Taxonomy Foundations
Please feel free to answer only the questions that are of the most interest and relevance to you.
1a. The first phase of taxonomy development will focus on climate mitigation. In subsequent phases of the Canadian taxonomy, which objectives should be prioritized? Please select your top choices.
- Circular economy
- Other (please specify)
1b. Specify priority
1) Corporate Measurement of Energy Productivity & Decoupling : [Taxonomy relevance] Demonstrates substantial contribution in increasing GDP per energy unit consumed through linked to a decoupling of value produced ($) from total energy consumption. Use the HEC Montréal Energy Productivity tool to benchmark performance and strategies to align with taxonomy thresholds that ensure economic output increases while total energy use stabilizes or declines.
See:
– HEC Report in Energy Productivity (in French) : https://energie.hec.ca/productiviteenergetique/
– PPT of the HEC report (in English) : https://drive.google.com/file/d/1qycZzhd1Gq4lQzjqc38uV5fAAb_t9Sil/view?pli=1
2) Measuring AI Energy, Environmental & GHG Impacts: [Taxonomy relevance] Given Canada's « AI for ALL » Goals, need to ensure AI data center and related investments are not incompatible with climate goals, create pressure on the electricity grid and don't prevent the electrification of existing electricity needs to decarbonize the economy.
See :
– Estimating GHG from AI Use : An open Framework for Corporate-Level Measurement : https://cdn.sanity.io/files/3ogo9b9g/production/a5c1f64ca5864e61b47e6384ee4d0ed31bc861f4.pdf
– Al, data, and computing: shaping infrastructures for a decarbonised world : https://theshiftproject.org/en/publications/al-data-and-computing-shaping-infrastructures-for-a-decarbonised-world/
– AI : From Energy Efficiency Gains to Rebound Effects : https://huggingface.co/papers/2501.16548
– Quantify training energy (MWh), inference energy (Wh/query), and data‑center overhead using standardized metrics (PUE, WUE, CUE).
– Convert energy use into location‑based and market‑based GHG emissions, reflecting grid carbon intensity.
– Include water use, hardware lifecycle, and e‑waste in environmental impact reporting (aligned with EU taxonomy DNSH).
– Use lifecycle frameworks from recent research to assess full environmental impacts of AI systems.
3) Measuring & Limiting the Rebound Effect : [Taxonomy relevance] Ensures efficiency gains translate into real emissions reductions and do not undermine substantial contribution claims.
See : Chapter 1 – HEC Report in Energy Productivity (in French) : https://energie.hec.ca/productiviteenergetique/
– Measure rebound using elasticity models (demand change vs efficiency gains) and behavioral indicators (comfort-taking, increased usage).
– Apply input–output analysis to capture indirect and supply‑chain rebound.
– Limit rebound through usage caps, internal carbon pricing, renewable coupling, and efficiency‑plus‑electrification strategies.
– Report rebound mitigation as part of DNSH documentation.
4) Measuring Economic Gains, Risk Reduction & Co‑Benefits from Reduced Oil & Gas Use in Industry (ex., via electrification, efficiency and fuel switch): [Taxonomy relevance] : Demonstrates substantial contribution to climate mitigation and supports transition finance by evidencing financial materiality, reduced exposure to transition risks, and positive externalities.
– Quantify fuel cost savings, maintenance reductions, and productivity gains from electrification, efficiency improvements, and fuel switching using standardized KPIs aligned with sustainable finance reporting frameworks.
– Apply Total Cost of Ownership (TCO) and Marginal Abatement Cost Curves (MACC) to demonstrate economic viability and alignment with taxonomy “substantial contribution” thresholds.
– Capture co‑benefits: ex., lower exposure to fossil‑fuel price volatility, improved air quality and worker health, reduced noise and vibration, and enhanced ESG performance.
It is proposed that the design and implementation of the Canadian taxonomy be informed by the following six principles: Credible; usable; interoperable; dynamic; regionally specific; and prioritized.
2a. Do you agree or disagree with these proposed principles as the foundation for the taxonomy?
Neutral
2b. Please share the reason for your answer in the space provide
I would add: Taxonomy must be guided by science‑based evidence, transparency and independent verification. Is it prioritized as a function of industry interest or scientific imperative?
The notions of ‘interoperability’ and ‘regional specificity’ can be inherently at odds. Interoperability requires harmonization across jurisdictions, while regional specificity demands adaptation to local contexts. Which of these two principles is intended to prevail?
3. Please rate each of the principles below on a scale from “very important” to “not at all important.”
- Credible
- Very important
- Usable
- Very important
- Interoperable
- Very important
- Dynamic
- Not that important
- Regionally Specific
- Important
- Prioritized
- Important
Do No Significant Harm and Minimum Social Safeguards
Please feel free to answer only the questions that are of the most interest and relevance to you.
The proposed taxonomy framework for Canada includes a set of Do No Significant Harm (DNSH) criteria and Minimum Social Safeguards (MSS), which will be designed to ensure that climate mitigation objectives do not adversely impact other environmental and social values, or legal rights. It is proposed that issuers must disclose the extent to which they meet specific criteria for both DNSH and MSS in order to claim taxonomy alignment.
4a. Do you agree or disagree with the proposed partial alignment approach, which would require entities to disclose the extent to which DNSH/MSS criteria are or are not met rather than requiring full alignment with all DNSH/MSS criteria?
Disagree
5a. Do you agree or disagree with the proposed scope of objectives to be covered under the DNSH and MSS frameworks?
Agree
6a. Do you agree or disagree with the proposed approach to grounding the DNSH/MSS frameworks in Canadian legislation and going beyond existing laws and standards where necessary?
Agree
7. In which areas, if any, is it necessary to go beyond existing laws and standards for the DNSH/MSS framework?
1) Corporate Measurement of Energy Productivity & Decoupling : See HEC report – https://energie.hec.ca/productiviteenergetique/
2) Measuring AI Energy, Environmental & GHG Impacts: Given Canada's « AI for ALL » Goals, need to ensure AI data center and related investments are not incompatible with climate goals, create pressure on the electricity grid and don't prevent the electrification of existing electricity needs to decarbonize the economy. Rebound effect must also be monitored.
8a. Do you agree or disagree that different DNSH/MSS standards should apply to projects or entities of different sizes? E.g. more comprehensive disclosures required for larger projects and/or entities, with more streamlined disclosures for smaller projects and/or entities?
Strongly agree
11. Is there any other feedback you would like to provide on the proposed MSS/DNSH framework?
Need to expand :
1) Corporate Measurement of Energy Productivity & Decoupling : [Taxonomy relevance] Demonstrates substantial contribution in increasing GDP per energy unit consumed through linked to a decoupling of value produced ($) from total energy consumption. Use the HEC Montréal Energy Productivity tool to benchmark performance and strategies to align with taxonomy thresholds that ensure economic output increases while total energy use stabilizes or declines.
See:
– HEC Report in Energy Productivity (in French) : https://energie.hec.ca/productiviteenergetique/
– PPT of the HEC report (in English) : https://drive.google.com/file/d/1qycZzhd1Gq4lQzjqc38uV5fAAb_t9Sil/view?pli=1
2) Measuring AI Energy, Environmental & GHG Impacts: [Taxonomy relevance] Given Canada's « AI for ALL » Goals, need to ensure AI data center and related investments are not incompatible with climate goals, create pressure on the electricity grid and don't prevent the electrification of existing electricity needs to decarbonize the economy.
See :
– Estimating GHG from AI Use : An open Framework for Corporate-Level Measurement : https://cdn.sanity.io/files/3ogo9b9g/production/a5c1f64ca5864e61b47e6384ee4d0ed31bc861f4.pdf
– Al, data, and computing: shaping infrastructures for a decarbonised world : https://theshiftproject.org/en/publications/al-data-and-computing-shaping-infrastructures-for-a-decarbonised-world/
– AI : From Energy Efficiency Gains to Rebound Effects : https://huggingface.co/papers/2501.16548
– Quantify training energy (MWh), inference energy (Wh/query), and data‑center overhead using standardized metrics (PUE, WUE, CUE).
– Convert energy use into location‑based and market‑based GHG emissions, reflecting grid carbon intensity.
– Include water use, hardware lifecycle, and e‑waste in environmental impact reporting (aligned with EU taxonomy DNSH).
– Use lifecycle frameworks from recent research to assess full environmental impacts of AI systems.
3) Measuring & Limiting the Rebound Effect : [Taxonomy relevance] Ensures efficiency gains translate into real emissions reductions and do not undermine substantial contribution claims.
See : Chapter 1 – HEC Report in Energy Productivity (in French) : https://energie.hec.ca/productiviteenergetique/
– Measure rebound using elasticity models (demand change vs efficiency gains) and behavioral indicators (comfort-taking, increased usage).
– Apply input–output analysis to capture indirect and supply‑chain rebound.
– Limit rebound through usage caps, internal carbon pricing, renewable coupling, and efficiency‑plus‑electrification strategies.
– Report rebound mitigation as part of DNSH documentation.
4) Measuring Economic Gains, Risk Reduction & Co‑Benefits from Reduced Oil & Gas Use in Industry (ex., via electrification, efficiency and fuel switch): [Taxonomy relevance] : Demonstrates substantial contribution to climate mitigation and supports transition finance by evidencing financial materiality, reduced exposure to transition risks, and positive externalities.
– Quantify fuel cost savings, maintenance reductions, and productivity gains from electrification, efficiency improvements, and fuel switching using standardized KPIs aligned with sustainable finance reporting frameworks.
– Apply Total Cost of Ownership (TCO) and Marginal Abatement Cost Curves (MACC) to demonstrate economic viability and alignment with taxonomy “substantial contribution” thresholds.
– Capture co‑benefits: ex., lower exposure to fossil‑fuel price volatility, improved air quality and worker health, reduced noise and vibration, and enhanced ESG performance.
Section 3: Activity Classification Framework
Please feel free to answer only the questions that are of the most interest and relevance to you.
The activity classification framework proposes that the green category should be limited to activities with zero or near-zero emissions that make a substantial contribution to climate change mitigation or to activities that directly enable zero to near-zero emissions climate solutions (e.g., electric vehicle manufacturing facilities)
12a. Do you agree or disagree with this approach?
Strongly agree
12b. Please explain your answer in the space below
The ultimate goal of the Framework must be for the financial system to ensure that capital allocation is compatible with planetary boundaries and with IPCC‑validated pathways that limit global temperature rise to 1.5 °C.
In practical terms, this means ending financial gains derived from the externalisation of environmental and social costs, and aligning all financial activity with the biophysical limits of the Earth – as defined by science.
The framework proposes that the transition category should include activities that have materially high emissions today but have the potential to decarbonize sufficiently to align with the green definition (i.e., zero to near-zero emissions) by 2050. These activities are likely to experience stable or growing demand in Paris-aligned representative pathways due to a lack of technically feasible low-carbon substitutes (e.g., major decarbonization investments in steel, cement, and chemicals manufacturing).
13a. Do you agree or disagree with this approach?
Agree
14a. Do you agree or disagree that the proposed guardrails for the transition category (both for the whole-of-activity and measures pathways) are sufficient to protect the science-based credibility of the category and reduce the risk of carbon lock-in, while supporting usability?
Neutral
The report proposes that initial research on the abatement measures category should be conducted in 2027 and include select emissions-reduction measures for activities that have high Scope 1 emissions today and are unable to achieve the life cycle emissions reductions necessary to align with Paris-aligned pathways (e.g. major decarbonization investments in the upstream production, refining, and distribution of oil and gas). These are activities that are highly likely to experience declining demand as the world transitions to clean energy and alternative technologies.
15a. Do you agree or disagree that the taxonomy should include a third “abatement” category, as proposed, that covers demand-decline activities?
Strongly disagree (i.e., disagree on fundamentals)
15b. Please explain your answer in the space below
I disagree. A third “abatement” category for demand‑decline activities would obscure the structural drivers of Canada’s rising emissions. In 2024, Canada emitted 689 Mt of GHGs, including 200 Mt from oil and gas—an 80 % increase since 1990. The Pathways MoU proposes capturing only 6 Mt by 2035 and 10 Mt by 2045, far less than the 20 Mt of new annual emissions enabled by pipeline expansion. Despite $37 billion in profits for oil‑sands producers in 2023, taxpayers are being asked to fund a $43.7 billion pipeline and a $20–30 billion CCS project that does not offset the emissions growth it enables. This expansion is driven by Alberta’s fiscal dependence on oil revenues, which locks national policy into a high‑carbon trajectory. Any future investment in oil and gas must therefore be conditional on genuine economic diversification in Alberta and reform of its fiscal model. Creating an “abatement” category risks legitimizing continued fossil‑fuel growth rather than aligning finance with science‑based pathways that actually reduce emissions.
The report proposes an illustrative list of guardrails for the abatement measures category that, if formally developed and finalized, would be designed to prevent carbon lock-in and ensure the abatement measures category remains credible within a Paris-aligned framework. When thinking about the taxonomy’s credibility and usability, please provide feedback on this illustrative list of guardrails.
16. Should any additional guardrails be considered?
Yes — additional guardrails are needed. The credibility of any abatement category cannot be ensured without addressing the structural driver of Canada’s carbon lock‑in: Alberta’s fiscal dependence on oil revenues. Oil and gas still supply nearly 28 % of Alberta’s budget, creating a powerful incentive for continued expansion and shaping national climate policy in ways that undermine Paris alignment. This dynamic has already led to costly federal concessions, including weakened climate regulations and public financing of high‑emission infrastructure such as the Trans Mountain pipeline. To prevent carbon lock‑in, any abatement guardrails must require that future investments in oil and gas be conditional on genuine economic diversification in Alberta and on reforms to its fiscal model. Without these conditions, abatement measures risk becoming a greenwashing mechanism that legitimizes continued fossil‑fuel growth rather than supporting a credible, Paris‑aligned taxonomy.
Section 4: Technical Screening Criteria
Please feel free to answer only the questions that are of the most interest and relevance to you.
18a. Do you agree or disagree with the proposed approach for developing technical screening criteria?
Neutral
18b. Please explain your answer in the space below
Technical screening criteria are essential, but they are not sufficient to ensure a credible and usable taxonomy. The approach must go beyond technical thresholds to include best‑practice energy management, continuous‑improvement processes, circularity in operations, and robust GHG‑mitigation practices via adoption of internationally recognized certifications such as « ISO standards ». Should also guarantee full transparency in data sharing. Without these additional guardrails, the taxonomy risks becoming a compliance exercise rather than a driver of genuine, verifiable science‑aligned transition.
19. Please rate each of the proposed elements for the screening criteria on a scale from “very important” to “not at all important.”
- Forward-looking wherever possible
- Very important
- Include sunset and/or sunrise dates where relevant
- Important
- Revise as necessary during regular review periods
- Very important
- Primarily use quantitative metrics
- Very important
- Primarily focus on Scope 1 emissions but address Scope 2 and 3 emissions where feasible and/or material
- Important
- Informed by a range of robust methods
- Very important
20. When thinking about the taxonomy’s credibility and usability, how technologically specific or prescriptive should the technical screening criteria be?
The technical screening criteria should be performance‑based, focused on measurable reductions in GHG emissions and energy consumption. A performance‑driven approach ensures maximum flexibility, allowing different technologies, efficient energy management processes and new circular business models to meet the criteria as long as they achieve the required outcomes. This strengthens credibility while avoiding overly prescriptive, technology‑specific rules.
21. Do you agree or disagree with the proposed approach of focusing primarily on Scope 1 emissions, and addressing Scope 2 and 3 emissions where feasible and/or material?
Only in the very short term. Focusing primarily on Scope 1 emissions is insufficient for a credible taxonomy. Meaningful GHG reduction requires understanding the entire value chain, since the most effective and economical mitigation opportunities sometimes occur in Scope 2 or Scope 3. The priority should be to map energy flows, GHG flows, and scope boundaries across each industrial sector to identify where investments deliver the greatest decarbonization impact. A clear timeline for integrating Scope 2 and 3 must be established, ensuring the taxonomy evolves toward full value‑chain accountability rather than remaining limited to direct emissions. Ex., see : https://energie.hec.ca/gesindustriels/
Final questions
22. Overall, how satisfied are you with the proposed sustainable finance taxonomy framework?
Satisfied