Pulling The Plug
The Legacy of Renewable Support
Draft Do Not Cite
M. Beck (Balsillie School of International Affairs)
Nic Rivers (University of Ottawa)
R. Wigle
Balsillie School of International Affairs, Wilfrid Laurier University)∗
September 26, 2014
Abstract
Although support for renewable electricity is motivated primarily
by environmental benefits, there is evidence of some amount of ‘industrial’ benefits from learning by doing which are not appropriable
by investors. This market failure is a second source of economic inefficiency. While there has been significant Computable General Equilibrium (CGE) modeling of alternative forms of public support for
renewable electricity, much less attention has been paid to what happens when such supports are phased out. Our longer-term goal is to
consider optimal paths for renewable support.
A recursive dynamic regional CGE model of Canada is used to
consider the path of adjustment after renewable electricity supports are
removed. The model features a learning by doing mechanism related
to historical total output of the renewable sector.
∗
The authors acknowledge support from Carbon Management Canada and Sustainable
Prosperity. The authors thank, without implicating, comments from participant’s in a
brown bag session at Wilfrid Laurier University’s Economics Department.
1
Contents
1 Introduction
3
2 Ontario’s Renewable Energy Policy
4
3 Learning Externalities
6
4 Model Overview
11
4.1 Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
5 Policies Considered
6 Central Case Findings
6.1 Temporary Support . . .
6.2 Comparison of Temporary
6.3 Economic Efficiency . . .
6.3.1 Sensitivity . . . . .
13
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and Permanent
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Support
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7 Summary, Conclusions and Extensions
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A Additional Tables: Temporary Support
27
B Sensitivity
29
2
1
Introduction
Support for new forms of renewable electricity generation such as solar and
wind have often been motivated by the desire to reduce GHG and local
air emissions. At the same time there is widespread belief that this sector
is also subject to another externality, namely the fact that efficiency gains
from learning by doing in the sector are not appropriable by the firms in the
sector (Neuhoff 2005). Rather the cost reductions associated with increasing historical output in the sector reduce cost for any firm, including new
entrants. As a result of this externality (cost reductions due to learning)
associated with the output side, the output level of renewable generators can
be lower than socially optimal. Since learning effects are normally modeled
as falling in historical output, this externality is likely to diminish over time,
leading to an optimal schedule of support that declines over time as well.
Our final objective is to derive an optimal schedule of support for renewable energy supports in Ontario using a recursive dynamic regional CGE
model of Canada. The model features a learning by doing mechanism related
to historical total output of the renewable sector.
This version of the paper represents a first step toward this goal. This
paper discusses some results comparing permanent to temporary supports,
detailed in Section 5. The policy context we are working in is the scale
back of renewable energy supports and dissatisfaction with the ‘high cost’
of renewable energy. Initially our focus is on considering the longer-term
impact of renewable electricity promotion, in particular if it that support
is only temporary.1 The current paper considers how the evaluation of
renewable support policy changes if the support is removed at some point in
the future. Building on these results, ongoing research is foreseen addressing
the following issues:
1. What is the optimal time profile of support for renewable energy?
2. What values of the key parameters justify renewable energy support
and at what level?
The findings of this paper include:
1. Though we only compare a simple on/off policy decision (the ending
of support after a give period) the case for renewable energy support
is significantly strengthened by the possibility that the support will
eventually end.
1
The fact that support should be ratcheted down over time is discussed in Melitz (2005)
and Neuhoff (2005).
3
2. The key parameters in determining the net welfare effect of renewable
supports are the degree of learning (learning rate) and the degree of
substitutability of renewable and conventional electricity.2 We provide an initial evaluation of how much learning and substitutability is
enough to make supports welfare-neutral.
2
Ontario’s Renewable Energy Policy
Ontario’s Green Energy and Green Economy Act (GEGEA) is the Province’s
core piece of legislation for the promotion of energy conservation and renewable energy generation. The GEGEA was adopted under a liberal government in May 2009. The legislation introduced a new stand-alone statute,
the Green Energy Act (GEA), and also entailed amendments to 15 other
statutes. The GEA’s three major components (a) the Feed-in-Tariff (FIT)
Program to promote renewable energy, (b) the establishment of the Renewable Energy Facilitation Office (REFO), and (c) the streamlining of the
environmental approval processes for renewable energy projects.
The Liberal government pursued multiple goals with the GEGEA legislation, including economic growth and employment, environmental protection, and energy security. The provincial government explicitly introduced
the Act as a strategy for local economic development and mitigation of
unemployment in the manufacturing sector. The feed-in tariff policy, and
specifically the included domestic content requirement, aimed at fostering
local equipment manufacturing, the creation of ’green jobs’ and a boost in
private investment. In short, the goal was to make Ontario a competitive
player and a North-American first mover in the global clean energy market. Ontario followed the example of multiple European countries that had
already pursued similar renewable energy policies, aimed at both environmental protection and industrial development. In particular, the German
Erneuerbare Energien Gesetz, inspired the Ontario model Mabee, Mannion,
and Carpenter (2012).
At the core of the GEGEA is the feed-in tariff (FIT) program, which
was first introduced in 2009. The FIT program seeks to reduce the financial
and regulatory risks for private renewable energy investors in order to boost
capacity development in the province. Renewable energy generators enter
FIT contracts, i.e. long-term power purchase agreements, with the Ontario
Power Authority (OPA). The OPA guarantees a fixed price for every kWh
2
We intend to incorporate the role of environmental damages more fully into the next
release of the paper.
4
generated from eligible renewable electricity technologies over a time period of 20 years (40 years for hydropower). Tariff rates vary by technology,
project size, and ownership. Eligible technologies include bioenergy (including on and off-farm biogas, biomass, and landfill gas), solar photovoltaic
(with project capacity below 10MW), waterpower with project capacity
below 50MW per project; and wind. A separate program for small-scale
project, called microFIT, was also introduced.
A review of the FIT program launched in October 2011 resulted in a
number of amendments. The legislation was generally deemed successful,
nearly 2,000 FIT contracts had been signed in the first two years, associated
with over 4,600 MW of new capacity (Ontario 2012). However, a number of shortcomings were identified including the low level of community
involvement, the increase in electricity prices, NIMBY-activism in Ontario
communities, regulatory uncertainties and process delays. The government
introduced multiple reforms to the program in response to the review. The
OPA essentially abandoned the FIT model for large wind projects, instead
moving to a competitive bidding arrangement for future supply.
In 2010, Japan filed a complaint with the World Trade Organization
(WTO) against the FIT program’s domestic content requirement. In order to be eligible under the FIT program, projects had to use up to 50%
inputs from local suppliers and service providers. Japan argued that this
rule was a protective measure breaking international free trade law. The
WTO ruled that the dometic content requirement violated Canada’s MFN
commitment.3 In May 2013 the government first announced plans to change
the local content rule. It was was formally abandoned in July 2014. Around
the same time, in May 2013, a new round of FIT reforms was announced.
Importantly, a new procurement process for large renewable energy projects
was introduced. These projects had so far been eligible for the FIT, so
that the change essentially limits the scope of the program. Ontario’s renewable energy market players perceive growing uncertainty around future
support policies and there are concerns about dwindling demand for new
development.
Taking wind development as an example, Ontario’s 2021 target to reach
renewable capacity of 10,700 MW should include around 6,500 MW of windpower (Government of Ontario 2013). As of March 2014 contracted capacity
for wind power was already at 5,742 MW (OPA 2014). With thousands of
projects already in the pipeline and in the absence of more ambitious future
3
The MFN principle requires all imports to be treated the same once border measures
are applied.
5
targets in sight, the market for new project developers has become fairly
competitive (Bailey 2012). As political commitment for renewable power
in Ontario is arguably fading at this moment, questions arise regarding
the timing and temporal structure of renewable support schemes and their
lasting effects once the support has been ceased.
3
Learning Externalities
Over the last decade, a large number of studies debated scope and causes of
learning externalities in innovative energy industries including offshore wind
power (van der Zwaan, Rivera-Tinoco, Lensink, and van der Oosterkamp
2012), photovoltaics (Wand and Leuthold 2011), clean coal (Nakata, Sato,
Wang, Kusunoki, and Furubayashi 2011), and CCS (Li, Zhang, Gao, and Jin
2012). These studies commonly explain the occurrence of cost reductions
and quality improvements with increasing output by reference to learning or
experience curve models, economies of scale, spillover effects from research
and development (R&D) or declining input factor prices. Most studies conclude that the benefits come from multiple possible sources. Increasing
output in a specific sector may generate benefits in the form of average or
marginal cost reductions due to scale effects and learning effects as accumulating experience can cause endogenous improvements in factor productivity
and/or quality.
Neij, per Dannemand Anderson, Durstewitz, Helby, Hoppe-Kilpper, and
Morthorst 2003 distinguish between three different sources of experiencebased learning:
Learning through research and development Knowledge from growing experience may feed back into the technology design process.
Learning through manufacturing Accumulated experience in equipment
manufacturing may lead to process improvements in purchasing, production, distribution etc., which may cause manufacturing costs to
drop.
Learning from utilization This type of learning occurs, for example, when
workers become more skilled in handling specific equipment, which reduces maintenance cost and down times.
Scale-based cost reductions can also have different sources as outlined by
(Junginger, Faaij, and Turkenburg):
6
Mass production Standardization of the product allows for upscaling of
the production facilities, which reduces the cost of each output unit.
In the renewable energy sector, one can think of two scale effects at
different points in the value chain. For example, the mass production
of wind turbines decreases the per unit production costs. Additionally,
the cost of wind electricity generation declines as the size of wind farms
increases.
Product redesign For example, increasing the size of the individual wind
turbine also leads to lower specific costs per turbine.
Lbd in the context of this study is understood as learning from experience in
the generation of electricity from renewable energy sources. The potential
for learning is higher at early development stages and decreases as the technology matures. At this point, scale effects due to mass production are more
likely to become the key driver of cost reductions. High marginal returns on
increases in production in a technology’s early development stages play an
important role in achieving competitiveness with incumbent technologies.
Policy interventions to internalize the learning externality have potential to
be welfare-improving, particularly in early stages, where the learning effects
are largest.
Endogenous improvements in productivity and factor quality with increasing output are typically formalized through experience curve models.
Experience curves are specified as logarithmic relations linking the percentage increase in output and the resultant percentage fall in average or
marginal cost. Traditional learning curves are one-factor models. They subsume all factors contributing to cost reductions over time in one parameter,
the learning rate as a function of cumulative experience. For learning occurring in electricity generation, experience levels are commonly approximated
by cumulative electricity produced and costs are commonly measured in $/
kWh generated. However, given the uncertainties around the sources of industrial benefits, this approach is likely to lead to biased interpretations.
Hence, some recent studies develop two- or multi-factor models to provide
a more disaggregate and accurate picture of the causes of declining costs.
Two-factor models commonly include public R&D investment as additional
independent variable (Yeh and Rubin 2012). (Söderholm and Sundqvist) develop a four-factor model to explain investment cost developments in wind
sector of four European countries by additionally considering scale effects
and feed-in tariff prices. The latter is expected to counter cost reductions
as it makes less efficient sites more attractive and generally lowers competition and thus the incentive to innovate. Some studies additionally include
7
a time trend to account for cost reductions due to exogenous technological progress that is independent of cumulative output (Ferioli and van der
Zwaan 2009). With every additional independent variable considered, the
omitted variable bias becomes less distorting. However, multi-factor models
require large amounts of detailed data that may not be available in many
cases. This is why (McDonald and Schrattenholzer 2001) questions the
added value of separating the two endogenous factors, learning and scaling,
in long-term energy models.
This discussion already indicates that estimated learning rates tend to
vary significantly across studies, depending on the used data set and model
specifications (Söderholm and Sundqvist 2007). Table 1 provides an overview
of recent studies on cost reductions in the wind energy sector. Estimated
learning rates range between 1.77% and 19%. The wide range can be explained by differences in the included time periods and geographies, the
degree of disaggregation of cost drivers and the ways in which cost and
experience are measured.
In economic equilibrium models like the one used for this analysis experience curves are used as a means to incorporate the effects of endogenous
technological change in order to better assess the full welfare impacts of renewable energy policies. Modelers need to make several decisions on how to
incorporate Lbd into the wider model structure, which will then determine
the choice of adequate learning rates. One set of modeling choices relates to
the assumed source of learning benefits while the other one relates to their
assumed scope.
Source of benefits The major decision is whether to disaggregate endogenous cost reductions into those driven by learning effects and those
driven by scaling effects. The appropriate learning rate needs to be
chosen accordingly, i.e. it needs to reflect the same level of aggregation.
Nature of benefits Mechanisms to be considered include those where the
benefits take the form of efficiency improvements (more output from a
given quantity and quality of inputs) and those that involve improved
factor quality. In this analysis learning effects are modelled as factor
efficiency improvements.
Electricity vs. equipment Lbd can occur at different levels in the renewable energy value chain. The literature distinguishes between Lbd in
equipment production, installation, and electricity generation. Modellers need to choose at which stage(s) they want to consider Lbd.
Empirical learning rates will differ across stages.
8
Table 1: Reported Learning Rates
Study
(Ek
Söderholm)
Scope
Wind, Europe,
1986-2002
LR
17%
(Qiu and Anadon)
Wind, China,
2003-2007
4.14.3%
(van der Zwaan,
Rivera-Tinoco,
Lensink,
and
van der Oosterkamp)
(Söderholm
and
Sundqvist)
Offshore Wind,
Europe, 20052011
3%
Wind, Europe
1.778.25%
(Junginger, Faaij,
and Turkenburg)
Wind, global,
1990-2001
18-19%
(Neij, per Dannemand Anderson,
Durstewitz, Helby,
Hoppe-Kilpper,
and Morthorst)
Wind,
mark,
2000
17%
and
Den1981-
9
4
Cost/Experience
investment
cost/global
installed capacity
electricity
cost/national
installed capacity
investment
cost/European
installed capacity
investment
cost/European
installed capacity
investment
cost/national
installed capacity
generation
cost/national
production
Factors included
public R&D with
lbs rate of 20%
lbd and lbs of manufacturers and developers; excluding
scale effects
scale and learning
effects
scale and R&D
and policy effects
scale and learning
effects
scale and learning
effects
Reference levels It is important to determine the relevant reference point
for these curves: Is it the global level of output, is it national levels of
output or here in the case of a Canadian regional model, is it, for example the level of output in the province. In this analysis the relevant
reference level was chosen to be the cumulated renewable electricity
generation in Ontario.
The scope learning externalities refers to the extent to which learning
benefits assumed to be restricted to the country, sector, and firm where the
increase in output occurs.
Embodied versus non-embodied Efficiency gains due to learning could
be embodied or non-embodied in the specific production factors of the
firm where the learning occurs, i.e. in the firm’s equipment and its
workers. In modelling terms, embodied learning changes the efficiency
of input factors, while disembodied learning causes alterations in the
production function itself. In this analysis learning is assumed to be
embodied.
Factor specificity Productivity gains from accumulated experience could
be associated with one factor or another, most importantly capital
or labour. This analysis assumes that productivity gains are neutral
across all factors.
Sector-specificity If output increases cause factors to be more productive,
they may be more productive only in the sector where the learning has
accrued, or the benefits could spill-over into other industries. In the
case of labour, the (embodied) skills learned by specific workers could
apply to just the sector where the worker learned them, or they might
be more generally applicable, in which case the worker would take
them her when moving between sectors. In this analysis, industrywide learning effects are assumed, while potential spill-overs into other
sectors are neglected.
Geographic specificity Similarly to learning externalities across sectors,
the extent of industrial benefits can be local, regional, national or
global in scope. In this analysis, only Lbd resulting from output increases within Ontario are considered.
For this paper, we adopt the most straightforward interpretation. Learning is assumed to be factor neutral and apply to firms within a given province
only. As this section shows, there is considerable scope for alternative implementations.
10
4
Model Overview
FiT-rd is a recursive dynamic multi-region CGE model of the Canadian
economy.5 It is designed to simulate immediate and transitional economic
impacts of different climate policy scenarios including renewable energy quotas and feed-in tariffs. In particular, the recursive dynamic character of
FiT-rd means that in each period, the representative household makes an
investment decision. One period’s purchases of investment goods cause the
sector-specific capital stocks to increase the next period. The model’s agents
can be considered myopic, since there is no mechanism whereby anticipation
of future events can affect current behaviour.
The following paragraphs describe the model and the used data sources.
Production Nested constant-elasticity-of-substitution (CES) production functions are used to model firms’ input choices regarding the key production factors capital, labour and a nested aggregate of all other inputs
such as energy and material.
For renewable energy firms an additional fixed factor (non-fossil sites)
is considered, which represents the finite availability of renewable energy sites in each year. Because the supply of sites is fixed, the supply
curve for this sector is upward sloping in each period. The factor share
is taken from Sue Wing (2008).6 This can be seen as representing exploitation of the best sites first.
Factor markets The groups of factors of production are distinguished:
capital, labour, and specific resources. Capital is assumed to be both
region and sector specific. This allows the capital stock of a specific sector in a specific region to accumulate over time. Labour is
considered mobile between sectors within a province, but immobile
between provinces. To determine total labour supply, each household
is assumed to trade off between leisure and consumption. The model
recognizes some equilibrium unemployment.
Government and taxation All government revenue (direct and indirect
taxes) is received by that province’s representative agent, and government spending is fixed in each region in all periods. Although in
this formulation, policy scenarios are likely to affect revenues, and
5
Further detail about the earlier model from which it was developed is available in Beck,
Rivers, and Wigle (2013b).
6
An earlier paper by Sue Wing in Energy Policy 2006 gives dramatically higher shares
to the resource (20% versus 6%). #34 pp 3847–69
11
thus the government deficit, the implementation of policy scenarios
are typically constrained to keep total government revenues (and thus
the deficit) to be kept constant.
Investment demand In FiT-rd the level of expenditure on investment responds to average rates of return with a constant elasticity formulation.
Higher average rates of return lead to higher levels of investment. The
default value of the parameter is i is 14 .
Total investment in a region from the previous period is allocated
among alternative sectors in the region according to a CET transformation function. Sectors earning higher rates of return to capital
receive a higher share of new vintage capital.
Consumer demand Household consumption is modeled in a rather aggregate fashion as distributional impacts are not the focus of this analysis.
One representative agent in each province receives factor income from
labour and government transfers. Household income is reduced by
federal and provincial taxes to fund public services. Given these budget constraints, the representative agent purchases consumption goods
according to a CES aggregate over all consumer goods.
International and inter-provincial trade FiT-rd allows for bilateral trade
between provinces and across national borders following the (Armington) approach. According to this common approach in applied studies,
domestic goods and imports from the rest of the world are nested in
a CES function. Goods produced in Canada are a CES aggregate
of goods produced in the home province and goods imported from
other provinces. Canada is assumed to be a price taker on the global
market, whereas relative prices between provinces are determined endogenously. An elasticity of transformation function is used to specify
the ease with which Canadian goods can be exported instead of sold
domestically when relative prices change. Overall, the models ensures
that trade surplus or deficit in each province, and therefore total foreign saving in Canada, remain fixed at a benchmark level.
Data The model is calibrated to the economic transactions (quantities and
prices) in a benchmark year as compiled in Statistic Canada’s symmetric provincial input-output tables for the year 2005. The benchmark set also includes data on production, intermediate use, final demands, sectoral capital earnings and sectoral expenditures on wages
and salaries as well as information on inter-provincial and international
12
trade flows. Elasticities are are assumed exogenously. Since renewable
energy technologies are not differentiated from conventional technologies in the benchmark input-output data set, this specification is made
using additional data on electricity generation technologies in combination with a forecast of electricity generation from renewable sources
to generate a technology profile for the renewable energy sector. Cost
and technology information on 18 different electricity generation technologies is provided by the US Environmental Protection Agency.
4.1
Learning
Learning by doing is modeled as a learning curve that confers factor neutral
cost savings (read efficiency enhancements) to all renewable electricity producers in the region. The amount of learning in a given region depends on
the total historical output of the sector in that region. Learning is restricted
in these model runs to the renewable generating sector. A key feature of the
enhancement is that it is assumed to be external to firms. In other words,
firms do not incorporate the learning by doing gains into their output or
investment behaviour because they do not capture the bulk of the efficiency
gains that result from increasing their own output. Further, the increased
productivity is assumed to be disembodied, meaning it does not enhance
the productivity of resources used outside of the renewable sector.
ct = ct−1
Yt
Yt−1
−γ
In the central case, γ is chosen such that a doubling of historical output (Yt )
leads to a 5% reduction in ct .
5
Policies Considered
We consider the introduction of a feed-in-tariff scheme (like that used in
Ontario) to double the share of renewable electricity in Ontario. In one case
(Permanent) we consider the policy to be in effect permanently, whereas
in the other (Temporary) the supports hit the same target up until 2035,
after which they are removed. The Ontario feed-in tariff scheme provides
a subsidy to production of renewable electricity financed through a tax on
all domestic consumption of electricity within Ontario. The subsidy is endogenously selected to hit the target share of renewable electricity in total
electricity generation.
13
Table 2: Central Case Parameters
Parameter
Renewable–Conventional Electricity Substitution
Learning Rate in Renewable Energy
Armington Elasticity (Domestic–Foreign)
Armington Elasticity (Among Domestic)
Elasticity of Substitution in Value Added
Output transformation between domestic and export
Discount Rate
Depreciation rate (all sectors except utilities/electricity)
Depreciation Rates (Utilities)a
a
6
σCR
ρ
σf
σd
σV
σT
r
δ
δU
includes all electricity generation
Central Case Findings
We start in Section 6.1 by looking at the characteristics of the temporary
policy scenario and then continue to a comparison of the temporary and
permanent experiments.
6.1
Temporary Support
An overview of the macro-level impacts of temporary support are provided
in Figure 1 (dollar-value magnitudes). The key observations are that consumption, welfare and GDP all fall relative to BAU as long as the policy is
in effect. Once support is removed, all three rise.
The design of the Ontario feed-in tariff policy regime supports purchases
of renewable electricity relative to conventional electricity, but is relatively
neutral regarding the treatment of electricity as a whole relative to other
goods. As a result, impacts on factor markets tend to be somewhat muted.
The small rise in the rental rate on capital when the support is introduced
results because the renewable electricity sector is slightly more capital intensive than the conventional electricity sector. Real wages fall (less than
1
4 of one percent as long as the support is in place, but recover once the
support ends.
Although the effect on the economy-wide rental rate (and thus the economywide rate of return) are very modest, the same cannot be said of key sectoral
rental rates. This is illustrated in Figure 3 for selected sectors. While sup14
Value
2.0
5%
3.0
6.0
0.7
3.0
5%
7%
5%
ports are in effect, rental rates rise markedly in the renewable sector, and
decline much more modestly in the conventional electricity sector. When
the supports are removed, the impacts on rental rates are reversed. The
rental rate declines in the mining sector (which includes coal, oil and gas
mining) while the support is in effect, but then rises slightly after support
is removed,
Table 3: Temporary: Sectoral Rental Rates (%)
2010
2015
2020
2025
2030
2035
2040
2045
2050
MIN
-0.1
-0.1
-0.2
-0.3
-0.3
-0.4
0.1
0.1
0.0
UTL
0.2
0.2
0.2
0.3
0.3
0.3
-0.1
-0.1
-0.1
ELE
-4.0
-4.1
-5.0
-6.0
-7.2
-8.4
3.9
1.8
0.9
REN
102.3
44.9
36.6
35.4
35.3
35.3
-37.5
-21.8
-11.1
units all items are % change relative to BAU
MIN Mining sector includes coal, oil and gas mining
UTL Utilities sector (includes telecomms, sewage, water utilities)
ELE conventional electricity sector
REN renewable electricity sector
Impacts on aggregate trade flows (both within Canada and abroad) are
very modest as detailed in Figure 9.
There is a very modest impact on the overall level of investment when the
support is initially provided, but the major impact on investment behaviour
is on the allocation of investment along sectors. Figure 3 shows the dynamics
in both subsectors of the electricity sector.
Figure 3 illustrates the dynamics of the two electricity sub-sectors. When
the policy is first introduced, the investment7 in the renewable electricity
rises dramatically relative to BAU in the first period and then converge
7
That is installation of capital into a given sector.
15
Figure 1: Temporary Support: Macro Impacts
$ M change versus BAU
to a 100% increase relative to BAU. The renewable sector’s output level
and employment both double while the policy is in effect. When the policy
is stopped in 2035, sectoral investment falls sharply at first but remains
below BAU values over our modeling horizon. Employment, output and
the sectoral capital stock converge back to their initial values. While the
output of the renewable sector will never quite reach the baseline value, it
would eventually get extremely close to the baseline values. Even though
the historical output will always be higher in the case of the temporary
policy (versus none), the difference in the learning effects between the two
situations will become progressively smaller. Looked at another way, even
without support, the learning by doing impacts as we model them will be
achieved eventually.
The dynamics in the conventional electricity sector mirror, the sign of
those in the renewable sector, but the percentage changes are much smaller.
This is due to the relative size of the two sectors (see Table 4. Investment
in the conventional electricity sector declines as do employment, output and
the capital stock. When supports end there is a (smaller) positive spike in
investment in the conventional sector. Output, employment and the capital
stock will eventually converge to the baseline values.
16
Figure 2: Temporary Support: Factor Prices (%)
• % change versus BAU
• Pct Rental Percentage change in real capital rental rate
• Pct Return Percentage change in rate of return to capital
• Pct Wage Percentage change in real wage rate
Table 4: Renewable Market Shares
Year
2010
2015
2020
2025
2030
2035
2040
2045
2050
Baseline
3.0
4.8
6.6
8.4
10.2
12.0
12.7
13.3
14.0
Permanent
6.0
9.6
13.2
16.8
20.4
24.0
25.3
26.7
28.0
17
Temporary
6.0
9.6
13.2
16.8
20.4
24.0
17.2
16.2
15.8
Figure 3: Electricity Sector Dynamics
• top is renewable generating sector, bottom is conventional generating
sector
• % change versus BAU
• K: Capital Stock, L: Labour employed, NK: Newly installed capital,
Q: Output
18
6.2
Comparison of Temporary and Permanent Support
Because the model is recursive dynamic, the model time paths are identical
up to the point where the policies diverge. This is shown in Figure 4. If
the support remains in place (permanent scenario), then after 2035 welfare
continues to fall to the end of the modeled period (2050). By contrast, with
temporary support, the welfare loss not only gets smaller but becomes positive in the next modeled period. That is to say that the welfare rises relative
to BAU after the support is lifted. This finding may seem counter-intuitive,
but results primarily from three facts. First, the the stock of renewable electricity capital is both larger and more productive (due to learning) than it
was in the BAU. The renewable sector capital is also more productive relative to the conventional capital. Recall that one motivation for the renewable
support is the production-side externality related to learning.
Figure 4: Welfare Comparison
• Units are $M (change relative to BAU)
• ofit-on-2-t: temporary ofit policy
• ofit-on-2: permanent ofit policy
The dynamics of the renewable generating sector are compared between
the two scenarios in Figure 5. Up to 2035 the dynamics for capital stock and
newly installed capital are the same. When the support is permanent, both
the capital stock and newly installed capital stay close to twice as high as
the BAU, whereas both new investment and the capital stock decline rather
19
sharply after support is cut.
Figure 5: Renewable Sector Dynamics Comparison (%)
• pct K refers to percent change of capital stock (% relative to BAU)a
• pct NK refers to percent change of newly installation of capital (%
relative to BAU)b
a
b
blue (permanent) and red (temporary) lines
yellow (permanent) and green (temporary) lines
Figure 6 shows the magnitude of changes in the learning effect induced
by policy. We assume that learning is also occurring in the baseline following
the same relation as in the counterfactuals. The figure shows the percentage
change in those learning effects between the baseline and the policy experiment. The difference reaches near 5% by 2050 in the Permanent policy. In
the Temporary policy, the difference in learning effects begins to erode after
2035.
6.3
Economic Efficiency
The welfare measures depicted in Figure 4 refers to welfare from consumption and leisure in each time period, ignoring both the impacts on the environment, and changes to the capital stock over the time period. A number
of approaches can be used to determine which policy scenario is most efficient. We calculate the present value of the welfare stream associated with
20
Figure 6: Learning Effects
each policy measure, inclusive also of leisure. Flows are discounted at a rate
of 5%.
Table 5: Present Value Calculations ($M, 2010)
Welfare
Welfare+Damages
Central Case
Temporary Permanent
-1072
-10409
3712
-4439
A key issue arises in the calculation of the present value for any recursive
dynamic model in terms of the treatment of ‘post-terminal’ periods. Our
explicit modeling ends in 2050. A standard way of treating such periods
(here periods after 2050) is to estimate a post-terminal time path based on
knowledge of the model and observed convergence in the model solution near
the terminal point.8 Our ‘Permanent’ experiment solutions are converging
in terms of percentage changes over the last three periods. It is not so clear
that our temporary solutions are converging. As a result, the findings in
Table 5 should be treated with some caution.
8
Rutherford (2008)
21
The value of reduced damages in each period is computed as the reduction in greenhouse gas emissions of the conventional electricity sector
multiplied by $50/t. Our ‘social cost of carbon’ is higher than the recently
re-evaluated US counterpart, but the damages associated with the conventional electricity sector would normally be dominated by those attributable
to local air emissions. Our baseline emissions do reflect the dramatic reduction in GHG emissions in Ontario associated with shutting down coal
generation.9
Given these two caveats, the results are only illustrative, but nonetheless
raise the possibility that key parameters are likely to affect not only the size,
but the sign of welfare evaluations of renewable support programs.
6.3.1
Sensitivity
Although most of our sensitivity analysis has been left to an appendix, some
sensitivity analysis of our overall present value calculations is presented here.
This sensitivity analysis is still subject to the same caveats mentioned before,
yet it illustrates the notion that the sign, as well as the magnitude of welfare
impacts is likely to be subject to parameter sensitivity.
Table 6: Present Value Calculations ($M, 2010)
Welfare
Welfare+Damages
Welfare
Welfare+Damages
Welfare
Welfare+Damages
Central Case
Temporary Permanent
-1072
-10409
3712
-4439
LSLL
Temporary Permanent
-5788
-23387
-1209
-17487
MSML
Temporary Permanent
4470
1594
9488
7473
9
Baseline GHG emissions of the Ontario generating sector fall by almost 55% between
2005 and 2015.
22
7
Summary, Conclusions and Extensions
As noted in the paper, several aspects of the modeling need refinement,
so our simulation results should be considered as illustrative. This section
briefly reviews some of our suggestive findings and directions for future research.
1. The key parameters to our findings include some whose processes are
not well understood (learning rates), those which are not easily estimated (ease of current and future substitution between renewable
and conventional electricity) and others which are subject to not only
quantitative debate, but also ethical debates (marginal damage costs).
2. Besides the parameters mentioned above, the optimal path and level
of support for renewables is likely to depend on other aspects of the
economy in question. This likely includes the emissions of the conventional electricity sector in the baseline. Thus, the environmental
benefits in Alberta (coal-fired) are likely greater than in Québec or
BC, where the existing conventional sector have very low fossil fuel
content.
3. Since the learning externality declines as cumulative output rises over
time, the optimal response to a learning by doing externality of the
type we consider would be a subsidy that declines over time (Melitz
2005). Given that their are also environmental externalities, it is possible that the optimal path will not go to zero. Searching for the optimal
support schedule is an obvious next step.
4. Once we can typify optimal support strategies, we can also inquire
into the conditions (in terms of model parameters) under which the
optimal support level is positive. Alternatively we can also consider
how these parameters affect the size of supports and the associated
welfare gains as well as its path.
23
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26
A
Additional Tables: Temporary Support
Figure 7: Temporary Support: Ontario Electricity Sector
% Change in sectoral output relative to BAU
27
Figure 8: Temporary Support: Electricity Trade (%)
% change versus BAU
Figure 9: Temporary Support: Ontario Trade Impacts (%)
% change versus BAU
28
B
Sensitivity
We consider three key sensitivity cases focusing on two values that are of
most importance to our results. Our central case is denoted CC10 LSLL
refers to a parameterization that is less conducive to renewable electricity.
Likewise, MSML is a parameterization that is more conducive to renewable
electricity.
Case
σCR
Learning Rate
LSLL
1
2%
CC
2
5%
MSML
5
10%
σCR is the elasticity of substitution between conventional and renewable
electricity. It represents how easily conventional and fossil electricity can
be substituted for one another. To illustrate how it works in the model, we
consider a value of σCR of 2. If the relative price of renewable electricity was
10% lower in the counterfactual than the baseline run, the demand share
of renewables relative to conventional electricity would rise by 20%. Interestingly, a number of earlier studies of the electricity sector have assumed
that conventional and renewable electricity were perfect substitutes at the
margin.
Figure 10: Welfare Temporary
Units are $M
10
In the pivot tables it is ULD because the depreciation rate is lower in the Utilities,
Renewable and Electricity sectors than the rest of the economy. This was previously not
a central case, but now is.
29
Figure 11: Welfare Permanent
Units are $M
Once support is removed, the renewable sector starts to decline in all
configurations, but does so noticeably slower as we parameterize the model
to be more conducive to renewable electricity.
30
Figure 12: Renewable Production (% vs BAU)
Figure 13: GDP Temporary Support ($M vs BAU)
31
Figure 14: Investment Sensitivity: Temporary Support
• Units are $M (change relative to BAU)
• ofit-on-2-t: temporary ofit policy
• ofit-on-2: permanent ofit policy
32