In order to understand the driving characteristics of electric heavy-duty trucks in practical application scenarios and promote their usage to replace diesel trucks, this study analyzed the actual usage of electric and diesel heavy-duty trucks in a steel factory based on vehicle-monitoring data and remote online monitoring data and estimated the emission reduction potential of the application of electric trucks by using a mileage-based method and the greenhouse gas emission model. The results showed that the electric heavy-duty trucks in the steel factory mostly operated for over 14 h, with a vehicle kilometers traveled (VKT) of 50–300 km each day, which could meet most of the demands of the transportation of the steel industry. The average daily energy consumption for most trucks falls within the range of 210–230 kWh/100 km, with higher consumption in winter than in summer, which can save approximately 18–26% in operating costs compared with diesel trucks. It is estimated that the usage of these electric heavy-duty trucks can achieve an annual reduction of 115.8 tons of NO_x emissions, 0.7 tons of PM emissions, and 18,000 tons of CO₂ emissions. To further promote the application of electric heavy-duty trucks in China, several policy suggestions, such as introducing priority road-right policies, promoting vehicle and battery leasing markets, and exempting zero-emission vehicles during heavy pollution days, were proposed.

The adoption of new energy vehicles (NEVs) is an effective strategy for pollution reduction, especially for high-emitting commercial vehicles. This paper systematically reviews the promotion policies and development status of zero-emission commercial vehicles (ZECVs) in China, with a focus on diverse application scenarios. Comprehensive policies, including subsidies, right-of-way, infrastructure development, and environmental protection incentives, have significantly advanced NEV adoption, as demonstrated by Shenzhen’s full electrification of buses and the extensive deployment of zero-emission trucks. Despite the overall slow development of ZECVs, regions in southern China and developed areas exhibit better progress. Medium and large passenger vehicles (MLPVs) have achieved a zero-emission rate of around 40%, contrasting with the significantly lower rates of 1.52% for mini and light trucks (MLTs) and 0.44% for medium and heavy trucks (MHTs). Electrification promotion varies significantly in different application scenarios, with buses leading at over 90% zero-emission rates, followed by the airport (24%) and port (16%) vehicles. The electrification of sanitation, logistics, and key industry transport, through lagging, is enhanced by targeted policies and local industry. Buses are designated as the highest priority (Level 1) for electrification transition while intercity logistics and vehicles in key industries are categorized as the lowest priority (Level 4). In addition, policy recommendations, including tailored strategies for ZECV promotion and emission reductions in traditional commercial vehicles, are put forward to provide guidance and reference for setting future zero-emission promotion goals and policy direction for commercial vehicles in subdivided application scenarios.

Promoting the use of zero-emission vehicles is an important measure for reducing pollutant and carbon dioxide emissions from medium- and heavy-duty trucks (MHDTs). This study took Hainan Island as an example. Based on big data such as industrial layout and traffic flow, it clarified that the main channels of freight transportation on Hainan Island are concentrated in the northern region, including the surrounding areas of Haikou; the important ports of Haikou, Yangpu, and Basuo; and Chengmai and Tunchang counties. Furthermore, pathways for the promotion of zero-emission MHDTs are proposed, which can reduce exhaust emissions by 1549 tons of NOx, 62 tons of particulate matter (PM), and 3.60 million tons of CO₂ by 2030. Compared with the vehicle type categorization plan, the spatial layout plan can achieve higher emission reduction benefits in the medium term (2025). In addition, in conjunction with existing policies and planning requirements, this study also puts forward policy suggestions for the promotion of zero-emission MHDTs.

Motor vehicle emissions are a major air quality concern in Chinese cities. However, traditional population-based emission inventory methods fail to capture the spatial and temporal variations in emissions for effective policy design. This study proposes a high-resolution approach for traffic flow prediction and vehicle emission inventory estimation, using Jinan City, China, as a case study. We leverage multi-source geospatial data and employ a two-fold random forest model to predict hourly traffic flow at a road-segment level. Speed-aligned emission factors were then combined with these data to calculate hourly and road-level vehicle emission estimates. Compared to traditional methods, our approach offers substantial improvements: (1) improved spatiotemporal resolution; (2) enhanced accuracy of traffic flow prediction; and (3) support for more effective vehicle emission control strategies. Results show that heavy-duty vehicles, particularly freight trucks operating on inter-regional corridors through Jinan, contribute 78% more to NO_X emissions than local light-duty vehicles. These transient emissions are typically overlooked in static inventories but constitute a significant source of urban pollution. This study offers valuable insights for combining geospatial data and machine learning to improve the accuracy and resolution of vehicle emission inventories, supporting urban air quality policy and planning.