APPENDIX EA-SCC. SOCIAL COST OF CARBON FOR REGULATORY IMPACT

ANALYSIS UNDER EXE CUTIVE ORDER 12866

TABLE OF CONTENTS

EA-SCC.1 EXECUTIVE SUMMARY

..................................................................................... 1

EA-SCC.2 MONETIZING CARBON DIOXIDE EMISSIONS

............................................... 2

EA-SCC.3 SOCIAL COST OF CARBON VALUES USED IN PAST

REGULATORY ANALYSES

............................................................................................ 4

EA-SCC.4 APPROACH AND KEY ASSUMPTIONS

............................................................ 5

EA-SCC.4.1 Integrated Assessment Models

................................................................. 5

EA-SCC.4.1.1 The DICE Model

.......................................................................... 7

EA-SCC.4.1.2 The PAGE Model

........................................................................ 8

EA-SCC.4.1.3 The FUND Model

........................................................................ 8

EA-SCC.4.1.4 Damage Functions

....................................................................... 9

EA-SCC.4.2 Global versus Domestic Measures of SCC

............................................. 11

EA-SCC.4.2.1 Global SCC

................................................................................ 11

EA-SCC.4.2.2 Domestic SCC

............................................................................ 12

EA-SCC.4.3 Valuing Non-CO 2 Emissions

.................................................................. 12

EA-SCC.4.4 Equilibrium Climate Sensitivity

............................................................. 13

EA-SCC.4.5 Socio-Economic and Emissions Trajectories

.......................................... 16

EA-SCC.4.6 Discount Rate

.......................................................................................... 18

EA-SCC.4.6.1 Historically Observed Interest Rates

......................................... 20

EA-SCC.4.6.2 The Ramsey Equation

................................................................ 21

EA-SCC.4.6.3 Accounting for Uncertainty in the Discount Rate

...................... 23

EA-SCC.4.6.4 The Discount Rates Se lected for Estimating SCC

..................... 24

EA-SCC.5 REVISED SCC ESTIMATES

............................................................................... 24

EA-SCC.6 LIMITATIONS OF THE ANALYSIS

.................................................................. 29

EA-SCC.7 A FURTHER DISCUSSION OF CATASTROPHIC IMPACTS AND

DAMAGE FUNCTIONS

.................................................................................................. 32

EA-SCC.7.1 Extrapolation of Climate Damages to High Levels of Warming

............ 32

EA-SCC.7.2 Failure to Incorporate Inter-Sectoral and Inter-Regional

Interactions

............................................................................................................. 33

EA-SCC.7.3 Imperfect Substitutabilit y of Environmental Amenities

......................... 33

EA-SCC.8 CONCLUSION

...................................................................................................... 34

EA-SCC.9 APPENDIX

............................................................................................................ 4 1

EA-SCC.9.1 Other (non-CO 2 ) Gases

........................................................................... 42

EA-SCC.9.2 Extrapolating Emi ssions Projections to 2300

......................................... 44

LIST OF TABLES

Table 1.1. Social Cost of CO 2 , 2010–2050 (2007$)

........................................................................ 2 Table 4.1. Summary Statistics fo r Four Calibrated Climate Sensitivity Distributions

.................. 14

EA-SCC.iTable 4.2. Socioeconomic and Emissions Pr ojections from Select EMF-22 Reference

Scenarios

................................................................................................................ 16 Table 5.1. Disaggregated Social Cost of CO 2 Values by Model, Socio-Economic Trajectory, and Discount Rate for 2010 (2007$)

................................................... 27

Table 5.2. Social Cost of CO 2 , 2010–2050 (2007$)

...................................................................... 28 Table 5.3. Changes in the Average Annual Growth Rates of S CC Estimates between

2010 and 2050

........................................................................................................ 29 Table 7.1. Probabilities of Various Tipping Points from Expert Elicitation

................................. 32 Table 9.1. Annual SCC Values: 2010–2050 (2007$)

.................................................................... 41

Table 9.2. 2010 Global SCC Estimates at 2.5 Percent Discount Ra te (2007$/ton CO 2 )

............... 49

Table 9.3. 2010 Global SCC Estimates at 3 Percent Discount Rate (2007$/ton CO 2 )

.................. 49

Table 9.4. 2010 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton CO 2 )

.................. 49

Table 9.5. Additional Summa ry Statistics of 2010 Global SCC Estimates

.................................. 50

LIST OF FIGURES

Figure 4.1. Annual Consumption Loss as a Fraction of Global GDP in 2100 Due to an Increase in Annual Global Temperat ure in the DICE, FUND, and PAGE

Models

.................................................................................................................... 10 Figure 4.2. Annual Consumption Loss for Lower Temperature Changes in DICE, FUND,

and PAGE

.............................................................................................................. 10 Figure 4.3. Estimates of the Probability De nsity Function for Equilibrium Climate Sensitivity

.............................................................................................................. 15 Figure 5.1. Level of Global GDP Across EMF Scenarios

............................................................. 28 Figure 9.1. Sulphur Dioxide Emission Scenarios

.......................................................................... 44 Figure 9.2. Global Population, 2000–2300 (pos t-2100 extrapolations assume the population growth rate changes linearly to reach a zero growth rate by 2200)

...................................................................................................................... 46 Figure 9.3. World GDP, 2000-2300 (post-2100 extr apolations assume GDP per capita growth declines linearly, reaching zero in 2300)

................................................... 46 Figure 9.4. Global Fossil and Industrial CO 2 Emissions, 2000-2300 (post-2100 extrapolations assume growth rate of CO 2 intensity (CO 2 /GDP) over 2090–2100 is maintained through 2300)

............................................................... 47 Figure 9.5. Global Net Land Use CO 2 Emissions, 2000–2300 (post-2100 extrapolations assume emissions decline linearly, reaching zero in 2200)

................................... 47 Figure 9.6. Global Non-CO 2 Radiative Forcing, 2000–2300 (post-2100 extrapolations

assume constant non-CO 2 radiative forcing after 2100)

........................................ 48 Figure 9.7. Global CO 2 Intensity (fossil & industrial CO 2 emissions/GDP), 2000–2300 (post-2100 extrapolations assume decline in CO 2 /GDP growth rate over 2090–2100 is maintained through 2300)

............................................................... 48 Figure 9.8. Histogram of Global SCC Estimates in 2010 (2007$/ton CO 2 ), by Discount

Rate*

....................................................................................................................... 50

EA-SCC.iiAPPENDIX EA-SCC. SOCIAL COST OF CARBON FOR REGULATORY IMPACT

ANALYSIS UNDER EXE CUTIVE ORDER 12866 Interagency Working Gr oup on Social Cost of Carbon , United States Government

With participation by

Council of Economic Advisers Council on Environmental Quality Department of Agriculture Department of Commerce

Department of Energy Department of Transportation Environmental Protection Agency

National Economic Council Office of Energy and Climate Change

Office of Management and Budget Office of Science and Technology Policy Department of the Treasury

EA-SCC.1 EXECUTIVE SUMMARY

Under Executive Order (E.O.) 12866, agencies are required, to the extent permitted by law, “to assess both the costs a nd the benefits of the intended regulation and, recognizing that some costs and benefits are difficult to qua ntify, propose or adopt a regulation only upon a

reasoned determination that the be nefits of the intended regulation justify its costs.” The purpose of the “social cost of carbon” (S CC) estimates presented here is to allow agencies to incorporate the social benefits of reducing carbon dioxide (CO 2 ) emissions into cost-benefit analyses of regulatory actions that have small, or “mar ginal,” impacts on cumulative global emissions. The estimates are presented with an acknowledgement of the many uncertainties involved and with a clear understanding that they should be updated ov er time to reflect increasing knowledge of the science and economics of climate impacts.

The SCC is an estimate of the monetized damages associated with an incremental increase in carbon emissions in a given year. Itis intended to include but is not limited to changes in net agricultural productivity, human health, property damages from increased flood risk, and the value of ecosystem services due to climate change.

This document presents a summary of the in teragency process that developed these SCC estimates. Technical experts from numerous agenci es met on a regular basis to consider public comments, explore the technical literature in re levant fields, and disc uss key model inputs and assumptions. The main objective of this process was to develop a range of SCC values using a defensible set of input assump tions grounded in the existing scie ntific and econo mic literatures. In this way, key uncertainties and model differenc es transparently and consistently inform the range of SCC estimates used in the rulemaking process.

EA-SCC.1The interagency group selected four SCC valu es for use in regulatory analyses (Table 1.1). Three values are based on the average SCC from three integrated assessment models, at discount rates of 2.5, 3, and 5 percent. The fourth value, which represents the 95 th percentile SCC

estimate across all three models at a 3 percent discount rate, is in cluded to represent higher-thanexpected impacts from temperature change furthe r out in the tails of the SCC distribution.

Table 1.1. Social Cost of CO 2 , 2010–2050 (2007$)

Year Discount Rate

%

5 3 2.5 3 Avg Avg Avg 95th

2010 4.7 21.4 35.1 64.9

2015 5.7 23.8 38.4 72.8

2020 6.8 26.3 41.7 80.7

2025 8.2 29.6 45.9 90.4

2030 9.7 32.8 50.0 100.0

2035 11.2 36.0 54.2 109.7

2040 12.7 39.2 58.4 119.3

2045 14.2 42.1 61.7 127.8

2050 15.7 44.9 65.0 136.2

EA-SCC.2 MONETIZING CARBON DIOXIDE EMISSIONS

The “social cost of carbon” (SCC) is an estimate of the monetized damages associated

with an incremental increase in carbon emissions in a given year. It is intended to include but is not limited to changes in net agricultural productivity, human health, property damages from increased flood risk, and the value of ecosystem se rvices. We report estimates of the social cost of carbon in dollars per metric ton of CO 2 throughout this document. a

When attempting to assess the incremental economic impacts of CO 2 emissions, the analyst faces a number of serious challenges. A recent report from the National Academies of Science (NRC 2009) points out that any assessm ent will suffer from uncertainty, speculation, and lack of information about (1) future emissi ons of greenhouse gases; (2) the effects of past and future emissions on the climate system; (3) the impact of changes in climate on the physical and biological environment; and (4) the tran slation of these environmental impacts into economic damages. As a result, any effort to quantify and monetize the harms associated with climate change will raise serious questions of science, economic s, and ethics and should be viewed as provisional.

Despite the serious limits of both quantifica tion and monetization, SCC estimates can be useful in estimating the social benefits of reducing CO 2 emissions. Under E.O. 12866, agencies are required, to the extent permitted by law, “to assess both the costs and the benefits of the intended regulation and, recognizing that some costs and benefits are difficult to quantify, propose or adopt a regulation only upon a reasoned determination that th e benefits of the

a In this document, we present all values of the SCC as the cost per metric ton of CO 2 emissions. Alternatively, one could report the SCC as the cost per metric ton of carbon emissions. The multiplier for translating between mass of

CO 2 and the mass of carbon is 3.67 (the molecular weight of CO 2 divided by the molecular weight of carbon = 44/12 = 3.67).

EA-SCC.2intended regulation justify its costs.” The purpose of the SCC estimates presented here is to make it possible for agencies to incorporate the social benefits from reducing CO 2 emissions into costbenefit analyses of regulatory actions that have small or “marginal” impacts on cumulative global emissions. Most Federal re gulatory actions can be expected to have marginal impacts on global emissions.

For such policies, the benefits from reduced (or costs from increased) emissions in any future year can be estimated by multiplying the change in emissions in that year by the SCC value appropriate for that year. The net present value of the benefits can then be calculated by multiplying each of these future benefits by an appropriate discount factor and summing across all affected years. This approach assumes that the marginal damages from increased emissions are constant for small departures from the ba seline emissions path, an approximation that is reasonable for policies that have effects on emissions that are small relative to cumulative global

CO 2 emissions. For policies that have a large (non-marginal) impact on global cumulative emissions, there is a separate que stion of whether the SCC is an appropriate tool for calculating the benefits of reduced emissions; we do not attempt to answer that question here.

An interagency group convened on a regular ba sis to consider public comments, explore

the technical literature in releva nt fields, and discuss key inputs and assumptions in order to generate SCC estimates. Agencies that actively participated in the interagency process include the Environmental Protection Agency, and the De partments of Agriculture, Commerce, Energy, Transportation, and Treasury. This process was convened by the Council of Economic Advisers and the Office of Management and Budget, with active participation and regular input from the Council on Environmental Quality, National Economic Council, O ffice of Energy and Climate Change, and Office of Science and Technology Polic y. The main objective of this process was to develop a range of SCC values using a defensible set of input assumptions that are grounded in the existing literature. In this way, key uncertainties and model differences can more transparently and consistently inform the range of SCC estimates used in the rulemaking process.

The interagency group selected four SCC estima tes for use in regulatory analyses. For 2010, these estimates are $4.7, $21.4, $35.1, and $64.9 ( 2007$). The first three estimates are based on the average SCC across models and soci o-economic and emissions scenarios at the 5, 3, and 2.5 percent discount rates, resp ectively. The fourth value is included to represent the higherthan-expected impacts from temperature change furt her out in the tails of the SCC distribution. For this purpose, we use the SCC value for the 95 th percentile at a 3 percent discount rate. The central value is the average SCC across models at the 3 percent discount rate. For purposes of capturing the uncertainties involved in regulatory impact analysis, we emphasize the importance and value of considering the full range. These SCC estimates also grow over time. For instance, the central value increases to $24 per ton of CO 2 in 2015 and $26 per ton of CO 2 in 2020. See Appendix A for the full range of annual SCC estimates from 2010 to 2050.

It is important to emphasize that the in teragency process is committed to updating these

estimates as the science and economic understanding of clima te change and its impacts on society improves over time. Specifically, we have set a preliminary goal of revisiting the SCC values within 2 years or at such time as substa ntially updated models beco me available, and to continue to support research in this area. In th e meantime, we will continue to explore the issues

EA-SCC.3raised in this document and consider public co mments as part of the ongoing interagency

process.

EA-SCC.3 SOCIAL COST OF CARBON VALUES USED IN PAST

REGULATORY ANALYSES

To date, economic analyses for Federal regula tions have used a wide range of values to estimate the benefits associated with reducing CO 2 emissions. In the final model year 2011 CAFE rule, the Department of Transportation (DOT) used bot h a “domestic” SCC value of $2 per ton of CO 2 and a “global” SCC va lue of $33 per ton of CO 2 for 2007 emission reductions (2007$), increasing both values at 2.4 percent per y ear. It also included a sensitivity analysis at $80 per ton of CO 2 . A domestic SCC value is meant to reflect the value of damages in the United States resulting from a unit change in CO 2 emissions, while a global SCC value is meant to reflect the value of damages worldwide.

A 2008 regulation proposed by DOT assumed a domestic SCC value of $7 per ton CO 2 (in 2006 dollars) for 2011 emission reductions (with a range of $0-$14 for sensitivity analysis), also increasing at 2.4 percent per year. A regulation finalized by DOE in October of 2008 used a domestic SCC range of $0 to $20 per ton CO 2 for 2007 emission reductions (2007$). In addition, EPA’s 2008 Advance Notice of Proposed Rulemaking for Greenhouse Gases identified what it described as “very preliminary” SCC estimates subject to revi sion. EPA’s global mean values were $68 and $40 per ton CO 2 for discount rates of approxima tely 2 percent and 3 percent, respectively (2006$ for 2007 emissions).

In 2009, an interagency process was initiate d to offer a preliminary assessment of how best to quantify the benefits from reducing CO 2 emissions. To ensure consistency in how benefits are evaluated across agencies, the Administration sought to develop a transparent and defensible method, specifically designed for the rulemaking process, to quantify avoided climate change damages from reduced CO 2 emissions. The interagency group did not undertake any original analysis. Instead, it combined SCC estimates from the existing literature to use as interim values until a more comprehe nsive analysis could be conducted.

The outcome of the preliminary assessment by the interagency group was a set of five interim values: global SCC estimates for 2007 (in 2006 dollars) of $55, $33, $19, $10, and $5 per

ton of CO 2. The $33 and $5 values represented model- weighted means of the published estimates produced from the most recently available versions of three integrated assessment models— DICE, PAGE, and FUND—at appr oximately 3 and 5 percent discount rates. The $55 and $10 values were derived by adjusting the published estimates for uncertainty in the discount rate (using factors developed by Newell and Pizer (2003)) at 3 and 5 percent discount rates, respectively. The $19 value was chosen as a central value between the $5 and $33 per ton estimates. All of these values were assumed to increase at 3 percent annually to represent growth in incremental damages over time as the magnitude of climate change increases.

These interim values represent the first sustained interagency effort within the U.S. government to develop an SCC for use in regulatory analysis. The results of this preliminary effort were presented in several proposed and fi nal rules and were offere d for public comment in

EA-SCC.4connection with proposed rules, including the joint EPA-DOT fuel economy and CO 2 tailpipe emission proposed rules.

EA-SCC.4 APPROACH AND KEY ASSUMPTIONS

Since the release of the interim values, interagency group has reconvened on a regular basis to generate improved SCC estimates. Specifically, th e group has considered public comments and further explored the technical literature in relevant fields. This section details the several choices and assumpti ons that underlie the resultin g estimates of the SCC.

It is important to recognize that a number of key uncertainties remain, and that current SCC estimates should be treated as provisional and revisable since they will evolve with improved scientific and economic understanding. The interage ncy group also recognizes that the existing models are imperfect and incomplete. The National Academy of Science (2009) points out that there is tension between the goal of producing quantified estimates of the economic damages from an incremental ton of carbon and the limits of existing efforts to model these effects. Throughout this document, we highlight a number of concerns and problems that should be addressed by the research community, including research programs housed in many of the agencies participating in the interagency process to estimate the SCC.

The U.S. Government will periodically review and reconsider estimates of the SCC used for cost-benefit analyses to reflect increasing knowledge of the science and economics of climate impacts, as well as improvements in modeling. In this context, statements recognizing the limitations of the analysis and calling for further research take on exceptional significance. The interagency group offers the new SCC values with all due humility a bout the uncertainties embedded in them and with a sincere pro mise to continue work to improve them.

EA-SCC.4.1 Integrated Assess ment Models

We rely on three integrated assessment models (IAMs) commonly used to estimate the SCC: the FUND, DICE, and PAGE models. b These models are frequently cited in the peerreviewed literature and used in the IPCC assessment. Each model is given equal weight in the SCC values developed through this process, bearing in mind their different limitations (discussed below).

These models are useful because they combine climate processes, economic growth, and feedbacks between the climate and the global economy into a single modeling framework. At the same time, they gain this advantage at the expense of a more detailed representation of the underlying climatic and economic systems. DICE, PAGE, and FUND all take stylized, reducedform approaches (see NRC 2009 for a more de tailed discussion; see Nordhaus 2008 on the

b The DICE (Dynamic Integrated Climate and Economy) model by William Nordhaus evolved from a series of energy models and was first presented in 1990 (Nordhaus and Boyer 2000, Nordhaus 2008). The PAGE (PolicyAnalysis of the Greenhouse Effect) model was developed by Chris Hope in 1991 for use by European decisionmakers in assessing the marginal impact of carbon emissions (Hope 2006, Hope 2008). The FUND (Climate Framework for Uncertainty, Negotiation, and Distribution) model, developed by Richard Tol in the early 1990s, originally to study international capital transfers in climate policy. is now widely used to study climate impacts ( e.g. , Tol 2002a, Tol 2002b, Anthoff et al. 2009, Tol 2009).

EA-SCC.5possible advantages of this approach). Other IAMs may better reflect the complexity of the science in their modeling frameworks but do not link physical impacts to economic damages. There is currently a limited amount of research linking climate impacts to economic damages, which makes this exercise even more difficult. Underlying the three IAMs selected for this exercise are a number of simplifying assumptions and judgments reflecting the various modelers’ best attempts to synthesize the available scientific and economic research characterizing these relationships.

The three IAMs translate emissions in to changes in atmospheric greenhouse concentrations, atmospheric conc entrations into changes in temperature, and changes in temperature into economic damages. The emissions projections used in the models are based on specified socio-economic (GDP and population) pa thways. These emissions are translated into concentrations using the carbon cycle built into ea ch model, and concentrations are translated into warming based on each model’s simplified representation of the climate and a key parameter, climate sensitivity. Each model uses a different approach to translate warming into damages. Finally, transforming the stream of economic damages over time into a single value requires judgments about how to discount them.

Each model takes a slightly different approa ch to model how changes in emissions result in changes in economic damages. In PAGE, for example, the consumpti on-equivalent damages in each period are calculated as a fraction of GDP, depending on the temperature in that period relative to the pre-industrial average temperat ure in each region. In FUND, damages in each period also depend on the rate of temperatur e change from the prior period. In DICE, temperature affects both consumption and investme nt. We describe each model in greater detail here. In a later section, we discuss key gaps in how the models account for various scientific and economic processes ( e.g. , the probability of catastrophe, and the ability to adapt to climate change and the physical changes it causes).

The parameters and assump tions embedded in the three models vary widely. A key objective of the interagency process was to enable a consistent exploration of the three models while respecting the different approaches to quant ifying damages taken by the key modelers in

the field. An extensive review of the literature was conducted to select three sets of input parameters for these models: c limate sensitivity, soci o-economic and emissions trajectories, and discount rates. A probability distri bution for climate sensitivity was sp ecified as an input into all three models. In addition, the interagency group us ed a range of scenarios for the socio-economic parameters and a range of values for the disc ount rate. All other mode l features were left unchanged, relying on the model developers’ best estimates and judgments. In DICE, these parameters are handled deterministically and re presented by fixed constants; in PAGE, most parameters are represented by probability distri butions. FUND was also run in a mode in which parameters were treated probabilistically.

The sensitivity of the results to other aspects of the models ( e.g. , the carbon cycle or damage function) is also important to explore in the context of future revisions to the SCC but has not been incorporated into these estimates. Areas for future research are highlighted at the

end of this document.

EA-SCC.6EA-SCC.4.1.1 The DICE Model

The DICE model is an optimal growth m odel based on a global pr oduction function with an extra stock variable (atmospheric CO 2 concentrations). Emission reductions are treated as analogous to investment in “natural capital.” By investing in natural capital today through reductions in emissions—implying reduced consum ption—harmful effects of climate change can

be avoided and future consum ption thereby increased.

For purposes of estimating the SCC, CO 2 emissions are a function of global GDP and the carbon intensity of economic output, with the latte r declining over time due to technological progress. The DICE damage f unction links global average temp erature to the overall impact on the world economy. It varies quadratically with temperature change to capture the more rapid increase in damages expected to occur under more extreme climate change, and is calibrated to include the effects of warming on the production of market and nonmarket goods and services. It incorporates impacts on agriculture, coastal areas (due to sea level rise), “other vulnerablemarket sectors” (based primarily on changes in energy use), human health (based on climaterelated diseases, such as mala ria and dengue fever, and pollu tion), non-market amenities (based on outdoor recreation), and human settlements and ecosystems. The DICE damage function also includes the expected value of damages associated with low probability, high impact “catastrophic” climate change. This last component is calibrated based on a survey of experts (Nordhaus 1994). The expected value of these impact s is then added to the other market and nonmarket impacts mentioned above.

No structural components of the DICE mode l represent adaptation explicitly, though it is included implicitly through the choice of studies us ed to calibrate the aggregate damage function. For example, its agricultural impact estimates assume that farmers can adjust land use decisions in response to changing climate conditions , and its health imp act estimates assume improvements in healthcare over time. In addition, the small impacts on forestry, water systems, construction, fisheries, and out door recreation imply optimistic a nd costless adaptation in these sectors (Nordhaus and Boyer, 2000; Warren et al ., 2006). Costs of resettleme nt due to sea level rise are incorporated into damage estimates, but their magnitude is not clearly reported. Mastrandrea’s (2009) review c oncludes that “in general, DI CE assumes very effective adaptation, and largely ignores adaptation costs.”

Note that the damage function in DICE has a somewhat different meaning from the damage functions in FUND and PAGE. Becaus e GDP is endogenous in DICE and because damages in a given year reduce investment in that year, damages propagate forward in time and reduce GDP in future years. In contrast, GDP is exogenous in FUND and PAGE, so damages in any given year do not propagate forward. c

c Using the default assumptions in DICE 2007, this effect generates an approximately 25 percent increase in the SCC relative to damages calculated by fixing GDP. In DICE2007, the time path of GDP is endogenous. Specifically, the path of GDP depends on the rate of saving and level of abatement in each period chosen by the optimizing representative agent in the model. We made two modifications to DICE to make it consistent with EMF GDP trajectories (see next section): we assumed a fixed rate of savings of 20%, and we re-calibrated the exogenous path of total factor productivity so that DICE would produce GDP projections in the absence of warming that exactly matched the EMF scenarios.

EA-SCC.7EA-SCC.4.1.2 The PAGE Model

PAGE2002 (version 1.4epm) treats GDP growth as exogenous. It divides impacts into economic, non-economic, and catastrophic categories a nd calculates these impacts separately for eight geographic regions. Damages in each regi on are expressed as a fraction of output, where the fraction lost depends on the temperature ch ange in each region. Damages are expressed as power functions of temperature change. The expone nts of the damage function are the same in all regions but are treated as uncertain, with values ranging from 1 to 3 (instead of being fixed at 2 as in DICE).

PAGE2002 includes the consequences of catast rophic events in a separate damage subfunction. Unlike DICE, PAGE2002 models these events probabilistically. The probability of a “discontinuity” ( i.e. , a catastrophic event) is assumed to increase with temperature above a specified threshold. The threshold temperature, th e rate at which the probability of experiencing a discontinuity increases above the threshold, a nd the magnitude of the resulting catastrophe are all modeled probabilistically.

Adaptation is explicitly included in PAGE. Impacts are assumed to occur for temperature increases above some tolerable level (2 °C fo r developed countries and 0 °C for developing countries for economic impacts, and 0 °C fo r all regions for non-economic impacts), but adaptation is assumed to reduce these impacts. Default values in PAGE2002 assume that the developed countries can ultimate ly eliminate up to 90 percent of all economic impacts beyond the tolerable 2 °C increase and that developing countries can eventually eliminate 50 percent of their economic impacts. All regions are assumed to be able to mitigate 25 percent of the noneconomic impacts through adaptation (Hope 2006).

EA-SCC.4.1.3 The FUND Model

Like PAGE, the FUND model treats GDP growth as exogenous. It includes separately calibrated damage functions for eight market and nonmarket sectors: agricu lture, forestry, water, energy (based on heating and cooling demand), sea level rise (based on the value of land lost and the cost of protection), ecosystems, human he alth (diarrhea, vector-borne diseases, and cardiovascular and respiratory mortality), and extreme weather. Each impact sector has a different functional form, and is calculated separately for sixteen geographic regions. In some impact sectors, the fraction of output lost or gained due to climate change depends not only on the absolute temperature change but also on the rate of temperature change and level of regional

income. d In the forestry and agricultural sect ors, economic damages also depend on CO 2 concentrations.

Tol (2009) discusses impacts not included in FU ND, noting that many are likely to have a relatively small effect on damage estimates (both positive and negative). However, he characterizes several omitted impacts as “big unknowns:” for instance, extreme climate scenarios, biodiversity loss, and effects on ec onomic development and political violence. With

d In the deterministic version of FUND, the majority of damages are attributable to increased air conditioning demand, while reduced cold stress in Europe, North America, and Central and East Asia results in health benefits in those regions at low to moderate levels of warming (Warren et al . 2006).

EA-SCC.8regard to potentially catastrophic events, he not es, “Exactly what would cause these sorts of changes or what effects they would have are not well-understood, although the chance of any one of them happening seems low. But they do have the potential to happen relatively quickly, and if they did, the costs could be substantial. Only a few studies of climate change have examined

these issues.”

Adaptation is included both implicitly and explicitly in FUND. Explicit adaptation is seen in the agriculture and sea level rise sectors. Implicit adaptation is included in sectors such as energy and human health, where wealthier popula tions are assumed to be less vulnerable to climate impacts. For example, the damages to agriculture are the sum of three effects: (1) those due to the rate of temperature change (damages are always positive); (2) those due to the level of temperature change (damages can be positive or negative depending on region and temperature); and (3) those from CO 2 fertilization (damages are generally negative but diminishing to zero).

Adaptation is incorporated into FUND by a llowing damages to be smaller if climate change happens more slowly. The combined effect of CO 2 fertilization in the agricultural sector, positive impacts to some regions from higher temperatures, and sufficiently slow increases in temperature across these sectors can result in negative economic damages from climate change.

EA-SCC.4.1.4 Damage Functions

To generate revised SCC values, we rely on the IAM modelers’ current best judgments of how to represent the effects of climate change (represented by the increase in global-average surface temperature) on the cons umption-equivalent value of bot h market and non-market goods (represented as a fraction of global GDP). We recognize that these representations are incomplete and highly uncertai n. Given the paucity of data linking the physical impacts to economic damages, we were not able to identify a better way to translate changes in climate into net economic damages, s hort of launching our ow n research program.

The damage functions for the three IAMs are pres ented in Figure 4.1 and Figure 4.2, using the

modeler’s default scenarios and mean input a ssumptions. There are significant differences between the three models both at lower (Figure 4.1) and higher (Figure 4.2) increases in globalaverage temperature.

EA-SCC.9Figure 4.1. Annual Consumption Loss as a Frac tion of Global GDP in 2100 Due to an Increase in Annual Global Temperature in the DICE, FUND, and PAGE Models e

Figure 4.2. Annual Consumption Loss for Lower Temperature Changes in DICE, FUND,

and PAGE

e The x-axis represents increases in annual, rather than equilibrium, temperature, while the y-axis represents theannual stream of benefits as a share of global GDP. Each specific combination of climate sensitivity, socioeconomic, and emissions parameters will produce a differe nt realization of damages for each IAM. The damage functions represented in Figure 4.1 and Figure 4.2 are the outcome of default assumptions. For instance, under alternate assumptions, the damages from FUND may cross from negative to positive at less than or greater than 3

°C.

EA-SCC.10The lack of agreement among the models at lower temperature increases is underscored by the fact that the damages from FUND are well below the 5 th percentile estimated by PAGE, while the damages estimated by DICE are roughly equal to the 95 th percentile estimated by PAGE. This is significant because at higher disc ount rates we expect that a greater proportion of the SCC value is due to damages in years with lower temperature increases. For example, when the discount rate is 2.5 percent, about 45 perc ent of the 2010 SCC value in DICE is due to damages that occur in years when the temperature is less than or equal to 3 °C. This increases to approximately 55 percent and 80 pe rcent at discount rates of 3 and 5 percent, respectively.

These differences underscore the need for a thorough review of damage functions—in particular, how the models incorporate adap tation, technological change, and catastrophic damages. Gaps in the literature make modifying these aspects of the models challenging, which highlights the need for additional research. As knowledge improves, the Federal government is committed to exploring how these (and other) models can be modified to incorporate more accurate estimates of damages.

EA-SCC.4.2 Global versus Domest ic Measures of SCC

Because of the distinctive nature of the climate change problem, we center our current attention on a global measure of SCC. This approach is the same as that taken for the interim values, but it otherwise represents a departure from past practices, which tended to put greater emphasis on a domestic measure of SCC (limited to impacts of climate change experienced within U.S. borders). As a matter of law, cons ideration of both global and domestic values is generally permissible; the relevant statutor y provisions are usually ambiguous and allow

selection of either measure. f

EA-SCC.4.2.1 Global SCC

Under current OMB guidance contained in Circular A-4, analysis of economically significant proposed and final regulations fro m the domestic perspective is required, while analysis from the international perspective is optional. However, the climate change problem is highly unusual in at least two respects. First, it involves a global externality: emissions of most greenhouse gases contribute to damages around the world even when they are emitted in the United States. Consequently, to address the global nature of the problem, the SCC must incorporate the full (global) damages cause d by GHG emissions. Second, climate change presents a problem that the Unite d States alone cannot solve. Even if the United States were to reduce its greenhouse gas emissions to zero, th at step would be far from enough to avoid substantial climate change . Other countries would also need to take action to reduce emissions if significant changes in the global climate are to be avoided. Emphasizing the need for a global solution to a global problem, the United Stat es has been actively involved in seeking international agreements to reduce emissions and in encouraging other nations, including emerging major economies, to take significant steps to reduce emissions. When these

f It is true that Federal statutes are presumed not to have extraterritorial effect, in part to ensure that the laws of the United States respect the interests of foreign sovereigns. But use of a global measure for the SCC does not give

extraterritorial effect to federal law and hence does not intrude on such interests.

EA-SCC.11considerations are taken as a whole, the intera gency group concluded that a global measure of the benefits from reducing U.S. emissions is preferable.

When quantifying the damages associated with a change in emissions, a number of analysts ( e.g. , Anthoff et al . 2009a) employ “equity weighting” to aggregate changes in consumption across regions. This weighting take s into account the relative reductions in wealth in different regions of the world. A per-capita loss of $500 in GDP, for instance, is weighted more heavily in a country with a per-capita GDP of $2,000 than in one with a per-capita GDP of $40,000. The main argument for this approach is that a loss of $500 in a poor country causes a greater reduction in utility or welfare than does the same loss in a wealthy nation. Notwithstanding the theoretical claims on behalf of equity weighting, the interagency group concluded that this approach would not be appropriate for estimating a SCC value used in domestic regulatory analysis. g For this reason, the group concluded that using the global (rather than domestic) value, without equity we ighting, is the appropriate approach.

EA-SCC.4.2.2 Domestic SCC

As an empirical matter, the development of a domestic SCC is greatly complicated by the relatively few region- or country-specific estima tes of the SCC in the literature. One potential source of estimates comes from the FUND model. The resulting estimates suggest that the ratio of domestic to global benefits of emission reductions varies with key parameter assumptions. For example, with a 2.5 or 3 percent discount rate, the U.S. benefit is about 7–10 percent of the global benefit, on average, across the scenarios analyzed. Alternatively, if the fraction of GDP lost due to climate change is assumed to be similar across countri es, the domestic benefit would be proportional to the U.S. share of globa l GDP, which is currently about 23 percent. h

On the basis of this evidence, the interagency workgroup determined that a range of values from 7 to 23 percent should be used to adjust the global SCC to calculate domestic effects. Reported domestic values should use this ra nge. It is recognized that these values are approximate, provisional, and highly speculative. There is no a priori reason why domestic

benefits should be a constant fraction of net global damages over time. Further, FUND does not account for how damages in other regions could affect the United States ( e.g. , global migration, economic and political destabilization). If more accurate methods for calculating the domestic SCC become available, the Federal government will examine these to determine whether to update its approach.

EA-SCC.4.3 Valuing Non-CO 2 Emissions

While CO 2 is the most prevalent greenhouse gas emitted into the atmosphere, the U.S. included five other greenhouse gases in its recent endangerment finding: methane, nitrous oxide,

g It is plausible that a loss of $X inflicts more serious harm on a poor nation than on a wealthy one, but development of the appropriate “equity weight” is challenging. Emissions reductions also impose costs, and hence a full account would have to consider that a given cost of emissions re ductions imposes a greater utility or welfare loss on a poor nation than on a wealthy one. Even if equity weighting—for both the costs and benefits of emissions reductions—is appropriate when considering the utility or welfare effects of international action, the interagency group concluded that it should not be used in developing an SCC for use in regulatory policy at this time.

h Based on 2008 GDP (in current US dollars) from the World Bank Development Indicators Report

.

EA-SCC.12hydrofluorocarbons, perfluorocarbons , and sulfur hexafluoride. The climate impact of these gases is commonly discussed in terms of thei r 100-year global warming potential (GWP). GWP measures the ability of different gases to trap heat in the atmosphere ( i.e. , radiative forcing per unit of mass) over a particular timeframe relative to CO 2 . However, because these gases differ in both radiative forcing and atmospheric lifetimes, their relative damages are not constant over time. For example, because methane has a short lifetime, its impacts occur primarily in the near term and thus are not discounted as heavily as t hose caused by longer-lived gases. Impacts other than temperature change also vary across gases in ways that are not captured by GWP. For instance, CO 2 emissions, unlike methane and other greenhouse gases, contribute to ocean acidification. Likewise, damages from methane emissi ons are not offset by the positive effect of

CO 2 fertilization. Thus, tran sforming gases into CO 2 -equivalents using GWP, and then multiplying the carbon-equivalents by the SCC, would not result in accurate estimates of the

social costs of non-CO 2 gases.

In light of these limitations, and th e significant contributions of non-CO 2 emissions to climate change, further research is required to link non-CO 2 emissions to economic impacts.

Such work would feed into efforts to deve lop a monetized value of reductions in non-CO 2 greenhouse gas emissions. As part of ongoing wo rk to further improve the SCC estimates, the interagency group hopes to devel op methods to value these othe r greenhouse gases. The goal is to develop these estimates by the time we issue revised SCC estimates for CO 2 emissions.

EA-SCC.4.4 Equilibrium Climate Sensitivity

Equilibrium climate sensitivity (ECS) is a key input parameter for the DICE, PAGE, and

FUND models. i It is defined as the long-term increase in the annual global-average surface temperature from a doubling of atmospheric CO 2 concentration relative to pre-industrial levels (or stabilization at a concentration of approx imately 550 parts per million (ppm)). Uncertainties in this important parameter have received substantial attention in the peer-reviewed literature.

The most authoritative statement about equ ilibrium climate sensitivity appears in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC):

Basing our assessment on a combination of seve ral independent lines of evidence...including observed climate change and th e strength of known feedbacks simulated in [g lobal climate models], we conclude that the global mean equilibrium warming for doubling CO 2 , or ‘equilibrium climate sensitivity,’ is likely to lie in the range 2 °C to 4.5 °C, with a most likely value of about 3 °C. Equilibri um climate sensitivity is very likely larger than 1.5 °C. j

For fundamental physical reasons as well as data limitations, values substantially higher than

4.5 °C still cannot be excluded, but agreement with observations and prox y data is generally

i The equilibrium climate sensitivity includes the response of the climate system to increased greenhouse gas concentrations over the short to medium term (up to 100–200 years), but it does not include long-term feedback effects due to possible large-scale changes in ice sheets or the biosphere, which occur on a time scale of many hundreds to thousands of years ( e.g. , Hansen et al . 2007).

j This is in accord with the judgment that it “is likely to lie in the range 2 °C to 4.5 °C” and the IPCC definition of “likely” as greater than 66 percent probability (Le Treut et al . 2007). “Very likely” indicates a greater than 90 percent probability.

EA-SCC.13worse for those high values than for valu es in the 2 °C to 4.5 °C range. (Meehl et al . 2007, p. 799)

After consulting with several lead authors of this chapter of the IPCC report, the interagency workgroup selected four candidate pr obability distributions and calibrated them to

be consistent with the above statement: Roe a nd Baker (2007), log-normal, gamma, and Weibull. Table 4.1 gives summary statistics for the four calibrated distributions.

Table 4.1. Summary Statistics for Four Calib rated Climate Sensit ivity Distributions

Rank Roe & Baker Log-Normal Gamma Weibull Pr(ECS < 1.5 °C) 0.013 0.050 0.070 0.102 Pr(2 °C < ECS < 4.5 °C) 0.667 0.667 0.667 0.667

5 t h Percentile 1.72 1.49 1.37 1.13

10 t h Percentile 1.91 1.74 1.65 1.48

Mode 2.34 2.52 2.65 2.90 Median (50 t h percentile) 3.00 3.00 3.00 3.00

Mean 3.50 3.28 3.19 3.07

90 t h Percentile 5.86 5.14 4.93 4.69

95 t h Percentile 7.14 5.97 5.59 5.17

Each distribution was calibrated by appl ying three constraints from the IPCC:

(1) a median equal to 3 °C, to reflect the judg ment of “a most likely value of about 3 °C;” k
(2) two-thirds probability that the equilibrium climate sensitiv ity lies between 2 and 4.5 °C;

and (3) zero probability that it is less than 0 °C or greater than 10 °C (Hegerl et al. 2006, p. 721).

We selected the calibrated Roe and Baker distribution from the four candidates for two reasons. First, the Roe and Baker distribution is the only one of the four that is based on a theoretical understanding of the response of th e climate system to increased greenhouse gas concentrations (Roe and Baker 2007; Roe 2008). In contrast, the other th ree distributions are mathematical functions that are arbitrarily chos en based on simplicity, c onvenience, and general shape. The Roe and Baker distribution results fro m three assumptions about climate response: (1) absent feedback effects, the equilibrium climate sensitivity is equal to 1.2 °C; (2) feedback factors are proportional to the change in surface temperature; and (3) uncertainties in feedback factors are normally distributed. There is widespread agreement on the first point and the second and third points are common assumptions.

k Strictly speaking, “most likely” refers to the mode of a distribution rather than the median, but common usage would allow the mode, median, or mean to serve as candidates for the central or “most likely” value and the IPCC report is not specific on this point. For the distributions we considered, the median was between the mode and the mean. For the Roe and Baker distribution, setting the median equal to 3 °C, rather than the mode or mean, gave a

95 th percentile that is more consistent with IPCC judgments and the literature. For example, setting the mean and mode equal to 3 °C produced 95 th percentiles of 5.6 and 8.6 °C, respectively, which are in the lower and upper end of the range in the literature. Finally, the median is closer to 3 °C than is the mode for the truncated distributions selected by the IPCC (Hegerl et al . 2006); the average median is 3.1 °C and the average mode is 2.3 °C, which is most consistent with a Roe and Baker distribution with the median set equal to 3 °C.

EA-SCC.14Second, the calibrated Roe and Baker distribu tion better reflects the IPCC judgment that “values substantially higher than 4.5°C still ca nnot be excluded.” Although the IPCC made no quantitative judgment, the 95 th percentile of the calibrated Roe & Baker distribution (7.1 °C) is much closer to the mean and the median (7.2 °C) of the 95 th percentiles of 21 previous studies summarized by Newbold and Daigneault (2009). It is also closer to the mean (7.5 °C) and median (7.9 °C) of the nine truncated di stributions examined by the IPCC (Hegerl et al . 2006)

than are the 95 th percentiles of the three other ca librated distributions (5.2–6.0 °C).

Finally, we note the IPCC judgment that the equilibrium climate sensitivity “is very likely larger than 1.5°C.” Although the calibrated Roe & Baker distribution, for which the probability of equilibrium climate sensitivity being greater than 1.5 °C is almost 99 percent, is

not inconsistent with the IPCC definition of “very likely” as “greater than 90 percent probability,” it reflects a greater degree of certain ty about very low values of ECS than was expressed by the IPCC.

To show how the calibrated Roe and Baker distribution compares to different estimates of the probability distribution function of equilibrium climate sensitivity in the empirical literature, Figure 4.3 overlays it on Figure 9.20 fro m the IPCC Fourth Assessment Report. These functions are scaled to integrate to unity betw een 0 °C and 10 °C. The horizontal bars show the respective 5 percent to 95 percent ranges; dots indicate the median estimate. l

Calibrated

Roe & Baker

Figure 4.3. Estimates of the Probability Dens ity Function for Equilibrium Climate Sensitivity

l The estimates based on instrumental data are from Andronova and Schlesinger (2001), Forest et al. (2002; dashed line, anthropogenic forcings only), Forest et al . (2006; solid line, anthropogenic and natural forcings), Gregory et al

. (2002), Knutti et al . (2002), Frame et al . (2005), and Forster and Gregory (2006). Hegerl et al . (2006) are based on multiple palaeoclimatic reconstructions of north hemisphere mean temperatures over the last 700 years. Also shown are the 5–95 percent approximate ranges for two estimates from the last glacial maximum (dashed, Annan et al

. 2005; solid, Schneider von Deimling et al . 2006), which are based on models with different structural properties.

EA-SCC.15EA-SCC.4.5 Socio-Economic and Emissions Trajectories

Another key issue considered by the interage ncy group is how to select the set of socioeconomic and emissions parameters for use in PAGE, DICE, and FUND. Socio-economic pathways are closely tied to climate damages because, all else equal, more and wealthier people tend to emit more greenhouse gases and also have a higher (absolute) willingness to pay to avoid climate disruptions. For this r eason, we consider how to model several input pa rameters in tandem: GDP, population, CO 2 emissions, and non-CO 2 radiative forcing. A wide variety of scenarios have been developed and used for climate change policy simulations ( e.g. , SRES 2000, CCSP 2007, EMF 2009). In determining which scenario s are appropriate for inclusion, we aimed to select scenarios that span most of the plausible ranges of outcomes for these variables.

To accomplish this task in a transparent way, we decided to rely on the recent Stanford Energy Modeling Forum exercise, EMF-22, which us es ten well-recognized models to evaluate substantial, coordinated global action to meet specific stabilization targets. A key advantage of relying on these data is that GDP, population, and emission trajectories are internally consistent

for each model and scenario evaluated. The EMF- 22 modeling effort also is preferable to the IPCC SRES due to their age (SRES were developed in 1997) and the fact that 3 of 4 of the SRES scenarios are now extreme outliers in one or more variables. Although the EMF-22 scenarios have not undergone the same leve l of scrutiny as the SRES scenarios, they are recent, peerreviewed, published, and publicly available.

To estimate the SCC for use in evaluating domestic policies that will have a small effect on global cumulative emissions, we use socio-economic and emission trajectories that span a range of plausible scenarios. Fi ve trajectories were selected from EMF-22 (Table 4.2). Four of these represent potential business-as-usual (BAU ) growth in population, wealth, and emissions

and are associated with CO 2 (only) concentrations ranging from 612 to 889 ppm in 2100. One represents an emissions pathway that achieves stabilization at 550 ppm CO 2 e ( i.e. , CO 2 -only concentrations of 425–484 ppm or a radiative forcing of 3.7 W/m 2 ) in 2100, a lower-than-BAU trajectory. m Out of the 10 models included in the EMF- 22 exercise, we selected the trajectories used by MiniCAM, MESSAGE, IMAGE, and the optimistic scenario from MERGE. For the BAU pathways, we used the GDP, population, and emission trajectories from each of these four models. For the 550 ppm CO 2 e scenario, we averaged th e GDP, population, and emission trajectories implied by these same four models.

Table 4.2. Socioeconomic and Emissions Projections from Select EMF-22 Reference

Scenarios

Reference Fossil and Industrial CO 2 Emissions

GtCO 2 /yr

EMF – 22 Based Scenarios 2000 2010 2020 2030 2050 2100

IMAGE 26.6 31.9 36.9 40.0 45.3 60.1 MERGE Optimistic 24.6 31.5 37.6 45.1 66.5 117.9

MESSAGE 26.8 29.2 37.6 42.1 43.5 42.7

MiniCAM 26.5 31.8 38.0 45.1 57.8 80.5

m

Such an emissions path would be consistent with widespread action by countries to mitigate GHG emissions, though it could also result from technological advances. It was chosen because it represents the most stringent case analyzed by the EMF-22 where all the models converge: a 550 ppm, not to exceed, full participation scenario.

EA-SCC.16550 ppm average 26.2 31.1 33.2 32.4 20.0 12.8

Reference GDP market exchange rates in trillion 2005$ n

EMF – 22 Based Scenarios 2000 2010 2020 2030 2050 2100

IMAGE 38.6 53.0 73.5 97.2 156.3 396.6 MERGE Optimistic 36.3 45.9 59.7 76.8 122.7 268.0

MESSAGE 38.1 52.3 69.4 91.4 153.7 334.9

MiniCAM 36.1 47.4 60.8 78.9 125.7 369.5 550 ppm average 37.1 49.6 65.6 85.5 137.4 337.9 Global Population

billions

EMF – 22 Based Scenarios 2000 2010 2020 2030 2050 2100

IMAGE 6.1 6.9 7.6 8.2 9.0 9.1 MERGE Optimistic 6.0 6.8 7.5 8.2 9.0 9.7

MESSAGE 6.1 6.9 7.7 8.4 9.4 10.4

MiniCAM 6.0 6.8 7.5 8.1 8.8 8.7 550 ppm average 6.1 6.8 7.6 8.2 8.7 9.1

We explore how sensitive the SCC is to va rious assumptions about how the future will evolve without prejudging what is likely to o ccur. The interagency group considered formally assigning probability weights to different states of the world, but this proved challenging to do in an analytically rigorous way given the dearth of information on the likelihood of a full range of future socio-economic pathways.

There are a number of caveats. First, EMF BAU scenarios represent the modelers’ judgment of the most likely pathway abse nt mitigation policies to reduce greenhouse gas emissions, rather than the wider range of possi ble outcomes. Nevertheless, these views of the most likely outcome span a wide range, from the more optimistic ( e.g. , abundant low-cost, lowcarbon energy) to more pessimistic ( e.g. , constraints on the availability of nuclear and renewables). o Second, the socio-economic trajector ies associated with a 550 ppm CO 2 e concentration scenario are not derived from an assessment of what policy is optimal from a benefit-cost standpoint. Rather, it is indicative of one po ssible future outcome. The emission trajectories underlying some BAU scenarios ( e.g. , MESSAGE’s 612 ppm) also are consistent

n While the EMF-22 models used market exchange rates (MER) to calculate global GDP, it is also possible to use purchasing power parity (PPP), which takes into account the different price levels across countries, so it more accurately describes relative standards of living across countries. MERs tend to make low-income countries appear poorer than they actually are. Because many models assu me convergence in per capita income over time, use of MER-adjusted GDP gives rise to projections of higher economic growth in low income countries. There is an ongoing debate about how much this will affect estimated climate impacts. Critics of the use of MER argue that it leads to overstated economic growth and hence a signif icant upward bias in projections of greenhouse gas emissions, and unrealistically high future temperatures ( e.g. , Castles and Henderson 2003). Others argue that convergence of the emissions-intensity gap across countries at least partially offset the overstated income gap so that differences in exchange rates have less of an effect on emissions (Holtsmark and Alfsen, 2005; Tol, 2006). Nordhaus (2007b) argues that the ideal appr oach is to use superlative PPP accounts ( i.e. , using cross-sectional PPP measures for relative incomes and outputs and national accounts price and quantity indexes for time-series extrapolations). However, he notes that it important to keep this debate in perspective; it is by no means clear that exchange-rate-conversion issues are as important as uncertainties about population, technological change, or the many geophysical uncertainties.

o For instance, in the MESSAGE model’s reference case total primary energy production from nuclear, biomass, and non-biomass renewables is projected to increase from about 15 percent of total primary energy in 2000 to 54 percent in 2100. In comparison, the MiniCAM reference case shows 10 percent in 2000 and 21 percent in 2100.

EA-SCC.17with some modest policy action to address climate change. p We chose not to include socioeconomic trajectories that achieve even lower GHG concentrations at this time, given the difficulty many models had in converging to meet these targets.

For comparison purposes, the Energy Information Agency in its 2009 Annual Energy

Outlook projected that global CO 2 emissions will grow to 30.8, 35.6, and 40.4 gigatons in 2010, 2020, and 2030, respectively, while world GDP is projected to be $51.8, $71.0 and $93.9 trillion (2005$ using market exchange rates) in 2010, 2020, and 2030, respectively. These projections

are consistent with one or more EMF-22 scenarios. Likewise, the United Nations’ 2008 Population Prospect projects population will grow from 6.1 billion people in 2000 to 9.1 billion people in 2050, which is close to the populati on trajectories for the IMAGE, MiniCAM, and

MERGE models.

In addition to fossil and industrial CO 2 emissions, each EMF scenario provides projections of methane, nitrous oxide, fl uorinated greenhouse gases, and net land use CO 2 emissions out to 2100. These assumptions also are used in the three models while retaining the default radiative forcings due to other factors ( e.g. , aerosols and other gases). See the Appendix for greater detail.

EA-SCC.4.6 Discount Rate

The choice of a discount rate , especially over long periods of time, raises highly contested and exceedingly difficult questions of science, economics, philosophy, and law. Although it is well understood that th e discount rate has a large influence on the current value of future damages, there is no consensus about wh at rates to use in this context. Because CO 2 emissions are long-lived, subsequent damages occu r over many years. In calculating the SCC, we first estimate the future damages to agri culture, human health, and other market and nonmarket sectors from an additional unit of CO 2 emitted in a particular year in terms of reduced consumption (or consumption equi valents) due to the impacts of elevated temperatures, as represented in each of the three IAMs. Then we discount the stream of future damages to its present value in the year when the additional unit of emissions was released using the selected discount rate, which is intended to reflect society’s marg inal rate of substitution between consumption in different time periods.

For rules with both intra- and intergenerational effects, agencies traditionally employ

constant discount rates of both 3 percent and 7 percent in accordance with OMB Circular A-4. As Circular A-4 acknowledges, however, the c hoice of discount rate for intergenerational problems raises distinctive problems and presents considerable challenges. After reviewing those challenges, Circular A-4 states, “If your rule will have important intergenerational benefits or costs you might consider a further sensitivity analysis using a lower but positive discount rate in addition to calculating net benef its using discount rates of 3 a nd 7 percent.” For the specific purpose of developing the SCC, we adapt and revise that approach here.

p For example, MiniCAM projects if all non-US OECD countries reduce CO 2 emissions to 83 percent below 2005 levels by 2050 (per the G-8 agreement) but all other countries continue along a BAU path CO 2 concentrations in 2100 would drop from 794 ppmv in its reference case to 762 ppmv.

EA-SCC.18Arrow et al. (1996) outlined two main approaches to determine the discount rate for climate change analysis, which they labeled “descriptive” and “prescriptive.” The descriptive approach reflects a positive (non-normative) pe rspective based on observations of people’s

actual choices— e.g. , savings versus consumption decisions over time, and allocations of savings among more and less risky investments. Advocates of this approach generally call for inferring

the discount rate from market rates of return “b ecause of a lack of justification for choosing a social welfare function that is any different than what decision makers [i ndividuals] actually use” (Arrow et al . 1996).

One theoretical foundation for the cost-benefit an alyses in which the social cost of carbon will be used—the Kaldor-Hicks potential-compensation test—also suggests that market rates

should be used to discount future benefits and costs, because it is the market interest rate that would govern the returns potentially set aside today to compensate future individuals for climate damages that they bear ( e.g. , Just et al . 2004). As some have noted, the word “potentially” is an important qualification; there is no assurance that such returns will actually be set aside to provide compensation, and the very idea of co mpensation is difficult to define in the intergenerational context. On the other ha nd, societies provide compensation to future generations through investments in human capita l and the resulting incr ease in knowledge, as well as infrastructure and other physical capital.

The prescriptive approach specifies a social welfare function that formalizes the normative judgments that the decision-maker wants explicitly to incorporate into the policy

evaluation— e.g. , how inter-personal comparisons of utilit y should be made, and how the welfare of future generations should be weighed against that of the present generation. Ramsey (1928), for example, has argued that it is “ethically indefensible” to apply a positive pure rate of time preference to discount values across gene rations, and many agree with this view.

Other concerns also motivate making adju stments to descriptive discount rates. In particular, it has been noted that the preferen ces of future generations with regard to consumption versus environmental amenities may not be the same as those today, making the current market rate on consumption an inappropriate metric by which to discount future climaterelated damages. Others argue that the discount ra te should be below market rates to correct for

market distortions and uncertaintie s or inefficiencies in intergenerational transfers of wealth, which in the Kaldor-Hicks logic are presumed to compensate future generations for damage (a potentially controversial assu mption, as noted above; Arrow et al . 1996, Weitzman 1999).

Further, a legitimate concern about both desc riptive and prescriptive approaches is that they tend to obscure important heterogeneity in the population. Th e utility function that underlies the prescriptive approach assumes a representative agent with perfect foresight and no credit constraints. This is an artificial rendering of th e real world that misses ma ny of the frictions that

characterize individuals’ lives a nd indeed the available descriptive evidence supports this. For instance, many individuals smooth consumption by borrowing with credit cards that have relatively high rates. Some are unable to access traditional credit markets and rely on payday lending operations or other high cost forms of smoothing consumption. Whether one puts greater weight on the prescriptive or descriptive approach, the high interest rates that credit-constrained

EA-SCC.19individuals accept suggest that some account should be given to the discount rates revealed by

their behavior.

We draw on both approaches but rely primarily on the descriptive approach to inform the

choice of discount rate. With reco gnition of its limitations, we find th is approach to be the most defensible and transparent give n its consistency with the sta ndard contemporary theoretical foundations of benefit-cost analys is and with the approach required by OMB’s existing guidance. The logic of this framework also suggests that ma rket rates should be used for discounting future consumption-equivalent damages. Regardless of the theoretic al approach used to derive the appropriate discount rate(s), we note the inherent conceptual and practical difficulties of adequately capturing consumption trade-offs over many decades or even centuries. While relying primarily on the descriptive approach in selec ting specific discount rates, the interagency group has been keenly aware of the deeply normative dimensions of both the debate over discounting in the intergenerational context and the consequen ces of selecting one disc ount rate over another.

EA-SCC.4.6.1 Historically Observed Interest Rates

In a market with no distortions, the return to savings would equal the private return on investment, and the market rate of interest would be the appr opriate choice for the social discount rate. In the real world risk, taxes, and other market imperfections drive a wedge between the risk-free rate of return on capital a nd the consumption rate of interest. Thus, the literature recognizes two conceptual discount conc epts—the consumption rate of interest and the opportunity cost of capital.

According to OMB’s Circular A-4, it is appropriate to use the rate of return on capital when a regulation is expected to displace or alter the use of capital in the private sector. In this case, OMB recommends Agencies use a discount rate of 7 percent. When regulation is expected to primarily affect private consumption—for inst ance, via higher prices for goods and services— a lower discount rate of 3 percen t is appropriate to reflect how private individuals trade-off current and future consumption.

The interagency group examined the econo mics literature and c oncluded that the consumption rate of interest is the correct concep t to use in evaluating the benefits and costs of a marginal change in carbon emissions (Lind 1990, Arrow et al ., 1996, Arrow 2000). The consumption rate of interest also is appropriate when the impacts of a regulation are measured in consumption (-equivalent) units, as is done in th e three integrated assessment models used for estimating the SCC.

Individuals use a variety of savings instrume nts that vary with ri sk level, time horizon, and tax characteristics. The standard analytic framework used to develop intuition about the discount rate typically assumes a representativ e agent with perfect foresight and no credit constraints. The risk-free rate is appropriate for discounting certain future benefits or costs, but the benefits calculated by IAMs are uncertain. To use the risk-free rate to discount uncertain benefits, these benefits first must be transformed into “certainty equivalents,” i.e. , the maximum certain amount that we would exchange for th e uncertain amount. However, the calculation of the certainty-equivalent requires first estimating the correlation between the benefits of the policy and baseline consumption.

EA-SCC.20If the IAM projections of future impacts represent expected values (not certaintyequivalent values), then the a ppropriate discount rate generally does not equal the risk-free rate. If the benefits of the policy tend to be high in those states of the world in which consumption is low, then the certainty-equivalent benefits will be higher than the expected benefits (and vice versa). Since many (though not necessarily all) of the important impacts of climate change will flow through market sectors such as agriculture and energy, and since willingness to pay for environmental protections typically increases with income, we might expect a positive (though not necessarily perfect) correlation between the net benefits from climate policies and market returns. This line of reasoning suggests that the proper discount rate would exceed the riskless rate. Alternatively, a negative correlation between the returns to climate policies and market returns would imply that a discount rate below the riskless rate is appropriate.

This discussion suggests that both the post- tax riskless and risky rates can be used to capture individuals’ consumption-e quivalent interest rate. As a measure of the post-tax riskless rate, we calculate the average real return from Treasury notes over the longest time period available (those from Newell and Pizer 2003) and adjust for Federal taxes (the average marginal rate from tax years 2003 through 2006 is around 27 percent). q This calculation produces a real interest rate of about 2.7 pe rcent, which is roughly cons istent with Circular A-4’s recommendation to use 3 percent to repres ent the consumption rate of interest. r A measure of the post-tax risky rate for investments whose returns are positively correlated with overall equity market returns can be obtained by adjusting pre-tax rates of household returns to risky investments (approximately 7 perc ent) for taxes yields a real rate of roughly 5 percent. s

EA-SCC.4.6.2 The Ramsey Equation

Ramsey discounting also provides a useful framework to inform the choice of a discount rate. Under this approach, the analyst applies either positive or normative judgments in selecting values for the key parameters of the Ramsey equation: η (coefficient of relative risk aversion or elasticity of the marginal utility of consumption) and ρ (pure rate of time preference). t These are then combined with g (growth rate of per-capita consumption) to equal the interest rate at which

qThe literature argues for a risk-free rate on government bonds as an appropriate measure of the consumption rate of interest. Arrow (2000) suggests that it is roughly 3-4 percent. OMB cites evidence of a 3.1 percent pre-tax rate for 10-year Treasury notes in the A-4 guidance. Newell and Pizer (2003) find real interest rates between 3.5 and 4 percent for 30-year Treasury securities.

r The positive approach reflects how individuals make allocation choices across time, but it is important to keep in mind that we wish to reflect preferences for society as a whole, which generally has a longer planning horizon.

s Cambell et al . (2001) estimates that the annual real return from stocks for 1900-1995 was about 7 percent. The annual real rate of return for the S&P 500 from 1950–2008 was about 6.8 percent. In the absence of a better way to population-weight the tax rates, we use the middle of the 20–40 percent range to derive a post-tax interest rate (Kotlikoff and Rapson 2006).

t The parameter ρ  measures the pure rate of time preference : people’s behavior reveals a preference for an increase in utility today versus the future. Consequently, it is standard to place a lower weight on utility in the future. The

parameter η captures diminishing marginal utility : consumption in the future is likely to be higher than consumption today, so diminishing marginal utility of consumption implies that the same monetary damage will cause a smaller reduction of utility for wealthier individuals, either in the future or in current generations. If η = 0, then a one dollar increase in income is equally valuable regardless of level of income; if η = 1, then a one percent increase in income is equally valuable no matter the level of income; and if η > 1, then a one percent increase in income is less valuable

to wealthier individuals.

EA-SCC.21future monetized damages are discounted: ρ + η ·g. u In the simplest version of the Ramsey model, with an optimizing representative agent with perfect foresight, what we are calling the “Ramsey discount rate,” ρ + η ·g, will be equal to the rate of return to capital, i.e. , the market interest rate.

A review of the literature provides some guidance on reasonable parameter values for the Ramsey discounting equation, based on both pr escriptive and descriptive approaches.

• η . Most papers in the climate change literature adopt values for η in the range of 0.5 to 3 (Weitzman cites plausible values as those ra nging from 1 to 4), although not all authors

articulate whether their choice is based on prescriptive or descriptive reasoning. v Dasgupta (2008) argues that η should be greater than 1 and may be as high as 3, since η equal to 1 suggests savings rates that do not conform to observed behavior.

• ρ . With respect to the pure rate of time preference, most papers in the climate change literature adopt values for ρ in the range of 0 to 3 percent per year. The very low rates tend to follow from moral judgments involving intergenerational neut rality. Some have argued that to use any value other than ρ = 0 would unjustly discriminate against future generations ( e.g. , Arrow et al . 1996, Stern 2006). However, even in an inter-generational setting, it may make sense to use a small positive pure rate of time preference because of the small probability of unforeseen cataclysmic events (Stern 2006).

• g. A commonly accepted approximation is around 2 percent per year. For the socioeconomic scenarios used for this exercise, the EMF models assume that g is about 1.5–2 percent to 2100.

Some economists and non-econo mists have argued for cons tant discount rates below 2 percent based on the prescriptive approach. When grounded in the Ramsey framework, proponents of this approach have argued that a ρ of zero avoids giving preferential treatment to one generation over another. The choice of η has also been posed as an ethical choice linked to

the value of an additional dolla r in poorer countries compared to wealthier ones. Stern (2006) applies this perspectiv e through his choice of ρ = 0.1 percent per year, η = 1 and g = 1.3 percent per year, which yields an annual discount rate of 1.4 percent. In the context of permanent income

u In this case, g could be taken from the selected EMF socioeconomic scenarios or alternative assumptions about the rate of consumption growth.

v Empirical estimates of η span a wide range of values. A benchmark value of 2 is near the middle of the range of values estimated or used by Szpiro (1986), Hall and Jones (2007), Arrow (2007), Dasgupta (2006, 2008), Weitzman (2007, 2009), and Nordhaus (2008). However, Chetty (2006) developed a method of estimating η using data on labor supply behavior. He shows that existing evidence of the effects of wage changes on labor supply imposes a tight upper bound on the curvature of utility over wealth (CRRA < 2) with the mean implied value of 0.71 and concludes that the standard expected utility model cannot generate high levels of risk aversion without contradicting established facts about labor supply. Recent work has jointly estimated the components of the Ramsey equation. Evans and Sezer (2005) estimate η = 1.49 for 22 OECD countries. They also estimate ρ = 1.08 percent per year using data on mortality rates. Anthoff et al . (2009b) estimate η = 1.18, and ρ = 1.4 percent. When they multiply the bivariate probability distributions from their work and Evans and Sezer (2005) together, they find η = 1.47, and ρ = 1.07.

EA-SCC.22savings behavior, however, Stern’ s assumptions suggest that individuals would save 93 percent

of their income. w

Recently, Stern (2008) revisited the values us ed in Stern (2006), stating that there is a case to be made for raising η due to the amount of weight lower values place on damages far in the future (over 90 percent of expected damages occur after 2200 with η = 1). Using Stern’s assumption that ρ = 0.1 percent, combined with a η of 1.5 to 2 and his original growth rate, yields a discount rate greater 2 percent.

We conclude that arguments made under the pr escriptive approach can be used to justify discount rates between roughly 1.4 and 3.1 percen t. In light of concerns about the most appropriate value for η , we find it difficult to justify rates at the lower end of this range under the Ramsey framework.

EA-SCC.4.6.3 Accounting for Uncertainty in the Discount Rate

While the consumption rate of interest is an important driver of the benefits estimate, it is uncertain over time. Ideally, we would formally model this uncertainty, just as we do for climate sensitivity. Weitzman (1998, 2001) showed theo retically and Newell and Pizer (2003) and Panipoulou et al. (2004) confirm empirically that discount rate uncertainty can have a large effect on net present values. A main result from these studies is that if there is a persistent element to the uncertainty in the discount rate ( e.g. , the rate follows a random walk), then it will result in an effective (or certainty-equivale nt) discount rate that declines over time. Consequently, lower discount rates tend to do minate over the very long term (Weitzman 1998, 1999, 2001; Newell and Pizer 2003; Panipoulou et al. (2004); Gollier 2008; Summers and Zeckhauser 2008; and Gollier and Weitzman 2009).

The proper way to model discount rate uncer tainty remains an active area of research. Newell and Pizer (2003) employ a model of how long-term interest rate s change over time to

forecast future discount rates. Their model inco rporates some of the basic features of how interest rates move over time, and its parameters are estimated based on historical observations of long-term rates. Subsequent work on this topic, most notably Panipoulou et al. (2004), uses more general models of interest rate dynamics to allow for better forecasts. Specifically, the volatility of interest rates depends on whether rates are currently low or high and variation in the level of persistence over time.

While Newell and Pize r (2003) and Panipoulou et al. (2004) attempt fo rmally to model uncertainty in the discount rate, others argue fo r a declining scale of di scount rates applied over time ( e.g. , Weitzman 2001, and the UK’s “Green Book” for regulatory analysis). This approach uses a higher discount rate initia lly, but applies a graduated scal e of lower discount rates further

out in time. x A key question that has emerged with rega rd to both of these approaches is the

w Stern (2008) argues that building in a positive rate of exogenous technical change over time reduces the implied savings rate and that η at or above 2 are inconsistent with observed behavior with regard to equity. (At the same time, adding exogenous technical change—all else equal—would increase g as well.)

x For instance, the UK applies a discount rate of 3.5 percent to the first 30 years; 3 percent for years 31–75; 2.5 percent for years 76–125; 2 percent for years 126–200; 1.5 percent for years 201–300; and 1 percent after 300 years. As a sensitivity, it recommends a discount rate of 3 percent for the first 30 years, also decreasing over time.

EA-SCC.23trade-off between potential time inconsistency and giving greater weight to far future outcomes (see the EPA Science Advisory Board’s recent comments on this topic as part of its review of

their Guidelines for Economic Analysis ). y

EA-SCC.4.6.4 The Discount Rates Selected for Estimating SCC

In light of disagreement in the literature on the appropriate market interest rate to use in this context and uncertainty about how interest rates may change over time, we use three discount rates to span a plausible range of cert ainty-equivalent consta nt discount rates: 2.5, 3, and 5 percent per year. Based on the review in the previous sections, the interagency workgroup

determined that these three rates reflect reasonable judgments under both descriptive and prescriptive approaches.

The central value (3 percent) is consis tent with estimates pr ovided in the economics literature and OMB’s Circular A-4 guidance for the consumption rate of interest. As previously mentioned, the consumption rate of interest is the correct discounting concept to use when future damages from elevated temperatures are estimated in consumption-equivalent units. Further, 3 percent roughly corresponds to the after-tax riskle ss interest rate. The upper value of 5 percent is included to represent the possibility that climate damages are positively correlated with market returns. Additionally, this discount rate may be justified by the high interest rates that many consumers use to smooth consumption across periods.

The low value (2.5 percent) is included to in corporate the concern that interest rates are highly uncertain over time. It represents the av erage certainty-equivalent rate using the meanreverting and random walk approaches from Newe ll and Pizer (2003) startin g at a discount rate of 3 percent. Using this approach, the certainty equivalent is about 2.2 percent using the random walk model and 2.8 percent using the mean reverting approach. z Without giving preference to a particular model, the average of the two rates is 2.5 percent. Further, a rate below the riskless rate would be justified if climate investments are negatively correlated with the overall market

rate of return. Use of this lower value also re sponds to certain judgments using the prescriptive or normative approach and to ethical objections that have been raised abou t rates of 3 percent or higher.

EA-SCC.5 REVISED SCC ESTIMATES

Our general approach to estimating SCC values is to run the three integrated assessment models (FUND, DICE, and PAGE) using the following inputs agreed upon by the interagency

group:

y Uncertainty in future damages is distinct from uncertainty in the discount rate. Weitzman (2008) argues that Stern’s choice of a low discount rate was “right for the wrong reasons.” He demonstrates how the damages from a low probability, catastrophic event far in the future dominate the effect of the discount rate in a present value calculation and result in an infinite willingness-to-pay for mitigation today. Newbold and Daigneault, (2009) and Nordhaus (2009) find that Weitzman’s result is sensitive to the functional forms chosen for climate sensitivity, utility, and consumption. Summers and Zeckhauser (2008) argue that uncertainty in future damages can also work in the other direction by increasing the benefits of waiting to learn the appropriate level of mitigation required.

z Calculations done by Pizer et al . using the original simulation program from Newell and Pizer (2003).

EA-SCC.24• A Roe and Baker distribution for the clima te sensitivity parameter bounded between 0

and 10 with a median of 3 °C and a cumula tive probability between 2 and 4.5 °C of twothirds.

• Five sets of GDP, population and carbon emissions trajectories based on EMF-22.

• Constant annual discount rate s of 2.5, 3, and 5 percent.

Because the climate sensitivity parameter is modeled probabilis tically, and because PAGE and FUND incorporate uncertainty in other model pa rameters, the final output from each model run is a distribution over the SCC in year t

.

For each of the IAMS, the basic computational steps for calculating the SCC in a particular year t

are:

1. Input the path of emissions, GDP, and popula tion from the selected EMF-22 scenarios, and the extrapolations based on th ese scenarios for post-2100 years.

2. Calculate the temperature effects and (consumption-equivalent) damages in each year resulting from the baseline path of emissions.

a. In PAGE, the consumption-equivalent damages in each period are calculated as a fraction of the EMF GDP forecast, depending on the temperature in that period relative to the pre-industrial average temperature in each region.

b. In FUND, damages in each period depe nd on both the level and the rate of temperature change in that period.

c. In DICE, temperature affects both consumpti on and investment, so we first adjust the EMF GDP paths as follows: Using the C obb-Douglas production function with the DICE2007 parameters, we extract the path of exogenous technical change implied by the EMF GDP and population paths, then we recalculate the baseline GDP path taking into account climate damages re sulting from the baseline emissions path.

3. Add an additional unit of carbon emissions in year t . (The exact unit varies by model.)

4. Recalculate the temperature effects and damages expected in all years beyond t resulting from this adjusted path of emissions, as in step 2.

5. Subtract the damages computed in step 2 from those in step 4 in each year. (DICE is run in 10 year time steps, FUND in annual time st eps, while the time steps in PAGE vary.)

6. Discount the resulting path of marginal damage s back to the year of emissions using the agreed upon fixed discount rates.

7. Calculate the SCC as the net present value of the discounted path of damages computed in step 6, divided by the unit of carbon emissi ons used to shock the models in step 3.

EA-SCC.258. Multiply by 12/44 to convert from dollars per ton of carbon to dollars per ton of CO 2 (2007 dollars) in DICE and FUND. (All calculations are done in tons of CO 2 in PAGE).

The steps above were repeated in each mode l for multiple future years to cover the time horizons anticipated for upcoming rulemaking analysis. To maintain consistency across the three IAMs, climate damages are cal culated as lost consumption in each future year.

It is important to note that each of the three models has a different default end year. The default time horizon is 2200 for PAGE, 2595 for DICE , and 3000 for the latest version of FUND. This is an issue for the multi-model approach because differences in SCC estimates may arise simply due to the model time horizon. Many consider 2200 too short a time horizon because it could miss a significant fraction of damages unde r certain assumptions about the growth of marginal damages and discounting, so each model is run here through 2300. This step required a small adjustment in the PAGE model only. This step also required assumptions about GDP, population, and greenhouse gas emission trajectories after 2100, the last year for which these data are available from the EMF-22 models. (A mo re detailed discussion of these assumptions is included in the Appendix.)

This exercise produces 45 separate distribu tions of the SCC for a given year, the product of 3 models, 3 discount rates, a nd 5 socioeconomic scenarios. This is clearly too many separate

distributions for consideration in a regulatory impact analysis.

To produce a range of plausible estimates that still reflects the uncertainty in the estimation exercise, the distributi ons from each of the models and scenarios are equally weighed and combined to produce three separate probability distributions for SCC in a given year, one for

each assumed discount rate. These di stributions are then used to de fine a range of point estimates for the global SCC. In this way, no integrated assessment model or socioeconomic scenario is given greater weight than another. Because the literature shows that the SCC is quite sensitive to assumptions about the discount ra te, and because no consensus exists on the appropriate rate to use in an intergenerational context, we present SCCs based on the average values across models

and socioeconomic scenarios for each discount rate.

The interagency group selected four SCC valu es for use in regulatory analyses. Three values are based on the average SCC across mode ls and socio-economic and emissions scenariosat the 2.5, 3, and 5 percent discount rates. The fourth value is included to represent the higherthan-expected economic impacts from climate change further out in the tails of the SCC distribution. For this purpose, we use the SCC value for the 95 th percentile at a 3 percent discount rate. (The full set of distributions by model and scenario combination is included in the Appendix.) As noted above, the 3 percent discount rate is the central value, and so the central value that emerges is the average SCC across models at the 3 percent discount rate. For purposes of capturing the uncertainties involved in re gulatory impact analysis, we emphasize the importance and value of considering the full range.

As previously discussed, low probability, high impact events are incorporated into the SCC values through explicit consideration of their effects in two of the three models as well as the use of a probability density function for equilibrium climate sensitivity. Treating climate

EA-SCC.26sensitivity probabilistically results in more high temperature outcomes, which in turn lead to higher projections of damages. Although FUND does not include catastrophic damages (in contrast to the other two models), its probabilistic treatment of the equilibrium climate sensitivity parameter will directly affect the non-catastrophic damages that are a function of the rate of temperature change.

In Table 5.1, we begin by presenting SCC estimates for 2010 by model, scenario, and discount rate to illustrate the variability in the SCC across each of these input parameters. As expected, higher discount rates consistently result in lower S CC values, while lower discount rates result in higher SCC values for each socioeconomic trajectory. It is also evident that there are differences in the SCC estimated across the three main models. For these estimates, FUND produces the lowest estimates, while PAGE generally produces the highest estimates.

Table 5.1. Disaggregated Social Cost of CO 2 Values by Model, Soci o-Economic Trajectory, and Discount Rate for 2010 (2007$)

Discount rate: 5% 3% 2.5% 3%

Model Scenario Avg Avg Avg 95th

DICE IMAGE 10.8 35.8 54.2 70.8

MERGE 7.5 22.0 31.6 42.1 Message 9.8 29.8 43.5 58.6

MiniCAM 8.6 28.8 44.4 57.9

550 Average 8.2 24.9 37.4 50.8

PAGE IMAGE 8.3 39.5 65.5 142.4

MERGE 5.2 22.3 34.6 82.4 Message 7.2 30.3 49.2 115.6

MiniCAM 6.4 31.8 54.7 115.4 550 Average 5.5 25.4 42.9 104.7

FUND IMAGE -1.3 8.2 19.3 39.7

MERGE -0.3 8.0 14.8 41.3 Message -1.9 3.6 8.8 32.1

MiniCAM -0.6 10.2 22.2 42.6 550 Average -2.7 -0.2 3.0 19.4

These results are not surprising when compared to the estimates in the literature for the latest versions of each model. For example, adjusting the values from the literature that were used to develop interim SCC va lues to 2007 dollars for the year 2010 (assuming, as we did for the interim process, that SCC grows at 3 per cent per year), FUND yields SCC estimates at or near zero for a 5 percent discount rate and ar ound $9 per ton for a 3 percent discount rate. There are far fewer estimates using the latest versions of DICE and PAGE in the literature: Using similar adjustments to genera te 2010 estimates, we calculate a SCC from DICE (based on Nordhaus 2008) of around $9 per ton for a 5 percent discount rate, and a SCC from PAGE (based on Hope 2006, 2008) close to $8 per ton fo r a 4 percent discount rate. Note that these comparisons are only approximate since the litera ture generally relies on Ramsey discounting,

while we have assumed constant discount rates. aa

aa Nordhaus (2008) runs DICE2007 with ρ = 1.5 and η = 2. The default approach in PAGE2002 (version 1.4epm)

treats ρ and η as random parameters, specified using a triangular distribution such that the min, mode, and max = 0.1, 1, and 2 for ρ , and 0.5, 1, and 2 for η , respectively. The FUND default value for η is 1, and Tol generates SCC

estimates for values of ρ = 0, 1, and 3 in many recent papers ( e.g. , Anthoff et al . 2009). The path of per-capita

EA-SCC.27The SCC estimates from FUND are sensitive to differences in emissions paths but relatively insensitive to differences in GDP paths across scenarios, while the reverse is true for DICE and PAGE. This likely occurs because of several structural differences among the models. Specifically in DICE and PAGE, the fraction of economic output lost due to climate damages increases with the level of temperature alone, wh ereas in FUND the fractional loss also increases with the rate of temperature change. Further, in FUND increases in income over time decrease vulnerability to climate change (a form of adaptation), whereas this does not occur in DICE and

PAGE. These structural differences among the models make FUND more sensitive to the path of emissions and less sensitive to GDP compared to DICE and PAGE.

Figure 5.1 shows that IMAGE has the highest GDP in 2100 while MERGE Optimistic has the lowest. The ordering of global GDP le vels in 2100 directly corresponds to the rank ordering of SCC for PAGE and DICE. For FUND , the correspondence is less clear, a result that is to be expected given its less direct re lationship between its da mage function and GDP.

450

400

350

Global 300 GDP (trillions 250

of U.S. dollars) 200

150

100

50

0

2000 2010 2020 2030 2050 2100 IMAGE

MERGE Optimistic

MESSAGE

MiniCAM

3.7 W/m2 radiative forcing

Figure 5.1. Level of Global GDP Across EMF Scenarios

Table 5.2 shows the four selected SCC valu es in 5-year increments from 2010 to 2050. Values for 2010, 2020, 2040, and 2050 are calculated by first combining all outputs (10,000 estimates per model run) from all scenarios and models for a given discount rate. Values for the years in between are calculated using a simple linear interpolation.

Table 5.2. Social Cost of CO 2 , 2010–2050 (2007$)

Discount Rate 5% 3% 2.5% 3%

Year Avg Avg Avg 95th

2010 4.7 21.4 35.1 64.9

2015 5.7 23.8 38.4 72.8

2020 6.8 26.3 41.7 80.7

consumption growth, g, varies over time but is treated deterministically in two of the three models. In DICE, g is endogenous. Under Ramsey discounting, as economic growth slows in the future, the large damages from climate change that occur far out in the future are discounted at a lower rate than impacts that occur in the nearer term.

EA-SCC.282025 8.2 29.6 45.9 90.4

2030 9.7 32.8 50.0 100.0

2035 11.2 36.0 54.2 109.7

2040 12.7 39.2 58.4 119.3

2045 14.2 42.1 61.7 127.8

2050 15.7 44.9 65.0 136.2

The SCC increases over time because future e missions are expected to produce larger incremental damages as physical and economic systems become more stressed in response to greater climatic change. Note that this approach allows us to estimate the growth rate of the SCC directly using DICE, PAGE, and FUND rather than assuming a constant annual growth rate as was done for the interim estimates (using 3 percent) . This helps to ensure that the estimates are internally consistent with ot her modeling assumptions. Table 5.3 illustrates how the growth rate

for these four SCC estimates varies over time . The full set of annua l SCC estimates between 2010 and 2050 is reported in the Appendix.

Table 5.3. Changes in the Average Annual Growth Rates of SCC Estimates between 2010

and 2050

Average Annual

Growth Rate 5% 3% 2.5% 3.0%

Year Range Avg Avg Avg 95th

2010–2020 3.6 2.1 1.7 2.2

2020–2030 3.7 2.2 1.8 2.2

2030–2040 2.7 1.8 1.6 1.8

2040–2050 2.1 1.4 1.1 1.3

While the SCC estimate grows over time, the future monetized value of emissions reductions in each year (the SCC in year t multiplied by the change in emissions in year t ) must be discounted to the present to de termine its total net present value for use in regulatory analysis. Damages from future emissions should be discounted at the same rate as that used to calculate the SCC estimates themselves to ensure internal consistency— i.e. , future damages from climate change, whether they result from emissions today or emissions in a later year, should be discounted using the same rate. For example, clima te damages in 2020 that are calculated using a SCC based on a 5 percent discount rate also should be discounted back to the analysis year using a 5 percent discount rate. bb

EA-SCC.6 LIMITATIONS OF THE ANALYSIS

As noted, any estimate of the SCC must be taken as provisional and subject to further refinement (and possibly significant change) in accordance with evolvi ng scientific, economic, and ethical understandings. During the course of our modeling, it became a pparent that there are several areas in partic ular need of additional explorati on and research. These caveats and additional observations in the following section ar e necessary to consider when interpreting and applying the SCC estimates.

bb However, it is possible that other benefits or costs of proposed regulations unrelated to CO 2 emissions will be discounted at rates that differ from those used to develop the SCC estimates.

EA-SCC.29Incomplete treatment of non-catastrophic damages . The impacts of climate change are expected to be widespread, diverse, and hetero geneous. In addition, the exact magnitude of these impacts is uncertain because of the inherent co mplexity of climate processes, the economic

behavior of current and future populations, and our inability to accurately forecast technological change and adaptation. Current IAMs do not assi gn value to all of the important physical, ecological, and economic impacts of climate change recognized in the climate change literature (some of which are discussed above) because of lack of precise information on the nature of damages and because the science incorporated in to these models understandably lags behind the most recent research. Our ability to quantif y and monetize impacts will undoubtedly improve with time. It is also likely that even in future applications, a number of potentially significant damage categories will rema in non-monetized. (Ocean acidifi cation is one example of a potentially large damage from CO 2 emissions not quantified by any of the three models. Species and wildlife loss is another example that is exceedingly difficult to monetize.)

Incomplete treatment of potential catastrophic damages . There has been considerable recent discussion of the risk of catastrophic impacts and how best to account for extreme scenarios, such as the collapse of the Atlantic Meridional Overturning Circulation or the West Antarctic Ice Sheet, or large releases of methane from melting permafrost and warming oceans. Weitzman (2009) suggests that catastrophic damages are extremely large—so large, in fact, that the damages from a low probability, catastrophic ev ent far in the future dominate the effect of the discount rate in a present value calculation an d result in an infinite willingness-to-pay for mitigation today. However, Nordhaus (2009) concluded that the conditions under which Weitzman’s results hold “are limited and do not apply to a wide range of potential uncertain

scenarios.”

Using a simplified IAM, Newbold and Daig neault (2009) confirmed the potential for large catastrophe risk premiums but also show ed that the aggregate benefit estimates can be highly sensitive to the shapes of both the climate sensitivity distribution and the damage function at high temperature changes. Pindyck (2009) also used a simplified IAM to examine high-impact low-probability risks, using a right-skewed gamma distribution for climate sensitivity as well as an uncertain damage coefficient, but in most cas es found only a modest risk premium. Given this difference in opinion, further research in this area is needed before its practical significance can be fully understood and a reasonable approach devel oped to account for such risks in regulatory analysis. (The next section discusses the scie ntific evidence on catastrophic impacts in greater detail.)

Uncertainty in extrapolation of damages to high temperatures . The damage functions in these IAMs are typically calibrated by estimating damages at moderate temperature increases ( e.g. , DICE was calibrated at 2.5 °C) and extrapolat ed to far higher temperatures by assuming that damages increase as some power of the temperature change. Hence, estimated damages are

far more uncertain under more extrem e climate change scenarios.

Incomplete treatment of adaptation and technological change . Each of the three integrated assessment models used here assumes a certain degree of low- or no-cost adaptation. For instance, Tol assumes a great deal of adapta tion in FUND, including widespread reliance on

EA-SCC.30air conditioning, so much so that the largest si ngle benefit category in FUND is the reduced electricity costs from not havi ng to run air conditioning as intensively (NRC 2009).

Climate change also will increase returns on investment to develop technologies that allow individuals to cope with adverse climate conditions, and IAMs to do not adequately

account for this directed technological change. cc For example, scientists may develop crops that are better able to withstand higher and more variable temperatures. Although DICE and FUND have both calibrated their agricultural sectors unde r the assumption that farmers will change land use practices in response to climate change (M astrandrea 2009), they do not take into account technological changes that lower the cost of this adaptation over time. On the other hand, the calibrations do not account for increases in climate variability, pests, or diseases, which could make adaptation more difficult than assumed by the IAMs for a given temperature change. Hence, models do not adequately account for poten tial adaptation or technical change that might alter the emissions pathway and resulting damages. In this respect, it is difficult to determine whether the incomplete treatment of adaptation and technological change in these IAMs under or overstate the likely damages.

Risk aversion . A key question unanswered during this interagency process is what to

assume about relative risk aversi on with regard to high-impact outcomes. These calculations do not take into account the possibility that individuals may have a higher willingness to pay to reduce the likelihood of low-probability, high-impact damages than they do to reduce the likelihood of higher-probability but lower-impact damages with the same expected cost. (The

inclusion of the 95 th percentile estimate in the final set of SCC values was largely motivated by this concern.) If individuals do show such a higher willingness to pay, a further question is

whether that fact should be taken into account fo r regulatory policy. Even if individuals are not risk-averse for such scenarios, it is possible th at regulatory policy should include a degree of

risk-aversion.

Assuming a risk-neutral representative agent is consistent with OMB’s Circular A-4,

which advises that the estimates of benefits and costs used in regulatory analysis are usually based on the average or the expected value and that “emphasis on these expected values is appropriate as long as society is ‘risk neutral’ with respect to the regulatory alternatives. While this may not always be the case, [analysts] shoul d in general assume ‘risk neutrality’ in [their] analysis.”

Nordhaus (2008) points to the need to expl ore the relationship between risk and income in the context of climate change across models and to explore the role of uncertainty regarding various parameters in the results. Using FUND, Anthoff et al. (2009) explored the sensitivity of the SCC to Ramsey equation parameter assumpti ons based on observed behavior. They conclude that “the assumed rate of risk aversion is at least as important as the assumed rate of time preference in determining the social cost of ca rbon.” Since Circular A-4 allows for a different assumption on risk preference in regulatory analysis if it is adequately justified, we plan to continue investigating this issue.

cc However these research dollars will be diverted from what ever their next best use would have been in the absence of climate change (so productivity/GDP would have been still higher).

EA-SCC.31EA-SCC.7 A FURTHER DISCUSSION OF CATASTROPHIC IMPACTS AND

DAMAGE FUNCTIONS

As noted above, the damage functions underlying the three IAMs used to estimate the SCC may not capture the economic effects of all possible adverse consequences of climate change and may therefore lead to underestimate s of the SCC (Mastrandr ea 2009). In particular, the models’ functional forms may not adequately capture: (1) pot entially discontinuous “tipping point” behavior in Earth systems; (2) inter-sectoral and inter-regional interactions, including global security impacts of high-end warming; and (3) limited near-term substitutability between damage to natural systems a nd increased consumption.

It is the hope of the interagency group that over time researchers a nd modelers will work to fill these gaps and that the SCC estimates used for regulatory analysis by the Federal government will continue to evolve with improvements in modeling. In the meantime, we

discuss some of the available evidence.

EA-SCC.7.1 Extrapolation of Climate Damage s to High Levels of Warming

The damage functions in the models are calibrated at moderate levels of warming and should therefore be viewed cautiously when extra polated to the high temperatures found in the upper end of the distribution. Recent science su ggests that there are a number of potential climatic “tipping points” at which the Earth system may exhibit discontinuous behavior with potentially severe social a nd economic consequences ( e.g. , Lenton et al. 2008, Kriegler et al. 2009). These tipping points include the disrupti on of the Indian Summer Monsoon, dieback of the Amazon Rainforest and boreal forests, colla pse of the Greenland Ice Sheet and the West Antarctic Ice Sheet, reorganization of the Atlantic Meridional Ov erturning Circulation, strengthening of El Niño-Southern Oscilla tion, and the release of methane from melting permafrost. Many of these tippi ng points are estimated to have thresholds between about 3 °C and 5 °C (Lenton et al. 2008). Probabilities of se veral of these tipping points were assessed through expert elicitatio n in 2005–2006 by Kriegler et al. (2009); results from this study are highlighted in Table 7.1. Ranges of probability ar e averaged across core experts on each topic.

Table 7.1. Probabilities of Various Tipping Points from Expert Elicitation

Possible Tipping Points Duration before effect is fully realized

years Additional Warming by 2100

%

0.5–1.5 C 1.5–3.0 C 3–5 C Reorganization of Atlantic Meridional Overturning Circulation about 100 0–18 6–39 18–67

Greenland Ice Sheet Collapse at least 300 8–39 33–73 67–96 West Antarctic Ice Sheet Collapse at least 300 5–41 10–63 33–88

Dieback of Amazon rainforest about 50 2–46 14–84 41–94 Strengthening of El Niño-Southern

Oscillation about 100 1–13 6–32 19–49

Dieback of Boreal Forests about 50 13–43 20–81 34–91

Shift in Indian Summer Monsoon about 1 not formally assessed Release of Methane from Melting

Permafrost less than 100 not formally assessed

EA-SCC.32As previously mentioned, FUND does not in clude potentially catastrophic effects. DICE assumes a small probability of ca tastrophic damages that increase s with increased warming, but the damages from these risks are incorporated as expected values ( i.e. , ignoring potential risk aversion). PAGE models catastrophic impacts in a probabilistic framework (Figure 1), so the high-end output from PAGE potentially offers the best insight into the SCC if the world were to experience catastrophic climate change. For instance, at the 95 th percentile and a 3 percent discount rate, the SCC estimated by PAGE ac ross the five socio- economic and emission trajectories of $113 per ton of CO 2 is almost double the value estimated by DICE, $58 per ton in2010. We cannot evaluate how well the three models account for catastrophic or noncatastrophic impacts, but this estimate highlights the sensitivity of SCC values in the tails of the distribution to the assumptions made about catastrophic impacts.

PAGE treats the possibility of a catastrophic event probabilistically, while DICE treats it deterministically ( i.e. , by adding the expected value of the damage from a catastrophe to the aggregate damage function). In part, this results in different probabilities being assigned to a catastrophic event across the two models. For inst ance, PAGE places a probability near zero on a catastrophe at 2.5 °C warming, while DICE assu mes a 4 percent probability of a catastrophe at

2.5 °C. By comparison, Kriegler et al. (2009) estimate a probability of at least 16

– 36 percent of crossing at least one of their primary climatic tipping points in a scenario with temperatures

about 2

– 4 °C warmer than pre-Industrial levels in 2100.

It is important to note that crossing a climatic tipping point will not necessarily lead to an economic catastrophe in the sense used in the IA Ms. A tipping point is a critical threshold across which some aspect of the Earth system starts to shifts into a qualitatively different state (for instance, one with dramatically reduced ice sheet volumes and higher sea levels). In the IAMs, a catastrophe is a low-probability environmen tal change with high economic impact.

EA-SCC.7.2 Failure to Incorporate Inter-Sectoral and Inter-Regional Interactions

The damage functions do not fully incorpor ate either inter-sectoral or inter-regional interactions. For instance, while damages to the agricultural sector are incorporated, the effects of changes in food supply on human health are not fully captured and depend on the modeler’s choice of studies used to calibrate the IAM. Likewise, the effects of climate damages in one region of the world on another region are not incl uded in some of the models (FUND includes the effects of migration from sea level rise). Th ese inter-regional interactions, though difficult to quantify, are the basis for climate-induced national and economic security concerns ( e.g. , Campbell et al. 2007; U.S. Department of Defense 2010) and are particularly worrisome at higher levels of warming. High-end warming scen arios, for instance, project water scarcity affecting 4.3

– 6.9 billion people by 2050, food scarcity affecting about 120 million additional people by 2080, and the creation of millions of climate refugees (Easterling et al. 2007; Campbell et al . 2007).

EA-SCC.7.3 Imperfect Substitutability of Environmental Amenities

Data from the geological record of past climate changes suggests that 6 °C of warming may have severe consequences for natural syst ems. For instance, during the Paleocene-Eocene Thermal Maximum about 55.5 million years ago, when the Earth experienced a geologically

EA-SCC.33rapid release of carbon associated with an approximately 5 °C increase in global mean temperatures, the effects included shifts of about 400

– 900 miles in the range of plants (Wing et
al. 2005), and dwarfing of both land mammals (Gingerich 2006) and soil fauna (Smith et al. 2009).

The three IAMs used here assume that it is possible to compensate for the economic consequences of damages to natural systems through increas ed consumption of non-climate goods, a common assumption in many economic m odels. In the context of climate change, however, it is possible that the damages to na tural systems could become so great that no increase in consumption of non-climate goods would provide complete compensation (Levy et
al. 2005). For instance, as water supplies become s carcer or ecosystems become more fragile and less bio-diverse, the services they provide may become increasingly more costly to replace. Uncalibrated attempts to incorporate the imperfect substitutability of such amenities into IAMs (Sterner and Persson 2008) indicate that the optimal degree of emissions abatement can be considerably greater than is commonly recognized.

EA-SCC.8 CONCLUSION

The interagency group selected four SCC estimates for use in regulatory analyses. For 2010, these estimates are $4.7, $21.4, $35.1, and $64.9 ( 2007$). The first three estimates are based on the average SCC across models and soci o-economic and emissions scenarios at the 5, 3, and 2.5 percent discount rates, resp ectively. The fourth value is included to represent the higherthan-expected impacts from temperature change furt her out in the tails of the SCC distribution. For this purpose, we use the SCC value for the 95 th percentile at a 3 percent discount rate. The central value is the average SCC across models at the 3 percent discount rate. For purposes of capturing the uncertainties involved in regulatory impact analysis, we emphasize the importance and value of considering the full range. These SCC estimates also grow over time. For instance, the central value increases to $24 per ton of CO 2 in 2015 and $26 per ton of CO 2 in 2020.

We noted a number of limitations to this analysis, including the incomplete way in which the integrated assessment models capture cat astrophic and non-catastrophic impacts, their incomplete treatment of adaptation and technological change, uncertainty in the extrapolation of damages to high temperatures, and assumptions regarding risk aversion. The limited amount of research linking climate impact s to economic damages makes this modeling exercise even more difficult. It is the hope of the interagency gr oup that over time research ers and modelers will work to fill these gaps and that the SCC estimates used for regulatory analysis by the Federal government will continue to evolve with improvements in modeling.

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EA-SCC.40EA-SCC.9 APPENDIX

This Appendix provides additional technical info rmation about the non-CO 2 emission projections used in the modeling and the method for extrapolating emissions forecasts through 2300, and shows the full distribution of 2010 SCC es timates by model and s cenario combination. Annual SCC values for the next 40 years are provided in Table 9.1.

Table 9.1. Annual SCC Values: 2010–2050 (2007$)

Discount Rate 5% 3% 2.5% 3%

Year Avg Avg Avg 95th

2010 4.7 21.4 35.1 64.9

2011 4.9 21.9 35.7 66.5

2012 5.1 22.4 36.4 68.1

2013 5.3 22.8 37.0 69.6

2014 5.5 23.3 37.7 71.2

2015 5.7 23.8 38.4 72.8

2016 5.9 24.3 39.0 74.4

2017 6.1 24.8 39.7 76.0

2018 6.3 25.3 40.4 77.5

2019 6.5 25.8 41.0 79.1

2020 6.8 26.3 41.7 80.7

2021 7.1 27.0 42.5 82.6

2022 7.4 27.6 43.4 84.6

2023 7.7 28.3 44.2 86.5

2024 7.9 28.9 45.0 88.4

2025 8.2 29.6 45.9 90.4

2026 8.5 30.2 46.7 92.3

2027 8.8 30.9 47.5 94.2

2028 9.1 31.5 48.4 96.2

2029 9.4 32.1 49.2 98.1

2030 9.7 32.8 50.0 100.0

2031 10.0 33.4 50.9 102.0

2032 10.3 34.1 51.7 103.9

2033 10.6 34.7 52.5 105.8

2034 10.9 35.4 53.4 107.8

2035 11.2 36.0 54.2 109.7

2036 11.5 36.7 55.0 111.6

2037 11.8 37.3 55.9 113.6

2038 12.1 37.9 56.7 115.5

2039 12.4 38.6 57.5 117.4

2040 12.7 39.2 58.4 119.3

2041 13.0 39.8 59.0 121.0

2042 13.3 40.4 59.7 122.7

2043 13.6 40.9 60.4 124.4

2044 13.9 41.5 61.0 126.1

2045 14.2 42.1 61.7 127.8

2046 14.5 42.6 62.4 129.4

2047 14.8 43.2 63.0 131.1

2048 15.1 43.8 63.7 132.8

2049 15.4 44.4 64.4 134.5

2050 15.7 44.9 65.0 136.2

EA-SCC.41EA-SCC.9.1 Other (non-CO 2 ) Gases

In addition to fossil and industrial CO 2 emissions, each EMF scenario provides projections of methane (CH 4 ), nitrous oxide (N 2 O), fluorinated gases, and net land use CO 2 emissions to 2100. These assumptions are used in all three IAMs while retaining each model’s default radiative forcings (R F) due to other factors ( e.g. , aerosols and other gases). Specifically,

to obtain the RF associated with the non-CO 2 EMF emissions only, we calculated the RF associated with the EMF atmospheric CO 2 concentrations and subtracted them from the EMF

total RF. dd This approach respects the EMF scenarios as much as possible and at the same time takes account of those components not included in the EMF projections. Since each model treats

non-CO 2 gases differently ( e.g. , DICE lumps all other gases into one composite exogenous input), this approach was applied slightly differently in each of the models.

FUND : Rather than relying on RF for these gases, the actual emissions from each scenario were used in FUND. The model default trajectories for CH 4 , N 2 O, SF 6 , and the CO 2 emissions from land were replaced with the EMF values.

PAGE : PAGE models CO 2 , CH 4 , sulfur hexafluoride (SF 6 ), and aerosols and contains an “excess forcing” vector that includes the RF for everything else. To include the EMF values, we

removed the default CH 4 and SF 6 factors, ee decomposed the excess forcing vector, and constructed a new excess forcing vector that includes the EMF RF for CH 4 , N 2 O, and fluorinated gases, as well as the model default values fo r aerosols and other factors. Net land use CO 2 emissions were added to the fossil and industrial CO 2 emissions pathway.

DICE : DICE presents the greatest challenge because all forcing due to factors other than

industrial CO 2 emissions is embedded in an exogenous non-CO 2 RF vector. To decompose this exogenous forcing path into EMF non-CO 2 gases and other gases, we relied on the references in DICE2007 to the Intergovernmental Panel on C limate Change’s (IPCC) Fourth Assessment Report (AR4) and the discussion of aerosol fo recasts in the IPCC’s Third Assessment Report (TAR) and in AR4, as explained below. In DICE 2007, Nordhaus assumes that exogenous forcing

from all non-CO 2 sources is -0.06 W/m 2 in 2005, as reported in AR4, and increases linearly to

0.3 W/m 2 in 2105, based on GISS projections, and th en stays constant after that time.

According to AR4, the RF in 2005 from CH 4 , N 2 0, and halocarbons (approximately similar to the F-gases in the EMF-22 scenarios) was 0.48 + 0.16 + 0.34 = 0.98 W/m 2 and RF

from total aerosols was -1.2 W/m 2 . Thus, the -.06 W/m 2 non-CO 2 forcing in DICE can be decomposed into: 0.98 W/m 2 due to the EMF non-CO 2 gases, -1.2 W/m 2 due to aerosols, and the remainder, 0.16 W/m 2 , due to other residual forcing.

dd

Note EMF did not provide CO 2 concentrations for the IMAGE reference sc enario. Thus, for this scenario, we fed the fossil, industrial and land CO 2 emissions into MAGICC (considered a “neutral arbiter” model, which is tuned to emulate the major global climate models) and the resulting CO 2 concentrations were used. Note also that MERGE assumes a neutral biosphere so net land CO 2 emissions are set to zero for all years for the MERGE Optimistic reference scenario, and for the MERGE component of the average 550 scenario ( i.e. , we add up the land use emissions from the other three models and divide by 4).

ee Both the model default CH 4 emissions and the initial atmospheric CH 4 is set to zero to avoid double counting the effect of past CH 4 emissions.

EA-SCC.42For subsequent years, we calculated the DICE default RF from aerosols and other nonCO 2 gases based on the following two assumptions:

(1) RF from aerosols declines linearly from 2005 to 2100 at the rate projected by the TAR and then stays constant thereafter, and

(2) With respect to RF from non-CO 2 gases not included in the EMF-22 scenarios, the share of non-aerosol RF matches the share implicit in the AR4 summary sta tistics cited above and

remains constant over time.

Assumption (1) means that the RF from aerosols in 2100 equals 66 percent of that in 2000, which is the fraction of the TAR projection of to tal RF from aerosols (including sulfates, black carbon, and organic carbon) in 2100 vs. 2000 under th e A1B SRES emissions scenario. Since the SRES marker scenarios were not updated for th e AR4, the TAR provides the most recent IPCC projection of aerosol forcing. We rely on the A1B projection from the TAR because it provides one of the lower aerosol forecasts among the SRES marker scenarios and is more consistent with the AR4 discussion of the post-SRES literature on aerosols:

Aerosols have a net cooling effect and the re presentation of aerosol and aerosol precursor emissions, including sulphur dioxide, black carbon and organic carbon, has improved in the post-SRES scenarios. Generally, these emissions are projected to be lower than reported in SRES. {WGIII 3.2, TS.3, SPM}. ff

Assuming a simple linear decline in aerosols from 2000 to 2100 also is more consistent

with the recent literature on these emissions. Fo r example, Figure 9.1 shows that the sulfur dioxide emissions peak over the short-term of some SRES scenarios above the upper bound

estimates of the more recent scenarios. gg Recent scenarios project sulfur emissions to peak earlier and at lower levels compared to the SRES in pa rt because of new information about present and planned sulfur legislation in some developing countries, such as India and China. hh The lower bound projections of the recent literature have also shifted downward slightly compared to the SRES scenario (IPCC 2007).

With these assumptions, the DICE aerosol forcing changes from -1.2 in 2005 to -0.792 in

2105 W/m 2 ; forcing due to other non-CO 2 gases not included in the EMF scenarios declines from

0.160 to 0.153 W/m 2

.

ff

AR4 Synthesis Report, p. 44, http://www.ipcc.ch/pdf/assessment-report/ar4/syr/ar4_syr.pdf

gg

See Smith, S.J., R. Andres, E. Conception, and J. Lurz. 2004. “Historical sulfur dioxide emissions, 1850-2000: methods and results.” Joint Global Research Institute, College Park, 14 pp.

hh See Carmichael, G., D. Streets, G. Calori, M. Amann, M. Jacobson, J. Hansen, and H. Ueda. 2002. “Changing trends in sulphur emissions in Asia: implications for acid deposition, air pollution, and climate.” Environmental Science and Technology 36(22):4707- 4713; Streets, D., K. Jiang, X. Hu, J. Sinton, X.-Q. Zhang, D. Xu, M. Jacobson, and J. Hansen. 2001. “Recent reductions in China’s greenhouse gas emissions.” Science 294(5548):1835­

1837.

EA-SCC.43.

Figure 9.1. Sulphur Dioxide Emission Scenarios
Notes: Thick colored lines depict the four SRES mark er scenarios and black dashed lines show the median,

5 th and 95 th percentile of the frequency distribution for the full ensemble of 40 SRES scenarios. The blue area (and the thin dashed lines in blue) illustrates individual scenarios and the range of Smith et al. (2004).

Dotted lines indicate the minimum and maximum of SO 2 emissions scenarios developed pre-SRES. Source: IPCC (2007), AR4 WGIII 3.2, http://www.ipcc.ch/publications_and_data/ar4/wg3/en/ch3-ens3-2

­ 2-4.html

.

Although other approaches to decomposin g the DICE exogenous forcing vector are possible, initial sensitivity analysis suggests that the differences among reasonable alternative approaches are likely to be minor. For example, adjusting the TAR aerosol projection above to

assume that aerosols will be maintained at 2000 levels throug h 2100 reduces average SCC values (for 2010) by approximately 3 percent (or less than $2); assuming all aerosols are phased out by 2100 increases average 2010 SCC values by 6–7 percent (or $0.50–$3), depending on the discount rate. These differences increase slightly for SCC values in late r years but are still well within 10 percent of each other as far out as 2050.

Finally, as in PAGE, the EMF net land use CO 2 emissions are added to the fossil and

industrial CO 2 emissions pathway.

EA-SCC.9.2 Extrapolating Emissions Projections to 2300

To run each model through 2300 requi res assumptions about GDP, population, greenhouse gas emissions, and radi ative forcing trajectories afte r 2100, the last year for which these projections are available from the EMF-22 models. These inputs were extrapolated from

2100 to 2300 as follows:

1. Population growth rate declines linearly, reaching zero in 2200.

2. GDP/per capita growth rate declines linearly, reaching zero in 2300.

3. The decline in the fossil and industrial carbon intensity (CO 2 /GDP) growth rate over 2090-2100 is maintained from 2100 through 2300.

EA-SCC.444. Net land use CO 2 emissions decline linearly, reaching zero in 2200.

5. Non-CO 2 radiative forcing remains constant after 2100.

Long run stabilization of GDP per capita was viewed as a more realistic simplifying assumption than a linear or exponential extrapol ation of the pre-2100 economic growth rate of each EMF scenario. This is based on the idea that increasing scarcity of natural resources and the degradation of environmental sinks ava ilable for assimilating pollution from economic production activities may eventually overtake the rate of technological progress. Thus, the overall rate of economic growth may slow over the very long run. The interagency group also considered allowing an exponential decline in the growth rate of GDP per capita. However, since this would require an additional assumption about how close to zero the growth rate would get by 2300, the group opted for the simpler and more transparent linear extrapolation to zero by

2300.

The population growth rate is also assumed to decline linearly, reaching zero by 2200. This assumption is reasonably c onsistent with the United Nations long run population forecast, which estimates global population to be fairly stable after 2150 in the medium scenario (UN 2004). ii The resulting range of EMF population traject ories (Figure A2) also encompass the UN medium scenario forecasts through 2300 – gl obal population of 8.5 billion by 2200, and 9 billion by 2300.

Maintaining the decline in the 2090–2100 carbon intensity growth rate ( i.e. , CO 2 per dollar of GDP) through 2300 assumes that tec hnological improvements and innovations in the areas of energy efficiency and other carbon re ducing technologies (possibly including currently unavailable methods) will continue to proceed at roughly the same pace that is projected to occur towards the end of the forecast period for each EMF scenario. This assumption implies that total cumulative emissions in 2300 will be between 5,000 and 12,000 GtC, which is within the range of the total potential global carbon stock estimated in the literature.

Net land use CO 2 emissions are expected to stabilize in the long run, so in the absence of any post 2100 projections, the group assumed a linear decline to zero by 2200. Given no a priori reasons for assuming a long run increase or decline in non-CO 2 radiative forcing, it is assumed to remain at the 2100 levels for each EMF scenario through 2300.

Figure 9.2 through Figure 9.7 show the paths of global population, GDP, fossil and

industrial CO 2 emissions, net land CO 2 emissions, non-CO 2 radiative forcing, and CO 2 intensity (fossil and industrial CO 2 emissions/GDP) resulting from these assumptions.

ii United Nations. 2004. World Population to 2300

. http://www.un.org/esa/population/publications/longrange2/worldpop2300final.pdf

.

EA-SCC.455.5 6.5 7.5 8.5 9.5 10.5 11.5

2000 2050 2100 2150 2200 2250 2300 IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

ExtrapolationsGlobalPopulation[billion]

Year

Figure 9.2. Global Population, 2000–2300 (post-2100 extrapolations assume the population growth rate changes lin early to reach a zero growth rate by 2200)
Note: In the fifth scenario, 2000–2100 population is equal to the average of the population under the 550 ppm CO 2 e, full-participation, not-to-e xceed scenarios considered by each of the four models.

0 500 1000 1500 2000

2000 2050 2100 2150 2200 2250 2300 IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

Extrapolations

YearWorldGDP[$trillion]

Figure 9.3. World GDP, 2000-2300 (post-2100 extr apolations assume GDP per capita growth declines linearly, reaching zero in 2300)
Note: In the fifth scenario, 2000–2100 GDP is equal to the average of the GDP under the 550 ppm CO 2 e, full-participation, not-to-exceed scenarios co nsidered by each of the four models.

EA-SCC.46Fossil&IndustrialCO2Emissions[GtCO2/decade] 200

150

100

50

0 IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

Extrapolations

2000 2050 2100 2150 2200 2250 2300

Year

Figure 9.4. Global Fossil and Industrial CO 2 Emissions, 2000-2300 (post-2100 extrapolations assume growth rate of CO 2 intensity (CO 2 /GDP) over 2090–2100 is maintained through 2300)
Note: In the fifth scenario, 2000–2100 emissions are equal to the average of the emissions under the 550 ppm CO2e, full-participation, not-to-exceed scenario s considered by each of the four models.

‐ 3.00 ‐ 2.00 ‐ 1.00 0.00 1.00 2.00 3.00 4.00

2000 2050 2100 2150 2200 2250 2300 IMAGE

MERGE

MESSAGE

MiniCAM

5th scneario

ExtrapolationsNetlandCO2emissions[GtCO2/yr]

Year z

Figure 9.5. Global Net Land Use CO 2 Emissions, 2000–2300 (post-2100 extrapolations

assume emissions decline lin early, reaching zero in 2200) jj

Note: In the fifth scenario, 2000–2100 emissions are equal to the average of the emissions under the 550 ppm CO2e, full-participation, not-to-exceed scenar ios considered by each of the four models.

jj MERGE assumes a neutral biosphere so net land CO 2 emissions are set to zero for all years for the MERGE Optimistic reference scenario, and for the MERGE component of the average 550 scenario ( i.e. , we add up the land use emissions from the other three models and divide by 4).

EA-SCC.470.00 0.75 1.50 2.25 3.00 3.75

2000 2050 2100 2150 2200 2250 2300 IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

ExtrapolationsnonCO2RF[W/m^2]

Year

Figure 9.6. Global Non-CO 2 Radiative Forcing, 2000–2300 (post-2100 extrapolations

assume constant non-CO 2 radiative forcing after 2100)
Note: In the fifth scenario, 2000–2100 emissions are equal to the average of the emissions under the 550 ppm CO2e, full-participation, not-to-exceed scenario s considered by each of the four models.

0 0 0 0 0 1 1 1 1

2000 2050 2100 2150 2200 2250 2300 IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

ExtrapolationsFossil&IndustrialCO2intensity[GtCO2/trillion$]

Year

Figure 9.7. Global CO 2 Intensity (fossil & industrial CO 2 emissions/GDP), 2000–2300 (post

2100 extrapolations assume decline in CO 2 /GDP growth rate over 2090–2100 is maintained through 2300)
Note: In the fifth scenario, 2000–2100 emissions are equal to the average of the emissions under the 550 ppm CO 2 e, full-participation, not-to-e xceed scenarios considered by each of the four models.

EA-SCC.48Table 9.2. 2010 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton CO 2 )

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 3.3 5.9 8.1 13.9 28.8 65.5 68.2 147.9 239.6 563.8 MERGE optimistic 1.9 3.2 4.3 7.2 14.6 34.6 36.2 79.8 124.8 288.3 Message 2.4 4.3 5.8 9.8 20.3 49.2 50.7 114.9 181.7 428.4

MiniCAM base 2.7 4.6 6.4 11.2 22.8 54.7 55.7 120.5 195.3 482.3

5th scenario 2.0 3.5 4.7 8.1 16.3 42.9 41.5 103.9 176.3 371.9

Scenario DICE

IMAGE 16.4 21.4 25 33.3 46.8 54.2 69.7 96.3 111.1 130.0 MERGE optimistic 9.7 12.6 14.9 19.7 27.9 31.6 40.7 54.5 63.5 73.3 Message 13.5 17.2 20.1 27 38.5 43.5 55.1 75.8 87.9 103.0

MiniCAM base 13.1 16.7 19.8 26.7 38.6 44.4 56.8 79.5 92.8 109.3

5th scenario 10.8 14 16.7 22.2 32 37.4 47.7 67.8 80.2 96.8

Scenario FUND

IMAGE -33.1 -18.9 -13.3 -5.5 4.1 19.3 18.7 43.5 67.1 150.7 MERGE optimistic -33.1 -14.8 -10 -3 5.9 14.8 20.4 43.9 65.4 132.9 Message -32.5 -19.8 -14.6 -7.2 1.5 8.8 13.8 33.7 52.3 119.2

MiniCAM base -31.0 -15.9 -10.7 -3.4 6 22.2 21 46.4 70.4 152.9

5th scenario -32.2 -21.6 -16.7 -9.7 -2.3 3 6.7 20.5 34.2 96.8

Table 9.3. 2010 Global SCC Estimates at 3 Percent Discount Rate (2007$/ton CO 2 )

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 2.0 3.5 4.8 8.1 16.5 39.5 41.6 90.3 142.4 327.4 MERGE optimistic 1.2 2.1 2.8 4.6 9.3 22.3 22.8 51.3 82.4 190.0 Message 1.6 2.7 3.6 6.2 12.5 30.3 31 71.4 115.6 263.0

MiniCAM base 1.7 2.8 3.8 6.5 13.2 31.8 32.4 72.6 115.4 287.0

5th scenario 1.3 2.3 3.1 5 9.6 25.4 23.6 62.1 104.7 222.5

Scenario DICE

IMAGE 11.0 14.5 17.2 22.8 31.6 35.8 45.4 61.9 70.8 82.1 MERGE optimistic 7.1 9.2 10.8 14.3 19.9 22 27.9 36.9 42.1 48.8 Message 9.7 12.5 14.7 19 26.6 29.8 37.8 51.1 58.6 67.4

MiniCAM base 8.8 11.5 13.6 18 25.2 28.8 36.9 50.4 57.9 67.8

5th scenario 7.9 10.1 11.8 15.6 21.6 24.9 31.8 43.7 50.8 60.6

Scenario FUND

IMAGE -25.2 -15.3 -11.2 -5.6 0.9 8.2 10.4 25.4 39.7 90.3 MERGE optimistic -24.0 -12.4 -8.7 -3.6 2.6 8 12.2 27 41.3 85.3 Message -25.3 -16.2 -12.2 -6.8 -0.5 3.6 7.7 20.1 32.1 72.5

MiniCAM base -23.1 -12.9 -9.3 -4 2.4 10.2 12.2 27.7 42.6 93.0

5th scenario -24.1 -16.6 -13.2 -8.3 -3 -0.2 2.9 11.2 19.4 53.6

Table 9.4. 2010 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton CO 2 )

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 0.5 0.8 1.1 1.8 3.5 8.3 8.5 19.5 31.4 67.2 MERGE optimistic 0.3 0.5 0.7 1.2 2.3 5.2 5.4 12.3 19.5 42.4 Message 0.4 0.7 0.9 1.6 3 7.2 7.2 17 28.2 60.8

MiniCAM base 0.3 0.6 0.8 1.4 2.7 6.4 6.6 15.9 24.9 52.6

5th scenario 0.3 0.6 0.8 1.3 2.3 5.5 5 12.9 22 48.7

Scenario DICE

IMAGE 4.2 5.4 6.2 7.6 10 10.8 13.4 16.8 18.7 21.1 MERGE optimistic 2.9 3.7 4.2 5.3 7 7.5 9.3 11.7 12.9 14.4

EA-SCC.49Message 3.9 4.9 5.5 7 9.2 9.8 12.2 15.4 17.1 18.8

MiniCAM base 3.4 4.2 4.7 6 7.9 8.6 10.7 13.5 15.1 16.9

5th scenario 3.2 4 4.6 5.7 7.6 8.2 10.2 12.8 14.3 16.0

Scenario FUND

IMAGE -11.7 -8.4 -6.9 -4.6 -2.2 -1.3 0.7 4.1 7.4 17.4 MERGE optimistic -10.6 -7.1 -5.6 -3.6 -1.3 -0.3 1.6 5.4 9.1 19.0 Message -12.2 -8.9 -7.3 -4.9 -2.5 -1.9 0.3 3.5 6.5 15.6

MiniCAM base -10.4 -7.2 -5.8 -3.8 -1.5 -0.6 1.3 4.8 8.2 18.0

5th scenario -10.9 -8.3 -7 -5 -2.9 -2.7 -0.8 1.4 3.2 9.2

0 2000 4000 6000 8000 10000 12000 14000 16000‐25‐12114274053667992105118131144157170183196209222235248261274

All 2010, 5.0% All 2010, 3.0% All 2010, 2.5%

Figure 9.8. Histogram of Global SCC Estimates in 2010 (2007$/ton CO 2 ), by Discount Rate*

* The distribution of SCC values ranges from -$5,192 to $66,116, but the X-axis has been truncated at approximately the 1 st and 99 th percentiles to better show the data.

Table 9.5. Additional Summary Statisti cs of 2010 Global SCC Estimates

Discount

Rate 5% 3% 2.5%

Scenario Mean Variance Skewness Kurtosis Mean Variance Skewness Kurtosis Mean Variance Skewness Kurtosis

DICE 9.0 13.1 0.8 0.2 28.3 209.8 1.1 0.9 42.2 534.9 1.2 1.1

PAGE 6.5 136.0 6.3 72.4 29.8 3,383 .7 8.6 151.0 49.3 9,546.0 8.7 143.8

FUND -1.3 70.1 28.2 1,479.0 6.0 16,382.5 128.0 18,976.5 13.6 150,732.6 149.0 23,558.3

EA-SCC.50