a case study where tax regimes based on tailpipe CO2 emission rates have ...... Number (1,000's) of registered private and light goods vehicles by engine size. .151 ...... balance of the research literature indicates the answer is âyes. ...... CERC, Source Apportionment for London using ADMS-Urban. prepared by Cambridge.
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 © Eric Mazzi, 2010
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 “first law” 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.
ii
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
2
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
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 iii
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 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
3
4
PATHWAYS FROM POLICY TO RISKS ................................................................................ 33
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
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
REGULATING CAR MASS FOR CONCURRENT TRAFFIC SAFETY AND CLIMATE MITIGATION BENEFITS ............................................................................................................................. 71
5
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
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 iv
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
v
APPENDIX B - SUPPORTING INFORMATION FOR CHAPTER 3 “TAILPIPE CO2 EMISSION REGULATIONS AND AUTO COLLISION RISKS: A UK CASE STUDY” ................................................................ 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: “FIRST LAW” 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
vi
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 “first law” 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 = . is calculated in the simulation assuming randomized collision events sampled from a mass distribution representative of the UK on-road car fleet. 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 “Euro” 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 vii
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’s) of registered private and light goods vehicles by engine size. .151
Table B7
“First law” 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
viii
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’ “first law” for two-car crashes [20]: RR = μλ (λ = 3.8), where μ 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 “large” also means “heavy,” and “small” also means “light”. 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 ix
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 19902005 is based on industry data. Projected shares from 2006-2007 are based on industry forecasts, and from 2008-2020 based on authors’ projections. The focus of this study is on the area between the actual/projection curve and the “no growth” 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. “Additional diesels” are defined as the number of petrol vehicles switched to diesel beyond the “no growth” 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 “postEuro IV” 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
x
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. “Lighter” group is comprised of engine size ranges 701-1,000 cubic centimeter (CC), 1,001-1,200 CC, and 1,201-1,500 CC. “Mid-mass” includes 1,501-1,800 CC and 1,801-2,000 CC. “Heavier” 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
“First law” 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
xi
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 ≈ 1 for mass ratio ≈ 1), the RR for purely front-to-driver side impacts has been observed to pass through 10 at the origin (i.e., RR ≈ 10 for mass ratio ≈ 1) [27]. Because of this relationship, the RR for side impact collisions is commonly fit to the equation RR = A * μλ, where statistical models reveal A ≈ 10.................................86
Figure A1
Model for estimating annual number of scrapped vehicles, based on UK deregistration 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 xii
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 “first law” RR regression model that relates mass ratio to RR fatalities in two car-collisions. Top panel models the relationship as RR = µλ. Bottom panel models the relationship as RR = α + βµ. µ = mass ratio. ………………………….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 “cars” and “Landrover/Jeep”, but excludes “light vans”. 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
xiii
LIST OF ACRONYMS
ACEA
European Automobile Manufacturers’ 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
xiv
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 “light trucks” 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 “cars.” 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 – 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 “CO2” 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 “collision” or “crash” are generally preferred over the term “accident.” 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, multiplecar, single-car, car-pedestrian, car-bicycle, car-heavy goods, car-motorcycle, and heavy goodspedestrian. Crashworthiness: relating to physical design or technology of vehicles aimed at minimizing injuries or fatalities when collisions occur (sometimes described as “secondary safety”). Crash prevention or avoidance: relating to the ability of the driver or vehicle technology to avoid a collision (sometimes described as “primary safety”). xv
Delta V (or ∆V): 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’clock point of impact) it can be expressed as: ∆V1 = (V1 + V2) * M2 / ( M1 + M2) or ∆V1 = SQRT ( 2 * Ea * M2 / [ M1 * ( M1 + M2) ] ) ∆V2 = (V1 + V2) * M1 / ( M1 + M2) or ∆V2 = SQRT ( 2 * Ea * M1 / [ M2 * ( M1 + M2) ] ) V ≡ velocity; M ≡ mass; Ea ≡ total kinetic energy absorbed in the crash subscript 1 is case car, subscript 2 is other car ∆V is computed for crashes in the U.S. Crashworthiness Data System (CDS), but no known ∆V 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. Onroad 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) precrash, (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. “Quasi-induced exposure” methods make use of data where one driver xvi
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 “kerb” in the UK) mass data beginning in 1996 includes 75 kg to represent the mass of the driver. In this study the words “light” or “lighter,” and “heavy” or “heavier” refer to lesser or greater mass, respectively. The U.S. EPA classifies cars for purposes of dynamometer testing according to their “inertia weight” 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 CMax, or BMW 318. Model Year: the year a vehicle is manufactured and sold as per the manufacturer. In this study, the “year first registered” 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 xvii
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 “first law” 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 “secondary safety”). 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 “overall length”), width (also called “overall width”), 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° turn (“turn distance”). In this study, the words “small” or “smaller” and “large” or “larger” 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 “other,” “partner,” or “bullet” car. Struck car: the first car of interest in a risk calculation, also called the “case”, “subject”, “self”, “own”, “target”, or “driven” 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. xviii
PREFACE
This thesis is written following the manuscript thesis format of the University of British Columbia (UBC). UBC’s 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 (“I”). The exception is the first person plural (“we”) 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 “The Dictionary of Epidemiology” by John Last (4th edition, Oxford University Press, 2000). As examples, the phrases “ecologic fallacy,” “relative risk,” and “disability adjusted life years” are all defined by Last. For terminology rooted in economics, I make use of the text “Economics of the Public Sector” by Joseph Stiglitz (3rd edition, W.W. Norton and Company, 2000). As examples, the phrases “rebound effect” and “social cost” are defined by Stiglitz. For general words, I have used Merriam-Webster’s online dictionary at www.merriamwebster.com.
1
See http://www.grad.ubc.ca/students/thesis/index.asp?menu=002,002,000,000 for more information.
xix
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’ll 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 – 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’t 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’ve frequently collaborated with and even some that I’ve 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’ve 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) “oh yes, Hadi cares about equity.” I don’t think it was meant as a compliment in particular. But it struck me right away – yes, I thought, he does care about equity which is one reason I’m privileged to work with him!
xx
DEDICATION
. I dedicate this thesis to my family: Theresa, Alex, and Angeline.
xxi
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.
xxii
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’s current finding is that “observational evidence from all continents and most oceans show that many natural systems are being affected by regional climate changes” and that “it is likely that anthropogenic warming has had a discernible influence on many physical and biological systems.” [3] Examples of policymakers’ 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 “The 2008 Climate Change Act made Britain the first country in the world to set legally binding ‘carbon budgets’, aiming to cut UK emissions by 34% by 2020 and at least 80% by 2050” [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 “bring together … energy policy … and … climate mitigation policy.” [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 1
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’ 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’s 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]. Million tonnes CO2 equivalent
900
180
800
160
700
140
600
120
500
100
400
80
300
60 total (left Y-axis)
200 100 0 1989
40
total transport (right Y-axis)
20
passenger cars (right Y-axis)
0 1991
1993
1995
1997
1999
2001
2003
2005
2007
2
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 “first law” 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’s 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].
3
The relationship between vehicle mass, tailpipe CO2/km, fuel type, PM10
Figure 1.2
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’ “first law” for two-car crashes [20]: RR = μλ (λ = 3.8), where μ 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].
RR ≡ fatality relative risk RR = (risk at variable mass) / (risk at mean mass) 10.0 where mean car mass = 1456 kg
500 450
grams/km CO2 or PM *10000
350 300 RR = 1.0
250
1.0
200 150
RR, fatality relative risk
400
100 50 0 500
0.1 1000
1500 2000 Curb mass (kg)
2500
3000
3500
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)
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
4
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’s 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 “large” also means “heavy,” and “small” also means “light”. In terms of fuel choice, alternative-fuelled vehicles have grown dramatically in recent years but still comprise less than 1% [29]. Small +30% 1998-2007
70%
Medium -15% 1998-2007 Large +55% 1998-2007
60% New registrations
Diesel +182% 1998-2007
50% 40% 30% 20% 10% 0% 1994
1996
1998
ACEA voluntary CO2 agreement
2000 UK CO2-based VED
2002
2004
UK CO2-based company car tax
2006
2008
5
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 “additional diesels” 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.
6
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’ “first law” 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.
7
m. Estimate changes in the “first law” 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 Research Task
1. Quantitative Integrated Assessment modeling of passenger car CO2 policy incorporating air quality, traffic safety, and other relevant risks
2. Assess the impact of diesel growth on CO2 emissions, air quality, and human health
Summary of research tasks, data sources, and analysis methods. Parameters or variables Demonstrate the importance of choice of decision criteria using UKspecific parameters Demonstrate the importance of a multiple risk framework using UK-specific parameters
Sources of data and information resources
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].
Outline a framework for quantitative policy analysis of multiple risks
Selected literature on quantitative policy analysis [38, 39].
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
Concentrationresponse factors
Peer reviewed literature [49, 50] and UK-specific studies [30, 51]
Analysis methods 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]. 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. Developed an influence diagram showing basic linkages between policies and risks.
Impact pathway analysis. Mortality for ages ≥ 30. Quantify health outcomes as annual counts.
Data gaps; research challenges; limitations; other comments
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.
Only PM10 mortality and partial morbidity is quantified. Work is completed and published [41].
8
Research Task
3. Assess the correlation between tailpipe CO2 emission rates and various traffic safety measures in the UK, using curb mass as the primary variable
1.4
Parameters or variables
Sources of data and information resources
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
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
Analysis methods 1. Replicated Evans’ method for calculating “first law” of relative fatality risk using UK data [20, 54]. 2. Graphically and numerically compared CO2/km vs. conditional fatality risk using DFT’s results [55]. 3. Using Mengert’s method [56] and policyrelevant scenario of varied fleet mass composition, estimated changes in total fatalities for: single-car, two-car, and carpedestrian crashes. 4. Using the same fleet composition scenarios for which changes in total fatalities are estimated, calculated “first law” relative risk using Latin Hypercube methods [57] to simulate two-car collisions.
Data gaps; research challenges; limitations; other comments
Data on casualties, vehicles, and crash modes for every individual UK car crash reported to police from 19942005 (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.
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
9
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’s (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 sparkignition gasoline and compression-ignition diesel cars, comprising 99.3% of all new
2
I use the term “conventional diesel technology” to include diesel cars with or without particle filters.
10
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’s 11
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 “that 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.” 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 “Substantial 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.” The European Environmental 12
Agency also published findings that “Action to combat climate change will deliver considerable ancillary benefits in air pollution abatement” [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 “environmental costs” because “diesels have high emissions of particulate matter”. 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’s 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 13
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 “be responsible for several thousand additional fatalities over the life of each model year’s cars.” 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 “mass may not be fundamental to safety” [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:
14
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.
15
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 “non-subtle” 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].
16
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 “yes.” But it is a conditional “yes” 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. Chapter 5 provides conclusions including relationship of the policy, air quality, and traffic safety analyses, discussion of my thesis research in relation to current research hypotheses, strengths and weaknesses, significance, and recommendations for further research.
17
1.6 1.
REFERENCES Pacala, S. and R. Socolow, Stabilization wedges: solving the climate problem for the next 50 years with current technologies. Science, 2004. 305: p. 968-972.
2.
Ribeiro, K., et al., Transport and its infrastructure, in Climate Change 2007: Mitigation. Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. 2007, Cambridge University Press.
3.
Parry, M., et al., Technical Summary. Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. 2007: Cambridge, UK.
4.
London. Mayor's Priorites. 2009 [cited September 20, 2009]; Available from: http://www.london.gov.uk/mayor/priorities/environment.jsp.
5.
HM-Government, The UK Low Carbon Transition Plan. 2009.
6.
Keith, D., Geoengineering. Nature, 2001. 409: p. 420.
7.
DTI, Meeting the energy challenge: a white paper on energy (May 2007). 2007, Department of Trade and Industry.
8.
DECC. UK Department of Energy & Climate Change. 2009 [cited; Available from: www.decc.gov.uk.
9.
CFIT, Transport and climate change. 2007, Commission for Integrated Transport (www.cfit.gov.uk).
10.
DEFRA, The UK Government Sustainable Development Strategy. 2005, UK Department for Environment, Food and Rural Affairs (DEFRA).
11.
DFT, Delivering better transport: priorities for 2007-08 and beyond. 2007, UK Department for Transport.
12.
HM-Treasury, Budget 2007 - Building Britain’s long-term future: prosperity and fairness for families. 2007, HM Treasury (UK).
13.
HM-Government. Office for Low Emission Vehicles. 2009 [cited; Available from: http://interactive.bis.gov.uk/lowcarbon/.
14.
MORI, Assessing the impact of Graduated Vehicle Excise Duty: quantitative report. 2003, UK Department for Transport (www.dft.gov.uk) and MORI (www.mori.co.uk).
15.
IR, Report on the evaluation of the company car tax reform: stage two. 2006, UK Inland Revenue (www.inlandrevenue.gov.uk).
18
16.
DECC, Statistics about climate change. 2009, Department of Energy & Climate Change.
17.
Fontaras, G. and Z. Samaras, A quantitative analysis of the European Automakers’ voluntary commitment to reduce CO2 emissions from new passenger cars based on independent experimental data. Energy Policy, 2007. 35: p. 2239-2248.
18.
DFT, UK Transport Statistics online database (www.dft.gov.uk/pgr/statistics). 2008, UK Department for Transport.
19.
NAEI, National Atmospheric Emission Inventory online database (www.naei.org.uk). 2008.
20.
Evans, L. and M. Frick, Mass ratio and relative driver fatality risk in two-vehicle crashes. Accident Analysis and Prevention, 1993. 25(2): p. 213-224.
21.
JATO. Jato Dynamics new car database for Great Britain. 2007 [cited June 3, 2007].
22.
Verboven, F., Quality-based tax discrimination and tax incidence: evidence from gasoline and diesel cars. The RAND Journal of Economics, 2002. 33(2): p. 275-297.
23.
Lane, B., Car buyer research report: consumer attitudes to low carbon and fuel-efficient passenger cars. 2005, Low Carbon Vehicle Partnership (UK).
24.
SMMT, New car CO2 report 2008. 2008, Society of Motor Manufacturers and Traders Ltd. (www.smmt.co.uk).
25.
DeCicco, J., F. An, and M. Ross, Technical options for improving the fuel economy of U.S. cars and light trucks by 2010–2015. 2001, American Council for an Energy-Efficient Economy www.aceee.org.
26.
Fulton, L., P. Cazzola, and F. Cuenot, IEA Mobility Model (MoMo) and its use in the ETP 2008. Energy Policy, 2009. 37: p. 3758-3768.
27.
Sperling, D. and D. Gordon, Two Billion Cars: Driving Toward Sustainability. 2009: Oxford University Press.
28.
Kahane, C., Vehicle weight, fatality risk and crash compatibility of model year 1991-99 passenger cars and light trucks. 2003, National Highway Traffic Safety Administration (U.S.).
29.
SMMT, Motor industry facts - 2008. 2008, Society of Motor Manufacturers and Traders (SMMT) www.smmt.co.uk.
30.
COMEAP, Quantification of the effects of air pollution on health in the United Kingdom. 1998: The Stationary Office London (www.tsonline.co.uk).
31.
Dargay, J. and M. Hanly, Volatility of car ownership, commuting mode and time in the UK. Transportation Research Part A: Policy and Practice, 2007. 41: p. 934-948.
19
32.
Sorrell, S., The rebound effect: an assessment of the evidence for economy-wide energy savings from improved energy efficiency. 2007, UK Energy Research Centre (www.ukerc.ac.uk).
33.
WHO, Environmental burden of disease: Country profiles (United Kingdom). 2007, World Health Organization.
34.
Parry, I., M. Walls, and W. Harrington, Automobile externalities and policies. Journal of Economic Literature, 2007. XLV: p. 373-399.
35.
Woodcock, J., et al., Energy and Health Series: Energy and Transport. The Lancet, 2007. 370: p. 1078-1088.
36.
Jacoby, H., R. Prinn, and R. Schmalensee, Kyoto's unfinished business. Foreign Affairs, 1998. July/August: p. 54-66.
37.
Levy, D. and S. Rothenberg, Heterogeneity and Change in Environmental Strategy: Technological and Political Responses to Climate Change in the Global Automobile Industry, in Organizations, Policy and the Natural Environment: Institutional and Strategic Perspectives, A. Hoffman and M. Ventresca, Editors. 2002, Stanford University Press.
38.
Morgan, M.G. and M. Henrion, Uncertainty: a guide to dealing with uncertainty in quantitative risk and policy analysis. 1992: Cambridge University Press.
39.
Morgan, M.G., et al., Why Conventional Tools for Policy Analysis are Often Inadequate for Problems of Global Change. Climatic Change, 1999. 41: p. 271-281.
40.
SMMT, New vehicle registrations in the UK. 2009, Society of Motor Manufacturers and Traders (www.smmt.co.uk).
41.
Mazzi, E. and H. Dowlatabadi, Air quality impacts of climate mitigation: UK policy and passenger vehicle choice. Environmental Science & Technology, 2007. 41: p. 387-392.
42.
DOH, Health statistics online data. 2006, UK Department of Health.
43.
GAD, Government Actuary's Department (GAD) online statistics. 2006, United Kingdom Government Actuary's Department (GAD).
44.
Hayman, G., et al., Modelling of Tropospheric Ozone (AEAT/ENV/R/1858 issue 2). 2005, Department for Environment, Food and Rural Affairs, the Scottish Executive, the National Assembly of Wales and the Northern Ireland Department of the Environment.
45.
Hayman, G., et al., Modelling of Tropospheric Ozone Formation. 2002: prepared by AEA Technology for UK Department for Environment, Food and Rural Affairs (DEFRA).
46.
Stedman, J., et al., Revised PM10 projections for the UK for PM10 objective analysis. 2002: prepared by AEA Technology for UK Department for Environment, Food and Rural Affairs
20
(DEFRA) and The National Assembly for Wales, The Scottish Executive and the Department of the Environment in Northern Ireland. 47.
Stedman, J., et al., Quantification of the health effects of air pollution in the UK for revised PM10 objective analysis. 2002: AEA Technology and UK Department of Health.
48.
Stedman, J., et al., Baseline PM10 and NOx projections for PM10 objective analysis. 2001: prepared by AEA Technology for UK Department for Environment, Food and Rural Affairs (DEFRA) and The National Assembly for Wales, The Scottish Executive and the Department of the Environment in Northern Ireland.
49.
Dockery, D., et al., An association between air pollution and mortality in six U.S. cities. New England Journal of Medicine, 1993. 329: p. 1753-1759.
50.
Pope, C.A., et al., Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. Journal of the American Medical Association, 2002. 287(9): p. 11321141.
51.
COMEAP, Statement on long-term effects of particles on mortality. 2001: UK committee on the medical effects of air pollutants (www.advisorybodies.doh.gov.uk/comeap).
52.
DFT, Make-Model crash database linkable to UK Data Archive data (electronic database). 2006, UK Department for Transport.
53.
DFT, Road 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, UK Data Archive (Colchester, Essex).
54.
Evans, L., Traffic Safety. 2004: Science Serving Society.
55.
DFT, Cars: make and model: the risk of driver injury in Great Britain: 2000-2004. 2006, Department for Transport (UK).
56.
Mengert, P. and S. Borener, Overall fatality risk to the public at large related to national weight mix of passenger cars (DOT-TSC-HS070-PM-89-27). 1989, National Highway Traffic Safety Administration (NHTSA).
57.
Lumina, Analytica Professional for Windows 4.1.2.4 User Guide. 2008.
58.
Globalcar, Online car specifications (www.globalcar.com). 2007.
59.
Street-car, Online car specifications (www.street-car.net). 2007.
60.
Whatcar, Online car specifications (www.whatcar.co.uk). 2007.
61.
Cambridge.Systematics.Inc., Moving Cooler: An Analysis of Transportation Strategies for Reducing Greenhouse Gas Emissions. 2009, Urban Land Institute: Washington, D.C.
21
62.
Kopp, R., Transport policies to reduce CO2 emissions from the light-duty vehicle fleet (Issue Brief 12), in Assessing U.S. Climate Policy Options. 2007, Resources for the Future: Washington, DC.
63.
Keefe, R., J. Griffin, and J. Graham, The Benefits and Costs of New Fuels and Engines for Light-Duty Vehicles in the United States. Risk Analysis, 2008. 28(5): p. 1141-1154.
64.
Sansom, T., et al., Surface transport costs and charges: Great Britain 1998. 2001, Department of the Environment, Transport and the Regions (DETR).
65.
DFT, Carbon and sustainability reporting within the renewable transport fuel obligation (RTFO). 2008, UK Department for Transport.
66.
Onoda, T., IEA policies - G8 recommendations and an afterwards. Energy Policy, 2009. 37: p. 3823–3831.
67.
DEFRA, Online database of GHG emissions (http://www.defra.gov.uk/environment/statistics/globatmos/alltables.htm). 2008, Department for Environment, Food and Rural Affairs.
68.
Lave, L., Conflicting objectives in regulating the automobile. Science, 1981. 212(893-899).
69.
Lave, L., Controlling contradictions among regulations. The American Economic Review, 1984. 74(3): p. 471-475.
70.
Maclean, H. and L. Lave, Life cycle assessments of automobile/fuel options. Environmental Science & Technology, 2003. 37(5445-5452).
71.
Bonilla, D., Fuel demand on UK roads and dieselisation of fuel economy. Energy Policy, 2009. 37: p. 3769-3778.
72.
Kohler, J., Y. Jin, and T. Barker, Integrated modelling of EU transport policy. Journal of Transport Economics and Policy, 2008. 42(1): p. 1-21.
73.
Ogden, J., R. Williams, and E. Larson, Societal lifecycle costs of cars with alternative fuels/engines. Energy Policy, 2004. 32(7-27).
74.
Litman, T., Integrating public health objectives in transportation decision-making American Journal of Health Promotion, 2003. 18(1): p. 103-109.
75.
ARB, California state motor vehicles pollution control standards; Request for waiver of federal preemption; Opportunity for Public Hearing, Docket ID #EPA-HQ-OAR2006-0173; 72 FR 21260 (April 30, 2007). 2007, California Air Resources Board.
76.
T&E, Danger ahead: Why weight-based CO2 standards will make Europe's car fleet dirtier and less safe. 2007, European Federation for Transport and Environment: Brussels, Belgium.
22
77.
Hull, A., Policy integration: what will it take to achieve more sustainable transport solutions in cities? Journal of Transport Economics and Policy, 2007. 15: p. 94-103.
78.
Lund, H. and E. Munster, Integrated transportation and energy sector CO2 emission control strategies. Transport Policy, 2006. 13: p. 426–433.
79.
Chatterjee, K. and A. Gordon, Planning for an unpredictable future: Transport in Great Britain in 2030. Transport Policy, 2006. 13: p. 254–264.
80.
Dowlatabadi, H., On integration of policies for climate and global change. Mitigation and Adaptation Strategies for Global Change, 2007. 12: p. 651–663.
81.
Maclean, H. and L. Lave, Evaluating automobile fuel/propulsion system technologies. Progress in Energy and Combustion Science, 2003. 29: p. 1–69.
82.
Cifuentes, L., et al., Hidden Health Benefits of Greenhouse Gas Mitigation. Science, 2001. 293: p. 1257-1259.
83.
Davis, D., et al., Short-term Improvements in Public Health from Global-Climate policies on Fossil-Fuel Combustion: An Interim Report. The Lancet, 1997. 350: p. 1341-1349.
84.
EEA, Air quality and ancillary benefits of climate change policies (EEA Technical report No 4/2006). 2006, European Environment Agency (EEA): Copenhagen, Denmark.
85.
IPCC, Climate Change 2001: Mitigation. 2001: Intergovernmental Panel on Climate Change (www.ipcc.ch).
86.
van Vuuren, D., et al., Exploring the ancillary benefits of the Kyoto Protocol for air pollution in Europe. Energy Policy, 2006. 34(4): p. 444-460.
87.
Mayeres, I. and S. Proost, Should diesel cars in Europe be discouraged? Regional Science and Urban Economics 2001. 31: p. 453–470.
88.
Swart, R., et al., A Good Climate for Clean Air: Linkages between Climate Change and Air Pollution. Climatic Change, 2004. 66: p. 263-269.
89.
Kavalov, B. and S. Peteves, Impacts of increasing automotive diesel consumption in the EU, I.f.E. Directorate General Joint Research Centre (DG JRC), Editor. 2004, European Commission
90.
Jacobson, M., et al., The effect on photochemical smog of converting the U.S. fleet of gasoline vehicles to modern diesel vehicles. Geophysical Research Letters, 2004. 31: p. L02116.
91.
Jacobson, M., Correction to ‘‘Control of fossil-fuel particulate black carbon and organic matter, possibly the most effective method of slowing global warming’’. Geophysical Research Letters, 2005. 110: p. D14105.
23
92.
Cifuentes, L., et al., Assessing the Health Benefits of Urban Air Pollution Reductions Associated with Climate Change Mitigation (2000-2020): Santiago, Sao Paulo, Mexico City, and New York City. Environmental Health Perspectives, 2001. 109(Supplement 3): p. 419425.
93.
Moon, P., A. Burnham, and M. Wang. Vehicle-cycle energy and emission effects of conventional and advanced vehicles (2006-01-0375). in 2006 World Congress. 2006. Detroit, Michigan: Society of Automotive Engineers (www.sae.org).
94.
Tolouei, R. and H. Titheridge, Vehicle mass as a determinant of fuel consumption and secondary safety performance. Transportation Research Part D: Transport and Environment, 2009. 14: p. 385-399.
95.
Broughton, J., The likely effects of downsizing on casualties in car accidents. 1999, Transport Research Laboratory (UK).
96.
Crandall, R. and J. Graham, The effect of fuel economy standards on automobile safety. Journal of Law & Economics, 1989. 32: p. 97-118.
97.
Evans, L. and M. Frick, Car size or car mass: which has greater influence on fatality risk? American Journal of Public Health, 1992. 82(8): p. 1105-1112.
98.
Noland, R., Motor vehicle fuel efficiency and traffic fatalities. The Energy Journal, 2004. 25(4): p. 1-22.
99.
Robertson, L., Blood and oil: vehicle characteristics in relation to fatality risk and fuel economy. American Journal of Public Health, 2006. 96(11): p. 1906-1909.
100.
Thomas, P. and R. Frampton, Car size in UK crashes: the effects of user characteristics, impact configuration, and the patterns of injury. Traffic Injury Prevention, 2002. 3: p. 275-282.
101.
Toy, E. and J. Hammitt, Safety impacts of SUVs, vans, and pickup trucks in two-vehicle crashes. Risk Analysis, 2003. 23(4): p. 641-650.
102.
Wenzel, T. and M. Ross, The effects of vehicle model and driver behavior on risk. Accident Analysis and Prevention, 2005. 37: p. 479-494.
103.
van Auken, R. and J. Zellner, A 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. 2003, Dynamic Research Inc.
104.
Bedard, M., et al., The independent contribution of driver, crash, and vehicle characteristics to driver fatalities. Accident Analysis and Prevention, 2002. 34: p. 717-727.
105.
Evans, L., Comment: the dominant role of driver behavior in traffic safety. American Journal of Public Health, 1996. 86(6): p. 784-786.
24
106.
NHTSA, Final regulatory impact analysis: corporate average fuel economy and CAFE reform for MY 2008-2011 light trucks. 2006, National Highway Traffic Safety Administration.
107.
Wood, D., C. Simms, and C. Glynn. Two car frontal collisions: the role of car mass, collision speed distribution, and frontal stiffness on occupant fatality and injury. in 2005 International IRCOBI Conference on the Biomechanics of Impact. 2005. Prague: International Research Council on the Biomechanics of Impact (IRCOBI).
108.
Padmanaban, J. Influences of vehicle size and mass and selected driver factors on odds of driver fatality. in 47th Annual Proceedings of the Association for the Advancement of Automotive Medicine. 2003.
109.
Austin, R., Vehicle aggressiveness in real world crashes. 2005, National Highway Traffic Safety Administration (U.S.).
110.
Evans, L. How to make cars lighter and safer (2004-01-1172). 2004: Soceity of Automotive Engineers.
111.
Ferguson, S., The effectiveness of electronic stability control in reducing real-world crashes: a literature review. Traffic Injury Prevention, 2007. 8: p. 329-338.
112.
Robertson, L., How to save fuel and reduce injuries in automobiles. Journal of Trauma, 1991. 31(1): p. 107-109.
113.
Joksch, H., Velocity change and fatality risk in a crash - a rule of thumb. Accident Analysis and Prevention, 1993. 25: p. 103-104.
114.
Mosedale, J. and A. Purdy, Excessive speed as a contributory factor to personal injury road accidents. 2003, United Kingdom Department for Transport.
115.
Patterson, T., et al., The effect of increasing rural interstate speed limits in the United States. Traffic Injury Prevention, 2002. 3: p. 316-320.
116.
Evans, L., Causal influence of car mass and size on driver fatality risk. American Journal of Public Health, 2001. 91: p. 1076-1081.
117.
Andersson, A., O. Bunketorp, and P. Allebeck, High rates of psychosocial complications after road traffic injuries. Injury, 1997. 28(8): p. 539-543.
118.
Dobson, A., et al., Women drivers’ behaviour, socio-demographic characteristics and accidents. Accident Analysis and Prevention, 1999. 31: p. 525-535.
119.
Evans, L., Safety-belt effectiveness: the influence of crash severity and selective recruitment. Accident Analysis and Prevention, 1996. 28(4): p. 423-433.
25
120.
Fernandes, R., R.F. Soames-Job, and S. Hatfield, A challenge to the assumed generalizability of prediction and countermeasure for risky driving: different factors predict different risky driving behaviors. Journal of Safety Research, 2007. 38: p. 59-70.
121.
Klauer, S., et al., The impact of driver inattention on near-crash/crash risk: an analysis using the 100-car naturalistic driving study data. 2006, National Highway Traffic Safety Administration.
122.
Krahe, B. and I. Fenske, Predicting aggressive driving behavior: the role of macho personality, age, and power of car. Aggressive Behavior, 2002. 28: p. 21-29.
123.
Langford, J. and S. Koppel, Epidemiology of older driver crashes – identifying older driver risk factors and exposure patterns. Transportation Research, 2006. Part F 9: p. 309-321.
124.
Lourens, P., J. Vissers, and M. Jessurun, Annual mileage, driving violations, and accident involvement in relation to drivers’ sex, age, and level of education. Accident Analysis and Prevention, 1999. 31: p. 593-597.
125.
Redelmeir, D., R. Tibshirani, and L. Evans, Traffic-law enforcement and risk of death from motor-vehicle crashes: case-crossover study. The Lancet, 2003. 361: p. 2177-2182.
126.
Shope, J., Influences on youthful driving behavior and their potential for guiding interventions to reduce crashes. Injury Prevention, 2006. 12(Suppl I): p. i9-i14.
127.
Vingilis, E. and S. Macdonald, Review: drugs and traffic collisions. Traffic Injury Prevention, 2002. 3: p. 1-11.
128.
Warner, H. and L. Aberg, Drivers' decision to speed: a study inspired by the theory of planned behavior. Transportation Research, 2006. Part F 9: p. 427-433.
129.
Yun, J., Offsetting behavior effects of the corporate average fuel economy standards. Economic Inquiry, 2002. 40(2): p. 260-270.
130.
Wenzel, T. and M. Ross, Increasing the fuel economy and safety of new light-duty vehicles. 2006, U.S. Department of Energy under Contract No. DE-AC03-76SF00098.
131.
O'Neill, B. and D. Mohan, Reducing motor vehicle crash deaths and injuries in newly motorized countries. British Medical Journal, 2002. 324(7346): p. 1142-1145.
132.
Noland, R. and M. Quddus, Improvements in medical care and technology and reductions in traffic-related fatalities in Great Britain. Accident Analysis and Prevention, 2004. 36: p. 103113.
26
2
INTEGRATED ASSESSMENT OF MULTIPLE RISKS TO ASSESS CURRENT AND FUTURE CLIMATE MITIGATION POLICIES FOR PASSENGER CARS3
2.1
INTRODUCTION
Policymakers in industrialized countries are increasingly tasked to design and implement climate mitigation policy for passenger cars [1-3]. Car fleets in developed countries are currently dominated by conventional gasoline and diesel technology [3, 4], and risk assessments incorporating social costs commonly assume 100% conventional technology [5, 6]. 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’s (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. “Integrated Assessment of Multiple
Risks to Assess Current and Future Climate Mitigation Policies For Passenger Cars,” 2009. Based on various reviewer feedback, it is being substantially revised for re-submission to an appropriate journal.
27
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 costbenefit [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 28
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’s 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
29
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
Public Health
Social Costs Primary
Congestion
New Public Policy Priorities
Traffic Safety, Air
Climate Change
Quality Secondary
Air Quality, Traffic
Environmental Noise
Safety
Congestion, Energy Security, Climate Change, Air Quality
Tertiary
Environmental Noise,
Congestion, Energy
Environmental
Climate Change,
Security, Climate
Noise
Energy Security
Change
Water Security, Water Quality, Land
often excluded
Use, Food Security
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 30
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
marginal external cost, pence per km travel .
range of the marginal external costs for each risk.
12 p/km 10 p/km 8 p/km 6 p/km 4 p/km 2 p/km 0 p/km global warming
noise pollution
local air quality
traffic congestion collision risks
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 31
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.
32
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’ 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 2.3.1
PATHWAYS FROM POLICY TO RISKS 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: 33
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.
passenger cars usage, fuels & technology economic actors VKT: km per car consumers ownership: cars/person passenger car policies
car manufacturers
vehicle technology
economic, ecological, and human health risks climate change
air quality
energy security
traffic safety
congestion fuel providers
fuel properties noise
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 34
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’s 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 carpedestrian 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.
35
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.
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.
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.
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).
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).
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 36
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.
Broad, multisector policies
Policy Category
Policy Description [2, 8284] economy-wide trade
cap
and
Upstream carbon tax [86]
fuel tax
Fuel-oriented policies
company car free fuel tax
fuel-specific mandate
low carbon fuel standard (lifecycle CO2 basis)
fuel feebate (↑ fee for ↑ life cycle gCO2/L; rebates below gCO2/L pivot point)
Example in the UK
CC = climate change, AQ = air quality, ES = energy security, TS = traffic safety, C = congestion, N = noise Benefit4 Disbenefit
EU ETS [85], but CC, ES not applicable to cars
AQ, C
AQ, C
AQ, C
AC, C
CC, C
AQ
CC, ES
gasoline and CC, ES diesel tax [21, 87, 88] income tax CC, ES through employer [21]
AQ, C
Renewable CC, ES Transport Fuel Obligation [14]
AQ
AQ, CC
AQ
AQ
AQ
AQ
CC, ES
CC, ES
direct 4
Benefits and disbenfits are classified as direct or indirect as follows.
indirect
37
Policy Category
Policy Description [2, 8284] fleet gCO2/km emission standards (option for tradable credits)
fleet CO2/km CC target per ACEA voluntary [64]
gCO2/km acquisition tax
CO2/km CC registration taxes flat fee of £38 [21]
vehicle technology- or ownership-based policies
gCO2/km circulation tax
vehicle
vehicle
CO2/km company car tax
green labeling
scrappage bounty[90]
CO2/km vehicle CC taxes VED tax bands [10] UK benefit-in-kind CC CO2/km tax bands [9]
AQ, ES, N
AQ, TS, C, N.
AQ, ES, C, N
AQ, C, N
AQ, ES, C, N
AQ, C, N
AQ, ES, C, N
AQ, C, N
AQ, ES, N
AQ, N
EC Directive CC 1999/94/EC [89]
General Motors AQ, TS EcoFLEX [90] CC, E
vehicle feebates (↑ fee for ↑ gCO2/km; rebates below gCO2/km pivot point) [91] gCO2/km vehicle-specific cap and trade
technology mandate (e.g., minimum sales of zero emission vehicles) gCO2/km emissions tax
VKT or usage oriented policies
CC = climate change, AQ = air quality, ES = energy security, TS = traffic safety, C = congestion, N = noise Benefit4 Disbenefit
Example in the UK
road pricing
per-km tax or insurance premiums [81, 93, 94]
CC AQ, ES, TS, C, N
AQ, C, N
AQ, ES, C, N
AQ, TS, C, N
CC
CC, AQ
CC ES, C, N
CC, AQ, TS, C, N
AQ, ES, C, N
AQ, C, N
CC, AQ, ES, N
CC, AQ, ES, TS
CC, AQ, ES, N
AQ
CC
London C congestion charge[48, 74, 92] C
38
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’ 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].
39
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.
Policies affecting rate and type of new cars
on road vehicle stock and patterns of use
Policies affecting rate and type of scrapped cars
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].
600
life cycle gCO2/km
500
400
300
200
100
7,000 km - Gasoline
14,000 km - Gasoline
28,000 km - Gasoline
7,000 km - Diesel
14,000 km - Diesel
28,000 km - Diesel
0 0
5
10
15
20
years
40
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’s 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.
41
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
mean dB(A) relative to conventional gasoline or diesel
2.0 1.5 1.0 0.5 0.0 -0.5 -1.0 -1.5
G CN
G LP g
ol in as
e
d br i y h
.
-2.0 -2.5 -3.0
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:
42
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’s 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’s 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 43
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’s 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’s 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 – 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’s) [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.
44
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-peerreviewed 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’s 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. 45
2.7 1.
REFERENCES Cambridge.Systematics.Inc., Moving Cooler: An Analysis of Transportation Strategies for Reducing Greenhouse Gas Emissions. 2009, Urban Land Institute: Washington, D.C.
2.
Kopp, R., Transport policies to reduce CO2 emissions from the light-duty vehicle fleet (Issue Brief 12), in Assessing U.S. Climate Policy Options. 2007, Resources for the Future: Washington, DC.
3.
Sperling, D. and D. Gordon, Two Billion Cars: Driving Toward Sustainability. 2009: Oxford University Press.
4.
Keefe, R., J. Griffin, and J. Graham, The Benefits and Costs of New Fuels and Engines for Light-Duty Vehicles in the United States. Risk Analysis, 2008. 28(5): p. 1141-1154.
5.
Parry, I., M. Walls, and W. Harrington, Automobile externalities and policies. Journal of Economic Literature, 2007. XLV: p. 373-399.
6.
Sansom, T., et al., Surface transport costs and charges: Great Britain 1998. 2001, Department of the Environment, Transport and the Regions (DETR).
7.
Fulton, L., P. Cazzola, and F. Cuenot, IEA Mobility Model (MoMo) and its use in the ETP 2008. Energy Policy, 2009. 37: p. 3758-3768.
8.
Ribeiro, K., et al., Transport and its infrastructure, in Climate Change 2007: Mitigation. Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. 2007, Cambridge University Press.
9.
IR, Report on the evaluation of the company car tax reform: stage two. 2006, UK Inland Revenue (www.inlandrevenue.gov.uk).
10.
MORI, Assessing the impact of Graduated Vehicle Excise Duty: quantitative report. 2003, UK Department for Transport (www.dft.gov.uk) and MORI (www.mori.co.uk).
11.
Woodcock, J., et al., Energy and Health Series: Energy and Transport. The Lancet, 2007. 370: p. 1078-1088.
12.
DFT, UK Transport Statistics online database (www.dft.gov.uk/pgr/statistics). 2008, UK Department for Transport.
13.
SMMT, New car CO2 report 2008. 2008, Society of Motor Manufacturers and Traders Ltd. (www.smmt.co.uk).
14.
DFT, Carbon and sustainability reporting within the renewable transport fuel obligation (RTFO). 2008, UK Department for Transport.
46
15.
Onoda, T., IEA policies - G8 recommendations and an afterwards. Energy Policy, 2009. 37: p. 3823–3831.
16.
DEFRA, Online database of GHG emissions (http://www.defra.gov.uk/environment/statistics/globatmos/alltables.htm). 2008, Department for Environment, Food and Rural Affairs.
17.
CFIT, Transport and climate change. 2007, Commission for Integrated Transport (www.cfit.gov.uk).
18.
DEFRA, The UK Government Sustainable Development Strategy. 2005, UK Department for Environment, Food and Rural Affairs (DEFRA).
19.
DFT, Delivering better transport: priorities for 2007-08 and beyond. 2007, UK Department for Transport.
20.
DTI, Meeting the energy challenge: a white paper on energy (May 2007). 2007, Department of Trade and Industry.
21.
HM-Treasury, Budget 2007 - Building Britain’s long-term future: prosperity and fairness for families. 2007, HM Treasury (UK).
22.
Lave, L., Conflicting objectives in regulating the automobile. Science, 1981. 212(893-899).
23.
Lave, L., Controlling contradictions among regulations. The American Economic Review, 1984. 74(3): p. 471-475.
24.
Maclean, H. and L. Lave, Life cycle assessments of automobile/fuel options. Environmental Science & Technology, 2003. 37(5445-5452).
25.
Bonilla, D., Fuel demand on UK roads and dieselisation of fuel economy. Energy Policy, 2009. 37: p. 3769-3778.
26.
Kohler, J., Y. Jin, and T. Barker, Integrated modelling of EU transport policy. Journal of Transport Economics and Policy, 2008. 42(1): p. 1-21.
27.
Ogden, J., R. Williams, and E. Larson, Societal lifecycle costs of cars with alternative fuels/engines. Energy Policy, 2004. 32(7-27).
28.
Litman, T., Integrating public health objectives in transportation decision-making American Journal of Health Promotion, 2003. 18(1): p. 103-109.
29.
ARB, California state motor vehicles pollution control standards; Request for waiver of federal preemption; Opportunity for Public Hearing, Docket ID #EPA-HQ-OAR2006-0173; 72 FR 21260 (April 30, 2007). 2007, California Air Resources Board.
47
30.
T&E, Danger ahead: Why weight-based CO2 standards will make Europe's car fleet dirtier and less safe. 2007, European Federation for Transport and Environment: Brussels, Belgium.
31.
Hull, A., Policy integration: what will it take to achieve more sustainable transport solutions in cities? Journal of Transport Economics and Policy, 2007. 15: p. 94-103.
32.
Lund, H. and E. Munster, Integrated transportation and energy sector CO2 emission control strategies. Transport Policy, 2006. 13: p. 426–433.
33.
Chatterjee, K. and A. Gordon, Planning for an unpredictable future: Transport in Great Britain in 2030. Transport Policy, 2006. 13: p. 254–264.
34.
Dowlatabadi, H., On integration of policies for climate and global change. Mitigation and Adaptation Strategies for Global Change, 2007. 12: p. 651–663.
35.
Maclean, H. and L. Lave, Evaluating automobile fuel/propulsion system technologies. Progress in Energy and Combustion Science, 2003. 29: p. 1–69.
36.
Morgan, M.G. and M. Henrion, Uncertainty: a guide to dealing with uncertainty in quantitative risk and policy analysis. 1992: Cambridge University Press.
37.
Rajagopal, D., et al., Challenge of biofuel: filling the tank without emptying the stomach? Environmental Research Letters, 2007. 2(044004): p. 1-9.
38.
Farrell, A., et al., Ethanol can contribute to energy and environmental goals. Science, 2006.
39.
Jacobson, M., Effects of ethanol (E85) versus gasoline vehicles on cancer and mortality in the United States. Environmental Science & Technology, 2007. 41: p. 4150-4157.
40.
Williams, R. and E. Larson, A comparison of direct and indirect liquefaction technologies for making fluid fuels from coal. Energy for Sustainable Development, 2003. VII(4): p. 103-129.
41.
Peirson, J., I. Skinner, and R. Vickerman, Estimating the external costs of UK passenger transport: the first step towards an efficient transport market. Environment and Planning A, 1995. 27: p. 1977-1993.
42.
Lindberg, G. Recent progress in the measurement of external costs and implications for transport pricing reforms. in Implementing Reform on Transport Pricing: Identifying ModeSpecific issues. 2002. Brussels, Belgium.
43.
Morgan, M.G., et al., Why Conventional Tools for Policy Analysis are Often Inadequate for Problems of Global Change. Climatic Change, 1999. 41: p. 271-281.
44.
Naess, P., Cost-benefit analyses of transportation investments - neither critical nor realistic. Journal of Critical Realism, 2006. 5(1): p. 32-60.
48
45.
Nash, C., Marginal cost and other pricing principles for user charging in transport: a comment. Transport Policy, 2003. 10: p. 345–348.
46.
Sturm, R., Economics and physical activity. American Journal of Preventive Medicine, 2005. 28(2S2): p. 141-149.
47.
HPA, Health Effects of climate change in the UK 2008: an update of the Department of Health report 2001/2002. 2008, UK Health Protection Agency (HPA).
48.
Beevers, S. and D. Carslaw, The impact of congestion charging on vehicle speed and its implications for assessing vehicle emissions. Atmospheric Environment, 2005. 39: p. 6875– 6884.
49.
Evans, L., Traffic Safety. 2004: Science Serving Society.
50.
den Boer, L. and A. Schroten, Traffic noise reduction in Europe: Health effects, social costs and technical and policy options to reduce road and rail traffic noise. 2007, CE Delft (www.ce.nl) commissioned by T&E (http://www.transportenvironment.org).
51.
DFT, Sources of Particulate Matter in Urban Areas: TRAMAQ Project UG 250. 2002, UK Department for Transport (www.dft.gov.uk).
52.
COMEAP, Quantification of the effects of air pollution on health in the United Kingdom. 1998: The Stationary Office London (www.tsonline.co.uk).
53.
WHO, Environmental burden of disease: Country profiles (United Kingdom). 2007, World Health Organization.
54.
DFT, Road 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, UK Data Archive (Colchester, Essex).
55.
WHO-Europe, Burden of disease from environmental noise (http://www.euro.who.int/Noise/activities/20021203_3 downloaded April 3, 2008). 2008, World Health Orgainization.
56.
GAD, Government Actuary's Department (GAD) online statistics. 2006, United Kingdom Government Actuary's Department (GAD).
57.
Cohen, A., et al., The global burden of disease due to outdoor air pollution. Journal of Toxicology and Environmental Health, Part A,, 2005. 68: p. 1301–1307.
58.
Kunzli, N., et al., Assessment of deaths attributable to air pollution: should we use risk estimates based on time series or cohort studies? American Journal of Epidemiology, 2001. 153: p. 1050-1055.
49
59.
Levy, J., et al., Assessing the public health benefits of reduced ozone concentrations. Environmental Health Perspectives, 2001. 109(12): p. 1215-1226.
60.
Knol, Trends in the environmental burden of disease in the Netherlands 1980 – 2020. 2005, RIVM (www.rivm.nl/en) commissioned by Netherlands Environmental Assessment Agency.
61.
Murray, C.J.L., and Lopez, A.D., ed. The Global Burden of Disease: A Comprehensive Assessment of Mortality and Disability from Diseases, Injuries, and Risk Factors in 1990 and Projected to 2020. Global Burden of Disease and Injury Series. 1996, Harvard University Press.
62.
Marsden, G. and P. Bonsall, Performance targets in transport policy. Transport Policy, 2006. 13(191-203).
63.
CFIT, A Review of Transport Appraisal. 2004, Commission for Integrated Transport (www.cfit.gov.uk).
64.
Fontaras, G. and Z. Samaras, A quantitative analysis of the European Automakers’ voluntary commitment to reduce CO2 emissions from new passenger cars based on independent experimental data. Energy Policy, 2007. 35: p. 2239-2248.
65.
Broughton, J., Monitoring progress toward the 2010 casualty reduction target - 2005 data. 2007, Transport Research Laboratory (www.trl.co.uk) for the UK Department for Transport (www.dft.gov.uk).
66.
Kwon, T., The determinants of the changes in car fuel efficiency in Great Britain (1978–2000). Energy Policy, 2006. 34: p. 2405–2412.
67.
FEHRL, Study SI2.408210 tyre/road noise - volume 1: final report. 2006, Forum of European National Highway Research Laboratories (FEHRL).
68.
Gouge, B., et al., An Integrated Approach to Transportation Policy in BC - assessing greenhouse gas reductions opportunities in freight transportation. 2008, Pacific Institute for Climate Solutions.
69.
Greening, P., European vehicle emission legislation – present and future. Topics in Catalysis, 2001. 16/17(1-4): p. 5-13.
70.
Schipper, L., Determinants of automobile use and energy consumption in OECD countries. Annual Review of Energy and Environment, 1995. 20: p. 325-386.
71.
Dargay, J., The effect of prices and income on car travel in the UK. Transportation Research Part A: Policy and Practice, 2006. 41(949-960).
72.
Dargay, J. and M. Hanly, Volatility of car ownership, commuting mode and time in the UK. Transportation Research Part A: Policy and Practice, 2007. 41: p. 934-948.
50
73.
Whelan, Modelling car ownership in Great Britain. Transportation Research Part A: Policy and Practice, 2007. 41: p. 205-219.
74.
Prud'homme, R. and J. Bocarejo, The London congestion charge: a tentative economic appraisal. Transport Policy, 2005. 12: p. 279–287.
75.
Verboven, F., Quality-based tax discrimination and tax incidence: evidence from gasoline and diesel cars. The RAND Journal of Economics, 2002. 33(2): p. 275-297.
76.
Boulter, P. and I. McCrae, The links between micro-scale traffic, emission and air pollution models. 2007, Transport Research Laboratory (www.trl.co.uk).
77.
Singh, R., A. Huber, and J. Braddock, Sensitivity analysis and evaluation of MicroFacPM: a microscale motor vehicle emission factor model for particulate matter emissions. Journal of the Air & Waste Management Association, 2007. 57: p. 420-433.
78.
Mazzi, E. and H. Dowlatabadi, Air quality impacts of climate mitigation: UK policy and passenger vehicle choice. Environmental Science & Technology, 2007. 41: p. 387-392.
79.
Mazzi, E., H. Dowlatabadi, and M. Kandlikar, Regulating Car Mass for Concurrent Traffic Safety and Climate Mitigation Benefits. 2010 (DRAFT).
80.
VCA, Online data of vehicle emissions for UK cars model years 2000-2006. 2007, Vehicle Certification Agency (www.vcacarfueldata.org.uk).
81.
Greenberg, A., Designing pay-per-mile auto insurance regulatory incentives. Transportation Research Part D: Transport and Environment, 2009. 14(6): p. 437-445.
82.
Acutt, M. and J. Dodgson, Policy instruments and greenhouse gas emissions from transport in the UK. Fiscal Studies, 1996. 17(2): p. 65-82.
83.
Glaister, S., UK transport policy 1997-2001. Oxford Review of Economic Policy, 2002. 18(2): p. 154-186.
84.
Potter, S. and G. Parkhurst, Transport policy and transport tax reform. Public Money & Management, 2005(June): p. 172-178.
85.
Grubb, M. and K. Neuhoff, Allocation and competitiveness in the EU emissions trading scheme: policy overview. Climate Policy, 2006. 6: p. 7-30.
86.
Nordhaus, W., After Kyoto: Alternative Mechanisms to Control Global Warming. The American Economic Review, 2006. 96(2): p. 31-34.
87.
COWI, Fiscal measures to reduce CO2 emissions from new passenger cars. 2002, European Commission.
51
88.
Kunert, U. and H. Kuhfeld, The diverse structures of passenger car taxation in Europe and the EU Commissions proposal for reform. Transport Policy, 2007. 14(4): p. 306-316
89.
DFT, New passenger cars - information on fuel consumption and CO2 emissions. 2005, UK Department for Transport.
90.
Just-Auto, UK: GM adds green scrapping incentive (June 6), in Just-Auto (www.justauto.com). 2006.
91.
Greene, D., Feebates, footprints and highway safety. Transportation Research Part D: Transport and Environment, 2009. 14: p. 375-384.
92.
TFL. Congestion charging (www.tfl.gov.uk/roadusers/congestioncharging/default.aspx). 2008 [cited April 14, 2008].
93.
Bordoff, J. and P. Noel, Pay-As-You-Drive Auto Insurance: a Simple Way to Reduce DrivingRelated Harms and Increase Equity, in The Hamilton Project. 2008, The Brookings Institution.
94.
Parry, I., On the costs of policies to reduce greenhouse gases from passenger vehicles (RFF DP 06-14). 2006, Resources for the Future.
95.
Gruenspecht, H., Zero emission vehicles: a dirty little secret. Resources, 2001(142): p. 7-10.
96.
Pickrell, D., Cars and clean air: a reappraisal. Transportation Research Part A: Policy and Practice, 1999. 33: p. 527-547.
97.
Broughton, J., Casualty rates by type of car (version 3). 2007, TRL Limited.
98.
Kimmo, K., Life-cycle energy consumption and carbon dioxide emissions of world cars (www2.lut.fi/~kklemola/dontfly/carsof2006.htm). 2006.
99.
Schafer, A. and H. Jacoby, Vehicle technology under CO2 constraint: a general equilibrium analysis. Energy Policy, 2006. 34: p. 975-985.
100.
Heywood, J., et al., The performance and future ICE and fuel cell powered vehicles and their potential fleet impact, in 2004 SAE World Congress. 2004, Society of Automotive Engineers.
101.
Golbuff, S. Design optimization of a plug-in hybrid electric vehicle (2007-01-1545). in 2007 World Congress. 2007. Detroit, Michigan, USA: Society of Automotive Engineers (www.sae.org).
102.
Samaras, C. and K. Meisterling, Life cycle assessment of greenhouse gas emissions from plug-in hybrid vehicles: implications for policy. Environmental Science & Technology, 2008. 42: p. 3170–3176.
52
103.
Bergerson, J. and L. Lave, Should We Transport Coal, Gas, or Electricity: Cost, Efficiency, and Environmental Implications. Environmental Science & Technology, 2006. 39(16): p. 5905-5910.
104.
Giere, R., Understanding Scientific Reasoning. 4th ed. 1998: Harcourt Brace College Publishing.
105.
Creswell, J., Research design: qualitative, quantitative, and mixed method approaches (2nd ed.). 2003: Sage Publications.
106.
De Jong, G., et al., Comparison of car ownership models. Transport Reviews, 2004. 24(4): p. 379–408.
107.
Page, M., G. Whelan, and A. Daly, Modeling the factors which influence new car purchasing, in European Transport Conference 2000. 2000: Cambridge, UK.
108.
MORI, Assessing the impact of Graduated Vehicle Excise Duty: qualitative report. 2003, UK Department for Transport (www.dft.gov.uk) and MORI (www.mori.co.uk).
109.
Last, J., A dictionary of epidemiology. 4th ed. 2001: International Epidemiological Association.
110.
Vedal, S., Ambient Particles and Health: Lines that Divide. Journal of the Air & Waste Management Association, 1997. 47: p. 551-581.
111.
Schipper, L., C. Marie-Lilliu, and L. Fulton, Diesels in Europe: Analysis of Characteristics, Usage Patterns, Energy Savings, and CO2 Emission Implications. Journal of Transport Economics and Policy, 2002. 36(2): p. 305-340.
112.
FleetNews, Diesel sales rocket with fuel price rise, in Fleet News. 2006.
113.
IR, Report on the evaluation of the company car tax reform. 2004, UK Inland Revenue (www.inlandrevenue.gov.uk).
114.
Lane, B., Car buyer research report: consumer attitudes to low carbon and fuel-efficient passenger cars. 2005, Low Carbon Vehicle Partnership (UK).
115.
Sher, E., ed. Handbook of Air Pollution from Internal Combustion Engines: Pollution Formation and Control. 1998, Academic Press.
116.
Scheid, E. Development trends in passenger car DI engines. in AVEEC 2001 (presentation downloaded from www.meca.org). 2001.
53
3
AIR QUALITY IMPACTS OF CLIMATE MITIGATION: UK POLICY AND PASSENGER VEHICLE CHOICE6
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 1520% 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’s company car benefit-in-kind tax was changed to a CO2based 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’s 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.
6
A version of this chapter has been published. Mazzi, E. and H. Dowlatabadi, “Air quality impacts of climate
mitigation: UK policy and passenger vehicle choice”. Environmental Science and Technology, 2007. 41: p. 387392.
54
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.
Diesel % of total new registrations
60% 50% 40%
1988 Fiat introduces EU's 1st turbocharged direct injection (TDI) diesel
EU without UK UK
30% 20% 10%
2002 CO2 based company car tax 2001 CO2 based vehicle excise duty
0% 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 Year 55
3.2
METHODS
We define “additional diesels” as the number of newly registered diesel vehicles additional to an estimated “no growth” 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 “no growth” 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. number of diesel company car registrations number of diesel private car registrations
50%
diesel % of total company cars
45%
diesel % of total private cars
40%
600 500
35% 30%
400
25% 300
20% 15%
200
10% 100
Percentage of new diesel registrations
Number of new diesel registrations, 1000's
700
5%
0
0% 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Year
56
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’s 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 20012020 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 57
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 µg/m3. This is a ratio of 0.0737 µg/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 – 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 µg/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 µg/m3 increase in PM2.5 for a cohort of subjects age 30 and older in the U.S. [33]. We 58
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 µg/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 µg/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 “Euro III interval” from 20012005 when Euro III emissions standards applied, a “Euro IV interval” from 2006-2008, and the “post-Euro IV interval” 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.
59
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’ projections. The focus of this study is on the area between the actual/projection curve and the “no growth” curve which is split into three time intervals defined by the applicable emission standard: Euro III, Euro IV, and post-Euro IV.
50% 45%
Euro IV standards apply in 2006
actual diesel share
Diesel % of total new registrations
projected diesel share
40%
Post-Euro IV diesels (with PM trap) in 2009
Most Euro III and Euro IV diesels scrapped by 2020
“no growth” diesel
35% CO2 tax and Euro III standards apply in 2000
30% 25%
Euro IV interval
post-Euro IV interval
Euro III interval
20% 15% 10% 5% 0% 1990
1995
2000
2005
2010
2015
2020
Year
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. 60
Figure 3.4
Summary results of the impact of additional diesels in the UK from 2001-2020 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
-20
-7
-4
-3
6
fuel, 10 bbl oil
box C: change in air quality (annual average) PM2.5
+0.043
↑
NO2 ozone CO
↑
or
g/m
3
g/m3
↓
↓
g/m
3
g/m
3
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
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.
61
Figure 3.5
Estimates of additional diesels in the UK 2001-2020 disaggregated by Euro emission class. “Additional diesels” are defined as the number of petrol vehicles switched to diesel beyond the “no growth” 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 “post-Euro IV” in this study. 7 post-Euro IV
Cumulative additional diesels, millions
Euro IV Euro III
6 CO2 based Vehicle Excise Duty (VED)
5
Proposed harmonization of petrol and diesel car emission limits (post-Euro IV)
4
Euro IV CO2 emission based limits Company apply Car Tax
3
2
1
0 2001
2003
2005
2007
2009
2011
2013
2015
2017
2019
Year
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
62
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.
0.5
10
0.0
0
-5
NOx
-10
-0.5
PM2.5
Change in CO 2 emissions, mega-tonnes/year
Change in PM 2.5, NOX, HC, and CO emissions, kilo-tonnes/year
5
HC CO
-15
CO2 -20 2000
-1.0 2002
2004
2006
2008
2010 2012 Year
2014
2016
2018
2020
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 µg/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 63
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 “NOx 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.” [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 postEuro 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’ 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 64
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 65
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’s 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.
66
3.6 1.
REFERENCES 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.
IPCC, Climate Change 2001: Mitigation. Intergovernmental Panel on Climate Change: 2001.
4.
van Vuuren, D.; Cofala, J.; Eerens, H.; Oostenrijk, R.; Heyes, C.; Klimont, Z.; den Elzen, M.; Amann, M., Exploring the ancillary benefits of the Kyoto Protocol for air pollution in Europe. Energy Policy 2006, 34, (4), 444-460.
5.
Schipper, L.; Marie-Lilliu, C.; Fulton, L., Diesels in Europe: Analysis of Characteristics, Usage Patterns, Energy Savings, and CO2 Emission Implications. Journal of Transport Economics and Policy 2002, 36 part 2, 305-340.
6.
IEA Energy Statistics Manual; International Energy Agency (www.iea.org): 2005.
7.
MORI Assessing the Impact of Graduated Vehicle Excise Duty: Quantitative Report; UK Department for Transport (www.dft.gov.uk) and MORI www.mori.co.uk: 2003.
8.
IR Report on the evaluation of the company car tax reform: stage two; UK Inland Revenue (www.inlandrevenue.gov.uk): 2006.
9.
Lane, B., Car buyer research report: consumer attitudes to low carbon and fuel-efficiency passenger cars.; Low Carbon Vehicle Partnership (UK): 2005.
10.
SMMT UK New Car Registrations by CO2 Performance 2005 Annual Report; The Society of Motor Manufacturers and Traders Ltd. (www.smmt.co.uk): 2006.
11.
SMMT, New vehicle registrations in the UK. In Society of Motor Manufacturers and Traders (www.smmt.co.uk): 2005.
12.
Jacobson, M.; Seinfeld, J.; Carmichael, G.; Streets, D., The effect on photochemical smog of converting the U.S. fleet of gasoline vehicles to modern diesel vehicles. Geophysical Research Letters 2004, 31, L02116.
67
13.
Krupnick, A.; Burtraw, D.; Markandya, A., In Ancillary Benefits and Costs of Greenhouse Gas Mitigation Strategies, Expert Workshop on the Ancillary Benefits and Costs of Greenhouse Gas Mitigation Strategies, Washington, D.C., March 27-29, 2000; Washington, D.C., 2000.
14.
Swart, R.; Amann, M.; Raes, F.; Tuinstra, W., A Good Climate for Clean Air: Linkages between Climate Change and Air Pollution. Climatic Change 2004, 66, 263-269.
15.
NAEI National Atmospheric Emission Inventory online database (www.naei.org.uk): September 22, 2006, 2005.
16.
TRL Data required to monitor compliance with the End of Life Vehicles Directive; PR SE/483/02; Transport Research Laboratory, UK: 2003.
17.
VCA, Vehicle Database. In Vehicle Certification Agency (www.vcacarfueldata.org): 2005.
18.
Delcan Strategies To Reduce Greenhouse Gas Emissions From Passenger Transportation In Three Large Urban Areas - Final Report (by Delcan Corporation, KPMG, and A.K. Socio-Technical Consultants Ottawa Inc.); Transport Canada (www.tc.gc.ca): 1999.
19.
DFT Transport Statistics (online data); UK Department for Transport (www.dft.gov.uk): September 15, 2006, 2005.
20.
Martuzzi, M.; Krzyzanowski, M.; Bertollini, R., Health impact assessment of air pollution: providing further evidence for public health action. European Respiratory Journal 2003, 21, (Supplement 40), 86s-91s.
21.
Künzli, N.; Kaiser, R.; Medina, S.; Studnicka, M.; Chanel, O.; Filliger, P.; Herry, M.; Horak Jr., F.; Puybonnieux-Texier, V.; Quénel, P.; Schneider, J.; Seethaler, R.; Vergnaud, J.-C.; Sommer, H., Public-health impact of outdoor and traffic-related air pollution: a European assessment. The Lancet 2000, 356, 795-801.
22.
Smith, K., Why Particles? Chemosphere 2002, 49, 867-871.
23.
WHO, Comparative Quantification of Health Risks: Global and Regional Burden of Disease due to Selected Major Risk Factors. World Health Organization: Geneva, 2004.
24.
McCubbin, D.; Delucchi, M., The health costs of motor vehicle related air pollution. Journal of Transport Economics and Policy 1999, 33, (3), 253-286.
25.
Watkiss, P.; Jones, G.; Kollamthodi, S., An Evaluation of the Air Quality Strategy Additional Analysis: Local Road Transport Measures. prepared for UK Department for Environment, Food and Rural Affairs (DEFRA): 2004. 68
26.
CERC, Source Apportionment for London using ADMS-Urban. prepared by Cambridge Environmental Research Consultants Ltd. for: UK Department for Environment, Food and Rural Affairs (DEFRA) and The National Assembly for Wales, The Scottish Executive and the Department of the Environment in Northern Ireland: 2004.
27.
Ntziachristos, L.; Giechaskiel, B.; Pistikopoulous, P.; Fysikas, E.; Samaras, Z., In Particle Emission Characteristics of Different On-Road Vehicles, PM Characterization in Diesel and Gasoline Exhaust Gas, Yokohama, Japan, 2003; Society of Automotive Engineers (SAE): Yokohama, Japan, 2003.
28.
Stedman, J.; Bush, T.; Murrells, T.; Hobson, M.; Handley, C.; King, K., Quantification of the health effects of air pollution in the UK for revised PM10 objective analysis. AEA Technology and UK Department of Health: 2002.
29.
Stedman, J.; Bush, T.; Murrells, T.; Hobson, M.; Handley, C.; King, K., Revised PM10 projections for the UK for PM10 objective analysis. Prepared by AEA Technology for UK Department for Environment, Food and Rural Affairs (DEFRA) and The National Assembly for Wales, The Scottish Executive and the Department of the Environment in Northern Ireland: 2002.
30.
Stedman, J.; Bush, T.; Murrells, T.; King, K., Baseline PM10 and NOx projections for PM10 objective analysis. Prepared by AEA Technology for UK Department for Environment, Food and Rural Affairs (DEFRA) and The National Assembly for Wales, The Scottish Executive and the Department of the Environment in Northern Ireland: 2001.
31.
Marshall, J.; Teoh, S.; Nazaroff, W., Intake fraction of nonreactive vehicle emissions in US urban areas. Atmospheric Environment 2005, 39, 1363-1371.
32.
Marshall, J.; Riley, W.; McKone, T.; Nazaroff, W., Intake fraction of primary pollutants: motor vehicle emissions in the South Coast Air Basin. Atmospheric Environment 2003, 37, 3455-3468.
33.
Pope, C. A.; Burnett, R.; Thun, M.; Calle, E.; Krewski, D.; Ito, K.; Thurston, G., Lung, Cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. Journal of the American Medical Association 2002, 287, (9), 1132-1141.
34.
Ostro, B., Outdoor air pollution: Assessing the environmental burden of disease at national and local levels; World Health Organization: 2004.
35.
COMEAP, Quantification of the effects of air pollution on health in the United Kingdom. The Stationary Office London (www.tsonline.co.uk): 1998. 69
36.
Kunzli, N.; Medina, S.; Kaiser, R.; Quenel, P.; Horak, F.; Studnicka, M., Assessment of deaths attributable to air pollution: should we use risk estimates based on time series or cohort studies? American Journal of Epidemiology 2001, 153, 1050-1055.
37.
Roosli, M.; Kunzli, N.; Brauen-Fahrlander, C.; Egger, M., Years of life lost attributable to air pollution exposure in Switzerland: dynamic exposure-response model. International Journal of Epidemiology 2005.
38.
Dockery, D.; Pope, C.; Xu, X.; Spengler, J.; Ware, J.; Fay, M.; Ferris, B.; Speizer, F., An association between air pollution and mortality in six U.S. cities. New England Journal of Medicine 1993, 329, 1753-1759.
39.
Jerrett, M.; Burnett, R.; Ma, R.; Pope, C.; Krewski, D.; Newbold, K.; Thurston, G.; Shi, Y.; Finkelstein, N.; Calle, E.; Thun, M., Spatial Analysis of Air Pollution and Mortality in Los Angeles. Epidemiology 2005, 16, (6), 727-736.
40.
Hayman, G.; Jenkin, M.; Pilling, M.; Derwent, R., Modeling of Tropospheric Ozone Formation. prepared by AEA Technology for UK Department for Environment, Food and Rural Affairs (DEFRA): 2002.
41.
Hayman, G.; Abbott, J.; Thomson, C.; Bush, T.; Kent, A.; Derwent, D.; Jenkin, M.; Pilling, M.; Richard, A.; Whitehead, L., Modeling of Tropospheric Ozone (AEAT/ENV/R/1858 issue 2); Department for Environment, Food and Rural Affairs, the Scottish Executive, the National Assembly of Wales and the Northern Ireland Department of the Environment: 2005.
42.
Greening, L.; Greene, D.; Difiglio, C., Energy efficiency and consumption - the rebound effect - a survey. Energy policy 2000, 28, 389-401.
43.
Jacobson, M., Correction to ‘‘Control of fossil-fuel particulate black carbon and organic matter, possibly the most effective method of slowing global warming’’. Geophysical Research Letters 2005, 110, D14105.
44.
Sato, M.; Hansen, J.; Koch, D.; Lacis, A.; Ruedy, R.; Dubovik, O.; Holben, B.; Chin, M.; Novakov, T., Global atmospheric black carbon inferred from AERONET. Proceedings of the National Academy of Sciences 2003, 100, (11), 6319-6324.
45.
Verboven, F., Quality-based tax discrimination and tax incidence: evidence from gasoline and diesel cars. The RAND Journal of Economics 2002, 33, (2), 275-297.
46.
Fleet News Diesel sales rocket with fuel price rise; March, 2006.
70
4
REGULATING CAR MASS FOR CONCURRENT TRAFFIC SAFETY AND CLIMATE MITIGATION BENEFITS7
4.1
INTRODUCTION
We examined the relationship between vehicle mass, traffic collision health risks, and tailpipe CO2 emissions for passenger cars using the UK as a case study. Traffic collisions continue to present substantial public health risks in the UK [1] and globally [2]. 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) drivetrain efficiency, and c) carbon intensity of the fuel. Mass reduction is potentially a win-win strategy with dual benefits of climate mitigation and nearterm 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,
“Regulating car mass for concurrent traffic safety and climate mitigation benefits” 2010.
71
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).
140
1,600 pedestrian, fatal
1,400
120
100
1,000 80 800 60 600 40
400
KSI per billion passenger-km
Fatal per billion passenger-km
cars, fatal
1,200
bicycles, fatal
motorcycles, fatal
pedestrian,KSI
cars, KSI
bicycles, KSI
motorcycles, KSI
20
0 1996
200
0 1998
2000
2002
2004
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.
Fatalities
Fatal crash events
other 5%
≥ 3 veh 16%
1 car 18%
bicycle 5% motorcycle 20%
cars 48%
2 veh (1 not car) 25%
2 car 14% car ped 13%
pedestrian 22%
1 veh (not car) 7%
other veh ped 7%
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. 73
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 (“accidents”), 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 “first law” of two-car collisions [12], then compared the RR and CO2 emission 74
rates [25, 26]. The “first law” is a power law relationship between vehicle mass and RR of driver fatality of the following form [12]: RR ≡ μλ where: RR ≡ n1/n2 = (number driver fatalities in lighter cars) / (number of driver fatalities in heavier cars) μ ≡ (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’s conditional KSI risk estimates for two-car collisions[28] and CO2 emission rates [25]. As they are only for makes and models, DFT’s 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’s 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.
75
Table 4.1
Comparison of key parameters for the “first law” RR analysis data set: heavier cars and their drivers cf. lighter cars. Variable
Heavier Car
Lighter Car
140
140
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%
Number of drivers (vehicles) Average car model year
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 20002005. 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 76
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 “Increase lighter car” scenario was developed to simulate policies that induce rapid growth of lighter cars, the “Increase lighter cars and prohibit heavier cars” to simulate prohibition of heavier cars (approximately 1,600 kg mass and larger), and the “Constant mass” 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 (λ) varied parametrically at values 2, 4, and 6. Analytica™ version 4.1 software was used to simulate randomized crashes using the Latin Hypercube sampling algorithm.
77
Baseline plus three alternative scenarios for years 2000-2005 used in the absolute risk analysis and the RR fleet composition simulation. “Lighter” group is comprised of engine size ranges 701-1,000 cubic centimeter (CC), 1,001-1,200 CC, and 1,201-1,500 CC. “Mid-mass” includes 1,501-1,800 CC and 1,801-2,000 CC. “Heavier” 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. 1501-1800 (M)
35%
1201-1500 (L)
30%
1801-2000 (M)
25%
1001-1200 (L)
20%
701-1000 (L)
15%
2001-2500 (H)
10%
2501-3000 (H)
40%
1995
1997
1999
2001
2003
2005
1501-1800 (M) 1201-1500 (L) 1801-2000 (M)
25%
1801-2000 (M)
30% 1001-1200 (L)
25% 701-1000 (L)
20% 2001-2500 (H)
15% 2501-3000 (H)
10%
3000 & over (H)
5%
700 & under
1995
40%
Scenario: Increase light cars and prohibit heavy (decrease mid-mass cars)
30%
1201-1500 (L)
35%
0% 1993
700 & under
35% Registered (on road) car shares
40%
3000 & over (H)
5% 0% 1993
1501-1800 (M)
Scenario: Increase light cars (BAU heavier cars, decrease mid-mass cars)
Baseline (actual)
1001-1200 (L)
20% 701-1000 (L)
15% 2001-2500 (H)
10%
1997
1999
2001
2003
2005
Scenario: Constant mass
1501-1800 (M)
35% Registered (on road) car shares
Registered (on-road) car shares
40%
Registered (on-road) car shares
Figure 4.3
1201-1500 (L)
30%
1801-2000 (M)
25% 1001-1200 (L)
20% 701-1000 (L)
15% 2001-2500 (H)
10%
2501-3000 (H)
2501-3000 (H)
5%
5% 3000 & over (H)
0% 1993
1995
1997
1999
2001
2003
2005
700 & under
3000 & over (H)
0% 1993
1995
1997
78
1999
2001
2003
2005
700 & under
4.3
RESULTS
The results of the “first law” two-car RR analysis are summarized in Figure 4.4. The data set was suitable to develop five groups with curb mass ratio ( ranging from 1.06-1.55. The regression analysis for the relationship RR = resulted in = 5.31 (95% CI 3.9-6.7; R2 = 0.71, p
