Online Resource 3: Additional Methodological Details for
the Estimation of Impacts and Adaptation Costs for Roads
Climatic Change article:
Climate Change Risks to US Infrastructure: Impacts on roads,
bridges, coastal development, and urban drainage
James E. Neumann, Jason Price, Paul Chinowsky, Leonard Wright, Lindsay Ludwig, Richard
Streeter, Russell Jones, Joel B. Smith, William Perkins, Lesley Jantarasami, Jeremy Martinich
Corresponding author:
James E. Neumann
Industrial Economics, Inc.
jneumann@indecon.com
Additional Methodological Details for the Estimation of Impacts and
Adaptation Costs for Roads
The methodology for assessing adaptation costs for roads, as described in Chinowsky et al.
(2013), accounts for four specific effects: (1) rutting of paved roads from precipitation, (2)
rutting of paved roads caused by freeze-thaw cycles, (3) cracking of paved roads during periods
of high temperatures, and (4) erosion of unpaved roads from precipitation. Adaptation costs
related to precipitation and freeze-thaw were modeled based on evidence suggesting that these
stressors affect the frequency of periodic maintenance activities, which in turn affects annual
maintenance costs. In contrast, the approach for heat focuses on changes in pavement design
likely to result from climate change and the corresponding changes in re-paving costs.
Depending on the nature of the changes in climate projected for a given area, the analysis of
these effects may suggest a net cost or a net cost savings.
This analysis assumes that society will implement adaptation measures to avoid the adverse
impacts of climate change. Economically, the optimal adaptation strategy would involve
implementation of only those adaptation measures that yield a net benefit to society. That is,
adaptation options that require resources exceeding the value of the benefits gained (i.e, the value
of the climate change impacts avoided) would not be implemented. Thus, under the most
efficient adaptation strategy, the total costs borne by society would include the costs of
adaptation plus the value of residual damages remaining after the implementation of those
adaptation measures that result in positive net benefits.1 Due to the limited information available
on the value society places on avoiding road degradation, we do not explicitly model this
optimization process. Instead, we assume that adaptation measures will be implemented to
maintain the current level of service such that residual impacts are zero, and report adaptation
costs only.
To model adaptation costs for precipitation and freeze-thaw, we assessed the extent to which
these stressors affect the frequency of periodic maintenance activities for paved and unpaved
roads. Empirical evidence suggests that precipitation and freeze-thaw accelerate the rutting of
1
These residual damages include the impact of foregone investment in roads on economic output. For example, the
American Society of Civil Engineers (2011) reports that deficiencies in pavement and bridge conditions and
highway congestion reduced U.S. gross domestic product by $37 billion in 2010.
paved roads (N.D. Lea International, 1995 and U.S. DOT, 2006), while precipitation leads to
erosion of unpaved roads (Dubé et al., 2004). Changes in rutting for paved roads may affect how
frequently these roads are re-sealed2, while changes in erosion may affect the frequency with
which unpaved roads are reshaped. As the frequency of these maintenance practices increases
(decreases), annual maintenance costs will also increase (decrease). For example, if paved roads
are re-sealed every five years under current climate but are re-sealed every four years due to
climate change, this would imply that the proportion of roads re-sealed on an annual basis
increased from 20 percent under current climate to 25 percent with climate change. The cost of
adapting to climate change in this scenario is the cost of re-sealing an additional 5 percent of the
paved road network each year. Abstracting from this example, we estimate adaptation costs
related to precipitation and freeze thaw for each individual grid cell as follows:
1
(1) 𝐶𝐴𝑀 = (𝐹
𝑅𝐶
1
− 𝐹 ) 𝑀𝑇𝑈 × 𝑅𝑈
𝑅𝐵
Where CAM = adaptation costs associated with changes in maintenance frequency (i.e., change in
annual maintenance costs);
FRC = frequency of resealing paved roads (or reshaping unpaved roads) under the climate change
scenario (i.e., every FRC years);
FRB = frequency of resealing (or reshaping) under current baseline climate (once every 10 years);
MTU = total miles of paved (unpaved) road; and
RU = re-sealing (or reshaping) unit cost.
1
Under this approach, the expression (
𝐹𝑅𝐶
−
1
𝐹𝑅𝐵
) 𝑀𝑇𝑈 represents the change in the miles
of road re-sealed (or reshaped) on an annual basis. We obtained MTU from the DOT/Tele Atlas
inventory for each grid cell and derived separate estimates of the change in the frequency of re1
sealing (𝐹
𝑅𝐶
1
− 𝐹 ) for paved and unpaved roads.
𝑅𝐵
As indicated by Equation 1 above, the change in the frequency of re-sealing paved roads is
estimated based on the difference between the frequency of re-sealing under current climate and
2
Re-sealing is a routine maintenance practice to prolong the life of a pavement.
the re-sealing frequency with climate change. The frequency of re-sealing under current climate
(FRB) varies depending on the type of seal coat employed, road use, and other local conditions.
Based on literature review ranges and data from state departments of transportation, we assume
that paved roads are, on average, re-sealed every seven years under current climate.
We estimate the frequency of re-sealing with climate change (FRC) based on the impact of
precipitation and freeze-thaw on the condition of paved roads, as measured by the pavement
condition index (PCI).3 A road’s PCI rating is typically at or near 100 at the beginning of its
lifecycle but degrades over time prior to rehabilitation.4 The precise level of PCI degradation
occurring over a road’s lifecycle varies, but for the purposes of this analysis, we assume that
roads are rehabilitated once their PCI rating declines to 55, which represents the threshold
between good and fair pavement conditions. Applying this assumption, a road’s PCI rating
declines by approximately 45 points during its lifecycle prior to rehabilitation.
To assess the frequency of re-sealing with climate change, we assume that a maximum level of
PCI degradation is acceptable between re-sealings. The ratio of this maximum level of
degradation to the annual rate of PCI degradation with climate change represents the frequency
of re-sealing with climate change, as outlined in Equation 2. As indicated in the numerator of
the right-hand side of Equation 2, the maximum value of PCI degradation between re-sealings
may be derived from the annual rate of PCI degradation under current climate (RB) and the
frequency of re-sealing (FRB). In addition, as indicated in the denominator, the annual amount of
PCI degradation with climate change is estimated as the sum of PCI degradation under current
climate (RB) and the incremental change in PCI degradation associated with climate change (RC).
(2) 𝐹𝑅𝐶 =
(𝑅𝐵 ×𝐹𝑅𝐵 )
(𝑅𝐵 +𝑅𝐶 )
Where FRC = frequency of re-sealing with climate change (i.e., every FRC years);
FRB = frequency of re-sealing under current baseline climate (once every 7 years);
RB = annual rate of PCI degradation under current climate; and
3
Developed by the U.S. Army Corps of Engineers, the PCI is a numerical index of pavement condition ranging in value from 0
(poor condition) to 100 (excellent condition).
4
At the end of their lifecycle, roads undergo rehabilitation, which involves replacement of both the road base and the asphalt
overlay.
RC= incremental PCI degradation per year due to climate change.
The annual rate of PCI degradation with current climate (RB) is likely to vary, but we
approximate this value based on the typical PCI degradation occurring during a road’s lifecycle
(45 points, as indicated above) and the typical duration of a road’s lifecycle. Based upon prior
analyses by the U.S. Department of Transportation (2006) and N.D. Lea International (1995) in
which a road’s lifecycle was modeled over a period of 20 to 30 years, we assume a road lifespan
of 20 years for roads located in wet climates (average monthly precipitation exceeding 5 cm) or
areas that experience at least 50 freeze days per year.5,6 A lifecycle of 30 years is applied to
roads located in all other areas where precipitation and freeze-thaw stress are not as significant.
Averaging the 45-point PCI degradation occurring during a road’s lifecycle over the 20 to 30year lifespan range, we estimate that annual PCI degradation under current climate (RB) is 1.5
points in wet areas and moderate or high-freeze areas7 and 2.25 points in other areas. Given this
range of values for RB and a re-sealing frequency (FRB) of seven years under current climate (see
above), we estimate that the acceptable level of PCI degradation between re-sealings (the
numerator in Equation 2) ranges from 10.5 to 15.75 PCI points.
Calculating the frequency of re-sealing with climate change (FRC) based on Equation 2 requires
estimation of the extent to which precipitation and freeze-thaw affect annual PCI degradation
(RC). To estimate RC, we draw from prior studies examining the rutting associated with
precipitation and freeze-thaw, and subsequently assess the changes in PCI associated with these
rutting impacts. N.D. Lea International (1995) indicates that rut depth over a road’s lifecycle
increases by approximately 3 millimeters with every 10 centimeter increase in mean monthly
rainfall. In addition, U.S. DOT (2006) shows that rut depths in moderate freeze areas (50 to 400
freeze days per year) and high-freeze areas (more than 400 freeze days per year) are
5
The number of freeze days for a given day is equal to the number of degrees below freezing. For example, if the temperature is
-4 degrees one day and -5 degrees the next, this would represent nine freeze days.
6
U.S. DOT (2006) examines the effects of freeze-thaw on rutting over a 20-year period. We therefore assume a 20-year lifespan
for roads located in moderate or high-freeze areas (i.e., areas where the annual number of freeze days exceeds 50). We also
assume a 20-year lifecycle for roads in wet areas based on findings in N.D. Lea International (1995) indicating that road wear is
more significant in areas where precipitation exceeds 5 cm per month.
7
Areas where the annual number of freeze days ranges from 50 to 400 are classified as moderate freeze areas, while areas with
more than 400 freeze days are considered high freeze areas.
approximately 3.25 and 2.75 millimeters higher, respectively, than in no-freeze zones. From
these figures, we also infer that lifetime rutting is approximately 0.5 millimeters higher in
moderate freeze areas than in high freeze areas. These values and those for precipitation
represent the incremental rutting occurring over a road’s lifetime. To convert to annual values,
these estimates are divided by a road’s lifespan (i.e., 20 or 30 years).
These rutting effects are translated into PCI changes based on the level of PCI degradation per
millimeter of rut depth. To derive this relationship, we use data for the total PCI degradation and
rutting occurring over a road’s lifecycle. As indicated above, we assume PCI degradation of 45
points during a road’s lifespan. Measurements of the degree of rutting occurring over a road’s
lifecycle vary across the identified studies. N.D. Lea International (1995) estimates 8
millimeters of rutting over a road’s lifecycle, while U.S. DOT (2006) estimates 5.75 millimeters
of rutting. Based on these values, PCI degradation of 5.625 points per millimeter of rutting is
estimated related to precipitation and 7.83 PCI points per millimeter of rutting related to freezethaw.8
Based on the estimated change in re-sealing frequency for paved roads, we estimate the
corresponding change in re-sealing costs assuming a unit re-sealing cost of $13,500 per lane
mile, as derived from Yamada and Dimas (1999).
Changes in precipitation patterns associated with climate change may affect the erosion of
unpaved roads and the frequency with which transportation agencies re-shape these roads. To
estimate this change in frequency, we follow an approach similar to that outlined above for
paved roads. More specifically, we estimate, by grid cell, the cumulative level of unpaved road
erosion that occurs during the typical reshaping cycle under current climate. Treating this level
of erosion as the maximum amount of erosion acceptable between reshapings, we then calculate
how quickly this cumulative amount of erosion occurs under each climate change scenario—this
represents our estimate of the frequency of reshaping with climate change.
8
We use two separate values for PCI degradation per millimeter of rutting to normalize for any differences between the N.D. Lea
International (1995) and U.S. DOT (2006) rutting measurement methods.
To apply this method, the annual rate of erosion is first estimated under current climate by grid
cell. These values are then multiplied by the frequency of reshaping under current climate to
derive estimates of the cumulative level of erosion that occurs between each reshaping. These
cumulative values are represented as the numerator in Equation 3. In calculating the numerator
value, we assume that the average frequency of reshaping under current climate (FRB) is once
every 10 years.9 Dividing the level of erosion that occurs between reshapings (FRB × EAB) by the
annual erosion that occurs with climate change (EAC) yields the frequency of re-shaping under
each climate change scenario (FRC).
(3) 𝐹𝑅𝐶 =
𝐹𝑅𝐵 ×𝐸𝐴𝐵
𝐸𝐴𝐶
Where FRC = frequency of reshaping under climate change scenario;
FRB = frequency of reshaping under current climate (i.e., every FRB years);
EAB = annual erosion with current baseline climate (tons/acre/yr) , and
EAC = annual erosion (tons/acre/yr) with climate change
Calculation of the EAB and EAC terms in Equation 3 requires estimation of annual unpaved road
erosion. Erosion is estimated as a function of precipitation, based on the findings of Dubé et al.
(2004) and Sheridan and Noske (2005):10
(4) 𝐸𝐴 = 0.25(0.0159𝑃𝐴1.5016 )
Where EA = annual erosion (tons/acre/yr), and
PA = annual precipitation (inches).
After estimating the change in reshaping frequency, the annual change in re-shaping costs is
estimated based on a unit re-shaping cost of $24,200 per lane mile derived from R.S. Means
(2008).11
To assess the cost of adapting roadways to changes in temperature, we examine the implications
of climate change for the design specifications of asphalt pavements. In areas where maximum
9
See Wyoming Technology Transfer Center (2010).
10
Dubé et al. (2004) estimate erosion as a function of annual precipitation for native-surfaced unpaved roads. Most unpaved
roads in the U.S., however, are gravel roads (Selim and Skorseth, 2001). Therefore, using data from Sheridan and Noske (2005),
we incorporated an adjustment parameter into the Dubé et al. (2004) erosion equation to so that it would reflect gravel roads
rather than native-surfaced unpaved roads.
11
This estimate reflects the costs of re-grading plus the cost of a two-inch top layer. We convert the costs from R.S. Means
(2008) to year 2010 dollars using the GDP deflator.
temperatures increase due to climate change, asphalt pavements will become susceptible to
increased cracking. This impact may be avoided by using a different binder in the surface
asphalt mix.12 To model adaptation through the use of different pavement binders, guidelines for
Superpave are utilized, which specify binder performance grades based on the maximum 7-day
pavement temperature during the course of the year.13 The thresholds between Superpave
performance grades occur in 6-degree increments between pavement temperatures of 46 and 82
degrees Centigrade, as shown in Table A.
Table 1. Superpave binder performance
grades
7-day Maximum
Pavement Temperature
Performance Grade (˚C)
PG-46
46
PG-52
52
PG-58
58
PG-64
64
PG-70
70
PG-76
76
PG-82
82
Source: Washington State DOT (undated)
The cost (savings) of adapting paved roads to higher (lower) temperatures is estimated as the
incremental cost of re-paving with a higher (lower) grade binder. As an initial step in this
process, the daily pavement temperature is estimated for each 0.5 degree by 0.5 degree grid cell
under current climate and under each climate change scenario using the following equation from
Lavin (2003):
(5) 𝑇𝑃 = 0.9545(𝑇𝐴 − 0.00618𝐿2 + 0.2289𝐿 + 42.2) − 17.78
Where TP = Pavement temperature (˚C)
TA = Air temperature (˚C)
L = Latitude
12
In the short term, transportation authorities may respond to increased cracking through more intensive maintenance, but an
analysis of temporary changes in maintenance practices was not feasible with the available data. Such an analysis would require
estimation of the change in cracking associated with increased (or reduced) temperatures. Although the literature includes studies
examining the causes of cracking, these studies estimate cracking as a function of temperature and several other variables (e.g.,
traffic load) that we are not able to account for in this national analysis.
13
"Superpave" is a broad term for the asphalt research results from the 1987 - 1993 Strategic Highway Research Program
(SHRP). Superpave consists of (1) an asphalt binder specification, (2) a hot mix asphalt (HMA) design method and (3) HMA
tests and performance prediction models.
Using daily pavement temperatures derived from Equation 5, the binder performance grade
appropriate to each grid cell is identified under current climate and with climate change. As
indicated above, the Superpave guidelines specify performance grades based on the maximum 7day pavement temperature during the course of the year. The maximum daily temperature is
used as an approximation of this value.
After specifying the binder performance grade appropriate to each grid cell under current climate
and with climate change, we identified grid cells where the performance grade is likely to change
due to climate change. For each of these areas, the incremental annual cost of re-paving is
estimated as a function of the total lane miles scheduled to be repaved and the incremental unit
cost of re-paving, as represented below:
1
(6) 𝐶𝑅 = 𝑀𝑇 (𝐹 ) (𝐵𝐶 − 𝐵𝐵 )
𝑃
Where CR = Incremental annual cost of re-paving;
MT= Total miles of road;
FP = Frequency of re-paving (i.e., every FP years);
BC = Asphalt unit cost with binder required with climate change; and
BB = Asphalt unit cost with binder required under baseline current climate.
The term MT(1/FP) in Equation 6 represents the lane miles of road scheduled to be repaved each
year, while the term BC - BB represents the change in the per unit cost of asphalt when switching
from one performance grade to another.
In applying Equation 6, the frequency of re-paving (FP) is set equal to once every 10 years,
consistent with observed practice.14,15
We estimate the cost of asphalts with varying binder performance grades (e.g., BC and BB) based
on construction project data from the Colorado Department of Transportation (2010), as
summarized in Table 1. While the costs of asphalt vary regionally, the Colorado data provided a
14
See Sacramento Area Council of Governments (2006).
The frequency of re-paving a road is not the same as its lifespan. During a road’s lifespan (i.e., between rehabilitations when
the asphalt and road base are replaced) the asphalt surface layer is replaced on a periodic basis.
15
complete set of asphalt costs, including cost variance by performance grade. This level of detail
was necessary to implement the approach summarized in Equation 6. Based on asphalt pricing
data from Argus (2011), asphalt costs in Colorado are 10 to 15 percent lower than in many other
parts of the U.S. Our adaptation cost estimates associated with switching to higher grade binders
are therefore likely to be conservative.
Table 1.
average costs of asphalt by performance grade
Cost (year 2010$ per lane
Performance Grade
mile)
PG-46
$197,000
PG-52
$210,000
PG-58
$225,000
PG-64
$241,000
PG-70
$258,000
PG-76
$276,000
PG-82
$295,000
Source: Derived from Colorado DOT (2010)
Figure 1: Distribution of annual adaptation costs for roads by cost type for the climate
projections spanning the range of precipitation futures in the US (billions of 2005$).
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