Chapter Twenty-Seven
THE COSTS OF CLIMATE CHANGE
TO THE UNITED STATES
James G. Titus
Office of Policy Analysis
U.S. Environmental Protection Agency
Washington, DC 20460
INTRODUCTION
Given the consequences that the other chapters of this book expect to result from global warming, it is
hard to imagine that we would deliberately alter our planet in such a fashion. Yet preliminary analyses on
the subject generally conclude that the value to society of avoiding these consequences is not as great as
the cost of decreasing emissions, especially when one "discounts" future benefits to present values (e.g.
Nordhaus 1990). This paper, however, makes two departures from previous studies, and thereby reaches a
much higher estimate of the cost of heating our planet.
First, we focus on the range of uncertainty, rather than merely the central estimate, of a given impact.
Focusing only on central estimates understates the value of an environmental risk because (a) our
uncertainty tends to be skewed; (b) the damage function is often nonlinear; and (c) people are risk-averse.
Suppose, for example, that our uncertainty regarding the impact of climate change on a farm is lognormal
with the most likely impact a loss of 9 acre feet with nine-fold uncertainty; that the cost to the farmer is
equal to number of acre feet lost, raised to the 1.5 power; and that people will pay 25 cents to avoid a risk
that has a standard deviation of $1. Elementary probability theory shows that the expected decline in
water would be 16.5 acre-feet; the expected cost would be $105, with a standard deviation of $395; hence,
people would be willing to pay about $200 to avoid such a risk. By contrast, using only the best estimates
and ignoring risk aversion would lead one to expect a loss of 9 acre-feet and a cost of only $27.
Second, we include environmental and other nonmarket impacts.1 Because estimates of these assets
range from poor to nonexistent, we assume that society will undertake the necessary costs to offset
environmental effects of global warming.
The following sections (1) develop equations that summarize the nationwide impact of effects that
have already been quantified; (2) develop state-by-state equations for four areas that have not been
previously estimated in detail; (3) project the costs of climate change for two alternate emissions
scenarios; and (4) calculate the benefits of reducing emissions. We assume that the U.S. economy will
grow 1.2 to 2.1 percent per year. For the most part, our estimates are based on the GISS (Goddard
Institute for Space Studies) and GFDL (Princeton Geophysical Fluid Dynamics Laboratory) models
whose implications are discussed in detail by Smith and Tirpak (1989). Our calculations suggest that a
CO2 doubling would cost the United States $37-351 billion per year, with $92-130 billion most likely. At
a 3% discount rate, the CO2 from burning one gallon of gasoline will cause damages of 16-36 cents.
NATIONWIDE CALCULATIONS BASED ON SMITH AND TIRPAK (1989)
A previous EPA Report to Congress (Smith and Tirpak 1989) quantified costs for agriculture, energy
consumption, and sea level rise. For those studies, our task is solely to interpret the results in a common
framework that enables us to estimate the costs for different years and amounts of climate change. The
EPA study also provided estimates of increased mortality, which can be readily monetized using existing
estimates of the regulatory cost of saving lives.
Titus, J.G. 1992. The Costs of Climate Change to the United States. Originally published in: Majumdar, S.K., L.S.
Kalkstein, B. Yarnal, E.W. Miller, andL.M. Rosenfeld (eds). Global Climate Change: Implications, Challenges,
and Mitigation Measures. Pennsylvania Academy of Sciences. The format of this document has been modified for
electronic distribution.
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Originally in Global Climate Change: Implications, Challenges and Mitigation Measures (1992)
Agriculture
Crop modelers have already examined many of the ways by which global warming could affect
agriculture, including longer growing seasons in colder areas, heat stress in the south, increased
evaporation, changes in precipitation, the CO2 fertilization effect, and changes in pests. Based on
available studies, Adams et al. (1989) estimated the impact for the year 2060, assuming that C02 doubles
and that the climate reaches the equilibria implied by the general circulation models. They reported that
the GISS and GFDL models imply annual losses of 7.45 and 42.4 billion ($1984), ignoring CO2
fertilization; and -$13.4 (net benefit) and $12.25 billion including C02 fertilization.2
Given these estimates, we need equations that can generalize the results for different climate
scenarios and different years. (We use the term "generalize" to refer to both interpolation of smaller
changes and extrapolation to larger changes). Our calculations employ separate equations for the GISS
and GFDL models; we discuss only the former. Our low and high equations are as follows:
Costmedian = 5.853 T/Teq2 - 16.4 C02*
Costlow = Costmedian " 3 .7 T/Teq2 " 6.129 T/Teq2 -4.1 C02*
Costhigh = Costmedian + 1 T/Teq2 + 6.3 T/Teq2 + 5.0 C02*
where C02* = (C02-330)/330 for CO2<660 ppm
= In (CO2/330)/ln(2) for CO2>660 ppm
T = transient global temperature increase;
Teq2 = The model's estimated equilibrium warming for 2x C02 and : We multiply these
results by 1.005 * * (year-1985) to account for population growth and 1.263 to
convert to $1990.
The median equation assumes that the climate impact is directly proportional to the change in global
temperatures. Up to a doubling, we assume that the beneficial impacts of C02 are also linear. However,
because crop modelers have noted that the beneficial impact of C02 eventually reaches a point of
diminishing returns, we assume that the impact is only logarithmic past the doubling point (i.e. a C02
quadrupling has twice the beneficial impact of a C02 doubling). The low and high equations include
Adams' yield uncertainties for climate and C02 fertilization.
Energy Consumption
Global warming would decrease energy consumption for space heating while increasing requirements
for cooling. However, the cost of cooling a house one degree is greater than the cost of heating it one
degree because (1) air conditioners require a more expensive form of energy (electricity) than home
heating (mostly oil and natural gas); and (2) air conditioning takes place during peak hours while heating
is mostly required at night.
Linder and Inglis (1989) developed a national model of how electricity consumption responds to
temperature changes based on daily demand and weather data for five utility regions. They estimate that
the increased consumption of electricity in the year 2060 implied by the GISS transient model would
increase costs $37 billion with low economic growth and 53-81 billion ($1986) with high economic
growth. Our generalizing equations are as follows:
Baseline = P*growth*79.6
electricmedian = (1.016T-l)*Baseline
electrichigh = electricmedian*o
electricbw = electricmedian/o
where
T is transient global temperature increase;
P accounts for increasing price, assuming that (real) electricity prices grow 1.37% through 2060
and are stable thereafter; growth accounts for growth in electricity consumption, assuming
2
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The Costs of Climate Change to the United States
that in the absence of climate change, consumption grows at 90 percent of the rate of general
economic growth; and
o represents a fourfold uncertainty for a given rate of growth.
These estimates fail to account for impacts of changing precipitation (e.g. pumping for irrigation) and
adaptation (e.g. more insulation). They ignore the impact of global warming on the cost of meeting the
increased demand, by assuming that it could be met by coal-fired utilities. If non-greenhouse gas sources
are required, the unit generating costs may be greater; reduced availability for cooling water may also
increase the costs of new capacity. We have not adjusted these calculations downward to account for
reductions in space heating.3
Sea Level Rise
Global warming could raise sea level by (1) expanding seawater, (2) melting mountain glaciers,
causing the ice sheets in (3) Greenland and (4) Antarctica to melt or discharge ice into the oceans, and by
(5) depleting groundwater tables. Our calculations consider only the first three categories.
We used the same equations as IPCC for mountain glaciers and Greenland4 for thermal expansion, we
used the model developed by Hoffert el al. (1980) to specify these equations, with B=0 for the low
scenario and B=1 for the high estimate (B is the ratio of temperature increase at the surface to temperature
increase at the bottom of the ocean). In both cases, we ran the model for 500 years for an assumed
warming of 2°C. In the low case, the rise was 18, 24, and 30 cm after 100, 200 and 500 years,
respectively. For the high case, the rise was 21, 33, and 59 cm.
We then estimated the following regression equations.
Expandio„(t) = 1.26T(t-l) - 1.24T(t-2) + 0.104T(t-3) + 0.0141T(t-4)
+ 0.003393T(t-5) + 1.712 Expandi0„(t-1) - 0.721 Expandi0„(t-2)
Expandhlgh(t) = 1.24T(t-l) - 1.3383T(t-2) + 0.1374T(t-3) + 0.023386T(t-4) + 0.009048T(t-5) +
0.003804T(t-6) + 1.796957 Expandiow(t-l) - 0.79832 Expandiow(t-2)
where T represents transient global temperature and both equations use time steps of 5 years. In
each case the R-squared was greater than 0.9999.
Note that these equations use first and second order lagged dependant variables. The effect of doing
so is to impose the assumption
that after the first 5 or 6 time
periods, the effect of a
temperature increase diminishes
as the sum of two declining
exponentials. The adjustment
times for the low and high
equations are 100 and 600 years.
Figure 1 shows the resulting sea
level rise projections.
Using those projections, we
estimated the costs of sea level
rise for wetland loss, dike
construction, land for dikes, loss
of dry land, beach nourishment,
and the cost of elevating
infrastructure in low areas, based
on Titus et al. (1991). For each of
the cost categories, that study had
reported low and high estimates FIGURE 1 . Sea level curves show the range of uncertainty given the trend for temperatures. The
for the 12 (baseline), 50, 100, and temperature curve starts at 0.5°C to account for past warming due to greenhouse gases.
Increase in Temperature
and Sea Level Over Time
Temp. ( C) — Sea Level (cm)
Year
3
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Originally in Global Climate Change: Implications, Challenges and Mitigation Measures (1992)
200 centimeter scenarios. For sea level rise less than 200 cm, this analysis interpolates those estimates to
calculate the total cost of sea level rise by a given year. For dry-land loss and dike costs, we assume that
development in coastal areas will grow at the same rate as the general economy; but we assume that does
not increase wetland loss or the cost of protecting barrier islands.
The slope of coastal lands is generally much steeper above the one-meter contour than below. Thus,
for sea level rise greater than 200 cm, our calculations for elevating structures and loss of dry land assume
costs rise at the same rate as for a rise of 100 to 200 cm. We assume that a 3.3 meter rise would inundate
the remaining coastal wetlands, and interpolate linearly. For sand, we assume that the incremental costs
stay the same. For dikes, we assume that costs rise with the 1.5 power of sea level rise.
Health
Heat- and cold-related mortality are the only health impacts of global warming that have been
quantified. Kalkstein (1989) estimates that given today's population in the 15 largest cities, a CO2
doubling would increase heat-related deaths 529-3878 among the elderly and 513-2368 among other age
groups, while reducing cold-related deaths by 59-123 and 25-68 among the two groups. Fisher et al.
(1989) estimate that the value of reducing the risk of a statistical death is between $1.6 and 8.5 million;
this estimate does not say how much a human life is worth, but rather the extent to which society
currently invests resources to avoid deaths from pollution and accidents.
We generalize the relationships as follows:
Deathshigh =6110T
Deathsiow = 1646 T
Costhh,„h = 8,500,000 Deaths X growth
Costbw = 8,500,000 Deaths X growth
where growth represents economic growth as described above.
STATE BY STATE CALCULATIONS OF EFFECTS NOT PREVIOUSLY QUANTIFIED
For the impacts not previously quantified, calculating impacts on a state-by-state basis seemed to
be an appropriate level of aggregation. The national level is too broad because it can not capture all the
ambiguities, such as (1) some areas becoming wetter while others become colder; and (2) a warmer or
dryer climate helping a cold area suffering from too much rainfall, while harming a warm area where
water supplies are only barely adequate. By contrast, state-level analyses can capture these regional
differences.
We generated our scenarios for annual and summer changes in climate by assuming that (1) the
climate of a state is characterized by the climate of its capital; (2) the change in temperature for a CO2
doubling is characterized by the difference between temperatures projected by the doubled CO2 and
control runs of the GISS and GFDL models; (3) the change in rainfall for a C02 doubling is characterized
by the ratio of changes implied by the doubled C02 and control runs; and (4) the change in rainfall or
temperature by a particular year is proportional to the change in global temperatures.
Automobile Air Conditioners
Warmer temperatures will lead motorists to run their air conditioners a greater percentage of the time.
General Motors estimates that the average automobile in the United States uses 20 gallons of gasoline to
run its air conditioner for every 10,000 miles driven. It also estimates that on days with a daily average
temperature of 50, 60, and 90 degrees (F), that 0, 30, and 90 percent of all miles driven by automobiles
with air conditioners are driven with the air conditioners on.5 These assumptions imply that the U.S.
currently consumes 3.84 billion gallons per year on automobile air conditioning.
Our calculations use those results along with the assumption that fuel consumption is proportional to
the difference between the daily average temperature and 50° F.
4
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The Costs of Climate Change to the United States
Air Pollution: Ozone
Climate plays an important role in determining whether air pollutants are transported out of harms
way or linger enough to threaten people's health. Of the many ways by which global warming could
change air quality, the only quantified so far is the impact of warmer temperatures on the formation of
tropospheric ozone.
Gerry (1987) estimated the possible impacts of 2° and 5° C warmings on ozone concentrations in Los
Angeles, New York, Philadelphia, and Washington,6 with ozone concentrations increasing 1.6875 + .3684
percent per degree warming. Morris et al. (1988) applied a regional transport model to central California
and the part of the United States (approximately) between 78° and 98°N longitude (that is, west of central
Maryland and east of San Antonio Texas.) They found that maximum ozone concentrations would
increase 1.4525 + .497 percent per degree warming.7 Because Gerry and Morris et al. reach similar
results, we assume that ozone concentrations increase 1.5+0.5 percent per degree warming.
Estimating the cost of preventing such an increase requires us to consider (a) the percentage
reduction in emissions, (b) the absolute level of emissions that would otherwise occur, and (c) the cost of
reducing emissions by one ton. Smith and Tirpak (1988) estimate that the 6% increase on ozone
concentrations implied by Morris et al. would require emissions of volatile organic compounds (VOC) to
be reduced 11.7%; hence we assume that the reduction in VOC would be 1 to 2% for every 1% increase
in ozone concentrations.
Pechan Associates (1990) estimate that the baseline emissions for 2005 would be approximately 14
million tons, and that emissions are increasing by about 3% per year. They also estimate that the
incremental cost of reducing emissions currently ranges from $1700 to $5000. Smith and Tirpak state that
the cost is $5000. EPA's Office of Air Quality Planning and Standards told us that even at the $5000/ton
cost, the available emission reductions are not unlimited. Nevertheless, we assume that emissions can be
controlled for $1700-5000 per ton.
WATER RESOURCES
Rising temperatures and reduced rainfall could increase the demand for irrigation water and diminish
the supply of water for all uses. The resulting reductions in river flows would reduce hydropower
production and worsen water quality (unless the discharge of pollutants was also reduced).8
Baseline Conditions
Our starting points are USGS data on withdrawals of ground and surface water by the agriculture,
residential, and industrial sectors of each state. Based on Gibbons (1988), we assume that the price of
surface water is currently $35-85 per acre foot, and that the elasticity of demand for water is between 0.5
and 1.0.9 We assume the same range for elasticity of supply, which is probably optimistic because (a) in
the west current supplies are already oversubscribed, and (b) in the east, water experts generally doubt
that any more major dams will be built due to the adverse environmental effects. We assume that
residential and industrial demand increase by the same rate as economic growth, but that irrigation
demand does not increase. As Table 1 shows, our baseline water prices range from $60 to $250 per acre-
foot by 2060.
Our approach is similar for groundwater. However, in those regions where the ratio of groundwater
overdraft to recharge was greater than 0.2, we assume that the current price of water is the same as the
surface price, and that there is a near-fixed supply elasticity of 0.1. Where there is no overdraft, we
assume that unlimited water can be pumped for $10 per acre-foot. For intermediate cases, we interpolate
between these two assumptions.
5
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Originally in Global Climate Change: Implications, Challenges and Mitigation Measures (1992)
TABLE 1
Regression Equations Predicting Irrigation Per Acre As A Function of Climate Change
SOUTHEAST (Results Assume 600 ppm C02 fertiliztion)
Aln (Soy Irrigation) = 0.05AT,ja - 0.619Aln (Pjja)
(0.365) (0.0891)
R2= 0.745 S.E. = 0.087
Aln (Corn Irrigation) = 0.0200ATjja - 0.592Aln(Pjja)
(0.372) (0.0808)
R2= 0.9400 S.E. = 0.0372
GREAT PLAINS (Results Ignore C02 fertilization)
Aln (Corn Irrigation) = 0.0172ATjja - 0.45567iln(Pjja)
(0.373) (0.00252)
R2= 0.833 S.E. = .0387
Aln (Wheat Irrigation) = 0.1763AT -1.25Aln(P) + 0.00918Aln(Yield)
(0.116) (0.250) (0.001911)
R2= 0.551 S.E. = 0.0348
GREAT LAKES
With 600 ppm C02 fertiliztion
A % Soy Irrigation = -0.684 + 0.193ATjja -0.687A%Pjja +0.165A%Yield
(0.0387) (0.467) (0.0432)
R2= 0.849 S.E. = 0.226
A % Corn Irrigation = -1.05 + 0.0712ATjja + 0.408A%Yield
(1.23) (0.127)
R2= 0.511 S.E. = 0.125
Without C02 fertilization
A % Soy Irrigation = 0.0855ATjrja -1.5523A%Pjrja +0.360A%Yield
(0.0137) (0.4113) (0.1253)
R2= 0.659 S.E.= 0.3186
A % Corn Irrigation = 0.0697ATjrja - 1.539 A%Pjrja + 1.0012A%Yield
(0.017) (0.127) (0.2445)
R2= 0.5353 S.E.= 0.331
Combined
A % Soy Ir = 0.0900ATjrja -1.5817A%Pjrja +0.202A%Yield - 0.000763A C02
(0.0111) (0.2647) (0.0484) (0.00022583)
R2= 0.736 S.E.= 0.2841
A % Corn Ir = 0.0641ATjrja - 0.729 A%Pjrja + 0.562A%Yield -0.00322A C02
(0.0126) (0.265) (0.143) (0.000201)
R2= 0.8004 S.E.= 0.2848
6
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The Costs of Climate Change to the United States
Changes in Supply and Demand
Previous studies have estimated changes in water requirements (per acre) for a dozen or so sites
that are already irrigated in the Southeast (Peart et al. 1989), Great Plains (Rosensweig), and the Great
Lakes Region (Ritchie).10 We estimate summary regression equations, shown in Table l.11 We used the
state-specific results of Peterson and Keller (1990) as low and high estimates of the elasticity of irrigated
acreage with respect to temperature and precipitation.
Finally, residential lawn watering and other outdoor uses would also change. Because only 25% of
residential use is consumptive, we assume that it would increase by 25% of the fraction by which
irrigation per acre increases in a given state. We include this impact within irrigation demand. We
calculate the increased cost of delivering more water to residential users, based on the current difference
between residential and market prices.
The supply of water could change for two reasons: (1) more (or less) rainfall would increase
(decrease) the amount of water flowing in rivers and recharging aquifers; and (2) higher temperatures
increase evaporation and hence reduce the availability of water if nothing else changes. We use the simple
model from Waggoner and Revelle (1990) which assumes that runoff decreases 2-4 percent if either
(a) precipitation decreases 1 percent or (b) temperature increases 0.4°. We assumed that in the west, river
flow, groundwater, and surface supplies available for withdrawal decline by the same fraction as runoff.
For the east, our assumptions are the same, except that because flows are very large relative to
withdrawals, we assume that runoff changes have no impact on surface supplies.12
If the change in climate is small, the cost to society can be calculated simply as the price of water
times the sum of the increase in demand plus the decrease in supply. But for large changes, this
assumption understates the impact because as prices rise, each additional shift in supply or demand costs
more than the previous shift. Figure 2 illustrates the general case. As demand increases, more water must
be delivered; prices will rise, which will lead some current users to conserve water and spend more on
labor, fertilizer, or some
other factor of production to Cost of Increased Demand or Decreased Supply of Water
achieve a harvest of a given
size.
The cost of a reduction in
supply can also be divided
into increased extraction cost
and the cost of conserving
water. In Figure 2c, the left
triangle shows the increased
cost of supplying water from
the preexisting pumps that
continue to operate. The right
triangle illustrates the
difference between the value
of using water for users who
choose to consume at a
higher price and the previous
cost of pumping from wells
that are closed due to the
increased cost.
We do not allow for
water being transported
between states; nor do we
allow for consumers to
switch between ground and
A. Initial Condition
B. Demand increases bv a factor of e
P0P^=RQh
P]=A/(eQ)ra
C. Supply of water decreases by factor of g D. Increase in demand and decrease in supply
' Existing supply or demand curve
' Old supply or demand curve
E3 Cost of operating new wells
YZR Costs of factors that replace water
Increased co.sts of operating wells that stay open
FIGURE 2.
7
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Originally in Global Climate Change: Implications, Challenges and Mitigation Measures (1992)
surface water. To prevent anomalies, in which dramatic supply and demand crunches cause implausible
price rises, we assume that a state can always build a project that provides all of the increased water
requirements for $300 to 700 per acre foot.
Water Pollution and Hydropower
We assume that the discharge of pollutants would change by the same proportion as does river flow,
to maintain current water quality. EPA's 1990 Report to Congress on the Cost of a Clean Environment
reports that state and local water pollution costs for point sources (mostly sewage treatment plants) are
growing at 3.6 percent per year and will be $18.8 billion by the year 2000. Other costs (mostly industrial)
are growing at 4.5 percent per year and will reach $45.3 billion by the year 2000. We assume that in the
baseline, these costs would only grow in step with the general economy after 2000.
According to EPA Region III, assuming that one has already achieved secondary (85%) control, the
cost elasticity for further control is about -1.0. For the last several years, each industry-specific water-
quality regulation issued by EPA has been accompanied by a Regulatory Impact Assessment, which must
estimate the marginal cost of controlling pollutants under the regulation, as well as an estimate of the
marginal cost of the next more stringent technology. Based on these analyses, we assume elasticities of
1.0 to 1.73.
We assume that hydropower production declines in proportion to the change in runoff, using data on
current hydropower from Edison Electric Institute (1985). We assumed that there will be no increase in
generating capacity.
Results
Tables 2 and 3 illustrate our intermediate calculations of surface water supply and demand, based on
the GISS general circulation model. We display our results at the state level only to permit the reader to
better understand the limitations of our calculations. Our nationwide results are presented both in (a)
$1990 and (b) $1990 scaled downward to account for economic growth. Note that for surface water, the
uncertainty surrounding our baseline assumptions is greater than the impact of climate change.
Nevertheless, our equations imply that increased demand for irrigation water could raise the total demand
for water by 10 percent for about 10 states. About three quarters of the states would experience declines
in water supplies while the rest would have increased availability. The scaled cost of these changes would
be $1-3 billion per year for the GISS scenario and $3-7 billion for GFDL.
Table 4 illustrates our groundwater calculations for both the low and high scenarios. Although the
low-growth scenario implies that none of the states would be paying over $200/acre foot, the high-growth
scenario has 24 states doing so, with 13 states paying over $500/acre-foot (at the margin). The scaled cost
under the GISS scenario would be $1.1-4.6 billion per year for the GISS scenario and $1.5-6 billion for
GFDL.
Finally, Table 5 illustrates our estimates for the other water resources and the total cost. The water
quality problems of global warming would be more expensive than the water quantity problems. Under
GISS, (scaled) pollution control costs would increase $15-52 billion (compared with a base of $64
billion), with the total water resource costs $21-60 billion. With GFDL, the costs is $31-87 billion, of
which $21-67 billion is for pollution control. Nevertheless, some areas might have more favorable
conditions. Under the GISS scenario, wetter conditions in the Pacific Northwest would increase
hydropower production by 2-5 billion (unsealed) dollars, more than offsetting declines in California and
Washington. However, the dry conditions of the GFDL scenario would lead hydropower production to
decline in every state for a total loss of 4-13 billion (unsealed) dollars.
8
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The Costs of Climate Change to the United States
Table 2
Change in Surface Water Supply and Demand for GISS C02 Doubling Low Scenario
Consumption Price Cost
State
Climate
% Change
% Change
% Change
Base
Change
PO
PI
Irrigation
Demand
Supply
Demand
Supply
AL
17.52
15.63
85
96
215.33
0.24
-20.64
3.59
.00
AZ
3.60
3.34
58
63
1.22
.97
-14.70
2.05
48.15
AR
4.76
4.46
80
94
110.41
10.64
-20.64
41.67
136.01
CA
29.97
27.85
60
76
24.91
17.21
-26.35
327.07
891.27
CO
16.11
16.63
60
72
34.11
23.65
-13.82
243.12
256.04
CT
2.09
1.82
85
98
89.54
0.56
-24.70
1.00
.00
DE
.10
.10
84
81
46.21
-0.47
6.43
-0.04
.00
FL
5.54
4.86
75
92
37.86
7.91
-28.57
33.55
.00
GA
9.54
9.88
84
82
63.09
0.83
6.43
6.67
.00
ID
24.57
26.40
66
59
-9.84
-4.43
20.84
-71.67
.00
IL
28.05
26.46
85
90
78.59
-0.06
-11.00
-1.38
.00
IN
22.78
21.50
85
90
78.93
0.03
-11.00
.59
.00
IA
4.02
3.80
85
90
130.56
0.37
-11.00
1.28
58.05
KS
1.47
1.34
73
106
124.31
31.69
-37.18
36.68
85.92
KY
7.71
7.95
85
82
53.17
-0.25
6.43
-1.64
.00
LA
18.60
17.10
82
97
159.06
8.29
-21.92
129.80
.00
ME
1.35
1.18
85
98
378.67
1.20
-24.70
1.39
.00
MD
1.70
1.75
85
82
82.31
0.26
6.43
-0.37
.00
MA
3.67
3.19
85
98
91.92
0.22
-24.70
0.70
.00
MI
24.49
20.29
85
103
168.38
0.65
-31.81
13.64
.00
MN
4.20
3.97
85
90
175.69
0.48
-11.00
1.72
.00
MS
2.39
2.20
83
96
182.69
7.36
-20.64
14.86
.00
MO
11.21
10.00
85
96
138.39
0.27
-20.64
2.55
309.08
MT
13.12
14.76
58
55
8.86
7.08
18.27
54.94
-217.84
NE
7.65
7.69
71
83
59.01
17.13
-13.82
97.95
136.76
NV
3.49
3.09
58
70
8.67
6.98
-26.35
14.52
91.06
NH
.56
.49
85
98
378.67
0.96
-24.70
.46
.00
NJ
3.67
3.21
85
99
109.41
1.28
-24.70
4.03
.00
NM
2.43
2.43
56
66
18.88
16.83
-14.70
24.08
36.42
NY
12.61
11.02
85
99
431.80
1.47
-24.70
15.90
.00
NC
12.75
13.17
85
82
68.00
0.27
6.43
2.88
.00
ND
15.91
14.86
85
92
311.99
1.19
-13.82
16.14
291.91
OH
22.79
18.83
85
103
167.98
0.13
-31.81
2.57
.00
OK
1.26
1.04
80
106
102.45
9.64
-37.18
10.02
67.30
OR
6.93
7.64
59
54
.80
.44
20.84
1.80
-125.31
PA
26.23
21.86
85
104
422.65
1.83
-31.81
41.12
.00
RI
.24
.21
84
98
90.15
1.27
-24.70
.26
.00
SC
9.80
10.12
85
82
77.58
.16
6.43
1.35
.00
SD
.44
.65
59
102
214.37
153.78
-13.82
51.71
14.30
TN
16.83
15.00
85
96
230.71
.08
-20.64
1.13
.00
TX
9.18
8.31
71
96
70.14
22.61
-33.20
155.25
426.99
UT
4.34
4.62
62
56
-6.58
-4.21
18.27
-11.28
-69.09
VT
.53
.46
85
98
321.27
.43
-24.70
0.19
.00
VA
9.11
9.39
85
82
31.32
-.08
6.43
-0.61
.00
WA
9.47
10.68
61
54
-1.32
-1.13
28.45
-6.56
.00
WV
9.47
9.76
85
82
-2.57
-.09
6.43
-.73
.00
WI
89.12
8.60
85
90
284.09
.03
-11.00
.23
.00
WY
5.62
5.80
57
68
27.71
23.84
-13.82
80.74
84.45
US
(Scaled and including price uncertainty)
36.5
551.5
Note: PO and PI = baseline and greenhouse-induced price of water ($/ac-ft)
Costs are in millions of dollars per year; quantities are in millions of acre-feet per year.
9
-------
Originally in Global Climate Change: Implications, Challenges and Mitigation Measures (1992)
Table 3
Change in Surface Water Supply and Demand for GISS C02 High Scenario
Consumption
Price
Cost
State
Climate
% Change
% Change
% Change
Base
Change
PO
PI
Irrigation
Demand
Supply
Supply
Supply
AL
24.42
19.95
259
389
232.94
.15
-33.35
9.82
.00
AZ
3.94
3.44
75
100
1.27
.85
-24.43
2.52
60.24
AR
6.48
5.49
217
351
148.08
7.71
-33.35
110.68
472.60
CA
34.30
28.12
88
174
27.00
14.71
-41.41
464.25
1530.54
CO
18.30
17.55
86
133
36.20
19.49
-23.06
321.94
385.16
CT
2.91
2.30
256
432
156.21
2.52
-39.13
18.96
.00
DE
.14
.14
252
223
50.53
-1.14
11.77
-0.39
.00
FL
5.54
4.86
75
92
37.86
7.91
-28.57
33.55
.00
GA
9.54
9.88
84
82
63.09
0.83
6.43
6.67
.00
ID
24.57
26.40
66
59
-9.84
-4.43
20.84
-71.67
.00
IL
28.05
26.46
85
90
78.59
-0.06
-11.00
-1.38
.00
IN
22.78
21.50
85
90
78.93
0.03
-11.00
.59
.00
IA
4.02
3.80
85
90
130.56
0.37
-11.00
1.28
58.05
KS
1.47
1.34
73
106
124.31
31.69
-37.18
36.68
85.92
KY
7.71
7.95
85
82
53.17
-0.25
6.43
-1.64
.00
LA
18.60
17.10
82
97
159.06
8.29
-21.92
129.80
.00
ME
1.35
1.18
85
98
378.67
1.20
-24.70
1.39
.00
MD
1.70
1.75
85
82
82.31
0.26
6.43
-0.37
.00
MA
3.67
3.19
85
98
91.92
0.22
-24.70
0.70
.00
MI
24.49
20.29
85
103
168.38
0.65
-31.81
13.64
.00
MN
4.20
3.97
85
90
175.69
0.48
-11.00
1.72
.00
MS
2.39
2.20
83
96
182.69
7.36
-20.64
14.86
.00
MO
11.21
10.00
85
96
138.39
0.27
-20.64
2.55
309.08
MT
13.12
14.76
58
55
8.86
7.08
18.27
54.94
-217.84
NE
7.65
7.69
71
83
59.01
17.13
-13.82
97.95
136.76
NV
3.49
3.09
58
70
8.67
6.98
-26.35
14.52
91.06
NH
.56
.49
85
98
378.67
0.96
-24.70
.46
.00
NJ
3.67
3.21
85
99
109.41
1.28
-24.70
4.03
.00
NM
2.43
2.43
56
66
18.88
16.83
-14.70
24.03
36.42
NY
12.61
11.02
85
99
431.80
1.47
-24.70
15.90
.00
NC
12.75
13.17
85
82
68.00
0.27
6.43
2.88
.00
ND
15.91
14.86
85
92
311.99
1.19
-13.82
16.14
291.91
OH
22.79
18.83
85
103
167.98
0.13
-31.81
2.57
.00
OK
1.26
1.04
80
106
102.45
9.64
-37.18
10.02
67.30
OR
6.93
7.64
59
54
.80
.44
20.84
1.80
-125.31
PA
26.23
21.86
85
104
422.65
1.83
-31.81
41.12
.00
RI
.24
.21
84
98
90.15
1.27
-24.70
.26
.00
SC
9.80
10.12
85
82
77.58
.16
6.43
1.35
.00
SD
.44
.65
59
102
214.37
153.78
-13.82
51.71
14.30
TN
16.83
15.00
85
96
230.71
.08
-20.64
1.13
.00
TX
9.18
8.31
71
96
70.14
22.61
-33.20
155.25
426.99
UT
4.34
4.62
62
56
-6.58
-4.21
18.27
-11.28
-69.09
VT
.53
.46
85
98
321.27
.43
-24.70
0.19
.00
VA
9.11
9.39
85
82
31.32
-.08
6.43
-0.61
.00
WA
9.47
10.68
61
54
-1.32
-1.13
28.45
-6.56
.00
WV
9.47
9.76
85
82
-2.57
-.09
6.43
-.73
.00
WI
89.12
8.60
85
90
284.09
.03
-11.00
.23
.00
WY
5.62
5.80
57
68
27.71
23.84
-13.82
80.74
84.45
US
Adding in the price uncertainty
1763.7
2715.6
Note: PO and PI = baseline and greenhouse-induced price of water ($/ac-ft)\
Costs are in millions of dollars per year; quantities are in millions of acre-feet per year.
10
-------
The Costs of Climate Change to the United States
Table 4
Change in Groundwater Supply and Demand for GISS C02 Doubling
State Overdraft Elasticity PO PI Demand Cost Supply Cost
L
H
L
H
L
H
L
H
Low
High
Low
High
AL
0.18
0.42
©o
0.1
50
50
500
1077
0.8
3.2
0
100.9
AZ
0.65
0.88
0.1
0.1
61
72
93
150
1.6
3.7
84.9
132.6
AR
1.68
2.6
0.1
0.1
67
135
131
598
165.4
472.6
205.6
741.7
CA
0.25
0.43
0.1
0.1
71
106
156
450
117.4
333.7
677.2
2291.8
CO
0
0
©o
©o
10
10
10
10
16.5
10.3
0
0
CT
0
0
©o
©o
10
10
10
10
0
0.1
0
0
DE
0.02
0.04
©o
©o
12
12
16
16
0
0
0
0
FL
0.16
0.36
©o
0.1
45
45
374
1097
27.2
115.2
0
1460.4
GA
0.12
0.2
©o
0.1
35
35
440
394
7.2
34.1
0
-77.5
ID
0.11
0.17
©o
0.3
34
34
187
121
-14
-6.7
0
-657.8
IL
0
0
©o
©o
10
10
10
10
-0.1
0.2
0
0
IN
0
0
©o
©o
10
10
10
10
1.3
0.9
0
0
IA
0.68
1.4
0.1
0.1
117
136
500
741
3.5
20.2
30.7
138.8
KS
0.9
1.47
0.1
0.1
61
174
88
1045
299.3
618.7
461.9
2084.1
KY
0
0
©o
©o
10
9
10
10
0
0
0
0
LA
0.29
0.51
0.1
0.1
89
156
315
969
58
211.8
106.8
556.5
ME
0
0
©o
©o
10
10
10
10
0
0
0
0
MD
0.02
0.04
©o
©o
12
12
17
17
0.1
0
0
0
MA
0
0
©o
©o
10
10
10
10
0
0.2
0
0
MI
0.07
0.17
©o
0.3
23
23
304
803
3.3
26.4
0
243.2
MN
0
0
©o
©o
10
10
10
10
2
1.9
0
0
MS
0.3
0.5
0.1
0.1
84
162
268
964
67.5
245.5
912.2
456
MO
0.76
1.69
0.1
0.1
109
150
500
#
4.9
47.2
26.7
158.6
MT
0.38
0.58
0.1
0.1
93
80
353
220
0.2
1.9
-23.6
-26.8
NE
0.64
1.27
0.1
0.1
105
128
499
824
38.9
200.4
268.7
1434.2
NV
0.83
1.51
0.1
0.1
73
101
176
460
1.7
6.2
27.5
99.8
NH
0
0
©o
©o
10
10
10
10
0
0
0
0
NJ
0.03
0.08
©o
0.8
15
15
79
116
0.9
3
0
56.9
NM
0.45
0.68
0.1
0.1
65
84
117
238
11.1
41.1
45.7
98.8
NY
0.03
0.08
©o
0.8
15
15
82
121
1.3
3.8
0
59.1
NC
0.13
0.25
©o
0.1
39
39
500
419
0.7
3.3
0
-57.2
ND
0.73
1.53
0.1
0.1
119
145
500
818
5.3
22.1
38.5
183.8
OH
0
0
©o
©o
10
10
10
9
0.1
0.3
0
0
OK
2.12
4.35
0.1
0.1
78
167
215
#
26.8
105.6
76.9
481.7
OR
0.1
0.14
©o
0.5
31
31
97
69
0
1
0
-38.8
PA
0.04
0.09
©o
0.7
16
16
100
173
1
2.9
0
121.1
RI
0
0
©o
©o
10
9
10
9
0
0
0
0
SC
0.13
0.24
©o
0.1
39
39
500
417
0.2
0.6
0
-16.3
SD
0.79
1.31
0.1
0.1
92
155
346
867
13.4
54.1
15.3
70.2
TN
0
0
©o
©o
10
10
10
10
1.1
1
0
0
TX
2.14
3.71
0.1
0.1
70
138
148
751
222.6
588
611.8
2584.1
UT
0.44
0.53
0.1
0.1
69
56
142
80
-2
-5.7
-56.7
-31.3
VT
0
0
©o
©o
10
10
10
10
0
0
0
0
VA
0.02
0.04
©o
©o
12
12
17
17
0
0
0
0
WA
0.12
0.2
©o
0.1
36
36
434
260
-0.6
71.1
0
-198.5
WV
0
0
©o
©o
10
10
10
9
0
0
0
0
WI
0
0
©o
©o
10
10
10
10
1.5
0.9
0
0
WY
0.43
0.64
0.1
0.1
67
89
127
260
4.1
13.9
11.9
26.7
US (Scaled, including price uncertainty)
223
1263
639
4479
Note: # Supply curve shift is unrealistically costly; hence we assume that project is built that delivers water for $800/acre-foot.
See Table 2 for explanation of other units.
11
-------
Originally in Global Climate Change: Implications, Challenges and Mitigation Measures (1992)
State
Residential
Table 5
Other Water Related Costs for GISS C02 Doubling
($ millions, scaled for economic growth)
Pollution
Hydropower
Total Water Costs (SBillions)
Low
High
Low
High
Low
High
Low
Medium
High
Mean
AL
13.5
33.8
277.2
858.9
39.2
114.1
.4
.6
1.0
.7
AZ
.1
.4
152.1
478.1
40.9
120.6
.2
.4
.7
.5
AR
3.2
16.0
162.3
502.8
9.9
28.8
.3
.6
1.2
.8
CA
15.6
77.5
2708.7
8275.9
198.7
571.8
3.8
6.3
10.5
7.2
CO
4.1
20.3
133.2
419.8
61.9
183.0
.4
.6
.9
.6
CT
-9.7
28.0
281.0
862.1
1.6
4.6
.3
.5
.9
.6
DE
.4
1.1
-30.6
-23.2
0.0
0.0
.0
.0
.0
.0
FL
1.5
3.8
1335.5
4058.5
1.1
3.0
1.6
2.6
4.3
3.0
GA
6.5
16.3
-294.3
-222.6
-4.8
-14.8
-.1
-.2
-.3
-.2
ID
.0
-.1
-130.4
-98.0
-50.7
-162.3
-.2
-.3
-.4
-.3
IL
-39.2
111.5
357.1
1132.8
0.2
0.7
.4
.7
1.2
.8
IN
-10.5
31.7
171.0
542.5
0.9
2.5
.2
.3
.6
.4
IA
1.5
7.5
87.0
276.1
1.8
5.4
.1
.2
.4
.3
KS
3.3
16.7
418.4
1245.4
0.0
0.1
.6
1.2
2.5
1.6
KY
-6.9
19.8
-173.1
-130.9
-4.1
-12.7
-.1
-.1
-.2
-.1
LA
8.3
20.9
324.0
1000.7
0.0
0.0
.5
.8
1.4
.9
ME
-4.8
16.7
105.3
322.9
8.7
25.2
.1
.2
.4
.2
MD
7.4
20.3
-241.3
-182.6
-2.4
-7.3
-.1
-.1
-.2
-.2
MA
-20.0
57.8
512.5
1572.0
1.0
2.9
.6
.9
1.6
1.1
MI
-40.0
128.7
1186.8
3578.1
5.3
15.0
1.3
2.2
3.7
2.5
MN
-13.9
36.6
132.6
420.5
1.6
4.8
0.1
.3
.4
.3
MS
1.2
2.9
177.1
548.8
0.0
.0
.2
.4
.8
.5
MO
16.2
88.7
347.5
1076.7
5.8
16.8
.6
1.0
1.6
1.1
MT
.2
1.6
-93.6
-70.5
-37.3
-118.7
-.1
-.2
-.3
-.2
NE
.8
4.2
64.6
203.5
3.3
9.8
.2
.4
1.0
0.6
NV
.3
1.6
100.5
306.9
25.8
74.4
.2
.3
.5
.3
NH
-2.6
9.0
94.0
288.3
4.8
13.7
.1
.2
.3
.2
NJ
11.9
30.3
671.7
2060.5
.0
.0
.7
1.2
2.1
1.4
NM
.1
.6
65.4
205.5
.3
1.0
.1
.2
.3
.2
NY
88.5
225.2
1557.4
4777.5
115.0
332.1
1.9
3.2
5.2
3.6
NC
6.7
16.9
-300.8
-227.5
-7.4
-23.0
.1
-.2
-.3
-.2
ND
1.3
6.5
26.6
84.0
5.8
17.1
.3
.4
.5
.4
OH
-48.5
142.7
1391.5
4201.6
0.9
2.6
1.5
2.5
4.2
2.9
OK
4.5
22.9
544.5
1620.5
14.9
42.1
.7
1.1
2.0
1.3
OR
.0
.3
-359.8
-270.5
-179.0
-573.1
-.4
-.6
-1.0
-.7
PA
56.9
142.6
1541.2
4646.9
8.0
22.7
1.8
2.902
4.784
3.288
RI
-3.6
10.3
86.1
264.2
.0
.0
.1
.2
.3
.2
SC
4.2
10.5
-161.0
-121.8
-3.7
-11.3
-.1
-.1
-.2
-.1
SD
.8
3.9
28.7
90.3
14.0
41.3
.1
.1
.2
.1
TN
10.1
25.8
330.6
1024.4
37.0
107.8
.4
.7
1.1
.8
TX
18.4
91.8
2323.3
6981.3
05.9
16.8
2.9
5.0
8.8
5.9
UT
-.2
-.9
-197.8
-148.8
-4.7
-14.9
-.1
-.2
-.3
-.2
VT
-1.7
5.8
47.9
146.8
3.8
11.0
.1
.1
.2
.1
VA
3.8
10.8
-278.9
-211.0
-1.3
-4.2
-.1
-.2
-.3
-.2
WA
-33.8
56.0
-771.9
-578.7
-439.5
-1432.9
-.4
-1.0
-2.1
-1.3
WV
-2.0
3.1
-86.8
-65.6
-0.5
-1.6
.0
-.1
-.1
-.1
WI
-14.4
48.1
149.2
473.2
4.1
12.1
.2
.3
.5
.3
WY
.4
2.0
19.0
59.8
3.2
9.3
.1
.1
.2
.1
US
40.0
1629.
14741.
52244.
-112.
-572.
21.310
35.83
60.46
41.15
NOTE: These results include a downward scaling by a factor of 3.4, so that proj ected economic growth does not make them look too high in relation to
today's economy. Tables 2, 3, and 4 omit this scaling so that the reader can better understand the intermediate calculations that they represent.
12
-------
The Costs of Climate Change to the United States
FORESTS
Although no one has estimated the nationwide loss of forests resulting from a change in climate,
several researchers have estimated the decline in forest biomass. Solomon (1986) applied a forest stand
simulation model to 24 sites in eastern Canada and the United States, using climate projections from
Mitchell (1983). In an EPA Report to Congress, Urban and Shuggart (1989) and Botkin et al. (1989)
examined sites in the southeastern United States and Great Lakes region, using the GISS, GFDL, and
Oregon State University Models. Figure 2 illustrates the results of the three studies.13
Our approach was to (1) develop generalizing regression equations that express the change in biomass
as a function of temperature and precipitation; and (2) use rough estimates of the value of forests to
estimate the value of the forest changes. We limited our efforts to the 30 states east of, or bordering, the
Mississippi River, excluding Florida.
Change in Biomass
We characterize the Solomon and EPA results separately, given the differences in approach, climate
assumptions, and quality of reported results.14 In specifying our regression equations, we focused
primarily on two alternate formulations: (1) modeling biomass as a function of climate and (2) modeling
the change in biomass as a function of both climate and the change in climate.
Table 6 shows our equations. The logarithmic equations treat biomass as extremely sensitive to
changes in precipitation (elasticities of 12 and 4), because this functional form places excess emphasis on
accuracy in cases where the loss is near 100%:15 Although an elasticity of 4-12 is too sensitive for small
changes in precipitation, it understates the sensitivity for large declines in precipitation by a similar
percentage.
Temperature was the primary driving factor for all of the modeling studies we used. Our regression
equations mostly suggest that the optimal biomass occurs with an annual average temperature of around 8
to 10 degrees, depending on rainfall.
We see no compelling reason to favor one equation over the other; so we treat them as equally valid
generalizations of the modeling studies. Because biomass can not be negative, we characterize our
uncertainty with a lognormal distribution. (We remind the reader that a best estimate that biomass will
decline can still imply that the mean estimate is for an increase in biomass.)16 Table 7 illustrate summary
statistics of the simulations.17; Figure 2 compares our best estimates with the estimates of previous
studies. We assumed that our simulations had two types of systematic error. (1) for a given state the four
projections each have a correlation of 0.5 with one another;18and (2) for a given equation, the state-
specific projections have a correlation of 0.5.19
Value of Forest Changes
Most economic information on the value of forests concerns the value of the timber. Very little has
been done to estimate the values of habitat, recreation, natural recharge of water supplies, reduction in air
pollution, scenic vistas and screening noise and unsightly infrastructure, providing shade for pedestrians,
parked vehicles, and buildings. Moreover, even if we had such studies, they probably would focus on the
value at the margin, not the total value. The proper question for someone in Mississippi is not so much
"How much would you pay to keep this forest alive" as it is "How much is it worth for the whole region
to not look like West Texas?" In the past, these decisions have not confronted us~hence existing analyses
would probably understate the values of forests.
Our baseline price assumption considers the observation that forests generally sell for $300 to $1000
per acre less after it is logged. Assuming a 33% tax rate and a 10% rate of return implies that the forests
are worth $45 to $150 per acre per year. We assume that the elasticity of demand for forest services is
unity.
13
-------
Originally in Global Climate Change: Implications, Challenges and Mitigation Measures (1992)
TABLE 6
Regression Equations Summarizing Forest Modeling Results
Using the results of Botkin el al. and Urban & Shuggart
1. ln(B+l) = -9.37 + 0.659T - 0.0396T2 + 9.121 ln(P) + 3.581n(Pjja)
(0.223) (0.00735) (2.16) (1.07)
>15
S.E. = 1.29 R2 = 0.557 D,, = 4to6 Tmax=8.2
2. A%B = (0.238 + 0.0686P - 0.0313T)T + 3.2449 %P
(0.108) (0.0294) (0.00317) (0.956)
S.E. = .350 R2 = 0.756 D15 = 0.9 Tmx=7.6 + 2.2P
Using Solomon's result
3. ln(B+l) = -4.08 + 0.547T - 0.0262T2 + 4.39 ln(Pjja)
(0.167) (0.00617) (1.79) (1.07)
D15
4. A%B = (-0.244 + 0.117P - 0.0146T)T + 0.373 %P
(0.111) (0.0371) (0.00259) (0.239)
S.E.=.166 R2 =0.49 D15 = 29 Tmx=-16.7 + 8P
Note. B = biomass, T = temperature (°C), P = rain (mm/day)
Annual values unless subscripted JJA. Tmax is the temperature at which the model predicts maximum biomass. D15 is the ratio of the
sensitivity to a one degree rise in temperature to a 1% increase in rainfall.21
Table 7
Costs of Forest Decline from a GISS Doubled C02
Current % Change in Cost Divided Cost
Forest Acres Biomass By Current Value ($ billions)
State
million Low
Medium
High
Low
Medium
High
Low
High
AL
21.73
-80.9
-93.6
-97.9
1.7
2.8
3.90
2.30
8.80
AR
16.99
-68.9
-89.9
-96.7
1.2
2.3
3.40
1.30
5.90
CT
1.82
31.0
-26.0
-58.2
-0.3
0.3
0.90
0.00
0.10
DE
0.40
66.4
-06.3
-47.2
-0.5
0.1
0.60
0.00
0.00
GA
23.91
-1.2
-50.4
-75.1
0.0
0.7
1.40
0.00
3.10
IL
4.26
5.5
-52.5
-78.6
-0.1
0.7
1.50
0.00
.60
IN
4.44
21.3
-32.7
-62.7
-0.2
0.4
1.00
-0.10
.40
IA
1.56
65.7
-36.9
-76.0
-0.5
0.5
1.40
-0.10
.20
KY
12.26
53.0
-14.6
-52.3
-0.4
-2.0
0.70
-0.40
.80
LA
13.88
-41.4
-76.8
-90.8
0.5
1.5
2.40
0.50
3.30
ME
17.71
85.9
09.6
-35.4
-0.6
-0.1
0.40
-0.90
.60
MD
2.63
49.6
-17.2
-54.1
-0.4
0.2
0.80
-.10
.20
MA
3.10
23.7
-30.5
-60.9
-0.2
0.4
0.90
-0.10
0.30
MI
18.22
65.9
-26.8
-67.7
-0.5
0.3
1.10
-0.70
1.80
MN
16.58
224.5
05.3
-65.8
-1.2
-0.1
1.10
-1.60
1.50
MS
16.69
-79.6
-93.3
-97.8
1.6
2.7
3.80
1.70
6.70
MO
12.52
-11.1
-74.8
-92.9
0.1
1.4
2.60
0.10
3.10
NH
5.02
72.0
00.0
-41.8
-0.5
0.0
0.50
-0.20
.20
NJ
1.99
-2.1
-48.4
-72.8
0.0
0.7
1.30
0.00
.20
NY
18.77
40.5
-19.6
-54.0
-0.3
0.2
0.80
-0.50
1.30
NC
18.89
3.7
-46.0
-71.9
0.0
0.6
1.30
-0.10
2.20
OH
7.31
-12.3
-57.2
-79.2
0.1
0.8
1.60
0.10
1.10
PA
17.00
-14.1
-57.4
-78.9
0.2
0.9
1.60
0.20
2.50
RI
0.40
22.1
-31.4
-61.5
-0.2
0.4
1.00
0.00
0.00
SC
12.26
-27.1
-64.8
-83.0
0.3
1.0
1.80
0.30
2.10
TN
13.26
-48.7
-75.7
-88.5
0.7
1.4
2.20
0.60
2.90
VT
4.48
87.4
05.7
-40.4
-0.6
-0.1
0.50
-.20
.20
VA
15.97
22.0
-34.2
-64.4
-0.2
0.4
1.00
-0.30
1.50
WV
11.94
37.6
-24.2
-58.3
-0.3
0.3
0.90
-0.30
0.90
WI
15.32
171.0
17.9
-48.7
-1.0
-0.2
0.80
-1.30
.80
US
(including price uncertainty)
-0.30
57.40
14
-------
The Costs of Climate Change to the United States
Our cost estimates ignore "substitution opportunities" across states. To the extent that timber is the
resource being valued, this assumption clearly tends to overstate the cost. However, most of the other
forest values are very site specific and hence substitution is not really an option in the short run. To
constrain the calculations from implying absurdly high costs for a given state, we assume that the total
cost per acre can not exceed five times the initial value, that is, $225 to 750/acre. On engineering grounds,
this assumption seems reasonable since it would probably be possible to irrigate forests at that price.2"
As Table 5 shows, the projected losses in the southeast are more than enough to offset any possible
gains elsewhere. Nevertheless, the reader might logically ask: How could we possibly lose $24 to 60
billion in forests. As a rough check, we note that even the partial studies of forest values are in the same
league. For example, annual consumer surplus associated with nonwater recreation in U.S. land is $81
billion (Bergstrom and Cordell, in press). Although not all of it is associated with forests (e.g. historic
sites), surely a large fraction is.
Kielbaso and Moll (1987) estimate that the value of trees along streets is $25 billion. The Council of
Tree and Landscape Appraisers estimates that street trees represent one-tenth the value of all urban trees
(e.g. backyards and parks), which implies that the total value of urban trees is $250 billion; Assuming a
real estate cost of capital of 10% implies that the annual services of urban trees is $25 billion. Given that
these studies examine but two of the many nontimber uses of forests, it seems reasonable that a large
scale loss of forests might be valued in the tens of billions of dollars.
Summary of Results for Doubled C02 by 2060
Table 8 summarizes our calculations for a CO2 doubling by 2060. The GISS scenario implies a
(scaled) cost of $37-229 billion; the GFDL scenario, $48-351 billion.
Our estimates for
Projected Declines in Forests Resulting
from CO2 Doubling
electricity and agriculture are
lower than the Smith and
Tirpak estimates primarily
because they reported actual
costs estimated for 2060,
while we have scaled these
estimates downward to
account for economic
growth; in addition, we
calculate the benefits of CO2
fertilization assuming a
concentration of 600 ppm,
whereas Smith and Tirpak
recommended 330 and 660
21
ppm.
Our calculations suggest
that the direct economic
effects considered by Smith
and Tirpak would
be overshadowed by the
environmental and "quality
of life" factors. The costs
associated with air pollution,
water pollution, lost forests,
and health account for 80
percent of the total.
GISS
A = 80 to 100% loss
E33 B = 50 to 80% loss
~~ C = 20 to 50% loss
DUD D = ± 20% gain
ES E = 20 to 40% gain
F = 40% or greater gain
FIGURE 3. Estimates from previous studies reported by Soloman (1986), Botkin et al. (1989), and
Urban and Shuggart (1989); states not shown were not examined in those studies. The projections
for GISS and GFDL were based on the equations shown in Table 6.
15
-------
Originally in Global Climate Change: Implications, Challenges and Mitigation Measures (1992)
THE BENEFITS OF SLOWING THE CHANGE IN CLIMATE
The preceding estimates illustrate our assumed sensitivity of various sectors to a change in climate.
But policymakers need to know the benefits of particular policies, which requires examining (1) how the
costs will rise through time with no policy; and (2) the benefit (reduction in costs) of implementing a
particular policy.
Costs of Climate Change Through Time
Like the IPCC, we relied on EPA's Atmospheric Stabilization Framework for estimates of
concentrations and temperatures through 2100,22 which assumes a three-degree temperature sensitivity,
and hence, less warming than the Smith and Tirpak study. The model projects a global warming of 4.6
degrees by 2100, with a change in concentrations sufficient to eventually cause a 6.8 degree warming. We
assume that concentrations remain constant after 2100, and that global temperatures approach the
equilibrium with an e-folding time of 40 years. Figure 3 illustrates the resulting estimates of temperature
and sea level; note that substantial warming has already occurred.
We estimated transient regional scenarios of temperature and precipitation by interpolating and
extrapolating the GISS and GFDL estimates according to the ratio of the transient temperature to the
equilibrium temperature of the model. Thus, we remove any (perceived) upward bias associated with
these two models; we only use GISS and GFDL to allocate climate change across regions, not to estimate
global warming.
Table 8
Scaled Cost in the Year 2060
GISS
GFDL
Low
Medium
High
Mean
Low
Medium
High
Mean
Agriculture
-7.60
-2.80
1.40
-3.10
-3.90
5.10
14.00
5.10
Electricity
2.40
5.60
13.30
8.10
2.40
5.60
13.30
8.10
Sea Level
1.70
5.70
19.10
11.80
1.70
5.70
19.10
11.80
Ozone
9.50
21.80
50.30
30.90
14.10
32.60
75.40
46.30
Mobile A/C
1.10
1.80
3.10
2.10
2.30
3.10
4.30
3.30
Health
2.50
9.90
31.80
20.00
2.50
8.90
31.80
20.00
Forests
-0.30
28.50
57.40
30.40
4.20
58.70
113.20
58.70
Water Resources
21.30
35.90
60.50
41.10
31.10
52.00
87.20
59.50
Surface Water
Demand
0.04
0.25
1.80
1.67
1.06
2.33
5.17
2.33
Supply
0.64
1.68
4.50
1.68
0.89
2.28
5.81
3.53
Ground Water
Demand
0.23
0.53
1.30
0.77
0.27
0.73
1.97
1.19
Supply
0.63
1.68
4.50
2.71
0.89
2.28
5.81
3.53
Water Pollution
Public
2.19
5.81
15.40
9.30
3.71
8.42
19.10
11.80
Industrial
12.50
21.50
36.70
24.80
18.06
29.40
47.90
33.10
Hydropower
-0.11
-0.35
-0.57
-0.35
1.30
2.21
3.77
2.55
Residential
0.04
0.25
1.60
1.38
0.10
0.50
2.50
2.21
Total
37
92
229
139
48
130
351
212
Figure 4 shows GISS transient estimates of the (scaled) costs of climate change. As expected, sea
level rise peaks after the year 2100 because by this time most wetlands have been inundated; a second
peak occurs a century later when the substantial concentration of development between 1 and 2 meters is
inundated.23 Under the GFDL scenario, agricultural costs diminish at first, as agriculture becomes a
smaller portion of the total economy; they later increase as the adverse impacts begin to grow more
rapidly than the general economy. By contrast, the GISS scenario shows substantial near-term benefits
from CO2 fertilization. The other impacts rise with the change in climate.
16
-------
The Costs of Climate Change to the United States
w
CO
= 150
Q
o
w)
E
O
m
10
Year
FIGURE 4.
Long Term Impact If Carbon Emissions Are
Reduced by 10 Billion Tons in the Next Decade
Impacts of an Example Policy
For illustrative purposes, the
easiest policy to consider is a
small temporary reduction in
emissions. We assume that during
the decade 1995-2005, emissions
of CO2 are reduced by 10 billion
metric tons of carbon, but that
emissions are the same after 2005
as in the baseline.24 As Figure 5
shows, the impact of such a
reduction reaches a peak a of
0.044 a few decades later.
However, because CO2 remains in
the atmosphere for centuries, the
impact declines very slowly.
Therefore, any reduction in
emissions today would yield
benefits well into the next
millennium. As Figure 5 shows,
the benefits would be about $1-2
billion during much of the next
century.
Discounting
How much should we be
willing to pay today to save to
save a billion or so dollars per
year for the next several
centuries? Ultimately, the answer
depends on whether or not we care
about future generations.
Economists generally assume that
public policies should make no
distinction between generations;
1.e. that we care as much about a
future generation as about our
own. However, a dollar invested
today will be worth $10 at some
future date. Therefore, when
economists say that we should not
spend $1 to save a future
generation $5, they are not saying
that future generations are less important than our own; they are saying that the future generation will be
better off if we invest the $1 in an investment that will yield $10.
The problems with this approach are that (1) we do not know the extent to which the funds for
reducing emissions will come from investment or current consumption; and (2) to the extent that it comes
out of investment, we do not know what the foregone investment would have otherwise yielded. There are
numerous solutions to each problem; but none are fully satisfactory. The theory of portfolio management
offers the most thorough and tested approach for deciding on the required return of an investment.
Cost
Temperature
-2.0
-1.0
CO
o
o
a>
o>
c
CO
.c
o
4-»
s
-------
Originally in Global Climate Change: Implications, Challenges and Mitigation Measures (1992)
Unfortunately, the approach has never been rigorously applied to determine the appropriate return for
global warming and other long-term environmental problems. For a stock to be held for one year, the
capital asset pricing model shows that the required return is a linear function of the extent to which the
investment increase the overall riskiness of the investor's portfolio. If there is no risk (e.g. U.S. Treasury
Bill), we assume that the investment has a pretax inflation adjusted return of 4 percent. Financial analysts
generally agree that a fully diversified portfolio (annual risk of about 15 percent) requires an extra 4-1/2
percent return to account for the risk, i.e. 8-1/2 percent25. A stock that rises or falls with, but twice as
much as the market would require twice the risk premium (9 percent), for a total return of 13 percent.
Selling short on the same investment implies that when the market falls 1 percent, the investment rises 2
percent; thus, there is a risk premium of -9 percent, implying a required return of -5 percent per year.
If a stock is to be held for 30 years, the investor does not bear 30 times the risk of holding the stock
for one year. For a random walk, the standard deviation rises with the square root of the number of years.
Thus, the required return for a typical investment being held for 100 years would be 1.04100*1.045
10=78.38 rather than 4117. Even this calculation overstates the risk over the period: The standard
deviation would be 160% of the initial investment; but unless one is buying options the risk in reality is
never greater than the investment itself.
The returns of environmental investments are probably not correlated with the stock market; the
scientific uncertainties have nothing to do with Wall Street. But extending the analysis to consider other
principal components of societal wealth suggests that there is probably a strong negative correlation
between the return from efforts to stop global warming and the state of the environment. The principals of
portfolio theory require us to consider how our uncertainty regarding an investment is related to our
overall portfolio, which includes the environment, income, and economic assets not traded in the market.
Efforts to slow global warming are least likely to be important if the environment is in good shape,
whether that condition results from a failure of global warming to materialize or environmental policies
that are strong enough to allow nature to survive, or a technological leap forward that give us the wealth
or reduce the costs to enable us to solve environmental problems that today seem prohibitively expensive.
Moreover, a strong economy is probably less vulnerable to climate change than a weak economy. Thus,
the use of financial theory would lead us to either (1) use a discount rate less than the risk-free rate; or (2)
estimate the value of reducing the uncertainty, and discount the result using the risk-free rate. To ensure
that we avoid anomalous negative discount rates, we opted for the latter approach. Our calculations
already estimate the standard deviation of our uncertainty for a given model. We assume that model error
and our ignorance about the future triples that uncertainty, and we apply that risk/return tradeoff implied
by the capital asset pricing model. For example, if our projected cots of climate change has a median of
$100 with a standard deviation of 15%, we would value that impact at $104.50; if the uncertainty is 150%
of the median, we would value the impact as $145.
Over the last century, the average rate of return on Treasury Bills has been less than 2 percent
adjusted for inflation; the last few decades, however, has seen a rate closer to 5 percent. Moreover, taxes
distort the picture: The after-tax rate may reflect how people weigh the present against the future.
However, the total cost to society of forgoing an investment must include the taxes that are lost as well.
Because most investments are taxed at a rate lower than the risk-free rate, however, the appropriate return
probably falls somewhere between these two rates. Thus, our calculations use the 2, 3, and 4 percent
discount rates.
Table 9 illustrates our calculations, with the results expressed in terms of cents of damages per gallon
of gasoline. Our results are consistent with the hypothesis of Cline (1991) that the impacts of global
warming are understated unless one looks at the very long run. With a 4 percent discount rate, most of the
costs occur before the year 2100. By contrast, with a 2 percent rate, the costs are 29-43 cents per gallon
through 2100, but the total cost is 97-135 cents per gallon. While we assume that concentrations are stable
after 2100, Cline (1991) assumed that they will continue to increase for another century. Had we adopted
his assumption, the additional costs of burning a gallon of gasoline would appear to be even greater.
18
-------
The Costs of Climate Change to the United States
Table 9
Marginal Costs of Climate Change from Burning One Gallon of Gasoline
(present value in cents per gallon)
Model Discount Rate
2% 3% 4%
GISS
Before 2100 28.7 16.5 10.3
Long Run 97.0 24.7 12.0
GFDL
Before 2100 42.6 25.2 14.0
Long Run 135.6 36.5 18.6
NOTE: These calculations assume that the ratio of worldwide to U.S. damages will be the same as the ratio of emissions (i.e.,
five). They also assume that the baseline concentration of greenhouse gases does not increase after the year 2100, implying an
equilibrium warming of 6.8 degrees.
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Botkin, D.B., R.A. Nisbet, and T.E. Reynales. 1989. "Effects of Climate Change on Forests of the Great Lake
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Cline, W.R. "Estimating the Benefits of Greenhluse Warming Abatement" (Draft). Institute for International
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NOTES
1. See parallel effort by Cline (1991).
2. We have adjusted their figures to 1990 dollars.
3. Nordhaus (1990) estimates that the savings would be about $1.6 billion per year.
4. The IPCC model probably overstates the short-term contribution of mountain glaciers, but is otherwise superior
to other efforts because the rate of melting depends upon the amount of snowcover remaining.
5. Even on a 90 degree day, some people will drive at night when it is cooler and hence not need their air
conditioner-, by contrast, even on a 60 degree day, a large portion of the driving will occur during the day.
6. We simply calculate the mean and standard deviation of the samples and divide the latter by the square root of
the sample size.
7. Note, that Smith and Tirpak's summary of these results indicates that ozone concentrations will rise about 10
percent.
8. For more details on our water resource and forest calculations, detailed appendices are available from the
author.
9. Elasticity refers to the percent change in quantify supplied or demanded for a 1 percent change in price.
10. The referenced publications describe the analysis undertaken and some of the general results. However, only
Rosensweig published the site-specific results.
11. We matched the Great Lakes equations with USGS water regions 1, 4, 5, 7, and 9, as well as the state of
Washington; the southeast with regions 2, 3,6, and 8; and the Great Plains results with the rest of the nation.
12. This distinction was necessary because the relatively fixed supply of dams in the midst of plentiful surface
water in the east implies that supply is inelastic when demand shifts but effectively elastic runoff changes.
13. Note that the figures for Solomon are estimates based on reading the graphs published in the article. Solomon
has changed jobs and thrown away the raw data on which the figures were based. We also had to estimate the
climate change that Dr. Solomon had 32 used in the model runs; the results were based on a 1983 run of the
United Kingdom Meteorological Office's model. Dr. Mitchell of the UKMO told us that they had thrown away
their raw data as well. Finally, the reader may note that Boikin failed to report a number of his results. We
attempted to secure from Boikin his unreported results, but he declined to cooperate.
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The Costs of Climate Change to the United States
14. We relied on historic climate data provided by Roy Jenne and Dennis Joseph of the National Center for
Atmospheric Research. We had to omit a few of the Solomon sites from our analysis because NCAR had no
available weather data at those sites.
15. The act of minimizing a sum of squares treats, the difference between a 10 and 50 per-cent loss in biomass as
less than the difference between a 98 and a 99 percent loss, even though for most purposes we would be more
interested in the former distinction. On the other hand, the arithmetic form treat the first 50% loss as the same as
going from 50% to zero. When considering the value of losing a forest, the last increment is particularly
important. The problem is that the model accuracy is probably closer to arithmetic; hence focussing on the loss
of that final 5% is pointless since the models we are trying to mimic are not that accurate anyway. One of our
reasons for using arithmetic percentage decline and logarithms is that neither form is ideal, but their weaknesses
are complementary.
16. Statistical theory show that the variance of a mean which declines with the sample size if observations are
independent. We assumed that the projections for a given state had 0.5 correlations with one another.
17. These statistics are for exposition and model evaluation only. As mentioned in the previous paragraph, our cost
estimates were based solely on the mean and variances of the logarithm of the change in biomass for each state.
18. On statistical grounds, the R-square values approaching 0.75 make a good case for a correlation of 0.5.
According to regression theory, oversimplifications and omitted explanatory variables only cause systematic
error if those variables are correlated with the variables included in the model. It seems that some of those
variables are fairly random, while others are systematic.
19. Given that each equation accounts for l/4of the projection, it is straightforward to show that assumption implies
[hat a correlation of 0.125 between the projections for differential states. Implied variance is thus: variance =
0.125* (Ea)1+ .875* Ea2
20. We may be assuming an artificially low price of water that climate change will invalidate. Future should
probably calculate the amount of water necessary to 33offset the impact of climate change and use the water
price assumed to result from climate change.
21. IPCC projects the C02, equilibrium climate to occur somewhat after 2060; by that time, it projects a C02,
concentration of 600 ppm.
22. We are grateful to Bill Pepper of ICF Incorporated, the model's principal investigator.
23. An additional 60 cm of baseline sea level rise also occurs by this time.
24. Such a reduction is equivalent to 12 percent of projected emissions for that period, or about 80 billion barrels of
oil.
25. E.G. Sharp, W. 1990. Investments, Chapter 23. New York: Prentice-Hall.
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