The Environmental Implications of China’s Rise in Africa
Bibliographic record
Abstract
As early as October 2006, the Chinese State increasingly came to realize the scale of the domestic and global tasks that China would have to take on in terms of tackling environmental issues. Domestically, as we have seen, thirty years of reforms saw huge improvements in China’s economic development and living standards. However, China’s natural resources have been the subject of widespread exploitation with significant implications for the pollution of the natural environment as documented by scholars such as Economy (2004, 2005), Hayes (2007), Magee (2006) and Smil (1980, 1998, 2004). China now emits more CO2 than the United States and Canada put together and its emissions are up by 171 per cent since the year 2000 (EIA 2011). China’s insatiable appetite for natural resources to fuel its domestic growth and satisfy its domestic energy needs has thus left an unparalleled and increasing footprint on the world’s environment (Liu and Diamond 2005; Mol 2011). Shortages of domestic commodities and global oil price spikes in 2004, 2006 and 2007 has led China to increasingly turn to resource-rich regions such as Africa and central Asia in search of these resources and in the interests of energy security. China relies on coal, for example, for almost 70 per cent of its total energy supply, yet it is estimated that in 2020 the shortage of coal in China will reach 1 billion metric tons per year, with significant implications for domestic manufacturing businesses (Dickinson 2010). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".