Python class creating Multi-Regional symmetric Environmentally Extended Input-Output (MREEIO) tables for Canada. OpenIO-Canada operates at the provincial level (13 provinces). It can thus be used to compare the environmental impacts of value chains or consumption by households from any specific province.
The database covers reference years from 2014 to 2022.
OpenIO-Canada covers 492 commodities, 33 GHGs, around 300 pollutants in 3 compartments (air, water and soil), 67 mineral resources, water consumption, energy use and plastic waste pollution.
OpenIO-Canada is connected to the Exiobase global MRIO database to model value chains happening outside Canada.
IMPORTANT While openIO-Canada v2.11 CAN operate with Exiobase versions 3.9.4, 3.9.5 and 3.9.6, we do NOT recommend using these versions of Exiobase, as there are significant mistakes in the modeling of the mining sector. This triggers a drastic overestimation of Canada's own footprint, through its imports of minerals (notably the American imports).
Clone the repository (or download it) and install the different libraries required to run this Python class (requirements.txt).
Note that we recommend working with version 3.9 of Python as we can ensure it works for that specific version.
Go to the doc folder and start with the Running_openIO-Canada.ipynb file to generate the IO tables. You can then explore
how to use openIO-Canada with the other notebooks.
Using openIO-Canada v2.11 (with capitals endogenized) and population estimates from StatCan, we can derive carbon footprints per capita for all 13 provinces of Canada, and Canada as a whole, from 2014 to 2022, following a consumption approach.
We can see that the national carbon footprint per capita is around 20tCO2eq, even though it slightly decreased during the COVID years (2019, 2020 & 2021). We can also note that there was virtually no progress nationally from 2014 to 2022. Per province, it follows a similar trend of virtually no progress. Two provinces stand out. Alberta significantly reduced its enormous carbon footprint per capita to 25tCO2eq, even though it is still one of the worst in Canada. Québec in 2022, on the other hand had the worst carbon footprint in over 8 years, even though it stays the best performing province.
We can also look at the same results, but through a territorial approach.
The national carbon footprint following a territorial approach is also around 20tCO2eq. This time however, we can see a small
decrease of that footprint in time. This could be explained by a greening of the operations throughout Canada, although
such a greening procedure would have most likely triggered a decrease of the consumption footprint as well. This thus
rather seems to indicate that Canada delocalized a part of its production, instead relying on imports.
Regarding provinces, we can see that the worst of the national production happens in Alberta and Saskatchewan.
The classification used by openIO-Canada is the Input-Output Commodity Classification (IOCC) from StatCan. Unfortunately, this classification does not provide a fully detailed structure. It is thus complicated to know where each commodity/service is actually classified. We recreated this structure based on comparisons with the NAPCS classification. You can find this structure in the doc/OpenIO-Canada-classification-structure.xlsx file.
OpenIO-Canada supports the endogenization of capitals, allowing the user to obtain the tables either with or without the
endogenization. Endogenization consists in integrating the use of capital goods (i.e., any good that is used more than a
year, such as a building, a machine or a software) in the value chain description of all commodities/services. In other words,
gross capital formation is typically a final demand and with endogenization it becomes part of the intermediate economy.
So what? After endogenization of capitals there are two main consequences:
- The emission factors include the impacts of capital goods on the environment. Without endogenization, it's like having a cradle-to-gate emission factor without the infrastructure. Not performing endogenization of capitals thus results in underestimated emission factors.
- The national/provincial emission estimates are underestimated. While endogenization of capitals does not (or really
marginally) affect the impact of domestic capital goods, it also affects capital goods from other countries. In other words,
national emission levels, following a consumption approach, without endogenization of capitals includes domestic capital
goods but excludes foreign capital goods that were used to produce imported commodities/services. Not endogenizing capitals
thus results in an underestimation once again.
Do note however, that endogenization introduces a lot of uncertainties and that studies currently typically do not endogenize capitals.
You can read more on how endogenization of capitals was achieved in the openIO-Canada model in the methodology document, in the doc folder.
For more information on endogenization in general, you can refer to these articles: https://doi.org/10.1021/acs.est.8b02791 https://doi.org/10.1111/jiec.12931
OpenIO-Canada operates at basic price, and so does exiobase, thus ensuring consistency. However, openIO-Canada can still
provide emission factors at purchaser price for its users. We use average impact of trade and downstream transportation
to estimate these purchaser price emission factors.
What's basic and purchaser price you ask?
The purchaser price is the price that is paid by the final consumer. The basic price is the price of the manufactured
commodity. These are different, since there are additional costs between the price of the manufactured commodity and the
final price paid by the consumer. The figure below illustrates the difference in the case of a t-shirt.
While the price of the manufactured t-shirt is 4$, the final consumer pays that t-shirt 10$ because the retailer/wholesaler
makes a profit on the sale. Then, said retailers also had to transport the t-shirt to its warehouse or directly to the
retail shop, and these costs are passed down to the final consumer. The final cost of the t-shirt could also simply
include a delivery to the consumer. Finally taxes are being paid on the t-shirt.
In most cases, users of openIO-Canada will not have access to the breakdown between basic price/retail margins/downstream
transportation margins/taxes, and so in the majority of cases, the purchaser price emission factors should be used. If
somehow you have access to this breakdown, using the basic price emission factors will allow for a more accurate assessment.
https://doi.org/10.5281/zenodo.8200655
- Santiago Zuluaga, Shoshanna Saxe (2025), Leveraging building permit data for large-scale embodied carbon and circularity assessment of residential building construction, Environ. Res.: Infrastruct. Sustain. 5 035015. https://doi.org/10.1088/2634-4505/adfc95
- Wambersie, L., & Ouellet-Plamondon, C. (2024). Developing a comprehensive account of embodied emissions within the Canadian construction sector. Journal of Industrial Ecology, 1–14. https://doi.org/10.1111/jiec.13548
- Yoffe, H., et al. (2024). Mapping construction sector greenhouse gas emissions: a crucial step in sustainability meeting increasing housing demands. Environmental research - Infrastructure and sustainability. https://doi.org/10.1088/2634-4505/ad546a
- Anne de Bortoli, Maxime Agez (2023), Environmentally-extended input-output analyses efficiently sketch large-scale environmental transition plans: Illustration by Canada's road industry, Journal of Cleaner Production, Volume 388, 2023, 136039, ISSN 0959-6526, https://doi.org/10.1016/j.jclepro.2023.136039.
