Variations and influential factors of agricultural carbon emissions in Gansu Province
Bibliographic record
Abstract
With the development of agricultural modernization,more and more people are paying attention to the environmental problems caused by agricultural carbon emissions.According to the statistical and survey data from1993 to 2011 from China Rural Statistical Yearbook and Gansu Rural Yearbook,based on six kinds of factors(carbon sources,chemical fertilizers,pesticide,farming films,agricultural diesels,irrigation and tillage in agricultural production),this paper calculates the amount of agricultural carbon emissions and analyzes the quantitative and the structure characteristics of the carbon emission in Gansu Province during the period from 1993 to 2011. The results show as follows:the amount of agricultural carbon emissions is in the gradual upward trend in 19 years,which increased from 66.37 ten thousand tons in 1993 to 207.92 ten thousand tons in 2011,the average annual growth rate is 6.67%;The agricultural carbon emission intensity is also increased year by year,which increased from 182.40 kg·hm-2in1993 to 510.93 kg·hm-2in 2011,the average annual growth rate is 6.01%;In terms of the structure of agricultural carbon emissions,fertilizers are the largest carbon source,the average ratio reaches 49.40%,the next is agricultural films,the average ratio reaches 30%. Further more,the paper decomposes the influencing factors of agricultural carbon emissions by using LMDI model. It is shown that agricultural economic development makes the key impact on carbon emissions. overall,agricultural economic development and agricultural labor scale play an active role in agricultural carbon emissions,compared with the carbon emission load in 1993,from 1994 to 2011 agricultural economic development increased 255.65 ten thousand tons of carbon emissions as well as agricultural labor scale increased2.45 ten thousand tons of carbon emissions. While the production efficiency and structure restrain carbon emission,which cut 114.70 ten thousand tons and 2.36 ten thousand tons of carbon emissions,respectively. Finally,according to the conclusion of this study,some advices of low carbon development of agriculture were put forward. The results of this study could provide scientific basis for making carbon-reduction policy and sustainable development of agriculture in Gansu province.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".