The commitment to decarbonization is driving city decision-makers and service providers to leverage emerging techniques to promote sustainability. Transport emissions, particularly exhaust emissions from road traffic, remain a major source of greenhouse gases and air pollutants. Precise, real-time, and high-resolution monitoring and prediction of road traffic emissions are crucial for implementing hyper-local, evidence-based emission mitigation policies. This study introduces the design and demonstration of a Digital Twin (DT) platform for road traffic emission nowcasting and forecasting. By integrating multi-source data collected from online repositories and Internet of Things (IoT) sensors, the platform provides a comprehensive, near-real-time view of road traffic emissions with high spatio-temporal resolution. A streamlined technology backend that connects various data mining and traffic simulation techniques is designed to facilitate knowledge-based decision-making, focusing on forecasting future emissions under different sustainability-oriented policy interventions. The DT platform is demonstrated for Kista, Stockholm, through a 3D interactive visualization platform. With its unified and modular design, the DT pipeline can be scaled to city-wide levels and adapted for a variety of other scenarios, such as congestion analysis and noise monitoring.