d i s c u s s i o n pa p e r n u m B e r 2 august 2010
d e v e l o p m e n t a n d c l i m at e c h a n g e d i s c u s s i o n pa p e r s 1
56660
d e v e l o p m e n t a n d c l i m a t e c h a n g e
The Costs of Adapting to
Climate Change for Infrastructure
d i s c u s s i o n pa p e r n u m B e r 2 august 2010
d e v e l o p m e n t a n d c l i m a t e c h a n g e
The Costs of Adapting to
Climate Change for Infrastructure
*gordon hughes
**paul chinowsky
***Ken strzepek
Note: This paper is based upon work that has been commissioned by the World Bank as part of the Economics of Adaptation to
Climate Change study. The results reported in the paper are preliminary and subject to revision. The analysis, results, and views
expressed in the paper are those of the authors alone and do not represent the position of the World Bank or any of its member
countries.
* Department of Economics, University of Edinburgh, UK
** Department of Civil , Environmental and Architectural Engineering, University of Colorado, Boulder, CO
*** MIT Joint Program on the Science and Policy of Global Change, Cambridge, MA
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taBle oF contents
abstract vii
section 1. setting the scene 1
section 2. data 5
section 3. climate change 5
section 4. econometric specifications 8
section 5. the effects of climate on demand for infrastructure 13
section 6. calculating the cost of adaptation 30
section 7. estimates of the costs of adaptation 34
section 8. conclusion 43
references 43
Tables
1. correlation matrix of climate variables and historic demographic indicators 7
2. projection equations for electricity generating capacity, fixed telephone lines,
and electricity network coverage 13
3. projection equations for municipal and industrial water demand 17
4. projection equations for water and sewer networks 19
5. projection equations for roads 20
6. projection equations for other transport 23
7. projection equations for health 25
8. projection equations for social infrastructure 27
9. projection equations for average household size 31
10. delta-p costs of adaptation by category and country class for 2010�50
(us$ billion per year at 2005 prices, no discounting) 34
iv t h e c os ts oF ad a p tin g to c limate c h an ge For in Fr as tr u c tu r e
11. delta-p costs of adaptation by infrastructure category and World Bank region for 2010�50
(us$ billion per year at 2005 prices, no discounting) 36
12. delta-p costs of adaptation by decade and World Bank region for all infrastructure
(us$ billion per year at 2005 prices, no discounting) 38
13. total costs of adaptation by infrastructure category and country class for 2010�50
(us$ billion per year at 2005 prices, no discounting) 40
14. total costs of adaptation by infrastructure category and region for 2010�50
(us$ billion per year at 2005 prices, no discounting) 41
appendix 1. derivation of the climate dose-response relationships 45
references 51
d e v e l o p m e n t a n d c l i m at e c h a n g e d i s c u s s i o n pa p e r s vii
aBstract
An approach to estimating the costs of adapting to costs. The second component measures the effect of
climate change is presented along with results for major climate changes on the long-run demand for infrastruc-
components of infrastructure. The analysis separates the ture. The results indicate that the price/cost element is
price/cost and quantity effects of climate change. The usually less than 1 percent of baseline costs, while the
first component measures how climate change alters the quantity effect may be negative for many countries.
cost of a baseline program of infrastructure develop-
ment via changes in design standards and operating
d e v e l o p m e n t a n d c l i m at e c h a n g e d i s c u s s i o n pa p e r s 1
1. Setting the Scene benchmark that depends upon factors such as popula-
tion and income.
This paper presents the results of a global analysis of
the costs of adapting infrastructure to climate change In the period from t to t+1, for example from 2010 to
over the period from 2010 to 2050. The analysis was 2015, the country will have to invest in order to meet
carried out as part of the World Bank's Economics of the efficient level of infrastructure in t+1 and to replace
Adaptation to Climate Change study. In this context, infrastructure in situ at date t, which reaches the end of
infrastructure has been given a rather broad definition. its useful life during the period. Thus, the total value of
It includes the usual types of infrastructure services, investment in infrastructure of type i in country j and
including transport (especially roads, rail, and ports), period t is
electricity, water and sanitation, and communications.1
In addition, urban and social infrastructure such as I ijt = Cijt [Qijt +1 - Qijt + Rijt ] (1)
urban drainage, urban housing, health and educational
facilities (both rural and urban), and general public
where Cijt is the unit cost of investment and Rijt is the
buildings have been included.
quantity of existing infrastructure of type i that has to
be replaced during the period. The change in the total
The basic approach is extremely simple. For any coun-
cost of infrastructure investment may be expressed in
try j and date t (t = 2010, 2015, ..., 2050), we start from
terms of the total differential of (1) with respect to the
the assumption that there is some "efficient" level of
relevant climate variables that affect either unit costs or
provision of infrastructure of type i, which will be
efficient levels of provision for infrastructure of type i:
denoted by Qijt . The efficient level of infrastructure is
that which would be reached if the country had invested
I ijt = Cijt [Qijt +1 - Qijt + Rijt ] + (Cijt + Cijt )[ (2) +1 - Qijt + Ri
Qijt
up to the point at which the marginal benefits of addi-
I marginal [Qijt +1 - Q
tional infrastructure just cover the ijt = Cijtcosts--bothijt + Rijt ] + (Cijt + Cijt )[ Qijt +1 - Qijt + Rijt ]
capital and maintenance--of increasing the stock of
infrastructure. It is often argued that developing coun- An equivalent equation may be derived for the costs of
tries tend to underinvest in infrastructure and that the operating and maintaining infrastructure. In the
extent of underinvestment is particularly large for the discussion that follows, the first part of the right-hand
poorest countries (AICD 2009). This is an important side of equation (2) is referred to as the Delta-P
development issue, which is not directly related to component of the cost of adaptation, while the second
climate change. Hence, the approach attempts to strip part is referred to as the Delta-Q component. These
out the effects of country differences in their actual components themselves cover a number of ways in
provision of infrastructure by establishing a common which climate change may cause changes in the costs or
quantities of providing infrastructure services.
1 Limitations on the availability of comparable data meant that it was
not possible to cover gas networks in the study. However, the costs of Delta-P. At the simplest level, changes in temperature,
adaptation are likely to be minimal apart from any impacts on the level precipitation, or other climate variables may alter the
of demand, which are likely to be similar to the pattern for electricity.
2 t h e c os ts oF ad a p tin g to c limate c h an ge For in Fr as tr u c tu r e
direct cost of constructing infrastructure to a standard climate and for a changing climate. A similar
specification. For example, seasonal weather variations exercise may be carried out for operating, mainte-
can increase the costs of building. However, this is a nance, and replacement costs in order to calculate
minor factor. More important is the impact of climate the increment in annualized infrastructure costs as
change on the design standards that are applied in order a consequence of climate change.
to maintain the quality of infrastructure services
provided by a unit of infrastructure, such as a kilometer b. Delta-Q. The quantities of infrastructure assets
of paved road or a fixed telephone connection (See required (holding income constant) will change as
Canadian Standards Association 2006 for a discussion a consequence of different climatic conditions.
of this issue). Again, this has two dimensions. The first is that
climate change may change the level or composi-
a. Changes in the frequency and/or the severity of tion of demand for energy, transport, and water at
storms, flooding, and other extreme weather given levels of income, so we need to calculate the
events may compromise the performance of infra- net impact of these changes in terms of capital
structure designed to existing standards. Hence, it and operating costs. The second is that climate
is common to refer to "climate proofing" invest- change will mean that countries have to invest in
ments or ensuring "climate resilience." The study specific additional assets in order to maintain
starts from the basis that design standards should specific standards of protection for non-infrastruc-
be adjusted so as to deliver the same level of ture activities.
performance as would have applied if climate
change had not occurred. Thus, if roads or build- The Delta-P dimension of the study is uncontroversial
ings are currently constructed to withstand a 1-in- in principle, though more or less difficult in practice.
50 or 1-in-100-year flood or wind storm, then the Various organizations have made broad brush estimates
same design standard should apply, but under the of the cost of "climate proofing" existing investment
circumstances of a changed frequency or severity programs in developing countries (UNFCCC 2007;
of those events. The changes in the unit costs-- McGray et al. 2008). Typically, the analysis starts from
Cijt --represent the costs of building infrastruc- a baseline program in investment by time of infrastruc-
ture that delivers the same level of performance in ture. Then, an estimate is made of the percentage
the face of different climatic stresses. The deriva- increase in unit costs required to ensure that invest-
tion of the cost changes, expressed as dose- ments are resilient to climate change.
response relationships for different climate
stressors, are described in Appendix 1. The dose- One problem with the "climate proofing" approach
response functions are applied to estimates of the concerns the investment program to which the cost of
average values of climate variables under a climate proofing should be applied. For some sectors or
scenario of a stable climate and alternative scenar- countries/regions, it is possible to start from a detailed
ios for climate change by country.2 This gives a inventory of infrastructure assets and then to ask what
series of cost increases--at constant 2005 prices-- investments will be required to meet future demand for
by type of infrastructure, country, and time period. infrastructure services. The best example of this
When applied to the baseline projection of infra- approach is a study of the costs of adaptation to climate
structure demand, we obtain the Delta-Q cost of change in Alaska (Larsen et al. 2008). However, this
adaptation; that is, the difference between the cost type of exercise requires an inventory of infrastructure
of the baseline investment program for a stable assets and it does not take account of future investment
in infrastructure.
2 Most climate models generate projections for 2� grid squares. For this
study, these projections have been downscaled to 0.5� grid squares In the case of developing countries, many institutions
and then population-weighted averages of the grid square values have that are concerned with adaptation to climate change
been computed for each country. Thus, references to climate variables
by country in this paper should be construed as referring to the popu- for infrastructure draw a distinction between (a) the
lation-weighted averages of, say, precipitation for the various grid cost of eliminating the "development deficit,"--that is,
squares that cover the country.
d e v e l o p m e n t a n d c l i m at e c h a n g e d i s c u s s i o n pa p e r s 3
the gap between the infrastructure that a country Relying either upon wish lists or on the envelope of
"ought" to have and the infrastructure that it actually what other countries at similar incomes have invested
has--and (b) the cost of adapting to climate change on ignores the trade-offs that all governments have to
the assumption that the country has an efficient level of make. Even if external assistance is available to fund
infrastructure. The former is seen as a development capital projects, it is common experience that lack of
problem, while the latter is a climate change problem. funds for operations and maintenance may lead to rapid
Even though it is understood that money is fungible, deterioration in the services provided by stocks of infra-
the two elements of total investment in infrastructure structure assets.
might be financed out of different pots of money.
Thus, it may be argued that the analysis should not be
The corollary of this distinction between adaptation and based on some notional "efficient" level of infrastructure,
the development budget is that the baseline program for but should start from the actual levels and growth of
infrastructure investment used in constructing the infrastructure based on decisions that reflect real
Delta-Q should not be derived from actual or planned constraints on budgets and the associated priorities. To
investment in infrastructure. Instead, it should reflect examine whether the distinction is important in prac-
the "efficient" demand for infrastructure.3 This is no tice, the full study has used two sets of baseline projec-
simple task. The World Bank has recently completed a tions of demand for infrastructure. The "frontier"
detailed assessment of infrastructure investment needs projection is derived by using frontier methods of esti-
in 22 African countries, assuming a catch-up from mation to estimate econometric equations that charac-
actual to efficient provision over a decade to 2020 terize the envelope of infrastructure demand given
(AICD 2009). That exercise involved very substantial exogenous variables such as income, population, urban-
work and cannot be extended to all countries in a short ization, etc. This is intended to provide an estimate of
period. Instead, the analysis has to be based on an the "efficient" level of infrastructure demand as envis-
econometric model that can be used to construct projec- aged in discussions of the development gap. In contrast,
tions of the efficient demand for infrastructure up to the "panel" projection uses conventional projections
2050. derived from econometric estimates of the average rela-
tionship between infrastructure and the exogenous
While the principle of drawing a distinction between variables.
the "development deficit" and adaptation to climate
change is widely followed in international negotiations, The difference between the Delta-P estimates using the
many economists consider that the distinction is either two sets of baseline projections is not as large as some
unworkable in practice or simply wrong as a matter of might expect. There is an important reason for this.
economic logic. The reason is that most assessments of We find that the relative gap between the frontier and
the "efficient" demand for infrastructure ignore the panel projections tends to narrow, because there appears
question of resources. A specific country might wish to to be convergence toward standard patterns of infra-
have more roads, schools, or hospitals than the stocks structure provision. Further, the income elasticity of
that are currently in situ, and the rest of the world demand for infrastructure is generally less than 1 for
might agree that this would be a desirable goal. But, the frontier demand equations and is lower than the
this is nothing more than a wish list independent of the equivalent income elasticities for the average demand
resources that are available. With limited resources equations. For the frontier baselines projection, these
some countries may choose to spend their funds on factors lead to a lower level of new investment in infra-
providing better roads or more healthcare services. structure, but a higher level of expenditure on replacing
and maintaining the initial level of infrastructure.
Under the panel baseline projection, lower levels of
3 This paper will refer to the (efficient) demand for infrastructure and spending on replacement and maintenance are offset by
will not attempt to address the question of how far the actual stocks of
infrastructure are constrained by the supply of infrastructure assets. In higher spending on new investment. Depending on the
effect, we assume that (a) we can identify an equation describing the initial development gap and the timing of new invest-
long-run demand for infrastructure, and (b) supply constraints are not
relevant when projecting the future investment program in calculating ment, it is possible--though not usual--for the cost of
adaptation costs.
4 t h e c os ts oF ad a p tin g to c limate c h an ge For in Fr as tr u c tu r e
adaptation to be larger for the average baseline than for will influence the nature of investment in roads. There
the frontier baseline. are more complex but potentially larger effects operat-
ing through the economic geography of urban life,
In this paper we will focus exclusively upon the panel industry, and commerce; that is, in the ways in which
projections derived from panel data models rather than we organize economic activity in space. Small changes
the frontier models. This is consistent with the view may have significant consequences for the level of
that the distinction between the development deficit investment in infrastructure.
and the adaptation deficit is difficult to draw under the
best of circumstances and may not be useful in practical While the principle that climate change may affect the
terms. demand for infrastructure seems straightforward, the
task of estimating the Delta-Q costs of adaptation is
The second, Delta-Q, aspect of this work concerns the much more difficult for two general reasons.
impact of changes in climate on the demand for infra-
structure. To approach this issue, we have to consider a. Many of the impacts of climate on demand for
the mechanisms by which changes in climate may affect infrastructure are long term in nature. This may
the demand for infrastructure and how we might iden- not be true for electricity, but any influence of
tify these consequences. For example, it is generally climate on the demand for roads will operate via
accepted that demand for electricity depends upon the path of economic development over a period
climate in general, but it is not so easy to identify the of one, two, or many decades. There are two
key climate parameters when estimating the demand for consequences. First, we should not think of the
electricity or for electricity-generating capacity. Note Delta-Q component of the costs of adaptation as
that even these two variables may be subject to different arising on a regular schedule every five years. The
influences because the seasonal or diurnal pattern of calculation merely identifies additions to and
electricity demand is strongly influenced by climate.4 subtractions from a liability (or asset) that will
Part of the difficulty is that the outcome depends upon materialize in future as economic activity adjusts
the relative weights assigned to different factors. An to the changes in climate that are taking place.
increase in average temperatures will lead to less Second, in planning for future infrastructure
demand for heating in the colder seasons but more development, governments need to consider how
demand for cooling in the warmer seasons. The overall climate change may affect the amount and type of
direction of change is not easy to predict and is likely to infrastructure that is required if it will influence
depend upon the way in which we set up the problem. future patterns of economic activity.
Electricity is simple to think about by comparison with b. In practice, there is no way of examining the
roads or other transport infrastructure because there is empirical impact of climate on the demand for
an intuitive sense of the mechanisms involved in a rela- infrastructure other than through some form of
tionship between climate variables and the stock of panel data analysis--pooling data for countries,
electricity-generating capacity. But it would be wrong regions, states, or other geographical units over
simply to impose the assumption that climate has no time. Inevitably, climate is a cross-sectional vari-
effect on the demand for roads. Patently, climate vari- able (since year-to-year variations are weather),
ables do affect the structure of economic activity hold- which may easily be confounded with other cross-
ing other factors constant --for example, through the section fixed effects. This has prompted various
level and composition of agricultural output--and this criticisms of the Ricardian approach to identifying
the impact of climate change on agriculture or
GDP on the grounds that climate variables are
4 There are also limitations on what one can obtain from climate projec-
tions. For example, it is conventional to include degree-days as a cli-
acting as a proxy for non-climate factors such as
mate variable in equations predicting energy demand because of institutions. Some economists draw the conclu-
heating requirements. The number of heating degree-days for a par-
ticular location is calculated from the truncated distribution of temper-
sion that climate variables should not be used in
atures below some threshold--often 18�C--either on an hourly or a this way. We do not accept this view, since it
daily basis.
d e v e l o p m e n t a n d c l i m at e c h a n g e d i s c u s s i o n pa p e r s 5
closes off any possibility of estimating the impact The WDI data has been supplemented with data on
of climate change on overall demand for infra- infrastructure availability from a wide variety of sources,
structure. Instead, we have carried an extensive including other international organizations (FAO, ITU,
econometric analysis of the role of climate vari- WHO, UNICEF, UPU), official country data (espe-
ables in modeling the demand for infrastructure. cially census data), and various systematic surveys such
The details are technical and take up a large as Demographic and Health Surveys (DHS) and Living
amount of space, so that they are reported in a Standards Measurement Surveys (LSMS), which are
separate paper (Hughes 2010). The issues and broadly consistent across countries. Even so, the final
results are summarized in sections 4 and 5 below. dataset is very patchy in terms of coverage, especially for
Our results suggest that the demand for some earlier periods. The panels are unbalanced and there are
categories of infrastructure is affected by different many missing values for intermediate years. Thus, it is
climate variables with important interactions with not possible to make use of econometric specifications
income per capita and urbanization. involving autoregressive or similar errors over time.
The results of our econometric analysis suggest that the A further remark concerns the nature of the data relat-
absolute magnitude of the Delta-Q component of adap- ing to different types of infrastructure. In a few cases,
tation should not be ignored in the long run. On the we have direct measures of the quantity of infrastruc-
other hand, the Delta-P component is much more ture assets--for example, kilometers of paved or all
predictable as a basis for discussing plans for adapting roads, kilometers of rail track, MW of generating capac-
to climate change. For these reasons, our detailed esti- ity. More commonly we have to rely upon measures of
mates of the costs of adaptation by 5-year period up to infrastructure output--for example, numbers of house-
2050 concentrate on the Delta-P component, while the holds connected to electricity, water, or sewer systems.
Delta-Q estimates are presented as indicative estimates In practice, the efficient levels of infrastructure assets
for the whole period. are closely linked to these output or input variables, so
we believe that it is reasonable to base our projections
on an analysis of these infrastructure indicators.
2. Data
The core data used in this study is the World 3. climate change
Development Indicators (WDI) database published in
2008 by the World Bank, which provides panel data for Describing the historic climate in a manner that is
up to 168 countries and the years 1960 to 2006. The compatible with macroeconomic data is far from
year 2005 is treated as the base year for all of our esti- straightforward without any of the complications of
mation. Our work relies on the 2008 version of the projecting climate change into the second half of the
database. One crucial consequence is that the purchas- 21st century. The literature on the influence of climate
ing power parity estimates of GDP per person rely on a on economic variables has tended to rely upon average
version of the 2005 ICP baseline due to appear as Penn values of climate variables, primarily temperature,
World Tables (PWT) Version 7. These estimates cover measured for the capital city of the country. The classic
the period 1980�2007 for a large set of countries. They dataset is the data compiled by NCAR--NOAA's
have been extended backwards to 1960 by splicing esti- National Center for Atmospheric Research in Boulder,
mates from PWT Version 6.2, which uses the 2000 Colorado--for weather stations around the world iden-
ICP baseline. Country gaps have been filled by the tified by their World Meteorological Organization
standard approach of using a quadratic equation linking reference code. The difficulty with this dataset is that
GDP per person in constant (2000) USD at market there is no consistency across stations in the data that is
exchange rates to GDP per person at constant (2005) reported. We have examined average data for capital
PPP exchange rates. cities derived from weather stations in or near the capi-
tal--including, for example, nearby airports. This is
6 t h e c os ts oF ad a p tin g to c limate c h an ge For in Fr as tr u c tu r e
used to obtain an average elevation for the capital city, weighted and inverse population-weighted climate vari-
but there are too many missing values to rely upon the ables are shown in Table 1 along with correlations with
climate variables for our econometric analysis. historic demographic variables used as instruments for
institutional development as discussed in Section 4.
The Climate Research Unit at the University of East
Anglia has compiled a series of historic weather data for The primary climate variables used in the econometric
0.5 degree grid squares for land areas of the globe. analyses are the two weighted means for (a) annual
Summary statistics have been computed for each grid average temperature (computed as the average of
cell for monthly average, maximum and minimum monthly average temperatures) in �C; (b) total annual
temperatures (in degrees C), and precipitation (in mm) precipitation (computed as the sum of monthly average
for the period 1901�2002. The distribution of tempera- precipitation); (c) the temperature range (the average
tures is generally accepted as being well-approximated maximum temperature in the hottest month, the aver-
by the normal distribution, so it was sufficient to age minimum temperature in the coldest month); and
compute the mean and standard deviation for each grid (d) the precipitation range (average precipitation in the
cell. For precipitation, the distribution is closer to the wettest month, average precipitation in the driest
log-normal, so the mean and standard deviation of month).
ln(precipitation+1mm) were calculated in addition to
the mean of precipitation.5 One point to note is that annual average temperature
measured in degrees C is negative or very small in a
Country estimates of the climate variables were number of countries, especially for the inverse popula-
constructed using grid cell means for monthly mean, tion-weighted means. Because of the use of the loga-
maximum, and minimum temperatures and precipita- rithmic transform, it is necessary either to exclude
tion. The primary variables are population-weighted countries with extreme temperatures or to apply some
averages using the population in each country in each linear shift to temperatures. The transformation
grid cell to weight the grid-cell means, thus reflecting adopted was to add 40�C to all temperatures. This
the average exposure for the population of each coun- value reflects the range from the minimum value of the
try.6 Alternative sets of country means weighted by (a) monthly minimum temperature (-29.1�C) and the
the land areas in each cell, and (b) the inverse of popu- maximum value of the monthly maximum temperature
lation in each cell were also constructed. The reason for (+46.9�C). Of course, the shift has no effect on the
doing this is linked to the demand for transport and temperature range.
other types of hard infrastructure. Consider a country
such as Australia. The population is concentrated in The choice and use of climate projections to 2050 and
the coastal areas of the continent, while the interior-- beyond is considerably more complex. Global climate
with very different climatic conditions--is very thinly models (GCMs) are programmed to produce projec-
populated. So the population-weighted averages will tions of different variables for different time periods.
reflect the climate on the coast whereas the inverse At a micro scale, there are large differences between the
population-weighted averages reflect the climate in the results generated by the various models, so that it is
interior, while the area-weighted averages fall in necessary to be very careful about relying upon a single
between.7 The correlations between the population- model. The standard deviation of projections for any
one grid cell is typically large relative to the mean value
of the projected change up to 2050 or even 2100.
5 The shift of +1mm is required because precipitation is zero for many Further, the problem is more serious than simple
months at some grid squares, which would generate missing values
without the shift.
models may suggest. Our econometric models suggest
6 There is one complication. Just over 10 percent of grid cells cover
more than one country, but the data only provide the land area of each
country in each grid cell plus total population in the grid cell. It is,
ed and area-weighted means instead of or in addition to the popula-
therefore, necessary to assume that population density is uniform over
tion-weighted means improves the performance of our equations. In
these grid cells so that population is split between countries in the
all of the cases that we have examined, the area-weighted climate vari-
same proportion as land area.
ables are dominated by the inverse population-weighted (ipop) climate
7 We have tested whether using either the inverse-population-weight- variables.
d e v e l o p m e n t a n d c l i m at e c h a n g e d i s c u s s i o n pa p e r s 7
table 1. correlation matrix of climate variableS anD hiStoric Demographic
inDicatorS
population-weighted climate inverse population-weighted climate
Average Temper- Precipi- Average Temper- Precipi-
temper- Precipi- ature tation temper- Precipi- ature tation Birth rate
ature tation range range ature tation range range 1950�54
Population-weighted
climate
average temperature 1.000
precipitation 0.229 1.000
temperature range -0.656 -0.650 1.000
precipitation range 0.600 0.774 -0.618 1.000
Inverse population-
weighted climate
average temperature 0.830 0.151 -0.630 0.433 1.000
precipitation 0.083 0.811 -0.576 0.525 0.070
temperature range -0.563 -0.612 0.943 -0.526 -0.598 -0.646 1.000
precipitation range 0.373 0.789 -0.630 0.775 0.283 0.885 -0.631 1.000
Historic demographic
indicators
Birth rate 1950-54 0.728 0.042 -0.373 0.510 0.637 -0.106 -0.293 0.215 1.000
infant mortality 1950-54 0.595 -0.027 -0.201 0.430 0.528 -0.118 -0.165 0.183 0.821
Source: authors' estimates using data for 157 countries with non-missing data for gdp, population, urbanization, and generating
capacity in 2005.
that the ranges between maximum and minimum For the main scenario analysis in this study, we have
monthly temperatures and precipitation are often the used results from the NCAR CCSM-3 and CSIRO-3
primary drivers of infrastructure demand. This means models (abbreviated to NCAR and CSIRO). These
that the projections used to calculate the Delta-Q costs have relatively similar changes in the global moisture
must be based upon climate scenarios that generate index, but they differ significantly in their patterns of
monthly maximum and minimum temperatures as well climate change at the regional and country level. The
as average temperatures, which restricts the set of models are part of a larger set of 26 GCMs that have
GCMs that can be used. But even more important, the been examined in detail by the MIT Joint Program on
variance of the difference between two variables is the the Science and Policy of Global Change. As part of
sum of their variances minus their covariance. Under their analysis, the MIT group has down-scaled the
most plausible outcomes, this will exceed the variance of climate projections to match the 0.5 degree grid cells
each element, so that the uncertainty about climate used for the historic climate data, so population- and
ranges will be higher than for climate means.8 area-weighted means were constructed for the countries
covered by our study for the NCAR and CSIRO
scenarios.
8 This is particularly the case for the precipitation range. Generally, cli- These projections are not sufficient for the Delta-P
mate change projections suggest that monthly maximum and mini- analysis, because design standards for certain types of
mum temperatures will move roughly in line with average
temperatures. That is certainly not the case for precipitation since in infrastructure are driven by extreme values rather than
many places it is expected that rainfall patterns will become more monthly average values. However, GCMs are not capa-
uneven with zero or even negative covariance between changes for the
driest and wettest months. ble of generating reliable estimates of daily maximum/
8 t h e c os ts oF ad a p tin g to c limate c h an ge For in Fr as tr u c tu r e
minimum temperatures, precipitation, or wind speed, so are not relevant when we wish to make projections 40
it is necessary to deal with this requirement in an indi- or more years into the future, since it is neither possible
rect manner. We have proceeded as follows: nor desirable to attempt to project how governance or
institutions will evolve over that period.
a. Use the normal or log-normal distributions of
monthly averages of maximum/minimum temper- To the extent that (a) institutional factors influence the
ature and monthly precipitation to estimate the current level of infrastructure provision, and (b) there is
99th percentile of monthly maximum temperature, a correlation between institutional development and
the 1st percentile of monthly minimum tempera- GDP per person or urbanization, then the impact of
ture, and the 99th percentile of maximum monthly institutional development will be (partly) captured by
precipitation. the coefficients on GDP per person or urbanization in
the reduced form discussed below. This is one reason
b. Express these percentiles as a ratio of the maxi- why the elasticities of infrastructure demand with
mum/minimum of monthly average maximum/ respect to these variables may be higher when estimated
minimum temperatures and the maximum using a sample of all countries than for a sample of
monthly precipitation and assume that these ratios high-income countries only. But, equally, there are
will remain broadly constant in the future. many other factors that may affect the reduced form
elasticities.
c. Apply the ratios of the 99th/1st percentiles to the
associated monthly extremes for 2050 in order to Quite apart from matters of econometric philosophy,
compute the change from extreme values for the the nature of the data available for the purpose of
historic climate to extreme values for the climate making projections of future demand for infrastructure
scenario in absolute units--degrees C or mm of has an important influence upon the specification of the
rainfall. models. There are a very limited number of variables
for which independent projections extending to 2050
d. In the case of wind speed, we have estimated the have been constructed and can be used. In addition to
elasticity of the 99th percentile of wind speed with the climate variables discussed above, these are total
respect to the 99th percentile of precipitation by population, the age structure of the population, urban-
fitting extreme value distributions to the historic ization, and growth in income (GDP per capita
climate data and used the change in maximum measured at purchasing power parity), plus a number of
precipitation to project changes in extreme wind geographical features, which act as country-fixed
events. effects.9
The basic approach for the econometric analysis is to
develop a reduced form specification of the efficient
4. econometric SpecificationS demand for the services provided by each type of
infrastructure--for example, paved roads or railways.10
In considering the specification of the econometric
analysis, it has to be remembered that the goal is to
9 The demographic projections are based on the medium fertility pro-
generate projections of the average demand for infra- jection in the UN Population Division's 2006 revision, which is linked to
structure up to 2050, whether or not these are affected the urbanization projections. The central scenario for growth rates for
GDP per person at purchasing power is computed by taking the aver-
by climate. We are not trying to examine the factors age of five economic integrated assessment models-- Hope (2003),
Nordhaus (2002), Tol (2007), IEA (2008) and EIA (2008). The average
that drive the actual amounts of infrastructure assets growth rate for world GDP in real terms is very close to the IPCC A1
supplied today or in the past. The key implication is SRES scenario, but the country growth rates are not based upon the
downscaled versions of that scenario since those were constructed
that it is not appropriate to include, for example, indica- with a base data of 1990 and the relative country weights are very out
tors of governance or institutional development in the of date. The sources of the population and income projections are
described in a separate note.
analysis. These may be relevant factors explaining
10 There is an extensive literature, much of it originating in the World
actual outcomes for individual countries today. But they Bank, on developing econometric models to identify links between
d e v e l o p m e n t a n d c l i m at e c h a n g e d i s c u s s i o n pa p e r s 9
We assume that the structural equation defining the Since there are no strong priors on the appropriate
efficient demand for infrastructure type i in country j in functional forms for fi{ }, ci{ }, and gi{ }, we can use a
period t may be written as: standard flexible functional form to represent the
demand equation hi{ } in terms of the explanatory vari-
ables. We have adopted a restricted version of the
Qijt = f i {Pjt , Y jt , Cijt , X jt , Z ijt ,V jt , t} (3)
translog specification for all variables other than popu-
lation. Using the notation xj=ln(Xj), the general trans-
The variables are defined as follows:
log function for infrastructure services may be written
as:
Pjt is the population of country j in period t;11
Yjt is average income per head for country j in d ijt = a i + b pi p jt + b yi y jt + b xim xmjt + b vir v rjt + g yi y 2 + g
(7)
period t; d ijt = a i + b pi p jt + b yi y jt jt b x
+
d cost a i infrastructure type i+ b xim xmjt + + + b virimrjt jt xg yi + jtjyb v rjtx s imnb mjtvxnjt++ y 2imr xmjt vg
Cijt is the unit ijt
= of + b pi p jt + b yi y jt for p jt + mjt y + ir xim x mjt
d ijt = a brpiv y + b yiy jt + gjt xim mjt + r xviry rjtxmjtg+ j+irjt vx
2 2+ f
y rjtr
i im jt yi jt
.
= i b mjt
d ijt a im+ jt pi p jt + b yiiry jtjt+rjt b xim ximn + im y bxmjt rjt + gjyi ir 2rjt+ g xims mjt xmjt xnjt + f imr xmjt v rjt
country j in period + t; r y x + j y v + s xr xnjt + + imr xmjt v v +
+ mjt jt vir v y y jt rjt x 2imn
f
mjt jt
Xjt is a vector of country characteristics for coun-
try j in period t;
+ r im y jt xmjt + j ir y jt v rjt + s x xnjt + f imr xmjt v rjt
imn mjt
Zijt is a vector of economic or other variables
that affect the demand for infrastructure type i
for country j in period t; and In practice, it is often difficult to estimate the full trans-
log specification using the more complex econometric
Vjt is a vector of climate variables for country j in models, so the approach adopted was to start with the
period t. log-linear specification and then test whether the coef-
ficients on the quadratic and cross-product terms are
We can observe or project values for some of these vari- significant. Because this involves repeated testing of
ables, notably P, Y, X, and V (dropping subscripts). For overlapping specifications, we have followed the spirit
the other variables we assume that: of the Bonferroni adjustment to test statistics by requir-
ing that any coefficients retained in the model are
Cijt = ci {Y jt , X j , Z ijt ,V jt , t} (4) significantly different from zero at the 1 percent level
using conventional statistical tests.12
and
We have noted that including climate variables in equa-
tions for the demand for infrastructure may be chal-
Z ijt = g i {Y jt , X jt ,V jt , t} . (5) lenged by some economists, especially if one goes on to
assume that future demand for infrastructure will be
Solving for Zijt and Cijt allows us to write the reduced
affected by projected changes in these climate variables.
form as
The reason for the debate is that climate variables are
believed to act as a proxy for institutional and other
Qijt = hi {Pjt , Y jt , X jt ,V jt , t} (6) factors that determine actual outcomes, partly as a
consequence of historical patterns of development
(Acemoglu et al. 2001; Albouy 2008; Dell et al. 2008;
Horowitz 2008. For example, attempts have been made
infrastructure investment and economic growth and to project future to estimate a relationship linking income per person
investment requirements for infrastructure in developing countries--
see Fay and Yepes (2003), Estache et al. (2005), and AICD (2009).
11 For some types of infrastructure, total population may be replaced by
population in each age group; i.e., the number of children (ages 0 to
14), the number of elderly (ages 65+). The country-fixed effects include 12 In fact almost all of the coefficients are significantly different from
country size and the proportions of land area that are desert, arid, zero at the 0.1 percent level. The exceptions to this procedure relate to
semiarid, steep, or very steep using standard FAO land classifications. linear terms in exogenous variables when one or more of the quadratic
In addition, we have used the proportion of land that has no significant terms is significant at the 0.1 percent level. In such cases the linear
soil constraints for agriculture. term is retained, since it may be important for scaling the predictions.
10 t h e c os ts oF ad a p tin g to c limate c h an ge For in Fr as tr u c tu r e
and average temperature as a basis for measuring the quadratic terms or cross-products with other
impact of climate change at highly aggregated level. explanatory variables. Consistently, one or both of
Indeed, any simple correlation of these variables appears the variables have coefficients that are significantly
to show that a higher average temperature (usually for different from zero at the 95 percent or 99 percent
the capital city of the country) is associated with lower levels. For this reason, the variables are included
average income per person. But even this relationship is in all of the models discussed below. So, it must
complicated by the role of natural resource endowments. be remembered that--even without further
Acemoglu et al. (2001) suggest that, in part, tempera- controls for the possible role of climate as an
ture is serving as an instrument for institutional devel- instrument for institutional development--the
opment, so they include historical mortality rates in analysis starts from a point that matches the state
their analysis on the grounds that this is an alterna- of the art in the current literature.
tive--and better--proxy for institutional development.
b. The role of climate as an instrument for institu-
The strategy adopted for our analysis relies on a number tional development is a geographical argument--
of alternative ways of dealing with this problem. that is, it is about the geography of regional
development--as much as it is about climate per
a. The Acemoglu et al (AJR) study used colonial se. Thus, the natural approach-- again made
(mostly 18th century) mortality as an instrument difficult in the past by data limitations--is to
for institutional development and found that this consider the use of spatial econometrics in which
had a very significant coefficient in their equations spatially weighted values of variables are used as
for recent economic growth. However, estimates instruments for institutional and other factors.
of colonial mortality are not available for more The standard model of spatial interaction (or
than one-half of the countries in our sample and, autocorrelation) is:
in any case, there is considerable controversy about
the reliability of the estimates that have been used.
Instead, we have used an alternative set of instru-
yi = a + j Wj i
ij y j + b xi + ei (8)
mental variables. The UN's population statistics
include a variety of demographic variables for the where the matrix W is a matrix of weights capturing
early 1950s for almost all countries. These the spatial influence of location j on location i, is
provide good instruments because they are closely called the spatial autocorrelation coefficient, and is the
correlated with the historical endowment of both error term whose distribution depends upon the model
institutions and infrastructure, but demographic specification. The inverse-distance model has been used
changes over the past 50 years mean they are less for this analysis for which the elements of W are
associated with current patterns.13 Two instru- proportional to the reciprocal of the distance between
ments have been used--the crude birth rate and the population centroids for countries i and j up to a
infant mortality. These two were chosen because maximum of 2,500 km.14 The W matrix is normalized
they capture the highest proportion of the cross- so that the row sums are equal to 1. The equations are
country variation of the demographic variables estimated using panel GMM with spatially weighted
examined. Reflecting their special role, these vari- values of population, GDP per person, urbanization,
ables were included on their own without and country size as instruments. The details of the
analysis are given in a separate paper, but the overall
13 The actual variable used in the AJR study is ln (settler mortality). For
63 countries in their samples (excluding Bahamas), the correlations
between ln (settler mortality) and our historic demographic variables 14 The distance band is chosen to ensure that all countries have at least
are 0.46 for ln (crude birth rate), 0.67 for ln (infant mortality), and -0.69 three "neighbors" within the band. This is a particular concern for
for ln (life expectancy). The correlations with AJR's proximate indicator large/isolated countries or territories such as Australia, Brazil, Canada,
of institutions (average protection against expropriation risk 1985�95) and Papua New Guinea. Reducing the distance band to 2,000 km
are -0.58 for ln (settler mortality), -0.57 for ln (crude birth rate), -0.69 for would mean that seven countries or territories have only one "neigh-
ln (infant mortality), and 0.65 for ln (life expectancy). Hence, our histor- bor" within the band, while reducing it to 1,500 km excludes Australia,
ic demographic indicators should provide better instruments for insti- Mongolia, Papua New Guinea, and Timor-Leste as having no "neigh-
tutional influences than AJR's use of settler mortality. bors."
d e v e l o p m e n t a n d c l i m at e c h a n g e d i s c u s s i o n pa p e r s 11
conclusion is that (a) the spatial interactions are consis- problem is that governance ratings change over
tently insignificant, and (b) including them does not time, whereas the climate variables are constant.
alter the role of the climate variables in our equations. To get around this, we have computed country
averages for the years for which data is available
c. Setting aside the spatial argument, the central and constructed the correlation matrices for both
econometric contention of the argument that population-weighted and inverse population-
climate variables do not reflect the role of climate weighted climate variables. Population-weighted
per se is that some or all of these variables are mean temperature and precipitation range would
correlated with the error terms in the regression. be the best instruments, as they have simple corre-
This is a classic econometric problem that may be lations of -0.41 to -0.49 for the main WGI gover-
caused by omitted variables, measurement errors, nance variables--notably government
or other factors. The standard solution is to treat effectiveness, regulatory quality, and rule of law.
the suspect climate variables as endogenous and Population-weighted precipitation and tempera-
look for instruments that are correlated with the ture range have very low correlations with the
climate variables but not with the error term--see, governance variables, and the correlations for the
for example, Cameron and Trivedi (2006, chapter inverse population-weighted variables are signifi-
4) or Baum (2005, chapter 8). It is not easy to cantly worse than for the population-weighted
find suitable instruments for all of the climate variables. With squared correlations of 0.2 or less,
variables, especially as a group, since physical char- climate variables would have high standard errors
acteristics of countries are included in the infra- if they were acting as instruments for gover-
structure demand equations. We have investigated nance--roughly 5 times the true standard errors
a range of potential instruments, such as the abso- for the governance variables--which is difficult to
lute value of latitude (the best instrument for reconcile with the relatively high t-ratios actually
temperature); internal renewable water resources; obtained. Further, including governance variables
numbers of bird, mammal, and plant species per sq in the tests reported below may reduce or increase
km; percentage covered by water and snow/ice; the F-values for the joint tests on the sets of
and spatially weighted physical characteristics for climate variables, but it does not alter the infer-
neighboring countries. These instruments ence. Overall, our results provide little support for
perform reasonably well for mean temperature and this interpretation.
temperature range (both population-weighted and
inverse population-weighted) on their own. In It is not possible to prove a negative. Our analysis
these cases, the use of instrumental variables does cannot demonstrate conclusively that the coefficients on
not alter our conclusions. The variables turn out our climate variables reflect the effects of climate per se
to be weak instruments for total precipitation and rather than the indirect influence of other, non-climate,
precipitation range on their own or for all climate factors. Nonetheless, we would argue that the cumula-
variables together, but no one has seriously tive weight of evidence is strong enough to shift the
proposed that either total precipitation or precipi- burden of proof. A key point is that the influence of
tation range act as proxies for other influences on climate in our infrastructure equations rarely depends
infrastructure demand. Finally, the analysis using upon a single climate variable on its own, whereas argu-
instrumental variables consistently fails to reject ments about the role of climate as a proxy or instrument
the hypothesis that the climate variables--either for other factors focus almost exclusively on mean
individually or as a group--can be treated as exog- temperature. There is even less reason to believe that
enous; that is, that the correlation between the inverse population-weighted climate variables act in this
climate variables and the error term is zero. way, since by definition these reflect climate patterns in
areas where people do not live and have not lived in
d. A final possibility is that climate variables act as
instruments for governance variables. One
12 t h e c os ts oF ad a p tin g to c limate c h an ge For in Fr as tr u c tu r e
large numbers.15 Hence, after extensive and careful significant fraction of observations are censored from
investigation, we conclude that the evidence supports above with the upper limit equal to logit (0.999).
the view that climate does and will have a significant
influence on future demand for infrastructure. In In addition to climate variables, the explanatory vari-
reaching this conclusion, we have been particularly strict ables in the base models are:
when considering the inclusion of mean temperature
(population-weighted and inverse population-weighted) � Log of population
in our projection models, so there has been a bias in
favor of omitting these variables unless there was unam- � Logs of GDP per person at 2005 PPP, country size,
biguous support for retaining them. On that basis, we and urban population as percentage of total popula-
believe it is reasonable to estimate the Delta-Q compo- tion plus quadratic terms in these variables
nent of adaptation over the full period from 2010 to � Log of a cross-country building cost index with the
2050 on the basis of the demand projections generated U.S.=1.0
by our equations.
� Logs of the proportions of land area that are desert,
The primary investigation of alternative specifications is arid, semi-arid, steep, very steep, and have no soil
carried out using pooled OLS with Driscoll-Kraay stan- constraints for agriculture--obtained from FAO's
dard errors, which allow for a general pattern of spatial Terrastat database
dependence between countries (Driscoll and Kraay � Logs of the birth rate and infant mortality for
1998; Hoechle 2007).16 In the case of the proportions 1950-54
of the population covered by electricity, water, and sewer
networks, the dependent variable is the logit of the rele- � Dummy variables for World Bank regions.
vant shares in order to translate values between 0 and 1
to the entire real line. It is necessary to censor values The last two groups of variables are retained in all
that are reported as either 0 or 1 in order to avoid models. Tests for the inclusion of non-climate and
degeneracy. Thus, the minimum and maximum values climate variables are performed separately. At the first
correspond to shares of 0.001 and 0.999, as the shares stage, the non-climate variables are tested for signifi-
are reported to the nearest 0.1 of a percentage point. A cance in a model containing the seven climate vari-
panel tobit model has been used to estimate the ables--pop and ipop variants other than temperature
demand equations for coverage rates for which a range. After dropping non-climate variables that do
not have significant coefficients, tests on the hypotheses
that the coefficients for (a) the population-weighted
15 The absolute values of the correlation coefficients between the logs climate variables, (b) the inverse population-weighted
of similarly weighted climate variables are less than 0.66 across our
sample of countries, with the sole exception of total precipitation and climate variables, and (c) all climate variables are all
precipitation range (see Table 1). Both temperature and precipitation equal to zero are carried out. If one or more of these
are negatively correlated with temperature range. The correlation
coefficients between population-weighted and inverse population- hypotheses are rejected, the set of climate variables
weighted variables range from 0.78 to 0.83, with the exception of tem- included in the model is reduced by first dropping
perature range, for which the value is 0.94. In view of this last
correlation, we have excluded the inverse-population weighted tem- either the pop or the ipop variants and then those vari-
perature range from the analysis. ables within each category that do not have significant
16 Driscoll-Kraay standard errors are robust to panel heteroscedasticity coefficients. Finally, interactions with GDP and urban-
and temporal autocorrelation as well as spatial interdependence. The
estimation is carried out using Hoechle's xtscc procedure in Stata, ization are tested for the climate variables that have
which generalizes the Driscoll-Kraay estimator to allow for unbalanced
panels. There is an important feature of the Driscoll-Kraay/Hoechle
been retained.
procedure that needs to be kept in mind. The method relies upon the
derivation of a robust covariance matrix for a sequence of cross-sec-
tional averages. The panels used for our analysis of some categories of Finally, we have used total, urban, or rural population
infrastructure are very unbalanced and do not span continuous periods weights (as appropriate) in estimating equations for
of time. Nonetheless, cross-sectional averages can be calculated for
more than 25 years. The sample of countries in each cross-sectional which the dependent variable is the log or logit of an
average differs, but this is consistent with the way in which the covari- infrastructure indicator per person or per household;
ance estimator is specified. Thus, even though the Driscoll-Kraay anal-
ysis relies upon asymptotics as T, the nature of our data is for example, municipal industrial water use per person,
consistent with its basic requirements.
d e v e l o p m e n t a n d c l i m at e c h a n g e d i s c u s s i o n pa p e r s 13
average household size, or the percentages of house- the inverse population-weighted climate variables. The
holds connected to electricity, water, or sewer networks. rejection of the hypothesis of zero coefficients is partic-
In all cases, the weights are normalized to sum to the ularly strong for the inverse-population weighted
number of observations used for the analysis. climate variables; this is reinforced by the higher values
of the t-ratios for these coefficients. The only climate
variable with a coefficient that is not significantly
different from zero is population-weighted mean
5. the effectS of climate on temperature. On the other hand, both temperature
range and inverse population-weighted mean tempera-
DemanD for infraStructure ture have coefficients that are highly significant. The
signs of the coefficients differ, but these variables are
Electricity generating capacity. Model (1) in Table 2
negatively correlated (see Table 1) so that warmer coun-
shows that the tests for the joint significance of the
tries tend to have less generating capacity, holding other
climate variables reject the hypothesis of zero coeffi-
factors constant.
cients decisively for both the population-weighted and
table 2. projection equationS for electricity generating capacity, fixeD tele-
phone lineS, anD electricity network coverage
Logit(Urban Logit(Rural
Ln(Generating capacity) Ln(Fixed telephone lines) electricity coverage) electricity coverage)
Variables (1) (2) (3) (4) (5) (6) (7) (8)
ln(population) 0.954*** 0.959*** 1.088*** 1.081*** 0.729** 0.641** 1.882*** 1.750***
(0.028) (0.021) (0.033) (0.030) (0.222) (0.217) (0.150) (0.152)
ln(gdp per per-
0.975*** 0.926*** 0.618*** 0.608*** 3.706*** 3.703*** 5.909*** 6.496***
son)
(0.141) (0.129) (0.024) (0.016) (0.562) (0.556) (0.587) (0.642)
ln(country size) 1.111*** 1.034*** -0.159*** -0.150*** 4.454*** 5.208***
(0.137) (0.117) (0.026) (0.026) (0.630) (0.697)
ln(% urban) 2.936*** 4.248*** 2.084*** 1.340 -10.59*** -10.43*** -7.703*** 8.478
(0.563) (0.711) (0.474) (1.593) (3.141) (3.122) (1.554) (4.349)
ln(% urban)
0.324*** 0.309***
squared
(0.050) (0.044)
ln(gdp per per-
-0.115*** -0.108*** -0.621*** -0.714***
son) *
ln(country
(0.014) (0.012) (0.070) (0.078)
size)
ln(gdp per per-
-0.239*** -0.226*** -0.203** -0.191** 1.683*** 1.660*** 0.627** 0.776***
son) *
ln(% urban) (0.056) (0.060) (0.066) (0.062) (0.428) (0.427) (0.211) (0.218)
ln(country size) * 0.131*** 0.106*** 1.001*** 1.091***
ln(% urban) (0.027) (0.026) (0.136) (0.140)
(continued)
14 t h e c os ts oF ad a p tin g to c limate c h an ge For in Fr as tr u c tu r e
table 2. projection equationS for electricity generating capacity, fixeD tele-
phone lineS, anD electricity network coverage (continued)
Logit(Urban Logit(Rural
Ln(Generating capacity) Ln(Fixed telephone lines) electricity coverage) electricity coverage)
Variables (1) (2) (3) (4) (5) (6) (7) (8)
ln(Building cost) -1.878*** -1.712*** 21.44** 18.21** 17.94*** 14.30***
(0.289) (0.284) (6.636) (5.967) (3.502) (3.348)
ln(% desert) 0.0276*** 0.0278*** 0.0250*** 0.0351***
(0.004) (0.005) (0.005) (0.007)
ln(% semi-arid) -0.0378*** -0.0297** 0.0296*** 0.0307***
(0.011) (0.011) (0.004) (0.004)
ln(% steep land) -0.107*** -0.111*** -1.509*** -0.321
(0.008) (0.012) (0.413) (0.350)
ln(% very steep
0.0947*** 0.0792*** 0.0647*** 0.0647***
land)
(0.008) (0.007) (0.004) (0.004)
ln(% no soil con-
-0.0522*** -0.0414*** 0.0365*** 0.0346*** -0.805*** -1.146***
straint)
(0.010) (0.008) (0.006) (0.007) (0.167) (0.142)
ln(temperature -
-0.837 -2.158*** -2.620*** -5.626 -7.115*
pop)
(0.480) (0.202) (0.376) (6.088) (3.084)
ln(precipitation -
0.395*** 0.152*** -0.001 -3.175** -3.884*** -1.541*
pop)
(0.069) (0.040) (0.085) (1.224) (1.098) (0.767)
ln(temp range -
0.313** 0.386*** -0.250** 0.144* -6.061** -3.735** -4.629***
pop)
(0.103) (0.074) (0.090) (0.067) (1.866) (1.228) (1.119)
ln(precip range -
-0.431*** -0.272*** -0.169** -0.0449** 3.574** 3.799*** 2.094**
pop) 1.388***
(0.055) (0.052) (0.065) (0.017) (1.342) (1.055) (0.756) (0.310)
ln(temperature -
-1.057*** -1.447*** -0.388*** 0.305*** -0.203 -10.53***
ipop) -13.65***
(0.137) (0.125) (0.111) (0.068) (2.396) (1.418) (1.946)
ln(precipitation -
-0.272*** -0.269*** -0.149 -1.771 -1.270**
ipop)
(0.045) (0.036) (0.098) (0.997) (0.490)
ln(precip range -
0.479*** 0.468*** 0.240** 0.0542* 1.018 1.012*
ipop)
(0.055) (0.048) (0.077) (0.025) (1.001) (0.509)
ln(% urban) * -1.006**
ln(temperature - (0.363)
pop)
d e v e l o p m e n t a n d c l i m at e c h a n g e d i s c u s s i o n pa p e r s 15
table 2. projection equationS for electricity generating capacity, fixeD tele-
phone lineS, anD electricity network coverage (continued)
Logit(Urban Logit(Rural
Ln(Generating capacity) Ln(Fixed telephone lines) electricity coverage) electricity coverage)
Variables (1) (2) (3) (4) (5) (6) (7) (8)
ln(% urban) * -0.388***
ln(precipitation - (0.050)
pop)
ln(% urban) * 0.328***
ln(temp range - (0.070)
pop)
ln(% urban) * 0.270*** 0.139***
ln(precip range - (0.066) (0.031)
pop)
ln(% urban) * 0.862*** -4.295***
ln(temperature - (0.102) (1.151)
ipop)
ln(% urban) * -0.0749***
ln(precip
(0.009)
range - ipop)
model pols pols pols pols tobit tobit tobit tobit
observations 6027 6027 5130 5130 906 906 853 853
number of coun-
165 165 186 186 130 130 127 127
tries
r-squared 0.923 0.924 0.938 0.939
log-likelihood -250.6 -253.2 -436.6 -438.4
dF 26 27 25 28 19 15 24 20
no of censored
716 716 661 661
obs
p-value for all cli-
mate variables = 0.000 0.000 0.008 0.000
0
p-value for pop
climate variables 0.000 0.000 0.005 0.000
=0
p-value for ipop
climate variables 0.000 0.000 0.167 0.000
=0
Note: standard errors are shown in brackets underneath the relevant coefficients with *** p < 0.001, ** p < 0.01, * p < 0.05. in addi-
tion to the variables shown, all of the equations include the following explanatory variables: ln(birthrate 1950), ln(infant mortality
1950) and dummy variables for World Bank regions.
Source: authors' estimates.
16 t h e c os ts oF ad a p tin g to c limate c h an ge For in Fr as tr u c tu r e
Overall, however, temperature and temperature range inclusion in the equation for urban coverage. For rural
are less important influences on the amount of generat- coverage, population-weighted precipitation range and
ing capacity than precipitation and precipitation range inverse population-weighted temperature--on its own
together with their interactions with urbanization. and interacted with urbanization--are the key climate
There are various mechanisms by which precipitation variables. The chi-square statistic for the test of zero
may affect installed capacity. One factor is the role of influence of the temperature variables is 59.7, so that
hydro power in total electricity supply, since utilization these cannot be excluded.
factors tend to be lower for hydro plants. Another is
the role of pumped irrigation systems and similar influ- Water use. The dependent variables for water use are
ences on patterns of electricity demand in countries the logs of water abstractions per person for municipal
with high interseasonal variations in rainfall. Even and industrial use, which are derived from FAO data.
though the absolute values of the coefficients for precip- This includes water that is lost in treatment and in
itation and precipitation range are smaller than the water supply networks. Models (1) and (2) in Table 3
equivalent coefficients for temperature, these variables summarize the results of the econometric analysis for
have an important effect in the calculation of the municipal water use per person. In this case, the tests
Delta-Q changes because the distributions of changes for the joint significance of the climate variables reject
in total precipitation and precipitation range are much the hypothesis of zero coefficients decisively for the
more dispersed and much larger relative to their historic population-weighted variables, but not for the inverse
values than are the equivalent distributions for population-weighted variables. The best specification
temperature. includes population-weighted precipitation and precipi-
tation range. Another point to note is the quadratic in
Fixed telephone lines. The projection equations for fixed GDP per person. The results seem to be intuitively
telephone lines are reported as Models (3) & (4) in reasonable, reflecting rainfall patterns where people live
Table 2. Again, the hypotheses of zero coefficients for and the effect of changes in GDP on water use. The
climate variables are decisively rejected, particularly for quadratic terms in GDP per person imply that water
the population-weighted variables, with mean tempera- consumption per person reaches a peak at an income of
ture, temperature range, and precipitation range all about $12,000 per capita in 2005 PPP, and falls gradu-
having significant coefficients. There are strong interac- ally as countries get richer beyond this point.
tions with urbanization, so that the impact of climate
change on demand for telephones varies markedly both Models (3) and (4) in Table 3 summarize the results for
within and across country classes. industrial water use per person. In this case, the tests
reject the hypotheses that the population-weighted and/
Electricity network coverage. Models (5) through (8) in or inverse population-weighted climate variables have
Table 2 show the estimated equations for the logits of zero coefficients. The detailed investigation identifies
electricity coverage for urban and rural households population-weighted temperature range and precipita-
weighted by the relevant populations in 2005.17 Panel tion range plus inverse population-weighted precipita-
tobit models are used with an upper censoring value tion and precipitation range as having significant
corresponding to a coverage of 99.9 percent. Since the coefficients. There are significant interactions between
majority of observations are censored, the number of the inverse-population weighted climate variables and
exogenous variables is reduced in each equation by GDP per person with urbanization. Use of water in
much more than for electricity generating capacity. industry is a derived demand, so the influence of
Nonetheless, population-weighted precipitation, precipi- climate variables operates through the scale and location
tation range, and temperature range clearly warrant of food processing and similar resource-based industries.
Hence, it is climate conditions in rural and thinly popu-
lated areas that have a significant influence.
17 For the purpose of projecting the total numbers of connections, it is
necessary to allow for non-household connections. We have assumed
that the total numbers of electricity connections are 108 percent of the
Water and sewer connections. Table 4 summarizes the
numbers of households connected to the network. This multiplier results for coverage rates of piped water supply and
reflects the typical ratio for upper-middle and high-income countries.
d e v e l o p m e n t a n d c l i m at e c h a n g e d i s c u s s i o n pa p e r s 17
table 3. projection equationS for municipal anD inDuStrial water DemanD
Ln(Municipal water use per person) Ln(Industrial water use per person)
Variables (1) (2) (3) (4)
ln(gdp per person) 2.159** 2.000** 3.455** 2.953*
(0.679) (0.632) (1.073) (1.197)
ln(country size)
ln(% urban) 0.530*** 0.559***
(0.071) (0.067)
ln(gdp per person) squared -0.115** -0.105** -0.191** -0.219***
(0.039) (0.036) (0.064) (0.064)
ln(Building cost) -2.477*** -2.342*
(0.662) (0.904)
ln(% steep land) 0.970*** 0.943***
(0.124) (0.100)
ln(% very steep land) 0.152*** 0.156*** -0.225*** -0.183**
(0.023) (0.032) (0.054) (0.059)
ln(% no soil constraint) -0.265*** -0.156***
(0.040) (0.027)
ln(temperature - pop) 0.923* -0.027
(0.391) (1.219)
ln(precipitation - pop) -0.150 -0.306*** 0.456
(0.173) (0.087) (0.354)
ln(temp range - pop) 0.459** 2.091*** 2.003***
(0.163) (0.294) (0.221)
ln(precip range - pop) 0.205 0.367*** -0.819* -0.594***
(0.175) (0.103) (0.323) (0.127)
ln(temperature - ipop) 0.079 -0.842
(0.239) (0.592)
ln(precipitation - ipop) 0.102 -0.512* -5.318***
(0.081) (0.240) (0.836)
ln(precip range - ipop) -0.054 0.902** 5.682***
(0.103) (0.287) (0.931)
ln(gdp per person) * 0.577***
ln(precipitation - ipop) (0.099)
ln(gdp per person) * -0.577***
ln(precip range - ipop) (0.107)
model pols pols pols pols
observations 368 368 337 337
number of countries 161 161 158 158
(continued)
18 t h e c os ts oF ad a p tin g to c limate c h an ge For in Fr as tr u c tu r e
table 3. projection equationS for municipal anD inDuStrial water DemanD
(continued)
Ln(Municipal water use per person) Ln(Industrial water use per person)
Variables (1) (2) (3) (4)
r-squared 0.980 0.979 0.954 0.955
log-likelihood
dF 19 14 19 18
no of censored obs
p-value for all climate variables = 0 0.000 0.000
p-value for pop climate
0.000 0.000
variables = 0
p-value for ipop climate
0.087 0.000
variables = 0
Note: standard errors are shown in brackets underneath the relevant coefficients with *** p < 0.001, ** p < 0.01, * p < 0.05. in addi-
tion to the variables shown, all of the equations include the following explanatory variables: ln(birthrate 1950), ln(infant mortality
1950) and dummy variables for World Bank regions.
Source: authors' estimates.
sewer networks in urban and rural areas. Models (1) to For the purpose of costing wastewater treatment, we
(6) are based upon panel tobit estimation, allowing for have assumed that the BOD/COD concentration and
the upper censoring of countries with reported coverage other characteristics of sewage handled by wastewater
of 99.9 percent or higher. In general, population- treatment plants correspond to typical values for munic-
weighted climate variables have a significant influence ipal wastewater. This implies that industries will be
on coverage rates in urban areas, while inverse popula- expected to process wastewater with high concentra-
tion-weighted climate variables are more important in tions of industrial pollutants. Further, it is assumed that
rural areas. The only exception is rural water supply, for wastewater treatment plants are scaled to process 80
which both sets of climate variables are significant. percent of the volume of water treated by water treat-
Interactions with GDP per person and urbanization are ment plants, allowing for network losses and wastewater
not significant. Since coverage rates for piped water that is not discharged to sewers.
supply are close to or equal to 99.9 percent in high
income countries, changes in climate variables will not Roads. Table 5 shows equations for the total length of
have any effect on costs of adaptation in many coun- roads (both paved and unpaved) and for the logit of the
tries. However, changes in average temperature--and share of paved roads in total road length, weighted by
precipitation for rural households--may affect the total road length in the latter case. The key climate
numbers of households connected to collective sewer variables affecting the length of roads are temperature
systems.18 and precipitation range--both population-weighted and
inverse population-weighted--plus population-weighted
temperature range. There are strong interactions with
18 It should be emphasized that this is not a matter of whether house-
holds have access to some form of adequate sanitation. The depen-
GDP per person for temperature and precipitation
dent variable is the proportion of households that are connected to
community sewers, rather than relying upon septic tanks or equivalent
individual arrangements. Community sewers are more expensive to
construct and the wastewater that is collected must be treated, so tions by assuming that the total numbers of water supply and sewer
costs of adaptation arise from shifts to or away from reliance on com- connection are 10 percent higher than the numbers of household con-
munity sewers. Again, we have allowed for non-household connec- nections, based on typical ratios for middle-income countries.
d e v e l o p m e n t a n d c l i m at e c h a n g e d i s c u s s i o n pa p e r s 19
table 4. projection equationS for water anD Sewer networkS
Logit(Urban water Logit(Rural water Logit(Urban sewer Logit(Rural water
coverage) coverage) coverage) coverage)
Variables (1) (2) (3) (4) (5) (6) (7) (8)
ln(population) 0.353** 0.300* 0.513*** 0.488*** 0.357** 0.278* 0.765*** 0.783***
(0.127) (0.118) (0.113) (0.113) (0.119) (0.117) (0.149) (0.230)
ln(gdp per person) 0.889*** 0.901*** 0.430 0.647 2.576*** 2.629*** 1.405*** 1.407***
(0.150) (0.148) (0.826) (0.824) (0.348) (0.350) (0.346) (0.335)
ln(country size) -0.580*** -0.539*** 1.327*** 1.439*** 2.035*** 2.161*** -0.636*** -0.635***
(0.112) (0.095) (0.318) (0.314) (0.407) (0.405) (0.102) (0.175)
ln(% urban) -3.744*** -3.797*** 1.388*** 1.371*** 1.247*** 1.226***
(0.995) (0.982) (0.230) (0.229) (0.339) (0.268)
ln(gdp per person)
0.157** 0.149**
squared
(0.053) (0.053)
ln(gdp per person) * -0.282*** -0.293*** -0.276*** -0.285***
ln(country size) (0.035) (0.034) (0.041) (0.042)
ln(gdp per person) * 0.451*** 0.462***
ln(% urban) (0.131) (0.130)
ln(Building cost) -7.790** -9.089*** 13.91** 14.16*
(2.878) (2.607) (4.162) (5.822)
ln(% desert) -0.188* -0.201***
(0.080) (0.051)
ln(% arid land) -0.421*** -0.295*** -0.254*** -0.266***
(0.082) (0.073) (0.053) (0.072)
ln(% semi-arid land) 0.141* 0.162* 0.375*** 0.449***
(0.064) (0.063) (0.075) (0.074)
ln(% no soil constraint) -0.282* -0.422***
(0.114) (0.091)
ln(temperature - pop) -8.469*** -8.000*** -0.351 -5.950** -7.603*** 0.281
(2.303) (1.352) (2.406) (2.128) (1.452) (3.102)
ln(precipitation - pop) -0.262 -1.690** -1.319*** 0.128 0.149
(0.530) (0.570) (0.214) (0.529) (0.625)
ln(temp range - pop) -0.693 -1.793* -1.498** -0.119 -0.023
(0.742) (0.767) (0.484) (0.747) (0.325)
ln(precip range - pop) -1.184* -1.500*** 0.870 0.288 -0.413
(0.517) (0.238) (0.593) (0.531) (0.823)
ln(temperature - ipop) -0.761 -6.472*** -6.385*** -1.098 -3.842*** -3.745***
(1.033) (0.947) (0.863) (0.889) (0.864) (0.485)
ln(precipitation - ipop) -0.017 0.131 -0.283 -0.981*** -0.855***
(0.375) (0.381) (0.356) (0.190) (0.085)
(continued)
20 t h e c os ts oF ad a p tin g to c limate c h an ge For in Fr as tr u c tu r e
table 4. projection equationS for water anD Sewer networkS (continued)
Logit(Urban water Logit(Rural water Logit(Urban sewer Logit(Rural water
coverage) coverage) coverage) coverage)
Variables (1) (2) (3) (4) (5) (6) (7) (8)
ln(precip range - ipop) -0.084 -0.486 -0.429 0.255
(0.416) (0.425) (0.430) (0.269)
model tobit tobit tobit tobit tobit tobit pols pols
observations 582 582 547 547 318 318 272 272
number of countries 157 157 155 155 140 140 124 124
r-squared 0.901 0.901
log-likelihood -452.1 -452.9 -461.5 -464.7 -327.0 -335.1
dF 21 16 21 17 21 15 20 15
no of censored obs 94 94 36 36 10 10
p-value for all climate
0.000 0.000 0.000 0.000
variables = 0
p-value for pop climate
0.000 0.000 0.024 0.021
variables = 0
p-value for ipop climate
0.854 0.001 0.002 0.000
variables = 0
Note: standard errors are shown in brackets underneath the relevant coefficients with *** p < 0.001, ** p < 0.01, * p < 0.05. in addi-
tion to the variables shown, all of the equations include the following explanatory variables: ln(birthrate 1950), ln(infant mortality
1950) and dummy variables for World Bank regions.
Source: authors' estimates.
table 5. projection equationS for roaDS
ln(total road length) logit(share of paved roads)
variables (1) (2) (3) (4)
ln(population) 0.584*** 0.590*** 0.599*** 0.668***
(0.004) (0.005) (0.057) (0.064)
ln(gdp per person) -0.042 2.070*** 1.980*** 8.010***
(0.034) (0.098) (0.094) (0.781)
ln(country size) -0.0931* -0.007 3.645*** 0.081
(0.045) (0.029) (0.473) (0.300)
ln(% urban) 0.395*** 0.786*** -2.207*** -0.845
(0.069) (0.074) (0.389) (0.520)
ln(country size) squared 0.0166*** 0.0175*** -0.108*** -0.0668***
(0.002) (0.001) (0.019) (0.013)
ln(% urban) squared -0.155*** -0.0581***
(0.015) (0.010)
(continued)
d e v e l o p m e n t a n d c l i m at e c h a n g e d i s c u s s i o n pa p e r s 21
table 5. projection equationS for roaDS (continued)
ln(total road length) logit(share of paved roads)
variables (1) (2) (3) (4)
ln(gdp per person) * 0.0331*** 0.0217*** -0.316*** 0.010
ln(country size) (0.003) (0.002) (0.023) (0.023)
ln(gdp per person) * -0.105*** -0.199***
ln(% urban) (0.006) (0.012)
ln(country size) * 0.434*** 0.036
ln(% urban) (0.053) (0.037)
ln(Building cost) -11.84*** -9.597***
(0.778) (0.527)
ln(% desert) 0.0347*** 0.0546*** -0.157*** -0.232***
(0.009) (0.006) (0.025) (0.033)
ln(% arid) 0.396*** 0.371***
(0.041) (0.065)
ln(% semi-arid) -0.0492*** -0.0503***
(0.005) (0.004)
ln(% steep land) 0.100*** 0.0660***
(0.013) (0.011)
ln(% very steep land) -0.0245*** -0.0111*** 0.196*** 0.174***
(0.004) (0.002) (0.040) (0.045)
ln(% no soil constraint) 0.0279*** 0.0402***
(0.006) (0.005)
ln(temperature - pop) -1.267*** 1.589*** 2.041
(0.190) (0.424) (1.121)
ln(precipitation - pop) 0.017 0.262
(0.030) (0.242)
ln(temp range - pop) -0.108** -0.208*** -2.074*** 16.46***
(0.038) (0.042) (0.106) (1.595)
ln(precip range - pop) -0.170** -0.154*** -2.099*** 0.663*
(0.055) (0.028) (0.319) (0.315)
ln(temperature - ipop) 0.593*** 0.684*** -2.213*** -1.829***
(0.141) (0.145) (0.530) (0.256)
ln(precipitation - ipop) 0.039 -0.646***
(0.069) (0.176)
ln(precip range - ipop) 0.156* 1.334*** 0.976***
(0.060) (0.070) (0.071)
ln(% urban) * 0.110***
ln(precip range - pop) (0.011)
ln(gdp per person) * -0.373***
ln(temperature - pop) (0.025)
(continued)
22 t h e c os ts oF ad a p tin g to c limate c h an ge For in Fr as tr u c tu r e
table 5. projection equationS for roaDS (continued)
ln(total road length) logit(share of paved roads)
variables (1) (2) (3) (4)
ln(gdp per person) * -2.150***
ln(temp range - pop) (0.192)
ln(gdp per person) * -0.190***
ln(precip range - pop) (0.038)
ln(gdp per person) * -0.127***
ln(precip range - ipop) (0.008)
model pols pols pols pols
observations 2040 2040 1790 1790
number of countries 182 182 179 179
r-squared 0.922 0.926 0.816 0.822
log-likelihood
dF 27 28 25 23
no of censored obs
p-value for all climate variables = 0 0.000 0.000
p-value for pop climate variables = 0 0.000 0.000
p-value for ipop climate variables = 0 0.000 0.000
Note: standard errors are shown in brackets underneath the relevant coefficients with *** p < 0.001, ** p < 0.01, * p < 0.05. in addi-
tion to the variables shown, all of the equations include the following explanatory variables: ln(birthrate 1950), ln(infant mortality
1950) and dummy variables for World Bank regions.
Source: authors' estimates.
range and with urbanization for precipitation range. GDP per person. In particular, higher temperatures in
These climate variables are directly linked to the cost of rural areas--that is, inverse population-weighted
building and maintaining roads--temperature is partic- temperature--lead to a lower share of paved roads,
ularly important for paved roads subject to heavy use, which is exactly what one would expect in view of the
while temperature and precipitation ranges affect the higher costs of construction and maintenance for rural
capital and maintenance costs of both paved and paved roads associated with higher temperatures.
unpaved roads. The fact that both population-weighted
and inverse-population weighted climate variables are Other transport. Table 6 shows the projection equations
significant reflects the impact of climate on all types of for rail track length, aircraft movements, and container
roads--rural, urban, and national. It is likely that traffic handled by ports. The last two are indicators
climate may also play a role through the structure of the used in estimating investments in airports and sea/river
economy--for example, the nature and role of agricul- ports. With one exception, the tests reject the hypothe-
tural production--and through geographical patterns of sis of zero coefficients for all climate variables decisively.
economic development. The exception is for inverse population-weighted
climate variables in the rail equation. The primary
The share of paved roads in total road length is influ- climate influences are:
enced by the same variables and their interactions with
d e v e l o p m e n t a n d c l i m at e c h a n g e d i s c u s s i o n pa p e r s 23
table 6. projection equationS for other tranSport
Ln(Rail track length) Ln(Aircraft movements) Ln(Container traffic)
Variables (1) (2) (3) (4) (5) (6)
ln(population) 0.484*** 0.472*** 0.540*** 0.540*** 0.649*** 0.649***
(0.043) (0.041) (0.035) (0.032) (0.034) (0.023)
ln(gdp per person) 0.259*** 0.971 0.710*** 0.692*** 0.005 -2.830***
(0.053) (0.741) (0.065) (0.066) (0.074) (0.748)
ln(country size) 0.425*** 0.405*** -0.184*** -0.167*** -2.751*** -3.495***
(0.076) (0.049) (0.030) (0.030) (0.195) (0.363)
ln(% urban) -0.315 -0.215 -0.376** 2.202*** 3.989*** -0.071
(0.172) (0.146) (0.143) (0.571) (0.389) (1.116)
ln(country size) squared 0.0337*** 0.0325*** 0.0299*** 0.0278***
(0.003) (0.003) (0.003) (0.003)
ln(% urban) squared -0.112* 1.090*** 1.563***
(0.047) (0.168) (0.171)
ln(gdp per person) * 0.222*** 0.302***
ln(country size) (0.015) (0.034)
ln(country size) * -0.507*** -0.513***
ln(% urban) (0.021) (0.026)
ln(Building cost) -3.062*** -2.936***
(0.349) (0.339)
ln(% desert) 0.0862*** 0.0728*** 0.266*** 0.223***
(0.021) (0.020) (0.009) (0.015)
ln(% arid) -0.333*** -0.363***
(0.012) (0.011)
ln(% semi-arid) 0.0404*** 0.0710***
(0.006) (0.004)
ln(% steep land) -0.132*** -0.144***
(0.029) (0.032)
ln(% very steep land) 0.0743*** 0.0819***
(0.010) (0.011)
ln(temperature - pop) -2.235*** 3.225* -0.466 0.712*
(0.660) (1.288) (0.334) (0.307)
ln(precipitation - pop) 0.362*** -1.378*** -0.396*** -0.351*** 0.591*** 0.909***
(0.035) (0.153) (0.102) (0.080) (0.140) (0.124)
ln(temp range - pop) 0.939*** -0.797 -1.038*** -1.400*** 0.232
(0.183) (0.512) (0.131) (0.111) (0.170)
ln(precip range - pop) -0.175 0.373*** 0.313*** 0.035
(0.123) (0.053) (0.044) (0.079)
ln(temperature - ipop) 0.814 -1.005*** -1.084*** -0.906*** -6.590***
(0.745) (0.114) (0.091) (0.093) (1.305)
(continued)
24 t h e c os ts oF ad a p tin g to c limate c h an ge For in Fr as tr u c tu r e
table 6. projection equationS for other tranSport (continued)
Ln(Rail track length) Ln(Aircraft movements) Ln(Container traffic)
Variables (1) (2) (3) (4) (5) (6)
ln(precipitation - ipop) 0.149 0.326*** 0.026 0.429*** 0.261***
(0.133) (0.046) (0.072) (0.075) (0.051)
ln(precip range - ipop) -0.217 -0.183*** 0.060 -0.457*** -0.326***
(0.174) (0.041) (0.059) (0.034) (0.058)
ln(% urban) * 0.707***
ln(precipitation - pop) (0.163)
ln(% urban) * -0.508***
ln(temp range - pop) (0.074)
ln(% urban) * -0.354***
ln(precipitation - ipop) (0.089)
ln(% urban) * 0.324***
ln(precip range - ipop) (0.058)
ln(gdp per person) * -0.580***
ln(temperature - pop) (0.156)
ln(gdp per person) * 0.167***
ln(precipitation - pop) (0.014)
ln(gdp per person) * 0.159**
ln(temp range - pop) (0.051)
ln(gdp per person) * 0.612***
ln(temperature - ipop) (0.139)
model pols pols pols pols pols pols
observations 1969 1969 5040 5040 407 407
number of countries 133 133 175 175 69 69
r-squared 0.741 0.740 0.831 0.833 0.805 0.822
log-likelihood
dF 19 18 23 24 25 24
no of censored obs
p-value for all climate variables = 0 0.000 0.000 0.000
p-value for pop climate variables = 0 0.000 0.000 0.000
p-value for ipop climate variables = 0 0.040 0.000 0.000
Note: standard errors are shown in brackets underneath the relevant coefficients with *** p < 0.001, ** p < 0.01, * p < 0.05. in addi-
tion to the variables shown, all of the equations include the following explanatory variables: ln(birthrate 1950), ln(infant mortality
1950) and dummy variables for World Bank regions.
Source: authors' estimates.
d e v e l o p m e n t a n d c l i m at e c h a n g e d i s c u s s i o n pa p e r s 25
a. Rail length--temperature, precipitation, and movement will be affected by factors such as the
temperature range, both on their own and inter- amount and distribution of tourism (both internal and
acted with GDP per person external), the dispersion and nature of natural-resource
based industries, and the availability of alternative
b. Aircraft movements--all climate variables other methods of transport.
than population-weighted temperature plus inter-
actions with urbanization Health care. Our analysis of adaptation costs relies
upon two health care inputs--the numbers of hospital
c. Container traffic--both precipitation variables, beds and physicians --as indicators used in assessing
inverse population-weighted temperature, and the baseline cost of health infrastructure (hospitals and
precipitation plus interactions with urbanization clinics) and the impact of climate change. The projec-
and GDP per person. tion equations are shown in Models (1) through (4) of
Table 7. In addition, Models (5) and (6) report equa-
In these cases, there is no easy explanation for the tions for one important indicator of health outcomes--
results since it is clear that there are multiple influences the log of the infant mortality rate.
on the indicators. For example, the number of aircraft
table 7. projection equationS for health
Ln(No of hospital beds) Ln(No of doctors) Ln(Infant mortality rate)
Variables (1) (2) (3) (4) (5) (6)
ln(population 0-14) 0.283** 0.323*** -0.520*** -0.558** 1.423*** 1.444***
(0.099) (0.060) (0.135) (0.187) (0.116) (0.129)
ln(population 15-64) 0.881*** 0.736*** 1.300*** 1.196*** -0.982*** -0.964***
(0.131) (0.064) (0.120) (0.182) (0.132) (0.143)
ln(population 65+) -0.224*** -0.128*** 0.275*** 0.391*** -0.475*** -0.508***
(0.032) (0.023) (0.045) (0.034) (0.035) (0.033)
ln(gdp per person) 1.721*** -2.680*** 0.246** -4.951*** 0.928* 0.947*
(0.327) (0.605) (0.080) (0.589) (0.390) (0.384)
ln(country size) -0.155*** -0.0984*** 0.364*** 0.147** 0.105*** 0.109***
(0.021) (0.021) (0.106) (0.046) (0.031) (0.031)
ln(% urban) -0.094 1.077*** 1.301*** -0.165*** 1.738**
(0.072) (0.211) (0.162) (0.031) (0.584)
ln(gdp per person) squared -0.100*** -0.0853*** -0.0661** -0.0661**
(0.019) (0.010) (0.023) (0.023)
ln(country size) squared 0.0173*** 0.0124*** 0.0111*** 0.0116***
(0.002) (0.001) (0.001) (0.001)
ln(gdp per person) * -0.0454*** -0.0185*** -0.0172*** -0.0186***
ln(country size) (0.011) (0.004) (0.004) (0.004)
ln(gdp per person) * -0.118*** -0.130***
ln(% urban) (0.027) (0.024)
ln(country size) * 0.0966*** 0.0642***
ln(% urban) (0.015) (0.010)
(continued)
26 t h e c os ts oF ad a p tin g to c limate c h an ge For in Fr as tr u c tu r e
table 7. projection equationS for health (continued)
Ln(No of hospital beds) Ln(No of doctors) Ln(Infant mortality rate)
Variables (1) (2) (3) (4) (5) (6)
ln(% arid) 0.0627*** 0.0462***
(0.011) (0.007)
ln(% very steep land) 0.0306*** 0.0244***
(0.005) (0.006)
ln(temperature - pop) -1.275*** -10.13*** -1.512*** -7.442*** 0.954*** 0.345
(0.084) (1.416) (0.173) (1.432) (0.142) (0.216)
ln(precipitation - pop) -0.275*** -0.357*** -0.244*** 0.054
(0.079) (0.040) (0.031) (0.047)
ln(temp range - pop) 0.011 -0.037 0.263*** 0.203***
(0.102) (0.044) (0.052) (0.053)
ln(precip range - pop) 0.168* 0.0722* -0.102*
(0.080) (0.035) (0.049)
ln(temperature - ipop) 0.102 -0.276* -5.061*** -0.262*** -0.208***
(0.110) (0.113) (1.114) (0.049) (0.047)
ln(precipitation - ipop) 0.164* 0.0756*** -0.0824* 0.0911** 0.0622**
(0.079) (0.021) (0.039) (0.034) (0.020)
ln(precip range - ipop) -0.039 0.104** -0.028
(0.058) (0.037) (0.044)
ln(% urban) * -0.463**
ln(temperature - pop) (0.139)
ln(gdp per person) * 1.022*** 0.709***
ln(temperature - pop) (0.159) (0.162)
ln(gdp per person) * 0.536***
ln(temperature - ipop) (0.125)
model pols pols pols pols pols pols
observations 1852 1852 2650 2650 2486 2486
number of countries 177 177 180 180 177 177
r-squared 0.936 0.939 0.950 0.955 0.917 0.917
log-likelihood
dF 22 17 25 23 24 22
no of censored obs
p-value for all climate vari-
0.000 0.000 0.000
ables = 0
p-value for pop climate vari-
0.000 0.000 0.000
ables = 0
p-value for ipop climate vari-
0.000 0.000 0.000
ables = 0
Note: standard errors are shown in brackets underneath the relevant coefficients with *** p < 0.001, ** p < 0.01, * p < 0.05. in addi-
tion to the variables shown, all of the equations include the following explanatory variables: ln(birthrate 1950), ln(infant mortality
1950) and dummy variables for World Bank regions.
Source: authors' estimates.
d e v e l o p m e n t a n d c l i m at e c h a n g e d i s c u s s i o n pa p e r s 27
Again, the hypothesis that climate variables have no for population-weighted temperature. How this
effect on either health inputs or health outcomes is works out country-by-country depends upon the
consistently rejected at very high confidence levels. temperature distribution across heavily and thinly
populated areas. In this case, the coefficient on
a. Hospital beds--Population-weighted temperature precipitation is negative and is linked to the popu-
on its own and interacted with GDP per person lation-weighted variable.
plus inverse population-weighted precipitation are
the key variables in this case. The overall coeffi- c. Infant-mortality--This is influenced by tempera-
cient (elasticity) on mean temperature increases-- ture, including an interaction with urbanization
from -3.07 for a low-income country with a GDP plus temperature range and inverse population-
per person of $1,000, to -0.72 for a middle- weighted precipitation. The signs of the coeffi-
income country with a GDP of $10,000 per cients on temperature can be misinterpreted.
person, and to +0.70 for a high-income country Assuming that temperature increases (or
with a GDP of $40,000 per person. Thus, it is decreases) uniformly throughout a country, the net
easy to be misled by simple assumptions about coefficient on temperature is +0.88 for a country
how climate "ought" to affect investment in with an urbanization rate of 20 percent, but +0.24
healthcare facilities that are based on experience in for a country with an urbanization rate of 80
a narrow range of countries. The coefficient on percent. Hence, an increase in mean temperature
precipitation is positive but quite small. It is is likely to increase infant mortality, but by more
possible that this reflects an increased need for in low-income countries with low levels of urban-
dispersed hospital facilities when communications ization than in middle- and high-income coun-
are subject to disruption caused by high rainfall. tries with higher levels of urbanization. These
results conform with a priori expectations. In
b. Doctors --The results for the number of doctors addition, a higher temperature range and higher
are similar to those for hospital beds, but the precipitation in rural areas tend to increase infant
influence of temperature is divided between popu- mortality, both of which seem reasonable.
lation and inverse population-weight variables.
This seems reasonable since hospitals are invari- Social infrastructure. The number of teachers is used as
ably located in urban areas, whereas doctors may the indicator for investment in schools, while the
be more dispersed, though this is not the case in number of post offices is used as one indicator for
the poorest countries. Again, the interactions with municipal infrastructure. The equations are shown in
GDP per person mean that the overall coefficients Table 8. As one would expect, one cannot reject the
switch from negative to positive at a GDP per hypothesis that the inverse population-weighted climate
person of $12,600 for inverse population-weighted variables have no effect on the number of post offices,
temperature, and at a GDP per person of $36,200 which are concentrated in areas of greater population
table 8. projection equationS for Social infraStructure
Ln(No of teachers) Ln(No of post offices)
Variables (1) (2) (3) (4)
ln(population 0-14) 0.360*** 0.397*** 0.276*** 0.366***
(0.062) (0.074) (0.065) (0.060)
ln(population 15-64) 0.670*** 0.544*** 0.363*** 0.048
(0.115) (0.132) (0.099) (0.091)
(continued)
28 t h e c os ts oF ad a p tin g to c limate c h an ge For in Fr as tr u c tu r e
table 8. projection equationS for Social infraStructure (continued)
Ln(No of teachers) Ln(No of post offices)
Variables (1) (2) (3) (4)
ln(population 65+) -0.059 0.029 0.231*** 0.445***
(0.042) (0.048) (0.032) (0.030)
ln(population)
ln(gdp per person) 0.0878*** 0.0851*** 1.977*** -1.124**
(0.022) (0.020) (0.155) (0.403)
ln(country size) -0.188*** -0.111*** -0.0338*** -0.018
(0.011) (0.014) (0.010) (0.011)
ln(% urban) -0.404*** -3.032*** -2.066*** -2.178***
(0.064) (0.429) (0.097) (0.078)
ln(gdp per person) squared -0.107*** -0.109***
(0.009) (0.009)
ln(country size) squared 0.00308*** 0.00191* 0.0139*** 0.0133***
(0.001) (0.001) (0.001) (0.001)
ln(% urban) squared -0.222*** -0.185*** -0.641*** -0.701***
(0.023) (0.025) (0.031) (0.027)
ln(gdp per person) * 0.0170*** 0.0114***
ln(country size) (0.001) (0.002)
ln(country size) * 0.0861*** 0.0729***
ln(% urban) (0.011) (0.011)
ln(% desert) 0.0318*** 0.013
(0.007) (0.007)
ln(% arid) -0.121*** -0.0989***
(0.006) (0.005)
ln(% semi-arid) 0.0423*** 0.0489***
(0.006) (0.007)
ln(% steep land) -0.0549*** -0.0488***
(0.012) (0.013)
ln(% very steep land) 0.0167*** 0.00934*** 0.0827*** 0.0667***
(0.003) (0.003) (0.006) (0.010)
ln(% no soil constraint) 0.0532*** 0.0314***
(0.003) (0.007)
ln(temperature - pop) -0.923*** -0.694*** -2.167*** -10.35***
(0.071) (0.084) (0.119) (0.859)
ln(precipitation - pop) -0.130*** -0.289*** -0.173** 0.650***
(0.027) (0.017) (0.059) (0.084)
ln(temp range - pop) -0.277*** -0.217*** -0.518*** -0.537***
(0.017) (0.026) (0.066) (0.047)
(continued)
d e v e l o p m e n t a n d c l i m at e c h a n g e d i s c u s s i o n pa p e r s 29
table 8. projection equationS for Social infraStructure (continued)
Ln(No of teachers) Ln(No of post offices)
Variables (1) (2) (3) (4)
ln(precip range - pop) -0.125*** 0.0871*** -0.058
(0.014) (0.014) (0.052)
ln(temperature - ipop) 0.222*** 0.751*** -0.110
(0.046) (0.056) (0.178)
ln(precipitation - ipop) 0.041 -0.0824*
(0.028) (0.038)
ln(precip range - ipop) -0.022 0.068
(0.031) (0.046)
ln(% urban) * -0.444***
ln(precipitation - pop) (0.043)
ln(% urban) * 0.485***
ln(precip range - pop) (0.036)
ln(% urban) * 0.825***
ln(temperature - ipop) (0.045)
ln(gdp per person) * 0.931***
ln(temperature - pop) (0.112)
ln(gdp per person) * -0.100***
ln(precipitation - pop) (0.007)
model pols pols pols pols
observations 950 950 3251 3251
number of countries 167 167 173 173
r-squared 0.979 0.982 0.909 0.911
log-likelihood
dF 25 26 29 27
no of censored obs
p-value for all climate variables = 0 0.000 0.000
p-value for pop climate variables = 0 0.000 0.000
p-value for ipop climate variables = 0 0.000 0.065
Note: standard errors are shown in brackets underneath the relevant coefficients with *** p < 0.001, ** p < 0.01, * p < 0.05. in addi-
tion to the variables shown, all of the equations include the following explanatory variables: ln(birthrate 1950), ln(infant mortality
1950) and dummy variables for World Bank regions.
Source: authors' estimates.
30 t h e c os ts oF ad a p tin g to c limate c h an ge For in Fr as tr u c tu r e
density. Otherwise the results show a mixture of Step 1--Construct baseline projections of infrastructure
climate interactions with urbanization for the number investment. The projection equations discussed in the
of teachers and with GDP per person for post offices. previous section are used to construct baseline projec-
Focusing again on mean temperature in the equation tions of the efficient stock of infrastructure assets for
for the number of teachers, the overall coefficients for a periods from 2010 to 2050 under the assumption of no
uniform increase in temperature are -0.55 for an urban- climate change. The projections of physical infrastruc-
ization rate of 20 percent and -0.02 for an urbanization ture demand are based upon standard assumptions
rate of 80 percent, so the effect of climate on teachers is about income and population growth, population struc-
much larger in low-income and rural countries. This is ture, and urbanization. The value of new investment
consistent with the well-established difficulty of equip- required for infrastructure type i for country j in period
ping and staffing rural schools. Of course, low-income t is obtained by multiplying Qijt = Qijt+1 - Qijt by
countries today are likely to be much more urban in Cij, the unit cost of infrastructure type i in country j at
2050, so that the cumulative impact of an increase in 2005 prices. The unit costs have been compiled from a
temperature on the number of teachers will be small large variety of World Bank and other sources. A stan-
even in these cases. dardized construction cost index has been used to allow
for broad cross-country differences in construction
Household size. In several cases, the amount of infra- costs, but allowances are also made for location (urban
structure is linked to the projected number of house- or rural) and other special factors. In addition to new
holds, so it is necessary to rely upon equations that investment, we have estimated the amount of invest-
project the average household size in urban and rural ment that would be required to replace infrastructure
areas. These are shown in Table 9. It seems that assets that reach the end of their economic life. There
climate does affect average household sizes. The is no realistic way of modeling the age structure of
primary mechanism is that higher temperatures are assets in situ at the beginning of the analysis.
associated with larger average sizes for both urban and Implementing a full vintage model of infrastructure is
rural households. There is also a significant but quanti- not sensible given the uncertainty about other parame-
tatively small impact of precipitation on rural household ters in the model. Hence, we have adopted a continu-
size. ous depreciation assumption--that is, in period t the
required replacement investment is (5/Li)*Qijt where Li
is the typical economic life of infrastructure of type i.
6. calculating the coSt of Step 2--Add alternative climate scenarios. The data used
for the baseline projections is supplemented with
aDaptation projections of the climate variables taken from the
climate scenarios that are being used for the whole
The calculation of the cost of adaptation involves a
EACC study. These are constructed as deltas at differ-
number of steps. The description that follows focuses
ent dates with respect to the no-climate-change base-
on investment or capital costs. A similar process is
line derived from calculations of monthly average,
required to estimate changes in the costs of operation
maximum, and minimum temperatures and precipita-
and maintenance, both for the baseline level of infra-
tion. To avoid instability in the projections arising from
structure and for changes in infrastructure resulting
path-dependency and other effects, the climate variables
from changes in climate conditions.19
for 2010 are 20-year averages centered on 2010. These
are computed for 2010, 2030, ... and then interpolated
to give the projections for the 5-year periods.
19 The analysis is formulated in terms of periods that are referred to by
the first year in the period--that is,2010�14 is shortened to 2010. No
attempt is made to allow for within-period changes in variables. Some Step 3--Project infrastructure quantities under the alterna-
of the demographic variables (urbanization and population age struc-
ture) used in the projection equations are based on period averages. tive climate scenarios. This is similar to the projection of
For other variables, such as income and total population, the added baseline infrastructure quantities in Step 1, but using
complexity of using period averages outweighs the benefits because
the main projection equations are frontier models and may overstate the climate variables for the alternative climate
the levels of infrastructure required to meet demand over relatively scenarios.
short periods.
d e v e l o p m e n t a n d c l i m at e c h a n g e d i s c u s s i o n pa p e r s 31
table 9. projection equationS for average houSeholD Size
ln(urban household size) ln(rural household size)
variables (1) (2) (3) (4)
ln(population 0-14) 0.295*** 0.353*** 0.285*** 0.304***
(0.051) (0.044) (0.047) (0.031)
ln(population 15-64) -0.247 -0.357** -0.208 -0.258*
(0.132) (0.120) (0.113) (0.109)
ln(population 65+) -0.119 -0.073 -0.120 -0.089
(0.099) (0.094) (0.084) (0.096)
ln(gdp per person) -0.038 -0.028 0.521*** 0.373**
(0.028) (0.027) (0.150) (0.112)
ln(country size) 0.0254* 0.0303* 0.0897*** 0.0798**
(0.011) (0.012) (0.022) (0.025)
ln(% urban) 0.109** 0.119** 0.017 0.044
(0.034) (0.036) (0.026) (0.023)
ln(gdp per person) squared -0.0292** -0.0200*
(0.009) (0.008)
ln(gdp per person) * -0.0112*** -0.0107***
ln(country size) (0.002) (0.003)
ln(% desert) -0.0196*** -0.0212***
(0.003) (0.003)
ln(% arid) 0.0152*** 0.0195***
(0.004) (0.004)
ln(% semi-arid) 0.0217*** 0.0248***
(0.003) (0.004)
ln(temperature - pop) 0.859*** 0.777*** 0.844*** 0.718***
(0.140) (0.138) (0.178) (0.111)
ln(precipitation - pop) -0.118* -0.118* -0.119***
(0.047) (0.053) (0.025)
ln(temp range - pop) 0.054 0.156
(0.079) (0.080)
ln(precip range - pop) 0.109 0.039
(0.066) (0.064)
ln(temperature - ipop) -0.166** -0.177*** 0.057
(0.063) (0.047) (0.043)
ln(precipitation - ipop) 0.0785*** 0.0795*** 0.0296**
(0.016) (0.022) (0.010)
ln(precip range - ipop) -0.0827*** -0.0496**
(0.019) (0.018)
model pols pols pols pols
(continued)
32 t h e c os ts oF ad a p tin g to c limate c h an ge For in Fr as tr u c tu r e
table 9. projection equationS for average houSeholD Size (continued)
observations 322 322 254 254
number of countries 126 126 112 112
r-squared 0.991 0.991 0.996 0.996
log-likelihood
dF 20 15 25 21
no of censored obs
p-value for all climate variables = 0 0.000 0.000
p-value for pop climate variables = 0 0.000 0.000
p-value for ipop climate variables = 0 0.000 0.001
Note: standard errors are shown in brackets underneath the relevant coefficients with *** p < 0.001, ** p < 0.01, * p < 0.05. in addi-
tion to the variables shown, all of the equations include the following explanatory variables: ln(birthrate 1950), ln(infant mortality
1950) and dummy variables for World Bank regions.
Source: authors' estimates.
Step 4--Apply the dose-response relationship to estimate b. Variant 2 assumes that the asset is designed to
changes in unit costs for alternative climate scenarios. We withstand the worst conditions that it might be
calculate the changes in unit costs for infrastructure exposed to over its life--that is:
type i in country j for period t, Cijt, using the climate
change deltas for the alternative climate scenarios and
the dose-response relationships discussed in Appendix Cijt = d [max(V jt ,...,V j ,t + Li )]Cij (10)
1. There is a complication that has to be considered.
This concerns the question of whether the design stan- on the assumption that the severity of storms
dards used for infrastructure are--or should increases monotonically with the relevant climate
be--forward looking. Normal engineering practice does variable(s) V.
not take account of changes in underlying climate
conditions. Thus, in designing for a 100-year storm, the
The difficulty with Variant 2 is that it implies that the
engineer looks at the characteristics of the 100-year
asset is significantly overdesigned for most of its work-
storm on the basis of evidence of storms up to the
ing life because it will only be exposed to the most
current date. Clearly, this does not allow for changes in
severe weather conditions at the very end of its life. In
the severity of the 100-year storm that might be
economic terms, Variant 2 is not the optimal solution
expected to occur over the life of the asset. There are
and it would be sensible to design for the 100-year
two possible approaches that can be adopted.
storm consistent with the expected climate at some
earlier date. There is no general solution, since the
a. Variant 1 assumes that the dose-response adjust- optimal period to look ahead depends upon both the
ment to unit costs is calculated using current expected increase in the severity of storms over the
climate conditions--that is: future and the shape of the dose-response relationship.
For consistency with the analysis of coastal protection,
we have modified Variant 2 to look ahead for a fixed
Cijt = d [V jt ]Cij (9) period of 50 years.
where d[ ] is the dose-response relationship.
d e v e l o p m e n t a n d c l i m at e c h a n g e d i s c u s s i o n pa p e r s 33
Step 5--Estimate the change in total investment costs for pro rata with maximum monthly precipitation.
the baseline projections. This yields the Delta-P estimates
of the cost of adaptation for each climate scenario with d. Changes in temperature affect the rate at which
two variants corresponding to the alternatives at Step 4 oxygen levels recover in rivers to which the efflu-
above. ent is discharged from waste water treatment
plants. Thus, a higher level of BOD removal is
Step 6--Estimate the change in investment costs due to the required to maintain the quality of receiving
difference between the baseline infrastructure quantities and waters. This implies higher consumption of elec-
the alternative climate scenario quantities. This yields the tricity or use of chemicals at treatment plants.
Delta-Q estimates of the cost of adaptation for each The increase in O&M costs is linked to the
climate scenario. increase in average temperatures and is incorpo-
rated in our estimates of the cost of adaptation.
Step 7--Special adjustments. We have incorporated some
special factors in the calculation of the costs of adapting These steps are followed in deriving the estimates of
to climate change that could not be represented by the Cijt used in calculating the Delta-P costs of adapta-
general dose-response relationships. These are: tion in the first part of equation (2):
a. For electricity generation, we have taken account
of the decrease in the operating efficiency of exist- I jt [1] = Cijt [Qijt +1 - Qijt + Rijt ] (11)
i
ing thermal power plants as the ambient tempera-
ture increases. The effect is documented in the with the Qijt, etc. given by the baseline projections of
literature for ambient temperatures above 15�C, infrastructure investment. The Delta-Q costs of adapta-
though it is possible to design new power plants tion are defined by:
to include absorption chillers to bring the ambient
temperature of the air entering turbines down to
15�C for a relatively minor penalty on operating
I jt [2] = (Cijt + Cijt )[Qijt +1 - Qijt + R(12)
ijt ]
i
costs.
I jt [2] = (Cijt + Cijt )[Qijt +1 - Qijt + Rijt ]
i
b. Another special factor for electricity generation
concerns the efficiency and feasibility of water in which Qijt are obtained from the changes in the
cooling as temperatures increase, because of limits baseline investments associated with the alternative
on the temperature rise that can be permitted in climate scenarios.
the receiving waters. Dry cooling can be adopted
either in parallel with wet cooling or as an alterna- Equation (12) yields engineering estimates of the
tive in particularly hot or dry locations. The Delta-Q costs, which reflect an assumption that coun-
model assumes that an increasing proportion of tries will respond to climate change by building more or
power plants will rely upon dry cooling as average less infrastructure. However, it should be noted that
temperatures rise. more cost-effective options may be available. In
another paper, we examine one of these options in more
c. The operating costs of water treatment plants may detail for the water sector (Hughes, Chinowsky, and
increase as a result of climate change. Primary Strzepek 2010). We show that the welfare cost of using
attention has focused on the amount of chemicals water abstraction fees to limit increases in demand for
used for flocculation if the levels of turbidity and water may be lower than the cost of building additional
suspended solids in raw water rise. This is likely capacity for water and wastewater treatment. Our
to be associated with changes in levels of peak results demonstrate that this economic approach can
flow in rivers from which water is abstracted, so reduce the cost of adaptation in the water sector by a
the model allows for cost of chemicals to increase substantial amount relative to the engineering approach
of building more infrastructure assets in response to an
34 t h e c os ts oF ad a p tin g to c limate c h an ge For in Fr as tr u c tu r e
increase in demand for water. 7. eStimateS of the coStS of
aDaptation
There is an obvious instrument--water abstraction
fees--available in the water sector. Similar policies Our estimates of the costs of adaptation for electricity
could be followed for some other types of infrastruc- and water services are shown in Tables 10 to 14. To
ture--for example, energy and transport. As a conse- facilitate comparisons all figures in the tables are
quence, the estimates of the Delta-Q costs of adaptation presented as average costs per year at 2005 prices over
will tend to overstate the economic costs of adaptation the relevant period--that is, for 2010�50 as a whole or
in countries that face an increase in demand for infra- for each decade--with no discounting. Figures are
structure as a consequence of climate change. Since the rounded to the nearest $1 billion per year to avoid any
effect is one-sided--that is, an economic approach can impression of spurious accuracy. As a consequence,
reduce costs when the demand for infrastructure sums of the separate numbers may differ from the rele-
increases, but would not be required when the demand vant totals due to rounding. The Delta-P increases in
for infrastructure decreases--it is safe to conclude that investment, O&M, and total costs for the two climate
the engineering estimates of the Delta-Q costs of adap- scenarios are shown by infrastructure category and
tation presented in the next section represent an upper country class in Table 10. The baseline costs without
bound on the costs of following a cost-effective strategy any climate change are shown as a point of reference.
of adaptation. In all cases, the costs of adaptation are substantially
table 10. Delta-p coStS of aDaptation by category anD country claSS for 2010�50
(uS$ billion per year at 2005 prices, no discounting)
Lower middle Upper middle
NCAR scenario Cost type Low income income income High income Total
1. power & telephones capital cost 0 0 0 1 2
o&m cost 0 0 0 0 0
total cost 0 1 0 1 2
Baseline cost 132 173 92 304 701
2. Water & sewers capital cost 0 0 0 0 0
o&m cost 0 0 0 0 1
total cost 0 0 0 0 1
Baseline cost 119 154 95 193 562
3. roads capital cost 3 2 1 6 11
o&m cost 0 0 0 0 0
total cost 3 2 1 7 12
Baseline cost 67 56 60 215 398
4. other transport capital cost 0 0 1 0 1
o&m cost 0 0 3 1 5
total cost 0 0 4 1 6
Baseline cost 8 18 86 31 142
5. health & schools capital cost 0 1 0 1 2
o&m cost 0 0 0 0 0
total cost 0 1 0 1 2
Baseline cost 36 121 92 302 551
(continued)
d e v e l o p m e n t a n d c l i m at e c h a n g e d i s c u s s i o n pa p e r s 35
table 10. Delta-p coStS of aDaptation by category anD country claSS for 2010�50
(uS$ billion per year at 2005 prices, no discounting) (continued)
Lower middle Upper middle
NCAR scenario Cost type Low income income income High income Total
6. urban infrastructure capital cost 8 6 2 5 20
o&m cost 0 0 0 0 0
total cost 8 6 2 5 20
Baseline cost 287 219 209 841 1,555
total capital cost 11 9 4 14 37
o&m cost 0 0 4 2 6
total cost 11 9 8 15 43
Baseline cost 649 740 634 1,887 3,910
CSIRO scenario
1. power & telephones capital cost 0 0 0 1 1
o&m cost 0 0 0 0 0
total cost 0 0 0 1 2
Baseline cost 132 173 92 304 701
2. Water & sewers capital cost 0 0 0 0 0
o&m cost 0 0 0 0 1
total cost 0 0 0 0 1
Baseline cost 119 154 95 193 562
3. roads capital cost 1 1 0 5 6
o&m cost 0 0 0 0 0
total cost 1 1 0 5 7
Baseline cost 67 56 60 215 398
4. other transport capital cost 0 0 0 0 1
o&m cost 0 0 2 1 3
total cost 0 0 2 1 4
Baseline cost 8 18 86 31 142
5. health & schools capital cost 0 0 0 1 2
o&m cost 0 0 0 0 0
total cost 0 0 0 1 2
Baseline cost 36 121 92 302 551
6. urban infrastructure capital cost 4 2 1 4 11
o&m cost 0 0 0 0 0
total cost 4 2 1 4 11
Baseline cost 287 219 209 841 1,555
total capital cost 5 4 2 10 21
o&m cost 0 0 2 1 4
total cost 5 4 4 11 25
Baseline cost 649 740 634 1,887 3,910
Source: authors' estimates
36 t h e c os ts oF ad a p tin g to c limate c h an ge For in Fr as tr u c tu r e
higher for the NCAR scenario than for the CSIRO the low-income countries, the costs of adaptation are
scenario, so we will focus on the NCAR figures. The highest for East Asia (EAP) and for South Asia (SAS),
total Delta-P cost of adaptation over 40 years is about 1 reflecting their populations and aggregate income. The
percent of the baseline cost for all countries. The ratio costs for Europe and Central Asia (ECA) are higher
of adaptation costs to baseline costs is highest for low- than might have been anticipated, but this reflects the
income countries at about 1.7 percent and is lowest for initial level of infrastructure leading to relatively high
high-income countries. O&M costs. Sub-Saharan Africa (SSA) has the high-
est ratio of adaptation costs to baseline costs at 2.3
Table 11 shows the same information for developing percent. Broken down by infrastructure category and
countries disaggregated by World Bank region. Outside region, the heaviest burden of adaptation is for other
table 11. Delta-p coStS of aDaptation by infraStructure category anD worlD
bank region for 2010�50 (uS$ billion per year at 2005 prices, no discounting)
NCAR scenario Cost type EAP ECA LCA MNA SAS SSA Total
capital
1. power & telephones 0 0 0 0 0 0 1
cost
o&m cost 0 0 0 0 0 0 0
total cost 0 0 0 0 0 0 1
Baseline
137 75 41 24 79 41 397
cost
capital
2. Water & sewers 0 0 0 0 0 0 0
cost
o&m cost 0 0 0 0 0 0 0
total cost 0 0 0 0 0 0 1
Baseline
115 71 54 25 81 22 368
cost
capital
3. roads 1 0 1 0 2 1 5
cost
o&m cost 0 0 0 0 0 0 0
total cost 1 0 1 0 2 1 5
Baseline
36 37 31 13 44 23 183
cost
capital
4. other transport 0 1 0 0 0 0 1
cost
o&m cost 0 3 0 0 0 0 4
total cost 0 4 0 0 0 0 5
Baseline
16 80 6 2 4 4 111
cost
capital
5. health & schools 1 0 0 0 0 0 1
cost
o&m cost 0 0 0 0 0 0 0
total cost 1 0 0 0 0 0 1
Baseline
93 49 52 20 25 9 249
cost
capital
6. urban infrastructure 5 1 2 0 5 2 15
cost
o&m cost 0 0 0 0 0 0 0
(continued)
d e v e l o p m e n t a n d c l i m at e c h a n g e d i s c u s s i o n pa p e r s 37
table 11. Delta-p coStS of aDaptation by infraStructure category anD worlD
bank region for 2010�50 (uS$ billion per year at 2005 prices, no discounting) (continued)
NCAR scenario Cost type EAP ECA LCA MNA SAS SSA Total
total cost 5 1 2 0 5 2 15
Baseline
163 159 78 32 252 31 714
cost
capital
total 8 2 3 1 8 3 24
cost
o&m cost 0 3 0 0 0 0 4
total cost 8 5 3 1 8 3 28
Baseline
560 470 262 116 485 130 2,023
cost
CSIRO scenario
capital
1. power & telephones 0 0 0 0 0 0 1
cost
o&m cost 0 0 0 0 0 0 0
total cost 0 0 0 0 0 0 1
Baseline
137 75 41 24 79 41 397
cost
capital
2. Water & sewers 0 0 0 0 0 0 0
cost
o&m cost 0 0 0 0 0 0 0
total cost 0 0 0 0 0 0 0
Baseline
115 71 54 25 81 22 368
cost
capital
3. roads 0 0 0 0 1 0 2
cost
o&m cost 0 0 0 0 0 0 0
total cost 0 0 0 0 1 0 2
Baseline
36 37 31 13 44 23 183
cost
capital
4. other transport 0 0 0 0 0 0 0
cost
o&m cost 0 2 0 0 0 0 2
total cost 0 2 0 0 0 0 3
Baseline
16 80 6 2 4 4 111
cost
capital
5. health & schools 0 0 0 0 0 0 1
cost
o&m cost 0 0 0 0 0 0 0
total cost 0 0 0 0 0 0 1
Baseline
93 49 52 20 25 9 249
cost
capital
6. urban infrastructure 2 1 1 0 3 1 7
cost
o&m cost 0 0 0 0 0 0 0
total cost 2 1 1 0 3 1 7
(continued)
38 t h e c os ts oF ad a p tin g to c limate c h an ge For in Fr as tr u c tu r e
table 11. Delta-p coStS of aDaptation by infraStructure category anD worlD
bank region for 2010�50 (uS$ billion per year at 2005 prices, no discounting) (continued)
CSIRO scenario
NCAR scenario Cost type EAP ECA LCA MNA SAS SSA Total
Baseline
163 159 78 32 252 31 714
cost
capital
total 3 1 1 0 4 1 11
cost
o&m cost 0 2 0 0 0 0 3
total cost 3 3 1 1 4 1 14
Baseline
560 470 262 116 485 130 2,023
cost
Source: authors' estimates.
transport in the ECA region, largely because of the Tables 13 and 14 give details of the costs of adaptation
high level of O&M costs. This is followed by roads in by infrastructure category and country class or region
South Asia, but in both cases the cost of adaptation is when the definition of the cost of adaptation is
little more than 5 percent of baseline costs. extended to include both the Delta-P and the Delta-Q
components in the analysis. Recall that the Delta-Q
Table 12 shows the breakdown of the Delta-P costs of costs are driven by the increase or decrease in the
demand for infrastructure associated with the projected
adaptation for all infrastructure by decade. The relative
changes in climate. Table 13 shows that the Delta-Q
cost of adaptation increases gradually from about 1
changes are negative for the world as a whole in both
percent of baseline costs for 2010�19 to about 1.6
scenarios. This means that total expenditure on infra-
percent for 2040�49. One component of this increase is structure will fall as a consequence of climate change,
the rise in O&M costs in the ECA region, which has though more investment may be required in some coun-
already been highlighted, but even for all regions other tries and some sectors. However, the fall in total expen-
than ECA there is an increase from about 1.2 percent diture is most important for high-income countries, so
of baseline costs in the first decade to 1.6 percent in the that the overall scale of the Delta-Q adjustments for
final decade. developing countries is similar to that of the Delta-P
table 12. Delta-p coStS of aDaptation by DecaDe anD worlD bank region for all
infraStructure (uS$ billion per year at 2005 prices, no discounting)
NCAR scenario Cost type EAP ECA LCA MNA SAS SSA Total
2010-19 capital cost 5 2 1 1 4 1 13
o&m cost 0 0 0 0 0 0 1
total cost 5 2 1 1 4 1 14
Baseline cost 417 395 197 77 273 79 1,438
2020-29 capital cost 7 2 2 1 6 2 21
o&m cost 0 2 0 0 0 0 3
total cost 7 5 2 1 7 2 24
Baseline cost 505 452 238 101 396 109 1,801
(continued)
d e v e l o p m e n t a n d c l i m at e c h a n g e d i s c u s s i o n pa p e r s 39
table 12. Delta-p coStS of aDaptation by DecaDe anD worlD bank region for all
infraStructure (uS$ billion per year at 2005 prices, no discounting) (continued)
NCAR scenario Cost type EAP ECA LCA MNA SAS SSA Total
2030-39 capital cost 9 2 3 1 9 3 27
o&m cost 0 5 0 0 0 0 6
total cost 9 7 3 1 9 3 33
Baseline cost 608 497 283 127 550 146 2,213
2040-49 capital cost 11 2 4 1 11 5 34
o&m cost 0 6 0 0 0 0 7
total cost 11 8 4 1 12 5 41
Baseline cost 710 538 330 156 719 187 2,641
CSIRO scenario
2010-19 capital cost 3 1 1 0 1 0 6
o&m cost 0 0 0 0 0 0 1
total cost 3 1 1 0 1 1 7
Baseline cost 417 395 197 77 273 79 1,438
2020-29 capital cost 3 1 1 0 2 1 7
o&m cost 0 1 0 0 0 0 2
total cost 3 2 1 1 2 1 9
Baseline cost 505 452 238 101 396 109 1,801
2030-39 capital cost 3 1 1 0 4 1 12
o&m cost 0 3 0 0 0 0 4
total cost 4 4 1 1 4 1 15
Baseline cost 608 497 283 127 550 146 2,213
2040-49 capital cost 4 2 1 1 8 2 19
o&m cost 0 3 0 0 0 0 4
total cost 4 6 2 1 8 2 23
Baseline cost 710 538 330 156 719 187 2,641
Source: authors' estimates.
costs. This is illustrated in the breakdown of adaptation The striking feature of the results--taking account of
costs by World Bank region in Table 14, which shows both Delta-P and Delta-Q costs of adaptation--is how
that the sum of Delta-P and Delta-Q costs of adapta- small the overall costs of adaptation are relative to the
tion is close to zero for all developing countries. The baseline costs. The impact of climate change is far from
net costs of adaptation per year over the full period vary evenly distributed, but even in the worst-affected
from a negative cost (that is, a saving) of $7 billion per region--MNA--the net cost is little more than 2
year for East Asia to a positive cost of $2 billion per percent of baseline expenditures. Thus, in practice the
year for the Middle East and North Africa (MNA). cost of adaptation for infrastructure is well within all of
the margins of error inherent in this type of exercise.
40 t h e c os ts oF ad a p tin g to c limate c h an ge For in Fr as tr u c tu r e
table 13. total coStS of aDaptation by infraStructure category anD country
claSS for 2010�50 (uS$ billion per year at 2005 prices, no discounting)
Lower middle Upper middle
NCAR scenario Cost type Low income High income Total
income income
1. power & telephones delta-p 0 1 0 1 2
delta-Q -4 -2 -2 -20 -29
delta-p+delta-Q -4 -2 -2 -19 -27
Baseline cost 132 173 92 304 701
2. Water & sewers delta-p 0 0 0 0 1
delta-Q -5 1 1 -1 -4
delta-p+delta-Q -5 1 1 -1 -3
Baseline cost 119 154 95 193 562
3. roads delta-p 3 2 1 7 12
delta-Q 0 -2 -3 -23 -27
delta-p+delta-Q 3 0 -2 -17 -16
Baseline cost 67 56 60 215 398
4. other transport delta-p 0 0 4 1 6
delta-Q 0 0 -4 -1 -6
delta-p+delta-Q 0 0 0 0 0
Baseline cost 8 18 86 31 142
5. health & schools delta-p 0 1 0 1 2
delta-Q 0 -1 1 3 2
delta-p+delta-Q 0 0 1 4 4
Baseline cost 36 121 92 302 551
6. urban infrastructure delta-p 8 6 2 5 20
delta-Q -3 -5 -3 -10 -21
delta-p+delta-Q 5 0 -1 -5 -1
Baseline cost 287 219 209 841 1,555
total delta-p 11 9 8 15 43
delta-Q -13 -10 -10 -53 -86
delta-p+delta-Q -1 -1 -2 -38 -43
Baseline cost 649 740 634 1,887 3,910
CSIRO scenario
1. power & telephones delta-p 0 0 0 1 2
delta-Q -4 1 -1 -4 -8
delta-p+delta-Q -4 2 0 -4 -6
Baseline cost 132 173 92 304 701
2. Water & sewers delta-p 0 0 0 0 1
delta-Q -2 1 1 -8 -8
delta-p+delta-Q -2 1 1 -7 -7
Baseline cost 119 154 95 193 562
3. roads delta-p 1 1 0 5 7
(continued)
d e v e l o p m e n t a n d c l i m at e c h a n g e d i s c u s s i o n pa p e r s 41
table 13. total coStS of aDaptation by infraStructure category anD country
claSS for 2010�50 (uS$ billion per year at 2005 prices, no discounting) (continued)
CSIRO scenario Lower middle Upper middle
NCAR scenario Cost type Low income High income Total
income income
delta-Q -2 -2 -4 -23 -30
delta-p+delta-Q -1 -1 -4 -18 -24
Baseline cost 67 56 60 215 398
4. other transport delta-p 0 0 2 1 4
delta-Q 0 0 -2 -1 -3
delta-p+delta-Q 0 0 0 1 0
Baseline cost 8 18 86 31 142
5. health & schools delta-p 0 0 0 1 2
delta-Q -1 -2 0 1 -2
delta-p+delta-Q -1 -1 0 1 0
Baseline cost 36 121 92 302 551
6. urban infrastructure delta-p 4 2 1 4 11
delta-Q -5 -5 -3 -10 -24
delta-p+delta-Q -1 -3 -3 -7 -13
Baseline cost 287 219 209 841 1,555
total delta-p 5 4 4 11 25
delta-Q -14 -7 -9 -45 -75
delta-p+delta-Q -8 -3 -5 -34 -50
Baseline cost 649 740 634 1,887 3,910
Source: authors' estimates.
table 14. total coStS of aDaptation by infraStructure category anD region for
2010�50 (uS$ billion per year at 2005 prices, no discounting)
NCAR scenario Cost type EAP ECA LCA MNA SAS SSA Total
1. power & telephones delta-p 0 0 0 0 0 0 1
delta-Q -5 -1 1 2 -3 -1 -9
delta-p+delta-Q -5 -1 1 2 -3 -1 -7
Baseline cost 137 75 41 24 79 41 397
2. Water & sewers delta-p 0 0 0 0 0 0 1
delta-Q -3 4 0 1 -4 -1 -3
delta-p+delta-Q -3 4 0 1 -4 -1 -3
Baseline cost 115 71 54 25 81 22 368
3. roads delta-p 1 0 1 0 2 1 5
delta-Q -1 -1 -1 0 0 0 -4
delta-p+delta-Q 0 -1 0 0 2 1 1
Baseline cost 36 37 31 13 44 23 183
(continued)
42 t h e c os ts oF ad a p tin g to c limate c h an ge For in Fr as tr u c tu r e
table 14. total coStS of aDaptation by infraStructure category anD region for
2010�50 (uS$ billion per year at 2005 prices, no discounting) (continued)
NCAR scenario Cost type EAP ECA LCA MNA SAS SSA Total
4. other transport delta-p 0 4 0 0 0 0 5
delta-Q 0 -4 0 0 0 0 -5
delta-p+delta-Q 0 0 0 0 0 0 0
Baseline cost 16 80 6 2 4 4 111
5. health & schools delta-p 1 0 0 0 0 0 1
delta-Q -1 1 0 0 0 0 -1
delta-p+delta-Q 0 1 0 0 0 0 1
Baseline cost 93 49 52 20 25 9 249
6. urban infrastructure delta-p 5 1 2 0 5 2 15
delta-Q -4 -2 -1 -1 -2 0 -11
delta-p+delta-Q 1 -2 0 -1 3 1 4
Baseline cost 163 159 78 32 252 31 714
total delta-p 8 5 3 1 8 3 28
delta-Q -15 -5 -2 1 -10 -2 -33
delta-p+delta-Q -7 0 1 2 -2 1 -5
Baseline cost 560 470 262 116 485 130 2,023
CSIRO scenario
1. power & telephones delta-p 0 0 0 0 0 0 1
delta-Q -1 0 0 1 -3 -2 -4
delta-p+delta-Q -1 1 0 1 -3 -2 -3
Baseline cost 137 75 41 24 79 41 397
2. Water & sewers delta-p 0 0 0 0 0 0 0
delta-Q -1 2 0 1 -2 0 0
delta-p+delta-Q -1 2 0 1 -2 0 0
Baseline cost 115 71 54 25 81 22 368
3. roads delta-p 0 0 0 0 1 0 2
delta-Q -1 -2 -2 -1 -1 -1 -8
delta-p+delta-Q -1 -2 -2 0 0 0 -6
Baseline cost 36 37 31 13 44 23 183
4. other transport delta-p 0 2 0 0 0 0 3
delta-Q 0 -2 0 0 0 0 -3
delta-p+delta-Q 0 0 0 0 0 0 0
Baseline cost 16 80 6 2 4 4 111
5. health & schools delta-p 0 0 0 0 0 0 1
delta-Q -1 0 0 -1 -1 0 -2
delta-p+delta-Q -1 0 0 0 0 0 -1
Baseline cost 93 49 52 20 25 9 249
6. urban infrastructure delta-p 2 1 1 0 3 1 7
(continued)
d e v e l o p m e n t a n d c l i m at e c h a n g e d i s c u s s i o n pa p e r s 43
table 14. total coStS of aDaptation by infraStructure category anD region for
2010�50 (uS$ billion per year at 2005 prices, no discounting) (continued)
NCAR scenario
CSIRO scenario Cost type EAP ECA LCA MNA SAS SSA Total
delta-Q -4 -3 -1 -1 -4 -1 -13
delta-p+delta-Q -2 -2 -1 -1 -1 0 -6
Baseline cost 163 159 78 32 252 31 714
total delta-p 3 3 1 1 4 1 14
delta-Q -8 -5 -3 0 -10 -3 -29
delta-p+delta-Q -5 -2 -1 1 -7 -2 -16
Baseline cost 560 470 262 116 485 130 2,023
Source: authors' estimates.
8. concluSion Delta-Q effects may be positive or negative--increasing
or decreasing the costs of adaptation--in different
The work reported in this paper represents the most countries. Summed by region, the Delta-Q totals are
extensive and careful effort that has been made to esti- negative in all regions except MNA. The results of our
mate the costs of adapting to climate change in the econometric analysis do not dictate that climate change
infrastructure sector at a global level. Our primary will have the effect of reducing demand for generating
conclusion is that the cost of adapting to climate capacity or roads. The equations contain complex inter-
change, given the baseline level of infrastructure provi- actions between income and various climate variables
sion, is no more than 1�2 percent of the total cost of --not merely temperature--with both population-
providing that infrastructure. While there are differ- weighted and inverse population-weighted variants. It
ences across regions and sectors, the pattern is clear and does not seem plausible that these effects are merely
unambiguous--the cost of adaptation is small in rela- capturing the influence of one or more omitted vari-
tion to other factors that may influence the future costs ables. Hence, estimates of the costs of adaptation that
of infrastructure. We accept that we may have omitted ignore the potential impact of climate change on the
or underestimated some of the costs of adaptation. On demand side may give a rather partial view of the over-
the other hand, we have consistently tried to err on the all picture.
generous side--increasing our estimates of probable
costs when there is reasonable doubt. Further, it can be
shown that an economic rather than an engineering
approach to adaptation when climate change increases referenceS
the demand for infrastructure will reduce the Delta-Q
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d e v e l o p m e n t a n d c l i m at e c h a n g e d i s c u s s i o n pa p e r s 45
appenDix 1. Derivation of 1. Estimation of Dose-Response Values for
the climate DoSe-reSponSe Construction Costs
relationShipS To generate dose-response values for infrastructure
construction costs, we employed two general
Paul Chinowsky approaches. The first estimates dose-response values
(Department of Civil, Environmental and Architec- based on the cost associated with the change in the
tural Engineering, University of Colorado) typical building code update, while the second more
directly estimates the incremental costs of climate stres-
Jason Price & Jim Neumann sors and design changes. We use the building code
(Industrial Economics Inc, Cambridge, Mass) approach to generate dose-response values for paved
roads, buildings, and transmission towers and the latter
for bridges and unpaved roads.
The dose-response relationship between climate change
and the cost of building and maintaining infrastructure Our assessment of dose-response values for infrastruc-
is a central component of the World Bank's assessment ture construction costs assumes perfect foresight with
of infrastructure adaptation costs. The magnitude of respect to climate change. Therefore, these dose-
the dose-response relationship is likely to vary both by response values represent the relationship between
infrastructure type and by country. Variation in this infrastructure construction costs at the time of
relationship by infrastructure type reflects, among other construction and the changes in climate projected
factors, differences in the materials with which different during the infrastructure's lifespan.
types of infrastructure are constructed and the ways in
which different types of infrastructure are used; for A. Building Code Methodology
example, buildings often provide heating and cooling.
In addition, variation in the dose-response relationship The building code methodology is based on the premise
by country reflects inter-country variation in labor and that a major update of design standards results in a 0.8
materials costs as well as terrain; for example, varying percent increase in construction costs (FEMA 1998).
degrees of flat versus mountainous terrain. The readily available data suggest that such code
updates would occur with every 10 centimeter (cm)
The data and methods supporting the World Bank's increase in precipitation for paved roads and buildings;
assessment of dose-response values by infrastructure therefore, we express the precipitation dose-response
type and country are outlined in the sections below. relationship for these specific types of infrastructure as
This information is presented separately for infrastruc- follows:
ture construction costs and infrastructure maintenance
costs. Exhibits 1 and 2 describe the specific dose-
response relationships analyzed. We note that the dose-
(1) C P , BPR = 0.8%(B BPR )
response values estimated for both construction costs where
and maintenance costs are based on the cost of building
and maintaining infrastructure in the United States. To CP,BPR = change in building and paved road construc-
develop dose-response values specific to individual tion costs associated with a 10 cm change in annual
developing countries, we scaled the U.S.-based cost esti- precipitation
mates using an inter-country construction cost index
published by Compass International Consultants Inc. BBPR = base construction costs for buildings and paved
(2009). The country-specific values that make up this roads
index represent average construction costs for each
country relative to costs in the United States. Based on published construction cost information, we
assume base construction costs of $185 per square foot
for medical buildings as a base for public facilities
46 t h e c os ts oF ad a p tin g to c limate c h an ge For in Fr as tr u c tu r e
exhibit 1 -- DoSe-reSponSe DeScriptionS for conStruction coStS
Precipitation Dose-Response Temperature Dose-Response Wind Dose-Response
Bridges change in construction costs per not estimated. impact likely to not estimated. impact likely to
bridge per 1 foot increase in bridge be minimal. be minimal.
height.
Paved Roads change in costs of constructing a km change in cost of constructing not estimated. impact likely to
of paved road per 10 cm change in a km of paved road per step- be minimal.
annual precipitation projected during wise increase in the maximum
lifespan relative to baseline climate. of monthly maximum tempera-
dose-response represents change in ture values projected during
costs for every 10 cm increment. lifespan relative to baseline cli-
mate. the first increase occurs
after a 1 degree celsius
change in maximum tempera-
ture. every other step occurs 3
degrees celsius beyond that.
Unpaved Roads change in construction costs per km not estimated. impact likely to not estimated. impact likely to
per 1% change in the maximum of the be minimal. be minimal.
monthly maximum precipitation values
projected during lifespan relative to
baseline climate.
Transmission Poles not estimated. impact likely to be not estimated. impact likely to percent change in costs per 15
minimal. be minimal. mph (~24 kmh) increase in the
maximum of the monthly maxi-
mum wind speeds projected
during lifespan, relative to base-
line climate.
Buildings change in costs per square foot, per change in costs per square not estimated. impact likely to
10 cm change in annual precipitation foot, per 0.5 degree change be minimal.
projected during lifespan. celsius in annual average tem-
perature during lifespan, rela-
tive to baseline climate.
exhibit 2 -- DoSe-reSponSe DeScriptionS for maintenance coStS
Precipitation Temperature
Paved Roads - Existing change in annual maintenance costs per change in annual maintenance costs per km per 1 degree
km per 10 cm change in annual rainfall change celsius in maximum of monthly maximum temper-
projected during lifespan relative to base- ature projected during lifespan.
line climate.
Paved Roads - Newly paved roads constructed after 2010 would have no maintenance impact if designed for changes in cli-
Constructed mate expected during their lifetime.
Unpaved Roads change in annual maintenance costs per not estimated. impact likely to be minimal.
1% change in maximum of monthly maxi-
mum precipitation projected during
lifespan.
Railroads not estimated. impact likely to be mini- change in annual maintenance costs per km per 1 degree
mal. celsius change in maximum of monthly maximum temper-
ature projected during lifespan.
Buildings - Existing change in annual maintenance costs per change in annual maintenance costs per square foot per
square foot per 10 cm change in annual 1 degree change celsius in annual average temperature
rainfall projected during lifespan. projected during lifespan.
Buildings - Newly Buildings constructed after 2010 would have no maintenance impact if designed for changes in climate
Constructed expected during their lifetime.
d e v e l o p m e n t a n d c l i m at e c h a n g e d i s c u s s i o n pa p e r s 47
(DCD 2007) and $621,000 per kilometer (km) for projected, we assume a 0.8 increase in construction costs
paved roads, the latter of which represents the average due to a design standard update.
cost per km of constructing a 2-lane collector road in
rural areas (FDOT 2009a).20 The readily available data suggests several relationships
will have no impact or minimal impact in these catego-
The code update methodology that we employed for ries as follows: 1) no impact from wind on paved roads
temperature effects is similar to the approach outlined or buildings, 2) no impact from temperature on trans-
in Equation 1 for precipitation. Unlike the code mission poles, and finally 3) no impact from precipita-
update approach for precipitation, we do not apply the tion on transmission poles.
full 0.8 percent cost increase of a code update to each
incremental change in temperature. Instead, we scale B. Example of Building Code Methodology
the 0.8 percent value to reflect the portion of construc-
tion costs likely to be associated with temperature Two examples are presented here to illustrate the appli-
effects. Based on data published by Whitestone cation of the building code methodology to new
Research (2008), we assume that 28 percent of the costs construction, a building example for precipitation and a
associated with a code update for buildings are related paved road example for temperature. For the former,
to HVAC equipment affected by temperature. assume that a new hospital is to be built in a location
Similarly, research into the effects of temperature on that has a base precipitation level of 100 cm per year. It
roads provides a guideline of 36 percent of the costs for is projected that due to climate change, the location will
a code update for roads is temperature-related (Miradi have a 15 cm increase during the 40-year anticipated
2004). Based on these values, we assume a 0.22 percent lifespan of the building. Given the 10cm threshold for
increase in building construction costs for each incre- a building code update, the design of the structure
mental change in temperature and a 0.29 percent would anticipate the precipitation increase and the asso-
increase in paved road construction costs for such ciated building code update. Essentially, the building
changes. Based on professional judgment and the will be overbuilt for Year 0 to anticipate the need later
design parameters for HVAC systems, which are typi- in the lifespan to accommodate the increased precipita-
cally based on the number of degree days per year tion. The cost of this overbuild will be the cost of one
(NOAA 2009), we assume that the 0.22 percent value is code update for the 10 cm increase, or 0.8 percent of
applied to building costs for each 0.5 degree Celsius the base construction costs.
increase in average annual temperature. For paved
roads, we apply the 0.29 percent increase as a step func- In the context of temperature, consider the example of
tion, with the first increase occurring after a 1 degree paved road construction. Using the 36 percent relative
Celsius increase in temperature and later increases impact discussed above, the standard 0.8 percent cost
occurring with each 3-degree increase in temperature. increase for a code update is modified by this percent-
This reflects the need for new pavement binders with age resulting in a modified value of 0.29 percent of base
every 3-degree increase in temperature and a change in construction costs. However, to apply this to new
practice for a 1-degree change as an initial safety factor construction, the guidelines for pavement design are
(Blacklidge Emulsions, Inc. 2009). brought into the equation. Specifically, temperature
increases require new pavement binders every 3 degrees
We also apply the building code methodology to trans- Celsius (Blacklidge Emulsions, Inc. 2009). Therefore,
mission line towers, but instead of precipitation and for a new construction scenario where the maximum
temperature, wind is the climate stressor of concern. temperature will increase 2 degrees over the 20-year
For every 15 mile per hour (~24 km per hour) increase lifespan of the road, a cost increase of 0.29 percent of
in the maximum of the maximum monthly wind speeds base construction costs is applied after the first 1 degree
to account for an initial safety factor built into the
design. Since the increase does not total an additional 3
20 Both of these base cost values represent the costs of construction in degrees, the total increase from the temperature impact
the United States. We developed values specific to other countries
based on an inter-country construction cost index published by
is 0.29 percent of base construction costs.
Compass International Consultants Inc. (2009), as indicated above.
48 t h e c os ts oF ad a p tin g to c limate c h an ge For in Fr as tr u c tu r e
C. Direct Response Methodology most significant design changes associated with an
increase in clearance would involve changes to bridge-
For bridges and unpaved roads, we use a more direct
deck support structures, which account for approxi-
approach for estimating the cost impact of changes in
mately 50 percent of bridge construction costs (Kinsella
climate stressors. Under this approach, we directly
and McGuire 2005). In addition, based on the standard
relate changes in infrastructure construction costs to
16-foot clearance for bridges on highways (FHWA
specific changes in climate or infrastructure design
2009), a one-foot increase in bridge clearance would
requirements. In general terms, this approach is
represent a 6.25 percent increase. Assuming that the
summarized by Equation 2.
increase in costs for bridge foundations would be
proportional to the change in clearance, we assume that
construction costs for the bridge support structures
(2) CURBT = M � BURBT
would increase by 6.25 percent with each 1-foot
where CURB = change in construction costs for increase in clearance. Because support structures repre-
bridges and unpaved roads associated with a unit sent approximately 50 percent of bridge construction
change in climate stress or design requirements costs, we assume that the total construction costs for a
bridge would increase by approximately 3.13 percent
M = cost multiplier (50 percent x 6.25 percent) with each one-foot increase
in clearance.
BURB = base construction costs for
bridges and unpaved roads The base cost of a bridge is likely to vary significantly
due to differences in the number of lanes per bridge and
Implementation of the approach represented by bridge length. For the purposes of this analysis, we use
Equation 2 is somewhat different for unpaved roads the costs of a 2-lane bridge spanning 100 feet.
than it is for bridges. For unpaved roads, we express the Assuming an average lane width of 12 feet, this trans-
dose-response relationship represented by Equation 2 as lates to a bridge deck with an area of approximately
the change in construction costs associated with a 1 2,400 square feet. Based on a unit cost of $220 per
percent change in maximum monthly precipitation. square foot (FDOT 2009b), we estimate that the total
Research findings have demonstrated that 80 percent of base construction costs for a bridge are approximately
degradation of unpaved roads can be attributed to $528,000. Applying the 3.13 percent value derived
precipitation (Ramos-Scharron and MacDonald 2007). above to this estimate, we assume an increase of
The remaining 20 percent is attributed to factors such $16,500 in bridge construction costs for each one-foot
as tonnage of traffic and traffic rates. Given this 80 increase in bridge clearance.21
percent attribution to precipitation, we assume that the
base construction costs for unpaved roads increase by 80
The readily available data suggest no impact or minimal
percent of the total percentage increase in maximum
impact will originate from wind or temperature
monthly precipitation; that is, a 0.8 percent increase in
increases for new construction of bridges or unpaved
costs for each 1 percent increase in maximum precipita-
roads.
tion. For example, if the maximum monthly precipita-
tion increases by 10 percent in a given location, then 80
percent of that increase is used (8 percent) as the 2. Estimation of Dose-Response Values for
increase in base construction costs. In addition, we Maintenance Costs
further assume a base construction cost of $13,000 per Similar to our development of dose-response values for
km for unpaved roads, based on published cost data infrastructure construction costs, we employed two basic
(Cerlanek et al. 2006). The readily available data methodologies to generate dose-response values relating
suggest no relationship between temperature and the
cost of building unpaved roads.
21 This value is based on U.S. construction cost data. We developed val-
For bridges, we estimate the climate-related change in ues specific to other countries based on an inter-country construction
cost index published by Compass International Consultants Inc. (2009),
costs per one-foot increase in bridge clearance. The as indicated above.
d e v e l o p m e n t a n d c l i m at e c h a n g e d i s c u s s i o n pa p e r s 49
changes in climate stressors to changes in infrastructure change in climate stress, scaled for the stressor's effect
maintenance costs. The first approach is based on on maintenance costs, as shown in Equation 4.
infrastructure lifespan decrements that could potentially
result from climate change if maintenance practices
S
remain unchanged following changes in climate stress. (4) LERB = (SMT )
We use this methodology to develop dose-response BaseS
values for existing paved roads and buildings.22 Newly where LERB = Potential percent change in lifespan for
constructed paved roads and buildings are assumed to existing paved roads and buildings associated with a
not be affected by climate stressors because forward- unit change in climate stress
looking design allows these structures to accommodate
future climate changes at the time of construction. For
S = Change in climate stress (i.e., precipitation or
railroads and unpaved roads (both existing and newly
temperature)
constructed), we use a separate methodology similar to
the direct dose-response approach outlined above for
BaseS = Base level of climate stress with no climate
bridge and unpaved road construction costs.
change
A. Avoided Lifespan Decrement Methodology SMT = Percent of existing paved road or building
To assess the relationship between climate stressors and maintenance costs associated with a given climate stres-
maintenance costs for existing paved roads and build- sor (i.e., precipitation or temperature)
ings, we use an approach based on the cost of prevent-
ing the reduction in lifespan that may result from As indicated in Equation 4, the potential change in
changes in climate-related stress. As indicated by lifespan is dependent on the change in climate stress.
Equation 3, implementation of this approach involves For precipitation effects, we assume a potential reduc-
two basic steps: (1) estimating the lifespan decrement tion in lifespan for existing paved roads and buildings
that would result from a unit change in climate stress for every 10 cm increase in annual rainfall. For temper-
and (2) estimating the costs of avoiding this reduction ature, we assume a potential lifespan reduction with
in lifespan. every 1 degree Celsius change in temperature (average
annual temperature for existing buildings and maximum
(3) M TERB = (LERB)(CERB) annual temperature for existing paved roads).
where MTERB = Change in maintenance costs for Equation 4 also illustrates that our estimate of the
existing paved roads and buildings associated with a potential reduction in lifespan associated with a given
unit change in climate stress change in climate stress reflects the contribution of that
stressor to baseline maintenance costs (i.e., variable
LERB = Potential percent change in lifespan for exist- SMT). For buildings, we assume that changes in
ing paved roads and buildings associated with a unit precipitation associated with climate change will affect
change in climate stress roofing and external enclosures and changes in temper-
ature will affect HVAC systems. Because roofing and
external enclosures represent 15 percent of building
CERB = Cost of preventing a given lifespan decrement
maintenance costs (Whitestone Research 2008), we
for existing paved roads and buildings
assumed that precipitation contributes 15 percent to a
building's maintenance costs. Similarly, because HVAC
To estimate the reduction in lifespan that could result
represents 28 percent of building maintenance costs
from an incremental change in climate stress (LERB),
(Whitestone Research 2008), we assume that tempera-
we assume that such a reduction is equal to the percent
ture effects are responsible for 28 percent of a building's
maintenance costs. We also identified similar data for
paved roads suggesting that precipitation-related main-
22 By existing roads and buildings, we mean those roads and buildings in
service as of 2010, the first year in the time horizon of this analysis. tenance represents 4 percent of maintenance costs and
50 t h e c os ts oF ad a p tin g to c limate c h an ge For in Fr as tr u c tu r e
that temperature-related maintenance represents 36 road construction costs. More specifically, we estimate
percent (Miradi 2004). the change in railroad and unpaved road maintenance
costs associated with a unit change in climate stress as a
After estimating the potential reduction in lifespan fixed percentage of baseline construction costs (for rail-
associated with a given climate stressor, we estimate the roads) or maintenance costs (for unpaved roads), as
costs of avoiding this reduction in lifespan. To estimate illustrated by Exhibit 5.
these costs, we assume that the change in maintenance
costs would be approximately equal to the product of (5) MTURR = M � BURR
(1) the potential percent reduction in lifespan (LERB)
and (2) the base construction costs of the asset. where MTURR = Change in maintenance costs for
Therefore, if we project a 10 percent potential reduction unpaved roads and railroads associated with a unit
in lifespan, we estimate the change in maintenance costs change in climate stress
as 10 percent of base construction costs. As indicated
above, we estimate base construction costs of $185 per
M = Cost multiplier
square foot for buildings and $621,000 per km for
paved roads in the U.S.23
BURR = Baseline maintenance (for unpaved roads) costs
or construction costs (for railroads)
B. Example of Avoided Lifespan Decrement
Approach Similar to the direct response methodology for
As an example of the avoided lifespan methodology, construction costs, implementation of this approach for
consider a country with baseline annual precipitation of maintenance costs also varies by infrastructure type.
80 cm without climate change. For such a country, a For railroads, we express the relationship described by
10-cm increase in annual precipitation would represent Equation 5 as the change in maintenance costs associ-
a 12.5 percent increase in precipitation. Because precip- ated with a 1 degree Celsius change in the maximum of
itation accounts for approximately 15 percent of build- the maximum monthly temperature projections for an
ing-related maintenance costs, we would assume a 1.9 area. Based on research on the effect of heat stress on
percent potential reduction in building lifespan (12.5% rails and the associated costs, we estimate that for every
x 15%). If baseline building construction costs in this 1 degree increase in maximum temperature, railroad
country are approximately $175 per square foot, we maintenance costs increase by 0.14 percent of railroad
would estimate an increase in maintenance costs of construction costs (DRPT 2008). Therefore, assuming
approximately $3.30 per square foot for every 10 cm construction costs of approximately $404,000 per km
increase in annual precipitation. If the country were to (in the U.S) (Railroad 2009; Vickers 1992), we estimate
experience a 15-cm increase in annual precipitation, we that railroad maintenance costs would increase by $565
would still assume a $3.30 per square foot increase for every 1 degree increase in maximum temperature.
because the 15-cm increase includes just one 10-cm
incremental change. However, if we were to project a For unpaved roads, we express the dose-response rela-
21-cm increase, we would assume an increase of $6.60 tionship represented by Equation 5 as the change in
per square foot. maintenance costs associated with a 1 percent change in
maximum monthly precipitation. As indicated above,
research has demonstrated that 80 percent of unpaved
C. Direct Response Methodology
road degradation can be attributed to precipitation,
To estimate dose-response values for railroad and while the remaining 20 percent is due to traffic rates
unpaved road maintenance costs, we follow an approach and other factors (Ramos-Scharron and MacDonald
similar to that outlined above for bridge and unpaved 2007). Given this 80 percent attribution to precipita-
tion, we assume that maintenance costs increase by 0.8
percent with every 1 percent increase in the maximum
23 As indicated above, we developed values specific to other countries of the maximum monthly precipitation values projected
based on these U.S. values and an inter-country construction cost index
published by Compass International Consultants Inc. (2009), as indicat- for any given year. Published data indicates that the
ed above.
d e v e l o p m e n t a n d c l i m at e c h a n g e d i s c u s s i o n pa p e r s 51
baseline cost of maintaining an unpaved road is approx- Washington, DC: Federal Emergency Management
imately $930 per km (Cerlanek et al. 2006). Therefore, Agency.
for every 1 percent increase in maximum temperature,
we assume a maintenance cost increase of $7.45 per km. FHWA. 2009. "Right of Passage: Controversy Over
Vertical Clearance on the Interstate System." Federal
The readily available data suggest climate stressors will Highway Administration. http://www.fhwa.dot.gov/
have no impact or minimal impact in these categories as infrastructure/50vertical.cfm, viewed May 31, 2009.
follows: 1) no impact from temperature on unpaved
roads, 2) no impact from precipitation on railroads. Kinsella, Y., and F. McGuire. 2005. "Climate Change
Impacts on the State Highway Network: A Moving
Target." Proceedings of the NIZHT Conference,
Christchurch, New Zealand, November 2005.
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The World Bank Group
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Washington, D.C. 20433 USA
Tel: 202-473-1000
Fax: 202-477-6391
Internet: www.worldbank.org/climatechange