Greenhouse gas emissions associated with urban water infrastructure: What we have learnt from China's practice
Bibliographic record
Abstract
Abstract Municipal water and wastewater services have complicated sources of greenhouse gas (GHG) emissions, and quantifying their roles is critical for tackling global environmental challenges. In this study we provide a systematic review of the state‐of‐the‐art on GHG emission characterizations of China's urban water infrastructure with the aim of shedding light on global implications for sustainable development. We started by synthesizing a framework on GHG emissions associated with water and wastewater infrastructure. Then we analyzed the different sources of GHG emissions in drinking water and wastewater treatment systems. In drinking water services, electricity consumption is the largest source of GHG emissions. A particular concern in China is the common use of secondary pumping for high‐rise buildings. Optimized pressure management with an efficient pumping system should be prioritized. In wastewater services, non‐CO2 emissions such as methane (CH4) and nitrous oxide (N2O) emissions are substantial, but vary greatly depending on regional and technological differences. Further research directions may include GHG inventory development for urban water systems at the plant level, quantifications of GHG emissions from sewer systems, emission reduction measures via water reclamation, renewable energy recovery, energy efficiency improvement, cost–benefit analyses, and characterizations of Scope 3 emissions. This article is categorized under: Engineering Water > Sustainable Engineering of Water Science of Water > Water and Environmental Change Engineering Water > Planning Water
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".