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.
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
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".