A review of China's road traffic carbon emissions

Identifying spatiotemporal characteristics and driving factors for road traffic CO2 emissions

How road network transformation may be associated with reduced carbon emissions: An exploratory analysis of 19 major Chinese cities

The "top-down" method was used to measure the traffic carbon emissions from 1985 to 2016 in the Yangtze River Economic Belt and analyze its spatial pattern and temporal evolution characteristics. Considering the unexpected output, a three-stage DEA model was constructed to evaluate and compare the traffic carbon emission efficiency of the Yangtze River Economic Belt, excluding the influence of external environment variables and random errors. The study found that first, the total traffic carbon emissions in the Yangtze River Economic Belt showed a rising trend, among which the carbon emissions from petroleum energy consumption accounted for the largest proportion. Sichuan, Hubei, and Hunan and the Su-Zhe-Hu Region were the high-value areas of traffic carbon emissions in the upper, middle, and lower reaches of the Yangtze River, respectively. Second, from the east to west, the center of traffic carbon emissions generally showed a changing track of moving east first and then west; from the north to south, it highlighted the characteristics of increasing concentrated distribution along the Yangtze River in space. Third, there was an obvious spatial differentiation in the traffic carbon emission efficiency values of different provinces; from 2007 to 2016, the efficiency value of the eastern region was the highest, but the value of the central region changed from higher than that in the western region to lower than that in the western region. Finally, external environmental factors had a significant impact on the efficiency of traffic carbon emissions, in which the optimization of industrial structure was found to be conducive to the improvement of traffic carbon emission efficiency, while the influence of government intervention was changed from "innovation compensation" effect to "compliance cost" effect.

Emissions of nitrogen oxides (NOx) from road traffic are dependent on a range of factors including vehicle type, speed, driving patterns and engine temperature. Recently a number of studies have indicated that ambient air temperature plays an important role in vehicle NOx emissions, mainly due to various technical challenges of diesel vehicles that occur at low ambient temperatures. This study aims to derive a correction formula to account for this temperature dependence when calculating emissions from road traffic in Norway. Measured NOx concentrations in the period 2016–2019 at 46 sites dominated by road traffic sources are compared to the NOx concentrations calculated with the chemistry-transport modelling system EMEP/uEMEP at the same sites. The model has good road traffic volume input data, but no temperature dependence in the emission factors. A clear temperature dependence in the observed-to-modelled ratio of NOx concentration is found. The ratio increases from 1.09 at high temperatures to 2.9 at low temperatures. The increase occurs gradually in the temperature range from -13 °C to +14 °C. Assuming this temperature dependence in the bias is due to the road traffic emissions, a correction formula for these emissions is derived. The correction factor is 1 at high temperatures and 3.28 at low temperatures, with a linear increase in the range from +12.4 °C to -12.9 °C. Thus, our results suggest that road traffic emissions should be 3.3 times higher at temperatures below -13 °C than at high temperatures, and 2.7 times higher at -7 °C. The temperature range and magnitude of this temperature dependence are consistent with the existing literature on emission measurement experiments performed on various models of diesel vehicles. The derived temperature dependence can be used to correct road traffic emissions. However, the parameter values in the correction are dependent on the vehicle fleet composition and are applicable only for the current Norwegian vehicle fleet.

Abstract. Road traffic is one of the major anthropogenic emission sectors for NOx, CO and NMHCs (non-methane hydrocarbons). We applied ECHAM4/CBM, a general circulation model coupled to a chemistry module, which includes higher hydrocarbons, to investigate the global impact of 1990 road traffic emissions on the atmosphere. Improving over previous global modelling studies, which concentrated on road traffic NOx and CO emissions only, we assess the impact of NMHC emissions from road traffic. It is revealed that NMHC emissions from road traffic play a key role for the impact on ozone. They are responsible for (indirect) long-range transport of NOx from road traffic via the formation of PAN, which is not found in a simulation without NMHC emissions from road traffic. Long-range transport of NMHC-induced PAN impacts on the ozone distribution in Northern Hemisphere regions far away from the sources, especially in arctic and remote maritime regions. In July total road traffic emissions (NOx, CO and NMHCs) contribute to the zonally averaged ozone distribution by more than 12% near the surface in the Northern Hemisphere midlatitudes and arctic latitudes. In January road traffic emissions contribute near the surface in northern and southern extratropics more than 8%. Sensitivity studies for regional emission show that effective transport of road traffic emissions occurs mainly in the free troposphere. In tropical latitudes of America up to an altitude of 200 hPa, global road traffic emissions contribute about 8% to the ozone concentration. In arctic latitudes NMHC emissions from road transport are responsible for about 90% of PAN increase from road transport, leading to a contribution to ozone concentrations of up to 15%.

China has allocated low-carbon targets into all regions and trades, and road traffic also has its own emission reduction targets. Congestion may increase carbon emissions from road traffic. It is worthwhile to study whether it is possible to achieve the goal of road traffic reduction by controlling congestion; that is, to achieve the equilibrium between traffic congestion and a low-carbon economy. The innovation of this paper is mainly reflected in the innovative topic selection, the introduction of a traffic index, and the establishment of the first traffic congestion and low-carbon economic equilibrium model. First, the relevant calculation method of the traffic index is introduced, and the traffic index is used to quantify the traffic congestion degree. Using the traffic index, GDP, and road passenger traffic volume, a nonlinear regression model of road traffic carbon emissions is constructed. Then, the calculation method of the carbon emission intensity of road traffic in the region is proposed. The equilibrium model of traffic congestion and a low-carbon economy is constructed to look for the degree of road traffic congestion that may occur under the permitted carbon emission intensity. Taking Beijing, where electric vehicles account for less than 3% of the total vehicles, as an example, it is difficult to achieve the equilibrium target between road traffic congestion and a low-carbon economy by alleviating traffic congestion in 2020. If the target of traffic carbon emission reduction in 2020 is adjusted from 40%–45% to 19.7% based on 2005, the equilibrium will be achieved. A negative correlation between road traffic carbon emissions and the reciprocal of the traffic index (1/TI) is found after eliminating the effects of GDP and PTV (road passenger traffic volume). As the traffic index decreases by units, the carbon emission reduction accelerates. The results show that carbon reduction targets cannot be simply allocated to various industries. The results of the research on the degree of the impact of traffic congestion on carbon emissions can be used as a basis for carbon reduction decisions of the traffic sector. The research method of this paper can provide a reference for the study of the equilibrium of traffic congestion and a low-carbon economy in other regions.

Mitigating traffic-related carbon emissions is pivotal for achieving carbon peaking targets and advancing sustainable urban development. This study employs spatial autocorrelation and high-low clustering analyses to analyze the spatial correlation and clustering patterns of urban road traffic carbon emissions in Shaanxi Province. The spatiotemporal evolution and structural impacts of emissions are quantified through a systematic framework, while the GTWR (Geographically Weighted Temporal Regression) model uncovers the multidimensional and heterogeneous driving mechanisms underlying carbon emissions. Findings reveal that road traffic CO2 emissions in Shaanxi exhibit an upward trajectory, with a temporal evolution marked by distinct phases: “stable growth—rapid increase—gradual decline”. Emission dynamics vary significantly across transport modes: private vehicles emerge as the primary emission source, taxi/motorcycle emissions remain relatively stable, and bus/electric vehicle emissions persist at low levels. Spatially, the province demonstrates a pronounced high-carbon spillover effect, with persistent high-value clusters concentrated in central Shaanxi and the northern region of Yan’an City, exhibiting spillover effects on adjacent urban areas. Notably, the spatial distribution of CO2 emissions has evolved significantly: a relatively balanced pattern across cities in 2010 transitioned to a pronounced “M”-shaped gradient along the north–south axis by 2015, stabilizing by 2020. The central urban cluster (Yan’an, Tongchuan, Xianyang, Baoji) initially formed a secondary low-carbon core, which later integrated into the regional emission gradient. By focusing on the micro-level dynamics of urban road traffic and its internal structural complexities—while incorporating built environment factors such as network layout, travel behavior, and infrastructure endowments—this study contributes novel insights to the transportation carbon emission literature, offering a robust framework for regional emission mitigation strategies.

In the context of urbanization and regional development differences in China, transportation carbon emissions in different cities show spatiotemporal heterogeneity. Although previous studies have primarily focused on carbon emissions from road transportation, research specifically examining the southeast coastal urban agglomerations in China, particularly Fujian Province, is lacking. Thus, this study selected Fujian Province as the research object and revealed the temporal and spatial distribution characteristics of carbon emissions in the urban agglomeration of Fujian Province based on the standard deviation ellipse （SDE）. Furthermore, the geographically and temporally weighted regression model （GTWR） and geographical detector were employed to examine the interaction and spatiotemporal heterogeneity of five driving factors, namely urbanization rate （UR）, infrastructure rate （IR）, passenger volume （PV）, freight volume （FV）, and per capita GDP （APGDP）, on road traffic carbon emissions in cities within Fujian Province. The results show that the road transportation carbon emissions in Fujian urban agglomeration have continued to grow over the past 20 years, with an average annual growth rate of 10.355%, and the spatial distribution of carbon emissions showed a significant "northeast-southwest" trend. Secondly, the carbon emissions focus during the research period was consistently located in Quanzhou City, Fujian Province, and the direction of carbon emissions showed a counterclockwise trend, continuously shifting towards the "northeast-southwest" direction. Furthermore, the spatiotemporal heterogeneity results of single driving factors indicated that APGDP played a predominantly positive role in driving road traffic carbon emissions in urban agglomerations, while UR, IR, PV, and FV had inhibitory or promoting effects within different cities. Lastly, the interaction results of dual driving factors from geographical detectors revealed that IR∩UR, IR∩PV, IR∩FV, and IR∩APGDP had the greatest contribution to the spatiotemporal heterogeneity of road traffic carbon emissions in Fujian Province's urban agglomerations. This study provides a new perspective for understanding differences in transportation-related carbon emissions among urban agglomerations and is significant for promoting low-carbon transformation in regional transportation.

Road traffic carbon dioxide emissions have a significant impact on the atmospheric environment, and changes in traffic conditions can affect these emissions; while changes in traffic state and traffic infarction can reflect traffic conditions, there are also differences in carbon dioxide emissions generated by vehicles operating on different fuels. Therefore, this paper sequentially determines quantitative indicators of traffic states and traffic infarction as well as macro control strategies to improve traffic carbon dioxide emissions. Furthermore, taking the confluence area as the object of analysis, the impact of traffic state and traffic infarction on carbon dioxide emissions is sequentially analysed with actual traffic flow data and statistical methods, and corresponding models are constructed. Meanwhile, based on the analysis results, mainline speed limit control strategies that consider the traffic state and traffic infarction, as well as the control strategy based on the combination of multiple fuel–vehicle combinations, are presented. The analysis results show that changes in both traffic states and traffic infarction can affect traffic carbon dioxide emissions from the mainline and the on-ramps, which can fundamentally affect traffic conditions. Moreover, total carbon dioxide emissions from a combination of gasoline and diesel minibuses decrease with an increase in the ratio of gasoline minibuses.

This paper mainly discusses the carbon emissions in Chinese county transportation planning. By using the traffic planning indicators, a method to estimate road traffic carbon emissions in Chinese counties based on STIRPAT is proposed. The result shows that: 1) The estimation method for county traffic carbon emissions links county carbon emission to county traffic planning. 2) The GDP and road mileage have the most positive effect on Chinese county traffic carbon emissions while the vehicle ownership and population also have some effect. 3) Chinese counties should pay attention to the road network planning and some supporting measures such as public transportation and new energy vehicles.KeywordsRoad traffic planningSTIRPATEstimation methodCarbon emissionCounty

Carbon emissions have become a focus of political and academic concern in the global community since the launch of the Kyoto Protocol. As the largest carbon emitter, China has committed to reaching the carbon peak by 2030 and carbon neutrality by 2060 in the 75th United Nations High-level Meeting. The transport sector needs to be deeply decarbonized in China to achieve this goal. Previous studies have shown that the carbon emissions of the railway sector are small compared to highways, waterways, and civil aviation. However, these studies only consider the operation stage and do not consider the carbon emissions caused by large-scale railway infrastructure construction during the construction stage. As an essential source of carbon emissions and the focus of emissions reduction, the carbon emission of railway construction projects (RCPs) is in urgent need of relevant research. Based on a systematic literature review (SLR), this paper sorts out the carbon emission factors (CEFs) related to RCPs; combines semi-structured expert interviews to clarify the carbon emissions measurement boundary of RCPs; modifies and calibrates CEFs; constructs the carbon emission measurement model of RCPs including building material production stage, building material transportation stage, and construction stage; and conducts empirical analysis to validate carbon emission factors and measurement models. This study effectively complements the theoretical research on CEFs and measurement models in the construction stage of railway engineering and contributes to guiding the construction of low-carbon railways practically.

Optimal Methodology to Generate Road Traffic Emissions for Air Quality Modeling

Economic growth, urban expansion, traffic dependence, and urban lifestyle have caused a large amount of energy consumption and greenhouse gas emissions in cities. The issue of urban greenhouse gas emissions has become a global focus and a research hotspot. This paper analysed the causality of driving factors and identified the key elements such as urban population, economy, energy and motor vehicles through system dynamics (SD) and econometric model. Then, it conducted empirical analysis and explored the impact of factors on urban transportation carbon emissions taking Beijing as an example. The research results showed that in addition to energy consumption, other factors were positively correlated with traffic carbon emissions. It believed that in order to control urban traffic carbon emissions, we must control the urban population, economy and the number of vehicles rationally. With the optimization of energy structure, the structural adjustment of traffic energy consumption is conducive to energy conservation and emission reduction in the transportation industry, and the decoupling of the total energy consumption of traffic and the carbon emissions from transportation.

The administrative units of 17 provinces （autonomous regions and municipalities directly under the Central Government） along the "Belt and Road" were selected as basic spatial units to calculate the provincial traffic carbon emissions along the "Belt and Road" from 2000 to 2021. On the basis of analyzing the spatial and temporal characteristics of traffic carbon emissions by using the spatial autocorrelation method, the spatial and temporal heterogeneity of influencing factors of traffic carbon emissions was explored by combining a fixed-effect regression model and geographic detector. The results show that： ① The provincial traffic carbon emissions along the "Belt and Road" had significant spatial positive correlation, and the overall trend was upward. Additionally, the cluster evolution of high and low values of traffic carbon emissions presented the characteristics of polarization in space. The high value cluster area was mainly distributed in the open leading area, and the low value cluster area was mainly distributed in the core area of the silk road. ② Opening-up level and vehicle ownership were the positive driving factors of carbon emissions from transportation, whereas energy intensity, transportation structure, industry development scale, and government intervention were the negative driving factors. ③ Energy intensity and transportation structure were the main driving factors for the spatial variation of transportation carbon emissions, and most of them would produce nonlinear enhancement when they were spatially superimposed with other factors, that is, there was strong synergy among driving factors. The results showed that the provincial traffic carbon emissions along the "Belt and Road" were affected by the surrounding areas, the influence degree was increasing, and there was synergy between the key driving factors of traffic carbon emissions. Therefore, it is suggested that the provinces along the "Belt and Road" should fully consider the spatial and temporal heterogeneity of traffic carbon emission influencing factors and formulate differentiated traffic carbon emission reduction policies.

Several studies have examined the success of populist leaders in recent decades. These studies focus on both supply side factors that concern the traits of populist actors and demand side factors in the form of characteristics of the supporters. However, we still lack a solid understanding of how these supply and demand side factors interact to explain the support of populist leaders. We contribute to this literature by examining the interplay of two central supply side factors, people-centeredness and anti-immigration policies, and two demand side factors, political dissatisfaction and generational differences, in determining populist support. We test these explanations by leveraging a choice-based conjoint analysis embedded in a representative sample of the Finnish population (n = 1030). The results show that while people-centeredness enhance the favourability of prospective political leaders among the general population, only anti-immigration policies appeal to the politically dissatisfied. In contrast to recent studies, we find no evidence that populist leader traits would be more favoured by younger generations. These results indicate that the interplay between supply and demand may well be more intricate than what previous studies suggest.