Does Islamic Sustainable Finance Support Sustainable Development Goals to Avert Financial Risk in the Management of Islamic Finance Products? A Critical Literature Review
Bibliographic record
Abstract
Policymakers, governments, and Islamic financial institutions are increasingly focusing on sustainable development, leading to an in-depth examination of current sustainable finance practices, projects, and product portfolios. This study examines the role of Islamic sustainable finance (ISF) in promoting Sustainable Development Goals (SDGs) to avert financial risk in the management of Islamic Finance Products (ISFP). Through qualitative analysis, the study conducts a critical literature review (CLR) that incorporates conceptual, theoretical, and empirical perspectives on ISF and SDGs and addresses two specific research questions. Our study examines over 48 journals from 2010 to 2024 and provides insights into how ISF advances the SDGs across all environmental, social, and economic dimensions. It also highlights that ISF promotes green entrepreneurship by investing in sustainable projects, supporting SMEs, and offering alternative financing. ISF also promotes financial stability, justice, and growth and is consistent with the principles of Maqasid al-Shari’ah. Key ISF mechanisms that promote the SDGs include Islamic Green Sukuk, Socially Responsible Investment Funds, Islamic Microfinance, and Islamic Impact Investing. Integrating Islamic ethical principles into financial activities is crucial for inclusive and sustainable economic development. These qualitative insights are critical for policymakers, Islamic financial institutions, Halal entrepreneurs, environmentalists, and investors to understand the potential of Islamic social finance (ISF) to support sustainable practices, projects, and portfolios. Furthermore, the ISFs alignment with Maqasid al-Shari’ah highlights its importance in promoting sustainable development while mitigating financial risk in ISFPs management. The study offers robust contributions to the existing literature to provide comprehensive insights into how ISF can be effectively used to promote SDGs.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".