### - 1 - Accompanying Notes and Documentation on Development of DICE-2007 Model:

Notes on DICE-2007.delta.v8 as of September 21, 2007

William Nordhaus

Yale University 

## October 5, 2007

Preliminary

I. Description of the Accompanying Notes ................................................................... - 3 -
II. Estimates of the backstop technology .................................................................. - 4 -
A. Description ............................................................................................................ - 4 -
B. Lab Notes .............................................................................................................. - 4 -
III. Estimates of the parameters of th e abatement cost function ............................ - 7 -

a. General Description ............................................................................................. - 7 - b. Lab notes ............................................................................................................... - 7 -
IV. Calibrating the preference func tion and discounting ........................................ - 9 -

a. General Description ............................................................................................. - 9 - b. Lab Notes on the Calibration ............................................................................. - 9 -

c. Further notes on analysis of the preference function ................................... - 13 -
V. Lab notes for other GH G forcings ...................................................................... - 18 -
VI. Lab notes for the Climate and Carbon-Cycle Module ..................................... - 19 -

a. Basic philosophy ................................................................................................ - 19 - b. MAGICC calibration: Me thodology ............................................................... - 19 -
VII. Lab notes for Impacts and Damage Function ............................................... - 23 -

a. General background .......................................................................................... - 23 - b. Lab notes ............................................................................................................. - 24 -
VIII. Lab notes for output and sigma estimates ..................................................... - 27 -

a. General philosophy ........................................................................................... - 27 - b. Lab notes on second round estimates ............................................................. - 27 -

c. First round esti mates ......................................................................................... - 31 - IX. Lab notes on population ...................................................................................... - 32 -

a. Background ......................................................................................................... - 32 - b. Lab notes ............................................................................................................. - 33 -
X. Lab notes specification of participation function ............................................. - 33 -
XI. Lab notes for calibration of length of no-control period ................................. - 34 - XII. Lab notes for Importance Of Backstop Price Revision ...................................... - 35 - XIII. Lab notes on the carbon cycle. .............................................................................. - 38 -

### - 2 - XIV. Lab notes on adjustment of climate module.................................................. - 40 - XV. Lab notes on changing time steps in climate module ........................................ - 51 -
XV. Miscellaneous Modeling Details ..................................................................... - 54 -

a. Initial Period Time Step .................................................................................... - 54 - b. Kyoto Runs ......................................................................................................... - 54 - XVI. Notes on uncertain parameter ......................................................................... - 55 -

a. The Monte Carlo estimates ............................................................................... - 55 - b. Estimation of uncert ain variables .................................................................... - 57 -

### - 3 - I. Description of the Accompanyi ng Notes

These notes are intended to provid e more detail on the derivation and calibration of the different components of the DICE-2007,delta.v8 model revision underlying the DICE model of September 5, 2007 underlying the DICE model. The current study, William Nordhaus, “The Ch allenge of Global Warming: Economic Models and Environmental Policy,”is dated September 11, 2007. These notes are necessarily incomplete because some of the steps involved detailed spreadsheets, but the major elements should be clear.

Where the entry is labeled “Lab notes,” these are drawn from contemporaneous notes on the procedures. They have sometimes been edited but not been rewritten for the present Accomp anying Notes. These lab notes will help users understand the reasonin g underlying the estimates.

Cautionary note on the detailed estimates in these Accompanying Notes: The data in these notes are not published at the present stage becaus e their quality is significantly less than the aggregated data in the published study. They are provided so that researchers can unders tand how the aggregate numbers were developed. The detailed estimates are often based on judg mental estimates, extrapolations from other estimates, or on less reliabl e source data.

### - 4 - II. Estimates of the ba ckstop technology

A. Description

This was a major new element in DI CE-2007. The mathematics of the backstop is straightforward. By solving the cost function for the derivative of cost w.r.t. emission, the marginal cost at miu = 1 is the backstop price.

The estimated level of the backstop te chnology was around $1170 per ton C in 2005, declining by a logistic function at an initial rate of 0.5 percent per year to an asymptotic cost of $585 per ton C. These are the cost of 100 percent displacement of carbon emissions.

B. Lab Notes

For empirical estimates, we looked to three sources. First, the IPCC study of sequestration is shown in the following table. The results are as follows. The numbers are quite a bit lower than our ba ckstop technologies. For electricity, including transportation and storage, the upper estimate is around $500 per ton C (for ocean storage). This is about half of our estimate. We can see the difference in the results from the following, which subs titutes a backstop of $500 per ton C:

CCS system components Cost of capture or injection (2002 $) Remarks per ton CO2 (range) per ton C (range)

Capture from a coal- or g as-fired 15 75 55 275 Net costs of captured CO2, compared to the same plant power plant without capture.

Capture from h y dro g en and 5.0 55 18 201 Applies to high-purity sources requiring simple drying and ammonia production or g as compression. processin g

Capture from other industrial sources 25 115 92 421 Range reflects use of a number of different technologies and

fuels.

Transportation 1.0 8.0 3.7 29 Per 250 km pipeline or shipping for mass flow rates of 5 (high end) to 40 (low end) MtCO2 yr-1.

Geolo g ical stora g e 0.5 8.0 1.8 29 Excluding potential revenues from EOR or ECBM.

Geolo g ical stora g e: monitorin g and 0.1 0.3 0.4 1.1 This covers pre-injection, injection, and post-injection verificatio n monitoring, and depends on the regulatory requirements.

Ocean stora g e 5.0 30 18 110 Including offshore transportation of 100-500 km, excluding monitoring and verification.

Mineral carbonation 50 100 183 366 Range for the best case

Source: IPCC Special Report: Carbon Dioxide Capture and Storage, Bert Metz, Ogunlade Davidson, Heleen de Coninck, Manuela Loos, and Leo Meyer, eds.,

### - 5 - Second, we looked at estimates provided by Jae Edmonds (personal communication, shown next. For this, we took the Edmonds estimates of marginal cost at high miu (personal communication, January 9, 2007, included at the end). Estimating Jae’s function, we get the foll owing. The estimated backstop for 2100 is also shown. The exponent is higher in th is round (closer to DICE 1994). Based on this, we set exponent = 2.8 and 2100 backstop = 800.

We have adjusted the model with this introduction. We then define the base run as no controls for 150 year s, then optimize after that. (Later, we adjusted that to 250 years.) The carbon tax with Jae’s parameterization is 19.18 v. 19.86, this is slightly lower. This is probably becaus e the initial cost is lower with this parameterization, and therefor e the initial miu is lower.

Finally, we examined the preliminary co mpilation of estimates from the draft report for the FAR. A review of prelimin ary results from second draft FAR IPCC (SDFAR) indicates that the Edmonds esti mates are relatively optimistic. The exponent is virtually identical the Edmond s runs (1.79+1), so the strong convexity seems to be realistic. The estimate of the cost of the backstop technology for 2050 is $1634 for the SDFAR and $621 for Edmonds for 2100. Note that few of the SDFAR scenarios go beyond 60 perc ent reduction, and the upper limit is $150 per t CO2 ($550 per t C). This further calibration sugg ests a great deal of uncertainty about the backstop technology cost and that the estima tes here are consistent with the existing model estimates. The following graph sho ws the model and formulaic estimates for the two sources. MC is Edmonds, SDFAR is from the review. The results are in jae_calibration.wf1.

### - 6 - 0 400 800 1200 1600 2000

0.0 0.2 0.4 0.6 0.8 1.0

MIU IPCCF_NEW MCF_NEW IPCC MCModel and functional MCemissions reductions (/tC)

### - 7 - III. Estimates of the parameters of the abatement cost function

a. General Description

The estimates of the abatement cost, or miu, function takes the abatement cost estimates described above and estimates a log-linear function. Since the data are marginal cost data, we can integrate this to get the cost function. The important finding is the great convexity of the functi on, with a well-determined exponent near 3 from all the data sets.

b. Lab notes

In early runs, we had limited the increase in miu at .25 per decade. This gave poor results in many of the constrained runs, and it was therefore dropped in the d.8 version. The only major difference is in the highly constrained runs.

Given the problems with the base run, it seems best to have that a variant of the optimal with the miu at the Hotelling or at zero for the first few periods. 150 years seems a reasonable guess. This al so keeps cumulative C consumption well below the limit.

obs MIU MC MCF MCFLN

1 0.88 635 602.55 662.50
2 0.78 420 470.30 522.68
3 0.52 221 204.47 235.59
4 0.2 54 28.7185 36.028
5 0.001 0.001 0.00053 0.0010
6 1 783.508 851.71

Dependent Variable: MC Method: Least Squares
Date: 01/11/07 Time: 15:10 Sample (adjusted): 1 5 Included observations: 5 after adjustments Convergence achieved after 1 iteration MC=C(1)*MIU^C(2)

Coefficient Std. Error t-Statistic Prob.

### - 8 - C(1) 783.5084 67.83342 11.55048 0.0014 C(2) 2.054282 0.344458 5.963815 0.0094

R-squared 0.983807 Mean dependent var 266.0002 Adjusted R-squared 0.978409 S.D. dependent var 263.4395 S.E. of regression 38.70945 Akaike info criterion 10.43922 Sum squared resid 4495.263 Schwarz criterion 10.28299 Log likelihood -24.09805 Durbin-Watson stat 2.675909

Dependent Variable: LOG(MC) Method: Least Squares
Date: 01/11/07 Time: 15:10 Sample (adjusted): 1 5 Included observations: 5 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

C 6.747257 0.152544 44.23158 0.0000 LOG(MIU) 1.965246 0.047851 41.07000 0.0000

R-squared 0.998225 Mean dependent var 2.994654 Adjusted R-squared 0.997633 S.D. dependent var 5.613898 S.E. of regression 0.273139 Akaike info criterion 0.531504 Sum squared resid 0.223815 Schwarz criterion 0.375279 Log likelihood 0.671241 F-statistic 1686.745
Durbin-Watson stat 2.274837 Prob(F-statistic) 0.000032

### - 9 - IV. Calibrating the preference function and discounting

a. General Description

Calibrating the utility function is one of the major new approaches in DICE2007. The major assumption is that the two taste parameters need to be set in a way that is consistent with obse rved market returns. This ra ises deep questions that are discussed in the literature.

b. Lab Notes on the Calibration

For this calibration, we adjust the elas ticity of the MU of consumption to fit the path of interest rates conditional on th e time discount rate. We begin with a time path of the real return on capital. The calib ration was that the first four period give an average rate of return of around 6.5 percent. The following shows a table with results:

prstp alpha r1 r2 r3 r4 av ctax

0.0% 2.93 6.80% 6.60% 6.30% 6.00% 6.43% 24.1651

1.0% 2.50 6.80% 6.80% 6.70% 6.40% 6.68% 1.0% 2.00 6.80% 6.20% 5.70% 5.40% 6.03% 1.5% 2.50 6.80% 7.20% 7.20% 7.10% 7.08% 

### 1.5% 2.00 6.800% 6.600% 6.300% 6.000% 6.425% 21.2003
1.0% 2.31 6.800% 6.600% 6.300% 6.000% 6.425% 21.8687

0.5% 2.62 6.800% 6.600% 6.300% 6.000% 6.425% 22.7858
0.1% 2.87 6.800% 6.600% 6.300% 6.000% 6.425% 23.8328 0.0% 2.93 6.800% 6.600% 6.300% 6.000% 6.425% 24.1651



### The columns are the time discount rate, cons umption elasticity, first four period real returns, average return, and carbon tax. (T hese were a little different from the final runs). The last five rows show that the initia l carbon tax is slight ly affected by the calibration, with zero discount rates givi ng about 15 percent higher initial carbon tax than our base run. There is a slight increase in the current C tax with lower PRSTP with given interest rates, presumably because of higher decline in r in the future. However, to a first approximation, it is the near-term interest rate that determines the current policy, not the disc ount rate. We can see the impact of the PRSTP on the current carbon tax as follows:

### - 10 - 21 21.5

22 22.5

23 23.5

24 24.5

0.0% 0.5% 1.0% 1.5% 2.0%



### The following shows how real returns di ffer with different parameters (these have slightly different other parameters, but the pattern is similar to the latest version):

Return on capital

5.5% 5.7% 5.9% 6.1% 6.3% 6.5% 6.7% 6.9% 7.1% 7.3% 7.5%

2005 2015 2025 2035 2045 2055 2065 r=.03 a=1 dr=.0025
r=.015 a=1.7 dr=0.005
r=.015 a=1.8 dr=.01
r=.015 a=1.6 dr=0.005

### - 11 - Return on capital



### 0% 1% 2% 3% 4% 5% 6% 7% 8%

2005 2015 2025 2035 2045 2055 2065 r=.03,a=1,dr r=.015,a=1

r=.035,a=1 r=.001,a=1

r=.015,a=2 r=.01,a=2.8

### - 12 - Carbon tax 2005 2015 2025 2035 2045 2055
r=.03 a=1 dr=.0025 19.3 29.7 43.6 61.4 83.5 110.4 r=.015 a=1.7 dr=0.005 20.2 31.8 47.6 68.7 95.9 129.9 r=.015 a=1.6 dr=0.005 22.6 35.5 53.0 76.1 105.6 142.1 r=.015 a=1.3 dr=.005 32.7 50.9 74.7 104.9 142.3 187.5 r=.02 a=1.5 dr=.01 23.6 37.8 57.8 85.0 120.2 164.7 r=.015 a=1.8 dr=.01 18.2 28.5 43.0 62.3 87.3 118.9
r=.005 a=2.1 19.6 30.6 45.4 65.0 89.9 120.9
r=.001 a=2.3 19.8 31.0 46.4 67.0 93.4 126.6

Savings rate r=.03 a=1 dr=.0025 22.2% 22.0% 21.8% 21.7% 21.7% 21.7%
r=.015 a=1.7 dr=0.005 21. 8% 21.7% 21.8% 21.9% 22.0% 22.2%
r=.015 a=1.6 dr=0.005 22. 4% 22.3% 22.4% 22.4% 22.6% 22.7% r=.015 a=1.3 dr=.005 24.6% 24.4% 24.2% 24.2% 24.3% 24.4% r=.02 a=1.5 dr=.01 21.8% 22.1% 22.3% 22.6% 22.9% 23.2% r=.015 a=1.8 dr=.01 21.2% 21.2% 21.2% 21.3% 21.5% 21.7% r=.005 a=2.1 21.9% 21.7% 21.7% 21.7% 21.8% 22.0% r=.001 a=2.3 21.8% 21.6% 21.6% 21.7% 21.9% 22.1%

Return on capital r=.03 a=1 dr=.0025 6.91% 6.99% 6.79% 6.52% 6.23% 5.94%
r=.015 a=1.7 dr=0.005 6. 91% 7.10% 6.92% 6.59% 6.22% 5.84%
r=.015 a=1.6 dr=0.005 6. 91% 6.90% 6.63% 6.28% 5.90% 5.54% r=.015 a=1.3 dr=.005 6.91% 6.26% 5.76% 5.35% 4.99% 4.67% r=.02 a=1.5 dr=.01 6.91% 7.09% 6.81% 6.38% 5.90% 5.44% r=.015 a=1.8 dr=.01 6.91% 7.29% 7.20% 6.91% 6.53% 6.14% r=.005 a=2.1 6.91% 7.06% 6.90% 6.60% 6.26% 5.92% r=.001 a=2.3 6.91% 7.10% 6.95% 6.65% 6.28% 5.91%

Emissions control rate r=.03 a=1 dr=.0025 10.2% 13.2% 16.5% 20.2% 24.3% 28.8%
r=.015 a=1.7 dr=0.005 10. 5% 13.7% 17.4% 21.6% 26.3% 31.5%
r=.015 a=1.6 dr=0.005 11. 2% 14.5% 18.4% 22.8% 27.7% 33.1% r=.015 a=1.3 dr=.005 13.7% 17.8% 22.3% 27.3% 32.7% 38.7% r=.02 a=1.5 dr=.01 11.4% 15.1% 19.3% 24.3% 29.8% 36.0% r=.015 a=1.8 dr=.01 9.9% 12.9% 16.4% 20.4% 25.0% 30.0% r=.005 a=2.1 10.3% 13.4% 16.9% 20.9% 25.4% 30.3% r=.001 a=2.3 10.4% 13.5% 17.1% 21.3% 25.9% 31.1%

Discount rate r=.03 a=1 dr=.0025 -2.96% -2.88% -2.81% -2.74% -2.67% r=.015 a=1.7 dr=0.005 -1.49% -1.42% -1.35% -1.28% -1.22% r=.015 a=1.6 dr=0.005 -1.49% -1.42% -1.35% -1.28% -1.22% r=.015 a=1.3 dr=.005 -1.49% -1.42% -1.35% -1.28% -1.22% r=.02 a=1.5 dr=.01 -1.98% -1.79% -1.62% -1.47% -1.33% r=.015 a=1.8 dr=.01 -1.49% -1.42% -1.35% -1.28% -1.22%
r=.005 a=2.1 -0.50% -0.50% -0.50% -0.50% -0.50% r=.001 a=2.3 -0.10% -0.10% -0.10% -0.10% -0.10%

### - 13 - c. Further notes on analysis of the preference function

Calibration of the utility function is a difficu lt issue that involves both the discount rate and the elasticity of the marginal ut ility of consumption. The background from the earlier model is the following. The or iginal DICE model publication had an extensive discussion of discounting issues. In Managing the Global Commons, I summarized as follows:

Beginning with the fundamentals, a discount rate is a pure number per unit time that allows us to convert values in the future into values to day. The most common form of discount rate is the nominal or money interest rate, which is applied to future dollar values so that they can be converted into present values. When the nomin al interest rate is corrected fo r inflation, we obtain the real interest rate, which represents the rate used to c onvert future constant-do llar values into today's dollars.

To understand the economics of real interest rates, economists often use the optimal-growth framework that underlies this study, namely, the Ra msey model. The Ramsey model derives the real interest rate, or the discount rate on goods , from a combination of time discounting, the elasticity of marginal utility, and growth in consumption. We begin with the fact that society shows different levels of concern about real incomes of differe nt generations. We call this phenomenon time discounting

. For example, if, as between equally well-off generati ons, society is indifferent between an increment of real income today and (1+ ρ ) 100 increments in 100 years, we sa y that the "pure rate of time preference" is ρ per year. Most economists and political philosophers find it hard to defend a pure rate of time preference above zero on ethical grou nds (just as it may be hard to defend the fact that the United States devotes only 1/20t h of one percent of its national income to overseas development assistance). On the other hand, it would be unrealistic to make decisions based on the premise that there is in fact no time preference given that many so cial decisions are in fact tilted in favor of present generations.

A second and more justifiable reason for favori ng present consumption, or in the context of this study postponing GHG control costs, comes fr om the fact that different generations enjoy different levels of consumption. Industrial countries have witnessed more or less continual growth in living standard for more than a centur y; thus the per capita real consumption in the U.S. has growth by a factor of four over the twentieth century. Soci ety might well feel that it is appropriate for later, richer generations to pay a larger fraction of GHG control costs, just as high-income people pay a larger fraction of their income in income taxes. We might then discount future costs if average living standards were improving -- a phenomenon we call growth discounting.

One must take into account both time and growth discounting to understand the phenomenon of goods discounting . Goods discounting refers to the real interest rate defined above and concerns the relative valuation of units of good s or consumption at different points in time; the real interest rate combines goods discounting and time discounting. If we follow the approach set out in the Ramsey mo del developed in Chapter 2, we can derive the condition for an optimal path of investment and consumption as:

(6.2) M {u'[c(t)]}/ M t = u'[c(t)] { M Y(t)/ M K(t) - δ K - ρ }

which states that the time rate of change of the marginal utility of consumption eq uals the marginal utility of consumption times the net marginal product of capital { M Y(t)/ M K(t) - δ K } minus the pure rate of

### - 14 - social time preference ( ρ ). Assuming riskless competitive markets, the net marginal product of capital will equal the instantaneous real intere st rate [r(t)], so (6.2) reduces to:

(6.3) r(t) = M {u'[c(t)]}/ M t / u'[c(t)] + ρ = α g(t) + ρ

where g(t) is the growth rate of per capita cons umption. In steady state, with stable population and a constant rate of growth of per capita consumption, (6.3) becomes:

(6.4) r* = α g* + ρ

where asterisks represent steady-state values. In equation (6.4), if ρ = .03 , g = .03, and α = 1 -- parameters which might reflect today's conditions -- the real interest rate on goods would be 6 percent. However, if all economic growth ceases and the growth discount evaporates, the real interest rate would fall to 3 percent. It is crucial to understand that a high real interest rate can be generated either by a high rate of time preference or by a high elasticity of marginal utility in a society where living standards are improving

.... The methodology of this study is to anchor t he parameters in actual observations wherever possible, and evidence on rates of return are review ed in the next section. We have constructed the DICE model so that the parameters on the right -hand side of equation (6.3) are constrained to be consistent with observed market returns as repr esented by the left-hand side of (6.3). The base parameters in the model determine a real interest ra te of around 6 percent per annum in the first few periods, with the rate declining slowly in the future as population and economic growth slows. In the next section, we discuss the appropriat eness of this approach. (p. 122-25)


### In other words, the calibration relied prim arily on an estimate of the rate of return on capital. Additionally, howeve r, there were difficulties in using an alternative elasticity, as I explained in the review of the Stern Review:

When the DICE model was constructed fifteen years ago, I assumed logarithmic utility for computational reason s – alternative utility functions would not converge numerically. This calibration led to a social discount rate of 3 percent per ye ar, which was calibrated to match the growth of consumption, savings rates, and market rates of return on capital. Because of improvements in computers and software, we can no w easily calibrate alterative utilit y functions. Experiments with the DICE-2006 model indicate that a social discount ra te of 0.1 percent per year is consistent with a utility curvature parameter of 2.25. However, the Review’s social discount rate of 0.1 percent per year is inconsistent with its utility curvature assumption of 1. The Review’s calibration gives too low a rate of return and too high a savings rate compared to macroeconomic data, but the alternative calibration proposed here fits the macroeconomic data underlying the DICE model. (Stern Review, pp. 16-17)



### We must not make a virtue out of ne cessity. Sound policy should not rely upon assumptions that are unnecessarily philo sophically justifiable. In this round of modeling, I therefore continue to rely on the fundamental empirical assumption that the rate of return must be calibrated to observed market levels . However, from the point of view of welfare economics, it seems preferable to change the assumption about the pure rate of social time preference to a level that is much closer to zero or generationally neutral. Many paradoxes and puzzles arise with discount rates near zero. We take a pure rate of time preference of 1 percent per year to be a reasonable

### - 15 - assumption for the current round. We also compare with time preference rates of 0.5 and 0.1 percent.

Lowering the PRSTP requires, however, that the elasticity of the marginal utility of consumption (EMUC) be changed. Fo r this purpose, I select a EMUC that, along with the assumed PRSTP, yi elds a rate of return on ca pital that is stable in the first three periods indicating long-run gr owth equilibrium. This yields a rate of return of approximately 7 percent per ye ar in the first three decades with the current data. This rate is consistent wi th safe returns on capital in advanced economies, although it may be lower than estimated returns in high-growth economies such as China (see particularly the rates of return on human capital estimated by Heckman, http://www.journals.uchicago.edu/EDCC/ journal/issues/v51 n4/510401/510401. html for a very big example).

The following shows the combination of parameters and values of key variables in early periods. There is not much operational difference among the calibrations for rho of 0.1, 0.5 and 1.0 percent per year for the first half century. The first-period carbon taxes are all between $19 and $20 per ton C, which is virtually identical to the base period. So it is ma inly a matter of aest hetics. The long-run temperature profiles are shown here. There is some difference in the profiles after a century or so because the savi ngs rates are slightly higher for the low discount rate cases as growth slows.

Taking all this information, un less the economic data are revised significantly, the best parameterization appears to be a PRSTP of 1 percent along with a EMUC of 1.85.

### - 16 - Mean temperature

0% 50% 100% 150% 200% 250% 300% 350%

20

05 2 0 25 20 45 2065 20 85 2105 21 25 21 45 21 65 21 85 r=.03,a=1,dr r=.03,a=1

r=.01,a=2 r=.001,a=1

r=.005,a=2 r=.01,a=1.9

### - 17 - Carbon tax 2005 2015 2025 2035 2045 2055 r=.03,a=1,dr 19.3 29.7 43.6 61.4 83.5 110.4 r=.03,a=1 16.9 25.6 36.6 50.3 66.8 86.3 r=.01,a=2 16.8 26.0 38.4 54.8 75.6 101.3 r=.001,a=1 117.1 169.2 225.0 285.5 351.3 422.1 r=.005,a=2 21.8 34.0 50.3 71.7 98.6 131.8 r=.01,a=1.9 19.5 30.1 44.4 62.9 86.2 114.6 r=.005,a=2.1 19.6 30.6 45.4 65.0 89.9 120.9 r=.001,a=2.3 19.8 31.0 46.4 67.0 93.4 126.6

Savings rate r=.03,a=1,dr 22.2% 22.0% 21.8% 21.7% 21.7% 21.7% r=.03,a=1 22.1% 21.7% 21.3% 21.0% 20.8% 20.6% r=.01,a=2 21.2% 21.0% 20.9% 20.9% 21.0% 21.1% r=.001,a=1 33.0% 31.7% 31.0% 30.5% 30.2% 30.0% r=.005,a=2 22.5% 22.3% 22.3% 22.3% 22.4% 22.5% r=.01,a=1.9 22.1% 21.9% 21.8% 21.7% 21.8% 21.8% r=.005,a=2.1 21.9% 21.7% 21.7% 21.7% 21.8% 22.0% r=.001,a=2.3 21.8% 21.6% 21.6% 21.7% 21.9% 22.1%

Return on capital r=.03,a=1,dr 6.91% 6.99% 6.79% 6.52% 6.23% 5.94% r=.03,a=1 6.91% 7.01% 6.91% 6.73% 6.55% 6.37% r=.01,a=2 6.91% 7.28% 7.24% 7.01% 6.70% 6.38% r=.001,a=1 6.89% 4.34% 3.36% 2.89% 2.61% 2.41% r=.005,a=2 6.91% 6.87% 6.63% 6.30% 5.96% 5.62% r=.01,a=1.9 6.91% 7.00% 6.83% 6.55% 6.23% 5.92% r=.005,a=2.1 6.91% 7.06% 6.90% 6.60% 6.26% 5.92% r=.001,a=2.3 6.91% 7.10% 6.95% 6.65% 6.28% 5.91%

Emissions control rate r=.03,a=1,dr 10.2% 13.2% 16.5% 20.2% 24.3% 28.8% r=.03,a=1 9.5% 12.1% 15.0% 18.1% 21.5% 25.1% r=.01,a=2 9.5% 12.2% 15.4% 19.0% 23.0% 27.5% r=.001,a=1 27.8% 34.6% 41.1% 47.6% 54.1% 60.6% r=.005,a=2 10.9% 14.2% 17.9% 22.1% 26.7% 31.8% r=.01,a=1.9 10.3% 13.3% 16.7% 20.5% 24.8% 29.4% r=.005,a=2.1 10.3% 13.4% 16.9% 20.9% 25.4% 30.3% r=.001,a=2.3 10.4% 13.5% 17.1% 21.3% 25.9% 31.1%

### - 18 - V. Lab notes for other GHG forcings

This is clearly an area of major scie ntific uncertainty. We adopted the other forcings from the FAR:

The total non-CO2 forcings are way do wn from the TAR, possibly because of cloud albedo effects. We lower the overal l level and keep the GISS change in the total over the 2000-2100 period the same. No te that CO2 forcings are more than 10 times other forcings over the period.

### - 19 - VI. Lab notes for the Climate and Carbon-Cycle Module

a. Basic philosophy

We decided to keep the same basic st ructure for the climate model, but we used the MAGICC program to tune the parameters. For this purpose, we did the tuning in two stages. First, we set the pa rameters of the carbon cycle to match the projections for concentrations in the MAGI CC model. We did this for a number of runs, but concentrated on the A1F1 run.

We next took the forcings from the CO2 and exogenous forcings and compared the DICE climate model with the various MAGICC models. We then tuned the DICE parameters to align wi th the MAGICC model projections. More details are in the next section.

b. MAGICC calibration: Methodology

The method of the new recalibration is the following:

1. We based the runs primarily off the A1F1 run in MAGICC. Using this, we looked at the carbon cycle, the forcin gs, and the temperature predictions for 2.6 and 4.5 degrees for 2x CO2. We could not get the user option with 3 oC to

run. 2. We then went step by step through th e various steps using a Excel version of the geophysics module. It is so mewhat messy, but the files are modelDICE_recalib_v4.4_a1f1.xls and modelDICE_recalib_v4.4_b1min.xls.
3. First, we looked at the carbon cycle from Magicc. We reoptimized the coefficients, assuming that the system was in equilibrium in 1750 (this imposes linearity). This yielded the follo wing comparison for the history and projection. These are imperfect, and part icularly the historical is not a good match, perhaps because of our emissions numbers.

### - 20 - 580 600 620 640 660 680 700 720

1760 1780 180 0 1820 1840 186 0 1880 1900 192 0 194 0 1960 dice
magicc

700 900 1100 1300 1500 1700 1900 2100

1 9 80 2 0 00 2 020 2 040 2 060 2 0 80 2 100 dice magicc

### - 21 - 4. We then checked the forcings, which were reasonable:

Forcings

0.000 1.000 2.000 3.000 4.000 5.000 6.000 7.000 8.000 9.000 10.000

1 2 3 4 5 6 7 8 9 10 DICE
magic 2.6
Magicc 4.5



### 5. We then checked the temperature pr ojections in magicc and dice and recalibrated. This ended up with the fo llowing. DICE is a little low, but this reflects the slightly lower forcings just shown. This calibration seems reasonable for now.

### - 22 - Temp 4.5

0.000 1.000 2.000 3.000 4.000 5.000 6.000 7.000

1 2 3 4 5 6 7 8 9 10 MAgicc

DICE

Temp 2.6

0.000 0.500 1.000 1.500 2.000 2.500 3.000 3.500 4.000 4.500 5.000

1 2 3 4 5 6 7 8 9 10 Magicc

DICE



### 6. We also looked at the B1min emission s scenario. These also were a close match for the carbon cycle, as shown below:

### - 23 - 700 800 900 1000 1100 1200 1300 1400 1500

1980 20 0

0 2020 2040 2 0 60 2080 2100 dice magicc



### VII. Lab notes for Impacts and Damage Function

a. General background

The impacts analysis follows the earlie r framework in DICE/RICE-2000. We recalibrated the damage estimates. No majo r changes in the accounting format were introduced. However, the earlier ve rsion was somewhat mechanical. We particularly were concerned that the ne gative damages for some regions were continued at high T increases, and that th e catastrophic was not carefully calibrated.

The changes were as follows: (1) We removed the risk aversion from the catastrophic because we would later intr oduce risk analysis. (2) We introduced Cline’s agricultural studies. These were generally more positive than our earlier estimates, and we took a judgmental av erage. (3) We recalibrated using the new estimates for GDP and population. (4) We t ook the output weighted as that is the
measurement convention.

The major summary table is the following:

### - 24 - Notes to 2007 revisions:

1. All revisions are in blue.
2. The basic sectoral breakdowns are the same.
3. There have been substantial chan g es in the re g ional definitions. These are indicated on pa g e "Re g ions." However, we have not carefull y redefined re g ions to match new re g ions. Problems are primaril y low and middle income. These were poorl y estimated in last round, so the estimates are probabl y about the same qualit y

. 4. All numbers have been updated to us e 2005 GDPs in PPP and 2005 US dollars per th e data on the spre adsheet macro_011807.xls.
5. The ma j or chan g es are in the catastrophic estimates. See that sheet for discussion. 6. We have parameterized b y assumin g that the dama g e function is to the 2th power. It is then calibrated to 2.5 de g rees C. The estimated elasticit y is ver y close to quadratic, so this is a reasonable specification.
7. Last round had ne g ative dama g es at 0. This is doubtful g iven all results and has been constrained so that new linear term is zero. Specification is therefore simpl y quadratic. 8. We use the output wei g hts.

The new summar y table is shown below (linked to the source).

Re g ion Other vul- market Total non-catastrophi c Catastrophic impac t TOTAL TOTAL

Agriculture nerable mkt Coastal Health time use Settlements [2.5 degree] [6 degrees] [2.5 degree] [6 degrees] [2.5 degree] [6 degrees]

US 0.03% 0.00% 0.10% 0.02% -0.28% 0.10% -0.03% 1.34% 0.94% 4.00% 0.70% 5.34% WE/Euro 0.03% 0.00% 0.46% 0.02% -0.43% 0.25% 0.33% 4.26% 1.09% 4.80% 3.65% 9.06% OHI -0.05% -0.32% 0.09% 0.02% -0.35% 0.10% -0.50% 4.19% 1.11% 4.80% -2.79% 8.99% Russia -0.82% -0.80% 0.05% 0.02% -0.75% 0.05% -2.25% 3.63% 1.12% 4.80% -16.36% 8.43% EE/FSU 0.03% 0.00% 0.01% 0.02% -0.36% 0.10% -0.21% 0.76% 0.94% 4.00% -0.66% 4.76% Japan 0.02% 0.00% 0.27% 0.02% -0.31% 0.25% 0.24% 4.04% 1.07% 4.80% 2.96% 8.84% China 0.02% 0.32% 0.08% 0.09% -0.26% 0.05% 0.30% 3.92% 1.04% 4.00% 3.33% 7.92% India 0.32% 0.29% 0.09% 0.40% 0.30% 0.10% 1.51% 6.94% 1.57% 6.00% 13.29% 12.94% MidEast 0.35% 0.20% 0.04% 0.23% 0.24% 0.05% 1.12% 4.41% 0.96% 4.00% 9.61% 8.41% SSA 0.67% 0.32% 0.02% 1.00% 0.25% 0.10% 2.35% 9.55% 1.78% 7.00% 20.00% 16.55% LA 0.42% 0.28% 0.10% 0.32% -0.04% 0.10% 1.18% 5.16% 1.30% 5.20% 10.47% 10.36% OthAsia 0.52% 0.21% 0.09% 0.32% -0.04% 0.10% 1.20% 5.00% 1.23% 5.00% 10.55% 10.00%

Global: 2105 wei g hts: 2.5 oC Output wei g hted 0.61% 3.51% 1.16% 4.72% 1.77% 8.23% Population wei g hted 0.97% 5.60% 1.26% 5.05% 2.24% 10.65%



### b. Lab notes

The major issue at this stage is that the database for impact studies continues to be relatively small.

One new finding that informed the revision was the Gecon results, W Nordhaus, Proc Nat Acad Sci (US) , Feb. 2006, which indicated that the damage function at the current climate was negative rather than positive.

Calibration is the following:

### - 25 - 6. To calibrate the new dama g e function, we set a power function to these estimates. (See below for calibration.) This y ields the followin g : Dama g e estimate Dama g e estimate at T* D/Y= 0.0028388 *T^ 2.00000 1.77% Calibrated at T = 2.5 D/Y= 0.0010262 *T^ 2.50000 9.05% Calibrated at T = 6 D/Y= 0.0031112 *T^ 1.90000 1.77% Calibrated at T = 2.5

Dama g e functions Ori g New Alt1 Alt2 Alt3
Calibrated at 2.5 2.5 2.5 6
a1 -0.0045 0 0 0 0
a2 0.0035 0.0028388 0.0031112 0.0010262
a3 2 2 1.9 2.5000000
T Ori g New Alt1 Alt2 Old 0 0.00% 0.00% 0.00% 0.00% 0.00% 1 -0.10% 0.28% 0.31% 0.10% 0.32% 2 0.50% 1.14% 1.16% 0.58% 1.27% 2.5 1.06% 1.77% 1.77% 1.01% 1.98%

3 1.80% 2.55% 2.51% 1.60% 2.85% 4 3.80% 4.54% 4.33% 3.28% 5.07% 5 6.50% 7.10% 6.62% 5.74% 7.93% 6 9.90% 10.22% 9.36% 9.05% 11.41%

### - 26 - Just for information, the following shows th e damage functions at a function of T, which suggested IPCC TAR and FAR range:

July 24, 2007. There was a small correct ion in the tabulation in the earlier version, but this did not affect the results. 0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 10% 11%

0 1 2 2.5 3 4 5 6

Mean T increaseDamage/Output 

### Orig New

Alt1 Alt2

IPCC

### - 27 - VIII. Lab notes for output and sigma estimates

a. General philosophy

The general approach was to start with a database on major countries and then to aggregate up to regions. We then further aggregated to the DICE global total. The current data base is 71 majo r countries totaling about 96 percent of emissions, output, and slightly less of popu lation. The database and calculations are contained in DATA SET 5.16.2007_WN adj_v5.xls.

b. Lab notes on second round estimates

We have the problem of adjusting our RICE country totals for the world total. We decided to keep the global emission s total and the population total. The question of how to treat the remainder is an issue because the missing countries are low income. We have decided to calibrate to the world totals, as shown below:

2004 2004 2004 2005 2005 2005 RICE Total ratio RICE Total ratio

Population 5456.27 6312.75 0.864325

GDP PPP 53928.25 57,262.56 0.941771

Emissions

7158.667 7375.654 0.970581 7457.55 7683.594 0.970581 26,247.97 27,043.57 0.970581

GDP/capita 9.883717 9.070933 1.089603

co2-gdp 0.138286 0.134182 1.03059

### - 28 - We adjusted China’s PPP GDP as it was unre alistically high and distorts the overall numbers. The following shows the calculatio ns. We have rescaled the level to 60 percent of the IMF PPP numbers.

PPP MER US data US-C trad e Ratio PPP MER Ratio

GDP, 2005 8817.00 2224.00 3.96 4497.82 2224.00 2.02

Exports G&S
0.368 818.43 762.00

Exports to US
0.47 384.66 244.00 243.00

GDP-exports 7998.57 1405.57 5.69 4497.82 1405.57 3.20

Ratio 0.51013

In earlier versions of PWT we have made estima tes for the PRC based upon Bold is recalculated. quasi-benchmark estimates of indivi dual researchers including Irving Kravis, Key assumption is the 3.2 PPP who, in the early 1980s, made PPP estimates for the ye ar, 1975. Because of it s ratio for non-exports. large population, much interest has attach ed to real product estimates for China. The present range of estimates for the PRC would put it as the 2nd or 3rd largest economy in the world. The wide range of PPP estimates for China and the large
size of their difference from the exch ange rate suggest that substantial uncertainty is associated with these nu mbers. China participated in an ESCAP benchmark study for 1993 limited to comparisons between Shanghai and Tokyo and Hong Kong and Guangdong. In the end only the Hong Kong-Guangdong comparison was completed. Currently there is a study of prices in 10 cities within China and discussions have taken place about a possible link to the OECD comparisons for 1996; however that has not yet been realized.

Draft: December, 2001
Alan Heston
Treatment of China in PWT 6.



### Redid the emissions and output data. Re tuned the population to the new IIASA, both by region and by total. The IIASA has more of a hump, which we have not tried to match, but the basic trend is the same.

Then we redid the output per capita and emissions per output using the new data. Both have major uncertainties, which ca nnot be overcome with given data and

### - 29 - methods. We have matched reasonably cl osely the new aggregates (see DATA SET 5.16.2007_WNadj_v3.xls as well as the abov e file). Output is very slightly lower ($284 tr in 2100 v. $300 in 2100). However, we did overestimate the sigma significantly, and the new 2095 sigma is 0.0681 v. 0.0 877. This came about because we did not take into account the long run trend.

The effect on the social cost of carbon in uncontrolled run is marginally changed, from 29.28 to 30.07. This paradoxical results arises because of consumption elasticity. The runs are pretty stable at this point, even though the trajectories change.

Here is the comparison of old and new data and DICE:

sigma

0.0600 0.0700 0.0800 0.0900 0.1000 0.1100 0.1200 0.1300 0.1400

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 DICE

New data

Old data ajdusted new ajdusted new



## CO2

0 5 10 15 20 25 30

2005 2010 2015 2 020

2025 2030 2035 2040 2045 2050 2 055

2060 2065 2070 2075 2 080

2085 2090 209

5 2100 2105CO2 (GtC) DICE New data

Old data

### - 30 - Pop

6,000 6,500 7,000 7,500 8,000 8,500 9,000 9,500

2005 2015 2 025 2035 20 45 2055 2065 2075 20 85 2095 DICE

new rescaled

Old data

IIASA



### GWP

0 50000 100000 150000 200000 250000 300000 350000 400000 45000020052010201520202025203020352040204520502055206020652070207520802085209020952100Output DICE

New data

Old data



### CO2-GWP

0.000 0.020 0.040 0.060 0.080 0.100 0.120 0.14020052010201520202025203020352040204520502055206020652070207520802085209020952100CO2-GDP

DICE

New data

Old data

### - 31 - c. First round estimates

We have revisited the output and emis sions projections. These are contained in macro_011807.xls. The ou tput data are taken from the IMF, World Development Report, although we use PPP aggregatio n. The TFP growth is adapted from Corderi’s estimates. The basic assumption s are the following with respect to the growth of per capita GDP:

Asymptotic output ratio for US 10 Asymptotic ratio of region to US: US 1.000 WE 1.000 OHI 1.000
Russia 0.500 EE/FSU 0.700 Japan 1.000 China 0.400 India 0.400 MidEast 0.400 SSA 0.333 LA 0.600 OthAsia 0.400 Rate of approach to long-run level (annual rate): US 1.000% WE 0.700% OHI 0.800% Russia 1.200% EE/FSU 1.200% Japan 0.500% China 3.000% India 2.500% MidEast 1.000% SSA 2.000% LA 2.000% OthAsia 1.500%

### - 32 - The estimates for emissions are taken from the EIA. We have then projected future emissions growth by region using the following logistics estimates:

Ratio to
US Asymptotic
ratio Ratio asym/initi
al Initial growth
1.00 0.03 0.196 -0.90% US
1.00 0.03 0.191 -0.90% WE/Euro
0.25 0.01 0.171 -0.90% OHI
3.00 0.08 0.256 -1.00% Russia
1.00 0.03 0.294 -1.00% EE/FSU
0.50 0.01 0.151 -0.50% Japan
1.50 0.04 0.312 -0.50% China
1.30 0.04 0.379 -0.60% India
3.00 0.08 0.361 -0.30% MidEast
1.20 0.03 0.176 -0.50% SSA
1.00 0.03 0.294 -0.50% LA
1.00 0.03 0.158 -0.50% OthAsia



### We then ran the model out to 2200 using the Hotelling estimates. We calibrated the sigmas and output levels of the new DICE model with the estimates from the aggregated regional RICE. This produced the following calibration, shown in sheet “calibrateDICE2005” of the above Excel sheet:

DICE MODEL RICE regi onal aggregated Ratio RICE to DICE year 2005 2105 2205 2005 2105 2205 output 61.063 291.445 587.23 61.036 246.571 496.974 1.000 0.846 0.846 pccon 7.432 27.531 savrate 0.22 0.22 indem 7.576 19.998 22.49743 7.619 20.282 26.709 1.006 1.014 1.187 sigma 0.125 0.072 0.0456 0.125 0.082 0.072 1.000 1.146 1.583



### The emissions are reasonably close, with the sigma and output errors offsetting. Note that the Hotelling rents are import ant by 2200, so those estimates will not match.

IX. Lab notes on population

a. Background

We got the new population estimates from Wolfgang Lutz. The totals are not all that different, but the distributi on is very different from the earlier. We tuned the totals and the uncert ainty estimates to the new IIASA projections. These are a little higher than our earlier. The main problem is the shape over the 21 st 

### century (see above).

### - 33 - b. Lab notes

The projections are in pop_compare.xls

*****

X. Lab notes specification of participation function

We next add a model for participation. The modeling equations are the following:

** Participation PARTFRACTper1 Fraction of emissions under control regime 2005 /.1/ PARTFRACTper21 Fraction of emissi ons under control regime 2205 /.5/ PARTFRACT(T) = PARTFRAC Tper1+ .05*(PARTFRACTper21-PARTFR ACTper1)*(ORD(T)-1)$(ORD(T) LT 22)+ (PARTFRACTper21-PARTFR ACTper1)$(ORD(T) GE 22); ABATEEQ(T).. ABATECOST(T) =E= (PARTFRACT(T)**(1- expcost2))*YGROSS(T)*(cost 1(t)*(MIU(T)**EXPcost2)); *ABATEEQ(T).. ABATECOST(T) =E= YGROSS(T)*(cost1(t)*(MIU(T)**EXPcost2)); YY(T).. Y(T) =E= YGROSS(T)*((1-(PARTFRACT(T)**(1- expcost2))*cost1(t)*(MIU(T)**EXPcost 2)))/(1+a1*TATM(T) + a2*TATM(T)**2);



### The general theory is the following: We assume that only a fraction of countries participates in the climate protocols. In this example, the fraction begins at 1 percent and rises to 50 percent in equal increments.

The impact is the following: Assume that the parameter π represents the participation rate. With this participation, if the control fraction of the participants in miu = 1, then the overall miu = π . The cost is the cost of that fraction, given by

C = a*miu^b

And the total cost is then the follo wing fraction of total reductions:

C(miu) = π a (miu/ π )^b = π ^(1-b) a*miu^b

So there is an inefficiency given by the parameter π ^(1-b). Because our estimate of b is relatively large (2.8), this means that nonparticipation is relatively costly.

### - 34 - XI. Lab notes for calibration of le ngth of no-control period

Because including carbon constraints is a problem, another approach is to have miu=0 for a limited period, after which the ca rbon constraints kick in. This is not as efficient as Hotellling pricing, but will get most of the impact. The following shows the impact, with objective function and period s of zero miu. This is very close to the solution where have Hoteling miu for 20 periods and then optimal (for that).

0 1 2 3 4 5 6

0 1 2 5 10 15 20 25

Case Obj
function Diff from opt (trill) Diff from opt (bill) opt 1332.656 0 - 1 period 1332.626 0.03 30 2 periods 1332.578 0.078 78 5 periods 1332.363 0.293 293 10 periods 1331.498 1.158 1,158 15 periods 1330.295 2.361 2,361 20 periods 1329.12 3.536 3,536 1327.677 4.979 4,979

Optimal after 20 perio d 1329.167 3.489 3,489

### - 35 - XII. Lab notes for Importance Of Backstop Price Revision

One of the major differences in the new DICE is the estimate of the backstop technology. This was not a central issue in the earlier DICE model as it was not a variable which affected the optimal policy (w hich is the central design strategy of the modeling).

In the DICE version of the last round, the backstop (miu=1) rose rapidly over time. The following shows a comparison of estimates of the backstop in the current and the old version (these are approximate):

. MC carbon reductio n

miu=.5 Old New

2050 323 $ 300 $ 2100 1,100 $ 275 $ 2200 1,480 $ 230 $

miu=1
2050 730 $ 915 $ 2100 1,250 $ 833 $ 2200 4,000 $ 740 $



### The current version is much more optimist ic about future carbon technologies. It reflects both a modeling error in the last round and much techno logical work on the
issue.

### - 36 - To test the importance of the backstop issue, we ran the new model with a rising rather than falling backstop price. The alternative model was D_011207d_opt.GMS, while the new model was D_011207c_opt.GMS. Some results are shown in test_base and opt_011207.xls.

The following shows a rough simulation where we use a rising backstop in the spirit of DICE-1998. The rising back stop means that carbon policies are much more expensive, so that constraining polic ies are more advantageous in the present
circumstance.

OLD

Run Ob j ective functio n Difference from base/150: Ob j ective functio n Base
150 y ear dela y 1327.345 -2.465 0
50 y ear dela y 1,329.28 -3.757 1.932 Optimal 1330.16 -3.893 2.815
Limit to 2 de g ree C 1322.167 -9.877 -5.178 Limit to 2.5 de g ree C 1327.451 -6.521 0.106
Limit to 2X CO2 1329.504 -4.549 2.159

NEW

Run Objective
function Difference
from base/150: Objective
function
Base
150 y ear dela y 1329.810 0.000
50 y ear dela y 1333.034 3.224 Optimal 1334.053 4.243
Limit to 2 de g ree C 1332.044 2.234 Limit to 2.5 de g ree C 1333.972 4.162
Limit to 2X CO2 1334.053 4.243



### Note that the initial carbon taxes are not as heavily affected, however, indicating that the long-term outlook is different but current optimal policy is relatively little affected:

### - 37 - NEW

Carbon tax comparison 2005.00 2055.00 2105.00 2205.00
Base 150 year delay 0.03 0.36 2.73 407.92 50 year delay Optimal 19.18 112.67 329.53 742.31 Limit to 2 degree C 28.75 271.68 677.79 745.95 Limit to 2.5 degree C 19.88 124.45 449.92 743.52
Limit to 2X CO2 19.18 112.67 329.53 742.31 Hotelling 0.03 0.36 2.73 64.53



### OLD

Carbon tax comparison 2005.00 2055.00 2105.00 2205.00

Base 150 year delay 0.00 0.00 0.00 0.00 50 year delay Optimal 19.79 120.2 380.2 1893.69 Limit to 2 degree C 36.94 405.81 1,578.71 3,779.72 Limit to 2.5 degree C 23.65 185.47 1048.94 3483.8

Limit to 2X CO2 20.32 130.6 506.4 2949.67



### The new model has slightly lower C taxes in the short run (because the long run is more optimistic). There is little short-ru n difference, but an enormous long-run difference in the optimal policy because em issions reductions in the long-run are economical. The long-run are unrealistically high and would only be applicable in a world where technology is worse that stagnant.

Note primarily that policy is now much mo re important because the long-run cost of reductions are much less expensive.

### - 38 - XIII. Lab notes on the carbon cycle.

We have recalibrated the carbon cycle to recent data on sources and sinks. We have used GISS data on atmospheric carb on concentrations. We assume that 110 GtC of deforestation has occurred from 18 50 to 2005, based on Marland. (IPCC SAR WGII Tables B.3 and B.4 in Sect B.3.3.1 and Gregg Marland, Oak Ridge National Laboratory.) The earlier carbon cycl e model overpredicts atmospheric concentrations by a substantial margin gi ven the revised data. The time pattern of land-use emissions is from Joos et al.

We then optimized the coefficients to minimize the squared errors over the last 20 years. The change for the 10-year model is the following for a baseline run with unlimited carbon resources.. The clim ate trajectory is lower (because the old model overpredicted atmospheric concentration s). The marginal retention rate is in line with other models. The impact on th e short-term carbon tax is lower by 4 percent.

Coefficient Old New

b11 0.66616 0.6608035
b12 0.33384 0.3391965
b21 0.27607 0.2764262

b22 0.60897 0.5968861
b23 0.11496 0.1266877
b32 0.00422 0.0038996

b33 0.99578 0.9961004

### - 39 - NEW year 2005 2105 2205 temp 0.71 2.76 4.65

conc 787.0 1,417.6 2,358.9

ctax 16.9 289.9 1,019.0
indem 75.2 221.9 349.8
Cumulative emissio n 75.2 1,630.6 4,577.5 Average atmos ret ratio 38.7% 34.3%

OLD 2005 2105 2205 temp 0.71 2.88 4.824

conc 787.00 1,481.30 2,485.61

ctax 17.6 292.4 1,007.5
indem 75.2 221.8 349.2
Cumulative emissio n 75.2 1,630.3 4,573.4 Average atmos ret ratio 42.6% 37.1%

Change (%) 2005 2105 2205 temp 0.00% 4.42% 3.65%
conc 0.00% 4.49% 5.37%
ctax 3.96% 0.88% -1.13%
indem 0.00% -0.05% -0.18%
Cumulative emissio n 0.00% -0.02% -0.09% Average atmos ret ratio 10.12% 8.16%

### - 40 - XIV. Lab notes on adjustme nt of climate module

The following are the lab notes on th e adjustment of the climate module:

The basic structure of the climate mode l has not been changed since the last round. However, there has been much carefu l analysis of GCMs, particularly in the IPCC TAR, and we have incorporated that in the latest model.

One first change is to incorporate the la test view on forcings. For this, we took the total forcings from GISS. We then examined the model using both GISS and Hadley global temperature record.

We first looked at the average temp erature (using the average of GISS and Hadley). We have estimated the model us ing both GISS and Hadley data. Using GISS total forcings, we get the following results. Constraining the adjustment parameter to .15, the key parameter is c(1 ), which is t2xco2. This is lower than the estimate from GCMs or our parameteri zation, which is 2.9. The parameter reasonably well determined in this tight specification. Estima tes using well-mixed GHGs are not significant.

The continuing puzzle is why the time series estimate of the temperaturesensitivity coefficient is lower than the models. The estimate is not significantly below 3. Time will tell.

Dependent Variable: TAV Method: Least Squares Date: 01/11/07 Time: 19:50 Sample (adjusted): 1891 2006 Included observations: 116 after adjustments Convergence achieved after 4 iterations TAV=TAV(-10)+.15*( TOTFORC_GISS-(4.1/C(1))*TAV(-10)-.44 *(TAV(-10)-TLOW(-10)) )

Coefficient Std. Error t-Statistic Prob.

C(1) 2.268675 0.445698 5.090166 0.0000

### - 41 - R-squared 0.712229 Mean dependent var -0.064793 Adjusted R-squared 0.712229 S.D. dependent var 0.243178 S.E. of regression 0.130451 Akaike info criterion -1.227048 Sum squared resid 1.957022 Schwarz criterion -1.203310 Log likelihood 72.16878 Durbin-Watson stat 0.906687



### We also estimate with t2 xco2=3 degrees, yielding:

Dependent Variable: TAV Method: Least Squares Date: 01/11/07 Time: 19:47 Sample (adjusted): 1891 2006 Included observations: 116 after adjustments TAV=TAV(-10)+C(2)*( TOTFORC_ GISS-1.366*TAV(-10)-.44*(TAV( -10)-TLOW(-10)) )

Coefficient Std. Error t-Statistic Prob.

C(2) 0.155163 0.017754 8.739675 0.0000

R-squared 0.708578 Mean dependent var -0.064793 Adjusted R-squared 0.708578 S.D. dependent var 0.243178 S.E. of regression 0.131276 Akaike info criterion -1.214443 Sum squared resid 1.981846 Schwarz criterion -1.190705 Log likelihood 71.43771 Durbin-Watson stat 0.943899



### Freeing both coefficients yields:

Dependent Variable: TAV Method: Least Squares Date: 01/11/07 Time: 19:55 Sample (adjusted): 1891 2006 Included observations: 116 after adjustments Convergence achieved after 4 iterations TAV=TAV(-10)+C(2)*( TOTFORC_ GISS-(4.1/C(1))*TAV(-10)-.44

### - 42 - *(TAV(-10)-TLOW(-10)) )

Coefficient Std. Error t-Statistic Prob.

C(2) 0.150269 0.018181 8.265170 0.0000 C(1) 2.271184 0.478918 4.742319 0.0000

R-squared 0.712229 Mean dependent var -0.064793 Adjusted R-squared 0.709705 S.D. dependent var 0.243178 S.E. of regression 0.131022 Akaike info criterion -1.209808 Sum squared resid 1.957018 Schwarz criterion -1.162333 Log likelihood 72.16889 Durbin-Watson stat 0.906993

ESTIMATING ALL THREE COEFFICIENTS

Dependent Variable: TAV Method: Least Squares Date: 01/11/07 Time: 19:57 Sample (adjusted): 1891 2006 Included observations: 116 after adjustments Convergence achieved after 4 iterations TAV=TAV(-10)+C(2)*( TOTFORC_ GISS-(4.1/C(1))*TAV(-10)-C(3) *(TAV(-10)-TLOW(-10)) )

Coefficient Std. Error t-Statistic Prob.

C(2) 0.172415 0.022721 7.588394 0.0000 C(1) 2.721348 0.647524 4.202697 0.0001 C(3) 1.037278 0.330157 3.141768 0.0021

R-squared 0.718649 Mean dependent var -0.064793 Adjusted R-squared 0.713670 S.D. dependent var 0.243178 S.E. of regression 0.130124 Akaike info criterion -1.215130 Sum squared resid 1.913357 Schwarz criterion -1.143916 Log likelihood 73.47752 Durbin-Watson stat 0.913028

### - 43 - ONE YEAR ADJUSTMENT:

Using one-year adjustment, we get the follo wing, with even lower temp sensitivity:

Dependent Variable: TAV Method: Least Squares Date: 01/11/07 Time: 20:06 Sample (adjusted): 1882 2006 Included observations: 125 after adjustments Convergence achieved after 6 iterations TAV=TAV(-1)+C(2)*( TOTFORC_GISS-(4.1/C(1))*TAV(-1)-.044 *(TAV(-1)-TLOW(-1)) )

Coefficient Std. Error t-Statistic Prob.

C(2) 0.050109 0.013030 3.845539 0.0002 C(1) 1.239215 0.314128 3.944936 0.0001

R-squared 0.824945 Mean dependent var -0.079852 Adjusted R-squared 0.823522 S.D. dependent var 0.241038 S.E. of regression 0.101258 Akaike info criterion -1.726415 Sum squared resid 1.261148 Schwarz criterion -1.681162 Log likelihood 109.9009 Durbin-Watson stat 2.356257

Dependent Variable: TAV-.07*.044*TLOW(-1) Method: Least Squares Date: 01/11/07 Time: 20:25 Sample (adjusted): 1882 2006 Included observations: 125 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

(1-.07*1.366-.07*0.044)*TAV(-1) 0.923321 0.046935 19.67229 0.0000
.07*TOTFORC_GISS 0.711085 0.184331 3.857664 0.0002

R-squared 0.825068 Mean dependent var -0.078965

### - 44 - Adjusted R-squared 0.823645 S.D. dependent var 0.241000 S.E. of regression 0.101207 Akaike info criterion -1.727423 Sum squared resid 1.259877 Schwarz criterion -1.682170 Log likelihood 109.9639 Durbin-Watson stat 2.359090

### - 45 - There are nasty AR and MA problems in the regressions however. For example:

Dependent Variable: TAV-.07*.044*TLOW(-1) Method: Least Squares Date: 01/11/07 Time: 20:27 Sample (adjusted): 1882 2006 Included observations: 125 after adjustments Convergence achieved after 13 iterations

Backcast: 1881

Variable Coefficient Std. Error t-Statistic Prob.

(1-.07*1.366-.07*0.044)*TAV(-1) 1.061581 0.018635 56.96689 0.0000
.07*TOTFORC_GISS 0.365049 0.077425 4.714897 0.0000 MA(1) -0.665235 0.073908 -9.000895 0.0000

R-squared 0.848090 Mean dependent var -0.078965 Adjusted R-squared 0.845600 S.D. dependent var 0.241000 S.E. of regression 0.094698 Akaike info criterion -1.852535 Sum squared resid 1.094066 Schwarz criterion -1.784656 Log likelihood 118.7835 Durbin-Watson stat 1.684536

Inverted MA Roots .67

These might be introduced by the data smoothing, but it is not clear. This makes it difficult to rely upon the regression analysis for parameterization. (I’m not sure if this has turned up in the statistical analysis.)

### - 46 - We tried an alternative specif 

### cation and adjusted it only to 0.15 as a compromise between models and data. The “NEWNEW” is with the adjusted and the “NEW” is with the carbon cycle adjustment only.

NEW year 2005 2105 2205 temp 0.71 2.758 4.654

conc 787 1417.594 2358.884

ctax 16.9 289.86 1019.02
indem 75.1822 221.9427 349.8124

Cum emi s 75.1822 1630.577 4577.455 average ret ratio 0.386731 0.343397

NEWNEW

temp 0.71 2.616 4.592

conc 787 1481.758 2488.904

ctax 13.49 239.69 871.04
indem 75.1822 222.0999 350.0969

temp 1 0.948513 0.986678

conc 1 1.045263 1.055119

ctax 0.798225 0.826916 0.854782
indem 1 1.000708 1.000813



### As additional runs, we tested the model against the IPCC TAR model simulations. Using the GISS forcings, we es timate the following patterns of model temperature and forcings:

1905 1915 1925 1935 1945 1955 1965 1975 1985 1995 2005 2000 Atmospheric temperature (degrees Celsius above preindustrial) 0.000 0.010 0.042 0.087 0.143 0.191 0.247 0.337 0.485 0.693 0.920 0.806 Total increase in radiative forcing since preindustrial (Watts per square meter) 0.042 0.160 0.278 0.408 0.476 0.603 0.855 1.277 1.816 2.289 2.539 2.414 Lower ocean temperature (degrees Celsius above preindustrial) 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000



### This matches very closely Figure 8.15 in TAR.

### - 47 - In addition, the 1% co2 growth scenar ios matches the TAR, but it has a oneperiod lag. That is, it gets to the 1.8 degr ee one period late because of the integration period.

### - 48 -

### - 49 -

### - 50 - Figure 9.5: (a) The time evolution of the globally averaged temperature change relative to the years (1961 to 1990) of the DDC simulati ons (IS92a). G: greenhou se gas only (top), GS: greenhouse gas and sulphate aerosols (botto m). The observed temperature change (Jones, 1994) is indicated by the black line. (Unit: °C). See Table 9.1 for more information on the individual models used here. (b) The time evolution of the globally averaged precipitation change relative to the years (1961 to 1990) of the DDC simulations. GHG: greenhouse gas only (top), GS: greenhouse gas and sulpha te aerosols (bottom). (Unit: %). See Table 9.1 for more information on the indivi dual models used here.

http://www.grida.no/climat e/ipcc_tar/wg1/fig9-5.htm

### - 51 - XV

. Lab notes on changing tim e steps in climate module

One of the early problems was the timing of the climate model. The lags in a 10-year time step mean that we cannot reproduce the results of climate models accurately. We therefore changed th e atmospheric equation to include contemporaneous rather than one-period lag.

The tests seemed to have no technica l problems. Temperature was higher as follows:

year 2005 2055 2105 2205
OLD OPT temp 0.71 1.671 2.534 2.703
ctax 16.4 93.24 277.93 631.61

NEW OPT temp 0.71 1.82 7 2.614 2.38
ctax 19.59 114.71 330.8 7 634.24



### This seems analytically superior. It needs, however, to have the climate model parameters adjusted.

We begin with Twentieth-century temperatur e and precipitation trends in ensemble climate simulations includ ing natural and anthropoge nic forcing, Anthony J. Broccoli,1 Keith W. Dixon, Thomas L. Delw orth, Thomas R. Knutson, and Ronald J. Stouffer, NOAA/Geophysical Fluid Dynami cs Laboratory, Princeton University, Princeton, New Jersey, USA Fanrong Zeng, RSIS, Princeton, New Jersey, USA. Their report is for basically the same forcings as described:

The time series of radiative forcing (relative to the pr eindustrial atmosphere) for the G ensemble (Figure 1a, red curve) features a slow rise from a value of

_ 0.4 W m

_ 2 in 1865 to

_ 1.2 W m

_ 2 in 1960. A more rapid rise follows thereafter, with the ra diative forcing approaching 3 W m

_ 2 by 1997.

### - 52 - In comparing with the Broccoli et al model, that model with the forcings leads to an increase of of about 1.05 oC over the peri od. This is consistent with the earlier calibration.

We therefore recalibrated as follows:

1. We changed the equilibrium T* to 3o C.
2. We then changed the atmos/ocean coe fficient to calibrate with both the doubling and the CFDL scenarios. Th e parameter of .215 yielded a good match, a little low for the CO2 doub ling and a little high for the GFDL
scenario:

Difference from 1880-1920

1880-1920 2000.000

0.245 1.296 1.051

CO2 doubling from IPCC

1.784 Speed of adjustment parameter for atmospheric temperature 0.215



### Comparing to the results pre-adjustment , we get the following. The adjustments tend to make the temp path a little high er (because simultaneous), increasing the carbon tax by about 10 percent to $20.

### - 53 - OPTIMAL

y ear 2005 2055 2105 2205 Carbon tax
Pre carbon and climate ad j usted 17.08 96.27 284.28 529.24
Latest 19.86 116.52 337.44 633.93

Temp Pre carbon and climate ad j usted 0.710 1.723 2.636 2.724
Latest 0.710 1.834 2.643 2.420

Concentrations Pre carbon and climate ad j usted 787.0 1055.2 1291.2 1094.1
Latest 787.0 1020.2 1218.9 990.3

BASE Temp Pre carbon and climate ad j usted 0.710 1.762 2.891 4.777
Latest 0.710 1.903 3.013 4.824

Concentrations Pre carbon and climate ad j usted 787.0 1086.7 1486.9 2406.1
Latest 787.0 1055.1 1422.9 2279.1

### - 54 - XV. Miscellaneous Modeling Details

a. Initial Period Time Step

The initial time step is a complication because it include largely the historical period. However, we still want to include it . We assume that the period includes the first two years of the KP, which we estimate to have a global reduction rate of 3 percent for the last two years, which is a decadal reduction rate of 0.3 percent. (macro_kyoto_032107.xls, shee t “Kyoto”, lines AZ:BA72 ). We raise this to 0.5 percent to capture anticipatory effects and non-participants. Note that this is the same in all runs. It means, however, that first-period prices are non-informative, and second-period ones are the useful ones.

b. Kyoto Runs

The Kyoto runs have a computational problem because the control rates are so low. The Hotelling rents and carbon taxe s cross relatively early, so the Kyoto “def” files need to ensure that the Hotelling rent takes over at the crossover point. The following shows an example for the Kyoto without US (from sheet “Economics”):

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

1 2 3 4 5 6 7 8 9 10 11 12 13 14 base25 kyoto kyoto w US strong kyoto



### c.

### - 55 - XVI. Notes on uncertain parameter

a. The Monte Carlo estimates

We have undertaken a preliminary Monte Carlo estimate to determine if a larger sample would produce higher varian ces or evidence of fat tails. For this purpose, we took the rand om 100 runs and estimated a “response surface” as a function of the random variables. The estimated equation was the following for a simple and for a polynomial function:

Dependent Variable: MARGY Method: Least Squares
Date: 07/18/07 Time: 16:41 Sample (adjusted): 1 100 Included observations: 100 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

A_CARBCYC 3298.726 285.3171 11.56161 0.0000 A_DAMCOEF 12678.03 5763.093 2.199865 0.0303

A_DECARB -1044.854 382.2601 -2.733360 0.0075 A_FOSSIL 0.008743 0.006374 1.371721 0.1735 A_PBACK 31.11352 16. 26451 1.912970 0.0589

A_POP 0.045886 0.003963 11.57901 0.0000 A_T2XCO2 9.297585 6.819875 1.363307 0.1761

A_TFPG -796.7031 191.9248 -4.151120 0.0001

R-squared 0.043463 Mean dependent var 1177.421 Adjusted R-squared -0.029317 S.D. dependent var 74.31845 S.E. of regression 75.39998 Akaike info criterion 11.56011 Sum squared resid 523034.4 Schwarz criterion 11.76852 Log likelihood -570.0055 Durbin-Watson stat 2.028405

Dependent Variable: MARGY Method: Least Squares
Date: 07/18/07 Time: 16:40 Sample (adjusted): 1 100 Included observations: 100 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

A_CARBCYC 9336.260 372.5297 25.06179 0.0000

### - 56 - A_DAMCOEF -1873.304 3373.255 -0.555340 0.5802

A_DECARB -566.9492 328.9890 -1.723308 0.0886 A_FOSSIL 0.020930 0.008258 2.534523 0.0132 A_PBACK 9.118749 11. 91313 0.765437 0.4462

A_POP 0.041152 0.004502 9.140391 0.0000 A_T2XCO2 4.457665 5.388065 0.827322 0.4105

A_TFPG -1288.599 260.3073 -4.950298 0.0000 A_CARBCYC^2 -24526.64 1040.204 -23.57867 0.0000 A_DAMCOEF^2 127666.9 563876.8 0.226409 0.8214

A_DECARB^2 -2896.838 2211.598 -1.309840 0.1939 A_FOSSIL^2 -1.71E-06 6.72E-07 -2.552498 0.0125 A_PBACK^2 -3.611497 4.924094 -0.733434 0.4654

A_POP^2 -5.81E-07 2.62E-07 -2.216236 0.0294 A_T2XCO2^2 0.124151 0.882171 0.140733 0.8884

A_TFPG^2 2867.141 3049.376 0.940239 0.3499 A_TFPG^3 -4023.028 10442.88 -0.385241 0.7011 A_T2XCO2*A_TFPG -65.35669 29.68070 -2.201993 0.0305

R-squared 0.976919 Mean dependent var 1177.421 Adjusted R-squared 0.972134 S.D. dependent var 74.31845 S.E. of regression 12.40599 Akaike info criterion 8.035784 Sum squared resid 12620.50 Schwarz criterion 8.504715 Log likelihood -383.7892 Durbin-Watson stat 2.104314



### We then predicted the major variables us ing a sample of 10,000 realizations of the random variables. The following shows the ma jor statistics for the SCC, temperature at 2205, and the marginal utility of consumption:

MARGYF MARGYF_QU AD SCC1F SCC1F_QUA D TEMP21F TEMP21F_QU AD
Mean 1178.760 1179.265 26.70280 26.57500 5.033002 4.967514
Median 1179.226 1180.559 26.71182 27.84185 5.043746 5.004213
Maximum 1447.814 1449.938 85.37925 85.83153 12.45189 11.38508
Minimum 899.5732 874.3436 -43.92759 -59.45535 -2.033592 -2.724209
Std. Dev. 73.51238 74.66149 17.13403 17.42335 1.823906 1.876188
Skewness -0.014920 -0.049511 -0.016256 -0.488811 -0.017054 -0.163506
Kurtosis 2.970143 3.067010 3.005680 3.633053 3.069014 3.160268

Jarque-Bera 0.742441 5.956634 0.453865 565.2080 2.469309 55.25962 Probability 0.689892 0.050878 0.796974 0.000000 0.290935 0.000000

Sum 11787603 11792648 267028.0 265750.0 50330.02 49675.14 Sum Sq. Dev. 54035302 55737812 2935455. 3035428. 33263.01 35197.29

Observations 10000 10000 10000 10000 10000 10000

### - 57 - Most of the specifications are close to normal, although two show high JarqueBera statistics, indicating significant kurtosis . There is no indication of an increasing variance as the sample size increases. The standard deviations ar e virtually identical for a sample of 100, 1000, and 10000. A te st of the coefficient of the Pareto distribution for the largest 1000 values of SCC gives a coefficient of -9, and similar results are found for the other variables. Th e tail has finite variance for a coefficient
> -2.

b. Estimation of uncertain variables

September 4, 2007 We recalculated the parameter calcul ation for the temperature-sensitivity coefficient and refined the description, adding the new description in the text.

July 18, 2007

We have incorporated the revisions of delta.8 runs in the uncertainty analysis.We first created the new file (in C:\Major\Resclim\DICE\DICE_delta8_unc\Bas e_newparams). We then verified that it gave the same results as the last runs. Note that we widened the upper and lower limits in the new runs.

The next step was to perform the “sigma tests.” These examine the impact of single variables on the outcome, with the variable going from -6sigma to +6sigma of its value.

June 7, 2007

The estimates of the uncertain parame ters were drawn from the existing literature as well as on the author’s judg ment. The starting point was the earlier uncertainty analysis in the study, Managing the Global Commons, Chapter 6. The top uncertain variables in terms of contribu ting to overall uncertainty were the following:

• decline of rate of growth of population (asymptotic population)

• decline of rate of growth of productivi ty (growth in total factor productivity)

• pure rate of social time preference (same)

• rate of decline in the CO2-output ratio (same)

• intercept of the damage function (same)

• climate-GHG sensitivity coefficient (same)

### - 58 - • intercept of the mitigation-cost fu nction (price of backstop technology)

• rate of atmospheric retention of CO2 (transfer coefficient)

We have added in parentheses the corresponding parameter in DICE-2007 model or “same” where the concept is the same. For the current runs, we omit the pure rate of time preference because that is a preference rather than a technological parameter. We have added an additional parameter, the resources of fossil fuels, which was not in the original DICE model. There are multiple sources for the estimates of the uncertainty of the parameters. One comparison is from DI CE 1994, and the following shows the coefficients of variation of the parameters which are easily compared. Additionally, a number of alternative estimates have b een used for comparison. We have found no useful comparative studies of the un certainty of damages. In general, the calibration is to the coefficient of variation in the different sources. It should be emphasized that the uncertainty esti mates are themselves very uncertain. Few of them have been calibrated to actual historical data, and some, like future productivity growth, are inherently unknowable.

Variable Definition Units Mean Standard deviation Coefficient of variation,
DICE-2007 Coefficient
of variation,
DICE-1994 Alternative
estimates

ga0 Rate of growth of total factor productivity Per year 0.9200 0.4000 0.435 0.680 0.19-0.54 (b, g)

gsigma Rate of decarbonization Per year 0.7000 0.2000 0.286 0.650 0.250 (b)

t2xco2 Equilibrium temperature sensitivity coefficient oC per CO2 doubling 3.0000 1.1100 0.370 0.354 0.372 (i)

a2 Damage parameter (intercept of damage equation) Fraction of global output 0.00278 0.00130 0.467 0.846

pback Price of backstop technology $ per ton C replaced 1,170 468 0.400 0.551 0.300 (b)

popasym Asymtotic global population Millions 8,600 1,892 0.220 0.224 (d)

b12 Transfer coefficient in carbon cycle Per decade 0.189 0.017 0.090 0.090 (e, f)

fosslim Total resources of fossil fuels Billions tons carbon 6,000 1,200 0.200 0.445 (g)

### - 59 - Source (a) Uncertaint y Anal y sis of Climate Chan g e and Polic y Response, M. Webster, C. Forest, J. Reill y , M. Babiker, D. Kicklighter, M. Mayer, R. Prinn, M. Sarofim, A. Sokolov, P. Stone and C. Wang, Report No. 95, December 2002, (b) Mort D. Webster, Uncertaint y in Future Carbon Emissions: A Preliminar y Exploration, Massachusetts Institute of Technology,Joint Program on the Science and Policy of Global Change, Report #30, November 1997 (d) IIASA stochastic population projections, personal communication, Wolf g an g Lutz, Ma y 2007. (e) The uncertaint y in DICE-2007 is set on the basis of the uncertaint y g enerated b y the MAGICC simulations, which have a standard deviation of approximately 40 ppm in 2100 for the A1F1 scenario. (f) From the cross-sectional uncertainties of the model comparison in Riahi, Ke y wan, Arnulf Gruebler, and Nebojsa Nakicenovic [2007]. “Scenarios of long-term socio-economic and enviro nmental development under climate stabilization,” Technological Fore casting & Social Change, forthcoming. ( g ) Nordhaus and Yohe, "Future Paths of Ener gy and Carbon Dioxide Emissions," NRC, 1982. (h) H. Ro g ner, Ann. Rev. Ener gy and Env. 22, p. 249. But this is the ran g e with and without speculative resources, which is interpreted as 4 sigma. (i) IPCC, Fourth Assessment Report, Science, Chap ter 10. For this estimate, we take the avera g e of results for the 18 AOGCMs and the estimates constrained by climatology, which yield a 5-95 percent range of 3.63 oC. This is