Lying flat city and rat race city: Chinese cities’ land development strategies
Bibliographic record
Abstract
A characteristic feature of China’s urbanization is the active role of city governments in organizing and financing land development. Through this process, local governments capture trillions of yuan in land value annually, channeling these revenues into urban infrastructure—a phenomenon widely known as land finance . While existing studies have extensively documented the socio-economic effects of land finance , less attention has been paid to its institutional origins, particularly how local governments shaped its development. This article adopts a historical institutionalist approach to examine the formation of land and infrastructure development institutions in Chongqing and Beijing since the late 1990s. By comparing these two contrasting cases, the study reveals what efforts city governments could make to create an active land development strategy, and how the strategy could remain passive without these efforts. This article argues that entrepreneurial municipalism best characterises Chinese city governments’ strategic efforts in land development. Key efforts include: setting ambitious urban development goals, centralising land management authority, and providing political and economic support to public asset corporations of land and infrastructure development. Revealing these efforts might help cities in other developing countries to devise their land development strategies for capturing land value in rapid urbanisation and industrialisation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".