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The Emissions Trading Scheme (ETS) is a market-based approach aiming at reducing greenhouse gas (GHG) emissions. It involves the allocation of emission allowances to entities that are permitted to emit certain levels of GHG, as well as the re-sale and re-allocation of these allowances among entities. Considering the uncertainty of urban traffic congestion, the problem of reserving and allocating Emission Trading Permits (ETP) under stochastic demand is investigated. The ETP reserve of each link is determined before congestion, the optimal allocation scheme of ETP is determined after congestion. A two-stage stochastic programming model is formulated to minimize the sum of the reserve cost of ETP before congestion and the expected total loss of ETP after congestion. First, the effectiveness of the stochastic programming model is verified by comparing with the traditional method. Second, the total cost increases with the increase of initial reserve cost and shortage cost, and decreases with the increase of selling price and the capacity of links by sensitivity analysis. The unit reserve cost has a negative effect on the reserve quantity, while shortage cost, selling price, and link capacity have a positive effect on the reserve quantity. At last, the validity of the stochastic programming solution can be verified by numerical analysis of the Nguyen-Dupuis network. The stochastic programming solution proposed in this study shows an 89% reduction in ETP reserve compared to the optimal initial capacity solution. When level E occurs, the total cost is correspondingly reduced by 11%.

A two-stage stochastic programming approach for reserving and allocating of emission trading permits under uncertainties