The most common decarbonization strategy for freight transport considered in the SD models is mode shift, with 15 models concerning this measure and 11 studies considering it a secondary option. Analyzing external forces, a high dependence on the infrastructure factor to implement this strategy can be observed. Social policy regarding infrastructure investments is usual among these models, although other policies are also applied, such as economic incentives, taxation, technologies, and legal requirements.
The second most common decarbonization strategy addressed by 13 SD models is alternative fuels, with the other 7 models considering this measure in conjunction with different strategies. As expected, the principal external forces needed to implement this strategy are technology and energy availability, although infrastructure availability and market acceptance are also of concern. The policies simulated in the models are mostly related to economic incentives (subsidies for alternative fuels, taxes on fossil fuels, and others) and knowledge-based investments in the R&D field. Social and legal policies were also found regarding refueling/recharging infrastructure investments and obligation schemes, such as blend targets (i.e., biodiesel with diesel).
Vehicle and asset utilization appears in eight SD models, and seven studies cite this as a secondary decarbonization strategy. Behavior is the main external force supporting this strategy. It depends on the business culture and willingness to establish partnerships for sharing assets, logistics centers, warehouses, transport infrastructure, load optimization, and consolidation. Policies simulated in the SD models include economic incentives to improve efficiency and encourage companies with financial benefits. Infrastructure and technology investments, as well as legal requirements, were also considered.
Reducing or managing the freight transport demand is the decarbonization strategy of 10 SD models and appears in 4 other studies as a secondary measure. The external force that supports this strategy is market acceptance, as freight transport demand is highly related to consumption patterns and prices that will affect demand according to the price elasticities of each product category. The policies simulated are related to economic measures, increasing fees and transport costs, reducing goods and transport demand, and knowledge-based instruments, for instance, simulating the impacts that information and communication technologies will have on freight transport demand.
Lastly, 4 SD models presented the strategy of improving vehicle efficiency, with 14 other studies considering it secondarily. Similarly to alternative fuel promotion, the implementation of this strategy requires the availability of technology and energy as external forces. Market acceptance, infrastructure, behavior, and regulation are of minor concern in these models. Simulated policies include knowledge-based instruments with technology investment, social instruments with infrastructure investment, and economic instruments with both incentives to adopt innovation and discourage old and outdated technologies.
The following subsections are related to the specific decarbonization strategies of the green logistics framework, describing the main impact on mechanisms and pathways, including how models deal with dynamics aspects.
3.1. Reducing Freight Transport Demand
Reducing the freight transport demand requires a range of processes to minimize the physical amount of goods to be delivered, such as material efficiency, including making products last longer, recycling, digitization, designing products with less material, and postponement of product customization [4
]. Other measures can include price increases, which affect transport demand according to cost elasticity. Table 3
summarizes the SD models’ objectives, policy elements, contributions, and limitations for reducing freight transport demand.
The models differ in terms of boundaries delimitation, inputs, and outputs. Consequently, distinct structures of causal loop diagrams or stock and flow diagrams were found according to their goals. In Refs. [43
], the authors assessed the rebound effects of efficiency gained with information and communication technologies over freight demand stimulation, which counterbalances or even outweighs positive environmental benefits.
As people get used to traveling more and having access to more goods due to gross domestic product (GDP) improvement, the social norm increases, influencing travel and consumption in a reinforcement feedback loop [45
]. Moreover, as fleet efficiency increases, travel costs decrease, leading to a rebound effect on transport demand. On the other hand, road congestion limits the growth in transport demand. A high volume of urban transport will lead to more traffic and reduce environmental quality [56
]. It would require legal regulations (e.g., higher taxes) to reduce transport demand. On the other hand, if the freight transport volume is high, the efficiency of logistics operations is likely to grow, improving economic performance and increasing freight transport demand.
In Refs. [74
], the authors modeled the interdependencies of relevant stakeholders in the freight transport market. The main focus was on decision processes regarding freight demand (e.g., private households, retailers, and shippers) and the resulting freight transport demand of the logistics service provider, which affects freight transport volume, traffic, and environment problems at an urban level. According to Ref. [30
], the increase in fuel price affects per capita income, thereby reducing vehicle purchases. However, due to population increase, positive economic growth, and annual investment in road infrastructure, changes in the fuel price are not sufficient to reduce transport demand.
According to Ref. [50
emissions are related to the urban freight volume powered by e-commerce. Their assumptions show that the urban freight volume will directly influence GDP, leading to higher product consumption, and e-commerce orders will likely increase, affecting urban freight volume in a reinforcing feedback loop. These factors will induce greater energy consumption and CO2
emissions, increasing urban logistics transport costs through the internalization of a carbon tax, resulting in a demand decrease.
The GDP increases transport investment, which will decrease traffic congestion, energy consumption, and emissions, leading to an improvement of GDP [79
]. Moreover, the increasing population will decrease GDP per capita, reducing the number of private cars, traffic volume, energy consumption, and emissions. It was also assumed that population growth would increase the use of non-motorized travel [79
], which is debatable, since slow modes depend on land use and suitable infrastructure. In Ref. [54
], the authors assessed the dynamics between product prices and freight demand. The authors argued that the mark-up variation might further lead to an increase or decrease in prices, causing an inverse effect on the product demand, which is also influenced by logistic costs.
Despite the differences found in the presented literature, some usual variables and assumptions can be highlighted regarding the dynamic relationships in freight transport demand modeling that form the feedback loops in Figure 3
. In a summarized form, emissions are affected directly by fleet efficiency and fuel consumption. Fleet efficiency depends on environmental regulations balancing freight emissions. However, fuel consumption varies according to transport demand, which is affected by other feedback loops, including those with delay effects.
Regarding the quantitative and simulation aspects, most of the studies did not present the model equations, except for Ref. [79
], which makes it challenging to analyze, replicate, or apply the models. Moreover, there is no information about integration techniques or time steps used. Another difficulty is the identification of delays. Although some delays are represented in the diagrams (arrows with hash marks), their estimations were not provided. Other relevant measures, such as the internalization of emission costs, are supposed to take some time to be implemented; however, their delays were not even pointed out in the diagrams. The discussion about time responses, an essential dynamic aspect, requires better exploration.
3.2. Shifting Freight to Lower-Carbon Transport Modes
It is important to increase the performance of railway, waterway, and combined multimodal transport in terms of the comparable price, quality, service, and flexibility of roadway transport to increase the use of alternative modes. Using synchromodality that focuses on optimal and flexible use of multiple modes is expected to contribute to this solution area [80
]. Table 4
summarizes the SD models’ objectives, policy elements, contributions, and limitations for shifting mode modeling.
Analyzing the SD diagrams of the models regarding shifting freight to lower-carbon transport modes, their boundaries, variables, and interrelations that form the feedback loops or stock and flow structures can be identified.
The system dynamics model for economic assessment of sustainability policies of transport (ESCOT) was developed by Ref. [68
] to assess the economic impacts of a sustainable transport system, considering macroeconomic, regional economic, transport, environmental, and policy aspects. The SD diagrams were not provided, but the results show that the growth of freight transport tends to be absorbed by rail and ship transport, since these alternative modes are attractive enough.
In Ref. [49
], the authors evaluated CO2
emissions, considering factors that affect the modal share, such as freight volume, network length, fuel price, and fuel intensity. However, no information regarding the dynamics of changes in the system was provided, compromising the interactions between policies, mode choice, and emission mitigation discussions. Additionally, the modal share modeling does not consider the mode’s capacity and its influence on the mode choice.
In Ref. [40
], the authors analyzed the modal shift process, driven by investment in the modes’ capacities. As the mode shift increases demand, it was assumed that increasing the competitiveness of the mode used would reinforce the mode shift. According to the authors, the inertia for the modal change is long; however, it was not evidenced how the pressure and policies to shift modes were quantified. The time to promote modal shift is randomly assumed as two years. However, its endogenous impact was not demonstrated, thus raising the question of how fast other decisions and actions must occur to achieve a good balance of modal share.
In Ref. [57
], the authors explored strategies for emission reductions and determined the barriers to the mode switch, taking into account company types, decision-making behavior, generalized cost by mode, reliability, functionality, dynamic fleet model, and bands of high-, medium-, and low-cost interventions. The model was then applied, and the results show that there is more perception of reliability than cost changes [58
]. The relationship between price and mode shift is not linear, capturing different companies’ responses, including their tolerance of cost increases, the time lag to implement the mode shift due to contractual considerations, and the need for implementing new systems.
In Ref. [77
], the authors simulated the modal shift from road to rail through increased investments in the rail network. The benefits of this shift would include the reduction in trucks using the road network, better pavement conditions, and road safety. Such a transition would require significant investment to upgrade and maintain the rail track. The dynamic relationships could not be analyzed, since the SD diagrams were not provided.
In Ref. [33
], the authors analyzed the emissions from the vehicle fleet on intercity highways. The scenario devoted to freight was to reduce vehicle kilometers traveled by increasing fuel price, promoting mode shift, and educating drivers to plan their routes and schedules. Therefore, this study does not provide the impact of the isolated freight scenario in freight transport demand reduction and emissions mitigation. The model description does not show the feedback loops described, and there is no clear relation between the fuel price and the average distance traveled. Moreover, it is not clear how assumptions or time lags for mode shift and route planning were designed, compromising policy evaluations.
The impact of policy measures on promoting the modal shift from road to rail, such as the increased road cost and containerization, was also examined [39
]. Increases in the imposition of taxes generally cause an increase in the total logistics cost of road transport. In contrast, containerization causes a decrease in the entire logistics cost of intermodal transport. The rate to implement the policy measures was not provided, but the results showed that the modal shift by containerization occurred more rapidly than by all kinds of road taxation.
The mode shift and sustainability of urban transportation were analyzed by Ref. [42
]. The model assumes that the increasing economy leads to more freight volume, truck trips, and vehicle kilometers traveled, which increases congestion, delivery travel time, and emissions, all impacting economic development. However, the increasing economy also leads to more rail investments; then, the truck trip is reduced together with vehicle kilometers traveled, congestion, delivery travel time, and emissions, resulting in better economic development. The results show that the high-density development of the rail network leads to the best performance of urban transport sustainability.
In Refs. [60
], the authors evaluated modal shift policies to eliminate overloaded trucking. According to the initial modal share, the freight volume by mode is converted into the modal traffic, impacting congestion levels and transport time and determining the next modal split. The results show that the modal shift increases sustainability. However, the reduced freight volume of highway systems would make highway carriers react, e.g., reducing trucking prices to compete with railway transport. Further studies could address the gaming processes of multiple stakeholders.
In Ref. [52
], the authors simulated logistics activities integrated into passenger rail networks. The growth of the rail network improves its competitiveness and market share. External benefits stimulate more investment and subsidies, which accelerate the modal shift. On the other hand, negative impacts, such as job reductions and decreases in fuel tax revenue, decrease the investments. Although dynamics have not been analyzed, the results show that the railway system mitigates emission costs.
In Ref. [76
], the authors explored the decarbonization goal, considering that economic development increases transportation demand and provides funds for infrastructure construction. The gap between supply and demand restricts the economic level, leading to more infrastructure investments increasing transport supply. It was also assumed that economic development guarantees technological investment, improves transportation efficiency, and reduces energy consumption using alternative modes and technological innovations. The results indicate that the mode shift is the most significant measure, although time lags were not taken into account.
In Ref. [53
], the authors simulated the mode shift by levying taxes on carbon emissions. The increasing economy leads the government to invest in railway freight transport. The government also imposes a carbon tax based on CO2
emissions, encouraging the modal shift, promoting the demand and growth of railway freight transport revenue, thereby raising the economy level and reducing road transport demand and CO2
emissions. The policies investigated have a positive effect on reducing emissions; however, exceeding the carbon levy rate will cause the transfer of short-distance trips from road to rail. This result indicates that the model could be improved by considering other relevant factors, such as trip distance and freight flow. Moreover, no time lag was mentioned for the mode shift, although the companies’ resistance, time for adaptation, and inertia play a role in the mode choice process.
Despite the differences presented in the literature, some common variables and feedback loops that rule the dynamic relationships in shifting freight to lower-carbon transport modes can be highlighted, as shown in Figure 4
. In this case, emissions and fuel consumption depend on the mode used, according to the modal share. Factors influencing modal share include logistics costs, freight volume, and mode competitiveness. Economic development and pressure to reduce emissions also play a role in the feedback loops.
Some studies only presented the main equations (not detailed) of their models [33
], making it challenging to analyze, replicate, or apply them. Moreover, there is no information about integration techniques or time steps used. Regarding the delays, no information was found; despite pressure to reduce emissions, the pressure to improve mode capacity, infrastructure investment, and fuel taxes may take time to be implemented. A general lack of discussion about the dynamic aspect of policy impacts in all mode choice SD models was found, i.e., how quickly or slowly the systems may change over time to achieve the results in a specific time.
3.3. Improving Assets Utilization
Optimizing assets utilization accommodates more freight transport demand with the same infrastructure and capital investment. It can be achieved through load optimization and consolidation, asset sharing, and better management of logistics centers, warehouses, and transport infrastructure. Transport predictability and flexibility are important enablers for this solutions area [80
]. Table 5
summarizes the SD models’ objectives, policy elements, contributions, and limitations for improving vehicle utilization modeling.
Different assumptions can be identified by analyzing the SD diagrams of the models, their variables, feedback loops, and stock and flow structures. In Ref. [41
], the authors evaluated the impacts of longer, heavier vehicles (LHVs) on emissions. For the market entry of LHVs, adaptation processes in logistic sectors have to take place. An unavoidable delay between legal permission and full market penetration occurs. This delay is longer for railways, since more complex logistics processes must be refined. The results show that depending on the rail freight demand and costs by transport unit, the modal shift may take place from rail to road, undermining CO2
reduction gains. However, the discussed delays for the logistics adaptation process were not estimated.
In Refs. [31
], the authors modeled logistics strategies toward more efficient transport operations and higher utilization of trucks. The shipment amount is influenced by the operating logistics concept (i.e., just in time), which affects the order cycle frequency and the amount per order cycle. Small shipment amount means a low utilization of trucks, which influences the transport distances traveled, the fuel consumption, emissions, transportation costs, and the pressure to consolidate. If consolidation pressure increases, the shipment amount also increases, which takes time, as companies have to identify consolidation potential. This response time has to be further explored.
emissions for inbound and outbound logistics based on shipment consolidation technique in an automotive assembly line were simulated by Ref. [65
emissions were calculated based on the total number of trips made by inbound and outbound transport vehicles and the type of fuel used. However, the shipment consolidation policy and the assumptions about how it should be implemented (i.e., increasing load factor and vehicle capacity) were not presented.
The effects of alternative truck weight regulation policies on the sustainability of a highway freight system, considering economic and social costs including pavement maintenance, traffic accidents, and emissions, were evaluated by Ref. [59
]. Three levels of weight regulation policies were considered. The best policy varies according to the importance of social costs. The model presents neither the SD diagrams nor the equations, delays, or time lags between policy implementation and results.
In Ref. [73
], the author considered that an increase in the truck–freight demand increases emissions, which are estimated based on the total transportation volume of each truck type (light, medium, and heavy) and the carbon density over the traveled distance. The freight volume and carbon emissions target are time dependent, but the discussion about how the change occurs over time was not provided. The results suggested increasing the use of medium and heavy trucks. Further exploration of whether large or heavy trucks can replace light trucks is necessary.
In Ref. [63
], the authors explored the sustainability of last-mile logistics with different distribution strategies. The centralized distribution case is profitable due to increased demand, while the operational and environmental costs increase. In the home delivery case, the emissions will be more significant, given a substantial increase in customers, increased transport distances, and a higher truck emission rate. The distributed network system considered crowd logistics operations relying on a sharing economy model, in which pollution will not increase sharply compared to previous options. The time that companies take to change their distribution strategies should be further explored.
In Ref. [48
], the authors analyzed freight flows in a distribution chain based on inventory and transport costs. The logistic decisions are taken at the supply chain level, as the choice of loading vehicle rates and order cycle frequency is generally taken lightly by the companies, whereas they influence distribution costs, transport demand, fuel consumption, and emissions. Low truck utilization involves a high number of shipments, which increases road use, reduces average speed, and increases the lead time and transport costs, impacting customer satisfaction, demand, and the order quantity per year.
presents the usual variables and feedback loops that rule the dynamic relationships in the models related to the improvement of asset utilization. The logistics concept of the supply chain dictating the order cycle frequency and amount per order cycle, the distribution costs impacting customer satisfaction and demand, and the pressure to consolidate are some of the key variables forming the feedback loops. Fuel consumption and emissions are influenced by distance traveled, which depends on vehicle utilization.
Regarding the quantitative phase of the SD models, the authors in Refs. [32
] presented the model equations in detail, and in Refs. [59
], the authors presented some main equations, while the other studies did not provide them, showing a lack of transparency. Integration techniques or time steps used were not revealed. The only delay reported (but not quantified) was between the pressure to consolidate and the shipment amount. In contrast, uncertainties may exist related, for example, to customer satisfaction and the influence of emissions on transport costs, which requires the internalization of external cost processes. Such dynamic aspects should be further investigated.
3.4. Increasing Energy Efficiency
Increasing vehicle efficiency involves using cleaner and more efficient technologies, fleet renewal, and driving behavior/eco-driving, among other measures. An increase in the variety of technologies reducing CO2
emissions in heavy commercial vehicles is expected; however, this market implies multiple stakeholders, which considerably affects market dynamics. Table 6
summarizes the SD models’ objectives, policy elements, contributions, and limitations for increasing energy efficiency modeling.
Analyzing the SD diagrams of the models related to increasing energy efficiency and their variables, feedback loops, and stock and flow structures, we can identify different assumptions made to model the system under study. In Ref. [55
], the authors simulated the diffusion of alternative fuels and drives within the truck market. There is a common link between the cost of trucks and their adoption, influencing the manufacturing costs via economies of scale. Investing in new technology is driven by economic forces considering the investment, maintenance, fuel, toll, taxes, and refueling costs.
In Ref. [69
], the authors also analyzed the diffusion of technologies reducing CO2
emissions in heavy commercial vehicles. The study identified that customer preferences change with gaining market shares of innovative technologies. Therefore, the adoption decision impacts the organization by influencing the social network, supplier’s efforts, governmental regulation, and the energy supply system. The causal loop diagram presents delays between some variables, such as governmental regulation, station construction, and market share, although they are not adequately discussed in the study.
In Ref. [70
], the authors modeled the penetration of alternative powertrain technologies to the heavy commercial vehicles market. The model presented some market dynamics and highlighted the sensitive factors of the diffusion process. However, there are several limitations due to missing dynamic empirical data.
In Ref. [46
], the authors interrelated the regional ship emissions, economic growth, and sustainable ecosystem development. Although the causal loop descriptions do not characterize feedback loops, the model provides assumptions that determine its behavior, divided into five sub-systems: shipping, energy, environment, economic, and policy components. The results show that ship speed should be suitably reduced to achieve more significant economic and environmental benefits. The model’s limitations include a lack of proper validation and uncertainties in the variable equations and parameter values.
presents the common variables and feedback loops that rule the dynamic relationships in the models related to increasing energy efficiency. R&D investment, influenced by both manufacturer interests and pressure to reduce emissions, increases vehicle efficiency and reduces emissions. The attractiveness of CO2
-saving technologies considers different factors, such as technology costs, consumer familiarity, refueling station coverage, and fuel prices.
In the quantitative phase of the SD models, only Ref. [69
] did not provide the equations, while the other studies provided some of them. Moreover, there is no information about integration techniques or time steps used. The diagrams represent some delays, although their estimations were not provided. Decisions related to the fleet renewal process and adoption of alternative technologies may take significant time to better investigate in future SD models.
3.5. Switching to Lower-Carbon Energy
Achieving deep carbon reductions will require a significant shift from fossil fuels to renewable energy. In this solution area, the focus is on reducing the carbon content of energy sources. The available options include using cleaner and lower-carbon fuels, such as biofuels, blended fuels, hydrogen, and electrification that ideally uses renewable energy, whose adoption will have significant challenges related to politics, economics, collaboration, awareness of technologies and methods, investment in renewable energy, acceptance of new technologies by societies, and type of governance [81
]. Table 7
summarizes the SD models’ objectives, policy elements, contributions, and limitations for promoting alternative energy sources.
Different assumptions were identified by analyzing the SD diagrams, their variables, feedback loops, and stock and flow structures. In Ref. [44
], the authors presented the assessment of transport strategies (ASTRA) model to assess energy scarcity, high oil prices, and technological investments in the transport sector, besides simulating transport taxation, infrastructure investments, incentives to accelerate fleet renewal, and increases in fuel prices. The typical results are projections of transport demand, CO2
emissions, and the evolution of vehicle fleet. However, the model structure is not presented; therefore, it is impossible to analyze its structure and feedback loops.
In Ref. [66
], the authors presented the global scale system dynamic simulation model for transport emissions (GLADYSTE) to estimate the impacts of policy and technological measures in transport-related sectors. The scenarios include new technologies, fuel quality, fiscal instruments, and traffic management policies. However, the SD diagrams and equations were not provided, making it unfeasible to replicate the model, evaluate the behavior or the assumptions between variables and the feedback loops, the delay equations, or the use of time-related variables.
In Ref. [72
], the authors modeled the interdependencies between the energy supply sector and road transport energy demand. The findings show that rising fossil fuel prices, carbon taxes, and investing in alternative fuel supply could reduce emissions. However, more stringent policies will be necessary for a carbon-neutral scenario, such as efficiency improvements, travel demand management, vehicle technology shifts, and fuel switches.
In Refs. [36
], the authors modeled the diffusion of a city logistics system based on electric and hybrid vehicles. The size of the fleet depends on freight demand, vehicle capacity, and load factor. The lower operating costs of alternative technologies generate savings and reinforce their adoption. However, the greater the number of vehicles, the more investment is needed, negatively affecting their purchase.
In Ref. [64
], the authors evaluated low-carbon strategies for the transport sector by using the SD model of for future inland transport systems (ForFITS). This model estimates the demand for each transport mode based on GDP, population, economic growth, price inflation, and other analyses. Policies adopted for freight transport include improving fuel efficiency and promoting the use of biofuels. The substitution of less efficient vehicles may occur slowly over time, although such delay was not addressed.
In Ref. [47
], the authors also employed ForFITS to estimate fuel use and emission reductions from electrified trucks and electric railways. Increasing the share of plug-in hybrid electric and fully electric trucks would reduce energy use and emissions, but it would not be enough to reverse current demand growth trends. Increasing the share of rail transport would lead to an additional reduction, while combining both mitigation options indicates the highest savings. This solution comes at the cost of providing the necessary electric charging infrastructure and clean energy mix to operate these vehicles effectively, which may not occur as quickly as desired.
In Refs. [34
], the authors analyzed CO2
emission mitigation in the road transport sector in response to social, economic, technical, and policy changes. Fuel consumption depends on fuel type, vehicle type, and distance traveled, while CO2
emissions depend on fuel consumed and emission factors. No single policy instrument could reduce emissions significantly, and a broad portfolio of policy measures is needed. SD diagrams were not provided, making it difficult to evaluate model structures and the assumptions made for the system’s dynamic behavior over time.
In Ref. [71
], the authors analyzed the road transportation sector’s energy consumption and CO2 emission reduction. Policies simulated fuel economy standards through efficiency improvements, mode shift from road to rail, and adoption of electric vehicles. Electric vehicle adoption is a good alternative, although the optimal result should include a mix of policies to reduce emissions. The paper does not provide the feedback loop descriptions and does not mention the assumptions made regarding the time responses from policy implementation to result achievement.
In Ref. [67
], the authors evaluated the impact of the railway electrification system. One dynamic factor included in the model is financial stability, which is very difficult to achieve, as railway operations require a lot of resources and an even flow of transport. At the beginning of the railway operations, there may be unavoidable delays, which will slow down the freight flow and lead to potential delays in investment return. The cost and the time of changing the locomotives were also considered. The electrification of railways has considerable potential to reduce emissions from the freight transport sector, helping to achieve climate targets, although the mentioned delays were not assessed.
In Ref. [78
], the authors analyzed the city logistics system, the policy-making process, and its connections to operational and economic factors. The level of emissions was analyzed considering policies promoting electric vehicles. As CO2
emissions rise, the financial incentives for green vehicles increase, making them more attractive to absorb transport demand. However, the model does not include technical issues, availability of charging stations, and time responses of policies related to alternative fuel adoption.
shows the common variables and relationships that form the feedback loops in the SD models related to alternative fuel adoption. Emissions depend on fuel consumption and efficiency, while alternative vehicle adoption takes into account regulations, fuel costs, refueling and recharging station availability, purchase and maintenance costs, as well as drivers’ experiences. On the other hand, transport demand and vehicle load lead to an expected fleet, influencing vehicle sales, while incentives to renew the fleet are another option to scrap polluting old vehicles and adopt alternative green technologies.
Most studies did not present the model equations in the quantitative phase of the SD models, while one study showed them entirely [38
], and three studies [35
] presented only the main equations. Integration techniques or time steps used were not disclosed. Any delay was discussed or represented in the models, although implementing regulations to adopt alternative fuels, incentives to renew the fleet, or drivers’ experience consolidation may not occur instantaneously. In general, there is a lack of discussion about the time responses related to policy enforcement and the willingness of companies to adopt innovations regarding alternative fuels and efficient vehicles. Thus, there is a research opportunity to deepen knowledge associated with the intrinsic dynamics of changing technological paradigms of this decarbonization strategy.