Climate Change 2001:
Working Group III: Mitigation
Other reports in this collection Comparison of Technology and/or Policy Measures and Assessment of Robustness

Assumed technology and/or policy options differ among models (Morita et al., 2000a). These differences are strongly dependent on the model structure. MESSAGE-MACRO, LDNE, and MARIA are dynamic optimization-type models that incorporate detailed supply-side technologies; once a constraint on CO2 emission or concentration is imposed, the optimal set of technology and/or policy measures (focusing on energy supply) is automatically selected in the model. AIM and IMAGE are recursive simulation-type models which integrate physical and land-use modules rather than focus on energy demand, so that highly detailed technology and/or policy measures are assumed for each region and time as exogenous scenarios. ASF, MiniCAM, PETRO, and WorldScan are other types of integrated models focusing on the economics of energy systems. In these models, only a carbon tax is used for the post-SRES analyses.

In order to reduce CO2 and other GHG emissions, each modelling team assumed specific technology and/or policy measures for its scenario quantification. The main reduction measures are:

Table 2.7 summarizes the contribution of these emission mitigation options and/or measures for the post-SRES scenarios. The table shows the emission reduction (in GtC) between the baseline and the mitigation and/or stabilization cases, corresponding to the first six points of the list above. For simplicity, the total ranges as well as the median value in 2100 are shown only for the 550ppmv stabilization case. As shown in Table 2.7, no single source will be sufficient to stabilize atmospheric CO2 concentrations. Across the scenarios, the contributions of demand reduction, substitution among fossil fuels, and switching to renewable energy are all relatively large. The contributions of nuclear energy, of CO2 scrubbing and removal differ significantly among the models and also across the post-SRES scenarios.

Table 2.7: Sources of emissions reduction for 550ppmv stabilization across the nine post-SRES models. Minimum-maximum and (median) at 2100 (GtC)
Substitution among fossil fuels
-0.1 – 2.2
0.2 – 11.8
0.1 – 0.1
2.4 – 5.4
0.0 – 0.2
0.6 – 2.7
Switch to nuclear
0.3 – 6.4
-2.4 – 1.9
0.0 – 2.0
0.3 – 1.7
0.0 – 3.1
-0.2 – 5.1
Switch to biomass
-0.8 – 1.5
-0.2 – 5.5
-0.2 – 0.3
1.1 – 3.8
0.0 – 4.3
-1.9 – 1.5
Switch to other renewables
0.1 – 2.5
0.6 – 15.1
-0.1 – 0.0
2.2 – 6.7
0.1 – 0.3
0.1 – 3.2
CO2 scrubbing and removal
0.0 – 4.7
0.0 – 23.8
0.5 – 1.6
0.0 – 5.8
0.0 – 1.1
0.0 – 3.0
Demand reduction
0.5 – 6.6
1.9 – 17.7
0.0 – 0.2
5.2 – 15.6
0.1 – 0.3
0.7 – 3.5
TOTAL reduction
7.1 – 11.9
21.7 – 30.5
0.3 – 4.4
21.7 – 26.9
0.2 –9.6
6.0 – 10.6
Note: Emission reductions are estimated by subtracting the mitigation value (in GtC) from the baseline value (in GtC) of each scenario.

With respect to the role of biofuels, it should be noted that the models assume trade in biofuels across regions; hence, biomass produced in Africa and/or South America can satisfy the fuel needs of Asia. In all mitigation scenarios, the additional role of biomass, as a mitigation option, is limited and the world supply never exceeds 400EJ/yr; this is possible because the other options (solar and/or wind, nuclear, and CO2 removal and storage) also play a key role in mitigation strategies. Table 2.8 shows the ranges in primary biomass use in 2050 in the post-SRES scenarios.

Table 2.8: Ranges of primary use of biomass in 2050 in the post-SRES scenarios (EJ)
Stabilization target
246 - 328
226 - 246
137 - 246
96 - 186
127 - 189
76 - 228
78 - 217
74 - 217
22 - 232
36 - 176
27 - 157
0 -180
143 -184
25 - 63
Note: As the PETRO model does not separate biomass energy from primary energy, no number is filled in (*).

To contribute to a synthesis of findings, each modelling team was asked to respond to the following questions about the policy implications of the scenarios:

As shown in Table 2.7, high emission worlds such as A1FI, A2, and A1B require a larger introduction of energy demand reduction, switching to renewable energy, and substitution among fossil fuels, in comparison to other SRES worlds. The contribution of CO2 scrubbing and removal is largest in the A1FI stabilization scenarios, while mitigation measures in the A1T world depend mainly on a switch to nuclear power as well as carbon scrubbing and removal. Biomass energy steadily contributes across the SRES worlds and also across stabilization targets.

The following summarizes more detailed differences in technology and/or policy measures across the regions as well as the different SRES worlds:

One of the major results of the post-SRES analyses is the identification of “robust climate policy options” across the different SRES worlds as well as across different stabilization targets. Most of the modelling teams have identified several such options based on their simulations. The following list summarizes the major findings:

The post-SRES analyses supplied several other findings from individual model simulations. The AIM and the MESSAGE-MACRO teams as well as other teams found that technological progress plays a very important role in stabilization, and that knowledge transfer to developing countries is a key issue in facilitating their participation in early CO2 emission reduction. With respect to policy integration, the AIM team found that integration between climate policies and domestic policies could effectively reduce GHGs in developing regions from their baselines, especially for the next two or three decades. On the other hand, the MESSAGE-MACRO team estimated that regional air pollution control with respect to sulphur emissions tends to: (1) amplify global climate change in the medium-term perspective, and (2) accelerate the shift towards less carbon (and sulphur) intensive fuels such as renewables. The MiniCAM team concluded that agriculture and land use and energy system controls need to be linked, and that failure to do this can lead to much larger than necessary costs.

The above results are found with robust technology and/or policy measures across the SRES worlds and across different stabilization targets, and many of them are common among different modelling teams. A part of these common results can be tested by more detailed analyses of emission reduction sources, shown in Table 2.7. This table as well as time series analyses of the contribution of sources clearly show that:

Other reports in this collection