The impact of sustainable Islamic banking financing for infrastructure projects on Malaysia’s economic growth
Bibliographic record
Abstract
Infrastructure is widely recognized as a catalyst for economic development in many countries; however, a significant funding gap persists. Alternative funding sources are required to address this deficit. Islamic finance presents one such alternative for infrastructure funding. Consequently, this study examines the impact of sustainable Islamic banking financing for infrastructure projects on Malaysia’s economic growth. The research employs a quantitative methodology utilizing Autoregressive Distributed Lag (ARDL) analysis to examine the long-run and short-run relationships between Islamic infrastructure financing in economic, environmental, and social sectors and Malaysia’s real Gross Domestic Products (GDP) using quarterly data from the first quarter of 2015 to the second quarter of 2024. The economic sectors include transportation and storage, and information and communication technology (ICT). The environmental sectors encompass electric, gas, and steam, and agriculture, forestry, and fishing. The social sectors comprise education, and human health and social work. The findings reveal that Islamic infrastructure financing in the transportation and storage, and ICT sectors demonstrates a positive but statistically insignificant effect on economic growth in the long run. Electric, gas, and steam financing also exhibits a positive but insignificant long-run impact, while agriculture, forestry, and fishing financing presents a negative and insignificant long-run effect. Notably, Islamic financing for education, and human health and social work sectors exhibits a positive and statistically significant long-run relationship with economic growth. The short-run analyses yield heterogeneous results across different lags for all sectors. Robustness checks utilizing FMOLS, DOLS, and CCR models corroborate the ARDL findings. The study elucidates the potential of Islamic financing from Islamic banking in promoting sustainable infrastructure development and economic growth, with implications for policymakers, Islamic financial institutions, and investors. It also identifies areas for future research, such as cross-country comparisons and sector-specific analyses.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".