Technology Implication of Agricultural Sectors in China: A CGE Analysis Based on CHINAGEM Model
Bibliographic record
Abstract
The primary goal of Chinese agricultural development is to guarantee national food security and the supply of major agricultural products. Hence, the improvement of agricultural technology plays a vital role in China for economic development. Technological change in agricultural sector results in higher future economic growth as well as food security, both in food consumption and availability. By constructing China’s agriculture general equilibrium model (CGE), this paper explains the impact of agricultural technology change. This paper constructs a dynamic CGE model based on CHINAGEM model for analyzing the technology increase in China Agricultural sector and then describes the construction of database and policy scenario. Model such as Computable General Equilibrium (CGE) model is used to conduct analysis of the economy-wide impacts of new agricultural technologies in China. In the general equilibrium model, some external variables are established; any part of structural changes caused by its exogenous variables can affect the entire system, resulting in general changes of goods, prices and quantity of factor. Simulation result of this paper indicates the agriculture sector output increases respectively; employment decreases; production cost decreases; and investment increases. Finally this paper describes the effects of the policy of technology changes by comparing policy scenario to baseline scenario and explains the impact of technology changes in China economy using CHINAGEM model.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".