Home / Economic Instruments for Long-term Reductions in Energy-based Carbon Emissions / Economic Instruments for Long-term Reductions in Energy-based Carbon Emissions – Appendix B

Economic Instruments for Long-term Reductions in Energy-based Carbon Emissions – Appendix B

Previous – Content – Next

Source: National Energy Board <www.neb.gc.ca/energy/SupplyDemand/2003/index_e.htm>.

2. Figures may not sum due to rounding.

*Includes many existing small hydro sites that may not

be EcoLogo-certifiable.

installed capacity for a given technology. For example,

if wind power has a technical potential of 100,000 MW,

it means that this is the maximum total generating capacity

that wind turbines could supply if they were installed

in every technically feasible location across the country.

potential for each technology. In each case, a range

is provided, which reflects the relatively high level

of uncertainty that exists.

It recognizes that the ability to capture the technical

potential within any given period will be affected by

factors such as grid access and capacity; zoning and

permitting; technological advances; financing; market

demand and acceptance; and design, manufacturing and

installation capacity.1

were developed based on broad consideration of a number

of factors, complemented by consultations with industry

and government personnel. As with all figures, the estimates

are given in ranges to reflect the high level of uncertainty.

potential numbers in 2010 and 2020. The growth rates

are not forecasts of a base case of renewable supply,

but rather the growth required on an annual basis to

satisfy the practical potential. Refer to the full case

study for details on the data presented (available at

<www.nrt-trn.ca>)

range of values reflects both the diversity of conditions

experienced and the high levels of uncertainty.

* Assuming logarithmic cost reductions

Source: IEA figures cited by Martin Tampier in “Background

Document for the Green Power Workshop Series, Workshop

4,” Prepared for Pollution Probe and the Summerhill

Group, February 2004, pp. 30-32.

the targeted grid-power RETs is presented in Table 5.

To ensure consistency among the technologies, all cost

data are derived from recent estimates provided by the

International Energy Agency (IEA). And, to reflect the

cost uncertainties involved, the data are expressed

as a range. Table 5 also provides a summary of IEA estimates

of forecast cost reductions for each technology over

the study period. The forecast cost reductions are based

on learning theory. This theory, which is well supported

by empirical data, defines the link between the increase

in installed capacity and the rate of cost decrease.

modelling the fiscal instruments. The results of the

modelling are discussed below in Section 4.

in 2010 covered under this case study is presented in

Table 6. As can be seen, the case study is concerned

only with 37% of electrical generation in Canada in

2010.

for Supply and Demand to 2025 (Techno-Vert Scenario)

<www.neb-one.gc.ca/energy/SupplyDemand/ 2003/index_e.htm>.

**1999. Natural Resources Canada, Canada’s Emissions

Outlook: An Update <www.nrcan.gc.ca/es/ceo/update.htm>.

fiscal instruments. The discussion is organized and

presented as follows:

• Overviewof the fiscal instruments that are assessed;
• Overviewof the Resources for the Future (RFF) model used toassess the instruments;
• Summaryof results (including a road map for understandingthe results);
• Detaileddiscussion of the base case and each
• Fiscalinstrument; and
• Sensitivityanalysis results.

instruments were selected and modelled. The five instruments

are:

1 An emissions price, which is analogous to an

emissions trading permit system or a carbon tax. Under

this scenario, a shadow price is placed on carbon

equivalent to $10/tonne CO2. This shadow price is

equivalent to the cost of an emissions trading permit

or the tax rate on carbon. The emissions price is

applied uniformly across all fossil fuel generation

in Canada in 2010.

 

2 A renewable portfolio standard (RPS), which requires

utilities to buy green certificates, or the equivalent,

so that renewable generation increases relative to

fossil fuel generation. The model compares renewable

generation attributable to an RPS with generation

from fossil fuels (i.e., not all electrical generation).

Constraints are not placed on technologies or regional

shares of the total RPS. Instead, the prevailing electricity

price determines the type of technology used to generate

electricity.

 

3 A renewable generation subsidy (RGS), which

is modelled as a direct subsidy from government to

grid-power RET producers on a per-kWh basis. In practice,

a subsidy could include any fiscal instrument that

lowers the cost of production for producers, such

as a direct production subsidy or a capital cost allowance.

 

4 A combination of RPS and generation subsidy,

modelled in tandem. We let the RPS be the dominant

policy, since the standard is meaningless if the subsidy

encourages more renewable generation than required.

A notable feature of this combination is that the

price of the green certificates is offset in part

by the subsidy, in contrast to the situation where

the instruments are implemented in isolation. This

outcome will therefore trigger some redistribution

of costs.

 

5 An R&D subsidy, which is a program to reduce

the future cost of renewable generation. As such,

the instrument can be anticipated to have a greater

impact in future periods. The model identifies the

annual increase in renewable energy R&D required

to achieve the emission reduction target.

 

RPS target (i.e., 10% of generation from renewables)

or a subsidy level (i.e., $0.01 per kWh), are solved

endogenously. Each instrument is required to achieve

a common emission reduction (or policy target), and

then the model indicates the policy level that would

achieve the carbon target.

the impacts of the fiscal instruments on reducing greenhouse

gas emissions, as well as the development and diffusion

of renewable energy. This model was developed and tested

for the U.S. Environmental Protection Agency to assess

the preferred fiscal instruments for promoting renewable

energy technologies. The analytical model includes two

sectors, one emitting and one non-emitting, and both

are assumed to be perfectly competitive and supplying

an identical product, electricity. Fossil fuel production

is the marginal technology, setting the overall market

price; thus, to the extent that renewable energy is

competitive, it displaces fossil fuel generation in

future policy periods.

to 2015, and a longer-term stage covering 2015 to 2030.

Electricity generation, consumption and emissions occur

in both, while investment in knowledge takes place in

the first stage, followed by technological change and

innovation that lowers the cost of renewable generation

in the second.

industry relies on fossil fuels. These are a mature

technology, and the productivity improvements available

through new R&D are assumed to be negligible.2 The

marginal production costs of the sector are assumed

to be constant with respect to output, increasing with

reductions in emission intensity. The representative

firm chooses an emission intensity to equate the additional

costs of abatement to the price of emissions. The full

marginal costs of generation then include both the marginal

production costs, given the emission intensity choice,

and any effective tax, such as the price of the emissions

or carbon embodied in an extra unit of output, or the

cost of green certificates under an RPS. As long as

fossil fuel generation occurs, the competitive market

price must equal the sum of these marginal costs.

using renewable resources. Unlike the fossil supply

curve, which is flat and set at the long-term marginal

cost of electricity, the renewable supply curve slopes

upward, reflecting marginal production costs that increase

with output. Because renewables are a young technology,

the costs of renewable power shift down over time as

the knowledge stock increases. There are two ways to

increase the knowledge stock: through investments in

R&D and “learning by doing,” which is

a function of total output during the first stage in

the model. The representative renewable energy firm

chooses output in each stage as well as R&D investment

to maximize profits. In the first stage, it produces

until the marginal cost of production equals the value

it receives from additional output, including the competitive

market price, any production subsidy, and the contribution

of such output to future cost reduction through learning

by doing. The firm also invests in research until the

discounted returns from R&D equal investment costs

on the margin.

the fiscal instruments, we hold the environmental effects

constant across the policy scenarios. While we calculate

the costs of achieving emission targets in this case

study, the benefits of the fiscal instruments are not

estimated. The fiscal instruments through their displacement

of fossil fuel can be expected to trigger a number of

environmental and economic benefits, including:

• improvedambient air quality and reduced carbon in the atmosphere;
• avoidedambient air quality impacts on sensitive ecosystemand health receptors and the associated economic valueof the avoided damages; and
• climatechange mitigation benefits such as avoided ecosystem,health and economic damages stemming from extremeweather events, temperature changes and sea-levelrise and the associated economic value of the avoided

  damages.

the fiscal instruments from a social perspective, the

benefits are in a sense fixed in the case study because

of the stipulation of a common emission target that

all instruments achieve.

that the outcomes are a function of how each instrument

influences the energy market. In the model, outcomes

differ due to changes in three decarbonization drivers:

renewable power penetration, the carbon intensity of

fossil fuel generation and total electricity demand.

instrument’s ability to affect one or all of the

three decarbonization drivers in the electricity market.

Generally speaking, an instrument will be more economically

efficient if it targets all of these three drivers.

For purposes of comparison, the base case indicators

are provided to allow for comparison with the policy

scenarios. In the no-policy base case, our model predicts

that renewable energy generation will increase from

13% to 17% of included generation in the second stage,

which corresponds to a 5% emission reduction. Subsequent

policy scenarios will target a 12% reduction overall

from the combined emissions in the two stages of the

no-policy case.

as follows:

required to achieve the carbon reduction target:

• Forthe emissions price, a tax of $10/tonne CO2 wouldachieve the 12% reduction in total carbon emissionsfrom the base case.
• Forthe RPS, a portfolio standard of 24% would achievethe 12% carbon reduction. This 24% is the final shareof renewable power generation in the generation coveredby this case study – which consists of both renewable

  and fossil fuel generation but excludes major hydro

  and nuclear.

• Forthe RGS, a value of about $0.006/kWh achieves thepolicy objective of a 12% carbon reduction.
• Whencombined with a subsidy of $0.002, the RPS needs tobe set at a slightly higher target of 24.2%.
• Forthe R&D subsidy, a program that increases R&Dspending by 61% annually above the base-case R&Dlevels would achieve the target.

of electricity in the first and second stages (2015

and 2030, respectively).

years in the first and second stages. Carbon reductions

are influenced by the three drivers in the following

ways:

• Renewablepower penetration displaces fossil generation whenan instrument reduces renewable production costs relativeto fossil generation costs.
• Thecarbon intensity of fossil fuel generation is reducedwhen carbon is priced in the fossil sector (i.e.,abatement from natural gas generation that displacescoal).
• Anincrease in the electricity price reduces total electricitydemand, which displaces output from fossil fuels.

the “on margin” emission intensity of fossil

fuel by the quantity of fossil fuel supplied.

Renewable output is a function of production cost differentials

between renewables and fossil fuels. Instruments affect

the cost differential through subsidizing renewable

generation, inducing renewable production cost decreases

through innovation, and/or taxing fossil fuel production.

Instruments that promote innovation reduce renewable

costs and carbon emissions in the second stage.

price changes in production costs. Fossil output is

also altered by total demand reductions, which occur

when an instrument increases the price of electricity.

that the instrument influences final demand through

electricity price increases.

by the public and private sectors.

supply below the base case.

9 Consumer

instrument measured as the change in the present value

of the total cost to consumers for both stages. The

consumer surplus is negative and is present when the

instrument increases the price of electricity.

10 Producer surplus ($M): This is the change in

for both stages. Renewable sector profits increase when

the instrument raises the price received by renewable

generation, either by a subsidy or a tax on fossil generation.

When this occurs, profits can be made if some renewable

production costs are below the instrument electricity

price in the scenario.