The Use Case of the Sustainable Development Goals for Impact Investment Measurement
Bibliographic record
Abstract
Investing private capital in projects designed to promote sustainable development is no new concept. Several models have been deployed such as social responsible investing, venture philanthropy and others. In 2007, a new system emerged called impact investing, which has three conditions to it. For-profit investments are made seeking financial returns. The ventures invested in must have positive impacts on society and/or the environment. These impacts need to be quantifiable and measurable. A framework to quantify the social and environmental impact created has yet to be developed. This paper will analyze how the United Nations Sustainable Development Goals (SDG) can be used as a resource to help develop an impact measurement system for impact investors. To examine the validity of the SDG indicators for impact investors, this project matches the SDG indicators with impact reports released by impact investment firms and associated businesses, as well as other impact measurement systems. The scope will cover a diversity of impact investment firms to test the flexibility of the SDGs. The current research surrounding impact investing focuses on defining impact investing, use cases, measurement strategies and implementation. For the SDGs, there is material that focuses on the validity and their practicality. This report will build on these theoretical frameworks for the specific case of using the SDGs to measure impact investing, and how a new framework can be developed out of the SDGs to create an effective impact measurement system for impact investors. This will help legitimize impact investing, bringing it to the forefront of sustainable development.
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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 | 0.018 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".