Tracking electricity use and scope2 CO2 emissions in 111 cities of Madagascar
Bibliographic record
Abstract
Several sources related that the electricity sector emits almost a quarter of greenhouse gases each year in the world. It is therefore one of the important sectors to take into account to limit global warming. Indian Ocean cities produce significant CO2 emissions during electricity consumption. Their volume and accuracy remain practically unknown and untested. Indeed, until now, there is no methodology suggested by the researchers to evaluate Fossil Fuel carbon dioxide (FFCO2) emission, and electricity consumption in this region. Aware of these crucial problems, this study was carried out to assess and analyse CO2 emissions coming from Electricity consumption (called Scope2) in 111 cities located in the Indian Ocean from 55 Power plants between 2015 and 2022 (08 years) and in four sectors (Residential, Commercial, Industrial, and On-road). To carry out a good comparison, all the data were grouped into three categories, before the lockdown measures due to COVID-19 (2015–2018); During the COVID-19-induced lockdown period (2019–2020); and after the lockdown period (2021–2022). The results showed that the CO2 emission difference is significant in the residential and commercial sectors. It was observed that CO2 emissions increased in 2019–2022 in the residential, industrial, and on-road sectors whereas, simultaneously during the same period, it decreased in the commercial sector. During the three periods, the CO2 emissions rate was the highest in the residential sector (around 52%), and the least on-road (around 1%). The significant difference in the commercial sector suggests a decrease in electricity consumption during the peak of the pandemic due to reduced business activities. Businesses adapted to new operating conditions, such as reduced hours or enhanced energy efficiency measures, which also contributed to the change in consumption patterns.
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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 | 0.002 | 0.001 |
| 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 | 0.001 |
| 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".