Xu Zhao

China’s national goals of peaking carbon emissions by 2030 and achieving carbon neutrality by 2060 necessitate a comprehensive transition of Chinese cities from high-carbon to low- and zero-carbon development. The transportation sector, a crucial area for energy conservation and emission reduction, faces significant challenges in achieving carbon neutrality due to rapid urbanization and motorization, which are projected to increase total emissions. Accurate monitoring of carbon emissions is vital for managing these emissions and achieving the “dual carbon” goals. This study provides a detailed analysis of Beijing’s integrated carbon emission monitoring system for urban mobility. It examines the innovative technical framework, which combines top-down and bottom-up approaches, enabling real-time and precise emissions tracking. The findings reveal that the system significantly improves data accuracy, supports effective governance strategies, and offers practical recommendations for scalability and adoption by other cities. In addition, the study evaluates the policy effectiveness of Beijing’s initiatives, providing evidence of substantial progress toward reducing carbon emissions. The research highlights the potential for replicability of Beijing’s system worldwide in transforming urban carbon governance. By addressing challenges such as data integration and resource limitations, the study contributes a comprehensive framework that advances the field of urban mobility emissions monitoring. This approach highlights the necessity of digital transformation in achieving sustainable urban development while setting a benchmark for other cities aiming to align with global climate goals.

The process of urbanization continues to accelerate, placing an increasing burden on the urban environment through the transportation sector. To address this challenge, the Beijing Municipal Transportation Bureau has introduced the Traffic Transport Environment Monitoring Information Platform, which aims to strengthen the monitoring and management of the transportation environment by facilitating flexible management and control of the transportation planning and implementation process through timely diagnostics and dynamic feedback. It represents a technologically innovative measure designed toward establishing and improving the dynamic predictive power of transport planning and monitoring in Beijing. This study thoroughly examines the design and implementation of the platform, providing detailed insights into its overall framework, data collection, and processing mechanisms, as well as strategies for data sharing and utilization. Through an analysis grounded in stakeholder theory, the study demonstrates the effectiveness and practicability of the platform. By prioritizing stakeholder demands, the study not only contributes to transportation planning technology theory but also holds pioneering significance for the practical implementation of transport environment monitoring information platforms in cities across China.

This study proposes a geographic information system (GIS)-based cellular automata (CA) model, which is designed for planning bikeways upon existing footpath networks within an urban area. The CA model was developed based on a GIS platform as a visual interface whereby spatiotemporal characteristics and spatial processing can be combined in a highly effective way. The host value of each CA cell is conditioned upon four indicator variables, namely cycling demand level, land-use nature, social value, and traffic safety. This model gives traffic planners a quick and intuitive framework to develop cycling facilities under limited land resources. A model prototype has been developed in a common desktop GIS and applied to a mid-sized rapidly developing area in Singapore.