The SRES scenarios generally cover the full range of GHG and sulfur emissions consistent with the storylines and the underlying range of driving forces from studies in the literature, as documented in the SRES database. This section summarizes the emissions of CO2 ,CH4 , and SO2 . For simplicity, only these three important gases are presented separately, following the more detailed exposition in Chapter 5 (see Table TS-4 for a summary of the ranges of emissions across the scenario groups).
Figure TS-6 illustrates the CO2 emissions across the SRES scenarios in relation to each of the three main scenario driving forces - global population, gross world product and primary energy requirements. The general tendencies across the driving forces are consistent with the underlying literature. All else being equal, the higher future global populations, higher gross world product, or higher primary energy requirements would be associated with higher emissions. However, it is important to note that the range of emissions is large across the whole range of driving forces considered in SRES, indicating the magnitude of the uncertainty associated with emission scenarios. For instance, emissions can range widely for any given level of future population (e.g. between 5 to 20 GtC in case of a low population scenario of seven billion by 2100). Conversely, emissions in the range of 20 GtC are possible with global population levels ranging from seven to 15 billion by 2100. While the SRES scenarios do not map all possibilities, they do indicate general tendencies, with an uncertainty range consistent with the underlying literature. This emphasizes an important SRES conclusion: alternative combinations of main scenario driving forces can lead to similar levels of GHGs emissions by the end of the 21st century. Alternatively, similar future worlds with respect to socio-economic developments can result in wide differences in future GHGs emissions, primarily as a result of alternative technological developments. This suggests that technology is at least as important a driving force of future GHG emissions as population and economic development across the set of 40 SRES scenarios.
|Figure TS-6a: Global carbon dioxide emissions (standardized) across SRES scenarios in relation to global population in 2100 for the four scenario families and six scenario groups. A1C and A1G have been combined into one fossil-intensive group A1F1. Shaded areas indicate scenario space for each scenario family and scenario group (in A1) (see Chapters 4 and 5).|
|Figure TS-6b: Global carbon dioxide emissions (standardized) across SRES scenarios in relation to gross world product in 2100 for the four scenario families and six scenario groups. A1C and A1G have been combined into one fossil-intensive group A1F1. Shaded areas indicate scenario space for each scenario family and scenario group (in A1) (see Chapters 4 and 5).|
|Figure TS-6c: Global carbon dioxide emissions (standardized) across SRES scenarios in relation to global primary energy requirements in 2100 for the four scenario families and six scenario groups. A1C and A1G have been combined into one fossil- intensive group A1F1. Shaded areas indicate scenario space for each scenario family and scenario group (in A1) (see Chapters 4 and 5).|
Figure TS-7: Global CO2 emissions from energy and industry in Figure TS-7a and from land-use change in Figure TS-7b - historical development from 1900 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 from the 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 probability of 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 (that result after A1C and A1G are combined into one fossil-intensive group A1FI, as in the SPM), which constitute four scenario families and three A1 scenario 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 colored vertical bars indicate the range of the four SRES scenario families in 2100. Also shown as vertical bars on the right of Figure TS-7a are the ranges of emissions in 2100 of IS92 scenarios and of scenarios from the literature 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"). 5 Three vertical bars in Figure TS-7b indicate the range of IS92 land-use emissions in 2025, 2050, and 2100.
Figure TS-7 illustrates the range of CO2 emissions of the SRES scenarios against the background of all the IS92 scenarios and other emissions scenarios from the literature documented in the SRES scenario database (blue shaded area). The range of future emissions is very large so that the highest scenarios envisage a tenfold increase of global emissions by 2100 while the lowest have emissions lower than today.
The literature includes scenarios with additional climate initiatives and policies, which are also referred to as mitigation or intervention scenarios. As shown in Chapter 2, many ambiguities are associated with the classification of emissions scenarios into those that include additional climate initiatives and those that do not. Many cannot be classified in this way on basis of the information available from the SRES scenario database and the published literature.
Figure TS-7a indicates the ranges of emissions in 2100
from scenarios that apparently include additional climate initiatives (designated
as "intervention" emissions range), those that do not ("non-intervention"),
and those that cannot be assigned to either of these two categories ("non-classified").
This classification is based on the subjective evaluation of the scenarios in
the database by the members of the writing team and is explained in Section
5 and in Chapter 2 in greater detail. It should be
noted that the distributions of emissions of scenarios from the literature is
asymmetric (see the emissions histogram in Figure 6-5
in Chapter 6) and that the thin tail that extends above
30 GtC by 2100 includes only a few scenarios.
Figure TS-7a shows the ranges of emissions of the four families (vertical bars next to each of the four marker scenarios), which illustrate that the scenario groups by themselves cover a large portion of the overall scenario distribution. Together, they cover much of the range of future emissions, both with respect to the scenarios in the literature and all SRES scenarios. Adding all other scenarios increases the covered range. For example, the SRES scenarios span jointly from the 95th percentile to just above the 5th percentile of the distribution of energy and industry emissions scenarios from the literature. This illustrates again that they only exclude the most extreme emissions scenarios found in the literature that are situated out in the tails of the distribution. What is perhaps more important is that each of the four scenario families covers a substantial part of this distribution. This leads to a substantial overlap in the emissions ranges of the four scenario families. In other words, a similar quantification of driving forces can lead to a wide range of future emissions and a given level of future emissions can result from different combinations of driving forces. This result is of fundamental importance for the assessments of climate change impacts and possible mitigation and adaptation strategies. Thus, it warrants some further discussion.
Another interpretation is that a given combination of the main driving forces, such as the population and economic growth, is not sufficient to determine the future emissions paths. Different modeling approaches and different specifications of other scenario assumptions overshadow the influence of the main driving forces. A particular combination of driving forces, such as specified in the A1 scenario family, is associated with a whole range of possible emission paths from energy and industry. The nature of climate change impacts and adaptation and mitigation strategies would be fundamentally different depending on whether emissions are high or low, given a particular combination of scenario driving forces. Thus, the implication is that the whole range needs to be considered in the assessments of climate change, from high emissions and driving forces to low ones.
The A1 scenario family explored variations in energy systems most explicitly and hence covers the largest part of the scenario distribution shown in Figure TS-7a, from the 95 th to just above the 10th percentile. The A1 scenario family includes four groups of scenarios that explore different structures of future energy systems, from carbon-intensive development paths to high rates of decarbonization. Two of the fossil-intensive groups were merged into one group, as in SPM, resulting in three A1 groups. All A1 groups otherwise share the same assumptions about the main driving forces (see Chapter 6 and for further detail Chapters 4 and 5). This indicates that different structures of the energy system can lead to basically the same variation in future emissions as generated by different combinations of the other main driving forces - population, economic activities, and energy consumption levels. The implication is that decarbonization of energy systems - the shift from carbon-intensive to less carbon-intensive and carbon-free sources of energy - is of similar importance in determining the future emissions paths as other driving forces. Sustained decarbonization requires the development and successful diffusion of new technologies. Thus investments in new technologies during the coming decades might have the same order of influence on future emissions as population growth, economic development, and levels of energy consumption taken together.
Figure TS-7b shows that CO2 emissions from deforestation peak in many SRES scenarios after several decades and subsequently gradually decline. This pattern is consistent with many scenarios in the literature and can be associated with slowing population growth and increasing agricultural productivity. These allow a reversal of current deforestation trends, leading to eventual CO2 sequestration. Emissions decline fastest in the B1 family. Only in the A2 family do net anthropogenic CO2 emissions from land use remain positive through to 2100. As was the case for energy-related emissions, CO2 emissions related to land-use in the A1 family cover the widest range. The range of land-use emissions across the IS92 scenarios is narrower in comparison.
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