Impacts of Climate Change on Seasonal Residential Electricity Consumption by 2050 and Potential Adaptation Options in Alexandria Egypt
Bibliographic record
Abstract
Climate change and associated more frequent, hot or cold, extreme/weather events, as well as increasing temperature may increase future residential demand for electricity for heating and cooling purposes. The paper in hand intends to assess potential impacts of increasing temperature attributed to climate change on seasonal residential electricity consumption in Alexandria city, Egypt. Additionally, it aims to identify and discuss potential soft and hard adaptation options to such impacts. For this purpose, seasonal changes in electricity consumption were investigated. For this purpose, data on monthly residential electricity consumption, population size and income levels at district level as well as maximum monthly temperature in Alexandria city, Egypt over the period 2007-2016 were collected. This is followed by developing a panel-data model to simulate influence of temperature on residential electricity consumption. It was found that there is a significant growth trend over the study period as well as considerable seasonal variation with summer season experienced significant increase in consumption. It was found that increasing temperature, under RCP 2.6 and RCP 8.5, may contribute to significant increase in residential summer electricity consumption by 2050. Different adaptation options to such an increase in consumption, both soft and hard, have been identified and assessed.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".