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Record W2766662908 · doi:10.22215/cjers.v11i2.2511

Carbon Emissions Embodied in Russia’s Trade: Implications for Climate Policy1

2017· article· en· W2766662908 on OpenAlexvenueno aff
И. А. Макаров, Anna Sokolova

Bibliographic record

VenueThe Canadian Journal of European and Russian Studies · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEconomic and Technological Developments in Russia
Canadian institutionsnot available
Fundersnot available
KeywordsGreenhouse gasProtectionismInternational tradeCommodityClimate changeInternational economicsNatural resource economicsEconomicsBusinessMarket economyEcology

Abstract

fetched live from OpenAlex

According to the current international climate change regime, countries are responsible for greenhouse gas (GHG) emissions that result from economic activities within their national borders, including emissions from producing goods for export. At the same time, imports of carbon-intensive goods are not addressed by international agreements, including the Paris Agreement that was adopted in 2015. This paper examines emissions embodied in Russia’s exports and imports based on the results of an input-output analysis. Russia is the second largest exporter of emissions embodied in trade and the large portion of these emissions is directed to developed countries. Because of the large amount of net exports of carbon-intensive goods, the current approach to emissions accounting does not suit Russia’s interests. On the one hand, Russia, as well as other large net emissions exporters, is interested in the revision of allocation of responsibility between exporters and importers of carbon-intensive products. On the other hand, both the commodity exports structure and relatively carbon inefficient technologies make Russia vulnerable to the policy of “carbon protectionism,” which can be implemented by its trade partners. Full text available at: https://doi.org/10.22215/rera.v11i2.1192

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.930
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.108
GPT teacher head0.360
Teacher spread0.251 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
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

Citations5
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueThe Canadian Journal of European and Russian StudiesSame topicEconomic and Technological Developments in RussiaFrench-language works237,207