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Record W3210585111 · doi:10.1002/eet.1965

Green finance for soft power: An analysis of China's green policy signals and investments in the Belt and Road Initiative

2021· article· en· W3210585111 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEnvironmental Policy and Governance · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEnergy, Environment, Economic Growth
Canadian institutionsnot available
FundersUniversity of WaterlooCentral University of Finance and Economics
KeywordsSoft powerChinaFinanceAction (physics)Power (physics)Government (linguistics)EconomicsBusinessPolitical science

Abstract

fetched live from OpenAlex

Abstract In this paper, I study why and how China uses green overseas finance in its Belt and Road Initiative (BRI) to build soft power. I apply Miskimmon et al.’s framework, which postulates that soft power is built on ‘signals' and ‘action’: I study eleven relevant Chinese BRI government and sector‐led signals in green BRI development and analyze Chinese green versus non‐green energy investments as actions. I find that Chinese regulators and financial institutions have provided multiple signals for greening finance in the BRI, while green finance action is insufficient with continued sponsoring of non‐green investments. The paper concludes that green finance is a tool for China to build soft power in the BRI, but it is applied insufficiently due to a lack of green finance action. The paper also finds that insufficient strength of soft power signals can lead to a dichotomy between soft power signals and action with possible negative consequences for soft power.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.340
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.017
GPT teacher head0.226
Teacher spread0.209 · 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