Progressive carbon tax and carbon emission reduction technology advancement in outsourced low-carbon supply chains
Bibliographic record
Abstract
This paper explores how carbon tax policies can motivate manufacturers in low-carbon supply chains (LCSCs) to enhance their carbon emission reduction (CER) efforts. Specifically, the study examines the effects of a progressive carbon tax policy on advancing CER technology within an LCSC, particularly when certain components are outsourced. A progressive carbon tax refers to a taxation policy in which the tax amount increases with higher carbon emissions. To address this, we develop a game-theoretic model for an LCSC consisting of an upstream low-carbon component supplier and a downstream manufacturer. We conduct a comparative analysis of CER effort, product pricing, market transaction volumes, and the profits of LCSC members under different CER models. Our analysis reveals three main findings. First, a progressive carbon tax policy can encourage the manufacturer to increase its CER effort, with stronger incentives observed when tax rates are higher and CER effort coefficients are lower. Second, advancing CER technology by the manufacturer can lead to higher profits, though LCSC profit under decentralized decision-making is lower than that under centralization. Third, enhancing CER technology can significantly reduce carbon emissions, with centralization leading to even lower emission levels. This study makes three key academic contributions. First, it clarifies how a progressive carbon tax can incentivize the advancement of CER technology within LCSCs. Second, it compares the profits under decentralized and centralized decision-making, offering valuable insights for LCSC coordination. Third, it provides theoretical support for governments to effectively leverage carbon tax policies in promoting CER.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".