Reappraisal of the Climate Change Challenge in the Congo Basin and Implications for the Cost of Adaptation
Bibliographic record
Abstract
The consensus on the reality of climate change is unequivocal. With the IPCC projecting that global greenhouse gas emissions will increase by 25–90% between the years 2000 and 2030, further warming and induced changes in the global climate system shall impact on many physical and biological systems. The Congo Basin countries are already experiencing climate change. Some local and regional studies have identified increasing temperatures, increasing wetness, significant variations in inter-seasonal and intra-seasonal climate, increase in floods and threats of landslides. Less sophisticated climate models have shown that Congo Basin countries will experience increases in rainfall of around 7.3% by 2050 and 13.5% by 2100. More sophisticated models predict significant increases in average annual precipitation up to 200 mm in the eastern portion of the Basin. The temperature change predictions between 2010 and 2050 consistently show that temperatures could rise by 1–3°C. Congo Basin countries’ vulnerability to climate change is owed to their geographical location, reliance on resources sensitive to climate and the low adaptive capacity of firms, households and the states. The most vulnerable people in the subregion are the urban poor and small-scale farmers. The most vulnerable sectors are agriculture, health, energy, coastal zones and water resources. Possibly the forests could be severely affected in the long run. There is need for urgent action. Tasks and activities will encompass managing natural resources, better resource management, and changes in laws, programmes, policies and investments. A potential national adaptation investment strategy for the Congo Basin countries will overlap with traditional development concerns and provide an opportunity to increase efficiency of the developmental efforts. New and additional financial resources shall be required to supplement current development plans, to ensure they are resilient to climate effects. This additional resource is the price for or cost of adaptation.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".