Impact of implementation of high-standard farmland construction policy on food production resilience: evidence from China
Bibliographic record
Abstract
Purpose The High-Standard Farmland Construction (HSFC) Policy, implemented by the Chinese Government in 2011, is a key initiative aimed at improving farmland quality and ensuring food security. However, does the construction of high-standard farmland (HSF) comprehensively safeguard food security? This paper systematically aims to evaluate the impact of the HSFC policy on food production. Design/methodology/approach Continuous difference-in-differences (DID) method is used in this study. 2011 marked a key turning point for the nationwide implementation of the HSFC policy. Given that the construction of HSF is a gradual process, with significant variation in the timing and extent of construction across regions, the traditional DID model does not accurately capture the impact of HSF construction on food production resilience. Therefore, this paper employs a continuous DID method to estimate the effects of HSF construction on food production resilience. Findings The implementation of the HSFC policy has significantly enhanced food production resilience. Heterogeneity analysis reveals that the policy has notably improved food production resilience in western regions and non-major grain-producing areas. Additionally, mechanism tests show that the policy affects food production resilience through two channels: increasing land transfer rates and raising agricultural loan balances. Originality/value This paper further enriches the body of research on the impact of the HSFC policy on food security. We suggest that the government could continue to promote the construction of HSF, effectively supervise and manage the construction process, ensure adequate funding and safeguard food security.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".