Survey of Commercial and Institutional Energy Use (SCIEU)
Detailed information for 2019
The purpose of this survey is to collect statistical information on the energy demand and consumption patterns of commercial and institutional buildings across Canada.
Data release - August 5, 2022
- Questionnaire(s) and reporting guide(s)
- Data sources and methodology
- Data accuracy
The purpose of this survey, sponsored by Natural Resources Canada (NRCan), is to collect statistical information on the energy demand and consumption patterns of commercial and institutional buildings across Canada. The survey collects data on the types and quantities of energy being used (such as electricity and natural gas) and building characteristics to better understand energy consumption.
Clear, credible information is the foundation on which to build the case for investments in energy efficiency. Understanding how much energy a building uses and for what reason is a necessary first step to designing effective measures to reduce energy use and track the effectiveness of those measures over time.
Aggregated data from this survey will be used by governments, utilities, industry associations, building managers and business owners to
develop programs and policies to improve the energy efficiency of commercial and institutional buildings and support Canada's climate change objectives
support programs such as ENERGY STAR® Portfolio Manager® to encourage energy efficiency practices and enable owners to track and compare their building's energy use against that of similar buildings.
Reference period: Calendar year
- Energy consumption and disposition
Data sources and methodology
The survey is split into two components: the building component and the institution component.
The target population of the building component is commercial and institutional buildings (excluding hospitals and postsecondary institutions) where the minimum floor area of the building is at least 50 square metres, where at least 50% of the floor space is used for commercial or institutional activities, and where the floor space was either partially or fully in use or available for use during the reference year. Military bases, embassies and standalone portable structures are excluded.
The targeted buildings can be classified by activity to the following 23 types:
- bank branch
- police station
- fire station
- assisted daily care facility or residential care facility
- hotel, motel, hostel or lodge
- preschool or daycare
- primary or secondary school
- food or beverage store
- retail store (non-food)
- office space (medical)
- office space (excluding medical)
- recreation centre
- ice rink
- performing arts
- place of worship
- museum or gallery
- library or archives
- vehicle dealership, repair or storage
- other activity or function.
The target population of the institution component contains all hospital and postsecondary institution campuses that include at least one building. The targeted campuses can be classified by activity to the following two types:
- hospital; North American Industry Classification System (NAICS) code 622
- postsecondary institution; NAICS 6112 (community college and C.E.G.E.P.) and 6113 (university).
In partnership with NRCan, four questionnaires were developed to collect information on energy use by commercial and institutional buildings across Canada.
Questionnaire design specialists were consulted in the design and testing of all four questionnaires. The questionnaires were tested with commercial and institutional buildings across Canada, sampled from the Statistical Building Register (SBgR) and the Business Register (BR). Comments by the contact persons on the design and content were incorporated into the final versions.
For the building component, the first collection instrument was a pre-contact survey conducted with sampled buildings to confirm that they were in scope for the survey, to identify contact persons for the collection of building information and to confirm the activity taking place in the building.
Following the pre-contact survey, the second collection instrument was a building questionnaire used to collect detailed information on the energy used by buildings.
For the institution component, the third collection instrument was a pre-contact survey with sampled hospitals and postsecondary institutions used to confirm that they were in scope for the survey; to identify contact persons for the collection of institution information; and to confirm the number, type and size of the campuses occupied by the institution. A campus is defined as having at least one building being used as either a postsecondary institution or a hospital.
Following the pre-contact survey, the fourth collection instrument was an institution questionnaire used to collect detailed information on the energy used by the institutions' campuses.
This is a sample survey with a cross-sectional design.
Two distinct samples are selected for the Survey of Commercial and Institutional Energy Use (SCIEU), one for the building component and the other for the institution component.
The SBgR was used as the sampling frame for the building component. The SBgR is a more exhaustive version of the current Address Register. It is a list of buildings and their units, compiled via various administrative data sources, which covers both residential and non-residential buildings in Canada.
Sample sizes were estimated using a previous version of the SBgR augmented by the BR. The sample sizes were calculated to obtain good quality estimates at the national level for the 23 building types or good quality estimates for provincial- and territorial-level estimates.
The fixed sample size was then allocated to the different strata created by crossing the province and the predicted building type as listed on the SBgR version delta 2.3.
This survey frame was built with the BR. The frame contained all establishments with a subsector of 622 (hospitals) or an industry group of 6112 (community colleges and C.E.G.E.P.s) or 6113 (universities). Business schools and computer and management training, technical and trade schools, and adult and continuing education schools were excluded from this survey. To be in scope for the institution component, the postsecondary institution or hospital must completely occupy at least one building. All postsecondary institutions or hospitals that only partially occupy a building are not in scope for this survey.
The sample was a census of all hospitals and postsecondary institutions in all provinces and territories. The sample design supports estimates by region for floor area, energy consumption and energy use intensity.
Data collection for this reference period: January 11, 2021 to April 12, 2021 (Building component); July 21, 2021 to November 23, 2021 (Institution component)
Responding to this survey is mandatory.
Data are collected directly from survey respondents.
The respondent is mailed or emailed a secure access code to respond to the electronic questionnaire.
View the Questionnaire(s) and reporting guide(s).
Error detection is an integral part of both collection and data processing activities. Automated edits are applied to data records during collection to identify reporting and capture errors. During data processing, other edits are used to automatically detect errors or inconsistencies that remain in the data following collection. These edits include value edits (e.g., value > 0, value > -500, value = 0), linear equality edits (e.g., value 1 + value 2 = total value), linear inequality edits (e.g., value 1 = value 2) and equivalency edits (e.g., value 1 = value 2). When errors are found, they can be corrected using imputation. Extreme values are also flagged as outliers using automated methods based on the distribution of the collected information. Following their detection, these values are reviewed to assess their reliability. Manual review of other units may lead to additional outliers identified. These outliers are excluded from use in the calculation of ratios and trends used for imputation and during donor imputation. In general, every effort is made to minimize the non-sampling errors of omission, duplication, misclassification, reporting and processing.
When respondents do not completely answer the questionnaire, or when reported data are considered incorrect during the error detection steps, imputation is used to fill in the missing information and modify the incorrect information. Many methods of imputation may be used to complete a questionnaire, including manual changes made by an analyst. The automated statistical techniques used to impute the missing data include deterministic imputation, replacement using auxiliary information available from other sources, replacement based on known data relationships for the sample unit and replacement using data from a similar unit in the sample (known as donor imputation). Usually, key variables are imputed first and are used as anchors in subsequent steps to impute other related variables.
Donor imputation is performed in classes, and those classes are created to obtain as homogeneous a pool of donors as possible for the variable being imputed.
Imputation generates a complete and coherent microdata file that covers all survey variables.
When a probability sample is used, as was the case for this survey, the principle behind estimation is that each building selected in the sample represents, in addition to itself, several other buildings not in the sample. For example, in a simple random sample of 2% of the population, each building in the sample represents 50 buildings in the population (itself and 49 others). The number of buildings represented by a given respondent is usually known as the weight or weighting factor.
In addition to the estimation weights, bootstrap weights have been created for the purpose of design-based variance estimation.
Estimates based on the survey data are also adjusted (by weighting) so that they are representative of the target population with regard to certain characteristics. To the extent that the characteristics are correlated with these independent estimates, this adjustment can improve the precision of estimates.
A census of campuses was sent to collection. However, not all campuses answered, nor were all in scope for the survey. Reweighting was performed so that each responding unit (either a true answer or one classified as out of scope) would represent itself plus some of the non-responding units. As a result, each responding campus represents itself and some other non-responding units. Estimates can be obtained by multiplying the information of interest by the weight found in the microdata file.
A statistical technique called calibration is used to adjust the final set of weights in such a way that the respondents represent, as closely as possible, the target population for this survey.
Prior to the data release, survey results were analyzed for quality. In general, this analysis included a detailed review of individual responses, coherence with results from related indicators and information from other external sources.
Statistics Canada is prohibited by law from releasing any information it collects that could identify any person, business, or organization, unless consent has been given by the respondent or as permitted by the Statistics Act. Various confidentiality rules are applied to all data that are released or published to prevent the publication or disclosure of any information deemed confidential. If necessary, data are suppressed to prevent direct or residual disclosure of identifiable data.
Revisions and seasonal adjustment
This methodology type does not apply to this statistical program.
Because the data are based on a sample, they are subject to sampling error. Estimates based on a sample vary from sample to sample and are typically different from the results that would have been obtained from a complete census with a 100% response rate.
Quality indicators were calculated using Statistics Canada's general estimation system, G-EST. The method used to calculate these indicators differed depending on the component.
Sampling variability of the building component estimates can be estimated using the bootstrap method. Bootstrap weights were created for the building component and were used to estimate the sampling variability of all produced estimates. This method was chosen for the building component because of the complexity of the sampling plan.
Taylor's linearization was used to calculate the sampling variability of the estimates.
The collection response rate for the building component is 25.6%.
The collection response rate for the institution component is 70.7%
Common sources of non-sampling errors include imperfect coverage, classification errors and non-response. Coverage errors, or imperfect coverage, arise when there are differences between the target population and the surveyed population. These differences can be caused by exclusion of units on the frame or inclusion of units outside of the target population in the sample. If the excluded population differs from the survey population, the results may be biased. In general, since these exclusions are small, one would expect the biases introduced to be small. Classification errors are related to stratification information such as the province and building type (or campus type). If these are not correct on the frame, units are selected in one stratum when they belong to another, and this can have an impact on the estimates. Non-response could occur either at pre-contact or during main collection. Survey estimates were adjusted (i.e., weighted) to account for non-response cases. Other types of non-sampling errors can include response and processing errors.
The main method used to reduce non-response bias involved a series of adjustments to the survey weights to account for non-response as much as possible.
The frame for the building component was the SBgR. This new Statistics Canada product is a list of all buildings in Canada with a physical address compiled from different administrative data sources. Unfortunately, not all buildings in Canada are represented by a physical address on an administrative data file. It is not possible to evaluate the coverage error related to these exclusions, but it is known that the impact is bigger in rural areas than in urban areas.
The frame for the institution component was the BR. Coverage error for the BR is low.
The 2019 SCIEU frame was built using the SBgR and was then stratified by building type and province or territory. The information available to classify buildings by activity type was not present for all buildings; therefore, some buildings were added in a supplementary stratum, and a sample was selected in that unknown stratum. If certain activity types of interest were more represented than others in that stratum, it is possible that not enough sample was selected to produce good estimates for that building type.
Other non-sampling errors
A significant effort was made to minimize bias by using a well-tested questionnaire, a proven methodology, specialized interviewers and strict quality control.