Green finance for soft power: An analysis of China's green policy signals and investments in the Belt and Road Initiative
Bibliographic record
Abstract
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.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".