6 SCENARIO ANALYSIS
6.5 C OMPARISON OF H ISTORICAL & S CENARIO D ATA
The right-hand graph in Figure 27 compares the normalised extent - rate relationships for nuclear power in the 8 scenarios with all the historical data points.38
Figure 28
Compared to the historical data, cumulative total capacities of nuclear power in future scenarios all have substantially higher ∆ts (i.e., slower rates of industry scaling). The comparison of ∆ts is made clearer in .
Figure 28. Rates of Industry Scaling for Nuclear Power: Historical & Scenarios. Δts in terms of cumulative total capacity (MW) globally for nuclear power both historically and in 8 scenarios. Bars for B2 480 & B2 670 scenarios are indicative only as scenario data reached < 60% of fitted K.
38 There are 6 historical data points in total. Global growth of wind power and CFLs is still exponential and so logistic fits are not reliable.
0 25 50 75 100 125
Δt (yrs) of cumulative total capacity
Scenario
Comparison of Δts (Global - nuclear power)
? (low % of K)
? (low % of K)
In the scenarios, nuclear power is projected to grow by two orders of magnitude in terms of cumulative total capacity (from ~0.4 TW in 2000 to 11-25 TW in 2100). But once this extent of industry scaling is normalised and assessed against the timescales over which it is reached, the scenario projections are conservative compared to known historical dynamics of industry scaling for energy technologies.
Put another way, scenario projections of nuclear power under carbon constraints show similar extents of scaling to those evidenced historically, but at much slower rates. To bring the nuclear scenario data points in line with the consistent historical extent - rate relationships, either normalised Ks would have to increase by another 3 orders of magnitude, or ∆ts would have to halve. This surprising finding is explained by the scenarios’ slow rates of scaling, shown starkly in Figure 28. Reasons for this are discussed in the next section.
The extent - rate relationships for natural gas, wind and solar power in the scenarios relative to the historical data points are similar to those for nuclear power: within the historical range of normalised extents, but with longer ∆ts. Figure 29 shows the
comparison for wind power. The relative position of the scenario data points below and to the right of the upwards sloping historical relationship is similar to that shown in Figure 27for nuclear power.
Figure 29. Extent - Rate Relationship of Industry Scaling in Scenarios: Wind Power.
Extent (normalised K) vs. rate (Δt) of industry scaling in terms of cumulative total capacity (MW) globally for all historical technologies (squares & circles) and for wind power in 8 scenarios (triangles). Unshaded data point for A2r base scenario is indicative only as scenario data reached < 60% of fitted K. Note log scale y-axis.
1.E+00
Δt (yrs) of cumulative total capacity
Cumulative Total Capacity (Global): normalised K vs Δt ALL TECHS HISTORICAL & WIND SCENARIOS - semi-log
REFINERIES (FCC)
Carbon capture & storage (‘CCS’) is the only technology in the scenarios that
approaches the historical extent - rate relationship. Figure 30 shows the comparison for all fossil-based CCS, i.e., coal power and natural gas power combined. The B2 670 data point is indicative only as the scenario data reached less than 60% of the fitted K.
Although the cluster of scenario data points still lies to the right of the upward sloping historical relationship, there is some overlap as the ∆ts are in the range of 24 - 66 years (compared to the historical range across all technologies of 19 - 64 years, with nuclear power and cars as the minimum and maximum respectively). As shown in Figure 31, industry scaling of CCS lies predominantly in the second half of the 21st century in the most carbon constrained scenarios (B1 & B2 480 ppmv). Its delayed scaling relative to other low carbon technologies is a function of modelling cost and performance
assumptions which reflect its relative immaturity.
Figure 30. Extent - Rate Relationship of Industry Scaling in Scenarios: Coal+Gas Power with CCS. Extent (normalised K) vs. rate (Δt) of industry scaling in terms of cumulative total capacity (MW) globally for all historical technologies (squares & circles) and for coal+natural gas power with carbon capture and storage ('CCS') in 8 scenarios
(triangles). Data point for B2 670 scenario is indicative only as logistic fit was poor.
Note log scale y-axis.
That industry scaling of renewables is more conservatively modelled than CCS is also surprising in light of the stronger ‘diffusion’ characteristics of CCS with its
requirements for a whole new distribution and storage infrastructure (Bielicki 2008).
Renewables, particularly centralised wind and solar, are clearer substitutes for conventional utility-scale power plants, requiring less ancillary changes to existing institutions and related technologies (Grübler et al. 1999)
1.E+00
Δt (yrs) of cumulative total capacity
Cumulative Total Capacity (Global): normalised K vs Δt ALL TECHS HISTORICAL & COAL+GAS CCS SCENARIOS - semi-log
REFINERIES (FCC)
Figure 31. Scaling of CCS in Future Scenarios. Scenario data and logistic fits for coal and natural gas power with carbon capture and storage ('CCS') globally.
Figure 32 brings together the historical and scenario data on industry scaling for all the technologies. The pattern is striking. With the exception of a small number of CCS scenarios (combining both coal and natural gas power), all the scenario data points lie to the right of the empirically-founded historical extent - rate relationship. Industry scaling of all technologies in all scenarios is therefore more conservative than the historical record indicates is feasible. This conservatism is attributable to the slow projected rates of industry scaling.
As with the historical data, this observed pattern holds if the global data are
disaggregated regionally. Figure 33 shows the same plot as Figure 32 but for the Core region and for a reduced number of scenario data points: nuclear power, all fossil CCS, and solar PV. Note that the historical extent – rate relationship is steeper than for the global data as the K – Δt relationship ‘accelerates’ or flattens out from Core to Rim (see Section 4.6 & 4.8.2 for discussion).
Two points from Figure 33 are salient. Firstly, the historical data points and scenario data points for each technology have the same relative position regionally as globally.
So the discussion above relating to the global data applies equally to the regional data.
Secondly, the fossil CCS data points are again the closest to the historical pattern, and in some cases are strongly overlapping. Again, this is for the same reasons as explained above with respect to the global data: concentration of CCS growth in the second half of the 21stcentury and so shorter Δts.
.
1980 2000 2020 2040 2060 2080 2100
Cumulative Total Capacity (GW)
Coal +Gas Power with CCS (Global, 1980-2100), Cumulative Total Capacity:
Historical Data + Scenarios (with logistic fits)
Coal+Gas with CCS (Global) - A2r base - Cumulative Total Capacity (GW) Coal+Gas with CCS (Global) - A2r base - Cumulative Total Capacity (GW) - logistic fit Coal+Gas with CCS (Global) - B1 base - Cumulative Total Capacity (GW) Coal+Gas with CCS (Global) - B1 base - Cumulative Total Capacity (GW) - logistic fit Coal+Gas with CCS (Global) - B2 base - Cumulative Total Capacity (GW) Coal+Gas with CCS (Global) - B2 base - Cumulative Total Capacity (GW) - logistic fit Coal+Gas with CCS (Global) - A2r 670 - Cumulative Total Capacity (GW) Coal+Gas with CCS (Global) - A2r 670 - Cumulative Total Capacity (GW) - logistic fit Coal+Gas with CCS (Global) - B1 670 - Cumulative Total Capacity (GW) Coal+Gas with CCS (Global) - B1 670 - Cumulative Total Capacity (GW) - logistic fit Coal+Gas with CCS (Global) - B2 670 - Cumulative Total Capacity (GW) Coal+Gas with CCS (Global) - B2 670 - Cumulative Total Capacity (GW) - logistic fit Coal+Gas with CCS (Global) - B1 480 - Cumulative Total Capacity (GW) Coal+Gas with CCS (Global) - B1 480 - Cumulative Total Capacity (GW) - logistic fit Coal+Gas with CCS (Global) - B2 480 - Cumulative Total Capacity (GW) Coal+Gas with CCS (Global) - B2 480 - Cumulative Total Capacity (GW) - logistic fit Coal with CCS (Global) - Historical - Cumulative Total Capacity (GW)
Figure 32. Extent - Rate Relationship of Industry Scaling in Scenarios: All Technologies. Extent (normalised K) vs. rate (Δt) of industry scaling in terms of cumulative total capacity (MW) globally for all historical technologies (black squares) and all technology-scenario combinations (diamonds, triangles, circles). Grey dotted lines show general upward-sloping pattern of historical extent - rate relationships.
Unshaded data points are indicative only. Note log scale y-axis.
Figure 33. Extent - Rate Relationship of Industry Scaling in Scenarios: All
Technologies (Core region). Extent (normalised K) vs. rate (Δt) of industry scaling in terms of cumulative total capacity (MW) in the Core region for all historical
technologies (black squares) and all technology-scenario combinations (diamonds, triangles, circles). Grey dotted lines show general pattern of historical extent - rate relationships. Unshaded data points are indicative only. Note log scale y-axis.
1.E+00
Δt (yrs) of cumulative total capacity
Cumulative Total Capacity (Global): normalised K vs Δt ALL TECHS: HISTORICAL & SCENARIOS - semi-log
HISTORICAL (ALL
Δt (yrs) of cumulative total capacity
Cumulative Total Capacity (Core region): normalised K vs Δt ALL TECHS: HISTORICAL & SCENARIOS - semi-log
HISTORICAL (ALL