"Science, Faculty of"@en . "Resources, Environment and Sustainability (IRES), Institute for"@en . "DSpace"@en . "UBCV"@en . "Mazzi, Eric"@en . "2010-04-19T14:23:46Z"@en . "2010"@en . "Doctor of Philosophy - PhD"@en . "University of British Columbia"@en . "Climate change mitigation policies applied to passenger cars can be effective in reducing tailpipe CO\u00E2\u0082\u0082 rates by changing vehicle mass, fuels, and drive-train technology. However, these same factors can lead to changes in vehicle emissions, vehicle safety, and, consequently, changes in health outcomes from air pollution and traffic collisions. These relationships are examined using the UK as a case study where tax regimes based on tailpipe CO\u00E2\u0082\u0082 emission rates have been in place since 2001.\nPolicymakers are tasked to design CO\u00E2\u0082\u0082 policies for passenger cars, but the effectiveness of new policies will depend on how well climate mitigation is balanced with other relevant risks. I examine the rationale and introduce the basic framework for an Integrated Assessment approach to quantitatively assess passenger car CO\u00E2\u0082\u0082 policies. As industrialized countries transition to more heterogeneous fleets with increasing uptake of alternative fuels and technologies, the importance of decision criteria choices, risk metrics, system boundaries, and inclusion of all relevant risks using an Integrated Assessment framework will be increasingly critical.\nSince 2001, there has been a strong growth in diesel car registrations in the UK. For 2001-2020, I estimate that switching from gasoline to diesel cars reduces CO\u00E2\u0082\u0082 emissions by 0.4 mega-tonnes annually. However, current diesel cars emit higher levels of PM10 and the switch from gasoline to diesel cars is estimated to result in 90 additional deaths annually (range 20-300) from 2001-2020.\nThe UK has also had an increase in registrations of lighter vehicles. The relationship between tailpipe CO\u00E2\u0082\u0082 emission rates, vehicle mass, and traffic safety risks were examined. The two-car \u00E2\u0080\u009Cfirst law\u00E2\u0080\u009D fatality risk ratio for drivers of lighter cars relative to drivers of heavier cars was estimated to be the mass ratio raised to the power 5.3. Independent estimates of driver killed or serious injury risk in two-car collisions were found to be inversely related to vehicle CO\u00E2\u0082\u0082 emission rates. Scenario analyses show that policies combining incentives for lighter cars with a 1,600 kg upper limit for new cars should simultaneously achieve traffic safety and climate mitigation goals more effectively than policies with no upper limit on mass."@en . "https://circle.library.ubc.ca/rest/handle/2429/23816?expand=metadata"@en . " AN INTEGRATED ASSESSMENT OF CLIMATE MITIGATION POLICY, AIR QUALITY AND TRAFFIC SAFETY FOR PASSENGER CARS IN THE UK by Eric Mazzi B.S. Mechanical Engineering, California State Polytechnic University, Pomona, 1987 M.S. Mechanical Engineering, University of Southern California, 1990 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE STUDIES (Resource Management and Environmental Studies) The University of British Columbia (Vancouver) April, 2010 \u00C2\u00A9 Eric Mazzi, 2010 ii ABSTRACT Climate change mitigation policies applied to passenger cars can be effective in reducing tailpipe CO2 rates by changing vehicle mass, fuels, and drive-train technology. However, these same factors can lead to changes in vehicle emissions, vehicle safety, and, consequently, changes in health outcomes from air pollution and traffic collisions. These relationships are examined using the UK as a case study where tax regimes based on tailpipe CO2 emission rates have been in place since 2001. Policymakers are tasked to design CO2 policies for passenger cars, but the effectiveness of new policies will depend on how well climate mitigation is balanced with other relevant risks. I examine the rationale and introduce the basic framework for an Integrated Assessment approach to quantitatively assess passenger car CO2 policies. As industrialized countries transition to more heterogeneous fleets with increasing uptake of alternative fuels and technologies, the importance of decision criteria choices, risk metrics, system boundaries, and inclusion of all relevant risks using an Integrated Assessment framework will be increasingly critical. Since 2001, there has been a strong growth in diesel car registrations in the UK. For 2001-2020, I estimate that switching from gasoline to diesel cars reduces CO2 emissions by 0.4 mega-tonnes annually. However, current diesel cars emit higher levels of PM10 and the switch from gasoline to diesel cars is estimated to result in 90 additional deaths annually (range 20-300) from 2001-2020. The UK has also had an increase in registrations of lighter vehicles. The relationship between tailpipe CO2 emission rates, vehicle mass, and traffic safety risks were examined. The two-car \u00E2\u0080\u009Cfirst law\u00E2\u0080\u009D fatality risk ratio for drivers of lighter cars relative to drivers of heavier cars was estimated to be the mass ratio raised to the power 5.3. Independent estimates of driver killed or serious injury risk in two-car collisions were found to be inversely related to vehicle CO2 emission rates. Scenario analyses show that policies combining incentives for lighter cars with a 1,600 kg upper limit for new cars should simultaneously achieve traffic safety and climate mitigation goals more effectively than policies with no upper limit on mass. iii TABLE OF CONTENTS ABSTRACT...................................................................................................................................... ii TABLE OF CONTENTS..................................................................................................................... iii LIST OF TABLES............................................................................................................................ vii LIST OF FIGURES ........................................................................................................................... ix LIST OF ACRONYMS ..................................................................................................................... xiv GLOSSARY ................................................................................................................................... XV PREFACE..................................................................................................................................... xix ACKNOWLEDGMENTS.................................................................................................................... xx DEDICATION ................................................................................................................................ xxi CO-AUTHORSHIP STATEMENT ..................................................................................................... xxii 1 INTRODUCTION, LITERATURE REVIEW, OBJECTIVES AND HYPOTHESES...................................... 1 1.1 INTRODUCTION ................................................................................................................ 1 1.2 RESEARCH OBJECTIVES BASED ON LINKS BETWEEN PASSENGER CAR CO2, AIR QUALITY, AND TRAFFIC SAFETY....................................................................................................... 3 1.3 RESEARCH QUESTIONS, HYPOTHESES, AND TASKS ........................................................... 6 1.4 CRITICAL HIGHLIGHTS FROM THE LITERATURE................................................................... 9 1.4.1 Climate Mitigation Policy: the Importance of Multiple Risk Assessment .................. 9 1.4.2 Air Quality and Ancillary Benefits of Climate Mitigation Policy............................... 12 1.4.3 Vehicle Mass, Traffic Safety, and CO2 Emissions from Passenger Cars............... 13 1.5 HOW THE THESIS CHAPTERS FIT TOGETHER................................................................... 17 1.6 REFERENCES ................................................................................................................ 18 2 INTEGRATED ASSESSMENT OF MULTIPLE RISKS TO ASSESS CURRENT AND FUTURE CLIMATE MITIGATION POLICIES FOR PASSENGER CARS .......................................................................... 27 2.1 INTRODUCTION .............................................................................................................. 27 2.2 IDENTIFYING RELEVANT RISKS AND CHOOSING DECISION CRITERIA.................................. 29 iv 2.2.1 Decision Criteria Based on Marginal External Costs ............................................. 30 2.2.2 Decision Criteria Based on Public Health Metrics.................................................. 31 2.2.3 Decision Criteria Based on New Public Policy Priorities ........................................ 32 2.2.4 Choosing the Appropriate Decision Criteria........................................................... 33 2.3 PATHWAYS FROM POLICY TO RISKS ................................................................................ 33 2.3.1 Linking Policies to Risks ........................................................................................ 33 2.3.2 Climate Mitigation Policy Options and Potential Influences on Risks..................... 36 2.3.3 Linking Policies to Risks with Alternative Fuels and Technologies ........................ 41 2.4 ESTABLISHING CAUSAL LINKS BETWEEN POLICIES AND OUTCOMES.................................. 42 2.4.1 Basic issues in Establishing Causal Links Between Policy and Outcomes............ 42 2.4.2 What Caused the Rapid Rise in Diesel Cars in the UK?........................................ 43 2.5 CONCLUDING REMARKS ................................................................................................. 45 2.6 ACKNOWLEDGEMENTS ................................................................................................... 45 2.7 REFERENCES ................................................................................................................ 46 3 AIR QUALITY IMPACTS OF CLIMATE MITIGATION: UK POLICY AND PASSENGER VEHICLE CHOICE54 3.1 INTRODUCTION .............................................................................................................. 54 3.2 METHODS...................................................................................................................... 56 3.3 RESULTS....................................................................................................................... 60 3.4 DISCUSSION .................................................................................................................. 64 3.5 ACKNOWLEDGEMENTS ................................................................................................... 66 3.6 REFERENCES ................................................................................................................ 67 4 REGULATING CAR MASS FOR CONCURRENT TRAFFIC SAFETY AND CLIMATE MITIGATION BENEFITS ............................................................................................................................. 71 4.1 INTRODUCTION .............................................................................................................. 71 4.2 DATA AND METHODS...................................................................................................... 74 4.3 RESULTS....................................................................................................................... 79 4.4 DISCUSSION .................................................................................................................. 84 4.5 ACKNOWLEDGEMENTS ................................................................................................... 88 4.6 REFERENCES ................................................................................................................ 89 5 CONCLUSIONS ...................................................................................................................... 92 5.1 RELATIONSHIP BETWEEN PPOLICY INTEGRATED ASSESSMENT, AIR QUALITY, AND TRAFFIC SAFETY RESEARCH........................................................................................................ 92 5.2 RESEARCH RELATIONSHIP TO CURRENT WORKING HYPOTHESES IN THE FIELD OF STUDY AS REFLECTED IN THE LITERATURE ..................................................................................... 94 v 5.2.1 Air Quality and CO2 Reduction in Transportation................................................... 94 5.2.2 Traffic Safety and CO2 Reduction in Transportation .............................................. 94 5.3 STRENGTHS AND WEAKNESSES OF THIS THESIS.............................................................. 96 5.3.1 Strengths ............................................................................................................... 96 5.3.2 Weaknesses .......................................................................................................... 97 5.4 SIGNIFICANCE AND POTENTIAL APPLICATIONS OF THIS THESIS ......................................... 98 5.5 RECOMMENDATIONS FOR FUTURE RESEARCH................................................................. 99 5.5.1 Mitigating Air Quality and Health Impacts from Pre-Euro V Diesel Cars in Europe 99 5.5.2 Technology and Policy Assessment of Imposing a 1,600 kg Cap on Passenger Car Mass ............................................................................................................. 100 5.5.3 Traffic Safety Risks Due to Variation in Vehicle Mass and Size: Do Consumers Understand and Internalize the Relative Risks of Purchasing Smaller, Lighter Vehicles? ............................................................................................................. 100 5.5.4 Public Health Risks from Environmental Noise and Hybrid Technology.............. 101 5.5.5 Development of Quantitative, Integrated Policy Models for Passenger Car Choice and Risks............................................................................................................. 101 5.6 REFERENCES .............................................................................................................. 102 APPENDIX A - SUPPORTING INFORMATION FOR AIR QUALITY IMPACTS OF CLIMATE MITIGATION: UK POLICY AND PASSENGER VEHICLE CHOICE .......................................................................... 107 A.1 INTRODUCTION ............................................................................................................ 108 A.2 METHODS.................................................................................................................... 109 A.3 RESULTS..................................................................................................................... 114 A.4 DISCUSSION ................................................................................................................ 116 A.4.1 NUMBER AND EMISSION CLASS OF ADDITIONAL DIESELS ......................................... 117 A.4.2 ANNUAL KILOMETERS TRAVELLED .......................................................................... 118 A.4.3 SPATIAL DISTRIBUTION OF VEHICLES ...................................................................... 121 A.4.4 PM10 EMISSIONS AND AMBIENT CONCENTRATIONS.................................................. 121 A.4.5 HEALTH EFFECTS .................................................................................................. 124 A.5 REFERENCES .............................................................................................................. 126 vi APPENDIX B - SUPPORTING INFORMATION FOR CHAPTER 3 \u00E2\u0080\u009CTAILPIPE CO2 EMISSION REGULATIONS AND AUTO COLLISION RISKS: A UK CASE STUDY\u00E2\u0080\u009D ................................................................ 129 B.1 INTRODUCTION ............................................................................................................ 130 B.2 METHODS.................................................................................................................... 138 B.2.1 METHODS: DATA SOURCES.................................................................................... 138 B.2.2 METHODS: SUMMARY TRAFFIC SAFETY STATISTICS................................................. 140 B.2.3 METHODS: USING SURROGATE DATA TO ASCERTAIN CURB MASS............................ 146 B.2.4 METHODS: \u00E2\u0080\u009CFIRST LAW\u00E2\u0080\u009D RR ANALYSIS.................................................................... 152 B.2.5 METHODS: ABSOLUTE RISK ANALYSIS .................................................................... 155 B.2.6 METHODS: STATISTICAL MODELS FOR CURB MASS AND CO2/KM.............................. 160 B.3 RESULTS..................................................................................................................... 165 B.4 DISCUSSION ................................................................................................................ 166 B.5 REFERENCES .............................................................................................................. 170 vii LIST OF TABLES Table 1.1 Summary of research tasks, data sources, and analysis methods. ..........................8 Table 2.1 Approximate ranking of risks in the UK for different decision criteria, based on the fleet dominated by spark-ignition gasoline and compression-ignition diesel passenger cars. ......................................................................................................30 Table 4.1 Comparison of key parameters for the \u00E2\u0080\u009Cfirst law\u00E2\u0080\u009D RR analysis data set: heavier cars and their drivers cf. lighter cars...............................................................................76 Table 4.2 Results of absolute risk analyses for single-car, car-pedestrian, and two-car collisions. The analysis simulated three alternate scenarios over the time period 2000-2005. Values shown are point estimates of the mean for year 2005. Time series of results including standard errors are plotted in Figure 4.6. ......................82 Table 4.3 Results of simulation of two-car collision relative risk fleet calculation for the year 2005. Relative risk is the driver fatality risk of lighter cars divided by driver fatality risk of heavier cars in the relationship RR = \u00EF\u0081\u00AD\u00EF\u0081\u00AC. \u00EF\u0081\u00AD is calculated in the simulation assuming randomized collision events sampled from a mass distribution representative of the UK on-road car fleet. \u00EF\u0081\u00AC is examined parametrically ranging from 2 to 6. .............................................................................................................84 Table A1 Comparison of the cost of ownership for matched pairs of petrol and diesel 2005 car models (all models shown meet Euro IV Emission Standards).......................108 Table A2 European Union \u00E2\u0080\u009CEuro\u00E2\u0080\u009D emission limits [10] and weighted average emission factors [11] for passenger vehicles in grams per kilometer (g/km). ..................................113 Table A3 Total estimated changes in emissions due to additional diesels 2001-2020. Intervals are defined by dates when new EU emission standards apply as shown in Figure 2.3. Diesels emit higher amounts of PM10, NOx, and 1,3 butadiene but lower amounts of CO, HC, benzene, and CO2. ..............................................................115 Table A4 Summary morbidity and mortality results..............................................................116 Table A5 Emission test failure rates for petrol and diesel passenger cars in the UK. ..........124 Table B1 Summary of selected studies which estimated the effect of vehicle mass on fatality and injury risk. ......................................................................................................131 viii Table B2 Summary description of electronic databases received........................................138 Table B3 Variation in curb mass and tailpipe CO2 emission rate for model year 2007 Ford Focus versions offered in the UK..........................................................................147 Table B4 Variation in curb mass and tailpipe CO2 emission rate for model year 2007 BMW Series 3 versions offered in the UK. BMW Series 3 was the 10th most newly registered UK car in 2006. ....................................................................................147 Table B5 Range of engine sizes, body types, and propulsion types associated with a model year 2003 Peugeot model 307S. ..........................................................................148 Table B6 Number (1,000\u00E2\u0080\u0099s) of registered private and light goods vehicles by engine size. .151 Table B7 \u00E2\u0080\u009CFirst law\u00E2\u0080\u009D RR analysis: comparison of data set statistics. ....................................153 Table B8 Single car Absolute risk analysis: comparison of data set statistics. ....................156 Table B9 Comparison of data set statistics for car-pedestrian absolute risk analysis. .........158 Table B10 Summary results of regression models relating curb mass (kg) and gCO2/km to explanatory variables using JATO data. ...............................................................161 ix LIST OF FIGURES Figure 1.1 UK trend in CO2 emissions for the total economy, transportation end use, and passenger cars [16]. .................................................................................................2 Figure 1.2 The relationship between vehicle mass, tailpipe CO2/km, fuel type, PM10 emissions, and relative fatality risk. CO2/km was regressed on curb mass for model year 2007 UK cars [21]. PM10 emission factors are for gasoline and diesel cars subject to Euro III, IV, or V emission standards [19]. Fatality relative risk (RR) is calculated based on Evans\u00E2\u0080\u0099 \u00E2\u0080\u009Cfirst law\u00E2\u0080\u009D for two-car crashes [20]: RR = \u00CE\u00BC\u00CE\u00BB (\u00CE\u00BB = 3.8), where \u00CE\u00BC is defined as the mass ratio. RR is the ratio of driver fatality risk for cars at a given mass divided by the fatality risk for cars with a mass of 1,456 kg, the mean of all 2007 models [21]..............................................................................................4 Figure 1.3 UK passenger car market trends in size and fuel choice 1995-2007. The legend includes % change in new registrations over 10 years from 1998-2007. Vehicle size and mass are strongly correlated (see Chapter 4), so \u00E2\u0080\u009Clarge\u00E2\u0080\u009D also means \u00E2\u0080\u009Cheavy,\u00E2\u0080\u009D and \u00E2\u0080\u009Csmall\u00E2\u0080\u009D also means \u00E2\u0080\u009Clight\u00E2\u0080\u009D. In terms of fuel choice, alternative-fuelled vehicles have grown dramatically in recent years but still comprise less than 1% [29]............................................................................................................................5 Figure 2.1 Marginal external social costs (pence per kilometer) in Great Britain adapted directly from reference [6]. Error bars represent the estimated range of the marginal external costs for each risk. .....................................................................31 Figure 2.2 Influence diagram of relationships between passenger car policies and risks in a market dominated by spark ignition gasoline and compression ignition diesel cars.34 Figure 2.3 Diagram illustrating passenger car that policy can influence the makeup of on-road vehicle stocks by regulating new cars, scrapped cars, or both. ..............................40 Figure 2.4 Effect of scrapping age and VKT on life cycle gCO2/km for 2006 models of gasoline and diesel cars in Europe [98]. .................................................................40 Figure 2.5 Mean noise rating of some 2006 models of alternative fuelled vehicles relative to conventional gasoline or diesel models [80]. The mean for gasoline models is the same for diesel models (72.3 dB(A) for both groups). CNG = compressed natural gas. LPG = liquefied petroleum gas........................................................................42 x Figure 3.1 Diesel share of new car registrations in the European Union (EU) and the UK. While aggregate EU demand for diesels began increasing in 1995, UK demand continued to decline until the first CO2 policy incentive came into effect in 2001 and has been increasing continuously...........................................................................55 Figure 3.2 Total number and percentage market share of new registrations of private and company diesel cars in the UK 1994-2005. During this period, the ratio of the price of petrol to diesel was remarkably stable averaging 0.98 (range 0.95-1.00) and fuel price advantages experienced elsewhere do not provide a plausible explanation for the observed changes in diesel registrations in the UK. .........................................56 Figure 3.3 Integrated framework for assessing emissions from additional diesels (i.e., diesels substituted for petrol vehicles). Actual diesel share of new registrations from 1990- 2005 is based on industry data. Projected shares from 2006-2007 are based on industry forecasts, and from 2008-2020 based on authors\u00E2\u0080\u0099 projections. The focus of this study is on the area between the actual/projection curve and the \u00E2\u0080\u009Cno growth\u00E2\u0080\u009D curve which is split into three time intervals defined by the applicable emission standard: Euro III, Euro IV, and post-Euro IV. ........................................................60 Figure 3.4 Summary results of the impact of additional diesels in the UK from 2001-2020 .....61 Figure 3.5 Estimates of additional diesels in the UK 2001-2020 disaggregated by Euro emission class. \u00E2\u0080\u009CAdditional diesels\u00E2\u0080\u009D are defined as the number of petrol vehicles switched to diesel beyond the \u00E2\u0080\u009Cno growth\u00E2\u0080\u009D estimate. Euro III and Euro IV emission standards apply in 2001 and 2006, respectively. Early adoption of some Euro IV diesels is incorporated into our estimates. Legislation to harmonize diesel and petrol particulate matter emission limits is proposed by 2009, described as \u00E2\u0080\u009Cpost- Euro IV\u00E2\u0080\u009D in this study...............................................................................................62 Figure 3.6 Estimated changes in emissions from 2001-2020 due to additional diesels. The solid lines (y-axis to left) show estimated changes in emissions of common air contaminants in kilo-tonnes, while the dashed line (y-axis to right) shows CO2 in mega-tonnes. Diesels emit higher rates of PM10 and NOx, and lower rates of HC, CO, and CO2. Emissions of common air contaminants are assumed to be harmonized for diesel and petrol vehicles beginning 2009, so differences in all emissions except CO2 approach zero from 2009-2020 as higher polluting diesels are scrapped...........................................................................................................63 xi Figure 4.1 1997-2006 time series of fatalities (left Y-axis) and KSI (right Y-axis) per billion passenger km for key road user groups in the UK. This illustrates the variation of casualty rates between road users (e.g., motorcycle occupant rates are 40-50 times car occupants), and that fatality rates do not always parallel injury (e.g., bicycle fatal and KSI rates 2003-2006). ..................................................................72 Figure 4.2 Distribution of 2,946 fatalities and 2,714 fatal crash events in the UK for 2007. There were also 30,720 KSI and 27,036 KSI events in 2007 with a similar distribution as fatalities. ..........................................................................................73 Figure 4.3 Baseline plus three alternative scenarios for years 2000-2005 used in the absolute risk analysis and the RR fleet composition simulation. \u00E2\u0080\u009CLighter\u00E2\u0080\u009D group is comprised of engine size ranges 701-1,000 cubic centimeter (CC), 1,001-1,200 CC, and 1,201-1,500 CC. \u00E2\u0080\u009CMid-mass\u00E2\u0080\u009D includes 1,501-1,800 CC and 1,801-2,000 CC. \u00E2\u0080\u009CHeavier\u00E2\u0080\u009D includes 2,001-2,500 CC, 2,501-3,000 CC, and 3,000 CC and over. 700 CC and under, were not included because there were too few vehicles, annual fatality counts were zero or near zero, and it was not possible to estimate risks. Scenario descriptions are provided in the text. .......................................................78 Figure 4.4 \u00E2\u0080\u009CFirst law\u00E2\u0080\u009D RR risk of driver fatality in two-car collisions and ratio of CO2 emission rates for UK cars 1995-2005. RR of driver fatality in two-car collisions is shown on the left vertical axis (mean +/- one standard error). Ratio of tailpipe CO2 emission rate (mean +/- one standard deviation) is shown on the right vertical axis. A horizontal line is drawn showing where CO2/km ratio =1.0. ....................................79 Figure 4.5 Relationship between driver two-car conditional KSI risk and vehicle CO2 emission rate in the UK. Model year passenger cars 1995-2004 for crash events during calendar years 2000-2004 are included. Increased CO2/km is a modest but significant predictor of decreased risk. Car make and model size key as defined by DFT: L/S = low/sports, S = small, S/M = small/medium, M = medium, L = large, MPV = multipurpose, 4WD = four wheel drive. .......................................................81 Figure 4.6 Single-car, car-pedestrian, and two-car absolute risk results, shown from top to bottom. Changes in annual fatalities are plotted as percentage change relative to the baseline. A horizontal line is plotted at 0% (no change). Error bars represent +/- one standard error. Points are plotted slightly offset in the time scale (x-axis) to make error bars visible. ..........................................................................................83 xii Figure 4.7 Comparison of RR calculated in this study (from Figure 4.4), to previous results from U.S. data sets. Both the horizontal and vertical axes are logarithmic scales. The effect of point of impact strongly affects the RR. When the front of a striking car crashes into the driver side of the struck car, the RR is much larger. While the RR for front-to-front collisions has been observed to pass through the origin (i.e., RR \u00E2\u0089\u0088 1 for mass ratio \u00E2\u0089\u0088 1), the RR for purely front-to-driver side impacts has been observed to pass through 10 at the origin (i.e., RR \u00E2\u0089\u0088 10 for mass ratio \u00E2\u0089\u0088 1) [27]. Because of this relationship, the RR for side impact collisions is commonly fit to the equation RR = A * \u00CE\u00BC\u00CE\u00BB, where statistical models reveal A \u00E2\u0089\u0088 10.................................86 Figure A1 Model for estimating annual number of scrapped vehicles, based on UK de- registration statistics. The annual rate of vehicles scrapped peaks at 10.7%, 14 years after the year the vehicle was initially registered. The area under the curve through 20 years is 85.7%. ...................................................................................109 Figure A2 Fleet average difference in CO2 emission factors (gCO2/km) between petrol and diesel passenger vehicles in the UK from 1997-2020...........................................111 Figure A3 Average annual travel distance for the first two years of ownership for diesel and petrol-fuelled passenger cars. Data are from the UK National Travel Survey. Sample sizes for individual years range from 12 to 563. Both company and privately owned vehicles are included. .................................................................119 Figure A4 Annual travel distance for diesel and petrol cars for NTS survey year 2004. This plot shows the effect of vehicle age on annual travel distance. ............................120 Figure B1 Historical traffic fatality rates for the UK................................................................140 Figure B2 Number of vehicles involved in fatal crashes 1994-2005......................................140 Figure B3 Road types for all fatal crashes 1994-2005. .........................................................141 Figure B4 Road class for all fatal crashes 1994-2005...........................................................142 Figure B5 Road speed limits for all fatal crashes 1994-2005. ...............................................142 Figure B6 Casualty types for all fatalities 1994-2005. ...........................................................143 Figure B7 Sex for all fatalities 1994-2005. ............................................................................143 Figure B8 Age and sex for all fatalities in 2005. ....................................................................144 xiii Figure B9 Crash mode for all single vehicle crashes 1994-2005. .........................................144 Figure B10 Objects struck off carriageway for all single vehicle crashes 1994-2005. .............145 Figure B11 Point of impact for two vehicle fatalities 1994-2005..............................................145 Figure B12 Curb mass (kg) histogram for model year 2007 Ford Focus versions. .................146 Figure B13 Diagram of the UK Data Archive data sorting process..........................................152 Figure B14 Regression model #2 line fit and residual plot. .....................................................163 Figure B15 Regression model #9 line fit and residual plot. .....................................................164 Figure B16 Line fit plot of \u00E2\u0080\u009Cfirst law\u00E2\u0080\u009D RR regression model that relates mass ratio to RR fatalities in two car-collisions. Top panel models the relationship as RR = \u00C2\u00B5\u00CE\u00BB. Bottom panel models the relationship as RR = \u00CE\u00B1 + \u00CE\u00B2\u00C2\u00B5. \u00C2\u00B5 = mass ratio. \u00E2\u0080\u00A6\u00E2\u0080\u00A6\u00E2\u0080\u00A6\u00E2\u0080\u00A6\u00E2\u0080\u00A6\u00E2\u0080\u00A6\u00E2\u0080\u00A6\u00E2\u0080\u00A6\u00E2\u0080\u00A6\u00E2\u0080\u00A6.165 Figure B17 Line fit plot of conditional risk model that relates two-car collision % risk of serious injury or fatality to CO2 emission rate....................................................................166 Figure B18 Mean annual travel distance by engine size category in 1998 and 2004 for cars in UK National Travel Survey [42]. Includes vehicles coded as \u00E2\u0080\u009Ccars\u00E2\u0080\u009D and \u00E2\u0080\u009CLandrover/Jeep\u00E2\u0080\u009D, but excludes \u00E2\u0080\u009Clight vans\u00E2\u0080\u009D. Error bars are +/- one standard deviation. n=8207 for year 2004; n=8692 for year 1998. ......................................167 Figure B19 A comparison of the proportions of registered cars and recorded crash involvements for year 2005, disaggregated by engine size [32, 33]. ....................168 Figure B20 Change in annual new car registrations in 2006 as compared to 1997 in the UK.169 xiv LIST OF ACRONYMS ACEA European Automobile Manufacturers\u00E2\u0080\u0099 Association (www.acea.be) AIS Abbreviated Injury Scale CO carbon monoxide DALY Disability Adjusted Life Years DFT UK Department for Transport EU European Union FARS Fatality Analysis Reporting System (U.S.) gCO2 grams of CO2 GHG greenhouse gases (CO2, CH4, N2O) HC hydrocarbon emissions (also known as volatile organic compounds) KSI killed and seriously injured IPCC Intergovernmental Panel on Climate Change NCAP New Car Assessment Program NHTSA U.S. National Highway Safety Administration NOx nitrogen oxides PM10 Particulate matter less than 10 microns aerodynamic diameter PM2.5 Particulate matter less than 2.5 microns aerodynamic diameter RR Relative risk VED Vehicle Excise Duty YLL Years of Life Lost xv GLOSSARY Aggressivity: the risk imposed on others (drivers/passenger/pedestrians) due to the combined effect of driver behavior and their vehicles. Cars: four-wheel passenger vehicles including all market segments. Vehicles that would be classified as \u00E2\u0080\u009Clight trucks\u00E2\u0080\u009D in the U.S. and Canada are included. In this study, cars strictly includes four-wheel passenger vehicles coded as vehicle type 9 in the UK Data Archive. All two-wheel vehicles, taxis, heavy goods, bicycles, etc,. are not included as \u00E2\u0080\u009Ccars.\u00E2\u0080\u009D Case car: see definitions for striking car and struck car. Casualty: slight injury, serious injury, or fatality Compatibility: design features that result in equalizing risks of fatality or injury between two cars; the goal of aggressivity programs are to make cars compatible. Aggressivity is generally considered to result from three design parameters: mass compatibility; structural or stiffness compatibility, and geometric compatibility (matching height of structural members that contact in collisions \u00E2\u0080\u0093 e.g., average height of force in crash tests). CO2 emission rate: average tailpipe greenhouse gas emissions per unit distance traveled, quantified as grams CO2 per kilometer (gCO2/km) where \u00E2\u0080\u009CCO2\u00E2\u0080\u009D includes CH4 and N2O based on equivalent global warming potential conversions. The gCO2/km emission factors employed in this study do not include other emissions that affect global warming such as black carbon, nor are life cycle emissions included. Crash, collision, or accident: an event where a vehicle strikes anything (e.g., another vehicle, pedestrian, or object). The terms \u00E2\u0080\u009Ccollision\u00E2\u0080\u009D or \u00E2\u0080\u009Ccrash\u00E2\u0080\u009D are generally preferred over the term \u00E2\u0080\u009Caccident.\u00E2\u0080\u009D Crash (or collision) type: in this study (and some other published studies, although the use of terminology is not universal) this phrase refers to basic crash groupings such as two-car, multiple- car, single-car, car-pedestrian, car-bicycle, car-heavy goods, car-motorcycle, and heavy goods- pedestrian. Crashworthiness: relating to physical design or technology of vehicles aimed at minimizing injuries or fatalities when collisions occur (sometimes described as \u00E2\u0080\u009Csecondary safety\u00E2\u0080\u009D). Crash prevention or avoidance: relating to the ability of the driver or vehicle technology to avoid a collision (sometimes described as \u00E2\u0080\u009Cprimary safety\u00E2\u0080\u009D). xvi Delta V (or \u00E2\u0088\u0086V): velocity change for two cars in a collision resulting from conservation of momentum. For the simple case of two cars colliding head-on (12 o\u00E2\u0080\u0099clock point of impact) it can be expressed as: \u00E2\u0088\u0086V1 = (V1 + V2) * M2 / ( M1 + M2) or \u00E2\u0088\u0086V1 = SQRT ( 2 * Ea * M2 / [ M1 * ( M1 + M2) ] ) \u00E2\u0088\u0086V2 = (V1 + V2) * M1 / ( M1 + M2) or \u00E2\u0088\u0086V2 = SQRT ( 2 * Ea * M1 / [ M2 * ( M1 + M2) ] ) V \u00E2\u0089\u00A1 velocity; M \u00E2\u0089\u00A1 mass; Ea \u00E2\u0089\u00A1 total kinetic energy absorbed in the crash subscript 1 is case car, subscript 2 is other car \u00E2\u0088\u0086V is computed for crashes in the U.S. Crashworthiness Data System (CDS), but no known \u00E2\u0088\u0086V estimates exist for UK data sets. Driver behavior: how, where, and when people drive, and how vehicles are maintained. Driver: person operating the vehicle. Euro NCAP: European New Car Assessment Programme (www.euroncap.com) which crash tests new cars to determine crashworthiness with four basic protocols: front impact, side impact, pole test, and pedestrian protection. Fatality or Mortality: death following a vehicle collision within 30 days (30 days in the UK, the number of days can vary in different countries and jurisdictions). Fuel economy: fuel consumption per unit distance (e.g., L/100km) or its inverse (miles per gallon). In this thesis, the term refers to the rated fuel economy such as listed in the JATO database. On- road fuel economy for UK cars is generally considered to be 10% less efficient than rated (tested) fuel economy. Haddon matrix: a Haddon matrix in traffic safety is a general way of modeling causes of traffic casualties and interventions that uses a 3 by 3 matrix. The horizontal row of the matrix is comprised of (i) human factors, (ii) vehicle, and (iii) environment. The vertical column is comprised of (i) pre- crash, (ii) during crash, and (iii) post-crash factors. Induced exposure: methods that estimate absolute risk of injury or fatality using indirect estimates of exposure based on collision data. Risk is quantified with the numerator being the count of outcome of interest (e.g., fatalities or injuries for chosen types of events and driver characteristics). The denominator is estimated based on various assumptions using the collision data set, such as assuming mathematical relationships between proportions of drivers involved in single-car events and multiple-vehicle events. \u00E2\u0080\u009CQuasi-induced exposure\u00E2\u0080\u009D methods make use of data where one driver xvii is recorded as being responsible for the event, while another driver is designated non-responsible (the non-responsible group is assumed to be representative of all drivers of a given type). Injury: The UK Data Archive uses a three-tier injury rating: slight injury (cuts, bruises, strains), serious injury (e.g., severe cuts, internal injury, any injury requiring hospitalization, injuries that lead to fatalities after 30 days or more), and fatality (traffic collision injury leading to death in less than 30 days). Globally the Abbreviated Injury Scale (AIS) is often used. The AIS is a 6 point scale ranging from AIS1 for minor injury to AIS6 for fatality. Make: the manufacturer of a vehicle (e.g., Ford, Renault, Vauxhall) Market segment: passenger car groups as commonly reported by the UK Society of Motor Manufacturers and Traders Ltd. (www.smmt.co.uk): mini, supermini, lower-medium, upper-medium, executive, luxury, specialty sports, 4X4/SUV, multi-purpose. Mass or curb mass: static vehicle mass with a full tank of fuel and other fluids. In Europe, curb (spelled \u00E2\u0080\u009Ckerb\u00E2\u0080\u009D in the UK) mass data beginning in 1996 includes 75 kg to represent the mass of the driver. In this study the words \u00E2\u0080\u009Clight\u00E2\u0080\u009D or \u00E2\u0080\u009Clighter,\u00E2\u0080\u009D and \u00E2\u0080\u009Cheavy\u00E2\u0080\u009D or \u00E2\u0080\u009Cheavier\u00E2\u0080\u009D refer to lesser or greater mass, respectively. The U.S. EPA classifies cars for purposes of dynamometer testing according to their \u00E2\u0080\u009Cinertia weight\u00E2\u0080\u009D which is the curb mass plus 300 lbs. Curb mass in the U.S. does not include driver or cargo (ANSI D16.1-1996). Although weight and mass are distinctly different quantities from Physics (weight being the force exerted by gravity acting on mass), these terms are considered synonymous for the purposes of this study. Model or model range: the basic name of a car design such as Volkswagen Golf, Ford Focus C- Max, or BMW 318. Model Year: the year a vehicle is manufactured and sold as per the manufacturer. In this study, the \u00E2\u0080\u009Cyear first registered\u00E2\u0080\u009D data field is considered equivalent to model year, even though this is unlikely to be correct for imported cars. Occupant: either the driver or passenger. Passenger: person in the vehicle other than the driver. Risk: three types are commonly quantified to measure traffic safety: 1. Absolute risk is used in this study to mean risk measured in fatalities (or injuries) per year (e.g., annual car-pedestrian fatalities in the UK), or fatalities per year per unit quantity of vehicles (e.g., millions registered) or vehicle-distance (e.g., billion vehicle-km). In general xviii other risk metrics are used such as casualty counts per unit driving time. Induced exposure methods are also used to estimate absolute risks. 2. Relative risk or risk ratio is used in this study to measure the risk of one group divided by the risk of another group. The \u00E2\u0080\u009Cfirst law\u00E2\u0080\u009D relationship described in this study is an example of relative risk because it quantifies the risk of one group of drivers divided by the risk of another group of drivers. 3. Conditional risk is used to measure the risk (e.g., probability of fatality or injury) given that a crash occurs. The conditional risk metric is perhaps the best indicator of crashworthiness (or \u00E2\u0080\u009Csecondary safety\u00E2\u0080\u009D). Road user: any persons using public roads who are subject to potential injury or fatality from car collisions; includes pedestrians, cyclists and occupants of cars, two-wheel vehicles, taxis, buses, heavy goods vehicles etc. Size: a physical measure of the vehicle that is commonly based on a characteristic length such as wheelbase (the distance between front and rear axles), length (also called \u00E2\u0080\u009Coverall length\u00E2\u0080\u009D), width (also called \u00E2\u0080\u009Coverall width\u00E2\u0080\u009D), or track width (the side-to-side distance between wheels). Many other direct measures of size have been assessed. Other indirect measures for size have been used such as the lateral distance needed to perform a 180\u00C2\u00B0 turn (\u00E2\u0080\u009Cturn distance\u00E2\u0080\u009D). In this study, the words \u00E2\u0080\u009Csmall\u00E2\u0080\u009D or \u00E2\u0080\u009Csmaller\u00E2\u0080\u009D and \u00E2\u0080\u009Clarge\u00E2\u0080\u009D or \u00E2\u0080\u009Clarger\u00E2\u0080\u009D refer to size, not mass. Stiffness: generally, the slope of the force (y-axis) versus displacement (x-axis) for a car as it crushes in a collision. Multiple definitions of stiffness have been employed and typically, but not consistently, stiffness has been strongly correlated with mass. Striking car: the second car of interest in a risk calculation, also called the \u00E2\u0080\u009Cother,\u00E2\u0080\u009D \u00E2\u0080\u009Cpartner,\u00E2\u0080\u009D or \u00E2\u0080\u009Cbullet\u00E2\u0080\u009D car. Struck car: the first car of interest in a risk calculation, also called the \u00E2\u0080\u009Ccase\u00E2\u0080\u009D, \u00E2\u0080\u009Csubject\u00E2\u0080\u009D, \u00E2\u0080\u009Cself\u00E2\u0080\u009D, \u00E2\u0080\u009Cown\u00E2\u0080\u009D, \u00E2\u0080\u009Ctarget\u00E2\u0080\u009D, or \u00E2\u0080\u009Cdriven\u00E2\u0080\u009D car. Traffic safety: relating to human health impacts as a result of vehicles operating on public roadways. Although property damage is generally included as a traffic safety impact, this study only considers human health. Version or trim: descriptors that define specific features of a car model or model range. Examples: Volkswagen Golf 2.0 TDI 140 PS sport, Ford Focus C-Max 2.0I Zetec auto, and BMW 318D M sport touring. xix PREFACE This thesis is written following the manuscript thesis format of the University of British Columbia (UBC). UBC\u00E2\u0080\u0099s manuscript thesis is comprised of one or more manuscripts suitable for journal publication (either as published or as intended for publication), with an Introduction at the beginning and a Conclusion at the end1. In order to fulfill the manuscript thesis requirements there is some redundancy in the Introduction and Conclusion chapters. Each journal requires an introduction to frame the problem, which is also required in the Introduction chapter. In general this thesis is written in the first person singular (\u00E2\u0080\u009CI\u00E2\u0080\u009D). The exception is the first person plural (\u00E2\u0080\u009Cwe\u00E2\u0080\u009D) is used in Chapters 3 and 4, (as well as Chapters 1 and 5 when referring to Chapters 3 and 4) because this material is based on journal articles for which I am the principal author, but there are one or more co-authors. Because this thesis employs multiple disciplines, it is critical for readers to understand the use of terminology. I have created a Glossary, mainly for the traffic safety research because I found no single source suitable to define terminology in this field. For terminology rooted in human health science, I make use of terms as defined in \u00E2\u0080\u009CThe Dictionary of Epidemiology\u00E2\u0080\u009D by John Last (4th edition, Oxford University Press, 2000). As examples, the phrases \u00E2\u0080\u009Cecologic fallacy,\u00E2\u0080\u009D \u00E2\u0080\u009Crelative risk,\u00E2\u0080\u009D and \u00E2\u0080\u009Cdisability adjusted life years\u00E2\u0080\u009D are all defined by Last. For terminology rooted in economics, I make use of the text \u00E2\u0080\u009CEconomics of the Public Sector\u00E2\u0080\u009D by Joseph Stiglitz (3rd edition, W.W. Norton and Company, 2000). As examples, the phrases \u00E2\u0080\u009Crebound effect\u00E2\u0080\u009D and \u00E2\u0080\u009Csocial cost\u00E2\u0080\u009D are defined by Stiglitz. For general words, I have used Merriam-Webster\u00E2\u0080\u0099s online dictionary at www.merriam- webster.com. 1 See http://www.grad.ubc.ca/students/thesis/index.asp?menu=002,002,000,000 for more information. xx ACKNOWLEDGMENTS Nothing is accomplished without the help of others, and this thesis is no exception. I have learned a lot and owe gratitude to many. I doubt I will remember everyone at this time of writing, so I\u00E2\u0080\u0099ll focus on my family, student colleagues, and faculty mentors. Acknowledgments of funding and reviewers for specific articles are provided at the end of chapters 2, 3, and 4. On a personal level, I have received patient support and inspiration from my wife Theresa, and my daughters Alex and Angeline \u00E2\u0080\u0093 thank you so much for all that you are. To Theresa I owe more gratitude than I can say, personally and academically (even going back to the old Chaffey College days!). Alex and Angeline, you have given me the gift of appreciating both the value of living in the moment and investing in our collective future, not to mention helping me learn from my many mistakes. I have learned and been inspired a great deal from my student colleagues at UBC at the Institute for Resources, Environment, and Sustainability (IRES), and the Bridge Program. I won\u00E2\u0080\u0099t list specific student colleagues to avoid generating an exhaustive list. But I will say that I have learned from and been inspired and impressed by many, including those I\u00E2\u0080\u0099ve frequently collaborated with and even some that I\u00E2\u0080\u0099ve seldom spoken with by virtue of seeing their work. I also owe a great deal to my thesis committee. I have truly been privileged with a world-class faculty committee. I would like to thank Dr. Milind Kandlikar, Dr. Michael Brauer, and Dr. Douw Steyn from whom I\u00E2\u0080\u0099ve learned a great deal through my exams, directed studies, and thesis support. I would also like to thank Dr. Kay Teschke for her outstanding teaching support through the Bridge Program. I also want to thank the university examiners, Dr. Steve Rogak and Dr. Karin Mickelson, for their helpful comments and questions. I also thank my external examiner, Dr. Lester Lave for his helpful, detailed comments on my thesis. Last, but first, I owe a great deal to Dr. Hadi Dowlatabadi. Beginning with our introduction eight years ago in his Integrated Assessment course, it has been quite a journey and I thank him for his tireless support. I will share one brief story about Hadi for the record. I had the chance to chat briefly with a world-renowned economist about my thesis to gain some insights. The subject of equity in public policy came up, to which my famous economist commenter said (paraphrase) \u00E2\u0080\u009Coh yes, Hadi cares about equity.\u00E2\u0080\u009D I don\u00E2\u0080\u0099t think it was meant as a compliment in particular. But it struck me right away \u00E2\u0080\u0093 yes, I thought, he does care about equity which is one reason I\u00E2\u0080\u0099m privileged to work with him! xxi DEDICATION . I dedicate this thesis to my family: Theresa, Alex, and Angeline. xxii CO-AUTHORSHIP STATEMENT Chapter 2. The idea for this study was jointly developed by me and Hadi Dowlatabadi. I developed the scope of the research, selected the methodology, performed the analyses, and prepared the manuscript. Chapter 3. Hadi Dowlatabadi proposed the idea for the study. I developed the scope of the research, selected the methodology, obtained all data, performed the analyses, and prepared the manuscript. Hadi helped make decisions on the scope of the analysis and contributed to the manuscript preparation. Chapter 4. Hadi Dowlatabadi proposed the idea for the study. I developed the scope of the research, selected the methodology, obtained all data, performed the analyses, and prepared the manuscript. Hadi helped make decisions on the scope of the analysis and contributed to the manuscript preparation. Hadi also created the initial fleet simulation model in AnalyticaTM, which I modified. Milind Kandlikar helped make decisions on the scope of the analysis and contributed to the manuscript preparation. Additional contributions to Chapters 2, 3, and 4 are provided in the acknowledgements for each article. 1 1 INTRODUCTION, LITERATURE REVIEW, OBJECTIVES AND HYPOTHESES 1.1 INTRODUCTION Climate policies applied to passenger vehicles can be effective in reducing CO2 emissions by changing vehicle mass, fuel choice, and technology [1]. However, these same factors that reduce CO2 emissions, such as reducing vehicle mass and switching from gasoline cars to diesel cars, can lead to changes in common air pollutant emissions, vehicle safety, and, subsequently, changes in health outcomes. This is an applied research study on the human health risks resulting from climate mitigation policies aimed at the passenger transportation sector. By necessity, most research to date has been based on scenarios or hypothetical changes in transportation choices [2]. Where it is feasible, this research emphasizes how actual changes in passenger car choices in the United Kingdom (UK) may have changed human health outcomes resulting from air quality and traffic safety. From both scientific and public policymaking perspectives, climate change is widely described as one of the most challenging and urgent problems in society today. The scientific evidence of climate change has been thoroughly evaluated by the Intergovernmental Panel on Climate Change (IPCC). The IPCC\u00E2\u0080\u0099s current finding is that \u00E2\u0080\u009Cobservational evidence from all continents and most oceans show that many natural systems are being affected by regional climate changes\u00E2\u0080\u009D and that \u00E2\u0080\u009Cit is likely that anthropogenic warming has had a discernible influence on many physical and biological systems.\u00E2\u0080\u009D [3] Examples of policymakers\u00E2\u0080\u0099 acceptance of the need to address climate change are ubiquitous, spanning all levels of government. To cite a municipal example, London has committed to reduce CO2 emissions by 60% from 1990 levels by the year 2020 [4]. At a national level, the UK government has stated that \u00E2\u0080\u009CThe 2008 Climate Change Act made Britain the first country in the world to set legally binding \u00E2\u0080\u0098carbon budgets\u00E2\u0080\u0099, aiming to cut UK emissions by 34% by 2020 and at least 80% by 2050\u00E2\u0080\u009D [5]. In principal there are three basic interventions that policymakers can target to address climate change [2, 6]: mitigation, adaptation, and geoengineering. This research specifically focuses on mitigation via reductions in CO2 emissions from passenger cars. In the UK public policymakers at multiple levels of government have targeted reductions of CO2 emissions [7]. In 2008 the UK created the Department of Energy and Climate Change with a mandate to \u00E2\u0080\u009Cbring together \u00E2\u0080\u00A6 energy policy \u00E2\u0080\u00A6 and \u00E2\u0080\u00A6 climate mitigation policy.\u00E2\u0080\u009D [8] Because of the importance of transportation in emissions, with passenger cars as a major contributor, multiple government agencies in the UK have identified climate mitigation for the 2 road transportation sector as a high priority [7, 9-12], including creating an Office for Low Emission Vehicles [13]. The UK was also an early adopter of climate mitigation policies for passenger cars. In 2001 it adopted a vehicle excise duty (VED) with annual fees based on a scale of certified CO2/km for car makes and models [14]. Approximately half of all new cars in the UK are sold as company cars. In 2002 a tax on the benefit-in-kind income from employee use of company cars was adopted, again based on certified CO2/km [15]. While the UK has reduced overall CO2 emissions, transportation remains an exception. Figure 1.1 illustrates trends in CO2 emission in the UK. From 1990 to 2007, CO2 emissions were reduced for every major end use except transport where emissions have risen by 8% [16]. Passenger cars have comprised 58 to 61% of transport emissions over this time period. The UK is the second largest new car market in the European Union (EU) where the European Automobile Manufacturers\u00E2\u0080\u0099 Association (ACEA) established a voluntary agreement with the European Commission in 1998. The ACEA agreed to reduce EU fleet-average passenger from 185 gCO2/km in 1995 to 140 gCO2/km by 2008, but the target was not met as ACEA\u00E2\u0080\u0099s 2008 average was 152 gCO2/km [17]. Moreover, reductions from passenger cars appear likely to remain difficult as car ownership continues to rise. The UK Department for Transport (DFT) forecasts ownership levels relative to the year 2000 of 122% by 2015, and 133% by 2025, a growth rate that is in excess of the population growth rate [18]. Figure 1.1 UK trend in CO2 emissions for the total economy, transportation end use, and passenger cars [16]. 0 100 200 300 400 500 600 700 800 900 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 0 20 40 60 80 100 120 140 160 180 Million tonnes CO2 equivalent total (left Y-axis) total transport (right Y-axis) passenger cars (right Y-axis) 3 1.2 RESEARCH OBJECTIVES BASED ON LINKS BETWEEN PASSENGER CAR CO2, AIR QUALITY, AND TRAFFIC SAFETY Figure 1.2 illustrates the essential relationships that led to the research objectives for this thesis. Empirical relationships for UK cars confirm that tailpipe CO2 emission rates increase linearly with curb mass for gasoline and diesel cars, and that diesel cars emit less CO2 than gasoline cars. Compared to gasoline cars, PM10 emission factors from diesel cars are 25, 12, and 2 times greater for cars meeting Euro III, IV, and V emission standards, respectively [19]. Theoretical and empirical \u00E2\u0080\u009Cfirst law\u00E2\u0080\u009D relationships for U.S. cars reveal that the relative risk of fatalities for drivers of lighter cars versus heavier cars in two-car collisions rises non-linearly with the mass ratio [20]. There have been two noteworthy trends in the UK as shown in Figure 1.3: (1) the growth in market share of diesel cars at the expense of gasoline cars, and (2) the growth in market share of both large (heavy) and small (light) cars at the expense medium class cars. Diesel cars registrations have increased 182% from 1998 to 2007, notably since 2001 when a sharp and sustained rise in diesel new registrations has occurred. New registrations of large cars, particularly sport utility and multipurpose, and small cars have increased over the same time period by 55% and 30%, respectively. Concurrently, registrations of medium class cars have declined 15%. Based on visual inspection of these time series trends, the rise in small cars is more temporally related to the ACEA CO2 agreement, while the diesel growth is more temporally related to the UK\u00E2\u0080\u0099s VED and company car tax regimes. However there are no known studies that have rigorously quantified the impact of these policies on new registrations such as with econometric methods [22]. There are other potential factors that have contributed to the growth in diesel and small cars, such as technological change, fuel taxation, and global oil prices. Nonetheless, the UK CO2 policies and ACEA CO2 agreement are commonly credited with making substantial contributions in reducing UK fleet-averaged emissions of newly registered cars from 190 gCO2/km in 1997 to 165 gCO2/km in 2007 [15, 17, 23, 24]. Learning from the UK experience is important because improved vehicle efficiency through changes in vehicle design (including fuel and mass) are promoted as viable strategies globally to mitigate climate change [1, 25-27]. 4 Figure 1.2 The relationship between vehicle mass, tailpipe CO2/km, fuel type, PM10 emissions, and relative fatality risk. CO2/km was regressed on curb mass for model year 2007 UK cars [21]. PM10 emission factors are for gasoline and diesel cars subject to Euro III, IV, or V emission standards [19]. Fatality relative risk (RR) is calculated based on Evans\u00E2\u0080\u0099 \u00E2\u0080\u009Cfirst law\u00E2\u0080\u009D for two-car crashes [20]: RR = \u00CE\u00BC\u00CE\u00BB (\u00CE\u00BB = 3.8), where \u00CE\u00BC is defined as the mass ratio. RR is the ratio of driver fatality risk for cars at a given mass divided by the fatality risk for cars with a mass of 1,456 kg, the mean of all 2007 models [21]. 0 50 100 150 200 250 300 350 400 450 500 500 1000 1500 2000 2500 3000 3500 Curb mass (kg) gr am s/ km C O 2 or P M *1 00 00 0.1 1.0 10.0 R R , f at al ity re la tiv e ris k gCO2/km Gasoline = -23.6 + 0.162 * kg gCO2/km Diesel = -28.4 + 0.132 * kg gPM/km Diesel Euro III gPM/km Diesel Euro IV gPM/km Diesel Euro V gPM/km Gasoline Euro III/IV/V RR, fatality relative risk (Y axis at right) RR \u00E2\u0089\u00A1 fatality relative risk RR = (risk at variable mass) / (risk at mean mass) where mean car mass = 1456 kg RR = 1.0 The intent of the first research objective was to examine how research on air quality and traffic safety risks could inform development of CO2 policies for passenger cars. However, there are many risks associated with passenger car use and thus the first research objective was chosen to be an examination of the rationale and basic framework for quantitative Integrated Assessment models of passenger car CO2 policy incorporating air quality, traffic 5 safety, and other relevant risks. Additional research context to support selection of the first research objective is provided in Section 1.4.1. The ACEA, VED, and company car tax policies provided incentives to manufacturers and consumers to reduce the average CO2/km of newly registered cars, including diesel [14, 15, 17]. Yet these policies were adopted 7 to 12 years prior to 2009 Euro V standards that were planned to harmonize the emissions of NOx, PM10, CO, and VOC\u00E2\u0080\u0099s for gasoline and diesel cars [19]. This mismatch in the timing of CO2 and air pollutant emission standards led to the second research objective, which was to assess the impact of diesel growth on CO2 emissions, air quality, and human health. Additional research context that supports the second research objective is provided in Section 1.4.2. The growth of larger (heavier) and smaller (lighter) cars at the expense of medium class cars (Figure 1.3) led to development of the third research objective. Given the prior research that vehicle mass is a determinant of both annual fatalities as well as the equity of risk in two car collisions [20, 28] and that vehicle mass directly influences CO2 emissions regardless of fuel type (Figure 1.2), the third research objective was chosen to examine relationships between tailpipe CO2 emissions and traffic safety risks, using vehicle mass as an intermediate variable. Section 1.4.3 provides an expanded discussion of the research context that supports the third research objective. Figure 1.3 UK passenger car market trends in size and fuel choice 1995-2007. The legend includes % change in new registrations over 10 years from 1998-2007. Vehicle size and mass are strongly correlated (see Chapter 4), so \u00E2\u0080\u009Clarge\u00E2\u0080\u009D also means \u00E2\u0080\u009Cheavy,\u00E2\u0080\u009D and \u00E2\u0080\u009Csmall\u00E2\u0080\u009D also means \u00E2\u0080\u009Clight\u00E2\u0080\u009D. In terms of fuel choice, alternative-fuelled vehicles have grown dramatically in recent years but still comprise less than 1% [29]. 0% 10% 20% 30% 40% 50% 60% 70% 1994 1996 1998 2000 2002 2004 2006 2008 N ew re gi st ra tio ns Small +30% 1998-2007 Medium -15% 1998-2007 Large +55% 1998-2007 Diesel +182% 1998-2007 ACEA voluntary CO2 agreement UK CO2-based VED UK CO2-based company car tax 6 1.3 RESEARCH QUESTIONS, HYPOTHESES, AND TASKS For each of the three research objectives, research questions, hypotheses and specific tasks for each research question are described below. This is followed by Table 1.1 which summarizes the data sources, methods, and limitations to achieving each objective RQ1 Question: In quantitative analysis of policies to achieve reductions in CO2 emissions from passenger cars, how important are Integrated Assessment methods and multiple risk frameworks, as opposed to separate, independent analyses of policies and risks (like air quality or traffic safety)? RQ1 Hypothesis: A broadening of the domain of mitigation policies for passenger cars can be more effective in reducing annual CO2 emissions, and also improve public health and other public risks associated with passenger car use. RQ1 Tasks: a. Review and summarize policy options that are designed to reduce annual CO2 emissions from passenger cars in the UK. b. Discuss the potential effects of choice of decision criteria and risks included or excluded from policy analyses. c. Assess the potential effects of choice of risk metrics. d. Outline an Integrated Assessment framework for analyzing CO2 reduction policies for passenger cars demonstrating how policies are linked to multiple public risks. RQ2 Question: How has the steep growth of diesel cars changed CO2 and PM10 emissions, and mortality related to PM10? RQ2 Hypothesis: Substitution of diesel cars for gasoline cars has produced savings in tailpipe CO2 emissions, with a resultant tradeoff in chronic exposure mortality due to traffic PM10 emissions. RQ2 Tasks: e. For the 2001-2020 study period, calculate annual CO2 reduction due to \u00E2\u0080\u009Cadditional diesels\u00E2\u0080\u009D based on the difference in average CO2 emission factors for gasoline and diesel cars, annual travel distance, and a UK-specific model for car scrap rates. 7 f. For the 2001-2020 study period, calculate reduction in petroleum fuel consumption due to additional diesels based on the fuel economy (L/100km) for the average diesel and average gasoline cars. g. For the 2001-2020 study period, estimate annual counts of additional diesels, and changes in air contaminant emissions (PM10, NOx, CO, and HC). h. Use the published results of existing air quality modeling studies to estimate the annual, population-weighted change in PM10 concentration. i. Quantify changes in annual mortality and hospitalizations using previously developed concentration-response functions most applicable to the UK. RQ3 Question: Climate mitigation policies such as the ACEA CO2 agreement and UK tax policies have provided incentives in favor of cars with lower certified CO2/km tailpipe emission rates, which are generally smaller and lighter cars. Simultaneously, consumer preferences for larger, heavier, higher CO2/km cars have kept these segments gaining market share. What is the relationship between tailpipe CO2/km emission rates of passenger cars and traffic safety risk? RQ3 Hypothesis: Occupants of smaller and lighter (lower CO2/km) will be subject to relatively larger risks of fatality or injury as compared to occupants of larger and heavier cars (higher CO2/km). Reducing on-road shares of heavier cars while increasing shares of lighter cars in the UK, compared to business-as-usual growth of heavier cars, will result in improvements in traffic safety. RQ3 Tasks: j. Replicate Evans\u00E2\u0080\u0099 \u00E2\u0080\u009Cfirst law\u00E2\u0080\u009D driver fatality risk for two-car crashes using UK data for recent crash years and model years [20]. k. The UK Department for Transport (DFT) has published the conditional risk of serious injury or fatality for makes and models of cars for model years 2000-2004 [30]. Using emissions data from independent sources, examine how the conditional risk relates to car CO2/km. l. Estimate changes in annual traffic fatalities in the UK during 2001-2005 if large and heavy cars had declined in market share, instead of risen. Estimate changes in fatalities for car-pedestrian, single-car, and two-car crash modes. 8 m. Estimate changes in the \u00E2\u0080\u009Cfirst law\u00E2\u0080\u009D fatality risk for two-car crashes in 2005 if large and heavy cars had declined in market share, instead of risen, and assuming randomized crash events. Table 1.1 Summary of research tasks, data sources, and analysis methods. Research Task Parameters or variables Sources of data and information resources Analysis methods Data gaps; research challenges; limitations; other comments Demonstrate the importance of choice of decision criteria using UK- specific parameters Tabulated relative ranking of risks using three decision criteria: i) economic social cost [34], ii) public health [35], and iii) social-political priorities [36, 37]. Demonstrate the importance of a multiple risk framework using UK-specific parameters Use the results of research task 1 (air quality and diesels), task 2 (traffic safety and curb mass), and other published UK-specific risk estimates [18, 31-33]. Showed how the set relevant risks can change and how the system boundary for risk analysis can change with choice of fuel and vehicle technology. 1. Quantitative Integrated Assessment modeling of passenger car CO2 policy incorporating air quality, traffic safety, and other relevant risks Outline a framework for quantitative policy analysis of multiple risks Selected literature on quantitative policy analysis [38, 39]. Developed an influence diagram showing basic linkages between policies and risks. The importance of Integrated Assessment, multiple risk quantitative modeling of passenger car CO2 policies has been demonstrated with UK-specific quantities and discussion. Building a quantitative policy model would be a separate research project, and beyond the scope of this thesis. Emission factors National Atmospheric Emission Inventory [19] Society of Motor Manufacturers and Traders Ltd. [24, 29, 40] Baseline mortality and morbidity rates, demographics Dept. of Health [42] UK Statistics Office Government Actuary's Department [43] Ambient concentrations Data archive studies [44-48] Dept. for Environment, Food, Rural Affairs 2. Assess the impact of diesel growth on CO2 emissions, air quality, and human health Concentration- response factors Peer reviewed literature [49, 50] and UK-specific studies [30, 51] Impact pathway analysis. Mortality for ages \u00E2\u0089\u00A5 30. Quantify health outcomes as annual counts. Only PM10 mortality and partial morbidity is quantified. Work is completed and published [41]. 9 Research Task Parameters or variables Sources of data and information resources Analysis methods Data gaps; research challenges; limitations; other comments Baseline traffic fatality rates, vehicle populations UK Data Archive [52, 53] Department for Transport [18] Curb mass JATO Dynamics [21] and available online, open source databases [58-60] Surrogate measures of mass: make, model, engine size, and fuel type 3. Assess the correlation between tailpipe CO2 emission rates and various traffic safety measures in the UK, using curb mass as the primary variable Fatality risk rates and coefficients Statistical estimates using individual collision data from the UK Data Archive, and both direct and indirect measures of vehicle curb mass 1. Replicated Evans\u00E2\u0080\u0099 method for calculating \u00E2\u0080\u009Cfirst law\u00E2\u0080\u009D of relative fatality risk using UK data [20, 54]. 2. Graphically and numerically compared CO2/km vs. conditional fatality risk using DFT\u00E2\u0080\u0099s results [55]. 3. Using Mengert\u00E2\u0080\u0099s method [56] and policy- relevant scenario of varied fleet mass composition, estimated changes in total fatalities for: single-car, two-car, and car- pedestrian crashes. 4. Using the same fleet composition scenarios for which changes in total fatalities are estimated, calculated \u00E2\u0080\u009Cfirst law\u00E2\u0080\u009D relative risk using Latin Hypercube methods [57] to simulate two-car collisions. Data on casualties, vehicles, and crash modes for every individual UK car crash reported to police from 1994- 2005 (inclusive) have been obtained from the UK Data Archive. A principal limitation is that curb mass data are not part of the UK Data Archive databases. This was overcome by manually entering curb mass, or using surrogate measures for curb mass. This reduced the precision of the estimates. 1.4 CRITICAL HIGHLIGHTS FROM THE LITERATURE Highlights of the literature emphasizing critical findings that are most relevant to my research objectives are provided here. Additionally, each chapter provides a review of the relevant literature. 1.4.1 CLIMATE MITIGATION POLICY: THE IMPORTANCE OF MULTIPLE RISK ASSESSMENT The first research objective was intended to set the research framework for the two public health case studies, to examine the lessons learned in designing climate mitigation policy for passenger cars to simultaneously address air quality and traffic safety risks. However, the first research question and tasks were proposed on the premise that a policy analysis 10 framework to incorporate additional risks (i.e., more than just air quality and traffic safety) is necessary. Policymakers in industrialized countries are increasingly tasked to design and implement climate mitigation policy for passenger cars [27, 61, 62]. Car fleets in developed countries are currently dominated by conventional gasoline and diesel technology2 [27, 63], and risk assessments incorporating social costs commonly assume 100% conventional technology [34, 64]. Integrated Assessment of multiple risks is growing in importance because industrialized countries are presently at a crossroads in fuel and drive-train choices. Scenarios for the near to distant future commonly project a much more heterogeneous mix of fuels and drive-train technologies. Examples from the International Energy Agency\u00E2\u0080\u0099s (IEA) current suite of transportation scenarios include steep uptake of plug-in hybrids beginning in 2015, 90% electric or fuel cell vehicles by 2050, and a range of advanced conventional cars (defined as 50-70% better fuel consumption) as high as 90% to as low as 10% [26]. While greenhouse gas reduction is a principal driver of these transitions, climate change is just one of many risks associated with passenger car use, and the effectiveness of new climate policies will depend on how well they balance climate change mitigation with other risks [2]. In designing policies to achieve transitions such as envisioned by IEA, it is clear that risk assessment models will need to adapt. In this study I examine the rationale, and present a basic framework, for Integrated Assessment models to incorporate multiple risks from a societal perspective in the analysis of climate mitigation policies for passenger cars. Using the UK as a case study, I illustrate the importance of decision criteria, risks included (or excluded), and how the choice of fuel and technology options influences system boundaries and relative ranking of multiple risks. The UK is a useful case study because UK circumstances are generally applicable to other developed countries (albeit not completely). The UK was an early adopter of mitigation policies for passenger cars with nationwide tax regimes in place since 2001-2002 [14, 15]. As in other industrialized countries passenger car ownership and road transport CO2 emissions continue to rise [35]. The UK Department for Transport (DFT) forecasts ownership levels relative to the year 2000 of 122% by 2015, and 133% by 2025, a growth rate that is in excess of the population growth rate [18]. Currently the UK car fleet is dominated by spark- ignition gasoline and compression-ignition diesel cars, comprising 99.3% of all new 2 I use the term \u00E2\u0080\u009Cconventional diesel technology\u00E2\u0080\u009D to include diesel cars with or without particle filters. 11 registrations [24]. However, alternative drive-trains, such as grid-independent hybrids and alternative-fuelled vehicles, such as ethanol-powered cars (E85), are already rapidly gaining market share [24]. Government policies such as the UK Renewable Transport Fuels Obligation [65] and voluntary and regulatory CO2 measures in Europe are expected to continue to stimulate the proliferation of alternative vehicle technologies and fuel types [66]. The UK is similar to other developed countries in its struggle to limit CO2 from transportation. From 1990 to 2005 CO2 emissions were reduced for every major sector, except transportation where emissions have risen by 12% [67]. Because of the importance of transportation in emissions, with passenger cars as a major contributor, multiple government agencies in the UK have identified climate mitigation for the road transportation sector as a high priority [7, 9-12]. The pitfalls of focusing on a single problem in policy design and analysis for passenger cars have long been established [68]. The array of regulations and policies affecting passenger cars is often in tension, and not always consistent with green design principles [69, 70]. A critical issue in Integrated Assessment of multiple risks is that analysis of fuels and vehicle technology alone is insufficient because ownership (cars per capita), annual vehicle kilometers traveled per car (VKT) and patterns of use (e.g., time of day and location) are essential factors that influence risks. A recent example from the UK is that steep growth of diesel cars has not yielded expected savings in fuel consumption and CO2 due to consumer choice of larger cars, driver behavior (speed), and VKT rebound effects [71]. Another example is the ongoing debate about fuel economy standards in the U.S. Considering only the principal environmental risks (air quality and climate change) and energy security, more stringent fuel economy standards are strongly beneficial. However, when considering traffic safety risks via disparate vehicle mass and economic risks of traffic congestion via VKT rebound effects, the likelihood of realizing net social benefits with more stringent fuel economy standards is less clear. While the need for Integrated Assessment in transportation policy is often acknowledged [2], much research has employed Integrated Assessment of multiple risks but applied one decision criterion such as social cost-benefit [35, 72, 73]. Other research has focused on integration of near-term public health risks but has not incorporated other risks such as climate change or congestion [35, 74]. Stakeholders involved in passenger car policy development are increasingly sophisticated in recognizing the interdependencies between fuels, technology, and vehicle ownership and VKT as well as the relevance of a multiple risk framework. For example, U.S. car manufacturers argued that fleet turnover effects (the rate of change of in-use technology over time) and rebound effects (i.e., VKT) might nullify welfare gains from California\u00E2\u0080\u0099s 12 proposed CO2 standards for passenger cars [75]. In Europe, proposals by the European Commission for car CO2 standards differentiated by mass classifications were refuted by a leading environmental organization on the basis of traffic safety concerns [76]. Therefore, robust Integrated Assessment of multiple risks in policy development can reasonably be expected to improve the social and political acceptability of new policies. There are inherent limitations in focusing primarily on Integrated Assessment of multiple risks. A truly comprehensive Integrated Assessment would explore the best strategy for meeting transportation needs within the context of all relevant local, social, and physical conditions. Additional domains that are relevant include organizational and institutional domains [77], interactions with other energy-intensive sectors [78], integration of transportation and land use planning [79], and integration of passenger car travel with other travel modes [35]. In the larger picture, the integration of climate change with other drivers of global change is relevant [80]. Another limitation of my research is that it focuses on risks and does not examine the positive attributes of consumer choice [81]. As an example, the relative benefits of gasoline-powered cars and all-electric cars are not equal. The travel range for a single refueling of a gasoline-powered car is much greater than an all-electric car, an attribute strongly valued by consumers. On the other hand, there could be traffic safety benefits due to more alert driving due to drivers taking a short-rest every few hours to charge batteries. Thus the relative benefits of fuels and technologies to consumers are excluded [63]. 1.4.2 AIR QUALITY AND ANCILLARY BENEFITS OF CLIMATE MITIGATION POLICY The second research objective was motivated by research literature on the ancillary benefits of climate mitigation policies. CO2 reduction policies have been promoted on the basis that reducing fossil fuel use provides dual benefits in terms of long-term climate change attenuation and short-term air quality improvements. Models predict that climate policies will reduce fossil fuel combustion and lower air pollutant emissions, and therefore provide substantial public health benefits [82-86]. For example, a study in Science [82] concluded \u00E2\u0080\u009Cthat GHG mitigation can provide considerable local public health benefits from air pollution reduction alone to countries that choose to abate GHG emissions by reducing fossil fuel combustion.\u00E2\u0080\u009D This and similar studies were cited by the IPCC in the 2001 Assessment that featured a chapter on ancillary air pollution benefits of climate policy, without any mention of the possibility of disbenefits [85] Similar findings were more recently published for the European context [86] which found that \u00E2\u0080\u009CSubstantial ancillary benefits were found for regional air pollution (SO2, NOx, VOC and particulate matter) of implementing the Kyoto Protocol (intended to control greenhouse gas emissions) in Europe.\u00E2\u0080\u009D The European Environmental 13 Agency also published findings that \u00E2\u0080\u009CAction to combat climate change will deliver considerable ancillary benefits in air pollution abatement\u00E2\u0080\u009D [84]. Neither of these European studies mentioned the potential air pollution disbenefits of using diesel technology for CO2 reduction from passenger cars. There is some acknowledgement in the literature of the potential impacts of diesel cars, in particular one economic study [87] which examined taxation policy for gasoline and diesel cars, including accounting of the \u00E2\u0080\u009Cenvironmental costs\u00E2\u0080\u009D because \u00E2\u0080\u009Cdiesels have high emissions of particulate matter\u00E2\u0080\u009D. Another editorial acknowledged the need for research into the climate mitigation and air quality effects of diesel car technology [88]. One study assessed the upstream CO2 and energy implications of increasing refinery production of diesel relative to gasoline fuels [89]. Studies in the U.S. context examined ambient ozone implications based on a hypothetical transition from gasoline cars to modern diesel technology [90], and the global warming implications of black carbon emissions from diesels [91]. While there are a handful of studies that partly fit the aim of the proposed research on diesels in the UK, the need for this research is still clear. A majority of research has omitted mention of the social costs of diesel cars in studies of transportation, CO2, and air pollution emissions. Moreover the studies that contain this omission were published in the highest impact publications such as Science [82], The Lancet [83], Environmental Health Perspectives [92], and IPCC\u00E2\u0080\u0099s Assessment Reports [85]. Where research has attempted to quantify CO2 and air quality tradeoffs of diesel cars, it has by necessity been based on scenarios, or hypothetical changes, in transportation choices. Where it is feasible, my research is based on actual changes in passenger car choices in the UK after enactment of climate mitigation policies targeting passenger cars. 1.4.3 VEHICLE MASS, TRAFFIC SAFETY, AND CO2 EMISSIONS FROM PASSENGER CARS The third objective was formulated on the premise that vehicle mass, CO2 emissions, and traffic safety are interrelated. The relationship between vehicle mass and tailpipe CO2 emissions is relatively straight forward [93, 94]. For any given vehicle design, a heavier vehicle consumes more fuel and results in greater CO2 emissions directly out of the tailpipe and indirectly upstream of the vehicle in the energy supply chain. The relationship between vehicle mass and traffic safety is less clear [28, 95-102]. Appendix B, Table B1 summarizes the methods and findings of several critical studies, and some of the critical findings will be discussed here. One of the earliest landmark studies of these 14 relationships assessed the linkage between U.S. CAFE standards and traffic safety with U.S. FARS data, using vehicle mass as the intermediary variable [96]. It was concluded that the CAFE standard would \u00E2\u0080\u009Cbe responsible for several thousand additional fatalities over the life of each model year\u00E2\u0080\u0099s cars.\u00E2\u0080\u009D Another landmark study by the U.S. National Highway Safety Administration (NHTSA) using FARS data found that reducing average vehicle mass by 45 kg (100 pounds) would result in a net increase in annual fatalities [28]. The NHTSA findings have been questioned by others who also used FARS data, but separately assessed the effects of mass and size to show that reducing average mass while holding size constant would decrease annual fatalities in the U.S. [103]. Other research examined model-specific fatality rates and argued that vehicle \u00E2\u0080\u009Cmass may not be fundamental to safety\u00E2\u0080\u009D [104, 105]. While these dominated the policy debates in the U.S., particularly around the issue of fuel economy standards [106], there are other studies that also reveal important relationships with respect to vehicle mass and traffic safety. For example, one study examined the independent effects of vehicle, driver, and collision variables for single vehicle crashes using FARS data and concluded that increased mass and size together reduce fatality risk [104]. Another examined the role of mass, size, and energy absorption in head-on two-car collisions for multiple casualty types and in multiple contexts (Germany, Japan, U.S.) and found size to be dominant, but mass still an important factor [107]. Yet another study examined driver fatality odds ratio in two-car U.S. collisions, separately assessing different crash modes and independent variables for mass, multiple size metrics, and driver characteristics [108]. It was found that mass ratio in two-vehicle collisions affected fatality risk more than any other vehicle variables, and that equalizing mass across the on-road fleet would reduce overall fatality risk. While the majority of research on the role of vehicle mass in traffic safety has used U.S. data, the relationship has been examined in the UK as well. One study examined the effect of a uniform 10% vehicle mass reduction in the UK and concluded this would reduce fatalities in single-car, two-car, and car-pedestrian crashes [95]. A critical review of the literature therefore, does not lead to a broad consensus on the role of vehicle mass in traffic safety. The basic underlying reason for this is that traffic safety is the net result of a large number of important variables and complex relationships that have constantly changed over time, and virtually all studies have important limitations. While every study makes some contribution, every study also possesses one or more of the following limitations: 15 1. Aggregation. Data is aggregated in one or more important ways: o Crash event aggregation: quantitative relationships are not based on individual crash data, but aggregated into groups leaving open the possibility of spurious findings due to the ecological fallacy (e.g., [102]). o Spatial aggregation: crash data are aggregated at a state (in the U.S.) or national level which combines data across important dimensions such as differing regulations for licensing and traffic enforcement, urban and rural road types, or other variables (e.g., [96]). o Crash type aggregation: crash data are aggregated across multiple types of crash events (e.g., car-heavy goods, single-car, two-car, car-pedestrian). For example, two frequently cited U.S. studies both aggregated crash data for car collisions with pedestrians, motorcycles, and bicyclists, even though these three types of crashes are very different [28, 103]. o Time aggregation: multiple crash years are aggregated leaving many potential confounders such as changes in roads, speed enforcement, or vehicle safety and technology. For example, one study that analyzed the role of mass and size included FARS data over the time period of 1975-1998 [28, 103, 107]. 2. Choice of risk metric. Two important risk metric choices influence the ability to generalize traffic safety research: o Risk type: absolute risk, relative risk (including risk ratio), or conditional risk are available options. Absolute risk (e.g., annual fatalities) is a common choice, but does not provide insights into the distribution of risk such as the relative risk between drivers of heavier (higher CO2/km) cars and lighter (lower CO2/km) cars. Risk equity between two groups can be measured by ratios, but ratios can vary without changing absolute risks. Conditional risk studies provide valuable insights into crashworthiness, but not crash avoidance. o Casualty type: fatalities and various degrees of injury are the available choices. Fatalities tend to be the metric of choice most often used, yet this choice excludes injury rate which is also critical to achieving traffic safety goals. 16 3. Vehicle and crash factors. Basic vehicle parameters such as mass, size, stiffness, and height affect safety, as well as specific safety equipment such as side air bags and electronic stability control [94, 97, 109-112]. For example, none of the aforementioned studies on the role of vehicle mass and traffic specifically adjusted risk estimates based on electronic stability control, even though this technology has been selectively adopted over time since the early 1990s and has been found to reduce fatal single-car crashes by 30-70%, and fatal rollover crashes by 70-90% [111]. Collision speed has been shown to be a critical crash factor in determining casualty severity [101, 113-115], yet most databases have only indirect indicators of collision speed such as road speed limit. Crash mode (points of impact, rollovers, hit objects) is yet another factor that substantially influences risks, but is not always adjusted in risk estimates [28, 102, 103, 116]. 4. Driver behavior. While there is wide agreement in the traffic safety literature on the critical importance of driver behavior as a determining factor in risk [102, 103, 105, 117-129], in practice it is difficult to adjust risk estimates for behavior and this correction factor is seldom used. Seat belt use, previous traffic citations, intoxication, and related variables have been employed in various analyses. While two U.S. studies [28, 103] have been compared [28, 103, 130], the comparisons have failed to mention that one study incorporated a relatively detailed sensitivity analysis using 9 behavioral variables [28], while the other did not adjust for behavior beyond basic age and sex indicators [103]. Others have claimed to have ruled out \u00E2\u0080\u009Cnon-subtle\u00E2\u0080\u009D behavior effects in their analysis [102], but this conclusion was derived by comparing aggregate descriptive statistics for a relatively small set of behavior variables. 5. Factors other than road environment, vehicle, and driver. In general, traffic safety risks can be viewed as a Haddon matrix which incorporates factors other than what is normally associated with the crash environment, vehicle, and driver [131]. For example, it has been argued that one-third of the reduction in fatalities in the UK may be due to more rapid response to serious collisions and new medical technologies [132]. 17 A critical question in the development of policies to improve traffic safety and climate mitigation from passenger cars is: can vehicle mass be reduced without compromising safety? The balance of the research literature indicates the answer is \u00E2\u0080\u009Cyes.\u00E2\u0080\u009D But it is a conditional \u00E2\u0080\u009Cyes\u00E2\u0080\u009D because it matters how mass is reduced. For example, the mass of individual vehicles can be reduced by eliminating safety equipment such as side air bag systems and electronic stability control components, but the safety tradeoffs would likely be negative. Average, on-road fleet mass can be reduced by introducing large numbers of the smallest car models while allowing numbers of large cars to grow rapidly, but this likely would have negative implications for the distribution of risk. While safety, design and material use is rapidly diffused through all vehicle types, and given similar rates of fleet turnover in different classes, vehicle mass will continue to be one of many important variables in traffic safety, and also directly influence vehicle CO2 emission rates. Thus further research into the relationships between vehicle mass, CO2 emissions, and traffic safety is warranted. 1.5 HOW THE THESIS CHAPTERS FIT TOGETHER The overall objective of this thesis is to assess the relationships between climate mitigation policy for passenger cars with case studies of the two largest public health risks: air quality and traffic safety. Before embarking on the two case studies, it was important to set the broader policy context first, emphasizing Integrated Assessment methods as the cornerstone of policy analysis and design; this is accomplished in Chapter 2. Subsequently Chapter 3 provides a case study on air quality and CO2 emissions, while Chapter 4 provides the traffic safety case study. 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Integrated Assessment of multiple risks is growing in importance because industrialized countries are presently at a crossroads in fuel and drive-train choices. Scenarios for the near to distant future commonly project a much more heterogeneous mix of fuels and drive-train technologies. Examples from the International Energy Agency\u00E2\u0080\u0099s (IEA) current suite of transportation scenarios include steep uptake of plug-in hybrids beginning in 2015, 90% electric or fuel cell vehicles by 2050, and a range of advanced conventional cars (defined as 50-70% better fuel consumption) rising as high as 90% to as low as 10% [7]. While greenhouse gas reduction is one principal driver of these transitions, climate change is just one of many risks associated with passenger car use, and the effectiveness of new climate policies will depend on how well they balance climate change mitigation with other risks [8]. In designing policies to achieve transitions such as envisioned by IEA, it is clear that risk assessment models will need to adapt. In this study I examine the rationale, and present a basic framework, for Integrated Assessment models to incorporate multiple risks from a societal perspective in the analysis of climate mitigation policies for passenger cars. Using the UK as a case study, I illustrate the importance of decision criteria, risks included (or excluded), and how the choice of fuel and technology options influences system boundaries and relative ranking of multiple risks. The UK is a useful case study because UK circumstances are generally applicable to other developed countries (albeit not completely). The UK was an early adopter of climate mitigation policies for passenger cars with nationwide tax regimes in place since 2001-2002 [9, 10]. As in other industrialized countries passenger car ownership and road transport CO2 emissions 3 A version of this chapter had been submitted for publication. Mazzi, E. \u00E2\u0080\u009CIntegrated Assessment of Multiple Risks to Assess Current and Future Climate Mitigation Policies For Passenger Cars,\u00E2\u0080\u009D 2009. Based on various reviewer feedback, it is being substantially revised for re-submission to an appropriate journal. 28 continue to rise [11]. The UK Department for Transport (DFT) forecasts ownership levels relative to the year 2000 of 122% by 2015, and 133% by 2025, a growth rate that is in excess of the population growth rate [12]. Currently the UK car fleet is dominated by spark-ignition gasoline and compression-ignition diesel cars, comprising 99.3% of all new registrations [13]; however, alternative drive-trains, such as grid-independent hybrids and alternative- fuelled vehicles, such as ethanol-powered cars (E85), are already rapidly gaining market share [13]. Government policies such as the UK Renewable Transport Fuels Obligation [14] and voluntary and regulatory CO2 measures in Europe are expected to continue to stimulate the proliferation of alternative vehicle technologies and fuel types [15]. The UK is similar to other developed countries in its struggle to limit CO2 from transportation. From 1990 to 2005 CO2 emissions were reduced for every major sector, except transportation where emissions have risen by 12% [16]. Because of the importance of transportation in emissions, with passenger cars as a major contributor, multiple government agencies in the UK have identified climate mitigation for the road transportation sector as a high priority [17-21]. The pitfalls of focusing on a single problem in policy design and analysis for passenger cars have long been established [22]. The array of regulations and policies affecting passenger cars is often in tension, and not always consistent with green design principles [23, 24]. A critical issue in Integrated Assessment of multiple risks is that analysis of fuels and vehicle technology alone is insufficient because ownership (cars per capita), annual vehicle kilometers traveled per car (VKT) and patterns of use (e.g., time of day and location) are essential factors that influence risks. A recent example from the UK is that steep growth of diesel cars has not yielded expected savings in fuel consumption and CO2 due to consumer choice of larger cars, driver behavior (speed), and VKT rebound effects [25]. Another example is the ongoing debate about fuel economy standards in the U.S. Considering only the principal environmental risks (air quality and climate change) and energy security, more stringent fuel economy standards are strongly beneficial. However, when considering traffic safety risks via disparate vehicle mass and economic risks of traffic congestion via VKT rebound effects, the likelihood of realizing net social benefits with more stringent fuel economy standards is less clear. While the need for Integrated Assessment in transportation policy is often acknowledged [8], much research has employed Integrated Assessment of multiple risks but applied one decision criterion such as social cost- benefit [5, 26, 27]. Other research has focused on integration of near-term public health risks but has not incorporated other risks such as climate change or congestion [11, 28]. Stakeholders involved in passenger car policy development are increasingly sophisticated in recognizing the interdependencies between fuels, technology, and vehicle ownership and VKT as well as the relevance of a multiple risk framework. For example, U.S. car manufacturers 29 argued that fleet turnover effects (the rate of change of in-use technology over time) and rebound effects (i.e., VKT) might nullify welfare gains from California\u00E2\u0080\u0099s proposed CO2 standards for passenger cars [29]. In Europe, proposals by the European Commission for car CO2 standards differentiated by mass classifications were refuted by a leading environmental organization on the basis of traffic safety concerns [30]. Therefore, robust Integrated Assessment of multiple risks in policy development can reasonably be expected to improve the social and political acceptability of new policies. There are inherent limitations in focusing primarily on Integrated Assessment of multiple risks. A truly comprehensive Integrated Assessment would explore the best strategy for meeting transportation needs within the context of all relevant local, social, and physical conditions. Additional domains that are relevant include organizational and institutional domains [31], interactions with other energy-intensive sectors [32], integration of transportation and land use planning [33], and integration of passenger car travel with other travel modes [11]. In the larger picture, the integration of climate change with other drivers of global change is relevant [34]. Another limitation of this study is that it focuses on risks and does not examine the positive attributes of consumer choice [35]. As an example, the travel range for a single refueling of a gasoline-powered car is much greater than an all-electric car, which is a positive attribute strongly valued by consumers. Thus the relative benefits of fuels and technologies to consumers are excluded [4]. In this study I first review the importance of identifying the risks of concern and choice of decision criteria in public policy affecting passenger car fuels, technology, ownership, and use. Next, I describe the key linkages between policies and the chosen set of risks, and illustrate how the pathways linking policies and risks depend on the types of technologies or fuels being analyzed. I conclude with summary remarks on development of policy. 2.2 IDENTIFYING RELEVANT RISKS AND CHOOSING DECISION CRITERIA Following Morgan and Henrion [36], it is emphasized that in the early phases of policy analysis the choice of decision criteria must be made and the principal risks of concern identified, and that these choices must be made iteratively throughout the policy analysis. When preparing to assess new or modified passenger car policies, identifying relevant risks is strongly dependent upon choice of decision criteria. I discuss three different approaches to choosing decision criteria and the associated risk priorities that result, primarily using the UK as a case study: (1) economic, utility-based approach based on marginal external costs, (2) public health approach based on various mortality and morbidity metrics, and (3) criteria based on public agency new policy 30 priorities or rights-based constrained risks. Each of these decision criteria approaches is valid, yet each can lead to different policy and risk mitigation priorities as summarized in Table 2.1. Table 2.1 Approximate ranking of risks in the UK for different decision criteria, based on the fleet dominated by spark-ignition gasoline and compression-ignition diesel passenger cars. Economic External Social Costs Public Health New Public Policy Priorities Primary Congestion Traffic Safety, Air Quality Climate Change Secondary Air Quality, Traffic Safety Environmental Noise Congestion, Energy Security, Climate Change, Air Quality Tertiary Environmental Noise, Climate Change, Energy Security Congestion, Energy Security, Climate Change Environmental Noise Water Security, Water Quality, Land Use, Food Security often excluded 2.2.1 DECISION CRITERIA BASED ON MARGINAL EXTERNAL COSTS I illustrate the relationship between relevant risks and decision criteria based on the current UK passenger car fleet that is dominated by spark ignition gasoline and compression ignition diesel cars. However, it is noted that the set of risks is also strongly affected by the choice of fuels and technology included in the analysis. For example, when considering biofuel-powered vehicle technology on a large scale, food security, water security, land use, and water quality risks rise in importance [37] while improvements in air quality and climate change remain uncertain [38, 39]. Scenarios that include coal-to-liquids fuels substantially mitigate energy security risks for countries that have large coal reserves but can exacerbate other risks such as air quality, water quality, ecosystem destruction and climate change [40]. The first comprehensive estimate of marginal external costs of transportation for the UK was published by Peirson and colleagues [41]. The most recent UK estimates are summarized in Figure 2.1 [6], and are generally similar to figures for the U.S. except that external costs of congestion, air quality, and traffic collisions are closer [5]. The most striking result of using social costing as the decision criterion in the UK is that the costs of congestion dominate all 31 environmental and health risks, including climate change which is assessed to be one order of magnitude lower. This is precisely why the VKT rebound effect is critical from an economic externality perspective; a relatively small increase in VKT occurring in urban centers at peak times may overwhelm estimated public welfare benefits from reducing CO2, noise, air pollutants or traffic safety risks. In fact, if one adopts economic externalities as the sole decision criterion, then arguably climate change risks could be omitted in the analysis of passenger car policies. Figure 2.1 Marginal external social costs (pence per kilometer) in Great Britain adapted directly from reference [6]. Error bars represent the estimated range of the marginal external costs for each risk. 0 p/km 2 p/km 4 p/km 6 p/km 8 p/km 10 p/km 12 p/km global warming noise pollution local air quality traffic collision risks congestion m ar gi na l e xt er na l c os t, pe nc e pe r k m tr av el . Like all single-metric approaches, externality costing has its limitations and criticisms such as sensitivity to assumptions about internalized risk, inadequate accounting of equity, and large uncertainties particularly due to choice of value of statistical life and discount rates [6, 42-46]. It is noted that motor vehicle transport also imposes physical inactivity risks [46], but quantifying this risk is only relevant when comparing modal shifts such as to cycling and walking. Physical inactivity risks do not change when comparing passenger car technology options against each other because all options equally promote physical inactivity. 2.2.2 DECISION CRITERIA BASED ON PUBLIC HEALTH METRICS Developing risk priorities from a public health perspective using mortality and morbidity produces a different ranking compared to externality costing. The top three priorities are clearly traffic safety, air quality, and noise. Climate change health risks within the UK have not yet been 32 quantified, and there is no indication the direct risks will be of similar magnitude to traffic safety or air quality in the near future, although these risks are expected to grow more rapidly than others over time [47]. Traffic congestion could be expected to induce some health risks such as increased exposure to air pollution, but congestion impacts on collision injuries and noise is difficult to assess. Where there is congestion, there is a slowing of traffic [48] and when collisions occur they are likely to be less injurious [49]. However, congestion can also lead to more aggressive driving and excess speeds to escape the congested area. Similarly mixed effects are expected in environmental noise health effects by increasing time of exposure but reducing peak noise levels because of slower speeds [50]. The relative ranking among the top three health risks in the UK depends upon the choice of metric. Choosing annual mortality as the metric, air quality is the top priority. In 2005, annual mortality in the UK due to urban air pollution (of which transport represents over 80% [51]) was estimated to be 12,400 due to particulate matter and 700 due to ozone [52, 53]. Traffic fatalities in 2005 were 3,201 [54]. For environmental noise, no UK-specific burden of disease estimates are yet available [55]; however, scaling estimates made for all EU-25 countries of 50,000 premature deaths annually [50] to the UK indicates roughly 6,000 deaths brought forward annually. Choosing years of life lost (YLL) as the metric, traffic safety becomes the top priority due to a high proportion of young casualties. Using UK life tables [56], traffic safety resulted in 127,000 YLL in 2005. Assuming 9 years of life lost per statistical death due to particulate matter [57, 58] and 1 year due to ozone [59], air pollution results in 112,000 YLL. A rough estimate for noise is 60,000 YLL, assuming 10 years of life lost per statistical death [60]. A further extension of YLL is to add morbidity effects to quantify disability adjusted life years (DALYs) [61]. I found no estimates comparing DALYs for all environmental health risks in the UK; however, estimates for the Netherlands show that air pollution DALYs are twice that of traffic safety, while traffic safety DALYs are twice that of environmental noise [60]. 2.2.3 DECISION CRITERIA BASED ON NEW PUBLIC POLICY PRIORITIES While climate change tends to subordinate to other risks when applying economic externality or public health decision criteria, indications are that climate change ranks highest from the perspective of new public policy priorities. Multiple UK government agencies identify climate change as a high priority while interrelated risks such as air quality and traffic congestion remain important but are emphasized less than climate change [17-21]. The CO2 reduction targets set by government agencies can be considered to be largely rights-based, constrained-risk criteria. 33 2.2.4 CHOOSING THE APPROPRIATE DECISION CRITERIA To summarize, Table 2.1 shows the relative rankings of risks based on choice of decision criteria. The example of the UK demonstrates there can be substantial differences in the ranking of risks and subsequently policy priorities. When choosing decision criteria, it is critical to recognize the distinction between policy analysis for research and public policymaking purposes. In research, it is common to adopt a single decision criterion (e.g., economic externality) or a specific metric (e.g., mortality as the health metric). However policy analysts in practice explicitly or implicitly are forced to adopt multiple decision criteria in order to accommodate the divergent interests of stakeholders[61]. Inevitably, agencies from different levels of government or agencies mandated to manage specific public risks typically employ different criteria [62]. In the UK, any transportation proposal that requires the funding or approval of the DFT must be appraised using economic, utility-based criteria [63], but different decision criteria prevail in constraining individual risks. For example, the Kyoto Protocol targets are an important determinant of CO2 emission constraints in the UK, and these targets were the outcome of a political process where circumstances dictated that an arbitrary constrained risk approach be employed[64]. Passenger car tailpipe CO2 emission rates are strongly influenced by the cost-effectiveness-based automobile manufacturers\u00E2\u0080\u0099 EU-wide voluntary targets [64], modified by the influence of UK-specific taxation policies on fuels and vehicle purchases [9, 10]. UK traffic safety goals are set according to various constrained risk casualty targets, partly influenced by technology-based criteria, and without any explicit mention of utility-based criteria [65]. Fuel economy standards are set based on technological criteria at the EU level [66]. Noise standards are determined by a hybrid of utility and technology-based EU standards for vehicle and tire noise [50, 67]. Clearly in the UK, where circumstances are generally similar to other industrialized countries, public policymakers aiming to reduce CO2 from passenger cars will have to design policies aimed to balance multiple risk targets developed from multiple decision criteria. Ultimately there is no clear, objective answer as to which decision criterion is best. The aim of public policymakers should be to design policy portfolios that perform acceptably well regardless of the decision criteria [68]. 2.3 PATHWAYS FROM POLICY TO RISKS 2.3.1 LINKING POLICIES TO RISKS A basic influence diagram illustrating the relationship between policies and risks for current generation gasoline and diesel cars is shown in Figure 2.2. Linking policies and risks, I present an influence diagram comprised of a framework with three basic economic actor groups: 34 consumers, manufacturers, and fuel providers, and four basic factors: ownership, annual VKT and patters of use, vehicle technology, and fuel properties [2, 5, 69, 70]. Figure 2.2 Influence diagram of relationships between passenger car policies and risks in a market dominated by spark ignition gasoline and compression ignition diesel cars. In response to policies, manufacturers and consumers determine vehicle ownership and technology choice as shown in Figure 2.2. However, beyond manufacturer pricing and consumer income, there are a variety of complex factors involved such as urban design, employment dynamics, expected annual VKT, congestion, and choice of alternative modes [71-73]. VKT is principally a consumer response, influenced by the marginal cost of driving through factors such as commuting times, fuel costs and road pricing [74]. Fuel properties (i.e., the choice of diesel or gasoline in this case) are determined principally by taxes, fuel providers, and auto manufacturers (through fuel specific changes in drive-train technologies) [75]. Ownership and VKT (including patterns of use) are linked to all risks in Figure 2.2 because more cars and more usage change emissions of contaminants and noise, congest roads, and raise the probabilities of collisions with other road users. It is noted, however, that these simple relationships can be complex and do not always change risks in the expected direction. Congestion charging in London has been estimated to increase average vehicle speeds by 2.1 climate change air quality energy security traffic safety congestion passenger cars usage, fuels & technology economic, ecological, and human health risks passenger car policies consumers fuel providers fuel properties economic actors noise VKT: km per car vehicle technology car manufacturers ownership: cars/person 35 km/hour, reducing NOx and PM10 in diesel cars, but increasing NOx in gasoline cars [48]. Air pollutant emissions vary at micro-timescales due to changes in vehicle speeds and use patterns (e.g., cold starts) [76], and also at macro-timescales due to fleet turnover effects that change the fleet composition mix of older and newer cars [77]. Increased vehicle speeds could also be expected to worsen traffic safety due to the power law influence of speed on casualty severity [49], although research on London\u00E2\u0080\u0099s congestion charge system concludes no obvious change in collision frequency. Increased speed could also possibly worsen noise because in the speed range of 30-50 km/hour tire noise begins to dominate motor and exhaust noise [50]. The fact that ownership and VKT influence all risks highlights the importance of modal shift (i.e., rail, bus, bicycle, and walking) in passenger transport policy. As illustrated in Figure 2.2, the combination of vehicle technology and fuel properties may be strongly linked to air quality and weakly linked to energy security (petroleum consumption) and CO2 emissions [25, 78]. Vehicle technology such as crash avoidance systems and occupant protection features, as well as fleet mass composition, all influence traffic safety risks [79]. Fuel properties alone are shown in Figure 2.2 as only influencing climate change, air quality, and energy security because the properties of fuel consumed do not alter congestion or traffic safety risks. I also note when considering conventional vehicle technologies that fuel and drive-train choices are not linked to changes in noise because the average diesel and gasoline cars in the UK are both 72.3 dB(A) [80], and both have been subject to the same 74 dB(A) regulation since 1998 [50]. One of the tasks in Integrated Assessment is to determine first-order and second-order effects, to ascertain which risks are of most concern and which are less important in evaluating policy and technology options[43]. Such decisions depend on research analyst choice and public agency targets and priorities. For example, research for the UK estimated a mortality increase of roughly 100 per year due to substitution of diesel cars for gasoline models [78]. Out of roughly 13,000 air pollution deaths in the UK annually, is 100 (0.8%) a first-order or second-order effect? Research on fleet mass reduction scenarios and traffic safety in the UK projected that car- pedestrian fatalities would be reduced 18% [79]. Again, is this a first-order or second-order effect? In general, iteration in the analysis and scrutiny of the results (e.g., to compare to published research and government priorities) may be required to ascertain whether a risk result is first-order or second-order. The direction of change estimated for a particular risk, whether things are improving or worsening, may itself trigger a risk to be classified as first-order or second-order even if the magnitude of change is small. 36 2.3.2 CLIMATE MITIGATION POLICY OPTIONS AND POTENTIAL INFLUENCES ON RISKS Table 2.2 provides a comprehensive list of policies that have the potential to reduce CO2 emissions from passenger cars with examples from the UK where applicable. A key concept is that every policy option listed, if sufficiently strong in its technical requirements or the price signal it induces or by the way policy mechanisms are structured, can potentially influence to a measurable degree ownership, VKT and patterns of use, vehicle technology, and fuel choice. Therefore all links to risk shown in Figure 2.2 are potentially affected by the policies in Table 2.2. Table 2.2 was developed based on the following assumptions. \u00EF\u0082\u00B7 Climate change. Most policies, by definition, should produce direct Climate change mitigation benefits. Mandating biofuels could reduce tailpipe CO2, but indirectly increase CO2 through land use. Policies aimed at VKT and usage can be expected to have indirect climate change effects, including potential disbenefits through increased vehicle ownership and life cycle increases in CO2. \u00EF\u0082\u00B7 Air quality. Most CO2 mitigation policies hold the potential to produce both indirect benefits and disbenefits depending on the technology and policy conditions such as degree of coordination of air emission and CO2 emission regulations for gasoline and diesel cars. Introducing biofuels or electricity to power cars could potentially improve or exacerbate ozone and airborne particles depending on meteorology and emissions from other sectors. \u00EF\u0082\u00B7 Energy security. Most policies are expected to improve (or at least not worsen) energy security via reduction in petroleum consumption or diversifying vehicle fuel mix (e.g., biofuels and electricity). \u00EF\u0082\u00B7 Traffic safety. Vehicle oriented policies that might induce bimodal vehicle fleets (like fleet average CO2 mandates, vehicle-specific cap and trade, or technology mandates) could result in traffic safety disbenefits. Conversely, policies that might minimize distribution of fleet mass, like feebates, might produce indirect benefits. Fleet turnover and traffic safety are related in that any policy that might retard or accelerate scrapping of the oldest cars could produce disbenefits or benefits, respectively. Induced changes in VKT and traffic safety are difficult to predict (e.g., reducing on road time, but increasing average speeds could cancel out). \u00EF\u0082\u00B7 Congestion. Broad, multi-sector policies could result in congestion benefits if vehicle usage is reduced, or disbenefits if non-transport sectors reduce CO2 to the extent that vehicle usage is allowed to increase. Road pricing and per-kilometer taxes should 37 produce congestion benefits as these are aimed at vehicle use; however road pricing that differentiates by time and place will be more effective than general per-kilometer taxes. Rebound effects from policies that reduce (e.g., technology policies) or increase (e.g., fuel policies) the marginal cost of driving could produce congestion disbenefits or benefits, respectively. Table 2.2 Policies with potential to reduce CO2 emissions from passenger cars. Every policy option has the potential to influence ownership, VKT, vehicle technology, and fuel properties. CC = climate change, AQ = air quality, ES = energy security, TS = traffic safety, C = congestion, N = noise Policy Category Policy Description [2, 82- 84] Example in the UK Benefit4 Disbenefit economy-wide cap and trade EU ETS [85], but not applicable to cars CC, ES AQ, C AQ, C B ro ad , m ul ti- se ct or p ol ic ie s Upstream carbon tax [86] CC, ES AQ, C AC, C fuel tax gasoline and diesel tax [21, 87, 88] CC, ES CC, C AQ company car free fuel tax income tax through employer [21] CC, ES AQ, C fuel-specific mandate Renewable Transport Fuel Obligation [14] CC, ES AQ AQ, CC low carbon fuel standard (lifecycle CO2 basis) CC, ES AQ AQ Fu el -o rie nt ed p ol ic ie s fuel feebate (\u00E2\u0086\u0091 fee for \u00E2\u0086\u0091 life cycle gCO2/L; rebates below gCO2/L pivot point) CC, ES AQ AQ 4 Benefits and disbenfits are classified as direct or indirect as follows. direct indirect 38 CC = climate change, AQ = air quality, ES = energy security, TS = traffic safety, C = congestion, N = noise Policy Category Policy Description [2, 82- 84] Example in the UK Benefit4 Disbenefit fleet gCO2/km emission standards (option for tradable credits) fleet CO2/km target per ACEA voluntary [64] CC AQ, ES, N AQ, TS, C, N. gCO2/km vehicle acquisition tax CO2/km registration taxes flat fee of \u00C2\u00A338 [21] CC AQ, ES, C, N AQ, C, N gCO2/km vehicle circulation tax CO2/km vehicle taxes VED tax bands [10] CC AQ, ES, C, N AQ, C, N CO2/km company car tax UK benefit-in-kind CO2/km tax bands [9] CC AQ, ES, C, N AQ, C, N green labeling EC Directive 1999/94/EC [89] CC AQ, ES, N AQ, N scrappage bounty[90] General Motors EcoFLEX [90] AQ, TS CC, E vehicle feebates (\u00E2\u0086\u0091 fee for \u00E2\u0086\u0091 gCO2/km; rebates below gCO2/km pivot point) [91] CC AQ, ES, TS, C, N AQ, C, N gCO2/km vehicle-specific cap and trade CC AQ, ES, C, N AQ, TS, C, N technology mandate (e.g., minimum sales of zero emission vehicles) CC, AQ ES, C, N CC CC, AQ, TS, C, N ve hi cl e te ch no lo gy - o r o w ne rs hi p- ba se d po lic ie s gCO2/km emissions tax CC AQ, ES, C, N AQ, C, N road pricing London congestion charge[48, 74, 92] C CC, AQ, ES, N CC, AQ, ES, TS V K T or u sa ge - or ie nt ed p ol ic ie s per-km tax or insurance premiums [81, 93, 94] C CC, AQ, ES, N AQ 39 \u00EF\u0082\u00B7 Noise. Any policy that increases uptake of relatively quiet hybrids might produce health benefits. However, it is unknown to what degree quiet cars might affect pedestrian and bicycle safety The far reaching influence of policy is illustrated by the London congestion charging scheme. While the main aim of the charge is to reduce congestion (and hence travel times) within the charging zone at peak times, the scheme is also expected to affect ownership. Various rules, such as exempting cars certified at less than 120 gCO2/km, penalizing cars over 225 gCO2/km, and exempting certain vehicle types (e.g., all electric cars or motor tricycles), are intended to influence consumers\u00E2\u0080\u0099 purchasing choices [81]. The scheme could also have the effect of increasing ownership if consumers purchase an exempt vehicle to be used for local travel. Market responses to the range of policies shown in Table 2.2 can influence risks via fleet turnover effects that are not always considered in policy analysis. The fleet turnover effect is illustrated in Figure 2.3 for gasoline and diesel cars to emphasize that public risks due to passenger car use are a function of the on-road vehicle stock, not just the new cars entering the stock. For example, air quality policies have been criticized on the basis that stringent emission standards may worsen public health due to consumers retaining older, higher polluting cars [95, 96]. Fleet turnover effects are also relevant to traffic safety as newer cars are shown to exhibit significantly improved occupant safety protection than models just a few years older [97]. Fleet turnover effects can also substantially change life cycle CO2 emissions as shown in Figure 2.4 [98]. For the average European car driven 14,000 km/year and average age 14 years, scrapping a car 3 years earlier increases average life cycle emissions by 12 gCO2/km, while scrapping 3 years later decreases life cycle emissions by 12 gCO2/km. For low travel distance cars, life cycle CO2/km is more sensitive to scrappage timing, while high travel distance cars (e.g., taxis, company cars) are less sensitive. Finally, it should be noted that fleet turnover rates can change over time. In the U.S. the mean lifetime of a car was 11 years for a 1966 model compared to 15 years for a 1990 model [99]. 40 Figure 2.3 Diagram illustrating passenger car that policy can influence the makeup of on-road vehicle stocks by regulating new cars, scrapped cars, or both. Figure 2.4 Effect of scrapping age and VKT on life cycle gCO2/km for 2006 models of gasoline and diesel cars in Europe [98]. 0 100 200 300 400 500 600 0 5 10 15 20 years lif e cy cl e gC O 2/ km 7,000 km - Gasoline 14,000 km - Gasoline 28,000 km - Gasoline 7,000 km - Diesel 14,000 km - Diesel 28,000 km - Diesel on road vehicle stock and patterns of use Policies affecting rate and type of new cars Policies affecting rate and type of scrapped cars 41 2.3.3 LINKING POLICIES TO RISKS WITH ALTERNATIVE FUELS AND TECHNOLOGIES This description of pathways from policies to risks has focused primarily on current gasoline and diesel cars. When evaluating alternative fuels or vehicle technologies, the risks and relationships shown in Figure 2.2, the impact of policies shown in Table 2.2, and the importance of life cycle CO2 emissions all change substantially. A complete description of these effects is beyond the scope of this study but I will mention a few examples. Figure 2.5 shows that, relative to current gasoline or diesel cars, CNG or hybrids would effect non-trivial changes in noise exposures of +1.7 dB(A) and -2.5 dB(A), respectively [80]. A noise reduction of 2.5 dB(A) could have a favorable impact on cardiovascular health, estimated to reduce the odds ratio of incidence of myocardial infarction from 1.20 to 1.14 relative to a no-effect level of 60 dB(A) [50]. Europe\u00E2\u0080\u0099s emissions trading scheme (ETS) does not include passenger cars but does include production of fuels and electricity [85]. Because the well-to-tank CO2 emissions per unit energy of diesel fuel is roughly 25% less than gasoline [35, 100], the ETS effect on fuel prices should moderately favor diesel cars over gasoline. However, the ETS could potentially be a much stronger influence on vehicle choice if plug-in hybrid technology is available, depending on the carbon intensity and cost of the electricity5 [101, 102]. In terms of life cycle CO2 emissions, comparing a future spark ignition car fuelled by lignocellulosic ethanol to a current generation spark ignition gasoline car could result in roughly one order of magnitude reduction from 300 gCO2/km to 30 gCO2/km [24]. The example of plug-in hybrids illustrates how rapidly impacts can change, because when consumers plugs in their car they have immediately and dramatically changed the system boundary of their energy use and associated impacts depending on the design of the electricity supply system [103]. 5 Currently, substantial taxes are levied on transportation fuels. Should electric vehicles grow in numbers, the failure to impose similar taxes on electricity for transportation would represent very large public subsidies. 42 Figure 2.5 Mean noise rating of some 2006 models of alternative fuelled vehicles relative to conventional gasoline or diesel models [80]. The mean for gasoline models is the same for diesel models (72.3 dB(A) for both groups). CNG = compressed natural gas. LPG = liquefied petroleum gas -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 CN G LPG gas olin e h ybr id . m ea n dB (A ) r el at iv e to co nv en tio na l g as ol in e or d ie se l 2.4 ESTABLISHING CAUSAL LINKS BETWEEN POLICIES AND OUTCOMES In Section 1.3 I discuss linkages between policies and risks. This leads to an important question in the analysis and design of public policy, which is to ascertain to what extent specific policies caused outcomes of interest. For example, in the analysis of tradeoffs between vehicle emissions of air pollutants and CO2 in the UK, an important question is: to what extent did climate mitigation policies cause the growth of diesel cars? I will illustrate the challenges in establishing a causal link between climate policy and outcomes of interest by briefly examining this question. First I will summarize basic issues in establishing causation, followed by a discussion of the various factors that may have caused the growth in diesel cars. 2.4.1 BASIC ISSUES IN ESTABLISHING CAUSAL LINKS BETWEEN POLICY AND OUTCOMES Employing a traditional scientific approach [104], I suggest there are three basic elements in establishing causation between a specific public policy and an outcome of interest: 43 1. Temporal relationship. Did the policy precede changes in the outcome of interest? In establishing causal links, it is important to attend to timing. As an example, in DFT\u00E2\u0080\u0099s evaluation of its VED policy impact on CO2 emissions, researchers were careful to choose subjects (e.g. private consumers and fleet operators) who made decisions after the VED was adopted in March, 2001[10]. 2. Quantitative and qualitative evidence linking the policy to the outcome. A general approach to examining causal evidence in research is to use visual diagrams to illustrate relationships between dependent variables (outcomes of interest) and independent variables, including the effects of intervening variables [105]. Quantitative evidence may come in the form of a statistical model such as done in econometric modeling of vehicle ownership (see [106] for a review of such models, and [107] for a UK-specific modeling example). Qualitative evidence may come in various forms such as survey interviews or focus groups to ascertain awareness of policies, for example as done in an examination of the VED tax in the UK [108]. 3. Reasoning and peer review. The presence of valid temporal relationships and quantitative or qualitative models cannot be divorced from reasoning in establishing causal linkages to policies. In practice, this means conducting a critical examination of all evidence guided by selected criteria. In health research, Bradford Hill\u00E2\u0080\u0099s eight criteria [109] form the reasoning that is often used to guide examinations of causal arguments (see [110] as an example). Are there confounding factors that have been overlooked in the analysis? To what degree are statistical relationships consistent with those found previously by other researchers, perhaps in other contexts? Subjecting the analysis of policy causation to peer review is a practical way of answering such questions and bolstering causal arguments in policy analysis [36]. 2.4.2 WHAT CAUSED THE RAPID RISE IN DIESEL CARS IN THE UK? As described in Section 3.4, there are multiple factors that may have contributed to rise in diesel market share in the UK. Three specific CO2 policies potentially contributed to the rise in diesel cars including the UK VED tax [10, 108], the UK company car tax [9], and the European-wide ACEA agreement[64]. Technological change by the introduction of turbo-charged direct injection (TDI) is another factor [111], as well as fuel prices [112]. All three CO2 policies could have plausibly contributed to the rise in diesels in the UK. The rise in diesel market share began in the UK in the year 2000, while a rise in the rest of the EU began in 1997 (see Figure 3.1). In terms of temporal relationships, the ACEA agreement was implemented as of 1997 and thus was more temporally related to the rise in the EU average diesel share than the UK. At first glance, neither the VED nor the company car tax could have 44 caused the rise of UK diesels because the rise initiated prior to policy implementation dates. However, it should be noted that these policies were announced in advance of the implementation dates. For example the company car tax had been announced in 1999, followed by release of the details of the CO2-based tax regime in 2000, with implementation beginning in 2002 [113]. The government\u00E2\u0080\u0099s analysis of the policy cited strong awareness amongst company decision-makers and that the policy led to direct increases in diesel cars [9, 113]. As well, an independent study attributed growth in diesels to the company car tax [114]. In contrast, it was determined that purchasers of private (non-company) cars had relatively low awareness of the VED policy, and that the VED had low impact on fleet composition based on qualitative [108] and quantitative [10] evidence. A research study of the ACEA agreement implicitly assumed that all changes in fleet composition affecting CO2 emissions were due to the manufacturer association\u00E2\u0080\u0099s voluntary agreement [64]. However, the objective of that research was to quantify CO2 emission rates due to changes in fleet composition rather than assess policy causation. In terms of modeling fleet composition, none of the CO2 policy impact assessments included econometric models. Amongst the aforementioned policy assessments [9, 10, 108, 113, 114], only the ACEA study was published in a peer-reviewed journal [64]. Fuel prices are another factor cited for the rise in diesel market share in the UK [112, 114]. In terms of the relative price of diesel versus gasoline, this is not a factor that could have contributed to the rise in diesel market share in the UK because the price ratio has been kept at unity over the time period where diesels have grown (see Figure 3.2 caption). Another hypothesis could be that a rise in the real price of both petroleum fuels led to consumers purchasing vehicles that consume less fuel per kilometre \u00E2\u0080\u0093 i.e. diesel cars and smaller gasoline cars. Examining year 2005 prices of fuels relative to 1996, average fuel costs rose 55%, while relative to the year 2000 prices rose 9% (UK fuel duties increased in the late 1990\u00E2\u0080\u0099s) [12]. Therefore it is plausible that the rise in the real price of fuels partly contributed to the rise in diesels, but again there is no known analysis of this factor. Technological change in the form of advanced TDI technology is yet another factor hypothesized to contribute to the rise in diesel cars. TDI advances for diesel cars are credited with improving efficiency, reducing noise, increasing acceleration and power, and reducing smoke and particle emissions [111]. TDI was first introduced to the European market in 1988 [115], although gains in TDI technology continued such as specific power of engines rising from roughly 40 to 60 kW per litre from 1991 to 2001 [116]. One of the temporal issues in examining causal linkages for TDI is that there is no indication of a difference in TDI technology offered in the UK compared to the rest of the EU [111], yet the UK lagged the EU by three years in terms of rising diesel shares. 45 My conclusion with regard to climate policy causal links to rising diesel share in the UK is that there are multiple explanatory factors that could have potentially contributed to this trend. The temporal relationships and available qualitative and quantitative evidence indicate that the company car tax clearly had some non-negligible causal influence on the growth of diesel cars. However the degree to which this tax regime influenced diesel growth as compared to other direct and indirect public policies has yet to be rigorously analyzed in either peer- or non-peer- reviewed literature. 2.5 CONCLUDING REMARKS I have argued for the growing importance of Integrated Assessment of multiple risks in the design of climate mitigation policies for passenger cars in industrialized countries. Using the UK context for illustration purposes, it is shown that three important choices in policy analysis can substantially change policy design to mitigate CO2 from passenger cars: (1) decision criteria, (2) choice of fuels and technology, and (3) the scope of risks included or excluded. These choices should be made iteratively during the policy analysis and the choices differ for researchers as compared to public policymakers. Employing Integrated Assessment it becomes clear that the direct implications of fuels and technology alone are insufficient to estimate changes in risks, because how policies affect ownership and VKT and patterns of use matters. While passenger car stocks in industrialized countries are presently dominated by spark ignition gasoline and compression ignition diesel cars, substantial growth of alternative fuels and vehicle technologies appears likely in the near future. The proliferation of these alternatives can drastically expand system boundaries affected by new policies in terms of energy use and CO2 emissions. With climate change as a growing policymaking priority and the concomitant changes in fuels and technologies, Integrated Assessment of multiple risks resulting from passenger car climate mitigation policy is useful and increasingly necessary. It can inform development of climate mitigation policy to show what is possible and, improve the social and political acceptance of policies, minimize unintended consequences, and improve the likelihood of realizing net public benefits. 2.6 ACKNOWLEDGEMENTS I thank Hadi Dowlatabadi, Michael Brauer, Milind Kandlikar, and Douw Steyn for comments on drafts of this article. I thank the University of British Columbia Bridge Program, AUTO21: B06 BLC, and Carnegie Mellon University\u00E2\u0080\u0099s NSF-supported Center for Integrated Study of the Human Dimensions of Global Change (SBR-9521914), and the Center for Climate Decision Making (SES-0345798). I am also grateful for generous support from the Exxon-Mobil Education Foundation. All errors are my responsibility. 46 2.7 REFERENCES 1. Cambridge.Systematics.Inc., Moving Cooler: An Analysis of Transportation Strategies for Reducing Greenhouse Gas Emissions. 2009, Urban Land Institute: Washington, D.C. 2. 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Development trends in passenger car DI engines. in AVEEC 2001 (presentation downloaded from www.meca.org). 2001. 54 3 AIR QUALITY IMPACTS OF CLIMATE MITIGATION: UK POLICY AND PASSENGER VEHICLE CHOICE6 6 A version of this chapter has been published. Mazzi, E. and H. Dowlatabadi, \u00E2\u0080\u009CAir quality impacts of climate mitigation: UK policy and passenger vehicle choice\u00E2\u0080\u009D. Environmental Science and Technology, 2007. 41: p. 387- 392. 3.1 INTRODUCTION Climate mitigation policies have been promoted on the basis that reducing fossil fuel use provides dual benefits in terms of long-term climate change attenuation and short-term air quality improvements. Models predict that climate policies result in reduced fossil fuel combustion, lower air emissions, and subsequently provide public health benefits [1-4]. In Europe, diesel cars are viewed as a promising option to reduce greenhouse gas emissions from personal transportation. Diesel fuel has a higher energy and carbon density than petrol (38.5 MJ/liter gross heating value and 778 gC/liter versus 34.9 MJ/liter and 659 gC/liter), but diesels have 25% better fuel economy based on matched pair vehicle models and thus emit 15- 20% less CO2 per kilometer [5-6]. Beginning March, 2001 a new vehicle excise duty (VED) was introduced in the UK whereby vehicles were taxed annually based upon certified CO2 emissions [7]. In April, 2002 the UK\u00E2\u0080\u0099s company car benefit-in-kind tax was changed to a CO2- based system as well [8]. Although a surcharge for diesel vehicles was applied to reflect their impact on air quality, the cost of owning diesel cars was and is lower (see illustrative cost comparison in Table A1 in Appendix A). These CO2 policies are credited with contributing to the UK\u00E2\u0080\u0099s success in reducing CO2 emissions [8-10]. Emissions of CO2 from new passenger vehicles registered in the UK between 2000 and 2005 have fallen from a fleet average of 181grams to 169 grams of CO2 per kilometer [10]. Diesel vehicles have made a contribution to the CO2 reductions as their market share has grown non-linearly at an annual compounded rate of 21% from 2001-2005. As shown in Figure 3.1, diesel new registrations in the UK had declined from 1995 onwards while new registrations of diesel cars were increasing in the rest of the European Union (EU) [11]. The changes in the tax regime are demonstrably the turning point for the resurgence of diesels in the UK. 55 On a parallel track and lagging the CO2 policies, new EU emission standards for passenger vehicles are being promulgated to converge emissions from diesels and petrol engines to the best that either technology can achieve. Thus, 2001 Euro III standards and 2006 Euro IV standards both drive down the higher emissions of PM10 (particulate matter less than 10 microns) and NOx (nitrogen oxides) from diesels, and force gasoline engines to lower their CO (carbon monoxide) and HC (hydrocarbons) emissions. Here we examine consumer switching from petrol-fuelled to diesel-fuelled passenger cars in the UK (all vehicles designed to carry passengers, but excluding freight vehicles). The tradeoffs between greenhouse gas reductions and air pollution impacts are assessed over a 20 year study period from 2001-2020. While diesel substitution for petrol cars scenarios have been assessed elsewhere [12], this is an empirical study in the context of an actual CO2 policy and targets an acknowledged gap in the climate policy literature [13,14]. These research findings have direct relevance to the design of climate policies in the transportation sector throughout the developed world. Figure 3.1 Diesel share of new car registrations in the European Union (EU) and the UK. While aggregate EU demand for diesels began increasing in 1995, UK demand continued to decline until the first CO2 policy incentive came into effect in 2001 and has been increasing continuously. 0% 10% 20% 30% 40% 50% 60% 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 Year D ie se l % o f t ot al n ew re gi st ra tio ns EU without UK UK 1988 Fiat introduces EU's 1st turbocharged direct injection (TDI) diesel 2002 CO2 based company car tax 2001 CO2 based vehicle excise duty 56 3.2 METHODS We define \u00E2\u0080\u009Cadditional diesels\u00E2\u0080\u009D as the number of newly registered diesel vehicles additional to an estimated \u00E2\u0080\u009Cno growth\u00E2\u0080\u009D diesel market share. Growth in new diesel registrations is estimated relative to historical average new registrations for private and company car registrations (roughly half of all newly registered cars in the UK are for company fleets). Figure 3.2 shows 1994-2005 data for new diesel registrations for private and company vehicles. To quantify the overall growth in diesel vehicles, we assign 15% as the \u00E2\u0080\u009Cno growth\u00E2\u0080\u009D diesel market share from 2001-2020. This is based on the average diesel share for 11 years from 1990-2000 leading up to the CO2 policies, and agrees closely with government projected diesel share [15]. Factors that likely have contributed to the growth in diesel market share, including CO2 policy, are described in the discussion section. Figure 3.2 Total number and percentage market share of new registrations of private and company diesel cars in the UK 1994-2005. During this period, the ratio of the price of petrol to diesel was remarkably stable averaging 0.98 (range 0.95-1.00) and fuel price advantages experienced elsewhere do not provide a plausible explanation for the observed changes in diesel registrations in the UK. 0 100 200 300 400 500 600 700 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Year Nu m be r o f n ew d ie se l r eg is tra tio ns , 1 00 0' s 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% Pe rc en ta ge o f n ew d ie se l r eg is tra tio ns number of diesel company car registrations number of diesel private car registrations diesel % of total company cars diesel % of total private cars 57 An annual time series of additional diesels for 2001-2020 is developed by adding in new registrations and subtracting estimates of scrapped cars. An estimate of the yearly number of scrapped cars is made using a model developed and calibrated using UK deregistration data as described in Figure A1 of Appendix A [16]. The annual time series of additional diesels is used to estimate changes in fuel consumption, emissions of CO2 and common air contaminants, and human health effects. Changes in fuel consumption and CO2 emissions are estimated based on the difference between petrol and diesel fleet average emission factors shown in Figure A2 of Appendix A. These emission factors use UK-specific, certified CO2/km [17] and are based on actual fleet average CO2 emissions from 2001 to 2005 [10], and projected fleet average emissions through 2020. Changes in fuel consumption are estimated using fleet CO2/km and conversion factors of 2,763 gCO2/liter for diesel and 2,504 g/liter for petrol [18]. We highlight that fleet average diesel CO2/km is only 4% lower than petrol as of model year 2005, because diesel technology\u00E2\u0080\u0099s inherent advantage has been offset by consumer preference for larger cars [10]. Changes in emissions of common air contaminants are estimated based on UK-specific emission factors for PM10, NOx, CO, HC, benzene, and 1,3 butadiene as provided in Table A2 in Appendix A. As with CO2, changes in emissions are quantified based on the difference between emission factors for petrol and diesel cars. Emissions are estimated for each year from 2001- 2020 based on the applicable EU emission class (Euro III, Euro IV, post-Euro IV), and using the UK-specific emission factors derived based on measurements under actual driving conditions [15]. Automobile usage is a constant 15,800 km annually in our analysis, based on average driving distances for all UK cars [19]. We assumed the same pattern of vehicle usage regardless of type (diesel or petrol) because of the high market share of diesels, and did not include a rebound effect (higher usage) due to the lower operating cost of diesels. A sensitivity estimate of the rebound effect is provided in the discussion. Further data and discussion of the annual travel distance are contained in Section A.4.2 of Appendix A. Human health impacts resulting from changes in common air contaminants are estimated solely on changes in particulate matter emissions employing a conventional impact pathway method [20, 21]. The rationale for using particulate matter is that it tends to dominate human health impacts from air pollution based on current science [20, 22-24]. Moreover, a recent study of transportation and air pollution in the UK concluded that reduction of PM10 should be the top priority if the goal is to reduce air pollution impacts on public health in the UK [25]. Another recent source apportionment study of London also concluded that controls on particulate matter 58 emissions from vehicles are most likely to result in the greatest improvements in ambient particulate matter concentrations [26]. The impact of excluding ambient ozone, NOx, CO, HC, benzene, and 1,3 butadiene in the quantification of health effects is discussed in the discussion section. Published emission factors and modeling studies for the UK are based exclusively on PM10; however, in general, more than 99% of particulate emissions by mass from diesel cars are PM2.5 [27]. Hence, for all practical purposes, changes in PM10 and PM2.5 emissions are equal. This allows the use of the health coefficients that are based on either PM10 or PM2.5. We use concentration-response coefficients based on PM10 and on PM2.5 where supporting evidence is available. To maintain clarity, for the remainder of this article we will only refer to particulate matter emissions or ambient concentrations as PM10, except where referring to original study findings that were based on PM2.5. We quantify changes in ambient PM10 concentrations by employing the results of atmospheric modeling studies of vehicle emission reduction measures in the UK [28-30]. The analysis scenario most applicable to our research estimated that an annual UK-wide reduction of 3.76 kilo-tonnes of particulate matter due to traffic emission controls would result in a UK-wide, population weighted change in average annual ambient concentration of 0.277 \u00C2\u00B5g/m3. This is a ratio of 0.0737 \u00C2\u00B5g/m3 change in annual mean ambient PM10 concentration per 1 kilo-tonne change in annual PM10 emissions, which includes a component of secondary nitrate particles as described in Section A.4.4 Appendix A [29]. This ratio was multiplied by our estimated annual change in PM10 emissions to estimate changes in population-weighted ambient PM10 concentrations. This PM10 ambient concentration estimate is equivalent to an intake fraction of 20 grams per million grams emitted \u00E2\u0080\u0093 in general agreement with estimates of vehicle emissions found elsewhere [31,32]. For the association between changes in ambient PM10 and mortality, we employ low, central, and high concentration-response coefficients. For our low mortality coefficient, we use 0.75% change in mortality annually per 10 \u00C2\u00B5g/m3 change in PM10, applied to the entire UK population. This coefficient was derived through meta-analyses by the UK Department of Health expert Committee on the Medical Effects of Air Pollution (COMEAP) applying the results of time series epidemiological studies [35]. We use this coefficient because it has been endorsed specifically for the UK; however, there are arguments that time series results are inappropriate for estimating long-term death rates [36, 37].Our central mortality coefficient uses the results of the American Cancer Society cohort study which found a 4% increase in chronic, all-cause exposure mortality per 10 \u00C2\u00B5g/m3 increase in PM2.5 for a cohort of subjects age 30 and older in the U.S. [33]. We 59 chose this study because it is by far the largest cohort study published. It estimated chronic rather than acute mortality, used the same exposure metric as we estimate in our study (annual average concentration), and is the preferred study for chronic exposure mortality estimates according to the latest World Health Organization guidelines [34]. For our high mortality coefficient, we use the results of the Harvard Six Cities study which found a 13% increase in chronic exposure mortality per 10 \u00C2\u00B5g/m3 change in PM2.5 for a cohort of about 8,000 subjects age 25 and older in the U.S. [38]. More recent research on intra-urban variation in PM2.5 and mortality in Los Angeles indicates that the true mortality coefficient may be somewhere between the central and high coefficients we employ in this study [39]. We quantify morbidity by estimating respiratory and cardiovascular hospitalizations using rate coefficients adopted by COMEAP [28]. Changes in both hospitalization rates are estimated at 0.8% per 10 \u00C2\u00B5g/m3 change in PM10 applied to the entire UK population. To tie together the linked steps in this analysis, an integrated framework is provided in Figure 3.3 that illustrates the timing of CO2 policy incentives, EU emission standards, and changes in diesel market share over time. To assess timing of the CO2 policies and EU vehicle emission standards, we divide the study time horizon into three intervals: a \u00E2\u0080\u009CEuro III interval\u00E2\u0080\u009D from 2001- 2005 when Euro III emissions standards applied, a \u00E2\u0080\u009CEuro IV interval\u00E2\u0080\u009D from 2006-2008, and the \u00E2\u0080\u009Cpost-Euro IV interval\u00E2\u0080\u009D from 2009-2020 when progressively higher emission standards apply to all new vehicle purchases. Early adoption of Euro IV diesels is incorporated into our estimates as described in Section A.2 of Appendix A. 60 Figure 3.3 Integrated framework for assessing emissions from additional diesels (i.e., diesels substituted for petrol vehicles). Actual diesel share of new registrations from 1990-2005 is based on industry data. Projected shares from 2006-2007 are based on industry forecasts, and from 2008-2020 based on authors\u00E2\u0080\u0099 projections. The focus of this study is on the area between the actual/projection curve and the \u00E2\u0080\u009Cno growth\u00E2\u0080\u009D curve which is split into three time intervals defined by the applicable emission standard: Euro III, Euro IV, and post-Euro IV. 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 1990 1995 2000 2005 2010 2015 2020 Year D ie se l % o f t ot al n ew r eg ist ra tio ns CO2 tax and Euro III standards apply in 2000 Euro IV standards apply in 2006 Post-Euro IV diesels (with PM trap) in 2009 Euro III interval Euro IV interval post-Euro IV interval Most Euro III and Euro IV diesels scrapped by 2020 actual diesel share projected diesel share \u00E2\u0080\u009Cno growth\u00E2\u0080\u009D diesel 3.3 RESULTS Figure 3.4 summarizes the main results of this study. The estimated additional diesel vehicle counts (Box A), emissions and fuel consumption (Box B), ambient concentrations (Box C), and health effects (Box D) are shown. 61 Figure 3.4 Summary results of the impact of additional diesels in the UK from 2001-2020 The estimated number of additional diesels over the 20 year study period is 0.7 million for Euro III, 1.6 million for Euro IV, and 7 million for post-Euro IV. Figure 3.5 shows the cumulative time series of additional diesels disaggregated by emission class, which is quantified by adding newly registered cars less the estimated number of scrapped cars. box A: number of cars petrol to diesel (2001-2020 total) Euro III +0.7 million diesel cars Euro IV +1.6 million diesel cars post-Euro IV +7.0 million diesel cars box B: change in emissions & fuel 2001-2020 total per 106 Euro III vehicles per 106 Euro IV vehicles per 106 post-Euro IV vehicles PM2.5, kt +12 8.3 3.7 0 NOx, kt +93 66 30 0 HC, kt -73 -34 -31 0 CO, kt -204 -126 -73 0 CO2, Mt -7.0 -2.3 -1.3 -0.9 fuel, 106 bbl oil -20 -7 -4 -3 box C: change in air quality (annual average) PM2.5 +0.043 \u00EF\u0081\u00ADg/m3 NO2 \u00E2\u0086\u0091 \u00EF\u0081\u00ADg/m3 ozone \u00E2\u0086\u0091 or \u00E2\u0086\u0093 \u00EF\u0081\u00ADg/m3 CO \u00E2\u0086\u0093 \u00EF\u0081\u00ADg/m3 box D: change in public health mortality (2001-2020 total) mortality per 106 vehicles mortality per Mt CO2 abated Euro III +910 +1,320 570 Euro IV +940 +590 870 post-Euro IV 0 0 0 62 Figure 3.5 Estimates of additional diesels in the UK 2001-2020 disaggregated by Euro emission class. \u00E2\u0080\u009CAdditional diesels\u00E2\u0080\u009D are defined as the number of petrol vehicles switched to diesel beyond the \u00E2\u0080\u009Cno growth\u00E2\u0080\u009D estimate. Euro III and Euro IV emission standards apply in 2001 and 2006, respectively. Early adoption of some Euro IV diesels is incorporated into our estimates. Legislation to harmonize diesel and petrol particulate matter emission limits is proposed by 2009, described as \u00E2\u0080\u009Cpost-Euro IV\u00E2\u0080\u009D in this study. 0 1 2 3 4 5 6 7 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019 Year C um ul at iv e ad di tio na l d ie se ls , m ill io ns post-Euro IV Euro IV Euro III Euro IV emission limits apply Proposed harmonization of petrol and diesel car emission limits (post-Euro IV) CO2 based Vehicle Excise Duty (VED) CO2 based Company Car Tax Over the 20 year study period, additional diesels are estimated to increase PM10 and NOx emissions by 12 kilo-tonnes and 93 kilo-tonnes, respectively. HC and CO emissions are estimated to decrease by 73 kilo-tonnes and 204 kilo-tonnes, respectively. CO2 emissions are estimated to decrease by 7 mega-tonnes. These total changes in emissions were obtained by integrating the annual time series of emissions shown in Figure 3.6 over the 20 year study 63 period. It is important to note that the lower polluting emission classes provide progressively lower CO2 and fuel saving benefits. Figure 3.6 Estimated changes in emissions from 2001-2020 due to additional diesels. The solid lines (y-axis to left) show estimated changes in emissions of common air contaminants in kilo-tonnes, while the dashed line (y-axis to right) shows CO2 in mega-tonnes. Diesels emit higher rates of PM10 and NOx, and lower rates of HC, CO, and CO2. Emissions of common air contaminants are assumed to be harmonized for diesel and petrol vehicles beginning 2009, so differences in all emissions except CO2 approach zero from 2009-2020 as higher polluting diesels are scrapped. As a result of the 12 kilo-tonne increase in particulate emissions, the average annual change in ambient PM10 concentration was estimated to be 0.043 \u00C2\u00B5g/m3 over the 20 year study period. Ambient concentration of NO2 is determined to increase by an un-quantified amount, while CO decreases. Changes in ambient ozone concentrations were not estimated because of the complexity and uncertainty in atmospheric chemistry that would result from an increase in NOx and a decrease in HC, the two principal precursors. No known UK studies have modelled such a -20 -15 -10 -5 0 5 10 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 Year C ha ng e in P M 2. 5, N O X , H C , a nd C O e m is si on s, k ilo -to nn es /y ea r -1.0 -0.5 0.0 0.5 C ha ng e in C O 2 e m is si on s, m eg a- to nn es /y ea r NOx PM2.5 HC CO CO2 64 scenario. The available studies conclude that ozone formation might be limited by NOx or HC under different conditions [40]. The most recent study indicated that reductions in HC usually improved ozone while \u00E2\u0080\u009CNOx emission control gave a more complex response, which was metric and region specific. Generally NOx emission control had an adverse effect on ozone air quality.\u00E2\u0080\u009D [41]. Therefore we show that ambient ozone levels may increase or decrease as indicated in Figure 3.4. Over the 20 year study period, the average annual mortality is estimated at 90 deaths per year applying the central concentration-response coefficient. The low and high mortality estimates are 20 and 300 deaths per year, respectively. Annual average hospitalizations total 32 per year. All mortality and morbidity impacts are associated with the Euro III and Euro IV additional diesels, based on the assumption of harmonization of emissions for diesel and petrol in the post- Euro IV standards. Included in Box D of Figure 3.4 are the ratios of the central mortality estimate per million additional diesels and per mega-tonne of CO2 abated, averaged over the 20 year study period. The additional mortality per million Euro III diesels and Euro IV diesels is estimated at 1,320 deaths and 590 deaths, respectively. The average mortality per mega-tonnes of CO2 abated is 570 for Euro III, 460 for Euro IV, and 0 for post-Euro IV. 3.4 DISCUSSION There are many uncertainties and limitations associated with this analysis. Section A.4 of Appendix A contains a detailed discussion of how these uncertainties and limitations are likely to have over- or underestimated fuel savings, emissions, exposure, and health effects estimates in the following five areas: 1. Number and emission class of additional diesels. The number of registered vehicles subject to successive EU emission standards is not stated in the data. We used the range of manufacturers\u00E2\u0080\u0099 offerings meeting the different standards as the guide to apportion registrations to each emission class. Our assumptions with respect to emission class likely result in a low bias of the estimated air quality and health impacts. Additional discussion is provided in Section A.4.1 of Appendix A. 2. Annual kilometers travelled. The assumption of equal annual travel distance for petrol and diesel vehicles does not account for variation in driver types (company car and high travel distance drivers), nor an economic rebound effect. Our assumption of driver types is consistent with a saturation of high travel distance drivers choosing diesels as described by Schipper [5]. In general there is a 65 measured rebound effect elasticity of 0.1-0.3% increase in annual travel distance for every 1% decrease in fuel costs per kilometer [42]. Therefore our annual travel distance assumptions likely underestimate air quality impacts and overestimate CO2 reductions and fuel savings. .Additional discussion is provided in Section A.4.2 of Appendix A. 3. Spatial distribution of vehicles. Available evidence indicates that our assumptions of spatial distribution of vehicles (urban/rural/motorway) does not substantially bias our estimates, either high or low. Additional discussion is provided in Section A.4.3 of Appendix A. 4. PM10 emissions and ambient concentrations. Our assumptions of PM10 emissions and ambient concentrations, including meteorology, secondary particulate matter (sulphate, nitrates, and organic), fuel quality, brake and tire wear, and vehicle age likely bias our estimates high in some cases and low in others. Overall, the air pollutant emissions and ambient concentrations are likely underestimated. Additional discussion is provided in Section A.4.4 of Appendix A. 5. Health effects. Health effects are likely underestimated, specifically the limitation of estimating health outcomes solely from PM10. The likely impacts on ambient levels and subsequently health effects of ozone, SO2, HC, NOx, CO, benzene, and 1,3 butadiene are discussed further in Section A.4.5 of Appendix A. Given these various sources of uncertainty, we think our basic findings are robust. Overall, the study may be biased in overestimating fuel savings and CO2 emissions and underestimating the health damaging emissions and associated health impacts. Additional disbenefits may also be present that have not been considered in this study, namely black carbon emissions from diesel engines and their exacerbation of climate change [43, 44]. We estimate that consumers switching from petrol to diesel cars in the UK over the time period of 2001-2020 will reduce CO2 by 7 Mt and saves 20 million barrels of oil. However, ancillary air quality effects hinge upon the fuel properties and conversion technology, not just the quantity of fuel consumed, and adverse air quality is estimated to result in 90 additional deaths annually (range 20-300). The CO2 reductions, fuel savings, and additional mortality are not necessarily all attributable to the CO2 policies. Econometric models are a valuable tool to explain changes in consumer choice of fuel and vehicle types [45], but to our knowledge no econometric models have been developed to accurately quantify UK CO2 policy influence on diesel growth. Various reports attribute no impact to the VED CO2 tax [7], while estimates of the impact of the company 66 car CO2 tax on company car diesel growth range from 33% [8] to 100% [9]. Other factors such as the European manufacturer voluntary CO2 program, oil prices, and technological change are potential influences as well [8, 10, 46], but not fuel prices or taxes (Figure 3.2). To the extent that CO2 policies contributed to diesel growth, coordinating CO2 controls with tightening of emission standards would save lives. Because of the uncertain CO2 policy impact, our estimates of emissions, fuel savings, and health impacts per unit quantity of additional diesel cars (Figure 3.4) are emphasized. As the rest of the EU and other developed countries prepare to integrate transportation into climate mitigation programs, the lessons learned from the UK experience can help inform the design of climate policies in the transportation sector to better balance near-term health effects with long-term climate mitigation. 3.5 ACKNOWLEDGEMENTS We thank Lester Lave, Michael Brauer, Julian Marshall, and two anonymous reviewers for insightful comments on drafts of this article. We thank the University of British Columbia Bridge Program, AUTO21: B06 BLC, and Carnegie Mellon University\u00E2\u0080\u0099s NSF-supported Center for Integrated Study of the Human Dimensions of Global Change (SBR-9521914), and the Center for Climate Decision Making (SES-0345798). We are also grateful for generous support from the Exxon-Mobil Education Foundation. All errors are a measure of the fallibility of the authors and of how much more we need to learn before making sound policy. 67 3.6 REFERENCES 1. Davis, D.; Kjellstrom, T.; Sloof, R.; McGartland, A.; Atkinson, W.; Barbour, W.; Hohenstein, W.; Nagelhout, P.; Woodruff, T.; Divita, F.; Wilson, J.; Deck, L.; Schwartz, J., Short-term Improvements in Public Health from Global-Climate policies on Fossil-Fuel Combustion: An Interim Report. The Lancet 1997, 350, 1341-1349. 2. Cifuentes, L.; Borja-Aburto, V.; Gouveia, N.; Thurston, G.; Davis, D., Hidden Health Benefits of Greenhouse Gas Mitigation. Science 2001, 293, 1257-1259. 3. 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Transportation is a large and growing source of greenhouse gases, and CO2 mitigation policies are being designed to encourage consumers to choose cars with better fuel efficiency and lower CO2 emission rates [3, 4]. Technical factors that determine tailpipe CO2 emission rates are: a) vehicle mass, b) drive- train efficiency, and c) carbon intensity of the fuel. Mass reduction is potentially a win-win strategy with dual benefits of climate mitigation and near- term public health because mass is a determinant of both traffic safety and tailpipe CO2 [5]. Mass is not an attribute desired by consumers [6], but is a tangible parameter that is amenable to regulation [7], and technology exists to reduce mass without compromising vehicle size [7, 8]. Amongst the myriad of technologies to improve vehicle fuel economy and CO2 emission rates, vehicle mass reduction via lightweight technology is likely the only option that also has the potential for traffic safety benefits without compromising consumer preferences. An additional consideration is that mass reduction synergizes well with other traffic safety and climate mitigation strategies. For any improvements in traffic safety technology (e.g., pedestrian safety) or management (e.g., enforcement [9]), reducing mass is expected to provide positive (albeit uncertain) incremental benefits [7]. Similarly, reducing mass in combination with vehicle technology to reduce tailpipe CO2 (e.g., biofuels, diesel, or hybrid) is expected to provide incremental positive benefits [8]. When examining car mass reduction as a policy strategy, we hold that it is critical to recognize two issues at the outset. First, recognize that traffic safety is not just measured by fatality counts for car occupants [10]. Traffic safety encompasses risk of both fatality and injury for multiple vehicle types, road users, injury rates, and crash events as illustrated in Figures 4.1 and 4.2 [11]. 7 A version of this chapter has been submitted for publication: Mazzi, E., H. Dowlatabadi, and M. Kandlikar, \u00E2\u0080\u009CRegulating car mass for concurrent traffic safety and climate mitigation benefits\u00E2\u0080\u009D 2010. 72 Moreover, traffic safety risks can be quantified as risk ratios [12], conditional risks [5], or absolute risks [13]. UK traffic safety goals include reductions in total killed or seriously injured (KSI), total children KSI, the slight injury rate, and vulnerable road user casualties such as bicyclists and pedestrians [11]. Second, it is critical to recognize that mass reduction is not just about uniform mass reductions, but also about the fleet distribution of mass [14]. Uniform mass reduction has not been conclusively shown to reduce overall fatalities [13, 15, 16]. However, the risks of fatality between specific mass groups are measurably impacted by uniform mass reduction, leading to a change in the incidence of fatality and injury risk across the public [14, 16, 17]. Figure 4.1 1997-2006 time series of fatalities (left Y-axis) and KSI (right Y-axis) per billion passenger km for key road user groups in the UK. This illustrates the variation of casualty rates between road users (e.g., motorcycle occupant rates are 40-50 times car occupants), and that fatality rates do not always parallel injury (e.g., bicycle fatal and KSI rates 2003-2006). 0 20 40 60 80 100 120 140 1996 1998 2000 2002 2004 2006 Fa ta l p er b ill io n pa ss en ge r-k m 0 200 400 600 800 1,000 1,200 1,400 1,600 K SI p er b ill io n pa ss en ge r-k m pedestrian, fatal cars, fatal bicycles, fatal motorcycles, fatal pedestrian,KSI cars, KSI bicycles, KSI motorcycles, KSI 73 Figure 4.2 Distribution of 2,946 fatalities and 2,714 fatal crash events in the UK for 2007. There were also 30,720 KSI and 27,036 KSI events in 2007 with a similar distribution as fatalities. A substantial body of research has found that vehicle mass is a key predictor of various measures of traffic safety risk [12, 15, 16]. However, mass is also strongly correlated with other traffic safety factors including physical metrics such as size and power [7], and behavioral metrics such as selling price [18]. Some authors have hypothesized that increased car mass itself (or its physical correlates like size and power) is partly causal in increasing risk taking behavior [19], thus illustrating the complexity of interactions between physical and behavioral factors in traffic safety. It has been argued that mass is not fundamental to traffic safety [18], while other research implies that mass offers a protective effect [12, 15]. We highlight that research consistently shows delta-V is a significant predictor of risk of injury and fatality [7, 20], and that the mass of vehicles is fundamental in calculating delta-V applying conservation of momentum. A more detailed listing of factors that influence traffic safety, along with references, is provided in Appendix Section B.1. Table B1 provides a list of important studies that evaluated the role of vehicle mass in traffic safety. Fatal crash events 1 car 18% 2 car 14% car ped 13% other veh ped 7% 1 veh (not car) 7% 2 veh (1 not car) 25% \u00E2\u0089\u00A5 3 veh 16% Fatalities cars 48% pedestrian 22% motorcycle 20% bicycle 5% other 5% 74 The scope of this research is strictly passenger cars, and CO2 emission rates as measured by equivalent gCO2/km (including N2O and CH4) emitted at the tailpipe. We do not examine all factors that determine annual CO2 emissions, such as annual travel distance. We primarily examine vehicle attributes independent of roads, behavior, and other variables. We also do not examine the relative merits of traffic safety policies aimed at road (e.g., congestion controls) or behavior (e.g., speed enforcement). Therefore we do not address, nor do we argue, that one of these domains is more or less important than the other. The contribution of this research is to quantify multiple traffic safety risk metrics and relate them to CO2 emissions, using UK data. The significance is to quantify potential benefits of regulating vehicle mass for concurrent benefits in traffic safety and climate mitigation. The UK is important as a global early adopter of climate mitigation policies for passenger cars with tax regimes in place since 2001-2002 [21, 22], as well as being the second largest market subject to the European automobile CO2 policies. Although every country (and regions within countries) will have different traffic safety priorities, we expect the UK results can be generalized to other developed countries. 4.2 DATA AND METHODS Our principal data source was the UK Data Archive which has three databases for each calendar year: collisions (\u00E2\u0080\u009Caccidents\u00E2\u0080\u009D), casualties, and vehicles [23]. We obtained these databases for calendar years 1994-2005, which have 5,354 driver fatalities. The UK Data Archive databases included only generic specification of vehicles such as cars, motorcycles, and bicycles. The UK Department for Transport (DFT) provided additional databases for years 1994-2005 with car make, car model, engine size, fuel type, body type, and other parameters [24] which we linked to the UK Data Archive fields. Using UK data we quantified traffic collision risks and car CO2/km rates in four different ways. Appendix Section B.2.1 provides more a detailed description of the data used in this study. Section B.2.2 with Figures B1 through B9 provides graphical descriptive statistics that is a useful reference for understanding specific trends in traffic safety in the UK. The UK Data Archive data bases do not have curb mass, which is a critical limitation. We were able to make use of independent databases and proxy indicators of vehicle mass as described in Appendix Sections B.2.3. First we calculated the relative risk (RR) of driver fatality for lighter cars versus heavier cars, described as the \u00E2\u0080\u009Cfirst law\u00E2\u0080\u009D of two-car collisions [12], then compared the RR and CO2 emission 75 rates [25, 26]. The \u00E2\u0080\u009Cfirst law\u00E2\u0080\u009D is a power law relationship between vehicle mass and RR of driver fatality of the following form [12]: RR \u00E2\u0089\u00A1 \u00CE\u00BC\u00CE\u00BB where: RR \u00E2\u0089\u00A1 n1/n2 = (number driver fatalities in lighter cars) / (number of driver fatalities in heavier cars) \u00CE\u00BC \u00E2\u0089\u00A1 (M2/M1) = (mass of heavier car) / (mass of lighter car) For the RR analysis, two-car collisions were included for years 1996-2005 and where both vehicles were also of model years 1996-2005. Consequently, the final dataset was comprised of 280 cars, 140 crash events, and 144 driver fatalities. Neither the UK Data Archive databases nor the supplemental databases provided by DFT included vehicle mass. We were able to determine curb mass using an independent data source 26]. Appendix Section B.2.4 provides a more detailed description of the study methods for estimating RR. RR is a robust metric as many confounding factors cancel out, and it is considered to be independent of behavior [12, 27]. In interpreting RR, the main consideration is whether the numerator and denominator groups differ substantially in major confounding factors such as car crashworthiness, seat belt use, age and sex of drivers, collision speed, and collision points of impact [12, 20]. In this study we have no data on seat belt use. We have surrogate data on car crashworthiness (model year) and collision speed (road speed limits). We have direct data on age and sex of drivers, and points of impact. Overall, based on comparisons summarized in Table 4.1, we expect confounding factors to be minimal in the RR calculation. Our second analysis is a comparison of DFT\u00E2\u0080\u0099s conditional KSI risk estimates for two-car collisions[28] and CO2 emission rates [25]. As they are only for makes and models, DFT\u00E2\u0080\u0099s estimates do not relate risk to more fundamental parameters of interest such as mass, size, stiffness, and safety equipment. For each make and model of car model years 1995-2004 in DFT\u00E2\u0080\u0099s analysis, we quantified the CO2 emission rate [25, 26]. We first examined the relationship between risk and CO2 graphically, then by regressing two-car conditional risk on the CO2 emission rate. 76 Table 4.1 Comparison of key parameters for the \u00E2\u0080\u009Cfirst law\u00E2\u0080\u009D RR analysis data set: heavier cars and their drivers cf. lighter cars. Variable Heavier Car Lighter Car Number of drivers (vehicles) 140 140 Average car model year 1998.8 1998.1 Average car curb mass, kg 1339 1044 Average car length, m 4.38 3.98 Average CO2 emission rate, gCO2/km 201 172 Average driver age 44.5 46.4 Driver % male 75.0% 73.6% Slightly injured 39.6% 10.7% Seriously injured 34.5% 12.1% Fatal 25.7% 77.1% KSI 60.2% 89.2% For our third analysis we estimated changes in absolute risk, measured by fatalities per year, as a result of mass distribution scenarios for the UK on-road car fleet over the time period 2000- 2005. We computed CO2 emission rates and changes in absolute fatality risk for three collision types: single-car, car-pedestrian, and two-car. We estimated fatality risk using a method originally developed by Mengert and Borener [29] because it satisfies the study objectives and is feasible given our available data fields. The U.S. National Highway Traffic Safety Administration has replicated this method and concluded results were consistent with its detailed statistical analysis of vehicle, road, and driver behavior variables [15]. Changes in fatality risk are estimated based on changes in the mass distribution for on-road car fleets. We derived estimates of curb mass using a database provided by JATO Dynamics Ltd [30] which included size (length, width, wheelbase), curb mass, fuel economy, and other parameters. We computed ordinary least squares regression relationships between mass, size, CO2 emission rate, and engine size. We were able to use engine size categories as a suitable proxy to create a mass category field to link to the UK Data Archive. Standard errors for fatality risk were estimated 77 assuming a poisson distribution [12]. We highlight that a key limitation of UK data, as compared to the U.S., is sample size. Total fatalities in the U.S. are over one order of magnitude greater, therefore standard errors as a percentage of the mean fatality counts are typically 3 times larger for UK data. Appendix Section B.2.5 provides a more detailed description of the study methods for the absolute risk analysis. Scenarios were developed for the absolute risk analysis to compare baseline fatality risk to alternate scenarios as shown in Figure 4.3. In our scenarios, the year 1999 is the initial condition and we simulate plausible changes in fleet mass composition over the years 2000-2005. All mass reduction strategies will reduce tailpipe CO2, but there are three options to reduce mass in a way that might produce beneficial traffic safety outcomes: impose a lower mass limit, manipulate the distribution without upper or lower limits based on traffic safety risks by group and collision type, or impose an upper mass limit [7]. A lower mass limit conflicts with climate and energy security goals. Manipulating the distribution by groups and collision types is infeasible due to large uncertainties and unrealistic administrative requirements. Hence an upper limit on mass is the most feasible option and is built into our scenarios. The \u00E2\u0080\u009CIncrease lighter car\u00E2\u0080\u009D scenario was developed to simulate policies that induce rapid growth of lighter cars, the \u00E2\u0080\u009CIncrease lighter cars and prohibit heavier cars\u00E2\u0080\u009D to simulate prohibition of heavier cars (approximately 1,600 kg mass and larger), and the \u00E2\u0080\u009CConstant mass\u00E2\u0080\u009D scenario to simulate changes in fatality risk independent of fleet average mass. A realistic upper limit on mass is 1,600 kg based on research that shows that 33% curb mass reduction for pickups, minivans, and sport utility can be achieved at vehicle price premiums well under 10% [8]. Our fourth risk analysis was an estimation of fleet average RR for two-car collisions using the same 2000-2005 scenarios as the absolute risk analysis shown in Figure 4.3. A distribution of mass for on-road cars for each scenario was developed using fleet distribution by engine size, and then mass distributions were estimated using the statistical relationships using the JATO data. Appendix Section B.2.6 provides a more detailed description of these statistical relationships. Two-car RR was estimated assuming fatal collisions occur randomly, with the power law exponent (\u00CE\u00BB) varied parametrically at values 2, 4, and 6. Analytica\u00E2\u0084\u00A2 version 4.1 software was used to simulate randomized crashes using the Latin Hypercube sampling algorithm. 78 Baseline (actual) 0% 5% 10% 15% 20% 25% 30% 35% 40% 1993 1995 1997 1999 2001 2003 2005 R e g i s t e r e d ( o n - r o a d ) c a r s h a r e s 1501-1800 (M) 1201-1500 (L) 1801-2000 (M) 1001-1200 (L) 701-1000 (L) 2001-2500 (H) 2501-3000 (H) 3000 & over (H) 700 & under Scenario: Increase light cars and prohibit heavy (decrease mid-mass cars) 0% 5% 10% 15% 20% 25% 30% 35% 40% 1993 1995 1997 1999 2001 2003 2005 R e g i s t e r e d ( o n r o a d ) c a r s h a r e s 1501-1800 (M) 1201-1500 (L) 1801-2000 (M) 1001-1200 (L) 701-1000 (L) 2001-2500 (H) 2501-3000 (H) 3000 & over (H) 700 & under Scenario: Constant mass 0% 5% 10% 15% 20% 25% 30% 35% 40% 1993 1995 1997 1999 2001 2003 2005 R e g i s t e r e d ( o n r o a d ) c a r s h a r e s 1501-1800 (M) 1201-1500 (L) 1801-2000 (M) 1001-1200 (L) 701-1000 (L) 2001-2500 (H) 2501-3000 (H) 3000 & over (H) 700 & under Scenario: Increase light cars (BAU heavier cars, decrease mid-mass cars) 0% 5% 10% 15% 20% 25% 30% 35% 40% 1993 1995 1997 1999 2001 2003 2005 R e g i s t e r e d ( o n - r o a d ) c a r s h a r e s 1501-1800 (M) 1201-1500 (L) 1801-2000 (M) 1001-1200 (L) 701-1000 (L) 2001-2500 (H) 2501-3000 (H) 3000 & over (H) 700 & under Figure 4.3 Baseline plus three alternative scenarios for years 2000-2005 used in the absolute risk analysis and the RR fleet composition simulation. \u00E2\u0080\u009CLighter\u00E2\u0080\u009D group is comprised of engine size ranges 701-1,000 cubic centimeter (CC), 1,001-1,200 CC, and 1,201-1,500 CC. \u00E2\u0080\u009CMid-mass\u00E2\u0080\u009D includes 1,501-1,800 CC and 1,801-2,000 CC. \u00E2\u0080\u009CHeavier\u00E2\u0080\u009D includes 2,001-2,500 CC, 2,501-3,000 CC, and 3,000 CC and over. 700 CC and under, were not included because there were too few vehicles, annual fatality counts were zero or near zero, and it was not possible to estimate risks. Scenario descriptions are provided in the text. 79 4.3 RESULTS The results of the \u00E2\u0080\u009Cfirst law\u00E2\u0080\u009D two-car RR analysis are summarized in Figure 4.4. The data set was suitable to develop five groups with curb mass ratio (\u00EF\u0081\u00AD\u00EF\u0080\u00A9 ranging from 1.06-1.55. The regression analysis for the relationship RR = \u00EF\u0081\u00AD\u00EF\u0081\u00AC resulted in \u00EF\u0081\u00AC\u00EF\u0080\u00A0= 5.31 (95% CI 3.9-6.7; R2 = 0.71, p <0.0005), and a line fit plot is provided in Figure B16). Figure 4.4 \u00E2\u0080\u009CFirst law\u00E2\u0080\u009D RR risk of driver fatality in two-car collisions and ratio of CO2 emission rates for UK cars 1995-2005. RR of driver fatality in two-car collisions is shown on the left vertical axis (mean +/- one standard error). Ratio of tailpipe CO2 emission rate (mean +/- one standard deviation) is shown on the right vertical axis. A horizontal line is drawn showing where CO2/km ratio =1.0. 0 2 4 6 8 10 12 14 1.0 1.2 1.4 1.6 mass ratio (\u00CE\u00BC): heavier car / lighter car RR \u00E2\u0089\u00A1 fa ta lit y ris k lig ht er c ar d riv er s / f at al ity ri sk h ea vi er ca r d riv er s 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 CO 2/ km ra tio : he av ie r c ar s / l ig ht er c ar s 2-car RR data +/- 1 std error (left Y-axis) RR fitted line, R^2 = 0.71, p<0.0005 (left Y-axis) CO2/km: heavy car / light car +/- 1 std dev (right Y-axis) CO2/km ratio = 1.0 (reference line) 80 Based on visual inspection of the data in Figure 4.4, we also attempted to fit the data to a linear statistical model with the following results (plot in Figure B16): RR = \u00CE\u00B1 + \u00CE\u00B2 * \u00C2\u00B5 (R2 = 0.96) \u00CE\u00B1 = - 14.1 [95% CI -20.9 to -7.4] p < 0.007 \u00CE\u00B2 = 14.5 [95% CI 9.1 to 19.9] p < 0.004 Appendix Section B.3, Figure B16 includes line fit plots for both the \u00E2\u0080\u009Cfirst law\u00E2\u0080\u009D and linear statistical equations. The results of the conditional risk analysis are provided in Figure 4.5. DFT-derived conditional KSI estimates are reported as a standardized percentage, rounded to whole numbers. Hence the reported risks can be viewed as a conditional risk category ranging from values 1-10. Car models are plotted in order of decreasing conditional risk category. Regressing percent risk on CO2/km revealed that decreasing CO2/km is a statistically significant predictor for increasing driver KSI risk: Driver % KSI risk = 8.4 \u00E2\u0080\u0093 0.020 * CO2/km (R2 = 0.22; p<0.0002; intercept 95% CI = 6.5-10.3; slope 95% CI = 0.010-0.030) Appendix Section B.3, Figure B17 provides a line fit plot for the conditional risk statistical model. The results of the absolute risk analysis are presented in Table 4.2 and Figure 4.6. The \u00E2\u0080\u009CIncrease lighter cars\u00E2\u0080\u009D scenario predicts declines in single-car, car-pedestrian, and two-car fatalities of 10%, 14%, and 13%, respectively by 2005 if rapid growth in lighter cars had begun in 2000 (with BAU heavier car shares). Under this scenario, our model predicts a reduction of 126 deaths per year could have been achieved by 2005 for these three collision types combined. Results are significant at the level of one standard error, which is an accepted level of significance commonly applied in traffic safety [12] and also described as \u00E2\u0080\u009Clikely\u00E2\u0080\u009D in climate policy analysis[3]. The \u00E2\u0080\u009CIncrease lighter cars and prohibit heavier cars scenario\u00E2\u0080\u009D predicts that rapid growth of lighter cars combined with prohibition of heavy cars entering the fleet beginning in 2000 produces the same 10% reductions in single-car fatalities, and steeper reductions in car-pedestrian and two-car fatalities of 18% and 17%, respectively. Under this scenario, our model predicts a reduction of 154 deaths per year could have been achieved by 2005 for these three collision types combined. Single-car and car- pedestrian results are significant at one standard error, while two-car results are significant at two standard errors. 81 Figure 4.5 Relationship between driver two-car conditional KSI risk and vehicle CO2 emission rate in the UK. Model year passenger cars 1995-2004 for crash events during calendar years 2000-2004 are included. Increased CO2/km is a modest but significant predictor of decreased risk. Car make and model size key as defined by DFT: L/S = low/sports, S = small, S/M = small/medium, M = medium, L = large, MPV = multipurpose, 4WD = four wheel drive. 0 50 100 150 200 250 300 350 0 5 10 15 20 25 D a e w o o M a t i z ( S 9 5 - 0 3 ) F i a t S e i c e n t o ( S 9 8 - 0 3 ) D a e w o o L a n o s ( S / M 9 7 - 9 9 ) H y u n d a i C o u p e ( L / S 9 5 - 0 1 ) V W L u p o ( S 9 9 - 0 2 ) P e u g e o t 2 0 6 ( S 9 8 - 0 4 ) M i t s u b i s h i C a r i s m a ( M 9 5 - 0 3 ) D a e w o o N u b i r a ( M 9 7 - 0 3 ) H o n d a C R V ( 4 W D 9 7 - 0 1 ) M G M G F ( L / S 9 5 - 0 3 ) R o v e r 2 5 / 4 5 ( S / M 9 9 - 0 4 ) S k o d a F e l i c i a ( S / M 9 5 - 0 0 ) T o y o t a Y a r i s ( S 9 9 - 0 4 ) F i a t P u n t o ( S 9 9 - 0 3 ) V a u x h a l l C o r s a ( S 9 9 - 0 3 ) H y u n d a i A t o z ( S 9 8 - 0 0 ) R e n a u l t C l i o B ( S 9 8 - 0 4 ) M i n i ( S 9 8 - 0 3 ) C i t r o e n X s a r a ( S / M 9 7 - 0 0 ) A u d i T T ( L / S 9 9 - 0 1 ) J a g u a r S T y p e ( L 9 9 - 0 4 ) T o y o t a A v e n s i s ( M 9 7 - 0 3 ) S k o d a O c t a v i a ( M 9 8 - 0 4 ) V a u x h a l l A s t r a ( S / M 9 8 - 0 4 ) S e a t L e o n ( S / M 0 0 - 0 3 ) F i a t B r a v o ( S / M 9 5 - 0 1 ) R e n a u l t M e g a n e ( S / M 9 5 - 0 4 ) F o r d K a ( S 9 6 - 0 4 ) P e u g e o t 3 0 7 ( S / M 0 1 - 0 4 ) S k o d a F a b i a ( S / M 0 0 - 0 4 ) V W P o l o ( S 9 8 - 0 4 ) M e r c e d e s A C L ( S 9 8 - 0 4 ) F o r d P u m a ( L / S 9 7 - 0 1 ) F o r d F o c u s ( S / M 9 8 - 0 3 ) A u d i A 3 ( S / M 9 6 - 0 3 ) B M W Z 3 ( L / S 9 6 - 0 1 ) F o r d G a l a x y ( M P V 9 5 - 0 3 ) L e x u s I S 2 0 0 ( M 9 9 - 0 3 ) V o l v o V 7 0 ( L 9 7 - 0 2 ) C i t r o e n S y n e r g i e ( M P V 9 5 - 0 0 ) V W B e e t l e ( S / M 9 9 - 0 3 ) P e u g e o t 4 0 6 ( M 9 5 - 0 3 ) S u z u k i B a l e n o ( S / M 9 5 - 0 0 ) N i s s a n A l m e r a ( S / M 9 5 - 0 3 ) C i t r o e n P i c a s s o ( M P V 0 0 - 0 4 ) C i t r o e n X s a r a ( S / M 0 0 - 0 3 ) C i t r o e n C 5 ( M 0 1 - 0 3 ) A u d i A 4 ( M 9 5 - 0 4 ) S a a b 9 - 3 ( M 9 8 - 0 3 ) B M W 3 0 0 C ( M 9 8 - 0 4 ) A u d i A 6 ( L 9 7 - 0 0 ) L R F r e e l a n d e r ( 4 W D 9 7 - 0 3 ) S a a b 9 - 5 ( L 9 7 - 0 3 ) A l f a 1 5 6 ( M 0 0 - 0 3 ) R o v e r 7 5 ( L 9 8 - 0 4 ) H o n d a A c c o r d ( 9 8 - 0 3 ) R e n a u l t S c e n i c ( M P V 9 6 - 0 2 ) C i t r o e n C 3 ( S 0 2 - 0 4 ) J a g u a r X T y p e ( M 9 6 - 0 3 ) M e r c e d e s M L ( 4 W D 9 8 - 0 3 ) g C O 2 / k m C o n d i t i o n a l r i s k o f d r i v e r s e r i o u s i n j u r y o r f a t a l i t y , % Standardized, conditional risk of serious injury or fatality, with biased 95% CI (lef t Y-axis) gCO2/km +/- 1 standard deviation (right Y-axis) 82 The \u00E2\u0080\u009CConstant mass\u00E2\u0080\u009D scenario predicts that prohibition of new models of heavy cars entering the fleet beginning in 2000, while increasing mid-mass shares and decreasing light cars to maintain baseline average mass, would increase fatalities for all three collision types. Fatality increases for single-car, car-pedestrian, and two-car collisions were estimated at 6%, 4%, and 7%, respectively. Under the \u00E2\u0080\u009Cconstant mass\u00E2\u0080\u009D scenario, our model predicts an increase of 61 fatalities per year by 2005 for these three collision types combined. However, results are not significant, even at one standard error. Table 4.2 Results of absolute risk analyses for single-car, car-pedestrian, and two-car collisions. The analysis simulated three alternate scenarios over the time period 2000-2005. Values shown are point estimates of the mean for year 2005. Time series of results including standard errors are plotted in Figure 4.6. Year 2005 fleet (simulation runs 2000- 2005) Baseline fleet (actual) Scenario: Increase lighter cars (BAU for heavier cars, decrease mid-mass cars) \u00E2\u0088\u0086 from baseline Scenario: Increase lighter cars and prohibit heavier cars (decrease mid-mass cars). \u00E2\u0088\u0086 from baseline Scenario: Constant mass (prohibit heavier cars while maintaining baseline mass) \u00E2\u0088\u0086 from baseline average mass, kg 1,341 1,270 -71 1,225 -115 1,341 0 std dev mass, kg 0 286 286 240 240 192 192 average gCO2/km 185 173 -12 166 -19 185 0 new car gCO2/km 169 162 -7 143 -26 191 22 single-car fatalities 426 384 -10% 382 -10% 452 6% car- pedestrian fatalities 306 263 -14% 251 -18% 317 4% two-car fatalities 325 283 -13% 270 -17% 348 7% sum of fatalities 1,057 931 -126 903 -154 1,118 61 83 Figure 4.6 Single-car, car-pedestrian, and two-car absolute risk results, shown from top to bottom. Changes in annual fatalities are plotted as percentage change relative to the baseline. A horizontal line is plotted at 0% (no change). Error bars represent +/- one standard error. Points are plotted slightly offset in the time scale (x-axis) to make error bars visible. -25% -20% -15% -10% -5% 0% 5% 10% 15% 1999 2000 2001 2002 2003 2004 2005 2006 si ng le c ar c ol lis io n an nu al fa ta lit ie s, % c ha ng e re la tiv e to b as el in e Baseline (actual) Scenario: Increase light cars (BAU for heavier cars, decrease mid-mass cars) Scenario: Increase light cars and prohibit heavy cars (decrease mid-mass cars). Scenario: Constant mass (prohibit heavy cars while maintaining baseline mass) -25% -20% -15% -10% -5% 0% 5% 10% 15% 1999 2000 2001 2002 2003 2004 2005 2006 ca r-p ed es tri an c ol lis io n an nu al fa ta lit ie s, % c ha ng e re la tiv e to b as el in e Baseline (actual) Scenario: Increase light cars (BAU for heavier cars, decrease mid-mass cars) Scenario: Increase light cars and prohibit heavy cars (decrease mid-mass cars). Scenario: Constant mass (prohibit heavy cars while maintaining baseline mass) -25% -20% -15% -10% -5% 0% 5% 10% 15% 1999 2000 2001 2002 2003 2004 2005 2006 tw o ca r c ol lis io n an nu al fa ta lit ie s, % c ha ng e re la tiv e to b as el in e Baseline (actual) Scenario: Increase light cars (BAU for heavier cars, decrease mid-mass cars) Scenario: Increase light cars and prohibit heavy cars (decrease mid-mass cars). Scenario: Constant mass (prohibit heavy cars while maintaining baseline mass) 84 Results for two-car relative risk (RR) simulation are summarized in Table 4.3. The fleet simulation for the \u00E2\u0080\u009CIncrease lighter car\u00E2\u0080\u009D scenario estimates that RR increases 14%, 36%, and 69% relative to the baseline scenario for \u00CE\u00BB values of 2, 4, and 6 respectively. For the \u00E2\u0080\u009CIncrease lighter cars and prohibit heavier cars\u00E2\u0080\u009D scenario, the estimated RR increases by 4%, 11%, and 22% for the same \u00CE\u00BB values. For the \u00E2\u0080\u009CConstant mass\u00E2\u0080\u009D scenario, the estimated RR decreases by 10%, 21%, and 33%. Table 4.3 Results of simulation of two-car collision relative risk fleet calculation for the year 2005. Relative risk is the driver fatality risk of lighter cars divided by driver fatality risk of heavier cars in the relationship RR = \u00EF\u0081\u00AD\u00EF\u0081\u00AC. \u00EF\u0081\u00AD is calculated in the simulation assuming randomized collision events sampled from a mass distribution representative of the UK on-road car fleet. \u00EF\u0081\u00AC is examined parametrically ranging from 2 to 6. Baseline Scenario: Increase lighter cars (BAU for heavier cars, decrease mid-mass cars) Scenario: Increase lighter cars and prohibit heavier cars (decrease mid-mass cars). Scenario: Constant mass (prohibit heavy cars while maintaining baseline mass) \u00CE\u00BB = 2 \u00CE\u00BB = 4 \u00CE\u00BB = 6 \u00CE\u00BB = 2 \u00CE\u00BB = 4 \u00CE\u00BB = 6 \u00CE\u00BB = 2 \u00CE\u00BB = 4 \u00CE\u00BB = 6 \u00CE\u00BB = 2 \u00CE\u00BB = 4 \u00CE\u00BB = 6 Mean 1.5 2.7 5.6 1.8 3.7 9.6 1.6 3.0 6.9 1.4 2.1 3.8 Std dev 0.6 2.6 11.2 0.8 4.1 19.9 0.7 3.3 16.1 0.5 1.8 6.8 % of baseline mean -- -- -- 14% 36% 69% 4% 11% 22% -10% -21% -33% 4.4 DISCUSSION We examined reductions in vehicle mass that might best achieve the dual goals of lowering CO2 emissions and traffic casualties. The \u00E2\u0080\u009Cfirst law\u00E2\u0080\u009D RR analysis for model year 1996-2005 cars in the UK resulted in a value of \u00CE\u00BB of 5.3. This implies, for example, that in multiple fatal collision events between 1,000 kg (135 gCO2/km) and 2,000 kg (200 gCO2/km) cars, the ratio of fatalities for drivers of lighter cars to drivers of heavier cars will be 39. The key finding is that drivers of linearly less CO2/km cars are 85 subject to non-linearly increased risk of fatality relative to drivers of higher CO2 /km cars in two- car collisions. An important secondary finding from our results in Table 4.1 is that there is a shift in the partition of risk in comparing lighter car/heavier car drivers from a 77%/26% split in fatalities, to a 12%/35% split for seriously injured. A \u00CE\u00BB value of 5.3 is higher than values found in the U.S. which ranges from 2.7-3.8 for primarily frontal collisions [12, 27]. This result is likely explained by the point of impact as illustrated in Figure 4.7. Compared to the complete UK Data Archive data set, our analysis data set had 9 percentage points fewer frontal impacts and 2 percentage points more driver-side impacts, but eliminating all side impact collisions was not feasible due to the aforementioned sample size limitation. We did attempt to fit our data to the same side-impact equation shown in Figure 4.7 (RR = A * \u00CE\u00BC\u00CE\u00BB), but the results were invalid (Appendix Section B.4). Increased collision speed is yet another factor that increases \u00CE\u00BB[12]. The only variable we have to indicate collision speed is road speed limit, which shows 69% and 15% of collisions occurring on 60 mph and 70 mph roads, respectively, compared to values of 63% and 12% in the complete Data Archive. Driver age and gender are additional factors that may have increased the value of \u00CE\u00BB, since older people and females tend to be more vulnerable to injury [17]. In lighter cars, the average driver was 1.9 years older and the percentage male was 1.4% lower as compared to heavier cars, trends that would be expected to increase \u00CE\u00BB. Finally, lighter cars were 0.7 years older on average than heavier cars that could make a small influence to increase \u00CE\u00BB. The linear statistical model that relates RR to mass ratio was also found to be significant. If this is the best approximation, then it indicates that in multiple fatal collision events between 1,000 kg (135 gCO2/km) and 2,000 kg (200 gCO2/km) cars, the ratio of fatalities for drivers of lighter cars to drivers of heavier cars will be 15; this is much lower than the value of 39 produced by the \u00E2\u0080\u009Cfirst law\u00E2\u0080\u009D model. We have no way of definitively determining which model is best approximation; however, based on the results found in the U.S. with larger data sets [12], we expect it is more likely that the power law relationship is a more accurate approximation. In either case, the fatality risk ratio, RR, is much greater than the mass ratio in two-car collisions. 86 Figure 4.7 Comparison of RR calculated in this study (from Figure 4.4), to previous results from U.S. data sets. Both the horizontal and vertical axes are logarithmic scales. The effect of point of impact strongly affects the RR. When the front of a striking car crashes into the driver side of the struck car, the RR is much larger. While the RR for front-to-front collisions has been observed to pass through the origin (i.e., RR \u00E2\u0089\u0088 1 for mass ratio \u00E2\u0089\u0088 1), the RR for purely front-to-driver side impacts has been observed to pass through 10 at the origin (i.e., RR \u00E2\u0089\u0088 10 for mass ratio \u00E2\u0089\u0088 1) [27]. Because of this relationship, the RR for side impact collisions is commonly fit to the equation RR = A * \u00CE\u00BC\u00CE\u00BB, where statistical models reveal A \u00E2\u0089\u0088 10. The analysis of two-car conditional KSI risk and CO2 emission rate shows that, for example, the average driver choosing a car that is 50 gCO2/km higher would be subject to a 1% point statistically significant reduction in KSI risk. Previous research has concluded that vehicle mass is an important determinant of both conditional KSI risk and fuel economy for these same car 1 10 100 1 mass ratio: heavy car / light car R R \u00E2\u0089\u00A1 fa ta lit y ris k lig ht c ar d riv er s / f at al ity ri sk h ea vy c ar d riv er s UK 2-car RR data +/- 1 std error UK RR fitted line, R2=0.71, p<0.0005 U.S. front-front crash modes (1991-95 data) U.S. front-driver side crash modes (1991-95 data) 2 . R R \u00E2\u0089\u00A1 fa ta lit y ris k lig ht c ar d riv er s / f at al ity ri sk h ea vy c ar d riv er s R R \u00E2\u0089\u00A1 fa ta lit y ris k lig ht c ar d riv er s / f at al ity ri sk h ea vy c ar d riv er s 87 models and risk estimates [5]. A limitation is that describing a car only by its make and model is imprecise, as there can be a large variation in important determinants of traffic safety and CO2 emission rate for a given make and model. For example, a BMW Series 3 model includes versions ranging in mass of 1,320-1,810 kg and emission rates of 135-310 gCO2/km[30]. We expect that the result of these blunt metrics is to reduce the magnitude of the effect of mass and its correlates on estimated risk. Our absolute risk analysis shows that rapid, yet realistic reductions in fleet mass over the years 2000-2005 in the UK could have reduced single-car, car-pedestrian, and two-car fatalities up to 10%, 18%, and 17%, respectively. Simultaneous reductions in fleet average CO2 emission rate of 12 gCO2/km and 19 gCO2/km could have been achieved based on our \u00E2\u0080\u009CIncrease lighter cars\u00E2\u0080\u009D and \u00E2\u0080\u009CIncrease lighter cars and prohibit heavier cars\u00E2\u0080\u009D scenarios, respectively. A key finding is that prohibiting entry of the heaviest cars into the fleet, vehicle mass roughly 1,600 kg and over in our scenario, was estimated to provide incremental reductions in three types of fatalities as well as tailpipe CO2. A strength of our analysis is that the results are not skewed by improving safety technology over time because our scenarios simulated changes in proportions of the actual UK fleet over the years 2000-2005, and estimated changes in fatality risk based on empirical rates for the same time period. A limiting assumption is that drivers shifting from heavier to lighter cars would not have altered their behavior, which conflicts with the \u00E2\u0080\u009Coffsetting behavior\u00E2\u0080\u009D hypothesis [19], but there is no known data or methods to quantify such behavior shifts. The fleet simulation of randomized collisions and RR shows that it makes a difference in how fleets are transitioned to lighter, lower CO2 cars. From 2000-2005, mean \u00E2\u0080\u009Cfirst law\u00E2\u0080\u009D RR in the UK would increase 14-69% (for \u00CE\u00BB range 2-6) under the \u00E2\u0080\u009CIncrease lighter cars\u00E2\u0080\u009D scenario, but increase only 4-22% under the \u00E2\u0080\u009CIncrease lighter cars and prohibit heavier cars\u00E2\u0080\u009D scenario. Therefore policy regimes that simultaneously increase shares of lighter cars and decrease heavier cars can be expected to both reduce CO2 and mitigate the equity of two-car fatality risk. The \u00E2\u0080\u009CConstant mass\u00E2\u0080\u009D scenario demonstrates that transitions towards a more homogeneous fleet, even without any reductions in average mass or CO2, improves the equity of fatality risk. These findings are consistent with other research that reducing the mass range of on-road car fleets is the most beneficial way to transition to a lower mass fleet[14, 16]. An examination of the assumption of randomized collisions is provided in Appendix Section B.4. It is important to highlight that vehicle size and mass correlate in our data [30] (n=3298) according to the following relationship: curb mass, kg = \u00CE\u00B1 * (wheelbase, m)\u00CE\u00B2 88 where: \u00CE\u00B1 = 109.9 +/- 8.2 (95% CI) and \u00CE\u00B2 = 2.62 +/- 0.07 (95% CI) Thus our results do not estimate the real effects of adopting technology that reduces mass without reducing size. With such technology it is plausible that KSI risk with decreasing mass as shown Figure 4.5 may not hold, that our estimates of fatality risk benefits would be larger, and that the value of fleet mean \u00CE\u00BB would be lower. The relationship between mass and size for current UK cars is statistically no different than 1980-1990 U.S. cars [12], which is clear evidence that potential benefits of mass reduction technology remain untapped. We conclude that reducing vehicle mass including phase in of an upper mass limit can yield public benefits simultaneously for traffic safety and climate mitigation, and that our UK results can be generalized to other developed countries. 4.5 ACKNOWLEDGEMENTS We thank the UK Department for Transport and JATO Dynamics Ltd. for provision of high quality data sets. We thank the University of British Columbia Bridge Program, AUTO21: B06 BLC, and Carnegie Mellon University\u00E2\u0080\u0099s NSF-supported Center for Integrated Study of the Human Dimensions of Global Change (SBR-9521914), and the Center for Climate Decision Making (SES-0345798). We are also grateful for generous support from the Exxon-Mobil Education Foundation. 89 4.6 REFERENCES 1. Broughton, J., \u00E2\u0080\u009CMonitoring progress toward the 2010 casualty reduction target - 2005 data.\u00E2\u0080\u009D 2007, Transport Research Laboratory (www.trl.co.uk) for the UK Department for Transport (www.dft.gov.uk). 2. 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Lave, \u00E2\u0080\u009CEvaluating automobile fuel/propulsion system technologies.\u00E2\u0080\u009D Progress in Energy and Combustion Science, 2003. 29: p. 1\u00E2\u0080\u009369. 7. Elvik, R. and T. Vaa, \u00E2\u0080\u009CThe Handbook of Road Safety Measures.\u00E2\u0080\u009D 2004: Elsevier. 8. DeCicco, J., F. An, and M. Ross, \u00E2\u0080\u009CTechnical options for improving the fuel economy of U.S. cars and light trucks by 2010\u00E2\u0080\u00932015.\u00E2\u0080\u009D 2001, American Council for an Energy Efficient Economy (www.aceee.org). 9. Redelmeir, D., R. Tibshirani, and L. Evans, \u00E2\u0080\u009CTraffic law enforcement and risk of death from motor vehicle crashes: case-crossover study.\u00E2\u0080\u009D The Lancet, 2003. 361: p. 2177- 2182. 10. Elvik, R., \u00E2\u0080\u009CDimensions of road safety problems and their measurement.\u00E2\u0080\u009D Accident Analysis and Prevention, 2008. 40: p. 1200\u00E2\u0080\u00931210. 11. DFT, \u00E2\u0080\u009CUK Transport Statistics online database (www.dft.gov.uk/pgr/statistics)\u00E2\u0080\u009D. 2008, Department for Transport (UK). 12. Evans, L., \u00E2\u0080\u009CTraffic Safety.\u00E2\u0080\u009D 2004: Science Serving Society. 90 13. Van Auken, R. and J. Zellner, \u00E2\u0080\u009CA further assessment of the effects of vehicle weight and size parameters on fatality risk in model year 1985-98 passenger cars and 1985-97 light trucks.\u00E2\u0080\u009D 2003, Dynamic Research Inc. 14. Buzeman-Jewkes, D., D. Viano, and P. L\u00C3\u00B6vsund, \u00E2\u0080\u009COccupant Risk, Partner Risk and Fatality Rate in Frontal Crashes: Estimated Effects of Changing Vehicle Fleet Mass in 15 Years.\u00E2\u0080\u009D Traffic Injury Prevention, 2000. 2(1): p. 1-10. 15. Kahane, C., \u00E2\u0080\u009CVehicle weight, fatality risk and crash compatibility of model year 1991-99 passenger cars and light trucks.\u00E2\u0080\u009D 2003, National Highway Traffic Safety Administration (U.S.). 16. Broughton, J., \u00E2\u0080\u009CThe likely effects of downsizing on casualties in car accidents.\u00E2\u0080\u009D 1999, Transport Research Laboratory (UK). 17. Padmanaban, J. \u00E2\u0080\u009CInfluences of vehicle size and mass and selected driver factors on odds of driver fatality.\u00E2\u0080\u009D in 47th Annual Proceedings of the Association for the Advancement of Automotive Medicine. 2003. 18. Wenzel, T. and M. Ross, \u00E2\u0080\u009CThe effects of vehicle model and driver behavior on risk.\u00E2\u0080\u009D Accident Analysis and Prevention, 2005. 37: p. 479-494. 19. Yun, J., \u00E2\u0080\u009COffsetting behavior effects of the corporate average fuel economy standards.\u00E2\u0080\u009D Economic Inquiry, 2002. 40(2): p. 260-270. 20. Wood, D., Veyrat, N., Simms, C., and Glynn, C. \u00E2\u0080\u009CLimits for survivability in frontal collisions: Theory and real life data combined.\u00E2\u0080\u009D Accident Analysis and Prevention, 2007. 39: p. 679-687. 21. MORI, \u00E2\u0080\u009CAssessing the impact of Graduated Vehicle Excise Duty: quantitative report.\u00E2\u0080\u009D 2003, UK Department for Transport (www.dft.gov.uk) and MORI (www.mori.co.uk). 22. IR, \u00E2\u0080\u009CReport on the evaluation of the company car tax reform: stage two.\u00E2\u0080\u009D 2006, UK Inland Revenue (www.inlandrevenue.gov.uk). 23. DFT, \u00E2\u0080\u009CRoad Accident Data [computer files] 1994 SN3461, 1996 SN3627, 1997 SN3817, 1998 SN4027, 1999 SN4080, 2000 SN4356, 2001 SN4501, 2002 SN4588, 2003 SN4979, 2004 SN5042, 2005 SN5244, 2006 SN5493. 2007,\u00E2\u0080\u009D UK Data Archive (Colchester, Essex). 24. DFT, \u00E2\u0080\u009CMake-Model crash database linkable to UK Data Archive data (electronic database).\u00E2\u0080\u009D 2006, UK Department for Transport. 91 25. VCA, Online data of vehicle emissions for UK cars model years 2000-2006. 2007, Vehicle Certification Agency (www.vcacarfueldata.org.uk). 26. Whatcar, \u00E2\u0080\u009COnline car specifications (www.whatcar.co.uk).\u00E2\u0080\u009D 2007. 27. Joksch, H., D. Massie, and R. Pichler, \u00E2\u0080\u009CVehicle aggressivity: fleet characterization using traffic collision data (DOT HS 808 679).\u00E2\u0080\u009D 1998, National Highway Traffic Safety Administration (NHTSA). 28. DFT, \u00E2\u0080\u009CCars: make and model: the risk of driver injury in Great Britain: 2000-2004.\u00E2\u0080\u009D 2006, Department for Transport (UK). 29. Mengert, P. and S. Borener, \u00E2\u0080\u009COverall fatality risk to the public at large related to national weight mix of passenger cars (DOT-TSC-HS070-PM-89-27).\u00E2\u0080\u009D 1989, National Highway Traffic Safety Administration (NHTSA). 30. JATO. \u00E2\u0080\u009CJATO Dynamics new car database for Great Britain.\u00E2\u0080\u009D 2007 [cited June 3, 2007]. 92 5 CONCLUSIONS Here I summarize and discuss my thesis contributions to the design of climate mitigation policy for passenger cars. First I summarize the relationship between my three thesis articles, and then comment on the results of my research relative to current hypotheses in the study of public policy. I summarize key strengths and weaknesses of my research, and summarize what I believe is the significance and potential application of my research. Specific recommendations for future research are provided. 5.1 RELATIONSHIP BETWEEN PPOLICY INTEGRATED ASSESSMENT, AIR QUALITY, AND TRAFFIC SAFETY RESEARCH Chapter 2 describes the rationale and basic framework for Integrated Assessment models of passenger car risks. Air quality and traffic safety are just two of the risks that are relevant in development of policy to reduce CO2 from passenger cars. The key issues and relationships in Integrated Assessment of passenger car CO2 policy (Chapter 2), air quality (Chapter 3), and traffic safety (Chapter 4) are summarized here. \u00EF\u0082\u00B7 The choice of decision criteria and risks included or excluded (see Table 2.1) influences the relative importance of air quality and traffic safety as compared to other risks. Risk rankings can change depending on the analyst\u00E2\u0080\u0099s choice of decision criterion and public risk metric. Moreover, available choices of decision criteria are different if the analyst is a researcher (more choice) as compared to a public policymaker (less choice). o For the current fleet dominated by gasoline and diesel cars, it was found that air quality and traffic safety outcomes rank highly regardless of whether monetized social costs, public health, or public agency new policy priority is chosen as the decision criterion (see Table 2.1). o In terms of health risk metrics, if annual fatalities is chosen as the metric, then air quality is dominant; if YLL is chosen, then traffic safety is the highest priority (see Section 2.2.2). \u00EF\u0082\u00B7 Public risks, including air quality and traffic safety, are influenced by policy through pathways (see Figure 2.2) that include three principal economic actors (consumers, 93 manufacturers, and fuel suppliers) and four basic factors: VKT and patterns of use, ownership, vehicle technology, and fuel properties. \u00EF\u0082\u00B7 Four categories of policy that could be leveraged to reduce passenger car CO2 emissions were identified (see Table 2.2): multi-sector policies, fuel-oriented policies, vehicle technology policy, and VKT and usage policy. Considering current gasoline and diesel cars, specific policies in all four categories are identified as potentially influencing air quality. However only vehicle technology and ownership, and vehicle VKT and usage categories are identified as potentially influencing traffic safety. \u00EF\u0082\u00B7 Compared to today\u00E2\u0080\u0099s car fleets dominated by conventional gasoline and diesel cars, the direction and magnitude of change in air quality, traffic safety, and other risks can be expected to change substantially with future fleets that are projected to be more heterogeneous. Future fleets are projected to have substantial uptake of one or more of the following [1]: biofuelled cars, plug-in hybrids, all-electric, and hydrogen fuel cell cars. The relative importance of air quality and traffic safety risks in more heterogeneous fleets is uncertain and there could be substantial shifts. For example, all-electric car air quality impacts would shift to the electricity generation sector which, in general, can vary greatly depending on the design of the electricity system [2]. The air quality impacts of current generation biofuels is regionally dependent and unclear. This was illustrated by research on ethanol versus gasoline in the U.S., which concluded that health impacts from ambient ozone varied across the country where it was sometimes positive and sometimes negative [3]. Therefore, my key conclusion about how the three parts of my thesis research relate is that the air quality and traffic safety research articles are risk-specific health and CO2 emission case studies that are essential elements to building the capacity to develop Integrated Assessment models of public risks due to passenger cars. 94 5.2 RESEARCH RELATIONSHIP TO CURRENT WORKING HYPOTHESES IN THE FIELD OF STUDY AS REFLECTED IN THE LITERATURE 5.2.1 AIR QUALITY AND CO2 REDUCTION IN TRANSPORTATION Approximately one decade ago, the working hypothesis in research on air quality and CO2 reduction was captured in the \u00E2\u0080\u009Cancillary benefits\u00E2\u0080\u009D literature. The prevailing research hypothesis was that reducing CO2 from transportation (and other sectors) would yield simultaneous benefits in air quality improvements, and no exceptions to this hypothesis were noted [4-6]. IPCC even sponsored a workshop to examine this hypothesis8 titled \u00E2\u0080\u009CWorkshop on Assessing the Ancillary Benefits and Costs of Greenhouse Gas Mitigation Strategies\u00E2\u0080\u009D which, by its title, implied that only benefits could be realized. In its 2001 Working Group III report, IPPC described these ancillary benefits without mentioning the possibility of disbenefits [7]. Our research on diesel substitution for gasoline cars in the UK has helped inform the evolution of the \u00E2\u0080\u009Cancillary benefits\u00E2\u0080\u009D research towards a more careful approach which considers that both benefits and disbenefits might be possible. While our research has caught some media attention, implying that tradeoffs were surprising [8], research that identified potential tradeoffs between CO2 reduction and air quality predates the ancillary benefits literature, particularly for biomass utilization [9]. Currently the literature in this area appears to have a more cautious approach such as the aforementioned potential ambient ozone disbenefits from ethanol-powered cars [3]. In its 2007 Working Group III report IPCC specifically acknowledged the potential air quality disbenefits of diesel cars [10]. 5.2.2 TRAFFIC SAFETY AND CO2 REDUCTION IN TRANSPORTATION Currently the prevailing research indicates there is a relatively weak link between traffic safety and CO2 from passenger cars on the basis that vehicle mass has uncertain impacts on traffic safety (see discussion in Section 1.4.2 and Section 3.1). However, in my view, this conclusion is strongly influenced by two factors: (1) a lack of clarity of what is meant by \u00E2\u0080\u009Ctraffic safety,\u00E2\u0080\u009D and (2) incomplete recognition of the changes in the mix of road users that researchers and policymakers alike are aiming to achieve. 8 See http://www.oecd.org/document/59/0,3343,en_2649_34361_1914811_1_1_1_1,00.html 95 \u00E2\u0080\u009CTraffic safety9\u00E2\u0080\u009D and related terms as implied by researchers is often oversimplified to a degree that traffic safety agencies cannot accept in practice. Some examples were previously described in Sections 1.4.2 and 3.1. Another example is a recently published article in a journal issue on \u00E2\u0080\u009CThe interaction of environmental and traffic safety policies\u00E2\u0080\u009D [11], one article summarizes the research on \u00E2\u0080\u009Chighway safety\u00E2\u0080\u009D and draws conclusions about \u00E2\u0080\u009Cthe safety issue\u00E2\u0080\u009D citing only findings on fatalities and largely based on two-car collisions [12]. Traffic safety encompasses risk of both fatality and injury for multiple vehicle types, road users, injury rates, and crash events (see Figures 4.1 and 4.2 [13]). Moreover, traffic safety risks can be quantified as risk ratios [14], conditional risks [15], or absolute risks (annual fatality count is a common measure of absolute risk) [16]. The UK DFT has specific traffic safety goals that include reductions in total KSI, total children KSI, the slight injury rate, and vulnerable road user casualty reductions such as bicyclists, motorcyclists, and pedestrians [13]. As such, concluding that traffic safety and passenger car CO2 are weakly linked largely based on inferences from two-car fatalities is not congruent with the needs of traffic safety authorities. Researchers and policymakers alike are aiming to dramatically change the mix of users that will share the roads with passenger cars. From a land use and public health perspective, researchers have identified steep growth in walking and bicycling (active transport) as a high priority [17]. Again using the DFT as an example, traffic safety authorities have recognized the need to improve road safety of multiple users that are expected to change over time due to CO2 reduction policies [18]. \u00E2\u0080\u009CA move towards a low carbon economy could also lead to a rise in the number of vulnerable road users. Potential increases in walking, cycling and use of powered two-wheelers mean that protection of these categories of road user should continue to be a priority in terms of vehicle engineering developments. In addition, an increase in car sharing could lead to an increase in rear seat passenger numbers, who do not currently enjoy equivalent safety protection to those in front seats.\u00E2\u0080\u009D In my view, there remains a gap between research on traffic safety and passenger cars CO2 reduction where research has yet to keep up with the complexity of current and future traffic safety needs that public agencies are mandated to address. The scope of our research (Chapter 9 The definition used in this thesis is \u00E2\u0080\u009Crelating to human health impacts as a result of vehicles operating on public roadways\u00E2\u0080\u009D as per the Glossary. 96 4) was designed to make progress towards closing this gap to help meet the needs of policymakers. 5.3 STRENGTHS AND WEAKNESSES OF THIS THESIS 5.3.1 STRENGTHS In my view there are two key strengths of this thesis research: (1) an emphasis on actual changes as compared to completely hypothetical scenarios, and (2) methodological strength that raises the bar on the ability to replicate integrated policy research results. The air quality and traffic safety studies both were developed with the goal to emphasize actual changes using the UK as a case study, while minimizing the use of hypothetical changes. While the use of hypothetical changes or scenarios is essential in policy research [19], deliberate choices were made to emphasize actual changes. In the air quality study, changes in emissions, ambient concentrations, and health outcomes were based on actual growth of diesel cars through the year 2005, with forecasted growth through the year 2020. While this is a long forecast period, all air quality mortality estimates were based on industry diesel projections just until the year 2009 when Euro V emission standards were expected to be in full compliance; forecasts from 2010 to 2020 only factored into the CO2 estimates. The traffic safety research was also developed to increase the emphasis on actual changes. An important choice was in the absolute risk analysis to use empirical fatality rates (car-pedestrian, single-car, and two-car) over the years 1999 to 2005 for realistic scenarios in how the fleet mix could have been changed. While we could have chosen to examine major changes in vehicle composition such as larger on-road shares of lighter cars and complete elimination of heavier cars, this would have drifted further from a realistic scenario. Instead, we chose to examine the elimination of new heavier cars (roughly 1,600 kg) combined with realistic increased shares of lighter cars beginning in the year 2000. In practice, policymakers have no way to simply eliminating all heavier cars (or any cars for that matter) in a short time frame because cars remain on the road until scrapped through normal retirement or by accelerated scrappage policies [20]. Projecting traffic safety outcomes into the future due to changes in vehicle design is also subject to much legitimate criticism because safety technology has been shown to improve substantially over time periods of as little as 3 years [21], thus such projections are weaker as compared to our approach. 97 The ability to replicate policy research has long been identified as a challenge [19]. A methodological strength of this thesis research is that analysis assumptions, methods, and data are sufficiently documented such that independent researchers would have a fair chance at replicating and validating the results. For the air quality research, careful attention was made to document basic vehicle counts, vehicle types, emission factors, ambient concentration estimates, and health risk coefficients. Similarly for the traffic safety analysis, careful attention was paid to document data sources and analytical methods. Although replication would be more challenging for the traffic safety based on the large database sizes (see Table B2), data sorting and reduction procedures were documented with sufficient detail to enable replication. It is my experience that other similar research is not as thoroughly documented to enable replication; one example is the aforementioned integrated study that examined electricity system design including CO2 and air quality [2]. 5.3.2 WEAKNESSES Chapters 2, 3, and 4 each have a number of weaknesses and limitations that are discussed in the respective chapters. I will highlight a primary weakness of each, and suggest how these might be overcome in the future. A principal weakness of the air quality study in Chapter 3 was the simplistic treatment of changes in CO2 emissions resulting from diesel substitution10. Annual changes in CO2 were estimated assuming average CO2/km emission rates based on certified emission factors [22], multiplied by assumed average annual distance traveled. This method overlooked factors that were known to be relevant, particularly VKT rebound effects [23]. In fact, since publication of Chapter 3, research on diesel substitution in the UK has concluded that CO2 savings were likely small due to the combined effects of consumer choice of larger cars, VKT rebound effects, and driver behavior (mainly speed) [24]. Even in the absence of specific research to make such estimates, this limitation could have been better overcome by employing quantitative techniques [19] to estimate a range of changes in annual CO2. This could have been done by using general estimates of rebound effects [25] and a deterministic or probabilistic sensitivity analysis by making reasonable assumptions about upsizing and driver behavior. 10 The two anonymous peer reviewers for the Chapter 2 articles both emphasized the weakness of the global warming benefits of diesel cars. One emphasized the annual travel distance issues, while the other emphasized black carbon effects. 98 A principal weakness of the traffic safety study in Chapter 4 is the absence of vehicle mass in the UK Data Archive data sets. Because curb mass (and its correlates) was the primary intermediate variable to examine changes in CO2/km and various traffic safety risks, this represented a substantial limitation. We were able to make adequate estimates of curb mass in two ways. Using the JATO data we found that engine size was a satisfactory proxy for curb mass (it is noted that our statistical model and coefficients are almost identical to those published by others \u00E2\u0080\u0093 see Figure 1 of [15]). For the \u00E2\u0080\u009Cfirst law\u00E2\u0080\u009D RR analysis, we were able to find curb mass for individual cars based on make, model, engine size, and body type using independent online databases [26-28]. Given the continued relevance of curb mass as a determinant of fuel consumption, CO2 emissions, and traffic safety, it would be useful if curb mass data were recorded for all vehicles involved in police-reported collisions in the UK; this is actually the case in the U.S. where the FARS database includes curb mass [14]. The Integrated Assessment methodology described in Chapter 2 is inherently limited by focusing primarily on Integrated Assessment of multiple risks for passenger cars. A truly comprehensive Integrated Assessment would explore the best strategy for meeting transportation needs within the context of all relevant local, social, and physical conditions [29]. Additional domains that are relevant include organizational and institutional domains[30], interactions with other energy- intensive sectors [31], integration of transportation and land use planning[32], and integration of passenger car travel with other travel modes [17]. In the larger picture, the integration of climate change with other drivers of global change is relevant [33]. Another limitation of this study is that it focuses on risks and does not examine the positive attributes of consumer choice [34]. As an example, the travel range for a single refueling of a gasoline-powered car is much greater than an all-electric car, which is a positive attribute strongly valued by consumers. Thus the relative benefits of fuels and technologies to consumers are excluded [35]. 5.4 SIGNIFICANCE AND POTENTIAL APPLICATIONS OF THIS THESIS The significance of this thesis is to demonstrate the potential public benefits (or, from another view, avoided consequences) of performing Integrated Assessment of multiple risks in the development of climate mitigation policies for passenger cars. The significance stems from the fact that public policymakers globally are currently tasked to develop these policies [1, 10, 36, 37]. Using the UK as a case study enhances the significance of this research because CO2 reduction policies for passenger cars have been implemented for many years. The first such policy that affected the UK was the manufacturers\u00E2\u0080\u0099 voluntary agreement to reduce fleet average 99 CO2/km for new sales in Europe from 185 gCO2/km in 1995 to 140 gCO2/km in 2008 [38], followed by the VED tax regime in 2001 [39], and the company car tax regime in 2002 [40]. The potential application of this thesis research is in the analysis of real world policies. In the UK, government agencies have been tasked analyze the effectiveness of its policies. Examples include the DFT\u00E2\u0080\u0099s analyses of the VED tax regime done in 2003 which included quantitative analysis of CO2 and air pollution emissions as factors in consumer decisions [39]. The UK Customs and Revenue department also performed similar analyses of the company car tax regime [40]. A local example is the London Transport Authority\u00E2\u0080\u0099s assessment of its transportation policy that focused on congestion control and the side effects on road safety (specifically identifying motorcycles, pedestrians, and bicyclists), CO2, air quality, and noise [41]. An international example is provided by analyses of transportation CO2 policies by the European Commission [42]. Non-government organizations could also utilize this research in its independent assessments of government policies. For example, the European Federation for Transport and Environment regularly assesses the effectiveness of government policies in Europe. This organization assessed proposed fuel economy standards with respect to traffic safety [43], and also recently released report on the effectiveness of the manufacturers\u00E2\u0080\u0099 CO2 agreement [44]. Lastly, researchers can build upon this thesis research to advance the analysis of real world policies. Integrated Assessment modeling continues to be a research priority [29], and the recent analysis of CO2 emissions from diesel cars is an example of the insights that can be revealed from such research [24]. 5.5 RECOMMENDATIONS FOR FUTURE RESEARCH 5.5.1 MITIGATING AIR QUALITY AND HEALTH IMPACTS FROM PRE-EURO V DIESEL CARS IN EUROPE The steep growth of pre-Euro V diesel cars (i.e. where particle filters were not required, and NOx limits differed greatly) experienced in the UK has occurred elsewhere in Western Europe where diesel registrations increased from 14% of all registrations in 1990 to 46% in 2009 [45]. It is likely that disbenefits, such as we estimated for the UK, are occurring in many other European cities. Evidence from the UK and elsewhere shows that diesel cars tend to remain in use longer than their gasoline counterparts [23], and that diesels also fail tailpipe emission tests at higher rates (see Table A5). For these reasons, it is likely that the trajectory of public health impacts from ambient particles (both primary and secondary particles) from pre-Euro V diesels will continue to persist, if not grow. Research into technology and policies to induce retrofit of particle filter technology and/or early retirement of higher polluting diesels is warranted. 100 5.5.2 TECHNOLOGY AND POLICY ASSESSMENT OF IMPOSING A 1,600 KG CAP ON PASSENGER CAR MASS We have contributed to the body of evidence that shows reducing vehicle mass can be accomplished while meeting traffic safety goals, but that it matters how on-road fleets are transitioned to lower vehicle mass. Specifically, Chapter 4 indicates that reducing the mass of the largest vehicles and narrowing the range and variation in vehicle mass of the on-road fleet is the most effective method to improve both absolute and relative traffic safety risks, while moving toward a less carbon-intensive fleet. Capping the mass of any new car is a method of achieving this, and current engineering research indicates that use of lightweight materials could enable retention of large size vehicles which are highly sought by consumers while keeping mass within a 1,600 kg limit [46-48]. Moreover, mass is not an attribute included in lists of consumer preferences [34, 49], and the impact of adopting lightweight technologies on vehicle price are estimated to be less than the current premium paid for hybrids [46-48]. Further research is needed to more definitively quantify changes in public health, lifecycle greenhouse gas emissions, and policy design to implement an upper limit on the mass of passenger cars. 5.5.3 TRAFFIC SAFETY RISKS DUE TO VARIATION IN VEHICLE MASS AND SIZE: DO CONSUMERS UNDERSTAND AND INTERNALIZE THE RELATIVE RISKS OF PURCHASING SMALLER, LIGHTER VEHICLES? Externality cost research commonly assumes that consumers internalize differential traffic safety risks when purchasing smaller, lighter passenger cars [50]. This assumption contributes to economic externality research findings that questions public welfare loss associated with car fleets with bimodal distributions in terms of mass and it correlates [51]. I could not locate any empirical validation of the assumption that consumers internalize the risk differential, and it appears to be rooted in expert opinion [52]. However, even the collision safety rating system used for informing consumers provides rankings within vehicle classes rather than across all vehicles. To quote the European NCAP [53]: \u00E2\u0080\u009CEuro NCAP\u00E2\u0080\u0099s frontal impact test simulates a car crashing into another of similar mass and structure. In real life, when two cars collide the vehicle with the higher mass has an advantage over the lighter one. Generally speaking, vehicles with higher structures tend to fare better in accidents than those with lower structures. Therefore, ratings are comparable only between cars of similar mass and with broadly similar structures. Euro NCAP groups cars into the following structural categories: passenger car, MPV, off-roader, roadster and 101 pickup. Within each of those categories, cars which are within 150 kg of one another are considered comparable.\u00E2\u0080\u009D Research is needed to examine this assumption that consumers are aware and have internalized (or voluntarily accepted) the differential risk, and explore alternatives for addressing the gap between subjective risk perceptions and objective risk estimates. 5.5.4 PUBLIC HEALTH RISKS FROM ENVIRONMENTAL NOISE AND HYBRID TECHNOLOGY Hybrid cars, whether grid independent or plug-in, produce substantially less environmental noise than conventional, internal combustion technology with approximately 2.5 dBA less for 2006 UK models as shown in Figure 2.5. Recent estimates of environmental noise indicate it is a growing public health risk of roughly similar order to traffic safety and air quality [54, 55]. While environmental noise from traffic is identified as a risk factor for cardiovascular disease [56], questions remain as to whether noise effects have been separated from other factors such as air pollution [57]. However, in terms of traffic safety, at least during the period of transition to a quieter fleet, pedestrians and bicyclists might be subjected to increased risk of traffic collisions with noiseless motor vehicles. These potential risks are also in need of quantification as well, and could inform the design of cars to compensate for noiseless operation such as incorporating awareness-level sound emissions into the vehicle design [58]. Subject to rigorous consideration of confounding factors and technology adaptations, the potential public health benefit through noise reduction from uptake of hybrid cars is recommended for further research. 5.5.5 DEVELOPMENT OF QUANTITATIVE, INTEGRATED POLICY MODELS FOR PASSENGER CAR CHOICE AND RISKS Policies are in place in industrialized countries to induce a transition from current fleets dominated by conventional gasoline and diesel passenger cars, to more heterogeneous fleets comprised of alternative fuels (e.g. biofuels) and technologies (e.g., plug-in hybrids) [1]. With this transition, the set of relevant risks, system boundaries, and relationships between policies and risks grow in size and complexity necessitating expansion of policy modeling capability to estimate risks. This research recommendation is essentially to move forward with actual, working quantitative models using the framework introduced in Chapter 2. 102 5.6 REFERENCES 1. Fulton, L., P. Cazzola, and F. 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Armstrong, and B. Brunekreef, \"The joint association of air pollution and noise from road traffic with cardiovascular mortality in a cohort study\". Journal of Occupational and Environmental Medicine, 2009. 66: p. 243-250. 58. Shim, S., A. Miller, and A. Marsh, \"The Emergence of New types of Powertrain and the Impact on the Insurance Industry\". 2009, KART/KIDI, Thatcham (Motor Insurance Repair Research Centre). 107 APPENDIX A - SUPPORTING INFORMATION FOR AIR-QUALITY IMPACTS OF CLIMATE MITIGATION: UK POLICY AND PASSENGER VEHICLE CHOICE 108 This Appendix contains Supporting Information published as part of Chapter 2 in Environmental Science and Technology. A.1 INTRODUCTION Table A1 illustrates the cost of ownership of matched pairs of petrol and diesel cars for model year 2005. Diesels have a higher purchase price but generally lower operating costs. Table A1 Comparison of the cost of ownership for matched pairs of petrol and diesel 2005 car models (all models shown meet Euro IV Emission Standards) Manufacturer and Model Engine and Transmission gCO2 per km VED CO2 Band VED Cost Fuel Liter per 100 km Purchase Price1 Fuel Cost 15,800 km Insur- ance2 Company Car CO2 Tax %3 1st Year Company Car Tax 22% Bracket) 1st Year Company Car Tax (40% Bracket) Ford Mondeo 4-door LX petrol 2.0 Duratec HE Saloon manual 187 F \u00C2\u00A3165 7.5 \u00C2\u00A316,067 \u00C2\u00A3955 \u00C2\u00A3910 23% \u00C2\u00A3821 \u00C2\u00A31,493 Ford Mondeo 4-door LX diesel 2.0 Duratorq TDCi (115PS) manual 159 D \u00C2\u00A3135 5.8 \u00C2\u00A316,594 \u00C2\u00A3750 \u00C2\u00A3910 17% \u00C2\u00A3626 \u00C2\u00A31,138 diesel \u00E2\u0080\u0093 petrol difference -15% -\u00C2\u00A330 23% \u00C2\u00A3527 -\u00C2\u00A3204 \u00C2\u00A30 -6% -\u00C2\u00A3196 -\u00C2\u00A3356 Ford Focus 4-door Ghia petrol 2.0i Duratec 16V Manual 173 E \u00C2\u00A3150 6.9 \u00C2\u00A315,803 \u00C2\u00A3883 \u00C2\u00A3781 20% \u00C2\u00A3702 \u00C2\u00A31,276 Ford Focus 4-door Ghia diesel 2.0i 16V TDCi (136PS) Manual 145 C \u00C2\u00A3115 5.2 \u00C2\u00A317,082 \u00C2\u00A3684 \u00C2\u00A3843 15% \u00C2\u00A3568 \u00C2\u00A31,032 diesel \u00E2\u0080\u0093 petrol difference -16% -\u00C2\u00A335 24% \u00C2\u00A31,280 -\u00C2\u00A3199 \u00C2\u00A363 -5% -\u00C2\u00A3134 -\u00C2\u00A3244 Vauxhaul Corsa petrol 1.4i SXi 5 Door 16V Manual 142 C \u00C2\u00A3115 5.7 \u00C2\u00A313,266 \u00C2\u00A3725 \u00C2\u00A3642 15% \u00C2\u00A3442 \u00C2\u00A3803 Vauxhaul Corsa diesel 1.3CDTi SXi 5 Door 16V Manual 115 B \u00C2\u00A385 4.2 \u00C2\u00A313,699 \u00C2\u00A3543 \u00C2\u00A3597 15% \u00C2\u00A3455 \u00C2\u00A3827 diesel \u00E2\u0080\u0093 petrol difference -19% -\u00C2\u00A330 -27% \u00C2\u00A3433 -\u00C2\u00A3182 -\u00C2\u00A345 0% \u00C2\u00A313 \u00C2\u00A324 1 Includes 17% value added tax (VAT), \u00C2\u00A325 for plates, and \u00C2\u00A338 for first registration cost. 2 Per person annual insurance cost for London, 2 drivers aged 40, no accident history, \u00C2\u00A3250 deductible, private use, nominal theft protection. 3 Beginning January 1, 2006 Euro IV diesel cars incurred a 3% surcharge, which is not included here. Note that lower medium segment and larger diesels, even with the 3% surcharge, still pay less company car tax. The company car benefit-in-kind tax bottoms out at 15% regardless of how low the CO2/km, and tops out at 35% regardless of how high the CO2/km or the diesel surcharge. 109 A.2 METHODS Here we present additional tables of estimated results as cited in the main article and some additional commentary on the study methods. Estimates for scrapped vehicles were made using the deregistration model shown in Figure A1 [1]. Figure A1 Model for estimating annual number of scrapped vehicles, based on UK de- registration statistics. The annual rate of vehicles scrapped peaks at 10.7%, 14 years after the year the vehicle was initially registered. The area under the curve through 20 years is 85.7%. Reductions in CO2 due to additional diesels are estimated using UK fleet wide average differences between petrol and diesel passenger vehicles shown in Figure A2. Actual differences in gCO2/km are applied through the year 2005 based on industry data which use conventional greenhouse gases (CO2, CH4, N2O) [2]. Global warming effects of black carbon are under review in the UK, but are not included in the CO2-emission factors at this time [3]. For the years 2006-2020, we estimated the differential by assuming a declining difference in the CO2-emission factor by 5% per year until 2015, and thereafter a constant difference of 0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 10% 11% 12% 0 5 10 15 20 25 Age (years) de re gi st er ed v eh ic le s as a % o f t ot al re gi st ra tio ns 110 4.4 gCO2/km as shown in Figure A2. The basis for this assumption for the years 2006-2020 is: \u00EF\u0082\u00B7 The CO2 advantage of diesel passenger vehicles increased rapidly through 2001 due to technological improvements achieved by auto manufacturers [4]. \u00EF\u0082\u00B7 In recent years more gains have been made in petrol vehicle design, and consumer preferences for larger and heavier diesel cars have resulted in a declining CO2 advantage for diesels based on fleet averages [2]. \u00EF\u0082\u00B7 The tightening of emission controls on diesel vehicles to harmonize with petrol vehicles is another factor that will likely close the gap between diesel and petrol vehicles, which is reflected in our projections. The addition of particle filter technology is expected to result in a fuel economy4 penalty of 3-5% [5, 6]. 4 In this study the term \u00E2\u0080\u009Cfuel economy\u00E2\u0080\u009D refers only to liters per kilometer, not energy efficiency. 111 Figure A2 Fleet average difference in CO2 emission factors (gCO2/km) between petrol and diesel passenger vehicles in the UK from 1997-2020. 0 2 4 6 8 10 12 14 16 18 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 Year P et ro l m in us D ie se l u ni t C O 2 em is si on s (g ra m s pe r ki lo m et re ) actual g/km CO2 projected g/km CO2 112 Fuel savings due to additional diesels were estimated as equivalent barrels (bbl) of crude oil saved. This was calculated directly from the fleet average differential CO2/km, then using emission factors (2,504 gCO2/L petrol and 2,763 gCO2/L diesel5), with a 25% additional energy savings expected in the upstream refining of diesel relative to petrol. Barrels of oil saved per unit quantity of additional diesels as shown in Figure 2.4 are based on the average over the vehicle lifetime (approximately 13 years and 205,000 km). Total barrels of oil saved over the 2001-2020 study period as shown in Figure 2.4 are not necessarily the unit fuel savings multiplied by the number of additional diesels, because the 20-year analysis period does not include the lifetime of every car, particularly post-Euro IV models (e.g., consider that some post-Euro IV cars enter the vehicle stock in our model in 2019, only 1 year before the end of the analysis period). Emissions estimates for common air contaminants are made by applying standard vehicle emission factors as derived by the UK National Atmospheric Emission Inventory (NAEI), which are shown in Table A2 along with the applicable emission limit. Euro III emission factors applied from 2001-2005 and Euro IV from 2006-2008. Beginning in 2009, \u00E2\u0080\u009Cpost-Euro IV\u00E2\u0080\u009D emissions of PM10 are assumed to be equal for diesel and petrol based on the European Commissions proposal to harmonize emission standards. Assumptions were made regarding early adoption of Euro IV vehicles registered in the UK prior to 2006 at 33% in 2004 and 66% in 2005. We assumed a proportion of kilometers driven on road types typical for the average of all passenger cars: 41% rural, 41% urban, and 18% motorway [7]. The calculation of intake fraction described in the Chapter 2 is based on a population of 60 million, an average breathing rate of 12.2 m3 per person-day, and the ratio of 0.0737 \u00EF\u0081\u00ADg/m3 change in annual mean ambient PM10 concentration per 1 kilo-tonne change in annual PM10 emissions [8, 9]. 5 These emission factors are CO2 equivalent that include CO2, CH4, and N2O tailpipe emissions. 113 Table A2 European Union \u00E2\u0080\u009CEuro\u00E2\u0080\u009D emission limits [10] and weighted average emission factors [11] for passenger vehicles in grams per kilometer (g/km)6. Standard (Date) Fuel Limit or Factor CO g/km HC7 g/km NOx8 g/km HC+ NOx g/km PM109 g/km Benzene g/km 1,3 Butadiene g/km limit 2.30 0.20 0.15 no limit no limit no limit no limit Petrol factor 0.730 0.205 0.246 0.451 0.002 0.00330 0.00026 limit 0.64 no limit 0.50 0.56 0.05 no limit no limit Euro III (2001) Diesel factor 0.085 0.029 0.582 0.611 0.044 0.00073 0.00036 limit 1.00 0.10 0.08 no limit no limit no limit no limit Petrol factor 0.487 0.196 0.128 0.324 0.002 0.00291 0.00020 limit 0.50 no limit 0.25 0.30 0.025 no limit no limit Euro IV (2006) Diesel factor 0.085 0.027 0.291 0.318 0.022 0.00067 0.00033 Petrol limit 1.0 0.075 0.06 none 0.00510 no limit no limit Proposed (2009) Diesel limit 0.5 no limit 0.20 0.25 0.005 no limit no limit 6 CO= carbon monoxide, HC= hydrocarbons, NOx= nitrogen oxides, PM10= 10 micron particulate matter. 7 Petrol HC emission factors include evaporative emissions applying NAEI equations for diurnal, hot soak, and running loss emissions. Equation inputs: diurnal rise = 9oC, maximum daily average = 15oC, annual mean = 11oC, and Reid Vapour Pressure = 90 kPa. Carbon canister control assumed for all vehicles. 8 NOx and PM10 emission factors include cold start emissions (grams per trip) averaged into the emission factor based on UK average 8.4 kilometer per trip, and assuming half of all starts are cold starts. 9 Emission standards are actually stated in terms of PM10. However as described in the Methods section of the paper, vehicles emit approximately 100% PM10. 10 Applicable only to petrol vehicles with lean-burn, direct-ignition engines. 114 We quantified morbidity by estimating respiratory and cardiovascular hospitalizations using rate coefficients adopted by COMEAP. Changes in both are estimated at 0.8% per 10 \u00EF\u0081\u00ADg/m3 change in PM10 applied to the entire UK population. We use a baseline rate for respiratory hospitalizations of 925 per 100,000 (ICD10 codes J00-J99, 2003), and 632 per 100,000 (I20- I52, 2003) for cardiovascular [12]. Baseline mortality estimates were the actual 2003 rates (www.gad.gov.uk). The baseline all cause mortality for all ages was 995 per 100,000 (used for the low-mortality estimate). For the central- and high-mortality estimates, we used a baseline mortality of 1,424 per 100,000 for ages 30 and older. The UK population was assumed to be a constant 60 million in our analysis, and a constant 37.7 million for those aged 30 and older. A more complete mortality and morbidity analysis would include additional outcomes such as acute effects, exacerbation of asthma, and chronic exposure-mortality for people under the age of 30 year [13]. However, estimating this additional suite of health outcomes would not, in our view, contribute to the study findings and could distract attention from other parts of the analysis. A.3 RESULTS Here we present additional tables to support material included in the Results section of Chapter 2. During a given year, the number of additional diesels is the sum of new registrations, plus those vehicles remaining on the road from previous years. Annual time series for new vehicle registrations (total vehicles, number of diesel vehicles, and number of petrol vehicles) through 2005 are based on industry statistics [2]. Projections from 2006-2007 are based on industry forecasts. We estimate total new passenger vehicle registrations from 2008-2020 based on a 1% (compounded) annual growth rate. We estimate diesel market share for new vehicles to increase 2% points annually from 2008-2010, and with no further growth from 2011-2020. The rationale for no further growth at that time is based on industry and government expectations [14], which also seems feasible because compliance with proposed Euro V emission standards to install particulate traps are expected to increase the cost of diesels substantially [10]. 115 Table A3 provides the estimated changes in common air contaminants over the 20-year study period. Table A4 provides the estimated changes in health outcomes. Table A3 Total estimated changes in emissions due to additional diesels 2001-2020. Intervals are defined by dates when new EU emission standards apply as shown in Figure 2.3. Diesels emit higher amounts of PM10, NOx, and 1,3 butadiene but lower amounts of CO, HC, benzene, and CO2. PM10 (kt) NOx (kt) CO (kt) HC (kt) CO2 (Mt) Benzene (kt) 1,3 butadiene (kt) Zone A : Euro III 5.7 45 -87 -24 -1.6 -0.35 0.013 Zone B : Euro IV 6.0 47 -117 -49 -2.1 -0.65 0.038 Zone C: post Euro-IV 0 0 0 0 -3.3 0 0 Total 2001-2020 11.7 93 -204 -73 -7.0 -1.00 0.051 Annual average 0.58 4.6 -10 -3.7 -0.35 -0.05 0.003 116 Table A4 Summary morbidity and mortality results. Hospitalizations Mortality Respiratory Cardio-vascular Central11 total Central per 106 Diesel Vehicles Central per Mt CO2 Reduced Low12 Total High13 Total Zone A: Euro III 190 130 910 1,320 570 190 2,950 Zone B: Euro IV 190 130 940 590 460 200 3,060 Zone C: post Euro-IV 0 0 0 0 0 0 0 Total 2001- 2020 380 260 1,850 200 270 390 6,010 Annual average 19 13 90 200 260 20 300 A.4 DISCUSSION Here we discuss the uncertainties and limitations associated with our methods and assumptions for each step of the analysis, and how these uncertainties and limitations are likely to have over- or underestimated emissions, fuel savings, exposure, and health outcome estimates. The issues discussed here are: 1) number and emission class of additional diesels, 2) annual travel distance, 3) spatial distribution of vehicles, 4) PM10 emissions and ambient concentrations, and 5) health effects. 11 Concentration response coefficient of 4% \u00E2\u0088\u0086mortality per \u00E2\u0088\u008610 \u00C2\u00B5g/m3 ambient PM10 (>age 30). 12 Concentration response coefficient of 0.75% \u00E2\u0088\u0086 mortality per \u00E2\u0088\u008610 \u00C2\u00B5g/m3 ambient PM10 (all ages). 13 Concentration response coefficient of 13% \u00E2\u0088\u0086 mortality per \u00E2\u0088\u008610 \u00C2\u00B5g/m3 ambient PM10 (>age 30). 117 A.4.1 Number and Emission Class of Additional Diesels The number of diesels assumed to be in each EU emission class affects the exposure and health impact estimates. As stated in the methods section, we assumed all additional diesels registered through 2003 were Euro III, declining to 67% in 2004, 34% in 2005, and 0% (i.e., 100% Euro IV) through 2008. Particulate traps (i.e., filters) on diesels are required to equalize emissions of PM10 with petrol (but not necessarily other emissions) [15, 16]. No diesel vehicles were assumed to have traps. In reality, there are three exceptions to these assumptions which we comment as follows [17]: Euro II vehicles in 2001-2002. PM10 emissions from Euro II diesel vehicles average 0.08 g/km (excluding cold start emissions), or about double the Euro III emission rate and quadruple the Euro IV rate. Our analysis assumes that no additional diesels were Euro II. However, in 2001 there were 24 manufacturers offering 131 different Euro II diesel models, compared to 26 manufacturers offering 328 Euro III models. In 2002 there were 10 manufacturers offering 31 different Euro II diesel models, compared to 31 manufacturers offering 459 Euro III models. Moreover, Euro II diesels would have been cheaper than Euro III models. \u00EF\u0082\u00B7 Early adopted Euro IV with particulate traps prior to 2009. We assume no early adoption of diesel vehicles equipped with particulate traps in our analysis. In 2004, 3 of 33 manufacturers (20 of 910 different diesel models) offering diesels equipped with traps and these were higher priced vehicles. There are currently no known projections for substantial increases in early adoption of diesel vehicles with particulate traps. \u00EF\u0082\u00B7 Sales of alternative fuel vehicles. The CO2 tax regime may have been at least partly responsible for sales of electric, electric hybrid, or liquefied petroleum gas (LPG) vehicles. However, these vehicles comprised only 0.3% of new registrations or less each year from 2001-2005 and there are currently no known projections for substantial increases. Our projected CO2/km emission factors (Figure A2) were developed in consideration that petrol hybrid vehicles would erode the CO2/km advantage of diesel over time. In our assessment, the assumptions regarding the number and emission class of diesel vehicles are more likely to underestimate rather than overestimate the PM10 emission estimates. 118 A.4.2 Annual Kilometers Travelled There are three main phenomena that affect annual travel distance of passenger cars: (1) the type of driver \u00E2\u0080\u0093 e.g., a high-travel distance driver versus a low-travel distance driver, (2) changes in vehicle travel distance with age, and (3) an economic rebound effect. Schipper and colleagues [18] have described how diesel vehicles in Europe have been used differently than petrol vehicles, including higher annual travel distance. For example diesels averaged 25,000 km annually compared to 14,000 km petrol in the UK for 1995 [18]. Schipper also described a saturation effect where \u00E2\u0080\u009Cas diesels gain market share in each country, the number of naturally high mileage drivers switching to diesel eventually is exhausted, and the later switchers are more likely to represent persons closer to average in their travel patterns.\u00E2\u0080\u009D Our analysis assumptions are consistent with this saturation effect for all diesel growth. To examine this assumption, we examined data from the UK National Travel Survey (NTS) in Figure A3 [19]. Plotted in this figure are the average annual travel distance values for the first two years14 of ownership using data from NTS survey years 1998-2004. The data show that average annual travel distance for newly registered vehicles has been declining for both petrol and diesel (a trend that also appears in the average vehicle stock data reported by the UK Department for Transport). Moreover, the average difference between annual travel distance for diesel and petrol has been declining. For the years 1998-2000 (i.e., prior to the CO2 policies, which had been announced in 1999 and implemented in 2001-02) the average difference for the data plotted in Figure A3 was 13,200 kilometers, compared to a difference of 11,600 kilometers for 2001-04 (i.e., 12% less). These data provide evidence of a trend towards this saturation effect. It should be noted that these data provide only indirect insights with limited sample sizes; a direct examination of this assumption would be obtain data for newly registered diesel cars for those owners that previously had purchased petrol cars, and then compare the annual travel distance, but identifying such owners is not possible with the NTS data. 14 Data is averaged over the first two years for two reasons: (1) in consideration of the small sample sizes, it provides a more robust average than a single year, and (2) it minimizes capturing changes in annual travel distance due to vehicle age (see Figure A4) or change of ownership (e.g., company cars are sold to non-company car drivers every 2.6 years on the average). 119 Figure A3 Average annual travel distance for the first two years of ownership for diesel- fuelled and petrol-fuelled passenger cars. Data are from the UK National Travel Survey. Sample sizes for individual years range from 12 to 563. Both company and privately-owned vehicles are included. Average annual kilometers for the first two years of ownership 0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 1998 1999 2000 2001 2002 2003 2004 NTS survey year ki lo m et er s pe r y ea r diesel petrol Linear (diesel) Linear (petrol) NTS = UK national travel survey Data on the effect of vehicle age on annual travel distance are presented in Figure A4. These data show that both diesel and petrol vehicles are driven less as the vehicle ages. Since we use the annual travel distance of the average vehicle stock in the UK, which includes old cars and new cars, then the effect of travel distance with age is averaged into our estimate. The higher travel distance for diesel shown in Figure A4 is likely the effect of different drivers \u00E2\u0080\u0093 i.e., high travel distance or company car drivers. Again, our assumption is that the driver types that switch from petrol to diesel represent the average driver in terms of annual travel distance. The third phenomenon is the economic rebound effect (i.e., consumers driving more kilometers annually due to reduced marginal cost of driving), which we do not include in our baseline analysis results as described in the main article. While actual, UK-specific fuel price differences could not induce a rebound effect (because fuel price ratio has not changed; see Figure 2.2), the fuel economy advantage of diesels could. UK rebound elasticities are not available, to our knowledge, and developing an econometric model of the rebound effect would be a research effort that would warrant a separate study. However, as a sensitivity analysis, we consider results from a review of rebound effect on vehicle kilometers travelled 120 (VMT) in the U.S. [20]. This review found that long-term, direct rebound effect on VMT to range from 10-30% (fuel cost per kilometer elasticities). Fuel-economy savings between diesel and petrol are proportional to the CO2/km savings, which vary year to year in our analysis (at least until year 2015). For example, a fuel savings of 0.36 liter/100km (equivalent to 10 gCO2/km diesel advantage \u00E2\u0080\u0093 see Figure A2) would be realized for a driver switching from petrol to diesel, amounting to a 5% savings in fuel cost per km. Using the general, long- term rebound effect elasticities cited above, this would result in 0.5-1.5% increase in VMT (85-255 km/year in our analysis using 17,000 km/yr average). Figure A4 Annual travel distance for diesel and petrol cars for NTS survey year 2004. This plot shows the effect of vehicle age on annual travel distance. Annual kilometers for survey year 2004 (effect of vehicle age on annual mileage) - 5,000 10,000 15,000 20,000 25,000 30,000 1990 1992 1994 1996 1998 2000 2002 2004 year car first registered ki lo m et er s pe r y ea r diesel petrol Linear (diesel) Linear (petrol) Overall, we expect that our assumptions with respect to annual travel distance underestimate PM10 exposure due to diesel growth potentially on the order of 20% or less; however, this level of uncertainty is very small compared to the uncertainty in the PM10 mortality health coefficient which is a factor of 15 (1,500%) difference between the low and the high. In terms 121 of fuel and CO2 savings, our annual travel distance assumptions may overestimate the actual benefits of diesels by 10-30%, but we lack the data to make a better estimate15. A.4.3 Spatial Distribution of Vehicles The spatial distribution of vehicles in our study is the same as the assumptions of the modelling studies used to estimate ambient PM10 [9, 21]. Distribution of emission sources spatially (and temporally) does influence ambient particle (and ozone) episodes. Higher or lower proportions of vehicles in urban areas will increase or decrease the population weighted ambient PM10 exposure estimate. At a smaller scale, proximity to road traffic by 100 meters or less has been shown to significantly increase cardiopulmonary mortality [22]. We have no evidence to expect the uncertainty in spatial distribution would substantially over- or underestimate PM10 exposure. A.4.4 PM10 Emissions and Ambient Concentrations Here we discuss assumptions and uncertainties in the PM10 emissions and ambient concentration estimates. The ambient model studies used in our study were complex analyses which applied many assumptions (e.g., 1996-1999 base years for emissions and meteorology, non-gravimetric ambient data converted to gravimetric using 1.3 scaling factor) and assessed many uncertainties (e.g., effect of roadside dust, meteorology, alternate secondary particulate assumptions). Readers are referred to the cited studies for discussions of these assumptions and uncertainty assessments [9, 23]. Contributions of secondary PM10 are included in our study, because they were included in the ambient model studies by way of scaling actual ambient data rather than modelling the atmospheric chemistry [9, 21]. Associated with 3.76 kilo-tonnes PM10 reductions estimated in the model studies were 23 kilo-tonnes of NOx reduction, which is roughly proportional to increases of 0.58 kilo-tonnes PM10 and 4.6 kilo-tonnes NOx in our study. So while NOx contributions to secondary PM10 are incorporated in an inexact manner in our results, the contribution of UK NOx emissions (as secondary nitrates) to mean annual PM10 concentrations is small. One of the reasons for this is that roughly half of the secondary 15 Schipper et al (2002) similarly noted the lack of robust estimates for rebound effects in Europe, and emphasized that fuel price differences (which exist in various EU countries, but not the UK) had the strongest effect in eliminating diesel\u00E2\u0080\u0099s efficiency advantage. 122 nitrates in the UK are attributed to atmospheric transport of emissions from mainland Europe [24]. Stedman and colleagues [9] found that 23 kilo-tonnes NOx reduction from illustrative traffic-control measures was predicted to reduce secondary PM10 an average of 0.015 \u00EF\u0081\u00ADg/m3, which represented 5.3% of total PM10 reduction. It should also be noted that total nitrate particle contributions may be small in terms of UK annual means but assessments of ambient data from local episodic events found nitrate particles to contribute substantially [24]. In terms of HC, our analysis shows a reduction of 3.7 kilo-tonnes per annum over the 20- year study period. What about the potential reductions in ambient PM10 from the secondary particles? UK ambient-modelling studies assumed the organic fraction of secondary particles to be negligible (i.e., that secondary particulate is comprised only of nitrates and sulphates) [9, 21]. The basis for this assumption is that organic aerosols were found, based on \u00E2\u0080\u009Cpreliminary models\u00E2\u0080\u009D, to comprise a maximum of 15% of total secondary particles, with a large portion from natural sources [9]. On the other hand, source-apportionment estimates using receptor models found secondary organic aerosols from urban traffic sources to contribute as much as 40% to specific episodic events in summer months [24]. Nonetheless, the studies generally agree that secondary organic particles are still relatively small contributors to total secondary particles based on annual means, and that primary particles still dominate ambient concentrations. Hence we reason that neglecting the reduction in HC due to diesel substitution for petrol vehicles results in a small overestimate of PM10 exposure. There are a few additional exclusions from our analysis which should be mentioned for completeness: \u00EF\u0082\u00B7 Upstream/downstream emissions and life cycle effects are excluded (e.g., differential emissions of PM10 and CO2 in diesel and petrol production, vehicle manufacturing, and vehicle scrap and salvage). The sole exception is that fuel savings (bbl crude oil) estimates include an adjustment for upstream differences in the production of diesel and petrol. 123 \u00EF\u0082\u00B7 Changes in diesel fuel sulphur content are ignored16, as this has been found to be very small or negligible in terms of reductions in ambient PM10 concentrations by 2010 [9]. \u00EF\u0082\u00B7 Changes in brake and tire wear in PM10 emission estimates are ignored. Combined, these comprise less than 0.01 g/km based on NAEI emission factors [11]. Estimating differences in brake and tire wear between petrol and diesel is highly uncertain, and is likely to be negligible compared to changes in tailpipe emission. \u00EF\u0082\u00B7 The assumption of constant annual travel distance means that modelled air-quality disbenefits occur later than the real situation, since vehicles are driven more kilometers in the early years of use (Figure A4). \u00EF\u0082\u00B7 We have not included any explicit increase in emission factors with vehicle age or emission control failures. Research has shown that a small percentage of vehicles can contribute a large proportion of fleet emissions [25]. On-board diagnostics and manufacturer legal requirements to maintain emissions were adopted with the goal of minimizing this phenomenon [26]. Euro III standards are required to be met for 80,000 km and Euro IV standards for 100,000 km. Table A5 provides failure rates for emission tests for petrol and diesel vehicles, although it is not stated which pollutant failed the test. Thus some increase in emissions is still very likely (e.g., even if emissions stay below the legal limit), so this leads to an underestimate of the emissions and exposure, which is expected to be small by comparison to the uncertainty in the mortality risk coefficients. 16 \u00E2\u0080\u009CUltra low sulphur\u00E2\u0080\u009D diesel and petrol not-to-exceed 50 mg/kg (50 ppm) are to be available as of 2005, while \u00E2\u0080\u009Csulphur free\u00E2\u0080\u009D fuels not-to-exceed 10 mg/kg (10 ppm) are to be available by 2009 (Department for Transport www.dft.gov.uk; [9]). The stated aim of the directive is to enable optimization of vehicle technology for low CO2 emissions, with secondary benefits by way of air pollutants such as NOx and PM10 such as through improved catalyst performance. 124 Table A5 Emission test failure rates for petrol and diesel passenger cars in the UK. 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 Petrol 6.0% 7.4% 7.9% 7.1% 6.4% 5.6% 4.3% 3.1% 2.4% 1.8% 1.4% Diesel 8.5% 10.6% 9.6% 8.5% 7.3% 5.9% 6.1% 5.5% 5.0% 4.4% 3.8% \u00EF\u0082\u00B7 Our assumption that post-Euro IV emissions of petrol and diesel vehicles are fully harmonized is not fully realistic. The proposed new standards for Euro V are shown in Table A2. Diesel cars will still be allowed to emit more than three times as much NOx as petrol cars (0.20 g/km diesel compared to 0.06 g/km petrol). Although the same PM10 standard is proposed, the emission factor for petrol vehicles is 0.002 g/km, which is already 60% below the proposed limit; hence it is possible that diesel cars will still emit double the mass emissions of petrol cars and remain within the proposed Euro V limits. A.4.5 Health Effects The principal, quantifiable air pollutants of concern on the basis of public health in the UK are PM10, ozone, and SO2 [27]. Given that vehicles contribute relatively little to ambient SO2, and PM10 was assessed quantitatively, the absence of quantitative estimates of changes in ozone health effects remains a substantial limitation of this study, in particular because ozone has been confirmed to be linked to mortality (i.e., not just morbidity) [28]. Ozone health effects were not estimated here because we determined it was not feasible using published studies to make direct quantitative estimates of changes in ambient ozone concentrations based the estimated changes in NOx and HC emissions, which is discussed in the main article. Separate from the contribution to secondary PM10 and ozone, NOx is itself associated with respiratory hospitalizations. An assessment of NOx health effects, independent of ozone or PM10, found that 23 kilo-tonnes annual reduction in NOx by traffic measures would result in 171 fewer respiratory hospitalizations (based on COMEAP dose-response factor of\u00EF\u0080\u00A0\u00EF\u0080\u00B0\u00EF\u0080\u00AE\u00EF\u0080\u00B5\u00EF\u0080\u00A5 increase in respiratory hospital admissions per\u00EF\u0080\u00A0\u00EF\u0080\u00B1\u00EF\u0080\u00B0\u00EF\u0080\u00A0\u00EF\u0081\u00ADg/m3 NO2) [23]. Scaled to our 4.6 kilo- tonnes annual increase in NOx over the 20-year study period, this translates to roughly 40 additional respiratory hospitalizations per annum. Hence our hospitalization projections are underestimates. 125 Carbon monoxide is another contaminant excluded from our health effect estimates. The estimated reduction due to additional diesels is 10 kilo-tonnes per annum over the 20-year study period. Quantifying the health effects of outdoor carbon monoxide in the UK has been discouraged for reasons of insufficient information [27]. Nonetheless carbon monoxide remains below the UK ambient objective of 10 mg/m3 (maximum daily running 8-hour mean). In 2004 there were no exceedances, and the maximum value at any monitoring station was 5.9 mg/m3. Reductions in carbon monoxide from vehicles has also been implicated in saving lives from accidental and suicidal poisonings in the U.S., which would be an ancillary benefit of diesel cars [29]. Hence, excluding carbon monoxide does overestimate the health effect impacts in our study, although the influence is expected to be minimal by comparison to the PM10 mortality effects. Finally, we discuss the exclusion of health-effect estimates for changes in toxic emissions. Changes in benzene due to additional diesels were estimated to decrease by 0.05 kilo- tonnes per annum over the study period, and 1,3 butadiene emissions were estimated to increase by 0.003 kilo-tonnes per annum. The health risk associated with these two contaminants are considered \u00E2\u0080\u009Clikely to be exceedingly small,\u00E2\u0080\u009D although no safe minimum level has been identified [30]. In terms of the UK air-quality objectives, actual measured levels in 2004 were [11]: \u00EF\u0082\u00B7 Benzene running annual mean objective is 16.25 \u00C2\u00B5g/m3. There was only one UK monitoring site in 2004, which recorded an annual mean of 10 \u00C2\u00B5g/m3. \u00EF\u0082\u00B7 1,3 butadiene running annual mean objective is 2.25 \u00C2\u00B5g/m3. There was only one UK monitoring site in 2004, which recorded an annual mean of 0.5 \u00C2\u00B5g/m3. 126 A.5 REFERENCES (1) TRL, \"Data required to monitor compliance with the End of Life Vehicles Directive,\" Transport Research Laboratory, UK, 2003. (2) SMMT, \"UK New Car Registrations by CO2 Performance 2005 Annual Report,\" The Society of Motor Manufacturers and Traders Ltd. (www.smmt.co.uk), 2006. (3) DEFRA, \"Air quality and climate change: a UK perspective (third report of the Air Quality Expert Group),\" UK Department for Environment, Food and Rural Affairs (DEFRA), 2006 draft. (4) Sher, E., Ed. Handbook of Air Pollution from Internal Combustion Engines: Pollution Formation and Control; Academic Press, 1998. (5) Pischinger, S., Topics in Catalysis 2004, 30/31, 5-16. (6) Sluder, C.; B. 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Redd, Journal of the American Medical Association 2006, 288, 988-995. (30) COMEAP, Statement on Transport and health in London; UK committee on the medical effects of air pollutants (www.advisorybodies.doh.gov.uk/comeap), 1999. 129 APPENDIX B - SUPPORTING INFORMATION FOR CHAPTER 3 \u00E2\u0080\u009CREGULATING CAR MASS FOR CONCURRENT TRAFFIC SAFETY AND CLIMATE MITIGATION BENEFITS\u00E2\u0080\u009D 130 This appendix documents various details for the Introduction, Methods, Results, and Discussion sections of Chapter 3. It will be submitted to The Lancet as an online supporting information document. B.1 INTRODUCTION The key premise of the analysis of traffic safety in relation to car CO2 emission rate emissions is that there is an association between traffic safety and car mass. Attempts to quantify the relationship between any parameter and traffic fatalities must consider many interrelated (and often correlated) factors that can confound apparent relationships. Here we list the key factors, followed by Table B1 summarizing several studies of the relationship between mass and traffic safety. 1. Vehicle design described in two categories: (1) basic features: structural stiffness, geometry, car size, engine size, power; and (2) safety technology: seat belts/restraints, airbags, braking systems, stability control, and others [1-6]. 2. Environment and road: urban/rural, number of lanes/separation, speed limits, weather, speed enforcement [6-9]. 3. Driver behavior: seat belt use, choice of speed, choice of environmental conditions to drive under, distractions (e.g., cell phones), alcohol and drug use, sleepiness [1, 2, 5, 10-17] . Examples of Indicator variables for behavior are: previous license suspensions, number of speeding violations, number of previous police-reported collisions, and previous drinking driving violations. 4. Occupant vulnerability: age, sex, health status, alcohol use [7, 11, 15, 18, 19]. 5. Crash event described in two categories: (1) crash mode: single-car, multiple-car, car-other vehicle types (heavy trucks, motor cycles, mopeds, bicycles), car- pedestrian and (2) crash type: points of impact, rollovers, hit objects [3, 17, 20, 21]. Note that some parameters are listed under more than one category. For example, seat belts are clearly a design feature of the vehicle with large variability in efficacy such as simple designs in many 1980\u00E2\u0080\u0099s vehicles to advanced tensioning systems in many new vehicles. However seat belt use is a behavior which has a very strong influence on the risk of fatalities [2, 22]. 131 Table B1 Summary of selected studies which estimated the effect of vehicle mass on fatality and injury risk. Study Description Key findings Data requirements Strengths Limitations U.S. National Highway Transportation Safety Administration (NHTSA) by Kahane[20] Quasi-induced exposure methods are employed, although the main results reported are absolute fatality risk using vehicle registration years for cars, and vehicle travel distance for \u00E2\u0080\u009Clight trucks.\u00E2\u0080\u009D Regression analysis to estimate the change in fatality risk for \u00E2\u0080\u009Ccase\u00E2\u0080\u009D cars for a 100 lbs (45 kg) decrease in mass. Compares cars of mass M to cars M-100 lbs, statistically adjusted for age, sex, road environment, impact point, etc.; includes adjustment for driver behavior variables. U.S. fatality analysis reporting system (FARS) and other U.S. data sources for calendar years 1995-2000, and model years 1991-1999. Excludes 2-door cars because of the documented association between risk taking drivers and 2-door cars. 100 lbs (45 kg) reductions in vehicle mass (and size, which is correlated to mass) increases overall fatality risk. Individual-level crash data for driver, environment, and cars. Curb mass is the principal independent variable of interest. Driver behavior data such as alcohol use, drug use, suspended license, crash frequency history, and various law violations. Careful sensitivity analysis for many factors, except size. Statistical adjustment of risk estimates for \u00E2\u0080\u009Cbad\u00E2\u0080\u009D driver behavior using 9 metrics. Large sample sizes and relatively low uncertainty. Includes risk estimates for all major crash modes and types, except that motorcycle, mopeds, and pedestrians are combined as one category. Does not assess the effects of size or other correlates such as power independent of mass. Hence all results are to be interpreted as the combined effect of mass, size, power, and other correlated attributes that were not separately accounted for in the analysis. Collision speed controlled based on speed limit of 55 miles/hour, an imprecise measure of speed. Evaluates only fatalities, excludes injuries. 132 Study Description Key findings Data requirements Strengths Limitations Mengert and Borener [23] Estimates absolute fatality risk. Analyzed changing mass mix of cars in the U.S. Directly calculates fatality risk factors for collisions of cars in various mass groups without adjustment for variables describing casualties, road, or vehicle. These fatality risk factors are used to estimate changes in risk due to changes in mass distribution for on-road cars. Decreasing fleet mass increases fatality risk for single and two-car crashes, reduces risk for pedestrians, with an overall net increase in risk. Individual-level crash data for cars and fatality counts including curb mass as the principal independent variable of interest. Driver data (age, sex, etc.) and road environment data are not required. Data requirements are minimal. Risk estimates using this method were compared to results using NHTSA\u00E2\u0080\u0099s detailed statistical models and found to compare reasonably well, thus validating this method [20]. Absolute risk estimates are not adjusted for many important variables (size, stiffness, seat belt use, behavior), other than mass and its correlates. Risk estimates are characterized by high degree of uncertainty. Evaluates only fatalities, excludes injuries. Toy and Hammit [24] Estimates conditional fatality and Abbreviated Injury Scale (AIS) risk, based on police-reported crash events. Uses \u00E2\u0080\u009Cdelta V\u00E2\u0080\u009D as an independent variable, estimated for each crash based on detailed crash event parameters. Increasing delta V, a measure of the combined effect of mass and crash severity (including speed), significantly increases fatality risk in two car collisions. But vehicle type and other factors are also important. Study used U.S. Crashworthiness Data System with detailed data on driver, road environment, and crash variables for roughly 6,500 fatalities. Relatively unique study in that it uses delta V as an independent variable, thus accounting for the combined effect of crash energy (surrogate for \u00E2\u0080\u009Ccrash severity\u00E2\u0080\u009D) and mass. Adjusts for restraint use. Body type, size, vehicle safety equipment, and other vehicle attributes are not included. Assessed only two- car crash modes. Being a conditional risk analysis, crash avoidance (including driver behavior) is not included. 133 Study Description Key findings Data requirements Strengths Limitations Wood and Simms [25] Estimates relative fatality and AIS risk. Model is developed to estimate the risk in car-to-car collisions considering three basic ratios: mass, length (size), and energy absorbed. For two-car frontal collisions, length ratio explains injury and fatality risk distributions better than mass ratio or collision energy. German, Japanese, and U.S. FARS empirical data for fatalities are used. Detailed accounting of vehicle attributes to separate the effects of mass, size, and crash severity. Crash avoidance (including driver behavior) is not included. Only two-car frontal collisions are assessed. Not clear if age and gender of casualties are adjusted in the risk estimates. Broughton [26] Estimates conditional fatality risk for UK model year 1991-94 cars. Vehicle mass is the fundamental independent variable. Conditional- fatality risks are used to estimate changes in downsizing of on-road fleets by assuming frequencies of crash events remain unchanged with changes in mass. Uniform 10% reduction in mass reduces fatality risk in single-car, two- car, and car- pedestrian crashes. Individual-level crash data for drivers, road environment, and cars using UK Data Archive. Curb mass was input from an independent source. Detailed data for on- road cars in the UK are used. Estimates risks for most crash modes (single-car, car- pedestrian, car-heavy goods, and car-car) and vulnerable road users (vehicle occupants, pedestrians). Method has been criticized for using mass ratio instead of mass difference. Size, stiffness, power, safety equipment, and other important variables not available. Crash avoidance (including driver behavior) is not included. Evaluates only fatalities, excludes injuries. 134 Study Description Key findings Data requirements Strengths Limitations Wenzel and Ross [6, 17] Risk defined as fatalities per quantity of registered cars. Risk to drivers, risk to others, and combined risk (sum of driver and others) are quantified for many makes and models of cars in the U.S., using primarily FARS data. Driver behavior and annual travel distance statistics are compared amongst different car groups. Mass is \u00E2\u0080\u0098not fundamental\u00E2\u0080\u009D traffic safety. Crash events and fatality rates disaggregated by make and model. Driver behavior variables. Vehicle design attributes. Annual travel distance data. Use of absolute fatality risk, divided into risk to drivers and risk to others, is a meaningful risk metric directly linked to common traffic safety policy agendas, including occupant protection and car compatibility. Provides convincing evidence linking observed crash data and underlying crash physics for the effect of some design features on driver risk such as unibody cf. body on frame, track width, and center of mass. Mass and risk relationship described as a \u00E2\u0080\u009Cpopular belief\u00E2\u0080\u009D without supporting assessment of previous research. High level analysis aggregating the effect of many factors into one result (e.g., crash avoidance and crashworthiness, multiple crash modes combined into one risk-to-driver estimate). Behavior assessed with descriptive statistics, not as control variables (e.g., as with NHTSA [20]). Choice of be behavior risk metric can affect the ability to generalize findings [27], therefore non- subtle effects may not be ruled out. The \u00E2\u0080\u009Cdesign quality\u00E2\u0080\u009D and country of origin effects explain risk variation for some important car groups but not all, and no connection to underlying physics (e.g., delta V) is provided. Evaluates only fatalities, excludes injuries. 135 Study Description Key findings Data requirements Strengths Limitations Evans [3] Estimates relative fatality risk for U.S. drivers of lighter cars versus drivers of heavier cars in head-on crashes. New equations are employed to separate the effects of size and mass. Makes use of added passenger mass to separate the effects of size and mass. Mass is the fundamental parameter assessed. Adding 75 kg mass, holding size constant, in two-car head-on crashes reduces driver fatality risk 8%, while increasing risk to collision partner driver by about 8%. Uses individual-level crash data for cars based on fatality counts, crash involvements, and vehicle mass. Age, sex, or other data are not used. Makes use of new equations to separate the effects of mass and size. Wide range of model years is employed, 1975-1998, therefore confounding by improved safety technology and vehicle design (e.g., Evans showed elsewhere that relationship between size and mass for 1975-1979 cars is significantly different than model year 1980-1990 cars [28]), yet these data points are all combined in the analysis. Moreover, one relationship between mass and size is used develop new equations to separate the effects of mass and size, even though the relationship between mass and size changes over the analysis period. No accounting for driver behavior. Evaluates only fatalities, excludes injuries. 136 Study Description Key findings Data requirements Strengths Limitations Van Auken [21] Research objectives were to separate the effects of mass, size, crashworthiness, crash avoidance, and compatibility. Uses data from 7 U.S. states for calendar years 1995-1999 crashes, and model years 1985-1998 cars and 1985- 1997 light trucks. Quasi-induced exposure methods were employed, with the main results reported based on vehicle registration years as the measure of exposure. Reducing mass, while holding wheelbase and track width constant, decreases fatality risk. Reducing wheelbase or track width, holding mass constant, increases fatality risk. Individual-level crash data for driver, environment, and cars. Relatively unique in quantitatively separating the effects of vehicle mass and size. Includes multiple crash modes: two-car, single-car (rollovers, hit objects), car- pedestrian/bicycle/ motorcycle, car-heavy goods/bus. Vehicle control variables included airbags and antilock brakes, vehicle age, four/all wheel drive, and two doors. Driver behavior variables excluded. Some important vehicle features such as stiffness and electronic stability control, were not accounted for. Used 3 relatively blunt variables for age and sex (young drivers, old males, old females). Evaluates only fatalities, excludes injuries. Collision speed controlled based on speed limit being 55 miles/hour, an imprecise measure of speed. Crandall [29] Landmark study of the relationship between U.S. CAF\u00C3\u0089 standards and traffic fatalities. Authors argued that CAF\u00C3\u0089 had strong influence on vehicle mass (assessed with economic models), then assessed effect of vehicle mass on total fatalities. CAFE responsible for ~ 225 kg (500 pounds) reduction in 1989 model cars, which in turn increased fatalities 2,200-3,900 for model year 1989 cars over 10 years (i.e., ~ 300 per year) Aggregate data on fuel economy, vehicle mass, engine size, fuel prices, steel prices, etc. Based on model years 1970- 1987. Robust assessment of the effect of CAFE policy on fuel economy and vehicle mass. Aggregate data. Correlates of vehicle mass (e.g., size, power) could confound effect of mass. Effects of safety technology over time not necessarily captured in the model. Evaluates primarily fatalities, with sensitivity analysis for injuries.. 137 Study Description Key findings Data requirements Strengths Limitations Bedard [30] Estimated the independent effects of vehicle, driver, and collision characteristics using U.S. FARS data for single vehicle crashes with fixed objects. Increased mass and size together reduces fatality risk in single-car, fixed- object crashes. Individual-level crash data for driver, environment, and cars. Accounts for some important behavior variables (alcohol and seat belt restraint use), collision speed, and vehicle characteristics (mass, model year, and age). Internal validity high based on assessment of single- vehicle, fixed object crashes. Independent effects of mass and size were not estimated (wheelbase and weight correlated r = 0.82, so wheelbase dropped). Ability to generalize is limited as many crash modes excluded (e.g., multi-vehicle, pedestrians, motorcycles, single-car rollovers). Evaluates only fatalities, excludes injuries. Padmanaban [31] Estimated driver-fatality odds (conditional risk) in two-vehicle collisions (car-car and car-light truck) in the U.S. based on various vehicle size metrics, controlled for mass. Used 1990-2000 calendar year data for model years 1981 and later. Evaluated separately frontal collisions, left-side impact, and right-side impact. Mass ratio of two- car collisions explains fatality risk more than any other vehicle variables, including various size metrics. \u00E2\u0080\u009CEqualizing\u00E2\u0080\u009D fleet mass will reduce overall fatality risk. 40 different vehicle parameters, plus seat belt use, drinking driving, and age/sex. Relatively unique in assessing 12 different size metrics for size such as overall length/width/height, volume, and front overhang. Evaluates only fatalities, excludes injuries. Effect of other critical vehicle variables omitted such as age and stiffness. Crash avoidance not assessed. 138 B.2 METHODS B.2.1 Methods: Data Sources Details for electronic databases received are provided in Table B2. At the end of this appendix is a data dictionary for this study. Figures B1 through B11 contain a summary of some important UK statistics and time trends for general reference. Table B2 Summary description of electronic databases received1. Database Name (for This Study) Database Names (Source) Number of Records Description/Comments UK Data Archive crash database [32] acc94, acc95, \u00E2\u0080\u00A6acc05 (UK DFT via the UK Data Archive) 2,719,913 total for 12 years data sets for year 1994-2005 (inclusive) with data fields describing each police- reported crash event in the UK UK Data Archive vehicle database [32] veh94, veh95, \u00E2\u0080\u00A6veh05 (UK DFT via the UK Data Archive) 4,967,866 total for 12 years data sets for year 1994-2005 (inclusive) with data fields describing vehicle types for all police-reported crash event in the UK; specific make/model/version data is not included 1 Complete database dictionary can be provided upon request. 139 Database Name (for This Study) Database Names (Source) Number of Records Description/Comments UK Data Archive casualty database [32] cas94, cas95, \u00E2\u0080\u00A6cas05 (UK DFT via the UK Data Archive) 3,698,606 total for 12 years data sets for year 1994-2005 (inclusive) with data fields describing each casualty (slight injury, serious injury, or fatality) for all police-reported crash events in the UK; excludes behavioral data such as previous motoring offences DFT make/model crash database [33] makemodel94-05 (direct from UK DFT December, 2006) 3,9168107 total for 12 years provides individual data on passenger cars linkable to the UK Data Archive data sets (linking fields = accref and vehref)2. Vehicle data is provided for roughly \u00C2\u00BE of all crash records (excludes heavy goods vehicles, bicycles, motorcycles, and others). Model years 1990 and later included (yr1stregi code \u00E2\u0089\u00A51990) 23,452 different make/model/versions (all casualties) 6,489 different make/model/versions (fatalities only) DFT make/model version database [33] 070611-D-make model 2006 (direct from DFT June, 2007) 293,902 total records make, model, make code, model code, body type, propulsion, year first registered, engine size group, and number of cars registered year end 2006 JATO database [34] JATO Dynamics Ltd. (latest version 4,304 total for years 8 years Make, model, and version data for UK cars. Years 2000-2007 are included, but with very few make/model/versions for earliest years (3 records for 2000; 186 records for 2004; 1,990 records for 2007). 2 Database variables: accref = accident reference number; vehref = vehicle reference number. 140 B.2.2 Methods: Summary Traffic Safety Statistics Figure B1 Historical traffic fatality rates for the UK. Figure B2 Number of vehicles involved in fatal crashes 1994-2005. 0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 1990 1992 1994 1996 1998 2000 2002 2004 Year Fa ta lit y Ra tio per billion vehicle km per 100,000 population Number of vehicles involved (numveh) for all fatal crashes 1994-2005 0% 10% 20% 30% 40% 50% 60% 1 2 3 4 \u00E2\u0089\u00A55 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 141 Figure B3 Road types for all fatal crashes 1994-2005. Road types (roadtype) for all fatal crashes 1994-2005 0% 10% 20% 30% 40% 50% 60% 70% 80% on e w ay st re et du al ca rri ag ew ay : 2 la ne s du al ca rri ag ew ay : 3+ la ne s si ng le ca rri ag ew ay : si ng le tr ac k si ng le ca rri ag ew ay : 2 la ne s si ng le ca rri ag ew ay : 3 la ne s si ng le ca rri ag ew ay : 4+ la ne s un kn ow n 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 142 Figure B4 Road class for all fatal crashes 1994-20053. Road class (1rdclass) for all fatal crashes 1994-2005 0% 10% 20% 30% 40% 50% 60% 70% Mo tor wa y A( M) A B C un cla ss ifie d 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Figure B5 Road speed limits for all fatal crashes 1994-2005. Road speed limits (speedlim) for all fatal crashes 1994-2005 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% \u00E2\u0089\u00A420 mph 30 mph 40 mph 50 mph 60 mph 70 mph 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 3 Database variable: 1rdclass is the first road class type (there can be more than one road involved, namely an intersection). Road class descriptions can be found at www.dft.gov.uk/matrix/forms/definitons.aspx . 143 Figure B6 Casualty types for all fatalities 1994-2005. Casualty types (typecas) for all fatalities 1994-2005 0% 10% 20% 30% 40% 50% 60% pedestrian cyclist car (excludes taxi) taxi motorcycle minibus, bus, or coach heavy goods vehicle other 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Figure B7 Sex for all fatalities 1994-2005. Sex for all fatalities 1994-2005 0% 10% 20% 30% 40% 50% 60% 70% 80% 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 male female 144 Figure B8 Age and sex for all fatalities in 2005. Age and sex for all fatalities in 2005 (n = 3,201) 0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 3.5% 4.0% 4.5% 0 10 20 30 40 50 60 70 80 90 100 age % o f a ll 20 05 fa ta lit ie s male female Figure B9 Crash mode for all single vehicle crashes 1994-2005. Single vehicle fatal crashes: skid, overturn, & jackknife 0% 10% 20% 30% 40% 50% 60% 70% no skid, overturn, or jackknife skid skid & overturn overturn jackknife & missing values 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 145 Figure B10 Objects struck off carriageway for all single vehicle crashes 1994-2005. Single vehicle fatal crashes: objects struck off the carriageway 0% 10% 20% 30% 40% 50% 60% 70% no h it ob je ct ou t ca rri ag ew ay si gn o r si gn al la m p po st ut ili ty p ol e tre e ce nt ra l o r si de b ar rie r di tc h & su bm er ge w at er bu s st op & ot he r pe rm an en t 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Figure B11 Point of impact for two vehicle fatalities 1994-2005 Two vehicle fatalities (numveh=2) and impact points (1stptimpac) 0% 10% 20% 30% 40% 50% 60% 70% did not impact front back offside nearside 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 146 B.2.3 Methods: Using Surrogate Data to Ascertain Curb Mass A critical limitation of the UK Data Archive and DFT databases is the lack of curb mass data. While there are independent data sources for curb mass that can be manually linked to our analysis data set, make/model alone is insufficient to look up a precise mass. As an example, Figure B12 shows the variation of curb mass amongst versions of the 2007 Ford Focus[34], which has consistently been the most registered make/model range in the UK in recent years. The overall standard deviation and range of curb mass are 62 kg and 268 kg, respectively. Table B3 similarly shows wide variation in curb mass for the 10th most registered make/model car, the BMW Series 3 [34]. Considering that a 45 kg (100 lb) change in mass has been shown to significantly affect injury and fatality risk [20], this limits the ability to account for changes in mass from the UK Data Archive data. Figure B12 Curb mass (kg) histogram for model year 2007 Ford Focus versions4. 4 Ford Focus was the 1st most newly registered UK car in 2006. (note that Ford Focus C-MAX is a different model range than the Ford Focus, and is not included in these data.) 0 5 10 15 20 25 30 35 1206 1241 1277 1312 1347 1383 1418 1453 1489 1524 1559 1595 More Fr eq ue nc y Bin Ford\u00C2\u00A0Focus\u00C2\u00A0\u00E2\u0080\u0090 curb\u00C2\u00A0mass\u00C2\u00A0histogram kg 147 Table B3 Variation in curb mass and tailpipe CO2 emission rate for model year 2007 Ford Focus versions offered in the UK. number of versions min kg max kg range kg mean kg std dev kg min gCO2/ km max gCO2/ km range gCO2/ km mean gCO2/ km std dev gCO2/ km diesel 62 1,323 1,630 307 1,401 54 130 163 33 144 54 gasoline 88 1,206 1,547 341 1,315 64 160 233 73 179 64 overall 150 1,206 1,630 424 1,351 73 130 233 103 165 23 Table B4 Variation in curb mass and tailpipe CO2 emission rate for model year 2007 BMW Series 3 versions offered in the UK. BMW Series 3 was the 10th most newly registered UK car in 2006. number of versions min kg max kg range kg mean kg std dev kg min gCO2/ km max gCO2/ km range gCO2/ km mean gCO2/ km std dev gCO2/ km diesel 30 1,415 1,810 395 1,594 109 135 213 77 173 20 gasoline 39 1,320 1,810 490 1,549 149 158 310 153 207 30 overall 69 1,320 1,810 490 1,568 134 135 310 175 192 31 For all analyses, except the \u00E2\u0080\u009Cfirst law\u00E2\u0080\u009D RR, we dealt with the lack of curb mass data in our study by using engine size as a surrogate measure for mass. Table B10 and Figure B14 illustrate with model year 2007 cars that engine size alone provides statistically significant explanatory power (R2 = 59%) for curb mass, independent of make/model or any other trim level variable. For the \u00E2\u0080\u009Cfirst law\u00E2\u0080\u009D RR analysis, the combination of make/model, engine size category, body type, and propulsion provided enough data to manually look curb mass for every make and model car, using mass data from various independent sources [34-37]. The DFT make/model version database (see Table B2) includes engine size (a categorical variable), body type (e.g., 5-door hatchback, saloon, estate), and propulsion (e.g., gasoline, diesel, electric). For reasons of determining a curb mass as well as other analysis tasks, we needed to link the UK Data Archive databases with the DFT make/model crash database and the DFT make/model version database. However, there was no code to specify the unique combinations of versions for a given 148 make, model, and model year car. The best available option was to concatenate the codes for make, model, and model year (year first registered assumed to be model year). However, this presented challenges. For example, consider the Peugeot (make code = L1) 307S (model code =866) with year first registered = 2003 (presumed to be model year). When we concatenate these fields (to create a new code = makmodregi), we get the following 13 version or trim combinations, all associated with the same make, model, and model year car as shown in Table B5. So the 2003 Peugeot 307S is associated with at least 3 different engine size ranges, 3 different body types, and 3 different propulsion types. Table B5 Range of engine sizes, body types, and propulsion types associated with a model year 2003 Peugeot model 307S. makmodregi5 engsizeDFT body type propulsion L1-866-2003 1,201-1,500 3 Door Hatchback Petrol L1-866-2003 1,501-1,800 Estate Petrol L1-866-2003 1,801-2,000 5 Door Hatchback Petrol L1-866-2003 1,201-1,500 5 Door Hatchback Petrol L1-866-2003 1,501-1,800 5 Door Hatchback Petrol L1-866-2003 1,501-1,800 3 Door Hatchback Petrol L1-866-2003 1,801-2,000 3 Door Hatchback Petrol L1-866-2003 1,801-2,000 Estate Petrol L1-866-2003 Over 30006 3 Door Hatchback Petrol L1-866-2003 1,801-2000 5 Door Hatchback Heavy Oil L1-866-2003 1,201-1500 5 Door Hatchback Heavy Oil L1-866-2003 1,201-1500 5 Door Hatchback Gas/Bi-Fuel L1-866-2003 1,801-2,000 Estate Gas/Bi-Fuel 5 Database variable that is concatenate of make, model, and year first registered. 6 This engine size is likely a coding error in the database for this particular record. 149 In terms of the complete study databases, there were 13,777 unique makmodregi codes which were part of a crash involving a fatality for years 1994-2005. All of these makmodregi codes needed to be matched with an engine size, yet 83% of these 13,777 makmodregi codes were associated with more than one engine size code. To assign a unique engine size (code = engsizeDFT) to each makmodregi, we used the following procedure: 1. Sort makmodregi codes: assign as \"unique\" or \"duplicate.\" a. \"unique\" for which only one engine size category is listed (2,310 codes) b. \"duplicate\" for which more than one engine size category is listed (11,467 codes or 83%) 2. For \u00E2\u0080\u009Cduplicate,\u00E2\u0080\u009D we used the number of registered cars in 2006 (code = numcars7) for a given makmodregi as the criteria to assign an engine size category. In other words, the engine size of the most populous makmodregi on the roads in 2006 was assumed to be the engine size for all cars in the analysis data set for a given makmodregi code. The details of this procedure follow: a. Data field named \u00E2\u0080\u009Cmax\u00E2\u0080\u009D was created to identify the most populous engine size code for each makmodregi code. b. Of the 11,467 makmodregi codes, 8,164 (71%) codes had only value for \u00E2\u0080\u009Cmax,\u00E2\u0080\u009D thus it was clear which engine size code to assign. c. Of the 11,467 makmodregi codes, 3,303 (29%) codes had more than value for \u00E2\u0080\u009Cmax\u00E2\u0080\u009D d. If \u00E2\u0080\u009Cmax\u00E2\u0080\u009D included more than four engine sizes, or four engine-size categories that were not continuous (e.g., 1,201-1,500, 1,501-1,800, 1,801-2,000, 2,501-3,000) we coded these as \u00E2\u0080\u009Cerror\u00E2\u0080\u009D for the engine size and these records were excluded from our analysis data set. This is because it is very unlikely that any given makmodregi spans over four different engine size categories (e.g., the Ford Focus spans only three, the Peugeot 307S spans four). The result is that 86 makmodregi codes were assigned as \u00E2\u0080\u009Cerror\u00E2\u0080\u009D for engine size. e. If \u00E2\u0080\u009Cmax\u00E2\u0080\u009D included four or less engine sizes, we used the total, on-road fleet numbers to assign an engine size. This created the following hierarchy in selecting the engine 7 Database variable: number of cars involved in the collision event. 150 size in these cases: 1,501-1,800, 1,201-1,500, 1,801-2,000, 1,001-1,200, 2,001- 2,500, 701-1,000, 2,501-3,000, >3,000, <701. If 1,001-1,200, 701-1,000, and 1,201- 1,500 were all coded as \u00E2\u0080\u009Cmax,\u00E2\u0080\u009D then 1,201-1,500 was the assigned engine size on the basis of having a larger % of all on-road cars in 2006. This procedure for assigning engine size would be unnecessary if a version code were included in the databases, in addition to first registered (i.e., model year), make, and model. The DFT has advised that a version or trim code can now be made available only for cars, and only cars initially registered after March, 2001 (Paul Syron personal communication, 8 August 2007). It should be noted that the scope for all of our analyses included cars first registered beginning in either 1996 (the \u00E2\u0080\u009Cfirst law\u00E2\u0080\u009D RR analysis) or 1994 (the absolute risk analysis), so having these codes would help somewhat, but would not be expected to make large differences in the precision of our study. It is noted here in the interest of documenting data availability for future research. To supplement the graphs of annual time series for registered cars used to develop scenarios, we provide here Table B6 of annual registrations for UK on-road numbers of cars, which is only available disaggregated by engine size (CC). This comprises the \u00E2\u0080\u009Cbaseline\u00E2\u0080\u009D scenario in our analysis. 151 Table B6 Number (in 1,000s) of registered private and light goods vehicles by engine size8. 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 CC \u00E2\u0089\u00A4 700 54 46 42 37 29 18 19 23 29 37 47 52 700 < CC \u00E2\u0089\u00A4 1,000 1,905 1,757 1,678 1,564 1,459 1,435 1,415 1,368 1,314 1,237 1,199 1,153 1,000 < CC \u00E2\u0089\u00A4 1,200 2,261 2,258 2,327 2,336 2,293 2,275 2,228 2,244 2,252 2,221 2,210 2,139 1,200 < CC \u00E2\u0089\u00A4 1,500 5,337 5,225 5,321 5,418 5,497 5,600 5,677 5,819 5,894 5,939 6,089 6,181 1,500 < CC \u00E2\u0089\u00A4 1,800 6,276 6,345 6,540 6,655 6,766 6,922 6,992 7,124 7,241 7,284 7,405 7,439 1,800 < CC \u00E2\u0089\u00A4 2,000 3,088 3,274 3,550 3,828 4,090 4,389 4,604 4,869 5,166 5,398 5,686 5,929 2,000 < CC \u00E2\u0089\u00A4 2,500 759 791 851 925 1,003 1,094 1,159 1,275 1,400 1,520 1,639 1,725 2,500 < CC \u00E2\u0089\u00A4 3,000 486 494 524 548 574 608 630 666 704 762 841 918 CC > 3,000 313 315 340 371 403 443 473 510 543 587 638 671 All private and light goods, excluding: \u00E2\u0080\u009Cother vehicles\u00E2\u0080\u009D and motorcycles, scooters, and mopeds 20,479 20,505 21,173 21,682 22,114 22,784 23,197 23,898 24,543 24,985 25,754 26,207 other vehicles 2,192 2,217 2,267 2,317 2,362 2,427 2,469 2,544 2,622 2,730 2,900 3,019 Motor cycles, scooters and mopeds 630 594 609 626 684 760 825 882 941 1,005 1,060 1,075 8 \u00E2\u0080\u009CPrivate and light goods\u00E2\u0080\u009D includes all vehicles used privately. The bulk of this group consists of private cars (whether owned by individuals or companies) and vans and light goods vehicles. The group also contains a number of important minority groups including private buses and coaches, private heavy goods vehicles, and some vehicles not exceeding 3,500 kg which, before 1st July 1995, were taxed in specialized taxation classes. \u00E2\u0080\u009COther vehicles\u00E2\u0080\u009D includes three-wheeled cars and vans not exceeding 450 kg unladen mass, recovery vehicles and general-haulage vehicles, as described above. Motorized tricycles are included but motor cycle combinations are included with motor cycles. 152 B.2.4 Methods: \u00E2\u0080\u009CFirst Law\u00E2\u0080\u009D RR Analysis The process of data sorting was an important part of the analysis and is illustrated in Figure B13. For various reasons, actual totals of casualties in the UK are believed to be less than recorded in the UK Data Archive [38, 39]. Table B7 is a comparison of critical statistics for the complete UK Data Archive, the \u00E2\u0080\u009Cfirst law\u00E2\u0080\u009D RR analysis data set, and various intermediate linking and sorting steps. Figure B13 Diagram of the UK Data Archive data sorting process.9 Table B7 provides a comparison of critical statistics for the complete UK Data Archive, the \u00E2\u0080\u009Cfirst law\u00E2\u0080\u009D RR analysis data set, and various intermediate linking and sorting steps. The second column shows statistics for the complete UK Data Archive set for crash years 1996-2005 including only driver fatalities (5,354 in total) involved in crashes between two cars (i.e., excluding taxis, motorcycles, 9 While the casualty totals in the analysis data set and the complete UK Data Archive have specific counts, the actual quantity of all road casualties in the UK is unclear as illustrated by the dashed line. All road casualties UK Archive casualties Analysis data set casualties 153 heavy goods, etc.). The next column shows that matching with the \u00E2\u0080\u009Cmake/model\u00E2\u0080\u009D database reduced the basic data set to 4,801 driver fatalities. The next step was to limit the data set to model year cars 1996-2005, resulting in 1,062 driver fatalities. Linking to the \u00E2\u0080\u009Cversion\u00E2\u0080\u009D database was necessary to provide engine size category and related data fields in order to accurately lookup curb weight, which reduced the driver fatalities in the data set to 946. Thus far, the data set includes 946 drivers who died in two-car collisions driving a case car of model year 1996-2005, but for most of these collisions the other car involved was 1995 or earlier. Limiting the data set further to include only driver fatalities in which the other cars was also model year 1996-2005 produced the analysis data set with 144 driver fatalities. Table B7 \u00E2\u0080\u009CFirst law\u00E2\u0080\u009D RR analysis: comparison of data set statistics. UK Data Archive UK Data Archive linked with makemodel data UK Data Archive linked with makemodel data UK Data Archive linked with makemodel 10 and engsize \"first law\" RR analysis data set numveh11 2 2 2 2 2 typecas 9 9 9 9 9 classcas 1 1 1 1 1 severcas 1 1 1 1 1 crash years 1996-2005 1996-2005 1996-2005 1996-2005 1996-2005 case car model year (yr1stregi) all (no data) <1978-2005 1996-2005 1996-2005 1996-2005 other car model year (yr1stregi) any any any any 1996-2005 median yr1stregi no data unknown 1998 1998 1998 mean yr1stregi no data unknown 1998.3 1998.1 1998.2 std dev yr1stregi no data unknown 2.0 1.9 1.8 number of driver fatalities 5,354 4,801 1,062 946 144 driver/fatality age, mean 44.7 44.7 45.5 45.7 49.8 driver/fatality age, std dev 21.5 21.6 21.4 21.5 22.6 10 makemodel is a database variable that is concatenate of make and model; engsize is variable for engine size. 11 Database variables: numveh = number of vehicles involved in collision event; typecas is the casualty type (9 = car occupant); classcas = casualty class (1 = driver); severcas is severity of casualty (1 = fatal). yr1stregi is year first registered, assumed to be the model year of the car. 154 UK Data Archive UK Data Archive linked with makemodel data UK Data Archive linked with makemodel data UK Data Archive linked with makemodel 10 and engsize \"first law\" RR analysis data set driver/fatality, % male 73.6% 73.5% 70.6% 70.5% 72.2% % front impact 59.3% 58.9% 56.1% 54.5% 50.0% % rear impact 3.6% 3.6% 3.0% 3.3% 2.1% % off-side impact 22.2% 22.6% 25.0% 25.7% 30.6% % near-/driver-side impact 14.9% 14.8% 15.8% 16.5% 16.7% speed limit 30 mph, % 13.9% 13.9% 12.1% 11.6% 6.9% speed limit 40 mph, % 7.4% 7.5% 7.2% 6.8% 4.2% speed limit 50 mph, % 3.7% 3.9% 3.8% 4.1% 4.2% speed limit 60 mph, % 62.8% 62.4% 62.2% 61.9% 69.4% speed limit 70 mph, % 12.1% 12.3% 14.8% 15.6% 15.3% speed limit, average 55.2 55.3 56.0 56.3 58.2 single carriageway, 2 lanes 78.6% 78.5% 75.7% 74.8% 75.7% We had intended in this study to quantify what Evans describes as a \u00E2\u0080\u009Csecond law\u00E2\u0080\u009D for two cars of equal mass involved in a collision12. However, we had too few data points for equal mass collisions to perform this analysis. The \u00E2\u0080\u009Csecond law\u00E2\u0080\u009D relationship follows. RRb \u00E2\u0089\u00A1 k / M where: RRb \u00E2\u0089\u00A1 (risk of matched pair at given curb mass) / (risk of 1400kg13 matched pair) k = constant determined empirically M = mass of car 1 = mass of car 2 This \u00E2\u0080\u009Csecond law\u00E2\u0080\u009D has direct implications for uniform downsizing of vehicle fleets, such as in response to policies mandating fleet-wide improvements in fuel economy. This relationship shows 12 RRb is used here to distinguish it from the first law, which was denoted as RR. 13 Evans\u00E2\u0080\u0099 choice of 1,400 kg is arbitrary, as one could choose another reference mass. 155 that uniform downsizing will, ceteris paribus, result in increased fatality rates even though the mass of both vehicles remains equal. This observed relationship is consistent with well known laws of physics (conservation of energy and momentum) in that less combined mass means less physical protection for vehicle occupants. B.2.5 Methods: Absolute Risk Analysis This method uses actual fatality risk rates for cars grouped by engine size (a surrogate measure for mass), and then predicts changes in total annual fatalities for hypothetical changes in the fleet composition. The basic data requirements, rationale for selecting this method, and on-road fleet scenarios for this method are provided in the main manuscript. Here we provide a description of the essential details of this method for the three crash modes included in our study: single-car, car- pedestrian, and two-car. Uncertainties were quantified assuming crash events and fatalities adhere to a poisson process as has been shown by Evans, and that standard errors for single quantities can be estimated as n\u00C2\u00BD as long as n is roughly 7 or larger [7]. The uncertainties for combined quantities such as ratios or differences of single quantities, then conventional error propagation formulas are employed. The method applied to single-car fatalities and car-pedestrian fatalities follows: \u00EF\u0082\u00B7 risk factor \u00E2\u0089\u00A1 Ki = Fi / Ri \u00EF\u0082\u00B7 i = mass categories \u00EF\u0082\u00B7 Fi = number of total single-car or car-pedestrian fatalities involving mass category i \u00EF\u0082\u00B7 Ri \u00E2\u0089\u00A1 baseline proportions of registrations for mass category i \u00EF\u0082\u00B7 scenarios are developed to produce new distributions of fleets \u00E2\u0089\u00A1 Ri\u00E2\u0080\u0099 \u00EF\u0082\u00B7 finally, new fatality counts are estimated Fi\u00E2\u0080\u0099 \u00E2\u0089\u00A1 Ki * Ri\u00E2\u0080\u0099 \u00EF\u0082\u00B7 note: Ki factors are proportional to the basic fatality rate per quantity of registered cars Example: F1 = 50 fatalities in a given year for mass group #1 R1 = 0.15 (15% of on road registrations for mass group #1) 156 K1 = 50 / 0.15 = 333 now assume a scenario with new distribution of cars: R1\u00E2\u0080\u0099= 0.10 F1\u00E2\u0080\u0099 = 333 * 0.10 = 33 fatalities (decrease of 17, or 34% for mass category #1) Statistics for the single-car and car-pedestrian analysis data sets as compared to the complete UK Data Archive are provided in Tables B8 and B9, respectively. Table B8 Single-car Absolute risk analysis: comparison of data set statistics. single-car analysis data set (linked with engsizeDFT14) UK Data Archive linked with makemodel data only UK Data Archive number of cars (numveh=1 ) 1 1 1 type of casualty (typecast=9) car car car class of casualty (classcas=1or2) driver or passenger driver or passenger driver or passenger severity of casualty (severcas=1) fatal fatal fatal crash years, range 1994-2005 1994-2005 1994-2005 car year 1st registered, range 1990-2005 1990-2005 all (no data) car year 1st registered, median 1991 1991 no data car year 1st registered, mean 1993 1993 no data car year 1st registered, std dev 3.5 3.4 no data fatalities, total number 5,104 5,135 5,371 fatalities, mean age 31.0 31.0 31.2 fatalities, std dev age 17.3 17.3 17.4 fatalities, % male 78.1% 78.1% 78.2% drivers, mean age 31.6 31.7 31.8 14 Variable from the make and model database provided by DFT: engsizeDFT is engine size field. 157 single-car analysis data set (linked with engsizeDFT14) UK Data Archive linked with makemodel data only UK Data Archive drivers, std dev age 16.4 16.5 16.5 drivers, % male 86.1% 86.1% 86.1% no skid, no overturn (skidoturn15 = 0) 38.0% 38.0% 38.0% skid only (skidoturn = 1) 29.6% 30.2% 30.2% skid and overturn (skidoturn = 2) 20.3% 19.7% 19.6% overturn only (skidoturn = 5) 12.1% 12.1% 12.1% hit object in carriageway: none 79.2% 79.0% 79.1% hit object out of carriageway: tree 35.7% 35.3% 35.1% road: single carriageway16, 2 lanes 71.0% 71.2% 70.9% speed limit 30 mph, % 22.9% 23.4% 23.1% speed limit 40 mph, % 8.7% 8.6% 8.3% speed limit 50 mph, % 2.7% 2.8% 3.0% speed limit 60 mph, % 49.7% 49.5% 49.6% speed limit 70 mph, % 15.9% 15.8% 16.0% speed limit average mph 52.7 52.6 52.7 15 Database variable: skidoturn defines the single car collision event. 16 \u00E2\u0080\u009Csingle carriageway\u00E2\u0080\u009D is defined as a road type without a physical barrier separating each direction of travel; \u00E2\u0080\u009Cdual carriageway\u00E2\u0080\u009D is a road type with some form physical barrier separating each direction of travel; single and dual carriageway roads can have multiple lanes in each direction (e.g., 3, 4, 5, 6 or more total lanes). 158 Table B9 Comparison of data set statistics for car-pedestrian absolute risk analysis. car-pedestrian analysis data set (linked with engsizeDFT) UK Data Archive linked with makemodel data only UK Data Archive number of cars (numveh ) 1-16 1-16 1-16 percentage involving just 1 car 86.2% 86.5% 86.6% type of casualty (typecast=9) car car car class of casualty (classcas=3) pedestrian pedestrian pedestrian severity of casualty (severcas=1) fatal fatal fatal crash years, range 1994-2005 1994-2005 1994-2005 car year 1st registered, range 1990-2005 1990-2005 all (no data) car year 1st registered, median 1991 1991 no data car year 1st registered, mean 1993 1993 no data car year 1st registered, std dev 3.5 3.4 no data fatalities, total number 5,192 6,385 7,275 fatalities, mean age 49.9 50.0 50.0 fatalities, std dev age 27.1 27.2 27.2 fatalities, % male 64.9% 64.6% 64.7% drivers, mean age 36.9 36.9 36.8 drivers, std dev age 15.9 15.9 15.8 drivers, % male 76.6% 77.3% 77.6% pedestrian location: crossing in carriageway outside of crossing 55.3% 55.3% 54.6% pedestrian physical facility: no crossing facility within 50m 77.3% 77.2% 77.2% road: single carriageway, 2 lanes 69.8% 70.1% 69.8% 159 The two-car analysis procedure is as follows. As there are two groups involved rather than one, a multiplicative risk model is employed. \u00EF\u0082\u00B7 risk factor \u00E2\u0089\u00A1 Ki-j = Fi-j / (Ri * Rj) \u00EF\u0082\u00B7 Fi-j \u00E2\u0089\u00A1 the number of fatalities between cars in mass categories i and j \u00EF\u0082\u00B7 Ri and Rj \u00E2\u0089\u00A1 baseline proportions of registrations for mass categories i and j \u00EF\u0082\u00B7 Scenarios are developed to produce new distributions of fleets \u00E2\u0089\u00A1 Ri\u00E2\u0080\u0099 and Rj\u00E2\u0080\u0099 \u00EF\u0082\u00B7 Finally, new fatality counts are estimated Fi-j\u00E2\u0080\u0099 \u00E2\u0089\u00A1 Ki-j * (Ri\u00E2\u0080\u0099 * Rj\u00E2\u0080\u0099) For example: F1-2 = 45 fatalities in a given year R1 = 0.10 (mass group #1 = 10% all cars) R2 = 0.05 (mass group #2 = 5% all cars) K1-2 = 70 / (0.10 * 0.05) = 9000 now assume a scenario with new distribution of cars: R1\u00E2\u0080\u0099= 0.15 and R2\u00E2\u0080\u0099 = R2 = 0.05 F1-2\u00E2\u0080\u0099 = 9000 * (0.15 * 0.05) = 68 (increased from 45) fatalities involving two-car collisions between cars of mass categories #1 and #2 For all three absolute risk analyses, the same 8 categories were included as described for the scenarios, using engine size as a surrogate for mass (i.e., 701-1,000 CC as group 1, \u00E2\u0080\u00A6 3,000 and over CC as group 8.). These groupings functioned well for the single-car and car-pedestrian analyses, but not for the two-car analysis. For two-car collisions, there were 64 K factors (i.e., K1-1, K1-2, \u00E2\u0080\u00A6 K8-8). It was found that statistically significant estimates of fatality risk could be made in only 13 of the 64 grouped pairs of collisions due to low fatality counts. This was strictly an outcome due to limited number of observations in the two-car data set. A work around solution was developed by creating just 3 categories: \u00E2\u0080\u009Clighter\u00E2\u0080\u009D (701-1,500 CC), \u00E2\u0080\u009Cmid-mass\u00E2\u0080\u009D (1,501-2,000 CC), and \u00E2\u0080\u009Cheavier\u00E2\u0080\u009D (2,001 to over 3,000 CC). With these coarser groupings, then the low fatality count problems were resolved. The fatality risk factors and scenario market shares for two-car collisions were calculated with these groupings, which enabled the use of the same scenario market shares for two-car collisions as used for the single-car and car-pedestrian analyses. 160 B.2.6 Methods: Statistical Models for Curb Mass and CO2/km Table B10 summarizes statistical models for curb mass and CO2/km. Regression analyses were all developed as ordinary least squares (OLS) fit using R statistical software (v2.5.1) or Microsoft Excel 2003. Regression models #2 and #9 were used in this study as follows. \u00EF\u0082\u00B7 Model #2 was used in the \u00E2\u0080\u009Cfirst law\u00E2\u0080\u009D RR analysis and the AnalyticaTM simulation analysis to estimate curb mass given engine size in the data set. \u00EF\u0082\u00B7 Model #9 was used in the \u00E2\u0080\u009Cfirst law\u00E2\u0080\u009D RR analysis to estimate gCO2/km given engine size in the data set. Additional models are included for general reference only. All OLS regression models are based on natural log transformed variables because this was required to satisfy the normality assumption. Homoskedacity was verified by visual review of residual plots. The ln model form also has the convenient property that the b1 and b2 slope coefficients represent elasticity. E.g., 1% change in diesel car mass results in a 1.113% change in CO2/km. 161 Table B10 Summary results of regression models relating curb mass (kg) and gCO2/km to explanatory variables using JATO data. intercept, a slope, b1 residual Comment model regression model n R2 p point 95% CI 95% CI std err point 95% CI 95% CI std err std err # 1 ln (kg curb mass) = a + b1 * ln (m wheelbase) + error 3,298 0.611 < 0.0001 4.695 4.625 4.765 0.036 2.619 2.547 2.690 0.036 0.0020 alternate equation: curb mass = exp(error) * exp(a) * (wheelbase)^b1 # 2 ln (kg curb mass) = a + b1 * ln (cc engine size) + error 3,298 0.587 < 0.0001 3.705 3.603 3.807 0.052 0.469 0.455 0.482 0.0069 0.0021 shows that engine size alone has substantial predictive power for mass # 3 ln (kg curb mass) = a + b1 * ln (cc engine size) + b2 * ln (m overall length) + error 3,298 0.769 < 0.0001 3.530 3.453 3.607 0.039 0.231 0.217 0.244 0.007 0.0016 b2 = 1.332 [1.281- 1.383 95% CI and std error=0.026] # 4 ln (petrol and diesel gCO2/km) = a + b1 * ln (kg curb mass) + error 3,242 0.494 < 0.0001 -1.321 -1.549 -1.092 0.117 0.902 0.870 0.933 0.016 0.0028 shows that mass has substantial predictive power for CO2/km, regardless of fuel (diesel/petrol) # 5 ln (diesel gCO2/km) = a + b1 * ln (kg curb mass) + error 1,294 0.780 < 0.0001 -3.016 -3.252 -2.779 0.121 1.113 1.081 1.146 0.016 0.0027 # 6 ln (petrol gCO2/km) = a + b1 * ln (kg curb mass) + error 1,948 0.728 < 0.0001 -2.224 -2.429 -2.020 0.104 1.040 1.011 1.068 0.014 0.0026 162 intercept, a slope, b1 residual Comment model regression model n R2 p point 95% CI 95% CI std err point 95% CI 95% CI std err std err # 7 ln (diesel kg curb mass) = a + b1 * ln (cc engine size) + error 1,317 0.614 < 0.0001 3.005 2.820 3.190 0.094 0.568 0.544 0.592 0.0124 0.0028 # 8 ln (petrol kg curb mass) = a + b1 * ln (cc engine size) + error 1,980 0.633 < 0.0001 3.897 3.785 4.009 0.057 0.439 0.424 0.454 0.0075 0.0026 # 9 ln (gCO2/km) = a + b1 * ln (cc engine size) + error 1,948 0.841 < 0.0001 0.984 0.901 1.068 0.043 0.568 0.557 0.579 0.0056 0.0020 163 Because models #2 and #9 were used in one of the fatality risk analyses in this study, we provide the line fit and residual plots in Figures B14 and B15. Figure B14 Regression model #2 line fit and residual plot. ln (engine size) Residual Plot -1.5 -1 -0.5 0 0.5 6.0 7.0 8.0 9.0 ln (engine size) Re si du al s outliers are: Caterham & Lotus ln (engine size) Line Fit Plot 6.0 7.0 8.0 9.0 6.0 7.0 8.0 9.0 ln (engine size) ln (k er b w t) ln (kerb wt) Predicted ln (kerb wt) C ur b kg curb kg curb kg 164 Figure B15 Regression model #9 line fit and residual plot. ln(gCO2/km) Line fit plot 4.0 4.5 5.0 5.5 6.0 6.5 6.0 7.0 8.0 9.0 ln (engine size) ln (g C O 2/ km ) ln(gCO2/km) Predicted ln(gCO2/km) ln (engine size) Residual Plot -0.4 -0.2 0 0.2 0.4 0.6 0.8 6.0 6.5 7.0 7.5 8.0 8.5 9.0 ln (engine size) Re si du al s 165 B.3 RESULTS Figure B16 is a plot of the \u00E2\u0080\u009Cfirst law\u00E2\u0080\u009D RR analysis for the five mass groupings. While the model is statistically significant, it is limited by the number of observational mass groupings that could be created. Figure B17 is a line fit plot of the conditional risk model that relates two-car collision % risk of serious injury or fatality to CO2 emission rate Figure B16 Line fit plot of \u00E2\u0080\u009Cfirst law\u00E2\u0080\u009D RR regression model that relates mass ratio to RR fatalities in two car collisions. Top panel models the relationship as RR = \u00C2\u00B5\u00CE\u00BB. Bottom panel models the relationship as RR = \u00CE\u00B1 + \u00CE\u00B2 * \u00C2\u00B5. \u00C2\u00B5 = mass ratio. 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 0.0 0.5 1.0 1.5 2.0 mass ratio R R RR Predicted RR 0.0 0.5 1.0 1.5 2.0 2.5 0.0 0.1 0.2 0.3 0.4 0.5 ln(wt ratio) ln (R R ) ln(RR) Predicted ln(RR) ln(mass ratio) 166 Figure B17 Line fit plot of conditional risk model that relates two-car collision % risk of serious injury or fatality to CO2 emission rate. 0 2 4 6 8 10 12 100 150 200 250 300 350 CO2/km mean ris k, % F or SI risk, % Predicted F or SI risk, % B.4 DISCUSSION To adequately examine a power law relationship then data spanning at least two orders of magnitude would be best. However our UK data span less than this. We cannot further subdivide our RR data into more mass categories to increase the span of our mass ratios because our fatality counts would become too low to employ the assumption of a poisson distribution [7]. To examine the effect of point of impact in interpreting the \u00E2\u0080\u009Cfirst law\u00E2\u0080\u009D RR results, we attempted to fit the data to the following model RR = A * \u00CE\u00BC\u00CE\u00BB with the following result. A = 0.15 [95% CI -0.66 to + 0.93] p < 0.63 \u00CE\u00BB= 4.84 [95% CI 1.56 to + 8.12] p < 0.018 R2 = 0.88 This model is invalid based on the p-value for the constant A. The ratio of river fatality risk has been found to fit the power law relationship in all published work that was found in the literature [25, 40, 41]. Nonetheless, we attempted to fit our data to a linear model as described in Section 3.3, and these data are plotted in Figure B16. With the linear model and \u00C2\u00B5 = 2, the point estimate of RR is 14.9 (i.e., ratio of driver fatality of lighter cars to heavier cars is 14.9 for a mass ratio of 2). 167 A limitation of our fleet simulation RR analysis (using AnalyticaTM) is that we assumed randomized collisions between two cars. In general, the issue of selective recruitment has been found in traffic safety where drivers exhibiting varied levels of risk-taking behaviors are associated with different uses of technology such as seat belt use [2]. This means that drivers of lighter and heavier cars may not be equally likely to be involved in collisions. Additionally, randomized collisions are based only on vehicle counts in our model, and annual distance traveled can vary by mass of cars. While we have no data on risk taking behavior, we were able to perform some validation of our assumption by comparing annual travel distance data [42] and crash involvements disaggregated by engine size groups [32, 33] as shown in Figures B18 and B19. Although the four largest engine size groups averaged approximately 40% higher annual travel than the four smallest engine size groups (under 700 CC group excluded due to small registration counts), a comparison of crash involvements reveals no obvious deviation from registration counts for all groups. Hence the assumption of randomized crash events appears to be reasonable. Figure B18 Mean annual travel distance by engine size category in 1998 and 2004 for cars in UK National Travel Survey [42]. Includes vehicles coded as \u00E2\u0080\u009Ccars\u00E2\u0080\u009D and \u00E2\u0080\u009CLandrover/Jeep\u00E2\u0080\u009D, but excludes \u00E2\u0080\u009Clight vans\u00E2\u0080\u009D. Error bars are +/- one standard deviation. n=8,207 for year 2004; n=8,692 for year 1998. 0 5,000 10,000 15,000 20,000 25,000 70 1-1 00 0 10 01 -12 00 12 01 -15 00 15 01 -18 00 18 01 -20 00 20 01 -25 00 25 01 -30 00 30 01 & ov er Engine size group, CC An nu al m ile s 1998 2004 168 Figure B19 A comparison of the proportions of registered cars and recorded crash involvements for year 2005, disaggregated by engine size [32, 33]. 0% 5% 10% 15% 20% 25% 30% 35% re gi st ra tio ns o r c ra sh e ve nt s engine size (CC) 2005 registrations 2005 crash events 169 This research indicates that the distribution of vehicle mass is important in achieving traffic safety goals. It is therefore important to note that the UK trend in registrations has seen a growing disparity in vehicle mass as evidenced by the changes shown in Figure B20 [43. 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SMMT, \u00E2\u0080\u009CMotor industry facts - 2007.\u00E2\u0080\u009D 2007, Society of Motor Manufacturers and Traders (SMMT) (www.smmt.co.uk). "@en . "Thesis/Dissertation"@en . "2010-05"@en . "10.14288/1.0069914"@en . "eng"@en . "Resource Management and Environmental Studies"@en . "Vancouver : University of British Columbia Library"@en . "University of British Columbia"@en . "Attribution-NonCommercial-NoDerivatives 4.0 International"@en . "http://creativecommons.org/licenses/by-nc-nd/4.0/"@en . "Graduate"@en . "An integrated assessment of climate mitigation policy, air quality and traffic safety for passenger cars in the UK"@en . "Text"@en . "http://hdl.handle.net/2429/23816"@en .