In the post-epidemic era, balancing epidemic prevention and control with sustainable economic development has become a serious challenge for all countries around the world. In China, a range of interventions include detection policies, clinical treatment policies, and most notably, traffic policies have been carried out for epidemic prevention and control. It has been widely confirmed that massive traffic restriction policies effectively brought the spread of the pandemic under control. However, restrictions on the use of transportation infrastructure undermine the smooth functioning of the economy. Particularly, China has a vast territory, with provinces differing in economic development, leading industries and transportation infrastructure; economic shock varies from region to region. In this case, targeted policies are the key to sustainable development. This paper sets forth advice for the Chinese government on its measures to boost the economy by analyzing regional differences in the impact of massive traffic restriction policies, based on large-scale human mobility data. After applying the Data Envelopment Analysis model, we classify Chinese provinces into different regions from the perspective of economic gradient, degree of internationalization and level of traffic convenience, respectively. Classification results are matched with the indicators of New Venues Created and the weekly Volumes of Visits to Venues from Baidu Maps. We find that the regional differences in the recovery of investment and consumption levels are striking. Based on the findings, we suggest that the government should adjust the intensity of traffic restrictions and economic stimulus policies dynamically according to regional differences to achieve sustainable economic development.
Public bikesharing is an environmentally friendly transportation mode that can remedy the urban “last mile” problem to some extents. Prior studies have investigated many predictors of the public bikesharing usage. For example, researchers find that gender, age, and physical conditions are significantly related to the public bikesharing usage. However, few studies have tested the characteristics of each ride and no integrative theoretical framework has been provided to explain these findings. In the current study, based on the conservation of resource theory, we suggest that the reason why these factors can predict public bikesharing usage is people’s inner needs of resource conservation. Based on this theoretical framework, we propose that: first, gender, age, and season will have direct impacts on public bikesharing usage (i.e., distance and user type); second, gender, age, and season will interactively predict public bikesharing usage as well. A relatively large sample with 1,383,773 rides in 2018 from New York City is used to test our hypotheses. The results indicate that old females indeed use public bicycle less intensively in the winter than young males do in other seasons and thus support the three-way interaction effect. Implications for the emerging public transport systems and limitations of this study are also discussed.
Since the China State Grid opened the market for infrastructure construction of electric charging stations and allowed Tesla, Potevio, BAIC BJEV and other enterprises to provide their own charging stations and other infrastructure construction, the development of electric vehicles has been greatly affected. How to maintain a sustainable governance in the opened electric vehicle charging and upgraded facilities market is an important policy issues. This paper presents a monopolistic competition model for the differentiated products market and addresses several issues related to Cournot equilibrium to illustrate why the expected free market actually operates in a monopolistic competition market structure. The analytic solution of the model shows that whether the extent of firm entry is insufficient, excessive or optimum is determined by consumers’ time preference, level of production differentiation and features of cost structure, including fixed cost and marginal cost. The sensitivity analysis has been performed among the above factors and tracked some other factors which would determine the effect of the new policy issues. The main policy suggestion is that the government should optimize entry regulations and lay down the criterion of charging interface standards for charging stations to avoid the electric vehicle charging and upgraded facilities marketization process of a one-size-fits-all solution and form a monopolistic competition market.
The growth of vehicle ownership not only brings opportunity for the economy, but also brings environment and transport problems, which is not good for sustainable transportation. It is of great significance to build supporting infrastructure and other services based on accurate forecasts of vehicle ownership in various provinces because of the variance of economic development stages, the carrying capacity of resources, and different degrees of transport planning in each province. We used the Gompertz model in order to predict China’s provincial vehicle ownership from 2018 to 2050. Considering the impact of the population structure, we summed up the growth rate of GDP per labor, the growth rate of population and the growth rate of employment rate to get the growth rate of GDP and then the GDP per capita of each province. We found that the vehicle ownership in each province will grow rapidly in the next 30 years; however, the change in the ranking of vehicle ownership among provinces varies. The ranking of some provinces with high or middle ranking now will decline in the following years, especially Beijing, Tianjin, Shanghai and Xinjiang. While the ranking of some provinces that locates in the middle and low ranking now will increase, such as Chongqing, Hubei, Anhui, Sichuan, Heilongjiang, Jiangxi, Hunan and Guangxi. We also investigate the reasons that affect the trend in each province and we find that the suitable vehicle growth pattern of each province, the stage of economic development and government policy, which are related to the growth rate of GDP per labor, employment rate, and GDP per capita, can affect vehicle ownership in the future.
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