Wholesale Trade Survey (monthly): CVs for total sales by geography - April 2021

Wholesale Trade Survey (monthly): CVs for total sales by geography - April 2021
Geography Month
202004 202005 202006 202007 202008 202009 202010 202011 202012 202101 202102 202103 202104
percentage
Canada 0.8 0.8 0.7 0.7 0.7 0.7 0.5 0.6 0.8 0.8 0.7 0.6 0.7
Newfoundland and Labrador 0.6 0.4 0.1 0.2 0.4 0.4 0.4 0.4 0.4 0.6 0.5 0.2 1.4
Prince Edward Island 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Nova Scotia 3.5 5.5 2.5 2.1 1.9 1.7 2.7 3.4 6.3 1.8 1.7 2.6 4.5
New Brunswick 2.2 3.2 2.7 2.0 3.6 3.5 2.9 5.0 3.5 3.4 2.6 1.1 1.3
Quebec 2.4 1.9 2.0 1.7 2.3 1.9 1.5 1.4 1.7 1.8 1.8 1.9 1.8
Ontario 1.2 1.2 1.1 1.0 0.9 1.0 0.8 0.9 1.3 1.2 1.1 0.9 1.1
Manitoba 2.5 2.6 1.1 1.2 1.8 2.8 1.7 1.4 2.5 1.7 2.4 1.8 3.0
Saskatchewan 1.2 0.6 0.7 1.2 1.4 0.7 0.9 0.9 1.0 1.0 1.6 1.2 0.8
Alberta 2.9 2.9 2.5 2.3 1.9 3.4 1.3 1.3 1.7 1.0 1.2 1.1 1.2
British Columbia 1.5 1.8 1.6 1.3 1.9 1.8 1.4 1.5 1.4 1.5 1.4 1.5 1.2
Yukon Territory 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Northwest Territories 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Nunavut 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

2021 Census: Enumerators now following up with dwellings

They are carrying out an important task on behalf of all Canadians

A Statistics Canada enumerator stands on the sidewalk holding a clipboard and pen, and wearing an a PPE mask as well as a Census 2021 red vest and an employee identification badge.
A census enumerator. Census enumerators safely collect the data that is vital to improving the lives of Canadians.

June 10, 2021 – Ottawa, ON – Statistics Canada

Statistics Canada thanks all Canadians who have completed their 2021 Census to date. Millions of households have responded to the questionnaires safely online, on paper or over the phone. Where needed, some census enumerators, who adhered to strict health and safety protocols, dropped off invitation letters to households that did not receive the invitation in the mail.

Statistics Canada enumerators are now following up with dwellings from which completed questionnaires have not yet been received. Every attempt is made by Statistics Canada employees to reach households by phone before enumerators conduct in-person visits to remind residents to complete the census and offer assistance.

Thousands of census enumerators have been hired across the country to collect the data that is vital to improving the lives of Canadians.

Enumerators visiting dwellings are following a new no-contact protocol. Under this protocol, no interviews are conducted inside the respondent's dwelling and no census employee from Statistics Canada is permitted to visit or enter institutional collective dwellings, especially the dwellings housing residents who are most vulnerable to COVID-19, such as seniors' residences. In accordance with guidelines from public health authorities, interviews take place outdoors and physically distanced and census employees are required to wear masks, and hand sanitizer is provided to employees so they may frequently disinfect their hands.

It's not too late for households to make their census contact-free by completing it online, on paper or over the phone. Households can still contact the Census Help Line at 1-855-340-2021 to request a secure access code or at 1-877-885-2021 to receive a paper questionnaire. Answers to many questions are also available on the census website.

Information from the census ensures that communities have the information they need to plan services that support employment, schools, public transit and hospitals. Millions of Canadians have counted themselves in already—have you?

Contacts

For more information, contact Media Relations at 613-951-4636, or at statcan.mediahotline-ligneinfomedias.statcan@statcan.gc.ca.

National Weighted Rates by Source and Characteristic, April 2021

National Weighted Rates by Source and Characteristic, April 2021
  Data source
Response or edited Imputed
%
Sales of goods manufactured 76.0 24.0
Raw materials and components 65.4 34.6
Goods / work in process 74.4 25.6
Finished goods manufactured 65.1 34.9
Unfilled Orders 83.9 16.1
Capacity utilization rates 61.2 38.8

From Exploring to Building Accurate Interpretable Machine Learning Models for Decision-Making: Think Simple, not Complex

By: Yadvinder Bhuller, Health Canada; Keith O’Rourke, Health Canada

In spite of an increasing number of examples, where both simple and complex prediction models have been used for decision-making, accurate prediction continues to be pertinent for both models. The added element is that the more complex a model, the more potential it has for less uptake from novice users who may not be familiar with machine learning (ML). Complex prediction models can arise from attempts to maximize predictive accuracy without regard to how difficult it would be for an individual to anticipate the predictions from the input data. However, even with a method considered as simple as linear regression, the complexity increases as more variables and their interactions are added. At the other extreme of using numerous non-linear functions for prediction, as with Neural Nets, it is possible that the results can be too complex to understand. Such models are usually called black box prediction models. Accurate interpretable models can also vary from accurate decision trees and rule lists that are so concise they can be fully described in a sentence or two for tabular data, through modern generalized additive models (e.g., for more challenging medical records), to methods to disentangled neural nets for unstructured data such as pixels. A recent notable addition is the use of Bayesian soft complexity constrained unsupervised learning of deep layers of latent structure that is then used to construct a concise rule list with high accuracy (Gu and Dunson, 2021).

An early example, over 20 years ago, of a simple method providing as accurate prediction as more complex models is the 1998 study, by Ennis et al., of various ML learning methods to the GUSTO-I database where none of the methods could outperform a relatively simple logistic regression model. More recent accounts of complex methods, even when simple ones could suffice, are noted in the 2019 article by Rudin and Radin. The often suggested simple remedy for this unmanageable complexity is just finding ways to explain these black box models; however, those explanations can sometimes miss key information. In turn, rather than being directly connected with what is going on in the black box model, they result in being "stories" for getting concordant predictions. Given that concordance is not perfect, they can result in very misleading outcomes for many situations.

Perhaps what is needed is a wider awareness of the increasing number of techniques to build simple interpretable models from scratch that achieve high accuracy. The techniques are not simple refinements of linear or logistic regression (by rounding their coefficient to integers which loses accuracy), but involve discernment of appropriate domain-based constraints and newer methods of constrained optimization. This results in a spectrum of ease of interpretability of prediction across different applications.

Understanding where and when to be simple!

While we need to accept what we cannot understand, we should never overlook the advantages of what we can understand. For example, we may never fully understand the physical world. Nor how people think, interact, create and or decide. In ML, Geoffrey Hinton's 2018 YouTube drew attention to the fact that people are unable to explain exactly how they decide in general if something is the digit 2 or not. This fact was originally pointed out, a while ago, by Herbert Simon, and has not been seriously disputed (Erickson and Simon, 1980). However, prediction models are just abstractions and we can understand the abstractions created to represent that reality, which is complex and often beyond our direct access. So not being able to understand people is not a valid reason to dismiss desires to understand prediction models.

In essence, abstractions are diagrams or symbols that can be manipulated, in error-free ways, to discern their implications. Usually referred to as models or assumptions, they are deductive and hence can be understood in and of themselves for simply what they imply. That is, until they become too complex. For instance, triangles on the plane are understood by most, while triangles on the sphere are understood by less. Reality may always be too complex, but models that adequately represent reality for some purpose need not be. Triangles on the plane are for navigation of short distances while on the sphere, for long distances. Emphatically, it is the abstract model that is understood not necessarily the reality it attempts to represent.

However, for some reason, a persistent misconception has arisen in ML that models for accurate prediction usually need to be complex. To build upon previous examples, there remains some application areas where simple models have yet to achieve accuracy comparable to black box models. On the other hand, simple models continue to predict as accurately as any state of the art black box model and thus, the question, as noted in the 2019 article by Rudin and Radin, is: "Why Are We Using Black Box Models in AI When We Don't Need To?"

In application areas where simple models can be as accurate, not using such models has unnecessarily led to recommendations that can impact areas including societal, health, freedom, and safety. An often-discussed hypothetical choice between the accurate machine-learning-based robotic surgeon and the less-accurate human surgeon is moot once someone builds an interpretable robotic surgeon that is as accurate as any other robot. Again, it is the prediction model that is understandable, not necessarily the prediction task itself.

Simple and interpretable models?

The number of application areas where accurate simple prediction models can be built to be understood has been increasing over time. Arguably, perhaps these models should be labeled as "interpretable" ML, as they are designed from scratch to be interpretable. They are purposely constrained so that their reasoning processes are more understandable to most if not all human users. This not only makes the connection between input data and predictions almost obvious, but it is also easier to troubleshoot and modify as needed. Interpretability is in the eye of the domain and interpretability constraints can include the following:

  • Sparsity of the model
  • Monotonicity with respect to a variable
  • Decomposability into sub-models
  • An ability to perform case-based reasoning
  • Disentanglement of certain types of information within the model's reasoning process
  • Generative constraints (e.g. biological processes)
  • Preferences among the choice of variables
  • Any other type of constraint that is relevant to the domain.

Some notable examples of interpretable models include sparse logical models (such as decision trees, decision lists, and decision sets) and scoring systems which are linear classification models that require users to add, subtract, and multiply only a few small numbers to make a prediction. These models can be much easier to understand than multiple regression and logistic regression, which can be difficult to interpret. Now, the intuitive simplification of these regression models, by restricting the number of predictors and rounding the coefficients, does not provide optimal accuracy. This is just a post hoc adjustment. It is better to build in interpretability from the very start.

There is increasing understanding based on considering numerous possible prediction models in a given prediction task. The not-too-unusual observation of simple models performing well for tabular data (a collection of variables, each of which has meaning on its own) was noted over 20 years ago and was labeled the "Rashomon effect" (Breiman, 2001). Breiman posited the possibility of a large Rashomon set in many applications; that is, a multitude of models with approximately the same minimum error rate. A simple check for this is to fit a number of different ML models to the same data set. If many of these are as accurate as the most accurate (within the margin of error), then many other untried models might also be. A recent study (Semenova et al., 2019), now supports running a set of different (mostly black box) ML models to determine their relative accuracy on a given data set to predict the existence of a simple accurate interpretable model—that is, a way to quickly identify applications where it is a good bet that accurate interpretable prediction model can be developed.

What's the impact on ML from full data science life-cycle?

The trade-off between accuracy and interpretability with the first fixed data set in an application area may not hold over time. In fact, it is expected to change as either more data accumulate, the application area becomes better understood, data collection is refined or new variables are added or defined and the application area changes. In a full data science process itself, even in the first data set, one should critically assess and interpret the results and tune the processing of the data, the loss function, the evaluation metric, or anything else that is relevant. More effectively turning data into increasing knowledge about the prediction task which can then be leveraged to increase both accuracy and likely generalization. Any possible trade-off between accuracy and interpretability therefore should be evaluated in the full data science process and life cycle of ML.

The full data science and life-cycle process likely is different when using interpretable models. More input is needed from domain experts to produce an interpretable model that make sense to them. This should be seen as an advantage. For instance, it is not too unusual at a given stage to find numerous equally interpretable and accurate models. To the data scientist, there may seem little to guide the choice between these. But, when shown to domain experts, they may easily discern opportunities to improve constraints as well as indications of which ones are less likely to generalize well. All equally interpretable and accurate models are not equal in the eyes of domain experts.

Interpretable models are far more trustworthy in that they can be more readily discerned where and when they should be trusted or not and in what ways. But, how can one do this without understanding how the model works, especially for a model that is patently not trustworthy? This is especially important in cases where the underlying distribution of data changes, where it is critical to trouble shoot and modify without delays, as noted in the 2020 article by Hamamoto et al. It is arguably much more difficult to remain successful in the ML full life cycle with black box models than with interpretable models. Even for applications where interpretable models are not currently accurate enough, interpretable models can be used a tool to help debug black box models.

Misunderstanding explanations

There is now a vast and confusing literature, which conflates interpretability and explainability. In this brief blog, the degree of interpretability is taken simply as how easily the user can grasp the connection between input data and what the ML model would predict. Erasmus et al. (2020) provide a more general and philosophical view. Rudin et al. (2021) avoid trying to provide an exhaustive definition by instead providing general guiding principles to help readers avoid common, but problematic ways of thinking about interpretability. On the other hand, the term "explainability" often refers to post hoc attempts to explain a black box by using simpler 'understudy' models that predict the black box predictions. However, as noted in the Government of Canada's (GoC's) Guideline on Service and Digital, prediction is not explanation, and when they are proffered as explanations they can seriously mislead (GoC, 2021). Often this literature assumes that one would just explain a black box without consideration of whether there is an interpretable model of the same accuracy, perhaps having uncritically bought into the misconception that only models that are too complex to understand can achieve acceptable accuracy.

The increasing awareness of the dangers of these "explanations" has led one group of researchers to investigate how misunderstanding can actually be purposefully designed in; something regulators may increasingly need to worry about (Lakkaraju and Bastani, 2019). It is also not uncommon for those who routinely do black box modeling to offer explanations of these models as an alternative or even a reason to forego learning about and developing interpretable models.

Keeping it simple

Interpretable ML models are simple and can be relied upon when relying upon ML tools for decision-making. On the other hand, even interpretability is probably not needed for decisions where humans can verify or modify the decision afterwards (e.g. suggesting options). Notwithstanding the desire for simple and accurate models, it is important to note that currently interpretable MLs cannot match the accuracy of black box models in all application areas. For applications involving raw data (pixels, sound waves, etc.) black box neural networks have a current advantage over other approaches. In addition, black box models allow users to delegate responsibility for grasping implications of adopting the model. Although a necessary trade-off between accuracy and interpretability does remain in some application areas, its ubiquity remains an exaggeration and the prevalence of the trade-off may continually decrease in the future. This has created a situation in ML where opportunities to understand and reap the benefits are often overlooked. Therefore, the advantages of newer interpretable modelling techniques should be fully considered in any ML application, at a minimum to determine if adequate accuracy is achievable. Perhaps and in the end it may boil down to the fact that if simple works, then why make things more complex.

Team members: Keith O'Rourke (Pest Management Regulatory Agency), Yadvinder Bhuller (Pest Management Regulatory Agency).

Keep on machine learning...

Breiman, L. (2001). Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author). Statist. Sci. 16(3): 199-231. DOI: 10.1214/ss/1009213726

Ennis, M., Hinton, G., Naylor, D., Revow, M., and Tibshirani, R. (1998). A Comparison of Statistical Learning Methods on the Gusto Database. Statistics. Med. 17, 2501-2508. A comparison of statistical learning methods on the GUSTO database

Erasmus, A., Bruent, T.D.P., and Fisher E. (2020). What is Interpretability? Philosophy & Technology. What is Interpretability?

Ericsson, K. A., & Simon, H. A. (1980). Verbal reports as data. Psychological Review, 87(3), 215–251. Verbal reports as data.

Government of Canada. (2021). Guideline on Service and Digital. Guideline on Service and Digital. [Accessed: May 13, 2021].

Gu, Y., and Dunson, D.B. (2021). Identifying Interpretable Discrete Latent Structures from Discrete Data. arXiv:2101.10373 [stat.ME]

Hinton, G. (2018). Why Is a Two a Two? Why Is A Two A Two? With Geoffrey Hinton and David Naylor [Accessed: May 13, 2021].

Hamamoto, R., Suvarna, K., Yamada, M., Kobayashi, K., Shinkai, N., Miyake, M., Takahashi, M., Jinnai, S., Shimoyama, R., Sakai, A., Taksawa, K., Bolatkan, A., Shozu, K., Dozen, A., Machino, H., Takahashi, S., Asada, K., Komasu, M., Sese, J., and Kaneko., S. (2020). Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine. Cancers. 12(12), 3532; Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine

Lakkaraju, H., and Bastani, O. (2019). "How do I fool you?": Manipulating User Trust via Misleading Black Box Explanations. arXiv:1911.06473 [cs.AI]

Rudin, C., Chen, C., Chen, Z., Huang, H., Semenova, L., and Zhong, C. (2021). Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges. arXiv:2103.11251 [cs.LG]

Rudin, C., & Radin, J. (2019). Why Are We Using Black Box Models in AI When We Don't Need To? A Lesson From An Explainable AI Competition. Harvard Data Science Review, 1(2). Why Are We Using Black Box Models in AI When We Don't Need To? A Lesson From An Explainable AI Competition

Semenova, R., Rudin, C., and Parr, R. (2019). A study in Rashomon curves and volumes: A new perspective on generalization and model simplicity in machine learning. arXiv:1908.01755 [cs.LG]

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Why are we conducting this survey?

This survey will provide information that will help Canadians understand how much waste is managed by governments and businesses in Canada. Data will be collected from businesses within the waste management industry, as well as from businesses that are engaged in handling some or all of their own waste, through partnerships and material recovery agreements. The results will assist businesses and policy makers in making sound decisions related to waste management, based on data that apply specifically to activities conducted in this area. The survey will provide a comprehensive picture of waste management in Canada.

Your information may also be used by Statistics Canada for other statistical and research purposes.

Your participation in this survey is required under the authority of the Statistics Act.

Other important information

Authorization to collect this information

Data are collected under the authority of the Statistics Act, Revised Statutes of Canada, 1985, Chapter S-19.

Confidentiality

By law, Statistics Canada is prohibited 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. Statistics Canada will use the information from this survey for statistical purposes only.

Record linkages

To enhance the data from this survey and to reduce the reporting burden, Statistics Canada may combine the acquired data with information from other surveys or from administrative sources.

Data-sharing agreements

To reduce respondent burden, Statistics Canada has entered into data-sharing agreements with provincial and territorial statistical agencies and other government organizations, which have agreed to keep the data confidential and use them only for statistical purposes. Statistics Canada will only share data from this survey with those organizations that have demonstrated a requirement to use the data.

Section 11 of the Statistics Act provides for the sharing of information with provincial and territorial statistical agencies that meet certain conditions. These agencies must have the legislative authority to collect the same information, on a mandatory basis, and the legislation must provide substantially the same provisions for confidentiality and penalties for disclosure of confidential information as the Statistics Act. Because these agencies have the legal authority to compel businesses to provide the same information, consent is not requested and businesses may not object to the sharing of the data.

For this survey, there are Section 11 agreements with the provincial and territorial statistical agencies of Newfoundland and Labrador, Nova Scotia, New Brunswick, Quebec, Ontario, Manitoba, Saskatchewan, Alberta, British Columbia and the Yukon. The shared data will be limited to information pertaining to business establishments located within the jurisdiction of the respective province or territory.

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Chief Statistician of Canada
Statistics Canada
Attention of Director, Enterprise Statistics Division
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Ottawa, Ontario
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You may also contact us by email at statcan.esdhelpdesk-dsebureaudedepannage.statcan@statcan.gc.ca or by fax at 613-951-6583.

For this survey, there are Section 12 agreements with Environment and Climate Change Canada, Government of Alberta Environment and Parks, the Recycling Council of Alberta, and the statistical agencies of Prince Edward Island, the Northwest Territories and Nunavut. For agreements with provincial and territorial government organizations, the shared data will be limited to information pertaining to business establishments located within the jurisdiction of the respective province or territory.

Reporting period information

1. Information should be reported for this Jurisdiction/Company 's most recent fiscal year that ended at any time between April 1, 20xx and March 31, 20xx .

Fiscal Year Start date:

Fiscal Year End date:

2. What is the reason the reporting period does not cover a full year?

Select all that apply.

  • Seasonal operations
  • New business
  • Change of ownership
  • Temporarily inactive
  • Change of fiscal year
  • Ceased operations
  • Other
    Specify other reason the reporting period does not cover a full year

Waste management services - Business

This survey applies to operating province.

3. Indicate which of the following waste management activities or services this company provides in operating province.

Non-hazardous waste (garbage):
Included in this category are materials, products or by-products for which the waste generator has no further use and which are received for disposal at waste disposal facilities or for processing at a waste processing facility.

Residential waste:
Residential waste refers to waste from primary and seasonal dwellings, which includes all single family, multi-family, high-rise and low-rise residences.

It includes:

  • the waste picked up by the municipality, (either using its own staff, or through contracted companies)
  • the waste from residential sources which is self-hauled to depots, transfer stations and landfills.

Construction, renovation and demolition waste (CR&D):
CR&D waste, also referred to as DLC (demolition, land clearing and construction waste), refers to waste generated by construction, renovation and demolition activities. It generally includes materials such as brick, painted wood, drywall, metal, cardboard, doors, windows, wiring. It excludes materials from land clearing on areas not previously developed. CR&D waste can come from residential sources such as house renovations or from non-residential sources for example the construction or demolition of office buildings.

Hazardous waste:
Includes materials or substances that given their corrosive, inflammable, infectious, reactive and toxic characteristics, may present a real or potential harm to human health or the environment. Due to their hazardous nature they require special handling, storing, transportation, treatment and disposal as specified by the Transportation of Dangerous Goods Regulations (1985), The Canadian Environmental Protection Act (1988), The Basel Convention (1989), or the Export and Import of Hazardous Waste Regulations (1992).

Industrial, commercial and institutional (IC&I) waste, non-residential:
IC&I waste is the waste generated by all non-residential sources in a municipality, and is excluded from the residential waste stream. This includes:

  • industrial waste, which is generated by manufacturing, and primary and secondary industries, and is managed off-site from the manufacturing operation. It is generally picked up under contract by the private sector
  • commercial waste is generated by commercial operations such as shopping centres, offices, etc. Some commercial waste (e.g., from small street-front stores) may be picked up by the municipal collection system along with the residential waste
  • institutional waste is generated by institutional facilities such as schools, hospitals, government facilities, senior homes, universities, etc. This waste is generally picked up under contract with the private sector.

Organic material collection, non-residential:
Collection of organic material (e.g., food scraps, leaves, grass, wood waste and paper products) from sources such as heavy and light industry, manufacturing, agriculture, warehousing, transportation, retail and wholesale commercial activities, restaurants, offices, educational or recreational facilities, health and other service facilities.

Organic material collection, residential:
Collection of organic material (e.g., food scraps, leaves, grass, yard trimmings) from dwellings, including apartment buildings and condominiums. Examples of collection methods are curbside collection, back door pick-up, and automated collection.

Organic processing services:
The breakdown of organic materials through either composting or anaerobic digestion processes.

Recyclable material collection services, non-residential (non-hazardous):
Collection of non-hazardous recyclable material, (e.g., cardboard, paper, plastics, metals, glass), from sources such as heavy and light industry, manufacturing, warehousing, transportation, retail and wholesale commercial activities, restaurants, offices, educational or recreational facilities, health and other service facilities. Recyclable material may be taken to an intermediate site such as a material recycling facility or transfer facility.

Recyclable material collection, residential (non-hazardous):
Collection of non-hazardous recyclable material (e.g., cardboard, paper, plastics, metals, glass) from dwellings, including apartment buildings and condominiums. Examples of collection methods are curbside collection, back door pick-up, and automated collection. Recyclable material may be taken to an intermediate site such as a material recovery facility or transfer facility. Recyclable material may be collected on a regular or flexible schedule.

Recycling services (non-hazardous waste):
Recovery and reprocessing of recyclable material (e.g., cardboard, paper, plastics, metals, glass) from the non-hazardous waste stream by baling, cleaning, sorting, reducing volume and preparing for shipment. Generally these activities take place in a material recycling facility (MRF).

Transfer station (non-hazardous):
A facility at which wastes transported by vehicles involved in collection are transferred to other vehicles that will transport the wastes to a disposal (landfill or incinerator) or recycling facility.

Waste collection services, non-residential (non-hazardous):
Collection of non-hazardous waste, garbage, rubbish, refuse, trash and commingled material from sources such as heavy and light industry, manufacturing, agriculture, warehousing, transportation, retail and wholesale commercial activities, restaurants, offices, educational or recreational facilities, health and other service facilities. Waste may be taken to an intermediate site or to a final disposal site.

Waste collection services, residential (non-hazardous):
Collection of non-hazardous waste, garbage, rubbish, refuse, trash and commingled material from dwellings, including apartment buildings and condominiums. Examples of collection methods are curbside collection, back door pick-up, and automated collection. Waste may be taken to an intermediate site or to a final disposal site.

Waste hauling or transportation:
The transportation of waste from one site or geographic area to another. This excludes the collection of waste and is limited to activities such as waste exporting or the shipping of wastes from transfer station to disposal or processing facility.

Select all that apply.

  • Non-hazardous solid waste (garbage), recyclables and organics
    • Waste collection, residential
    • Waste collection, non-residential
  • Industrial, commercial and institutional (IC&I)
  • Construction, renovation and demolition (CR&D)
  • Waste hauling or transportation
  • Recyclable material collection or organic material collection, residential
  • Recyclable material collection or organic material collection, non-residential
  • Recycling or organic processing services
    e.g., material recycling facility, composting facility
  • Waste transfer station
  • Waste disposal or processing facility
  • Other non-hazardous waste services
    • Specify other non-hazardous waste services
  • Hazardous waste
    e.g., waste collection, waste transfer facility, waste treatment, waste recycling, waste disposal facility
  • Other waste management activities or services
    e.g., waste collection, waste transfer facility, waste treatment, waste recycling, waste disposal facility
    • Specify other waste management activities or services

Waste management services - Government

4. In 20xx, did this jurisdiction provide waste management services for itself?

  • Yes
  • No

Which services do you provide?

Select all that apply.

  • Collection
  • Disposal or processing
  • Recycling or organic material processing
  • Waste management planning or administration

5. In 20xx, did this jurisdiction provide waste management services to any other municipalities, cities, villages, towns or townships?

  • Yes
  • No

How many municipalities, cities, villages, towns or townships? (Maximum of 15)

6. Specify the names of the municipalities, cities, villages, towns and townships.

Municipality

7. Which waste management services were provided by your jurisdiction?

Select all that apply.

  • Organic processing services:
    The breakdown of organic materials through either composting or anaerobic digestion processes.
  • Recycling services (non-hazardous waste):
  • Recovery and reprocessing of recyclable material (e.g., cardboard, paper, plastics, metals, glass) from the non-hazardous waste stream by baling, cleaning, sorting, reducing volume and preparing for shipment. Generally these activities take place in a material recycling facility (MRF).
  • Waste collection services, non-residential (non-hazardous):
    Collection of non-hazardous waste, garbage, rubbish, refuse, trash and commingled material from sources such as heavy and light industry, manufacturing, agriculture, warehousing, transportation, retail and wholesale commercial activities, restaurants, offices, educational or recreational facilities, health and other service facilities. Waste may be taken to an intermediate site or to a final disposal site.
  • Waste collection services, residential (non-hazardous):
    Collection of non-hazardous waste, garbage, rubbish, refuse, trash and commingled material from dwellings, including apartment buildings and condominiums. Examples of collection methods are curbside collection, back door pick-up, and automated collection. Waste may be taken to an intermediate site or to a final disposal site.
  • Collection
  • Disposal or processing
  • Recycling or organic material processing
  • Waste management planning or administration

Collection or transportation of non-hazardous waste (garbage)

8. In 20xx, was waste in this jurisdiction collected or transported by this jurisdiction's employees to a landfill, incinerator or energy from waste facility, residual waste processor or a transfer station?

Energy from waste (EFW):
EFW refers to any waste treatment that creates energy in the form of electricity or heat from a waste source. Most EFW processes produce electricity directly through combustion, or produce a combustible fuel commodity, such as methane, methanol, ethanol or synthetic fuels.

Incineration/thermal treatment:
Incineration, in the context of waste, refers to the burning of waste. Incineration of waste materials converts the waste into incinerator bottom ash, flue gases, particulates, and heat, which can in turn be used to generate electric power. Most jurisdictions in Canada consider incineration to be disposal.

Landfill:
A site, on land, that is used primarily for the disposal of waste materials. The contents of landfills can include garbage which is not processed, and also residual material from processing operations (e.g., MRF residues, incinerator ash, organic processing residues).

Residual waste processing (non-hazardous):
An operation in which the physical or chemical properties of non-recyclable or compostable wastes are changed to reduce size and/or volume. Examples of waste processing are shredding, compaction & transformation.

Transfer station (non-hazardous):
A facility at which wastes transported by vehicles involved in collection are transferred to other vehicles that will transport the wastes to a disposal (landfill or incinerator) or recycling facility.

  • Yes
  • No

9. In 20xx , was waste in this jurisdiction collected or transported by contractors hired by this jurisdiction to a landfill, incinerator or energy from waste facility, residual waste processor or a transfer station?

  • Yes
  • No

How many contractors? (Maximum of 15)

10. Specify the names of contractors hired by this jurisdiction.

Contractor

11. In 20xx , was waste in this jurisdiction collected or transported by another jurisdiction to a landfill, incinerator or energy from waste facility, residual waste processor or a transfer station?

  • Yes
  • No

How many other jurisdictions? (Maximum of 15)

12. Specify the names of the jurisdictions.

Jurisdiction

13. How many facilities were used to dispose the collected waste?

Do not specify transfer stations unless it is the final destination (e.g., incinerators/energy from waste facilities, landfills, residual waste processors).

Number of facilities (Maximum of 15)

14. Specify the names of the facilities where the waste was taken for disposal.

Do not specify transfer stations unless it is the final destination.

Facility

Collection or transportation of non-hazardous recyclable materials

15. In 20xx, did this jurisdiction have a curbside collection program for recyclable materials?

Recyclable material collection services, non-residential (non-hazardous):
Collection of non-hazardous recyclable material, (e.g., cardboard, paper, plastics, metals, glass), from sources such as heavy and light industry, manufacturing, warehousing, transportation, retail and wholesale commercial activities, restaurants, offices, educational or recreational facilities, health and other service facilities. Recyclable material may be taken to an intermediate site such as a material recycling facility or transfer facility.

Recyclable material collection, residential (non-hazardous):
Collection of non-hazardous recyclable material (e.g., cardboard, paper, plastics, metals, glass) from dwellings, including apartment buildings and condominiums. Examples of collection methods are curbside collection, back door pick-up, and automated collection. Recyclable material may be taken to an intermediate site such as a material recovery facility or transfer facility. Recyclable material may be collected on a regular or flexible schedule.

  • Yes
  • No

16. Were recyclable materials collected or transported by this jurisdiction's employees?

  • Yes
  • No

17. Were recyclable materials collected or transported by contractors hired by this jurisdiction?

  • Yes
  • No

How many contractors? (Maximum of 15)

18. Specify the names of contractors hired by this jurisdiction.

Contractor

19. Were recyclable materials collected or transported by another jurisdiction?

  • Yes
  • No

How many other jurisdictions? (Maximum of 15)

20. Specify the names of the jurisdictions.

Jurisdiction

21. How many facilities were used to process recyclable materials?

Number of facilities (Maximum of 15)

22. Specify the names of the facilities where the recyclable materials were taken for processing.

Facility

Collection or transportation of organic materials

23. In 20xx, did this jurisdiction have a curbside collection program for organic materials destined for processing?

Organic materials:
Materials that are or were once living, such as leaves, grass, yard trimmings, agricultural crop residues, wood waste, and paper and paperboard products or food scraps.

Organic material collection, residential:
Collection of organic material (e.g., food scraps, leaves, grass, yard trimmings) from dwellings, including apartment buildings and condominiums. Examples of collection methods are curbside collection, back door pick-up, and automated collection.

Organic material collection, non-residential:
Collection of organic material (e.g., food scraps, leaves, grass, wood waste and paper products) from sources such as heavy and light industry, manufacturing, agriculture, warehousing, transportation, retail and wholesale commercial activities, restaurants, offices, educational or recreational facilities, health and other service facilities.

  • Yes
  • No

24. Were organic materials collected or transported by this jurisdiction's employees?

  • Yes
  • No

25. Were organic materials collected or transported by contractors hired by this jurisdiction?

  • Yes
  • No

How many contractors? (Maximum of 15)

26. Specify the names of the contractors hired by this jurisdiction.

Contractor

27. Were organic materials collected or transported by another jurisdiction?

  • Yes
  • No

How many other jurisdictions? (Maximum of 15)

28. Specify names of the other jurisdictions.

Jurisdiction

29. How many facilities were used to process these organic materials?

Number of facilities (Maximum of 15)

30. Specify the names of the facilities where the organic materials were taken for processing.

e.g., composting, anaerobic digestion

Organic materials:
Materials that are or were once living, such as leaves, grass, yard trimmings, agricultural crop residues, wood waste, and paper and paperboard products or food scraps.

Organic processing services:
The breakdown of organic materials through either composting or anaerobic digestion processes.

Facility

Waste management services

31. In 20xx, did this company provide waste management services to a municipality, waste management commission, or other waste management body?

Waste management services include the following:
Residential and non-residential non-hazardous recyclable material collection and processing

Residential and non-residential non-hazardous organic material collection and processing

Residential and non-residential non-hazardous waste, garbage, rubbish, refuse and trash collection and processing

  • Yes
  • No

How many municipalities, waste management commissions, or other waste management bodies? (Maximum of 15)

32. Specify the names of the municipalities, waste management commissions or waste management bodies.

Municipality, waste management commission or waste management body

Collection or transportation of non-hazardous waste (garbage)

33. In 20xx , did this company provide or sub-contract services for the collection or transportation of non-hazardous waste to a landfill, incinerator, energy from waste facility, residual waste processor or a transfer station?

Waste collection services, non-residential (non-hazardous):
Collection of non-hazardous waste, garbage, rubbish, refuse, trash and commingled material from sources such as heavy and light industry, manufacturing, agriculture, warehousing, transportation, retail and wholesale commercial activities, restaurants, offices, educational or recreational facilities, health and other service facilities. Waste may be taken to an intermediate site or to a final disposal site.

Waste collection services, residential (non-hazardous):
Collection of non-hazardous waste, garbage, rubbish, refuse, trash and commingled material from dwellings, including apartment buildings and condominiums. Examples of collection methods are curbside collection, back door pick-up, and automated collection. Waste may be taken to an intermediate site or to a final disposal site.

Waste hauling or transportation:
The transportation of waste from one site or geographic area to another. This excludes the collection of waste and is limited to activities such as waste exporting or the shipping of wastes from transfer station to disposal or processing facility.

  • Yes
  • No

To how many facilities was the waste taken? (Maximum of 15)

34. Specify the names of the facilities where the non-hazardous waste was taken for processing.

Include only final destinations such as landfills, incinerators, energy from waste facilities, residual waste processors.

Exclude transfer stations unless it is the final destination for waste collected/transported by this company.

Facility

Collection or transportation of non-hazardous recyclable materials

35. In 20xx, did this company collect or transport non-hazardous recyclable materials?

Recyclable material collection services, non-residential (non-hazardous):
Collection of non-hazardous recyclable material, (e.g., cardboard, paper, plastics, metals, glass), from sources such as heavy and light industry, manufacturing, warehousing, transportation, retail and wholesale commercial activities, restaurants, offices, educational or recreational facilities, health and other service facilities. Recyclable material may be taken to an intermediate site such as a material recycling facility or transfer facility.

Recyclable material collection, residential (non-hazardous):
Collection of non-hazardous recyclable material (e.g., cardboard, paper, plastics, metals, glass) from dwellings, including apartment buildings and condominiums. Examples of collection methods are curbside collection, back door pick-up, and automated collection. Recyclable material may be taken to an intermediate site such as a material recovery facility or transfer facility. Recyclable material may be collected on a regular or flexible schedule.

  • Yes
  • No

To how many facilities was this material brought? (Maximum of 15)

36. Specify the names of the facilities where the recyclable materials were taken.

Facility

Collection or transportation of organic materials

37. In 20xx, did this company collect or transport organic materials?

Organic materials:
Materials that are or were once living, such as leaves, grass, yard trimmings, agricultural crop residues, wood waste, and paper and paperboard products or food scraps.

Organic material collection, non-residential:
Collection of organic material (e.g., food scraps, leaves, grass, wood waste and paper products) from sources such as heavy and light industry, manufacturing, agriculture, warehousing, transportation, retail and wholesale commercial activities, restaurants, offices, educational or recreational facilities, health and other service facilities.

Organic material collection, residential:
Collection of organic material (e.g., food scraps, leaves, grass, yard trimmings) from dwellings, including apartment buildings and condominiums. Examples of collection methods are curbside collection, back door pick-up, and automated collection.

  • Yes
  • No

To how many facilities was the material taken? (Maximum of 15)

38. Specify the names of the facilities where the organic material was taken for processing.

Facility

Organic material processing

39. In 20xx, did this Jurisdiction/Company own or operate a facility where organic materials were processed?

Organic materials:
Materials that are or were once living, such as leaves, grass, yard trimmings, agricultural crop residues, wood waste, and paper and paperboard products or food scraps.

Organic material collection, residential:
Collection of organic material (e.g., food scraps, leaves, grass, yard trimmings) from dwellings, including apartment buildings and condominiums. Examples of collection methods are curbside collection, back door pick-up, and automated collection.

Organic material collection, non-residential:
Collection of organic material (e.g., food scraps, leaves, grass, wood waste and paper products) from sources such as heavy and light industry, manufacturing, agriculture, warehousing, transportation, retail and wholesale commercial activities, restaurants, offices, educational or recreational facilities, health and other service facilities.

Include landfills or sites where organic materials were composted.

  • Yes
  • No

How many facilities? (Maximum of 15)

40. Please provide the name and owner of each processing facility.

Facility

Facility name

Facility owner

Organic material processing

41. For the specified facility, indicate which organic materials are processed at the facility.

Estimating sources of waste (garbage), recyclables and organic materials:
It is acknowledged that it is often very difficult to track the quantities of waste and recyclable materials by source unless the business or local government collects or prepares materials from only one source (e.g., a firm that collects waste only from IC&I sources).

In this survey, you are being asked to estimate the proportion of materials by source of material at three points (if applicable and known): at the facility where organic material is processed, at the facility where recyclables are prepared and at disposal. If you engage in one or more of these activities, you will be asked to estimate the proportion of waste, recyclable or organic materials from residential, non-residential and construction and demolition sources. While it is recognized that such estimates may be difficult to make, you are asked to be as accurate as possible.

Sources of materials:
Refers to the sources of generation of the waste or recyclable material. These sources are classified as residential, industrial, commercial and institutional (IC&I) and construction, renovation and demolition (CR&D). It is sometimes difficult to ascertain the source of a given material because of lack of tracking or complex collection arrangements (e.g., when collection is contracted out or when collection vehicles pick up materials from a mix of sources on their routes).

Food waste:
Includes food wastes and food scraps from households and non-residential sources such as grocery stores, restaurants, etc., destined for composting or anaerobic digestion.

Source separated organic materials (SSO):
Source separation of organics is the setting aside of organic waste materials at their point of generation (the home, office, or other place of business) by the generator. Examples of SSO materials are food scraps, soiled paper packaging such as ice cream boxes, muffin paper, flour and sugar bags, paper coffee cups and paper plates.

Leaf and yard waste:
Includes any waste collected from a yard or garden such as leaves, grass clippings, plants, tree trimmings and branches.

Forestry waste:
The debris or leftover waste from the management of forests. This would include trees, stumps, branches, etc., that were discarded.

Wood waste:
The primary constituents of wood waste are used lumber, trim, trees, branches, and other wood debris from construction and demolition clearing and grubbing activities. It includes: dimensional lumber, plywood, particle board and fibre board, crating, wood fencing, pressure treated lumber, wood shingles, wooden doors, creosoted wood products, demolition wood waste, painted wood.

Agricultural waste:
All waste materials produced as a result of agricultural activities, including, for example, residues from the application of pesticides, herbicides, fertilizers and other chemicals, wastewater, bedding material, etc.

Biosolids:
Includes solid or semisolid material obtained from treated wastewater.

Include all quantities of food waste, materials from source separated organics programs (SSO), leaf and yard waste as well as Christmas trees and pumpkins.

Leaf and yard waste

Please provide the amount and source of leaf and yard waste processed at this facility.

When precise figures are not available, please provide your best estimate.

Quantity of materials

Unit of measure (UOM)

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure

Residential

If the exact source breakdown is not available, please provide your best estimate for each source.

Industrial, commercial and institutional (IC&I)

If the exact source breakdown is not available, please provide your best estimate for each source.

Construction, renovation and demolition (CR&D)

If the exact source breakdown is not available, please provide your best estimate for each source.

Food waste and SSO material

Please provide the amount and source of food waste and SSO material processed at this facility.

When precise figures are not available, please provide your best estimate.

Quantity of materials

Unit of measure (UOM)

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure

Residential

If the exact source breakdown is not available, please provide your best estimate for each source.

Industrial, commercial and institutional (IC&I)

If the exact source breakdown is not available, please provide your best estimate for each source.

Construction, renovation and demolition (CR&D)

If the exact source breakdown is not available, please provide your best estimate for each source.

Forestry waste and wood waste

Please provide the amount and source of forestry waste and wood waste processed at this facility.

When precise figures are not available, please provide your best estimate.

Quantity of materials

Unit of measure (UOM)

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure

Residential

If the exact source breakdown is not available, please provide your best estimate for each source.

Industrial, commercial and institutional (IC&I)

If the exact source breakdown is not available, please provide your best estimate for each source.

Construction, renovation and demolition (CR&D)

If the exact source breakdown is not available, please provide your best estimate for each source.

Agricultural waste

Please provide the amount and source of agricultural waste processed at this facility.

When precise figures are not available, please provide your best estimate.

Quantity of materials

Unit of measure (UOM)

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure

Residential

If the exact source breakdown is not available, please provide your best estimate for each source.

Industrial, commercial and institutional (IC&I)

If the exact source breakdown is not available, please provide your best estimate for each source.

Construction, renovation and demolition (CR&D)

If the exact source breakdown is not available, please provide your best estimate for each source.

Biosolids

Please provide the amount and source of biosolids processed at this facility.

When precise figures are not available, please provide your best estimate.

Quantity of materials

Unit of measure (UOM)

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure

Residential

If the exact source breakdown is not available, please provide your best estimate for each source.

Industrial, commercial and institutional (IC&I)

If the exact source breakdown is not available, please provide your best estimate for each source.

Construction, renovation and demolition (CR&D)

If the exact source breakdown is not available, please provide your best estimate for each source.

Other type of organic material 1

Please provide the amount and source of other type of organic material 1 processed at this facility.

When precise figures are not available, please provide your best estimate.

Specify other type of organic material 1

Quantity of materials

Unit of measure (UOM)

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure

Residential

If the exact source breakdown is not available, please provide your best estimate for each source.

Industrial, commercial and institutional (IC&I)

If the exact source breakdown is not available, please provide your best estimate for each source.

Construction, renovation and demolition (CR&D)

If the exact source breakdown is not available, please provide your best estimate for each source.

Other type of organic material 2

Please provide the amount and source of other type of organic material 2 processed at this facility.

When precise figures are not available, please provide your best estimate.

Specify other type of organic material 2

Quantity of materials

Unit of measure (UOM)

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure

Residential

If the exact source breakdown is not available, please provide your best estimate for each source.

Industrial, commercial and institutional (IC&I)

If the exact source breakdown is not available, please provide your best estimate for each source.

Construction, renovation and demolition (CR&D)

If the exact source breakdown is not available, please provide your best estimate for each source.

Other type of organic material 3

Please provide the amount and source of other type of organic material 3 processed at this facility.

When precise figures are not available, please provide your best estimate.

Specify other type of organic material 3

Quantity of materials

Unit of measure (UOM)

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure

Residential

If the exact source breakdown is not available, please provide your best estimate for each source.

Industrial, commercial and institutional (IC&I)

If the exact source breakdown is not available, please provide your best estimate for each source.

Construction, renovation and demolition (CR&D)

If the exact source breakdown is not available, please provide your best estimate for each source.

Recycling

42. In 20xx, did this Jurisdiction/Company own or operate a facility (municipally or privately operated) that prepared materials for recycling?

e.g., material recycling facility (MRF), recycling centre, drop-off depot

Recycling:
Recycling is defined as the process whereby a recyclable material (e.g., glass, metal, plastic, paper) is diverted from the waste stream in order to be remanufactured into a new product, or is used as a raw material substitute.

Recycling centre/drop off depot:
A facility or site where the public can bring materials for recycling or re-use. In some cases, household hazardous waste or special waste is accepted at these sites.

Material Recycling Facility (MRF):
A facility where materials that are collected for recycling are prepared or processed. The preparation or processing can include sorting, baling, cleaning, crushing, volume reduction and storing until shipment.

  • Yes
  • No

How many facilities? (Maximum of 15)

43. Please provide the name and owner of each recycling facility.

Facility

Facility name

Facility owner

44. For the specified facility, indicate your best estimate of the sources of recycled materials.

Estimating sources of waste (garbage), recyclables and organic materials:
It is acknowledged that it is often very difficult to track the quantities of waste and recyclable materials by source unless the business or local government collects or prepares materials from only one source (e.g., a firm that collects waste only from IC&I sources).

In this survey, you are being asked to estimate the proportion of materials by source of material at three points (if applicable and known): at the facility where organic material is processed, at the facility where recyclables are prepared and at disposal. If you engage in one or more of these activities, you will be asked to estimate the proportion of waste, recyclable or organic materials from residential, non-residential and construction and demolition sources. While it is recognized that such estimates may be difficult to make, you are asked to be as accurate as possible.

Sources of materials:
Refers to the sources of generation of the waste or recyclable material. These sources are classified as residential, industrial, commercial and institutional (IC&I) and construction, renovation and demolition (CR&D). It is sometimes difficult to ascertain the source of a given material because of lack of tracking or complex collection arrangements (e.g., when collection is contracted out or when collection vehicles pick up materials from a mix of sources on their routes).

Ferrous metals:
These are metals which contain iron. They may have small amounts of other metals or other elements added, to yield specific properties. All ferrous metals are magnetic and give little resistance to corrosion. Steel is an example of a ferrous metal. The recycling of ferrous metals include but is not limited to the processing of tin/steel cans, strapping, as well as the extraction of metals from appliances.

Scrap metal:
Any metal cutting or reject of a manufacturing operation, which may be suitable for recycling.

Textiles:
Material composed of natural or synthetic fibers, including any combination of animal derived material such as wool or silk, plant-derived material such as linen and cotton, and synthetic material such as polyester or nylon (e.g., towels, shoes, purses, clothing, curtains and carpets).

White goods:
Includes metal items such as: stoves, fridges, freezers, air conditioners, dehumidifiers, washers, dryers, hot water tanks, metal sinks, microwaves, and various other metal items.

Plastics:
PET (1): Polyethylene Terephthalate, commonly abbreviated as PET or PETE, is a polymer resin of the polyester family. PET is identified by the number 1 recycling symbol. Commonly recyclable PET materials include 2 litre soda bottles, water bottles, cooking oil bottles, peanut butter jars.

HDPE (2): High Density Polyethylene is a polyethylene thermoplastic made from petroleum. HDPE is identified by the number 2 recycling symbol. Some commonly recycled HDPE materials include detergent bottles, milk jugs, and grocery bags.

All other plastics (3-7): Polyvinyl Chloride - PVC (3), Low Density Polyethylene - LDPE (4), Polypropylene - PP (5), Polystyrene - PS (6), Other (7). Common uses: (3) plastic pipes, outdoor furniture, shrink wrap, water bottles, (4) dry cleaning bags, produce bags, trash can liners, (5) aerosol caps, drinking straws, (6) packaging pellets, Styrofoam cups, (7) food containers.

Electronics:
Electronics are items that function through the use of electricity and/or batteries. Also included are items that have a circuit board but do not necessarily require electricity from an outlet (such as telecommunication equipment). Examples are personal computers, laptops, monitors, peripheral devices (printers, scanners, etc.), telephones, cell phones, facsimile machines, stereos, portable music players and children's toys containing electronic components.

Construction, renovation and demolition waste (CR&D):
CR&D waste, also referred to as DLC (demolition, land clearing and construction waste), refers to waste generated by construction, renovation and demolition activities. It generally includes materials such as brick, painted wood, drywall, metal, cardboard, doors, windows, wiring. It excludes materials from land clearing on areas not previously developed. CR&D waste can come from residential sources such as house renovations or from non-residential sources for example the construction or demolition of office buildings.

Only count quantities once. Exclude organic materials reported in question 41.

Newsprint and magazines

Quantity of materials

Unit of measure

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure

Residential

Industrial, commercial and institutional (IC&I)

Construction, renovation and demolition (CR&D)

Corrugated cardboard

Quantity of materials

Unit of measure

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure

Residential

Industrial, commercial and institutional (IC&I)

Construction, renovation and demolition (CR&D)

Mixed paper fibre and boxboard

Quantity of materials

Unit of measure

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure

Residential

Industrial, commercial and institutional (IC&I)

Construction, renovation and demolition (CR&D)

Glass

Quantity of materials

Unit of measure

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure

Residential

Industrial, commercial and institutional (IC&I)

Construction, renovation and demolition (CR&D)

Ferrous metals

Include ferrous scrap metal.

Quantity of materials

Unit of measure

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure

Residential

Industrial, commercial and institutional (IC&I)

Construction, renovation and demolition (CR&D)

White goods

e.g., household appliances

Quantity of materials

Unit of measure

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure

Residential

Industrial, commercial and institutional (IC&I)

Construction, renovation and demolition (CR&D)

Aluminum

Quantity of materials

Unit of measure

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure

Residential

Industrial, commercial and institutional (IC&I)

Construction, renovation and demolition (CR&D)

Copper

Quantity of materials

Unit of measure

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure

Residential

Industrial, commercial and institutional (IC&I)

Construction, renovation and demolition (CR&D)

Mixed metals

Quantity of materials

Unit of measure

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure

Residential

Industrial, commercial and institutional (IC&I)

Construction, renovation and demolition (CR&D)

Plastics

Quantity of materials

Unit of measure

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure

Residential

Industrial, commercial and institutional (IC&I)

Construction, renovation and demolition (CR&D)

Aseptic containers and tetra packs

Unit of measure

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure

Residential

Industrial, commercial and institutional (IC&I)

Construction, renovation and demolition (CR&D)

Gable top containers

e.g., milk cartons

Unit of measure

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure

Residential

Industrial, commercial and institutional (IC&I)

Construction, renovation and demolition (CR&D)

Electronics

Quantity of materials

Unit of measure

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure

Residential

Industrial, commercial and institutional (IC&I)

Construction, renovation and demolition (CR&D)

Tires

Quantity of materials

Unit of measure

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure

Residential

Industrial, commercial and institutional (IC&I)

Construction, renovation and demolition (CR&D)

Construction, renovation and demolition (CR&D) material

Quantity of materials

Unit of measure

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure

Residential

Industrial, commercial and institutional (IC&I)

Construction, renovation and demolition (CR&D)

Textiles

Quantity of materials

Unit of measure

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure

Residential

Industrial, commercial and institutional (IC&I)

Construction, renovation and demolition (CR&D)

Other 1

Specify type of material

Quantity of materials

Unit of measure

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure

Residential

Industrial, commercial and institutional (IC&I)

Construction, renovation and demolition (CR&D)

Other 2

Specify type of material

Quantity of materials

Unit of measure

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other
    Specify other unit of measure

Residential

Industrial, commercial and institutional (IC&I)

Construction, renovation and demolition (CR&D)

Other 3

Specify type of material

Quantity of materials

Unit of measure

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure

Residential

Industrial, commercial and institutional (IC&I)

Construction, renovation and demolition (CR&D)

Transfer stations

45. In 20xx, did this Jurisdiction/Company own or operate a transfer station for non-hazardous waste?

  • Yes
  • No

How many transfer stations? (Maximum of 15)

46. Please provide the following information for the transfer stations reported in Question 45.

Transfer station

Station name

Owner of this facility

Type of facility

  • Transfer station

Unit of measure

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure

Was a weigh scale present?

  • Yes
  • No

Quantity of waste managed through this transfer station

Residential

Industrial, commercial and institutional (IC&I)

Construction, renovation and demolition (CR&D)

Waste (garbage) disposal or processing

47. Did this Jurisdiction/Company own or operate a facility where waste was disposed or processed in 20xx?

Include:

  • all types of landfills, e.g., sanitary, stabilized, bioreactor
  • incineration or thermal treatment, e.g., energy from waste, gasification
  • residual waste processing, e.g., conversion of non-recyclable waste to alternative fuel source.
  • Yes
  • No

How many facilities? (Maximum of 15)

48. Please provide the following information for the facilities reported in Question 47.

Facility

Facility name

Owner of this facility

Type of facility

  • Landfill
  • Processor
  • Incinerator

Unit of measure

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure

Was a weigh scale present?

  • Yes
  • No

Quantity of waste disposed or processed in this facility

Residential

Industrial, commercial and institutional (IC&I)

Construction, renovation and demolition (CR&D)

Landfills

49. For the landfills reported in question 48, were any of the following materials received?

Bottom ash:
The residue ash that remains after the incineration of a waste material.

Contaminated soil:
Soils containing materials that, by their nature, require controlled disposal.

Clean fill:
Uncontaminated inert solid material including soil, rock, stone, dredged material, used asphalt, and brick, block or concrete. The soil is considered "clean" because it has not been contaminated or affected, for example by a spill or release of toxic materials.

Bottom ash from sewage sludge or solid waste incineration

  • Yes
  • No

Report the quantity of bottom ash from sewage sludge or solid waste incineration.

Quantity received at landfill

Unit of measure

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure
  • Yes
  • No

Contaminated soil

  • Yes
  • No

Report the quantity of contaminated soil.

Quantity received at landfill

Unit of measure

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure
  • Yes
  • No

Clean fill

  • Yes
  • No

Report the quantity of clean fill.

Quantity received at landfill

Unit of measure

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other
    Specify other unit of measure
  • Yes
  • No

Household hazardous or special waste and waste reduction

50. In 20xx, did this jurisdiction, in cooperation or conjunction with another jurisdiction or solely, administer a program to collect household hazardous and special waste?

Household hazardous/special waste (HHW):
Materials generated by residential households that cannot be collected in standard residential recycling programs and present a risk to municipal waste management systems because of their hazardous and/or toxic nature. This includes solid or liquid materials, or containers holding gases which have outlived their usefulness. This waste may be flammable, corrosive, explosive or toxic and therefore should not be disposed in landfills or sewage systems.

  • Yes
  • No

Waste reduction

51. In 20xx, did this jurisdiction conduct any of the following programs to encourage the reduction of waste?

Select all that apply.

  • Bag limits
  • Distribution of backyard composters (subsidized)
  • Reduced garbage collection frequency
    e.g., every two weeks
  • User fees or bag tags
  • Clear bag program for garbage
  • Other 1
    • Specify program 1
  • Other 2
    • Specify program 2
  • Other 3
    • Specify program 3
  • Other 4
    • Specify program 4
  • OR
    None of the above

Exports of waste for disposal or processing

52. In 20xx, did this Jurisdiction/Company own or operate a facility in operating province that transported or exported non-hazardous waste for disposal or processing to another province or territory or to another country?

Include direct shipments and shipments from transfer stations.

  • Yes
  • No

How many facilities? (Maximum of 15)

53. Provide the names of facilities handling non-hazardous waste for the purpose of exporting to another province or territory or to another country.

Facility

Facility name

Facility operator

Unit of measure

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other
    Specify other unit of measure

Quantity of waste exported to another province or territory

Quantity of waste exported to the United States

Quantity of waste exported to countries other than the United States

Imports of waste for disposal or processing

54. In 20xx, was non-hazardous waste from outside this province or territory disposed or processed in this Jurisdiction/Company 's facility/facilities in operating province ?

e.g., landfill facility, incinerator and energy from waste facility, or residual waste processing facility

  • Yes
  • No

Unit of measure

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure

55. Report the quantity of non-hazardous waste disposed or processed in operating province from other provinces or territories or from the United States.

Quantity of non-hazardous waste received from other provinces or territories in

Quantity of non-hazardous waste received from the United States in

Exports of recyclable materials

56. In 20xx, did this Jurisdiction/Company own or operate a facility in operating province that transported or exported recyclable materials to a material recycling facility (MRF) in another province or territory or in another country?

Exclude exports of recyclable materials to end markets in other provinces or the United States.

  • Yes
  • No

How many facilities? (Maximum of 15)

57. Provide the names of facilities handling recyclable materials for the purpose of exporting to another province or territory or to another country.

Facility

Facility name

Facility operator

Unit of measure

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other
    Specify other unit of measure

Quantity of recyclables exported to another province or territory

Quantity of recyclables exported to the United States

Quantity of recyclables exported to countries other than the United States

Imports of recyclable materials

58. In 20xx, were recyclable materials from outside operating province processed in this Jurisdiction/Company's material recycling facility/facilities (MRF)?

  • Yes
  • No

Unit of measure

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other
    Specify other unit of measure

59. Report the quantity of recyclable materials processed from other provinces or territories or from the United States.

Quantity of recyclables processed from other provinces or territories in

Quantity of recyclables processed from the United States in

Employment information

60. In 20xx, what was the average number of full-time (30 or more hours per week) and part-time (less than 30 hours per week) employees in operating province whose primary function is to work on the waste management activities of this Jurisdiction/Company ?

Exclude full-time equivalents for part-time employees, contract employees or sub-contractor's employees.

Average number of full-time employees in 20xx

Average number of part-time employees in 20xx

Waste management services - Business

61. Did this company provide waste management services in more than one province or territory in 20xx?

  • Yes
  • No

62. Indicate the other provinces or territories in which this company provides waste management services.

Select all that apply.

  • Alberta
  • British Columbia
  • Manitoba
  • New Brunswick
  • Newfoundland and Labrador
  • Northwest Territories
  • Nova Scotia
  • Nunavut
  • Ontario
  • Prince Edward Island
  • Québec
  • Saskatchewan
  • Yukon

Attach files

63. If you wish to report or submit additional documents using a separate attachment, please follow the instructions below.

Steps to attach a document

Step 1. Press the Attach files button

Step 2. Choose the file to attach

Step 3. Double click on the file to attach

To attach more than one document, repeat steps 1 to 3.

The name and size of each file attached will be displayed on the page.

Note: Each file attached must not exceed 5 MB and for the entire questionnaire, the total size must not exceed 50 MB.

Waste management and materials recovery

64. Is this business involved in the purchase or resale of metal (ferrous and non-ferrous), glass, plastic or other material?

Ferrous metals are metals containing iron. They may contain other metals to yield specific properties, are magnetic and subject to corrosion. Steel is an example of a ferrous metal.

Non-ferrous metals include non-iron containing metals, such as copper and aluminum.

  • Yes
  • No

65. Which of the following materials were sold by this business in 20xx?

Include materials recovered from items such as automobiles, household appliances and furniture, construction materials, etc.

Ferrous metals:
These are metals which contain iron. They may have small amounts of other metals or other elements added, to yield specific properties. All ferrous metals are magnetic and give little resistance to corrosion. Steel is an example of a ferrous metal.

Non-ferrous metals:
Non-ferrous metals include non-iron containing metals, such as copper and aluminum.

Plastics:PET (1):
Polyethylene Terephthalate, commonly abbreviated as PET or PETE, is a polymer resin of the polyester family. PET is identified by the number 1 recycling symbol. Commonly recyclable PET materials include 2 litre soda bottles, water bottles, cooking oil bottles, peanut butter jars.

HDPE (2):
High Density Polyethylene is a polyethylene thermoplastic made from petroleum. HDPE is identified by the number 2 recycling symbol. Some commonly recycled HDPE materials include detergent bottles, milk jugs, and grocery bags.

All others plastics (3-7):
Polyvinyl Chloride – PVC (3), Low Density Polyethylene – LDPE (4), Polypropylene – PP (5), Polystyrene – PS (6), Other (7). Common uses: (3) plastic pipes, outdoor furniture, shrink wrap, water bottles, (4) dry cleaning bags, produce bags, trash can liners, (5) aerosol caps, drinking straws, (6) packaging pellets, Styrofoam cups, (7) food containers.

Include materials recovered from items such as automobiles, household appliances and furniture, construction materials, etc.

Select all that apply.

Ferrous Metal

Please indicate the amount of ferrous metal prepared for market and sold by this facility in 20xx.

Quantity of materials

Unit of measure (UOM)

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure

Non-ferrous metals

Please indicate the amount of non-ferrous metal prepared for market and sold by this facility in #{ReferencePeriod}.

Quantity of materials

Unit of measure (UOM)

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure

Glass (include automotive)

Please indicate the amount of glass prepared for market and sold by this facility in 20xx.

Quantity of materials

Unit of measure (UOM)

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure

Plastic

Please indicate the amount of plastic prepared for market and sold by this facility in 20xx.

Quantity of materials

Unit of measure (UOM)

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure

Other material

Please indicate the amount of other material prepared for market and sold by this facility in 20xx.

Specify other material

Quantity of materials

Unit of measure (UOM)

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure

Waste management and materials recovery

66. What does your company do with its waste?

All of the waste is picked up by a waste management company

Some of the waste is picked up by a waste management company, but a portion is picked up by or sent to a third-party for recycling/recovery

May include: paper products sent to a pulp and paper processor; bottle return programs; metal brought to a scrap dealer, etc.

A third-party picks up the waste for recycling/recovery
i.e., a waste management company does not handle our waste

Important: These questions pertain to the portion of your company's waste that is not sent to or picked up by a waste management company.

Please include waste materials that are picked up by or sent directly to a third-party establishment for recovery/recycling.

Examples might include: paper products sent to a pulp and paper processor; bottle return programs; metal sent to a scrap dealer, etc.

67. Which of the following materials were picked up by or sent to a third-party in 20xx?

Plastics: PET (1):
Polyethylene Terephthalate, commonly abbreviated as PET or PETE, is a polymer resin of the polyester family. PET is identified by the number 1 recycling symbol. Commonly recyclable PET materials include 2 litre soda bottles, water bottles, cooking oil bottles, peanut butter jars.

HDPE (2):
High Density Polyethylene is a polyethylene thermoplastic made from petroleum. HDPE is identified by the number 2 recycling symbol. Some commonly recycled HDPE materials include detergent bottles, milk jugs, and grocery bags.

All others plastics (3-7):
Polyvinyl Chloride – PVC (3), Low Density Polyethylene – LDPE (4), Polypropylene – PP (5), Polystyrene – PS (6), Other (7). Common uses: (3) plastic pipes, outdoor furniture, shrink wrap, water bottles, (4) dry cleaning bags, produce bags, trash can liners, (5) aerosol caps, drinking straws, (6) packaging pellets, Styrofoam cups, (7) food containers.

Food waste: Includes food wastes and food scraps from households and non-residential sources such as grocery stores, restaurants, etc., destined for composting or anaerobic digestion.

Ferrous metals:
These are metals which contain iron. They may have small amounts of other metals or other elements added, to yield specific properties. All ferrous metals are magnetic and give little resistance to corrosion. Steel is an example of a ferrous metal. The recycling of ferrous metals include but is not limited to the processing of tin/steel cans, strapping, as well as the extraction of metals from appliances.

Non-ferrous metals:
Non-ferrous metals include non-iron containing metals, such as copper and aluminum.

Appliances (White goods): Includes metal items such as: stoves, fridges, freezers, air conditioners, dehumidifiers, washers, dryers, hot water tanks, metal sinks, microwaves, and various other metal items.

A third-party could include independent contractors, scrap metal company, etc.

Select all that apply.

Paper products

e.g., paper, mixed paper, cardboard, boxboard, etc.

Please provide the amount of paper products picked up by or sent to a third-party processor in 20xx.

Quantity of materials

Unit of measure (UOM)

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure

Destination (e.g., company name)

Plastic

e.g., products and packaging made of plastic that are sent for recovery/recycling

Please provide the amount of plastic picked up by or sent to a third-party processor in 20xx.

Quantity of materials

Unit of measure (UOM)

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure

Destination (e.g., company name)

Food waste

Please provide the amount of food waste picked up by or sent to a third-party processor in 20xx.

Quantity of materials

Unit of measure (UOM)

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure

Destination (e.g., company name)

Glass

Please provide the amount of glass picked up by or sent to a third-party processor in 20xx.

Quantity of materials

Unit of measure (UOM)

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure

Destination (e.g., company name)

Metal

Include ferrous and non-ferrous metals

Please provide the amount of metal picked up by or sent to a third-party processor in 20xx.

Quantity of materials

Unit of measure (UOM)

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure

Destination (e.g., company name)

Appliances

Please provide the amount of appliances picked up by or sent to a third-party processor in 20xx.

Quantity of materials

Unit of measure (UOM)

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure

Destination (e.g., company name)

Other material

Please provide the amount of other material picked up by or sent to a third-party processor in 20xx.

Specify other material

Quantity of materials

Unit of measure (UOM)

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure

Destination (e.g., company name)

Waste management and materials recovery

68. Does this establishment collect material as part of a take-it back program (e.g., used tank return, used/old electronic products)?

Include deposit-return programs and materials returned through Extended Producer Responsibility (EPR) programs.

Definition: Extended Producer Responsibility (EPR) is a program in which manufacturers of products are responsible for managing the end-of-life recycling/disposal of their products, post consumer use.

  • Yes
  • No

69. Which of the following materials did your company collect as part of a take-it back program in 20xx?

Electronics:
Electronics are items that function through the use of electricity and/or batteries. Also included are items that have a circuit board but do not necessarily require electricity from an outlet (such as telecommunication equipment). Examples are personal computers, laptops, monitors, peripheral devices (printers, scanners, etc.), telephones, cell phones, facsimile machines, stereos, portable music players and children's toys containing electronic components.

Appliances (White goods):
Includes metal items such as: stoves, fridges, freezers, air conditioners, dehumidifiers, washers, dryers, hot water tanks, metal sinks, microwaves, and various other metal items.

Select all that apply.

Electronic waste

Please indicate the amount of electronic waste your company collected in 20xx.

Quantity of materials

Unit of measure (UOM)

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure

Tires

Please indicate the amount of tires your company collected in 20xx.

Quantity of materials

Unit of measure (UOM)

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure

Appliances

Include items such as washers, dryers, stoves, fridges, etc.

Please indicate the amount of appliances your company collected in 20xx.

Quantity of materials

Unit of measure (UOM)

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure

Glass

Please indicate the amount of glass your company collected in 20xx.

Quantity of materials

Unit of measure (UOM)

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure

Other material

Please indicate the amount of other material your company collected in 20xx.

Specify other material

Quantity of materials

Unit of measure (UOM)

Note: If the UOM is not listed, select "Other specify".

  • Metric tonnes
  • Kilograms
  • Pounds
  • Cubic meters
  • Cubic yards
  • Short ton
  • Truck load
  • Bale
  • Units or items
  • Other specify
    Specify other unit of measure

An image is worth a thousand words: let your dashboard speak for you!

By: Chatana Mandava, Statistics Canada

A dashboard is a visual display of the most important information needed to achieve one or more objectives; information is consolidated and arranged on a single screen so that it can be monitored at a glance. Dashboards play a major role in the data science field as they are one of the most powerful ways to summarize results and communicate them to users. Statistics Canada's analysts often spend their time searching for information from the data provided or deriving insights from the data manually. Presenting a user-friendly and interactive dashboards reduces the workload for analysts as they can get the relevant information just by using filters. Dashboards are a powerful way of representing information that can be used to observe trends and monitor the performance of models to facilitate decision-making within an organization. Presented effectively, dashboards increase the productivity of users by reducing the landscape of information that needs to be parsed. Presented ineffectively, they can make finding the right information a daunting endeavour.

There are multiple tools like Tableau, Power BI, Dash Plotly and Kibana that can be used to build an effective and interactive dashboard. The choice of tool is dependent on multiple factors like type of data (e.g. text, numerical), size of data, parameters and also requirements of users. This article explores examples of two different problem statements and two different dashboards built based on their requirements.

Dashboards using Elasticsearch and Kibana

For a recent project, data scientists at Statistics Canada were tasked with building a dashboard to visualize COVID-19-related news articles and to be able to filter articles based on company name, source name, extraction source name, date, and a few important keywords such as COVID, Personal Protective Equipment, etc. The data extracted to solve this problem were text data. Millions of news articles were extracted through web scraping and various other means. Multiple machine learning and deep learning models such as SVM (Support vector Machines) and BERT (Bidirectional encoder representation from transformers) were applied to classify the news articles. The optimal way to display the consolidated results was to build an interactive dashboard. For this project, a Kibana dashboard was built to display all the news articles and visual representations of the output.

Elasticsearch is a search engine based on the Lucene Library. Elasticsearch allows the storage of huge volumes of data, and allows users to search through the data quickly and display the results in milliseconds. Instead of searching the text directly, it searches through an index and delivers results within a short amount of time. Kibana is a visualization and management tool for Elasticsearch. Given the objective of the project, Kibana was chosen as the appropriate tool. There are many interesting features in Kibana that allow users to build histograms, pie charts, bar charts, maps and so forth. The interactivity is automatically handled in Kibana. There are many other visualization tools (like Vega or Timelion) which can help create aggregate-based visualizations. Another interesting feature in Kibana is the ability to customize visualizations. The new Vega component allows users to create a variety of data visualizations available from the Vega library. The data can be ingested into Elasticsearch with the help of a Python library. The main objective of using the Elasticsearch Python library is to access news articles and store them in Elasticsearch for searching and analytics purpose. The data are scraped and ingested into Elasticsearch by creating a strict schema or mapping to make sure the data are being indexed in the correct format and type. Querying can be performed once the record is indexed.

A Kibana dashboard to provide multiple interfaces for interrogating data. This example demonstrates the use of a word cloud, a time series chart to show the number of articles extracted in a given time period, and tables to expose data elements within the

Description - Figure 1An example Kibana dashboard with multiple filters for analysts to quickly find items of interest.

The inbuilt options in Kibana Dashboard were utilized to build an interactive dashboard from the news articles. Over a period of approximately 10 days, a time series chart was built to analyze the total number of articles that were extracted during the period of time. This is an interactive time series plot that allows users to click on any year and the articles are filtered accordingly. The table option was used to visualize the title, body, snippet, extraction source, company name, source name and media name of the news articles. All the required filtrations were included in the dashboard, along with some aggregation visualizations. A few other features, such as word clouds, were also included which helped the analysts to understand the important words in those news articles and filter the required articles based on those words. These words change when filters are applied and new sets of words appear as per the sub-selected articles. One of the features allows users to save the results in a CSV file in case the analyst needs to incorporate other tools for further analysis. Users can also share the dashboard with others by sending them a URL.

The index patterns feature in Kibana helps to modify the index patterns and to update the index pattern field accordingly. An index pattern allows you to define properties of the field. For example, when the data are ingested into the database few fields will become searchable and some fields will be aggregable. The index pattern allows users to enable these features for all the fields. In the above problem case there is a field which contains the URL of the articles. When ingested this URL field format changed to Text. The format was modified to a clickable URL with the help of an index pattern.

Dash Plotly

In another example of the uses of dashboards, a second project at Statistics Canada set out to detect events from news articles. Working with users over a 35 day period, the final results were displayed in a dashboard built using Dash Plotly. Dash is an open source tool developed by Plotly for building machine learning and data science web apps. It allows users to insert various widgets, choosing dispositions and style as Dash's layout is HTML-based.

An example of a dashboard created using Dash Plotly and customized with a theme to show Government of Canada branding. This dashboard has a series of filters based on dates and topics, as well as a graph to display topic counts over time, article summarie

Description - Figure 2An example dashboard created using Dash Plotly. This example shows events found in news articles.

This dashboard also included a table that displays news articles, a summary of other articles, interactive buttons to move to next and previous article, a time series graph and a drop-down menu to filter articles based on company name, date, and division. Dash doesn't include the same number of inbuilt features as Kibana. It is meant to provide a lower level of functionality for building custom web applications. Through Dash Plotly, users can highlight the location, organization, date and time in the news articles. The entire script for the dashboard is written in Python, meaning special consideration needs to be given if it is to be shared outside your organization.

Pick the approach based on end-user needs

While this article examined dashboards built using Kibana and Dash Plotly, there are many other interactive dashboards that can be built using various tools. Both of the projects discussed leveraged cloud-based systems, but desktop tools also exist that can be connected to systems with less demanding requirements. Both Kibana and Plotly provide flexible systems, but they integrate into workflows in different ways. Elasticsearch is heavily focused on high performance text indexing and searching, making it a good choice for interaction with large amounts of text data. Dash Plotly incorporates all the power and convenience of Pandas for manipulating the data. Which tool you choose to use depends heavily on the context and requirements of the project and the needs of the end-users.

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Responsible use of automated decision systems in the federal government

By: Benoit Deshaies, Treasury Board of Canada Secretariat; Dawn Hall, Treasury Board of Canada Secretariat

Automated decision systems are computer systems that automate part or all of an administrative decision-making process. These technologies have foundations in statistics and computer science, and can include techniques such as predictive analysis and machine learning.

The Treasury Board Directive on Automated Decision-Making ("the Directive") is a mandatory policy instrument which applies to most federal government institutions, with the notable exception of the Canada Revenue Agency (CRA). It does not apply to other levels of government such as provincial or municipal governments. The Directive supports the Treasury Board Policy on Service and Digital and sets out requirements that must be met by federal institutions to ensure the responsible and ethical use of automated decision systems, including those using artificial intelligence (AI).

Data scientists play an important role in assessing data quality and building models to support automated decision systems. An understanding of when the Directive applies and how to meet its requirements can support the ethical and responsible use of these systems. In particular, the explanation requirement and the guidance (Guidance on Service and Digital, section 4.5.3.) from the Treasury Board of Canada Secretariat (TBS) on model selection are of high relevance to data scientists.

Potential issues with automated decisions

The use of automated decision systems can have benefits and risks for federal institutions. Bias and lack of explainability are two areas where issues can arise.

Bias

In recent years, data scientists have become increasingly aware of the "bias" of certain automated decision systems, which can result in discrimination. Data-driven analytics and machine learning can accurately capture both the desirable and undesirable outcomes of the past, and project them forward. Algorithms based on historic data can in some cases amplify race, class, gender and other inequalities of the past. As well, algorithms trained on datasets with a lack of, or disproportionate, representation can impact the accuracy of the systems. For example, many facial recognition systems don't work equally well, depending on the skin, colour or gender of the personFootnote 1, Footnote 2. Another common example is a model to support recruitment developed by Amazon which disproportionately favored male applicants. The underlying issue was identified to be that the model had been trained using the résumés of previous tech applicants to Amazon, who were predominantly menFootnote 3,Footnote 4.

Lack of explainability

Another potential issue with automated systems is when one cannot explain how the system arrived at its predictions or classifications. In particular, it can become difficult to produce an easily understood explanation when the systems grow in complexity, such as when neural networks are usedFootnote 5. In the context of the federal government, being able to explain how administrative decisions are made is critical. Individuals denied services or benefits have a right to a reasonable and understandable explanation from the government, which goes beyond indicating that it was a decision made by a computer. A strong illustration of this problem was seen when an algorithm started reducing the amount of medical care received by patients, leading to consequences that affected peoples' health and well-being. In this case, users of the system could not explain why this reduction occurredFootnote 6.

Objectives of the Directive

The issues described above are mitigated in conventional ("human") decision-making by laws. The Canadian Charter of Rights and Freedoms defines equality rights and precludes discrimination. Core administrative law principles of transparency, accountability, legality and procedural fairness define how decisions need to be made and what explanations must be provided to those impacted. The Directive interprets these principles and protections in the context of digital solutions making or recommending decisions.

The Directive also aims to ensure that automated decision systems are deployed in a manner that reduces risks to Canadians and federal institutions, and leads to more efficient, accurate, consistent and interpretable decisions. It does so by requiring an assessment of the impacts of algorithms, quality assurance measures for the data and the algorithm, and proactive disclosures about how and where algorithms are being used, to support transparency.

Scope of the Directive

The Directive applies to automated decision systems used for decisions that impact the legal rights, privileges or interests of individuals or businesses outside of the government—for example, the eligibility to receive benefits, or who will be the subject of an audit. The Directive came into force on April 1, 2019, and applies to systems procured or developed after April 1, 2020. Existing systems are not required to comply, unless an automated decision is added after this date.

Awareness of the scope and applicability of the Directive can enable data scientists and their supervisors to support their organization in implementing the requirements of the Directive to enable the ethical and responsible use of these systems.

For example, it is important to note that the Directive applies to the use of any technology, not only artificial intelligence or machine learning. This includes digital systems making or recommending decisions, irrelevant of the technology used. Systems automating calculations or implementing criteria that do not require or replace judgement could be excluded, if what they are automating is completely defined in laws or regulations, such as limiting the eligibility of a program to those 18 years or above in age. However, seemingly simple systems could be in scope if they are designed to replace or automate judgement. For example, a system which supports the detection of potential fraud by selecting targets for inspections using simple indicators, such as a person making deposits in three or more different financial institutions in a given week (a judgement of "suspicious behavior"), could be in scope.

The Directive applies to systems that make, or assist in making, recommendations or decisions. Having a person make the final decision does not remove the need to comply with the Directive. For example, systems that provide information to officers who make the final decisions could be in scope. There are multiple ways in which algorithms can make, or assist in making, recommendations or decisions. The list below illustrates some of these, reflecting how automating aspects of the fact finding or analysis process may influence subsequent decisions.

Some of the ways in which algorithms can support and influence decision-making processes:

  • present relevant information to the decision-maker
  • alert the decision-maker of unusual conditions
  • present information from other sources ("data matching")
  • provide assessments, for example by generating scores, predictions or classifications
  • recommend one or multiple options to the decision-maker
  • make partial or intermediate decisions as part of a decision-making process
  • make the final decision.

Requirements of the Directive

The following requirements of the Directive are foundational in enabling the ethical and responsible use of automated decision systems. Each section includes a brief description of the requirement and relevant examples that can enable their implementation.

Algorithmic Impact Assessment

It is important to understand and measure the impact of using automated decision systems. The Algorithmic Impact Assessment Tool (AIA) is designed to help federal institutions better understand and manage the risks associated with automated decision systems. Completing an AIA before production and when system functionality changes is required by the Directive.

The AIA provides the impact level for a system based on the responses federal institutions provide to a number of risk and mitigation questions, many of which are of high relevance to data scientists and their supervisors. This includes questions on potential risks related to the algorithm, the decision, and the source and type of data, as well as mitigation efforts such as consultation and the identification of processes and procedures in place to assess data quality.

The output of the AIA assigns an impact level ranging from Level I (little impact) to Level IV (very high impact). For example, a simple system deciding on the eligibility of receiving a $2 rebate for purchasing an energy-efficient light bulb might be Level I, whereas a complex neural network incorporating multiple data sources deciding to grant a prisoner parole would be a Level IV. The impact assessment is multi-faceted and was established through consultations with academia, civil society and other public institutions.

The impact level determined by the AIA supports the Directive in matching the appropriate requirements with the type of application being designed. While some of the Directive requirements apply to all systems, others vary according to the impact level. This ensures that the requirements are proportional to the potential impact of the system. For instance, at impact Level I decisions can be fully automated, whereas at Level IV the final decision must be made by a person. This supports the Directive requirements on "Ensuring human intervention" for more impactful decisions.

Demonstrates the flow an AI project will take, starting with the Algorithmic Impact Assessment to measure the impact level and determine the requirements outlined in Appendix C of the Directive.

Description - Figure 1The impact level calculated by the AIA determines the scaled requirements of the Directive.

Finally, the Directive requires publication of the final results of the AIA on the open government portal—an important transparency measure. This serves as a registry of automated decision systems in use in government, informs the public of when algorithms are used, and provides basic details about their design and the mitigation measures that were taken to reduce negative outcomes.

Transparency

The Directive includes a number of requirements aimed at ensuring that the use of automated decision systems by federal institutions is transparent. As mentioned above, publication of the AIA on the open government portal serves as a transparency measure. As clients will rarely consult this portal before accessing services, the Directive also requires that a notice of automation be provided to clients through all service delivery channels in use (Internet, in person, mail or telephone).

Another requirement which supports transparency, and is of particular relevance to data scientists, is the requirement to provide "a meaningful explanation to affected individuals of how and why the decision was made". This article presented how certain complex algorithms will be more difficult to explain, making this requirement more difficult to meet. In its guidance, TBS says to favor "easily interpretable models" and "the simplest model that will provide the performance, accuracy, interpretability and lack of bias required", distinguishing between interpretability and explainability (Guideline on Service and Digital, section 4.5.3.). This guidance is aligned with the work of others on the importance of interpretable models such as RudinFootnote 7 and MolnarFootnote 8.

Likewise, when the source code is owned by the Government of Canada, it needs to be released as open source, where possible. In the case of proprietary systems, the Directive requires that all versions of the software are safeguarded to retain the right to access and test the software and to authorize external parties to review and audit these components as necessary.

Beyond the publication of source code, additional transparency measures support the communication of the use of automated decision systems to a broad audience. Specifically, at impact levels III and IV, the Directive requires the publication of a plain language description of how the system works, how it supports the decision and the results of any reviews or audits. The latter could include the results of Gender-based Analysis Plus, Privacy Impact Assessments, and peer reviews, among others.

Quality assurance

Quality assurance plays a critical role in the development and engineering of any system. The Directive includes a requirement for testing before production, which is a standard quality assurance measure. However, given the unique nature of automated decision systems, the Directive also requires development of processes to test data for unintended biases that may unfairly impact the outcomes, and ensure that the data are relevant, accurate and up-to-date.

Quality assurance efforts should continue after the system is deployed. Operating the system needs to include processes to monitor the outcomes on a scheduled basis to safeguard against unintentional outcomes. The frequency of those verifications could depend on a number of factors, such as the impact and volume of decisions, and the design of the system. Learning systems that are frequently retrained may require more intense monitoring.

There is also direct human involvement in quality assurance, such as the need to consult legal services, provide human oversight of decisions with higher levels of impacts (often referred to as having a "human-in-the-loop"), and ensure sufficient training for all employees developing, operating and using the system.

Finally, the Directive requires a peer review from a qualified third party. The goal of this review is to validate the algorithmic impact assessment, the quality of the system, the appropriateness of the quality assurance and risk mitigation measures, and to identify the residual risk of operating the system. The peer review report should be considered by officials before making the decision to operate the system. A collaboration between TBS, the Canada School of Public Service and the University of Ottawa resulted in a guide proposing best practices for this activityFootnote 9.

Conclusion

The automation of service delivery by the government can have profound and far reaching impacts, both positive and negative. The adoption of data-driven technologies presents a unique opportunity to review and address past biases and inequalities, to build a more inclusive and fair society. Data scientists have also seen that automated decision systems can present some issues with bias and lack of explainability. The Treasury Board Directive on Automated Decision-Making provides a comprehensive set of requirements that can serve as the basic framework for the responsible automation of services and for preserving the basic protection of law in the digital world. In administrative law, the degree of procedural fairness for any given decision-making process increases or decreases with the significance of that decision. Likewise, the requirements of the Directive scale according to the impact level calculated by the algorithmic impact assessment.

Data scientists of the federal public service can play a leadership role in this transformation of government. By supporting the Directive in ensuring that decisions are efficient, accurate, consistent and interpretable, data scientists have the opportunity to identify ways to improve and optimize service and program delivery. Canadians also need data scientists to lead efforts to identify undesired biases in data, and support the responsible adoption of automation by building interpretable models, providing the transparency, fairness and explainability required.

Note: Stay tuned for an upcoming article on Statistics Canada’s Responsible Machine Learning Framework. The Data Science Network welcomes submissions for additional articles on this topic. Don’t hesitate to send us your article!

Team members
Benoit Deshaies, Dawn Hall

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Monthly Survey of Manufacturing: National Level CVs by Characteristic - April 2021

National Level CVs by Characteristic
Month Sales of goods manufactured Raw materials and components inventories Goods / work in process inventories Finished goods manufactured inventories Unfilled Orders
%
April 2020 0.87 0.99 1.20 1.41 1.10
May 2020 0.80 1.04 1.13 1.37 1.06
June 2020 0.69 1.05 1.19 1.38 1.06
July 2020 0.69 1.02 1.15 1.43 1.10
August 2020 0.64 1.05 1.23 1.50 1.20
September 2020 0.67 1.05 1.22 1.54 1.20
October 2020 0.69 1.02 1.18 1.53 1.15
November 2020 0.72 1.09 1.20 1.46 1.33
December 2020 0.69 1.04 1.18 1.46 1.37
January 2021 0.80 1.01 1.22 1.59 1.49
February 2021 0.76 1.01 1.49 1.68 1.36
March 2021 0.70 1.03 1.43 1.70 1.43
April 2021 0.72 1.06 1.52 1.70 1.40

Statistics Canada's publishing initiatives

Canadians are increasingly using mobile devices, such as smartphones and tablets, to access Government of Canada information and services.

To keep pace with this demand and to better serve users, Statistics Canada is now in the early stages of developing a mobile application to modernize the way data are published.

This consultation helped us gain insight on users' online behaviours and preferences and will support the agency's objectives to provide information in multiple formats and from multiple access points.

Respondent profile

When asked which user group they identify with, 49% of respondents identified in the program, policy and advocacy group, 18% as the education and research group, 18% as members of the general population, 10% as businesses, 7% as data services, 6% as other, and 1% as media.

Results

The 1004 responses to the consultation revealed that 44% of users read the news online in the early morning (5:00am to 8:30am); 44% in the morning (8:30am to 12:00pm); 18% in the afternoon (12:00pm to 5:00pm); 30% in the early evening (5:00pm to 9:00pm); 24% in the evening (9:00pm to midnight); and 3% overnight (midnight to 5:00am).

Furthermore, it was shown that the most important features or functionalities if Statistics Canada had a mobile application would be to ability to search for content (74%), save content (48%), share content (46%), receive notifications (44%), and be able to read offline (37%).

Respondents mobile applications preferences are that 54% would download a Statistics Canada mobile application; 63% would like to be notified of relevant news stories as they are published; 44% prefer mobile notifications over email notifications; and 40% prefer to download a mobile application on their mobile device to access news information, rather than navigate to a website.

Finally, 53% of respondents answered that they want to be notified if Statistics Canada publishes new data or articles throughout the day.

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Retail Commodity Survey: CVs for Total Sales (March 2021)

Retail Commodity Survey: CVs for Total Sales (March 2021)
NAPCS-CANADA Month
202012 202101 202102 202103
Total commodities, retail trade commissions and miscellaneous services 1.17 0.67 0.72 0.63
Retail Services (except commissions) [561] 1.15 0.67 0.72 0.63
Food at retail [56111] 0.90 0.98 0.99 0.65
Soft drinks and alcoholic beverages, at retail [56112] 0.59 0.68 0.63 0.53
Cannabis products, at retail [56113] 0.00 0.00 0.00 0.00
Clothing at retail [56121] 1.52 1.60 1.22 1.21
Footwear at retail [56122] 1.94 3.48 3.13 1.96
Jewellery and watches, luggage and briefcases, at retail [56123] 2.94 7.73 3.47 5.34
Home furniture, furnishings, housewares, appliances and electronics, at retail [56131] 0.70 0.94 0.97 0.76
Sporting and leisure products (except publications, audio and video recordings, and game software), at retail [56141] 1.67 2.68 2.87 2.27
Publications at retail [56142] 7.64 8.74 6.01 7.72
Audio and video recordings, and game software, at retail [56143] 6.88 3.16 7.15 5.54
Motor vehicles at retail [56151] 5.14 2.21 2.68 2.07
Recreational vehicles at retail [56152] 6.21 6.19 3.87 5.84
Motor vehicle parts, accessories and supplies, at retail [56153] 2.99 1.66 1.80 1.77
Automotive and household fuels, at retail [56161] 2.26 2.19 2.06 1.58
Home health products at retail [56171] 3.44 3.83 2.39 2.83
Infant care, personal and beauty products, at retail [56172] 3.14 2.33 2.32 2.29
Hardware, tools, renovation and lawn and garden products, at retail [56181] 1.69 1.77 2.11 1.79
Miscellaneous products at retail [56191] 2.12 2.57 2.44 3.24
Total retail trade commissions and miscellaneous servicesFootnote 1 2.43 1.48 1.66 1.85

Footnotes

Footnote 1

Comprises the following North American Product Classification System (NAPCS): 51411, 51412, 53112, 56211, 57111, 58111, 58121, 58122, 58131, 58141, 72332, 833111, 841, 85131 and 851511.

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