Effects of multi policies on electric vehicle diffusion under subsidy policy abolishment in China: A multi-actor perspective
Introduction
The transportation sector, which plays a vital role in economic and social growth, is facing enormous challenges (particularly road transportation) in energy security and dependence, and air pollution [1], [2], which are related considerably to the wide diffusion of conventional vehicles (CVs) [3]. Against this background, many countries, such as China (the focus of this paper), consider the use of electric vehicles (EVs), as a fundamental approach to addressing these persistent unsustainable problems [4], [5]. However, despite the environmental benefits of EVs compared to those of CVs, EVs’ penetration process has not been smooth in most countries [6]. To date, the diffusion of EVs worldwide is still limited to a niche market and penetration to the mainstream regime is hindered. For example, the sales of EVs in China accounted for only 4.47% of the total car sales in 2018, as shown in Fig. 1. By the end of 2019, the EV parc reached 3.81 million, accounting for only 1.46% of the total car parc in China [7]. This issue initiated a range of discussions on how the diffusion process can be facilitated. Most of the related literature attributes this issue to the immature technologies and the high initial purchase cost, and reports that entering into a more sustainable EV configuration requires technology redirection and a new market selection environment, in which governments are regarded widely as a critical agent [8], [9], [10].
Since 2010, a series of policy measures have been introduced and implemented by the Chinese government, to boost the penetration rate of EVs. Among these policies, the purchase subsidy scheme has attracted the most attention and gained prominence. The positive influence of this policy on the adoption of EVs has been demonstrated in much literature [12], [13]. However, this incentive-subsidy policy has caused an unpredicted “subsidies fraud” phenomenon in China because EV manufacturers deceived massive subsidies by falsely reporting their EV production numbers to the government [14], [15]. According to the survey conducted by the Ministry of Finance, more than 70% of the 93 automobile enterprises swindled the subsidies [16]. Some firms even form “subsidy inertia.” Additionally, the structure of policy instruments is unbalanced, and non-subsidy policies are relatively lacking [17]. Under this background, the Chinese central government is launching a new round of policy system adjustment and reform to achieve policy diversification and balance fiscal and non-fiscal policies [18]. Two questions thus emerge. How would the abolishment of the purchase subsidy influence the market share of EVs? What effects are the other multiple policies on the EV diffusion in the era of “post subsidy”?
Though plenty of studies have investigated the EV-related policies, most of them discussed one single policy measure in isolation [19]. However, the large-scale diffusion of EVs in practice can never be brought about by a single policy instrument, but is often the result of multiple policies and policy instrument interactions [20]. Moreover, there remains a lack of empirical research on the impact of policy packages other than purchase subsidy policy [21].
With this paper, we aim to advance this research stream by elaborating on the dynamics between multiple policies and EV’s market diffusion through the interaction among multiple actors. Consequently, the three steps are adopted. (1) Based on the EV policies in China, two types of policies (manufacturers- and consumers-oriented policies) are chosen, including both command-and-control and incentive-based instruments. Consequently, the two main actors, namely, manufacturers and consumers, are determined. (2) We establish a SD model-based framework explicitly that considers the government–enterprise–consumer interaction to discuss how the government’s multi-policy affects the activities and behavior of enterprises and consumers and how these changes affect the diffusion of EVs. (3) We simulate empirically the diffusion results of EVs from 2020 to 2030 under different scenarios. Hence, the results of this study can provide implications for the government to promote EV development with more efficient policy instruments under the gradual withdrawal of purchase subsidy, and for firms to fully understand the impact of different government policies, effectively utilize the policy benefits and formulate more effective corporate strategies.
This paper selects the Chinese EV-related policies as a case study mainly because China is an important actor in the global EV market. From 2010 to 2018, the sales of EVs in China increased from 0.72 million to 125.62 million, ranking China first in the world in terms of EV deployment [22]. The whole development process of EVs in China demonstrated an obvious policy-led characteristic. Its policy assessment can serve an interesting example for the EV industry worldwide. The modeling method of multi-actor and multi-policy and guidance of the research results are also applicable to other countries, if the development of EVs in these countries faces the same challenges, such as low consumer awareness, declining purchase subsidies, traffic congestion and policy optimization.
The remainder of this paper is organized as follows: Section 2 presents the research background, including the literature review on EV-related policy effectiveness and the illustration of multiple policies and multiple actors regarding Chinese EV. Section 3 proposes the construction of the SD simulation model and describes the interaction among government, firms and consumers. These are followed by simulation and multi-scenario analysis of possible policy effects in Section 4. Section 5 discusses the main findings and outlines the concluding remarks.
Section snippets
Literature review
In the past few years, an increasing number of studies have analyzed the policies on EV [8]. Some studies focused on the contents of specific policy measures or comparisons between different countries, while others assessed the effect of policies on the development of EVs [9]. Although many insights have been generated by latter studies, what still challenging is in gaining a better understanding of the influence of the policy mix on the diffusion of EVs [8], [9].
In the latter literature, most
SD model description
In this study, the SD model is adopted to simulate the development process of China’s EV market generation. This model is composed of six main modules: the consumer market module, which describes the evolution of market diffusion through market share research; the economic development module, which describes the needs of the society to develop the automotive industry; the cost modules (EV and CV), which stimulate the trend of the total life cycle cost (LCC) of EVs and CVs; the carbon emission
Model validation
To ensure the validation of the proposed SD model, the following three steps are performed.
Discussion and conclusions
The policy effect on emerging industries has always been a popular research topic. Existing research related to electric vehicle policies focuses mainly on a single policy, while the mechanism analysis of multiple policies is relatively insufficient. Given the limitations of the single policy research and the “black box” problem of the mechanism of industrial policy, this work views industrial policy as an organic system, considers the influence of the interaction between multi actors and
CRediT authorship contribution statement
Deyang Kong: Conceptualization, Writing - review & editing. Quhong Xia: Methodology, Software. Yixi Xue: Writing - original draft. Xin Zhao: Investigation.
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.
Acknowledgments
This work was supported by the Youth Program of National Natural Science Foundation of China (Grant No. 71802127) and the Key Projects in Soft Science Research of Shanghai Science and Technology Commission (Grant No. 19692102800).
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