Bibliographic record
Abstract
Through a systematic literature review and empirical analysis, this study explores the differences and linkages between ESG portfolios and traditional portfolios in terms of risk-return dimensions from the perspectives of different market environments, industry characteristics and risk measures. First, the study reviews the Modern Portfolio Theory (MPT) and the Capital Asset Pricing Model (CAPM) and analyzes their limitations in extreme market environments and the neglect of non-financial factors. Second, the study examines the role of ESG factors in corporate value creation and risk management, showing that ESG practices can enhance corporate reputation, reduce financial risk, and increase portfolio stability. In addition, the study compares the construction logic, risk characteristics and financial performance of ESG portfolios with those of traditional portfolios, and finds that ESG investments generally exhibit greater risk resistance during periods of market turbulence, although inconsistencies in ESG rating standards remain a challenge for investors. The findings suggest that ESG investments should focus more on data transparency and standardization, and combine ESG factors with quantitative investment strategies. At the same time, adherence to a long-term investment strategy is critical to realizing the sustainable value of ESG investing.
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 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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".