Socio-Economic Effects of Load Shedding on Poor Urban Households and Small Business Enterprises in Lusaka, Zambia
Bibliographic record
Abstract
Zambia has in the recent past witnessed an increase in economic activities which has led to an increased energy demand. This increased demand for energy has overshot the hydroelectric power generating capacity. Consequently, the national power utility company, the Zambia Electricity Supply Corporation (ZESCO) instituted nationwide load shedding schedules that last up to 12 hours daily. This development has potentially far reaching social and economic effects on the lives and operations of poor urban residents and small scale business enterprises (SMEs) that routinely depend on stable access to electricity. With a focus on two low income residential areas, namely Ng’ombe and Kalingalinga residential areas, this study explored how residents and SMEs of the capital city, Lusaka have been affected by the recent spate of load shedding in the city. A total of 200 households and 14 SMEs from Ng’ombe and Kalingalinga were interviewed. Results show that load shedding, which occurs daily in the two study sites has caused massive disruptions to the daily lives and operations of the households and small businesses respectively. Over time, the load shedding phenomena has gotten worse and become a major political issue, reflecting the hardships for households and businesses in Zambia. On this basis, this study recommends that the government provides subsidies on alternative energy appliances such as portable diesel solar generators for small business enterprises and more favourable electric tariff rates for business that shift their manufacturing activities to night time so as to reduce demand for electricity during peak periods.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".