Study on optimization of multimodal transportation path of Jiamusi grain considering cargo loss under low carbon policy
Time sensitivity analysis
Considering the customer’s requirements on cargo delivery time, the optimal transportation scheme for each loading mode under the low-carbon policy is analyzed by adjusting the time window, assuming that other conditions remain unchanged. The time window is set to start at [40,45] hours with a step size of 5 hours, and multiple time periods are tested.
The effect of time constraints on different loading methods from the loading method is shown in Fig. 6. With the relaxation of the time window, the overall carbon emissions show a fluctuating downward trend, with the process of decreasing, then increasing and then decreasing, which corresponds to a stepwise upward trend in the transportation time. When the time constraint is relaxed to [75,80] hours, the carbon emissions of all loading modes reach the lowest value. As shown in Fig. 6(a), when the time constraint is [50,55] hours, the bag transport mode is shifted from road to rail; after the time constraint is relaxed to [70,75] hours, the transport mode is shifted to waterway transport which has lower carbon emission. Figure 6(b) and (c) show that both bulk and containerized cargoes are mainly transported by rail, but both shift to waterborne transport at the time window of [70,75] hours. Under this time window, carbon emissions from bag, bulk and containerized transport decrease by 86%, 86% and 18% respectively from the peak.
Fig. 6
Carbon emissions and transportation time of the optimal transportation paths for each loading mode under different time constraints. (a) Bag transport results. (b) Bulk transport results. (c) Containerized transport results.
The effect of time constraints on the pairs of transportation schemes under different carbon emission policies is analyzed as shown in Fig. 7. When with the time window constraint gradually relaxed, the total cost of the optimal transportation scheme of loading mode under each carbon emission policy shows a fluctuating downward trend. When the time constraints are at [70,75] and [75,80], the transportation scheme shifts from the more costly public-rail intermodal transportation to the more affordable rail-water intermodal transportation. In Fig. 7(b), the total cost of the transportation scheme of each loading mode is the lowest under the ETS policy, and it has a lower cost advantage under each time window constraint. When the time constraint is between 50 and 80 hours, the cost of carbon emissions for each loading method is transformed into a profit benefit. In Fig. 7(c), under the COP, when the time constraint is above 50 hours, the carbon emission cost of each loading method is 0. In contrast, as in Fig. 7(a), the CTP generates the highest total cost. It is evident that, with the gradual relaxation of time windows, transportation options are increasingly shifting toward cleaner and lower-cost rail-water intermodal modes under different carbon policy regimes. Consequently, carbon emission costs will vary depending on the specific policy mechanisms implemented.
Fig. 7
Changes in total cost and carbon emission cost of three optimal transportation scenarios with low-carbon policies under different time window constraints. (a) CTP transportation results. (b) ETS policy transportation results. (c) COP transportation results.
Carbon price sensitivity analysis under the CTP
Analyzing the impact of carbon price volatility on transportation decisions under the CTP. As shown in Fig. 8, with the increase of carbon tax price, the total cost of each transportation mode shows an upward trend. It is worth noting that when the carbon tax price rises to RMB 2/kgCO\(_2\) and RMB 3/kgCO\(_2\), bagged and bulk transportation, in an effort to reduce carbon emissions and the cost of carbon emissions, shifts its mode of transportation from rail to waterborne transportation, which is more advantageous in terms of low carbon emissions, resulting in a reduction of carbon emissions by 26.7% and 26.8%, respectively.
Therefore, when the carbon price remained at the relatively low level of RMB 0.05/kg to RMB 1.5/kgCO\(_2\), the change in carbon emission costs was not sufficient to stimulate a shift in transportation modes to low-emission waterborne transport modes. However, when the carbon price starts to rise to RMB 2/kgCO\(_2\), the increase in carbon emission cost prompts some transportation options to reduce carbon emissions, initially showing the effect of carbon emission reduction. Therefore, the implementation of a reasonable carbon pricing strategy can effectively promote the transition of grain transportation to low-carbon modes.
Fig. 8
Sensitivity analysis of transportation options to carbon tax price under different loading modes.
Sensitivity analysis under the COP
Under the COP, the cost of carbon emissions is mainly affected by both the degree of carbon quota and the carbon price, This paper sets the carbon quota interval threshold based on the carbon emission data of the optimal transportation scheme under each loading mode. Assume that \(E_{\text {max}} = 39,\!776 \, \text {kgCO}_{2}\) is the high quota lower limit and \(E_{\text {min}} = 29,\!040 \, \text {kgCO}_{2}\) is the low quota upper limit. Identify \(E_g \le E_{\text {min}}\), \(E_{\text {min}}, and \(E_{\text {max}} \le E_g\) as the low, medium, and high carbon quota scenarios, respectively.
In the low carbon quota scenario, the total cost shows a continuous upward trend with the carbon price, as shown in Fig. 9(a). When the carbon price reaches RMB 2/kgCO\(_2\), the transportation mode of bags is shifted from railroad to relatively low-carbon waterways, and carbon emissions drop by 26.7%, with a better carbon emission reduction effect, but carbon emissions are still higher than carbon quotas, so when the carbon price is higher than RMB 2/kgCO\(_2\) still generates carbon emission costs, and the growth rate of total costs slows down significantly. In the medium quota, as shown in Fig 9(b), the total cost of bagging first shows a linear growth trend, and when the carbon price is RMB 2/kgCO\(_2\), the mode of bagging transportation shifts from railroad to rail-water intermodal transportation, the carbon emissions below carbon allowances, and the cost of carbon emission is zero. Thereafter, the total cost remains unchanged. In the high quota scenario, as shown in Fig. 9(c), the optimal transportation scenarios for all loading modes emit less carbon than the quota cap and therefore do not incur a carbon cost, and the total cost remains stable and unaffected by fluctuations in the carbon price.
Fig. 9
Carbon price sensitivity analysis for different carbon allowance scenarios for transportation under the COP. (a) Transportation results under low quota scenario. (b) Transportation results under medium quota scenario. (c) Transportation results under the high quota scenario.
Sensitivity analysis under the ETS policy
Carbon price sensitivity analysis under different carbon quota scenarios
The carbon trading market utilizes the same low-medium-high allowance mechanisms as the COP. When actual emissions are lower than allowances, companies can gain from selling the remaining allowances, stimulating a modal shift to more environmentally friendly transportation options through a reduction in total costs.
In the low carbon allowance scenario, as in Fig. 10(a), actual emissions continue to exceed the allowance limit, no carbon benefits are generated, and the total cost keeps growing as the carbon price rises. In the medium allowance scenario, as in Fig. 10(b), when the carbon price reaches RMB 2/kgCO\(_2\), bag transport shifts to rail-water intermodal transport, which reduces emissions below the allowance, and carbon benefits begin to partially offset total costs. In the high quota scenario, as shown in Fig 10(c), when the carbon price rises to RMB 2/kgCO\(_2\) and RMB 2.5/kgCO\(_2\), bag and bulk transportation shift to rail-water intermodal transportation, which achieves carbon emission reductions of 26.7% and 26.8%, respectively. Since the actual emissions are lower than the allowances, the continuous growth of carbon benefits leads to a significant reduction in the total cost of all transportation modes, and the total cost of all loading modes reaches the lowest value in this scenario. The study shows that a reasonable carbon price and high quota setting can not only effectively promote emission reduction, but also achieve synergistic optimization of environmental and economic benefits.
Fig. 10
Carbon price sensitivity analysis of transportation scenarios with different carbon allowance scenarios under the ETS policy. (a) Transportation results under low quota scenario. (b) Transportation results under medium quota scenario. (c) Transportation results under the high quota scenario.
Preference analysis
In the process of transport scheme development, the enterprise’s preference for different cost factors will also have an impact on the decision-making, due to the more obvious cost advantage of the transport scheme under each loading mode under the ETS policy, with the goal of the optimal total cost, other assumptions remain unchanged, in-depth study of the carbon trading mechanism, to analyze the impact of different preference values on the transport scheme.
The weight coefficients of each part in the composition of the objective function are \(\omega _1, \omega _2, \omega _3, \omega _4 \in (0, 1)\), with a sum of 1 and a step size set to 0.1. After testing a variety of combinations, in order to study the impact of the degree of weight change on the transportation plan, this paper keeps eight different weight combinations. In addition, the initial weight is set to \(\omega _1 = \omega _2 = \omega _3 = \omega _4 = 0.25\) as a reference to analyze the impact of freight cost, cargo damage cost, time cost and carbon emission cost on the development of transportation plan under three loading modes.
Different cost weights have a significant impact on the choice of transportation options, and the results are shown in Table 9. From the single weighting analysis, in Fig. 11(a), when the cost of cargo damage is prioritized, the all- weighting factor transportation scheme is adopted to reduce the cargo damage generated in the transportation process, but it leads to a surge in carbon emissions, which is 12 times of the minimum carbon emissions. When focusing on freight cost, the transportation scheme of each loading mode selects rail-water intermodal transportation, and the proportion of waterway transportation reaches 32.4%. When the cost of carbon emissions is dominant, the rail-water intermodal transport scheme at this time achieves carbon emissions minimization, and the carbon emissions of bag, bulk, and container transport are reduced by 27.6%, 27.7%, and 1.2%, respectively, compared with those at the time of initial weighting.
Fig. 11
Sensitivity analysis of transportation options to carbon tax price under different loading modes.(a) Results under single weights. (b) Results under combined weights.
When analyzing based on combination weights, freight cost and cargo damage cost play a dominant role in the decision making of transportation options, and their share in the total cost reaches, respectively, 99% for bags, 99% for bulk, and 93% for containers. The freight and cargo damage costs are divided into one group. With the change of weight coefficients, the results are shown in Table 9. The level of firms’ risk preference for freight and cargo damage is positively correlated with the share of waterway transportation modes for bag and bulk transportation. In Fig. 11(b), when the sum of freight and cargo weights is 0.8, the lowest carbon emitting rail-water intermodal transportation option is selected for each loading mode. When the weight of time and carbon emission cost is 0.8, the bulk transportation time is optimized to 66.7 hours, which satisfies the time window constraint and avoids the time penalty cost. Overall, container transportation has significant advantages and maintains better cost advantages and environmental benefits under various weight combinations.
Table 9 Solutions for different weight coefficients under the ETS policy.
