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

Appendix B: Executive Summary: Case Study on Renewable Grid-Power Electricity

1 INTRODUCTION

This case study analyzes the role that fiscal policy can play in promoting the long-term development of Canada's renewable energy sector. Ecological fiscal reform (EFR) is recognized as a lever for promoting and, where appropriate, accelerating the use of renewable energy technologies in order to make long-term reductions in energy-based carbon emissions. This case study addresses the renewable energy sector and explores the ability or "traction" of five fiscal instruments to improve the uptake or deployment of grid-power renewable energy technologies (RETs) in Canada.

2 THE RENEWABLE ENERGY CONTEXT

The focus of the current study is on renewable energy technologies. However, the term renewable energy technologies is commonly used interchangeably throughout the literature with terms such as clean energy, green power, alternative energy and low-impact technologies. While there is considerable overlap in the technologies included within each group, they are not identical. In practice, these definitional differences can become quite important when dealing with the RET policy and technology eligibility issues.

After some discussion of the scope of RETs to be used in this case study, it was concluded that the Environmental Choice Program (ECP)'s EcoLogo definition provided the best available match with the overall goals of this study. This conclusion was based on consideration of two factors:

• the goal of the NRTEE's EFR initiative clearly states that "long-term carbon emissions reduction" should not result in increased loading of other pollutants; and
• an implied goal of this initiative is the promotion of innovation.

In addition, to provide a focused output, the NRTEE directed the study team to examine only those RETs that generate electrical power (as opposed to thermal technologies such as solar hot water heaters). In a similar vein, the NRTEE directed the study team to look only at those RETs that are, or will be, tied into the national electricity grid (as opposed to stand-alone systems).

Consequently, the following technologies are considered in this case study:

• wind turbines (onshore and offshore);
• low-impact hydro;
• grid-connected photovoltaics (PV);
• landfill gas (for electricity generation);
• biomass (for electricity generation);
• ocean energy, including wave and tidal power conversion technologies; and
• geothermal.

For this case study, the term RET refers to renewable grid-power technologies or grid-power RETs.

3 RENEWABLE GRID POWER IN CANADA

The study addresses three key areas with respect to grid-power RETs:

• Current status: What is the current status of each technology in terms of installed Canadian grid electricity generating capacity, technical maturity and costs?
• Future potential in Canada: What is considered the long-term upper-limit capacity for each technology and how much of this upper limit is practically achievable by 2010 and 2020?
• Renewable power technology costs and learning trends: What are the current and projected future costs for the targeted technologies and what are the learning trends that impact these costs?

3.1 Current Status

Table 1 shows the current total installed electricity generation capacity in Canada as well as the total share of electricity generated by each source in 2003. As illustrated, if the estimate includes large hydro and all biomass installations, then Canada's total installed base of renewable electricity generation capacity is over 70,000 MW, or about 60% of the total; virtually all of this capacity is large hydro.

If the more stringent low-impact environmental criteria defined by the Environmental Choice Program (ECP) are used, then large hydro and some of the biomass facilities are excluded. (A summary of the ECP criteria is presented at the end of this appendix.) A breakdown of the estimated current (2003) installed base of EcoLogo-certifiable grid-power RETs is shown in Table 2. In 2003, these renewable energy technologies generated an estimated 12,100 GWh of electricity or about 2% of Canada's total electricity generation.

Table 1: Installed Electricity Capacity and Annual Electricity Generation in Canada in 2003

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%

Note: Figures may not sum due to rounding.
Source: National Energy Board <www.neb.gc.ca/energy/SupplyDemand/2003/index_e.htm>.

Table 2: Current Installed Base of ECP Grid-Power RETs in Canada in 2003

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%

Notes:
1. Installed capacities are for grid-power electricity and potentially could be EcoLogo-certifiable.
2. Figures may not sum due to rounding.
*Includes many existing small hydro sites that may not be EcoLogo-certifiable.

3.2 Future Potential in Canada

Technical potential refers to the long-term upper limit of total 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.

Table 3 provides an indication of the estimated technical potential for each technology. In each case, a range is provided, which reflects the relatively high level of uncertainty that exists.

Practical potential is necessarily a subset of technical potential. 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

Table 4 provides the estimated practical potential. The estimates 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.

Table 3: Technical Resource Potential of Grid-Power RETs in Canada

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

*Offshore not included due to lack of independent estimates.

Table 4: Estimated Practical Resource Potential of Grid-Power RETs in Canada

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

* Assuming logarithmic growth and based on practical resource 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.nrtee-trnee.ca>)

Table 5: IEA Cost Reduction and Estimates for Targeted Grid-Power RETs

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

Note: Cost estimates are for all OECD countries; the wide 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.

3.3 Renewable Energy Technology Costs and Learning Trends

A summary of the expected levelized costs for each of 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.

The practical potential and levelized costs are used in modelling the fiscal instruments. The results of the modelling are discussed below in Section 4.

Finally, the share of total electricity generation in Canada 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.

Table 6: Projected Share of Grid-Power RETs and Fossil Fuel Generation in 2010

Electricity-Generating Technology	Projected Electricity Generation in 2010 [GWh]	Percent of Total Generation
Grid-Power RETs (as included in this study)	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%

*2003. National Energy Board, Canada's Energy Future: Scenarios 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>.

4 ECONOMIC AND POLICY ANALYSIS - APPLICATION TO CANADA

This section presents the modelling results for each of the fiscal instruments. The discussion is organized and presented as follows:

• Overview of the fiscal instruments that are assessed;
• Overview of the Resources for the Future (RFF) model used to assess the instruments;
• Summary of results (including a road map for understanding the results);
• Detailed discussion of the base case and each
• Fiscal instrument; and
• Sensitivity analysis results.

4.1 Fiscal Instruments Assessed

In collaboration with the NRTEE, a base case and five fiscal 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.

In the model, the levels of the instruments, such as an 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.

4.2 Overview of RFF Renewable Energy Uptake Model

The RFF unified analytical model was employed to assess 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.

The model has two stages: a short-term stage covering 2010 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.

The carbon-emitting sector of the electrical generation 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.

Another sector of the industry generates without emissions by 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.

Since we target equivalent emission reductions for each of 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:

• improved ambient air quality and reduced carbon in the atmosphere;
  
  
• avoided ambient air quality impacts on sensitive ecosystem and health receptors and the associated economic value of the avoided damages; and
  
  
• climate change mitigation benefits such as avoided ecosystem, health and economic damages stemming from extreme weather events, temperature changes and sea-level rise and the associated economic value of the avoided damages.

While they are important in assessing the desirability of 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.

4.3 Summary Results

When reviewing the summary results, it is useful to understand 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.

The outcomes listed in Table 7 can be traced back to an 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.

The numbered items in the first column of Table 7 are defined as follows:

1 Policy level for 12% emission reduction: This row provides an estimate of the size of the fiscal instrument required to achieve the carbon reduction target:

• For the emissions price, a tax of $10/tonne CO2 would achieve the 12% reduction in total carbon emissions from the base case.
• For the RPS, a portfolio standard of 24% would achieve the 12% carbon reduction. This 24% is the final share of renewable power generation in the generation covered by this case study - which consists of both renewable and fossil fuel generation but excludes major hydro and nuclear.
• For the RGS, a value of about $0.006/kWh achieves the policy objective of a 12% carbon reduction.
• When combined with a subsidy of $0.002, the RPS needs to be set at a slightly higher target of 24.2%.
• For the R&D subsidy, a program that increases R&D spending by 61% annually above the base-case R&D levels would achieve the target.

2 Electricity price ($/kWh): This row indicates the impact of the fiscal measure on the annual price of electricity in the first and second stages (2015 and 2030, respectively).

3 Carbon emissions (Mt): Carbon emissions are presented as annual estimates in megatonnes of CO2 for the last years in the first and second stages. Carbon reductions are influenced by the three drivers in the following ways:

• Renewable power penetration displaces fossil generation when an instrument reduces renewable production costs relative to fossil generation costs.
  
• The carbon intensity of fossil fuel generation is reduced when carbon is priced in the fossil sector (i.e., abatement from natural gas generation that displaces coal).
• An increase in the electricity price reduces total electricity demand, which displaces output from fossil fuels.

For each scenario, carbon emissions are estimated by multiplying the "on margin" emission intensity of fossil fuel by the quantity of fossil fuel supplied.

4 Renewable output (MWh 10^11): This row indicates the output of renewable generation in the two stages. 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.

5 Fossil output (MWh 10^11): As with renewable output, fossil fuel output is altered by the instruments through price changes in production costs. Fossil output is also altered by total demand reductions, which occur when an instrument increases the price of electricity.

6 Total electricity output (MWh 10^11): Total generation includes fossil and renewable output; changes indicate that the instrument influences final demand through electricity price increases.

7 Renewable R&D ($M): Expenditures are expressed in millions of dollars annually in total R&D spending by the public and private sectors.

8 Additional renewable cost reduction: This row indicates the percent reduction in the cost of the renewable supply below the base case.

9 Consumer surplus ($M): This is the net consumer cost of the 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 the measure of total profit in the renewable sector 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.

11 Transfers ($M): This is the change in government revenues, where a positive number is revenue and a negative one is a disbursement. Again, the estimate is a total cost for both stages.

12 Welfare (excluding environmental benefits) ($M): This is the change in social welfare and is a proxy for the societal cost of the instrument. It is the sum of consumer and producer surpluses and transfers. It is an important metric, since all scenarios achieve the same carbon reduction target, yet have differing social costs.

13 Welfare relative to emissions price: This is simply a ratio that indicates the welfare costs of each scenario compared with the emissions price scenario. The emissions price is selected as the basis for comparison since it has the lowest welfare cost.

Table 7: Summary of Modelling Results for Fiscal Instruments (2000 $)

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               

		Base Case	Emissions Price	Renewable Portfolio Standard	Renewable Generation Subsidy	Combination RPS and RGS	Renewable Research Subsidy
1. Policy level for 12% emissions reduction			10 $/t CO2	24% of generation in case *	$0.006	RPS = 24.21%, RGS = $0.002	61%
2. Electricity price (in $/kWh)
1st stage	$0.092	$0.097	$0.095	$0.092	$0.095	$0.092
2nd stage	$0.092	$0.097	$0.093	$0.092	$0.092	$0.092
3. Carbon emissions (MT CO2)
1st stage	106	98.10	91.00	98.97	91.08	104.00
2nd stage	101	84.40	91.90	83.50	91.95	77.40
4. Renewable output (MWh 10^11)
1st stage	0.29	0.40	0.54	0.42	0.55	0.31
2nd stage	0.38	0.66	0.55	0.72	0.55	0.83
5. Fossil output (MWh 10^11)
1st stage	2.00	1.85	1.71	1.87	1.72	1.98
2nd stage	1.91	1.59	1.73	1.57	1.73	1.46
6. Total electricity output (MWh 10^11)
1st stage	2.29	2.25	2.26	2.29	2.27	2.29
2nd stage	2.29	2.25	2.28	2.29	2.29	2.29
7. Renewable R&D ($M)	$129	$450	$320	$533	$325	$1,576
8. Additional renewables cost reduction	0%	15%	13%	16%	13%	26%
9. Consumer surplus ($M)	$0	($11,690)	($4,521)	$0	($3,533)	$0
10. Producer surplus ($M)	$0	$2,215	$3,480	$2,846	$3,547	$1,590
11. Transfers ($M)	$0	$8,896	$0	($3,557)	($1,072)	($3,890)
12. Welfare - no benefits measured ($M) (9+10+11=12)	$0	($579)	($1,041)	($711)	($1,058)	($2,300)
13. Welfare relative to emissions price	-	1.00	1.80	1.23	$1.83	3.97

Figures may not sum due to rounding.
* This is 9% of all annual Canadian generation.
Source: Marbek Resource Consultants and Resources for the Future.

4.4 Detailed Results by Instrument

4.4.1 Base Case

The base case provides the reference from which
the percentage changes are estimated in Table 7. Renewable power penetration is forecast based on the relative costs of fossil fuel and renewable production. The baseline penetration of renewables increases over time, reflecting decreasing renewable power production costs due to innovation.

Total electricity output remains fixed in both periods in the base case3, and thus increased penetration of renewables decreases the carbon intensity of overall generation. This reduction is captured as a decrease in carbon emissions over time, from an annual level of 106 Mt in the first stage to 101 Mt in the second stage.

4.4.2 Emissions Price

An emissions price works to reduce emissions by reflecting their cost, either in environmental damage (as with an environmental levy) or opportunity cost elsewhere in the economy (as with an emissions
cap-and-trade system). This price sends a signal to everyone in the energy market to conserve carbon. Fossil energy producers can reduce costs by boosting efficiency or switching to lower-carbon fuels and processes. Since the price of fossil energy will then incorporate the cost of the carbon associated with that form of generation, the price of electricity will also rise, creating two effects. First, it signals consumers to conserve and take advantage of opportunities to reduce their demand by, for example, adopting more energy-efficient appliances. Second, it increases the price received by renewable energy producers, encouraging production and investment in non-emitting generation technologies. From a distributional perspective:

1 Consumers incur the highest electricity price increase and consumer surplus loss under the emissions price. Since consumers are also
taxpayers, the use of the revenues (i.e., transfers) is important in assessing the net effect on households.

2 For renewable energy producers, the emissions price has a modest but significant impact on renewable output, production cost decreases and producer surplus. The impact is also relatively consistent across stages.

3 For fossil fuel electricity generators, the emissions price is the only policy with an incentive to reduce emission intensity. Although profits for the fossil sector are not modelled - rather, they are assumed to be driven to zero in the long run by the market - the potential costs to the fossil sector under an emissions price would depend on its ability to pass along the production cost increases due to carbon abatement (i.e., coal to gas) to consumers, as well as any windfall gains from permit allocation.

4 For government, significant transfers or revenue could be raised under the emissions price, either through a tax-based system that collects revenue or through the allocation or auctioning of carbon permits under an emissions trading system. This is the only modelled scenario where significant government revenue potential exists. It also represents the value of the emissions rents, which are available to be allocated to consumers, generators and their shareholders, funds for transition assistance, or taxpayers more generally.

5 For society as a whole, welfare costs are lowest with the emissions price, making it the preferred option. One negative consequence of this scenario, not incorporated into this single-sector analysis, is that the increase in electricity prices could lead to economy-wide competitiveness. Reserving some permits for allocation to trade-exposed sectors that are electricity-intensive could mitigate these impacts.

6 An advantage of a cap-and-trade system is certainty in reaching the carbon target; however, uncertainty will then manifest itself in the price. All the other policies face challenges in setting a policy level that would achieve the emissions target with certainty.

4.4.3 Renewable Portfolio Standard

The renewable portfolio standard requires total electricity generation to be based on a minimum share of renewable sources. Although such a market share requirement can be implemented in several ways - quota obligations for retailers, green certificates for fossil generators - the general effect is the same. As long as the market would not meet the requirement on its own, renewable energy producers receive a price premium (the value of the green certificates they generate), while fossil energy producers receive a negative one (the cost of the green certificates they must buy in proportion to their generation). Moreover, the total subsidy to renewable energy producers is equal to the total effective tax paid by fossil energy generators, so no net revenues are raised or lost by the government.

Since the RPS does not distinguish among fossil generation technologies, there is no incentive to reduce emission intensity in that sector. Consumer prices rise due to the effective tax on fossil energy to fund the renewable subsidy (i.e., buy green certificates), but not as much as with the emissions price instrument. Although under the RPS more renewable energy is generated than under the emissions price, the timing of that generation is changed. Normally, when prices are fixed, as costs fall over time renewable generation expands. However, the RPS fixes the share of renewables in both periods, and over time this becomes easier to meet; hence, the effective tax and subsidy fall (i.e., the price of green certificates falls), while total electricity generation increases with the reduced price (the market price is equal to the price of electricity plus the price of green certificates, which fall due to innovation over time; therefore, electricity prices fall and final demand increases). Renewables then get a bigger boost in the first period and less in the second. The larger current subsidy may enable more learning by doing; however, recognizing that the support will fall in the future, investment in cost-reducing R&D may be smaller (this result is borne out in our scenarios). From a distributional perspective:

1 Consumers experience some electricity price increase and consumer surplus loss under the RPS. This effect is about 80% as large as with the emissions price in the first stage, and nearly negligible in the second. The electricity price rise is due to the purchase of renewable power in the form of green certificates (or the equivalent) by the fossil sector. Since renewables become cheaper with technical innovation, the cost of green certificates (and thereby consumer prices) is higher in the first stage but lower in the second as the cost of renewable supply decreases.

2 For renewable energy producers, the RPS induces a high uniform penetration through both periods, which is not surprising since the RPS fixes the share of renewables in both periods. Producer profits are also high, indicating the potential for the sector to benefit under an RPS. While there is certainty in terms of market share for the renewable sector, there is less stability in prices and less flexibility in the timing of renewable energy generation. Furthermore, the fact that the implicit subsidy falls over time with cost decreases means that incentives for innovation may be muted - indeed, our model predicts less R&D spending than under the emissions price. Although more renewable generation is needed overall, so much is done in the first stage that the return on lowering costs in the second stage is reduced, both because of the lower second-stage output (relative to the other policy scenarios) and also possibly because of greater learning by doing in the first stage, which can substitute for R&D.

3 For fossil fuel generators, output shares remain steady in the two periods, with lower output in the first stage and higher output in the second compared with other scenarios. In other words, cost reductions in renewables allow for fossil sector expansion. Still, short-term transitional costs could be expected to be greater under the RPS than in other scenarios. Actual potential costs to the fossil sector under an RPS will be higher if it is not fully able to pass along the costs of green certificates to consumers.

4 For government, the RPS has a neutral impact, with no revenue and no program disbursements.

5 For society as a whole, the welfare costs are greater than with the emissions price and generation subsidy, but lower than with the
combination and R&D subsidy. This ranking does not necessarily hold in all circumstances but rather depends on the particular trade-off between the extra costs of encouraging more effort upfront and the inefficiencies of not giving consumers incentives to conserve. Indeed, if one coped with the former problem by optimally designing the RPS requirement to increase over time, the RPS could be made to dominate the subsidy always, due to the presence of the modest conservation incentive.

6 Looking beyond the electricity sector, the increase in electricity prices risks causing some economy-wide competitiveness impacts such as decreased productivity or reduced exports, but these effects will be less severe than with the emissions price, particularly in the second stage.

4.4.4 Renewable Generation Subsidy

This fiscal instrument includes a range of possible policies that subsidize renewable energy generation (e.g., tax credits or direct subsidies) to encourage the expansion of carbon-free generation; however, they do nothing to encourage conservation or reduce the emission intensity of fossil generators. As well, there is no impact on the price of electricity, and thus consumers are not encouraged to reduce demand and therefore carbon emissions. Hence, much more effort must be expended on higher-priced renewables to displace fossil generation and meet the carbon reduction target. From a distributional perspective:

1 Consumer prices are not affected in the subsidy scenario, since all of the reductions are supplied through lower renewable energy costs, which do not affect the fossil fuel sector directly. Consumers would be indirectly affected since it is their tax revenue that funds some portion of the subsidy transferred to the renewable sector.

2 For renewable energy producers, the generation subsidy has the largest impact on profits, since they must be encouraged to displace more fossil output than in the preceding scenarios. Ongoing innovation is stimulated by the greater scope for reducing production costs at the higher output levels induced by the price premium.

3 For fossil fuel generators, the impact of the generation subsidy on fossil output is similar to that of the emissions price, since the additional renewable energy generation is partly offset by additional demand. The decline in output is slightly larger in the second stage, due to the more dramatic increase in the competitiveness of renewables from innovation. It might seem surprising that fossil output may be lower with the subsidy than with the emissions price, since the electricity price increase is absent. However, since the fossil sector lacks an opportunity to adjust its own emissions, the full burden of reductions falls on renewables to displace fossil output.

4 For government, the subsidy required to achieve the emission reduction target is a significant disbursement.

5 For society as a whole, welfare costs are greater than with the emissions price. With respect to reaching the emissions target, the renewable subsidy is likely to suffer from greater uncertainty than the preceding policies. Although not modelled, the reasoning is twofold:

• First, the uncertainty over the scope and speed of cost reductions in renewables is likely to be greater than uncertainty surrounding the costs of abatement in the fossil sector or the extent of conservation by consumers.
• Second, even if all cost uncertainties were similar, the reliance on only one method of emission reductions raises overall uncertainty. In a broader scenario, if innovation does not lower renewable energy production costs significantly, one could engage in relatively more emission abatement or conservation, whichever turns out to have the lower costs.

The renewable subsidy alone has more uncertainty regarding how much emissions will be reduced. It also has more uncertainty regarding the revenue requirement. If costs fall more than expected, a high subsidy would induce an oversupply relative to the carbon target, reflecting additional efficiency loss, as well as lost public funds. If costs do not fall as expected, either the emissions targets will not be met (and some public funds will be saved) or the subsidy must be increased even more to meet them, requiring greater-than-expected outlays.

4.4.5 A Combination of RPS and RGS

Renewable energy is often addressed by a combination of policies. Reasons include the overlapping jurisdictions of the federal, provincial and local governments and, perhaps, a desire for diversification. We have estimated the effects of placing a portfolio standard and a renewable production subsidy in place simultaneously. The key result is that the subsidy weakens the effect of the portfolio standard and raises costs slightly.

With both policies, the fossil fuel producer must still purchase green certificates for every unit of electricity generated. For the renewable energy producer, there are now two subsidies - the value of a green certificate and the direct subsidy. Since the direct subsidy boosts renewable supply, the equilibrium price of a green certificate does not need to be as high to reach the portfolio standard (compared with the RPS implemented in isolation). Consequently, when the policy target is a portfolio share, a direct subsidy to renewables primarily offsets the burden to fossil producers and consumers instead.

Since we assume the RPS is the driving policy instrument in our combination scenario, the distributional effects are quite similar to those of the RPS alone. The slight differences are as follows:

1 Consumer prices are slightly lower. Despite the additional electricity demand, emissions are also lower in the first stage - this is because the standard must be raised to offset the loss of conservation incentive, leading to even more reduction in the first stage and less in the second.

2 Renewable energy production is 0.5% higher and R&D spending is 1.5% higher.

3 For fossil fuel generators, output is nearly unchanged relative to the portfolio standard alone. This is because, even though the fossil fuel producer has to buy more certificates, the cost of these certificates is lower because the subsidy has generated a greater supply of them.

4 Perhaps the most telling effect is that the government in this combination scenario spends just over $1 billion on a subsidy that has little or no effect on behaviour, given the presence of the RPS.

5 From society's perspective, to the extent the subsidy does affect behaviour, it tends to lower prices and raise overall welfare costs. The weaker conservation incentive and the additional frontloading of emission reduction efforts (through increases in the RPS) lead to an increase in welfare costs, from 1.80 to 1.83 times that of the emissions price.

4.4.6 Renewable Research Subsidy

The renewable research subsidy uses current investments in reducing costs to increase future renewable energy production. Since it does not change any price incentives for demand or production, or change current costs, all the burden of emission reductions is placed on future displacement of fossil by renewable energy generation. Furthermore, given the lack of future production incentives, the required cost reductions are large and the required investments even larger. The ability of an R&D subsidy alone to deliver all of this is clearly an area of uncertainty. From a distributional perspective:

1 Consumers do not experience electricity price increases and consumer surplus losses under the R&D subsidy. As with the generation subsidy, they indirectly contribute to the renewable
sector through tax contributions to fund the R&D subsidy.

2 For renewable energy producers, the R&D subsidy induces the highest penetration in the second stage. This penetration is driven exclusively by innovation and cost decreases from renewable production. An important caveat is the degree to which Canadian learning by doing and R&D can drive cost decreases in renewables. While such production cost decreases are observed in Canada and internationally, it is not certain that Canadian R&D alone can reduce costs sufficiently to achieve the high levels of renewable power penetration predicted in this scenario. As a general observation, innovation in renewable production occurs internationally and is imported into Canada. This uncertainty regarding the ability of domestic R&D subsidies to achieve the penetration predicted in the model only reinforces the conclusion that this policy is a much more costly method for achieving emission reductions.

3 For fossil fuel generators, the R&D subsidy does not affect electricity price, but it does significantly reduce fossil output in the second stage. Although not modelled, costs associated with stranded assets or variable costs due to lower capacity utilization could arise. But transaction costs associated with decreased fossil demand are likely lower in this scenario, since a majority of reductions occur in the second stage. Thus, the transition period for the fossil sector to adjust to decreased demand is long and there is potential for costs to be minimized.

4 For government, the R&D subsidy requires the largest disbursement of the instruments. That said, promoting innovation is a government policy, and therefore R&D programs are generally part of a desirable policy approach to long-term carbon emission reductions. However, given the longer-term nature of the reductions associated with R&D, a government faced with a carbon reduction target would likely not achieve significant reductions in the short term under an R&D program.

5 From society's perspective, welfare costs are greatest under the R&D subsidy. Another negative consequence of this scenario is uncertainty.
As with the renewable generation subsidy, the uncertainty of renewable cost reductions makes this a relatively risky policy for promoting carbon reductions - all the more so, since in the absence of cost reductions, there is no incentive for additional uptake of renewables in either stage. Given the uncertainty over innovation success in general and the impact of domestic efforts in particular, it is highly uncertain that a domestic R&D program alone could achieve a significant carbon reduction target through renewable generation. Instead, an R&D subsidy can be seen as a complementary instrument that could be used to achieve longer-term societal goals such as promoting innovation.

4.5 Sensitivity Analysis

To further test the robustness of the results presented in the preceding discussion, a sensitivity analysis was conducted for the following factors:

1 An increase in the baseline price of electricity:
The sensitivity analysis shows that the differential between the renewables price and the electricity price is an important determinant of the size of the welfare cost. As well, this differential affects the desirability of an RPS compared with a renewable generation subsidy. These results can also be expected when the price of renewables changes; that is, a decrease in the price of renewables would produce results that are directionally similar to an increase in the electricity price.

2 An increase in the baseline price of natural gas: The sensitivity results indicate that increasing natural gas prices have a minimal impact on the outcomes with respect to the reference case. As discussed in the previous scenario, however, increasing gas prices could increase the price of electricity, and the response would be similar to an electricity price increase.

The sensitivity testing concludes that the results are robust to changing key variable assumptions. Indeed, our core observation holds: the economic efficiency and environmental effectiveness of the EFR instruments are linked to their ability to influence the entire electricity market and the three decarbonizing drivers in particular. As a general rule, an EFR instrument will be more efficient and effective if it signals to multiple agents in the electricity market that carbon is more expensive: fossil producers will reduce their emission intensity; renewable power producers will supply more output when the price differential between renewable and fossil generation decreases; and consumers will take measures to conserve electricity, reduce demand and displace fossil output. This finding holds under multiple input assumptions and explains why an emissions price is preferable to either an RPS or RGS. A good example of the increased risk in using a single instrument is highlighted by the R&D instrument scenario, where the emission reduction is entirely dependent on the ability of R&D investments to reduce renewable energy costs through innovation. While cost reductions can be expected from R&D spending, the scope and scale of the cost reductions are questionable, thus increasing the overall uncertainty in the instrument.

5 LESSONS LEARNED

Unquestionably, EFR instruments have traction with respect to decarbonizing electricity and increasing the uptake of renewables. Our results indicate that a wide range of EFR instruments can be used to decarbonize the economy and increase the installed capacity of renewable technologies. Important lessons learned include the following:

1 Instruments are most economically efficient and environmentally effective if they are comprehensively applied and target all actors in a market: Each EFR instrument outlined in the case study has different impacts on the three principal elements of an electricity market:

• renewable power penetration, which is how much of total Canadian electricity generation is supplied by renewable power;
  
  
• the carbon intensity of fossil fuel generation, which is how much carbon a unit of electricity generated by fossil fuels contains. Carbon intensity can be reduced by using natural gas instead of coal, for example; and
• total electricity demand, where consumers can reduce their electricity demand by practising energy conservation.
  

The success of one or more EFR instruments will rest on their ability to continue to influence the entire electricity market and these three decarbonization drivers in particular. Of the EFR options presented:

• the emissions price is the most effective at influencing the market and its drivers. It provides the means for attenuating negative effects;
  
  
• the renewable portfolio standard ensures a high penetration rate for renewables in the short and longer terms but only marginally influences consumer behaviour;
  
  
• the renewable generation subsidy ensures an even higher penetration rate for renewables, but it does not influence consumer behaviour or encourage electricity producers to work toward lower, permanent carbon emission intensities;
  
  
• a mix of RPS and RGS produces a slightly better result than the RPS or the RGS alone - however, the welfare cost is very high due to significant government disbursements to achieve the
  result; and
  
  
• the renewable research subsidy has considerable positive impact on the renewable sector, but it does nothing to influence the other drivers or assure market penetration in the long run.

2 A small portion of renewable energy technologies are competitive with fossil fuel generation now: Given that some renewables are competitive now, EFR instruments can be expected to increase the installed capacity of renewables in Canada to some degree. However, ambitious carbon reductions will require binding EFR instruments that close the price gap between fossil generation and renewable technologies.

3 Innovation reduces renewable energy costs: Innovation in renewable technologies will primarily come from international sources and ultimately reduce renewable supply costs in Canada. Thus the installed capacity of renewables in Canada can be expected to grow over time in the absence of EFR polices.

4 Renewables are an immature technology with uncertain costs and practical potential: Any modelling effort that targets renewables is faced with significant uncertainty in forecasting price and practical potential. This uncertainty is unavoidable, and the modelling should address uncertainty.

5 Renewables are at different stages of technological development: This implies that some instruments, such as an RPS, can be effective in deploying renewable technologies that are commercially viable in the short term, whereas R&D subsidies are better suited to targeting technologies still in the developmental stage.

6 The temporal impacts of the EFR instruments differ: The path of emission reductions and renewable power penetration can vary significantly between instruments. Instruments that require reductions from renewables in the short term will necessarily be more costly than instruments that target longer-term reductions. This effect occurs when the price of renewable supply drops over time.

7 The distributional consequences of the EFR instruments differ significantly: Comparing overall instrument costs can mask the distributional consequences of an EFR instrument. Table 8 provides an overview of the distributional consequences of the instruments included in this case study.

8 Program design and detail matter, but they are not captured in the analysis: We assessed the EFR instruments at a high level but observe that enabling conditions significantly affect outcomes. Enabling conditions such as local permitting, regulations, transmission distance and access to the grid all affect the technical and economic feasibility of the renewable supply and ultimately the predicted results of the EFR instruments. Simply assuming that the EFR instruments will achieve cost-effective carbon reductions without a clear understanding of the enabling conditions for and barriers to the uptake of renewables is highly risky policy.

9 Policy certainty and durability over the longer term are important: Policy certainty or the durability of the EFR instrument over the longer term is an important driver of renewable uptake. This is particularly the case for renewables where startup capital costs are high and investment returns must be established prior to project implementation.

Table 6: Summary of Distributional Results

                            

		I. Base case	II. Emissions price	III. Renewable portfolio standard	IV. Renewable generation subsidy	V. Combination RPS and RGS	VI. Renewable research subsidy
To achieve a 12% carbon emission reduction target from 2010 to 2030, you would see	(No attempt to reach target)	Emitters pay $10 for each tonne of CO2	Renewables have a 24% share of case study generation -9% of annual total Canadian generation	Government subsidy of $0.006 for each kWh generated by renewables	An RPS at 24.21% and an RGS at $0.002	The public and private sectors increase their R&D spending by 61%
Impacts on electricity generation	Renewables gain some market share; carbon is reduced by 5%	Renewables penetrate slightly more quickly than in I; electricity producers work hardest on reducing carbon emissions	A greater penetration of renewables than in II; costly for electricity producers at first but costs fall over time	A greater penetration of renewables than in II; not a driver of lower emissions intensity(= higher efficiency)	Slightly more renewables in the mix; fossil fuel - generated output remains unchanged	High penetration of renewables near end of time frame only
Impacts on consumers	Status quo	Electricity prices rise the most; conservation emphasized; negative impacts on some sectors	Overall electricity prices are lower than in II, but rise and then fall; conservation not emphasized	Electricity prices remain the same; conservation not emphasized	Electricity prices slightly lower than in IV; conservation not emphasized	Electricity prices remain unchanged; conservation not emphasized
Impacts on government	Status quo	Government revenues raised (as government collects on emissions price); redistribution to affected sectors is possible	No government revenues raised, lost or transferred	Government makes significant disbursements to fund the subsidy	Government makes disbursements ($1 billion) to fund the subsidy	Government makes significant disbursements to fund R&D in renewables
Impacts on the renewable sector	Status quo; some continued penetration	Output up; production cost down; some profit; R&D levels high	Output up more than in II; slightly more profit than in II; but less R&D is done	Greater profits as more production lowers costs; high investment in R&D	Output slightly higher; R&D also higher	Highest potential penetration near end of time frame with high R&D
Impacts on Canadian societal welfare*	Status quo	Overall lowest welfare costs of the five options	Greater welfare costs than in II and lower than in IV	Second highest welfare costs	Welfare costs slightly lower than in IV	Highest welfare costs
Level of uncertainty in reaching target	Target is not achieved	Low; all long-term carbon emission reduction drivers are acted on to work toward target	Medium; only two long-term carbon emission reduction drivers affected	Medium- high; only one long-term carbon emission reduction driver affected	Medium; only two long-term carbon emission reduction drivers affected	High due to reliance on one long-term carbon emission reduction driver (penetration not assured

* = adding together (1) costs to consumers and (2) losses/profits of electricity producers (both renewable and fossil fuel) and (3) net government revenues, but excluding environmental costs/benefits (e.g., the costs of adapting to climate change are not included here).

ENVIRONMENTAL CHOICE CRITERIA FOR RENEWABLE LOW-IMPACT ELECTRICITY

Summary only; for full technical criteria, see "Electricity Generation" at
<www.environmentalchoice.com>.

Renewable Low-Impact Electricity

From a consumer perspective, electricity is clean, cheap and has no visible environmental consequences. If we look beyond the outlets in our walls, however, environmental costs become apparent. In Canada, the major methods of generating electricity include burning fossil fuels, harnessing the power of water and using nuclear power. Each power source has consequences for the environment, from creating acid rain to flooding lands to disposing of radioactive waste. The Environmental Choice Program has made a commitment to promoting electrical energy sources that have greatly reduced environmental impacts. The ECP recognizes electricity that has been generated from naturally occurring energy sources (such as the wind and the sun) and from power sources that, with the proper controls, add little in the way of environmental burdens (such as less intrusive hydro and certain biomass combustion).

Certification Criteria

All Sources

• The facility must be operating, reliable, non-temporary and practical.
• During project planning and development, appropriate consultation with communities and stakeholders must have occurred, and prior or conflicting land use, biodiversity losses and scenic, recreational and cultural values must
  have been addressed.
• No adverse impacts can be created for any species recognized as endangered or threatened.
• Supplementary non-renewable fuels must not be used in more than 2% of the fuel heat input required for generation.
• Sales levels of ECP-certified electricity must not exceed production/supply levels.

Specific Sources (additional criteria to those listed above)

• Solar (cadmium-containing wastes must be properly disposed of or recycled)
• Wind (protection of concentrations of birds including endangered bird species)
• Water (compliance with regulatory licences; protection of indigenous species and habitat; requirements for head pond water levels, water flows, water quality and water temperature; measures to minimize fish mortality and to
  ensure fish migration patterns)
• Biomass (use only wood wastes, agricultural wastes and/or dedicated energy crops; requirements for rates of harvest and environmental management systems/practices; maximum levels for emissions
  of air pollutants)
• Biogas (maximum levels for emissions of air pollutants; leachate management)
• Other technologies that use media such as hydrogen or compressed air to control, store and/or convert renewable energy
• Geothermal technologies