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Cross-Border Carbon Regulation and Forests in Russia: From Expectations and Myth to Realization of Interests

2022· article· en· W4381572498 on OpenAlexaboutno aff
Е. А. Шварц, Andrey V. Ptichnikov, Alexey Kokorin, A. N. Krenke

Bibliographic record

VenueEconomic Policy · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsReforestationAdditionalityCarbon sequestrationNatural resource economicsBusinessCarbon offsetForest managementCarbon neutralityForestryEconomicsClimate changeGeographyGreenhouse gasEnvironmental economicsEcology

Abstract

fetched live from OpenAlex

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

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.008
Scholarly communication0.0100.006
Open science0.0010.004
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.016
GPT teacher head0.383
Teacher spread0.368 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2022
Admission routes1
Has abstractyes

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