Top of Page
Previous page | ToC | Next page
This annex describes the approaches taken to calculate estimated reductions from the measures detailed in the Plan. Two types of estimation procedures were used. Reduction estimates have been calculated on a case-by-case basis for the individual measures in the document as per section 5 (1) (b) (ii) of the Act. In addition, Environment Canada’s integrated Energy, Emissions, and Economy Model for Canada (E3MC) was used to estimate the overall emission reductions for the integrated package of measures and the modelled results were used to report on Canada’s emission reductions and total remaining emission levels for 2008-2012, thereby satisfying section 5 (1) (c) of the Act.
Lead departments have developed and applied the methodologies for the calculation of emissions reductions associated with individual measures under their respective responsibility. These individual program methodologies have been provided to Environment Canada and are reproduced below. Environment Canada also has developed and applied specific methodologies for the estimation of overall emissions reductions from the combined effect of these individual programs. This allows for the incorporation of negative and positive interaction effects among government measures in order to construct a robust estimate of their combined impact on national emissions.
The advice of the National Round Table on the Environment and the Economy is a key factor in the Government’s methods for estimating reductions. The “Response of the National Round Table on the Environment and the Economy to its Obligations Under the Kyoto Protocol Implementation Act” (September 2007) suggested certain methodological improvements for the development and presentation of reasonably expected emission reductions. These included the following:
This section describes the methodologies used to generate emission reductions from individual measures, as well the resulting emissions levels for Canada in 2008-2012 that are required under paragraphs 5 (1) (b) (ii) of the Act.
Expected reductions from individual measures were estimated by the responsible department, with related parameters incorporated into E3MC. The methodologies for each individual measure are described below.
Regulations setting minimum energy performance standards effectively remove products that do not meet the standard from the marketplace, with the impact on energy use realized as the existing stock of those products is exhausted. The estimates provided in the Regulatory Impact Analysis Statement (RIAS) for these standards are the source of the estimated actual reductions of these measures.
For each product proposed for regulation, Natural Resources Canada calculates an initial estimate of the energy savings based on the following factors:
The methodology is the same for actual and projected reductions, but the timing of regulatory amendments is subject to the regulatory process and thus may vary between the two. Information provided during the commentary period following pre-publication may also have an effect on the impact estimates. Impacts for equipment labelling are a percentage of regulatory impacts.
Program methodology combines third-party sector-level data and forecasts, results of published studies and evaluations, and GHG conversion factors and, in doing so, the methodology for calculating the energy impacts of minimum energy performance standards employs conservative assumptions. Over the course of the regulatory process, planning estimates are revised as further analysis and validation through consultation occurs. To create a range for this Plan, qualitative self-assessment considered the uncertainty surrounding the calculated actual reductions to be low, hence a range of +/- 10% is provided.
The methodology is the same for actual and projected reductions.
In addition, impacts for labelling programs for equipment are estimated as a percentage of regulatory impacts, based on program analysis.
The estimated energy savings are converted to GHG reductions using standard GHG conversion factors.
Reductions are considered fully incremental for this time period. Regulations address sales of inefficient products that continue to be traded despite the availability of more effective, commercially-acceptable alternatives, while labelling provides consumers with information required to identify the more efficient choices for the type of product they wish to buy.
With respect to section 5 (b) (ii) of the Act, it is not immediately possible to make comparisons between the actual and projected emission reductions achieved by the equipment, buildings and houses, retrofit and industry programs and the National Emissions Inventory, primarily with regard to emission reductions achieved from electricity savings. Natural Resources Canada will work with Environment Canada to resolve questions related to input data and definitions to determine the appropriate relationship between electricity savings and emission reductions from electricity generation.
Projected reductions are provided as reflected in the RIAS (December 24, 2008; April 16, 2011). The impacts are adjusted as required to account for changes to regulatory timing (e.g., the inclusion of general service lighting under Amendment 10 resulted in a longer consultation period).
It should be recognized that though the estimated reduction profile (by year) has changed in response to regulatory and market conditions, the long-term GHG impacts (to 2020) of energy efficiency regulations are expected to be greater than previously estimated. The decline in expected reductions in the early years of the regulatory framework should be considered deferred rather than lost.
The passenger automobile and light truck GHG emission regulations apply to companies that manufacture or import new passenger automobiles and light trucks of the 2011 and subsequent model years for the purpose of sale in Canada. The standards will require substantial environmental improvements from new vehicles and would put Canadian GHG emission standards at par with U.S. national standards. Effectively, therefore, there will be common Canada-U.S. GHG emission standards beginning in 2012.
Through the implementation of the proposed standards, it is anticipated that the average GHG emission performance of the 2016 Canadian fleet of new cars and light trucks would achieve an average level of 157 g CO2/km (252 g CO2/mile). This would represent an approximate 25% reduction compared to the new vehicle fleet that was sold in Canada in 2008.
The follow steps were taken to estimate “actual” emissions related to the implementation of the passenger automobile and light-duty truck GHG emission regulations:
The issue of additionality was addressed by comparing the emissions under a modelled scenario where only the passenger automobile and light-duty truck GHG emission regulations are modelled to a scenario where the regulations are modelled in combination with other measures aimed at reducing road transportation emissions (e.g., ecoTRANSPORT measures and biofuel regulations). This provides the “incremental” impact of the regulation, taking all other road transportation-related measures into consideration.
The uncertainty ranges generated for the mandatory passenger automobile and light-duty truck GHG emission regulations were generated using the lowest and highest alternative emission scenarios. In addition, a low and high consumer response in terms of purchase of new vehicles was also modelled.
Environment Canada’s E3MC model was used to estimate the emissions reductions from the mandatory passenger automobile and light-duty truck GHG emission regulations. Actual sales-weighted and on-road fleet fuel economy performance for 2008 and 2009 is included in the E3MC reference case. The sales-weighted fuel economy performance for cars and light-duty trucks was provided by Transport Canada23, while the on-road performance was provided by the Office of Energy Efficiency at Natural Resources Canada. E3MC has four broad vehicle categories: small cars, large cars, light-duty trucks, and SUVs. For each category, E3MC has new vehicle sales, price of new vehicles, on-road stock, survival rates, average fuel economy performance, vehicle-kilometres travelled, energy use, and associated emissions. These parameters are reported on an annual basis.
The modelling of the targeted reductions under the mandatory passenger automobile and light-duty truck GHG emission regulations was approached as follows:
With respect to additionality:
| Actual Reductions (Mt)26 | Projected Reductions (Mt) | ||||
|---|---|---|---|---|---|
| 2008 | 2009 | 2010 | 2011 | 2012 | |
| GHG Reductions | 0 | 0 | 0.04 | 0.15 | 0.32 |
As the emissions reduction will only start in 2010, a comparison between this measure and the National Emissions Inventory is not possible at this time.
The analysis of the impact of the mandatory passenger automobile and light-duty truck GHG emission regulations is sensitive to assumptions regarding vehicle sales, technology options and associated costs, gasoline and diesel prices, and market (consumer and manufacturer) behaviour. Given the numerous combinations and permutations, sensitivity analysis was done using the lowest and highest alternative emission scenarios. In addition, the low and high consumer response was also modelled.
Reductions start in 2010. See Projected Reductions.
Reductions start in 2010. See Projected Reductions.
The estimated emission reductions were calculated by multiplying emission factors by the renewable fuel volumes required to meet the federal and existing provincial mandates. The emission factors used were based on Natural Resources Canada’s GHGenius model. The emissions factors are presented in the following table.
| Renewable fuel type | Emission factor Mt CO2e per billion litres renewable fuel |
|---|---|
| Corn ethanol | 1.190 |
| Wheat ethanol | 1.470 |
| U.S. central ethanol (corn) | 0.740 |
| Canola biodiesel | 3.012 |
| Soy biodiesel | 2.704 |
| Tallow biodiesel | 3.228 |
| Diesel – west | 3.663 |
| Diesel – east | 3.456 |
| Kerosene | 3.348 |
| Kerosene reduction – west | 0.415 |
| Kerosene reduction – east | 0.203 |
| Nestle HVO | 1.470 |
| U.S. soy biodiesel | 2.463 |
The anticipated reductions are based on the total volume of renewable fuels that would be required by the federal regulations, minus the volume of renewable fuels from provincial regulations that had been finalized at the time (ethanol: British Columbia, Saskatchewan, Ontario, and Manitoba; biodiesel: British Columbia, Alberta, and Manitoba). The reduction estimate for the 2011 KPIA is an approximation based on a proposed July 2011 start date for the federal biodiesel regulation amendments. As for previous estimates, ethanol and biodiesel volumes in the market prior to 2010 are not accounted for.
The business as usual (BAU) scenario is based on an estimated growth in demand for gasoline, diesel fuel, heating distillate, and renewable fuels. The demand volumes for these fuels were calculated by starting with the actual demand for 2008 and applying growth rates. These growth rates were based on annual increases in demand, predicted in Natural Resources Canada’s “Canadian Energy Outlook for the years 2008-2020.”
The emission reductions attributable to provincial mandates were subtracted from the overall reductions to arrive at the incremental reductions due to federal actions.
A range of GHG emission reductions was estimated based on either including or excluding the effect of provincial regulations on the incremental volume of renewable fuel volume required by the federal Renewable Fuels Regulations. The high estimate is the expected GHG reductions based on the total volume of renewable fuels that would be required by the federal regulations. The low estimate is the expected GHG emission reductions based on the total volume of renewable fuels that would be required by the federal regulations, minus the volume of renewable fuels from finalized provincial regulations (British Columbia, Saskatchewan, Ontario, Alberta, and Manitoba).
In calculating estimated emission reductions, assumptions were made regarding the distribution of the various types of renewable fuels, based on the current and planned production of renewable fuels in Canada. It was also assumed that some level of imports of renewable fuels, primarily from U.S., would be needed during the first three years of the regulations coming into force while domestic production capacity expands.
Natural Resources Canada’s GHGenius tool also models life-cycle emissions based on various input parameters and pathway assumptions.
Actual reductions are defined as those GHG reductions actually achieved as of the end of December 2010, that is, those reductions resulting from projects that were physically completed during 2010 that resulted in a change in operating conditions, relative to pre-project conditions, during that calendar year. The reductions presented include a “direct” and an “indirect” component. Annual reductions were calendarized by multiplying by the proportion of the year over which a project was operational (e.g. if a project was physically completed at the end of June 2010 - halfway through the year - calculated annual emission reductions were divided by two).
“Direct reductions” are those GHG reductions resulting from reduced usage of fossil fuels on mill sites. To calculate “direct reductions”, each mill’s annual post-project usage of fossil fuels and biomass (physical quantities) was compared to its pre-project annual usage. Each change in the physical quantity of a fuel used was converted to GHG (tonnes of CO2e) to yield the net reductions using the National Council for Air and Stream Improvement’s accepted factors for pulp and paper facilities (NCASI v3.2).
“Indirect reductions” are those GHG reductions resulting from increased production of electricity from renewable sources (biomass or spent pulping liquor) or electricity savings from energy efficiency improvements. It is assumed that the production of renewable electricity and electricity savings offset the production of electricity by conventional producers. To calculate “indirect reductions” each mill’s post-project production of renewable electricity and usage of electricity was compared to its pre-project production/usage. Each change in electricity production/usage was converted to GHGs (tonnes of CO2e) using a national emissions factor for marginal electricity generation (0.46588 tonnes CO2e/MWh).
Information on mill energy usage was provided by the proponents through their project proposals, a series of post-project reports, and confirmed through technical evaluation by the PPGTP. In addition, selected projects are subject to site audits by PPGTP technical staff.
All GHG reduction figures provided for the 2010 calendar year are based on data available as of early March 2011. Proponents may continue to submit amendments to approved projects past this date, thus there exists the potential for GHG reductions to change. In addition, figures may be adjusted based on the results of project technical reviews and/or audits (program experience to date has demonstrated the potential for a project’s GHG reduction estimates to be adjusted in either direction). For this reason, actual GHG reductions are presented as a range (±5% of calculated reductions). Low estimates were selected as the actual expected reductions, as they reflect conservative program estimates.
Projected reductions are defined as those reductions expected to be achieved during the 2011 and 2012 calendar years, based on project proposals received as of March 2011. To date, proposals for $876 million (92%) of available PPGTP funds have been received by the program. Thus, these projects are deemed a sufficient basis on which to calculate projections for the final years of the program.
Projected reductions are those reductions resulting from PPGTP projects that are expected to be physically completed in either 2011 or 2012, summed with the reductions attributable to projects that were completed in the previous year(s) (e.g., projects that generate GHG reductions that were completed in 2010 are considered to generate GHG reductions in 2010, 2011, and 2012). Emission reductions are expected to persist throughout the operational lifespan of the project equipment. All PPGTP projects are expected to be completed in 2012.
The calculation of “direct” and “indirect” projected reductions was completed using the same methodology as described for actual reductions. Projected GHG reductions were calendarized in the same manner as the actual GHG reductions, based on forecasted project completion dates. Changes in fuel consumption and electricity production/usage were taken from project proposals as submitted by proponents. The majority of projects used in calculations have been approved by the PPGTP and were therefore subject to technical verification.
It was not considered appropriate to adjust projected reductions for additionality given the scope and nature of the PPGTP. The PPGTP funds projects with environmental improvements in Canadian pulp and paper mills. Given the lack of investment capital available to these companies, the likelihood of these projects, many of which have very low returns-on-investment, being implemented was considered extremely minimal. This assumption is further supported by the fact that even projects demonstrating high returns-on-investment were not being implemented prior to the introduction of the PPGTP. Extreme capital constraints (worsened by the economic downturn) force mills to devote their limited resources to emergency maintenance, rather than the type of system-level improvements funded by this program. Based on this, all of the projected emissions reductions associated with PPGTP projects are considered directly attributable to this program.
PPGTP funding is offered to Canadian pulp and paper companies based on their production of black liquor, a by-product of the chemical pulping process. As such, it is targeted at only one segment of the wider Canada pulp and paper industry, making a direct comparison of the GHG produced by the industry and the reductions attributable to the PPGTP less informative. That being said, the 2008 National Inventory Report (NIR) listed the GHG emissions from the Canadian pulp and paper sector (stationary sources) as 4.54 Mt CO2e. Direct reductions27, from lower fossil fuel usage on mill sites, attributable to the PPGTP over the Kyoto reporting period (2008-2012) are estimated at 0.59 Mt (13% of total sector emissions). The PPGTP estimate treats emissions from biomass combustion (black liquor and hog fuel) in a manner consistent with the NIR, that is, only CH4 and N2O emissions are considered. Emission reductions are calculated by subtracting mills’ post-project GHG emissions from their pre-project emissions (the baseline).
All projected GHG reductions provided for the 2011 and 2012 calendar years are based on estimates provided by proponents as of March 2011. Proponents may continue to submit amendments to approved projects past this date, thus there exists the potential for GHG reductions to change. Figures may also be adjusted based on the results of project technical reviews and/or audits (program experience to date has demonstrated the potential for a project’s GHG reduction estimates to be adjusted in either direction), as well as in the event of project delay. Projected GHG reductions are presented as a range (±5% of calculated reductions). Because not all project proposals have been submitted, and the PPGTP expects additional GHG emission reductions will be associated with at least some of these expected proposals, the calculated projected GHG reductions were selected as the expected reductions (the low estimates were deemed too low, given that more project proposals are expected to be received).
Renewable power projects supported under the ecoENERGY for Renewable Power (ecoRP) program provide quarterly invoices for each project showing actual metered production measured during each quarter. Yearly production is calculated by adding production of the four quarters in each year.
The GHG emission factor used for the estimates of GHG reductions is based on the GHG emission intensity of marginal electricity generation in each province (since it is assumed that incremental renewable power generation replaces existing generation at the margin). To obtain a national factor, the emissions factor for the marginal fuel in each province was weighted by the provincial share of electricity generation and then summed. Thus, a cross-Canada GHG emission factor of 465.88 t/GWh was used. The value of the emission factor used directly influences the estimate of GHG reductions. Any uncertainties in the emission factor therefore have a direct impact on the uncertainty of the emissions estimate.
GHG emission reductions were estimated using the following equation:
GHG emission reductions = Actual renewable energy production × GHG emission factor
These are actual reductions based on actual production. The program considers that all projects funded are incremental and would not have been done without program support.
To calculate GHG emission reduction for future years, “renewable energy production” is estimated based on expected production provided for each project.
The program is designed to encourage 14.3 terawatt-hours of electricity production per year by 2011-12 (translates to about 4,000 megawatts of renewable power capacity). The terawatt-hour target is directly related to the program’s transfer payment budget of $1.43 billion through a production incentive equivalent to 1 cent/kWh. Consequently, the calculations of GHG emissions are related to the amount of electricity produced on a yearly basis, i.e. GWh or TWh, and the transfer payments made to recipients.
The amount of electricity produced is dependent on two key factors:
At program inception, the following assumptions were used:
| Fiscal year | Cumulative expected capacity | Cumulative average capacity factor | Expected production per fiscal year | High forecast GHG reduction |
|---|---|---|---|---|
| MW | % | GWh/yr | Mt CO2 | |
| 2007-08 | 1,120 | 37.37% | 2,139 | 0.996 |
| 2008-09 | 2,020 | 40.33% | 4,726 | 2.20 |
| 2009-10 | 2,970 | 42.42% | 8,023 | 3.74 |
| 2010-11 | 4,000 | 43.64% | 11,689 | 5.45 |
| 2011-12 | 4,000 | 43.64% | 14,314 | 6.67 |
These numbers were later refined as projects came online and more certainty about their production was available. A switch to calendar year was also made in 2011.
| Year of production | Annual expected production | Annual actual production | Annual expected GHG reduction | Annual actual/expected GHG reduction |
|---|---|---|---|---|
| GWh/yr | GWh/yr | (Mt CO2/yr) | (Mt CO2/yr) | |
| 2008 | 2,742.83 | 2,435.40 | 1.28 | 1.13 |
| 2009 | 5,461.49 | 4,713.15 | 2.54 | 2.20 |
| 2010 | 9,027.08 | 9,027.08 | 4.21 | 4.21 |
| 2011 | 12,974.41 | 12,974.41 | 6.04 | 6.04 |
| 2012 | 13,952.88 | 13,952.88 | 6.50 | 6.50 |
Note: 2008 and 2009 numbers are actual, 2010 to 2012 are expected. The numbers are all on a calendar-year basis.
The uncertainties surrounding these factors and how they were mitigated at the program development stage are described below.
The uncertainties surrounding the low and expected emission reductions take these two factors into consideration.
Contribution agreements are signed for each project based on the maximum allowable production that a project will generate. This constitutes the high projection level of the expected production and GHG reductions.
Under the WPPI program, the predecessor program to ecoRP for wind power, the actual production level has been shown to be on average 90% of this high projection level. Because the ecoRP program includes deterrents for underproduction, it is believed that this 90% level constitutes a low projection level.
Expected projection is estimated at 93% of the high level based on past performances of projects.
In summary: The high projection level for GHG reductions is the total production estimate for all projects as shown under their contribution agreements multiplied by the GHG emission factor discussed above.
It is not feasible to make comparisons between the actual and projected emission reduction calculations achieved via ecoENERGY for Renewable Power and the National Emissions Inventory due to fundamental differences in the scope of analysis and methodology between the national inventory data and that of the program. Program objectives are specific to renewable energy deployment projects and the associated GHG emission reductions are achieved on a project specific basis. Conversely, the National Emissions Inventory for the electricity subsector captures large and periodic fluctuations in year-to-year emissions that can result from numerous factors (e.g., economic slowdowns, fluctuations in the national electricity supply mix), which fall outside the scope of the program and therefore make a direct comparison very difficult.
For the projects in the industrial, commercial, and institutional sectors, GHG emission reductions were estimated based on:
Specifically, GHG reductions are calculated for each system in the following manner. The energy saved or displaced per year for each system is determined using one of the following modelling software: RETScreen, SWIFT, WATSUN, TRNSYS, T*Sol, Enerpool, Polysun, or F-Chart. These models have been tested and evaluated for accuracy. The calculated energy saved is multiplied by a factor based on the fuel displaced. Presented in terms of tonnes CO2 equivalent per GJ saved or displaced, the factors used were: 0.05069 (natural gas); 0.06275 (propane); 0.07328 (fuel oil); and 0.150 (electricity). The factors used for natural gas, propane, fuel oil, and electricity were 0.05069, 0.06275, 0.07328, and 0.150 tonnes CO2 equivalent per GJ saved or displaced, respectively.
It was assumed that all systems deployed under the program are incremental and would not have been deployed in absence of the program. This assumption is reasonable since in the past, when an incentive was not available, the systems were not deployed.
The projects that contribute to actual reductions have been completed and, given that the size of the systems and the modelled energy savings and fuel displaced are known, uncertainties have been addressed. Therefore, low, high, and expected GHG emissions reduction numbers are same.
For the projects in the industrial, commercial, and institutional sectors, GHG emission reductions were estimated based on:
The number of systems expected to be completed annually and the associated energy savings of individual systems were based on the funding level and the program experience acquired under the Renewable Energy Deployment Initiative (REDI) program. Under the REDI program, the systems also estimated energy savings per year for each system using one of the following modelling software: RETScreen, SWIFT, WATSUN, TRNSYS, T*Sol, Enerpool, Polysun, or F-Chart. Presented in terms of tonnes CO2 equivalent per GJ saved or displaced, the factors used were: 0.05069 (natural gas); 0.06275 (propane); 0.07328 (fuel oil); and 0.150 (electricity). The emissions factor used for natural gas, propane, fuel oil, and electricity were 0.05069, 0.06275, 0.07328, and 0.150 tonnes CO2 equivalent per GJ saved or displaced, respectively.
For deployments under the residential pilot initiative, the typical residential hot water system is expected to result in 1.5 tonnes of GHG emission reductions. The number of projects expected was multiplied by 1.5 to determine the estimated GHG emission reductions.
It is not feasible to make comparisons between the actual and projected emission reduction calculations achieved via ecoENERGY for Renewable Heat and the National Emissions Inventory due to fundamental differences in the scope of analysis and methodology between the national inventory data and that of the program. Program objectives are specific to renewable energy deployment projects and the associated GHG emission reductions are achieved on a project-specific basis. Conversely, the National Emissions Inventory for the heat subsector captures large and periodic fluctuations in year-to-year emissions that can result from numerous factors (e.g., economic slowdowns, fluctuations in the national electricity supply mix), which fall outside the scope of the program and therefore make a direct comparison very difficult.
The uncertainties surrounding these factors and how they were mitigated at the program development stage are described below:
This program has several elements, the impacts of which were calculated individually.
Home energy ratings (actual observations) are submitted to the program by energy advisors. For existing housing and new housing, the impact assumptions described under Projected Reductions Methodology are applied to monitored results. Also for new housing, actual housing starts are monitored and applied to the impact assumptions noted under Projected Reductions Methodology annually. Program results are calculated with actual observations recorded in the program database.
New Buildings
Existing Buildings
Program methodology combines monitored participation, third-party sector-level data and forecasts, results of published studies and evaluations, and GHG conversion factors. Participation is accurately measured, but the other elements seldom elaborate on the accuracy of their quantitative conclusions. Thus conservative assumptions are made. To create a range for this Plan, qualitative self-assessment considered the uncertainty surrounding the calculated actual reductions to be medium; hence a range of +/- 25% is provided.
The estimated energy savings are converted to GHG reductions using standard GHG conversion factors.
Reductions are considered fully incremental for this time period, and are based on conservative assumptions. The development process and publication of an updated energy code for buildings encourage and enable more energy efficient building codes. The information, training, and capacity building elements address what is sometimes referred to as the “significant status quo bias” of energy consumers.
With respect to section 5 (b) (ii) of the Act, it is not immediately possible to make comparisons between the actual and projected emission reductions achieved by the equipment, buildings and houses, retrofit and industry programs and the National Emissions Inventory, primarily with regard to emission reductions achieved from electricity savings. Natural Resources Canada will work with Environment Canada to resolve questions related to input data and definitions to determine the appropriate relationship between electricity savings and emission reductions from electricity generation.
Expected reductions represent conservative estimates of program impacts. For this Plan, qualitative self-assessment considered the uncertainty surrounding the calculated actual reductions to be medium; hence a range of +/- 25% is provided.
In the building sector, the estimates face uncertainties that are explained below:
Homes Component: Actual grant applications provide the basis for this calculation. Eligible retrofits are verified on-site by a Natural Resources Canada certified Energy Advisor. File submissions are evaluated for risk and may be subjected to three levels of quality assurance. Energy savings are based on pre- and post-retrofit evaluations and use computer generated calculations based on standard operating conditions normalized for lifestyle and average weather conditions.
Small and Medium Organizations – Buildings and Industry: Actual contribution applications provide the basis for this calculation, with energy savings calculated by a certified technologist or professional engineer.
Given the amount of actual data that is collected, the level of quality assurance, and the 3% standard accuracy of the blower door test that is a key element of the home energy evaluation, the uncertainty surrounding the calculated energy savings from retrofit projects is low. For the program as a whole, however, the findings of an evaluation conducted in 2010 indicated a net-to-gross ratio of 0.70 to 0.78, which is used here to create a new range for the estimated impacts. The adjusted expected actual reductions are set at the mid-point of the range for 2008 and 2009.
Projected reductions from this program were estimated using information from technical and past program files, specifically, the average savings and participation rates for each sub-component of the initiative, subject to the limitations of the program design.
The estimated energy savings are converted to GHG reductions using standard GHG conversion factors.
The baseline assumption is that grant applicants would not have made investments to realize the expected incremental energy savings without encouragement from the program. However, a range is provided considering the uncertainty of this assumption.
With respect to section 5 (b) (ii) of the Act, it is not immediately possible to make comparisons between the actual and projected emission reductions achieved by the equipment, buildings and houses, retrofit and industry programs and the National Emissions Inventory, primarily with regard to emission reductions achieved from electricity savings. Natural Resources Canada will work with Environment Canada to resolve questions related to input data and definitions to determine the appropriate relationship between electricity savings and emission reductions from electricity generation.
The unadjusted reductions, comparable to those stated in previous Plans, are presented in the table below.
| Actual Reductions (Mt) | Projected Reductions (Mt) | |||
|---|---|---|---|---|
| 2008 | 2009 | 2010 | 2011 | 2012 |
| 0.39 | 0.89 | 1.58 | 1.75 | 1.75 |
Free-ridership was initially expected to have minimal influence on expected GHG reductions. This is due to incentive eligibility being designed to minimize this practice (e.g., requiring a minimum one-year project payback period for those Small and Medium Organization projects receiving funding; requiring a pre-project energy assessment or audit; not incenting projects that begin prior to official approval being received from Natural Resources Canada).
Projected reductions are provided as a range to reflect the inherent uncertainty and risks involved in program delivery. Expected reductions represent a conservative estimate of program impacts.
A 2010 evaluation that covered the elements of this program concluded that net-to-gross ratios for their impacts ranged from 0.26 to 0.84. The net-to-gross at the higher end of this range was for the homes component of the program. Given that the homes component is the largest element of program impacts, the combined impact for the program overall may result in a net-to-gross ratio of 0.70 to 0.78. Some further analysis would help determine the most appropriate number for adjusting estimates and ranges of actual and potential reductions. However, for this Plan, the range of projected reductions has been adjusted to 0.70-0.78 of the previous expected levels, and the adjusted expected reduction in each year is set at the mid-point of the range.
Actual reductions were calculated by multiplying the average energy savings per participating facility (based on technical studies and past program files) by the actual number of participants for the informational and the instructional elements of the program. Energy savings (by fuel) were converted to GHG reductions using standardized conversion factors.
Program methodology combines monitored participation, results of published studies and evaluations, and GHG conversion factors. Participation is accurately measured, but the other elements seldom elaborate on the accuracy of their quantitative conclusions. Thus conservative assumptions are made. To create a range for this Plan, qualitative self-assessment considered the uncertainty surrounding the calculated actual reductions to be low on participation and medium on energy savings, hence a range of +/- 20% is provided.
Estimated reductions were calculated by multiplying the average energy savings per participating facility (based on technical studies and past program files) by the expected number of participants for the informational and the instructional elements of the program. The average of energy savings across participating facilities recognizes that some will take no action while others will achieve higher savings. Energy savings (by fuel) were converted to GHG reductions using standardized conversion factors.
These calculations for estimating avoided emissions were done separately for the two program components: (1) energy savings from the Canadian Industry Program for Energy Conservation (CIPEC) and (2) energy savings from site-specific energy assessments. The baseline assumption is that the participants would not otherwise have undertaken actions to achieve these incremental energy savings.
With respect to section 5 (b) (ii) of the Act, it is not immediately possible to make comparisons between the actual and projected emission reductions achieved by the equipment, buildings and houses, retrofit and industry programs and the National Emissions Inventory, primarily with regard to emission reductions achieved from electricity savings. Natural Resources Canada will work with Environment Canada to resolve questions related to input data and definitions to determine the appropriate relationship between electricity savings and emission reductions from electricity generation.
Preliminary expected reductions are provided as a range to reflect two possible scenarios regarding the types of industrial firms that participate in both the CIPEC program and the site assessments. High-end expected reductions include large final emitters (LFEs) in both sub-initiatives, while the low-end expected reductions include non-LFE participation only. The expected reductions represent conservative estimates of program impacts.
Actual reductions have been defined by the program as those that are a result of measures that have been fully commissioned (operational) and therefore are displacing fossil fuels or electricity provided via the electrical grid. It is assumed that GHG emission reductions will begin the month after the date of commission. For projects implemented/commissioned during 2008 and 2009, the reductions from this program are calculated from GHG emission estimates submitted in project proposals and final project reports, not always through monitored data.
The issue of additionality was not formally addressed for the actual reductions reported for 2008 and 2009. However, the majority of the reductions are a result of measures implemented within Aboriginal and northern community infrastructure, which falls under the mandate of INAC. In future reporting years, INAC will work with Natural Resources Canada to identify any projects that may have received funding from multiple funding programs to ensure that the additionality issue has been addressed.
The ecoENERGY for Aboriginal and Northern Communities Program’s main objective is to reduce GHG emissions in Aboriginal and northerncommunities. The program’s goal is to support projects in Aboriginal and northern communities that will result in an estimated 1.3 Mt GHG reduction over a 20-year period once projects have been commissioned. Due to the scale of the program, a comparison of the actual reductions resulting from the ecoENERGY for Aboriginal and Northern Communities Program to the National Emissions Inventory is not possible.
All proponents are required to submit detailed data in their proposals, with separate requirements for renewable power projects and energy efficiency projects. A data requirements document is provided to proponents to assist in the collection of this information. It is recommended that proponents submit a RETScreen30 analysis with their proposals. The GHG reduction estimates that are submitted by proponents are then sent for technical review by a third party.
GHG emission reductions = Project electricity production × GHG emission factor
The project electricity production is calculated using the maximum power capacity, capacity factor, and project lifetime for the project.
GHG emissions reductions = Baseline emissions – Calculated project emissions
The project proponent decides if the baseline energy consumption will be estimated using system specifications or if historic metered data will be used. Baseline emissions are calculated as follows:
Baseline emissions = Total energy consumption x GHG emission factor
The GHG emission factor for renewable energy and energy efficiency projects is based on the source of base case electricity. There are three possibilities in Aboriginal and northern communities as follows (and in some cases multiple sources may apply):
For Aboriginal and northern communities, there are three general possibilities for electricity sources: central grid system (e.g., provincial grid), isolated grid system, and off-grid/on-site generation. This information is critical for the program’s GHG estimates since the source of electricity (coal, diesel, hydro) has an impact on the GHG emission factor for both renewable energy and energy efficiency projects as it is based on avoided fossil fuels.
The amount of electricity produced by a renewable energy project is dependent on the following factors:
The following assumptions have been made for reporting low, high, and expected emissions for the program:
High – The original GHG reduction estimates provided in project proposals.
Exception: Third-party technical review sometimes results in a higher GHG reduction estimate than that submitted in the project proposal. In this case, the third-party review estimate is recorded as the high estimate for the project.
Low – The GHG emission reductions calculated during the third-party technical review.
Exception: Third-party technical review sometimes results in a higher GHG reduction estimate than that submitted in the project proposal. In this case, the project proposal estimate is recorded as the low estimate for the project.
Expected – The GHG emission reductions calculated for the project by the third-party technical review.
Exception: In the absence of a third-party technical review, the GHG reduction estimates submitted in the project proposal are reported as expected estimates for the project.
The ecoENERGY for Aboriginal and Northern Communities Program will assist in the development of installed electrical generation in Aboriginal and northern communities. It is anticipated that once all projects supported by the program are commissioned, the resulting displacement of natural gas, coal, and diesel-electric generation will produce a 1.3 Mt reduction of GHG emissions over the project life-cycle (assumed to be 20 years).
Estimated reductions from this program are estimated using information provided in project proposals submitted by proponents. Each proponent is required to submit detailed data in their proposals, with separate requirements for renewable power projects and energy efficiency projects. A data requirements document is provided to proponents to assist in the collection of this information. It is recommended that proponents submit a RETScreen analysis with their proposals. The GHG reduction estimates that are submitted by proponents are then sent for technical review by a third party.
The issue of additionality was not formally addressed for the projected reductions reported for 2010, 2011, and 2012. For future reporting periods, INAC will work with Natural Resources Canada to identify any projects that may have received funding from multiple funding programs to ensure that the additionality issue has been addressed.
Renewable Energy Projects
GHG emission reductions = Project electricity production × GHG emission factor
The project electricity production is calculated using the maximum power capacity, capacity factor, and project lifetime for the project.
Energy Efficiency Projects
GHG emissions reductions = Baseline emissions – Calculated project emissions
The project proponent decides if the baseline energy consumption will be estimated using system specifications or if historic metered data will be used. Baseline emissions are calculated as follows:
Baseline emissions = Total energy consumption x GHG emission factor
The GHG emission factor for renewable energy and energy efficiency projects is based on the source of base case electricity. There are three possibilities in Aboriginal and northern communities as follows (and in some cases multiple sources may apply):
For Aboriginal and northern communities, there are three general possibilities for electricity sources: central grid system (e.g. provincial grid), isolated grid system, and off-grid/on-site generation. This information is critical for the program’s GHG estimates since the source of electricity (coal, diesel, hydro) has an impact on the GHG emission factor for both renewable energy and energy efficiency projects as it is based on avoided fossil fuels.
The amount of electricity produced by a renewable energy project is dependent on the following factors:
The following assumptions have been made for reporting low, high, and expected emissions for the program:
High – The original GHG reduction estimates provided in project proposals.
Exception: Third-party technical review sometimes results in a higher GHG reduction estimate than that submitted in the project proposal. In this case, the third-party review estimate is recorded as the high estimate for the project.
Low – The GHG emission reductions calculated during the third-party technical review.
Exception: Third-party technical review sometimes results in a higher GHG reduction estimate than that submitted in the project proposal. In this case, the project proposal estimate is recorded as the low estimate for the project.
Expected – The GHG emission reductions calculated for the project by the third-party technical review.
Exception: In the absence of a third-party technical review, the GHG reduction estimates submitted in the project proposal are reported as expected estimates for the project.
Actual GHG emissions reductions from the ecoAUTO Rebate Program can only be estimated, not measured directly, as the only data available are the total number of vehicles sold in Canada during the program, as well as the number of ecoAUTO-eligible vehicles that were sold. The incremental impact of the program on those sales was estimated using assumptions about the impact of the program and other important factors, such as fuel price and general economic conditions, on the behaviour of manufacturers and consumers. The North American Feebate Analysis Model (NAFAM), developed by Transport Canada, was used.
The range of estimates for 2008 and 2009 represents the most up-to-date estimate of the impact of the program, based on information available at the time of the preparation of the Plan. A complete evaluation of the performance of the ecoAUTO Rebate Program is currently underway, the results of which were not available in time for inclusion in this Plan.
To calculate the actual GHG emission reductions from the ecoAUTO Rebate Program, Transport Canada relied on:
Like Environment Canada’s Energy, Emissions, and Economy Model for Canada (E3CM), the NAFAM model approximates consumers’ and manufacturers’ decisions using Qualitative Choice Theory. These decisions are based on the price of buying and operating a vehicle compared with the perceived trade-off between energy savings through improved fuel efficiency and the incremental capital and operating costs. In order to determine the impact of the policies on GHG emissions, Transport Canada’s model incorporates a simplified version of Natural Resources Canada’s Champagne model, a light-duty vehicle stock accounting framework.
In the NAFAM model, the impact of the policy is estimated against a “base case” scenario where the model is run without any policy intervention. With everything else being held constant, all the changes in the values observed are associated with the policy. The model compares the characteristics of a vehicle, its use, and actual sales number, with or without the policy. This is how the analysis takes into account the free-rider issue. The estimate of annual GHG emission reductions due to the ecoAUTO Rebate Program is calculated by using the difference between the annual emissions estimate calculated for the base case and the annual estimate calculated for the policy scenario. The resulting difference gives the incremental annual emission reductions attributed to the ecoAUTO Rebate Program.
The NAFAM model was calibrated to the most up-to-date database available reflecting the characteristics of 2003 model-year vehicles available for sale in the North American market (Canada and United States). These vehicles are then “modified” with new fuel efficient technologies through time, using assumptions about consumer preferences, fuel price, technology cost, fuel consumption improvements, and industry production plans reflecting decision making in a North American market.
In the NAFAM model, the manufacturers’ response is calculated by estimating how 2003 model-year vehicles evolve through time, given assumptions about how often vehicles are modified, and what the costs associated with increasing a vehicle’s fuel efficiency are. Given the ecoAUTO program was announced in Budget 2007 and was in effect for only two years, the model did not attempt to assess the impact of the program with respect to manufacturers’ decisions on the vehicles made available over this period.
The estimate of annual GHG emission reductions due to the ecoAUTO Rebate Program was calculated by using the difference between the annual emissions estimate calculated for the base case and the annual estimate calculated for the policy scenario, making assumptions about the impact of the program on:
The resulting difference gave the incremental annual emission reductions attributed to the ecoAUTO Rebate Program. Results from the ex-post analysis indicated a likely program impact of between 0.02 and 0.04 Mt in 2008 and 2009, which led to a decision not to update the analysis using the NAFAM model given that both analyses yielded very similar results.
The analysis of the impact of the ecoAUTO Rebate Program is sensitive to assumptions regarding vehicle operating cost and market (consumer and manufacturer) behaviour.
Uncertainty analysis was conducted to estimate the potential impact of variations in fuel price and rebound assumptions on the GHG emission reduction estimates. The following is a description of the assumptions made by Transport Canada for the Low, Expected and High cases. Those cases represent sensitivities to the most recent development in fuel prices and the impact of changes in operating costs on vehicle use (the rebound effect).
In Transport Canada’s model, consumer behaviour is represented by assumptions about consumers’ price elasticity of demand, their valuation of potential fuel savings, and the rebound effect.
Changes in fuel costs have a direct impact on the potential fuel savings achieved when reducing a vehicle’s fuel consumption – for a given change in fuel consumption, a higher fuel price will lead to higher savings. The $0.80 per litre fuel price represents the Canadian average motor gasoline prices for the 12-month period ending in November 2004, which was the time period when the 2003 model-year vehicles were manufactured and sold. The fuel price of $1.10 per litre represents the average gasoline prices observed in Canada from March 2007 (introduction of the ecoAUTO program) to December 2008.
The combination of the high price and not allowing manufacturers to implement incremental technology improvements defines the low and expected impact case as it is expected that the policy will have a smaller incremental effect on consumers in this situation.
In addition, for all cases, the analysis assumes that the rebound effect of better fuel efficiency is 15%, rather than the 23% that was used in the preliminary estimates done when the program was developed in 2006. This change stems from recent studies suggesting that the rebound effect is lower than previously thought. In addition, in making its fuel economy ruling for model year 2011, the National Highway Traffic Safety Administration in the United States has also chosen to use a 15% rebound effect as its expected value.
| Low Case | Expected Case | High Case | |
|---|---|---|---|
| Fuel prices (¢ per litre) | 110 | 110 | 80 |
| Rebound effect | -0.15 | -0.15 | -0.15 |
In the case of the ex-post analysis, uncertainty analysis was conducted assuming a range of 60% to 80% for the free-rider effect, the 80% representing the number of successful ecoAUTO applicants that did notstate that they would have purchased a different vehicle in the absence of the rebate, and 60% being a relatively conservative estimate based on the estimates available in the literature.
The projected emission reductions reported under the ecoAUTO Rebate Program are calculated by taking into account only new gasoline- and diesel-powered light-duty vehicles, while the National Emissions Inventory calculates emissions for all gasoline- and diesel-powered light-duty vehicles. The methodology used to calculate projected emission reductions reported under the ecoAUTO Rebate Program has been peer-reviewed and was based on up-to-date data and assumptions reflecting the state of knowledge at the time of the analysis. Thus, while comparisons cannot directly be made between the projected emission reduction calculations and the National Emissions Inventory, it would be valid to compare the estimated emissions reductions with the level of emissions reported in the Inventory to provide a sense of the relative impact of the program on emissions from the light-duty vehicle fleet.
Emissions reductions due to the ecoAUTO Rebate Program for the 2010-2012 period were estimated both using the NAFAM model and actual program data, as is described in the “Actual Reductions” section.
The uncertainty analysis related to the potential impact of the ecoAUTO Rebate Program for the 2010-2012 period was conducted both using the NAFAM model and actual program data, as is described in the “Actual Reductions” section.
Actual GHG emissions reductions from the Green Levy can only be estimated, not measured directly, as the only data available is the total number of vehicles sold in Canada during the policy, as well as the number of vehicles subject to the Green Levy that were sold. The incremental impact of the Green Levy on those sales was estimated using assumptions about the impact of the policy and other important factors, such as fuel price and general economic conditions, on the behaviour of manufacturers and consumers.
The range of estimates provided for 2008 and 2009 represents the most up-to-date estimate of the impact of the policy, based on available information at the time of the preparation of the Plan.
To estimate actual GHG emission reductions from the Green Levy, Transport Canada used the North American Feebate Analysis Model (NAFAM). Like Environment Canada’s Energy, Emissions, and Economy Model for Canada, the model used by Transport Canada approximates consumers’ and manufacturers’ decisions using Qualitative Choice Theory. These decisions are based on the price of buying and operating a vehicle compared with the perceived trade-off between energy savings through improved efficiency and the incremental capital and operating costs. In order to determine the impact of the policies on GHG emissions, Transport Canada’s model incorporates a simplified version of Natural Resources Canada’s Champagne model, a light-duty vehicle stock-accounting framework.
In the NAFAM model, the impact of the policy is estimated against a “base case” scenario where the model is run without any policy intervention. With everything else being held constant, all the changes in the values observed are associated with the policy. The model compares the characteristics of a vehicle, its use, and actual sales number, with or without the policy. This is how the analysis takes into account the free-rider issue. The estimate of annual GHG emission reductions due to the Green Levy are calculated by using the difference between the annual emissions estimate calculated for the base case and the annual estimate calculated for the policy scenario. The resulting savings are incremental annual emission reductions attributed to the Green Levy.
The model used for this analysis was calibrated to the most up-to-date data available reflecting the characteristics of 2003 model year vehicles available for sale in the North American market (Canada and United States). These vehicles are then “modified” with new fuel efficient technologies through time, using assumptions about consumer preferences, fuel price, technology cost, fuel consumption improvements, and industry production plans reflecting decision making in a North American market.
The analysis of the impact of the Green Levy program is sensitive to assumptions regarding vehicle operating cost and market (consumer and manufacturer) behaviour. Uncertainty analysis was conducted to estimate the potential impact of variations to those assumptions on the GHG reduction estimates. The following is a description of the assumptions made by Transport Canada for the low, expected, and high cases. These cases represent sensitivities to the most recent development in fuel prices and the impact of changes in operating costs on vehicle use (the rebound effect).
In Transport Canada’s model, manufacturers’ technology response is estimated by simulating how 2003 model year vehicles are modified through time, given assumptions about how often vehicles are retrofitted (generally over a four- to five-year schedule), and what the costs associated with increasing a vehicle’s fuel efficiency are. The sensitivity analysis of the Green Levy now includes a technology response of the policy for the high case. Inclusion of the technology effect in the analysis has the consequence of progressively increasing the impact of the program, as more retrofitted vehicles enter the fleet.
Consumer behaviour is represented by assumptions about consumers’ elasticity of demand, their valuation of potential fuel savings, and the rebound effect.
Changes in fuel costs have a direct impact on the potential fuel savings achieved when reducing a vehicle’s fuel consumption – for a given change in fuel consumption, a higher fuel price will lead to higher savings. The $0.80 per litre price represents the Canadian average motor gasoline prices for the 12-month period ending in November 2004, which was the time period when the 2003 model year vehicles were manufactured and sold. The fuel price of $1.10 per litre represents the average gasoline prices observed in Canada from March 2007 (introduction of the Green Levy) to December 2008. In 2009 and 2010, average retail fuel prices in Canada were approximately 94.5¢ and 103.5¢ per litre, respectively, well within our range of estimates.
The combination of the high price and allowing manufacturers to implement incremental technology improvements defines the high case, as it is expected that the policy will have a larger incremental effect on consumers in this situation. The assumptions made in the high scenario lead to the greatest impacts by 2012 due to technology adoption. The low and expected scenario assumptions yield a greater initial impact in 2008 due to lower fuel prices, but do not yield as much impact over the longer term.
In addition, for the high case, the analysis now assumes that the rebound effect of better fuel efficiency is 15%, rather than the 23% that was used for the preliminary estimates that were provided in 2006. This change stems from recent studies suggesting that the rebound effect is lower than previously thought. In addition, in making its fuel economy ruling for model year 2011, the National Highway Traffic Safety Administration in the United States has also chosen to use a 15% rebound effect as its expected value.
| Low Case | Expected Case | High Case | |
|---|---|---|---|
| Fuel prices (¢ per litre) | 80 | 80 | 110 |
| Rebound effect | -0.23 | -0.23 | -0.15 |
In addition, in 2011, Transport Canada reviewed the market share of vehicles with a combined fuel consumption rating of 13 litres or more per 100 kilometres for the 2007 to 2009 model years and found that their market share has fallen considerably since the introduction of the Green Levy. In fact, this share has fallen more than what was anticipated in the analysis conducted using the NAFAMl. This could indicate that the analysis might have slightly underestimated the impacts of the Green Levy on GHG emissions.
Of course, the introduction of the Green Levy is not the only factor that could have had an impact on the sales of those vehicles. Other factors, such as the rise in fuel prices observed since 2008 and the Passenger Automobile and Light Truck Greenhouse Gas Emission Regulations, are also likely to have played a role.
Increasing fuel prices could have prompted consumers to stay away from vehicles consuming more fuel, while manufacturers could be expected to start changing the vehicles they offer on the market ahead of the entry into force of the regulations, as it typically takes a manufacturer about four years to bring a new vehicle to the market.
Given the uncertainty associated with evaluating the individual impact of all of these factors, it is expected that the range of estimates of the impact of the Green Levy provided by the ex-ante analysis still represents the most likely range of impact of the Levy.
The projected emission reductions reported under the Green Levy are calculated by taking into account only new gasoline- and diesel-powered light-duty vehicles, while the National Emissions Inventory calculates emissions for all gasoline- and diesel-powered light-duty vehicles. The methodology used to calculate projected emission reductions reported under the Green Levy has been peer-reviewed and was based on up-to-date data and assumptions reflecting the state of knowledge at the time of the analysis. Thus, while comparisons cannot directly be made between the projected emission reduction calculations and the National Emissions Inventory, it would be valid to compare the estimated emission reductions with the level of emissions reported in the Inventory to provide a sense of the relative impact of the initiative emissions from the light-duty vehicle fleet.
Emissions reductions due to the Green Levy for the 2010-2012 period were estimated using the NAFAM model, as is described in the “Actual Reductions” section.
The uncertainty analysis related to the potential impact of the Green Levy for the 2010-2012 period was conducted using the NAFAM model, as is described in the “Actual Reductions” section.
The program interventions include a number of elements, the impacts of which were calculated individually. The actual energy savings of program interventions were calculated based on the actual number of drivers reached by the program, the expected changes in their behaviour resulting from the program, and the fuel saved because of these changes (as described in under Projected Reductions Methodology).
The goal under the MOU with automobile manufacturers of reducing GHG emissions by 5.3 Mt was a negotiated target that was initially based on the emissions reductions that could be expected from a 25% improvement in fuel efficiency by 2010. The 5.3 Mt target was measured from a reference case level of emissions that was designed to reflect the actions of the automotive industry that would have occurred in the absence of action on climate change. In 2009-10, a third-party verification of the data collection and analysis methodologies used to calculate GHG reductions attributable to the MOU concluded that the methodologies were sound and applied accurately. The 2007 MOU Interim Report concluded that the MOU triggered GHG emission reductions equal to between 3.1 and 3.4 Mt. Note that emissions reductions from the MOU are excluded from KPIA reporting.
Program methodology combines monitored participation, third-party sector-level data and forecasts, results of published studies and evaluations, and GHG conversion factors. Participation is accurately measured; however, information from secondary sources is seldom accompanied by robust explanations of underlying quantitative conclusions. Thus conservative assumptions are made. Where secondary sources have been used to populate variables, the lower end of published ranges was used. Variables were updated as new and more robust information became available, either through case studies or other government and third-party studies. To create a range for this Plan, qualitative self-assessment considered the uncertainty surrounding the calculated actual reductions to be medium, hence a range of +/- 25% is provided.
The program interventions include a number of elements, the impacts of which were calculated individually. The estimated energy savings of program interventions were calculated based on the expected number of drivers reached by the program, the changes in their behaviour resulting from the program, and the fuel saved because of these changes. That is, multiplying the following variables: expected number of vehicles reached, average annual kilometres travelled per vehicle, estimated percent savings from intervention, estimated retention rate, and GHG emissions per kilometre.
Average fuel consumption data from Statistics Canada for the period prior to the interventions was used as the baseline measure. The baseline assumption is that drivers would not have adopted more fuel-efficient behaviours without the instruction afforded by the program, and thus would not have achieved the incremental fuel savings. The methodology includes a variable for grams of carbon equivalent emissions per kilometre travelled.
Government publications, accepted models, technical studies, and past program files provided information regarding these variables and were the basis for the estimates of participation, rates of adoption, retention of fuel-efficient practices, and the average impact of these practices.
In support of reduced GHG emissions from transportation energy use, total projected emissions reductions from the ecoENERGY for Personal Vehicles program in 2010 were 0.20 Mt, which is equivalent to 0.23% of total emissions from the light-duty vehicle sector reported in the (most recently available) 2008 National Emissions Inventory.
The Government of Canada has a number of programs designed to reduce GHG emissions from the transportation sector. These programs are designed to be complementary. Projected reductions represent conservative estimates of program impacts.
For this Plan, qualitative self-assessment considered the uncertainty surrounding the calculated actual reductions to be medium, hence a range of +/- 25% is provided.
Because the selection of projects under the program was initially delayed to allow for more national consultations in 2007, it was not assumed that the project implementation would be sufficiently advanced to yield GHG reduction prior to 2010.
The 13 projects funded under the program are subject to an extensive measurement approach based on reductions in distance travelled and fuel used. Actual emissions are not available yet and will be available once the program is completed.
Not applicable at this time.
Transportation Demand Management (TDM) is the application of strategies and policies to reduce automobile travel demand, or to redistribute this demand to other modes. The program will achieve its GHG impact by funding TDM initiatives that reduce the distance (VKT) travelled by passenger vehicles in urban areas. Various locational and socio-economic factors influence VKT, including land-use, urban sprawl, fuel prices, and car ownership. It is important to note that the effect of the ecoMOBILITY program is linked with the availability of alternatives to personal vehicles. Certain transit-based TDM strategies must be implemented in close collaboration with transit investments, while other strategies such as teleworking and other workplace programs can be implemented more independently.
In 2006, it was assumed that the program could support a reduction in total VKT in urban areas by 3% in 2010 through the direct and indirect (transformative) effects of the program activities. This assumption came from the “high TDM” option outlined in a study commissioned by Transport Canada (“The Impact of Transit Improvements on GHG Emissions: A National Perspective”, Transport Canada, March 2005). This option assumed that both transit and non-transit TDM measures would be implemented by municipalities in combination with significant transit infrastructure investments. The 3% reduction was applied to historical VKT data available from Natural Resources Canada, the results were translated into reductions in fuel use and subsequently GHG reductions using EC conversion factors. This methodology yielded a preliminary estimate of 1.6 Mt in 2012.
It was assumed that emission reductions were obtained from specific TDM initiatives stimulated by the program. The reductions were based on the following assumptions:
The ecoMOBILITY program was redirected in 2009 to focus its activities exclusively on non-transit-based TDM. While transit-based TDM can reduce VKT in the short and longer terms, non-transit-based TDM targeting travel behaviour has less short-term impact. The revised program approach that focuses on a narrower range of non-transit-based TDM strategies necessarily lowered the GHG emission reductions that could be attributed to the program in 2012.
Results from the above-mentioned study on transit investment were used to project the GHG impacts of the softer non-transit-based TDM measures such as workplace travel plans, car sharing, or travel awareness campaigns.
A revised expected scenario assumes a 0.2% reduction of VKT in 2012, yielding an estimated 0.112 Mt reduction.
With respect to additionality, Natural Resources Canada’s Canada Energy Outlooks include program funding committed by the federal and provincial governments and by public utilities. Because these energy efficiency growth rates are incorporated in the methodology assumptions, the program’s estimated reduction may be considered incremental.
The emission reduction estimates are based on fuel savings due to program activities. Fuel saving results were translated into GHG emission reductions using EC conversion factors by fuel types published in the National Emissions Inventory.
While direct comparisons cannot be made between the projected emission reduction calculations and the National Emissions Inventory, it would be valid to compare the estimated emission reductions with the level of emissions reported in the Inventory to provide a sense of the relative impact of the program.
A scenario was considered as part of the uncertainty analysis that assumes a VKT reduction of 0.4%, yielding an estimated 0.223 Mt in 2012. This scenario would occur if the replication or multiplier effect of the program was greater than expected.
Projected GHG reductions are only a co-benefit, as the focus of the program is on reducing smog-forming emissions, not GHGs. GHG emission reductions are the result of individuals retiring their old vehicle and choosing sustainable forms of transportation, such as public transit or membership in a car-sharing program, or replacing their old vehicle with a more fuel-efficient one, and/or driving less.
It is assumed that the program accelerates vehicle retirement by one year and reduction calculations only consider vehicles retired through the program and only the year following vehicle retirement.
The baseline is emissions produced if a vehicle retired by the program had remained on the road for a year. The actual and projected reductions consider how this vehicle was replaced, either by another mode of transportation or another vehicle.
“Actual” means that emissions reductions reflect measured program results, such as the number of vehicles retired and a survey of program participants to determine post-vehicle retirement behaviour (conducted 6 to 12 months after they retire their old vehicles). Projected reductions are based on anticipated program results.
A database developed specifically to manage the program and track results is used for the calculations. Published data for emission factors for vehicles (by model and model year), annual vehicle use (average distance driven), and transit data (distance travelled and fuel consumed by buses) are the basis for the calculations. Emission factors and vehicle usage data are as consistent as feasible with parameters used for the National Emissions Inventory.
Considering only the year following the retirement of the vehicle limits the calculations to the impact of the program, eliminating “additionality” issues. Without the scrappage program, older vehicles would have reached the “normal” end of their natural life and been replaced by other vehicles or other means of transportation. These emissions are already factored into the National Emissions Inventory and by other emissions reduction measures.
Emissions estimates vary depending on the number of program participants, the incentive selected, and personal transportation behaviours after the old vehicle has been retired. Post-vehicle retirement behaviour is assumed to remain the same as previous results.
The key assumption is that the vehicle scrappage program accelerated the retirement of an older vehicle by one year. Although this is difficult to measure, this assumption is more conservative than the three years used by the California Air Resource Board to assess potential vehicle retirement initiatives.
The range of expected emission reductions reflects minimum and maximum projections for vehicles to be retired.
Projected and actual emissions reductions are calculated in a similar manner. The projected number of vehicles retired is used in the calculations rather than actual results.
See “Actual Reductions” section above.
Actual GHG reductions will not be reported until the program results measurement is completed in 2011-12.
Not applicable.
Direct and transformative GHG savings for the ecoTECHNOLOGY for Vehicles Program (ETVP) were based on estimates calculated from the previous pilot Advanced Technology Vehicle Program, which followed a similar program model on a smaller scale. “Direct savings” refers to reductions from incremental advanced technologies that are embedded in conventional vehicles in the Canadian market. “Transformative savings” refers to reductions from non-conventional advanced vehicles (e.g., hybrids, electric, etc.).
For direct GHG savings it was assumed that 1% of the sales of new vehicles include technologies targeted by the program due to public outreach and education activities of ETVP.
Transformative emissions savings estimates were based on the forecast market shares of advanced technology vehicles over the relevant period. Advanced technology vehicles were defined as vehicles presenting an 11.5% improvement. In comparison, the average improvement of new vehicles was estimated at 7.5%. It was assumed that 20% of these advanced technology vehicle sales were influenced by the ETVP.
In both cases, vehicles were assumed to save 1.6 litres per 100 kilometres and travel 23,500 kilometres per year.
In 2010, the original estimates were reviewed to take into account the impact of the economic downturn by factoring in lower vehicle sales and slower advanced technology penetration rate. Preliminary estimates were based on assumptions made about new vehicle sales, technology penetration, and vehicle distance travelled forecasts. The economic downturn has had a significant impact on vehicle sales. These factors contributed to slower market penetration of advanced technologies and reduced the overall impact of the program within the program timelines to 0.2 Mt in 2012. Initial reduction estimates are not expected to be achieved until at least 2 to 3 years after the end of the program.
With respect to additionality, it was assumed that emission reductions were incremental where they were obtained from technology adoption stimulated by the program.
The emission reduction estimates are based on fuel savings due to program activities. Fuel saving results were translated into GHG emission reductions using EC conversion factors by fuel types published in the National Emissions Inventory.
While comparisons cannot directly be made between the projected emission reduction calculations and the National Emissions Inventory, it would be valid to compare the estimated emission reductions with the level of emissions reported in the Inventory to provide a sense of the relative impact of the program.
An uncertainty analysis was undertaken to consider both a low and a high scenario. A scenario was developed to further take into account the impact of the economic downturn by factoring in lower vehicle sales and slower advanced technology penetration rate. The low scenario was reviewed to assume lower market penetration of advanced technologies (12% vs. 20%), lower fuel saving (1.2 l/100 km vs. 1.6 l/100 km) applied to lower sales figures (1.49 million vs. 1.52 million), yielding a reduction of only 0.09 Mt in 2012. A higher scenario was estimated based on more optimistic assumptions on fuel saving (1.9 l/100 km vs. 1.6 l/100 km), yielding a reduction of 0.6 Mt in 2012.
The estimated actual energy savings were calculated based on the actual number of transportation professionals reached by the program, the changes in their behaviour resulting from the program, and the fuel saved because of these changes, as described in the “Projected Reductions” section below.
Program methodology combines monitored participation, third-party sector-level data and forecasts, results of published studies and evaluations, and GHG conversion factors. Participation is accurately tracked. Secondary sources, however, seldom elaborate on underlying methodologies supporting their quantitative conclusions. Thus, for impact reporting purposes, where secondary sources have been consulted to populate variables, conservative assumptions are made and therefore the low end of published ranges has been used. For some activities, case studies were conducted to verify Natural Resources Canada’s assumptions with regards to the impact of training in various commercial and institutional fleet sectors. Findings from these studies were employed to qualify impact forecasting and measurement models. Variables have been updated as new and more robust information became available, either through case studies or other government and third-party studies.
To create a range for this Plan, a qualitative self-assessment considered the uncertainty surrounding the calculated actual reductions to be medium, hence a range of +/- 25% is provided.
This program contains a number of elements, the impacts of which were calculated individually.
The estimated energy savings were calculated based on the expected number of transportation professionals reached by the program, the changes in their behaviour resulting from the program, and the fuel saved because of these changes. That is, the following variables were multiplied: expected number of vehicles reached, average fuel consumption per vehicle, estimated percent savings from intervention, estimated retention rate, and GHG emissions per unit of fuel.
Average fuel consumption data from Statistics Canada for the period prior to the interventions was taken as the baseline measure. The baseline assumption for projected reductions was that drivers and fleet managers would not have adopted more fuel-efficient behaviours without the instruction and tools afforded by the program, and thus would not have achieved the incremental fuel savings. Follow-up with participants has been conducted in order to verify this assumption. Adjustments in the estimation models were made based on the findings from these follow-up exercises in order to determine actual reductions. The methodology also includes a factor for grams of carbon dioxide equivalent emissions per kilometre travelled.
Government publications, accepted models, technical studies, and past program case studies provided variables and served as the basis for the estimates of participation, rates of adoption of fuel-efficient practices, and the average impact of these practices.
In support of reduced GHG emissions from transportation energy use, total projected emission reductions from the ecoENERGY for Fleets program in 2010 were 0.38 Mt, which is equivalent to 0.47% of total emissions from the heavy-duty and off-road sectors reported in the (most recently available) 2008 National Emissions Inventory.
The Government of Canada has a number of programs to reduce GHG emissions from the freight transportation sector. Natural Resources Canada’s ecoENERGY for Fleets program aims to reduce emissions from freight transportation through behavioural change as a result of training and awareness campaigns. Transport Canada’s ecoFREIGHT program aims to reduce emissions from freight transportation through partnerships, promotion of technologies, and addressing regulatory barriers that limit the uptake of emission-reducing technologies. These programs are designed to be complementary; however, there is a potential for overlap between the impacts of the programs. For example, if an emission-reducing technology reduces a truck’s overall emissions by 4%, the total remaining emissions is 96%. Any further measures can only impact the remaining 96%. The impact of the overlap is deemed to be very small because the impact of each individual measure is small. Nonetheless, in order to account for potential overlap between Natural Resources Canada’s programs and Transport Canada’s programs, expected reductions represent conservative estimates of program impacts in all cases.
For this Plan, qualitative self-assessment considered the uncertainty surrounding the calculated actual reductions to be medium, hence a range of +/- 25% is provided.
The 38 projects funded under the program are subject to an extensive GHG emission measurement approach based on fuel saved through the use of clean technology. Actual program emission reduction measurements are not available yet and will be available once the program results measurement is completed in 2011-12.
Not applicable at this time.
The ecoFREIGHT GHG emission reduction estimates include reductions expected directly from projects funded under the program, as well as reductions from the broader market adoption of technologies (multiplier or transformative effect) fostered by the program’s information dissemination activities. Emission reductions from program activities relating to partnership MOUs (from 0.5 Mt in 2009 to 0.9 Mt in 2012) are included incrementally.
In 2007, the GHG preliminary estimates were based on data supplied in the current and previous generations of program applications, actual project Contribution Agreements, and project progress and final reports as available. The data from the previous generation of programs was extrapolated to form the preliminary impact estimates for the ecoFREIGHT program by pro-rating the direct GHG impacts according to the amount of project funding available to the ecoFREIGHT program.
The ecoFREIGHT direct impact was calculated from the forecasted number of projects and their estimated GHG impacts by mode and by technology. The ecoFREIGHT indirect (i.e. multiplier) impact was then calculated by applying a multiplier factor ranging from 1.75 in 2008 to 2.4 in 2012 according to the projected mix of technologies to the direct impact of a particular year. The multiplier effect was primarily influenced by the payback period of the technologies.
In 2010, the preliminary estimates were reviewed and updated with the estimated emission reductions from actual projects receiving funding under the program, rather than information from predecessor programs. Revised assumptions regarding the multiplier effect of the program also took into account the impact of the economic downturn, which reduced the ability/willingness of industry to invest in clean technology projects during the economic recession and thus reduced the number of projects under the program. Finally, the projected reductions were adjusted upward to include the expected GHG emission reductions from: 1) the mandatory implementation of truck speed limiters in Ontario and Quebec (0.4 Mt in 2012), which was achieved with the support of the NHITI initiative of ecoFREIGHT, and 2) the MOUs (0.9 Mt in 2012).
The 2010 expected scenario only includes projected direct reductions of technology projects funded under the ecoFREIGHT program (57.3 kilotonnes of GHG emission reductions in 2012), the reduction from the introduction of speed limiters in Ontario and Quebec supported by the NHITI (0.4 Mt in 2012) and reduction from the MOUs (0.9 Mt in 2012). In this scenario, the multiplier, or indirect effect, was assumed to be delayed until after 2012 due to the economic downturn.
A baseline scenario was not necessary since the emission reduction estimates are based on expected reduction in fuel use and GHG emissions from specific technologies where adoption is stimulated by the program.
With respect to additionality, it was assumed that emission reductions were incremental where they were obtained from projects directly funded by the program. The indirect impact was also assumed to be incremental because it resulted from technology adoption stimulated by the program.
The emission reduction estimates are based on fuel savings due to program activities. Fuel saving results were translated into GHG emission reductions using EC conversion factors by fuel types published in the National Emissions Inventory.
While direct comparisons cannot be made between the projected emission reduction calculations and the National Emissions Inventory, it would be valid to compare the estimated emission reductions with the level of emissions reported in the Inventory to provide a sense of the relative impact of the program.
An uncertainty analysis was conducted including a high scenario in which indirect or multiplier reductions are assumed to occur through replication of program projects in the freight industry. The ecoFREIGHT indirect (i.e. multiplier) impact under this scenario was assumed to be achieved within the Kyoto compliance period (2008-2012) and calculated by applying the same factors described earlier to the revised direct impact of a particular year as described above.
Because the selection of program projects and the program funding round were not held until after amendments to the Canadian Marine Act came into force in 2008, it was assumed that the project implementation would not be sufficiently advanced to yield actual measured GHG reductions prior to 2012.
The two projects funded under the program are subject to an extensive measurement approach based on reductions in fuel used. Actual emissions measurements are not available yet and will be available once the program is completed.
Not applicable at this time.
The information used to project GHG emission reductions for the Marine Shore Power Program (MSPP) comes from Transport Canada’s “Feasibility Study to Determine Suitable Locations for Marine Shore Power Pilot Projects in Canada” (Final Report, July 2005). In this study, 15 sites were analyzed and GHG estimates were calculated based on reductions in fuel use.
The approach averaged out the GHG savings of 11 of the 15 projects analyzed (excluding four projects that were considered to be too expensive to implement). The average net annual GHG savings used was 1.3 kt per project. Savings were calculated primarily based on estimations of marine fuel displaced by electricity.
It was assumed that the funding received would allow four projects to be implemented under the MSPP, each achieving an average net annual GHG reduction of 1.3 kt, for a total of 5.3 kt in 2010. (It was anticipated that there could be a mix of larger and smaller projects together.)
For the “transformative” impact of the program, it was assumed that two more projects would be implemented after 2010 (one in 2010 and one in 2012) as a result of the demonstrations, each also achieving a net annual GHG reduction of 1.3 kt, for a total of 2.6 kt per year in 2012.
Thus, the preliminary reductions initially estimated in 2006 assumed the implementation of a total of six projects of varying sizes. The number and/or size of projects were dependent on increases in the costs of equipment and/or the ability/willingness of promoters to invest in such projects due to changes in economic activity.
In 2010, the original estimates were reviewed to include updated estimated emission reductions from one project underway in the Port of Vancouver and to take into account the impact of the economic downturn by reducing the number of projects that would take place under the program.
Projections were revised downwards such that the expected scenario assumed only two projects would be funded under the program, with an estimated GHG reduction of 4.5 kt in 2012: the Vancouver project with an estimated GHG emission annual reduction of 3.2 kt starting in 2009 and one other project with an estimated GHG annual reduction of 1.3 kt. At that time, program staff were aware that a third project had been developed then withdrawn by the proponent due to the economic recession.
With respect to additionality, it was assumed that emission reductions were incremental where they were obtained from projects directly funded by the program. The indirect impact was also assumed to be incremental because it resulted from technology adoption stimulated by the program.
The emission reduction estimates are based on fuel savings due to program activities. Fuel saving results were translated into GHG emission reductions using EC conversion factors by fuel types published in the National Emissions Inventory.
While direct comparisons cannot be made between the projected emission reduction calculations and the National Emissions Inventory, it would be valid to compare the estimated emission reductions with the level of emissions reported in the Inventory to provide a sense of the relative impact of the program.
A high scenario was estimated as part of the uncertainty analysis, which included four projects with an estimated GHG emission reduction of 7.1 kt in 2012. It is assumed that two projects would be funded under the program with an estimated GHG reduction of 4.5 kt per year (as per the above low scenario). In addition, two other projects would take place in 2012 with an estimated GHG reduction of 2.6 kt in 2012 as a result of the replication or multiplier effect of the program.
The estimated emission reductions for the 2011 Plan use the same basic methodology as was used to calculate the estimated emissions reductions for the 2008, 2009, and 2010 Plans. While the methodology is similar, this year’s approach differs in the following manners:
Given the nature of the measures, it is virtually impossible to obtain a true actual reduction estimate and compare this with the National Emissions Inventory. Therefore, “actual” reductions are based on an approach that estimates the reduction based on:
If the measure is assumed to affect the behaviour of all claimants for the Public Transit Tax Credit, then the actual emission reductions due to the measure would be about 0.82 Mt. However, it is highly likely that a significant majority of the claimants would have continued to use public transit in the absence of the measure.
The assumed behavioural impact is based on a study by Litman for the Victoria Transport Policy Institute. The incremental trips are estimated by
Using the Litman approach, the behavioural impact is estimated to be about 7%. Applying this estimate to the total avoided emissions based on tax claims received for the Public Transit Tax Credit, the actual reduction from the measure is estimated to be 0.032 Mt.
The estimated emission reductions from this measure are dependent on key assumptions, including the growth in ridership, the elasticity with respect to transit fare increases, the number of trips, the estimated reduction in vehicle trips (transit-to-auto passenger conversion), and the number of tax claims received for the Public Transit Tax Credit.
Moreover, there is the issue of behaviour. If all the claimants were taking public transit prior to the introduction of the Public Transit Tax Credit, then the actual reductions due to the measure would be zero. In this situation, there would still be avoided emissions if the claimant had access to a car.
The uncertainty analysis focused on a couple of issues:
The estimated emission reductions for the 2011 Plan use the same methodology that was used to calculate the estimated emission reductions for the 2008, 2009, and 2010 Plans.
The calculation used information from a variety of sources. The data on public transit trips (ridership) was obtained from Canadian Urban Transit Association statistics. The GHG emission factors were obtained from the Climate Change Transportation Table. A constant 2.5% annual growth in ridership (average of the last four years) was used to project baseline levels of ridership over the 2010-2012 period. Based on a calculation that the tax credit would result in an effective fare reduction of 9%, and using a short-term own-price elasticity for the overall market of 2.5%, which is based on a study by Litman for the Victoria Transport Policy Institute, new (incremental) trips resulting from the tax credit were calculated. These new trips were adjusted to estimate reduced vehicle trips based on information on vehicle occupancy from Transport Canada, and appropriate emissions factors were applied to these figures to produce the emission reduction estimates for each year.
The emissions estimate was also influenced by several other factors:
The estimated emission reductions from this measure are dependent on key assumptions, including the growth in ridership, the elasticity with respect to transit fare increases, the number of trips, the estimated reduction in vehicle trips (transit-to-auto passenger conversion), and the number of tax claims received for the Public Transit Tax Credit.
Moreover, there is the issue of behavioural impact. If all the claimants were taking the public transit prior to the introduction of the tax credit, then the actual reductions would be zero. In this situation, there would still be avoided emissions if the claimant had access to a car.
The uncertainty analysis focused on a couple of issues:
All provincial actions, such as Ontario’s phase-out of coal-fired power plants, provincial renewable promotion programs31, Quebec’s carbon levy, the British Columbia carbon tax and the Clean Energy Act (the CEA became law on June 3, 2010), Alberta’s Climate Change Emissions Management Amendment Act, and Nova Scotia’s emissions cap on electricity generation, are included in the business-as-usual base case32. Hence, the impact of these programs is reflected in the total emissions estimated for both the core and alternative scenarios examined.
The information used to calculate GHG emission reductions from the various provincial policies comes from provincial legislation and budget documents. For example, the modelling reflects the specific tax rates, emission caps or intensity targets, and penalties for the following major provincial initiatives, among others:
The Government of Canada is applying Environment Canada’s integrated Energy, Emissions, and Economy Model for Canada (E3MC) to estimate the reduction for the overall integrated package of measures. The modelled runs incorporate individual parameters for each of the initiatives reported here – as provided by lead departments – and aggregate the results to report on Canada’s net emission reductions and total remaining emission levels for 2008-2012. The use of the model responds to the NRTEE’s suggested methodological improvement for an “integrative accounting of the emission reduction estimates.”
The E3MC model incorporates an updated energy, emissions, and economy baseline that includes the latest GHG emissions inventory published by Environment Canada. This baseline already incorporates many measures and trends currently underway across Canada. The date of January 1, 2006, has been applied as the cut-off point for defining existing measures that are to be included in the baseline. Some of the measures included in the baseline are complementary to the federal policies presented in this Plan. As such, to avoid double-counting, the impacts from these measures are not included in the total emissions reductions. Since integrated modelling has been done from a baseline that includes provincial and territorial programs, some of which complement federal actions, the impact of federal actions may in some cases be understated since all interaction effects between provincial/territorial and federal programs are netted from estimated federal reductions. Some key assumptions in the baseline that effect federal policies in the 2011 KPIA Plan include:
The Act requires that the expected emission reductions be compared to the levels in the most recently available emissions inventory for Canada. This stipulation implies that reductions must be “additional to any that would otherwise occur.” As the 2008 and 2009 inventories include the impact of actions from all levels of government, it is necessary to specify the hypothetical, unobservable baseline level of emissions (i.e. the baseline without the actions from governments).
In order to ensure that all requirements of the Act are met, the methodology for estimating emission reductions was slightly modified. Specifically, a modified approach was used to develop a “No Federal Programs” baseline that excludes all federal actions announced after January 1, 2006. This counter-factual baseline provides an emissions level in the absence of federal government programs, and was constructed as follows.
The No Federal Programs baseline was then compared to the “actual” 2008 and 2009 emissions level reported in the National Inventory Report. The difference in emissions is attributable to the incremental impact of federal government GHG reduction actions for 2008 and 2009.
To capture the effects of the Government’s climate change programs, the assumptions used for the individual measures as detailed in this Plan were built into the E3MC model. E3MC assumes that consumers of energy respond to the program parameters by altering their decisions regarding purchases and investments based on Qualitative Choice Theory.38 That is to say, the model assumes that decisions are based on the price of fuel combined with the perceived trade-off between energy cost savings through improved efficiency and capital and operating costs. For example, a program such as the ecoENERGY Retrofit Initiative provides financial support to reduce the cost of implementing an energy efficiency project, encouraging investment by improving the trade-off between the long-term value of energy savings and up-front investment costs.
The 2010-2012 emission levels for Canada were generated by simultaneously modelling the individual emissions reduction measures detailed in this Plan in E3MC. This ensures that measures were assessed in an integrated manner, thereby accounting for any positive and negative interactions between measures and regulations. In addition, the electricity sector in E3MC reflects a North American approach. If there are reductions in domestic electricity demand, there can be increased exports across the United States if relative power costs make it advantageous.
The methodological approach for developing the 2010-2012 emissions levels is similar to that used for estimating “actual” emissions reductions for 2008 and 2009, with the exception that 2008 and 2009 data are aligned to the National Inventory and an emissions projection is produced for 2010 to 2012.
There are a number of key determinants that influence energy supply and demand and emissions. These determinants include: the pace of economic growth; population and household formation; energy prices (e.g., world oil price and price of refined petroleum products, regional natural gas prices, and electricity prices); technological change, policy decisions, and consumer response to policy price and government actions. Varying any one of these assumptions could have a material impact on the energy and emission reduction estimates contained in this Plan.
As a basis for assessing the additional reductions required to achieve the GHG emission reduction targets implied by the Kyoto Protocol Implementation Act, nine alternative baselines of projected emissions excluding government measures were constructed. Given a projection period of 2010 to 2012 and that preliminary economic growth rates for 2010 are in the public domain, it was decided to use only one set of economic growth rates – those reported in Budget 2011 – and sensitivity analysis was performed around energy price and program effectiveness.
Given the uncertainty concerning key modelling assumptions, a set of alternative baselines was developed focused on the following drivers:39
In these alternative scenarios:
Environment Canada’s E3MC has two components: a bottom-up model of Canada’s energy supply and demand structure and a macroeconomic model of the Canadian economy.
The energy supply and demand model is an integrated multi-region, multi-sector North American model that simulates the supply, price, and demand for all fuels. The model can determine energy output and prices for each sector, both in regulated and unregulated markets. It simulates how factors like energy prices and government policies affect the choices that consumers and businesses make in the purchase and use of energy. The model’s outputs, which include changes in energy use, energy prices, GHG emissions, investment costs, and possible cost savings from policies, are used to identify the direct effects stemming from GHG reduction measures. The resulting savings and investments from the energy supply and demand model are then used as inputs into the macroeconomic model.
The macroeconomic model is used to examine consumption, investment, production, and trade decisions in the whole economy. It captures not only the interaction among industries, but also the implications for changes in producer prices, relative final prices, and income. It also factors in government fiscal balances, monetary flows, and interest and exchange rates.
More specifically, the macroeconomic model incorporates 133 industries at a provincial and territorial level. It also has an international component to account for exports and imports, covering approximately 100 commodities. The model projects the direct impacts on the economy’s final demand, output, employment, price formation, and sectoral income that result from various policy choices. These, in turn, permit an estimation of the effect of climate change policy and related impacts on the national economy.
The analytical approach permitted by E3MC addresses several key modelling challenges, namely additionality, free ridership, rebound effects, and policy-interaction effects.
The additionality issue refers to the question of what would have happened without the initiative in question. Problems of additionality arise when the stated emission reductions do not reflect the difference in emissions between equivalent scenarios with and without the initiative in question. This will be the case if stated emission reductions from an initiative have already been included in the reference case – emission reductions will effectively be double-counted in the absence of appropriate adjustments. In the E3MC model, additionality is controlled for by the fact that model structure is based on incremental or marginal decision making. The E3MC model assumes a specific energy efficiency or emission intensity profile at the sector and end-use point (e.g., space heating, lighting, auxiliary power, etc.). Under the E3MC modelling philosophy, if the initiative in question was to increase the efficiency of a furnace, only the efficiency of a new furnace would be changed. The efficiency of older furnaces would not change unless those furnaces are retired and replaced with higher efficiency ones. As such, any change in the model is incremental to what is reflected in the business-as-usual assumptions.
A related problem, free ridership, arises when stated reductions include the results of behaviour that would happen regardless of the policy. This can occur when subsidies are paid to all purchasers of an item (e.g., a high efficiency furnace), regardless of whether they purchased the item because of the subsidy. Those who would have purchased the product regardless are termed free-riders. In our model, the behaviour of free-riders has already been accounted for in the reference case. Their emissions are not counted, therefore, toward the impact of the policy. Instead, it is only the incremental take-up of the emissions-reducing technology that is counted.
The rebound effect describes the increased use of a more efficient product resulting from the implied decrease in the price of its use. For example, a more efficient car is cheaper to drive and so people may drive more. Emission reductions will generally be overestimated by between 5% and 20%, if estimates do not account for increased consumption due to the rebound effect. Within the model, there are mechanisms for fuel choice, process efficiency, device efficiency, short-term budget constraints, and cogeneration, which all react to changes in energy and emissions costs in different time frames.40 All these structures work to simulate the rebound effect – in the example above, the impact of extra kilometres that may be driven as a result of improved fuel efficiency are automatically netted out of the associated emission reduction estimates. Finally, emission reduction policies such as the ones defined in the Government’s plan interact with each other, with a resulting impact on their overall effectiveness. A policy package containing more than one measure or policy would ideally take into account this impact to understand the true contribution the policy package is making (in this case to emission reductions). This impact is described through what are known as policy interaction effects.
As E3MC focuses on the marginal decisions being made by consumers, industry, and energy producers, the issue of additionality, free-ridership, rebound effects, and policy-interaction effects are addressed in both the business-as-usual case and when analyzing policies and measures.
E3MC is a comprehensive and integrated model focusing on the interactions between sectors and policies. In the demand sectors, the fuel choice, process efficiency, device efficiency, and level of self-generation are all integrally combined in a consistent manner. The model has detailed equations to ensure that all the interactions between these structures are simulated with no loss of energy or efficiency. For example, the electric generation sector responds to the demand for electricity from the energy demand sectors, so any policy to reduce electricity demand in the consumer sectors will impact the electricity generation sector. The model accounts for the emissions in the electricity generation sector, as well as the consumer demand sectors. As the electricity sector reduces its emissions intensity, policies designed to reduce electricity demand in the consumer sectors will cause less of an emissions reduction. The natural gas and oil supply sectors similarly respond to the demands from the consumer sectors, including the demands for refined petroleum products for transportation. As well, the export by supply sectors of their products is also simulated.
Taken as a whole, the E3MC model provides a detailed representation of technologies that produce goods and services throughout the economy and can realistically simulate capital stock turnover and choices among technologies. It also includes a representation of equilibrium feedbacks, such that supply and demand for goods and services adjust to reflect policy. Given its comprehensiveness, E3MC covers all the GHG emissions sources, including those unrelated to energy use.
As a technology vintage model, E3MC tracks the evolution of capital stocks over time through retirements, retrofits, and new purchases, in which consumers and businesses make sequential acquisitions with limited foresight about the future. This is particularly important for understanding the implications of alternative time paths for emission reductions. The model calculates energy costs (and emissions) for each energy service in the economy, such as heated commercial floorspace or person-kilometre travelled. In each time period, capital stocks are retired according to an age-dependent function (although the retrofitting of un-retired stocks is possible, if warranted by changing economic conditions). Demand for new stocks grows or declines depending on the initial exogenous forecast of economic output (i.e. a forecast that is external to the model and not explained by it) and the subsequent interplay of energy supply-demand with the macroeconomic module. A model simulation iterates between energy supply-demand and the macroeconomic module until there is a convergence. The global convergence criterion is set at 0.1% between iterations. This convergence procedure is repeated for each year over the simulation period.41 E3MC simulates the competition of technologies at each energy service node in the economy based on a comparison of their cost and some technology-specific controls, such as a maximum market share limit in cases where a technology is constrained by physical, technical, or regulatory means from capturing all of a market. The technology choice simulation reflects the financial costs, as well as the consumer and business preferences revealed by real-world technology acquisition behaviour.
While E3MC is a very sophisticated analytical tool, no model can fully capture the complicated interactions associated with given policy measures between and within markets or between firms and consumers. Unlike computable general equilibrium models, however, the E3MC model does not fully equilibrate government budgets and the markets for employment and investment. That is, the modelling results reflect rigidities such as unemployment and government surpluses/deficits. Furthermore, the model, as used by Environment Canada, does not generate changes in nominal interest rates and exchange rates, as would occur under a monetary policy response to a major economic event.
23 The Government of Canada, in conjunction with the motor vehicle industry, sets Company Average Fuel Consumption (CAFC) targets annually. The CAFC targets represent the maximum weighted average fuel consumption numbers for new light-duty vehicles. There are two annual CAFC targets for new light-duty vehicles – one for passenger cars and another for trucks. Historically, Canada’s CAFC targets have been harmonized with the Corporate Average Fuel Economy (CAFE) standards in the U.S. The current CAFC goal is 8.6 litres per 100 km for passenger cars and 10.2 litres for light-duty trucks. For 2008, the sales-weighted performance for the fleet is 7.1 litres per 100 km for passenger cars and 9.5 litres per 100 km for light-duty trucks.
24 Population over the age of 18 was chosen as a key driver for projecting energy use and associated emissions from passenger vehicles and light-duty trucks. Statistical analysis shows that population over the age of 18 is the most highly correlated driver, and hence represents the “best-fit”.
25 Based on industry sources, some 40% of a model year is sold in the previous calendar year, with the remaining 60% being sold in the model year calendar year. As such, reductions are anticipated for 2010, resulting from the purchase of model year 2011 vehicles sold in 2010.
26 No reductions were realized in 2008 and 2009, as the regulations came into effect in 2010.
27 Please note this is only the “direct” contribution - it does not include the emissions offset as the result of electricity savings or green energy production.
28 The capacity factor relates actual electricity produced to the theoretical total capacity of a power installation and is expressed in percent. The higher the capacity factor, the higher the production of electricity per megawatt of capacity. As a simplified example, if the wind is expected to blow 30% of the time, a wind turbine would have a capacity factor of 30%.
29 The sub-bullets for new and existing buildings are multiplicative.
30 RETScreen Clean Energy Project Analysis Software is a unique decision support tool developed with the contribution of numerous experts from government, industry, and academia. The software is used to evaluate the energy production and savings, costs, emission reductions, financial viability, and risk for various types of Renewable-energy and Energy-efficient Technologies (RETs).
31 Modelling shows that there is significant interaction between provincial renewable promotion programs and the ecoENERGY for Renewable Power program. As such, the integrated modelling does not reflect significant incremental reductions from the ecoENERGY for Renewable Power program.
32 Saskatchewan’s Bill No. 126 “An Act respecting the Management and Reduction of Greenhouse Gases and Adaptation to Climate Change”, received Royal Assent in 2010, but has not yet been proclaimed, pending the approval of the accompanying regulations, which are under development. The actions itemized in this Act will be included in the business-as-usual case once the regulations and actions are fully funded and implemented.
33 B.C. Ministry of Finance (2008): http://www.leg.bc.ca/38th4th/3rd_read/gov37-3.htm and http://www.sbr.gov.bc.ca/documents_library/notices/BC_Carbon_Tax_Update.pdf.
34 Government of Alberta (2008): http://environment.alberta.ca/02486.html.
35 http://www2.publicationsduquebec.gouv.qc.ca/dynamicSearch/telecharge.
php?type=5&file=2006C46A.PDF
36 http://www.gov.ns.ca/just/regulations/regs/envgreenhouse.htm
37 In using actual GDP and energy price parameters while excluding government climate change policy measures, this methodology implies that government programs have no impact on these core economic parameters. While this is likely not the case, and potentially leads to a small downwards bias in the resulting estimates of actual emission reductions from government programs for 2008 and 2009, this is not expected to materially affect the resulting estimates of actual reductions.
38 Qualitative Choice Theory is based on the work of the Nobel Laureate, Daniel McFadden. Using Dr. McFadden’s theory, several other leading economists such as Kenneth Train have applied this theory to estimating demand in key energy using sectors of the economy such as transportation and the built environment.
39 Consideration was given to using alternative economic growth rates for the projection. However, as the preliminary economic growth rates for 2010 are in the public domain, it was decided to use the economic growth rates reported in Budget 2011.
40 A shift in energy prices will cause cogeneration to shift in the short to medium term, device efficiency to adjust over the short to mid-term, process efficiency to adjust in the mid-term, and fuel choice to react in the mid- to long-term. The actual adjustment times depend on the particular sector.
41 The energy technology simulation component of the E3MC model (i.e. Energy 2020) does not have an explicit test for convergence because of the algorithm used in the model. The macroeconomic component of the E3MC model (i.e. The Informetrica Model or TIM) is used to test for convergence between the two models because, logically, if one model continues to send the identical information to the other model, then necessarily the other model should find the exact same solution as before. As the initial testing showed that after about three iterations most of the variables in TIM were very close to convergence, the maximum iteration for convergence is set to five.