Economic-Mathematical Modeling of Social and Environmental Risks Management of Projects of Urbanized Territories Development
Bibliographic record
Abstract
The article covers the problem of development of management methods of social and environmental risks of urbanized territories development. The main purpose of research is a development and scientific substantiation of economic-mathematical model, allowing to increase the efficiency of risk management of urban development and selection for the practical implementation of the most efficient investment and town-planning projects. During the investigation the conditions and possible consequences of socio-environmental violations of natural and antropogenic environment in urban areas were examined, the characteristics of the information base of the assessment and objects of socio-ecological and economic risks of urban development were identified, the peculiarities of the negative occurrences of the deterioration of environmental quality were distinguished. As a base for the source data array composition, the authors propose to use the common system of integral parameters of sustainable development and environmental indicators. As basic function of socio-ecological damage is proposed to use the direct dependency of the health level of the population from the most significant parameters of the environment, expressed in the econometric equation. For formal description of the dependency of economic-mathematical models, the whole complex of factors affecting the value of socio-ecological damage were studied, the drawbacks of simple regression models for description of interaction between the mentioned factors were examined, the assumption about the need to include in a formal description the factors of socio-economic risks of the interrelated equations system was made. The main result of the study is developed economic-mathematical model describing the process of formation of socio-environmental risk during implementation of investment and construction projects of urbanized territories development. The main conclusion of the study is the justification for the need to identify environmental and social risks and due to their presence the economic damage in the process of developing programmes and projects of urban territories development. The use of the proposed model assists with pictorial rendition of the correlation between environmental problems and development of city economy, as well as more objective substantiation of efficiency of investments in the urban environment development.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".