Bibliographic record
Abstract
Climate change has emerged as a major issue of financial risk for Canadian pension funds when determining where to place investments. The author argues that while such pension funds recognize climate change as an issue that holds the potential for significant financial risk, the funds’ current approach to climate-related risks faces critical limitations. The author identifies the current practices of the five largest pension funds in Canada when faced with climate-related financial risks, then discusses the key shortcomings in current practices among the pension funds in three main areas.First, the author examines organizational governance, which seeks to understand investment policies and guidelines related to climate risk, as well as the involvement of senior management and the pensions’ boards of directors in guiding their funds in the face of these risks. Second, the author considers the funds’ strategy and risk management, which encompasses any specific climate strategies adopted by the pension funds, as well as any tools or metrics used to manage and mitigate climate-related financial risk. Third, the author canvasses pension funds’ engagement and advocacy, which includes any stewardship practices that monitor or seek to improve the climate practices of investee companies.The author concludes by discussing the remaining challenges to pension funds and defining a path forward. The remaining challenges are approached by comparing Canadian funds to their international peers’ approaches to climate-related financial risk, and by examining the position of Canadian pension funds within Canada’s wider climate policy implementing the Paris Agreement. The author defines the path forward for pension funds as requiring a strong policy signal from government that could accelerate their transition to investments promoting the low-carbon economy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".