Spatio–Temporal Patterns and Driving Mechanisms of Urban Land High-Quality Use: Evidence from the Greater Pearl River Delta Urban Agglomeration
Bibliographic record
Abstract
Efficient urban land use plays a crucial role in promoting regional development and ensuring economic growth. Analyzing the spatio–temporal pattern of urban land high-quality use (ULHU) and identifying its key influencing factors and pathways can contribute to enhancing land use efficiency and achieving high-quality development. This study established a comprehensive measurement indicator system for evaluating ULHU using the entropy method and investigated the spatio–temporal evolution pattern of ULHU in the Greater Pearl River Delta (PRD) urban agglomeration in the years 2005, 2010, 2015 and 2020. Furthermore, a multi-period fuzzy-set qualitative comparative analysis method was adopted to explore the concurrent driving mechanisms that impact ULHU from the configuration perspective. The findings reveal that the level of ULHU has exhibited a significant improvement, increasing from 0.1150 in 2005 to 0.2758 in 2020, with an annual growth rate of 8.739%. The spatial pattern in the PRD region exhibits an incremental distribution, characterized by higher values in the central region and lower values in the peripheral area, as this pattern reveals significant heterogeneity across the region. The configurations for ULHU in the PRD were identified, such as Population-industry-talent driven, Openness-fiscal-talent driven, Population-led, and Population-industry driven. Among these configurations, population density and industrial structure were identified as the core driving factors, while the role of talent resources is gradually diminishing. This study provides practical guidance for promoting the ULHU and optimizing national spatial planning.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".