Analysis on the Agricultural Green Production Efficiency and Driving Factors of Urban Agglomerations in the Middle Reaches of the Yangtze River
Bibliographic record
Abstract
As one of the main grain-producing areas in China, urban agglomeration in the middle reaches of the Yangtze River plays an important role in the development of agricultural production for China’s grain supply. The existing studies about agricultural production efficiency lack of regional coordination analysis at both macro and micro levels, and only few studies consider the impact of agricultural production environment pollution and other undesirable outputs. Based on the input–output index system of agricultural green production, Slacks-based model (SBM) was adopted to measure the agricultural green production efficiency of 31 prefecture level cities in the middle reaches of the Yangtze River from 2008 to 2018, and the Tobit model of panel fixed effect was used to analyze the driving effect of external factors that affect the agricultural green production efficiency of urban agglomeration in the middle reaches of the Yangtze River. At the same time, the research methods at both macro and micro levels provide ideas for the research of transregional production efficiency. The results showed that: (1) the agricultural green production efficiency of urban agglomeration in the middle reaches of the Yangtze river is relatively low, with 2009 and 2013 as the inflection points, showing a stable trend of rise and decline; (2) The green agricultural production efficiency of urban agglomeration in the middle reaches of the Yangtze River presents the spatial distribution characteristics of “high in the west and low in the east”. The regional efficiency difference is obvious, the gap gradually expands, develops from the equilibrium to the polarization; (3) Urbanization development and government intervention has a significant restraining effect on the improvement of agricultural green production efficiency, and opening to the outside world produces a remarkable influence on the improvement of agricultural green production efficiency, however, economic development and industrial structure have little impact on the improvement of agricultural green production efficiency. Therefore, it is necessary to increase investment in technological innovation, promote agricultural transformation and upgrading, promote rational factors allocation and promote coordinated development of agriculture based on regional production differences.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".