Emissions Scenarios


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4.4.2. Translation of Storylines into Scenario Drivers

Table 4-4 gives a summary overview of the main scenario assumptions and characteristics (see also Table 4-2 above). To facilitate comparability, the summary format adopted is similar to the previous IS92 scenario series (Pepper et al., 1992). Specific assumptions about the quantification of particular scenario drivers, such as population and economic growth, technological change, resource availability, land-use changes, and local and regional environmental policies, are summarized in this Section (GHG emissions are reported in detail in Chapter 5). The assumptions are based on the range of driving forces identified in Chapter 2 and their relationships as summarized in Chapter 3. For simplicity these drivers are presented separately, but it is important to keep in mind that the evolution of these scenario drivers is to a large extent interrelated, as reflected in the SRES scenarios.


Table 4- 4a: Overview of main driving forces for the four SRES marker scenarios for 2100 if not indicated otherwise. Numbers in brackets show the range across all other scenarios from the same scenario family as the marker. Units are given in the table. (IND regions includes industrialized countries consisting of OECD90 and REF regions; and DEV region includes developing countries consisting of ASIA and ALM regions, see Appendix IV).

  Population In Billion Economic Growth, GDPmexa Per Capita Income, GDPmex/capita Primary Energy Use Hydrocarbon Resource Useb Land- Use Changec

A1 Lutz (1996)
Low
~7 billion
1.4 IND
5.6 DEV
Very high

1990- 2020: 3.3 (2.8- 3.6)
1990- 2050: 3.6 (2.9- 3.7)
1990- 2100: 2.9 (2.5- 3.0)
Very high

in IND:
US$ 107,300 (60,300- 113,500)
in DEV:
US$ 66,500 (41,400- 69,800)
Very high

2.226 (1,002- 2,683) EJ

Low energy intensity of 4.2 MJ/ US$
(1.9- 5.1)
Varied in four scenario groups:
Oil: Low to very high 20.8 (11.5- 50.8) ZJ
Gas: High to very high 42.2 (19.7- 54.9) ZJ
Coal: Medium to very high 15.9 (4.4- 68.3) ZJ
Low.
1990- 2100:
3% cropland,
6% grasslands
2% forests

A2 Lutz (1996)
High
~15 billion
2.2 IND
12.9 DEV
Medium

1990- 2020: 2.2 (2.0- 2.6)
1990- 2050: 2.3 (1.7- 2.8)
1990- 2100: 2.3 (2.0- 2.3)
Low in DEV
Medium in IND
in IND:
US$ 46,200 (37,100- 64,500)
in DEV:
US$ 11,000 (10,300- 13,700)
High

1,717 (1,304- 2.040) EJ

High energy intensity of 7.1 MJ/ US$
(5.2- 8.9)
Scenario dependent:
Oil: Very low to medium 17.3 (11.0- 22.5) ZJ
Gas: Low to high 24.6 (18.4- 35.5) ZJ
Coal: Medium to Very high 46.8 (20.1- 47.7) ZJ
Medium

n.a. from ASF

B1 Lutz (1996)
Low
~7 billion
1.4 IND
5.7 DEV
High

1990- 2020: 3.1 (2.9- 3.3)
1990- 2050: 3.1 (2.9- 3.5)
1990- 2100: 2.5 (2.5- 2.6)
High

in IND:
US$ 72,800 (65,300- 77,700)
In DEV:
US$ 40,200 (40,200- 45,200)
Low.

514 (514- 1,157) EJ
Very low energy intensity of
1.6 EJ/ US$ (1.6- 3.4)
Scenario dependent:
Oil: Very low to high 19.6 (15.7- 19.6) ZJ
Gas: Medium to high 14.7 (14.7- 31.8) ZJ
Coal: Very low to high 13.2 (3.3- 27.2) ZJ
High
1990- 2100:
-28% cropland
-45% grassland
+30% forests

B2 UN (1998)
Median
~10 billion
1.3 IND
9.1 DEV
Medium

1990- 2020: 3.0 (2.2- 3.1)
1990- 2050: 2.8 (2.1- 2.9)
1990- 2100: 2.2 (2.0- 2.3)
Medium

in IND:
US$ 54,400 (42,400- 61,100)
In DEV:
US$ 18,000 (14,200- 21,500)
Medium

1,357 (846- 1,625) EJ
Medium energy intensity of 5.8 MJ/ US$ (4.3- 6.5)
Oil: Low to medium
19.5 (11.2- 22.7) ZJ by 2100
Gas: Low to medium 26.9 (17.9- 26.9) ZJ by 2100
Coal: Low to very high 12.6 (12.6- 44.4) ZJ by 2100
Medium
1990- 2100:
+22% cropland
+9% grasslands
+5% forestsd

A. Exponential growth rates after World Bank (1999) method (given on pages 371 to 372) are calculated using the different base years from the models.

B. Resource availability is generally combined with scenario specific rates of technological change.

C. Residual and other land- use categories are not shown in the Table.

D. Land- use data for B2 marker taken from AIM land- use B2 scenario run.



As discussed above, the SRES scenarios were designed to reflect inherent uncertainties of future developments by adopting a range of salient input assumptions, but without attempting to cover the extremes from the scenario literature. Given the nature of the SRES open process and its multi-model approach, as well as the need for documented input assumptions, published scenario extremes are difficult to reproduce using alternative model approaches or insufficiently documented input data. (For instance, many long-term emission scenarios do not report their underlying population assumptions (see Chapters 2 and 3), which is especially true for extreme scenarios that are usually performed within the context of model sensitivity analysis.)

Compared to the previous IS92 scenario series there are important similarities, but also important differences. For instance, three different future population scenarios were adopted, albeit that the future population levels are somewhat lower and the range more compressed than those in IS92 this reflects advances in demographic modeling and population projections. Conversely, the range of assumptions that concern resource availability and future technological change is much wider compared to earlier scenarios, reflecting in particular the results of the IPCC WGII Second Assessment Report (SAR; Watson et al., 1996). Another distinguishing characteristic of the SRES scenarios is an attempt to reflect the most recent understanding on the relationships between important scenario driving-force variables. For instance, no scenario combines low fertility with high mortality assumptions, which reflects the consensus view from demographers (see Chapter 3). Equally, all SRES scenarios assume a qualitative relationship between demographics and social and economic development trends, which reflects both the literature (see Chapter 3) and the results of the evaluation of the IS92 scenario series (Alcamo et al., 1995). All else being equal, fertility and mortality trends are thus lower in scenarios with high-income growth assumptions, but the multidimensionality of the causal linkages must be recognized and so no particular cause-effect model is postulated here. Finally, the scenarios also attempt to reflect recent advances (as reviewed in Chapter 3) in understanding of the evolution of macro-economic and material productivity (e.g., their coupling via capital turnover rates), uncertainties in future levels of "dematerialization" (reflected in the difference between the B1 and A1 scenarios), and the likely evolution of local and regional environmental policies (e.g., all scenarios assume various degrees of sulfur-control policies).

The main aspects of translating the storylines into scenario drivers are summarized below. For each scenario family an overview of all scenario quantifications is given. Scenarios that share harmonized input assumptions with the respective scenario marker in terms of global population and GDP profiles (see Tables 4-1 and 4-3) are indicated in italics in the subsequent discussion. Altogether, 26 scenarios in the four scenario families share similar assumptions about population and GDP at the global level. The other 14 scenarios either do not fully comply with the agreed common input assumptions concerning global population and GDP or explore important sensitivities of future demographic and economic developments beyond that described in the 24 scenarios. These sensitivities include resource availability, technology development, or land-use changes and describe similar demographic and economic development patterns as other scenarios within a family, even if they do not fall within the range suggested by the harmonization criteria (see Table 4-1). Combined, the SRES scenario set comprises 40 scenarios grouped into four scenario families and different scenario groups (see Table 4-3).

Each scenario family is illustrated by a designated marker scenario. A marker is not necessarily the mean or mode of comparable scenario quantifications, nor would it be possible to construct an internally consistent scenario reflecting medians/modes of all salient scenario characteristics (both in terms of scenario input assumptions as well as scenario outcomes, i.e. emissions). Marker scenarios should also not be interpreted as being the more likely alternative scenario quantifications. However, only the four marker scenarios were subjected to the SRES open process through the SRES website and they have also received closest scrutiny by the entire writing team.


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