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Record W2041383858 · doi:10.3386/w14460

China's Participation in Global Environmental Negotiations

2008· preprint· en· W2041383858 on OpenAlexaff
Huifang Tian, John Whalley

Bibliographic record

VenueNational Bureau of Economic Research · 2008
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsWestern University
Fundersnot available
KeywordsChinaNegotiationGeographyPolitical scienceArchaeology

Abstract

fetched live from OpenAlex

In the paper we discuss China's participation in both the 2009 Copenhagen negotiations on a post-Kyoto global climate change regime currently under way and out beyond Copenhagen in further negotiations likely to follow. China is now both the largest and most rapidly growing carbon emitter, and has much higher emission intensity relative to GDP than OECD countries. In the Copenhagen negotiation, there will be strong pressure on China to take on emissions reduction commitments and China's concern will be to do so in ways that allow continuation of a high growth rate and fast development. Central to this will be maintaining access to OECD markets for manufactured exports in face of potential environmental protectionism. Thus the broad approach seems likely to be to take on environmental commitments in part in return for stronger guarantees of access to export markets abroad. This involves directly linked trade and environmental commitments although how linkage can be made explicit is a major issue. More narrowly, the issues that seem likely to dominate the climate change negotiating agenda from China's viewpoint are the interpretation of the common but differentiated responsibilities (CBDR) principle adopted in Kyoto, the choice of negotiating instruments and form of emission commitments, and the size (and form) of accompanying financial funds for adaptation and innovation. We suggest that a possible interpretation of CBDR reflecting China's desire to leave room to grow when undertaking emission reduction commitments might be for China to take on emission intensity commitments while OECD countries take on emission level commitments. Larger funds and flexibility in their use will also raise China's willingness to make commitments.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.254
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.460
GPT teacher head0.488
Teacher spread0.028 · 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; both teacher heads agree on what is shown here.

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

Citations12
Published2008
Admission routes1
Has abstractyes

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