Are households willing to adopt solar home systems also likely to use electricity more efficiently? Empirical insights from Accra, Ghana
Bibliographic record
Abstract
The diffusion of renewable energy technology, such as solar home systems (SHS), has great potential to reduce GHG emissions. However, households’ energy efficiency (EE) and curtailment behavior (CB) play a crucial role in this process. This study examines the rooftop solar PV potential, households’ willingness to adopt SHS, and their EE/CB implications for mitigating CO2 emissions through SHS adoption. A survey of 216 households was carried out alongside rooftop solar PV potential analysis in a high-income gated estate and a middle-class neighborhood using secondary data. First, we find that rooftop solar PV has the potential to offset all grid electricity and its associated CO2 emissions for at least 63.5% of households. Secondly, the willingness to adopt SHS is lower in the high-income neighborhood than the middle-class ones. This dynamic is explained by the occupancy status, where most of those in the high-income neighborhood tend to be renters – a group known to have a low willingness to adopt SHS. Thirdly, our results affirm that energy-saving behavior is more common in a middle-class neighborhood where the propensity to adopt SHS is also high. Our results suggest that households willing to adopt SHS are more likely to engage in EE/CB. However, this tendency is common among middle-class households, who, in practice, may not be able to afford the SHS. Our findings underscore the need for more targeted policy interventions for SHS, and EE and CB among homeowners, high-income neighborhoods, and real estate developers.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".