Shifting Electricity Demand Under Temperature Extremes in Bangladesh
Why this work is in the frame
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Bibliographic record
Abstract
Bangladesh is among the world’s most climate-vulnerable countries, facing recurrent hazards that disrupt lives and livelihoods. Among these, heatwaves and cold snaps strongly affect electricity consumption, representing a key socio-economic impact of climate extremes. In this study, we used meteorological and electricity data from six sub-regions of Bangladesh to examine long-term changes in extreme temperature days and their effects on electricity usage. Results showed that western inland stations (Chuadanga, Jashore) experienced hotter summers and colder winters, whereas coastal sites (Barishal, Patuakhali) were moderated by maritime influences. Trend analysis revealed significant increases in hot-day frequency since 1961 (up to 1.8 days yr−1 at coastal areas, while cold-day frequencies generally declined but with regional variability. Electricity demand followed a clear pattern, being highest on hot days, lowest on cold days, and intermediate on normal days. Among the regions, Khulna consistently recorded the greatest demand (up to 161 MWh), while Patuakhali remained the lowest (~19–32 MWh). Regression analysis further showed that demand rises with maximum temperature, with slopes up to 5.7 MWh °C−1 and moderate correlations (r = 0.27–0.47). Importantly, the temperature–demand relationship has strengthened in recent years, as similar climatic conditions now correspond to higher electricity use, reflecting both climatic pressures and socio-economic growth. These findings highlight the challenge of temperature extremes for electricity demand and the need to integrate climate–energy linkages into adaptation planning.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it