Equilibrium study of logistics demand and logistics resource allocation in Guangdong Province
Bibliographic record
Abstract
Logistics serve as a vital link between production and consumption. The balanced allocation of logistics demand and resources can promote the harmonious development of the logistics system, thereby fostering regional economic growth. As a leading region in China's reform and opening-up, Guangdong Province has experienced high economic growth. The equitable allocation of logistics demand and resources within a province is crucial for sustaining its economic development. This paper investigates the regional characteristics of logistics demand and resource allocation in Guangdong Province by analyzing the spatial distribution and evolutionary trends of logistics demand alongside the equilibrium of logistics resource allocation. First, the entropy weight method is utilized to examine the development trends of logistics demand and resource levels in Guangdong Province. Second, spatial autocorrelation analysis is applied to the spatiotemporal evolution characteristics of logistics demand across various cities in Guangdong Province for the years 2011, 2016, and 2021. Using 2021 cross-sectional data, an inconsistency index, which is based on geographic concentration, is employed to assess the mismatch between logistics demand and resource allocation across cities in Guangdong. The study reveals that logistics demand in Guangdong Province has been steadily increasing, with significant regional disparities. The spatial distribution exhibited a degree of correlation, with clustering patterns. However, logistics resource allocation remains imbalanced, with a certain degree of correspondence to logistics demand levels. Specifically, areas with higher logistics demand tend to have a higher concentration of logistics resources. The Pearl River Delta region holds the most abundant logistics resources, whereas many cities in northern and western Guangdong face severe shortages and are unable to meet the normal logistics demand.
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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".