How do African SMEs respond to climate risks? Evidence from Kenya and Senegal
Bibliographic record
Abstract
This paper investigates to what extent and how micro, small and medium-sized enterprises (SMEs) in developing countries are adapting to climate risks. We use a questionnaire survey to collect data from 325 SMEs in the semi-arid regions of Kenya and Senegal and analyze this information to estimate the quality of current adaptation measures, distinguishing between sustainable and unsustainable adaptation. We then study the link between these current adaptation practices and adaptation planning for future climate change. We find that financial barriers are a key reason why firms resort to unsustainable adaptation, while general business support, access to information technology and adaptation assistance encourages sustainable adaptation responses. Engaging in adaptation today also increases the likelihood that a firm is preparing for future climate change. The finding lends support to the strategy of many development agencies who use adaptation to current climate variability as a way of building resilience to future climate change. There is a clear role for public policy in facilitating good adaptation. The ability of firms to respond to climate risks depends in no small measure on factors such as business environment that can be shaped through policy intervention.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".