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 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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.023 | 0.014 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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.
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".