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Integrated Analysis of Ghgs and Public Health Damage Mitigation For Transportation Research Part D 35 (2015) 84–103 Contents lists available at ScienceDirect Transportation Research Part D journal homepage: www.elsevier.com/locate/trd 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).
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