Government Subsidies and Revenue Sharing Decisions for Port and Shipping Service Supply Chain in Emission Control Areas
Bibliographic record
Abstract
As the core of the port and shipping service supply chain system, the port and shipping companies must urgently solve the problem of how to balance emissions, costs, and benefits with the gradual extension of China’s emission control area (ECA) and the stringent emission requirements. From the perspective of system optimization, this research constructs a revenue sharing model of the port and shipping service supply chain and deals with the revenue sharing decision-making problem of the secondary service supply chain after port enterprises and shipping enterprises joining the government subsidy mechanism driven by ECA’s policy. Research shows that the government subsidy mechanism directly affects the profit of the port and shipping service supply chain, which is the key factor in implementing the ECA’s policy and promoting the emission reduction of the port and shipping enterprises. The revenue sharing of the port and shipping enterprises plays a decisive role in the revenue of the shipping enterprises. Cooperative emission reduction mechanism should be led by port enterprises to promote the balance between revenue and emission reduction in the supply chain system. Results provide a reference for the Chinese government to formulate corresponding incentives and subsidy policy under the new ECA’s regulations as well as solving the problems of how to balance emissions reduction and cost improvement for port and shipping enterprises.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".