Annual Industrial Consumption of Energy Survey (ICE)
Detailed information for 2022
The Industrial Consumption of Energy survey (ICE), which is funded by Natural Resources Canada and Environment and Climate Change Canada, provides estimates of energy consumption by manufacturing establishments in Canada. These estimates serve as an important indicator of Canadian economic performance and are used by all levels of government in establishing informed policies in the energy area.
Data release - October 30, 2023
The Industrial Consumption of Energy survey (ICE) collects data on electricity generation, energy consumption and steam sales. It requests data on the consumption of various energy commodities such as electricity, natural gas, propane, diesel, wood and steam. It also asks information on the different usages of energy commodities: as fuel, to produce steam for sale, to produce electricity and for non-energy use.
The survey results are used by Natural Resources Canada to track energy efficiency improvements and by Environment and Climate Change Canada to calculate carbon dioxide emissions. Industry also uses the information to monitor the results of their energy reduction efforts and to measure their contributions to Canada's climate change goals. Within Statistics Canada, the data are used as an input into the environmental accounts and statistics as well as into the annual Report on Energy Supply and Demand in Canada.
Natural Resources Canada and Environment and Climate Change Canada are provided with data on a regular basis. Statistics Canada has also entered into data sharing agreements with various agencies and government departments for this survey.
The survey is administered as part of the Integrated Business Statistics Program (IBSP). The IBSP has been designed to integrate approximately 200 separate business surveys into a single master survey program. The IBSP aims at collecting industry and product detail at the provincial level while minimizing overlap between different survey questionnaires. The redesigned business survey questionnaires have a consistent look, structure and content.
The integrated approach makes reporting easier for firms operating in different industries because they can provide similar information for each branch operation. This way they avoid having to respond to questionnaires that differ for each industry in terms of format, wording and even concepts. The combined results produce more coherent and accurate statistics on the economy.
Reference period: The calendar year
- Energy consumption and disposition
Data sources and methodology
The target population comprises manufacturing establishments in Canada. Under the North American Industry Classification System (NAICS 2017), manufacturing establishments are classified to NAICS 31, 32 and 33.
The electronic questionnaire for the Industrial Consumption of Energy survey (ICE) has been in use since reference year 2014. Prior to its implementation, the questionnaire was tested on focus groups made up of survey respondents. For reference year 2015 the questionnaire was revised to include a question on electricity generation. This question was tested using a focus group of respondents to the survey. For reference year 2018 the questionnaire's format was revised to reduce its length and facilitate processing.
This is a sample survey with a cross-sectional design.
The survey has a cross-sectional design featuring a random stratified sample of establishments that are classified both geographically and according to the North American Industry Classification System (NAICS) Canada 2017.
The sampling unit is the establishment as defined in the Business Register.
Sampling units are classified into homogeneous groups (i.e. groups with the same NAICS codes and same geographic region) based on the characteristics of their establishments. Each group is divided into strata (i.e. small, medium, and large) based on the annual revenue of the establishment.
This division of groups into strata is done using the Geometric Stratification method. A special Must-Take stratum is created for units whose presence in the sample is critical.
Once the population is stratified, a sample of predetermined size is allocated into each stratum using the Power Allocation method. The objective of this method is to allocate sample while optimizing overall quality within industrial and geographical groupings while respecting the available resources.
The sample is selected using Bernoulli sampling. For reference year 2022, the total sample size for the survey was 4,982 establishments.
Data collection for this reference period: 2023-01-03 to 2023-03-30
Responding to this survey is mandatory.
Data are collected directly from survey respondents.
Data were collected using the electronic questionnaire (EQ) and Computer Assisted Telephone Interviews (CATI). The collection began by emailing invitation letters and Secure Access Code letters to respondents. Phone and email follow-ups started in February for enterprises that had not yet responded. The Regional Office collected data from late reporters using CATI. The average time required to complete the interview was 26.36 minutes, respondents were offered the choice of responding in the English or French language.
Energy consumption data pertaining to the Pulp and Paper and the Wood manufacturing sectors are provided by the Forest Products Association of Canada (FPAC) pursuant to section (13) of the Statistics Act. These data, will be used solely for the purposes of the Act, that is, for statistical and research purposes only.
Energy consumption data pertaining to the Petroleum and Coal Products manufacturing sectors are provided by the Canadian Energy and Emissions Data Centre (CEEDC) pursuant to section (13) of the Statistics Act. These data, will be used solely for the purposes of the Act, that is, for statistical and research purposes only.
The concepts and definitions of these administrative data align with those used in the Industrial Consumption of Energy survey. When these data arrive at Statistics Canada they are combined with data collected directly by the survey and undergo the same processing.
View the Questionnaire(s) and reporting guide(s) .
The following methods are used to detect errors:
The electronic questionnaire contains edits to help respondents correct for inconsistencies. For example, if a respondent checks off that they used a particular fuel, but then do not respond to questions regarding how they used that fuel, an edit will point out this inconsistency.
Historical edits are used to identify large year over year reporting changes. For establishments that have no previous data to compare with, an outlier detection method using Statistics Canada's generalized system BANFF compares the energy consumption of establishments with similar revenue within the establishment's industry to identify whether the data are reasonable or not.
The above describes the edits which are applied to microdata. Edits are not applied to macrodata.
Imputation is used to determine plausible values for all variables that are missing or inconsistent with historical data. A number of statistical techniques are employed for this purpose that use survey data collected during the current and previous cycles.
For records for which historical data are available, missing fuel consumption values are calculated using a production trend.
As of 2006, an automated donor imputation program has been in use. Donor imputation involves identifying a respondent record that is similar to a non-respondent based on information that is available for both establishments (such as industry, revenue and province). The data available for the donor establishment are then used for the non-respondent.
The Generalized Estimation System is applied to calculate energy consumption estimates for each of the 88 manufacturing NAICS of interest. The most recent revenue values for these industries available from Statistic Canada's Business Register are used to help correct sampling errors and to include an adjustment for the uncovered portion of each industry that was excluded from the sample, that is, the take-none stratum.
Starting in 2006, coal, coal coke, petroleum coke, coke on catalytic cracking catalyst, refinery fuel gas, butane and steam purchased are weighted, while they were not previously. This change allows the production of estimates that represent the population.
Throughout the collection and processing of the data, every effort was made to ensure that the results would be of superior quality. As part of the quality evaluation stage, initial survey results are validated by industry experts such as the Canadian Industry Program for Energy Conservation and the Canadian Energy and Emissions Data Centre. Natural Resources Canada and Environment and Climate Change Canada are also important partners in this data validation stage. Data quality indicators are included in the CANSIM tables in order to provide users with information about the reliability of the data.
Statistics Canada is prohibited by law from releasing any information it collects which 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.
Confidentiality analysis includes the detection of possible direct disclosure, which occurs when the value in a tabulation cell is composed of a few respondents or when the cell is dominated by a few companies.
Revisions and seasonal adjustment
On an annual basis, the Industrial Consumption of Energy survey provides preliminary estimates of energy consumption for the current reference year (RY) and revised estimates for the previous reference year (RY-1).
The magnitude of revisions to the RY-1 data will vary from year to year. As RY-1 is preliminary, further editing of the data occurs prior to its finalization. This editing includes the use of the newer current year data to review previously reported and imputed data.
While considerable effort is made to ensure high standards throughout all stages of collection and processing, the resulting estimates are inevitably subject to a certain degree of error. These errors can be broken down into two major types: non-sampling and sampling.
Non-sampling error is not related to sampling and may occur for many reasons. For example, non-response is an important source of non-sampling error. Population coverage, differences in the interpretation of questions, incorrect information from respondents, and mistakes in recording, coding and processing data are other examples of non-sampling errors.
For the 2022 Annual Industrial Consumption of Energy Survey, the non-sampling errors were controlled through a careful design of the questionnaire, the use of a minimal number of simple concepts and consistency checks. Of the units contributing to the estimate, the unweighted response rate was 82.29%.
Sampling error occurs because population estimates are derived from a sample of the population rather than the entire population. Sampling error depends on factors such as sample size, sampling design, and the method of estimation. An important property of probability sampling is that sampling error can be computed from the sample itself by using a statistical measure called the coefficient of variation (CV). The range of acceptable data values yielded by a sample is called a confidence interval. Confidence intervals can be constructed around the estimate using the CV. The standard error is calculated by multiplying the sample estimate by the CV. The sample estimate plus or minus twice the standard error is then referred to as a 95% confidence interval.
For the 2022 Annual Industrial Consumption of Energy Survey, CVs were calculated for each estimate. Generally, the more commonly reported variables obtained very good CVs (of less than 10%), while the less commonly reported variables were associated with higher but still acceptable CVs (under 25%). Some data might not be released because of poor data quality.