The Impacts on Illegal Farmland Conversion of Adopting Remote Sensing Technology for Land Inspection in China
Bibliographic record
Abstract
While China’s central government has adopted remote sensing technology in land inspection since 2000, little empirical research has been done on its effect. This study aims to measure the effect of satellite imagery-based land inspection (SIBI) on illegal farmland conversion. The data used in this study were collected for the period from 1997 to 2010 at the province-level. The econometrics approach for panel data model was used in this research. The results showed that SIBI has a deterrent effect of approximately 2.42 ha for every increase of 1% in proportion to the area of prefecture-level regions inspected in a province-level region. The results also indicate land inspections with RS (Remote Sensing) technology saved approximately 11,880 ha farmland from illegal conversion during 2000–2010 with an estimated contribution of reducing illegal farmland conversion by nearly 11%. Furthermore, the governance structure change for land inspection has also contributed to deterring illegal farmland conversion. The deterrent effects due to land inspection by the Supervisor of State Land (SSL) are about 7332 ha during 2008–2010 with an estimated contribution of reducing illegal farmland conversion by nearly 33%. In conclusion, although SIBI has strengthened China’s central capacity to uncover illegal farmland conversion and weakened local governments’ abilities to hide illegal farmland conversion, it has limited impact on illegal farmland conversion since it is just a technical tool. Improvements in the land inspection governance structure have also helped to deter illegal farmland conversion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".