How Digitalization Enhances Fiscal Sustainability in Local Governments: Findings from Systematic Literature Review
Bibliographic record
Abstract
The study examines the impact of digitalization on fiscal sustainability in local governments to assess how digital tools influence revenue mobilization, public service efficiency, and governance transparency. A systematic literature review was conducted, focusing on 17 peer-reviewed empirical studies published between 2015 and 2025. Employing the PRISMA framework, the review synthesizes quantitative, qualitative and mixed methods studies. Key findings reveal that digitalization, particularly through e-tax systems, increases tax compliance, streamlines service delivery, and reduces corruption. Commonly reported digitalization efforts include electronic tax filing systems, and smart governance platforms. Despite these advancements, several challenges persist such as limited technological infrastructure, data security risks, institutional resistance and low digital literacy, especially in developing rural areas. A major research gap identified is the limited exploration of long-term fiscal impacts and digital inclusion. These gaps are largely due to the absence of longitudinal studies and the underrepresentation of low-income or geographically remote municipalities in existing research. To address these limitations, the study recommends further investment in digital infrastructure, strengthening of cybersecurity frameworks, and expansion of digital literacy initiatives. Future research could focus on cross-country comparisons, environmental implications of digitalization, and long-term fiscal effects to inform more inclusive and resilient local governance strategies.
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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 | 0.029 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.022 | 0.026 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".