Yuanyuan Liu

Low-carbon construction of highway projects constitutes a critical pathway toward achieving the carbon peaking target in the transportation sector. Existing studies have been unable to simultaneously address the identification of carbon emission sources across different engineering types and the delineation of responsible entities for implementing management strategies. This study employs the emission factor method to conduct construction-phase carbon emission accounting for a mountainous expressway in Guangdong Province, China, and reveals significant clustering characteristics of highway construction carbon emissions along two dimensions: the proportion of total emissions and the proportion of material-derived carbon emission sources. Based on K-Means cluster analysis, nine engineering categories—temporary works, subgrade works, pavement works, bridge/culvert works, tunnel works, intersection works, traffic engineering works, greening works, and other works—are classified into four types. Accordingly, a dual-factor classification framework is established, comprising “Core–Material Dominant (CMD)”, “Core–Mixed Balanced (CMB)”, “Peripheral–Material Dominant (PMD)”, and “Peripheral–Mixed Balanced (PMB)”. Differentiated carbon abatement strategies are proposed for each engineering type, with explicit definition of implementation stages and primary responsible entities. Application of the proposed framework to the case project achieved a total carbon abatement of 5.2% during the construction phase. This research provides systematic methodological support for differentiated carbon emission mitigation in highway construction.

Reducing carbon emissions from highway construction is vital for mitigating climate change. Based on an expressway project in central China, this study conducted a fine-grained CO₂ emission accounting and analysis. The findings reveal that bridges and culverts rank first in emissions among all subprojects, emerging as a “carbon hotspot” with an exceptionally high emission intensity, followed by subgrade and pavement. Additionally, traffic safety facilities, accounting for 6.40% of total emissions, are also not negligible. Regarding emission sources, material emissions exhibit an extreme dominance at 89.44%, whereas off-road machinery and transport vehicles demonstrate distinct patterns of input-emission asymmetry and single-vehicle-type dominance, respectively. Consequently, this study delineates decarbonization pathways, quantitatively verifies the low-carbon efficacy of four mitigation technologies, and focuses on evaluating the environmental-economic dual feasibility of substituting core equipment with pure electric alternatives. The results demonstrate that the resin-bonded gravel sound barrier achieves an emission reduction rate of 73.26%; furthermore, under a 50% penetration rate, deploying 30-ton class EV dump trucks can cut transportation emissions by 9.98%, maintaining exceptional financial resilience even under a 30% plunge in diesel prices. This study provides a replicable, scientific decision-making basis for the deep decarbonization of the transportation infrastructure sector.

Current methods for defining traffic low emission control zones (TLEZ) often face limitations that hinder their widespread implementation and effectiveness. This study addresses these challenges by employing a comprehensive approach to analyze PM_2.5 concentration levels within TLEZ. This study utilizes PM_2.5 data collected by taxi fleets, integrating static road network features and dynamic time series features to gain a detailed understanding of pollution distribution patterns across different urban areas. To capture these complex distribution patterns of PM_2.5, a sophisticated deep learning model that combines Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and Attention Mechanism (AM) is deployed. This model adeptly identifies spatial and temporal variations in PM_2.5 concentrations, allowing for a more accurate and responsive analysis of pollution levels. A multi-objective optimization model is developed to minimize the overall impact on residents' daily lives, which considers both environmental and social factors in the delineation of TLEZ. The optimization model is solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), which is a robust evolutionary algorithm that facilitates the identification of Pareto-optimal solutions. These solutions can help define the optimal boundaries for Low, Ultra-Low, and Zero Emission Zones. By establishing a framework for assessing and optimizing these zones, this study provides valuable insights and actionable guidance for policymakers and urban planners.

Understanding the CO₂ emission characteristics and key mitigation pathways of intercity passenger transport is crucial for achieving sustainable development in the transport system. Using origin–destination data on travel between city pairs by various transportation modes, we employ the life cycle assessment (LCA) method to estimate route-level CO₂ emissions from intercity multimodal passenger transport corridors, considering infrastructure construction and vehicle operation phases. Subsequently, a sensitivity analysis is conducted to assess the impact of 39 parameters associated with the construction phase, operation phase, and transportation modes on CO₂ emissions from corridors. Trend analysis is employed to explore the future emission mitigation potential for the parameters that have the most significant impact on corridor emissions. Four intercity multimodal passenger corridors in China are selected as case studies. Results indicate that the CO₂ emissions per passenger-kilometer from these corridors exhibit an approximate negative linear relationship with corridor lengths. The proportion of construction-related CO₂ emission intensity of various intercity passenger transport modes varies significantly, ranging from 2.5 to 32.9%. In the medium term, effective emission-mitigation strategies should focus on decreasing private car gasoline consumption in three corridors under 200 km in length, as well as decreasing private car gasoline consumption and promoting clean electricity in the Xi’an–Yan’an corridor. In the long term, efforts should be placed on increasing electric private car share and promoting clean electricity. This study lays a crucial foundation for the refined management of CO₂ emissions from future intercity passenger transport.

In this study, we aimed to provide a practical method to estimate the economic and environmental impact of vehicle and work activities throughout the entire service life of a pavement area to support pavement management strategies and decisions. To achieve this, we integrated two key life cycle analysis methods, life cycle assessment (LCA)) and life cycle cost analysis (LCCA). The integrated model not only considers CO₂ emissions associated with the four main modules—the materials module, the work activities module, the work zone module and the usage module—mentioned in LCA, it also considers agency costs and user costs related to highway usage, work activities, work zone traffic delays and detours in the LCCA process. We used detailed and integrated methods to compute CO₂ emissions and costs based on the four modules and two components of agency and users mentioned above. A case study based on a real freeway project in China was used to verify the applicability of the integrated model. The results of the application of the integrated LCA-LCCA model indicate that maintaining the typical activity profile could be beneficial in terms of both CO₂ emissions and cost, with savings of 36.8 ton/lane/km of CO₂ emissions and 10,530 USD/lane/km (in 2007 dollars) representing the total benefits during the pavement’s service life. This means that timely maintenance could help to achieve savings in terms of financial costs and CO₂ emissions simultaneously.

As a market-based instrument for transportation demand management, a transport fee-charging policy can not only effectively reduce traffic congestion, but also improve air quality. Considering the urgent need to improve urban transport fee-charging policies and reduce transport carbon emissions, the paper focuses on the role of the performance of fee-charging policies in reducing the carbon emissions of urban transport. In this study, we propose a methodological framework for the performance evaluation of urban traffic carbon emission fee-charging policies. First, we analyze the current situation of the implementation of fee-charging policies and their relationship with urban traffic congestion. Subsequently, we analyze changing trends of carbon emissions associated with transportation travel in Beijing in recent years, to identify the main sources of carbon emissions from transport. Finally, we used the DEA method to evaluate the performance of the fee policies for urban transport, which are meant to reduce carbon emissions, analyze their implementation efficiency, and then discuss the main factors affecting their efficiency. The results show that with the implementation of fee-charging policies, urban traffic congestion has eased. The overall carbon dioxide (CO₂) emissions from transportation in Beijing grew rapidly. CO₂ emissions generated by car travel are the main source of carbon emissions from transportation in Beijing. The average value of the overall technical efficiency (TE) of Beijing’s fee-charging policies to reduce transportation carbon emissions from 2006 to 2018 is 0.962, indicating that the overall implementation of Beijing’s fee-charging policies has been effective. Adjustments to the fee structure reduce motor vehicle travel to an extent, increase the proportion of green travel, and reduce the intensity of transportation carbon emissions. The technical non-efficiency in Beijing’s fee-charging policy is mainly due to non-efficiency of scale, followed by pure technical non-efficiency. Appropriately adjusting the fee structures imposed by different policies would help to improve the efficiency of policy implementation.

Exploring key impact factors and their effects on urban residents’ transport carbon dioxide (CO₂) emissions is significant for effective low-carbon transport planning. Researchers face the model uncertainty problem to seek a rational and better explanatory model and the key variables in the model set containing various factors after they are arranged and combined. This paper uses the Bayesian Model Averaging method to solve the above problem, explore the key variables, and determine their relative significance and averaging effects. Beijing, Xi’an, and Wuhan are selected as three case cities for their representation of developing Chinese cities. We found that the initial key factor increasing transport emissions is the high dependence on cars, and the second is the geographical location factor that much more suburban residents suffer longer commuting. Developing satellite city rank first for reducing transport emissions due to more local trips with an average short distance, the second is the metro accessibility, and the third is polycentric form. Key planning strategies and policies are proposed: (i) combining policies of car restriction based on vehicle plate number, encouraging clean fuel cars, a carbon tax on oil uses, and rewarding public transit passengers; (ii) fostering subcenters’ strong industries to develop self-contained polycentric structures and satellite cities, and forming employment and life circle within 5 km radius; and (iii) integrating bus and rail transit services in the peripheral areas and suburbs and increasing the integration level of muti-modes transferring in transport hubs. The findings will offer empirical evidence and reference value in developing cities globally.

Understanding transport carbon dioxide (CO₂) emission impact factors’ effects is important for the rational planning strategy making in reducing the emissions. This study determines transport emission impact factors’ heterogeneous effects and proposes urban and transport planning strategies in typical developing cities. Quantile regression is applied to overcome the insufficiency of factors’ mean effects and to avoid the biased estimations when the outcome variable is non-normally distributed and heteroscedastic. It is found that, from the low emitters at the 10th quantile to the high emitters at the 90th quantile, transport emissions’ increasing rates are 8.8 times and 79.6 times that of car availability and home-to-center/subcenter distance (HCD/HSD), respectively. When commute distance reaches 5.8 km or farther, and car availability percentage is 41.2% or greater, the effects that metro services have on reducing emissions decrease by 37.8%. Polycentric and satellite city forms can greatly reduce emission increases, which are caused by HCD growth when HCD is more than 10–15 km. According to these findings, the following planning strategies are recommended, including limiting oil-fueled car use to about 40% among the urban residents, forming employment and life circles within a 5–6 km radius, allocating better public transit services around metro stations, providing high service levels of bicycle lanes, pedestrian streets, and greenways to attract more transfers to metros, controlling urban radius within 10–15 km under the monocentric pattern, and fostering polycentric structures and satellite cities when city continuously sprawls. This study can provide empirical evidence and reference value globally.