Study on the Operation Efficiency of Toll Roads in China from the Perspective of Scale Economy
Bibliographic record
Abstract
Although China’s toll highways are world-renowned, they suffer from indisputable operational inefficiencies. Operationally, China’s toll highway sector is characterized by an administrative monopoly. In particular, governmental loan-repayment highways have such characteristics as franchising, monopoly, and “one highway by one company.” Hence, this study concentrates on the relationship between economic performance, administrative monopoly, and scale efficiency with respect to toll highways, and explores how the China-specific administrative monopoly affects the transformation of toll highways from scale to efficiency. Using the globally referenced data envelopment analysis- (DEA-) Malmquist Index, this study first measures the operational efficiency of China’s toll highway sector from 2010 to 2017. Based on provincial panel data, this paper then discusses the relationship between toll highway scale and economic performance through system-generalized method of moments estimation and verifies the status quo of the transformation of toll highways from scale to efficiency. From the provincial and industrial perspectives, this study further verifies how an administrative monopoly restricts the transformation from scale to total factor productivity and scale efficiency through the unique operation pattern in the toll highway sector. Finally, this study conducts an extended analysis of the relationship between operational efficiency and debt in the toll highway sector. The administrative monopoly is found to increase the debt burden of the toll highway sector and to have a negative impact on the long-term sustainability of the sector.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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".