Routing and adopting of green technology in road freight transportation under the carbon cap-and-trade scheme

The carbon cap-and-trade (CCT) scheme is one of the market-based measures widely adopted in many industries, which can effectively reduce carbon emissions and promote green technology by internalizing external costs. This paper investigates the routing of freight carriers and the behavior of the adoption of green technology in a general transportation network under the CCT scheme. Under the scheme, the government sets the carbon quotas between each origin–destination (OD) pair for a unit of freight. The carriers jointly make their routing and green technology adopting choices (such as purchasing low-emission vehicles) to minimize their total cost, including the transportation cost, the capital cost of green technology, and the trading cost or revenue from the inner carbon market. The routing and green technology adopting problem under the CCT scheme can be modeled as the traditional minimum-cost network flow problem with carbon trading equilibrium constraint. Their collective behavior endogenously determines the trading price of carbon emissions between carriers. We found that the route and technology choice between each OD pair must be Pareto optimal in the sense of simultaneous minimization of the cost (transportation cost and capital cost) and emissions. Therefore, the relationship between the trading price and the carrier’s choice can be built. Two algorithms based on the relationship are proposed to calculate the optimal solutions under any carbon cap level. The conclusion and algorithm are also extended to various cases with multiple green technologies. Furthermore, we compare the CCT scheme and the environmental tax scheme. We conclude that the CCT scheme is more efficient in promoting green technology, since the trading revenue is redistributed among carriers to reduce their carbon abatement cost. Case studies based on a large-scale real-world road network validate the practicality and effectiveness of the model.