Transportation Research Part D 35 (2015) 84–103
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Transportation Research Part D
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Integrated analysis of GHGs and public health damage mitigation for developing urban road transportation strategies ⇑ Xiongzhi Xue a, Yan Ren a,b,c, Shenghui Cui b,c, , Jianyi Lin b,c, Wei Huang b,c, Jian Zhou d a College of the Environment & Ecology, Xiamen University, 361102, China b Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China c Xiamen Key Lab of Urban Metabolism, Xiamen 361021, China d Low Carbon & Ecological Research Center, Guangdong Provincial Academy of Environmental, Guangdong 510045, China article info abstract
Keywords: This study attempts to present an urban road transportation strategy focusing on the mit- Transportation igation of both GHGs emission and public health damage, taking Xiamen City as a case GHGs emission study. We developed a Public Health and GHGs Emission model to estimate the impacts Air pollution of direct energy-consumption-related GHGs emissions and public health damage in Xia- Cost men’s road transportation strategies from 2008 to 2025, considering the environmental Urban area benefits and economic costs. Two scenarios were designed to describe future transporta- tion strategies for Xiamen City, and mitigation potentials for both GHGs emission and pub- lic health costs were estimated from 2008 to 2025 under a series of options. The results show that enacting controls on private vehicles would be most effective to GHGs mitiga- tion, while enacting controls on government and rental vehicles would contribute the most
to NO2 and PM2.5 reductions. Compared with the Business as Usual scenario, the Inte- grated scenario would achieve about a 68% energy consumption reduction and save 0.23 billion yuan (95% CI: 0.16, 0.32) in health costs in 2025. It is clear that integrated and advisable strategies need to mitigate the adverse impacts of urban road vehicles on GHGs emissions and public health and economic costs, particularly in regions of rapid urbanization. Ó 2014 Elsevier Ltd. All rights reserved.
Introduction
Energy consumption in the transport sector occupies the second largest one with 30% of total world delivered energy and almost 60% of the world’s oil demand (Atabania et al., 2011). Although large mitigations in greenhouse gases (GHGs) are needed to prevent climate change, emissions from transportation are rising faster than from any of the other energy-con- suming sectors, and are predicted to increase by 80% between 2007 and 2030 (Kahn et al., 2007). Within the transport sector, road transportation leads oil consumption with 81% of total transport energy need (Atabania et al., 2011). In China, trans- portation energy demand grew to 191.24 million tonnes of oil equivalent (Mtoe) in 2007 from 25.2 Mtoe in 1980, with 7% of average annual growth rate (Wang and Huo, 2009). The IEA (2008) has forecast that, without aggressive strategies and
⇑ Corresponding author at: Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China. E-mail address: [email protected] (S. Cui). http://dx.doi.org/10.1016/j.trd.2014.11.011 1361-9209/Ó 2014 Elsevier Ltd. All rights reserved. X. Xue et al. / Transportation Research Part D 35 (2015) 84–103 85 management to reduce energy demand in the coming two decades, oil need would exceed 800 Mtoe by 2030 in which road transportation account for 43%. Many researches have attached GHGs mitigation to energy consumption of the road transportation in the future with dif- ferent options measures. (Singha et al., 2008; Morrow et al., 2010; Cui et al., 2010, 2011; Chavez-Baeza and Sheinbaum-
Pardo, 2014). Traffic-related emissions are a complex mix of pollutants composed of NOx, PM, CO, SO2, VOCs,O3, and mul- titudinous toxic trace chemicals (Uhereka et al., 2010). Public health has been close attached to traffic-related air pollution in a lot of worldwide researches (Burnett et al., 1998; Laden et al., 2006; Pope III and Dockery, 2006; Woodcock et al., 2009; Chen et al., 2013). Quantifying the links between air pollution and human health damage makes it probable not only to con- sider the cost-effectiveness of past and current pollution control strategies, but also to assess the benefits of local conserva- tion actions (Aunan et al., 2004). Hill et al. combined air pollution dose–response relationships to estimate health effects under PM2.5 emissions (Hill et al., 2008). To estimate the health damage caused by air pollution in China, exposure–mortal- ity coefficients was applied to assess the expected deaths attributable to PM2.5 with a meta-analysis (Shang et al., 2013). Health risks was calculated by linking estimated exposures to the relevant dose–response relationships in traffic-related air pollution as indicated by NO2 (Zhang and Batterman, 2013). It was estimated that between 350,000 and 500,000 people died prematurely each year from ambient air pollution in China (Chen et al., 2013). Policies for GHGs mitigations often reduce emissions of air pollutants simultaneously, attaching co-benefits to air quality and public health. The early studies have estimated that the GHGs mitigation co-benefits of public health by controlling and reduction pollution emissions (Woodcock et al., 2009; West et al., 2013; Geng et al., 2013). Furthermore, government applaud to develop a series of policies for the multiple and long-term profits of green-energy reform and innovation, in which GHG mitigation strategies for promoting energy sustainability should be a key point (McCollum et al., 2011). Although several studies have presented evidence that various measures for controlling emissions from the transportation sector (motor vehicle controls, cleaner fuels and energy-saving vehicle technologies) may result in co-benefits, these literatures have so far failed to formulate convincing, policy-relevant estimates of co-benefits (Jack and Kinney, 2010). Accordingly, more and more researches (in particular at the local scale) are so urgently needed that government could facilitate to make decision as well as take account for mitigation of environmental pollution and public health loss (Geng et al., 2013). Consid- ering these circumstances, this study aims to analyze the co-benefits of road transportation strategies and to discuss oppor- tunities to mitigate GHG and air pollution emissions, based on an integrated analysis, taking Xiamen City as an example. Xiamen City, a sub-provincial city in Fujian Province, is one of the six special economic zones. Since the establishment of special economic zones, the GDP of Xiamen City has grown from 6.4 million yuan in 1980 to 156.002 billion yuan in 2008, an average annual growth rate of 17.6%. In 2008, the city consumed a total of 0.86 million tons in transportation energy, with carbon dioxide emissions of 9675 million tons accounting for 18% of the city’s total carbon emissions. The same year, the number of road vehicles had reached 576,100, an average annual growth rate of nearly 20% (XCBS, 2009). The resulting trans- portation energy consumption growth has directly resulted in the increase of pollutants, including PM2.5, SO2, and NO2. PM2.5 comes mainly from motor vehicle exhaust, industrial pollution emissions and road dust, in which motor vehicle exhaust contributes the highest contribution proportion—more than 40% (XCBS, 2009; Zhao et al., 2010; Ye et al., 2006). Therefore, transportation energy conservation has become critical for reducing the carbon emissions in Xiamen City. Xiamen City has been identified as one of the national low-carbon pilot cities, and the Xiamen municipal government has taken a variety of measures to reduce GHGs emissions. Facing above earlier studies for GHGs and air pollution emissions in China’s road transportation and future trends under various policy scenarios, the current researches are confused by the limited estimation of policy of implementing or prep- aration and potential co-benefits and lake of integration of benefits for related policies. An integrated analysis is necessary to build sophisticated modeling of policies, atmospheric transport chemistry, climate science and public health effects (Jack and Kinney, 2010; Bell et al., 2008). This study intends to highlight and analyze the trade-off of transport, traffic-related GHGs and air pollution emission, public health, policy scenarios and policy costs. We build a PHAGE (Public Health and GHGs Emission) model to analyze the co-benefits of public health, GHGs reduction and policies costs under mitigation scenarios for the transportation sector, taking Xiamen City as an example. The goal of this study is to analyze the effects of GHGs and air pollution from transport emissions and public health with different scenarios, to assess the contribution of traffic-related options for air quality and health under taking account for each option implementary cost.
Methods
Model structure
The PHAGE model We constructed a PHAGE (Public Health and GHG Emission) model to analyze the impact of the integrated effects of energy consumption in the road transportation sector. This model includes two types of impacts: public health damage and GHGs emission effects. First, the emissions of pollutants and GHGs were calculated using the Input–output (I–O) method to analyze and appreciate each alternative scenarios’ energy requirements (Park and Heo, 2007; Peter and Hertwich, 2008; Zhang et al., 2014). The LEAP model is a scenario-based modeling tool to track energy consumption for various energy anal- ysis and GHGs emission effects which developed by the Stockholm Environment Institute (SEI, 2008; Huang and Lee, 2009; 86 X. Xue et al. / Transportation Research Part D 35 (2015) 84–103
Lin et al., 2010; Zhang et al., 2011). It has been extensively used on the local, national, and global scales to display energy supply and demand, estimate the environmental impact of energy policies, and recognize hidden obstacle (Priece et al., 2008; Zhang et al., 2011). Based on analysis data, we can explore which overall strategy is preferable with respect to emis- sions reduction potential, energy consumption, overall costs, and how the strategy contributes to national development objectives. The LEAP model has also been used to analyze traffic-related energy and environment impacts (Dhakal, 2003; Pradhan et al., 2006), to estimate the energy demand of transport and air pollution emissions and also to estimation and analysis the natural gas consumption for future in Beijing (Li, 2009; Lin et al., 2010). Second, concentration change functions and exposure–response functions are used to calculate changes in the concentrations of pollutants and health economic damage respectively (Wang, 2010; He and Chen, 2013). Meanwhile, this study estimates the costs of purchasing vehicles, maintenance, fuel consumption and development of fuel-saving technology. Finally, integrated assessments of both types of impacts were used to recommend comprehensive and appropriate strategies, combining an economic lost analysis. Opti- mization of road transportation strategies was developed to synthesize the adverse influence urban road transportation on both GHGs mitigation and air pollution. The structure of PHAGE model is shown in Fig. 1.
Scenario design This study classified road transportation vehicles into six types: public transit (PT), private vehicles (PVs), taxis, govern- ment and rental vehicles (GRVs), motorcycles (MCs) and trucks. Vehicles were further divided into eleven categories based on five fuel types (seen Appendix A). For the sake of analysis the results of potential options for energy sustainability and emissions reduction, this study takes the BAU (business as usual) and the INT (integrated) scenarios into account. The BAU scenario is supposed not to implement any measures during the study period for controlling energy consumption or air pollutant emissions in the urban transportation sector. The INT scenario assumes a series of policies and measures will be implemented for mitigat- ing traffic-related GHGs and air pollution emissions. The INT scenario is developed with five sets of options—Motor Vehicle Controls (MVC), Fuel Economy Regulations (FER), Promotion of New Energy Vehicles (PNEV), Fuel Tax (FT), and Promotion of Biofuels (PB). Among these options, the MVC option is to control the intensity of private vehicles and reinforce the transport infrastructure; the FER option includes: mandatory control of oil consumption for vehicles (AQSIQ and SAC, 2004), light-duty vehicles (AQSIQ and SAC, 2006) and further enhancing regulations; the PNEV option is to improve new energy vehicles including CNG-vehicles, electric-vehicles, Hybrid electric vehicles; the FT option sources from the ‘‘fuel pricing and taxation reform’’ in 2009 and the FT implementation is supposed to control gasoline and diesel demand without the other fuel (Mao et al., 2012); the PB option refers to utilize the biofuel for oil fuel saving in road transporta- tion. The sets of conditions about policies, parameters of different vehicles types and each fuel are shown in Table 1, Appendices B and C.
Data sources There are three data sources for this study: the Xiamen Statistical Yearbooks (1999–2013), the comprehensive urban plan and special sector planning documents, and the investigation data from governments. The Xiamen Statistical Yearbooks pro- vide data about society and economy (SYXSEZ, 1999–2013). There are equally important data which are from the Xiamen City’s Master Plan (2004–2020), the eleventh five-year planning of Xiamen, the Xiamen Integrate Transport Planning
Fig. 1. Structure of the PHAGE (Public Health and GHGs Emission) model. X. Xue et al. / Transportation Research Part D 35 (2015) 84–103 87
Table 1 Policy options and assumptions for the INT scenario.
Options Definition Basis MVC Proportion of private cars and motorcycles to total travel decreases 20%a; Xiamen’s Master Plan in 2004–2020c buses’ proportion of total passenger miles decrease 78.5% by 2015, 70% Xiamen’s Comprehensive Transportation Plan for 2006–2020d by 2020, 65% by 2025; the average annual mileage of private cars Xiamen’s Outline Plan for Building an Eco-citye decreases 6%a; the BRT contribution rate to passenger capacity in public transports reaches 35% by 2025 FER The annual vehicle kilometers traveled and the average fuel economy of Xiamen Energy saving Centref; AQSIQ and SAC (2004, 2006)g; all models increases 12% and 40% Shabbir and Ahmad (2010)h; Zhu and Jiang (2010)i; Zhang et al. PNEV New energy vehicles proportion of public transport reaches 50%; (2007)j proportion of private cars, government vehicles and rental vehicles reaches 25%; there are equal proportions of new energy vehicles: hybrids, CNG, and electric vehicles. FT In addition to the electric car, the fuel tax of all vehicles increases 10%; new energy vehicle ratios reach 5%; there are equal proportions of new energy vehicles: hybrids, CNG, and electric vehicles PB The bioenergy the E10b substitute for gasoline usage increases 60%; the B20b bus substitute for regular diesel increases to 30%, diesel trucks decrease to 40%
The years from 2008 to 2025 were chosen as the future scenario period. First a business-as-usual (BAU) scenario was established. Secondly, five alternative fuel development scenarios focusing on the reduction of oil and emissions were put forward. Finally the integrated scenario combined the advantages of the five aforementioned alternative cases’ reductions in pollutant emissions. a Unless otherwise specified, all reached their target values in 2025. b E10 is a fuel with ethanol and gasoline volume ratio of 1:9 mixed fuel, B20 is ordinary diesel mixed with 20% biodiesel. c The plan was designed by the Urban Planning Bureau of Xiamen in 2004. d The plan was designed by the Urban Planning Bureau of Xiamen in 2006. e The plan was designed by the Environmental Protection Bureau of Xiamen in 2004. f The policy came from the energy-saving public service network in Xiamen in 2010, http://xmecc.xmsme.gov.cn/. g AQSIQ and SAC is General Administration of Quality Supervision, Inspection and Quarantine of the PRC (AQSIQ), Standardization Administration of the PRC (SAC) about fuel economy regulations of vehicles. h,i,j These documents related measures on urban energy savings (Shabbir and Ahmad, 2010; Zhu and Jiang, 2010; Zhang et al., 2007).
(2006–2020), the Xiamen Telecommunications and Traffic Postal Service Annual Report (1999–2008) as well as the Outline Planning of Building an Ecological urban in Xiamen. Meanwhile, the Development and Reform Commission provides a series of data about oil fuel, compressed natural gas, liquefied petroleum gas, electricity, gasoline and diesel, etc. In addition, the Municipal Construction & Gardens Bureau of Xiamen, the Xiamen Statistical Bureau, the Transport Bureau of Xiamen, the Xiamen Public Transport Group and the Xiamen Energy Control Center are also continued to support for achieving survey.
Direct energy demand and pollutant emission calculations
Energy consumption The LEAP model is able to realize the key target in this study by analytical prediction of energy consumption and a series of air pollution and GHGs emissions. These could be calculated from traffic-related factors, including the vehicle-kilometers traveled, the number of motor vehicles, the fuel economy and the passenger or freight-tonnage-kilometers. Based on the data and the model parameter set, energy consumption was calculated for ordinary motor vehicles (PVs, Taxis, GRVs and MCs), buses (PT) and freight transport (Trucks).
(1) Ordinary motor vehicles
The equation for energy consumption of ordinary motor vehicles is as follows: X EDiðtÞ¼ V iðtÞVKTiðtÞFiðtÞ ð1Þ where ED is the energy consumption of ordinary motor vehicles, in Mtons; i is the vehicle type; t is the vintage; Vi(t) is vehi- 4 cle type i in the given vintage t (10 t); VKTi(t) is the average annual travel distance of automobile type i in the given vintage t, in km; and Fi(t) is the economic fuel of automobile type i in the given vintage t. In this paper per-mile fuel consumption refers to MJ km 1.
(2) Bus The equation of energy consumption of a public-transport bus is as follows: X EDPiðtÞ¼ EPassiðtÞ TDTPiðtÞ ð2Þ 88 X. Xue et al. / Transportation Research Part D 35 (2015) 84–103
where EDPi(t) is the energy consumption for bus type i in the given vintage t, in Mtons; EPassi(t) is the per-person energy 1 consumption for bus type i in the given vintage t, as MJ person ; TDTPi(t) is the total number of daily passengers traveling in bus type i in the given vintage t, in number of persons.
(3) Freight transport The equation of energy consumption of a freight-transport vehicle is as follows: X
EDFiðtÞ¼ EFreightiðtÞ TEFiðtÞ ð3Þ where EDFi(t) is the energy consumption of freightage for automobile type i in the given vintage t, in Mtons; EFreighti(t) is the energy consumption of per unit cargo turnover for automobile type i in the given vintage t, as MJ; and TEFi(t) is the cargo turnover for vehicle type i in the given vintage t, in ton km 1.
GHG and pollutant emissions The equation of pollutant emissions of a vehicle is as follows: X EjðtÞ¼ TEDiðtÞ EFijðtÞ ð4Þ where E is the pollutant emissions, in tons (toe); j is the pollutant type; t is the vintage; TEDi(t) is the energy consumption of vehicle type i in the given vintage t, in tons; and EFij(t) is the pollutant emissions of pollutant type j in the given vintage t,as kg km 1. Appendix D lists each fuel of emission factors and caloric values.
Change in air pollutant concentration
The main contributors to human health damage are both the exposure time and pollutants concentration. Therefore, it is necessary to translate pollutant emissions into changes in the concentrations of pollutants. Pollutant concentration is rela- tive to the emissions from fuel consumption and the emission height. The calculation function according to Garbaccio et al. (2000) is as follows:
C ¼ clow Elow ð5Þ where C is the pollutant’s actual concentration, in lgm 3, and c is the transfer coefficient for a given level: high, medium, and low refer to different emission heights; and E is the emissions of the pollutant. In this study, the height of emissions from road transportation is at the low level. Concentration coefficients are listed in Appendix E. c is as defined by Lvovsky and Hughes (1997) and is used to reduce form coefficients to estimate concentrations.
Exposure–response (ER) coefficients and the economic value of health damage
A set of data, covered 113 major cities, from Ministry of Environmental Protection of China reveal the average air pollu- 3 3 3 tants concentrations were 87 lgm for PM10, 38 lgm for NO2 and 42 lgm for SO2 in 2009 (CMEP, 2010), and over 50% of NOx is from traffic-related emissions. Especially in extremely large conurbations, this proportion had reached to 80% (Zhou, 2009). In Beijing, the share of total PM10 concentration from motor vehicles is 30% (Guo et al., 2010). Single-factor Poisson regression analysis showed that there were significant positive relationship between all air pollu- tants and daily mortality, except for the associations between TSP and coronary heart disease (CHD) deaths (Kan et al., 2012).
Diseases caused by PM2.5, SO2 and NO2 emissions were therefore calculated using functions (6)–(8):
ðbj CjkÞ Hij ¼ðe 1Þ C0j P ERij ð6Þ HD ¼ H VER ð7Þ ij Xij ij HDV j ¼ HDij ð8Þ i where i is the type of disease; j is the type of pollutant; k is the scenario (BAU or INT); Hij is the number of cases of disease i caused by the pollutant j; bj is the estimated coefficient for pollutant j, see Appendix F; Cjk is the concentration of pollutant j for scenario k (BAU or INT); C0j is the reference concentration of pollutant j; P is the population; ERij is the coefficient of expo- sure–response; HDij refers to the public health damage of Hij; VERij refers to economic value of the damage Hij per case showed in the last column of Appendix G.1–3; and HDVj refers to the total public health damage by air pollutant-related energy demand. The economic value of damage is adjusted in the light of income level differences among in the different 3 3 3 areas. The concentration parameters were set as: 12 lgm for PM2.5 (EPA, 2011), 5 lgm for SO2 (Guo, 2007), 40 lgm for NO2 (WHO, 2008), and 20.5% for NO2/NOx (Boulter et al., 2007).
Cost estimation
Co-benefits of reducing air pollutants and GHGs emissions for sustainable transportation strategies have been proved. However, economic cost is a key factor to determine whether such a strategy should be implemented in the reality. There X. Xue et al. / Transportation Research Part D 35 (2015) 84–103 89 may be benefits of public health or environment for promoting transportation strategies. But if the costs are considered to be too high, such an effort may not be adopted. So it is necessary to estimate the economic cost of policies implementation. Based on definitions of these options, the economic costs of MVC, PNEV and FT options are suitable to estimate with the cost of each vehicle form Geng et al. (2013). This method, considering the acquiring vehicle cost, fuel cost and maintenance cost, is calculated using functions (9), (10):
Ctotal r YEAV ¼ ð9Þ 1 ð1 þ rÞ n
YAVC ¼ YEAV þ CAMC þ Cfuel ð10Þ where YEAV is the vehicle acquiring costs within the service life; YAVC is the total cost containing the vehicle acquiring cost, maintenance cost as well as fuel cost. Ctotal is total vehicles acquiring cost; CAMC is annual average maintenance cost; Cfuel denotes annual average fuel cost; r is depreciation rate, which is set 0.064 in this study by Xiamen Local Taxation Bureau (XLTB, 2014); n is service life. The price of fuel in the future is from IEA (see Appendix I). The data for vehicle costs (see Appendix H) are offered from Xiamen Public Transport Group, vehicle market analysis (CCID Consulting, 2010) and inter- views with local drivers. The economic costs of PB and FER options are calculated by the costs of biofuel production and tech- nology of improving fuel efficiency, respectively. The cost of biofuel production is 5.63 yuan l 1 (Zhang et al., 2008). Currently to improve vehicle’s fuel efficiency is mainly through the implication of highly turbocharged diesel engines (Binder and Schwarz, 2004; Abd-Alla, 2002). From the survey of vehicles market, we found that the vehicle with turbo- charged diesel engines could save 1.6 l fuel per hundred kilometers and had a 30,000 yuan price higher than traditional vehi- cle. Therefore the cost of improving fuel efficiency for the FER option can be calculated through total travel distance per year (see Appendix C).
Results and discussion
Energy demand
The energy demand results in Xiamen’s road transportation sector from 2008 to 2025 are shown in Fig. 2. The total energy demand under the BAU scenario grows from 0.86 million tons in 2008 to 2.78 million tons in 2025, with an annual increase rate of 7.15%. Under the INT scenario, the energy demand in 2025 is 1.88 million tons, with a 4.71% annual growth rate. The series of new fuel-utilization, energy-saving measures and options would play an obvious role in restraining the growth of traffic-related energy demand by 2.43% annually.
GHGs and air pollution emissions
GHGs and air pollution reductions under the different scenarios and options are displayed in Fig. 3.InFig. 3a, the GHG emissions are in line with energy-demand under the BAU and INT scenarios. GHG emissions were 2.59 million tons CO2e in 2008, and under the BAU scenario these would increase to 8.37 million tons CO2e in 2025, with an average annual growth rate of 7.14%. Under the INT scenario, the emissions would rise to 5.35 million tons CO2e in 2025, with a dramatically lower annual increase rate of 2.78%. Fig. 3b shows the potential mitigation of GHGs emissions and air pollution for each option. The results indicate that by saving energy and utilizing new fuels, mitigation effects would be amplified with the implementation of a series of measures and options planned for Xiamen. Among these options, the greatest contribution to GHGs emission reduction in 2025 is the MVC option, which achieves nearly 22% contribution rate, followed closely by the FT option, also about 20%, while the PB option would contribute only about 5%. However, the greatest contribution to air pollution reduction is from the PB option, with a contribution rate of nearly 30%, followed by the MVC option, while other options contribute less than 14%. The MVC option contributes the largest reduction for GHG emissions because the transportation sector relies primarily on fossil fuels. However, the PB option contributes the most for air pollution reduction and the least for GHGs reduction because biofuels are mostly ethanol-gasoline blended fuels and therefore produce air pollution emissions but not GHG emissions.
Public health costs and transport options economic costs
According to analysis the costs of the public health and traffic strategies, the results are display in Fig. 4. The Fig. 4a illus- trates the public health costs attributable to traffic-related air pollution emissions under different scenarios from 2008 to 2025. A 3% discount rate is used (the base year 2008), based on WHO guidelines (WHO, 2001). The public health costs were 0.17 billion yuan (95% CI: 0.08, 0.31) in 2008, and under the BAU scenario would be 0.49 billion yuan (95% CI: 0.71, 1.18) and the average annual growth rate is 9.75% in 2025. Under the INT scenario, this would reach only 0.26 billion yuan (95% CI: 0.24, 0.64) which has a 5.64% of average annual growth rate during the same period. In terms of public health costs, those caused by NO2 emissions are the largest. Comparing the BAU scenario to the INT scenario, however, the benefit to public health caused by PM2.5 reduction would significantly increase, from 5.86% to 69.3%, during the period 2009–2025. The
NO2 reduction contribution is less, although still measurable, while the SO2 contribution is negligible. 90 X. Xue et al. / Transportation Research Part D 35 (2015) 84–103
Fig. 2. Total direct energy demand in Xiamen’s road transportation sector under the BAU and INT scenarios for the period 2008–2025.
The Fig. 4b indicates the results of economic costs and public health benefits in the INT scenario with five options in 2025. Economic costs are analyzed which consider budgets on change types of vehicle and fuel (namely diesel, gasoline, CNG, elec- tricity and HEV), as well as the cost of production biofuel and new technology development for improving fuel economy. Due to different options with different adjustment, there exists a great costs disparity between these five options. In the INT sce- nario, the total economic cost is about 1775 million yuan in 2025. Among the options, the greatest contribution to economic cost in 2025 is the FER option, which contributes more than 50%, followed by PB option (24%), PNEV option (16%), MVC option (4%) and FT option (1%). However, there is an obviously different performance in public health benefits comparing to economic costs for these options. The greatest contribution to public health benefit is the PB option with a 30% contribu- tion rate, followed by MVC option (23%), FT option (19%), PNEV option (17%) and FER option (14%).
Integrated analysis of mitigation measures
Fig. 5a illustrates that the structure of Xiamen road transportation is capable of achieving energy demand mitigation and fuel economy under the INT scenario. Compared with BAU scenario, the INT scenario could achieve an energy-consumption reduction of about 68%, and save 0.397 billion yuan (95% CI: 0.27, 0.537) in health costs in 2025. The controls on PVs would achieve the largest effect, leading to GHG emission and energy decreases of more than 70% and 60% of the total reductions, respectively, in 2025, while the controls on GRVs would result in NO2 and PM2.5 reduction contributions of more than 40% and 50%, respectively. The controls on trucks would reduce SO2 emissions by about 58%, but would slightly increase the GHG X. Xue et al. / Transportation Research Part D 35 (2015) 84–103 91
Fig. 3. GHG and air pollution reductions under different scenarios and options. (a) GHG and air pollution emissions in Xiamen’s road transportation sector under the BAU and INT scenarios during the period 2008–2025. (b) GHG and air pollution emission reductions for different options under the INT scenario compared with the BAU scenario, in 2008 and 2025. emissions and energy consumption, in 2025. These mixed results are due mainly to the implementation of the biofuel option for trucks, which would result in slightly higher GHG emissions and energy consumption. Combining the environmental co-benefits and economic costs under a series of options and measures, Fig. 5b displays the best combination of effects on GHGs and air pollution reductions with public health benefits and economic costs. The results show that the MVC and FT options of enacting control on trucks, GRVs and PVs would give the largest co-benefits. In addi- tion, FER option which would tighten the control on trucks presents good performance on reduction SO2 emissions. Although the PB option plays an important role in air pollution reduction, the economic cost of biofuel production is about 3 times larger than benefit. Therefore, the PB option is not the best choice, while the MVC and FT options are the potential choice due to their good comprehensive performance.
Discussion
Fig. 2 indicates that control of energy demand potential in Xiamen will gradually increase, with the implementation of a series of measures and options for energy demand reduction. Among all these options, the MVC option would account for 92 X. Xue et al. / Transportation Research Part D 35 (2015) 84–103
Fig. 4. Public health costs impacts of traffic-related air pollution and measures economic costs under different scenarios and options. (a) Public health cost of traffic-related air pollution under the BAU and INT scenarios from 2008 to 2025. Public health costs caused by SO2 are negligible and not shown in the figure. (b) The economic costs and public health benefits in the INT scenario with the five options in 2025. nearly 43%, which dominates the GHGs reduction effects from 2009 to 2025, followed by the FT, FER and PNEV options. The PB option contribution would be less significant. Comparing Fig. 3 data with Fig. 2, under the BAU scenario, both the energy consumption and GHGs emissions would maintain about a 7% annual growth rate. Under the INT scenario, however, the energy demand annual growth rate, obviously lower than the rate of the BAU scenario, would be 4.71%, while the GHGs emissions annual growth rate would be 2.78%, lower than the rate of energy demand, due to the introduction of both the MVC and FT options. This result argues that policy measures and options play a more considerable role in GHGs reduction than in energy utilization reduction. By 2025, the relative reduction contributions of GHG emissions and air pollution will have changed to 22% for MVC, 14% for FER, 14% PNEV, 20% for FT, and 5% for PB, respectively, and details in Appendix J. Total air pollution emission is estimated to increase from 65.9 thousand tons in 2008 to 176.5 thousand tons in 2025 under the BAU scenario, with an average annual growth rate of 5.97%. Under the INT scenario, the air pollution emission is estimated to grow to up to 109.9 thousand tons in 2025, with a 3.05% annual growth rate—almost 3% lower than under
BAU. In terms of air pollution emissions, NO2 emissions are dominant. Under the BAU scenario, NO2 emissions would increase from 56.9 thousand tons in 2008 to 153.5 thousand tons in 2025, with a 6.01% annual growth rate, followed by
SO2 emissions with a 5.38% annual increase. PM2.5 emissions would be less than the other two air pollutants, but have the largest annual growth rate of 6.39%. Under the INT scenario, NO2 emissions would comprise the largest pollutant and increase to 97.1 thousand tons in 2025, with an annual increase rate of 3.19%, followed by SO2 emissions with an annual increase rate of 2.35%. However, due to implementation of a series of measures and options for air pollution reduction, PM2.5 shows a significant reduction effect with an increase rate of only 1.42% annually. For 2008, we estimated that traffic-related air pollution led to a 0.37% mortality rate, compared to all causes. Guo et al. (2010) estimated that 2207 mortality cases from Beijing were caused by traffic-related air pollution—a nearly 3% mortality compared to all causes in 2008, about 10 times the Xiamen public health damage. This difference is due mainly to the size of the vehicle fleet in Beijing, which has reached 3.5 million (BTMB, 2008), compared with 576,100 in Xiamen (XCBS, 2009). In addition, Beijing’s population is 16.95 million (BSB, 2009), almost 7 times that of Xiamen (XCBS, 2009). This dramatic X. Xue et al. / Transportation Research Part D 35 (2015) 84–103 93
Fig. 5. Mitigation of emissions and energy utilization under different road transportation sector scenarios and options with economic costs in 2025. (a) Energy and emission reduction ratios compared between the INT and BAU scenarios in 2025. (b) Optimal scenarios of air pollutant and GHG emission mitigations under the different options in 2025. The lines present which air pollutant and GHG reductions have the best effects on the road transportation sector. The blue line represents optimal scenarios of SO2 emission reductions with truck controls; the orange line represents optimal scenarios of NO2 and PM2.5 (both have the same performance) emission reductions with GRV controls; the green line represents optimal scenarios of GHG emission reductions with PV controls. difference results in a huge challenge for motor vehicular emissions management. Based on the public heath endpoints and the mitigation of air pollution emissions, Table 2 lists the avoided cases of death and disease attributable to NO2,SO2 and PM2.5 reduction by 2025, by each of the five road transportation options, as well as the forecasting public health benefits under the different energy utilization and road transportation control options. The results suggest that the best public health benefits will be achieved under the PB option, with a contribution rate of more than 30%, followed by the MVC option. Over- all, the INT scenario, if implemented, would notably impact public health, avoiding 571 (95% CI: 180, 682) PM2.5-related deaths and details in Appendix K. Our result is similar to that found by Grabow et al. (2011), a reduction in PM2.5-related mortality across the Midwest in the USA, with a total impact of 525 fewer deaths through reducing automobile use for short urban and suburban trips. This integrated assessment of the implementation of air pollution control measures focusing on road transportation has shown how ambitious urban transportation strategies can result in both co-benefits and trade-offs, including lower GHG emissions, improved public health and economic costs. For the MVC option, the public health benefit is a little larger than the economic cost, which suggests that the MVC option implication is necessary. However, two parallel strategies are required to implement this option. First, urban planning should attempt to decrease the transportation demand. Fortunately, the Xiamen municipal government is already aware of this need and has recently employed mixed-use functional planning. 94 X. Xue et al. / Transportation Research Part D 35 (2015) 84–103
Table 2
Health benefits of air pollution reduction for five options under the INT scenario compared with the BAU scenario in 2025 (PM2.5 and SO2 with 95% CI, NO2 with 90% CI).
Transport options Reduction in cases Cost benefit for public health (million yuan)
NO2 (death cases) PM2.5 (death cases) SO2 (all cases) NO2 (death cases) PM2.5 (death cases) SO2 (all cases) MVC 1259 467 150,145 78 29 0.12 (551, 2051) (356, 575) (13,571, 16,458) (33, 127) (19, 38) (0.06, 0.19) FER 819 241 150,145 51 16 0.12 (357, 1337) (136, 346) (13571, 16458) (22, 83) (10, 20) (0.06, 0.19) PNEV 966 287 68,895 60 18 0.06 (421, 1576) (180, 400) (65,859, 71935) (26, 98) (11, 24) (0.05, 0.07) FT 1070 332 150,145 66 21 0.12 (467, 1744) (224, 438) (13,571, 16,458) (29, 108) (11, 31) (0.05, 0.19) PB 1722 571 249,775 106 35 0.20 (755, 2797) (459, 682) (232,413, 267,150) (46, 173) (22, 48) (0.13, 0.27)
Secondly, public transportation needs to be improved to meet the increased travel demand. In Xiamen, public transportation accounted for 31% of travel, and private cars and motorcycles for 8.21% and 12.56%, respectively, in 2009 (XCBS, 2010). There is also further potential through the implementation of transit-oriented development. And finally, the government’s rigid regulations for private car usage can also cut down on private motor vehicle use. For instance, the Beijing and Shanghai Municipal governments have both adopted policies to restrict the emption and use of private vehicles. Our results show that fuel economy regulations, either for public health benefit or economic cost, will be a less effective strategy than the MVC option. Nevertheless, FER is a critical option for decreasing vehicular energy-related demand and emissions. The vehicle fuel economy standard implemented by the Chinese government is already more rigorous than those in Canada, the USA and Australia, although it is less severe than those in the European Union and Japan (Yan and Crookes, 2010). Wagner and An (2009) indicated that technologies promoting fuel economy have been more frequently adopted in vehicles produced in China than anywhere else; the vehicles with enhancing fuel economy were estimated to have improved from 9.11 to 7.95 l/100 km between 2002 and 2006. Considering all the significant potential technical solutions, FER option will have con- siderable impact on fuel demand and emissions over the next few decades. Strengthened fuel economy regulations are likely to be introduced in the near future in China (Yan, 2008). Another key factor, for both air pollution and GHG reductions, is fuel quality, because the better this matches the level of vehicle emission control technologies, the greater the emission reduction that can be achieved (Liu et al., 2008). Fig. 5 shows that the PNEV option for the road transportation sector would not, by itself, produce the best performance for reducing GHG and air pollution emissions. Moreover based on economical analysis, the cost of PNEV option is the third largest options (Fig. 4b). Therefore, the government should improve vehicle requirements to meet air pollutant emissions standards that could advance from the Euro III to the Euro IV models in China (Wang et al., 2010). In addition, this might encourage on research green vehicle with lower cost and find a reasonable balance in investment and return. The Xiamen City Transportation Sys- tem should be dominated by LPG, CNG, electricity and other clean fuels, instead of relying mainly on fossil fuel consumption, as is currently the case. The government should implement vehicle fuel-saving rate subsidies, especially electric cars. These could support the development of hybrid cars and encourage consumers to buy new energy-efficient vehicles. The FT option, tightening controls on PVs, GRVs and trucks, would be able to have a dramatic integrated performance on air pollution and
GHG emissions (seen in Fig. 5b) and its economic cost is greatly less than other options. The PB option is significant for SO2, NOx, and PM2.5 emission reductions from automobiles, although it has little effect on GHG reduction. While it is agreed that the reduction of air pollution is a laudable goal, especially in the developing world, and that the promotion of biofuel use would increase diversity of energy sources and encourage sustainable environmental stewardship (Hubbard, 2010), the main barrier to implementing the PB option is the high production cost for biofuel and the increasing awareness of its adverse consequences on food security, which has always been of paramount importance to the Chinese government. The best solu- tion to this quandary would be to switch the source of biofuels from feedstock grain to non-food sources. Although the MVC and FT options assume good results for mitigation co-benefits, Shaw et al. (2014) argued that soft pol- icies such as personalized travel planning should be encouraged for behavior change in transportation. Because soft policies are most commonly implemented, or because they are discrete projects that seem more amenable to evaluation. Therefore, we should concern more some soft policies in transportation reform to achieve a better environmental and public health benefits in future work. Furthermore, in this study we only consider the economic benefit of public health and neglect the economic benefit of GHGs mitigation. The reason is that there has not carbon market in Xiamen and no carbon trade occurs yet. In China the carbon market has launched in some pilot cities such as in Beijing, Shanghai and Shenzhen. There- fore, with the development of carbon market in Xiamen, this part of benefit should also be analyzed in the estimation of co- benefits. Considering that, with the rapid urbanization of the past decade or so, the number of private vehicles skyrocketed to 93.56 million in 2011 from 16.09 million in 2000 (Chen et al., 2013), it is critical to conduct a quantitative study on the road X. Xue et al. / Transportation Research Part D 35 (2015) 84–103 95 transportation sector at the city scale, so that appropriate strategies can be pursued toward mitigating the consequent neg- ative effects. This study has evaluated only the direct emissions of road vehicles, and near-term and local co-benefits of air pollutant reductions. Some studies have found that, even with improvements in the road transportation sector, emissions would still be high, due to the dominant utilization of coal in power grid (Geng et al., 2013) and the influence on health of the long-range cumulative effects of exposure to air pollutants (West et al., 2013). Therefore, our evaluation of mitigation co-benefits might be an underestimation.
Conclusions and policy implications
Although a lot of literatures link public transport use (i.e., Badland et al., 2014; Department of Infrastructure and Transport, 2013; Daniels and Mulley, 2013) and driving with effects of environment and health (i.e., Solis et al., 2013; Kelly et al., 2009; Kousoulidou et al., 2008; Deng, 2006), not many of studies focus on the trade-off with transport energy consumption, traffic-related GHGs and air pollution emission, benefits for public health, policy scenarios and the cost of implementation options. In this study, we build a PHAGE (Public Health and GHGs Emission) model to research the multi dimension relationship, including transport energy consumption, traffic-related GHGs and air pollution emission, benefits for public health, policy scenarios and the cost of implementation options. Furthermore, we analyze and discuss which one of traffic-related options has most effective to which pollution reduction and to which kind of vehicles. The results of our study indicate that the traffic-related energy use under different scenarios could not only impact the air quality and climate change, but also lead to substantial public health benefits in Xiamen. Co-benefits of air quality and public health can provide strong complementary motivations for transitioning to a future low carbon city. Our study shows that the MVC and FT options focus on the control of trucks, GRVs and PVs, and would have the best effect on GHGs and air pollution mitigation. Although the addition of the PB option would further increase air pollution reduction, the high cost for biofuel production and impact on food security may be a great stumbling block. Therefore, important health gains and reductions in GHGs and air pollution emissions could be achieved through a major overhaul of urban transportation structure, by decreasing the use of private motor vehicles, trucks, and government and rental vehicles, and that further improvements could be achieved by encouraging the use of biofuels. Increased use of more fuel-efficient vehicles might reduce emissions, but the health effects of this option (PNEV) would be smaller. Included in this last option might be increased incentives for motorcycle use, as that could result in more active travel and consequent health benefits. Overall, a successful strategy must include an integration of the above elements. Although there are some uncertainties in the estimate of the benefits for public health and the costs of implementation measures for GHGs and air pollutants emission, it is an important scenario-based health benefit evaluation of air pollution reduction for Xiamen City. Many of studies have sighted on megalopolis with more vehicle and wider region, example for Beijing, Shanghai, Guangzhou, etc. However, few studies focus on some second-tier cities with limit region and rapidly growing population, i.e. Xiamen. Therefore, our study also could provide formulation and implementation of transportation police as reference for some small-medium cities. These policy scenarios give us great promise in co-benefits of environment and public health, but achieving these options in really might have many obstacles, especially the developing country-China. Although the central government has realized the importance to build ‘‘energy saving, environment friendly’’ society, the local government is under pressured by economic development to promote private vehicles market which become a critical barrier for improving and control overfull private vehicles (Pan, 2011). China has paid ten million Yuan to carry out new technology for development electrical vehicles. How- ever, growing EVs market would require stable grid systems, adequate training of professionals and lower prices to ensure safety and achievement which could gain consumer confidence and favor (Brown et al., 2010). Biofuels have be able to reduce GHG emissions by more than 80% (Biofuels & greenhouse emissions: myths versus facts, http://www.energy.gov/), but these biofuels from crop which become growing concern due to food security. Therefore, it is critical important to diver- sify the raw materials of production biofuels to balance biofuel demand and food security (Yan and Crookes, 2009). Promo- tion implementing of fuel tax would achieve the co-benefits of environment and health and might change consumer behavior (Mao et al., 2012), but the key barrier to realizing the FT option is the government attitude. The Chinese growing economic could be slow down because of higher fuel prices and lower competitiveness of export goods. Moreover, it is dif- ficult to distinguish road vehicle fuel use and non-road vehicle fuel use (Yan and Crookes, 2009); and if government takes the refund after collection way for all vehicles fuel use, how to guarantee the non-road vehicle fuel use of taxes ‘‘back’’ is also a difficult problem in a large and populated country like China. Therefore, in a developing country like China with rapid increasing population and economic, how to formulate effective measures to improve transportation is really a problem. The realization of sustainable urban transportation development should encourage and implement valid transport management policies, build friendly and economic infrastructure construc- tion and develop speedy and accurate traffic information technologies. Furthermore, the low-carbon transport should be achieve to use clear fuel and biofuel and change transport modes (i.e. cycling and walking) for cut down GHGs and pollution emissions. The third, the government should introduce the concept of energy-saving day by day toward public and encour- age to purchase more efficient and economic vehicles. More attention needs to be paid to estimation of implementation pol- icies and infrastructure relative to transport strategies. These strategies, along with changes in urban form and land use, may deliver larger and more enduring change, and thus greater health and GHGs benefits, which redress the current pervasive favoring of unsustainable travel. Certainly this assumption needs to be studied empirically. Although further research will 96 X. Xue et al. / Transportation Research Part D 35 (2015) 84–103 undoubtedly provide more robust estimates, the methods and findings presented in this study can be helpful in developing integrated transportation strategies that can achieve the genuine co-benefits.
Acknowledgments
We thank the anonymous reviewers for their useful comments and discussion. This study is supported by National Key Technology R&D Program, China (2012BAC21B03) and National Nature Science Foundation of China (41371205).
Appendix A. Vehicle classifications for Xiamen City
Section End-use Abbreviation Fuel type Public transit (PT) Bus Rapid Transit BRT Diesel/CNGa/Elec Bus – Diesel/LPGb/Ele
Private vehicle (PV) Hybrid electric vehicle HEV Gasoline Compressed natural gas vehicle CNG-V CNG Electric vehicle EV Ele Gasoline vehicle GV Gasoline
Taxi Liquefied petroleum gas vehicle LPG-V LPG Compressed natural gas vehicle CNG-V CNG Electric vehicle EV Ele
Government and rental vehicle (GRV) Light-duty vehicle LV Gasoline/CNG/Ele Large or medium-sized vehicle L-MV Diesel/CNG/Ele
Motorcycle (MC) Motorcycle MC Gasoline
Truck Diesel vehicle DV Diesel
a Compressed natural gas. b Liquefied petroleum gas. c Electricity.
Appendix B. Major parameters used in the LEAP-Xiamen model
Scenario fuel/technology CUR BAU INT (%) 2008 2015 2020 2025 2015 2020 2025 PT Existinga Bus 90.3 90.0 87.3 83.5 78.5 70.0 65.0 Gasoline 100 100 100 100 52.3 34.8 17.4 CNG 0 0 0 0 15.0 20.0 25.0 Electricity 0 0 0 0 10.0 11.7 13.3 HEV 0 0 0 0 10.0 11.7 13.3 B20 0 0 0 0 12.7 21.8 30.9 BRT 9.7 10.0 12.7 16.5 21.5 30.0 35.0 Dieseline 100 100 100 100 59.1 39.4 19.7 CNG 0 0 0 0 15.0 20.0 25.0 Electricity 0 0 0 0 3.2 5.5 7.7 HEV 0 0 0 0 10.0 13.3 16.7 B20 0 0 0 0 12.7 21.8 30.9
PV Gasoline 100 100 100 100 62.7 41.8 20.9 CNG 0 0 0 0 3.0 5.3 7.7 HEV 0 0 0 0 9.0 9.3 9.7 Electricity 0 0 0 0 3.0 5.3 7.7 E10 0 0 0 0 22.3 38.2 54.1 X. Xue et al. / Transportation Research Part D 35 (2015) 84–103 97
Appendix B (continued) Scenario fuel/technology CUR BAU INT (%) 2008 2015 2020 2025 2015 2020 2025
Taxi Gasoline 53 53 53 53 37.3 28.2 19.1 CNG 0 0 0 0 15.0 20.0 25.0 LPG 47 47 47 47 20.0 10.0 0.0 Electricity 0 0 0 0 15.0 20.0 25.0 E10 0 0 0 0 12.7 21.8 30.9
GRV Gasoline 50 50 50 50 31.4 20.9 10.5 CNG 0 0 0 0 3.0 5.3 7.7 HEV 0 0 0 0 9.0 9.3 9.7 Electricity 0 0 0 0 3.0 5.3 7.7 E10 0 0 0 0 22.3 38.2 54.1 Dieseline 50 50 50 50 34.6 26.4 18.2 CNG 0 0 0 0 3.0 5.3 7.7 HEV 0 0 0 0 9.0 9.3 9.7 Electricity 0 0 0 0 3.0 5.3 7.7 B20 0 0 0 0 15.9 27.3 38.6
Truck Dieseline 100 100 100 100 84.1 72.7 61.4 B20 0 0 0 0 15.9 27.3 38.6
PV Growth rate 16.8 16.8 13.0 11.0 8.0 2.4 1.3 GRV Growth rate 16.8 16.8 13.0 11.0 8.0 2.4 1.3 MC Growth rate 14.6 14.6 11.2 8.9 1.6 0.8 0.1 Truck Growth rate 14.6 14.6 11.2 8.9 1.6 0.8 0.1
a Existing vehicles refers to ordinary appliances, as compared with energy-efficient vehicles.
Appendix C. Parameter about travel distances in the LEAP-Xiamen model
Scenario fuel/technology (billion km) CUR BAU INT 2008 2015 2020 2025 2015 2020 2025 PT Existinga Bus Gasoline 0.53 0.67 0.78 0.84 0.3 0.21 0.11 CNG 0 0 0 0 0.09 0.12 0.15 Electricity 0 0 0 0 0.06 0.07 0.08 HEV 0 0 0 0 0.06 0.07 0.08 B20 0 0 0 0 0.07 0.13 0.19 BRT Dieseline 0.06 0.07 0.08 0.09 0.09 0.1 0.06 CNG 0 0 0 0 0.02 0.05 0.08 Electricity 0 0 0 0 0.005 0.01 0.03 HEV 0 0 0 0 0.02 0.03 0.05 B20 0 0 0 0 0.02 0.06 0.1
PV Gasoline 3.35 9.08 13.89 15.34 4.82 4.62 2.53 CNG 0 0 0 0 0.23 0.59 0.93 HEV 0 0 0 0 0.69 1.03 1.17 Electricity 0 0 0 0 0.24 0.59 0.93 E10 0 0 0 0 1.66 3.96 6.02
Taxi Gasoline 0.19 0.28 0.35 0.39 0.2 0.19 0.14 CNG 0 0 0 0 0.08 0.13 0.19 LPG 0.16 0.25 0.31 0.35 0.11 0.07 0 Electricity 0 0 0 0 0.08 0.13 0.19 E10 0 0 0 0 0.07 0.14 0.23
(continued on next page) 98 X. Xue et al. / Transportation Research Part D 35 (2015) 84–103
Appendix C (continued) Scenario fuel/technology (billion km) CUR BAU INT 2008 2015 2020 2025 2015 2020 2025
GRV Gasoline 0.44 1.26 2.03 2.35 0.63 0.60 0.33 CNG 0 0 0 0 0.03 0.08 0.12 HEV 0 0 0 0 0.09 0.13 0.15 Electricity 0 0 0 0 0.03 0.08 0.12 E10 0 0 0 0 0.1 0.24 0.37 Dieseline 0.44 1.26 2.03 2.35 0.69 0.76 0.57 CNG 0 0 0 0 0.03 0.08 0.12 HEV 0 0 0 0 0.09 0.13 0.15 Electricity 0 0 0 0 0.03 0.08 0.12 B20 0 0 0 0 0.16 0.41 0.66
Truck Dieseline 2.62 4.36 5.06 5.45 3.44 3.61 3.21 B20 0 0 0 0 0.65 1.36 2.02
Motorcycle Gasoline 2.45 3.19 3.52 3.70 3.04 3.30 3.44
a Existing vehicles refers to ordinary appliances, as compared with energy-efficient vehicles.
Appendix D. Calorific values and emission factors of fuel types
1 1 1 Calorific values (kJ kg ) SO2 emission factor (kg TJ ) NO2 emission factor (kg TJ ) Gasoline 43,070 0 200 Diesel 42,652 0.01 200 LPG 50,179 0 200 Natural gas (including LNG) 38,931 0 150 CNG 43,000 0 0
The calorific value of the energy coefficient from the ‘‘general principles of calculation of energy consumption’’ (GB/T-2589-2008); SO2 and NO2 emission factors from the LEAP model come with a parameter table; PM2.5 emission factor from Fujian emission factors in Wang and Huo (2009).
Appendix E. Concentration coefficients resulting from increases in related pollutant emissions
3 3 a 3 3 a 3 3 b Emission height level PM2.5 (lgm 10 t) SO2 (lgm 10 t) NO2 (lgm 10 t) Low 0.09474 0.03364 0.0945
a Wang (2010). b Jenkin (2004).
Appendix F. Coefficients estimated (standard error in parenthesis) (Fernando et al., 2012)
Criteria PM2.5 NO2 SO2 ⁄⁄ ⁄ ⁄⁄ ßj 0.051 0.041 0.070 (95% CI: 0.046, 0.055) (95% CI: 0.033, 0.047) (95% CI: 0.058, 0.078)
⁄ p < 0.05. ⁄⁄ p < 0.001. X. Xue et al. / Transportation Research Part D 35 (2015) 84–103 99
Appendix G.1. Expose–response and valuation estimates
Health effect Cases per 1 mil. People with a 1 lg/m3 Economic losses/single increase case a Due to PM2.5 Mortality (deaths) 7.14 105262.00 Respiratory hospital admissions (cases) 12 3912.00 Acute bronchitis (cases) Emergency room visits 235 4.1 (cases) Lower respiratory infection/child asthma (cases) 23.00 58.0 Asthma attacks (cases) 2608.00 58.0 Chronic bronchitis (cases) 61.20 1470.0 Respiratory symptoms (cases) 183.00 0.966 Restricted activity days (days) 575.0 d 1.75 yuan/d
b Due to SO2 Chest discomfort 10,000 1.38 Respiratory systems/child 5 1.38
c Due to NO2 Cardiovascular disease (deaths) 1.360 105262.00 Respiratory diseases (deaths) 0.523 105262.00 a,b Dose–response data are from the World Bank (World Bank, 1997), updated. c Data are from the Yearbook of the Xiamen Special Economic Zone (XCBS, 2009).
Appendix G.2. Expose–response coefficients
Health effect Change in mortality risk per Reference lg/m3 (95% CI)a a Due to PM2.5 Mortality (deaths) 4.3E 03 (2.6E 03, 6.1 E 03) Xu et al. (1994), Pope and Dockery (1996) and Dockery and Pope (1996) Respiratory hospital admissions 1.2E 03 (1.0E 03, 1.6E 03) Aunan et al. (2004) (cases) Acute bronchitis (cases) Emergency 2.4E 03(1.3 E 03, 3.4E 03) Samet et al. (1981) room visits (cases) Lower respiratory infection/child 1.6E 03 (0.8E 03, 2.4E 03) Dockery et al. (1989) asthma (cases) Asthma attacks (cases) 3.9E 03 (1.9E 03, 5.9E 03) Wang and Mullahy (2006) and Zhang et al. (2008) Chronic bronchitis (cases) 4.0E 03(0.0E 03, 6.0E 03) Abbey et al. (1993) Respiratory symptoms (cases) 0.18 (0.09, 0.27) Krupnick et al. (1990)
Restricted activity days (days) 9.4 E 03 (7.9 E 03, 1.1 Ostro (1990) E 02)
b Due to SO2 Chest discomfort 2.9 E 03 (2.2 E 03, 4.2 Hiltermann et al. (1998) E 03)
Respiratory systems/child 5E 03 (1 E 03, 9 E 03) Atkinson et al. (2001) and Zhou (1997)
c Due to NO2 Cardiovascular disease (deaths) 1.1E 02 (8.1E 03, 1.3E 02) Kan et al. (2008), Qian et al. (2007) and Jia et al. (2004) Respiratory diseases (deaths) 1.5E 02 (9.8E 03, 2.0E 02) Kan et al. (2008), Qian et al. (2007) and Wong et al. (2001)
a 3 The coefficient for mortality is expressed in mortality percent increase per lg/m change in PM2.5, SO2 and NO2, while the coefficient for morbidity is in 3 terms of annual morbidity cases per lg/m annual average change in PM2.5, SO2 and NO2. 100 X. Xue et al. / Transportation Research Part D 35 (2015) 84–103
Appendix G.3. Unit value (per case) for different health endpoints
Health effect Cost per case (95% CI) Reference a Due to PM2.5 Mortality (deaths) 105,262 (67,764, 143,408) CMH (2003–2005) Respiratory hospital admissions (cases) 3912 (3456, 4818) CMH (2003–2005) Acute bronchitis (cases) Emergency room visits (cases) 4.1 (1.9, 10.2) Kan et al. (2004) and CMH (2003–2005) Lower respiratory infection/child asthma (cases) 58 World Bank (1997) Asthma attacks (cases) 58 World Bank (1997) Chronic bronchitis (cases) 1470 (938, 7302) Kan and Chen (2004) Respiratory symptoms (cases) 0.966
b Due to SO2 Chest discomfort 1.38 World Bank (1997) Respiratory systems/child 1.38 World Bank (1997)
c Due to NO2 Cardiovascular disease (deaths) 105,262a Respiratory diseases (deaths) 105,262a
a The available data in Xiamen did not provide the distribution of the values. The approach is willingness to pay (WTP).
Appendix H. Basic information about transportation in the INT scenario
Section Type Cost/104 Yuan Passenger/ Life serves/ Energy intensity/Megajoule Maintenance annual cost/ per unit Person Year per person (or vehicle-km) Thousand Yuan per unit BRT Diesel 90–180 120 4 2.3 6.8–13.7 CNG 85–90 95 4 2.2 3.2–6.8 Electricity 170 66 4 0.3 5.2–14.2 HEV 80 93 4 1.9 4.8–8.9 B20 – – – 2.5 –
BUS Diesel 45–65 52 6 9.5 5.7–11.9 CNG 55 95 6 4.4 3.2–6.8 Electricity 170 66 6 0.7 5.2–14.2 HEV 80 92 6 7.9 4.8–8.9 B20 – – – 10.2 –
PV Gasoline 10 5 15 3.0 2.2–3.8 CNG 8 5 15 2.4 0.9–1.8 Electricity 30 5 15 0.002 2.5–4.5 HEV 12 5 15 0.43 2.8–4.3 E10 – – – 3.25 –
GRV Gasoline 10 5 15 3.0 2.2–3.8 CNG 6 5 15 2.4 0.9–1.8 Electricity 30 5 15 0.43 2.5–4.5 HEV 12 5 15 0.007 2.8–4.3 E10 – – – 3.25 –
Taxi Gasoline 12 5 6 3.0 2.3–4.6 CNG 6 5 6 2.4 2.3–4.6 LPG 11 5 6 2.1 2.7–5.0 Electricity 30 5 6 0.43 3.8–5.7 E10 – – – 3.25 –
MC Gasoline 0.6 2 8 0.57 1.0–4.0
Truck Gasoline 7 2 8 3.8 0.4–0.6 B20 – – – 4.0 –
Biofuel: Only consider as the fuel. Source: interview and market survey performed by authors. X. Xue et al. / Transportation Research Part D 35 (2015) 84–103 101
Appendix I. Fuel price (yuan Megajoule 1) in 2025 (EIA, 2014)
Fuel LPG Biofuel Gasoline Diesel Fuel Natural Gas Electricity Price 0.16 0.17 0.18 0.18 0.11 0.19
Appendix J. Reduction of pollutant emissions and reduction rates of various conservation measures
Pollutant reduction and reduction rate of each option Reduction (104 t) Reduction rate of each option (%) Year 2015 2020 2025 2015 2020 2025 GHG 122.04 234.07 302.22 MVC 13.06 17.86 21.80 FER 4.37 7.54 13.85 PNEV 5.93 8.37 13.67 FT 6.45 11.19 20.19 PB 1.45 2.73 5.07
PM2.5 0.15 0.31 0.41 MVC 13.04 18.95 26.27 FER 4.35 7.37 13.56 PNEV 8.70 10.53 16.10 FT 5.80 10.53 18.64 PB 10.14 17.89 32.20
SO2 0.26 0.43 0.61 MVC 8.77 9.09 14.12 FER 4.39 7.69 14.12 PNEV 1.75 4.20 6.47 FT 4.39 7.69 14.12 PB 8.77 13.29 23.53
NO2 0.50 0.86 1.16 MVC 11.83 15.51 21.99 FER 4.58 7.72 14.23 PNEV 9.16 11.62 16.81 FT 5.92 10.25 18.64 PB 11.16 17.60 30.22
Appendix K. Benefit to public health in air pollution reduction and reduction rate of each option
Benefit to public health (108 yuan) Reduction rate of each option (%)
(PM2.5 and SO2 with 95% CI, NO2 with 90% CI) 2015 2020 2025 Reduction of air pollution 2015 2020 2025 MVC 12.19 16.60 23.36 PM2.5 0.32 0.69 1.04 FER 4.52 7.61 14.01 (0.22, 0.41) (0.03, 1.35) (0.5, 1.83)
SO2 0.0011 0.0021 0.0033 PNEV 9.02 11.26 16.56 (0.0004,0.0018) (0.0008, 0.0032) (0.002, 0.0047)
NO2 0.41 0.83 1.29 FT 5.89 10.34 18.63 (0.18, 2.02) (0.37, 2.85) (0.56, 2.09)
Benefit 0.72 1.53 2.34 PB 10.86 17.69 30.85
Appendix L. Supplementary material
Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/ j.trd.2014.11.011. 102 X. Xue et al. / Transportation Research Part D 35 (2015) 84–103
References
Abbey, D.E., Petersen, P.K., Mills, F., Beeson, W.L., 1993. Long term ambient concentrations of total suspended particulates, ozone, and sulfur dioxide and respiratory symptoms in a non-smoking population. Arch. Environ. Health 46 (1), 33–46. Abd-Alla, G.H., 2002. Using exhaust gas recirculation in internal combustion engines: a review. Energy Convers. Manage. 43, 1027–1042. AQSIQ, SAC, 2006. Limits of Fuel Consumption for Light Duty Commercial Vehicles (Draft).
Boulter, P.G., McCrae, I.S., Green, J., 2007. Primary NO2 Emissions from Road Vehicles in the Hatfield and Bell Common Tunnels. Transport Research Laboratory. Brown, S., Pyke, D., Steenhof, P., 2010. Electric vehicles: the role and importance of standards in an emerging market. Energy Policy 38 (7), 3797–3806. Burnett, R.T., Cakmak, S., Brook, J.R., 1998. The effect of the urban ambient air pollution mix on daily mortality rates in 11 Canadian cities. Can. J. Public Health. Revue canadienne de santepublique 89 (3), 152–156. CCID Consulting, 2010. China’s New Energy Vehicle Market Analysis and Forecasting in 2010–2011 (in Chinese). Chavez-Baeza, C., Sheinbaum-Pardo, C., 2014. Sustainable passenger road transport scenarios to reduce fuel consumption, air pollutants and GHG (greenhouse gas) emissions in the Mexico City Metropolitan Area. Energy, 1–11. Chen, Z., Wang, J.N., Ma, G.X., et al, 2013. China tackles the health effects of air pollution. Lancet 382, 1959–1960. China Ministry of Health (CMH), 2003–2005. China Statistical Yearbook of Public Health. Peking Union Medical College, Beijing. Chinese Ministry of Environmental Protection (CMEP), 2010. China Environmental Yearbook. China Environmental Yearbook Inc., Beijing. Cui, S.H., Meng, F.X., Wang, W., et al, 2011. GHG accounting for public transport in Xiamen city, China. Carbon Manage. 2 (4), 383–395. Cui, S.H., Niu, H.B., Wang, W., et al, 2010. Carbon footprint analysis of the Bus Rapid Transit (BRT) system: a case study of Xiamen City. Int. J. Sustain. Dev. World Ecol. 17 (4), 329–337. Daniels, R., Mulley, C., 2013. Explaining walking distance to public transport: the dominance of public transport supply. J. Transport Land Use 6, 5–20. Department of Infrastructure and Transport, 2013. Walking, Riding, and Access to Public Transport: Supporting Active Travel in Australian Communities. Commonwealth of Australia, Canberra. Dhakal, S., 2003. Implications of transportation policies on energy and environment in Kathmandu Valley, Nepal. Energy Policy 31, 1793-1760. Dockery, D., Pope III, C.A., 1996. Epidemiology of Acute Health Effects: Summary of Time-Series Studies. Harvard University Press. Dockery, D., Ware, J.W., Ferris, B.G., et al, 1989. Effects of inhalable particles on respiratory health of children. Am. Rev. Respir. Dis. 139, 587–594. Fernando, R.S., Ubiratan, P.S., Paulo, S., et al, 2012. Health risks and economic cost of absenteeism due to air pollution in Sao Paulo, Brazil. Aerosol Air Quality Res. 12, 826–833. Garbaccio, R.F., Ho, M.S., Jorgenson, D.W., 2000. Modeling the Health Benefits of Carbon Emissions Reductions in China. Kennedy School of Government, Harvard University, Cambridge. General Administration of Quality Supervision, Inspection and Quarantine of the PRC (AQSIQ), Standardization Administration of the PRC (SAC), 2004. Limits of Fuel Consumption for Passenger Cars, National Standard of the PRC: GB 19578-2004 (in Chinese). Geng, Y., Ma, Z.X., Xue, B., et al, 2013. Co-benefit evaluation for urban public transportation sector—a case of Shenyang, China. J. Cleaner Prod., 1–10. Grabow, M.L., Spak, S.N., Holloway, T., et al, 2011. Air quality and exercise-related health benefits from reduced car travel in the mid-western United States. Environ. Health Perspect. 120, 68–76. Guo, X.B., 2007. Some new researches of air pollutions on health effects. J. Peking Univ. (Health Sci.) 39 (2), 116–118 (in Chinese). Guo, X.R., Cheng, S.Y., Chen, D.S., et al, 2010. Estimation of economic costs of particulate air pollution from road transport in China. Atmos. Environ. 44, 3369–3377. He, L.Y., Chen, Y., 2013. Thou shalt drive electric and hybrid vehicles: scenario analysis on energy saving and emission mitigation for road transportation sector in China. Transp. Policy 25, 30–40. Hill, J., Polasky, S., Nelson, E., et al, 2008. Climate change and health costs of air emissions from biofuels and gasoline. Proc. Natl. Acad. Sci. USA 106 (6), 2077–2082. Hiltermann, T.J., Stolk, J., Brunekreef, B., et al, 1998. Asthma severity and susceptibility to air pollution. Eur. Respir. J. 11, 686–693. Huang, W.M., Lee, G.W.M., 2009. GHG legislation: lessons from Taiwan. Energy Policy 37, 2696–2707. Hubbard, P., 2010. Biofuels and the developing word. Bologna Center J. Int. Aff. 13. International Energy Agency (IEA), 2008. World Energy Outlook 2008. OECD/IEA, Pairs. Jack, D.W., Kinney, P.L., 2010. Health co-benefits of climate mitigation in urban areas. Curr. Opin. Environ. Sustain. 2 (3), 172–177.
Jenkin, M.E., 2004. Analysis of sources and partitioning of oxidant in the UK—Part 1: The NOX-dependence of annual mean concentrations of nitrogen dioxide and ozone. Atmos. Environ. 38 (30), 5117–5129. Jia, J., kan, H.D., Chen, B.H., et al, 2004. Association of air pollution with daily mortality in Zhabei District of Shanghai: a case-crossover analysis. J. Environ. Health 21, 279–282 (in Chinese). Kahn, R., Kobayashi, S.S., Beuthe, M., et al, 2007. Transport and its infrastructure. In: Metz, B., Davidson, O.R., Bosch, P.R., Dave, R., Meyer, L.A. (Eds.), Climate Change 2007: Mitigation Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, New York. Kan, H., Chen, B., 2004. Particulate air pollution in urban areas of Shanghai, China: health-based economic assessment. Atmos. Environ. 322, 71–79. Kan, H., Chen, R.J., Tong, S.L., 2012. Ambient air pollution, climate change, and population health in China. Environ. Int. 42, 10–19. Kan, H., London, S.J., Chen, G.H., et al, 2008. Season, sex, age, and education as modifiers of the effects of outdoor air pollution on daily mortality in Shanghai, China: the public health and air pollution in Asia (PAPA) study. Environ. Health Perspect. 116 (9), 1183–1188. Kelly, J.A., Ryan, L., Casey, E., et al, 2009. Profiling road transport activity: emissions from 2000 to 2005 in Ireland using national car test data. Transp. Policy 16 (4), 183–192. Kousoulidou, M., Ntziachristos, L., Mellios, G., et al, 2008. Road-transport emission projections to 2020 in European urban environments. Atmos. Environ. 42 (32), 7465–7475. Krupnick, A.J., Harrington, W., Ostro, B.D., 1990. Ambient ozone and acute health effects: evidence from daily data. J. Environ. Econ. Manage. 18, 1–18. Laden, F., Schwartz, J., Speizer, F.E., et al, 2006. Reduction in fine particulate air pollution and mortality: extended follow-up of the Harvard Six Cities study. Am. J. Respir. Crit. Care Med. 173, 667–672. X. Xue et al. / Transportation Research Part D 35 (2015) 84–103 103
Li, P., 2009. Study on North China’s Natural Gas Market and LEAP Model Application. Daqing Petroleum Institute. Lin, J.Y., Cao, B., Cui, S.H., et al, 2010. Evaluating the effectiveness of urban energy conservation and GHG mitigation measures: the case of Xiamen city, China. Energy Policy 38 (9), 5123–5132. Liu, H.A., He, K.B., He, D.Q., et al, 2008. Analysis of the impacts of fuel sulfur on vehicle emissions in China. Fuel 87 (13–14), 3147–3154. Lvovsky, Kseniya, Hughes, G., 1997. An Approach to Projecting Ambient Concentrations of SO2 and PM-10. Unpublished Annex 3.2 to World Bank.
Mao, X.Q., Yang, S.Q., Liu, Q., et al, 2012. Achieving CO2 emission reduction and the co-benefits of local air pollution abatement in the transportation sector of China. Environ. Sci. Policy 21, 1–13. McCollum, D.L., Krey, V., Riahi, K., 2011. An integrated approach to energy sustainability. Nat. Climate Change 1 (9), e428–e429. Morrow, W.R., Gallagher, K.S., Collantes, G., et al, 2010. Analysis of policies to reduce oil consumption and greenhouse-gas emissions from the U.S. transportation sector. Energy Policy 38 (3), 1305–1320. Ostro, B., 1990. Associations between morbidity and alternative measures of particulate matter. Risk Anal. 10, 421–427. Pan, H.X., 2011. Implementing sustainable urban travel policies in China. International Transport Forum, 25–27 May in Leipzig, Germany. Park, H.C., Heo, E., 2007. The direct and indirect household energy requirements in the Republic of Korea from 1980 to 2000: an input–output analysis. Energy Policy 35 (5), 2839–2851.
Peters, G.P., Hertwich, E.G., 2008. CO2 embodied in international trade with implications for global climate policy. Environ. Sci. Technol. 42 (5), 1401–1407. Pope III, C.A., Dockery, D., 1996. Epidemiology of chronic health effects: cross-sectional studies. In: Wilson, Richard, Spengler, John (Eds.), Particles in Our Air: Concentrations and Health Effects. Harvard School of Public Health, Harvard University, Harvard University Press, MA. Pope III, C.A., Dockery, D.W., 2006. Health effects of fine particulate air pollution: lines that connect. J. Air Waste Manage. Assoc. 56 (6), 709–742. Priece, L., de la Rue, S., Sinton, J., 2008. Sectoral trends in global energy use and greenhouse gas emissions. Energy Policy 36, 1386–1403. Qian, Z., He, Q., Lin, H.M., et al, 2007. Short-term effects of gaseous pollutants on cause-specific mortality in Wuhan, China. J. Air Waste Manage. Assoc. 57 (7), 785–793. Samet, J.M., Bishop, Y., Speizer, F.E., et al, 1981. The relationship between air pollution and emergency room visits in an industrial community. J. Air Pollut. Control Assoc. 31, 236–240. SEI, 2008. User Guide, LEAP: Long Range Energy Alternative Planning System. Stockholm Environment Institute: Boston, MA, USA. Shabbir, R., Ahmad, S.S., 2010. Monitoring urban transport air pollution and energy demand in Rawalpindi and Islamabad using leap model. Energy 35 (5), 2323–2332. Shang, Y., Sun, Z.W., Cao, J.J., et al, 2013. Systematic review of Chinese studies of short-term exposure to air pollution and daily mortality. Environ. Int. 54, 100–111. Shaw, C., Hales, S., Chapman, P.H., Edwards, R., 2014. Health co-benefits of climate change mitigation policies in the transportation sector. Nat. Climate Change 4, 427–433. Singha, A., Gangopadhyaya, S., Nandaa, P.K., et al, 2008. Trends of greenhouse gas emissions from the road transport sector in India. Sci. Total Environ. 390, 124–131. Solis, J.C., Sheinbaum, C., et al, 2013. Energy consumption and greenhouse gas emission trends in Mexican road transport. Energy Sustain. Dev. 17, 280–287. U.S. Energy Information Administration, 2014. Energy Prices by Sector and Source.