11.3.2 Comparing bottom-up and top-down sectoral potentials for 2030

Table 11.5 and Figure 11.3 bring together the ranges of economic potentials synthesized from Chapters 4 to 10, as discussed in 11.3.1, with the ranges of top-down sectoral estimates for 2030 presented in Chapter 3. The bottom-up estimates are shown with the potentials from end-use electricity savings attributed (1) to the end-use sectors, i.e. to the buildings and industry sectors primarily responsible for the electricity use and (2) upstream, at the point of emission to the energy supply sector. The top-down ranges are provided by an analysis of the data from multi-gas studies for 2030 reported in Section 3.6. A relationship has been estimated between the absolute reductions in total GHGs and the carbon prices required to achieve them (see Appendix 3.1). Ranges for mitigation potential have been calculated for a 68% confidence interval for carbon prices at 20 and 100 US$(2000)/tCO₂-eq. The ranges are shown in the last two columns of Table 11.5.

Figure 11.3: Economic mitigation potential in different cost categories as compared to the baseline

Notes: The ranges reported in Tables 3.13 and 3.14 were used for comparison for the top-down studies. ‘High’ and ‘low’ refer to the high and low ends

of the economic potential range reported.

The ranges of bottom-up and top-down aggregate estimates of potentials overlap substantially under all cost ceilings except for the no-regrets bottom-up options. This contrasts with the comparison in the TAR, where top-down costs were higher. It is not the case that bottom-up approaches systematically generate higher abatement potentials. This change comes largely from lower costs in the top-down models, because some have introduced multi-gas abatement and have introduced more bottom-up features, such as induced technological change, which also tend to reduce costs.

Two further points can be made with regard to the comparison of bottom-up and top-down results:

1) Sector definitions differ between top-down and bottom-up approaches. The sectoral data presented here are not fully comparable. The main difference is that the electricity savings are allocated to the power sector in the top-down models compared to the end-use sectors in Table 11.3. Both allocation approaches are presented in Table 11.5.

2) At a sector level however, there are some systematic and striking discrepancies:

Energy supply. The top-down models indicate a higher emission reduction. This can be explained in part by differences in the mitigation options that are included in the top-down models and not included in the bottom-up approach. Examples are: reductions in extraction and distribution, reductions of other non-CO₂ emissions, and reductions through the increased use of CHP. Further, different estimates of the inertia of the substitution are expected to play a role. In bottom-up estimates, fuel substitution is assumed only after end-use savings whereas top-down models adopt a more continuous approach. Finally, the top-down estimates include the effects of energy savings in other sectors and structural changes. For example, a reduction in oil use also implies a reduction in emissions from refineries. These effects are excluded from the bottom-up estimates.

Buildings. Top-down models give estimates of reduction potentials from the buildings sector that are lower than those from bottom-up assessments. This is because the top-down models look only at responses to price signals, whereas most of the potential in the buildings sector is thought to be from ‘negative cost’ measures that would be primarily realized through other kinds of interventions (such as buildings or appliance standards). Top-down models assume that the regulatory environments of ‘reference’ and ‘abatement’ cases are similar, so that any negative cost poten- tial is either neglected or assumed to be included in baseline.

Agriculture and forestry. The estimates from bottom-up assessments were higher than those found in top-down studies, particularly at higher cost levels. These sectors are often not covered well by top-down models due to their specific character. An additional explanation is that the data from the top-down estimates include additional deforestation (negative mitigation potential) due to biomass energy plantations. This factor is not included in the bottom-up estimates.

Industry. The top-down models generate higher estimates of reduction potentials in industry than the bottom-up assessments. One of the reasons could be that top-down models allow for product substitution, which is often excluded in bottom-up sector analysis; equally, top-down models may have a greater tendency to allow for innovation over time.

The overall bottom-up potential, both at low and high carbon prices, is consistent with that of 2030 results from top-down models as reported in Chapter 3, Section 3.6.2 for a limited set of models. For carbon prices <20 US$/tCO₂-eq, the ranges are 10–17 GtCO₂-eq/yr for bottom-up, as opposed to 9–18 GtCO₂-eq/yr for top-down studies. For carbon prices <50 US$/tCO₂-eq, the ranges are 14–25 GtCO₂-eq/yr for bottom-up versus 14–23 GtCO₂-eq/yr for top-down studies. For carbon prices <100 US$/tCO₂-eq the ranges are 16–30 GtCO₂-eq/yr and 17–26 GtCO₂-eq/yr for bottom-up and top-down respectively. As explained above, the differences between bottom-up and top-down are larger at the sector level.