Exploring the Research Landscape of Impact Investing and Sustainable Finance: A Bibliometric Review
Bibliographic record
Abstract
Impact investing and sustainable finance are crucial in addressing social and environmental issues while developing a more resilient, equitable, and sustainable world. The purpose of this article is to analyze, synthesize, and evaluate the existing literature on the impact investing and sustainable finance research domain. Using PRISMA protocol, data was extracted from the Web of Science and Scopus databases, resulting in the compilation of 498 documents. Researchers use Biblioshiny and VOSviewer to analyze the bibliographic meta data. The findings show that the number of publications in this field has increased significantly over the last five years. In terms of journal productivity, Sustainability is the most prominent source, followed by Resources Policy and Journal of Cleaner Production. The results indicate that China published 189 articles, securing the first position, followed by India with 82 articles and the UK with 72 articles. Thematic map analysis underscores the significance of impact investing in renewable energy for sustainable economic growth. In addition, four research themes have emerged from the co-occurrence of keywords analysis. These themes are “sustainable finance for sustainable economic development”; “the rise of ESG investing in the changing world”; “corporate governance and CSR in enhancing firm performance”; and “mobilizing sustainable finance to tackle climate changes”. Furthermore, the research gives a complete summary of current research trends, future research directions and policy recommendations to assist academic researchers, investors, policymakers, business organizations and financial institutions in better understanding the impact investment and sustainable finance.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.015 | 0.022 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".