The impact of carbon tax and green credit on competing manufacturers economic and environmental performances

Shuting Xu et al.

Asia-Pacific Journal of Operational Research2026https://doi.org/10.1142/s0217595926500181article
AJG 1
Weight
0.50

What the paper says

With increasingly stringent global environmental governance, carbon tax and green credit have become two typical policy instruments widely used to promote green transformation. However, their combined effects on technology upgrading in competitive markets remain underexplored. This paper examines a policy setting where two instruments are implemented in a market comprising both technology.maintaining and technology.upgrading manufacturers. Two game.theoretic models are developed: a Nash model for simultaneous competition and a Stackelberg model for sequential competition. The key findings are as follow: (1) Under both competition scenarios, the technologyupgrading manufacturer exhibits superior economic performance. However, excessively high carbon tax rate may erode its environmental performance advantage. Moreover, while lower green credit discount can facilitate technology upgrading, it does not necessarily yield higher profits for the manufacturer. (2) When the technology-upgrading manufacturer shifts from competing simultaneously to taking a leading role, it will lower its technology upgrading level, whereas both manufacturers’ profit increases. Numerical experiments reveal that under high carbon tax rate or low green.credit discount, such a change in the competitive sequence may increase total carbon emissions. The policymakers should dynamically increase carbon tax rate or reduce green credit discount to balance economic and environmental performances.

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https://doi.org/https://doi.org/10.1142/s0217595926500181

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@article{shuting2026,
  title        = {{The impact of carbon tax and green credit on competing manufacturers economic and environmental performances}},
  author       = {Shuting Xu et al.},
  journal      = {Asia-Pacific Journal of Operational Research},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1142/s0217595926500181},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.