Assessing energy consumption, CO2 and pollutant emissions and health benefits from China's transport sector through 2050

Energy Policy 116 (2018) 382–396

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Assessing energy consumption, CO2 and pollutant emissions and health benefits from China's transport sector through 2050 ⁎

T

⁎

Lei Liua,b, Ke Wanga,b, , Shanshan Wanga,b, Ruiqin Zhanga,b, , Xiaoyan Tangb a b

College of Chemistry and Molecular Engineering, Zhengzhou University, Zhengzhou 450001, China Research Institute of Environmental Science, Zhengzhou University, Zhengzhou 450001, China

A R T I C L E I N F O

A B S T R A C T

Keywords: Energy demand Emission reduction LEAP model Scenario analysis Intake fraction method Health benefits

With the accelerating process of urbanization, energy consumption and emissions of the transport sector in China have increased rapidly. In this paper, we employed the LEAP (Long-range Energy Alternatives Planning system) model to estimate the energy consumption, CO2 (carbon dioxide) and air pollutant emissions of the transport sector between 2010 and 2050 under four scenarios: Business as Usual (BAU), Energy Efficiency Improvement (EEI), Transport Mode Optimization (TMO), and Comprehensive Policy (CP). Furthermore, the intake fraction method was adopted to assess the health benefits of reducing pollutant emissions. The results showed that energy consumption will reach 509–1284 Mtce under the different scenarios by 2050. The emissions of CO2, carbon monoxide (CO), sulfur dioxide (SO2), nitrogen oxide (NOX) and particulate matter (PM10 and PM2.5) will be 2601, 173, 3.4, 24.0, 0.94 and 0.78 Mt, respectively, under the BAU scenario in 2050. Regarding health benefits, economic losses caused by mortality will be reduced by 47, 40 and 72 billion USD in 2050 under the EEI, TMO and CP scenarios, respectively, compared to those under the BAU scenario. Among the health outcomes associated with PM10, acute bronchitis exhibits the worst outcome. Considering health impacts, policy implications are suggested to reduce CO2 and pollutant emissions.

1. Introduction According to the International Energy Agency, energy consumption by the global transport sector accounts for approximately 19% of the overall consumption and the associated CO2 emissions account for 23% of the overall emissions (IEA, 2012). In developed countries, the energy consumption shares of the transport sector are approximately 30% (Han et al., 2012). However, China's energy consumption from the transport sector accounted for 8% of the total energy consumption in 2010 (CSB, 2012), which is far below the level of developed countries. Based on data from the National Bureau of Statistics, the CO2 emissions by the transport sector accounted for an estimated 6% of the overall emissions in 2010 (Ji, 2012). Additionally, with the improvement of people's living standards, energy consumption by the transport sector will increase dramatically. In many areas, the environmental problems and health effects caused by the transport sector have become increasingly serious. Air pollutants from the transport sector, including carbon monoxide (CO), sulfur dioxide (SO2), nitrogen oxide (NOX) and particulate matter (PM10 and PM2.5), play a significant role in worsening air quality and pose a

serious threat to public health (Huang and Guo, 2014). Abundant epidemiology studies have confirmed that the increase of respiratory diseases and air pollution are closely related (Tao et al., 2014). Such pollution can lead to various respiratory disorders, such as chronic bronchitis, acute bronchitis and asthma attack. Moreover, air pollutants not only have significant impacts on the human respiratory system but also have a certain degree of impact on the cardiovascular system and nervous system, and more importantly, the traffic emissions of PM may cause cell mutations and increase the incidence of cancer (de Kok et al., 2006). The Chinese government has implemented a series of policies and plans to mitigate air pollutant emissions, including those from the transport sector. The 12th Five-Year Development Plan for Transport clearly proposed the sustainable development of integrated transport, road transport, waterway transport and civil aviation. As a result, the Ministry of Transport required that the city should vigorously develop public transport. Subsequently, the Ministry of Transport formulated the Action Plan for Climate Change in the Transport Sector, improving the system and setting standards for energy conservation and environmental protection including introducing more than twenty green

⁎ Correspondence to: College of Chemistry and Molecular Engineering, Research Institute of Environmental Science, Zhengzhou University, No.100 Science Avenue, Zhengzhou, Henan 450001, China. E-mail addresses: [email protected] (L. Liu), [email protected] (K. Wang), [email protected] (R. Zhang).

https://doi.org/10.1016/j.enpol.2018.02.019 Received 4 September 2017; Received in revised form 10 February 2018; Accepted 12 February 2018 0301-4215/ © 2018 Elsevier Ltd. All rights reserved.

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including some uncertain meteorological parameters. Therefore, we adopted a simplified approach to calculate health risk without considering meteorological conditions. In fact, the intake fraction (IF) method, defined as the mass fraction of pollutant inhaled by a population divided by the total mass of pollutants emitted (Bennett et al., 2002), has been commonly used for evaluating health benefits (Marshall et al., 2003, 2005; Wang et al., 2006, 2016; Fang et al., 2012; Zhang et al., 2015). Hence, the IF method was employed in this work to estimate the health effects of China's transport sector. In this study, we focused on the entire transport sector using the LEAP model to build a complex yet easily understandable dendritic structure of the transport sector and forecast the energy consumption, CO2 emissions and air pollutant emissions from 2010 to 2050. In the China - U.S. Joint Announcement on Climate Change in 2014, the Chinese government pledged to reach peak carbon emissions by 2030; thus, we sought to determine when the transport sector will achieve its carbon emissions peak. In addition, the emissions from the transport sector will certainly have a significant impact on air quality. Therefore, based on the air pollutant emissions, health impacts are estimated using the IF method in this study. The paper is organized as described below. After the introduction section, the research methodology is presented in Section 2. The data sources and scenario design are described in Section 3, and the research results and a discussion are presented in Section 4. The uncertainty and sensitivity analyses are discussed in Section 5, and the conclusions and implications of the study are presented in Section 6.

traffic standards and norms. In 2013, the Ministry of Transport identified 26 cities as green and low-carbon traffic pilot cities. The use of large-scale new energy buses and multi-field passenger cars, online monitoring of energy consumption, intelligent management of urban passenger vehicle coverage, and other measures to ease traffic pressure can improve the ecological environment, enhance air quality and promote the coordinated sustainable development of the transport sector. More importantly, the Air Pollution Prevention and Control Law that was revised in 2015 has formulated measures for the environmental management of motor vehicles, non-road mobile machinery and ships as well as fuel management measure. The effect of the implementation of the national green transport policy on air pollutants has been evaluated (He and Ou, 2016; Qiu and He, 2017a, 2017b). Previous findings indicate that Chinese transport policies are effective for reducing air pollutants emissions in the transport sector. Nonetheless, there is a necessity for further reduction of emissions from the transport sector to mitigate related health problems. Previous studies related to energy consumption and pollutant emissions by the transport sector have primarily centered on three topics: (1) predicting only the emissions of greenhouse gases (GHGs) (Saboori et al., 2014; Hao et al., 2015a, 2015b; Yin et al., 2015); (2) estimating the emissions of both GHGs and air pollutants (Takeshita, 2012; Chavez-Baeza and Sheinbaum-Pardo, 2014; Dhar and Shukla, 2015); and (3) evaluating pollutant emissions via health assessments (Li and Crawford-brown, 2011; Mena-Carrasco et al., 2012; Tobollik et al., 2016). In terms of CO2 emissions from the transport sector, certain studies in China only focused on current emission estimates and evaluated the impact factors of carbon emissions (Xu and Lin, 2015). Most studies focused on forecasting GHG emissions have used a bottom-up approach (Wang et al., 2007a; He et al., 2013; Zheng et al., 2015), cointegration method (Lin and Xie, 2014), or system dynamics approach (Liu et al., 2015). Other studies have evaluated the effects of fuel efficiency improvements and associated costs (Wang et al., 2007a) or urban development strategies and patterns (He et al., 2013) on CO2 emissions and sought to determine methods of constraining national emissions from a provincial-level perspective (Zheng et al., 2015). For estimating CO2 and air pollutant emissions in the transport sector, investigators have used different models (i.e., the LEAP (Long-range Energy Alternatives Planning system) model, International Vehicle Emission model and hybrid energy-economy model) to calculate air pollutant emissions by dividing the transport sector into different vehicle types from a bottom-up perspective (e.g., Peng et al., 2015; Zhang et al., 2013; Mao et al., 2012). No studies combining CO2 and air pollutant emissions and health assessments have yet been performed in China, although some studies have focused on cities (Ren et al., 2016; Xue et al., 2015); however, to the best of our knowledge, only one study has covered all of China (He and Qiu, 2016). Unfortunately, only the passenger transport sector and not the freight transport sector was covered in the latter study; thus a comprehensive analysis of the transport sector in China is lacking. In general, most of the above researchers focused on emissions reductions from part of the transport sector (e.g., freight transport, intercity passenger transport or urban passenger transport) but ignored the comprehensive effect of the entire transport sector. Thus, studies dealing with the overall emissions for both CO2 and air pollutants are almost non-existent for China's entire transport sector. As previously mentioned, studies on the health effects of the transport sector have evaluated only a particular city or region. Usually, pollutant concentrations based on an air quality model are used to calculate health risk (Hao et al., 2007; Mena-Carrasco et al., 2012; Hasanbeigi et al., 2013; Sharma and Patil, 2016). The modeling component is an extremely complicated process. To simplify the modeling, some researchers have employed a fixed box model whereby the study region is represented by a parallelepiped with uniform pollutant dispersion to calculate the concentration (Chen and He, 2014; Yang and He, 2016; He et al., 2017). The fixed box model has some constraints,

2. Methodology 2.1. LEAP model To analyze and forecast energy consumption and its related emissions under different scenarios for the transport sector of China, the LEAP model was selected. This model is an energy-planning system developed by the Stockholm Environment Institute and the University of Boston, and it is widely used to analyze energy policy and assess climate change mitigation (LEAP, 2008). LEAP contains the Technology and Environmental Database, which describes the characteristics of various energy technologies and their impact on the environment, thus providing extensive information for users. The major advantage of the LEAP model is its more flexible model structure and data framework. Users can decompose a research object to different degrees from the bottom-up and can also adjust the model based on the obtained data. Based on different policy and technology options, researchers design energy consumption patterns under different development scenarios to predict the energy consumption and environmental impacts of different sectors. The LEAP model has been widely used in the power plants (Cai et al., 2007; Mcpherson and Karney, 2014), the iron and steel industry (Wang et al., 2007a; Wang et al., 2007b), the cement industry (Ke et al., 2012), the transport sector (He and Chen, 2013; He et al., 2016), and for multi-sectoral energy supply and demand (Cai et al., 2008; Amirnekooei et al., 2012; Huang et al., 2011; Yu et al., 2015). Generally, the LEAP framework is disaggregated in a bottom-up tree structure including four activity levels: sector, sub-sector, end use, and devices. According to the classification of national statistical systems, this study divided the transport sector into three parts: intercity passenger transport, freight transport and urban passenger transport. The intercity passenger and freight transport parts were further subdivided into railway, highway, waterway and civil aviation sub-sectors. The urban passenger sector was divided into public transport and private transport, and each sector was further divided to different traffic types (e.g., different fuels and different vehicles). The specific sector classifications are shown in Fig. 1. Therefore, the present study includes 3 major sectors, 10 sub-sectors and 28 traffic types. Compared to other studies, the bottom line of our application is that we fully take 383

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Sector

Sub-sector

Traffic type Diesel vehicles

Highway Gasoline vehicles Diesel locomotive Railway Electric locomotive Freight transport

Inland river Waterway Ocean Civil Aviation

Aircraft

Diesel vehicles Highway Gasoline vehicles Diesel locomotive Railway Electric locomotive Transport

Intercity passenger transport

Waterway

Inland river

Civil Aviation

Aircraft

Bus_Gasoline Bus_Diesel Bus_LPG Bus_CNG Bus_Electricity Bus_Hybrid- power Subway

Public transport

Taxi_Gasoline Taxi_CNG

Urban passenger transport

Taxi_LPG Taxi_Electricity Private car_Gasoline Private car_Diesel

Private transport

Private car_Electricity Private car_Hybrid- power Fig. 1. Constructed dendritic structure of the transport sector in the LEAP model.

2.2. Calculation of energy consumption and emissions

advantage of LEAP decomposition by using a total of 28 traffic types (decomposed objects). In previous studies, the decomposed objects ranged from only 12 traffic types (He and Chen, 2013) to 13 (Peng et al., 2015) and up to 14 (Shabbir and Ahmad, 2010). In the end-use analysis, the calculation of energy demand mainly includes two factors: the activity level and the energy intensity. The activity and energy intensity levels depend on the traffic turnover and the unit turnover of energy consumption, respectively. The emissions can be calculated from the energy consumption and the emission factors, which depend on the class of vehicles and the fuel types.

2.2.1. Energy consumption The energy demand of the transport sector is calculated based on the volume of traffic turnover and unit turnover of energy consumption in each sub-sector as shown below:

EC = ∑Tri∙Tmij∙Efij

(1)

where EC is the total energy demand (Mtce); Tri is the traffic turnover in sub-sector i (billion passenger-km (Bp-km) or billion ton-km (Bt-km)); Tmij is the proportion of traffic type j in sub-sector i; Efij is the unit traffic turnover of energy consumption of traffic type j in sub-sector i 384

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of a pollutant released from a specified source or source class, which is expressed by the following equation:

Table 1 Energy intensity used in this study in 2010. Sector

Sub-sector

Traffic type

Energy intensity

Freight transport (kgce/tkm)

Highway

Diesel vehicles Gasoline vehicles Diesel locomotive Electric locomotive Aircraft

0.0058 0.0070 0.0041 0.0014 0.0502

Inland river Ocean

0.0106 0.0034

Diesel vehicles Gasoline vehicles Diesel locomotive Electric locomotive Aircraft

0.0092 0.0105 0.0041 0.0014 0.0366

Inland river BUS_Gasoline BUS_Diesel BUS_LPG BUS_CNG BUS_Electricity BUS_Hybridpower Taxi:Gasoline Taxi:CNG Taxi:LPG Taxi:Electricity Subway Private car_Gasoline Private car_Diesel Private car_Electricity Private car_Hybridpower

0.0037 0.0058 0.0051 0.0074 0.0103 0.0012 0.0047 0.0778 0.0776 0.0726 0.0059 0.0110 0.0473

Railway Civil Aviation Waterway

Intercity passenger transport (kgce/pkm)

Urban passenger transport (kgce/pkm)

Highway Railway Civil Aviation Waterway Public transport

Private transport

References

IFn =

HEhn = ∑DOSEhn×DRhn

(5)

HBhn = ∑DOSEhn×DRhn×UEh

(6)

where HEhn is the number of cases of health outcome h caused by pollutant n (case); DRhn is the dose-response coefficient of health outcome h caused by pollutant n (case/t); HBhn are the health benefits (USD); and UEh is the unit value for the health outcome h (USD/case). According to Zhang et al. (2015), the dose-response coefficient has a certain relationship with the concentration-response coefficient as shown in (7). Combining (5), (6) and (7), the health benefit h caused by reducing the pollutant n can be formulated as (8).

DRhn=

CRhn×fhn×1012 365×BR

HBhn = ∑

0.0319 0.0059

IFn×APn×CRhn×fhn×UEh×1012 365×BR

(7) (8)

where CRnh is the concentration-response coefficient for the health outcome h of pollutant n (case × m3/μg); fnh is the baseline of mortality or morbidity incidence rate for the health outcome h of pollutant n; and BR is the breathing rate, which has a standard value of 20 m3/d.

0.0213

3. Data sources and scenario design 3.1. Data sources

2.2.2. CO2 emissions The CO2 emission calculation was based on the energy consumption and the emission factor. The formula is expressed as follows:

Transport turnover: The primary data for traffic turnover of intercity passenger transport and freight transport were collected from national statistical yearbooks (CSB, 2012). The transport turnover of intercity passenger transport and freight transport according to Han et al. (2012) are forecast in Section SM-1.1 of the Supplemental material. Additionally, the traffic turnover of public transport and private transport for urban passenger are presented in Section SM-1.2. Energy intensity: Energy intensity represents the unit turnover of energy consumption, and it was obtained from China Statistical Yearbook of Transportation (CTYS, 2012) and other studies. The detailed data are shown in Table 1. Transport mode: The transport mode percentage as a traffic type was collected from national and transportation statistical yearbooks (CSB, 2012; CTYS, 2012; MTC, 2012). The detailed data are given in Section SM-2. Emission factor: The emission factors of CO2 and air pollutants were derived from the literature, as shown in Table 2. Intake fraction: According to Wang et al. (2016), the original IF (Ying et al., 2002; Ren et al., 2016) can be adjusted based on the population density to derive the value for the present study. In 2050, the IFs of SO2, NOX, PM10 and PM2.5 for the transport sector are 1.1 × 10−6, 7.9 × 10−6, 9.0 × 10−6 and 8.9 × 10−6, respectively. The calculation details are illustrated in Section SM-3. Unit value of health outcomes: According to Hasanbeigi et al. (2013), the unit values for health outcomes for Shanghai in 2001 (Kan and Chen, 2004) can be adjusted based on GDP per capita as shown in

(2)

where CE is the amount of CO2 emissions (Mt); ECij is the energy demand of traffic type j in sub-sector i (Mtce); and EFij is the CO2 emission factor of traffic type j in sub-sector i (t/tce). 2.2.3. Pollutant emissions CO, SO2, NOX, and PM10 emissions are four air pollutants considered in this study. The pollutant emissions can be calculated as follows:

APn = ∑ECij ∙EFijn

(4)

where IFn is the IF of pollutant n and DOSEn is the amount of inhaled pollutant n by an individual (Mt). Several types of health damage are caused by air pollutants. We considered nine health outcomes caused by PM10 and two outcomes each caused by SO2 and NOX. The health effects (HEhn) are assessed by the human inhalation dose and dose-response relations, which can be determined from Eq. (5). The health benefits (HBhn) are estimated by the unit economic valuation of health effects h and health effects as shown in Eq. (6).

CECERG, 2009; CTYS, 2012; Hao et al., 2015a; Hao et al., 2015b; Yin et al., 2015 CECERG, 2009; CTYS, 2012; Pan, 2014; Yin et al., 2015 Zhang et al., 2012; Pan, 2014; Tang et al., 2015; Yin et al., 2015

(kgce/p-km or kgce/t-km); i is the sub-sector type (e.g., freight highway); and j is the traffic type (e.g., diesel vehicle).

CE = ∑ECij ∙EFij

DOSEn APn

(3)

where APn represents the emissions of air pollutant n (Mt); ECij represents the energy demand of traffic type j in sub-sector i (Mtce); EFijn represents the emission factor of the air pollutant n of traffic type j in sub-sector i (t/tce); and n represents the emission type (e.g., CO, SO2, etc.). 2.3. Health benefits estimation Based on studies that have assessed the health effects caused by air pollutant (Zhang et al., 2015; Wang et al., 2016), we used the IF method to estimate the health benefits from reducing air pollutant emissions. Bennett et al. (2002) defined the IF as the integrated incremental intake 385

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3.2.2. EEI scenario In the EEI scenario, based on the transport development policies and plans in recent years, we assume that the energy consumption per unit turnover of passenger highway transport, freight highway transport, water transport and civil aviation will decrease by 3%, 7%, 6% and 4%, respectively, every five years in the future. Moreover, the average annual unit energy consumption of the private transport, railway and public transport sub-sectors will be reduced by 1%, 0.1% and 0.1%, respectively. With the improvement of transport technology, the energy efficiency level will be further improved in the future.

Table 2 Emission factors for CO2 and air pollutant in 2010 (unit: kg/tce). Sector type

Fuel type

CO2a

COb

SO2b

NOXb

PM10c

PM2.5d

Highway

Gasoline Diesel LPG CNG Diesel Diesel kerosene

2027 2145 1861 1625 2124 2124 2092

234 29 234 12 29 29 3

0.6 5.4 0.0 0.0 5.4 5.8 5.8

17.6 23.4 16.6 18.6 35.1 8.8 43.9

0.57 1.35 0.01 0.04 0.01 1.00 0.82

0.441 1.061 0.008 0.031 0.008 0.956 0.791

Railway Waterway Civil Aviation a b c d

Data source: Yan and Crookes (2009) and Ou et al. (2010). Data source: Pan (2014). Data source: Zhang et al. (2010), Zhao et al. (2012) and Peng et al. (2015). Data source: Zhao et al. (2012).

3.2.3. TMO scenario Based on national policies and the proportion of travel modes in different transport sub-sectors, additional transport methods will use more clean energy, such as electricity. In the highway and railway subsectors, the proportions of diesel vehicles and electric locomotives will gradually increase. In the public transport sub-sector, the use of taxis will gradually decrease and travel by buses and subways will be promoted. In the private transport sector, the use of private cars will be restricted by raising fuel taxes and implementing a vehicle limit. In addition, hybrids and electric cars and other alternative energy vehicle will be promoted in the public and private transport sub-sectors. For example, electric and hybrid power buses will account for 80% and 20% of the sector by 2050, respectively. Correspondingly, diesel buses will be reduced to 0% by 2050. The detailed changes of the transport mode are shown in Section SM-2.

Table 3 Unit value of health outcomes (95% confidence interval (CI)). Health outcome

Total mortality Respiratory hospital admission Cardiovascular hospital admission Asthma attack Acute bronchitis Chronic bronchitis

Value (USD/case) 2010

2050

67,689 (63,628, 71,807) 443 650

393,393 (369,789, 417,323) 2573 3780

3 (1, 5) 4 (2, 7) 3774 (5,03, 12,558)

18 (8, 30) 26 (9, 43) 21,935 (2926, 72986)

3.2.4. CP scenario A comprehensive policy scenario is considered as a combination of the EEI and TMO scenarios. Under this scenario, the energy demand and emissions will be reduced to a greater degree than under the above three scenarios.

Table 3. The specific calculation details are illustrated in Section SM-4. Concentration-response coefficient: Concentration-response coefficients and baselines of mortality or morbidity incidence rates used in this study were collected from different sources and are shown in Table 4.

4. Results and discussion 4.1. Transport turnover

3.2. Scenario design With the accelerating process of urbanization and motorization, the transport turnover will continue to increase in the future. The predicted results under the BAU scenario are shown in Fig. 2. In the freight transport sector, the transport turnover of railway, highway, inland river, ocean and civil aviation will reach 8986, 31859, 20423, 19851 and 572 Bt-km in 2050, respectively (Fig. 2a). In addition, the transport turnover of railway, highway, waterway and civil aviation of the intercity passenger transport sector will reach 2458, 1639, 13 and 1352 Bp-km in 2050, respectively (Fig. 2b). The results predicted for freight and intercity passenger transport are nearly equivalent to those in other reports (CECERG, 2009). Moreover, the transport turnover of public transport and private transport for the urban passenger subsector will reach 8628 and 8982 Bp-km in 2050, respectively (Fig. 2c). Although the increase of private traffic turnover is inextricably linked to the increase of private vehicle ownership, the amount of turnover will be further reduced in the TMO scenario by limiting the travel frequency and levying fuel taxes.

Since the initiation of the 12th Five-Year Plan, China has enacted a series of energy-saving and emission reduction measures to improve the energy efficiency of various sectors and reduce unit energy consumption. The Ministry of Transport has also proposed the decreasing goals of the unit energy consumption of integrated transport, road transport, waterway transport and civil aviation. The reduction of unit energy consumption can certainly reduce total fossil energy consumption and subsequent environmental problems (e.g., CO2 and other pollutant emissions). As for the transport structure, China has also enacted many policies to build green and low-carbon transportation, such as promoting public transport and clean energy vehicles to improve the traffic structure. In short, energy and transport structure are the major elements to be considered in our scenario selection. Based on the energy and air pollutant emission limit policies, four scenarios were constructed to evaluate the energy consumption and related emissions for the transport sector. We projected the transport sector behavior up to 2050, with 2010 used as the base year. The four scenarios include the (1) Business as Usual (BAU) scenario, (2) Energy Efficiency Improvement (EEI) scenario, (3) Travel mode Optimization (TMO) scenario, and (4) Comprehensive Policy (CP) scenario. The detailed descriptions for the four scenarios are given below:

4.2. Energy consumption Because of increased traffic turnover, energy consumption by the transport sector will continue to increase in the next 40 years under the BAU, EEI and TMO scenarios, which are shown in Fig. 3. Under the BAU scenario, the energy consumption in 2010 is 262 Mtce and increases approximately 4.9 times to 1284 Mtce in 2050, with an average annual growth rate of 4%. Incidentally, the value of the energy consumption in 2010 is nearly equivalent to that reported by the National Bureau of Statistics of China, which was determined in a more complex bottom-up approach. Under the EEI, TMO and CP scenarios, the energy

3.2.1. BAU scenario In the BAU scenario, we assume that energy efficiency and transport modes are maintained at the current levels. No measures will be implemented during the scenario period. 386

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Table 4 Concentration-response coefficients and baseline of mortality or morbidity incidence rates used in the analysis. Health outcomes

Air pollutant

Coefficients (mean and 95% CI)

References

Mortality or morbidity incidence rates

References

Total mortality

SO2

0.0008 (0.0004, 0.0012) 0.0014 (0.0003, 0.0025) 0.0004 (0.0002, 0.0007) 0.0004 (0.0001, 0.0008) 0.0004 (0.00005, 0.0008) 0.0004 (−0.00003, 0.0009) 0.0012 (0.0001, 0.0016) 0.0002 (−0.0001., 0.0006) 0.0016 (0.0006, 0.0026) 0.0013 (0.0007, 0.002) 0.0007 (0.0003, 0.0011) 0.0008 (0.0006, 0.001) 0.0029 (−0.0003, 0.0062) 0.0033 (0.0013, 0.0054) 0.0039 (0.0019, 0.0059) 0.0021 (0.0015, 0.0027) 0.0055 (0.0019, 0.0091) 0.0079 (0.0027, 0.0130) 0.0045 (0.0013, 0.0077) 0.0101 (0.0037, 0.0156)

Shang et al., 2013

0.00711

NHFPC, 2016

Cao et al., 2011

0.00711

NHFPC, 2016

Shang et al., 2013

0.00711

NHFPC, 2016

Shang et al., 2013

0.00711

NHFPC, 2016

Dab et al., 1996

0.0413

NHFPC, 2016

Dab et al., 1996

0.0413

NHFPC, 2016

Aunan and Pan, 2004

0.0413

NHFPC, 2016

Bell et al., 2008

0.0413

NHFPC, 2016

Wong et al., 1999

0.0102

NHFPC, 2016

Wong et al., 1999

0.0102

NHFPC, 2016

Aunan and Pan, 2004

0.0102

NHFPC, 2016

Bell et al., 2008

0.0102

NHFPC, 2016

Galan et al., 2003

0.0693

Chen et al., 2002

Galan et al., 2003

0.0561

Chen et al., 2002

Künzli et al., 2000

0.0561

Kan et al., 2004

Ko et al., 2007

0.0561

Tong et al., 2015

Jing et al., 2000

0.3908

Wang et al., 1994

Tong et al., 2015

0.3105

Tong et al., 2015

Kan and Chen, 2004

0.0027

NHFPC, 2016

Tong et al., 2015

0.0007

Tong et al., 2015

NOX PM10 PM2.5 Respiratory hospital admission

SO2 NOX PM10 PM2.5

Cardiovascular hospital admission

SO2 NOX PM10 PM2.5

Asthma attack

SO2 NOX PM10 PM2.5

Acute bronchitis

PM10 PM2.5

Chronic bronchitis

PM10 PM2.5

gasoline, diesel consumption will increase under the TMO and CP scenarios because of the increased proportion of diesel vehicles. With the energy consumption of different sub-sectors or different fuel type mentioned above, we further analyze the consumption of different fuel types for different sectors as shown in Fig. 6. In the intercity passenger transport sub-sector, the kerosene consumption of civil aviation will increase from 47% in 2010 to 70% in 2050 because of the improvement of living standards (Fig. 6a). For freight transport, the main fuel type is diesel, which will account for more than 90% of the fuel in 2030 (Fig. 6b). For the urban passenger transport sector, the proportion of different fuels shows greater changes. For example, gasoline accounts for 79% in 2010 and will decrease to 74% and 58% by 2030 and 2050, respectively (Fig. 6c). The share of diesel and electricity will increase steadily during the scenario period.

consumption will be 755, 816 and 509 Mtce in 2050, respectively. Compared to the BAU scenario, energy consumption under the other three scenarios will decrease by 41%, 36% and 60%, which suggests that improving energy efficiency is better than the travel structure optimization for reducing energy consumption. The energy consumption of different sub-sectors under different scenarios is shown in Fig. 4. Under the BAU scenario, the fastest growing energy consumption is the private transport of urban passenger sub-sector, which increases from 96 Mtce in 2010 to 412 Mtce in 2040, with the proportion of the entire transport sector increasing from 36% to 39% (Fig. 4a). In the other three scenarios, the energy consumption of private transport is controlled to varying degrees because of policy adoption, which shows that private transport has a relatively large energy-saving potential. In the future, the reduction of energy consumption for private cars can be achieved by adopting vehicle restrictions, collecting fuel taxes and levying congestion charges. In addition to analyzing the energy consumption of different subsectors, Fig. 5 shows the energy consumption by fuel type in four scenarios. Under the BAU and EEI scenarios, gasoline consumption will continue to dominate the total energy consumption in the future and will account for 50% or more of the energy in 2050 (Figs. 5a and 5b) because private vehicles are the main energy consumers, and the number of private vehicles is considerably larger than the number of vehicles for urban passenger transport. With the involvement of electric buses and electric vehicles in the TMO and CP scenarios, the ratio of gasoline consumption declines (Figs. 5c and 5d). In addition to

4.3. CO2 emissions The carbon dioxide emissions related to energy consumption in the transport sector under the four scenarios from 2010 to 2050 are shown in Fig. 7 and Table 5, and a similar trend to that of the energy consumption in Fig. 3 is observed. Under the BAU scenario, the amount of CO2 emissions will be increased from 530 Mt in 2010 to 2601 Mt in 2050 at an average annual growth rate of 4%. For comparison, Zhang et al. (2013) reported that the CO2 emissions of the transport sector in China were 770 Mt in 2009 and 2660 Mt in 2030, whereas Lin and Xie (2014) demonstrated that the CO2 emissions were 500 Mt in 2010. The 387

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Transport turnover (Bp-km)

Transport turnover (Bp-km)

Transport turnover (Bt-km)

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80000 70000 60000 50000 40000 30000 20000 10000 0

Civil Aviation Ocean Inland river Highways Railways

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urban passenger transport sector were approximately 890 Mt in 2050 (He et al., 2013) and 1606 Mt in 2050 (Hao et al., 2015c).

a

4.4. Pollutant emissions

Civil Aviation Waterways Highways Railways

b

Private transport Public transport

c

4.4.1. CO emissions Fig. 9a and Table 5 show that under the BAU scenario, the amount of CO emissions will increase from 37 Mt in 2010 to 173 Mt in 2050 at an average annual growth rate of 4%. Zhang et al. (2013) reported that the CO emissions of the transport sector in China would be 106 Mt in 2030 under their BAU scenario compared to 127 Mt based on our results. Under the EEI, TMO and CP scenarios, the amount of CO emissions will be 98, 55 and 30 Mt in 2050, respectively. Compared with the CO emissions under the BAU scenario, the CO emissions under the other three scenarios will decrease by 44%, 68% and 82%. Under the TMO and CP scenarios, the peak times of CO emissions will occur in 2024 and 2020 because of the increased proportion of new energy vehicles and public transport, which have lower CO emission factors than traditional transport. Among the four pollutants considered, the CO emission factor is relatively large compared to that of the other pollutants, because CO is a major pollutant by-product of combustion engines. Fig. 10a shows the CO emissions of the different sub-sectors under four scenarios. Obviously, the CO emissions are mainly obtained from urban passenger transport, especially private transport. These CO emissions account for approximately 50% from the overall transport sector under the different scenarios.

3000 2000 1000 0 18000 15000 12000 9000 6000 3000 0 2010

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4.4.2. SO2 emissions Under the BAU scenario, the amount of SO2 emissions in 2010 is 0.7 Mt, and it will increase 5.2 times to 3.4 Mt in 2050 at an annual growth rate of 4% (Fig. 7b and Table 5). He and Chen (2013) reported that the SO2 emissions associated with the road transportation sector were 0.3 Mt in 2030, whereas our results indicated 0.4 Mt in 2030 for the road transport sector. Under the EEI, TMO and CP scenarios, the amount of SO2 emissions will be 1.8, 3.4 and 1.7 Mt in 2050, respectively. Compared with the BAU scenario, the SO2 emissions under the EFI and CP scenarios will decrease by 47% and 49% in 2050. However, the SO2 emissions under the TMO scenarios will increase by 1% because SO2 is the main product of diesel engines and its emission factor for diesel vehicles is much larger than that of other fuel vehicles. The proportion of diesel vehicles will be increased for the highway sub-sector under the TMO scenario, which will result in a lack of SO2 emission reductions. Under the EEI scenario, the SO2 emissions will be reduced compared with those under the BAU scenario because of the use of energy-saving technology. Although increasing the proportion of diesel vehicles in the CP scenario will increase the SO2 emissions, improving energy efficiency will also decrease the SO2 emissions. The reduction of SO2 emissions related to improving energy efficiency is more obvious. The above situation can also be seen in Fig. 10b. Moreover, the SO2 emissions are mainly observed in the freight waterway sub-sector.

2050

Fig. 2. Traffic turnover in China during 2010–2050 under the BAU scenario, (a) Freight transport; (b) Intercity passenger transport; (c) Urban passenger transport.

Energy consumption (Mtce)

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900

600

300

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Fig. 3. Energy consumption of the transport sector under the different scenarios during 2010–2050.

different results are mainly because of the difference between the statistical scope and the calculation method. Under the EEI, TMO and CP scenarios, the amount of CO2 emissions will be 1605, 1698 and 757 Mt in 2050, respectively. Compared with emissions under the BAU scenario, the CO2 emissions under the other three scenarios will decrease by 38%, 35% and 71%. Under the CP scenarios, the carbon emissions of the transport sector in China will peak in 2030, which can meet the promise of a carbon emission peak according to the China - U.S. Joint Announcement on Climate Change, whereas the other three scenarios have no peak. The CO2 emissions of intercity passenger transport, freight transport and urban passenger transport under different scenarios are shown in Fig. 8, and the trends are also similar to that of the energy consumption in Fig. 4. In all scenarios, the emissions of the urban passenger sector still account for the bulk, which are from 260 Mt to 1517 Mt in 2050. In the other studies, the carbon emissions of the

4.4.3. NOX emissions Fig. 7c and Table 5 show the amounts of NOX emissions under the BAU, EEI, TMO and CP scenarios. Under the BAU scenario, the amount of NOX emissions in 2010 is 5.0 Mt and increases 4.8 times to 24.0 Mt in 2050 at an annual growth rate of 4%. The China Vehicle Environmental Management Annual Report demonstrated that the NOX emissions of vehicles reached 5.9 Mt in 2015 (CEM, 2016) compared to 6.3 Mt in our study. Under the EEI, TMO and CP scenarios, the amount of NOX emissions will be 14.6, 16.2 and 7.4 Mt in 2050, respectively. The NOX emissions under the three scenarios will decrease by 39%, 33% and 69% compared with those under the BAU scenario. The above results show that the reduction of NOX emissions under the EEI scenario is greater than that under the TMO scenario. Energy efficiency improvements have the most significant effect on NOX emission reductions. As shown in Fig. 10c, NOX emissions are mainly concentrated in the sub388

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Fig. 4. Energy consumption by sub-sector type under the four scenarios, (a) BAU scenario; (b) EEI scenario; (c) TMO scenario; (d) CP scenario.

CP scenarios, the amount of PM10 emissions will be 0.51, 0.75 and 0.38 Mt in 2050, respectively. Compared with PM10 emissions under the BAU scenario, those under the other three scenarios will decrease by 46%, 20% and 60%, which shows that the improvement of energy efficiency has a great effect on the reduction of PM10 emissions. Moreover, PM10 emissions are mainly concentrated in the sub-sectors urban passenger private transport, urban passenger public transport, freight highway and freight waterway (Fig. 10d). These vehicles mainly use diesel fuel, which generates PM10 emissions; therefore, improving the quality of diesel is essential. For public transport, changing the vehicle

sectors urban passenger private transport, urban passenger public transport, freight highway and intercity passenger civil aviation.

4.4.4. PM10 and PM2.5 emissions Under the BAU scenario, the amount of PM10 emissions in 2010 is 0.19 Mt and increases 5 times to 0.94 Mt in 2050 at an annual growth rate of 4% (Fig. 7d and Table 5). For comparison, Zhang et al. (2013) predicted that the PM10 emissions of the transport sector in China would be approximately 0.90 Mt in 2030 under their BAU scenario, whereas our results indicate 0.59 Mt in 2030. Under the EEI, TMO and

Fig. 5. Energy consumption by fuel type under the four scenarios, (a) BAU scenario; (b) EEI scenario; (c) TMO scenario; (d) CP scenario.

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Fig. 6. Fuel type in the different transport sectors under the CP scenario, (a) Intercity passenger transport; (b) Freight transport; (c) Urban passenger transport.

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Table 5 CO2 and air pollutants predicted emission from the transport sector (unit: Mt).

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Emission in 2010

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0 2010

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EEI

TMO

CP

BAU

EEI

TMO

CP

1708 127 2.0 15.6 0.59 0.49

1196 98 1.5 10.9 0.44 0.35

1287 65 1.9 12.5 0.48 0.41

845 50 1.4 8.2 0.35 0.29

2601 173 3.4 24.0 0.94 0.78

1605 98 1.8 14.6 0.51 0.42

1698 55 3.4 16.2 0.75 0.65

757 30 1.7 7.4 0.38 0.33

4.5. Policy analysis

2050

Fig. 7. CO2 emissions of the transport sector under the different scenarios.

Based on the above results, to summarize, the significant effects on energy saving are the following, in a descending order: CP scenario, EEI scenario, and TMO scenario, which is also for CO2 and pollutant reduction (except SO2). In this regard, we conducted comparisons between the different scenarios: 1) BAU scenario vs EEI scenario; 2) BAU scenario vs TMO scenario; and 3) EFI scenario vs TMO scenario. Comparing the BAU and EEI scenarios, the largest energy-saving sector is freight transport, which is characterized by a long annual driving distance. The contributions to energy-saving and CO2 reduction are 47% and 57% in 2050, respectively. This is because the energy intensity of this sector is set to decrease more. However, the reduction

fuel type is the most important factor. As for PM2.5, under the BAU scenario, the amount of PM10 emissions in 2010 was 0.15 Mt and increases 5 times to 0.78 Mt in 2050 at an annual growth rate of 4% (Fig. 7e and Table 5). Under the EEI, TMO and CP scenarios, the amount of PM10 emissions will be 0.42, 0.65 and 0.33 Mt in 2050, respectively. The emission trends with time and the departmental distribution characteristics are the same as PM10 (Fig. 9e and Fig. 10e).

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0 2010

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Fig. 8. CO2 emissions by sub-sector type under the four scenarios, (a) BAU scenario; (b) EEI scenario; (c) TMO scenario; (d) CP scenario.

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a

BAU EEI TMO CP

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CO emissions (Mt)

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NOX emissions (Mt)

CO2 emissions (Mt)

Urban passenger Private transport Urban passenger Public transport Civil Aviation Freight Waterway Freight Highway Freight Railway Intercity passenger Civil Aviation Intercity passenger Waterway Intercity passenger Highway Intercity passenger Railway

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CO2 emissions (Mt)

Urban passenger Private transport Urban passenger Public transport Civil Aviation Freight Waterway Freight Highway Freight Railway Intercity passenger Civil Aviation Intercity passenger Waterway Intercity passenger Highway Intercity passenger Railway

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e

BAU EEI TMO CP

0.6

2015

0.4 0.2 0.0 2010

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Fig. 9. Air pollutant emissions of the transport sector under the different scenarios, (a) CO; (b) SO2; (c) NOX; (d) PM10; (e) PM2.5.

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Fig. 10. Air pollutant emissions by sub-sector type under the four scenarios, (a) CO; (b) SO2; (c) NOX; (d) PM10; (e) PM2.5.

SO2, NOX, PM10 and PM2.5 are 70%, 27%, 51%, 69%, 65% and 64% in 2050, respectively. Therefore, the use of electric vehicles gains more attention by society. With the subsidy policy for private purchase on new energy vehicles issued in China, the national development direction of new energy vehicles is inclined toward electric vehicles. Moreover, electric vehicles are not affected by the driving restriction policy. In terms of urban transport policy, public transport may be the key for pollutant emission reduction, which results in decreases of 57%

in the energy efficiency of compact cars is more difficult. In the category of the freight transport, only the railway transport sub-sector can be electrified, and others still rely on the burning of oil. For the BAU scenario and TMO scenario, however, the most energysaving sector is urban passenger transport. This is mainly due to new energy alternatives, especially electric vehicles. Using the electric vehicle as an example, the emission reductions that are introduced by using electric vehicles for CO2 (only including direct emissions), CO, 392

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Table 6 Net gain of health benefits of the EFI, TMO and CP scenarios compared with the BAU scenario in 2050 (95% CI). Health outcomes

SO2 Total mortality Respiratory hospital admission Cardiovascular hospital admission Asthma attack Subtotal (SO2) NOX Total mortality Respiratory hospital admission Cardiovascular hospital admission Asthma attack Subtotal (NOX) PM10 Total mortality Respiratory hospital admission Cardiovascular hospital admission Asthma attack Acute bronchitis Chronic bronchitis Subtotal (PM10) PM2.5 Total mortality Respiratory hospital admission Cardiovascular hospital admission Asthma attack Acute bronchitis Chronic bronchitis Subtotal (PM2.5) a b

Reduction in casesa

Cost benefit for public healthb

EEI

TMO

CP

EEI

TMO

CP

1.4 (0.7, 2.1) 4.0 (0.5, 7.9) 3.9 (1.5, 6.4) 48 (−5, 103)

−0.02 (−0.01, −0.03) −0.06 (−0.01, −0.09) −0.06 (−0.02, −0.09) −0.7 (0.07, −1.5)

1.4 (0.7, 2.2) 4.2 (0.5, 8.4) 4.1 (1.6, 6.7) 51 (−5, 109)

0.5 (0.3, 0.8) 1.6 (0.2, 3.1) 1.5 (0.6, 2.5) 18.9 (−2.0, 40.5) 22.6 (−0.9, 46.9)

−0.01 (0, −0.01) −0.02 (0, −0.04) −0.02 (−0.01, −0.04) −0.3 (0.03, −0.6) −0.3 (0.01, −0.7)

0.6 (0.3, 0.9) 1.7 (0.2, 3.3) 1.6 (0.6, 2.6) 20.0 (−2.1, 42.8) 23.9 (−1.0, 49.6)

116 192 154 215

101 (22, 180) 168 (−13, 377) 134.66 (31, 114) 188 (74, 308)

178 295 237 331

45.4 (9.7, 81.2) 75.4 (−5.7, 169.7) 60.5 (13.9, 51.2) 84.5 (33.3, 138.3) 265.9 (51.4, 440.4)

39.8 (8.5, 71.0) 66.0 (−5.0, 148.5) 52.9 (12.23, 44.8) 73.9 (29.14, 121.0) 232.7 (44.9, 385.4)

69.9 (14.9, 124.9) 116.1 (−8.7, 261.3) 93.2 (21.5, 78.9) 130.1 (51.3, 212.9) 409.4 (79.0, 677.9)

1.5 (0.8, 26) 26(2.2, 35) 3.8 (1.6, 6.0) 117 (57, 177) 1148 (395, 1902) 6.5 (1.9, 11)

0.7 (0.3, 12) 12 (1.0, 16) 1.7 (0.7, 2.6) 51 (25, 78) 506 (174, 838) 2.9 (0.8, 4.9)

2.0 (1.0, 35) 34 (2.9, 46) 5.0 (2.1, 7.8) 152 (74, 230) 1492 (513, 2471) 8.4 (2.4, 14)

0.6 (0.3, 10.5) 10.4 (0.87, 13.9) 1.5 (0.64, 2.4) 45.9 (22.4, 69.6) 451.8 (155.3, 748.3) 2.5 (0.74, 4.4) 512.8 (180.2, 848.9)

0.26 (0.13, 4.61) 4.6 (0.38, 6.1) 0.66 (0.28, 1.1) 20.3 (9.8, 30.6) 198.9 (68.4, 329.5) 1.1 (0.32, 1.9) 225.9 (79.4, 373.7)

0.78 (0.39, 13.6) 13.5 (1.1, 18.0) 1.9 (0.84, 3.1) 59.7 (29.1, 90.4) 586.9 (201.7, 972.2) 3.3 (0.96, 5.7) 666.2 (234.1, 1102.9)

1.3 (0.3, 2.5) 3.7 (−1.8, 11) 3.6 (2.7, 4.5) 52 (37, 67) 1087 (372, 1789) 3.1 (1.2, 4.8)

0.5 (0.1, 1.0) 1.4 (−0.7, 4.2) 1.4 (1.1, 1.7) 20 (14, 26) 414 (142, 682) 1.2 (0.4, 1.8)

1.6 (0.4, 3.2) 4.6 (−2.3, 14) 4.6 (3.4, 5.7) 66 (47, 85) 1374 (470, 2261) 4.0 (1.5, 6.1)

0.50 (0.12, 0.99) 1.4 (−0.7, 4.3) 1.4 (1.1, 1.8) 20.5 (14.7, 26.4) 427.7 (146.2, 703.9) 1.2 (0.45, 1.9) 452.9 (161.8, 739.3)

0.19 (0.05, 0.38) 0.55 (−0.3, 1.7) 0.54 (0.41, 0.68) 7.8 (5.6, 10.1) 162.9 (55.7, 268.2) 0.47 (0.17, 0.73) 172.6 (61.7, 281.7)

0.63 (0.16, 1.3) 1.8 (−0.9, 5.5) 1.8 (1.4, 2.3) 25.9 (18.5, 33.4) 540.5 (184.7, 889.5) 1.6 (0.57, 2.4) 572.3 (204.5, 934.2)

(25, 206) (−14, 431) (41, 150) (85, 352)

(38, 318) (−22, 664) (55, 201) (130, 541)

Unit: thousands of cases. Unit: billions of USD.

because the number of adverse outcomes decreases except for that of SO2 under the TMO scenario. For the economic costs, under the CP scenario, the reductions in economic losses of SO2, NOX and PM10, PM2.5 are estimated to be 23.9, 409.4, 662.2 and 572.3 billion USD, respectively. Moreover, by comparison, the health benefits under the EEI scenario are better than those under the TMO scenario. In all, the health impacts associated with air pollution generated by the transport system has a direct relationship with the air pollutant concentration, and the concentration of air pollutants is directly affected by the air pollutant emissions of the transport sector. Under the four scenarios, the largest reduction in air pollutant emissions occurs under the CP scenario, followed by the EEI and TMO scenarios. This finding indicates that energy efficiency improvements can significantly reduce the health outcomes caused by air pollutants.

CO2, 51% CO, 59% SO2, 61% PM10 and 61% PM2.5 in 2050. Furthermore, a comparison between the EEI scenario and the TMO scenario is made. From the perspective of energy consumption and emission mitigation, the difference is not great; the CO2 reduction predictions are 38% and 35% in 2050, respectively. The other pollutants (except CO) have a similar decreasing trend under the two scenarios. When both are promoted to a certain extent, the effect can be the same. In the medium and long term, the TMO scenario is more effective. After all, electricity does not directly emit harmful gases. When the structure is adjusted to a certain level, the improvement of energy efficiency will be more important.

4.6. Health benefit assessment The massive emissions of air pollutants cause serious impacts on residents’ health. Therefore, based on the estimation of air pollutant emissions, the health impacts are evaluated in this study. According to the relevant literature, we considered four health outcomes caused by SO2 and NOX. For PM10 and PM2.5, six health outcomes are considered. The results of the health benefit assessment of the EEI, TMO and CP scenarios compared with the BAU scenario in 2050 are shown in Table 6. A comparison of the four pollutants shows that the reduction in health outcomes caused by PM10 is the greatest in all three scenarios. For the EEI scenario, 1148 thousand cases of acute bronchitis outcomes caused by PM10 will be avoided, which is the greatest reduction among the nine health outcomes. As under the EEI scenario, the number of acute bronchitis cases is larger than that of the other outcomes under the TMO (506 thousand cases) and CP (1492 thousand cases) scenarios. This finding implies that the impact of air pollutants is most serious for acute bronchitis. In addition, the mortality outcomes caused by the four pollutants will decrease by 120, 102 and 183 thousand cases under the three scenarios. Each scenario has a positive impact on human health

5. Uncertainty and sensitivity analysis 5.1. Uncertainty analysis Parameter uncertainty could affect the health benefit results. In this study, the overall uncertainties are based on the following three aspects: (1) calculation of energy consumption, (2) estimation of CO2 and pollutant emissions, and (3) health benefit assessment. Uncertainties related to the base year in the first calculation of energy consumption originate from three factors: traffic turnover (Tr), the proportion of traffic type (Tm) and the unit traffic turnover of energy consumption (Ef). Traffic turnover data of intercity passenger transport and freight transport and the data of the proportion of traffic type are obtained from the Chinese statistical yearbook. The statistics departments mainly use the bottom-up approach to generate a progressive summary, so there is a certain degree of acceptable error. Turnover data of the urban passenger transport are calculated using the relevant parameters (e.g., urbanization rate, private car ownership and trip 393

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pollutants (He and Qiu, 2016). Furthermore, in the available literature, concentration-response coefficients of health outcome are the same as in the original research. The mortality or morbidity incidence rate may have more uncertainties because the available data consider the whole society and not just the transport sector. Moreover, because of a lack of data (such as for IFs and the unit value of health outcome), certain parameters are primarily based on regional data and adjusted for China; therefore, the health benefits may be overestimated. In addition, we evaluated SO2, NOX, PM10 and primary PM2.5 emissions without considering the health effects of secondary pollutants such as secondary PM2.5, acid rain and O3. A possible method of improving data accuracy is to reduce uncertainties via literature reviews and data localization. In addition to the above, the long-term projection up to 2050 may have great uncertainty because policies may change with social development and technological progress. Therefore, our transport structure is only representative of a possibility. As an example, if we use more public transport, the proportion of private transport will decrease in the urban transport subsector. In addition, the use of public transport will reduce road congestion and petroleum-driven cars (including diesel). Therefore, air pollutant emissions from vehicles will be significantly reduced. In terms of health risk assessment, the pollutant concentration (mg/ m3) (not pollutant emissions (kt)) is usually used to calculate health risk. There is a mismatch between pollutant concentration and pollutant emission. Nevertheless, the emission amount was used in the current study. Therefore, the errors associated with health benefits certainly exist. However, the extent of uncertainty remains to be investigated.

Table 7 Sensitivity analysis of variable parameters on the health benefit under the CP scenario. parameter

Energy demand Turnover GDP growth rate

Population level Urbanization rate Private car ownership Energy efficiency Energy intensity Emission Emission factor Health benefit Health outcome number

CR coefficient

UE

Intake fraction mortality or morbidity incidence rate

Change range

Health benefit change, %

1% per five years lower than original value 1% per five years higher than original value 90% of original value 110% of original value 90% of original value 110% of original value 90% of original value 110% of original value

−21

90% of original value 110% of original value

−10 10

90% of original value 110% of original value

−10 10

Only four health outcomes (mortality, Respiratory, Cardiovascular and Asthma attack) Lower values of 95% confidence Higher values of 95% confidence Lower values of 95% confidence Higher values of 95% confidence 90% of original value 110% of original value 90% of original value 110% of original value

−74

21 −1 1 −1 1 −2 2

−66 66 −6 6

5.2. Sensitivity analysis

−10 10 −10 10

To address the uncertainty of the health benefit results, a sensitivity analysis for eleven parameters (e.g., GDP growth rate, energy intensity, etc.) based on three aspects was conducted. For each calculation, only one input parameter was changed while all others were kept constant. The ranges of the sensitivity analysis for each input parameter are shown in Table 7. Then, the resulting health benefits were compared to the health benefits determined under the CP scenario. For energy demand, we set the GDP growth rate to 1% lower or higher than the original value every five years, which caused the health benefits to decrease or increase by 21%. As for the other parameters associated with energy demand, upward and downward movements (within a 10% band) of the population level, urbanization rate, private car ownership and energy intensity cause health benefits to decrease and increase by 1%, 1%, 2% and 10%, respectively. Obviously, the greatest effect on the health benefits estimates was GDP growth, which was followed by energy intensity. For the evaluation of pollutant emissions, only one parameter (emission factor) was involved, and it was also set within a 10% band. The results show that the emission factor changes the health benefits by ± 10%. When considering the health benefits assessment, we chose five parameters (health outcome number, CR coefficient, UE, IF and mortality or morbidity incidence rate) for the analysis. For the health outcome number, we mainly reduced the health outcomes caused by PM10 and PM2.5 so that they remained consistent with outcomes caused by SO2 and NOX. Hence, only four health outcomes were observed, total mortality, respiratory disease, cardiovascular disease and asthma attack, and a −74% change was observed. The CR coefficients and UEs used data at the 95% confidence interval obtained from the literature. As a result, the CR coefficients have a greater effect on health benefits. Moreover, the IF and mortality or morbidity incidence rates show upward and downward movements (within a 10% band), and both change the health benefits by ± 10%. Overall, health benefits are more sensitive to the health outcome number than to the other parameters, although they are also sensitive to the CR coefficients ( ± 66%) and GDP growth ( ± 21%).

distance) obtained from the literature. Most data for the unit turnover of energy consumption were also obtained from the literature and Chinese statistical yearbooks. For example, the energy intensity of diesel locomotives ranges from 0.0040 to 0.0047 kgce/t-km and that for diesel vehicles for highway use in intercity passenger transport ranges from 0.0078 to 0.0115 kgce/p-km. We also compared the energy intensity of the relevant national studies to reduce uncertainty. In future predictions, our assumptions for the parameters will depend mainly on national policy plans and available research, e.g. the energy intensity of passenger highway transport, freight highway transport, water transport and civil aviation will be decreased by 3%, 7%, 6% and 4%, respectively, every five years in the future. Differences in the degree of reduction can lead to changes in emissions, further affecting health benefits. For the estimates of CO2 and pollutant emissions, the main uncertainty is the emission factor. Current studies indicate that the range of the CO2 emission factor for gasoline is from 2006 to 2145 kgCO2/tce. To reduce the uncertainty regarding emission factors, we also performed comparisons with data from the CO2 and pollutant emission factor database in the LEAP model. Moreover, future changes in emission factors will depended on our assumptions (CO2 and CO decrease by 1% annually, and other pollutants decrease by 5% annually). Regarding the evaluation of health benefits, five parameters are involved (Eq. (8)): the number of health outcomes, the concentrationresponse coefficient, the mortality or morbidity incidence rates, the IF, and the unit value of the health outcome. The numbers of health outcomes caused by pollutants are based on the work of Wang et al. (2016). In this work, we considered six health outcomes caused by PM10 and PM2.5 as well as only four by SO2 and NOX, whereas other studies have considered six health outcomes caused by each of the four 394

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6. Conclusions and policy implications

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In this paper, the energy consumption, air pollutant emissions, and health effects caused by the transport sector were estimated using the LEAP model under different scenarios for 2010–2050. The main conclusions drawn from the study are summarized as follows. With rapid growth of transport turnover, energy consumption under the four scenarios will rapidly increase as well, and in 2050, energy consumption will reach 1284, 755, 815 and 509 Mtce, respectively. The corresponding carbon dioxide emissions will also increase at an average annual growth rate of 4% under the BAU scenario. Under the CP scenario, carbon emissions of the transport sector in China will peak in 2030. Regarding air pollutant emissions, (1) under the EEI, TMO and CP scenarios, the CO emissions will be 98, 55 and 30 Mt in 2050, which represent decreases of 44%, 68% and 82%, respectively, compared with the corresponding emissions under the BAU scenario; (2) the SO2 emissions will increase to 3.4 Mt under the BAU scenario in 2050 at an annual growth rate of 4%, and compared with the SO2 emissions under the BAU scenario, those under the EEI and CP scenarios will decrease by 47% and 49%, respectively; (3) under the EEI, TMO and CP scenarios, the NOX emissions will be 14.6, 16.2 and 7.4 Mt in 2050, which represent decreases of 39%, 32% and 69%, respectively, compared with the BAU scenario; (4) the PM10 emissions will be 0.94, 0.51, 0.75 and 0.38 Mt in 2050, respectively; and (5) the PM2.5 emissions will be 0.78, 0.42, 0.65 and 0.33 Mt in 2050, respectively. For the health benefits, mortalities will be reduced by 120, 102 and 183 thousand cases under the EEI, TMO and CP scenarios, respectively, compared with the mortality under the BAU scenario, and the associated economic losses will be reduced by 47, 40 and 72 billion USD in 2050, respectively. Among the six health outcomes, acute bronchitis for human beings exhibits the worst outcome, it will eventually account for 60% of the total economic losses. Based on these research findings for the transport sector in China, certain policy recommendations are proposed. Although the number of vehicles using oil in the future will decrease to 11 types after the structural adjustment, it is also necessary to improve oil quality. Hence, the first recommendation is to improve oil quality. The quality of transport fuel is related to the level of pollution emissions, and it also affects the operation of relevant pollution control equipment. Clean oil, especially with reduced sulfur content for diesel vehicles, is particularly critical. Second, urban car ownership must be controlled. Restricting the purchase rights, increasing the purchase tax and collecting vehicle congestion taxes are possible measures. In addition, the technical efficiency of transport vehicles should be improved to reduce the unit energy consumption level of the vehicle, especially passenger vehicles. Moreover, the promotion of public transport and non-motorized traffic can reduce the use of cars to achieve lower emissions. Furthermore, the replacement of fossil-fuel based energy with electricity and hybrid power can greatly reduce air pollutant emissions from vehicles. Acknowledgements Authors are grateful for the financial support from the China Sustainable Energy Project of U.S. Energy Foundation (No. G-141022231) and Clean Development Mechanism Funds (No. 2014036). Appendix A. Supplementary material Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.enpol.2018.02.019. References Amirnekooei, K., Ardehali, M.M., Sadri, A., 2012. Integrated resource planning for Iran: Development of reference energy system, forecast, and long-term energy-environment plan. Energy 46, 374–385.

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