Environmental Assessment of Changes in Regional Industrial Structures in Russia at the Beginning of the 21st Century
Bibliographic record
Abstract
Abstract— Structural changes in industry in Russian regions for 2005–2019 were assessed from an environmental standpoint. The decrease in the share of the extractive industry and hazardous activities in the manufacturing industry was seen as an environmentally progressive change in regional industrial structure (its greening), and a change for the opposite, as degreening. There was an increase in mineral resources extraction in the absolute majority of Russia’s main producing regions, while in half of them, it increased by more than 1.5 times, and in a quarter, it more than doubled. A northeastern vector of development of the country’s extractive industry has clearly emerged, causing a relative shift of large-scale impacts on nature to Eastern Siberia, the Far East, and the European North to ecologically significant and easily vulnerable landscapes of the permafrost zone, as well as to shelf areas. The number of regions where the share of mining in industrial output exceeds 50% increased from 9 to 14. In two-fifths of Russian regions, the share of environmentally hazardous industries in the manufacturing sector has significantly increased. In regions where nature-intensive production is significantly reduced (Khanty-Mansi Autonomous Okrug and Tatarstan), industries of primary processing of raw materials have appeared, which are also not environmentally friendly. Only in Belgorod, Kaliningrad, and Murmansk oblasts have both industrial structures in general and their manufacturing sectors become more environmentally friendly. Interregional differences in the level of environmental friendliness of industrial structures increased. Methodological and informational difficulties prevented the author from establishing a relationship between structural changes in the industry of Russian regions and dynamics of impacts on natural components.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".