Environmental Assessment of Changes in Regional Industrial Structures in Russia at the Beginning of the 21st Century
Why this work is in the frame
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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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it