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## The Costs Of Adapting To

 
    Climate Change for **Infrastructure** 

## 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
© 2010 The International Bank for Reconstruction and Development / THE WORLD BANK 1818 H Street, NW Washington, DC 20433,  U.S.A. Telephone: 202-473-1000 Internet: www.worldbank.org/climatechange E-mail: feedback@worldbank.org All rights reserved. August 2010
This volume is a product of the staff of the International Bank for Reconstruction and Development / The World Bank. The findings, interpretations, and conclusions expressed in this volume do not necessarily reflect the views of the Executive Directors of The World Bank or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgement on the part of the World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries.

## R I G H T S  A N D  P E R M I S S I O N S

The material in this publication is copyrighted. Copying and/or transmitting portions or all of this work without permission may be a violation of applicable law. The International Bank for Reconstruction and Development / The World Bank encourages dissemination of its work and will normally grant permission to reproduce portions of the work promptly. For permission to photocopy or reprint any part of this work, please send a request with complete information to the Copyright Clearance Center Inc., 222 Rosewood Drive, Danvers, MA 01923, USA; telephone 978-750-8400; fax 978-750-4470; Internet: www.copyright.com. Cover photo: Darjeeling, India.  © Shutterstock Images, LLC Note: All dollars are U.S. dollars unless otherwise indicated.

## 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 |
| 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   |
|---|---|---|------|
|                                                                   |                                                                                    | 	 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                                                                                 |                                                       |                                                                                              |

## Abstract

An approach to estimating the costs of adapting to climate change is presented along with results for major components of infrastructure. The analysis separates the price/cost and quantity effects of climate change. The first component measures how climate change alters the cost of a baseline program of infrastructure development via changes in design standards and operating 

costs. The second component measures the effect of 
climate changes on the long-run demand for infrastruc-
ture. The results indicate that the price/cost element is 
usually less than 1 percent of baseline costs, while the 
quantity effect may be negative for many countries.

## 1.	 Setting The Scene

This paper presents the results of a global analysis of 
the costs of adapting infrastructure to climate change 
over the period from 2010 to 2050. The analysis was 
carried out as part of the World Bank's Economics of 
Adaptation to Climate Change study.  In this context, 
infrastructure has been given a rather broad definition.  
It includes the usual types of infrastructure services, 
including transport (especially roads, rail, and ports), 
electricity, water and sanitation, and communications.1  
In addition, urban and social infrastructure such as 
urban drainage, urban housing, health and educational 
facilities (both rural and urban), and general public 
buildings have been included.  

The basic approach is extremely simple. For any counter-only jard put $\alpha$ ($\leftarrow$ 2010, 2015, $\ldots$, 2050), we start from the assumption (that we ignore the "efficient" level of provision of infrastructure is often), which will become is denoted by $Q_{ij}$. The correction of type i, which will become is not the which would be reached if the country had invested up to the point at which the marginal benefits of addition, but it is different for the marginal cost--both empirical infrastructure. It is often found that the loss of information due to the fact that the original cost--the distribution of interest is introduced that the extent of underninsert is in infrastructure and that the boost counters (ALG 2009). This is an important development issue, which is not directly related to climate change. Hence, the approach attempts to stop out the effects of country differences in brief actual provision of infrastructure by establishing a common 
benchmark that depends upon factors such as population and income.

In the period from t to t+1, for example from 2010 to 
2015, the country will have to invest in order to meet 
the efficient level of infrastructure in t+1 and to replace 
infrastructure in situ at date t, which reaches the end of 
its useful life during the period.  Thus, the total value of 
investment in infrastructure of type i in country j and 
period t is

$$I_{i j t}=C_{i j t}[Q_{i j t+1}-Q_{i j t}+R_{j t}]\tag{1}$$

where Cijt is the unit cost of investment and Rijt is the 
quantity of existing infrastructure of type i that has to 
be replaced during the period.  The change in the total 
cost of infrastructure investment may be expressed in 
terms of the total differential of (1) with respect to the 
relevant climate variables that affect either unit costs or 
efficient levels of provision for infrastructure of type i:

$$\Delta I_{j i t}=\Delta C_{i j t}[Q_{j i t+1}-Q_{j i t}+R_{i j t}\,]+\qquad\qquad\qquad(2)$$

An equivalent equation may be derived for the costs of 
operating and maintaining infrastructure.  In the 
discussion that follows, the first part of the right-hand 
side of equation (2) is referred to as the Delta-P 
component of the cost of adaptation, while the second 
part is referred to as the Delta-Q component.  These 
components themselves cover a number of ways in 
which climate change may cause changes in the costs or 
quantities of providing infrastructure services.   

Delta-P.  At the simplest level, changes in temperature, 
precipitation, or other climate variables may alter the 

direct cost of constructing infrastructure to a standard specification.  For example, seasonal weather variations can increase the costs of building.  However, this is a minor factor.  More important is the impact of climate change on the design standards that are applied in order to maintain the quality of infrastructure services provided by a unit of infrastructure, such as a kilometer of paved road or a fixed telephone connection (See Canadian Standards Association 2006 for a discussion of this issue).

## Changes In The Frequency And/Or The Severity Of A.

storms, flooding,  and other extreme weather events may compromise the performance of infrastructure designed to existing standards.  Hence, it is common to refer to "climate proofing" investments or ensuring "climate resilience."  The study starts from the basis that design standards should be adjusted so as to deliver the same level of performance as would have applied if climate change had not occurred.  Thus, if roads or buildings are currently constructed to withstand a 1-in- 50 or 1-in-100-year flood or wind storm, then the same design standard should apply, but under the circumstances of a changed frequency or severity of those events.  The changes in the unit costs—
ΔCijt —represent the costs of building infrastructure that delivers the same level of performance in the face of different climatic stresses.  The derivation of the cost changes, expressed as doseresponse relationships for different climate stressors, are described in Appendix 1.  The doseresponse functions are applied to estimates of the average values of climate variables under a scenario of a stable climate and alternative scenarios for climate change by country.2  This gives a 
series of cost increases—at constant 2005 prices— by type of infrastructure, country, and time period. 
 
When applied to the baseline projection of infrastructure demand, we obtain the Delta-Q cost of adaptation; that is, the difference between the cost of the baseline investment program for a stable 

climate and for a changing climate.  A similar 
exercise may be carried out for operating, mainte-
nance, and replacement costs in order to calculate 
the increment in annualized infrastructure costs as 
a consequence of climate change.

Delta-Q. 
b.	
 The quantities of infrastructure assets 
required (holding income constant) will change as a consequence of different climatic conditions. Again, this has two dimensions.  The first is that climate change may change the level or composition of demand for energy, transport, and water at 
given levels of income, so we need to calculate the net impact of these changes in terms of capital and operating costs.  The second is that climate change will mean that countries have to invest in specific additional assets in order to maintain specific standards of protection for non-infrastructure activities.  

The Delta-P dimension of the study is uncontroversial 
in principle, though more or less difficult in practice.  
Various organizations have made broad brush estimates 
of the cost of "climate proofing" existing investment 
programs in developing countries  (UNFCCC 2007; 
McGray et al. 2008).  Typically, the analysis starts from 
a baseline program in investment by time of infrastruc-
ture.  Then, an estimate is made of the percentage 
increase in unit costs required to ensure that invest-
ments are resilient to climate change.  

One problem with the "climate proofing" approach 
concerns the investment program to which the cost of 
climate proofing should be applied.  For some sectors or 
countries/regions, it is possible to start from a detailed 
inventory of infrastructure assets and then to ask what 
investments will be required to meet future demand for 
infrastructure services.  The best example of this 
approach is a study of the costs of adaptation to climate 
change in Alaska  (Larsen et al. 2008).  However, this 
type of exercise requires an inventory of infrastructure 
assets and it does not take account of future investment 
in infrastructure.

In the case of developing countries, many institutions 
that are concerned with adaptation to climate change 
for infrastructure draw a distinction between (a) the 
cost of eliminating the "development deficit,"—that is, 

the gap between the infrastructure that a country "ought" to have and the infrastructure that it actually has—and (b) the cost of adapting to climate change on the assumption that the country has an efficient level of infrastructure.  The former is seen as a development problem, while the latter is a climate change problem. Even though it is understood that money is fungible, the two elements of total investment in infrastructure might be financed out of different pots of money.

The corollary of this distinction between adaptation and 
the development budget is that the baseline program for 
infrastructure investment used in constructing the 
Delta-Q should not be derived from actual or planned 
investment in infrastructure.  Instead, it should reflect 
the "efficient" demand for infrastructure.3  This is no 
simple task.  The World Bank has recently completed a 
detailed assessment of infrastructure investment needs 
in 22 African countries, assuming a catch-up from 
actual to efficient provision over a decade to 2020 
(AICD 2009). That exercise involved very substantial 
work and cannot be extended to all countries in a short 
period.  Instead, the analysis has to be based on an 
econometric model that can be used to construct projec-
tions of the efficient demand for infrastructure up to 
2050.

While the principle of drawing a distinction between 
the "development deficit" and adaptation to climate 
change is widely followed in international negotiations, 
many economists consider that the distinction is either 
unworkable in practice or simply wrong as a matter of 
economic logic.  The reason is that most assessments of 
the "efficient" demand for infrastructure ignore the 
question of resources.  A specific country might wish to 
have more roads, schools, or hospitals than the stocks 
that are currently in situ, and the rest of the world 
might agree that this would be a desirable goal.  But, 
this is nothing more than a wish list independent of the 
resources that are available.  With limited resources 
some countries may choose to spend their funds on 
providing better roads or more healthcare services.  

Relying either upon wish lists or on the envelope of 
what other countries at similar incomes have invested 
ignores the trade-offs that all governments have to 
make.  Even if external assistance is available to fund 
capital projects, it is common experience that lack of 
funds for operations and maintenance may lead to rapid 
deterioration in the services provided by stocks of infra-
structure assets.

Thus, it may be argued that the analysis should not be 
based on some notional "efficient" level of infrastructure, 
but should start from the actual levels and growth of 
infrastructure based on decisions that reflect real 
constraints on budgets and the associated priorities.  To 
examine whether the distinction is important in prac-
tice, the full study has used two sets of baseline projec-
tions of demand for infrastructure.  The "frontier" 
projection is derived by using frontier methods of esti-
mation to estimate econometric equations that charac-
terize the envelope of infrastructure demand given 
exogenous variables such as income, population, urban-
ization, etc.  This is intended to provide an estimate of 
the "efficient" level of infrastructure demand as envis-
aged in discussions of the development gap.  In contrast, 
the "panel" projection uses conventional projections 
derived from econometric estimates of the average rela-
tionship between infrastructure and the exogenous 
variables.

The difference between the Delta-P estimates using the 
two sets of baseline projections is not as large as some 
might expect.  There is an important reason for this.  
We find that the relative gap between the frontier and 
panel projections tends to narrow, because there appears 
to be convergence toward standard patterns of infra-
structure provision.  Further, the income elasticity of 
demand for infrastructure is generally less than 1 for 
the frontier demand equations and is lower than the 
equivalent income elasticities for the average demand 
equations.  For the frontier baselines projection, these 
factors lead to a lower level of new investment in infra-
structure, but a higher level of expenditure on replacing 
and maintaining the initial level of infrastructure.  
Under the panel baseline projection, lower levels of 
spending on replacement and maintenance are offset by 
higher spending on new investment.  Depending on the 
initial development gap and the timing of new invest-
ment, it is possible—though not usual—for the cost of 

adaptation to be larger for the average baseline than for the frontier baseline.

In this paper we will focus exclusively upon the panel projections derived from panel data models rather than the frontier models.  This is consistent with the view that the distinction between the development deficit and the adaptation deficit is difficult to draw under the best of circumstances and may not be useful in practical terms.

The second, Delta-Q, aspect of this work concerns the 
impact of changes in climate on the demand for infra-
structure.  To approach this issue, we have to consider 
the mechanisms by which changes in climate may affect 
the demand for infrastructure and how we might iden-
tify these consequences.  For example, it is generally 
accepted that demand for electricity depends upon 
climate in general, but it is not so easy to identify the 
key climate parameters when estimating the demand for 
electricity or for electricity-generating capacity.  Note 
that even these two variables may be subject to different 
influences because the seasonal or diurnal pattern of 
electricity demand is strongly influenced by climate.4  
Part of the difficulty is that the outcome depends upon 
the relative weights assigned to different factors.  An 
increase in average temperatures will lead to less 
demand for heating in the colder seasons but more 
demand for cooling in the warmer seasons.  The overall 
direction of change is not easy to predict and is likely to 
depend upon the way in which we set up the problem.

Electricity is simple to think about by comparison with 
roads or other transport infrastructure because there is 
an intuitive sense of the mechanisms involved in a rela-
tionship between climate variables and the stock of 
electricity-generating capacity.  But it would be wrong 
simply to impose the assumption that climate has no 
effect on the demand for roads.  Patently, climate vari-
ables do affect the structure of economic activity hold-
ing other factors constant —for example, through the 
level and composition of agricultural output—and this 

ticular location is calculated from the truncated distribution of temperatures below some threshold—often 18°C—either on an hourly or a daily basis. 

will influence the nature of investment in roads.  There 
are more complex but potentially larger effects operat-
ing through the economic geography of urban life, 
industry, and commerce; that is, in the ways in which 
we organize economic activity in space.  Small changes 
may have significant consequences for the level of 
investment in infrastructure.

While the principle that climate change may affect the 
demand for infrastructure seems straightforward, the 
task of estimating the Delta-Q costs of adaptation is 
much more difficult for two general reasons.

## Many Of The Impacts Of Climate On Demand For A.

infrastructure are long term in nature.  This may 
not be true for electricity, but any influence of 
climate on the demand for roads will operate via 
the path of economic development over a period 
of one, two, or many decades.  There are two 
consequences.  First, we should not think of the 
Delta-Q component of the costs of adaptation as 
arising on a regular schedule every five years.  The 
calculation merely identifies additions to and 
subtractions from a liability (or asset) that will 
materialize in future as economic activity adjusts 
to the changes in climate that are taking place.  
Second, in planning for future infrastructure 
development, governments need to consider how 
climate change may affect the amount and type of 
infrastructure that is required if it will influence 
future patterns of economic activity.

In practice, there is no way of examining the 
b.	

empirical impact of climate on the demand for 
infrastructure other than through some form of 
panel data analysis—pooling data for countries, 
regions, states, or other geographical units over 
time.  Inevitably, climate is a cross-sectional vari-
able (since year-to-year variations are weather), 
which may easily be confounded with other cross-
section fixed effects.  This has prompted various 
criticisms of the Ricardian approach to identifying 
the impact of climate change on agriculture or 
GDP on the grounds that climate variables are 
acting as a proxy for non-climate factors such as 
institutions.  Some economists draw the conclu-
sion that climate variables should not be used in 
this way.  We do not accept this view, since it 

closes off any possibility of estimating the impact 
of climate change on overall demand for infra-
structure.  Instead, we have carried an extensive 
econometric analysis of the role of climate vari-
ables in modeling the demand for infrastructure.  
The details are technical and take up a large 
amount of space, so that they are reported in a 
separate paper (Hughes 2010).  The issues and 
results are summarized in sections 4 and 5 below.  
Our results suggest that the demand for some 
categories of infrastructure is affected by different 
climate variables with important interactions with 
income per capita and urbanization.    

The results of our econometric analysis suggest that the absolute magnitude of the Delta-Q component of adaptation should not be ignored in the long run.  On the other hand, the Delta-P component is much more predictable as a basis for discussing plans for adapting to climate change.  For these reasons, our detailed estimates of the costs of adaptation by 5-year period up to 2050 concentrate on the Delta-P component, while the Delta-Q estimates are presented as indicative estimates for the whole period. 

## 2.	 Data

The core data used in this study is the World Development Indicators (WDI) database published in 
2008 by the World Bank, which provides panel data for up to 168 countries and the years 1960 to 2006.  The year 2005 is treated as the base year for all of our estimation.  Our work relies on the 2008 version of the database.  One crucial consequence is that the purchasing power parity estimates of GDP per person rely on a version of the 2005 ICP baseline due to appear as Penn World Tables (PWT) Version 7.  These estimates cover the period 1980–2007 for a large set of countries.  They have been extended backwards to 1960 by splicing estimates from PWT Version 6.2, which uses the 2000 ICP baseline.  Country gaps have been filled by the standard approach of using a quadratic equation linking GDP per person in constant (2000) USD at market exchange rates to GDP per person at constant (2005) PPP exchange rates.

The WDI data has been supplemented with data on 
infrastructure availability from a wide variety of sources, 
including other international organizations (FAO, ITU, 
WHO, UNICEF, UPU), official country data (espe-
cially census data), and various systematic surveys such 
as Demographic and Health Surveys (DHS) and Living 
Standards Measurement Surveys (LSMS), which are 
broadly consistent across countries.  Even so, the final 
dataset is very patchy in terms of coverage, especially for 
earlier periods.  The panels are unbalanced and there are 
many missing values for intermediate years.  Thus, it is 
not possible to make use of econometric specifications 
involving autoregressive or similar errors over time.  

A further remark concerns the nature of the data relat-
ing to different types of infrastructure.  In a few cases, 
we have direct measures of the quantity of infrastruc-
ture assets—for example, kilometers of paved or all 
roads, kilometers of rail track, MW of generating capac-
ity.  More commonly we have to rely upon measures of 
infrastructure output—for example, numbers of house-
holds connected to electricity, water, or sewer systems.  
In practice, the efficient levels of infrastructure assets 
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.

## 3.	 Climate Change

Describing the historic climate in a manner that is 
compatible with macroeconomic data is far from 
straightforward without any of the complications of 
projecting climate change into the second half of the 
21st century.  The literature on the influence of climate 
on economic variables has tended to rely upon average 
values of climate variables, primarily temperature, 
measured for the capital city of the country.  The classic 
dataset is the data compiled by NCAR—NOAA's 
National Center for Atmospheric Research in Boulder, 
Colorado—for weather stations around the world iden-
tified by their World Meteorological Organization 
reference code.  The difficulty with this dataset is that 
there is no consistency across stations in the data that is 
reported.  We have examined average data for capital 
cities derived from weather stations in or near the capi-
tal—including, for example, nearby airports.  This is 

used to obtain an average elevation for the capital city, but there are too many missing values to rely upon the climate variables for our econometric analysis.

The Climate Research Unit at the University of East 
Anglia has compiled a series of historic weather data for 
0.5 degree grid squares for land areas of the globe.  
Summary statistics have been computed for each grid 
cell for monthly average, maximum and minimum 
temperatures (in degrees C), and precipitation (in mm) 
for the period 1901–2002.  The distribution of tempera-
tures is generally accepted as being well-approximated 
by the normal distribution, so it was sufficient to 
compute the mean and standard deviation for each grid 
cell.  For precipitation, the distribution is closer to the 
log-normal, so the mean and standard deviation of 
ln(precipitation+1mm) were calculated in addition to 
the mean of precipitation.5  

Country estimates of the climate variables were 
constructed using grid cell means for monthly mean, 
maximum, and minimum temperatures and precipita-
tion.  The primary variables are population-weighted 
averages using the population in each country in each 
grid cell to weight the grid-cell means, thus reflecting 
the average exposure for the population of each coun-
try.6  Alternative sets of country means weighted by (a) 
the land areas in each cell, and (b) the inverse of popu-
lation in each cell were also constructed.  The reason for 
doing this is linked to the demand for transport and 
other types of hard infrastructure.  Consider a country 
such as Australia.  The population is concentrated in 
the coastal areas of the continent, while the interior—
with very different climatic conditions—is very thinly 
populated.  So the population-weighted averages will 
reflect the climate on the coast whereas the inverse 
population-weighted averages reflect the climate in the 
interior, while the area-weighted averages fall in 
between.7  The correlations between the population-

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, therefore, necessary to assume that population density is uniform over these grid cells so that population is split between countries in the same proportion as land area. 
7	
  We have tested whether using either the inverse-population-weight-
weighted and inverse population-weighted climate variables are shown in Table 1 along with correlations with historic demographic variables used as instruments for institutional development as discussed in Section 4. 

The primary climate variables used in the econometric 
analyses are the two weighted means for (a) annual 
average temperature (computed as the average of 
monthly average temperatures) in °C; (b) total annual 
precipitation (computed as the sum of monthly average 
precipitation); (c) the temperature range (the average 
maximum temperature in the hottest month, the aver-
age minimum temperature in the coldest month); and 
(d) the precipitation range (average precipitation in the 
wettest month, average precipitation in the driest 
month).  

One point to note is that annual average temperature 
measured in degrees C is negative or very small in a 
number of countries, especially for the inverse popula-
tion-weighted means.  Because of the use of the loga-
rithmic transform, it is necessary either to exclude 
countries with extreme temperatures or to apply some 
linear shift to temperatures.  The transformation 
adopted was to add 40°C to all temperatures.  This 
value reflects the range from the minimum value of the 
monthly minimum temperature (-29.1°C) and the 
maximum value of the monthly maximum temperature 
(+46.9°C).  Of course, the shift has no effect on the 
temperature range.  

The choice and use of climate projections to 2050 and 
beyond is considerably more complex.  Global climate 
models (GCMs) are programmed to produce projec-
tions of different variables for different time periods.  
At a micro scale, there are large differences between the 
results generated by the various models, so that it is 
necessary to be very careful about relying upon a single 
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.  
Further, the problem is more serious than simple 
models may suggest.  Our econometric models suggest 

ed and area-weighted means instead of or in addition to the popula-
tion-weighted means improves the performance of our equations.  In 
all of the cases that we have examined, the area-weighted climate vari-
ables are dominated by the inverse population-weighted (ipop) climate 
variables.

| Population-weighted climate   |   Inverse population-weighted climate |
|-------------------------------|---------------------------------------|
| Average                       |                                       |
| temper-                       |                                       |
| ature                         |                                       |
| Precipi-                      |                                       |
| tation                        |                                       |
| Temper-                       |                                       |
| ature                         |                                       |
| range                         |                                       |
| Precipi-                      |                                       |
| tation                        |                                       |
| range                         |                                       |
| Population-weighted           |                                       |
| climate                       |                                       |
| Average temperature           |                                 1     |
| Precipitation                 |                                 0.229 |
| Temperature range             |                                -0.656 |
| Precipitation range           |                                 0.6   |
| Inverse population-           |                                       |
| weighted climate              |                                       |
| Average temperature           |                                 0.83  |
| Precipitation                 |                                 0.083 |
| Temperature range             |                                -0.563 |
| Precipitation range           |                                 0.373 |
| Historic demographic          |                                       |
| indicators                    |                                       |
| Birth rate 1950-54            |                                 0.728 |
| Infant mortality 1950-54      |                                 0.595 |

 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 
monthly temperatures and precipitation are often the 
primary drivers of infrastructure demand.  This means 
that the projections used to calculate the Delta-Q costs 
must be based upon climate scenarios that generate 
monthly maximum and minimum temperatures as well 
as average temperatures, which restricts the set of 
GCMs that can be used.  But even more important, the 
variance of the difference between two variables is the 
sum of their variances minus their covariance.  Under 
most plausible outcomes, this will exceed the variance of 
each element, so that the uncertainty about climate 
ranges will be higher than for climate means.8

Average 
temperature
Precipitation
Temperature 
range
Precipitation 
range
Birth rate
1950–54

For the main scenario analysis in this study, we have 
used results from the NCAR CCSM-3 and CSIRO-3 
models (abbreviated to NCAR and CSIRO).  These 
have relatively similar changes in the global moisture 
index, but they differ significantly in their patterns of 
climate change at the regional and country level.  The 
models are part of a larger set of 26 GCMs that have 
been examined in detail by the MIT Joint Program on 
the Science and Policy of Global Change.  As part of 
their analysis, the MIT group has down-scaled the 
climate projections to match the 0.5 degree grid cells 
used for the historic climate data, so population- and 
area-weighted means were constructed for the countries 
covered by our study for the NCAR and CSIRO 
scenarios.

These projections are not sufficient for the Delta-P 
analysis, because design standards for certain types of 
infrastructure are driven by extreme values rather than 
monthly average values.  However, GCMs are not capa-
ble of generating reliable estimates of daily maximum/

minimum temperatures, precipitation, or wind speed, so it is necessary to deal with this requirement in an indirect manner.  We have proceeded as follows:

Use the normal or log-normal distributions of 
a.	
monthly averages of maximum/minimum temperature and monthly precipitation to estimate the 
99th percentile of monthly maximum temperature, the 1st percentile of monthly minimum temperature, and the 99th percentile of maximum monthly 
precipitation.
Express these percentiles as a ratio of the maxib.	
mum/minimum of monthly average maximum/ minimum temperatures and the maximum monthly precipitation and assume that these ratios will remain broadly constant in the future. Apply the ratios of the 99
c.	
th/1st percentiles to the 
associated monthly extremes for 2050 in order to compute the change from extreme values for the historic climate to extreme values for the climate scenario in absolute units—degrees C or mm of rainfall. In the case of wind speed, we have estimated the 
d.	

elasticity of the 99th percentile of wind speed with 
respect to the 99th percentile of precipitation by 
fitting extreme value distributions to the historic 
climate data and used the change in maximum 
precipitation to project changes in extreme wind 
events. 

## 4.	 Econometric Specifications

In considering the specification of the econometric analysis, it has to be remembered that the goal is to generate projections of the average demand for infrastructure up to 2050, whether or not these are affected by climate.  We are not trying to examine the factors that drive the actual amounts of infrastructure assets supplied today or in the past.  The key implication is that it is not appropriate to include, for example, indicators of governance or institutional development in the analysis.  These may be relevant factors explaining actual outcomes for individual countries today.  But they 

are not relevant when we wish to make projections 40 
or more years into the future, since it is neither possible 
nor desirable to attempt to project how governance or 
institutions will evolve over that period.  

To the extent that (a) institutional factors influence the 
current level of infrastructure provision, and (b) there is 
a correlation between institutional development and 
GDP per person or urbanization, then the impact of 
institutional development will be (partly) captured by 
the coefficients on GDP per person or urbanization in 
the reduced form discussed below.  This is one reason 
why the elasticities of infrastructure demand with 
respect to these variables may be higher when estimated 
using a sample of all countries than for a sample of 
high-income countries only.  But, equally, there are 
many other factors that may affect the reduced form 
elasticities.  

Quite apart from matters of econometric philosophy, 
the nature of the data available for the purpose of 
making projections of future demand for infrastructure 
has an important influence upon the specification of the 
models.  There are a very limited number of variables 
for which independent projections extending to 2050 
have been constructed and can be used.  In addition to 
the climate variables discussed above, these are total 
population, the age structure of the population, urban-
ization, and growth in income (GDP per capita 
measured at purchasing power parity), plus a number of 
geographical features, which act as country-fixed 
effects.9  

The basic approach for the econometric analysis is to 
develop a reduced form specification of the efficient 
demand for the services provided by each type of 
infrastructure—for example, paved roads or railways.10  

9	
  The demographic projections are based on the medium fertility projection in the UN Population Division's 2006 revision, which is linked to the urbanization projections.  The central scenario for growth rates for GDP per person at purchasing power is computed by taking the average of five economic integrated assessment models— Hope (2003), Nordhaus (2002), Tol (2007), IEA (2008) and EIA (2008).  The average growth rate for world GDP in real terms is very close to the IPCC A1 SRES scenario, but the country growth rates are not based upon the downscaled versions of that scenario since those were constructed with a base data of 1990 and the relative country weights are very out of date.  The sources of the population and income projections are described in a separate note.  
10	   There is an extensive literature, much of it originating in the World 
Bank, on developing econometric models to identify links between 
We assume that the structural equation defining the efficient demand for infrastructure type i in country j in period t may be written as:

$$Q_{i j t}=f_{i}\{P_{j t},Y_{j t},C_{i j t},X_{j t},Z_{i j t},V_{j t},t\}\qquad\qquad(3)$$
The variables are defined as follows:

Pjt is the population of country j in period t;11 Yjt is average income per head for country j in  period t;  d p y x v y x = + + + + + + a b b b b g g 2 2 ijt i pi jt yi jt xim mjt vir rjt yi jt xim mjt y x y v x x x v r j s f
Cijt is the unit cost of infrastructure type i for country j in period t; 

im jt mjt ir jt rjt imn mjt njt imr mjt rjt + + + + ∑ ∑ ∑ ∑ ∑ ∑ ∑ d p y x v y x = + + + + + + a b b b b g g + + + + ∑ ∑ ∑ ∑ ∑ ∑ ∑ 2 2 ijt i pi jt yi jt xim mjt vir rjt yi jt xim mjt y x y v x x x v r j s f im jt mjt ir jt rjt imn mjt njt imr mjt rjt $\mathbf{X_{jt}}$ is a vector of country characteristics for country j in period t; $\sum\mathbf{r}_{\text{\emph{inv}}}\mathbf{y}_{\text{\emph{jt}}}\mathbf{x}_{\text{\emph{mijt}}}+\sum\mathbf{j}_{\text{\emph{iv}}}\mathbf{y}_{\text{\emph{jt}}}\mathbf{v}_{\text{\emph{jt}}}$ - $\sum\mathbf{s}_{\text{\emph{inv}}}\mathbf{x}_{\text{\emph{mijt}}}\mathbf{x}_{\text{\emph{jt}}}+\sum\mathbf{f}_{\text{\emph{inv}}}\mathbf{x}_{\text{\emph{mijt}}}\mathbf{v}_{\text{\emph{jt}}}$
Zijt is a vector of economic or other variables that affect the demand for infrastructure type i for country j in period t; and Vjt is a vector of climate variables for country j in period t.

We can observe or project values for some of these variables, notably P, Y, X, and V (dropping subscripts).  For the other variables we assume that:

$$C_{ijt}=c_{i}\{Y_{jt},X_{j},Z_{ijt},V_{jt},t\}\tag{4}$$
and

$$Z_{ijt}=g_{i}\{Y_{jt},X_{jt},V_{jt},t\}\,.\tag{5}$$
Solving for Zijt and Cijt allows us to write the reduced form as 

$$Q_{ijt}=h_{i}\{P_{jt},Y_{jt},X_{jt},V_{jt},t\}\tag{6}$$

Since there are no strong priors on the appropriate 
functional forms for fi{ }, ci{ }, and gi{ }, we can use a 
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 
translog specification for all variables other than popu-
lation.  Using the notation xj=ln(Xj), the general trans-
log function for infrastructure services may be written 
as:

d p y x v y = + + + + + + a b b b b g g (7) 2 ijt i pi jt yi jt xim mjt vir rjt yi jt d p y = + + + a b b b ijt i pi jt yi jt x y x y v x x x v r j s f im jt mjt ir jt rjt imn mjt njt imr mjt rjt + + + + ∑ ∑ ∑ ∑ ∑ ∑ ∑ d p y x v y = + + + + + + a b b b b g g y x y v r j 2 ijt i pi jt yi jt xim mjt vir rjt yi jt x im jt mjt ir jt r + + ∑ ∑ ∑ .	 y x y v x x x v r j s f im jt mjt ir jt rjt imn mjt njt imr mjt rjt

In practice, it is often difficult to estimate the full trans-
log specification using the more complex econometric 
models, so the approach adopted was to start with the 
log-linear specification and then test whether the coef-
ficients on the quadratic and cross-product terms are 
significant.  Because this involves repeated testing of 
overlapping specifications, we have followed the spirit 
of the Bonferroni adjustment to test statistics by requir-
ing that any coefficients retained in the model are 
significantly different from zero at the 1 percent level 
using conventional statistical tests.12 

We have noted that including climate variables in equa-
tions for the demand for infrastructure may be chal-
lenged by some economists, especially if one goes on to 
assume that future demand for infrastructure will be 
affected by projected changes in these climate variables.  
The reason for the debate is that climate variables are 
believed to act as a proxy for institutional and other 
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 
to estimate a relationship linking income per person 

and average temperature as a basis for measuring the impact of climate change at highly aggregated level.  
Indeed, any simple correlation of these variables appears to show that a higher average temperature (usually for the capital city of the country) is associated with lower average income per person.  But even this relationship is complicated by the role of natural resource endowments. Acemoglu et al. (2001) suggest that, in part, temperature is serving as an instrument for institutional development, so they include historical mortality rates in their analysis on the grounds that this is an alternative—and better—proxy for institutional development.

The strategy adopted for our analysis relies on a number of alternative ways of dealing with this problem.

The Acemoglu et al (AJR) study used colonial a.	

(mostly 18th century) mortality as an instrument 
for institutional development and found that this 
had a very significant coefficient in their equations 
for recent economic growth.  However, estimates 
of colonial mortality are not available for more 
than one-half of the countries in our sample and, 
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-
mental variables.  The UN's population statistics 
include a variety of demographic variables for the 
early 1950s for almost all countries.  These 
provide good instruments because they are closely 
correlated with the historical endowment of both 
institutions and infrastructure, but demographic 
changes over the past 50 years mean they are less 
associated with current patterns.13  Two instru-
ments have been used—the crude birth rate and 
infant mortality.  These two were chosen because 
they capture the highest proportion of the cross-
country variation of the demographic variables 
examined.  Reflecting their special role, these vari-
ables were included on their own without 

quadratic terms or cross-products with other 
explanatory variables.  Consistently, one or both of 
the variables have coefficients that are significantly 
different from zero at the 95 percent or 99 percent 
levels.  For this reason, the variables are included 
in all of the models discussed below.  So, it must 
be remembered that—even without further 
controls for the possible role of climate as an 
instrument for institutional development—the 
analysis starts from a point that matches the state 
of the art in the current literature.

The role of climate as an instrument for institub.	
tional development is a geographical argument— that is, it is about the geography of regional development—as much as it is about climate per se.  Thus, the natural approach— again made difficult in the past by data limitations—is to consider the use of spatial econometrics in which spatially weighted values of variables are used as instruments for institutional and other factors. The standard model of spatial interaction (or autocorrelation) is:
$$y_{i}={\bf a}\ +{\bf j}\ \sum_{j\neq i}W_{ij}y_{j}+{\bf b}\,x_{i}+{\bf e}_{i}\tag{8}$$

where the matrix W is a matrix of weights capturing 
the spatial influence of location j on location i, φ is 
called the spatial autocorrelation coefficient, and ε is the 
error term whose distribution depends upon the model 
specification.  The inverse-distance model has been used 
for this analysis for which the elements of W are 
proportional to the reciprocal of the distance between 
the population centroids for countries i and j up to a 
maximum of 2,500 km.14  The W matrix is normalized 
so that the row sums are equal to 1.  The equations are 
estimated using panel GMM with spatially weighted 
values of population, GDP per person, urbanization, 
and country size as instruments.  The details of the 
analysis are given in a separate paper, but the overall 

bors."
conclusion is that (a) the spatial interactions are consistently insignificant, and (b) including them does not alter the role of the climate variables in our equations.

Setting aside the spatial argument, the central c.	

econometric contention of the argument that 
climate variables do not reflect the role of climate 
per se is that some or all of these variables are 
correlated with the error terms in the regression.  
This is a classic econometric problem that may be 
caused by omitted variables, measurement errors, 
or other factors.  The standard solution is to treat 
the suspect climate variables as endogenous and 
look for instruments that are correlated with the 
climate variables but not with the error term—see, 
for example, Cameron and Trivedi (2006, chapter 
4) or Baum (2005, chapter 8).  It is not easy to 
find suitable instruments for all of the climate 
variables, especially as a group, since physical char-
acteristics of countries are included in the infra-
structure demand equations. We have investigated 
a range of potential instruments, such as the abso-
lute value of latitude (the best instrument for 
temperature); internal renewable water resources; 
numbers of bird, mammal, and plant species per sq 
km; percentage covered by water and snow/ice; 
and spatially weighted physical characteristics for 
neighboring countries.  These instruments 
perform reasonably well for mean temperature and 
temperature range (both population-weighted and 
inverse population-weighted) on their own.  In 
these cases, the use of instrumental variables does 
not alter our conclusions.  The variables turn out 
to be weak instruments for total precipitation and 
precipitation range on their own or for all climate 
variables together, but no one has seriously 
proposed that either total precipitation or precipi-
tation range act as proxies for other influences on 
infrastructure demand.  Finally, the analysis using 
instrumental variables consistently fails to reject 
the hypothesis that the climate variables—either 
individually or as a group—can be treated as exog-
enous; that is, that the correlation between the 
climate variables and the error term is zero.

A final possibility is that climate variables act as 
d.	
instruments for governance variables.  One 

problem is that governance ratings change over 
time, whereas the climate variables are constant.  
To get around this, we have computed country 
averages for the years for which data is available 
and constructed the correlation matrices for both 
population-weighted and inverse population-
weighted climate variables.  Population-weighted 
mean temperature and precipitation range would 
be the best instruments, as they have simple corre-
lations of -0.41 to -0.49 for the main WGI gover-
nance variables—notably government 
effectiveness, regulatory quality, and rule of law.  
Population-weighted precipitation and tempera-
ture range have very low correlations with the 
governance variables, and the correlations for the 
inverse population-weighted variables are signifi-
cantly worse than for the population-weighted 
variables.  With squared correlations of 0.2 or less, 
climate variables would have high standard errors 
if they were acting as instruments for gover-
nance—roughly 5 times the true standard errors 
for the governance variables—which is difficult to 
reconcile with the relatively high t-ratios actually 
obtained.  Further, including governance variables 
in the tests reported below may reduce or increase 
the F-values for the joint tests on the sets of 
climate variables, but it does not alter the infer-
ence.  Overall, our results provide little support for 
this interpretation.

It is not possible to prove a negative.  Our analysis 
cannot demonstrate conclusively that the coefficients on 
our climate variables reflect the effects of climate per se 
rather than the indirect influence of other, non-climate, 
factors.  Nonetheless, we would argue that the cumula-
tive weight of evidence is strong enough to shift the 
burden of proof.  A key point is that the influence of 
climate in our infrastructure equations rarely depends 
upon a single climate variable on its own, whereas argu-
ments about the role of climate as a proxy or instrument 
for other factors focus almost exclusively on mean 
temperature.  There is even less reason to believe that 
inverse population-weighted climate variables act in this 
way, since by definition these reflect climate patterns in 
areas where people do not live and have not lived in 

large numbers.15  Hence, after extensive and careful 
investigation, we conclude that the evidence supports 
the view that climate does and will have a significant 
influence on future demand for infrastructure.  In 
reaching this conclusion, we have been particularly strict 
when considering the inclusion of mean temperature 
(population-weighted and inverse population-weighted) 
in our projection models, so there has been a bias in 
favor of omitting these variables unless there was unam-
biguous support for retaining them.  On that basis, we 
believe it is reasonable to estimate the Delta-Q compo-
nent of adaptation over the full period from 2010 to 
2050 on the basis of the demand projections generated 
by our equations. 

The primary investigation of alternative specifications is 
carried out using pooled OLS with Driscoll-Kraay stan-
dard errors, which allow for a general pattern of spatial 
dependence between countries (Driscoll and Kraay 
1998; Hoechle 2007).16  In the case of the proportions 
of the population covered by electricity, water, and sewer 
networks, the dependent variable is the logit of the rele-
vant shares in order to translate values between 0 and 1 
to the entire real line.  It is necessary to censor values 
that are reported as either 0 or 1 in order to avoid 
degeneracy.  Thus, the minimum and maximum values 
correspond to shares of 0.001 and 0.999, as the shares 
are reported to the nearest 0.1 of a percentage point.  A 
panel tobit model has been used to estimate the 
demand equations for coverage rates for which a 

15	   The absolute values of the correlation coefficients between the logs 
of similarly weighted climate variables are less than 0.66 across our sample of countries, with the sole exception of total precipitation and precipitation range (see Table 1).  Both temperature and precipitation are negatively correlated with temperature range.  The correlation coefficients between population-weighted and inverse populationweighted variables range from 0.78 to 0.83, with the exception of temperature range, for which the value is 0.94.  In view of this last correlation, we have excluded the inverse-population weighted temperature range from the analysis.  
16	   Driscoll-Kraay standard errors are robust to panel heteroscedasticity 
and temporal autocorrelation as well as spatial interdependence.  The estimation is carried out using Hoechle's xtscc procedure in Stata, which generalizes the Driscoll-Kraay estimator to allow for unbalanced panels.  There is an important feature of the Driscoll-Kraay/Hoechle procedure that needs to be kept in mind.  The method relies upon the derivation of a robust covariance matrix for a sequence of cross-sectional averages.  The panels used for our analysis of some categories of infrastructure are very unbalanced and do not span continuous periods of time.  Nonetheless, cross-sectional averages can be calculated for more than 25 years.  The sample of countries in each cross-sectional average differs, but this is consistent with the way in which the covariance estimator is specified.  Thus, even though the Driscoll-Kraay analysis relies upon asymptotics as T→∞, the nature of our data is 
significant fraction of observations are censored from above with the upper limit equal to logit (0.999). 

In addition to climate variables, the explanatory variables in the base models are:

Log of population
- 
Logs of GDP per person at 2005 PPP, country size, 
- 
and urban population as percentage of total population plus quadratic terms in these variables Log of a cross-country building cost index with the 
- 
U.S.=1.0 Logs of the proportions of land area that are desert, 
- 
arid, semi-arid, steep, very steep, and have no soil constraints for agriculture—obtained from FAO's Terrastat database Logs of the birth rate and infant mortality for 
- 
1950-54 Dummy variables for World Bank regions.
- 

The last two groups of variables are retained in all 
models.  Tests for the inclusion of non-climate and 
climate variables are performed separately.  At the first 
stage, the non-climate variables are tested for signifi-
cance in a model containing the seven climate vari-
ables—pop and ipop variants other than temperature 
range.  After dropping non-climate variables that do 
not have significant coefficients, tests on the hypotheses 
that the coefficients for (a) the population-weighted 
climate variables, (b) the inverse population-weighted 
climate variables, and (c) all climate variables are all 
equal to zero are carried out.  If one or more of these 
hypotheses are rejected, the set of climate variables 
included in the model is reduced by first dropping 
either the pop or the ipop variants and then those vari-
ables within each category that do not have significant 
coefficients.  Finally, interactions with GDP and urban-
ization are tested for the climate variables that have 
been retained. 

Finally, we have used total, urban, or rural population 
weights (as appropriate) in estimating equations for 
which the dependent variable is the log or logit of an 
infrastructure indicator  per person or per household; 
for example, municipal industrial water use per person, 

average household size, or the percentages of households connected to electricity, water, or sewer networks.  
In all cases, the weights are normalized to sum to the number of observations used for the analysis.  

## 5.	 The Effects Of Climate On Demand For Infrastructure

the inverse population-weighted climate variables.  The 
rejection of the hypothesis of zero coefficients is partic-
ularly strong for the inverse-population weighted 
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 
temperature.  On the other hand, both temperature 
range and inverse population-weighted mean tempera-
ture have coefficients that are highly significant.  The 
signs of the coefficients differ, but these variables are 
negatively correlated (see Table 1) so that warmer coun-
tries tend to have less generating capacity, holding other 
factors constant.

Electricity generating capacity.  Model (1) in Table 2 shows that the tests for the joint significance of the climate variables reject the hypothesis of zero coefficients decisively for both the population-weighted and 

| Ln(Generating capacity)   | Ln(Fixed telephone lines)   |
|---------------------------|-----------------------------|
| Logit(Urban               |                             |
| electricity coverage)     |                             |
| Logit(Rural               |                             |
| electricity coverage)     |                             |
| Variables                 | (1)                         |
| Ln(Population)            | 0.954***                    |
| (0.028)                   | (0.021)                     |
| Ln(GDP per per-           |                             |
| son)                      |                             |
| 0.975***                  | 0.926***                    |
| (0.141)                   | (0.129)                     |
| Ln(Country size)          | 1.111***                    |
| (0.137)                   | (0.117)                     |
| Ln(% urban)               | 2.936***                    |
| (0.563)                   | (0.711)                     |
| Ln(% urban)               |                             |
| squared                   |                             |
| 0.324***                  | 0.309***                    |
| (0.050)                   | (0.044)                     |
| Ln(GDP per per-           |                             |
| son) *                    |                             |
| -0.115***                 | -0.108***                   |
| Ln(Country                |                             |
| size)                     |                             |
| (0.014)                   | (0.012)                     |
| Ln(GDP per per-           |                             |
| son) *                    |                             |
| -0.239***                 | -0.226***                   |
| Ln(% urban)               | (0.056)                     |
| Ln(Country size) *        | 0.131***                    |
| Ln(% urban)               | (0.027)                     |
| Ln(Generating capacity)   | Ln(Fixed telephone lines)   |
|---------------------------|-----------------------------|
| Logit(Urban               |                             |
| electricity coverage)     |                             |
| Logit(Rural               |                             |
| electricity coverage)     |                             |
| Variables                 | (1)                         |
| Ln(Building cost)         | -1.878***                   |
| (0.289)                   | (0.284)                     |
| Ln(% desert)              | 0.0276***                   |
| (0.004)                   | (0.005)                     |
| Ln(% semi-arid)           | -0.0378***                  |
| (0.011)                   | (0.011)                     |
| Ln(% steep land)          | -0.107***                   |
| (0.008)                   | (0.012)                     |
| Ln(% very steep           |                             |
| land)                     |                             |
| 0.0947***                 | 0.0792***                   |
| (0.008)                   | (0.007)                     |
| Ln(% no soil con-         |                             |
| straint)                  |                             |
| -0.0522***                | -0.0414***                  |
| (0.010)                   | (0.008)                     |
| Ln(Temperature -          |                             |
| pop)                      |                             |
| -0.837                    | -2.158***                   |
| (0.480)                   | (0.202)                     |
| Ln(Precipitation -        |                             |
| pop)                      |                             |
| 0.395***                  | 0.152***                    |
| (0.069)                   | (0.040)                     |
| Ln(Temp range -           |                             |
| pop)                      |                             |
| 0.313**                   | 0.386***                    |
| (0.103)                   | (0.074)                     |
| Ln(Precip range -         |                             |
| pop)                      |                             |
| -0.431***                 | -0.272***                   |
| 1.388***                  |                             |
| (0.055)                   | (0.052)                     |
| Ln(Temperature -          |                             |
| ipop)                     |                             |
| -1.057***                 | -1.447***                   |
| -13.65***                 |                             |
| (0.137)                   | (0.125)                     |
| Ln(Precipitation -        |                             |
| ipop)                     |                             |
| -0.272***                 | -0.269***                   |
| (0.045)                   | (0.036)                     |
| Ln(Precip range -         |                             |
| ipop)                     |                             |
| 0.479***                  | 0.468***                    |
| (0.055)                   | (0.048)                     |
| Ln(% urban) *             | -1.006**                    |
|                           |                             |
| Ln(Temperature -          |                             |
| pop)                      |                             |
| (0.363)                   |                             |
| Ln(Generating capacity)   | Ln(Fixed telephone lines)   |
|---------------------------|-----------------------------|
| Logit(Urban               |                             |
| electricity coverage)     |                             |
| Logit(Rural               |                             |
| electricity coverage)     |                             |
| Variables                 | (1)                         |
| Ln(% urban) *             | -0.388***                   |
|                           |                             |
| Ln(Precipitation -        |                             |
| pop)                      |                             |
| (0.050)                   |                             |
| Ln(% urban) *             | 0.328***                    |
|                           |                             |
| Ln(Temp range -           |                             |
| pop)                      |                             |
| (0.070)                   |                             |
| Ln(% urban) *             | 0.270***                    |
|                           |                             |
| Ln(Precip range -         |                             |
| pop)                      |                             |
| (0.066)                   | (0.031)                     |
| Ln(% urban) *             | 0.862***                    |
|                           |                             |
| Ln(Temperature -          |                             |
| ipop)                     |                             |
| (0.102)                   | (1.151)                     |
| Ln(% urban) *             | -0.0749***                  |
| Ln(Precip                 |                             |
| range - ipop)             |                             |
| (0.009)                   |                             |
| Model                     | POLS                        |
| Observations              | 6027                        |
| Number of coun-           |                             |
| tries                     |                             |
| 165                       | 165                         |
| R-squared                 | 0.923                       |
| Log-likelihood            | -250.6                      |
| DF                        | 26                          |
| No of censored            |                             |
| obs                       |                             |
| 716                       | 716                         |
| P-value for all cli-      |                             |
| mate 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 addition 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.

Overall, however, temperature and temperature range are less important influences on the amount of generating capacity than precipitation and precipitation range together with their interactions with urbanization. There are various mechanisms by which precipitation may affect installed capacity.  One factor is the role of hydro power in total electricity supply, since utilization factors tend to be lower for hydro plants.  Another is the role of pumped irrigation systems and similar influences on patterns of electricity demand in countries with high interseasonal variations in rainfall.  Even though the absolute values of the coefficients for precipitation and precipitation range are smaller than the equivalent coefficients for temperature, these variables have an important effect in the calculation of the Delta-Q changes because the distributions of changes in total precipitation and precipitation range are much more dispersed and much larger relative to their historic values than are the equivalent distributions for temperature. Fixed telephone lines.  The projection equations for fixed telephone lines are reported as Models (3) & (4) in Table 2.  Again, the hypotheses of zero coefficients for climate variables are decisively rejected, particularly for the population-weighted variables, with mean temperature, temperature range, and precipitation range all having significant coefficients.  There are strong interactions with urbanization, so that the impact of climate change on demand for telephones varies markedly both within and across country classes.   

Electricity network coverage.  Models (5) through (8) in 
Table 2 show the estimated equations for the logits of 
electricity coverage for urban and rural households 
weighted by the relevant populations in 2005.17  Panel 
tobit models are used with an upper censoring value 
corresponding to a coverage of 99.9 percent.  Since the 
majority of observations are censored, the number of 
exogenous variables is reduced in each equation by 
much more than for electricity generating capacity.  
Nonetheless, population-weighted precipitation, precipi-
tation range, and temperature range clearly warrant 

inclusion in the equation for urban coverage.   For rural 
coverage, population-weighted precipitation range and 
inverse population-weighted temperature—on its own 
and interacted with urbanization—are the key climate 
variables.  The chi-square statistic for the test of zero 
influence of the temperature variables is 59.7, so that 
these cannot be excluded.  

Water use.  The dependent variables for water use are 
the logs of water abstractions per person for municipal 
and industrial use, which are derived from FAO data.  
This includes water that is lost in treatment and in 
water supply networks.  Models (1) and (2) in Table 3 
summarize the results of the econometric analysis for 
municipal water use per person.  In this case, the tests 
for the joint significance of the climate variables reject 
the hypothesis of zero coefficients decisively for the 
population-weighted variables, but not for the inverse 
population-weighted variables.  The best specification 
includes population-weighted precipitation and precipi-
tation range.  Another point to note is the quadratic in 
GDP per person.  The results seem to be intuitively 
reasonable, reflecting rainfall patterns where people live 
and the effect of changes in GDP on water use.  The 
quadratic terms in GDP per person imply that water 
consumption per person reaches a peak at an income of 
about $12,000 per capita in 2005 PPP, and falls gradu-
ally as countries get richer beyond this point.

Models (3) and (4) in Table 3 summarize the results for 
industrial water use per person.  In this case, the tests 
reject the hypotheses that the population-weighted and/
or inverse population-weighted climate variables have 
zero coefficients.  The detailed investigation identifies 
population-weighted temperature range and precipita-
tion range plus inverse population-weighted precipita-
tion and precipitation range as having significant 
coefficients.  There are significant interactions between 
the inverse-population weighted climate variables and 
GDP per person with urbanization.  Use of water in 
industry is a derived demand, so the influence of 
climate variables operates through the scale and location 
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.

Water and sewer connections.  Table 4  summarizes the 
results for coverage rates of piped water supply and 

|                            |           |           | 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                                   |
|                                       |       |       | 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               |       |       |                                      |                                       |
| variables = 0                         |       |       |                                      |                                       |
| 0.000                                 | 0.000 |       |                                      |                                       |
| P-value for ipop climate              |       |       |                                      |                                       |
| variables = 0                         |       |       |                                      |                                       |
| 0.087                                 | 0.000 |       |                                      |                                       |

 Note: Standard errors are shown in brackets underneath the relevant coefficients with *** p < 0.001, ** p < 0.01, * p < 0.05.  In addition 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 (6) are based upon panel tobit estimation, allowing for the upper censoring of countries with reported coverage of 99.9 percent or higher.  In general, populationweighted climate variables have a significant influence on coverage rates in urban areas, while inverse population-weighted climate variables are more important in rural areas.  The only exception is rural water supply, for which both sets of climate variables are significant. Interactions with GDP per person and urbanization are not significant.  Since coverage rates for piped water supply are close to or equal to 99.9 percent in high income countries, changes in climate variables will not have any effect on costs of adaptation in many countries.  However, changes in average temperature—and precipitation for rural households—may affect the numbers of households connected to collective sewer systems.18

For the purpose of costing wastewater treatment, we 
have assumed that the BOD/COD concentration and 
other characteristics of sewage handled by wastewater 
treatment plants correspond to typical values for munic-
ipal wastewater.  This implies that industries will be 
expected to process wastewater with high concentra-
tions of industrial pollutants.  Further, it is assumed that 
wastewater treatment plants are scaled to process 80 
percent of the volume of water treated by water treat-
ment plants, allowing for network losses and wastewater 
that is not discharged to sewers.

Roads.  Table 5 shows equations for the total length of 
roads (both paved and unpaved) and for the logit of the 
share of paved roads in total road length, weighted by 
total road length in the latter case.  The key climate 
variables affecting the length of roads are temperature 
and precipitation range—both population-weighted and 
inverse population-weighted—plus population-weighted 
temperature range.  There are strong interactions with 
GDP per person for temperature and precipitation 

tions by assuming that the total numbers of water supply and sewer 
connection are 10 percent higher than the numbers of household con-
nections, based on typical ratios for middle-income countries. 

Logit(Urban water  
coverage)
Logit(Rural water  
coverage)
Logit(Urban sewer  
coverage)
Logit(Rural water  
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) squared
0.157**
0.149**
(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)
Logit(Urban water  
coverage)
Logit(Rural water  
coverage)
Logit(Urban sewer  
coverage)
Logit(Rural water  
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 variables = 0
0.000
0.000
0.000
0.000
P-value for pop climate variables = 0
0.000
0.000
0.024
0.021
P-value for ipop climate variables = 0
0.854
0.001
0.002
0.000

Note: Standard errors are shown in brackets underneath the relevant coefficients with *** p < 0.001, ** p < 0.01, * p < 0.05.  In addition 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.

|                          |           |            | 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)   |            |                         |                               |
|                          |            |            | 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***  |                         |                               |
| Ln(% desert)             | 0.0347***  | 0.0546***  | -0.157***               | -0.232***                     |
| (0.009)                  | (0.006)    | (0.025)    | (0.033)                 |                               |
| Ln(% arid)               | 0.396***   | 0.371***   |                         |                               |
| 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)    |            |                         |                               |
| (0.778)                  | (0.527)    |            |                         |                               |
| (0.041)                  | (0.065)    |            |                         |                               |
|                                        |           |       | 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 addition 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.

GDP per person.  In particular, higher temperatures in 
rural areas—that is, inverse population-weighted 
temperature—lead to a lower share of paved roads, 
which is exactly what one would expect in view of the 
higher costs of construction and maintenance for rural 
paved roads associated with higher temperatures. 

range and with urbanization for precipitation range. These climate variables are directly linked to the cost of building and maintaining roads—temperature is particularly important for paved roads subject to heavy use, while temperature and precipitation ranges affect the capital and maintenance costs of both paved and unpaved roads.  The fact that both population-weighted and inverse-population weighted climate variables are significant reflects the impact of climate on all types of roads—rural, urban, and national.   It is likely that climate may also play a role through the structure of the economy—for example, the nature and role of agricultural production—and through geographical patterns of economic development.

Other transport.  Table 6 shows the projection equations 
for rail track length, aircraft movements, and container 
traffic handled by ports.  The last two are indicators 
used in estimating investments in airports and sea/river 
ports.  With one exception, the tests reject the hypothe-
sis of zero coefficients for all climate variables decisively. 
                                                       
The exception is for inverse population-weighted 
climate variables in the rail equation.  The primary 
climate influences are:

The share of paved roads in total road length is influenced by the same variables and their interactions with 

|                          |           |           |           | 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***               |                          |                         |
| Ln(% urban) squared      | -0.112*   | 1.090***  | 1.563***  |                         |                          |                         |
| 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***                |                          |                         |
| Ln(% arid)               | -0.333*** | -0.363*** |           |                         |                          |                         |
| Ln(% semi-arid)          | 0.0404*** | 0.0710*** |           |                         |                          |                         |
| Ln(% steep land)         | -0.132*** | -0.144*** |           |                         |                          |                         |
| Ln(% very steep land)    | 0.0743*** | 0.0819*** |           |                         |                          |                         |
| 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)                 |                          |                         |
| (0.003)                  | (0.003)   | (0.003)   | (0.003)   |                         |                          |                         |
| (0.047)                  | (0.168)   | (0.171)   |           |                         |                          |                         |
| (0.021)                  | (0.020)   | (0.009)   | (0.015)   |                         |                          |                         |
| (0.012)                  | (0.011)   |           |           |                         |                          |                         |
| (0.006)                  | (0.004)   |           |           |                         |                          |                         |
| (0.029)                  | (0.032)   |           |           |                         |                          |                         |
| (0.010)                  | (0.011)   |           |           |                         |                          |                         |
|                                        |           |           |         | 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 addition 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.

Rail length—temperature, precipitation, and 
a.	
temperature range, both on their own and interacted with GDP per person

movement will be affected by factors such as the 
amount and distribution of tourism (both internal and 
external), the dispersion and nature of natural-resource 
based industries, and the availability of alternative 
methods of transport.  

Aircraft movements—all climate variables other 
b.	
than population-weighted temperature plus interactions with urbanization Container traffic—both precipitation variables, 
c.	
inverse population-weighted temperature, and precipitation plus interactions with urbanization and GDP per person.

Health care.  Our analysis of adaptation costs relies 
upon two health care inputs—the numbers of hospital 
beds and physicians —as indicators used in assessing 
the baseline cost of health infrastructure (hospitals and 
clinics) and the impact of climate change.  The projec-
tion equations are shown in Models (1) through (4) of 
Table 7.  In addition, Models (5) and (6) report equa-
tions for one important indicator of health outcomes—
the log of the infant mortality rate.

In these cases, there is no easy explanation for the results since it is clear that there are multiple influences on the indicators.  For example, the number of aircraft 

|                            |            |            |            | 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)    |            |                           |                     |                             |
|                                |           |           |           | 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-  |           |           |           |                           |                     |                             |
| ables = 0                      |           |           |           |                           |                     |                             |
| 0.000                          | 0.000     | 0.000     |           |                           |                     |                             |
| P-value for pop climate vari-  |           |           |           |                           |                     |                             |
| ables = 0                      |           |           |           |                           |                     |                             |
| 0.000                          | 0.000     | 0.000     |           |                           |                     |                             |
| P-value for ipop climate vari- |           |           |           |                           |                     |                             |
| ables = 0                      |           |           |           |                           |                     |                             |
| 0.000                          | 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 addition 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.

Again, the hypothesis that climate variables have no effect on either health inputs or health outcomes is consistently rejected at very high confidence levels.

Hospital beds—Population-weighted temperature 
a.	

on its own and interacted with GDP per person 
plus inverse population-weighted precipitation are 
the key variables in this case.  The overall coeffi-
cient (elasticity) on mean temperature increases—
from -3.07 for a low-income country with a GDP 
per person of $1,000, to -0.72 for a middle-
income country with a GDP of $10,000 per 
person, and to +0.70 for a high-income country 
with a GDP of $40,000 per person.  Thus, it is 
easy to be misled by simple assumptions about 
how climate "ought" to affect investment in 
healthcare facilities that are based on experience in 
a narrow range of countries.  The coefficient on 
precipitation is positive but quite small.  It is 
possible that this reflects an increased need for 
dispersed hospital facilities when communications 
are subject to disruption caused by high rainfall. 

Doctors —The results for the number of doctors 
b.	

are similar to those for hospital beds, but the 
influence of temperature is divided between popu-
lation and inverse population-weight variables.  
This seems reasonable since hospitals are invari-
ably located in urban areas, whereas doctors may 
be more dispersed, though this is not the case in 
the poorest countries.  Again, the interactions with 
GDP per person mean that the overall coefficients 
switch from negative to positive at a GDP per 
person of $12,600 for inverse population-weighted 
temperature, and at a GDP per person of $36,200 

|                      |          |          | 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)              |                          |

for population-weighted temperature.  How this 
works out country-by-country depends upon the 
temperature distribution across heavily and thinly 
populated areas.  In this case, the coefficient on 
precipitation is negative and is linked to the popu-
lation-weighted variable.

     Infant-mortality—This is influenced by tempera-
c.	

ture, including an interaction with urbanization 
plus temperature range and inverse population-
weighted precipitation.  The signs of the coeffi-
cients on temperature can be misinterpreted.  
Assuming that temperature increases (or 
decreases) uniformly throughout a country, the net 
coefficient on temperature is +0.88 for a country 
with an urbanization rate of 20 percent, but +0.24 
for a country with an urbanization rate of 80 
percent.  Hence, an increase in mean temperature 
is likely to increase infant mortality, but by more 
in low-income countries with low levels of urban-
ization than in middle- and high-income coun-
tries with higher levels of urbanization.  These 
results conform with a priori expectations.  In 
addition, a higher temperature range and higher 
precipitation in rural areas tend to increase infant 
mortality, both of which seem reasonable. 

Social infrastructure. The number of teachers is used as 
the indicator for investment in schools, while the 
number of post offices is used as one indicator for 
municipal infrastructure.  The equations are shown in 
Table 8.  As one would expect, one cannot reject the 
hypothesis that the inverse population-weighted climate 
variables have no effect on the number of post offices, 
which are concentrated in areas of greater population 

|                            |            |            | 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)              |                          |
|                                        |           |           | 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 addition 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.

density.  Otherwise the results show a mixture of climate interactions with urbanization for the number of teachers and with GDP per person for post offices. Focusing again on mean temperature in the equation for the number of teachers, the overall coefficients for a uniform increase in temperature are -0.55 for an urbanization rate of 20 percent and -0.02 for an urbanization rate of 80 percent, so the effect of climate on teachers is much larger in low-income and rural countries.  This is consistent with the well-established difficulty of equipping and staffing rural schools.  Of course, low-income countries today are likely to be much more urban in 
2050, so that the cumulative impact of an increase in temperature on the number of teachers will be small even in these cases. Household size.  In several cases, the amount of infrastructure is linked to the projected number of households, so it is necessary to rely upon equations that project the average household size in urban and rural areas.  These are shown in Table 9.  It seems that climate does affect average household sizes.  The primary mechanism is that higher temperatures are associated with larger average sizes for both urban and rural households.  There is also a significant but quantitatively small impact of precipitation on rural household size.

## 6.	 Calculating The Cost Of Adaptation

The calculation of the cost of adaptation involves a number of steps.  The description that follows focuses on investment or capital costs.  A similar process is required to estimate changes in the costs of operation and maintenance, both for the baseline level of infrastructure and for changes in infrastructure resulting from changes in climate conditions.19  

attempt is made to allow for within-period changes in variables.  Some of the demographic variables (urbanization and population age structure) used in the projection equations are based on period averages.  

Step 1—Construct baseline projections of infrastructure 
investment.  The projection equations discussed in the 
previous section are used to construct baseline projec-
tions of the efficient stock of infrastructure assets for 
periods from 2010 to 2050 under the assumption of no 
climate change.  The projections of physical infrastruc-
ture demand are based upon standard assumptions 
about income and population growth, population struc-
ture, and urbanization.  The value of new investment 
required for infrastructure type i for country j in period 
t is obtained by multiplying ΔQijt = Qijt+1 - Qijt by 
Cij, the unit cost of infrastructure type i in country j at 
2005 prices.  The unit costs have been compiled from a 
large variety of World Bank and other sources.  A stan-
dardized construction cost index has been used to allow 
for broad cross-country differences in construction 
costs, but allowances are also made for location (urban 
or rural) and other special factors.  In addition to new 
investment, we have estimated the amount of invest-
ment that would be required to replace infrastructure 
assets that reach the end of their economic life.  There 
is no realistic way of modeling the age structure of 
assets in situ at the beginning of the analysis.  
Implementing a full vintage model of infrastructure is 
not sensible given the uncertainty about other parame-
ters in the model.  Hence, we have adopted a continu-
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.

Step 2—Add alternative climate scenarios. The data used 
for the baseline projections is supplemented with 
projections of the climate variables taken from the 
climate scenarios that are being used for the whole 
EACC study.  These are constructed as deltas at differ-
ent dates with respect to the no-climate-change base-
line derived from calculations of monthly average, 
maximum, and minimum temperatures and precipita-
tion.  To avoid instability in the projections arising from 
path-dependency and other effects, the climate variables 
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.

Step 3—Project infrastructure quantities under the alterna-
tive climate scenarios.  This is similar to the projection of 
baseline infrastructure quantities in Step 1, but using 
the climate variables for the alternative climate 
scenarios.

|                            |            |            | 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                       |
| 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     |   0     |         |         |
| P-value for pop climate variables = 0  |   0     |   0     |         |         |
| P-value for ipop climate variables = 0 |   0     |   0.001 |         |         |

Note: Standard errors are shown in brackets underneath the relevant coefficients with *** p < 0.001, ** p < 0.01, * p < 0.05.  In addition 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.

Variant 2 
b.	
assumes that the asset is designed to 
withstand the worst conditions that it might be exposed to over its life—that is:
$$\Delta C_{ijt}=d[\max(V_{jt},...,V_{j,t+L_{j}})]C_{ij}\tag{10}$$
on the assumption that  the severity of storms increases monotonically with the relevant climate variable(s) V.

Step 4—Apply the dose-response relationship to estimate changes in unit costs for alternative climate scenarios.  We calculate the changes in unit costs for infrastructure type i in country j for period t, ΔCijt, using the climate change deltas for the alternative climate scenarios and the dose-response relationships discussed in Appendix 1.  There is a complication that has to be considered. This concerns the question of whether the design standards used for infrastructure are—or should be—forward looking.  Normal engineering practice does not take account of changes in underlying climate conditions.  Thus, in designing for a 100-year storm, the engineer looks at the characteristics of the 100-year storm on the basis of evidence of storms up to the current date.  Clearly, this does not allow for changes in the severity of the 100-year storm that might be expected to occur over the life of the asset.  There are two possible approaches that can be adopted.

Variant 1 
a.	
assumes that the dose-response adjustment to unit costs is calculated using current climate conditions—that is: 
$$\Delta C_{i j i}=d[V_{j i}]C_{i j}$$

      The difficulty with Variant 2 is that it implies that the 
      asset is significantly overdesigned for most of its work-
      ing life because it will only be exposed to the most 
      severe weather conditions at the very end of its life.  In 
      economic terms, Variant 2 is not the optimal solution 
      and it would be sensible to design for the 100-year 
      storm consistent with the expected climate at some 
      earlier date.  There is no general solution, since the 
      optimal period to look ahead depends upon both the 
      expected increase in the severity of storms over the 
      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 
      period of 50 years.  
(9)

         where d[ ] is the dose-response relationship.

Step 5—Estimate the change in total investment costs for the baseline projections.  This yields the Delta-P estimates of the cost of adaptation for each climate scenario with two variants corresponding to the alternatives at Step 4 above. Step 6—Estimate the change in investment costs due to the difference between the baseline infrastructure quantities and the alternative climate scenario quantities.  This yields the Delta-Q estimates of the cost of adaptation for each climate scenario. 

Step 7—Special adjustments.  We have incorporated some special factors in the calculation of the costs of adapting to climate change that could not be represented by the general dose-response relationships.  These are:
For electricity generation, we have taken account a.	

of the decrease in the operating efficiency of existing thermal power plants as the ambient temperature increases.  The effect is documented in the literature for ambient temperatures above 15°C, though it is possible to design new power plants 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 costs.  
$$\Delta Q_{ijt}+\Delta R_{ijt}\,\big]\,\big[$$

A and b are equal. 
Another special factor for electricity generation 
b.	
concerns the efficiency and feasibility of water cooling as temperatures increase, because of limits 
on the temperature rise that can be permitted in the receiving waters.  Dry cooling can be adopted either in parallel with wet cooling or as an alternative in particularly hot or dry locations.  The model assumes that an increasing proportion of power plants will rely upon dry cooling as average temperatures rise. The operating costs of water treatment plants may 
c.	
increase as a result of climate change.  Primary attention has focused on the amount of chemicals used for flocculation if the levels of turbidity and suspended solids in raw water rise.  This is likely to be associated with changes in levels of peak flow in rivers from which water is abstracted, so the model allows for cost of chemicals to increase 
pro rata with maximum monthly precipitation.

Changes in temperature affect the rate at which d.	

oxygen levels recover in rivers to which the efflu-
ent is discharged from waste water treatment 
plants.  Thus, a higher level of BOD removal is 
required to maintain the quality of receiving 
waters.  This implies higher consumption of elec-
tricity or use of chemicals at treatment plants.  
The increase in O&M costs is linked to the 
increase in average temperatures and is incorpo-
rated in our estimates of the cost of adaptation.

These steps are followed in deriving the estimates of 
ΔCijt used in calculating the Delta-P costs of adapta-
tion in the first part of equation (2):

$$\Delta I_{j i}[1]=\sum_{i}\Delta C_{i j i}[Q_{i j i+1}-Q_{j i i}+R_{i j i}\,]\qquad\qquad(11)$$
with the Qijt, etc. given by the baseline projections of infrastructure investment. The Delta-Q costs of adaptation are defined by:

$$\Delta I_{ji}[2]=\sum_{i}(C_{ji}+\Delta C_{ji})[\Delta Q_{ji+1}-\tag{12}$$
in which ΔQijt are obtained from the changes in the baseline investments associated with the alternative climate scenarios.

Equation (12) yields engineering estimates of the 
Delta-Q costs, which reflect an assumption that coun-
tries will respond to climate change by building more or 
less infrastructure.  However, it should be noted that 
more cost-effective options may be available.  In 
another paper, we examine one of these options in more 
detail for the water sector (Hughes, Chinowsky, and 
Strzepek 2010).  We show that the welfare cost of using 
water abstraction fees to limit increases in demand for 
water may be lower than the cost of building additional 
capacity for water and wastewater treatment.  Our 
results demonstrate that this economic approach can 
reduce the cost of adaptation in the water sector by a 
substantial amount relative to the engineering approach 
of building more infrastructure assets in response to an 

## 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 could be followed for some other types of infrastructure—for example,  energy and transport.  As a consequence, the estimates of the Delta-Q costs of adaptation will tend to overstate the economic costs of adaptation in countries that face an increase in demand for infrastructure as a consequence of climate change.  Since the effect is one-sided—that is, an economic approach can reduce costs when the demand for infrastructure increases, but would not be required when the demand for infrastructure decreases—it is safe to conclude that the engineering estimates of the Delta-Q costs of adaptation presented in the next section represent an upper bound on the costs of following a cost-effective strategy of adaptation.   

Our estimates of the costs of adaptation for electricity 
and water services are shown in Tables 10 to 14.  To 
facilitate comparisons all figures in the tables are 
presented as average costs per year at 2005 prices over 
the relevant period—that is, for 2010–50 as a whole or 
for each decade—with no discounting.  Figures are 
rounded to the nearest $1 billion per year to avoid any 
impression of spurious accuracy.  As a consequence, 
sums of the separate numbers may differ from the rele-
vant totals due to rounding.  The Delta-P increases in 
investment, O&M, and total costs for the two climate 
scenarios are shown by infrastructure category and 
country class in Table 10. The baseline costs without 
any climate change are shown as a point of reference.  
In all cases, the costs of adaptation are substantially 

| NCAR scenario          | Cost type    | Low income   |
|------------------------|--------------|--------------|
| Lower middle           |              |              |
| income                 |              |              |
| Upper middle           |              |              |
| income                 | High income  | Total        |
| 1.  Power & telephones | Capital cost | 0            |
| O&M cost               | 0            | 0            |
| Total cost             | 0            | 1            |
| Baseline cost          | 132          | 173          |
| 2.  Water & sewers     | Capital cost | 0            |
| O&M cost               | 0            | 0            |
| Total cost             | 0            | 0            |
| Baseline cost          | 119          | 154          |
| 3.  Roads              | Capital cost | 3            |
| O&M cost               | 0            | 0            |
| Total cost             | 3            | 2            |
| Baseline cost          | 67           | 56           |
| 4.  Other transport    | Capital cost | 0            |
| O&M cost               | 0            | 0            |
| Total cost             | 0            | 0            |
| Baseline cost          | 8            | 18           |
| 5.  Health & schools   | Capital cost | 0            |
| O&M cost               | 0            | 0            |
| Total cost             | 0            | 1            |
| Baseline cost          | 36           | 121          |
| (                      | continued    | )            |
| NCAR scenario            | Cost type    | Low income   |
|--------------------------|--------------|--------------|
| Lower middle             |              |              |
| income                   |              |              |
| Upper middle             |              |              |
| income                   | High income  | Total        |
| 6.  Urban infrastructure | Capital cost | 8            |
| O&M cost                 | 0            | 0            |
| Total cost               | 8            | 6            |
| Baseline cost            | 287          | 219          |
| Total                    | Capital cost | 11           |
| O&M cost                 | 0            | 0            |
| Total cost               | 11           | 9            |
| Baseline cost            | 649          | 740          |
| CSIRO scenario           |              |              |
| 1.  Power & telephones   | Capital cost | 0            |
| O&M cost                 | 0            | 0            |
| Total cost               | 0            | 0            |
| Baseline cost            | 132          | 173          |
| 2.  Water & sewers       | Capital cost | 0            |
| O&M cost                 | 0            | 0            |
| Total cost               | 0            | 0            |
| Baseline cost            | 119          | 154          |
| 3.  Roads                | Capital cost | 1            |
| O&M cost                 | 0            | 0            |
| Total cost               | 1            | 1            |
| Baseline cost            | 67           | 56           |
| 4.  Other transport      | Capital cost | 0            |
| O&M cost                 | 0            | 0            |
| Total cost               | 0            | 0            |
| Baseline cost            | 8            | 18           |
| 5.  Health & schools     | Capital cost | 0            |
| O&M cost                 | 0            | 0            |
| Total cost               | 0            | 0            |
| Baseline cost            | 36           | 121          |
| 6.  Urban infrastructure | Capital cost | 4            |
| O&M cost                 | 0            | 0            |
| Total cost               | 4            | 2            |
| Baseline cost            | 287          | 219          |
| Total                    | Capital cost | 5            |
| O&M cost                 | 0            | 0            |
| Total cost               | 5            | 4            |
| Baseline cost            | 649          | 740          |

Source: Authors' estimates higher for the NCAR scenario than for the CSIRO scenario, so we will focus on the NCAR figures.  The total Delta-P cost of adaptation over 40 years is about 1 percent of the baseline cost for all countries.  The ratio of adaptation costs to baseline costs is highest for lowincome countries at about 1.7 percent and is lowest for high-income countries.

the low-income countries, the costs of adaptation are 
highest for East Asia (EAP) and for South Asia (SAS), 
reflecting their populations and aggregate income.  The 
costs for Europe and Central Asia (ECA) are higher 
than might have been anticipated, but this reflects the 
initial level of infrastructure leading to relatively high 
O&M costs.  Sub-Saharan Africa (SSA) has the high-
est ratio of adaptation costs to baseline costs at 2.3 
percent.   Broken down by infrastructure category and 
region, the heaviest burden of adaptation is for other 

NCAR scenario
Cost type
EAP
ECA
LCA
MNA
SAS
SSA
Total
    1.  Power & telephones
Capital cost
0 
0 
0 
0 
0 
0 
1 
O&M cost
0 
0 
0 
0 
0 
0 
0 
Total cost
0 
0 
0 
0 
0 
0 
1 
Baseline cost
137 
75 
41 
24 
79 
41 
397 
    2.  Water & sewers
Capital cost
0 
0 
0 
0 
0 
0 
0 
O&M cost
0 
0 
0 
0 
0 
0 
0 
Total cost
0 
0 
0 
0 
0 
0 
1 
Baseline cost
115 
71 
54 
25 
81 
22 
368 
    3.  Roads
Capital cost
1 
0 
1 
0 
2 
1 
5 
O&M cost
0 
0 
0 
0 
0 
0 
0 
Total cost
1 
0 
1 
0 
2 
1 
5 
Baseline cost
36 
37 
31 
13 
44 
23 
183 
    4.  Other transport
Capital cost
0 
1 
0 
0 
0 
0 
1 
O&M cost
0 
3 
0 
0 
0 
0 
4 
Total cost
0 
4 
0 
0 
0 
0 
5 
Baseline cost
16 
80 
6 
2 
4 
4 
111 
    5.  Health & schools
Capital cost
1 
0 
0 
0 
0 
0 
1 
O&M cost
0 
0 
0 
0 
0 
0 
0 
Total cost
1 
0 
0 
0 
0 
0 
1 
Baseline cost
93 
49 
52 
20 
25 
9 
249 
    6.  Urban infrastructure
Capital cost
5 
1 
2 
0 
5 
2 
15 
O&M cost
0 
0 
0 
0 
0 
0 
0 
NCAR scenario
Cost type
EAP
ECA
LCA
MNA
SAS
SSA
Total
Total cost
5 
1 
2 
0 
5 
2 
15 
Baseline cost
163 
159 
78 
32 
252 
31 
714 
    Total
Capital cost
8 
2 
3 
1 
8 
3 
24 
O&M cost
0 
3 
0 
0 
0 
0 
4 
Total cost
8 
5 
3 
1 
8 
3 
28 
Baseline cost
560 
470 
262 
116 
485 
130 
2,023 
CSIRO scenario
    1.  Power & telephones
Capital cost
0 
0 
0 
0 
0 
0 
1 
O&M cost
0 
0 
0 
0 
0 
0 
0 
Total cost
0 
0 
0 
0 
0 
0 
1 
Baseline cost
137 
75 
41 
24 
79 
41 
397 
    2.  Water & sewers
Capital cost
0 
0 
0 
0 
0 
0 
0 
O&M cost
0 
0 
0 
0 
0 
0 
0 
Total cost
0 
0 
0 
0 
0 
0 
0 
Baseline cost
115 
71 
54 
25 
81 
22 
368 
    3.  Roads
Capital cost
0 
0 
0 
0 
1 
0 
2 
O&M cost
0 
0 
0 
0 
0 
0 
0 
Total cost
0 
0 
0 
0 
1 
0 
2 
Baseline 
cost
36 
37 
31 
13 
44 
23 
183 
    4.  Other transport
Capital cost
0 
0 
0 
0 
0 
0 
0 
O&M cost
0 
2 
0 
0 
0 
0 
2 
Total cost
0 
2 
0 
0 
0 
0 
3 
Baseline cost
16 
80 
6 
2 
4 
4 
111 
    5.  Health & schools
Capital cost
0 
0 
0 
0 
0 
0 
1 
O&M cost
0 
0 
0 
0 
0 
0 
0 
Total cost
0 
0 
0 
0 
0 
0 
1 
Baseline cost
93 
49 
52 
20 
25 
9 
249 
    6.  Urban infrastructure
Capital cost
2 
1 
1 
0 
3 
1 
7 
O&M cost
0 
0 
0 
0 
0 
0 
0 
Total cost
2 
1 
1 
0 
3 
1 
7 
CSIRO scenario
NCAR scenario
Cost type
EAP
ECA
LCA
MNA
SAS
SSA
Total
Baseline cost
163 
159 
78 
32 
252 
31 
714 
Total
Capital cost
3 
1 
1 
0 
4 
1 
11 
O&M cost
0 
2 
0 
0 
0 
0 
3 
Total cost
3 
3 
1 
1 
4 
1 
14 
Baseline cost
560 
470 
262 
116 
485 
130 
2,023 

Source: Authors' estimates.

transport in the ECA region, largely because of the 
high level of O&M costs.  This is followed by roads in 
South Asia, but in both cases the cost of adaptation is 
little more than 5 percent of baseline costs.

Table 12 shows the breakdown of the Delta-P costs of 
adaptation for all infrastructure by decade.  The relative 
cost of adaptation increases gradually from about 1 
percent of baseline costs for 2010–19 to about 1.6 
percent for 2040–49.  One component of this increase is 
the rise in O&M costs in the ECA region, which has 
already been highlighted, but even for all regions other 
than ECA there is an increase from about 1.2 percent 
of baseline costs in the first decade to 1.6 percent in the 
final decade.  

Tables 13 and 14 give details of the costs of adaptation 
by infrastructure category and country class or region 
when the definition of the cost of adaptation is 
extended to include both the Delta-P and the Delta-Q 
components in the analysis.  Recall that the Delta-Q 
costs are driven by the increase or decrease in the 
demand for infrastructure associated with the projected 
changes in climate.  Table 13 shows that the Delta-Q 
changes are negative for the world as a whole in both 
scenarios.  This means that total expenditure on infra-
structure will fall as a consequence of climate change, 
though more investment may be required in some coun-
tries and some sectors.  However, the fall in total expen-
diture is most important for high-income countries, so 
that the overall scale of the Delta-Q adjustments for 
developing countries is similar to that of the Delta-P 

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 
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 costs by World Bank region in Table 14, which shows that the sum of Delta-P and Delta-Q costs of adaptation is close to zero for all developing countries.  The net costs of adaptation per year over the full period vary from a negative cost (that is, a saving) of $7 billion per year for East Asia to a positive cost of $2 billion per year for the Middle East and North Africa (MNA).   

The striking feature of the results—taking account of 
both Delta-P and Delta-Q costs of adaptation—is how 
small the overall costs of adaptation are relative to the 
baseline costs.  The impact of climate change is far from 
evenly distributed, but even in the worst-affected 
region—MNA—the net cost is little more than 2 
percent of baseline expenditures.  Thus, in practice the 
cost of adaptation for infrastructure is well within all of 
the margins of error inherent in this type of exercise.  

| NCAR scenario            | Cost type   |   Low income |
|--------------------------|-------------|--------------|
| Lower middle             |             |              |
| income                   |             |              |
| Upper middle             |             |              |
| income                   |             |              |
| High income              | Total       |              |
| 1.  Power & telephones   | Delta-P     |           0  |
| Delta-Q                  | -4          |          -2  |
| Delta-P+Delta-Q          | -4          |          -2  |
| Baseline cost            | 132         |         173  |
| 2.  Water & sewers       | Delta-P     |           0  |
| Delta-Q                  | -5          |           1  |
| Delta-P+Delta-Q          | -5          |           1  |
| Baseline cost            | 119         |         154  |
| 3.  Roads                | Delta-P     |           3  |
| Delta-Q                  | 0           |          -2  |
| Delta-P+Delta-Q          | 3           |           0  |
| Baseline cost            | 67          |          56  |
| 4.  Other transport      | Delta-P     |           0  |
| Delta-Q                  | 0           |           0  |
| Delta-P+Delta-Q          | 0           |           0  |
| Baseline cost            | 8           |          18  |
| 5.  Health & schools     | Delta-P     |           0  |
| Delta-Q                  | 0           |          -1  |
| Delta-P+Delta-Q          | 0           |           0  |
| Baseline cost            | 36          |         121  |
| 6.  Urban infrastructure | Delta-P     |           8  |
| Delta-Q                  | -3          |          -5  |
| Delta-P+Delta-Q          | 5           |           0  |
| Baseline cost            | 287         |         219  |
| Total                    | Delta-P     |          11  |
| Delta-Q                  | -13         |         -10  |
| Delta-P+Delta-Q          | -1          |          -1  |
| Baseline cost            | 649         |         740  |
| CSIRO scenario           |             |              |
| 1.  Power & telephones   | Delta-P     |           0  |
| Delta-Q                  | -4          |           1  |
| Delta-P+Delta-Q          | -4          |           2  |
| Baseline cost            | 132         |         173  |
| 2.  Water & sewers       | Delta-P     |           0  |
| Delta-Q                  | -2          |           1  |
| Delta-P+Delta-Q          | -2          |           1  |
| Baseline cost            | 119         |         154  |
| 3.  Roads                | Delta-P     |           1  |
CSIRO scenario
NCAR scenario
Cost type
Low income
Lower middle 
income
Upper middle 
income
High income
Total
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.

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 
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 
CSIRO scenario
NCAR 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

The work reported in this paper represents the most extensive and careful effort that has been made to estimate the costs of adapting to climate change in the infrastructure sector at a global level.  Our primary conclusion is that the cost of adapting to climate change, given the baseline level of infrastructure provision, is no more than 1–2 percent of the total cost of providing that infrastructure.  While there are differences across regions and sectors, the pattern is clear and unambiguous—the cost of adaptation is small in relation to other factors that may influence the future costs of infrastructure.  We accept that we may have omitted or underestimated some of the costs of adaptation.  On the other hand, we have consistently tried to err on the 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 the demand for infrastructure will reduce the Delta-Q costs by a substantial amount in some cases.  Thus, in our view it is extremely unlikely that revised estimates will alter our conclusion about the relative magnitude of the costs of adaptation. The second conclusion of our study is that the impact of climate change on the overall demand for infrastructure may be more important than the increase in the cost of providing the baseline level of provision.  These 

Delta-Q effects may be positive or negative—increasing 
or decreasing the costs of adaptation—in different 
countries.  Summed by region, the Delta-Q totals are 
negative in all regions except MNA.  The results of our 
econometric analysis do not dictate that climate change 
will have the effect of reducing demand for generating 
capacity or roads.  The equations contain complex inter-
actions between income and various climate variables 
—not merely temperature—with both population-
weighted and inverse population-weighted variants.  It 
does not seem plausible that these effects are merely 
capturing the influence of one or more omitted vari-
ables.  Hence, estimates of the costs of adaptation that 
ignore the potential impact of climate change on the 
demand side may give a rather partial view of the over-
all picture.

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## Appendix 1. Derivation Of The Climate Dose-Response Relationships

Paul Chinowsky
(Department of Civil, Environmental and Architectural Engineering, University of Colorado)
Jason Price & Jim Neumann
(Industrial Economics Inc, Cambridge, Mass)
The dose-response relationship between climate change and the cost of building and maintaining infrastructure is a central component of the World Bank's assessment of infrastructure adaptation costs.  The magnitude of the dose-response relationship is likely to vary both by infrastructure type and by country.  Variation in this relationship by infrastructure type reflects, among other factors, differences in the materials with which different types of infrastructure are constructed and the ways in which different types of infrastructure are used; for example, buildings often provide heating and cooling. In addition, variation in the dose-response relationship by country reflects inter-country variation in labor and materials costs as well as terrain; for example, varying degrees of flat versus mountainous terrain. The data and methods supporting the World Bank's assessment of dose-response values by infrastructure type and country are outlined in the sections below.  
This information is presented separately for infrastructure construction costs and infrastructure maintenance costs.  Exhibits 1 and 2 describe the specific doseresponse relationships analyzed.  We note that the doseresponse values estimated for both construction costs and maintenance costs are based on the cost of building and maintaining infrastructure in the United States.  To develop dose-response values specific to individual developing countries, we scaled the U.S.-based cost estimates using an inter-country construction cost index published by Compass International Consultants Inc. (2009).  The country-specific values that make up this index represent average construction costs for each country relative to costs in the United States.

## 1.  Estimation Of Dose-Response Values For Construction Costs

To generate dose-response values for infrastructure 
construction costs, we employed two general 
approaches.  The first estimates dose-response values 
based on the cost associated with the change in the 
typical building code update, while the second more 
directly estimates the incremental costs of climate stres-
sors and design changes.  We use the building code 
approach to generate dose-response values for paved 
roads, buildings, and transmission towers and the latter 
for bridges and unpaved roads. 

Our assessment of dose-response values for infrastruc-
ture construction costs assumes perfect foresight with 
respect to climate change.  Therefore, these dose-
response values represent the relationship between 
infrastructure construction costs at the time of 
construction and the changes in climate projected 
during the infrastructure's lifespan. 

## A.  Building Code Methodology

The building code methodology is based on the premise 
that a major update of design standards results in a 0.8 
percent increase in construction costs (FEMA 1998).  
The readily available data suggest that such code 
updates would occur with every 10 centimeter (cm) 
increase in precipitation for paved roads and buildings; 
therefore, we express the precipitation dose-response 
relationship for these specific types of infrastructure as 
follows:

$$\quad(1)\qquad C_{P,B P R}=0.8\%_{0}\bigl(B_{B P R}\,\bigr)$$
where 

CP
 ,BPR = change in building and paved road construc-
tion costs associated with a 10 cm change in annual 
precipitation

BBPR = 	
     base construction costs for buildings and paved 
roads 

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 

## Exhibit 1 - Dose-Response Descriptions For Construction Costs

| Precipitation Dose-Response            | Temperature Dose-Response            |
|----------------------------------------|--------------------------------------|
| Not estimated.  Impact likely to       |                                      |
| be minimal.                            |                                      |
| Bridges                                | Change in construction costs per     |
| bridge per 1 foot increase in bridge   |                                      |
| height.                                |                                      |
| Paved Roads                            | Change in costs of constructing a km |
| of paved road per 10 cm change in      |                                      |
| annual precipitation projected during  |                                      |
| lifespan relative to baseline climate. |                                      |
| Dose-response represents change in     |                                      |
| costs for every 10 cm increment.       |                                      |
| Change in cost of constructing         |                                      |
| a km of paved road per step-           |                                      |
| wise increase in the maximum           |                                      |
| of monthly maximum tempera-            |                                      |
| ture values projected during           |                                      |
| 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.           |                                      |
| Not estimated.  Impact likely to       |                                      |
| be minimal.                            |                                      |
| Unpaved Roads                          | Change in construction costs per km  |
| per 1% change in the maximum of the    |                                      |
| monthly maximum precipitation values   |                                      |
| projected during lifespan relative to  |                                      |
| baseline climate.                      |                                      |
| Transmission Poles                     | Not estimated.  Impact likely to be  |
| minimal.                               |                                      |
| Not estimated.  Impact likely to       |                                      |
| be minimal.                            |                                      |
| Buildings                              | Change in costs per square foot, per |
| 10 cm change in annual precipitation   |                                      |
| projected during lifespan.             |                                      |
| Change in costs per square             |                                      |
| foot, per 0.5 degree change            |                                      |
| 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    |
| km per 10 cm change in annual rainfall                                                               |                                           |
| projected during lifespan relative to base-                                                          |                                           |
| line climate.                                                                                        |                                           |
| Paved Roads - Newly                                                                                  |                                           |
| Constructed                                                                                          |                                           |
| Paved roads constructed after 2010 would have no maintenance impact if designed for changes in cli-  |                                           |
| mate expected during their lifetime.                                                                 |                                           |
| Unpaved Roads                                                                                        | Change in annual maintenance costs per    |
| 1% change in maximum of monthly maxi-                                                                |                                           |
| mum precipitation projected during                                                                   |                                           |
| lifespan.                                                                                            |                                           |
| Railroads                                                                                            | Not estimated.  Impact likely to be mini- |
| mal.                                                                                                 |                                           |
| Buildings - Existing                                                                                 | Change in annual maintenance costs per    |
| square foot per 10 cm change in annual                                                               |                                           |
| rainfall projected during lifespan.                                                                  |                                           |
| Buildings - Newly                                                                                    |                                           |
| Constructed                                                                                          |                                           |
| Buildings constructed after 2010 would have no maintenance impact if designed for changes in climate |                                           |
| expected during their lifetime.                                                                      |                                           |
| Not estimated.  Impact likely to                                                                     |                                           |
| be minimal.                                                                                          |                                           |
| Not estimated.  Impact likely to                                                                     |                                           |
| be minimal.                                                                                          |                                           |
| Not estimated.  Impact likely to                                                                     |                                           |
| be minimal.                                                                                          |                                           |
| Percent change in costs per 15                                                                       |                                           |
| mph (~24 kmh) increase in the                                                                        |                                           |
| maximum of the monthly maxi-                                                                         |                                           |
| mum wind speeds projected                                                                            |                                           |
| during lifespan, relative to base-                                                                   |                                           |
| line climate.                                                                                        |                                           |
| Not estimated.  Impact likely to                                                                     |                                           |
| be minimal.                                                                                          |                                           |
| Change in annual maintenance costs per km per 1 degree                                               |                                           |
| change Celsius in maximum of monthly maximum temper-                                                 |                                           |
| ature projected during lifespan.                                                                     |                                           |
| Not estimated.  Impact likely to be minimal.                                                         |                                           |
| Change in annual maintenance costs per km per 1 degree                                               |                                           |
| Celsius change in maximum of monthly maximum temper-                                                 |                                           |
| ature projected during lifespan.                                                                     |                                           |
| Change in annual maintenance costs per square foot per                                               |                                           |
| 1 degree change Celsius in annual average temperature                                                |                                           |
| projected during lifespan.                                                                           |                                           |

(DCD 2007) and $621,000 per kilometer (km) for paved roads, the latter of which represents the average cost per km of constructing a 2-lane collector road in rural areas (FDOT 2009a).20    

The code update methodology that we employed for 
temperature effects is similar to the approach outlined 
in Equation 1 for precipitation.   Unlike the code 
update approach for precipitation, we do not apply the 
full 0.8 percent cost increase of a code update to each 
incremental change in temperature.  Instead, we scale 
the 0.8 percent value to reflect the portion of construc-
tion costs likely to be associated with temperature 
effects.  Based on data published by Whitestone 
Research (2008), we assume that 28 percent of the costs 
associated with a code update for buildings are related 
to HVAC equipment affected by temperature.  
Similarly, research into the effects of temperature on 
roads provides a guideline of 36 percent of the costs for 
a code update for roads is temperature-related (Miradi 
2004).  Based on these values, we assume a 0.22 percent 
increase in building construction costs for each incre-
mental change in temperature and a 0.29 percent 
increase in paved road construction costs for such 
changes.  Based on professional judgment and the 
design parameters for HVAC systems, which are typi-
cally based on the number of degree days per year 
(NOAA 2009), we assume that the 0.22 percent value is 
applied to building costs for each 0.5 degree Celsius 
increase in average annual temperature.  For paved 
roads, we apply the 0.29 percent increase as a step func-
tion, with the first increase occurring after a 1 degree 
Celsius increase in temperature and later increases 
occurring with each 3-degree increase in temperature.  
This reflects the need for new pavement binders with 
every 3-degree increase in temperature and a change in 
practice for a 1-degree change as an initial safety factor 
(Blacklidge Emulsions, Inc. 2009).

We also apply the building code methodology to trans-
mission line towers, but instead of precipitation and 
temperature, wind is the climate stressor of concern.  
For every 15 mile per hour (~24 km per hour) increase 
in the maximum of the maximum monthly wind speeds 

projected, we assume a 0.8 increase in construction costs due to a design standard update. 

The readily available data suggests several relationships 
will have no impact or minimal impact in these catego-
ries as follows:  1) no impact from wind on paved roads 
or buildings, 2) no impact from temperature on trans-
mission poles, and finally 3) no impact from precipita-
tion on transmission poles. 

## B.  Example Of Building Code Methodology

Two examples are presented here to illustrate the appli-
cation of the building code methodology to new 
construction, a building example for precipitation and a 
paved road example for temperature.  For the former, 
assume that a new hospital is to be built in a location 
that has a base precipitation level of 100 cm per year.  It 
is projected that due to climate change, the location will 
have a 15 cm increase during the 40-year anticipated 
lifespan of the building.  Given the 10cm threshold for 
a building code update, the design of the structure 
would anticipate the precipitation increase and the asso-
ciated building code update.  Essentially, the building 
will be overbuilt for Year 0 to anticipate the need later 
in the lifespan to accommodate the increased precipita-
tion.  The cost of this overbuild will be the cost of one 
code update for the 10 cm increase, or 0.8 percent of 
the base construction costs.

In the context of temperature, consider the example of 
paved road construction.  Using the 36 percent relative 
impact discussed above, the standard 0.8 percent cost 
increase for a code update is modified by this percent-
age resulting in a modified value of 0.29 percent of base 
construction costs.  However, to apply this to new 
construction, the guidelines for pavement design are 
brought into the equation.  Specifically, temperature 
increases require new pavement binders every 3 degrees 
Celsius (Blacklidge Emulsions, Inc. 2009).  Therefore, 
for a new construction scenario where the maximum 
temperature will increase 2 degrees over the 20-year 
lifespan of the road, a cost increase of 0.29 percent of 
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 
degrees, the total increase from the temperature impact 
is 0.29 percent of base construction costs.    

## C.  Direct Response Methodology

For bridges and unpaved roads, we use a more direct approach for estimating the cost impact of changes in climate stressors.  Under this approach, we directly relate changes in infrastructure construction costs to specific changes in climate or infrastructure design requirements.  In general terms, this approach is summarized by Equation 2.

$${\mathrm{(2)}}\qquad C_{U R B T}=M\times B_{U R B T}$$
where  *CURB* =	 change in construction costs for bridges and unpaved roads associated with a unit change in climate stress or design requirements

$$\begin{array}{r l}{M={\mathrm{cost~multiplier}}}\\ {B_{U R B}~=~{\mathrm{base~construction~costs~for~}}}\\ {{\mathrm{~bridges~and~unproved~roots}}}\end{array}$$
Implementation of the approach represented by Equation 2 is somewhat different for unpaved roads than it is for bridges.  For unpaved roads, we express the dose-response relationship represented by Equation 2 as the change in construction costs associated with a 1 percent change in maximum monthly precipitation. Research findings have demonstrated that 80 percent of degradation of unpaved roads can be attributed to precipitation (Ramos-Scharron and MacDonald 2007). The remaining 20 percent is attributed to factors such as tonnage of traffic and traffic rates.  Given this 80 
percent attribution to precipitation, we assume that the base construction costs for unpaved roads increase by 80 percent of the total percentage increase in maximum monthly precipitation; that is, a 0.8 percent increase in costs for each 1 percent increase in maximum precipitation.  For example, if the maximum monthly precipitation increases by 10 percent in a given location, then 80 percent of that increase is used (8 percent) as the increase in base construction costs.  In addition, we further assume a base construction cost of $13,000 per km for unpaved roads, based on published cost data (Cerlanek et al. 2006).  The readily available data suggest no relationship between temperature and the cost of building unpaved roads. For bridges, we estimate the climate-related change in costs per one-foot increase in bridge clearance.   The 

most significant design changes associated with an 
increase in clearance would involve changes to bridge-
deck support structures, which account for approxi-
mately 50 percent of bridge construction costs (Kinsella 
and McGuire 2005).  In addition, based on the standard 
16-foot clearance for bridges on highways (FHWA 
2009), a one-foot increase in bridge clearance would 
represent a 6.25 percent increase.  Assuming that the 
increase in costs for bridge foundations would be 
proportional to the change in clearance, we assume that 
construction costs for the bridge support structures 
would increase by 6.25 percent with each 1-foot 
increase in clearance.  Because support structures repre-
sent approximately 50 percent of bridge construction 
costs, we assume that the total construction costs for a 
bridge would increase by approximately 3.13 percent 
(50 percent x 6.25 percent) with each one-foot increase 
in clearance.

The base cost of a bridge is likely to vary significantly 
due to differences in the number of lanes per bridge and 
bridge length.  For the purposes of this analysis, we use 
the costs of a 2-lane bridge spanning 100 feet.  
Assuming an average lane width of 12 feet, this trans-
lates to a bridge deck with an area of approximately 
2,400 square feet.  Based on a unit cost of $220 per 
square foot (FDOT 2009b), we estimate that the total 
base construction costs for a bridge are approximately 
$528,000.  Applying the 3.13 percent value derived 
above to this estimate, we assume an increase of 
$16,500 in bridge construction costs for each one-foot 
increase in bridge clearance.21

The readily available data suggest no impact or minimal 
impact will originate from wind or temperature 
increases for new construction of bridges or unpaved 
roads.  

## 2.  Estimation Of Dose-Response Values For Maintenance Costs

Similar to our development of dose-response values for infrastructure construction costs, we employed two basic methodologies to generate dose-response values relating 

changes in climate stressors to changes in infrastructure 
maintenance costs.  The first approach is based on 
infrastructure lifespan decrements that could potentially 
result from climate change if maintenance practices 
remain unchanged following changes in climate stress.  
We use this methodology to develop dose-response 
values for existing paved roads and buildings.22    Newly 
constructed paved roads and buildings are assumed to 
not be affected by climate stressors because forward-
looking design allows these structures to accommodate 
future climate changes at the time of construction.  For 
railroads and unpaved roads (both existing and newly 
constructed), we use a separate methodology similar to 
the direct dose-response approach outlined above for 
bridge and unpaved road construction costs.

## A.  Avoided Lifespan Decrement Methodology

To assess the relationship between climate stressors and maintenance costs for existing paved roads and buildings, we use an approach based on the cost of preventing the reduction in lifespan that may result from changes in climate-related stress.  As indicated by Equation 3, implementation of this approach involves two basic steps: (1) estimating the lifespan decrement that would result from a unit change in climate stress and (2) estimating the costs of avoiding this reduction in lifespan.

(3) $M$ $T_{\rm{ERB}}$ = ($L_{\rm{ERB}}$)($C_{\rm{ERB}}$)
where *MTERB* = Change in maintenance costs for existing paved roads and buildings associated with a unit change in climate stress LERB  = Potential percent change in lifespan for existing paved roads and buildings associated with a unit change in climate stress CERB =	Cost of preventing a given lifespan decrement for existing paved roads and buildings To estimate the reduction in lifespan that could result from an incremental change in climate stress (*LERB*), we assume that such a reduction is equal to the percent change in climate stress, scaled for the stressor's effect on maintenance costs, as shown in Equation 4.  

$${\mathrm{(4)}}\qquad L_{E R B}={\frac{\Delta S}{B a s e S}}(S M T)$$
where LERB = Potential percent change in lifespan for existing paved roads and buildings associated with a unit change in climate stress ΔS  =	
Change in climate stress (i.e., precipitation or temperature)
BaseS =	 Base level of climate stress with no climate change SMT  =	 Percent of existing paved road or building maintenance costs associated with a given climate stressor (i.e., precipitation or temperature)

As indicated in Equation 4, the potential change in 
lifespan is dependent on the change in climate stress.  
For precipitation effects, we assume a potential reduc-
tion in lifespan for existing paved roads and buildings 
for every 10 cm increase in annual rainfall.  For temper-
ature, we assume a potential lifespan reduction with 
every 1 degree Celsius change in temperature (average 
annual temperature for existing buildings and maximum 
annual temperature for existing paved roads).

Equation 4 also illustrates that our estimate of the 
potential reduction in lifespan associated with a given 
change in climate stress reflects the contribution of that 
stressor to baseline maintenance costs (i.e., variable 
SMT).  For buildings, we assume that changes in 
precipitation associated with climate change will affect 
roofing and external enclosures and changes in temper-
ature will affect HVAC systems.  Because roofing and 
external enclosures represent 15 percent of building 
maintenance costs (Whitestone Research 2008), we 
assumed that precipitation contributes 15 percent to a 
building's maintenance costs.  Similarly, because HVAC 
represents 28 percent of building maintenance costs 
(Whitestone Research 2008), we assume that tempera-
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-
tenance represents 4 percent of maintenance costs and 

that temperature-related maintenance represents 36 percent (Miradi 2004).

After estimating the potential reduction in lifespan associated with a given climate stressor, we estimate the costs of avoiding this reduction in lifespan.  To estimate these costs, we assume that the change in maintenance costs would be approximately equal to the product of 
(1) the potential percent reduction in lifespan (*LERB*) 
and (2) the base construction costs of the asset. Therefore, if we project a 10 percent potential reduction in lifespan, we estimate the change in maintenance costs as 10 percent of base construction costs.  As indicated above, we estimate base construction costs of $185 per square foot for buildings and $621,000 per km for paved roads in the U.S.23

## B.  Example Of Avoided Lifespan Decrement Approach

As an example of the avoided lifespan methodology, consider a country with baseline annual precipitation of 80 cm without climate change.  For such a country, a 10-cm increase in annual precipitation would represent a 12.5 percent increase in precipitation.  Because precipitation accounts for approximately 15 percent of building-related maintenance costs, we would assume a 1.9 percent potential reduction in building lifespan (12.5% x 15%).  If baseline building construction costs in this country are approximately $175 per square foot, we would estimate an increase in maintenance costs of approximately $3.30 per square foot for every 10 cm increase in annual precipitation.  If the country were to experience a 15-cm increase in annual precipitation, we would still assume a $3.30 per square foot increase because the 15-cm increase includes just one 10-cm incremental change.  However, if we were to project a 21-cm increase, we would assume an increase of $6.60 per square foot.

## C.  Direct Response Methodology

To estimate dose-response values for railroad and unpaved road maintenance costs, we follow an approach similar to that outlined above for bridge and unpaved 

road construction costs.  More specifically, we estimate 
the change in railroad and unpaved road maintenance 
costs associated with a unit change in climate stress as a 
fixed percentage of baseline construction costs (for rail-
roads) or maintenance costs (for unpaved roads), as 
illustrated by Exhibit 5.

$${\mathrm{(5)}}\qquad M T_{U R R}=M\times B_{U R R}$$
where *MTURR* = Change in maintenance costs for unpaved roads and railroads associated with a unit change in climate stress M  = Cost multiplier BURR =	Baseline maintenance (for unpaved roads) costs or construction costs (for railroads)

Similar to the direct response methodology for 
construction costs, implementation of this approach for 
maintenance costs also varies by infrastructure type.  
For railroads, we express the relationship described by 
Equation 5 as the change in maintenance costs associ-
ated with a 1 degree Celsius change in the maximum of 
the maximum monthly temperature projections for an 
area.  Based on research on the effect of heat stress on 
rails and the associated costs, we estimate that for every 
1 degree increase in maximum temperature, railroad 
maintenance costs increase by 0.14 percent of railroad 
construction costs (DRPT 2008).  Therefore, assuming 
construction costs of approximately $404,000 per km 
(in the U.S) (Railroad 2009; Vickers 1992), we estimate 
that railroad maintenance costs would increase by $565 
for every 1 degree increase in maximum temperature.   

For unpaved roads, we express the dose-response rela-
tionship represented by Equation 5 as the change in 
maintenance costs associated with a 1 percent change in 
maximum monthly precipitation.  As indicated above, 
research has demonstrated that 80 percent  of unpaved 
road degradation can be attributed to precipitation, 
while the remaining 20 percent is due to traffic rates 
and other factors (Ramos-Scharron and MacDonald 
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 
of the maximum monthly precipitation values projected 
for any given year.  Published data indicates that the 

Washington, DC:  Federal Emergency Management Agency.

baseline cost of maintaining an unpaved road is approximately $930 per km (Cerlanek et al. 2006).  Therefore, 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 
Highway Administration. http://www.fhwa.dot.gov/
infrastructure/50vertical.cfm, viewed May 31, 2009.

The readily available data suggest climate stressors will have no impact or minimal impact in these categories as 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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Vickers, R.A. 1992. Cost Effective Maintenance of Railway Tracks. London:  Institute of Civil Engineers.

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The World Bank Group 1818 H Street, NW Washington, D.C.  20433  USA Tel: 202-473-1000 Fax: 202-477-6391 Internet:  www.worldbank.org/climatechange