Download a critical assessment of the ipcc sres scenarios

Project no. GOCE-CT-2003-505539
Project acronym: ENSEMBLES
Project title: ENSEMBLE-based Predictions of Climate Changes and their Impacts
Instrument: Integrated Project
Thematic Priority: Global Change and Ecosystems
D7.1b Critical assessment of the IPCC SRES scenarios
Due date of milestone:
Actual submission date: March 2005
Start date of project: 1 September 2004
Duration: 60 Months
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Project co-funded by the European Commission within the Sixth Framework Programme (2002-2006)
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
A CRITICAL ASSESSMENT OF THE IPCC SRES SCENARIOS
Richard S.J. Tol, Brian O’Neill, Detlef P. van Vuuren
March 14, 2005
1.
Introduction
Emissions scenarios are a crucial part of climate change research. Emissions scenarios are
derived from population, economic and technology scenarios, which also shape vulnerability
to and hence impacts of climate change. Emissions scenarios drive scenarios of climate
change, which in turn drive impacts research. The Intergovernmental Panel on Climate
Change (IPCC) Special Report on Emissions Scenarios (SRES) developed a set of scenarios,
which currently form the core of most climate change research. This paper critically assesses
the SRES, as currently analysed with General Circulation Models (GCMs), including those in
the ENSEMBLES Integrated Project.
The IPCC SRES scenarios have been used extensively since their publication, and a
considerable amount of this work was evaluated in IPCC’s Third Assessment Report
(Houghton et al., 2001; McCarthy et al., 2001; Metz et al., 2001). This included research into
possible climatic change by climate modelers, impact and adaptation studies and analysis of
potential mitigation policies for climate change. The SRES scenarios will also serve as the
basis for many of the impact studies to be assessed in the IPCC Fourth Assessment Report
now underway. Interestingly, the SRES scenarios have also served as a basis or inspiration for
numerous other exercises at global, regional and national levels – where often new modelling
was consistent with the overall SRES storylines and published parameters (UNEP, 2002;De
Mooij and Tang, 2003; Kainuma et al., 2003; Van Vuuren et al., 2003).
The paper is structured as follows. In Section 2, we give a brief overview of how the SRES
scenarios came to be, what they are, and what they are not. In Section 3, we contrast scenarios
and observations. In Section 4, we critically look at the population scenarios considered. In
Section 5, we consider the recent discussion on purchasing power parity versus market
exchange rates. In Section 6, we discuss the assumptions of unconditional convergence
embedded in the SRES scenarios. Section 7 concludes.
2.
A brief overview of SRES
In 2000, the IPCC published a new set of emission scenarios in the Special Report on
Emission Scenarios (SRES), designed to serve as a basis for assessments of climate change
and possible response strategies (Nakicenovic, 2000). The SRES scenarios were developed in
a relatively open process that started in 1996. Modelling groups and individuals were invited
to contribute to the scenario development. Six modelling teams accepted this invitation and
participated in this exercise.1
The IPCC scenarios cover very long time periods (1990-2100) so as to capture the large
inertia involved in the climate system. Uncertainties obviously play a major role over such a
long time period. Future greenhouse gas emissions result from complex dynamic processes,
1
In addition, draft results of the modelling groups were put on a website for comments by outside reviewers. The
scenarios were also reviewed by both experts and government representatives.
including demographic and socio-economic development, and technological change. The
future evolution of these factors is highly uncertain, with various development patterns
capable of introducing very different futures. For example, the question of whether innovation
will drive the cost of non-emitting energy technologies down to a level below that of fossil
fuels is crucial. The SRES process addressed uncertainty in two ways. First, the scenarios
were based on a set of storylines describing alternative broad development patterns. Each
storyline was intended to represent consistent demographic, social, economic, technological,
and environmental developments. Thus, in contrast to earlier IPCC scenarios, the new
scenarios described not only plausible greenhouse gas emission trajectories, but also
consistent trajectories of human activities (‘drivers’). Second, the SRES process included six
different models for creating quantitative interpretations of each of the storylines. The multimodel approach was intended to capture uncertainties related to model structure.
There are four base scenarios: A1, A2, B1, and B2. The A scenarios place more emphasis on
economic growth, the B scenarios on environmental protection; the 1 scenarios assume more
globalisation, the 2 scenarios more regionalisation. The A1 scenario has three variants, A1(B),
A1FI, and A1T.
Population growth is highest in A2 (15 billion people in 2100), followed by B2 (10 billion)
and A1 and B1 (7 billion). Economic growth is most rapid in the A1 scenario, followed by
B1, B2 and A2. All four scenarios assume that developing countries grow faster than
developed ones; the gap between rich and poor closes most rapidly in the A1 scenario,
followed by B1, B2 and A2. Energy intensity falls in all four scenarios, most rapidly in the B1
scenario, followed by A1, B2 and A2. Coal as an energy source is almost phased out in B1,
A1B and A1T, roughly constant (as a share in total energy supply) in A1FI and B2, and
increasing in A2. Annual arbon dioxide emissions from fossil fuel combustion reach 30 GtC
in the A1FI scenario, 29 GtC in A2, 14 GtC in B2, 13 GtC in A1, 5 GtC in B1 and 4 GtC in
A1T in 2100. Carbon dioxide emissions from land use range from 0 to –2 GtC in 2100,
equalizing the differences between A1FI and A2, between A1B and B2, and between A1T
and B1. Sulphur emissions fall in all scenarios, fastest in A1T and slowest in A2. Methane
and nitrous oxide emissions rise in some scenarios (A1F1, A2, B2) but fall in others (A1B,
A1T, B1); A1FI has the highest emissions, B1 (for methane) and A1T (for nitrous oxide) the
lowest. Emissions of other greenhouse gas increase in all scenarios, except for B1, where they
are roughly constant. Precursors follow the same pattern as methane and nitrous oxide.
3.
Scenarios and observations
Although the SRES scenarios were published in 2000, most models used to develop the
scenarios were calibrated on 1990 and 1995 data, and most of the calculations were done well
before 1999 (when the review process started). Actual developments through 2000 were
obviously not known at that time. Now, information has become available that can be
compared against the SRES scenarios. First, the SRES report includes scenario data for both
1990 and 2000, allowing a factual comparison against new data for these time periods.
Second, since the publication of SRES, new projections have been published by different
sources. These new outlooks can be expected to include the latest (scientific) knowledge and
insights into events that have occurred since the development of SRES. They can also be used
to judge if these events could have substantially changed possible future trends. Such outlooks
often focus on shorter time frames than SRES (20-30 years instead of 100 years). In some
cases, however, new long-term scenarios have been published. Van Vuuren and O’Neill
(submitted) investigate the consistency of the IPCC SRES scenarios (which start in 1990) in
comparison with available 1990-2000 data and recent projections, primarily short-term
outlooks. The most important inconsistencies are summarized in Table 7.
In almost all the cases of (now) historical development through to and including the 21st
century, the SRES assumptions for 1990 and 2000 are reasonably consistent with available
data. This certainly holds for the global projections for income, population, energy and nonCO2 gases. Only small differences were found for these variables on the regional scale, in
particular, for income trends and energy trends in Asia and REF. For CO2 emissions, the
SRES scenarios indicate a slightly more rapid global increase between 1990 and 2000 than is
now apparent from emission inventories (15% versus an average of 11%), but the difference
in terms of absolute emissions in 2000 is small (mostly caused by the decline in coal use in
the late 1990s in China). Finally, for sulfur emissions, there seems to be a clear difference
between the assumed change in SRES in the 1990-2000 period and the trend in current
inventories (a global 3% versus 20% decline, respectively), mostly resulting from diverging
trends in the Asia and REF regions. In both the case of CO2 and sulfur is should be noted
trends in China in the late 1990s are not fully known.
Comparing the SRES scenarios to current near-term projections shows the SRES scenarios in
most cases to be within the range of these projections, both globally and for individual
regions. It should also be noted, however, that the range of population and economic
projections has shifted downward since SRES publication. While assumptions regarding these
drivers in most SRES scenarios still fall within this range, in a few cases, SRES assumptions
rise above it. In addition, the low end of the current range is under-represented in the SRES
scenarios for both population and economic growth. Revisions of the SRES scenarios based
on the same storylines could therefore be based on somewhat lower population projections
and near-term economic projections. This would be more important in particularly regions
and scenarios. In the case of economic growth, assumptions for the ALM region, the A1
scenario in particular, deserve the most attention. In the case of population, the assumptions
for the Asia and ALM regions in the A2 scenario would be the most important to consider for
revision since they differ the most from the updated range of projections. In addition, our
results show the differences between SRES and more recent population projections for the
medium term (2050) to be magnified in the long term (2100) due to the path dependency of
population growth. Lower population pathways are likely to lead to slightly lower greenhouse
gas emissions, in particular for A1 and B1. For economic growth, this relationship is
somewhat less direct, as downward revisions of economic growth in the ALM region might
have consequences for technology development and possibilities for important in this region.
At the same time, it should be noted that except for the first two decades for A1 – in terms of
emissions the SRES scenarios still seem to be fully consistent with the current range of more
recent outlooks. This might imply that along with revisions in driving forces, also revisions
might be made in the relationship between these drivers and greenhouse gas emissions.
Comparison on the regional scale shows that the most important differences between SRES
and the current near-term projections occur for the ALM region. Here, the range of SRES
economic growth assumptions and resulting growth rates for energy use and CO2 emissions
are near the upper end of current projections or beyond. By now, the assumed rapid change in
conditions for economic growth in this region seem to have become (even more)
questionable. The impact of this region on the global emissions projections is limited.
Although to a much smaller degree, the GDP and emissions growth rates of the Asia region in
the A1 scenario are also high compared to the recent projections.
Another important difference between the SRES scenarios and more recent insights globally
occurs for sulfur. As a result of the rapid decline in the 1990-2000 period and expectation
about desulfurization policies in low-income countries, a rapid increase in sulfur emissions of
some of the SRES scenarios for sulfur between 2000 and 2030 has become very unlikely.
Despite the fact that the exact trend in Chinese emissions during the 1990s remains an
important uncertainty, a revision of scenarios is likely to result in lower sulfur emissions. It
should be noted that this, other factors being equal, implies an increase in the expected
temperature change associated with the SRES scenarios.
Finally, there are a few elements that have not been included in the SRES scenarios in much
detail – and which recently have become much more important for climate change
projections; one of these is the emissions of black and organic carbon, while another is gridbased land-use projections. Non-official projections consistent with SRES assumptions have
now become available from individual modelling teams.
Regarding the question of whether the SRES scenarios have become outdated or not, there are
obviously no hard criteria. With a few exceptions, the study reported here has shown the
assumed SRES trends to still be plausible. In addition, there is no evidence that underlying
axioms of the storylines have been falsified. As a result, at this point in time there seems to be
no need for a large-scale IPCC-led update of the SRES scenarios on the sole basis of their
performance in the 1990-2000 period, or on the comparison with more recent projections. At
the same time, however, individual modelling groups could decide to update their scenarios,
making them fully consistent with current trends, while still preserving the connection with
the SRES storylines and harmonization criteria. Such an approach has been taken, for
instance, by the IMAGE group when it published its detailed elaboration of the SRES
scenarios in 2001. Variants of SRES scenarios could also be developed by independent
research teams to cover parts of the range of drivers or outcomes that are less well represented
in SRES; the low end of the range of future population size is one example. In fact, the SRES
report itself allowed for a great diversity of elaboration of the same scenarios – indicating
particular criteria for scenarios to meet in order to maintain consistency. Most of these criteria
are formulated for the longer term (first criteria to be applied in 2025). The option of updating
SRES scenarios (by individual modeling groups), while upholding the connection with the
SRES storylines and criteria, will, in general, keep results compatible with earlier work. This
will be based on SRES and allow for more comparability (and easier communication) in
assessment, for instance, in IPCC’s Fourth Assessment Report. At the same time, the SRES
updating option will allow research groups to produce long-term scenarios that are also well
suited to shorter term applications.Table 1. Main inconsistencies found between the SRES
scenarios and more recent scenarios and data
Parameter
Population
GDP
Energy
CO2
Non-CO2 gasses
Sulfur
4.
Population
Inconsistencies noted in comparison
 SRES does not include a representation of the current low-end
population scenarios
 The A2 scenarios outside the current 95% probability estimate
 For specific regions (in particular sub-Sahara Africa and China),
differences between SRES and current projections larger.
 Global economic projection for A1 is outside range of current
projections in the first 2 decades
 For the ALM region, the set of SRES scenarios seem to be
representative of the upper end scenarios only.
 No major anomalies with historical data
 See GDP
 See GDP for short-term projections
 Slightly too high for 2000 emissions
 Historically seen, somewhat too high for the F-gases
 Several forcing agents (black carbon) not included yet
 2000 sulfur emissions too high.
 Emissions in the first decades of the high emission scenarios
unlikely
The population projections used in SRES were consistent with the demographic outlook at
that time (Gaffin, 1998). The projection used in the B2 scenarios was the UN medium variant
(UN, 1998). The A1 and B1 scenarios all shared a common, relatively low, population
projection from IIASA, while the A2 scenario used a relatively high population projection
from IIASA (Lutz et al., 1996). These two projections spanned, at the global level,
approximately the 90% uncertainty interval associated with the IIASA probabilistic
projections (i.e. a level just within the 5th and 95th percentiles of the distribution).
Updated projections, however, generally anticipate less global population growth than the
projections used in the SRES scenarios. Since the early 1990s, birthrates in many parts of the
world have fallen surprisingly fast and the AIDS epidemic has taken an unexpectedly large
toll. These changes have led demographers to revise their outlook on future population
downward, toward smaller, older populations than previously anticipated. For example,
Figure 1a compares the projections for 2050 used in SRES to the most recent IIASA (Lutz et
al., 2001), UN (2003), World Bank (2003) and the US Census Bureau (US.BoC, 2003)
projections for the world and the four SRES macro regions. For comparability, the figure
plots all population sizes relative to the projected population in the SRES B2 scenario for each
region (i.e. the UN medium scenario produced in 1996).
For the world as a whole, population was projected at 9.4 billion in 2050 in the SRES B2
scenario. This figure shows that the A2 scenario anticipated a 21% larger global population,
and the A1 and B1 scenarios, a 7% smaller population than the B2 scenario. When these
scenarios are compared to more recent projections for the world, a small downward revision
to the medium (or “best guess”) projections and to the high end of the uncertainty range is
shown, along with a relatively large downward revision to the low end of the uncertainty
range. As a group, updated medium projections foresee a 3-6% (0.3 to 0.6 billion people)
smaller global population in 2050 relative to the SRES B2 projection; similarly, the high end
of the range has shifted down, so that the SRES A2 scenario now no longer falls within the
90% uncertainty interval. Rather, it falls 6-7 percentage points (0.5 – 0.7 billion people) above
the updated UN high scenario and the 95th percentile of the IIASA uncertainty range. At the
low end of the range changes are much larger: the SRES A1/B1 assumptions fall 14-18
percentage points (1.3 – 1.7 billion people) above the UN low scenario and the 5th percentile
of the IIASA uncertainty range.
Considering the four SRES macro regions, Asia and ALM are shown – due to their very large
absolute sizes - to drive the global results. They also share a similar pattern of change (large
downward revision to the low end of the uncertainty range and a smaller revision to medium
and high end). Analysis of smaller sub-regions (not shown) indicates that these changes are
primarily due to changes in the outlook for Sub-Saharan Africa, the Middle East and North
Africa region, and the East Asia region. Recent data showing lower than expected fertility in
these regions has led to less projected population growth. In addition, a much more
pessimistic view on the extent and duration of the HIV/AIDS crisis in sub-Saharan Africa has
also lowered anticipated growth in that region.
Changes in the outlook in the industrialized countries differ substantially from the global
pattern. In the OECD region, the UN projections are actually about 10% higher than
previously, despite continuing low fertility in these regions, due mainly to changes in
assumptions regarding migration. Previous UN projections did not attempt to project
migration beyond 2025, assuming instead that it was zero afterwards; the updated projections
assume non-zero migration through 2050. Updated IIASA projections are also slightly higher
in this region, due mainly to a more optimistic projection of future life expectancy. In the REF
region, projections from both institutions have been revised downward, especially in the UN
projections and for the high end of the uncertainty range. These changes have been driven by
recent data showing very low fertility levels and mortality that is quite high relative to other
industrialized countries, particularly in the Former Soviet Union.
It should be noted that the SRES A1/B1 assumptions for the industrialized countries (OECD
and REF regions) cannot be directly compared to the low-end range of more recent scenarios,
because SRES did not assume a low population growth projection for these regions (even
though growth was relatively low in A1/B1 for the world as a whole). Rather, SRES assumed
a medium fertility scenario coupled with relatively low mortality in these regions, which in
combination resulted in a future growth in these regions that was actually somewhat high
relative to a “best guess” projection.
Because population growth is a path-dependent process, changes in the estimates for the base
year and in the short-term outlook can have important implications for the plausibility of
long-term population growth paths. Therefore it is worth comparing the SRES population
assumptions to updated projections for the end of the century. IIASA and the UN are the only
institutions that have produced updated projections for the world that extend to 2100, shown
in comparison to the SRES assumptions in Figure 1b. Patterns are qualitatively similar to
those found for 2050, but larger in magnitude: a general downward shift in the full range of
projections that is somewhat larger at the lower end. For example, the most recent central
projections for global population are 13-19% (1.4-2.0 billion people) lower than the medium
population scenario used in the SRES B2 scenarios. Similarly, the SRES A2 population
assumption of 15 billion in 2100 is now 10-16 percentage points (1.1 – 1.7 billion) above the
UN high and IIASA 95th percentile. At the low end differences are larger: the UN low and
IIASA 5th percentile are 15-22 percentage points (1.6 – 2.2 billion) below the SRES A1/B1
assumptions.
As was the case with the outlook for 2050, the long term changes at the global level are driven
by the developing country regions (Asia and ALM), with the changes particularly large in the
China region, Middle East and North Africa, and Sub-Saharan Africa.
Although the range of projected population sizes has shifted down since the development of
the SRES scenarios, this does not automatically imply that the SRES population assumptions
are no longer credible. For example, the assumptions used in the SRES B2 and A1/B1
scenarios still fall within the plausible range of population outcomes according to more recent
outlooks (see Fig. 1). What is clearly missing, however, in the current SRES set is a
representation of a population projection that is representative of the lowest end of the current
range of population projections. This implies that if new scenarios were to be developed today
it would make sense to choose lower population growth assumptions, and for this reason some
researchers have produced revised versions of the SRES population assumptions. For example
Hilderink (2004) provides an alternative interpretation of the demographic implications of the
SRES storylines, and produces four new global population projections that span a range of 8
to 12 billion in 2100 (as compared to 7 to 15 billion in SRES).
At the high end of the range, the comparison of SRES to the updated outlook is less
favourable. The population projection used in the A2 scenarios now falls above the 95th
percentile in the IIASA projections and above the most recent UN high scenario. In particular
regions, the differences are especially large such as East Asia, Middle East, North Africa and
the Former Soviet Union. In these particular regions, the SRES assumptions now strain
credibility. Researchers using the SRES population scenarios should take these differences
into consideration. It is advisable to use revised projections for the regions with the largest
differences if possible. For example, IIASA has recently produced a new population scenario
for use in a stabilization variant of an A2 storyline that results in a population of about 12
billion in 2100.
It should also be kept in mind that while in the few regions discussed above there is a clear
inconsistency between the SRES A2 population assumptions and the more recent outlook,
there is, in general, a substantial range of population outcomes that is consistent with any
given SRES storyline (O’Neill, 2004). As long as there is consistency between the population
assumptions used to generate emissions and to evaluate impacts, researchers need not feel
tightly bound to a single population projection for each storyline.
(a)
Ratios to SRES Medium: World and SRES 4 Regions in 2100
1.4
SRES A2
1.2
Ratio to SRES B2
SRES B2
1.0
SRES A1/B1
0.8
0.6
0.4
UN Low & IIASA 90%
uncertainty interval (lower
bound)
0.2
UN/WB/USCB Medium &
IIASA Median
OECD90
REF
UN 2003
USBC 2003 & WB 2003
IIASA 2001
SRES
USBC 2003 & WB 2003
UN 2003
SRES
ASIA
IIASA 2001
USBC 2003 & WB 2003
UN 2003
IIASA 2001
SRES
USBC 2003 & WB 2003
UN 2003
SRES
WORLD
IIASA 2001
USBC 2003 & WB 2003
UN 2003
IIASA 2001
SRES
0.0
UN High & IIASA 90%
uncertainty interval (upper
bound)
USBC 2003
WB 2003
ALM
Ratios to SRES Medium: World and SRES 4 Regions in 2100
(b)
2.0
SRES A2
1.8
1.6
SRES B2
Ratio to SRES B2
1.4
1.2
SRES A1/B1
1.0
0.8
UN Low & IIASA 90%
uncertainty interval (lower
bound)
0.6
UN Medium &
IIASA Median
0.4
0.2
WORLD
OECD90
ASIA
REF
UN 2003
IIASA 2001
SRES
UN 2003
IIASA 2001
SRES
UN 2003
IIASA 2001
SRES
UN 2003
IIASA 2001
SRES
UN 2003
IIASA 2001
SRES
0.0
UN High & IIASA 90%
uncertainty interval (upper
bound)
ALM
Figure 1: Population size worldwide and for four SRES macro regions, relative to the
population size in the SRES B2 projection for (a) 2050 and (b) 2100. Source: (Nakicenovic
and Swart, 2000;Lutz et al., 2001;UN, 2003;US.BoC, 2003;WorldBank, 2003)
5.
Exchange rates
Recently, the IPCC SRES scenarios have been severely criticised (Castles and Hendersen,
2003a,b; Castles, 2004). The data underlying the SRES scenarios are largely based on market
exchange rates (MER) for converting national currencies into US dollars. Castles and
Henderson argue that the appropriate conversion should be based on purchasing power parity
exchange rates2 (PPP). Under PPP, the gap between rich and poor countries is smaller. Castles
and Henderson argue that therefore the economic growth rate and hence the emissions growth
rate would have been smaller had the SRES scenarios been based on PPP. Climate change
would be much less of a problem. This conclusion and the ensuing debate, commenced by the
defensive attitude of the IPCC (IPCC, 2003; Nakicenovic et al., 2003; Grübler et al., 2004),
attracted the attention of climate change policy makers, the press (Economist, 2003a,b, 2004),
and the sceptics (Michaels, 2003).
Per capita income in poor countries tends to be higher when measured in PPP (or RER) than
when measured in MER. Castles and Henderson (2003a,b) deduce from this that developing
country growth in the future would be slower under PPP than under MER. This implicitly
assumes convergence of per capita income (see Section 6). The SRES scenarios and most
economic models used for climate change policy analysis indeed assume convergence, so the
critique by Castles and Henderson is valid.
Besides convergence of per capita income, the SRES scenarios also assume convergence of
emissions intensities. Castles and Henderson (2003a,b) overlook this, but as Holtsmark and
Alfsen (2004a,b, 2005) and Tol (submitted) point out, switching from MER to PPP exchange
rates would increase emission projections, at least partially offsetting the drop in emissions
due to the income effect.
Manne and Richels (2004) and Manne et al. (forthcoming) use the MERGE model to show
that the choice between PPP and MER indeed alters carbon dioxide emissions, but that the
differences are small compared to other uncertainties, and that the differences get smaller if
one moves from emissions to concentrations to temperatures. Holtsmark and Alfsen (2004a,b,
2005) and Tol (submitted) concur. Tol (submitted) adds that the drop in global carbon dioxide
emissions when switching from MER to PPP is entirely due to a drop in emissions from
developing countries. This would change the political debate. Moreover, it would also imply a
different pattern of SO2 emissions. This effect has yet to be quantified.
6.
Convergence
The discussion on the exchange rate used highlights the SRES assumptions about the
convergence of per capita income and emission intensities.
In the economic growth literature (Barro and Sala-i-Martin, 1995), absolute or σ-convergence
of per capita income means that poorer countries grow faster than do richer countries. It does
not mean that the income gap between rich and poor would be closed by a specific date (e.g.,
2100), only that it would tend to fall and would close eventually. Absolute convergence
would occur because, if all else is equal, investments in poorer countries would yield a higher
return. Unsurprisingly, empirical studies have convincingly demonstrated that all else is not
equal, and absolute convergence has not happened. Conditional, club or β-convergence is
2
Strictly speaking, PPP exchange rates are not exchange rates in the sense that one can swap currencies at this
rate; rather, PPP “exchange rates” are measures of cross-country differences in price levels.
observed, however. Here, similar countries converge to the same income level. The clubs may
diverge, however.
Each of the SRES scenarios assume absolute convergence of per capita incomes (Nakicenovic
and Swart, 2000).
Miketa and Mulder (forthcoming) test the convergence assumption in energy intensities. They
find weak evidence for absolute convergence in the amount of energy used per dollar of
income generated, but strong evidence for conditional convergence.
Each of the SRES scenarios assume absolute convergence of emission intensities
(Nakicenovic and Swart, 2000).
As with PPP v MER, the erroneous assumption on convergence of emission intensities at least
partly offsets the erroneous assumption on convergence of per capita incomes. Again, the
implication would be slower economic growth in developing countries, and slightly slower
growth in their emissions. This would more pronounced in regional emissions of greenhouse
gases and aerosols than in global emissions, less pronounced in greenhouse gas
concentrations, and even less in the global mean temperature and sea level.
7.
Conclusions
The IPCC SRES emissions scenarios leave much to be desired. However, they constitute the
standard scenarios, and their quality is not worse, and often better than alternative emissions
scenarios. Moreover, much of the critique is directed at the demographic and economic details
of the scenarios. This may have lead to a small upward bias of emissions projection. The
range of future greenhouse gas emissions is undisputed, however. It is therefore appropriate
that the ENSEMBLES GCMs run the SRES scenarios.
Acknowledgements
The authors thank the comments received from Philip Bagnoli, Arnulf Grübler, David
Henderson, Jean-Charles Hourcade, Tom Kram, Keywan Riahi, Roberto Roson, Bill
Nordhaus, Hom Pant, Rich Richels, Jim Sweeney, Bert de Vries and John Weyant for their
comments contributed in various production stages of this paper. The CEC DG Research
through the NEMESIS/ETC and ENSEMBLES projects (ENG2-CT01-000538; GOCE-CT2003-505539) and the Michael Otto Foundation for Environmental Protection provided
welcome financial support.
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