Atmospheric CO$_2$ and total electricity production before and during the nation-wide restriction of activities as a consequence of the COVID-19 pandemic
Bibliographic record
Abstract
In this paper, we analysed real-time measurements of atmospheric CO$_2$ with total electricity production and nation-wide restrictions phases due to the novel coronavirus COVID-19 pandemic, and its effects on atmospheric CO$_2$ concentrations. A decline of 3.7% in the global energy demand at about 150 million tonnes of oil equivalent (Mtoe) in the first quarter (Q1), of 2020 was recorded, as compared to the same first quarter (Q1), of 2019, due to the cutback on global economic activities. Our results showed that: 1) electricity production for the same period in the years 2018, 2019, and 2020, shrunk at an offset of about 9.20%, which resulted in the modest reduction of about (-1.79%), in the atmospheric CO$_2$, to that of 2017-2018 CO$_2$ level; 2) a non-seasonal abrupt; but brief, atmospheric CO$_2$ decrease by about 0.85% in mid-February 2020, could be due to the Phase 1 movement restrictions in China. The results showed that, the reduction in electricity production is significant to the short-term variability of atmospheric CO$_2$. It also highlights the significant contributions from China to the atmospheric CO$_2$, which suggests that, without the national restriction of activities, CO$_2$ concentration are set to exceed 2019 by 1.79%, but it quickly decreased due to the lockdown, and sustained the reduction for two consecutive months. The results underscore the atmospheric CO$_2$ reductions on the monthly time scale that can be achieved, if electricity production from combustible sources were slashed, which could be useful for cost-benefit analyses of the reduction in electricity production from combustible sources, and the impact of these reduction to the atmospheric CO$_2$.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| 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".