WBGUWISSENSCHAFTLICHER BEIRAT DER BUNDESREGIERUNGGLOBALE UMWELTVERÄNDERUNGEN

materialien
Prof. Nebosja Nakicenovic und
Dr. Keywan Riahi:
Model Runs With MESSAGE in theContext of the Further Development of the
Kyoto-Protocol

Externe Expertise für das WBGU-Sondergutachten"Welt im Wandel: Über Kioto hinausdenken.
Klimaschutzstrategien für das 21. Jahrhundert"
Berlin 2003

Externe Expertise für das WBGU-Sondergutachten"Welt im Wandel: Über Kioto hinausdenken. Klimaschutzstrategien für das 21. Jahrhundert"
Berlin: WBGUISBN 3-936191-03-4
Verfügbar als Volltext im Internet unter http://www.wbgu.de/wbgu_sn2003.html
Autor: Prof. Nebosja Nakicenovic, Dr. Keywan Riahi
Titel: Model Runs With MESSAGE in the Context of the Further Development of the Kyoto-Protocol
Laxenburg, Österreich 2003
Veröffentlicht als Volltext im Internet unter http://www.wbgu.de/wbgu_sn2003_ex03.pdf

Wissenschaftlicher Beirat der Bundesregierung Globale Umweltveränderungen
Geschäftsstelle
Reichpietschufer 60–62, 8. OG.
10785 Berlin
Telefon (030) 263948 0
Fax (030) 263948 50
E-Mail wbgu@wbgu.de
Internet http://www.wbgu.de
Alle WBGU-Gutachten können von der Internetwebsite http://www.wbgu.de in deutscher und englischer Sprache herunter geladen werden.

© 2003, WBGU

Model runs with MESSAGE in thecontext of the further developmentof the Kyoto-Protocol

FINAL REPORT

submitted to theSecretariat of theGerman Advisory Council on Global Change
Contract Nr. WBGU II/2003

Principal Investigators

Prof. Nebojsa Nakicenovic and Dr. Keywan Riahi
IIASA Contract No. 03-116

August 25, 2003

International Institute forApplied Systems Analysis
Schlossplatz 1
A-2361 Laxenburg, Austria

Tel: +43 2236 807 0
Fax: +43 2236 71313
E-mail: info@iiasa.ac.at
Web: www.iiasa.ac.atModel runs with MESSAGE in thecontext of the further developmentof the Kyoto-Protocol

FINAL REPORT

submitted to theSecretariat of theGerman Advisory Council on Global Change
Contract Nr. WBGU II/2003

Principal Investigators

Prof. Nebojsa Nakicenovic and Dr. Keywan Riahi
IIASA Contract No. 03-116

August 25, 2003

This paper reports on work of the International Institute for Applied Systems Analysis and has received
only limited review. Views or opinions expressed in this report do not necessarily represent those of theInstitute, its National Member Organizations, or other organizations sponsoring the work.

1

Introduction

The objective of this study is to analyze the feasibility and the technologic andeconomic implications of scenarios that fulfill the stated objectives of the Article 2 of
the United Nations Framework Convention on Climate Change (UNFCCC, 1992),
namely to lead to a stabilization of atmospheric concentrations of greenhouse gases(GHGs), as exemplified by the main anthropogenic greenhouse gas – CO2.
The stabilization levels are to be achieved by 2100 in this study and are very
ambitious and stringent (400 to 450 ppmv). They embrace a precautionary principle
approach at the lower bounds of atmospheric stabilization levels, assumed1 to beconsistent with the UNFCCC language of a “not dangerous anthropogenic
interference with the climate system”. These “climate stabilization” scenarios are
imposed on a number of “background” scenarios of overall demographic, economic,
and technologic development drawing on the Special Report on Emissions Scenarios(SRES, Nakicenovic et al., 2000) by the Intergovernmental Panel on Climate Change(IPCC), that assessed the uncertainties on future GHGs in absence of climate policies.
Altogether three SRES background scenarios are further analyzed in this study:
SRES-A1, SRES-B1, and SRES-B2. Scenarios A1 and B1 embrace a “sustainable
development” paradigm, with the SRES-A1 scenario focusing of the economic andsocial dimensions (income growth and disparity reduction) and the SRES-B1 scenario
focusing in addition also on the environmental dimension (resource conservation andcontrol of traditional pollutants with exception of GHGs) of the “three pillars” (social,economic, environmental) of sustainable development. The more intermediate,“dynamics as usual” scenario SRES-B2 is also analyzed as a means of comparison –even if its assumed stringent climate stabilization target (400 ppmv atmosphericconcentration of CO2) may not necessarily be consistent with the more cautious
geopolitical, economic and technologic outlook described in the SRES-B2
background scenario storyline.
Compared to the IPCC SRES report (reporting on so-called climate non-intervention
scenarios) and the IPCC Third Assessment Report (TAR) that analyzed various levelsof CO2 stabilization imposed on the SRES background scenarios, the present
scenarios differ in a number of aspects.
First of all, only three SRES background scenarios are analyzed in this study,
reflecting the interest of the study sponsor (WBGU) as well as time and financial
constraints for the analysis. In contrast, IPCC recommended that at least six so-called
illustrative SRES scenarios be used in the assessments of climate change as they spanmuch of the uncertainties in emissions and their underlying driving forces.
Second, both the background as well as the stabilization scenarios differ to both the
IPCC SRES and TAR scenarios with respect to a number of additional constraints
imposed on deployment of zero-carbon options (nuclear, biomass, hydropower, andcarbon sequestration), again reflecting the interest of the study sponsor (andhenceforth referred to as “WBGU constraints”).
Third, the scenarios differ (slightly) from those presented in the IPCC SRES and TAR
in terms of continued model improvement such as a different calibration of the year
1

This assumption is to a degree arbitrary as conclusive scientific evidence is lacking of what could constitute alevel of “dangerous interference with the climate system” due to persistent uncertainties on climate sensitivity andon the impacts (market and non-market) of any given level of realized global warming.

1

2000 values for which (contrary to SRES and TAR) actual energy and GHG
emissions statistics are now available as well a full reflection of the current outlook on
the implementation of the UNFCCC Kyoto Protocol in case of the climate
stabilization scenarios.
Finally, the scenarios reported in this study are characterized by a number of specificmethodological features:First, and most importantly, all of them embrace a social planner, inter-temporal
optimization framework, reflecting current state-of-art in climate policy modelingconsistent with IPCC SRES and TAR methodology.
Second, in case of climate stabilization, the scenarios assume (again consistent withprevailing economic theory) a strict separation of the economic issues of equity andefficiency. Thus, the issue of allocation emission rights is separated from the issue of
economic efficiency in achieving prescribed emissions reduction profiles, leading toatmospheric CO2 stabilization. In other words, the scenarios assume international
agreement on ultimate climate stabilization goals (and hence on cumulative carbon
emissions) as well as on the allocation of resulting GHG emission entitlements (where
two variants of a model suggested by the study sponsor WBGU labeled “contraction
and convergence” of per capita emission entitlements are analyzed here), whereas theamount of actual emission reduction is assumed to operate under the criterion of
economic efficiency (global cost minimization, or rather international marginal
abatement cost equalization), assuming the existence of a perfect global market of
tradable emission permits.
Third, a distinguishing (and pioneering) feature of the scenario methodology
developed at IIASA is the coupling of both “top-down” (macroeconomic) and“bottom-up” (engineering) perspectives of global optimization models addressing
climate change mitigation policies. Thus, the scenarios presented in this study, nolonger suffer from the customary dichotomy and discrepancy in the interpretation ofclimate policies between macro-economic and engineering modeling approaches.
These methodological issues need to be borne in mind when interpreting the study
results in-as-far as the triple postulates of a global social planner, cost minimization
under existence of an agreement on emission entitlements as well as of perfectly
functioning markets in emission permits trade result in a rather optimistic outlook on
feasibility (and costs) of climate stabilization scenarios, compounded by the fact thatthe “sustainable development” base case scenarios (with exception of the SRES B2
scenario) on which these climate stabilization scenarios are imposed already portray
an optimistic baseline projection of availability and costs of environmentally benigntechnology, easing subsequently the achievement of ambitious climate stabilization
targets.
The plan for the remainder of the study report is as follows. After the introductory
Chapter 1, the methodology underlying the present scenario study is described in
more detail in Chapter 2, presenting both an overview of the IPCC SRES background
scenarios as well as of the IIASA modeling framework used in this study. Chapter 3
presents more detail on the assumptions underlying the three IPCC SRES background
scenarios that serve as baselines for the subsequent analysis of climate stabilization
targets. Critical input assumptions in terms of demographic, economic, andtechnological development, as well as in terms of constraints on resource availability
and additional constraints on the availability of zero-carbon options that differ fromthe IPCC SRES and TAR reports (“WBGU constraints”) are outlined. Chapter 42presents the climate stabilization scenarios in more detail, outlining the various
atmospheric CO2 concentration targets assumed as well as the regional allocation
criteria for emission entitlements suggested by the study sponsor WBGU. The
Chapter continues with an analysis of the different emissions and climate change
implications of the scenarios as well of the magnitude and type of emission reduction
measures suggested by the different scenarios modeled, including issues ofinternational trade in carbon emission permits and an assessment of the costs and themacroeconomic impacts of emission reductions and trade of the climate stabilization
scenarios compared with the (unconstrained) modified IPCC SRES background
scenarios. Finally, Chapter 5 concludes, highlighting in particular robust findings
from the analysis performed here as well as important limitations embedded in the
study design and methodology deployed.

2

Methodology

2.1

IPCC Emission Scenarios and the SRES Process

There are more than 500 global emissions scenarios in the literature (Morita and Lee,1998). They are the main tools for assessing future anthropogenic climate change,
possible impacts on human- and ecosystems, and alternative response strategies andpolicies such as mitigation and adaptation. It is for these reasons that emissions
scenarios constitute an important component of the IPCC assessments. The first set ofthree emissions scenarios was developed by the IPCC in 1990 (Houghton et al., 1990)
and the second set of six in 1992 (Leggett et al., 1992; Pepper et al., 1992). The main
purpose of the 1990 scenarios was to serve as input for climate models. The second setof six so-called IS92 scenarios were developed by an integrated model and were
published two years later. They covered a wide range of main driving forces andemissions outcomes. The IS92 scenarios and especially the central variant IS92a wereamong the most widely used in the literature and have been reproduced by many of
the global energy and emissions models.
In 1994 the IPCC formally evaluated the 1992 scenario set (Alcamo et al., 1995) and, in
1996, based on this review and its findings, it initiated the effort that resulted in a new
set of 40 scenarios by six different modeling groups published as IPCC Special Reporton Emissions Scenarios (SRES, Nakicenovic et al., 2000). This new set of emissions
scenarios was developed for use in future IPCC assessments and by wider scientificand policymaking communities. During Third Assessment Report (TAR, 2001) of the
IPCC 80 so-called Post-SRES CO2 stabilization scenarios were developed by ninemodeling groups based on 40 SRES baseline scenarios. The Post-SRES scenarios
stabilize CO2 concentrations at various levels ranging from 450 to 750 ppmv by around2150, half a century later than assumed in this study.
SRES scenarios span 5th to 95th percentile of most important driving forces and GHG
emissions ranges from 1990 to 2100 of some 500 scenarios in the literature (that wereassembled into a unique database as part of the SRES scenario literature review, see
Morita and Lee, 1998). SRES scenarios are based on four narrative storylines
developed by the writing team based on the extensive review of quantitative anddescriptive scenarios in the literature. Climate change policies were not considered inany of the SRES scenarios as specified by the SRES terms of reference while CO2mitigation policies and measures were included explicitly in the Post-SRES CO2stabilization scenarios. The four SRES storylines were quantified by six alternative3

integrated assessment models (IAM) resulting in 40 SRES reference scenarios and bynine IAMs resulting in 80 Post-SRES CO2-stabilization scenarios. The scenarios arereported for four “macro” world regions but individual models provide more detailed
information at spatial resolutions of dozen and more world regions.
The emissions profiles of the SRES and Post-SRES scenarios have provided inputs
for GCMs and simplified models of climate change. They contain information, suchas the level of economic activities, rates of technological change, and demographic
developments in different world regions, required to assess climate-change impacts
and vulnerabilities, adaptation strategies and policies. The same kind of information,in conjunction with emissions trajectories, can serve as a benchmark for theevaluation of alternative mitigation measures and policies. Post-SRES scenarios
provide information on the mitigation efforts necessary to stabilize CO2 atmosphericconcentrations at alternative levels. Finally, the SRES scenarios can provide acommon basis and an integrative element across the three working groups for the
IPCC Fourth Assessment Report (AR4). In this study three of the 40 SRES scenarios(SRES-A1, SRES-B1, and SRES-B2) are used a background scenarios for achieving
CO2 atmospheric concentrations stabilization at a very low levels of 400 to 450 ppmv
through a very limited and restricted number of mitigation measures and options.

2.2

Modeling Framework

The principal models and data sets used to develop the scenario projections for the
IPCC SRES and TAR are shown in Figure 2.1. They are the Scenario Generator(Nakicenovic et a1., 1998a), the bottom-up systems engineering model MESSAGE IV(Messner and Strubegger, 1995), the top-down macroeconomic model MACRO(Messner and Schrattenholzer, 2000), the climate impact model MAGICC (Wigley
and Raper, 1997 and 2002; Hulme et al., 2000), and several databases, mostimportantly the energy technology database CO2DB (Strubegger et al., 1999). Each is
described in turn. For further details on the modeling framework see Riahi and Roehrl(2000a,b).
For the purpose of this study and the development scenarios presented in Chapter 3
and 4, a subset of the above models was used. Specifically, MESSAGE was adapted
for the estimation of detailed regional energy system development paths consistent
with the specifications and constraints defined by the WBGU. In addition, themacroeconomic model MACRO was applied to assess the economic impact and priceinduced changes of energy demand due to carbon abatement policies. Climate
indicators, such as the scenario’s atmospheric CO2 concentrations, temperaturechange, and sea level rise, were calculated with the newest version of the MAGICC
model (version 3.0) using an updated parametrization to derive consistent climate
projections with the IPCC TAR (2001). The models are used in an iterative fashion,
which permits the endogenization of internally consistent energy-economic-climate
indicators from a macroecomomic and energy systems perspective.
We shall now describe each of the models.

4

Storyline•Economic Development•Demographic Projections•Technological Change•Environmental Policies•Energy Intensity

Take directy from AIM Model(Morita et al.) emulation runs:•Landuse change data
•Landuse related CO2•Non-energy related CH4 and N2OSCENARIO
GENERATOR
Economic and Energy
Development Model

MESSAGE IV - MACRO
Energy Systems Engineering andMacroeconomic Energy Model

Spreadsheet Emissions
Model

MAGICC
Model for the Assessmentof GHG Induced Climate
ChangeCalculate additional non-energy
related emissions

Common Databases
Energy, Economy, Resources
Technology Inventory CO2DB,EDGAR Database (emissions
factors)

Figure 2.1: The IIASA modeling framework used for the IIASA-SRES scenarios, including the
Scenario Generator, MESSAGE IV, MACRO, and associated databases. The climate impact
model MAGICC was used in addition to calculate GHG concentrations and changes inradiative forcing, global temperature, and sea level rise.

2.2.1

The Scenario Generator

The Scenario Generator (SG, Nakicenovic et al., 1998a, and 1998b) is a simulation
model to help formulate scenarios of economic and energy development for elevenworld regions analyzed by MESSAGE IV. Its main objective is to allow the scenario
formulation and documentation of key scenario assumptions, and to provide common,
consistent input data for MESSAGE IV and MACRO.
Within the SG there are, first, consistent sets of economic and energy data for the baseyear 1990 and 2000, plus time series of such data for prior years. Second, the SGcontains a set of regression equations estimated using the economic and energy data
sets. These equations represent key relationships between economic and energy
development, based on empirical data, that can be used selectively in formulating
particular scenarios. To allow adjustments for different storylines and variants, allimportant variables are formulated so that a user can overwrite the values suggestedby the equations of the SG.
Inputs to the SG are future population trajectories for eleven world regions used by
MESSAGE IV plus key parameters determining regional per capita GDP growth. The
SG first calculates growth rates of total GDP for each world region. Second, it
calculates total final energy trajectories for each region by combining the populationand per capita GDP growth trajectories with final energy intensity profiles based on
the SG’s set of empirically derived equations. The resulting final energy demands are5then disaggregated, again based on combining regional per capita income growth with
the SG’s set of empirically derived equations, into the six demand sectors used by
MESSAGE IV and listed below. In the list, “specific” energy demands are those that
require electricity (or its substitutes such as, in the long term, hydrogen). “Nonspecific” energy demands are mainly thermal requirements that can be fulfilled by any
energy form.
•
•
•
•
•
•

industrial specificindustrial non-specificresidential/commercial specificresidential/commercial non-specifictransportation
non-commercial (e.g, fuelwood)

2.2.2

The Systems Engineering Model MESSAGE IV

MESSAGE (Model for Energy Supply Strategy Alternatives and their General
Environmental Impact) is a systems-engineering optimization model used for
medium- to long-term energy system planning, energy policy analysis, and scenario
development (Messner and Strubegger, 1995). The model provides a framework forrepresenting an energy system with all its interdependencies from resource extraction,
imports and exports, conversion, transport, and distribution, to the provision of energy
end-use services such as light, space conditioning, industrial production processes,
and transportation. The model’s current version, MESSAGE IV, provides informationon the utilization of domestic resources, energy imports and exports and trade-relatedmonetary flows, investment requirements, the types of production or conversion
technologies selected (technology substitution), pollutant emissions, inter-fuelsubstitution processes, as well as temporal trajectories for primary, secondary, final,
and useful energy.
The degree of technological detail in the representation of an energy system is flexible
and depends on the geographical and temporal scope of the problem being analyzed.
A typical model application is constructed by specifying performance characteristics
of a set of technologies and defining a Reference Energy System (RES) that includes
all the possible energy chains that the model can make use of. In the course of amodel run MESSAGE IV will then determine how much of the available technologies
and resources are actually used to satisfy a particular end-use demand, subject tovarious constraints, while minimizing total discounted energy system costs. An
illustration of the MESSAGE Reference Energy System is given in Figure 2.2.

6

mining

crude
oil
nat. gas

extraction
extraction

coal(H2FC)

oil
gas

use
use
use
use

resid.oillight oil

coal_ppl
IPCC
coal FCoil pplelectr.

bio stc
bio gtc
nuclearhydrowindgeothrm

importsolar thsolar pv

electricity
coal_gas

INDUSTRY
specific
furnaceboilerfeedstocks

RESIDENTIAL/
COMMERCIAL(H2FC)

gas std
gcc
gas fcgas

DEMANDRENEW-WC
RENEW-0C
COAL
RESID. OIL
LIGHT OILGASELECTRICITYHEATHYDROGENHYDROGENHEAT

meth_bioC
meth_bio0C

coal
oil

light oil

ELECTRICITY

meth_ngrenewables withcarbon emissions(renew_WC)(renew_0C)
renewables withoutcarbon emissions

residual oil

RESID. OIL
LIGHT OIL

refinery
ref_adv
syn_ligexportminingligniteFINALtransport & distribution

hard
coal

SECONDARYGASPRIMARY
RENEW-WC
RENEW-0C
COAL
OIL

RESOURCE

coal_hpl
oil_hplgas-hplbioC_hpl
po_turb.

use

specificuse
use

thermalrural

TRANSPORT(H2FC)

use
use
use

air
road
rail/water/
pipeliquefaction

electrolysis
steam-refblending

REFERENCE ENERGY SYSTEM,
1990 and 2020
1990

2020

h:\tnt2000\kewan-lucytr-tx13n.dsf

Figure 2.2: Schematic diagram of the basic energy system structure in the MESSAGE model.

2.2.3

The Macroeconomic Model MACRO

MACRO is a top-down macroeconomic model (Manne and Richels, 1992, Messnerand Schrattenholzer, 2000). Its objective function is the total discounted utility of asingle representative producer-consumer. The maximization of this utility function
determines a sequence of optimal savings, investment, and consumption decisions. Inturn, savings and investment determine the capital stock. The capital stock, available
labor, and energy inputs determine the total output of an economy according to anested constant elasticity of substitution (CES) production function. Energy demandin two categories (electricity and non-electric energy) is determined within the model,
consistent with the development of energy prices and the energy intensity of GDP.
The main determinants of energy demand are the reference GDP growth input into themodel and the development of the overall energy intensity of GDP. Energy supply isrepresented by two quadratic cost functions, one for each of MACRO’s two demand
categories, and is determined so as to minimize costs. MACRO’s outputs include
internally consistent projections of world and regional realized GDP (i.e., taking into
account the feedback that changing energy and other costs have on economic growth)
including the disaggregation of total production into macroeconomic investment,
overall consumption, and energy costs.
2.2.4

Iterating MESSAGE with MACRO

Linking MACRO with MESSAGE permits the estimation of internally consistentprojections of energy prices and energy systems costs – derived from a detailed
systems engineering model (MESSAGE) – with economic growth and energy demand
projections obtained from a macroeconomic model (MACRO). The scenario results
7

are calculated in an iterative fashion (see Figure 2.3). As the initial step MESSAGE
calculates energy prices and system costs for a given energy demand, and passes thisinformation to MACRO. MACRO then calculates the optimal allocation of the
production factors (capital, labor and energy) and the imputed effect on GDP andenergy demand. The corrected demand from MACRO is returned to MESSAGE,
which initiates again the calculation of the energy prices and system costs. This
iteration ends as soon as convergence between the two models is achieved.
This approach is particularly important in the case of policy scenarios that assume
trading of emissions permits, since the associated monetary transfers have asignificant impact on the regional economic development. In the WBGU scenarios
this effect is taken into account by adding the costs from carbon trading to the energy
systems costs of MESSAGE.2 MACRO uses the corrected systems costs as an input
and calculates the implied effect on GDP and the total economic production byadjusting the optimal allocation of the production factors (capital, labor, and energy).
As a result scenarios are obtained, where the prices of energy and carbon as well as
the price-induced changes of GDP and energy demand are endogenized and internally
fully consistent.

Scenario Generator

Reference final
energy demand

Reference GDP
Energy intensities

Conversion

Useful energy
demandConversion
Conversion
Useful energy
demandFinal energy
demandMACRO

MESSAGE
Final energy shadow prices
Final energy demand

Cost functions

Total energy system cost
Conversion

Figure 2.3: Schematic illustration of MESSAGE-MACRO iterations.
2.2.5

The Climate Change Model MAGICC

To estimate aggregate climate impacts of the scenarios Version 3.0 of the climatechange model MAGICC (Model to Assess Greenhouse-gas Induced Climate Change:
Wigley and Raper, 2002) was used. MAGICC includes a carbon cycle model that
relates atmospheric inputs (emissions) and outputs (physical and chemical sinkprocesses) to changes in the atmospheric carbon concentration. It uses carbon dioxide(CO2), methane (CH4), sulfur dioxide (SO2), and nitrogen oxide (NOx) energy-related
emissions from MESSAGE together with emission profiles for other greenhouse2

In the case a region gains revenues from carbon trading, they are subtracted from the respective energy systems
costs, reducing thus energy costs, rising regional GDP growth.

8

gases and non-energy related activities as described in SRES.3 The model estimatesnet carbon flows and atmospheric CO2 concentrations, changes in radiative forcing
and temperature relative to 1990, and sea level rise.

3

Baseline Scenarios

3.1

Introduction to IPCC SRES Baseline Scenarios

The three GHG stabilization scenarios presented in this study are based on the SRES
background scenarios. This section reviews the SRES baseline scenarios. SRES
background or “reference” scenarios provide 40 GHG emissions baselines based on
different future worldviews. During the IPCC Third Assessment Report (TAR), SRES
scenarios served as background scenarios to develop the 80 Post-SRES stabilization
scenarios. The approach in this study is similar in the sense that three of the 40 SRES
scenarios provide the background information for developing the GHG stabilization
scenarios for the IPCC TAR. (The main difference to this study is that the TAR
scenarios stabilize CO2 concentrations at levels ranging from 450 to 750 ppmv byaround 2150, whereas in this study, the lowest range of 400 to 450 ppmv is chosen for
all GHGs in conjunction with half-a-century earlier stabilization of concentrations.)
The basic approach of the SRES writing team was to construct scenarios that wereboth qualitative and quantitative (SRES, Nakicenovic et al., 2000). The process
involved first the formulation of the qualitative scenario characteristics in the form offour narrative storylines and then their quantification by six different modelling
approaches. The qualitative description gives background information about the
global setting of the scenarios, which can be used, like in this study, to assess thecapability of society to adapt to and mitigate climate change, or for linking theemission scenarios with sustainability and equity issues. The quantitative descriptionof emission scenarios can be used as input to models for computing the future extent
of climate change, and for assessing strategies to reduce emissions. Again, they areuse in the same way in this study, first a simple climate model (MAGICC) is used toassess future GHG concentrations and climate change implication and than the IIASA
integrated modelling framework was used to (iteratively) achieve the concentrations
stabilization at very low levels of 400 to 450 ppmv CO2 equivalent by 2100 through avery restricted set of mitigation measures and options.
The relation between qualitative and quantitative scenarios can be characterized interms of Figure 3.1.
The SRES writing team developed four scenario “families”, because an even number
helps to avoid the impression that there is a “central” or “most likely” case. Box 3.1
provides an explanation of terminology used in SRES and Figure 3.2 illustrates this
scenario terminology schematically. There are four scenario families that are branchout into 6 scenario groups that include altogether 40 emissions scenarios. The
scenarios cover a wide range – but not all possible futures. In particular, there are no“global disaster” scenarios where the poor parts of the world become even poorer orwhere catastrophic events endanger human survival in general. None of the SRES
scenarios include new explicit climate policies such as the fulfilment of Kyoto
Protocol.
3

For the stabilization scenarios the emission profiles for other GHGs than CO2 were obtained from equivalentstabilization scenarios based on SRES as reported in Swart et al., 2001, and in Rao and Riahi, 2003. These nonenergy, non-industry GHGs do not form part of the cost minimization model used for the stabilization scenarios
here, but are exogenous study input assumptions.

9Models

Scenarios

StoriesFigure 3.1: Schematic illustration of alternative scenario formulations ranging fromnarrative storylines to quantitative formal models (source: Nakicenovic et al., 2000).

Figure 3.2: Schematic illustration of SRES scenarios. The set of scenarios consists of the four
scenario families A1, A2, B1, and B2. Each family consists of a number of scenarios, some ofwhich have “harmonized” driving forces and share the same prespecified population andgross world product (a few that also share common final energy trajectories are called “fully
harmonized”). These are marked as “HS” for harmonized scenarios. One of the harmonized
scenarios, originally posted on the open-process web site, is called a “marker scenario.” Allother scenarios of the same family based on the quantification of the storyline chosen by themodeling team are marked as “OS.” Six modeling groups developed the set of 40 emissions
scenarios. The GHG and SO2 emissions of the scenarios were standardized to share the samedata for 1990 and 2000 on request of the user communities. The time-dependent standardized
emissions were also translated into geographic distributions.

10Box 3.1: IPCC SRES Scenario Terminology (Source: SRES, Nakicenovic et al., 2000).
Model:

a formal representation of a system that allows quantification of relevant
system variables.
Storyline:
a narrative description of a scenario (or a family of scenarios) highlighting
the main scenario characteristics, relationships between key driving forces,
and the dynamics of the scenarios.
Scenario:a description of a potential future, based on a clear logic and a quantified
storyline.
Family:scenarios that have a similar demographic, societal, economic, andtechnical-change storyline. Four scenario families comprise the SRES: A1,
A2, B1, and B2.
Group:
scenarios within a family that reflect a variation of the storyline. The A1
scenario family includes three groups designated by A1T, A1FI, and A1B
that explore alternative structures of future energy systems. The otherthree scenario families consist of one group each.
Category:
scenarios are grouped into four categories of cumulative CO2 emissions
between 1990 and 2100: low, medium-low, medium-high, and highemissions. Each category contains scenarios with a range of different
driving forces yet similar cumulative emissions.
Marker:a scenario that was originally posted on the SRES website to represent agiven scenario family. A marker is not necessarily the median or mean
scenario.
Illustrative:a scenario that is illustrative for each of the six scenario groups reflected in
the Summary for Policymakers of this report. They include four revised"scenario markers" for the scenario groups A1B, A2, B1, and B2, and two
additional illustrative scenarios for the A1FI and AIT groups. See also“(Scenario) Groups” and “(Scenario) Markers”.
Harmonized:
harmonized scenarios within a family share common assumptions for
global population and GDP while fully harmonized scenarios are within
5% of the population projections specified for the respective marker
scenario, within 10% of the GDP and within 10% of the marker scenario’s
final energy consumption.
Standardized:
emissions for 1990 and 2000 are indexed to have the same values.
Other scenarios: scenarios that are not harmonized.

Each family has a unifying theme in the form of a “storyline” or narrative that
describes future demographic, social, economic, technological, and policy trends.
Four storylines were developed by the whole writing team that identified driving
forces, key uncertainties, possible scenario families, and their logic. Six global
modelling teams then quantified the storylines. The quantification consisted of firsttranslating the storylines into a set of quantitative assumptions about the driving
forces of emissions (for example, rates of change of population and size of theeconomy and rates of technological change). Next, these assumptions were input tosix integrated, global models that computed the emissions of GHGs and sulphur
dioxide (SO2). As a result, a total of 40 scenarios were produced for the fourstorylines. The large number of alternative scenarios showed that a single storyline
could lead to a large number of feasible emission pathways (Nakicenovic et al., 2000;
Morita et al., 2001).
In all, six models were used to generate the 40 scenarios that comprise the four
scenario families. Six of these scenarios, which should be considered equally sound,
were chosen to illustrate the whole set of scenarios. They span a wide range of11

uncertainty, as required by the SRES Terms of Reference. These encompass fourcombinations of demographic change, social and economic development, and broad
technological developments, corresponding to the four families (A1, A2, B1, B2),
each with an illustrative “marker” scenario. Two of the scenario groups of the A1
family (A1FI, A1T) explicitly explore energy technology developments, alternative tothe “balanced” A1B group, holding the other driving forces constant, each with anillustrative scenario. Rapid growth leads to high capital turnover rates, which meansthat early small differences among scenarios can lead to a large divergence by 2100.
Therefore, the A1 family, which has the highest rates of technological change andeconomic development, was selected to show this effect.
To provide a scientific foundation for the scenarios, the writing team extensively
reviewed and evaluated the scenario literature. Results of the review were publishedin the scientific literature (Alcamo and Nakicenovic, 1998), and made available to thescientific community in the form of an Internet scenario database (Morita and Lee,1998). The background research by the six modelling teams for developing the 40
scenarios was also published in the scientific literature (Nakicenovic, 2000).

3.2

A short description of the SRES Scenarios

3.2.1

Introduction

Ranges of possible future emissions and their driving forces are very large so that
there are an infinite number of alternative futures to explore. Since there is noagreement on how the future will unfold, the SRES tried to sharpen the view ofalternatives by assuming that individual scenarios have diverging tendencies – oneemphasizes stronger economic values, the other stronger environmental values; one
assumes increasing globalisation, the other increasing rationalization. Combining
these choices yielded four different scenario families as illustrated schematically in
Figure 3.3. This two-dimensional representation of the main SRES scenario
characteristics is an oversimplification (Nakicenovic et al., 2000). It is shown just as
an illustration. In fact, to be accurate, the space would need to be multi-dimensional,
listing other scenario developments in many different social, economic, technological,
environmental, and policy dimensions.
The titles of the four scenario storylines and families have been kept simple: A1, A2,
B1, and B2. There is no particular order among the storylines; they are listed inalphabetical and numerical order:•

The A1 storyline and scenario family describes a future world of very rapid
economic growth, global population that peaks in mid-century and declines
thereafter, and the rapid introduction of new and more efficient technologies.
Major underlying themes are convergence among regions, capacity building, andincreased cultural and social interactions, with a substantial reduction in regional
differences in per capita income. The A1 scenario family develops into threegroups that describe alternative directions of technological change in the energy
system. The three A1 groups are distinguished by their technological emphasis:
fossil intensive (A1FI), non-fossil energy sources (A1T), or a balance across all
sources (A1B).4

4

Balanced is defined as not relying too heavily on one particular energy source, on the assumption that similar
improvement rates apply to all energy supply and end-use technologies.

12SRES Scenarios

Economic

A2

A1
GlobalRegional

B1

B2

A
y

og

Tec h n

ivin

ol

er g

g Forci c u ltu re- u se)

( La

nd

Dr

y

gr

P

o pu a t io nlEn

Eco nom

Environmentalyes

Figure 3.3: Schematic illustration of SRES scenarios. The four scenario “families” areshown, very simplistically, for illustrative purposes, as branches of a two-dimensional tree.
The two dimensions shown indicate global and regional scenario orientation, anddevelopment and environmental orientation, respectively. In reality, the four scenarios sharea space of a much higher dimensionality given the numerous driving forces and otherassumptions needed to define any given scenario in a particular modelling approach. The
schematic diagram illustrates that the scenarios build on the main driving forces of GHG
emissions. Each scenario family is based on a common specification of some of the main
driving forces. Source: SRES, Nakicenovic et al., 2000.

•

The A2 storyline and scenario family describes a very heterogeneous world. The
underlying theme is self-reliance and preservation of local identities. Fertility
patterns across regions converge very slowly, which results in continuously
increasing global population. Economic development is primarily regionallyoriented and per capita economic growth and technological change are more
fragmented and slower than in other storylines.

•

The B1 storyline and scenario family describes a convergent world with the same
global population that peaks in mid-century and declines thereafter, as in the A1storyline, but with rapid changes in economic structures toward a service andinformation economy, with reductions in material intensity, and the introductionof clean and resource-efficient technologies. The emphasis is on global solutions
to economic, social, and environmental sustainability, including improved equity,
but without additional climate initiatives.

•

The B2 storyline and scenario family describes a world in which the emphasis is
on local solutions to economic, social, and environmental sustainability. It is aworld with a continuously increasing global population at a rate lower than A2,
intermediate levels of economic development, and less rapid and more diverse
13

technological change than in the B1 and A1 storylines. While the scenario is alsooriented toward environmental protection and social equity, it focuses on local andregional levels.
In all, six models were used to generate the 40 scenarios that comprise the four
scenario families. They are listed in Box 3.2. These six models are representative ofemissions scenario modelling approaches and different integrated assessment
frameworks in the literature, and include so-called top-down and bottom-up models.
Box 3.2: Models used to generate the SRES scenarios.
•

Asian Pacific Integrated Model (AIM) from the National Institute of Environmental
Studies in Japan (Morita et al., 1994);•

Atmospheric Stabilization Framework Model (ASF) from ICF Consulting in the USA(Lashof and Tirpak, 1990; Pepper et al., 1992, and 1998; Sankovski et al., 2000);•

Integrated Model to Assess the Greenhouse Effect (IMAGE) from the National Institutefor Public Health and Environmental Hygiene (RIVM) (Alcamo et al., 1998; de Vries etal., 1994, 1999, 2000), used in connection with the Dutch Bureau for Economic Policy
Analysis (CPB) WorldScan model (de Jong and Zalm, 1991), the Netherlands;•Multiregional Approach for Resource and Industry Allocation (MARIA) from the Science
University of Tokyo in Japan (Mori and Takahashi, 1999; Mori, 2000);•Model for Energy Supply Strategy Alternatives and their General Environmental Impact(MESSAGE) from the International Institute of Applied Systems Analysis (IIASA) in
Austria (Messner and Strubegger, 1995; Riahi and Roehrl, 2000a); and the•Mini Climate Assessment Model (MiniCAM) from the Pacific Northwest National
Laboratory (PNNL) in the USA (Edmonds et al., 1994, 1996a and 1996b).

3.2.2

Emissions and Other Results of the SRES Scenarios

Figure 3.4 illustrates the range of global energy-related and industrial CO2 emissions
for the 40 SRES scenarios against the background of all the 500 emissions scenarios
from the literature documented in the SRES scenario database. The six SRES scenariogroups are represented by the six illustrative scenarios. Figure 3.4 also shows a rangeof emissions of the six scenario groups next to each of the six illustrative scenarios.
Figure 3.4 shows that the four marker and two illustrative scenarios by themselves
cover a large portion of the overall scenario distribution. This is one of the reasonsthat the SRES Writing Team recommended the use of all four marker and two
illustrative scenarios in future assessments. Together, they cover most of theuncertainty of future emissions, both with respect to the scenarios in the literature andthe full SRES scenario set. Figure 3.4 also shows that they are not necessarily close to
the median of the scenario family because of the nature of the selection process. For
example, A2 and B1 are at the upper and lower bounds of their scenario families,
respectively. The range of global energy-related and industrial CO2 emissions for thesix illustrative SRES scenarios is generally somewhat lower than the range of the
IPCC IS92 scenarios (Leggett et al., 1992; Pepper et al., 1992). Adding the other 34
SRES scenarios increases the covered emissions range. Jointly, the SRES scenarios
cover the relevant range of global emissions, from the 95th percentile at the high endof the distribution all the way down to very low emissions just above the 5th percentileof the distribution. Thus, they only exclude the most extreme emissions scenarios

14found in the literature – those situated out in the tails of the distribution. What isperhaps more important is that each of the four scenario families covers a sizable partof this distribution, implying that a similar quantification of driving forces can lead toa wide range of future emissions. More specifically, a given combination of the main
driving forces is not sufficient to uniquely determine a future emission path. There are
too many uncertainties. The fact that each of the scenario families covers a substantial
part of the literature range also leads to an overlap in the emissions ranges of the fourfamilies. This implies that a given level of future emissions can arise from very
different combinations of driving forces. This result is of fundamental importance forassessments of climate change impacts and possible mitigation and adaptation
strategies.

Total database range

8

A1
2

0
1900

B2
1990 range

B1
Minimum in Database

1950

2000

2050

Non-classified

4

IS92 range

A2

Non-intervention

Maximum in
Database

6

Intervention

Global Carbon Dioxide Emissions
Energy and Industry(index, 1990=1)

10

2100

Figure 3.4: Global CO2 emissions from energy and industry, historical development from1900 to 1990 and in 40 SRES scenarios from 1990 to 2100, shown as an index (1990 = 1).
The range is large in the base year 1990, as indicated by an “error” bar, but is excluded fromthe indexed future emissions paths. The dashed time-paths depict individual SRES scenarios
and the blue shaded area the range of scenarios from the literature (as documented in the
SRES database). The median (50th), 5th, and 95th percentiles of the frequency distribution
are shown. The statistics associated with the distribution of scenarios do not imply probabilityof occurrence (e.g., the frequency distribution of the scenarios in the literature may be
influenced by the use of IS92a as a reference for many subsequent studies). The 40 SRES
scenarios are classified into six groups. Jointly the scenarios span most of the range of the
scenarios in the literature. The emissions profiles are dynamic, ranging from continuous
increases to those that curve through a maximum and then decline. The coloured vertical bars
indicate the range of the four SRES scenario families in 2100. Also shown as vertical bars on
the right are the ranges of emissions in 2100 of IS92 scenarios, and of scenarios from theliterature that apparently include additional climate initiatives (designated as “intervention”
scenarios emissions range), those that do not (“non-intervention”), and those that cannot be
assigned to either of these two categories (“non-classified”).

15

An important feature of the SRES scenarios obtained using the SAR methodology isthat their overall radiative forcing is higher than the IS92 range despite comparatively
lower GHG emissions (Wigley and Raper, 1992; Wigley et al., 1994; Houghton et al.,
1996; Smith et al., 2000; IPCC, 2001). This is owing to the loss of sulphur-induced
cooling during the second half of the 21st century. On one hand, the reduction in
global sulphur emissions reduces the role of sulphate aerosols in determining futureclimate, and therefore reduces one aspect of uncertainty about future climate change(because the precise forcing effect of sulphate aerosols is highly uncertain). On theother hand, uncertainty increases because of the diversity in spatial patterns of SO2
emissions in the scenarios. Future assessments of possible climate change need toaccount for these different spatial and temporal dynamics of GHG and sulphuremissions, and they need to cover the whole range of radiative forcing associated withthe scenarios.
In summary, the SRES scenarios lead to the following findings:•

Alternative combinations of driving-force variables can lead to similar levels andstructure of energy use and land-use patterns, as illustrated by the various scenariogroups and scenarios. Hence, even for a given scenario outcome, for example, interms of GHG emissions, there are alternative combinations and alternative
pathways that could lead to that outcome. For instance, significant global changes
could result from a scenario of high population growth, even if per capita incomes
would rise only modestly, as well as from a scenario in which a rapid
demographic transition (low population levels) would coincide with high rates ofincome growth and affluence.

•

Important possibilities for further bifurcations in future development trends exist
within one scenario family, even when adopting certain values for importantscenario driving force variables to illustrate a particular possible development
path.

•

Emissions profiles are dynamic across the range of SRES scenarios. They portray
trend reversals and indicate possible emissions crossover among different
scenarios. They do not represent mere extensions of a continuous increase of
GHGs and sulphur emissions into the future. This more complex pattern of future
emissions across the range of SRES scenarios reflects the recent scenario
literature.

•

Describing potential future developments involves inherent ambiguities anduncertainties. One and only one possible development path (as alluded to for
instance in concepts such as “business-as-usual scenario”) simply does not exist.
And even for each alternative development path described by any given scenario,
there are numerous combinations of driving forces and numerical values that canbe consistent with a particular scenario description. This particularly applies to theA2 and B2 scenarios that imply a variety of regional development patterns that are
wider than in the A1 and B1 scenarios. The numerical precision of any model
result should not distract from the basic fact that uncertainty abounds. However, inthe opinion of the SRES writing team, the multi-model approach increases thevalue of the SRES scenario set, since uncertainties in the choice of model input
assumptions can be more explicitly separated from the specific model behaviour
and related modelling uncertainties.

•

Any scenario has subjective elements and is open for various interpretations.
While the SRES writing team as a whole has no preference for any of the16scenarios, and has no judgement about the probability or desirability of the
scenarios, the open process and reactions to SRES scenarios have shown that
individuals and interest groups do have such judgements. This will stimulate an
open discussion in the political arena about potential futures and choices that canbe made in the context of climate change response. For the scientific community,
the SRES scenario exercise has led to the identification of a number ofrecommendations for future research that can further increase understanding about
potential development of socio-economic driving forces and their interactions, andassociated GHG emissions.

3.3

WGBU Baseline Scenarios

3.3.1

Introduction to WGBU Scenarios

Three background scenarios are analyzed in this study that are subject a stringent
climate stabilization constraints (400 and 450 ppmv stabilization by 2100). They also
analyze feasibility and costs of meeting climate stabilization at such low levels under
additional constraints on zero-carbon energy technologies and carbon sequestration(referred below as “WBGU constraints”).
The three (no-climate-control) base case scenarios are based on the IPCC SRES
scenarios: SRES-A1, SRES-B1, and SRES-B2 (that were described above). Scenarios
A1 and B1 embrace a “sustainable development” paradigm, with the SRES-A1
scenario focusing of the economic and social dimensions (income growth anddisparity reduction) and the SRES-B1 scenario focusing in addition also on theenvironmental dimension (resource conservation and control of traditional pollutants
with exception of GHGs) of the “three pillars” (social, economic, environmental) of
sustainable development. Furthermore, the original SRES scenario sets contained anumber of scenario groups embedded within the overall scenario family A1,
essentially depending on rates and direction of technological change spanning all theextremes from fossil fuel intensive (SRES A1FI) to low- and zero-emission
technology intensive (A1T). For the purposes of this study, the SRES A1T scenario
was adopted as background scenario as it was structurally quite close to the B1
scenario (albeit at much higher levels of energy demand) in order to explore somedegree of uncertainty that may surround a sustainable development scenario. In otherwords for the purposes of this study both SRES scenarios A1T as well as B1 areconsidered describing similar overall global developments: a transition towards
sustainability. In this study, the primary focus is on climate change issues. Hencethese two “non-intervention” scenarios are analyzed for two levels of climate
stabilization: 450 ppmv (in case of the A1T scenario) and 400 ppmv in case of the B1
scenario much in line to similar modeling exercises performed within IPCC TAR.
A third SRES scenario (B2) is also analyzed, primarily as a means of comparison for
the two other scenarios. The more intermediate, “dynamics as usual” scenario SRESB2 provides a contrast of a baseline that provides less favorable conditions for climate
policies compared to its sustainable development scenario counterparts A1T and B1.
For this scenario we also assume a stringent climate stabilization target of 400 ppmv –even if this very ambitious target may not necessarily be consistent with the morecautious geopolitical, economic and technologic outlook described in the SRES-B2
background scenario storyline.

173.3.2

Input Assumptions

In the following sections important input assumptions, characterizing the threebackground scenarios and their corresponding policy scenarios are described. In-asfar as the scenarios have retained the original IPCC SRES assumptions they areextensively documented in the literature and Internet (e.g., see Nakicenovic et al.,2000)5. Instead, the most important features of the scenario input assumptions are
summarized, and documented in tabular form with an indication where they departfrom the original IPCC SRES numbers. In addition, some of numerical assumptions
identical to SRES are also reproduced, as is the case for the GDP scenarios, because
they are important for the understanding of the quantitative study results presented in
Chapter 4.
Population
The three background scenarios are based on two population projections. One(underlying the SRES-B2) scenario is a medium population projection in which global
fertility patterns are assumed to converge to replacement level by the end of the 21st
century (based on the corresponding UN Medium projection). World population
continues to rise in order to stabilize at a level of approximately 10 billion people by2100 (and to remain at that level thereafter). The two sustainable development
scenarios share the same, low population projection. In this (sub-replacement fertility)
scenario world population would peak below 9 billion people by the mid 21st century
in order to decline to some 7 billion by 2100 (declining further thereafter). It should
be noted that this choice of base case scenarios excludes the possibility of highpopulation growth from the analysis reported here.
Economic Growth
For reasons of consistency and comparability to both IPCC SRES and TAR, theoriginal SRES GDP growth scenarios were used for the three baseline scenarios inthis study (see Table 3.1).6 These original economic development paths are notreached due to the costs of stabilization, so that the resulting rates of economic
development are lower in the WGBU stabilization scenarios. In order to ease thesubsequent comparison of the GDP “losses” associated with the WGBU climate
stabilization scenarios, we replicate the corresponding SRES GDP input assumptions
for both measures of economic growth reported in SRES: GDP at market exchange
rates (mer) and GDP expressed at purchasing power parities (ppp).
It should be noted that two out of the three WGBU scenarios adopt a normative andoptimistic outlook on the development catch-up of developing countries. This is an
outcome of the original IPCC SRES scenario characteristics that serve as baselines for
the WGBU scenarios. As such these scenarios not only reflect fulfillment of theaspirations of the economic dimensions of sustainable development, but are also interms of resource and technology availability rather optimistic, providing a positive5

In addition to extensive documentation of SRES scenarios in the public domain, all detailed scenario assumptions
and results have been provided in electronic form to WBGU. Thus, there is no need to reproduce this statisticalinformation extensively here. Instead, here we focus on the main driving forces and developments in the three
scenarios.
6
Strictly speaking the GDP growth in the WBGU scenarios A1T and B1 would be slightly lower than in theoriginal SRES scenarios, as the additional WBGU constraints imposed on these scenarios would lead to someincreases in energy prices, thus somewhat reducing energy demand and GDP growth. Quantitative model
simulations have however indicated that this macro-economic effect would be so small as to be insignificant over
the time period and magnitude of the GDP growth reported here.

18and receptive environment for the implementation of climate stabilization policies.
Scenario B2 is more cautious than the two “ideal worlds” scenario counterparts, buteven in this scenario, the development aspirations of the South are largely fulfilledeven if occurring at a slower pace and with greater disparities to the industrializedcountries. It should be noted, that none of the scenarios analyzed here considers thepossibility of a development failure or of widening North-South development gaps.
Table 3.1 GDP scenarios 1990 to 2100 expressed at market exchange rates (mer) andpurchasing power parities (ppp) for the three IPCC SRES scenarios analyzed in this study. In
trillion US$(1990).
1990

2000

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

mex

20.9

26.7

37.9

56.5

89.1

135.2

181.3

247.6

313.8

383.3

455.9

528.5

ppp

25.7

33.3

47.1

66.6

96.6

138.9

181.0

240.7

304.2

372.2

443.0

513.9

mex

16.4

20.5

25.3

31.0

38.0

46.1

54.1

65.5

76.9

90.3

105.7

121.1

ppp

14.1

17.7

21.8

26.9

33.0

40.1

47.2

57.2

67.3

79.2

92.9

106.6

mex

1.1

0.8

1.5

2.9

5.3

8.8

12.4

16.2

20.0

24.4

29.3

34.2

ppp

2.6

2.2

3.1

4.3

6.0

8.8

12.4

16.2

20.0

24.4

29.3

34.2

mex

1.5

2.7

5.8

12.3

26.2

44.5

62.7

91.9

121.0

150.0

178.6

207.3

ppp

5.3

8.2

13.5

21.6

35.2

51.8

67.5

93.3

121.0

150.0

178.6

207.3

mex

1.9

2.7

5.3

10.3

19.5

35.8

52.0

73.9

95.8

118.6

142.3

165.9

ppp

3.8

5.1

8.6

13.8

22.4

38.1

53.9

73.9

95.8

118.6

142.3

165.9

mex

20.9

26.8

36.2

52.1

73.1

100.7

135.6

171.6

208.5

249.8

290.0

328.4

ppp

25.7

33.3

44.6

61.6

82.2

108.4

140.0

171.8

204.1

242.5

281.3

318.8

mex

16.4

20.6

26.0

32.4

38.3

43.9

49.9

55.4

59.8

66.3

73.9

82.3

ppp

14.1

17.7

22.4

28.1

33.3

38.3

43.6

48.5

52.5

58.4

65.1

72.7

mex

1.1

0.8

1.1

1.7

2.8

4.3

6.2

8.2

10.3

12.8

15.3

18.1

ppp

2.6

2.2

2.6

3.3

4.3

5.3

6.4

8.2

10.3

12.8

15.3

18.1

mex

1.5

2.7

4.8

8.7

15.1

24.9

37.9

51.4

64.8

78.7

91.7

103.1

ppp

5.3

8.2

12.0

17.3

24.6

34.1

46.1

57.4

67.7

79.3

91.7

103.1

mex

1.9

2.7

4.4

9.3

17.0

27.6

41.6

56.7

73.6

92.0

109.1

124.8

ppp

3.8

5.1

7.6

12.8

20.1

30.6

44.0

57.8

73.6

92.0

109.1

124.8

mex

20.9

28.3

38.6

50.7

66.0

85.5

109.5

134.8

161.5

186.3

210.3

234.9

ppp

25.7

34.8

46.9

60.2

75.5

93.2

113.9

136.8

160.7

183.8

207.4

231.8

mex

16.4

21.1

26.5

30.3

33.1

35.8

38.3

40.9

44.4

47.9

52.0

56.6

ppp

14.1

18.3

23.0

26.3

28.8

31.3

33.5

35.9

39.2

42.4

46.1

50.4

mex

1.1

1.0

1.2

1.8

2.8

4.5

6.6

8.6

10.5

11.9

13.2

14.5

ppp

2.6

2.4

2.7

3.3

4.3

5.6

7.2

9.5

11.6

13.3

14.8

16.2

mex

1.5

3.5

7.2

13.2

21.3

30.7

41.8

52.7

64.1

75.0

85.8

97.1

ppp

5.3

9.3

15.1

22.4

30.7

39.3

49.3

59.0

68.7

78.5

89.2

100.4

mex

1.9

2.7

3.7

5.5

8.8

14.6

22.8

32.6

42.6

51.4

59.3

66.8

ppp

3.8

4.9

6.2

8.2

11.7

17.0

23.9

32.4

41.2

49.6

57.2

64.9

A1
World
OECD90
REF
ASIA
ALM
B1
World
OECD90
REF
ASIA
ALM
B2
World
OECD90
REF
ASIA
ALM

Resource Availability
Table 3.2 summarizes the resource availability assumptions for all the SRES scenariofamilies and scenario groups within the A1 scenario family. Eight categories of
conventional and unconventional oil and gas reserves, resources and additional
occurrences are listed as are the assumptions of their availability as used in the SRES
scenarios. For comparison also historical and future scenario consumption figures areshown.

19

Table 3.2 Oil and gas resource availability assumptions underlying the IPCC SRES
scenarios. Eight different categories of conventional and unconventional reserves, resourcesand occurrences are shown as well as which resource category is assumed to be available inwhich scenario. For comparison also historic, cumulative resource extraction as well as
future use levels as resulting from the SRES scenarios are shown. In ZJ (1021 J or 1,000 EJ).
Conventional
reserves andresources
Category
Oil
Gas

I,II,III
12.4
16.5

Scenario/
Category
SRES
A1B
A1T
A1O&G
A1C
A2
B1
B2

Unconventional
EnhancedrecoveryRecoverable
IV
V
VI
5.8
1.9
14.1
2.3
5.8
10.8

Unconventional
and additional
occurrences
VII
24.6
16.2

VIII
35.2
800

Scenario assumptions

Total
94
852

Historical
Consumption
1860-1998
5.1
2.4
Consumption
1990-2100

I,II,III

IV

V

VI

VII

VIII

Oil

Gas

gas/oil
gas/oil
gas/oil
gas/oil
gas/oil
gas/oil
gas/oil

gas/oil
gas/oil
gas/oil
gas/oil
gas/oil
gas/oil
gas/oil

gas/oil
gas/oil
gas/oil
gas/oil
gas/oilGas
gas/oil

gas/oil
gas/oil
gas/oil
--gas
gas
gas

gas
gas
gas/oil-------------gas---------

25.5
20.8
34.4
18.5
19.6
17.2
19.4

31.8
24.9
49.1
20.5
24.5
23.9
26.9

The resource availability assumptions of the three base case scenarios analyzed here
can be characterized as typical “middle of the ground”. Thus, no scenario in thepresent study assumes either extremely large (e.g., in form of methane hydrates) orextremely low availability of oil and gas resources.
Technology
The tree base case scenarios adopted follow the technology assumptions as outlined
for the IPCC SRES scenarios. The main characterization (next to efficiency andemissions) of technologies for the SRES scenarios concerns their costs over time.
Costs are treated as dynamic and in addition, embrace an endogenous technologicalchange perspective, i.e., improvements in costs are linked to (cumulative) deployment
rates. In other words, an initially expensive technology can get only less expensive incases it is deployed (learning by doing hypothesis). If it remains “on the shelf”, there
is no endogenous mechanism of cost improvement. A summary of selected advancedenergy technologies and their levelized costs is given in Table 3.3.
It should be noted that not all technology and costs assumptions from the SRES reportare pertinent to the scenarios analyzed here due to additional constraints such as a ban
on nuclear energy (see next section). Furthermore, it is important to emphasize thatthe very nature of the two sustainable development base case scenarios (A1T and B1)
implies a very dynamic (or in using a value statement extremely optimistic) outlook
on the possibilities to improve efficiency, versatility, and above all the economics ofadvanced energy technologies. Only scenario B2 is more moderate in its technology
assumptions, but it is important to retain that the analysis reported here does notinclude technology scenarios in which advanced energy technologies are notforthcoming or its costs are not improving at all.7 Given this dynamic technology
7This in fact continues to be a characteristic of many climate policy models, in particular those (top-down) models
deploying the concept of a (static) back-stop technology.

20

outlook, the comparatively modest costs of climate stabilization reported in the next
chapter should therefore not be surprising – even if the limits imposed by analyzing
only three scenarios (in fact in technology terms only two, i.e., A1T/B1 versus B2)
compresses the inevitable uncertainty innovation and long-term technology
development entails.
Table 3.3 Range (min/max) of levelized costs for selected advanced energy technologies by2020 and 2050 for the three scenarios analyzed in this report. In US cents(1990) per kWh(excluding fuel costs).
Costs of electricity generation technologies (UScent/kWh)
2020

2050

A1-450
IGCC
NGCC
Biomass (including CC
and single steam cycles)
Wind
Hydro (including large
and small hydro)
Solar thermal
PV
Geothermal
Nuclear

min
3.2
1.1

max
3.2
1.1

min
2.8
0.9

max
2.8
0.9

2

3.1

1.9

2.4

3.5

3.5

1.7

1.7

0.9

8

0.9

8

3.2
3.7
2.7
2.6

4.8
5.4
2.9
3.7

1.9
1
2.5
2.5

2.8
1.5
2.9
4

B1-400
IGCC
NGCC
Biomass (including CC
and single steam cycles)
Wind
Hydro (including large
and small hydro)
Solar thermal
PV
Geothermal
Nuclear

min
3
1

max
3
1.3

min
2.8
0.8

max
2.8
1

2

3.1

1.9

2.6

3.9

3.9

2.6

2.6

0.9

8

0.9

8

3.2
4.2
2.7
2.6

4.8
6.1
2.9
4

2
1.6
2.5
2.6

3.1
2.4
2.9
4.5

B2-400
IGCC
NGCC
Biomass (including CC
and single steam cycles)
Wind
Hydro (including large
and small hydro)
Solar thermal
PV
Geothermal
Nuclear

min
3.2
1.6

max
3.3
1.6

min
3
1.2

max
3.1
1.2

2.1

3.4

2.1

3

4.5

4.5

3.4

3.4

0.9

8

0.9

8

3.5
4.9
2.9
2.6

5.2
7.1
2.9
4

2.6
2.7
2.9
2.6

3.9
4.2
2.9
3.8

2020

2050

2020

2050

21

3.3.3

Additional WBGU Constraints

Both, the A1T and B1 WBGU background scenarios as well as the respective
stabilization scenarios differ to their IPCC SRES and TAR counterparts with respect
to a number of additional constraints imposed on the deployment of zero-carbon
options (nuclear, biomass, hydro power, and carbon sequestration). The constraints
were specified by the WBGU in order to ensure sustainability of zero-carbon energyuse, addressing also the risk of non-permanent CO2 sequestration (see the forthcoming“WBGU Sondergutachten” for a more detailed discussion).
The following WBGU constraints are considered (quantifications are given for theworld):•The potential of biomass use (including non-commercial biomass) is limited to
100 EJ•

Hydropower is limited to 12 EJ in the medium term and 15 EJ in the long term•

All nuclear plants are phased out globally until the year 2050•

CO2 capture and storage is assumed to be a tentative solution for the next
hundred years (phase out 2100), and cumulative CO2 capture and storage from2000 to 2100 is constraint to 300 GtC.8

The WBGU constraints represent stringent limitations for zero-carbon energy, andhave a considerable impact on the carbon mitigation potential of the stabilization
scenarios. The aggregated effect of the constraints was too restrictive in the case of
A1T-450 scenario and led to model infeasibilities. In order to achieve a robust model
solution under the given constraints, it was, hence, necessary to introduce additionalchanges to the A1T assumptions as compared to the original A1T SRES scenario. In
this respect, the penetration of renewable hydrogen production technologies and the
diffusion hydrogen fuel cell vehicles in the transportation sector are assumed to takeplace at a much higher pace than in A1T-SRES. In addition, the WBGU scenario
assumes also higher shares of electricity-based transportation for the medium term(enabling the decarbonization of the transportation sector via renewable electricity
and/or fossil power with carbon capture and sequestration). Most importantly
however, the WBGU A1T scenario assumes also considerably higher improvementsof energy intensities of GDP, and does not include the WBGU constraint with respect
to carbon capture.
None of the above-mentioned WBGU constraints was considered during thedevelopment of the WBGU B2 scenarios, particularly because the SRES B2 baseline
scenario describes a comparatively pessimistic development, assuming thecontinuation of historical trends along a “dynamics-as-usual” and non-sustainable
path. It is, hence, unlikely that the stringent stabilization target (400 ppmv) can beachieved under the restrictions for the energy portfolio as suggested by the WBGU
constraints.

8

One of the main differences to the comparable scenarios reported in the IPCC TAR is that the present WBGU
scenarios in addition also consider carbon sequestration from biomass derived electricity and hydrogen production
facilities.

22

4

Policy Scenarios

4.1

CO2 Stabilization Levels

GHG concentrations stabilization levels in the WGBU scenarios correspond to thestabilization of atmospheric CO2 concentrations at 400 and 450 ppmv. This climate
stabilization target is based on limiting future climate change to below global mean
temperature change of 2 degrees C and assuming an intermediary climate sensitivity.
This climate change target yields a CO2 concentrations stabilization level of some 400
ppmv.9For the high demand scenario SRES-A1T this climate stabilization target is not
feasible for the range of original input assumptions and additional constraints andmodel parameters adopted within the framework of this study. Hence, a somewhat
higher stabilization target of 450 ppmv (consistent with the scenarios reported in
IPCC TAR albeit a half-a-century earlier achievement of the stabilization earlier in
the WBGU scenarios) was adopted.
It is important to recall that while similar climate stabilization targets have beenfrequently advanced in the literature, there remains considerable scientific uncertainty
with respect to what constitutes any particular threshold value of “acceptable” or “notdangerous” climate change. This is due to both the non-linear responses of the climate
system (e.g., abrupt temperature change versus gradual, smooth changes) to any givenchange in atmospheric concentrations of GHGs that remain to be poorly understood,
as well as to the considerable uncertainties surrounding estimates of vulnerability andresilience of natural (managed and unmanaged) ecosystems and societies (humanactivities) vis à vis any given level of climate change. Hence the scenarios reported
here should be considered as illustrative – even though very stringent – precautionary
response strategies to future climate change. The model calculations reported here
should therefore not be interpreted as endorsement or preference of these particularstabilization levels over alternative ones, including less stringent scenarios; nor,conversely as an interpretation that these 400 to 450 ppmv stabilization scenarios
constitute indeed a level of “non-dangerous” interference with the climate system.
The uncertainties are too large to be able to derive such conclusions. For example,
most of the 40 SRES emissions scenarios can lead to a 2 degrees Celsius average
global mean temperature change by 2100 assuming a choice of “appropriate” climate
sensitivity. In other words, climate sensitivity accounts for as much uncertainty of
future climate change range as do future emissions as described by the full range of
SRES scenarios (IPCC, TAR Synthesis Report, 2002). The range of stabilization
scenarios reported here should therefore simply be interpreted as illustrative model
calculations for particular climate stabilization target levels chosen as exogenous
study assumptions, reflecting the research interests for this study, rather than ascientific endorsement of the particular stabilization levels analyzed.
Given the exogenously defined stabilization levels of atmospheric CO2
concentrations, both the MESSAGE and the MAGICC model were used iteratively toderive global, cost minimal CO2 emission trajectories consistent with the climate
stabilization targets (see Figure 4.1). In turn, this forms the input for the subsequent
9

The calculations for the stabilization scenarios were done with the MAGICC model and a calculation horizon up
to the year 2300. The resulting climate sensitivities consistent with the goal of remaining below 2 degrees realized
global warming and for the stabilization levels analyzed here ranges between 2 to 2.9 degrees C per doubled CO2
concentration.

23

step of emission entitlements allocation that constitutes the basis for the model
calculations of international trade in carbon emission entitlements and for actuallyrealized emission reductions under a marginal abatement cost minimization criterion.

Global realized CO2-emissions
16
14

B2-base12
A1T-base

GtC

10
B1-base8
A1T-450

6
B1-4004
B2-4002
01990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
Figure 4.1: Global CO2 emissions of three alternative baseline scenarios: WBGU-A1T,
10
WBGU-B1, and WBGU-B2 and corresponding emission profiles consistent withstabilization at 400 ppmv (B1-400, B2-400) and 450 ppmv (A1T-450) respectively. In GtC.

4.2

Allocation Criteria

The three stabilization scenarios are based on a separation of the economic issues of
equity and efficiency (which consistent with the prevailing economic theory and mostof stabilization scenarios in the literature). Thus, the issue of allocation emission
rights is separated from the issue of economic efficiency in achieving pre-scribedemissions reduction profiles, leading to atmospheric CO2 stabilization. In other words,
the scenarios assume international agreement on ultimate climate stabilization goals(in this case on 400 and 450 ppm CO2 by 2100 and hence on cumulative carbon
emissions) as well as on the allocation of resulting GHG emission entitlements.
Grübler and Nakicenovic (1994) have proposed a number of alternative allocation
mechanisms within the context of international burden sharing in GHG emission
reductions. They proposed a two-tired classification of alternative burden sharing
schemes based on reductive versus distributive criteria.

10

These three baseline scenarios differ from those presented in the IPCC SRES in terms ofcontinued model improvement such as a different calibration of the year 2000 values forwhich (contrary to SRES and TAR) actual energy and GHG emissions statistics are now
available as well a full reflection of the current outlook on the implementation of the
UNFCCC Kyoto Protocol in case of the climate stabilization scenarios, and most importantly
also with respect to the implementation of the WBGU constraints.

24

Reductive criteria follow the traditional command-and-control environmental policy
approach, in terms that a given environmental (emission reduction) target is first
defined and then the required emission reduction is distributed according to various
schemes. Two of these reductive criteria have figured prominently in the literature(for a detailed overview see Grübler and Nakicenovic, 1994; and IPCC SAR,
1996:83-124): equal percentage emission cuts (also referred to as emission“grandfathering” due to its insensitivity to differences in initial conditions and initialpreservation of emission status quo, particularly for large emittors), and cutbacks
proportional to historical contribution (a responsibility based approach of burden
sharing underlying for instance the Brazilian proposal for a sequel to the Kyoto
protocol).
Conversely, distributive criteria take a different point of departure. They consider theassimilative capacity of the atmosphere as a global commons resource to be
distributed according to different equity principles. A prominent example for this typeof criterion are equal per capita emission entitlements. Due to the enormous
asymmetries between population and GHG emissions between UNFCCC Annex-I and
non-Annex-I countries, such a distributive criterion necessarily implies the existenceof a functioning global market for trade in emission permits, as an immediate
implementation of such an allocation scheme as emission reduction rule would
require such drastic emission reductions in Annex-I countries to be infeasible
technically, economically, as well as politically. However, such simplistic criteria
ignore important aspects of intergenerational equity, as ignoring the “common but
differentiated” responsibility (UNFCCC, 1992) for creating the environmental
externality in the first place. An interesting model has been developed by Fujii (1990),
who postulates that access to the global commons such as how much carbon canultimately be released into the atmosphere should be distributed equally among all
people inhabiting the planet, irrespective of place and time they live. In other words
per capita emissions need to be equalized not only across countries but also acrosssubsequent generations.11
It goes without saying that the debate about alternative burden sharing schemes andvarious equity concepts these are based on continues with no scientific consensus insight. As a result, most studies explore a range of alternative burden sharing (emission
entitlements allocation) models that are then tested for their implications on carbon
trade and financial transfer flows. Given the methodologies available for this type ofanalysis, it is not surprising that nearly the entirety of the literature follows theprinciple of economics of sharply separating equity from efficiency. In other words,
whereas many alternative emission entitlement models are possible (and indeed
explored in the literature), the actual emission reduction calculations invariableembrace a marginal cost equalization (minimization) approach. In other words,
regional with higher emissions than their allocation have the economic choice ofeither reducing emissions domestically or to buy emission “credits” from regions thathave either lower emissions than their entitlements (often referred to by skeptics as
trade in “hot air”), or to invest in emission reductions in regions that have lower
emission reduction costs, accruing corresponding emission credits. Due to enormous
differences in initial conditions, projected growth in future emissions, technology
availability, etc. a vigorous formal modeling framework is required to perform actual
11

For the low stabilization scenarios analyzed here, the Fuji model when calculated over the period 1800-2100
would imply that emission entitlements for Western Europe would immediately drop to some 0.5 tC per capita andfor North America even would become immediately negative (to compensate for over-proportional high historical
per capita emissions), implying vast trade (and resource transfers) flows from Annex-I to non-Annex-I countries.

25

calculations of levels of emission reductions and trade in carbon emission credits. The
IIASA integrated assessment methodology has the added advantage of also taking
macro-economic feedback effects from resulting energy price increases andrevenues/losses from carbon trade formally into account.
In this study two variants of a “hybrid” burden sharing allocation mechanism are
explored following a proposal from the study sponsor (WBGU). This burden sharing
scheme is referred to as a “contraction and convergence” scheme in the literature (seee.g., den Elzen, 2002). In essence, all regions need to converge to a common percapita emission entitlement by a given pre-specified date (2050, and 2100 respectively
in this study, hence the scenario designation as c&c2050 and c&C2100 respectively).
For regions with above world average per capita emissions this implies immediatelinear reductions (hence the term “contraction”) until the convergence criterion is
fulfilled, however starting from very different initial conditions. (Thus this criterion
also contains certain elements of emissions “grandfathering”, this being the reason itis referred to here as “hybrid” approach). For (developing) regions below world
average per capita emissions, emissions can rise initially until the world average percapita emission level is reached. Thereafter, also developing regions need to“contract” to the pre-specified convergence level.12 Depending on the demographic
scenario underlying the base case scenario (low in the A1T and B1 scenarios, medium
in the B2 scenario), as well as on the required stabilization level (450 ppmv for A1T
and 400 ppmv for the other two scenarios), per capita emissions converge to different,
but overall very low levels. For instance in the c&c2050 scenarios, global per capita
carbon emissions converge to levels between 0.3 (B1-400) to 0.8 (A1T-450)
tC/capita, in order to decline further thereafter to as low as 0.2 tC/capita by 2100.
The differentiation between 2050 and 2100 as alternative years for per capita emission
entitlements convergence results however in comparatively little difference.
Postponing the convergence year to 2100 eases the transition for the Annex-I
countries somewhat at the expense of the developing countries, but differences incumulative emissions are comparatively small: 24-37 GtC difference for the Annex-I
countries between the two c&c schemes across the three base case scenarios analyzed,
to be compared with cumulative (1800-2100) emissions of between 370 and 413 GtC
for Annex-I countries in the c&c2050 scenarios. Figure 4.2 presents the results of the
carbon emission entitlement allocation for the three scenarios and the two c&cemission allocation schemes.

12

Formally, the algorithm used is Sy+1 = Sy - (Sy - Py+1) (t / tconv) where Sy is the emissions share of a region in theyear y, Py is its share of the global population (subject to the emission cap) in year y, t is the time elapsed between
2010 and the target year, and tconv is the total time until convergence has to be achieved (if convergence target is2050, tconv is 40 years). See also http://www.gci.org.uk/contconv/cc.html on this type of allocation scheme andcalculus.

26

Regional emissions entitlements
2000

Regional emissions entitlements

NAM

A1T- 450 (c&c2100)

2000

WEU

NAM

A1T- 450 (c&c2050)

PAO
1500

PAO
1500

FSU

FSU

CPA

EEU
MtC

MtC

EEU
1000

CPA

1000

SAS

SAS

PAS

500

PAS

500

Regional emissions entitlements
2000

00

90

21

80

20

70

20

60

20

20

40

50
20

30

20

20

10

20

20

20

Regional emissions entitlements

NAM

B1- 400 (c&c2100)

20

AFR

00

MEA

90

LAM
0

19

0
20
50
20
60
20
70
20
80
20
90
21
00

30

20
4

20

20
20

00

20

20

10

AFR

19

MEA

90

LAM
0

2000

WEU

NAM

B1- 400 (c&c2050)

PAO
1500

FSU
EEU

MtC

MtC

EEU
CPA

CPA

1000

SAS

SAS

PAS

500

PAS

500

00
21

90

80

20

70

20

60

20

20

50

40

20

20

30
20

20
20

20

20

Regional emissions entitlements

NAM

B2- 400 (c&c2100)

10

AFR

00

MEA

19

0

90

00

90

Regional emissions entitlements
2000

LAM

21

20

70

80

20

60
20

20

50
20

40
20

30

20

20

20

10
20

20

00

AFR

19

MEA

90

LAM
0

2000

WEU

NAM

B2- 400 (c&c2050)

PAO
1500

FSU
EEU

MtC

MtC

EEU
CPA

CPA

1000

SAS
PAS

500

SAS
PAS

500

00
21

90

70

80

20

20

60

20

20

50

40

20

20

30
20

20
20

20

20

10

AFR

00

MEA

90

LAM
0

19

00
21

90
20

80

70

20

20

60
20

50
20

40
20

30
20

20
20

10
20

20

00

AFR

19

MEA

90

LAM
0

WEU
PAO

1500

FSU

1000

WEU
PAO

1500

FSU

1000

WEU

Figure 4.2: Regional emission entitlements for three climate stabilization scenarios (A1T450, B1-400, B2-400) under two allocation schemes (c&C2050, and c&c2100). In MtC.

Given the state of the scientific and policy debate, there is no single widely acceptedburden sharing scheme, and it is also highly unlikely that the “contraction andconvergence” (c&c) scheme analyzed here for WBGU has the potential to gain thenecessary wide “buy in” required for an international agreement on such an intricate
issue. Each scheme proposed has its individual strengths and weaknesses. The c&cscheme here quite legitimately attracts criticisms from Annex-I countries of not
sufficiently considering differences in initial starting conditions and for providing an
incentive for unfettered population growth in the “South”. Conversely, developing
countries quite legitimately object such schemes because they ignore the“differentiated” responsibility in creating the climate change externality in the firstplace. For instance, Annex-I countries have caused about half of all CO2 emissions(and about two thirds of energy-related emissions) since the onset of the Industrial
Revolution while accounting for only some 20 percent of global population, adisparity that largely remains preserved under the two WBGU emission allocation
schemes analyzed here (see Table 4.1).

27

Table 4.1 Share of different regions in cumulative population and cumulative carbon
emissions over the period 1800 to 2100 for the scenarios analyzed in this study. In percent.

5

Cumulative population

A1T
B1
B2
OECD
15
15
13
REF
7
7
6
ASIA
49
49
51
ALM
28
28
30
Cumulative emissions in Baseline
A1T
B1
B2
OECD
33
37
36
REF
13
14
13
ASIA
32
28
30
ALM
22
22
21
Cumulative emission entitlements (c&c2050)
A1T-450
B1-400B2-400
OECD
36
40
40
REF
12
13
13
ASIA
31
28
28
ALM
21
20
19
Cumulative emission entitlements (c&c2100)
A1T-450
B1-400B2-400
OECD
39
42
43
REF
13
14
14
ASIA
29
26
26
ALM
19
18
17

For instance, the stabilization scenarios lead to the almost paradoxical situation thatthe share of Annex-I countries in cumulative emissions (and hence in contribution toradiative forcing change) increases compared to the unabated baseline scenarios. It
appears thus that the critical participation of developing countries in climate
mitigation efforts cannot be considered as probable, unless some alternative formula
is found that also incorporates considerations of historical responsibility and thatassures that global emissions inequities do at least not increase in a climate control
regime.
The fact, that only one particular burden sharing scheme is explored in more detail inthis study, has therefore to be considered as important shortcoming, calling for further
in-depth analysis of alternative proposals and should not be interpreted asendorsement of this particular type of burden sharing scheme over other, alternative
reductive or distributive criteria (or combinations thereof).

4.3

Results

4.3.1

Emissions, Concentrations and Climate Change Implications

Before discussing the structure, measures, and economic implications of the climate
stabilization scenarios in more detail below we summarize first the climate
implications of the scenarios analyzed.

28

Figure 4.1 above showed the global carbon emissions in the six main scenarios
analyzed (three base case and three additional climate stabilization scenarios).
Cumulative historical and future carbon emissions (as a first rough proxy variable forchanges in radiative forcing) range between 1200 (B1) and 1400 (B2) GtC over theperiod 1800 to 2100, compared to a range between 700 (B2-400) and 900 (A1T-450)
GtC in the stabilization scenarios. In this context however, one needs to keep in mind
that two of the three scenarios (with exception of scenario B2) embrace a sustainable
development paradigm, leading even in absence of climate policies to comparatively
low emissions, as well as the invariably low stabilization levels (400 and 450 ppmv)
analyzed in this study. The limitation of assumed mitigation options and measures
also leads to an “upper limit” on the cumulative emissions “avoided” across the threestabilization scenarios.
The “simple” climate model, MAGICC, has been integrated into the IIASA
assessment framework. It was used to assess climate change implications of the GHG
and aerosol emissions of the three WGBU scenarios. Figure 4.3 shows the resulting
changes in atmospheric CO2 concentrations, global mean temperature change, as well
as sea level rise (Figures 4.3a, 4.3b, and 4.3c).

CO2-Concentrations
Figure 4.3a: Atmospheric
CO2 concentrations for the three unconstrained base scenarios(A1T, B1,
650B2) and the corresponding climate stabilization scenarios (A1T-450, B1-400, B2400) analyzed in this study. The shaded area indicates the model uncertainty forrange
the three
A1T-450
stabilization scenarios. In ppmv.
B1-400600
CO2 (ppmv)

B2-400A1T-WBGU

550

B1-WBGU
B2-WBGU

500
450
400
350
1980

2000

2020

2040

2060

2080

2100

29

Tempearature change relative to 1961-1990 mean
3

range
A1T-450
B1-400degree Celsius

2

B2-400
Historic
A1T-WBGU
B1-WBGU

1

B2-WBGU

0

-1
1860

1900

1940

1980

2020

2060

2100

Figure 4.3b: Global mean temperature change, compared to the 1961 to 1990 mean for thesix scenarios analyzed. The shaded area indicates the model uncertainty for the threestabilization scenarios. In degrees Celsius.

Sea level rise
80

range

70

A1T-45060

B2-400B1-400A1T-WBGU

cm

50

B1-WBGU
B2-WBGU

40
30
20
10
0
1980

2000

2020

2040

2060

2080

2100

Figure 4.3c: Sea level rise resulting from global warming (Figure 4.3b) for the six scenarios
analyzed. The shaded area indicates the model uncertainty for the three stabilization
scenarios. Note in particular the continuous rising trends of sea level rise beyond the study
horizon (2100) and the substantial uncertainties. In cm sea level rise compared to 2000.

30

The three figures confirm a phenomenon frequently discussed in the literature, i.e., theeffect of compounding uncertainties. With each successive step in the analysis, i.e.,translating carbon emissions to atmospheric CO2 concentrations, these in turn toglobal mean temperature change, and the latter into sea level rise, uncertainties
increase substantially.
Whereas atmospheric concentration uncertainties in the three climate stabilization
scenarios are substantial, they nonetheless invariably remain below the best guessestimates of their corresponding no-policy scenarios. Conversely, uncertainties inprojected global mean temperature change are much larger: embracing even the best
guess projections of the no-policy base scenarios. In other words, based on current
scientific understanding of climate sensitivity uncertainties, it is impossible to deducethat the climate stabilization scenarios analyzed here would indeed lead to lower
levels of global warming than their unabated scenario counterparts, if the latter would
unfold under a low range of climate sensitivity, but the stabilization scenarios would
have to face a situation of high climate sensitivity. Nonetheless, even given inevitable
uncertainties, the model calculations confirm that lower emissions indeed lead ceterisparibus (which in this case is an important qualifier) to lower levels of realized global
mean temperature change and thus to reduced risks of adverse climate change
impacts.
Finally, it is instructive to consider the uncertainties in projected sea level rise (Figure4.3c). By 2100, the best guess estimates of sea level rise reduce the risk from some 50
cm increase in the no-policy scenarios to below 40 cm in the policy scenarios.
Considering the enormous inertia embedded in the climate system and its impacts on
the oceans this difference between the scenarios should be less surprising, as is theenormous uncertainty range (less than 10 cm to well above 70 cm by 2100 in the
climate stabilization scenarios). More importantly, constraining the analysis to thestudy horizon of 2100 (as done in Figure 4.3c) would give a wrong sense ofcomplacency, as due to inertia sea levels would continue to rise even under strictclimate stabilization scenarios for hundreds of years into the future. The best guess
model calculations indicate that by 2300 sea levels could continue to rise in thestabilization scenarios to levels between 62 to 75 cm above today's levels, with anuncertainty spanning an extremely wide range: 8 cm to 164 cm (i.e., from a nonproblem to an extremely serious issue for low lying islands and coastal areas). Thus,
the example illustrates well the deep uncertainties and the importance of inertia andtime lags in climate change responses inherently involved in the climate change
debate. In addition, the uncertainties, particularly on the climate change impact side
compound to such a wide range as leaving all the room for policy interpretations,spanning the extremes of “do nothing” complacency to an alarmist call for “hitting thebrake as fast as possible” policy intervention.
4.3.2

Emissions, Reductions, and Carbon Trade

As mentioned above, the starting point for the analysis of climate stabilization
scenarios are three unabated scenarios, modified after their corresponding “parent”(baseline) scenarios from the IPCC SRES report (SRES-A1T, SRES-B1, and SRESB2). The climate stabilization scenarios constrain global carbon emissions as to leadto the pre-scribed stabilization levels of 450 and 400 ppmv respectively (A1T-450,
B1-400, B2-400). In turn these global allowable emissions levels are allocated overtime based on two variants to the “contraction&convergence” algorithm describedabove. Subsequently the MESSAGE model is used to calculate actual regional andintertemporal emission reduction measures consistent with the global constraint but
31

assuming full flexibility to actually perform emission reduction measures where andwhen13 they are cheapest (cost minimization criterion). Figure 4.4 summarizes theresulting emissions cumulated over the entire time period 2000 to 2100 for the nine
scenarios analyzed and compares these cumulative emissions with historic emissions
over the period 1800 to 2000. In order to illustrate the effect of flexibility, the figure
shows both emission allocations as well as realized emissions, with the difference
reflecting international carbon trade.
Given the nature of the c&c emission entitlements schemes, a general pattern is that ofimports of emission credits into the OECD regions from the developing regions(ASIA and ALM). This is graphically visible in the Figure 4.4 where realized
emissions in the OECD region systematically surpass those allocated as emission
entitlements. A further robust finding from the analysis, is that by postponing theconvergence date of per capita emission entitlements from 2050 to 2100, larger
emission entitlements are allocated to the OECD countries, reducing thus the need forcarbon trading.
Contrasting the difference between emission entitlements and realized emissions, withthat of the baseline scenarios indicates the relative importance of “domestic” emission
reduction, versus carbon trade. Given the long-term and stringent emission reduction
goal underlying the scenarios analyzed, it is not surprising to see that cumulatively
trade remains a comparatively small measure compared to “domestic” emissions
reduction, indicating in addition that the frequently discussed problem of carbon“leakage” due to tradable emission permits should remain comparatively low under
the scenarios analyzed, furthering their political viability.
However, the cumulative aggregates in Figure 4.4 mask important inter-temporalvariability in carbon trade over time. As a rule, carbon emission credits imports into
the OECD peak in the period 2020 to 2050, especially around 2050 when the
additional WBGU constraints, such as a phase out of nuclear power, render thecompliance to the climate stabilization goals very difficult. This is also the period of atemporary price “hike” in the carbon permits traded (cf. discussion below). However,also trade within developing countries is important in the scenarios (masked in theregional aggregates of Figure 4.4).
For instance, in the second half of the 21st century, China and the Middle East would
emerge as major importers of carbon permits from the Indian and African continents
with revenue flows of equal order of magnitude than for the some OECD regions(e.g., North America). These detailed regional carbon trade and revenue flows are
however not robust as arising from the specific assumptions and constraints
characterizing the scenarios analyzed here. For a more comprehensive picture
additional scenarios would have to be examined and resulting trade and revenue
patterns aggregated to arrive at more robust conclusions with respect to financial“winners” and “losers” from carbon trade under stringent climate stabilization
scenarios. Figures 4.5 and 4.6 give more detail on the international trade in carbon andthe resulting resource transfers (revenue/investment flows from OECD to developing
countries).

13

A discount rate of 5 percent is used in all model calculations reported here.

32

400

OECD

Cumulative emissions (2000-2100)
Realized emissions
Emission entitlements

200

100

0

400

1800-2000

GtC

300

Historic

baseline
A1T

B1

B2

c&c2050

c&c2100

A1T450 B1 400 B2 400

A1T 450 B1 400 B2400

REF

Cumulative emissions (2000-2100)
Realized emissions
Emission entitlements

200

100

0

400

1800-2000

GtC

300

Historic

baseline

A1T

B1

B2

c&c2050

c&c2100

A1T450 B1 400 B2 400

A1T450 B1 400 B2 400

ASIA

Cumulative emissions (2000-2100)
Realized emissions
Emission entitlements

200

100

0

400

1800-2000

GtC

300

Historic

baseline
A1T

B1

B2

c&c2050

c&c2100

A1T450 B1 400 B2 400

A1T450 B1 400 B2 400

ALM

Cumulative emissions (2000-2100)
Realized emissions
Emission entitlements

200

100

0

1800-2000

GtC

300

Historic

baseline
A1T

B1

B2

c&c2050

c&c2100

A1T450 B1 400 B2 400

A1T450 B1 400 B2 400

Figure 4.4: Cumulative energy and industry carbon dioxide emissions, historic (1800 to2000) and future (2000 to 2100) for the three baseline scenarios and the six climate policy
scenarios analyzed for four macro-regions. In GtC.

33

Cumulative Carbon trade (2000-2100)
C&C2050-scenarios

90
70

-30
-50

OECD

REF

ASIA

ALM

B2-400B1-400

A1T-450

B2-400B1-400

A1T-450

B2-400B1-400B2-400-10

A1T-450

B1-400
A1T-450

B2-40010

B1-40030
A1T-450

GtC

50

World

Cumulative Carbon trade (2000-2100)
C&C2100-scenarios

9070

B2-400B1-400

A1T-450

B2-400B1-400
A1T-450

B1-400B2-400

A1T-450
B2-400-10

B1-400

A1T-450

B2-40010

B1-40030
A1T-450

GtC

50

-30
-50

OECD

REF

ASIA

ALM

World

Figure 4.5: Cumulative (2000 to 2100) carbon trade flows for the six climate stabilization
scenarios and for the four macro-regions assuming convergence of per capita emission
entitlements by 2050 (top panel) or by 2100 (bottom panel). In GtC.

34

Cumulative Revenue of Carbon Trade (2000-2100)
C&C2050-scenarios

B2-400B1-400

A1T-450-5000

B2-400

A1T-450

0B1-400B2-400B1-400

A1T-450

B2-400B1-400Billion US$90

5000

A1T-450

10000-10000-15000

OECD

REF

ALM

ASIA

Cumulative Revenue of Carbon Trade (2000-2100)
C&C2100-scenarios

B2-400B2-400B1-400B1-400

A1T-450

A1T-450-5000

B1-400

A1T-450

0B2-400B2-400B1-400Billion US$90

5000

A1T-450

10000-10000-15000

OECD

REF

ASIA

ALM

Figure 4.6: Cumulative (2000 to 2100) revenues from carbon trade for the six climate
stabilization scenarios and for the four macro-regions assuming convergence of per capita
emission entitlements by 2050 (top panel) or by 2100 (bottom panel). In Billion US$(1990).

The revenue flows from carbon trade (Figure 4.6) mirror the picture already shown
above (Figure 4.5) for the physical volumes of carbon trade, except of course thatwhen the comparison is made on a intertemporal basis, revenues increase generally
faster than physically traded volumes due to the increasing carbon permit prices.
It constitutes an interesting finding of this study that the “contraction&convergence”
algorithm of emission permit allocation – that in the literature is invariably assumed to35

favor net resource transfers to developing countries is in fact not unambiguous. Onlyin the case of an early convergence date of 2050 resource transfers remain
systematically in direction from the OECD region to the developing and the reforming
economies countries – easing thus their respective economic burden of emission
reductions amidst economic development catchup. Conversely, if the convergencedate is pushed out to 2100, the OECD region remains its position of “net payer”,
however the situation for developing countries and for the economies in transition
deteriorates significantly. In fact, these regions – in their aggregate—become “netpayers” in two of the three scenarios analyzed here. This variation is not scenario
specific, but in its aggregate the results suggests that the variant of a contraction andconvergence in per capita emission entitlements as late as 2100 is unlikely to find thewider acceptance from both transition economy as well as developing countries,
required for the political sustainability of the proposed emission permit allocation
regime.
Another significant finding from this analysis refers to the transition economies. Evenas trade patterns in carbon emission permits are diverse across the scenarios
examined, financial flows are in a persistent direction: the reforming economies
would under the proposed emission allocation scheme c&c2050 examined become inaggregate (i.e., cumulatively over the period 2000 to 2100) net payers, even in
scenarios where cumulative trade in emission permits is positive cumulated over theentire time horizon.
The reason for these seemingly paradox model results lie in the vast natural gas
resources of the Former Soviet Union. After initially being able to sell vast amountsof “hot air”, the transition away from reliance on natural gas towards the second halfof the 21st century proves to be invariably extremely difficult across all scenarios,
requiring the purchase of emission credits. Even as total trade flows remain positive(i.e., reforming economies remain net exporters of carbon credits cumulatively overthe study horizon), the rising carbon price results in net resource transfers away fromreforming economies. In other words: Russia sells low (before 2050) but buys high(after 2050).
Thus all scenarios indicate net resource transfers from both OECD and for mostscenarios also from the reforming economies14 away in direction of developing
countries. The absolute magnitude of these resource transfers at first sight seems to be
staggering: Over the period 2000 to 2100, Annex-I countries would transfer between
US$8 to 13 trillion in direction of developing countries to buy emission credits (or to
pay for mitigation measures) in developing countries, with the latter being thebeneficiaries of these resource transfers (to be balanced however against the muchhigher mitigation costs arising from the stringent climate stabilization scenarios
examined here). Considering however, that such resource transfer numbers are
cumulative estimates over a 100 year period, annual averages are much less daunting:transfers from Annex-I countries would average between US$84 to 128 billion
annually, with however substantial intertemporal variation. Nonetheless such numbers– even if small in comparison to the respective GDPs –- do not present financial“peanuts”. After all, official development assistance (ODA) globally in the year 2000

14

Perhaps this model result helps to explain the reluctance of Russia to ratify the Kyoto Protocol. An importantresearch task remains to investigate under which emission permit allocation regime and under which base casescenarios this unfavorable result for the transition economies could be eased; which would be an obvious precondition for acceptance of a stringent international climate control regime by the countries of this region.

36

amounted to some US$53 billion (UN, 200315), significantly below the numbers forcarbon permit trade suggested above.
4.3.3

Carbon shadow prices and macroeconomic implications

It is one of the outstanding features of the methodology developed at IIASA to resolve
the much discussed dichotomy between “bottom-up” and “top-down” models inclimate policy analysis. This is achieved by an iterative linkage procedure between theengineering type energy supply model MESSAGE that calculates energy systems
costs, including marginal carbon abatement costs (shadow prices), and themacroeconomic model MACRO that balances changes in prices with resulting
changes in energy demand as well as the impacts of rising energy and carbon prices
on GDP. It is through this iterative approach that it became finally possible to resolve
the discrepancies between the “bottom-up” and “top-down” perspectives, an
important methodological issue that has haunted the climate change policy literature
for more than a decade.
In essence, the discrepancy between “bottom-up” and “top-down” models arises fromthe myopia (or ignorance) of each class of models for representation of technology
detail and the dynamics of technological change (a traditional strength of “bottom-up”
models) on one hand, and for the consistent equilibration of prices versus quantities (atraditional strength of “top-down” models) on the other. The modeling framework
developed at IIASA continues to be the only integrated assessment model available
worldwide to have resolved this modeling dichotomy. This is particularly important
for the present study as in terms of climate mitigation traditional “bottom-up” (B-U)
and “top-down” (T-D) models continue to exhibit persistent biases ranging from theoptimistic (B-U) to the pessimistic (T-D) as concerns feasibility and costs of climate
mitigation measures. From this perspective, the modeling results presented here canclaim significantly higher credence than that arrived with traditional modelingframeworks, subject of course to the limitations imposed by the type of scenarios
analyzed as well as the assumption on intertemporal cost minimization that are sharedinvariably by all climate change policy analysis model available.
Figure 4.7 shows the calculated carbon permit price, or the globally equalized
marginal carbon abatement costs for the six policy scenarios analyzed. A firstimportant observation concerning Figure 4.7 is that differences in base case scenarios
are more important than differences in the two emission permit allocation regimes
studied. For all practical purposes of policy analysis, the economic cost differences
between the two emission permit allocation schemes are negligible (even as was
discussed above, resulting in trade patterns that are quite different between the two
allocation models). This modeling result confirms our earlier observation that thecosts of meeting a particular climate stabilization target is more dependent on the typeof base scenario analyzed (high- versus low-emission futures) and the range of
mitigation technologies available (unconstrained versus – as in this study –constrained) than on the absolute level of emission reduction or the particular modelof emission permit allocation chosen.

15

See http://www.un.org/reports/financing/profile.htm

37Permit price
1200

US$(1990)/tC

1000
800
600

B2- 400

400
A1T- 450200
B1- 400
02000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

Figure 4.7: Carbon permit price for the six climate stabilization policy scenarios analyzed.
Bold lines refer to the c&c2050 emission permit allocation scheme, thin lines to the c&c2100
allocation model. In constant 1990 US$ per tC.

A robust finding from the analysis reported here is that the marginal carbon abatement
costs are quite close over the near term (to 2040) across the scenarios, gradually rising
to some US$200$/tC. Between 2040 to 2060, a first bifurcation occurs in the price ofcarbon permits (marginal carbon abatement costs): Prices/costs remain low in
scenarios in which zero-carbon options (e.g., nuclear) or carbon sequestration areunconstrained (B2-400 scenarios), but “spike” around 2050 for scenarios in whichthese options are assumed to be constrained (the B1-400 and A1T-450 scenarios
incorporating e.g., the WBGU nuclear phaseout constraint). These scenario
differences are gradually reduced by 2060, beyond which the base case scenario
difference sets in. Costs/prices continue to rise in scenarios of a more modest outlook
on the dynamics of new energy technologies (even if unconstrained) (B2-400
scenarios), whereas in scenarios with a more dynamic technology outlook,costs/prices remain comparatively low, to the tune of US$200/tC.16
The results also confirm the importance of exploring a wide range of baseline
scenarios. For instance in considering only the technology optimistic, sustainable
development baseline scenarios B1 and A1T could result in the misleadinginterpretation that the costs of meeting even extremely stringent climate stabilization
goals are comparatively modest (some US$200/tC on average). This picture changes
however drastically, when considering a more modest outlook on technology
improvements as done in the B2 scenario, where costs continue to rise reaching some16

The carbon permit price hike after 2090 in the B1-400 scenarios is due to the imposition of the WBGU
constraint for carbon capture and sequestration assuming that the contribution of these technologies is zero from2100 onwards. The model is flexible to chose an optimal deployment path for carbon capture under the given
constraint, and is relying on this mitigation option until the very last moment when the constraint becomes binding(2100). To some extent the extreme price hike arises from modeling end-period truncation effects, and would
presumably be less drastic, when the model calculations would be extended beyond 2100.

38

US$1,000/tC towards the end of the 21st century. This result confirms yet anew thatan important element of any precautionary climate policy needs to pay particularattention to widening the portfolio of low cost abatement options (rather thanconstraining it). The larger the technology portfolio available, and the cheaper its
costs, the easier (read: cheaper) it becomes to meet any (uncertain) ultimate climate
stabilization target over the long term. Conversely, due to technological inertia, costsof meeting stringent climate stabilization goals are almost “pre-programmed” over the
next couple of decades (i.e., are baseline scenario independent) and are essentiallydependent on emissions constraints levels and existence (or absence) of flexibility
mechanisms such as the international trade in carbon permits.
After having established the costs of meeting the carbon emission constraints assumed
for the scenarios analyzed, we now can turn to the macroeconomic implications(costs) of climate stabilization. These costs include the costs of carbon emission
reduction in a direct, narrow sense (e.g., through carbon sequestration and disposal),the costs of switching to more expensive alternative energy sources, the costs ofenergy conservation, as well as the macroeconomic costs (or benefits) of the resource
transfers that go along with emission trading.
As mentioned above, the unique coupling of technology-rich engineering models withmacroeconomic models achieved in the IIASA methodology results in a morebalanced view of the macro-economic costs of climate stabilization at challenging low
levels. Table 4.2 presents the numerical results.
Table 4.2 Change in regional GDP (compared to unconstrained baseline) of meeting climate
stabilization targets of 450 ppmv (A1T) and 400 ppmv (B1, and B2) for two allocation
schemes of carbon emission permits: c&c2050 and c&c2100 (in parentheses) for the years
2020, 2050, and 2100. In percent change compared to respective scenario baseline.
Change of GDP(% of baseline)

OECD

REFS

ASIA

ALM

WORLD

2050
2100

-0.4 (-0.3)-1.2 (-0.9)-0.2 (-0.2)

0.8 ( 1.7)
-2.0 (-1.7)-0.2 (-0.2)-0.7 (-1.0)-1.9 (-2.1)-0.4 (-0.4)-0.3 (-0.5)-1.4 (-1.7)-0.4 (-0.4)-0.4 (-0.4)-1.5 (-1.6)-0.3 (-0.3)

B1-400

2020
2050
2100

-0.7 (-0.6)-1.1 (-0.8)-0.4 (-0.4)

0.3 ( 0.9)
-3.6 (-2.7)-2.2 (-2.1)-2.5 (-2.8)-1.8 (-2.1)
0.1 ( 0.1)

-2.0 (-2.2)-1.7 (-2.0)-0.3 (-0.3)-1.1 (-1.1)-1.5 (-1.5)-0.3 (-0.3)

B2-400

2020
2050
2100

-0.3 (-0.4)-2.0 (-1.7)-2.1 (-2.0)-1.8 (-1.7)-5.9 (-5.3)-0.8 (-0.7)-0.5 (-0.6)-2.4 (-2.6)-2.0 (-2.1)-1.9 (-2.0)-1.8 (-2.1)-1.5 (-1.5)-0.6 (-0.6)-2.2 (-2.3)-1.7 (-1.7)

A1T-450 2020

This analysis that includes a full coupling of the costs of climate mitigation with themacro-economy confirms earlier observations on the importance of base case scenario
assumptions. Ceteris paribus, macroeconomic costs for any given level of climate
stabilization are higher in scenarios characterized by low economic productivity
growth (GDP per capita) as well as in scenarios with a less dynamic technology
outlook. This is clearly illustrated by the fact the scenario with the lowest GDP and
the most conservative technology outlook analyzed here (B2) has the highest macroeconomic costs of climate stabilization, both in absolute (US$) terms as well as interms of percentage GDP losses.
39

An important finding of this analysis is the invariably17 negative macro-economic
effects of climate stabilization for all regions even after considering the impacts(losses/benefits) from emissions trading. Revenues from selling emissions credits areout-weighted by the costs of emission reductions even in developing countries. Thisobviously is a result of the stringent nature of the climate stabilization scenarios
analyzed here, but it casts important doubts on the feasibility of full international
cooperation for such strict climate regimes, unless it were possible to demonstrate thatthe economic losses from adverse climate impacts would exceed the numbers of GDP
losses indicated in Table 4.2 for developing countries.18 Especially noteworthy are themodel results for the transition economies, in which GDP losses are generally in thelonger term (2050) the highest among all regions concerned – and that for a region
where the climate impact literature almost invariably suggests positive economic
impacts of climate change.
Hence, more important than the absolute magnitude of the macroeconomic costs ofclimate stabilization, which given the ambitious stabilization goals are comparatively
modest on a global scale, are issues of regional differences and disparities inbalancing costs and benefits from climate change mitigation. In the aggregate theGDP loss of the climate stabilization ranges between 0.3 to 1.7 percent of GDP, whichmeans of resetting the global economic output in the year 2100 to the value prevailingin the year 2099, which is not a daunting perspective indeed (even if a comparison inabsolute numbers of the future GDP loss to current GDP would yield rather an
impressive figure19). The fact, that the estimates reported here are at the low end of
the comparable literature (drawing mostly on the results of macro-economic models)
and that we feel confident about these results, is yet another illustration of the benefitsof a comprehensive methodological framework that integrates both systems
engineering and macro-economic model perspectives. And yet, even ourcomparatively optimistic global outlook confirms the importance of looking into thedetails of regional disparities in the burden sharing of the costs of combating climatechange. Unless more satisfactory burden sharing schemes for transition economies
and developing countries can be devised than analyzed here, there remain important
doubts on the political and economic feasibility of stringent climate stabilization
scenarios that put a proportionally higher economic burden on those that have notcaused the problem in the first place.
4.3.4

Emission reduction measures

Figure 4.8 summarizes the various measures for emission reduction at the global levelbased on the model results obtained with the coupled MESSAGE and MACRO
models. The multitude of emission reduction measures are summarized under threemajor categories for ease of comparison: energy demand reduction (conservationinduced by rising energy prices due to the carbon constraints); structural change(primarily inter-fuel substitution either within fossil fuels, e.g., substitution of coal by
natural gas use, or between fossil fuels and non-fossils, i.e., renewables in this case);
17

The only exception being the region Asia for the year 2100 in the B1-400 scenario, where modest economic
gains are indicated.
18
Based on the available climate impact literature this seems a difficult proposition indeed as GDP losses fromdeveloping countries due to climate stabilization typically range between one to two percent of GDP in the
scenarios analyzed, whereas the literature on market impacts suggests typical values of negative climate change
impacts below one percent of GDP in developing countries.
19
Depending on the scenario analyzed the GDP loss by 2100 amounts to between 1 (B1-400) to 4 (B2-400)
Trillion $ (constant 1990 US$) compared to a world GDP in the year 2000 of some 27 trillion $ and a respective
world GDP by 2100 of between 235 (B2) to 529 (A1T) trillion $.

40

Demand Reductions
Structural Change
CO2 Capture & Storage

2100

2090

2080

2070

2060

2050

2040

2030

0
2020

2100

2090

2080

2070

2060

2050

16000
B2-WBGU

12000

8000
B2-4004000
Demand Reductions
Structural Change

2100

2050

2060

2040

2030

2020

2010

2000

CO2 Capture & Storage

0
1990

2040

Carbon Dioxide Emissions (MtC) Energy & Industry

2030

2010

2000

2020

CO2 Capture & Storage

4000

2090

Structural Change

2080

Demand Reductions

A1T-450

2000

B1-400

4000

8000

2010

8000

12000

2070

12000

A1T-WBGU

1990

B1-WBGU

016000

Carbon Dioxide Emissions (MtC) Energy & Industry

16000

1990

Carbon Dioxide Emissions (MtC) Energy & Industry

and finally carbon capture (scrubbing) and storage (sequestration). It should be notedthat in this study only carbon emissions from industrial sources were considered as
potential target for emission reductions, as insufficient information is currently
available to estimate detailed emission reduction costs for non-industrial sources orfor GHGs outside carbon.

Figure 4.8: Magnitude of emission reductions between unconstrained baseline and climate
stabilization scenarios, by type of measures. In MtC (Million or mega tons of elemental
carbon).

Depending on the baseline scenario, existence of additional WBGU constraints on
zero-carbon options and carbon capture (for scenarios B1 and A1T), as well as
ultimate stabilization target to be achieved (450 ppmv for A1T and 400 ppmv for theother two scenarios), the relative importance of mitigation measures varies across the
scenarios. Overall, demand reductions due to rising energy and carbon prices are oflesser importance compared to structural change and carbon sequestration, as theoverall costs of meeting the stabilization targets remain comparatively modest. (Forthe scenario with much higher costs (B2-400) energy conservation plays consequently
a much larger role.) The second differentiation between the scenarios concerns therole of carbon sequestration versus other interfuel substitution measures. In casecarbon capture is assumed to be constrained (B1-400 scenario) this technologicaloption is a transient one (cf. Figure 4.8); in case the option is unconstrained, carbon

41

capture plays an important role in the mitigation technology portfolio throughout the
21st century.20
Given these findings structural changes in the energy supply chains assume adominant role in the WGBU scenarios, particularly in the B2 scenario in which thebaseline scenario describes more modest structural changes compared for instance tothe rather extreme A1T scenario that postulates already a secular transition away fromfossil fuels already in the base case scenario without climate change (cf. Figure 4.9).
100% Oil/Gas

Shares inPrimary Energy

B2- 400

1990
1970
B1-SRESA1T-SRES
B2-SRES1950

B1-400B1-WBGUA1T- 450

B2-WBGUA1T-WBGU

100%
Carbon1920

1900

historical

1850

100% Zero-carbon

Figure 4.9: Energy triangle. Share of coal, oil/gas, and zero-carbon options in global
primary energy supply: Historical (since 1850) and in the scenarios analyzed in this study to2100. Note in particular the similarity of almost all scenarios in terms of their primary energy
structure towards the end of the 21st century resulting from the relatively limited choice of
three baseline scenarios and the stringent climate stabilization targets adopted. In percent.

The fact that two of the three base case scenarios as well as invariably all the three
climate stabilization scenarios display such similar structural changes in energy
supply is related to the assumptions of this study concerning choice of base casescenarios as well as technological constraints. The resulting technological“monoculture” can be considered both a limiting factor of the analysis presented here
(as illustrating a too constrained choice of base case scenarios, compressing
uncertainty), but at the same time also an interesting signal for energy technology
20

Note that without unconstrained carbon sequestration a 400 ppmv stabilization scenario imposed upon the
SRES-B2 scenario with its more moderate technology dynamics would be infeasible.

42

policy that has to date largely ignored the risks associated with technological “lockin” in a few critical energy technologies. It is almost ironic that present arguments forenergy sector diversification that should favor a larger share of renewables in theenergy portfolio could in the distant future be turned against renewables themselves,at least in scenarios that follow the lines of the (admittedly) extreme examples
illustrated here.
The most important renewable “backstop” technologies constitute intermittent
renewables for electricity generation (PV and wind) as well as solar hydrogen, of
critical importance for the transportation energy demands. These technologies, while
disposing of the highest “option value” for climate stabilization in the scenarios
analyzed, are at the same time those technologies, that if turning out as infeasible atthe scale described by the scenarios pose the most serious risk of not attaining thedesired climate stabilization levels. Therefore, these critical technological options
should be analyzed in much more detail in future with respect to technological andeconomic feasibility as well as potential environmental impacts. This early,
anticipatory technology assessment is considered as an important element of any
enhanced research and development (R&D) strategy for climate friendly energy
technologies.

4.4

Limitations of the Analysis

After presenting the study’s main results and findings it may be also useful tosummarize the main limitations of the analysis that need to be borne in mind before
drawing definitive policy conclusions. The robustness of the study findings inparticular are limited by a number of factors, most notably:•

The limited number of base scenarios analyzed, that artificially compresses
uncertainty;•

The use of a singular burden sharing scheme in distributing emission permits,again compressing uncertainty as well as reducing the likelihood of the climate
stabilization scenarios (lack of potential buy-in from developing countries);•

The large number of constraints imposed on future energy technologies,
significantly reducing the portfolio for low or zero-carbon technologies. This
increases the vulnerability (and decreases the likelihood of feasibility of theclimate stabilization scenarios) in case one of the dominant energy sector“backstop” technologies (most notably solar hydrogen and to a lesser degree
decarbonization of hydrocarbon sources of energy) should prove infeasible fortechnological, economic, or ecological reasons.

Finally, whereas above limitations of the analysis reported here can be remediated byadditional, in-depth studies of a larger set of scenarios, burden sharing schemes, andextensive energy technology portfolio analysis, there remain also important
limitations inherent to the study methodology deployed (and for which no practicalalternatives are available in the literature yet):•

The main methodological limitation of the present study relates to both themethodological nature of inter-temporal optimization models assuming both asocial planner operating under perfect foresight with no uncertainty (e.g., on theultimate climate stabilization target) as well as under perfectly functioning43

global markets (enabling to separate neatly issues of equity and efficiency, andignoring the important problem of possible un-cooperative behavior or “freeriding”).
We address above four areas of limitations in more detail below.
1. Limited number of base case scenarios
In this study three base case scenarios served as background scenarios to analyze
feasibility, costs, and trade flows of ambitious climate stabilization targets. Two ofthese scenarios, based on the IPCC SRES-B1 and A1T scenarios, are in fact
structurally quite close. Both scenarios explore different dimensions of sustainable
development, with an emphasis of closure of the North-South development gap andthe rapid development and diffusion of post-fossil technologies. Only one scenario,
based on SRES-B2, adopts a more “moderate” (or pessimistic) outlook. Whereas all
three scenarios are interesting and valuable, they nonetheless constitute a too limitedbase to judge feasibility and costs of ambitious climate stabilization as artificiallyreducing the large uncertainties inherent in projection a multitude of developments
some hundred years out into the future.
Another factor has constrained the choice of available background scenarios in thisstudy. The significant constraints imposed on zero-carbon options in the stabilization
scenarios have meant that practically only a single original SRES scenario wasconsistent with both a 400 ppmv stabilization scenario as well as the WBGU zerocarbon technology constraints (with the A1T conditionally consistent, and the B2
scenario only feasible without WBGU constraints.) Thus there is an inherent tradeoff
existing between exploring uncertainties of scenario baselines along with uncertainties
of technology constraints. Both uncertainty spaces cannot be explored together at theirextreme end distributions (e.g., high emission scenarios with stringent technology
constraints but under ambitious climate stabilization targets), leading to a compression
in base case scenario uncertainty.
It has been a frequent observation in the literature (e.g., Roehrl and Riahi, 2000), thatfor climate policy baseline scenario differences are more important in determining
feasibility and costs of climate stabilization than the ultimate level of the stabilization
goals. In other words, it matters more in which different future world climate policies
are assumed to be implemented, than the degree of ambition these climate policies
pursue (400 ppmv or even 550 ppmv stabilization). For both scenarios A1T and B1,
the 400-450 ppmv stabilization targets are both achievable comparatively easily and atcomparatively modest costs (generally below US$200/tC).21
This illustrates the multiple benefits of sustainable development scenarios, but it also
provides for a potential pitfall in climate change policy, namely to assume thatpolicies can easily be implemented assuming that everything else is also developing in
the right direction. Conversely, a critical question of uncertainty remains: are theseclimate stabilization targets and policies as feasible and cheap in case the world would
not move uniformly in the direction described by sustainable development scenarios?
21

We speak of “comparatively easily” achievement of the stabilization targets especially considering theadditional constraints imposed on zero-carbon energy technologies (nuclear and biomass) as well as carbon
sequestration assumed for these WBGU scenarios. These constraints show up as “spikes” in the carbon permit
price around the year 2050 (reaching 600 $/tC) in all scenarios due to the nuclear phase-out constraint and around2100 (reaching some 1200 $/tC) due to the carbon sequestration constraint in B1-400. But these price hikes aretransient phenomena.

44

The answer obviously is not, as for instance indicated by the costs of meeting anequally stringent target in a world as described by the SRES-B2 scenario (where costs
could easily rise beyond US$1,000/tC). Therefore, a wider set of scenarios would
need to be analyzed (including for instance also examples from the SRES-A2 scenariofamily) to avoid an unjustified sense of optimism or complacency. After all, themultiple dimensions of sustainable development and the key question of what
constitutes a sustainable development scenario are even more contentious anduncertain than extent, and timing of climate policies. From this perspective one could
argue that scenarios of climate stabilization might be easier to be agreed upon andimplemented internationally than scenarios of comprehensive sustainable
development.
2. Use of a single burden-sharing scheme
The burden sharing scheme analyzed here has – like all alternative schemes – anumber of advantages as well as disadvantages as discussed in Section 4.2 above.
Given that a wide international agreement would be necessary before such a burden
sharing scheme could come into operation, the potential objections to such a schemefrom both the perspectives of the “North” as well as the “South” need carefulconsideration. For instance, Annex-I countries may quite legitimately object to any
per capita allocation criterion as proving an incentive for unfettered population
growth. Conversely, non-Annex-I countries quite legitimately might object the factthat historical responsibility in creating the climate change externality remains
ignored, as well as that the resulting emission permit allocation leads to an
aggravation in the North-South disparities in GHG emissions. It was observed in
Section 4.2 that the particular allocation scheme analyzed gives Annex-I countries aneven higher share in global emissions, than in case of their unconstrained base casescenarios.
Evidently, no individual burden sharing scheme is likely to be ever developed thatwill find universal appeal. It seems therefore even more important to inform thepolicy debate by a systematic analysis of a number of alternative burden sharing
schemes on potential sources of conflict, but also on potential areas of confluence ofinterests. Such an analysis certainly should also include “transitional” burden sharing
schemes, e.g., schemes that combine a variety of allocation principles and criteria,
depending on timing and development status of the respective parties involved. For
instance, a pragmatic approach could start from an allocation scheme combining
elements of emission status quo (“grandfathering”) with a gradual phase in ofemission reduction proportional to historical responsibility, finally to gradually phasein per capita emission entitlements as additional criteria. The fact that it was notpossible in this study to systematically explore alternative emission allocation
schemes therefore remains an important shortcoming and constitutes a priority areafor future research.
3. Constrained technology portfolios
Combined with the choice of only a limited number of base case scenarios, theimposition of a larger number of additional constraints (compared to the original
IPCC SRES scenarios) on the potential supply of zero-carbon energy or of carbon
sequestration constrains the technology portfolios analyzed in this study significantly.
This does not reduce the soundness of any individual scenario explored as being
consistent with latest theories of path dependency in technological systems (see e.g.,
Arthur, 1989, Grübler, 1998) but constitutes a limitation across the range of scenarios
45explored as resulting in a significant reduction of the technological diversity futureclimate policy initiatives can draw upon and in considering full uncertainty. It also
increases vulnerability of any particular climate stabilization regime in cases thedominant technologies options carrying the backbone of the energy system in the
scenarios explored here (intermittent renewables and solar hydrogen in particular)
would turn out infeasible at the scale assumed in the scenarios due to economic,
technological, or environmental reasons.
Thus, the analysis of the potential “option value” of various technological systems forclimate stabilization scenarios is limited given the constrained technology portfolios
analyzed in this study. Analysis of a wider range of technology options as well as
addressing the issue of spatial heterogeneity, in which certain technologies (e.g.,
nuclear) could be banned in some regions, but furthered in others with different
resource endowment conditions, remains thus an important future research task.
4. Methodological limitations
As mentioned above, a main methodological limitation (which as inherent in thestatus quo of available methodologies not only applies to the present study, but toalmost the entirety of the climate change policy literature) is the potential mismatch inthe main assumptions on existence of a social planner, perfect foresight, and no
uncertainty underlying the modeling and economic calculus of climate policy
scenarios. This study (as well as the bulk of the relevant literature) has also assumed
the existence of perfectly functioning global markets in emission permits, allowing forequalization of regionally diverse marginal emission reduction costs. Evidently,
decision making in the real world is far more complex and fuzzy than these
simplifying modeling assumptions suggest. As is evidenced for instance by the ongoing discussion on the Kyoto Protocol, the modalities for crediting joint
implementation measures or investments under the clean development mechanism
continue to be under discussion, and the perspective of an emerging global
functioning market on emission permits after 2010 (as assumed in this study) seems
challenging to say the least. If any of above mentioned assumption is relaxed,feasibility of climate stabilization declines and costs of meeting climate targets
increase. The results presented here should therefore be interpreted as minimum costestimates under an optimistic implementation outlook, ignoring important aspects ofpotential uncooperative behavior and of free-riding under any climate stabilization
regime. An important research task remains therefore, to extend both analysis andmodels in direction of uncertainty, imperfect information, and heterogeneous agentswith potentially non-cooperative behavior.

5

References

Alcamo, J., Bouwman, A., Edmonds, J., Grübler, A., Morita, T., and Sugandhy, A.,
1995: An evaluation of the IPCC IS92 emission scenarios. In Climate Change
1994, Radiative Forcing of Climate Change and An Evaluation of the IPCC IS92
Emission Scenarios, Cambridge University Press, Cambridge, pp. 233-304.
Alcamo, J. and Nakicenovic, N. (eds.), 1998: Long-Term Greenhouse Gas Emissions
Scenarios and Their Driving Forces, Special Issue, Mitigation and Adaptation
Strategies for Global Change, 3(2-4), 95-457.

46

Alcamo, J., Leemans, R., and Kreileman, E., eds., 1998: Global Change Scenarios ofthe 21st Century. Results from the IMAGE 2.1 model. Elsevier Science, London.
de Jong, A. and Zalm, G., 1991: Scanning the future: A long-term scenario study of
the world economy 1990-2015. In Long-term Prospects of the World Economy.
OECD, Paris, pp. 27-74.
de Vries B, Bollen, J., Bouwman, L., den Elzen, M., Janssen, M., and Kreileman, E.,
2000: Greenhouse-gas emissions in an equity-, environment- and service-oriented
world: an IMAGE-based scenario for the next century. Technological Forecasting
and Social Change, 63(2-3).
de Vries, B., Janssen, M., and Beusen, A., 1999: Perspectives on global energy futures– simulations with the TIME model. Energy Policy, 27, 477-494.
de Vries, H.J.M., Olivier, J.G.J., van den Wijngaart, R.A., Kreileman, G.J.J., andToet, A.M.C., 1994: Model for calculating regional energy use, industrial
production and greenhouse gas emissions for evaluating global climate scenarios.
Water, Air Soil Pollution, 76, 79-131.
den Elzen, M.G.J., 2002: Exploring climate regimes for differentiation of futurecommitments to stabilise greenhouse gas concentrations. Integrated Assessment
00(0):1-17.
Edmonds, J., Wise, M., and MacCracken, C., 1994: Advanced energy technologies
and climate change. An Analysis Using the Global Change Assessment Model(GCAM). PNL-9798, UC-402, Pacific Northwest Laboratory, Richland, WA,USA.
Edmonds, J., Wise, M., Pitcher, H., Richels, R., Wigley, T., and MacCracken, C.,
1996a: An integrated assessment of climate change and the accelerated
introduction of advanced energy technologies: An application of MiniCAM 1.0.
Mitigation and Adaptation Strategies for Global Change, 1(4), 311-339.
Edmonds, J., Wise, M., Sands, R., Brown, R., and Kheshgi, H., 1996b: Agriculture,land-use, and commercial biomass energy. A Preliminary integrated analysis of thepotential role of Biomass Energy for Reducing Future Greenhouse Related
Emissions. PNNL-11155, Pacific Northwest National Laboratories, Washington,
DC.
Fujii, Y., 1990: An assessment of the responsibility for the increase in the CO2
concentration and inter-generational carbon accounts, WP-90-55, International
Institute for Applied Systems Analysis, Laxenburg, Austria.
Grübler, A., and Nakicenovic, N., 1994: International burden sharing in greenhousegas reduction, RR-94-9, Laxenburg, Austria: International Institute for Applied
Systems Analysis.
Houghton, J.T., Jenkins, G.J., and Ephraums, J.J., eds., 1990: Climate Change: The
IPCC Scientific Assessment. Cambridge University Press, Cambridge, UK, 365 pp.
Houghton, J.T., Meira Filho, L.G., Callander, B.A., Harris, N., Kattenberg, A., andMaskell, K., eds., 1996: Climate Change 1995. The Science of Climate Change.
Contribution of Working Group I to the Second Assessment Report of theIntergovernmental Panel on Climate Change, Cambridge University Press,
Cambridge, UK 572 pp.

47

Hulme, M., Wigley, T., Barrow, E., Raper, S., Centella, A., Smith S., and Chipanski,
A., 2000. Using a climate scenario generator for vulnerability and adaptationassessments: MAGICC and SCENGEN version 2.4 workbook. Norwich, UK. 52
pp.
IPCC (Intergovernmental Panel on Climate Change) 1996: Climate Change 1995:
Economic and Social Dimensions of Climate Change. J.P. Bruce, H. Lee, E.F.
Haites (eds.), Contribution of Working Group III to the Second Assessment of theIntergovernmental Panel on Climate Change. Cambridge University Press, UK. pp448, ISBN: 0521560519.
IPCC (Intergovernmental Panel on Climate Change) 2001: Climate Change 2001:
Climate Change 2001: The Scientific Basis - Contribution of Working Group I to
the Third Assessment Report of IPCC, Cambridge University Press, UK. pp 944,
ISBN: 0521014956
IPCC (Intergovernmental Panel on Climate Change) 2002: Climate Change 2001:
Synthesis Report. Third Assessment Report of the Intergovernmental Panel on
Climate Change, Robert T. Watson (ed.), Cambridge University Press, Cambridge,
UK, 408 pp. ISBN 0521015073
Lashof, D. and Tirpak, D.A., 1990: Policy Options for Stabilizing Global Climate.
21P-2003. U.S. Environmental Protection Agency, Washington, DC.
Leggett, J., Pepper, W.J., and Swart, R.J., 1992: Emissions Scenarios for IPCC: An
Update. In: Climate Change 1992. The Supplementary Report to the IPCC
Scientific Assessment. J.T. Houghton, B.A. Callander and S.K. Varney (eds.),
Cambridge University Press, Cambridge, UK, pp. 69-95.
Manne A. and Richels, R., 1992: Buying Greenhouse Insurance: The Economic Costs
of CO2 Emission Limits. MIT Press, Cambridge, USA.
Messner, S. and Schrattenholzer, L., 2000: MESSAGE-MACRO: Linking an energy
supply model with a macroeconomic module and solving it iteratively. Energy
25:267-282.
Messner, S. and Strubegger, M., 1995: User’s Guide for MESSAGE III, WP-95-69.
International Institute for Applied Systems Analysis, Laxenburg, Austria.
Mori, S., 2000: The development of greenhouse gas emissions scenarios using an
extension of the MARIA model for the assessment of resource and energy
technologies. Technological Forecasting & Social Change, 63(2-3).
Mori, S., and Takahashi, M., 1999: An integrated assessment model for the evaluationof new energy technologies and food productivity. International Journal of Global
Energy Issues, 11(1-4), 1-18.
Morita, T. and Lee, H.-C., 1998a: Appendix to Emissions Scenarios Database andReview of Scenarios. Mitigation and Adaptation Strategies for Global Change, 3(24), 121-131.
Morita, T., Nakicenovic, N., and Robinson, J., 2000, Overview of mitigation scenarios
for global climate stabilization based on new IPCC emissions scenarios,
Environmental Economics and Policy Studies, 3(2), 65-88.

48

Morita, T., Robinson, J., Adegbulugbe, A., Alcamo, J., Herbert, D., La Rovere, E.L.,
Nakicenovic, N., Pitcher, H., Raskin, P., Riahi, K., Sankovski, A., Sokolov, V.,
Vries, H.J.M., Dadi, Z., 2001: Greenhouse Gas Emission Mitigation Scenarios and
Implications. In: Metz, B., O. Davidson, R. Swart and J. Pan (eds.), 2001, Climate
Change 2001: Mitigation, Contribution of Working Group III to the Third
Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge
University Press, Cambridge, UK. 700 pp, ISBN: 0521015022.
Morita, Y., Matsuoka, Y., Kainuma, M., and Harasawa, H., 1994: AIM - Asian
Pacific integrated model for evaluating policy options to reduce GHG emissions
and global warming impacts. In Global Warming Issues in Asia. S. Bhattacharya etal. (eds.), AIT, Bangkok, pp. 254-273.
Nakicenovic, N. and Riahi, K., 2001: An assessment of technological change across
selected energy scenarios. In: Energy Technologies for the Twenty-First Century,
World Energy Council (WEC), London, UK.
Nakicenovic, N., ed., 2000: Global Greenhouse Gas Emissions Scenarios: Five
Modeling Approaches, Special Issue. Technological Forecasting & Social Change
63(2-3):105-372.
Nakicenovic, N., Alcamo, J., Davis, G., de Vries, B., Fenhann, J., et al., 2000: Special
Report on Emissions Scenarios (SRES), Working Group III, Intergovernmental
Panel on Climate Change (IPCC), Cambridge University Press, Cambridge, UK.
ISBN 0-521-80493-0.
Nakicenovic, N., Grübler, A., and McDonald, A., eds., 1998a: Global Energy
Perspectives. Cambridge University Press, Cambridge, UK.
Nakicenovic, N., Amann, M., and Fischer, G., 1998b: Global Energy Supply and
Demand and their Environmental Effects, Report to the Central Research Instituteof the Electric Power Industry, International Institute for Applied Systems
Analysis, Laxenburg, Austria.
Pepper, W.J., Barbour, W., Sankovski, A., and Braaz, B., 1998: No-policy greenhousegas emission scenarios: revisiting IPCC 1992. Environmental Science & Policy, 1,
289-312.
Pepper, W.J., Leggett, J., Swart, R., Wasson, J., Edmonds, J., and Mintzer, I., 1992:
Emissions scenarios for the IPCC. An update: Assumptions, methodology, andresults. Support document for Chapter A3. In Climate Change 1992:
Supplementary Report to the IPCC Scientific Assessment. J.T. Houghton, B.A.
Callandar, S.K. Varney, eds., Cambridge University Press, Cambridge, UK.
Rao, S. and Riahi, K., 2003: Long Term Multigas Mitigation Strategies Using
MESSAGE. Presented at the International Energy Workshop (IEW), June 2003,
Laxenburg, Austria.
Riahi, K. and Roehrl R.A., 2000a: Greenhouse gas emissions in a dynamics-as-usual
scenario of economic and energy development. Technological Forecasting andSocial Change 63:175-205.
Riahi, K. and Roehrl, R.A., 2000b: Energy technology strategies for carbon dioxide
mitigation and sustainable development. Environmental Economics and Policy
Studies 63:89-123.

49

Sankovski, A., Barbour, W., and Pepper, W., 2000: Quantification of the IS99
emission scenario storylines using the atmospheric stabilization framework (ASF).
Technological Forecasting & Social Change, 63(2-3):263-287.
Smith, S.J., Wigley, T.M.L., Nakicenovic, N., and Raper, S.C.B., 2000: Climate
Implications of Greenhouse Gas Emissions Scenarios. Technological Forecasting
and Social Change 65(2):195-204.
Strubegger, M., McDonald, A., Gritsevskii, A., and Schrattenholzer, L., 1999:
CO2DB Manual, Version 2.0, April, 1999, IIASA, Laxenburg, Austria, 17 pp.
Swart, R., Mitchell, J., Morita, T., and Raper, S., 2002: Stabilization Scenarios for
Climate Impact Assessment. Global Environmental Change 12(3), pp 155-165
UNFCCC, 1992: United Nations Framework Convention on Climate Change,
http://www.unfccc.org/resource/convkp.html.
Wigley, T.M.L. and Raper, S.C.B., 2002: Reasons for larger warming projections in
the IPCC Third Assessment Report. J Climate 15(20):2945-2952.
Wigley, T.M.L., and Raper, S.C.B., 1997: Model for the Assessment of Greenhousegas Induced Climate Change (MAGICC Version 2.3.), The Climate Research Unit,
University of East Anglia, UK.
Wigley, T.M.L. and Raper, S.C.B., 1992: Implications for climate and sea level ofrevised IPCC emissions scenarios. Nature, 357, 293-300.
Wigley, T.M.L., et al., 1994: Model for the Assessment of Greenhouse-gas Induced
Climate Change Version 1.2. Climate Research Unit, University of East Anglia,
UK.

50