Development of the logistics system of the economic region “polissya” in the context of the green economy: ecological problems and perspectives
Bibliographic record
Abstract
Modern business conditions require the implementation of the financial support mechanism for the transformation of transport and logistics systems using non-traditional sources of funding, including "green" investments. The key instruments of "green" financing for transport infrastructure modernization, which are effectively used in different countries, include: "green" bonds, "green" loans, grants, guarantees, technical assistance, money of "green" investment funds. This paper is devoted to the analyzes of the dynamics of environmental indicators of the regional logistics system taking as an example the economic region "Polissya". On this basis, modern environmental problems of the district's logistics system have been identified. An analysis of the development of world markets for "green" bonds, "green" loans and sustainable investment assets is made. Peculiarities and characteristic features of "green" financing instruments for the development of logistics systems of different levels are considered. As a result of the research it is established that in the Ukrainian realities it is expedient to apply the advanced international experience of realization of the "green" financing of infrastructure projects mechanism in economic areas. This will successfully transform regional logistics systems in the context of the green economy and achieve sustainable development of transport infrastructure.
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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 | 0.000 | 0.000 |
| 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) | 0.002 | 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".