Development of the Financial Flow Model for the Sustainable Development of an Industrial Enterprise
Bibliographic record
Abstract
The review of modern methodological approaches to assessing the sustainable development of an industrial enterprise revealed the absence of generally accepted integral tools and the connection sustainable development trends with financial flows. To fill this gap this, taking into account the principle of balanced development economic, environmental and social components aimed. The purpose of the study is the development of a financial flow management model for the sustainable development of an industrial enterprise (using the example of a large Russian petrochemical enterprise). To achieve the goal of the purpose, the following methods implemented systematic approach, analysis and synthesis, comparative analysis, analysis of dynamics series, correlation analysis, regression analysis, solving the linear programming problem. As a result of the study, we came to conclusion about the shift of the enterprise’s focus on environmental issues; the growth of the integral indicator of sustainable development of an industrial enterprise; the negative impact of credit resources on the aggregate indicator. The novelty of the study lies in the development of a new methodological solution, which is the basis of the financial management model for the sustainable development of the enterprise: it is adequate to the level of microeconomic system; covers three ways of measuring sustainable development and the possibility of choosing the best quality; allows to implement a proactive approach to managing financial flows with the principles of sustainable development of the enterprise (existing approaches either represent only a set of indicators or addressed the diagnosis of a specific subsystem, either do not consider the relationship between financial flows and the aggregated indicator of sustainable development of the enterprise).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".