The role of gender in firm-level climate change adaptation behaviour: Insights from small businesses in Senegal and Kenya
Bibliographic record
Abstract
Literature on gender and climate change adaptation tends to propose that women are both especially vulnerable to climate change and especially valuable to climate change adaptation, but these ideas have been little considered in the context of adaptation within small businesses and have rarely been tested through quantitative empirical analysis. This paper responds to this gap within existing literature and explores how female representation in the ownership or management structures of micro and small businesses shapes firm-level adaptive capacity, as implied through adaptation behaviour. Using firm-level survey data from semi-arid regions of Senegal and Kenya, we employ a Poisson regression model to empirically investigate how female representation in ownership and management of micro and small businesses affects adoption of firm-level sustainable and unsustainable adaptation strategies, with increasing exposure to extreme weather events. Our results show that businesses with female leadership that faced a larger number of extreme events adopt more sustainable and fewer unsustainable strategies than those with only male leadership. We interpret this result recognising that unsustainable adaptation strategies, such as selling business assets, require a business to have access to business assets and resources and thus are an outcome of a business’ coping capacity. Consistent with literature, we then identify that adaptation assistance can mitigate some of the harmful effects of climate shocks and additionally support micro and small businesses with female leadership to adopt more adaptation strategies (both sustainable and unsustainable) – and to a greater extent than businesses with only male leadership. Results evidence the value and efficiency of developing an inclusive business enabling environment for adaptation that targets women entrepreneurs, not just for delivering on equitable climate justice agendas, but also for strategic upscaling of resilience.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".