Transit-oriented development (TOD) has been widely accepted in recent years as an important urban development policy. This article reviews the existing TOD literature pertinent to conditions in China, introduces TOD practices in China, and evaluates land development impacts of TOD across 50 Chinese cities that either have metro systems already or expect to have operating metro systems by 2020. The evaluation analysis contributes to the existing literature because most research on TOD in Chinese cities has focused on large, national or provincial capitals such as Beijing, Shanghai, and/or Guangzhou. Based on simulation analysis, we evaluate TOD’s land development impacts across all Chinese cities that are expected to have metro systems by 2020. Our results show that the second- as well as the third-class cities of China will have more potential for TOD implementation than the first-class cities in the next five years.

China's rapid economic development has led to inequality in terms of property, education, and health. Equal access to basic public facilities has become a key concern of inclusive development policies. However, previous studies have paid little attention to the effects of different travel modes on the accessibility of basic public facilities. The present research fills this gap. Taking Xiamen city as a case study, it explores the degrees of horizontal and vertical equity by examining the accessibility of various basic public facilities, paying attention to different travel modes and travel times. The results for Xiamen city show that disadvantaged groups experience a greater level of inequity. By taking these aspects into account, one is better equipped to identify areas in the city where access to basic public facilities is in need of substantial improvement.

In response to the Paris Agreement, a growing number of countries, 123 in total, have committed to carbon reduction targets. While existing research has assessed various policies’ effectiveness in achieving these targets, the potential of the act of goal-setting itself as a policy instrument has been underexplored. Here we leveraged a comprehensive panel dataset spanning 163 countries from 2011 to 2022 and employed a rigorous difference-in-difference model. Empirical findings reveal that both proposing carbon reduction targets and setting higher targets effectively reduce emissions intensity. The mechanism driving the impact of carbon reduction targets on CO₂ emissions centers on the level of education, rather than renewable energy capacity, requiring further investigation to the mechanism. Subsequent analysis establishes connections between target levels and renewable energy capacity. Our results advocate for dynamic updates of carbon reduction targets aligned with renewable energy capacity when formulating climate objectives.

This paper introduces FAST-LiDAR-SLAM: a robust, fast, and versatile LiDAR SLAM framework, which is specifically designed for urban scenarios with unstable GPS signals. The framework establishes a flexible and compact factor graph model that incorporates various relative and absolute measurements (including loop closure and GPS) as factors to optimize state estimation and dense mapping. Two key innovations are presented in this work. Firstly, it adopts a direct point cloud registration and map updating system in contrast to the traditional feature-based SLAM. In this way, the system can effectively exploit the fine structure of the environment and maintain robustness when confronted with non-structural road scenes. Secondly, an improved strong tracking extended Kalman filter (STEKF) is proposed to exclude anomalous GPS data in the back-end processing. Experiments show that FAST-LiDAR-SLAM achieves single-frame pose estimation in 20 $ms$ for solid-state LiDAR tests, outperforming Faster-LIO in accuracy. For rotating LiDAR, FAST-LiDAR-SLAM achieves comparable accuracy to LIO-SAM while significantly improving computational efficiency.