Vehicle Emission Changes in China under Different Control Measures over Past Two Decades

Abstract

Vehicle emissions have become a significant source of air pollution in urban cities, especially in China. Mobile sources account for 45% of local fine particle emissions in the Chinese capital Beijing. The Beijing–Tianjin–Hebei (BTH) area, one of China’s most representative urban clusters, is suffering from severe air pollution. With the rapid growth of vehicle ownership in the past two decades, vehicle emissions in China have also undergone great changes under various management measures. The BTH region is also a place where mobile source emission management was carried out earlier. It is of important research value to understand the evolution trend of the vehicle ownerships in the BTH region and the actual effects of various management measures for the control of vehicle emissions. Due to the imperfect evaluation of the current vehicle emission limitation measures from 2000 to 2019, the vehicle emission inventory of the BTH region was established, and the major control measures in the BTH region were evaluated. Results showed that the vehicle ownership has been increasing year by year over the past 20 years, from 2.39 million in 2000 to 25.32 million in 2019, with an average annual growth rate of 13.24%. However, the pollutants discharged by motor vehicles showed a trend of first rising and then falling due to various measures except CO₂. The unsynchronized control measures have resulted in huge differences in vehicle growth trends and emissions among Beijing, Tianjin and Hebei. The emissions of carbon monoxide (CO), volatile organic compounds (VOCs), nitrogen oxides (NO_X), and particulate matter (PM₁₀) in Beijing showed a trend of increasing first and then decreasing. The changes in these pollutants in Tianjin were similar to those in Beijing, but there was a secondary increase for NO_X and PM₁₀ in the later period. The discharge of all pollutants in Hebei Province showed a growing trend except sulfur dioxide (SO₂). The major emission source of CO and VOCs in BTH was PCs, and the contribution rate of PCs to VOCs, reached 86.0–89.6% in 2019. Heavy-duty trucks (HDTs) and buses were the main sources of NO_X emissions, contributing 78.2–85.4% of NO_X in 2019. Eliminating high emission vehicles was the best control measure in the BTH Region, which had a good emission reduction effect on all pollutants. For sustainable development of the BTH region, it is suggested that Beijing, Tianjin and Hebei province implement vehicle control policies simultaneously and establish a joint management mechanism.

1. Introduction

With the rapid development of the economy and the continuous advancement of urbanization, the number of motor vehicles in China has been increasing year by year. The air pollution caused by vehicle exhaust is becoming more and more serious [1,2]. Previous research has demonstrated that vehicle emissions have become one of the most significant sources pollution in China, and are the primary cause of regional smog [3]. The statistics have shown that the number of vehicles in China had reached 348 million by the end of 2019. The national emissions of carbon monoxide (CO), hydrocarbon (HC), nitrogen oxides (NO_X), and particulate matter (PM) from vehicles were 6.94 million tons, 1.71 million tons, 6.22 million tons, and 0.069 million tons, respectively, in 2019. Among them, gasoline vehicles are the main source of CO and HC emissions, whose contributions exceed 80% and 70%, respectively. Diesel vehicles emit more than 80% NO_X and 90% PM [4]. In addition, NO_X and volatile organic compounds (VOCs) generated by vehicles are important precursors of ozone (O₃) [5,6,7]. They are crucial factors causing regional compound pollution. Vehicles have become a major contributor to the deterioration of the urban atmospheric environment [8]. However, vehicle emissions not only affect air quality. Since vehicles are mostly driven in densely populated areas, the emission height is close to people’s breathing height, which causes more serious health hazards to the human body [9,10,11]. After entering the human body through the respiratory system, CO will combine with the hemoglobin. It could aggravate tissue hypoxia and cause harm to the nervous system. VOCs have a stimulating effect on human respiratory organs, and some aromatic hydrocarbons contained in them will stimulate the central nervous system. NO is the main component of NO_X emitted from vehicle exhaust, which is toxic to the human body. PM not only causes asthma, pneumonia, respiratory infections and other diseases, but also increases the risk of cancer [12,13].

With the increasing ecological and health issues caused by mobile emissions, vehicle pollution has become a hot topic in the field of environmental research in recent years [14,15]. Many scholars have successfully carried out a large number of studies on the prevention of vehicle pollution, mainly focusing on vehicle emission factors and emission inventory [16,17,18,19]. The emission factor refers to the pollutant concentration emitted into the atmosphere per unit mileage of fuel consumption by the vehicle. It is the most basic parameter reflecting the equivalent emission of vehicle pollutants. The emission inventory refers to the total amount of pollutants discharged into the atmosphere by the vehicles in a certain time span and space area. It accurately reflects the spatial and temporal distribution characteristics of vehicle emissions. It is also the calculation basis for analyzing the emission control policies and their reduction benefits [20,21]. Wang et al. studied the vehicle emission trends in Chinese megacities (Beijing, Shanghai and Guangzhou) from 1995 to 2005 based on vehicle emission inventories [22]. Based on the MOBILE-China model, Wu et al. established a vehicle emission inventory for Beijing from 1995 to 2009. Based on the inventory, they conducted a detailed assessment of the emission control strategies and policies for seven categories since the mid-1990s [23]. In terms of urban agglomeration research, Wu et al. established the inventories of light vehicle energy consumption and CO₂ emissions in three developed areas, including the Beijing–Tianjin–Hebei (BTH) region, the Yangtze River Delta (YRD) region and the Pearl River Delta (PRD) region. In addition, they evaluated the emission reduction benefits of the electrification of light vehicle markets [24]. Further, based on emissions inventory data, vehicle pollution reduction and development trends have become a hotspot in the field of mobile source pollution [25,26]. These studies not only contribute to a comprehensive understanding of vehicle exhaust pollution, but also provide support for government management decisions. The Beijing–Tianjin–Hebei (BTH) region was the largest and most dynamic region of the economy in northern China, with serious air pollution and the highest pollution emission intensity in the country. The BTH region is characterized by a large number of vehicles and a high population density. Due the early start of motor vehicle management, BTH is a representative area in which to study the evolution of motor vehicle emissions from various measures. A great deal of research has been carried out in this area. Lu et al. evaluated the emission reduction effects of two measures, including the early elimination of ‘yellow-label’ cars (they refer to the gasoline cars whose emission standard is lower than China I and diesel cars whose emission standard is lower than China III) and the prohibition of ‘yellow-label’ cars. In addition, they found the elimination of ‘yellow-label’ cars brought more obvious emission reduction benefits [27]. Xie et al. analyzed the environmental and economic benefits of fuel quality upgrading in the BTH region. They stated that the improvement of fuel quality could effectively reduce vehicle emissions, especially for VOCs and PM [28]. Yang et al. set thirteen scenarios to evaluate the effect of vehicle pollution prevention and control policies in the BTH region. They claimed that eliminating low-emission vehicles had a better emission reduction effect, which could reduce CO and HC emissions by nearly 50% [29]. Guo et al. (2016) analyzed five control scenarios and proved that upgrading emission standards could significantly reduce pollutant emissions. In addition, the removal of high-emission vehicles in the short term will effectively reduce emissions [30]. However, these studies assess either the mitigation effects of a single policy or the mitigation effects of a preset scenario. It was very important to evaluate the emission reduction effect of various implemented policies for selecting the optimal policies and formulating future policies. However, few studies have conducted a comprehensive assessment of the evolution of motor vehicles and multiple policies in a region. Additionally, few studies have focused on the changes in vehicle ownership, which is the most direct reflection of the management effect of various measures. It is of great significance to comprehensively analyze the changing trend of vehicles in China under various policies.

The Calculate Emissions from Road Transport (COPERT) model was introduced by the European Environment Agency (EEA) in 1989. The model is based on the energy conservation relationship between fuel consumption and driving conditions, and the average speed is used to characterize the driving characteristics of motor vehicles [31]. This model is suitable for vehicle emission inventory research [32]. The COPERT model has been widely accepted and applied in Europe, Australia and China [33]. Since China’s emission standards are formulated with reference to European standards, the COPERT model could be better applied to study the emission of vehicles emissions in China. Based on this model, this study established the vehicle emission inventory in the BTH region from 2000 to 2019. The evolution trend of vehicle ownership during the study period is discussed in detail. According to the vehicle emission management policies in the BTH region in recent years, the vehicle control measures from 2000 to 2019 are classified into three categories, including eliminating high-emission vehicles, improving emission standards, and regulating the total number of vehicles. The vehicle emission reduction effect under each control measure is evaluated. Our study aims to illustrate the changing trend of vehicle ownerships and evaluate the vehicle emission reduction measures over the last couple of decades, that hope to provide a reference for future policy development.

2. Materials and Methods

2.1. Calculation of Emission Inventory

The emission inventory is the most visual indication of the emissions of vehicles in a certain area over a period of time. Vehicle kilometers travelled (VKT) is the average distance traveled by a certain type of vehicle throughout the year. It provides basic data for urban traffic planning, vehicle exhaust emission prediction, energy consumption prediction and other traffic-related fields. It is the necessary data for calculating vehicle pollutant emission lists and pollution sharing rate of all types of vehicles, and it is also the necessary basic parameter of a variety of vehicle exhaust emission factor prediction models. The calculation of vehicle emission inventory based on the average annual mileage is one of the most widely used calculation methods in the world. It is more suitable for large-scale emission inventory calculation and has strong applicability [34]. The formula used in this study for calculating motor vehicle emissions was as follows:

$$Q_{m,n}={{\displaystyle \sum }}_i{{\displaystyle \sum }}_j{P}_{m,i,j}\times VK{T}_{m,i}\times E{F}_{i,j,n}$$

(1)

In which:

m was the study region;

n was the pollutant type (CO, NO_X, PM_10, SO_2, VOCs and CO₂);

i was the type of motor vehicle, including light-duty passenger car (PC), light-duty vehicles (LDVs), heavy-buses (Buses) and heavy-duty truck (HDTs);

j was the emission standard for various types of motor vehicles from 2000 to 2019 (China 0, China I, China II, China III, China IV and China V);

$Q_{m,n}$ was the emission of type n in the m region, tons;

$P_{m,i,j}$ was the number of type i in the m region under the j emission standards;

$VK{T}_{m,i}$ was the annual average mileage of type i in the m region, km;

$E{F}_{i,j,n}$ was the emission factor of type n, i under the j emission standard, g·km⁻¹ per car.

2.2. Emission Factors

In this study, the COPERT V model, which, based on reliable experimental data, was used to simulate the emission factors of motor vehicles in the BTH region. The input parameters of the COPERT V model mainly include vehicle type, average driving speed and weather data. The average speed data of vehicles came from a previous study [35]. The fuel parameters, including sulfur content and vapor pressure, are derived from national and local vehicle fuel standards. The meteorological data including relative humidity and monthly temperatures were obtained from the National Data Center for Meteorological Sciences (http://data.cma.cn/, last received: 15 November 2022). The emission factor data are from the model output results.

2.3. Vehicle Ownership

The data of vehicle population came from National Statistics (http://www.stats.gov.cn/, last received: 15 November 2022) and the China Automotive Industry Yearbook [36]. The population of various types of vehicles in each year under different emission standards was calculated with the reference of previous method, the specific calculation method has been detailed in a previous study [37]. The schedule for the implementation of different emission standards for various vehicles types are shown in

Table 1. Schedule of vehicle emission standards of different vehicle types in the study area.

Region	Type of Vehicle	China I	China II	China III	China IV	China V
Beijing	PC, LDV	1999	2003	2006	2008	2013
	HDT, Bus	2000	2003	2006	2013	2015
Tianjin	PC, LDV	2000	2005	2008	2011	2015
	HDT, Bus	2001	2004	2008	2013	2017
Hebei	PC, LDV	2000	2005	2008	2011	2016
	HDT, Bus	2001	2004	2008	2013	2017

.

2.4. VKT Evolution Trend

The VKT was an important parameter to describe the activity level of motor vehicles. Due to the differences in economic level, industrial layout, and policy changes in each region, there may be some difference in the VKT of vehicle of the same type in different regions and years. In this study, the VKT of different vehicles in the BTH region from 2000 to 2019 was obtained from statistical data. Figure 1 shows the VKT changes of different vehicles types in Beijing, Tianjin, and Hebei during the study period.

3. Results and Discussions

3.1. Vehicle Ownership and Emission Inventory

3.1.1. Evolution Trend of Vehicle Ownership

The BTH region has seen great changes in vehicle ownership in the past two decades. Figure 2 shows the variation tendency of vehicle ownership for each province from 2000 to 2019. The vehicle ownership in this region has increased year by year over the past 20 years, from 2.39 million in 2000 to 25.32 million in 2019, with an average annual growth rate of 13.24%. The number of vehicles in Beijing, Tianjin, and Hebei provinces increased from 1.03, 0.46 and 0.90 million to 5.84, 3.07 and 16.41 million, respectively, with an average annual growth rate of 9.58%, 10.51% and 16.54%, respectively.

The growth of vehicles in Beijing, Tianjin, and Hebei provinces showed different trends. Since 2011, Beijing had allocated vehicles through a lottery system. The growth rate of vehicle population dropped sharply from 22.38% in 2010 to 4.61%. In addition, it has been maintained at a low growth level. Then since 2014, Tianjin had allocated vehicles by lottery, and the growth rate of motor vehicle ownership dropped sharply from 18.41% in 2013 to 4.84%. However, Hebei Province had not implemented the management measures for the total amount of PCs, and the growth rate of vehicle ownership has always been maintained at a high level. Regarding the vehicle type distribution, the fraction of PCs has increased the most significantly. The fraction of PCs in Beijing, Tianjin, and Hebei province increased from 71.49%, 47.76% and 44.24% to 89.47%, 87.10% and 86.60%, respectively, from 2000 to 2019. The fraction of LDVs, HDTs, and Buses all showed a downward trend. At the end of 2019, the proportion of LDVs in BTH was 6.74%, 1.40%, and 2.39%, the proportion of HDTs was 8.66%, 2.96%, and 1.28%, and Buses accounted for 8.20%, 4.66%, and 0.55%, respectively. The rapid increase in the share of PCs resulted in the decline of the fraction of LDVs, HDTs and Buses.

3.1.2. Emission Inventories

Vehicle emissions of different pollutants in the BTH region from 2000 to 2019 are shown in Figure 3. On the whole, the variation tendency of vehicle emissions in Beijing and Tianjin were consistent, while there were obvious differences in Hebei Province. The emissions of CO, VOCs, NO_X, and PM₁₀ in Beijing all showed a trend of first rising and then falling from 2000 to 2019. This downward trend was mainly attributed to the measures that controlled the amounts of PCs and the scrapping of ‘yellow-label’ vehicles. The NO_X and PM₁₀ emissions grew at an average rate of 10.1% and 11.8% per year in the first five years, and reached to a peak of 15.3 × 10⁴ t and 1.0 × 10⁴ t in 2012. Then they gradually declined to 12.8 × 10⁴ t and 0.7 × 10⁴ t at the end of 2019. The sharp fall in their emissions is thought to be largely due to the regulation of diesel vans. In addition, the decline of NO_X in Beijing was significantly smaller than PM₁₀, indicating that the existing control measures had an inferior effect on NO_X.

In Tianjin, both CO and VOCs emissions showed a trend of increasing first and then decreasing. Their emissions increased dramatically from 2000 to 2013. Then the emissions reduced to 6.9 × 10⁴ and 2.0 × 10⁴ t in 2019, with an average annual decline rate of 9.8% and 3.6%. For NO_X and PM₁₀, emissions rose with fluctuation from 5.6 × 10⁴ t and 0.35 × 10⁴ t in 2000 to 7.3 × 10⁴ t and 0.42 × 10⁴ t in 2019. The growth of NO_X and PM₁₀ emissions in the past five years were mainly related to the increase in the amount of LDVs and HDTs. The emissions of CO, VOCs, NO_X, and PM₁₀ in Hebei province showed a fluctuating upward trend. A large increase in vehicle ownership is the main reason for this phenomenon [34]. However, the average annual growth rate of pollutants in Hebei were lower than the growth rate of vehicle ownership, indicating that various emission reduction measures had achieved certain effects. However, they are not enough to offset the increase in pollution caused by the huge increase in car ownership. The SO₂ emissions from Beijing, Tianjin, and Hebei fluctuated during 2000–2019, with five sudden drops in 2003, 2007, 2011, 2013, and 2017. This is mainly because SO₂ emissions were closely related to sulfur content in fuels, which continuously decreases as fuel quality improves. Different from other pollutants, CO₂ emissions in Beijing, Tianjin, and Hebei sustained high growth at an average annual rate of 7.10%, 7.56% and 14.20% between 2000 and 2019. As the engine burns, nearly 99% of the carbon in the fuel is converted to CO₂. The annual increase in vehicle ownership in each province is responsible for the continuous growth of CO₂ emissions. Although vehicle emission standards continue to improve, the vehicle fuel economy improvements have been relatively limited. Therefore, coping with the rapid growth of vehicle CO₂ emissions will be an important challenge for China to achieve carbon emission reduction goals.

3.1.3. Contributions of Different Vehicles

To further understand the emissions of different types of motor vehicles, we quantified the contribution of different vehicle types to various pollutants. For CO and VOCs, PCs was the major emission source from 2000 to 2019. In 2000, the contribution of PCs to VOCs were 64.7%, 51.8%, and 58.2%. In recent years, the number of PCs has increased rapidly, resulting in a continuous increase in its contribution to various pollutants. In 2019, its contribution to VOCs had reached 89.6%, 88.6% and 86.0%. VOCs emitted from vehicle exhaust mainly come from vehicle exhaust, fuel volatilization and liquid fuel. Many previous studies based on on-board tests and tunnel tests have shown that, compared with gasoline vehicles, diesel vehicles emit about 10 to 100 times lower VOCs concentrations [38,39]. For VOCs contribution, gasoline cars are much higher than diesel cars. In addition, diesel vehicles emit far less VOCs than other fuel models. Meanwhile, the contribution of PCs to CO emissions in Tianjin and Hebei also increased significantly in 2019, accounting for 73.6% and 65.1%, respectively.

For NO_X, Buses and HDTs are the main emitters, and their contribution always remained at a stable level from 2000 to 2019. In 2000, their contribution to NO_X in Beijing, Tianjin, and Hebei province were 82.3%, 73.3% and 85.4%. Then in 2019, they were 80.1%, 78.2% and 85.4%. In terms of regional distribution, the Buses contribution in Beijing was higher than HDTs, while the reverse is true in Tianjin and Hebei. This situation is partly influenced by the functional orientation of the cities. For PM₁₀, CO₂ and SO₂, HDTs are the primary contributors. From 2000 to 2019, the contributions of Buses and HDTs to PM₁₀, CO₂, and SO₂ showed an overall downward trend. In the case of SO_2, their contribution reduced from 69.7%, 67.5% and 77.9% to 39.3%,42.8% and 56.9%. From the perspective of regional distribution, the contribution of Buses in Beijing is higher than that of HDTs, while in Tianjin and Hebei it is the opposite. In addition, compared with 2000, the contribution of PCs to SO₂ increased by 470.0%, 151.4% and 148.4% in 2019.

3.2. Evaluation of Emission Reduction Effects

Based on previous studies, the measures for the prevention and control of vehicle emissions issued in the BTH region between 2010 and 2019 were classified, including eliminating high-emission vehicles, tightening emission standards, and regulating the total number of vehicles. This study takes the actual emissions of vehicles in the BTH region from 2000 to 2019 as the “benchmark emissions”. Then the emissions under the reduction measures are called “controlled emissions”. The vehicle ownership under “control emissions” is the same as the corresponding “benchmark emissions”, and the emission reduction effect of each measure is evaluated by comparing the “benchmark emissions” and “control emissions”.

Regarding the elimination of high-emission vehicles, Beijing published a policy in 2009 to provide subsidies for ‘yellow-label’ vehicles, which refers to gasoline cars with emission standards below China I and diesel cars below China III. Tianjin issued relevant policies in 2012 and Hebei in 2013. In the following years, the BTH region accelerated the elimination of high-emission vehicles through subsidies and restrictions on traffic. This study evaluated the emission reduction effect based on the number of eliminated vehicles caused by the implementation of the policy. The total number of vehicles with a certain emission standard for a certain type at the end of the year minus the total number of vehicles with the emission standard for that type at the end of the previous year, and a natural elimination rate of 5% were deducted as the elimination of high-emission vehicles caused by the implementation of the policy.

In terms of improving emission standards and fuel quality, the policies in the BTH region were synchronized with the state and even implemented earlier than the state. To maintain the comparability with the policy of eliminating high-emission vehicles, 2009 is selected as the base year. In this year, both diesel and gasoline complied with China II standard. The LDVs in Beijing were compliant with China IV; standards, and HDV were compliant with China III standards. All new vehicles after 2009 implemented the 2009 emission standards and fuel quality, while other measures remain unchanged. The difference between the actual emissions and the emission reduction effect of this measure was taken as the reduction effect of this measure.

Regarding the regulation and control of PCs, Beijing was the first city to implement such policies. Since 2011, PCs registration would be allocated by a lottery system. Then in 2014, the same measure was implemented in Tianjin. Relevant policies have not yet been implemented in Hebei Province.

In general, the prediction methods of vehicle ownership include regression analysis, trend extrapolation, artificial neural network, and logical analysis methods. Based on the actual vehicle growth trend in this region, the trend extrapolation method is used in this study. The average increase in the PC ownerships of this region in the five years before the implementation of the policy is used as the theoretical increase. Then the difference between the actual increase and the theoretical increase is used to evaluate the emission reduction effect of the policy.

3.2.1. Eliminating High-Emission Vehicles

Eliminating high-emission vehicles (referring to gasoline cars with emission standards below China I and diesel cars below China III) was an important measure to reduce vehicle emissions. Figure 4a shows the reduction amount of various pollutants in different regions since the implementation of this policy, including CO, VOC_S, NO_X, PM₁₀ and SO₂. The CO₂ emission reduction is depicted in Figure 4b. The emission reductions under this measure had increased year by year, mainly due to the excellent emission reduction effect on CO, VOCs, NO_X, and CO₂. The emission reductions are higher especially in Beijing and Tianjin, which is mainly attributed to the elimination of more diesel trucks in the both places. In 2019, the emission reductions of CO, VOCs, NO_X, PM₁₀, SO₂, and CO₂ were 18.0 × 10⁴ t, 4.6 × 10⁴ t, 13.4 × 10⁴ t, 0.9 × 10⁴ t, 0.1 × 10⁴ t, 2.0 × 10⁷ t in Beijing, and 7.5 × 10⁴ t, 1.7 × 10⁴ t, 4.9 × 10⁴ t, 0.3 × 10⁴ t, 0.0 × 10⁴ t, 0.7 × 10⁷ t in Tianjin. In Hebei province, they were 16.3 × 10⁴ t, 3.9 × 10⁴ t, 1.7 × 10⁴ t, 1.7 × 10⁴ t, 0.5 × 10⁴ t, 4.3 × 10⁷ t, respectively.

3.2.2. Improving Emission and Fuel Standards

In 2009, both diesel and gasoline in the BTH region were in line with the China II standard. The LDVs adopted the China IV standard and HDTs adopted the China III standard in Beijing. Then the LDVs and HDTs in Tianjin and Hebei Province were implemented at the China III standard. Assuming that emission standards and fuel quality were the same as in 2009 and other conditions were consistent with baseline emissions under this measure. The actual emission reductions are shown in Figure 5. In 2019, the emission reductions of CO, VOCs, NO_X, PM₁₀, and SO₂ were 0.7 × 10⁴ t, 0.5 × 10⁴ t, 2.5 × 10⁴ t, 0.2 × 10⁴ t and 1.0 × 10⁴ t in Beijing, and 7.9 × 10⁴ t, 0.6 × 10⁴ t, 2.2 × 10⁴ t, 0.1 × 10⁴ t and 0.5 × 10⁴ t in Tianjin. The improvement of emission standards has a certain emission reduction effect on all pollutants, especially CO. CO is the product of incomplete combustion of automobile gasoline, which is closely related to the combustion efficiency of fuel. Increasingly stringent emissions standards for vehicles are placing greater demands on the combustion efficiency of vehicles, forcing carmakers to increase their engine combustion efficiency. The emission of SO₂ depends directly on the amount of S in the fuel. Rising fuel standards have steadily reduced the sulfur content of fuel. Therefore, the measures to raise fuel standards have achieved a certain clean burning effect.

3.2.3. Regulating and Controlling of PCs

The total volume control policy was an important method to curb the rapid growth of vehicles. Beijing and Tianjin have both issued policies to limit the purchase of PCs. In December 2010, Beijing began to implement car purchase restrictions. Tianjin issued a purchase restriction order for automobiles in December 2013. Therefore, the growth rate of PCs in Beijing and Tianjin decreased significantly after the issuance of the car purchase restriction order. On the contrary, due to the lack of relevant policies, the automobile growth rate of Hebei Province is significantly higher than that of Beijing and Tianjin. Furthermore, the growth rate of vehicle ownerships in Hebei Province has increased to a certain extent after Beijing and Tianjin issued relevant policies. The emission reduction effect under this policy was shown in Figure 6. This measure has a clear effect on various pollutant, especially for CO, VOCs and CO₂. In 2019, the emission reductions of CO, VOCs, NO_X, PM₁₀ and CO₂ were 4.1 × 10⁴ t, 1.6 × 10⁴ t, 0.3 × 10⁴ t, 0.1 × 10⁴ t and 1.0 × 10⁷ t in Beijing, and 1.9 × 10⁴ t, 0.8 × 10⁴ t, 0.1 × 10⁴ t, 0.1 × 10⁴ t and 0.4 × 10⁷ t in Tianjin respectively. The implementation of the control measures for PCs has achieved a great restriction effect on the vehicle emissions. However, the unsynchronized implementation time leads to inconsistent emission reduction effect.

3.2.4. Comparison of Different Control Measures

The above study results show that various vehicle emission reduction policies have achieved certain emission reduction effects in the BTH region, but there are certain differences of each measure. The emission reduction contributions brought by different policies in 2019 are explained in detail in Figure 7. The elimination of high-emission vehicles was the best measure, and CO, VOCs, NO_X, PM₁₀, and SO₂ had been achieved a marked decrease. The second is standard upgrade policy, which had significant effects on SO₂, NO_X, and PM₁₀ emission reduction. Meanwhile, the standard upgrade measure had achieved an obvious effect in reducing CO emission in the Tianjin and Hebei Provinces, which exceeded the reductions effects of eliminated high-emission vehicles. It was mainly related to the implementation of China III standards for light-duty vehicles in 2009. Finally, regulating and controlling PCs had a significant effect on CO and VOCs emission reduction. The above results were consistent with previous studies [30,40].

4. Conclusions

In this study, the vehicle emission inventory in the BTH region from 2000 to 2019 was established based on COPERT model. The evolution trends of vehicle ownership during this period was presented. The emission characteristics of each pollutant and the contribution of each vehicle type are also described in detail. The major motor vehicle management policies in the BTH region during the past 20 years have been comprehensively evaluated.

Over the past 20 years, the vehicle ownership in the BTH region increased gradually, especially for PCs. However, the fleet structure has changed significantly. The proportion of PCs has increased significantly, while the others have decreased. After the implementation of the policy that regulated and controlled PCs, the growth rate of the vehicle population in Beijing and Tianjin dropped significantly. There are some regional differences in pollutant emission characteristics in the BTH region. In Beijing, the emission of CO, VOCs, NO_X, and PM₁₀ show a trend of rising first and then falling. In Tianjin, CO and VOCs showed the same trend, while NO_X and PM₁₀ showed a secondary rise scenario. The emissions of CO₂ throughout the region have been showing an increasing trend. Except for SO₂, other pollutants in Hebei increased gradually during the study period. PCs were the major contributors to CO and VOCs emissions. HDTs and Buses were the main sources of NO_X emissions. LDVs and HDTs contributed more PM₁₀, CO₂, and SO₂. Therefore, emission reduction measures for different pollutants should be formulated according to major contributing vehicle types. The best vehicle exhaust control measure in the BTH region was to eliminate high-emission vehicles, which had good emission reduction effects on various pollutants. The following is the standard upgrade policy, which had obvious emission reduction effect on SO₂, NO_X, and PM₁₀. The implementation of total quantity control for PCs caused an obvious reduction CO and VOCs emissions. In general, various emission reduction policies have achieved great emission reduction benefits. However, serious vehicle pollution still exists in the BTH region. Vehicle emission management in Hebei Province lags behind that of Beijing and Tianjin. The asynchronism of policy implementation makes the effect of vehicle emission reduction in different provinces uneven. Although vehicle emission reduction has achieved good effects under the implementation of the policy, mobile sources are still an important emission source in this region. For the sustainable development of the BTH region, Beijing, Tianjin, and Hebei should establish a joint management system for vehicles. The joint reduction of mobile source pollution will be achieved through the realization of policy synchronization. In the next step, we will assess the trend in vehicle ownerships and emissions in the BTH region over the next two decades under a joint management scenario. The benefits of motor vehicle management measures on air quality will be evaluated.