The Impact of Stringent Fuel and Vehicle Standards on Premature Mortality and Emissions ! Cristiano Façanha, Sarah Chambliss, Josh Miller, Ray Minjares, Kate Blumberg ! ICCT Roadmap Webinar Series ! December 4 th , 2013 !
Webinar Structure ! ! Introduction and report overview ! ! 15 min, Cristiano Façanha ! ! Emissions methodology ! ! 10 min, Josh Miller ! ! Health impact methodology ! ! 20 min, Sarah Chambliss ! ! Q&A ! ! 15 min ! 1 !
Global Transportation Roadmap Series ! THE IMPACT OF STRINGENT FUEL AND VEHICLE STANDARDS ON PREMATURE MORTALITY AND EMISSIONS ICCT’S GLOBAL TRANSPORTATION HEALTH AND CLIMATE ROADMAP SERIES AUTHORS: Sarah Chambliss, Josh Miller, Cristiano Façanha, Ray Minjares, Kate Blumberg www.theicct.org communications@theicct.org BEIJING | BERLIN | BRUSSELS | SAN FRANCISCO | WASHINGTON 2 !
Most advanced controls can reduce emissions by over 99% ! 3 !
There is wide discrepancy regarding the stringency of vehicle emission standards worldwide ! Standards shown for LDVs ! Grey: no standards/ 1 / 2 / 3 / 4 / 5 / 6 / import standards or I II III IV V VI unknown. ! 4 !
A global focus on health impacts from transportation is critical to provide policy insights ! BEST PRACTICE NON-EU EUROPE AND RUSSIA AUSTRALIA, CANADA, EU-28 JAPAN, SOUTH KOREA, US CHINA AND INDIA AFRICA MIDDLE EAST ASIA-PACIFIC-40 LATIN AMERICA 5 !
The study relies on a well-informed policy roadmap towards cleaner vehicles and fuels ! EU-28 Canada U.S. Japan Australia South Korea China- Metro buses China India (early adopters) India (nat’l) Brazil Mexico Latin America-31 Russia Non-EU Europe Asia-Pacific-40 Africa Middle East 1990 1995 2000 2005 2010 2015 2020 2025 2030 Baseline Standards Pre-Euro Euro 1 Euro 2 Euro 3 Euro 4 Euro 5 Euro 6 Accelerated Standards Euro III Euro IV Euro V Euro VI Next-Generation 6 ! HDV Standards Timeline !
Latest vehicle controls can reduce emissions and premature mortality worldwide by 75% !! Non-EU Europe, Russia, & Best Practice China & India Latin America Other Countries 110,000 At a global level, health Globally new 100,000 impacts from urban vehicle standards could save particle emissions will 90,000 210,000 early deaths increase 50% by 2030 in 2030 and 25 million 80,000 unless new vehicle and fuel of years of life through standards are adopted. 2030. Early deaths 70,000 60,000 -79% -80% 50,000 40,000 30,000 -74% 20,000 -7% 10,000 0 2000 2005 2010 2 01 5 2020 2025 2030 2000 2005 2010 2 01 5 2020 2025 2030 2000 2005 2010 2 01 5 2020 2025 2030 2000 2005 2010 2 01 5 2020 2025 2030 Baseline Accelerated (%) Data labels indicate percent reduction from Baseline in 2030 7 !
And they can cut short-lived climate pollutants by over 80% ! Baseline Accelerated 400 350 Total non-CO 2 GHG emissions (MtCO 2 e) 300 80% 250 200 150 100 50 0 2000 2005 2010 2015 2020 2025 2030 2000 2005 2010 2015 2020 2025 2030 Best Practice China & India Latin America Non-EU Europe & Russia Other Countries 8 !
Framework for evaluating the health impacts of transportation emissions ! SOURCE EMISSIONS CONCENTRATION HEALTH EFFECTS Early Deaths Vehicle Tons of PM Urban Air and Years of Activity Emitted Quality Life Lost Emission Urban Concentration- Factors Intake Response Fractions Function ! Source: vehicle-km traveled by road vehicles in urban areas ! ! Historical data from government agencies in major markets, IEA in other countries ! ! Projected based on changes in population and PPP-GDP ! ! Emission factors: grams per vehicle-km ! ! Consider vehicle fleet composition, fuel type, emission control technology ! ! Influenced by emission standards and diesel sulfur content ! ! Emissions: metric tons, product of activity and emission factors ! 9 !
Emission factors ! ! Reasons for using emission factors ! ! Reflect policy effects on real-world emissions ! ! Lifetime average emission factors include deterioration ! ! Depend on speed, temperature, road grade, vehicle types ! ! Reasons for applying COPERT factors across regions ! ! Most countries follow European classification scheme for vehicle standards (Euro 1/I through Euro 6/VI) ! ! Developed by strong research/academic team ! ! Well-supported, up-to-date standards and technologies ! ! Comprehensive, public documentation ! ! Emission factors broadly in line with other models !
Vehicle emission limits and ultra-low sulfur diesel are key drivers of PM emission reduction ! Diesel: 2,000 ppm 500 ppm 350 ppm 50 ppm 10 ppm 0.45 -99% HHDT LDV 0.40 Fuel Sulfur Level -25% Average lifetime emission factor 0.35 0.30 (grams PM 2.5 /km) 0.25 -38% -99% 0.20 -22% -68% 0.15 0.10 -77% -20% -33% 0.05 -23% -90% -95% 0.00 Uncontrolled Euro 1/I Euro 2/II Euro 3/III Euro 4/IV Euro 5/V Euro 6/VI DPF ! DPF ! 11 !
Vehicle turnover translates standards into fleetwide emission reductions ! Baseline Accelerated ! Figure: HDV 1400 activity and NOx 1200 Vehicle-km traveled (billion) emissions by 1000 control level in 800 China ! 600 400 ! Baseline: China IV 200 yields initial 0 reductions, 4,000 outpaced by VKT NO X (metric kiloton) 3,000 growth ! ! Accelerated: 2,000 China V and VI 1,000 result in sustained NOx reductions ! 0 2000 2005 2010 2015 2020 2025 2030 2000 2005 2010 2015 2020 2025 2030 Uncontrolled Euro I Euro II Euro III Euro IV Euro V Euro VI 12 !
Accelerated standards drive convergence in average emissions per vehicle-km ! Baseline Accelerated 0.10 Bars = variation in emissions (g/km) ! 0.08 PM (g/km) 0.06 0.04 0.02 0 2.0 NO X (g/km) 1.5 1.0 0.5 0 2010 2015 2020 2025 2030 2010 2015 2020 2025 2030 Best Practice China & India Latin America Non-EU Europe & Russia Other Countries 13 !
Emissions projections ! Non-EU Europe ! Figure: (top row) Best Practice China & India Latin America & Russia Other Countries vehicle-km, (below) Vehicle-km traveled 1.3% 7.4% 10,000 (billion) PM, NOx, HC 4.3% 5,000 emissions ! 3.4% 3.5% ! 0 Baseline: sustained Health effects quantified 300 PM (metric kiloton) decreases in Best 200 Practice regions ! 100 -76% ! -83% Accelerated policies -4% -76% -83% 0 reverse emission NO X (metric kiloton) 10,000 trends in many 5,000 regions (2020-2030) ! -55% -71% -71% ! -82% By 2030, 80% -40% 0 4,000 reduction in PM HC (metric kiloton) 3,000 compared to 2,000 baseline in regions 1,000 -78% -47% yet to adopt best -54% -66% -70% 0 2000 2005 2010 2015 2020 2025 2030 2000 2005 2010 2015 2020 2025 2030 2000 2005 2010 2015 2020 2025 2030 2000 2005 2010 2015 2020 2025 2030 2000 2005 2010 2015 2020 2025 2030 practices ! (%) Data labels indicate annualized growth in vehicle activity Baseline Accelerated (%) Data labels indicate percent reduction from Baseline in 2030 14 !
Urban concentration with intake fractions ! SOURCE EMISSIONS CONCENTRATION HEALTH EFFECTS Gridded Gridded Air Emissions Quality Inventory Chemical Transport Aggregate Model Health Disaggregation Impacts Concentration- Response Urban Vehicle Tons of PM Function Intake Activity Emitted Fractions Emission Urban Air Factors Quality 15 !
Intake fraction ! ! Intake fraction is the ratio of the mass of pollutant inhaled to mass emitted ! ! ! ! ! ! ! Intake fraction varies by source and setting ! ! Size of exposed population ! ! Proximity of emissions to population ! ! Environmental persistence of pollutant ! 16 !
Variation in intake fraction worldwide (Apte 2012) ! 17 !
Calculating concentration from intake fraction ! Breathing rate constant (Q) ! 18 !
Estimating impacts from exposure ! ! The “relative risk” predicts how much more often deaths will occur at higher concentrations ! ! The size of the urban population and the baseline disease rate both influence the final estimate of total early deaths ! ! RRs are estimated for 3 disease categories that lead to premature mortality ! ! Lung cancer, adults over 30 ! ! Cardiopulmonary disease, adults over 30 ! ! Acute respiratory infection (ARI), children under 5 ! 19 !
Estimating impacts: nuances of the concentration- response function ! 1.8 1.7 Relative Risk 1.6 ∆ C = 10 μ g / m 3 1.5 ∆ RR = 0.038 1.4 1.3 ∆ C = 10 μ g / m 3 1.2 ∆ RR = 0.110 1.1 1 5 15 25 35 45 55 65 75 Ambient PM 2.5 ( μ g/m 3 ) ! Two forms of concentration-response functions, linear and log-linear (Ostro et al. 2005) ! ! The background concentration can influence the increase in relative risk ! ! We take the average of the change in risk near the counterfactual and at the background concentration ! 20 !
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