This Annex describes the approaches taken to calculate estimated reductions from the measures detailed in the report. 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 paragraphs 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 emissions reduction for the overall integrated package of measures and the modeled results were used to report on Canada’s emission reductions and total remaining emission levels for 2008-2012, thereby satisfying paragraph section 5(1)(c) of the Act.
The advice of the National Round Table on the Environment and the Economy is a key factor in the Governments’ 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 methodology used to generate emissions 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.
The March 2008 Regulatory Framework set an initial required reduction of 18% from 2006 emission intensity levels in 2010 for existing facilities. Every year thereafter, a 2% continuous improvement in emission intensity will be required. By 2015, therefore, an emission-intensity reduction of 26% from 2006 levels will be required, with a further reduction to 33% by 2020. New facilities, which are defined as those whose first year of operation is 2004 or later, will be granted a three-year commissioning period before they will face an emission-intensity reduction target. After the third year, new facilities will be required to improve their emission intensity each year by 2%. A cleaner fuel standard will be applied, thereby setting the target as if they were using the designated fuel. New coal-fired electricity generation and oil sands facilities coming into operations in 2012 or later, will be required to achieve an emission intensity target which reflects the use of carbon capture and storage.
Environment Canada’s E3MC model was used to estimate the emissions intensity reductions. Actual and estimated emissions for 2006 are available in the E3MC reference case for most of the covered industrial sectors.9 GHG emissions are disaggregated in three broad categories: combustion-related, process and non-energy. For each of those categories, the share of what is fixed process emissions has been estimated. Those shares have been applied to the reference case GHG emissions in 2006 to produce a net figure for covered GHG emissions by covered sectors. The modelling of the targeted reductions under the Regulatory Framework for Industrial Greenhouse Gas Emissions was approached as follows:
Reductions under the Regulatory Framework presented in this report represent the aggregation of reductions credited under all compliance options available to industry, in accordance with the targets contained in the Regulatory Framework released in 2008. Actual in-year reductions will vary from these amounts, depending on the specific compliance options chosen by individual firms. E3MC modeling indicates that choice of compliance option is in turn influenced by differences in marginal costs that they present to regulated industries. The expected emissions reductions totals also include reductions associated with complementary regulatory measures being taken by the Government of Alberta.
As noted in the main body of this report the Government has indicated that it is refining its regulatory approach to reflect the new realities of the global economic downturn and the opportunities represented by a new Administration in the United States. The Government has committed to releasing detailed plans by year’s end. Given the KPIA deadlines for reporting, the 2009 Plan cannot reflect the new regulatory approach. Therefore, to comply with the Act this report includes the expected emissions reductions for the industrial regulations as described in Turning the Corner though the final regulatory regime will differ from Turning the Corner.
The Regulatory Framework mandates the application of best practices with respect to the control of unintentional fugitive emissions and HFCs.
The reductions under the Best Practices for the Capture of Unintentional Fugitive Emissions and HFCs were estimated using the reference and alternative base case. This captures the manner in which the economic growth and world oil prices impact the industrial sector.
It should be recognized that though the estimated reduction profile (by year) has changed in response to regulatory and market conditions, the long-term greenhouse gas impacts 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 greenhouse gas emission reductions are based on emission factors provided from Natural Resource Canada’s GHGenius model. The factors used were:
1.25 Mt GHG reduction / billion litres of ethanol
2.2 Mt GHG reduction / billion litres of biodiesel
Emission factors contained within the model were applied to the estimated renewable fuel volumes to determine the resulting emission reductions.
Data sources used include NRCan’s Canada's Energy Outlook: The Reference Case 2006, Statistics Canada Catalogue no. 45-004-XIE The Supply and Disposition of Refined Petroleum Products in Canada (April 2007) for 2006 volumesand emission factors based on NRCan’s GHGenius.
The reduction estimate for 2010 reflects a September start date for the federal regulation. Ethanol and biodiesel volumes in the market prior to 2010 are not accounted for. For 2011 and 2012, respectively, the low estimate shows a 0.2 megatonne per year greater benefit, than the estimate provided for the original KPIA report. In the new estimate, proposed renewable mandates in Alberta and Manitoba are no longer counted against the total renewable volume of the federal regulation, as they have not yet been finalized as of this writing.
The emission factors contained within the model were applied to the estimated renewable fuel volumes to determine the resulting emission reductions. The emissions factors used for the anticipated GHG emissions reductions of the regulations reflect factors such as lower emissions from dominant cultivation practices for biofuels feedstocks such as corn, wheat, canola and soy. The Government has also indicated that its calculations use more recent data for biofuels processing than many other studies, thus reflecting recent improvements in efficiency.
New fuel demand growth and emission factors are currently being developed that will be used in the renewable fuels regulations regulatory impact assessment and subsequent updates. This will provide greater certainty on actual future performance in GHG emission reductions through increased use of biofuels.
The program is designed to encourage 14.3 terawatt-hours of electricity production per year by 2012-2013 (translates to about 4000 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 per kilowatt hour. 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:
The uncertainties surrounding these factors and how they were mitigated at the program development stage are described below:
This program has several elements whose impacts were calculated individually. Technical and past program files provided the information for average savings growth and participation for each element.
The stated reduction expectations are based on historical levels of code-compliance. The impact of free-ridership is expected to be minimal in terms of the major program components (i.e., the building code updates).
Preliminary expected reductions are provided as a range to reflect the inherent risks involved in program implementation. Expected reductions represent conservative estimates of program impacts.
Homes Component: The estimate of emissions saved was based on the expected energy savings per house multiplied by the number of houses expected to participate in the program. Energy savings were based on NRCan’s program experience in this area, while the number of houses was estimated using a combination of past program participation and current funding levels.
Small and Medium Organizations – Buildings and Industry: The impact estimates represent the expected average energy savings per project multiplied by the expected number of projects, based on experience with past program participation and subject to current funding levels.
Uncertainty concerning the emissions reductions estimates associated with the ecoEnergy Retrofit initiative is consistent with the following specified variables. 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 1 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 NRCan). The significance of free-ridership will be assessed as part of an NRCan study in 2009.
Preliminary expected 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.
The methodology employed to calculate the expected ranges presented above varies between the three components of the ecoENERGY Retrofit Initiative. For ecoENERGY Retrofit – Homes, the range is based on different calculated GHG reductions per house, specifically Low: 3.0 t/house, High: 3.5 t/house and Expected: 3.2 t/house (methodology accounts for budget increase). For ecoENERGY Retrofit – SMO and the Existing Buildings Initiative, 10% is subtracted from the calculated program reductions to account for implementation risks.
Technical studies and past program files provided average savings and participation for both elements of the program. Energy savings (by fuel) were converted to greenhouse gas reductions using standardized conversion factors.
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.
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.
Preliminary expected reductions are provided as a range to reflect two possible scenarios regarding the types of industrial firms which participate in both the CIPEC program and the site assessments. High-end expected reductions are based on the assumption that large final emitters (LFEs) participate in both sub-initiatives, while the low-end expected reductions assume LFEs are not involved in either. The expected reductions in the Preliminary Expected Reductions table represent conservative estimates of program impacts.
To calculate the anticipated greenhouse gas emissions reduction from the ecoAUTO rebate program, Transport Canada used the North American Feebate Analysis Model. Like Environment Canada’s Energy-Economy-Environment Model for Canada, the model used by Transport Canada approximates consumer's and manufacturer's 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 greenhouse gas 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 North American Feebate Analysis 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 will compare the characteristics of a vehicle, its use, and actual sales number, with or without the policy. This in essence is how the analysis takes into account the free-rider issue. The estimate of annual greenhouse gas emissions savings 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 savings are incremental, annual emission reductions attributed to the ecoAUTO rebate program.
The model used for this analysis 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.
Manufacturers’ response is estimated by estimating how 2003 model-year vehicles evolve through time, given assumptions about how often vehicles are modified, and what are the costs associated with increasing a vehicle’s fuel efficiency. Given the fact that the ecoAUTO program was announced in Budget 2007 and was only in effect for less than two years, the assumption has been that the program has not prompted manufacturers to modify their vehicles in any significant way given the short lead time and the 2 year length of the program. Although there is anecdotal evidence that some manufacturers did make some marginal modifications to their vehicles during the course of the program to qualify for the rebate, the assumption, in all cases considered is that the program had no impact on manufacturers’ decisions about the vehicles they made available to consumers over the last two model years.
The analysis of the impact of the ecoAUTO rebate program is sensitive to assumptions regarding vehicle operating cost and market (consumers and manufacturers) behaviour. New analysis was conducted this year to estimate the potential impact of variations to those assumptions on the greenhouse gas reduction estimates. Following is a description of the assumptions made by Transport Canada for the “Expected” and “High” cases. The expected and high 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 similar 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 without allowing manufacturers to implement incremental technology improvements defines the expected impact case as it is expected that the policy will have less incremental effect on consumers in this situation.
In addition, for both cases, the analysis now assumes that the rebound effect of better fuel efficiency is 15 percent, rather than the 23 percent 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 it fuel economy ruling for model-year 2011, the National Highway Traffic Safety Administration in the United States has also chosen to use a 15 percent rebound effect as its expected value.
| Expected Case | High Case | |
|---|---|---|
| Fuel Prices (¢ per litre) | 110 | 80 |
| Rebound effect | -0.15 | -0.15 |
To calculate anticipated greenhouse gas emissions reductions from the Green Levy, Transport Canada used the North American Feebate Analysis Model. Like Environment Canada’s Energy-Economy-Environment Model for Canada, the model used by Transport Canada approximates consumer's and manufacturer's 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 greenhouse gas 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 North American Feebate Analysis 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 will compare the characteristics of a vehicle, its use, and actual sales number, with or without the policy. This in essence is how the analysis takes into account the free-rider issue. The estimate of annual greenhouse gas emissions savings due to the Green Levy 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 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 (consumers and manufacturers) behaviour. New analysis was conducted this year to estimate the potential impact of variations to those assumptions on the greenhouse gas reduction estimates. Following is a description of the assumptions made by Transport Canada for the Expected and High cases. The expected and high 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 years schedule), and what are the costs associated with increasing a vehicle’s fuel efficiency. 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 similar 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.
The combination of the high price while allowing manufacturers to implement incremental technology improvements defines the high case as it is expected that the policy will have more 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 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 percent, rather than the 23 percent 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 it fuel economy ruling for model-year 2011, the National Highway Traffic Safety Administration in the United States has also chosen to use a 15 percent rebound effect as its expected value.
| Expected Case | High Case | |
|---|---|---|
| Fuel Prices (¢ per litre) | 80 | 110 |
| Rebound effect | -0.23 | -0.15 |
The program interventions include a number of elements whose impacts 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.
Government publications, accepted models, technical studies and past program files provided information regarding these variables and the basis for the estimates of participation, rates of adoption and retention of fuel-efficient practices, and the average impact of these practices.
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. Preliminary expected reductions represent conservative estimates of program impacts.
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) traveled by passenger vehicles in urban areas. It can be a cost-effective alternative to increasing road infrastructure capacity, and can help maximize the benefits of existing infrastructure. 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. Canada's Economic Action Plan, including the $4 billion Intrastructure Stimulus announced in Budget 2009, will support accelerated investments in public transit infrastructure that should contribute to greenhouse reductions over the longer term. The ecoMOBLITY program will focus its activities on non-transit based TDM strategies that can be delivered in parallel to accelerated infrastructure projects rather than seek to introduce complexity or delays in these major projects through the demonstration and impacts reporting of incremental transit-based TDM strategies.
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 NRCan, 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. The current program approach to focus on a narrower range of non-transit based TDM strategies will necessarily lower GHG emission reductions that will be attributable to the program in 2012.
Sensitivity analysis was conducted on the assumptions made about VKT reductions. An expected scenario assumes a 0.2% reduction of VKT in 2012 yielding an estimated 0.112 Mt reduction. A higher scenario assumes a VKT reduction of 0.4% yielding an estimated 0.223 Mt in 2012. Because the selection of projects under the program was initially delayed to allow for more national consultations in 2007, it is also unlikely that the project implementation will be sufficiently advanced to yield GHG reduction in 2009.
Projected GHG reductions are small as the focus of the program is on reducing smog-forming emissions, not greenhouse gas emissions. Expected GHG emissions 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.
GHG reductions are the difference between emissions from the older, retired vehicle and its replacement (assumed to be the reward chosen by program participant). Estimates published here are based on anticipated program uptake and incentive selection. However actual reductions will be estimated individually for each participant through a database developed specifically to manage the program and track results. Published data for emission factors, annual vehicle usage, and transit data are the basis for the calculations.
Emissions estimates will vary with number of program participants, reward selection, and personal transportation behaviour after the older vehicle has been retired.
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 20% of sales of new vehicles with less than 6 L/100 kms fuel efficiency would be influenced by 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 attributable to the ETVP.
In both cases, vehicles were assumed to save 2 L/100 kms and travel 23,500 kms per year.
Preliminary estimates were based on assumptions made about new vehicle sales, technology penetration and vehicle distance travelled forecasts. The economic downturn has impacted significantly on vehicle sales. In addition, fuel prices are also lower that expected making advanced technologies less attractive due to longer payback period. These factors will contribute to a lower market penetration of advanced technologies and reduce the overall impact of the program within the program timelines. Initial reductions estimates are expected to be achieved 2 to 3 years after the end of the program.
The low scenario assumes lower market penetration of advanced technologies, lower fuel saving applied to lower sales figures.
This program contains a number of elements whose impacts 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.
Government publications, accepted models, technical studies and past program files provided variables and the basis for the estimates of participation, rates of adoption of fuel-efficient practices, and the average impact of these practices.
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 and there is some overlap in terms of the target audience. As such, this provides a small degree of uncertainty in this analysis.
Preliminary expected reductions are provided as a range to reflect the inherent risks involved in program implementation. Expected reductions represent conservative estimates of program impacts.
The GHG preliminary estimates are based on the data supplied by the project proponents in historical or previous program proposals, Contribution Agreements, progress and final reports.
The historical data was adapted to form the preliminary impact estimates for the current ecoFREIGHT programs by pro-rating the direct GHG impacts on the basis of the magnitude of the funding allocated to the new programs.
The ecoFreight direct impact was calculated from the forecasted number of projects and their GHG impacts. The ecoFreight indirect (i.e: transformative) impact was calculated by applying a factor of approximately 2 (2008: 1.75 to 2012: 2.4) to the direct impact of a particular year. The factors were obtained from the indirect calculation assumptions based on the simple payback period of the technologies. If the direct impact was estimated at 100 Kt in 2012, the indirect impact was estimated at 240 kt for a total impact of 340 kt in 2012. Where appropriate, reduction associated with the Memoranda of Understanding and with speed limiters activities were added to the estimates.
The scenarios were developed by updating the preliminary estimates for direct impacts with information drawn from the actual projects now receiving funding under the program, rather than information from historical projects. The current technology projects will be completed progressively by 2010/11 under the program. A 0.4 Mt annual reduction was included in the reductions from the activities of the NHTSI, reflecting estimated impacts of the truck speed limiter regulations in Ontario and Quebec. (Note that there is no consensus among jurisdictions at this time to proceed with a national mandate on such regulations.)
The uptake of technology may differ due to increases in the costs of equipment and/or the ability/willingness of promoters to invest in such project during the economic downturn.
In addition, fuel prices are also lower that expected making energy-saving technologies less attractive due to longer payback period. These factors may reduce the overall market penetration of energy-reducing technologies and reduce the overall impact of the program. The key uncertainty addressed in the scenarios is linked with the transformative or indirect impacts.
The expected scenario is based only on the expected direct reductions of projects selected under ecoFREIGHT program funding Rounds 1 and 2, which are expected to contribute an estimated 57.3 kilotonnes of GHG emission reductions in 2012 to the impact of ecoFREIGHT. It is also based on the introduction of speed limiters in 2 provinces. No replication, or indirect effect, is assumed by 2012. In the high scenario, indirect reductions are assumed to occur through replication of program projects in the freight industry. The ecoFREIGHT indirect (ie: transformative) impact was calculated by applying a factor of approximately 2 (2009: 1.75 to 2012: 2.4) to the revised direct impact of a particular year as described in the methodology section.
Both scenarios also include targeted reductions (from 0.5 Mt in 2009 to 0.9 Mt in 2012) under the voluntary agreements.
The information used to calculate GHG emission reduction for the Marine Shore Power Program 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 for which GHG estimates were calculated.
The approach averaged out the GHG savings of 11 of the 15 projects analyzed (excluding 4 projects considered to be too expensive to implement). The average net annual GHG savings used was 1.3 kt per project.
It was assumed that the funding received would allow for 4 projects to be implemented under the MSDP, each achieving an average net annual GHG reduction of 1.3 kt for a total of 5.3 kt in 2010. (Note that in reality it could be a mix of larger and smaller projects together.)
For the “transformative” impact of the program, we assumed that 2 more projects would be implemented after 2010 (1 in 2010 and 1 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.
Preliminary estimates assumed the implementation of a total of 6 projects of varying sizes. The number and/or size of projects may differ due to increases in the costs of equipment and/or the ability/willingness of promoters to invest in such project due to the economic downturn.
The low scenario has been estimated to reflect a scenario where only one additional project is selected and implemented under the program (for a total of 2 projects), with an expected annual reduction of 4.5 kt (ie 1.3 kt added to the current project estimate of 3.2 kt).
The high scenario has been estimated on the assumption that 2 additional projects are funded under the Program (for a total of three) and two others are implemented as an indirect result of the demonstrations.
The estimated emissions reductions for the 2009 Plan use the same methodology as the one used to calculate the estimated emissions reductions for the 2008 Plan.
The calculation used information on public transit trips (ridership) and greenhouse gas emissions factors from the Climate Change Transportation Table. A constant 2.65% annual growth (avg. of the last five years) in ridership was used to project baseline levels of ridership over the 2008-2012 period. Based on a calculation that the tax credit would result in an effective fare reduction of 9.0%, 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.
There are many factors at play which makes it virtually impossible to assign greenhouse gas emissions reductions to this measure with any certitude. Vehicle operating cost increases (fuel price, parking costs, etc.), and transit supply or service improvements are just two factors that can influence ridership. Moreover, improvements to vehicle fuel economy and the increased penetration of lower-emitting fuels, would work to lower the overall emissions reduction potential. Therefore, the estimated reductions are likely representative of the upper bound of potential reductions for this measure.
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 modeled runs incorporated individual parameters for each of the initiatives reported here, as provided by lead departments, and aggregated 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 National Round Table’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 greenhouse gas 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 these measures included in the baseline are complimentary to federal policies presented in this report. As such, to avoid double-counting, the impacts from these measures are not included in the total emissions reductions. Some key assumptions in the baseline that effect federal policies in the 2009 Kyoto Protocol Implementation Act Plan include:
To capture the effects of the Government’s climate change programs, the assumptions used for the individual measures were built into the closely replicated E3MC model. In the model, consumers of energy respond to the program parameters by making decisions regarding investments using Qualitative Choice Theory.10 These decisions are based on the price of fuel combined with the perceived trade-off between energy 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 efficiency and investment costs.
The 2008-2012 emission levels for Canada were generated by combining the individual emissions reductions measures in E3MC. This ensured that measures were assessed in an integrated manner, thereby accounting for positive and negative interactions between measures and regulations.
An alternative scenario has been constructed as a component of Environment Canada’s sensitivity analysis. In this alternative scenario, the economy is projected to grow at 1.7 percent per year over the 2008 to 2012 period (as opposed to 2.2 percent under the reference case). Over the same period, the world oil prices are assumed to average about $98 per barrel (in US$2008) instead of $75 per barrel under the reference case.
Under the alternative scenario, with the measures included in this Plan, the modeling also makes use of the "low" expected reductions where available, as indicated in this report.
Environment Canada's E3MC has two components: Energy 2020, which incorporates Canada's energy supply and demand structure, and TIM, Informetrica's macroeconomic model of the Canadian economy.
Energy 2020 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, greenhouse gas emissions, investment costs and possible cost savings from policies, are used to identify the direct effects stemming from greenhouse gas reduction measures. The resulting savings and investments from Energy 2020 are then used as inputs into TIM.
TIM 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, interest and exchange rates.
More specifically, TIM 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 modeling 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 emissions 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 emissions reductions from an initiative have already been included in the reference case – emissions 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 modeling 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. Emissions 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, we have 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.11 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 emissions reduction estimates. Finally, emissions-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.
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 electric generation sector. The model accounts for the emission in the electric generation sector as well as the consumer demand sectors. As the electric sector reduces its emissions intensity, policies designed to reduce electric 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 greenhouse gas 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 emissions reductions. The model calculates energy costs (and emissions) for each energy service in the economy, such as heated commercial floor space or person kilometre traveled. 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 per cent between iterations. This convergence procedure is repeated for each year over the simulation period.12 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 modeling 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.
9 Actual emissions are informed by several Statistics Canada surveys and reports, including the Report on Energy Supply and Demand and the Industrial Consumption of Energy Survey and Environment Canada’s National Inventory Report on Greenhouse Gas Emissions.
10 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.
11 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.
12 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 for 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.