This section explores the efficiency of various sets of policy scenarios under extreme condi- tions of natural resources availability. Policy sets are compared in terms of abatement, and expenditure. The global power sector scenarios assume the application of different policies
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to support the adoption of low carbon technologies, including subsidies (Subs), feed-in-tariffs (FiT), carbon pricing (CarbP), regulation (Reg), and any possible combination of them.2 Each of the 15 policy sets are simulated under two different conditions of natural resources availability: the ‘High CSC’ group corresponds to scenarios with high availability of renew- able resources, while the ‘Low CSC’ group corresponds to scenarios with low availability of renewable resources. In the creation of these scenarios, the following assumptions are made:
• In all the scenarios, the default learning rates for all the technologies are used (see section 4.5).
• In all the scenarios, the maximum electricity demand reductions are assumed, in line with the definition of the DEC scenario (see sections 5.2.4 and 5.5).
• For the ‘Low CSC’ group of scenarios, the lower limits of the cost supply curves of the most relevant renewable energy resources are used: hydroelectricity, wind onshore, biomass and solar energy. For the ‘High CSC’ group, the higher limits of the cost supply curves of the same resources are used (see sections 4.4 and 6.2.2). For the other technologies, the most likely cost supply curve are used on each scenario.
• On each scenario, only the policies indicated in the name (labels) are applied, at the maximum intensity (decarbonisation intensity equal 1). The rest are deactivated (policies are deactivated using decarbonisation intensity equal 0, see sections 5.2.1 and 5.2.2 for more details).
7.4.1
Abatement by policy set
Figure 7.2 shows the cumulative emission reductions (with respect to BAU, see section 5.4) between 2016 and 2050 for the 15 policy scenarios, corresponding to all possible combinations of subsidies, feed-in-tariffs, carbon pricing and regulation. As it might be expected, the scenarios with higher availability of renewable energy resources achieve higher emission reductions than their lower availability of renewable resources counterpart. If single policies are ranked by emission reductions, regulation is the most effective by far, followed by carbon pricing, feed-in-tariffs and subsidies. The reason why regulation performs better in terms of emission reductions is simple: the replacement of carbon intensive power stations is forced in the case of regulation, while with the other mechanisms, the low price of fossil fuels (particularly coal), undermines the performance of the market based decarbonisation
incentives. The relative ranking of the policies based on market mechanisms, i.e. carbon pricing, feed-in-tariffs and subsidies, is not a strong indicator in this case, because it depends on the underlying level of the incentives (rate of the subsidies, feed-in-tariffs, and the price of carbon).
Figure 7.2 Reduction in cumulative emissions for the global power sector between 2016 and 2050, under optimistic (High CSC) and pessimistic (Low CSC) assumptions regarding the availability of hydroelectricity, wind onshore and biomass energy. All the possible combinations of the four policy instruments of FTT:Power are presented: subsidies, feed- in-tariffs, carbon pricing and direct regulation. For a detail description of the the policy instruments, please refer to section 5.2.
As described in section 5.6, the marginal abatement potential of each policy depends on the concurrent policies being implemented. Therefore, the impact of policy instruments is different if used individually or in combination, given their underlying synergies (Mercure et al., 2014). This phenomenon emerges from the complex representation of the power sector embedded in FTT:Power, based on non-linear, hysteretic dynamics (Mercure, 2015). While in general, larger the number of policies being implemented, larger the abatement achieved, there are some particular cases when the opposite happens. For instance, in figure 7.2, the inclusion of feed-in-tariff policies in the scenarios with regulation, produces a decrease (instead of an increase) in abatement. While this might seem contradictory, a detailed analysis of those scenarios shows that the reason for the increase in emissions is the stability constraints of FTT:Power. As explained in section 4.6, the composition of the electricity matrix is constrained by lower and upper limits of baseload, flexible and variable electricity generation technologies. When there is a large increase of variable electricity in the system (due, for
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instance, to the extensive adoption of wind or solar energy, supported by feed-in-tariffs), FTT:Power activates a control mechanism that hinders the rapid replacement of flexible and baseload electricity sources. When the control mechanism is activated, the regulatory framework that puts a ban in the construction of new coal power stations is superseded, in order to keep the balance between baseload, flexible and variable electricity generation technologies. This phenomenon only happens on extreme decarbonisation scenarios, and is explained in detail in section 4.6.2. Consequently, the policy set with the larger reduction in cumulative emissions in this chapter is the one that combines carbon pricing, subsidies and regulation, without feed-in-tariffs.3
Figure 7.3 shows the emission trajectories for all the policy set scenarios presented in this chapter. The trajectories can be divided in two clear groups: those from scenarios without direct regulation policy (top trajectories, excluding BAU), and those from scenarios including regulation policy (bottom trajectories). Highlighted in green, blue, red and magenta are the trajectories associated with scenarios with single carbon pricing, subsidies, feed-in-tariffs and regulation policies, respectively. It is clear, from figure 7.3, the differences in abatement performance of the policy sets with and without regulation.
7.4.2
Electricity expenditure by policy set
The different emission trajectories and abatement performances presented in the previous section, are a representation of the impact of the different decarbonisation policies in the composition of the power sector. To analyse these impacts in more detail, figure 7.4 presents the cumulative electricity generation between 2016 and 2050, for all the 15 policy sets, under low (upper chart) and high (bottom chart) availability of renewable energy resources.
Similar to the charts presented in the previous section, figure 7.4 shows a clear separation between the scenarios that include regulation in the policy mix, with those that do not include it. Without regulation, most of the electricity is generated by fossil fuels, with the associated impacts in emissions presented in figure 7.3. When regulation is incorporated in the policy mix, the electricity matrix becomes more diverse, with a larger participation of renewable energies. Consequently, emissions decrease in scenarios that include regulation, as shown in the previous section.
3In chapter 9, the effect of the stability constraints of FTT:Power in extreme decarbonisation scenarios, is
analysed in detail. In particular, section 9.8 presents an analysis of the impact of the capability of the grid to balance baseload, flexible and variable electricity on decarbonisation scenarios.
Figure 7.3 Emission trajectories for the 15 policy set scenarios presented in figure 7.2, under low and high availability of renewable energy resources. Green, blue, red and magenta trajectories correspond to single CarbP, Subs, FiT and Reg policies, respectively. The emission trajectories for combined policies, are shown in gray. The top black trajectory corresponds to BAU, and the bottom trajectory corresponds to the one with the highest emission reductions (CarP+Subs+Reg, see figure 7.2).
The lower chart of figure 7.4 shows a larger participation of hydro and wind energy in the electricity matrix than the upper chart. This is consistent with the larger availability of those resources in the HighCSC scenarios. In the case of solar energy, the difference is more subtle between the two groups of scenarios. The reason behind this subtle difference is the large availability of solar resources, even in the LowCSC scenario. As it can be seen in the cost supply curves of figure 6.6, the lower limit of the technical potential of solar energy is larger than the most likely technical potential of wind, and larger than the upper limit of the technical potential of hydro. In the case of biomass, it only participates marginally in all the scenarios, therefore it was not included as a single category in figure 7.4.
Decarbonisation policies require resources to support the deployment of low carbon tech- nologies (such as subsidies and feed-in-tariffs), as well as for pricing the externality (carbon pricing policies). Moreover, the replacement of low-cost carbon intensive power stations with more expensive low-carbon units, has an impact in the price of electricity. In this context, it is relevant to analyse the economic resources required to implement these decarbonisation policies, in the same way that we study their abatement potential. The economic resources associated with each policy scenario, are defined in this context as the total expenditure on electricity, corresponding to the sum of the following three quantities:
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Figure 7.4 Cumulative electricity generation, by policy set, between 2016 and 2050. Top and bottom chart correspond to scenarios of low and high availability of renewable energy resources, respectively.
Public Revenue is the undiscounted sum of the money paid by consumers and collected by the government in the form of carbon taxation or emission allowances, embedded in the price of electricity. In FTT:Power, the price of carbon is included in the calculation of the levelised cost of electricity, as shown in section 4.6.1.
Public Expenditure is the undiscounted sum of the money spent by the governments on subsidies and feed-in-tariffs, to support the deployment of low carbon technologies.
Private Expenditure corresponds to the undiscounted sum of money spent on electricity by final consumers, calculated as the price of electricity times the electricity generated
Figure 7.5 Electricity expenditure for the global power sector between 2016 and 2050, under low availability (Low CSC, top) and high availability (High CSC, bottom) assumptions regarding hydroelectricity, wind onshore, biomass and solar energy. All the possible com- binations of the four policy instruments of FTT:Power are presented, same as in figure 7.2. Electricity expenditure is divided in private expenditure (blue), public revenues (red) and public expenditure (yellow).
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between 2016 and 2050. Private expenditure does not include the carbon price (which is accounted as public revenue), or subsidies and feed-in-tariffs (which are accounted as public expenditure).
The total expenditure on electricity is the sum of private expenditure on electricity, public expenditure on subsidies and feed-in-tariffs and public revenues from carbon pricing. No assumptions are made regarding the use of public revenues, because the economy is not explicitly modelled in FTT:Power. However, its is important to highlight that those revenues could potentially be used to support government spending on technology subsidies, or be redistributed to households in the form of income tax reductions, increasing their disposable income (Mercure et al., 2014). Given that no assumption regarding the use of those revenues are made here, the expenditure on electricity for scenarios with carbon pricing can be taken as an upper limit on expenditure.
The top and bottom charts of figure 7.5 present the (cumulative) total expenditure on electric- ity, from 2016 and 2050, when there is limited availability (former) and abundant amount (latter) of hydroelectricity, wind onshore biomass and solar energy. The total expenditure on electricity is separated on its three components: the private expenditure on electricity is shown in blue, the money spent on subsidies and feed-in-tariffs is shown as public expen- diture in yellow, and the carbon allowances and taxes paid by the consumers are shown as public revenues in red.4
Despite the significant differences in emission reductions (shown in figure 7.2), the expendi- ture on electricity does not change considerably among policy sets, under the same conditions of availability of natural energy resources. However, cumulative electricity expenditure between the two groups of policy sets (top versus bottom charts of figure 7.5) presents large differences. In the context of stringent decarbonisation scenarios, such as those analysed in this section, low availability of resources has a negative impact on the price of electricity. Due to the use of the lower limits of the cost supply curves for hydropower, wind, biomass and solar energy, the levelised cost of electricity rises under scenarios of large adoption of these resources. This is particularly relevant in the case of hydroelectricity, which represents by far the largest share among renewables in the electricity mix at present: 16.6% in 2014, compared with 6.2% corresponding to the share of all other renewable energies combined (REN21, 2015).
4Notice that the charts of figure 7.5 have different scales, in order to appreciate better the differences within
The long-lasting life of hydropower stations constrains their rate of replacement, produc- ing technological lock-ins and inertia in the system.5 This phenomenon is captured by FTT:Power, through the cost supply curves: in scenarios with limited amount of hydrologi- cal resources, grids with high levels of hydroelectricity are forced to pay higher prices of electricity when the hydro cost supply curve approaches its technical potential. Even if the technology is replaced, the technology diffusion process occurs at the rate of replacement of existing technology as it ages, which is inversely related to its life span (Mercure et al., 2014). This is the main reason for the large difference in electricity expenditure between the two groups of scenarios of figure 7.5.
7.4.3
Efficiency by policy set
An appropriate measure of policy efficiency, should include both the amount of emission reductions, and the total expenditure on electricity associated with each policy set. If considerations such as energy poverty or fiscal austerity are at stake, then it is relevant to separate the sources of revenues and expenditures: public expenditure in policy support for subsidies and feed-in-tariffs, public revenues from carbon pricing and private expenditure on electricity. However, in the context of this thesis, the aforementioned considerations are out of the scope of the analysis. Therefore, the total expenditure on electricity, defined as the sum of private expenditure on electricity, public revenues and public expenditure, is used. Figure 7.6 shows how policy efficiency, measured as the ratio between reduction in emissions and total expenditure on electricity, changes between scenarios of low and high availability of renewable energy resources.
The brown bars in figure 7.6 correspond to the differences between scenarios of high and low availability of renewable energy resources, by policy set. Changes in the availability of energy resources have, as the brown bars show, a large impact on the efficiency of policy. Larger the efficiency of the policy set, larger the impact that uncertainty on energy resources has on its efficiency. There are no considerable differences in electricity expenditure between policy sets, therefore the ranking produced by policy efficiency is not significantly different to the one produced by emission reductions (figure 7.2).
In terms of single policy comparisons, regulation is the one with the highest efficiency, despite producing the highest increase in the price of electricity (see figure 7.5). As discussed in section 7.4.1, regulation produces a large reduction in emissions, due to the enforced
5Examples of discussions on lock-ins include Arthur (1989), Unruh (2000). For a discussion about inertia
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Figure 7.6 Policy efficiency, measured as emission reductions normalised by total expenditure on electricity, by policy set between 2016 and 2050. Scenarios of low and high availability of hydroelectricity, wind onshore, biomass and solar, are presented in dark and light green, respectively. In brown, the differences between the two sets of scenarios, corresponding to the effect that availability on renewable energy resources has on policy efficiency.
replacement of carbon intensive power stations (see figure 7.2). The increase in cost is produced by the same reason: low cost fossil fuel power plants (particularly based on coal) are replaced by more expensive clean alternatives.
The policy efficiency ranking follows closely the emission reductions ranking, because the differences in electricity expenditure are considerably smaller than the differences in abate- ment. These results suggest that the policy conclusions are robust with respect to changes in electricity demand, even though electricity demand is exogenous in these scenarios.
7.4.4
Policy implications
The first and foremost policy frequently indicated as the solution to the challenge of de- carbonise the economy is the introduction of a price on carbon (Campiglio, 2016). There is a vast amount of literature on carbon taxation, and how it can optimally internalise the externalities associated with anthropogenic GHG emissions (see, e.g., Cooper (2008); Hsu (2012); Metcalf and Weisbach (2009); Nordhaus (2015); Weitzman (2013)). While con- ceptually the need for carbon pricing has long been understood, the difficulty has been in
translating concepts into real policies (Ferdi and CEPR, 2015, p. 12). The design and choice of a specific policy instrument (or mix of instruments) depend on many economic, social, cultural, ethical, institutional, and political factors (IPCC, 2014b, p. 235), and countries normally rely on a combination of several instruments with different targets simultaneously (Ferdi and CEPR, 2015, p. 254). In this context, the study presented in this chapter, focuses on different portfolios of policy instruments using regionally differentiated intensities and timings.
The results presented in this chapter, suggest that direct regulation (in the form of caps on installed capacity) can have a larger impact than market-based instruments in the de- carbonisation of the power sector. These results are aligned with the evidence that suggest that when the efficiency of market-based instruments is constrained, regulatory approaches are a more suitable alternative (IPCC, 2014b, p. 240). The superlative performance of regulation policies in the simulations, however, has some caveats. As explained in section 5.6, the relative performance of market versus non-market based mechanisms is influenced by the assumptions of the model. The assumptions of complete feasibility of regulation as well as the strong inertia produced by the shares equation formulation might produce an overestimation of the relative performance of non-market based over market based policies, and consequently the results presented above must be analysed cautiously.
It is important to highlight that technology diffusion in FTT:Power takes place following S- shaped curves (Mercure, 2012). Therefore, the influence of policy instruments is constrained by the rate of replacement of the different technologies, which depends not only on cost considerations, but also on limited access to technology and information, which is embedded in the mathematical dynamics of FTT:Power (Mercure, 2015; Mercure et al., 2014). In this context, the modelling exercise presented above contributes as a positive description of the power sector, capturing some of the limitations associated with the policy diffusion process and decarbonisation. Based on this analysis, some policy recommendations are presented below.
• Scenarios with direct regulation exhibit larger emission reductions than those without regulation. In terms of scenarios with single policy instruments, direct regulation is the one with the largest abatement, followed by carbon pricing, feed-in-tariffs and subsidies. The combination of policies do not follow an additive pattern on emission reductions, due to the complexities of the system. On the one hand, policy instruments have synergies, therefore the marginal abatement of each policy depends on the concurrent policies being implemented (see section 5.6). On the other hand, stability constraints on FTT:Power could hinder, on extreme scenarios, the phase-out of carbon-intensive
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technologies (see section 4.6.2 for a detailed analysis of this phenomenon). In this