Παρασκευή 16 Νοεμβρίου 2018

IJERPH, Vol. 15, Pages 2570: The Optimal Carbon Emission Reduction and Prices with Cap and Trade Mechanism and Competition

IJERPH, Vol. 15, Pages 2570: The Optimal Carbon Emission Reduction and Prices with Cap and Trade Mechanism and Competition

International Journal of Environmental Research and Public Health doi: 10.3390/ijerph15112570

Authors: Linghong Zhang Hao Zhou Yanyan Liu Rui Lu

More and more countries employ the Carbon Cap and Trade mechanism (CCT-mechanism) to stimulate the manufacturer to produce much more eco-friendly products. In this paper, we study how the CCT-mechanism affects competitive manufacturers’ product design and pricing strategies. Assume that there are two competitive manufacturers; we give the optimal closed form solutions of the carbon emission reduction rates and retail prices in the Nash game model and the Stackelberg game model with CCT-mechanism, respectively. Additionally, we also discuss the impacts of CCT-mechanism, consumer environmental awareness (CEA), and the sensitivity of switchovers toward price on the optimal carbon emission reduction rates, retail prices, and manufacturers’ profits. We find that (i) when the carbon quota is not enough, there is a trade off between investing in producing much greener product and purchasing carbon quota; when the carbon price is not high, the manufacturer tends to purchase the carbon quota; and when the carbon price is much higher, the manufacturer is more willing to increase the environmental quality of the product; (ii) manufacturer’s size affects product’s emission reduction rate and manufacturer’s optimal profit; larger manufacturer tends to produce much greener product, but it does not mean that he could obtain much more money than the small manufacturer; and (iii) the decision sequence changes manufacturer’s strategies; the optimal emission reduction rate in Nash and Stackelberg game models are almost the same, but the differences of prices and profits between Nash and Stackelberg model’s are much bigger.



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