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Advanced Energy Technologies and Climate Change: An Analysis Using the Global Change Assessment Model (GCAM)

May 1994

Global Studies Program
Pacific Northwest Laboratory

DISCLAIMER

This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor Battelle Memorial Institute, nor any of their employees, makes any warranty, express ed or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency there of, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

PACIFIC NORTHWEST LABORATORY
operated by
BATTELLE MEMORIAL INSTITUTE
for the
UNITED STATES DEPARTMENT OF ENERGY
under Contract DE-AC06-76RLO 1830

PNL-9798
UC402

J.A. Edmonds
M.A. Wise
C.N. MacCracken

May 1994

Prepared for
the U.S. Department of Energy
under contract DE-AC06-76RLO 1830.

Pacific Northwest Laboratory
Richland, Washington 99352

SUMMARY

We report results from a "top down" energy-economy model employing "bottom up" assumptions embedded in an integrated assessment framework, the Global Change Assessment Model (GCAM). The analysis shows that from the perspective of long-term energy system development, differences in results from the "top down" and "bottom up" research communities would appear to be more closely linked to differences in assumptions regarding the economic cost associated with advanced technologies than to differences in modeling approach.

The adoption of assumptions regarding advanced energy technologies were shown to have a profound effect on the future rate of anthropogenic climate change. The cumulative effect of the five sets of advanced energy technologies is to reduce annual emissions from fossil fuel use to levels which stabilize atmospheric concentrations below 550 ppmv, the point at which atmospheric concentrations are double those that existed in the middle of the eighteenth century.

While all energy technologies play roles in reducing future fossil fuel carbon dioxide emissions, the introduction of advanced biomass energy production technology plays a particularly important role. If biomass energy can be made available at $2.40/GJ or less in quantities sufficient to make it the core energy supply technology In the middle of the next century, then emissions can be cut dramatically relative to the reference case. The problem of emissions reduction becomes one of technology development and deployment in this case, and not one of fiscal and regulatory intervention.

ACKNOWLEDGEMENTS

The preparation of this report was supported by the United States Department of Energy (DOE) under contract number DE-AC06-76RLO 1830. We are indebted to Ted Williams of DOE for supporting our participation in the IPCC process and making this research pos sible. We are also indebted to John Houghton of the United States DOE and Rich Richels of the Electric Power Research Institute (EPRI) for supporting the development of GCAM. The authors wish to express special appreciation to Tom Wigley, without whom we would have been unable to conduct the analysis of atmospheric concentrations and climate change associated with greenhouse gas emissions. We are also indebted to Howard Gruenspecht for constructive comments on an earlier draft. In addition, the authors ar e grateful to Bob Williams and Hans-Holger Rogner for supplying technical data for the analysis. We are also grateful to Thomas Johansson, Rich Richels and Hadi Dowlatabadi, who have provided counsel in this endeavor. While these readers have improved the quality of the paper, the authors retain full responsibility for accuracy and validity of the results reported here. The views expressed here are those of the authors alone.

CONTENTS

I. INTRODUCTION

II. APPROACH

Overview
Energy-Related Emissions
Atmosphere, Climate and Sea Level

III. THE REFERENCE CASE

IV. FIVE ADVANCED ENERGY SUPPLY SCENARIO

Defining Five Advanced Energy Technology Cases
The Effects of Advanced Energy Technologies on Energy Use and Emissions
Comparison to Results of a Traditional "Bottom-up" Analysis
The Value of Advanced Energy Technologies in Stabilizing Future Fossil Fuel CO2
Emissions
Implications for Atmospheric Composition
Implications for Climate Change and Sea Level

V. CONCLUSIONS

VI. REFERENCES.

TABLES

Table 1. Key Assumptions
Table 2. Summary of Assumptions Used by Case
Table 3. Present Discounted Incremental Value of Adding Advanced Energy Technologies

FIGURES

Figure 1. Comparison of IS92a with Case 1: Total Energy Production
Figure 2. Comparison of IS92a with Case 1: Total Fossil Fuel CO2
Figure 3. Global Energy Production and Use by Fuel: Case 1
Figure 4. Global Fossil Fuel Carbon Emissions by Region: Case 1
Figure 5. Global Annuel Fossil Fuel CO2
Figure 6. Global Annual Energy Production and Use
Figure 7. Global Energy Production and Use by Fuel: Case 5
Figure 8. Global Fossil Fuel CO2 Emissions: Comparison to LEESS
Figure 9. Global Energy Production and Use: Comparison to LEESS
Figure 10. Carbon Taxes Required for Stabilization of Global CO2 Emissions at 1992 Levels: IS92a
Figure 11. Global Cummulative Carbon Emissions from 1990, by Scenario
Figure 12. Atmospheric Carbon Dioxide Concentration, by Scenario
Figure 13. Mean Global Temperature Increase, by Scenario
Figure 14. Mean Global Sea Level Rise, by Scenario

I. INTRODUCTION

Much has been made of the issue of the cost of reducing future fossil fuel carbon emissions in the past few years. The literature has grown to be enormous. Several reviews (e.g., Grubb et al. 1993) have surveyed the field. Two schools of thought with regard to the cost of emissions reductions have developed. One school, referred to as the "top-down" modelers, deals with the aggregate economy using models based on economic principles. In general, but not always, these models begin with the hypothesis th at the energy system is in something close to competitive equilibrium, and that markets are relatively efficient. These models have positive costs associated with reductions of emissions from a reference trajectory. The second school is referred to as the "bottom-up" school of thought. This approach emphasizes engineering-economic calculations, and focuses on the potential for advanced energy technologies to replace existing technologies, with concurrent increases in efficiency and profitability, and emissions reductions. Emissions reductions costs are generally found to be negative by this school.

The purpose of this paper is to examine the potential for advanced energy technologies such as those proposed by Johansson et al. (1993), to reduce reference case greenhouse gas emissions. The paper further considers the impact of these technologies on the rate and timing of climate change. This paper documents work undertaken in support of the Intergovernmental Panel on Climate Change (IPCC), Working Group Two (WG2), Energy Supply Mitigation Chapter.

II. APPROACH

Overview

The purpose of this paper is to examine the impact of advanced energy technologies on greenhouse gas emissions, atmospheric composition, and climate change. This analysis requires tools which cover both economic and bio-geophysical relationships. Our tool, the Global Change Assessment Model (GCAM), is an integrated set of models that address complementary facets of the problem. We rely on the Edmonds-Reilly-Barns Model (ERB) for energy related greenhouse gas emissions. Other emissions trajectories, part icularly land-use related emissions, are taken from the IPCC, IS92 scenarios. Atmospheric composition, radiative forcing, global mean temperature change, and sea level rise are developed following Wigley and Raper (1992).

Energy-Related Emissions

We have used the ERB, version 4.15, to model energy-economy-greenhouse emissions relationships. The ERB is a well-documented (Edmonds and Reilly, 1985; Edmonds et al., 1986), frequently used, long-term model of global energy and fossil fuel greenhouse ga s emissions. The model can be thought of as consisting of four parts: supply, demand, energy balance, and greenhouse gas emissions. The first two modules determine the supply of and demand for each of six major primary energy categories in each of nine gl obal regions. The energy balance module ensures model equilibrium in each global fuel market. (Primary electricity is assumed to be untraded; thus supply and demand balance in each region.) The greenhouse gas emissions module is a set of three post-processors which calculate the energy-related emissions of carbon dioxide (CO2), nitrous oxide (N2O), and methane (CH4). The original version of the model is documented in Edmonds and Reilly (1985), while major revisions are discussed in Edmonds et al. (1986). The model is currently configured to develop scenarios for benchmark years: 1990, 2005, 2020, 2035, 2050, 2065, 2080, and 2095.

Energy demand for each of the six major fuel types is developed for each of the nine regions. Five major exogenous inputs determine energy demand: population; labor productivity; exogenous energy end-use intensity; energy prices; and energy taxes, subsidies, and tariffs.

The model calculates base GNP directly as a product of labor force and labor productivity. An estimate of base GNP for each region is used both as a proxy for the overall level of economic activity and as an index of income. The base GNP is, in turn, modified within the model to be consistent with energy-economy interactions. The GNP feedback elasticity is regional, allowing the model to distinguish energy supply dominant regions, such as the Mideast, where energy prices and GNP are positively related, from the rest of the world where the relationship is inverse.

The exogenous end-use energy-intensity improvement parameter is a time-dependent index of energy productivity. It measures the annual rate of growth of energy productivity that would continue independent of such other factors as energy prices and real inc ome changes. In the past, technological progress and other non-price factors have had an important influence on energy use in the manufacturing sector of advanced economies. Including an exogenous end-use energy-intensity improvement parameter allows scenarios to be developed that incorporate either continued improvements or technological stagnation assumptions as an integral part of these scenarios.

The final major energy factor influencing demand is energy prices. Each region has a unique set of energy prices derived from world prices (determined in the energy balance component of the model) and region-specific taxes and tariffs. The model can be modified to accommodate nontrading regions for any fuel or set of fuels. The model assumes that regions do not trade solar, nuclear, or hydroelectric power, but all regions trade fossil fuels.

The energy-demand module performs two functions: it establishes the demand of energy, and its services, and it maintains a set of energy flow accounts for each region. Oil and gas are transformed into secondary liquids and gases that are used either dire ctly in end-use sectors or indirectly as electricity. Hydro, nuclear, and solar electric or fusion are accounted for directly as electricity. Nonelectric solar energy is included with conservation technologies as a reduction in the demand for marketed fuels.

The four secondary fuels are consumed to produce energy services. In each region of the model, energy is consumed by three end-use sectors: residential/commercial, industrial, and transportation.

The demand for energy services in each region's end-use sectors is determined by the cost of providing these services and by the levels of income and population. The mix of secondary fuels used to provide these services is determined by the relative costs of providing these services using each alternative fuel. The demand of fuels to provide electric power is then determined by the relative costs of production, as is the share of oil and gas transformed from coal and biomass.

Energy supply is disaggregated into two categories, renewable and non-renewable. Energy supply from all fossil fuels is related directly to the resource base by grade, the cost of production (both technical and environmental) and to the historical production capacity. The introduction of a graded resource base for fossil fuel (and nuclear) supply allows the model to explicitly test the importance of fossil fuel resource constraints as well as to represent fuels such as shale oil, in which only small amounts are likely available at low costs but for which large amounts are potentially available at high cost.

Note here that nuclear is treated in the same category as fossil fuels. Nuclear power is constrained by a resource base as long as light-water reactors are the dominant producers of power. Breeder reactors, by producing more fuel than they consume, are modeled as an essentially unlimited source of fuel that is available at higher cost.

A rate of technological change is also introduced on the supply side. This rate varies by fuel and is expected to be both higher and less certain for emerging technologies.

The supply and demand modules each generate energy supply and demand estimates based on exogenous input assumptions and energy prices. If energy supply and demand match when summed across all trading regions in each group for each fuel, then the global en ergy system balances. Such a result is unlikely at an arbitrary set of energy prices. The energy balance component of the model is a set of rules for choosing energy prices which, on successive attempts, bring supply and demand nearer a system-wide balance. Successive energy price vectors are chosen until energy markets balance within a prespecifed bound.

Given the solution of the energy balance component of the model, greenhouse gas emissions for CO2, CH4, and N2O are calculated by applying emissions coefficients. Emissions coefficients for CO2 are as follows:

* liquids 19.2 TgC/EJ
* gases 13.7 TgC/EJ
* solids 23.8 TgC/EJ
* carbonate rock mining 27.9 TgC/EJ Modern biomass is treated as if its carbon absorption occurred in the year of release. This approximation can either underestimate or overestimate actual net annual fluxes depending upon whether the underlying stock of biomass is either expanding or contracting. (See Edmonds and Barns 1990).

Atmosphere, Climate and Sea Level

The analysis of atmospheric composition, climate change, and sea level rise uses the MAGICC model following Wigley and Raper (1992).

The concentration of CO2 in the atmosphere is determined using a reduced form carbon cycle model. The model is balanced. That is, the model reproduces current atmospheric concentrations in a manner which does not resort to directly pairing emission sourc es and sinks. Two sink terms are considered, ocean and terrestrial. The ocean sink employs a convolution integral representation, based on Maier-Reimer and Hasselmann (1987). The terrestrial sink is modeled as four linked boxes. An important feature of the model is that it provides a pathway by which atmospheric CO2 concentrations affect terrestrial carbon storage. This pathway allows the carbon cycle to be balanced, though it should be noted that this mechanism is a gross oversimplification of what is cu rrently known regarding the carbon cycle, and great uncertainty remains as to the disposition of anthropogenic emissions. (See for example, Wisniewski and Lugo 1992 and IPCC 1992.)

The atmospheric concentration of CH4, N2O, and the halocarbons are determined using a mass balance equation of the form:

where

E = the emission rate b = a units conversion term C = the concentration, and T = the removal rate for the each sink.

Methane has two sinks: atmospheric chemical reactions and soils. For nitrous oxide and the halocarbons only the atmospheric sink is considered. It is well known that atmospheric sink rates are not constant. For methane, the availability of hydroxyl radicals is a governing factor which in turn depends on the concentration of CH4 and the emission rates for carbon monoxide (CO), oxides of nitrogen (NOx), and volatile organic compounds (VOCs). The model explicitly considers the effect of CO, NOx, and VOC emis sions on the atmospheric removal rate.

Sulphur emissions are short lived, but their effect on climate is thought to be significant. Unlike the "greenhouse" gases, they exert a cooling effect. Because of their short lifetimes, no atmospheric stock model is needed.

Radiative forcing varies by gas. Carbon dioxide effects on radiative forcing are given by

 

The changes in radiative forcing associated with methane and nitrous oxide are computed as per Shine et al (1990). These methods consider direct and indirect effects on radiative forcing, as well as the effects of absorption band overlaps.

The radiative forcing associated with halocarbons has two components, a direct component and an indirect ozone component. The direct component derives from the direct warming properties of halocarbon molecules, while the indirect ozone effect takes into account the cooling associated with the destruction of ozone subsequent to the dissociation of the halocarbons. The indirect ozone effect is computed as follows:

The direct effect is given by

The total effect is the sum of the direct and ozone effects.

The total effect is the sum of the direct and ozone effects.

The presence of sulphate aerosols in the atmosphere is presently felt to have a strong local cooling effect. This effect is manifest through three pathways: scattering and absorption of shortwave (solar) radiation effects, cloud reflectivity effects, and cloud persistence effects (IPCC, 1992). The effect on radiative forcing is computed as

where E is the emissions rate and Eo is the initial emissions rate.

The change in global mean temperature depends on the sum of the changes in radiative forcing, climate sensitivity, and ocean thermal inertia. The climate sensitivity is reflected by the change in global mean temperature associated with a doubling of the preindustrial concentration of atmospheric CO2, after direct and feedback effects (for example, water vapor, ice albedo, and clouds) are taken into account. The most commonly cited range of climate sensitivity is 1.5 to 4.5 deg. C, with a "best guess" valu e of 2.5 deg. C. Ocean thermal lag is computed using an upwelling-diffusion model. The model in turn depends critically on parameters for mixed-layer depth, oceanic vertical diffusivity, the upwelling rate, and the temperature change of high-latitude sinking water relative to the global-mean change.

Sea level rise is computed as the sum of two terms: thermal expansion and meltwater runoff (Wigley and Raper, 1992). Thermal expansion is computed from the oceanic upwelling-diffusion model referenced above. Meltwater is the sum of contributions from thre e sources: small glaciers, Greenland, and Antarctica. These in turn are driven by equilibrium temperature change.

III. THE REFERENCE CASE

To provide a basis for comparison, a reference case was constructed which we call Case 1. Because this work was done in support of the IPCC WG2, an attempt has been made to tune the model to reflect the IPCC developed emissions scenario, IS92a, IPCC (1992 ). Consistency has been maintained with population and GNP assumptions as well as global energy and fossil fuel carbon emissions. We report some of the key assumptions which govern the behavior of the model in Table 1. A full description of reference case model inputs and outputs is given in an appendix which is available upon request.

The biomass resource constraint was developed to be consistent with the estimated population resource base and rising income levels, Edmonds and Reilly (1985). While an enormous supply of energy is available in the form of shale oil. this resource is gene rally quite expensive to produce with current technologies and therefore is not a major factor in the reference scenario. The uranium resource base is assumed to be extended if a breeder reactor technology is adopted.

Energy and fossil fuel carbon emissions closely mirror those developed for IS92a. This is shown in Figure 1 and Figure 2. Figure 3 and Figure 4 show the fuel mix of global energy production and use and the regional distribution of fossil fuel CO2 emissions respectively.

While total energy production and fossil fuel CO2 emissions closely mirror those of IS92a, the scenarios differ in their details. For example, Case 1 uses more conventional gas and less conventional oil than IS92a. Similarly, Case 1 includes more coal use after the year 2025 than in IS92a. Other differences in the fuel mix are less noteworthy.

The geographic distribution of primary energy consumption differs by region. While there is relatively close agreement between Case 1 and IS92a for the OECD region, there is significantly more energy use in Case 1 for China than for IS92a. On the other hand, there is significantly less energy use in Case 1 for other developing nations than in IS92a. Thus, as presently configured, the aggregate measures of energy and CO2 emissions in Case 1 are in good overall agreement with IS92a, but real differences exist regarding the details of the case.

In Case 1 the energy system grows from its 1990 level of approximately 340 EJ/year to approximately 1380 EJ/year. Conventional oil and gas production peaks in the year 2035, and declines thereafter. Coal production grows steadily from approximately 95 EJ/ year in 1990 to more than 790 EJ/year in 2095. Both biomass and solar electric(a) technologies show significant growth. By the year 2095 they provide 250 and 173 EJ/year respectively .

The regional distribution of fossil fuel CO2 emissions changes greatly over the period of analysis. OECD regional emissions peak in the year 2050 and decline slightly thereafter. (They rise slightly in IS92a.) Emissions in the former Soviet Union and Eastern Europe grow, but only slowly after the year 2050. Chinese robust economic growth fuels rapid emissions growth. By the year 2095, the Chinese economy has grown by a factor of more than 50, and it represents half of global fossil fuel carbon emissions in Case 1. (They rise by only half this amount in IS92a.)

IV. FIVE ADVANCED ENERGY SCENARIOS

Defining Five Advanced Energy Technology Cases

To examine the potential of advanced energy technologies to reduce greenhouse related emissions, we have constructed five alternative scenarios each of which adopts the same assumptions as those used in the reference scenario (closely following IS92a), with the exception of the assumed wide availability of additional energy supply and transformation technologies. While these technologies are assumed to be widely available, they must compete in the marketplace against other fuel prod uction technologies, including both conventional and other advanced technologies.

Case 1. Reference Case (IS92a (ERB)): Developed to be consistent with IS92a.

Case 2. Advanced Fossil Fuel Technologies (Adv FF): The efficiency of fossil fuel powered electric-generation technologies are assumed to reach 66 percent by the year 2095. Other assumptions are the same as for Case 1.

Case 3. Advanced Liquefied Hydrogen Fuel Cells (Adv FF, LH2): Hydrogen fuel cells are used to power transportation. Hydrogen is available from natural gas, biomass and electrolysis at the following costs:

Natural Gas Steam Reforming: $1.71 + Pgas/0.901 Biomass BCL Gasifier: $4.83 + Pbiomass/0.784 Electrolysis: $2.36 + Pelec/0.900

Costs from Williams 1994a .

Hydrogen is assumed to be liquefied and used in fuel cells on board vehicles. Associated costs are as follows:

Liquefaction: $3.90/ GJ; Fuel Storage: $1.16/ GJ; Fuel Cell lifecycle capital cost: $138/GJ.M.

The cost of liquefying, transporting, and operating fuel cells from Rogner.(b)

The cost of Solar/wind power reaches a busbar cost of $0.04/kWh by 2020 and the cost decreases at 0.5 percent/year thereafter, Williams 1994b.

Other assumptions are the same as for Case 2.

Case 4. Advanced Hydrogen Fuel Cells (Adv FF, H2): Assumptions are the same as in Case 3, except that there are no costs of liquefying hydrogen, and no additional costs for vehicles or infrastructure.

Case 5. Low Cost Biomass (Low Bio Cost): Same as Case 4 except that biomass costs are reduced. By the year 2020, 20 percent of the biomass resource is available at $1.40/EJ, and 80 percent is available at $2.40/EJ, Williams (1994a).

Case 6. Accelerated Rate of Exogenous End-use Energy Intensity Improvement (3E21): Same as Case 5 except that it assumes that the exogenous end-use energy intensity improvement rate reaches 2.0 percent/year by 2050.

Click for Table 2.

The Effects of Advanced Energy Technologies on Energy Use and Emissions

Figure 5 and Figure 6 show emissions and total primary energy trajectories associated with each of the six cases outlined above. Both energy and emissions are reduced by the introduction of a dvanced fossil fuel combustion technologies, Case 2. Interestingly, the effect of these technologies on total primary energy use is greater than the effect on fossil fuel carton emissions. The differential effect, of between two and five percent, is due at least in part to the "take back" effect of lower electricity prices associated with the advanced energy technologies. Note that this effect is of second order, modifying emissions reductions. but never reversing the direction of the change in emissions reductions.

The introduction of advanced transportation technologies, Cases 3 and 4, significantly reduced fossil fuel C02 emissions. Reductions become increasingly important after the year 2005. While the lower cost, non-cryogenic hydrogen fuel cell technology has lower emissions in addition to lower cost, the incremental impact on emissions is not as great as that obtained from the introduction of hydrogen fuel cell vehicles; that is the difference between Case 2 and Case 3. The effect of advanced transportation technologies on energy is less pronounced than on fossil fuel carbon emissions. For example, cryogenic hydrogen technology reduces fossil fuel carbon emissions by more than 25 percent on the year 2095, yet total primary energy use increases by only 1 percent. The non-cryogenic technology case, Case 4, lowers energy use, but never by more than 10 percent relative to the advanced fossil fuel case, Case 2. This contrasts sharply with the 20 to 30 percent reductions in fossil fuel carbon emissions between Cases 2 and 4.

The most important feature of Case 5 is the introduction of significantly lower biomass energy costs. The appearance of this technology has a moderate impact on global primary energy use, reducing it 10 percent in the year 2095 relative to Case 4, though there is no noticeable effect prior to 2060. The effect on global fossil fuel emissions is dramatic, however. The low cost biomass fuel quickly drives coal from the market shortly after the turn of the next century. Contrast Figures 3 and 7 which show Cases 1 and 5 respectively.

Case 6 differs from Case 5 only in the fact that the rate of exogenous energy intensity improvement has been raised to 2 percent/year. From the perspective of fossil fuel CO2 emissions, there is some additional reduction in fossil fuel emissions over Case 5, but that difference is trivial by the year 2095. The effect on energy use is dramatically different. The impact of the increased rate of exogenous end-use energy intensity improvement is to lower energy use by 44 percent by the year 2095 relative to C ase 5. As a consequence, biomass energy production declines from 420 EJ in the year 2095 in Case 5 to 250 EJ in Case 6.

Click for Figure 7.

Comparison to Results of the LEESS Scenario

The low CO2 Emitting Energy Supply Systems (LEESS) scenario developed for the IPCC WGII (Johansson et. al., 1993) uses the same set of assumptions about the costs of advanced energy technologies, as those adopted in Case 6. The LEESS is a "bottom-up" scenario based on engineering estimates of the future costs of advanced low-carbon energy supply, while the results developed here employ a classical "top-down" model. Nevertheless, both scenarios give similar results.

Figures 8 and 9 compare the LEESS scenario to Cases 1-6 with respect to global fossil fuel carbon emissions and energy production. From Figure 8, the LEESS emissions are not substantially different from Cases 5 and 6. In Figure 9, global energy supply in the LEESS scenario is less than Case 5: however, Case 6 actually has a slightly lower energy supply total than LEESS by the end of the next century. These figures demonstrate that the "top-down" and "bottom-up" approaches do not produce inconsistent results when given common assumptions. This suggests that differences in results between the two approaches are not primarily due to methodological differences but instead caused by differences in assumptions about the economic costs and availability of technologies.

The Value of Advanced Energy Technologies in Stabilizing Future Fossil Fuel CO2 Emissions

One way to consider the climate-related value of advanced energy technologies is to examine their effect on the cost of achieving some climate-related goal. We have arbitrarily chosen as a reference goal the stabilization of fossil fuel CO2 emissions as a target. The tax rate (assumed to be applied globally), or marginal cost of stabilizing fossil fuel carbon emissions for Case 1, is shown in Figure 10. These rates are equivalent to the market values of tradable permits.

Costs of stabilizing fossil fuel CO2 emissions are computed using the consumer plus producer surplus method described in Edmonds and Barns (1992). Note that costs reflect only those associated with fossil fuel carbon emissions reductions, and make no attempt to include value for other greenhouse related gaseous emissions. The cost of global emissions reductions grow from approximately $35 billion per year in 2005 to $1387 billion per year in 2095. At the same, time global GNP grows from an estimate $20 t rillion in 1990 to $230 trillion in 2095. To compute a present value, we have discounted costs at 5 percent per year. Summing discounted costs and comparing these to the sum of present discounted GNP yields a present discounted burden of 0.22 percent for Case 1.

The incremental value of adding each bundle of technologies to Case 1 is given in Table 3. The total value of the technologies increases steadily, indicating that each reduces emissions. The second column is computed by looking at the difference between individual cases and can be interpreted as the incremental value in terms of stabilizing emissions of the next component to the bundle. It is important not to make too much of this number, because component values are not independent of the order of computation. That is, the incremental value of advanced fossil fuel technologies might be very different if it were the last item added to the bundle rather than the first. And one would expect that diminishing returns to addi ng emissions reducing technologies would apply in this case.

Quite clearly, great value is gained by adding low cost biomass to the bundle. Even with advanced fossil fuels, low cost solar electric power, and low cost fuel cell vehicles, the present discounted value of adding this technology is still almost half a trillion dollars. The present discounted value of the advanced energy technologies embodied in Cases 1-5 is $1.8 trillion.

Implications for Atmospheric Composition

The ERB provides information with regard to fossil fuel CO24, and N2O emissions. These emissions represent only a partial set of anthropogenic fluxes of these gases, not the complete array of greenhouse gases. Case 1 emissions rates for all gases are consistent with those of IS92a. For Cases 2 through 6, emissions streams for the IS92a scenario have been modified by taking the change in emissions relative to Case 1 for CH4 and N2O and applying these to the global streams. Land-use change emissions are assumed unchanged, as are emissions rates for chlorofluorocarbons (CFCs) and CFC substitutes. Emissions rates for SO2 are modified in proportion to changes in the sum coal and oil emissions.

Cumulative emissions for the fossil fuel CO2 are shown in Figure 11. These amount to 1300 PgC between 1990 and 2095 in Case 1 and 1000 PgC in Case 4, but decline sharply to a bit more than 400 PgC in Case 5. It is interest ing that while annual emissions vary by a factor of almost 40 between Cases 1 and 5 (or 6) in the year 2095, cumulative emissions to that date vary by less than a factor of 4.

Even less variation occurs in atmospheric concentration, as shown in Figure 12. Concentrations of atmospheric CO2 stabilize below 450 ppmv in both cases, while they rise above 700 ppmv in Case 1, and exceed 550 ppmv in Case 4.

Implications for Climate Change and Sea Level

Results for mean global temperature change were surprising. A significant cooling effect, related to emissions of SO2, is embedded in the IS92a case. The introduction of advanced energy technologies leads to reductions in SO2 emissions and consequently to a reduced cooling effect. This in turn leads to the ironic result that Case 5 has higher mean global temperature before the year 2050 than Case 1, Figure 13. Before the year 2050, there is very little difference between the other cases. And in the year 2050, all cases' temperature change, relative to 1990, lie within the range 1.0 deg.C to 1.2 deg.C. Beyond the year 2050, temperature scenarios diverge sharply.

Figure 14 shows the corresponding change in sea level. There is great inertia in the ocean-glacier system. The difference in sea level rise varies within only approximately 2 cm before the year 2050, though the cases diverge by 13 cm by the year 2095, and that divergence is widening.

V. CONCLUSIONS

We report results from a "top down" energy-economy model employing "bottom up" assumptions and embedded in an integrated assessment framework, the GCAM. The analysis shows that from the perspective of long-term energy system development, differences in results from the "top down" and "bottom up" research communities would appear to be more closely linked to differences in assumptions regarding the economic cost associated with advanced technologies than to differences in modeling approach.

The adoption of assumptions regarding advanced energy technologies were shown to have a profound effect on the future rate of anthropogenic climate change. The cumulative effect of the five sets of advanced energy technologies is to reduce annual emissions from fossil fuel use to levels which stabilize atmospheric concentrations below 550 ppmv, the point at which atmospheric concentrations are double those that existed in the middle of the eighteenth century.

This work has led us to the conclusion that differences in the long term results obtained by "top-down" and "bottom-up" modelers can be attributed largely to differences in assumptions. Methodology plays a less important role in explaining differences. The single most important question is whether or not the advanced energy supply technologies described in Cases 2 through 6 will be available in the quantities and at the costs assumed in this study.

While all energy technologies introduced in Cases 2 through 6 play roles in reducing future fossil fuel CO2 emissions, the introduction of advanced biomass energy production technology plays a critical role. If biomass energy can be made available at $2.4 0/GJ or less in quantities sufficient to make it the core energy supply technology in the middle of the next century, then emissions can be cut dramatically relative to Case 1. The problem of emissions reduction becomes one of technology development and d eployment in this case, and not one of fiscal and regulatory intervention.

Because SO2 has a cooling effect on the atmosphere, policies which reduce fossil fuel use are not as effective as a simple greenhouse calculation might imply. The proper treatment of SO2 is therefore an important consideration in the analysis of climate consequences of technology development and deployment.

(a) Solar electricity is a general category which includes technologies all non-carbon emitting electricity technologies other than nuclear, hydro, and biomass. Thus fusion, wind, geothermal, and OTEC are included in addition to photovoltaic and power tower technologies.

(b) Personal communication. 1994.

VI. REFERENCES

Edmonds, J. and Barns, D.W. 1990. Estimating the Marginal Cost of Reducing Global Fossil Fuel CO2 Emissions: PNL-SA-18361, Pacific Northwest Laboratory, Richland, Washington.

Edmonds, J. and Barns, D.W. 1992. "Factors Affecting the Long-term Cost of Global Fossil Fuel CO2 Emissions Reductions," International Journal of Global Energy Issues,4(3): 140-166.

Edmonds, J. and Reilly, J. 1985. Global Energy: Assessing the Future, Oxford University Press, New York.

Edmonds. J.A., Reilly, J.M., Gardner, R.H., and Brenkert, A. 1986. Uncertainty in Future Global Energy Use and Fossil Fuel C02 Emissions 1975 to 2075. TR036, DO3/NBB-0081 Dist. Category UC-11, National Technical Information Service, U.S. Department of Commerce. Springfield Virginia 22161.

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Sources

Parson, E.A. and K. Fisher-Vanden, Searching for Integrated Assessment: A Preliminary Investigation of Methods, Models, and Projects in the Integrated Assessment of Global Climatic Change. Consortium for International Earth Science Information Network (CIESIN). University Center, Mich. 1995.

 

Suggested Citation

Consortium for International Earth Science Information Network (CIESIN). 1995. Thematic Guide to Integrated Assessment Modeling of Climate Change [online]. University Center, Mich.
CIESIN URL: http://sedac.ciesin.org/mva/iamcc.tg/TGHP.html

 

 

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