Central China Region Research Articles

Effect of Vertical Wind Shear on PM2.5 Changes over a Receptor Region in Central China

Vertical wind shear (VWS) significantly impacts the vertical mixing of air pollutants and leads to changes in near-surface air pollutants. We focused on Changsha (CS) and Jingmen (JM), the upstream and downstream urban sites of a receptor region in central China, to explore the impact of VWS on surface PM2.5 changes using 5-year wintertime observations and simulations from 2016–2020. The surface PM2.5 concentration was lower in CS with higher anthropogenic PM2.5 emissions than in JM, and the correlation between wind speed and PM2.5 was negative for clean conditions and positive for polluted conditions in both two sites. The difference in the correlation pattern of surface PM2.5 and VWS between CS and JM might be due to the different influences of regional PM2.5 transport and boundary layer dynamics. In downstream CS, the weak wind and VWS in the height of 1–2 km stabilized the ABL under polluted conditions, and strong northerly wind accompanied by enhanced VWS above 2 km favored the long-range transport of air pollutants. In upstream JM, local circulation and long-range PM2.5 transport co-determined the positive correlation between VWS and PM2.5 concentrations. Prevailed northerly wind disrupted the local circulation and enhanced the surface PM2.5 concentrations under polluted conditions, which tend to be an indicator of regional transport of air pollutants. The potential contribution source maps calculated from WRF-FLEXPART simulations also confirmed the more significant contribution of regional PM2.5 transport to the PM2.5 pollution in upstream region JM. By comparing the vertical profiles of meteorological parameters for typical transport- and local-type pollution days, the northerly wind prevailed throughout the ABL with stronger wind speed and VWS in transport-type pollution days, favoring the vertical mixing of transported air pollutants, in sharp contrast to the weak wind conditions in local-type pollution days. This study provided the evidence that PM2.5 pollution in the Twain-Hu Basin was affected by long-distance transport with different features at upstream and downstream sites, improving the understanding of the air pollutant source–receptor relationship in air quality changes with regional transport of air pollutants.

The Analysis of Carbon Emission’s Characteristics and Dynamic Evolution Based on the Strategy of Unbalanced Regional Economic Development in China

Analyzing the evolution law of carbon emissions is particularly important for the designation of policies on energy conservation and emission reduction. Based on the regional division of China, this paper uses a spatial panel model to find the causes of the differences in carbon emission, and the non-parametric model, logarithmic mean Divisia index (LMDI) model and the extended STIRPAT model to analyze the relevant influencing factors in detail. From the studies in this paper, there come the following conclusions: (1) The environmental Kuznets curve (EKC) in the eastern region resembles the national EKC, demonstrating the same “N” pattern. However, the “upside-down U” pattern in the middle and western regions not only confirms the assumption of EKC in some Chinese regions but also demonstrates the effective restraint in high energy consumption and high emission levels when narrowing down the gaps between the central and western regions and the eastern regions. (2) In addition, good education can effectively suppress the increase in carbon emission, and every 1% increase in the proportion of educated people (college and above) results in emission reduction, respectively, by 0.22%, 0.51% and 0.44% in the eastern, central and western regions of China. (3) Significantly, the effect of tertiary industry structure on carbon emissions is positive, reflecting the trend of “deterioration” of China’s industrial structure over long time scales. This study functions positively in understanding the evolutionary pattern of regional carbon emissions and proposing differentiated policies on emission reduction.

Causes and Evolution Characteristics of Green Innovation Efficiency Loss: The Perspective of Factor Mismatch under Local Government Competition

The objective of this paper is to measure the efficiency loss of green innovation caused by local government competition and explore its causes and evolution characteristics. Based on the equimarginal principles such as the deviation of the allocation of green innovation output factors, this paper uses the panel data of China’s provinces (excluding Tibet, Hong Kong, Macao and Taiwan) from 2000 to 2020 and employs the spatial panel measurement model and the Kernel density estimation. The study finds that first, local government competition causes the mismatch of local innovation factors not only locally but also in neighboring regions. Second, the mismatch of innovative talents and capital caused by local government competition lowers the green innovation efficiency. Third, the shortage of innovative talents caused by local government competition is the main reason for the loss of green innovation efficiency in Beijing, Tianjin, and Shanghai. Fourth, the degree of efficiency loss of green innovation at the provincial level in China is heterogeneous in government competition strategies, and the loss due to tax competition is the most significant. Fifth, although the loss of green innovation efficiency generally decreases yearly, in the future, the institutional competition will still hinder the improvement of green innovation efficiency in the eastern, central and western regions of China. Our policy suggestions include promoting regional cooperation and cultivating innovative talents to further improve the efficiency of green innovation.

Spatiotemporal variations and sources of PM2.5 in the Central Plains Urban Agglomeration, China.

The Central Plains Urban Agglomeration (CPUA) is the largest region in central China and suffers from serious air pollution. To reveal the spatiotemporal variations and the sources of fine particulate matter (PM2.5, with an aerodynamic diameter of smaller than 2.5 μm) concentrations of CPUA, multiple and transdisciplinary methods were used to analyse the collected millions of PM2.5 concentration data. The results showed that during 2017 ~ 2020, the yearly mean concentrations of PM2.5 for CPUA were 68.3, 61.5, 58.7, and 51.5 μg/m3, respectively. The empirical orthogonal function (EOF) analysis suggested that high PM2.5 pollution mainly occurred in winter (100.8 μg/m3, 4-year average). The diurnal change in PM2.5 concentrations varied slightly over the season. The centroid of the PM2.5 concentration moved towards the west over time. The spatial autocorrelation analysis indicated that PM2.5 concentrations exhibited a positive spatial autocorrelation in CPUA. The most polluted cities distributed in the northern CPUA (Handan was the centre) formed a high-high agglomeration, and the cities located in the southern CPUA (Xinyang was the centre) formed a low-low agglomeration. The backward trajectory model and potential source contribution function were employed to discuss the regional transportation of PM2.5. The results demonstrated that internal-region and cross-regional transport of anthropogenic emissions were all important to PM2.5 pollution of CPUA. Our study suggests that joint efforts across cities and regions are necessary.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11869-022-01178-z.

Relationship between the Chinese Main Air Transport Network and COVID-19 Pandemic Transmission

The COVID-19 pandemic had an unprecedented impact on the civil aviation passenger transport industry. This study analyzes the scale change and spatial distribution of the Chinese main air transport network (CMATN) and its role in the early spread of the pandemic using a complex network analysis method and econometric model. The result shows that CMATN is mainly located in the economically developed and densely populated central and eastern regions of China. The normalized degree, closeness, and betweenness centralities of CMATN node cities show an overall increasing trend, indicating that the air transport network is constantly improving. There was a significant positive relationship between the centrality of node cities, the duration of the COVID-19 pandemic, and the number of confirmed cases, indicating that air transport networks were crucial in the pandemic’s early spread. Furthermore, social and economic variables such as GDP and population had a significant positive impact on the duration of the pandemic, indicating that higher levels of social and economic development increased the seriousness of the pandemic. Our findings are expected to supplement existing research and serve as a point of reference for pandemic prevention and control.

Can China's carbon emissions trading scheme promote balanced green development? A consideration of efficiency and fairness

The carbon emission trading scheme (ETS) is an important market-based tool for achieving carbon neutrality. The existing research on the policy effect of ETS focuses on emission reduction effect and economic effect respectively. In view of this, based on the connotations of green development, we embed fairness toward the environment into the green development system to construct a balanced green development index to measure two dimensions, namely, efficiency and fairness. Theoretically understand the synergistic development effect of economic growth and ecological environment quality produced by the implementation of ETS, and analyze the impact of carbon market on balanced green development. A multiperiod difference-in-differences (DID) model explores the impact of China's carbon ETS on the green equilibrium level by using Chinese provincial panel data for the period 2008–2018. First, the results demonstrate that China's balanced green development level is weakly coordinated overall and there is a global spatial agglomeration effect. Second, the carbon ETS has significantly improved the level of balanced green development which promotes the degree of coordination between green economic growth and environmental fairness, offering an effective path that includes efficiency and fairness for green development. Third, there are regional differences in the impact of the carbon ETS on green equilibrium levels. The effect on the western region of China was significantly higher than that of the central and eastern regions of China. Compared with the existing research, the study breaks the single research perspective. From the perspective of efficiency and fairness, we propose improvements in the construction of a national unified carbon emissions trading market to strengthen the balanced green development. This paper provides a theoretical basis and policy reference for deepening China's green and high-quality development under the “carbon peaking and carbon neutrality” goal.

Influence of Multi-Scale Meteorological Processes on PM2.5 Pollution in Wuhan, Central China

Heavy PM2.5 (particulate matter with an aerodynamics diameter less than 2.5 μm) pollution frequently happens in Wuhan under unfavorable meteorological conditions. To comprehensively understand the complex impact of both regional-scale synoptic forcing and local-scale processes within the planetary boundary layer (PBL) on air quality in Wuhan, this study analyzed long-term PM2.5 concentration measurement, near-surface and upper-air meteorological observations from March 2015 to February 2019, in combination with the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2). We found that in winter the day-to-day change of PM2.5 pollution level in Wuhan was governed by the synoptic warm/cold advection, in addition to the high emissions of pollutants. The synoptic condition can largely determine both the vertical development of PBL and horizontal transport of pollutants. When a high-pressure system is located to the north of Wuhan at the 900-hPa level, the induced warm advection above 1,000 m can enhance the thermal stability of lower troposphere and inhibit the development of daytime PBL, leading to a decreased dispersion volume for pollutants. Meanwhile, within the PBL the pollutants emitted from Henan, Shandong, and Anhui provinces can be transported to Wuhan, further worsening the pollution. Our results highlight the importance of coordinated pollution controls in Central China and adjacent north regions under the unfavorable synoptic condition.

Modifiable risk factors associated with cardiovascular disease and mortality in China: a PURE substudy.

To examine the incidence of cardiovascular disease (CVD) and mortality in China and in key subpopulations, and to estimate the population-level risks attributable to 12 common modifiable risk factors for each outcome. In this prospective cohort of 47 262 middle-aged participants from 115 urban and rural communities in 12 provinces of China, it was examined how CVD incidence and mortality rates varied by sex, by urban-rural area, and by region. In participants without prior CVD, population-attributable fractions (PAFs) for CVD and for death related to 12 common modifiable risk factors were assessed: four metabolic risk factors (hypertension, diabetes, abdominal obesity, and lipids), four behavioural risk factors (tobacco, alcohol, diet quality, and physical activity), education, depression, grip strength, and household air pollution. The mean age of the cohort was 51.1 years. 58.2% were female, 49.2% were from urban areas, and 59.6% were from the eastern region of China. The median follow-up duration was 11.9 years. The CVD was the leading cause of death in China (36%). The rates of CVD and death were 8.35 and 5.33 per 1000 person-years, respectively, with higher rates in men compared with women and in rural compared with urban areas. Death rates were higher in the central and western regions of China compared with the eastern region. The modifiable risk factors studied collectively contributed to 59% of the PAF for CVD and 56% of the PAF for death in China. Metabolic risk factors accounted for the largest proportion of CVD (PAF of 41.7%), and hypertension was the most important risk factor (25.0%), followed by low education (10.2%), high non-high-density lipoprotein cholesterol (7.8%), and abdominal obesity (6.9%). The largest risk factors for death were hypertension (10.8%), low education (10.5%), poor diet (8.3%), tobacco use (7.5%), and household air pollution (6.1%). Both CVD and mortality are higher in men compared with women, and in rural compared with urban areas. Large reductions in CVD could potentially be achieved by controlling metabolic risk factors and improving education. Lowering mortality rates will require strategies addressing a broader range of risk factors.

A Dynamic Analysis of Retail Infrastructure and Tourism Demand: Evidence from a Casino Destination

ABSTRACT This study used a panel cointegration approach to investigate the effect of retail infrastructure/facilities on tourism demand in the casino gaming destination, Macao, from 23 provinces (and municipalities) of mainland China. Numbers of retail outlets in general and retail establishments in integrated resorts (IRs) are used as proxies for retail infrastructure/facilities. Dynamic Ordinary Least Square (DOLS) estimations reveal that a positive long-run relationship exists between tourism demand and retail from both dimensions. Heterogeneous results on the individual province estimates suggest visitors from the emerging central and western regions of China respond more strongly to retail facilities than the more saturated southern and eastern regions. The bidirectional causality between retail infrastructure and tourism demand also validates a positive cycle in the long-term growth of the destination. The results suggest that casino gaming destinations should seek to diversify entertainment and capitalize on the competitive advantage of their non-gaming facilities to sustain long-term development.

Can Green Credit Contribute to Sustainable Economic Growth? An Empirical Study from China

Green development is an inevitable trend of sustainable development: how does it affect green economic growth as the main channel of green project financing and the core force of building a green financial system? The present article measures the relationship between green credit and sustainable economic growth using a benchmark regression analysis model and explores the main influencing factors and regional characteristics that affect the coupling development of green credit and sustainable economic growth by combining mechanism and heterogeneity tests. The results of the study show that: (i) Green credit has a significant positive contribution to sustainable economic growth. (ii) In terms of the transmission mechanism, industrial upgrading and environmental regulation have a significant impact on sustainable economic growth. (iii) In terms of heterogeneity, the effect of green credit on sustainable economic growth is the most pronounced in the west, followed by the central and eastern regions of China. The policy implications of this study are that green credit in China is an inevitable trend, and that a sound policy supporting the legal system and information communication mechanisms should be promulgated to ensure the effective allocation of resources, thereby promoting the coordinated, sustainable and stable development of environmental protection and the economy.

Socio-Economic Development of Tibet (1951-1959)

In May 2021, China celebrated the 70th anniversary of the liberation of the Tibet Autonomous Region. In its socio-economic development, Tibet has gone through a number of stages. The article explored the first stage: 1951–1959, during which the central government of the PRC practically did not interfere in the internal affairs of the region, limiting its help to creation of the foundations of modern economy and social infrastructure. Firstly, it was necessary to link Tibet with the central regions of China by reliable transportation system through the construction of highways, airlines and water routes. Secondly, it was necessary to lay the foundations of modern industry and services, which were almost nonexistent in Tibet. In 1951, 98 % of the region’s economy consisted of primitive agriculture and animal husbandry. Thirdly, it was required to create a system of education, health care, press and radio almost from scratch. To accomplish those goals it was necessary to prepare qualified personnel and labor force.

Social Support and Drug Abstention Motivation among Chinese Male Drug Addicts: A Moderated Mediation Model of Self-Control and Sensation-Seeking.

The goal of this study is to examine how social support affects Chinese male drug addicts’ abstention motivation. To elucidate the mechanism as well as the boundary condition of the aforesaid influence, self-control and sensation-seeking were induced. Using the questionnaire method, the cross-sectional data were collected from 498 male drug addicts from one hospital and four compulsory isolation drug abstention centers in Central China region. The results indicated that social support has a positive direct and indirect effect on abstention motivation. The indirect influence is that the impact of social support on abstention motivation is mediated by self-control. The direct effect was moderated by sensation-seeking. Specifically, for individuals with low sensation-seeking, social support can significantly increase drug abstinence motivation, but this effect was not significant for those with high sensation-seeking. Theoretical and practical implications of the results were discussed.

Applying a Bayesian approach for refining the chronostratigraphy of the Yumidong site in the Three Gorges region, central China

Yumidong (Corn Cave) is a newly discovered Paleolithic site in the Three Gorges region of central China. Numerous Paleolithic artifacts have been excavated from the sedimentary deposits of the cave in association with faunal remains attributed to the Middle-Late Pleistocene Ailuropoda - Stegodon fauna of southern China. To establish the chronology of the sedimentary sequence (>5 m thick), 14 C dating was applied to bone and charcoal samples (n = 6); the U-series method was used to date in situ precipitated speleothems (n = 12), transported speleothem samples (n = 6) and 18 subsamples of a fossil tooth; and the coupled ESR/U-series method was used to date fossil teeth (n = 6). The derived dates were combined using a hierarchical Bayesian approach to generate a unified chronostratigraphy for the Yumidong sequence. In our Bayesian analyses, the 14 C and coupled ESR/U-series dates were considered to provide direct age estimates for the target layers, while the U-series dates of the in situ precipitated speleothems and fossil tooth were used as minimum age constraints and those of the transported speleothem fragments as maximum age constraints. The Bayesian analyses provided robust time intervals for the archeological layers: L2-Upper (14–23 ka), L2-Lower (27–63 ka), L3 (106–171 ka), L4 (140–192 ka), L10 (157–229 ka), L11 (181–256 ka), and L12 (214–274 ka) with a probability of 95%, allowing the establishment of a ∼300 ka long geological and archeological history for the Yumidong site and placing it as a reference site for Paleolithic cultural evolution in the Three Gorges region from the late Middle Pleistocene to Late Pleistocene.

The Impact of Political Connections on Corporate Green Innovation: The Mediating Effect of Corporate Social Responsibility and the Moderating Effect of Environmental Public Opinion

The construction industry is the lifeblood of the national economy; thereby, to some extent, the green transformation of the construction industry is representative of the industrialization levels of modern construction, especially in China. Based on the panel data of A-share listed companies in China’s construction industry from 2014 to 2019, this work studies the influence mechanism of political connection on corporate green innovation by establishing a multiple regression model, analyzes the realization path of corporate social responsibility as an mediating variable on the impact of political connections on corporate green innovation, and reveals the role boundary of environmental public opinion as a moderating variable on the impact of political connections on corporate green innovation. The results show that political connection has a significant positive impact on corporate green innovation, and this impact exists in both state-owned and non-state-owned enterprises, as well as in the eastern region and the central and western regions of China. Moreover, corporate social responsibility plays a partial mediating role in the relationship between political connection and corporate green innovation, and serves as an effective value transfer intermediary, a benefit balance mechanism, and a risk avoidance method. Political connections urge enterprises to be more socially responsible, thus affecting green innovation. Additionally, environmental public opinion strengthens the positive impact of political connections on corporate green innovation, especially in non-state-owned enterprises and in the eastern regions with a higher degree of marketization. The research conclusions provide a new theoretical reference for promoting the transformation of green innovation and achieving high-quality development in the construction industry.

Investigating the eco-efficiency of China's textile industry based on a firm-level analysis

Industrial eco-efficiency has been an invaluable measurement for the relationship between production activities and environmental depletion, which is important to investigate when aiming for sustainable development. For the textile industry, however, limited rigorous studies have comprehensively evaluated the eco-efficiency from a firm-level perspective, and research on multi-level comparisons of the sub-sectors has also been lacking. Given the differences in environmental impacts due to the selection of various raw materials and unstandardized production processes in the textile industry, we focused on three sub-sectors, i.e., the cotton, chemical fibers textile sector (CCTS), the non-cotton textile sector (NCTS), and the printing and dyeing sector (PDS). By applying the slacks-based measure (SBM) model based on the principle of the data envelopment analysis (DEA), i.e., the SBM-DEA model, the eco-efficiency of China's textile industry was measured at the firm level from 2001 to 2011 using a large sample dataset. We then further analyzed the factors affecting eco-efficiency using the Tobit regression model. An empirical analysis showed an upward trend of eco-efficiency over time in the eastern, central, and western regions of China with great disparities for the three sub-sectors. Analyses of the typical provinces in the eastern region all showed increasing trends in eco-efficiency, with Shandong Province consistently performing the best. We also found that large-scale firms had the highest annual average eco-efficiency than that of small- and medium-scale firms. For the influencing factors on the eco-efficiency, our results indicated that the economic scale and export delivery value both had a significant positive correlation with the eco-efficiency, while foreign direct investment and environmental regulation were both significantly negative for the eco-efficiency in all three sub-sectors. These findings provide valuable insights into helping the textile industry address high-quality green development and sustainability.

Potential geographic distribution of relict plant Pteroceltis tatarinowii in China under climate change scenarios.

Pteroceltis tatarinowii (Pteroceltis: Ulmaceae) is a deciduous tree that has a cultivation history of more than 2000 years in China. As an excellent afforestation tree species and rare and endangered tertiary relic plant, P. tatarinowii has high ecological protection value. Due to the forest destruction caused by predatory logging and natural environmental factors, the population of P. tatarinowii in China has decreased significantly. In this study, the potential geographical distribution of P. tatarinowii in China under climate change was predicted using MaxEnt model and ArcGIS based on 223 effective distribution points of P. tatarinowii and 11 environmental variables. The results showed that: (1) the prediction accuracy of MaxEnt model was extremely high, and the areas under curve (AUC) value of the training data was 0.936; The area of the potential suitable habitat area of P. tatarinowii under current climate condition was 180.84×104 km2, and mainly located in the central and southeast regions of China. (2) The domain environmental variables affecting the potential geographical distribution of P. tatarinowii were min temperature of coldest month (12.1~22.7°C), isothermality (26.6~35.8), mean diurnal range 6.9~9.3°C and precipitation of wettest month (189.5 ~955.5 mm). (3) In 2050s and 2070s, compared with current (4.19×104 km2), the area of highly suitable habitat will increase by 0.2%-0.3% (RCP2.6) and 1.22%-3.84% (RCP8.5) respectively. while the poorly, moderately and total suitable habitats will decrease. The gravity center of P. tatarinowii showed a trend of migration to higher latitudes and northern regions in the future. These results will provide theoretical basis for cultivation management and resource protection of P. tatarinowii.

Spatio-Temporal Differentiation Characteristics and Urbanization Factors of Urban Household Carbon Emissions in China.

This paper explores the spatial relationship between urbanization and urban household carbon emissions at the prefectural level and above cities in China and uses Exploratory Spatial Data Analysis (ESDA) and Geographically Weighted Regression (GWR) to reveal the extent of the impact of urbanization on urban household carbon emissions and the spatial and temporal variation characteristics. The results show that: Overall carbon emissions of urban households in cities of China showed a decreasing trend during the study period, but there were significant differences in the carbon emissions of urban households in the four major regions. In terms of the spatial and temporal characteristics of urban household carbon emissions, the urban “head effect” of urban household carbon emissions is obvious. The high-high clustering of urban household carbon emissions is characterized by a huge triangular spatial distribution of “Beijing-Tianjin-Hebei, Chengdu-Chongqing, and Shanghai”. The level of urbanization in Chinese cities at the prefecture level and above shows a spatial pattern of decreasing levels of urbanization in the east, middle, and west. The four subsystems of urbanization are positively correlated with urban household carbon emissions in the same direction. The urbanization factors have a contributory effect on some cities’ carbon emissions of urban households, but there are significant regional differences in the impact of urbanization factors on urban household carbon emissions in the eastern, central, and western regions of China, as they are at different stages of rapid urbanization development.

Network approach reveals the spatiotemporal influence of traffic on air pollution under COVID-19.

Air pollution causes widespread environmental and health problems and severely hinders the quality of life of urban residents. Traffic is critical for human life, but its emissions are a major source of pollution, aggravating urban air pollution. However, the complex interaction between traffic emissions and air pollution in cities and regions has not yet been revealed. In particular, the spread of COVID-19 has led various cities and regions to implement different traffic restriction policies according to the local epidemic situation, which provides the possibility to explore the relationship between urban traffic and air pollution. Here, we explore the influence of traffic on air pollution by reconstructing a multi-layer complex network base on the traffic index and air quality index. We uncover that air quality in the Beijing–Tianjin–Hebei (BTH), Chengdu–Chongqing Economic Circle (CCS), and Central China (CC) regions is significantly influenced by the surrounding traffic conditions after the outbreak. Under different stages of the fight against the epidemic, the influence of traffic in some regions on air pollution reaches the maximum in stage 2 (also called Initial Progress in Containing the Virus). For the BTH and CC regions, the impact of traffic on air quality becomes bigger in the first two stages and then decreases, while for CC, a significant impact occurs in phase 3 among the other regions. For other regions in the country, however, the changes are not evident. Our presented network-based framework provides a new perspective in the field of transportation and environment and may be helpful in guiding the government to formulate air pollution mitigation and traffic restriction policies.

Research on Smart City Construction Management Evaluation System Based on Ternary Subject Theory

In this paper, the existing problems and optimization principles of the current smart city construction management evaluation indicator system in China are analyzed. The evaluation indicator system is selected for two rounds based on the membership and utility and constructed systematically. The distribution of weight of smart city construction management evaluation indicators in China is obtained based on empirical investigation of the government officials, experts, scholars and the public participating in the evaluation from different typical regions in eastern, central, and western China by means of surveys and questionnaire investigations. Finally, the existing problems of the evaluation indicator system are analyzed and the optimization path is proposed.

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.