Area Differences in Regional Logistics Efficiency and the Law Governing Its Temporal and Spatial Evolution
Bibliographic record
Abstract
Because logistics is an important service industry with regard to national economic development, the development level of this industry has emerged as one of the most significant indicators of a country or region’s comprehensive strength. An in-depth study of the regional differences in China’s logistics efficiency and its evolution law in time and space can help promote the high-quality development of the logistics industry. To this end, this study collects data pertaining to the development of the logistics industry in China’s eight economic regions during the period 2009–2018. Moreover, it uses DEA models to calculate the logistics efficiency of 31 provinces and cities in these eight economic regions. Results indicate the following: (1) affected by external environmental factors, the pure technical efficiency is underestimated and the scale efficiency and integrated technical efficiency are overestimated; (2) it reveals that residents’ consumption level and the total retail sales of social consumer goods are negatively correlated with regional logistics efficiency, whereas the regional per capita gross domestic product is positively correlated with regional logistics efficiency; (3) in terms of space, the logistics industry in the same region displays characteristics of high aggregation and cooperation, whereas serious segmentation and opposition are found among different regions. The logistics efficiency of China’s eight economic zones is unbalanced. It displays a pattern of high efficiency in the east and low efficiency in the west on the whole. Moreover, valuable suggestions were propounded to improve regional logistics efficiency in China.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".