Drivers of Rural Households’ Choices and Intensity of Sustainable Energy Sources for Cooking and Lighting in Ondo State, Nigeria
Bibliographic record
Abstract
Poverty reduction and the promotion of sustainable human development are fundamentally dependent on having access to modern energy services. Energy supplies that are dependable, reasonably priced, and sustainable are vital to modern societies. In achieving the sustainable development goals (SDG7) and access to clean energy supplies, this study, using cross-sectional data from 180 randomly sampled rural households, analyzed the key factors determining the choice and intensity of energy sources used for lighting and cooking in rural Nigeria. Both descriptive and inferential statistics (multivariate probit (MVP) and zero-truncated Poisson (ZTP models)) were employed for the analyses. The result showed that there is evidence of fuel stacking in their choice of cooking and lighting energy, and it increases with rising income levels but is more pronounced for lighting than cooking. The result also revealed that reliable access to clean energy (9% of sampled households for LPG and 23% of the households for grid electricity) is very low, as these households still rely on fuelwood (70%) for cooking, but the predominant usage of kerosene (39%) for lighting, as reported in the literature, has drastically changed to dry cell battery (51%). The results using a multivariate probit model to capture the multiple fuel usage phenomenon among rural households show that access to clean energy, improvement in rural poverty, usage of indoor kitchens, household size, and an increase in the education of household heads’ spouses significantly influence the use of clean energy in the rural areas. In the same vein, the result of the ZTP model showed that income, access to energy sources, and occupation of the household head were the drivers of the intensity of cooking and lighting energy sources. Thus, it is recommended that any policy interventions that are targeted at encouraging rural households to use clean energy should start by improving rural access to these clean energy sources, improving their poverty status while also increasing the level of education and awareness of rural women concerning the risks of using dirty energy sources.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".