From a global perspective, the transportation industry is the crucial and fastest-growing sector contributing to greenhouse emissions, which are the main factor contributing to climate change. The International Council on Clean Transportation (ICCT) estimated that global carbon dioxide emissions related to transportation will increase significantly in the future, growing more than 70% by 2050.
The transportation sector, therefore, is facing the challenge of improving energy efficiency and reducing vehicle emissions while also enhancing the performance of the urban transportation system and meeting increasing travel demand. Shared autonomous vehicles (SAVs), which integrate automation and on-demand mobility services, could play an important role in achieving such goals. Governments worldwide have high expectations for autonomous vehicles, which have great potential for development.
However, there are still many uncertainties about the environmental and energy effects of SAVs, and some researchers and policymakers have concerns about whether SAVs are beneficial for sustainable social development. Although some studies have modeled SAVs and estimated their energy and environmental effects, they only utilized independent transportation demand models with an externally specified travel demand. Such approaches do not account for the interaction between transportation and land-use systems (Fig. 1), which causes these studies to ignore an essential fact—namely, that in the long-term, SAVs will not only affect the residents’ travel behaviors and traffic flow, but also change the spatial location decisions for residents and enterprises, thus changing travel distribution and ultimately affecting emissions and energy consumption.
Fig. 1 Feedback loops between urban transportation and land use. Source: Zhong S., Sun J. Logic-Driven Traffic Big Data Analytics: Methodology and Applications for Planning, Springer, 2022.
In a recent paper published in npj Urban Sustainability, we assess the long-term effects of shared autonomous vehicles on land use, transportation, energy, and the environment by a land-use and transportation integrative approach (Fig. 2). Moreover, to facilitate efficient SAV introduction, the optimal SAV pricing strategy was determined based on multiple effects. Our study further analyzed the benefits of vehicle electrification—another important future transportation trend—to give policymakers a better understanding of future transportation.
Fig. 2 A conceptual overview of the sequential modeling processes. A sequential model is applied to assess the energy and environmental impacts of different regional development scenarios quantitatively. The model incorporates the quasi-dynamic land use and transport integrated model, vehicle emission model, entropy-weighted TOPSIS method, and electrification scenarios analysis.
We first analyzed SAVs’ effects on urban transportation and land-use systems. Our results revealed that although SAVs will increase travel demand and decrease the proportion of trips made using public transportation, SAV introduction will have more advantages than disadvantages. SAVs’ effects on promoting transportation efficiency can overcome the negative effects to reduce regional total flow time and improve accessibility.
Based on the transportation and land-use results, we examined the energy and environmental effects of different SAV pricing scenarios. Our results indicated that SAV promotion can decrease car-use demand, and optimize urban land-use and transportation systems, thereby significantly reducing energy consumption and emissions (Fig. 3). Although low SAV pricing may produce the opposite results, we can nevertheless conclude that SAVs have great potential for promoting the sustainable development of urban transportation.
Fig. 3 The impact of SAVs on PM2.5 emission. The spatial distribution of relative PM2.5 emission between different SAV pricing scenarios and Scenario S0 across the study area. SAV pricing scenarios incorporate Scenario S1 (a), Scenario S2 (b), Scenario S3 (c), Scenario S4 (d), and Scenario S5(e). Orange indicates increased PM2.5 emission in SAV pricing scenarios, and green indicates reduced PM2.5 emission in SAV pricing scenarios. Jiangyin City, Jiangsu Province, China, is the background. This city, as a county-level city in Wuxi City, is one of six pilot smart cities under construction and was selected in our study to evaluate the impacts of SAVs.
Our results show the importance of making an appropriate SAV pricing strategy. We identified a nonlinear relationship between SAV fares and SAVs’ effects on transportation, land use, energy use, and the environment (Fig. 4). When SAV fares are low, owing to the drastic reduction in travel costs, residents’ travel demand will increase significantly, leading to an increase in energy consumption and emissions, which is detrimental to saving energy and reducing emissions. An appropriate SAV fare, meanwhile, can offset the induced travel demand, give full play to SAVs’ effects on saving energy and reducing emissions, and maximize sustainable urban development.
Fig. 4 The impact of different development scenarios on energy and environment. Green columns: PM2.5 emission under different regional development scenarios across the study area. Blue columns: the impact of different development scenarios on energy consumption.
Furthermore, we examined the effect of vehicle electrification on the environmental and energy benefits of SAVs. We proved that vehicle electrification plays a significant role in further mitigating vehicle emission problems (Fig. 5). If vehicle electrification is coordinated with technological innovations in electric power structure and EV (electric vehicle) energy efficiency, the environmental benefits of vehicle electrification will be further amplified (Fig. 5).
Fig. 5 Reduction ratios of NOx, CO2, and PM2.5 emissions under different electrification scenarios relative to Scenario S3. Scenario E1 further considers vehicle electrification based on Scenario S3, and EV energy efficiency and emission levels from electricity generation are both at the current level. Scenarios E2 and E3 build on Scenario E1 by considering a cleaner generation mix in the electric power structure and advances in EV energy efficiency, respectively. Scenario E4 takes into account three technical innovations: vehicle electrification, EV energy consumption, and electric power structure. Grey icons represent current levels, and green icons represent future levels.
Overall, our study’s findings can help transportation authorities gain a deeper understanding of future transportation trends—namely, vehicle automation, shared mobility, and vehicle electrification. Our study can also help authorities formulate policies for contributing to the transportation sector pollution prevention and carbon emission reduction in the context of sustainable city development. Such policies can cover upcoming changes to existing policies before SAV deployment, as well as future policy incentives aimed at bringing the great potential of SAVs into play. For example, our fleet electrification results show that the government should formulate incentives to accelerate the transition to EVs and adopt stricter fuel economy standards to push vehicles and power grids toward cleaner, more efficient development.
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