Biennial Industrial Water Survey (IWS)
Detailed information for 2017
Every 2 years
This survey is being conducted to provide Canadians with national and regional indicators related to the use of water in industry.
Data release - October 23, 2019 (Thermal-electric power producers); August 18, 2020 (Manufacturing and mining); January 16, 2023 (2020 data)
This survey provides information about the intake, costs, sources, treatments and discharge of water used for the manufacturing, mining and thermal-electric power generating industries in Canada. These data are used in the development of environmental accounts and fulfill the requirements for producing water-related indicators as part of the Canadian Environmental Sustainability Indicators (CESI) published by Environment and Climate Change Canada.
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: Calendar year
- Environmental quality
Data sources and methodology
The target population consists of locations primarily involved in manufacturing, coal mining, metal ore mining, non-metallic mineral mining (excluding sand and gravel quarrying), and thermal-electric power production. Thermal-electric power production includes fossil-fuel and nuclear electric power generation.
The Industrial Water Survey uses three questionnaires to collect data from respondents. A separate questionnaire was designed for each of the three sectors surveyed, i.e., one for manufacturing, one for the mineral extraction industries, and another for fossil-fuel and nuclear electric power generation. 2017 is the first year that IWS is using an electronic questionnaire (EQ). Prior to the implementation of the EQ, the questionnaire was tested on survey respondents to ensure that the concepts were clearly understood and that respondents were able to answer the questions correctly.
The questionnaires collect data on the volume of water brought into the facility, including information on the source, purpose, treatment, and possible recirculation of this water, by industrial users. As well, data are collected on the volumes of water discharged and treatment of this discharged water by industrial users. Cost information on the intake and discharge of water is also collected.
The questionnaires were developed in collaboration with data users in order to meet their statistical needs. Respondents were also consulted through individual meetings to ensure the information being asked was available and that the questionnaire could be filled out within a reasonable timeframe.
All questionnaires were revised in 2017 after they were tested on focus groups made up of survey respondents.
This is a sample survey with a cross-sectional design.
The frame used for sampling purposes is the Statistics Canada Business Register, with the observed population comprised of all manufacturing, selected mining and all thermal-electric locations. The statistical unit is the location. The location, is defined as a producing unit at a single geographical site at which economic activity is conducted and for which, employment data are available.
The population size was 126 608 manufacturing locations (NAICS 31-33), 736 mining locations (NAICS 2121, 2122, 2123, excl. 21232) and 100 thermal-electric power generating plants (NAICS 221112, 221113).
There is an independent sampling strategy for each of the three sectors.
- A census approach is used to obtain information from each of the approximately 100 thermal-electric power generating stations in Canada. A stratified Bernoulli random design is used for sample selection in the manufacturing and mineral extraction sectors.
- In the mining sector, locations are stratified by province and by 4-digit NAICS industry. All of the approximately 383 in-sample units receive a questionnaire.
- In the manufacturing sector, locations are stratified by drainage region, by industry, and by size group (four groups) based on water consumption. To reduce response burden on small units, the smallest units of the industries of interest are excluded from sampling. In each combination of industries, locations that make up the bottom 5% of the revenue from the Business Register by drainage region were excluded. Some specific industries, identified as large consumers of water are selected with certainty; the rest of the population is sampled with varying sampling fractions, depending on the industry. All of the approximately 5 073 in-sample units receive a questionnaire.
Data collection for this reference period: 2018-05-02 to 2018-09-26
Responding to this survey is mandatory.
Data are collected directly from survey respondents.
Data are collected using the electronic questionnaire (EQ) and Computer Assisted Telephone Interviews (CATI). The collection begins by emailing invitation letters and Secure Access Code letters to respondents. Phone and email follow-ups will be done for enterprises that had not yet responded.
Mail-out occurs in the year following the reference year and is directed to an "environment manager or coordinator". Respondents are asked to return the completed questionnaires within 28 days of receipt. Mail out is only done for paper on a request basis.
Telephone and fax follow-up are used to obtain data from respondents who returned incomplete questionnaire or whose questionnaires are outstanding 28 days after the mail-out
View the Questionnaire(s) and reporting guide(s).
Many factors affect the accuracy of data produced in a survey. For example, respondents may make errors in interpreting questions, answers may be incorrectly entered on the questionnaires, and errors may be introduced during the tabulation process. Every effort was made to reduce the occurrence of such errors in the survey.
The electronic questionnaire contains edits to help respondents correct for inconsistencies (e.g., total equal the sum of their components).
Returned data are first checked using the edit software. This procedure verifies that all core cells have been filled in, that certain values lie within acceptable ranges, that questionnaire flow patterns have been respected, that certain transformed and derived variables are assigned values, that blanks are set to zeroes where appropriate, and that totals equal the sum of their components. Consistency edit rules are performed on the data for each usable record. These rules ensure that all the variables have valid responses and are complete and coherent both within the questionnaire and across questionnaires.
Collection officers evaluate the edit failures and concentrate follow-up efforts accordingly. Phone follow-ups are performed to verify information in cases where edit checks fail.
Further data checking was performed by subject matter officers who compared historical data with returned data to determine if differences between survey cycles were reasonable. If not, collection officers were asked to confirm with respondents their responses. Subject matter officers also researched companies (using annual reports, websites, etc.) in an effort to verify information submitted by respondents.
Outlier values were identified after collection, outside of Integrated Business Statistics Program, and only real outliers were removed from the imputation process.
Statistical imputation is used for total non-response and partial non-response records. Six methods of imputation are used:
- deductive imputation (there is only one possible value for the field to impute);
- historical imputation (when available);
- imputation by current ratio;
- imputation by current mean;
- donor imputation (using a nearest neighbour approach to find, for each record requiring imputation, the valid record that is most similar to it);
- manual imputation.
The criteria used for ratio, mean and donor imputation are various combinations of industry group and geographic area. Statistics Canada's generalized edit and imputation system (Banff) is used for this process. Usually, key variables are imputed first and are used as anchors in subsequent steps to impute other related variables.
Imputation generates a complete and coherent microdata file that covers all survey variables.
The Generalized Estimation System (G-Est) developed at Statistics Canada is used to produce domain estimates and quality indicators. It is a SAS based application for producing estimates (totals, ratios, percentages) for domains of a population based on a sample.
An initial sampling weight (the design weight) is calculated for each unit in the survey and is the inverse of the probability of selection. The weight calculated for each sampling unit indicates how many other units it represents. Sampling units which are selected with certainty (must-take units) have sampling weights of one and only represent themselves; outlier units with larger than expected size are seen as misclassified and their weight is usually adjusted so that they only represent themselves, and the weights of other units are adjusted accordingly to take into account the existence of outliers. The final weights are usually either one or greater than one.
Estimates are computed at several levels of interest such as the North American Industry Classification System code, region or province and drainage region, based on the most recent classification information for the statistical entity and the survey reference period.
Sampling error is measured by the coefficient of variation (CV) which represents the proportion of the estimate that comes from the variability associated to it. The calculated CVs are indicated in the data tables with quality indicators.
The manufacturing industry also underwent an adjustment to account for the not-covered population.
Micro data evaluation and error detection are important processes used to ensure good quality data. However, the final estimates obtained through the use of this micro data must also be evaluated in order to ensure accuracy. The quality of the estimates produced from a survey can be assessed through comparison to the trends obtained from other data sources and/or through a historical comparison to data obtained previously through the same survey. Estimates for the Industrial Water Use Survey are compared with the estimates from previous reference periods. This historical comparison is made to ensure that the estimates are coherent.
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.
Statistics Canada's generalized G-Confid system is used to prevent the identification of all data points that are confidential as well as those data points that needed to be suppressed to prevent the residual disclosure of those confidential data points.
A discretionary disclosure order (DDO) pursuant to paragraph 17(2)(g) of the Statistics Act was obtained to allow increased disclosure of aggregate information from the Biennial Industrial Water Survey: Fossil-Fuel and Nuclear Electric Power Generating Plants. The DDO permits the release of industrial water use by region and province, allowing the dissemination of a complete national profile of water use for this sector.
Revisions and seasonal adjustment
Revisions are made for the previous survey reference period, with the initial release of the current data, as required. The purpose is to address any significant issues with the data that were found between survey cycles. The actual period of revision depends on the nature of the issue. For the most current data refer to tables 38-10-0037-01 to 38-10-0041-01; 38-10-0055-01 to 38-10-0085-01. The data are not seasonally adjusted.
The accuracy of data collected in a sample survey is affected by both sampling and non-sampling errors.
Sampling errors arise from the fact that the information obtained from a sample of the population is applied to the entire population. The sampling method as well as the estimation method, the sample size, and the variability associated to each measured variable determine the sampling error.
A possible measure of sampling error is the coefficient of variation (CV). It represents the proportion of the estimate that comes from the variability associated to it. To take into account that imputation has occurred, both the sampling error and the non-response rate are combined into one quality rating code for each estimate. This code uses letters that range from A to F where A means the estimate is of excellent quality and F means too unreliable to be published.
B. Very good
E. Use caution
F. Too unreliable to be published
X. Suppressed to meet the confidentiality requirements of the Statistics Act
Non-sampling errors arise from coverage error, data response error, non-response error, and processing errors. Every effort is made to reduce these types of errors including verification of keyed data, consistency and validity edits and follow up for non-response.
Data response error may be due to questionnaire design, the characteristics of a question, inability or unwillingness of the respondent to provide correct information, misinterpretation of the questions or definitional problems. These errors are controlled through careful questionnaire design and testing and the use of simple concepts and consistency checks.
Processing errors may occur at various stages of processing such as data entry, editing and tabulation. All efforts are undertaken to minimize non-sampling errors through extensive edits, quality control steps and data analysis, but some of these errors are outside the control of Statistics Canada.
Non-response error results when respondents refuse to answer, are unable to respond or are too late in reporting. Missing data items are imputed for partial non-responses (i.e., when only some questions are left unanswered) and total non-response (i.e., when all questions from the survey are left unanswered).
The total intake water response rate for the 2017 reference year for the thermal-electric component of the survey was 98.8%.