3.2.2.1 CO₂ emissions from energy and industry

This category of emissions encompasses CO₂ emissions from burning fossil fuels, and industrial emissions from cement production and sometimes feedstocks.^[5] Figure 3.8 compares the range of the pre-SRES and SRES baseline scenarios with the post-SRES baseline scenarios. The figure shows that the scenario range has remained almost the same since the SRES. There seems to have been an upwards shift on the high and low end, but careful consideration of the data shows that this is caused by only very few scenarios and the change is therefore not significant. The median of the recent scenario distribution has shifted downwards slightly, from 75 GtCO₂ by 2100 (pre-SRES and SRES) to about 60 GtCO₂ (post SRES). The median of the recent literature therefore corresponds roughly to emissions levels of the intermediate SRES-B2 scenarios. The majority of scenarios, both pre-SRES and post-SRES, indicate an increase in emissions across most of the century, resulting in a range of 2100 emissions of 17–135 GtCO₂ emissions from energy and industry (90^th percentile of the full scenario distribution). Also the range of emissions depicted by the SRES scenarios is consistent with the range of other emission scenarios reported in the literature; both in the short and long term (see Van Vuuren and O’Neill, 2006).

Figure 3.8: Comparison of the SRES and pre-SRES energy-related and industrial CO₂ emissions scenarios in the literature with the post-SRES scenarios.

Note: The two vertical bars on the right extend from the minimum to maximum of the distribution of scenarios and indicate the 5^th, 25^th, 50^th, 75^th and the 95^th percentiles of the distributions by 2100.

Source: After Nakicenovic et al., 2006

Several reasons may contribute to the fact that emissions have not declined in spite of somewhat lower projections for population and GDP. An important reason is that the lower demographic projections are only recently being integrated into emission scenario literature. Second, indirect impacts in the models are likely to offset part of the direct impacts. For instance, lower energy demand leads to lower fossil fuel depletion, thus allowing for a higher share of fossil fuels in the total energy mix over a longer period of time. Finally, in recent years there has been increasing attention to the interpretation of fossil fuel reserves reported in the literature. Some models may have decreased oil and gas use in this context, leading to higher coal use (and thus higher emissions).

Analysis of scenario literature using the Kaya identity shows that pre-SRES and post-SRES baseline scenarios indicate a continuous decline of the primary energy intensity (EJ/GDP), while the change in carbon intensity (CO₂/E) is much slower – or even stable (see Figure 3.6 and Section 3.2.1.5) in the post-SRES scenarios. In other words, in the absence of climate policy, structural change and energy efficiency improvement do contribute to lower emissions, but changes in the energy mix have a much smaller (or even zero) contribution. This conclusion is true for both the pre-SRES, SRES, as well as the post-SRES scenario literature.

Baseline or reference emissions projections generally come from three types of studies:

1. Studies meant to represent a ‘best-guess’ of what might happen if present-day trends and behaviour continue.

2. Studies with multiple baseline scenarios under comprehensively different assumptions (storylines).

3. Studies based on a probabilistic approach.

In literature, since the TAR, there has been some discussion of the purpose of these approaches (see Schneider, 2001; Grübler et al., 2002; Webster et al., 2002). Figure 3.9 (left panel) shows a comparison of the outcomes of some prominent examples of these approaches by comparing the outcome of baselines scenarios reported in the set of EMF-21 scenarios, representing the ‘best-guess’ approach, to the outcomes of the SRES scenarios, representing the storyline approach. In the right panel the SRES range is compared to the probabilistic approach (see Webster et al., 2002; Richels et al., 2004, for the probability studies).

Figure 3.9: Comparison of various long-term scenario studies for CO₂ emissions. Left panel: IPCC SRES, EMF-21 range (grey area), indicating the range of the lowest and highest reported values in the EMF-21 study (Weyant et al., 2006). Right panel: Webster et al. (2002) and Richels et al. (2004), indicating the mean (markers) and 95% intervals of the reported ranges of these studies (for the latter, showing the 95% interval of the combined range for optimistic and pessimistic technology).

The figure shows that the range of different models participating in the EMF-21 study is somewhat smaller than those from SRES and the probabilistic approach. The range of EMF-21 scenarios result from different modelling approaches and from modeller’s insights into ‘the mostly likely values’ for driving forces. The two probabilistic studies and SRES explicitly assume more radical developments, but the number of studies involved is smaller. This leads to the low end of scenarios for the second category having very specific assumptions on development that may lead to low greenhouse gas emissions. The range of scenarios in the probabilistic studies tends to be between these extremes. Overall, the three different approaches seem to lead to consistent results, confirming the range of emissions reported in Figure 3.8 and confirming the emission range of scenarios used for the TAR.