3.6.2 Evaluation of short-term mitigation opportunities in long-term stabilization scenarios
220.127.116.11 Studies reporting short-term sectoral reduction levels
While there are many potential emissions pathways to a particular stabilization target from a specific year, it is possible to define emissions trajectories based on short-term mitigation opportunities that are consistent with a given stabilization target. This section assesses scenario results (by sector) from top-down models for the year 2030, to evaluate the range of short-term mitigation opportunities in long-term stabilization scenarios. To put these identified mitigation opportunities in context, Chapter 11, Section 11.3 compares the short-term mitigation estimates across all of the economic sectors.
Many of the modelling scenarios represented in this section were an outcome from the Energy Modelling Forum Study 21 (EMF-21), which focused specifically on multi-gas strategies to address climate change stabilization (see De la Chesnaye and Weyant, 2006). Models that were evaluated in this assessment are listed in Table 3.12.
Table 3.12: Top-down models assessed for mitigation opportunities in 2030
|Model ||Model type ||Solution concept ||Time horizon ||Modelling team and reference |
|AIM (Asian-Pacific Integrated Model) ||Multi-Sector General Equilibrium ||Recursive Dynamic ||Beyond 2050 ||NIES/Kyoto Univ., Japan Fujino et al., 2006. |
|GRAPE (Global Relationship Assessment to Protect the Environment) ||Aggregate General Equilibrium ||Inter-temporal Optimization ||Inter-temporal Optimization ||Institute for Applied Energy, Japan Kurosawa, 2006. |
|IMAGE (Integrated Model to Assess The Global Environment) ||Market Equilibrium ||Recursive Dynamic ||Beyond 2050 ||Netherlands Env. Assessment Agency Van Vuuren et al., Energy Journal, 2006a. (IMAGE 2.2) Van Vuuren et al., Climatic Change, 2007. (IMAGE 2.3) |
|IPAC (Integrated Projection Assessments for China) ||Multi-Sector General Equilibrium ||Recursive Dynamic ||Beyond 2050 ||Energy Research Institute, China Jiang et al., 2006. |
|MERGE (Model for Evaluating Regional and Global Effects of GHG Reduction Policies) ||Aggregate General Equilibrium ||Inter-temporal Optimization ||Beyond 2050 ||EPRI & PNNL/Univ. Maryland, U.S. USCCSP, 2006. |
|MESSAGE-MACRO (Model for Energy Supply Strategy Alternatives and Their General Environmental Impact) ||Hybrid: Systems Engineering & Market Equilibrium ||Inter-temporal Optimization ||Beyond 2050 ||International Institute for Applied Systems Analysis, Austria Rao and Riahi, 2006. |
|MiniCam (Mini-Climate Assessment Model) ||Market Equilibrium ||Recursive Dynamic ||Beyond 2050 ||PNNL/Univ. Maryland, U.S. Smith and Wigley, 2006. |
|SGM (Second Generation Model) ||Multi-Sector General Equilibrium ||Recursive Dynamic ||Up to 2050 ||PNNL/Univ. Maryland and EPA, U.S. Fawcett and Sands, 2006. |
|POLES (Prospective Outlook on Long-Term Energy Systems) ||Market Equilibrium ||Recursive Dynamic ||Up to 2050 ||LEPII-EPE & ENERDATA, France Criqui et al., 2006. |
|WIAGEM (World Integrated Applied General Equilibrium Model) ||Multi-Sector General Equilibrium ||Inter-temporal Optimization ||Beyond 2050 ||Humboldt University and DIW Berlin, Germany Kemfert et al., 2006. |
For each model, the resulting emissions in the mitigation case for each economic sector in 2030 were compared to projected emissions in a reference case. Results were compared across a range of stabilization targets. For more detail on the relationship between stabilization targets defined in concentrations, radiative forcing and temperature, see Section 3.3.2.
Key assumptions and attributes vary across the models evaluated, thus having an impact on the results. Most of the top-down models evaluated have a time horizon beyond 2050 such as AIM, IPAC, IMAGE, GRAPE, MiniCAM, MERGE, MESSAGE, and WIAGEM. Top-down models with a time horizon up to 2050, such as POLES and SGM, were also evaluated. The models also vary in their solution concept. Some models provide a solution based on inter-temporal optimization, allowing mitigation options to be adopted with perfect foresight as to what the future carbon price will be. Other models are based on a recursive dynamic, allowing mitigation options to be adopted based only on today’s carbon price. Recursive dynamic models tend to show higher carbon prices to achieve the same emission reductions as in inter-temporal optimization models, because emitters do not have the foresight to take early mitigation actions that may have been cheaper (for more discussion on modelling approaches, refer to Section 3.3.3).
Three important considerations need to be remembered with regard to the reported carbon prices. First, these mitigation scenarios assume complete ‘what’ and ‘where’ flexibility (i.e. there is full substitution among GHGs and reductions take place anywhere in the world, according to the principle of least cost). Limiting the degree of flexibility in these mitigation scenarios, such as limiting mitigation only to CO2, removing major countries or regions from undertaking mitigation, or both, will increase carbon prices, all else being equal. Second, the carbon prices of realizing these levels of mitigation increase in the time horizon beyond 2030. See Figure 3.25 for an illustration of carbon prices across longer time horizons from top-down scenarios. Third, at the economic sector level, estimated emission reduction for all greenhouse gases varies significantly across the different model scenarios, in part because each model uses sector definitions specific to that type of model.
Across all the models, the long-term target in the stabilization scenarios could be met through the mitigation of multiple greenhouse gases (CO2, CH4, N2O and high-GWP gases). However the specific mitigation options and the treatment of technological progress vary across the models. For example, only some of the models include carbon capture and storage as a mitigation option (GRAPE, IMAGE, IPAC, MiniCAM, and MESSAGE). Some models also include forest sinks as a mitigation option. The model results shown in Table 3.13 do not include forest sinks as a mitigation option, while the results shown in Table 3.14 do include forest sinks, as described in further detail below.
Table 3.13: Global emission reductions from top-down models in 2030 by sector for multi-gas scenarios.
|Model ||POLES ||IPAC ||AIM ||GRAPE ||MiniCAM ||SGM ||MERGE ||WIAGEM |
|Stabilization category ||Category VI ||Category II ||Category I |
|Stabilization target ||550 ppmv ||550 ppmv ||4.5 W/m2 from pre-Industrial ||4.5 W/m2 from pre-Industrial ||4.5 W/m2 from pre-Industrial ||From MiniCAM trajectory ||3.4 W/m2 from pre-Industrial ||2% from pre-Industrial |
|Carbon price in 2030 (2000 US$/tCO2-eq) ||57 ||14 ||29 ||2 ||12 ||21 ||192 ||9 |
|Reference emissions 2030 Total all gases (GtCO2-eq) ||53.0 ||55.3 ||49.4 ||57.0 ||54.2 ||53.5 ||47.2 ||43.1 |
|Sector Mitigation estimates in 2030 (total all gases GtCO2-eq) ||Energy supply: electric ||9.5 ||6.4 ||5.2 ||0.5 ||7.3 ||3.1 ||9.5 ||7.0 |
|Energy supply: non-electric ||3.0 ||0.6 ||1.1 ||0.0 ||1.5 ||1.6a ||3.2 ||1.7 |
|Transportation demand ||0.5 ||0.8 ||0.5 ||0.1 ||0.2 ||0.4a ||Included in Energy supply ||Included in Energy supply |
|Buildings demand ||1.0 ||0.6 ||0.5 ||0.4 ||0.3 ||Included in Energy supply ||Included in Energy supply ||Included in Energy supply |
|Industry demand ||1.9 ||1.2 ||0.5 ||Included in Buildings demand ||1.7 ||Included in Energy supply ||Included in Energy supply ||Included in Energy supply |
|Industry production ||0.8 ||0.0 ||0.8 ||0.3h ||0.2d ||1.7a ||3.6b ||3.6 |
|Agriculture ||(0.2) ||(1.0)e ||2.0 ||0.6 ||0.3 ||1.7 ||Included in industry production ||1.1 |
|Forestry ||No mitigation options modelled ||No mitigation options modelled |
|Waste management ||Included in another sector ||0.0g ||Included in Buildings demand ||0.0f ||0.3 ||0.5 ||Included in Industry production ||No mitigation options modelled |
|Global total ||16.4 ||8.7 ||10.6 ||1.9 ||11.9 ||11.2a ||16.3 ||15.5c |
|Mitigation as % of reference emissions ||31% ||16% ||21% ||3% ||22% ||21% ||35% ||35% |
Table 3.13 illustrates the amount of global GHG mitigation reported by sector for the year 2030 across a range of multi-gas stabilization targets. Across the higher Category IV stabilization target scenarios, emission reductions of 3–31% from the reference case emissions across all greenhouse gases can be achieved for a carbon price of 2–57 US$/tCO2-eq. The results from the POLES models fall into the higher end of the price range, in part due to the recursive dynamic nature of the model, and also due to its shorter time horizon over which to plan. The results from the GRAPE model fall into the lower end of the price range, which is the only inter-temporally optimizing model shown in the higher stabilization scenarios. In the GRAPE results, only 3% of the emissions are reduced by 2030, implying that the majority of the mitigation necessary to meet the target is undertaken beyond 2030. In scenarios with lower Category I and II stabilization targets, higher levels of short-term mitigation are required to achieve the target in the long run, resulting in a higher range of prices. Emission reductions of approximately 35% can be achieved at a price of 9–92 US$/tCO2-eq.
Several of the models included in the EMF-21 study also ran multi-gas scenarios that included forest sinks as a mitigation option. Table 3.14 shows the 2030 mitigation estimates for these scenarios that model net land-use change (including forest carbon sinks) as a mitigation option. When terrestrial sinks are modelled as a mitigation option, it can lessen the pressure to mitigate in other sectors. Further discussion of forest sequestration as a mitigation option is presented in Section 18.104.22.168. Across the higher Category IV stabilization target scenarios, emission reductions of 4–24% from the reference case emissions across all greenhouse gases can be achieved at a price of 2–21 US$/tCO2-eq. In scenarios with lower Category I and II stabilization targets, emission reductions of 26–40% can be achieved at a price of 31–121 US$/tCO2-eq.
Table 3.14: Global emission reductions from top-down models in 2030 (by sector) for multi-gas plus sinks scenarios.
|Model ||GRAPE ||IMAGE 2.2 ||IMAGE 2.3 ||MESSAGE ||MESSAGE ||IMAGE 2.3 ||IMAGE 2.3 ||MESSAGE |
|Stabilization categories ||Category VI ||Category III ||Category I/II |
|Stabilization target ||4.5 Wm2 from pre-Industrial ||4.5 Wm2 from pre-Industrial ||4.5 Wm2 from pre-Industrial ||B2 scenario, 4.5 Wm2 from pre-Industrial ||A2 scenario, 4.5 Wm2 from pre-Industrial ||3.7 Wm2 from pre-Industrial ||3.0 Wm2 from pre-Industrial ||B2 scenario, 3.0 Wm2 from pre-Industrial |
|Carbon price in 2030 (2000 US$/tCO2-eq) ||2 ||18 ||21 ||6 ||15 ||50 ||121 ||31 |
|Reference emissions 2030 Total all gases (GtCO2-eq) ||57.0 ||65.5 ||59.7 ||57.8 ||70.9 ||59.7 ||59.7 ||57.8 |
|Sector mitigaiton estimates in 2030 (total all gases GtCO2-eq) ||Energy supply: electric ||0.5 ||2.4 ||1.7 ||1.1 ||7.3 ||3.9 ||8.7 ||4.3 |
|Energy supply: non-electric ||0.0 ||2.2 ||1.6 ||0.5 ||3.5 ||2.3 ||3.7 ||2.2 |
|Transportation demand ||0.0 ||1.3 ||0.7 ||0.3 ||1.0 ||1.5 ||2.8 ||2.2 |
|Buildings demand ||0.3 ||0.8 ||0.3 ||0.5 ||1.2 ||0.5 ||1.0 ||1.4 |
|Industry demand ||Included in Buildings demand ||0.8 ||0.5 ||0.1 ||0.4 ||1.6 ||3.2 ||0.8 |
|Industry production ||0.1b ||1.1 ||0.8 ||0.3 ||0.6 ||1.1 ||2.0 ||0.8 |
|Agriculture ||0.3 ||0.7 ||0.6 ||0.6 ||1.5 ||1.0 ||1.2 ||1.7 |
|Forestry ||0.9 ||1.4 ||0.3 ||0.0 ||0.2 ||0.2 ||0.2 ||0.6 |
|Waste management ||0.0a ||0.7 ||1.0 ||0.9 ||1.1 ||1.0 ||1.1 ||0.9 |
|Global total ||2.1 ||11.5 ||7.6 ||4.4 ||16.8 ||13.0 ||24.0 ||15.0 |
|Mitigation as % reference emissions ||4% ||18% ||13% ||8% ||24% ||40% ||40% ||26% |