Enabling private sector adaptation to climate change in sub‐Saharan Africa
Bibliographic record
Abstract
Abstract The private sector is increasingly recognized as having important potential to help society adapt and become more resilient to climate change. Yet there is limited research examining how to promote and facilitate private sector adaptation in developing countries and in particular how governments can create an enabling environment to stimulate and incentivize domestic private sector adaptation. In this paper, we address this gap through a review of the key factors required to provide an enabling environment for the private sector denoted by existing adaptation literatures. We do this with a focus on adaptation by small and medium enterprises (SMEs) in sub‐Saharan Africa (SSA). To advance this review, we draw insights from a much larger, yet generally independent, literature on enabling environments for private sector development. This literature disaggregates the private sector and highlights key constraints to the development and growth of SMEs in SSA, including deficient infrastructure and evidence of an African gap in access to and use of finance. Both areas of scholarship are then combined in a framework identifying key “building blocks” constituting enabling conditions for private sector adaptation. The framework could be applied in many ways including to focus strategies to enhance private sector adaptation and to identify trade‐offs and interactions between policies or initiatives surrounding private sector development. By combining these literatures, we call for a more holistic approach to develop enabling environments for SME adaptation and climate resilient development that addresses the broader structural deficits that condition vulnerability and barriers that limit adaptive capacity. This article is categorized under: Vulnerability and Adaptation to Climate Change > Institutions for 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 | 0.002 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| 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".