China’s Greenfield Investment and African Countries’ Green Growth Under the Belt and Road Initiative
Bibliographic record
Abstract
Though in recent years African countries have experienced rapid economic growth, there is a growing need for them to accelerate the process of green growth to address challenges like climate change and depletion of natural resources. Under the framework of Belt and Road Initiative, this paper empirically examines the impact of China’s greenfield investment on green growth of African countries based on the STIRPAT model, using panel data of 37 African countries from 2003 to 2020. The results show that China’s greenfield investment can significantly contribute to green growth of African countries, including the improvement of energy productivity, CO2 productivity and non-energy material productivity, which confirms the validity of the “Pollution Halo Hypothesis” in the African region, and improvements in institutional quality can increase the contribution of greenfield investments. Compared with global greenfield investment, China is playing an important role in the green growth of African countries. The research in this paper expands the existing literature on investment and green growth, helps to grasp the reality of the environmental effects of China’s greenfield investment in Africa, and provides empirical evidence and policy support for Sino-African economic and trade cooperation and the high-quality green development of the “Belt and Road”.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".