Long-term implications of electric vehicle penetration in urban decarbonization scenarios: An integrated land use–transport–energy model

https://doi.org/10.1016/j.scs.2021.102800Get rights and content

Highlights

  • The framework of an integrated land use–transport–energy model is demonstrated.

  • The integrated model depicts land use–transport–energy interactions, with explicit spatial representations.

  • A stringent EV penetration scenario up to 2050 was analyzed using the model.

  • Impacts on land use, transport, energy profiles, and welfare associated with EV adoption varied spatially across zones.

  • The spatial differentiation of policy effectiveness deserves more attention.

Abstract

Electric vehicles (EVs) are considered a promising technology and an attractive solution for a low-carbon future. It is therefore necessary to model the market penetration of EVs and determine the role of EV adoption in future urban decarbonization scenarios. This study developed an integrated land use–transport–energy model to examine interactions between location choice, land use, transport patterns, energy profiles, and economy when implementing a stringent EV policy. Two scenarios were structured to investigate the long-term (to 2050) impacts of EV adoption on population distribution, land use patterns, transport demand, energy mix, emission profiles, and social welfare. Scenario simulations showed that ambitious market diffusion of EVs is likely to have significant positive effects on emission reduction in city centers, while economic benefits tend to occur in suburban areas, implying that EV adoption will play an important role in the spatial organization and structure of cities. The spatial heterogeneity of different urban zones requires more attention when evaluating the effectiveness of sustainable urban policies. Because disaggregated spatial interactions can be handled by the new integrated model, the methodology proposed here will be a useful tool for sustainable urban planning.

Introduction

Due to the huge increase in greenhouse gas (GHG) emissions associated with rapid urbanization and economic growth, climate change has become a serious threat worldwide (Kalnay & Cai, 2003; Lou, Jayantha, Shen, Liu, & Shu, 2019). The ambitions of the Paris Agreement require the rapid and massive decarbonization of cities (Solecki et al., 2018), with cities worldwide becoming agents of climate change mitigation (Creutzig, Mühlhoff, & Römer, 2012). Cities are responsible for about 70 % of total GHG emissions (Rashidi, Stadelmann, & Patt, 2017) and, hence, have a primary responsibility in addressing this challenge while offering some of the best opportunities for decarbonization in certain sectors, such as building, transport, water, and waste (Echeverri, 2018). Urban transport systems alone account for 68 % of total GHG emissions in cities (Menezes, Maia, & de Carvalho, 2017), and many countries around the world are promoting environmentally sustainable transport in the hope of achieving energy-efficient and low-carbon development in cities. However, the rapidly growing demand for mobility and private-vehicle ownership have counteracted efforts to reduce emissions of GHGs and other air pollutants in regions with large populations, such as urban areas. Transport emissions have continued to grow, while other sectors have decarbonized; thus, decarbonization of the transport sector plays a key role in achieving the target of carbon-dioxide-free cities.

Transport electrification has been identified as a key strategy to reduce GHG emissions (McCollum, Krey, Kolp, Nagai, & Riahi, 2014). Electric vehicles (EVs) offer an alternative to the conventional internal combustion engine vehicle (ICEV) and are often considered a promising solution for a green future. The deployment of EVs has been growing rapidly around the world (Weiss, Dekker, Moro, Scholz, & Patel, 2015), with many governments establishing increasingly stringent and ambitious targets in support of EV adoption. Several strong policy interventions have been proposed to facilitate the market diffusion of EVs, such as ICE bans and purchase subsidies. Meanwhile, the automotive industry has continued to promote next-generation EV technologies and launch new EV models. Stimulated by the ambitious targets set by governments and continuous improvements in industrial technology, the market diffusion rate of EVs has increased rapidly across the world, with the global electric car fleet exceeding seven million in 2019. China remained the world’s largest EV market, with 2.3 million electric vehicles in active use, followed by 1.2 million in Europe and 1.1 million in the United States (IEA, 2020). Therefore, in the near future, it is possible that EVs could gain significant market penetration, especially in urban areas (Wu, Freese, Cabrera, & Kitch, 2015). Therefore, because transport electrification offers a promising long-term option for a decarbonized transport sector, it is imperative to understand the long-term projections of energy use and GHG emissions with the stringent market diffusion of EVs.

The objective of this study was to develop an integrated urban model that could represent interplays between land use, transport, and energy, to examine the interactive mechanisms between location choices, travel activities, energy mix, emission profiles, and economy at the city scale when implementing a stringent EV policy. Model integration of the energy system into land use–transport interactions could improve the representation of energy issues, such as EV diffusion in the urban environment. Transport demand and modal shares are affected by the location choices of each economic agent, which can be formalized by the utility and profit maximizing principle, and their interactions with the transport system, rather than the use of a regression model with aggregated data such as GDP and population. Furthermore, it is also possible to investigate how the impacts of EV policies on land use, transport, energy use, emissions, and urban economy are spatially differentiated across zones, because an integrated urban model would be able to consider land use–transport–energy interactions with explicit spatial representations at the zonal scale.

This paper is structured as follows. An overview of the related literature is presented in section 2. Section 3 describes the methodology of the integrated land use–transport–energy model. Section 4 introduces the study area and data sources, with two scenarios outlined to illustrate the impact of EV penetration. Section 5 presents the simulation results and analyzes the most important findings. Section 6 is a discussion and conclusion that summarizes the main points and also briefly outlines suggested future work, with the policy implications for integrated land use–transport–energy planning and management.

Section snippets

Literature review

Many cities are pursuing low-carbon development targets to reduce GHG emissions in the long term, and the emergence and discussion of decarbonized transport systems and policies (such as widespread and massive EV diffusion into the market) have led to many influential research achievements in the field of sustainable urban studies. To achieve the long-term goals of climate change mitigation and reduction of fossil fuel dependence, an electrified transport sector is considered a climate-friendly

Model structure

We developed an integrated land use–transport–energy model in the tradition of a partial equilibrium model. The new model explicitly formulates the location choice decisions, travel behaviors, and energy technology selections. The framework of the integrated model follows the main structure of a computable urban economic (CUE) model (Ueda, Tsutsumi, Muto, & Yamasaki, 2013; Zhang, Matsushima, & Kobayashi, 2017), which is used to represent the interactions between land use and transport systems

Study area and data sources

A case study of Changzhou, China, was conducted to investigate the impacts of EV penetration using the integrated urban model. As one of the core cities of the Yangtze River Delta in China, Changzhou is located in the most urbanized and industrialized region of China (Fig. 2). Data for parameter estimation and model calibration were mainly collected from a Person Trip (PT) survey in 2008. The PT survey divided Changzhou into six traffic areas, 40 medium traffic regions, and 438 traffic analysis

Spatial patterns of the main indicators in the REF scenario

Fig. 3 presents the simulation results of the main indicators across all zones in 2050 for the REF scenario. The population density is high in the core and central zones and low in suburban and peripheral zones, while the zones with a high workforce density are distributed mainly inside the beltway area. Similar to the spatial patterns of population, the high values of transport demand density are concentrated in the core and central zones rather than in suburban and peripheral zones,

Discussion and conclusions

In this study, an integrated land use–transport–energy model consistent with microeconomic theory was used to estimate transport demands, modal split, and technology selection, as well as energy consumption and welfare levels based on the framework of land use–transport interactions. Energy use and emission profiles were coupled with location choice decisions, urban mobility, and land use equilibrium to investigate the influence of EV penetration and how its impact would vary spatially across

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgment

The author acknowledges support from the Japan Society for the Promotion of Science (JSPS) KAKENHI Grant Number 19K20507.

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