Nitrogen Footprint in China: Food, Energy, and Nonfood Goods
Bibliographic record
Abstract
The nitrogen (N) footprint is a novel approach to quantify losses to the environment of reactive N (Nr; all species of N except N2) derived from human activities. However, current N footprint models are difficult to apply to new countries due to the large data requirement, and sources of Nr included in calculating the N footprint are often incomplete. In this study, we comprehensively quantified the N footprint in China with an N mass balance approach. Results show that the per capita N footprint in China increased 68% between 1980 and 2008, from 19 to 32 kg N yr(-1). The Nr loss from the production and consumption of food was the largest component of the N footprint (70%) while energy and nonfood products made up the remainder in approximately equal portion in 2008. In contrast, in 1980, the food-related N footprint accounted for 86% of the overall N footprint, followed by nonfood products (8%) and energy (6%). The findings and methods of this study are generally comparable to that of the consumer-based analysis of the N-Calculator. This work provides policy makers quantitative information about the sources of China's N footprint and demonstrates the significant challenges in reducing Nr loss to the environment.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".