of the(tyre/road) noise at the source (Sandberg 2001). The potentialreduction of noise levels will depend on speed levels, averageannual daily traffic (ADT) and this traffic’s composition oflight (andmedium) and heavy vehicles (Morgan 2006,Ch 11).The tyre/road noise (vs. propulsion noise) will be moredominant at higher speeds. Still, a pavement producing lesstyre/road noise may yield a noticeable effect on total vehiclenoise at speed levels as low as 40 km/h for light vehicles.Expected noise reduction from low-noise pavements, atdifferent speeds, is assessed either at test sites or at roadside,and the reference pavement is some standard dense asphalttype (Morgan 2006, Ch 4, Table 4.2). The CBA shouldprincipally also compare the profitability of noise-reducingasphalt to other possible noise-reducing measures, e.g. noisebarriers or facade insulation.

6. Test values from the Nordic ball mill test (NB) enter theestimation of a pavement wearing parameter: wearingparameter ¼ (NB / aggregate size) £ 100. The Nordic ballmill test is a wet abrasion test that determines resistance towear by abrasion from studded tyres (CEN 1998). The testhas shown high correlation with studded tyre wear, applyingdifferent pavement stone materials (Snilsberg 2008). Analternative test considered was the Troger test, wheremeasures of the resistance to studded tyres are based onasphalt dust formed from hammering asphalt cores by steelthreads, yielding a Troger value (Dk) defined as: Dk ¼Dm=rd; where Dm is the total particulate matter produced(lost) over a given period, and rd is the density (Raitanen2005, Lerfald 2008). Notwithstanding the test then type,yielding estimates of dust from different pavements, itshould be remarked that there is not necessarily very highcorrelation between the amount of (airborne) dust and theamount of PM10 (Snilsberg 2008) and it is the latter thatenters the cost-benefit analysis.

7. The Norwegian Institute of Public Health has applied thefollowing equation for estimating the change in healtheffects for a given area: PM ¼ D0 £ DRR £ DEx/10mg/m

3,where PM is the number of incidents caused by PM10

increase, D0 is the number of incidents, per year in the givenarea, DRR is the increase in relative risk, and DEx is theincrease (mg/m3) in PM10 (NIPH 2005, Snilsberg 2008).

8. The simulations will yield estimates of the expected values –the more the iterations, the closer we get to these expectedvalues; i.e. the simulation error (1) is proportional to the

number of iterations (N), 1 ¼ 3s=ffiffiffiffi

Np

; where s is thestandard deviation. Thus, increasing the number of iterationsabove 1000 will yield an error lower than 10%.

9. Earlier versions of the spreadsheet model were applied underthe EU-project SILVIA – ‘Sustainable Road Surfaces forTraffic Noise Control’ (Morgan 2006, Veisten et al. 2007a)and the Norwegian projects ‘Societal consequences and cost-benefit analyses of low-noise pavements’ (Veisten 2006) and“‘TORNADO” – a tool for strategic cost-benefit analyses ofnoise control measures and other environmental measures’(Veisten et al. 2007b).

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