Transport Sector In China Research Articles

Temperature shocks and low-carbon performance: Evidence from the transportation sector in China

This study identifies how temperature affects the transportation sector’s low-carbon performance, measured by carbon total factor productivity (CTFP). The findings reveal that an additional day with temperatures surpassing 30 °C results in a 0.25 % decrease in CTFP compared to days ranging from 15 °C to 20 °C. The mechanism analysis indicates that high temperatures primarily reduce CTFP by undermining technical efficiency, especially in reducing energy- and carbon-oriented technical efficiency. To combat heat stress, the transportation sector markedly increases the proportion of coal and oil consumption, thus shifting its energy structure towards a more carbon-intensive configuration. The heterogeneous analysis uncovers that the adverse impacts of high temperatures are more pronounced in transportation sectors located in the eastern region, in those that are energy- and labor-intensive, and in those heavily reliant on road transport. Moreover, we find that high-speed railways can effectively mitigate the negative impacts of high temperatures.

Unequal Health Risks and Attributable Mortality Burden of Source-Specific PM2.5 in China.

Anthropogenic emissions, originating from human activities, stand as the primary contributors to PM2.5, which is recognized as a global health threat. The disease burden associated with PM2.5 has been extensively documented. However, the prevailing estimations have predominantly relied on PM2.5 exposure-response functions, neglecting the distinct risks posed by PM2.5 from various sources. China has experienced a significant reduction in the PM2.5 concentration due to stringent emission controls. With diverse sources and abundant mortality data, this situation provides a unique opportunity to estimate short-term source-specific attributable mortality. Our approach involves an integrated unequal health risk-oriented modeling in China, incorporating a source-oriented Community Multiscale Air Quality model, an adjustment and downscaling method for exposure measurement, a generalized linear model with random-effects meta-analysis, and premature mortality estimation. Adhering to the unequal health risk concept, we calculated the attributable mortality of multiple PM2.5 sources by determining the source risk-adjusted factor. In this study, we observed varying excess risks associated with multiple PM2.5 sources, with transportation-related PM2.5 exhibiting the most substantial association. An interquartile range increase (7.65 μg/m3) was linked to a 1.98% higher daily nonaccidental mortality. Residential use- and transportation-related PM2.5 emerged as the two principal sources of premature mortality. In 2018, a remarkable 53,381 avoiding deaths were estimated compared to 2013, and over 67% of these were attributed to reductions in coal-dependent sources. Notably, transportation-related PM2.5 emerged as the largest contributor to premature mortality in 2018. This study underscores the significance of a new source-oriented health risk assessment to support actions aimed at reducing air pollution. It strongly advocates for heightened attention to PM2.5 reductions in the transportation sector in China.

Are electric vehicles really the optimal option for the transportation sector in China to approach pollution reduction and carbon neutrality goals?

Profound worldwide fleet electrification is thought to be the primary route for achieving the target of carbon neutrality. However, when and how electrification can help mitigate environmental impacts and carbon emissions in the transport sector remains unclear. Herein, the overall life-cycle environmental impacts and carbon saving range of two typical A-class vehicles in China, including electric vehicle (EV) and internal combustion engine vehicle (ICEV), were quantified by the life cycle assessment model for endpoint damage with localization parameters. The results showed that the EV outperformed the ICEV for the total environment impact after a travel distance of 39,153 km and for carbon emissions after 32,292 km. The ICEV was more carbon-friendly only when the driving distance was less than 3229 km/a. Considering a full lifespan travel distance of 150,000 km, the whole life-cycle average environmental impacts of EV and ICEV were calculated as 8.6 and 17.5 mPt/km, respectively, but the EV had 2.3 times higher impacts than the ICEV in the production phase. In addition, the EV unit carbon emission was 140 g/km, 46.8% lower than that of the ICEV. Finally, three potential reduction scenarios were considered: cleaner power mix, energy efficiency improvement and composite scenario. These scenarios contributed 19.1%, 13.0% and 32.1% reductions, respectively. However, achieving carbon peak and neutrality goals in China remains a great challenge unless fossil fuels are replaced by renewable energy. The research can provide scientific reference for the method and practice of emission reduction link identification, eco-driving choice and emission reduction path formulation.

Policy scenario analysis of achieving the "dual carbon" goal for the road transportation sector in China

The goal of "dual carbon" has brought the pressure of reducing carbon emissions to the road transportation sector in China. This study aims to identify and analyze possible policy measures that would help to achieve this goal from the perspective of the whole life cycle. We build a bottom-up computing framework based on the LEAP model. Six policy scenarios are designed to quantitatively assess their effects. The results show that promoting pure electric vehicles (PEV) and plug-in hybrid electric vehicles (PHEV) is appropriate for the passenger vehicles (PV), light-duty trucks (LDT), and mini trucks (MT) sub-sectors, while hydrogen fuel cell vehicles (FCV) are more suitable for the promotion of the heavy-duty trucks (HDT), heavy-duty buses (HDB), and medium-duty buses (MDB) sub-sectors. Additionally, the recycling of vehicle materials can effectively reduce the carbon emissions during the life cycle of motor vehicles. Furthermore, reducing the carbon emissions from the vehicle cycle of PV and the fuel cycle of HDT should be taken seriously. The main contribution of this study lies in establishing a comprehensive list of vehicle cycle carbon emission factors, covering various sub-sectors of China's road transportation sector. Besides, this study considers China's strategic planning and "dual carbon" goal, and proposes policy recommendations specifically tailored to the practical issues faced by China.

Congestion effects of energy and its influencing factors: China's transportation sector

This paper constructs a two-stage undesirable and desirable congestion model, which distinguishes purely technical inefficiency from congestion, to study the energy congestion of 30 transportation sectors in China from 2011 to 2020. The Heckman model is used to analyze the influence mechanism of various factors on the undesirable congestion of energy, which avoids sample selection bias. The results show that: (1) The undesirable congestion of energy mainly exists in the transport sector of the western region, while the desirable congestion of energy only exists in the transport sectors of the eastern and central regions. (2) Purely technical inefficiency in the transportation sector does not usually occur in isolation. (3) Population density and the proportion of clean freight turnover have a negative impact on the undesirable congestion of energy.

Establish of air pollutants and greenhouse gases emission inventory and co-benefits of their reduction of transportation sector in Central China

Recently, the transportation sector in China has gradually become the main source of urban air pollution and primary driver of carbon emissions growth. Considering air pollutants and greenhouse gases come from the same emission sources, it is necessary to establish an updated high-resolution emission inventory for the transportation sector in Central China, the most polluted region in China. The inventory includes on-road mobile, non-road mobile, oil storage and transportation, and covers 9 types of air pollutants and 3 types of greenhouse gases. Based on the Long-range Energy Alternatives Planning System (LEAP) model, the emissions of pollutants were predicted for the period from 2020 to 2035 in different scenarios. Results showed that in 2020, emissions of SO2, NOx, CO, PM10, PM2.5, VOCs, NH3, BC, OC, CO2, CH4, and N2O in Henan Province were 27.5, 503.2, 878.6, 20.1, 17.4, 222.1, 21.5, 9.4, 2.9, 92,077.9, 6.0, and 10.4 kilotons, respectively. Energy demand and pollutant emissions in Henan Province are simulated under four scenarios (Baseline Scenario (BS), Pollution Abatement Scenario (PA), Green Transportation Scenario (GT), and Reinforcing Low Carbon Scenario (RLC)). The collaborative emission reduction effect is most significant in the RLC scenario, followed by the GT scenario. By 2035, under the RLC scenario, energy consumption and emissions of SO2, NOx, CO, PM10, PM2.5, VOCs, NH3, CO2, CH4, and N2O are projected to decrease by 72.0%, 30.0%, 55.6%, 56.0%, 38.6%, 39.7%, 51.5%, 66.1%, 65.5%, 55.4%, and 52.8%, respectively. This study provides fundamental data support for subsequent numerical simulations.

Analysis of the Determinants of Work-Life Balance (WLB) on Transportation Employees in China

The transportation sector in China plays a crucial role in the nation’s economy. This survey covered 708 valid transportation employees who worked in Beijing, China. This research aimed to investigate the employees’ perceptions and the factors affecting their job satisfaction towards work-life balance, mediated by job characteristics. 12 determinants were identified including fair compensation, opportunity for growth, work environment, supervisor support, justice and equality, reward and recognition, job security, autonomy and control, participation in decision-making, effective communication, professional respect and work challenge. This study was significant as the researchers found that all the determinants were supported in the research study.

Intellectual capital and firm performance: the moderating effect of auditor characteristics

PurposeThis study aims to examine the impact of intellectual capital (IC) and its three components (human, structural and relational capital) on corporation performance in the Chinese transportation industry. In addition, this study also investigates auditor characteristics (both Big-N and non-Big-N auditors) as a moderating role to examine the relationship between IC and corporate performance.Design/methodology/approachThe data include 398 firm-year observations of transportation companies listed on the Shanghai and Shenzhen Stock Exchange from 2011 to 2020. Value-added intellectual coefficient (VAIC) model and its modified version (MVAIC) are applied to measure IC efficiency. Finally, the fixed effects regression analysis is used to mitigate the endogeneity issue. To investigate the moderating effect of auditor characteristics, the authors divide the samples based on the clients audited by Big-4 and non-Big-4 firms.FindingsThis study reveals that IC can enhance firm performance in China’s transportation sector. Overall, findings indicate that on the whole, IC has a positive and significant impact on corporation profitability and productivity. Human capital and physical and financial assets (capital employed) play highly important roles, but structural capital has no significant impact. The authors also found that auditor characteristics play an important moderating role in the connection between IC and corporate performance. For example, the positive association between IC and corporate performance is more pronounced when Big-4 auditors audit client firms. At the same time, the authors found a negative relationship between IC and firm performance when non-Big-4 auditors audit client firms.Practical implicationsManagers must understand that several components of IC have a total effect on corporate financial performance. Therefore, managers can dedicate more resources to such components based on the performance outcomes to emphasize their business strategies.Originality/valueThis study is the first empirical analysis of the impact of IC and its components on corporation performance in the transportation sector in China, an emerging market. Previous studies mainly focus on developed countries’ high technology and financial industries sectors but the impact of IC in transportation industry largely remains unknown. Thus, the present findings contribute to IC literature by revealing several underlying mechanisms by which the components of IC help achieve good firm performance.

Transportation carbon emission reduction potential and mitigation strategy in China

Effectively reducing transportation carbon emissions is greatly significant to achieve the carbon peaking and neutral goals of China. On the basis of considering regional technology heterogeneity, we employ the parametric meta-frontier approach to analyze the carbon emission performance and reduction potential of the transportation sector in China. Then, we further decompose the emission reduction potential's contributors into removing management inefficiencies and filling technology gaps. The estimated potential carbon emission reductions from transportation sector in China are 12.3 million tons, accounting for 8.4 % of the annual transportation carbon emissions. Specifically, the eastern regions, especially Shandong, Shanghai, and Liaoning have the greatest carbon emission reduction potential; while Qinghai, Jiangxi, and Ningxia have the smallest potential. As the major contributors to the potential emission reductions, filling technology gaps and removing management inefficiencies account for 57.5 % and 42.5 % of the total potential, respectively. Moreover, removing management inefficiencies dominate for the eastern region and filling technology gaps for the central and western regions. Finally, we provide provincial-specific emission mitigation strategies based on the identification of the reduction potential and its contributors. Our policy implications help decision-makers to facilitate the low-carbon development of transportation sector.

Spatio-temporal heterogeneity of transportation carbon emissions and its driving factors in China’s main urban agglomerations

The transportation sector in China has the characteristics of large total carbon emissions, high level, and unbalanced spatial distribution. For carbon emissions reduction, it is of great significance to study the carbon emissions of transportation in China in different regions. Focusing on China’s three urban agglomerations: Beijing-Tianjin-Hebei Region, Yangtze River Delta Region and Pearl River Delta Region, this paper explores and compares the spatio-temporal heterogeneity of China’s traffic carbon emissions from 2000 to 2019 by using methods such as GWR and ESDA. The results show that: 1) As for carbon emissions, the total carbon emissions and per capita carbon emissions of the three urban agglomerations have shown a significant growth trend. The high-value aggregation in Beijing-Tianjin-Hebei Region has weakened, the high-value and low-value aggregation in the Yangtze River Delta Region has increased, and the change in the Pearl River Delta Region is not obvious. 2) As for influencing factors, motor vehicle ownership has the greatest impact on regional carbon emissions, but the impact intensity of motor vehicle ownership on carbon emissions of the three urban agglomerations is different. 3) As for spatio-temporal heterogeneity, after 2010, the spatial correlation of carbon emissions of the three urban agglomerations was lower than that of the surrounding areas, and all of them were weakened.

Understanding the determinants for predicting citizens' travel mode change from private cars to public transport in China.

Rapid urbanization and motorization have generated increasing social and environmental challenges to the urban transport sector in China, such as traffic congestion, car accidents, air pollution, and global warming. Prioritizing the development of urban public transport system has been adopted as a primary strategy by Chinese government. However, the problems caused by large numbers of private cars are still far from being solved, and the ridership of public transport in China is relatively low. Therefore, the current study proposes a new comprehensive framework by enhancing the norm activation model (NAM) and theory of planned behavior (TPB) with the perceived accessibility to public transport, to better understand the determinants for predicting citizens’ choice of public transport as a sustainable travel mode. An online survey concerning travel mode change was conducted among citizens in Hangzhou, China. Based on 341 valid samples, partial least squares structural equation modeling (PLS-SEM) was employed to verify the proposed framework. The analytical results confirm that awareness of consequences and subjective norms are the two key constructs for connecting the two theories as a whole. Further, the examination of intention-behavior relationship shows that perceived accessibility to public transport could strengthen the relationship between behavioral intention and actual behavior in using public transport. The findings not only contribute to the development of pro-environmental theories, but also have meaningful implications for governments to develop relevant policies to encourage citizens to use public transport as a sustainable travel mode.

Cost–Benefit Analysis of Synergistic CO2 and NOx Energy-Efficient Technologies for the Road Transport Sector in China

The transportation sector is a major source of greenhouse gases and air pollutants, and it has a crucial effect on the synergistic reduction of NOx and carbon. In order to find the energy-efficient vehicle technologies with the highest net reduction potential and lowest net reduction cost over the life cycle, this study traced the CO2 and NOx emission streams of 33 energy-efficient technologies, hidden in the supply chain during the production phase, through structural path analysis, and measured the emission reductions during the use phase using the emission factor method. Moreover, we applied structural decomposition analysis to quantify the three main drivers, including emission intensity, industrial structure, and final demand, of changes in CO2 and NOx emissions from 11 transport subsectors during 2012–2018. Results indicate that CO2 emissions of the transport sector more than doubled from 2012 to 2018; however, the influence of NOx was less significant. The final demand of the road subsector was the most significant driver contributing to CO2 emission changes, with an increase of 109.27 Mt. The emission intensity of road transportation caused the greatest mitigation effect on NOx emission changes, with a decrease of 1902 Kt. The findings of the scenario analysis demonstrate that the most efficient action of the pure electric technology for passenger cars reduces 20.92 Mt NOx emissions, and the parallel hybrid technology for heavy trucks offers the greatest cost effectiveness with a net abatement of 2577 Mt CO2 over its life cycle. Consequently, the aggressive development of new energy technology has become a prerequisite strategy to synergistically reduce CO2 and NOx emissions.

Cross-cutting scenarios and strategies for designing decarbonization pathways in the transport sector toward carbon neutrality

The transport sector will play a pivotal role in achieving China’s carbon neutrality goal by 2060. This study develops a regional transport-energy integrated model to analyze the long-term pathways and strategies toward the carbon-neutral ground transport sector in China. A set of scenarios are created to identify the effectiveness and feasibility of low-carbon policy measures based on the well-known transport strategies within the Avoid–Shift–Improve framework. Our simulations shed light on synergistic coupling and trade-offs among different strategies and instruments for prescribing a desirable mix of policy measures that maximize the synergies and minimize the trade-offs. Here, we show that a region-specific policy package designed from a balanced perspective under the Avoid–Shift–Improve framework has the potential to realize a deep decarbonization in the transport sector and will greatly assist in achieving China’s carbon neutrality by 2060.

Dynamic spatial spillover effect of new energy vehicle industry policies on carbon emission of transportation sector in China

In order to reduce vehicle carbon emission, China launched many policies to develop new energy vehicles (NEVs). Using the panel data of 30 provinces in China from 2009 to 2018, this paper constructs a dynamic spatial panel data model based on spatial Dubin model (SDM) to empirically analyze the dynamic spatial spillover effect of NEV industry policies on the carbon emissions of China's transportation sector. The results are as follows: Firstly, low carbon emissions of transportation sector in one region can promote the realization of low carbon emissions in spatially adjacent or economically similar regions, and this relationship shows a significant dynamic inertia and increasing trend. Secondly, the NEV industry policies have both short-term and long-term effects on carbon emissions of transportation sector in local region. Thirdly, NEV industry policies in one region can produce positive spatial spillover effect on carbon emissions of transportation sector in spatially adjacent or economically similar regions, but this effect needs long-term transmission to show significant effect. Fourthly, both the education level of regional residents and the investment scale of transportation sector are negatively correlated with the carbon emission of transportation sector. Based on the results, some suggestions are put forward.

A novel method for carbon emission forecasting based on EKC hypothesis and nonlinear multivariate grey model: evidence from transportation sector.

Greenhouse gas emissions have brought a serious challenge to the global environment and climate. Efficient and accurate prediction of carbon emissions is essential for the decision-making sectors to control growth and formulate policies. Firstly, considering the economic, demographic, and energy factors, a novel nonlinear multivariate grey model (ENGM(1,4)) based on environmental Kuznets curve (EKC) is proposed with respect to the data characteristics of the incomplete information of carbon emission of transportation sector. The model integrates the IPAT (“Influence = Population, Affluence, Technology”) equation and the extended atochastic impacts by regression on population, affluence, and technology model (STIRPAT). Secondly, the derivation method is used to solve the time response equation of the model and the quantum particle swarm optimization algorithm (QPSO) is designed to optimize the model parameters. Then, 18 years of carbon emission data from China, the USA, and Japan are selected as the validation set. Comparative analysis indicates that the prediction accuracy of the statistical models and the intelligent models depends on sufficient samples and complex variables, and has certain limitations in limited sample prediction. The calculation results show that the new model outperforms other models in various evaluation indicators, indicating that its prediction accuracy is higher. Finally, the projections show that in 2019–2025, the average increase in carbon emissions from the transport sector in China and the USA was 2.837% and 2.394%, respectively, while Japan shows a downward trend with an average decline rate of 1.2231%. The analyzed prediction results are consistent with current situation of the three countries and the transport sectors, demonstrating the high accuracy and reliability of the new model.

Decoupling relationship between economic growth and PM2.5 emissions in the transportation sector in China: regional differences and influencing factors

PM2.5 emissions from the transportation sector are a source of haze pollution in China, to which, however, less attention is paid by society. The decoupling relationships between PM2.5 emissions and economic growth from the transportation sector in the eastern, central, and western regions of China from 2010 to 2017 are analyzed by using the Tapio decoupling model. On this basis, in the transportation sector, socioeconomic factors influencing PM2.5 emissions and effective means of controlling PM2.5 emissions are studied by using a logarithmic mean Divisia index model. The results indicate that: (a) in China’s transportation sector, the decoupling relationships of the two aspects in the eastern, central, and western regions show an N-shaped trend, that is, the rate of change in PM2.5 emissions from the transportation sector gradually exceeds that of economic development. The strong decoupling changes into an expansive coupling in the eastern and central regions, while the strong decoupling becomes an expansive negative decoupling in the western region. (b) Economic growth and population growth mainly contribute to the increase of PM2.5 emissions. Improvements of the energy structure and a decrease in transport intensity are the main factors driving a reduction in PM2.5 emissions. (c) Due to regional differences in the ‘rebound effect’ and ‘technological effect’, technological progress has increased PM2.5 emissions from the transportation sector in the central region, while reduced such emissions in the eastern and western regions. This research provides targeted policy reference for regional governance of PM2.5 emissions from the transportation sector.

Research on energy saving potential and countermeasures in China’s transport sector

As the people’s living condition improves, the energy demand in China’s transport sector has grown dramatically and will keep increasing in the short term. In this research, we adopted a bottom-up approach to conduct the energy conservation analysis in China’s transport sector during the 14th Five-Year Plan Period based on the LEAP model. This study aims to estimate the energy saving potential of the transport sector in China under three scenarios: the Business as Usual (BAU) scenario, the Energy Conservation (EC) scenario, and the Enhanced Energy Conservation (ECC) scenario. The results indicated that the total energy demand in China’s transport sector would continue to grow over the 14th Five-Year Plan Period, and it would come up to 665.29 million tons of coal equivalent (Mtce) by 2025 in the BAU scenario. The EC scenario and the EEC scenario would save 27.80 and 40.99 Mtce by 2025 compared to the total energy demand in the BAU scenario. Besides, the energy demand of freight transport, intercity passenger transport and urban passenger transport would reach 250.45, 60.75 and 353.89 Mtce by 2025 in the BAU scenario. The corresponding energy saving potential of freight transport, intercity passenger transport and urban passenger transport would be 20.45, 2.22 and 5.12 Mtce by 2025 in the EC scenario, and would be 27.64, 3.34 and 10.01 Mtce by 2025 in the EEC scenario. Considering the significant energy saving potential of the transport sector in China, several major tasks and policy recommendations were then proposed to further reduce the transportation energy consumption.

The economic feasibility of green hydrogen and fuel cell electric vehicles for road transport in China

In September 2020, the Chinese central government announced a new policy to develop hydrogen energy and fuel cell applications. It emphasized fuel cell commercial vehicles rather than passenger vehicles. Such an emphasis is drastically different from the hydrogen and fuel cell strategies of other leading economies. This study aims at providing insights into the justification behind it. It develops quantitative models to conduct economic assessments of the feasibility of hydrogen energy produced from renewable energy and subsequently applied in the road transport sector in China. A well-to-wheel model is developed to estimate the carbon emissions of the hydrogen supply chain as well as that of the fuel cell electric vehicles. In the meantime, a levelised cost of hydrogen model is adopted to analyze the cost of hydrogen as storage for renewable energy. These are followed by a total cost of ownership model applied to assess the cost of owning and driving fuel cell electric vehicle, fueled by the hydrogen produced from renewables, compared to alternative vehicle powertrains, especially those fossil fuel-based. On such a basis, the relation between energy policies and the competitiveness of hydrogen produced from renewable energy and the fuel cell electric vehicle is discussed.

Impact of COVID-19 on transportation and logistics: a case of China

Recent research has shown that the COVID-19 pandemic has holistically affected the financial and industrial sectors of China, in a considerably adverse manner. This paper aims to examine the impact of the COVID-19 virus on the transportation and logistics sector in China. In this regard, the study opted for a quantitative method of research design, along with primary data collection. The explanatory variable here is the COVID-19 virus, whereas, the dependent variables are air freight, ocean freight, and land freight. The data analysis technique used is the Structural Equation Modelling (SEM) analysis, comprising of the Confirmatory Factor Analysis (CFA) and the path assessment method. A structured survey questionnaire was also used, and the survey questionnaire scale was set from strongly agree to disagree strongly. The findings revealed that the effect of COVID-19 on air freight is statistically negative and significant. Moreover, the impact of COVID-19 on land freight is statistically negative and significant, whereas the effect of COVID-19 is statistically insignificant in the context of ocean freight, particularly during the period of the COVID-19 pandemic. Therefore, keeping these results in consideration, policymakers can enhance their support, so as to increase the performance of the logistics and transportation sector in China.

Transportation Sustainability, Macroeconomics, and Endogeneity in China: A Hybrid Neural-Markowitz-Variable Reduction Approach

This paper analyzes the sustainability of the transportation sector in China by investigating feedbacks or endogenous effects among energy consumption, transportation, and major macroeconomic variables and their consequent CO2 emission levels related to fiscal policy, monetary policy, inflationary pressure, and economic activity. A novel combination of Alternative Variable Reduction Techniques, a Markowitz Portfolio Selection Model, and Neural Networks is applied on monthly data from January 1999 to December 2017. The main idea is to develop a hybrid approach to unveil endogeneity in China's transportation footprint, which is mostly impacted by energy consumption and CO2 emission levels of different transportation modes and major macro-economic factors while simultaneously exploring the epistemic uncertainty that surrounds this issue as captured by Information Entropy, Variance Inflation Factor, and Covariance matrix. It can be concluded that road transportation has played a dominant role in decreasing the entire sustainability level, which is mainly driven by trade and fixed-asset investment as well as monetary and fiscal policy, while consumer expectations and cornerstone economic prices have shown limited impact on the transportation footprint. Railways and waterways help mitigate the harmful impacts of increased economic activity and expansionist monetary and fiscal policy on the transportation footprint.