| Age group | Sex | Combined response rate (%) |
|---|---|---|
| Ages 6 to 11 | Males | 33.7 |
| Females | 27.2 | |
| Ages 12 to 19 | Males | 35.3 |
| Females | 35.2 | |
| Ages 20 to 39 | Males | 35.0 |
| Females | 37.2 | |
| Ages 40 to 59 | Males | 39.3 |
| Females | 36.6 | |
| Ages 60 to 79 | Males | 41.6 |
| Females | 38.9 |
| Age Group | Sex | Average Glucose (mmol/L) | |
|---|---|---|---|
| Average (mmol/L) | c.v. (%) | ||
| Ages 6 to 11 | Males | 4.99 | 1.17 |
| Females | 4.9 | 1.22 | |
| Ages 12 to 19 | Males | 5.1 | 0.56 |
| Females | 4.96 | 1.20 | |
| Ages 20 to 39 | Males | 5.22 | 0.88 |
| Females | 4.94 | 0.79 | |
| Ages 40 to 59 | Males | 5.78 | 1.88 |
| Females | 5.48 | 2.27 | |
| Ages 60 to 79 | Males | 6.28 | 4.18 |
| Females | 5.77 | 2.70 | |
| Geography | Month | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 202004 | 202005 | 202006 | 202007 | 202008 | 202009 | 202010 | 202011 | 202012 | 202101 | 202102 | 202103 | 202104 | |
| percentage | |||||||||||||
| Canada | 1.21 | 0.75 | 0.34 | 0.35 | 0.19 | 0.21 | 0.21 | 0.20 | 0.25 | 0.20 | 0.19 | 0.44 | 1.00 |
| Newfoundland and Labrador | 2.03 | 1.30 | 1.05 | 0.82 | 0.36 | 0.62 | 1.53 | 0.30 | 0.48 | 1.08 | 0.48 | 2.23 | 2.41 |
| Prince Edward Island | 52.43 | 11.92 | 9.11 | 8.73 | 0.95 | 0.63 | 0.84 | 1.08 | 1.81 | 1.63 | 1.04 | 1.07 | 18.65 |
| Nova Scotia | 4.09 | 3.94 | 0.88 | 1.50 | 1.39 | 0.37 | 0.77 | 0.36 | 1.03 | 0.91 | 0.40 | 0.88 | 3.06 |
| New Brunswick | 2.39 | 2.08 | 0.82 | 0.60 | 2.28 | 0.50 | 0.33 | 0.39 | 0.49 | 0.98 | 0.50 | 0.44 | 1.26 |
| Quebec | 1.93 | 1.66 | 0.70 | 0.77 | 0.48 | 0.56 | 0.65 | 0.55 | 0.79 | 0.68 | 0.67 | 0.42 | 1.04 |
| Ontario | 2.24 | 1.33 | 0.63 | 0.70 | 0.26 | 0.31 | 0.25 | 0.28 | 0.45 | 0.34 | 0.24 | 0.99 | 2.16 |
| Manitoba | 5.60 | 2.47 | 0.81 | 0.70 | 0.34 | 0.34 | 0.72 | 0.93 | 0.78 | 0.89 | 0.46 | 0.48 | 1.53 |
| Saskatchewan | 5.72 | 3.08 | 0.58 | 1.55 | 0.67 | 0.99 | 0.91 | 1.04 | 0.75 | 0.91 | 0.52 | 0.52 | 1.60 |
| Alberta | 2.62 | 1.76 | 0.63 | 0.53 | 0.23 | 0.55 | 0.33 | 0.36 | 0.54 | 0.52 | 0.33 | 0.79 | 1.87 |
| British Columbia | 3.21 | 2.19 | 1.03 | 0.83 | 0.67 | 0.58 | 0.72 | 0.68 | 0.39 | 0.33 | 0.56 | 0.97 | 2.75 |
| Yukon Territory | 10.07 | 3.77 | 3.06 | 1.41 | 1.57 | 1.64 | 1.72 | 1.71 | 4.34 | 5.07 | 1.96 | 3.13 | 73.01 |
| Northwest Territories | 6.95 | 3.24 | 2.48 | 1.43 | 1.94 | 2.14 | 2.10 | 2.04 | 1.97 | 6.05 | 1.83 | 3.05 | 79.73 |
| Nunavut | 315.64 | 5.07 | 3.93 | 1.82 | 0.56 | 2.60 | 2.45 | 67.48 | 2.75 | 2.54 | 2.39 | 2.53 | 4.52 |
| Geography | Month |
|---|---|
| 202104 | |
| % | |
| Canada | 0.7 |
| Newfoundland and Labrador | 1.3 |
| Prince Edward Island | 2.2 |
| Nova Scotia | 1.9 |
| New Brunswick | 2.1 |
| Quebec | 1.6 |
| Ontario | 1.3 |
| Manitoba | 1.0 |
| Saskatchewan | 2.2 |
| Alberta | 1.5 |
| British Columbia | 1.2 |
| Yukon Territory | 1.7 |
| Northwest Territories | 0.6 |
| Nunavut | 1.1 |
With the arrival of spring and warmer weather comes a sense of hope with the development of new vaccinations across the globe as we move beyond the one-year mark since the first COVID-19 lockdown. The Data Access Division (DAD) would like to take a moment to thank its beloved staff. The success of our program comes from the hard work and dedication that each member has continued to show collectively throughout these changing times. We could not have achieved our advancements without your great efforts and continued collaboration. We would also like to thank our clients and friends for their continued patience and support as we are constantly reminded of how fortunate we are to be part of such a strong community. We remain devoted to continuing our work to ensure that you are provided with the real-time data and services that you need.
DAD would like to highlight and celebrate some of its greatest accomplishments within the last few months. The Self-Serve Access (SSA) section provided virtual access to clients and successfully onboarded new users to the Public Use Microdata File (PUMF) Collection, provided free access and created accounts for research data centre (RDC) researchers for Real Time Remote Access (RTRA), and had 82 Data Liberation Initiative (DLI) member institutions. The Virtual Data Lab (VDL) team successfully onboarded its first set of users in the new cloud environment for its first pilot project back in February, bringing the team one step closer to a production environment. DAD, in partnership with the Canadian Research Data Centre Network (CRDCN), recently opened two new RDCs! One centre is located at Carleton University in Ottawa, and a new satellite centre opened its doors to researchers at the University of Calgary. In addition, the first Business Research Microdata (BRM) file that uses real, not synthetic, data was released to the RDCs. To help support researchers working in RDCs and in the VDL, we have produced a series of short training videos to help support researchers in producing their statistical output for release.
For the upcoming year, DAD will continue to focus on collaboration efforts with various teams and partners. We will focus on leveraging new technologies to help drive Statistics Canada's modernization efforts by developing new and innovative ways to access microdata, such as developing the Virtual Research Data Centre (vRDC) and the VDL, increasing granularity while meeting researcher needs, and continuing to provide the research community increased and faster access to data to support better decision making for policies and programs across the country. In the RDC Program, we will be opening another centre to researchers, as well as increasing our business data holdings.
Welcome to another DLI program membership year. The SSA section has added the following new services to the program:
The External Advisory Committee (EAC) sent a call-out in February to the Listserv for two volunteers to represent the Atlantic and Ontario regions. The SSA section and the EAC would like to welcome Jane Fry as an Ontario Region representative and to thank two members who have stepped down from the committee: Peter Webster, Co-Chair of the EAC and Atlantic Region representative, and Claire Wollen, Ontario Region representative.
The chair of the committee, Alex Cooper, sent an email to the Listserv confirming to the DLI community that face-to-face training has been cancelled again this year, but we will be doing national training again with regional sessions.
The Professional Development Committee (PDC) sent a call-out to the Listserv in March for a volunteer to represent the Quebec Region.
The SSA section and the PDC would like to take this opportunity to welcome Vivek Jadon from McMaster University as the new Ontario Regional Training Coordinator.
The PDC is working on several initiatives:
The newly designed DLI website is now live! The website includes the updated DLI Contact and Alternate's Survival Guide, information on the program, and resources. You can also easily navigate through the different data access programs.
We are working on putting PUMFs online in a downloadable format. Newly released PUMFs are being added to the website as they become available, and older PUMFs are being added in phases. As part of this project, digital object identifiers are being assigned to PUMFs.
A list of all DLI products is available on the website: Data Liberation Initiative.
RDC researchers have had their access extended to March 31, 2022.
The graphical user interface has been launched! The number of surveys available is currently limited. However, more surveys will be added throughout the year.
The SAS Assistant will help users with little SAS experience to generate successful tables. You will be able to use buttons and dropdown menus to build your SAS code, and your code is created as you select the variables.
A list of all RTRA products is available on the website: Real Time Remote Access.
While RDCs are still operating under reduced capacity because of COVID-19 restrictions, we are excited to announce the opening of a new centre at Carleton University and new satellite centre at the University of Calgary. Researchers now have access to two sites in both Ottawa and Calgary to facilitate demand for access across the cities.
The high-level Joint Task Force, co-chaired by Martin Taylor (Executive Director, CRDCN) and Jacques Fauteux (Assistant Chief Statistician, StatCan), focused on developing and aligning data access strategies for academic researchers and provided a report to the CRDCN executive board and the Chief Statistician in February. The report gave an overview of the technical infrastructure of the vRDC, identified possible intersections with the VDL platform, and highlighted critical business questions that will be explored in partnership between the CRDCN and StatCan in the coming months. Presentations on the report and next steps will be provided to StatCan staff and CRDCN academic directors in April.
We are pleased to announce that our pilot to test the VDL access platform with academic researchers was launched in March! Starting with selected projects in four universities, a StatCan cloud infrastructure platform will make StatCan microdata securely accessible outside RDC facilities. This pilot project involves the University of Toronto, Université de Montréal, McMaster University, and University of Calgary to start, but will be implemented on a broader scale after pilot testing is complete and the vRDC infrastructure is available. For more information on the pilot testing, please see the Modernization of Access section below.
On February 23, the CRDCN hosted a very well-attended webinar on the BRM. This will be the first business dataset released to the RDCs using real, not synthetic, data. It is expected that it will take extra time for researchers to become familiar with the data. As well, vetting will take longer than for a typical social data project because we will be using the BRM to create and test business data vetting rules. For these reasons, it is not recommended that students undertake their thesis or dissertation work with the BRM. Researchers can start applying for access in April.
A total of 25 products were added to our data holdings in the fourth quarter of the 2020/2021 fiscal year. These include two new surveys (Survey of Digital Technology and Internet Use and Impacts of COVID-19 on Health Care Workers: Infection Prevention and Control), one new linked data file (Survey of Approaches to Educational Planning linked to Postsecondary Student Information System–Registered Apprenticeship Information System–T1 Family File), as well as updated survey cycles and administrative files.
For a complete list of data available in RDCs and government access centres, visit Data available at the Research Data Centres.
We will soon be releasing our Data Access Training Video Series! These short videos will provide practical instruction to StatCan microdata users on a variety of topics related to access and data analysis. Our initial series of videos will cover how to prepare your output for confidentiality vetting, such as applying rounding techniques and testing for homogeneity and dominance using a variety of statistical software packages.
The Government Data Access team has started plans to merge the Federal Research Data Centre (FRDC) and the Social Data Access Centre and the Business Data Access Centre (formerly known as CDER), both located at Tunney's Pasture, into one location by the end of spring 2021. This new centre will provide access to both social and business data for federal government users. The integration of business and social data access into one physical location is a significant step in completing the full integration of the Business Data Access program under the FRDC umbrella.
DAD is also working with Employment and Social Development Canada (ESDC) to facilitate access and address research needs for the emergency response data linked to StatCan datasets. ESDC will undergo an accreditation process and training, and data access will be available from home for researchers.
Two provincial secure access points continue to operate in British Columbia and Alberta. Two more sites will open in spring 2021 in Ontario at the Ministry of Finance and the Ministry of Children, Community and Social Services. For more information about these initiatives, please contact statcan.maddlidamidd.statcan@statcan.gc.ca.
We would like to welcome Shelley Jeglic, who has joined DAD as the new chief of the Government Data Access Program. Shelley previously worked in the Centre for Population Health Data and is excited to make the switch from client to service provider.
The VDL team is excited to announce that eight researchers from the University of Toronto, University of Calgary, McMaster University and Université de Montréal, and four researchers from the Public Health Agency of Canada are now accessing the VDL environment as part of the academic pilot! They are accessing microdata files with low to medium levels of sensitivity. This is the first set of users to be granted access to anonymized microdata in the cloud environment using the VDL. The VDL team has been working hard to establish and expand governance and system capabilities to support this virtual access. This is a big milestone for the team, and a step forward in enabling secure virtual access to data for more accessible research, and, in turn, better decision making. We could not have reached this level of success on this project and our pilots without the collaborative effort of many teams and individuals.
Going forward, the VDL project will continue to onboard users identified for our pilots. The established pilots will help evaluate the nuances of onboarding different types of researchers, as well as help inform how the user experience of the environment can be improved leading up to production. Once the VDL team has successfully conducted the pilots using data with low to medium levels of sensitivity, the team will conduct additional pilots using data with medium to high levels of sensitivity, as this is more representative of the data that are typically used by researchers. StatCan will select pilots based on several criteria to learn about and improve the VDL process to meet the project's objectives.
Overall, with the VDL, StatCan will be better positioned to advance its user-centricity by introducing this new mode of access and contribute to the agency's modernization efforts.
The VDL will vastly improve access to statistical information for researchers by providing users 24/7 remote access to data housed at StatCan using a secure IT connection and a protected cloud environment. Progress is ongoing on a number of key initiatives to increase virtual data access and promote collaboration. These include the development of analytics platforms and monitoring capabilities, and continued assessment and development of the Client Relationship Management System (CRMS) and the Microdata Search Tool.
A number of monitoring mechanisms have been approved and are available to StatCan for deemed employees to access protected microdata in the cloud environment. Staff from DAD will use a variety of mechanisms to monitor for potential security incidents and follow the established incident protocol when required, which supplements the Information and Privacy Breach Protocol at StatCan.
Development on the CRMS corporate project continues under the Dissemination Division. The Dissemination team is currently working with IT to establish development priorities and DAD for pilot assessments. Stay tuned for more information!
Questions or comments? Visit Access to microdata.
Check out the StatCan Blog.
| 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 |
They are carrying out an important task on behalf of all 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?
For more information, contact Media Relations at 613-951-4636, or at statcan.mediahotline-ligneinfomedias.statcan@statcan.gc.ca.
| 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 |
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.
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.
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:
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.
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.
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.
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).
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]
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.
Data are collected under the authority of the Statistics Act, Revised Statutes of Canada, 1985, Chapter S-19.
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.
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.
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.
Section 12 of the Statistics Act provides for the sharing of information with federal, provincial or territorial government organizations. Under Section 12, you may refuse to share your information with any of these organizations by writing a letter of objection to the Chief Statistician, specifying the organizations with which you do not want Statistics Canada to share your data and mailing it to the following address:
Chief Statistician of Canada
Statistics Canada
Attention of Director, Enterprise Statistics Division
150 Tunney's Pasture Driveway
Ottawa, Ontario
K1A 0T6
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.
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.
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:
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:
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.
4. In 20xx, did this jurisdiction provide waste management services for itself?
Which services do you provide?
Select all that apply.
5. In 20xx, did this jurisdiction provide waste management services to any other municipalities, cities, villages, towns or townships?
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.
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.
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?
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?
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
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.
16. Were recyclable materials collected or transported by this jurisdiction's employees?
17. Were recyclable materials collected or transported by contractors hired by this jurisdiction?
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?
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
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.
24. Were organic materials collected or transported by this jurisdiction's employees?
25. Were organic materials collected or transported by contractors hired by this jurisdiction?
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?
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
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
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
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.
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
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.
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
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.
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
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.
How many facilities? (Maximum of 15)
40. Please provide the name and owner of each processing facility.
Facility
Facility name
Facility owner
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.
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".
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.
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".
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.
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".
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.
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".
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.
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".
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.
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".
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.
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".
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.
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".
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.
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.
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.
Quantity of materials
Unit of measure
Note: If the UOM is not listed, select "Other specify".
Residential
Industrial, commercial and institutional (IC&I)
Construction, renovation and demolition (CR&D)
Quantity of materials
Unit of measure
Note: If the UOM is not listed, select "Other specify".
Residential
Industrial, commercial and institutional (IC&I)
Construction, renovation and demolition (CR&D)
Quantity of materials
Unit of measure
Note: If the UOM is not listed, select "Other specify".
Residential
Industrial, commercial and institutional (IC&I)
Construction, renovation and demolition (CR&D)
Quantity of materials
Unit of measure
Note: If the UOM is not listed, select "Other specify".
Residential
Industrial, commercial and institutional (IC&I)
Construction, renovation and demolition (CR&D)
Include ferrous scrap metal.
Quantity of materials
Unit of measure
Note: If the UOM is not listed, select "Other specify".
Residential
Industrial, commercial and institutional (IC&I)
Construction, renovation and demolition (CR&D)
e.g., household appliances
Quantity of materials
Unit of measure
Note: If the UOM is not listed, select "Other specify".
Residential
Industrial, commercial and institutional (IC&I)
Construction, renovation and demolition (CR&D)
Quantity of materials
Unit of measure
Note: If the UOM is not listed, select "Other specify".
Residential
Industrial, commercial and institutional (IC&I)
Construction, renovation and demolition (CR&D)
Quantity of materials
Unit of measure
Note: If the UOM is not listed, select "Other specify".
Residential
Industrial, commercial and institutional (IC&I)
Construction, renovation and demolition (CR&D)
Quantity of materials
Unit of measure
Note: If the UOM is not listed, select "Other specify".
Residential
Industrial, commercial and institutional (IC&I)
Construction, renovation and demolition (CR&D)
Quantity of materials
Unit of measure
Note: If the UOM is not listed, select "Other specify".
Residential
Industrial, commercial and institutional (IC&I)
Construction, renovation and demolition (CR&D)
Unit of measure
Note: If the UOM is not listed, select "Other specify".
Residential
Industrial, commercial and institutional (IC&I)
Construction, renovation and demolition (CR&D)
e.g., milk cartons
Unit of measure
Note: If the UOM is not listed, select "Other specify".
Residential
Industrial, commercial and institutional (IC&I)
Construction, renovation and demolition (CR&D)
Quantity of materials
Unit of measure
Note: If the UOM is not listed, select "Other specify".
Residential
Industrial, commercial and institutional (IC&I)
Construction, renovation and demolition (CR&D)
Quantity of materials
Unit of measure
Note: If the UOM is not listed, select "Other specify".
Residential
Industrial, commercial and institutional (IC&I)
Construction, renovation and demolition (CR&D)
Quantity of materials
Unit of measure
Note: If the UOM is not listed, select "Other specify".
Residential
Industrial, commercial and institutional (IC&I)
Construction, renovation and demolition (CR&D)
Quantity of materials
Unit of measure
Note: If the UOM is not listed, select "Other specify".
Residential
Industrial, commercial and institutional (IC&I)
Construction, renovation and demolition (CR&D)
Specify type of material
Quantity of materials
Unit of measure
Note: If the UOM is not listed, select "Other specify".
Residential
Industrial, commercial and institutional (IC&I)
Construction, renovation and demolition (CR&D)
Specify type of material
Quantity of materials
Unit of measure
Note: If the UOM is not listed, select "Other specify".
Residential
Industrial, commercial and institutional (IC&I)
Construction, renovation and demolition (CR&D)
Specify type of material
Quantity of materials
Unit of measure
Note: If the UOM is not listed, select "Other specify".
Residential
Industrial, commercial and institutional (IC&I)
Construction, renovation and demolition (CR&D)
45. In 20xx, did this Jurisdiction/Company own or operate a transfer station for non-hazardous waste?
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
Unit of measure
Note: If the UOM is not listed, select "Other specify".
Was a weigh scale present?
Quantity of waste managed through this transfer station
Residential
Industrial, commercial and institutional (IC&I)
Construction, renovation and demolition (CR&D)
47. Did this Jurisdiction/Company own or operate a facility where waste was disposed or processed in 20xx?
Include:
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
Unit of measure
Note: If the UOM is not listed, select "Other specify".
Was a weigh scale present?
Quantity of waste disposed or processed in this facility
Residential
Industrial, commercial and institutional (IC&I)
Construction, renovation and demolition (CR&D)
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.
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".
Report the quantity of contaminated soil.
Quantity received at landfill
Unit of measure
Note: If the UOM is not listed, select "Other specify".
Report the quantity of clean fill.
Quantity received at landfill
Unit of measure
Note: If the UOM is not listed, select "Other specify".
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.
51. In 20xx, did this jurisdiction conduct any of the following programs to encourage the reduction of waste?
Select all that apply.
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.
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".
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
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
Unit of measure
Note: If the UOM is not listed, select "Other specify".
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
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.
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".
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
58. In 20xx, were recyclable materials from outside operating province processed in this Jurisdiction/Company's material recycling facility/facilities (MRF)?
Unit of measure
Note: If the UOM is not listed, select "Other specify".
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
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
61. Did this company provide waste management services in more than one province or territory in 20xx?
62. Indicate the other provinces or territories in which this company provides waste management services.
Select all that apply.
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.
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.
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.
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".
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".
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".
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".
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".
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.
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".
Destination (e.g., company name)
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".
Destination (e.g., company name)
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".
Destination (e.g., company name)
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".
Destination (e.g., company name)
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".
Destination (e.g., company name)
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".
Destination (e.g., company name)
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".
Destination (e.g., company name)
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.
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.
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".
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".
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".
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".
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".
