Sustainable investment : factors influencing the adoption of a sustainable investment strategy : examining how different factors influence the adoption of sustainable investment strategies.
Bibliographic record
Abstract
With the growth of ESG investing across the finance industry, this paper seeks to explore how different factors influence the adoption of a sustainable investment strategy, with a predominant focus on large asset managers. This study aims to fill the theoretical gap between practitioners and academics on what motivates asset managers to engage in sustainable investment in addition to exploring the barriers they face, factors integral to their success, the strategies they use to invest and opinions on the future of sustainable finance. Using a content analysis of survey responses and interviews with senior individuals who work at large funds in Canada, the United States, the United Kingdom and Hong Kong, the results show that the primary motivations for engaging in sustainable investment are that it 1) adds comprehensiveness to the investment decision process, 2) mitigates investment risk and creates opportunity for long-term risk-adjusted returns, and 3) satisfies stakeholder/client demand and fulfills perception of fiduciary duties. Based on the results, this paper indicates that investment managers may want to pay further attention to developing their sustainable investment strategy in order to achieve a competitive advantage in the market.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".