Regarding the study of the time-dependent green vehicle path problem (TDGVRP), most of the literature uses the step function to represent the vehicle speed change in order to reduce the computation, ignoring the continuity of vehicle speed, which leads to the lack of accurate carbon emissions measurement. This study represents the vehicle speed variation as a continuous function to make the constructed model more consistent with the actual situation, in order to promote the reduction of carbon emissions generated in the logistics and distribution process, improve the greenhouse effect and ecological environment, and ultimately promote sustainable development. In this paper, a simulated annealing-genetic hybrid algorithm (GA-SA) is proposed to solve the constructed optimization model, and two sets of comparison experiments are designed. The experimental results show that compared with the two classical algorithms, the simulated annealing-genetic hybrid algorithm (GA-SA) has better solution performance, inherits the robustness and potential parallelism of the genetic algorithm, and has a higher practical value. Meanwhile, although the total driving distance of the vehicle path considering carbon emissions increases by 3.52 km, the carbon emission cost and the total cost decrease by 5.6% and 3.4%, respectively, which confirms that the path optimization model considering carbon emissions constructed in this study can not only play the role of restraining carbon emissions but also reduce the total distribution cost and the waste of resources. In this study, a continuous function is used to represent the vehicle speed variation, and two classical optimization algorithms (the genetic algorithm and simulated annealing algorithm) are combined and parameter-optimized, and certain innovations are made in the processing of vehicle speed and the solution algorithm. Finally, the effectiveness of the model and algorithm is verified by experiments.

With the Chinese government promoting the “Double Carbon” policy and high-economic development, environmental regulation and economic efficiency research has become a hot topic. However, existing research focuses on industrial economic efficiency, ignoring logistics industry, which has high emissions and energy consumption. Based on provincial panel data from China’s Yangtze River Economic Belt from 2009 to 2020, this paper uses a super-SBM, which includes undesirable outputs and is combined with the Global Malmquist-Luenberger (GML) index, to measure and analyze the regional logistics efficiency. Panel regression and threshold regression models are used to test the impact of formal and informal environmental regulations on logistics efficiency, rather than the common single perspective. The results indicate that: (1) Environmental regulation has a significant impact on the overall logistics efficiency of the Yangtze River Economic Belt during the study period, and there is a problem of regional development incongruity. The maximum difference in logistics efficiency between provinces is 0.5637. (2) The impact mechanism of formal and informal environmental regulations differs, and each percentage of informal environmental regulation has a promotion effect on logistics efficiency that is 0.0827% higher than that of formal environmental regulation. (3) Formal environmental regulation has a single threshold effect on logistics efficiency, whereas informal environmental regulation has a double threshold effect, demonstrating that overregulation can have the opposite effect. Based on different regulatory tools, this paper makes pertinent recommendations to promote the coordinated development of the logistics industry’s economic and environmental benefits in the Yangtze River Economic Belt.