Hybrid Solar-Wind System: A Green Energy Alternative to Fossil-Fuel Generators for Women Entrepreneurs in Northern Nigeria.
Bibliographic record
Abstract
This study examines the potential of hybrid solar wind systems as a sustainable alternative to fossil fuel generators for women entrepreneurs in northern Nigeria, a region where more than 85 million people lack reliable electricity access. Using a mixed methods approach, the research investigates energy usage patterns, renewable energy awareness levels, and the feasibility of adopting solar-wind hybrid systems among women-led micro, small, and medium enterprises (MSMEs). Findings reveal that while the national grid remains the primary energy source, 2.2% of 2,537 participants surveyed use wind energy, and 19.3% adopt some form of solar energy. The survey evaluated monthly energy consumption patterns from lower- and middle-income earners, revealing that a smaller percentage (15.6%) spent between N25,000 (USD 16.30) and N50,000 (USD 32.60), while 27.1% spent between N15,000 (USD 9.78) and N25,000 (USD 16.30) on energy every month. This implies that a significant portion of the income generated by women-owned businesses in Northern Nigeria is spent on energy costs rather than being used for business investment readiness activities. The paper highlights the necessity of specific environmental policies, such as local production and circular economy initiatives for solar and wind hybrid materials and pay-as-you-go financing models. The inclusion of vulnerable community groups for gender-sensitive business thinking models will contribute to Nigeria’s Sustainable Development Goals (SDGs), fostering inclusive economic growth. Solar-wind hybrid systems can become a significant alternative to reduce energy poverty and empower women-led businesses by utilising Northern Nigeria’s abundant solar radiation and wind speed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".