The Driving Forces of Point Source Wastewater Emission: Case Study of COD and NH4-N Discharges in Mainland China
Bibliographic record
Abstract
Excess consumption of water resources and environmental pollution have become major challenges restricting sustainable development in China. In order to prevent the pollution of water resources, policymakers should have reliable emission reduction strategies. This paper aims to contribute new knowledge by analyzing the spatial-temporal characteristics and driving forces of point source emission. The chemical oxygen demand (COD) and ammonia nitrogen (NH4-N) emission variations in 31 provinces and municipalities of mainland China during the years 2004–2017 are analyzed. The results obtained using the logarithmic mean Divisia index (LMDI) method indicate that: (1) the COD and NH4-N emission effects have similar temporal characteristics. Technology improvement and pollutant emission intensity are the main factors inhibiting the incremental COD and NH4-N emission effects, while economic development is the main driving factor of COD and NH4-N emission effects. Population increases play a relatively less important role in COD and NH4-N emission effects. (2) The spatial features of COD and NH4-N emission effects show differences among provinces and municipalities. The reduction of COD emission effects in each province and municipality is obviously better than that of NH4-N emissions. (3) In the eastern, central, and the western regions of China, the total COD emission effect shows a downward trend, while apart from the central region, the NH4-N emission effect appears to be rising in the east and west of China. Therefore, increasing investment into pollution treatment, promoting awareness of water conservation, strengthening technological and financial support from the more developed eastern to the less developed central and western regions, can help to reduce the COD and NH4-N 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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".