Feng Liu

Public transport equity is usually assessed after a network has been designed rather than treated as a requirement that shapes the design itself. This study develops an equity-oriented optimization framework for hierarchical multimodal transit networks in which a fixed rail backbone, main-bus routes, and feeder-bus routes are designed jointly. The model minimizes an integrated objective combining total system cost and the modal accessibility gap between public transport and private cars, while spatial equity is imposed as a binding constraint through two alternative Gini-based standards: a demand-proportional (horizontal) index and a need-sensitive (vertical) index. The problem is solved by a genetic algorithm that embeds a strategy-based passenger assignment with crowding effects and tracks the best-so-far solution across generations. Experiments on Mandl’s benchmark network across eight scenarios, combining the two equity standards with four threshold levels, yield average travel times of 11.8–13.9 min. The two standards produce structurally different networks, and stricter equity does not necessarily degrade performance: the strictest need-sensitive scenario attains the lowest average travel time, although it also records the largest modal gap relative to cars. The framework thus supports scenario-based comparison, in which the equity formulation and threshold serve as explicit policy levers rather than fixed technical bounds.

I. Foreign direct investment (FDI) has been central to China’s growth, but its environmental implications remain debated. Recent evidence suggests that the net environmental effect of FDI in China ...

To address the COVID-19 pandemic, governments worldwide implemented mandatory restrictions. As an unintended consequence of these responses, significant air pollution reductions have been recorded across the world. We provide cross-national evidence on the causal impact of pandemic-induced lockdowns on air quality. Using daily air pollution data between 1 January and 31 December 2020, covering 596 major cities in 77 countries, we analyzed the data with a generalized difference-in-differences approach. The results show that lockdown restrictions reduced global concentrations of NO₂ by 21~35%, PM₁₀ by 14~26%, PM_2.5 by 9~18%, CO by 6~16%, and SO₂ by 5~16%, while the O₃ concentrations increased by 15~29% under eight specific lockdown measures. Furthermore, a simultaneous equations model suggests that reductions in public and private mobility, measured by changes in public transportation and car ridership, partly explain the observed decreases in air pollution. These findings have significant implications for ongoing global efforts to mitigate air pollution and underscore the pivotal role of public transit in achieving this goal.