Carbon Tax Policy and Technological Innovation for Low-Carbon Emission

Author(s):  
Lina Wang
2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Lei Yang ◽  
Jingna Ji ◽  
Chenshi Zheng

Through the establishment of the leading manufacturer Stackelberg game model under asymmetric carbon information, this paper investigates the misreporting behaviors of the supply chain members and their influences on supply chain performance. Based on “Benchmarking” allocation mechanism, three policies are considered: carbon emission trading, carbon tax, and a new policy which combined carbon quota and carbon tax mechanism. The results show that, in the three models, the leader in the supply chain, even if he has advantages of carbon information, will not lie about his information. That is because the manufacturer’s misreporting behavior has no effect on supply chain members’ performance. But the retailer will lie about the information when he has carbon information advantage. The high-carbon-emission retailers under the carbon trading policy, all the retailers under the carbon tax policy, and the high-carbon-emission retailers under combined quotas and tax policy would like to understate their carbon emissions. Coordination of revenue sharing contract is studied in supply chain to induce the retailer to declare his real carbon information. Optimal contractual parameters are deduced in the three models, under which the profit of the supply chain can be maximized.


2021 ◽  
Author(s):  
Homeyra Akter ◽  
Harun Or Rashid Howlader ◽  
Ahmed Y. Saber ◽  
Ashraf M. Hemeida ◽  
Hiroshi Takahashi ◽  
...  

2020 ◽  
Vol 12 (19) ◽  
pp. 8118
Author(s):  
Tu Peng ◽  
Xu Yang ◽  
Zi Xu ◽  
Yu Liang

The sustainable development of mankind is a matter of concern to the whole world. Environmental pollution and haze diffusion have greatly affected the sustainable development of mankind. According to previous research, vehicle exhaust emissions are an important source of environmental pollution and haze diffusion. The sharp increase in the number of cars has also made the supply of energy increasingly tight. In this paper, we have explored the use of intelligent navigation technology based on data analysis to reduce the overall carbon emissions of vehicles on road networks. We have implemented a traffic flow prediction method using a genetic algorithm and particle-swarm-optimization-enhanced support vector regression, constructed a model for predicting vehicle exhaust emissions based on predicted road conditions and vehicle fuel consumption, and built our low-carbon-emission-oriented navigation algorithm based on a spatially optimized dynamic path planning algorithm. The results show that our method could help to significantly reduce the overall carbon emissions of vehicles on the road network, which means that our method could contribute to the construction of low-carbon-emission intelligent transportation systems and smart cities.


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