Does Risk Reduction Mitigate the Costs of Going Green? - An Empirical Study of Sustainable Investing
Bibliographic record
Abstract
According to classic economic views of social responsibility as esposed by Milton Friedman, one would expect markets to penalize companies for undertaking social or environmental initiatives beyond minimal compliance with legal requirements, because such activities arguably may divert a firm's limited resources from the central goal of increasing profits to shareholders. In contrast to this traditional outlook, management theory and scholarship in recent decades has come to view CSR more strategically through the lens of business practices. This paper adds to the growing body of sustainability literature by more carefully examining the intersection between sustainability and risk management as a key arena where companies can apply sustainability principles to preserve value and gain potential competitive advantage. More specifically, we theorize that a focus on ecologically and socially sustainable business management should also enhance the company’s ability to proactively identify and minimize various forms of ecological, social, legal, and regulatory risks. More specifically, we theorize that, if sustainable companies are better at identifying and mitigating a wider range of risks, this should also be reflected in trends of lower volatility coupled with long term continued growth. Thus, we design an event study to perform a comparison of Dow Jones Sustainability Index US (DJSI-US) data to the market at large to see if the DJSI-US actually demonstrated these trends of lower volatility and long term growth as compared to the US stock market at large. Our empirical results generally support the theory that sustainable firms listed on the DJSI-US have shown less volatility and have an attractive risk-return profile. Data suggest that the DJSI-US stocks provide stable long-term returns comparable to the market over time, and tend to out-perform the market during times of financial downturn.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".