Monthly Renewable Fuel Survey (MRFS)
Detailed information for August 2023
This survey collects information on the supply and demand of renewable fuels in Canada. This information serves as an important indicator of Canadian economic performance and is used by all levels of government to establish informed energy-related policies. The private sector also uses this information in the corporate decision-making process.
Data release - November 10, 2023
This monthly survey covers the activities of all establishments in Canada engaged in the production and disposition of renewable biofuel liquids. These data are required for integration into the Canadian System of National Accounts' input-output tables. Under the authority of the Statistics Act, data are made available to other federal departments and provincial authorities through data sharing agreements that are subject to the embodied principles of data confidentiality. Data are also intended for use by survey respondents, industry associations, industry analysts and the general public.
Reference period: Month
Collection period: 10 days after the reference period.
- Energy consumption and disposition
Data sources and methodology
The survey is comprised of ethanol and renewable diesel fuel producers in Canada. The survey units will be establishments.
Questionnaire Design Resource Centre (QDRC) testing was completed and approved.
The questionnaires were designed according to Statistics Canada's questionnaire design standards. They were designed in consultation with survey stakeholders, such as industry associations (e.g., Renewable Industries Canada), government departments and key respondents. The questionnaire is completed by respondents in Excel format and delivered in electronic format.
The questionnaire will be subject to regular revision to reflect changes in business activities and practices.
This survey is a census with a cross-sectional design.
A census using survey specific flags on the Business Register.
The sampling unit is the establishment as defined in Statistics Canada's Business Register.
Sampling and sub-sampling
The sample size for the January 2020 reference period is projected to be 30 establishments. This is subject to change based on changes in the industry.
Responding to this survey is mandatory.
Data are collected directly from survey respondents and extracted from administrative files.
The collection method is an Excel template in an electronic questionnaire shell. The capture method is the Business Collection Portal and the Integrated Business Statistics Program's Response Analysis Follow-up Survey. The initial method of contact is email, and follow-up will be done by phone or email. Both official languages will be offered as a choice to potential respondents. The average time to complete the interview and survey is less than 30 minutes. The survey unit is establishments.
Data from this survey will be combined with other energy statistics to create a consolidated energy report.
View the Questionnaire(s) and reporting guide(s) .
Error detection is an integral part of both data collection and data processing. Edits are applied to data records during collection to detect reporting and capture errors. These edits detect potential errors based on year-over-year changes in key variables, totals and ratios that exceed the tolerance thresholds. They also detect problems in the consistency of the collected data (e.g., a total variable does not equal the sum of its parts). During data processing, other edits are used to detect errors or inconsistencies that remain in the data following collection. These edits include value edits (e.g., value > 0 and 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 the failed edit follow-up process during collection or imputation. Extreme values are also flagged as outliers using methods based on the distribution of the collected information. After they are detected, these values are reviewed to assess their reliability. A manual review of other units may lead to additional outliers being identified. These outliers are excluded from use in the calculation of ratios and trends used for imputation. In general, every effort is made to minimize the non-sampling errors of omission, duplication, misclassification, reporting and processing.
When non-response occurs, when a questionnaire is incomplete or when reported data are deemed incorrect during the error detection process, imputation is used to fill in any missing information and modify incorrect information. Many imputation methods can be used to complete a questionnaire, including manual changes made by an analyst. The statistical techniques used to impute the missing data include deterministic imputation, replacement using historical data (with a calculated trend, where appropriate), replacement using auxiliary information from other sources, replacement based on known data relationships for the sample unit and replacement using data from a similar unit in the sample (donor imputation). Key variables are usually imputed first and are used as anchors in subsequent steps to impute other related variables.
All units in the observed population are being surveyed. The estimation of totals is done through a simple aggregation of the values of all estimation units found in the domain of estimation. Estimates are computed for several domains of interest, such as industrial groups and provinces and territories, based on the most recent classification information available for the estimation unit and survey reference period. It should be noted that this classification information may differ from the original sampling classification, as the size, industry or location included in the records may have changed. Classification changes are immediately reflected in the estimates.
This methodology does not apply.
By law, Statistics Canada is prohibited 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 the direct or residual disclosure of identifiable data.
Revisions and seasonal adjustment
This methodology type does not apply to this survey.
The survey is a census of the target population. As a result, undercoverage is minimal, and minimal bias resulting from non-response is introduced. If changes are received from respondents, the data are incorporated and the published data are revised accordingly.