Using the Return on Sustainability Investment (ROSI) Framework to Value Accelerated Decarbonization
Bibliographic record
Abstract
A major barrier to companies' more effective integration of sustainability into their corporate strategies is finding ways to estimate and communicate the full value of their business cases. In the authors' experience in working with or for companies, they find that most do not track the value sustainability delivers for an organization. And when companies do track and measure their returns on investments in sustainability, the estimates tend to be focused almost exclusively on those benefits that are most direct and tangible, and show up on the corporate P&L, as opposed to other benefits like employee commitment and regulatory forbearance, which are more likely to show up in a lower cost of capital. To help companies quantify the expected value of their sustainability programs, the authors have developed a Return on Sustainability Investment (ROSI™) framework. The study presented here describes the outcomes of a recent analysis in which the NYU Stern Center for Sustainable Business in collaboration with ALO Advisors worked with Capital Power Corporation, a North American power producer, to estimate the value likely to be created by accelerating its transition to clean energy. Through their work with the Chief Sustainability Officer, Chief Financial Officer, and senior managers from several key business functions, the authors identified seven major sources of benefits, and quantified the expected effects on value of four of them, to produce an estimated contribution to the value of the company of about $30 million. The ROSI™ framework and methodology has since been incorporated into CPX's investment decision‐making process, and played an important role in management's decision to commit to the operating changes required to accelerate the company's transition away from coal‐generated electricity.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| 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".