Industrial Water Survey (IWS)
Detailed information for 2013
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 27, 2015
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.
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 observed population came from the Generic Survey Universe File (GSUF) created by Statistics Canada's Statistical Registers and Geography Division and contains all locations in Canada existing in January following the reference year. From this frame, only locations with a North American Industry Classification System (NAICS) Canada code belonging to manufacturing, coal mining, metal ore mining, non-metallic mineral mining (excluding sand and gravel quarrying), and fossil-fuel and nuclear electric power generation were retained.
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.
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.
The manufacturing and thermal-electric power generation questionnaires were revised in 2007, while the mining questionnaire was last revised in 2011.
This is a sample survey with a cross-sectional design.
The frame was constructed using Statistics Canada's Business Register. A census was used for the thermal-electric power generation industry. For each of the mining and manufacturing industries a stratified sample of locations classified to the North American Industry Classification System (NAICS) Canada 2012 and to geographical regions was selected randomly.
For the manufacturing industry, to reduce response burden on small units, the smallest units were excluded from sampling. Specifically, in each combination of industry by drainage region, locations that made up the bottom 5% of the size measure (revenue) were excluded. Adjustments were made during the estimation process to account for the small units which were excluded from the sample.
The statistical unit is the location, which refers to a production unit located at a single geographical location at or from which economic activity is conducted and for which a minimum of employment data are available. The Industrial Water Survey samples locations because this is the production unit for which water data are most commonly available and water use characteristics are often location specific.
An independent stratification strategy was used for each of the three industries:
¿ Thermal-electric power generation: No stratification was applied since a census was used for this industry.
¿ Mining: Locations were stratified by region and by the four-digit NAICS code.
¿ Manufacturing: Locations were stratified by drainage region (25 in total), by industry (three- or five-digit NAICS code) and by size group (four groups) based on water consumption.
Sampling and sub-sampling
An independent sampling strategy was used for each of the three industries:
¿ Thermal-electric power generation: A census was used for this industry.
¿ Mining: A stratified simple random sample design was used to select the sample. Some sub-industries , identified as large consumers of water, were selected with certainty; the remaining population was sampled randomly.
¿ Manufacturing: A stratified simple random sample design was used to select the sample. Certain industries, identified as large consumers of water, were selected with certainty; the remaining population was sampled randomly at a rate that varied according to industry.
The Generalized Sampling System (GSAM) was used for the sample selection. The sample size has been relatively stable from cycle to cycle; in 2013 the sample size was approximately 128 units for thermal-electric power generation (NAICS 221112 and 221113), 378 units for mining (NAICS 2121, 2122, and 21233 excluding 21232) and 5037 units for manufacturing (NAICS 31-33).
Data collection for this reference period: 2014-04-01 to 2014-09-15
Responding to this survey is mandatory.
Data are collected directly from survey respondents.
Data are collected using English and French mail-out/mail-back paper questionnaires.
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 30 days of receipt. A letter explaining the purpose of the survey, the requested return date and the legal requirement to respond are included in the mail-out package.
Telephone and fax follow-up are used to obtain data from respondents who returned incomplete questionnaire or whose questionnaires are outstanding 45 days after the mail-out. Information is automatically captured and entered using image character recognition software.
View the Questionnaire(s) and reporting guide(s).
A number of factors can 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 data capture or tabulation process. Every effort is made to reduce the occurrence of such errors in the survey.
Upon receipt, questionnaires were scanned using an imaging system that captured the data for transfer into a database. Captured data were first checked using an automated edit-check program (BLAISE). This program verified that all mandatory cells were filled in, certain values fell within acceptable ranges, questionnaire flow patterns were respected and totals equalled the sum of their components. Collection officers evaluated the edit failures and concentrated follow-up efforts accordingly. Follow-up for non-response and for data validation was conducted by telephone or fax.
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, web sites, etc.) in an effort to verify information submitted by respondents.
If a record had no response for at least one mandatory variable after editing, the record was not processed any further and was considered a total non-response.
Outliers were identified after collection and were removed from the imputation process.
Statistical imputation was used for partial response records. Five methods of imputation were used for the Industrial Water Survey: Deterministic imputation (only one possible value for the field to impute), historical imputation, imputation by ratio, donor imputation (using a "nearest neighbour" approach to find a valid record that is most similar to the record requiring imputation) and manual imputation. Ratios were calculated and donors were selected for imputation purposes based on the same or closest industry group within specified geographic areas.
Statistics Canada generalized edit and imputation system (BANFF) was used for this process.
The response values for sampled units were multiplied by a sampling weight in order to provide an estimate for the entire population. The sampling weight was calculated using a certain number of factors, such as the probability for a unit to be selected in the sample, and adjustment of the units that could not be contacted or that refused to respond. The manufacturing industry also underwent an adjustment to account for the non-covered population.
Statistics Canada generalized estimation system (GES) was used for this process.
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 were compared with the estimates from previous reference periods. This historical comparison was made to ensure that the estimates were 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 was 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 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 CANSIM tables (153-0047 to 153-0051; 153-0067 to 153-0097). 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. Typically the sampling error is measured by the expected variability of the estimate from the true value, expressed as a percentage of the estimate. This measure is referred to as the coefficient of variation (CV) or the standard deviation. Coefficients of variation of the final estimates were computed for the Industrial Water Survey and are indicated on the statistical tables.
The quality of the estimates was classified as follows:
A. Excellent data quality (CV is 0.01% to 4.99%)
B. Very good data quality (CV is 5.00% to 9.99%)
C. Good data quality (CV is 10.00% to 14.99%)
D. Acceptable data quality (CV is 15.00% to 24.99%)
E. Use caution (CV is 25.00% to 49.99%)
F. Too unreliable to be published (CV is > 49.99%, data are suppressed)
X. Suppressed to meet the confidentiality requirements of the Statistic Act
Processing errors may occur at various stages of processing such as data entry, editing and tabulation. All efforts were 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. Total non-response, i.e., when all questions from the survey are left unanswered, was dealt with by adjusting the weights assigned to the responding records, such that one responding record might also represent other non-responding units with similar characteristics (i.e., size, province, industry). Missing data items were imputed for partial non-responses (i.e., when only some questions are left unanswered).
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