Survey of Environmental Goods and Services

Detailed information for 2012

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 goods and services.

Data release - March 13, 2015

• Questionnaire(s) and reporting guide(s)
• Description
• Data sources and methodology
• Data accuracy

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 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

Data sources and methodology

Target population

The target population includes business establishments with a minimum of $750,000 in total revenues (as indicated on Statistics Canada's Business Register) in select Manufacturing industries (NAICS 325, 326, 332, 333, 335, 336, 339), Wholesale Trade industries (NAICS 416, 417), the Waste Management and Remediation Services industry (NAICS 562), the Engineering Services industry (54133) and the Environmental Consulting Services industry (NAICS 54162).

Instrument design

The questionnaire was developed by the Environmental Accounts and Statistics Division (EASD) with input from Industry Canada, the survey's primary stakeholder. Compared to its predecessor, the Environment Industry Survey, the content has been pared-down to reflect a more focused array of environmental products and services. The questions cover the following categories of goods and services :
- Renewable energy production
- Management of non-hazardous waste
- Management of industrial air pollution or flue gas
- Industrial wastewater treatment and municipal sewage treatment
- Remediation of ground water, surface water and leachate
- Remediation of soil, sediment and sludge
- Site remediation services and environmental emergency response services
- Revenues generated through exports
Questionnaire testing was performed by EASD and the Questionnaire Design Resource Center (Statistics Canada). A series of one-on-one interviews was conducted with firms in Ottawa, Toronto and Montreal.

Sampling

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

The Business Register was used as the survey frame for the 2012 reference year.

The portion of the sample receiving the SEGS questionnaire was derived through a two-phase sampling process: an initial sample size of 6,053 business establishments answered a series of screening questions in an effort to identify probable in-scope respondents; those that answered "Yes" to any one of the screening questions were added to the second phase of the sample and received a questionnaire. Businesses in NAICS 562xxx were not part of the first phase screening, as they were assumed by default to participate in environmental activities (Remediation Services). Respondents from reference years 2008 and 2010 who reported revenues from sales of environmental goods and services were also directly included in the second phase for 2012 and were not part of the first-phase screening. As a result, 2,119 establishments were considered in-scope for the final sample.

Data sources

Data collection for this reference period: 2013-10-15 to 2014-06-13

Responding to this survey is mandatory.

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

Data are collected through a mail-out/mail-back process. Follow-up procedures are applied when a questionnaire has not been received after a pre-specified period of time.

Data for the Management Consulting Services industry are derived from the Annual Survey of Service Industries: Consulting Services (record #4717, see the link below), administered by Service Industries Division.

• Additional documentation

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.

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.

If a record had no response for at least one mandatory cell after editing, the record was not processed any further and was considered a total non-response.

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.

Outliers were identified after collection and were removed from the imputation process.

Imputation

Statistical imputation was used for partial non-response records. Three methods of imputation were used: deterministic imputation (there is only one possible value for the field to impute), historical imputation and manual imputation.

Estimation

Estimates for the target population were calculated by multiplying the response values for the sampled units by their sampling weight. The sampling weight was calculated using a number of factors, including the probability of the unit being selected in the sample. A raising factor or weight adjustment was used in the estimation process to account for sampled units who could not be contacted or who refused to respond to the survey.

Quality evaluation

Since the 2008 Survey of Environmental Goods and Services is a complete redesign of the former Environment Industry Survey, historical comparisons were not possible as an added verification of data quality and consistency. The results of the 2008 reference year will be used as a new benchmark for future iterations of the survey.

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.

In order to prevent any data disclosure, confidentiality analysis is done using the Statistics Canada Generalized Disclosure Control System (G-Confid). G-Confid is used for primary suppression (direct disclosure) as well as for secondary suppression (residual disclosure). Direct disclosure occurs when the value in a tabulation cell is composed of or dominated by few enterprises while residual disclosure occurs when confidential information can be derived indirectly by piecing together information from different sources or data series.

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. The CVs were calculated and are indicated as ranges in the data tables.

The following criteria were used to report CVs:

A = excellent (0.00% to 4.99%)
B = very good (5.00% to 9.99%)
C = good (10.00% to 14.99%)
D = acceptable (15.00% to 24.99%)
E = use with caution (25.00% to 49.99%)
F = too unreliable to be published (50.00% and more, data are suppressed).
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).