The likelihood of staying below a threshold temperature increase of 2◦C over the 21st century, relative to 1900 depends on key driving factors of potential energy futures, many of which are uncertain. Issues such as technological progress, availability and cost of energy resources, and rate of implementation and success of decarbonisation policies, are just a few of the uncertain aspects that will play a relevant role in the future of our energy systems (O’Neill et al., 2014). It is, therefore, appropriate to use a range of scenarios to represent potential futures of the power sector, that allow an appropriate uncertainty analysis. Using FTT:Power, two extreme emission scenarios for the power sector were created, to define the range of plausible futures to be analysed. This is presented in figure 5.1 in the form of a range of potential emission trajectories.
The trajectory with the upper limit of emissions in figure 5.1 is BAU, and the trajectory with the lower level of emissions is DEC. A middle ground scenario (MID) is also presented in figure 5.1, as a dashed line. The two extreme trajectories represent limits of what could be achieved in the power sector, in an environmentally friendly (DEC) or fossil fuel intensive (BAU) context. They represent the upper and lower limits in terms of emissions, within a range of scenarios that can be considered feasible in the FTT:Power model domain. To pro- duce the range of scenarios represented in figure 5.1, the policy domain space of FTT:Power was divided into the following sets of variables:
• Carbon pricing, in U S$/tonC per region.
• Subsidies and feed-in-tariffs, as a percentage of the overnight cost of investment and the price of electricity, respectively, per region per technology.
• Regulation in the form of a cap in units of new installed capacity (in GW ) for some electricity generation technologies (per region per technology).
5.2 Exploring the Policy Domain Space in FTT:Power 73
Figure 5.1 Range of emission’s trajectories, based on two extreme FTT:Power scenarios. The top curve represents the emission’s trajectory of the Business As Usual (BAU) scenario, which depicts a fossil fuel intensive future. The bottom curve represents the emission’s trajectory of the Decarbonisation (DEC) scenario, which depicts a future where strong efforts are made to decarbonise the global power sector. The dashed line in the middle represents a middle ground scenario (MID), which is a trade-off between the two extremes. The shading represents the decarbonisation intensity: BAU (decarbonisation intensity equal 0) is brown, MID (decarbonisation intensity equal 0.5) is yellow, and DEC (decarbonisation intensity equal 1) is green.
All of these variables work on different units and have a different scope. With the aim of simplifying the scenario analysis, the domain range was normalised between zero and one for each one of these sets of variables, using a decarbonisation intensity variable. The minimum decarbonisation intensity scenario (intensity equal zero) corresponds to the case of minimum decarbonisation efforts, in terms of energy policy (no support for low-carbon technologies) and energy demand (no energy efficiency policies in place). In figure 5.1, it corresponds to the upper limit of the emission trajectories. The maximum decarbonisation intensity scenario (intensity equal one) corresponds to the case of maximum decarbonisation efforts, in terms of energy policy and energy demand. In figure 5.1, it corresponds to the lower limit of the emission trajectories. In the maximum decarbonisation effort scenario, emissions in 2050 are less than half the emission in 2000, in line with the RCP2.6 trajectory (IPCC, 2014b, p. 52).
The minimum decarbonisation intensity scenario corresponds to the BAU scenario, and the maximum decarbonisation intensity scenario corresponds to the DEC scenario. The MID scenario was formulated such that it coincides with the middle of the range (intensity equal 0.5). The next sections describe the way that these sets of variables are mapped into the decarbonisation intensity ([0 1] range), and how the range of scenarios described by figure 5.1 was created.1
5.2.1
Carbon Pricing
FTT:Power assumes that each region has its own independent carbon price, which can (but it does not have to) be the same for all (or some) regions. From a mathematical perspective, the price of carbon is accounted in the model as the carbon cost component of the Levelised Cost of Electricity (see equation 4.6 in the previous chapter). Mathematically, there is no difference if the price of carbon comes from a cap-and-trade system, from taxation, or any other policy mechanism. Therefore, in the context of the stylised version of the power sector within the model, it is not required the specification of the type of instrument behind the carbon price.
Naturally, reality is more complex than stylised models. When imperfect markets, incomplete access to information and uncertainty are taken into account, then taxes and cap-and-trade mechanisms do not produce the same results, and therefore the policy instrument matters. On the one hand, a cap ensures an upper limit for emissions, but generates uncertainty on the price. On the other hand, a tax generates a certain price, but its impact on emissions is uncertain (Grubb, 2014, p. 223). The efficiency and effectiveness of both mechanism (or any hybrid combination of them), will depend on various factors, including short versus long term objectives, industry expectations on investment and how consumers may perceive the different instruments (ibid.). Despite these important differences, the FTT:Power model does not differentiate between a carbon price defined by taxation, by a cap-and-trade system, or any other policy instrument. It is, therefore, relevant to keep in mind this limitation when
1The mapping of the policy variables into the range [0 1] facilitates the creation of a large number of
scenarios, within the context of the policies analysed in this dissertation. For instance, the emission scenarios for Brazil presented in figure 8.8 (chapter 8: “Hydropower Resources and Policy Performance in Brazil”) correspond to 120 samples of the decarbonisation intensity variable, under specific conditions (see section 8.4 for the details about those conditions). The capability of the system for creating an arbitrary number of simulations, using samples of the decarbonisation intensity variable, played an important role in the analysis stage of this dissertation. Due to the large number of scenarios under analysis, only the extreme cases of the decarbonisation intensity variable (0 or 1) are shown in most of the simulations presented in this thesis, with a few exceptions, such as the aforementioned case of chapter 8.
5.2 Exploring the Policy Domain Space in FTT:Power 75
the relative impacts of the different policy instruments are analysed. This issue, with a particular focus on the abatement contribution of the different policy instruments (and their combinations), is discussed below in sections 5.3 and 5.6.
The price of carbon in FTT:Power is defined exogenously, with an annual price between 2005 and 2050 (in 2008 USD). In the case of EU (regions 1-27), carbon price is taken from E3ME2between 2005 and 2015, while all the other regions are assumed to have no carbon price before 2015. From 2016 onwards, the carbon price varies according to the scenario analysed.
From 2016 until 2050, a base curve for the carbon price is defined exogenously. The curve rises gradually from 35 to 184 euros per ton of carbon (e/tC) between 2016 and 2050, respectively (red solid line in figure 5.2). This curve corresponds to the carbon price for the FTT:Power regions within the EU (regions 1-27), and it is assumed to remain constant between the BAU and the MID scenarios (decarbonisation intensity in the interval [0 0.5]). The other FTT:Power regions (28-53) increase their carbon price from zero (in the BAU scenario) up to a fraction of the base curve (in the MID scenario): the fraction is 1 for European countries outside the EU (regions 28-33, red solid line in figure 5.2), 0.75 for advanced economies outside Europe3(regions 34-38, blue solid line), and 0.5 for the rest of the world (regions 39-53, green solid line).
The changes in carbon price between the MID and DEC scenarios (decarbonisation intensities in the range [0.5 1]) follow a very simple pattern. The carbon price increases from one to four times the value in the MID scenario, proportionally to the decarbonisation intensity. So, European countries reach a maximum price of carbon of 736 [e/tC] (red dashed line in figure 5.2), advanced economies reach 552 [e/tC] (blue dashed line) and developing economies reach 368 [e/tC] (green dashed line).
It is important to emphasize that BAU and DEC are feasible limits within the borders of what FTT:Power can analyse. While in the BAU scenario only Europe has a carbon price, in reality there are already several countries outside Europe which have implemented carbon pricing policies. For instance, the Regional Greenhouse Gas Initiative (RGGI) in the Northeast
2E3ME (www.e3me.com) is a well established global non-equilibrium macroeconometric model developed
and maintained at Cambridge Econometrics. It is frequently used by the European Commission for impact assessment of environmental policy, including the assessment of the 2030 climate and energy targets and other policy analyses (Barker et al., 2012; Pollitt et al., 2015a,b).
3The group of advanced economies outside Europe includes USA, Japan, Canada, Australia and New
Figure 5.2 Carbon pricing for the BAU, MID and DEC scenarios between 2008 and 2050. For the BAU scenario, EU has a carbon price defined by the solid red curve, while the rest of the regions have no carbon price (purple line, at the bottom of the chart). Between the BAU and MID scenarios, carbon price in EU remains the same, while in the other regions it rises, proportionally with the increase of the decarbonisation intensity. In the MID scenario (decarbonisation intensity = 0.5), all European countries have the same carbon price (solid red curve), while the advanced economies outside EU and the rest of the world follow the carbon price described by the solid blue and green lines, respectively. Between MID and DEC scenarios, carbon price in all regions increase proportionally to the increase of the decarbonisation intensity. The carbon price in the DEC scenario (dashed lines, decarbonisation intensity = 1), is 4 times the price in the MID scenario. The carbon price for the scenarios between the MID and DEC limits in 2050 are within the ranges presented in IPCC (2014b, p. 59), for the scenarios between 430 and 580 ppm CO2eq.
and Mid-Atlantic U.S. states;4the California’s Cap-and-Trade Program;5the New Zealand Emissions Trading Scheme (NZ-ETS);6the Quebec Cap-and-Trade System7and the Chinese pilot ETS,8are some examples of carbon markets outside Europe. The small size of some of these markets (such as New Zealand), or the fact that some of them are still sub-national and
4The RGGI is the first mandatory emissions trading scheme in the United States, operating since January
1, 2009. The programme initially covered CO2emissions from power plants in the states of Connecticut,
Delaware, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Rhode Island, and Vermont (Ecofys, 2013)
5The California’s Cap-and-Trade Program started in 2012, and entered into its first compliance period as of
January 1, 2013 (ICAP, 2016b)
6The NZ-ETS started in 2008, and is now under its second statutory review (ICAP, 2016a)
7The Quebec Cap-and-Trade System was officially launched in 2013, and one year later it was linked with
California’s scheme, as part of the Western Climate Initiative (Ecofys, 2013)
8China’s National Development and Reform Commission (NDRC) approved in October 2011 seven pilot
carbon trading schemes for Beijing, Shanghai, Tianjin, Chongqing, Guangdong, Hubei, and Shenzhen (Ecofys, 2013)
5.2 Exploring the Policy Domain Space in FTT:Power 77
do not cover the whole country (such as the cases of USA, China and Canada), are part of the reasons why they are not yet incorporated in FTT:Power. It is expected that for the next versions, some non-European carbon markets will be included.
5.2.2
Subsidies and Feed-in-Tariffs
Subsidies and feed-in-tariffs are defined exogenously in FTT:Power, per technology per region per year. Each scenario requires to define 24 x 53 x 43 = 54,696 values for subsidies, and other 54,696 values for feed-in-tariffs. If the decarbonisation intensity range was sampled 100 times, then 10,939,200 exogenous points would be required. In order to keep tractability, some simple rules to automatically create subsidies and feed-in-tariffs scenarios were implemented.
Subsidies
Subsidies in FTT:Power are defined as a percentage of the investment cost,9granted by the regulator to investors, to decrease the LCOE of favoured technologies. So, a subsidy of 10% for technology i in year t, means that the investment cost of technology i in year t is 10% cheaper. The corresponding impact on the LCOE can be calculated using the equation 4.7.
A maximum nominal rate of 30% subsidy is defined for the following technologies:10
• Biogas in all regions
• Geothermal energy in all regions
• Technologies using carbon capture and storage (CCS) in all regions
• Tidal energy in Spain, France, Ireland, Portugal and UK
• Nuclear energy in China, India, Korea and Taiwan
The nominal rates change proportionally to the decarbonisation intensity value, between zero (BAU) and the maximum nominal rate (DEC). So, in the BAU scenario, all subsidies are zero, while in DEC scenario, all subsidies are at the maximum nominal rate, changing proportionally to the decarbonisation intensity in the intermediate scenarios. In all the
9Total investment costs include owner’s cost, EPC (engineering, procurement and construction), contingency
and interests during construction (IDC), and exclude refurbishment or decommissioning (IEA et al., 2010).
scenarios, subsidies are maintained at the nominal rate between 2015 and 2025. From 2026 until 2044, subsidies decline linearly to zero.
Similarly to the carbon price, subsidies in FTT:Power are not required to be defined at the policy instrument level. In the model, the effectiveness of these policies is fully determined by the corresponding impact on the Levelised Cost of Electricity (LCOE). In reality, however, the impact of subsidies on investment decisions changes depending on the specific policy instrument being applied. For instance, subsidies can be implemented in the form of grants, favourable loans or fiscal incentives (such as reduced taxes, accelerated depreciation, tax credits and tax deductions), all of which have different levels of acceptance among investors on specific sectors (IPCC, 2007, p. 481). In the context of the stylised scenarios analysed in this thesis, a detailed specification of the subsidy instrument is not required. However, it is important to keep this aspect in mind when analysing the relative impact of policy instruments in the decarbonisation scenarios. The differences between the FTT:Power representation of subsidies and the real instruments is discussed below in section 5.3.
Feed-in-tariffs (FiTs)
FiTs are the most popular form of renewable energy regulatory support policy worldwide (REN21, 2015, p. 88). Consequently, it is not surprising that FiT instruments are anal- ysed separately from traditional subsidy instruments, although they are equivalent from a theoretical perspective. In the case of FTT:Power, FiTs are defined as a percentage of the retail price of electricity, and granted to investors as the difference between the LCOE of a specific technology, and the retail price of electricity, times a premium. Therefore, FiTs can be considered as a subsidy proportional to the amount of electricity generated. In contrast, traditional subsidies in FTT:Power are assumed to be proportional to investment costs (see previous section). Given these differences, it makes sense to study their effects separately.
In reality, FiTs instruments are more complex than their stylised representation on FTT:Power. Real feed-in-tariff instruments can be divided into two main groups: fixed tariff and premium tariff. In the former, generators receive a fixed remuneration per unit of energy produced, independently of the market price of electricity. In the latter, generators receive a premium per unit of energy produced proportional to the price of electricity, which can be either fixed or variable. On the one hand, the fixed tariff scheme represents lower investment risk for investors. On the other hand, the premium tariff scheme shows higher compatibility with liberalised electricity markets (Couture et al., 2010; Held et al., 2014). For the sake of simplicity, all the regions using FiTs in FTT:Power are assumed to have the same instrument:
5.2 Exploring the Policy Domain Space in FTT:Power 79
a premium tariff scheme, with a variable premium proportional to the difference between the price and the levelised cost of electricity. The premium varies proportionally to the decarbonisation intensity value: in the BAU scenario, the premium is zero (equivalent to not having FiT), and it increases gradually from 0 to 1 between the BAU and MID scenarios (decarbonisation intensity between 0 and 0.5). Premium values between zero and one work, effectively, as a subsidy: the producer of electricity receives a grant, proportional to the difference between the LCOE and the market price of electricity. From the MID to the DEC scenario (decarbonisation intensity between 0.5 and 1), the premium increases gradually from 1 to 1.1, making the LCOE less expensive than the market price of electricity.11 The technologies that are subjected to FiT are solar PV, concentrated solar power (CSP), wind onshore and wind offshore, in all regions.
5.2.3
Regulation: limits on new installed capacity for specific electric-
ity generation technologies
The 2◦C target proposed by the IPCC requires the decarbonisation of a large part of the global power sector in a time scale of decades (Mercure et al., 2014). The scenario becomes even more stringent if the 1.5◦C target agreed in Paris is pursued (UNFCCC, 2015). The strong dominance of fossil fuels in the global electricity sector makes it difficult for low carbon technologies to achieve the degree of market penetration required by these scenarios. For that reason, limitations in the construction of new power plants for specific electricity generation technologies are imposed in FTT:Power, to accelerate the decarbonisation of the global power sector.
In this dissertation, the policy mechanism implemented in FTT:Power to limit the construction of new power plants is denominated direct regulation (or simply regulation), following the terminology used in the FTT literature (see for instance Mercure et al. (2014)). The term regulation, however, may arguably lead to some confusion, given that IPCC (2014b, p. 1158) associates regulatory approaches to energy standards, equitable access to grid and legal status of long-term CO2storage. Therefore, it is important to clarify the nature of the ‘regulation
policy instrument’ analysed in this dissertation (see section 5.3 for more details).
In FTT:Power, regulation policies control the construction of new units of specific tech- nologies, an can be used to phase out particular types of systems (Mercure et al., 2014). In
11In FTT:Power, a FiT rate of 1.1 means that the price of the technology, from the investor’s perspective, is
less expensive than the price of electricity by 10% of the difference between the price of electricity and the cost of the technology (LCOE).
the context of this dissertation, regulation policies are used for limiting the construction of new coal and gas power stations on specific regions. Similar to the case of the other policy instruments, FTT:Power does not require a specific definition of the legal instrument behind the limitation on constructing new power stations. Consequently, regulation is assumed to be effective in the model, and the limitations on the use of specific technologies are assumed to hold. The stylised policy representation in FTT:Power has, however, a more complex counterpart in reality. For instance, a partial limitation or a complete ban in the use of a specific technology usually requires the coordination of several institutional agents, and faces strong opposition from the affected industry. While these considerations are not part of the model representation of regulations, it is important to have them in mind when the effectiveness and the efficiency of the policy instruments are analysed. The differences