Appendix A. Inventory and Modelling Methodology

• Stationary Energy: Propane, Wood and Fuel Oil – Residential Buildings. Propane, and wood GHG emissions were estimated using linear regression methods. The data used in the estimates included historical propane and wood energy data published in the 2007, 2010 and 2012 CEEIs, and heating degree days (HDD) published by Environment Canada. This approach was also applied to the estimate of heating oil for all local governments, except the City of Victoria and District of Saanich. For the District of Saanich and the City of Victoria, heating oil GHG emissions were estimated based on the number of known tanks, average heated floor areas and estimated average fuel volumes.
• Stationary Energy: Electricity and Natural Gas Consumption - All Buildings. Prior to releasing the electricity and natural gas consumption data, the Province completes a series of quality assurance and control checks which has resulted in the re-allocation of energy between local governments. This data is then published on the Province’s website. When the published 2007-2018 natural gas data was trended, several unexplained data anomalies and trends were identified for several local governments in the CRD. As these data anomalies and trends could not readily be explained, the raw natural gas data sets were acquired from FortisBC, reviewed and compared to the published data. In the 2007 and 2010 reporting years, the published data was under reporting natural gas volumes by upwards of 17% at the CRD level and had several large allocations between the City of Victoria and other local governments in 2012. Based on the issues with the published data, and on the basis the annual raw natural gas consumption trends align with the reported 2018 consumption data and align with historical raw data provided to the City of Victoria and the District of Saanich for their energy and GHG emissions inventories, the raw FortisBC dataset was used to estimate GHG emissions. A similar issue was noted for the Juan de Fuca electoral area and electricity data for the 2007, 2010 and 2012 reporting years (i.e., the under reporting of energy consumption) in the published data. As such, the raw electricity data from BC Hydro was used to estimate GHG emissions.
• Stationary Energy: Fugitives. FortisBC provided total fugitive emissions for the 2018 reporting year at the regional level. To estimate local government fugitive emissions, the value was prorated based on the number of reported natural gas connections (provided by Fortis BC). Since no historical numbers were provided, the 2018 value was applied to the 2007 base year as well. The estimate of fugitive emissions is an understatement of GHG emissions as FortisBC did not estimate the upstream GHG emissions as recommended by the GPC Protocol.
• Transportation: On-Road. The Province of BC provided Insurance Corporation of BC (ICBC) vehicle registration data from April 1, 2018 – March 31, 2019. When compared to local government population trends, there appears to be a high degree of uncertainty as to the accuracy of the 2018 vehicle registration data in terms of total registered vehicles. Without having reliable historical (e.g. 2011-2017) and current (2019) data to compare this dataset against, the reasonableness of the data was too uncertain to be applied in the estimation of GHG emissions for the 2018 reporting year. Therefore, to estimate on-road energy and GHG emissions for the 2018 reporting year, 2010 vehicle populations were grown in proportion to the reported changes in local government populations. Each of the local government vehicle profiles were then adjusted to match the proportion of vehicle classes reported in the 2018 ICBC data.
• Transportation: On-Road. In cases where vehicle registration counts were 10 or less, the Province assigned a value of “<10” rather than report the actual number. In these cases, the inventory assumes there was 10 vehicles of that particular classification. This is likely to result in an over-estimation of GHG emissions, but it will be immaterial to the overall GHG inventory.
• Transportation: On-Road. Vehicle fuel consumption rates and Vehicle Kilometers Travelled (VKT) were taken from the activity data summary for British Columbia on-road transportation from the 2018 National Inventory Report (1990-2018) as prepared by Environment Canada. Based on the clear diesel and clear gasoline consumption values reported by the Province of BC for the Victoria region, the VKT and fuel efficiency values are reasonable and result in a similar estimate of fuel consumption for the Region.
• Transportation: Aviation. 2018 aviation GHG emissions were estimated using 2015 aircraft flight profiles (the last available data), and the total number of aircraft movements reported in 2018. The emissions were prorated to each local government on a per capita basis.
• Transportation: Waterborne Recreational Watercraft. GHG emissions from recreational watercraft and US/Canada ferries were estimated based on a publicly available year 2000 study for the Victoria, Vancouver, and Washington harbors. These GHG emissions were prorated to each local government on a per capita basis.
• Transportation: Cruise Ships. The Greater Victoria Harbour Authority reported on cruise ship emissions for the 2018 reporting year but did not provide an estimate for 2007. As a result, no cruise ship emissions are included in the 2007 base year inventory.
• Waste: Solid Waste. To quantify GHG emissions from the Hartland Landfill, the CRD utilized the waste-in-place (WIP) method which is accepted under the GPC Protocol. The WIP assigns landfill emissions based on total waste deposited during that year. It counts GHGs emitted that year, regardless of when the waste was disposed. Except for the City of Victoria, who claims 31% of the CRD’s landfill GHG emission, the remaining landfill GHG emissions were allocated to each local government on a per capita basis. Using this allocation method, the CRD members may over, or underestimate associated solid waste GHG emissions as the current year landfill GHG emissions are based upon cumulative waste over time, and each member may have contributed more waste in past years than the current year (and vice versa).
• AFOLU: Aggregate Sources And Non-CO2 Emission Sources On Land. These emissions are based on the 2019 NIR as prepared by ECCC and the total area of farmland BC in 2016 as reported by Statistics Canada. These GHG emissions were assigned to each local government on a per hectare (ha) of cropland basis.
• AFOLU: Land-Use. The land cover change analysis requires a consistent land-use category attribution and spatial resolution for the 2007 base and 2018 reporting years. For the land use change analysis, land cover data was available for the 2007, 2011 and 2017 years for only part of the CRD. Unfortunately, no more recent or higher quality data source was available to represent the land cover consistently for all three years. Furthermore, since annual data was not available, the change between land cover data years (2007-2011, 2011-2017) was averaged and may not represent actual changes in each year.
• AFOLU: Land-Use. There was limited land-use datasets for the Juan de Fuca, Salt Spring Island and Gulf Island Electoral Areas and this data was only available for 2007 and 2011. On this basis, land-use GHG emissions estimates for these electoral areas has been withheld.

Business As Usual Projection

Data describing the community’s emissions profile are provided for 2007 and 2018 only. Emissions for the years in between are assumed to have followed a linear trajectory. From 2019 onwards, all of the data is an estimate as a BAU projection.

The assumption is that energy consumption and emissions will increase proportionally with increases to population, although the impact of policies from higher levels of government are also incorporated, and other assumptions. Only policies that have already been adopted and that will have quantifiable impacts are incorporated.

Assumptions related to projections are as follows:

• The Province’s incremental steps to net zero energy ready buildings by 2032, via the BC Energy Step Code
• Federal and provincial tailpipe emissions standards: new light duty vehicle emissions decline from 200 g CO2e/km in 2015 to 119 g CO2e/km in 2025 (federal policy), and then decline again to 105 g CO2e/km in 2030 (provincial strengthening of this policy). This is for new vehicles, and is included in the projections taking account of vehicle turnover rates
• Renewable & low carbon transportation fuel standards: 20% by 2030, as in CleanBC Plan
• An average annual decrease of 0.6-1.1% in natural gas consumption per residential connection is included, to align with FortisBC planning
• The Province’s CleanBC Roadmap commitment to Zero Emission Vehicle Mandate of 100% of new vehicles by 2035. From the impacts of this, in our BAU scenario we assume that the proportion of electric vehicles on View Royal’s roads will be:
  • 5% in 2025
  • 15% in 2030
  • 68% in 2040
  • 96% in 2050 (even with 100% of all new vehicles sold having zero emissions, there is still a lag with vehicle turnover rates)
• How the impacts of a changing climate will affect building energy consumption:
  • Climate change data for the region was obtained from ClimateData.ca. CEA obtained this from the “downloads” section of the website, selected the BCCAQv2 (annual) dataset, Heating Degree Days (HDD’s) or Cooling Degree Days (CDD’s) variables, and the location on the map to be analysed
  • Projected global emissions to 2030 currently places the world in the range for the IPCC’s Fifth Assessment Report’s Representative Concentration Pathway (RCP) 6.0 scenario. As RCP 6.0 scenario not available on ClimateData.ca, RCP 4.5 (median values) were used as a proxy even though this is a more conservative scenario
  • Decreases in residential and commercial natural gas consumption are assumed to be proportional to decreases in HDD’s and the proportions of natural gas consumed for space heating for each sector, with this data obtained from the Navigant 2017 Conservation Potential Review for FortisBC Gas
  • Based on ClimateData.ca RCP 4.5 median values, the 30-year average of HDD’s around 2018 are 2,333, and in 2050 they will be 1,864
  • Decreases in residential and commercial electricity consumption are assumed to be proportional to decreases in HDD’s and the proportions of electricity consumed for space heating for each sector. However, for residential this is partially offset by, and for commercial more than offset by the proportions of electricity consumed for space cooling by each sector and how this will increase proportional to projected increases to CDD’s. These proportions were obtained from the Navigant 2016 Conservation Potential Review for BC Hydro
  • Based on ClimateData.ca RCP 4.5 median values, the 30-year average of CDD’s around 2018 are 30, and in 2050 they will be 101

Modelling the Big Moves

The QuickStart model estimates the impacts of the Big Moves compared to the BAU trajectory. The impacts of the Big Moves varies greatly between communities, and depend on the assumptions made. The assumptions made for each Big Move are based on research that CEA has conducted and are tailored for individual communities.

GHG emission reductions by Big Move are described in the main body of this report.

The QuickStart model inputs for View Royal are detailed below. Actions are assumed to be implemented in 2022.