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

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Source	Installed capacity		Generation
	MW	Share	GWh	Share
Hydro	68,100	58%	346,000	59%
Nuclear	12,600	11%	81,700	14%
Coal	16,600	14%	109,400	19%
Oil	7,500	6%	14,200	2%
Natural gas	11,000	9%	29,100	5%
Wind and biomass	2,200	2%	9,100	2%
Total	118,000	100%	589,500	100%

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

Grid-power RET (EcoLogo-certifiable)	Current installed base
	Cap factor	Capacity (MW)	Supply (GWh/yr)	Share of total grid-power RET supply
Wind (on shore)	35%	316	970	8%
Hydro*	60%	1,800	9,460	78%
Solar PV	14%	0.092	0.1	0%
Landfill gas (LFG)	90%	85	670	6%
Biomass	80%	128	900	7%
Wave	35%	0	0	0%
Tidal	35%	0	0	0%
Geothermal (large)	95%	0	0	0%
Total		2,300	12,100	100%

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.    

Grid-power RET (EcoLogo-certifiable)	Cap factor	Technical resource potential (total, not additional)
		Capacity (MW)		Supply (GWh/yr)
		Low	High	Low	High
Wind (offshore)*	35%	28,000	100,000	85,800	306,600
Low-impact hydro	60%	11,000	14,000	57,800	73,600
Solar PV	14%	9,800	100,000	12,000	122,600
Landfill gas (LFG)	90%	350	700	2,700	5,500
Biomass	80%	6,800	79,300	47,700	555,600
Wave	35%	10,100	16,100	31,000	49,400
Tidal	35%	2,500	23,500	7,700	72,100
Geothermal (large)	95%	No data	3,000	No data	25,000

Grid-Power RET (EcoLogo Certifiable)	Cap Factor	Practical Resource Potential
		Annual Growth in Deployment to Fill Practical Potential [%] *		Capacity [MW]				Supply [GWh/yr]
				2010		2020		2010		2020
		Min	Max	Low	High	Low	High	Low	High	Low	High
Wind (Onshore)	35%	25%	64%	5,000	10,000	15,000	40,000	15,300	30,700	46,000	122,600
Low-Impact Hydro	60%	18%	27%	5,600	9,000	9,800	no data	29,400	47,300	51,500	no data
Solar PV	14%	152%	347%	60	265	225	3,295	100	300	300	4,000
Landfill Gas (LFG)	90%	10%	17%	170	no data	250	no data	1,300	no data	2 000	no data
Biomass	80%	42%	73%	1,500	2,000	no data	6,000	10,500	14,000	néant	42,000
Wave	35%	0%	infinite	0	20	4	no data	0	60	12	no data
Tidal	35%	infinite	infinite	4	300	50	2,000	12	900	200	6,100
Geothermal (Large)	95%	infinite	infinite	100	600	1,500	no data	800	5,000	12 500	no data

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>)  

Grid-Power RET (EcoLogo Certifiable)	Cap Factor	Cost Reduction				Cost Estimates
		Cost Reduction every 10 Yrs [%]*		Annual Cost Reduction [%]*		Levelized Cost Estimates [CDN cents 2000/kWh]
		Min	Max	Min	Max	2003		2010		2020
						Low	High	Low	High	Low	High
Wind (Onshore)	35%	25%	25%	3%	3%	3.8	15.1	3.0	11.3	1.9	8.5
Low-Impact Hydro	60%	0%	13%	0%	1%	2.5	18.8	2.5	16.3	2.3	15.2
Solar PV	14%	30%	50%	4%	7%	22.6	100.3	12.5	50.2	7.5	30.1
Landfill Gas (LFG)	90%	0%	20%	0%	2%	2.5	18.8	2.5	15.1	2.3	13.5
Biomass	80%	0%	20%	0%	2%	2.5	18.8	2.5	15.1	2.3	13.5
Wave	35%	no data	no data	no data	no data	4.4	7.6	no data	no data	no data	no data
Tidal	35%	no data	no data	no data	no data	4.7	9.6	no data	no data	no data	no data
Geothermal (Large)	95%	10%	25%	1%	3%	2.5	15.1	2.5	12.5	2.1	10.3

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.  

Electricity-Generating Technology	Projected Electricity Generation in 2010 [GWh]	Percent of Total Generation
Grid-Power RETs(as included in this study)M	31,000*	5%
Fossil Fuels (coal, gas, oil as included in this study)	198,000**	32%
Other (nuclear and renewables excluded from this study)	394,000	63%
TOTAL	623,000**	100%

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.   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.   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.