The Futures of Environmental, Social, and Governance (ESG) by 2043 in Canada, and the Potential Implications on Large and Public Companies
Bibliographic record
Abstract
It has been almost 20 years since Environmental, Social, and Governance (ESG) was first coined and introduced as a vehicle to incentivize businesses to make tangible contributions to global challenges, such as the ones outlined in the United Nations Sustainable Development Goals. Since then, ESG assets and ESG-driven investment have steadily grown in the capital markets around the world, including Canada. However, there is also mounting criticism of ESG efforts regarding continued greenwashing practices, poor quality data, and lack of transparency, among others. \n \nThis Major Research Project considers some of the most relevant dynamics around ESG and explores the current operating system of ESG in Canada to produce a set of possible scenarios for ESG by 2043. Additionally, this report also articulates high-level potential implications for public and large companies in each one of those possible futures. \n \nA combination of primary and secondary research methods has been undertaken to achieve the project's goal, following principles of strategic foresight, design thinking, and systems thinking. Unstructured interviews and participatory design methods were conducted with knowledgeable individuals in relevant areas for this study to collect primary data. Literature review, environmental scan, horizon scan, and other research methods were also undertaken to collect secondary data and inform various frameworks for sensemaking and scenario generation. \n \nThe different analyses and scenarios in this report can be used to strengthen and inform future-oriented strategic plans and help build resilience for the challenges ahead. Some key insights in this report include i) a set of systemic archetypes used to identify key patterns in a highly complex, dynamic topic, such as ESG, ii) an updated map of key ESG actors in the Canadian context, iii) a set of relevant trends potentially shaping the future of ESG, and iv) a set of four possible and yet distinctive futures of ESG in Canada, with high-level potential implications for each scenario.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.021 | 0.009 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".