An Approach for Assessing Human Respiration CO<sub>2</sub> Emissions Using Radiocarbon Measurements and Bottom‐Up Data Sets
Bibliographic record
Abstract
Abstract Carbon dioxide (CO2) is a major greenhouse gas in the atmosphere and has large impacts on climate change. Its fossil fuel (CO2ff) and biogenic (CO2bio) sources are well investigated, while CO2 emissions from human respiration (CO2hr), a subset of CO2bio, have received less attention. Especially as a source of carbon emissions in densely populated megacities, the role of CO2hr emissions in the carbon cycles was largely neglected. Here we fully characterize the respiratory CO2 emission rates (CERs) of Chinese people for the first time. Using the example of the megacity Beijing in China, we estimate the CO2hr emissions and present a method for quantifying its fraction in the atmospheric CO2 based on radiocarbon (14C) measurements and inventory data sets. The results show that males and females have similar age trends in CERs, but the gender difference is significant, especially between the ages of 20 and 60, the average CERs was 33% higher for males than for females (P < 0.05). The CO2hr emissions were about 22.2 ± 0.6 kt CO2 per day, which was equivalent to 7.5% of daily CO2ff emissions in winter. The proportion is likely to be twice in summer due to the seasonal fluctuations of fossil fuel emissions. More importantly, the respiratory emissions could increase atmospheric CO2 concentration by about 2 ppm, accounting for 14% ± 6% of average CO2bio concentration in winter. This study highlights the importance of human respiration in carbon emissions in megacities and has implications for a better understanding of the regional carbon budget.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".