Survey of Environmental Goods and Services (SEGS)

Detailed information for 2020

Status:

Active

Frequency:

Annual

Record number:

1209

The purpose of the survey is to produce estimates of the production of environmental goods and services by industry. This survey collects data on sales of environmental and clean technology goods and services.

Data release - March 23, 2022

Description

The purpose of the survey is to produce estimates of the production of environmental goods and services by industry. This survey collects data on sales of environmental and clean technology goods and services.

This information can be used by businesses for market analysis, by trade associations to study the performance of the environment industry, by governments to develop policies and by researchers.

Subjects

  • Environment
  • Environmental protection

Data sources and methodology

Target population

The target population for the survey is NAICS based however, the questions pertain to a suite of products and services which are part of the Canadian clean technology taxonomy. More on these products and services can be found in the associated technical reference guide (16-511-X).

The target population includes business establishments operating in Canada, excluding head offices, in select; Support activities for agriculture and forestry industries (NAICS 115), Oil and gas extraction industries (NAICS 211), Utilities industries (NAICS 221), Construction industries (NAICS 236, 237, 238), Manufacturing industries (NAICS 311, 321, 324, 325, 326, 327, 332, 333, 334, 335, 336, 339), Wholesale trade industries (NAICS 412, 415, 416, 417, 418, 419), Software publishers industry (NAICS 511), the Data processing, hosting, and related services industry (NAICS 518), the Professional, scientific, and technical services industry (NAICS 541), the Management, scientific and technical consulting services industry (NAICS 5416), the Administrative and support, waste management and remediation services industries (NAICS 561, 562), and the Repair and maintenance industry (NAICS 811).

For the purpose of this survey, the smallest units of the industries of interest are excluded from the population. In each combination of 3-digits NAICS, establishments that make up the bottom 10% of the revenue by region were excluded.

The observed population comes from the Generic Survey Universe File (GSUF) created by Statistics Canada's Statistical Registers and Geography Division in January 2021. It contains all establishments in Canada existing in January 2021. From this file, establishments respecting the criteria above are retained.

Instrument design

The questionnaire was developed by the Environment and Energy Statistics Division (EESD), with input from Innovation, Science and Economic Development Canada and Natural Resources Canada, the survey's primary stakeholders. Compared to its predecessor, the Environment Industry Survey, the content has been pared-down to reflect a more focused array of environmental and clean technology products and services. The questions cover the following categories of goods and services:

- Clean energy production
- Management of non-hazardous waste
- Management of industrial air pollution or flue gas
- Monitoring and reduction of greenhouse gas emissions and air pollution
- Industrial wastewater treatment and municipal sewage treatment
- Water management, recycling and treatment of drinking water technologies
- Remediation of ground water, surface water and leachate
- Remediation of soil, sediment and sludge
- Smart grid and energy storage
- Bioenergy and biomaterial production
- Precision agriculture technologies
- Energy efficiency technologies
- Transportation technologies
- Site remediation services and environmental emergency response services
- Energy efficiency and industrial design services
- Monitoring and reduction of greenhouse gas emissions and air pollution services
- Clean energy services
- Water management and efficiency services
- Sustainable resource services
- Transportation services
- Smart grid services
- Environmental employment
- Revenues generated through exports
- Investments

Sampling

This is a sample survey with a cross-sectional design.

The Generic Survey Universe File (GSUF) is used as the survey frame.

A stratified sample of establishments classified to the 3-digit level of the North American Industry Classification System (NAICS) Canada 2017 and to geographical regions was selected. Respondents from reference year 2019 who reported total environmental revenues and a list of establishments provided by subject matters were selected with certainty.

Data sources

Data collection for this reference period: 2021-03-12 to 2021-10-15

Responding to this survey is mandatory.

Data are collected directly from survey respondents and derived from other Statistics Canada surveys.

Data are collected using an electronic questionnaire.

A secure access code is mailed or emailed to respondents directing them to the electronic questionnaire.

Telephone follow-up is used to obtain data from establishments who return incomplete questionnaires or who fail to respond.

View the Questionnaire(s) and reporting guide(s) .

Error detection

Many factors affect the accuracy of data produced in a survey. For example, respondents may have made errors in interpreting questions, answers may have been incorrectly entered on the questionnaires, and errors may have been introduced during the data capture or 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., totals equal the sum of their components).

Returned data were first checked using an automated edit-check program immediately after capture. This first procedure verified that all mandatory cells had been filled in, that certain values were within acceptable ranges, that questionnaire flow patterns had been respected, and that totals equaled the sum of their components. Collection officers evaluated the edit failures and concentrated follow-up efforts accordingly. Consistency edit rules were performed on the data for each usable record. These rules ensured that all the variables had valid responses and were complete and coherent both within the questionnaire and across questionnaires.

Further data checking was performed by subject matter officers who research companies (annual reports, web sites, etc.) in an effort to verify information submitted by respondents.

Outlier values were identified after collection and reviewed by the client division for verification. Only real outliers were removed from the imputation process.

Imputation

Statistical imputation is used for total non-response and partial non-response records. Six methods of imputation are used:

- deterministic imputation (there is only one possible value for the field to impute);
- historical imputation (when available);
- imputation by mean;
- imputation by diftrend (when historical information is available, they are adjusted by the annual rate of change);
- 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 mean, diftrend and donor imputation are various combinations of industry group, and geographical location (province, region, or Canada). 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.

Estimation

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 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.

Sampling error was measured by the coefficient of variation (CV) which represents the proportion of the estimate that comes from the variability associated to it. The CVs were calculated and are indicated in the data tables.

Quality evaluation

Data evaluation and error detection during data collection is an important way to ensure that the final estimates that are produced are of good quality. Post-collection, the survey results and estimates are evaluated as a further method of evaluating data quality. One way to assess data quality is to compare it to the trends of other data collected.

Disclosure control

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.

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 need to be suppressed to prevent the residual disclosure of those confidential data points.

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, but rarely exceeds three years. The data are not seasonally adjusted.

Data accuracy

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.

A = excellent
B = very good
C = good
D = acceptable
E = use with caution
F = too unreliable to be published
X = Suppressed to meet the confidentiality requirements of the Statistics Act

As for non-sampling errors, they arise from coverage error, data response error, non-response error, and processing errors. Every effort was made to reduce these types of errors including verification of keyed data, consistency and validity edits, and extensive follow up with respondents.

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. Measures have been taken to minimize these errors.

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 were left unanswered).

The weighted response rate is 81%. This response rate was calculated using the revenues from sales of environmental and clean technology goods manufactured in Canada variable.

For RY2020, the SEGS recorded 15,059 establishments in Canada providing environmental goods or services, with 1,419 of them having exports.

Documentation

Report a problem on this page

Is something not working? Is there information outdated? Can't find what you're looking for?

Please contact us and let us know how we can help you.

Privacy notice

Date modified: