Cross-Border Carbon Regulation and Forests in Russia: From Expectations and Myth to Realization of Interests
Bibliographic record
Abstract
Introduction of the EU Carbon Border Adjustment Mechanism over the period from 2023 to 2026 together with corporate commitments to achieve carbon neutrality and carry out commercial decarbonization have markedly increased interest in assessing the potential of carbon sequestration by Russian forests as a possible way to achieve decarbonization and facilitate Russian exports. The prevailing opinion in business circles is that a significant net positive carbon balance from Russia’s forests could circumvent the need for businesses to make costly reductions in their direct CO2 emissions. However, international decarbonization strategies and standards do not concur with that idea. Direct emissions will have to be reduced. Offset mechanisms, whose benefits are calculated as the difference between a baseline and an improved scenario for forest management (the principle of additionality), will compensate for only a part of the emissions. The experience of Canada is indicative, as it consistently implements measures to decarbonize industry without regard to the absorption of CO2 by its forests. Even though Canada has climatic conditions, forest growth, and population density similar to Russia’s, its policy is not dependent upon revising estimates of net CO2 absorption by forests upward. Forestry priorities in Russia, including reforestation, should instead be gradually shifted from managing commercial forests for harvesting timber to reducing all forest fires. Leased and non-leased forests should both be included, and reforestation that favors deciduous species and mixed forests should be given a higher priority. It is also necessary to remove barriers to forestry in agricultural forests and to plan for implementation of projects directed at improving both forestry and climate on the land leased out from the holdings of the State Forest Fund as well as on agricultural tracts, including those now overgrown by forests
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| 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".