Global Governance Implications for Responsible Investing in Canada: An Analysis of Public Service Pension Plans Investment Policies
Bibliographic record
Abstract
Responsible investing (RI) has become a hot topic in the global investment world and has gained momentum over the last decade.1 Attempts by global governance actors, such as the United Nations, to develop standardized policies for RI integration have been taken up by national governments.In Canada, for example, crown corporations are required to adopt the recommendations and disclosures of the Task Force for Climate Related Financial Disclosures (TCFD) by 2022. 2 Despite growing international and domestic governance approaches for RI incorporation, in the summer of 2021, five of Canada's largest investment funds, including the management funds for provincial pension plans in both Quebec (QC) and British Columbia (BC) as well as the Ontario Teachers' Pension Plan, increased their investments in Canada's oil sands.3 This comes despite promises made by these portfolios to decrease their environmental impact and commit to RI, which, importantly, they note may come from exercising their voting rights to act as advocates for responsible business practices in the companies they are invested in, rather than simply divesting their funds.4 This paper will evaluate a number of public service pension plans from across Canada to understand how RI is incorporated into their investment policies.Statements of Investment Policy and Procedures (SIPPs) and Responsible Investing Policies (RIPs) for public service pension plans across the country will be analyzed.The three central questions are: How do public pension funds incorporate international and national best practices of responsible investing set by global governance actors into their investment policies?How are recent actions in line with their current investment policies?Why do variations in alignment with global governance bodies occur between public service pension plans?Findings from this preliminary sample show it is portfolio size, rather than geography, that increases specificity and action with regard to RI practices.These findings will be of interest to practitioners in the investment field seeking to incorporate RI into their practice.This preliminary data aims to contribute to the ongoing discussion on RI across public sectors by providing insight into RI integration across the selected plans. RI ContextRI is an approach to investing that incorporates environmental, social and governance (ESG) criteria into the investment decision-making process.5 Although RI began as a response to stocks of companies that profited from vices such as gambling, tobacco and alcohol, 6 more modern RI sees investments made based on investor values, not necessarily withdrawn in response to specific negative actions.7 Much of the debate around current RI, however, focuses on both its necessity and its ability to produce returns for the investor.Supporters of RI have noted that RI such as selecting investments based on their ESG performance, has the potential to increase returns compared to choosing investments without such considerations. 8Opponents of RI suggest that utilizing RI screening inherently lowers portfolio diversity, creating unnecessary risk for investors.9 Regardless of its impacts, investment professionals have often noted that they are unequipped to incorporate RI properly. 10
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".