Transportation In China Research Articles

Impacts of regional development on emissions in China's transport sector.

The CO2 emissions in China's transport sector increased from 349.00 Mt in 2005 to 723.87 Mt in 2017. Thus, a number of climate change policies are being implemented to adjust regional structure and to decrease the emissions in China's transport sector at the regional level. However, few studies explored the impact of changes in regional structure (that is, measured regional share of the added value of transport sector) on emissions in China's transport sector. Therefore, based on the Kaya identity and LMDI analysis, we decompose 8 factors (including carbon intensity, energy structure, energy intensity, turnover intensity, transport intensity, regional structure, per-capita traffic activity, and population size) to analyze the driving factors of emissions in China's transport sector. The period 1997-2017 is divided into four phases according to the growth rate of emissions. The results show that regional structure increased CO2 emissions in China's transport sector between 2013 and 2017. The fast transport development in the Southwest region, reflected by the increase in the share of total transport value added, resulted in emissions growth during 2013-2017. Moreover, the change in the growth rate of the regional transport sector's value added is positively correlated with the change in the regional share of value added, which is positively correlated with the change in regional emissions.

Spatial Spillover Effects and Regional Differences in China's Transportation Carbon Emissions

Spatial Spillover Effects and Regional Differences in China's Transportation Carbon Emissions

Carbon Productivity in China's Transportation Industry

Carbon Productivity in China's Transportation Industry

Endogenous energy efficiency and rebound effect in the transportation sector: Evidence from China

The transportation industry is one of the main energy consumers, thus, the effectiveness of energy conservation efforts in this industry exerts an important impact on the sustainable development of both economy and society. Technological progress is commonly considered to be a key tool for energy saving and emission reduction; however, its own energy saving effect may be weakened by the rebound effect. Thus, the accurate identification of the rebound effect in the transportation sector is of great practical significance. Based on the idea of “learning by doing”, this study constructed a rebound effect model under the framework of endogenous growth theory. Furthermore, the nonlinear least squares method is used to measure the rebound effect of the transportation sector of 30 Chinese provinces from 2003 to 2017. The results show that the mean short-term rebound effect of China's transportation sector is 82%, while the mean long-term rebound effect is 123%. From a regional perspective, both the short-term and long-term rebound effects of China's transportation sector follow a decreasing trend from eastern to western regions. Mechanistically, the rebound effect of China's transportation sector is primarily driven by factor substitution, and the resulting substitution effect contributes 86% to the rebound effect. This study provides policy implications for the formulation and implementation of energy policies in the transportation sector.

ANALYSIS ON OPTIMIZATION DECISION OF PAVEMENT MAINTENANCE MODE BASED ON ANALYTIC HIERARCHY PROCESS

With the rapid development of China's transportation and the improvement of people's living standards, China's highways have entered the peak maintenance period. According to the old road conditions and financial conditions, how to choose the appropriate maintenance technology to meet the technical and economic requirements of highway maintenance and construction projects has become a difficult problem faced by highway maintenance management departments. Therefore, based on the principle of index selection, this study comprehensively considered the impact of various factors on maintenance decision-making, determined four evaluation indexes, and systematically studied the compre-hensive decision-making system of asphalt pavement recycling maintenance. Based on the subjective-objective linkage and probability statistics method, the eigenvalue of the applicability of recycling mode was transformed to the same scale of 0 to 100, and the fuzzy scoring interval of the four bids was proposed. The calculation method of pavement quality recovery index (PQRI) and the analysis basis of economic benefits were clarified. Finally, the weight of each index was calculated by analytic hierarchy process, and the multi-index comprehensive decision-making method of pavement maintenance is constructed and used to theoretically verify and guide the actual road section. Results demonstrate that the proposed PQRI can realize the operability of quantifying the later operation of pavement. The weight of each index from big to small is PQRI > economic benefit > applicability of recycling mode. The recycled pavement quality recovery indices and applicability of the four recycling modes have small differences, and the proposed four bids interval makes the decision-making evaluation process more simple and explicit. The decision evaluation results of actual road section verify the rationality and practicability of the decision method. The conclusions are important for the maintenance and management of asphalt pavement recycling technology. Keywords: road engineering, recycling maintenance, intelligent decision-making, fuzzy evaluation

Thoughts and Countermeasures of Speeding up the Construction of China's Transportation Service

Thoughts and Countermeasures of Speeding up the Construction of China's Transportation Service

From efficiency to equity: Changing patterns of China's regional transportation systems from an in‐use steel stocks perspective

AbstractTransportation serves as a bridge for social production, distribution, exchange, and consumption and, thus, plays an important role in boosting the economy. In recent years, the rapid development of China's transportation system has driven a continuous accumulation of steel. In this study, we estimated the in‐use steel stocks of 32 types of infrastructure and vehicles in three subsystems (railway, road, and subway) in 31 provinces of China from 1990 to 2019 based on a bottom‐up approach. The results show that China's in‐use steel stock in the transportation system has increased to 575 Mt in 2019 (34.4% for infrastructure, 65.6% for vehicle). Spatially, this increasing trend shows six diversified patterns among 31 provinces; steel stocks tend to decrease from the east to central and western inland areas. The Gini coefficient curve of the provinces’ stocks shows an inverted “U” shape over time, indicating a shrinking regional gap in recent years; this reflects a transition from efficiency to equity. We also measured the steel stock productivity (GDP per stock) in different regions and observed its diverse evolution types. The stock productivities in all regions climbed up initially, but have dropped in recent years, and the most obvious decrease is observed in western China. For eastern regions, vehicles have been the main growth point in recent years, while western regions are vigorously developing infrastructure. Our results help to illustrate the development status and spatial distribution of China's transportation system and could facilitate improved efficiency and the management of future challenges in China's transformation and development.

Economic structural change and freight transport demand in China

China's rapidly rising freight transport demand has been one of the primary contributors to global oil consumption growth since 2000 and has remained the dominant source of domestic transport energy consumption during the same period. The objective of this paper is to investigate the main structural driving forces of China's freight transport demand during its fastest growing period (1997–2012), and to analyze its potential future scenario. The results suggest that strong growth of total final demand, an increasingly freight-intensive economic structure, and lengthening inter-industry production linkages, which is consistent with China's investment and export driven development model from 1997 to 2012, have been the main propellers of rapid freight transport demand growth during this time. Looking ahead, as the Chinese economy gradually shifts to a consumption driven model in an increasingly uncertain global economic environment, policymakers need to be aware of both unexpected demand shocks and the inherent changes of the Chinese economy while considering their implications for China's transport energy demand and China's carbon emission mitigation strategy.

China's transportation sector carbon dioxide emissions efficiency and its influencing factors based on the EBM DEA model with undesirable outputs and spatial Durbin model

The threat of global climate change has caused the international community to pay close attention to atmospheric levels of greenhouse gases such as carbon dioxide. Transportation sector carbon dioxide emissions efficiency (TSCDEE) is a key indicator used to prioritize sustainable development in the transportation sector. In this paper, the epsilon-based measure data envelopment analysis model with undesirable outputs is applied to estimate TSCDEE for 30 provinces in China from 2010 to 2016. We also analyze influencing factors using the spatial Durbin model. Research shows that the overall TSCDEE of the Chinese provinces studied was 0.618, indicating that most regions are still in need of improvements. The provinces with the highest TSCDEE are located in developed coastal regions of China. This study shows that factors such as transportation structure, traffic infrastructure level, and technological progress have prominent positive effects on TSCDEE, while both urbanization level and urban population density exert significantly negative effects on TSCDEE. The findings should have a far-reaching impact on the sustainable development of global transportation.

What causes spatial inequalities of low-carbon development in China's transport sector? A newly proposed meta-frontier DEA-based decomposition approach

This paper proposes a new index to measure the spatial carbon intensity inequality. To further explore factors causing the inequalities of carbon intensity, meta-frontier DEA-based decomposition approach is used in this study. Using this method, carbon intensity inequality of China's transport sector from 2004 to 2018 were decomposed into nine factors, including two newly proposed technological gap factors. Study shows that: (1) there are spatial inequalities in carbon intensity of China's transport sector, which peaked in 2009 and then decreased; (2) potential energy intensity, production technology gap, and production technology contributed the most to spatial inequalities of low-carbon development in Chinas transport sector, followed by pure production efficiency and energy structure. Energy-saving technology, energy technology gap, and pure energy efficiency contributed have relatively little impacts; and (3) production technology gap is the determinant factor for the transport carbon intensity inequality between eastern and western regions. The proposed approach can also be used for many other similar low-carbon development studies.

Carbon emission reduction effect of transportation structure adjustment in China: an approach on multi-objective optimization model.

This study explores the carbon emission reduction effect of the transportation structure adjustment from the perspectives of transportation structure optimization and transportation mode shift. A multi-objective optimization model of China's transportation structure is constructed, and the transportation structure is then adjusted by combing the multi-objective particle swarm optimization (MOPSO) algorithm and compromise programming technique. Under the premise of ensuring the economic and social effects, the largest carbon emission reduction effect of the transportation structure is acquired. The results indicate that the transportation structure optimization reduces carbon emissions by 12.70%. Optimization in the western region has the largest carbon emission reduction effect, followed by that of the central and eastern regions. Furthermore, the carbon emission reduction effect of the shift mode "road to rail" increases uninhibitedly with the increase in the shift range. However, the same effect of "road to water" has an upper limit. "Road to rail" is found to be more effective in the eastern region, whereas "road to water" is more effective in the central and western regions.

Regional inequality of total factor CO2 emission performance and its geographical detection in the China's transportation industry.

The total factor CO2 emission performance (TFCEP) of transportation industry has received increasing research interests, while existing literature pays little attention to its regional inequality and driving factors. In order to uncover the regional inequality of TFCEP in China's transportation industry, this paper used Theil index and combined with geographical detector model (GDM) to explore the driving factors and their interactions on TFCEP in Chinese transportation industry. The results revealed that the TFCEP of transportation industry showed a promising increase during 2003-2017 with an annual growth rate of 0.12%, and the improvement was contributed by the technical efficiency change. The TFCEP in the Eastern region performed better than that in the Northeast, Central, and Western region. Regional inequality of TFCEP did exist and exhibited an obvious downward trend. The within-region inequality had a greater impact on the inequalities than between region. Freight turnover was the main driving factor of TFCEP in the transportation industry, followed by the energy intensity and per-capita GDP. In the Eastern and Western regions, freight turnover had the greatest impact on TFCEP, while in the Central and Northeastern regions, urbanization rate and energy intensity were the dominant factors, respectively. The interactions between energy intensity and freight turnover were highly influential. This paper provides important insights for different regions to formulate targeted carbon emission reduction policies.

Assessing co-benefits incentivizes climate-mitigation action

To lessen the myriad impacts of climate change, the world faces the daunting task of transitioning away from fossil-fuel-based infrastructure. In this issue of <i>One Earth</i>, Peng et al. demonstrate that such a transition in China's transportation and power-generation sectors could additionally provide substantial health and economic benefits, further incentivizing sustainable development pathways.

Costs and potentials of reducing CO2 emissions in China's transport sector: Findings from an energy system analysis

As China's transportation system expands it is producing a large amount of end-use emissions. To derive cost-effective measures for CO2 mitigation, the authors of this paper develop a marginal abatement cost (MAC) curve for China's transport sector. Using the TIMES model, MAC curves are derived which identify the linkages among all transport modes, considering, among other uncertainties, the impacts of a carbon peaking policy and driver preferences for plug-in hybrid electric vehicles (PHEVs). The results underline that the intercity passenger transport sector has a cost advantage, with a huge potential for emissions reduction (accounting for almost 38.8% of all CO2 emissions mitigation). The freight sector is another potentially significant contributor to CO2 reduction, underlining its vital role for emission mitigation in the longer run. The carbon peak policy, while making alternative fuel vehicles (AFV) more efficient, is found to increase the overall cost of emissions reduction. A higher ratio of electricity to fossil fuel use by PHEVs drivers would help the transport sector to reduce CO2 much faster. The sensitivity analysis of these and other factors is also estimated in this paper. The final MAC curves provide valuable insights for future low-carbon energy development strategies of China's transport sector.

Influencing factors of carbon emissions in transportation industry based on C[sbnd]D function and LMDI decomposition model: China as an example

Globally, the transportation industry is one of the leading fields that generate the largest share of greenhouse gas emissions. While undergoing rapid development, countries worldwide aim to solve the problems involved in high energy consumption. Taking China as an example, this paper studies the main factors of carbon emissions in the transport sector and analyses the decoupling states between carbon emission and economic growth, making energy efficiency policies accordingly. In order to better demonstrate the dependence of the economy on the carbon emissions in China's transportation industry comprehensively, combined with the CD production function, this paper develops the decomposition and decoupling technology based on the LMDI approach. Additionally, it quantifies seven effects: energy emission intensity effect, energy structure effect, energy intensity effect, transportation intensity effect, technology state effect, labor input effect and capital input effect. The results show three major points: (1) From 2001 to 2018, the cumulative carbon emissions of China's transportation industry increased by 633.46 million tons, in which the capital input effect is the key factor driving carbon emissions, accounting for 157.70% of the total cumulative increased emissions, followed by energy structure effect at 10.39%. The labor input effect accounted for the smallest proportion at 2.26%. In this case, the technology state effect is the primary factor in restraining carbon emissions. During the study period, it reduced carbon emissions by 292.27 million tons, accounting for 46.14%. To a certain extent, energy intensity effect, transportation intensity effect and energy emission intensity inhibited carbon emissions, representing 16.67%, 5.32% and 2.22%, respectively. (2) During the research period, two decoupling states existed between carbon emissions and economic growth in China's transportation industry, specifically weak decoupling and expansive coupling. (3) The analysis of decomposition and decoupling state of influencing factors of carbon emissions shows that, on the one hand, factors promoting carbon emissions (capital input effect, energy structure effect and labor input effect) hinder the decoupling process. On the other hand, factors restraining carbon emissions (technology state effect, transportation intensity effect, energy intensity effect and energy emission intensity effect) accelerate the decoupling process. The research findings provide a new perspective for achieving carbon emission reduction in the transportation industry and curbing energy consumption growth.

Simulation research on co-controlling pollutants and greenhouse gases emission in China's transportation sector

Simulation research on co-controlling pollutants and greenhouse gases emission in China's transportation sector

Impact of COVID-19 pandemic on energy consumption and carbon dioxide emissions in China's transportation sector

The COVID-19 pandemic has put the whole world in a great lockdown and greatly changed our lives. The lockdown caused by the COVID-19 pandemic has greatly affected the transportation industry in China. In this paper, energy consumption and carbon dioxide emissions of fuel vehicles and battery electric vehicles in China during the COVID-19 pandemic was estimated and analyzed. The impact of COVID-19 pandemic was analyzed. The study period is taken from September 2019 to April 2020, and the period from September 2018 to August 2019 is taken for comparison. Moreover, energy consumption and CO2 emissions of fuel vehicles during SARS epidemic was also analyzed. The related study period is taken from January 2003 to August 2003. The results show COVID-19 has a much larger impact on transportation energy consumption and CO2 emissions than SARS. After the COVID-19 pandemic, energy conservation and emission reduction still have a long way to go. Determining the energy consumption and emissions in transportation sector during the COVID-19 pandemic will provide the government with a reference for future energy conservation and emission reduction in transportation sector and the further promotion of battery electric vehicles.

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.

Analysis of the Relationship Between Railway and Highway Transportation and China's Economic Development

China has a vast land area and frequent interconnections between various regions. China's transportation industry is faced with tremendous pressure. This article combines China’s railway and highway transportation conditions to predict China’s economic development, uses stepwise regression to screen explanatory variables, and finally determines railway passenger turnover, road freight volume and passenger car ownership as the explanatory variables, and GDP as the dependent variable, and also analyzes China’s economic development by establishing a multiple regression model.

Research on application of a hybrid heuristic algorithm in transportation carbon emission.

China's transportation industry is entering a stage of high-quality development. Carbon emissions and environmental protection issues have put pressure on the construction of a green and low-carbon transportation system, and the transportation industry has become one of the industries with the fastest growth in carbon emissions. Therefore, it is of great significance to study the influencing factors of carbon dioxide emissions in the transportation industry and predict its carbon emissions. This article first thoroughly analyzes the main sources of carbon emissions in the transportation industry, including nine major energy consumption sources such as coal, gasoline, and diesel, and obtains the carbon emission values ​​from 2000 to 2017. Secondly, a linear regression analysis was performed on 13 pre-selected influencing factors and CO2 emissions in the transportation industry. In order to obtain the potential similarities between the factors, factor 13 is divided into four categories: economic scale, population size, transportation structure, and energy consumption. Each category and factor analysis is divided into four potential factors. Third, a training model was established based on the data from 2000 to 2012. Four algorithms, neural network (BP), extreme learning machine (ELM), genetic algorithm optimized neural network (GA-BP), and genetic algorithm optimized extreme learning machine (GA-ELM) are used to predict 2013 to 2017 and compare the predicted value of its respective algorithm with the actual value. Finally, the results show that the genetic algorithm optimized extreme learning machine (GA-ELM) hybrid heuristic algorithm has the highest degree of fit between the predicted value and the true value, which further illustrates the carbon emissions of the hybrid heuristic algorithm in the transportation industry. For the superiority of the prediction, the study also shows that the four influencing factors seriously affect the carbon emissions of the transportation industry. Therefore, accelerating the upgrading of the transportation structure and changing the proportion of energy consumption will be important measures for the transportation sector to control carbon emissions in the next step, so as to promote the sustainable development of the transportation system.