Assessment of state supports and subsidies efficiency in ensuring financial security of Ukrainian agricultural enterprises
Bibliographic record
Abstract
The study aimed to assess the impact of state subsidies and loans on the financial stability and competitiveness of Ukrainian agricultural enterprises in the face of economic challenges. Comparative analysis, content analysis of reports and data on state support for the agricultural sector for 2025, and theoretical research methods were used to identify possible areas for improving existing financial instruments. An analysis of the budgetary allocations for state subsidies shows that UAH 4.726 billion is earmarked for the support of farmers in 2025, which will help reduce production costs and increase the competitiveness of agricultural enterprises. An assessment of tax privileges, particularly the special VAT regime, has shown its importance for reducing costs and maintaining the competitiveness of farmers. The study also included an analysis of concessional loans, in particular the Affordable Loans 5-7-9% programme, which is an important tool for supporting agricultural enterprises in Ukraine. In 2024, according to PrivatBank, more than 3,000 loans worth UAH 10.6 billion were granted to agricultural enterprises. The mechanisms of state support in Ukraine and Canada were compared. The Canadian experience demonstrates that certain mechanisms can be adapted, such as subsidies for machinery and support for small farms. The results showed that government subsidies, loans and tax privileges significantly impact the financial stability of agricultural enterprises in Ukraine, providing access to finance, reducing costs and increasing market competitiveness
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".