Risks and Threats for Economic Security in Forest-Based Sector, Generated by Possible Climate Changes
Bibliographic record
Abstract
Due to expected climate changes, there is a forecast for arising of variety of interconnected and interdependent threats, leading to growth of risks in forest-based sector of economy. Study of risk, as an economic category, has substantial significance for scientific and practical activity in forest-based sector, as it allows identifying the most dangerous threats for economic security. This research includes the systematization of risk factors, caused by possible climate changes, with the allocation of the most dangerous ones which generate threats in forest-based sector of economy. Analysis of revealed risks is conducted on the basis of statistical methods of expert evaluation. It has been established that most of threats for economic security in the sphere of forest use are related to climate change and have stochastic nature, which is determined by a large share of uncertainty of existing factors (air temperature, air and soil humidity, extreme weather phenomena). All of this significantly complicates the planning of work of forest relations members which in this case are the subjects of economic security. The article emphasizes that the main problems of future forest-based sector will be caused by the change of forests productivity. Climate change, which could lead to forests’ drying out, change of forest ecosystems, increase of the level of fires and broad-scale natural disasters will inevitably cause the difficulties in material and technical provision of forest-based sector and increase of expenses for all forestry operations. Consequently, there is a forecast for growth of risk factors in entrepreneurial activity, which will lead to risks of decrease of employment of population in forest husbandry, aggravation of labor conditions, reduction of profits of the sector, and decrease of employees’ income. Economic and ecological consequences of climate change will have a vivid regional character.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".