Sustainability in Private Capital Investing: A Systematic Literature Review
Bibliographic record
Abstract
The private capital asset class has grown to over $10 trillion in assets under management and has significant potential to contribute to environmental, social, and governance (ESG) goals. However, there is a dearth of academic research about ESG with regards to private capital investing. This literature review adopted a mixed-methods approach, combining a quantitative (bibliometric) analysis with a qualitative review of the articles. It was found that less than 1% of the literature, written in English, between 1960-2020 on private equity and venture capital addresses topics related to sustainability. It was also observed that the 46 papers which address sustainability topics can be categorized into 13 themes, including certifications and standards, impact investing, and corporate social responsibility. Investment in private securities grew at twice the rate as public securities during the end of this time-period and interest in sustainability integration in private capital investing is growing. Incentives for private equity and venture firms to engage with sustainable investments are being driven by institutional investors, such as pension funds and insurance companies. The focus of sustainability research has typically been on public markets, hindering the potential of private capital investment to influence sustainable policy and practices. The objective of this paper is to provide evidence of the dearth of academic literature on the topic of private capital markets and sustainable investment, while identifying current themes in the existing literature so that future work may address gaps in research.
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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 | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.030 | 0.026 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".