Evaluation of the role of urban domestic wastewater treatment systems for greenhouse gases emissions in China
Bibliographic record
Abstract
Rapid urbanization has exacerbated the dual challenge of mitigating water pollution and reducing greenhouse gas (GHG) emissions. The present study offers insights into the actual role of urban domestic wastewater treatment systems by shedding light on their capacity to act as GHG emitters. We introduce a modelling framework to calculate GHG emissions from wastewater treatment systems in China over the past two decades. Our analysis showed that treated wastewater volume increased by over 4.5 times, but GHG emissions also increased by 2.9 times. The annual emissions from wastewater treatment were -on average- nearly 60 Tg CO2-eq over the past two decades, accounting for <1% of the total national emissions. We also found a significant spatial variability with thirteen developed areas contributing >70% of the GHG emissions. Constructions and operations of wastewater treatment systems approximately accounted for 17% and 83% of the GHG emissions, respectively. Our study also proposes a hierarchical governance framework based on ten major regions that could maximize the efficiency in mitigating water pollution and GHG emissions in China.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".