Mapping the Evolution of Sustainable Financial Inclusion: A Bibliometric Analysis of Global Trends (2007–2025)
Bibliographic record
Abstract
Sustainable financial inclusion is an essential factor for economic development, social justice, and environmental sustainability. The primary objective of this bibliometric analysis is to investigate trends in sustainable financial inclusion publications using 1467 Scopus and WoS-indexed documents published between 2007 and 2025. The review visualized major trends, intellectual structures, and thematic clusters using VOSviewer and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. This analysis identified eight thematic clusters, including digital finance, Environmental, Social, and Governance (ESG) integration, green finance, and financial literacy, which demonstrate the multidimensional nature of the field. Since 2017, research on sustainable financial inclusion has grown, led by China, India, and the USA, revealing geographic imbalances and underrepresentation of the Sub-Saharan Africa and Central Asia regions. Major barriers identified were financial illiteracy and uncoordinated regulations among institutions. This review suggests critical insights for scholars, policymakers, and practitioners should align inclusive finance with the Sustainable Development Goals (SDGs) and advocate for a shift from mere financial access to systemic, sustainability-driven models. It calls for collaboration between decision-makers and financial institutions to foster inclusive, fair, sustainable, and environmentally responsible financial ecosystems.
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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 | 0.015 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.197 | 0.254 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".