The electric vehicle has been a hot topic in recent years. Many countries have announced a clear plan to stop selling gas cars in 10 or 20 years. Naturally, with the emergence of EV, the deployment of the charging station is a problem of interest. Different from refueling, which could be done in a few minutes, recharging of the EV’s battery may need half an hour or even a longer time. Thus, people may more likely to leave their car while recharging. Thus, the deployment of the charging station needs to be organized carefully to benefit the majority, and this deployment may even affect the sales of EV or at least the speed of upgrading from traditional gas car to electric vehicle. After all, despite the mandatory policy made by the government, rational people only care about their own convenience.
The paper written by Yiyi Zhou and Shanjun Li focused on the interdependence relationship between EV adoption and charging station deployment. They found that the “indirect network effects between them could lead to multiple equilibria in the steady state.” To illustrate their idea, the authors used a “stylized theoretical model to examine the dynamics of EV adoption .” It is a rather sample which only considers one EV model and without automaker’s strategies. Even though it is a highly simplified model, it conveys an important idea that the number of sales of EV and installment of charging station must pass through their critical mass to make sure the economy would adopt this new technology instead of abandoning it. Later, “to bring the model to the data, they parameterize their theoretical model and derive a simultaneous system of two equations: an EV demand equation that quantifies the effect of the availability of charging stations on EV adoption; and a charging station supply equation that quantifies the effect of the installed base of EVs on the deployment of charging stations.” Their “data includes quarterly EV sales by vehicle model and the number of charging stations in each of the 354 MSAs from 2011 to 2013. ” The methods are OLS and 2SLS. Using their model, they predicted the targeting critical-mass, the fixed subsidy amount per EV and its different effect in different states in the US.
This paper used a rather traditional method to reveal the relationship between EV, charging station and the effect of subsidy policy in the US. Considering that China is already the biggest EV market in the world, there is great practical significance in doing similar research using China’s data. Different from the US, China’s EV market is still in its initial stage and there is still an argument between charging stations and battery swapping stations. The biggest EV company in China -- Beijing Electric Vehicle of Beijing Automotive Group Co., Ltd. just announced their plan which is called the Optimus Prime Plan and the key point is that BJEV would build 3000 battery swapping stations in China over the next five years. Therefore, in my model, I would like to include the role of battery swapping station besides charging station and using dynamic programming to predict what result would happen in the future. I will try to use the initial investment, profitability and the dynamic payback period to show their difference. Will they exist together in the future? Which type of station will have the leading role? Alternatively, only one of them will survive, which one will win? Also, considering I will use a large dataset from different provinces with different types of EV (taxi, bus and private cars), I would prefer to use GMM or SMM rather than traditional OLS and 2SLS. Also, we all know how strong the Chinese government is and the power of the domestic state-owned enterprises in China, I need to add some variables in my model to represent the effect of government on China’s EV market.
I hope my paper could give some suggestions about the development planning of China’s EV market and give an overall judgment about China’s current policies.
Yiyi Zhou and Shanjun Li, “Technology Adoption and Critical Mass: The Case of the U.S. Electric Vehicle Market”, The Journal of Industrial Economics, Volume66, Issue2, June 2018, Pages 423-480
Extension: Yiyi Zhou and Shanjun Li (2018)
The electric vehicle has been a hot topic in recent years. Many countries have announced a clear plan to stop selling gas cars in 10 or 20 years. Naturally, with the emergence of EV, the deployment of the charging station is a problem of interest. Different from refueling, which could be done in a few minutes, recharging of the EV’s battery may need half an hour or even a longer time. Thus, people may more likely to leave their car while recharging. Thus, the deployment of the charging station needs to be organized carefully to benefit the majority, and this deployment may even affect the sales of EV or at least the speed of upgrading from traditional gas car to electric vehicle. After all, despite the mandatory policy made by the government, rational people only care about their own convenience.
The paper written by Yiyi Zhou and Shanjun Li focused on the interdependence relationship between EV adoption and charging station deployment. They found that the “indirect network effects between them could lead to multiple equilibria in the steady state.” To illustrate their idea, the authors used a “stylized theoretical model to examine the dynamics of EV adoption .” It is a rather sample which only considers one EV model and without automaker’s strategies. Even though it is a highly simplified model, it conveys an important idea that the number of sales of EV and installment of charging station must pass through their critical mass to make sure the economy would adopt this new technology instead of abandoning it. Later, “to bring the model to the data, they parameterize their theoretical model and derive a simultaneous system of two equations: an EV demand equation that quantifies the effect of the availability of charging stations on EV adoption; and a charging station supply equation that quantifies the effect of the installed base of EVs on the deployment of charging stations.” Their “data includes quarterly EV sales by vehicle model and the number of charging stations in each of the 354 MSAs from 2011 to 2013. ” The methods are OLS and 2SLS. Using their model, they predicted the targeting critical-mass, the fixed subsidy amount per EV and its different effect in different states in the US.
This paper used a rather traditional method to reveal the relationship between EV, charging station and the effect of subsidy policy in the US. Considering that China is already the biggest EV market in the world, there is great practical significance in doing similar research using China’s data. Different from the US, China’s EV market is still in its initial stage and there is still an argument between charging stations and battery swapping stations. The biggest EV company in China -- Beijing Electric Vehicle of Beijing Automotive Group Co., Ltd. just announced their plan which is called the Optimus Prime Plan and the key point is that BJEV would build 3000 battery swapping stations in China over the next five years. Therefore, in my model, I would like to include the role of battery swapping station besides charging station and using dynamic programming to predict what result would happen in the future. I will try to use the initial investment, profitability and the dynamic payback period to show their difference. Will they exist together in the future? Which type of station will have the leading role? Alternatively, only one of them will survive, which one will win? Also, considering I will use a large dataset from different provinces with different types of EV (taxi, bus and private cars), I would prefer to use GMM or SMM rather than traditional OLS and 2SLS. Also, we all know how strong the Chinese government is and the power of the domestic state-owned enterprises in China, I need to add some variables in my model to represent the effect of government on China’s EV market.
I hope my paper could give some suggestions about the development planning of China’s EV market and give an overall judgment about China’s current policies.
Yiyi Zhou and Shanjun Li, “Technology Adoption and Critical Mass: The Case of the U.S. Electric Vehicle Market”, The Journal of Industrial Economics, Volume66, Issue2, June 2018, Pages 423-480