Islamic Finance for Sustainable Development: A Mixed-Methods Systematic Review and Bibliometric Analysis of SDG Alignment
Bibliographic record
Abstract
The intersection of Islamic finance and sustainable development presents a frontier of opportunity in addressing global challenges.This study offers a comprehensive, mixedmethods investigation into the role of Islamic finance in achieving the United Nations Sustainable Development Goals (SDGs).Employing a novel combination of systematic literature review following PRISMA guidelines, Preferred Systematic Approach for Literature Selection and Ranking (PSALSAR) methodology, and advanced bibliometric analysis, we provide a multilayered examination of this rapidly evolving field.Our systematic review covers 33 high-quality studies and identifies seven key factors contributing to SDG achievement through Islamic finance.The dominant factor, environmental sustainability, appears in 51.52% of the reviewed studies.The PSALSAR ranking reflects a robust distribution of evidence, with 12 studies (36.36%) classified as Tier 1 (Highly Relevant), 15 studies (45.45%) as Tier 2 (Moderately Relevant), and 6 studies (18.18%) as Tier 3 (Somewhat Relevant).Bibliometric analysis of 156 publications reveals significant temporal trends, geographical concentrations, and evolving research networks.Key findings include:(1) a marked increase in research output post-2020, indicating growing recognition of Islamic finance's potential in sustainable development; (2) strong interconnections between Islamic finance principles and environmental, social, and governance (ESG) criteria; (3) geographical concentration of research in Indonesia, South Asia, China and Nigeria, highlighting opportunities for broader global engagement; and (4) emerging focus areas such as green sukuk and financial technology integration.This study contributes to the literature by offering the first comprehensive mixed-methods analysis in this domain, providing a refined understanding of the current research landscape and identifying critical gaps.
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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: Empirical About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.087 | 0.212 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.101 | 0.060 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.005 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".