As we move through 2020, our world has been transformed by the spread of COVID-19 in many aspects. A large number of cities across the world entered “sleep mode” sequentially due to the stay-at-home or lockdown policies. This study exploits the impact of pandemic-induced human mobility restrictions, as the response to COVID-19 pandemic, on the urban air quality across China. Different from the “traditional” difference-in-differences analysis, a human mobility-based difference-in-differences method is used to quantify the effect of intracity mobility reductions on air quality across 325 cities in China. The model shows that the air quality index (AQI) experiences a 12.2% larger reduction in the cities with lockdown. Moreover, this reduction effect varies with different types of air pollutants (PM_2.5, PM₁₀, SO₂, NO₂, and CO decreased by 13.1%, 15.3%, 4%, 3.3%, and 3.3%, respectively). The heterogeneity analysis in terms of different types of cities shows that the effect is greater in northern, higher income, more industrialized cities, and more economically active cities. We also estimate the subsequent health benefits following such improvement, and the expected averted premature deaths due to air pollution declines are around 26,385 to 38,977 during the sample period. These findings illuminate a new light on the role of a policy intervention in the pollution emission, while also providing a roadmap for future research on the pollution effect of COVID-19 pandemic.

The purpose of this research is to assess the distribution of commuting trips and the level of jobs-housing balance with Nanjing smart card data. A new approach is presented using the Lorenz curve and Gini coefficient based on the commuting time. This article also quantifies and visualizes Nanjing’s jobs-housing balance in each urban, suburban and exurban district. The core findings from this research are summarized as follows. First, the Gini coefficient of commuting time is 0.251 in urban areas, 0.258 for suburban areas and 0.267 for exurban areas. The gap of each non-urban district in commuting time is larger than urban districts. Second, the result of jobs-housing ratio (JHR) shows that jobs of Xuanwu district are far more than the working population of this district, whereas jobs and working population in other urban districts are relatively matched. The value of JHR is less than 0.8 in all suburban and exurban districts but Yuhuatai district, which suggests that jobs in these suburban districts (excluding Yuhuatai district) are in short supply compared with their working population. Third, the JHR within a particular district may be different according to the specific locations, especially those areas close to the boundary between two different kinds of districts.

With China’s rapid economic development, particularly its accelerated urbanization, air pollution has been one of the serious environmental issues across China. Most major cities in China expand their subway systems to handle this problem. This study takes both long- and short-term effects of subway expansions on air quality and its corresponding health implications into account based on a network density-based time series analysis and a distance-based difference-in-differences analysis. The daily and hourly monitor-level air quality data on Nanjing from 13 May 2014 to 31 December 2018, combining with corresponding weather variables, are used to quantify the effect of subway expansions on local air pollution caused by eight new subway lines in Nanjing. The results reveal that subway expansions result in a statistically significant decrease in the air pollution level; specifically, the air pollution level experiences a 3.93% larger reduction in the areas close to subway lines. Heterogeneous analysis of different air pollutants indicates that the air pollution reduction effect of subway expansions is more significant in terms of Particulate Matter (PM_2.5) and CO. A back-of-the-envelope analysis of the health benefits from this air improvement shows that the total number of yearly averted premature deaths is around 300,214 to 443,498. A set of alternative specifications confirm the robustness of our results. These results provide strong support for putting more emphasis on the environmental effect of subway expansions in the cost-benefit analysis of subway planning.

The feeder bus, as an important component of urban public transportation systems, is capable of filling the existing service gap left by urban rail transit to satisfy feeder demand. Two challenges to taking advantage of the feeder bus are identified. The first challenge is to identify the service gap, and the second one is to strengthen the coordination of multilevel urban traffic modes. Given these two challenges, this paper established a route design model that introduced a feeder bus demand coefficient to quantify feeder demand for each bus stop. To assist the design of feeder bus routes, an optimization approach based on the tabu search algorithm was proposed. Considering the sizeable combinational optimization problem, the algorithm decomposed this problem into three phases: feeder area partition, candidate stop selection, and route design problem. In the first phase, the entire area was partitioned into several subareas and the specific boundary nodes were moved into an adjacent subarea. Then, the stops with the minimum overall travel cost from every subarea were chosen as candidate ones under the limit of operating costs. The last step was to find the shortest circular route to visit each candidate stop and determine the operating direction. The proposed optimization had been implemented in a downtown area of Suzhou, China, in order to verify its effectiveness through the performance in terms of the quality of optimal solutions and computing efficiency. In addition, a possibility of simplifying a multiple circular route design problem to a single route design problem utilizing spatial clustering is provided to enhance the computing efficiency in a large-scale network.

This paper argues that the scale effect and substitution effect in the labor demand for environmental regulations should not be ignored in the pursuit of environmental improvements. It is necessary to analyze the influential mechanism of environmental regulations on employment. Based on the pooled cross-section data combined by CHIP (Chinese Household Income Project) data and macro data at the city level, this paper investigates the impacts of environmental regulations on an individual’s employment probability in China. The results show that there exists a U-shaped relationship between environmental regulations and an individual’s employment probability. The employment effect on workers from different regions and industries or with different hukou (Household Registration System) is heterogeneous. Specifically, the regulations are more stringent in the east, more significant in secondary and tertiary industries, and stronger on urban workers’ employment. The findings are robust to alternative measures.