China Belt and Road Initiative (BRI) Investment Report 2023 H1 – Green Finance & Development Center
Bibliographic record
Abstract
In April 2023, the Ministry of Commerce (MOFCOM) released new BRI engagement statistics covering the period of January to March 2023. According to these data, Chinese enterprises invested about 5.76 billion in non-financial direct investments in countries “along the Belt and Road” in the first quarter of 2023 (a year-on-year increase of 9.5%). For this report, the definition of BRI countries includes 148 countries that had signed a cooperation agreement with China to work under the framework of the Belt and Road Initiative by June 2023. We base our data on the China Global Investment Tracker and our own data research at the Green Finance & Development Center affiliated with FISF Fudan University, Shanghai. The data mostly includes deals with a size of over USD100 million and we count BRI engagements as those in countries that had an MoU with China to cooperate under the BRI (thus, if the Syrian Republic signed a BRI MoU in 2022, we also count prior investments into Syria as BRI investments). As with most data, they tend to be imperfect and need regular updating.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".